Policy Assessment 

for the Review of the Particulate Matter 

National Ambient Air Quality Standards 

Second External Review Draft 

June 2010 
 

DISCLAIMER

 This draft document has been prepared by staff from the Ambient Standards Group, 

Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. Any 

opinions, findings, conclusions, or recommendations are those of the authors and do not 

necessarily reflect the views of the EPA. This document is being circulated to obtain review and 

comment from the Clean Air Scientific Advisory Committee (CASAC) and the general public. 

Comments on this draft document should be addressed to Beth Hassett-Sipple, U.S. 

Environmental Protection Agency, Office of Air Quality Planning and Standards, C504-06, 

Research Triangle Park, North Carolina 27711 (email: hassett-sipple.beth@epa.gov). 
EPA-452/P-10-007 

June 2010 

Policy Assessment 

 for the Review of the Particulate Matter 

National Ambient Air Quality Standards 

Second External Review Draft 

U.S. Environmental Protection Agency 

Office of Air and Radiation 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, North Carolina 27711 
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EXECUTIVE SUMMARY 

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

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

(OAQPS) in conjunction with the Agency’s ongoing review of the national ambient air 

quality standards (NAAQS) for particulate matter (PM), which include primary (healthbased) and secondary (welfare-based) standards. It presents staff conclusions regarding 

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

standards for consideration in this review, based on the scientific and technical 

information assessed in other EPA documents, including the Integrated Science 

Assessment for Particulate Matter (Final Report) (ISA, US EPA, 2009a), the 

Quantitative Health Risk Assessment for Particulate Matter (Final Report) (RA, US 

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

Report) (UFVA, US EPA, 2010b). 

This PA is intended to “bridge the gap” between the relevant scientific evidence 

and technical information and the judgments required of the EPA Administrator in 

determining whether, and if so how, to revise the PM NAAQS. The current and potential 

alternative PM standards are considered in terms of the basic elements of the NAAQS: 

indicator, averaging time, form, and level. 

Primary standards for fine particles (Chapter 2): 

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

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

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

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

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

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

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

supplemental protection for days with high peak concentrations. On this basis, staff 

concludes that consideration should be given to alternative annual PM2.5 standard levels 

in the range of 13 to 11 μg/m3

, in conjunction with retaining the current 24-hour PM2.5

standard level of 35 μg/m3

, and that consideration could also be given to an alternative 

24-hour PM2.5 standard level of 30 μg/m3

 particularly in conjunction with an annual 

standard level of 11 μg/m3

. 

Primary standard for thoracic coarse particles (Chapter 3): 

 In assessing the adequacy of the current primary 24-hour PM10 standard meant to 

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

concludes that consideration should be given to retaining or revising the current standard, 

depending on the relative weight placed on the evidence supporting associations with 

PM10-2.5 and the uncertainties associated with this evidence. With regard to potential 

alternative standards, staff concludes that, to the extent consideration is given to revising 

the current standard to increase public health protection, consideration should be given to 

retaining the PM10 indicator and the 24-hour averaging time and revising the form and 
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level, with consideration of levels from 85 g/m3

 down to about 65 g/m3 in conjunction 

with a 98th percentile form. 

Secondary standards for PM-related visibility impairment (Chapter 4): 

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

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

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

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

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

public welfare protection. In considering alternative standards, staff concludes that 

consideration should be given to establishing a new indicator based on using speciated 

PM2.5 mass and relative humidity to calculate PM2.5 light extinction. Staff also concludes 

that consideration should be given to a 1-hour averaging time, considering only daylight 

hours with relative humidity no higher than 90 %, and a level, defined in terms of PM2.5

light extinction, in the range of 191 to 64 Mm-1 to target protection against visibility 

impairment related to fine particles. 

Secondary standards for non-visibility welfare effects (Chapter 5):

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

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

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

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

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

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

standards. Staff also concludes that there is insufficient information at this time to base a 

national ambient standard on climate impacts associated with current ambient 

concentrations of PM or its constituents. 
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TABLE OF CONTENTS 

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

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

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

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

1.2.2 Previous PM NAAQS Reviews .......................................................................1-3 

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

1.2.4 Current PM NAAQS Review........................................................................... 1-9 

1.3 GENERAL APPROACH AND ORGANIZATION OF THIS DOCUMENT........ 1-11 

1.4 REFERENCES ........................................................................................................ 1-13 

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

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

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

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

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

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

2.1.3 Current Approach............................................................................................. 2-7 

2.2 ADEQUACY OF CURRENT STANDARDS ........................................................ 2-13 

2.2.1 Evidence-based Considerations ..................................................................... 2-13 

2.2.2 Risk-based Considerations............................................................................. 2-31 

2.2.3 Staff Conclusions on Adequacy of Current Standards .................................. 2-42 

2.3 CONSIDERATION OF ALTERNATIVE STANDARDS ..................................... 2-43 

2.3.1 Indicator ......................................................................................................... 2-44 

2.3.2 Averaging Times............................................................................................2-50 

2.3.3 Forms ............................................................................................................. 2-52 

2.3.3.1 Form of the Annual Standard........................................................2-52 

2.3.3.2 Form of the 24-Hour Standard......................................................2-54 

2.3.4 Alternative Levels..........................................................................................2-56 

2.3.4.1 Evidence-based Considerations .................................................... 2-56 

2.3.4.2 Risk-based Considerations............................................................ 2-76 

2.3.4.3 Integration of Evidence-based and Risk-based Considerations....2-82 

2.4 STAFF CONCLUSIONS ON ALTERNATIVE FINE PARTICLE STANDARDS 

..........................................................................................................................................2-84 

2.5 KEY UNCERTAINTIES AND AREAS FOR FUTURE RESEARCH AND DATA 

COLLECTION ........................................................................................................ 2-86 

2.6 REFERENCES ........................................................................................................ 2-91 
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3 REVIEW OF THE PRIMARY STANDARD FOR THORACIC COARSE 

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

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

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

3.1.2 Review Completed in 2006.............................................................................. 3-3 

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

3.1.4 Current Approach............................................................................................. 3-6 

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

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

3.2.2 Staff Conclusions on Adequacy of Current PM10 Standard .........................3-23 

3.3 CONSIDERATION OF POTENTIAL ALTERNATIVE STANDARDS .............. 3-26 

3.3.1 Indicator ......................................................................................................... 3-26 

3.3.2 Averaging Time .............................................................................................3-29 

3.3.3 Form............................................................................................................... 3-30 

3.3.4 Level .............................................................................................................. 3-32 

3.4 STAFF CONCLUSIONS ON A PRIMARY THORACIC COARSE PARTICLE 

STANDARD............................................................................................................ 3-44 

3.5 KEY UNCERTAINTIES AND AREAS FOR FUTURE RESEARCH AND DATA 

COLLECTION ........................................................................................................ 3-45 

3.6 REFERENCES ........................................................................................................ 3-47 

4 REVIEW OF THE SECONDARY STANDARDS FOR VISIBILITY-RELATED 

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

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

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

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

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

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

4.1.3 Current Approach............................................................................................. 4-9 

4.2 ADEQUACY OF CURRENT STANDARDS ........................................................ 4-11 

4.2.1 Evidence-based Considerations ..................................................................... 4-11 

4.2.2 Summary........................................................................................................ 4-29 

4.3 CONSIDERATION OF ALTERNATIVE STANDARDS ..................................... 4-30 

4.3.1 Nature of the Indicator...................................................................................4-30 

4.3.2 Averaging and Applicable Times ..................................................................4-37 

4.3.3 Alternative Levels/Forms...............................................................................4-38 

4.3.4 Performance of Alternative Standards........................................................... 4-41 

4.4 STAFF CONCLUSIONS ON ALTERNATIVE SECONDARY STANDARDS FOR 

VISIBILITY-RELATED EFFECTS .......................................................................4-48 
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4.5 KEY UNCERTAINTIES AND AREAS FOR FUTURE RESEARCH AND DATA 

COLLECTION ........................................................................................................ 4-50 

4.6 REFERENCES ........................................................................................................ 4-52 

5 REVIEW OF THE SECONDARY STANDARDS FOR OTHER WELFARE 

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

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

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

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

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

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

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

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

5.1.3 Current Approach............................................................................................. 5-5 

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

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

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

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

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

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

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

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

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

5.3.4 Key Uncertainties and Areas for Future Research and Data Collection........5-24 

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

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

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

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

5.4.4 Key Uncertainties and Areas for Future Research and Data Collection........5-28 

5.5 REFERENCES ........................................................................................................ 5-29 

CHAPTER APPENDICES: 

APPENDIX 2A: Table 2A-1. Predicted Percent of Counties with Monitors (and percent of 

population in counties with monitors) Not Likely to Meet Alternative Annual and 24-hour PM2.5 

Standards ...................................................................................................2A-1 

APPENDIX 3A: Table 3A-1. Predicted Percent of Counties with Monitors (and percent of 

population in counties with monitors) Not Likely to Meet Alternative 24-hour PM10 

Standard.....................................................................................................3A-1 

APPENDIX 4A: Information Regarding the 1-hour PM2.5 Mass Indicator ......................4A-1 
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APPENDIX 4B: Simplified Approaches to Calculate Hourly PM Light Extinction Values from 

Hourly PM2.5 Mass and Relative Humidity Data plus 24-hour Mean PM Composition.........4B-1 

APPENDIX 4C: Assessment of the PM Components Responsible for the Largest Hourly PM 

Light Extinction and PM2.5 Mass Hours Selected as Maximum Daily and Using All Hours for 14 

Urban Areas ............................................................................................ 4C-1 
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LIST OF TABLES 

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

Matter 1971-2006 ...................................................................................................... 1-4 

Table 2-1. Summary of Causal Determinations for PM2.5 ........................................................ 2-15 

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

(for IHD Mortality based on 2007 PM2.5 Concentrations) ......................................2-40 

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

mortality and hospital admissions based on 2007 PM2.5 concentrations)................ 2-41 

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

Table 3-2. Counties with PM10 Concentration-Equivalent Design Values > 150 g/m3 and 98th

percentile PM10 concentrations > 87 g/m3 ............................................................ 3-41 

Table 4-1. Logit model estimated VAQ values corresponding to various percent acceptability 

values for the four cities. .........................................................................................4-21 

Table 4-2. Percentage of daily maximum hourly values of daylight PM10 light extinction 

exceeding CPLs (excluding hours with relative humidity greater than 90 percent)4-26 

Table 4-3. Percentage of daily maximum hourly values of daylight PM10 light extinction 

exceeding CPLs when “just meeting” the current PM2.5 NAAQS (15/35 μg/m3) 

(excluding hours with relative humidity greater than 90 percent) ..........................4-29 

Table 4-4. PM10 light extinction design values for “just meeting” secondary NAAQS scenarios 

based on measured PM10 light extinction (excluding hours with relative humidity 

greater than 90 percent)........................................................................................... 4-44 

Table 4-5. 90th percentile maximum daily 1-hour PM light extinction design values (Mm1

) after 

rollback to meet alternative standards of 60 μg/m3

, 40 μg/m3, 30 μg/m3,20 μg/m3 and 

10 μg/m3 maximum daylight 1-hour PM mass concentration for the 90th percentile 

 ..................................................................................................4-47 

Table 5-1. Scope of the current secondary PM NAAQS review and current NOx/SOx secondary 

review. ....................................................................................................................... 5-4 
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LIST OF FIGURES 

Figure 2-1. Overview of Approach for Review of Primary PM2.5 Standards ............................2-12 

Figure 2-2. Distribution of site-level variation in 98th and 99th percentile concentrations, as 

measured by coefficient of variation (SD/Mean) computed by site across years, 

2000-2008................................................................................................................ 2-55 

Figure 2-3. Confidence Bounds on PM2.5 Concentration-Response Relationships from Multi-city 

Epidemiological Studies..........................................................................................2-59 

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

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

Older Adults ............................................................................................................ 2-62 

Figure 2-5. Summary of Effect Estimates (per 10 μg/m3

) and Air Quality Distributions for 

Long-term PM2.5 Exposure Multi-City Studies of Children....................................2-63 

Figure 2-6. Summary of Effect Estimates (per 10 μg/m3

) and Air Quality Distributions for 

Short-term PM2.5 Exposure Multi-City Studies of the General Population and 

Older Adults ............................................................................................................ 2-64 

Figure 2-7. Air Quality and Population Distributions: Long-term PM2.5 Exposure Studies .....2-66 

Figure 2-8. Air Quality and Population Distributions: Short-term PM2.5 Exposure Studies.....2-67 

Figure 2-9. Distribution of Site-Level Ratio of Annual 98th Percentile Concentration/Annual 

Mean, 2000-2008..................................................................................................... 2-74 

Figure 2-10. Site-level 24-hour DVs versus Annual DVs, 2006-2008..................................... 2-75 

Figure 2-11. Percent Reduction in Long-term Exposure-related Mortality Risk .................... 2-77 

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

Risk....................................................................................................................... 2-78

Figure 3-1. Approach to Considering the Current and Potential Alternative Thoracic Coarse 

Particle Standards ...................................................................................................... 3-8 

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

Mortality Studies ..................................................................................................... 3-19 

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

 Morbidity Studies............................................................................3-20 

Figure 3-4. Site-Level Ratio of Daily PM10-2.5 to Daily PM10 ................................................... 3-27 

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

.................................................................................................................................3-35 

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

.................................................................................................................................3-36 

Figure 3-7. Composite 3-year PM10 98th percentile 24-hour average concentration versus the 

PM10 expected exceedance concentration-equivalent design value ........................3-39 

Figure 4-1. Overview of Approach for Review of Secondary PM2.5 Standards .......................4-10 

Figure 4-2. Summary of results of urban visibility studies in four cities, showing the identified 

range of the 50% acceptance criteria ....................................................................4-20 

Figure 4-3. Distribution of estimated maximum daily daylight 1-hour PM10 light extinction 

across the 2005-2007 period, by study area (excluding hours with relative humidity 

greater than 90 percent). (Adapted from Figure 3-8 in UFVA) ..............................4-25 
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Figure 4-4. Distribution of daylight 1-hour PM10 light extinction when rolled back to just meet 

current PM fine NAAQS across the 2005-2007 period, by study area (excluding 

hours with relative humidity greater than 90 percent). ..........................................4-28 

Figure 4-5. Scatter plots of PM10 light extinction versus PM2.5 mass concentration of two cities 

(from UFVA Appendix D, Figure D2)....................................................................4-32 

Figure 4-6. Daily maximum daylight 1-hour PM10 light extinction after rollback to just meet a 

scenario with daily maximum of 112 Mm-1 for the 90th percentile excluding hours 

with relative humidity greater than 90 percent........................................................4-43 

Figure 4-7. Maximum daily daylight 1-hour PM10 light extinction under "just meet" conditions 

for a NAAQS scenario based on 1-hour PM2.5 mass of 30 μg/m3, 90th percentile, 

excluding relative humidity >90%...................................................................... 4-45
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LIST OF ACRONYMS/ABBREVIATIONS 

ACS American Cancer Society 

AOD Aerosol optical depth 

ANS Autonomic nervous system 

AQCD Air Quality Criteria Document 

AQS EPA’s Air Quality System 

AR4 Fourth Assessment Report of the Intergovernmental Panel on Climate Change 

BC Black carbon 

BP Blood pressure 

C Carbon 

Ca Calcium 

CAA Clean Air Act 

CAPs Concentrated ambient particles

CASAC Clean Air Scientific Advisory Committee 

CBVD Cerebrovascular disease 

CCN Cloud Condensation Nuclei 

CCSP US Climate Change Science Program 

Cd Cadmium 

CFR Code of Federal Regulations 

CHD Coronary heart disease 

CHF Congestive heart failure 

CHS Childrens Health Study 

CO Carbon monoxide 

CO2 Carbon dioxide 

COPD Chronic obstructive pulmonary disease 

C-R Concentration-response relationship 

CRP C-reactive protein 

CSN Chemical Speciation Network 

Cu Copper 

CV Cardiovascular 
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CVD Cardiovascular disease 

DE Diesel exhaust 

DEP Diesel exhaust particles 

dv deciview 

DV Design value 

DVT Deep vein thrombosis 

EC Elemental carbon 

ECG Electrocardiogram 

EPA Environmental Protection Agency 

FEM Federal Equivalent Method 

FEV1 Change in forced expiratory volume in one second 

FRM Federal Reference Method 

GEOS Global Scale Air Circulation Model 

GHG Greenhouse gas 

GI Group interviews 

Hg Mercury 

HR Heart rate 

HRV Heart rate variability 

IHD Ischemic heart disease 

IMPROVE Interagency Monitoring of Protected Visual Environment 

IPCC Intergovernmental Panel on Climate Change 

IRP Integrated Review Plan 

ISA Integrated Science Assessment 

IT Intratracheal 

Lag Time between one event and another 

Lag 0 Same day as the death, test, hospital, ED, clinic, physician visit; that 

occurs on the same day as the exposure to the pollutant(s) 

Lag 0-x All the deaths test, hospital, ED, clinic, physician visit; that occurs on the 

same day as the exposure to the pollutant(s) and the x days following the 

day of exposure 

MCAPS Medicare Air Pollution Study 

MEA Millennium Ecosystem Assessment 
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x 

MI Myocardial infarction 

Mm Megameter 

MSA Metropolitan Statistical Area 

N Nitrogen 

NAAQS National Ambient Air Quality Standards 

NCEA National Center for Environmental Assessment 

NCore National Core Monitoring Network 

Ni Nickel 

NMMAPS National Morbidity, Mortality, and Air Pollution Study 

NO Nitric oxide 

NO2 Nitrogen dioxide 

NO3

- Nitrate 

NOx Nitrogen oxides 

NRC National Research Council 

O3 Ozone 

OAQPS Office of Air Quality Planning and Standards 

OAR Office of Air and Radiation 

OC Organic carbon 

OR Odds ratio 

ORD Office of Research and Development 

PA Policy Assessment 

PAH Polyaromatic Hydrocarbon 

PAR Photosynthetically active radiation 

Pb Lead 

PBDES Polybrominated diphenyl ethers 

PM Particulate matter 

PM10 Particles with an upper 50% cut-point of 10± 0.5 μm aerodynamic 

diameter and a penetration curve as specified in the Code of Federal 

Regulations. 

PM10-2.5 Particles with an upper 50% cut-point of 10 μm aerodynamic diameter 

and a lower 50% cut-point of 2.5 μm aerodynamic diameter. 
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PM2.5 Particles with an upper 50% cut-point of 2.5 μm aerodynamic diameter 

and a penetration curve as specified in the Code of Federal Regulations. 

PMX The legal definition for PMX, as defined in the Code of Federal 

Regulations, includes both a 50% cut-point and a penetration curve. A 

50% cut-point of X μm diameter means that 50% of particles with 

aerodynamic diameter of X are removed by the inlet and 50% pass 

through the inlet and are collected on the filter. Depending on the specific 

penetration curve specified, particles larger than X μm aerodynamic 

diameter are collected with an efficiently than decreases rapidly for 

particles larger than X while the collection efficiency for particles smaller 

than X increases rapidly with decreasing size until 100 % efficiency is 

reached. 

POP Persistant organic pollutants 

PRB Policy-Relevant Background 

RA Risk Assessment 

REA Risk and Exposure Assessment 

RF Radiative forcing 

RH Relative humidity 

RR Relative risk 

SCAB South Coast Air Basin 

SD Standard deviation 

SES Socioeconomic status 

Si Silicon 

S Sulfur 

SO2 Sulfur Dioxide 

SO4

2- Sulfate 

SOx Sulfur Oxides 

SOCs Semi-volatile organic compounds 

TSP Total suspended particulate 

UFPs Ultrafine particles 

UFVA Urban-Focused Visibility Assessment 

V Vanadium 

VAQ Visual Air Quality 

VOC Volatile organic compounds 
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WACAP Western Airborne Contaminants Assessment Project 

WHI Women’s Health Initiative 

Zn Zinc 
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1 1. INTRODUCTION 

2 1.1 PURPOSE 

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

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

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

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

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

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

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

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

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

determining whether, and if so, how, it is appropriate to revise the NAAQS for PM.1 12 This 

13 second draft PA presents factors relevant to EPA’s review of the primary (health-based) and 

14 secondary (welfare-based) PM NAAQS. It focuses on both evidence- and risk-based 

15 information in evaluating the adequacy of the current PM NAAQS and in identifying potential 

16 alternative standards for consideration. 

17 In this second draft PA, we consider the scientific and technical information available in 

18 this review as assessed in the Integrated Science Assessment for Particulate Matter (Final 

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

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

21 Assessment (Final Report) (UFVA, US EPA, 2010b). In so doing, we focus on information that 

is most pertinent to evaluating the basic elements of NAAQS: indicator2

, averaging time, form,3 22 

23 and level. These elements, which together serve to define each standard, must be considered 

24 collectively in evaluating the health and welfare protection afforded by the PM standards. 

25 Although this second draft PA should be of use to all parties interested in this PM 

26 NAAQS review, it is written with an expectation that the reader has familiarity with the technical 

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

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

 1

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

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

current NAAQS review process. 

2

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

whether an area attains the standard. 

3

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

determining whether an area attains the standard. 
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1 1.2 BACKGROUND 

2 1.2.1 Legislative Requirements 

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

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

5 pollutants that meet three specified criteria, including air pollutants “emissions of which, in his 

6 judgment, cause or contribute to air pollution which may reasonably be anticipated to endanger 

7 public health and welfare” and whose “presence . . . in the ambient air results from numerous or 

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

9 quality criteria are to “accurately reflect the latest scientific knowledge useful in indicating the 

10 kind and extent of all identifiable effects on public health or welfare which may be expected 

11 from the presence of [a] pollutant in the ambient air . . .” 42 U.S.C. § 7408(b). 

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

13 promulgate “primary” and “secondary” NAAQS for pollutants for which air quality criteria are 

14 issued. Section 109(b)(1) defines a primary standard as one “the attainment and maintenance of 

15 which in the judgment of the Administrator, based on [air quality] criteria and allowing an 

adequate margin of safety, are requisite to protect the public health.”4 16 A secondary standard, as 

17 defined in Section 109(b)(2), must “specify a level of air quality the attainment and maintenance 

18 of which, in the judgment of the Administrator, based on such [air quality] criteria, is requisite to 

19 protect the public welfare from any known or anticipated adverse effects associated with the 

presence of [the] pollutant in the ambient air.”5 20 

21 The requirement that primary standards include an adequate margin of safety was 

22 intended to address uncertainties associated with inconclusive scientific and technical 

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

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

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

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

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

28 2009). Both kinds of uncertainties are components of the risk associated with pollution at levels 

29 below those at which human health effects can be said to occur with reasonable scientific 

 4

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

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

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

to a single person in such a group.” S. Rep. No. 91-1196, 91st Cong., 2d Sess. 10 (1970). 5

 Welfare effects as defined in section 302(h) (42 U.S.C. section 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.” 
Draft- Do Not Quote or Cite 1-3 June 2010

1 certainty. Thus, in selecting primary standards that include an adequate margin of safety, the 

2 Administrator is seeking not only to prevent pollution levels that have been demonstrated to be 

3 harmful but also to prevent lower pollutant levels that may pose an unacceptable risk of harm, 

4 even if the risk is not precisely identified as to nature or degree. 

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

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

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

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

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

10 In setting standards that are “requisite” to protect public health and welfare, as provided 

11 in section 109(b), EPA’s task is to establish standards that are neither more nor less stringent 

12 than necessary for these purposes. In so doing, EPA may not consider the costs of implementing 

13 the standards. See generally Whitman v. American Trucking Associations, 531 U.S. 457, 471, 

14 475-76 (2001). 

15 Section 109(d) (1) of the CAA requires that “not later than December 31, 1980, and at 5-

16 year intervals thereafter, the Administrator shall complete a thorough review of the criteria 

17 published under section 108 and the national ambient air quality standards . . . and shall make 

18 such revisions in such criteria and standards and promulgate such new standards as may be 

19 appropriate in accordance with section [108]... and subsection (b) . . .” 42 U.S.C. § 7409(d)(1). 

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

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

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

23 criteria and standards as may be appropriate . . .” 42 U.S.C. § 7409(d)(2). Since the early 

24 1980's, this independent review function has been performed by the Clean Air Scientific 

25 Advisory Committee (CASAC). 

26 1.2.2 Previous PM NAAQS Reviews 

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

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

expanding scientific information, particularly with respect to the selection of indicator6 29 and level. 

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

31 decisions are briefly discussed below. 

 6

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

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

historically been defined in terms of particle size ranges. 
Draft- Do Not Quote or Cite 1-4 June 2010

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

Particulate Matter 1971-20067 2 

3 

Final Rule Indicator Ave. Time Level Form

24-hour 

260 μg/m3 

(primary) 

150 μg/m3

(secondary) 

Not to be exceeded more than once 

per year 

1971 

36 FR 8186 

April 30, 1971 

TSP 

Annual 75 μg/m3

(primary) 

Annual average 

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

per year on average over a 3-year 

period 

1987 

52 FR 24634 

July 1, 1987 

PM10

Annual 50 μg/m3 Annual arithmetic mean, averaged 

over 3 years 

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

PM2.5 Annual 15 μg/m3 Annual arithmetic mean, averaged 

over 3 years8

24-hour 150 μg/m3

Initially promulgated 99th percentile, 

averaged over 3 years; when 1997 

standards were vacated, the form of 

1987 standards remained in place 

(not to be exceeded more than once 

per year on average over a 3-year 

period) 

1997 

62 FR 38652 

July 18, 1997 PM10

Annual 50 μg/m3 Annual arithmetic mean, averaged 

over 3 years 

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

PM2.5 Annual 15 μg/m3 Annual arithmetic mean, averaged 

over 3 years9

2006 

71 FR 61144 

October 17, 2006 PM10 24-hour 150 μg/m3

Not to be exceeded more than once 

per year on average over a 3-year 

period 

4 

 7

 When not specified, primary and secondary standards are identical. 8

 The level of the standard was to be compared to measurements made at sites that represent “community-wide air 

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

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

areas may average measurements from multiple community-oriented monitors to determine compliance (see 71 FR 

61165-61167). 
Draft- Do Not Quote or Cite 1-5 June 2010

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

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

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

4 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 5 , 24-hour average, 

not to be exceeded more than once per year, and 75 μg/m3 6 , annual geometric mean. The 

secondary standard was 150 μg/m3 7 , 24-hour average, not to be exceeded more than once per 

8 year. 

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

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

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

12 including particles with a median aerodynamic diameter less than or equal to 10 μm, which 

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

14 beyond the larynx to the thoracic region of the respiratory tract). The EPA also revised the 

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

150 μg/m3 16 with no more than one expected exceedance per year; and (2) replacing the annual 

TSP standard with a PM10 standard of 50 μg/m3 17 , annual arithmetic mean. The secondary 

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

19 the primary standards. The revisions also included a new reference method for the measurement 

20 of PM10 in the ambient air and rules for determining attainment of the new standards. On 

21 judicial review, the revised standards were upheld in all respects. Natural Resources Defense 

22 Council v. EPA, 902 F. 2d 962 (D.C. Cir. 1990), cert. denied, 498 U.S. 1082 (1991). 

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

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

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

26 continue to focus on particles less than or equal to 10 μm in diameter, the fine and coarse 

27 fractions of PM10 should be considered separately. New standards were added, using PM2.5, 

28 referring to particles with a nominal median aerodynamic diameter less than or equal to 2.5 μm, 

29 as the indicator for fine particles. The PM10 standards were retained for the purpose of 

30 regulating the coarse fraction of PM10 (referred to as thoracic coarse particles or coarse-fraction 

31 particles; generally including particles with a nominal median aerodynamic diameter greater than 

32 2.5 μm and less than or equal to 10 μm, or PM10-2.5). The EPA established two new PM2.5

standards: an annual standard of 15 μg/m3 33 , based on the 3-year average of annual arithmetic 

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

air quality; and a 24-hour standard of 65 μg/m3 35 , based on the 3-year average of the 98th 

36 percentile of 24-hour PM2.5 concentrations at each population-oriented monitor within an area. 
Draft- Do Not Quote or Cite 1-6 June 2010

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

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

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

24-hour PM10 standard was revised to be based on the 99th 4 percentile of 24-hour PM10

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

6 them identical in all respects to the primary standards. 

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

8 filed by a large number of parties, addressing a broad range of issues. In May 1998, a three9 judge panel of the U.S. Court of Appeals for the District of Columbia Circuit issued an initial 

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

11 growing empirical evidence demonstrating a relationship between fine particle pollution and 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

27 promulgated on the same day, effected “an unconstitutional delegation of legislative authority.” 

28 Id. at 1034-40. Although the panel stated that “the factors EPA uses in determining the degree of 

29 public health concern associated with different levels of ozone and PM are reasonable,” it 

30 remanded the rule to EPA, stating that when EPA considers these factors for potential non31 threshold pollutants “what EPA lacks is any determinate criterion for drawing lines” to 

32 determine where the standards should be set. Consistent with EPA’s long-standing interpretation 

33 and D.C. Circuit precedent, the panel also reaffirmed its prior holdings that in setting NAAQS 

34 EPA is “not permitted to consider the cost of implementing those standards” Id. at 1040-41. 

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

36 American Trucking Associations v. EPA, 195 F. 3d 4 (D.C. Cir. 1999). The full Court of 
Draft- Do Not Quote or Cite 1-7 June 2010

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

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

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

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

5 531 U.S. 457, 464, 475-76. On the constitutional issue, the Court held that the statutory 

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

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

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

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

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

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

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

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

14 Cir. 2002). 

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

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

17 standards and the 1987 PM10 standards. After CASAC and public review of several drafts, EPA’s 

18 National Center for Environmental Assessment (NCEA) finalized the Air Quality Criteria 

19 Document for Particulate Matter (henceforth, AQCD or the "Criteria Document") in October 

20 2004 (U.S. EPA, 2004) and OAQPS finalized an assessment document, Particulate Matter 

21 Health Risk Assessment for Selected Urban Areas (Abt, 2005), and a “Staff Paper,” Review of the 

22 National Ambient Air Quality Standards for Particulate Matter: Policy Assessment of Scientific 

23 and Technical Information, in December 2005 (U.S. EPA, 2005). In conjunction with their 

24 review of the Staff Paper, CASAC provided advice to the Adminstrator on revisions to the PM 

25 NAAQS (Henderson, 2005a). In particular, most CASAC PM Panel members favored revising 

the level of the 24-hour PM2.5 primary standard in the range of 35 to 30 μg/m3

 with a 98th 26 

27 percentile form, in concert with revising the level of the annual PM2.5 standard in the range of 14 

to 13 μg/m3 28 (Henderson, 2005a, p.7). For thoracic coarse particles, the Panel had reservations in 

29 recommending a 24-hour PM10-2.5 primary standard, and agreed that there was a need for more 

30 research on the health effects of thoracic coarse particles (Henderson, 2005b). With regard to 

31 secondary standards, most Panel members strongly supported establishing a new, distinct 

32 secondary PM2.5 standard to protect urban visibility (Henderson, 2005a, p. 9). 

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

34 (71 FR 2620) and solicited comment on a broad range of options. Proposed revisions included: 

revising the level of the 24-hour PM2.5 primary standard to 35 μg/m3 35 ; revising the form, but not 

36 the level, of the annual PM2.5 primary standard by tightening the constraints on the use of spatial 
Draft- Do Not Quote or Cite 1-8 June 2010

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

terms of a new indicator, PM10-2.5

10 set at a level of 70 μg/m3 2 based on the 3-year average of the 

98th 3 percentile of 24-hour PM10-2.5 concentrations; revoking the annual PM10 primary standard; 

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

suite of primary standards for fine and coarse particles.11 5 Subsequent to the proposal, CASAC 

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

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

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

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

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

11 secondary standards for fine particles, EPA revised the level of the 24-hour PM2.5 standard to 35 

μg/m3

, retained the level of the annual PM2.5 standard at 15 μg/m3 12 , and revised the form of the 

13 annual PM2.5 standard by adding further constraints on the optional use of spatial averaging. The 

14 EPA revised the secondary standards for fine particles by making them identical in all respects to 

15 the primary standards. With regard to the primary and secondary standards for thoracic coarse 

16 particles, EPA retained the level and form of the 24-hour PM10 standard (such that the standard 

remained at a level of 150 μg/m3 17 with a one expected exceedance form), and revoked the annual 

18 PM10 standard. The EPA also established a new Federal Reference Method (FRM) for the 

19 measurement of PM10-2.5 in the ambient air (71 FR 61212-13). Although the standards for 

20 thoracic coarse particles were not defined in terms of a PM10-2.5 indicator, the new FRM for 

21 PM10-2.5 was established to provide a basis for approving Federal Equivalent Methods (FEMs) 

22 and to promote gathering scientific data to support future reviews of the PM NAAQS. 

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

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

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

serious concerns regarding the decision to retain the level of the standard at 15 μg/m3 26 . With 

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

28 CASAC acknowledged concerns associated with retaining this standard while recognizing the 

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

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

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

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

ambient mix of particles dominated by rural windblown dust and soils and agricultural and mining sources. 

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

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

impairment, within the range of 20 to 30 μg/m3 and with a form within the range of the 92nd to 98th percentile (71 FR 

2685). 
Draft- Do Not Quote or Cite 1-9 June 2010

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

2 expressed concernsthat CASAC’s advice to establish a distinct secondary standard for fine 

3 particles to address visibility impairment was not followed. 

4 1.2.3 Litigation Related to the 2006 PM Standards 

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

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

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

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

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

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

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

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

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

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

15 Farm Bureau Federation v. EPA, 559 F. 3d 512, (D.C. Cir. 2009). With regard to the standards 

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

17 protection from thoracic coarse particle exposures and to revoke the annual PM10. standard. 

18 American Farm Bureau Federation at 533-38. With regard to the secondary PM2.5 standards, the 

19 court remanded the standards to EPA because the Agency failed to adequately explain why 

20 setting the secondary PM standards identical to the primary standards provided the required 

21 protection for public welfare, including protection from visibility impairment. 

22 The decisions of the court with regard to these three issues are discussed in chapters 2, 3 

23 and 4, respectively. The EPA is responding to the court’s remands as part of the current review 

24 of the PM NAAQS. 

25 1.2.4 Current PM NAAQS Review 

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

27 general call for information (72 FR 35462, June 28, 2007). In July 2007, EPA held two “kick28 off” workshops on the primary and secondary PM NAAQS, respectively (72 FR 34003 and 

34005, June 20, 2007).12 29 These workshops provided an opportunity for a public discussion of 

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

 12 See workshop materials http://www.regulations.gov/search/Regs/home.html#home Docket ID numbers EPA-HQOAR-2007-0492-008; EPA-HQ-OAR-2007-0492-009; EPA-HQ-OAR-2007-0492-010; and EPA-HQ-OAR-2007-

0492-012. 
Draft- Do Not Quote or Cite 1-10 June 2010

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

2 issues. 

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

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

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

6 2007). On November 30, 2007, EPA held a consultation with CASAC on the draft IRP (72 FR 

7 63177, November 8, 2007), which included the opportunity for public comment. The final IRP 

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

draft plan as well as input from senior Agency managers.13 9 

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

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

12 external review draft ISA (US EPA, 2008b) was reviewed by CASAC and the public at a 

13 meeting held in April 2009 (74 FR 2688, February 19, 2009). Based on CASAC and public 

14 comments, NCEA prepared a second draft ISA (US EPA, 2009b), which was reviewed by 

15 CASAC and the public at a meeting held on October 5-6, 2009 (74 FR 46586, September 10, 

16 2009). Based on CASAC and public comments, NCEA prepared the final ISA (US EPA, 

17 2009a; 74 FR 66353, December 15, 2009). 

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

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

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

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

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

23 Plans, US EPA, 2009c,d). These planning documents outlined the scope and approaches that 

24 staff planned to use in conducting quantitative assessments as well as key issues that would be 

25 addressed as part of the assessments. In designing and conducting the initial health risk and 

26 visibility impact assessments, we considered CASAC comments (Samet 2009a,b) on the Scope 

27 and Methods Plans made during an April 2009 consultation (74 FR 7688, February 19, 2009) as 

28 well as public comments. Two draft assessment documents, Risk Assessment to Support the 

29 Review of the PM2.5 Primary National Ambient Air Quality Standards: External Review Draft - 

30 September 2009 (US EPA 2009e) and Particulate Matter Urban-Focused Visibility Assessment - 

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

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

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

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

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

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

description of the major elements of the process for reviewing NAAQS see Jackson (2009). 
Draft- Do Not Quote or Cite 1-11 June 2010

1 External Review Draft - September 2009 (US EPA, 2009f) were reviewed by CASAC and the 

2 public at a meeting held on October 5-6, 2009. Based on CASAC (Samet 2009c,d) and public 

3 comments, OAQPS staff revised these draft documents and released second draft assessment 

4 documents (US EPA, 2010d,e) in January and February 2010 (75 FR 4067, January 26, 2010) for 

5 CASAC and public review at a meeting held on March 10-11, 2010. Based on CASAC (Samet, 

6 2010a, b) and public comments on the second draft assessment documents, we revised these 

7 documents and released final assessment documents in June 2010 (US EPA, 2010a,b). 

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

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

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

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

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

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

14 2010c) that built upon the information presented and assessed in the final ISA and two second 

15 draft REAs. The EPA presented an overview of the first draft PA at a CASAC meeting on 

16 March 10, 2010. CASAC and public review of the first draft PA was discussed during public 

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

18 FR19971, April 16, 2010). 

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

20 developing this second draft PA which will be reviewed by CASAC at an upcoming meeting 

21 scheduled for July 26-27, 2010 (75 FR 32763, June 9, 2010). We will consider CASAC and 

22 public comments on this second draft PA in preparing a final PA which will include final staff 

23 conclusions related to the adequacy of the current PM standards and alternative standards for 

24 consideration that are supported by the currently available scientific evidence and quantitative 

25 assessments. We plan to release the final PA in September 2010. 

26 1.3 GENERAL APPROACH AND ORGANIZATION OF THIS DOCUMENT 

27 This second draft PA includes staff’s evaluation of the policy implications of the 

28 scientific assessment of the evidence presented and assessed in the ISA and the results of 

29 quantitative assessments based on that evidence presented and assessed in the REAs. Taken 

30 together, this information informs staff conclusions and the identification of policy options for 

31 consideration in addressing public health and welfare effects associated with exposure to ambient 

32 PM. 

33 Since the last review, much new information is now available on PM air quality and 

34 human health effects directly in terms of PM2.5 and, to a much more limited degree, PM10-2.5 and 

35 ultrafine particles (UFPs). Since the purpose of this review is to evaluate the adequacy of the 
Draft- Do Not Quote or Cite 1-12 June 2010

1 current standards, which separately address fine and thoracic coarse particles, staff is focusing 

2 this policy assessment and associated quantitative analyses primarily on the evidence related 

3 directly to PM2.5 and PM10-2.5. In so doing, we are considering PM10-related evidence primarily 

4 to help inform our understanding of key issues and to help interpret and provide context for 

5 understanding the public health and welfare impacts of ambient fine and coarse particles. We are 

6 also considering the currently available evidence related to UFPs as well as PM2.5 components to 

7 aid in considering whether there is support to consider standards with a different size fraction 

8 and/or distinct standards focused on regulating specific PM2.5 components or categories of fine 

9 particle sources. 

10 Following this introductory chapter, this document is organized into two main parts: 

11 review of the primary PM NAAQS (chapters 2 and 3) and review of the secondary PM NAAQS 

12 (chapters 4 and 5). Chapters 2 and 3 present staff observations and conclusions related to review 

13 of the primary standards for fine and thoracic coarse particles, respectively. Each chapter 

14 includes background information on the rationale for previous reviews and the policy assessment 

15 approaches followed in the current review, focusing on evidence-based considerations and, as 

16 appropriate, quantitative risk-based considerations. Staff conclusions are presented with regard 

17 to the adequacy of the current primary standards and potential alternative primary standards for 

18 consideration, in terms of indicators, averaging times, forms, and levels. Chapter 4 focuses on 

19 PM-related visibility impairment, and presents staff observations and conclusions with regard to 

20 the adequacy of the current standards and potential distinct secondary standards for 

21 consideration, in terms of alternative indicators, averaging times, forms, and levels. Chapter 5 

22 focuses on other PM-related welfare effects, including effects on climate, ecological effects, and 

23 effects on materials, and presents staff observations and conclusions with regard to the adequacy 

24 of the current standards and the extent to which information is available to support consideration 

25 of alternative standards. 
Draft- Do Not Quote or Cite 1-13 June 2010

1 1.4 REFERENCES 

2 Abt Associates (2005). Particulate Matter Health Risk Assessment for Selected Urban Areas. Final Report. 

3 Bethesda, MD. Prepared for the Office of Air Quality Planning and Standards, U.S. Environmental 

4 Protection Agency, Contract No. 68-D-03-002. EPA 452/R-05-007A. Available: 0 

5 Henderson, R. (2005a). EPA’s Review of the National Ambient Air Quality Standards for Particulate Matter 

6 (Second Draft PM Staff Paper, January 2005): A review by the Particulate Matter Review Panel of the EPA 

7 Clean Air Scientific Advisory Committee. June 6, 2005. Available: http://www.epa.gov/sab/pdf/casac-05-

8 007.pdf. 

9 Henderson, R. (2005b). Clean Air Scientific Advisory Committee (CASAC) Review of the EPA Staff 

10 Recommendations Concerning a Potential Thoracic Coarse PM Standard in the Review of the National 

11 Ambient Air Quality Standards for Particulate Matter: Policy Assessment of Scientific and Technical 

12 Information (Final PM OAQPS Staff Paper, EPA-452/R-05-005). September 15, 2005. Available: 

13 http://www.epa.gov/sab/panels/casacpmpanel.html. 

14 Henderson, R. (2006a). Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee to the 

15 Honorable Stephen L. Johnson, Administrator, US EPA. Clean Air Scientific Advisory Committee 

16 Recommendations Concerning the Proposed National Ambient Air Quality Standards for Particulate 

17 Matter. March 21, 2006. Available: http://www.epa.gov/sab/pdf/casac-ltr-06-002.pdf 

18 Henderson, R.; Cowling, E.; Crapo, J.D..; Miller, F. J.; Poirot, R.L.; Speizer, F.; Zielinska, B. (2006b). Letter from 

19 Clean Air Scientific Advisory Committee to the Honorable Stephen L. Johnson, Administrator, US EPA. 

20 Clean Air Scientific Advisory Committee Recommendations Concerning the Final National Ambient Air 

21 Quality Standards for Particulate Matter. September 29, 2006. Available: 

22 http://www.epa.gov/sab/pdf/casac-ltr-06-002.pdf

23 Henderson, R. (2008). Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee to the 

24 Honorable Stephen L. Johnson, Administrator, US EPA. Clean Air Scientific Advisory Committee 

25 Particulate Matter Review Panel’s Consultation on EPA’s Draft Integrated Review Plan for the National 

26 Ambient Air Quality Standards for Particulate Matter. January 3, 2008. Available: 

27 http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/76D069B8191381DA8

28 52573C500688E74/$File/EPA-CASAC-08-004-unsigned.pdf 

29 Jackson (2009). Memo from Administrator Lisa P. Jackson to Elizabeth Craig, Acting Assistant Administrator for 

30 OAR and Lek Kadeli, Acting Assistant Administrator for ORD. Process for Reviewing the National 

31 Ambient Air Quality Standards. May 21, 2009. Available: 

32 http://www.epa.gov/ttn/naaqs/pdfs/NAAQSReviewProcessMemo52109.pdf. 

33 Samet, J. (2009a). Letter from Clean Air Scientific Advisory Committee to the Honorable Lisa P. Jackson, 

34 Administrator, US EPA. Consultation on EPA’s Particulate Matter National Ambient Air Quality 

35 Standards: Scope and Methods Plan for Health Risk and Exposure Assessment. May 21, 2009. Available: 

36 http://yosemite.epa.gov/sab/sabproduct.nsf/723FE644C5D758DF852575BD00763A32/$File/EPA37 CASAC-09-009-unsigned.pdf

38 Samet, J. (2009b). Letter from Clean Air Scientific Advisory Committee to the Honorable Lisa P. Jackson, 

39 Administrator, US EPA. Consultation on EPA’s Particulate Matter National Ambient Air Quality 

40 Standards: Scope and Methods Plan for Urban Visibility Impact Assessment. May 21, 2009. Available: 

41 http://yosemite.epa.gov/sab/sabproduct.nsf/0F63D7995F5850D5852575BD0077869C/$File/EPA-CASAC42 09-010-unsigned.pdf 

43 Samet, J. (2009c). Letter from Clean Air Scientific Advisory Committee to the Honorable Lisa P. Jackson, 

44 Administrator, US EPA. Review of the Risk Assessment to Support the Review of the Particulate Matter 
Draft- Do Not Quote or Cite 1-14 June 2010

1 (PM) Primary National Ambient Air Quality Standards – External Review Draft (September 2009). 

2 November 24, 2009. Available: 

3 http://yosemite.epa.gov/sab/sabproduct.nsf/BC1ECC5D539EF72385257678006D5754/$File/EPA4 CASAC-10-003-unsigned.pdf

5 Samet, J. (2009d). Letter from Clean Air Scientific Advisory Committee to the Honorable Lisa P. Jackson, 

6 Administrator, US EPA. Review of Particulate Matter Urban-Focused Visibility Assessment (External 

7 Review Draft, September 2009). November 24, 2009. Available: 

8 http://yosemite.epa.gov/sab/sabproduct.nsf/15872217938041F685257678006A26E3/$File/EPA-CASAC9 10-002-unsigned.pdf 

10 Samet, J. (2010a). Letter from Clean Air Scientific Advisory Committee to the Honorable Lisa P. Jackson, 

11 Administrator, US EPA. CASAC Review of Quantitative Health Risk Assessment for Particulate matter – 

12 Second External Review Draft (February 2010). April 15, 2010. Available: 

13 http://yosemite.epa.gov/sab/sabproduct.nsf/BC4F6E77B6385155852577070002F09F/$File/EPA-CASAC14 10-008-unsigned.pdf 

15 Samet, J. (2010b). Letter from Clean Air Scientific Advisory Committee to the Honorable Lisa P. Jackson, 

16 Administrator, US EPA. CASAC Review of Particulate Matter Urban-Focused Visibility Assessment 

17 (Second External Review Draft, January 2010). April 20, 2010. Available: 

18 http://yosemite.epa.gov/sab/sabproduct.nsf/0D5CB76AFE7FA77C8525770D004EED55/$File/EPA19 CASAC-10-009-unsigned.pdf 

20 Samet, J. (2010c). Letter from Clean Air Scientific Advisory Committee to the Honorable Lisa P. Jackson, 

21 Administrator, US EPA. CASAC Review of Policy Assessment for the Review of the PM NAAQS – First 

22 External Revew Draft (March 2010). May 17, 2010. Available: 

23 http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/E504EE3276D87A9E8

24 525772700647AFB/$File/EPA-CASAC-10-011-unsigned.pdf/. 

25 US Department of Health, Education and Welfare (DHEW). (1969). Air Quality Criteria for Particulate Matter. 

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

27 US EPA (2004). Air Quality Criteria for Particulate Matter. National Center for Environmental Assessment, Office 

28 of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711; 

29 report no. EPA/600/P-99/002aF and EPA/600/P-99/002bF. October 2004. Available: 

30 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_cd.html

31 US EPA (2005). Review of the National Ambient Air Quality Standards for Particulate Matter: Policy Assessment 

32 of Scientific and Technical Information, OAQPS Staff Paper. Research Triangle Park, NC 27711: Office 

33 of Air Quality Planning and Standards; report no. EPA EPA-452/R-05-005a. December 2005. Available: 

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

35 US EPA (2007). Draft Integrated Review Plan for the National Ambient Air Quality Standards for Particulate 

36 matter. National Center for Environmental Assessment and Office of Air Quality Planning and Standards, 

37 U.S. Environmental Protection Agency, Research Triangle Park, NC. Report No. EPA 452/P-08-006. 

38 Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pd.html. 

39 US EPA (2008a). Integrated Review Plan for the National Ambient Air Quality Standards for Particulate Matter. 

40 National Center for Environmental Assessment and Office of Air Quality Planning and Standards, U.S. 

41 Environmental Protection Agency, Research Triangle Park, NC. Report No. EPA 452/R-08-004. March 

42 2008. Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pd.html. 

43 US EPA (2008b). Integrated Science Assessment for Particulate Matter: First External Review Draft. National 

44 Center for Environmental Assessment-RTP Division, Office of Air Quality Planning and Standards, 
Draft- Do Not Quote or Cite 1-15 June 2010

1 Research Triangle Park, NC. EPA/600/R-08/139 and 139A. December 2008. Available: 

2 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html. 

3 US EPA (2009a). Integrated Science Assessment for Particulate Matter: Final Report. National Center for 

4 Environmental Assessment-RTP Division, Office of Air Quality Planning and Standards, Research 

5 Triangle Park, NC. EPA/600/R-08/139F. December 2009. Available: 

6 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html. 

7 US EPA (2009b). Integrated Science Assessment for Particulate Matter: Second External Review Draft. National 

8 Center for Environmental Assessment-RTP Division, Office of Air Quality Planning and Standards, 

9 Research Triangle Park, NC. EPA/600/R-08/139B. July 2009. Available: 

10 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html. 

11 US EPA (2009c). Particulate Matter National Ambient Air Quality Standards: Scope and Methods Plan for Health 

12 Risk and Exposure Assessment. Office of Air Quality Planning and Standards, U.S. Environmental 

13 Protection Agency, Research Triangle Park, NC. EPA-452/P-09-002. February 2009. Available: 

14 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pd.html. 

15 US EPA (2009d). Particulate Matter National Ambient Air Quality Standards: Scope and Methods Plan for Urban 

16 Visibility Impact Assessment. Office of Air Quality Planning and Standards, U.S. Environmental 

17 Protection Agency, Research Triangle Park, NC. EPA-452/P-09-001. February 2009. Available: 

18 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pd.html. 

19 US EPA (2009e). Risk Assessment to Support the Review of the PM Primary National Ambient Air Quality 

20 Standards - External Review Draft. Office of Air Quality Planning and Standards, U.S. Environmental 

21 Protection Agency, Research Triangle Park, NC. EPA-452/P-09-006. September 2009. Available: 

22 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 

23 US EPA (2009f). Particulate Matter Urban-Focused Visibility Assessment – External Review Draft. Office of Air 

24 Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

25 EPA-452/P-09-005. September 2009. Available: 

26 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 

27 US EPA (2009g). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality 

28 Standards – Preliminary Draft. Office of Air Quality Planning and Standards, U.S. Environmental 

29 Protection Agency, Research Triangle Park, NC. EPA-452/P-09-007. September 2009. Available: 

30 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html. 

31 US EPA (2010a). Quantitative Risk Assessment for Particulate Matter – Final Report. Office of Air Quality 

32 Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-452/R33 10-005. June 2010. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 

34 US EPA (2010b). Particulate Matter Urban-Focused Visibility Assessment – Final Report. Office of Air Quality 

35 Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-452/R36 10-004. June 2010. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 

37 US EPA (2010c). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality 

38 Standards- First External Review Draft. Office of Air Quality Planning and Standards, U.S. Environmental 

39 Protection Agency, Research Triangle Park, NC. EPA 452/P-10-003. March 2010. Available: 

40 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html. 

41 US EPA (2010d). Quantitative Risk Assessment for Particulate Matter – Second External Review Draft. Office of 

42 Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

43 EPA-452/P-10-001. February 2010. Available: 

44 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 
Draft- Do Not Quote or Cite 1-16 June 2010

1 US EPA (2010e). Particulate Matter Urban-Focused Visibility Assessment – Second External Review Draft. Office 

2 of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, 

3 NC. EPA-452/P-10-002. January 2010. Available: 

4 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 
Draft – Do Not Quote or Cite 2-1 June 2010

1 2 REVIEW OF THE PRIMARY STANDARDS FOR FINE PARTICLES 

2 This chapter presents staff conclusions with regard to the adequacy of the current suite of 

3 primary PM2.5 standards and the alternative primary standards for fine particles that are 

4 appropriate for consideration in this review. Our assessment of these issues is framed by a series 

5 of key policy-relevant questions, which expand upon those presented at the outset of this review 

6 in the IRP. Answers to these questions will inform decisions by the Administrator on whether, 

7 and if so how, to revise the current suite of primary fine particle standards. 

8 Staff notes that final decisions regarding the primary standards must draw upon scientific 

9 information and analyses about health effects and risks, as well as judgments made about how to 

10 deal with the uncertainties that are inherent in the scientific evidence and analyses. Ultimately, 

11 the final decisions are largely public health policy judgments. Our approach to informing these 

12 judgments recognizes that the available health effects evidence generally reflects a continuum 

13 consisting of ambient levels at which scientists generally agree that health effects occur through 

14 lower levels at which the likelihood and magnitude of the response become increasingly 

15 uncertain. 

16 Our approach for reviewing the primary standards for fine particles is presented in 

17 section 2.1. Staff conclusions regarding the adequacy of the current suite of primary PM2.5 

18 standards are presented in section 2.2, focusing on both evidence-based and quantitative risk19 based considerations. Section 2.3 presents our conclusions with respect to alternative fine 

20 particle standards, focusing on each of the basic elements of the standards: pollutant indicator 

21 (section 2.3.1), averaging time (section 2.3.2), form (section 2.3.3), and level (section 2.3.4). 

22 Section 2.4 summarizes staff conclusions on the primary fine particle standards. Key 

23 uncertainties and areas for future research and data collection efforts are included in section 2.5. 

24 2.1 APPROACH 

25 Staff’s approach for reviewing the current primary PM2.5 standards, which involves 

26 translating scientific and technical information into the basis for addressing key policy-relevant 

27 questions, takes into consideration the approaches used in previous PM NAAQS reviews and the 

28 court’s remand of the primary annual PM2.5 standard set in 2006. The past and current 

29 approaches described below are all based most fundamentally on using information from 

30 epidemiological studies to inform the selection of PM standards that, in the Administrator’s 

31 judgment, protect public health with an adequate margin of safety. Such information can be in 

32 the form of air quality distributions over which health effect associations have been observed, or 

33 in the form of concentration-response (C-R) functions that support quantitative risk assessment. 

34 However, evidence- and risk-based approaches using information from epidemiological studies 
Draft – Do Not Quote or Cite 2-2 June 2010

1 to inform decisions on PM standards are complicated by the recognition that no population 

2 threshold, below which it can be concluded with confidence that PM-related effects do not occur, 

3 can be discerned from the available evidence. As a result, any approach to reaching decisions on 

4 what standards are appropriate necessarily requires judgments about how to translate the 

5 information available from the epidemiological studies into a basis for appropriate standards, 

6 which includes consideration of how to weigh the uncertainties in reported associations across 

7 the distributions of PM concentrations in the studies or in quantitative estimates of risk. Such 

8 approaches are consistent with setting standards that are neither more nor less stringent than 

9 necessary, recognizing that a zero-risk standard is not required by the CAA. 

10 2.1.1 Approaches Used in Previous Reviews 

11 2.1.1.1 Review Completed in 1997 

12 In setting the 1997 primary PM2.5 annual and 24-hour standards, the Agency relied 

13 primarily on an evidence-based approach that focused on epidemiological evidence, especially 

14 from short-term exposure studies of fine particles judged to be the strongest evidence at that 

15 time. The EPA did not place much weight on quantitative risk estimates from the very limited 

16 risk assessment conducted, but did conclude that the risk assessment results confirmed the 

17 general conclusions drawn from the epidemiological evidence that a serious public health 

18 problem was associated with ambient PM levels allowed under the then current PM10 standards 

19 (62 FR 38665/1, July 18, 1997). 

20 The EPA considered the epidemiological evidence and data on air quality relationships 

21 to set an annual PM2.5 standard that was intended to be the “generally controlling” standard; i.e., 

the primary means of lowering both long- and short-term ambient concentrations of PM2.5.

1 22 In 

23 conjunction with the annual standard, EPA also established a 24-hour PM2.5 standard to provide 

24 supplemental protection against days with high peak concentrations, localized “hotspots,” and 

25 risks arising from seasonal emissions that might not be well controlled by a national annual 

26 standard (62 FR 38669/3). Recognizing that there are various ways to combine two standards to 

27 achieve an appropriate degree of public health protection, such as an approach that only 

28 considered short- and long-term exposure evidence, analyses, and standards independently, EPA 

29 concluded that the selected approach based on a generally controlling annual standard was the 

 1

 In so doing, EPA noted that because an annual standard would focus control programs on annual average PM2.5

concentrations, it would not only control long-term exposure levels, but would also generally control the overall 

distribution of 24-hour exposure levels, resulting in fewer and lower 24-hour peak concentrations. Alternatively, a 

24-hour standard that focused controls on peak concentrations could also result in lower annual average 

concentrations. Thus, EPA recognized that either standard could provide some degree of protection from both shortand long-term exposures, with the other standard serving to address situations where the daily peaks and annual 

averages are not consistently correlated (62 FR 38669). 
Draft – Do Not Quote or Cite 2-3 June 2010

1 most effective and efficient approach. This conclusion was based in part on one of the key 

2 observations from the quantitative risk assessment that most of the aggregated annual risk 

3 associated with short-term exposures through a year results from the large number of days during 

4 which the 24-hour average concentrations are in the low- to mid-range, well below the peak 24-

5 hour concentrations. As a result, lowering a wide range of ambient 24-hour PM2.5 concentrations 

6 by means of a generally controlling annual standard, as opposed to focusing on control of peak 

7 24-hour concentrations, was determined to be the most effective and efficient way to reduce total 

8 population risk (62 FR 38670 to 38671). 

9 In setting the level of the annual standard in 1997, EPA first determined a level for the 

10 annual standard based on the short-term exposure studies, and then considered whether the key 

11 long-term exposure studies suggested the need for a lower level. While recognizing that health 

12 effects may occur over the full range of concentrations observed in the studies, EPA concluded 

13 that the strongest evidence for short-term PM2.5 exposure-related effects occurs at concentrations 

14 near the long-term (e.g., annual) average in the short-term exposure studies. The EPA selected a 

15 level for the annual standard at or below the long-term mean concentrations in studies providing 

16 evidence of associations with short-term exposures, placing greatest weight on those short-term 

17 exposure studies that reported clearly statistically significant associations with mortality and 

18 morbidity effects (62 FR 38676/1). Further consideration of the average PM2.5 concentrations 

19 across the cities in the key long-term exposure studies of mortality and respiratory effects in 

20 children did not provide a basis for establishing a lower annual standard level. Because the 

21 annual standard level selected was below the range of annual concentrations most strongly 

22 associated with both short- and long-term exposure effects at that time, and because even small 

23 changes in annual means in this concentration range could make a significant difference in 

24 overall risk reduction and total population exposures, EPA concluded that this standard would 

25 provide an adequate margin of safety against effects observed in these epidemiological studies 

26 (62 FR 68676/3). 

27 The selection of the level of the annual standard was done in conjunction with having 

28 first selected the form of the annual standard to be based on the concentration measured at a 

29 single monitor sited to represent community-wide air quality, or a value resulting from an 

30 average of measurements from multiple community-wide air quality monitoring sites that met 

31 specific criteria and constraints (“spatial averaging”). This decision emphasized consistency 

32 with the types of air quality measurements that were used in the relevant epidemiological studies. 

33 In reaching this decision, EPA recognized the importance of ensuring that spatial averaging 

34 would not result in inequities in the level of protection provided by the PM2.5 standards in some 

35 areas. Because the annual standard, defined in terms of single or averaged community-wide air 

36 quality monitoring sites, could not be expected to offer an adequate margin of safety against the 
Draft – Do Not Quote or Cite 2-4 June 2010

1 effects of all potential short-term exposures in areas with strong local or seasonal sources that 

2 could not be directly evaluated in the epidemiological studies, EPA set the level of the 24-hour 

3 standard to supplement the control afforded by the annual standard based on air quality 

4 relationships between annual and 24-hour concentrations. This approach was intended to 

5 provide an adequate margin of safety against infrequent or isolated peak concentrations that 

6 could occur in areas that attain the annual standard (62 FR 38677). 

7 2.1.1.2 Review Completed in 2006 

8 In 2006, EPA used a different evidence-based approach to assess the appropriateness of 

9 the levels of the 24-hour and annual PM2.5 standards. Based on an expanded body of 

10 epidemiological evidence that was stronger and more robust, including both short- and long-term 

11 exposure studies, the Administrator decided that using evidence of effects associated with 

12 periods of exposure that were most closely matched to the averaging time of each standard was 

13 the most appropriate public health policy approach for evaluating the scientific evidence to 

14 inform selecting the level of each standard. Thus, the Administrator relied upon evidence from 

15 the short-term exposure studies as the principal basis for selecting the level of the 24-hour PM2.5 

16 standard that would protect against effects associated with short-term exposures. The 

17 Administrator relied upon evidence from long-term exposure studies as the principal basis for 

18 selecting the level of the annual PM2.5 standard that would protect against effects associated with 

19 long-term exposures. 

20 With respect to quantitative risk-based considerations, the Administrator determined that 

21 the estimates of risks likely to remain upon attainment of the 1997 suite of PM2.5 standards were 

22 indicative of risks that could be reasonably judged important from a public health perspective, 

23 and, thus, supported revision of the standards. However, the Administrator judged that the 

24 quantitative risk assessment had important limitations and did not provide an appropriate basis 

25 for selecting the levels of the revised standards (71 FR 61174/1-2). The Administrator more 

26 heavily weighed the implications of the uncertainties associated with the quantitative risk 

27 assessment than the Clean Air Scientific Advisory Committee (CASAC) did in their comments 

28 on the proposed rulemaking, where CASAC stated, “[w]hile the risk assessment is subject to 

29 uncertainties, most of the PM Panel found EPA’s risk assessment to be of sufficient quality to 

30 inform its recommendations...The risk analyses indicated that the uncertainties would increase 

rapidly below an annual level of 13 μg/m3 31 – and that was the basis for the PM Panel’s 

recommendation of 13 μg/m3 32 as the lower bound for the annual PM2.5 standard level” 

33 (Henderson, 2006, p.3). 

34 With regard to the primary annual PM2.5 standard, the Administrator placed the greatest 

35 weight on the long-term means of the concentrations associated with mortality effects in two key 
Draft – Do Not Quote or Cite 2-5 June 2010

1 long-term exposure studies in the record, the American Cancer Society (ACS) and Harvard Six 

2 Cities studies (71 FR at 61172 to 61177). Important validation and reanalyses of the original 

3 studies provided “evidence of generally robust associations and provide[d] a basis for greater 

4 confidence in the reported associations than in the last review,” and the extended ACS study 

5 provided “new evidence of mortality related to lung cancer and further substantiate[d] the 

6 statistically significant associations with cardiorespiratory-related mortality observed in the 

7 original studies” (71 FR 61172/1-2). The Administrator also recognized the availability of long8 term exposure studies that provided evidence of respiratory morbidity, including changes in lung 

9 function measurements and decreased growth in lung function as reported in the 24-Cities study 

10 and the Southern California Children’s Health Study, respectively (Dockery et al. 1996, 

Gauderman et al. 2002). In retaining the level of the annual standard at 15 μg/m3 11 , the 

12 Administrator selected a level that was “appreciably below” the long-term average 

13 concentrations reported in the key long-term mortality studies and “basically at the same level” 

14 as the long-term average concentrations in the long-term respiratory morbidity studies. In the 

15 judgment of the Administrator, the two long-term respiratory morbidity studies did “not warrant 

16 setting a lower level for the annual standard than the level warranted based on the key mortality 

17 studies” (71 FR 61176/3). 

18 In considering the form of the primary annual PM2.5 standard, the Administrator 

19 strengthened the standard by tightening the criteria for use of spatial averaging. Based on a 

20 much larger set of PM2.5 air quality data than was available in the 1997 review, analyses were 

21 conducted concerning the potential for disproportionate impacts on potentially vulnerable 

22 subpopulations. These analyses suggested that “the highest concentrations in an area tend to be 

23 measured at monitors located in areas where the surrounding population [was] more likely to 

24 have lower education and income levels, and higher percentages of minority populations” (71 FR 

25 61166/2, see also US EPA, 2005, section 5.3.6.1; Schmidt et al., 2005, Attachment A/Analysis 

7).2 26 

In revising the level of the 24-hour PM2.5 standard from 65 μg/m3

 to 35 μg/m3 27 , the 

28 Administrator placed greatest weight on the much expanded body of epidemiological evidence 

29 from U.S. and Canadian short-term PM2.5 exposure studies with more reliable air quality data 

30 that was reanalyzed to address statistical modeling issues. The Administrator recognized that 

 2

 As summarized in footnote 29 at 71 FR 61166/2, the 2004 AQCD noted that some epidemiologic studies, most 

notably the ACS study of associations between long-term PM2.5 exposure and mortality, reported larger effect 

estimates in the cohort subgroup with lower education levels (US EPA, 2004, p 8-103). The 2004 AQCD also noted 

that lower education level may be a marker for lower socioeconomic status (SES) that may be related to increased 

vulnerability to the effects of fine particle exposures, for example, as a result of greater exposure from proximity to 

sources such as roadways and industry, as well as other factors such as poorer health status and limited access to 

health care (US EPA, 2004, section 9.2.4.5). 
Draft – Do Not Quote or Cite 2-6 June 2010

1 these studies provided no evidence of clear effect thresholds or lowest-observed effect levels. 

Nonetheless, in focusing on the 98th 2 percentile air quality values in these studies, the 

3 Administrator sought to establish a standard level that would require improvements in air quality 

4 generally in areas in which the distribution of daily short-term exposure to PM2.5 could 

5 reasonably be expected to be associated with serious health effects. The Administrator 

6 concluded that although future air quality improvement strategies in any particular area were not 

7 yet defined, most such strategies were likely to move a broad distribution of PM2.5 air quality 

8 values in an area lower, resulting in reductions in risk associated with exposures to PM2.5 levels 

across a wide range of concentrations and not just at the 98th 9 percentile concentrations (71 FR 

10 61168/3). 

11 2.1.2 Remand of Primary Annual PM2.5 Standard 

12 As noted above in section 1.2.4, several parties filed petitions for review following 

13 promulgation of the revised PM NAAQS in 2006. These petitions challenged several aspects of 

14 the final rule including the selection of the level of the primary PM2.5 annual standard. More 

15 specifically, petitioners representing public health and environmental groups (American Lung 

16 Association, Environmental Defense, and the National Parks Conservation Association) and 

17 several states and state agencies argued that the decision to retain the level of the annual PM2.5 

standard at 15 μg/m3 18 was “arbitrary, capricious, an abuse of discretion, or otherwise not in 

19 accordance with law.” 42 U.S.C. 7607(d)(9). The primary 24-hour PM2.5 standard was not 

20 challenged by any of the litigants and, thus, not considered in the court’s review and final 

21 decision. 

22 On judicial review, the D.C. Circuit remanded the primary annual PM2.5 NAAQS to EPA 

23 because the Agency failed to adequately explain why the annual standard provided the requisite 

24 protection from both short- and long-term exposures to fine particles including protection for 

25 susceptible populations. American Farm Bureau Federation v. EPA, 559 F. 3d 512 (D.C. Cir. 

26 2009). With respect to human health protection from short-term PM2.5 exposures, the court 

27 considered the different approaches used by EPA in the 1997 and 2006 PM NAAQS decisions, 

28 as summarized above. The court found that EPA failed to adequately explain why a 24-hour 

29 PM2.5 standard by itself would provide the protection needed from short-term exposures and 

30 remanded the annual PM2.5 standard to EPA “for further consideration of whether it is set at a 

31 level requisite to protect the public health while providing an adequate margin of safety from the 

32 risk of short-term exposures to PM2.5.” American Farm Bureau Federation, 559 F. 3d at 520-24. 

33 With respect to protection from long-term exposure to fine particles, the court found that 

34 EPA failed to adequately explain how the primary annual PM2.5 standard provided an adequate 

35 margin of safety for children and other susceptible populations. The court found that EPA did 
Draft – Do Not Quote or Cite 2-7 June 2010

1 not provide a reasonable explanation of why certain morbidity studies, including a study of 

2 children in Southern California showing lung damage associated with long-term PM2.5 exposure 

3 (Gauderman et.al, 2000) and a multi-city study (24-Cities Study) evaluating decreased lung 

4 function in children associated with long-term PM2.5 exposures (Raizenne et al., 1996), did not 

5 call for a more stringent annual PM2.5 standard. Id. at 522-23. Specifically, the court found that: 

6 

7 EPA was unreasonably confident that, even though it relied solely upon long-term 

8 mortality studies, the revised standard would provide an adequate margin of safety with 

9 respect to morbidity among children. Notably absent from the final rule, moreover, is 

10 any indication of how the standard will adequately reduce risk to the elderly or to those 

11 with certain heart or lung diseases despite (a) the EPA’s determination in its proposed 

12 rule that those subpopulations are at greater risk from exposure to fine particles and (b) 

13 the evidence in the record supporting that determination. Id. at 525. 

14 

15 In remanding the primary annual standard for reconsideration, the court did not vacate the 

16 annual standard. Id. at 530. 

17 2.1.3 Current Approach 

18 This review is founded on an assessment of a much expanded body of epidemiological 

19 evidence, more extensive air quality data and analyses, and a more comprehensive risk 

20 assessment relative to the information available in past reviews, as presented in the ISA and RA. 

21 As a result, staff’s approach to reaching conclusions about the adequacy of the current suite of 

22 PM2.5 standards and potential alternative standards that are appropriate for consideration is 

23 broader and more integrative than in past reviews. Our approach also reflects consideration of 

24 the issues raised by the court in its remand of the primary annual PM2.5 standard, since decisions 

25 made in this review, and the rationales for those decisions, will comprise the Agency’s response 

26 to the remand. 

27 Our approach in this second draft PA takes into account both evidence-based and risk28 based considerations, and the uncertainties related to both types of information, as well as advice 

29 from CASAC and public comments on the first draft PA. In so doing, we are seeking to provide 

30 as broad an array of policy options as is supportable by the available information, recognizing 

31 that the selection of a specific approach to reaching final decisions on the primary PM2.5

32 standards will reflect the judgments of the Administrator as to what weight to place on the 

33 various approaches and types of information presented in the final PA. 

34 We believe it is most appropriate to consider the protection against PM2.5-related 

35 mortality and morbidity effects, associated with both long- and short-term exposures, afforded by 

36 the annual and 24-hour standards taken together, as was done in the 1997 review, rather than to 

37 consider each standard separately, as was done in the 2006 review. This approach reflects the 
Draft – Do Not Quote or Cite 2-8 June 2010

1 recognition that changes in PM2.5 air quality designed to meet an annual standard would likely 

2 result not only in lower annual average concentrations but also in fewer and lower peak 24-hour 

3 concentrations. It also reflects the recognition that setting the annual standard to be the 

4 “generally controlling” standard, and setting the 24-hour standard to provide supplemental 

5 protection, continues to be seen as the most effective and efficient way to reduce total population 

6 risk associated with both long- and short-term exposures and provide requisite protection in areas 

7 across the country. While we recognize that changes designed to meet a 24-hour standard would 

8 result not only in fewer and lower peak 24-hour concentrations but also in lower annual average 

9 concentrations, we note that simulated changes in annual average concentrations associated with 

10 just meeting alternative 24-hour standards are highly sensitive to how air quality changes are 

11 simulated and are more uncertain and variable across areas. The extent to which these two 

12 standards are interrelated in any given area depends in large part on the relative levels of the 

13 standards, the peak-to-mean ratios that characterize air quality patterns in an area, and whether 

14 changes in air quality designed to meet a given suite of standards are likely to be of a more 

15 regional or more localized nature. 

16 Our consideration of the protection afforded by the current and alternative suites of 

17 standards focuses on PM2.5-related health effects associated with long-term exposures, for which 

18 quantitative estimates of risks to public health are appreciably larger, and also considers effects 

19 and estimated risks associated with short-term exposures. In both cases, we place greatest 

20 weight on associations that have been judged in the ISA to be causal and likely causal, while also 

21 considering associations judged to be suggestive of a causal relationship or that focus on specific 

22 susceptible populations. We focus on studies conducted in the U.S. and Canada, as studies in 

23 other countries reflect air quality and exposure patterns that are not necessarily typical of the 

U.S.,3 24 and place relatively greater weight on statistically significant associations that yield 

25 relatively more precise effect estimates and that are judged to be robust to confounding by other 

26 air pollutants. In the case of short-term exposure studies, we place greatest weight on large 

27 multi-city studies, while also considering associations in single-city studies. 

28 In translating information from epidemiological studies into the basis for reaching staff 

29 conclusions on the adequacy of the current suite of standards (section 2.2), we have considered a 

30 number of factors. As an initial matter, we have considered the extent to which the currently 

31 available evidence and related uncertainties strengthens or calls into question conclusions from 

32 the last review regarding associations between fine particle exposures and health effects. We 

33 have also considered evidence on susceptible populations and potential impacts on such 

34 populations. Further, we have explored the extent to which PM2.5-related health effects have 

 3

 Nonetheless, we recognize the importance of all studies, including international studies, in the ISA’s assessment of 

the weight of the evidence that informs causality determinations. 
Draft – Do Not Quote or Cite 2-9 June 2010

1 been observed in areas where air quality distributions extend to lower levels than previously 

2 reported or in areas that would likely meet the current suite of standards. 

3 In translating information from epidemiological studies into the basis for reaching staff 

4 conclusions on alternative standard levels for consideration (section 2.3.4), we have explored 

5 various approaches that reflect the absence of discernible thresholds in the C-R functions from 

6 long- and short-term exposure studies. In so doing, we recognize that there is no single criterion 

7 that comprises the “correct” approach, but rather there are various approaches that are reasonable 

8 to consider. Identifying the implications of various approaches in reaching conclusions on the 

9 range of alternative standard levels that is appropriate to consider can help inform decision 

10 making, which will necessarily also take into account the limitations and uncertainties in the 

11 evidence and public health policy judgments as to the degree of health protection that is to be 

12 achieved. 

13 In reaching staff conclusions on the range of annual standard levels that is appropriate to 

14 consider in setting a generally controlling annual standard intended to provide protection from 

15 effects associated with long-and short-term exposures, we have explored two types of 

16 approaches. One such approach looks directly at confidence bounds on the C-R relationships 

17 from both long- and short-term exposure studies. The other approach is based on the use of 

18 statistical metrics that characterize air quality distributions from multi-city studies. We 

19 recognize that these two types of approaches are intrinsically related, since C-R functions are 

20 most certain across a concentration range with the greatest data density, in which the bulk of the 

21 data exist. 

22 In considering the first type of approach, we have explored the extent to which analyses 

23 characterizing confidence bounds on C-R relationships have been published for studies of health 

24 effects associated with long- or short-term exposures to PM2.5. Such analyses could potentially 

25 be used to characterize a concentration below which uncertainty in a C-R relationship 

26 substantially increases or is judged to be indicative of an unacceptable degree of uncertainty 

27 about the existence of a continuing C-R relationship. 

28 In considering statistical air quality metrics, we first recognize that there are two air 

29 quality distributions that are relevant to consider. One is the distribution based on concentrations 

30 averaged across ambient monitors within each area included in a given study, which we refer to 

31 as a composite monitor distribution. This is the air quality data typically used in analyses of C-R 

32 relationships presented in epidemiological studies of both long- and short-term exposures, in 

33 both multi- and single-city studies. The second relevant air quality distribution is based on 

34 concentrations measured at the monitor within each area that records the highest concentration. 

35 This maximum monitor distribution is relevant because this is the distribution that is generally 

36 used to determine whether a given standard is met in an area, and to determine the extent to 
Draft – Do Not Quote or Cite 2-10 June 2010

1 which ambient PM2.5 concentrations need to be reduced in order to bring an area into attainment 

2 with the standard. For composite monitor distributions, we have compiled distributional 

3 statistics drawn from multi-city studies, as supplemented by information obtained from study 

4 authors. For maximum monitor distributions, we were able to obtain information for a much 

5 more limited set of studies from EPA’s public air quality data base, the Air Quality System 

(AQS).4 6 

7 More specifically, to the extent available, we have compiled data for long- and short-term 

8 exposure multi-city studies on the long-term mean PM2.5 concentration over the time period of 

each study, as well as the standard deviation5 and lower percentiles of the distribution (e.g., 25th 9 

and 10th 10 percentiles). We have explored the implications of considering these various air quality 

11 metrics as a basis for reaching staff conclusions as to the range of concentrations that is 

12 appropriate to consider for alternative annual standard levels. This approach also includes a 

13 broader consideration of uncertainties related to the C-R relationships from long- and short-term 

14 exposure multi-city studies, most notably uncertainties related to our currently limited 

15 understanding of the heterogeneity of relative risk estimates in areas across the country, with 

16 may be attributed in part to the potential for different components within the mix of ambient fine 

17 particles to differentially contribute to health effects observed in the studies and to exposure18 related factors. 

19 In reaching staff conclusions on alternative 24-hour standard levels that are appropriate 

20 to consider in setting a 24-hour standard intended to supplement the protection afforded by 

21 potential alternative annual standards, we have focused on comparing 24-hour and annual air 

22 quality statistics for areas across the U.S. Consistent with the aim of the approach used in 1997, 

23 such a standard would be intended to provide supplemental protection against the effects of 

24 short-term PM2.5 exposures, especially in areas with high peak-to-mean ratios, possibly 

25 associated with strong local or seasonal sources, or for potential PM2.5-related effects that may be 

26 associated with shorter-than-daily exposure periods. By considering information on peak-to27 mean air quality ratios, we reach conclusions as to the level of a 24-hour standard would be 

28 appropriate to consider for this purpose. 

29 Based on the evidence-based considerations outlined above, we then develop integrated 

30 conclusions with regard to alternative suites of standards, including both annual and 24-hour 

31 standards that we believe are appropriate for consideration in this review based on the currently 

32 available evidence and air quality information. In so doing, we discuss the roles that each 

33 standard might be expected to play in the protection afforded by alternative suites of standards. 

 4

 Focusing on multi-city studies, AQS data was available from 1999 and onwards. 

5

A range of one standard deviation around the mean represents approximately 68% of normally distributed data, and 

below the mean falls between the 25th and 10th percentiles. 
Draft – Do Not Quote or Cite 2-11 June 2010

1 Beyond these evidence-based considerations, we also consider the quantitative risk 

2 estimates and the key observations presented in the RA. This assessment included an evaluation 

3 of 15 urban case study areas and estimated risk associated with a number of health endpoints 

4 associated with long-term and short-term PM2.5 exposures (US EPA, 2010a). As part of our risk5 based considerations, we have considered estimates of the magnitude of PM2.5-related risks 

6 associated with recent air quality levels and air quality simulated to just meet the current and 

7 alternative suites of standards using alternative simulation approaches. We have also 

8 characterized the risk reductions, relative to the risks remaining upon just meeting the current 

9 standards, associated with just meeting alternative suites of standards. In so doing, we recognize 

10 the uncertainties inherent in such risk estimates, and have taken such uncertainties into account 

11 by considering the sensitivity of the “core” risk estimates to alternative assumptions and methods 

12 likely to have substantial impact on the estimates. In addition, we have conducted additional 

13 analyses to characterize the representativeness of the urban study areas within a broader national 

14 context. We have considered this risk-based information to help inform our conclusions on the 

15 adequacy of the current suite of standards, potential alternative suites of standards that are 

16 appropriate for consideration in this review, and on the roles that the annual and 24-hour 

17 standards may play in affording protection against effects related to both long- and short-term 

18 PM2.5 exposures. 

19 Staff conclusions reflect our understanding of both evidence-based and risk-based 

20 considerations to inform two overarching questions related to: (1) the adequacy of the current 

21 suite of PM2.5 standards and (2) potential alternative standards for consideration in this review to 

22 provide appropriate protection from the effects associated with both long- and short-term 

23 exposures to fine particles. In addressing these broad questions, we have organized the 

24 discussions below around a series of more specific questions reflecting different aspects of each 

25 overarching question. When evaluating the health protection afforded by the current or any 

26 alternative suites of standards considered, we have taken into account the four basic elements of 

27 the NAAQS (e.g., indicator, averaging time, form, and level). Figure 2-1 provides an overview 

28 of the policy-relevant questions that frame our review, as discussed more fully below. 

29 We believe that the approach outlined above, when presented in the final PA, will 

30 provide a comprehensive basis to help inform the judgments required of the Administrator in 

31 reaching decisions about the current and potential alternative primary PM2.5 standards and in 

32 responding to the remand of the 2006 annual PM2.5 standard. 
Figure 2-1. Overview of Approach for Review of Primary PM2.5 Standards

Evidence-based Considerations

 Does currently available evidence and related uncertainties

strengthen or call into question nature of associations?

 Expanded understanding of susceptible populations?

 Effects at lower concentrations then previously seen or in 

areas that would likely meet current standards?

Risk-based Considerations

 Nature, magnitude, and uncertainties of estimated risks 

remaining upon just meeting the current suite of standards?

 Roles of annual and 24-hour standards; annual standard 

as “generally controlling” standard?

 Importance of remaining risks from public health 

perspective?

Alternative Suites of Standards Supported by Current Information?

Indicator

 PM2.5 indicator?

 Other indicators?

• Ultrafine particles?

• Components?

Averaging Times

 Annual and 24-hour?

 Other averaging times?

• Sub-daily exposures?

• Seasonal exposures?

Levels

For a generally controlling annual standard?

For a supplemental 24-hour standard?

Evidence-based Considerations

Epidemiological and air quality data

•Relative weight placed on information?

•Confidence bounds around C-R relationships?

•Statistical air quality metrics?

•Peak-to-mean ratios?

Forms

Annual standard: spatial averaging?

24-hour standard: 98th or 99th

percentiles?

Support for 

retaining current 

suite of PM2.5 standards

Alternative suites of standards for consideration

YES

NO

Adequacy of Current Suite of PM2.5 Standards?

Does information call into 

question adequacy of current 

suite of PM2.5 standards?

Risk-based Considerations

Nature, magnitude, and uncertainties of estimated risks 

remaining upon just meeting alternative standards?

•Alternative annual standards?

•Alternative 24-hour standards?

Confidence in risk estimates?

Draft-Do Not Quote or Cite 2-12 June 2010
Draft – Do Not Quote or Cite 2-13 June 2010

1 2.2 ADEQUACY OF CURRENT STANDARDS 

2 In considering the adequacy of the current suite of PM2.5 standards, staff addresses the 

3 following overarching question: 

4 Does the currently available scientific evidence and risk-based information, as reflected in 

5 the ISA and RA, support or call into question the adequacy of the protection afforded by 

6 the current suite of fine particle standards? 

7 To inform the answer to this broad question, we address a series of more specific 

8 questions to aid in considering the currently available scientific evidence and the results of recent 

9 quantitative risk analyses in a policy-relevant context, as discussed below. In considering the 

10 scientific and technical information, we reflect upon both the information available in the last 

11 review and information that is newly available since the last review as assessed and presented in 

12 the ISA and the RA (US EPA, 2009a; US EPA, 2010a). 

13 2.2.1 Evidence-based Considerations 

14 Our review of the adequacy of the current suite of PM2.5 primary standards begins by 

15 considering the strength of the evidence, susceptible populations, and the air quality distributions 

16 over which health effects associations have been reported. 

17  To what extent does the currently available scientific evidence and related uncertainties 

18 strengthen or call into question evidence of associations between ambient fine particle 

19 exposures and health effects? 

20 In considering the strength of the associations between long- and short-term exposures to 

21 PM2.5 and health effects, we note that in the last review EPA concluded that there was “strong 

22 epidemiological evidence” for PM2.5 linking long-term exposures with cardiovascular and lung 

23 cancer mortality and respiratory morbidity and short-term exposures with cardiovascular and 

24 respiratory mortality and morbidity (US EPA, 2004, p. 9-46; US EPA, 2005, p. 5-4). Overall, 

25 the epidemiological evidence supported “likely causal associations” between PM2.5 and both 

26 mortality and morbidity from cardiovascular and respiratory diseases, based on “an assessment 

27 of strength, robustness, and consistency in results” (US EPA, 2004, p. 9-48). 

28 In looking across the extensive new scientific evidence available in this review, our 

29 overall understanding of health effects associated with fine particle exposures has been greatly 

30 expanded. The currently available evidence is stronger in comparison to evidence available in 

31 the last review because of its breadth and the substantiation of previously observed health effects 

32 (US EPA, 2009a, section 2.3.1). A number of large multi-city epidemiological studies have been 

33 conducted throughout the U.S. including extended analyses of studies that were important to 

34 inform decisionmaking in the last review. These studies have reported consistent increases in 
Draft – Do Not Quote or Cite 2-14 June 2010

1 morbidity and/or mortality related to ambient PM2.5 concentrations, with the strongest evidence 

2 reported for cardiovascular-related effects. In addition, the findings of new toxicological and 

3 controlled human exposure studies provide stronger support for a number of potential biologic 

4 mechanisms or pathways for PM-related cardiovascular and respiratory effects (US EPA, 2009a, 

5 chapter 5; Figures 5-4 and 5-5). In summary, the ISA concludes, “[t]he new evidence ... greatly 

6 expands upon the evidence available in the 2004 PM AQCD particularly in providing greater 

7 understanding of the underlying mechanisms for PM2.5 induced cardiovascular and respiratory 

8 effects for both short- and long-term exposures” (US EPA, 2009a, p. 2-17). 

9 With regard to causal inferences described in the ISA, we note that since the last review 

10 EPA has developed a more formal framework for reaching causal determinations that draws 

11 upon the assessment and integration of evidence from across epidemiological, controlled human 

12 exposure, and toxicological studies, and the related uncertainties, that ultimately influence our 

13 understanding of the evidence (US EPA, 2009a, section 1.5). This framework employs a five14 level hierarchy that classifies the overall weight of evidence and causality using the following 

15 categorizations: causal relationship, likely to be a causal relationship, suggestive of a causal 

16 relationship, inadequate to infer a causal relationship, and not likely to be a causal relationship 

17 (US EPA, 2009a, Table 1-3). 

