Quantitative Health Risk Assessment for 

Particulate Matter 

Second External Review Draft 
 

DISCLAIMER

This draft document has been prepared by staff from the 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 Dr. Zachary Pekar, U.S. 

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

06, Research Triangle Park, North Carolina 27711 (email: pekar.zachary@epa.gov). 

Elements of this report (see Acknowledgements below) have been provided to the 

U.S. Environmental Protection Agency (EPA) by Abt Associates, Inc. in partial 

fulfillment of Contract No. EP-D-08-100, Work Assignment 0-02. Any opinions, 

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

reflect the views of the EPA or Abt Associates, Inc. Any analyses, interpretations, or 

conclusions presented in this report based on emergency department, hospitalization and 

mortality baseline incidence data obtained from outside sources, are credited to the 

authors and not the institutions providing the raw data. 

ACKNOWLEDGEMENTS

In addition to EPA staff, personnel from Abt Associates, Inc. contributed to the 

writing of this document. Specific chapters and appendices where Abt Associates, Inc. 

made contributions include: chapter 3 (Overview of Methods Used in Estimating Risk for 

the Urban Case Studies), and appendices A (Air Quality Assessment), C 

(Epidemiological – Epi - Study Specific Information On PM2.5), E (Risk Analysis - Core 

Analysis), and F (Sensitivity Analysis Results). 

 

 
 

 EPA-452/P-10-001 

February 2010 

Quantitative Health Risk Assessment for 

Particulate Matter 

Second External Review Draft 

US Environmental Protection Agency 

Office of Air and Radiation 

Office of Air Quality Planning and Standards 

Research Triangle Park, North Carolina 27711 
 

1 Table of Contents 

2 

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

4 List of Figures............................................................................................................................... vi

5 List of Acronyms/Abbreviations...............................................................................................viii

6 1 Introduction.................................................................................................................... 1-1

7 1.1 Background............................................................................................................... 1-3 

8 1.2 Current risk Assessment: Goals and Planned Approach ..........................................1-5 

9 1.3 Organization of Document ....................................................................................... 1-6 

10 2 Scope................................................................................................................................2-1

11 2.1 Overview of Risk Assessment from Last Review ....................................................2-2 

12 2.2 Original Assessment Plan.........................................................................................2-3 

13 2.2.1 Risk Assessment ............................................................................................. 2-3 

14 2.2.2 Population Exposure Analysis ........................................................................2-5 

15 2.3 Current Scope and Key Design Elements.................................................................2-6 

16 2.4 Alternative Suites of PM2.5 Standards Evalutated .................................................... 2-9 

17 3 Urban Case Study Analysis Methods........................................................................... 3-1

18 3.1 General Approach.....................................................................................................3-1 

19 3.1.1 Basic Structure of the Risk Assessment..........................................................3-1 

20 3.1.2 Calculating PM2.5-Related Health Effects Incidence ...................................... 3-7 

21 3.1.2.1 Short-term vs. Long-term Exposure ........................................................3-9 

22 3.1.2.2 Calculating Annual Incidence................................................................3-10 

23 3.2 Air Quality Inputs...................................................................................................3-11 

24 3.2.1 Characterizing Recent Conditions ................................................................3-11 

25 3.2.2 Estimating Policy Relevant Background ......................................................3-13 

26 3.2.3 Simulating Air Quality to Just Meet Current and Alternative Standards .....3-14 

27 3.2.3.1 Proportional Rollback Method............................................................... 3-15 

28 3.2.3.2 Hybrid Rollback Method ....................................................................... 3-19 

29 3.2.3.3 Peak Shaving Rollback Method............................................................. 3-19 

30 3.3 Selection of Model Inputs....................................................................................... 3-20 

31 3.3.1 Health Endpoints........................................................................................... 3-20 

32 3.3.2 Selection and Delineation of Urban Study Areas ......................................... 3-22 

33 3.3.3 Selection of Epidemiological Studies and Concentration-response (C-R) 

34 Functions within those Studies .....................................................................3-26 

35 3.3.4 Summary of Selected Health Endpoints, Urban Areas, Studies, and C-R 

36 Functions.......................................................................................................3-32 
February 2010 Draft – Do Not Quote or Cite ii

1 3.4 Baseline Health Effects Incidence Data .................................................................3-47 

2 3.4.1 Data Sources ................................................................................................. 3-47 

3 3.4.1.1 Mortality ................................................................................................ 3-47 

4 3.4.1.2 Hospital Admission and Emergency Department Visits .......................3-47 

5 3.4.1.3 Populations............................................................................................. 3-49 

6 3.4.2 Calculation of Baseline Incidence Rates.......................................................3-52 

7 3.5 Addressing Uncertainty and Variability .................................................................3-56 

8 3.5.1 Overview.......................................................................................................3-56 

9 3.5.2 Treatment of Key Sources Of Variability .....................................................3-59 

10 3.5.3 Qualitative Assessment of Uncertainty.........................................................3-62 

11 3.5.4 Single and Multi-Factor Sensitivity Analyses ..............................................3-72 

12 3.5.4.1 Sensitivity Analyses for Long-Term Exposure-Related Mortality........3-72 

13 3.5.4.2 Sensitivity Analyses for Short-Term Exposure-Related Mortality and 

14 Morbidity ...............................................................................................3-76 

15 3.5.4.3 Multi-factor Sensitivity Analyses ..........................................................3-77 

16 3.5.5 Summary of Approach to Addressing Variability and Uncertainty.............. 3-78 

17 4 Urban Case Study Results............................................................................................. 4-1

18 4.1 Assessment of Health Risk Associated with Recent Conditions (core analysis) ...4-16 

19 4.2 Assessment of Health Risk Associated with Just Meeting the Current and 

20 Alternative Suites of Standards (core analysis)...................................................... 4-19 

21 4.2.1 Core Risk Estimates for Just Meeting the Current Suite of Standards .........4-21 

22 4.2.2 Core Risk Estimates for Just Meeting Alternative Suites of Standards........4-22 

23 4.3 Sensitivity Analysis Results ...................................................................................4-28 

24 4.3.1 Sensitivity Analysis Results to Identify Potentially Important Sources of 

25 Uncertainty and Variability...........................................................................4-28 

26 4.3.1.1 Single-factor Sensitivity Analysis..........................................................4-35 

27 4.3.1.2 Multi-factor Sensitivity Analysis Results ..............................................4-41 

28 4.3.2 Additional Set of Reasonable Risk Estimates to Inform Consideration of 

29 Uncertainty in Core Risk Estimates ..............................................................4-43 

30 4.4 Evaluating the representativeness of the Urban Study Areas in the National 

31 Context.................................................................................................................... 4-48

32 4.5 Consideration of Design Values and Patterns of PM2.5 Monitoring Data in 

33 Intrepreting Core Risk Estimates............................................................................4-64 

34 4.5.1 Design Values ............................................................................................... 4-65 

35 4.5.2 Patterns in PM2.5 Monitoring Data................................................................4-71 
February 2010 Draft – Do Not Quote or Cite iii

1 5 National-Scale Assessment of Long-Term Mortality Related to PM2.5

2 Exposure .........................................................................................................................5-1

3 5.1 Overview...................................................................................................................5-1 

4 5.2 Methods .................................................................................................................... 5-3 

5 5.2.1 Population Estimates.......................................................................................5-3 

6 5.2.2 Population Exposure .......................................................................................5-3 

7 5.2.3 Premature Mortality Estimates........................................................................5-5 

8 5.3 Results.......................................................................................................................5-5 

9 6 Integrative Discussion of Urban Case Study Analysis of PM2.5-related Risks.........6-1

10 6.1 Key Analytical Elements in this Risk Assessment...................................................6-2 

11 6.2 Interpreation of Urban Study Area Results ..............................................................6-4 

12 6.2.1 Simulation of Just Meeting the Current Suite of PM2.5 Standards..................6-7 

13 6.2.2 Simulation of Just Meeting Alternative Annual Standards...........................6-10 

14 6.2.3 Simulation of Just Meeting Alternative Suites of Annual and 24-hour 

15 Standards.......................................................................................................6-12 

16 6.3 National Perspective on PM2.5-related Risks.......................................................... 6-16 

17 6.4 Key Observations....................................................................................................6-18 

18 7 REFERENCES............................................................................................................... 7-1

19 

20 Appendix A. Air Quality Assessment 

21 Appendix B. Hybrid (Non-Proportional) Rollback Approach 

22 Appendix C. Epidemiological Study Specific Information for PM Risk Assessment 

23 Appendix D. Supplement to Representativeness of the 15 Urban Study Areas 

24 Appendix E. Risk Estimates (Core Analysis)

25 Appendix F. Sensitivity Analysis Results 

26 Appendix G. Supplement to the National-Scale Assessment of Long-Term Mortality Related to 

27 PM2.5 Exposure 

28 Appendix H. Consideration off Risk Associated with Exposure to Thoracic Coarse PM 

29 (PM10-2.5 ) 
February 2010 Draft – Do Not Quote or Cite iv

1 List of Tables 

2 

3 Table 3-1. Numbers of Monitors in Risk Assessment Locations From Which Composite 

4 Monitor Values Were Calculated*..........................................................................3-13 

5 Table 3-2 Regional Policy-Relevant Background Estimates Used in the Risk Assessment. ..3-14 

6 Table 3-3. EPA Design Values for Annual and \24-hour PM2.5 Standards for the Period 2005-

7 2007.* ...................................................................................................................... 3-16 

8 Table 3-4. Urban Study Areas Selected for the Risk Assessment. ...........................................3-24 

9 Table 3-5. Locations, Health Endpoints, and Short-Term Exposure Studies Included in the PM2.5 

10 Risk Assessment*.................................................................................................... 3-34 

11 Table 3-6. Locations, Health Endpoints, and Long-Term Exposure Studies Included in the PM2.5

12 Risk Assessment*.................................................................................................... 3-35 

13 Table 3-7. Summary of Locations, Health Endpoints, Studies and Concentration-Response 

14 Functions Included in the Core Analysis.* .............................................................3-36 

15 Table 3-8. Summary of Locations, Health Endpoints, Studies and Concentration-Response 

16 Functions Included in Sensitivity Analyses. ........................................................3-44 

17 Table 3-9. Sources of Hospital Admissions (HA) and Emergency Department (ED) Visit Data.

18 .................................................................................................................................3-48 

19 Table 3-10. Relevant Population Sizes for PM Risk Assessment Locations..............................3-50 

20 Table 3-11. Baseline Mortality Rates (Deaths per 100,000 Relevant Population per Year) for 

21 2006 for PM Risk Assessment Locations.*............................................................3-53 

22 Table 3-12. Baseline Hospital Admission (HA) and Emergency Department (ED) Rates 

23 (Admissions/Visits per 100,000 Relevant Population per Year) for 2007 for PM Risk 

24 Assessment Locations.* ..........................................................................................3-55 

25 Table 3-13. Summary of Qualitative Uncertainty Analysis of Key Modeling Elements in the PM 

26 NAAQS Risk Assessment. .................................................................................. 3-64 

27 Table 4-1. Estimated Annual Incidence of Selected Mortality and Morbidity Endpoints 

28 Associated with Long- and Short-Term Exposure to Ambient PM2.5 Concentrations 

29 that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations. 

1,2 30 ................................................................................................................................ 4-6 

31 Table 4-2 Estimated Percent of Total Annual Incidence of Selected Mortality and Morbidity 

32 Endpoints Associated with Long- and Short-Term Exposure to Ambient PM2.5

33 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5

34 Concentrations. 1,2.................................................................................................... 4-7 

35 Table 4-3 Overview of Sensitivity Analysis Results ...............................................................4-31 

36 Table 4-4 Derivation of a set of reasonable alternative risk estimates to supplement the core 

37 risk estimates (Los Angeles and Philadelphia, current standards, for long-term IHD 

38 mortality).................................................................................................................4-45

39 Table 4-5 Data Sources for PM NAAQS Risk Assessment Risk Distribution Analysis.....4-50 

40 Table 4-6 Summary Statistics for Selected PM Risk Attributes.............................................. 4-52 

41 Table 4-7 Results of Kolomogrov-Smirnoff Tests for Equality Between National and Urban 

42 Study Area Distributions for Selected National Risk Characteristic Variables ..4-56 

43 Table 4-8 Identification of controlling standard (24-hour or annual) for alternative suites of 

44 standard levels .........................................................................................................4-70 
February 2010 Draft – Do Not Quote or Cite v

1 Table 5-1 Estimated PM2.5-related premature mortality associated with incremental air 

2 quality differences between 2005 ambient mean PM2.5 levels and lowest measured 

level from the epidemiology studies or policy relevant background (90th 3 percentile 

4 confidence interval)...................................................................................................5-6 

5 

6 
February 2010 Draft – Do Not Quote or Cite vi

1 List of Figures 

2 

3 Figure 3-1. Major components of particulate matter health risk assessment...............................3-2 

4 Figure 3-2. Flow diagram of risk assessment for short-term exposure studies...........................3-5 

5 Figure 3-3.Flow diagram of risk assessment for long-term exposure studies. ............................3-6 

6 Figure 3-4 15 urban study areas included in the risk assessment (including seven PM regions 

7 used to guide selection of study areas)....................................................................3-25 

8 Figure 4-1 Percent reduction in long-term exposure-related mortality risk (alternative standards 

9 and recent conditions relative to the current standards) (Note: inset shows PM2.5

10 related incidence and percent of total incidence for IHD mortality under the current 

11 suite of standards)...................................................................................................... 4-8 

12 Figure 4-2 Percent reduction in long-term exposure-related mortality risk (recent conditions 

13 relative to the current standards) (Note: inset shows PM2.5 related incidence and 

14 percent of total incidence for IHD mortality under the current suite of standards) ..4-9 

15 Figure 4-3 Percent reduction in long-term exposure-related mortality risk (alternative standards 

16 relative to the current standards) (Note: inset shows PM2.5 related incidence and 

17 percent of total incidence for IHD mortality under the current suite of standards) 4-10 

18 Figure 4-4 Percent reduction in short-term exposure-related mortality and morbidity risk 

19 (alternative standards and recent conditions relative to the current standards) (Note: 

20 inset shows PM2.5 related incidence and percent of total incidence for CV under the 

21 current suite of standards) ....................................................................................... 4-11 

22 Figure 4-5 Percent reduction in short-term exposure-related mortality and morbidity risk (recent 

23 conditions relative to the current standards) (Note: inset shows PM2.5 related 

24 incidence and percent of total incidence for CV under the current suite of standards)

25 .................................................................................................................................4-12 

26 Figure 4-6 Percent reduction in short-term exposure-related mortality and morbidity risk 

27 (alternative standards relative to the current standards) (Note: inset shows PM2.5

28 related incidence and percent of total incidence for CV under the current suite of 

29 standards)................................................................................................................. 4-13

30 Figure 4-7 Comparison of core risk estimates with reasonable alternative set of risk estimates for 

31 Los Angeles and Philadelphia (IHD mortality).......................................................4-46 

32 Figure 4-8 Comparison of core risk estimates with reasonable alternative set of risk estimates for 

33 Los Angeles and Philadelphia (all cause mortality)................................................4-46 

34 Figure 4-9 Comparison of distributions for key elements of the risk equation: total population. 4-

35 57 

Figure 4-10 Comparison of distributions for key elements of the risk equation: 98th 36 percentile 24-

37 hour average PM2.5 .................................................................................................. 4-58 

38 Figure 4-11Comparison of distributions for key elements of the risk equation: all use mortality 

39 rate. 4-59 

40 Figure 4-12Comparison of distributions for key elements of the risk equation: Mortality risk 

41 effect estimate from Zanobetti and Schwartz (2008). .............................................4-60 

42 Figure 4-13Comparison of distributions for selected variables expected to influence the relative 

43 risk from PM2.5: long term average July temperature. ............................................4-61 

44 Figure 4-14Comparison of distributions for selected variables expected to influence the relative 

45 risk from PM2.5: percent of population 65 and older..............................................4-62 
February 2010 Draft – Do Not Quote or Cite vii

1 Figure 4-15Comparison of distributions for selected variables expected to influence the relative 

2 risk from PM2.5: per capita annual personal income. .............................................4-63 

3 Figure 4-16Comparison of distributions for selected variables expected to influence the relative 

4 risk from PM2.5: per capita annual personal income. .............................................4-64 

5 Figure 4-17Design values in 15 urban study areas and broader set of U.S. urban areas relative to 

6 the current suite of standards (15/35) ......................................................................4-67 

7 Figure 4-18Design values in 15 urban study areas and broader set of U.S. urban areas relative to 

8 the 12/35 alternative suite of standards ...................................................................4-68 

9 Figure 4-19Design values in 15 urban study areas and broader set of U.S. urban areas relative to 

10 the 12/25 alternative suite of standards)..................................................................4-69 

11 Figure 4-20Annual and 24-hour design values (for individual monitors and at the study-area 

12 level) for the 15 urban study areas (with the presentation of values scaled to reflect 

13 current standard of 15/35) ....................................................................................... 4-73 

14 Figure 4-21Annual and 24-hour design values (for individual monitors and at the study-area 

15 level) for the 15 urban study areas (with the presentation of values scaled to reflect 

16 current standard of 12/25) ....................................................................................... 4-74 

17 Figure 5-1 Conceptual diagram of data inputs and outputs for national long-term mortality risk 

18 assessment .................................................................................................................5-3 

19 Figure 5-2 2005 fused surface baseline PM2.5 concentrations .................................................... 5-4 

20 Figure 5-3 Percentage of premature mortality attributable to PM2.5 exposure at various 2005 

21 annual average PM2.5 levels* .................................................................................... 5-7 

22 Figure 5-4 Cumulative distribution of county-level percentage of total mortality attributable to 

23 PM2.5 for the U.S. with markers identifying where along that distribution the urban 

24 case study area analysis fall*.....................................................................................5-8 

25 
February 2010 Draft – Do Not Quote or Cite viii

1 List of Acronyms/Abbreviations 

2 

3 A/C Air conditioning 

4 ACS American Cancer Society 

5 Act Clean Air Act 

6 AMI Acute Myocardial Infarction 

7 AQS EPA's Air Quality System 

8 β Slope coefficient 

9 BenMAP Benefits Mapping Analysis Program 

10 BMI Body Mass Index 

11 BRFSS Behavioral Risk Factor Surveillance System 

12 CASAC Clean Air Scientific Advisory Committee 

13 CAA Clean Air Act 

14 CBSA Core-based Statistical Area 

15 CDC Centers for Disease Control 

16 CDF Cumulative Distribution Function 

17 CFR Code of Federal Regulations 

18 CHD Coronary Heart Disease 

19 CMAQ Community Multiscale Air Quality 

20 CO Carbon Monoxide 

21 COPD Chronic Obstructive Pulmonary Disease 

22 CPD Cardio-pulmonary Disease 

23 C-R Concentration-response 

24 CSA Consolidated Statistical Area 

25 CV Cardiovascular 

26 CVD Cardiovascular Disease 

27 df Degrees of freedom 

28 ED Emergency Department 

29 ER Emergency Room 

30 EPA United States Environmental Protection Agency 

31 FACA Federal Advisory Committee Act 

32 FIPS Federal Information Processing System 

33 GAM Generalized additive model 

34 GEOS-CHEM Goddard Earth Observing System-Chemical Model 

35 GLMs Generalized linear model 
February 2010 Draft – Do Not Quote or Cite ix

1 HA Hospital Admissions 

2 HCUP Healthcare Cost and Utilization Project 

3 HEI Health Effects Institute 

4 HS High School 

5 ICD International Classification of Diseases 

6 IHD ischemic heart disease 

7 INF Influence of uncertainty on risk estimates 

8 IRP Integrated Review Plan 

9 ISA Integrated Science Assessment Document 

10 KB Knowledge Base 

11 km Kilometer 

12 K-S Kolmogorov-Smirnov 

13 LML Lowest Measured Level 

14 MCAPS Medicare Air Pollution Study 

15 MSA Metropolitan Statistical Area 

16 NA Not Applicable 

17 NAAQS National Ambient Air Quality Standards 

18 NCEA National Center for Environmental Assessment 

19 NEI National Emissions Inventory 

20 NCHS National Center for Health Statistics 

21 NMMAPS National Morbidity, Mortality, and Air Pollution Study 

22 NOx Nitrogen oxides 

23 O3 Ozone 

24 OAQPS Office of Air Quality Planning and Standards 

25 PA Policy Assessment Document 

26 PM Particulate Matter 

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

28 Regulations, includes both a 50% cut-point and a penetration 

29 curve. A 50% cut-point of X μm diameter means that 50% of 

30 particles with aerodynamic diameter of X are removed by the inlet 

31 and 50% pass through the inlet and are collected on the filter. 

32 Depending on the specific penetration curve specified, particles 

33 larger than X μm aerodynamic diameter are collected with an 

34 efficiently than decreases rapidly for particles larger than X while 

35 the collection efficiency for particles smaller than X increases 

36 rapidly with decreasing size until 100 % efficiency is reached. 

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

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

39 Federal Regulations. 
February 2010 Draft – Do Not Quote or Cite x

1 

2 PM2.5 Particles with a 50% upper cut-point of 2.5 μm aerodynamic 

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

4 Federal Regulations. 

5 PM10-2.5 Particles with a 50% upper cut-point of 10 μm aerodynamic 

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

7 diameter. 

8 PRB Policy-Relevant Background 

9 RA Risk Assessment Document 

10 RR Relative risk 

11 REA Risk and Exposure Assessment 

12 SAB Science Advisory Board 

13 SEDD State Emergency Department Databases 

14 SID State Inpatient Database 

15 SO2 Sulfur Dioxide 

16 SOx Sulfur Oxides 

17 SES Socio-economic Status 

18 TRIM Total Risk Integrated Methodology 

19 TRIM.Risk Total Risk Integrated Methodology - Risk Assessment component 

20 UFP Ultrafine Particles 

21 USDA U.S. Department of Agriculture 

22 VNA Voronoi Neighbor Averaging 

23 WHI Women's Health Initiative 

24 WHO World Health Organization 

25 ZCA Zip Code Area 
February 2010 1-1 Draft – Do Not Quote or Cite 

1 1 INTRODUCTION 

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

3 the national ambient air quality standards (NAAQS) for particulate matter (PM). Sections 108 

4 and 109 of the Clean Air Act (Act) govern the establishment and periodic review of the NAAQS. 

5 These standards are established for pollutants that may reasonably be anticipated to endanger 

6 public health and welfare, and whose presence in the ambient air results from numerous or 

7 diverse mobile or stationary sources. The NAAQS are to be based on air quality criteria, which 

8 are to accurately reflect the latest scientific knowledge useful in indicating the kind and extent of 

9 identifiable effects on public health or welfare that may be expected from the presence of the 

10 pollutant in ambient air. The EPA Administrator is to promulgate and periodically review, at 

11 five-year intervals, “primary” (health-based) and “secondary” (welfare-based) NAAQS for such 

12 pollutants. Based on periodic reviews of the air quality criteria and standards, the Administrator 

13 is to make revisions in the criteria and standards, and promulgate any new standards, as may be 

14 appropriate. The Act also requires that an independent scientific review committee advise the 

15 Administrator as part of this NAAQS review process, a function performed by the Clean Air 

Scientific Advisory Committee (CASAC).1 16 

17 The current NAAQS for PM include a suite of standards to provide protection for 

18 exposures to fine and coarse particles using PM2.5 and PM10, as indicators, respectively (71 FR 

19 61144, October 17, 2006). With regard to the primary and secondary standards for fine particles, 

in 2006 EPA revised the level of the 24-hour PM2.5 standard to 35 μg/m3 20 (calculated as a 3-year 

average of the 98th 21 percentile of 24-hour concentrations at each population-oriented monitor), 

retained the level of the annual PM2.5 annual standard at 15 μg/m3 22 (calculated as the 3-year 

23 average of the weighted annual mean PM2.5 concentrations from single or multiple community24 oriented monitors), and revised the form of the annual PM2.5 standard by narrowing the 

constraints on the optional use of spatial averaging.2 25 With regard to the primary and secondary 

standards for PM10, EPA retained the 24-hour PM10 standard at 150 μg/m3 26 (not to be exceeded 

 1

 The Clean Air Scientific Advisory Committee (CASAC) was established under section 109(d)(2) of the Clean Air 

Act (CAA or Act) (42 U.S.C. 7409) as an independent scientific advisory committee. CASAC provides advice, 

information and recommendations on the scientific and technical aspects of air quality criteria and NAAQS under 

sections 108 and 109 of the CAA. The CASAC is a Federal advisory committee chartered under the Federal 

Advisory Committee Act (FACA). See 

http://yosemite.epa.gov/sab/sabpeople.nsf/WebCommitteesSubcommittees/CASAC%20Particulate%20Matter%20R

eview%20Panel for a list of the CASAC PM Panel members and current advisory activities. 

2 In the revisions to the PM NAAQS finalized in 2006, EPA tightened the constraints on the spatial averaging 

criteria 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, October 17, 2006) . 
February 2010 1-2 Draft – Do Not Quote or Cite 

1 more than once per year on average over 3 years) and revoked the annual standard because 

2 available evidence generally did not suggest a link between long-term exposure to current 

3 ambient levels of coarse particles and health or welfare effects. These standards were based 

4 primarily on a large body of epidemiological evidence relating ambient PM concentrations to 

5 various adverse health endpoints. Secondary standards for PM2.5 and PM10 were revised to be 

6 identical to the primary standards. 

The next periodic review of the PM NAAQS is now underway.3 7 The review process 

8 includes four key phases: planning, science assessment, risk assessment, and policy 

9 assessment/rulemaking. A planning document, the Integrated Review Plan for the National 

10 Ambient Air Quality Standards for Particulate Matter (IRP; EPA, 2008a), outlined the science11 policy questions that frame this review, the process and schedule for the review, and descriptions 

of the purpose, contents, and approach for developing the other key documents for this review.4 12 

13 The science assessment document, the Integrated Science Assessment for Particulate Matter 

14 (ISA; EPA, 2009a and b), includes an evaluation of the scientific evidence on the health effects 

15 of PM, including information on exposure, physiological mechanisms by which PM might 

16 damage human health, and an evaluation of the epidemiological evidence including information 

17 on reported concentration-response (C-R) relationships for PM-related morbidity and mortality 

associations, including consideration of effects on at-risk populations.5 18 

19 This second draft quantitative health risk assessment (RA) presents the quantitative 

20 assessments of PM-related risks to public health being conducted by staff in EPA’s Office of Air 

21 Quality Planning and Standards (OAQPS) to support the review of the primary PM standards. 

22 The development of this document is described below in chapter 2. This draft RA is being 

23 released for review by the CASAC PM Panel and the public at a public meeting to be held on 

24 March 10-11, 2010. Comments received on this draft will be taken into consideration in 

25 preparing a final quantitative health RA for PM, which is scheduled to be completed in April 

26 2010. 

27 The final ISA and final quantitative health RA will inform the policy assessment and 

28 rulemaking steps that will lead to final decisions on the primary PM NAAQS. A policy 

29 assessment (PA) is now being prepared by OAQPS staff to provide a staff analysis of the 

30 scientific basis for alternative policy options for consideration by senior EPA management prior 

 3

 See http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_index.html for more information on the current and 

previous PM NAAQS reviews. 

4

 On November 30, 2007, EPA held a public consultation with the CASAC PM Panel on the draft IRP. The final 

IRP took into consideration comments received from CASAC and the public on the draft plan as well as input from 

senior Agency managers. 

5

 On October 5-6, 2009, the CASAC PM Panel met to review the second draft ISA (EPA, 2009a). The final ISA 

took into consideration CASAC and public comments received on that draft. 
February 2010 1-3 Draft – Do Not Quote or Cite 

1 to rulemaking. The PA is intended to help “bridge the gap” between the Agency’s scientific 

2 assessments, presented in the ISA and RA, and the judgments required of the Administrator in 

3 determining whether it is appropriate to retain or revise the standards. The PA will integrate and 

4 interpret information from the ISA and the RA to frame policy options and to facilitate 

5 CASAC’s advice to the Agency and recommendations on any new standards or revisions to 

6 existing standards as may be appropriate, as provided for in the Clean Air Act. The first draft PA 

7 is planned for release around the end of February 2010 for review by the CASAC PM Panel and 

8 the public during a public teleconference being planned for late March. Proposed and final 

9 rulemaking notices are now scheduled for November 2010 and July 2011, respectively. 

10 1.1 BACKGROUND 

11 As part of the last PM NAAQS review completed in 2006, EPA’s OAQPS conducted a 

12 quantitative risk assessment to estimate risks of various health effects associated with exposure 

13 to ambient PM2.5 and PM10-2.5 in a number of urban study areas selected to illustrate the public 

14 health impacts of these pollutants (U.S. EPA, 2005, chapter 4; Abt Associates, 2005). The 

15 assessment scope and methodology were developed with considerable input from the CASAC 

16 Review Panel and the public, with CASAC concluding that the general assessment methodology 

17 and framework were appropriate (Hopke, 2002). The final quantitative risk assessment took into 

18 consideration CASAC advice (Hopke, 2004; Henderson, 2005) and public comments on two 

19 drafts of the risk assessment. 

20 The extensive quantitative assessment conducted for fine particles in the last review 

21 included estimates of risks of mortality (total non-accidental, cardiovascular, and respiratory), 

22 morbidity (hospital admissions for cardiovascular and respiratory causes), and respiratory 

23 symptoms (not requiring hospitalization) associated with recent short-term (daily) ambient PM2.5 

24 levels and risks of total, cardiopulmonary, and lung cancer mortality associated with long-term 

25 exposure to PM2.5 in nine urban study areas. The quantitative risk assessment included estimates 

26 of: (1) risks of mortality, morbidity, and symptoms associated with recent ambient PM2.5 levels; 

27 (2) risk reductions and remaining risks associated with just meeting the existing suite of PM2.5 

28 NAAQS (1997 standards); and (3) risk reductions and remaining risks associated with just 

29 meeting various alternative PM2.5 standards. 

30 The quantitative risk assessment conducted in the last review for thoracic coarse particles 

31 was much more limited than the analyses conducted for fine particles. The PM10-2.5 risk 

32 assessment included risk estimates for just three urban areas for two categories of health 

33 endpoints related to short-term exposure to PM10-2.5: hospital admissions for cardiovascular and 

34 respiratory causes and respiratory symptoms. While one of the goals of the PM10-2.5 risk 

35 assessment was to provide estimates of the risk reductions associated with just meeting 
February 2010 1-4 Draft – Do Not Quote or Cite 

1 alternative PM10-2.5 standards, OAQPS staff concluded that the nature and magnitude of the 

2 uncertainties and concerns associated with this portion of the risk assessment weighed against 

3 use of these risk estimates as a basis for recommending specific standard levels (U.S. EPA, 2005, 

4 p. 5-69). 

5 Prior to the issuance of a proposed rulemaking in the last review, CASAC presented 

6 recommendations to the Administrator supporting revisions of the PM2.5 primary standards. 

7 These recommendations placed substantial reliance on the results of the quantitative risk 

8 assessment (Henderson, 2005, pp 6-7). In a letter to the Administrator following the 2006 

9 proposed rule (71 FR 12592, January 17, 2006), CASAC requested reconsideration of the 

10 Agency’s proposed decisions and reiterated and elaborated on the scientific bases for its earlier 

11 recommendations which included placing greater weight on the result of the Agency’s risk 

12 assessment. With regard to the quantitative risk assessment, CASAC concluded, “While the risk 

13 assessment is subject to uncertainties, most of the PM Panel found EPA’s risk assessment to be 

14 of sufficient quality to inform its recommendations.” (Henderson, 2006a, p. 3). 

15 In the 2006 final rule, the EPA Administrator recognized that the quantitative risk 

16 assessment for fine particles was based upon a more extensive body of data and was more 

17 comprehensive in scope than the previous assessment conducted for the review completed in 

18 1997. However, as presented in the final rulemaking notice, the Administrator was mindful of 

19 significant uncertainties associated with the risk estimates for fine particles. More specifically, 

20 

21 Such uncertainties generally related to a lack of clear understanding of a number of 

22 important factors, including, for example, the shape of the concentration-response 

23 functions, particularly when, as here, effect thresholds can neither be discerned nor 

24 determined not to exist; issues related to selection of appropriate statistical models for the 

25 analysis of the epidemiologic data; the role of potentially confounding and modifying 

26 factors in the concentration-response relationships; issues related to simulating how PM2.5 

27 air quality distributions will likely change in any given area upon attaining a particular 

28 standard, since strategies to reduce emissions are not yet defined; and whether there 

29 would be differential reductions in the many components within PM2.5 and, if so, whether 

30 this would result in differential reductions in risk. In the case of fine particles, the 

31 Administrator recognized that for purposes of developing quantitative risk estimates, 

32 such uncertainties are likely to [be] amplified by the complexity in the composition of the 

33 mix of fine particles generally present in the ambient air. (72 FR 61168, October 17, 

34 2006). 

35 

36 As a result, the Administrator viewed that the quantitative risk assessment provided supporting 

37 evidence for the conclusion that there was a need to revise the PM2.5 primary standards, but he 

38 judged that the assessment did not provide an appropriate basis to determine the level of the 

39 standards (72 FR 61168, October 17, 2006). 
February 2010 1-5 Draft – Do Not Quote or Cite 

1 In a letter to the EPA Administrator following the issuance of the final rule, CASAC 

2 expressed “serious scientific concerns” regarding the final PM standards. In particular, CASAC 

3 was concerned that the Agency “did not accept our finding that the annual PM2.5 standard was 

4 not protective of human health and did not follow our recommendation for a change in that 

5 standard” (Henderson et al, 2006b, p.1). With respect to the use of the risk assessment to inform 

6 EPA’s decision on the primary PM2.5 standard, CASAC stated, “While there is uncertainty 

7 associated with the risk assessment for the PM2.5 standard, this very uncertainty suggests a need 

8 for a prudent approach to providing an adequate margin of safety” (Henderson et al., 2006b, p.2) 

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

10 NAAQS in 2006. These petitions for review addressed the following issues with regard to the 

11 primary PM NAAQS: (1) selecting the level of the annual primary PM2.5 standard, (2) retaining 

12 PM10 as the indicator for coarse particles and retaining the level and form of the 24-hour PM10

13 standard, and (3) revoking the PM10 annual standard. On judicial review, the D.C. Circuit 

14 remanded the annual primary PM2.5 NAAQS to EPA because the Agency failed to adequately 

15 explain why the standard provided the requisite protection from both short- and long-term 

16 exposures to fine particles including protection for at-risk populations. The court upheld the 

17 Agency’s use of the quantitative risk assessment to inform the decision to revise the PM2.5 

standards but not to inform the selection of level.6 18 The court also upheld the decision to retain 

19 the 24-hour PM10 standard and revoke the annual PM10 standard. American Farm Bureau 

20 Federation v. EPA, 559 F. 3d 512, (D.C. Cir. 2009). 

21 1.2 CURRENT RISK ASSESSMENT: GOALS AND PLANNED APPROACH 

22 The goals of the current quantitative health risk assessment remain largely the same as 

23 those articulated in the risk assessment conducted in the last review. These goals include: (a) to 

24 provide estimates of the potential magnitude of premature mortality and/or selected morbidity 

25 effects in the population associated with recent ambient levels of PM and with just meeting the 

26 current and alternative suites of PM standards considered in selected urban study areas, 

27 including, where data are available, consideration of impacts on at-risk populations; (b) to 

28 develop a better understanding of the influence of various inputs and assumptions on the risk 

29 estimates to more clearly differentiate among alternative suites of standards, including potential 

30 impacts on various at-risk populations; and (c) to gain insights into the distribution of risks and 

31 patterns of risk reductions and the variability and uncertainties in those risk estimates. In 

 6

 One petition for review addressed the issue of setting the secondary PM2.5 standards identical to the primary 

standards. On judicial review, the court remanded the secondary PM2.5 NAAQS to EPA because the Agency failed 

to adequately explain why the standards provided the required protection from visibility impairment. American 

Farm Bureau Federation v. EPA, 559 F. 3d 512, (D.C. Cir. 2009). 
February 2010 1-6 Draft – Do Not Quote or Cite 

1 addition, this assessment includes nationwide estimates of the potential magnitude of premature 

2 mortality associated with long-term exposure to recent levels of ambient PM2.5 to more broadly 

3 characterize this risk on a national scale and to support the interpretation of the more detailed 

4 risk estimates generated for selected urban study areas. The overall scope and design of this 

5 quantitative risk assessment, discussed below in chapters 2 and 3, reflect efforts to achieve these 

6 goals. 

7 This current quantitative risk assessment builds on the approach used and lessons learned 

8 in the last PM risk assessment and attempts to reduce and better characterize overall uncertainty 

9 associated with the analysis by incorporating a number of enhancements, in terms of both the 

10 methods and data used in the analyses. This assessment covers a variety of health endpoints for 

11 which, in staff’s judgment, there is adequate information to develop quantitative risk estimates 

12 that can meaningfully inform the review of the primary PM NAAQS. Evidence of relationships 

13 between PM and other health endpoints for which, in staff’s judgment, there currently is 

14 insufficient information to develop meaningful quantitative risk estimates will be more generally 

15 considered in the PA as part of the evidence-based considerations that inform staff’s assessment 

16 of policy options. 

17 1.3 ORGANIZATION OF DOCUMENT 

18 The remainder of this document is organized as follows. Chapter 2 provides an overview 

19 of the scope of the quantitative risk assessment, including a summary of the previous risk 

20 assessment, the original planned approach and the key design elements reflected in this second 

21 draft assessment, and the rationale for the alternative standard levels evaluated in this 

22 assessment. Chapter 3 describes the analytical approach, methods, and data used in conducting 

23 the risk assessment, including the approach used to generate risk estimates for the set of urban 

24 case studies included in this analysis and the approaches used in addressing variability and 

25 uncertainty (Appendices A, B, and C provide supplemental information regarding the data and 

26 methods used). Chapter 4 presents selected risk estimates generated for the urban case studies, 

27 including the results of single- and multi-factor sensitivity analyses and a national-scale analysis 

28 of the representativeness of relevant risk-related factors (Appendix D provides supplemental 

29 information on risk-related factors; Appendices E and F provide detailed risk estimates and 

30 sensitivity analysis results, respectively). Chapter 5 presents the approach used and results of a 

31 national-scale assessment of PM2.5-related long-term mortality risks associated with recent air 

32 quality (Appendix G provides supplemental information to the national-scale mortality analysis). 

33 Chapter 6 provides an integrative discussion of the various risk estimates generated in these 

34 assessments that draws on the results of the urban area case studies, the uncertainty/variability 
February 2010 1-7 Draft – Do Not Quote or Cite 

1 characterization, and the national-scale analyses to inform our quantitative characterization of 

2 PM-related risks to public health.
February 2010 Draft – Do Not Quote or Cite 2-1

1 2 SCOPE 

2 This chapter provides an overview of the scope and key design elements of this 

3 quantitative health risk assessment. The design of this assessment began with a review of the 

4 risk assessment completed during the last PM NAAQS review (Abt Associates, 2005; EPA, 

5 2005, chapter 4), with an emphasis on considering key limitations and sources of uncertainty 

6 recognized in that analysis. 

7 As an initial step in the this PM NAAQS review, EPA invited outside experts, 

8 representing a broad range of expertise (e.g., epidemiology, human and animal toxicology, 

9 statistics, risk/exposure analysis, atmospheric science) to participate in a workshop with EPA 

10 staff to help inform EPA’s plan for the review. The participants discussed key policy-relevant 

11 issues that would frame the review and the most relevant new science that would be available to 

12 inform our understanding of these issues. One workshop session focused on planning for 

13 quantitative risk/exposure assessments, taking into consideration what new research and/or 

14 improved methodologies would be available to inform the design of a quantitative health risk 

15 assessment and whether, and if so how, it might be appropriate to conduct a quantitative 

16 exposure assessment. These workshop discussions informed the preparation of the IRP, which 

17 included initial plans for quantitative risk and exposure assessments. 

18 As a next step in the design of these quantitative assessments, OAQPS staff developed a 

19 more detailed planning document, Particulate Matter National Ambient Air Quality Standards: 

20 Scope and Methods Plan for Health Risk and Exposure Assessment (Scope and Methods Plan; 

21 EPA, 2009b). This Scope and Methods Plan was the subject of a consultation with the CASAC 

22 PM Panel and public review on April 1-2, 2009 (at which the first draft ISA was also reviewed). 

23 Based on consideration of CASAC and public comments on the Scope and Methods Plan and 

24 information in the first draft ISA, we modified the scope and design of the risk assessment and 

25 completed initial analyses that were presented in an initial draft of this RA (first draft RA; EPA, 

26 2009e). The CASAC PM Panel met on October 5-6, 2009 to review the first draft RA (as well as 

the second draft ISA).7 27 Based on consideration of CASAC (Samet, 2009) and public comments 

28 on the first draft RA, together with ongoing refinement of elements of the risk assessment 

29 approach informed by the second draft ISA, we have prepared this second draft RA. 

30 In presenting the scope and key design elements of the current risk assessment, this 

31 chapter first provides a brief overview of the risk assessment completed for the previous PM 

32 NAAQS review in section 2.1, including key limitations and uncertainties associated with that 

 7

 A public teleconference was held on November 12, 2009, during which CASAC reviewed the draft comment letter 

prepared by the CASAC PM Panel. 
February 2010 Draft – Do Not Quote or Cite 2-2

1 analysis. Section 2.2 provides a summary of the initial design of the risk assessment as outlined 

2 in the Scope and Methods Plan. Section 2.3 provides an overview of key design elements 

3 reflected in this second draft risk assessment that reflect consideration of previous CASAC and 

4 public comments. Section 2.4 provides a summary of the alternative air quality scenarios 

5 simulated in this assessment, including recent air quality and the current and alternative suites of 

6 PM2.5 24-hour and annual standards. 

7 2.1 OVERVIEW OF RISK ASSESSMENT FROM LAST REVIEW 

8 The quantitative risk assessment from the last review included a broad assessment of 

9 PM2.5-related risk and a much more limited treatment of PM10-2.5-related risk. That assessment 

10 included estimates of risks of mortality (total non-accidental, cardiovascular, and respiratory), 

11 morbidity (hospital admissions for cardiovascular and respiratory causes), and respiratory 

12 symptoms (not requiring hospitalization) associated with short-term (24-hour) exposure to 

13 ambient PM2.5 and risks of total, cardiopulmonary, and lung cancer mortality associated with 

14 long-term exposure to PM2.5 in selected urban areas. Nine urban areas were selected across the 

15 U.S.: Boston, MA; Detroit, MI; Los Angeles, CA; Philadelphia, PA; Phoenix, AZ; Pittsburgh, 

16 PA; San Jose, CA; Seattle, WA; and St. Louis, MO. 

17 The EPA recognized that there were many sources of uncertainty and variability inherent 

18 in the inputs to the assessment and that there was a high degree of uncertainty in the resulting 

19 PM2.5 risk estimates. Such uncertainties generally related to a number of important factors, 

20 including: (a) the shape of the concentration-response (C-R) function (and whether or not a 

21 population threshold exists); (b) issues related to the selection of appropriate statistical models 

22 for the analysis of epidemiological data; (c) the role of potentially confounding and modifying 

23 factors in the C-R relationships; (d) methods for simulating how daily PM2.5 ambient 

24 concentrations would likely change in any given area upon meeting a particular suite of 

25 standards; and (e) the potential for differences in the relative toxicity of the components within 

26 the mix of ambient PM2.5. 

27 While some of these uncertainties were addressed quantitatively in the form of estimated 

28 confidence ranges around central risk estimates, other uncertainties and the variability in key 

29 inputs were not reflected in these confidence ranges, but rather were addressed through separate 

30 sensitivity analyses or characterized qualitatively (EPA, 2005, chapter 4; Abt Associates, 2005). 