18 Using this causal framework, the ISA concludes that the collective evidence is largely 

19 consistent with past studies and substantially strengthens what was known in the last review to 

20 reach the conclusion that a causal relationship exists between both long- and short-term 

21 exposures to PM2.5 and mortality and cardiovascular effects including cardiovascular-related 

22 mortality. The ISA also concludes that the collective evidence continues to support likely causal

23 associations between long- and short-term PM2.5 exposures and respiratory effects, including 

24 respiratory-related mortality. Further, the ISA concludes that available evidence is suggestive of 

25 a causal relationship between long-term PM2.5 exposures and other health effects, including 

26 developmental and reproductive effects (e.g., low birth weight) and carcinogenic, mutagenic, and 

27 genotoxic effects (e.g., lung cancer mortality). Table 2-1 summarizes these causal 

28 determinations (US EPA, 2009a, sections 2.3.1 and 2.6). 
Draft – Do Not Quote or Cite 2-15 June 2010

1 Table 2-1. Summary of Causal Determinations for PM2.5

Exposure 

Duration Outcome 

Causality 

Determination 

Mortality Causal 

Cardiovascular Effects Causal 

Respiratory Effects Likely to be Causal 

Reproductive and Developmental Effects Suggestive 

Long-term 

Cancer, Mutagenicity, Genotoxicity Effects Suggestive 

Mortality Causal 

Cardiovascular Effects Causal 

Respiratory Effects Likely to be Causal 

Short-term 

Central Nervous System Effects Inadequate 

2 Source: adapted from US EPA, 2009, Table 2-6. 

3 Health Effects Associated with Long-term PM2.5 Exposure

4 With regard to mortality, the ISA concludes that newly available evidence significantly 

5 strengthens the link between long-term exposure to PM2.5 and mortality, while providing 

6 indications that the magnitude of the PM2.5-mortality association may be larger than previously 

7 estimated (US EPA, 2009a, sections 7.2.10, 7.2.11, 7.6.1; Figures 7-6 and 7-7). A number of 

8 large U.S. cohort studies have been published since the last review, including extended analyses 

9 of the American Cancer Society (ACS) and Harvard Six Cities studies (US EPA, 2009a, pp 7-84 

10 to 7-85; Figure 7-6; Krewski et al., 2009; Pope et al., 2004; Jerrett et al., 2005; Laden et al., 

11 2006). In addition, new long-term PM2.5 exposure studies evaluating mortality impacts in 

12 additional cohorts are now available (US EPA, 2009a, section 7.6). For example, the Women’s 

13 Health Initiative (WHI) reported effects of PM2.5 on cardiovascular-related mortality in post14 menopausal women with no previous history of cardiac disease (Miller et al., 2007), while 

15 multiple studies observed PM2.5-associated mortality among older adults using Medicare data 

16 (Eftim et al., 2008; Zeger et al., 2007, 2008). Collectively, these new studies, along with 

17 evidence available in the last review, provide us with consistent and stronger evidence of 

18 associations between long-term exposure to PM2.5 and mortality (U.S. EPA, 2009a, sections 2.3.1 

19 and 7.6). 

20 The strength of the causal association between long-term PM2.5 exposure and mortality 

21 also builds upon new studies providing evidence of improvement in community health following 
Draft – Do Not Quote or Cite 2-16 June 2010

1 reductions in ambient fine particles. Pope et al. (2009) have documented the population health 

2 benefits of reducing ambient air pollution by correlating past reductions in ambient PM2.5 

3 concentrations with increased life expectancy. These investigators report that reductions in 

4 ambient fine particles during the 1980s and 1990s account for as much as 15 percent of the 

5 overall improvement in life expectancy in 51 U.S. metropolitan areas, with the fine particle 

6 reductions reported to be associated with an estimated increase in mean life expectancy of 

7 approximately 5 to 9 months (US EPA, 2009a, p. 7-95; Pope et al., 2009). An extended analysis 

8 of the Harvard Six Cities study found that as cities cleaned up their air, locations with the largest 

9 reductions in PM2.5 saw the largest improvements in reduced mortality rates, while those with the 

10 smallest decreases in PM2.5 concentrations saw the smallest improvements (Laden et al., 2006). 

11 Another extended follow-up to the Harvard Six Cities study investigated the delay between 

12 changes in ambient PM2.5 concentrations and changes in mortality (Schwartz et al., 2008) and 

13 reported that the effects of changes in PM2.5 were seen within the 2 years prior to death (US 

14 EPA, 2009a, p. 7-92; Figure 7-9). 

15 With regard to cardiovascular effects, several new studies have examined the association 

16 between cardiovascular effects and long-term PM2.5 exposures in multi-city studies conducted in 

17 the U.S. and Europe. The ISA concludes that the strongest evidence comes from recent studies 

18 investigating cardiovascular-related mortality. This includes evidence from a number of large, 

19 multi-city U.S. long-term cohort studies including extended follow-up analyses of the ACS and 

20 Harvard Six Cities studies, as well as the WHI (US EPA, 2009a, sections 7.2.10 and 7.6.1; 

21 Krewski et al., 2009; Pope et al., 2004; Laden et al., 2006; Miller et al., 2007). Pope et al. (2004) 

22 reported a positive association between mortality and long-term PM2.5 exposure for a number of 

23 specific cardiovascular diseases, including ischemic heart disease (IHD), dysrhythmia, heart 

24 failure, and cardiac arrest (US EPA, 2009a, p. 7-84; Figure 7-7). Krewski et al. (2009) provides 

25 further evidence for IHD-related mortality associated with long-term PM2.5 exposure (US EPA, 

26 2009a, p. 7-84, Figure 7-7). 

27 With regard to cardiovascular-related morbidity associated with long-term PM2.5 

28 exposures, studies were not available in the last review. Recent studies, however, have provided 

29 new evidence linking long-term exposure to PM2.5 with cardiovascular outcomes that has 

30 “expanded upon the continuum of effects ranging from the more subtle subclinical measures to 

31 cardiopulmonary mortality” (US EPA, 2009a, p. 2-17). In the current review, studies are now 

32 available that evaluated a number of endpoints ranging from subtle indicators of cardiovascular 

33 health to serious clinical events associated with coronary heart disease (CHD) and 

34 cerebrovascular disease (CVD), including myocardial infarction (MI), coronary artery 

35 revascularization (e.g., bypass graft, angioplasty, stent, atherectomy), congestive heart failure 

36 (CHF), and stroke. The most significant new evidence comes from the WHI study which 
Draft – Do Not Quote or Cite 2-17 June 2010

1 provides evidence of nonfatal cardiovascular events including both coronary and cerebrovascular 

2 events (Miller et al., 2007; US EPA, 2009a, sections 7.2.9 and 7.6.1). Toxicological studies 

3 provide supportive evidence that the cardiovascular morbidity effects observed in long-term 

4 exposure epidemiological studies are biologically plausible and coherent with studies of 

5 cardiovascular-related mortality as well as with studies of cardiovascular-related effects 

6 associated with short-term exposures to PM2.5, as described below (US EPA, 2009a, p 7-19). 

7 With regard to respiratory effects, the ISA concludes that extended analyses of studies 

8 available in the last review as well as new epidemiological studies conducted in the U.S. and 

9 abroad provide stronger evidence of respiratory-related morbidity associated with long-term 

10 PM2.5 exposure. The strongest evidence for respiratory-related effects available in this review is 

11 from studies that evaluated decrements in lung function growth, increased respiratory symptoms, 

and asthma development (U.S. EPA, 2009a, sections 2.3.1.2, 7.3.1.1, and 7.3.2.1).6 12 Specifically, 

13 extended analyses of the Southern California Children’s Health Study (CHS) provided evidence 

that clinically important deficits in lung function7 14 associated with long-term exposure to PM2.5 

15 persisted into early adulthood (U.S., EPA, 2009a, p. 7-27; Gauderman et al., 2004). Additional 

16 analyses of the CHS cohort reported an association between long-term PM2.5 exposure and 

17 bronchitic symptoms (US EPA, 2009a, p. 7-24; McConnell et al., 2003) and a strong modifying 

18 effect on the association between lung function and asthma incidence (US EPA, 2009, 7-25; 

19 Islam et al., 2007). The outcomes observed in these more recent reports from the Southern 

20 California CHS, including evaluation of a broader range of endpoints and longer follow-up 

21 periods, were larger in magnitude and more precise. Supporting these results are new 

22 longitudinal cohort studies conducted by other researchers in varying locations using different 

23 methods (U.S. EPA, 2009a, section 7.3.9.1). New evidence from a U.S. cohort of cystic fibrosis 

24 (CF) patients provided evidence of association between long-term PM2.5 exposures and 

25 exacerbations of respiratory symptoms resulting in hospital admissions or use of home 

26 intravenous antibiotics (US EPA, 2009a, p. 7-25; Goss et al., 2004). 

27 Toxicological studies provide coherence and biological plausibility for the respiratory 

28 effects observed in epidemiological studies (US EPA, 2009a, p. 7-42). For example, pre- and 

29 postnatal exposure to ambient levels of urban particles has been found to affect lung 

30 development in an animal model (US EPA, 2009a, section 7.3.2.2; p. 7-43). This finding is 

 6

 Supporting evidence comes from studies “that observed associations between long-term exposure to PM10 and an 

increase in respiratory symptoms and reductions in lung function grown in areas where PM10 is dominated by PM2.5” 

(US EPA, 2009a, p. 2-12). 7

 Clinical significance was defined as a FEV1 below 80% of the predicted value, a criterion commonly used in 

clinical settings to identify persons at increased risk for adverse respiratory conditions (US EPA, 2009a, p. 7-29-7-

30). The primary NAAQS for SO2 also includes this interpretation for FEV1 (75 FR 35525, June 22, 2010). 
Draft – Do Not Quote or Cite 2-18 June 2010

1 important because impaired lung development is one mechanism by which PM exposure may 

2 decrease lung function growth in children (US EPA, 2009a, p. 2-12; section 7.3). 

3 With regard to respiratory-related mortality associated with long-term PM2.5 exposure, 

4 the ISA concludes that the evidence is “limited and inconclusive” (US EPA, 2009a, p. 7-41). 

5 The extended follow-up of the Harvard Six Cities study reported a positive but non-statistically 

6 significant association between long-term PM2.5 exposure and respiratory-related mortality 

7 (Laden et al., 2006), whereas Pope et al. (2004) found no association (US EPA, 2009a, p. 7-84). 

8 There is emerging but limited evidence for an association between long-term PM2.5 exposure and 

9 respiratory mortality in post-neonatal infants where long-term exposure was considered as 

10 approximately one month to one year (US EPA, 2009a, pp. 7-54 to 7-55). Emerging evidence of 

11 short- and long-term exposure to PM2.5 and respiratory morbidity and infant mortality provide 

12 some support for the weak respiratory mortality effects observed. 

13 Beyond effects considered to have causal or likely causal associations with long-term 

14 PM2.5 exposure discussed above, the following health outcomes are classified as having evidence 

15 suggestive of a causal association with long-term PM2.5 exposure: (1) reproductive and 

16 developmental effects and (2) cancer, mutagenicity, and genotoxicity effects (US EPA, 2009a, 

17 Table 2-6). With regard to reproductive and developmental effects, the ISA notes, “[e]vidence is 

18 accumulating for PM2.5 effects on low birth weight and infant mortality, especially due to 

19 respiratory causes during the post-neonatal period” (US EPA, 2009a, section 2.3.1.2). New 

20 evidence available in this review reports a significant association between exposure to PM2.5 

21 during pregnancy and lower birth weight, pre-term birth, and intrauterine growth restriction, and 

22 a significant association between post-natal exposure to PM2.5 and an increased risk of infant 

23 mortality (US EPA, 2009a, section 7.4). The ISA further notes that “[i]nfants and fetal 

24 development processes may be particularly vulnerable to PM exposure, and although the 

25 physical mechanisms are not fully understood, several hypotheses have been proposed involving 

26 direct effects on fetal health, altered placenta function, or indirect effects on the mother’s health” 

27 (US EPA, 2009a, section 7.4.1). While toxicological studies provide some evidence that 

28 supports an association between long-term PM2.5 exposure and adverse reproductive and 

29 developmental outcomes, there is “little mechanistic information or biological plausibility for an 

30 association between long-term PM exposure and adverse birth outcomes (e.g., low birth weight 

31 or infant mortality)” (US EPA, 2009a, p. 2-13). 

32 With regard to cancer, mutagenic and genotoxic effects, “[m]ultiple epidemiologic 

33 studies have shown a consistent positive association between PM2.5 and lung cancer mortality, 

34 but studies have generally not reported associations between PM2.5 and lung cancer incidence” 

35 (US EPA, 2009a, p. 2-13 and sections 2.3.1.2 and 7.5; Table 7-7; Figures 7-6 and 7-7). The 

36 extended follow-up to the ACS study reported an association between long-term PM2.5 exposure 
Draft – Do Not Quote or Cite 2-19 June 2010

1 and lung cancer mortality (US EPA, 2009a, p. 7-71; Krewski et al., 2009) as did the extended 

2 follow-up to the Harvard Six Cities study when considering the entire 25-year follow-up period. 

3 There is some evidence, primarily from in vitro studies, providing biological plausibility for the 

4 PM-lung cancer relationships observed in epidemiological studies (US EPA, 2009a, p. 7-80), 

5 although toxicological studies of carcinogenicity, mutagenicity, and genotoxicity generally 

6 reported mixed results (US EPA, 2009a, section 7.5).

7 Health Effects Associated with Short-term PM2.5 Exposure

8 In considering effects associated with short-term PM2.5 exposure, the body of currently 

9 available scientific evidence has been expanded greatly by the publication of a number of new 

10 multi-city time-series studies that have used uniform methodologies to investigate the effects of 

11 short-term fine particle exposures on public health. This body of evidence provides a more 

12 expansive data base and considers multiple locations representing varying regions and seasons 

13 that provide evidence on the influence of climate and air pollution mixes on PM2.5-associated 

14 health effects. These studies provide more precise estimates of the magnitude of effects 

15 associated with short-term PM2.5 exposure than most smaller-scale single-city studies that were 

16 more commonly available in the last review (U.S. EPA 2009a, chapter 6). 

17 With regard to mortality, extended and expanded analyses of a multi-city study available 

18 in the last review as well as new U.S. multi-city and single-city short-term PM2.5 exposure 

19 studies have found generally consistent positive associations between short-term PM2.5 exposures 

20 and cardiovascular- and respiratory-related mortality as well as all-cause (non-accidental) 

21 mortality (US EPA, 2009a, sections 2.3.1.1, 6.2.11 and 6.5.2.2; Figures 6-26, 6-27, and 6-28). A 

22 Canadian multi-city study available in the last review was expanded from 8 to 12 cities (Burnett 

23 and Goldberg, 2003; Burnett et al., 2004), with consistent findings of a positive and statistically 

24 significant association between short-term PM2.5 exposure and mortality (US EPA, 2009a, p 6-

25 182, Figure 2-1), although the influence of NO2 and limited PM2.5 data for several years during 

26 the study period somewhat diminish these findings. In an analysis of National Morbidity, 

27 Mortality, and Air Pollution Study (NMMAPS) data, Dominici et al. (2007a) reported 

28 associations between fine particle exposures and all-cause and cardio-respiratory mortality (US 

29 EPA, 2009a, p. 6-175, Figure 6-26). In a study of 112 U.S. cities, Zanobetti and Schwartz 

30 (2009) reported positive associations (in 99% of the cities) and frequently statistically significant 

31 associations (in 55% of the cities) between short-term PM2.5 exposure and total (non-accidental) 

32 mortality (US EPA, 2009a, pp 6-176 to 6-179; Figures 6-23 and 6-24). Collectively, these 

33 studies provide generally consistent and much stronger evidence for PM2.5-associated mortality 

34 than the evidence available in the last review. 
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1 With regard to cardiovascular effects, new multi-city as well as single-city short-term 

2 PM2.5 exposure studies conducted since the last review supports a largely positive and frequently 

3 statistically significant relationship between short-term exposure to PM2.5 and cardiovascular4 related disease and mortality, substantiating prior findings. For example, among a multi-city 

5 cohort of older adults participating in the Medicare Air Pollution Study (MCAPS) investigators 

6 reported evidence of a positive association between short-term PM2.5 exposures and hospital 

7 admissions related to cardiovascular outcomes (US EPA, 2009a, pp. 6-57 to 58; Dominici et al, 

8 2006a; Bell et al, 2008). The strongest evidence for cardiovascular morbidity effects has been 

9 observed predominately for ischemic heart disease (IHD) and congestive heart failure (CHF) 

10 hospital admissions and emergency department visits, and cardiovascular-related mortality (US 

11 EPA, 2009a, Figure 2-1, p. 6-79, sections 6.2.10.3, 6.2.10.5, and 6.2.11; Bell et al., 2008; 

12 Dominici et al., 2006a; Tolbert et al., 2007; Zanobetti and Schwartz, 2009). Furthermore, these 

13 findings are supported by a recent study of a multi-city cohort of women participating in the 

14 WHI that reported a positive but non-statistically significant association between short-term 

15 exposure to PM2.5 and ECG measures of myocardial ischemia (Zhang et al., 2009). 

16 In focusing on respiratory effects, the strongest evidence from short-term PM2.5 exposure 

17 studies has been observed for respiratory-related emergency department visits and hospital 

18 admissions for chronic obstructive pulmonary disease (COPD) and respiratory infections (U.S. 

19 EPA, 2009a, sections 2.3.1.1 and 6.3.8.3; Figures 2-1 and 6-13; Dominici et al., 2006a;). 

20 Evidence for PM2.5-related respiratory effects has also been observed in panel studies, which 

21 indicate associations with respiratory symptoms, pulmonary function, and pulmonary 

22 inflammation among asthmatic children. Although not consistently observed, some controlled 

23 human exposure studies have reported small decrements in various measures of pulmonary 

24 function following controlled exposures to PM2.5 (US EPA, 20009a, p. 2-10). Furthermore, the 

25 comparatively larger body of toxicological evidence since the last review is coherent with the 

26 evidence from epidemiological and controlled human exposure studies that examined short-term 

27 exposures to PM2.5 and respiratory effects (US EPA 2009a, section 6.3.10.1). 

28 Uncertainties in the Evidence

29 With respect to understanding the nature and magnitude of PM2.5-related risks, as 

30 discussed above, we recognize that epidemiological studies evaluating health effects associated 

31 with long- and short-term PM2.5 exposures have reported heterogeneity in responses both within 

32 and between cities and geographic regions within the U.S. This heterogeneity may be attributed, 

33 in part, to differences in the fine particle composition. However, the currently available evidence 

34 and limited availability of city-specific PM2.5 speciation data does not allow conclusions to be 

35 drawn regarding the relative toxicity of PM2.5 components, combinations of PM2.5 components, 
Draft – Do Not Quote or Cite 2-21 June 2010

1 or sources of fine particles in different locations. Overall, the ISA concludes “that many 

2 constituents of PM2.5 can be linked with multiple health effects, and the evidence is not yet 

3 sufficient to allow differentiation of those constituents or sources that are more closely related to 

4 specific health outcomes” (US EPA, 2009a, p. 2-17.) 

5 Measurement error is also an important source of uncertainty. Variability in the 

6 associations observed across PM2.5 epidemiological studies may be due in part to exposure error 

7 related to the use of county-level air quality data and our limited understanding of factors that 

8 may influence exposures (e.g., topography, the built environment, climate, source characteristics, 

9 ventilation usage, personal activity patterns). As noted in the ISA, biases and uncertainties in 

10 exposure estimates can result in biases and uncertainties in associated health effect estimates (US 

11 EPA, 2009a, p. 2-17). 

12 We note challenges with interpreting differences in health effects observed in the eastern 

13 versus western parts of the U.S. As noted in section 2.3.2 of the ISA, western U.S. counties tend 

14 to be larger and more topographically diverse than eastern U.S. counties and regional differences 

15 in climate and infrastructure can effect time spent outdoors or indoors, housing characteristics 

16 including air conditioning usage, and personal activity patterns. In light of these differences, the 

17 ISA notes “...the available evidence and the limited amount of city-specific speciated PM2.5 data 

18 does not allow conclusions to be drawn that specifically differentiate effects of PM in different 

19 locations “ (US EPA 2009a, p. 2-17). Therefore, we recognize that important uncertainties 

20 remain in this review related to understanding the temporal and spatial variability in PM2.5 

21 concentrations, including PM2.5 components, and associated health impacts across different 

22 geographic areas. 

23 Summary

24 In considering the extent to which newly available scientific evidence strengthens or calls 

25 into question evidence of associations identified in the last review between ambient fine particle 

26 exposures and health effects, we recognize that much progress has been made in assessing some 

27 key uncertainties related to our understanding of health effects associated with short- and long28 term exposure to PM2.5. As briefly discussed above as well as in the more complete discussion of 

29 the evidence as assessed in the ISA, we note that the newly available information combined with 

30 information available in the last review provides substantially stronger confidence in a causal 

31 association between short- and long-term exposures to PM2.5 and mortality and cardiovascular 

32 effects. In addition, the newly available evidence reinforces and expands the evidence 

33 supporting the likely causal nature of the associations between short- and long-term exposure to 

34 PM2.5 and respiratory effects. Causal inferences, as discussed in the ISA, are based not only on 

35 the more expansive epidemiological evidence available in this review but also reflects 
Draft – Do Not Quote or Cite 2-22 June 2010

1 consideration of important progress that has been made to advance our understanding of a 

2 number of potential biologic modes of action or pathways for PM-related cardiovascular and 

3 respiratory effects (US EPA 2009a, chapter 5). With respect to suggestive evidence for a 

4 broader range of effects, the body of scientific evidence is somewhat expanded but is still limited 

5 with respect to associations between long-term PM2.5 exposures and developmental and 

6 reproductive effects as well as cancer, mutagenic, and genotoxic effects. Thus, we conclude that 

7 there is stronger and more consistent and coherent support for associations between short- and 

8 long-term PM2.5 exposure and a broader range of health outcomes than was available in the last 

9 review, providing the basis for fine particle standards at least as protective as the current PM2.5 

10 standards. 

11 Having reached this initial conclusion, we then consider how the new evidence informs 

12 our understanding of susceptible populations by addressing the following question: 

13  To what extent does the currently available scientific evidence expand our 

14 understanding of susceptible populations? 

15 Specific groups within the general population, referred to as susceptible populations, are 

at increased risk for experiencing adverse health effects related to PM exposures.8 16 These groups 

17 could exhibit a greater risk of PM-related health effects than the general population for a number 

18 of reasons including, being affected by lower concentrations of PM, experiencing a larger health 

19 impact at a given PM concentration, and/or being exposed to higher PM concentrations than the 

20 general population. Given the heterogeneity of individual responses to PM exposures, the 

21 severity of the health effects experienced by a susceptible population may be much greater than 

22 that experienced by the population at large. 

23 As summarized below, the currently available epidemiological and controlled human 

24 exposure evidence expands our understanding of previously identified susceptible populations 

25 (e.g., children, older adults, and individuals with pre-existing heart and lung disease) and 

26 supports the identification of additional susceptible populations (e.g., persons with lower 

27 socioeconomic status (SES), genetic differences) (US EPA, 2009a, section 2.4.1, Table 8-2). In 

28 addition, toxicological studies provide underlying support for the biological mechanisms that 

29 potentially lead to increased susceptibility to PM-related health effects. These studies also. 

30 Lifestages: Children and Older Adults 

31 Two different lifestages have been associated with increased susceptibility to PM-related 

32 health effects: childhood (i.e., less than 18 years of age) and older adulthood (i.e., 65 years of 

33 age and older). Childhood represents a lifestage where susceptibility to PM exposures may be 

 8

 Although studies have primarily used exposures to PM10 or PM2.5, the available evidence suggests that the 

identified factors also increase risk from PM10-2.5 (US EPA, 2009a, section 8.1.8). 
Draft – Do Not Quote or Cite 2-23 June 2010

1 related to the following observations: children spend more time outdoors; children have greater 

2 activity levels than adults; children have exposures resulting in higher doses per body weight and 

3 lung surface area; and the developing lung is prone to damage, including irreversible effects, 

4 from environmental pollutants as it continues to develop through adolescence (US EPA, 2009a, 

5 section 8.1.1.2). Older adults represent a lifestage where susceptibility to PM-associated health 

6 effects may be related to the higher prevalence of pre-existing cardiovascular and respiratory 

7 diseases found in this age group compared to younger age groups as well as the gradual decline 

8 in physiological processes that occur as part of the aging process (US EPA, 2009a, section 

9 8.1.1.1). 

10 With regard to mortality, recent epidemiological studies have found that older adults are 

11 at greater risk of all-cause (non-accidental) mortality associated with short-term exposure to both 

12 PM2.5 and PM10, providing consistent and stronger evidence of effects in this susceptible 

13 population compared to the last review. Epidemiological studies that examined the association 

14 between mortality and long-term exposure to PM2.5 that stratified the results by age (i.e., less 

15 than 65 years of age compared to aged 65 and older; different age groups within the aged 65 and 

16 older population) reported results that are generally consistent with the findings of these short17 term exposure studies while also providing some evidence that risk declines with increasing age 

18 starting at age 60, such that there is no evidence of an association among persons 85 years and 

19 older (US EPA, 2009a, Figure 7-7, section 8.1.1.1, Zeger et al., 2008). 

20 With regard to morbidity effects, currently available studies provide evidence that older 

21 adults have heightened responses, especially for cardiovascular-related effects, and children have 

22 heightened responses for respiratory-related effects associated with long- and short-term PM2.5

23 exposures. With regard to older adults, epidemiological studies provide evidence of increases in 

PM2.5-related risk of MI, coronary revascularization,9 24 and their combination with CHD-related 

25 death for participants free of CVD at baseline (Miller et al., 2007) as well as cardiovascular26 related hospitalization (Dominici et al., 2006; Bell et al., 2008). Further, dosimetry studies have 

27 shown a depression of fine and coarse PM clearance in all regions of the respiratory tract with 

28 increasing age beyond young adulthood, suggesting that older adults are at greater risk of PM29 related respiratory health effects (US EPA, 2009a, section 8.1.1). 

30 With regard to respiratory-related effects in children associated with long-term PM 

31 exposures, our understanding of effects on lung development has been strengthened based on 

32 newly available evidence that is consistent and coherent across different study designs, locations, 

33 and research groups. For example, the strongest evidence comes from the extended follow-up 

34 for the Southern California CHS which includes several new studies that report positive 

 9

 Coronary revascularization includes percutaneous coronary interventions, such as angioplasty. 
Draft – Do Not Quote or Cite 2-24 June 2010

1 associations between long-term exposure to PM2.5 and respiratory morbidity, particularly for 

2 such endpoints as lung function growth, respiratory symptoms (i.e., bronchitic symptoms), and 

3 respiratory disease incidence (US EPA, 2009a, section 7.3; Gauderman et al., 2004). These 

4 analyses provide evidence that PM2.5–related effects persist into early adulthood and are more 

5 robust and larger in magnitude than previously reported. 

6 With regard to respiratory effects in children associated with short-term exposures to PM, 

7 currently available studies provide stronger evidence of respiratory-related hospitalizations with 

8 larger effect estimates observed among children. In addition, reductions in lung function (FEV1) 

9 and an increase in respiratory symptoms and medication use associated with PM exposures has 

10 been reported among asthmatic children (US EPA, 2009a, sections 6.3.1, 6.3.2.1, 8.4.9). 

11 In addition, accumulating evidence suggests that the developing fetus may also represent 

12 an additional lifestage that is susceptible to PM exposures. The ISA further notes that “[i]nfants 

13 and fetal development processes may be particularly vulnerable to PM exposure, and although 

14 the physical mechanisms are not fully understood, several hypotheses have been proposed 

15 involving direct effects on fetal health, altered placenta function, or indirect effects on the 

16 mother’s health” (US EPA, 2009a, section 7.4.1). Evidence is accumulating for PM2.5-related 

17 effects on low birthweight and infant mortality, especially due to respiratory causes during the 

18 post-neonatal period (US EPA, 2009a, sections 2.3.1.2 and 7.4). 

19 Pre-existing Diseases/Health Conditions

20 A number of health conditions have been found to put individuals at greater risk for 

21 adverse effects following exposure to PM. The currently available evidence confirms and 

22 strengthens evidence in the last review that individuals with underlying cardiovascular and 

23 respiratory diseases are more susceptible to PM exposures. 

24 The majority of the epidemiological studies that examined associations between short25 term PM exposures and cardiovascular outcomes focused on cardiovascular-related hospital 

26 admissions and emergency department visits (US EPA, section 8.1.6.1). There is some new 

27 evidence that individuals with pre-existing IHD are at greater risk of PM-associated hospital 

28 admissions and emergency department visits related to cardiovascular effects. Additional studies 

29 have focused on hypertension and on the effects of PM on cardiac function in individuals with 

30 dysrhythmia with mixed results. One epidemiological study (US EPA, 2009a, section 7.2.9; 

31 Zanobetti and Schwartz, 2007) investigated associations between long-term exposure to PM10

32 and the progression of disease or reduced survival in a 21-city study of people discharged 

33 following an acute MI, finding significant associations for mortality, CHF and new 

34 hospitalization for MI. 
Draft – Do Not Quote or Cite 2-25 June 2010

1 With regard to individuals with pre-existing respiratory illnesses (e.g, asthma, COPD), 

2 the ISA presents and assesses a number of studies that evaluate a broad range of health outcomes 

3 (e.g., mortality, asthma symptoms) in response to PM exposures (US EPA, 2009a, section 

4 8.1.6.2). Evidence in asthmatics is stronger and more consistent, while studies of persons with 

5 COPD have reported mixed results. Epidemiological studies have examined the effect of short6 term exposure to PM in asthmatics finding an increase in medication use, asthma attacks, and 

7 respiratory symptoms (i.e., asthma symptoms, cough, shortness of breath, and chest tightness). 

8 Controlled human exposure studies report that healthy and asthmatic subjects exposed to coarse, 

9 fine and ultrafine CAPs, exhibit similar respiratory responses, although these studies excluded 

10 moderate and severe asthmatics that would be expected to show increased susceptibility to PM 

11 exposure. Toxicological studies using diesel exhaust particles (DEPs) provide mechanistic 

12 support that PM exposure results in allergic sensitization, and individuals with allergic airway 

13 conditions are at greater risk of adverse effects upon exposure to PM2.5. Further, there is 

14 emerging but limited evidence which suggests that non-allergic respiratory morbidities may also 

15 increase the susceptibility of an individual to PM-related respiratory effects (US EPA, 2009a, p. 

16 8-12). 

17 There is also emerging evidence that suggests the influence of additional pre-existing 

18 diseases or health conditions, including diabetes and obesity on the manifestation of PM-related 

19 health effects. The ISA notes that additional research exploring the effect of PM exposures on 

20 obese individuals and identifying the biological pathway(s) that could increase the susceptibility 

21 of diabetic and obese individuals to PM could improve our understanding of these potentially 

22 susceptible populations (US EPA 2009a, pp. 2-23-2-24). 

23 Socioeconomic Status

24 Stronger evidence is available in this review indicating that people from lower 

25 socioeconomic strata are a susceptible population relative to PM exposures (US EPA, 2009a, 

section 8.1.7). Persons with lower SES10 26 have been generally found to have a higher prevalence 

27 of pre-existing diseases; limited access to medical treatment; and limited access to fresh foods 

28 leading to a reduced intake of antioxidants, polyunsaturated fatty acids and vitamins, which can 

29 increase this population’s risk to PM-related effects. Evidence available in the last review from 

30 the ACS and Harvard Six Cities cohort studies indicated increased mortality risk with long-term 

31 exposure to PM2.5 in the cohort subgroups with lower education levels (US EPA 2004, section 

32 9.2.4.5). In this review, additional support is available to identify persons with lower SES as a 

33 susceptible population. For example, Krewski et al. (2009) found moderate evidence for 

 10 SES is a composite measure that usually consists of economic status, measured by income; social status measured 

by education; and work status measured by occupation (US EPA, 2009a, p. 8-14). 
Draft – Do Not Quote or Cite 2-26 June 2010

1 increased lung cancer mortality in individuals with a high school education or less in response to 

2 long-term exposure to PM2.5. However, IHD-related mortality associated with long-term PM2.5 

3 exposures was most strongly associated with individuals with higher education levels (US EPA, 

4 2009a, p. 8-15). 

5 Genetic Factors 

6 Investigation of potential genetic susceptibility has provided evidence that individuals 

7 with null alleles or polymorphisms in genes that mediate the antioxidant response to oxidative 

8 stress (e.g., GSTM1), regulate enzyme activity (i.e., MTHFR and cSHMT), or regulate levels of 

9 procoagulants (i.e., fibrinogen) are more susceptible to PM-related effects. However, some 

10 evidence suggests that polymorphisms in genes (e.g., HFE) may provide protection for PM11 related effects. Emerging evidence also suggests that PM exposure can impart epigenetic effects 

12 (i.e., DNA methylation) (US EPA, 2009a, p. 8-16). More research is needed to better understand 

13 the relationship between genetic effects and potential susceptibility to PM-related effects. 

14 Summary

15 In summary, we conclude that there are several susceptible populations that are likely to 

16 be at increased risk of PM-related effects, including the lifestages of childhood and older 

17 adulthood, those with preexisting heart and lung diseases, and those of lower SES. We also 

18 recognize that there is emerging, though still limited evidence for additional potentially 

19 susceptible populations, such as those with diabetes, people who are obese, or those with genetic 

20 factors. We note that the available evidence does not generally allow distinctions to be drawn 

21 between the PM indicators in terms of whether populations are more susceptible to a particular 

22 size fraction (i.e., PM2.5 and PM10-2.5). 

23  To what extent does the currently available scientific evidence report associations that 

24 extend to air quality concentrations that are lower than had previously been observed 

25 or that are observed in areas that would likely meet the current suite of PM2.5 

26 standards? 

27 In focusing our attention on whether the available evidence supports consideration of 

28 standards that are more protective than the current suite of PM2.5 standards, we first recognize 

29 that the ISA concludes there is no evidence to support the existence of a discernible threshold 

30 below which effects would not occur (US EPA, 2009a, section 2.4.3). Next, we consider 

31 whether the evidence provides information for health effects associated with air quality 

32 concentrations that are lower than had previously been observed, or if epidemiological studies 

33 have reported effects in areas that would likely meet the current suite of PM2.5 standards. 
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1 Associations with Long-term PM2.5 Exposure

2 Extended follow-up analyses of the ACS and Harvard Six Cities studies provide 

3 consistent and stronger evidence of a causal association with mortality at lower air quality 

4 distributions than had previously been observed. The original and reanalysis of the ACS study 

5 reported positive and statistically significant effects associated with a long-term mean PM2.5 

concentration of 18.2 μg/m3 6 across 50 metropolitan areas for 1979-1983 (Pope et al., 1995; 

Krewski et al., 2000).11 7 In extended analyses, positive and statistically significant effects of 

8 approximately similar magnitude were associated with declining PM2.5 concentrations, from an 

aggregate long-term mean in 58 metropolitan areas of 21.2 μg/m3 9 in the original monitoring 

period (1979-1983) to 14.0 μg/m3 10 for 116 metropolitan areas in the most recent years evaluated 

11 (1999-2000), with an overall average across the two study periods in 51 metropolitan areas of 

17.7 μg/m3 12 (Pope et al., 2002; Krewski et al., 2009). With regard to the Harvard Six Cities 

13 Study, the original and reanalysis reported positive and statistically significant effects associated 

with a long-term mean PM2.5 concentration of 18.0 μg/m3 14 for 1980-1985 (Dockery et al., 1993; 

15 Krewski et al., 2000). In an extended follow-up of this study, the aggregate long-term mean 

concentration across all years evaluated was 16.4 μg/m3

 for 1980-198812 16 (Laden et al., 2006). 

17 In an additional analysis of the extended follow-up of the Harvard Six Cities cohort study, 

18 investigators reported the C-R relationship was linear and “clearly continuing below the level” of 

19 the current annual standard (US EPA, 2009a, p. 7-92; Schwartz et al., 2008). 

20 We then consider new cohort studies that provide evidence of mortality associated with 

21 air quality distributions that are generally lower than those reported in the ACS and Harvard Six 

22 Cities studies, with effect estimates that were similar or greater in magnitude. The Women’s 

23 Health Initiative (WHI) reported positive and most often statistically significant associations 

24 between long-term PM2.5 exposure and cardiovascular-related mortality, with much larger 

25 relative risk estimates than in the ACS and Harvard Six Cities studies, as well as morbidity 

effects at an aggregate long-term mean PM2.5 concentration of 13.5 μg/m3 26 for 2000 (Miller et al., 

27 2007). Using the Medicare cohort, Eftim et al. (2008) reported somewhat higher effect estimates 

28 than in the ACS and Harvard Six Cities studies with aggregate long-term mean concentrations of 

13.6 μg/m3 and 14.1 μg/m3 29 , respectively, for 2000-2002. The MCAPS reported associations 

30 between long-term PM2.5 exposure and mortality for the eastern region of the U.S. at an 

aggregated long-term PM2.5 median concentration of 14.0 μg/m3 31 , although no association was 

32 reported for the western region with an aggregated long-term PM2.5 median concentration of 13.1 

 11 The study periods referred to in this document reflect the years of air quality data that were included in the 

analyses, whereas the study periods identified in the ISA reflect the years of health status data that were included. 12 Aggregate mean concentration provided by study author (Laden, 2009). 
Draft – Do Not Quote or Cite 2-28 June 2010

μg/m3 (US EPA, 2009a, p. 7-88; Zeger et al., 2008)13 1 . Premature mortality in children reported 

2 in a national infant mortality study as well as mortality in a cystic fibrosis cohort including both 

3 children and adults reported positive but not statistically significant effects associated with longterm aggregate mean concentrations of 14.8 μg/m3

 and 13.7 μg/m3 4 , respectively (Woodruff et al., 

5 2008; Goss et al., 2004). 

6 With respect to respiratory morbidity effects associated with long-term PM2.5 exposure, 

7 the across-city mean of 2-week average PM2.5 concentrations reported in the initial Southern 

California CHS was approximately 15.1 μg/m3 8 (Peters et al., 1999). These results were found to 

9 be consistent with results of cross-sectional analyses of the 24-City Study (Dockery et al., 1996; 

10 Raizenne et al., 1996), which reported a long-term cross-city mean PM2.5 concentration of 14.5 

μg/m3 11 . In this review, extended analysis of the Southern California CHS provided stronger 

12 evidence of PM2.5-related respiratory effects, at lower air quality concentrations than had 

previously been reported, with a four-year aggregate mean concentration of 13.8 μg/m3 13 across 

14 the 12 study communities (McConnell et al., 2003; Gauderman et al., 2004, US EPA, 2009a, 

15 Figure 7-4). 

16 Broadening our consideration to effects for which evidence is suggestive of a causal 

17 association, we note birth outcome studies that reported positive and statistically significant 

effects related to aggregate long-term mean PM2.5 concentrations of approximately 12 μg/m3 18 (US 

19 EPA, 2009a, Table 7-5; Bell et al., 2007; Liu et al., 2007; Parker et al, 2005). In contrast, Parker 

20 and Woodruff (2008) reported no overall association with birthweight with an aggregate longterm mean concentration of 13.5 μg/m3 21 (US EPA, 2009a, section 7.4.1.1). 

22 Collectively, the currently available evidence provides support for associations between 

23 long-term PM2.5 exposure and mortality and morbidity effects that extend to air quality 

24 concentrations that are lower than had previously been observed, with aggregated long-term 

25 mean PM2.5 concentrations extending to well below the level of the current standard. These 

26 studies evaluated a broader range of health outcomes in the general population and in susceptible 

27 populations than were considered in the last review, and include extended follow-up for 

28 prospective epidemiological studies that were important in the last review as well as additional 

29 evidence in important new cohorts. 

 13 Zeger et al. (2008) also reported positive and statistically significant effects for the central region, with an 

aggregate long-term mean PM2.5 concentration of 10.7 μg/m3

. However, in contrast to the eastern and western risk 

estimates, the central risk estimate increased with adjustment for COPD (used as a proxy for smoking status). Due 

to the potential for confounding bias influencing the risk estimate for the central region, we have not focused on the 

results reported in the central region to inform the adequacy of the current suite of standards. 
Draft – Do Not Quote or Cite 2-29 June 2010

1 Associations with Short-term PM2.5 Exposure

2 In considering long-term average ambient concentrations from short-term PM2.5 exposure 

3 studies, an expanded follow-up analyses of a Canadian multi-city study provides evidence of a 

4 causal association between short-term exposures to PM2.5 and mortality at lower air quality 

5 distributions than had previously been observed. Specifically, the expansion of the multi-city 

6 Canadian study from 8 (Burnett and Goldberg, 2003; aggregate long-term mean PM2.5

concentration of 13.3 μg/m3 7 ) to 12 Cities (Burnett et al 2004) yielded results consistent with 

8 prior findings of a positive and statistically significant association, but at a lower aggregate longterm mean PM2.5 concentration of 12.8 μg/m3 9 . In a multi-city time-series analysis of 112 U.S. 

10 cities published since the last review, Zanobetti and Schwartz (2009) reported a positive and 

11 statistically significant association with all-cause, cardiovascular-related (e.g., MI, stroke), and 

12 respiratory-related mortality and short-term PM2.5 exposure, in which the aggregate long-term 

mean PM2.5 concentration was 13.2 μg/m3 (ranging from 6.6 μg/m3to 24.7 μg/m3 13 ) (US EPA, 

14 2009a, Figure 6-24). Furthermore, city-specific effect estimates indicate the association between 

15 short-term exposure to PM2.5 and total mortality and cardiovascular- and respiratory-related 

16 mortality is consistently positive for an overwhelming majority (99%) of the 112 cities across a 

17 wide range of air quality concentrations (US EPA, 2009a, Figure 6-24, p. 6-178 to 179). The 

18 authors report that for all-cause mortality, city-specific effect estimates were statistically 

19 significant for 55% of the 112 cities, with long-term city-mean PM2.5 concentrations ranging 

from 7.8 μg/m3 to 18.7 μg/m3 and 24-hour PM2.5 city-mean 98th 20 percentile concentrations 

ranging from 18.4 to 64.9 μg/m3 21 . 

22 With regard to cardiovascular and respiratory morbidity effects, in the first analysis of the 

23 MCAPS cohort conducted by Dominici et al. (2006a) across 204 US counties, investigators 

24 reported a statistically significant association with hospitalizations for cardiovascular and 

25 respiratory diseases and short-term PM2.5 exposure, in which the aggregate long-term mean 

PM2.5 concentration was 13.4 μg/m3

 (ranging from 4 μg/m3to 23 μg/m3 26 ). Furthermore, a subanalysis restricted to days with 24-hour average concentrations of PM2.5 at or below 35 μg/m3 27 

28 indicated that, in spite of a reduced statistical power from a smaller number of study days, 

29 statistically significant associations were still observed between short-term exposure to PM2.5

and hospital admissions for cardiovascular and respiratory diseases (Dominici, 2006b14 30 ). These 

results, along with the observation that approximately 50% of the 204 county-specific mean 98th 31 

32 percentile PM2.5 concentrations aggregated across all years were below the 24-hour standard of 

 14 This sub-analysis was not included in the original publication (Dominici et al., 2006a). Authors provided subanalysis results for the Administrator’s consideration as a letter to the docket following publication of the proposed 

rule in January 2006. 
Draft – Do Not Quote or Cite 2-30 June 2010

35 μg/m3 1 ,suggests that the overall health effects observed across the U.S. are not primarily 

2 driven by the higher end of the PM2.5 air quality distribution (Bell, 2009, personal 

3 communication from Dr. Michelle Bell regarding air quality data for Bell et al., 2008 and 

4 Dominici et al., 2006). In an extended analysis of the MCAPS study, Bell et al (2008) reported a 

5 positive and statistically significant increase in cardiovascular hospitalizations associated with 

6 short-term PM2.5 exposure, in which the aggregate long-term mean PM2.5 concentration was 12.9 

μg/m3 (ranging from 4 μg/m3to 20 μg/m3 7 ). 

8 In considering single-city short-term PM2.5 exposure studies that would likely have met 

9 the current suite of standards, the following studies reported positive and statistically significant 

10 associations in areas: Mar et al. (2004) reported an association for short-term PM2.5 exposures 

11 in relation to respiratory symptoms among children in Phoenix; and two studies noted in the last 

12 review, Peters et al. (2001) and Delfino et al. (2007), reported an association with short-term 

13 exposure to PM2.5 and MI- and respiratory-related hospital admissions in Boston and Montreal, 

14 Canada, respectively. Single-city studies that reported positive but statistically non-significant 

15 associations for cardiovascular and respiratory endpoints include a number of studies reporting a 

16 wide range of air quality distributions conducted in Saint John (Steib et al., 2000), Phoenix 

17 (Wilson et al., 2007), Denver (Rabinovitch et al., 2006), Edmonton (Villeneuve et al., 2006), and 

18 Nueces County, TX (Lisabeth et al., 2008). Other single-city short-term PM2.5 exposure 

19 analyses reported null findings for cardiovascular and respiratory morbidity effects in association 

20 with short-term exposure to PM2.5 in areas that would likely have met the current suite of 

21 standards, including Phoenix (i.e., respiratory symptoms in adults; Mar et al., 2004); Spokane 

22 (Slaughter et al. 2005), Denver (Rabinovitch et al., 2004) and Edmonton (Villeneuve et al., 

23 2006). In light of the mixed findings reported in single-city studies, particularly for studies 

24 conducted in areas such as Phoenix, Denver, and Edmonton that report both positive and null 

25 findings, we place comparatively greater weight on the results from multi-city studies in 

26 considering the adequacy of the current suite of standards. 

27 Collectively, the findings from multi-city short-term PM2.5 exposure studies together with 

28 support from single city studies at concentrations that would likely have met the current suite of 

29 standards provide evidence of PM2.5 associated health effects. These findings are further 

30 bolstered by evidence of statistically significant PM2.5 associated health effects occurring in 

analyses restricted to days in which 24-hour average PM2.5 concentrations were below 35 μg/m3 31 

32 (Dominici, 2006b). 

33 Summary 

34 In evaluating the currently available scientific evidence, we conclude that the evidence 

35 from long and short-term PM2.5 exposure studies calls into question whether the current suite of 
Draft – Do Not Quote or Cite 2-31 June 2010

1 PM2.5 primary standards protects public health with an adequate margin of safety from effects 

2 associated with long- and short-term exposures to PM2.5. We also conclude that this evidence 

3 provides strong support for considering fine particle standards that would impart increased 

4 protection beyond that afforded by the current annual and 24-hour PM2.5 standards. More 

5 protective standards would reflect the substantially stronger and broader body of evidence for 

6 mortality and cardiovascular and respiratory morbidity effects now available in this review both 

7 at lower concentrations of air quality than had previously been observed and at levels allowed by 

8 the current suite of PM2.5 standards.

9 2.2.2 Risk-based Considerations 

10 Looking beyond evidence-based considerations, staff also has considered the extent to 

11 which health risks estimated to occur upon just meeting the current suite of PM2.5 standards may 

12 be judged to be important from a public health perspective. For this review, we have estimated 

13 risk for a set of health effect endpoints based on a number of selection criteria (US EPA, 2010a, 

14 section 3.3.1). Specifically, we have estimated risks for (a) all-cause, IHD, cardiopulmonary and 

15 lung cancer mortality related to long-term PM2.5 exposure, (b) non-accidental, cardiovascular 

16 (CV) and respiratory mortality related to short-term PM2.5 exposure, and (c) cardiovascular17 related and respiratory-related hospital admissions and asthma-related emergency department 

18 visits associated with short-term PM2.5 exposure. In the discussion below, we focus on 

19 cardiovascular-related endpoints, since the causal association for these endpoints based on 

20 available literature is assessed in the ISA to be the strongest of the endpoints considered. The 

21 modeled risks for the broader set of health effect endpoints are included in the RA (US EPA, 

22 2010a). 

23 As discussed below, three factors figure prominently in the interpretation of the risk 

24 estimates associated with simulating just meeting the current suite of standards, including: (1) the 

25 importance of changes in annual mean PM2.5 concentrations in a study area in estimating changes 

26 in risks for both long- and short-term exposures, (2) the peakiness of ambient PM2.5

concentrations in a study area,15 27 and (3) the spatial pattern of ambient PM2.5 reductions that result 

28 from using different approaches to simulate just meeting the current standard levels (i.e., rollback 

29 approaches). The latter two factors are interrelated and influence the degree of risk reduction 

30 estimated under the current suite of standards. 

31 The magnitude of both long- and short-term exposure-related risk estimated to remain 

32 upon just meeting the current suite of standards is strongly associated with the simulated change 

 15 The term “peakiness” refers to air quality distributions across urban areas that have high peak-to-mean ratios (i.e. 

the ratio of the 24-hour design value to the annual mean design value) relative to distributions in other urban study 

areas in the U.S. 
Draft – Do Not Quote or Cite 2-32 June 2010

1 in annual mean PM2.5 concentrations. The role of annual mean PM2.5 concentrations in driving 

2 long-term exposure-related risk estimates is intuitive given that risks are modeled using the 

annual mean air quality metric.16 3 The fact that short-term exposure-related risk estimates are 

4 also driven by changes in long-term mean PM2.5 concentrations is less intuitive, since changes in 

mean 24-hour PM2.5 concentrations are used to estimate changes in risk for this time period.17 5 

6 Analyses show that short-term exposure-related risks are not primarily driven by the small 

7 number of days with PM2.5 concentrations in the upper tail of the air quality distribution, but 

8 rather by the large number of days with PM2.5 concentrations at and around the mean of the 

9 distribution (US EPA, 2010a, section 3.1.2.2). Consequently, changes in annual mean PM2.5

10 concentrations are related, to a large extent, to changes in short-term exposure-related risk. 

11 Therefore, we focus on changes in annual mean PM2.5 concentrations to inform our 

12 understanding of patterns of both long-term and short-term exposure-related risk estimates across 

13 the set of urban study areas evaluated in the quantitative RA. 

14 The peakiness of ambient PM2.5 concentrations within a study area informs the type of 

15 rollback approach used to simulate just meeting the current suite of standards to determine the 

16 magnitude of the reduction in annual mean PM2.5 concentrations for that study area and 

consequently the degree of risk reduction.18 17 For example, study areas with relative peaky PM2.5

18 distributions are likely to have greater estimated risk reductions for the current suite of standards 

19 (depending on their mix of 24-hour and annual design values), and such locations can be 

20 especially sensitive to the type of rollback approach used, with the proportional rollback 

21 approach resulting in notably greater estimated risk reduction compared with the locally-focused 

22 rollback approach. In contrast, study areas with less peaky PM2.5 concentrations typically 

23 experience greater simulated risk reductions for the current annual-standard level compared with 

24 24-hour standard levels (again depending on the mix of 24-hour and annual design values). In 

25 addition, the type of rollback approach used will tend to have less of an impact on the magnitude 

26 of risk reductions for study areas with less peaky PM2.5 concentrations. Rigorous consideration 

 16 As noted in section 3.2.1 of the RA (U.S.EPA, 2010a), estimates of long-term exposure-related mortality are 

actually based on an annual mean PM2.5 concentration across monitors in a study area (i.e., the composite monitor 

mean). Therefore, in considering changes in long-term exposure-related mortality, it is most appropriate to compare 

composite monitor estimates generated for a study area under each suite of standards. The annual mean at the 

highest reporting monitor (i.e., maximum monitor) for a study area (i.e., the annual design value) determines the 

percent reduction in PM2.5 concentrations required to meet a particular standard. Both types of air quality estimates 

are provided in Table 3-4 of the RA and both are referenced in this discussion of core risk estimates, as appropriate. 17 Estimates of short-term exposure-related mortality and morbidity are based on composite monitor 24-hour PM2.5

concentrations. However, similar to the case with long-term exposure-related mortality, it is the 98th percentile 24-

hour concentration estimated at the maximum monitor (the 24-hour design value) that will determine the degree of 

reduction required to meet a given 24-hour standard. 

18 The peakiness of ambient PM2.5 levels also has a direct bearing on whether the 24-hour or annual standard will be 

controlling for a particular study area, with more peaky distributions generally being associated with locations where 

the 24-hour standard is controlling. 
Draft – Do Not Quote or Cite 2-33 June 2010

1 of these two factors, allowed us to better understand the nature and pattern of estimated risk 

2 reductions and risk remaining upon simulation of just meeting the current suite of standards 

3 across the urban study areas (see U.S.EPA, 2010a, section 5.2.1). 

4 We have considered a series of questions to inform our understanding of the adequacy of 

5 the current suite of fine particle standards based on insights obtained from the quantitative RA. 

6 We begin by considering the overall confidence associated with the RA and the degree to which 

7 the set of urban study areas included in the RA is representative of urban areas across the U.S.. 

8 We then consider the nature and magnitude of risk estimated to remain based on simulating just 

9 meeting the current suite of standards. 

10  What is the level of confidence associated with risk estimates generated for simulating 

11 just meeting the current suite of PM2.5 standards? 

12 A number of design elements were included in the quantitative RA to increase the overall 

13 confidence in the risk estimates generated for the 15 urban study areas. These elements 

14 included: (a) use of a deliberative process in specifying components of the risk model that 

15 reflects consideration of the latest research on PM2.5 exposure and risk (US EPA, 2010a, section 

16 5.1.1), (b) integration of key sources of variability into the design as well as the interpretation of 

17 risk estimates (U.S.EPA, 2010a, section 5.1.2), (c) assessment of the degree to which the urban 

18 study areas are representative of areas in the U.S. experiencing higher PM2.5-related risk 

19 (U.S.EPA, 2010a, section 5.1.3), and (d) identification and assessment of important sources of 

20 uncertainty and the impact of these uncertainties on the core risk estimates (U.S.EPA, 2010a, 

section 5.1.4).19 21 Two additional analyses examined potential bias and overall confidence in the 

22 risk estimates. The first analysis explored potential bias in the core risk estimates by considering 

23 a set of alternative reasonable risk estimates generated as part of a sensitivity analysis. The 

24 second analysis compared the annual mean PM2.5 concentrations associated with simulating just 

25 meeting the current suite of standards with the air quality distribution used in deriving the C-R 

functions applied in modeling mortality risk.20 26 Greater confidence is associated with risk 

27 estimates based on annual mean PM2.5 concentrations that are within the region of the air quality 

28 distribution used in deriving the C-R functions where the bulk of the data reside (i.e., within one 

29 standard deviation (SD) around the mean). Each of the design elements listed above together 

30 with the two additional analyses is discussed below. 

31 Staff used a deliberative process to specify each of the key analytical elements 

32 comprising the core risk model, including: selection of urban study areas; selection of health 

 19 The “core” risk estimates produced in this assessment refer to those generated using the combination of modeling 

elements and input datasets in which we had the highest confidence relative to other modeling choices considered 

(note, that alternative modeling elements have been included as part off the sensitivity analyses completed). 

20 This analysis also considered simulations of alternative standards as discussed in section 2.4.2. 
Draft – Do Not Quote or Cite 2-34 June 2010

1 endpoints, including specification of the C-R functions to use in modeling those endpoints; and 

2 choice of rollback approach used to simulate just meeting the current suite of standards. This 

3 deliberative process involved rigorous review of the currently available literature addressing both 

4 PM2.5 exposure and risk combined with the application of a formal set of criteria to guide 

development of each of the key analytical elements in the RA (US EPA, 2010a, section 5.1.1)21 5 

6 The application of this deliberative process increases overall confidence in the risk estimates by 

7 insuring that the estimates are based on the best available science and data characterizing PM2.5

8 exposure and risk, and that they reflect consideration of input from external experts on PM 

9 exposure and risk through CASAC and public reviews. 

10 We considered key sources of variability that can impact the nature and magnitude of 

11 risks associated with the current standard levels across the urban study areas. These sources of 

12 uncertainty include those that contribute to differences in risk across urban study areas, but do 

13 not directly affect the degree of risk reduction associated with the simulation of the current 

14 standard levels (e.g., differences in baseline incidence rates, demographics and population 

15 behavior). We also focused on factors that not only introduce variability into risk estimates 

16 across study areas, but also play an important role in determining the magnitude of risk 

17 reductions upon simulation of current standard levels (e.g., peakiness in ambient PM2.5

18 concentrations within individual urban study areas and the nature of the rollback approach used 

19 to simulate just meeting the current standards – see earlier discussion). 

20 Single and multi-factor sensitivity analyses were combined with a qualitative analysis to 

21 assess the impact of potential sources of uncertainty on the core risk estimates. The qualitative 

22 uncertainty analysis supplemented the quantitative sensitivity analyses by allowing coverage for 

23 sources of uncertainty which could not be readily included in the sensitivity analysis (US EPA, 

24 2010a, section 3.5.3). The quantitative sensitivity analyses informed our understanding of 

25 sources of uncertainty which may have a moderate to large impact on the core risk estimates. 

26 With respect to the long-term exposure-related mortality risk estimates, the most important 

27 sources of uncertainty identified in the quantitative sensitivity analyses included: selection of CR function;22 28 modeling risk down to policy-relevant background (PRB) versus lowest measured 

29 level (LML); and the choice of rollback approach used. With regard to the qualitative analysis of 

30 uncertainty the following sources were identified as potentially having a large impact on core 

31 risk estimates for the long-term exposure-related mortality: characterization of inter-urban 

 21 In addition, as discussed in section 1.2.4, the RA design reflects consideration of CASAC and public comments 

on the Scope and Methods Plan and two draft assessment documents. 22 In the case of long-term exposure-related mortality, we considered both alternative C-R functions from the 

epidemiological study providing the C-R function used in the core analysis (i.e., alternative functions obtained from 

the Krewski et al. (2009) study involving the ACS dataset) as well as alternative C-R functions identified in other 

studies (i.e., C-R functions obtained from Krewski et al. (2000) based on the Harvard Six Cities dataset). 
Draft – Do Not Quote or Cite 2-35 June 2010

1 population exposures; impact of historical air quality; and potential variation in effect estimates 

2 reflecting differences in PM2.5 composition. Together, the qualitative analysis of uncertainty and 

3 quantitative sensitivity analyses provided us with a comprehensive understanding of which 

4 sources of uncertainty could have a significant impact on the core risk estimates. This 

5 information proved useful in interpreting core risk estimates and increased our overall 

confidence in the analysis.23 6 

7 In addition to identifying sources of uncertainty with a moderate to large impact on the 

8 core risk estimates, the single and multi-element sensitivity analyses also produced a set of 

9 reasonable alternative risk estimates that allowed us to place the results of the core analysis in 

context with regard to uncertainty and potential bias.24 10 Most of the alternative model 

11 specifications supported by available literature produced risk estimates that are higher (by up to a 

12 factor of 2 to 3) than the core risk estimates. This was not unexpected. The C-R functions used 

13 in the core analysis for estimating mortality risks associated with long-term PM2.5 exposures 

14 were selected from the extended analysis of the ACS study (Krewski et al., 2009). The C-R 

15 functions used in the sensitivity analysis were from the reanalysis and validation of the Harvard 

16 Six Cities study (Krewski et al., 2000). In generalizing the results of the extended analyses of 

17 the ACS and Harvard Six Cities studies across the broader national population, we recognize 

18 differences in the underlying populations enrolled in these long-term cohort studies, specifically 

19 related to socioeconomic status (SES), a factor in considering impacts on susceptible 

20 populations. As noted in the last review, the ACS study population has a higher SES status (e.g., 

21 educational status) relative to the Harvard Six Cities study population (12% versus 28% of the 

22 cohort had less than a high school education, respectively) (US EPA, 2004a, p. 8-118). The 

23 Harvard Six Cities cohort may provide a more representative sample of the broader national 

24 population than the ACS cohort. 

25 As discussed above, lower SES groups have been identified as a susceptible population. 

26 Therefore, use of effect estimates reported in the ACS study which does not provide 

27 representative coverage for lower-SES groups, may result in risk estimates that are biased low. 

28 In contrast, risk estimates developed in the sensitivity analysis based on the Harvard Six Cities 

 23 Given increased emphasis placed in this analysis on long-term exposure-related mortality, the uncertainty analyses 

completed for this health endpoint category are more comprehensive than those conducted for short-term exposurerelated mortality and morbidity, which to some extent reflects limitations in study data available for addressing 

uncertainty in the later category (U.S.EPA 2010a, section 3.5.4.2). 24 The alternative set of reasonable risk estimates are based on alternative model specifications to those used in the 

core risk model. These alternative reasonable risk estimates were only generated for long-term exposure-related 

mortality and not for any of the short-term exposure-related mortality or morbidity endpoints. Consequently, 

consideration of overall confidence (and potential bias) in the core risk estimates based on consideration for these 

alternative risk estimates is limited to estimates of mortality associated with long-term PM2.5 exposures. 
Draft – Do Not Quote or Cite 2-36 June 2010

1 study data set, provide better coverage for lower SES populations and therefore result in higher 

2 risk estimates (US EPA, 2010a, section 5.1.5).

3 While being mindful that the use of C-R functions from Krewski et al. (2009) introduces 

4 potential for low bias in the core risk estimates, we also recognize many strengths of this study 

5 and reasons for continued use in generating the core risk estimates, including: the large number 

6 of metropolitan statistical areas (MSAs), inclusion of two time periods for the air quality data 

7 which allowed us to consider different exposure windows; and analysis of a wide range of C-R 

8 function models. Therefore, we concluded that C-R functions obtained from this study had the 

9 greatest overall support and should be used in the core risk model. Consideration of the 

10 alternative set of reasonable risk estimates provided several observations relevant to the 

11 interpretation of the core risk estimates including: (a) the core estimates are unlikely to 

12 underestimate risk and (b) the degree of potential bias in the core risk estimates could range up to 

at least a factor of 2-3 higher.25 13 

14 In considering the overall confidence in the core risk estimates, we have compared the 

15 PM2.5 concentrations simulated under the current suite of standard levels across the urban study 

16 areas to the distribution of PM2.5 concentrations used in deriving the C-R functions used for 

17 long-term exposure-related mortality (as presented in Krewski et al., 2009). Specifically, this 

18 assessment compared the composite monitor annual mean PM2.5 concentrations used in modeling 

19 long-term exposure-related mortality risk in the core analysis to the distribution of annual mean 

PM2.5 concentrations from the 1999-2000 ACS exposure period.26 20 Generally, when composite 

21 monitor annual mean concentrations were within one SD of the mean of the ACS dataset (i.e., in 

the range of 14+/-3 μg/m3 22 ), we had relatively high confidence in the risk estimates, since they 

23 were based on PM2.5 concentrations that roughly matched those used in deriving the C-R 

24 functions. However, as composite monitor annual mean PM2.5 concentrations extend below this 

 25 We note that these findings regarding potential bias in the core risk estimates were based on modeling PM2.5-

attributable IHD and all-cause mortality associated with long-term PM2.5 exposure for the current suite of standards. 

However, we would expect these observations regarding overall confidence in the core risk estimates to hold for 

other long-term exposure-related mortality endpoints modeled in the RA for both the alternative annual and 24-hour 

standard levels considered. Furthermore, given increased emphasis placed in this analysis on long-term exposurerelated mortality, as noted earlier, the uncertainty analyses completed for this health endpoint category are more 

comprehensive than those conducted for short-term exposure-related mortality and morbidity effects, which to some 

extent reflects limitations in study data available for addressing uncertainty in the later category. Therefore, an 

alternative set of reasonable risk estimates was not generated to supplement core risk estimates generated for shortterm PM2.5 exposure. 26 As discussed in sections 3.3.3 and 4.0 of the RA (U.S.EPA, 2010a), each category of long-term exposure-related 

mortality was estimated using separate C-R functions derived from the 1979-1983 and 1999-2000 ACS monitoring 

periods. For purposes of comparing composite monitor annual mean PM2.5 concentrations to the ACS data sets used 

in deriving the C-R functions, we focused on the later monitoring period (1999-2000), since ambient PM2.5 

concentrations from this period more closely matched those associated with the study areas in our consideration of 

recent air quality conditions. The 1999-2000 ACS monitoring period had a mean PM2.5 concentration of 14 μg/m3

, a 

SD of 3.0 μg/m3 and an LML of 5.8 μg/m3 (see Table 1 in Krewski et al., 2009). 
Draft – Do Not Quote or Cite 2-37 June 2010

1 range, our confidence in the risk estimates decreased, with our confidence being significantly 

2 reduced when composite monitor annual mean concentrations approach the LML of the ACS 

dataset (i.e., 5.8 μg/m3 3 ). 

4  How representative is the set of urban study areas for the broader set of urban areas in 

5 the U.S. expected to experience elevated risk from ambient PM2.5 exposure? 

6 The goal in selecting urban study areas was to provide coverage for the range of larger 

7 urban areas in the U.S. expected to experience relatively elevated risk due to ambient PM2.5

8 exposure and other factors associated with PM2.5 risk (e.g., elevated baseline incidence rates for 

9 relevant health endpoints, relatively larger susceptible populations). As part of considering our 

10 overall confidence in the quantitative RA, we assessed the representativeness of the 15 urban 

11 study areas in the broader national context.. Three separate analyses were used to explore 

12 representativeness: 

13  A comparison of PM2.5-risk-related attributes of the 15 urban study areas against 

14 national distributions of these same attributes suggested that the urban study areas 

15 likely reflect the distribution of risk for the nation, with the potential for better 

characterization at the high end of that distribution (US EPA, 2010a, section 4.4.1).27 16 

17  An analysis of where the 15 urban study areas fall along the distribution of U.S. 

18 counties included in a national-scale mortality analysis suggested that we have 

19 captured counties likely to experience elevated PM2.5-related risk (US EPA, 2010a, 

20 section 4.4.2). 

21  An evaluation of the mix of design values across the 15 urban study areas as 

22 contrasted with design values for the broader set of urban study areas in the U.S. This 

23 analysis suggested that (a) the 15 urban study areas reasonably capture the key 

24 groupings of urban areas in the U.S. likely to experience elevated risk due to PM2.5 

25 exposures and (b) we have included study areas likely to experience relatively greater 

26 degrees of PM2.5-related risk (US EPA, 2010a, section 4.5.1). 

27 Based on these analyses, we conclude that these study areas are generally representative 

28 of urban areas in the U.S. likely to experience relatively elevated levels of risk related to ambient 

29 PM2.5 exposure. 

30  What is the nature and magnitude of the long-term and short-term exposure-related 

31 risks remaining upon just meeting the current suite of PM2.5 standards? 

32 In considering PM2.5-related risks likely to remain upon just meeting the current PM2.5

33 annual and 24-hour standards in the 15 urban study areas included in the quantitative RA, we 

 27 This representativeness analysis also showed that the urban study areas do not capture areas with the highest 

baseline morality risks or the oldest populations (both of which can result in higher PM2.5-related mortality 

estimates). However, some of the areas with the highest values for these attributes have relatively low PM2.5

concentrations (e.g., urban areas in Florida) and consequently failure to include these areas in the set of urban study 

areas is unlikely to exclude high PM2.5-risk locations. 
Draft – Do Not Quote or Cite 2-38 June 2010

1 focus on the 13 areas that would not meet the current standards based on recent air quality (2005-

2 2007). These 13 areas have annual and/or 24-hour design values that are above the levels of the 

current standards (see Tables 2-2 and 2-3).28 3 Based on the core risk estimates for these areas, 

4 using the proportional rollback approach, we make the following key observations regarding the 

5 magnitude of risk remaining upon simulation of just meeting the current suite of standards: 

6  Long-term exposure-related mortality risk remaining: IHD-related mortality 

7 attributable to long-term PM2.5 exposure was estimated to range from less than 100 to 

8 approximately 2,000 cases per year. The variability in these estimates reflect, to a 

9 great extent, differences in the size of study area populations. These estimates 

10 represent from 4 to 17% of all IHD-related mortality in a given year for the urban 

11 study areas, representing a measure of risk that takes into account differences in 

12 population size and baseline mortality rates (see Table 2-2). 

13  Short-term exposure-related mortality risk remaining: CV-related mortality 

14 associated with short-term PM2.5 exposure was estimated to range from less than 10 to 

15 500 cases per year. These estimates represent approximately 1 to 2% of total CV16 related mortality in a given year for the urban study areas (see Table 2-3). 

17  Short-term exposure-related morbidity risk remaining: CV-related hospitalizations 

18 were estimated to range from approximately 10 to 800 cases per year across the study 

19 areas, which is approximately equivalent to less than 1% of total CV-related 

20 hospitalizations (see Table 2-3). 

21 Although long- and short-term exposure-related mortality have similar patterns (in terms 

22 of the subset of urban study areas experiencing risk reductions for the current suite of standard 

23 levels), the magnitude of risk remaining is substantially lower for short-term exposure-related 

24 mortality. These findings were expected, since, as noted earlier, changes in annual mean PM2.5

25 concentrations were expected to drive both long- and short-term exposure-related risk, resulting 

26 in similar overall patterns in risk reduction for both exposure periods (in terms of the subset of 

27 study areas experiencing risk reductions). We note, however, that the variability in the effect 

28 estimates used to model short-term exposure-related health endpoints across urban study areas 

29 introduced additional variation into the pattern of risk reduction across study areas. 

30  Substantial variability exists in the magnitude of risk remaining across study areas: 

31 Estimated risks remaining upon just meeting the current suite of standards vary 

32 substantially across study areas, even when considering risks normalized for 

33 differences in population size and baseline incidence rates. This variability is a 

34 consequence of the substantial differences in the annual mean PM2.5 concentrations 

35 across study areas that result from simulating just meeting the current standards. This 

36 is important because, as discussed above, annual mean concentrations are highly 

37 correlated with both long- and short-term exposure-related risk. This variability in 

 28 Of the 15 study areas, only Dallas and Phoenix have both annual and 24-hour design values below the levels of 

the current standards based on 2005-2007 air quality. 
Draft – Do Not Quote or Cite 2-39 June 2010

1 annual mean PM2.5 concentrations occurred especially in those study areas in which 

2 the 24-hour standard is the “controlling” standard. In such areas, the variability in 

3 estimated risks across study areas was largest when regional patterns of reductions in 

4 PM2.5 concentrations were simulated, using the proportional rollback approach, as 

5 was done in the core analyses. Less variability was observed when more localized 

6 patterns of PM2.5 reductions were simulated using the locally-focused rollback 

7 approach, as was done in a sensitivity analysis. When simulations were done using 

8 the locally-focused rollback approach, estimated risks remaining upon just meeting 

9 the current suite of standards were appreciably larger than those estimated in the core 

10 analysis (US EPA, 2010a, section 4.3.1.1). 

11  Simulation of just meeting the current suite of standards results in annual mean PM2.5 

12 concentrations well below the current standard for some study areas: In simulating 

13 just meeting the current suite of standards, the resulting composite monitor annual 

mean PM2.5 concentrations ranged from about 15 μg/m3 14 (for those study areas in 

which the annual standard was controlling) down to as low as about 8 μg/m3 15 (for 

16 those study areas in which the 24-hour standard was controlling or the annual mean 

concentration was well below 15 μg/m3 17 based on recent air quality). As discussed 

18 above, as the composite monitor annual mean PM2.5 concentrations used in generating 

risk estimates extend below 11.0 μg/m3 19 (one SD below the mean for the 1999-2000 

20 ACS monitoring period, Krewski et al., 2009) we have increasingly less confidence in 

21 the risk estimates, with confidence decreasing significantly as composite monitor 

concentrations approach the LML for the ACS dataset (5.8 μg/m3 22 ). Typically, for the 

23 15 urban study areas assessed, the locations where the 24-hour standard was the 

24 controlling standard were simulated to have the lowest composite monitor annual 

25 mean PM2.5 concentrations. We observe that all four of the urban study areas with 

simulated composite monitor annual mean PM2.5 concentrations below 11 μg/m3 26 have 

27 the 24-hour standard controlling (U.S.EPA, 2010a, Table 3-4). While such locations 

28 often are estimated to have the greatest risk reductions, there is also reduced 

29 confidence associated with these risk estimates.

30  To what extent are the risks remaining upon simulation of the current suite of 

31 standards important from a public health perspective? 

32 Estimates of long-term exposure-related IHD mortality risk under simulation of the 

33 current suite of standard levels range from less than 100 deaths per year for the urban study area 

34 with the lowest risk to approximately 2,000 deaths per year for the urban study areas with the 

35 greatest risk. Estimates of risk for the urban areas included in the RA suggest that IHD mortality 

36 related to long-term PM2.5 exposure would likely be in the thousands of deaths per year on a 

37 national scale. Based on these risk estimates for IHD mortality alone, we conclude that risks 

38 estimated to remain upon simulation of the current suite of standards are important from a public 

39 health standpoint. This reflects consideration of both the severity of the effect and the magnitude 

40 of the effect. We have also estimated risks for long-term exposure related mortality risk related 

41 to cardiopulmonary effects and lung cancer, which increase the total annual incidence of 
Draft Do Not Quote or Cite June 2010 

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

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

Standards (for IHD mortality based on 2007 PM2.5 Concentrations)1,2 3 

4 

Exposure Period: 

1979-1983

Exposure Period: 

1999-2000

Exposure Period: 

1979-1983

Exposure Period: 1999-

2000

220 277 13.2% 16.7%

(180 - 258) (227 - 324) (10.9% - 15.5%) (13.7% - 19.5%)

297 374 11.7% 14.7%

(243 - 349) (307 - 440) (9.6% - 13.7%) (12.1% - 17.3%)

131 165 10.9% 13.8%

(107 - 154) (135 - 194) (8.9% - 12.9%) (11.3% - 16.2%)

195 247 9% 11.4%

(159 - 230) (202 - 291) (7.3% - 10.6%) (9.3% - 13.4%)

377 478 9.1% 11.5%

(308 - 445) (390 - 563) (7.4% - 10.7%) (9.4% - 13.5%)

77 98 6.7% 8.5%

(63 - 92) (80 - 116) (5.5% - 8%) (7% - 10.1%)

344 434 10.7% 13.6%

(281 - 405) (355 - 511) (8.8% - 12.6%) (11.1% - 16%)

860 1094 6.1% 7.7%

(701 - 1018) (890 - 1296) (4.9% - 7.2%) (6.3% - 9.1%)

1755 2222 9.3% 11.8%

(1435 - 2070) (1814 - 2620) (7.6% - 11%) (9.6% - 13.9%)

261 330 10.5% 13.2%

(214 - 308) (270 - 389) (8.6% - 12.3%) (10.8% - 15.6%)

317 402 6.7% 8.5%

(258 - 374) (327 - 476) (5.5% - 7.9%) (6.9% - 10.1%)

256 324 9.3% 11.8%

(209 - 302) (264 - 382) (7.6% - 11%) (9.6% - 13.9%)

15 19 2.9% 3.7%

(12 - 18) (16 - 23) (2.4% - 3.4%) (3% - 4.4%)

446 563 11.2% 14.2%

(365 - 525) (461 - 662) (9.2% - 13.2%) (11.6% - 16.7%)

38 49 3.7% 4.7%

(31 - 46) (40 - 58) (3% - 4.4%) (3.8% - 5.6%)

Risk Assessment 

Location

 Incidence of Ischemic Heart Disease 

Mortality Associated with Long-term 

Exposure to PM2.5

3

Percent of Incidence of Ischemic Heart Disease 

Mortality Associated with Long-term Exposure 

to PM2.5

3

 Atlanta, GA

 Baltimore, MD

 Birmingham, AL

 Dallas, TX

 Detroit, MI

 Fresno, CA

 Houston, TX

 Los Angeles, CA

 New York, NY

 Philadelphia, PA

 Phoenix, AZ

 Pittsburgh, PA

 Salt Lake City, UT

 St. Louis, MO

 Tacoma, WA

1 The current primary PM2.5 standards include an annual standard set at 15 μg/m3 and a 24-hour standard set at 35 μg/m3.

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical 

uncertainty surrounding the PM coefficient.

3 Estimates based on Krewski et al. (2009), using ambient PM2.5 concentrations from 1979 - 1983 and from 1999-2000, respectively. Incidence is 

5 for 30+ year olds within each urban study area.

6 

2-40
Draft 'o 1ot Quote or Cite June 2010 

1 Table 2-3. Estimated Incidence and Percent of Total Annual Incidence Associated with 

2 Short-Term PM2.5 Exposure Based on Simulation of the Current Suite of 

3 Standards (CV mortality and hospital admissions based on 2007 PM2.5

concentrations)1,2 4 

 5 

32 41 0.8% 0.4%

(-33 - 95) (-27 - 109) (-0.8% - 2.4%) (-0.2% - 1%)

62 216 1.6% 1.3%

(-4 - 126) (159 - 273) (-0.1% - 3.2%) (1% - 1.7%)

-1 16 0% 0.3%

(-42 - 40) (-11 - 43) (-1.5% - 1.5%) (-0.2% - 0.9%)

29 28 0.8% 0.3%

(-19 - 76) (-18 - 73) (-0.5% - 2.2%) (-0.2% - 0.7%)

60 233 1% 1.1%

(-8 - 127) (171 - 295) (-0.1% - 2.2%) (0.8% - 1.4%)

12 23 0.7% 0.5%

(-9 - 33) (0 - 46) (-0.5% - 2%) (0% - 0.9%)

46 56 0.9% 0.3%

(-31 - 122) (-37 - 149) (-0.6% - 2.4%) (-0.2% - 0.8%)

-30 258 -0.2% 0.5%

(-132 - 72) (3 - 511) (-0.7% - 0.4%) (0% - 0.9%)

473 752 2.1% 1.2%

(276 - 668) (552 - 951) (1.2% - 3%) (0.8% - 1.5%)

84 203 2.1% 1.3%

(22 - 145) (149 - 257) (0.5% - 3.6%) (0.9% - 1.6%)

84 108 1.3% 0.5%

(-4 - 170) (1 - 215) (-0.1% - 2.7%) (0% - 1%)

43 140 1.1% 1.1%

(-9 - 93) (103 - 177) (-0.2% - 2.3%) (0.8% - 1.4%)

9 9 0.8% 0.4%

(-2 - 20) (0 - 18) (-0.2% - 1.7%) (0% - 0.7%)

106 178 1.9% 1.3%

(24 - 187) (131 - 225) (0.4% - 3.3%) (0.9% - 1.6%)

11 19 0.7% 0.5%

(-6 - 27) (-46 - 82) (-0.4% - 1.8%) (-1.3% - 2.3%)

Risk Assessment 

Location

Percent of Incidence 

of Cardiovascular 

Mortality Associated 

with Short-term 

Exposure to PM2.53

Percent of Incidence of 

Cardiovascular Hospital 

Admissions Associated 

with Short-term 

Exposure to PM2.54

Incidence of 

Cardiovascular 

Mortality Associated 

with Short-term 

Exposure to PM2.53

Incidence of 

Cardiovascular 

Hospitalizations 

Associated with Shortterm Exposure to 

PM2.54

 Atlanta, GA

 Baltimore, MD

 Birmingham, AL

 Dallas, TX

 Detroit, MI

 Fresno, CA

 Houston, TX

 Los Angeles, CA

 New York, NY

 Philadelphia, PA

 Phoenix, AZ

 Pittsburgh, PA

 Salt Lake City, UT

 St. Louis, MO

 Tacoma, WA

1 The current primary PM2.5 standards include an annual standard set at 15 μg/m3 and a 24-hour standard set at 35 μg/m3.

2 Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty 

surrounding the PM coefficient.

3 Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been 

"shrunken" towards the appropriate regional means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti 

via email. 

4 Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. 

6 (2008). Location-specific C-R function estimates were not available from this study.

 7 

8 

9 

10 

11 .

2-41
Draft – Do Not Quote or Cite 2-42 June 2010

1 mortality attributable to long-term PM2.5 exposure (see U.S. EPA, 2010a, section 4.2.1). 

2 In addition to long-term exposure-related mortality, we estimated cardiovascular and 

3 respiratory-related mortality risk associated with short-term PM2.5 exposure. We note that these 

4 mortality estimates are up to an order of magnitude smaller than estimates related to long-term 

exposure-related mortality.29 5 As part of the RA, we also estimated respiratory and 

6 cardiovascular-related hospital admissions as well as asthma-related emergency department 

7 visits associated with short-term exposure to PM2.5, with estimates of cardiovascular and 

8 respiratory hospital admissions together ranging up to approximately 1,000 admissions per year 

9 across the urban study areas, with the estimated incidence of asthma-related emergency 

10 department visits being several fold higher. Further, as discussed in section 2.2.1, we recognize 

11 that the currently available scientific information includes evidence for a broader range of health 

12 endpoints and susceptible populations beyond those included in the quantitative RA, including 

13 lung function growth and respiratory symptoms in children and reproductive and developmental 

14 effects. Taken together, the set of quantitative risk estimates related to long- and short-term 

15 PM2.5 exposure, together with consideration of the health endpoints which could not be 

16 quantified, further strengthen the conclusion that risks estimated to remain following simulated 

17 attainment of the current suite of standards are important from a public health perspective, both 

18 in terms of severity and magnitude. 

19 2.2.3 Staff Conclusions on Adequacy of Current Standards 

20 Collectively, taking into consideration the responses to specific questions focusing on 

21 different ways to address the adequacy of the current suite of PM2.5 standards, we revisit the 

22 overarching policy question: does the currently available scientific evidence and risk-based 

23 information support or call into question the adequacy of the protection afforded by the current 

24 suite of fine particle standards? 

25 With respect to evidence-based considerations, the currently available evidence provides 

26 stronger evidence beyond what was available in the last review, that associations between short27 and long-term PM2.5 exposures and a broad range of adverse health effects exist. The currently 

28 available information strengthens the associations between PM2.5 and mortality and 

29 cardiovascular and respiratory morbidity effects observed in the last review and expands our 

30 understanding of a broader range of health outcomes as well as our understanding of effects in 

31 susceptible populations. The currently available evidence provides support for associations that 

32 extend to lower concentrations than what had been observed in the last review, including at 

 29 Estimates of short-term exposure-related and long-term exposure-related mortality should not be added because 

there is the potential for overlap (i.e., the long-term exposure-related mortality estimate picking up some of the 

short-term exposure-related signal on a daily basis, aggregated over the year). 
Draft – Do Not Quote or Cite 2-43 June 2010

1 ambient concentrations below the levels of the current standards providing the basis for 

2 consideration of alternative standards that would provide increased protection beyond that 

3 afforded by the current PM2.5 standards. 

4 In relation to risk-based considerations for informing our understanding of the adequacy 

5 of the current fine particle standards, we focus on the estimates of PM2.5-related mortality and 

6 morbidity effects likely to remain upon simulations of just meeting the current standards in a 

7 number of example urban areas. In considering the core risk estimates together with our 

8 understanding of the uncertainties in these estimates, based upon extensive sensitivity analyses, 

9 we conclude that the risks estimated to be associated with just meeting the current standards can 

10 reasonably be judged to be important from a public health perspective. We further conclude that 

11 these estimated risks provide support for consideration of alternative standards that would 

12 provide increased protection beyond that afforded by the current PM2.5 standards. 

13 We recognize that important uncertainties and research questions remain when 

14 considering both evidence- and risk-based approaches. Nonetheless, we note that much progress 

15 has been made in reducing some key uncertainties since the last review, including important 

16 progress in advancing our understanding of potential mechanisms by which ambient PM2.5 is 

17 causally linked with mortality, cardiovascular, and respiratory effects observed in 

18 epidemiological studies. Additional information continues to emerge for a broader range of 

19 health effects including reproductive and development effects and more information is available 

20 to understand susceptible populations including children, older adults, individuals with pre21 existing cardiovascular and respiratory disease, persons at lower SES, and persons with genetic 

22 susceptibility. 

23 As was true in the last review, we recognize that as the body of available evidence has 

24 expanded, it has added greatly both to our knowledge of health effects associated with fine 

25 particle exposures, as well as to the complexity inherent in interpreting the evidence in a policy26 relevant context as a basis for setting appropriate standards. In evaluating both evidence-based 

27 and risk-based considerations, along with associated limitations and uncertainties, we reach the 

28 conclusion that the available information clearly calls into question the adequacy of the current 

29 suite of PM2.5 standards and provides strong support for giving consideration to revising the 

30 current standards to provide increased public health protection. 

31 2.3 CONSIDERATION OF ALTERNATIVE STANDARDS 

32 Having reached the conclusion that the currently available scientific evidence calls into 

33 question the adequacy of the current suite of PM2.5 standards, staff considers a second 

34 overarching question: 
Draft – Do Not Quote or Cite 2-44 June 2010

1 What alternative suites of fine particle standards are supported by the currently available 

2 scientific evidence and risk-based information, as reflected in the ISA and RA? 

3 To inform the answer to this overarching question, we have posed a series of more 

4 specific questions to inform decisions regarding the basic elements of the NAAQS: indicator 

5 (section 2.3.1), averaging time (section 2.3.2), form (section 2.3.3), and level (section 2.3.4). 

6 These elements are considered collectively in evaluating the health protection afforded by 

7 alternative suites of standards under consideration. In considering the currently available 

8 scientific and technical information, we consider both the information available in the last review 

9 and information that is newly available since the last review as assessed and presented in the ISA 

10 and RA prepared for this review (US EPA, 2009a; US EPA, 2010a). 

11 2.3.1 Indicator 

12 In initially setting standards for fine particles in 1997, EPA concluded it was more 

13 appropriate to control fine particles as a group, rather than singling out any particular component 

14 or class of fine particles for which only very limited evidence was available. In establishing a 

15 size-based indicator to distinguish fine particles from particles in the coarse mode, EPA noted 

16 that the available epidemiological studies of fine particles were based largely on PM2.5 and also 

17 considered monitoring technology that was generally available. The selection of a 2.5 μm size 

18 cut reflected the regulatory importance of defining an indicator that would more completely 

19 capture fine particles under all conditions likely to be encountered across the U.S., especially 

20 when fine particle concentrations are likely to be high, while recognizing that some small coarse 

21 particles would also be captured by current methods to monitor PM2.5 (62 FR 38667 to 38668, 

22 July, 18, 1997). In the last review, based on the same considerations, EPA again recognized that 

23 the available information supported retaining the PM2.5 indicator and remained too limited to 

24 support a distinct standard for any specific PM2.5 component or fine particle source (71 FR 61162 

25 to 61164, October 17, 2006). 

26  Does the currently available information provide support for the continued use of a 

27 PM2.5 mass-based indicator for fine particles? 

28 In this review, epidemiological studies linking cardiovascular and respiratory effects as 

29 well as mortality with long- and short-term fine particle exposures continue to be largely indexed 

30 by PM2.5. Based on the same considerations that informed the last two reviews, we again 

31 conclude that it is appropriate to retain a PM2.5 indicator to provide protection associated with 

32 long- and short-term exposure to fine particles. 
Draft – Do Not Quote or Cite 2-45 June 2010

1 We also look to the expanded body of evidence available in this review to consider 

whether there is sufficient evidence to support a separate standard for ultrafine particles30 2 (UFPs) 

3 and whether there is sufficient evidence to establish distinct standards focused on regulating 

4 specific PM2.5 components or sources of fine particles, as addressed below. 

5  To what extent does the currently available information provide support for 

6 considering a separate indicator for UFPs? 

7 A number of studies available in this review have focused on UFPs, as a subset of PM2.5. 

8 As noted in the ISA, the enormous number and larger surface area of UFPs are important 

9 considerations for focusing on this subfraction of fine particles in assessing potential public 

health impacts (US EPA, 2009a, p. 6-83).31 10 Per unit mass, UFPs may have more opportunity to 

11 interact with cell surfaces due to their greater surface area and their greater particle number 

12 compared with larger particles (US EPA, 2009a, p. 5-3). Greater surface area also increases the 

13 potential for soluble components (e.g., transition metals, organics) to adbsorb to UFPs and 

14 potentially cross cell membranes and epithelial barriers (US EPA, 2009a, p. 6-83). . In 

15 addition, evidence available in this review suggests that the ability of particles to enhance 

16 allergic sensitization is associated more strongly with particle number and surface area than 

particle mass (US EPA, 2009a, p. 6-127).32 17 

18 New evidence, primarily from controlled human exposure and toxicological studies, 

19 expands our understanding of UFP-related cardiovascular and respiratory effects. However, this 

20 evidence is still very limited and largely focused on exposure to diesel exhaust (DE), for which 

21 the ISA concludes “it is unclear if the effects observed are due to UFP, larger particles (i.e., 

22 PM2.5), or the gaseous components of DE” (US EPA, 2009a, p. 2-22). In addition, the ISA notes 

23 uncertainties associated with the controlled human exposure studies as concentrated ambient 

24 particle (CAP) systems have been shown to modify the composition of UFPs (US EPA, 2009a, p. 

25 2-22, see also section 1.5.3). Relatively few epidemiological studies have examined potential 

26 cardiovascular and respiratory effects associated with short-term exposures to UFPs. These 

27 studies have reported inconsistent and mixed results (US EPA, 2009a, section 2.3.5). 

28 Collectively, in considering the body of scientific evidence available in this review, the 

29 ISA concludes that the currently available evidence is suggestive of a causal association between 

30 short-term exposures to UFPs and cardiovascular and respiratory effects. Furthermore, the ISA 

 30 Ultrafine particles, generally including particles with a nominal aerodynamic diameter less than 0.1 μm, are 

emitted directly to the atmosphere or are formed by nucleation of gaseous constituents in the atmosphere (US EPA, 

2009a, p. 3-3). 

31 Particle number is most highly concentrated in the UFP fraction with volume (or mass) most concentrated in the 

larger size fractions (US EPA, 2009a, p. 3-2). 

32 More information on possible modes of action for effects associated with UFPs exposures is discussed in sections 

5.1 and 5.4 of the ISA. 
Draft – Do Not Quote or Cite 2-46 June 2010

1 concludes that evidence is inadequate to infer a causal association between short-term exposure 

2 to UFPs and mortality as well as long-term exposure to UFPs and all outcomes evaluated (US 

3 EPA, 2009a, sections 2.3.5, 6.2.12.3, 6.3.10.3, 6.5.3.3, 7.2.11.3, 7.3.9, 7.4.3.3, 7.5.4.3, and 

4 7.6.5.3; Table 2-6). 

5 With respect to our understanding of ambient UFP concentrations, at present, there is no 

6 national network of UFP samplers; thus, only episodic and/or site-specific data sets exist (US 

EPA, 2009a, p. 2-2).33 7 Therefore, a national characterization of concentrations, temporal and 

8 spatial patterns, and trends is not possible, and the availability of ambient UFP measurements to 

9 support health studies are extremely limited. In general, measurements of UFPs are highly 

10 dependent on monitor location and, therefore, more subject to exposure error than accumulation 

11 mode particles (US EPA, 2009a, p. 2-22). The UFP number concentrations fall off sharply 

12 downwind from sources, as UFPs may grow into the accumulation mode by coagulation or 

13 condensation (US EPA, 2009a, p. 3-89). Limited studies of UFP ambient measurements suggest 

14 these particles exhibit a high degree of spatial and temporal heterogeneity driven primarily by 

15 differences in nearby source characteristics (US EPA, 2009a, p. 3-84). Internal combustion 

16 engines and, therefore, roadways are a notable source of UFPs, so concentrations of UFPs near 

17 roadways are generally expected to be elevated (US EPA, 2009a, p. 2-3). Concentrations of 

18 UFPs have been reported to drop off much more quickly with distance from roadways than 

19 larger particle sizes (US EPA, 2009a, p. 3-84). 

20 In considering both the currently available health effects evidence and the air quality data 

21 for UFPs, we conclude that this information is still too limited to support a distinct PM standard 

22 for UFPs. 

23  To what extent does the currently available information provide support for 

24 considering a separate indicator for a specific PM2.5 component or source category of 

25 fine particles? Conversely, to what extent does the currently available information 

26 provide support for eliminating any component or source category from the mix of fine 

27 particles included in the PM2.5 indicator? 

28 In addressing the issue of particle composition, the ISA concludes that, “[f]rom a 

29 mechanistic perspective, it is highly plausible that the chemical composition of PM would be a 

30 better predictor of health effects than particle size” (US EPA, 2009a, p. 6-202). Heterogeneity of 

31 ambient concentrations of PM2.5 constituents (e.g., elemental carbon (EC), organic carbon (OC), 

32 sulfates, and nitrates) observed in different geographical regions as well as regional 

33 heterogeneity in PM2.5-related health effects reported in a number of epidemiological studies are 

34 consistent with this hypothesis (US EPA, 2009a, section 6.6). 

 33 The ISA contains a review of the current scientific information related to measurements of UFPs (US EPA, 

2009a, sections 3.5.1 and 3.5.2). 
Draft – Do Not Quote or Cite 2-47 June 2010

1 With respect to the availability of ambient measurement data for fine particle components 

2 in this review, there are now more extensive ambient PM2.5 speciation measurement data 

available through the Chemical Speciation Network (CSN). 34 3 Data from the CSN monitoring 

4 network provide further evidence of spatial and seasonal variation in both PM2.5 mass and 

5 composition among cities/regions (US EPA, 2009a, pp. 3-50 to 3-60; Figures 3-12 to 3-18; 

6 Figure 3-47). Some of this variation may be related to regional differences in meteorology, 

7 sources, and topography (US EPA, 2009a, p. 2-3). 

8 The currently available epidemiological, toxicological, and controlled human exposure 

9 studies have evaluated the health effects associated with ambient PM2.5 constituents and 

10 categories of fine particle sources, using a variety of quantitative methods applied to a broad set 

11 of PM2.5 constituents, rather than selecting a few constituents a priori (US EPA, 2009a, p. 2-26). 

12 Epidemiological studies have used measured ambient PM2.5 speciation data, including 

13 monitoring data from the CSN, while all of the controlled human exposure and most of the 

14 toxicological studies have used CAPs and analyzed the constituents therein (US EPA, 2009a, p. 

6-203).35 15 The CSN provides PM2.5 speciation measurements generally on a one-in-three or one16 in-six day schedule and, thus, do not capture data every day at most sites. To expand our 

17 understanding of the role of specific PM2.5 components and sources with respect to the observed 

18 health effects, researchers have expressed a strong interest in having access to PM2.5 speciation 

measurements collected more frequently.36 19 

20 With respect to epidemiological studies evaluating short-term exposures to fine particle 

21 constituents, several new multi-city studies are now available. These studies continue to show an 

22 association between mortality and cardiovascular and/or respiratory morbidity effects and short-

 34The CSN consists of 54 Speciation Trends Network (STN) sites as well as about 150 SLAMS supplemental sites 

across the country measuring over 40 chemical species. A limited number of CSN monitors began collecting 

ambient data in 2000 with the majority of sites collecting data starting in 2001. These sites collect aerosol samples 

over 24 hours on filters that are analyzed for PM2.5 mass, trace elements, major ions (e.g., sulfates, nitrates, 

ammonium), and EC/OC. 

35 Most studies considered between 7 to 20 ambient PM2.5 constituents, with EC, OC, sulfate, nitrate, and metals 

most commonly measured. Many of the studies grouped the constituents with various factorization or source 

apportionment techniques to examine the relationship between the grouped constituents and various health effects. 

However, not all studies labeled the constituent groupings according to their presumed source and a small number of 

controlled human exposure and toxicological studies did not use any constituent grouping. These differences across 

studies substantially limit any integrative interpretation of these studies (US EPA, 2009a, p. 6-203). 36 As outlined in section 6.6.2.11of the ISA, some investigators have circumvented the issue of less than daily 

speciation data by using the PM2.5 chemical species data in a second stage regression to explain the heterogeneity in 

PM10 or PM2.5 mortality risk estimates across cities and assuming that the relative contributions of PM2.5 have 

remained the same over time (US EPA, 2009a, p. 6-206). In April 2008, EPA co-sponsored a workshop to discuss 

modifications to the current ambient air quality monitoring networks that would advance our understanding of the 

impacts of PM exposures on public health/welfare in the most meaningful way, including improving our 

understanding of fine particle components. A summary of the workshop recommendations, including 

recommendations for daily PM2.5 speciation measurements in large urban areas, is available at 

www.epa.gov/ORD/npd/pdfs/FINAL-April-2008-AQ-Health-Research-Workshop-Summary-Dec-2008.pdf. 
Draft – Do Not Quote or Cite 2-48 June 2010

1 term exposures to various PM2.5 components including nickel (Ni), vanadium (V), EC, OC, and 

2 sulfates (US EPA, 2009a, sections 6.5.2.5 and 6.6). Lippmann et al. (2006) and Dominici et al. 

3 (2007) evaluated the heterogeneity in the PM10–mortality association as evaluated in the 

4 NMMAPS data by analyzing the PM2.5 speciation data. Nickel and V were identified as 

5 significant predictors of variation in PM10-related mortality across cities, with Ni levels in New 

York City being reported as particularly high (US EPA, 2009a, section 6.5.2.5; Figure 6-31).37 6 

7 Bell et al. (2009) and Peng et al. (2009) conducted similar analyses focusing on the variation in 

8 PM2.5-related cardiovascular and respiratory hospital admissions in older adults. Peng et al. 

9 (2009) focused on the components that make up the majority of PM2.5 mass and using multi10 pollutant models reported only EC and OC were significantly associated with risk of 

11 hospitalization for cardiovascular disease. Bell et al. (2009) used data from twenty PM2.5 

12 components and found that EC, Ni, and V were most positively and significantly associated with 

13 the risk of PM2.5-related hospitalizations suggesting that the observed associations between PM2.5 

14 and hospitalizations may be primarily due to particles from oil combustion and traffic (US EPA, 

15 2009a, section 6.2.10.1). In a study of 25 U.S. cities, Franklin et al. (2008) focused on a time16 series regression of mortality related to PM2.5 mass by season and also examined effect 

17 modification due to various PM2.5 species. They concluded that Al, As, Ni, Si and sulfates were 

18 significant effect modifiers of PM2.5 mortality risk estimates, and “simultaneously including Al, 

19 Ni, and sulfates together or Al, Ni, and As together further increased explanatory power. Of the 

20 species examined, Al and Ni explained the most residual heterogeneity” (US EPA, 2009a, p. 6-

194; Table 6-17).38 21 Furthermore, Ostro et al (2006) examined associations between PM2.5 

22 components and mortality in six California counties and found an association between mortality, 

23 especially cardiovascular-related mortality and several PM2.5 components including EC, OC, 

24 nitrates, iron (Fe), potassium (K), and titanium (Ti) at various lags (US EPA, 2009a, p. 6-195).

25 Limited evidence is available to evaluate the health effects associated with long-term 

26 exposures to PM2.5 components (US EPA, 2009a, section 7.6.2). The most significant new 

27 evidence is provided by a study that evaluated multiple PM2.5 components and an indicator of 

28 traffic density in an assessment of health effects related to long-term exposure to PM2.5 (Lipfert 

29 et al., 2006). Using health data from a cohort of U.S. military veterans and PM2.5 data from 

30 EPA’s CSN, Lipfert et al. (2006) reported positive associations between mortality and long-term 

31 exposures to nitrates, EC, Ni and V as well as traffic density and peak O3 concentrations. 

32 Additional evidence from a long-term exposure study conducted in a Dutch cohort provides 

 37 However, as noted in the ISA, in a sensitivity analysis when selectively removing cities from the overall estimate, 

the significant association between the PM10 mortality risk estimate and the PM2.5 Ni fraction was diminished upon 

removing New York City from the analysis, which is consistent with the results presented by Dominici et al. (2007) 

(US EPA, 2009a, section 6.5.2.5; Figure 6-32). 

38 We note that New York City was not included in the 25 cities examined by Franklin et al. (2008). 
Draft – Do Not Quote or Cite 2-49 June 2010

1 supportive evidence that long-term exposure to traffic-related particles is associated with 

2 increased mortality (Breelen et al., 2008). 

3 With respect to source categories of fine particles associated with a range of health 

4 endpoints, the ISA reports that currently available evidence suggests associations between 

5 cardiovascular effects and a number of specific PM2.5–related source categories, specifically oil 

6 combustion, wood or biomass burning, motor vehicle emissions, and crustal or road dust sources 

7 (US EPA, 2009a, section 6.6; Table 6-18). In addition, a few studies have evaluated associations 

8 between PM2.5–related source categories and mortality. These studies included a reported 

9 association between mortality and a PM2.5 coal combustion factor (Laden et al., 2000), while 

10 others linked mortality to a secondary sulfate long-range transport PM2.5 source (Ito et al., 2006; 

11 Mar et al., 2006) (US EPA, 2009a, section 6.6.2.1). There is less consistency in associations 

12 observed between PM2.5 sources and respiratory health effects, which may be partially due to the 

13 fact that fewer studies have evaluated respiratory-related outcomes and measures. However, 

there is some evidence for associations with secondary SO4

2– 14 .and decrements in lung function in 

15 asthmatic and healthy adults(US EPA, 2009a, p. 6-211; Gong et al., 2005; Lanki et al., 2006). 

16 Respiratory effects relating to the crustal/soil/road dust and traffic sources of PM have been 

17 observed in asthmatic children and adults (US EPA, 2009a, p. 6-205; Gent et al., 2009; Penttinen 

18 et al., 2006). 

19 Recent studies have shown that source apportionment methods have the potential to add 

20 useful insights into which sources and/or PM constituents may contribute to different health 

21 effects. Of particular interest are several epidemiological studies that compared source 

22 apportionment methods and reported consistent results across research groups (US EPA, 2009a, 

23 p. 6-211; Hopke et al., 2006; Ito et al., 2006; Mar et al., 2006; Thurston et al., 2005). These 

24 studies reported associations between total mortality and secondary sulfate in two cities for two 

25 different lag times. The sulfate effect was stronger for total mortality in Washington D.C. and 

26 for cardiovascular-related morality in Phoenix (US EPA, 2009a, p. 6-204). These studies also 

27 found some evidence for associations with mortality and a number of source categories (e.g., 

28 biomass/wood combustion, traffic, copper smelter, coal combustion, sea salt) at various lag times 

29 (US EPA, 2009a, p. 6-204). Sarnat et al., (2008) compared three different source apportionment 

30 methods and reported consistent associations between ED visits for cardiovascular diseases with 

31 mobile sources and biomass combustion as well as increased respiratory-related ED visits 

32 associated with secondary sulfate (US EPA, 2009a, pp. 6-204 and 6-211; Sarnat et al., 2008). 

33 In summary, in considering the currently available evidence for health effects associated 

34 with chemical components and source categories of PM2.5 as presented in the ISA, we conclude 

35 that additional information available in this review continues to provide evidence that many 

36 different constituents of the fine particle mixture as well as specific source categories of fine 
Draft – Do Not Quote or Cite 2-50 June 2010

1 particles are linked to adverse health effects. However, as noted in the ISA, while “[t]here is 

2 some evidence for trends and patterns that link particular ambient PM constituents or sources 

3 with specific health outcomes...there is insufficient evidence to determine whether these patterns 

4 are consistent or robust” (US EPA, 2009a, p. 6-210). Furthermore, the ISA concludes that “the 

5 evidence is not yet sufficient to allow differentiation of those constituents or sources that are 

6 more closely related to specific health outcomes” (US EPA, 2009a, pp. 2-26 and 6-212). 

7 Therefore, we conclude that the currently available evidence is not sufficient to support 

8 consideration of a separate indicator for a specific PM2.5 component or source category of fine 

9 particles. We also conclude that the evidence is not sufficient to support eliminating any 

10 component or source from the mix of fine particles included in the PM2.5 indicator.

11 Summary 

12 In considering whether currently available evidence provides support for retaining, 

13 revising, or supplementing the current PM2.5 mass-based indicator, we first conclude that it is 

14 appropriate to retain PM2.5 as the indicator for fine particles. Secondly, we conclude that the 

15 currently available evidence does not provide a sufficient basis for supplementing the mass16 based PM2.5 indicator by considering a separate indicator for ultrafine particles as a subfraction 

17 of fine particles. We also conclude that the currently available evidence is too limited to provide 

18 support for considering a separate indicator for a specific PM2.5 component or source category of 

19 fine particles or for eliminating any individual component or source category from the mix of 

20 fine particles included in the PM2.5 mass-based indicator. 

21 2.3.2 Averaging Times 

22 In 1997, EPA initially set both an annual standard, to provide protection from health 

23 effects associated with both long- and short-term exposures to PM2.5, and a 24-hour standard to 

24 supplement the protection afforded by the annual standard. In the last review, EPA retained both 

25 annual and 24-hour averaging times (62 FR 38667 to 38668, July, 18, 1997). 

26 In this review, we consider whether the currently available information provides support 

27 for maintaining standards with annual and 24-hour averaging times and whether there is 

28 sufficient evidence to support setting standards with other averaging times to address subdaily or 

29 seasonal exposures. 

30  To what extent does the currently available information continue to provide support for 

31 annual and 24-hour averaging times? 

32 The overwhelming majority of studies conducted since the last review continue to utilize 

33 annual and 24-hour averaging times, and largely contribute to the body of evidence for health 

34 effects related to both short-term (from less than 1 day to up to several days) and long-term 
Draft – Do Not Quote or Cite 2-51 June 2010

1 (from a year to several years) measures of PM2.5. Consequently, we conclude that the currently 

2 available evidence continues to support annual and 24-hour averaging times. 

3  To what extent does the currently available scientific evidence provide support for 

4 considering a standard with an averaging time less than 24 hours to address health 

5 effects associated with subdaily fine particle exposures? 

6 Relative to information available in the last review, recent studies provide additional 

7 evidence for cardiovascular effects associated with subdaily (e.g., one to several hours) exposure 

8 to PM, especially effects related to cardiac ischemia, vasomotor function, and more subtle 

9 changes in markers of systemic inflammation, hemostasis, thrombosis and coagulation (US EPA, 

2009a, section 6.2).39 10 Because these studies have used different indicators (e.g., PM2.5, PM10, 

11 PM10-2.5, UFPs), averaging times (e.g., 1, 2, and 4 hours), and health outcomes, it is difficult to 

12 draw conclusions about cardiovascular effects associated specifically with subdaily exposures to 

13 PM2.5. 

14 With regard to respiratory effects associated with subdaily PM2.5 exposures, the currently 

15 available evidence is much sparser than for cardiovascular effects and continues to be very 

16 limited. The ISA concludes that for several studies of hospital admissions or medical visits for 

17 respiratory diseases, the strongest associations were observed with 24-hour average or longer 

18 exposures, not with less then 24-hour exposures (US EPA, 2009a, section 6.3). 

19 Collectively, we conclude that this information is too unclear, with respect to the 

20 indicator, averaging time and health outcome, to serve as a basis for establishing a shorter-than21 24-hour PM2.5 primary standard at this time. 

22  To what extent does the currently available scientific evidence provide support for 

23 considering separate standards with distinct averaging times to address effects 

24 associated with seasonal fine particle exposures? 

25 With regard to health effects associated with PM2.5 exposure across varying seasons in 

26 this review, Bell et al. (2008) reported higher PM2.5 risk estimates for hospitalization for 

27 cardiovascular and respiratory diseases in the winter compared to other seasons. In comparison 

28 to the winter season, smaller statistically significant associations were also reported between 

29 PM2.5 and cardiovascular morbidity for spring and autumn, and a positive non-significant 

30 association was observed for the summer months. In the case of mortality, Zanobetti and 

31 Schwartz (2009) reported a 4-fold higher effect estimate for PM2.5 associated mortality for the 

32 spring as compared to the winter. Taken together, these results are inconsistent, suggesting 

 39 A limited number of additional studies have also provided evidence of reported electrocardiogram changes 

typically representative of cardiac ischemia (S-T segment depression) or reported changes in heart rate variability 

(HRV) (US EPA, 2009a, sections 6.2.1.2 and 6.2.12.1), however, these changes are often variable and difficult to 

interpret the PM2.5 etiologically relevant mechanism underlying the observed effects. 
Draft – Do Not Quote or Cite 2-52 June 2010

1 individuals are at greater risk of dying from higher exposures to PM2.5 in the warmer months, and 

2 at greater risk of PM associated hospitalization for cardiovascular and respiratory diseases during 

3 colder months of the year. 

4 Overall, we observe that there are few studies presently available to deduce a general 

5 pattern in PM2.5-related risk across seasons. In addition, these studies utilized 24-hour exposure 

6 periods within each season to assess the PM2.5 associated health effects, and do not provide 

7 information on health effects associated with a season-long exposure to PM2.5. Due to these 

8 limitations in the currently available evidence, we conclude that there is no basis to consider a 

9 seasonal averaging time separate from a 24-hour averaging time. 

10 Summary

11 We recognize that the currently available evidence informs our understanding of 

12 exposure durations of concern and continues to provide strong support for standards that provide 

13 protection for both short- and long-term exposures. In considering the possibility of effects 

14 associated with subdaily PM2.5 exposures (i.e., less than 24-hour exposures), we recognize that 

15 there is additional evidence available in this review, primarily focused on cardiovascular effects 

16 with more limited evidence for respiratory effects. However, because these studies have used 

17 different indicators of PM exposure (e.g., PM2.5, PM10, UFPs), averaging times, and a broad 

18 range of health outcomes, it is difficult to use this evidence to serve as a basis for establishing a 

19 standard with a shorter-than-24-hour averaging time. With respect to seasonal effects, while we 

20 recognize there is some new evidence for PM2.5- related effects differentiated by season, we 

21 conclude that this evidence is too limited to use as a basis for establishing a PM2.5 standard with a 

22 seasonal averaging time. Based on the above considerations, we conclude that the currently 

23 available information provides strong support for retaining the current annual and 24-hour 

24 averaging times but does not provide support for considering alternative averaging times. 

25 2.3.3 Forms 

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

27 level of the standard in determining whether an area attains the standard. In this review, we 

28 consider whether currently available information supports consideration of alternative forms for 

29 the annual or 24-hour PM2.5 standards. 

30 2.3.3.1 Form of the Annual Standard 

31 In 1997, EPA established the form of the annual PM2.5 standard as an annual arithmetic 

32 mean, averaged over 3 years, from single or multiple community-oriented monitors. This form 

33 was intended to represent a relatively stable measure of air quality and to characterize area-wide 

34 PM2.5 concentrations. The level of the standard was to be compared to measurements made at 
Draft – Do Not Quote or Cite 2-53 June 2010

1 the community-oriented monitoring site recording the highest level, or, if specific constraints 

were met40 2 , measurements from multiple community-oriented monitoring sites could be 

3 averaged (62 FR 38671 to 38672, July 18, 1997). The constraints were intended to ensure that 

4 spatial averaging would not result in inequities in the level of protection (62 FR 38672). This 

5 approach was consistent with the epidemiological studies on which the PM2.5 standard was 

6 primarily based, in which air quality data were generally averaged across multiple monitors in an 

7 area or were taken from a single monitor that was selected to represent community-wide 

8 exposures, not localized “hot spots.” 

In the last review, EPA tightened the criteria for use of spatial averaging41 9 to provide 

10 increased protection for vulnerable populations exposed to PM2.5. This change was based in part 

11 on an analysis of the potential for disproportionate impacts on potentially vulnerable populations, 

12 which found that the highest concentrations in an area tend to be measured at monitors located in 

13 areas where the surrounding population is more likely to have lower education and income 

14 levels, and higher percentages of minority populations (71 FR 61166/2; US EPA, 2005, section 

15 5.3.6.1). 

16 In this review, we again consider the potential impact of allowing for spatial averaging, 

17 noting that persons from lower socioeconomic strata have been identified as an additional 

18 susceptible population (see section 2.2.1). 

19  Does the currently available evidence provide support for the continued use of spatial 

20 averaging as part of the form of the annual standard? 

21 In considering the potential for disproportionate impacts on potentially vulnerable 

22 populations, we updated analyses conducted for the last review. Specifically, we evaluated 

23 whether persons with a lower SES status are more likely than the general population to live in 

24 census tracts in which the monitors recording the highest air quality values in an area are located. 

25 Data used in this analysis included demographic parameters measured at the census tract level, 

26 including income level and percent minority population. Data from the census tract in each area 

27 in which the highest air quality value was monitored were compared to the area-wide average 

28 value (consistent with the constraints on spatial averaging provided by the current standard) in 

29 each area (Schmidt, 2010). 

30 Recognizing the limitations of such cross-sectional analyses, we observe that the highest 

31 concentrations in an area tend to be measured at monitors located in areas where the surrounding 

 40 The original criteria for spatial averaging included: (1) the annual mean concentration at each site shall be within 

20 percent of the spatially averaged annual mean, and (2) the daily values for each monitoring site pair shall yield a 

correlation coefficient of at least 0.6 for each calendar quarter. 41 The current criteria for spatial averaging include: (1) the annual mean concentration at each site shall be within 10 

percent of the spatially averaged annual mean, and (2) the daily values for each monitoring site pair shall yield a 

correlation coefficient of at least 0.9 for each calendar quarter (71 FR 61167/2-3, October 17, 2006). 
Draft – Do Not Quote or Cite 2-54 June 2010

1 populations are more likely to have lower income levels and higher percentage of minority levels. 

2 Based upon this analysis, we believe that the existing constraints on spatial averaging, as 

3 modified in 2006, may not be adequate to avoid substantially greater exposures in some areas, 

4 potentially resulting in disproportionate impacts on persons with lower SES levels and 

5 minorities. Therefore, we conclude that it is appropriate to consider a form for the annual PM2.5 

6 standard that does not allow for the use of spatial averaging across monitors, such that the level 

7 of the annual PM2.5 standard would be compared to measurements made at the monitoring site 

8 that represents “community-wide air quality” recording the highest PM2.5 concentrations. 

9 2.3.3.2 Form of the 24-Hour Standard 

In 1997, EPA established the form of the 24-hour PM2.5 standard as the 98th 10 percentile of 

11 24-hour concentrations at each population-oriented monitor within an area, averaged over three 

years (62 FR at 38671 to 38674, July 18, 1997). The Agency selected the 98th 12 percentile as an 

13 appropriate balance between adequately limiting the occurrence of peak concentrations and 

14 providing increased stability which, when averaged over 3 years, facilitated the development of 

15 more stable implementation programs. By basing the form of the standard on concentrations 

16 measured at population-oriented monitoring sites, EPA intended to provide protection for people 

17 residing in or near localized areas of elevated concentrations. In the last review, in conjunction 

18 with lowering the level of the 24-hour standard, EPA retained this form based in part on a 

comparison with the 99th percentile form.42 19 

In this review, we have again considered the relative stability of the 98th and 99th 20 

21 percentile forms. 

 Does the currently available evidence provide support for the continued use of the 98th 22 

23 percentile form of the 24-hour standard? 

24 We recognize that the selection of the appropriate form of the 24-hour standard includes 

25 maintaining adequate protection against peak 24-hour concentrations while also providing a 

26 stable target for risk management programs, which serves to provide for the most effective 

public health protection in the long run.43 27 As in previous reviews, we recognize that a 

28 concentration-based form provides proportionally greater weight to days when concentrations are 

 42 In reaching this final decision, EPA recognized a technical problem associated with a potential bias in the method 

used to calculate the 98th percentile concentration for this form. EPA adjusted the sampling frequency requirement 

in order to reduce this bias. Accordingly, the Agency modified the final monitoring requirements such that areas 

that are within 5 percent of the standards are required to increase the sampling frequency to every day (71 FR 61164 

to 61165, October 17, 2006). 

43 See ATA III, 283 F.3d at 374-375 which concludes it is legitimate for EPA to consider promotion of overall 

effectiveness of NAAQS implementation programs, including their overall stability, in setting a standard that is 

requisite to protect the public health. 
Draft – Do Not Quote or Cite 2-55 June 2010

1 well above the level of the standard than to days when the concentrations are just above the 

2 standard. Further, a concentration-based form compensates for missing data and less-than-every3 day monitoring; and, when averaged over 3 years, it has greater stability and, thus, facilitates the 

4 development of more stable implementation programs. 

In revisiting the stability of a 98th versus 99th 5 percentile form for a 24-hour standard 

6 intended to provide supplemental protection for a generally controlling annual standard, we 

7 consider air quality data reported in 2000 to 2008 to update our understanding of the ratio 

between peak-to-mean PM2.5 concentrations.44 As illustrated in Figure 2-2, the 98th 8 percentile 

value is a more stable metric than the 99th 9 percentile. 

10 Figure 2-2. Distribution of site-level variation in 98th and 99th percentile concentrations, as 

measured by coefficient of variation (SD/Mean) 45 11 computed by site across years, 2000-2008 Coefficient of variation

98th percentile 

concentrations

99th percentile 

concentrations

16.13

18.76

12 

On this basis, we conclude that it is appropriate to consider retaining the current 98th 13 

14 percentile form of the 24-hour standard as it represents an appropriate balance between 

15 adequately limiting the occurrence of peak concentrations and providing increased stability 

relative to an alternative 99th 16 percentile form. In addition, by basing the form of the standard on 

17 concentrations measured at population-oriented monitoring sites, the standard would continue to 

18 focus on providing protection for people residing in or near localized areas of elevated 

19 concentrations. 

 44 We consider a coefficient of variation instead of simply the standard deviation because the 99th percentile values 

have higher concentration levels and dividing by the mean normalizes the data. In focusing on three years of recent 

air quality (2006 to 2008), we see a similar pattern of peak-to-mean ratios (Schmidt, 2010). 45 Coefficient of variation x 100. 
Draft – Do Not Quote or Cite 2-56 June 2010

1 2.3.4 Alternative Levels 

2 In reaching staff conclusions for alternative standard levels that are appropriate to 

3 consider, we take into account both evidence-based (section 2.3.4.1) and risk-based 

4 considerations (section 2.3.4.2) as well as the related limitations and uncertainties associated 

5 with this information as presented and discussed more fully in the ISA and RA (US EPA, 2009a; 

6 US EPA, 2010a). Alternative levels are discussed in conjunction with staff conclusions on other 

7 elements of the standard presented above, notably, retaining PM2.5 as the indicator for fine 

8 particles (see section 2.3.1); retaining the current annual and 24-hour averaging times (see 

9 section 2.3.2); modifying the current form of the annual standard (see section 2.3.3.1) and 

10 retaining the current form of the 24-hour standard (see section 2.3.3.2). Specifically, we address 

11 the following overarching question: 

12 What alternative standard levels are appropriate to consider to provide requisite 

13 protection for long- and short-term PM2.5 exposures? 

14 Staff conclusions based on the integration of the evidence-based and risk-based 

15 approaches are presented in section 2.3.4.3.

16 2.3.4.1 Evidence-based Considerations 

17 In translating information from epidemiological studies into the basis for reaching staff 

18 conclusions on alternative standard levels, we apply the policy framework outlined in section 

19 2.1.3. In doing so, we focus on identifying levels for a generally controlling annual standard and 

20 a 24-hour standard that provides supplemental protection against days with high peak 

21 concentrations especially in areas with high peak-to-mean ratios, possibly associated with strong 

22 local or seasonal sources, or for potential PM2.5-related effects that may be associated with 

23 shorter-than-daily exposure periods, that might not be well controlled by an annual standard. 

24 We address a series of specific questions beginning with consideration of the relative 

25 weight to place on different evidence. Using the available epidemiological evidence, we then 

26 consider two approaches for identifying alternative annual standard levels: (1) looking directly 

27 at confidence bounds on C-R relationships reported in long- and short-term exposure studies and 

28 (2) exploring different statistical metrics that characterize air quality distributions from multi-city 

29 epidemiological studies. We also consider information on peak-to-mean air quality ratios in 

30 reaching conclusions regarding 24-hour standard levels that are appropriate to consider. 

31  What factors do we weigh in placing emphasis on epidemiological evidence to translate 

32 this information into staff conclusions on alternative standard levels? 

33 As discussed in section 2.1.3, we initially focus on long- and short-term PM2.5 multi-city 

34 exposure studies conducted in the U.S. and Canada and place the greatest weight on associations 
Draft – Do Not Quote or Cite 2-57 June 2010

1 that have been judged in the ISA to be causal or likely causal. We also consider the evidence for 

2 a broader range of health outcomes judged in the ISA to have suggestive evidence of a causal 

3 association, specifically studies that focus on effects in susceptible populations, to evaluate 

4 whether this evidence provides support for considering lower alternative levels. 

5 We take several factors into account in placing relative weight on the body of available 

6 epidemiological studies, for example, study characteristics, including study design (e.g., time 

7 period of air quality monitoring, control for potential confounders); strength of the study (in 

8 terms of statistical significance and precision of result); and availability of air quality distribution 

9 data. We place greatest weight on information from multi-city epidemiological studies. These 

studies have a number of advantages compared to single-city studies46 10 that include providing 

11 representation of ambient PM2.5 concentrations and potential health impacts across a range of 

12 diverse locations providing spatial coverage for different regions across the country, reflecting 

13 differences in PM2.5 sources, composition, and potentially other exposure-related factors which 

14 might impact PM2.5-related risks; lack of ‘publication bias’ (US EPA, 2004a, p. 8-30); and 

15 consideration of larger study populations that afford the possibility of generalizing to the broader 

16 national population and provide higher statistical power than single-city studies to detect 

17 potentially statistically significant associations with relatively more precise effect estimates. 

18  To what extent have confidence bounds on concentration-response relationships 

19 reported in epidemiological studies been characterized? 

20 As outlined in section 2.1.3, to the extent analyses characterizing confidence bounds on 

21 concentration-response (C-R) relationships have been published from both long- and short-term 

22 exposure studies we have explored this evidence in reaching staff conclusions on the range of 

23 annual standard levels that is appropriate to consider. We note that although epidemiological 

24 studies reporting C-R functions and associated 95% confidence intervals provide information on 

25 the precision of the effect estimates across the air quality distribution, these analyses do not 

26 provide evidence of a concentration below which the confidence interval becomes notably wider 

27 and uncertainty in a C-R relationship substantially increases. If an analysis characterizing the 

28 confidence bounds on the C-R relationships is available and indicates an unacceptable degree of 

29 uncertainty about the existence of a continuing C-R relationship, the possibility that an effects 

30 threshold may exist becomes more likely. 

 46 As discussed in section 2.2.1, we recognize that single-city studies provide ancillary evidence to multi-city studies 

in support of calling into question the adequacy of the current suite of standards. However, in light of the mixed 

findings reported in single-city short-term PM2.5 exposure studies, and the likelihood that these results are influenced 

by localized events and not representative of air quality across the country, we place comparatively greater weight 

on the results from multi-city studies in considering alternative annual standard levels. 
Draft – Do Not Quote or Cite 2-58 June 2010

1 Although we have identified a number of PM10 studies reporting confidence bounds 

2 around C-R functions (e.g., Daniels et al., 2004; Samoli et al., 2005; Wong et al., 2008), we have 

3 identified only three PM2.5 studies reporting confidence bounds around C-R functions (i.e., Pope 

4 et al., 2002; Miller et al., 2007; Schwartz et al., 2008; see Figure 2-3). We found that these 

5 studies analyzed the C-R function not to characterize the confidence bounds associated with the 

6 C-R function, but primarily to determine if a linear curve most appropriately represented the C-R 

7 relationship. In particular, one long-term exposure study utilized a nonparametric smoothing 

8 model to depict the shape of the C-R function and associated confidence intervals using mortality 

9 data from the ACS cohort (Pope et al., 2002). Goodness-of-fit testing indicated these data 

10 supported a log-linear C-R relationship. We observe (and observed in the last review) that there 

11 was an appreciable widening of confidence bounds on the smoothing plot for all-cause mortality 

at approximately 13 to 12 μg/m3 12 (US EPA, 2005, p. 3-56; Figure 3-4) which is somewhat below 

where the bulk of the air quality distribution in the study occurs (i.e., 14 μg/m3 13 is one standard 

deviation below the long-term mean concentration of 17.7 μg/m3 14 ). The widening of the 

15 confidence bounds in the smoothing plot is likely a consequence of the relative paucity of air 

16 quality data at lower PM2.5 concentrations in this study, and does not suggest the possibility that 

17 an effects threshold may exist at lower PM2.5 concentrations. 

18 Additional evidence continues to support log-linear C-R relationships (US EPA, 2009a, 

19 section 2.4.3). A recent long-term exposure study also provides information on the shape of the 

20 C-R function and associated confidence intervals using cardiovascular mortality and morbidity 

21 data from the WHI cohort (Miller et al., 2007). This analysis of cardiovascular events in relation 

22 to long-term PM2.5 exposure is indicative of a continued reduction in risk at lower concentrations 

of PM2.5. However, since the reference value for the C-R function in this analysis is 11μg/m3

, 23 we 

24 are unable to ascertain a point on the C-R relationship where there is an appreciable widening of 

25 confidence bounds and greater uncertainty in the C-R relationship in this study. 

26 Statistical analyses have been conducted to examine the PM-mortality C-R relationship 

27 and whether a threshold exists. Schwartz et al. (2008) analyzed the shape of the concentration 

28 response relationship associated with long-term PM2.5 exposure using a variety of statistical 

29 methods. Similar to the above noted studies, the C-R function reported by Schwartz et al. (2008) 

30 was found to be linear, with “....little evidence for a threshold in the association between 

31 exposure to fine particles and the risk of death...”. Although confidence bounds were provided 

32 in Schwartz et al. (2008) for the association between PM2.5 and mortality, widening of 

confidence bounds occurred just below the long-term mean concentration of 17.5 μg/m3 33 , and 

34 continued broadening of confidence bounds at lower PM2.5 concentrations is likely attributable to 

35 the comparative lack of data at the lower end of the air quality distribution for this analysis (i.e., 
Draft-Do Not Quote or Cite June 2010 

Figure 2-3. Confidence Bounds on PM2.5 Concentration-Response Relationships 

from Multi-city Epidemiological Studies

Source: Pope et al., 2002 

Source: Miller et al., 2007 

Source: Schwartz et al., 2008

2-59
Draft – Do Not Quote or Cite 2-60 June 2010

10.7 μg/m3 1 is one standard deviation below the long-term mean concentration). Therefore we 

2 are unable to identify from this study where uncertainty in the C-R relationship substantially 

3 increases at lower PM2.5 concentrations and the potential increases for an effects threshold. 

4 While these long-term studies provide information on the lack of any discernible 

5 threshold and on the precision of PM2.5-associated health effects, we have found no new 

6 evidence to inform our understanding of the confidence intervals around the estimated PM2.5 C-R 

7 functions. Consequently, without sufficient information available at this time we are unable to 

8 use this type of approach as the basis for reaching conclusions on a range of alternative PM2.5 

9 standard levels that would be appropriate for consideration in this review. 

10 In the absence of this type of evidence, we focus on the second approach by addressing 

11 the following questions: 

12  How do we consider different statistical air quality metrics for identifying alternative 

13 levels to be considered for a generally controlling annual standard? 

14 We first recognize that health effects may occur over the full range of concentrations 

15 observed in the long- and short-term epidemiological studies and that the ISA concluded no 

16 discernible threshold for any effects can be identified based on the currently available evidence 

17 (US EPA 2009a, section 2.4.3). As outlined in section 2.1.3, there is no single criterion that is 

18 recognized as the “correct” approach to translate air quality metrics from epidemiological studies 

19 into the basis for considering alternative standard levels, therefore, we explore various statistical 

20 metrics that are reasonable to consider using two air quality distributions that are relevant to 

21 inform alternative standard levels. 

22 We first consider air quality distributions based on concentrations averaged across 

23 ambient monitors within each area included in a given study (i.e., composite monitor) and then 

24 averaged across study areas for an overall study mean concentration. The composite monitor 

25 distribution is representative of the air quality data typically used in epidemiological analyses 

26 and provides a direct link between PM2.5 concentrations and the observed health effects. 

27 We then consider a second air quality distribution based on concentrations estimated at 

28 the monitor within each area that records the highest concentration (i.e., maximum monitor) and 

29 then averaged across study areas for an overall study mean concentration. The maximum 

30 monitor distribution is relevant because it reflects the distribution that is generally used to 

31 determine whether a given standard is met in an area, and determines the extent to which 

32 ambient PM2.5 concentrations need to be reduced in order to bring an area into attainment with 

33 the standard.

34 In identifying alternative standard levels that are appropriate to consider, we recognize 

35 that the protection provided depends on whether these levels are set just below or substantially 

36 below the long-term mean concentrations reported in the epidemiological studies and whether a 
Draft – Do Not Quote or Cite 2-61 June 2010

1 composite or maximum monitor distribution is used as the basis for selecting the levels. 

2 Statistical metrics based upon composite monitor distributions are below, and in some places 

3 well below, the same statistical metrics based upon maximum monitor distributions. A policy 

4 approach that uses data based on composite monitor distributions to identify alternative standard 

5 levels, and then compares those levels to concentrations at maximum monitors to determine if an 

6 area meets a given standard, inherently builds in a margin of safety. In recognition of this 

7 margin of safety, we believe it is appropriate to consider a policy approach that places 

8 appreciably greater weight on focusing on alternative levels that are just somewhat below the 

9 long-term mean concentrations reported in the epidemiological studies using the composite 

monitor distributions.47 10 

11 Alternatively, a policy approach that uses maximum monitor distributions to identify 

12 alternative standard levels that are then compared to concentrations at maximum monitors has no 

13 inherent margin of safety. Therefore, to identify alternative standard levels using maximum 

14 monitor distributions that provide a margin of safety, staff believes it is appropriate to consider a 

15 policy approach that places greater weight on identifying alternative levels substantially below 

16 the long-term mean concentrations from long- and short-term exposure studies. Using this 

17 approach, we believe it is reasonable to focus on the lower end of the region within which the C18 R relationship is strongest, in which the bulk of the data reside. In doing so, we believe that 

19 focusing on concentrations down to approximately one standard deviation below the long-term 

20 mean concentrations using the maximum monitor distribution is also appropriate. 

21 For the reasons discussed above, we focus our consideration of the long-term means and 

22 associated standard deviations from multi-city epidemiological studies (see Figures 2-4 through 

23 2-6). The studies included in these figures represent long- and short-term exposure studies that 

24 evaluated endpoints classified in the ISA as having a causal or likely causal association at long25 term mean concentrations close to or below the level of the current annual PM2.5 standard (i.e., 

below 17 μg/m3).48 26 In addition, Figure 2-5 includes long-term exposure studies of children that 

27 evaluated effects classified as having evidence suggestive of an association below the level of the 

28 current annual standard. These figures present long-term mean concentrations and associated 

29 standard deviations to the extent they are available for the composite monitor and maximum 

30 monitor distributions. 

31 Beyond looking at standard deviations, we also have considered distributions from 

32 epidemiological studies that provide a more continuous measure of the density of the data from 

 47 A similar approach was followed in the SO2 primary NAAQS review (75 FR 35547 to 35548, June 22, 2010). 48 We note that additional studies presented and assessed in the ISA report effects at higher long-term mean PM2.5 

concentrations. 
Figure 2-4. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for Long-term PM2.5 Exposure Multi-City Studie

 Older Adults 

Air Quality Data (μg/m3) 

Author Reported Data EPA Analysis (Max Monitor)a Study Cite Geographic Area Years of Air Quality Data Endpoint Mean Mean - 1SD Range Mean Mean - 1SD Range 

General Population

Mortality-CV 

All CVD 

Incident MI 

Revascularization 

Stroke 

WHI Miller et al. (2007) 36 US cities 2000 

CBVD 

13.5 10.2 3.4 – 28.3 16.8 12.5 5-28 

Mortality-All-cause 

Cystic Fibrosis Goss et al. (2004)b

6 US regions (NE, SE, NC, SC, NW, SW) 2000 Pulmonary exacerbation 13.7 9.5 11.8-15.9 (IQR) - - - 

Mortality-all cause 

Mortality-IHD 

Mortality-CPD ACS-Reanalysis II Krewski et al. (2009) 51 US MSAs 1999-2000 

Mortality-Lung cancer

14.0 11.0 5.8 - 22 15.4 11.3 9-25 

VA Lipfert et al. (2006) 1999-2001 Mortality-all cause 14.3 11.3 5.0 - ? - - - 

Mortality-all cause 

Mortality-CV 

Mortality-Respiratory

Harvard Six Cities 

(SCS)-Extended Laden et al. (2006) 

6 US cities 

(Northeast/ 

Midwest) 

1979-1998 

Mortality-Lung cancer

16.4c 10.8 10-22 - - - 

Older Adults

MCAPS-Western US Zeger et al. (2008) 62 US counties 2000-2005 Mortality-all cause 13.1d - 10.4-18.5 (IQR) - - - 

Medicare-ACS Eftim et al. (2008) 51 US MSAs 2000-2002 Mortality-all cause 13.6 10.8 6.0-25.1 14.9 11.3 9-24 

MCAPS-Eastern US Zeger et al. (2008) 421 US counties 2000-2005 Mortality-all cause 14.0d - 12.3-15.3 (IQR) - - - 

Medicare -SCS Eftim et al. (2008) 6 US cities 2000-2002 Mortality-all cause 14.1 11.0 9.6-19. 1 - - - 

aMaximum monitor calculations noted in Hassett-Sipple et al. (2010) 0.8 1 1.2 b cohort included persons with cystic fibrosis age 6 and older, mean age: 18.4 yrs cEstimated from data provided by study author dMedian (IQR: Interquartile range); overall US reported median (IQR) of 13.2 μg/m3 (11.1-14.9)

Draft-Do Not Quote or Cite 2-62 
Figure 2-5. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for Long-term PM2.5 Exposure Multi-City Studie

Children

Air Quality Data (μg/m3) Study Cite Geographic Area Years of Air Quality Data Endpoint Mean Mean – 

1SD Range Effect Estimate (95% C

Bell et al. 

(2007) CT,MA 1998-2002 Low Birth Weight 11.9 10.3 

IUGR -1nd trimesterIUGR -2nd Liu et al. trimester

(2007) 3 Canadian cities 1985-1999 

IUGR – 3rd trimester

12.2 - 6.3-15 

Parker and 

Woodruff 

(2008) 

Continental US 2000-2003 Low Birth Weight 13.5 - 10.9-16.1 (IQR) 

S CA CHS McConnell et al. (2003) 12 communities – S CA 1996-1999 Bronchitic Symptoms 13.8 6.1 6-29 

24-Cities Dockery et al. (1996) 24 communities – US, Canada 1988-1991 Bronchitis 14.5 10.3 5.8-20.7 

Woodruff et al. 

(2008) 96 US counties 1999-2002 Infant mortality 14.9a -- 12.0-18.6 (IQR) 

amedian for all cause mortality; median (IQR: interquartile range) for survivors = 14.8 (11.7-18.7) μg/m3 0.8 1 1.2 1.4 1.6 1.8 

Draft-Do Not Quote or Cite 2-63 
Figure 2-6. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for Short-term PM2.5 Exposure Multi-City Studie

 Older Adults 

Air Quality Data (μg/m3) 

Author Reported Data EPA Analysis (Max Monitor)a Study/Cite Geographic Area Years of Air Quality Data Endpoint Mean Mean - 1SD Range Mean Mean - 1SD Range 

General Population

Burnett et al. (2004) 12 Canadian Cities 1981-1999 Nonaccidental mortality 12.8 - - - - - 

Zanobetti & Schwartz (2009) 112 US counties 1999-2005 Nonaccidental mortality 13.2b 10.3b 6.6-24.7 14.1 10.9 6.7-26.4 

Burnett & Goldberg (2003) 8 Canadian Cities 1986-1996 Nonaccidental mortality 13.3 3.9 c - - - -

Harvard Six Cities/ Klemm and Mason 

(2003) 

6 US cities 

(Northeast/ 

Midwest) 

1979-1988 Nonaccidental mortality 14.7d - 9-23 (IQR) - - -

Franklin et al. (2008) 25 US communities 2000-2005 Nonaccidental mortality 14.8b - 9.9-27.4b - - - Franklin et al. (2007) 

27 US 

communities 1997-2002 

Nonaccidental 

mortality 

15.6b - 8.8-23.9 - - - 

Older Adults

CVD HA MCAPS/Bell et al. (2008) 202 US counties 1999-2005 Resp HA 12.9b 10.2b 4-20 13.4 10.3 5-26 

IHD HA 

CHF HA 

Dysrhythmia HA 

CBVD HA 

PVD HA 

COPD HA 

MCAPS/Dominici et al. 2006 204 US counties 1999-2002 

RTI HA 

13.4b 10.5b 4-23 13.9 10.5 5-29 

0.99 1 

aMaximum monitor calculations noted in Hassett-Sipple et al. (2010) bEstimated from data provided by study author or published study cEstimated from coefficient of variation reported in original study by Burnett et al. (2000) dMean value not reported in study, median presented from original study by Schwartz et al. (1996) 

IQR: interquartile range 

Draft-Do Not Quote or Cite 2-64 
Draft – Do Not Quote or Cite 2-65 June 2010

1 multi-city studies as a function of annual mean concentrations (from both composite and 

2 maximum monitor distributions) across the cities in each study, as shown in Figures 2-7 and 2-8. 

3 Specifically, we consider distributions of air quality data as well as distributions of population 

4 data across air quality concentrations, which serve as a surrogate for the density of the health 

5 effects data. Taken together, statistics from these distributions can inform our understanding of 

6 the relative density of air quality and health effects data in the lower region of the C-R 

7 relationship. We focus on the lower quartile of these distributions, and consider the range from 

the 25th to 10th 8 percentiles as a range within which the data become more sparse and that the C-R 

relationship becomes appreciably more uncertain.49 9 In so doing, we recognize that focusing on 

10 any specific percentile from such distributions is somewhat arbitrary, although we believe the 

25th and 10th 11 percentiles are reasonable to consider. In staff's view, placing weight on PM2.5

concentrations from air quality and population distributions below the range from the 25th to 10th 12 

13 percentiles would be a highly uncertain basis for selecting alternative standard levels for 

14 consideration. 

15 We note the interrelatedness of these distributional statistics and a range of one standard 

16 deviation around the mean, which contains approximately 68% of normally distributed data, in 

that one standard deviation below the mean falls between the 25th and 10th 17 percentiles of the air 

18 quality distribution. Having considered both these distributional statistics and the concentrations 

19 that are one standard deviation below study mean concentrations, we conclude that they provide 

20 similar information to inform selection of alternative standard levels for consideration. In the 

21 following discussion, we focus on the standard deviation statistic as being a more comparable 

22 statistical measure across studies, rather than focusing on what are somewhat arbitrary 

23 distributional percentiles, to inform our translation of the epidemiological evidence into the basis 

24 for alternative standard levels that are appropriate to consider. 

25 Composite Monitor Distributions

26 Information on composite monitor distributions is available for each epidemiological 

27 study considered and, therefore, represents the most robust data set available to inform our 

28 conclusions regarding alternative standard levels. In addition, these data were used in the 

 49 In looking at the distributions between the 25th and 10th percentiles of either the air quality or population data, we 

note that the difference between the maximum and composite monitor distributions is much smaller for the three 

short-term exposure studies (Figure 2-8) than for the three long-term exposure studies (Figure 2-7). This difference 

is most likely associated with the greater number of years of air quality data considered in the short-term exposure 

studies (3 to 6 years) compared to the long-term exposure studies (1 to 3 years) as well as the greater number of 

study areas considered in the short-term exposure studies (112 to 204 counties) compared to the long-term exposure 

studies (36 to 51 cities/MSAs).
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Cumulative Frequency (%) Cumulative Population (% of total study population)

Annual Mean (μg/m3)

Annual Mean (μg/m3

)

- Eftim et al., 2008

- Krewski et al., 2009

- Miller et al, 2007

• Composite monitor

° High site

Figure 2-7. Air Quality and Population Distributions: Long-Term PM2.5 Exposure Studies

Draft-Do Not Quote or Cite 2-66 June 2010
Cumulative Frequency (%) Cumulative Population (% of total study population)

Annual Mean (μg/m3)

Annual Mean (μg/m3

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- Bell et al., 2008

- Dominici et al., 2006

- Zanobetti Zanobetti & Schwartz, 2009 & Schwartz, 2009

• Composite monitor

° High site

Figure 2-8. Air Quality and Population Distributions: Short-Term PM2.5 Exposure Studies

Draft-Do Not Quote or Cite 2-67 June 2010
Draft – Do Not Quote or Cite 2-68 June 2010

1 epidemiological studies to generate PM2.5 effect estimates and, therefore, provide a direct link to 

2 health evidence.

3 By applying the policy approach discussed above, we focus on alternative levels that are 

4 just somewhat below the long-term mean concentrations reported in the epidemiological studies 

5 using the composite monitor distributions. We initially consider studies that evaluated mortality 

6 (causal association), cardiovascular effects (causal association) or respiratory effects (likely 

7 causal association). In the long-term exposure studies, we observe positive and statistically 

significant associations at long-term mean concentrations ranging from 16.4 to 13.5 μg/m3 8 

9 (Laden et al., 2006; Lipfert et al., 2006; Krewski et al., 2009; Goss et al., 2004; Miller et al.; 

10 2007; Zeger et al., 2008; Eftim et al., 2008; Dockery et al., 1996; McConnell et al., 2003; see 

11 Figures 2-4 and 2-5). In then considering information from short-term exposure studies 

12 reporting positive and statistically significant associations with these same broad health effect 

13 categories, we observe positive and statistically significant associations at long-term mean 

concentrations in a somewhat lower range of 15.6 to 12.8 μg/m3 14 (Franklin et al., 2007, 2008; 

15 Klemm and Mason, 2003; Burnett and Goldberg, 2003; Zanobetti and Schwarts, 2009; Burnett et 

al., 2005; Bell et al., 2008; Dominici et al., 2006; See Figure 2-6).50 16 

17 In taking into consideration specific susceptible populations (i.e., children), we expand 

18 our evaluation of the long-term exposure studies to include a broader range of health outcomes 

19 judged in the ISA to have suggestive evidence of a causal association. This evidence is taken 

20 into account to evaluate whether it provides support for considering lower alternative levels than 

21 if weight were only placed on studies for which associations have been judged in the ISA to be 

22 causal or likely causal. We make note of studies that provide emerging evidence for low birth 

23 weight and infant mortality, especially related to respiratory causes during the post-neonatal 

24 period (see Figure 2-5). This more limited body of evidence indicates positive and often 

25 statistically significant effects associated with long-term mean concentrations in the range of 

14.9 to 11.9 μg/m3 26 (Woodruff et al., 2008; Liu et al., 2009; Bell et al., 2007). As illustrated in 

27 this figure, although Parker and Woodruff (2008) did not observe an association between 

28 quarterly estimates of exposure to PM2.5 and low birth weight in a multi-city U.S. study, other 

29 U.S. and Canadian studies did report positive and statistically significant associations between 

PM2.5 and low birth weight at lower ambient concentrations (Bell et al., 2007; Liu et al., 2007).51 30 

 50 When integrating evidence from short-term exposure studies reporting positive and statistically significant 

associations with mortality, cardiovascular, and respiratory effects, the ISA concluded that these associations are 

generally consistent and precise at long-term mean PM2.5 concentrations of 12.8 μg/m3 and above (US EPA, 2009a, 

pp. 2-10 to 2-11) 

51 As noted in section 7.4 of the ISA. Parker et al. (2005) reported that over a 9 month exposure period (mean 

concentrations of 15.4 μg/m3

) a significant decrease in birth weight was associated with infants in the highest 

quartile of PM2.5 exposure as compared to infants exposed in the lowest quartile. 
Draft – Do Not Quote or Cite 2-69 June 2010

1 There remain significant limitations (e.g., identifying the etiologically relevant time period) in 

2 the evaluation of evidence on the relationship between PM2.5 exposures and birth outcomes (US 

3 EPA, 2009a, pp. 7-48 and 7-56); nonetheless, we believe it is important to give some weight to 

4 these studies in taking into account potential impacts on specific susceptible populations and in 

5 considering alternative standard levels that provide protection with a margin of safety. 

6 With respect to carcinogenicity, mutagenicity, and genotoxicity (suggestive evidence of a 

7 causal association), the strongest evidence currently available is from long-term prospective 

8 cohort studies that report positive relationships between PM2.5 and lung cancer mortality. At this 

9 time, the PM2.5 concentrations reported in studies evaluating these effects reported ambient 

10 concentrations that are equal to or greater than ambient concentrations observed in studies that 

11 reported mortality and cardiovascular and respiratory effects (US EPA, 2009a, section 7.5). 

12 Therefore, in selecting alternative levels, we note that in providing protection for mortality and 

13 cardiovascular and respiratory effects, it is reasonable to anticipate that protection will also be 

14 provided for carcinogenicity, mutagenicity, and genotoxicity effects. 

15 A different policy approach might place greater weight on selecting alternative levels that 

16 are substantially below the long-term mean concentrations reported in the epidemiological 

17 studies based on composite monitor distributions. In focusing on effects classified as having a 

18 causal or likely causal association with long- or short-term PM2.5 exposures, we observe 

19 concentrations one standard deviation below the long-term means for long-term exposure studies 

generally in the range of about 11 to 9.5 μg/m3 20 (Laden et al., 2006; Lipfert et al., 2006; Krewski 

21 et al., 2009; Goss et al., 2004; Miller et al., 2007; Eftim et al., 2008; Dockery et al., 1996; see 

Figures 2-4 and 2-5).52 22 As presented in Figure 2-6, one standard deviation below the long-term 

mean concentrations for short-term exposure studies is generally about 10 μg/m3.

53 23 In expanding 

24 our consideration to include effects classified as having evidence suggestive of a causal 

25 association with long-term PM2.5 exposures, information is available for one study which 

reported one standard deviation below the mean at 10.3 μg/m3 26 (Bell et al., 2007; see Figure 2-5). 

27 We believe focusing on statistical metrics looking at levels that are substantially below the long28 term mean concentrations reported in the epidemiological studies using the composite monitor 

 52 With respect to considering air quality concentrations reported in the extended follow-up to the Southern 

California CHS, we recognize that one standard deviation below the long-term mean concentration aggregated 

across the 12 study communities is a significantly lower concentration than looking at one standard deviation below 

the long-term means reported in other long-term exposure studies, if fact, this concentration is close to or below the 

LML reported in the majority of the long-term exposure studies. This reflects the large standard deviation reported 

in this study related to the wide range of ambient concentrations considered in the 12 study communities. 

(McConnell et al., 2003). 

53 Burnett and Goldberg (2003) reported a comparatively greater variability in the air quality distribution across 8 

Canadian cities: estimated SD from coefficient of variation reported in original study by Burnett et al.(2000) is 9.4 

μg/m3

. 
Draft – Do Not Quote or Cite 2-70 June 2010

1 distributions could potentially result in standard levels that are lower than necessary to provide 

2 appropriate protection given the margin of safety already provided by the approach discussed 

3 above. 

4 Therefore, in focusing on composite monitor distributions, staff believes it is most 

5 appropriate and reasonable to consider an alternative annual PM2.5 standard level just somewhat 

6 below the lowest long-term mean concentrations reported in the multi-city epidemiological 

studies, that is, in the range of 13 to 11 μg/m3 7 . 

 An alternative level of 13 μg/m3 8 would be somewhat below the lowest long-term 

9 mean concentrations reported in the multi-city long-term exposure studies that 

10 provide evidence of positive and statistically significant associations with health 

11 effects classified as having a causal or likely causal association. This level would 

12 also be below the long-term mean concentrations reported in many, but not all, short13 term exposure studies that provided similar evidence. 

 An alternative level of 12 μg/m3 14 would be somewhat below the lowest long-term 

15 mean concentrations reported in the multi-city long- and short-term exposure studies

16 that provide evidence of positive and statistically significant associations with health 

17 effects classified as having a causal or likely causal association. This level would 

18 also be below the long-term mean concentrations reported in two of the three long19 term exposure studies that provide suggestive evidence of positive and often 

20 statistically significant associations with low birth weight and infant mortality. 

 An alternative level of 11 μg/m3 21 would place appreciably greater weight on 

22 information from multi-city, long-term exposure studies for which we have 

23 suggestive evidence of low birth weight and infant mortality by considering a level 

24 somewhat below the lowest long-term mean concentrations reported in such studies 

25 that provide evidence of positive and statistically significant associations. 

26 Maximum Monitor Distributions

27 With regard to maximum monitor distributions, we first note that this data set represents 

28 a far less robust metric compared to the data set using composite monitor distributions because it 

29 is based upon far fewer epidemiological studies. Furthermore, as previously noted, maximum 

30 monitor distributions are not used in analyses of associations between air quality data and PM2.5-

31 associated health effects in epidemiological studies. 

32 Maximum monitor distributions were calculated by OAQPS for studies for which we 

33 could reasonably match geographic study areas and air quality data from recent years assessed in 

34 epidemiological studies that considered mortality (causal association), cardiovascular effects 

35 (causal association) or respiratory effects (likely causal association) (Hassett-Sipple et al., 2010). 

36 This data set includes consideration of three long-term exposure studies, specifically the 

37 extended follow-up analyses of the ACS study (Krewski et al., 2009), the WHI study (Miller et 

38 al., 2007), and the MCAPS cohort using the ACS study sites (Eftim et al., 2008) as well as three 
Draft – Do Not Quote or Cite 2-71 June 2010

1 short-term exposure studies including the MCAPS cohort (Dominici et al., 2006; Bell et al., 

2 2008), and a national mortality study (Zanobetti and Schwartz, 2009). Information for maximum 

3 monitor distributions for studies that evaluated health outcomes judged in the ISA to have 

4 suggestive evidence of a causal association were not calculated and, thus, are not considered in 

5 the discussion below. 

6 By applying the policy approach discussed above, in using the maximum monitor 

7 distributions, we focus on alternative levels that reflect consideration of concentrations 

8 substantially below the long-term mean concentrations reported in the epidemiological studies to 

9 provide a margin of safety. In considering concentrations that are one standard deviation below 

10 the long-term mean concentrations for the three long-term exposure studies, we observe 

concentrations of 12.5, 11.3 and 11.3 μg/m3 11 (Miller et al., 2007; Krewski et al., 2009; Eftim et 

12 al., 2008; see Figure 2-4) from these studies that report positive and statistically significant 

13 associations. In considering this same statistical metric for the three short-term exposure studies, 

we observe somewhat lower concentrations of 10.9, 10.3, and 10.5 μg/m3 14 (Zanobetti and 

15 Schwartz, 2009; Bell et al., 2008; Dominici et al., 2006; see Figure 2-6). 

16 A different policy approach might focus on alternative levels that are only just somewhat 

17 below the long-term mean concentrations for epidemiological studies using the maximum 

18 monitor distributions. For the three long-term exposure studies, positive and statistically 

19 significant effects were associated with long-term mean concentrations of 16.8, 15.4, and 14.9 

μg/m3 20 (Miller et al., 20007; Krewski et al., 2009; Eftim et al., 2008; see Figure 2-3). For the 

21 three short-term exposure studies, positive and primarily statistically significant effects were 

reported at lower long-term mean concentrations of 14.1, 13.4, and 13.9 μg/m3 22 ((Zanobetti and 

23 Schwartz, 2009; Bell et al., 2008; Dominic et al., 2006; Figure 2-5). In staff’s view, focusing on 

24 statistical metrics looking at levels that are just below the long-term mean concentrations 

25 reported in the epidemiological studies using the maximum monitor distributions would 

26 potentially result in standard levels that are too high to provide protection with a margin of 

27 safety. 

28 Therefore, in focusing on maximum monitor distributions, we consider it most 

29 appropriate to focus on a policy approach that considers alternative annual PM2.5 standard levels 

30 that are substantially below the long-term mean concentrations reported in the multi-city 

epidemiological studies, specifically, at 11 or 10 μg/m3 31 . 

 An alternative level of 11 μg/m3 32 would be approximately one standard deviation 

33 below the long-term mean concentrations reported in the multi-city long-term 

34 exposure studies reporting positive and statistically significant associations with 

35 health effects classified as having a causal or likely causal association. 
Draft – Do Not Quote or Cite 2-72 June 2010

 An alternative level of 10 μg/m3 1 would be approximately one standard deviation 

2 below the long-term mean concentrations reported in the multi-city long- and short3 term exposure studies reporting positive and statistically significant associations with 

4 health effects classified as having a causal or likely causal association. 

5 Summary of Evidence-based Considerations 

6 In considering the currently available evidence, we have identified a set of alternative 

7 annual standard levels that we conclude are appropriate to consider. Based upon composite 

8 monitor distributions, we conclude it is appropriate to consider alternative annual standard levels 

from 13 to 11 μg/m3 9 . Alternatively, based upon maximum monitor distributions, we conclude it 

is appropriate to consider alternative annual standard levels of 11 or 10 μg/m3 10 . We also observe 

11 that these alternative standard levels are generally within the range of levels that would be 

identified based on considering distributional statistics, within the range of the 25th to 10th 12 

13 percentiles, of distributions of air quality data and population data across air quality 

14 concentrations, as discussed above. 

15 While we believe alternative annual standard levels based on either composite monitor 

16 and maximum monitor distributions are appropriate to consider, we think it is reasonable to place 

17 more weight on an approach based on composite monitor distributions. As noted above, 

18 information on composite monitor distributions is available for each epidemiological study 

19 considered and, therefore, represents the most robust data set available to inform our conclusions 

20 regarding alternative standard levels. Further, this distribution represents the air quality data 

21 typically used in epidemiological analyses and provides a direct link between PM2.5

22 concentrations and the observed health effects. In contrast, maximum monitor distributions 

23 represent a far less robust metric because they are available for only a few studies. Moreover, 

24 while this distribution is generally used to determine whether a given standard is met in an area, 

25 it is not used to link air quality data with PM2.5-associated health effects in the epidemiological 

26 evidence. 

27 Therefore, in considering the emphasis to place on the two policy approaches considered, 

28 staff concludes it is most appropriate to place weight on the composite monitor approach and 

29 focus on alternative levels that are just somewhat below the lowest long-term mean 

30 concentrations reported in the multi-city studies. As noted above, by basing this level on the 

31 composite monitor distribution but by applying it at monitors that represent the maximum 

32 monitor distribution, this approach inherently builds in a margin of safety. Thus, based upon the 

33 currently available evidence, we conclude alternative annual standard levels in the range of 13 to 

11 is μg/m3 34 are most strongly supported by the available evidence and are appropriate to 

35 consider. 
Draft – Do Not Quote or Cite 2-73 June 2010

1 We next take into account evidence-based considerations and air quality data to inform 

2 alternative standard levels for the 24-hour PM2.5 standard by addressing the following question: 

3  What alternative standard levels are appropriate to consider for setting a 24-hour 

4 standard intended to supplement the protection afforded by a generally controlling 

5 annual standard? 

6 As recognized above, a generally controlling annual standard intended to serve as the 

7 primary means for providing protection for effects associated with both long- and short-term 

8 PM2.5 exposures cannot be expected to offer an adequate margin of safety against the effects of 

9 all short-term PM2.5 exposures, especially in areas with high peak-to-mean ratios, possibly 

10 associated with strong local or seasonal sources, or for potential PM2.5-related effects that may be 

11 associated with shorter-than-daily exposure periods. As a result, in conjunction with such an 

12 annual standard, we believe it is appropriate to consider alternative 24-hour PM2.5 standard levels 

13 that would provide supplemental protection while still maintaining the annual standard as the 

14 generally controlling standard. Such a 24-hour standard can reasonably be based on considering 

15 air quality information based on site-level 24-hour and annual design values. Such information is 

presented in Figure 2-9 in terms of distributions of the ratios of 98th 16 percentile design values to 

17 annual mean design values by region across the U.S. A different way of looking at such 

18 information is presented in Figure 2-10, which is a scatter plot of 24-hour and annual design 

19 values in urban areas across the U.S., color-coded by region.

In general, as illustrated in Figure 2-9, 98th 20 percentile PM2.5 concentrations are on average 

21 approximately 2.5 times greater than annual concentrations in almost all regions of the U.S. with 

22 the notable exception of the Northwest, where the ratio is approximately 3.5. In narrowing this 

23 analysis to three recent years of air quality data (2006 to 2008), we see the same patterns 

24 throughout the U.S. (Schmidt, 2010). Based on this information, we believe it is reasonable to 

25 focus on 24-hour standard levels that are at least 2.5 times higher than the annual standard level. 

26 Based on this consideration, we conclude that it is appropriate to consider retaining the current 

24-hour standard level of 35 μg/m3 in conjunction with annual standard levels of 13 to 11 μg/m3 27 . 

28 We have also considered whether it is also appropriate to consider a lower 24-hour 

29 standard level in conjunction with any annual standard levels within this range, while still 

30 maintaining the annual standard as the generally controlling standard. As illustrated in Figure 2-

10, we have examined the implications of a 24-hour standard level of 30 μg/m3 31 as compared to a 

level of 35 μg/m3

 in conjunction with an annual standard of 12 μg/m3 32 . As seen in Figure 2-10, 

combining an annual standard of 12 μg/m3 with a 24-hour standard of 30 μg/m3 33 will result in 

34 many more areas outside the Northwest in which the 24-hour standard would become the 

35 controlling standard. This can be seen by looking to the left of the “12/30” and “12/35” lines, 
Draft – Do Not Quote or Cite 2-74 June 2010

which is the region on the graph in which the 24-hour standard would be controlling.54 1 In 

2 contrast, based on the same type of consideration, we observe that a 24-hour standard level of 30 

μg/m3 in conjunction with an annual standard of 11 μg/m3 3 would not result in the 24-hour 

4 standard becoming the controlling standard in an appreciable number of areas. Based on these 

5 considerations, we conclude that it is appropriate to also consider a 24-hour standard level of 30 

μg/m3 in conjunction with an annual standard of 11 μg/m3 6 . 

7 In summary, based on the above considerations, we conclude that it is appropriate to 

consider retaining the current 24-hour standard level of 35 μg/m3 8 in conjunction with annual 

standard levels of 13 to 11 μg/m3 9 , and that consideration could also be given to an alternative 24-

hour standard level of 30 μg/m3 10 particularly in conjunction with an annual standard level of 11 

μg/m3 11 . Such combinations of 24-hour and annual standards levels would be expected to result in 

12 the annual standard being the generally controlling standard, except in areas in the Northwest 

13 with particular high peak-to-mean ratios, to protect against PM2.5-related health effects 

14 associated with long- and short-term exposures, with the 24-hour standard providing appropriate 

15 supplemental protection. 

Figure 2-9. Distribution of Site-Level Ratio of Annual 98th 16 Percentile 

17 Concentration/Annual Mean, 2000-2008 

1

2

3

4

5

6

Overall

U.S.

Industrial Southeast Northeast

Midwest

Upper 

Midwest

So. CA Southwest Northwest Outlying 

Sites 18 

 54Figures similar to Figure 2-10 are presented in the RA (US EPA 2010a, section 4.5.1) together with a more 

detailed discussion of interpreting the information in such figures. 
 12/30 line

12/35 line 

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Annual Design Value (μg/m3)

24-hour Design Value (μg/m

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Sites in this area of the plotexceed the 24-hr level only

Sites in this area of the plot exceed the annual level only

Sites in this area of the plot exceed neither level

24-hr levels

Annual level

OtheSo. CNortUppNortInduSoutSout

Symbo

geog

Figure 2-10. Site-level 24-hour DVs versus Annual DVs, 2006-2008

Sites in this area of the plot exceed both levels

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1 2.3.4.2 Risk-based Considerations 

2 Beyond looking directly at the relevant epidemiologic evidence, staff has also considered 

3 the extent to which specific levels of alternative PM2.5 standards are likely to reduce both long4 term exposure-related mortality risk and short-term exposure-related mortality and morbidity 

5 risk. In addition to considering the nature and magnitude of PM2.5-attributable risk remaining 

6 under each set of alternative standards, we have also considered the nature and magnitude of risk 

7 reductions under the alternative standards considered. These risk estimates for the set of 

8 alternative standard levels considered are based on the same methodology used in estimating risk 

9 for the current suite of standard levels (see section 2.2.2). 

10 The RA initially included analyses of alternative annual standard levels of 14, 13, and 12 

μg/m3

 paired with either the current 24-hour standard level of 35 μg/m3 11 or with alternative 24-

hour standard levels of 30 and 25 μg/m3 12 . The specific combinations of alternative standard 

13 levels assessed in the RA included: (a) suites focusing on alternative annual standard levels alone 

14 including combinations of alternative standard levels denoted by 14/35, 13/25 and 12/35 and (b) 

15 combinations of alternative annual and 24-hour standard levels including combinations denoted 

16 by 13/30 and 12/25. In addition, subsequent to the release of the second draft RA, we expanded 

the range of alternative annual standard levels evaluated to include a level of 10 μg/m3 17 . In 

18 simulating ambient PM2.5 levels associated with these alternative standard levels, we included a 

19 more regional spatial pattern of reductions (reflected in the use of a proportional rollback 

20 approach) as well as more localized spatial patterns of reductions (reflected in the use of a hybrid 

21 approach and to an even greater extent in the use of a locally focused rollback approach) (see 

22 U.S. EPA, 2010a, section 3.2.3). While the proportional rollback approach was used in 

23 generating the core risk estimates, the other two more localized rollback approaches were 

24 considered as part of sensitivity analyses. 

25 Results for the alternative suites of standard levels considered are presented in Figures 2-

26 11 and 2-12, which depict patterns in risk reduction for long-term exposure-related risk (Figure 

27 2-11) and short-term exposure-related risk (Figure 2-12) using different combinations of 

28 alternative standard levels relative to the risk under the current standard. These figures include 

29 results for each of the 15 urban study areas, thereby allowing patterns in risk reduction across 

alternative standard levels and urban study areas to be considered together.55 30 The discussion 

 55 Patterns of risk reduction across alternative annual standard levels (in terms of percent change relative to risk for 

the current annual standard level) are similar for all health endpoints modeled for a particular exposure duration (i.e., 

patterns of percent risk reduction will be similar for long-term exposure related all-cause, IHD and cardiopulmonary 

mortality). This reflects the fact that the C-R functions used in the quantitative RA are close to linear across the 

range of ambient air concentrations evaluated.. 
Draft-Do Not Quote or Cite Ju 

1 Figure 2-11 Percent reduction in long-term exposure-related mortality risk (alternative standards relative to the 2 (Note: inset shows PM2.5 related incidence and percent of total incidence for IHD mortality under the current suite of 3 

 Detroit, MI

 New York, NY

 Philadelphia, PA

 Phoenix, AZ

 Salt Lake City, UT

Tacoma, W

0%

20%40%60%

15/35** 14/35 13/35 12/35 13/30 

Current and Alternative Standards

Long-Term Exposure-Related Risk: 

Percent Reduction from Current Standard

 Atlanta, GA 277 (227 - 324); 16.7% (13.7% - 19.5%) Baltimore, MD 374 (307 - 440); 14.7% (12.1% - 17.3%) Birmingham, AL 165 (135 - 194); 13.8% (11.3% - 16.2%) Dallas, TX 247 (202 - 291); 11.4% (9.3% - 13.4%) Detroit, MI 478 (390 - 563); 11.5% (9.4% - 13.5%) Fresno, CA 98 (80 - 116); 8.5% (7% - 10.1%) Houston, TX 434 (355 - 511); 13.6% (11.1% - 16%) Los Angeles, CA 1094 (890 - 1296); 7.7% (6.3% - 9.1%) New York, NY 2222 (1814 - 2620); 11.8% (9.6% - 13.9%) Philadelphia, PA 330 (270 - 389); 13.2% (10.8% - 15.6%) Phoenix, AZ 402 (327 - 476); 8.5% (6.9% - 10.1%) Pittsburgh, PA 324 (264 - 382); 11.8% (9.6% - 13.9%) Salt Lake City, UT 19 (16 - 23); 3.7% (3% - 4.4%) St. Louis, MO 563 (461 - 662); 14.2% (11.6% - 16.7%) Tacoma, WA 49 (40 - 58); 4.7% (3.8% - 5.6%)

4 5 6 *Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (7 estimate of percent of total incidence (and 95% CI) under the current standards. While incidence and percent of total incidence estimate8 specifically for IHD-related mortality, the percent reduction plots provided in the figure apply to all long-term exposure-related mortali9 see text. 

**The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3 10 . Combinations of an annual standar

11 (m) are denoted n/m in this figure. Note, that the percent reductions for Salt Lake City and Tacoma at the 12/25 standard are 100% and 

2-77
Draft-Do Not Quote or Cite Ju 

1 Figure 2-12. Percent Reduction in Short-term Exposure-related Mortality and Morbidity Risk (alternative sta2 current standards) (Note: inset shows PM2.5 related incidence and percent of total incidence for CV mortality under th3 standards*) 4 

A

 Baltimore,

 MD

 Birmingham, AL

Detroit, MI 

 Fresno, CA 

 Houston, TX 

Lo

 New York,

 NY

 Philadelphia,

PA

 Pittsburgh, PA Phoenix, AZ 

Salt Lake City, UT 

St. Louis, MO 

 Tacoma,

 WA

0%5%

10%15%20%25%30%35%

15/35** 14/35 13/35 12/35 13/30 12/2

Recent Air Quality, Current Standard and Alternative Standards

Short-Term Exposure-Related Risk: 

Percent Reduction from Current Standard

 Atlanta, GA 32 (-33 - 95); 0.8% (-0.8% - 2.4%) Baltimore, MD 62 (-4 - 126); 1.6% (-0.1% - 3.2%) Birmingham, AL -1 (-42 - 40); 0% (-1.5% - 1.5%) Dallas, TX 29 (-19 - 76); 0.8% (-0.5% - 2.2%) Detroit, MI 60 (-8 - 127); 1% (-0.1% - 2.2%) Fresno, CA 12 (-9 - 33); 0.7% (-0.5% - 2%) Houston, TX 46 (-31 - 122); 0.9% (-0.6% - 2.4%) Los Angeles, CA -30 (-132 - 72); -0.2% (-0.7% - 0.4%) New York, NY 473 (276 - 668); 2.1% (1.2% - 3%) Philadelphia, PA 84 (22 - 145); 2.1% (0.5% - 3.6%) Phoenix, AZ 84 (-4 - 170); 1.3% (-0.1% - 2.7%) Pittsburgh, PA 43 (-9 - 93); 1.1% (-0.2% - 2.3%) Salt Lake City, UT 9 (-2 - 20); 0.8% (-0.2% - 1.7%) St. Louis, MO 106 (24 - 187); 1.9% (0.4% - 3.3%) Tacoma, WA 11 (-6 - 27); 0.7% (-0.4% - 1.8%)

`

5 6 7 *Based on Zanobetti and Schwartz (2009). The legend contains, for each urban area, the incidence estimate (and 95% CI) and the estim8 incidence (and 95% CI) under the current standards. While incidence and percent of total incidence estimates are provided specifically 9 the percent reduction plots provided in the figure apply to all short-term exposure-related mortality and morbidity categories assessed – 

**The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3 10 . Combinations of an annual standar

11 standard (m) are denoted n/m in this figure.

2-78
Draft – Do Not Quote or Cite 2-79 June 2010

1 below of the magnitude of risk remaining under simulated attainment of the alternative standard 

2 levels is based on risk estimates presented in U.S.EPA, 2010a, section 4.2.2. 

3  What is the nature and magnitude of risk associated with just meeting the alternative 

4 annual PM2.5 standards considered? 

5 In characterizing estimates of PM2.5-related risk associated with simulation of the 

6 alternative annual standards combined with the current 24-hour standard level (i.e., 14/35, 13/35 

7 and 12/35), we estimated both the magnitude of risk reductions (relative to risk remaining upon 

8 just meeting the current suite of standards), as well as the risk estimated to remain upon just 

9 meeting the alternative standards. Our analysis included the assessment of risk associated with 

an alternative annual standard level of 10 μg/m3 10 . In discussing these risks, we focus on the set 

11 of urban study areas experiencing risk reductions under each alternative annual standard. Key 

12 policy-relevant observations associated with these risk estimates include: 

13  Magnitude of estimated reductions in long-term exposure-related mortality risk: 

14 Upon simulation of just meeting the alternative annual standard levels considered (14, 

13, and 12 μg/m3 15 ) in conjunction with the current 24-hour standard (denoted as 14/35, 

16 13/35 and 12/35 suites of standards), the core analysis estimates reductions in long17 term exposure-related mortality for 12 of the 15 urban study areas, with the degree of 

18 estimated risk reduction increasing incrementally across the alternative standard 

19 levels (both in terms of the number of study areas experiencing risk reduction and the 

magnitude of those reductions). For the alternative annual standard level of 12 μg/m3 20 

21 (in conjunction with the current 24-hour standard), the core analysis estimates that 

22 these study areas have reductions in risk (relative to risk remaining upon just meeting 

23 the current suite of standards) ranging from about 11 to 35%. 

24 For some of those areas in which the 24-hour standard is the generally controlling 

25 standard, larger risk reductions would have been estimated in this case (12/35 suite of 

26 standards) if the locally-focused rollback approach had been used to simulate just 

27 meeting the current suite of standards. This result would be expected since the 

28 magnitude of risk remaining upon just meeting the current suite of standards would 

29 have been higher than that estimated based on the proportional rollback approach 

30 used in the core analysis. Therefore, while the absolute risks would not change, the 

31 percentage difference would have been greater if we had started with higher risks 

32 related to simulation of just meeting the current annual standard. 

33  Long-term exposure-related mortality risk remaining: For an annual standard level of 

14 μg/m3 34 , the percent of total incidence of long-term exposure-related IHD mortality 

35 attributable to PM2.5 (i.e., risk remaining) in the 5 urban study areas experiencing risk 

36 reductions ranges from an estimate of 9 to 15%. For an alternative annual standard of 

12 μg/m3 37 , estimated risk remaining in the 12 urban study areas experiencing risk 

38 reductions ranges from 6 to 11% in terms of PM2.5-attributable long-term exposure39 related mortality. This translates into estimates of between 90 and 300 cases per year 

40 attributable to long-term PM2.5 exposure for those study areas experiencing the 
Draft – Do Not Quote or Cite 2-80 June 2010

1 greatest reductions in risk under the lowest alternative annual standard level 

2 simulated. 

3  Short-term exposure-related mortality and morbidity risk: For the alternative annual 

standard level of 12 μg/m3 4 (in conjunction with the current 24-hour standard), the 

5 core analysis estimates that reductions in both short-term exposure-related CV 

mortality and morbidity risk ranged from 5 to 23%.56 6 In terms of risk remaining upon 

simulation of 12 μg/m3 7 (in conjunction with the current 24-hour standard), the urban 

8 study areas with the greatest percent reduction have CV-related mortality estimates 

9 ranging from 25 to 50 deaths per year. 

 Simulation of risks for an alternative annual standard level below 12 μg/m3 10 : 

Simulation of risks for an alternative annual standard of 10 μg/m3 11 suggests that 

12 additional risk reductions could be expected with alternative annual standards below 

12 μg/m3 13 . However, we recognize that there is potentially greater uncertainty 

14 associated with these risk estimates compared with estimates generated for the higher 

15 alternative annual standards considered in the RA, since these estimates require 

16 simulation of relatively greater reductions in ambient PM2.5 concentrations. As lower 

17 ambient PM2.5 concentrations are simulated (i.e., ambient concentrations further from 

18 recent conditions), potential variability in such factors as the spatial pattern of 

19 ambient PM2.5 reductions (rollback) increases, thereby introducing greater uncertainty 

20 into the simulation of composite monitor annual mean PM2.5 concentrations and, 

21 consequently, risk estimates (US EPA, 2010a, Appendix J). 