31 The C-R relationships used in the quantitative risk assessment were based on findings from 

32 human epidemiological studies that relied on fixed-site, population oriented, ambient monitors as 

33 a surrogate for actual ambient PM2.5 exposures. The assessment included a series of base case 

34 estimates that, for example, included various cutpoints intended as surrogates for alternative 

35 potential population thresholds. Other uncertainties were addressed in various sensitivity 
February 2010 Draft – Do Not Quote or Cite 2-3

1 analyses (e.g., the use of single- versus multi-pollutant models, use of single versus multi-city 

2 models, use of a distributed lag model) and had a more moderate and often variable impact on 

3 the risk estimates in some or all of the cities. 

4 These same sources of uncertainty and variability were also applicable to the quantitative 

5 risk assessment conducted for PM10-2.5 in the last review. However, the scope of the risk 

6 assessment for PM10-2.5 was much more limited than that for PM2.5 reflecting the much more 

7 limited body of epidemiological evidence and air quality information available for PM10-2.5. The 

8 PM10-2.5 risk assessment included risk estimates for just three urban areas for two categories of 

9 health endpoints related to short-term exposure to PM10-2.5: hospital admissions for 

10 cardiovascular and respiratory causes and respiratory symptoms. While one of the goals of the 

11 PM10-2.5 risk assessment was to provide estimates of the risk reductions associated with just 

12 meeting alternative PM10-2.5 standards, EPA staff concluded that the nature and magnitude of the 

13 uncertainties and concerns associated with this portion of the risk assessment weighed against 

14 use of these risk estimates as a basis for recommending specific standard levels (EPA, 2005, see 

15 p. 5-69). These uncertainties and concerns were summarized in the proposal notice (see FR 71 

16 2662, January 17, 2006) and discussed more fully in the Staff Paper (EPA, 2005, chapter 4) and 

17 associated technical support document (Abt Associates Inc., 2005). 

18 2.2 ORIGINAL ASSESSMENT PLAN 

19 The Scope and Methods Plan outlined a planned approach for conducting the current 

20 quantitative PM risk assessment, including broad design issues as well as more detailed aspects 

21 of the analyses. That document also outlined plans for a population exposure analysis based on 

22 micro-environmental exposure modeling. The planned approaches for conducting both analyses 

23 are briefly summarized below. 

24 2.2.1 Risk Assessment 

25 Key design elements for the quantitative risk assessment, as presented in the Scope and 

26 Methods Plan, included: 

27  PM size fractions: We planned to focus primarily on estimating risk associated with 

28 exposure to PM2.5 with a much more limited assessment of PM10-2.5. Regarding PM 

29 components and ultrafine particles, we concluded that, based on review of evidence in 

30 the first draft ISA, there was insufficient data to support quantitative risk assessment 

31 at this time. 

32  Selection of health effects categories (PM2.5): We planned to focus primarily on 

33 categories for which the evidence supports a judgment that there is at least a likely 

34 causal relationship. We also planned to consider including additional categories for 

35 which evidence supports a judgment that there is a suggestive causal relationship 
February 2010 Draft – Do Not Quote or Cite 2-4

1 (e.g., reproductive, developmental outcomes), if sufficient information was available 

2 to develop meaningful risk estimates for these additional categories. 

3  Selection of health effect categories (PM10-2.5): We planned to build on the limited 

4 risk assessment conducted in the last review (EPA, 2005) with a focus on health 

5 effect categories that staff judged to be sufficiently suggestive of a causal relationship 

6 with short-term exposure to warrant analysis. 

7  Selection of urban study areas: We planned to expand the number of urban study 

8 areas to between 15 and 20, with selection of these study areas being based on 

9 consideration of a number of factors (e.g., availability of location-specific C-R 

10 functions and baseline incidence data, coverage for geographic heterogeneity in PM 

11 risk-related attributes, coverage for areas with more vulnerable populations). We also 

12 discussed the possibility of including more refined risk assessments for locations 

13 where more detailed exposure studies had been completed (e.g., L.A., where a zip 

14 code level analysis of long-term PM2.-exposure related mortality was presented in 

15 Krewski et al., 2009). 

16  Simulation of air quality levels that just meet current or alternative suites of 

17 standards: We planned to consider the use of non-proportional air quality 

18 adjustment methods in addition to the proportional approach that has been used 

19 previously. These non-proportional adjustment methods could be based on (a) 

20 historical patterns of reductions in urban areas, if these result in support for non21 proportional reductions across monitors and/or (b) model-based (e.g., CMAQ) 

22 rollback designed to more realistically reflect patterns of PM reductions across 

23 monitors in an urban area. 

24  Characterization of policy relevant background (PRB): We planned to use 

25 modeling (combination of the global-scale circulation model, GEOS-Chem, with the 

26 regional scale air quality model, CMAQ) as presented in the first draft ISA, rather 

27 than empirical data to characterize PRB levels for use in the risk assessment model. 

28  Selection of epidemiological studies to provide C-R functions: We planned to 

29 include both multi- and single-city studies (given advantages associated with both 

30 designs) as well as multi- and single-pollutant studies, placing greater weight on the 

31 use of C-R functions reflecting adjusted single-city estimates obtained from multi-city 

32 studies. 

33  Shape of the functional form of the risk model: We planned to emphasize non34 threshold C-R functions in the risk assessment model, based on the first draft ISA 

35 conclusion that there was little support in the literature for population thresholds for 

36 mortality effects associated with either long-term or short-term PM2.5 ambient 

concentrations.8 37 We also stated that we may consider population thresholds as part of 

38 the sensitivity analysis. 

 8

 In discussing short-term exposure mortality studies, the first draft ISA (U.S. EPA, 2009a) indicated support for nothreshold log-linear models, while acknowledging that the possible influence of exposure error and heterogeneity of 

shapes across cities remains to be resolved. 
February 2010 Draft – Do Not Quote or Cite 2-5

1  Modeling of risk down to PRB versus lowest measured level (LML): We planned 

2 to model risk down to LML for estimating risk associated with long-term PM2.5 

3 exposures and down to PRB for estimating risks associated with short-term PM2.5 

4 exposures. 

5  Characterization of uncertainty and variability: We planned to include a 

6 discussion in the risk assessment report on the degree to which the risk assessment 

7 covers key sources of variability related to PM risk. For uncertainty, we planned to 

8 include a qualitative discussion of key sources of uncertainty and provide ratings 

9 (low, medium and high) in terms of their potential impact on risk estimates. We also 

10 described the use of sensitivity analysis methods planned both to characterize the 

11 potential impact of sources of uncertainty on risk estimates and to provide an 

12 alternative set of reasonable estimates to supplement the main (“core”) set of risk 

13 estimates generated for the urban study areas. 

14  National-scale assessment: We planned to conduct a limited national-scale 

15 assessment of mortality associated with long-term exposure to recent ambient PM2.5

16 levels. 

17  Representativeness analysis for the urban study areas: We planned to conduct an 

18 analysis to evaluate the representativeness of the selected urban study areas against 

19 national distributions for key PM risk-related attributes to determine whether they are 

20 nationally representative or more focused on a particular portion of the distribution 

21 for a given attribute. 

22 2.2.2 Population Exposure Analysis 

23 The Scope and Methods Plan also described a population exposure analysis based on 

24 micro-environmental exposure modeling using the Air Pollution Exposure Model (APEX). The 

25 planned analysis would have focused on PM2.5 and have involved a subset of the urban study 

26 areas included in the risk assessment. The results of this analysis were planned to focus on 

27 providing insights on population exposure with respect to informing the interpretation of 

28 available epidemiological studies. 

29 Following release of the Scope and Methods Plan, we continued development of the 

30 approach for conducting a population exposure analysis, with the goal of completing the analysis 

31 as part of the current PM review. However, this additional design work highlighted the need to 

32 more clearly define the intended purpose of the analysis, including specific ways in which the 

33 results would be used to interpret the estimates generated from the risk assessment (e.g., 

34 potentially identifying sources of exposure measurement error associated with the 

35 epidemiological studies from which C-R functions were drawn for the risk assessment and the 

36 magnitude of the impact of those sources of error on risk estimates). Taking CASAC comments 

37 into consideration, which emphasized the same point regarding the importance of more clearly 

38 defining how the exposure assessment results would be used, as well as the complexities 

39 associated with designing and conducting such an assessment, we decided to continue methods 
February 2010 Draft – Do Not Quote or Cite 2-6

1 development work rather than attempt to complete a preliminary population exposure analysis as 

2 part of this review. Development of the population exposure analysis methodology is ongoing, 

3 and we anticipate that such an assessment could be conducted as part of the next PM NAAQS 

4 review. 

5 2.3 CURRENT SCOPE AND KEY DESIGN ELEMENTS 

6 An overview of the scope and key design elements that are the basis for this second draft 

7 RA are presented below, focusing on those aspects of the risk assessment approach which differ 

8 from the originally planned approach. 

9  PM size fractions: This quantitative risk assessment characterizes risk associated 

10 with PM2.5-related exposures only. With regard to PM10-2.5 , we have concluded that 

11 continued limitations in data available for characterizing PM10-2.5 exposure and risk 

12 would introduce significant uncertainty into a PM10-2.5 risk assessment such that the 

13 risk estimates generated would be of limited utility in informing review of the 

14 standard. This conclusion was reached by reviewing the set of limitations cited in the 

15 last PM NAAQS risk assessment for not using the PM10-2.5 risk estimates in 

16 recommending specific standard levels. We then considered whether health effects 

17 data released since the last review (as summarized in the final PM ISA) as well as any 

18 enhancements to the PM10-2.5 monitoring network would fundamentally address these 

19 limitations. We concluded that significant limitations in both health effects data and 

20 the PM10-2.5 monitoring network continue to exist such that a quantitative risk 

21 assessment for PM10-2.5 is not supported at this time (a more in-depth discussion of the 

22 rationale behind the decision not to conduct a quantitative risk assessment for PM10-2.5

23 is presented in Appendix H). Furthermore, based on the final PM ISA, we continue to 

24 conclude that available data are too limited to support a quantitative risk assessment 

25 for any specific PM components or for ultrafine particles (UFPs). We note, however, 

26 that the evidence for health effects associated with thoracic coarse particles, PM 

27 components, and UFPs will be included in the evidence-based considerations that will 

28 be presented in the draft PA.. 

29  Selection of health effects categories (PM2.5): A multi-factor decision framework 

30 was used to select the final set of health effects categories included in the risk 

31 assessment for PM2.5 (section 3.3.1). This set of endpoints is consistent with those 

32 outlined in the Scope and Methods Plan for PM2.5 (i.e., all of the selected endpoints 

33 are from categories classified in the ISA as having a causal or likely causal

34 relationship with PM2.5 exposure), although selecting endpoints limited to these two 

35 classifications is a consequence of applying our multi-factor decision framework and 

36 not the sole determining factor. A number of health effect categories classified as 

37 suggestive of a casual relationship in the ISA (e.g., reproductive effects) were 

38 considered, but were not selected for inclusion due in part to limited information 

39 available to support selection of C-R functions for specific endpoints within these 

40 health effect categories and/or lack of available baseline incidence data. Inn addition, 

41 CASAC members expressed differing views as to the appropriateness of including 

42 these categories. 
February 2010 Draft – Do Not Quote or Cite 2-7

1  Selection of urban study areas: We have included 15 urban study areas in the risk 

2 assessment, with the selection of these areas being based on a number of criteria 

3 including: (a) consideration of urban study areas evaluated in the last PM risk 

4 assessment; (b) consideration of locations evaluated in key epidemiological studies; 

5 (c) preference for locations with relatively elevated 24-hour and/or annual PM2.5

6 monitored levels so that the assessment can provide potential insights into the degree 

7 of risk reduction associated with just meeting the current and alternative suites of 

8 standards; and (d) preference to include locations in different regions across the 

9 country, reflecting potential differences in PM sources, composition, and potentially 

10 other factors which might impact PM-related risk (section 3.3.2). Due in part to time 

11 and resource limitations, we have not included a specialized analysis of risk based on 

12 epidemiology studies using more highly-refined exposure analysis (e.g., the study of 

13 L.A. involving zip code-level effect estimates, as presented in Krewski et al., 2009). 

14 We have included consideration of studies with more refined surrogate measures of 

15 exposure in our discussion of uncertainty related to long-term mortality, since they 

16 can inform our interpretation of the degree of potential bias associated with the effect 

17 estimates used to model risks (section 3.5.3). 

18  Method used to develop composite monitor values: Ongoing methods 

19 development has resulted in revisions to the methods used to derive composite 

20 monitor values for both the annual and 24-hour distributions (section 3.2.1). The 

21 revised methods ensure that monitors contributing to a composite calculation in a 

22 particular study area are given equal weight, in contrast to the approach used in the 

23 first draft RA, which effectively weighted monitors by their sampling frequency, 

24 potentially leading to estimates that were biased high. 

25  Simulation of air quality levels that just meet current or alternative suites of 

26 standards: In addition to applying the proportional rollback approach used in the 

27 first draft RA (and in the last risk assessment) to simulate PM2.5 ambient levels that 

28 would “just meet” the current and alternative suites of standards, we have developed 

29 and applied two alternative approaches (hybrid and peak-shaving) to help characterize 

30 the uncertainty associated with this aspect of the assessment (section 3.2.3). We have 

31 also refined our rollback approach for the Pittsburgh study area, using a dual-zone 

32 approach to take into account monitor locations and the related topography in that 

33 area (section 3.2.3). 

34  Characterization of PRB: Consistent with the planned approach, we have used 

35 regional PRB estimates generated using a combination of GEOS-Chem and CMAQ 

36 modeling as presented in the ISA (section 3.2.2). 

37  Selection of epidemiological studies to provide C-R functions: In modeling risk 

38 associated with both short-term and long-term PM2.5 exposures, we have focused on 

39 larger multi-city studies based on our conclusion that these studies provided more 

40 defensible effect estimates. In modeling short-term exposure-related mortality and 

41 morbidity, we obtained more spatially-refined effect estimates at the city- and 

42 regional-levels, respectively (in both cases, these effects estimates are based on 

43 application of Bayesian methods). We also included C-R functions selected from 

44 several single city studies to provide coverage for additional health effect endpoints 
February 2010 Draft – Do Not Quote or Cite 2-8

1 associated with short-term PM2.5 exposures (e.g., emergency department visits). 

2 Modeling of long-term exposure-related mortality focused on the latest reanalysis of 

3 the ACS dataset (Krewski et al., 2009). This study expands upon previous 

4 publications presenting evaluations of the ACS long-term cohort study and in 

5 particular includes rigorous examination of different model forms for estimating 

6 effects estimates (in addition to including updated and expanded datasets on 

7 incidence and exposure). Our rationale for selecting the specific studies used in the 

8 assessment, as well as our rationale for not selecting alternative studies, is discussed 

9 below in section 3.3.3. 

10  Characterization of uncertainty and variability: Our approach to characterizing 

11 uncertainty and variability is based on application of the WHO Guidance on 

12 Characterizing and Communicating Uncertainty In Exposure Assessment (WHO, 

13 2008). This guidance provides a four-tiered approach for characterizing uncertainty 

14 (and to a lesser extent variability) in the context of a risk assessment, with tiers 

15 ranging from qualitative characterization (Tier 1) to use of full-probabilistic Monte 

16 Carlo-based simulation (Tier 3). Sensitivity analysis methods, which are used in the 

17 RA to assess sources of uncertainty and variability, represent a Tier 2 approach. The 

18 application of single- and multi-factor sensitivity analysis methods in the RA serves 

19 two purposes: (a) to characterize the potential magnitude of impact that a source(s) of 

20 uncertainty and/or variability can have on risk estimates and (b) to provide an 

21 additional set of reasonable risk estimates to supplement the “core” risk estimates in 

22 characterizing the potential magnitude of uncertainty in the risk estimates. The 

23 “core” risk estimates produced in this assessment refer to those generated using the 

24 combination of modeling elements and input datasets in which we had the highest 

25 confidence relative to other modeling choices (section 3.5.1 and 3.5.4). 

26  National-scale assessment: As planned, we have conducted a limited national-scale 

27 assessment of (chapter 5). This analysis provides estimates of mortality associated 

28 with long-term exposure to recent ambient PM2.5 levels at the national scale, which 

29 provides some context for considering the risks estimated for the urban study areas. 

30 We continue to conclude that any expansion of this assessment (e.g., to include 

31 additional health endpoints or additional air quality scenarios that simulate just 

32 meeting alternative suites of standards), as suggested by some CASAC Panel 

33 members, was beyond the scope of what was needed or could reasonably be done 

34 within the time and resources available for this review (section 5.1). 

35  Representativeness analysis for the urban study areas: As planned, we have 

36 conducted an analysis to evaluate the representativeness of the selected urban study 

37 areas against national distributions for key PM risk-related attributes to determine 

38 whether they are nationally representative or more focused on a particular portion of 

39 the distribution for a given attribute (section 4.4). 

40  Consideration of patterns in design values and ambient PM2.5 monitoring data 

41 across urban areas: We have included in this second draft assessment an 

42 examination of how 24-hour and annual design values, together with patterns in PM2.5

43 monitoring data within an area, can influence the degree of risk reduction estimated to 

44 occur upon just meeting the current or alternative suites of standards. This analysis 
February 2010 Draft – Do Not Quote or Cite 2-9

1 has resulted in a better understanding of the factors behind specific patterns of risk 

2 reduction. We have also compared patterns of design values for the urban study areas 

3 with patterns across the broader set of urban areas in the U.S. in order to help place 

4 core risk estimates generated for the set of urban study areas in a broader national 

5 context. 

6  Integrated discussion of results and key observations: To enhance the utility of the 

7 risk estimates generated for the 15 urban study areas in supporting the review of the 

8 PM NAAQS, we have added a new chapter 6: Integrative Discussion of PM2.5-

9 Related Risks. This chapter integrates the core risk estimates generated for the 15 

10 urban study areas with information from the sensitivity analyses and the qualitative 

11 analysis of uncertainty, analyses of representativeness and patterns of design values, 

12 and the national-scale mortality analysis. 

13 2.4 ALTERNATIVE SUITES OF PM2.5 STANDARDS EVALUTATED 

14 In developing estimates of risks associated with just meeting alternative suites of PM2.5

15 standards, we selected alternative levels for the annual and 24-hour PM2.5 standards during the 

16 development of the first draft RA that we judged to be appropriate, drawing from the information 

17 available to us at that time from the second draft ISA. In defining alternative suites of standards 

18 to be evaluated, we identified alternative standard levels in conjunction with the averaging times 

(24-hour and annual) and forms for the current suite of standards.9 19 We note that all of the basic 

20 elements of the standards (e.g., indicator, averaging time, level, and form) will be discussed in a 

21 forthcoming draft Policy Assessment which will present staff conclusions based on both 

22 evidence-based and risk-based considerations to inform judgments that the EPA Administrator 

23 must make in deciding whether to retain or revise the existing suite of PM standards. 

24 In selecting alternative levels for the annual and 24-hour PM2.5 standards for the purpose 

25 of evaluation in the quantitative risk assessment, we considered ambient air quality levels 

26 associated with health effects in epidemiological studies of long- and short-term exposure to 

27 PM2.5, as assessed in the second draft ISA. As discussed further below (section 3.3.3), in 

28 selecting alternative levels for consideration in the risk assessment, we placed emphasis on air 

29 quality information from multi-city studies because these studies have a number of advantages 

30 compared to single-city studies including: (1) multi-city studies reflect ambient PM2.5 levels and 

31 potential health impacts across a range of diverse locations; (2) multi-city studies “clearly do not 

32 suffer from potential omission of negative analyses due to ‘publication bias’” (EPA, 2004a, p. 8-

33 30); and (3) multi-city studies generally have higher statistical power. 

 9

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

whether an area attains the standard. The form of the 24-hour PM2.5 standard is the 98th percentile of the distribution 

of 24-hour PM2.5 concentrations at each population-oriented monitor within an area, averaged over 3 years. The 

form of the annual PM2.5 standard is an annual arithmetic mean, averaged over 3 years, from single or multiple 

community-oriented monitors. 
February 2010 Draft – Do Not Quote or Cite 2-10

1 Specifically, regarding alternative levels for the annual PM2.5 standard to be evaluated in 

2 this risk assessment, we first considered long-term average PM2.5 concentrations associated with 

3 health effects observed in long-term epidemiological studies, as summarized in Figure 2-2 of the 

4 second draft ISA. The second draft ISA concluded that the association between increased risk of 

5 mortality and long-term PM2.5 exposure becomes more precise and consistently positive in 

locations with mean PM2.5 concentrations of 13.5 μg/m3 6 and above. (EPA, 2009a, section 

7 2.3.1.2). The second draft ISA also concluded that the strongest evidence for cardiovascular8 related effects related to long-term PM2.5 exposures has been reported in large, multi-city U.S.-

9 based studies and, specifically, one of these studies, the Women’s Health Initiative (WHI) Study, 

10 reports associations between PM2.5 and cardiovascular effects among post-menopausal women 

with a mean annual average PM2.5 concentration of 13.5 μg/m3 11 (EPA, 2009a, section 2.3.1.2). In 

12 addition, we evaluated long-term average PM2.5 concentrations in short-term exposure studies 

13 that reported statistically significant effects. More specifically, as reported in the second draft 

14 ISA, both cardiovascular and respiratory morbidity effects (e.g., emergency department visits, 

15 hospital admissions) have been observed and become more precise and consistently positive in 

locations with mean PM2.5 concentrations of 13 μg/m3 16 and above (EPA, 2009a, section 2.3.1; 

also see Figure 2-1).10 17 

18 Based on the available epidemiological evidence indicating effects associated with a 

19 range of annual averaged PM2.5 concentrations, as briefly described above, we selected levels of 

12 and 13 μg/m3 20 as the alternative annual standard levels to be evaluated in the quantitative risk 

assessment. We have added 14 μg/m3 21 to the set of annual levels evaluated in this second draft 

22 RA to provide fuller coverage for the range of values between the current annual standard level 

of 15 μg/m3 23 and the lowest level evaluated. 

24 In identifying alternative levels for the 24-hour PM2.5 standard to be evaluated in this risk 

25 assessment, we considered the ambient PM2.5 levels associated with mortality and morbidity 

effects as reported in key short-term epidemiological studies. We focused on the 98th 26 percentile 

27 PM2.5 ambient levels reported in two multi-city studies that provided C-R functions used in the 

28 core risk assessment, Zanobetti and Schwartz (2009) and Bell et al. (2008). The focus on the 

98th 29 percentile of the 24-hour PM2.5 concentrations observed in the epidemiological studies is 

30 consistent with the approach used in the prior PM NAAQS review and is consistent with the 

31 current form of the 24-hour PM2.5 standard. 

 10 We note that the association between long-term mean ambient PM2.5 levels and statistically-significant health 

effects reported in short-term exposure studies would be dependent on the specific relationship between day-to-day 

variation in the 24-hour PM2.5 levels (in the underlying study counties) and the associated long-term mean PM2.5

levels (i.e., the association between mean PM2.5 levels and short-term health effects, would not hold for counties 

with notably different relationships between short-term day-to-day variation and longer-term mean PM2.5 levels). 
February 2010 Draft – Do Not Quote or Cite 2-11

The second draft ISA presented 98th 1 percentile 24-hour PM2.5 values for each of the 112 

2 urban areas included in the Zanobetti and Schwartz (2009) short-term mortality study (EPA, 

2009a, Figure 6-22). We evaluated the trend in these county-level 98th 3 percentile 24-hour PM2.5

4 levels in conjunction with the statistical significance of the associated county-level effect 

5 estimates. If we had found an association between the air quality levels and statistically 

significant effect estimates (i.e., higher 98th 6 percentile PM2.5 levels were consistently associated 

7 with statistically significant effect estimates), then it would have been reasonable to consider the 

lowest 98th 8 percentile PM2.5 level associated with the set of counties for which a statistically 

9 significant effect estimates was observed as the basis for selecting an alternative standard level 

10 for evaluation in this risk assessment. However, no such association was observed. Rather, we 

observed mixed results with no clear correlation between 98th 11 percentile air quality levels and 

statistically significant effect estimates. Therefore, we focused on the overall range of 98th 12 

13 percentile values across the entire set of counties and considered the lower quartile of that 

14 distribution as representative of a reasonably precautionary approach for identifying alternative 

levels for consideration in the risk assessment. The 10th and 25th 15 percentiles values were 25.5 

and 29.8 μg/m3, respectively (Zanobetti, 2009). We note that the overall 98th 16 percentile value 

across the entire set of urban areas analyzed in Zanobetti and Schwartz. (2009) was 34.3 μg/m3 17 

18 (EPA, 2009a, Figure 2-1; Zanobetti and Schwartz, 2009) 

19 We also completed a similar analysis of the county-level ambient air quality data (Bell, 

20 2009) for the 202 counties associated with the Bell et al. (2008) study. Analysis of the overall 

distribution of 98th percentile values across the entire dataset resulted in identifying 10th and 25th 21 

percentile values of about 24.4 and 29.3 μg/m3, respectively. We note that the overall 98th 22 

percentile value across the entire set of counties analyzed in Bell et al. (2008)) was 34.2 μg/m3 23 

24 (EPA, 2009a, Table 6-11; Bell, 2009). 

25 Based on the available epidemiological evidence indicating effects associated with a 

range of 98th 26 percentile 24-hour PM2.5 concentrations, as briefly described above, we selected 

levels of 25 and 30 μg/m3 27 as the alternative 24-hour standard levels to be evaluated in this 

28 quantitative risk assessment. 

29 Once alternative levels were identified for the annual and 24-hour PM standards, we then 

30 identified specific combinations of these standard levels to be considered in evaluating suites of 

31 alternative standards in the risk assessment. In selecting the pairing of annual and 24-hour 

32 standard levels, we considered which standard was likely to be controlling across the set of 15 

33 urban study areas (either the annual or 24-hour standard will be the “controlling standard” at a 
February 2010 Draft – Do Not Quote or Cite 2-12

given location, depending on the design value associated with that location).11 1 For this risk 

2 assessment, the goal was to select combinations of annual and 24-hour levels that would result in 

3 a mixture of behavior in terms of which standards would control across the various urban study 

areas. For example, with the 12/35 combination (i.e., an annual standard level of 12 μg/m3 4 and a 

24-hour standard level of 35 μg/m3

), the annual level of 12 μg/m3 5 is the controlling standard for 

6 all 15 urban study areas, while with the 12/25 combination, the annual standard is the controlling 

7 standard at some locations and the 24-hour standard is the controlling standard at other locations. 

8 Consideration of these factors resulted in a set of five alternative combinations of annual and 24-

9 hour standards being identified for inclusion in the risk assessment. 

10 The full set of air quality scenarios included in the risk assessment, including the recent 

11 conditions air quality scenario and current standards scenario along with the five alternative sets 

12 of standards are as follows:

13  Recent conditions (risk estimates based on ambient PM2.5 monitoring data for the 

14 analysis period – 2005 to 2007) 

 Current PM2.5 NAAQS: annual 15 μg/m3; 24-hour 35 μg/m3 15 

 Alternative PM2.5 standards: annual 14 μg/m3; 24-hour 35 μg/m3 16 

 Alternative PM2.5 standards: annual 13 μg/m3; 24-hour 35 μg/m3 17 

 Alternative PM2.5 standards: annual 12 μg/m3; 24-hour 35 μg/m3 18 

 Alternative PM2.5 standards: annual 13 μg/m3

; 24-hour 30 μg/m3 19 

 Alternative PM2.5 standards: annual 12 μg/m3; 24-hour 25 μg/m3 20 .

 11 The controlling standard is the standard which requires the greatest percentage reduction to get the design value 

monitor to meet that standard - see section 3.3.3 for additional detail on the issue of controlling standards. 
February 2010 Draft – Do Not Quote or Cite 3-1

1 3 URBAN CASE STUDY ANALYSIS METHODS 

2 This chapter provides an overview of the methods used in the risk assessment. Section 

3 3.1 discusses the basic structure of the risk assessment, identifying the modeling elements and 

4 related sources of input data needed for the analysis. Section 3.2 discusses air quality 

5 considerations. Section 3.3 discusses the selection of health endpoints, urban study areas and C6 R functions from key epidemiological studies used in modeling those endpoints. Section 3.4 

7 discusses baseline health effects incidence rates. Finally, section 3.5 describes how uncertainty 

8 and variability are addressed in the risk assessment. 

9 3.1 GENERAL APPROACH 

10 3.1.1 Basic Structure of the Risk Assessment 

11 The general approach used in both the prior and the current PM risk assessment relies 

12 upon C-R functions which have been estimated in epidemiological studies. Since these studies 

13 estimate C-R functions using ambient air quality data from fixed-site, population-oriented 

14 monitors, the appropriate application of these functions in a PM risk assessment similarly 

15 requires the use of ambient air quality data at fixed-site, population-oriented monitors. 

16 The general PM health risk model, illustrated in Figure 3-1, combines information about 

17 PM air quality for specific urban areas with C-R functions derived from epidemiological studies, 

18 baseline health incidence data for specific health endpoints, and population estimates to derive 

19 estimates of the annual incidence of specified health effects attributable to ambient PM 

20 concentrations under different air quality scenarios. This assessment was implemented within 

21 TRIM.Risk, the component of EPA’s Total Risk Integrated Methodology (TRIM) model that 

estimates human health risks.12 22 

23 The analyses conducted for this review focused on estimating risks associated with recent 

24 PM2.5 air quality and estimating changes in these risks associated with air quality simulated to 

25 reflect just meeting the current suite of PM2.5 ambient standards, as well as any additional 

26 reductions in incidence estimated to occur upon just meeting alternative suites of PM2.5

27 standards. 

28 Consistent with past risk assessments for NAAQS reviews, this risk assessment is 

29 intended to estimate risks attributable to anthropogenic sources and activities only. Therefore, for 

30 all health endpoints associated with short-term exposure to PM2.5, the risk assessment considers 

31 only the incidence of health effects associated with PM2.5 concentrations in excess

 12 For more detailed information about TRIM.Risk, see: http://www.epa.gov/ttn/fera/trim_risk.html 
February 2010 Draft – Do Not 

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Figure 3-1. Major components of particulate matter health risk assessment. 
February 2010 Draft – Do Not Quote or Cite 3-3

of policy relevant background (PRB) levels. In the studies estimating a relationship between 

mortality and long-term exposure to PM2.5, however, the lowest measured levels (LMLs) 

reported in the epidemiological studies were substantially above PRB. Thus, estimating risk 

down to PRB would have required substantial extrapolation of the estimated C-R functions 

below the range of the data on which they were estimated. Therefore, we estimated risk only 

down to the LML to avoid introducing additional uncertainty related to this extrapolation into 

this analysis. To provide consistency for the different C-R functions selected from the long-term 

exposure studies, and, in particular, to avoid the choice of LML unduly influencing the results of 

the risk assessment, we selected a single LML – 5.8 μg/m3 from the later exposure period 

evaluated in Krewski et al. (2009) -- to be used in estimating risks associated with long-term 

PM2.5 exposures. 

For each health effect that has been associated with PM2.5, the risk assessment may be 

viewed as assessing the incidence of the health effect associated with PM2.5 concentrations under 

a given air quality scenario (e.g., a scenario in which PM2.5 concentrations just meet a specified 

suite of standards) above PRB or the LML . Equivalently, the risk assessment may be viewed as 

assessing the change in incidence of each health effect associated with a change in PM2.5

concentrations from some higher level (e.g., PM2.5 concentrations that just meet a specified suite 

of standards) to specified lower levels (PRB levels or the LML). 

 The risk assessment procedures described in more detail below are diagramed in Figure 

3-2 for analyses based on short-term exposure studies and in Figure 3-3 for analyses based on 

long-term exposure studies. To estimate the change in incidence of a given health effect 

resulting from a given change in ambient PM2.5 concentrations in an assessment location, the 

following analysis inputs are necessary: 

 Air quality information including: (1) PM2.5 air quality data from one or more recent 

years from population-oriented monitors in the assessment location, (2) estimates of 

PM2.5 PRB concentrations appropriate to this location, and (3) a method for adjusting the 

air quality data to reflect patterns of air quality changes to simulate just meeting the 

current or alternative suite of PM2.5 standards. (These air quality inputs are discussed in 

more detail in section 3.2). 

 C-R function(s) which provide an estimate of the relationship between the health 

endpoint of interest and PM2.5 concentrations (preferably derived in the assessment 

location, although functions estimated in other locations can be used at the cost of 

increased uncertainty -- see section 3.5.3). For PM2.5, C-R functions are available from 

epidemiological studies that assessed PM2.5-related health effects associated with either 

short- or long-term exposures. (Section 3.1.2 describes the role of C-R functions in 

estimating health risks associated with PM2.5). 

 Baseline health effects incidence rate and population. The baseline incidence rate 

provides an estimate of the incidence rate (number of cases of the health effect per year, 
February 2010 Draft – Do Not Quote or Cite 3-4

usually per 10,000 or 100,000 general population) in the assessment location 

corresponding to recent ambient PM2.5 levels in that location. To derive the total baseline 

incidence per year, this rate must be multiplied by the corresponding population number 

(e.g., if the baseline incidence rate is number of cases per year per 100,000 population, it 

must be multiplied by the number of 100,000s in the population). (Section 3.4 

summarizes considerations related to the baseline incidence rate and population data 

inputs to the risk assessment). 
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Figure 3-2. Flow diagram of risk assessment for short-term exposure studies. 
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Figure 3-3. Flow diagram of risk assessment for long-term exposure studies. 
February 2010 Draft – Do Not Quote or Cite 3-7

1 The risk assessment was carried out using three years of recent air quality data from 

2 2005, 2006, and 2007 (see section 3.2.1). We matched the population data used in the risk 

3 assessment to the year of the air quality data. For example, when we used 2005 air quality data, 

4 we used 2005 population estimates. It was not possible to obtain the necessary data to calculate 

5 baseline incidence rates separately for each of the three years for each of the risk assessment 

6 locations, therefore, we calculated these rates for a single year, under the assumption that these 

7 rates are unlikely to have changed significantly from 2005 to 2007. The calculation of baseline 

8 incidence rates is described in detail in section 3.4. 

9 For this risk assessment, we developed a core (primary) set of risk results based on the 

10 application of modeling element choices (e.g., C-R functions, lag periods) that we believe have 

11 the greatest overall support in the literature (hereafter referred to as the “core” results). While it 

12 is not possible at this time to assign quantitative levels of confidence to these core risk estimates, 

13 we do believe these estimates are generally based on inputs having higher overall levels of 

14 confidence relative to risk estimates that could have been generated using other inputs identified 

15 in the literature. 

16 In addition, as discussed above in section 2.1 and later in section 3.5, we have also used 

17 single-element and multi-element sensitivity analysis techniques to generate a set of reasonable 

18 alternative risk estimates based on the application of alternative modeling element choices that, 

19 while not having as much support in the literature as those used in the core analysis, do still 

20 represent plausible inputs. The results of these sensitivity analyses allow us to gain insights into 

21 which sources of uncertainty and variability may have the greatest impact on risk estimates when 

22 acting alone, or in combination with other sources of uncertainty. The sensitivity analysis-based 

23 risk estimates also provide us with an additional set of reasonable risk results that allow us to 

24 place the results of the core analysis in context with regard to uncertainty. A number of 

25 modeling elements were used in differentiating core analyses from sensitivity analyses (e.g., C-R 

26 function shape, alternative effect estimates, alternative lag structures, different methods used to 

27 rollback air quality to simulate attainment to current or alternative standard levels, application of 

28 PRB versus LML). Specific choices made in relation to individual modeling elements in 

29 differentiating the core analysis from sensitivity analyses are described, as appropriate, in the 

30 sections that follow, which cover specific aspects of the risk assessment design. The potential 

31 utility of the sensitivity analysis-based risk estimates in informing consideration of uncertainty 

32 and variability in the core results is discussed in section 4.5.2. 

33 3.1.2 Calculating PM2.5-Related Health Effects Incidence 

34 The C-R functions used in the risk assessment are empirically estimated relations 

35 between average ambient concentrations of PM2.5 and the health endpoints of interest (e.g., 
February 2010 Draft – Do Not Quote or Cite 3-8

1 mortality or hospital admissions reported by epidemiological studies for specific locations). This 

2 section describes the basic method used to estimate changes in the incidence of a health endpoint 

3 associated with changes in PM2.5, using a “generic” C-R function of the most common functional 

4 form. 

5 Although some epidemiological studies have estimated linear C-R functions and some 

6 have estimated logistic functions, most of the studies used a method referred to as “Poisson 

7 regression” to estimate exponential (or log-linear) C-R functions in which the natural logarithm 

8 of the health endpoint is a linear function of PM2.5: 

9 

 x y Be  10  (1) 

11 

12 where x is the ambient PM2.5 level, y is the incidence of the health endpoint of interest at 

13 PM2.5 level x, β is the coefficient of ambient PM2.5 concentration, and B is the incidence at x=0, 

14 i.e., when there is no ambient PM2.5. The relationship between a specified ambient PM2.5 level, 

15 x0, for example, and the incidence of a given health endpoint associated with that level (denoted 

16 as y0) is then 

17 

 0

0

x y Be  18  (2) 

19 

20 Because the log-linear form of a C-R function (equation (1)) is by far the most common 

21 form, we use this form to illustrate the “health impact function” used in the PM2.5 risk 

22 assessment. 

23 If we let x0 denote the baseline (upper) PM2.5 level, and x1 denote the lower PM2.5 level, 

24 and y0 and y1 denote the corresponding incidences of the health effect, we can derive the 

25 following relationship between the change in x, Δx= (x0- x1), and the corresponding change in y, 

Δy, from equation (1).13 26 

  

yyy y e x    ( ) [ ]. 01 0 1  27 (3) 

28 

29 Alternatively, the difference in health effects incidence can be calculated indirectly using 

30 relative risk. Relative risk (RR) is a measure commonly used by epidemiologists to characterize 

31 the comparative health effects associated with a particular air quality comparison. The risk of 

32 mortality at ambient PM2.5 level x0 relative to the risk of mortality at ambient PM2.5 level x1, for 

 13 If Δx < 0 – i.e., if Δx = (x1- x0) – then the relationship between Δx and Δy can be shown to be 

( ) [ 1]   1  0  0  x y y y y e  . If Δx < 0, Δy will similarly be negative. However, the magnitude of Δy will be the 

same whether Δx > 0 or Δx < 0 – i.e., the absolute value of Δy does not depend on which equation is used. 
February 2010 Draft – Do Not Quote or Cite 3-9

1 example, may be characterized by the ratio of the two mortality rates: the mortality rate among 

2 individuals when the ambient PM2.5 level is x0 and the mortality rate among (otherwise identical) 

3 individuals when the ambient PM2.5 level is x1. This is the RR for mortality associated with the 

4 difference between the two ambient PM2.5 levels, x0 and x1. Given a C-R function of the form 

5 shown in equation (1) and a particular difference in ambient PM2.5 levels, Δx, the RR associated 

with that difference in ambient PM2.5, denoted as RRΔx, is equal to eβΔx 6 . The difference in health 

7 effects incidence, Δy, corresponding to a given difference in ambient PM2.5 levels, Δx, can then 

8 be calculated based on this RRΔx as: 

9 

( ) [1 (1/ )] 0 1 0 RR x y y y y       10 . (4) 

11 

12 Equations (3) and (4) are simply alternative ways of expressing the relationship between 

13 a given difference in ambient PM2.5 levels, Δx > 0, and the corresponding difference in health 

14 effects incidence, Δy. These health impact equations are the key equations that combine air 

15 quality information, C-R function information, and baseline health effects incidence information 

16 to estimate ambient PM2.5 health risk. 

17 3.1.2.1 Short-term vs. Long-term Exposure 

18 Concentration-response (C-R) functions that use as input annual average PM2.5 levels (or 

19 some function of these, such as the average over a period of several years) relate these to the 

20 annual incidence of the health endpoint – i.e., in such studies x in equation (1) above is the 

21 average PM2.5 concentration over a period of one or more years, meant to represent long-term 

22 exposure, and y is the annual incidence of the health effect associated with that long-term 

23 exposure. 

24 Concentration-response (C-R) functions that use as input 24-hour average PM2.5 levels (or 

25 some function of these, such as the average over one or more days) relate these to the daily 

26 incidence of the health endpoint – i.e., in such studies x in equation (1) above is the average 

27 PM2.5 concentration over a period of one or a few days (short-term exposure), and y is the daily 

28 incidence of the health effect associated with that short-term exposure. 

29 There are several variants of the short-term (daily) C-R function. Some C-R functions 

30 were estimated by using moving averages of ambient PM2.5 to predict daily health effects 

31 incidence. Such a function might, for example, relate the incidence of the health effect on day t

32 to the average of PM2.5 concentrations on days t and (t-1). Some C-R functions consider the 

33 relationship between daily incidence and daily average PM2.5 lagged a certain number of days. 

34 For example, a study might estimate the C-R relationship between mortality on day t and average 

35 PM2.5 on a prior day (t-1). A few studies have estimated distributed lag models, in which health 
February 2010 Draft – Do Not Quote or Cite 3-10

1 effect incidence is a function of PM2.5 concentrations on several prior days – that is, the incidence 

2 of the health endpoint on day t is a function of the PM2.5 concentration on day t, day (t-1), day (t3 2), and so forth. Such models can be reconfigured so that the sum of the coefficients of the 

4 different PM2.5 lags in the model can be used to predict the changes in incidence on several days. 

5 For example, corresponding to a change in PM on day t in a distributed lag model with 0-day, 1-

6 day, and 2- day lags considered, the sum of the coefficients of the 0-day, 1-day, and 2-day lagged 

7 PM2.5 concentrations can be used to predict the sum of incidence changes on days t, (t+1) and 

8 (t+2). 

9 Most daily time-series epidemiological studies estimated C-R functions in which the PM10 related incidence on a given day depends only on same-day PM concentration(i.e. lag 0), the 

11 previous-day PM concentration (i.e. lag 1), or some variant of those, such as a two-day average 

12 concentration (e.g. lag 0-1). Such models necessarily assume that the longer pattern of PM 

13 levels preceding the PM concentration on a given day does not affect mortality or morbidity on 

14 that day. To the extent that PM-related mortality on a given day is affected by PM concentrations 

15 over a longer period of time, then these models would be mis-specified, and this mis16 specification would affect the predictions of daily incidence based on the model. 

17 The extent to which time-series studies using single-day PM2.5 concentrations may under 

18 or over-estimate the relationship between short-term PM2.5 exposure and risk of mortality is 

19 unknown. However, there is some evidence, based on analyses of PM10 data, that mortality or 

20 morbidity on a given day is influenced by prior PM exposures up to more than a month before 

21 the date of death (Schwartz, 2000). The extent to which short-term exposure studies (including 

22 those that consider distributed lags) may not capture the full impact of long-term exposures to 

23 PM2.5 is similarly not adequately understood, although the current evidence (e.g., Krewski et al., 

24 2009; Krewski et al., 2000) suggests that there is a substantial impact of long-term exposures on 

25 health effects that is not picked up in the short-term exposure studies. 

26 3.1.2.2 Calculating Annual Incidence 

27 The risk assessment estimated health effects incidence, and changes in incidence, on an 

28 annual basis, for 2005, 2006, and 2007. For mortality, both short-term and long-term exposure 

29 studies have reported estimated C-R functions. As noted above, most short-term exposure C-R 

30 functions estimated by daily time-series epidemiological studies relate daily mortality to same31 day PM2.5 concentration or previous-day PM2.5 concentration (or some variant of those). 

32 To estimate the daily health impacts of 24-hour average ambient PM2.5 levels above PRB, 

33 C-R functions from short-term exposure studies were used together with estimated changes in 

34 24-hour ambient PM2.5 concentrations to calculate the daily changes in the incidence of the 
February 2010 Draft – Do Not Quote or Cite 3-11

1 health endpoint. After daily changes in health effects were calculated, an annual change was 

2 calculated by summing the daily changes. 