22  Substantial variability in magnitude of estimated risk reduction across urban study 

23 areas: While there is a consistent pattern of estimated risk reduction across the 

24 alternative annual standards with lower alternative standard levels resulting in more 

25 urban study areas experiencing increasingly larger risk reductions, there is 

26 considerable variability in the magnitude of these reductions across study areas for a 

27 given alternative annual standard level. This variability in estimated risk reflects 

28 differing degrees of reduction in annual mean concentrations across the study areas, 

29 which results, in part, because the study areas began with varying annual mean PM2.5

30 concentrations after simulating just meeting the current suite of standards. Therefore, 

31 even if study areas have similar “ending” annual mean PM2.5 concentrations after 

32 simulation of just meeting a specific alternative annual standard, because the starting 

33 point in the calculation (the annual mean PM2.5 concentrations upon just meeting the 

34 current suite of standards) can be variable, the overall reduction in annual mean PM2.5

35 concentrations across the standards can also be variable. This translates into variation 

36 in reductions in long-term exposure-related risk upon just meeting alternative annual 

37 standard levels across the study areas. 

38  The nature of the spatial pattern in PM2.5 reductions (reflected in the rollback 

39 approach used) can impact the magnitude of estimated risk reductions: The 

40 sensitivity analysis involving application of locally focused rollback revealed that the 

41 pattern of reductions in ambient PM2.5 concentrations upon just meeting the current 

 56 Because the same air quality metric (annual distributions of 24-hour PM2.5 concentrations) was used in generating 

short-term exposure-related mortality and morbidity endpoints, patterns of risk reduction (as a percent of risk under 

the current suite of standards) are similar for both sets of endpoints (see US EPA, 2010a, section 4.2.2). 
Draft – Do Not Quote or Cite 2-81 June 2010

1 suite of standards can impact the magnitude of additional risk reductions estimated 

2 for just meeting alternative (lower) annual standard levels. Specifically, for those 

3 study areas with more peaky PM2.5 distributions, application of the locally focused 

4 rollback approach resulted in higher annual mean PM2.5 concentrations remaining 

5 upon just meeting the current suite of standards. If a proportional rollback approach 

6 was then used to simulate just meeting alternative annual standard levels, a greater 

7 degree of reduction in composite monitor annual mean PM2.5 concentrations will 

8 result, since the “starting point” for the calculation (annual mean PM2.5 concentrations 

9 upon just meeting the current suite of standards) would be higher. These findings 

10 highlight the important roll played by variability in the spatial pattern of ambient 

11 PM2.5 concentrations in influencing the magnitude of risk reductions under alternative 

12 annual standard levels. 

13  Based on consideration of the composite monitor annual mean PM2.5 concentrations 

14 involved in estimating long-term exposure-related mortality, we have varying levels 

15 of confidence in risk estimates generated for the three alternative annual standard 

16 levels considered: With the exception of one study area, those study areas estimated 

to have risk reductions under the alternative annual standards of 14 and 13 μg/m3 17 

18 have simulated composite monitor annual mean PM2.5 concentrations ranging from 

just below 10.6 to over 13.3 μg/m3 19 (see US EPA, 2010a, Table 3-4). In other words, 

20 these composite monitor annual mean PM2.5 concentrations generally fall well within 

21 the range of ambient PM2.5 concentrations considered in fitting the C-R functions 

22 used (i.e., within one SD of the mean PM2.5 concentration from 1999-2000 ACS 

23 dataset). The urban study areas estimated to have risk reductions under the lower 

alternative annual standard level of 12 μg/m3 24 have lower composite monitor annual 

mean values ranging from 9.0 to over 11.4 μg/m3 25 . These values generally extend to 

26 below one SD of the mean of the ACS dataset and therefore, we have somewhat 

27 lower confidence in these risk estimates, relative to those generated for the two higher 

28 alternative annual standards. By contrast, urban study areas estimated to have risk 

reductions under the alternative standard level of 10 μg/m3 29 (paired with the current 

30 24-hour standard) have simulated composite monitor annual estimates ranging from 

7.6 to 8.9 μg/m3 31 (see US EPA, 2010a, Table 3-4). These concentrations are towards 

32 the lower end of the range of ACS data used in fitting the C-R functions (in some 

33 cases approaching the LML) and, therefore, we have substantially less confidence in 

34 these risk estimates, compared with those for the higher alternative annual standards 

35 assessed. 

36  What is the nature and magnitude of risk associated with simulating different 

37 combinations of alternative annual and 24-hour PM2.5 standards? 

38 In characterizing PM2.5-related risks associated with simulation of alternative annual 

39 standards combined with alternative 24-hour standards (13/30 and 12/25), we estimated both the 

40 magnitude of risk remaining upon just meeting these alternative standards, as well as the 

41 magnitude of risk reductions (relative to risk remaining upon just meeting the current suite of 

42 standards). While the alternative 24-hour standard levels considered did result in estimated risk 
Draft – Do Not Quote or Cite 2-82 June 2010

1 reductions, we have lower confidence in these risk estimates because of the relatively low 

2 annual-average PM2.5 concentrations associated with simulation of these standard levels. 

3 Of the 11 urban study areas estimated to have risk reductions under the alternative 24-

hour standard of 30 μg/m3 4 (with the 24-hour standard controlling – see US EPA 2010a, Table 3-

4), composite monitor annual mean PM2.5 concentrations ranged from 6.6 to 11.3 μg/m3 5 with 

most of the urban study areas having concentrations in the 8 to 10 μg/m3 6 range. These 

7 concentrations extend into the lower range of PM2.5 concentrations considered in the ACS study 

8 to fit the C-R functions and therefore, we have somewhat lower confidence in these estimates. 

9 When we consider composite monitor concentrations for urban study areas assessed to 

have risk reductions under the alternative 24-hour standard level of 25 μg/m3 10 (again, where the 

11 24-hour standard is controlling), we observed composite monitor annual mean PM2.5 

concentrations that are even lower, ranging from 5.6 to 11.2 μg/m3 12 with most study areas having 

concentrations in the range of 7 to 9 μg/m3 13 . Because this range extends well into the lower range 

14 of PM2.5 concentrations considered in the ACS study to fit the C-R functions (in some cases 

15 extending below the LML), we have substantially lower confidence in these risk estimates. 

16 Furthermore, we find that those urban study areas with the greatest degree of estimated risk 

17 reduction under these alternative 24-hour standard levels also had the lowest composite monitor 

18 annual average PM2.5 levels, and therefore we have the lowest overall confidence in these results. 

19 2.3.4.3 Integration of Evidence-based and Risk-based Considerations 

20 In considering the epidemiological evidence, estimates of risk reductions associated with 

21 just meeting alternative annual and/or 24-hour standards, air quality analyses, and related 

22 limitations and uncertainties, staff concludes that there is clear support for considering revisions 

23 to the suite of current PM2.5 standards to provide additional protection against health effects 

24 associated with long- and short-term exposures. We recognize that health effects may occur over 

25 the full range of concentrations observed in the long- and short-term epidemiological studies and 

26 that no discernible threshold for any effects can be identified based on the currently available 

27 evidence. In reaching staff conclusions regarding appropriate alternative standard levels to 

28 consider, we have examined where the evidence of associations is strongest and, conversely, 

29 where we have substantially less confidence in C-R relationships and in quantitative estimates of 

30 risk. 

31 As an initial matter, based on the available evidence and insights from the quantitative 

32 risk assessment, we conclude that it is appropriate to consider alternative annual and 24-hour 

33 standard levels consistent with the goal of establishing a suite of standards that includes a 

34 “generally controlling” annual standard and a 24-hour standard that provides supplemental 

35 protection, especially for areas with high peak-to-mean ratios, possibly associated with strong 
Draft – Do Not Quote or Cite 2-83 June 2010

1 local or seasonal sources, or for potential PM2.5-related effects that may be associated with 

2 shorter-than-daily exposure periods. Based upon the currently available evidence, we conclude 

alternative annual standard levels in the range of 13 to 11 μg/m3 3 are appropriate to consider. 

4 This conclusion reflects the much stronger body of scientific evidence available in this review 

5 supporting a causal association between long- and short-term PM2.5 exposures and mortality and 

6 cardiovascular effects and a likely causal association between long- and short-term PM2.5

7 exposures and respiratory effects, as well as evidence that is suggestive of a causal association 

8 with other health outcomes such as low birth weight and infant mortality and cancer, 

9 mutagenicity, and genotoxicity effects. In conjunction with an annual standard within this range, 

10 we conclude it is appropriate to consider retaining the current 24-hour standard level of 35 

μg/m3

, as well as considering an alternative 24-hour standard level of 30 μg/m3 11 , particularly in 

conjunction with consideration of an annual standard level of 11 μg/m3 12 . 

13 Beyond evidence-based considerations, we have also considered the extent to which the 

14 quantitative risk assessment supports consideration of these alternative standard levels or 

15 provides support for lower levels. We first conclude that risks estimated to remain upon 

16 simulation of just meeting the current suite of standards are important from a public health 

17 perspective, considering both the severity and estimated magnitude of effects. In considering 

simulations of just meeting alternative annual standard levels within the range of 13 to 11 μg/m3 18 

in conjunction with the current 24-hour standard level of 35 μg/m3 19 , we conclude that important 

20 public health improvements are associated with risk reductions estimated for standard levels of 

13 and 12 μg/m3, noting that the level of 11 μg/m3 21 was not included in the quantitative risk 

22 assessment. Our overall confidence in the quantitative risk estimates is strongest for the 

alternative annual standard level of 13 μg/m3 23 . We have somewhat lower confidence in risk 

estimates for the alternative annual standard of 12 μg/m3 24 . We also estimated risks likely to 

remain upon just meeting an annual standard level of 10 μg/m3 25 , although we have substantially 

26 lower confidence in those estimates. We also have somewhat lower confidence in our risk 

estimates for an alternative 24-hour standard level of 30 μg/m3 27 , and substantially lower 

confidence in our estimates of risks for an alternative 24-hour standard level of 25 μg/m3 28 . Based 

29 on the above considerations, we conclude that the quantitative risk assessment provides support 

for considering an alternative annual standard within a range of 13 to 11 μg/m3 30 , in conjunction 

with a 24-hour standard of 35 or 30 μg/m3 31 , but does not provide strong support for considering 

32 lower alternative levels. 

33 In identifying this range of annual standard levels, staff has taken into consideration the 

34 importance of balancing the strength of the currently available evidence and risk-based 

35 information with the remaining uncertainties and limitations associated with this information. 

The upper end of this range of annual standard levels (13 μg/m3 36 ) reflects placing appreciably 
Draft – Do Not Quote or Cite 2-84 June 2010

1 more weight on the uncertainties and limitations in the information which would serve to reduce 

2 the potential to overestimate public health risks and protection likely to be associated with just 

3 meeting a standard set at this level. This policy option would reflect placing greater weight on 

4 the remaining uncertainties in the evidence, including uncertainties associated with 

5 understanding the heterogeneity observed in the epidemiological studies such as those associated 

6 with the role of specific components, sources, and subfractions (e.g., UFPs) within the current 

7 PM2.5 mass-based indicator, the role of fine particles and co-pollutants within the broader 

8 ambient mixture, and exposure-related factors that influence the magnitude and duration of fine 

particle exposures. The lower end of this range (11 μg/m3 9 ) reflects placing much less weight on 

10 uncertainties and limitations in the information which would serve to reduce the potential to 

11 underestimate public health improvements likely to be associated with just meeting a standard 

12 set at this level. This policy option would reflect placing considerably more weight on limited 

13 evidence of serious effects in susceptible populations such as potential developmental effects, 

14 while recognizing that significant limitations remain in assessing the relationship between PM2.5 

15 exposures and these effects, specifically, understanding the nature of the association and 

16 exposure windows of concern. 

17 To provide some perspective on the implications of applying various combinations of 

18 alternative annual and 24-hour standards, staff assessed (based on 2006 to 2008 air quality data) 

19 the percentage of counties, and the population in those counties, that would not likely attain 

20 varies alternative suites of PM2.5 standards. This assessment, shown in Appendix 2A, Table 2A21 1, was not considered as a basis for the above staff conclusions. 

22 2.4 STAFF CONCLUSIONS ON ALTERNATIVE FINE PARTICLE STANDARDS 

23 In reaching conclusions on potential alternative standards to provide requisite protection 

24 for health effects associated with long- and short-term fine particle exposures, staff has 

25 considered these standards in terms of the basic elements of the NAAQS: indicator, averaging 

26 time, form, and level. In considering the scientific and technical information, we reflect upon the 

27 information available in the last review integrated with information that is newly available as 

28 assessed and presented in the ISA and RA (US EPA, 2009a; US EPA, 2010a) and as summarized 

29 in sections 2.2 and 2.3. We also consider the issues raised by the court in its remand of the 

30 primary annual PM2.5 standard. 

31 As outlined in section 2.1.3, our approach to reaching conclusions about the adequacy of 

32 the current suite of PM2.5 standards and potential alternative standards that are appropriate for 

33 consideration is broader and more integrative than approaches used in past reviews. Our 

34 approach integrates a much expanded body of health effects evidence, more extensive air quality 

35 data and analyses, and a more comprehensive quantitative risk assessment, and considers the 
Draft – Do Not Quote or Cite 2-85 June 2010

1 combined protection against PM2.5-related mortality and morbidity effects associated with both 

2 long- and short-term exposures afforded by the annual and 24-hour standards. 

3 We recognize that selecting from among alternative standards will necessarily reflect 

4 consideration of the qualitative and quantitative uncertainties inherent in the relevant evidence 

5 and in the assumptions that underlie the quantitative risk assessment. In identifying these 

6 alternative suites of primary standards and ranges of levels for consideration, we are mindful that 

7 the Clean Air Act (CAA) requires standards to be set that are requisite to protect public health 

8 with an adequate margin of safety, such that the standards are to be neither more nor less 

9 stringent than necessary. Thus, the CAA does not require that the NAAQS be set at zero-risk 

10 levels, but rather at levels that avoid unacceptable risks to public health. 

11 (1) Consideration should be given to revising the current PM2.5 primary standards to provide 

12 increased public health protection from the effects of both long- and short-term exposures to 

13 fine particles in the ambient air. This conclusion is based in general on the evaluation in the 

14 ISA of the currently available epidemiological, toxicologic, dosimetric, and exposure-related 

15 evidence, and on air quality information and analyses related to the epidemiological 

16 evidence, together with judgments as to the public health significance of the estimated 

17 incidence of effects upon just meeting the current suite of standards. 

18 (2) The indicator for fine particle standards should continue to be PM2.5. We conclude that the 

19 available evidence does not provide a sufficient basis for replacing or supplementing the 

20 PM2.5 indicator with an indicator defined in terms of UFPs or for any specific fine particle 

21 component or source category of fine particles, nor does it provide a basis for excluding any 

22 component or source category from the mix of particles included in the PM2.5 indicator. 

23 (3) Averaging times for PM2.5 standards should continue to include annual and 24-hour averages 

24 to protect against health effects associated with long-term (seasons to years) and short-term 

25 (hours to days) exposure periods. Consideration of other averaging times, including an 

26 averaging time less than 24 hours to address health effects associated with subdaily 

27 exposures or a longer averaging time to address effects associated with seasonal exposures, 

28 was limited by the relatively small amount of relevant information available. 

29 (4) Consideration should be given to revising the form of the annual standard to one based on the 

30 highest community-oriented monitor in an area rather than a form that allows averaging 

across monitors (e.g., spatial averaging). The 98th 31 percentile form of the current 24-hour 

32 standard should be retained. 

33 (5) Consideration should be given to alternative suites of PM2.5 standards to provide protection 

34 against effects associated with both long- and short-term exposures, taking into account both 

35 evidence-based and risk-based considerations. 

36 (a) Consideration should be given to setting the levels of the annual and 24-hour 

37 standards such that the annual standard would be the “generally controlling” 

38 standard to provide protection for both long- and short-term PM2.5 exposures in 

39 conjunction with a 24-hour standard to provide supplemental protection against 

40 days with high peak concentrations to limit peak concentrations in areas with high 

41 peak-to-mean ratios, possibly associated with strong local or seasonal sources, or 
Draft – Do Not Quote or Cite 2-86 June 2010

1 for potential PM2.5-related effects that may be associated with shorter-than-daily 

2 exposure periods. We conclude that this is the most effective and efficient way to 

3 reduce total population risk associated with both long- and short-term exposures 

4 and provide requisite protection in areas across the country. 

5 (b) Consideration should be given to alternative annual standard levels in the range of 

13 to 11 μg/m3 6 . We conclude that this range is most strongly supported by the 

7 currently available scientific evidence and risk-based information 

8 (c) Consideration should be given to retaining the current 24-hour standard level of 

35 μg/m3 9 in conjunction with annual standard levels in this range; consideration 

could also be given to an alternative 24-hour standard level of 30 μg/m3 10 

particularly in conjunction with an annual standard level of 11 μg/m3 11 . 

12 2.5 KEY UNCERTAINTIES AND AREAS FOR FUTURE RESEARCH AND DATA 

13 COLLECTION 

14 In this section, we briefly discuss key uncertainties and areas for future health-related 

15 research, model development, and data collection activities. These efforts, if undertaken, could 

16 provide important evidence for informing future PM NAAQS reviews and, in particular, 

17 consideration of possible alternative indicators, averaging times, and/or levels. In some cases, 

18 research in these areas can go beyond aiding standard setting to informing the development of 

19 more efficient and effective control strategies. We note, however, that a full set of research 

20 recommendations to meet standards implementation and strategy development needs is beyond 

21 the scope of this discussion. 

22 As has been presented and discussed in the PM ISA, particularly in Chapters 4 through 7, 

23 the scientific body of evidence informing our understanding of health effects associated with 

24 long- and short-term exposures to fine particles has been broadened and strengthened since the 

25 last review. In reviewing the adequacy of the current suite of primary PM2.5 standards and in 

26 evaluating alternative health-based fine particle standards appropriate for consideration, we 

27 identify the following key uncertainties and areas for future research and data collection efforts 

28 that have been highlighted in this review. We recognize that some research could be available to 

29 inform the next PM NAAQS review, while other research may require longer-term efforts. 

30 Interpretation of Epidemiological Evidence

31 Additional research focused on identifying the most important factors contributing to the 

32 observed heterogeneity in the epidemiological evidence could provide insights for interpreting 

33 these studies. We encourage research and data collection efforts directed at improving our 

34 understanding of the nature of the exposures contributing to the observed health effects, for 

35 example, the role of specific components, sources, and subfractions (e.g., UFPs) within the 

36 current PM2.5 mass-based indicator and the role of fine particles and co-pollutants within the 

37 broader ambient mixture, as well as improving our understanding of exposure-related factors that 
Draft – Do Not Quote or Cite 2-87 June 2010

1 influence the magnitude and duration of fine particle exposures. Much of this research may 

2 depend on the availability of increased monitoring data, as discussed below. 

3  Components/Sources. The currently available scientific evidence continues to be largely 

4 indexed by aggregate PM2.5 mass-based concentrations which vary in composition both 

5 regionally and seasonally. Source characterization, exposure, epidemiological, and 

6 toxicological research focused on improving our understanding of the relative toxicity of 

7 different fine particle components, properties, and sources that may be more closely linked 

8 with various health effects. Critical to this better understanding of the impacts of 

9 components and their associated sources are data that refines the temporal and spatial 

10 variability of the fine particle mixture.. This research would reduce the uncertainties in 

11 estimating risks. It could also inform consideration of alternative indicators in future PM 

12 NAAQS reviews as well as aid in the development of efficient and effective source control 

13 strategies for reducing health risks. 

14  Ultrafine Particles (UFPs). Additional monitoring methods development work, health 

15 research, and ambient monitoring data collection efforts are needed to expand the currently 

16 available scientific data base for UFPs. UFP measurements should include surface area as 

17 well as number, mass and composition. It would be most useful for a UFPs monitoring 

18 network to be designed to inform our understanding of the spatial and temporal variability of 

19 these particles, including in near-roadway environments. This information would improve 

20 our ability to explore consideration of a separate indicator for UFPs as a subfraction of fine 

21 particles in future PM NAAQS reviews. 

22  Co-pollutant Exposures. Research focused on furthering our understanding of the extent to 

23 which an association between fine particles and specific health effects can be modified by 

24 one or more co-pollutants would inform our ability to discern the role of PM in the complex 

25 ambient mixture. For example, does the magnitude of a PM2.5-related effect estimate differ 

26 on days when O3 levels are higher compared to days when O3 levels are lower? 

27  Exposure-related Factors. Additional research and analyses would be useful to provide 

28 insights on population exposures, specifically in improving our understanding of intra-city 

29 and inter-city differences related to various PM components, source contributions and 

30 personal and building-related factors (e.g., air conditioning use; residence near roadways) 

31 that may enhance our interpretation of the epidemiological evidence. This research could 

32 focus on different size fractions in PM2.5 (i.e.,UFPs) as well as components. Coordination 

33 between exposure and health studies could advance our understanding of exposure-related 

34 factors. For example, epidemiological panel studies might use measured personal exposure 

35 measurements to explore differences in personal exposures related to (1) indoor generated 

36 fine particles, (2) fine particle exposures measured by community monitors, and (3) fine 

37 particle exposures not captured by community monitors (i.e., personal exposures during 

38 commuting). 

39 Health Outcomes, Exposure Durations of Concern, and Susceptible Populations

40 New information available in this review reinforces and expands the evidence of 

41 associations between long- and short-term PM2.5 exposures and mortality and cardiovascular and 

42 respiratory effects. Less evidence is available to understand other health effects (e.g., 
Draft – Do Not Quote or Cite 2-88 June 2010

1 developmental/reproductive effects; central nervous system effects). Additional research could 

2 expand our understanding of the associations between PM2.5 and a broader range of health 

3 outcomes; reduce uncertainties associated with our current understanding of concentration4 response relationships; improve our understanding of exposure durations of concern; and 

5 improve our understanding of the potential public health impacts of fine particle exposures in 

6 susceptible populations. 

7  Health Effects. Research on a broader range of cardiovascular and respiratory endpoints 

8 could improve our understanding of the mechanisms by which these effects occur. In 

9 addition, future research could expand the scientific data base for health effects that are 

10 currently less understood including effects categorized within the ISA as having evidence 

11 suggestive of a causal relationship or for which currently available evidence is inadequate. 

12 To the extent that research supports a link between fine particles and adverse effects on the 

13 nervous system, reproduction, development, or other endpoints, such effects could play an 

14 increased role for informing future PM NAAQS reviews including expanding the health 

15 endpoints that could potentially be evaluated in future quantitative risk assessments. 

16  Concentration-Response Relationships. Research focused on improving our understanding 

17 of the shape of the C-R relationships, especially at lower ambient fine particle 

18 concentrations, as well as the confidence bounds around these C-R relationships, could 

19 reduce uncertainties associated with estimating and characterizing risks throughout the full 

20 range of air quality distributions. As more information becomes available on fine particle 

21 components and sources, it will be important to understand the C-R relationships for key 

22 constituents of the fine particle mixture, as well. 

23  Exposure Durations of Concern. Research should be directed at broadening the scientific 

24 data base to improve our understanding of health effects associated with short-term, peak 

25 exposures, such as those related to traffic-related sources, wildfires, agricultural burning, or 

26 other episodic events, as well as to improve our understanding of health effects associated 

27 with seasonal-length exposures, such as those related to wintertime wood-burning emissions. 

28 Additional quantitative measures of exposure might take into account factors including the 

29 magnitude and duration of sub-daily and seasonal length PM2.5 exposures and the frequency 

30 of health impacts associated with repeated peak exposures. More research is needed to better 

31 understand effects that occur at longer lag times than have historically been studied (e.g., 0 to 

32 2 day lags). 

33  Susceptible Populations. Improving our understanding of the populations that are more 

34 likely to experience adverse health effects related to fine particle exposures and the 

35 concentrations at which these effects may occur is important for informing future PM 

36 NAAQS reviews and for developing programs to reduce related public health risks. This 

37 evidence may also provide insights into the biologic modes of action for toxicity. 

38 o Pre-existing Health Conditions. While currently identified susceptible populations 

39 include persons with pre-existing cardiovascular and respiratory disease, evidence 

40 continues to emerge related to additional health conditions that may increase 

41 susceptibility to fine particle exposures (e.g., diabetes, obesity, neurological 

42 disorders). Research to replicate or extend these findings would enhance our 

43 understanding of these and other potentially susceptible populations. 
Draft – Do Not Quote or Cite 2-89 June 2010

1 o Children. Epidemiological and toxicological studies provide evidence that children 

2 are more susceptible to PM exposures, primarily for respiratory-related effects. 

3 Evidence of developmental effects associated with PM exposures continues to 

4 emerge. Additional research exploring issues to better understand key windows of 

5 development impacted by PM exposures could enhance our understanding of this 

6 important susceptible population. 

7 o Genetic Susceptibility. Research to expand our understanding of genetic 

8 susceptibility could inform our understanding of potentially susceptible populations 

9 and provide additional information for identifying the specific pathways and 

10 mechanisms of action by which PM initiates health effects. 

11 o Socioeconomic status (SES). Additional research is needed to identity what factors 

12 (e.g., general health status, diet, medication, stress, unmeasured pollution) cause SES 

13 differences in response to pollution measured at a community site. 

14 Data Collection Needs and Methods Development Activities

15 Additional research and data collection efforts focused on expanding current monitoring 

16 methods and networks as well as continued development of exposure models expand data 

17 available for health studies could improve our understanding of potential alternative indicators, 

18 averaging times, and levels to consider in future PM NAAQS reviews. In particular, staff 

19 encourages work to enhance our understanding of the temporal and spatial variability of PM2.5, 

20 PM2.5 components, and subfractions (e.g., UFPs). 

21  Monitoring Measurements. In order to improve our understanding of the association 

22 between fine particles and health effects, more frequent measurement data could be collected. 

23 This would provide information that could inform our understanding of alternative lags. 

24 o PM2.5 Components. With respect to improving our understanding of the impacts of 

25 PM2.5 components, enhancements to the Chemical Speciation Network (CSN), 

26 including more frequent measurement schedules and the development and 

27 deployment of continuous monitoring methods for specific fine particle components 

28 (e.g., EC/OC, sulfates), could enhance our understanding of the temporal and spatial 

29 variability of specific components. Furthermore, identifying chemical species within 

30 the mix of OC would improve our understanding of the artifacts associated with semi31 volatile PM components and aid in designing toxicological experiments. 

32 o Ultrafine Particles. In order to improve our understanding of the public health 

33 impacts of UFPs, consideration should be given to establishing a Federal Reference 

34 Method (FRM) for UFPs and establishing a national UFP monitoring network. 

35 o Source Apportionment. Composition data with better time resolution (e.g., 1 to 6 

36 hour) and better size resolution (e.g., UFPs, accumulation mode particles, coarse 

37 particles in PM2.5 and PM10-2.5) could provide more precise and accurate information 

38 on sources of fine particles to inform health research as well as development of more 

39 efficient and effective control strategies. 

40 o Areas of Peak Exposures. Some portion of the required PM2.5 monitoring network 

41 could be dedicated to improving our ability to characterize community-wide air 
Draft – Do Not Quote or Cite 2-90 June 2010

1 quality at a neighborhood or urban scale that represents where we expect to find peak 

2 exposures. A portion of the required network could be targeted to monitor short-term 

3 PM2.5 concentrations in areas that are not affirmatively designated as “community4 wide” air quality. Requiring monitors in areas not designated as “community-wide” 

5 would ensure protection for those populations whose ambient exposures may be 

6 affected by unique local sources or terrain which are often located in areas with 

7 smaller spatial scales. These areas, such as population-oriented micro- and middle8 scale “hot spots,” are not required to be monitored in the current network design. 

9  Model Development. Continuing work to improve models for estimating PM2.5 mass and 

10 composition in areas with only every third or sixth day measurements, and by space, where 

11 measurements are not available could enhance our understanding of the temporal and spatial 

12 variability of fine particles. Refinement of these models to finer spatial scales may improve 

13 exposure estimates in epidemiological studies as well as in quantitative risk and exposure 

14 assessments 

15  Air Quality Distributions Reported in Epidemiological Studies. Most epidemiological 

16 studies provide some information on the distribution of ambient measurement data evaluated, 

17 however, published information is often generally limited in scope and the descriptive 

18 statistics reported vary from one study to another. Understanding the air quality distributions 

19 at which effects have been observed is important for informing consideration of the adequacy 

20 of the current NAAQS as well as potential alternative indicators, averaging times, and levels 

21 to consider. Working with intramural and extramural research groups, we plan to encourage 

22 a more comprehensive and more consistent reporting of air quality data. 
Draft – Do Not Quote or Cite 2-91 June 2010

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14 Zhang Z, Whitsel E, Quibrera P, Smith R, Liao D, Anderson G, Prineas R (2009). Ambient fine particulate matter 

15 exposure and myocardial ischemia in the Environmental Epidemiology of Arrhythmogenesis in the 

16 Women's Health Initiative (EEAWHI) study. Environ Health Perspect, 117: 751-756. 

17 Zeger S, McDermott A, Dominicit F, Samet J. (2007). Mortality in the Medicare population and chronic exposure 

18 to fine particulate air pollution. Johns Hopkins University. Baltimore. 

19 http://www.bepress.com/jhubiostat/paper133. 

20 Zeger S, Dominici F, McDermott A, Samet J. (2008). Mortality in the Medicare population and chronic exposure to 

21 fine particulate air pollution in urban centers (2000-2005). Environ Health Perspect, 116: 1614. 
Draft-Do Not Quote or Cite 3-1 June 2010

1 3 REVIEW OF THE PRIMARY STANDARD FOR THORACIC 

2 COARSE PARTICLES 

3 This chapter presents staff conclusions with regard to the adequacy of the current primary 

4 PM10 standard, which is intended to protect public health against exposures to thoracic coarse 

5 particles (PM10-2.5), and potential alternative primary standards for consideration in this review. 

6 Our assessment of these issues is framed by a series of key policy-relevant questions, which 

7 expand upon those presented in the IRP (US EPA, 2008a). The answers to these questions will 

8 inform decisions on whether, and if so how, to revise the current PM10 standard. 

9 Our approach for reviewing the primary PM10 standard is presented in section 3.1. Our 

10 considerations and conclusions regarding the adequacy of the current PM10 standard are 

11 presented in section 3.2. Section 3.3 presents our considerations and conclusions with respect to 

12 potential alternative standards, focusing on each of the basic elements of the standards: pollutant 

13 indicator (section 3.3.1), averaging time (section 3.3.2), form (section 3.3.3), and level (section 

14 3.3.4). Section 3.4 summarizes staff conclusions on the current and potential alternative 

15 standards. Section 3.5 discusses key uncertainties and suggested future research areas and data 

16 collection efforts. 

17 3.1 APPROACH 

18 Staff’s approach for reviewing the current primary PM10 standard builds upon the 

19 approaches used in previous PM NAAQS reviews. The past and current approaches described 

20 below are all based most fundamentally on using information from epidemiologic studies to 

21 inform the selection of PM standards that, in the Administrator’s judgment, protect public health 

22 with an adequate margin of safety. Evidence-based approaches to using information from 

23 epidemiologic studies to inform decisions on PM standards are complicated by the recognition 

24 that no population threshold, below which it can be concluded with confidence that PM-related 

25 effects do not occur, can be discerned from the available evidence (ISA, section 2.4.3). As a 

26 result, any approach to reaching decisions on what standards are appropriate requires judgments 

27 about how to translate the information available from the epidemiologic studies into a basis for 

28 appropriate standards, which includes consideration of how to weigh the uncertainties in reported 

29 associations across the distributions of PM concentrations in the studies. Our approach to 

30 informing these decisions, discussed more fully below, recognizes that the available health 

31 effects evidence reflects a continuum consisting of ambient levels at which scientists generally 

32 agree that health effects are likely to occur through lower levels at which the likelihood and 

33 magnitude of the response become increasingly uncertain. Such an approach is consistent with 
Draft-Do Not Quote or Cite 3-2 June 2010

1 setting standards that are neither more nor less stringent than necessary, recognizing that a zero2 risk standard is not required by the CAA. 

3 3.1.1 Reviews Completed in 1987 and 1997 

4 The PM NAAQS have always included some type of a primary standard to protect 

5 against effects associated with exposures to thoracic coarse particles. In 1987, when EPA first 

6 revised the PM NAAQS, EPA changed the indicator for PM from Total Suspended Particles 

7 (essentially applicable to particles smaller than 25-45 micrometers) to focus on inhalable 

8 particles, those which can penetrate into the trachea, bronchi, and deep lungs (52 FR 24634). 

9 EPA changed the PM indicator to PM10 based on evidence that the risk of adverse health effects 

10 associated with particles of 10 micrometers or less was significantly greater than that associated 

11 with larger particles (52 FR at 24639). 

12 In the 1997 review, in conjunction with establishing new fine particle (i.e., PM2.5)

13 standards (see above, sections 1.2.3, 2.1.1), EPA concluded that continued protection remained 

14 warranted against potential effects associated with thoracic coarse particles in the size range of 

15 2.5 to 10 μm. This conclusion was based on particle dosimetry, toxicological information, and 

16 on limited epidemiologic evidence from studies that measured PM10 in areas where coarse 

17 particles were likely to dominate the distribution (62 FR 38677, July 18, 1997). Thus, EPA 

18 concluded that the existing PM10 standards would provide requisite protection against effects 

19 associated with particles in the size range of 2.5 to 10 μm. Although EPA considered a more 

20 narrowly defined indicator for thoracic coarse particles in that review (i.e., PM10-2.5), EPA 

21 concluded that it was more appropriate, based on existing evidence, to continue to use PM10 as 

22 the indicator. This decision was based, in part, on the recognition that the only studies of clear 

23 quantitative relevance to health effects most likely associated with thoracic coarse particles used 

24 PM10 in areas where the coarse fraction was the dominant fraction of PM10, namely two studies 

25 conducted in areas that substantially exceeded the 24-hour PM10 standard (62 FR 38679). In 

26 addition, there were only very limited ambient air quality data then available specifically for 

27 thoracic coarse particles, in contrast to the extensive monitoring network already in place for 

28 PM10, Therefore, it was judged more administratively feasible to use PM10 as an indicator. EPA 

29 also stated that the PM10 standards would work in conjunction with the PM2.5 standards by 

30 regulating the portion of particulate pollution not regulated by the PM2.5 standards. 

31 As explained in chapter 1, in May 1998, a three-judge panel of the U.S. Court of Appeals 

32 for the District of Columbia Circuit found "ample support" for EPA's decision to regulate coarse 

33 particle pollution, but vacated the 1997 PM10 standards, concluding that EPA had failed to 

34 adequately explain its choice of PM10 as the indicator for thoracic coarse particles pointing to the 

35 lack of reasoned explanation for the variable level of allowable concentrations of thoracic coarse 
Draft-Do Not Quote or Cite 3-3 June 2010

1 particles (varying by levels of PM2.5) and the consequent double regulation of PM2.5. American 

2 Trucking Associations v. EPA , 175 F. 3d 1027, 1054-56 (D.C. Cir. 1999). The court also 

3 rejected considerations of administrative feasibility as justification for a NAAQS, which are to 

4 be based exclusively on health and welfare considerations. Id. at 1054. Pursuant to the court’s 

5 decision, EPA removed the vacated 1997 PM10 standards from the Code of Federal Regulations 

6 (CFR) (69 FR 45592, July 30, 2004) and deleted the regulatory provision (at 40 CFR section 

7 50.6(d)) that controlled the transition from the pre-existing 1987 PM10 standards to the 1997 

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

9 remained in place. Id. at 80777. 

10 3.1.2 Review Completed in 2006 

11 In the review of the PM NAAQS that concluded in 2006, EPA considered the growing, 

12 but still limited, body of evidence supporting associations between health effects and thoracic 

coarse particles measured as PM10-2.5.

1 13 The new studies available in the 2006 review included 

14 epidemiologic studies that reported associations with health effects using direct measurements of 

15 PM10-2.5, as well as dosimetric and toxicological studies. In considering this growing body of 

16 PM10-2.5 evidence, as well as evidence from studies that measured PM10 in locations where the 

17 majority of PM10 was in the PM10-2.5 fraction (US EPA, 2005, section 5.4.1), staff concluded that 

18 that the level of protection afforded by the existing 1987 PM10 standard remained appropriate 

19 (US EPA, 2005, p. 5-67), but recommended that the indicator for the standard be revised. 

20 Specifically, staff recommended urban coarse particles in the size range of 10-2.5 micrometers as 

21 the indicator, thus focusing on those thoracic coarse particles that are generally present in urban 

22 environments (US EPA, 2005, p. 5-71). The agency proposed to retain a standard for a subset of 

23 thoracic coarse particles, proposing a qualified PM10-2.5 indicator to focus on the mix of thoracic 

24 coarse particles generally present in urban environments. More specifically, the proposed 

25 revised thoracic coarse particle standard would have applied only to an ambient mix of PM10-2.5 

26 dominated by resuspended dust from high-density traffic on paved roads and/or by industrial and 

27 construction sources. The proposed revised standard would not have applied to any ambient mix 

28 of PM10-2.5 dominated by rural windblown dust and soils. In addition, agricultural sources, 

29 mining sources, and other similar sources of crustal material would not have been subject to 

30 control in meeting the standard (71 FR 2667 to 2668, January 17, 2006). 

31 The Agency received a large number of comments overwhelmingly opposed to the 

32 proposed qualified PM10-2.5 indicator (71 FR 61188 to 61197). After careful consideration of the 

 1

The PM Staff Paper (US EPA, 2005) also presented results of a quantitative assessment of health risks for PM10-2.5. 

However, staff concluded that the nature and magnitude of the uncertainties and concerns associated with this risk 

assessment weighed against its use as a basis for recommending specific levels for a thoracic coarse particle 

standard.
Draft-Do Not Quote or Cite 3-4 June 2010

1 scientific evidence and the recommendations contained in the 2005 Staff Paper, the advice and 

2 recommendations from CASAC, the public comments received regarding the appropriate 

3 indicator for coarse particles, and after extensive evaluation of the alternatives available to the 

4 Agency, the Administrator decided it would not be appropriate to adopt a qualified PM10-2.5 

5 indicator. Underlying this determination was the decision that it was requisite to provide 

6 protection from exposure to all thoracic coarse PM, regardless of its origin, rejecting arguments 

7 that there are no health effects from community-level exposures to coarse PM in non-urban areas 

8 (71 FR 61189). The EPA concluded that dosimetric, toxicological, occupational and 

9 epidemiologic evidence supported retention of a primary standard for short-term exposures that 

10 included all thoracic coarse particles (i.e. both urban and non-urban), consistent with the Act’s 

11 requirement that primary NAAQS provide an adequate margin of safety. At the same time, the 

12 agency concluded that the standard should target protection toward urban areas, where the 

13 evidence of health effects from exposure to PM10-2.5 was strongest (71 FR at 61193, 61197). The 

14 proposed indicator was not suitable for that purpose. Not only did it inappropriately provide no 

15 protection at all to many areas, but it failed to identify many areas where the ambient mix was 

16 dominated by coarse particles contaminated with urban/industrial types of coarse particles for 

17 which evidence of health effects was strongest (71 FR 61193). 

18 The agency ultimately concluded that the existing indicator, PM10, was most consistent 

19 with the evidence. Although PM10 includes both coarse and fine PM, the Agency concluded that 

20 it remained an appropriate indicator for thoracic coarse particles because, as reported by Schmidt 

21 et al. (2005), fine particle levels are generally higher in urban areas and, therefore, a PM10

22 standard set at a single unvarying level will generally result in lower allowable concentrations of 

23 thoracic coarse particles in urban areas than in non-urban areas. EPA considered this to be an 

24 appropriate targeting of protection given that the strongest evidence for effects associated with 

25 thoracic coarse particles came from epidemiologic studies conducted in urban areas and that 

26 elevated fine particle concentrations in urban areas could result in increased contamination of 

27 coarse fraction particles by PM2.5, potentially increasing the toxicity of thoracic coarse particles 

28 in urban areas (71 FR 61195-96). Given the evidence that the existing PM10 standard afforded 

29 requisite protection with an ample margin of safety, the Agency retained the level and form of 

the 24-hour PM10 standard.2 30 

31 The Agency also revoked the annual PM10 standard, in light of the conclusion in the PM 

32 Criteria Document (US EPA, 2004) that the available evidence does not suggest an association 

33 with long-term exposure to PM10-2.5 and the conclusion in the Staff Paper (US EPA, 2005) that 

34 there is no quantitative evidence that directly supports an annual standard. 

 

2

Thus, the standard is met when a PM10 concentration of 150 g/m3 is not exceeded more than once per year on 

average over a three-year period. 
Draft-Do Not Quote or Cite 3-5 June 2010

1 In the same rulemaking, EPA also included a new Federal Reference Method (FRM) for 

2 the measurement of PM10-2.5 in the ambient air (71 FR 61212 to 61213). Although the standard 

3 for thoracic coarse particles does not use a PM10-2.5 indicator, the new FRM for PM10-2.5 was 

4 established to provide a basis for approving Federal Equivalent Methods (FEMs) and to promote 

5 the gathering of scientific data to support future reviews of the PM NAAQS. 

6 3.1.3 Litigation of 2006 Final Rule for Thoracic Coarse Particles 

7 A number of groups filed suit in response to the final decisions made in the 2006 review. 

8 See American Farm Bureau Federation and the National Pork Producers Council v. EPA (DC 

9 Cir. 2009). Among the petitions for review were challenges from industry groups on the 

10 decision to retain the PM10 indicator and the level of the PM10 standard and from environmental 

11 and public health groups on the decision to revoke the annual PM10 standard. The court upheld 

12 both the daily PM10 standard and the decision to revoke the annual standard. 

13 First, the court upheld EPA’s decision for a standard to cover all thoracic coarse PM, 

14 both of urban and non-urban origin. The court rejected arguments that the evidence showed 

15 there are no risks from exposure to non-urban coarse PM. The court further found that EPA had 

16 a reasonable basis not to set separate standards for urban and non-urban coarse PM, namely the 

17 inability to reasonably define what ambient mixes would be included under either ‘urban’ or 

18 ‘non-urban;’ and that the evidence in the record supported EPA’s cautious decision to provide 

19 “some protection from exposure to thoracic coarse particles... in all areas.” 559 F. 3d at 532-33. 

20 Specifically, the court stated, 

21 

22 Although the evidence of danger from coarse PM is, as EPA recognizes, 

23 “inconclusive,” (71 FR 61193, October 17, 2006), the agency need not wait for 

24 conclusive findings before regulating a pollutant it reasonably believes may pose 

25 a significant risk to public health. The evidence in the record supports the EPA’s 

26 cautious decision that “some protection from exposure to thoracic coarse particles 

27 is warranted in all areas.” Id. As the court has consistently reaffirmed, the CAA 

28 permits the Administrator to “err on the side of caution” in setting NAAQS.

29 559 F. 3d at 533. 

30 

The court also upheld EPA’s decision to retain the level of the standard at 150 μg/m3 31 and 

32 to use PM10 as the indicator for a standard meant to protect against exposures to thoracic coarse 

33 particles. In upholding the level of the standard, the court referred to the conclusion in the Staff 

34 Paper that there is “little basis for concluding that the degree of protection afforded by the 

35 current PM10 standards in urban areas is greater than warranted, since potential mortality effects 

36 have been associated with air quality levels not allowed by the current 24-hour standard, but 

37 have not been associated with air quality levels that would generally meet that standard, and 
Draft-Do Not Quote or Cite 3-6 June 2010

1 morbidity effects have been associated with air quality levels that exceeded the current 24-hour 

2 standard only a few times.” 559 F. 3d at 534. The court also rejected arguments that a PM10

3 standard established at an unvarying level will result in arbitrarily varying levels of protection 

4 given that the level of coarse PM would vary based on the amount of fine PM present. The court 

5 agreed that the variation in allowable coarse PM accorded with the strength of the evidence: 

6 typically less coarse PM would be allowed in urban areas (where levels of fine PM are typically 

7 higher), in accord with the strongest evidence of health effects from coarse particles. 559 F. 3d 

8 at 535-36. In addition, such regulation would not impermissibly double regulate fine particles, 

9 since any additional regulation of fine particles (beyond that afforded by the primary PM2.5 

10 standard) would be for a different purpose: to prevent contamination of coarse particles by fine 

11 particles. 559 F. 3d at 535, 536. These same explanations explained the choice of PM10 as an 

12 indicator, and provided the reasoned explanation for that choice lacking in the record for the 

13 1997 standard. 559 F. 3d at 536. 

14 With regard to the challenge from environmental and public health groups, the court 

15 upheld EPA’s decision to revoke the annual PM10 standard. Specifically, the court stated the 

16 following: 

17 The EPA reasonably decided that an annual coarse PM standard is not necessary 

18 because, as the Criteria Document and the Staff Paper make clear, the latest 

19 scientific data do not indicate that long-term exposure to coarse particles poses a 

20 health risk. The CASAC also agreed that an annual coarse PM standard is 

21 unnecessary. 559 F. 3d at 538-39. 

22 

23 3.1.4 Current Approach 

24 Our approach relies most heavily on the health evidence, primarily the epidemiologic 

25 evidence, assessed in the ISA (US EPA, 2009a) and on available PM air quality information. As 

26 discussed in more detail in the Quantitative Health Risk Assessment for Particulate Matter – 

27 Final (US EPA, 2010a), we have not conducted a quantitative assessment of health risks 

28 associated with PM10-2.5. Staff concluded that limitations in the monitoring network and in the 

29 health studies that rely on that monitoring network, which would be the basis for estimating 

30 PM10-2.5 health risks, would introduce significant uncertainty into a PM10-2.5 risk assessment such 

31 that the risk estimates generated would be of limited value in informing review of the standard. 

32 Therefore, staff concluded in the RA that a quantitative risk assessment for PM10-2.5 is not 

33 supportable at this time (US EPA, 2010a, p. 2-6). 

34 For purposes of this policy assessment, we seek to provide as broad an array of options 

35 for consideration as is supportable by the available evidence and air quality information, 

36 recognizing that the final decisions on the primary PM10 standard will reflect the judgments of 

37 the Administrator. In developing these options for consideration, we consider the available 
Draft-Do Not Quote or Cite 3-7 June 2010

1 evidence and air quality information that informs overarching questions related to: (1) the 

2 adequacy of the current 24-hour PM10 standard to protect against effects associated with 

3 exposures to thoracic coarse particles and (2) what potential alternative standard(s), if any, 

4 should be considered in this review. In addressing these broad questions, we have organized the 

5 discussions below around a series of more specific questions reflecting different aspects of each 

6 overarching question. When evaluating the health protection afforded by the current or potential 

7 alternative standards, we have taken into account the four basic elements of the NAAQS (e.g., 

8 indicator, averaging time, form, and level). 

9 Figure 3-1 provides an overview of the policy-relevant questions that frame our review, 

10 as discussed more fully below. We believe the approach outlined here, when presented in the 

11 final Policy Assessment, will provide a comprehensive basis to help inform the judgments 

12 required of the Administrator in reaching decisions about the current and potential alternative 

13 primary standards meant to protect public health against exposures to thoracic coarse particles. 
Draft-Do Not Quote or Cite 3-8 June 2010

1 Figure 3-1. Approach to Considering the Current and Potential Alternative Thoracic 

2 Coarse Particle Standards 

3 
Draft-Do Not Quote or Cite 3-9 June 2010

1 3.2 ADEQUACY OF THE EXISTING PM10 STANDARD 

2 In considering the adequacy of the current 24-hour PM10 standard to protect against 

3 effects associated with exposures to thoracic coarse particles, we address the following 

4 overarching question: 

5 Does the available scientific evidence, as reflected in the ISA, support or call into question 

6 the adequacy of the protection afforded by the current 24-hour PM10 standard against 

7 effects associated with exposures to thoracic coarse particles? 

8 To inform our consideration of this overarching question, we consider the scientific 

9 evidence for associations between PM10-2.5 and mortality and morbidity, evidence linking 

10 PM10-2.5 toxicity to specific sources/locations, uncertainties in the evidence, and available PM10

11 air quality concentrations in PM10-2.5 study locations (section 3.2.1). Evidence for populations 

12 that are particularly susceptible to PM exposures is discussed in detail in section 2.2.1 above, and 

13 is not repeated here. Staff conclusions regarding the adequacy of the current standard are 

14 presented in section 3.2.2. 

15 3.2.1 Evidence-Based Considerations 

16 In considering the currently available body of scientific evidence for health effects of 

17 thoracic coarse particles, we consider the following question: 

18  To what extent does the available scientific evidence support or call into question 

19 associations between ambient thoracic coarse particle exposures and adverse health 

20 effects? 

21 Since the conclusion of the last review, the Agency has developed a more formal 

22 framework for reaching causal inferences from the body of scientific evidence. As discussed 

23 above in section 2.2.1, this framework uses a five-level hierarchy that classifies the overall 

24 weight of evidence using the following categorizations: causal relationship, likely to be a causal 

25 relationship, suggestive of a causal relationship, inadequate to infer a causal relationship, and not 

26 likely to be a causal relationship (ISA, section 1.5, Table 1-3). Applying this framework to 

27 thoracic coarse particles, the ISA concludes that the existing evidence is suggestive of a causal 

28 relationship between short-term PM10-2.5 exposures and mortality, cardiovascular effects, and 

29 respiratory effects (US EPA, 2009a, section 2.3.3; see Table 3-1 below). In contrast, the ISA 

30 concludes that available evidence is inadequate to infer a causal relationship between long-term 

31 PM10-2.5 exposures and various health effects (US EPA, 2009a, section 2.3; Table 3-1 below). 

32 Similar to the judgment made in the AQCD regarding long-term exposures (US EPA, 2004), the 

33 ISA states, “To date, a sufficient amount of evidence does not exist in order to draw conclusions 

34 regarding the health effects and outcomes associated with long-term exposure to PM10-2.5” (US 
Draft-Do Not Quote or Cite 3-10 June 2010

1 EPA, 2009a, section 2.3.4). Given these weight of evidence conclusions in the ISA, our 

2 evidence-based considerations regarding the adequacy of the current 24-hour PM10 standard 

3 focus on effects that have been linked with short-term exposures to PM10-2.5. 

4 As noted above, in the last review of the PM NAAQS, PM10 studies conducted in 

5 locations where PM10 is comprised predominantly of PM10-2.5 were also considered (US EPA, 

6 2005, pp. 5-49 to 5-50). However, PM10 studies are difficult to interpret within the context of a 

7 standard meant to protect against exposures to PM10-2.5 because PM10 is comprised of both fine 

8 and coarse particles, even in locations with the highest concentrations of PM10-2.5 (see below). In 

9 light of the considerable uncertainty in the extent to which PM10 effect estimates reflect 

10 associations with PM10-2.5, together with the availability in this review of recent studies that 

11 evaluate associations with PM10-2.5 specifically and the fact that the ISA weight of evidence 

12 conclusions for thoracic coarse particles were based on studies of PM10-2.5, we focus in this 

13 Policy Assessment on studies that have specifically evaluated PM10-2.5. The evidence supporting 

14 a link between short-term thoracic coarse particle exposures and adverse health effects is 

15 discussed in detail in the ISA (US EPA, 2009a, Chapter 6) and is summarized briefly below for 

16 mortality, cardiovascular effects, and respiratory effects. 

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

Exposure Duration Outcome Causal Determination

Cardiovascular Effects Suggestive 

Respiratory Effects Suggestive 

Short-term Mortality Suggestive 

Central Nervous System Effects Inadequate 

Cardiovascular Effects Inadequate 

Respiratory Effects Inadequate 

Mortality Inadequate 

Reproductive and Developmental Effects Inadequate 

Long-term 

Cancer Mutagenicity, Genotoxicity Effects Inadequate 

18 Source: adapted from US EPA, 2009a; Table 2-6 

19 Short-Term PM10-2.5 and Mortality 

20 The ISA assesses a number of multi-city and single-city epidemiologic studies that have 

21 evaluated associations between mortality and short-term PM10-2.5 concentrations (US EPA, 

22 2009a, Figure 6-30 presents PM10-2.5 mortality studies assessed in the last review and the current 

23 review). Different studies have used different approaches to estimate ambient PM10-2.5. Some 

24 studies have used the difference between PM10 and PM2.5 mass, either measured at co-located 
Draft-Do Not Quote or Cite 3-11 June 2010

1 monitors (e.g., Lipfert et al., 2000; Mar et al., 2003; Ostro et al., 2003; Sheppard et al., 2003; 

2 Wilson et al., 2007) or as the difference in county-wide average concentrations (Zanobetti and 

3 Schwartz, 2009), while other studies have measured PM10-2.5 directly with dichotomous samplers 

4 (e.g., Burnett and Goldberg, 2003; Fairley et al., 2003; Burnett et al., 2004; Klemm et al., 2004). 

5 Despite differences in the approaches used to estimate ambient PM10-2.5 concentrations, the 

6 majority of multi- and single-city studies have reported positive associations between PM10-2.5 

7 and mortality, though most of these associations were not statistically significant (US EPA, 

8 2009a, Figure 6-30). When considered as a whole, the ISA concluded that epidemiologic studies 

9 have reported consistent, positive associations between short-term PM10-2.5 and mortality (US 

10 EPA, 2009a, section 6.5.2.3). 

11 In considering specific mortality studies, we note that the U.S. multi-city study by 

12 Zanobetti and Schwartz (2009) reported positive and statistically significant associations with 

13 PM10-2.5 for all-cause, cardiovascular, and respiratory mortality (US EPA, 2009a, section 6.5.2.3) 

14 while other multi-city studies have reported positive, but not statistically significant, PM10-2.5 

15 effect estimates (US EPA, 2009a, Figure 6-30, Burnett and Goldberg, 2003; Klemm et al., 2003; 

16 Burnett et al., 2004). In the study by Zanobetti and Schwartz, the effect estimates for all-cause 

17 and respiratory mortality remained statistically significant in co-pollutant models that included 

18 PM2.5, while the effect estimate for cardiovascular mortality remained positive but not 

19 statistically significant. When risk estimates in this study were evaluated by climatic region (US 

20 EPA, 2009a, Figure 6-28), the “dry continental” region, which included areas with relatively 

21 high PM10-2.5 concentrations (see US EPA, 2009a, Figure 6-29; Schmidt and Jenkins, 2010; and 

22 discussion of regional differences in PM10-2.5 concentrations below) such as Salt Lake City, 

23 Provo, and Denver, showed the largest risk estimates. However, the “dry” region, which 

24 included Phoenix and Albuquerque, two locations that also have relatively high PM10-2.5 

25 concentrations, did not show positive associations with all-cause or respiratory mortality and 

26 only a relatively small positive association for cardiovascular mortality. In addition, the 

27 “Mediterranean” region (which included cities in California, Oregon, and Washington) did not 

28 show positive associations while the other three regions (i.e., “hot summer, continental,” “warm 

29 summer, continental,” and “humid, subtropical and maritime”), which included cities that 

30 correspond to the mid-west, northeast, and southeast geographic regions, all showed positive 

31 associations (US EPA, 2009a, Figure 6-28). 

32 The ISA also presents empirical Bayes-adjusted city-specific effect estimates for cities 

33 evaluated by Zanobetti and Schwartz (US EPA, 2009a, Figure 6-29). City-specific estimates 

34 were positive, though generally not statistically significant, for cardiovascular mortality in all 47 

35 cities evaluated. Effect estimates were positive for all-cause and respiratory mortality in all 

36 cities except Los Angeles (negative for all-cause and respiratory) and Phoenix (negative for 
Draft-Do Not Quote or Cite 3-12 June 2010

1 respiratory) (US EPA, 2009a, Figure 6-29). In addition, positive and statistically significant 

2 associations between mortality (all-cause, cardiovascular, and/or respiratory) and PM10-2.5 were 

3 reported for six locations (St. Louis, MO; Salt Lake City, UT; Chicago, IL; Pittsburgh, PA; 

4 Detroit, MI; and Birmingham, AL). 

5 In considering single-city PM10-2.5 mortality studies, we note that all of the studies 

6 included in Figure 6-30 of the ISA (US EPA, 2009a) reported positive PM10-2.5 effect estimates, 

7 with three single-city studies reporting effect estimates that were statistically significant (Mar et 

8 al., 2003; Ostro et al., 2003; Wilson et al., 2007). One study reported a negative PM10-2.5 effect 

9 estimate for respiratory mortality (Villeneuve et al., 2003), though effect estimates for all-cause 

10 and cardiovascular mortality were positive in this study (US EPA, 2009a, Figure 6-30). 

11 Short-Term PM10-2.5 and Cardiovascular Effects 

12 The ISA assesses a number of studies that have evaluated the link between short-term 

13 ambient concentrations of thoracic coarse particles and cardiovascular effects. In considering the 

14 available epidemiologic evidence, the ISA concludes that single- and multi-city epidemiologic 

15 studies generally report positive associations between short-term PM10-2.5 concentrations and 

16 hospital admissions or emergency department visits for cardiovascular causes (US EPA, 2009a, 

17 section 2.3.3, 6.2.12.2). Some of these studies have reported positive and statistically significant 

18 PM10-2.5 effect estimates in co-pollutant models while others report that PM10-2.5 effect estimates 

19 remain positive, but not statistically significant (US EPA, 2009a, Figure 6-5). 

20 These studies include a recent U.S. multi-city study evaluating hospital admissions and 

21 emergency department visits for cardiovascular disease in Medicare patients (Peng et al., 2008). 

22 In this study of older adults, the authors reported a positive and statistically significant 

23 association between 24-hour PM10-2.5 concentrations and cardiovascular disease hospitalizations 

24 in a single pollutant model using air quality data for 108 U.S. counties with co-located PM10 and 

25 PM2.5 monitors. The effect estimate was reduced only slightly in a two-pollutant model that 

26 included PM2.5, but it was no longer statistically significant (US EPA, 2009a, sections 2.3.3, 

27 6.2.10.9). Regional and/or county-specific analyses were not conducted for the locations 

28 evaluated by Peng. Therefore, it is not possible, based on the data from this study, to consider 

29 differences in PM10-2.5 effect estimates in specific locations or regions. In addition to this U.S. 

30 multi-city study, positive associations reported for short-term PM10-2.5 and cardiovascular 

31 morbidity reached statistical significance in a multi-city study in France (Host et al., 2007) and 

32 single-city studies in Detroit and Toronto (US EPA, 2009a, Figures 6-2, 6-3). In contrast, 

33 associations were positive but not statistically significant in single-city studies conducted in 

34 Atlanta and Boston (and for some endpoints in Detroit) (US EPA, 2009a, Figures 6-1 to 6-3, 6-

35 5). 
Draft-Do Not Quote or Cite 3-13 June 2010

1 The plausibility of the positive associations reported for PM10-2.5 and cardiovascular2 related hospital admissions and emergency department visits is supported by a small number of 

3 controlled human exposure studies that have reported alterations in heart rate variability 

4 following exposure to PM10-2.5; by short-term PM10-2.5 epidemiologic studies reporting positive 

5 associations with cardiovascular mortality; by a small number of recent epidemiologic studies 

6 that have examined dust storm events and reported increases in cardiovascular-related 

7 emergency department visits and hospital admissions (see below); and by associations with other 

8 cardiovascular effects including heart rhythm disturbances and changes in heart rate variability 

9 (US EPA, 2009a, sections 2.3.3, 6.2.12.2). The few toxicological studies that examined the 

10 effect of PM10-2.5 on cardiovascular health effects used intratracheal instillation and, as a result, 

11 provide only limited evidence on the biological plausibility of PM10-2.5 induced cardiovascular 

12 effects (US EPA, 2009a, sections 2.3.3, 6.2.12.2). 

13 Short-Term PM10-2.5 and Respiratory Effects

14 The ISA also assesses a number of studies that have evaluated the link between short15 term ambient concentrations of thoracic coarse particles and respiratory effects. This includes 

16 recent studies conducted in the U.S., Canada, and France (US EPA, 2009a, section 6.3.8), 

17 including the U.S. multi-city study of Medicare patients by Peng et al. (2009). As discussed 

18 above, Peng estimated PM10-2.5 concentrations as the difference between PM10 and PM2.5 

19 concentrations measured by co-located monitors. The authors reported a positive, but not 

20 statistically significant, PM10-2.5 effect estimate for respiratory-related hospital admissions. 

21 Single-city studies have reported positive, and in some cases statistically significant, PM10-2.5 

22 effect estimates for respiratory-related hospital admissions and emergency department visits (US 

23 EPA, 2009a, Figures 6-10 to 6-15). Some of these PM10-2.5 respiratory morbidity studies have 

24 reported positive and statistically significant PM10-2.5 effect estimates in co-pollutant models that 

25 included gaseous pollutants while others reported that PM10-2.5 effect estimates remain positive, 

26 but not statistically significant, in such co-pollutant models (US EPA, 2009a, Figure 6-15). 

27 A limited number of epidemiologic studies have focused on specific respiratory 

28 morbidity outcomes and reported both positive and negative, but generally not statistically 

29 significant, associations between PM10-2.5 and lower respiratory symptoms, wheeze, and 

30 medication use (US EPA, 2009a, sections 2.3.3.1 and 6.3.1.1; Figures 6-7, 6-8, 6-9). Although 

31 controlled human exposure studies have not observed an effect on lung function or respiratory 

32 symptoms in healthy or asthmatic adults in response to short-term exposure to PM10-2.5, healthy 

33 volunteers have exhibited an increase in markers of pulmonary inflammation. Toxicological 

34 studies using inhalation exposures are still lacking, but pulmonary injury and inflammation has 

35 been reported in animals after intratracheal instillation exposure (US EPA, 2009a, section 

36 6.3.5.3) and, in some cases, PM10-2.5 was found to be more potent than PM2.5. 
Draft-Do Not Quote or Cite 3-14 June 2010

1 PM10-2.5 Toxicity: Impacts of Sources and Composition 

2 As discussed above, positive, and in some cases statistically significant, associations 

3 between short-term PM10-2.5 concentrations and mortality and morbidity have been reported in a 

4 number of different locations. Little is known about how PM10-2.5 composition varies across 

5 these locations and how that variation could affect particle toxicity (US EPA, 2009a, sections 

6 2.3.3, 2.3.4, 2.4.4). However, the limited available evidence suggests that specific components 

7 of thoracic coarse particles tend to comprise different fractions of PM10-2.5 mass in different 

8 environments (e.g., urban versus rural environments) (US EPA, 2009a, section 3.5.1.1; Schmidt 

9 et al., 2005; Edgerton et al., 2009). It is possible that such differences in particle composition 

10 could affect particle toxicity, though the ISA concludes that existing evidence is not sufficient to 

11 draw distinctions in toxicity based on composition and notes that recent studies have reported 

12 that PM from different sources, including crustal sources, is associated with adverse health 

13 effects (US EPA, 2009a, section 2.4.4). The evidence for associations with particles originating 

14 from different types of sources and in different locations is discussed briefly below. 

15 As discussed above, most PM10-2.5 epidemiologic studies have been conducted in urban 

16 locations in the U.S., Canada, and Europe while a small number of studies have examined the 

17 health impacts of dust storm events (US EPA, 2009a, sections 6.2.10.1, 6.5.2.3). Although these 

18 dust storm studies do not link specific particle constituents to health effects, it is useful to 

19 consider them within the context of the toxicity of particles of non-urban crustal origin. Several 

20 studies have reported positive and statistically significant associations between dust storm events 

21 and morbidity or mortality, including the following: 

22  Middleton et al. (2008) reported that dust storms in Cyprus were associated with a 

23 statistically significant increase in risk of hospitalization for all causes and a non24 significant increase in hospitalizations for cardiovascular disease. 

25  Chan et al. (2008) studied the effects of Asian dust storms on cardiovascular hospital 

26 admissions in Taipei, Taiwan and reported a statistically significant increase associated 

27 with 39 Asian dust events. Evaluating the same data, Bell et al. (2008) also reported 

28 positive and statistically significant associations between hospitalization for ischemic 

29 heart disease and PM10-2.5. 

30  Perez et al. (2008) tested the hypothesis that outbreaks of Saharan dust exacerbate the 

31 effects of PM10-2.5 on daily mortality in Spain. During Saharan dust days, the PM10-2.5 

32 effect estimate was larger than on non-dust days and it became statistically significant, 

33 whereas it was not statistically significant on non-dust days. 

34 In contrast to the studies noted above, some dust storm studies have reported associations that 

35 were not statistically significant. Specifically, Bennett et al. (2006) reported on a dust storm in 

36 the Gobi desert that transported PM across the Pacific Ocean, reaching western North America in 
Draft-Do Not Quote or Cite 3-15 June 2010

1 the spring of 1998. The authors reported no excess risk of cardiac or respiratory hospital 

2 admissions associated with the dust storm in the population of British Columbia’s Lower Fraser 

3 Valley (Bennett et al., 2006). In addition, Yang et al. (2009) reported that hospitalizations for 

4 congestive heart failure were elevated during or immediately following 54 Asian dust storm 

5 events, though effect estimates were not statistically significant. The implications of these 

6 studies for the current review, for consideration of potential standard indicators, are discussed 

7 below. 

8 Next we consider uncertainties associated with the evidence by addressing the following 

9 question: 

10  What are the important uncertainties associated with the currently available scientific 

11 evidence that should be considered in evaluating the adequacy of the current PM10 

12 standard? 

13 The majority of the health evidence supporting the link between short-term thoracic 

14 coarse particle exposures and mortality and morbidity comes from epidemiologic studies. 

15 Although new studies have become available since the last review and have expanded our 

16 understanding of the association between PM10-2.5 and adverse health effects (see above and U.S. 

17 EPA, 2009a, Chapter 6), important uncertainties remain. These uncertainties, and their 

18 implications for interpreting the scientific evidence, are discussed below. 

19 The ISA (sections 2.3.3, 2.3.4) concludes that an important uncertainty in the PM10-2.5

20 epidemiologic literature is that associated with the air quality estimates used in these studies. 

21 Specifically, the ISA concludes that there is greater error in estimating ambient exposures to 

22 PM10-2.5 than to PM2.5 and that such uncertainty is a particularly relevant consideration when 

23 interpreting PM10-2.5 epidemiologic studies. Contributing to this uncertainty is the relatively 

24 limited spatial coverage provided by existing PM10-2.5 monitors (US EPA, 2009a, sections 2.2.3, 

25 2.3.3, 2.3.4, 3.5.1.1 and see above). As discussed above, a national network to monitor PM10-2.5

26 is not in place, limiting the spatial area over which PM10-2.5 concentrations are measured. In 

27 addition, based on the limited available evidence, the ISA concluded that “there is greater spatial 

28 variability in PM10-2.5 concentrations than PM2.5 concentrations, resulting in increased exposure 

29 error for the larger size fraction” (US EPA, 2009a, p. 2-8) and that available measurements do 

30 not provide sufficient information to adequately characterize the spatial distribution of PM10-2.5 

31 concentrations (US EPA, 2009a, section 3.5.1.1). The net effect of these uncertainties on 

32 epidemiologic studies of PM10-2.5 is to bias the results of such studies toward the null hypothesis. 

33 That is, as noted in the ISA, these limitations in estimates of ambient PM10-2.5 concentrations 

34 “would tend to increase uncertainty and make it more difficult to detect effects of PM10-2.5 in 

35 epidemiologic studies” (US EPA 2009a, p. 2-21). 
Draft-Do Not Quote or Cite 3-16 June 2010

1 Given these limitations in PM10-2.5 monitoring, different epidemiologic studies have 

2 employed different approaches to estimating PM10-2.5 concentrations, further contributing to 

3 uncertainty in interpreting these studies. For example, as discussed above, the multi-city study 

4 by Peng et al. (2008) estimated PM10-2.5 by taking the difference between collocated PM10 and 

5 PM2.5 monitors while the study by Zanobetti and Schwartz (2009) used the difference between 

6 county average PM10 and PM2.5 concentrations. A small number of studies have directly 

7 measured PM10-2.5 concentrations with dichotomous samplers (e.g., Burnett et al., 2004; 

8 Villeneuve et al., 2003; Klemm et al., 2004). It is not clear how computed PM10-2.5

9 measurements, such as those used by Zanobetti and Schwartz, compare with the PM10-2.5

10 concentrations obtained in other studies either by direct measurement with a dichotomous 

11 sampler or by calculating the difference using co-located samplers (US EPA, 2009a, section 

6.5.2.3).3 12 Given the use of these different approaches to estimating PM10-2.5 concentrations 

13 across studies, and their inherent limitations, the distributions of thoracic coarse particle 

14 concentrations over which reported health outcomes occur remain highly uncertain. 

15 The ISA also notes that the potential for confounding by co-occurring pollutants, 

16 particularly PM2.5, has been addressed in only a relatively small number of PM10-2.5

17 epidemiologic studies, introducing additional uncertainty into the interpretation of these studies 

18 (US EPA, 2009a, section 2.3.3). This is a particularly important consideration given the 

19 relatively limited body of experimental evidence available to support the plausibility of 

20 associations between PM10-2.5 itself and health effects reported in epidemiologic studies. As 

21 discussed above, many epidemiologic studies that have evaluated co-pollutant models have 

22 reported that PM10-2.5 effect estimates remain positive, but lose precision and are not statistically 

23 significant in these models (US EPA, 2009a, Figures 6-5, 6-9, 6-15). The net effect of this 

24 limitation in the number of epidemiologic studies that have evaluated co-pollutant models, 

25 combined with the limited number of supporting experimental studies, is to increase the 

26 uncertainty associated with estimates of the extent to which PM10-2.5 itself, rather than one or 

27 more co-occurring pollutants, is responsible for reported health effects. 

28 Another uncertainty results from the relative lack of information on the chemical and 

29 biological composition of PM10-2.5, and the effects associated with the various components (ISA, 

30 section 2.3.4). As discussed above, a few recent studies have evaluated associations between 

31 health effects and particles of non-urban, crustal origin by evaluating the health impacts of sand 

32 storm events. Though these studies provide some information on the health effects of particles 

33 that likely differ in composition from the particles of urban origin that are typically studied, 

 3

In addition, when the difference between PM2.5 and PM10 is calculated, the potential for differences among 

operational flow rates and temperatures for PM10 and PM2.5 monitors add to the potential for exposure 

misclassification. 
Draft-Do Not Quote or Cite 3-17 June 2010

1 without more information on the chemical speciation of PM10-2.5, the apparent variability in 

2 associations with health effects across locations is difficult to characterize (US EPA, 2009a, 

3 section 6.5.2.3). 

4 As discussed above, a 24-hour PM10 standard is in place to protect the public health 

5 against exposures to thoracic coarse particles. Therefore, in further considering the adequacy of 

6 the current PM10 standard, we ask the following question: 

7  To what extent does the available scientific evidence report associations between 

8 PM10-2.5 and morbidity and mortality in areas that would likely meet the current PM10 

9 standard? 

 In addressing this question, we have used EPA’s Air Quality System (AQS)4 10 to 

11 characterize PM10 concentrations in U.S. locations where both single-city and multi-city PM10-2.5

12 studies have been conducted (see U.S. EPA, 2009a, Figures 6-1 to 6-30 for studies). When 

13 compared to single-city studies, we note that multi-city studies assess PM10-2.5-associated health 

14 effects among larger study populations, providing enhanced power to detect PM10-2.5-associated 

15 health effects. In addition, multi-city studies often provide spatial coverage for different regions 

16 across the country, reflecting differences in PM10-2.5 sources, composition, and potentially other 

17 factors that could impact PM10-2.5-related effects. These factors make multi-city studies 

18 particularly important when drawing conclusions about health effect associations. However, 

19 multi-city studies often present overall effect estimates rather than single-city effect estimates, 

20 while short-term air quality can vary considerably across cities. Therefore, the extent to which 

21 effects reported in multi-city studies are associated with the short-term air quality in any 

22 particular location is uncertain, especially when considering short-term concentrations at the 

23 upper end of the distribution of daily concentrations for pollutants with relatively heterogeneous 

24 spatial distributions such as PM10-2.5 and PM10 (US EPA, 2009a, section 2.1.1.2). In contrast, 

25 single-city studies are more limited in terms of power and geographic coverage but the link 

26 between reported health effects and the short-term air quality in a given city is more 

27 straightforward to establish. As a result, in considering 24-hour PM10 concentrations in locations 

28 of epidemiologic studies, we have focused below primarily on single-city studies (Figures 3-2 

29 and 3-3) and single-city analyses of the locations evaluated in the multi-city study by Zanobetti 

30 and Schwartz (US EPA, 2009a, Figure 6-29). 

31 The current PM10 standard has a form of one-expected-exceedance per year, averaged 

32 over 3 years. In order to compare PM10 concentrations in study locations to the level of the 

33 current standard, we have identified the PM10 3-year expected exceedance concentration34 equivalent design value for each study period (labeled “DV” in Figures 3-2 and 3-3 below) using 

 4 http://www.epa.gov/ttn/airs/airsaqs/ 
Draft-Do Not Quote or Cite 3-18 June 2010

1 the protocol specified in the PM10 State Implementation Plan (SIP) Development Guidelines (US 

EPA, 1987).5 2 Some studies (indicated by a * in Figure 3-3) covered time periods of less than 

3 three years. For these study areas, to characterize ambient PM10 concentrations relative to 

4 concentrations allowed under the current PM10 standard, we averaged the second highest 24-hour 

5 PM10 concentrations for each year of the study (i.e., second highest concentration measured at 

6 the single monitor in the study area recording the highest such concentration). The identification 

7 of concentration-equivalent design values and second highest PM10 concentrations for each study 

8 area are described in more detail in the memo by Schmidt and Jenkins (2010). 

 5

Specifically, the PM10 3-year expected exceedance concentration-equivalent design value is identified as the highest 

24-hour average concentration (i.e., from a single monitor in the study area) over a 3-year period when there are 347 

or fewer samples reported for that time-frame, the second highest 24-hour average concentration when there are 348 

to 695 samples in the 3-year period, the third highest 24-hour average concentration when there are 696 to 1042 

samples in the 3-year period, and the fourth highest 24-hour average concentration when there are 1043 or more 

samples reported over the 3-year period. Concentration-equivalent design values were not identified for study 

periods less than 3 years. 
Draft-Do Not Quote or Cite 3-19 June 2010

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

1 

2 

++ 3 Studies in Figures 3-2 and 3-3 are a combination of those assessed in the last review and those assessed in the ISA in the current revie

4 figures are ordered by increasing PM10 concentration-equivalent design values.
Draft-Do Not Quote or Cite 3-20 June 2010

1 Figure 3-3. PM10 Air Quality PM10-2.5 Effect Estimates in Locations of U.S. PM10-2.5 Morbidity Studies 

2 

3 * Concentration-equivalent design values were not identified for study periods less than three years. For study periods of less than thre4 second highest 24-hour PM10 concentrations for each year of the study (i.e., second highest concentration measured by the single monit5 recording the highest such concentration). 
Draft-Do Not Quote or Cite 3-21 June 2010

1 In addition to the single-city studies included in Figures 3-2 and 3-3 above, multi-city 

2 averages of the 3-year expected exceedance concentration-equivalent design values for U.S. 

multi-city studies were 114 g/m3 3 (for the locations evaluated by Zanobetti and Schwartz, 2009) 

and 100 g/m3 4 (for the locations evaluated by Peng et al., 2008). As discussed above, the extent 

5 to which overall PM10-2.5 effect estimates reported in multi-city studies are associated with the air 

6 quality in any particular location is uncertain. However, the ISA also presents Bayes-adjusted 

7 single-city effect estimates for each of the cities evaluated by Zanobetti and Schwartz (US EPA, 

8 2009a, Figure 6-29), providing the opportunity to consider associations between PM10-2.5 and 

9 mortality, and to consider the PM10 air quality, in each of the individual cities evaluated in this 

10 study. 

11 As discussed above, in single-city analyses in the locations evaluated by Zanobetti and 

12 Schwartz, PM10-2.5 effect estimates for mortality were generally positive but not statistically 

13 significant, and most were similar in magnitude and precision, particularly for cardiovascular 

14 mortality, across a wide range of estimated PM10-2.5 concentrations (US EPA, 2009a, Figure 6-

15 29). Three-year PM10 expected exceedance concentration-equivalent design values in these 

cities ranged from 50 g/m3 (Davie, FL) to 381 g/m3 16 (El Paso, TX). In most of the cities 

evaluated (36 of 47), concentration-equivalent design values were below 150 g/m3 17 (Schmidt 

18 and Jenkins, 2010). In the six cities where positive and statistically significant PM10-2.5 mortality 

19 effect estimates were reported (see above), concentration-equivalent design values were as 

20 follows (Schmidt and Jenkins, 2010): 

 Chicago: 113 g/m3 21 

 Pittsburgh: 139 g/m3 22 

 Birmingham: 154 g/m3 23 

 Detroit: 165 g/m3 24 

 St. Louis: 165 g/m3 25 

 Salt Lake City: 283 g/m3 26 

27 Therefore, while PM10-2.5 effect estimates in single-city analyses were not statistically significant 

28 for most locations evaluated by Zanobetti and Schwartz, including some locations with PM10

29 concentrations well above those allowed by the current 24-hour PM10 standard, positive and 

30 statistically significant PM10-2.5 effect estimates were reported in two locations (Chicago, 

Pittsburgh) with concentration-equivalent design values below 150 g/m3 31 . 

32 In considering PM10-2.5 epidemiologic studies conducted in Canada and elsewhere outside 

33 the U.S., we note that we generally do not have access to PM10 air quality information beyond 

34 that published by the study authors. Many of these studies report PM concentrations averaged 

35 across monitors, rather than from the highest monitor in the study area, and/or report only mean 
Draft-Do Not Quote or Cite 3-22 June 2010

1 or median concentrations. Lin et al. (2002) reported positive and statistically significant 

2 associations between PM10-2.5 and asthma hospital admissions in children in Toronto (US EPA, 

3 2009a; Figures 6-12, 6-15). The authors reported a maximum PM10 concentration measured at a 

single monitor in the study area of 116 g/m3 4 , suggesting that the PM10 air quality in Toronto 

5 during this study would have been permitted by the current 24-hour PM10 standard. In contrast 

6 Middleton et al. (2008), who reported that dust storms in Cyprus were associated with a 

7 statistically significant increase in risk of hospitalization for all causes and a non-significant 

8 increase in hospitalizations for cardiovascular diseases, reported a maximum 24-hour PM10

concentration of 1,371 g/m3 9 . Thus, the dust storm-associated increases in hospitalizations 

10 reported in this study occurred in an area with PM10 concentrations that were likely well above 

11 those allowed by the current standard. Other dust storm studies did not report maximum 24-hour 

12 PM10 concentrations from individual monitors, though the studies by Chan et al. (2008) and Bell 

13 et al. (2008), who reported positive and statistically significant associations between dust storm 

14 metrics and cardiovascular-related hospital admissions, reported that 24-hour PM10

concentrations, averaged across monitors, exceeded 200 g/m3 15 . It is likely that peak 

16 concentrations measured at individual monitors in these studies were much higher and, therefore, 

17 24-hour PM10 concentrations in these study areas were likely above those allowed by the current 

18 standard. 

19 Summary of Evidence-Based Considerations

20 New evidence supporting an association between PM10-2.5 and mortality and morbidity 

21 has become available since the last review of the PM NAAQS. The available evidence was 

22 judged in the ISA to be suggestive of a causal relationship between short-term PM10-2.5 

23 exposures and mortality, cardiovascular effects, and respiratory effects while the evidence was 

24 judged inadequate to infer a causal relationship with long-term PM10-2.5 exposures (US EPA, 

25 2009a, section 2.3.3; see Table 3-1 above). The evidence supporting a link between short-term 

26 thoracic coarse particle exposures and adverse health effects comes primarily from 

27 epidemiologic studies, with limited supporting evidence from controlled human exposure studies 

28 and, to a lesser extent, animal instillation studies. This evidence includes several recent (i.e., 

29 published since the last review of the PM NAAQS) multi-city epidemiologic studies conducted 

30 in the U.S., Canada, and Europe and a small number of recent studies of particles of non-urban 

31 origin. In general, epidemiologic studies have reported positive, and in some cases statistically 

32 significant, PM10-2.5 effect estimates. In the limited number of studies that have evaluated co33 pollutant models that include either gaseous pollutants or fine particles, PM10-2.5 effect estimates 

34 generally remain positive, and in a few cases statistically significant. 

35 Positive associations between PM10-2.5 and mortality and morbidity have been reported in 

36 a number of locations across the U.S. with a wide range of PM10-2.5 and PM10 concentrations. 
Draft-Do Not Quote or Cite 3-23 June 2010

1 Among single-city analyses, PM10-2.5 effect estimates were positive and statistically significant in 

2 a few U.S. cities and at least one Canadian city with ambient PM10 concentrations that would be 

3 allowed by the current 24-hour PM10 standard. However, most PM10-2.5 effect estimates, even 

4 those reported in locations with PM10 concentrations above the concentrations allowed by the 

5 current standard, were not statistically significant. 

6 3.2.2 Staff Conclusions on Adequacy of Current PM10 Standard 

7 In light of the available PM10-2.5 health evidence and the PM10 air quality concentrations 

8 in study locations, as discussed above, we revisit the overarching question: Does the currently 

9 available scientific evidence, as reflected in the ISA, and air quality information support or call 

10 into question the adequacy of the protection afforded by the current 24-hour PM10 standard 

11 against effects associated with exposures to thoracic coarse particles? 

12 In considering the evidence and information as they relate to the adequacy of the current 

13 24-hour PM10 standard we note that, as discussed above, this standard is meant to protect the 

14 public health against effects associated with short-term exposures to PM10-2.5. In the last review, 

15 it was judged appropriate to maintain such a standard given the “growing body of evidence 

16 suggesting causal associations between short-term exposure to thoracic coarse particles and 

17 morbidity effects, such as respiratory symptoms and hospital admissions for respiratory diseases, 

18 and possibly mortality” (71 FR 61185, October 17, 2006). Given the expanded body of evidence 

19 available in the current review, discussed in detail in the ISA (US EPA, 2009a, Chapter 6) and 

20 summarized above, we conclude that the evidence continues to support the appropriateness of a 

21 standard to protect the public health against effects associated with short-term exposures to 

22 PM10-2.5. In addition, when considering the evidence for associations with PM10-2.5 from different 

23 types of sources and in different locations (e.g., urban/industrial as well as windblown dust of 

24 non-urban origin), we conclude that it remains appropriate to provide some measure of 

25 protection against exposures to all thoracic coarse particles. 

26 In considering the evidence, we note that a decision on the adequacy of the public health 

27 protection provided by the current PM10 standard will be a public health policy judgment in 

28 which the Administrator weighs that evidence and its inherent uncertainties. Therefore, 

29 depending on the emphasis placed on different aspects of the evidence and uncertainties, 

30 consideration of different conclusions on adequacy could be supported. 

31 For example, one approach to considering the evidence and its associated uncertainties 

32 would be to place emphasis on the following: 

33  Several multi-city epidemiologic studies conducted in the U.S., Canada, and Europe, as 

34 well as a number of single-city studies, have reported generally positive, and in some 

35 cases statistically significant, associations between short-term PM10-2.5 concentrations and 
Draft-Do Not Quote or Cite 3-24 June 2010

1 adverse health endpoints including mortality and cardiovascular- and respiratory-related 

2 hospital admissions and emergency department visits. 

3 

4  A number of single-city analyses (including the majority of the Bayes-adjusted single5 city analyses in locations evaluated by Zanobetti and Schwartz (2009)), using different 

6 approaches to estimate ambient PM10-2.5 concentrations, have reported positive PM10-2.5 

7 effect estimates in locations with 24-hour PM10 concentrations that are allowed by the 

8 current PM10 standard. In a few cases, these PM10-2.5 effect estimates were statistically 

9 significant. 

10 

11  While limited in number, studies that have evaluated co-pollutant models have generally 

12 reported that PM10-2.5 effect estimates remain positive, and in a few cases statistically 

13 significant, when these models include gaseous pollutants or fine particles. 

14 

15  Support for the plausibility of the associations reported in epidemiologic studies is 

16 provided by a limited number of controlled human exposure studies reporting that short17 term (i.e., 2-hour) exposures to PM10-2.5 decrease heart rate variability and increase 

18 markers of pulmonary inflammation. 

19 

20 Such an approach to considering the evidence would place substantial weight on the generally 

21 positive PM10-2.5 effect estimates that have been reported for mortality and morbidity, even those 

22 effect estimates that are not statistically significant. This could be judged appropriate given that 

23 consistent results have been reported across multiple studies using different approaches to 

24 estimate ambient PM10-2.5 concentrations and that exposure measurement error, which is likely to 

25 be larger for PM10-2.5 than for PM2.5, tends to bias the results of epidemiologic studies toward the 

26 null hypothesis, making it less likely that associations will be detected. To the extent that a 

27 decision on the adequacy of the current 24-hour PM10 standard were to place emphasis on the 

28 considerations noted above, it could be judged that the current 24-hour PM10 standard does not 

29 provide adequate public health protection and that it should be revised in order to increase 

30 protection against effects associated with short-term exposures to thoracic coarse particles. 

31 Another approach to considering the evidence and its uncertainties would be to place 

32 emphasis on the following: 

33  While most of PM10-2.5 effect estimates reported for mortality and morbidity were 

34 positive, many were not statistically significant, even in single-pollutant models. This 

35 includes effect estimates reported in study locations with PM10 concentrations well36 above those allowed by the current 24-hour PM10 standard. 

37  The number of epidemiologic studies that have employed co-pollutant models to 

38 address the potential for confounding, particularly by PM2.5, remains limited. 

39 Therefore, the extent to which PM10-2.5 itself, rather than one or more co-pollutants, 

40 contributes to reported health effects remains uncertain. 
Draft-Do Not Quote or Cite 3-25 June 2010

1  Only a limited number of experimental studies provide support for the associations 

2 reported in epidemiologic studies, resulting in further uncertainty regarding the 

3 plausibility of the associations between PM10-2.5 and mortality and morbidity reported 

4 in epidemiologic studies. 

5  Limitations in PM10-2.5 monitoring and the different approaches used to estimate 

6 PM10-2.5 concentrations across epidemiologic studies result in uncertainty in the 

7 ambient PM10-2.5 concentrations at which the reported effects occur. 

8  The chemical and biological composition of PM10-2.5, and the effects associated with 

9 the various components, remains uncertain. Without more information on the chemical 

10 speciation of PM10-2.5, the apparent variability in associations across locations is 

11 difficult to characterize. 