3 The mortality associated with long-term exposure is likely to include mortality related to 

4 short-term exposures as well as mortality related to longer-term exposures. As discussed 

5 previously, estimates of daily mortality based on the time-series studies also are likely influenced 

6 by prior PM exposures. Therefore, the estimated annual incidences of mortality calculated based 

7 on the short- and long-term exposure studies are not likely to be completely independent and 

8 should not be added together. While we can characterize the statistical uncertainty surrounding 

9 the estimated PM2.5 coefficient in a reported C-R function, there are other sources of uncertainty 

10 associated with the C-R functions used in the risk assessment that are addressed via sensitivity 

11 analyses and/or qualitatively discussed in section 3.5.3. 

12 3.2 AIR QUALITY INPUTS 

13 3.2.1 Characterizing Recent Conditions 

14 As noted earlier, a major input to the PM2.5 risk assessment is ambient PM2.5 air quality 

15 data for each assessment location. Twenty-four hour PM2.5 air quality data for 2005, 2006, and 

16 2007 were obtained for each of the urban study areas from monitors in EPA’s Air Quality 

17 System (AQS). To characterize PM2.5 air quality in each risk assessment location as accurately 

18 as possible, we used only those monitors that were located within the county or counties that 

19 were analyzed in the epidemiological studies used to select C-R functions. In a few cases, an 

20 urban area was delineated differently by two or more epidemiological studies used in the risk 

21 assessment. For example, Birmingham, AL was defined as Blount, Jefferson, Shelby, St. Clair, 

22 and Walker Counties in one study and as only Jefferson County in another study. In such cases, 

23 we matched our delineation of the urban study area to that used in each study, resulting in two or 

24 more different delineations of the urban study area and identified them as, for example, 

25 Birmingham 1 and Birmingham 2. The counties and the number of air quality monitors included 

26 within each urban area are given in Table 3-1. 

27 In order to be consistent with the approach generally used in the epidemiological studies 

28 that estimated PM2.5 C-R functions, the average ambient PM2.5 concentration on each day for 

29 which measured data were available was deemed most appropriate for use in the risk assessment 

30 (i.e., we created a composite monitor average). Consistent with the approach used in the prior 

31 PM risk assessment, a composite monitor data set was created for each assessment location 

32 based on a composite of all monitors located within each urban study area. For this risk 

33 assessment, we have used an approach for creating composite monitors (see description below) 

34 that reflects equal weighting of monitors in computing both 24-hour and annual composite 

35 monitor values. (This reflects a change from the approach used in the first draft RA which 
February 2010 Draft – Do Not Quote or Cite 3-12

1 weighted monitors by sampling frequency – an approach which could result in bias being 

2 introduced into the analysis.) 

3 To calculate daily averages at the composite monitor for a location, we first checked the 

4 number of observations at each monitor at that location. If a monitor had fewer than 11 

5 observations in a quarter of the year (three months, the first quarter being January, February, and 

6 March), we left the days in that quarter without observations as missing. If a monitor had at least 

7 11 observations in a quarter, we filled in the missing days at that monitor in that quarter as 

8 follows: For each series of seven or fewer consecutive days with missing values, we took the 

9 average of the closest day with a reported value before the missing days and the closest day with 

10 a reported value after the missing days, and we assigned that average to all days in the series of 

11 missing days. If a series of consecutive missing days was greater than seven, we did not fill 

12 them in. After the missing days at monitors had been filled in as described, we calculated the 

13 composite monitor value for a given day as the average of values across all monitors for that day. 

14 If there were any days for which the composite monitor value was missing, we filled them in 

15 with 7-day moving averages (i.e., an average of the 3 days before and the 3 days after the 

16 missing day). Given the approach for interpolating missing days at individual monitors (just 

17 described), the incidence of missing days at composite monitors was very low. The numbers of 

18 monitors in the risk assessment locations are given in Table 3-1. 

19 To calculate annual averages at the composite monitor for a location, we first checked the 

20 number of observations in each quarter of each year at each monitor at the location. If a monitor 

21 had fewer than 11 observations in a quarter of the year, we set the quarterly average at that 

22 monitor to “missing.” If the monitor had at least 11 observations in a quarter, we calculated the 

23 quarterly average at the monitor as the average of the reported observations at the monitor in that 

24 quarter. For each quarter of the year, we then calculated the composite monitor quarterly 

25 average as the average of the monitor-specific quarterly averages. The annual average at the 

26 composite monitor was then calculated as the average of the four composite monitor quarterly 

averages.14 27 

28 

 14 Pittsburgh was treated somewhat differently from the other locations because there are effectively two attainment 

areas in Pittsburgh – one containing ten of the monitors we’re using in the risk assessment (“Pittsburgh-1”), and the 

other containing the remaining 2 monitors (“Pittsburgh-2”). We treated each of these two sets of monitors as a 

separate “location,” and calculated both daily and annual composite monitor values in each “location.” We then 

calculated composite monitor values for Pittsburgh as weighted averages of the composite monitor values for 

“Pittsburgh-1” and “Pittsburgh-2”, where the weights were the proportion of the monitors in each (i.e., 10/12 and 

2/12). 
February 2010 Draft – Do Not Quote or Cite 3-13

1 Table 3-1. Numbers of Monitors in Risk Assessment Locations From Which Composite 

2 Monitor Values Were Calculated* 

Risk Assessment 

Location Counties Number of Monitors 

Atlanta, GA - 1 Cobb, De Kalb, Fulton, Gwinnett 8 

Atlanta, GA - 2 Cobb, De Kalb, Fulton 7 

Atlanta, GA - 3 20-County MSA** 10 

Baltimore, MD Baltimore city, Baltimore county 8 

Birmingham, AL – 1 Blount, Jefferson, Shelby, St. Clair, Walker 10 

Birmingham, AL – 2 Jefferson 8 

Dallas, TX Dallas 6 

Detroit, MI Wayne 9 

Fresno, CA Fresno 3 

Houston, TX Harris 6 

Los Angeles, CA Los Angeles 10 

New York, NY – 1*** Kings, New York City (Manhattan), Queens, Richmond, Bronx 12 

Philadelphia, PA Philadelphia 7 

Phoenix, AZ Maricopa 5 

Pittsburgh, PA Allegheny 12 

Salt Lake City, UT Salt Lake 7 

St. Louis, MO - 1 Jefferson, Madison (IL), St. Louis, St. Louis City, St. Clair (IL) 15 

St. Louis, MO - 2 Madison (IL), St. Louis, St. Louis City, St. Clair (IL) 14 

Tacoma, WA Pierce 1 

3 * Calculation of composite monitor values is described in the text above. 

4 ** Barrow, Bartow, Carroll, Cherokee, Clayton, Cobb, Coweta, DeKalb, Douglas, Fayette, Forsyth, Fulton, Gwinett, 

5 Henry, Newton, Paulding, Pickens, Rockdale, Spalding, and Walton. 

6 *** The sets of monitors for New York (Manhattan) have 1-in-3 day sampling, with sampling schedules synced 

7 across monitors. This means that for the three year simulation period, roughly 2/3 of the days (i.e., 731) had no 

8 monitor coverage for the New York urban study area, resulting in a need to interpolate estimates for these days (for 

9 the composite monitor) using the approach described above. Similarly, with Tacoma, the single monitor at that 

10 location also has 1 in 3 day sampling, resulting again, in 2/3 of the days not having data with interpolation being 

11 used to derive estimates for those days (for the composite monitor). 

12 

13 Appendix A summarizes the PM2.5 air quality data that were used in each of the 

14 assessment locations, including quarterly and annual counts, quarterly and annual averages, and 

the 98th 15 percentile of the daily (24-hour) averages. 

16 3.2.2 Estimating Policy Relevant Background 

17 Policy-relevant background estimates used in the risk assessment model (see Table 3-2 

18 below) were obtained from the ISA (Table 3-23, final ISA, EPA, 2009d). These values were 

19 generated based on a combination of Community Multiscale Air Quality model (CMAQ) and 

20 Goddard Earth Observing System (GEOS)-Chem modeling as described in the draft ISA (see 

21 section 3.7.1.2). Annual values presented in Table 3-2 were used in modeling health endpoints 

22 associated with long-term exposure (in those sensitivity analysis scenarios where risk was 

23 modeled down to PRB – see section 3.5.4). For health endpoints associated with short-term 

24 exposure (which involved modeling down to PRB, exclusively), quarterly values presented in 

25 Table 3-2 were used to represent the appropriate block of days within a simulated year. 
February 2010 Draft – Do Not Quote or Cite 3-14

1 Table 3-2 Regional Policy-Relevant Background Estimates Used in the Risk 

2 Assessment. 

U.S. Region Annual 

JanuaryMarch April-June JulySeptember OctoberDecember 

Northeast 0.74 0.85 0.78 0.67 0.68 

Southeast 1.72 2.43 1.41 1.41 1.64 

Industrial Midwest 0.86 0.89 0.89 0.94 0.73 

Upper Midwest 0.84 0.79 0.93 0.99 0.66 

Southwest 0.62 0.61 0.76 0.70 0.40 

Northwest 1.01 0.48 0.81 1.42 1.32 

Southern California 0.84 0.54 0.92 1.21 0.67 

3 

4 3.2.3 Simulating Air Quality to Just Meet Current and Alternative Standards 

5 This section describes the methodologies used to simulate ambient PM2.5 levels in an area 

6 that would just meet specified PM2.5 standards. The form of the current PM2.5 standards requires 

that the 3-year average (rounded to the nearest 0.1 μg/m3 7 ) of the annual means from each single 

8 monitor or the average of multiple monitors must be at or below the level of the annual standard 

and the 3-year average (rounded to the nearest 1 μg/m3 9 ) of the ninety-eighth percentile values at 

10 each monitor cannot exceed the level of the 24-hour standard. In determining attainment of the 

11 annual average standard, an area may choose to use either the spatially averaged concentrations 

12 across all population-oriented monitors, subject to meeting certain criteria detailed in Part 50, 

13 Appendix N, of the CFR, or it may use the highest 3-year average based on individual monitors. 

14 The most realistic simulation of just meeting both the annual and the 24-hour PM2.5 standards in 

15 a location would require changing the distribution of 24-hour PM2.5 concentrations at each 

16 monitor separately, based on the specific mix of local and regional controls impacting that 

17 particular location. This would require extensive analysis and assumptions about the nature of 

18 future control strategies that is beyond the scope of quantitative risk assessments done as part of 

the review of the NAAQS.15 19 

20 In the last PM risk assessment, just meeting the current or alternative PM2.5 standards was 

21 simulated by changing 24-hour PM2.5 concentrations at a “composite monitor,” which 

22 represented the average of the monitors in a location. In the current PM risk assessment, just 

23 meeting the current or alternative PM2.5 standards was simulated by changing 24-hour PM2.5

24 concentrations at each monitor separately. This change was made because the current PM risk 

25 assessment considers three alternative approaches to simulating PM2.5 concentrations that just 

 15 Such modeling analyses are done by States in developing state implementation plans that demonstrate how areas 

will come into attainment with standards that have been promulgated. 
February 2010 Draft – Do Not Quote or Cite 3-15

1 meet a given suite of standards (i.e., proportional, hybrid and peak-shaving – see below), and two 

2 of these methods (hybrid and peak-shaving) involve making monitor-specific changes of 24-hour 

3 PM2.5 concentrations to simulate just meeting standards. All three of these methods start with 

4 monitor-specific series of PM2.5 concentrations in which missing days have been filled in as 

5 described above. 

6 In simulating ambient PM2.5 levels that would just meet current and alternative suites of 

7 standards, we have applied the following approaches to rolling back air quality levels: (a) 

8 proportional rollback, in which the same proportional adjustment is applied to all monitors in a 

9 study area, has traditionally been used in the NAAQS risk assessments since it generally reflects 

10 historical patterns in how air quality has changed over time, (b) hybrid rollback, which involves 

11 an initial localized reduction to bring higher monitors down to the range of their neighbors, 

12 followed by proportional reduction, if needed, to just meet a given suite of standards; and (c) 

13 peak-shaving rollback, in where each monitor that exceeds the 24-hour standard is simulated to 

14 just meet the 24-hour standard through proportional reduction of its annual 24-hour PM2.5

15 distribution (with no impact on monitors that are meeting the 24-hour standard). The 

16 proportional rollback approach is applied to each of the 15 urban study areas, while the other two 

17 rollback approaches are applied to a subset of areas as appropriate (e.g., the peak-shaving 

18 approach is only used for those study areas where the 24-hour standard is both controlling and 

19 being exceeded by one or more monitors). 

20 The proportional rollback approach was used in generating the core risk estimates in light 

21 of its use in past risk assessments, while the other two rollback approaches (hybrid and peak 

22 shaving) were considered in sensitivity analyses to characterize potential variability in the way 

23 urban areas may respond to suites of current or alternative standards. As described below, the 

24 proportional rollback reflects a regional pattern of ambient PM2.5 reduction, the hybrid approach 

25 reflects a combination of local and regional patterns in ambient PM2.5 reduction, and the peak 

26 shaving approach reflects a localized pattern of ambient PM2.5 reduction. We have not ascribed 

27 greater confidence to the proportional approach, since we have no basis for predicting which 

28 approach would likely be most reflective of future patterns of ambient PM2.5 reductions in each 

29 study area. 

30 3.2.3.1 Proportional Rollback Method 

31 The proportional approach, which reflects a regional pattern of reductions in ambient 

32 PM2.5 concentrations, was used in previous PM2.5 risk assessments. This approach involves 

33 proportional adjustments to monitor levels, in which PM2.5 concentrations are reduced (“rolled 

34 back”) by the same percentage each day. When this approach is used, it does not matter whether 

35 (1) PM2.5 concentrations are first rolled back by the same percentage each day at each monitor, 
February 2010 Draft – Do Not Quote or Cite 3-16

1 and then the composite monitor values are calculated from these monitor-specific values or (2) 

2 first the composite monitor values are calculated and then these are rolled back by the same 

3 percentage each day – the results will be the same. 

4 The percent reduction of 24-hour PM2.5 concentrations in the proportional rollback 

5 approach (and in the second step of the hybrid rollback approach, described below) at each 

6 monitor each day to simulate just meeting current and alternative set of standard levels is 

determined by the PM2.5 annual and 24-hour design values. The annual design value (in μg/m3 7 ) 

8 was calculated as follows: 

9  At each monitor, the annual average PM2.5 concentration was calculated for each of the 

10 years 2005, 2006, and 2007, and these three annual average concentrations were then 

11 averaged. 

12  The maximum of these monitor-specific 3-year averages of annual averages is the annual 

13 design value, denoted dvannual; 

The 24-hour design value (in μg/m3 14 ) was similarly calculated as follows: 

 At each monitor, the 98th 15 percentile 24-hour PM2.5 concentration was calculated for each 

of the years 2005, 2006, and 2007, and these three 98th 16 percentile concentrations were 

17 then averaged. 

 The maximum of these monitor-specific 3-year averages of 98th 18 percentile concentrations 

is the 24-hour design value, denoted dvdaily 98 (note, we will refer to the 98th 19 percentile 

20 design value as the 24-hour design value throughout the rest of the document). 

21 The annual and 24-hour design values used in assessing the current and alternative 

22 standards for PM2.5 are given in Table 3-3. Note that monitors that were closed in 2005 (and 

23 therefore, did not include monitoring data for the majority of the three year simulation period), or 

24 which were missing an entire year’s worth of monitoring data during any of the three simulation 

25 years (2005, 2006 or 2007) were excluded from consideration as design value monitors, although 

26 these monitors were still used to construct composite monitors for purposes of estimating risks. 

27 

28 Table 3-3. EPA Design Values for Annual and \24-hour PM2.5 Standards for the Period 

29 2005-2007.* 

Location 

Annual 

 (μg/m3) 

24-hour 

(μg/m3) 

Atlanta 16.2 35 

Baltimore 15.6 37 

Birmingham 18.7 44 

Dallas 12.8 26 
February 2010 Draft – Do Not Quote or Cite 3-17

Location 

Annual 

 (μg/m3) 

24-hour 

(μg/m3) 

Detroit 17.2 43 

Fresno 17.4 63 

Houston 15.8 31 

Los Angeles 19.6 55 

New York 15.9 42 

Philadelphia 15.0 38 

Phoenix 12.6 32 

Pittsburgh 19.8 60 

Salt Lake City 11.6 55 

St. Louis 16.5 39 

Tacoma 10.2 43 

1 *The calculation of design values is explained in the text above. 

2 

3 The percent reduction required to meet a standard (annual or 24-hour) was determined by 

4 comparing the design value for that standard with the level of the standard. Because pollution 

5 abatement methods are applied largely to anthropogenic sources of PM2.5, rollbacks were applied 

6 only to PM2.5 above estimated PRB levels. The percent reduction was determined by the 

7 controlling standard. For example, suppose both annual and 24-hour PM2.5 standards are being 

8 simulated. Suppose pa is the percent reduction required to just meet the annual standard (i.e., the 

9 percent reduction of daily PM2.5 above background necessary to get the annual design value 

10 down to the current or alternative annual standard). Suppose pd is the percent reduction required 

11 to just meet the 24-hour standard (i.e., the percent reduction of daily PM2.5 above background 

12 necessary to get the 24-hour PM2.5 design value down to the 24-hour standard). If pd is greater 

13 than pa, then all 24-hour average PM2.5 concentrations above background are reduced by pd

14 percent. If pa is greater than pd, then all 24-hour average PM2.5 concentrations are reduced by pa

15 percent. The method of rollbacks to meet a set of annual and 24-hour PM2.5 standards is 

16 summarized as follows: 

17 1. The percent by which the above-PRB portion of all daily PM2.5 concentrations (at the 

18 composite monitor) would have to be reduced to just meet the annual standard (denoted 

19 stda) is 

)

( ) 1

annual avg

a avg

a dv PRB

std PRB

p 

 20   , 
February 2010 Draft – Do Not Quote or Cite 3-18

1 

 where PRBavg is the average of the daily PRB concentrations.16 2 

3 

4 

5 2. The percent by which the above-PRB portion of all 24-hour PM2.5 concentrations (at the 

6 composite monitor) would have to be reduced to just meet the current or alternative 24-

7 hour standard (denoted stdd98) is: 

8 

)

( ) 1

98

98

98

daily avg

d avg

d dv PRB

std PRB

p 

 9  

10 

Let pmax = maximum of (maximum of pa and pd98) and zero.17 11 

12 

13 

14 3. Then if PMo denotes the original PM value on a given day (at the composite monitor), the 

15 rolled back PM value on that day, denoted PMrb, is: 

16 

17 PMrb = PRB +(PMo − PRB)*(1− pmax ). 

18 

19 Results of the simulations done in each urban study area using the proportional rollback 

20 approach, as well as the hybrid and peak shaving approaches discussed below, are presented in 

21 Appendix F, Tables F-49 and F-50. For each urban study area and suite of standards, two sets of 

22 values are presented in each table based on application of each rollback approach including: (a) 

23 the maximum monitor-specific three-year (2005-2007) annual average (i.e., “Max. M-S” in both 

24 tables) and (b) the composite monitor value for 2007 (i.e., “2007 CM” in both tables). The first 

25 estimate (Max M-S) allows us to see how the design value changes in just meeting each suite of 

26 standards based on application of the different rollback methods, while the second estimate 

27 (2007 CM) is the surrogate for long-term exposure-related mortality, as described below in 

28 section 3.5.4. The tables differ in terms of the information presented in the last set of columns, 

29 with Table F-49 showing the percent reduction in the composite monitor values given 

30 application of a particular rollback approach (allows comparison of the pattern of risk reduction 

 16 In the previous PM risk assessment, a constant PRB level was assumed for all days, and that constant PRB level 

was used in the formulas to calculate percent rollbacks necessary to just meet a standard. It can be shown that, if 

PRB levels vary from day to day, the average PRB level takes the place of the constant PRB level in the previous 

formula, as shown in the above equation. 

17 If the percent rollback necessary to just meet the annual standard and the percent rollback necessary to just meet 

the 24-hour standard were both negative -- i.e., if both standards were already met -- then the percent rollback 

applied in the risk assessment was zero. That is, PM values were never increased, or “rolled up.” 
February 2010 Draft – Do Not Quote or Cite 3-19

1 across standard levels generated using each rollback approach), and Table F-50 showing the 

2 percent difference in the composite monitor values in comparing the hybrid and peak shaving 

3 results to that obtained with the proportional rollback approach for a given standard level (allows 

4 comparison of residual risk estimates generated using the different rollback approaches for each 

5 standard level). The information in the last set of columns in each table is considered below in 

6 the sensitivity analysis (section 3.5.4). 

7 3.2.3.2 Hybrid Rollback Method 

8 The hybrid rollback approach reflects a combination of first localized and then regional 

9 patterns of reductions in ambient PM2.5 concentrations. In comparison to the proportional 

10 rollback approach, this approach has two steps: (1) first PM2.5 concentrations are reduced at a 

11 specific monitor location within an urban study area and then additional monitors within that 

12 urban study area are adjusted to a lesser extent (with the magnitude of adjustment based on a 

13 distance-decay function); then (2) a proportional rollback of the adjusted PM2.5 concentrations at 

14 all of the different monitors is carried out, as described in Section 3.2.3.1 above. Because the 

15 initial step reflecting localized controls is non-proportional, this needs to be completed on the 

16 monitor datasets (associated with a particular study area) prior to construction of the composite 

17 monitor. However, once those non-proportional reductions have been implemented, a composite 

18 monitor can then be constructed (as described earlier) and the second step of conducting 

19 proportional adjustment to simulate the current or alternative suites of standards can be 

20 calculated for the composite monitor. New design values are calculated for the hybrid rollback 

21 approach based on the PM2.5 concentrations that have been adjusted in the first step of the twostep process.18 22 The hybrid approach is described in more details in Appendix B. 

23 3.2.3.3 Peak Shaving Rollback Method 

24 The peak shaving approach reflects localized patterns of reduction in ambient PM2.5

25 concentrations and has only been applied in cases where the 24-hour standard is controlling (i.e., 

26 the percent rollback necessary to meet the daily standard is greater than the percent rollback 

27 necessary to meet the annual standard in that location). This approach was used to calculate 

28 annual averages for 2005, 2006, and 2007 at composite monitors for comparison with the 

29 composite monitor annual averages calculated using the proportional and hybrid rollback 

 18 As with the composite monitor values representing recent air quality, “rolled back” composite monitor values in 

Pittsburgh, for both the proportional rollback and the hybrid rollback methods, were calculated based on the division 

of monitors into the 10 in “Pittsburgh-1” and the remaining 2 in “Pittsburgh-2” (see footnote in Section 3.2.1). 

Daily and annual composite monitor values in “Pittsburgh-1” and “Pittsburgh-2” were rolled back as described in 

Sections 3.2.3.1 and 3.2.3.2; rolled back composite monitor values for Pittsburgh were calculated as weighted 

averages of the rolled back composite monitor values for “Pittsburgh-1” and “Pittsburgh-2”, where the weights were 

the proportion of the monitors in each (i.e., 10/12 and 2/12). 
February 2010 Draft – Do Not Quote or Cite 3-20

1 approaches. Because of time constraints, we did not calculate health risks with the application of 

2 the peak shaving rollback approach. Because the C-R functions used in the risk assessment are 

3 almost linear, a comparison of annual averages at composite monitors using the three different 

4 approaches for simulating just meeting alternative standards provides a good surrogate for 

5 estimates of health risks when alternative standards are just met (see Section 3.5.4 for additional 

6 detail on the composite monitor-based comparison of the three rollback strategies completed as 

7 part of the sensitivity analysis). 

8 As with the proportional and hybrid rollback approaches, the peak shaving approach for 

9 calculating annual averages at composite monitors starts with monitor-specific quarterly 

10 averages that have been calculated as described above in Section 3.2.1. In contrast to the 

11 proportional and hybrid rollback approaches, the peak shaving method uses monitor-specific 

12 design values. For each monitor, we compared the monitor-specific 24-hour design value to the 

13 level of the 24-hour standard and calculated the percent rollback necessary to reduce the 

14 concentration at each monitor to the standard level (using a formula that is analogous to the 

15 proportional rollback formula given above in Section 3.2.3.1). We then rolled back each 

16 quarterly average at the monitor by this percent rollback. We calculated the average quarterly 

17 average across all monitors in the location, for each quarter. Finally, we calculated the annual 

18 average at the composite monitor under the standard by averaging the four quarterly averages 

calculated on the previous step.19 19 

20 3.3 SELECTION OF MODEL INPUTS 

21 3.3.1 Health Endpoints 

22 The selection of health effect endpoints reflects consideration for a number of factors. 

23 The specific set of factors considered in selecting health effects endpoints to model in this 

24 assessment included: 

25  The overall weight of evidence from the collective body of epidemiological, controlled 

26 human exposure, and toxicological studies and the determination made in the final ISA 

27 regarding the strength of the causal relationship between PM2.5 and the more general 

28 health effect category; 

 19 As with the rolled back composite monitor values in Pittsburgh using both the proportional and hybrid rollback 

methods, rolled back composite monitor values in Pittsburgh using the peak shaving method were calculated based 

on the division of monitors into the 10 in “Pittsburgh-1” and the remaining 2 in “Pittsburgh-2” (as explained in the 

footnote in Section 3.2.3.2). However, unlike in the other locations, if the annual standard was controlling in one of 

the Pittsburgh attainment areas (i.e., in “Pittsburgh-1” or “Pittsburgh-2”), monitor-specific quarterly averages in that 

attainment area were rolled back by the percent rollback necessary to just meet the annual standard there. Once 

monitors in “Pittsburgh-1” and “Pittsburgh-2” were rolled back, the procedure to calculate annual composite 

monitor values in Pittsburgh was the same as in the other risk assessment locations. 
February 2010 Draft – Do Not Quote or Cite 3-21

1  The extent to which particular health effect endpoints within these broader health effect 

2 categories are considered significant from a public health standpoint; 

3  The availability of well-conducted epidemiological studies providing C-R functions for 

4 specific health effect endpoints; 

5  The availability of sufficient air quality monitoring data in areas that were evaluated in 

6 the epidemiological studies; 

7  The availability of baseline incidence data to support population risk (incidence) 

8 modeling; and 

9  The anticipated value of developing quantitative risk estimates for the health effect 

10 endpoint(s) to inform decision-making in the context of the PM NAAQS review. 

11 

12 In selecting the set of health effect endpoint categories (and associated endpoints and 

13 related at-risk populations) to include in the PM2.5 risk assessment, we considered the health 

14 effects evidence presented in the final ISA (EPA, 2009d), as well as CASAC (Samet, 2009a) and 

15 public comments received on the Scope and Methods Plan and CASAC (Samet, 2009b) and 

16 public comments received on the first draft RA. In reviewing the final ISA in relation to PM2.5, 

17 we focused on the following sections: (a) section 2.3.1.1 (Effects of Short-Term Exposure to 

18 PM2.5), (b) section 2.3.1.2 (Effects of Long-Term Exposure to PM2.5), (c) section 2.3.2 

19 (Integration of PM2.5 Health Effects), and (d) subsections in Chapter 6 and 7 of the final ISA 

20 providing summaries of causal determination (for both morbidity and mortality endpoints) 

21 related to short-term and long-term exposure, respectively. We also considered information in 

22 the ISA on at-risk populations, which identified the life stages of children and older adults, 

23 people with pre-existing cardiovascular and respiratory diseases, and people with lower 

24 socioeconomic status as populations at increased risk for PM-related health effects. 

25 Based on the evidence presented in the ISA and application of the above criteria, we 

26 identified the following health effects endpoints for inclusion in the risk assessment: 

27 Health effects associated with short-term PM2.5 exposure: 

28  Mortality (causal relationship) 

29 o non-accidental, 

30 o cardiovascular-related 

31 o respiratory-related, 

32  Cardiovascular effects (causal relationship) 

33 o cardiovascular-related hospital admissions 

34  Respiratory effects (likely causal relationship) 

35 o respiratory-related hospital admissions 
February 2010 Draft – Do Not Quote or Cite 3-22

1 o asthma-related emergency department visits 

2 Health effects associated with long-term PM2.5 exposure:

3  Mortality (causal relationship) 

4 o all-cause 

5 o ischemic heart disease (IHD)-related 

6 o cardiopulmonary-related 

7 o lung cancer 

8 While we selected specific health effect endpoints that were all within broad health effect 

9 categories classified in the ISA as having a “causal” or “likely causal” association with PM2.5

10 exposure, our selection is a based on applying the multi-factor approach described above. 

11 The evidence available for these selected health effect endpoints generally focused on 

12 the entire population, although some information was available that allowed us to consider 

13 differences in estimated risk for the at-risk populations of older adults and people with pre14 existing cardiovascular and respiratory diseases. While evidence of effects in other important at15 risk populations, including children and people with lower socioeconomic status, was not judged 

16 to be sufficient to support quantitative risk assessment, this evidence will be part of the evidence17 based considerations to be discussed in the policy assessment document currently being 

18 developed. 

19 3.3.2 Selection and Delineation of Urban Study Areas 

20 This section describes the approach used in selecting the 15 urban study areas included in 

21 this risk assessment (see Table 3-3 for a listing of the urban study areas). This approach builds 

22 upon and expands the approach for selecting urban study areas from the prior risk assessment 

23 (EPA, 2005, section 3.2, p. 37). 

24 Criteria used in the prior risk assessment and updated in this analysis include: 

25  Availability of sufficient air quality data: Sufficient air quality data was 

26 identified as having at least 11 observations per quarter for a one year period and 

27 at least 122 observations per year. We assessed prospective study areas by 

28 insuring that there was at least one PM2.5 monitor within the boundaries of the 

29 prospective study area that met these completeness criteria for the period 2005 to 

30 2007 with additional preference given to locations with more than one PM2.5 

31 monitor meeting completeness criteria, since this provided a better 

32 characterization of ambient air levels for that urban location. 

33 

34  Inclusion in epidemiology study: Coverage of the location within one of the key 

35 epidemiology studies included in the risk assessment (at or close to the location 

36 where at least one C-R function for one of the recommended health endpoints has 

37 been estimated by a study satisfying the selection criteria used in the risk 

38 assessment). In this review, because the current risk assessment primarily utilizes 
February 2010 Draft – Do Not Quote or Cite 3-23

1 multi-city studies to evaluate risk for short-term and long-term PM2.5 exposures 

2 (whereas the prior risk assessment used city-specific studies in modeling 

3 endpoints associated with short-term exposures), this criterion no longer applies 

4 for most prospective areas. 

5 

6  Availability of city-specific baseline incidence data: Regarding sufficiency of 

7 baseline health effects incidence data, an ongoing effort by EPA to collect county8 level hospital and emergency department admissions data from states to support 

9 this risk assessment (see section 3.5) has resulted in enhanced health effects 

10 baseline incidence data, largely addressing this criterion (i.e., most urban areas in 

11 the U.S. now have coverage with the updated baseline health effects incidence 

12 data). 

13 

14 Two additional factors considered in selecting locations to model in the current 

15 assessment included: 

16  Potential for risk reductions using alternative standard levels: Specifically, 

17 we focused on those urban areas with PM2.5 monitoring levels suggesting the 

18 potential for risk reduction under alternative (24-hour or annual) standards under 

19 consideration, particularly focusing on urban locations with at least one monitor 

having an annual average above 12 μg/m3

 and/or a 24-hour value above 25 μg/m3 20 . 

21 Furthermore, locations with ambient PM2.5 level significantly higher than these 

22 levels were favored (with several urban study areas selected having both annual 

23 and 24-hour design values exceeding the current standards – Table 3-4). 

24 

25  Regional representation: The second criterion we added for study area selection 

26 focused on providing coverage for factors believed to play a role in influencing 

27 risk heterogeneity at the national-level (e.g., PM2.5 source characteristics and 

28 composition, demographics, SES status, air conditioner use). Building on the 7 

29 regions originally identified in the 1996 PM Criteria Document (EPA, 1996, 

30 section 6.4) (i.e., PM regions), we evaluated several urban locations from each of 

31 these PM regions with the goal to identify one or more candidate urban study 

32 areas in each region. Ultimately, consideration of the criteria described here 

33 resulted in an urban study area not being identified for one of the PM regions (the 

34 Upper Midwest), however, the remaining six PM regions each included at least 

35 one urban study areas evaluated in the risk assessment. While the PM regions 

36 were originally defined focusing primarily on differences in PM composition, size 

37 and seasonality, by selecting urban study areas from regions across the continental 

38 U.S., we recognize the potential for covering regional differences in other factors 

39 related to risk heterogeneity as well (e.g., demographics, SES). The 

40 representativeness analysis (section 4.4) specifically assesses the degree to which 

41 the 15 urban study areas provide coverage for national trends in key risk-related 

42 factors such as those listed here. 

43 

44 Based on consideration of the above criteria, 15 study areas were selected for inclusion in 

45 this risk assessment. Table 3-4 presents the 15 urban study areas including (a) whether the urban 
February 2010 Draft – Do Not Quote or Cite 3-24

1 study area was included in the prior risk assessment, (b) which PM region the urban study area is 

2 located in, and (c) the 24-hour and annual design values using 2005-2007 air quality data. Figure 

3 3-4 identifies each of the 15 urban study areas in relation to the 7 regions used to guide the 

4 selection of the urban study areas. 

5 

6 Table 3-4. Urban Study Areas Selected for the Risk Assessment. 

Urban study 

area 

State 

Modeled in last 

NAAQS review 

PM 

region* 

Annual design 

value (μg/m3) 

24-hour design 

value (μg/m3) 

Atlanta GA SE 16.2 35 

Baltimore MD NE 15.6 37 

Birmingham AL SE 18.7 44 

Dallas TX SE 12.8 26 

Detroit MI X IM 17.2 43 

Fresno CA SCA 17.4 63 

Houston TX SE 15.8 31 

LA CA X SCA 19.6 55 

New York NY NE 15.9 42 

Philadelphia PA X NE 15.0 38 

Phoenix AZ X SW 12.6 32 

Pittsburgh PA X IM 19.8 60 

Salt Lake City UT NW 11.6 55 

St. Louis MO X IM 16.5 39 

Tacoma WA X NW 10.2 43 

7 * SE (Southeast), IM (industrial Midwest), SCA (Southern California), NE (Northeast), NW (Northwest), SW 

8 (Southwest) (See, EPA, 1996, section 6.4 for description of these regions).

9 

10 
February 2010 Draft – Do Not Quote or Cite 3-25

Northwest

Southwest Southeast

Northeast

Upper Midwest Industrial Midwest

Southern California

Dallas

Fresno

Tacoma

Atlanta

Detroit

Houston

Phoenix

New York

Baltimore St. Louis

Birmingham Los Angeles

Salt Lake City Pittsburgh Philadelphia

1 

2 Figure 3-4 15 urban study areas included in the risk assessment (including seven PM 

3 regions used to guide selection of study areas). 

4 

5 Once the 15 urban study areas were selected, the next step was to identify the spatial 

6 template to use in defining each study area (i.e., the geographical area associated with each study 

7 area that would be used in identifying which counties and PM2.5 monitors were associated with a 

8 particular study area). For 12 of the 15 urban study areas, we either used a combined statistical 

9 area (CSA) as the basis for the spatial template, or if that was not available, we used a core-based 

10 statistical area (CBSA). The three remaining urban study areas were special cases and were 

11 handled as follows: 

12  Baltimore: We used counties in the Baltimore CBSA only and did not consider the 

13 larger Baltimore-DC CSA since we felt it unlikely that the entire larger CSA would 

14 behave similarly with regard to PM2.5 emissions reduction strategies; 

15  Philadelphia: We used the Philadelphia CSA, but excluded Berks County (Reading), 

16 and 

17  Tacoma: we only used Pierce County (since we felt it unlikely that efforts to reduce 

18 emissions at the “elevated” monitor in Pierce County, would significantly impact 

19 monitors in Seattle). 
February 2010 Draft – Do Not Quote or Cite 3-26

1 As noted above, in a few instances, two or more epidemiological studies used different 

2 geographic boundaries for determining which populations were included in their studies. For 

3 example, in one study conducted in Birmingham, AL populations from Blount, Jefferson, 

4 Shelby, St. Clair, and Walker Counties were included, while another study included the 

5 population residing in only Jefferson County. In such cases, we matched our delineation of the 

6 urban area to that of each study, resulting in two or more different delineations of the urban area. 

7 As we discuss below, two of the studies on which we rely for our core analysis – 

8 Zanobetti and Schwartz (2009) and Bell et al. (2008) – are multi-location studies. Zanobetti and 

9 Schwartz (2009) specified the county or counties included in each of the urban areas they 

10 included in their analysis. Bell et al. (2008), however, did not focus on urban areas, but instead 

11 focused on counties with populations above a specified threshold number. To limit the number 

12 of different “versions” of a risk assessment location, wherever possible we specified the counties 

13 in a risk assessment location for Bell et al. (2008) to match the set specified for Zanobetti and 

14 Schwartz (2009). This was possible in those cases in which Zanobetti and Schwartz (2009) 

15 identified an urban area as a single county, and that county was also included in Bell et al. 

16 (2008). This was the case for several of the risk assessment locations. In some cases, however, 

17 Zanobetti and Schwartz (2009) used a multi-county delineation of an urban area where at least 

18 one of the counties was not among those included in Bell et al. (2008). In those cases, we had to 

19 delineate two definitions of the urban area – one corresponding to Zanobetti and Schwartz (2009) 

20 and the other corresponding to Bell et al. (2008). This was the case for Atlanta, Birmingham, 

21 and St. Louis. In both Atlanta and New York, other delineations by other studies forced 

22 additional delineation of these urban areas, as shown in Table 3-1 above. 

23 Finally, we applied the studies of mortality associated with long-term exposure to PM2.5

24 to the urban areas as defined by the short-term exposure mortality study, Zanobetti and Schwartz 

25 (2009), to enable meaningful comparisons between estimates of premature morality associated 

26 with short-term and long-term exposure to PM2.5. 

27 3.3.3 Selection of Epidemiological Studies and Concentration-response (C-R) Functions 

28 within those Studies 

29 As discussed above, we included in the PM2.5 risk assessment only those health effect 

30 endpoint categories (and specific health effects) that met the set of criteria reflected in the multi31 factor approach we developed for selecting health effect endpoints (see section 3.3.1). One of 

32 these factors was the strength of evidence supporting a causal association between PM2.5

33 exposure and the endpoint of interest. Thus, in cases where the majority of the available studies 

34 did not report a statistically significant relationship, the effect endpoint was not included. Once 

35 it had been determined that a health endpoint would be included in the analysis, however, 

36 inclusion of a study on that health endpoint was not based on statistical significance alone, but 
February 2010 Draft – Do Not Quote or Cite 3-27

1 considered other factors (e.g., overall design of the study including degree of control for 

2 confounders, method used to characterize exposure to PM2.5 within the risk assessment). 

3 A significant change since the previous PM risk assessment is the addition to the relevant 

4 epidemiological literature of several multi-city studies. This type of study has several 

5 advantages over single-city studies. First, multi-city studies use the same study design in each of 

6 the cities included in the study, so that city-specific results are readily comparable. Second, 

7 when they are estimating a single C-R function based on several cities, multi-city studies also 

8 tend to have more statistical power and provide effect estimates with relatively greater precision 

9 than single city studies due to larger sample sizes, reducing the uncertainty around the estimated 

10 coefficient. Moreover, in a multi-city study the statistical power to detect an effect in any given 

11 city can be supplemented by drawing statistical power from data across all the cities included in 

12 the study (or all the cities in the same region) to adjust city-specific estimates towards the mean 

13 across all cities included in the analysis (or in the same region). This is particularly useful in 

14 those instances, where a city has relatively less data resulting in a larger standard error for the 

15 effect estimate. In this situation, the information on the C-R relationship in all the other cities 

16 included in a multi-city study can be used to help inform an assessment of the C-R relationship 

17 in the city in question. Finally, multi-city studies tend to avoid the often-noted problem of 

18 publication bias that single-city studies confront (in which studies with statistically insignificant 

19 or negative results are less likely to get published than those with positive and/or statistically 

20 significant results). 

21 For this risk assessment, we selected what we considered to be the best study to assess 

22 the C-R relationship between PM2.5 and a given health endpoint, and we included other studies 

23 for that health endpoint only if they were judged to contribute something above and beyond what 

24 we could learn from the primary study selected. 

25 A primary study for a given health endpoint had to satisfy the study selection criteria that 

26 we have used in past PM (and other) risk assessments. In particular: 

27  It had to be a published, peer-reviewed study that has been evaluated in the PM ISA and 

28 judged adequate by EPA staff for purposes of inclusion in this risk assessment based on 

29 that evaluation. 

30  It had to directly measure, rather than estimate, PM2.5 on a reasonable proportion of the 

31 days in the study. 
February 2010 Draft – Do Not Quote or Cite 3-28

1 It had to either not rely on Generalized Additive Models (GAMs) using the S-Plus 

2 software to estimate C-R functions or to appropriately have re-estimated these functions using 

revised methods.20 3 

4 Because of the advantages noted above, we selected multi-city studies as our primary 

5 studies for assessing the risks of premature non-accidental, cardiovascular, and respiratory 

6 mortality (Zanobetti and Schwartz, 2009) and cardiovascular and respiratory hospital admissions 

7 (Bell et al., 2008) associated with short-term exposure to PM2.5 in our core analysis. In each of 

8 these studies, the 15 urban areas selected for the PM risk assessment were among the locations 

9 included in their analysis. These two multi-city studies are based on more recent air quality and 

10 health effects incidence data for short-term exposure-related mortality and morbidity and 

11 therefore represent the best studies to use in deriving C-R functions for this risk assessment. 

12 Dominici et al. (2007) was considered as an alternative study in identifying C-R functions for 

13 modeling short-term exposure-related mortality, however its study period and the underlying air 

14 quality data and disease incidence data (1987-2000) are not as current as that of Zanobetti and 

15 Schwartz et al., 2009 (study period of 2001-2005), and therefore, we decided to focus on 

16 Zanobetti and Schwartz et al. (2009) as the source of C-R functions for modeling short-term 

17 exposure-related mortality. 

18 Studies often report more than one estimated C-R function for the same location and 

19 health endpoint. Sometimes models including different sets of co-pollutants are estimated in a 

20 study; sometimes different lag structures are used. Sometimes different modeling approaches are 

21 used to fit weather and temporal variables in the model. Once a study has been selected, the next 

22 step is to select one or more C-R functions from among those reported in the study. 

23 Zanobetti and Schwartz (2009) divided the United States into six regions, based on the 

24 Köppen climate classification (Kottek 2006; Kottek et al. 2006)(http://koeppeneiger.vuwien.ac.at/).21 25 They estimated the coefficient of PM2.5 in single-pollutant log-linear 

26 models using Poisson regression for each of 112 cities, as well as in two-pollutant models with 

27 coarse PM. They estimated annual models (which assume that the relationship between 

28 mortality and PM2.5 is the same through the year), as well as four seasonal models per location. 

29 They then used a random effects meta-analysis to combine the city-specific results (Berkey et al. 

 20 The GAM S-Plus problem was discovered prior to the recent final PM risk assessment carried out as part of the 

PM NAAQS review completed in 2006. It is discussed in the 2004 PM Criteria Document (EPA, 2004), PM Staff 

Paper (EPA, 2005c), and PM Health Risk Assessment Technical Support Document (Abt Associates, 2005). 21 Zanobetti and Schwartz delineate regions as follows: “region 1: humid subtropical climates and maritime 

temperate climates (Cfa, Cfb), which includes FL, LA TX, GA, AL, MS, AR, OK, KS, MO, TN, SC, NC, VA, WV, 

KY; region 2: warm summer continental climates (Dfb), including ND, MN, WI, MI, PA, NY, CT, RI, MA, VT, 

NH, ME; region 3: hot summer continental climates (Dfa) with SD, NE, IA, IL, IN, OH; region 4: dry climates 

(BSk) (NM, AZ, NV); region 5: dry climates together with continental climate (Dfc, BSk) with MT, ID, WY, UT, 

CO; region 6: Mediterranean climates which includes CA, OR, WA (Csa, Csb)” (p. 10). 
February 2010 Draft – Do Not Quote or Cite 3-29

1 1998). Pooling of city-specific results was done at the national level as well as at the regional 

2 level, and separately for each season as well as for the annual functions. 

3 With respect to the multi-city study for short-term exposure mortality, at the request of 

4 EPA, the authors produced Empirical Bayes “shrunken” city-specific estimates, adjusted towards 

5 the appropriate regional mean, using the approach described in Le Tertre et al. (2005). This was 

done for the annual estimates as well as for each season-specific estimate.22 6 The annual cityspecific “shrunken” estimates were used in our core analysis.23 7 The seasonal estimates were 

8 used in a sensitivity analysis. City-specific estimates have the advantage of relying on city9 specific data; however, as noted above, such estimates can have large standard errors (and thus 

10 be unreliable); “shrinking” city-specific estimates towards the regional mean estimate is a more 

efficient use of the data.24 11 Such “shrinking” can be thought of as combining the advantages of a 

12 single-city study (in which the estimation of a city-specific coefficient is not influenced by data 

13 from other locations) with the advantages of a multi-city study (in which there is much greater 

14 statistical power to detect small effects). 

15 In Zanobetti and Schwartz (2009) all PM2.5 models used the same lag structure (i.e., an 

16 average of same-day and the previous day’s PM2.5). The study did, however, examine both 

17 single-pollutant and two-pollutant models (with coarse PM). We selected the single-pollutant 

18 models, in part to avoid collinearity problems, and in part to be consistent with most of the other 

19 studies used in the risk assessment, which were single-pollutant studies. 

20 Bell et al. (2008) estimated log-linear models relating short-term exposure to PM2.5 and 

21 hospital admissions for cardiovascular and respiratory illnesses among people 65 and older, 

22 using a 2-stage Bayesian hierarchical model, for each of 202 counties in the United States. They 

23 reported both annual and season-specific results, nationally and regionally (for four regions: 

24 Northeast, Southeast, Northwest, and Southwest), but not at the local (city-specific) level. All 

25 cardiovascular hospital admissions models were single-pollutant, 0-day lag models; for 

26 respiratory hospital admissions, both single-pollutant 0-day models and single-pollutant 2-day 

27 models were estimated. We used the regional, annual C-R functions in our core analysis 

(identifying the appropriate region for each of our 15 risk assessment locations).25 28 For 

 22 These city-specific “shrunken” estimates were provided to EPA (see Zanobetti, 2009) . 23 One reason we selected the annual functions over the season-specific functions for the core analysis is that, while 

we can sum the season-specific mortality estimates across the four seasons, we cannot do the same for the upper and 

lower bounds of 95% confidence intervals around those estimates. To produce correct confidence bounds around 

annual mortality estimates based on seasonal functions, we would need the covariance matrix of the season-specific 

estimates, separately for each location, which we do not have. 

24 The degree to which a city-specific estimate is “shrunken” towards the regional mean depends on the size of the 

standard error of the city-specific estimate relative to that of the regional mean estimate. The larger the city-specific 

estimate relative to the regional mean estimate, the less shrinkage toward the regional mean. 

25 The region into which each of the 202 counties in Bell et al. (2008) falls is given at: 

http://www.biostat.jhsph.edu/MCAPS/estimates-full.html. 
February 2010 Draft – Do Not Quote or Cite 3-30

1 respiratory hospital admissions (for the core analysis), we selected the 2-day lag models, based 

2 on evidence that for respiratory effects the strongest associations with PM exposure may be 

associated with longer lag periods (on the order of 2 days or more).26 3 We used the regional 

4 season-specific functions in a sensitivity analysis. 

5 We identified two studies that estimated C-R relationships between short-term exposure 

6 to PM2.5 and emergency department (ED) visits for cardiovascular and/or respiratory illnesses. 

7 (There were no multi-city studies for this category of health endpoint.) Tolbert et al. (2007) 

8 examined both cardiovascular and respiratory ED visits in Atlanta, GA, using single-pollutant 

9 log-linear models with a 3-day moving average (0-day, 1-day, and 2-day lags) of PM2.5. Ito et al. 

10 (2007) estimated the relationship between short-term exposure to PM2.5 and ED visits for asthma 

11 in New York City (Manhattan). They estimated two single-pollutant models, one for the whole 

12 year and one for the period from April through August; in addition, they estimated several two13 pollutant models for the period from April through August. We selected the single-pollutant 

14 model for the whole year for the core analysis, and we explored the impacts of using the annual 

15 versus the April-through-August model, as well as the single- versus multi-pollutant models in 

16 sensitivity analyses. 

17 For the purpose of conducting a sensitivity analysis to show the impact of different lag 

18 structures, different modeling approaches, and single- versus two-pollutant models on estimates 

19 of the risks of premature mortality and hospital admissions associated with short-term exposure 

20 to PM2.5, we selected Moolgavkar (2003). This study reported results for premature non21 accidental, cardiovascular, and respiratory mortality and for cardiovascular and respiratory 

22 hospital admissions associated with short-term exposures to PM2.5 in Los Angeles, using several 

23 different lag structures and several different approaches to modeling the effects of weather and 

24 temporal variables. 

25 In modeling premature mortality associated with long-term exposure to PM2.5 in our core 

26 analysis, we selected Krewski et al. (2009) as our primary study. This study is an extension of 

27 the ACS prospective cohort study (Pope et al., 2002), used in the previous PM risk assessment,. 

28 The Krewski et al., 2009 study (and the underlying ACS dataset) has a number of advantages 

29 which informed our selection of this study as the basis for C-R functions used in the core 

30 analysis, including: (a) extended air quality analysis incorporating data from 1989 to 2000 

31 (extending the period of observation to eighteen years: 1982-2000), which increases the power of 

32 the study and allows the study authors to examine the important issue of exposure time windows, 

 26 The ISA states that, “Generally, recent studies of respiratory HAs that evaluate multiple lags, have found effect 

sizes to be larger when using longer moving averages or distributed lag models. For example, when examining HAs 

for all respiratory diseases among older adults, the strongest associations where observed when using PM 

concentrations 2 days prior to the HA.” (EPA, 2009d, section 2.4.2.2). 
February 2010 Draft – Do Not Quote or Cite 3-31

1 (b) rigorous examination of a range of model forms and effect estimates, including consideration 

2 for such factors as spatial autocorrelation in specifying response functions, (c) coverage for a 

3 range of ecological variables (social, economic and demographic) which allows for consideration 

4 for whether these confound or modify the relationship between PM2.5 exposure and mortality, (d) 

5 inclusion of a related analysis (focusing on Los Angeles), which allowed for consideration of 

6 spatial gradients in PM2.5 and whether they effect response models (by addressing effect 

7 modification, for example) and (e) large overall dataset with over 1.2 million individuals and 156 

8 MSAs. To provide coverage for one of the other larger datasets used in prospective cohort 

9 analyses of long-term mortality (the six-cites dataset), we selected the Krewski et al. (2000) 

10 study to provide C-R functions that were used in the sensitivity analysis completed for this risk 

11 assessment. 

12 A number of other studies were considered as candidates for use in modeling long-term 

13 exposure-related mortality in this analysis. For purposes of transparency, we have included a 

14 brief summary here of our rationale for not selecting a number of the more high-profile studies 

15 for use in the core analysis. The Laden et al. (2006) study (which focused on the six-cities 

16 dataset) was not selected because it used visibility data to estimate ambient PM2.5 levels. The 

17 Goss et al. (2004) study (based on the cystic fibrosis data), while addressing an at-risk population 

18 of concern, was not selected because of a lack of baseline incidence data for this population 

19 which prevents quantitative modeling of mortality incidence. The Miller et al. (2007) study 

20 (focusing on the Women’s Health Initiative dataset) while providing coverage for a population of 

21 particular interest, was not used, again due to an absence of baseline incidence data (which is 

22 particularly important for this population which is typically healthier than the general 

23 population). And finally, the Eftim et al. (2008) study (focusing on the Medicare population) 

24 was not included because this study did not include representative confounder control for 

25 smoking, which introduces uncertainty into C-R functions obtained from the study. 

26 Krewski et al. (2009) (the study selected as the basis for C-R functions used in the core 

27 analysis) considered mortality from all causes, as well as cardiopulmonary mortality, mortality 

28 from ischemic heart disease, and lung cancer mortality. The study presents a variety of C-R 

29 functions, in an effort to show how the results vary with various changes to the method/model 

30 used. It was not readily apparent from review of the HEI report, that the authors of the study 

31 recommended any one of these as clearly superior to the others. Therefore, we corresponded 

32 with the authors of the Krewski et al. (2009) study to obtain additional clarification regarding 

33 specific aspects of the study and associated results as presented in the HEI report (Krewski et al., 

34 2009). In response to the our question of whether the study authors had a preference for a 

35 particular model (in the context of using that model and its hazard ratio(s) in risk assessment), 

36 the authors stated that they had “refrained from expressing a preference among the results for 
February 2010 Draft – Do Not Quote or Cite 3-32

1 their use in quantitative risk assessment,” preferring to “explore several plausible statistical 

2 models that we have fit to the available data.” However, the authors go on to state that “...if one 

3 had to choose a model for use in practical applications involved in air quality management, one 

4 could argue that a random effects model (which accounts for apparent spatial autocorrelation in 

5 the data) might be preferable. A model that included ecological covariates, which has the effect 

6 of reducing the residual variation in mortality, might also be of interest. If forced to pick a single 

7 model for risk assessment applications in air quality management, our random effects model with 

8 ecological covariates might be selected” (Krewski, 2009). 

9 In addition to these statements from the study authors regarding the model form to use, 

10 EPA staff also considered the results of an analysis presented in the study examining the 

11 importance of exposure time windows in deriving C-R functions. This analysis suggested that 

12 models developed using both exposure time windows considered in the analysis (1979-1983 and 

13 1999-2000) were equally effective at representing the relationship between PM2.5 exposure and 

14 long-term exposure-related mortality. Therefore, we concluded that C-R functions used in the 

15 core analysis should include functions fitted to both exposure time windows. However, the study 

16 does not provide random effects models with ecological covariates for both exposure time 

17 windows (this form of model is only provided with a fit to the latter exposure window). 

18 Therefore, for the core analysis, we decided to use the Cox proportional hazard model with 44 

19 individual and 7 ecological variables fitted to both exposure time windows (note, that if the 

20 Krewski et al. (2009) study had provided a random effects model with ecological covariates (for 

21 both PM monitoring periods – 1979-1983 and 1999-2000), then we would have used those 

22 models in our core analysis). 

 In specifying effect estimates for each set of models, the relative risks for a 10 μg/m3 23 

24 change in PM2.5 were back-calculated from Table 33 of Krewski et al. (2009). We selected 

25 several additional C-R functions from Krewski et al. (2009) to use in sensitivity analyses carried 

26 out in two risk assessment locations (Los Angeles and Philadelphia), including the random 

27 effects form (section 3.5.4), as described below. In addition, as mentioned earlier, we used C-R 

28 functions obtained from Krewski et al. (2000) [reanalysis of the Six Cities Study] in the 

29 sensitivity analysis. 

30 3.3.4 Summary of Selected Health Endpoints, Urban Areas, Studies, and C-R Functions 

31 A summary of the selected health endpoints, urban areas, and epidemiological studies used 

32 in the risk assessment is given below in Tables 3-5 and 3-6 for short-term and long-term 

33 exposure studies, respectively. A more detailed overview of the locations, health endpoints, 

34 studies, and C-R functions included in the core analysis is given in Table 3-7. An overview of 
February 2010 Draft – Do Not Quote or Cite 3-33

1 the locations, health endpoints, studies, and C-R functions included in sensitivity analyses is 

2 given in Table 3-8. 
February 2010 Draft – Do No

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Table 3-5. Locations, Health Endpoints, and Short-Term Exposure Studies Included in the PM

Assessment* 

Premature Mortality Hospital Admissions 

Urban Area Non-Accidental Cardiovascular Respiratory Cardiovascular Respiratory CardiovaAtlanta, GA Tolbert 

(2007

Baltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TX

Zanobetti and 

Schwartz (2009)

Zanobetti and 

Schwartz (2009)

Zanobetti and 

Schwartz (2009)

Bell et al. (2008) Bell et al. (2008) 

Los Angeles, CA

Moolgavkar 

(2003)

Moolgavkar 

(2003)

Moolgavkar 

(2003)

New York, NY Philadelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTSt. Louis, MOTacoma, WA

Zanobetti and 

Schwartz (2009) 

Zanobetti and 

Schwartz (2009) 

Zanobetti and 

Schwartz (2009) 

Bell et al. (2008) Bell et al. (2008) 

*Studies in italics are used only in sensitivity analyses. 
February 2010 Draft – Do No

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Table 3-6. Locations, Health Endpoints, and Long-Term Exposure Studies Included in the PM

Assessment*

Premature Mortality Urban Area

All-Cause Cardiopulmonary Ischemic Heart Disease 

Atlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXNew York, NY Phoenix, AZ Pittsburgh, PA Salt Lake City, UT St. Louis, MO Tacoma, WA 

Krewski et al. (2009) [extension 

of the ACS study] 

Krewski et al. (2009) [extension 

of the ACS study] 

Kre

Los Angeles, CA Philadelphia, PA

Krewski et al. (2009) [extension 

of the ACS study] 

Krewski et al. (2000) [reanalysis 

of the Six Cities Study]

Krewski et al. (2009) [extension 

of the ACS study] 

Krewski et al. (2000) [reanalysis 

of the Six Cities Study]

Krewski et al. (2009) [extension 

of the ACS study] 

Kre

Kre

*Studies in italics are used only in sensitivity analyses. 
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Table 3-7. Summary of Locations, Health Endpoints, Studies and Concentration-Response Fu

the Core Analysis.*

Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Zanobetti and Schwartz (2009)1 Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) 1 Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) 1 Short-term exposure respiratory mortality Krewski et al. (2009) 2 Long-term exposure all-cause mortality Krewski et al. (2009) 2 Long-term exposure cardiopulmonary mortality Krewski et al. (2009) 2 Long-term exposure ischemic heart disease mortalit

Cobb, De Kalb, Fulton, Gwinnett 

Krewski et al. (2009) 2 Long-term exposure lung cancer mortality 

Bell et al. (2008)3 Cobb, DeKalb, Fulton, Short-term exposure HA (unscheduled), cardiovasc

Bell et al. (2008)3 Short-term exposure HA (unscheduled), respiratory 

Tolbert et al. (2007) Short-term exposure Emergency room (ED) visits, 

cardiovascular 

Atlanta 

Barrow, Bartow, Carroll, Cherokee, Clayton, Cobb, Coweta, DeKalb, Douglas, Fayette, Forsyth, Fulton, Gwinett, Henry, Newton, Paulding, Pickens, Rockdale, Spalding, Walton 

Tolbert et al. (2007) Short-term exposure Emergency room (ED) visits, 

respiratory 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality 

Baltimore Baltimore city, 

Baltimore county 

Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality 
February 2010 Draft – Do No

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Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalitKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovascBell et al. (2008) Short-term exposure HA (unscheduled), respiratory Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalit

Blount, Jefferson, Shelby, St. Clair, Walker 

Krewski et al. (2009) Long-term exposure lung cancer mortality 

Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Birmingham 

Jefferson 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality 

Dallas Dallas 

Krewski et al. (2009) Long-term exposure all-cause mortality 
February 2010 Draft – Do No

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Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalitKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovascBell et al. (2008) Short-term exposure HA (unscheduled), respiratory Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalitKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Detroit Wayne 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalit

Fresno Fresno 

Krewski et al. (2009) Long-term exposure lung cancer mortality 
February 2010 Draft – Do No

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Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovascBell et al. (2008) Short-term exposure HA (unscheduled), respiratory Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalitKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Houston Harris 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalitKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Los Angeles Los Angeles 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 
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Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalitKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Kings, New York City (Manhattan), Queens, Richmond, Bronx 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

New York 

New York City (Manhattan) 

Ito et al. (2007) Short-term exposure Emergency room (ED) visits, 

asthma 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalitKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Philadelphia Philadelphia 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

Phoenix Maricopa Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality 
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Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalitKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovascBell et al. (2008) Short-term exposure HA (unscheduled), respiratory Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalitKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Pittsburgh Allegheny 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality 

Salt Lake City Salt Lake 

Krewski et al. (2009) Long-term exposure ischemic heart disease mortalit
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Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Krewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovascBell et al. (2008) Short-term exposure HA (unscheduled), respiratory Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalit

Jeffferson, Madison (IL), St. Louis, St. Louis city, St. Clair (IL)

Krewski et al. (2009) Long-term exposure lung cancer mortality 

Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

St. Louis 

Madison (IL), St. Louis, St. Louis city, St. Clair (IL) Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortalitKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Tacoma Pierce 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

*All C-R functions in the core analysis are single-pollutant, log-linear models; all are for a full year. The exposure metric for all shor

functions is the 24-hour average; the exposure metric for all long-term exposure C-R functions is the annual average. 1 This is a multi-city study; city-specific estimates “shrunken” towards the mean across all cities in a region were supplied to EPA (Za

2 Two C-R functions were used for the core analysis – one corresponding to the earlier exposure period, from 1979 – 1983, and the ot

later exposure period, from 1999 – 2000. Both C-R functions were based on follow-up of the cohort through 2000. Both used the st
February 2010 Draft – Do No

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hazards model, with 44 individual and 7 ecologic covariates. The relative risks for a 10 μg/m3 change in PM2.5 from which the PM2.5 

calculated were taken from Table 33 of Krewski et al. (2009). 3 This study estimated four regional C-R functions – for the Northeast, Southeast, Northwest, and Southwest – for each health endpoi

assessment location, we used the regional C-R function for the region containing the risk assessment location. The designation of cou

four regions can be found at http://www.biostat.jhsph.edu/MCAPS/estimates-full.html . 
February 2010 Draft – Do No

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Table 3-8. Summary of Locations, Health Endpoints, Studies and Concentration-Response Fu

 Sensitivity Analyses. 

Sensitivity Analysis Study/C-R Function Health Endpoint** 

Single-Factor Sensitivity Analyses: Impact of using different model choices – fixed effects log-linear vs. random effects log-linear vs. random effects log-log C-R function* 

random effects log-linear: Krewski et al. (2009) [Table 9, "Autocorrelation at MSA and ZCA levels" group - "MSA & Diff" row] random effects log-log: Krewski et al. (2009) [Table 11, "MSA and DIFF" rows] 

All-cause, cardiopulmonary, ischemic heart disease, and lung cancer mortaliassociated with long-term exposure 

Impact of using copollutant models in modeling longterm exposure-related mortality Krewski et al., 2000 (reanalysis of ACS) – provides 2-pollutant 

models combining PM2.5 with 

CO, NO2 

, O3 or SO2. 

All-cause mortality associated with long-term exposure 

Impact of estimating risks down to PRB rather than down to LML 

Krewski et al. (2009) – C-R functions for each of two exposure periods 

Long-term exposure all-cause mortali

Impact of C-R function from alternative long-term exposure study 

Krewski et al. (2000) [reanalysis of the Harvard Six Cities study] 

All-cause, cardiovascular, respiratorylung cancer mortality associated with long-term exposure 

Impact of using alternative hybrid rollback approach (note, that as discussed in section 3.2.3, in addition to the hybrid rollback approach, we have also included a peak-shaving rollback approach as an alternative to the proportional rollback approach).27

Krewski et al. (2009) All-cause mortality associated with 

long-term exposure 

Impact of using season-specific C-R functions (vs. an annual C-R function) 

Zanobetti and Schwartz (2009) – seasonal functions vs. annual function 

Non-accidental mortality, cardiovascular mortality, respiratory mortality associated with short-term 

27 However, as noted in section 3.2.3 and in section 3.5.4, quantitative risk estimates were not generated using the peak-shav

instead, composite monitor values (acting as surrogates for long-term exposure-related risk) were used as the basis for the sensitivity 

peak-shaving rollback approach. 
February 2010 Draft – Do No

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Sensitivity Analysis Study/C-R Function Health Endpoint** 

exposure 

Impact of using season-specific C-R functions (vs. an annual C-R function) 

Bell et al. (2008) – seasonal functions vs. annual function 

HA (unscheduled), cardiovascular and respiratory, associated with short-term 

exposure

Impact of using an annual C-R function (applied to the whole year) vs. a seasonal function for April through August (applied only to that period) (using a single pollutant model). 

Ito et al. (2007) Asthma ED visits 

Impact of model selection (e.g., log-linear GAM with 30 df; log-linear GAM with 100 df; and log-linear GLM with 100 df) 

Moolgavkar (2003) Non-accidental and cardiovascular 

mortality; and cardiovascular and COPD+ HA associated with short-terexposure 

Impact of lag structure (0-day, 1-day, 2-day) Moolgavkar (2003) Non-accidental and cardiovascular an

COPD+ HA associated with short-terexposure 

Impact of single- vs. multi-pollutant models (PM2.5

with CO) 

Moolgavkar (2003) Non-accidental and cardiovascular 

mortality; and cardiovascular and COPD+ HA associated with short-terexposure 

Impact of using alternative hybrid rollback approach Zanobetti and Schwartz (2009) Non-accidental mortality associated 

with short-term exposure 

Impact of lag structure (0-day, 1-day, 2-day) Bell et al., 2008 Cardiovascular and respiratory hospit

admissions associated with short-term exposure 

Multi-Factor Sensitivity Analyses: Impact of using a fixed effects log-linear vs. a random effects log-log model, estimating incidence down to the lowest measured level (LML) in the study vs. down to PRB, and using a proportional vs. hybrid rollback to estimate incidence associated with long-term exposure to PM2.5 concentrations that just meet the current 

standards 

 All-cause and ischemic heart disease 

mortality associated with long-term exposure 

Impact of using season-specific vs. all-year C-R functions and proportional vs. hybrid rollbacks to estimate incidence associated with short-term exposure to PM2.5 concentrations that just meet the current 

Zanobetti and Schwartz (2009) Non-accidental mortality associated 

with short-term exposure 
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Sensitivity Analysis Study/C-R Function Health Endpoint** 

standards *This “single-factor” sensitivity analysis is actually two factors – first the change from a fixed effects log-linear model to a random ef

and then the change from a random effects log-linear model to a random effects log-log model. These were combined into a single se

because Krewski et al. (2009) did not present the results of a fixed effects log-log model (to compare to the core analysis fixed effects 

**”HA” = hospital admissions, “ED” = emergency department visits, “COPD+” = chronic obstructive pulmona
February 2010 Draft – Do Not Quote or Cite 3-47

1 3.4 BASELINE HEALTH EFFECTS INCIDENCE DATA 

2 As noted in section 3.2.1 above, the form of C-R function most commonly used in 

3 epidemiological studies on PM, shown in equation (1), is log-linear. To estimate the change in 

4 incidence of a health endpoint associated with a given change in PM2.5 concentrations using this 

5 form of C-R function requires the baseline incidence (often calculated as the baseline incidence 

6 rate times the population) of the health endpoint, that is, the number of cases per unit time (e.g., 

7 per year) in the location before a change in PM2.5 air quality (denoted y0 in equations 3 and 4). 

8 Incidence rates express the occurrence of a disease or event (e.g., asthma episode, death, 

9 hospital admission) in a specific period of time, usually per year. Rates are expressed either as a 

10 value per population group (e.g., the number of cases in Philadelphia County) or a value per 

11 number of people (e.g., the number of cases per 10,000 residents in Philadelphia County), and 

12 may be age- and sex-specific. Incidence rates vary among geographic areas due to differences in 

13 population characteristics (e.g., age distribution) and factors promoting illness (e.g., smoking, air 

14 pollution levels). 

15 3.4.1 Data Sources 

16 3.4.1.1 Mortality 

17 We obtained individual-level mortality data for 2006 for the whole United States from 

18 the Centers for Disease Control (CDC), National Center for Health Statistics (NCHS). The data 

19 are compressed into a CD-ROM, which contains death information for each decedent, including 

20 residence county FIPS, age at death, month of death, and underlying causes (ICD-10 codes). 

21 The detailed mortality data allow us to generate cause-specific death counts at the county level 

22 for selected age groups. Below we describe how we generated the county-level death counts. 

23 3.4.1.2 Hospital Admission and Emergency Department Visits 

24 For hospital admissions (HA) and emergency department (ED) visits, there are multiple 

25 data sources: 

26  Healthcare Cost and Utilization Project (HCUP) Central Distributor. HCUP is a 

27 family of health care databases developed through a Federal-State-Industry partnership 

28 and sponsored by the Agency for Healthcare Research and Quality (AHRQ). The HCUP 

29 databases are based on the data collection efforts of data organizations in participating 

30 states. We used two HCUP databases: the State Inpatient Database (SID) and the State 

31 Emergency Department Database (SEDD) respectively. SID/SEDD include detailed 

32 HA/ED information for each discharge, including patient county FIPS, age, admission 

33 type (e.g., emergent, urgent), admission/discharge season, and principle diagnosis (ICD-9 

34 codes). The HCUP databases can be purchased from the HCUP Central Distributor, 

35 although not all participant states release the data to the Central Distributor. 
February 2010 Draft – Do Not Quote or Cite 3-48

1  HCUP State Partners. For those HCUP participating states that don’t release their data 

2 to the Central Distributor, we contacted the HCUP state partners to obtain the HA and/or 

3 ED data. 

4  Communication with the author(s) of selected epidemiological studies. The ED data 

5 for Atlanta in 2004 were sent to EPA by one of the authors of Tolbert et al. (2007). 

6 Table 3-9 shows the states for which we obtained data from the HCUP Central 

7 Distributor and the HCUP State Partners. The data are at the discharge level if not otherwise 

8 noted, and the data year is 2007 for all the states in the table. The column “PM RA Location” 

9 indicates the selected risk assessment location(s) where the incidence rate is applied. 

10 The necessary baseline incidence data were not available for Atlanta, Birmingham, 

11 Philadelphia, Pittsburgh and St. Louis. Therefore, for each of these five risk assessment 

12 locations EPA instead used the baseline incidence rate for a designated surrogate location. 

13 Surrogate locations were chosen if they were deemed to be sufficiently similar to the urban area 

14 whose baseline incidence data were not available. Surrogate locations are noted in Table 3-9. 

15 

16 Table 3-9. Sources of Hospital Admissions (HA) and Emergency Department (ED) 

17 Visit Data. 

States 

HCUP 

Central 

Distributor 

HCUP State 

Partner 

PM RA 

Location Notes 

Arizona HA data -- Phoenix 

California NA* HA data Fresno, Los 

Angeles 

 Due to privacy concerns, CA state 

agency provided county level data. 

Illinois NA HA data St. Louis 

1. Due to privacy concerns, IL state 

agency provided county level data. 

2. Two IL counties (Madison and St. 

Clair) serve as the surrogate for the 

St. Louis metropolitan region. 

Maryland HA data -- Baltimore, 

Philadelphia 

Baltimore serves as the surrogate for 

Philadelphia. 

Michigan HA data -- Detroit 

New York NA HA and ED data New York, 

Pittsburgh 

 Buffalo, NY serves as the surrogate 

for Pittsburgh. 

North Carolina HA data -- Atlanta and 

Birmingham 

Charlotte, NC serves as the surrogate 

for both Atlanta and Birmingham. 

Texas NA HA data Dallas, 

Houston 

Utah HA data -- Salt Lake 

City 

Washington HA data -- Tacoma 
February 2010 Draft – Do Not Quote or Cite 3-49

1 *NA denotes “not available, or not available with all variables required for our analysis. If data were not available 

2 from the HCUP Central Distributor, we contacted the HCUP State Partner. 

3 

4 3.4.1.3 Populations 

5 To calculate baseline incidence rate, in addition to the health baseline incidence data we 

6 also need the corresponding population. We obtained population data from the U.S. Census 

7 Bureau (http://www.census.gov/popest/counties/asrh/). These data, released on May 14, 2009, 

8 are the population estimates of the resident populations by selected age groups and sex for 

9 counties in each U.S. state from 2000 to 2008. We used 2007 populations for calculating most 

10 incidence rates except for the ED visit rate in Atlanta. Because the ED visit data obtained from 

11 the authors of Tolbert et al. (2007) are for 2004, we used 2004 population estimates for the 20-

12 county Metropolitan area used in the Tolbert et al. study for the Atlanta area to calculate the ED 

13 incidence rates to be applied when using that study in the risk assessment; we then applied the 

14 2004 rates to the 2007 population, assuming the ED incidence rates in Atlanta did not change 

15 significantly from 2004 to 2007. The sizes of the populations in the assessment locations that are 

16 relevant are shown below in Table 3-10. 
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Table 3-10. Relevant Population Sizes for PM Risk Assessment Locations. 

Population (Year 2006 and 2007)* 

City Counties All Ages Ages $30 

2006 2007 2006 2007 

Atlanta, GA - 1 Cobb, De Kalb, Fulton, Gwinnett 3,126,000 3,198,000 1,817,000 1,865,000 23Atlanta, GA - 2 Cobb, De Kalb, Fulton 2,376,000, 2,421,000 1,400,000 1,433,000 19Atlanta, GA - 3 20-County MSA** 4,975,000 5,123,000 2,831,000 2,918,000 39Baltimore, MD Baltimore city, Baltimore county 1,429,000 1,426,000 849,000 848,000 19Birmingham, AL - 1 Blount, Jefferson, Shelby, St. Clair, 

Walker 1,037,000 1,044,000 619,000 625,000 13

Birmingham, AL - 2 Jefferson 660,000 659,000 397,000 397,000 8Dallas, TX Dallas 2,338,000 2,367,000 1,285,000 1,308,000 19Detroit, MI Wayne 2,012,000 1,985,000 1,176,000 1,168,000 23Fresno, CA Fresno 886,000 899,000 444,000 452,000 8Houston, TX Harris 3,876,000 3,936,000 2,097,000 2,139,000 29Los Angeles, CA Los Angeles 9,881,000 9,879,000 5,544,000 5,579,000 1,0New York, NY - 1 Kings, New York City (Manhattan), 

Queens, Richmond, Bronx 8,251,000 8,275,000 4,940,000 4,975,000 1,0

New York, NY - 2 New York city (Manhattan) 1,613,000 1,621,000 1,061,000 1,074,000 20Philadelphia, PA Philadelphia 833,000 1,450,000 833,000 833,000 18Phoenix, AZ Maricopa 3,779,000 3,880,000 2,103,000 2,167,000 41Pittsburgh, PA Allegheny 1,225,000 1,219,000 790,000 786,000 20Salt Lake City, UT Salt Lake 991,000 1,010,000 504,000 517,000 8St. Louis, MO - 1 Jefferson, Madison (IL), St. Louis, St. 

Louis city, St. Clair (IL) 2,093,000 2,091,000 1,259,000 1,261,000 27
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Population (Year 2006 and 2007)* 

City Counties All Ages Ages $30 

2006 2007 2006 2007 

St. Louis, MO - 2 Madison (IL), St. Louis, St. Louis city, 

St. Clair (IL) 1,879,000 1,875,000 1,134,000 1,134,000 25

Tacoma, WA Pierce 764,000 773,000 437,000 444,000 7

* Not all populations listed in the table were used for calculating the incidence rates. As noted above, the population year needs to ma

data and the population age group needs to match what is used in the epidemiological studies. In addition, 2004 population (all ages) is Atlanta-3, which is 4,663,946. Populations in this table are rounded to the nearest 1,000. 

** The 20 counties are Barrow, Bartow, Carroll, Cherokee, Clayton, Cobb, Coweta, DeKalb, Douglas, Fayette, Forsyth, Fulton, Gwinett, Henry, NewRockdale, Spalding, and Walton.
February 2010 Draft – Do Not Quote or Cite 3-52

1 3.4.2 Calculation of Baseline Incidence Rates 

2 To calculate a baseline incidence rate to be used with a C-R function from a given study, 

3 we matched the counties, age groupings, and ICD codes used in that study. For example, Bell et 

4 al. (2008) designated Dallas, TX as Dallas County and estimated a C-R function for ICD-9 codes 

5 490–492, 464–466, and 480–487 (respiratory HA) among ages 65 and up; we therefore selected 

6 only those HA records that had corresponding ICD codes for ages 65 and up in Dallas County 

7 and also selected the population for the same age group in the same county. The incidence rate 

8 is simply the ratio of the selected HA count to the population. The same procedure was used to 

calculate baseline incidence rates for all of the risk assessment locations.28 9 

10 If a C-R function was estimated for a specific season, we selected only those HA records 

11 within that season. The season definitions are: winter (December, January, and February), spring 

12 (March, April, and May), summer (June, July, and August) and fall (September, October, and 

13 November). Note that the HA data for some states didn’t include information about admission 

14 season but only discharge season or discharge quarter. The admission season was then 

approximated using discharge season or discharge quarter.29 15 

16 Some studies (e.g., Bell et al., 2008) look at the unscheduled HAs only, so we excluded 

17 scheduled admissions from the analyses to match the study. A HA is unscheduled if the 

18 admission type is emergency or urgent. 

19 The baseline mortality rates are given in Table 3-11. The baseline HA and ED visit rates 

20 are given in Table 3-12. 

 28 For Atlanta, Birmingham, Philadelphia, Pittsburgh and St. Louis, the HA data are not available. We calculated the 

hospital admission rates for the surrogate cities. These cities are listed in Table 3-7. 

29 Based on communication with the HCUP state partner in Texas, patients are normally admitted and discharged in 

the same season. 
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Table 3-11. Baseline Mortality Rates (Deaths per 100,000 Relevant Population per Year) for 2006 for PM R

 Locations.* 

Type of Mortality (ICD-10 or ICD-9 Codes) 

City Age Group All-Cause 

Non-accidental 

(A00-R99) 

Cardiovascular 

(I01-I59) 

Respiratory 

(J00-J99) 

Cardiopulmonary 

(401-440, 460-

519) 

Ischemic 

Heart 

Disease 

(410-414) 

Atlanta, GA - 1 All ages --- 480 120 41 --- --- Atlanta, GA - 1 

≥ 30 860 --- --- --- 330 89 

Atlanta, GA - 2 --- --- --- --- --- --- --- Atlanta, GA - 3 --- --- --- --- --- --- --- Baltimore, MD All ages --- 950 270 85 --- --- Baltimore, MD 

≥ 30 1,700 --- --- --- 690 300 

Birmingham, AL - 1 All ages --- 920 260 85 --- --- Birmingham, AL - 1 

≥ 30 1,600 --- --- --- 680 190 

Birmingham, AL - 2 --- --- --- --- --- --- --- Dallas, TX All ages --- 540 150 48 --- --- Dallas, TX 

≥ 30 1,020 --- --- --- 420 170 

Detroit, MI All ages --- 850 300 67 --- --- Detroit, MI 

≥ 30 1,500 --- --- --- 700 360 

Fresno, CA All ages --- 620 190 67 --- --- Fresno, CA 

≥ 30 1,300 --- --- --- 590 260 

Houston, TX All ages --- 480 130 37 --- --- Houston, TX 

≥ 30 920 --- --- --- 370 150 

Los Angeles, CA All ages --- 560 190 57 --- --- Los Angeles, CA 

≥ 30 1,030 --- --- --- 510 250 

New York, NY - 1 All ages --- 630 270 52 --- --- 
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Type of Mortality (ICD-10 or ICD-9 Codes) 

City Age Group All-Cause 

Non-accidental 

(A00-R99) 

Cardiovascular 

(I01-I59) 

Respiratory 

(J00-J99) 

Cardiopulmonary 

(401-440, 460-

519) 

Ischemic 

Heart 

Disease 

(410-414) 

New York, NY - 1 

≥ 30 1,0800 --- --- --- 580 380 

New York, NY - 2 --- --- --- --- --- --- --- Philadelphia, PA All ages --- 970 280 83 --- --- Philadelphia, PA 

≥ 30 1,700 --- --- --- 720 300 

Phoenix, AZ All ages --- 600 160 67 --- --- Phoenix, AZ 

≥ 30 1,100 --- --- --- 470 220 

Pittsburgh, PA All ages --- 1,090 330 96 --- --- Pittsburgh, PA 

≥ 30 1,800 --- --- --- 770 350 

Salt Lake City, UT All ages --- 480 110 45 --- --- Salt Lake City, UT 

≥ 30 980 --- --- --- 350 101 

St. Louis, MO - 1 All ages --- 870 270 83 --- --- St. Louis, MO - 1 

≥ 30 1,500 --- --- --- 680 320 

St. Louis, MO - 2 --- --- --- --- --- --- --- Tacoma, WA All ages --- 660 190 66 --- --- Tacoma, WA 

≥ 30 1,200 --- --- --- 510 240 

National All ages 810 750 220 76 340 140 National 

≥ 30 1,300 1,300 370 130 580 240 

* Figures in this table are rounded to a two-integer level of precision. 
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Table 3-12. Baseline Hospital Admission (HA) and Emergency Department (ED) Rates (Admissions

Relevant Population per Year) for 2007 for PM Risk Assessment Locations.* 

* Figures in this table are rounded to a two-integer level of precision. ** These are 2004 incidence rates because Tolbert et al. (2007) provided 2004 ED visit data in a 20-county delineation of Atlanta. Howe

applied to the appropriate year population in the risk assessment.

Health Endpoints (ICD-9 Codes) 

City Age Group 

HA, cardiovascular (390-

429) 

HA (unscheduled), 

cardiovascular(426

–429, 430–438, 

410–414, 440–449) 

HA, COPD 

(490-496) 

HA (unscheduled), 

respiratory (490–492, 

464–466, 480–487) 

ED visits, 

cardiovascular (410–

414, 427, 428, 433–

437, 440, 443–445, 

451–453) 

ED visits

(460–465, 

466.19, 477

493, 496, 7

Atlanta, GA - 1 --- --- --- --- --- --- Atlanta, GA - 2 

≥ 65 --- 5,700 --- 2,020 --- 

Atlanta, GA - 3 All ages --- --- --- --- 690** 26Baltimore, MD

≥ 65 --- 8,600 --- 2,600 --- 

Birmingham, AL - 1 --- --- --- --- --- --- Birmingham, AL - 2 

≥ 65 --- 5,700 --- 2,020 --- 

Dallas, TX

≥ 65 --- 5,000 --- 2,000 --- 

Detroit, MI

≥ 65 --- 8,800 --- 3,000 --- 

Fresno, CA

≥ 65 --- 5,600 --- 2,100 --- 

Houston, TX

≥ 65 --- 5,900 --- 2,200 --- 

Los Angeles, CA All ages --- --- 223 --- --- Los Angeles, CA

≥ 65 5,500 5,500 --- 2,000 --- 

New York, NY - 1

≥ 65 --- 6,400 --- 2,030 --- 

New York, NY - 2 All ages --- --- --- --- --- Philadelphia, PA

≥ 65 --- 8,600 --- 2,600 --- 

Phoenix, AZ

≥ 65 --- 5,020 --- 1,600 --- 

Pittsburgh, PA

≥ 65 --- 6,100 --- 1,900 --- 

Salt Lake City, UT

≥ 65 --- 3,030 --- 1,200 --- 

St. Louis, MO - 1 --- --- --- --- --- --- St. Louis, MO - 2 

≥ 65 --- 5,600 --- 2,600 --- 

Tacoma, WA 

≥ 65 --- 4,500 --- 1,600 --- 
February 2010 Draft – Do Not Quote or Cite 3-56

1 3.5 ADDRESSING UNCERTAINTY AND VARIABILITY 

2 3.5.1 Overview 

3 An important component of a population health risk assessment is the characterization of 

4 both uncertainty and variability. Variability refers to the heterogeneity of a variable of interest 

5 within a population or across different populations. For example, populations in different 

6 regions of the country may have different behavior and activity patterns (e.g., air conditioning 

7 use, time spent indoors) that affect their exposure to ambient PM and thus the population health 

8 response. The composition of populations in different regions of the country may vary in ways 

9 that can affect the population response to exposure to PM – e.g., two populations exposed to the 

10 same levels of PM might respond differently if one population is older than the other. In 

11 addition, the composition of the PM to which different populations are exposed may differ, with 

12 different levels of toxicity and thus different population responses. Variability is inherent and 

13 cannot be reduced through further research. Refinements in the design of a population risk 

14 assessment are often focused on more completely characterizing variability in key factors 

15 affecting population risk – e.g., factors affecting population exposure or response – in order to 

16 produce risk estimates whose distribution adequately characterizes the distribution in the 

17 underlying population(s). 

18 Uncertainty refers to the lack of knowledge regarding the actual values of inputs to an 

19 analysis. Models are typically used in analyses, and there is uncertainty about the true values of 

20 the parameters of the model (parameter uncertainty) – e.g., the value of the coefficient for PM2.5

21 in a C-R function. There is also uncertainty about the extent to which the model is an accurate 

22 representation of the underlying physical systems or relationships being modeled (model 

23 uncertainty) – e.g., the shapes of C-R functions. In addition, there may be some uncertainty 

24 surrounding other inputs to an analysis due to possible measurement error—e.g., the values of 

25 daily PM2.5 concentrations in a risk assessment location, or the value of the baseline incidence 

26 rate for a health effect in a population. In any risk assessment, uncertainty is, ideally, reduced to 

27 the maximum extent possible through improved measurement of key variables and ongoing 

28 model refinement. However, significant uncertainty often remains, and emphasis is then placed 

29 on characterizing the nature of that uncertainty and its impact on risk estimates. The 

30 characterization of uncertainty can be both qualitative and, if a sufficient knowledgebase is 

31 available, quantitative. 

32 The selection of urban study areas for the PM2.5 risk assessment was designed to cover 

33 the range of PM2.5-related risk experienced by the U.S. population and, in general, to adequately 

34 reflect the inherent variability in those factors affecting the public health impact of PM2.5

35 exposure. Sources of variability reflected in the risk assessment design are discussed in section 
February 2010 Draft – Do Not Quote or Cite 3-57

1 3.5.2, along with a discussion of those sources of variability which are not fully reflected in the 

2 risk assessment and consequently introduce uncertainty into the analysis. 

3 The characterization of uncertainty associated with risk assessment is often addressed in 

4 the regulatory context using a tiered approach in which progressively more sophisticated 

5 methods are used to evaluate and characterize sources of uncertainty depending on the overall 

6 complexity of the risk assessment (WHO, 2008). Guidance documents developed by EPA for 

7 assessing air toxics-related risk and Superfund Site risks (USEPA, 2004b and 2001, respectively) 

8 as well as recent guidance from the World Health Organization (WHO, 2008) specify multi9 tiered approaches for addressing uncertainty. 

10 The WHO guidance presents a four-tiered approach, where the decision to proceed to the 

11 next tier is based on the outcome of the previous tier’s assessment. The four tiers described in the 

12 WHO guidance include: 

13  Tier 0 – recommended for routine screening assessments, uses default uncertainty factors 

14 (rather than developing site-specific uncertainty characterizations); 

15  Tier 1 – the lowest level of site-specific uncertainty characterization, involves qualitative 

16 characterization of sources of uncertainty (e.g., a qualitative assessment of the general 

17 magnitude and direction of the effect on risk results); 

18  Tier 2 – site-specific deterministic quantitative analysis involving sensitivity analysis, 

19 interval-based assessment, and possibly probability bound (high- and low-end) 

20 assessment; and 

21  Tier 3 – uses probabilistic methods to characterize the effects on risk estimates of sources 

22 of uncertainty, individually and combined. 