12 To the extent that a decision on the adequacy of the current 24-hour PM10 standard were to place 

13 emphasis on the considerations noted above, it could be judged that, while it remains appropriate 

14 to maintain a standard to protect against short-term exposures to thoracic coarse particles, the 

15 available evidence does not provide a sound basis for concluding that the current 24-hour PM10

16 standard fails to protect public health with an adequate margin of safety. Such an approach to 

17 considering the evidence could also note the ISA conclusions that, when considered as a whole, 

18 the available evidence is “suggestive” of a causal relationship between short-term PM10-2.5 

19 exposures and mortality, cardiovascular effects, and respiratory effects. These weight-of20 evidence conclusions contrast with those for the relationships between PM2.5 exposures and 

21 adverse health effects, which were judged in the ISA to be either “causal” or “likely causal” for 

22 mortality, cardiovascular effects, and respiratory effects. Thus, while this approach to 

23 considering the evidence would recognize the positive, and in some cases statistically significant, 

24 associations between PM10-2.5 and mortality and morbidity by maintaining a standard to protect 

25 against exposures to thoracic coarse particles, it would place relatively greater emphasis on the 

26 limitations and uncertainties noted above, which tend to complicate the interpretation of that 

27 evidence. 

28 Given all of the above, we conclude that consideration could be given to either retaining or 

29 revising the current 24-hour PM10 standard, depending on the approach taken to considering the 

30 available evidence and information. Therefore, we judge that it is appropriate in this Policy 

31 Assessment to consider what potential alternative standards, if any, could be supported by the 

32 available scientific evidence in order to increase public health protection against exposures to 

33 PM10-2.5. 
Draft-Do Not Quote or Cite 3-26 June 2010

1 3.3 CONSIDERATION OF POTENTIAL ALTERNATIVE STANDARDS 

2 Staff next considers the following overarching question: 

3 What potential alternative standard(s) could be supported by the currently available 

4 scientific evidence and air quality information to increase public health protection against 

5 exposures to PM10-2.5? 

6 In addressing this overarching question, we consider how the currently available 

7 scientific evidence and air quality information could inform decisions regarding the basic 

8 elements of the NAAQS: indicator (section 3.3.1), averaging time (section 3.3.2), form (section 

9 3.3.3), and level (section 3.3.4). 

10 3.3.1 Indicator 

11 As discussed above, PM10 includes both PM10-2.5 and PM2.5, with the relative contribution 

12 of each to PM10 mass varying across locations (see below). In the most recent review completed 

13 in 2006, EPA concluded that the PM10 indicator remained appropriate because a PM10 standard 

14 would be expected to provide appropriate protection against effects associated with exposures to 

15 PM10-2.5. In particular, a PM10 indicator would be expected to target protection to urban areas, 

16 where the evidence of effects from exposure to coarse PM is the strongest (71 FR at 61196). In 

17 considering potential alternative standards in the current review, we have considered the 

18 following question with regard to indicator: 

19  To what extent does the available evidence and/or air quality information provide 

20 support for retaining or revising the current PM10 indicator?

21 In addressing this question, we focus on the following considerations: 

22  The extent to which PM10 is comprised of PM10-2.5 

23 

24  The appropriateness of a standard that allows lower PM10-2.5 concentrations in areas with 

25 higher fine particle concentrations (i.e., urban areas) than areas with lower fine particle 

26 concentrations (i.e., rural areas) (see Schmidt, 2005 for urban-rural comparison) 

27 

28 As an initial matter, we consider the proportion of PM10 mass in different regions of the 

29 U.S. that is PM10-2.5 (see US EPA, 2009a, section 3.5.1.1; Schmidt and Jenkins, 2010). Schmidt 

30 and Jenkins (2010) divided the U.S. into climatic regions using the same approach as used in the 

multi-city epidemiologic study by Zanobetti and Schwartz (2009) (see above).6 31 Consistent with 

 6

The Mediterranean region includes CA, OR, WA. The Dry region includes NM, AZ, NV. The Dry Continental 

region includes MT, ID, WY, UT, CO. The Hot Summer Continental region includes SD, NE, IA, IL, IN, OH. The 

Warm Summer Continental region includes ND, MN, WI, MI, PA, NY, CT, RI, MA, VT, NH, ME. The Humid 

Subtropical and Maritime region includes FL, LA, TX, GA, AL, MS, AR, OK, KS, MO, TN, SC, NC, VA, WV, 

KY, NJ, DE, DC, MD. 
Draft-Do Not Quote or Cite 3-27 June 2010

1 the air quality analyses in the ISA (US EPA, 2009a, section 3.5.1.1) and the concentration 

2 estimates of Zanobetti and Schwartz (2009), PM10-2.5 concentrations were higher in the western 

3 U.S. than the east, with the highest concentrations in the southwest (data not shown, from 

4 Schmidt and Jenkins, 2010). On average, ratios of PM10-2.5 concentrations to PM10 

5 concentrations were also higher in the western U.S. than in the eastern U.S., with the southwest 

6 (i.e., Dry region in Figure 3-4) having the highest ratios. Consistent results were reported in the 

7 ISA analyses of PM air quality (US EPA, 2009a, compare Tables 3-9 and 3-10). While the same 

8 general pattern persisted when the analysis was restricted to days with PM10 concentrations at the 

high end of the distribution of daily concentrations (i.e., at or above 95th 9 percentile), ratios of 

10 PM10-2.5 to PM10 on these days tended to be somewhat higher, on average, across most regions of 

11 the U.S. (see right-hand columns in Figure 3-4). 

12 Figure 3-4. Site-Level Ratio of Daily PM10-2.5 to Daily PM10* 

13 

14 *Blue stars represent mean concentrations, horizontal lines represent median concentrations, boxes 

15 represent 75% confidence intervals, and error bars represent 95% confidence intervals. N values 

16 equal the number of site years of monitoring data for each region. 

17 

18 Thus, on average across the U.S., PM10-2.5 comprises a larger portion of PM10 on days with 

19 relatively high PM10 concentrations than on days with more typical PM10 concentrations. This 

20 suggests that elevated PM10 concentrations across much of the U.S. are due in large part to 

21 elevations in PM10-2.5 mass. Given this, we conclude that a PM10 standard would be most 

22 effective at limiting PM10-2.5 concentrations if it focuses on the upper end of the distribution of 

23 daily PM10 concentrations. This issue is discussed further below in the context of the standard 

24 form (section 3.3.3). 

25 Given the above conclusion, in further considering the issue of indicator we note that 

26 most of the evidence for positive associations between PM10-2.5 and morbidity and mortality, 
Draft-Do Not Quote or Cite 3-28 June 2010

1 particularly evidence for these associations at relatively low concentrations of PM10-2.5, continues 

2 to come from studies conducted in locations where the PM10-2.5 is expected to be largely of urban 

3 origin. While some studies have reported positive associations between relatively high 

4 concentrations of particles of non-urban origin (i.e., crustal material from windblown dust in 

5 non-urban areas, see above) and mortality and morbidity, we note that the extent to which these 

6 associations would remain at the lower particle concentrations more typical of U.S. and 

Canadian urban study locations remains uncertain.7 7 Given these considerations, and given the 

8 increased potential for coarse particles in urban areas to become contaminated by toxic 

9 components of fine particles from urban/industrial sources (US EPA, 2004), we conclude that it 

10 remains appropriate to maintain a standard that allows lower ambient thoracic coarse particle 

11 concentrations in urban areas than in non-urban areas. 

12 Given this conclusion, we note that it would be reasonable to consider an indicator that 

13 targets control on areas with the types of ambient mixes generally present in urban areas. Such 

14 an indicator would focus control on areas with ambient mixes known with greater certainty to be 

15 associated with adverse health effects and, therefore, would provide public health benefits with 

16 the greatest degree of certainty. As noted in the last review of the PM NAAQS, a PM10 standard 

17 would allow lower concentrations of PM10-2.5 in areas with higher fine particle concentrations, 

18 which tend to be urban locations, than areas with lower fine particle concentrations, which tend 

19 to be rural locations (Schmidt et al., 2005). Therefore, we reach the conclusion that a PM10

20 indicator would appropriately target protection to those locations where the evidence is strongest 

21 for associations between adverse health effects and exposures to thoracic coarse particles. In 

22 contrast, we note that a PM10-2.5 indicator, for a standard set at a single unvarying level, would 

23 not achieve this targeting, given that allowable thoracic coarse particle concentrations would be 

24 the same regardless of the location or the likely sources of PM. Therefore, given the currently 

25 available evidence, one possible result of using a PM10-2.5 indicator would be a standard that is 

26 overprotective in rural areas and/or underprotective in urban areas. 

27 In addition, while we note that administrative feasibility is not an appropriate basis for 

28 informing decisions on the NAAQS (see above), we also note that PM10-2.5 concentrations are not 

29 routinely measured and reported at present (US EPA, 2009a, section 3.5.1.1). In the last review 

30 of the PM NAAQS, EPA required monitoring of PM10-2.5 mass and expects approximately 80 

31 stations to be reporting mass concentrations by January 1, 2011, as part of the National Core 

 7

Other than the dust storm studies, we note that the study in Coachella Valley by Ostro et al. (2003) reported 

statistically significant associations in a location where coarse particles are expected to be largely due to windblown 

dust. Specifically, we note the CASAC conclusion in the last review that “studies from Ostro et al. showed 

significant adverse health effects, primarily involving exposures to coarse-mode particles arising from crustal 

sources” (Henderson, 2005). In considering this study, we also note the relatively high PM10 concentrations in the 

study area (see Figure 3-2 above). 
Draft-Do Not Quote or Cite 3-29 June 2010

1 (NCore) network (http://www.epa.gov/ttnamti1/ncore/index.html). However, data from those 

2 monitors are not yet available for locations and time periods of PM10-2.5 health studies. 

3 Given all of the above considerations, we conclude that the available evidence supports 

4 consideration in the current review of a PM10 indicator for a standard that protects against 

5 exposures to thoracic coarse particles. We further conclude that consideration of alternative 

6 indicators (e.g., PM10-2.5) in future reviews is desirable and could be informed by additional 

7 research, as described below (section 3.4). 

8 3.3.2 Averaging Time 

9 Based primarily on epidemiologic studies that reported positive associations between 

10 short-term (24-hour) PM10–2.5 concentrations and mortality and morbidity, the Administrator 

11 concluded in the last review that the available evidence supported a 24-hour averaging time for a 

12 standard intended to control thoracic coarse particles. In contrast, given the relative lack of 

13 studies supporting a link between long-term exposures to thoracic coarse particles and morbidity 

14 or mortality (US EPA, 2004a, Chapter 9), the Administrator further concluded that an annual 

15 coarse particle standard was not warranted at that time (71 FR 61198-61199). 

16 In the current review, we consider the extent to which the available evidence provides 

17 information relevant for decisions on averaging time by considering the following question: 

18  To what extent does the available evidence continue to support a 24-hour averaging 

19 time for a standard meant to protect against effects associated with exposures to 

20 PM10-2.5? 

21 With regard to this question, we note the conclusions from the ISA regarding the weight 

22 of evidence for short-term and long-term PM10-2.5 exposures as well as the studies on which those 

23 conclusions are based. Specifically, as discussed above (see Table 3-1 above), the ISA 

24 concludes that the existing evidence is suggestive of a causal relationship between short-term 

25 PM10-2.5 exposures and mortality, cardiovascular effects, and respiratory effects (ISA, section 

26 2.3.3). This conclusion is based largely on epidemiologic studies which have primarily 

27 evaluated associations between 24-hour PM10-2.5 concentrations and morbidity and mortality 

28 (e.g., see ISA, Figure 2-3), though a small number of controlled human exposure studies have 

29 reported effects following shorter exposures (i.e., 2-hours) to PM10-2.5 (e.g., see ISA, sections 

30 6.2.1.2, 6.3.3.2). In contrast, with respect to long-term exposures, the ISA concludes that 

31 available evidence is inadequate to infer a causal relationship with all health outcomes 

32 evaluated (US EPA, 2009a, section 2.3). Specifically, the ISA states, “To date, a sufficient 

33 amount of evidence does not exist in order to draw conclusions regarding the health effects and 

34 outcomes associated with long-term exposure to PM10-2.5” (US EPA, 2009a, section 2.3.4; see 

35 Table 3-1 above). 
Draft-Do Not Quote or Cite 3-30 June 2010

1 In considering these weight of evidence determinations in the ISA, we conclude that, at a 

2 minimum, they suggest the importance of maintaining a standard that protects against short-term 

3 exposures to thoracic coarse particles. Given that the majority of the evidence supporting the 

4 link between short-term PM10-2.5 and morbidity and mortality is based on 24-hour average 

5 thoracic coarse particle concentrations, we conclude that the evidence available in this review 

6 continues to support consideration of a 24-hour averaging time for a PM10 standard meant to 

7 protect against effects associated with short-term exposures to PM10-2.5. We further conclude that 

8 the available evidence does not support consideration of an annual thoracic coarse particle 

9 standard at this time. In reaching this conclusion, we also note that, to the extent a short-term 

10 standard requires areas to reduce their 24-hour ambient particle concentrations, long-term 

11 concentrations would also be expected to decrease. Therefore, a 24-hour standard meant to 

12 protect against short-term exposures to thoracic coarse particles would also be expected to 

13 provide some protection against any potential effects of long-term exposures at ambient 

14 concentrations. 

15 3.3.3 Form 

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

17 level of the standard in determining whether an area attains that standard. In identifying a single 

18 statistic for the form, we note that although future air quality improvement strategies in any 

19 particular area are not yet defined, most such strategies are likely to affect a broad distribution of 

20 PM air quality concentrations in an area. Therefore, although the form of the standard defines a 

21 single statistic, any resulting reductions in health risks are likely to occur across a wide range of 

22 concentrations. 

23 As discussed above, in the last review the Administrator retained the one-expected 

24 exceedance form of the primary 24-hour PM10 standard. This decision was linked to the overall 

25 conclusion that “the level of protection from coarse particles provided by the current 24-hour 

26 PM10 standard remains requisite to protect public health with an adequate margin of safety” (71 

27 FR 61202). Because revising either the level or the form of the standard would have altered the 

28 protection provided, it was concluded that such changes “would not be appropriate based on the 

29 scientific evidence available at this time” (71 FR 21202). Therefore, the decision in the last 

30 review to retain the one-expected-exceedance form was part of the broader decision that the 

31 existing 24-hour standard provided requisite public health protection. 

32 In the current review, we are also considering the form of the standard within the context 

33 of the overall decision on whether, and if so how, to revise the current 24-hour PM10 standard. 

34 Given the conclusions above regarding the appropriate indicator and averaging time for 
Draft-Do Not Quote or Cite 3-31 June 2010

1 consideration for potential alternative standards, we consider potential alternative forms for a 24-

2 hour PM10 standard. To frame our consideration of this issue, we pose the following question: 

3  To what extent does available evidence and information support consideration of an 

4 alternative form for a 24-hour PM10 standard? 

5 Although the selection of a specific form must be made within the context of decisions on 

6 the other elements of the standard, EPA generally favors concentration-based forms for shortterm standards. In 1997 EPA established a 98th 7 percentile form for the 24-hour PM2.5 standard 

and in 2010 EPA established a 98th 8 percentile form for the 1-hour NO2 standard (62 FR 38671; 

75 FR 6474).8 9 In making these decisions, EPA noted that, as compared to an exceedance-based 

10 form, such as the form of the current PM10 standard, a concentration-based form is more 

11 reflective of the health risks posed by elevated pollutant concentrations because it gives 

12 proportionally greater weight to days when concentrations are well above the level of the 

13 standard than to days when the concentrations are just above the level of the standard. In 

14 addition, when averaged over three years, a concentration-based form can provide a stable 

15 regulatory target, facilitating the development of stable implementation programs. 

16 In further considering the issue of form in the current review of the 24-hour PM10

17 standard, we note that a concentration-based form better compensates for missing data and less 

18 than-daily monitoring than an exceedance-based form. This is a particularly important 

19 consideration in the case of PM10 because, depending on ambient PM10 concentrations, the 

20 frequency of PM10 monitoring differs across locations (US EPA, 1987). With a concentrationbased form, such as a 98th 21 percentile form, attainment status would be determined based on PM10

22 concentrations from the same part of the annual distribution of 24-hour PM10 concentrations, 

23 despite the potential for differences in the frequency of PM10 monitoring across locations. In 

24 contrast, with the current one-expected-exceedance form, differences in monitoring frequency 

25 can result in attainment status in some locations being based on estimates of the number of days 

26 per year that the standard level is expected to be exceeded (i.e., in locations with less-than-daily 

27 monitoring), rather than actual counts of the number of exceedances (see Appendix K to Part 50 

28 of the CFR). The extent to which these estimates reflect actual exceedances becomes more 

29 uncertain as the monitoring frequency decreases. 

30 In considering specific concentration-based forms for a 24-hour PM10 standard, we note 

31 that, as discussed above, in past reviews (62 FR 38671; 75 FR 6474) EPA has judged it 

32 appropriate to define a standard that provides a balance between adequately limiting the 

 8

In the recently completed review of the SO2 primary NAAQS, a 99th percentile form was adopted. In the case of 

SO2, there was specific evidence that a 99th percentile form would be more effective than a 98th percentile form for 

controlling 5-minute peak SO2 concentrations, which were of concern because controlled human exposure studies 

have linked 5-minute SO2 exposures to respiratory effects (See 75 FR at 35541 (June 22, 2010)). 
Draft-Do Not Quote or Cite 3-32 June 2010

1 occurrence of peak pollutant concentrations and providing a stable regulatory target for 

2 implementation programs. For short-term standards, this balance has been achieved by setting 

3 the form of the standard at the upper end of the distribution of pollutant concentrations, but not 

4 using the most extreme statistics, and by averaging pollutant concentrations over three years. 

5 We conclude that similar considerations are relevant in the current review for a decision on the 

6 form of a PM10 standard. Specifically, given that PM10-2.5 is likely to make a larger contribution 

7 to PM10 mass on days with relatively high PM10 concentrations than on days with more typical 

8 PM10 concentrations (see above), we conclude that it is appropriate to set the standard form at the 

9 upper end of the annual distribution of 24-hour PM10 concentrations. In addition, given the 

10 potential for local sources to have important impacts on monitored PM10 concentrations (US 

11 EPA, 2009a, section 2.1.1.2), we also conclude that it is desirable to select a form that promotes 

stability of local implementation programs.9 12 

13 Based on all of the above considerations, we conclude that, to the extent it is judged 

14 appropriate to revise the current 24-hour PM10 standard, it would be appropriate to consider a 

range of potential alternative standard levels based on 98th 15 percentile PM10 concentrations. In 

16 drawing this conclusion we note that, because of the current expected-exceedance-based form 

17 and the differences in monitoring frequency across locations, the stringency of a standard with a 

98th 18 percentile form, relative to the stringency of the current standard, will differ across locations. 

One consequence of this is that, compared to the current standard, a standard with a 98th 19 

20 percentile form (or any other concentration-based form) could be more stringent than the current 

21 standard in some locations and less stringent in other locations, depending on the level of the 

22 standard. This issue is discussed in more detail below within the context of specific potential 

23 alternative standard levels. 

24 3.3.4 Level 

25 As noted above, to the extent it is judged in the current review that the 24-hour PM10

26 standard does not provide adequate public health protection against exposures to thoracic coarse 

27 particles, potential alternative standard levels could be considered. Given the conclusions 

28 described above for indicator, averaging time, and form, we conclude that it would be 

appropriate to consider potential alternative levels for a 24-hour PM10 standard with a 98th 29 

 9

We note that in the most recent review of the NO2 primary NAAQS, a 98th percentile form was adopted, rather than 

a 99th percentile form, due to the potential for “instability in the higher percentile concentrations” near local sources 

(i.e., mobile sources on major roads) (75 FR 6493). See also, ATA III, 283 F. 3d at 374-75 (upholding 98th

percentile form since “otherwise States would have to design their pollution control programs around single high 

exposure events that may be due to unusual meteorological conditions alone, rendering the programs less stable – 

and hence, we assume, less effective – than programs designed to address longer-term average conditions.”). 
Draft-Do Not Quote or Cite 3-33 June 2010

1 percentile form. To inform our consideration of this issue, we have considered the following 

2 question: 

3  To what extent does available evidence and air quality information support 

consideration of alternative standard levels for a 24-hour PM10 standard with a 98th 4 

5 percentile form? 

6 Evidence-based Considerations

7 In considering the evidence as it relates to potential alternative standard levels for 

8 consideration, we first consider the relative weight to place on specific epidemiologic studies, 

9 including the weight to place on the uncertainties associated with those studies. We have 

10 considered several factors in placing weight on specific epidemiologic studies including the 

11 extent to which studies report statistically significant associations with PM10-2.5 and the extent to 

12 which the reported associations are robust to co-pollutant confounding. 

13 In addition, we consider the extent to which associations with PM10-2.5 can be linked to 

14 the air quality in a specific location. With regard to this, we place greatest weight on information 

15 from single-city analyses. Although, as discussed above, multi-city studies have advantages in 

16 terms of power to detect associations and geographic coverage, the extent to which effects 

17 reported in multi-city studies are associated with the short-term air quality in any particular 

18 location is highly uncertain, especially when considering short-term concentrations at the upper 

19 end of the distribution of daily concentrations for pollutants with relatively heterogeneous spatial 

20 distributions such as PM10-2.5 and PM10 (US EPA, 2009a, section 2.1.1.2). In contrast, single-city 

21 studies are more limited in terms of power and geographic coverage but the link between 

22 reported health effects and the air quality in a given city is more straightforward to establish (US 

23 EPA, 2009a, section 2.1.1.2). Given this, in considering PM10 concentrations in locations of 

24 epidemiologic studies, we place the most weight on single-city studies (Figures 3-2 and 3-3) and 

25 single-city analyses of the locations evaluated in the multi-city study by Zanobetti and Schwartz 

26 (US EPA, 2009a, Figure 6-29). 

27 In considering PM air quality in study locations, we also note that the available evidence 

28 does not support the existence of thresholds, or lowest-observed-effects levels, in terms of 24-

29 hour average concentrations (US EPA, 2009a, section 2.4.3). In the absence of an apparent 

30 threshold, for purposes of identifying a range of standard levels potentially supported by the 

31 health evidence, we focus on the range of PM10 concentrations that have been measured in 

32 locations where U.S. epidemiologic studies have reported associations with PM10-2.5 (see U.S. 

33 EPA, 2009a, Figures 6-1 to 6-30 for studies). In characterizing PM10 air quality in PM10-2.5 study 

locations, we have used EPA’s AQS to identify the highest 98th 34 percentile 24-hour PM10

35 concentrations for each year in each study location (i.e., from the monitor in the study area 

recording highest 98th 36 percentile concentration), as described in Schmidt and Jenkins (2010). 
Draft-Do Not Quote or Cite 3-34 June 2010

The 98th 1 percentile concentrations from each study year were averaged together and these 

2 averages are presented below in Figures 3-5 (PM10-2.5 mortality studies) and 3-6 (PM10-2.5 

3 morbidity studies) for locations of single-city studies. 
Draft-Do Not Quote or Cite 3-35 June 2010

Figure 3-5. 98th 1 Percentile PM10 Concentrations in Locations of U.S. PM10-2.5 Mortality Studies* 

2 

3 *Studies in Figures 3-5 and 3-6 are a combination of those assessed in the last review and those assessed in the ISA in the current revie

figures are ordered by increasing 98th 4 percentile PM10 concentrations.
Draft-Do Not Quote or Cite 3-36 June 2010

Figure 3-6. 98th 1 Percentile PM10 Concentrations in Locations of U.S. PM10-2.5 Morbidity Studies 

2 
Draft-Do Not Quote or Cite 3-37 June 2010

In addition to the single-city study locations in Figures 3-5 and 3-6, 98th 1 percentile PM10

2 concentrations averaged across the study locations evaluated in the multi-city studies by 

Zanobetti and Schwartz (2009) and Peng et al. (2008) were 78 g/m3

 and 68 g/m3 3 , respectively. 

4 Multi-city effect estimates remained positive, and in some cases (i.e., Zanobetti and Schwartz for 

5 all-cause and respiratory mortality) statistically significant, in co-pollutant models that included 

6 fine particles. 

7 Bayes-adjusted single-city effect estimates for the 47 cities evaluated by Zanobetti and 

8 Schwartz (US EPA, 2009a, Figure 6-29), which were generally positive but not statistically 

9 significant, can provide some additional insight into the PM10 concentrations in specific locations 

where associations between PM10-2.5 and mortality have been reported. The 98th 10 percentile PM10

concentrations in these cities ranged from 39 g/m3

 (Davie, FL) to 195 g/m3 11 (El Paso, TX), and 

12 in the 6 cities where positive and statistically significant PM10-2.5 mortality effect estimates were 

reported, 98th 13 percentile PM10 concentrations were as follows (Schmidt and Jenkins, 2010): 

 Chicago: 92 g/m3 14 

 Salt Lake City: 98 g/m3 15 

 Detroit: 105 g/m3 16 

 Pittsburgh: 110 g/m3 17 

 Birmingham: 122 g/m3 18 

 St. Louis: 138 g/m3 19 

20 Thus, in the single-city mortality studies in Figure 3-5 above, as well as the Bayes-adjusted 

21 single-city analyses of the locations evaluated by Zanobetti and Schwartz, positive and 

statistically significant PM10-2.5 effect estimates were reported in some locations with 98th 22 

percentile PM10 concentrations as low as 92 g/m3 23 (i.e., locations evaluated by Mar et al., 2003; 

24 Ostro et al., 2003; Wilson et al., 2007; and the cities listed above, US EPA 2009a, Figure 6-29). 

25 Similarly, among the U.S. morbidity studies, Ito (2003) reported a positive and statistically 

26 significant PM10-2.5 effect estimate for hospital admissions for ischemic heart disease in Detroit, 

where the 98th percentile PM10 concentration was well above 92 g/m3 27 . PM10-2.5 effect estimates 

28 in this study remained positive, and in some cases statistically significant, in co-pollutant models 

29 with gaseous pollutants (US EPA, 2009a, Figures 6-5 and 6-15). Other morbidity studies 

30 generally did not report statistically significant PM10-2.5 effect estimates, though effect estimates 

31 were marginally significant (i.e., the lower confidence limit was 0) for asthma hospital 

32 admissions in the Bronx (NYSDOH, 2006) and for respiratory infection-related admissions in 

33 Detroit (Ito et al., 2003). 
Draft-Do Not Quote or Cite 3-38 June 2010

1 Air Quality-based Considerations

2 In addition to the evidence-based considerations described above, we have used EPA’s 

AQS to estimate the level of a 24-hour PM10 standard with a 98th 3 percentile form that would 

4 approximate the degree of protection, on average across the country, provided by the current 24-

hour PM10 standard with its one-expected-exceedance form. Based on regression of 98th 5 

6 percentile PM10 concentrations onto one-expected-exceedance concentration equivalent design 

values, a 98th percentile PM10 concentration of 87 g/m3 7 is, on average, equivalent to a oneexpected-exceedance design value of 150 g/m3 8 (see Figure 3-7 below and Schmidt and Jenkins, 

9 2010). However, as indicated in Figure 3-7, the range of equivalent concentrations varies 

considerably across monitoring sites (95% confidence interval ranges from 63 to 111 g/m3 10 ) (see 

Schmidt and Jenkins, 2010). As a consequence, while we note that a 98th 11 percentile 24-hour 

PM10 standard with a level of 87 g/m3 12 would be expected to provide public health protection 

13 that is, on average across the U.S., equivalent to the protection provided by the current standard, 

in some locations such a 98th 14 percentile standard could be more protective than the current 

15 standard while in other locations it could be less protective than the current standard. 
Draft-Do Not Quote or Cite 3-39 June 2010

1 Figure 3-7. Composite 3-year PM10 98th percentile 24-hour average concentration versus 

2 the PM10 expected exceedance concentration-equivalent design value 

3 

4 

5 In considering this issue in the specific locations where we have evidence for associations 

6 between PM10-2.5 and mortality or morbidity, we note that, as described above, positive and 

7 statistically significant PM10-2.5 effect estimates have been reported in the following U.S. 

8 locations: 

9  Coachella Valley (Ostro et al., 2003) 

10  Detroit (Ito et al., 2003) 

11  Phoenix (Mar et al., 2003; Wilson et al., 2007) 

12  Chicago, Salt Lake City, Detroit, Pittsburgh, Birmingham, St. Louis (US EPA, 2009a, 

13 Figure 6-29) 

In considering the PM10 air quality, we note that 98th 14 percentile PM10 concentrations were above 

87 g/m3 15 in all of these cities, even those with PM10 concentration-equivalent design values 

below 150 g/m3 16 (Detroit, Chicago, Pittsburgh) (see section 3.2.1 above for PM10 concentration17 equivalent design values). 
Draft-Do Not Quote or Cite 3-40 June 2010

1 To provide a broader perspective, we also used EPA’s AQS to compare the number of 

2 U.S. counties with PM10 one-expected-exceedance concentration-equivalent design values above 

150 g/m3

 to the number with 98th percentile PM10 concentrations above 87 g/m3 3 (for the years 

4 2006 to 2008) (see Schmidt and Jenkins, 2010). The results in Table 3-2 below indicate the 

5 following: 

6  In the eastern U.S., the number of counties with PM10 one-expected-exceedance 

concentration-equivalent design values above 150 g/m3 is the same as that with 98th 7 

percentile PM10 concentrations above 87 g/m3 8 (i.e, none in the northeast and three in the 

9 southeast). 

10 

11  Some counties in the western U.S. (i.e., ten counties total in the southwest, northwest, and 

12 Southern California) had PM10 one-expected-exceedance concentration-equivalent design 

values above 150 g/m3 but had 98th percentile PM10 concentrations below 87 g/m3 13 . 

14 

15  A small number of counties in the midwest (i.e., four counties total in the industrial midwest 

16 and upper midwest) had one-expected-exceedance concentration-equivalent design values 

below 150 g/m3 but had 98th percentile PM10 concentrations above 87 g/m3 17 . 
Draft-Do Not Quote or Cite 3-41 June 2010

1 

Table 3-2. Counties with PM10 Concentration-Equivalent Design Values > 150 g/m3 and 98th 2 percentile P

> 87 g/m3 3 

Region > All U.S. Northeast Southeast Industrial Midwest Upper Midwest Southwest Northw

Total # of counties > 318 40 60 57 35 24 79 

Total population > 122,582 15,753 27,009 23,646 5,143 11,037 16,18

 Statistic Numbers of counties, populations, and percentages of total 

# counties 38 0 3 3 3 12 8 

population 22,450 0 4,159 1,789 710 6,106 1,36

% # counties 12% 0% 5% 5% 9% 50% 10% 

3-year average 

98th percentile > 

87 μg/m3

% population 18% 0% 15% 8% 14% 55% 8% 

# counties 45 0 3 1 1 14 15 

population 32,169 0 4,159 348 28 7,675 2,07

% # counties 14% 0% 5% 2% 3% 58% 19% 

3-Year Expected 

Exceedance 

Equivalent 

Design Value > 

150 μg/m3 % population 26% 0% 15% 1% 1% 70% 13% 

4 
Draft-Do Not Quote or Cite 3-42 June 2010

1 Integration of evidence- and air quality-based considerations

2 In considering the evidence and air quality information within the context of identifying 

3 potential alternative standard levels for consideration, we note the following: 

 Although a 98th percentile 24-hour PM10 concentration of 87 g/m3 4 is equivalent, on 

5 average across the country, to a PM10 concentration-equivalent design value of 150 

g/m3

, the 98th 6 percentile standard could be more stringent than the current standard in 

7 some locations (e.g., in the industrial and upper midwest) and less stringent than the 

8 current standard in other locations (e.g., northwest, southwest, and southern California). 

 A 98th percentile 24-hour PM10 standard with a level at or below 87 g/m3 9 would be 

10 expected to maintain PM10 and PM10-2.5 concentrations below those present in U.S. study 

11 locations where PM10-2.5 effect estimates have been reported to be positive and 

12 statistically significant. 

 While studies generally reported positive PM10-2.5 effect estimates in locations with 98th 13 

percentile PM10 concentrations below 87 g/m3 14 , these effect estimates were not 

15 statistically significant. 

16 To the extent the above considerations are emphasized, the approach to considering 

17 potential alternative standard levels would place more weight on potential alternative standard 

18 levels for which the evidence is strongest (e.g., multiple single-city studies conducted in different 

19 locations, using different approaches, and reporting positive and statistically significant 

20 associations with PM10-2.5). In particular, such an approach would place more weight on positive 

21 and statistically significant PM10-2.5 effect estimates that have been reported in single-city 

22 analyses and less weight on PM10-2.5 effect estimates that are not statistically significant and/or 

23 that reflect estimates across multiple cities. It could be judged appropriate to place less weight 

24 on PM10-2.5 effect estimates that are not statistically significant given the relatively large amount 

25 of uncertainty that is associated with the broader body of PM10-2.5 health evidence, including 

26 uncertainty in the extent to which health effects evaluated in epidemiologic studies result from 

27 exposures to PM10-2.5 itself, rather than one or more co-occurring pollutants. In addition, it could 

be judged appropriate to place less weight on 98th 28 percentile PM10 concentrations averaged 

29 across multiple cities, given the uncertainty in linking multi-city effect estimates with the air 

30 quality in any particular location. To the extent this approach to considering the evidence and 

uncertainties is applied, we conclude that a standard level around 85 g/m3 31 would be appropriate 

32 to consider. 

33 In contrast, alternative approaches to considering the evidence could lead to consideration 

of standard levels below 85 g/m3 34 . For example, given that exposure error is particularly 

35 important for PM10-2.5 epidemiologic studies and can bias the results of these studies toward the 

36 null hypothesis, it could be judged appropriate to place more weight on positive associations 

37 reported in epidemiologic studies, even when those associations are not statistically significant. 
Draft-Do Not Quote or Cite 3-43 June 2010

1 In addition, despite the uncertainties associated with interpreting multi-city average air quality 

2 concentrations, it could be judged appropriate to place weight on PM10-2.5 multi-city effect 

estimates and, therefore, on the multi-city averages of 98th 3 percentile PM10 concentrations in 

4 study locations. To the extent that these considerations are emphasized, we conclude that a 

standard level as low as 65 g/m3

 could potentially be considered. A level of 65 g/m3 5 would be 

6 expected to maintain PM10 and PM10-2.5 concentrations below those in all of the single-city 

7 mortality study locations in Figure 3-5 above, including those where PM10-2.5 effect estimates 

were not statistically significant. In addition, a standard level of 65 g/m3 8 would be expected to 

maintain PM10 and PM10-2.5 concentrations below the multi-city averages of 98th 9 percentile PM10

10 concentrations in the locations evaluated by Zanobetti and Schwartz (2009) and Peng et al. 

11 (2008), below concentrations in most locations of single-city morbidity studies (including studies 

12 conducted in Atlanta reporting both positive and negative effect estimates), and below 

13 concentrations in many locations where Bayes-adjusted single-city PM10-2.5 effect estimates were 

14 positive, though not statistically significant. 

In considering potential alternative standard levels below 65 g/m3 15 , we note that, as 

16 discussed above, the overall body of PM10-2.5 health evidence is relatively uncertain, with 

somewhat stronger support in U.S. studies for associations with PM10-2.5 in locations with 98th 17 

percentile PM10 concentrations above 87 g/m3 than in locations with 98th 18 percentile PM10

concentrations below 65 g/m3 19 . Specifically, we note the following: 

20  Epidemiologic studies, either single-city or multi-city, have not reported positive and 

statistically significant PM10-2.5 effect estimates in locations with 98th 21 percentile PM10

concentrations (multi-city average 98th 22 percentile concentrations in the case of multi-city 

studies) at or below 65 g/m3 23 . 

24 

25  Although single-city morbidity studies have reported positive, but not statistically 

significant, associations with PM10-2.5 in locations with 98th 26 percentile PM10

concentrations below 65 g/m3 27 , the results of U.S. morbidity studies were generally less 

28 consistent than those of mortality studies, with some PM10-2.5 effect estimates being 

29 positive while others were negative (i.e., negative effect estimates were reported in 

several studies conducted in Atlanta, where the 98th 30 percentile PM10 concentrations 

ranged from 67 g/m3 to 71 g/m3 31 ). 

32 

33  Although Bayes-adjusted single-city PM10-2.5 effect estimates were positive, but not 

statistically significant, in some locations with PM10 concentrations below 65 g/m3 34 , 

35 these effect estimates were based on the difference between community-wide PM10 and 

36 PM2.5 concentrations. As discussed above, it is not clear how these estimates of PM10-2.5 

37 concentrations compare to those more typically used in other studies to calculate PM10-2.5 

38 effect estimates. At present, corroborating studies are lacking that use other approaches 

39 (i.e., co-located monitors, dichotomous samplers) to estimate/measure PM10-2.5 in 

40 locations with the lowest PM10 concentrations. 
Draft-Do Not Quote or Cite 3-44 June 2010

1 In light of these limitations in the evidence for a relationship between PM10-2.5 and 

2 adverse health effects in locations with relatively low PM10 concentrations, along with the 

3 overall uncertainties in the body of PM10-2.5 health evidence as described above and in the ISA, 

4 we conclude that while it could be judged appropriate to consider standard levels as low as 65 

g/m3 5 , it is not appropriate, based on the currently available body of evidence, to consider 

standard levels below 65 g/m3 6 . 

7 To provide some perspective on the potential implications of standard levels from different 

parts of the range of 65 to 85 g/m3 8 , staff assessed (based on 2006 to 2008 air quality data) the 

9 percentage of counties, and the population in those counties, that would likely not attain different 

10 standard levels within this range. The results of this analysis are presented in appendix 3A 

11 (Table 3A-1), but these results were not considered as a basis for the above staff conclusions. 

12 3.4 STAFF CONCLUSIONS ON A PRIMARY THORACIC COARSE PARTICLE 

13 STANDARD 

14 In reaching conclusions on the adequacy of the current PM10 standard and potential 

15 alternative standards to provide requisite protection against health effects associated with short16 term exposures to thoracic coarse particles, staff has considered the basic elements of the 

17 NAAQS: indicator, averaging time, form, and level (section 3.3.1 to 3.3.4 above). In 

18 considering available scientific evidence and air quality information, we reflect upon the 

19 evidence and information available in the last review integrated with evidence and information 

20 that has become available since that review as assessed and presented in the ISA (US EPA, 

21 2009a) and summarized above in sections 3.2 and 3.3. 

22 We recognize that selecting from among potential alternative standards will necessarily 

23 reflect consideration of the evidence as well as the uncertainties inherent in that evidence. In 

24 considering the current PM10 standard and identifying potential alternative primary standards for 

25 consideration, we are mindful that the Clean Air Act requires standards to be set that are 

26 requisite to protect public health with an adequate margin of safety, such that the standards are to 

27 be neither more nor less stringent than necessary. Thus, the Act does not require that the 

28 NAAQS be set at zero-risk levels, but rather at levels that avoid unacceptable risks to public 

29 health. 

30 (1) Consideration should be given to retaining or revising the current 24-hour PM10 primary 

31 standard, depending on the relative weight placed on the evidence supporting associations 

32 with PM10-2.5 and the uncertainties associated with this evidence. 

33 (2) The indicator for thoracic coarse particles should continue to be PM10. This conclusion is 

34 based on our assessment of the evidence for effects related to particles of urban and non35 urban origins. We also conclude that future research should be targeted so as to inform 

36 consideration of different indicators in future reviews. 
Draft-Do Not Quote or Cite 3-45 June 2010

1 (3) The averaging time should be 24-hours for a PM10 standard meant to protect against short2 term exposures to thoracic coarse particles. This conclusion reflects the body of 

3 epidemiologic studies, which are most often based on 24-hour average PM10-2.5 

4 concentrations. 

5 (4) To the extent consideration is given to revising the current standard: 

(a) Consideration should be given to a 98th 6 percentile form for a 24-hour PM10

7 standard. This conclusion is based on consideration of providing a balance between 

8 limiting peak concentrations and providing a stable regulatory target, compensating 

9 for differences in monitoring frequency across locations, and focusing the standard 

10 on days when PM10-2.5 is likely to make a relatively larger contribution to PM10

11 mass. 

(b) Consideration should be given to PM10 standard levels from 85 g/m3 12 down to 

about 65 g/m3

. This range of levels is based on consideration of 98th 13 percentile 

14 PM10 concentrations in U.S. study locations where PM10-2.5 epidemiologic studies 

15 have been conducted. We conclude that standard levels around the upper end of 

16 this range are most strongly supported by the evidence given the availability of 

17 multiple single-city analyses in different locations and using different approaches to 

18 estimate PM10-2.5, with some reporting positive and statistically significant PM10-2.5 

effect estimates. Standard levels as low as 65 g/m3 19 could be considered to the 

20 extent more weight is placed on positive, but not statistically significant, 

21 associations and on multi-city PM10-2.5 effect estimates. 

22 3.5 KEY UNCERTAINTIES AND AREAS FOR FUTURE RESEARCH AND 

23 DATA COLLECTION 

24 In this section, we highlight key uncertainties and areas for future health-related research, 

25 model development, and data collection activities. These efforts, if undertaken, could provide 

26 important evidence for informing future PM NAAQS reviews and, in particular, consideration of 

27 possible alternative indicators, averaging times, forms, and/or levels. In some cases, research in 

28 these areas can go beyond aiding standard setting to informing the development of more efficient 

29 and effective control strategies. 

30 As an initial matter, we note that many of the research needs identified for fine particles 

31 (see above, section 2.5) are also relevant for thoracic coarse particles. This includes research in 

32 the following areas: 

33  Sources and components of PM10-2.5, including source apportionment modeling; 

34 monitoring of components; linking specific sources/components to health outcomes; 

35 linking sources/components to intra- and inter-city differences in health effects; and 

36 linking sources/components to population exposures 

37  Understanding the extent to which an association between thoracic coarse particles and 

38 specific health effects can be modified by co-pollutants 
Draft-Do Not Quote or Cite 3-46 June 2010

1  Understanding associations with a broad range of cardiovascular and respiratory 

2 endpoints as well as adverse effects in the nervous system, on reproduction, and/or on 

3 development 

4  Understanding C-R relationships and the confidence bounds around these relationships, 

5 especially at lower ambient thoracic coarse particle concentrations 

6  Understanding air quality distributions in locations of epidemiologic studies 

7  Identifying populations susceptible to PM10-2.5-related health effects 

8  Modeling to estimate PM10-2.5 mass and composition in areas with less-than-daily 

9 monitoring 

10 These uncertainties and areas for future research are discussed above in section 2.5 and 

11 that discussion will not be repeated here. In addition to the above, there are several areas for 

12 future research that are particularly relevant for thoracic coarse particles. These include the 

13 following: 

14  The body of experimental inhalation studies (e.g., controlled human exposure and animal 

15 toxicology studies) is currently relatively sparse. Additional well-conducted experimental 

16 studies could play an important role in weight of evidence judgments in future ISAs. 

17 Therefore, experimental evaluation of effects (e.g., vasomotor function, airways 

18 responsiveness, pulmonary function/inflammation) of concentrated ambient PM10-2.5 from 

19 specific sources (e.g., traffic, industrial, non-industrial) would be useful, particularly if 

20 exposure-response relationships are evaluated. 

21  Exposure error is of particular concern for thoracic coarse particles, given the relative lack of 

22 monitoring and its less homogeneous atmospheric distribution compared to fine particles (US 

23 EPA, 2009a, section 2.1.1.2). Therefore, short-term studies with well-characterized personal 

24 exposures (e.g., panel studies) would be useful. Such studies could examine indicators of 

25 cardiovascular and respiratory morbidity (e.g., arrhythmia, ischemia, vasomotor function, 

26 respiratory symptoms, pulmonary inflammation, pulmonary function, pulmonary injury) and 

27 would be particularly useful if they evaluated concentration-response relationships and/or 

28 effects of repeated peak exposures. 

29  Epidemiologic studies currently use a variety of approaches to measure/estimate PM10-2.5 

30 concentrations. It is important that we better understand the relationship between results 

31 from studies that estimate PM10-2.5 concentrations using either (1) difference method of co32 located monitors, (2) difference method of county-wide averages of PM10 and PM2.5, or (3) 

33 direct measurement of PM10-2.5 using a dichotomous sampler. In addition, as described 

34 above, PM10-2.5 monitoring will be required at NCORE sites by 2011. It would be useful for 

35 future epidemiologic studies to make use of these new PM10-2.5 monitoring sites. 

36  Very little information is available to inform weight of evidence conclusions for endpoints 

37 associated with long-term PM10-2.5 exposures. Epidemiologic and animal toxicological 

38 studies of long-term exposures (i.e., months to years) to PM10-2.5 would be helpful. Such 

39 studies could evaluate links with cardiovascular and respiratory morbidity, reproductive and 

40 developmental outcomes, cancer, and mortality. 
Draft-Do Not Quote or Cite 3-47 June 2010

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1 4 REVIEW OF THE SECONDARY STANDARDS FOR VISIBILITY2 RELATED EFFECTS 

3 This chapter presents staff conclusions with regard to the adequacy of the current suite of 

4 secondary PM2.5 standards to protect against PM-related visibility impairment as well as 

5 alternative secondary PM standards that are appropriate for consideration in this review. Our 

6 assessment of these issues is framed by a series of key policy-relevant questions, which expand 

7 upon those presented at the outset of this review in the IRP. The answers to these questions will 

8 inform decisions on whether, and if so how, to revise the current suite of secondary PM2.5 

9 standards for the purpose of providing appropriate protection from PM-related visibility 

10 impairment. 

11 In presenting staff conclusions on a range of alternative secondary standards that are 

12 appropriate for consideration, we note that the final decision is largely a public welfare policy 

13 judgment. A final decision must draw upon scientific information and analyses about PM14 related visibility impairment and related impacts on public welfare, as well as judgments about 

15 how to deal with the range of uncertainties that are inherent in the scientific evidence and 

16 analyses. Our approach to informing these judgments is discussed more fully below. 

17 Information on the approaches used to set the secondary PM2.5 standards in past reviews 

18 as well as our current approach for this review are presented in section 4.1. Our conclusions 

19 regarding the adequacy of the current suite of secondary PM2.5 standards to protect against PM20 related visibility impairment are presented in section 4.2. Section 4.3 presents our conclusions 

21 with respect to alternative PM standards by focusing on each of the basic elements of the 

22 standards: pollutant indicator (section 4.3.1), averaging time (section 4.3.2), and level and form 

23 (section 4.3.3). The performance of alternative standards, with a focus on the uniformity of 

24 protection from visibility impairment afforded by the alternative standards, is evaluated in 

25 section 4.3.4. Section 4.4 summarizes all staff conclusions on the secondary PM standards for 

26 visibility protection. This chapter concludes with an overview of areas of key uncertainties and 

27 suggested future research areas and data collection efforts (section 4.5) 

28 4.1 APPROACH 

29 Staff’s approach for reviewing the current suite of secondary PM2.5 standards builds upon 

30 and broadens the approaches used in previous PM NAAQS reviews. We first present a brief 

31 summary of the approaches used to review and establish secondary PM standards in the last two 

32 reviews of the PM NAAQS (section 4.1.1). Recent litigation on the 2006 standards has resulted 

33 in the remand of the secondary annual and 24-hour PM2.5 NAAQS to EPA as discussed in section 
Draft-Do Not Quote or Cite 4-2 June 2010

1 4.1.2. Our current approach for evaluating the secondary PM2.5 standards using both evidence2 and impact assessment-based considerations is outlined in section 4.1.3. 

3 4.1.1 Approaches Used in Previous Reviews 

4 The original suite of secondary PM2.5 standards was established in 1997 and revisions to 

5 those standards were made in 2006. The approaches used in making final decisions on 

6 secondary standards in those reviews, as well as the current review, utilize different ways to 

7 consider the underlying body of scientific evidence. They also reflect an evolution in our 

8 understanding of the nature of the effect on public welfare from visibility impairment, from that 

9 focused only on Class I area visibility impacts to a more multifaceted approach that also 

10 considers PM-related impacts on non-Class I area visibility, such as in urban areas. This 

11 evolution has occurred in conjunction with the expansion of available PM data and information 

12 from associated studies of public perception, valuation and personal comfort and well being. 

13 4.1.1.1 Review Completed in 1997 

14 In 1997, EPA revised the identical primary and secondary PM10 NAAQS in part by 

15 establishing new identical primary and secondary PM2.5 standards. In revising the secondary 

16 standards, EPA recognized that PM produces adverse effects on visibility and that impairment of 

17 visibility was experienced throughout the U.S., in multi-state regions, urban areas, and remote 

18 mandatory Class I Federal areas alike. However, in considering an appropriate level for a 

19 secondary standard to address adverse effects of PM2.5 on visibility, EPA concluded that the 

20 determination of a single national level was complicated by regional differences in several 

21 factors that influence visibility such as background and current levels of PM2.5, composition of 

22 PM2.5, and average relative humidity. Variations in these factors across regions could thus result 

23 in situations where attaining an appropriately protective concentration of fine particles in one 

24 region might or might not provide adequate protection in a different region. The EPA also 

25 determined that there was insufficient information at that time to establish a level for a national 

26 secondary standard that would represent a threshold above which visibility conditions would 

27 always be adverse and below which visibility conditions would always be acceptable. 

28 Based on these considerations, EPA assessed potential visibility improvements in urban 

29 areas and on a regional scale that would result from attainment of the new primary standards for 

30 PM2.5. The agency concluded that the spatially averaged form of the annual PM2.5 standard was 

31 well suited to the protection of visibility, which involves effects of PM2.5 throughout an extended 

32 viewing distance across an urban area. Based on air quality data available at that time, many 

33 urban areas in the Northeast, Midwest, and Southeast, as well as Los Angeles, were expected to 

34 see perceptible improvement in visibility if the annual PM2.5 primary standard was attained. The 

35 EPA also concluded that in some areas attainment of the 24-hour PM2.5 standard would be 
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1 expected to reduce, to some degree, the number and intensity of ‘‘bad visibility’’ days, i.e., the 

2 20% of days having the greatest impairment over the course of a year. 

3 Having concluded that attainment of the annual and 24-hour PM2.5 primary standards 

4 would lead to visibility improvements in many eastern and some western urban areas, EPA also 

5 considered whether these standards could provide potential improvements to visibility on a 

6 regional scale. Based on information available at the time, EPA concluded that attainment of 

7 PM2.5 secondary standards set identical to the primary standards would be expected to result in 

8 visibility improvements in the eastern U.S. at both urban and regional scales, but little or no 

9 change in the western U.S., except in and near certain urban areas. 

10 The EPA then considered the potential effectiveness of a regional haze program, required 

by sections 169A and 169B of the Act1 11 to address those effects of PM on visibility that would 

12 not be addressed through attainment of the primary PM2.5 standards. The regional haze program 

13 would be designed to address the widespread, regionally uniform type of haze caused by a 

14 multitude of sources. The structure and requirements of sections 169A and 169B of the Act 

15 provide for visibility protection programs that can be more responsive to the factors contributing 

16 to regional differences in visibility than can programs addressing a nationally applicable 

17 secondary NAAQS. The regional haze visibility goal is more protective than a secondary 

18 NAAQS since the goal addresses any man-made impairment rather than just impairment at levels 

19 determined to be adverse. Thus, an important factor considered in the 1997 review was whether 

20 a regional haze program, in conjunction with secondary standards set identical to the suite of 

21 PM2.5 primary standards, would provide appropriate protection for visibility in non-Class I areas. 

22 The EPA concluded that the two programs and associated control strategies should provide such 

23 protection due to the regional approaches needed to manage emissions of pollutants that impair 

24 visibility in many of these areas. 

25 For these reasons, EPA concluded that a national regional haze program, combined with 

26 a nationally applicable level of protection achieved through secondary PM2.5 standards set 

27 identical to the primary PM2.5 standards, would be an appropriate approach for addressing 

28 regional variations in the adverse effects of PM2.5 on visibility than establishing national 

29 secondary standards for PM at lower levels than the primary PM2.5 standards. The EPA further 

30 recognized that people living in certain urban areas may place a high value on unique scenic 

31 resources in or near these areas, and as a result might experience visibility problems attributable 

32 to sources that would not necessarily be addressed by the combined effects of a regional haze 

 1

 In 1977, Congress established as a national goal ‘‘the prevention of any future, and the remedying of any existing, 

impairment of visibility in mandatory Class I Federal areas which impairment results from manmade air pollution’’, 

section 169A(a)(1) of the Act. The EPA is required by section 169A(a)(4) of the Act to promulgate regulations to 

ensure that ‘‘reasonable progress’’ is achieved toward meeting the national goal. 
Draft-Do Not Quote or Cite 4-4 June 2010

1 program and PM2.5 secondary standards. The EPA concluded that in such cases, state or local 

2 regulatory approaches, such as past action in Colorado to establish a local visibility standard for 

3 the City of Denver, would be more appropriate and effective in addressing these special 

4 situations because of the localized and unique characteristics of the problems involved. 

5 Visibility in an urban area located near a mandatory Class I Federal area could also be improved 

6 through state implementation of the current visibility regulations, by which emission limitations 

7 can be imposed on a source or group of sources found to be contributing to ‘‘reasonably 

8 attributable’’ impairment in the mandatory Class I Federal area. 

9 Based on these considerations, EPA set secondary PM2.5 standards identical to the 

10 primary PM2.5 standards, in conjunction with a regional haze program under sections 169A and 

11 169B of the Act, as the most appropriate and effective means of addressing the welfare effects 

12 associated with visibility impairment. Together, the two programs and associated control 

13 strategies were expected to provide appropriate protection against the effects of PM on visibility 

14 and enable all regions of the country to make reasonable progress toward the national visibility 

15 goal. 

16 4.1.1.2 Review Completed in 2006 

17 In 2006, EPA revised the secondary PM2.5 standards to address visibility impairment by 

18 making them identical to the revised primary standards. The EPA’s decision regarding the need 

19 to revise the secondary PM2.5 standards reflected a number of new developments and sources of 

20 information that had occurred and/or become available following the 1997 review. First, EPA 

21 promulgated a regional haze program in 1999 (65 FR 35713) which required states to establish 

22 goals for improving visibility in Class I areas and to adopt control strategies to achieve these 

23 goals. Second, extensive new information from visibility and fine particle monitoring networks 

24 had become available, allowing for updated characterizations of visibility trends and PM levels 

25 in urban areas, as well as Class I areas. These new data allowed EPA to better characterize 

26 visibility impairment in urban areas and the relationship between visibility and PM2.5 

27 concentrations. Finally, additional studies in the U.S. and abroad provided the basis for the 

28 establishment of standards and programs to address specific visibility concerns in a number of 

29 local areas. These studies (e.g., in Denver, Phoenix, British Columbia) utilized photographic 

30 representations of visibility impairment and produced reasonably consistent results in terms of 

31 the visual ranges found to be generally acceptable by study participants. EPA considered the 

32 information generated by these studies useful in characterizing the nature of particle-induced 

33 haze and for informing judgments about the acceptability of various levels of visual air quality in 

34 urban areas across the U.S. Based largely on this information, the Administrator concluded that 

35 it was appropriate to revise the secondary PM standards to provide increased protection from 
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1 visibility impairment principally in urban areas, in conjunction with the regional haze program 

2 for protection of visual air quality in Class I areas. 

3 In so doing, the Administrator recognized that PM-related visibility impairment is 

4 principally related to fine particle levels, such that it was appropriate to focus the review on 

5 whether the current secondary PM2.5 standards should be revised. The Administrator also 

6 recognized that perception of visibility impairment is most directly related to instantaneous 

7 levels of visual air quality, such that in considering whether the current suite of secondary 

8 standards would provide the appropriate degree of protection, he concluded that it was 

9 appropriate to focus on just the 24-hour secondary PM2.5 standard to provide requisite protection. 

10 The EPA then considered whether PM2.5 remained the appropriate indicator for a 

11 secondary standard to protect visibility, primarily in urban areas. EPA noted that PM-related 

12 visibility impairment is principally related to fine particle levels. Hygroscopic components of 

13 fine particles, in particular sulfates and nitrates, contribute disproportionately to visibility 

14 impairment under high humidity conditions. Particles in the coarse mode generally contribute 

15 only marginally to visibility impairment in urban areas. With the substantial addition to the air 

16 quality and visibility data made possible by the national urban PM2.5 monitoring networks, an 

17 analysis conducted for the 2006 review found that, in urban areas, visibility levels showed far 

18 less difference between eastern and western regions on a 24-hour or shorter time basis than 

19 implied by the largely non-urban data available in the 1997 review. In analyzing how well PM2.5 

20 concentrations correlated with visibility in urban locations across the U.S., the 2005 Staff Paper 

21 (US EPA, 2005) concluded that clear correlations existed between 24-hour average PM2.5 

22 concentrations and reconstructed light extinction, which is directly related to visual range. These 

23 correlations were similar in the eastern and western regions of the U.S. These correlations were 

24 less influenced by relative humidity and more consistent across regions when PM2.5

25 concentrations are averaged over shorter, daylight time periods (e.g., 4 to 8 hours) when relative 

26 humidity was generally lower and less variable. The 2005 Staff Paper noted that a standard set at 

27 any specific PM2.5 concentration would necessarily result in visual ranges that vary somewhat in 

28 urban areas across the country, reflecting the variability in the correlations between PM2.5 

29 concentrations and light extinction. The 2005 Staff Paper concluded that it was appropriate to 

30 use PM2.5 as an indicator for standards to address visibility impairment in urban areas, especially 

31 when the indicator is defined for a relatively short period (e.g., 4 to 8 hours) of daylight hours. 

32 Based on their review of the Staff Paper, most CASAC Panel members also endorsed such a 

33 PM2.5 indicator for a secondary standard to address visibility impairment (Henderson, 2005a). 

34 Based on the above considerations, the Administrator concluded that PM2.5 should be retained as 

35 the indicator for fine particles as part of a secondary standard to address visibility protection, in 

36 conjunction with averaging times from 4 to 24 hours. 
Draft-Do Not Quote or Cite 4-6 June 2010

1 In considering what level of protection against PM-related visibility impairment would be 

2 appropriate, the Administrator took into account the results of the public perception and attitude 

3 surveys in the U.S. and Canada, state and local visibility standards within the U.S., and visual 

4 inspection of photographic representations of several urban areas across the U.S. In the 

5 Administrator’s judgment, these sources provided useful but still quite limited information on the 

6 range of levels appropriate for consideration in setting a national visibility standard primarily for 

7 urban areas, given the generally subjective nature of the public welfare effect involved. Based 

8 on photographic representations of varying levels of visual air quality, public perception studies, 

and local and state visibility standards, the 2005 Staff Paper concluded that 30 to 20 μg/m3 9 PM2.5 

10 represented a reasonable range for a national visibility standard primarily for urban areas, based 

11 on a sub-daily averaging time. The upper end of this range was below the levels at which 

12 illustrative scenic views are significantly obscured, and the lower end was around the level at 

13 which visual air quality generally appeared to be good based on observation of the illustrative 

14 views. This concentration range generally corresponded to median visual ranges in urban areas 

15 within regions across the U.S. of approximately 25 to 35 km, a range which was bounded above 

16 by the visual range targets selected in specific areas where state or local agencies placed 

17 particular emphasis on protecting visual air quality. In considering a reasonable range of forms 

18 for a PM2.5 standard within this range of levels, the 2005 Staff Paper concluded that a 

19 concentration-based percentile form was appropriate, and that the upper end of the range of 

concentration percentiles should be consistent with the 98th 20 percentile used for the primary 

standard and that the lower end of the range should be the 92nd 21 percentile, which represented the 

22 mean of the distribution of the 20 percent most impaired days, as targeted in the regional haze 

23 program. While recognizing that it was difficult to select any specific level and form based on 

24 then currently available information (Henderson, 2005a), the CASAC Panel was generally in 

25 agreement with the ranges of levels and forms presented in the 2005 Staff Paper. 

26 The Administrator also considered the level of protection that would be afforded by the 

27 proposed suite of primary PM2.5 standards (71 FR 2681), on the basis that although significantly 

28 more information was available than in the 1997 review concerning the relationship between fine 

29 PM levels and visibility across the country, there was still little available information for use in 

30 making the relatively subjective value judgment needed in selecting the appropriate degree of 

31 protection to be afforded by such a standard. In so doing, the Administrator compared the extent 

32 to which the proposed suite of primary standards would require areas across the country to 

33 improve visual air quality with the extent of increased protection likely to be afforded by a 

34 standard based on a sub-daily averaging time. Based on such an analysis, the Administrator 

35 observed that the predicted percent of counties with monitors not likely to meet the proposed 

36 suite of primary PM2.5 standards was actually somewhat greater than the predicted percent of 
Draft-Do Not Quote or Cite 4-7 June 2010

1 counties with monitors not likely to meet a sub-daily secondary standard with an averaging time 

2 of 4 daylight hours, a level toward the upper end of the range recommended in the 2005 Staff 

3 Paper, and a form within the recommended range. Based on this comparison, the Administrator 

4 concluded that revising the secondary 24-hour PM2.5 standard to be identical to the proposed 

5 revised primary PM2.5 standard (and retaining the current annual secondary PM2.5 standard) was a 

6 reasonable policy approach to addressing visibility protection primarily in urban areas. In 

7 proposing this approach, the Administrator also solicited comment on a sub-daily (4- to 8-hour 

8 averaging time) secondary PM2.5 standard (71 FR 2675-2781). 

9 In commenting on EPA’s proposal, the CASAC requested that a sub-daily standard to 

10 protect visibility be favorably reconsidered (Henderson, 2006). The CASAC noted three 

11 cautions regarding the Agency’s proposed reliance on a secondary PM2.5 standard identical to the 

12 proposed 24-hour primary PM2.5 standard: (1) PM2.5 mass measurement is a better indicator of 

13 visibility impairment during daylight hours, when relative humidity is generally low; the sub14 daily standard more clearly matches the nature of visibility impairment, whose adverse effects 

15 are most evident during the daylight hours; using a 24- hour standard as a proxy introduces error 

16 and uncertainty in protecting visibility; and sub-daily standards are used for other NAAQS and 

17 should be the focus for visibility; (2) CASAC and its monitoring subcommittees have repeatedly 

18 commended EPA’s initiatives promoting the introduction of continuous and near-continuous PM 

19 monitoring, and expanded deployment of continuous PM2.5 monitors is consistent with setting a 

20 sub-daily standard to protect visibility; (3) The analysis showing a similarity between 

21 percentages of counties not likely to meet what they considered to be a lenient 4- to 8-hour 

22 secondary standard and a secondary standard identical to the proposed 24-hour primary standard 

23 was a numerical coincidence that was not indicative of any fundamental relationship between 

24 visibility and health. The CASAC Panel further stated that ”visual air quality is substantially 

impaired at PM2.5 concentrations of 35 μg/m3 25 ” and that ‘‘it is not reasonable to have the visibility 

26 standard tied to the health standard, which may change in ways that make it even less appropriate 

27 for visibility concerns.” 

28 In reaching a final decision, the Administrator focused on the relative protection provided 

29 by the proposed primary standards and the limitations in the information available concerning 

30 studies of public perception and attitudes regarding the acceptability of various degrees of 

31 visibility impairment in urban areas, as well as on the subjective nature of the judgment required. 

32 In so doing, the Administrator concluded that caution was warranted in establishing a distinct 

33 secondary standard for visibility impairment and that the available information did not warrant 

34 adopting a secondary standard that would provide either more or less protection against visibility 

35 impairment in urban areas than would be provided by secondary standards set equal to the 

36 proposed primary PM2.5 standards. 
Draft-Do Not Quote or Cite 4-8 June 2010

1 4.1.2 Remand of Secondary PM2.5 Standards 

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

3 NAAQS in 2006. These petitions challenged several aspects of the final rule including EPA’s 

4 decision to set the secondary NAAQS for fine PM, which protect the public welfare from 

5 adverse visibility effects, at the same level as the primary NAAQS, which protect public health. 

6 On judicial review, the D.C. Circuit remanded to EPA for reconsideration the secondary 

7 NAAQS for fine PM because the Agency’s decision was unreasonable and contrary to the 

8 requirements of section 109(b)(2). The petitioners argued that EPA’s decision lacked a reasoned 

9 basis. First, they asserted that EPA never determined what level of visibility was “requisite to 

10 protect the public welfare”. They argued that EPA unreasonably rejected the target level of 

11 protection provided by its staff, while failing to provide a target level of its own. The court 

12 stated “the EPA’s failure to identify such a level when deciding where to set the level of air 

13 quality required by the revised secondary fine PM NAAQS is contrary to the statute and 

14 therefore unlawful. Furthermore, the failure to set any target level of visibility protection 

15 deprived the EPA’s decision-making of a reasoned basis.” American Farm Bureau Federation v. 

16 EPA, 559 F. 3d 512 (D.C. Cir. 2009). 

17 Second, the petitioners challenged EPA’s method of comparing the protection expected 

18 from potential standards. They contended that the EPA relied on a meaningless numerical 

19 comparison, ignored the effect of humidity on the usefulness of a standard using a daily 

20 averaging time, and unreasonably concluded that the primary standards would achieve a level of 

21 visibility roughly equivalent to the level the EPA staff and CASAC deemed “requisite to protect 

22 the public welfare.” Again, the court found that EPA’s equivalency analysis failed on its own 

23 terms. The same table showing the alternative secondary standard used for comparison to the 

24 alternative primary to show equivalency also included six other standards within the 

25 recommended CASAC range that would be more “protective” under EPA’s definition than the 

26 primary standards. Two-thirds of the potential standards within the CASAC’s recommended 

27 range would be substantially more protective than the primary standards. The court found that 

28 EPA failed to explain why it looked only at one of the few potential standards that would be less 

29 protective and only slightly so than the primary standards. More fundamentally, however, the 

30 court found that EPA’s equivalency analysis demonstrated nothing about the relative protection 

31 offered by the different standards, and that the tables offered no valid information about the 

32 relative visibility protection provided by the standards. 

33 Finally, the Staff Paper made clear that a visibility standard using a daily averaging time 

34 would be confounded by regional differences in humidity. The EPA acknowledged this 

35 problem, yet did not address this issue in concluding that the primary standards would be 

36 sufficiently protective of visibility. Therefore, the court granted the petition for review and 
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1 remanded for reconsideration the secondary fine PM NAAQS. American Farm Bureau 

2 Federation, 559 F. 3d at 520-24. 

3 4.1.3 Current Approach 

4 The staff’s approach in this review broadens the general approaches used in the last two 

5 PM NAAQS reviews by utilizing, to the extent available, enhanced tools, methods, and data to 

6 more comprehensively characterize visibility impacts. As such, staff is taking into account both 

7 evidence-based and impact-based considerations to inform staff conclusions related to the 

8 adequacy of the current secondary PM2.5 standards and alternative standards that are appropriate 

9 for consideration in this review. In so doing, we are seeking to provide as broad an array of 

10 options as is supportable by the available information, recognizing that the selection of a specific 

11 approach to reaching final decisions on the secondary PM2.5 standards will reflect the judgments 

12 of the Administrator. Figure 4-1 below provides a diagram of our approach. 

13 For the purposes of this second draft Policy Assessment (PA), staff has drawn from the 

14 qualitative evaluation of all studies discussed in the Integrated Science Assessment for 

15 Particulate Matter (Final Report) (ISA, USEPA, 2009a). The discussions presented in this 

16 chapter consider the extensive new air quality and source apportionment information available 

17 from the regional planning organizations, long-standing evidence of PM effects on visibility, and 

18 public preference studies from four urban areas, as discussed in chapter 9 of the ISA, as well as 

19 the integration of evidence across disciplines presented in chapter 2 of the ISA. In addition, 

20 limited information that has become available regarding the characterization of public 

21 preferences in urban areas has provided some new perspectives on the usefulness of this 

22 information in informing the selection of target levels of urban visibility protection. On these 

23 bases, we are again focusing our assessments in this review on visibility conditions in urban 

24 areas. 

25 Our conclusions reflect our understanding of both evidence-based and impact-based 

26 considerations to inform two overarching questions related to: (1) the adequacy of the current 

27 suite of PM2.5 standards and (2) what potential alternative standards, if any, should be considered 

28 in this review to provide appropriate protection from PM-related visibility impairment. In 

29 addressing these broad questions, we have organized the discussions below around a series of 

30 more specific questions reflecting different aspects of each overarching question. When 

31 evaluating the visibility protection afforded by the current or any alternative standards 

32 considered, we have taken into account the four basic elements of the NAAQS (e.g., indicator, 

33 averaging time, level, and form). 
Draft – Do Not Quote or Cite 4-10 June 2010

Figure 4-1. Overview of Approach for Review of Secondary PM2.5 Standards 
Draft-Do Not Quote or Cite 4-11 June 2010

1 4.2 ADEQUACY OF CURRENT STANDARDS 

2 In considering the adequacy of the current suite of PM2.5 standards, staff addresses the 

3 following overarching question: 

4 Does the currently available scientific evidence and visibility impact information, as 

5 reflected in the ISA and UFVA, support or call into question the adequacy of the visibility 

6 protection afforded by the current suite of fine particle standards? 

7 To inform the answer to this overarching question, we have posed a series of more 

8 specific questions to aid in considering the currently available scientific evidence and the results 

9 of recent quantitative visibility impact analyses in a policy-relevant context, as discussed below. 

10 In considering the scientific and technical information, we reflect upon both the information 

11 available in the last review and information that is newly available since the last review as 

12 assessed and presented in the final ISA and final UFVA (US EPA, 2009a; US EPA, 2010b). 

13 4.2.1 Evidence-based Considerations 

14 In reviewing the adequacy of the current suite of PM2.5 standards, we have taken into 

15 account evidence-based considerations, primarily as presented in the ISA, by considering causal 

16 inference, impacts on susceptible populations, and the kind of visibility effects that have been 

17 observed in urban areas that would likely meet the current standards. 

18  To what extent does the newly available scientific evidence strengthen or call into 

19 question evidence of associations between ambient fine particle exposures and visibility 

20 effects? 

21 New research conducted by regional planning organizations in support of the Regional 

22 Haze Rule, as discussed in chapter 9 of the ISA, continues to support and refine our 

23 understanding of the nature of the PM visibility effect and the source contributions to that effect 

24 in rural and remote locations. Additional byproducts of this research include new insights 

25 regarding the regional source contributions to urban visibility and better characterization of the 

26 urban excess that occurs in many cities above regional background. Ongoing urban PM2.5

27 speciated and mass monitoring has produced new information that has allowed for updated 

28 characterization of visibility trends and current levels in urban areas. Information from both of 

29 these sources of PM data, while useful, has not however changed the fundamental and long 

30 understood science characterizing the contribution of PM, especially fine particles, to visibility 

31 impairment from the last review. This science, briefly summarized below, provides the basis for 

32 the ISA designation of the relationship between PM and visibility impairment as causal. 

33 Visibility impairment is caused by the scattering and absorption of light by suspended 

34 particles and gases in the atmosphere. The combined effect of light scattering and absorption by 
Draft-Do Not Quote or Cite 4-12 June 2010

1 both particles and gases is characterized as light extinction, (i.e. the fraction of light that is 

2 scattered or absorbed per unit of distance in the atmosphere). Light extinction is measured in 

3 units of 1/distance, which is often expressed in the technical literature as 1/(million meters) or 

inverse megameters (abbreviated Mm-1 4 ). When PM is present in the air, its contribution to light 

5 extinction typically greatly exceeds that of gases. 

6 The amount of light extinction contributed by PM depends on the particle size 

7 distribution and composition, as well as its concentration. If details of the ambient particle size 

8 distribution and composition (including the mixing of components) are known, Mie theory can 

9 be used to accurately calculate PM light extinction (ISA chapter 9). However, routine 

10 monitoring rarely includes measurements of particle size and composition information with 

11 sufficient detail for such calculations. A much simpler algorithm can be used to estimate light 

12 extinction using routinely monitored fine particle (PM2.5) speciation and coarse particle mass 

13 (PM10 – 2.5) data, plus relative humidity information needed to estimate the contribution by liquid 

14 water in solution with hygroscopic PM components (ISA section 9.2.2.2 and UFVA chapter 3). 

15 The concentration of each of the major aerosol components is multiplied by a dry 

16 extinction efficiency value and for the hygroscopic components (e.g., ammoniated sulfate and 

17 ammonium nitrate) an additional multiplicative term to account for the water growth to estimate 

18 these components’ contribution to light extinction. Both the dry extinction efficiency and water 

19 growth terms are developed by some combination of empirical assessment and theoretical 

20 calculation using typical particle size distributions associated with each of the major aerosol 

21 components, and they are evaluated by comparing the algorithm estimates of light extinction 

22 with coincident optical measurements. Summing the contribution of each component gives the 

23 estimate of total light extinction. The most commonly used algorithm is referred to as the 

24 IMPROVE algorithm because it was developed specifically to use the aerosol monitoring data 

25 generated at network sites and with equipment specifically designed to support the Interagency 

26 Monitoring of Protected Visual Environments (IMPROVE) program and was evaluated using 

27 IMPROVE optical measurements at the subset of sites that make those measurements (Malm et 

28 al., 1994). The formula for the traditional IMPROVE algorithm is shown below. 

29 

30 bext ≈ 3 x f(RH) x [Sulfate] 

31 + 3 x f(RH) x [Nitrate] 

32 + 4 x [Organic Mass] 

33 + 10 x [Elemental Carbon] 

34 + 1 x [Fine Soil] 

35 + 0.6 x [Coarse Mass] 

36 + 10 

37 
Draft-Do Not Quote or Cite 4-13 June 2010

Light extinction (bext) is in units of Mm-1 1 , the mass concentrations of the components 

indicated in brackets are in μg/m3 2 , and f(RH) is the unitless water growth term that depends on 

3 relative humidity. The dry extinction efficiency for particulate organic mass is larger than those 

4 for particulate sulfate and nitrate principally because the density of the dry inorganic compounds 

5 is higher than that assumed for the PM organic mass components. Since IMPROVE does not 

6 include ammonium ion monitoring, the assumption is made that all sulfate is fully neutralized 

7 ammonium sulfate and all nitrate is assumed to be ammonium nitrate. Though often reasonable, 

8 neither assumption is always true (see Section 9.2.3.1). In the eastern U.S. during the summer 

9 there is insufficient ammonia in the atmosphere to neutralize the sulfate fully. Fine particle 

10 nitrates can include sodium or calcium nitrate, which are the fine particle fraction of generally 

11 much coarser particles due to nitric acid interactions with sea salt at near-coastal areas (sodium 

12 nitrate) or nitric acid interactions with calcium carbonate in crustal aerosol (calcium nitrate). 

13 Despite the simplicity of the algorithm, it performs reasonably well and permits the contributions 

14 to light extinction from each of the major components (including the water associated with the 

15 sulfate and nitrate compounds) to be separately approximated. 

16 The f(RH) terms inflate the particulate sulfate and nitrate light scattering for high relative 

17 humidity conditions. For relative humidity below 40% the f(RH) value is 1, but it increases to 2 

18 at ~66%, 3 at ~83%, 4 at ~90%, 5 at ~93% and 6 at ~95% relative humidity. The result is that 

19 both particulate sulfate and nitrate are more efficient per unit mass than any other aerosol 

20 component for relative humidity above ~85% where its total light extinction efficiency exceeds 

the 10 m2 21 /g associated with elemental carbon (EC). Based on this algorithm, particulate sulfate 

22 and nitrate are estimated to have comparable light extinction efficiencies (i.e., the same dry 

23 extinction efficiency and f(RH) water growth terms), so on a per unit mass concentration basis at 

24 any specific relative humidity they are treated as equally effective contributors to visibility 

25 effects. 

26 Inspection of the PM component-specific terms in the simple algorithm shows that most 

27 of the PM2.5 components contribute 5 times or more light extinction than a similar concentration 

28 of PM10 – 2.5. We also know that particles with hygroscopic components (e.g. particulate sulfate 

29 and nitrate) contribute more light extinction at higher relative humidity than at lower relative 

30 humidity because they change size in the atmosphere in response to ambient relative humidity 

31 conditions. PM containing elemental or black carbon absorbs light as well as scattering it, 

32 making it the component with the greatest light extinction contributions per unit of mass 

33 concentration, except for the hygroscopic components under high relative humidity conditions. 

34 As a result of better characterization of PM in rural and remote locations, refinements in 

35 the original IMPROVE algorithm have been made to better account for the aging of the organic 

36 aerosols that occurs during transport from urban to more distant areas, and to add a component 
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1 for sea salt. However, the focus on urban areas in this review made these refinements 

2 unnecessary due to the fact that urban areas contain a higher proportion of newly emitted 

3 aerosols, and because particulate sea salt, while expected to contribute to visibility impairment in 

4 coastal areas, is natural in origin and not controllable through regulation. Therefore, the 

5 assessments done in support of this review have relied on the original IMPROVE algorithm, 

6 As mentioned above, particles are not the only contributor to ambient visibility 

7 conditions. Light scattering by gases also occurs in ambient air. Under pristine atmospheric 

8 conditions, naturally occurring gases such as N2 and O2 cause what is known as Rayleigh 

9 scattering. Rayleigh scattering, which depends on the density of air as a function primarily of the 

10 elevation above sea level, can be treated as a site-dependent constant. Rayleigh contribution to 

11 light extinction is only significant under pristine conditions. The only other commonly occurring 

12 atmospheric gas to appreciably absorb light in the visible spectrum is NO2. NO2 forms in the 

13 atmosphere from NO emissions associated with combustion processes. These combustion 

14 processes also emit PM at levels that generally contribute much higher light extinction than the 

15 NO2 (i.e. NO2 absorption is generally less than approximately 5% of the light extinction, except 

16 where emission controls remove most of the PM prior to releasing the remaining gases to the 

17 atmosphere). The remainder of this document focuses on the contribution of PM, which is 

18 typically much greater than that of gases, to ambient light extinction, unless otherwise specified. 

19  To what extent does the available evidence inform our understanding of the temporal 

20 nature of the PM visibility effect, including relevant exposure periods, associated 

21 atmospheric conditions, and diurnal patterns of exposure? 

22 Diurnal Periods of Interest 

23 Typically, we think of visibility associated with daytime periods. We recognize, 

24 however, that PM light extinction behaves the same at night as during the day, enhancing the 

25 scattering of anthropogenic light, contributing to the “skyglow” within and over populated areas, 

26 adding to the total sky brightness, and contributing to the reduction in contrast of stars against 

27 the background. These effects produce the visual result of a reduction of the number of visible 

28 stars and the disappearance of diffuse or subtle phenomena such as the Milky Way. The 

29 extinction of starlight is a secondary and minor effect also caused by increased PM scattering 

30 and absorption. 

31 However, there are significant and important differences between daytime and nighttime 

32 visual environments that potentially make the nighttime period inappropriate to address at this 

33 time. First, daytime visibility has dominated the attention of those who have studied the 

34 visibility effects of air pollution. As a result, little research has been conducted on nighttime 

35 visibility and the state of the science is not yet comparable to that associated with daytime 
Draft-Do Not Quote or Cite 4-15 June 2010

1 visibility impairment. Second, in addition to air pollution, nighttime visibility is affected by the 

2 addition of light into the sight path from numerous sources, including anthropogenic light sources 

3 such as artificial outdoor lighting, which varies dramatically across space, and natural sources 

4 including the moon, planets, and stars. Light sources and ambient conditions are typically five to 

5 seven orders of magnitude dimmer at night than in sunlight. Moonlight, like sunlight, introduces 

6 light throughout an observer’s sight path at a constant angle. On the other hand, dim starlight 

7 emanates from all over the celestial hemisphere while artificial lights are concentrated in cities 

8 and illuminate the atmosphere from below. These different light sources will yield variable 

9 changes in visibility as compared to what has been established for the daytime scenario, in which 

10 a single source, the sun, is by far the brightest source of light. Third, the human psychophysical 

11 response (e.g., how the human eye sees and processes visual stimuli) at night is expected to 

12 differ (ISA, section 9.2.2).

13 Given the above, we do not believe that the science is available at this time to support 

14 adequate characterization of nighttime PM light extinction effects. In addition, we are not aware 

15 of preference or valuation studies providing information on public preferences for nighttime 

16 visual air quality (VAQ). Thus, we limit our consideration of PM visibility impacts to daylight 

17 hours only. 

18 Exposure Durations of Interest

19 Very little is known about the role exposure duration plays in determining the 

20 acceptability or unacceptability of a given level of VAQ on the public welfare. We do know 

21 from preference and/or valuation studies that atmospheric visibility conditions can be quickly 

22 assessed and preferences determined. These studies show that a momentary glance at an image 

23 of a scene (i.e. less than a minute) is enough for study participants to consistently judge the 

24 acceptability or unacceptability of the viewed visual air quality conditions. Outside these 

25 controlled settings, we are unaware of any studies that characterize the extent to which different 

26 frequencies and durations of exposure to visibility conditions contribute to the degree of public 

27 welfare impact that occurs. 

28 Recognizing the need for further research in this area, it is useful to consider a variety of 

29 circumstances that are commonly expected to occur and to evaluate the potential impact of 

30 visibility impairment on the public welfare based on the information we do have. In some 

31 circumstances, such as infrequent visits to scenic vistas in natural or urban environments, people 

32 are motivated specifically to take the opportunity to view a valued scene and are likely to do so 

33 for many minutes to hours to appreciate various aspects of the vista they choose to view. In such 

34 circumstances, the viewer may consciously evaluate how the VAQ at that time either enhances 

35 or diminishes the experience or view. However, the public also has many more opportunities to 
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1 notice visibility conditions on a daily basis in settings associated with performing daily routines 

2 (e.g. during commutes, while walking the dog, or when taking out the recyclables). These 

3 scenes, whether iconic or generic, may not be consciously viewed for their scenic value, but their 

4 VAQ may still affect a person’s sense of wellbeing. Research has demonstrated that people are 

5 emotionally affected by low VAQ, that perception of pollution is correlated with stress, 

6 annoyance and symptoms of depression, and that VAQ is deeply intertwined with a “sense of 

7 place,” affecting peoples sense of the desirability of a neighborhood (ISA section 9.2.4). Though 

8 we do not know the extent to which these emotional effects are linked to different periods of 

9 exposure to poor VAQ, providing additional protection against short-term exposures to levels of 

10 VAQ considered unacceptable in the context of the preference studies would be expected to 

11 provide some degree of protection against the risk of loss in the public’s “sense of wellbeing”. 

12 Some people have mostly intermittent opportunities on a daily basis (e.g. during morning 

13 and/or afternoon commutes) to experience ambient visibility conditions as they spend much of 

14 their time indoors without access to windows. For such people a view of poor VAQ during their 

15 morning commute may provide their perception of the day’s visibility conditions until the next 

16 time they venture outside during daylight hours later or perhaps the next day. Other people have 

17 exposure to visibility conditions throughout the day, so that a day with multiple hours of 

18 visibility impairment would likely be judged as having a greater impact on their wellbeing than a 

19 day with just one such hour. 

20 We have no information or studies on the fraction of the public that has only one or a few 

21 opportunities to experience visibility during the day, or information or studies on the duration of 

22 the effect on wellbeing from exposure to different durations of poor VAQ conditions. However, 

23 it is logical to conclude that people with limited opportunities to experience visibility conditions 

24 on a daily basis would receive the entire impact of the day’s VAQ based on the visibility 

25 conditions that occur during the short time period when they can see it. Since this group could 

26 be affected on the basis of observing VAQ conditions for periods as short as one hour or less, 

27 and because in some areas the worst PM VAQ of the day occurs during the morning (see UFVA, 

28 figure 3-12) when many people are commuting, we believe it is appropriate to consider 

29 characterizing PM visibility conditions in terms of the worst or maximum hourly value during 

30 daylight hours for each day for purposes of evaluating the adequacy of the current suite of 

31 secondary standards. 

32 For another group of observers, those who have access to visibility conditions often or 

33 continuously throughout the day, the entire impact of the day’s visibility conditions may be 

34 based on the varying visibility conditions they observe throughout the day. For this group, it 

35 might be more appropriate to evaluate the adequacy of the current suite of secondary standards in 

36 terms of all daylight hours in the day. 
Draft-Do Not Quote or Cite 4-17 June 2010

1 Temporal Variations of Visibility Impacts

2 While visibility conditions can change quickly (i.e., less than a minute), atmospheric 

3 sight path averaged light extinction which is pertinent to visibility impacts generally changes 

4 more slowly (i.e. tens of minutes generally). Sub-hourly variations in light extinction 

5 determined at any point in the atmosphere are likely the result of small-scale spatial pollution 

6 features (i.e. plumes) being transported by the wind across that point. At typical wind speeds 

7 found in U.S. cities, an hour corresponds to a few tens of kilometers of air flowing past a point, 

8 which is similar to visibility sight path lengths of interest in urban areas. 

9 PM concentrations and light extinction in urban environments vary from hour to hour 

10 throughout the day due to a combination of diurnal meteorological conditions and systematic 

11 changes in emissions activity (e.g. rush hour traffic). Generally low mixing heights at night and 

12 during the early morning hour tend to trap locally produced emissions, which are diluted as the 

13 mixing height increases due to heating during the day. Low temperatures and high relative 

14 humidity at night are conducive to the presence of ammonium nitrate particles and water growth 

15 by hygroscopic particles compared with the generally higher temperatures and lower relative 

16 humidity later in the day. These combine to make early morning the most likely time for peak 

17 urban visibility impacts. Superimposed on such systematic time of day variations are the effects 

18 of synoptic meteorology (i.e., those associated with changing weather) and regional-scale air 

19 quality that can generate peak visibility impacts any time of day. The net effects of the 

20 systematic urban and larger scale variations are that peak daytime PM light extinction can occur 

21 any time of day though more often in early morning hours (UFVA section 3.4.2 and 3.4.3 and 

22 shown in Figures 3-9, 3-10 and 3-12). Use of multi-hour averaging of PM light extinction would 

23 suppress peak hourly conditions that are expected to represent peak visibility impacts. 