23 With this four-tiered approach, the WHO framework provides a means for systematically 

24 linking the characterization of uncertainty to the sophistication of the underlying risk assessment. 

25 Ultimately, the decision as to which tier of uncertainty characterization to include in a risk 

26 assessment will depend both on the overall sophistication of the risk assessment and the 

27 availability of information for characterizing the various sources of uncertainty. EPA staff has 

28 used the WHO guidance as a framework for developing the approach used for characterizing 

29 uncertainty in this risk assessment. 

30 The overall analysis in the PM NAAQS risk assessment is relatively complex, thereby 

31 warranting consideration of a full probabilistic (WHO Tier 3) uncertainty analysis. However, 

32 limitations in available information prevent this level of analysis from being completed at this 

33 time. In particular, the incorporation of uncertainty related to key elements of C-R functions 

34 (e.g., competing lag structures, alternative functional forms, etc.) into a full probabilistic WHO 

35 Tier 3 analysis would require that probabilities be assigned to each competing specification of a 

36 given model element (with each probability reflecting a subjective assessment of the probability 
February 2010 Draft – Do Not Quote or Cite 3-58

1 that the given specification is the “correct” description of reality). However, for many model 

2 elements there is insufficient information on which to base these probabilities. One approach that 

3 has been taken in such cases is expert elicitation; however, this approach is resource- and time4 intensive and consequently, it was not feasible to use this technique in the current PM NAAQS 

review to support a WHO Tier 3 analysis.30 5 

6 For most elements of this risk assessment, rather than conducting a full probabilistic 

7 uncertainty analysis, we have included qualitative discussions of the potential impact of 

8 uncertainty on risk results (WHO Tier1) and/or completed sensitivity analyses assessing the 

9 potential impact of sources of uncertainty on risk results (WHO Tier 2). Note, however, that in 

10 conducting sensitivity analyses, we have used both single- and multi-factor approaches (to look 

11 at the individual and combined impacts of sources of uncertainty on risk estimates). Also, as 

12 discussed below in section 3.5.4, in conducting sensitivity analyses, we used only those 

13 alternative specifications for input parameters or modeling approaches that were deemed to have 

14 scientific support in the literature (and so represent alternative reasonable input parameter values 

15 or modeling options). This means that the alternative risk results generated in the sensitivity 

16 analyses represent reasonable risk estimates that can be used to provide a context, with regard to 

17 uncertainty, within which to assess the set of core (base case) risk results (see section 4.5.3). 

18 The sensitivity analysis also includes coverage for potential variability in the pattern of 

19 reductions in ambient PM2.5 concentrations associated with simulations of just meeting the 

20 current and alternative suites of standards. Specifically, as discussed above in section 3.2.3, we 

21 have included three alternative rollback methods (proportional, hybrid and peak shaving) to 

22 provide coverage for variability in this potentially important factor influencing risk estimates. 

23 In addition to the qualitative and quantitative treatment of uncertainty and variability 

24 which are described here, we have also completed two additional analyses intended to place the 

25 risk results generated for the 15 urban study areas in a broader national context. The first is a 

26 representativeness analysis (described in section 4.4) which evaluates the set of urban study areas 

27 against national-distributions of key PM risk-related attributes (with the goal of determining the 

28 degree to which the study areas are representative of national trends in these parameters). The 

29 second is a national-scale assessment of long-term mortality related to PM2.5 exposures 

30 (discussed in chapter 5). In addition to providing an estimate of the national impact of PM2.5 on 

31 long-term mortality, this analysis also evaluates whether the set of 15 urban study areas generally 

32 represents the broader distribution of risk across the U.S., or a more focused portion of the 

33 national risk distribution (e.g., the higher-end). 

 30 Note, that while a full probabilistic uncertainty analysis was not completed for this risk assessment, we were able 

to use confidence intervals associated with effects estimates (obtained from epidemiological studies) to incorporate 

statistical uncertainty associated with sample size considerations in the presentation of risk estimates. 
February 2010 Draft – Do Not Quote or Cite 3-59

1 The remainder of this section is organized as follows. Key sources of variability which 

2 are reflected in the design of the risk assessment, along with sources excluded from the design, 

3 are discussed in section 3.5.2. A qualitative discussion of key sources of uncertainty associated 

4 with the risk assessment (including the potential direction, magnitude and degree of confidence 

5 associated with our understanding of the source of uncertainty – the knowledge base) is 

6 presented in section 3.5.3. The methods and results of the single- and multi-factor sensitivity 

7 analyses completed for the risk assessment are presented in section 3.5.4. An overall summary 

8 of the methods used to address uncertainty and variability for the 15 urban study areas (including 

9 the two assessments intended to place the urban study areas in a broader national context) is 

10 presented in section 3.5.5. 

11 3.5.2 Treatment of Key Sources Of Variability 

12 The risk assessment was designed to cover the key sources of variability related to 

population exposure and exposure response, to the extent supported by available data.31 13 

14 However, as with all risk assessments, there are sources of variability which have not been fully 

15 reflected in the design of the risk assessment and consequently introduce a degree of uncertainty 

16 into the risk estimates. While different sources of variability were captured in the risk 

17 assessment, it was generally not possible to separate out the impact of each factor on population 

18 risk estimates, since many of the sources of variability are reflected collectively in a specific 

19 aspect of the risk model. For example, inclusion of urban study areas from different PM regions 

20 likely provides some degree of coverage for a variety of factors associated with PM2.5 risk (e.g., 

21 air conditioner use, PM2.5 composition, differences in population commuting and exercise 

22 patterns, weather). However, the model is not sufficiently precise or disaggregated to allow the 

23 individual impacts of any one of these sources of variability on the risk estimates to be 

24 characterized. 

25 Key sources of potential variability that are likely to affect population risks are discussed 

26 below, including the degree to which they are (or are not) fully captured in the design of the risk 

27 assessment: 

 31 The term “key sources of variability” refers to those sources that the EPA staff believes have the potential to play 

an important role in impacting population incidence estimates generated for this risk assessment. Specifically, EPA 

staff has concluded that these sources of uncertainty, if fully addressed and integrated into the analysis, could result 

in adjustments to the core risk estimates which might be relevant from the standpoint of interpreting the risk 

estimates in the context of the PM NAAQS review. The identification of sources of variability as “key” reflects 

consideration for sensitivity analyses conducted for previous PM NAAQS risk assessments, which have provided 

insights into which sources of variability (reflected in different elements of those earlier sensitivity analyses) can 

influence risk estimates, as well as information presented in the final PM ISA. For example, chapter 2 of the final 

PM ISA addresses such issues as: ambient PM variability and correlations (section 2.1.1), trends and temporal 

variability (section 2.1.2), correlations between pollutants (section 2.1.4), and source contributions to PM (section 

2.1.6). These discussions were carefully considered by staff in identifying key sources of variability to address both 

in the risk assessment and in the qualitative discussion of variability presented in this section. 
February 2010 Draft – Do Not Quote or Cite 3-60

1  PM2.5 composition: While information was not available to support modeling risk 

2 associated with different components of PM2.5, the assessment did use effect estimates 

3 (for a number of the short-term exposure-related health endpoints) differentiated by 

4 region of the country, or differentiated for specific urban locations (sections 3.3.3 and 

5 3.3.4). While many factors may contribute to differences in effect estimates (for the 

6 same health endpoint) across different locations, compositional differences in PM2.5

7 may be partially responsible. Therefore, while the analysis did not explicitly address 

8 compositional differences in generating risk estimates, potential differences in PM2.5

9 composition may be reflected in those effect estimates that are differentiated by region 

10 and/or urban study area. The effect estimates for mortality associated with long-term 

11 exposure to PM2.5 are not regionally differentiated and instead, a single national-scale 

12 estimate is used. This means that any differences in risks of mortality associated with 

13 long-term exposure to PM2.5 that are linked to differences in PM2.5 composition (or to 

14 any other differences across regions or locations) would not be discernable, since a 

15 single national-scale risk estimate is generated for each mortality category. In addition 

16 to using region- or location-specific effect estimates for health effects associated with 

17 short-term exposures, the selection of urban areas to include in the risk assessment was 

18 designed in part to ensure that areas in different regions of the country, with different 

19 PM2.5 composition, were included. 

20  Intra-urban variability in ambient PM2.5 levels: Several recent studies (e.g., Jerrett 

21 et al., 2005) have addressed the issue of heterogeneity of PM concentrations within 

22 urban areas and its potential impact on the estimation of premature mortality associated 

23 with long-term exposure to PM2.5. Most recently, the HEI Reanalysis II (Krewski et 

24 al., 2009), focusing on the ACS dataset, discusses epidemiological analyses completed 

25 for Los Angeles and New York City which included more highly-refined (zip code 

26 level) characterizations of spatial gradients in population exposure within each urban 

27 area based on land-use regression methods and/or kriging. While both analyses 

28 provide insights into the issue of intra-urban heterogeneity in PM2.5 concentrations and 

29 its potential implications for epidemiology-based health assessments, due to the time 

30 and resource necessary to integrate them into the risk assessment, we were not able to 

31 incorporate these studies quantitatively. The implications of these studies for 

32 interpretation of long-term mortality C-R functions and potential exposure error 

33 associated with those functions is discussed below in section 3.5.3. 

34  Variability in the patterns of ambient PM2.5 reduction as urban areas: In 

35 simulating just meeting the current or alternative suites of standards, there can be 

36 considerable variability in the patterns of ambient PM2.5 reductions that result from 

37 different simulation approaches (i.e., they can be more localized, more regional, or 

38 some combination thereof). To address this issue in the risk assessment, we have 

39 included three rollback approaches as part of the sensitivity analysis including: 

40 proportional (reflecting regional patterns of reduction), hybrid (reflecting a 

41 combination of localized and regional patterns of reduction), and peak shaving 

42 (reflecting localized patterns of reduction) (see section 3.2.3 for additional detail on 

43 these rollback methods and section 3.5.4 for a description of how this factor is 

44 addressed in the sensitivity analysis). 
February 2010 Draft – Do Not Quote or Cite 3-61

1  Copollutant concentrations: Inclusion of copollutant models in short-term exposure2 related time series studies has produced mixed results in terms of the degree of 

3 attenuation of the PM2.5 signal that results from inclusion of other pollutants (see final 

4 PM ISA, sections 6.2.10.9 and 6.3.8.5). The PM ISA (section 6.2.10.9) suggests that 

5 these inconsistent findings associated with controlling for gaseous pollutants are likely 

6 due to differences in the correlation structure among pollutants as well as differing 

7 degrees of exposure measurement error related to the copollutants. Further, the PM 

8 ISA (section 2.1.3) notes that correlations between PM and copollutants (including CO, 

9 O3, SO2 and NO2) can vary both seasonally and spatially. Therefore, it is possible that 

10 the degree of attenuation of PM2.5-related risk by copollutants may differ across study 

11 areas. However, because the multi-city studies used in the core risk assessment 

12 (Zanobetti and Schwartz., 2009; Bell et al., 2008; and Krewski et al., 2009) provide 

13 single pollutant models, our analysis does not directly address the issue of copollutant 

14 confounding (see section 3.5.3 for additional discussion of uncertainty introduced into 

15 the analysis as a result of not including copollutant models in the core risk assessment). 

16 We did explore the issue of copollutant modeling in the context of modeling long-term 

17 exposure-related mortality as part of the sensitivity analysis (section 3.5.4). In 

18 addition, the potential impact of copollutant confounding on short-term exposure19 related mortality and morbidity was explored in the Moolgavkar et al., 2003 study, as 

20 discussed below in section 4.3.1.1 (although they have limited applicability to the core 

21 risk estimates generated in this RA). 

22  Demographics and socioeconomic-status (SES)-related factors: Variability in 

23 population density particularly in relation to elevated levels of PM2.5 has the potential 

24 to influence population risk. In addition, other aspects of demographics such as age of 

25 housing stock (which can influence rates of air conditioner use thereby impacting rates 

26 of infiltration of PM indoors) can impact exposure and therefore risk (discussed in PM 

27 ISA – sections 2.2.1 and 2.3.2). While risk modeling completed for this analysis is 

28 based on concentrations measured at central-site monitors used as surrogates for 

29 population exposure and does not explicitly consider more detailed patterns of PM 

30 exposure by different subpopulations, potential differences in exposure to PM2.5

31 reflecting demographic and SES-related factors is covered to some degree by the use of 

32 urban study area-differentiated effects estimates (for short-term exposure-related 

33 mortality) and regionally-differentiated effects estimates (in the case of short-term 

34 exposure-related morbidity). In the case of long-term exposure-related mortality, while 

35 the modeling for this group of endpoints does not utilize location-specific or 

36 regionally-differentiated effects estimates, the national-scale effects estimates that are 

37 used do reflect differences in exposure and health response across urban study areas 

38 (which will reflect, to some extent, differences in demographics and SES-related 

39 factors to the extent that these factors influence the relationship between PM2.5

40 exposure and mortality response, as detected by the underling cohort studies). 

41  Behavior affecting exposure to PM2.5: We have incorporated, where available, 

42 region- and/or city-specific effect estimates in order to capture behavioral differences 

43 across locations that could affect population exposures to PM2.5 (e.g., time spent 

44 outdoors, air conditioning use). However, while these location-specific effect 

45 estimates may be capturing differences in behavior, they may also be capturing other 
February 2010 Draft – Do Not Quote or Cite 3-62

1 differences (e.g., differences in the composition of PM2.5 to which populations are 

2 exposed). As noted above, it was not possible to separate out the impact of these 

3 different factors, which may vary across locations and populations, on effect estimates. 

4  Baseline incidence of disease: We collected baseline health effects incidence data (for 

5 mortality and morbidity endpoints) from a number of different sources (see section 

6 3.4). Often the data were available at the county-level, providing a relatively high 

7 degree of spatial refinement in characterizing baseline incidence given the overall level 

8 of spatial refinement reflected in the risk assessment as a whole. Otherwise, for urban 

9 study areas without county-level data, either (a) a surrogate urban study area (with its 

10 baseline incidence rates) was used, or (b) less refined state-level incidence rate data 

11 were used. 

12  Longer-term temporal variability in ambient PM2.5 levels (reflecting meteorological 

13 trends, as well as future changes in the mix of PM2.5 sources and regulations impacting 

14 PM2.5): Risk estimates for the PM2.5 NAAQS review have been generated using recent 

15 years of air quality data. In other words, efforts have not been made to simulate 

16 potential future changes in either the concentrations or composition of ambient PM2.5

17 in the risk assessment locations based on possible changes in economic activity, 

18 demographics or meteorology. Actual risk levels potentially experienced in the future 

19 as a result of implementing alternative standard levels may differ from those presented 

20 in this report due, in part, to potential changes in these factors related to ambient PM2.5. 

21 3.5.3 Qualitative Assessment of Uncertainty 

22 As noted in section 3.5.1, we have based the design of the uncertainty analysis carried out 

23 for this risk assessment on the framework outlined in the WHO guidance document (WHO, 

24 2008). That guidance calls for the completion of a Tier 1 qualitative uncertainty analysis, 

25 provided the initial Tier 0 screening analysis suggests there is concern that uncertainty associated 

26 with the analysis is sufficient to significantly impact risk results (i.e., to potentially affect 

27 decision making based on those risk results). Given previous sensitivity analyses completed for 

28 prior PM NAAQS reviews, which have shown various sources of uncertainty to have a 

29 potentially significant impact on risk results, we believe that there is justification for conducting 

30 a Tier 1 analysis. In fact, as argued earlier, given the complexity of the overall risk assessment, a 

31 full Tier 3 uncertainty analysis is warranted for consideration under the WHO guidelines 

32 (although as discussed later, limitations in available data preclude completion of this level of 

33 more-refined uncertainty analysis at this time). 

34 For the qualitative uncertainty analysis, we have described each source of uncertainty and 

35 qualitatively assessed its potential impact (including both the magnitude and direction of the 

36 impact) on risk results, as specified in the WHO guidance. As shown in Table 3-13, for each 

37 source of uncertainty, we have (a) provided a description, (b) estimated the direction of influence 

38 (over, under, both, or unknown) and magnitude (low, medium, high) of the potential impact of 

39 each source of uncertainty on the risk estimates, (c) assessed the degree of uncertainty (low, 
February 2010 Draft – Do Not Quote or Cite 3-63

1 medium, or high) associated with the knowledge-base (i.e., assessed how well we understand 

2 each source of uncertainty), and (d) provided comments further clarifying the qualitative 

3 assessment presented. Table 3-13 includes all key sources of uncertainty identified for the PM2.5

4 NAAQS risk assessment. A subset of these sources has been included in the Tier 2 quantitative 

5 assessment discussed in section 3.5.4. 

6 The categories used in describing the potential magnitude of impact for specific sources 

7 of uncertainty on risk estimates (i.e., low, medium, or high) reflect EPA staff consensus on the 

8 degree to which a particular source could produce a sufficient impact on risk estimates to 

influence the interpretation of those estimates in the context of the PM NAAQS review.32 9 

10 Sources classified as having a “low” impact would not be expected to impact the interpretation 

11 of risk estimates in the context of the PM NAAQS review; sources classified as having a 

12 “medium” impact have the potential to change the interpretation; and sources classified as “high” 

13 are likely to influence the interpretation of risk in the context of the PM NAAQS review (if those 

14 sources of uncertainty are reduced or more fully characterized). Because this classification of 

15 the potential magnitude of impact of sources of uncertainty is qualitative and not informed 

16 directly by any type of analytical results, it is not possible to place a quantitative level of impact 

on each of the categories. 33 17 Therefore, the results of the qualitative analysis of uncertainty have 

18 limited utility in informing consideration of overall confidence in the core risk estimates and, 

19 instead, serve primarily as a means for guiding future research to reduce uncertainty related to 

20 PM2.5 risk assessment. 

21 As with the qualitative discussion of sources of variability included in the last section, the 

22 characterization and relative ranking of sources of uncertainty addressed here is based on 

23 consideration by EPA staff of information provided in previous PM NAAQS risk assessments 

24 (particularly sensitivity analyses), the results of the sensitivity analyses completed for the current 

25 PM NAAQS risk assessment and information provided in the final PM ISA as well as earlier PM 

26 Criteria Documents. Where appropriate, in Table 3-13, we have included references to specific 

27 sources of information considered in arriving at a ranking and classification for a particular 

28 source of uncertainty. 

 32 For example, if a particular source of uncertainty were more fully characterized (or if that source was reduced, 

potentially reducing bias in a core risk estimate), would the estimate of incremental risk reduction in going from the 

current to an alternative standard level change sufficiently to produce a different conclusion regarding the magnitude 

of that risk reduction in the context of the PM NAAQS review? 

33 Thematically, the categories used in the qualitative uncertainty analysis are similar to the categories used in 

categorizing the results of the single- and multi-factor sensitivity analyses completed for this analysis (section 4.3). 

However, in the context of the sensitivity analysis results, because we do have quantitative estimates of the impact 

of individual modeling elements, it is possible to categorize the modeling elements included in the sensitivity 

analysis based on magnitude of impact on risk estimates. This is not possible for the qualitative uncertainty analysis 

described in this section. 
February 2010 Draft – Do 

3-64

1 Table 3-13. Summary of Qualitative Uncertainty Analysis of Key Modeling Elements in the P2 Assessment. 

Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

A. Characterizing ambient PM2.5

levels for study populations using the existing ambient monitoring network 

If the set of monitors used in a particular urban study area to characterize population exposure as part of an ongoing risk assessment do not match the ambient monitoring data used in the original epidemiological study, then uncertainty can be introduced into the risk estimates. 

Both Low- medium Low-medium 

KB and INF: In modeling risk, we were included in the epidemiologicunderlying C-R functions. This meendpoints modeled using C-R funcstudies, there is likely a close assocnetwork used in the risk assessmenstudy supplying the C-R function(sinstances where the networks are dstudies are used, resulting in an inchave changed), uncertainty may be assessment and it is challenging to magnitude of the impact that that uestimates, given the complex interpmismatched monitoring networks (monitors used in modeling risk and epidemiological study). 

B. Characterizing policy-relevant background (PRB) 

For this analysis, we have used modeling to estimate PRB levels for each urban study area. Depending on the nature of errors reflected in that modeling, uncertainty (in both directions) may be introduced into the analysis. 

Both Low Low 

INF: Given that the risk assessmenreduction in risk associated with mto alternative standard levels, the imlevels on the risk estimates is expeclong-term exposure related mortalianalysis on modeling risk down to reduces the significance of the PRB 

modeling long-term exposure-relat

C. Characterizing intra-urban population exposure in the context of epidemiology studies linking 

Exposure misclassification within communities that is associated with the use of generalized population monitors (which may miss important patterns of exposure within urban study areas) introduces uncertainty into the 

Under 

(generally) 

Mediumhigh High 

KB and INF: Recent analyses in Lbased on ACS data (as reported in demonstrate the relatively significauncertainty can have on effect estimresults). These analyses also illustrspecific nature of this source of uncAngeles analysis suggest that expoestimates that are biased low and th
February 2010 Draft – Do 

3-65

Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

PM2.5 to 

specific health effects 

effect estimates obtained from epidemiology studies. 

underestimation of risk. Specificallevel analysis based on ACS data c(Jerrett et al., 2005), the final ISA sexposure analysis reported in the Jeimproved exposure assessment and sources of fine particle pollution. S

PM2.5 and mortality from all causes 

were reported with the magnitude ogreater than those reported in previthe associations for PM2.5 and mort

[kriging and land-use regression] fosimilar, though the use of land use somewhat smaller hazard ratios and (see Table 7-9). This indicates that not the cause of the associations fostudies. This provides evidence tharesult in stronger associations betw

generally observed in broader studi(final ISA, section 7.6.3, p. 7-90). 

D. Statistical fit of the C-R functions 

Exposure measurement error combined with other factors (e.g., size of the effect itself, sample size, control for confounders) can effect the overall level of confidence associated with the fitting of statistical effect-response models in epidemiological studies. 

Both 

 Lowmedium 

(long-term health endpoints) 

 Medium 

(short-term health endpoints) 

Medium 

INF: Long-term mortality studies bsample sizes (given that large natioin deriving national-scale models), models used appears to be subject t(see next row below) and (c) our nospecific effects estimates (but insteestimates). These factors combine ttend to be statistically robust (as reKrewski et al., 2009). In addition, regarding exposure misclassificatiogenerally we do not believe that the consistently biased in a particular dterm mortality and morbidity health uncertainty associated with the fit osample sizes often involved, difficuetiologically relevant time period fthe fact that models tend to be fitte
February 2010 Draft – Do 

3-66

Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

urban areas (which introduces the pconfounding and effects modificaticontrast to the long-term mortality mortality and morbidity endpoints estimates that are not statistically sfor this risk assessment, in modelinmorbidity endpoints, we are not relmodels. In the case of short-term mspecific effects estimates derived ucombine national-scale models witcommunication with Zanobetti, 200we are using regional effects estimcases, while effects estimates are at significant, these models do beneficompared to city-specific models. 

E. Shape of the C-R functions 

Uncertainty in predicting the shape of the C-R function, particularly in the lower exposure regions which are often the focus in PM NAAQS regulatory reviews. 

Both Medium Low-medium 

INF: Regarding long-term mortalitlinear non-threshold model is best modeling both short-term and longAlthough consideration for alternatal., 2009) does suggest that differenestimates to a certain extent, generamoderate source of overall uncertacase in this risk assessment, we are lowest measured levels found in thstudies. With regard to long-term mconcludes that, “In addition to examresponse relationship between shormortality, Schwartz et al. (2008, 15the shape of the concentration-respwith long-term exposure to PM. Usmethods, the concentration-responsindistinguishable from linear, and, 

observed to suggest that a threshold between long-term exposure to PM

(Section 7.6).” (section 2.4.3, p. 2-2morbidity, the final ISA states that, 
February 2010 Draft – Do 

3-67

Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

further support the use of a no-threadditional issues such as the influenestimates between cities, and the efdifferences in PM on the concentrarequire further investigation.” (sect

F. Addressing co-pollutants 

The inclusion or exclusion of co-pollutants which may confound, or in other ways, affect the PM effect, introduces uncertainty into the analysis. 

Both Low- medium Medium 

INF: With regard to long-term healstates that, “Given similar sources traffic), disentangling the health rechallenge in the study of ambient a7.5.1, p. 7-57). The final ISA also consideration of copollutants can hestimates. For example, the more reas reported in Krewski et al., 2009 ozone in the model along with PM2

significant results for long-cancer mmortality remained statistically signTable 23). With regard to short-terfinal ISA generally concludes that robust to the inclusion of copolluta(see ISA, sections 6.3.8, 6.3.9, and considering multi-pollutant models 

risk for short-term and long-term eus to conclude that the potential imuncertainty is low-medium (dependunder consideration). The epidemibasis for selecting C-R functions fonot include multi-pollutant models 

and PM2.5 combined models in Zan

However, we have included copollanalysis (see Section 4.3). 

G. Potential variation in effects estimates reflecting compositional 

The composition of PM can differ across study areas reflecting underlying differences in primary and secondary PM2.5 sources (both 

natural and anthropogenic). If 

Both Medium- High Medium-High 

KB and INF: Epidemiology studies differences in PM2.5-related health in the magnitude of those effects (sthe draft ISA). While these may be composition (e.g., different degrees composition remains one potential 
February 2010 Draft – Do 

3-68

Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

differences for PM 

these compositional differences in fact translate into significant differences in public health impact (per unit concentration in ambient air) for PM2.5 then 

significant uncertainty may be introduced into risk assessments if these compositional differences are not explicitly addressed. 

term exposure morbidity and mortacity-specific and/or regional-specifassessment may well reflect differethus consideration of differences in differences in composition may alrestimates for these endpoints to som

H. Specifying lag structure (short-term exposure studies) 

Different lags may have varying degrees of association with a particular health endpoint and it may be difficult to clearly identify a specific lag as producing the majority of a PM-related effect (recently, distributed lags have been recommended since they allow for a distribution of the impact across multiple days of PM exposure prior to the health outcome). A lack of clarity regarding the specific lag(s) associated with a particular health endpoint adds uncertainty into risk estimates generated for that endpoint. 

Both Medium Medium 

KB and INF: With regard to lag peattempt has been made to identify wmore strongly associated with specepidemiologic evidence evaluated i

supported the use of lags of 0-1 daylonger moving averages or distribu(U.S. EPA, 2004a). However, curreto the most appropriate a priori lag morbidity and mortality outcomes.24). This suggests that uncertainty identification of appropriate lags, arelevant time period for exposure to endpoints. 

I. Transferability of C-R functions from study locations to urban study area locations 

The use of effects estimates based on data collected in a particular location(s) as part of the underlying epidemiological study in different locations (the focus of the risk assessment) introduces uncertainty into the analysis. 

Both 

Medium (for 

long-term 

exposure 

mortality) 

Not 

applicable 

(for shortMedium (for long-term exposure mortality) Low (for short- term exposure mortality) INF: This issue has been ameliorat

assessment since we are now using short-term endpoints with effects eapplied only to urban study areas munderlying epidemiological study. exposure mortality studies, these argeneralized national signal and thertransferability of functions between 
February 2010 Draft – Do 

3-69

Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

term 

exposure 

health effect 

risk 

estimates) 

J. Use of single-city versus multicity studies in the derivation of C-R functions 

Often both single-city and multi-city studies are available (for a given health effect endpoint) for the derivation of C-R functions. Each of these study designs has advantages and disadvantages which should be considered in the context of assessing uncertainty in a risk assessment (Note, that generally this issue applies more to the modeling of short-term exposure-related endpoints then to the modeling of long-term exposure related endpoints, since the latter is typically based on multi-city prospective cohort studies). 

Both Medium High 

KB: Because many health endpoint

both single-city and multi-city studselection of single city studies and consider in examining this issue. INF: For reasons presented in sectifocus on multi-city studies as a soucore risk assessment, reflecting adv(e.g., they tend to have more statistestimates with relatively greater prdue to larger sample sizes, reducing estimated coefficient, and reducing choice of multi-city studies is well-introduce uncertainty since single crange of C-R functions (and associreflecting greater variation in study composition, human behavior, and the input datasets used (e.g., ambiebaseline incidence data). Even if thR functions obtained from multi-cithose C-R functions may be reflectof C-R functions seen across single

K. Impact of historical air quality on estimates of health risk from long-term PM2.5

exposures 

Long-term studies of mortality suggest that different time periods of PM exposure can produce significantly different effects estimates, raising the issue of uncertainty in relation to determining which exposure window is most strongly associated with mortality. 

Both Medium Medium 

INF: The latest HEI Reanalysis II sat exposure windows (1979-1983 aexposure in relation to mortality, dto which window was more stronglHowever, the study did suggest thaestimates are associated with the dithe more recent period having largeevidence for determining the windoeffects of long-term pollution expo
February 2010 Draft – Do 

3-70

Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

period of up to five years, with the the first few years after intervention 95). 

L. Characterizing baseline incidence rates 

Uncertainty can be introduced into the characterization of baseline incidence in a number of different ways (e.g., error in reporting incidence for specific endpoints, mismatch between the spatial scale in which the baseline data were captured and the level of the risk assessment). 

Both Low- medium Low 

INF: The degree of influence of thirisk estimates likely varies with the consideration. There is no reason tsystematic biases in estimates of thinfluence on risk estimates that are reductions between alternative stanunaffected by this source of uncertaKB: The county level baseline incat the county level were obtained frrelative degree of uncertainty is low

1 * Refers to the degree of uncertainty associated with our understanding of the phenomenon, in the context of assessing and charact2 (specifically in the context of modeling PM risk) 
February 2010 Draft – Do Not Quote or Cite 3-71

1 The results presented in Table 3-13 consider only the potential impact of each source of 

2 uncertainty when acting in isolation to impact core risk estimates. However, it is likely that a 

3 number of these sources of uncertainty could act in concert to impact risk estimates and 

4 furthermore, that these combined effects could be more than additive in certain circumstances. 

5 EPA staff has identified several combinations of sources of uncertainty addressed in Table 3-13 

6 that should be highlighted due to their potential to produce significant impacts on core risk 

7 estimates when acting in concert. These are briefly described below: 

8  Uncertainty source D (statistical fit of the C-R functions), Source E (shape of the 

9 C-R functions), Source F (addressing copollutants), and Source J (use of single10 city versus multi-city studies in the derivation of C-R functions): Consideration of 

11 uncertainty associated with the shape of C-R functions needs to be considered in light 

12 of overall confidence (uncertainty) associated with a particular model. A number of 

13 factors contribute to an interpretation of confidence in a model including: statistical fit 

14 of the model, degree to which potential confounding by copollutants is considered, and 

15 other aspects of study design including single- versus multi-city study design. While 

16 choice of a particular model (e.g., threshold model, or log-log model) may produce a 

17 significant impact on risk estimates relative to alternative model forms, the overall 

18 scientific support for that particular model form (informed by consideration of the 

19 factors listed above) is an important consideration in assessing overall uncertainty both 

20 from a qualitative and quantitative standpoint. 

21 In addition, there is the potential for sources of uncertainty discussed in Table 3-13 to 

22 interact with sources of variability covered in section 3.5.2 in impacting core risk estimates. One 

23 such interaction is discussed below: 

24  Uncertainty source A (characterizing ambient PM2.5 levels for study populations 

25 using the existing ambient monitoring network) and variability related to the 

26 pattern of ambient PM2.5 reductions at urban study areas (see section 3.5.2): The 

27 estimation of a composite monitor value to use in modeling risk for a study area under 

28 an alternative suite of standards is dependent both on the specification of the 

29 monitoring network and the approach used in adjusting the concentrations for the 

30 monitors in that network (i.e., the rollback approach used to simulate the pattern of 

31 ambient PM2.5 reductions associated with just meeting the current or alternative suites 

32 of standards). As we have seen in modeling risk for Pittsburgh, refinements in the 

33 approach used to simulate air quality just meeting alternative suites of standards (in the 

34 case of Pittsburgh transitioning from a single study area to two distinct study areas 

35 each with different design values and separate assessments of rollback) produced 

36 significant differences in composite monitor values for the study area. Therefore, both 

37 of these factors (the definition of the monitoring network and rollback approach) can 

38 work in concert to impact ambient PM2.5 levels and hence risk estimates. 

39 
February 2010 Draft – Do Not Quote or Cite 3-72

1 3.5.4 Single and Multi-Factor Sensitivity Analyses 

2 We quantitatively examined the impact of several inputs to the risk assessment in a series 

3 of single-factor sensitivity analyses summarized above in Table 3-8. A number of these sources 

4 of uncertainty were also examined in-concert to assess their combined impact on core risk 

5 estimates through the multi-factor sensitivity analysis. In addition, the sensitivity analysis 

6 considered variability in the pattern of reductions in ambient PM2.5 associated with just meeting 

7 the current and alternative suites of standards (i.e., consideration of variability in the simulation 

8 of rollback). This section focuses on providing additional detail on the sources of alternative 

9 model specifications and input datasets used in the sensitivity analysis (as alternative to the core 

10 modeling approach). 

11 Rather than present results for each sensitivity analysis for all of the air quality scenarios 

12 considered in the core analysis, we selected a single air quality scenario – PM2.5 concentrations 

13 that just meet the current standards – to use for the sensitivity analyses. The one exception to 

14 this was the sensitivity analyses examining the impact of alternative approaches to simulating 

just meeting alternative standards (the hybrid and peak-shaving rollback methods).34 15 

16 In discussing the approach used in conducting the sensitivity analysis, we focus first on 

17 methods used in assessing long-term exposure related health endpoints followed by the methods 

18 used in assessing short-term exposure related health endpoints. We then discuss multi-factor 

19 sensitivity analyses completed for both short-term and long-term exposure-related health 

20 endpoints. Note, that the results of the sensitivity analyses (including both single- and multi21 factor analyses) are presented and discussed in section 4.3. 

22 3.5.4.1 Sensitivity Analyses for Long-Term Exposure-Related Mortality 

23 Because Krewski et al. (2009) presented results based on alternative model specifications 

24 only for the later exposure period (1999 – 2000), our sensitivity analyses focusing on the 

25 estimates of health effects incidence associated with long-term exposure to PM2.5 similarly used 

26 the C-R functions based on this later exposure period. Krewski et al. (2009) considered several 

27 alternative modeling approaches to estimate the relationship between mortality (both all cause 

28 and cause-specific) and long-term exposure to PM2.5, providing us the opportunity to examine 

29 the impact of alternative modeling approaches on the estimate of mortality risk associated with 

30 long-term exposure. In particular, we examined the impact of using a random effects log-linear 

model and of using a random effects log-log model35 31 (rather than the standard fixed effects log32 linear model used in the core analysis) to estimate the risks of all cause mortality, 

 34 Sensitivity analyses focusing on the hybrid and peak-shaving rollback approach (relative to the proportional 

rollback approach used in the core analysis) involved the full set of alternative standard levels, in order to assess 

potential differences in risk across the range of standard levels. 

35 In the log-log model, the natural logarithm of mortality is a linear function of the natural logarithm of PM2.5. 
February 2010 Draft – Do Not Quote or Cite 3-73

1 cardiopulmonary mortality, ischemic heart disease mortality, and lung cancer mortality 

associated with long-term exposure in Los Angeles and Philadelphia.36 2 The coefficient of PM2.5

3 in the random effects log-linear model was back-calculated from the relative risk reported in 

4 Table 9 (“Autocorrelation at MSA and ZCA levels” group – “MSA & DIFF” row) of Krewski et 

5 al. (2009). The coefficient of PM2.5 in the random effects log-log model was back-calculated 

6 from the relative risks reported in Table 11 (“MSA and DIFF” rows) of Krewski et al. (2009). 

7 As noted above, for all health endpoints associated with long-term exposure to PM2.5 we 

estimated risk associated with PM2.5 concentrations above 5.8 μg/m3 8 (the LML for the later 

9 exposure period used in Krewski et al., 2009). In a sensitivity analysis we examined the impact 

10 of that limitation by comparing those mortality risk estimates to the mortality risk estimates 

11 obtained when we estimated risk associated with PM2.5 concentrations above estimated PRB 

12 levels. This sensitivity analysis was carried out for all cause mortality in all 15 risk assessment 

13 urban areas. 

14 In addition, we compared the impact of using the primary C-R functions used in the risk 

15 assessment, taken from Table 33 of Krewski et al. (2009), versus C-R functions for mortality 

16 associated with long-term exposure reported in another study, Krewski et al. (2000), which was 

17 based on a reanalysis of the Harvard Six Cities Study. The C-R functions estimated in Krewski 

18 et al. (2000) from the Harvard Six Cities cohort were estimated for ages 25 and up, while the C19 R functions estimated in Krewski et al. (2009) from the ACS cohort were for ages 30 and up. 

20 For purposes of consistency in the comparison, however, we applied the C-R functions from 

21 Krewski et al. (2000) to ages 30 and up (and used the baseline incidence rates for that age group 

as well).37 22 This sensitivity analysis was carried out for all cause mortality, cardiopulmonary 

23 mortality, and lung cancer mortality in Los Angeles and Philadelphia. 

24 We also considered the impact of using multi-pollutant models in estimating long-term 

25 exposure-related mortality. Specifically, we obtained 2-pollutant models (considering CO, NO2 , 

26 O3 or SO2 together with PM2.5) from Krewski et al., 2000, which is an earlier reanalysis of the 

27 ACS dataset and used them in generating alternative estimates of all-cause mortality to contrast 

28 with the core estimates generated using Krewski et al., 2009. 

29 For all of the sensitivity analyses involving alternative C-R functions, in addition to 

30 calculating the incidence of the health effect when an alternative approach is taken, we 

 36As noted in Table 3-8, we combined both of these alternative modeling approaches in a single sensitivity analysis. 

In changing from a fixed effects log-linear model to a random effects log-log model, two changes are actually being 

made – the change from a fixed effects log-linear model to a random effects log-linear model, and the change from a 

random effects log-linear model to a random effects log-log model. However, because Krewski et al. (2009) did not 

present results for a fixed effects log-log model, it was not possible to compare the impact of making the single 

change from a fixed effects log-linear model (our core analysis selection) to a fixed effects log-log model. We thus 

instead present a two-stage sensitivity analysis incorporating both of the changes. 

37 The baseline incidence rates for ages 25 and up and ages 30 and up are likely to be very similar. 
February 2010 Draft – Do Not Quote or Cite 3-74

1 calculated the percent difference in estimates from the core analysis resulting from the change in 

2 analysis input. So for example, when we calculated the incidence of all cause mortality 

3 associated with long-term exposure to PM2.5 using a random effects log-log model (instead of the 

4 fixed effects log-linear model used in the core analysis), we calculated the percent difference in 

5 the result as (incidence estimated using a random effects log-log model - incidence estimated 

6 using a fixed effects log-linear model)/( incidence estimated using a fixed effects log-linear 

7 model). 

8 Finally, we also examined the issue of variability in estimating the pattern of reductions 

9 in ambient PM2.5 levels under the current and alternative standard levels (i.e., conducting 

10 rollback). For the first draft RA, we considered the impact of using a hybrid rollback approach in 

11 addition to the proportional rollback approach which has been more traditionally used in PM 

12 NAAQS risk assessment (this sensitivity analysis was implemented including the generation of 

13 quantitative risk estimates for a full suite of long-term exposure-related mortality categories). 

14 For this second draft, as discussed above in sections 2.6, and 3.2.3, we have included 

15 consideration of a peak shaving rollback approach in addition to the hybrid as non-proportional 

16 methods to contrast with proportional rollback. As discussed in Section 3.2.3, for the second 

17 draft risk assessment, rather than generating quantitative risk estimates, we have calculated 

18 composite monitor estimates using the different rollback methods (proportional, hybrid and peak 

19 shaving). The composite monitor values are surrogates for long-term exposure-related mortality. 

20 Therefore, by comparing composite monitor values generated for the same study area/standard 

21 level combination (using different rollback methods), we can obtain insights into the potential 

22 impact of the rollback method used on long-term exposure-related mortality. Specifically, for 

23 this sensitivity analysis, we compared composite monitor values in two ways: 

24  Potential difference in composite monitor values at the current or alternative standard 

25 level (for the same study area) given application of alternative rollback methods: We 

26 compared the absolute magnitude of composite monitors values produced using different 

27 rollback methods for the same study area/standard level combination to provide insights 

28 into differences in the magnitude of residual risk for a given suite of standards in a study 

area using different rollback methods (Appendix F, Table F-50).38 29 For example, in Table 

30 F-50, for Los Angeles, we see that for the current standard suite of standards, use of 

31 proportional rollback and peak shaving rollback methods results in composite monitor 

values of 9.5 μg/m3 and 12.0 μg/m3 32 , respectively, with the peak shaving value being 40% 

33 higher than the value derived using proportional rollback. Given that the composite 

34 monitor values are surrogates for long-term exposure-related mortality, we conclude that 

35 for this combination of urban study area and suite of standards, use of the peak shaving 

36 rollback method could produce PM2.5-attributable long-term mortality risk estimates that 

37 are approximately 40% higher than use of the proportional rollback method. 

 

38 This calculation reflects the fact that we model long-term exposure-related mortality down to LML. 
February 2010 Draft – Do Not Quote or Cite 3-75

1  Potential difference in the pattern of reduction in composite monitor values across 

2 alternative standards: We compared differences in the percent reduction in composite 

3 monitor values across alternative suites of standards for the same study area using 

4 different rollback methods to provide insights into differences in incremental risk 

5 reduction resulting from the use of different rollback approaches (Appendix F, Table F49).39 6 For example, in Table F-49, for Baltimore, we see that the proportional rollback 

7 and hybrid rollback approaches resulted in composite monitor values for the 13/35 

alternative suite of standards of 11.6 μg/m3

 and 11.8 μg/m3 8 , respectively, with these 

9 translating into a percent reduction (compared with their respective values under the 

10 current suite of standards) of 21% and 16%, respectively. Given that the composite 

11 monitor values are surrogates for long-term exposure-related mortality, we conclude that 

12 use of the two rollback methods (in the case of Baltimore for these two suites of 

13 standards) does not appear to produce notably different patterns of risk reduction (in 

14 terms of percent reduction), although residual risk could differ using the two approaches. 

15 The peak-shaving and hybrid rollback approaches were not applied to all study areas, 

since they are primarily applicable in certain situations.40 16 The sensitivity analysis results 

17 described above (presented in Appendix F, Tables F-49 and F-50) form the basis for summary 

18 information related to rollback approaches presented in Table 4-3. 

19 In addition to the above insights regarding potential impacts on residual risk and the 

20 degree of risk reduction across standard levels, inclusion of multiple rollback approaches also 

21 allowed us to more fully examine the degree to which alternative 24-hour standards can produce 

22 reductions in annual-average PM2.5 concentrations, thereby producing reductions in long-term 

23 exposure-related mortality. As discussed below in section 6.2, alternative 24-hour standards, 

24 when controlling, can result in reductions in annual average PM2.5 concentrations, particularly if 

25 proportional rollback is used. In this case, the assumption of more regional patterns of PM2.5

26 reduction in reducing PM2.5 concentrations to just meet alternative 24-hour standards results in 

27 an equivalent magnitude of reduction in the annual average. However, in simulating more 

28 localized patterns of PM2.5 reductions to just meet alternative 24-hour standards, the PM2.5

29 reductions can be more limited to the monitor(s) (and areas) exceeding the 24-hour standard, and 

30 other monitors may not be effected, resulting in a smaller impact on the annual average. 