24 Meteorological Causes of Visibility Impacts 

25 Visibility is also reduced directly by the presence of precipitation and fog regardless of 

26 the presence or absence of PM. A secondary PM NAAQS is not meant to protect against such 

27 sources of visibility impairment that occur irrespective of PM levels. Therefore, one 

28 consideration in the development of alternative standard forms (discussed below in section 4.3) 

29 was the inclusion of a relative humidity screen in an effort to avoid misinterpretation of the direct 

30 effect of meteorological conditions on visibility and those caused by PM air quality. 

31  Based on currently available information, what range of levels of visibility impairment 

32 is reasonable to consider in reaching judgments about the adequacy of the current 

33 NAAQS? 

34 In order to identify levels of visibility impairment appropriate for consideration in setting 

35 secondary PM NAAQS to protect the public welfare, we comprehensively examined information 
Draft-Do Not Quote or Cite 4-18 June 2010

1 that was available in this review regarding people’s stated preferences for acceptable and 

2 unacceptable visual air quality. 

3 Light extinction is an atmospheric property that by itself does not directly translate into a 

4 public welfare effect. Instead, light extinction becomes meaningful in the context of the impact 

5 of visibility on the human observer. This has been studied in terms of the acceptability or 

6 unacceptability expressed for it by a human observer. The perception of the visibility impact of 

7 a given level of light extinction occurs in conjunction with the associated characteristics and 

8 lighting conditions of the viewed scene. Thus, a given level of light extinction may be perceived 

9 differently by an observer looking at a different scene or the same scene with different lighting 

10 characteristics. Likewise, different observers looking at the same scene with the same lighting 

11 may have different preferences regarding the associated VAQ. When scene and lighting 

12 characteristics are held constant, the perceived appearance of a scene (i.e., how well the scenic 

13 features can be seen and the amount of visible haze) depends only on changes in light extinction. 

14 This has been demonstrated using the WinHaze model that uses image processing technology to 

15 apply user-specified changes in light extinction values to the same base photograph with set 

16 scene and lighting characteristics. 

17 Much of what we know about the acceptability of levels of visibility comes from survey 

18 studies in which participants were asked questions about their preference or the value they place 

19 on various visibility levels as displayed to them in scenic photographs and/or WinHaze images 

20 with a range of known light extinction levels. Urban visibility preference studies for four urban 

21 areas were reviewed in the UFVA (chapter 2) to assess the light extinction levels judged by the 

22 participant to have acceptable visibility. While the results differed among the four urban areas, 

23 results from a rating exercise showed that within each preference study, survey participants 

24 consistently distinguish between different levels of light extinction and prefer and value visibility 

25 associated with lower light extinction levels among the scenic images they are shown. 

26 The reanalysis of urban preference studies included three completed urban visibility 

27 preference survey studies plus a pair of smaller focus studies designed to explore and further 

28 develop urban visibility survey instruments. The three western studies included one in Denver, 

29 Colorado (Ely et al., 1991), one in the lower Fraser River valley near Vancouver, British 

30 Columbia (BC), Canada (Pryor, 1996), and one in Phoenix, Arizona (BBC Research & 

31 Consulting, 2003). A pilot focus group study was also conducted for Washington, DC (Abt 

32 Associates Inc., 2001). In response to an EPA request for public comment on the Scope and 

33 Methods Plan (74 FR 11580, March 18, 2009), we received comments (Smith, 2009) about the 

34 results of a new Washington, DC focus group study that had been conducted using methods and 

35 approaches similar to the method and approach employed in the EPA pilot study (Smith and 

36 Howell, 2009). When taken together, these studies from the four different urban areas included a 
Draft-Do Not Quote or Cite 4-19 June 2010

1 total of 852 individuals, with each individual responding to a series of questions answered while 

2 viewing a set of images of various urban VAQ conditions. 

3 The approaches used in the four studies are similar and are all derived from the method 

4 first developed for the Denver urban visibility study. In particular, the studies all used a similar 

5 group interview type of survey to investigate the level of visibility impairment that participants 

6 described as “acceptable.” While each study asked the basic question, “What level of visibility 

7 degradation is acceptable?”, the term “acceptable” was not defined, so that each person’s 

8 response was based on his/her own values and preferences for VAQ. Given the similarities in 

9 the approaches used, we concluded that it is reasonable to compare the results to identify overall 

10 trends in the study findings and that this comparison can usefully inform the selection of a range 

11 of levels for use in further analyses. However, variations in the specific materials and methods 

12 used in each study introduce uncertainties that should also be considered when interpreting the 

13 results of these comparisons. Key differences between the studies include: 1) image presentation 

14 methods (e.g., projected slides of actual photos, projected images generated using WinHaze (a 

15 significant technical advance in the method of presenting VAQ conditions), or use of computer 

16 monitor screen; 2) number of participants in each study; 3) participant representativeness of the 

17 general population of the relevant metropolitan area; and 4) specific wording used to frame the 

18 questions used in the group interview process. 

19 In the UFVA, each study was evaluated separately and figures developed to display the 

20 percentage of participants that rated each photograph as “acceptable”. The horizontal axis was in 

21 terms of light extinction (deciview or dv) and the vertical axis in terms of percent of participants 

22 rating “acceptable”. Ely et al. (1991) introduced a “50% acceptability” criteria analysis of the 

23 Denver preference study results. The 50% acceptability criteria is designed to identify the VAQ 

24 level that best divides the photographs into two groups: those with a VAQ rated as acceptable by 

25 the majority of the participants, and those rated not acceptable by the majority of participants. 

26 We adopted the criteria as a useful index for comparison between studies. The results of each 

27 individual analysis were then combined graphically to allow for visual comparison. Figure 4-2 

28 (Figure 2-16 in UFVA) presents the graphical summary of the results of the studies in the four 

29 cities and draws on results previously presented in Figures 2-3, 2-5, 2-7 and 2-11 of chapter 2 in 

30 the UFVA. Figure 4-2 also contains lines at 20 dv and 30 dv that effectively and pragmatically 

31 identify a range where the 50% acceptance criteria occur across all four of the urban preference 

32 studies. Out of the 114 data points shown in Figure 4-2, only one photograph (or image) with a 

33 VAQ below 20 dv was rated as acceptable by less than 50% of the participants who rated that 
Draft-Do Not Quote or Cite 4-20 June 2010

photograph.2 1 Similarly, only one image with a VAQ above 30 dv was rated acceptable by more 

than 50% of the participants who viewed it.3 2 

 3 

Figure 4-2. Summary of results of urban visibility studies in four cities, showing the 

identified range of the 50% acceptance criteria .4 

 4 

5 As can be seen in the figure, each urban area has a separate and unique response curve 

6 that appears to indicate that it is distinct from the others. These curves are the result of a 

7 logistical regression analysis using a logit model of the greater than 19,000 ratings of haze 

8 images as acceptable or unacceptable. The model results can be used to estimate the VAQ 

9 deciview values where the estimated response functions cross the 50% acceptability level, as 

10 well as any alternative criteria levels. Selected examples of these are shown in Table 4-1 (Table 

11 2-4 in UFVA, Chapter 2). These results show that the logit model data also support the upper 

and lower ends of the range of 50th 12 percentile acceptability values (e.g. near 20 dv for Denver 

13 and near 30 dv for Washington, DC) already identified in Figure 4-2 (see Table 2-4 UFVA). 

 2

 Only 47% of the BC participants rated a 19.2 dv photograph as acceptable. 3

 In the 2001 Washington, D.C. study, a 30.9 dv image was used as a repeated slide. The first time it was shown 

56% of the participants rated it as acceptable, and 11% rated it as acceptable the second time it was shown. The 

same VAQ level was rated as acceptable by 42% of the participants in the 2009 study (Test 1). 4

 Top scale shows light extinction in inverse megameter units; bottom scale in deciviews. Logit analysis estimated 

response functions are shown as the color-coded curved lines for each of the four urban areas 
Draft-Do Not Quote or Cite 4-21 June 2010

1 Table 4-1. Logit model estimated VAQ values corresponding to various percent 

2 acceptability values for the four cities. 

3 

 Denver British 

Columbia 

Phoenix Washington, 

DC 

90% Acceptability criteria 14.21 16.80 24.15 23.03 

75% Acceptability criteria 17.05 19.63 21.80 26.03 

50% Acceptability 

Criteria 

19.90 22.45 24.15 29.03 

25% Acceptability criteria 22.74 25.28 26.51 32.03 

10% Acceptability criteria 25.59 28.10 28.87 35.03 

4 

Based on the composite results and the effective range of 50th 5 percentile acceptability 

6 across the four urban preference studies shown in Figure 4-2 and Table 4-1, benchmark levels 

have been selected in a range from 20 dv to 30 dv (74 Mm-1 to 201 Mm-1 7 ) for the purpose of 

8 provisionally assessing whether visibility conditions would be considered acceptable (i.e., less 

9 than the low end of the range, unacceptable (i.e., greater than the high end of the range, or 

potentially acceptable (within the range). A midpoint of 25 dv (122 Mm-1 10 ) was also selected for 

use in the assessment. This level is also very near to the 50th 11 percentile criteria value from the 

12 Phoenix study (i.e. 24.3 dv), which is by far the best of the four studies in terms of least noisy 

13 preference results and the most representative selection of participants. Based on the currently 

14 available information, we conclude that the use of 25 dv to represent the middle of the 

15 distribution of results seems well supported. 

16 These three benchmark values provide a low, middle, and high set of light extinction 

17 conditions that are used to provisionally define daylight hours with urban haze conditions that 

18 have been judged unacceptable by the participants of these preference studies. As discussed 

19 above, PM light extinction is taken to be light extinction minus the Rayleigh scatter (i.e. light 

scattering by atmospheric gases which is on average about 10 Mm-1 20 ), so the low, middle and 

high levels correspond to PM light extinction levels of about 64 Mm-1, 112 Mm-1 and 191 Mm-1 21 . 

22 In the UFVA, these three levels were called Candidate Protection Levels (CPLs). We continue 

23 to use this term in this document. However, it is important to note that the degree of protection 

24 provided by a secondary NAAQS is not determined solely by the level of the standard but by all 

25 the components (e.g., indicator, form, level, averaging time) being applied together. Therefore, 

26 the reader should keep in mind that the term CPL is meant only to indicate levels within a range 

27 that we feel are appropriate for consideration that could, in conjunction with other aspects of the 

28 standard, provide an appropriate degree of visibility protection. 
Draft-Do Not Quote or Cite 4-22 June 2010

1 In characterizing our degree of confidence in each CPL and across the range, a number of 

2 issues were considered. Looking first at the two studies that define the upper and lower bounds 

3 of the range, we considered whether they represent a true regional distinction in preferences for 

4 urban visibility conditions between western and eastern U.S. There is little information available 

5 to help evaluate this, especially given that we have preference studies in only one eastern urban 

6 area. Smith and Howell (2009) found little difference in preference response to Washington, DC 

7 haze photographs between the study participants from Washington, DC and those from Houston, 

8 TX. This provides some limited evidence that the value judgment of the public in different areas 

9 of the country may not be an important factor in explaining the differences in these study results. 

10 In further considering what factors could explain the observed differences in preferences 

11 across the four urban areas, we noted that the urban scenes used in each study had different 

12 characteristics. For example, each of the western urban visibility preference study scenes 

13 included mountains in the background while the single eastern urban study did not. It is also true 

14 that each of the western scenes included objects at greater distances from the camera location 

15 than in the Washington, DC study. There is no question that objects at a greater distance have a 

16 greater sensitivity to perceived visibility changes as light extinction is changed compared to 

17 otherwise similar scenes with objects at a shorter range. This alone might explain the difference 

18 between the results of the Washington DC, study and those from the Western urban studies. 

19 Also, it seems likely that people value the views of mountains in the background more than 

20 generic distant buildings in the foreground of the western scenes; just as it seems likely that the 

21 Capital Mall and Washington Monument were the likely objects of greatest interest for the 

22 Washington, DC study base photograph. Having scenes with the object of greatest intrinsic 

23 value nearer and hence less sensitive for Washington compared with more distant objects of 

24 greatest intrinsic value in the western urban areas could further explain the difference in 

25 preference results. 

26 Another question that we considered was whether the high CPL value that is based on the 

27 Washington DC preference results is likely to be generally representative of urban areas that do 

28 not have associated mountains or other valued objects visible in the distant background. Such 

29 areas would include the middle of the country and many areas in the eastern US. In order to 

30 examine this issue, an effort would have to be made to see if scenes in such areas could be found 

31 that would be generally comparable to the western scenes (e.g., contain valued scenic elements at 

32 more sensitive distances than that used in the Washington, DC study). This is only one of a 

33 family of issues concerning how exposure to urban scenes of varying sensitivity affects public 

34 perception, for which no information is currently available. Additional urban visibility 

35 preference studies employing images selected of potentially more sensitive scenes could help 

36 evaluate whether a lower value for the high CPL is supportable. Other investigations to 
Draft-Do Not Quote or Cite 4-23 June 2010

1 determine how common such scenes are in various regions of the country would also be 

2 informative. Until such information becomes available, the high end of the CPL range (30 dv) 

3 seems to be an appropriate level to consider. 

4 With respect to the low end of the range, we considered factors that might further refine 

5 our understanding of the robustness of this level. We concluded that additional urban preference 

6 studies, especially with a greater variety in types of scenes, including potentially more sensitive 

7 western urban scenes, could help evaluate whether a lower CPL value than the currently selected 

8 20 dv is supportable. Further, the reason for the noisiness in data points around the curves 

9 apparent in both the Denver and British Columbia results compared to the smoother curve fit of 

10 Phoenix study results could be explored. One possible explanation that we identified is that 

11 these older studies used photographs taken at different times of day and on different days to 

12 capture the range of light extinction levels needed for the preference studies. In contrast, the use 

13 of WinHaze in the Phoenix (and Washington, DC) study reduced variations in scene appearance 

14 that affects preference rating and avoided the uncertainty inherent in using ambient 

15 measurements to represent sight path averaged light extinction values. Reducing these sources 

16 of noisiness and uncertainty in the results of future studies of sensitive urban scenes could 

17 provide more certainty in the selection of a low CPL value. 

18  To what extent does the available information demonstrate or suggest that PM-related 

19 visibility impairment (within the range of CPLs) is occurring at current ambient 

20 conditions or at levels that would meet the current standards? 

21 Current Visibility Levels

22 Chapter 3 of the UFVA characterized current visibility conditions in terms of both PM2.5 

and light extinction levels for the 14 urban areas5 23 selected and compared them to the CPLs 

24 identified above. 

25 As an initial matter, we note that visibility impairment during periods with fog or 

26 precipitation occurs irrespective of the presence or absence of PM. In order to avoid 

27 precipitation and fog confounding estimates of PM visibility impairment, and as advised by 

28 CASAC as part of its comments on the first public review draft of the UFVA, we restricted our 

29 assessment of visibility conditions to daylight hours with relative humidity less than or equal to 

30 90% (UFVA section 3.3.5 and Appendix G). However, not all periods with relative humidity 

31 above 90% have fog or precipitation. Removing those hours from application of a secondary PM 

 5

 Comments on the second review draft of the UFVA from those familiar with the monitoring sites in St. Louis 

indicate that the site selected to provide continuous PM10 monitoring, though less than a mile from the site of the 

PM2.5 data is not representative of the urban area and resulted in unrealistically large PM10-2.5 values. The EPA staff 

considers these comments credible and removed the St. Louis assessment results, leaving 14 of the 15 urban areas 

from the UFVA to be included in the Policy Assessment. 
Draft-Do Not Quote or Cite 4-24 June 2010

1 standard involves a tradeoff between the benefits of avoiding many of the hours with 

2 meteorological causes of visibility impacts and the cost of not counting some hours with high 

3 relative humidity without fog or precipitation, where the growth of hygroscopic PM into large 

4 solution droplets results in enhanced PM visibility impacts. For the 14 urban areas included in 

5 the assessment for which updated meteorological data were obtained, a 90% relative humidity 

6 cutoff criterion is effective in that on average less than 6% of the hours are removed from 

7 consideration, yet those hours have on average ten times the likelihood of rain, six time the 

8 likelihood of snow/sleet, and 34 times the likelihood of fog compared with hours with 90% or 

9 lower relative humidity. 

10 Figure 4-3 (Figure 3-8 in UFVA) presents box-and-whisker plots to illustrate the 

11 distributions of the estimates of daylight 1-hour reconstructed PM10 light extinction levels in 

12 each area during the 2005-2007 time period. The distribution of the daily maximum 1-hour 

13 values is shown. The horizontal dashed lines in the plots represent the low, middle, and high 

CPLs for PM10 light extinction of 64, 112, and 191 Mm-1 14 , corresponding to the benchmark VAQ 

15 values of 20 dv, 25 dv and 30 dv as discussed above. Table 4-2 (Table 3-7 in UFVA) provides 

16 the percentages of days (across all of 2005-2007, unweighted) in which the daily maximum 

17 daylight 1-hour PM10 light extinction level was greater than each of the three CPLs (excluding 

18 hours with relative humidity greater than 90 percent). 

19 From these displays it can be seen that among the 14 urban areas, those in the East and in 

20 California tend to have a higher frequency of visibility conditions above the high CPL compared 

21 with those in the western US. Both Figure 4-3 and Table 4-2 indicate that all 14 urban areas 

22 have daily maximum hourly PM10 light extinctions that exceed even the highest CPL some of the 

23 time. Again, the non-California western urban locations have the lowest frequency of maximum 

24 hourly PM10 light extinction with values in excess of the high CPL for 8 percent or fewer of the 

25 days. Except for the two Texas and the non-California western urban areas, all of the other 

26 urban areas exceed that high CPL from about 20 percent to over 60 percent of the days. Based 

27 on these estimated maximum hourly PM10 light extinction estimates, all 14 of the urban areas 

28 exceed the low CPL for about 40 percent to over 90 percent of the days. Based on all of the 

29 above, we conclude that current levels of PM10 light extinction associated with recent PM air 

30 quality exceed levels that could reasonably be considered as protective of the public welfare. 
Draft-Do Not Quote or Cite 4-25 June 2010

1 Figure 4-3. Distribution of estimated maximum daily daylight 1-hour PM10 light extinction 

2 across the 2005-2007 period, by study area (excluding hours with relative humidity greater 

3 than 90 percent). (Adapted from Figure 3-8 in UFVA)* 

4 

*In the box-and-whisker plot, the box represents the 25th to 75th 5 percentile range and the whiskers 

represent the 10th and 90th percentile points of the data; individual data points below the 10th 6 percentile 

and above the 90th 7 percentile are graphed as small circles. The three dashed horizontal lines represent 

the three CPL levels of 64, 112, and 191 Mm-1 8 . 
Draft-Do Not Quote or Cite 4-26 June 2010

1 

2 Table 4-2. Percentage of daily maximum hourly values of daylight PM10 light extinction 

3 exceeding CPLs (excluding hours with relative humidity greater than 90 percent). (adapted 

4 from Table 3-7 in UFVA) 

5 

Candidate Protection Level

64 Mm-1 112 Mm-1 191 Mm-1

Study Area 

Number of Days with 

Estimates (a) Percentage of Daily Maximum Hourly 

Values Exceeding CPL

Tacoma 109 52 22 4 

Fresno 324 75 52 30 

Los Angeles 300 90 83 62 

Phoenix 86 42 7 1 

Salt Lake City 306 44 17 8 

Dallas 273 80 41 10 

Houston 148 79 45 11 

Birmingham 349 89 65 34 

Atlanta 279 91 75 31 

Detroit 141 87 68 43 

Pittsburgh 277 85 57 26 

Baltimore 181 80 50 23 

Philadelphia 143 86 64 31 

New York 225 83 59 28 

Average 224 75 50 25 

6 Visibility Levels That Just Meet Current Standards

7 In the UFVA, we modeled the “what if” scenario based on simulating just meeting the 

current suite of PM2.5 secondary standards: 15 μg/m3 8 annual average PM2.5 concentration and 35 

μg/m3 24-hour average PM2.5 concentration with a 98th 9 percentile form, averaged over three 

10 years. The steps needed to model the “what if” conditions involve explicit consideration of 

11 changes in PM2.5 components and are described here. First, we applied proportional rollback to 

12 all the PM2.5 monitoring sites in each study area, taking into account policy relevant background 

13 (PRB) PM2.5 mass, to “just meet” the NAAQS scenario for the area as a whole, not just at the 

14 visibility assessment study site. The health risk assessment document (EPA 2010a) describes 

15 this procedure in detail. The degree of rollback is controlled by the highest annual or 24-hour 

16 design value, which in most study areas is from a site other than the site used in this visibility 

17 assessment. The relevant result from this analysis is the percentage reduction in non-PRB PM2.5 

18 mass need to “just meet” the NAAQS scenario, for each study area. These percentage reductions 

19 are shown in Table 4-4 of the UFVA. Note that Phoenix and Salt Lake City meet the 15/35 
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1 NAAQS scenario under current conditions and require no reduction. PM2.5 levels in these two 

2 cities were not “rolled up.” Second, for each day and hour for each PM2.5 component, we 

3 subtracted the PRB concentration from the current conditions concentration, to determine the 

4 non-PRB portion of the current conditions concentration. Third, we applied the percentage 

5 reduction from step 1 to the non-PRB portion of each of the five PM2.5 components and added 

6 back the PRB portion of the component. Finally, we applied the IMPROVE algorithm, using the 

7 reduced PM2.5 component concentrations, the current conditions PM10-2.5 concentration for the 

8 day and hour, and relative humidity for the day and hour to calculate the PM10 light extinction. 

9 We then included the term for Rayleigh scattering. 

10 Figure 4-4 and Table 4-3 display the results of the rollback procedure as a box and 

11 whisker plot of daily maximum daylight 1-hour PM10 light extinction and the percentage of daily 

12 maximum hourly PM10 light extinction values exceeding the CPLs when just meeting the current 

PM2.5 NAAQS scenario of 15/35 μg/m3 13 (excluding hours with relative humidity greater than 90 

14 percent. These displays show that at the current PM NAAQS level (i.e., 15/35) all of the eastern 

15 urban areas and Los Angeles exceed the least restrictive CPL more than 10% of the time and that 

16 only Tacoma would not exceed the least restrictive CPL more than 2% of the time. 
Draft-Do Not Quote or Cite 4-28 June 2010

1 

2 Figure 4-4. Distribution of daylight 1-hour PM10 light extinction when rolled back to just 

3 meet current PM fine NAAQS across the 2005-2007 period, by study area (excluding hours 

4 with relative humidity greater than 90 percent). * 

NAAQS Scenario: 15 μg/m3

 annual; 35 μg/m3 5 24-hour 

6 Displayed: Daily Max Daylight Light Extinction (excluding hours >90% RH) 

7 

8 

 * In the box-and-whisker plot, the box represents the 25th to 75th 9 percentile range and the 

whiskers represent the 10th and 90th percentile points of the data; individual data points below the 10th 10 

percentile and above the 90th 11 percentile are graphed as small circles. The three dashed horizontal lines 

represent the three CPL levels of 64, 112, and 191 Mm-1 12 
Draft-Do Not Quote or Cite 4-29 June 2010

1 

2 Table 4-3 Percentage of daily maximum hourly values of daylight PM10 light extinction 

exceeding CPLs when “just meeting” the current PM2.5 NAAQS (15/35 μg/m3 3 ) (excluding 

4 hours with relative humidity greater than 90 percent). (Adapted from Table 4-7 in UFVA). 

5 

Candidate Protection Level

64 Mm-1 112 Mm-1 191 Mm-1

Study Area 

Number of Days with 

Estimates (a) Percentage of Daily Maximum Hourly 

Values Exceeding CPL

Tacoma 109 43 10 1 

Fresno 324 54 30 10 

Los Angeles 300 85 69 39 

Phoenix 86 44 6 1 

Salt Lake City 306 24 9 4 

Dallas 273 81 41 10 

Houston 148 75 41 11 

Birmingham 349 84 55 24 

Atlanta 279 90 71 25 

Detroit 141 80 61 33 

Pittsburgh 277 78 48 16 

Baltimore 181 78 48 19 

Philadelphia 143 85 61 28 

New York 225 76 45 19 

Average 224 70 43 17

6 4.2.2 Summary 

7 In summary, we conclude that the available information in this review, as described 

8 above and in the UFVA and ISA, clearly calls into question the adequacy of the current suite of 

9 PM2.5 standards in the context of public welfare protection from visibility impairment, primarily 

10 in urban areas, and supports consideration of alternative standards to provide appropriate 

11 protection. 

12 This conclusion is based first on the large percentage of days that exceed the range of 

13 CPLs identified for consideration under both current and just meet PM air quality conditions. In 

particular, under just meet conditions for the suite of secondary PM NAAQS (i.e. 15/35 μg/m3 14 ) 

greater than 10% of the days exceed the highest, least protective CPL of 191 Mm-1 15 for 9 of the 

14 urban areas. When the middle CPL of 112 Mm-1 16 is considered, 11 of the 14 cities have 

17 greater than 10% of their days exceeding with a range of 30 to 74% of days exceeding this level. 
Draft-Do Not Quote or Cite 4-30 June 2010

At the lowest CPL of 64 Mm-1 1 , the percentage of days exceeding this level range from 24 to 90% 

2 across the 14 urban areas. 

3 Second, we conclude that the averaging times associated with the current suite of PM2.5

4 NAAQS are not well suited to protect against PM-related visibility impairment on the basis that, 

5 in the context of the studies, short-term exposure (e.g., 1 hour or less) is sufficient for an 

6 unacceptable level of visual air quality to be observed and the associated impacts of that 

7 observation to be registered by the observer. Since some portion of the population may only 

8 have the opportunity to observe one hour or less of ambient daylight visibility conditions, relying 

9 on an averaging time as long as that of the current 24-hour and annual PM2.5 standards would 

10 make it difficult to identify a requisite level of protection that would translate into appropriate 

11 protection against the maximum daily value. In addition, these longer averaging times also result 

12 in the inclusion of nighttime conditions, for which the science needed to support identification of 

13 appropriate levels of visibility protection is not well developed or understood. 

14 In reaching the conclusion that the current suite of PM2.5 standards is inadequate to 

15 provide the appropriate protection of the public welfare from known and/or anticipated adverse 

16 effects by calling into question the adequacy of the current levels and averaging times, we also 

17 considered whether the current indicator of PM2.5 remains useful in relating ambient PM to the 

18 public welfare effect of visibility impairment. Section 4.3 below discusses these and other 

19 considerations in its discussion of alternative standards for consideration. 

20 4.3 CONSIDERATION OF ALTERNATIVE STANDARDS 

21 Having reached the conclusion that just meeting the current suite of PM2.5 standards 

22 continues to allow levels of visual air quality impairment that, based on the scientific evidence 

23 and information available in this review, can reasonably be considered adverse to the public 

24 welfare, this section will discuss alternative standards that could potentially provide requisite 

25 public welfare protection from known and/or anticipated adverse effects. This section discusses 

26 indicator, averaging time and form, including how they relate to the characteristics of the 

27 visibility effect of interest. 

28 4.3.1 Nature of the Indicator 

29  To what extent does currently available information provide support for considering a 

30 different indicator(s) for PM to replace or supplement the PM2.5 mass-based indicator? 

31 As described below, EPA staff has considered three alternative indicators: PM2.5 mass, 

32 directly measured PM2.5 light extinction, and speciated PM2.5 mass-calculated light extinction. 

33 PM2.5 mass is the same indicator as is used by the current suite of PM2.5 NAAQS. Directly 

34 measured PM2.5 light extinction is a measurement of the light extinction caused by PM2.5 under 

35 ambient conditions. Speciated PM2.5 mass-calculated light extinction uses the IMPROVE 
Draft-Do Not Quote or Cite 4-31 June 2010

1 algorithm to calculate PM2.5 light extinction using measured dry PM2.5 speciation and relative 

2 humidity data. We believe that consideration of the use of either directly measured PM2.5 light 

3 extinction or speciated PM2.5 mass-calculated light extinction as an indicator is justified because 

4 light extinction is a physically meaningful measure of the ambient PM2.5 characteristic that is 

5 most relevant and directly related to visibility effects. Further, PM2.5 is the component of PM 

6 responsible for most of the visibility impairment in most urban areas. In these areas, the 

7 contribution of PM10 – 2.5 is a minor contributor to visibility impairment most of the time, 

8 although at some locations (see UFVA Figure 3-13 for Phoenix) PM10 – 2.5 can be a major 

9 contributor to urban visibility effects. In the absence of PM air quality information from a much 

10 larger number of urban areas across the country, it is not possible at this time to know how many 

11 urban areas fall into this category, though it is reasonable to presume that other urban areas in the 

12 desert southwestern region of the country may have conditions similar to the conditions shown 

13 for Phoenix. A possible solution to this issue would be to add a companion PM10 – 2.5 standard to 

14 provide control under those circumstances. Insufficient information is available at this time to 

15 consider this latter refinement. 

16 The basis for considering each of these three indicators is discussed below. The 

17 discussion also addresses monitoring data requirements for the directly measured PM2.5 light 

18 extinction and for speciated PM2.5 mass-calculated light extinction. 

19 PM2.5 Mass Indicator

20 PM2.5 mass monitoring methods are in wide spread use, including the Federal Reference 

21 Method (FRM) involving the collection of periodic (1 day in 6 and 1 day in 3) 24-hour filter 

22 samples. These samples are then subsequently analyzed to determine 24-hour PM fine mass. 

23 The Chemical Speciation Network (CSN) and continuous PM2.5 monitoring produce hourly 

24 average mass concentrations and are conducted at many locations. At a few locations, 

25 continuous speciation sampling produces measures of some of the major PM2.5 chemical 

26 components. These routine monitoring activities do not include measurement of the water 

27 content of the ambient PM that contributes, often significantly, to visibility impacts. Further, the 

28 PM mass concentration monitoring does not provide information on the composition of the 

29 ambient PM which also contributes to the variability in the amount of visibility impact associated 

30 with any ambient PM mass concentration. 

31 The overall performance of PM2.5 mass as a predictor of visibility effects as indicated by 

32 PM10 light extinction can be seen in scatter plots shown in Figure 4-5 for Pittsburgh and 

33 Philadelphia, PA (Similar plots for all 14 urban areas are in Appendix D, Figure D-2 of the 

34 UFVA). These demonstrate the variation in hourly PM10 light extinction corresponding to any 
Draft-Do Not Quote or Cite 4-32 June 2010

1 specific level of PM2.5 mass concentration as well as statistical differences of the average 

2 relationships (depicted as the best fit lines) between cities. 

3 Figure 4-5 Scatter plots of PM10 light extinction versus PM2.5 mass concentration of two 

4 cities (from UFVA Appendix D, Figure D2). 

5 

6 Directly Measured PM2.5 Light Extinction Indicator

PM light extinction6 7 is the major component of or contributor to light extinction, which is 

8 the property of the atmosphere that is most directly related to visibility effects. It differs from 

9 light extinction by the nearly constant contributions for Rayleigh (or clean air) light scattering 

10 and the minor contributions by NO2 light absorption. The net result is that PM light extinction 

11 has a one-to-one relationship to light extinction, unlike PM2.5 mass concentration which, as 

shown above, does not have this characteristic. PM2.5 light extinction7 12 can be directly measured. 

13 Direct measurement of PM2.5 light extinction can be accomplished using several instrumental 

14 methods, some of which have been used for decades to routinely monitor the two components of 

15 PM2.5 light extinction (light scattering and absorption) or to jointly measure both as total light 

16 extinction (from which Rayleigh scattering is subtracted to get PM2.5 light extinction). There are 

17 a number of advantages to direct measurements for use in a secondary standard relative to 

18 calculated PM2.5 light extinction that was used to generate hourly values for the UFVA. These 

19 include the greater accuracy of direct measurements with shorter averaging times and the overall 

 6

 PM light extinction is used here to refer to the light extinction caused by PM regardless of particle size, so it 

includes the contributions from particle larger that 10m that are additive to the PM10 light extinction. 7

 PM2.5 light extinction is the contribution to PM light extinction by particles sampled through an inlet with a particle 

size 50% cutpoint of 2.5m diameter. 
Draft-Do Not Quote or Cite 4-33 June 2010

1 greater simplicity when compared to the need for measurements of multiple parameters needed 

2 to generate the calculated PM light extinction level for a standard. 

3 In evaluating whether direct measurement of PM10 light extinction is appropriate to 

4 consider in the context of this PM NAAQS review, EPA produced a White Paper on Particulate 

5 Matter (PM) Light Extinction Measurements (US EPA, 2010f), and solicited comment on the 

6 white paper from the Ambient Air Monitoring and Methods Subcommittee (AAMMS) of 

7 CASAC. In its review (Russell and Samet, 2010) of the white paper, the AAMMS made the 

8 recommendation to EPA that direct measurement be limited to PM2.5 light extinction as this can 

9 be accomplished by a number of commercially available instruments and because PM2.5 is 

10 generally responsible for most of the PM visibility impairment in urban areas. They indicated 

11 that it is technically more challenging at this time to accurately measure the PM10 – 2.5 component 

12 of light extinction. 

13 Speciated PM2.5 Mass-Calculated Light Extinction Indicator

14 PM2.5 light extinction can be estimated from PM2.5 mass and speciation data plus relative 

15 humidity data, as is presently routinely done on a 24-hour average basis under the Regional Haze 

16 Program using data from the rural IMPROVE monitoring network. Applying a conceptually 

17 similar approach on an hourly basis using data from the urban CSN monitoring network would 

18 permit the use of routinely collected data from the current CSN network, and possibly similar 

19 new monitoring sites, as input to a simple algorithm to calculate PM2.5 light extinction for use in 

20 a secondary PM NAAQS. 

21 The approach used to generate hourly PM10 light extinction for the UFVA was one 

22 particular implementation of such a conceptual approach. It involved the use of the original 

23 IMPROVE algorithm with estimates of hourly PM2.5 components derived from day-specific 24-

24 hour and hourly measurements of PM2.5 mass, 24-hour measurements of PM2.5 composition, and 

25 (for some but not all study sites) hourly PM10-2.5 mass, along with daily temperature data and 

hourly relative humidity information (UFVA, Section 3.3).8 26 The UFVA approach entailed 

27 numerous and complex data processing steps to generate hourly PM composition information 

28 from these less time-resolved data, including application of the SANDWICH approach to adjust 

29 for nitrate losses and to estimate organic carbonaceous material via mass balance. EPA staff 

30 employed complex custom software to do these data processing steps. The complexity of the 

31 approach used was reasonable for assessment purposes at 15 urban areas. While the UFVA 

32 approach conceivably could also be the basis for defining a national visibility standard, a simpler 

33 approach would have greater transparency, and simplicity, and thus should be considered for that 

 8

 Daily temperature data enters the UFVA approach via the SANDWICH method. This fact was not highlighted in 

the UFVA. 
Draft-Do Not Quote or Cite 4-34 June 2010

1 purpose. Therefore, we evaluated the degree to which simpler approaches would work as well as 

2 the more complex method used in the UFVA. This evaluation (described briefly below and in 

3 more detail in Appendix 4B, especially Table 4B-2) demonstrated that the PM10 light extinction 

4 values developed for the UFVA can be well approximated using the same IMPROVE algorithm 

5 applied to hourly PM2.5 composition values that were much more simply generated in locations 

6 where PM10-2.5 does not contribute significantly to PM VAQ. 

7 The simplified approach favored by EPA staff (i.e., approach F in Appendix 4B), is 

8 aimed at calculating hourly PM2.5 light extinction using the original IMPROVE equation (see 

9 section 4.2.1 above) excluding the Rayleigh term for light scattering by atmospheric gases (i.e. 

10 Mm-1 10 ) and the term for light scattering by PM10-2.5 (i.e., 0.6 x [Coarse Mass]). This leaves 

11 five PM2.5 species concentration terms (i.e., sulfate, nitrate, organic carbonaceous mass, 

12 elemental carbon, and fine soil/crustal), plus relative humidity that need to be determined on an 

13 hourly basis from monitoring data. Table 4B-2 in Appendix 4B describes ten steps in this 

14 simplified approach (denoted approach F in Appendix 4B) in detail, which are summarized here: 

15 i. Estimate 24-hour organic carbonaceous mass from the CSN organic carbon 

16 measurement, via a multiplier, rather than by the SANDWICH method. 

17 ii. Calculate 24-hour fine soil/crustal from the CSN measurements of the related PM2.5

18 crustal elements. 

19 iii. Calculate 24-hour neutralized, dry sulfate and nitrate in the same manner as in the 

20 IMPROVE network and Regional Haze program, by multiplication by factors of 

21 1.375 and 1.29 respectively. 

22 iv. Sum the five components, for each day. 

23 v. Calculate the five component fractions (i.e., percentages) of the sum-of-five, for 

24 each day. 

25 vi. Looking across all CSN sampling days in a given calendar month, average the daily 

26 component fractions for sulfate, for nitrate, etc. These monthly-average component 

27 fractions will be applied on every day in that month. 

28 vii. For each day, determine a scaling factor to reconcile hourly PM2.5 mass

29 measurements from a continuous instrument with the 24-hour PM2.5 mass 

determined by FRM sampling.9 30 

31 viii. For each day, apply this scaling factor to the 1-hour measurements of PM2.5 mass 

32 from the continuousinstrument. 

33 ix. For each day and daylight hour in the month, multiply the scaled value of hourly 

34 PM2.5 mass by the component fractions from step (v). 

 9

 Steps (vii) and (viii) were performed in the analysis that is presented in Appendix 4B, but perhaps would not need 

to part of the approach for actual implementation. The Comments column of Table 4B-2 discusses this aspect in 

more detail. 
Draft-Do Not Quote or Cite 4-35 June 2010

1 x. These hourly speciated PM2.5 concentrations are then combined with collocated 

2 hourly averaged relative humidity as input to the IMPROVE algorithm to calculate 

3 the speciated PM2.5 mass-calculated light extinction. 

4 A more detailed description of the sources of the data and steps required to determine 

5 speciated PM2.5 mass-calculated light extinction by this approach is contained in Appendix 4B 

6 (see Table 4B-2). Also, Table 4B-1 of Appendix 4B compares/contrasts this approach with the 

7 UFVA approach and with another approach of intermediate complexity. Use of this approach 

8 would require establishing Federal Reference Methods for sampling and laboratory analysis for 

9 each of the PM2.5 components that is measured, which could be based on existing CSN 

10 procedures, including the IMPROVE-like procedures for elemental carbon and organic carbon 

11 that have recently been fully implemented in the CSN network. 

12 The PM2.5 light extinction values generated by using this simplified approach are 

13 comparable to those developed for use in the UFVA as indicated by the regression statistics for 

scatter plots of the paired data (i.e., slopes of the regression equation and R2 14 values are near 1 as 

15 show in Tables 4B-3 and 4B-4 in Appendix 4B). We believe that this simplified approach 

16 provides reasonably good estimates of PM2.5 light extinction. 

17 Comparison of PM Components: PM2.5 Mass and PM10 Light Extinction Indicators 

18 Given our understanding of the causal relationship between ambient PM and PM visual 

19 air quality and the contribution of various PM components to PM light extinction under ambient 

20 conditions, an environmentally-relevant indicator for PM VAQ should be able to appropriately 

21 order the hours/days by the magnitude of PM VAQ impairment. One benefit of selecting such 

22 an indicator would be to identify the components of PM that are most important for improving 

23 PM VAQ in a given area. 

24 Because the PM component contributions to PM10 light extinction are weighted 

25 according to their light extinction efficiency while their contributions to PM2.5 mass are equally 

26 weighted (i.e., the sum of the component concentrations is the total PM2.5 mass concentration), 

27 our initial hypothesis was that the choice of indicator could significantly influence the relative 

28 contributions by PM components for the percent of hours selected above the level of a standard. 

29 In order to evaluate this hypothesis, EPA staff conducted an assessment which explored 

30 the PM10 components contributing to the top 10% of the maximum daily 1-hour and top 2% of 

31 all hours for PM2.5 mass and PM10 light extinction (see Appendix 4C, Indicator Comparisons and 

32 Component Apportionment Comparisons). While this comparison used calculated PM10 light 

33 extinction instead of the directly measured PM2.5 light extinction or speciated PM2.5 mass34 calculated light extinction indicators described above, we believe that comparisons done with 

35 these latter indicators would produce sufficiently similar results for those areas in which PM10-2.5
Draft-Do Not Quote or Cite 4-36 June 2010

1 is not a concern. These findings, described below and in Appendix 4C, are considered useful in 

2 informing judgments regarding the implications of indicator selection. 

3 A careful examination of stacked bar charts for each hour included in the top 10% of the 

4 maximum daily 1-hour and top 2% of all hours forms for both the PM2.5 mass and PM10 light 

5 extinction indicators shows that the two indicators often select the same hour or another hour 

6 during the same day as top contributors (compare plot a to plot d and plot e to plot h in Figures 

7 4C-1 to 4C-14 and see a tally of the numbers of common and unique days in Table 4C-1). When 

8 the two indicators select unique hours among the top contributors, the PM2.5 mass selected hours 

9 necessarily have lower PM10 light extinction values than those selected by PM10 light extinction 

10 as the indicator, generating a bias. However, the average results for each of the 14 urban areas 

11 show a remarkably similar identification of the relative amounts of the PM10 components 

12 responsible for the highest values regardless of which indicator is used to select the hours 

13 (compare plot a to d and plot e to plot h in Figure 4C-15). 

14 For any selected hour, the relative contributions of components to PM mass is not the 

15 same as the relative contributions of the components to PM light extinction because the differing 

16 light extinction efficiencies of the components. These differences can be seen by comparing 

17 paired plots in Figure 4C-1 to 4C-15 (i.e. compare a and b, c and d, e and f, etc.). The result is 

18 that components that contribute little to PM light extinction are more prominent contributors to 

19 PM mass concentration (e.g., fine soil). Similarly the relative importance of the hygroscopic PM 

20 components (i.e., PM sulfates and nitrates) compared to that of the non-hygroscopic components 

21 (i.e. primarily organic and elemental carbon) to PM mass is generally different than it is to PM 

22 light extinction and that difference changes as a function of relative humidity. 

23 In summary, this comparison demonstrates that the selection of indicator has little effect 

24 on which hours/days contribute to the top 10% of the maximum daily 1-hour and top 2% of all 

25 hours for PM2.5 mass and PM10 light extinction. However, in terms of properly identifying the 

26 PM2.5 species that are the largest contributors to the visibility impairment in those selected 

27 hours/days for the purpose of targeting controls, a calculated PM2.5 light extinction indicator 

28 would correctly weight the different components in terms of their differing contributions to 

29 visibility impairment, while the PM2.5 mass indicator would not. Likewise, a direct measurement 

30 of PM2.5 light extinction could not, by itself, provide speciated component information useful for 

31 targeting controls. Therefore, we conclude that the speciated PM2.5 mass-calculated light 

32 extinction indicator has advantages over the PM2.5 mass indicator in the context of this PM 

33 NAAQS review. 
Draft-Do Not Quote or Cite 4-37 June 2010

1 4.3.2 Averaging and Applicable Times 

2 Consideration of appropriate averaging times, diurnal periods, and ambient conditions 

3 over which the indicator would be measured were informed by consideration of the nature of PM 

4 visibility effects. 

5 With respect to consideration of appropriate averaging times, as discussed above (section 

6 4.2.1), we took into account what we know from the studies concerning how quickly people 

7 experience and judge visibility conditions, the possibility that some fraction of the public 

8 experience infrequent or short periods of exposure to ambient visibility conditions, and the 

9 typical rate of change of the path averaged PM light extinction over urban areas. While 

10 perception of change in visibility can occur in less than a minute, meaningful changes to path11 averaged light extinction occur more slowly and can be well represented by hourly averaging. 

12 Multi-hour averaging times, on the other hand, would have the effect of reducing and masking 

13 the magnitude of hourly peak visibility values which can change significantly from one hour to 

14 the next (see UFVA Figure 3-12). Reduction of peak values through multi-hour averaging 

15 reduces the ability of the indicator to accurately characterize the visibility effects experienced by 

16 the segment of the population that experiences infrequent short-term exposures during peak 

17 periods. Therefore, we conclude that a 1-hr averaging time is appropriately suited to characterize 

18 meaningful short-term variations in visibility conditions. 

19 With respect to selection of an appropriate diurnal period, staff recognized that nighttime 

20 visibility impacts, described in the ISA (section 9.2.2) are significantly different from daytime 

21 impacts and not sufficiently well understood to be included at this time. As a result, we conclude 

22 that a secondary standard to protect visibility should only apply to daylight hours at this time. In 

23 the UFVA, daylight hours were defined to be those morning hours having no minutes prior to 

24 local sunrise and afternoon hours having no minutes after local sunset. This definition ensures 

25 the exclusion of periods of time where the sun is not the primary outdoor source of light to 

26 illuminate scenic features. 

27 In considering the well-known interaction of PM with ambient relative humidity 

28 conditions, staff acknowledges that PM is not necessarily the primary source of visibility 

29 impairment during periods with fog or precipitation. In order to reduce the probability that hours 

30 identified as having a high degree of visibility impairment for purposes of determining 

31 compliance with a standard were caused by fog or precipitation, staff determined that a relative 

32 humidity screen that excludes daylight hours with average relative humidity above 90% is 

appropriate (UFVA section 3.3.5 and Appendix G). For example, for the 15 urban areas10 33 

 10 The 90% relative humidity cap assessment was conducted as part of the UFVA on all 15 of the urban areas, 

including St. Louis that is not displayed in the Policy Assessment. 
Draft-Do Not Quote or Cite 4-38 June 2010

1 included in the final UFVA, a 90% relative humidity cutoff criterion proved effective in that on 

2 average less than 6% of the daylight hours were removed from consideration, yet those same 

3 hours had on average 10 times the likelihood of rain, 6 times the likelihood of snow/sleet, and 34 

4 times the likelihood of fog compared with hours with 90% or lower relative humidity. However, 

5 not all periods with relative humidity above 90% have fog or precipitation. We recognize that 

6 removing those hours from consideration involves a tradeoff between the benefits of avoiding 

7 many of the hours with meteorological causes of visibility impacts and not counting some hours 

8 without fog or precipitation in which high humidity levels (> 90%) lead to the growth of 

9 hygroscopic PM to large solution droplets resulting in enhanced PM visibility impacts. 

10 4.3.3 Alternative Levels/Forms 

11 Candidate Protection Levels

12 The results from the visibility preferences studies conducted in four urban areas define a 

13 range of low, middle and high CPLs of 20 dv, 25 dv and 30 dv which are equivalent to PM10 

light extinction of values of 64 Mm-1, 112 Mm-1, and 191 Mm-1 14 (see section 4.2 above). With 

15 only the four preference study results, the individual low and high CPL are in fact reflective of 

16 the results from the Denver and Washington, DC studies in particular, and the middle CPL is 

very near to the 50th 17 percentile criteria result from Phoenix. 

18 Determining PM2.5 mass concentration values that correspond to the low, middle and 

19 high CPL is complicated by the lack of a one-to-one relationship between PM2.5 light extinction 

20 and PM2.5 mass. By considering the extinction efficiency values (i.e., the ratio of PM2.5 light 

extinction to PM2.5 mass) used in the original IMPROVE algorithm that range from 3 m2 21 /g to 10 

m

2 22 /g, a range of PM2.5 concentration levels that roughly corresponds to a low estimate of the low 

23 CPL and high estimate of the high CPL values can be determined. The full range is from ~6 

μg/m3

 to ~60 μg/m3 24 . Keeping in mind that there is no exact one-to-one correspondence between 

25 PM2.5mass concentration and PM2.5 light extinction, no individual value in this range of 

26 conditions corresponds to the CPL values expressed in terms of PM2.5 light extinction, so a 

27 number of alternate values within the range were selected for assessing their visibility protection 

28 performance when combined with alternative forms (as described below). Five alternative PM2.5

mass concentration values were selected for this purpose: 10 μg/m3

, 20 μg/m3, 30 μg/m3 29 , 40 

μg/m3 and 60 μg/m3 30 . 

31 Alternative Forms

32 The form of the current 24-hour PM2.5 NAAQS is set so that the level of the standard is 

compared to the three -year average of the annual 98th 33 percentile of the measured indicator. The 

34 purpose in averaging for three years is to provide stability from the occasional effects of inter-
Draft-Do Not Quote or Cite 4-39 June 2010

1 annual meteorological variability that can result in unusual high pollution levels for a particular 

2 year that is otherwise typical. The use of a percentile form, among other things, makes the 

3 standard less subject to the possibility of inappropriate violations caused by statistical outlier 

4 indicator values. A percentile form can also be used to take into account the number of times an 

5 exposure might occur as part of the judgment on protectiveness in setting a NAAQS. For all of 

6 these reasons, we believe it is appropriate to consider incorporating the use of a three-year 

7 average of a specified percentile for the PM secondary standard. 

8 The urban visibility preference studies that provided results leading to the range of CPLs 

9 being considered in this document, offer no information that addresses the frequency of time that 

10 visibility levels should be below those values. Given this lack of information and recognizing 

11 that the nature of the public welfare effect is one of aesthetics and/or feelings of wellbeing, we 

12 believe that it is not necessary to consider eliminating all exposures above the level of the 

13 standard and that allowing some number of hours/days with reduced visibility can reasonably be 

considered. In the UFVA, 90th, 95th and 98th 14 percentile forms are assessed in the PM light 

15 extinction NAAQS scenarios (Chapter 4). The hourly PM2.5 mass concentration scenarios that 

are described and assessed below and in Appendix 4A include only the 90th and 95th 16 percentile 

forms.11 17 

18 Another aspect of the form that needs to be considered is whether to include all daylight 

19 hours or only the maximum daily daylight 1-hour. The maximum daily daylight 1-hour form is 

20 more appropriate for protecting the welfare of people who have limited, infrequent or 

21 intermittent exposure to visibility during the day (e.g. during commutes), but spend most of their 

22 time without an outdoor view. For such people a view of poor visibility during their morning 

23 commute may represent their perception of the day’s visibility conditions until the next time they 

24 venture outside during daylight, hours later or perhaps the next day. Other people have exposure 

25 to visibility conditions throughout the day. For those people it might be more appropriate to 

26 include every daylight hour in assessing compliance with a standard, since a day with multiple 

27 hours with visibility impairment is likely to be judged to have a greater impact on their wellbeing 

28 than a day with just one such hour. 

29 We do not have information regarding the fraction of the public that has only one or a 

30 few opportunities to experience visibility during the day, nor do we have information on the role 

31 the duration of the observed visibility conditions has on wellbeing effects associated with those 

32 visibility conditions. However, it is logical to conclude that people with limited opportunities to 

33 experience visibility conditions on a daily basis would experience the entire impact associated 

34 with visibility based on their short term exposure. The impact of visibility for those who have 

 11 Assessment of the 98th percentile with PM2.5 mass was thought to be unnecessarily restrictive so it was not 

conducted to simply and speed the assessment process. 
Draft-Do Not Quote or Cite 4-40 June 2010

1 access to visibility conditions often or continuously during the day may be based on varying 

2 conditions throughout the day. Based on these considerations, the segment of the population 

3 with infrequent access to visibility could be characterized as a susceptible population relative to 

4 peak visibility impairment, while those with longer exposures are a susceptible population for 

5 somewhat longer-term visibility impairment. 

6 In light of these considerations, the UFVA assessment of the various PM10 light 

7 extinction scenarios included both the maximum daily and all daylight hours forms. We noticed 

8 a close correspondence between the level of protection afforded for all 15 urban areas in the 

assessment by the maximum daily daylight1-hour using the 90th 9 percentile form and the all 

daylight 1-hour combined with the 98th 10 percentile form (UFVA section 4.1.4). On this basis, 

11 staff notes that the reductions in visibility impairment required to meet either form of the 

12 standard would provide protection to both fractions of the public (i.e., those with limited 

13 opportunities and those with greater opportunities to view PM-related visibility conditions). 

14 Another way to assess the two forms (i.e., maximum daily and all daylight hours) is to 

15 compare the PM components for hours that would be targeted for reduction by the use of each. 

16 This was done for both indicators (i.e. PM2.5 mass concentration and PM10 light extinction) by 

17 examining and comparing the component contributions for current conditions (i.e., 2005 – 2007) 

18 for the hours with the highest levels for each indicator. Stacked bar plots showing the PM10

19 component composition of the top 10% of hours for the maximum daily 1-hour and of the top 

20 2% of all daylight hours for both indicators and each of the 14 urban areas are described in 

21 Appendix 4C. 

22 For Figures 4C-1 to 4C-14 composition displayed in plots labeled a and e for the light 

23 extinction indicator and c and g for the PM2.5 mass indicator can be compared to assess the effect 

24 of using the maximum daily compared with the all daylight hours forms. Some of the same 

25 hours are selected by both forms. By definition the top 10% maximum daily hour form never 

26 has more than 1 hour selected per day. There are very nearly twice as many hours in the top 2% 

27 of all hours compared to the top 10% of maximum daily hours. However, it is not unusual to 

28 have multiple hours selected in the same day by the top 2% of all hours form. These hours 

29 selected in the same day tend to have nearly identical composition. The top 2% of all hours may 

30 select hours on a greater number of days compared with the top 10% of maximum daily hours, or 

31 if there are enough instances of multiple hours per day, it could select hours on a smaller number 

32 of days compared with the top 10% of maximum daily hours. A list of the numbers of common 

33 and unique days selected comparing the two forms for both indicators and the number of days 

34 selected by each combination of indicator and form is shown in Table 4C-2. 

35 For most of the urban areas the number of common days is much larger than the number 

36 of unique days. Generally, the PM component mix is less similar for sites with a lower number 
Draft-Do Not Quote or Cite 4-41 June 2010

1 of common and a greater number of unique days. For most of the 14 urban areas, the differences 

2 in components contributions for the two forms are minor. The greatest difference between the 

3 two forms is for Salt Lake City. Average effects of the choice of form for each urban areas is 

4 displayed in Figure 4C-15 (compare plot a to plot e and plot c to plot g). This shows that the 

5 differences in PM component contributions differ more depending on the choice of indicator 

6 than the choice of form. Thus, these two specific percentile levels correspond to the nearly 

equivalent levels of protection afforded by the maximum daily 90th 7 percentile and the all daylight 

hours 98th 8 percentile forms. 

9 4.3.4 Performance of Alternative Standards 

10 We conducted assessments of alternative standards based both on hourly PM10 light 

11 extinction as the indicator (see Chapter 4 of the UFVA) and on the hourly PM2.5 mass 

12 concentration indicator (see Appendix 4A in this document). In the context of these assessments 

13 of the 14 urban areas, PM10 light extinction is dominated by PM2.5 light extinction except for 

14 Phoenix, and therefore we consider these assessments reasonably comparable to either the 

directly measured or speciated PM2.5 mass-calculated light extinction indicators.12 15 There are 

16 nine PM10 light extinction alternative standards discussed here (i.e., daily maximum daylight 1-

hour PM10 light extinction at the low, middle and high CPL for each of the 90th, 95th and 98th 17 

18 percentile forms) and there are ten PM2.5 mass concentration alternative standards (i.e., five 

concentration levels for each of the 90th and 95th 19 percentile forms). In both cases the assessment 

20 involves rolling back non-policy relevant background (PRB) PM2.5 light extinction or PM2.5 mass 

21 concentration values until these specific alternative standards are just met. 

22 In considering the performance of alternative standards, we focused on the consistency 

23 across the 14 urban areas of the resulting visibility conditions, as measured in terms of light 

24 extinction, when the alternative scenarios are just met. Because of the one-to-one 

correspondence between light extinction and PM10 light extinction,13 25 the PM10 light extinction 

26 indicator based standards should produce visibility conditions exactly as prescribed. The ability 

27 to structure a PM10 light extinction based alternative standard to the level of visibility protection 

28 desired is an advantage because it could afford the most uniform degree of visibility protection 

29 nationwide. 

30 Figures similar to Figure 4-5 for the other PM10 light extinction scenarios are shown in 

31 Appendix F of the UFVA. Table 4-4 shows the design values for the 9 scenarios based on 

32 maximum daily 1-hour PM10 light extinction. When an area just meets a scenario, its design 

 12 These assessments were conducted prior to the decision to focus on PM2.5 light extinction indicators. 13 In this assessment light extinction = PM10 light extinction + 10Mm-1, where the last term is Rayleigh or clean air 

light scattering that in fact can range from about 8Mm-1 to 12Mm-1 depending on average sight path elevation above 

sea level. 
Draft-Do Not Quote or Cite 4-42 June 2010

1 value in principle should exactly equal the NAAQS level, so preparation of this table serves as a 

2 check against calculation errors. Note that the design values in Table 4-4, resulting from the 

3 rollback steps described in section 4.1.4 of the UFVA, in some cases do not exactly equal the 

4 assumed level of the NAAQS, although all are quite close. In some cases (e.g. Phoenix for 191 

Mm-1/90th and 95th 5 percentile), current conditions already meet the scenario specifications so no 

6 rollback was necessary and current design values are shown in Table 4-4 and reflected in box 

7 and whisker plot figures. The minor differences between prescribed and assessed design values 

8 seen for some applications of the rollback assessment are due to hours switching in the PM10 

9 light extinction frequency distribution that is purely an artifact of the rollback methodology as 

10 described in the UFVA (section 4.3). These discrepancies were judged too small to justify 

11 iterative rollback that could have been used to eliminate them. 
Draft-Do Not Quote or Cite 4-43 June 2010

1 

2 Figure 4-6 Daily maximum daylight 1-hour PM10 light extinction after rollback to just meet 

a scenario with daily maximum of 112 Mm-1 for the 90th 3 percentile excluding hours with 

4 relative humidity greater than 90 percent. 

5 

6 
Draft-Do Not Quote or Cite 4-44 June 2010

1 

2 Table 4-4. PM10 light extinction design values for “just meeting” secondary NAAQS 

3 scenarios based on measured PM10 light extinction (excluding hours with relative humidity 

4 greater than 90 percent) 

Secondary NAAQS Scenarios Based on Daily Maximum 

(a) (b) (c) (d) (e) (f) (g) (h) (i) 

Level (Mm-1) 191 191 191 112 112 112 64 64 64 

Percentile 

Form 90th 95th 98th 90th 95th 98th 90th 95th 98th 

PM light extinction Design Value 

(based on same percentile form as the NAAQS scenario) 

Tacoma, WA 140 157 191 112 112 108 66 70 60 

Fresno, CA 191 191 191 112 112 112 64 64 64 

Los Angeles, 

CA 191 191 191 112 112 112 65 64 64 

Phoenix, AZ 105 144 185 105 112 112 64 64 64 

Salt Lake City, 

UT 164 191 191 112 112 112 64 64 64 

Dallas, TX 183 191 191 113 113 112 64 66 66 

Houston, TX 191 191 191 115 112 112 67 61 67 

Birmingham, 

AL 191 192 191 113 114 112 64 66 64 

Atlanta, GA 191 191 191 112 111 112 64 63 65 

Detroit, MI 191 191 191 112 112 112 64 64 65 

Pittsburgh, PA 191 191 191 112 112 112 64 64 64 

Baltimore, MD 191 191 191 111 112 112 63 64 65 

Philadelphia, 

PA 191 191 191 112 112 112 65 64 64 

New York, NY 192 191 191 113 112 112 65 64 64 

5 

6 A complete description of the rollback assessment for the ten maximum daily 1-hour 

7 PM2.5 mass concentration scenarios is available in Appendix 4A of this document. The process 

8 used is very similar to that used in the UFVA for rollback of the PM10 light extinction based 

9 scenarios. 
Draft-Do Not Quote or Cite 4-45 June 2010

1 Since PM2.5 mass does not have a one-to-one correspondence to light extinction, the PM10 

2 light extinction conditions resulting from just meeting standards based on PM2.5 mass are not as 

3 uniform as those shown above for PM10 light extinction scenarios. This is demonstrated in the 

4 box and whisker plot of the maximum daily 1-hour PM10 light extinction based on 1-hr PM2.5

mass for the 30 μg/m3

, 90th 5 percentile scenario shown below (Figure 4-7), which shows greater 

6 variation across the urban areas than is seen in the PM10 light extinction based scenarios. Similar 

7 plots for all ten PM2.5 mass based scenarios are shown in Appendix 4A (Figure 4A-2). 

8 Figure 4-7. Maximum daily daylight 1-hour PM10 light extinction under "just meet" 

conditions for a NAAQS scenario based on 1-hour PM2.5 mass of 30 μg/m3 9 , 90th percentile, 

10 excluding relative humidity >90%.

11 

The 90th percentile PM10 light extinction design values corresponding to the 90th 12 

13 percentile PM2.5 mass concentration based scenarios for the five mass concentration levels are 

14 shown in Table 4-5. Values in the table that exceed each of the CPL values are highlighted using 

15 different colors. The values in the table are the same for some urban areas for the less restrictive 

PM2.5 mass concentration standard (e.g. Tacoma at 60 μg/m3

 and 40 μg/m3 16 , or Phoenix at 60 

μg/m3, 40 μg/m3, 30 μg/m3 and 20 μg/m3 17 ), because those areas required no rollback to meet the 

18 less restrictive PM2.5 mass concentration standards. In order for most or all urban areas to 
Draft-Do Not Quote or Cite 4-46 June 2010

achieve even the highest CPL at the 90th 1 percentile, a PM2.5 mass based standard set below 40 

μg/m3 2 would be necessary. Based on the CPL’s considered, the higher PM2.5 mass levels of 60 

μg/m3 and 40 μg/m3 3 clearly are not sufficiently protective, since they permit 10 and 9 of the 14 

areas, respectively, to have design values larger than the high CPL value (i.e.,191 Mm-1 4 ). The 30 

μg/m3 5 standard level has PM10 light extinction values that are marginally above the high CPL at 

3 of the 14 urban areas and has a mean value below the high CPL. At the 20 μg/m3 6 PM2.5

7 standard level, all areas meet the highest CPL benchmark, and the mean PM10 light extinction 

value across the 14 urban areas is nearer to the middle CPL (i.e., 112 Mm-1 8 ) than to the high 

CPL. The 10 μg/m3 standard level produces a mean PM10 light extinction value of 79.1 Mm-1 9 , 

which is greater than the low CPL (i.e., 64 Mm-1 10 ) but nearer to it than the middle CPL. 
Draft-Do Not Quote or Cite 4-47 June 2010

Table 4-5. 90th percentile maximum daily 1-hour PM light extinction design values (Mm1 1 ) 

after rollback to meet alternative standards of 60 μg/m3, 40 μg/m3, 30 μg/m3,20 μg/m3 2 and 

10 μg/m3 3 maximum daylight 1-hour PM mass concentration for the 90th percentile.* 

4 

60 μg/m3 40 μg/m3 30 μg/m3 20 μg/m3 10 μg/m3

Tacoma, WA 140 Mm-1 140 Mm-1 140 Mm-1 128 Mm-1 82 Mm-1

Fresno, CA 338 Mm-1 248 Mm-1 190 Mm-1 132 Mm-1 74 Mm-1

Los Angeles, CA 403 Mm-1 284 Mm-1 220 Mm-1 156 Mm-1 105 Mm-1

Phoenix, AZ 105 Mm-1 105 Mm-1 105 Mm-1 105 Mm-1 86 Mm-1

Salt Lake City, UT 164 Mm-1 164 Mm-1 153 Mm-1 107 Mm-1 59 Mm-1

Dallas, TX 183 Mm-1 183 Mm-1 183 Mm-1 146 Mm-1 80 Mm-1

Houston, TX 194 Mm-1 194 Mm-1 179 Mm-1 125 Mm-1 73 Mm-1

Birmingham, AL 357 Mm-1 266 Mm-1 208 Mm-1 152 Mm-1 102 Mm-1

Atlanta, GA 249 Mm-1 249 Mm-1 191 Mm-1 134 Mm-1 76 Mm-1

Detroit, MI 291 Mm-1 202 Mm-1 157 Mm-1 120 Mm-1 88 Mm-1

Pittsburgh, PA 278 Mm-1 243 Mm-1 185 Mm-1 127 Mm-1 69 Mm-1

Baltimore, MD 246 Mm-1 246 Mm-1 201 Mm-1 138 Mm-1 76 Mm-1

Philadelphia, PA 258 Mm-1 175 Mm-1 134 Mm-1 98 Mm-1 63 Mm-1

New York, NY 306 Mm-1 281 Mm-1 212 Mm-1 141 Mm-1 74 Mm-1

Mean Values 250.9 Mm-1 212.9 Mm-1 175.6 Mm-1 129.2 Mm-1 79.1 Mm-1 

5 

6 *Colored highlighting shows which of the CPL levels the values are near, using the 

following definitions: PM light extinction equal or above the high CPL >191 Mm-1 7 ; above the 

middle CPL, 112 Mm-1 – 190 Mm-1; above the low CPL, 64 Mm-1 – 111 Mm-1 8 ; below the low 

CPL, < 64 Mm-1 9 . Values without color highlighting indicate no rollback, so no information 

10 about the effectiveness of the hourly PM mass based alternative standards

11 

The range of 90th 12 percentile PM2.5 light extinction design levels resulting from rollback to 

just meet a 90th percentile 20 μg/m3

 PM2.5 mass concentration is from 98 Mm-1 13 for Philadelphia 

to 156 Mm-1 for Los Angeles with most values near the middle CPL value of 112 Mm-1 14 . A 

similar range for the 30 μg/m3 PM mass concentration is from 134 Mm-1 to 220 Mm-1 15 for the 

16 same two cities. This demonstrates the amount of variability in visibility conditions that would 

17 likely result from using a standard with PM2.5 mass concentration instead of PM2.5 light 

18 extinction as the indicator. While this degree of variation is not particularly large, it does mean 

19 that some areas would be required to further reduce ambient levels to meet a secondary standard 

20 though they have visibility conditions that are as good or better than other areas which meet the 

21 PM2.5 mass based standard. Some measure of the extent to which this occurs may be gained by 
Draft-Do Not Quote or Cite 4-48 June 2010

1 comparing the percent rollback values required to meet the various PM mass and PM light 

2 extinction values. 

3 Based on inspection of the design values for individual urban areas and means shown in 

Table 4-5, among the PM2.5 mass concentration levels that were assessed, 30 μg/m3 4 provides 

protection somewhat comparable to the high CPL (191 Mm-1), 20 μg/m3 5 provides protection 

somewhat comparable to the middle CPL (112 Mm-1) and 10 μg/m3 6 provides protection 

somewhat comparable to the low CPL (64 Mm-1 7 ). Additional assessments could be conducted to 

8 refine the PM2.5 mass concentration values to better correspond to the three CPL PM light 

9 extinction values for these 14 urban areas, though this would not much change the variations in 

10 performance across urban areas. 

11 Care must be taken to avoid misinterpretation of this suggestion of approximate 

12 comparability of protection afforded the various urban areas by PM mass at the three 

concentration levels in terms of the three CPLs. The values in Table 4-5 are the 90th 13 percentile 

14 values of the PM2.5 light extinction distribution that results from transforming the hourly PM2.5

15 mass and composition values of the rolled back PM distributions for each urban area. Hours 

with values above the 90th 16 percentile of PM mass do not necessarily have PM light extinction 

above the 90th 17 percentile and visa versa. As discussed earlier (section 4.3.1) for any individual 

18 hourly PM mass concentration there is a substantial range of corresponding PM light extinction 

19 values possible. 

20 4.4 STAFF CONCLUSIONS ON ALTERNATIVE SECONDARY STANDARDS FOR 

21 VISIBILITY-RELATED EFFECTS 

22 In reaching conclusions on potential alternative standards to provide requisite protection 

23 of PM-related visibility impairment, staff has considered the basic elements of the NAAQS: 

24 indicator, averaging time, form and level. In considering the scientific and technical 

25 information, we considered the information available in the last review integrated with 

26 information that is newly available as assessed and presented in the ISA and UFVA and as 

27 summarized in sections 4.2, 4.3, and Appendices 4A, 4B and 4C. 

28 As outlined in section 4.1.3, we emphasize a policy approach that broadens the general 

29 approaches used in the last two PM NAAQS reviews by utilizing, to the extent available, 

30 enhanced tools, methods, and data to more comprehensively characterize visibility impacts. As 

31 such, we have taken into account both evidence-based and impact assessment-based 

32 considerations to inform our conclusions related to the adequacy of the current PM2.5 secondary 

33 standards and alternative standards that are appropriate for consideration in this review. In so 

34 doing, we are seeking to provide as broad an array of options as is supportable by the available 

35 information, recognizing that the selection of a specific approach to reaching final decisions on 
Draft-Do Not Quote or Cite 4-49 June 2010

1 the secondary PM standards for protection from PM-related visibility impairment will reflect the 

2 judgments of the Administrator. We recognize that selecting from among alternative standards 

3 will necessarily reflect consideration of the qualitative and quantitative uncertainties inherent in 

4 the relevant evidence and in the assumptions that underlie the quantitative visibility impact 

5 assessment. 

6 (1) Consideration should be given to revising the current suite of PM2.5 secondary standards to 

7 provide increased public welfare protection from PM2.5-related visibility impairment, 

8 primarily in urban areas. This conclusion is based in general on the evaluation in the ISA of 

9 the currently available information, including a more extensive characterization of the 

10 sources contributing to visibility impairment in both rural and urban locations; a refined 

11 understanding of the contributions of various PM components in such areas; exposure-related 

12 evidence supporting a causal relationship between ambient PM and impaired VAQ, evidence 

13 that a significant number of days with levels of VAQ that could reasonably be considered 

14 unacceptable would continue to occur in areas where the current standards were met; and 

15 judgments as to the public welfare significance of these occurrences upon just meeting the 

16 current suite of PM2.5 standards. 

17 (2) Consideration should be given to establishing a new speciated PM2.5 mass-calculated light 

18 extinction indicator. This conclusion takes into consideration the available evidence that 

19 demonstrates a one-to-one correspondence between directly measured or calculated PM light 

20 extinction and PM-related visibility impairment as well as the significant degree of 

21 variability in visibility protection across the U.S. allowed by a PM2.5 mass indicator. While a 

22 secondary standard that uses a PM 2.5 mass indicator could be set to provide additional 

23 protection from PM2.5-related visibility impairment, we conclude that the advantages of using 

24 a speciated PM 2.5 mass-calculated light extinction indicator (as described above in section 

25 4.3.1) make it the preferred choice. 

26 (3) Consideration should be given to a one hour averaging time to protect against daytime PM2.5

27 -related visibility impairment that takes into account the short-term (instantaneous) nature of 

28 the perception of visibility impairment, short-term variability in PM-related VAQ (partial 

29 hour to hourly), and the short-term nature of relevant exposure periods for the viewing public 

30 (partial hour to multiple hours). Due to limitations in the science related to assessing public 

31 welfare impacts associated with nighttime visibility impairment, consideration should be 

32 given to including daylight hours only. Further, consideration should be given to applying a 

33 90% relative humidity screen to remove hours in which fog or precipitation is much more 

34 likely to contribute to the observed visibility impairment. 

35 (4) In conjunction with a 1-hour averaging time, consideration should be given to either a 

36 maximum daily daylight 1-hour form or a form that includes all daylight hours. 

Consideration should be given to a form defined in terms of the 90th, 95th or 98th 37 percentile of 

38 the distribution of calculated PM2.5 light extinction, averaged over three years. 

39 (5) Consideration should be given to selecting a target level of acceptable PM-related VAQ in 

40 terms of PM2.5 light extinction. Further, consideration should be given to a range of 

alternative candidate protection levels from down to 64 Mm-1 and up to 191 Mm-1 41 , with a 
Draft-Do Not Quote or Cite 4-50 June 2010

midpoint level of 112 Mm-1 1 to provide appropriate protection against PM2.5-related visibility 

2 impairment. 

3 (6) Consideration should be given to the following alternative secondary PM2.5 standards to 

4 provide protection against PM2.5 -related visibility impairment during daylight hours: 

5 (a) Consideration should be given to establishing a new 1-hour daily maximum 

6 speciated PM2.5 mass-calculated light extinction secondary standard set at a level 

within the range of 64 to 191 Mm-1 in combination with a 90th or 95th 7 percentile 

8 form, averaged over 3 years. 

9 (b) Alternatively, consideration could be given to a new all daylight hours speciated 

10 PM2.5 mass-calculated light extinction secondary standard set at a level within the 

range of 64 to 191 Mm-1 in combination with a 98th 11 percentile form, averaged 

12 over 3 years. 

13 4.5 KEY UNCERTAINTIES AND AREAS FOR FUTURE RESEARCH AND DATA 

14 COLLECTION 

15 This section will be organized into two overarching topic areas: refining current 

16 understanding of visibility preferences and characterization of ambient urban visibility 

17 conditions. The first deals principally with how the public reacts to and values visibility 

18 conditions, while the second is more concerned with determining ambient visibility conditions 

19 and the relationships between PM component concentrations and light extinction. 

20 Visibility Preferences 

21  Levels: The results of the reanalysis of the four urban preference studies (UFVA Chapter 2) 

22 demonstrated well defined though significant statistical differences in visibility impairment 

23 levels that divide acceptable from unacceptable conditions across the study areas. A number 

24 of hypothesis concerning why the results differed for each area are discussed in chapter 2 of 

25 the UFVA, but the current state of knowledge does not support a definitive explanation for 

26 the range of results. A better understanding of the reasons for the differences in preference 

27 response among the studies of the four urban areas could influence the design of future 

28 visibility preference survey studies and the interpretation of their results ultimately leading to 

29 a better defined range of CPLs for the next PM NAAQS review. 

30  Averaging Times/Forms: Additional information would also be helpful in deciding among 

31 the various forms and averaging times to develop an effective visibility based secondary PM 

32 NAAQS and to assess the overall benefits of visibility improvements. Our current 

33 understanding of urban visibility effects does not provide insights concerning: 

34 o relative importance of intensity versus frequency of visibility impairment, 

35 o strength of preference for different distributions of visibility conditions, and 

36 o public exposure rates and mechanisms. 

37 Future research to address these deficiencies should include designing and conducting 

38 additional preference, valuation and exposure studies to: 
Draft-Do Not Quote or Cite 4-51 June 2010

1 o expand the number and geographic coverage of urban area preference results, 

2 o evaluate the sensitivity of results to the differences in survey study methodology, 

3 o apply consistent methodology at multiple urban areas to better understand reasons for 

4 preference difference among results in different urban areas, 

5 o develop information on the strength of preference and relative importance of intensity 

6 versus frequency of visibility impairment 

7 o identify the types of scenic elements that are most influential for informing public 

8 visibility impact awareness, and 

9 o provide insights concerning visibility impact exposure duration, intensity and timing 

10 and their relationship to the degree and longevity of public welfare effects. 

11 Additionally, prior to the next PM NAAQS review and as part of the planning for 

12 additional preference and valuation survey studies, a literature review of recent social science 

13 literature could usefully be conducted to assess the state of knowledge of view exposure 

14 mechanisms, and the psychological and behavioral effects associated with viewed stimuli. 

15 Urban Visibility Conditions 

16 The paucity of light extinction monitoring data for urban areas lead to the use of the 

17 IMPROVE algorithm to calculate hourly light extinction from continuous PM mass and 24-hour 

18 PM2.5 component and relative humidity data (UFVA Chapter 3). The steps used to apportion 24-

19 hour PM2.5 component to calculate hourly averaged values used monthly mean diurnal PM 

20 component variations from regional air quality modeling. The mass balance methods used to 

21 estimate organic concentration and the loss of nitrate are reasonable but not likely to be precise. 

22 The IMPROVE algorithm was originally developed for remote area application to estimate 24-

23 hour light extinction and it was not verified for use in generating urban hourly estimates. The 

24 resulting hourly PM light extinction data set is thought to be reasonably representative of the 

25 hourly PM light extinction levels that is adequate for assessment purposes, but these data are less 

26 accurate than those from direct measurements. 

27 A pilot PM light extinction monitoring program could usefully be designed and deployed 

28 at some number of locations selected to cover a range of PM air quality conditions with emphasis 

29 given to locations with continuous PM mass and speciation monitoring as well as 24-hour mass 

30 and speciation sampling. Information from such a pilot monitoring program could be used to: 

31 o evaluate the performance of PM light extinction monitoring methods that could 

32 ultimately be use as an FRM, 

33 o evaluate and refine approaches for apportioning 24-hour PM species to hourly values 

34 (needed for sites without continuous PM speciation monitoring), 

35 o evaluate and refine light extinction calculation algorithms for use in urban settings, 

36 and 

37 o conduct the visibility effects assessment for the next PM secondary NAAQS. 
Draft-Do Not Quote or Cite 4-52 June 2010

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1 US EPA (2008a). Integrated Review Plan for the National Ambient Air Quality Standards for Particulate Matter. 

2 National Center for Environmental Assessment and Office of Air Quality Planning and Standards, U.S. 

3 Environmental Protection Agency, Research Triangle Park, NC. Report No. EPA 452/R-08-004. March 

4 2008. Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pd.html. 

5 US EPA (2009a). Integrated Science Assessment for Particulate Matter: Final Report. National Center for 

6 Environmental Assessment-RTP Division, Office of Air Quality Planning and Standards, Research 

7 Triangle Park, NC. EPA/600/R-08/139F. December 2009. Available: 

8 http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html. 

9 US EPA (2010 a). Quantitative Risk Assessment for Particulate Matter – Final Report. Office of Air Quality 

10 Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-452/R11 10-005. June 2010. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html

12 

13 US EPA (2010b). Particulate Matter Urban-Focused Visibility Assessment – Final Report. Office of Air Quality 

14 Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-452/R15 10-004. June 2010. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 

16 

17 US EPA (2010f). White Paper on PM Light Extinction Measurements. Office of Air Quality Planning and 

18 Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. January 2010. Available 

19 at: 

20 http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/823a6c8842610e76852

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1 5 REVIEW OF THE SECONDARY STANDARDS FOR OTHER 

2 WELFARE EFFECTS 

3 This chapter presents staff conclusions with regard to the current suite of secondary PM

4 standards to protect against PM-related welfare effects other than visibility impairment. 

5 Specifically, staff has assessed the relevant information related to effects of atmospheric PM on 

6 the environment, including effects on climate, ecological effects, and effects on materials. Our 

7 assessment is framed by a series of key policy-relevant questions, which expand upon those 

8 presented in the Integrated Review Plan (IRP) (US EPA, 2008a, section 3.2). The answers to 

9 these questions will inform decisions on whether to retain or revise the current suite of secondary 

10 PM standards. 

11 In presenting staff conclusions with regard to the current secondary standards relative to 

12 PM-related effects on climate, ecological effects, and materials, we note that the final decision is 

13 largely a public welfare policy judgment. A final decision must draw upon scientific information 

14 and analyses about non-visibility PM-related effects and related impacts on public welfare, as 

15 well as judgments about how to deal with the range of uncertainties that are inherent in the 

16 scientific evidence and analyses. Our approach to informing these judgments is discussed more 

17 fully below. This approach is consistent with the requirements of the NAAQS provisions of the 

18 Act and with how EPA and the courts have historically interpreted the Act. These provisions 

19 require the Administrator to establish secondary standards that, in the Administrator’s judgment, 

20 are requisite to protect public welfare from any known or anticipated adverse effects associated 

21 with the presence of the pollutant in the ambient air. In so doing, the Administrator seeks to 

22 establish standards that are neither more nor less stringent than necessary for this purpose. The 

23 Act does not require that secondary standards be set at a zero-risk level, but rather at a level that 

24 avoids unacceptable public welfare impacts. 

25 Information on the approaches used to set the secondary PM standards in past reviews as 

26 well as our current approach for this review are presented in section 5.1. A discussion of the 

27 scope of the review as related to non-visibility welfare effects of PM is included in section 5.1.2. 

28 This chapter considers each of the non-visibility welfare effects separately. The discussion of 

29 PM-associated effects on climate (section 5.2), ecological effects (section 5.3), and materials 

30 (section 5.4) are each followed by a consideration of key uncertainties and areas for future 

31 research and data collection. 

32 5.1 APPROACH 

33 Background information on the approaches used to establish the PM secondary standards 

34 in 1997 and revisions to those standards in 2006 are summarized below. This section also 
 

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1 includes a discussion of the ongoing joint review of ecological effects of oxides of nitrogen and 

2 sulfur (NOx/SOx secondary review) for clarity, since depositional effects of PM components of 

3 NOx and SOx to ecosystems were historically considered as a component of the PM secondary 

4 review. Lastly, there is a discussion of the current approach for evaluating the effects of PM on 

5 climate, ecosystems, and materials using evidence-based considerations to inform our 

6 understanding of the key policy-relevant issues. 

7 5.1.1 Approaches Used in Previous Reviews 

8 5.1.1.1 Review Completed in 1997 

9 In the 1997 review, as discussed in section 2.1.1.1, EPA determined that for the primary 

10 standard the fine and coarse fractions of PM10 should be considered separately and added a suite 

11 of new primary standards, using PM2.5, as the indicator for fine particles, and retaining PM10 as 

12 the indicator for regulating thoracic coarse particles. The EPA established two new PM2.5 

standards: an annual standard of 15 μg/m3 13 , based on the 3-year average of annual arithmetic 

14 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 15 percentile of 24-hour PM2.5 

16 concentrations at each population-oriented monitor within an area (62 FR 38652, July 18, 1997). 

17 With respect to the secondary PM standards, EPA concluded in 1997, that the available 

18 evidence on effects of PM on non-visibility welfare endpoints was not sufficient to warrant a 

19 separate secondary standard. Therefore, the secondary standards were set equal to the primary 

20 PM2.5 and PM10 standards in the final rule to provide protection against effects on visibility as 

21 well as materials damage and soiling effects related to fine and coarse particles (62 FR 38683). 

22 5.1.1.2 Review Completed in 2006 

23 In 2006, the Administrator concluded that there was insufficient information to consider a 

24 distinct secondary standard based on PM-related impacts to ecosystems, materials damage and 

25 soiling, and climatic and radiative processes (71 FR 61144, October 17, 2006). Specifically, 

26 there was a lack of evidence linking various non-visibility welfare effects to specific levels of 

27 ambient PM. To provide a level of protection for welfare-related effects, the secondary 

28 standards were set equal to the revised primary standards to directionally improve the level of 

29 protection afforded vegetation, ecosystems and materials (71 FR 61210). 

30 In the last review, the 2004 AQCD concluded that regardless of size fraction, particles 

31 containing nitrates and sulfates have the greatest potential for widespread environmental 

32 significance (US EPA, 2004, sections 4.2.2 and 4.2.3.1). Considerable supporting evidence was 

33 available that indicated a significant role of NOx, SOx, and transformation products in 
 

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1 acidification and nutrient enrichment of terrestrial and aquatic ecosystems (71 FR 61209). The 

2 recognition of these ecological effects, coupled with other considerations detailed below, led 

3 EPA to initiate a joint review of the NO2 and SO2 secondary NAAQS that will consider the 

4 gaseous and particulate species of NOx and SOx with respect to the ecosystem-related welfare 

5 effects that result from the deposition of these pollutants and transformation products. 

6 5.1.2 Scope of Current NAAQS Reviews 

7 Non-visibility welfare-based effects of oxides of nitrogen and sulfur are divided between 

8 two NAAQS reviews; (1) the PM NAAQS review and, (2) the joint NOx/SOx secondary 

9 NAAQS review. The scope of each document and the components of N and S considered in 

10 each review are detailed in this section and summarized in Table 5-1. 

11 5.1.2.1 Scope of the Current Secondary PM NAAQS Review 

12 In reviewing the current suite of secondary PM standards to address visibility impairment 

13 (chapter 4), climate forcing effects (section 5.2), and other welfare-related effects (sections 5.3 

14 and 5.4), all PM-related effects that are not being covered in the NOx/SOx review are 

15 considered. With regard to the materials section (5.4), the discussion has been expanded to 

16 include particles and gases that are associated with the presence of ambient NOx and SOx, as 

17 well as NOy, NH3 and NHX for completeness. By excluding the effects associated with 

18 deposited particulate matter components of NOx and SOx and their transformation products 

19 which are addressed fully in the NOx/SOx secondary review, as outlined below, the discussion 

20 of ecological effects of PM has been narrowed to focus on effects associated with the deposition 

21 of metals and, to a lesser extent, organics (section 5.3). 
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1 Table 5-1. Scope of the current secondary PM NAAQS review and current NOx/SOx secondary review. 

 NOx/SOx Secondary 

Review 

PM Secondary Review

 Materials 

Welfare Effect 

Acidifying deposition, nutrient enrichment 

Direct effects of gas-phase NOx/SOx on vegetation 

Visibility impairment 

Climate Forcing effects 

Ecological effects Damage 

Documents  ISA NOx/SOx NOx/SOx PM PM PM 

PM and NOx/SOx Annex E 

 REA NOx/SOx NOx/SOx 

PM (Urban focused visibility assessment) 

 PA NOx/SOx NOx/SOx PM PM PM PM Components 

Deposited particulate and gaseous forms of oxides of nitrogen and sulfur and related N and S containing compounds. 

Gaseous forms of oxides of nitrogen and sulfur and related N and S containing compounds in the ambient air. 

All particles 10 microns or smaller in the ambient air. 

Climate-related particles (aerosols) in the ambient air. 

Deposited components of PM, including metals and organics but not N and S containing compounds. 

Particles and gases associated with ambient NOx and SOx including NOy, NH3 and NHx. 

2 
 

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1 5.1.2.2 Scope of the Current NOx/SOx Secondary NAAQS Review 

2 This is the first time since the NAAQS were established in 1971 that a joint review of the 

3 secondary NAAQS for NOx and SOx, has been conducted. This review is being conducted 

4 because the atmospheric chemistry and environmental effects of NOx, SOx, and their associated 

5 transformation products are linked, and because the National Research Council (NRC) has 

6 recommended that EPA consider multiple pollutants, as appropriate, in forming the scientific 

7 basis for the NAAQS. The NOx/SOx secondary review focuses on the welfare effects associated 

8 with exposures from deposited particulate and gaseous forms of oxides of nitrogen and sulfur 

9 and related N and S containing compounds and transformation products on ecosystem receptors. 

10 An assessment of the complex ecological effects associated with N deposition requires 

11 consideration of multiple forms of N. These include evaluation of data on inorganic reduced 

forms of N (e.g., ammonia [NH3] and ammonium ion [NH4 12 +]), inorganic oxidized forms (e.g., 

NOx, nitric acid [HNO3], nitrous oxide [N2O], nitrate [NO3 13 −]), and organic N compounds (e.g., 

14 urea, amines, proteins, nucleic acids). In addition to acidification and N-nutrient enrichment, 

15 other welfare effects related to deposition of N-and S-containing compounds are discussed, such 

16 as SOx interactions with mercury (Hg) methylation. In addition, the NOx/SOx secondary review 

17 includes evidence related to direct ecological effects of gas-phase NOx and SOx since the direct 

18 effects of gas-phase SOx on vegetation formed a primary basis for the initial establishment of the 

19 secondary NAAQS for SO2. 

20 Effects of acidifying deposition associated with particulate N and S are covered in the 

21 recent Integrated Science Assessment for Oxides of Nitrogen and Sulfur-Ecological Criteria

22 (Final Report (US EPA, 2008c). The Risk and Exposure Assessment for Review of the 

23 Secondary National Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur 

24 (Final)(NOx/SOx REA) (US EPA, 2009h) considers four main targeted ecosystem effects 

25 considered in the review of secondary effects of NOx and SOx: (1) aquatic acidification due to N 

26 and S, (2) terrestrial acidification due to N and S, (3) aquatic nutrient enrichment, including 

27 eutrophication and (4) terrestrial nutrient enrichment. In the draft Policy Assessment for Review 

28 of the Secondary National Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of 

29 Sulfur (US EPA 2010c) ecologically-based indicators that link atmospheric concentrations to 

30 deposition are being considered. 

31 5.1.3 Current Approach 

32 The remainder of this chapter summarizes and highlights key aspects of the policy 

33 relevant information from the ISA to help inform the Administrator’s judgments regarding the 

34 adequacy of the current suite of secondary PM NAAQS in relation to climate processes, 
 

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1 ecological effects, and materials damage. The ISA uses a five-level hierarchy that classifies the 

2 weight of evidence for causation, not just association, into a qualitative statement about the 

3 overall weight of evidence and causality (US EPA, 2009a, section 1.5.5, Table 1-3): causal 

4 relationship; likely to be a causal relationship; suggestive of a causal relationship; inadequate to 

5 infer a causal relationship; not likely to be a causal relationship (see US EPA, 2009a, Table 1-3). 

6 Staff is evaluating evidence-based considerations primarily by assessing the evidence of 

7 associations identified in the ISA. All relationships between PM and climate, ecological effects, 

8 and materials damage effects identified in the ISA are considered to be either “likely causal” or 

9 “causal”. The staff’s approach in this review of non-visibility welfare effects of PM is to 

10 consider information regarding particulate matter effects on climate, ecological endpoints and 

11 materials. This includes new literature available since the last review as well as existing, 

12 relevant information as presented in the ISA (US EPA 2009a). 

13 5.2 CLIMATE 

14 5.2.1 Scope 

15 Information and conclusions about what is currently known about the role of PM in 

16 climate is summarized in Chapter 9 of the PM ISA (US EPA, 2009a). The ISA concludes; “that 

17 a causal relationship exists between PM and effects on climate, including both direct effects on 

18 radiative forcing and indirect effects that involve cloud feedbacks that influence precipitation 

19 formation and cloud lifetimes” (US EPA, 2009a, section 9.3.10). Material from the climate 

20 section of the ISA is principally drawn from the U.S. Climate Change Science Program 

21 Synthesis and Assessment Product 2.3, Atmospheric Aerosol Properties and Climate Impacts, by 

22 Chin et al., (CCSP 2009) and Chapter 2, Changes in Atmospheric Constituents and in Radiative 

23 Forcing,(Forster et al., 2007) in the comprehensive Working Group I report in the Fourth 

24 Assessment Report (AR4) from the Intergovernmental Panel on Climate Change (IPCC), Climate 

25 Change 2007: The Physical Science Basis. Sections 9.3.7 (Fire as a Special Source of PM 

26 Welfare Effects), 9.3.9 (Other Special Sources and Effects), 9.3.9.1 (Glaciers and Snowpack) 

27 and 9.3.9.3 (Effects on Local and Regional Climate) of the ISA were written by NCEA staff. 

28 This section of the PA summarizes and synthesizes the policy-relevant science in the ISA for the 

29 purpose of helping to inform consideration of climate aspects in the review of the secondary PM 

30 NAAQS. 

Atmospheric PM (referred to as aerosols1 31 in the remainder of this section to be consistent 

32 with the ISA) affects multiple aspects of climate. These include absorbing and scattering of 

 

1 In the sections of the ISA included from IPCC AR4 and CCSP SAP2.3, ‘aerosols’ is more frequently used than 

“PM” and that word is retained. 

 
 

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1 incoming solar radiation, alterations in terrestrial radiation, effects on the hydrological cycle, and 

2 changes in cloud properties (US EPA, 2009a, section 9.3.1). Major aerosol components that 

3 contribute to climate processes include black carbon (BC), organic carbon (OC), sulfates, nitrates 

4 and mineral dusts. There is a considerable ongoing research effort focused on understanding 

5 aerosol contributions to changes in global mean temperature and precipitation patterns. The 

6 Climate Change Research Initiative identified research on atmospheric concentrations and effects 

7 of aerosols as a high research priority (National Research Council, 2001) and the IPCC 2007 

8 Summary for Policymakers states that anthropogenic contributions to aerosols remain the 

9 dominant uncertainty in radiative forcing (IPCC 2007). The current state of the science of 

10 climate alterations attributed to PM is in flux as a result of continually updated information. 

11 5.2.2 Adequacy of the Current Standard 

12 In considering the adequacy of the suite of secondary standards, staff addresses the 

13 following overarching question: 

14 Does currently available scientific information, as reflected in the ISA, support or call into 

15 question the adequacy of the protection for climate effects afforded by the current suite of 

16 secondary PM standards? 

17 To inform the answer to this overarching question, staff has posed specific questions to 

18 aid in assessing the available scientific evidence as related to climate effects attributed to 

19 aerosols. In considering the currently available scientific and technical information, we included 

20 both the information available from the last review and information that is newly available since 

21 the last review synthesized in Chapter 9 of the ISA (US EPA, 2009a). 

22  What new techniques are available to improve our understanding of climate effects of 

23 aerosols? 

24 Global climate change has increasingly been the focus of intense international research 

25 endeavors. Major efforts are underway to understand the complexities inherent in atmospheric 

26 aerosol interactions and to decrease uncertainties associated with climate estimations. Two recent 

27 reports, the US CCSP Product 2.3 and sections of the IPCC AR4 were combined to form the 

28 climate discussion in the ISA (CCSP 2009; Forster et al., 2007). A review of the most recently 

29 available techniques for assessing climate-aerosol relationships is presented in the ISA. Aerosol 

30 measurement capabilities reviewed in the ISA include a discussion of the increasingly 

31 sophisticated instrumentation and techniques available for quantifying aerosols, the enhanced 

32 sensing capabilities of satellites, development of remote sensing networks and synergy of 

33 measurements with model simulations (US EPA 2009a, section 9.3.2). Advances in measured 

34 aerosol properties as related to modeling as well as outstanding issues remaining in these 

35 measurement-based studies are elaborated in the ISA (US EPA 2009a sections 9.3.3 and 9.3.4). 
 

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1 Section 9.3.6 of the ISA, “Global Aerosol Modeling” considers the capabilities of climate 

2 modeling that have developed over the last decade and limitations of the techniques currently in 

3 use (US EPA 2009a). 

4  To what extent does newly available evidence improve our understanding of the nature 

5 and magnitude of climate responses to PM (aerosols)? 

6 Aerosols have direct and indirect effects on climate processes. The direct effects of 

7 aerosols on climate result mainly from particles scattering light away from earth into space, 

8 directly altering the radiative balance of the Earth-atmosphere system. This reflection of solar 

9 radiation back to space decreases the transmission of visible radiation to the surface of the earth 

10 and results in a decrease in the heating rate of the surface and the lower atmosphere. At the same 

11 time, absorption of either incoming solar radiation or outgoing terrestrial radiation by particles, 

12 primarily BC, results in an increased heating rate in the lower atmosphere. Global estimates of 

13 aerosol direct radiative forcing (RF) were recently summarized using a combined model-based 

14 estimate (Forster et al., 2007). The overall, model-derived aerosol direct RF was estimated in the 

IPCC AR4 as -0.5 (-0.9 to -0.1) watts per square meter (W/m2 15 ), with an overall level of scientific 

16 understanding of this effect as “medium low” (Forster et al., 2007), indicating a net cooling 

17 effect in contrast to greenhouse gases (GHGs) which have a warming effect. 

18 The contribution of individual aerosol components to total aerosol direct radiative forcing 

19 is more uncertain than the global average (US EPA, 2009a, section 9.3.6.6). The direct effect of 

20 radiative scattering by atmospheric particles exerts an overall net cooling of the atmosphere, 

21 while particle absorption of solar radiation leads to warming. For example, the presence of OC 

22 and sulfates decrease warming from sunlight by scattering shortwave radiation back into space. 

23 Such a perturbation of incoming radiation by anthropogenic aerosols is designated as aerosol 

24 climate forcing, which is distinguished from the aerosol radiative effect of the total aerosol 

25 (natural plus anthropogenic). The aerosol climate forcing and radiative effect are characterized 

26 by large spatial and temporal heterogeneities due to the wide variety of aerosol sources, the 

27 spatial non-uniformity and intermittency of these sources, the short atmospheric lifetime of 

28 aerosols (relative to that of the greenhouse gases), and processing (chemical and microphysical) 

29 that occurs in the atmosphere. For example, OC can be warming (positive forcer) when 

30 deposited on or suspended over a highly reflective surface such as snow or ice but, on a global 

31 average, is a negative forcer in the atmosphere. 

32 More information has also become available on indirect effects of aerosols. Particles in 

33 the atmosphere indirectly affect both cloud albedo (reflectivity) and cloud lifetime by modifying 

34 the cloud amount, and microphysical and radiative properties (US EPA, 2009a, section 9.3.6.4). 

35 The RF due to these indirect effects (cloud albedo effect) of aerosols is estimated in the IPCC 

AR4 to be -0.7(-1.8 to -0.3) W/m2 36 with the level of scientific understanding of this effect as 
 

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1 “low” (Forster et al., 2007). Aerosols act as cloud condensation nuclei (CCN) for cloud 

2 formation. Increased particulates in the atmosphere available as CCN with no change in 

3 moisture content of the clouds have resulted in an increase in the number and decrease in the size 

4 of cloud droplets in certain clouds that can increase the albedo of the clouds (the Twomey 

5 effect). Smaller particles slow the onset of precipitation and prolong cloud lifetime. This effect, 

6 coupled with changes in cloud albedo, increase the reflection of solar radiation back into space. 

7 The altitude of clouds also effects cloud radiative forcing. Low clouds reflect incoming sunlight 

8 back to space but do not effectively trap outgoing radiation, thus, cooling the planet, while higher 

9 elevation clouds reflect some sunlight but more effectively can trap outgoing radiation and act to 

10 warm the planet (US EPA, 2009a, section 9.3.3.5). 

11 The total negative RF due to direct and indirect effects of aerosols computed from the top 

of the atmosphere, on a global average, is estimated at -1.3 (-2.2 to -0.5) W/m2 12 in contrast to the 

positive RF of +2.9 (+3.2 to +2.6) W/m2 13 for anthropogenic GHGs (IPCC 2007, pg. 200). 

14 The understanding of the magnitude of aerosol effects on climate has increased 

15 substantially in the last decade. Data on the atmospheric transport and deposition of aerosols 

16 indicate a significant role for PM components in multiple aspects of climate. Aerosols can 

17 impact glaciers, snowpack, regional water supplies, precipitation and climate patterns (US EPA 

18 2009a section 9.3.9). Aerosols deposited on ice or snow can lead to melting and subsequent 

19 decrease of surface albedo (US EPA 2009a, section 9.3.9.2). Aerosols are potentially important 

20 agents of climate warming in the Arctic and other locations (US EPA, 2009a, section 9.3.9). 

21 Incidental fires and biomass burning are being recognized as having a significant impact on 

22 PM2.5 concentrations and climate forcing. Intermittent fires can occur at large enough scales to 

23 affect hemispheric aerosol concentrations (US EPA 2009a, section 9.3.7). 

24 A series of studies available since the last review examine the role of aerosols on local 

25 and regional scale climate processes (US EPA, 2009a, section 9.3.9.3). Studies on the South 

26 Coast Air Basin (SCAB) in California indicate aerosols may reduce near-surface wind speeds, 

27 which, in turn reduce evaporation rates and increase cloud lifetimes. The overall impact can be a 

28 reduction in local precipitation (Jacobson and Kaufmann, 2006). Conditions in the SCAB impact 

29 ecologically sensitive areas including the Sierra Nevadas. Precipitation suppression due to 

30 aerosols in California (Givati and Rosenfield, 2004) and other similar studies in Utah and 

31 Colorado found that orographic precipitation decreased by 15-30% downwind of pollution 

32 sources. Evidence of regional-scale impacts of aerosols on meteorological conditions in other 

33 regions of the U.S. are lacking. 
 

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1  To what extent does the currently available information provide evidence of association 

2 between specific PM constituents (i.e. BC, OC, sulfates) and climate-related effects? 

3 Advances in the understanding of aerosol components and how they contribute to climate 

4 change have enabled refined global forcing estimates of individual PM constituents. The global 

5 mean radiative effect from individual components of aerosols was estimated for the first time in 

the IPCC AR4 where they were reported to be (all in W/m2 6 units): -0.4 (+0.2) for sulfate, -0.05 

7 (+0.05) for fossil fuel-derived OC, +0.2 (+0.15) for fossil fuel derived BC, +0.03 (+0.12) for 

8 biomass burning, -0.1 (+0.1) for nitrates, and -0.1 (+0.2) for mineral dust (US EPA, 2009a, 

9 section 9.3.10). Sulfate and fossil fuel-derived OC cause negative forcing whereas BC causes 

10 positive forcing because of its highly absorbing nature (US EPA, 2009a, 9.3.6.3). Although BC 

11 comprises only a small fraction of anthropogenic aerosol mass load and aerosol optical depth 

12 (AOD), its forcing efficiency (with respect to either AOD or mass) is an order of magnitude 

13 stronger than sulfate and particulate organic matter (POM), so its positive shortwave forcing 

14 largely offsets the negative direct forcing from sulfate and POM (IPCC, 2007; US EPA 2009a, 

15 9.3.6.3). Global loadings for nitrates and anthropogenic dust remain very difficult to estimate, 

16 making the radiative forcing estimates for these constituents particularly uncertain (US EPA, 

17 2009a, section 9.3.7). 

18 Improved estimates of anthropogenic emissions of some aerosols, especially BC and OC, 

19 have promoted the development of improved global emissions inventories and source-specific 

20 emissions factors useful in climate modeling (Bond et al. 2004). Recent data suggests that BC is 

21 one of the largest individual warming agents after carbon dioxide (CO2) and perhaps methane 

22 (CH4) (Jacobson 2000; Sato et al., 2003; Bond and Sun 2005). There are several studies 

23 modeling BC effects on climate and/or considering emission reduction measures on 

24 anthropogenic warming detailed in section 9.3.9 of the ISA. Fires release large amounts of BC, 

25 CO2, CH4 and OC (US EPA, 2009a, section 9.3.7). 

26 5.2.3 Staff Conclusions 

27  Aerosols alter climate processes directly through radiative forcing and by indirect 

28 effects on cloud brightness, changes in precipitation and possible changes in cloud 

29 lifetimes. 

30  Individual components of aerosols differ in their reflective properties, and direction of 

31 climate forcing. Overall, based on current estimates of aerosol radiative forcing, 

32 aerosols have a net climate cooling effect. 

33  Most climate model simulations are based on global scale scenarios. These models 

34 may fail to consider the local variations in climate forcing due to emissions sources and 

35 local meteorological patterns. 
 

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1  Most of the warming aerosols in the U.S. are emitted by biomass burning and internal 

2 engine combustion. Much of the cooling aerosols are formed in the atmosphere by 

3 oxidation of SO2 or VOC’s. The relative mix and sources of warming and cooling 

4 components will vary in areas across the U.S. and over time. Thus, a set of controls to 

5 reduce warming PM would not necessarily reduce cooling PM and vice versa. 

6 Collectively taking into consideration the responses to specific questions regarding the 

7 adequacy of the current secondary PM standards for climate effects, we revisit the overarching 

8 question: “does available scientific information, as reflected in the ISA, support or call into 

9 question the adequacy of the protection for climate effects afforded by the current suite of 

10 secondary PM standards?” As an initial matter, we considered the appropriateness of the current 

11 secondary standard defined in terms of PM2.5 and PM10 indicators, for providing protection 

12 against potential climate effects of aerosols. Newly available scientific information on climate13 aerosol relationships has improved our understanding of direct and indirect effects of aerosols 

14 and aerosol properties. The major aerosol components that contribute to climate processes 

15 include BC, OC, sulfate, nitrate and mineral dusts. These components vary in their reflectivity, 

16 forcing efficiencies and even in the direction of climate forcing. The current standards that are 

17 defined in terms of aggregate size mass cannot be expected to appropriately target controls on 

18 components of fine and coarse particles that are related to climate forcing effects. Thus, the 

19 current mass-based PM2.5 and PM10 secondary standards are not an appropriate or effective 

20 means of focusing protection against PM-associated climate effects due to these differences in 

21 components. 

22 Overall, there is a net climate cooling associated with aerosols in the global atmosphere 

23 (US EPA, 2009a, section 9.2.10). Staff recognizes that some individual aerosol components, 

24 such as BC, are positive climate forcers, whereas others, such as OC and sulfates, are negative 

25 climate forcers. The relative mix of components will vary in areas across the U.S. and over 

26 time. Due to the spatial and temporal heterogeneity of PM components that contribute to climate 

27 forcing, uncertainties in the measurement of aerosol components, inadequate consideration of 

28 aerosol impacts in climate modeling, insufficient data on local and regional microclimate 

29 variations and heterogeneity of cloud formations, it is not currently feasible to conduct a 

30 quantitative analysis for the purpose of informing revisions of the current NAAQS PM standard 

31 based on climate. Based on these considerations, we conclude that there is insufficient 

32 information at this time to base a national ambient standard on climate impacts associated with 

current ambient concentrations of PM or its constituents2 33 . 

 2

 Given the reasons discussed above, this conclusion would apply for both the secondary (welfare based) and the 

primary (health based) standards. 
 

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1 5.2.4 Key Uncertainties and Areas for Future Research and Data Collection 

2 Although considerable progress is being made in estimating aerosol contributions to 

3 radiative forcing and climate fluctuations, significant uncertainties remain that preclude 

4 consideration of climate effects as a basis for establishing a separate NAAQS secondary 

5 standard. Further research into the effects of aerosols on climate could provide important 

6 information to reduce these uncertainties. 

7 A major impediment at this time to establishing a secondary standard for PM based on 

8 climate is the lack of accurate measurement of aerosol contributions, specifically quantification 

9 of aerosol absorption and inability to separate the anthropogenic component from total aerosol 

10 forcing. Section 9.3.4 of the ISA details the current limitations in aerosol measurement. Most 

11 measurement studies focus on the sum of natural and anthropogenic contributions under clear 

12 sky conditions, however, this scenario is simplistic when effects of cloud cover and differing 

13 reflective properties of land and ocean are considered. Satellite measurements do not currently 

14 have the capability to distinguish anthropogenic from natural aerosols. Due to a lack of data on 

15 the vertical distribution of aerosols, above-cloud aerosols and profiles of atmospheric radiative 

16 heating are poorly understood (US EPA, 2009a, section 9.3.4). 

17 Another uncertainty in considering climate effects of PM in the NAAQS review is the 

18 spatial and temporal heterogeneity of aerosols. In regions having high concentrations of 

19 anthropogenic aerosols, aerosol forcing is greater than the global average, and can exceed 

20 warming by GHGs, locally reversing the sign of the forcing (US EPA, 2009a, section 9.3.1). 

21 The contributions of policy-relevant background (PRB) concentrations to aerosol climate forcing 

22 are not sufficiently characterized (US EPA, 2009a, section 3.7). Emissions of carbonaceous 

23 aerosols from intermittent fires and volcanic activity can further complicate regional climate 

24 forcing estimates (US EPA, 2009a, sections 9.3.7 and 9.3.8). Individual components of aerosols 

25 may either be positive or negative climate forcers. Airborne PM components may be directly 

26 emitted or undergo a variety of physical and chemical interactions and transformations. These 

27 result in changes in particle size, structure and composition which alter aerosol reflective 

28 properties. Aerosols can grow in size in the atmosphere because ambient water vapor condenses 

29 on individual particles, a phenomenon known as hygroscopic growth (US EPA, 2009a, section 

30 9.3.6.2). Atmospheric lifetimes of individual aerosol components vary greatly confounding 

31 tracking source receptor relationships. 

32 Improved representation of aerosols in climate models is essential to more accurately 

33 predict the role of PM in climate forcing (US EPA, 2009a, section 9.3.6.7). The influence of 

34 aerosols on climate is not yet adequately taken into account in computer predictions although 

35 considerable progress in being made in this area. For example, PM components underrepresented 

36 or missing from many models include nitrate aerosols and anthropogenic secondary aerosols (US 
 

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1 EPA, 2009a, section 9.3.6.7). The modeling of aerosol indirect effects and absorption are 

2 difficult due to the high level of uncertainty associated with these climate factors. 

3 The interaction of PM with clouds remains a large source of uncertainty in climate 

4 estimates. The interactions of aerosols with clouds and linkages between clouds and the overall 

5 climate system are complex and limit the feasibility of conducting quantitative analysis for the

6 purpose of establishing a secondary PM standard based on welfare effects on climate processes. 

7 There are uncertainties associated with the potential effects of the alternative standards 

8 for visibility discussed in Chapter 4 on regional radiative forcing and climate. A secondary 

9 standard for visibility based on light extinction would result in reduced emissions that affect PM 

10 in areas where monitoring shows exceedance of the standard. The extinction budget work 

11 conducted for the UFVA (Figure 3-13, U.S. EPA 2010b) and second draft PA (Appendix B) 

12 indicates that most of the current visibility impact contributions on worst days comes from light 

13 scattering particles (e.g., nitrates, sulfates) that are negative climate forcers, and a smaller portion 

14 from absorbing aerosols (e.g., black carbon) that are positive climate forcers. The relative 

15 proportions of scattering and absorbing particles vary by location and some major contributing 

16 emission sources contribute to both scattering and absorbing PM, so it is unclear how the ratio of 

17 scattering to absorption might change in response to a secondary standard for visibility affects. 

18 However, since the prevailing mixture of aerosol is thought to have a net cooling effect on 

19 regional climate, reducing PM and light scattering aerosols could lead to increased radiative 

20 forcing and regional climate warming while having a beneficial effect on visibility. 

21 5.3 ECOLOGICAL EFFECTS 

22 5.3.1 Scope 

23 Information on what is currently known about ecological effects of PM is summarized in 

24 Chapter 9 of the ISA (US EPA 2009a). Four main categories of ecological effects are identified 

25 in the ISA: direct effects, effects of PM-altered radiative flux, indirect effects of trace metals and 

26 indirect effects of organics. Exposure to PM for direct effects occur via deposition (e.g. wet, dry 

27 or occult) to vegetation surfaces, while indirect effects occur via deposition to ecosystem soils or 

28 surface waters where the deposited constituents of PM then interacts with biological organisms. 

29 Both fine and coarse-mode particles may affect plants and other organisms; however, PM size 

30 classes do not necessarily relate to ecological effects (U.S. EPA, 1996). More often the chemical 

31 constituents drive the ecosystem response to PM (Grantz et al., 2003). The trace metal 

32 constituents of PM considered in the ecological effects section of the ISA are cadmium (Cd), 

33 copper (Cu), chromium (Cr), mercury (Hg), nickel (Ni) and zinc (Zn). Ecological effects of lead 

34 (Pb) in particulate form are covered in the Air Quality Criteria Document for Lead (US EPA, 

35 2006). The organics included in the ecological effects section of the ISA are persistent organic 
 

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1 pollutants (POPs), polyaromatic hydrocarbons (PAHs) and polybromiated diphenyl ethers 

2 (PBDEs). 

3 Ecological effects of PM include direct effects to metabolic processes of plant foliage; 

4 contribution to total metal loading resulting in alteration of soil biogeochemistry and 

5 microbiology, plant and animal growth and reproduction; and contribution to total organics 

6 loading resulting in bioaccumulation and biomagnification across trophic levels. It is important 

7 to emphasize that the metal and organic constituents of PM contribute to total metal and organic 

8 loads in ecosystems. 

9 The ISA states that overall, ecological evidence is sufficient to conclude that a causal 

10 relationship is likely to exist between deposition of PM and a variety of effects on individual 

11 organisms and ecosystems based on information from the previous review and limited new 

12 findings in this review (US EPA 2009a, sections 2.5.3 and 9.4.7). However the ISA also finds, 

13 in many cases, it is difficult to characterize the nature and magnitude of effects and to quantify 

14 relationships between ambient concentrations of PM and ecosystem response due to significant 

15 data gaps and uncertainties as well as considerable variability that exists in the components of 

16 PM and their various ecological effects. 

17 Ecological effects of PM must then be evaluated to determine if they are known or 

18 anticipated to have an adverse impact on public welfare. Characterizing a known or anticipated 

19 adverse effect to public welfare is an important component of developing any secondary 

20 NAAQS. The most recent secondary NAAQS reviews have assessed changes in ecosystem 

21 structure or processes using a weight-of-evidence approach that uses both quantitative and 

22 qualitative data. For example, the 2008 ozone (O3) final rule and 2010 O3 proposal conclude that 

23 a determination of what constitutes an “adverse” welfare effect in the context of secondary 

24 NAAQS review can appropriately occur by considering effects at higher ecological levels 

25 (populations, communities, ecosystems) as supported by recent literature. In the 2008 

26 rulemaking and current ozone proposal, the interpretation of what constitutes an adverse effect 

27 on vegetation can vary depending on the location and intended use of the plant. The degree to 

28 which O3-related effects are considered adverse depends on the intended use of the vegetation 

29 and its significance to public welfare (73 FR 16496). Therefore, effects (e.g. biomass loss, foliar 

30 injury, impairment of intended use) may be judged to have a different degree of impact on public 

31 welfare depending, for example, on whether that effect occurs in a Class I area, a city park, 

32 commercial cropland or private land. 

33 A paradigm useful in evaluating ecological adversity is the concept of ecosystem 

34 services. Ecosystem services identify the varied and numerous ways that ecosystems are 

35 important to human welfare. Ecosystems provide many goods and services that are of vital 

36 importance for the functioning of the biosphere and provide the basis for the delivery of tangible 
 

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1 benefits to human society. An EPA initiative to consider how ecosystem structure and function 

2 can be interpreted through an ecosystem services approach has resulted in the inclusion of 

3 ecosystem services in the NOx/SOx REA (US EPA, 2009h). The Millennium Ecosystem 

4 Assessment (MEA) defines these to include supporting, provisioning, regulating and cultural 

5 services (Hassan et al., 2005): 

6  Supporting services are necessary for the production of all other ecosystem services. 

7 Some examples include biomass production, production of atmospheric O2, soil 

8 formation and retention, nutrient cycling, water cycling, and provisioning of habitat. 

9 Biodiversity is a supporting service that is increasingly recognized to sustain many of 

10 the goods and services that humans enjoy from ecosystems. These provide a basis for 

11 three higher-level categories of services. 

12  Provisioning services, such as products (Gitay et al., 2001) i.e., food (including game, 

13 roots, seeds, nuts, and other fruit, spices, fodder), fiber (including wood, textiles), and 

14 medicinal and cosmetic products (including aromatic plants, pigments). 

15  Regulating services that are of paramount importance for human society such as (a) C 

16 sequestration, (b) climate and water regulation, (c) protection from natural hazards 

17 such as floods, avalanches, or rock-fall, (d) water and air purification, and (e) disease 

18 and pest regulation. 

19  Cultural services that satisfy human spiritual and aesthetic appreciation of ecosystems 

20 and their components. 

21 An important consideration in evaluating biologically adverse effects of PM and linkages 

22 to ecosystem services is that many of the MEA categories overlap and any one pollutant may 

23 impact multiple services. For example, deposited PM may alter the composition of soil24 associated microbial communities, which may affect supporting services such as nutrient 

25 cycling. Changes in available soil nutrients could result in alterations to provisioning services 

26 such as timber yield and regulating services such as climate regulation. If enough information is 

27 available, these alterations can be quantified based upon economic approaches for estimating the 

28 value of ecosystem services. Valuation may be important from a policy perspective because it 

29 can be used to compare the benefits of altering versus maintaining an ecosystem. Knowledge 

30 about the relationships linking ambient concentrations and ecosystem services can be used to 

31 inform a policy judgment on a known or anticipated adverse public welfare effect. 

32 This review seeks to build upon and focus this body of science using the concept of 

33 ecosystem services to qualitatively evaluate linkages between biologically adverse effects and 

34 particulate deposition. This approach is similar to that taken in the NOx/SOx REA in which the 

35 relationship between air quality indicators, deposition of N and S, ecologically relevant 

36 indicators and effects on sensitive receptors are linked to changes in ecosystem structure and 

37 services (US EPA, 2009h). This approach considers the benefits received from the resources and 
 

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1 processes that are supplied by ecosystems. Ecosystem components (e.g. plants, soils, water, 

2 wildlife) are impacted by PM air pollution, which may alter the services provided by the 

3 ecosystems in question. The goals of this policy assessment are to (1) identify ecological effects 

4 associated with PM deposition that can be linked to ecosystem services and (2) qualitatively 

5 evaluate ecological endpoints when possible. Keeping these goals and guidelines in mind, 

6 limited new data on PM effects on plants, soil and nutrient cycling, wildlife and water are 

7 evaluated in the context of ecosystem services to qualitatively evaluate linkages between 

8 biologically adverse effects and particulate deposition for the purpose of evaluating the adequacy 

9 of the current standard. 

10 5.3.2 Adequacy of the Current Standard 

11 In considering the adequacy of the suite of secondary standards, staff addresses the 

12 following overarching question: 

13 Does available scientific information, as reflected in the ISA support or call into question 

14 the adequacy of the protection afforded by the current suite of secondary PM standards for 

15 vegetation and ecosystems from the effects of deposited particulate metals and organics? 

16 To inform the answer to this overarching question, staff has posed specific questions to 

17 aid in assessing the available scientific evidence as related to ecosystem effects attributed to PM 

18 deposition as presented in the ISA (US EPA, 2009a). 

19  To what extent has key scientific evidence become available to improve our 

20 understanding of the nature and magnitude of ecosystem responses, the variability 

21 associated with these responses, and the impact of PM on ecosystem services? 

22 Key scientific evidence regarding PM effects on plants, soil and nutrient cycling, wildlife 

23 and water available since the last review is summarized below to evaluate how this information 

24 has improved our understanding of ecosystem responses to PM. 

25 Plants

26 As primary producers, plants play a pivotal role in energy flow through ecosystems. 

27 Ecosystem services derived from plants include all of the categories (supporting, provisioning, 

28 regulating, cultural) identified in the MEA (Hassan et al., 2005). Vegetation supports other 

29 ecosystem processes by cycling nutrients through food webs and serving as a source of organic 

30 material for soil formation and enrichment. Trees and plants provide food, wood, fiber, and fuel 

31 for human consumption. Flora help to regulate climate by sequestering CO2, control flooding by 

32 stabilizing soils and cycling water via uptake and evapotranspiration. Plants are significant in 

33 aesthetic, spiritual and recreational aspects of human interactions. 
 

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1 Particulate matter can adversely impact plants and ecosystem services provided by plants 

2 by deposition to vegetative surfaces (US EPA, 2009a, section 9.4.3). Particulates deposited on 

3 the surfaces of leaves and needles can block light, altering the radiation received by the plant. 

4 PM deposition can obstruct stomata limiting gas exchange, damage leaf cuticles and increase 

5 plant temperatures. This level of PM accumulation is typically observed near sources of heavy 

6 deposition such as smelters and mining operations (US EPA, 2009a, section 9.4.3). Plants 

7 growing on roadsides exhibit impact damage from near-road PM deposition, having higher levels 

8 of organics and heavy metals, and accumulate salt from road de-icing during winter months (US 

9 EPA, section 2009a, sections 9.4.3.1 and 9.4.5.7). 

10 In addition to damage to plant surfaces, deposited PM can be taken up by plants from soil 

11 or foliage. The ability of vegetation to take up heavy metals and organics is dependent upon the 

12 amount, solubility and chemical composition of the deposited PM. Uptake of PM by plants from 

13 soils and vegetative surfaces can disrupt photosynthesis, alter pigments and mineral content, 

14 reduce plant vigor, decrease frost hardiness and impair root development. The ISA indicates that 

15 there are little or no effects on foliar processes at ambient levels of PM (sections 9.4.3 and 9.4.7) 

16 however, damage due to atmospheric pollution can occur near point-sources or under conditions 

17 where plants are subjected to multiple stressors. 

18 Though all heavy metals can be directly toxic at sufficiently high concentrations, only 

19 Cu, Ni, and Zn have been documented as being frequently toxic to plants (U.S. EPA, 2004), 

20 while toxicity due to Cd, Co, and Pb has been observed less frequently (Smith, 1990; US EPA 

21 2009a, section 9.4.5.3). In general, plant growth is negatively correlated with trace metal and 

22 heavy metal concentration in soils and plant tissue (Audet and Charest, 2007). Trace metals, 

23 particularly heavy metals, can influence forest growth. Growth suppression of foliar microflora 

24 has been shown to result from Fe, Al, and Zn. These three metals can also inhibit fungal spore 

25 formation, as can Cd, Cr, Mg, and Ni (see Smith, 1990). Metals cause stress and decreased 

26 photosynthesis (Kucera et al., 2008) and disrupt numerous enzymes and metabolic pathways 

27 (Strydom et al., 2006). Excessive concentrations of metals result in phytotoxicity through: (i) 

28 changes in the permeability of the cell membrane; (ii) reactions of sulfydryl (-SH) groups with 

29 cations; (iii) affinity for reacting with phosphate groups and active groups of ADP or ATP; and 

30 (iv) replacement of essential ions (Patra et al., 2004). 

31 New information since the last review provides additional evidence of plant uptake of 

32 organics (US EPA, 2009a, section 9.4.6). An area of active study is the impact of PAHs on 

33 provisioning ecosystem services due to the potential for human and other animal exposure via 

34 food consumption (US EPA, 2009a, section 9.4.6 page 9-190). The uptake of PAHs depends on 

35 the plant species, site of deposition, physical and chemical properties of the organic compound 

36 and prevailing environmental conditions. It has been established that most bioaccumulation of 
 

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1 PAHs by plants occurs via leaf uptake, and to a lesser extent, through roots. Differences 

2 between species in uptake of PAHs confound attempts to quantify impacts to ecosystem 

3 provisioning services. For example, zucchini (Cucurbita pepo) accumulated significantly more 

4 PAHs than related plant species (Parrish et al., 2006). 

5 Plants as ecosystem regulators can serve as passive monitors of pollution (US EPA, 

6 2009a, section 9.4.2.3). Lichens and mosses are sensitive to pollutants associated with PM and 

7 have been used with limited success to show spatial and temporal patterns of atmospheric 

8 deposition of metals (US EPA, 2009a, section 9.4.2.3). For example, the presence or absence of 

9 a specific species of lichen can be used as a bioindicator of metal or organics contamination. 

10 PBDEs detected in moss and lichens in Antarctica indicate long-range transport of PM 

11 components (Yogui and Sericano 2008). In the U.S. Blue Ridge Mountains, a study linked metal 

12 concentrations in mosses to elevation and tree canopy species at some sites but not with 

13 concentrations of metals in the O horizon of soil (Schilling, 2002). A limitation to employing 

14 mosses and lichens to detect for the presence of air pollutants is the difference in uptake 

15 efficiencies of metals between species. The European Moss Biomonitoring Network has been 

16 shown to be useful in Europe for estimating general trends in metal concentrations and 

17 identification of some sources of trace contaminants, however, quantification of ecological 

18 effects is not possible due to the variability of species responses (US EPA, 2009a, section 

19 9.4.2.3). 

20 A potentially important regulating ecosystem service of plants is their capacity to 

21 sequester contaminants (US EPA, 2009a, section 9.4.5.3). Ongoing research on the application 

22 of plants to environmental remediation efforts are yielding some success in removing heavy 

23 metals and organics from contaminated sites (phytoremediation) with tolerant plants such as the 

24 willow tree (Salix spp.) and members of the family Brassicaceae (US EPA, 2009a, section 

25 9.4.5.3). Tree canopies can be used in urban locations to capture particulates and improve air 

26 quality (Freer-Smith et al., 2004). Plant foliage is a sink for Hg and other metals and this 

27 regulating ecosystem service may be impacted by atmospheric deposition of trace metals. 

28 An ecological endpoint (phytochelatin concentration) associated with presence of metals 

29 in the environment has been correlated with the ecological effect of tree mortality (Grantz et al., 

30 2003). Metal stress may be contributing to tree injury and forest decline in the Northeastern U.S. 

31 where red spruce populations are declining with increasing elevation. Quantitative assessment of 

32 PM damage to forests potentially could be conducted by overlaying PM sampling data and 

33 elevated phytochelatin levels. However, limited data on phytochelatin levels in other species 

34 currently hinders use of this peptide as a general biomarker for PM. 

35 The presence of PM in the atmosphere affects ambient radiation as discussed in the ISA 

36 which can impact the amount of sunlight received by plants (US EPA, 2009a, section 9.4.4). 
 

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1 Atmospheric PM can change the radiation reaching leaf surfaces through attenuation and by 

2 converting direct radiation to diffuse radiation. Diffuse radiation is more uniformly distributed in 

3 a tree canopy, allowing radiation to reach lower leaves. The net effect of PM on photosynthesis 

4 depends on the reduction of photosynthetically active radiation (PAR) and the increase in the 

5 diffuse fraction of PAR. Decreases in crop yields (provisioning ecosystem service) have been 

6 attributed to regional scale air pollution, however, global models suggest that the diffuse light 

7 fraction of PAR can increase growth (US EPA, 2009a, section 9.4.4). 

8 Soil and Nutrient Cycling

9 Many of the major indirect plant responses to PM deposition are chiefly soil-mediated 

10 and depend on the chemical composition of individual components of deposited PM. Major 

11 ecosystem services impacted by PM deposition to soils include support services such as nutrient 

12 cycling, products such as crops and regulating flooding and water quality. Upon entering the soil 

13 environment, PM pollutants can alter ecological processes of energy flow and nutrient cycling, 

14 inhibit nutrient uptake to plants, change microbial community structure and, affect biodiversity. 

15 Accumulation of heavy metals in soils depends on factors such as local soil characteristics, 

16 geologic origin of parent soils, and metal bioavailability. It can be difficult to assess the extent 

17 to which observed heavy metal concentrations in soil are of anthropogenic origin (US EPA, 

18 2009a, section 9.4.5.1). Trace element concentrations are higher in some soils that are remote 

19 from air pollution sources due to parent material and local geomorphology. 

20 Heavy metals such as Zn, Cu, and Cd and some pesticides can interfere with 

21 microorganisms that are responsible for decomposition of soil litter, an important regulating 

22 ecosystem service that serves as a source of soil nutrients (US EPA, 2009a, sections 9.4.5.1 and 

23 9.4.5.2). Surface litter decomposition is reduced in soils having high metal concentrations. Soil 

24 communities have associated bacteria, fungi, and invertebrates that are essential to soil nutrient 

25 cycling processes. Changes to the relative species abundance and community composition can 

26 be quantified to measure impacts of deposited PM to soil biota. A mutualistic relationship exists 

27 in the rhizophere (plant root zone) between plant roots, fungi, and microbes. Fungi in 

28 association with plant roots form mycorrhizae that are essential for nutrient uptake by plants. 

29 The role of mychorrizal fungi in plant uptake of metals from soils and effects of deposited PM 

30 on soil microbes is discussed in section 9.4.5.2 of the ISA. 

31 Wildlife

32 Animals play a significant role in ecosystem function including nutrient cycling and crop 

33 production (supporting ecosystem service), and as a source of food (provisioning ecosystem 

34 service). Cultural ecosystem services provided by wildlife include bird and animal watching, 

35 recreational hunting and fishing. Impacts on these services are dependent upon the 
 

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1 bioavailability of deposited metals and organics and their respective toxicities to ecosystem 

2 receptors. Pathways of PM exposure to fauna include ingestion, absorption and trophic transfer. 

3 Bioindicator species (known as sentinel organisms) can provide evidence of contamination due 

4 to atmospheric pollutants. Use of sentinel species can be of particular value because chemical 

5 constituents of deposited PM are difficult to characterize and have varying bioavailability (US 

6 EPA, 2009a, section 9.4.5.5). Snails readily bioaccumulate contaminants such as PAHs and 

7 trace metals. These organisms have been deployed as biomonitors for urban pollution and have 

8 quantifiable biomarkers of exposure including growth inhibition, impairment of reproduction, 

9 peroxidomal proliferation and induction of metal detoxifying proteins (metallothioneins) 

10 (Gomet-de Vaufleury, 2002; Regoli, 2006). Earthworms have also been used as sensitive 

11 indicators of soil metal contamination. 

12 Evidence of deposited PM effects on animals is limited (US EPA, 2009a, section 9.4.5.5). 

13 Trophic transfer of pollutants of atmospheric origin has been demonstrated in limited studies. 

14 PM may also be transferred between aquatic and terrestrial compartments. There is limited 

15 evidence for biomagnifications of heavy metals up the food chain except for Hg which is well 

16 known to move readily through environmental compartments (US EPA, 2009a section 9.4.5.6). 

17 Bioconcentration of POPs and PBDEs in the Arctic and deep-water oceanic food webs indicates 

18 the global transport of particle-associated organics (US EPA, 2009a, section 9.4.6). Salmon 

19 migrations are contributing to metal accumulation in inland aquatic systems, potentially 

20 impacting the provisioning and cultural ecosystem service of fishing (US EPA, 2009a, section 

21 9.4.6). Stable isotope analysis can be applied to establish linkages between PM exposure and 

22 impacts to food webs, however, the use of this evaluation tool is limited for this ecological 

23 endpoint due to the complexity of most trophic interactions (US EPA 2009a, section 9.4.5.6). 

24 Foraging cattle have been used to assess atmospheric deposition and subsequent bioaccumulation 

25 of Hg and trace metals and their impacts on provisioning services (US EPA, 2009a, section 

26 9.4.2.3). 

27 Water

28 New limited information on impacts of deposited PM on receiving water bodies indicate 

29 that the ecosystem services of primary production, provision of fresh water, regulation of climate 

30 and floods, recreational fishing and water purification are adversely impacted by atmospheric 

31 inputs of metals and organics (US EPA, 2009a, sections 9.4.2.3 and 9.4.5.4). Deposition of PM 

32 to surfaces in urban settings increases the metal and organic component of storm water runoff 

33 (US EPA, 2009a, sections 9.4.2.3). This atmospherically-associated pollutant burden can then be 

34 toxic to aquatic biota. 
 

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1 Atmospheric deposition can be the primary source of some organics and metals to 

2 watersheds. The contribution of atmospherically deposited PAHs to aquatic food webs was 

3 demonstrated in high elevation mountain lakes with no other anthropogenic contaminant sources 

4 (US EPA, 2009a, section 9.4.6). Metals associated with PM deposition limit phytoplankton 

5 growth, impacting aquatic trophic structure. Long-range atmospheric transport of 47 pesticides 

6 and degradation products to the snowpack in seven national parks in the Western U.S. was 

7 recently quantified indicating PM-associated contaminant inputs to receiving waters during 

8 spring snowmelt (Hageman et al., 2006). 

9  What new techniques are available to improve our understanding of ecosystem effects 

10 associated with metal and organic components of PM? 

11 The recently completed Western Airborne Contaminants Assessment Project (WACAP) 

12 is the most comprehensive database on contaminant transport and PM depositional effects on 

13 sensitive ecosystems in the U.S. In this project, the transport, fate, and ecological impacts of 

14 anthropogenic contaminants from atmospheric sources were assessed from 2002 to 2007 in seven 

15 ecosystem components (air, snow, water, sediment, lichen, conifer needles and fish) in eight 

16 core national parks (Landers et al., 2008). The goals of the study were to identify where the 

17 pollutants were accumulating, identify ecological indicators for those pollutants causing 

18 ecological harm, and to determine the source of the air masses most likely to have transported 

19 the contaminants to the parks (US EPA, 2009a, section 9.4.6). Collected data were analyzed to 

20 identify probable local, regional and/or global sources of deposited PM components and their 

21 concurrent effects on ecological receptors. The study concluded that bioaccumulation of semi22 volatile organic compounds (VOCs) was observed throughout park ecosystems (Landers et al., 

23 2008). Findings from this study included the observation of an elevational gradient in PM 

24 deposition with greater accumulation at higher altitude areas of the parks. Furthermore, specific 

25 ecological indicators were identified in the WACAP that can be useful in assessing 

26 contamination on larger spatial scales. For example, quantification of concentrations of selected 

27 pesticides in second-year conifer needles served as a method for regional-scale comparison of 

28 pollutant distribution (Landers et al., 2008). 

29 In the WACAP study, bioaccumulation and biomagnification of airborne contaminants 

30 were demonstrated on a regional scale in remote ecosystems in the Western United States. 

31 Contaminants were shown to accumulate geographically based on proximity to individual 

32 sources or source areas, primarily agriculture and industry (Landers et al., 2008). This finding 

33 was counter to the original working hypothesis that most of the contaminants found in western 

34 parks would originate from Eastern Europe and Asia (Landers et al., 2008 p 6-8). The WACAP 

35 study represents an experimental design in which ecological effects could be correlated to 

36 ambient pollutant levels on a regional scale. Although this assessment focuses on chemical 
 

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1 species that are components of PM, it does not specifically assess the effects of particulates 

2 versus gas-phase forms; therefore, in most cases it is difficult to apply the results to this 

3 assessment based on particulate concentration and size fraction (US EPA, 2009a, section 9.4.6). 

4 There is a need for ecological modeling of PM components in different environmental 

5 compartments to further elucidate links between PM and ecological indicators. 

6 Europe and other countries are using the critical load approach to assess pollutant effects 

7 at the level of the ecosystem. This type of assessment requires site-specific data and information 

8 on individual species responses to PM. In respect to trace metals and organics, there are 

9 insufficient data for the vast majority of U.S. ecosystems to calculate critical loads, however, a 

10 methodology is being presented in the NOx/SOx Secondary REA (US EPA 2009h) to calculate 

11 atmospheric concentrations from deposition that may be applicable to other environmental 

12 contaminants. 

13  Is there currently available information on ambient levels of PM that cause adverse 

14 effects on ecosystem components? 

15 As reviewed above, there is considerable data on impacts of PM on ecological receptors, 

16 but few studies that link ambient PM levels to observed effect. This is due, in part, to the nature, 

17 deposition, transport and fate of PM in ecosystems. PM is not a single pollutant, but a 

18 heterogeneous mixture of particles differing in size, origin and chemical composition (US EPA, 

19 2009a, section 9.4.1). The heterogeneity of PM exists not only within individual particles or 

20 samples from individual sites, but to even a greater extent, between samples from different sites. 

21 Since vegetation and other ecosystem components are affected more by particulate chemistry 

22 than size fraction, exposure to a given mass concentration of airborne PM may lead to widely 

23 differing plant or ecosystem responses, depending on the particular mix of deposited particles. 

24 Many of the PM components bioaccumulate over time in organisms or plants, making 

25 correlations to ambient levels of PM difficult. For example, in the WACAP study, SOC 

26 accumulation in vegetation and air showed different patterns, possibly because each medium 

27 absorbs different types of SOCs with varying efficiencies (Landers et al., 2008). 

28 Bioindicator organisms demonstrated biological effects including growth inhibition, 

29 metallothionein induction and reproductive impairment when exposed to complex mixtures of 

30 ambient air pollutants (US EPA 2009a, section 9.4.5.5). Other studies quantify uptake of metals 

31 and organics by plants or animals. However, due to the difficulty in correlating individual PM 

32 components to a specific physiological response, these studies are limited. Furthermore, there 

33 may be differences in uptake between species such as differing responses to metal uptake 

34 observed in mosses and lichens (US EPA 2009a, section 9.4.2.3). PM may also biomagnify 

35 across trophic levels confounding efforts to link atmospheric concentrations to physiological 

36 endpoints (US EPA, 2009a, section 9.4.5.6). 
 

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1 Evidence of PM effects that are linked to a specific ecological endpoint can be observed 

2 when ambient levels are exceeded. Most direct ecosystem effects associated with particulate 

3 pollution occur in severely polluted areas near industrial point sources (quarries, cement kilns, 

4 metal smelting) (US EPA, 2009a, sections 9.4.3 and 9.4.5.7). Extensive research on biota near 

5 point sources provide some of the best evidence of ecosystem function impacts and demonstrates 

6 that deposited PM has the potential to alter species composition over long time scales. 

7 Ecological field studies conducted in proximity to Cu-Ni smelter in Harjavalta, Finland indicated 

8 ecological structure and community composition are altered in response to PM and these effects 

9 decrease with increasing distance from the point source (US EPA 2009a, section 9.4.5.7). The 

10 ISA indicates at 4 km distance, species composition of vegetation, insects, birds, and soil 

11 microbiota changed, and within 1 km only the most resistant organisms were surviving (US 

12 EPA, 2009a, section 9.4.5.7). Heavy metal concentrations were quantified in understory plant 

13 species growing at varying distance from the Harjavalta smelter (Salemaa et al., 2004). Heavy 

14 metal concentrations were highest in bryophytes, followed by lichens and were lowest in 

15 vascular plants. At the Harjavalta smelter there are clear links between PM deposition levels, 

16 ecological endpoints and compromised ecosystem structure. However, these conditions are not 

17 reflective of ambient concentrations of PM in the majority of US ecosystems (US EPA, 2009a, 

18 section 9.4.7). 

19 5.3.3 Staff Conclusions 

20  A number of significant environmental effects that either have already occurred or are 

21 currently occurring are linked to deposition of chemical constituents found in ambient 

22 PM. 

23  Ecosystem services can be adversely impacted by PM in the environment, including 

24 supporting, provisioning, regulating and cultural services. 

25  The lack of sufficient information to relate specific ambient concentrations of 

26 particulate metals and organics to a degree of impairment of a specific ecological 

27 endpoint hinders our ability to identify a range of appropriate indicators, levels, forms 

28 and averaging times of a distinct secondary standard to protect against associated 

29 effects. 

30  Data from regionally-based ecological studies can be used to establish probable local, 

31 regional and/or global sources of deposited PM components and their concurrent 

32 effects on ecological receptors. 

33 Taking into consideration the responses to specific questions regarding the adequacy of 

34 the current secondary PM standards for ecological effects, we revisit the overarching question: 

35 “does available scientific information, as reflected in the ISA, support or call into question the 

36 adequacy of the protection for ecosystems afforded by the current suite of secondary PM 

37 standards?” Staff concludes that the available information is insufficient to assess the adequacy 
 

Draft-Do Not Quote or Cite June 2010

5-24

1 of the protection for ecosystems afforded by the current suite of PM secondary standards. 

2 Ecosystem effects linked to PM are difficult to determine because the changes may not be 

3 observed until pollutant deposition has occurred for many decades. Because the high levels 

4 necessary to cause injury occur only near a few limited point sources and/or on a very local 

5 scale, protection against these effects alone may not provide sufficient basis for considering a 

6 separate secondary NAAQS based on the ecological effects of particulate metals and organics. 

7 Data on ecological responses clearly linked with atmospheric PM is not abundant enough to 

8 perform a quantitative analysis although the WACAP study may represent an opportunity for 

9 quantification at a regional scale. At this time, we conclude that available evidence is not 

10 sufficient for establishing a distinct national standard for ambient PM based on ecosystem effects 

11 of particulates not addressed in the NOx/SOx secondary review (e.g. metals, organics). 

12 Staff considered the appropriateness of continuing to use the PM2.5 and PM10 size 

13 fractions as the indicators for protection of ecological effects of PM. Though the chemical 

14 constitution of individual particles can be strongly correlated with size, the relationship between 

15 particle size and particle composition can also be quite complex, making it difficult in most cases 

16 to use particle size as a surrogate for chemistry. At this time it remains to be determined as to 

17 what extent PM secondary standards focused on a given size fraction would result in reductions 

18 of the ecologically relevant constituents of PM for any given area. Nonetheless, in the absence of 

19 information that provides a basis for specific standards in terms of particle composition, 

20 observations continue to support retaining an appropriate degree of control on both fine and 

21 coarse particles to help address effects to ecosystems and ecosystem components associated with 

22 PM. 

23 5.3.4 Key Uncertainties and Areas for Future Research and Data Collection 

24 The above discussions identify linkages between ecological effects of deposited PM and 

25 potential impacts to ecosystem services. Unfortunately, our ability to relate ambient 

26 concentrations of PM to ecosystem response is hampered by a number of significant data gaps 

27 and uncertainties. These limitations include the presence of multiple ecological stressors 

28 confounding attempts to link specific ecosystem responses to PM deposition. These stressors 

29 can be anthropogenic (e.g. habitat destruction, eutrophication, other pollutants) or natural (e.g. 

30 drought, fire, disease). Deposited PM interacts with other stressors to affect ecosystem patterns 

31 and processes. Furthermore, the environmental effects of deposited PM are decoupled in space 

32 and time from the point of emission confounding efforts to identify ecological perturbations 

33 attributed to PM deposition. 

34 A second source of uncertainty lies in predicting the amount of PM deposited to sensitive 

35 receptors from measured concentrations of PM in the ambient air. This makes it difficult to 
 

Draft-Do Not Quote or Cite June 2010

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1 relate a given air concentration to a receptor response, an important factor in being able to set a 

2 national ambient air quality standard. A multitude of factors such as the mode of deposition 

3 (wet, dry and occult), wind speed, surface roughness or stickiness, elevation, particle 

4 characteristics (e.g. size, shape, chemical composition), and relative humidity exert varying 

5 degrees of influence on the deposition velocities for different PM components in any point in 

6 time. Composition of ambient PM varies in time and space and the particulate mixture may have 

7 synergistic, antagonistic or additive effects on ecological receptors depending upon the chemical 

8 species present. Furthermore, presence of co-occurring pollutants make it difficult to attribute 

9 observed effects to ecological receptors to PM alone or one component of deposited PM. 

10 Third, each ecosystem has developed within a context framed by the topography, 

11 underlying bedrock, soils, climate, meteorology, hydrologic regime, natural and land use history, 

12 and species composition that make it unique from all others. Sensitivity of ecosystem response 

13 is highly variable in space and time. Because of this variety and lack of sufficient baseline data 

14 on each of these features for most ecosystems, it is currently not possible to extrapolate with 

15 confidence any effect from one ecosystem to another. Further research is needed to decrease the 

16 uncertainties associated with ambient PM effects on ecosystems and ecosystem components. 

17 5.4 MATERIALS 

18 5.4.1 Scope 

19 Welfare effects on materials associated with deposition of PM include both physical 

20 damage (materials damage effects) and impaired aesthetic qualities (soiling effects). Because the 

21 effects of PM are exacerbated by the presence of acidic gases and can be additive or synergistic 

22 due to the complex mixture of pollutants in the air and surface characteristics of the material, this 

23 discussion will also include those particles and gases that are associated with the presence of 

24 ambient NOx and SOx, as well as NH3 and NHX for completeness. Building upon the 

25 information presented in the last Staff Paper (US EPA, 2005), and including the limited new 

26 information presented in Chapter 9 of the PM ISA (US EPA, 2009a) and Annex E. Effects of 

27 NOy, NHx, and SOx on Structures and Materials of the Integrated Science Assessment for 

28 Oxides of Nitrogen and Sulfur-Ecological Criteria (NOx/SOx ISA) (US EPA, 2008c) the 

29 following sections consider the policy-relevant aspects of physical damage and aesthetic soiling 

30 effects of PM on materials including metal and stone. 

31 The ISA concludes that evidence is sufficient to support a causal relationship between 

32 PM and effects on materials (US EPA, 2009a, sections 2.5.4 and 9.5.4). The deposition of PM 

33 can physically affect materials, adding to the effects of natural weathering processes, by 

34 potentially promoting or accelerating the corrosion of metals, by degrading paints and by 

35 deteriorating building materials such as stone, concrete and marble (US EPA, 2009a, section 
 

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5-26

1 9.5). Particles contribute to these physical effects because of their electrolytic, hygroscopic and 

2 acidic properties, and their ability to sorb corrosive gases (principally SO2). In addition, the 

3 deposition of ambient PM can reduce the aesthetic appeal of buildings and objects through 

4 soiling. Particles consisting primarily of carbonaceous compounds cause soiling of commonly 

5 used building materials and culturally important items such as statues and works of art. Soiling 

6 is the deposition of particles on surfaces by impingement, and the accumulation of particles on 

7 the surface of an exposed material results in degradation of its appearance (US EPA 2009a, 

8 section 9.5). Soiling can be remedied by cleaning or washing, and depending on the soiled 

9 material, repainting. 

10 5.4.2 Adequacy of the Current Standard 

11 In considering the adequacy of the suite of secondary standards, staff addresses the 

12 following overarching question: 

13 Does available scientific information, as reflected in the ISA support or call into question 

14 the adequacy of the protection for materials afforded by the current suite of secondary PM 

15 standards? 

16 To inform the answer to this overarching question, staff has posed a specific question to 

17 aid in assessing the available scientific evidence as related to materials damage and soiling 

18 attributed to PM deposition as presented in the ISA (US EPA, 2009a).

19  What new evidence is available to improve our understanding of effects of PM on 

20 materials and linking ambient concentrations to materials damage? 

21 The majority of available new studies on materials effects of PM are from outside the 

22 U.S., however, they provide limited new data for consideration of the secondary standard. 

23 Metal and stone are susceptible to damage by ambient PM. Considerable research has 

24 been conducted on the effects of air pollutants on metal surfaces due to the economic importance 

25 of these materials, especially steel, zinc, aluminum, and copper. Chapter 9 of the PM ISA and 

26 Annex E of the NOx/SOx ISA summarize the results of a number of studies on the corrosion of 

27 metals (US EPA, 2009a; US EPA, 2008c). Moisture is the single greatest factor promoting metal 

28 corrosion, however, deposited PM can have additive, antagonistic or synergistic effects. In 

29 general, SO2 is more corrosive than NOx although mixtures of NOx, SO2 and other particulate 

30 matter corrode some metals at a faster rate than either pollutant alone (US EPA, 2008c, Annex 

31 E.5.2). Information from both the PM ISA and NOx/SOx ISA suggest that the extent of damage 

32 to metals due to ambient PM is variable and dependent upon the type of metal, prevailing 

33 environmental conditions, rate of natural weathering and presence or absence of other pollutants. 

34 The PM ISA and NOx/SOx ISA summarize the results of a number of studies on PM and 

35 stone surfaces. While it is clear from the available information that gaseous air pollutants, in 
 

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1 particular SO2, will promote the deterioration of some types of stones under specific conditions, 

2 carbonaceous particles (non-carbonate carbon) and particles containing metal oxides may help to 

3 promote the decay process. Studies on metal and stone summarized in the ISA do not show an 

4 association between particle size, chemical composition and frequency of repair. 

5 A limited number of new studies available on materials damage effects of PM since the 

6 last review consider the relationship between pollutants and biodeterioration of structures 

7 associated with microbial communities that colonize monuments and buildings (US EPA 2009a, 

8 section 9.5). Presence of air pollutants may synergistically enhance microbial deterioration 

9 processes. The role of heterotrophic bacteria, fungi and cyanobacteria in biodeterioration varied 

10 by local meterological conditions and pollutant components. In a comparative study of 

11 biodeterioration processes on monuments in Latin America, limestone deterioration at the Mayan 

12 site of Uxmal was enhanced by biosolubilization by metabolic acids from bacteria and fungi 

13 while destruction of the Cathedral of La Plata was attributed primarily to atmospheric pollutants 

14 (Herrera and Videla, 2004). 

15 PM deposition onto surfaces such as metal, glass, stone and paint can lead to soiling. 

16 Soiling results when PM accumulates on an object and alters the optical characteristics 

17 (appearance). The reflectivity of a surface may be changed or presence of particulates may alter 

18 light transmission. These effects can impact the aesthetic value of a structure or result in 

19 reversible or irreversible damage to statues, artwork and architecturally or culturally significant 

20 buildings. Due to soiling of building surfaces by PM, the frequency and duration of cleaning 

21 may be increased. Soiling affects the aesthetic appeal of painted surfaces. In addition to natural 

22 factors, exposure to PM may give painted surfaces a dirty appearance. Pigments in works of art 

23 can be degraded or discolored by atmospheric pollutants, especially sulfates (US EPA, 2008c, 

24 Annex E-15). 

25 Formation of black crusts due to carbonaceous compounds and buildup of microbial 

26 biofilms results in discoloration of surfaces. Black crust includes a carbonate component derived 

27 from building material and organic carbon (OC) and elemental carbon (EC). In limited new 

28 studies quantifying the OC and EC contribution to soiling by black crust, OC predominated over 

29 EC at almost all locations (Bonazza et al., 2005). Limited new studies suggest that traffic is the 

30 major source of carbon associated with black crust formation (Putaud et al., 2004) and that 

31 soiling of structures in Oxford, UK showed a relationship with traffic and NO2 concentrations 

32 (Viles and Gorbushina, 2003). These findings attempt to link atmospheric concentrations of PM 

33 to observed damage. However, no data on rates of damage are available and all studies were 

34 conducted outside of the U.S. 
 

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1 5.4.3 Staff Conclusions 

2 Available evidence in regards to materials damage and soiling supports the following 

3 observations: 

4  Materials damage and soiling that occur through natural weathering processes are 

5 enhanced by exposure to atmospheric pollutants, most notably SO2 and particulate 

6 sulfates. 

7  While ambient particles play a role in the corrosion of metals and in the weathering of 

8 materials, no quantitative relationships between ambient particle concentrations and 

9 rates of damage have been established. 

10  While soiling associated with fine and course particles can result in increased cleaning 

11 frequency and repainting of surfaces, no quantitative relationships between particle 

12 characteristics and the frequency of cleaning or repainting have been established. 

13  Limited new data on the role of microbial colonizers in biodeterioration processes and 

14 contributions of black crust to soiling are not sufficient for quantitative analysis. 

15  While several studies in the PM ISA and NOx/SOx ISA suggest that particles can 

16 promote corrosion of metals there remains insufficient evidence to relate corrosive 

17 effects to specific particulate levels or to establish a quantitative relationship between 

18 ambient PM and metal degradation. With respect to damage to calcareous stone, 

19 numerous studies suggest that wet or dry deposition of particles and dry deposition of 

20 gypsum particles can enhance natural weathering processes. 

21 Revisiting the overarching policy question as to whether the available scientific evidence 

22 supports or calls into question the adequacy of the protection for materials afforded by the 

23 current suite of secondary PM standards, we conclude that no new evidence in this review calls 

24 into question the adequacy of the protection for materials afforded by the current standard. PM 

25 effects on materials can play no quantitative role in considering whether any revisions of the 

26 secondary PM NAAQS are appropriate at this time. Nonetheless, in the absence of information 

27 that provides a basis for establishing a different level of control, observations continue to support 

28 retaining an appropriate degree of control on both fine and coarse particles to help address 

29 materials damage and soiling associated with PM. 

30 5.4.4 Key Uncertainties and Areas for Future Research and Data Collection 

31 Quantitative relationships are needed between particle size, concentration, chemical 

32 concentrations and frequency of repainting and repair. Deposition rates of airborne PM to 

33 surfaces would provide an indication of rate and degree of damage to surfaces. There is 

34 considerable uncertainty with regard to interaction of co-pollutants in regards to materials 

35 damage and soiling processes. 
 

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5-29

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33 NC, EPA-452/P-10-006 

34 Viles HA; Gorbushina AA. (2003). Soiling and microbial colonisation on urban roadside limestone: A three year 

35 study in Oxford, England. , 38: 1217-1224. 

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APPENDICES 
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Appendix 2A

All U.S. Northeast Southeast

Industrial 

Midwest

Upper 

Midwest Southwest Northwest

Southern 

California

Outlying 

areas

526 87 150 134 44 21 63 16 12

182,075 42,841 40,904 38,646 7,361 9,311 18,446 22,610 2,099

annual

μg/m3

24-hour

μg/m3 Statistic

# counties 62 9 6 15 0 4 19 9 1

population 47,896 5,639 5,157 8,551 0 1,040 9,847 17,721 83

% # counties 12% 10% 4% 11% 0% 19% 30% 56% 8%

% population 26% 13% 13% 22% 0% 11% 53% 78% 4%

annual

μg/m3

24-hour

μg/m3 Statistic

# counties 151 19 45 53 0 4 20 10 1

population 77,390 10,202 13,678 22,128 0 1,040 9,866 20,535 83

% # counties 29% 22% 30% 40% 0% 19% 32% 63% 8%

% population 43% 24% 33% 57% 0% 11% 53% 91% 4%

# counties 211 40 49 72 0 4 34 11 2

population 104,539 26,228 14,063 28,417 0 1,040 14,266 20,553 114

% # counties 40% 46% 33% 54% 0% 19% 54% 69% 17%

% population 57% 61% 34% 74% 0% 11% 77% 91% 5%

# counties 239 36 79 90 0 4 20 10 1

population 102,274 21,725 18,913 30,255 0 1,040 9,866 20,535 83

% # counties 45% 41% 53% 67% 0% 19% 32% 63% 8%

% population 56% 51% 46% 78% 0% 11% 53% 91% 4%

# counties 270 46 79 95 0 4 34 11 2

population 114,610 27,991 18,913 31,876 0 1,040 14,266 20,553 114

% # counties 51% 53% 53% 71% 0% 19% 54% 69% 17%

% population 63% 65% 46% 82% 0% 11% 77% 91% 5%

# counties 343 53 115 119 5 5 34 11 2

population 132,785 30,053 26,400 36,179 1,250 4,112 14,266 20,553 114

% # counties 65% 61% 77% 89% 11% 24% 54% 69% 17%

% population 73% 70% 65% 94% 17% 44% 77% 91% 5%

# counties 393 71 116 130 11 7 44 12 3

population 147,943 39,345 26,547 38,494 2,164 4,317 15,739 21,306 173

% # counties 75% 82% 77% 97% 25% 33% 70% 75% 25%

% population 81% 92% 65% 100% 29% 46% 85% 94% 8%

# counties 391 62 133 128 14 7 34 12 2

population 146,671 35,932 29,392 38,335 2,611 4,856 14,266 21,306 114

% # counties 74% 71% 89% 96% 32% 33% 54% 75% 17%

% population 81% 84% 72% 99% 35% 52% 77% 94% 5%

# counties 417 71 133 130 17 8 44 12 3

population 151,975 39,345 29,392 38,494 2,782 4,887 15,739 21,306 173

% # counties 79% 82% 89% 97% 39% 38% 70% 75% 25%

% population 83% 92% 72% 100% 38% 52% 85% 94% 8%

Numbers of counties, populations, and percentages of total

Current levels

Alternative levels

Numbers of counties, populations, and percentages of total

15 35

11 25

12 35

30

11 30

12

Region > 

Total # of counties >

Total population (x 1,000)>

Table 2A-1. Predicted Percent of Counties with Monitors (and percent of Population in counties with monitors)

 Not Likely to Meet Alternative Annual and 24-hour PM2.5 Standards

10 25

10 30

13 30

13 35

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Appendix 3A

All U.S. Northeast Southeast

Industrial 

Midwest

Upper 

Midwest Southwest

Northwes

t

Californi

a

Outlying 

areas

318 40 60 57 35 24 79 18 8

122,582 15,753 27,009 23,646 5,143 11,037 16,188 22,695 1,525

μg/m3 Statistic

# counties 45 0 3 1 1 14 15 11 2

population 32,169 0 4,159 348 28 7,675 2,075 17,724 320

% # counties 14% 0% 5% 2% 3% 58% 19% 61% 25%

% population 26% 0% 15% 1% 1% 70% 13% 78% 21%

μg/m3 Statistic

# counties 38 0 3 3 3 12 8 10 1

population 22,450 0 4,159 1,789 710 6,106 1,366 8,220 260

% # counties 12% 0% 5% 5% 9% 50% 10% 56% 13%

% population 18% 0% 15% 8% 14% 55% 8% 36% 17%

# counties 41 0 3 3 4 12 9 11 1

population 22,736 0 4,159 1,789 734 6,106 1,381 8,467 260

% # counties 13% 0% 5% 5% 11% 50% 11% 61% 13%

% population 19% 0% 15% 8% 14% 55% 9% 37% 17%

# counties 48 0 4 4 5 12 11 12 2

population 25,914 0 4,176 1,838 781 6,106 1,499 11,313 360

% # counties 15% 0% 7% 7% 14% 50% 14% 67% 25%

% population 21% 0% 15% 8% 15% 55% 9% 50% 24%

# counties 60 1 6 4 6 12 18 13 2

population 37,835 74 4,441 1,838 885 6,106 3,458 20,832 360

% # counties 19% 3% 10% 7% 17% 50% 23% 72% 25%

% population 31% 0% 16% 8% 17% 55% 21% 92% 24%

# counties 75 1 7 7 8 15 22 13 4

population 47,280 74 4,462 9,094 986 7,745 3,798 20,832 450

% # counties 24% 3% 12% 12% 23% 63% 28% 72% 50%

% population 39% 0% 17% 38% 19% 70% 23% 92% 29%

# counties 100 4 8 12 9 15 35 14 5

population 54,170 666 4,776 12,527 1,003 7,745 5,538 21,585 490

% # counties 31% 10% 13% 21% 26% 63% 44% 78% 63%

% population 44% 4% 18% 53% 20% 70% 34% 95% 32%

87

Alternative 24-hour level (3-year average 98th percentile)

65

75

70

85

80

Numbers of counties, populations, and percentages of total

Table 3A-1. Predicted Percent of Counties with Monitors (and percent of Population in counties with monitors)

 Not Likely to Meet Alternative 24-hour PM10 Standards

Current 3-Year Expected Exceedance Equivalent Design Value

Numbers of counties, populations, and percentages of total

150

Total # of counties >

Total population >

Region > 

Draft-Do Not Quote or Cite 3A-1 June 2010
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Chapter 4 Appendices 

Appendix 4A: Information Regarding the 1-Hour PM 2.5 Mass Indicator 

Appendix 4B: Simplified Approaches to Calculate Hourly PM Light Extinction 

Values from Hourly PM2.5 Mass and Relative Humidity Data plus 

24-hour Mean PM Composition Data 

Appendix 4C: Assessment of the PM Components Responsible for the Largest 

Hourly PM Light Extinction and PM2.5 Mass Hours Selected as 

Maximum Daily and using All Hours for 14 Urban Areas 
This page left intentionally blank. 
Draft-Do Not Quote or Cite June 2010 4A-1

1 Appendix 4A

2 

3 Information Regarding the 1-hour PM2.5 Mass Indicator 

4 

5 This Appendix presents information on 2005-2007 levels of 1-hour PM2.5 mass 

6 concentrations in the 14 urban study areas and on the “what if” PM10 light extinction conditions 

7 that would exist if the study areas met each of 10 alternative secondary PM NAAQS scenarios 

8 based on a 1-hour PM2.5 mass indicator. With respect to the latter subject, this Appendix is 

9 therefore similar to Chapter 4 of the Particulate Matter Urban-Focused Visibility Assessment 

10 (UFVA), which presented similar information for 18 secondary PM NAAQS scenarios based on 

11 PM10 light extinction as the indicator, for the current annual and 24-hour PM2.5 NAAQS, and for 

a scenario with an annual NAAQS of 12 μg/m3 and a 24-hour NAAQS of 25 μg/m3 12 . 

13 

14 1.0 Indicator and Monitoring Method 

15 

16 As in Chapter 4 of the UFVA, this Appendix excludes from all NAAQS scenarios and 

17 results all non-daylight hours and all daylight hours with relative humidity greater than 90 

18 percent. This applies to both the definition of 10 secondary NAAQS scenarios, and to graphics 

19 and tables that characterize ambient conditions. While ambient humidity should not affect 

20 conventional measurement approaches for 1-hour PM2.5 mass, the issue of co-occurrence of high 

21 humidity levels with light extinction due to natural conditions would still apply. See section 

22 3.3.5 of the UFVA. The assumed hours of daylight are the same as those used in the UFVA, as 

23 shown in Table 3-5 of the UFVA. 

24 

25 All values for 1-hour PM2.5 mass concentration in this appendix come from the 

26 continuous instruments at the 14 urban study sites, with no adjustment to make these values 

27 consistent with the collocated 24-hour FRM measurement of PM2.5 mass. Appendix A of the 

28 UFVA provides details on the type of continuous instrument at each study site. TEOMs were 

29 used at all sites except for beta attenuation instruments in Fresno and Philadelphia, nephelometer 

30 instruments in Tacoma and Phoenix, and an FDMS instrument in Salt Lake City. 

31 

32 For conciseness in this second external review draft, only the daily maximum daylight 1-

33 hour PM2.5 mass concentration indicator is considered in this Appendix. It would also be 

34 possible to construct alternative NAAQS scenarios of an all-hours type, which could be analyzed 

35 in the same manner as presented in this Appendix. 

36 

37 2.0 Current Conditions of 1-hour PM2.5 Mass 

38 

39 Figure 4A-1 is a box plot of 2005-2007 daily maximum daylight 1-hour PM2.5 mass 

40 concentrations for the 14 study areas, excluding hours with relative humidity greater than 90 

41 percent, to give a sense of the range and central tendency of this parameter. The horizontal 

reference lines are at 10, 20, 30, 40 and 60 μg/m3

.

 42 The relative positions of the 90 percentile 

43 concentrations (indicated by the horizontal stroke at the top of the whisker) are generally 

44 consistent with the relative ranking of these sites according to their design values for the 24-hour 

45 PM2.5 NAAQS (see Table 3-2 of the UFVA); similarly, the relative positions of the median 
Draft-Do Not Quote or Cite June 2010 4A-2

1 concentrations are generally consistent with the annual PM2.5 design values. Table 4A-1, based 

2 on the same data as Figure 4A-1, presents the percentage of days in 2005-2007 on which the 

3 daily maximum daylight 1-hour PM2.5 concentration exceeded the reference levels represented 

4 by the horizontal lines in Figure 4A-1. 

5 

6 Figure 4A-1. 2005-2007 daily maximum daylight 1-hour PM2.5 mass concentrations 

(μg/m3 7 ) for the 14 study areas (excluding hours with relative humidity greater than 90 

8 percent) 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 
Draft-Do Not Quote or Cite June 2010 4A-3

1 Table 4A-1. Percentage of days with daily maximum daylight 1-hour PM2.5 mass 

2 concentration exceeding reference levels in 2005-2007 (excluding hours with relative 

3 humidity greater than 90 percent) 

4 

1-hour PM2.5 Mass Reference Level (μg/m3) 

Study Area Number of Days 

with Estimates 10 20 30 40 60 

Tacoma 109 50 11 1 0 0 

Fresno 324 88 62 37 20 8 

Los Angeles 300 92 81 67 46 20 

Phoenix 86 60 8 1 1 1 

Salt Lake City 306 64 20 11 7 2 

Dallas 273 75 25 5 0 0 

Houston 148 80 42 14 5 0 

Birmingham 349 92 60 37 23 8 

Atlanta 279 86 56 28 10 1 

Detroit 141 92 72 52 36 13 

Pittsburgh 277 94 57 28 15 3 

Baltimore 181 90 46 22 8 1 

Philadelphia 143 99 84 63 45 20 

New York 225 75 43 25 13 3 

5 

6 3.0 Alternative NAAQS Scenarios Based on 1-hour PM2.5 Mass as the Indicator 

7 

8 To ensure examination of a wide enough range of alternative standards based on 1-hour 

9 PM2.5 mass to encompass the range of standards that might be considered as alternatives to the 

10 PM10 light extinction NAAQS scenarios examined in Chapter 4 of the UFVA, we considered 

levels of 10, 20, 30, 40, and 60 μg/m3 11 . Only the daily maximum daylight hour form was 

12 considered. Each level was combined with two statistical forms: the three-year average of the 

annual 90th percentile value and the three-year average of the annual 95th 13 percentile value. For 

14 ease of reference, these scenarios are designated by letters from “aa” to “jj” and listed in Table 

15 4A-2. Looking somewhat ahead to results presented below, the scenarios are arranged in Table 

16 4A-2 in order of least to most stringent in terms of the reductions in ambient PM2.5 needed from 

17 current levels to meet the current and alternative NAAQS levels and forms. 

18 

19 Table 4A-2. Alternative NAAQS scenarios based on daily maximum daylight 1-hour PM2.5

20 mass, averaged over three years (excluding hours with relative humidity greater than 90 

21 percent) 

22 

NAAQS Scenario Level (μg/m3) Statistical Form 

aa 60 3-year average of 90th percentile 

bb 60 3-year average of 95th percentile 

cc 40 3-year average of 90th percentile 

dd 40 3-year average of 95th percentile 

ee 30 3-year average of 90th percentile 

ff 30 3-year average of 95th percentile 

gg 20 3-year average of 90th percentile 

hh 20 3-year average of 95th percentile 

ii 10 3-year average of 90th percentile 

jj 10 3-year average of 95th percentile 
Draft-Do Not Quote or Cite June 2010 4A-4

1 4.0 Approach to Modeling “What If” Conditions of PM10 Light Extinction for 

2 Alternative Secondary NAAQS Based on 1-hour PM2.5 Mass 

3 

4 Before modeling “what if” conditions, we augmented the data set described in Table 4 of 

5 the UFVA in the same manner as described in Section 4.1.4 of the UFVA, to achieve seasonal 

6 balance despite the lack of monitoring data for one quarter in each of Houston and Phoenix. In 

7 Tacoma and Phoenix, which had data only for two years in the 2005-2007 period, we averaged 

8 the percentile values from the only two available years rather than the three years defined for the 

9 statistical form of the NAAQS scenarios. 

10 

11 The modeling of daily maximum daylight 1-hour PM2.5 mass under each of the scenarios 

12 listed in Table 4A-2 used a rollback approach that combined relevant concepts and steps from 

13 the rollback methods described in sections 4.1.4 (for PM10 light extinction scenarios) and 4.2.2 

14 (for scenarios based on annual average and 24-hour average PM2.5) of the UFVA. The following 

15 are the steps in the modeling. 

16 

1. Identify the 90th 17 percentile daily maximum daylight 1-hour PM2.5 mass value in each of 

18 2005, 2006, and 2007 for a study area. Average these to determine the 3-year average design 

value for that percentile form. Repeat for the 95th 19 percentile form. These design values are 

presented in Table 4A-3. They range from 22 to 81 μg/m3 20 , indicating that some study areas meet 

21 some of the NAAQS scenarios under current conditions. In such cases, PM2.5 concentrations 

22 were not adjusted, i.e., there was no “roll up” for any area in any scenario. 

23 

2. Using the same days and hours as contributed by the three annual 90th 24 percentile values 

25 for actual 1-hour PM2.5 mass, find the three corresponding values of policy relevant background 

26 (PRB) 1-hour PM2.5 mass. Average these three annual values of PRB 1-hour PM2.5 to obtain the 

3 year average PRB portion of the actual 1-hour PM2.5 design value for the 90th 27 percentile form. 

Repeat for the 95th 28 percentile form. 

29 

30 In the modeling for the NAAQS scenarios examined in the UFVA, PRB for 1-hour PM2.5

31 mass was not explicitly calculated because it was not needed in the rollback modeling for the 

32 scenarios addressed in the UFVA. Therefore, it was necessary to reconstruct this parameter by 

33 adding the values for the PRB concentrations of the five components of PM2.5: nitrate, sulfate, 

34 elemental carbon, organic carbon material, and soil. The method for estimating PRB for these 

35 five components is described in Appendix C of the UFVA. 

36 

37 3. Subtract the value from step 2 from the value from step 1, to determine the non-PRB 

38 portion of the 1-hour PM2.5 mass design value. 

39 

40 4. Calculate the percentage reduction required in non-PRB 1-hour PM2.5 mass in order to 

41 reduce the design value to the level that defines the NAAQS scenario, using the following 

42 equation: 

43 

44 Percent reduction required = 1 – (NAAQS level – PRB portion of the design value)/(non-PRB 

45 portion of the design value) 

46 
Draft-Do Not Quote or Cite June 2010 4A-5

1 The percentage reductions determined in step 4 are shown in Table 4A-4. Note that for 

2 some combinations of area and scenario no reduction is required because the 2005-2007 design 

3 value already meets the NAAQS scenario. 

4 

5 5. Turning to the entire set of day/hour-specific actual and PRB daylight 1-hour 

6 concentrations of the five PM2.5 components for the three (or two) year period, determine the 

7 non-PRB portion of each of the five components in an hour by subtracting the PRB value from 

8 actual value, reduce it by the percentage determined in step 4, and add back in the PRB 1-hour 

9 concentration of the component. 

10 

11 6. Finally, calculate PM10 light extinction using the reduced values of the five components, 

12 the original value of 1-hour PM10-2.5, and the 1-hour value of f(RH), according to the following 

13 equation for PM10 light extinction (see section 3.2.3 of the UFVA for an explanation of the 

14 variables in this equation). 

15 

16 bextPM = 3 x f(RH) x [Sulfate] 

17 + 3 x f (RH) x [Nitrate] 

18 + 4 x [Organic Mass] 

19 +10 x [Elemental Carbon] 

20 + 1 x [Fine Soil] 

21 + 0.6 x [Coarse Mass] 

22 

23 These steps assume that in order to meet a PM NAAQS scenario based on 1-hour PM2.5

24 as the indicator, each component of PM2.5 is reduced by an equal percentage, across the five 

25 components and across all hours. In actual implementation of such a NAAQS, each state would 

26 develop an attainment strategy, which might result in unequal percentage reductions of the 

27 components. If the strategy emphasized reductions in the fine soil component, for example, PM 

28 light extinction levels would remain high relative to those estimated by these steps, because fine 

29 soil is not efficient in terms of reducing visibility compared to the other four components on a 

30 dry mass-to-mass basis. On the other hand, a strategy that involves relatively large reductions in 

31 sulfate or nitrate would achieve greater reductions in PM light extinction than estimated by these 

32 steps. The uncertainty in how the results of this rollback method compare to the results of actual 

33 attainment strategies should be kept in mind when comparing the results of “what if” scenarios 

34 for NAAQS based on PM2.5 mass as the indicator versus scenarios based on PM10 light 

35 extinction. Unlike the effect of humidity variation between areas, this source of uncertainty is 

36 not reflected in any of the results presented in this Appendix and will not be apparent in 

37 comparisons of results in this Appendix to results presented in the UFVA for NAAQS scenarios 

38 based on PM10 light extinction. 

39 

40 These steps also assume no change in PM10-2.5 concentrations between current conditions 

41 and “what if” conditions. While reductions in PM10-2.5 would not be needed to meet a secondary 

42 NAAQS based on 1-hour PM2.5 mass, it is possible that strategies to control PM2.5 concentrations 

43 might also achieve reductions in PM10-2.5 concentrations because some sources emit both and 

44 some control methods achieve some reductions in both. However, in most of the 14 study areas, 

45 PM10-2.5 makes a small contribution to estimated PM10 light extinction, in part because in many 

46 of the areas no local data on PM10-2.5 concentrations were available and the method used to fill 
Draft-Do Not Quote or Cite June 2010 4A-6

1 this gap (application of a factor to PM2.5 concentration) simply could not produce a high estimate 

2 of PM10-2.5. 

3 

Table 4A-3. 2005-2007 design values for 1-hour PM2.5 mass (μg/m3 4 )

5 

Study Area Percentile Form 

90th 95th 

Tacoma 22 27 

Fresno 55 66 

Los Angeles 72 81 

Phoenix 20 24 

Salt Lake City 32 45 

Dallas 26 29 

Houston 33 37 

Birmingham 55 74 

Atlanta 40 45 

Detroit 64 79 

Pittsburgh 46 51 

Baltimore 37 43 

Philadelphia 67 77 

New York 44 55 

6 

7 

8 Table 4A-4. Percentage reductions in non-PRB PM2.5 components required to meet 

9 NAAQS scenarios based on 1-hour PM2.5 mass 

10 

Scenario aa bb cc dd ee ff gg hh ii jj 

Level (μg/m3 ) 60 60 40 40 30 30 20 20 10 10 

Percentile Form 90 95 90 95 90 95 90 95 90 95 

Study Area Percentage Reduction 

Tacoma 0 0 0 0 0 0 11 27 60 69 

Fresno 0 10 28 40 46 55 65 71 83 86 

Los Angeles 17 26 45 51 59 64 73 76 87 88 

Phoenix 0 0 0 0 0 0 0 15 51 58 

Salt Lake City 0 0 0 12 7 34 39 56 70 78 

Dallas 0 0 0 0 0 0 23 34 64 69 

Houston 0 0 0 0 9 20 40 49 71 78 

Birmingham 0 19 28 46 46 60 65 74 84 87 

Atlanta 0 0 0 12 25 34 51 57 77 80 

Detroit 7 24 38 50 54 63 70 75 85 88 

Pittsburgh 0 0 13 22 35 42 57 62 79 81 

Baltimore 0 0 0 8 19 31 47 55 74 78 

Philadelphia 10 22 40 49 55 62 71 75 86 88 

New York 0 0 8 28 32 46 55 65 78 83 

11 

12 
Draft-Do Not Quote or Cite June 2010 4A-7

1 5.0. 1-hour PM2.5 Mass Results for “Just Meeting” Alternative Secondary NAAQS 

2 Scenarios Based on 1-hour PM2.5 Mass 

3 

4 As a check on the reasonableness of the rollback method described in section 4.0 and on 

5 the accuracy of the code used to implement it, it is of interest to examine the distribution of the 

6 levels of 1-hour PM2.5 that result from the method. Ideally, after rollback any area that had a 

7 non-zero required reduction should have a post-rollback design value for 1-hour PM2.5 mass that 

8 is exactly equal to the target design value. Also, there should be a progression of reductions in 1-

9 hour PM2.5 medians and other percentile points on the distribution as progressively more 

10 stringent scenarios are modeled. 