31 Inclusion of rollback approaches reflecting more localized patterns of ambient PM2.5 reduction 

32 (i.e., the hybrid and particularly the peak shaving methods) allows us to assess the degree to 

33 which alternative 24-hour standards (when controlling) produce appreciable reductions in 

 39 We note that this analysis also reflects calculation of long-term exposure-related mortality down to LML. 

40 For the hybrid rollback approach, only select study areas had the mix of local sources in proximity to monitor 

with elevated levels necessary to support consideration of a hybrid local/regional attainment strategy (i.e., 

application of the hybrid rollback) (i.e., Baltimore, Birmingham, Detroit, Los Angeles, New York, St. Louis). In the 

case of the peak sharing approach, only those locations where the 24-hour standard was controlling were considered 

for this sensitivity analysis (i.e., Atlanta, Baltimore, Birmingham, Detroit, Fresno, Los Angeles, New York, 

Philadelphia, Phoenix, Pittsburgh, St. Louis, Tacoma). 
February 2010 Draft – Do Not Quote or Cite 3-76

1 annual-average PM2.5 concentrations and consequently in long-term exposure-related mortality. 

2 This issue is revisited in discussing the results of the sensitivity analysis (section 4.3.1.1) and in 

3 the integrative discussion of the core risk estimates (section 6.2). 

4 3.5.4.2 Sensitivity Analyses for Short-Term Exposure-Related Mortality and 

5 Morbidity 

6 The scope of the sensitivity analysis completed for short-term exposure-related mortality 

7 and morbidity is more limited than that completed for long-term exposure-related mortality. 

8 This reflects, in part, the much greater magnitude of long-term exposure-related mortality. An 

9 additional factor is that while there has been considerable research in the area of short-term 

10 exposure-related mortality and morbidity which sheds light on uncertainty in such factors as C-R 

11 function specification, this information is not directly applicable in a sensitivity analysis. In 

12 order to complete a quantitative sensitivity analysis, we need alternative C-R function 

13 specifications that produce risk estimates that can be directly compared to the core risk estimates. 

14 Ideally, this is done by identifying alternative model forms in the epidemiological study used in 

15 the core risk model. However, in the case of short-term exposure-related mortality, the studies 

16 providing our core risk models (Zanobetti and Schwartz et al., 2009 and Bell et al., 2008), only 

17 provide limited alternative model specifications, as described below. Further, alternative 

18 epidemiological studies, such as Moolgavkar et al., 2003, while providing useful insights into 

19 which factors can impact risk estimates (e.g., lag, multipollutant forms), cannot generate 

20 alternative risk estimates that can be readily compared with the core risk estimates given 

21 differences in the underlying study designs and datasets employed. 

22 The primary studies selected to assess mortality risk and risk of hospitalization associated 

23 with short-term exposure to PM2.5 (Zanobetti and Schwartz, 2009, and Bell et al., 2008, 

24 respectively) both provided all-year C-R functions as well as season-specific C-R functions. We 

25 examined the impact of using season-specific functions by applying these functions to each 

season, as defined by the study authors,41 26 and summing the estimated season-specific incidences 

27 of mortality and hospitalizations. We compared these estimates to the estimates obtained by 

applying the corresponding all-year C-R functions to a year of air quality data.42 28 This sensitivity 

29 analysis was carried out for all 15 of the risk assessment urban areas. 

 41 Both studies defined each season as three months, beginning with winter defined as December, January, and 

February. In applying a season-specific function to a year of air quality data, we chose to keep a calendar year 

together, so that, for example, winter 2005 was defined as December 2005, January 2005, and February 2005. 

42 The mean season-specific incidence estimates can be summed to produce an all-year estimate of incidence. 

However, the 2.5th and 97.5th percentile season-specific estimates cannot be summed. To calculate the 2.5th and 

97.5th percentile estimates of all-year incidence from the season-specific estimates would require the variancecovariance matrix of the season-specific coefficient estimators, which was not available. Therefore our comparison 

of all-year estimates based on summed season-specific estimates versus estimates based on an all-year C-R function 

was carried out only using the mean estimates. 
February 2010 Draft – Do Not Quote or Cite 3-77

1 In addition, Ito et al. (2007) estimated an annual C-R function as well as a seasonal 

2 function for April through August for asthma ED visits in New York City. We compared the 

3 results of applying the annual C-R function to a whole year of air quality data to the results of 

4 applying the seasonal function to only those months (April through August) for which it was 

5 estimated. 

6 Moolgavkar (2003) estimated C-R functions for several health endpoints – non-accidental 

7 and cardiovascular mortality; and cardiovascular and respiratory HAs – associated with short8 term exposures to PM2.5 in Los Angeles using different lag structures, different modeling 

9 approaches to incorporating weather and temporal variables, and single-pollutant versus multi10 pollutant models. This study thus provided an opportunity to show the impact of lag structure, 

11 modeling approach, and single- vs. multi-pollutant models, individually, for several health 

12 endpoints associated with short-term exposures, although it is difficult to generalize to other 

13 locations since the study was only conducted in a single urban area. As noted earlier, differences 

14 in study design and the underlying datasets used prevent the results based on application of 

15 models from Moolgavkar et al., 2003 from being compared directly to the core risk estimates. 

16 Finally, as with estimates of long-term exposure-related mortality, we also considered the 

17 impact of variability related to simulating ambient PM2.5 levels under the suite of current 

18 standard levels (i.e., variability in conducting rollback) on estimates of non-accidental mortality 

19 associated with short-term exposures to PM2.5 (using Zanobetti and Schwartz, 2009). However, 

20 in this case, we only considered the hybrid model (consideration of peak shaving focused on the 

21 impact on long-term exposure-related mortality). We note however, that sensitivity analysis 

22 findings based on consideration for peak shaving generally will hold for short-term exposure23 related mortality and morbidity since both categories of health endpoints are also driven primary 

24 by annual-average PM2.5 levels (see section 6.2). . 

25 In all cases except the ED visits sensitivity analysis, in addition to calculating the 

26 incidence of the health effect when an alternative approach is taken, we calculated the percent 

difference in estimates from the core analysis resulting from the change in analysis input.43 27 

28 3.5.4.3 Multi-factor Sensitivity Analyses 

29 Each single-element sensitivity analysis shows how the estimates of PM2.5–related health 

30 effects incidence change as we change a single element of the analysis (such as the form of the 

31 C-R function or the way we simulate just meeting a set of standards). Because each of the 

32 alternative modeling choices is considered to be a reasonable choice, the results of these single-

 43 We did not calculate percent different for the ED visits sensitivity analysis because the two different C-R 

functions (all-year in the core analysis vs. April through August in the sensitivity analysis) are also being applied to 

different portions of the year (all year vs. April through August), so it is something of an “apple to oranges” 

comparison. 
February 2010 Draft – Do Not Quote or Cite 3-78

1 element sensitivity analyses provide a set of reasonable alternative estimates that may similarly 

2 be considered plausible (see section 4.3). The results of the single-element sensitivity analysis 

3 are presented and discussed in section 4.3.1. 

4 The single-element sensitivity analyses provide insight into which sources of uncertainty 

5 may have the greatest impact on risk estimates when acting alone. However, there are several 

6 sources of uncertainty in estimating PM2.5–related health effects. To provide a more complete 

7 picture of the uncertainty surrounding estimates of PM2.5–related health effects incidence – and 

8 to expand the set of reasonable alternative estimates – we next carried out multi-element 

9 sensitivity analyses. The results of the multi-factor sensitivity analysis are presented and 

10 discussed in section 4.3.1.2. 

11 The choice of uncertain analysis elements to include in the multi-element sensitivity 

12 analyses was guided by the single-element sensitivity analyses. In particular, we selected those 

13 modeling choices that had the greatest impacts on the estimates of health effects incidence in the 

14 single-element sensitivity analyses to provide insight into the scope of possible estimates that, 

15 while perhaps not based on our first choice of analysis elements, are nevertheless plausible 

16 alternative estimates. 

17 We identified three analysis elements that substantially affected the estimates of mortality 

18 associated with long-term exposure to PM2.5 -- the model choice (fixed effects log linear vs. 

19 random effects log-log), whether effects are estimated associated with PM2.5 concentrations 

down to the LML in the study (5.8 μg/m3 20 ) or down to PRB, and whether a proportional or a 

21 hybrid rollback is used to simulate PM2.5 concentrations that just meet a given set of standards. 

22 This resulted in 2 x 2 x 2 = 8 different estimates of mortality, all of which could be considered 

23 plausible, based on the fact that the underlying model choices are all considered reasonable. 

24 We identified two analysis elements that substantially affected the estimates of mortality 

25 associated with short-term exposure to PM2.5 – whether season-specific or all-year C-R functions 

26 were used and whether a proportional or a hybrid rollback approach was used to simulate just 

27 meeting the current and alternative standards. 

28 3.5.5 Summary of Approach to Addressing Variability and Uncertainty 

29 The characterization of uncertainty and variability associated with the risk assessment 

30 includes a number of elements, which have been discussed in detail above. These include: 

31  Identification of key sources of variability associated with PM2.5-related population 

32 exposure and hazard response and the degree to which they are captured in the risk 

33 assessment (see section 3.5.2). When important sources of variability in exposure 

34 and/or hazard response are not reflected in a risk assessment, significant uncertainty 

35 can be introduced into the risk estimates that are generated. While not explicitly 

36 referenced in the WHO guidance, this assessment (focused on coverage for key sources 
February 2010 Draft – Do Not Quote or Cite 3-79

1 of variability) could be considered part of a Tier 1 analysis (i.e., the qualitative 

2 characterization of sources of uncertainty). 

3  Qualitative assessment of uncertainty, including both an assessment of the magnitude 

4 of potential impact of each source on risk estimates (along with the potential direction 

5 of that impact) as well as an assessment of overall confidence associated with our 

6 understanding of that source of uncertainty (see section 3.5.3). This represents a WHO 

7 Tier 1 analysis. 

8  Single-factor sensitivity analysis intended to evaluate the impact of individual sources 

9 of uncertainty and variability on risk estimates (see section 3.5.4). The goal of this 

10 assessment is to evaluate the relative importance of these sources of uncertainty and 

11 variability in impacting core risk estimates. The single-factor sensitivity analysis 

12 represents a WHO Tier 2 analysis. In conducting these assessments, we have used 

13 alternative representations of modeling elements that have support in the literature to 

14 ensure that the risk estimates that are generated represent reasonable alternate estimates 

15 that can supplement the core risk estimates generated in the analysis (see section 4.5.3). 

16  Multi-factor sensitivity analysis intended to assess the combined impact of multiple 

17 sources of uncertainty and variability on risk estimates (see section 3.5.4). By 

18 considering the combined effect of multiple sources of uncertainty and variability, this 

19 analysis has the potential to identify any non-linearities which can magnify the impact 

20 of uncertainty and variability on risk estimates, especially if several non-linear factors 

21 act in concert. This also represents a WHO Tier 2 analysis. As with the single-factor 

22 sensitivity analysis results, these risk estimates are also generated using modeling 

23 inputs which have support in the literature and consequently, they also represent 

24 reasonable alternate estimates that supplement the core risk estimates (see section 

25 4.5.2). 

26 As noted above, since information was not available to characterize overall levels of 

27 confidence in alternative model inputs, the uncertainty characterization completed for this risk 

28 assessment did not include a full probabilistic assessment of uncertainty and its impact on core 

29 risk estimates (i.e., a WHO Tier 3 analysis was not completed). Further, the risk estimates 

30 generated using the single- and multi-factor sensitivity analyses do not represent uncertainty 

31 distributions, but rather additional plausible point estimates of risk (i.e., we do not know whether 

32 they represent risk estimates near the upper or lower bounds of a true but undefined uncertainty 

33 distribution and we do not know the actual population percentiles that they represent). The 

34 appropriate use for these reasonable alternate risk estimates in informing consideration of 

35 uncertainty in the core risk estimates is discussed in section 4.5.3. 

36 In addition to the specific analyses discussed above, we have also completed two 

37 additional analyses intended to place the 15 urban study areas in a broader national context with 

38 regard to risk. These include a representativeness analysis which evaluates the way the 15 urban 

39 study areas compare to national distributions for key PM-related risk attributes (discussed in 

40 section 4.4). We have also completed a national-scale assessment of long-term mortality related 
February 2010 Draft – Do Not Quote or Cite 3-80

1 to PM2.5 exposures (chapter 5), which, in addition to providing an estimate of the national impact 

2 of PM2.5 on long-term mortality, also evaluates whether the set of 15 urban study areas generally 

3 represents the broader distribution of risk across the U.S., or a more focused portion of the 

4 national risk distribution (e.g., the higher-end). 

5 A third set of analyses that has been added to this second draft RA focuses on evaluating 

6 patterns in the design values (including both 24-hour and annual) and underlying PM2.5

7 monitoring data for the 15 urban study areas (see Section 4.5). The goal of this analysis is to use 

8 this information to enhance our understanding of patterns in risk reduction seen under both the 

9 current and alternative suites of standards across the urban study areas. The interplay of design 

10 values and underlying PM2.5 monitoring data play a key role in determining whether a location 

11 will experience risk reductions when just meeting any given suite of standards is simulated and, 

12 if so, the magnitude of those reduction. As part of this analysis, we contrast patterns in design 

13 values for the 15 urban study areas with patterns seen more broadly across urban areas in the 

14 U.S. with the goal of placing the urban study areas in a national context with regard to this key 

15 factor influencing risk.
February 2010 Draft – Do Not Quote or Cite 4-1

1 4 URBAN CASE STUDY RESULTS 

2 For this risk assessment, we have developed a core set of risk estimates supplemented by 

3 an alternative set of risk results generated using single-factor and multi-factor sensitivity 

4 analysis. The core set of risk estimates was developed using model inputs that staff judge to 

5 have a greater degree of support in the literature relative to inputs used in the sensitivity analyses 

6 (the rationale for selection of specific epidemiological studies and associated C-R functions for 

7 the core analysis is discussed above in section 3.3.3). This chapter presents and discusses the 

8 core set of risk estimates generated for the urban case study area, and also discusses the results of 

9 the sensitivity analyses which serve to augment the core risk estimates. The results of the 

10 sensitivity analyses allow us to evaluate and rank the potential impact of key sources of 

11 uncertainty on the core risk estimates. In addition, because the sensitivity analyses were 

12 conducted using alternative modeling inputs having some degree of support in the literature, the 

13 results of the sensitivity analysis also represent a set of reasonable alternatives to the core set of 

14 risk estimates that can be used to inform characterization of uncertainty in the core results (see 

15 section 4.3 below). 

16 As discussed above in section 2.2 and 3.2, this risk assessment includes consideration of 

17 the following air quality scenarios: 

18  Recent conditions: based on PM2.5 concentrations characterized through monitoring for 

19 the period 2005-2007 at each urban case study location; 

20  Current NAAQS: based on rolling back PM2.5 concentrations to just meet the current 

suite of standards in each urban study area (annual standard of 15 μg/m3 21 and a 24-hour 

standard of 35 μg/m3 22 , denoted 15/35); 

23  Alternative NAAQS: based on rolling back PM2.5 concentrations to just meet alternative 

24 suites of standards in each urban study area: 

o annual standard of 14 μg/m3 and a 24-hour standard of 35 μg/m3 25 (denoted 13/35); 

o annual standard of 13 μg/m3 and a 24-hour standard of 35 μg/m3 26 (denoted 13/35); 

o annual standard of 12 μg/m3 and a 24-hour standard of 35 μg/m3 27 (denoted 12/35); 

o annual standard of 13 μg/m3 and a 24-hour standard of 30 μg/m3 28 (denoted 13/30); 

o annual standard of 12 μg/m3 and a 24-hour standard of 25 μg/m3 29 (denoted 12/25). 

30 In simulating both current and alternative suites of standards, for the core analysis, we 

31 used a proportional roll-back approach (see section 3.2.3), while a hybrid roll-back approach 

32 reflecting the potential for local source control was used for a subset of urban study areas as part 

33 of the sensitivity analysis conducted for this assessment (see section 3.2.3). In addition, we have 

34 considered the peak-shaving approach as a further alternative to proportional rollback in 
February 2010 Draft – Do Not Quote or Cite 4-2

1 simulating just meeting the current and alternative suites of standards. While we did not generate 

2 risk estimates based on application of the peak-shaving approach, we did generate composite 

3 monitor-based annual average PM2.5 levels which allow us to assess how long-term exposure4 related risk could vary if this alternative roll-back method was used (see Section 4.3). 

5 As described in section 2.1 and 3.3.2, we assessed risk for 15 urban study areas chosen to 

6 provide coverage for the diversity of urban settings across the U.S. that reflect areas with 

7 elevated annual and/or daily PM2.5 concentrations. At a minimum, all areas selected had recent 

8 air quality levels at or above the lowest annual and/or 24-hour standards analyzed. In addition, 

9 our goal was to select areas reflecting the heterogeneity in PM risk-related attributes such as 

10 sources, composition, demographics, and population behavior. 

11 Risk estimates were generated for the following health effects endpoints: (a) long-term 

12 exposure-related mortality (all-cause, cardiopulmonary disease-related (CPD), ischemic heart 

13 disease-related (IHD) and lung cancer-related), (b) short-term exposure-related mortality (non14 accidental, cardiovascular disease-related (CVD), respiratory), and (c) short-term exposure15 related morbidity (hospital admissions (HA) for CVD and respiratory illness and emergency 

16 department (ED) visits). Risk estimates are presented separately for each of these 15 study areas, 

17 although in certain circumstances, risk estimates may be restricted to a subset of these locations 

18 if, for example, an endpoint is modeled using a C-R function derived from an epidemiological 

19 study that was conducted only in a subset of the urban areas. For the core analysis, long-term 

20 exposure mortality risk was modeled down to lowest measured level (LML), because the LML 

21 was higher than estimated PRB and because there is substantial uncertainty as to the shape of the 

22 concentration-response (C-R) function at concentrations below the LML. For long-term 

23 exposure mortality a sensitivity analysis was conducted that estimated risk down to policy24 relevant background (PRB). In contrast, all short-term exposure health effects endpoints were 

25 modeled down to PRB, since this was higher than the LML across all studies and for purposes of 

26 NAAQS decision making, EPA is focused on risks associated with PM2.5 levels that are due to 

27 anthropogenic sources that can be controlled by U.S. regulations (or through international 

28 agreements with neighboring countries). 

29 In modeling long-term exposure mortality, for the core analysis, we have based estimates 

30 on the latest reanalysis of the American Cancer Society (ACS) dataset, with two sets of risk 

31 estimates being generated; one using a C-R function derived by fitting PM2.5 monitoring data 

32 from 1979-1983 and a second set based on fitting PM2.5 monitoring data from 1999-2000 

33 (Krewski et al., 2009) (see section 3.3.3). In presenting core risk estimates for long-term 

34 mortality, both sets of estimates are given equal weight. 

35 In modeling short-term exposure mortality and morbidity for the core analysis, we have 

36 used the latest multi-city studies (Zanobetti and Schwartz, 2009; Bell et al., 2008) (see section 
February 2010 Draft – Do Not Quote or Cite 4-3

1 3.3.3). In the case of short-term exposure mortality, we obtained and used city-specific effects 

2 estimates derived using empirical Bayes methods from the study authors (Zanobetti, 2009). 

3 Multi-city studies were favored for the core analysis, since these studies are not subject to 

4 publication bias and because they reflect a diverse set of locations with regard to the observed 

5 relationship between short-term PM2.5 exposure and health affect response in the population. 

6 Additional detail on the specific C-R functions and related modeling elements such as effects 

7 estimates and lag periods used in the core analysis relative to the sensitivity analysis are 

8 presented above in sections 3.3 and 3.4 and called out where appropriate below as specific risk 

9 estimates are discussed. 

10 The pattern of mortality incidence across the urban study areas is markedly different for 

11 short-term exposure-related mortality compared with long-term exposure-related mortality 

12 reflecting a number of factors including: (a) differences in patterns of daily PM2.5 levels versus 

13 annual average values across the urban study areas and (b) the fact that urban study area-specific 

14 effect estimates are used in modeling short-term exposure-related mortality, while a single effect 

15 estimate is used for all study areas for long-term exposure-related mortality (for a particular 

16 mortality category). Further, effect estimates for short-term exposure-related mortality can be 

17 notably small for some study areas (e.g., the effect estimates for non-accidental mortality for Los 

18 Angeles is significantly smaller than effect estimates for the other study areas, thereby 

19 accounting for the relatively small total incidence estimate for this study area – see Appendix C, 

20 Table C-1). 

21 Because the recent conditions air quality scenario spans three years (2005-2007), risk 

22 estimates are generated for each of these years, reflecting the underlying air quality data for a 

23 particular year. Risk metrics generated for the above health effects endpoints include: 

24  Annual incidence of the endpoint due to PM2.5 exposure (annual incidence): 

25 Generated for the population associated with a given urban study area (for a given 

26 simulation year), in most cases, these risk estimates include both a point estimate as well 

as a 95th 27 percentile confidence interval, the latter reflecting sampling error as 

28 characterized in the underlying epidemiological study. 

29  Percent of total annual incidence for the health endpoint due to PM2.5 exposure 

30 (percent of total incidence attributable to PM2.5): Again, generated for the population 

31 associated with a given urban study area (and simulation year), this metric characterizes 

32 the fraction of total incidence that is associated with PM2.5 exposure. As with the 

underlying PM-related incidence estimates, this risk metric also typically includes a 95th 33 

34 percentile confidence interval reflecting sampling error associated with the effects 

35 estimate. Compared with the annual incidence metric which reflects underlying 

36 population size for each study area, this risk metric has the advantage of not being 

37 dependent on the size of the underlying population, thereby allowing direct comparison 

38 of the potential impact of PM2.5 for the health effect endpoint of interest across urban 

39 study area locations. For this reason, in discussing risk estimates in this section, the 
February 2010 Draft – Do Not Quote or Cite 4-4

1 percent of total incidence attributable to PM2.5 risk metric is given greater emphasis than 

2 the absolute measure of annual incidence attributable to PM2.5. 

3  Percent reduction in PM2.5-related health effect incidence for an alternative set of 

4 standards or the recent conditions scenario, relative to the current standards 

5 (percent change from the current set of standards): Also estimated separately for each 

6 urban study area and simulation year, this metric characterizes the degree of risk 

7 reduction (for alternative standard levels) or increased risk (for the recent conditions 

8 scenario) relative to the current NAAQS. For this metric, a negative value represents an 

9 increase in risk (this is the case for the recent conditions scenario, where risks are higher 

10 than those associated with just meeting the current suite of standards). This metric is 

11 positive, or zero, for alternative suites of standards since they either produce no risk 

12 reduction (if ambient air levels under recent conditions are already at or below that 

13 alternative standard levels), or a positive risk reduction for alternative standards resulting 

14 in a reductions in ambient PM2.5 concentrations. Because this metric is incremental, it 

was not possible to generate the 95th 15 percentile confidence intervals included with the 

16 other two “absolute” risk metrics described above. As with the previous risk metric, this 

17 metric is not dependent on the underlying population size and therefore, allows direct 

18 comparison across urban study areas. 

19 In addition to presenting the central-tendency (highest confidence) estimates for each of 

these metrics, we also include 95th 20 percentile confidence intervals, reflecting statistical 

21 uncertainty surrounding the estimated coefficients in the reported C-R functions used in deriving 

22 the risk estimates (note, that these confidence intervals only capture this statistical fit uncertainty 

23 – other sources of uncertainty including shape and form of the function, are addressed separately 

24 as part of the sensitivity analysis – see Section 4.3.1.1 and the qualitative analysis of uncertainty 

25 – see Section 3.5.3). 

26 Detailed tables presenting estimates for these risk metrics for the complete set of air 

27 quality scenarios (for all 15 urban study areas) are included in Appendix E and referenced as 

28 needed in the discussion of risk estimates presented in the following sections. To support the 

29 discussion of risk estimates presented in this chapter, we have included a subset of tables and 

30 summary figures including: 

31  Tables summarizing risk for the current standard levels: Two tables are included 

32 which summarize both long-term and short-term exposure-related risk for the 15 urban 

33 study areas associated with just meeting the current suite of standards. Both tables 

34 include a subset of the health endpoints believed to have the greatest support in the 

35 literature including IHD mortality for long-term exposure, cardiovascular mortality and 

36 hospital admissions for short-term exposure. Table 4-1 presents total incidence 

37 attributable to PM2.5 exposure for the endpoints and Table 4-2 presents percent of total 

38 incidence attributable to PM2.5 exposure for these endpoints. Together, these tables 

39 inform consideration for the magnitude of public health impact (related to both long-term 

40 and short-term exposure to PM2.5) associated with just meeting the current suite of 

41 standards in the 15 urban study areas. 
February 2010 Draft – Do Not Quote or Cite 4-5

1  Figures illustrating the percent reduction in long-term and short-term exposure2 related risk for the alternative standard levels relative to the current standard (as 

3 well as increases in risk under recent conditions relative to the current standard): 

4 Figures 4-1 and 4-4 provide a snapshot of trends in risk reduction for long-term exposure5 related risk (Figure 4-1) and short-term exposure-related risk (Figure 4-4) across 

6 alternative standard levels relative to the risk under the current standard. These figures 

7 include plots for each of the 15 urban study areas, thereby allowing trends in risk 

reduction across standard levels (and urban study areas) to be assessed simultaneously.44 8 

9 Each of these figures is presented in additional detail by splitting each into (a) 

10 comparison of the recent conditions risk against the current standard level and (b) 

11 comparison of risk under alternative standard level against the current standard, in order 

12 to allow a more detailed look at patterns in risk reduction for individual urban study areas 

13 (splitting Figures 4-1 and 4-4 in this fashion allows greater resolution in tracing the linear 

14 risk plots for each study area). Specifically, Figures 4-2 and 4-3 provide these higher15 resolution plots for long-term exposure-related risk and Figures 4-5 and 4-6 provide 

16 higher-resolution plots for short-term exposure related risk. 

17 Although risk estimates were generated for all three simulation years, in this chapter core risk 

18 estimates primarily from 2007 are presented and discussed for both the recent conditions air 

19 quality scenario and just meeting current and alternative suites of standards. This reflects the 

20 observation that in generally 2007 represents a reasonable central year (in terms of the magnitude 

21 of risk generated for the three simulated years), when considering results for all modeled health 

22 effect endpoints across the 15 study areas. In addition, 2007 is the most recent year of the three 

23 simulated. We note, however, that while we do focus on 2007 in presenting and discussing risk 

24 estimates, we include an assessment of general trends across the three simulation years to gain 

25 perspective on year-to-year variation in PM2.5-related risk estimates as assessed here. 

26 

27 

28 

29 

30 

31 

32 

33 

 44 Note, that importantly, patterns of risk reduction across standard levels (in terms of percent change relative to risk 

for the current 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 this risk assessment are close to linear across the 

range of ambient air levels evaluated. This allows us to present these figures plotting changes in risk more generally 

for short-term exposure-related endpoints and long-term exposure related endpoints without having to provide 

figures for each specific endpoint category. 
February 2010 Draft – Do Not Quote or Cite 4-6

1 Table 4-1. Estimated Annual Incidence of Selected Mortality and Morbidity Endpoints 

2 Associated with Long- and Short-Term Exposure to Ambient PM2.5 

3 Concentrations that Just Meet the Current Standards, Based on Adjusting 

2007 PM2.5 Concentrations. 1,2 4 

Exposure Period: 

1979-1983

Exposure Period: 

1999-2000

220 277 32 41

(180 - 258) (227 - 324) (-33 - 95) (-27 - 109)

297 374 62 216

(243 - 349) (307 - 440) (-4 - 126) (159 - 273)

131 165 -1 16

(107 - 154) (135 - 194) (-42 - 40) (-11 - 43)

195 247 29 28

(159 - 230) (202 - 291) (-19 - 76) (-18 - 73)

377 478 60 233

(308 - 445) (390 - 563) (-8 - 127) (171 - 295)

77 98 12 23

(63 - 92) (80 - 116) (-9 - 33) (0 - 46)

344 434 46 56

(281 - 405) (355 - 511) (-31 - 122) (-37 - 149)

860 1094 -30 258

(701 - 1018) (890 - 1296) (-132 - 72) (3 - 511)

1755 2222 473 752

(1435 - 2070) (1814 - 2620) (276 - 668) (552 - 951)

261 330 84 203

(214 - 308) (270 - 389) (22 - 145) (149 - 257)

317 402 84 108

(258 - 374) (327 - 476) (-4 - 170) (1 - 215)

256 324 43 140

(209 - 302) (264 - 382) (-9 - 93) (103 - 177)

15 19 9 9

(12 - 18) (16 - 23) (-2 - 20) (0 - 18)

446 563 106 178

(365 - 525) (461 - 662) (24 - 187) (131 - 225)

38 49 11 19

(31 - 46) (40 - 58) (-6 - 27) (-46 - 82)

Risk Assessment 

Location

 Incidence of Ischemic Heart Disease 

Mortality Associated with Long-term 

Exposure to PM2.53

Incidence of 

Cardiovascular 

Mortality Associated 

with Short-term 

Exposure to PM2.54

Incidence of 

Cardiovascular 

Hospitalizations 

Associated with Short

term Exposure to 

PM2.55

 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

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3.

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical 

uncertainty surrounding the PM coefficient.

3Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 1983 and from 1999-2000 respectively.

4Based 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. 

5Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et 

5 al. (2008). Location-specific C-R function estimates were not available from this study.

6 

7 

8 

9 

10 

11 

12 

13 

14 
February 2010 Draft – Do Not Quote or Cite 4-7

1 Table 4-2 Estimated Percent of Total Annual Incidence of Selected Mortality and 

2 Morbidity Endpoints Associated with Long- and Short-Term Exposure to 

3 Ambient PM2.5 Concentrations that Just Meet the Current Standards, Based 

4 on Adjusting 2007 PM2.5 Concentrations. 1,2 

Exposure Period: 

1979-1983

Exposure Period: 

1999-2000

13.2% 16.7% 0.8% 0.4%

(10.9% - 15.5%) (13.7% - 19.5%) (-0.8% - 2.4%) (-0.2% - 1%)

11.7% 14.7% 1.6% 1.3%

(9.6% - 13.7%) (12.1% - 17.3%) (-0.1% - 3.2%) (1% - 1.7%)

10.9% 13.8% 0% 0.3%

(8.9% - 12.9%) (11.3% - 16.2%) (-1.5% - 1.5%) (-0.2% - 0.9%)

9% 11.4% 0.8% 0.3%

(7.3% - 10.6%) (9.3% - 13.4%) (-0.5% - 2.2%) (-0.2% - 0.7%)

9.1% 11.5% 1% 1.1%

(7.4% - 10.7%) (9.4% - 13.5%) (-0.1% - 2.2%) (0.8% - 1.4%)

6.7% 8.5% 0.7% 0.5%

(5.5% - 8%) (7% - 10.1%) (-0.5% - 2%) (0% - 0.9%)

10.7% 13.6% 0.9% 0.3%

(8.8% - 12.6%) (11.1% - 16%) (-0.6% - 2.4%) (-0.2% - 0.8%)

6.1% 7.7% -0.2% 0.5%

(4.9% - 7.2%) (6.3% - 9.1%) (-0.7% - 0.4%) (0% - 0.9%)

9.3% 11.8% 2.1% 1.2%

(7.6% - 11%) (9.6% - 13.9%) (1.2% - 3%) (0.8% - 1.5%)

10.5% 13.2% 2.1% 1.3%

(8.6% - 12.3%) (10.8% - 15.6%) (0.5% - 3.6%) (0.9% - 1.6%)

6.7% 8.5% 1.3% 0.5%

(5.5% - 7.9%) (6.9% - 10.1%) (-0.1% - 2.7%) (0% - 1%)

9.3% 11.8% 1.1% 1.1%

(7.6% - 11%) (9.6% - 13.9%) (-0.2% - 2.3%) (0.8% - 1.4%)

2.9% 3.7% 0.8% 0.4%

(2.4% - 3.4%) (3% - 4.4%) (-0.2% - 1.7%) (0% - 0.7%)

11.2% 14.2% 1.9% 1.3%

(9.2% - 13.2%) (11.6% - 16.7%) (0.4% - 3.3%) (0.9% - 1.6%)

3.7% 4.7% 0.7% 0.5%

(3% - 4.4%) (3.8% - 5.6%) (-0.4% - 1.8%) (-1.3% - 2.3%)

Risk Assessment 

Location

Percent of Incidence of Ischemic Heart 

Disease Mortality Associated with Long-term 

Exposure to PM2.53

Percent of Incidence 

of Cardiovascular 

Mortality Associated 

with Short-term 

Exposure to PM2.54

Percent of Incidence 

of Cardiovascular 

Hospital Admissions 

Associated with Short

term Exposure to 

PM2.55

 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

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3.

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty 

surrounding the PM coefficient.

3Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 1983 and from 1999-2000 respectively

4Based 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. 

5Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et 

5 al. (2008). Location-specific C-R function estimates were not available from this study.
February 2010 4-8 Draft – 

1 Figure 4-1 Percent reduction in long-term exposure-related mortality risk (alternative standards and recent conditions relativ2 (Note: inset shows PM2.5 related incidence and percent of total incidence for IHD mortality under the current suite of 

Detroit, MI 

 New York, NY

Philadelphia, PA Phoenix, AZ 

S

-50%-30%-10%

10%30%50%70%90%

2007 air

quality

15/35** 14/35 13/35 12/35 13/30 

Recent Air Quality, Current Standard 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%)

3 4 

5 *Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (6 estimate of percent of total incidence (and 95% CI) under the current standards. 

**The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3 7 . Combinations of an annual standar

8 (m) are denoted n/m in this figure.
February 2010 4-9 Draft – 

1 Figure 4-2 Percent reduction in long-term exposure-related mortality risk (recent conditions relative to the current standards2 PM2.5 related incidence and percent of total incidence for IHD mortality under the current suite of standards) 

Atlanta, GA 

Baltimore, MD 

 Birmingham, AL

Dallas, TX 

 Detroit, MI

 Fresno, CA

Houston, TX 

Los Angeles, CA 

 New York, NY

Philadelphia, PA 

Pittsburgh, PA Salt Lake City, UT 

 St. Louis, MO

Tacoma, WA 

Phoenix, AZ 

-250%-200%-150%-100%

-50%

0%

2007 air quality 15/35**

Recent Air Quality and Current Standard

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% 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%) Phoenix, AZ 402 (327 - 476); 8.5% (6.9% - 10.1%)

3 

4 5 *Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (6 estimate of percent of total incidence (and 95% CI) under the current standards. 

**The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3 7 . Combinations of an annual standar

8 (m) are denoted n/m in this figure. 
February 2010 4-10 Draft – 

1 Figure 4-3 Percent reduction in long-term exposure-related mortality risk (alternative standards relative to the current stand2 PM2.5 related incidence and percent of total incidence for IHD mortality under the current suite of standards)

 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%)

3 

4 *Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (5 estimate of percent of total incidence (and 95% CI) under the current standards. 

**The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3 6 . Combinations of an annual standar

7 (m) are denoted n/m in this figure. 8 ***The percent reductions for Salt Lake City and Tacoma at the 12/25 standard are 100% and 93%, respectively. 9 
February 2010 4-11 Draft – 

1 Figure 4-4 Percent reduction in short-term exposure-related mortality and morbidity risk (alternative standards and recent 2 current standards) (Note: inset shows PM2.5 related incidence and percent of total incidence for CV under the current 

 Atlanta, GA 

 Baltimore, MD

 Birmingham,

 AL

D

 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

-50%-40%-30%-20%-10%

0%

10%20%30%

2007 air

quality

15/35** 14/35 13/35 12/35 13/30 1

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% -  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%)

`

3 

4 5 *Based on Zanobetti and Schwartz (2009). The legend contains, for each urban area, the incidence estimate (and 95% CI) and the estim6 incidence (and 95% CI) under the current standards. 

**The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3 7 . Combinations of an annual standar

8 (m) are denoted n/m in this figure. 

9 *** The percent reductions from 2007 air quality to the current standard for Salt Lake City and Fresno are -58% and -81%, respectively
February 2010 4-12 Draft – 

1 Figure 4-5 Percent reduction in short-term exposure-related mortality and morbidity risk (recent conditions relative to the 2 inset shows PM2.5 related incidence and percent of total incidence for CV under the current suite of standards)

Atlanta, GA 

 Baltimore, MD

 Birmingham, AL

Fresno, CA 

Houston, TX 

Los Angeles, 

CA 

 New York,

 NY

 Philadelphia,PA

Phoenix, AZ 

 Pittsburgh, PA

Salt Lake City, UT 

St. Louis, MO 

Dallas, TX 

Tacoma, WA Detroit, MI 

-90%-80%-70%-60%-50%-40%-30%-20%-10%

0%

2007 air quality 15/35**

Recent Air Quality and Current Standard

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% -  Birmingham, AL -1 (-42 - 40); 0% (-1.5% -  Fresno, CA 12 (-9 - 33); 0.7% (-0.5% - 2% Houston, TX 46 (-31 - 122); 0.9% (-0.6% -  Los Angeles, CA -30 (-132 - 72); -0.2% (-0 New York, NY 473 (276 - 668); 2.1% (1.2%  Philadelphia, PA 84 (22 - 145); 2.1% (0.5%  Phoenix, AZ 84 (-4 - 170); 1.3% (-0.1% - 2 Pittsburgh, PA 43 (-9 - 93); 1.1% (-0.2% - 2 Salt Lake City, UT 9 (-2 - 20); 0.8% (-0.2%  St. Louis, MO 106 (24 - 187); 1.9% (0.4% -  Dallas, TX 29 (-19 - 76); 0.8% (-0.5% - 2.2 Detroit, MI 60 (-8 - 127); 1% (-0.1% - 2.2% Tacoma, WA 11 (-6 - 27); 0.7% (-0.4% - 1.

3 

4 *Based on Zanobetti and Schwartz (2009). The legend contains, for each urban area, the incidence estimate (and 95% CI) and the estim5 incidence (and 95% CI) under the current standards. 

**The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3 6 . Combinations of an annual standar

7 (m) are denoted n/m in this figure.
February 2010 4-13 Draft – 

1 Figure 4-6 Percent reduction in short-term exposure-related mortality and morbidity risk (alternative standards relative to 2 (Note: inset shows PM2.5 related incidence and percent of total incidence for CV under the current suite of standards)

 Baltimore,

 MD

 Birmingham, AL

 Detroit, MI 

 Fresno, CA 

 Houston, TX 

 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 

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%)

`

3 

4 *Based on Zanobetti and Schwartz (2009). The legend contains, for each urban area, the incidence estimate (and 95% CI) and the estim5 incidence (and 95% CI) under the current standards. 

**The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3 6 . Combinations of an annual standar

7 (m) are denoted n/m in this figure.
February 2010 Draft – Draft Do Not Quote or Cite 4-14

1 As noted above, the risk assessment includes risk estimates for a range of short-term and 

2 long-term exposure-related health effect endpoints. To focus the discussion of these risk 

3 estimates, we have selected a subset of the health endpoints as examples to help illustrate 

4 patterns in the risk estimates that might be of interest from a policy standpoint. Specifically, we 

5 have focused on those endpoints that the ISA identifies as having the greatest support in the 

6 literature (i.e., endpoints related to cardiovascular effects, including both mortality and 

7 morbidity). The subset of health effect endpoints selected as illustrative examples for this 

8 overview include: IHD-related mortality (for long-term exposure) and CV-related mortality and 

9 HA (for short-term exposure). While the discussion does focus on these cardiovascular-related 

10 endpoints, we do address other endpoints modeled in the risk assessment to a limited extent. The 

11 full set of risk estimates generated is presented in the detailed tables in Appendix E. 

12 For a subset of the urban case studies (e.g., Dallas and Phoenix), incremental reductions 

13 across alternative standards are initially very low (or even zero) reflecting the fact that recent 

14 ambient PM2.5 concentrations for these study areas are well below the current annual standard 

15 levels. For these study areas, meaningful reductions in risk may not be seen until relatively 

16 lower alternative standards are assessed (and results in the percent reduction from the current set 

17 of standards tables and figures may be zero for several of the less stringent, alternative sets of 

18 standards). The pattern of risk reductions across alternative standard levels for a given urban 

19 study area is an important factor that is discussed in the integrative discussion in Chapter 6. To 

20 set up that later discussion, in summarizing risk estimates below, we provide observations 

21 regarding trends in risk estimates across alternative suites of standards (for a given urban study 

22 area). 

23 For a number of the urban study areas, confidence intervals (and in some instances, point 

24 estimates) for short-term mortality and morbidity incidence and related risk metrics include 

25 values that fall below zero. Population incidence estimates with negative lower-confidence 

26 bounds (or point estimates) do not imply that additional exposure to PM2.5 has a beneficial effect, 

27 but only that the estimated PM2.5 effect estimate in the C-R function was not statistically 

28 significantly different from zero. In the case of short-term exposure mortality, where study area29 specific effects estimates were used (see section 3.4), several of the urban locations have non30 statistically significant effects estimates; these result in incidence estimates with non-positive 

31 lower bounds and/or best estimates (e.g., Birmingham, Detroit, and Los Angeles for non32 accidental mortality). In the case of short-term morbidity (e.g., HAs), where regional effects 

33 estimates were used, one of the regional coefficients (for the southeast) is not statistically 

34 significant, producing incidence estimates including negative values in the confidence interval 

35 for urban study areas falling within that region (e.g., Atlanta, Dallas, and Houston, for CV36 related HAs). Lack of statistical significance could mean that there is no relationship between 
February 2010 Draft – Draft Do Not Quote or Cite 4-15

1 PM2.5 and the health endpoint or it could mean that there was not sufficient statistical power to 

2 detect a relationship that actually exists. In the case of PM2.5 and both short-term exposure 

3 mortality and morbidity, recognizing that the ISA has concluded that there is either a causal or 

4 likely causal relationship between short-term PM2.5 exposure and these health effects (see section 

5 3.3.1), we believe it is reasonable to assume that instances where effects estimates are not6 statistically significant are likely to reflect insufficient sample size, rather than the absence of an 

7 actual association. We note, however, that (as discussed in section 3.6.3) many factors can 

8 potentially result in variations in the magnitude of effect estimates. In addition to sample size, 

9 these include: source and compositional differences for PM2.5, exposure error associated with the 

10 use of ambient monitors as a surrogate for actual exposure, and differences in population 

11 susceptibility and vulnerability. 

12 An important theme in discussing risk associated with both current and alternative 

13 standard levels is the linkage between the nature and magnitude of risk reductions seen for a 

14 particular study area (for a particular suite of 24-hour and annual standards) and the specific mix 

15 of 24-hour and annual design values associated with that study area. Because design values 

16 determine the degree to which the PM2.5 monitors in a study area are adjusted in simulating 

17 attainment of both current and alternative standard levels, they play a central role in determining 

18 the degree of risk reduction associated with a particular suite of standard levels. Given the 

19 importance of design values in determining risk reduction under both current and alternative 

20 standard levels, we have examined patterns in design values (specifically the relationship 

21 between 24-hour and annual design values) across the 15 urban study areas, as a means for 

22 enhancing our interpretation of patterns in risk reductions for the standard levels modeled. In 

23 addition, we have contrasted the patterns of design values for the 15 urban study areas with 

24 patterns of design values for the broader set of urban areas in the U.S.; this supporting efforts to 

25 place risk estimates for the urban study areas in a broader national context. This exploration of 

26 design values is discussed in section 4.5. 

27 An additional factor to consider in examining patterns in risk estimates is the overall 

28 spread in PM2.5 measurements across monitors at a particular urban study area, including 

29 distributions of both 24-hour distributions and annual averages. This factor works in concert 

30 with the patterns in design values mentioned earlier in determining the degree of risk reduction 

31 associated with a particular suite of standard levels. In addition, the spread in monitor values for 

32 a particular urban study area can also determine the degree to which alternative rollback methods 

33 (proportional, hybrid and peak shaving) produce differences in risk estimates for a given study 

34 area. Consequently, in concert with examining patterns in design values (see above) we have 

35 also explored patterns in PM2.5 monitoring data for the 15 urban study areas in an effort to better 
February 2010 Draft – Draft Do Not Quote or Cite 4-16

1 understand how application of different rollback methods results in differing impacts on core risk 

2 estimates. This topic is discussed in section 4.5. 