11 

12 Table 4A-5 shows the post-rollback 1-hour PM2.5 mass design values for the scenarios, 

13 with percentile forms matched. Design values for area-scenario combinations for which the 

14 required reductions were zero have been omitted, because the current conditions design values 

15 for these combinations would not be expected to reflect the target design value. It can be seen 

16 that the design values progress as expected and are in the vicinity of the target design values, but 

17 are not always exactly equal to the targets. EPA staff attributes this to the fact that PRB 

18 concentrations of 1-hour PM2.5 mass vary from hour to hour. It is possible for the daily 

19 maximum PM2.5 mass concentration on a certain day in 2005 with a percentile rank of, for 

example, 96th 20 to have a relatively small PRB portion and a large non-PRB portion compared to 

the daily maximum concentration that ranks 95th 21 . When an equal reduction is made to the non22 PRB portion of each total concentration, the two values may switch rank positions, and so a new 

23 day and hour becomes the 2005 contributor to the rolled back three-year design value. Since this 

24 day and hour was not used to determine the required percentage reduction, the resulting design 

25 value will not exactly meet the target design value. It would be possible to iterate with higher 

26 and lower percentage reductions until the rolled back design value exactly matched the target 

27 design value, but EPA considered this degree of refinement to be unnecessary in order to meet 

28 the objectives of the Policy Assessment Document, given other uncertainties in the underlying 

29 data and in the assumptions used to estimate PM10 light extinction values. 

30 

31 EPA staff also generated and examined box plots of daily maximum daylight 1-hour 

32 PM2.5 mass concentrations as a check for conceptual or programming errors, and found them to 

33 match expectations. They are not included here, for conciseness.
Draft-Do Not Quote or Cite June 2010 4A-8

1 Table 4A-5. Post-rollback design values for daily maximum 1-hour PM2.5 mass. Design 

2 values are shown only for combinations of study area and scenario for which the study area 

3 does not meet the scenario under current conditions, such that reductions were made 

4 during the rollback modeling.

5 

Scenario aa bb cc dd ee ff gg hh ii jj 

Level (μg/m3 ) 60 60 40 40 30 30 20 20 10 10 

Statistical Form 90th 95th 90th 95th 90th 95th 90th 95th 90th 95th 

Study Area Corresponding Design Value (μg/m3 ) (same percentile form as the scenario) 

Tacoma 20 21 11 12 

Fresno 63 40 42 30 31 20 21 10 10 

Los Angeles 53 53 35 35 26 26 18 18 9 9 

Phoenix 19 10 10 

Salt Lake City 38 29 28 19 19 10 10 

Dallas 23 23 12 11 

Houston 29 27 19 18 10 9 

Birmingham 58 42 39 32 29 21 20 11 10 

Atlanta 36 28 27 19 18 10 10 

Detroit 52 59 34 39 26 29 17 20 9 10 

Pittsburgh 33 33 24 25 16 17 8 9 

Baltimore 38 31 28 21 19 10 10 

Philadelphia 46 44 31 30 23 22 16 15 8 8 

New York 42 40 32 30 21 20 11 10 

6 

7 

8 6.0 PM10 Light Extinction Results for “Just Meeting” Alternative Secondary NAAQS 

9 Scenarios Based on 1-hour PM2.5 Mass 

10 

11 The rollback steps described in section 4.0 resulted in estimates of PM10 light extinction 

12 for each day and hour in each study area, for each of the 10 NAAQS scenarios based on 1-hour 

13 PM2.5 mass as the indicator. Two summaries of these conditions are presented here. 

14 

15 Figure 4A-2 presents a box plot of daily maximum daylight 1-hour PM10 light extinction 

16 for each NAAQS scenario based on 1-hour PM2.5 mass. These can be compared to Figure 3-8(a) 

17 of the UFVA representing pre-rollback daily maximum PM10 light extinction, and to the upper 

18 panel of the figures in Appendix F of the UFVA representing the daily maximum PM10 light 

19 extinction levels resulting from the 20 NAAQS scenarios examined in the UFVA (18 scenarios 

20 based on PM10 light extinction as the indicator, the current annual and 24-hour PM2.5 NAAQS, 

and a scenario with an annual NAAQS of 12 μg/m3

 and a 24-hour NAAQS of 25 μg/m3 21 ). It can 

22 be seen that the distribution of PM2.5 mass in a given study area shifts downward as the NAAQS 

23 scenarios progress from least to most stringent (as indicated by the required percentage 

24 reduction) and in most cases become more similar to other areas (once the progression of 

25 scenarios begins to require reductions in a given area). 

26 

27 Table 4A-6 presents the percentage of days in 2005-2007 on which daily maximum 1-hour PM10

28 light extinction exceeded each of the CPLs, under each of the 10 secondary PM NAAQS 

29 scenarios based on 1-hour PM2.5 mass. These percentages are necessarily based on the days for 

30 which data to estimate PM10 light extinction were available, but are best estimates of the 

31 percentage of all days in the year given that the days with data were well distributed across the 
Draft-Do Not Quote or Cite June 2010 4A-9

1 year on either a one-in-three or one-in-six sampling schedule. These percentages can be 

2 compared to the same-basis percentages presented in Table 4-7 of the UFVA.
Draft-Do Not Quote or Cite June 2010 4A-10

1 Figure 4A-2. Distributions of maximum daily daylight 1-hour PM10 light extinction under 

2 “just meet” conditions for NAAQS scenarios based on 1-hour PM2.5 mass (excluding hours 

3 >90% RH)

4 

(aa) NAAQS Scenario: 60 μg/m3 and 90th 5 percentile

6 

(bb) NAAQS Scenario: 60 μg/m3 7 and 95th percentile 

8 
Draft-Do Not Quote or Cite June 2010 4A-11

1 Figure 4A-2 (cont). Distributions of maximum daily daylight 1-hour PM10 light extinction 

2 under “just meet” conditions for NAAQS scenarios based on 1-hour PM2.5 mass (excluding 

3 hours >90% RH) (continued)

4 

(cc) NAAQS Scenario: 40 μg/m3 and 90th 5 percentile 

6 

(dd) NAAQS Scenario: 40 μg/m3 and 95th 7 percentile 

8 
Draft-Do Not Quote or Cite June 2010 4A-12

1 Figure 4A-2 (cont). Distributions of maximum daily daylight 1-hour PM10 light extinction 

2 under “just meet” conditions for NAAQS scenarios based on 1-hour PM2.5 mass (excluding 

3 hours >90% RH) (continued)

4 

(ee) NAAQS Scenario: 30 μg/m3 and 90th 5 percentile

6 

(ff) NAAQS Scenario: 30 μg/m3 and 95th 7 percentile 

8 
Draft-Do Not Quote or Cite June 2010 4A-13

1 Figure 4A-2 (cont). Distributions of maximum daily daylight 1-hour PM10 light extinction 

2 under “just meet” conditions for NAAQS scenarios based on 1-hour PM2.5 mass (excluding 

3 hours >90% RH) (continued) 

4 

(gg) NAAQS Scenario: 20 μg/m3 and 90th 5 percentile 

6 

(hh) NAAQS Scenario: 20 μg/m3 and 95th 7 percentile 

8 
Draft-Do Not Quote or Cite June 2010 4A-14

1 Figure 4A-2 (cont). Distributions of maximum daily daylight 1-hour PM10 light extinction 

2 under “just meet” conditions for NAAQS scenarios based on 1-hour PM2.5 mass (excluding 

3 hours >90% RH) (continued) 

4 

(ii) NAAQS Scenario: Daily Max: 10 μg/m3 and 90th 5 percentile

6 

(jj) NAAQS Scenario: Daily Max: 10 μg/m3 and 95th 7 percentile 

8 
Draft-Do Not Quote or Cite June 2010 4A-15

Table 4A-6. Percentage of days across three years (two years in the case of Phoenix and Houston) with 

daylight PM10 light extinction above CPLs when “just meeting” NAAQS scenarios based on 1-hour PM2.5 

indicates no reduction required from current conditions. 

Days with max hour above 

64 Mm-1

Days with max hour above 

112 Mm-1

Days wi

Scenario aa bb cc dd ee ff gg hh ii jj aa bb cc dd ee ff gg hh ii jj aa bb cc dd 

NAAQS 

Level 

(μg/m3

) 60 60 40 40 30 30 20 20 10 10 60 60 40 40 30 30 20 20 10 10 60 60 40 40 

NAAQS 

Percentile 

Form 90 95 90 95 90 95 90 95 90 95 90 95 90 95 90 95 90 95 90 95 90 95 90 95 

Area Percentage of days Percentage of days Per

Tacoma 53 53 53 53 53 53 53 53 43 35 23 23 23 23 23 23 23 23 11 6 4 4 4 4 

Fresno 76 73 65 57 69 60 55 44 28 17 52 48 37 31 44 32 29 18 9 4 30 27 17 11 

Los Angeles 89 87 84 81 84 79 74 69 41 30 78 76 65 57 65 53 41 31 11 7 52 46 30 24 

Phoenix 44 44 44 44 44 44 44 44 37 32 6 6 6 6 6 6 6 6 6 6 1 1 1 1 

Salt Lake 

City 45 45 45 37 45 45 45 26 17 10 17 17 17 15 17 17 17 11 8 5 8 8 8 7 

Dallas 81 81 81 81 81 81 81 71 41 29 41 41 41 41 41 41 41 32 8 5 10 10 10 10 

Houston 79 79 79 79 79 79 74 65 32 27 44 44 44 44 44 44 35 28 6 3 11 11 11 11 

Birmingham 89 85 80 68 87 80 72 62 41 34 65 56 51 36 58 51 40 30 15 12 34 26 21 13 

Atlanta 91 91 91 89 91 89 82 77 47 34 75 75 75 68 74 66 51 35 3 3 31 31 31 21 

Detroit 84 80 74 72 76 73 65 60 40 33 67 57 51 43 53 48 34 21 9 6 43 28 13 7 

Pittsburgh 85 85 81 77 81 77 63 55 27 19 57 57 51 45 52 44 29 22 3 0 26 26 18 14 

Baltimore 81 81 81 76 81 74 64 56 31 20 51 51 51 45 51 44 31 23 4 3 23 23 23 18 

Philadelphia 84 78 71 62 72 63 55 43 17 10 60 54 33 29 37 31 16 10 3 3 26 17 8 5 

New York 83 83 80 71 81 73 63 56 27 19 60 60 56 39 56 40 32 22 6 3 29 29 25 16 

Average 76 75 72 68 73 69 64 56 34 25 50 48 43 37 45 38 31 23 7 4 23 21 15 11 
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Draft – Do Not Quote or Cite 4B- June 2010 1

1 Appendix 4B 

2 Simplified Approaches to Calculate Hourly PM2.5 Light Extinction 

3 Values from Hourly PM2.5 Mass and Relative Humidity Data plus 

4 24-hour Mean PM2.5 Composition Data 

5 

6 Overview: The goal of the assessment described in this appendix is to identify a simpler 

7 procedure for calculating PM2.5 light extinction that produces hourly values that are comparable 

to those developed for the 15-city1 8 assessment (UFVA Section 3.3). The expected benefits of 

9 moving to a simpler approach include an increase the numbers of sites at which it could be 

10 applied (i.e., by not requiring so many collocated monitors), and periods of time (i.e., by not 

11 using sample day-specific CSN data) for which the calculation of PM light extinction could be 

12 conducted. 

13 The UFVA approach used the following: 

14 1. SANDWICH mass balance model to estimate organic carbonaceous material and nitrate 

15 mass concentrations on the FRM filter for each Chemical Speciation Network (CSN) 

16 sample day, and thus also organic carbonaceous material mass on the CSN filter. Other 

17 component concentrations on the CSN filter are taken by direct measurement. 

18 2. Monthly mean diurnal PM2.5 variations of each of the major PM2.5 components from 

19 CMAQ air quality simulation modeling results applied to sample day specific CSN 24-

20 hour samples. 

21 3. Hourly FRM-consistent PM2.5 mass developed by normalizing continuous PM2.5

22 measurements to the 24-hour FRM filter mass. 

23 4. Hourly sum of the FRM-consistent components scaled to hourly PM2.5 mass. 

24 5. An adjustment to the hourly PM2.5 nitrate component concentration to reflect actual 

25 atmospheric concentration, which is assumed to be represented by the CSN filter nitrate 

26 measurement. (This step in effect un-does the FRM nitrate loss introduced by the 

27 SANDWICH mass balance model as part of estimating organic carbonaceous material 

28 mass.) 

 

1 The PM10-2.5 values for St. Louis were determined by difference between two nearby monitoring sites. Based on 

comments received on the UFVA to the effect that the PM10 monitoring site was not representative of the St. Louis 

urban area, EPA does not consider the PM10-2.5 concentrations to be credible. However, for this assessment of 

various simplified methods to calculate PM2.5 light extinction, St. Louis data were not excluded since all but one of 

the comparisons are for calculated PM2.5 light extinction. 
Draft – Do Not Quote or Cite 4B- June 2010 2

1 6. Hourly PM10-2.5 data estimated by difference at sites with continuous monitors for both 

2 PM2.5 and PM10-2.5 or by using typical regional ratios of PM10-2.5 to PM2.5 times the PM2.5

3 concentrations where there are no PM10 measurements. 

4 7. IMPROVE algorithm to estimate hourly PM2.5 light extinction from hourly PM2.5

5 component and hourly relative humidity values. 

6 The process that was followed for this analysis was to examine the difference between 

7 calculated hourly PM10 light extinction values used in the UFVA and values generated using 

8 simpler approaches, explored in a step-wise fashion. All of the simpler approaches (designated 

9 by letter of the alphabet and described below) use the IMPROVE algorithm and measured hourly 

10 PM2.5 mass and relative humidity data. 

11 A. Omission of PM10-2.5 contributions to PM light extinction (Step 6 above). (This was done 

12 primarily to simplify comparisons with other simplified approaches; coarse mass 

13 contributions were separately examined in approach C.) For clarity, light extinction 

14 estimates are referred to as either PM2.5 or PM10 light extinction in the remainder of this 

15 Appendix to indicate whether the PM10-2.5 contribution to PM light extinction is included. 

16 B. Use of a simpler approach to organic carbonaceous mass and nitrate than in the 

17 SANDWICH-based UFVA approach briefly described in the steps listed above. Table 

18 4B-1 contrasts the steps in this simpler approach B (and approach F, below) to the steps 

19 of approach A. The key distinguishing features of approach B are (i) a multiplier of 1.7 

20 is applied to the CSN measurement of organic carbon (after a correction for blank filter 

21 artifact) rather than estimating organic carbonaceous material by the mass-balance 

22 SANDWICH approach, and (ii) the hourly PM2.5 mass is normalized to match the FRM 

23 value for the 24-hour PM2.5 mass and then speciated using the CSN-measured component 

24 mix, with no correction for nitrate loss from the FRM Teflon filter. 

25 C. Same as approach B except that it includes PM10-2.5 contributions to PM10 light 

26 extinction, 

27 D. Same as B except that the diurnal component distribution is assumed to be flat 

28 (unvarying) for each sample day. 

29 E. Same as B except that monthly averaged PM2.5 component distributions are used in place 

30 of the CSN sample period-specific values. 

31 F. Same as B except both flat diurnal PM2.5 component variation are assumed and monthly 

32 averaged PM2.5 component distribution are used in place of sample period specific values 

33 (i.e., combines simplification of approaches D and E). Because staff recommends 

34 consideration of this particular approach, for greatest clarity the data sources and 

35 calculation steps used in this approach are listed in Table 4B-2. 
Draft – Do Not Quote or Cite 4B- June 2010 3

1 I. Simply uses the PM2.5 mass concentration times a single constant. (This approach was 

2 included to show the performance of using PM2.5 mass without adjusting for particle 

composition and humidity effects on hygroscopic particles.)2 3 

 

2

 Note that the letter designations for the approaches are not contiguous. This is because EPA staff tested additional 

approaches that are not included in this discussion appendix. Note also that all of the figures include approach 

designators, as plan designators.
Draft – Do Not Quote or Cite June 2010 4B-4

Table 4B-1. Detailed Comparative Description of Approaches A, B, and F for Estimating 1-hour PM2.5 L

UFVA Step‡ Aspect of Approach Approach A Approach B 

1 Estimation of 24-hour organic 

carbonaceous mass 

The SANDWICH method (Frank, 2006) is used to subdivide the 24-hour PM2.5

mass reported by the FRM for each day and site into sulfate, nitrate elemental carbon, organic carbonaceous mass, and fine soil/crustal mass. This is done using information from the CSN measurements, physical models, and day-specific temperatures and relative humidty. The primary purpose of this SANDWICH step is to estimate organic carbonaceous mass on the FRM Teflon filter. 

Organic carbonaceous mass is assumed to equal the organic carbon value reported from CSN sampling, minus a network-wide blank filter correction, times 1.7. 

Sam

1, continued Estimation of 24-hour 

elemental carbon mass 

CSN elemental carbon concentration 

Same as A Sam

1, continued Estimation of 24-hour sulfate 

mass 

CSN sulfate concentration, with day-specific SANDWICH estimates of associated ammonium and water. 

Sulfate ion measurement from the CSN filter is multiplied by 1.375 to represent dry, neutralized sulfate. 

Sam

1, continued Estimation of 24-hour nitrate 

mass 

Nitrate ion on the FRM Teflon filter is estimated by SANDWICH, with dayspecific estimates of associated ammonium and water. 

Nitrate ion measurement from the CSN filter is multiplied by 1.29 to represent dry, neutralized nitrate. 

Sam

‡ The numbering of steps follows that used to describe the UFVA approach in section 3.3.1 of the UFVA.
Draft – Do Not Quote or Cite June 2010 4B-5

UFVA Step‡ Aspect of Approach Approach A Approach B 

1, continued Estimation of 24-hour fine 

soil/crustal mass 

Calculated from CSN elements, but without Al (a difference from the IMPROVE approach) 

Same as A Sam

2 Diurnal pattern of PM2.5

components 

The CMAQ-derived monthly normalized diurnal profiles for the sulfate, nitrate, elemental carbon, organic carbon and fine soil/crustal components (which average to 1.0 across 24 hours) were multiplied by the day-specific SANDWICH-based estimates of the 24-hour average concentrations of the five PM2.5 components, to get 

intermediate day-specific hourly estimates of the five components (including ammonium and water associated with sulfate and nitrate ion). 

CMAQ-derived profiles were applied to the five components to get intermediate estimates. However, as described above, in Approach B the sulfate, nitrate, and organic carbonaceous material components are defined and estimated differently than in Approach A. 

No Thithe 

comhouthe filtefor 

3 Sum the 5 components The hourly concentrations of 

these five components (including day-specific ammonium and water associated with sulfate and nitrate ion when the FRM Teflon filter is weighed) were added together, to get a sumof-components estimate of hourly PM2.5 mass for the day 

of the FRM/CSN sampling. 

The hourly concentrations of the five components were added together, to get a sumof-components estimate of hourly PM2.5 for the day of 

FRM/CSN sampling. Note that water is not included. 

Calave5 P

follFor samcomfraccomfracacroday
Draft – Do Not Quote or Cite June 2010 4B-6

UFVA Step‡ Aspect of Approach Approach A Approach B 

4 Hourly PM2.5 concentration, 

consistent with 24-hour FRM concentration. 

The hourly data from the continuous PM2.5 instrument 

on the day of the FRM sampling were normalized by their 24-hour average, to get a normalized diurnal profile. This profile was applied to the 24-hour PM2.5 mass 

reported by the FRM sampler, to get a preliminary, FRMconsistent estimate of hourly PM2.5 mass for the day of the 

FRM sampling. This keeps the average of the valid 1-hour PM2.5 values equal to the 

24-hour value from the FRM sampler. 

Same as A Sam

comTab

5, 6 Adjust preliminary estimates 

of hourly PM2.5 component 

concentrations (reflecting CMAQ diurnal profiles and 24-hour measurements) to be consistent with estimate of hourly PM2.5 mass. 

The two estimates of hourly PM2.5 mass from steps 3 and 

4 were compared, hour-byhour. Within each hour, the estimates of all five components from step 2 were increased or decreased by a common percentage (referred to below as Ai where the 

subscript i indicates the hour) so that the sum of the five components after this adjustment was equal to the estimate of the hourly PM2.5

mass from step 4. The adjustment percentage varied from hour-to-hour. 

Same as A Not 

Momix 

direspehou
Draft – Do Not Quote or Cite June 2010 4B-7

UFVA Step‡ Aspect of Approach Approach A Approach B 

7 Adjust the FRM-consistent 

estimate of sulfate to the CSN/IMPROVE-consistent basis expected by the IMPROVE algorithm. 

Each hourly estimate of sulfate concentration on the FRM filter from step 6 (which includes estimates of associated ammonium and particle bound water) was adjusted so that it excludes water and reflects full neutralization and therefore is consistent with the reporting practices of the IMPROVE program and the IMPROVE algorithm. 

No adjustment is needed, given that the factors of 1.375 and 1.29 already assume full neutralization and no water. In effect, the mass of water on the FRM filter is allocated among the 5 components. 

Sam

8 Adjust the FRM-consistent 

estimate of nitrate to the CSN/IMPROVE-consistent basis expected by the IMPROVE algorithm. 

A similar adjustment as in step 7 (for sulfate) was made to each hour’s nitrate concentration from step 6, so that the estimate of hourly nitrate would reflect actual atmospheric conditions and be consistent with the IMPROVE algorithm. This can result in the estimate of nitrate used in the IMPROVE algorithm being higher than the FRMconsistent estimate, for days on which the SANDWICH method predicts a loss of nitrate from the FRM filter. 

No adjustment is made. Implication: On warm days when the FRM filter has lost nitrate mass, the estimates of hourly PM2.5 will 

be lower than actual atmospheric mass. All hourly PM2.5 components will be 

reduced, by the fraction that the lost nitrate is of total PM2.5 mass. 

.Sam
Draft – Do Not Quote or Cite June 2010 4B-8

UFVA Step‡ Aspect of Approach Approach A Approach B 

Not numbered in UFVA Estimation of PM2.5 light 

extinction from estimates of hourly concentrations of PM2.5 components. 

Original IMPROVE algorithm, including f(RH) determined from hourly RH. Hours with RH >90% were excluded from design values and from most graphical displays of results. 

Same as A Sam
Draft – Do Not Quote or Cite 4B- June 2010 9

1 Table 4B-2. Data Sources and Calculation Steps for Approach F 

2 

Approach F 

(As performed for results shown in this 

Appendix) 

Comments 

Data Sources 

CSN values for sulfate ion (S), nitrate ion (N), 

elemental carbon (EC), organic carbon (OC), 

and the crustal elements Si, Ca, Fe, and Ti. 

CSN sampling is currently performed at about 

200 sites, some with one-in-six-days operation 

and some with one-in-three-days operation. In 

2005-2007, several different sampler models 

were in use among the 15 study sites. 

Prospectively, all CSN sampling for S, N, EC, 

and the crustal elements will be based the Met 

One SASS or Met One SuperSASS sampler, 

and all CSN sites now use the URG2000 

sampler for carbon measurements. 

If the revised IMPROVE algorithm were to be 

used in Step (ix) instead of the original 

IMPROVE algorithm, an option the 

Administrator may wish to consider, additional 

analysis results from CSN would be needed, in 

particular chlorine or chloride. 

Fixed external estimates of organic carbon 

artifact on quartz filters, based on networkwide field blank data. Estimate depends on 

sampler model used for carbon sampling. 

These values ranged from 0.32 to 1.53 μg/m3

, 

depending on sampler model. 

The artifact adjustment for the URG2000 

sampler is of most interest prospectively, 

because it is the single sampler now in use for 

carbon sampling in CSN. The URG2000 was 

used only at about one-half of the 15 study 

sites and only in the second half of 2007. For 

those sites and days, an organic carbon artifact 

of 0.5 ug/m3

 was assumed for the purposes of 

the UFVA and this document, based on early 

experience with this sampler. Should the 

Administrator choose to invite public comment 

on an approach like Approach F, this value 

could be updated prior to NPRM based on a 

more recent, larger field blank data set. 

Hourly PM2.5 mass concentration from 

continuous instrument. 

FRM or FEM 24-hour PM2.5 mass 

concentration 

Steps (vii) and (viii) involve the scaling of 

hourly PM2.5 mass concentrations from a 

continuous instrument to match the same-day 

24-hour concentration reported by a collocated 

FRM/FEM filter-based sampler. Including this 

scaling in a regulatory program based on 

Approach F would mean that estimates of 

PM2.5 light extinction could only be developed 
Draft – Do Not Quote or Cite 4B- June 2010 10

for days and locations with FRM/FEM filterbased sampling, implying the need for daily 

filter-based FRM/FEM sampling if estimates 

of light extinction are required on a daily basis. 

The Administrator may wish to consider 

requiring that only continuous instruments 

approved as federal equivalent methods (FEM) 

(for purposes of measuring 24-hour PM2.5 mass 

concentrations) may be used for PM2.5 light 

extinction estimation, and omitting the scaling 

steps. None of the continuous PM2.5

instruments used to collect the 2005-2007 data 

used in the UFVA analysis had been approved 

as FEM. Several models have now been 

approved. If the scaling steps are omitted, then 

FRM measurements would not be a required 

data source for this approach. 

Steps

(i) For each CSN sampling day, subtract OC 

artifact from OC measurement, and multiply 

by 1.7 to estimate organic carbonaceous 

material (OCM). 

(ii) For each CSN sampling day, calculate fine 

soil/crustal PM2.5 (FS) from CSN 

measurements of crustal elements Si, Ca, Fe, 

and Ti, using the formula 

Fine soil PM2.5 = 3.73 × [Si] + 1.63 × [Ca] + 

2.42 × [Fe] + 1.94 × [Ti] 

(iii) For each CSN sampling day, multiply 

CSN measurement of sulfate ion by 1.375, and 

multiply CSN measurement of nitrate ion by 

1.29, to reflect associated ammonium under an 

assumption of full neutralization. 

(iv) Sum the above estimates of the 5 

components of PM2.5: 

Sum = 1.375*S + 1.29*N + OCM + EC + FS 

(v) For each CSN sampling day, calculate the 5 

component factions: 

sulfate fraction = 1.375*S/Sum 

Nitrate fraction = 1.29*N/Sum 

OCM fraction = OCM/Sum 

EC fraction = EC/Sum 

FS fraction = FS/Sum 

(vi) Average the fraction for sulfate from step 
Draft – Do Not Quote or Cite 4B- June 2010 11

(v) across the CSN sampling days of that 

calendar month of that calendar year. Repeatn 

for the other 4 components. Call these the 

monthly-average component fractions. 

(vii) For each CSN sampling day, average the 

24 values of 1-hour PM2.5 mass from the 

continuous instrument. Divide the 24-hour 

FRM value for PM2.5 mass by this average. 

Call this the “instrument scaling factor”. 

(viii) For each CSN sampling day, multiply 

each 1-hour PM2.5 mass value from the 

continuous instrument by the instrument 

scaling factor. 

(ix) For each daylight hour of each day of that 

month (including days without CSN sampling) 

multiply the value of hourly PM2.5 mass from 

step (viii) by the monthly-average component 

fractions from step (vi). 

(x) Insert the results from step (ix) into the 

original IMPROVE algorithm, along with 

f(RH) calculated based on same-hour RH. 

Omit the Rayleigh scattering term and the 

contribution from PM10-2.5. Estimates of PM2.5

light extinction in hours with RH greater than 

90% are not used in design value calculations 

and graphics presented in this document. 

The Administrator may wish to consider 

proposing or seeking comment on the 

alternative of using the revised IMPROVE 

algorithm instead of the original algorithm, 

omitting the terms for Rayleigh scattering and 

NO2 absorption. This approach that would 

allow explicit consideration of sea salt effects 

on light extinction. 

The exclusion of hours with RH>90% 

originated out of concern that an instrumental 

measurement of PM light extinction might 

results in high values of light extinction due to 

natural conditions of fog or precipitation. In as 

much as Approach F does not employ a light 

extinction instrument, the Administrator may 

wish to consider not excluding these hours 

from the form of the NAAQS. However, the 

accuracy of RH measurements at such high 

values of RH would still be a consideration. 
Draft – Do Not Quote or Cite 4B- June 2010 12

1 As the results below show, approaches B, D, E, and F produce hourly PM2.5 light 

2 extinction values that are quite comparable to the hourly PM2.5 light extinction values in the 

3 original UFVA (i.e., approach A). EPA staff favors consideration of approach F because it is the 

4 most simplified of the methods tested that make use of CSN measurements, and its use of 

5 monthly averaged PM2.5 components information means that it can be applied every day, not just 

on days with CSN monitoring data (i.e., 1 day in 3 or 1 day in 6).4 6 

7 Comparative Performance of Simplified PM2.5 Light Extinction Approaches: The 

8 performance assessment of simplified approaches for calculated PM2.5 light extinction was 

9 accomplished by comparing hourly values of PM2.5 light extinction generated by each approach 

10 to their corresponding paired values generated using the original UFVA method, which is labeled 

11 above as approach A. Annual box and whisker plots of the percentage differences between the 

12 paired values, as well as annual and monthly scatter plots and regression analysis of these paired 

13 data, were generated. Selected graphs and summary tables of regression statistics are included 

14 below to show the degree of comparability between the various approaches to the original 

15 estimates of PM2.5 light extinction. The complete set of these figures is available on EPA’s web 

16 site (http://www.epa.gov/ttn/analysis/pm_pad.htm). 

17 The box and whisker plots of the differences between calculated hourly PM2.5 light 

18 extinction by approaches B, D, E, and F are shown in Figures 4B-1 and 4B-2. In the box and 

19 whisker plots of percentage difference, the percentage difference is calculated as follows: 

20 

21 Percentage difference = [(“A” estimate) – (simpler approach estimate)]/(“A” estimate) * 100% 

22 

23 The patterns of relatively small bias for the 15 urban areas are notably similar in each of the 

24 plots. Keeping in mind that the differences in the approach are iterative with approach B 

25 differing from approach A only by not using the SANDWICH model to estimate organic 

26 component mass concentration and to adjust nitrate concentrations, while the three other 

27 approaches (i.e., D, E, and F) add additional simplifications, it is perhaps surprising that the city28 to-city pattern of the box positions and sizes are remarkably similar for these four approaches. 

29 This suggests that the simplifications between approaches A and B (i.e., replacing the 

30 SANDWICH model) is responsible for the greatest amount of differences in calculated hourly 

31 PM2.5 light extinction between approach A and D, E, and F, or in other words, the additional 

 

4 Note that Approach F as applied for this Appendix required the availability of filter-based FRM/FEM data for 24-

hour concentration, and that most filter-based PM2.5 monitoring sites do not operate every day. However, see the 

related comment in Table 4B-2.
Draft – Do Not Quote or Cite 4B- June 2010 13

1 changes introduced in the remaining three approaches did not much affect the resulting hourly 

values.5 2 

3 The degree of comparability for paired hourly PM2.5 light extinction between approach F 

4 and A, and between approach F and B values by month and urban area is evident in regression 

statistics (Tables 4B-3 and 4B-4). In both tables the regression lines have slopes and R2 5 values 

6 near one with small intercepts for most urban areas and months implying that the values are 

7 highly comparable. The western urban areas (e.g., Fresno, Houston, Los Angeles, Phoenix, Salt 

Lake City and Tacoma) have slopes and R2 8 values for some months that imply a bias and/or 

9 noisier relationship between values calculated by approaches F and A. As expected the 

10 regression relationships show that values calculated by approach F are more similar to those of 

11 approach B (Table 4B-4) since neither uses the SANDWICH model, than to those of approach A 

12 (Table 4B-3) which included the SANDWICH model estimates. Four scatter plots of calculated 

13 hourly PM2.5 light extinction for all months that compare approach F to approach A and 

14 approach F to approach B for Baltimore and Fresno are show in Figure 4B-3 as examples of the 

15 degree of comparability for eastern and western urban areas. 

16 A comprehensive assessment of the reasons that there are greater differences between 

17 approaches A and B than between approaches B and any of D, E, and F has not been conducted. 

18 However, some explanations are suggested by the results themselves. The fact that hour-to-hour 

19 and day-to-day variations in the dry PM2.5 composition can be replaced by monthly averaged 

20 values without much loss of precision in calculated hourly PM2.5 light extinction suggests that 

21 these shorter term variations within a single month at a single monitoring site are not very 

22 influential. This may imply that the differences between approach A and the other approaches 

23 considered here are probably not due to the relative composition changes caused by use of the 

24 SANDWICH model and the simpler approach B. This suggests that the difference may be due to 

25 the adjustment to the FRM measured PM2.5 mass concentration that was done to account for 

26 negative sampling artifact for ammonium nitrate. This is consistent with the behavior for sites 

27 that have high ammonium nitrate (e.g., Fresno and Los Angeles). Depending on the results of 

28 this more complete assessment of why the difference in hourly values are higher between 

29 approach A and B then between those of B and any of D, E, and F, another approach that retains 

30 the SANDWICH model but otherwise has the simplifications of approach F might be developed 

31 as the better method to calculate PM2.5 light extinction. 

 

5 In the box and whisker plots, some extreme values of the percentage change are due to rounding effects when the 

values involved were very small.
Draft – Do Not Quote or Cite 4B- June 2010 14

Figure 4B-1 Box and whisker plot of the percent difference in calculated hourly PM2.5 1 light extinction between 

2 approaches B and A (top plot) and D and A (bottom plot) by urban area.

3 

4 

5 
Draft – Do Not Quote or Cite 4B- June 2010 15

1 Figure 4B-2 Box and whisker plot of the percent difference in calculated hourly PM2.5 light extinction 

2 between approaches E and A (top plot) and F and A (bottom plot) by urban area. 

3 
Draft – Do Not Quote or Cite 4B- June 2010 16

1 

2 Figure 4B-3 Scatter plots of calculated PM2.5 light extinction by approach F versus approach A (left) and B 

3 (right) for Baltimore (top) and Fresno (bottom). 

4 
Draft – Do Not Quote or Cite June 2010 4B-17

Table 4B-3 Linear regression equation and R2 values for relating hourly PM2.5 light extinction values 

approach F (x in the equation) to those using approach A (y in the equation) by month for 15 ur

Month 1 2 3 4 5 6 7 8 9 10 Atlanta,GA

y=1.09*x+‐0.74;Rsq=0.98

y=1.03*x+1.85;Rsq=0.98

y=1.01*x+‐1.53;Rsq=0.97

y=1.06*x+‐4.65;Rsq=0.98

y=1.02*x+‐4.41;Rsq=0.97

y=0.99*x+‐3.52;Rsq=0.97

y=0.96*x+‐1.38;Rsq=0.96

y=0.98*x+‐4.01;Rsq=0.98

y=0.98*x+‐2.8;

Rsq=0.98

y=0.97*x+0.42;Rsq=0.99

Baltimore, MD

y=0.99*x+1.31;Rsq=0.99

y=1.07*x+‐3.64;Rsq=1

y=0.97*x+3.35;Rsq=0.99

y=1.02*x+0.36;Rsq=0.96

y=0.99*x+0.83;Rsq=0.9

y=0.98*x+‐3.78;

Rsq=0.96

y=0.94*x+‐2.59;Rsq=0.98

y=0.88*x+2.1;Rsq=0.99

y=0.96*x+‐3.25;Rsq=0.98

y=1.05*x+‐2.77;Rsq=0.98

Birmingham, AL

y=1.1*x+‐2.32;Rsq=0.97

y=1.04*x+0.59;Rsq=0.97

y=1.06*x+‐2.86;Rsq=0.97

y=1.06*x+‐3.91;Rsq=0.97

y=0.94*x+2.05;Rsq=0.97

y=0.98*x+‐4.9;Rsq=0.98

y=1.02*x+‐6.87;Rsq=0.97

y=0.9*x+2.31;Rsq=0.93

y=1*x+‐6.31;Rsq=0.98

y=1.06*x+‐3.87;Rsq=0.97

Dallas,TX

y=1.17*x+‐1;Rsq=0.97

y=1.1*x+0.12;Rsq=0.98

y=1.15*x+‐4.59;Rsq=0.96

y=1.04*x+‐2.64;

Rsq=0.97

y=1.03*x+‐3.1;Rsq=0.96

y=0.93*x+0.75;Rsq=0.95

y=0.96*x+‐0.01;Rsq=0.96

y=0.98*x+‐0.63;Rsq=0.91

y=1.01*x+‐3.02;Rsq=0.93

y=1.06*x+‐2.35;Rsq=0.97

Detroit,MI

y=1.02*x+2.64;Rsq=0.97

y=0.96*x+3.31;Rsq=0.99

y=1*x+1.92;Rsq=0.99

y=1.1*x+‐0.43;Rsq=0.96

y=0.92*x+5.49;Rsq=0.96

y=0.96*x+2.14;Rsq=0.96

y=0.99*x+0.17;Rsq=0.93

y=1.29*x+‐18.07;Rsq=0.9

y=1.11*x+‐8.62;Rsq=0.97

y=1.18*x+‐3.49;Rsq=0.95

Fresno,CA

y=1.05*x+‐0.61;Rsq=0.98

y=1.06*x+2.02;Rsq=0.99

y=1.17*x+4.27;Rsq=0.96

y=1.32*x+‐4.32;Rsq=0.86

y=1.27*x+‐2.92;Rsq=0.83

y=1.24*x+‐5.26;Rsq=0.87

y=1.06*x+‐0.97;Rsq=0.97

y=1.3*x+‐6.59;Rsq=0.86

y=1.39*x+‐6.67;Rsq=0.89

y=1.51*x+‐8.53;Rsq=0.94

Houston,TX

y=0.83*x+4.6;Rsq=0.96

y=0.97*x+‐1.12;Rsq=0.99

y=0.9*x+0.8;Rsq=0.99

y=1.02*x+‐3.72;Rsq=0.98

y=0.97*x+‐2.87;Rsq=0.97

y=0.84*x+7.59;Rsq=0.92

y=0.76*x+11.38;Rsq=0.85

y=0.88*x+4.71;Rsq=0.94

y=0.99*x+‐2.92;Rsq=0.98

y=1.06*x+‐3.39;Rsq=0.99

LosAngeles,CA

y=1.21*x+‐0.76;Rsq=0.98

y=1.13*x+1.34;Rsq=0.98

y=1.1*x+7.13;Rsq=0.96

y=1.16*x+1.82;Rsq=0.96

y=1.05*x+13.01;

Rsq=0.96

y=1.35*x+‐4.71;

Rsq=0.86

y=1.29*x+‐4.96;Rsq=0.91

y=1.4*x+‐14.24;Rsq=0.82

y=1.43*x+‐10.81;Rsq=0.91

y=1.27*x+‐1.2;

Rsq=0.95

NewYork, NY

y=1.04*x+1.72;Rsq=0.98

y=1.03*x+‐1.34;Rsq=1

y=1.01*x+3.48;Rsq=0.99

y=1.11*x+‐0.74;

Rsq=0.98

y=1.06*x+‐1.45;Rsq=0.97

y=1.11*x+‐6.56;Rsq=0.96

y=1.02*x+‐7.96;Rsq=0.96

y=0.98*x+‐1.61;Rsq=0.97

y=1.03*x+0.24;Rsq=0.96

y=1.22*x+‐7.88;Rsq=0.97
Draft – Do Not Quote or Cite June 2010 4B-18

Philadelphia, PA

y=0.98*x+2.25;Rsq=0.99

y=1.01*x+‐1.64;Rsq=0.99

y=1*x+1.32;Rsq=0.99

y=0.96*x+2.4;Rsq=0.94

y=0.9*x+1.44;Rsq=0.98

y=0.92*x+‐0.44;Rsq=0.98

y=0.91*x+1.75;Rsq=0.98

y=0.85*x+5.59;Rsq=0.98

y=0.89*x+1.3;Rsq=0.96

y=0.95*x+3.87;Rsq=0.97

Phoenix,AZ

y=1.21*x+‐2.37;Rsq=0.89

y=1.13*x+‐1.38;Rsq=0.97

y=0.88*x+1.25;Rsq=0.96

y=0.85*x+1.94;Rsq=0.94

y=0.93*x+0.87;Rsq=0.9

y=0.88*x+1.65;Rsq=0.97

y=0.77*x+4.02;

Rsq=0.69

y=0.92*x+0.62;Rsq=0.99

y=0.86*x+0.48;Rsq=0.97

y=0.92*x+1.18;Rsq=0.72

Pittsburgh, PA

y=1.1*x+‐3.59;Rsq=0.95

y=0.99*x+1.91;Rsq=0.97

y=0.99*x+‐0.27;Rsq=0.99

y=1.09*x+‐2.34;Rsq=0.96

y=1.07*x+‐5.67;Rsq=0.92

y=0.95*x+‐1.28;Rsq=0.97

y=1.03*x+‐9.91;Rsq=0.96

y=0.94*x+‐1.93;Rsq=0.97

y=1.04*x+‐7.42;Rsq=0.94

y=1.02*x+‐1.66;Rsq=0.97

Salt LakeCity, UT

y=0.91*x+8.57;Rsq=1

y=0.97*x+4.76;Rsq=0.99

y=1.12*x+1.93;Rsq=0.97

y=1.14*x+‐1.25;Rsq=0.88

y=0.98*x+0.92;Rsq=0.83

y=1.02*x+0.99;Rsq=0.87

y=1*x+0;Rsq=0.99

y=0.98*x+‐0.43;Rsq=0.97

y=0.88*x+5.49;Rsq=0.87

y=1.09*x+0.11;Rsq=0.93

St Louis,

MO

y=1.01*x+1.63;Rsq=0.99

y=1.03*x+‐0.41;Rsq=1

y=1.07*x+‐0.47;Rsq=0.98

y=1.14*x+‐3.93;

Rsq=0.94

y=1.06*x+‐2.9;Rsq=0.92

y=0.92*x+0.97;Rsq=0.95

y=0.98*x+‐

2.77;Rsq=0.97

y=1*x+‐7.97;Rsq=0.98

y=0.94*x+‐0.89;Rsq=0.97

y=1.2*x+‐4.17;Rsq=0.95

Tacoma,WA

y=1.02*x+‐0.05;Rsq=1

y=0.98*x+0.71;Rsq=0.94

y=1.07*x+‐0.1;Rsq=0.97

y=1.22*x+‐3.78;

Rsq=0.96

y=1.18*x+‐4.43;Rsq=0.92

y=1.09*x+‐0.14;Rsq=0.9

y=1.1*x+‐1.28;

Rsq=0.94

y=1.22*x+‐4.35;Rsq=0.95

y=1.06*x+0.02;Rsq=0.96

y=0.97*x+1.2;Rsq=0.98
Draft – Do Not Quote or Cite June 2010 4B-19

Table 4B-4 Linear regression equation and R2 values for relating hourly PM2.5 light extinction values 

approach F (x in the equation) to those using approach B (y in the equation) by month for15 ur

Month 1 2 3 4 5 6 7 8 9 10 Atlanta, GA y=1.01*x +0.4;

Rsq=0.98

y=1.06*x+0.26;Rsq=0.96

y=1.02*x+‐0.15;

Rsq=0.99

y=1.02*x+‐0.72;Rsq=0.99

y=1.02*x+‐0.83;

Rsq=0.99

y=1.02*x+‐1.06;Rsq=0.99

y=1.02*x+‐0.38;

Rsq=0.95

y=1.04*x+‐3.54;

Rsq=0.98

y=1.02*x+‐0.74;Rsq=0.99

y=1*x+111;Rsq=0.9

Baltimore,MD

y=0.96*x+1.84;Rsq=0.99

y=1.09*x+‐5.5;Rsq=0.99

y=1.04*x+‐1.21;

Rsq=1

y=1.01*x+1.15;Rsq=0.98

y=1.02*x+‐1.15;

Rsq=1

y=0.99*x+0.73;Rsq=0.99

y=1.01*x+‐0.36;

Rsq=0.98

y=1*x+0.25;Rsq=1

y=1.02*x+‐1;

Rsq=1

y=0.97*+2.23;Rsq=0.9

Birmingham, AL

y=1.08*x+‐2.37;Rsq=0.98

y=0.98*x+3.39;Rsq=0.96

y=1.02*x+0;Rsq=0.99

y=1.07*x+‐2.22;Rsq=0.98

y=1*x+1.38;Rsq=0.99

y=1.01*x+‐0.71;Rsq=0.99

y=1.06*x+‐4.76;

Rsq=0.98

y=0.98*x+2.12;Rsq=0.95

y=1.02*x+‐2.17;

Rsq=0.98

y=1*x+146;Rsq=0.9

Dallas, TX y=1.08*x +‐1.32;

Rsq=0.99

y=1.05*x+‐1.39;

Rsq=0.99

y=1.06*x+‐1.42;

Rsq=0.99

y=1.08*x+‐2.66;Rsq=0.99

y=1.04*x+‐1.58;

Rsq=0.99

y=0.98*x+1.45;Rsq=0.95

y=0.99*x+0.46;Rsq=0.96

y=0.94*x+2.44;Rsq=0.91

y=1*x+‐0.52;Rsq=0.99

y=1.02*+‐0.48;Rsq=0.9

Detroit, MI y=1.06*x +‐4.03;

Rsq=0.98

y=1.05*x+‐2.66;

Rsq=1

y=1.03*x+‐1.35;Rsq=0.99

y=1.01*x+0.23;Rsq=0.99

y=1*x+‐0.08;Rsq=1

y=0.98*x+0.79;Rsq=0.99

y=1.02*x+‐0.65;

Rsq=0.99

y=1.09*x+‐5.76;

Rsq=0.97

y=1.01*x+‐0.83;Rsq=0.99

y=1.05*+‐1.29;

Rsq=0.9

Fresno, CA

y=1.06*x+‐5.57;Rsq=0.99

y=1.05*x+‐4.18;

Rsq=0.99

y=1.03*x+‐0.85;

Rsq=1

y=1.02*x+‐0.57;Rsq=0.99

y=1.02*x+‐0.24;

Rsq=0.98

y=1.03*x+‐0.55;Rsq=0.99

y=1*x+‐0.76;Rsq=1

y=1.02*x+‐0.58;

Rsq=0.99

y=1.04*x+‐1.01;

Rsq=0.99

y=1.1*x‐3.18;Rsq=0.9

Houston,TX

y=0.97*x+2.57;Rsq=0.99

y=1.08*x+‐1.78;

Rsq=0.97

y=1.01*x+0.8;

Rsq=0.99

y=1.04*x+‐0.93;Rsq=0.99

y=1.02*x+‐0.48;Rsq=0.98

y=0.9*x+8.05;Rsq=0.93

y=0.77*x+13.92;Rsq=0.81

y=0.92*x+6.43;Rsq=0.94

y=1.01*x+‐0.91;

Rsq=0.99

y=1.05*+‐1.77;

Rsq=0.9

LosAngeles, CA

y=1.08*x+‐3.01;Rsq=0.99

y=1.04*x+‐1.62;

Rsq=1

y=1.02*x+0.27;Rsq=0.99

y=1.05*x+‐2.99;Rsq=0.99

y=1*x+0.88;Rsq=1

y=1.05*x+‐2.15;Rsq=0.99

y=1.03*x+‐0.32;

Rsq=0.99

y=1.03*x+‐0.34;

Rsq=0.99

y=1.06*x+‐1.65;

Rsq=0.99

y=1.08*+‐3.22;

Rsq=0.9
Draft – Do Not Quote or Cite June 2010 4B-20

New York,

NY

y=1*x+‐0.52;Rsq=1

y=1.01*x+‐0.82;

Rsq=1

y=0.99*x+0.86;Rsq=1

y=1.03*x+‐0.26;Rsq=1

y=1*x+0.17;Rsq=0.99

y=1.07*x+‐3.4;Rsq=0.99

y=1.03*x+‐4.03;Rsq=0.99

y=1.02*x+‐2.43;

Rsq=0.99

y=1.01*x+‐0.94;Rsq=1

y=1.06*+‐3.4;

Rsq=0.9

Philadelphia, PA

y=1.03*x+‐1.63;Rsq=0.99

y=1.02*x+‐2.07;

Rsq=0.99

y=1.02*x+‐0.65;

Rsq=1

y=1.05*x+‐1.63;Rsq=0.98

y=0.99*x+0.1;Rsq=1

y=0.99*x+0.42;Rsq=1

y=1.02*x+0.32;Rsq=0.98

y=0.99*x+2.35;Rsq=0.99

y=0.96*x+1.39;Rsq=0.99

y=1.01*+0.84;Rsq=1

Phoenix,AZ

y=1.17*x+‐3.94;Rsq=0.93

y=0.95*x+1.15;Rsq=0.99

y=0.84*x+1.91;

Rsq=0.97

y=0.9*x+1.35;Rsq=0.94

y=0.98*x+0.6;Rsq=0.96

y=0.98*x+0.63;Rsq=0.98

y=0.88*x+3.02;Rsq=0.81

y=0.94*x+1.07;Rsq=0.99

y=0.97*x+0.28;Rsq=0.98

y=0.98*+1.01;Rsq=0.8

Pittsburgh, PA

y=1.04*x+‐0.5;Rsq=0.97

y=1*x+0.78;Rsq=0.99

y=1.01*x+0.29;

Rsq=1

y=1.05*x+‐0.59;Rsq=0.99

y=1.01*x+‐0.89;

Rsq=0.99

y=1*x+0.02;Rsq=0.99

y=1.02*x+‐3.12;

Rsq=0.99

y=1.04*x+‐3.96;

Rsq=0.99

y=1.02*x+‐1.58;

Rsq=0.99

y=0.98*+2.1;Rsq=0.9

Salt LakeCity, UT y=1*x+

1; Rsq=1

y=1.02*x+‐0.94;

Rsq=1

y=1.07*x+‐0.81;Rsq=0.99

y=1.05*x+‐0.51;Rsq=0.95

y=0.98*x+0.79;Rsq=0.96

y=1.02*x+‐0.44;

Rsq=0.99

y=0.97*x+0.77;Rsq=0.99

y=0.96*x+0.74;Rsq=0.96

y=0.75*x+8.1;Rsq=0.8

y=0.97*+1.37;Rsq=0.9

St Louis,

MO

y=1.04*x+‐0.73;Rsq=0.99

y=1.03*x+‐0.62;

Rsq=1

y=1.08*x+‐4.01;Rsq=0.99

y=1.08*x+‐2.89;Rsq=0.98

y=1.03*x+‐0.95;Rsq=0.97

y=0.99*x+0.5;Rsq=0.99

y=1.03*x+‐1.96;

Rsq=0.99

y=1.02*x+‐3.12;

Rsq=0.99

y=1*x+‐0.23;Rsq=0.99

y=1*x+103;Rsq=0.9

Tacoma,WA

y=0.97*x+1.51;Rsq=1

y=1.08*x+3.92;Rsq=0.73

y=1.02*x+0.31;

Rsq=0.97

y=1.03*x+‐0.72;Rsq=0.99

y=1.02*x+‐0.71;

Rsq=0.98

y=1.02*x+‐0.21;Rsq=0.99

y=1.04*x+‐0.37;

Rsq=0.98

y=1.03*x+‐0.58;

Rsq=0.99

y=0.98*x+1.41;Rsq=0.98

y=0.97*+0.92;Rsq=0.9
Draft – Do Not Quote or Cite 4B- June 2010 21

PM10-2.5 Light Extinction: The source of the PM10-2.5 data used to calculate its 

contribution to PM10 light extinction for the 14 cities varies by urban area. As described in the 

UFVA (section 3.2.1), for some urban areas hourly PM10-2.5 was estimated by the difference 

method. Birmingham, Detroit, Baltimore and Philadelphia had collocated continuous PM10 and 

PM2.5 monitors, while Tacoma, Los Angeles, Phoenix, Atlanta, and New York had two separate 

sites with continuous PM10 and PM2.5 monitors. The other urban areas used a regional ratio of 

PM10-2.5 to PM2.5 multiplied by hourly PM2.5 to estimate PM10-2.5. While this approach provides 

some insight about the long-term contributions to light extinction by PM10-2.5, the hourly 

estimates using a ratio times PM2.5 do not provide meaningful hourly information, so the results 

for those urban areas are not discussed in this assessment. Figure 4B-4 is a box plot of the 

percent difference between the estimate of hourly PM10 light extinction estimated by approach C 

(includes coarse PM contributions) and the estimate of hourly PM2.5 light extinction by approach 

A (does not include coarse PM contributions). Urban areas that used the ratio method to 

estimate PM10-2.5 are labeled with an asterisk above the figure box. The other difference between 

calculated PM light extinction between the two approaches is the use of the SANDWICH model 

in approach A that is not used in Approach C. It is this difference that is responsible for the 

negative values in the box plots. 
Draft – Do Not Quote or Cite 4B- June 2010 22

Figure 4B-4 Box and whisker plot of the percent difference between approaches C and approach A paired 

estimates of hourly PM10 light extinction. Urban areas that used the ratio method to estimate PM10-2.5 are 

designated with an asterisk (*) above the figure box. 

The relative importance of PM10-2.5 to light extinction is more clearly shown by 

comparing hourly calculated PM10 light extinction values from approach C to paired hourly 

PM2.5 light extinction from approach B, which is identical except for the PM10-2.5 contributions. 

As can be seen in example scatter plots 4Figure B-5 (urban areas with collocated sampler) and 

Figure 4B-6 (urban areas using two different sites), PM10 light extinction equals or exceeds 

PM2.5 when approaches C and B are compared. Not surprisingly these show that PM10-2.5

contributions to PM light extinction are not often important for most of the urban areas, 

especially those in the Eastern U.S., though they can on rare occasions contribute as much as 100 

Mm-1 to the PM2.5 light extinction at some sites (e.g., Birmingham, Detroit, Philadelphia, and 

Atlanta). Based on these scatter plots PM10-2.5 light extinction is a much more frequent and 

relatively large contributor to PM10 light extinction at Phoenix (i.e., regression slope of 1.55) and 

an infrequent but large contributor to otherwise low PM10 light extinction hours at Los Angeles. 

However, the Los Angeles PM10-2.5 data should be treated skeptically, since the PM10 monitoring 

was done at a site outside the LA basin that may be unrepresentative of LA conditions. The 

PM10-2.5 contribution to PM light extinction at Tacoma is about 50 Mm-1, contributing about 7% 

(i.e., regression slope is 1.07). 
Draft – Do Not Quote or Cite 4B- June 2010 23

Figure 4B-5 Scatter plots comparing hourly PM light extinction for approaches C and B, for the four urban 

areas with collocated PM10 and PM2.5 monitoring. 
Draft – Do Not Quote or Cite 4B- June 2010 24

Figure 4B-6 Scatter plots comparing hourly PM light extinction for approaches C and B, for the five urban 

areas with different sites for PM10 and PM2.5 monitoring. 
Draft – Do Not Quote or Cite 4B- June 2010 25

PM2.5 Mass Concentration Used Alone to Calculate PM2.5 Light Extinction: Approach I 

predicts PM2.5 light extinction by merely multiplying the same hourly PM2.5 values as used in 

approach A (continuous PM2.5 instrument normalized to match the FRM 24-hour concentration) 

by a constant (i.e., 4.35 m2

/g).6 As shown in Figures 4B-7 and 4B-8, the results are not nearly as 

comparable as those calculated using approach F (or any of the methods that include composition 

and hourly relative humidity data). In Figures 4B-7 and 4B-8, the percentage difference is 

calculated as follows, which is different than for the earlier box and whisker plots: 

Percentage difference = [(“C” estimate) – “A” estimate)]/(“C” estimate) * 100% 

The variations in the relationships are even more evident in the monthly regression 

equations as shown in Table 4B-5. The slopes range from about one half to nearly two with R2

values that are often below 0.9 and as low as 0.4. 

 

Figure 4B-7 Box and whisker plot of the percent difference in calculated hourly PM2.5 light extinction 

between approaches I and A by urban area. 

 

6 The value of 4.35 m2

/g has no derivation of particular importance. It was chosen by EPA staff as an initial 

estimate of the value that would make the mean PM2.5 light extinction estimated by approach I be the same as in 

approach B in one particular city. 
Draft – Do Not Quote or Cite 4B- June 2010 26

Figure 4B-8 Scatter plots of hourly calculated PM2.5 light extinction for approach I versus approach A for 

four selected urban areas. 
Draft – Do Not Quote or Cite June 2010 4B-27

Table 4B-5 Linear regression equation and R2 values for relating hourly PM2.5 light extinction values calculated using approac

those using approach A (y in the equation) by month for 15 urban areas.

Month 1 2 3 4 5 6 7 8 9 10 Atlanta,GA

y=1.49*x+‐

6.02;

Rsq=0.91

y=1.46*x+‐

6.61;

Rsq=0.85

y=1.25*x+‐

8.03;

Rsq=0.85

y=1.33*x+‐

11.91;

Rsq=0.85

y=1.2*x+‐

7.04;

Rsq=0.77

y=1.06*x+5

.99;

Rsq=0.65

y=0.87*x+2

5.82;

Rsq=0.55

y=1.06*x+8

.1;

Rsq=0.62

y=1.21*x+‐

2.64;

Rsq=0.75

y=1.22*x+‐

2.26;

Rsq=0.89

Baltimore, MD

y=1.28*x+‐

8.03;

Rsq=0.88

y=1.65*x+‐

31.13;

Rsq=0.84

y=1.47*x+‐

17.1;

Rsq=0.79

y=1.03*x+

0.11;

Rsq=0.79

y=1.05*x+

6.54;

Rsq=0.62

y=1.23*x+‐

6.26;

Rsq=0.7

y=1.1*x+‐

1.47;

Rsq=0.74

y=1.09*x+7

.77;

Rsq=0.85

y=1.33*x+‐

8.8;

Rsq=0.81

y=1.49*x+‐

7.7;

Rsq=0.72

Birmingham, AL

y=1.29*x+‐

5.86;

Rsq=0.8

y=1.31*x+‐

7.3;

Rsq=0.85

y=1.11*x+‐

5.08;

Rsq=0.89

y=1.05*x+‐

2.93;

Rsq=0.85

y=1.06*x+‐

6.34;

Rsq=0.9

y=1*x+‐

2.72;

Rsq=0.82

y=1.09*x+‐

0.42;

Rsq=0.77

y=0.95*x+2

.44;

Rsq=0.77

y=1.16*x+‐

13.85;

Rsq=0.8

y=1.24*x+‐

7.38;

Rsq=0.87

Dallas,TX

y=1.69*x+‐

8.95;

Rsq=0.84

y=1.94*x+‐

21.97;

Rsq=0.88

y=1.54*x+‐

13.47;

Rsq=0.79

y=1.34*x+‐

10.87;

Rsq=0.75

y=1.16*x+‐

2.83;

Rsq=0.69

y=0.86*x+3

.04;

Rsq=0.74

y=0.86*x+3

.72;

Rsq=0.75

y=0.96*x+‐

2.36;

Rsq=0.7

y=1.04*x+‐

2.03;

Rsq=0.71

y=1.19*x+‐

2.94;

Rsq=0.81

Detroit,MI

y=1.18*x+1

3.01;

Rsq=0.7

y=1.26*x+

5.26;

Rsq=0.84

y=1.12*x+

7.51;

Rsq=0.66

y=1.17*x+‐

5.53;

Rsq=0.85

y=0.79*x+

11.4;

Rsq=0.82

y=0.78*x+1

4.2;

Rsq=0.84

y=0.99*x+8

.98;

Rsq=0.73

y=1.22*x+1

.21;

Rsq=0.55

y=1.22*x+‐

11.05;

Rsq=0.72

y=1.42*x+‐

6.44;

Rsq=0.79

Fresno,CA

y=1.2*x+2.

75;

Rsq=0.82

y=1.28*x+

1.8;

Rsq=0.84

y=1.46*x+‐

0.46;

Rsq=0.83

y=1.29*x+‐

2.15;

Rsq=0.66

y=0.98*x+

4.24;

Rsq=0.54

y=1.01*x+‐

1.51;

Rsq=0.68

y=0.94*x+‐

1.1;

Rsq=0.93

y=1.13*x+‐

5.1;

Rsq=0.73

y=1.24*x+‐

4.61;

Rsq=0.73

y=1.63*x+‐

8.37;

Rsq=0.7

Houston,TX

y=1.29*x+‐

2.32;

Rsq=0.87

y=1.35*x+‐

6.35;

Rsq=0.81

y=1.23*x+‐

7.63;

Rsq=0.85

y=1.38*x+‐

18.36;

Rsq=0.82

y=1.33*x+‐

19.81;

Rsq=0.76

y=1.07*x+‐

2.13;

Rsq=0.77

y=0.54*x+2

0.69;

Rsq=0.57

y=0.75*x+1

1.34;

Rsq=0.43

y=1.21*x+‐

12.05;

Rsq=0.82

y=1.3*x+‐

10.7;

Rsq=0.95

LosAngeles,CA

y=1.45*x+‐

6.19;

Rsq=0.87

y=1.22*x+

3.21;

Rsq=0.85

y=1.26*x+

6.69;

Rsq=0.79

y=1.43*x+‐

6.52;

Rsq=0.78

y=1.41*x+‐

3.21;

Rsq=0.78

y=1.72*x+‐

25.83;

Rsq=0.56

y=1.24*x+4

.44;

Rsq=0.57

y=1.59*x+‐

21.91;

Rsq=0.49

y=1.42*x+‐

7.87;

Rsq=0.71

y=1.46*x+‐

14.3;

Rsq=0.81

NewYork, NY

y=1.5*x+‐

14.28;

Rsq=0.89

y=1.16*x+‐

5.06;

Rsq=0.92

y=1.39*x+‐

5.96;

Rsq=0.87

y=1.22*x+‐

1.7;

Rsq=0.89

y=1.2*x+‐

2.33;

Rsq=0.8

y=1.36*x+‐

9.36;

Rsq=0.78

y=1.17*x+0

.71;

Rsq=0.65

y=1.29*x+‐

13.03;

Rsq=0.79

y=1.37*x+‐

5.7;

Rsq=0.89

y=1.89*x+‐

21.18;

Rsq=0.83
Draft – Do Not Quote or Cite June 2010 4B-28

Philadelphia, PA

y=1.38*x+‐

9.12;

Rsq=0.76

y=1.1*x+2.

37;

Rsq=0.78

y=1.03*x+

1.61;

Rsq=0.83

y=0.81*x+

7.81;

Rsq=0.68

y=0.88*x+

4.55;

Rsq=0.85

y=0.97*x+3

.07;

Rsq=0.78

y=1.03*x+1

.63;

Rsq=0.82

y=0.79*x+3

4.86;

Rsq=0.69

y=0.8*x+1

0.76;

Rsq=0.64

y=1.01*x+

8.02;

Rsq=0.86

Phoenix,AZ

y=0.83*x+‐

2.5;

Rsq=0.89

y=0.76*x+‐

1.53;

Rsq=0.97

y=0.65*x+

1.46;

Rsq=0.96

y=0.56*x+

2.12;

Rsq=0.92

y=0.6*x+1.

7;

Rsq=0.88

y=0.62*x+1

.11;

Rsq=0.96

y=0.5*x+5.

73;

Rsq=0.61

y=0.69*x+0

.51;

Rsq=0.97

y=0.6*x+0.

85;

Rsq=0.95

y=0.6*x+2

35;

Rsq=0.58

Pittsburgh, PA

y=1.6*x+‐

7.31;

Rsq=0.75

y=1.22*x+

0.13;

Rsq=0.76

y=1.19*x+‐

2.09;

Rsq=0.76

y=1.06*x+

0.19;

Rsq=0.74

y=1.06*x+

0.15;

Rsq=0.56

y=0.97*x+1

1.89;

Rsq=0.67

y=1.06*x+1

0.46;

Rsq=0.51

y=1.28*x+‐

9.71;

Rsq=0.74

y=1.18*x+‐

2.72;

Rsq=0.62

y=1.16*x+

2.65;

Rsq=0.72

Salt LakeCity, UT

y=1.38*x+‐

4.78;

Rsq=0.93

y=1.25*x+‐

0.98;

Rsq=0.91

y=1.23*x+‐

0.88;

Rsq=0.87

y=0.92*x+

2.06;

Rsq=0.75

y=0.73*x+

4.3;

Rsq=0.69

y=0.83*x+2

.89;

Rsq=0.79

y=0.9*x+0.

11;

Rsq=0.98

y=0.88*x+‐

0.16;

Rsq=0.96

y=0.79*x+

5.83;

Rsq=0.87

y=1.1*x+2

23;

Rsq=0.82

St Louis,,

MO

y=1.5*x+‐

0.2;

Rsq=0.9

y=1.7*x+‐

23.42;

Rsq=0.87

y=1.79*x+‐

23.76;

Rsq=0.83

y=1.29*x+‐

5.93;

Rsq=0.69

y=1.24*x+‐

7.67;

Rsq=0.73

y=0.88*x+8

.95;

Rsq=0.74

y=1.12*x+‐

5.02;

Rsq=0.75

y=1.07*x+‐

2.69;

Rsq=0.72

y=1.03*x+

2.61;

Rsq=0.74

y=1.71*x+‐

15.49;

Rsq=0.79

Tacoma,WA

y=1.11*x+2

.25;

Rsq=0.98

y=1.51*x+‐

2.18;

Rsq=0.94

y=1.28*x+

0.12;

Rsq=0.92

y=1.37*x+‐

3.6;

Rsq=0.86

y=1.15*x+‐

1.92;

Rsq=0.71

y=1.1*x+2.

17;

Rsq=0.63

y=1.03*x+2

.64;

Rsq=0.81

y=1.3*x+‐

2.74;

Rsq=0.68

y=1.07*x+

2.74;

Rsq=0.84

y=1.35*x+‐

0.62;

Rsq=0.92
Draft-Do Not Quote or Cite June 2010 4C-1

1 Appendix 4C 

2 Assessment of the PM Components Responsible for the Largest Hourly PM10

3 Light Extinction and PM2.5 Mass Hours Selected as Maximum Daily and 

4 Using All Hours for 14 Urban Areas 

5 

6 As discussed in the UFVA, light extinction is considered a better indicator of visibility 

7 impacts than PM2.5 mass concentration. The UFVA also considers two different forms: the 

8 maximum daily and all daylight hours for possible use in a secondary PM NAAQS to protect 

9 visibility. One way to explore the ramifications of the selection of indicator and form is to assess 

10 how much difference these choices make to the types of sources that might need to be controlled 

11 to improve visibility conditions. The purpose of this assessment is to provide a better 

12 understanding of the dependency of the mix of PM components (i.e., a reflection of the types of 

13 sources) responsible for the high haze values on the choice of form and indicator used. This was 

14 done using the hourly PM10 light extinction and PM2.5 speciated mass composition data set as 

developed for the UFVA for the 14 urban areas1 15 . As was demonstrated in the UFVA (section 

4.1.4) for PM10 light extinction, the 90th 16 percentile maximum daily daylight hour has design 

values that are nearly identical to the 98th 17 percentile of all daylight hours for each of the urban 

18 areas. In order to reduce the numbers of cases for comparison, only these two forms, which give 

19 comparable design values, are included in this assessment. 

20 Stacked bar plots that show the PM species contributions to PM10 light extinction for 

21 each hour that is in the top 10% of daily maximum daylight hours of PM10 light extinction and 

22 the top 2% of all daylight hours of PM10 light extinction for each of the urban areas are displayed 

23 in Figure 3-13 of the UFVA. These show the components that contribute to light extinction for 

the hours that would need to be reduced in order to meet the 90th 24 percentile maximum daily and 

98th 25 percentile of all daylight hours PM10 light extinction. Similar figures were generated for this 

26 comparison to display the mass components of each hour that is in the top 10% daily maximum 

27 daylight hours and top 2% of all daylight hours for PM2.5 mass. To aid making the comparisons, 

28 the plots already available in the UFVA are repeated in this appendix and each set of plots is 

29 shown both as components contributing to light extinction and those contributing to PM2.5

30 speciated mass concentration. This leads to eight separate stacked bar plots (i.e., 2 indicators x 2 

31 forms x 2 component displays) for each of the 14 urban areas (see Figures 4C1 to 4C14). 

 

1 Due to the inappropriate coarse mass values developed for the St. Louis dataset, it was excluded in this assessment. 
Draft-Do Not Quote or Cite June 2010 4C-2

1 Figure 4C-1 Tacoma PM components contributing to the top 10% of maximum daily 1-hour PM10 light 

2 extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 light extinction (e 

3 & f) and for PM2.5 mass (g & h). 

4 
Draft-Do Not Quote or Cite June 2010 4C-3

1 
Draft-Do Not Quote or Cite June 2010 4C-4

Figure 4C-2 Fresno PM components contributing to the top 10% of maximum daily 1-hour PM10 1 light 

extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 2 light extinction (e 

3 & f) and for PM2.5 mass (g & h). 

4 
Draft-Do Not Quote or Cite June 2010 4C-5

1 

2 
Draft-Do Not Quote or Cite June 2010 4C-6

Figure 4C-3 Los Angeles PM components contributing to the top 10% of maximum daily 1-hour PM10 1 light 

extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 2 light extinction (e 

3 & f) and for PM2.5 mass (g & h). 

4 
Draft-Do Not Quote or Cite June 2010 4C-7

1 
Draft-Do Not Quote or Cite June 2010 4C-8

Figure 4C-4 Phoenix PM components contributing to the top 10% of maximum daily 1-hour PM10 1 light 

extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 2 light extinction (e 

3 & f) and for PM2.5 mass (g & h). 

4 
Draft-Do Not Quote or Cite June 2010 4C-9

1 

2 
Draft-Do Not Quote or Cite June 2010 4C-10

1 

Figure 4C-5 Salt Lake City PM components contributing to the top 10% of maximum daily 1-hour PM10 2 light 

extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 3 light extinction (e 

4 & f) and for PM2.5 mass (g & h). 

5 
Draft-Do Not Quote or Cite June 2010 4C-11

1 

2 
Draft-Do Not Quote or Cite June 2010 4C-12

1 

Figure 4C-6 Dallas PM components contributing to the top 10% of maximum daily 1-hour PM10 2 light 

extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 3 light extinction (e 

4 & f) and for PM2.5 mass (g & h).

5 
Draft-Do Not Quote or Cite June 2010 4C-13

1 

2 
Draft-Do Not Quote or Cite June 2010 4C-14

1 

Figure 4C-7 Houston PM components contributing to the top 10% of maximum daily 1-hour PM10 2 light 

extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 3 light extinction (e 

4 & f) and for PM2.5 mass (g & h). 

5 
Draft-Do Not Quote or Cite June 2010 4C-15

1 

2 
Draft-Do Not Quote or Cite June 2010 4C-16

1 

Figure 4C-8 Birmingham PM components contributing to the top 10% of maximum daily 1-hour PM10 2 light 

extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 3 light extinction (e 

4 & f) and for PM2.5 mass (g & h). 

5 
Draft-Do Not Quote or Cite June 2010 4C-17

1 
Draft-Do Not Quote or Cite June 2010 4C-18

1 

Figure 4C-9 Atlanta PM components contributing to the top 10% of maximum daily 1-hour PM10 2 light 

extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 3 light extinction (e 

4 & f) and for PM2.5 mass (g & h). 

5 
Draft-Do Not Quote or Cite June 2010 4C-19

1 

2 
Draft-Do Not Quote or Cite June 2010 4C-20

1 

Figure 4C-10 Detroit PM components contributing to the top 10% of maximum daily 1-hour PM10 2 light 

extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 3 light extinction (e 

4 & f) and for PM2.5 mass (g & h). 

5 
Draft-Do Not Quote or Cite June 2010 4C-21

1 

2 
Draft-Do Not Quote or Cite June 2010 4C-22

1 

Figure 4C-11 Baltimore PM components contributing to the top 10% of maximum daily 1-hour PM10 2 light 

extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 3 light extinction (e 

4 & f) and for PM2.5 mass (g & h). 

5 
Draft-Do Not Quote or Cite June 2010 4C-23

1 

2 
Draft-Do Not Quote or Cite June 2010 4C-24

1 

Figure 4C-12 Pittsburgh PM components contributing to the top 10% of maximum daily 1-hour PM10 2 light 

extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 3 light extinction (e 

4 & f) and for PM2.5 mass (g & h). 

5 
Draft-Do Not Quote or Cite June 2010 4C-25

1 

2 
Draft-Do Not Quote or Cite June 2010 4C-26

1 

Figure 4C-13 Philadelphia PM components contributing to the top 10% of maximum daily 1-hour PM10 2 light 

extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 3 light extinction (e 

4 & f) and for PM2.5 mass (g & h). 

5 
Draft-Do Not Quote or Cite June 2010 4C-27

1 

2 
Draft-Do Not Quote or Cite June 2010 4C-28

1 

2 Figure 4C-14 New York City PM components contributing to the top 10% of maximum daily 1-hour PM10 

light extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10 3 light 

4 extinction (e & f) and for PM2.5 mass (g & h).

5 
Draft-Do Not Quote or Cite June 2010 4C-29

1 

2 
Draft-Do Not Quote or Cite June 2010 4C-30

1 In each set of the 8 stacked bar plots, the plots labeled (a) and (b) are the PM10 light 

2 extinction and PM2.5 mass components for hours selected as the top 10 percent of the maximum 

3 daily PM10 light extinction; and the plots labeled (c) and (d) are the PM2.5 mass and PM10 light 

4 extinction components for the hours selected as the top 10 percent of the maximum daily PM2.5

5 mass concentration. The plots labeled (e) through (h) are in the same order as (a) through (d) 

6 except these hours were selected based on their being in the top 2% of all daylight hours for 

7 PM10 light extinction (e) and (f), and the top 2% of all daylight hours for PM2.5 mass (g) and (h). 

8 In other words, there are four unique selections of hours based on two different indicators (i.e., 

9 PM10 light extinction and PM2.5 mass) and two different forms (i.e. top 10% of maximum hourly 

10 and top 2% of all daylight hours). Each of these four selections of hours is displayed first as the 

11 components of the indicator used to make the selection, then in the following plot as the 

12 components of the indicator not used in the selection of the hours. 

13 Three sets of comparisons are possible using the plots in Figures 4C1 to 4C14. The first 

14 two are the comparisons of the PM composition for hours selected for the two PM indicators 

(i.e., light extinction and mass) and the two forms (i.e., 90th percentile maximum daily and 98th 15 

16 percentile of all daylight hours). The key difference for these first two types of comparisons has 

17 to do with the PM components for the hours selected by these four combinations of indicator and 

18 form. The third set of comparisons is the apportionments of components using PM10 light 

19 extinction versus using PM2.5 mass where the hours selected are identical. 

20 For any selected hour, the relative contributions of components to PM mass is not the 

21 same as the relative contributions of the components to PM light extinction because of the 

22 differing light extinction efficiencies of the components. Similarly, the relative importance of 

23 the hygroscopic PM components (i.e., PM sulfates and nitrates) compared to that of the non24 hygroscopic components (i.e. primarily organic and elemental carbon) to PM mass is generally 

25 different than it is to PM light extinction and that difference changes as a function of relative 

26 humidity. 

27 Indicator Comparisons: To assess the similarity of light extinction components selected 

28 by the two indicators compare plots (a) and (d) for the daily maximum form, as well as plots (e) 

29 and (h) for the all hours form for each urban area. A corresponding set of comparisons of the 

30 PM components can be done by comparing plots (b) and (c), as well as plots (f) and (g) for each 

31 urban area. For each of the urban areas there are multiple examples of identical or very nearly 

32 identical contributions among the top contributors for PM10 light extinction and PM2.5 mass. A 

33 more careful examination shows that the two indicators have selected the same hour or another 

34 hour during the same day as top contributors (note that the dates and times are listed below each 

35 stacked bar). 

36 To more efficiently identify the degree to which common and unique periods are selected 

37 by the two indicators, Table 4C-1 lists the numbers of common and unique days selected by the 
Draft-Do Not Quote or Cite June 2010 4C-31

1 two. The greater the number of common days compared to the number of unique days, the more 

2 similar are the components of PM selected by the two indicators. For example, Tacoma, Fresno, 

3 Salt Lake City, Birmingham and New York each have almost twice as many common days as 

4 unique day for the maximum daily form, though only Tacoma and Fresno have this characteristic 

5 for the all hours form. Most of the other urban areas have nearly comparable number of unique 

6 and common days for the 1-hour maximum daily form, and comparable to as much as three 

7 times (Dallas) the number of unique days compared to common days for the all hours form. 

8 Locations with relatively large numbers of unique days selected are more likely to have the 

9 greatest differences of the mix of PM components. For example, Los Angeles and Dallas have 

10 greater contributions by organics when PM2.5 mass is the indicator than when PM10 light 

11 extinction is used. In addition, many of the eastern urban areas have higher contributions of 

12 organics and less contributions by PM nitrate when PM2.5 mass is the indicator than when PM10

13 light extinction is used. 

14 Even if the unique hours selected by PM2.5 mass have similar composition they 

15 necessarily have lower light extinction than those selected based on being in the top percentage 

16 of light extinction. This can be seen by comparing the magnitude of the lowest selected light 

17 extinction hour in plots (a) or (e) with the light extinction values for the hours selected using 

18 PM2.5 mass in corresponding plots (d) or (h) for Figures 4C-1 to 4C-14. For example, notice that 

19 for Birmingham (Figure 4C-8) there are many hours selected by PM2.5 mass with light extinction 

20 values well below the minimum PM10 light extinction value for hours selected based on PM10

21 light extinction (i.e. compare (d) to (a) and (h) to (e)). 
Draft-Do Not Quote or Cite June 2010 4C-32

1 

2 Table 4C-1 The numbers of common and unique days selected for each urban area 

3 comparing PM10 light extinction versus PM2.5 mass for the top 10% of daily maximum and 

4 for the top 2% of all hours. 

10% maximum 2% all hours 

common unique common unique 

Tacoma 9 4 11 6 

Fresno 27 10 20 10 

Los Angeles 18 24 14 38 

Phoenix 5 8 9 18 

Salt Lake City 25 12 14 12 

Dallas 17 20 13 43 

Houston 11 7 16 23 

Birmingham 27 16 32 26 

Atlanta 18 19 19 27 

Detroit 9 10 8 22 

Baltimore 12 12 6 22 

Pittsburgh 19 18 16 30 

Philadelphia 9 10 8 17 

New York City 18 10 17 16 

5 

6 Form Comparisons: A similar set of comparisons of PM component mixtures can be 

7 made between hours selected based on their being in the top 10% of daily maximum or the top 

8 2% of all daylight hours. For Figures 4C-1 to 4C-14 composition displayed in plots labeled (a) 

9 and (e) for the PM10 light extinction indicator and (c) and (g) for the PM2.5 indicator can be 

10 compared. Some of the same hours are selected by both forms. By definition the top 10% 

11 maximum daily hour form never has more than 1 hour selected per day. There are very nearly 

12 twice as many hours in the top 2% of all hours compared to the top 10% of maximum daily 
Draft-Do Not Quote or Cite June 2010 4C-33

1 hours. However, it is not unusual to have multiple hours selected in the same day by the top 2% 

2 of all hours form. These hours selected in the same day tend to have nearly identical 

3 composition (e.g., Figure 4C-1 (e) has 4 consecutive hours on 11/08/2007, which is also selected 

4 with a 10% maximum daily form (a)). The top 2% of all hours may select hours on a greater 

5 number of days compared with the top 10% of maximum daily hours, or if there are enough 

6 instances of multiple hours per day, it could select hours on a smaller number of days compared 

7 with the top 10% of maximum daily hours. A list of the numbers of common and unique days 

8 selected comparing the two forms for both indicators and the number of days selected by each 

9 combination of indicator and form is shown in Table 4C-2. 

10 In addition to affecting the component mixture for the top hours selected, the number of 

11 multiple hours per day selected among the top 2% of all hours affects whether its design values 

12 are greater than or less than the corresponding design values for the top 10% of maximum daily 

13 hours. If the number of multiple hours per day is small enough that the 2% of all hours form has 

14 more days selected than the top 10 percent of all hours, then the design value for the 2% form is 

15 smaller than that of the 10% daily maximum form. An example of this is light extinction for 

16 Birmingham where the 2% of all hours form selects hours on 47 days and has a PM light 

extinction design value of 309 Mm-1 17 compared to the 35 days that have hours in the top 10% of 

daily maximum light extinction with a design value of 357 Mm-1 18 . Among the 14 urban areas, 

19 Salt Lake City light extinction is the most extreme example of the opposite situation with only 16 

days having the top 2% of all hours and a design value of 225 Mm-1 20 compared with 31 days 

selected by the top 10% maximum daily light extinction and a design value of 164 Mm-1 21 . 

22 For most of the urban areas the number of common days is much larger than the number 

23 of unique days. Generally, the PM component mix is less similar for sites with a lower number 

24 of common and a greater number of unique days. For example, the 2% of all hours form for Salt 

25 Lake City selected only one hour of one of the days where the PM carbonaceous components 

26 was the major contributor compared with the 4 hours with high PM carbonaceous components 

27 selected in the top 10% of the maximum daily form (compare Figure 4C-5 (a) with (e)). By 

28 comparison for Salt Lake City most of the multiple hours in single days had high contributions 

29 from PM nitrate. Composition of the hours selected for the two forms by either indicator are 

30 generally very similar. To facilitate comparisons among the PM component contributions for the 

31 two forms and indicators, Figure 4C-15 shows stacked bar plots of the average PM component 

32 contributions by city. 
Draft-Do Not Quote or Cite June 2010 4C-34

1 Table 4C-2 The numbers of common and unique days selected for each urban area comparing the top 10% 

2 of 1-hr daily maximum to the top 2% of all hours for PM10 light extinction and PM2.5 mass indicators. Also 

3 shown are the number of days selected for each form and indicator. 

PM10 Light Extinction PM2.5 Mass 

10% vs. 2% Number of days 10% vs. 2% Number of days 

common unique 10% 2% common unique 10% 2% 

Tacoma 11 3 11 14 11 1 11 12 

Fresno 25 7 32 25 20 12 32 20 

Los Angeles 30 8 30 38 18 12 30 18 

Phoenix 9 6 9 14 9 5 9 14 

Salt Lake City 16 15 31 16 18 13 31 18 

Dallas 25 2 27 25 23 4 27 23 

Houston 14 11 14 23 15 8 15 23 

Birmingham 35 14 35 47 35 1 35 46 

Atlanta 27 6 27 33 27 1 28 27 

Detroit 13 1 14 13 13 1 14 13 

Baltimore 12 6 18 12 11 7 18 11 

Pittsburgh 28 3 28 31 24 4 28 24 

Philadelphia 14 0 14 14 12 2 14 12 

New York City 23 0 23 23 19 4 23 19 

4 
Draft-Do Not Quote or Cite June 2010 4C-35

1 

2 Figure 4C-15 Average by urban areas of the PM components contributing to the top 10% of maximum daily 

3 1-hour PM10 light extinction (a & b) and PM2.5 mass (c & d), and to the top 2% of all daylight hours for PM10

4 light extinction (e & f) and for PM2.5 mass (g & h). 

5 
Draft-Do Not Quote or Cite June 2010 4C-36

1 

2 
Draft-Do Not Quote or Cite June 2010 4C-37

1 As discussed above the greatest differences between the two forms is for Salt Lake City. 

2 For most of the 14 urban areas, the differences in results comparing the two forms are minor 

3 (compare (a) to (e) and (c) to (g)). Figure 4C-15 also facilitates comparisons of the average 

4 effects on PM component mixtures depending on which of the two indicators is used (compare 

5 (a) to (d) and (e) to (h)). It shows that the difference in PM component contributions differ more 

6 depending on the choice of indicator than the choice of form. 

7 Component Apportionment Comparisons: This is the simplest of the three types of 

8 comparisons because the exact same hours are compared and the only difference is in the relative 

9 contributions of the components to PM10 light extinction compared to the relative component 

10 contributions to PM2.5 mass. To see these differences for individual hours or averages by urban 

11 areas compare plots labeled (a) and (b), (c) and (d), (e) and (f), or (g) and (h) in Figures 4C-1 to 

12 4C-15. The differences directly result from the application of the IMPROVE algorithm which 

13 weights the various PM mass components differently when expressing them as components of 

14 light extinction. For example, notice that the soil component of PM10 light extinction is barely 

15 discernable for Birmingham and Detroit, but is easily discerned as a component of PM2.5 mass. 

16 In many of the urban areas the relative contributions by soil and organic mass PM components 

17 are greater and the relative contributions by elemental carbon, sulfate and nitrate components are 

18 less when expressed as contributions to PM2.5 mass than when expressed as PM10 light 

19 extinction. 

20 The differences in the relative apportionment of the PM components are of concern when 

21 emission control efforts are designed to reduce visibility impacts. Use of the relative 

22 contribution by PM10 light extinction components recognizes that some PM components cause 

23 more visibility impacts per unit of mass concentration, whereas the use of relative contributions 

24 to PM2.5 mass treat the most and least effective components as though they contributed equally 

25 on a per unit mass contribution basis. 
This page left intentionally blank. 
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, NC 

Publication N