3 The remainder of this section is organized as follows. Core modeling results for the 

4 recent conditions air quality scenario are presented in section 4.1. Core modeling results for just 

5 meeting the current NAAQS and just meeting alternative NAAQS are presented in section 4.2. 

6 The results of the sensitivity analysis (including single-factor and multi-factor results) are 

7 presented in section 4.3. The results of a representativeness analysis involving comparison of 

8 counties associated with the 15 urban study area locations against the national distribution of 

9 counties with regard to a set of PM-risk related attributes are presented in section 4.4. Section 

10 4.5 discusses the consideration of design values in interpreting risk estimates generated for the 

11 15 urban study areas and helping to place them in a broader national context (section 5.4.1), as 

12 well as consideration for the patterns in ambient PM2.5 data within study areas as a factor 

13 influencing patterns of risk estimates (section 4.5.2). Chapter 6 provides an integrative 

14 discussion of the results of the core risk assessment for the 15 urban study areas informed by 

15 consideration of: (a) the single- and multi-factors sensitivity analysis, (b) the qualitative analysis 

16 of sources of variability and uncertainty, (c) the representativeness analysis (d) the national-scale 

17 mortality analysis (presented in chapter 5), and (e) the role of design values (and patterns in 

18 ambient PM2.5 monitoring data) in influencing overall patterns of risk estimates across alternative 

19 suites of standards. 

20 4.1 ASSESSMENT OF HEALTH RISK ASSOCIATED WITH RECENT CONDITIONS 

21 (CORE ANALYSIS) 

22 This section discusses core risk estimates generated for the recent conditions air quality 

23 scenario, focusing on the 2007 simulation year. Specifically, it provides a set of key observations 

24 regarding core risk estimates generated for the recent conditions air quality scenario. Note, that 

25 while the focus of this section is on identifying key risk-related observations potentially relevant 

26 to the current review of the PM NAAQS, additional review of the risk estimates provided in 

27 Appendix E is likely to result in additional observations that might be relevant to the PM 

28 NAQQS review (EPA staff will continue to review those results as they work on completing the 

29 summary of the RA presented in the first draft PA). 

30 In discussing results for the recent conditions air quality scenario, we have focused on 

31 absolute risk (either above PRB or LML, depending on the health effect endpoint). This reflects 

32 the fact that this air quality scenario represents recent conditions within the urban study areas and 

33 therefore, does not lend itself to an incremental assessment. The section is organized by health 

34 endpoint category, with results discussed in the following order: long-term exposure mortality, 
February 2010 Draft – Draft Do Not Quote or Cite 4-17

short-term exposure mortality and short-term exposure morbidity.45 1 In summarizing estimates 

2 for each endpoint category, we fist focus on the central-tendency risk estimates (these are what is 

3 discussed in each of the bullets focusing on a particular endpoint category). A discussion of the 

broader risk range reflecting consideration for 95th 4 % confidence interval risk estimates is 

5 presented as a separate bullet towards the end of the discussion. Key observations include: 

6  Long-term exposure-related mortality: Total incidence of PM2.5-related all-cause mortality 

7 ranges from 50-60 (Salt Lake City) to 2,380-3,000 (New York) (Appendix E, Table E-21 and 

8 E-30), with this range reflecting not only differences in baseline incidence across urban study 

9 areas, but also the size of study populations which vary considerably across the study areas. 

10 The percent of total incidence of IHD-related mortality attributable to PM2.5 ranges from 6.3-

11 8.0% (Tacoma) to 17.7-22.2% (Fresno) (Appendix E, Table E-24 and E-33). Total PM2.5-

12 attriutable incidence for all-cause mortality and cardiopulmonary mortality is larger than IHD 

13 (for a given study area under recent conditions), while total PM2.5-attributable incidence for 

14 lung-cancer mortality is lower than for IHD. However, the percent of total incidence 

15 attributable to PM2.5 exposure is larger for IHD-related mortality than for any of the other 

16 mortality categories modeled (Appendix E, Tables E-24 and E-33). 

17  Short-term exposure-related mortality: Total incidence of PM2.5-related mortality for 

18 short-term exposure (for all categories modeled) is substantially smaller than estimates for 

19 long-term exposure-related mortality. Estimates for CV mortality for short-term exposure 

20 ranges from 14 (Salt Lake City) to 570 (New York) (Appendix E, Table E-84). The percent 

21 of total non-accidental mortality attributable to PM2.5 ranges from 0.9% (Tacoma) to 2.5% 

22 (New York). (Appendix E, Table E-87). Percent of total incidence attributable to PM2.5

23 exposure is generally lower for total non-accidental mortality (compared with CV), ranging 

24 from 0.2% (Los Angeles) to 1.8% (Baltimore) (Appendix E, Table E-78). Estimates for 

25 respiratory mortality are usually higher than for CV mortality, ranging from 0.9% (Dallas) to 

26 2.8% (Fresno and New York) (Appendix E, Table E-96). Of the 15 urban study areas 

27 modeled for CV mortality, 12 locations had negative lower bound estimates of incidence 

28 (and two of these head negative point estimates), reflecting use of non-statistically significant 

29 effects estimates (see section 4.0 for additional discussion). The number of study areas 

30 modeled with non-statistically significant effects estimates was lower for the other two short31 term exposure-related mortality endpoints. 

32  Short-term exposure-related morbidity (hospital admissions for respiratory and 

33 cardiovascular illness): Total incidence of PM2.5-related cardiovascular HA range from 15 

34 (Salt Lake City) to 910 (New York City) and are significantly larger than estimates of 

35 respiratory HA attributable to PM2.5 exposure (Appendix E, Tables E102 and E-111). 

36 Similarly, the percent of total cardiovascular HA attributable to PM2.5 is larger than estimates 

37 for respiratory HA and ranges from 0.28% (Dallas) to 1.6% (Pittsburgh) (Appendix E, Table 

38 E-105). In this case, the pattern of risk across urban study areas reflects both differences in 

 45 Note, that as discussed earlier, for long-term exposure-related mortality, two risk estimates are provided for each 

urban study area, reflecting application of the two C-R functions used in modeling each mortality endpoint in the 

core analysis - i.e., C-R function derived using 1979-1983 PM2.5 monitoring data and the C-R function derived using 

1999-2000 data, with the latter function having the larger effect estimates and therefore, producing higher risk 

estimates. 
February 2010 Draft – Draft Do Not Quote or Cite 4-18

1 underlying baseline incidence for these endpoints as well as the use of regionally2 differentiated effect estimates obtained from Bell et al., 2008 (see Appendix C, Table C-1). 

3 Of the 15 urban study areas modeled for cardiovascular-related HAs, five locations had 

4 negative lower bound estimates of incidence, reflecting use of non-statistically significant 

5 effects estimates (see section 4.0 for additional discussion). 

6  Patterns of recent conditions risk across the three simulation years: A comparison of 

7 IHD mortality incidence estimates (based on the C-R function derived using 1979-1982 

8 monitoring data) across the three years (see Appendix E, Tables E-22 through E-24) shows 

9 that, while 2007 does produce incidence estimates that fall between those estimated for 2005 

10 and 2006 for some urban areas (e.g., Tacoma, St. Louis, LA), results for 2007 can be the 

11 highest of the three years (e.g., Fresno) or the lowest (e.g., Baltimore) for some locations. 

12 Generally, results for the same urban study area across the three years are fairly similar 

13 (results for Birmingham vary by less than 7% across the years), although they can vary by as 

14 much as 30% or more in some locations (see results for Tacoma in 2005 and 2006). All of 

15 this temporal variation results from year-to-year variation in the annual average PM2.5 levels 

16 for the study areas (see Appendix A). This is because other candidate input parameters, 

17 which could also involve temporal variability (e.g., demographics and baseline incidence 

18 rates) were not modeled with year-specific values, but rather using one representative year 

19 (see section 3.4.1.3 and 3.5 for demographics and baseline health effects incidence rates, 

20 respectively). In terms of short-term exposure-related morbidity and mortality endpoints, the 

21 pattern is similar to that described above for long-term mortality, with risk estimates for 2007 

22 generally falling between those generated for 2005 and 2006 (in terms of magnitude), 

23 although the magnitude of variations across the three simulation years for a given health 

24 endpoint/case study combination was notably lower for the short-term exposure-related 

25 endpoints than for the long-term endpoints. For example, with CV mortality, one of the 

26 urban study area with the greatest variation across the three years (New York) had a 15% 

27 difference in PM2.5 –related risk across the three years (see Appendix E, Tables E-82 through 

28 E-84). This compares with a spread of 30% for some of the urban study areas modeled for 

29 long-term exposure-related IHD mortality – see above. As with the long-term mortality risk 

30 metrics, all of this temporal variation results from year-to-year variation in the daily PM2.5

31 levels for the study areas (see Appendix A), given that other candidate input parameters, 

32 which could have temporal variability (e.g., demographics and baseline incidence rates) were 

33 not modeled with year-specific values, but rather using one representative year. 

 Consideration for the 95th 34 percentile confidence interval risk estimates in assessing 

35 uncertainty related to the statistic fit of effect estimates: As noted above, all of the risk 

metrics generated for this analysis include 95th 36 percentiles, reflecting uncertainty in the 

37 statistical fit of the underlying effect estimates in the C-R functions. These results suggest 

38 that this source of uncertainty can be notable. In the case of recent conditions risk estimates, 

39 for long-term mortality, while the central tendency risk estimate for all-cause (long term 

exposure-related) mortality incidence in New York range from 2,380-3,000, the 95th 40 

41 percentile confidence interval for this estimates is 1,960 to 3,500 (Appendix E, Table E-21 

42 and E-30). In this case, this source of uncertainty results in estimates that are ~18% lower to 

43 ~17% higher than the central tendency estimate range. Using the criteria we applied in 

44 assessing the results of the sensitivity analysis, these would translate as having a “small” 

45 impact on the core risk estimate (see Section 4.3.1). The impact of statistical fit uncertainty 
February 2010 Draft – Draft Do Not Quote or Cite 4-19

1 on the IHD-related long-term exposure-related mortality results (see Appendix E, Tables E2 24 and E-33) are similar in magnitude to those seen for all-cause mortality and also results in 

3 a classification of this uncertainty having a “small” impact on core risk estimates. For short4 term exposure-related mortality and morbidity, the impact of statistical fit (as reflected in the 

95th 5 percentile CI risk estimate ranges) is greater than for long-term mortality. For example 

6 with CV-related mortality, the central tendency estimate for New York is 570 cases, while 

the 95th 7 percentile CI is 332 to 902 (i.e., ~40% lower and ~40% higher than the core central8 tendency estimates). This translates into a “moderate” impact by this source of uncertainty 

9 on core risk estimates using the classification scheme developed for the sensitivity analysis. 

10 This suggests that uncertainty related to the statistical fig of effect estimates used in risk 

11 characterization has twice as greater an impact on short-term mortality as long-term mortality 

12 risk estimates. 

13 4.2 ASSESSMENT OF HEALTH RISK ASSOCIATED WITH JUST MEETING THE 

14 CURRENT AND ALTERNATIVE SUITES OF STANDARDS (CORE ANALYSIS) 

15 This section discusses core risk estimates generated for just meeting the current suite of 

16 standards and alternative suites of standards, focusing on the 2007 simulation year (although 

17 general trends in observations across the three simulated years are discussed to a limited extent). 

18 In discussing risk estimates for the current and alternative suites of standards, we include 

19 discussion of risk metric which characterize both incremental reductions in risk (across standard 

20 levels) as well as absolute risk for a particular standard level. In presenting these two categories 

21 of risk metric, we recognize that there is greater uncertainty in estimates of absolute risk relative 

22 to estimates of incremental risk. This reflects the fact that we have greater confidence in the 

23 ability of the risk models to differentiate risk between sets of standards, since this requires the 

24 models to estimate risk for ambient air PM2.5 levels likely near or within the range of ambient air 

25 quality data used in the underlying epidemiology studies. By contrast, estimates of absolute risk 

26 (for a given air quality scenario) require the models to perform at the lower boundary of ambient 

27 air PM2.5 levels reflected in the studies (i.e., down to the LML reflected in the long-term 

28 exposure mortality epidemiology studies or down to PRB levels in the short-term exposure 

29 morbidity and mortality studies). There is greater overall uncertainty in risk estimates generated 

30 based on the contribution to risk of exposures at these lower ambient air PM2.5 levels. While 

31 there is greater uncertainty associated with estimates of absolute risk, these estimates are of 

32 potential use in informing consideration of the magnitude of risk (and therefore public health 

33 impact) for a particular standard level. The overall level of confidence associated with different 

34 risk metrics (and implications for informing their use in the context of the PM NAAQS review) 

35 is discussed in Chapter 6. 

36 This section discusses risk estimates generated for the current standard level first, 

37 followed by discussion of risk estimates associated the set of alternative standard levels assessed. 

38 Each of these discussions is further organized by health endpoint category, with results discussed 
February 2010 Draft – Draft Do Not Quote or Cite 4-20

1 in the following order: long-term exposure mortality, short-term exposure mortality and short2 term exposure morbidity. Observations presented in the previous section regarding the statistical 

3 significance of effects estimates used in generating risk estimates and their implications for 

4 interpretation of those risk estimates also hold for estimates presented in this section. 

5 Consequently, observations regarding risk results with confidence intervals including negative 

6 estimates are not presented here and the reader is referred back to the earlier discussion in section 

7 4.1. 

8 We note that the lower magnitude of risk reductions (in terms of percent change in PM2.5-

9 attributable risk) generally seen for short-term exposure-related endpoints relative to long-term 

10 exposure-related endpoints primarily reflects the fact that PM2.5-attributable risk is modeled 

11 down to PRB for short-term, but only down to LML for long-term. This means that an 

12 incremental change (reduction) in long-term risk will be a larger fraction of overall risk 

13 compared with short-term risk and hence, the magnitude of risk reductions for long-term 

14 exposure-related risk is notably larger compared with short-term risk 

15 An important factor to consider in interpreting the risk estimates for both the current set 

16 of standards and sets of alternative standards is whether the annual or 24-hour standard for a 

given pairing of standards is controlling for a particular area.46 17 This factor can have a significant 

18 impact on the pattern of risk reductions predicted for a given location under the simulation of just 

19 meeting a specific set of standards. In addition, the approach used to simulate ambient PM2.5

20 levels under current and alternative standard levels (i.e., use of proportional, hybrid, or peak 

21 shaving) can significantly impact the magnitude risk reduction seen across standard levels 

22 (particularly the degree to which a particular standard produces notable reductions in long-term 

exposure-related mortality).47 23 The potential for different rollback strategies (reflecting 

24 potentially different combinations of local and/or regional controls) to impact patterns of risk 

25 reduction is not discussed here, but rather reserved for discussion as part of the sensitivity 

26 analysis (section 4.3) and the integrative chapter (chapter 6). 

27 An overview of which urban study areas are predicted to have risk reductions under the 

28 current and alternative suites of standards included in the risk assessment is presented below 

 46 For a given pairing of standard levels (e.g., 13/35), the controlling standard can be identified by comparing these 

levels to the design values for a given study area (see section 4.5.1). The controlling standard is the standard (annual 

or 24 hr) that requires the greatest percent reduction in the matching design value to meet that standard. 

47 Approaches such as hybrid rollback or peak-shaving which simulate more localized control strategies have the 

potential to reduce PM2.5 levels at monitors exceeding the daily standard, while leaving other monitors (which may 

have elevated annual-average PM2.5 levels) relatively or totally unadjusted. This can result in the 24-hour standard 

not providing coverage for the annual standard, even when the 24-hour standard is controlling (i.e., additional 

reduction focused on monitors with high annual design values may be required to attain the annual) - see discussion 

in Section 4.3 and Chapter 6. 
February 2010 Draft – Draft Do Not Quote or Cite 4-21

1 (Appendix E contains tables presenting the full set of detailed core risk estimates generated for 

2 the current and alternative suites of standards). 

3 4.2.1 Core Risk Estimates for Just Meeting the Current Suite of Standards 

4 This section summarizes risk estimates generated for the 15 urban study areas based on 

5 simulating just meeting the current suite of standards (including the magnitude of risk reductions 

6 relative to recent conditions, where applicable). 

7  Long-term exposure-related mortality: Total incidence of PM2.5-related IHD mortality 

8 ranges from 15-20 (Salt Lake City) to 1,760-2,220 (New York) (Table 4-1). The percent of 

9 total incidence of IHD mortality attributable to PM2.5 ranges from 3.7-4.7% (Tacoma) to 

10 13.2-16.7% (Atlanta) (Table 4-2). These levels of IHD mortality risk attributable to PM2.5

11 exposure reflect reductions in risk relative to recent condition ranging from 8.7% (Houston) 

12 to 68.6% (Salt Lake City). Two of the urban study areas (Dallas and Phoenix) do not exhibit 

13 reductions in risk in simulating just meeting the current suite of standards since these two 

14 locations meet the current suite of standards based on recent air quality data. As referenced 

15 above for the recent conditions scenario, total PM2.5-attriutable incidence for all-cause 

16 mortality and cardiopulmonary mortality is larger than IHD (for a given study area under 

17 recent conditions), while total PM2.5-attributable incidence for lung-cancer mortality is lower 

18 than for IHD. However, the percent of total incidence attributable to PM2.5 exposure is larger 

19 for IHD-related mortality than for any of the other mortality categories modeled (Appendix 

20 E, Tables E-24 and E-33). 

21  Short-term exposure-related mortality: As with the recent conditions analysis, total 

22 incidence of PM2.5-related mortality for short-term exposure is substantially smaller than 

23 estimates for long-term exposure-related mortality. Estimates for CV mortality for short24 term exposure ranges from 9 (Salt Lake City) to 470 (New York) (Table 4-1). The percent of 

25 CV mortality attributable to PM2.5 ranges from 0.7% (Fresno) to 2.1% (Philadelphia and New 

26 York). (Table 4-2). The level of risk reduction (comparing risk under the current standard 

27 with risk under recent conditions) is generally lower for short-term exposure-related CV 

28 mortality compared with long-term exposure-related all-cause mortality and ranges from 

29 5.5% (Baltimore) to 36.9% (Los Angeles). As mentioned for long-term exposure-related 

30 risk, both Phoenix and Dallas did not exhibit any risk reduction since these two locations 

31 meet the current suite of standards based on recent air quality data. Percent of total incidence

32 attributable to PM2.5 exposure is generally lower for total non-accidental mortality (compared 

33 with CV), ranging from 0.1% (Los Angeles) to 1.7% (Baltimore) (Appendix E, Table E-78). 

34 Estimates for respiratory mortality are usually higher than for CV, ranging from 0.9% 

35 (Dallas) to 2.6% (Baltimore) (Appendix E, Table E-96). As noted above, of the 15 urban 

36 study areas modeled for CV mortality, 12 locations had negative lower bound estimates of 

37 incidence (and two of these had negative point estimates), reflecting use of non-statistically 

38 significant effects estimates (see section 4.0 for additional discussion). 

39  Short-term exposure-related morbidity (hospital admissions for respiratory and 

40 cardiovascular illness): Total incidence of PM2.5-related cardiovascular HA range from 9 

41 (Salt Lake City) to 750 (New York City) and are significantly larger than estimates of 

42 respiratory HA attributable to PM2.5 exposure (Appendix E, Tables E102 and E-111). 

43 Similarly, the percent of total cardiovascular HA attributable to PM2.5 is larger than estimates 
February 2010 Draft – Draft Do Not Quote or Cite 4-22

1 for respiratory HA and ranges from 0.28% (Dallas) to 1.33% (Baltimore). As noted above, 

2 the pattern of risk across urban study areas reflects both differences in underlying baseline 

3 incidence for these endpoints as well as the use of regionally-differentiated effect estimates 

4 obtained from Bell et al., 2008 (see Appendix C, Table C-1). The level of risk reduction 

5 (comparing risk under the current standard with risk under recent conditions) for both 

6 respiratory and cardiovascular hospital admissions ranges from 5.5% (Baltimore) to 44.8% 

7 (Fresno), again with Phoenix and Dallas not exhibiting any risk reduction since these two 

8 locations meet the current suite of standards based on recent air quality data. As noted above, 

9 of the 15 urban study areas modeled for cardiovascular-related HAs, five locations had 

10 negative lower bound estimates of incidence, reflecting use of non-statistically significant 

11 effects estimates (see section 4.0 for additional discussion). 

12  Patterns of recent conditions risk across the three simulation years: Observations made 

13 earlier regarding patterns of risk across the three simulation years for the recent conditions 

14 simulations generally hold for the current standard level analysis. In other words, (a) 2007 

15 generally represents risks in between the other two years in terms of magnitude, (b) there are 

16 exceptions where 2007 had the highest risks and lowest risk (depending on study area and 

17 endpoint), and (c) generally, long-term exposure-related mortality endpoints showed greater 

18 cross year variation then the short-term exposure-related endpoints (with the magnitude of 

19 this variation similar to what is reported above for the recent conditions simulation). 

 Consideration for the 95th 20 percentile confidence interval risk estimates in assessing 

21 uncertainty related to the statistic fit of effect estimates: Uncertainty related to the 

22 statistical fit of effect estimates has the same magnitude of effect in modeling risk under the 

23 current standard as it did under recent conditions (i.e., an impact of about +/-18% on the core 

24 risk estimates, translating into a “small” impact based on classification used in the sensitivity 

25 analysis) (see (Appendix E, Table E-21 and E-30 for risk estimates used to reach this 

26 conclusion). The impact of this source of uncertainty on short-term exposure-related CV 

27 morality was similar (although slightly larger) compared with what was seen with risk 

28 estimates generated for the recent conditions air quality scenarios (i.e., 48% lower to 42% 

29 higher than the core risk estimate – see estimates in Appendix E, Table E-84). This results in 

30 a classification of “moderate” for this source of uncertainty and its impact on short-term 

31 exposure-related mortality, based on the classification scheme developed for the sensitivity 

32 analysis. 

33 4.2.2 Core Risk Estimates for Just Meeting Alternative Suites of Standards 

34 This section summarizes risk estimates generated for the 15 urban study areas when 

35 ambient PM2.5 levels under the alternative standard levels are simulated. As noted in section 4.2, 

36 this discussion focuses on the magnitude of incremental risk reductions for individual standard 

37 levels relative to the current standard, given that overall confidence in incremental risk metrics is 

38 considered higher than estimates of absolute risk for a given standard level. Note, however, that 

39 we do provide limited discussion of absolute risk levels attributable to PM2.5 exposure for 

40 alternative standard levels, with the provision that these be interpreted in the context of their 

41 greater levels of uncertainty. In discussing risk estimates for the alternative standard levels, we 

42 focus first on patterns of risk reduction across alternative annual levels (i.e., 14/35, 13/35 and 
February 2010 Draft – Draft Do Not Quote or Cite 4-23

1 12/35) and then discuss patterns across a combination of alternative 24 hour and annual 

2 standards (i.e., 13/30 and 12/25).

3 As noted in Section 4.1, although reductions in absolute incidence will differ for health 

4 effect endpoints associated with a particular averaging period across alternative suites of 

5 standards for a given urban study area, the patterns of reduction in terms of percent change in 

6 PM2.5-attributable risk are very similar for a given urban study area across health endpoints. This 

7 reflects the fact that the C-R functions used in the core analysis are close to linear across the 

8 range of ambient PM2.5 levels considered in this analysis, and consequently the main factor 

9 producing percent reductions in risk across alternative standards is the reduction in the air quality 

10 metric for a given study area (i.e., reductions in annual average PM2.5 concentrations or 

11 reductions in the distribution of 24-hour estimates for a year). Consequently, in discussing 

12 incremental risk reduction in terms of percent change relative to the current suite of standards, 

13 we speak more generally in terms of the category of annual-average risk or 2-4hour average 

14 risk, with the assumption that these observations hold for individual health effects endpoints 

15 assessed for each averaging period. These observations regarding patterns of percent risk 

16 reduction for the two averaging periods are reflected in Figures 4-1 through 4-6 which are 

17 referenced in the discussion below. 

Alternative annual standard levels (14/35, 13/35, and 12/35) 48 18 

19  Percent reductions in long-term exposure-related mortality: Reductions in all long-term 

20 exposure-related mortality categories were more limited under the 14/35 alternative standard, 

21 with only 5 of the 15 urban study areas demonstrating notable reductions ranging from 9% 

22 (Baltimore) to 12% (Houston and Birmingham) (see Figure 4-3 and Appendix E, Table E-9). 

Reducing the annual standard level to 13 μg/m3 23 (i.e., the 13/35 alternative suite of standards) 

24 produced a notable increase in the number of locations (9 of the 15) with risk reductions 

25 relative to the current standard ranging from 5% (New York) to 24% (Houston and 

Birmingham). The lowest annual standard evaluated (12 μg/m3 26 as reflected in the 12/35 

27 alternative suite of standards) resulted in additional study areas (now 12 of the 15 study 

28 areas) experiencing risk reductions with percentage risk reductions now ranging from 11% 

29 (Phoenix) to 26% (Houston and Baltimore). Note, that even in the 12/35 case, three of the 

30 urban study areas (Tacoma, Fresno and Salt Lake City) did not experience any decreases in 

31 risk, although risk reductions were seen for these three study areas when alternative 24-hour 

32 standards were considered – see below. The specific pattern of risk reduction (including 

33 importantly, the magnitude of risk reduction as well as residual risk associated with a 

 48 The three alternative annual standards considered in the risk assessment (12, 13 and 14 μg/m3 ) were each paired 

with the current 24-hour standard of 35 μg/m3 for purposes of generating risk estimates. A separate set of 

alternative suites of standards (i.e., 13/30 and 12/25) were also considered – see next section below. In discussing 

risk estimates associated with the alternative annual standards, each alternative annual standard level was paired 

with the current 24-hour standard of 35 μg/m3 in determining which standard level was controlling and, 

consequently, whether the alternative annual standard would produce any notable reductions in risk.
February 2010 Draft – Draft Do Not Quote or Cite 4-24

1 particular standard level) reflects whether daily or annual standard levels were controlling – 

2 see discussion below regarding patterns of risk reduction. 

3  Percent reduction in short-term exposure-related mortality and morbidity: The pattern 

4 of reductions in the percent of risk attributable to PM2.5 for mortality and morbidity 

5 associated with short-term exposure is similar to that described above for long-term mortality 

6 (see Figures 4-4 through 4-6). Specifically, the same five urban study areas (Atlanta, 

7 Baltimore, Birmingham, Houston and St. Louis) had notable risk reductions under the full set 

8 of alternative annual standards, with the degree of risk reduction for PM2.5-related 

9 cardiovascular mortality for the lowest alternative annual standard level (12/35) compared to 

10 the current standard level, ranging from 20% (St. Louis) to 23% (Birmingham) (see Figure 4-

11 4 and 4-6 and Appendix E, Table E-90). A number of the other study areas did not exhibit 

12 notable risk reductions until the lowest alternative annual standard was considered (i.e., 

13 Detroit, Los Angeles, New York, Philadelphia, Pittsburgh), with the degree of reduction in 

14 risk for the lowest alternative suite of standards (12/35) compared with the current standards 

15 ranging from 5% (Phoenix) to 16% (Detroit) (see Figure 4-4 and 4-6 and Appendix E, Table 

16 E-90). As with long-term exposure-related mortality, a number of additional study areas 

17 (Fresno, Salt Lake City, Tacoma) did not exhibit any notable risk reduction under the set of 

18 alternative annual standards considered and only experienced risk reductions when the 24-

19 hour standard level was reduced. Because the same air quality metric (annual distributions of 

20 24-hour PM2.5 concentrations) is used in generating short-term exposure-related mortality 

21 and morbidity endpoints, patterns of risk reduction are similar for both sets of endpoints (see 

22 Figures 4-4 through 4-6. Specifically, the same groups of urban study areas experience the 

23 same magnitude of risk reductions (in terms of percent changes in PM2.5-related risk relative 

24 to the current standard level) across the alternative standard levels for short-term exposure25 related morbidity (HAs). The specific pattern of risk reduction reflects whether daily or 

26 annual standard levels are controlling – see discussion below regarding patterns of risk 

27 reduction. 

28  Pattern of risk reduction linked to design values: The patterns of risk reduction across the 

29 15 urban study areas for the set of alternative annual standard levels considered here depends 

on whether the alternative annual (12, 13 or 14 μg/m3 30 ) or the current 24-hour standard of 35 

μg/m3 31 is controlling. The approach used to simulate just meeting alternative 24-hour 

32 standards (i.e., proportional, hybrid, or peak shaving) can have an impact on the magnitude 

33 of risk reduction, although it does not influence whether the annual or 24-hour design value 

34 was controlling for a given alternative suite of standards (see sensitivity analysis discussion 

35 in 4.3 and the integrative discussion in Chapter 6). The pattern in risk reduction seen across 

36 the 15 urban study areas (given the set of alternative annual standards considered) can be 

37 divided into three categories: (a) all of the alternative annual standard levels are controlling, 

38 resulting in notable risk reductions for all of the annual standard levels considered 

39 (Birmingham, Atlanta, Houston), (b) alternative annual standards only control at lower levels 

40 (i.e., 13/35 and/or 12/35) and consequently notable risk reductions are only seen at the lower 

41 or lowest annual standard level(s) considered (Dallas, Los Angeles, New York, Philadelphia, 

42 Phoenix, Pittsburgh), and (c) none of the alternative annual standard levels is controlling and 

43 therefore there is no estimated risk reduction for the alternative annual standard levels 

44 considered (Salt Lake City, Tacoma, Fresno). 
February 2010 Draft – Draft Do Not Quote or Cite 4-25

1  Absolute levels of PM2.5-attributable risk under alternative annual standards: As

2 discussed above, we have greater confidence in estimating incremental reductions in risk 

3 between the current and alternative suites of standards, then the estimation of absolute 

4 incidence under a given suite of standards. Nonetheless, we provide a summary of that risk 

5 metric here for long-term and short-term exposure-related mortality and short-term exposure6 related morbidity endpoints: 

7 o Long-term exposure-related mortality: The four study areas displaying the greatest 

8 degree of reduction across the alternative annual standards (Atlanta, Baltimore, 

9 Birmingham and Houston) have PM2.5-related IHD mortality estimates (under the 

10 lowest alternative annual standard of 12/35) ranging from 85-110 (Birmingham) to 

11 220-280 (Houston) (see Appendix E, Table E-21 and E-30). The two urban study 

12 areas with the greatest degree of PM2.5-related risk in absolute terms (Los Angeles 

13 and New York) do not exhibit significant reductions in risk until the lowest annual 

14 standard level of 12/35 is considered, with PM2.5-related IHD mortality estimated at 

15 750-950 and 1,420-1,800, respectively under that alternative standard (see Appendix 

16 E, Table E-21 and E-30). 

17 o Short-term exposure-related mortality: The four study areas displaying the greatest 

18 degree of reduction across the alternative annual standards (Atlanta, Baltimore, 

19 Birmingham and Houston), have PM2.5-related CV mortality estimates (under the 

20 lowest alternative standard of 12/35) ranging from 25 (Atlanta) to 50 (Baltimore) (see 

21 Appendix E, Table E-84). We note that Birmingham has an incidence estimate of -1, 

22 reflecting application of a non-statistically significant effect estimate in modeling this 

23 endpoint (see section 4.1). The urban study area with the greatest degree of PM2.5-

24 related risk in absolute terms (New York) does not exhibit significant reductions in 

25 risk until the lowest annual standard level of 12/35 is considered with PM2.5-related 

26 CV mortality estimated at 420 under that alternative standard level (see Appendix E, 

27 Table E-84). 

28 o Short-term exposure-related morbidity: The four study areas displaying the greatest 

29 degree of reduction across the alternative annual standard levels (Atlanta, Baltimore, 

30 Birmingham and Houston), have PM2.5-related cardiovascular HA (under the lowest 

31 alternative standard of 12/35) ranging from 12 (Birmingham) to 170 (Baltimore) (see 

32 Appendix E, Table E-102). The two urban study areas with the greatest degree of 

33 PM2.5-related risk in absolute terms (Los Angeles and New York) do not exhibit 

34 significant reductions in risk until the lowest annual standard level of 12/35 is 

35 considered with PM2.5-related all-cause mortality estimated at 240 and 670, 

36 respectively under that alternative standard level (see Appendix E, Table E-102). 

37  Patterns of recent conditions risk across the three simulation years: Observations made 

38 above regarding patterns of risk across the three simulation years for the recent conditions 

39 and current standards simulations generally hold for the alternative standards analysis. In 

40 other words, (a) 2007 generally represents risks between the other two years in terms of 

41 magnitude, (b) there are exceptions where 2007 had the highest risks and lowest risk 

42 (depending on study area and endpoint), and (c) generally, long-term exposure-related 

43 mortality endpoints showed greater cross-year variation then the short-term exposure-related 

44 endpoints in terms of both absolute PM2.5 risk for a particular alternative suite of standards, 

45 as well as incremental risk reductions relative to the current suite of standards. 
February 2010 Draft – Draft Do Not Quote or Cite 4-26

 Consideration of the 95th 1 percentile confidence interval risk estimates in assessing 

2 uncertainty related to the statistic fit of effect estimates: Continuing the pattern seen with 

3 the current standard level, uncertainty related to the statistical fit of effect estimates has the 

4 same magnitude of effect in modeling risk under alternative standards involving reduction of 

5 the annual level as it did under recent conditions (i.e., an impact of about +/-18% on the core 

6 risk estimates, translating into a “small” impact based on classification used in the sensitivity 

7 analysis) (see Appendix E, Table E-21 and E-30 for risk estimates used to reach this 

8 conclusion). Similarly, the pattern of impact this source of uncertainty on short-term 

9 exposure-related CV morality continues to be similar compared with what was seen for risk 

10 estimates generated for the recent conditions air quality scenarios (i.e., 42% lower to 

11 42%higher than the core risk estimate – see estimates in Appendix E, Table E-84). This 

12 continues to result in a classification of “moderate” for this source of uncertainty based on 

13 the classification scheme developed for the sensitivity analysis. 

14 Combinations of alternative 24-hour and annual standard levels (13/30, 12/25)

15  Percent reductions in long-term exposure-related mortality: The combination of suites 

16 of alternative 2-hour and annual standards produced notable reductions in long-term 

17 exposure-related mortality for 14 of the 15 urban study areas, with the lower combination 

18 (12/25) producing a notable reduction in risk relative to the first combination of 13/30. The 

19 only study area that did not exhibit a reduction in risk under the first combination (13/30) 

was Dallas, reflecting the fact that its 24-hour and annual design values are below 30 μg/m3 20 

and 13 μg/m3 21 , respectively (and consequently, the 13/30 did not produce a reduction in 

22 ambient air PM2.5, or a resulting reduction in risk). Reductions in long-term exposure-related 

23 mortality (across all endpoints) under the 13/30 combination ranged from 14% (Phoenix) to 

24 55% (Salt Lake City), while reductions for the 12/25 combination ranged from 12% (Dallas) 

25 to ~100% (Salt Lake City) (see Figure 4-1 and 4-3 and Appendix E, Table E-27). The 

26 reduction for Salt Lake City reflects a very high 24-hour design value which, when reduced 

to meet the 24-hour standard of 25 μg/m3 27 produced a very large reduction in the annual 

28 design value (given application of the proportional adjustment to simulate rollback), such 

that the value was very close to 5.8 μg/m3 29 (the LML below which long-term exposure-related 

30 mortality is not estimated). The specific pattern of risk reduction reflects whether the 24-hour 

31 or annual standard was controlling – see discussion below regarding patterns of risk 

32 reduction. 

33  Percent reduction in short-term exposure-related mortality and morbidity: The pattern 

34 of reductions in the percent of risk attributable to PM2.5 for mortality and morbidity 

35 associated with short-term exposure is similar to that described above for long-term mortality 

36 in terms of the ordering of sites, however the magnitude of risk reduction (in terms of percent 

37 change in PM2.5-related risk) is lower for short-term exposure-related health endpoints 

38 compared with long-term exposure-related mortality (see Figures 4-4 through 4-6). 

39 Specifically, 14 of the 15 urban study areas (Dallas being the exception), had notable risk 

40 reductions under both the 13/30 and 12/35 alternative suites of standards (Dallas only was 

41 estimated to have reductions in risk under the lower 12/25 combination - see Figure 4-4 and 

42 4-6 and Appendix E, Table E-108). Reductions in short-term exposure-related mortality and 

43 morbidity (across all endpoints) under the 13/30 combination ranged from 6% (Phoenix) to 

44 15% (Salt Lake City), while reductions for the 12/25 combination ranged from 7% (Dallas) 

45 to 30% (Birmingham). 
February 2010 Draft – Draft Do Not Quote or Cite 4-27

1  Pattern of risk reduction linked to design values: As with the set of alternative annual 

2 standards discussed in the previous section, the pattern of risk reduction seen for the two 

3 combinations of alternative 24-hour and annual standards described here depends on which 

4 standard is controlling. In addition, the magnitude of the reduction in risk reflects (a) the 

5 magnitude of the difference between the controlling design value and the standard level 

6 (which determines the degree of reduction in ambient air PM2.5 levels) and (b) the method 

7 used to simulate ambient PM2.5 levels under alternative suites of standards (i.e., proportional, 

8 hybrid or peak shaving). For this set of alternative suites of standards, 10 of the 15 study 

9 areas had the alternative 24-hour standard controlling under the 13/30 case and that number 

10 was increased to 12 out of the 15 study areas with the 12/25 case (Table 3-5). As expected, 

11 those study areas with the greatest reduction in risk (in terms of percent reduction compared 

12 with the current suite of standards) under the 12/25 case had a controlling 24-hour standard 

13 (e.g., Tacoma, Salt Lake City, Los Angeles and Fresno - see Figure 4-4 and 4-6 and 

14 Appendix E, Table E-90). 

15  Absolute levels of PM2.5-attributable risk under alternative suites of annual and 24-

16 hour standards: As with the alternative annual standards, below we provide a brief 

17 overview of the magnitude of PM2.5-attributable risk (i.e., absolute risk) associated with the 

18 two alternative suites of annual and 24-hour standards: 

19 o Long-term exposure-related mortality: The four study areas displaying the greatest 

20 degree of reduction across these two alternative suites of standards (Tacoma, St. 

21 Louis, Los Angeles and Fresno), have PM2.5-related IHD mortality estimates (under 

22 the 12/25 case) ranging from 3-4 (Tacoma) to 290-360 (Los Angeles) (see Appendix 

23 E, Table E-21 and E-30). The other urban study area with the greatest degree of 

24 PM2.5-related risk in absolute terms besides New York (New York) has PM2.5-related 

25 all-cause mortality estimated at 820-1,040 under the 12/25 case. 

26 o Short-term exposure-related mortality: eleven of the 15 study areas had percent 

27 reductions in risk for the 12/25 case (relative to the current standards) of 

28 approximately 29% (the other study areas had lower percent reductions). Of the 

29 locations with ~29% reductions in risk, PM2.5-attributable CV mortality for the 12/25 

30 case ranged from 6 (Salt Lake City) to 340 (New York) (see Appendix E, Table E31 84). New York City also represents the study area with the greatest residual risk for 

32 short-term exposure-related mortality under the 12/25 case. 

33 o Short-term exposure-related morbidity Of the 11 urban study areas with ~29% 

34 reduction in risk (for the 12/25 case relative to the current standards), the incidence of 

35 PM2.5-attributable cardiovascular HA emissions ranges from 7 (Salt Lake City) to 530 

36 (New York) (see Appendix E, Table E-102). New York City also represents the 

37 study area with the greatest residual risk for short-term exposure-related morbidity 

38 under the 12/25 case. 

 Consideration for the 95th 39 percentile confidence interval risk estimates in assessing 

40 uncertainty related to the statistic fit of effect estimates: As with the alternative standards 

41 considering lower annual levels, risk estimates generated for the two standards considering 

42 lower annual and 24-hour levels also suggest that uncertainty related to the statistical fit of 

43 effect estimates will have a greater impact on short-term exposure-related mortality (+/- 

44 ~40%) compared with long-term exposure-related mortality (+/- ~18%) (see Appendix E, 
February 2010 Draft – Draft Do Not Quote or Cite 4-28

1 Tables E-84 and E-21 plus Table E-30, respectively). Again, this results in a classification of 

2 this source of uncertainty as having a “lower” impact for long-term exposure-related 

3 mortality and a “moderate” impact on short-term exposure-related mortality. 

4 4.3 SENSITIVITY ANALYSIS RESULTS 

5 As noted in section 3.6.4 and section 4.1, the sensitivity analysis was conducted in order 

6 to gain insights into which of the identified sources of uncertainty and variability in the risk 

7 assessment model may have significant impacts on risk estimates. A second goal of the 

8 sensitivity analysis was to generate an additional set of reasonable risk estimates to supplement 

9 the core set of risk estimates to inform staff's characterization of uncertainty and variability 

10 associated with those core estimates. 

11 The first goal can be achieved by considering the magnitude of the impact of individual 

12 modeling elements based on results from the sensitivity analysis and identifying those elements 

13 which have the greatest impact on the core risk estimates. Use of the sensitivity analysis results 

14 in this context is addressed in section 4.3.1. Use of the results of the sensitivity analysis as an 

15 additional set of reasonable risk estimates to augment the core risk estimates in considering the 

16 impact of uncertainty and variability in the core risk model is discussed in section 4.3.2. 

17 In conducting the sensitivity analysis we modeled 2 of the 15 urban study areas 

18 (Philadelphia and Los Angeles - representing east and west coast urban areas, respectively) for 

19 most simulations. For some modeling elements (e.g., the hybrid and peak shaving alternative 

20 rollback approaches) we included a larger number of urban study areas that were applicable to 

21 the topic being assessed. In conducting the sensitivity analysis, we have also focused on long22 term exposure mortality and to a lesser extent on short-term exposure mortality and morbidity. 

23 Although the sensitivity analysis simulations were completed for all three simulation 

24 years (as reported in Appendix F), we have focused on results for 2007 in this presentation for 

25 comparability with the core results discussed in sections 4.1 and 4.2. 

26 4.3.1 Sensitivity Analysis Results to Identify Potentially Important Sources of Uncertainty 

27 and Variability 

28 The results of the sensitivity analysis are summarized in Table 4-3 (detailed results tables 

29 are presented in Appendix F). In presenting the results of the sensitivity analysis, we have 

30 compared the risk estimates for the particular simulation to the core set of risk estimates 

31 generated for the same health effect endpoint/urban study area combination. Specifically, we 

32 have calculated a percent difference between the sensitivity analysis result and the associated 

33 core risk estimate to compare the results of the sensitivity analysis across the different modeling 

34 elements that were considered. These percent difference results are emphasized in Table 4-1 and 

35 in the discussion presented below. 
February 2010 Draft – Draft Do Not Quote or Cite 4-29

1 In discussing the results of the sensitivity analysis, we have developed four descriptive 

2 categories, based on the general magnitude of the percent difference estimate generated for a 

3 particular modeling element: 

4  Modeling elements estimated to have percent differences of 20% or smaller (i.e., they 

5 produced risk estimates that differed from the core risk estimates by no more than 

6 20%) are classified as having a small contribution to uncertainty in the core risk 

7 estimates. 

8  Modeling elements estimated to have percent difference estimates in the range of 20 to 

9 50% are classified as having a moderate contribution to uncertainty in the core risk 

10 estimates. 

11  Modeling elements estimated to have percent difference estimates in the range of 50 to 

12 100% are classified as having a moderate-large contribution to uncertainty in the core 

13 risk estimates. 

14  Modeling elements estimated to have percent difference results >100% are classified as 

15 having a large contribution to uncertainty in the core risk estimates. 

16 The sensitivity analysis based on Moolgavkar’s (2003) study in Los Angeles addressing 

17 model specifications for both short-term mortality and morbidity (e.g., model selection, lag 

18 structure and co-pollutant models) are discussed together as a group. This reflects the fact that 

19 the Moolgavkar-based simulations were based on the same underlying dataset and focused on 

20 Los Angeles. Furthermore, the discussion of the Moolgavkar-based sensitivity analysis results 

21 presented below, as well as the summary of results presented in Table 4-1, focus on the 

22 difference in the spread of risk results across the Moolgavkar-based model specifications (for a 

23 particular endpoint), rather than the percent difference results based on comparison against the 

core result that are emphasized with the other sensitivity analyses.49 24 

25 The sensitivity analysis examining the impact of alternative rollback approaches for 

26 simulating ambient PM2.5 concentrations in urban study areas under both the current and 

27 alternative suites of standards also deserves additional discussion before presenting the results. 

28 For the first draft RA, we considered the impact of using a hybrid rollback approach in addition 

29 to the proportional rollback approach which has been traditionally used in PM NAAQS risk 

 49 Comparison of the Moolgavkar-based risk estimates with the core risk estimates consistently produce percent 

difference estimates that range to levels well above +100%, resulting in a general conclusion, based on this metric, 

that all of the factors considered in the Moolgavkar-based sensitivity analysis are large contributors to uncertainty in 

the core risk estimates. However, there is significant uncertainty in assuming that the behavior of the Moolgavkarbased risk models (reflecting consideration for alternate design elements) would be representative of how models 

derived from either of the key short-term studies considered in this risk assessment (Zanobetti and Schwartz., 2009 

and Bell et al., 2008) would respond to variations in design. Therefore, while sensitivity analysis results based on 

comparing Moolgavkar-based risk estimates against the core risk estimates are included in the detailed sensitivity 

analysis results tables presented in Appendix F (see Tables F-31 through F-33), we do not discuss these results here 

due to the degree of uncertainty associated with them. 
February 2010 Draft – Draft Do Not Quote or Cite 4-30

1 assessments. For this second draft, as discussed in sections 2.6, 3.2.3 and 3.5.4, we have 

2 included consideration of a peak shaving rollback approach in addition to the hybrid as nonproportional methods to contrast with proportional rollback.50 3 

4 As discussed in Section 3.2.3, for the second draft risk assessment, we have calculated 

5 composite monitor estimates based on proportional rollback and hybrid and/or peak shaving, 

6 where appropriate. The composite monitor values are surrogates for long-term exposure-related 

mortality.51 7 Therefore, by comparing composite monitor values generated for the same study 

8 area/suite of standards (using different rollback methods), we can obtain insights into the 

9 potential impact of the rollback method used on long-term exposure-related mortality (see 

10 Section 3.5.4 for additional discussion of how the composite monitor values generated using the 

11 different rollback methods are used in the sensitivity analysis). These sensitivity analysis results 

12 based on consideration for composite monitor values generated using the different rollback 

13 methods (which are presented in detail in Appendix F, Tables F-49 and F-50) form the basis for 

14 summary information related to rollback presented in Table 4-3. Due to the complexity of the 

15 sensitivity analysis conducted examining the issue of rollback, the discussion of results from that 

16 particular analysis presented in section 4.3.1.1 is more detailed than for the other factors 

17 considered as part of the sensitivity analysis. 

18 In discussing the results of the sensitivity analysis, results of the single-factor simulations 

19 are presented first (section 4.3.1.1), followed by the results of the multi-factor simulations 

20 (section 4.3.1.2). Within these categories, results are further organized by health effect endpoint 

21 with results for long-term exposure mortality discussed first and then short-term exposure 

22 mortality, followed by short-term exposure morbidity. An overall conclusion regarding which of 

23 the factors included in the sensitivity analysis represent potentially significant sources of 

24 uncertainty and variability impacting the core risk estimates is presented at the end of each sub25 section. 

 50 The peak shaving approach involves proportional reduction in 24-hour PM2.5 levels only at those urban study 

areas where the 24-hour standard is controlling (and only at those specific monitors with design values exceeding 

that 24-hour standard level) – see Section 3.2.3 for additional detail. 

51 The composite monitor is essentially the mean of the annual averages across the PM2.5 monitors in a study area. It 

is this air quality metric that is used in calculating long-term exposure-related mortality. Given that the same C-R 

function is used across all study areas, differences in long-term mortality across study areas (and/or across standard 

levels) reflect to a great extent underlying differences in the composite monitor values. Therefore, comparison of 

composite monitors (in terms of percent difference for example) can provide insights into potential percent 

differences in long-term mortality related to PM2.5 exposure across study areas and/or standard levels (see Section 

3.5.4). 
February 2010 4-31 Draft – 

1 Table 4-3 Overview of Sensitivity Analysis Results 

Sensitivity Analysis1

Health Endpoint and Risk 

Assessment Location 

Summary of Results 

(percent difference in risk estimate rela

to the core estimate)

Single-Factor Sensitivity Analyses (long-term exposure mortality): Impact of using different model choices: fixed effects log-linear (the core) vs. random effects log-linear C-R function 

 All-cause, CPD, IHD  Los Angeles and 

Philadelphia 

Random effects log-linear C-R model:  all-cause: +23% 

 IHD: +12% 

Impact of using different model choices: fixed effects log-linear (the core) vs. random effects log-log C-R function 

 All-cause, CPD, IHD  Los Angeles and 

Philadelphia 

Random effects log-log C-R model:  All-cause: +123 to +159%  CPD: +50 to +74%  IHD: +80 to +111%  Lung Cancer: +67 to +94% 

Impact of using different model choices: Single vs. multi-pollutant models 

 All-cause  Los Angeles and 

Philadelphia 

 Model with CO: +45% 

 Model with NO2: +73% 

 Model with O3: +45% 

 Model with SO2: -74% 

Impact of estimating risks down to PRB rather than down to LML (the core) 

 All cause  All 15 urban study areas 

 All-cause: +47 to +273% 

Impact of using alternative C-R function from another long-term exposure mortality study 

 All-cause, CPD, lung 

cancer 

 Los Angeles, Philadelphia 

 All-cause: +119 to +121%  CPD: +29 to +30%  Lung cancer: +29 to +30% 

Impact of using alternative hybrid rollback approach reflecting more localized patterns of ambient PM2.5

reduction (evaluated across current and alternative standard levels) – based on the composite monitor analysis described in Section 3.5.4 considering both hybrid and peak shaving approaches as alternatives to proportional rollback 

 Surrogate for long-term 

mortality (composite monitor-based analysis) 

 All study areas except 

Dallas had either hybrid and/or peak shaving applied as an alternative 

 Trend in incremental risk reduction 

(alternative standard level compared tcurrent standard): rollback method diappear to have a significant impact on metric (those urban study areas with different trends in reduction did not demonstrate a consistent pattern relat
February 2010 4-32 Draft – 

Sensitivity Analysis1

Health Endpoint and Risk 

Assessment Location 

Summary of Results 

(percent difference in risk estimate rela

to the core estimate)

rollback method to the proportional 

the type of hybrid method used) 

 Absolute risk for a given standard lev

use of alternative rollback methods diappear to impact estimation of PM2.5 

remaining for a given standard level: to >+50% 

 Has implications for degree to which 

hour standard levels produce reductioin annual-average PM2.5 levels (and 

consequently on long-term and short-exposure-related risk). Results suggesthat use of peek shaving rollback metcan result in smaller degree of reductiin annual-average values compared wproportional rollback, (see discussion text – section 4.3.1.1) 

Single-Factor Sensitivity Analyses (short--term exposure mortality):Impact of using season-specific C-R functions (vs. an annual C-R function) 

 Non-accidental mortality, 

CV, respiratory 

 All 15 urban study areas 

 Non-accidental: -116 to +179% 

 CV: -82 to +500%  Respiratory: -48 to +162% 

(Note, overall incidence estimates, particufor the locations with higher percent chanestimates, is very low, raising concerns ovthe stability of these sensitivity analysis results) 

Impact of using alternative hybrid rollback approach reflecting a combination of more localized and regional patterns of ambient PM2.5 reduction (note, 

this analysis is based exclusively on the hybrid rollback – the composite monitor analysis described 

 Non-accidental mortality  Baltimore, Birmingham, 

Detroit, Los Angeles, New York and St. Louis 

 Results for all seven urban study area

(across the current and alternative standard levels) do not exceed +17%, with most <+10%. 
February 2010 4-33 Draft – 

Sensitivity Analysis1

Health Endpoint and Risk 

Assessment Location 

Summary of Results 

(percent difference in risk estimate rela

to the core estimate)

above pertains only to long-term mortality-related risk) Single-Factor Sensitivity Analyses (short-term morbidity: hospital admissions (HA) and ED visits):Impact of using season-specific C-R functions (vs. an annual C-R function) 

 HA (unscheduled), CV 

and respiratory 

 All 15 urban study areas 

 HA (CV): -105 to +9% 

 HA (respiratory): -54 to +74% 

(Note, overall incidence estimates, particufor the locations with higher percent chanestimates, is very low, raising concerns ovthe stability of these sensitivity analysis results) 

Impact of using an annual C-R function (applied to the whole year) vs. a seasonal function for April through August (applied only to that period) (using a single pollutant model) 

 Asthma ED visits  New York 

NA (although incidence estimates were generated for this simulation, “percent difference from the core” were not generasince the alternate simulation focused on a subset of the year). 

Impact of considering models with different lags  HA (CV and respiratory 

 LA and New York 

NA (although incidence estimates were generated for this simulation, “percent difference from the core” were not generasince the lag-differentiated C-R functions are not regionally-differentiated, and therefore, do not allow a focused consideration of the lag factor alone in impacting risk estimates) 

Single-Factor Sensitivity Analysis (short-term exposure mortality and morbidity in LA based on Moolgavkar, 2003 study model o

presented here reflect spread in risk estimates across Moolgavkar-based model specifications and not percent difference from cunless so stated – see text) 

Impact of model selection (e.g., log-linear GAM with 30 df; log-linear GAM with 100 df; and loglinear GLM with 100 df) 

 Mortality (non-accidental, 

CV); HA (CV) 

 Los Angeles 

 Non-accidental mortality: +80% 

 CV mortality: +49  CV HA: +36% 
February 2010 4-34 Draft – 

Sensitivity Analysis1

Health Endpoint and Risk 

Assessment Location 

Summary of Results 

(percent difference in risk estimate rela

to the core estimate)

Impact of lag structure (0-day, 1-day, 2-day, 3-day, 4-day, 5-day) 

 Mortality (non-accidental)  Los Angeles 

 Non-accidental mortality: +55% 

Impact of single- vs. multi-pollutant models (PM2.5 with CO) 

 Mortality (CV); HA (CV)  Los Angeles 

 CV mortality: +106% 

 CV HA: +140% 

Multi-Factor Sensitivity Analyses (long-term mortality): Impact of using a fixed effects log-linear vs. a random effects log-log model, estimating incidence down to the lowest measured level (LML) in the study vs. down to PRB, and using a proportional vs. hybrid rollback to estimate incidence associated with long-term exposure to PM2.5 concentrations that just 

meet the current standards (note consideration of rollback in the multi-factor analysis did not incorporate the hybrid-based rollback approach) 

 All-cause, IHD long-term 

mortality 

 Los Angeles and 

Philadelphia 

 All-cause: +27 to +1,089%  IHD: +256to +673% 

Multi-Factor Sensitivity Analyses (short--term mortality):Impact of using season-specific vs. all-year C-R functions and proportional vs. hybrid rollbacks to estimate incidence associated with short-term exposure to PM2.5 concentrations that just meet the 

current standards 

 Non-accidental  Baltimore, Birmingham, 

Detroit, Los Angeles, New York and St. Louis 

 Non-accidental (four seasons + hybrid

116 to +179% 

1 1 Unless otherwise noted, sensitivity analysis results are based on the scenario reflecting just meeting the current suite of PM2.5 standard2 2 This metric is the percent spread in risk estimates across the Moolgavkar-based model specifications (not the percent difference estim

3 above). 
February 2010 Draft – Do Not Quote or Cite 4-35

1 4.3.1.1 Single-factor Sensitivity Analysis 

2 This section presents the results of the single-factor sensitivity analysis, which involved 

3 consideration of alternate model inputs on the core risk estimates, when those alternate inputs are 

4 considered one at a time (consideration of the combined effect of several model inputs being 

5 varied is covered by the multi-factor sensitivity analysis discussed in section 4.3.1.2). The 

6 results of the single-factor sensitivity analysis are characterized qualitatively using the four7 category approach described above (i.e., low, moderate, moderate-large and large, with each of 

8 these representing a defined range of percent difference from the core risk estimates). 

9 Long-term exposure mortality

10 This section summarizes the results of the sensitivity analysis focused on long-term 

11 exposure-related mortality endpoints (see Table 4-1 for the specific modeling elements 

12 considered in the sensitivity analysis). 

13  Impact of using different model choices for C-R function - fixed effects log-linear (the core 

14 approach) vs. random effects log-linear or random effects log-log models: This simulation 

15 considered two alternative C-R model forms obtained from Krewski et al., 2009 for modeling 

16 all-cause, CPD, IHD and lung cancer mortality, including (a) random effects log-linear 

17 model and (b) a random effects log-log model (note, the core effect estimate was derived 

18 using a fixed effects log-linear model obtained from Krewswki et al., 2009). The simulation 

19 also considered the use of multi-pollutant models that control for CO, NO2, O3 or SO2. The 

20 results of the simulation suggest that the use of a random effects log-linear model, rather than 

21 the core fixed effects model, has a relatively small effect on risk estimates, increasing them 

22 by 12 to 23% across the mortality categories and urban study areas modeled (Appendix F, 

23 Table F-3). However, use of a random effects log-log model has a larger impact on risk 

24 estimates, increasing them by 50 to 159% (Appendix F, Table F-3). The greater impact of 

25 the log-log model results from this function having an incrementally steeper slope at lower 

26 PM levels, which quickly increases incidence estimates compared with the core log-linear 

27 model (whose slope has a much more gradual incremental increase in slope at lower PM 

28 levels). The use of multi-pollutant models that control for co-pollutants was shown to have 

29 moderate-large impact on risk estimates, with control for CO, NO2, or O3 resulting in 

30 increased PM2.5-attributable risk estimates, while control for SO2 resulted in a moderate-large 

decrease in estimated PM2.5 risk.52 31 

32  Impact of estimating risks down to PRB rather than down to LML: This simulation 

33 compared long-term exposure mortality incidence associated with modeling risk down to 

34 PRB (which varies by region – see section 3.2.1) with the core approach of modeling down 

to LML (5.8 μg/m3 35 for long-term mortality – see section 3.1). This simulation involved all 

36 15 urban study areas, given that PRB is stratified by region and therefore, results of the 

 52 Sensitivity analysis results generated using the copollutant model involving PM2.5 and SO2 have been deemphasized since it is likely that control for SO2 may be capturing a portion of PM2.5 -attributable risk related to the 

secondary formation of sulfate, which is a component of the PM2.5 mixture (i.e., the two pollutants are often highly 

correlated). 
February 2010 Draft – Do Not Quote or Cite 4-36

1 simulation could differ significantly across the 15 urban study areas, or at least across the six 

2 PM regions represented by those study areas. The results of this simulation suggest that 

3 modeling risk down to PRB could have a moderate to large impact on long-term exposure 

4 mortality incidence, with estimates ranging from 47 to 273% higher than the core estimates 

5 (for matching urban locations) (Appendix F, Table F-6). Note, however, that risk metrics 

6 based on considering the incremental reduction in risk (incidence) between two alternative 

7 suites of standards would not be impacted by this source of uncertainty, since it only affects 

8 estimates of absolute risk. 

9  Impact of C-R function from alternative long-term exposure mortality study: This simulation 

10 considered use of alternative C-R functions (and effect estimates) based on the reanalysis of 

11 the Six Cities study (Krewski et al., 2000). The results suggest that use of the alternative C-R 

12 function could have a moderate to moderate-large effect on CPD mortality (+45 to +74%), a 

13 large effect on all-cause mortality (+123 to +159%), a moderate-large to large effect on IHD 

14 mortality (+80 to +111%) and a moderate-large effect on lung cancer mortality (+67 to 

15 +94%) (Appendix F, Table F-9). The results of this simulation suggest that (at least with 

16 regard to application of C-R functions obtained from the Six Cities study) the potential 

17 impact of functions from alternative studies on long-term exposure mortality depends on the 

18 mortality category being considered. In this analysis, use of the alternative C-R functions 

19 was shown to have a significant impact on all of the long-term mortality categories 

20 considered. 

21  Impact of using alternative rollback approaches (hybrid and peak shaving) to simulate just 

22 meeting the current and alternative suites of standards. This sensitivity analysis assessed the 

23 impact of estimating risk for the current and alternative sets of standards using two 

24 alternatives to the proportional rollback strategy: (a) the hybrid rollback approach that 

25 reflects an initial localized pattern of ambient PM2.5 reduction (resulting in non-proportional 

26 rollbacks of monitored PM2.5 concentrations) with a second phase of more regional 

27 reductions in ambient PM2.5 levels (based on proportional adjustments) and (b) peak shaving 

28 which represents a primarily local pattern of reductions in ambient PM2.5 (see Section 3.5.4 

29 for additional discussion of how these alternative rollback methods were integrated into the 

30 sensitivity analysis). We note that the core analysis utilized proportional rollback exclusively 

31 in simulating conditions for the current and alternative sets of standards, with this approach 

32 representing a regional pattern of ambient PM2.5 reduction. A number of observations can be 

33 drawn from this sensitivity analysis including: 

34 o Impact on estimates of PM2.5-related risk remaining after simulation of just 

35 meeting a given suite of standards: The sensitivity analysis results suggest that 

36 the use of alternative rollback methods can have a notable impact on estimates of 

37 the PM2.5-attributable risk remaining after simulation of a given suite of standards 

38 (see Appendix F, Table F-50 and discussion in section 3.5.4). Generally, use of 

39 the hybrid approach had a small to moderate impact on absolute PM2.5-

40 attributable risk estimates, compared with the core approach of using proportional 

41 rollback. By contrast, use of the peak shaving approach had a moderate to 

42 moderate-large impact on absolute PM2.5-attributable risk estimates. For example, 

43 Los Angeles had composite monitor values for the current suite of standards and 

44 several of the alternative suites of standards that were 40 to 60% greater when the 

45 peak shaving rollback method was used, compared with the proportional rollback 
February 2010 Draft – Do Not Quote or Cite 4-37

1 method (see Appendix F, Table F-50). By contrast, composite monitor values 

2 generated using hybrid rollback for Los Angeles, were between 13 and 38% 

3 higher than the proportional rollback methods. 

4 o Impact on degree of reduction across alternative suites of standards: When the 

5 same rollback methods is used to simulate both the current and any alternative 

6 suite of standards, the pattern of risk reduction across alternative standards is 

7 generally similar regardless of the rollback approaches used (see Table F-49, in 

8 Appendix F). However, if one looks at meeting the current suite of standards with 

9 application of the peak-shaving approach, followed by application of proportional 

10 rollback to simulate alternative suites of standards, we can see notable differences 

11 in the pattern of risk reduction. This is particularly true for areas with peaky PM2.5

12 distributions (i.e., areas with relatively high 24-hour design values and lower 

13 annual average design values). For example, with Los Angeles, which represents 

14 a study area with a relatively peaky PM2.5 distribution, application of proportional 

15 rollback in simulating both the current suite of standards and the alternative 

annual standard of 12 μg/m3 16 results in a 13% reduction in long-term exposure17 related mortality (see Figure 4-3 and Table E-27 in Appendix E). By contrast, 

18 application of peak shaving in simulating the current suite of standard levels 

19 followed by proportional reduction in simulating the same alternative annual 

20 standard results in an estimated 48% reduction in long-term exposure-related 

mortality.53 21 

22 Based on the simulations discussed above covering potential sources of uncertainty and 

23 variability impacting long-term mortality, we conclude that the following factors contribute 

24 potentially large sources of uncertainty to the core risk estimates: (a) use of alternative form of 

25 the C-R function, specifically use of a random-effects log-log model form obtained from the 

26 updated ACS study (Krewski et al., 2009) (b) use of an alternative C-R function with effects 

27 estimates obtained from the reanalysis of the Six Cities study (Krewski et al. 2000), and (c) 

estimation of risk down to PRB.54 28 Other factors considered in the sensitivity analysis had 

29 smaller impacts on core risk estimates. 

 53 The difference in risk reductions based on application of different rollback methods in simulating the current suite 

of standards reflects the fact that peak shaving rollback, when applied to a location where the 24hr standard level is 

controlling , such as Los Angeles, will produce a smaller degree of reduction in the composite monitor annualaverage PM2.5 level. By contrast, application of proportional rollback will produce a larger degree of rollback in the 

composite monitor annual-average (i.e., a level equal to that needed to get the 24hr design value to meet the 24hr 

standard). We also note that the risk reductions cited here reflecting application of peak-shaving in simulating the 

current suite of standards are based on comparison of composite monitor annual-averages presented in Table F-49 in 

Appendix F. In generating this surrogate for reduction in long-term exposure-related mortality between the two 

standard levels, we compared composite monitor annual-averages with consideration for the fact that long-term 

exposure-related mortality is only calculated down to LML. 

54 Use of peak-shaving as an alternative method for simulating ambient PM2.5 concentrations for alternative 

standards had a moderate-large impact on risk estimates. 
February 2010 Draft – Do Not Quote or Cite 4-38

1 Short-term exposure mortality

2 This section summarizes the results of the sensitivity analysis focused on short-term 

3 exposure-related mortality endpoints (see Table 5-1 for the specific modeling elements 

4 considered in the sensitivity analysis). 

5  Impact of using season-specific C-R functions (vs. an annual C-R function): This 

6 simulation considered the impact on short-term exposure mortality risk of using seasonally7 differentiated effects estimates rather than the core approach of using a single C-R function 

8 for the whole year (note, that the seasonal models were based on the same study as the model 

9 used in the core analysis – Zanobetti and Schwartz, 2009). The results of the simulation 

10 suggest that this source of uncertainty can have a wide range of effects across urban study 

11 areas (including not only variation in the magnitude of effect, but also in the direction). 

12 Percent changes compared with the core risk estimate were large, ranging from -116% (Los 

13 Angeles) to +179% (Birmingham) (these results are for non-accidental mortality – see 

14 Appendix F, Table F-15). We note that these two locations also have relatively low overall 

15 incidence estimates, which does raise concerns over the degree of stability in the sensitivity 

16 analysis estimates. Furthermore, for 9 of the 15 urban study areas (for non-accidental 

17 morality), percent changes from the core were small, with absolute values of 12% or less 

18 (Appendix F, Tables F-15). The results for CV and respiratory mortality also demonstrate 

19 considerable variation across locations, but are generally smaller than results cited above for 

20 non-accidental, with one exception. Birmingham is estimated to have short-term CV 

21 mortality that is +500% higher using seasonal effects estimates compared with the core 

22 results (We note, however, that this endpoint category also has very small incidence, again 

23 raising concerns over the stability of the sensitivity analysis results) (see Table F-18). The 

24 results for respiratory-related mortality also demonstrate considerable variability with results 

25 that could suggest a moderate to large impact (i.e., -48 to +162% - see Appendix F, Table F26 21). We note, however, that small incidence estimates again raise concerns regarding the 

27 stability of these percent difference results. 

28  Impact of using alternative hybrid rollback approach: This simulation evaluates the 

29 potential impact of using the hybrid (non-proportional) approach for simulating just meeting 

30 current and alternative sets of standards, as an alternative to the proportional approach used 

in the core analysis.55 31 The results of this simulation (as contrasted with the impact of using 

32 the hybrid approach on long-term exposure mortality) suggest that use of the hybrid rollback 

33 approach has relatively little effect on short-term mortality risk (e.g., percentage differences 

34 relative to the core risk estimates were in the low single digits for most locations, with one 

35 location having a difference of +17% - see Appendix F, Table F-36). 

 55 Note, that the peak shaving rollback method was only assessed in the context of the composite monitor values 

used in generating long-term exposure-related mortality estimates. Consequently, consideration of the peak shaving 

rollback method is only assessed in terms of its impact on long-term risk and not short-term exposure-related 

mortality. Note, however, that the impact of using peak shaving versus proportional rollback on short-term 

exposure-related risk is expected to be smaller than the impact on long-term exposure-related risk, since the latter is 

linked to composite annual averages which are expected to experience the greatest impact from application of 

alternative rollback methods. 
February 2010 Draft – Do Not Quote or Cite 4-39

1 The sensitivity analysis results discussed above, result in a number of overall 

2 observations regarding sources of uncertainty potentially impacting short-term exposure morality 

3 endpoints. The results of using the seasonally-differentiated effect estimates in modeling short4 term exposure mortality appear to generally have a relatively small impact (e.g., <15%) in most 

5 study areas. For some study areas, the impact does appear to be much larger, with results 

6 including both substantial negative and positive percent differences from the core estimates. 

7 However, in all of these cases, the total incidence estimates involved are very small, raising 

8 concerns over the stability of the risk estimates generated as part of this particular sensitivity 

9 analysis (in many of these instances, the estimates include negative lower bounds, reflecting the 

10 use of non-statistically significant effects estimates). For these reasons, the results of this 

11 sensitivity analysis, while initially appearing to be notable in terms of magnitude in some study 

12 areas, need to be interpreted with care. At this point, we are uncertain as to how important this 

13 source of uncertainty is in the context of short-term exposure mortality estimation. Regarding 

14 the use of the alternative hybrid (non-proportional) approach for simulating conditions under 

15 alternative standard levels, the results suggest that this factor has a modest impact on short-term 

16 exposure mortality (significantly less impact than with the use of the hybrid approach in 

17 estimating long-term exposure mortality). With the exception of factors examined using the 

18 Moolgavkar et al., (2003) study in Los Angeles (see section 4.3.1.4), it would appear that the 

19 factors examined here do not have a large impact on risk estimates generated for short-term 

20 exposure mortality. However, we note that the overall scope of the sensitivity analysis completed 

21 for short-term exposure-related mortality and morbidity is far more limited than that completed 

22 for long-term exposure-related mortality. 

23 Short-term exposure morbidity

24 This section summarizes the results of the sensitivity analysis focused on short-term 

25 exposure-related morbidity endpoints (see Table 5-1 for the specific modeling elements 

26 considered in the sensitivity analysis). The results of individual sensitivity analysis simulations 

27 are presented below, with overall observations presented at the end of the section. 

28  Impact of using season-specific C-R functions (vs. an annual C-R function): This 

29 simulation considered the impact on short-term exposure morbidity (HAs) of using 

30 seasonally-differentiated effects estimates rather than the core approach of using a single C-R 

31 function for the whole year (we note that the seasonal models were obtained from the same 

32 study as the model used in the core analysis – Bell et al, 2008). The results of the simulation 

33 suggest that, as with short-term exposure mortality this source of uncertainty can have a wide 

34 range of impacts on the risk estimates across urban study areas (including not only variation 

35 in the magnitude of risk, but also in the direction) depending on the specific health endpoint 

36 examined. We note, however, that the magnitude of impact appears to be less for short-term 

37 morbidity than for short-term mortality. Percent changes for most of the 15 urban study 
February 2010 Draft – Do Not Quote or Cite 4-40

1 areas were small for CV HAs (generally less than a 20% difference in either direction, 

2 although there was a large impact for Tacoma (-105%)) (see Appendix F, Table F-24). This 

3 source of uncertainty has a moderate to moderate-large impact for respiratory-related HAs 

4 with most locations having greater than a 54% to 74% absolute effect (see Appendix F, Table 

5 F-27). 

6  Impact of using a seasonal function for April through August (applied only to that 

7 period) in modeling asthma-related ED visits in New York, relative to the core 

8 approach of using a single annual effect estimate (and applying that to the whole year): 

9 This sensitivity analysis involved the approach of using a season-specific estimate to model 

10 incidence for the period April through August (obtained from Ito et al., 2007). Because this 

11 sensitivity analysis estimate covers a period shorter than a year, we have not directly 

12 compared it with the annual estimate generated for this endpoint in the core risk assessment 

13 (i.e., we have not generated percent difference estimates as is done with other sensitivity 

14 analysis simulations). However, the results of this sensitivity analysis do suggest that the use 

15 of seasonally-differentiated estimates in modeling this endpoint can impact risk.

16  Impact of considering models with different lags: To examine the impact of lag on 

17 modeling of short-term exposure-related morbidity, we used a range of effects estimates 

18 obtained from Bell et al., 2008 based on application of different lags, including 0-, 1- and 2-

19 day lags, (for both respiratory and cardiovascular-related morbidity). Because lag20 differentiated effects estimates were only available as national-averages and were not 

21 regionally-differentiated, we could not directly compare the results using different lag models 

22 to the results generated for the core analysis (i.e., the sensitivity analysis results would have 

23 mixed both the lag effect and the effect of regional differentiation, thereby preventing clear 

24 assessment of the importance of either factor considered in isolation). However, 

25 consideration of the magnitude of the risk estimates generated using different lag models, for 

26 the same endpoint at the same urban study are, suggests that choice of lag does effect 

27 estimates of short-term exposure-related morbidity (see Appendix F, Table F-48).

28 Given the results of the set of simulations completed for short-term exposure morbidity, 

29 both of which focused on the use of seasonally-differentiated effects estimates, it would appear 

30 that this factor does not have a substantial impact on risk estimates. The analysis considering 

31 different lag models does suggest that this factor could have a notable impact on risk estimates 

32 and should be carefully considered when specifying C-R functions to use in the risk assessment. 

33 Additional factors potentially impacting short-term exposure morbidity are addressed below in 

34 relation to the sensitivity analysis based on alternative models from Moolgavkar et al. (2003). As 

35 noted earlier, the scope of the sensitivity analysis completed for short-term exposure-related 

36 morbidity is limited. 

37 Short-term exposure-related mortality and morbidity (Moolgavkar et al., 2003 study-based 

38 analysis)

39 As noted earlier in the introduction to section 4.3, the results of sensitivity analysis based 

40 on Moolgavkar et al., (2003) include percent difference estimates based on considering the range 
February 2010 Draft – Do Not Quote or Cite 4-41

1 of risk estimates generated using alternative model specifications from this study for a given 

2 health endpoint and it is these results that are discussed below. 

3  Impact of model selection (e.g., log-linear GAM with 30df, log-linear GAM with 100df, 

4 and log-linear GLM with 100df) on estimating short-term exposure mortality and 

5 morbidity: Application of models obtained from Moolgavkar et al., (2003) with various 

6 formulations related to model selection (degrees of freedom, GLM vs. GAM) to the Los 

7 Angeles urban case study location results in a range of short-term exposure mortality 

8 estimates (for non-accidental and CV) that differ by 80% and 49%, respectively (see 

9 Appendix F, Table F-33). In the case of short-term exposure morbidity (specifically, CV10 related HAs), incidence estimates differ by 36% (see Appendix F, Table F-33). These results 

11 suggest that these elements of model specification represent a moderate source of uncertainty 

12 in estimating short-term mortality and morbidity. 

13  Impact of lag structure (0-day through 5-day) on estimating short-term exposure 

14 mortality: Consideration of the range of risk estimates for non-accidental mortality 

15 generated using different lag structures (and associated effect estimates) provided in 

16 Moolgavkar et al., (2003), suggest that this factor could have a moderate impact on risk (in 

17 the range of 55% when comparing the lowest and highest positive incidence estimates 

18 generated). (see Appendix F, Table F-33). 

19  Impact of considering multi-pollutant models on estimating short-term exposure 

20 mortality and morbidity: The results of the Moolgavkar-based simulations (when 

21 considering the spread in risk estimates specifically across these simulations) suggest that the 

22 multi-pollutant versus single-pollutant model issue (i.e., including CO in addition to PM2.5), 

23 could have a large impact on the estimation of short-term exposure mortality (106% for all24 cause) and morbidity (140% for CV-related HAs). 

25 Overall observations regarding key sources of uncertainty impacting short-term exposure 

26 mortality and morbidity risk estimates (based on the Moolgavkar et al., 2003 study) include the 

27 following. The spread in risk estimates generated across the Moolgavkar-based model 

28 specifications suggests that factor related to specifying the C-R model may have a moderate to 

29 large impact. More specifically, variation in the lag structure has a moderate impact on risk and 

30 use of single versus multi-pollutant models could have a potentially large impact on risk. Note, 

31 however, that as discussed earlier, the relevance of these sensitivity analysis results to the 

32 interpretation of core risk estimates is not clear and may be relatively low (see Section 4.3.1). 

33 4.3.1.2 Multi-factor Sensitivity Analysis Results 

34 The results of the multi-factor sensitivity analyses are intended to support both goals of 

35 the sensitivity analysis: (a) identify which factors (now in combination), appear to have a 

36 significant impact on estimation of the core estimates and (b) to derive a set of reasonable 

37 alternative risk estimates for use in considering uncertainty and variability associated with the 

38 core risk estimates. Regarding the latter application, because these multi-factor simulations 

39 combine multiple factors reflecting uncertainty and variability together in generating alternative 
February 2010 Draft – Do Not Quote or Cite 4-42

1 risk estimates, they are likely to produce the highest sensitivity analysis results. Therefore, it is 

2 particularly important to consider the reasonableness of the results of these multi-factor 

3 simulations, to insure that only credible estimates are included in the set of reasonable alternative 

4 risk estimates. Consequently, we emphasize consideration for the reasonableness of these multi5 factor simulations in the discussion presented below. 

6 Long-term exposure mortality

7 This section summarizes the results of the sensitivity analysis focused on long-term 

8 exposure-related mortality endpoints (see Table 4-1 for the specific modeling elements 

9 considered in the sensitivity analysis). 

10  Impact of using log-linear vs. log-log C-R model with fixed or random effects, 

11 estimating incidence down to the LML vs. PRB, and using proportional vs. hybrid 

12 rollback to estimate long-term exposure mortality: This multi-factor sensitivity 

13 analysis focused on a number of model design choices related to modeling long-term 

14 exposure mortality (all-cause and IHD). Modeling elements reflected in the 

15 simulations included: (a) model form (log-linear vs log-log and random vs fixed 

16 effects), (b) modeling risk down to PRB (vs LML), and (c) use of an alternative hybrid 

17 rollback approach (vs proportional rollback) to simulate just meeting the current and 

18 alternative sets of standards. Various permutations of these design elements choices 

19 (relative to the elements selected for the core analysis) were considered. Percent 

20 difference estimates (for all-cause mortality) ranged from 27% (for a model estimating 

21 risk down to PRB and use of the hybrid rollback approach) to 1,089% (for a model 

22 with random effects log-log model, risk estimated down to PRB, and use of the hybrid 

23 rollback approach). 

24 We believe that application of a log-log model with random effects is a reasonable 

25 alternative to the core model (fixed-effects log-linear model), based on our review of the 

26 discussion in Krewski et al. (2009). Similarly, the use of a hybrid rollback approach involving 

27 non-proportional adjustment where there is the potential for greater use of local control strategies 

28 to address local-sources is a reasonable alternative to solely using a proportional rollback 

29 approach in all study areas. Therefore, we believe that the combinations of modeling elements 

30 including these alternative choices are reasonable. However, there is more concern in predicting 

31 risk down to PRB. This is not because there is evidence for a threshold, but rather because we 

32 do not have data to support characterization of the nature of the C-R function in the vicinity of 

33 PRB. Specifically, there is increasing uncertainty in predicting the nature of the C-R function as 

34 you move below the LML. So, while we believe it is reasonable conceptually to estimate risk 

35 down to PRB, the quantitative process of doing this requires use of a function with very high 

36 uncertainty. Therefore, we concluded that those alternative risk estimates generated using risk 

37 estimated down to PRB should not be used in creating the reasonable alternative set of risk 

38 estimates in considering uncertainty associated with the core risk estimates. 
February 2010 Draft – Do Not Quote or Cite 4-43

1 A key limitation of the multi-factor sensitivity analysis is that the approach used did not 

2 allow us to consider the peak-shaving rollback method in concert with the other modeling 

3 elements described above. This means that the combined impact of peak shaving (which has a 

4 greater impact than the hybrid rollback method) with other model specifications is not 

5 characterized. However, as part of the integrative discussion in Chapter 6, we will consider the 

6 results of the single-factor sensitivity analysis examining rollback (with its consideration for 

7 peak shaving) along with the multi-factor sensitivity analysis results described here. 

8 Short-term exposure mortality

9 This section summarizes the results of the sensitivity analysis focused on short-term 

10 exposure-related mortality endpoints (see Table 4-1 for the specific modeling elements 

11 considered in the sensitivity analysis). 

12  Impact of using season-specific vs. annual effect estimates and proportional vs. 

13 hybrid rollback approaches in modeling short -term exposure mortality: This 

14 multi-factor sensitivity analysis focused on a number of model design choices related 

15 to modeling short-term mortality (non-accidental). Modeling elements included in this 

16 sensitivity analysis were use of seasonal vs. annual effects estimates and use of hybrid 

17 vs proportional rollback to simulate just meeting current and alternative standard 

18 levels. Percent difference estimates (for non-accidental mortality) across the 7 urban 

19 study areas included in the simulation ranged from -109% (LA) to +119% 

20 (Birmingham) (see Appendix F, Table F-42). However, we note that the total 

21 incidence estimates associated with these higher-impact locations were relatively low, 

22 again raising the concern for the stability in relative differences with the core estimates. 

23 Because of the more limited scope of the multi-factor sensitivity analysis completed for 

24 short-term exposure-related mortality, we have concluded that these results should not be used as 

25 an additional set of reasonable risk estimates to inform consideration of uncertainty associated 

26 with this category of risk estimates. 

27 4.3.2 Additional Set of Reasonable Risk Estimates to Inform Consideration of 

28 Uncertainty in Core Risk Estimates 

29 This section discusses the use of the output of the sensitivity analysis completed as part 

30 of this risk assessment as an additional set of reasonable risk estimates to inform consideration of 

31 uncertainty associated with the core risk estimates. Specifically, in the case of long-term 

32 exposure-related mortality endpoints, staff has concluded that the results of the sensitivity 

33 analysis represent a reasonable set of alternate risk estimates that fall within an overall set of 

plausible risk estimates surrounding the core estimates.56 34 

 56 As noted in section 4.3.2 and in the integrative discussion in Section 6.4, while staff believes that the sensitivity 

analysis does provide insights into the potential impact of certain sources of uncertainty on short-term exposurerelated mortality and morbidity risk, the sensitivity analysis conducted for short-term exposure-related endpoints 
February 2010 Draft – Do Not Quote or Cite 4-44

1 While not representing a formal uncertainty distribution, the output of the sensitivity 

2 analysis, when combined with the core risk estimates, represent a set of plausible risk estimates, 

3 which reflect consideration for uncertainty in various elements of the risk assessment model. 

4 Therefore, while the discussion of risk estimates in the context of assessing the degree of risk 

5 reduction associated with suites of alternative standards (see Chapter 6) does focus on the core 

6 risk estimates since these are judged to have the greatest overall confidence, the output of the 

7 sensitivity analysis can be used to provide additional perspective on the potential range of 

8 uncertainty around the core estimates. Note however, that we do not know the confidence 

9 interval captured by this uncertainty set, or the specific percentiles of the risk distribution are 

10 represented by points within that set. 

11 As noted earlier, the quantitative single- and multi-factors sensitivity analyses generated 

12 an additional set of risk estimates for a subset of the urban study areas, air quality scenarios and 

13 health endpoints included in the core risk analysis (i.e., Los Angeles and Philadelphia assessed 

14 for the current standard level). However, the part of the sensitivity analysis focusing on 

15 alternative methods for simulating ambient PM2.5 levels (i.e., rollback), did consider a larger 

16 number of study areas and air quality scenarios. In presenting the alternative sets of reasonable 

17 risk estimates, we focus on Los Angeles and Philadelphia for many of the modeling elements, 

18 although we expand the discussion in the context of discussing results related to conducting 

19 rollback.. 

20 In using the additional set of reasonable risk results to augment the core risk estimates, 

21 we begin by presenting both the core and alternative sets of estimates for Los Angeles and 

22 Philadelphia in Table 4-4. Then, in Figures 4-7 and 4-8, we present graphical display of the full 

23 uncertainty set comprising the core plus additional reasonable risk estimates for Los Angeles and 

24 Philadelphia, differentiated by mortality category (Figure 4-7 present results for IHD and Figure 

25 4-8 presents results for all cause mortality). This section concludes with a set of observations 

26 resulting from consideration of information depicted in Table 4-4 and Figures 4-7 and 4-8 in the 

context of interpreting uncertainty in the core risk estimates.57 27 

 

was not as comprehensive as that conducted for long-term exposure-related endpoints. Therefore, we do not believe 

that the results of the sensitivity analysis can be used as an additional set of reasonable risk estimates to supplement 

the core set in the case of short-term exposure-related endpoints. 

57 As noted earlier in 3.4.1, we have excluded several of the sensitivity analysis results in defining the set of 

alternative reasonable risk estimates. Specifically, we consider estimates based on modeling risk down to PRB to be 

less reasonable than the other scenarios included in the sensitivity analysis, since there is substantial uncertainty 

associated with the C-R function shape below the LML. In addition, as discussed in Section 4.3.1.1 risk estimates 

generated using the copollutant model involving PM2.5 and SO2 have been de-emphasized since it is likely that 

control for SO2 may be capturing a portion of PM2.5-attributable risk related to the secondary formation of sulfate. 
February 2010 Draft – Do Not Quote or Cite 4-45

1 Table 4-4 Derivation of a set of reasonable alternative risk estimates to 

2 supplement the core risk estimates (Los Angeles and Philadelphia, 

3 current standards, for long-term IHD mortality). 

Sensitivity analysis 

Core risk estimate 

Description of 

simulation 

Results 

(percent difference: 

sensitivity analysis versus 

core estimate)4

Adjusted set of risk estimate 

to supplement core risk 

estimates1 

Single-element sensitivity analysis results 

(A) Impact of using 

different model 

choices: random 

effects log-linear 

model 

Los Angeles and Philadelphia: 

IHD: +12%; 

All cause: +23% 

Los Angeles and 

IHD: 8.6%, All cause: 2.5% 

Philadelphia: 

IHD: 14.8%, All cause: 4.4% 

(B) Impact of using 

different model 

choices: random 

effects log-log model 

Los Angeles: 

IHD: +111%; All cause: +159 

Philadelphia: 

IHD: +80%; All cause: +123% 

Los Angeles and 

IHD: 16.2%, All cause: 5.2% 

Philadelphia: 

IHD: 23.8%, All cause: 8.0% 

(C) Impact of using 

different model 

choices (single vs. 

multi-pollutant – NO2 

Vs O3/CO)3

Los Angeles and Philadelphia: 

All cause: +45 to +74% 

(O3/CO and NO2 , respectively) 

and -74% for SO2 

Los Angeles and 

All cause: 2.9% and 3.5% 

(for O3/CO and NO2 , 

respectively), 0.52% (SO2) 

Philadelphia: 

All cause: 5.2% and 6.3% (for 

O3/CO and NO2 , 

respectively), 0.94% (SO2) 

(D) Impact of C-R 

function from 

alternative long-term 

exposure study 

(Krewski et al., 2000) 

Los Angeles: 

All cause: +121% 

Philadelphia: 

All cause: +119% 

Los Angeles: 

All cause: 4.4% 

Philadelphia: 

All cause: 7.9% 

(E) Impact of using 

alternative roll-back 

approach (hybrid and 

peak shaving) to 

simulate just meeting 

alternative standards 

Los Angeles: 

Both all cause & IHD: +21 to 

+40% (hybrid and peak 

shaving, respectively) 

Philadelphia: 

Both all cause & IHD: +8% 

(peak shaving only) 

Los Angeles and 

Hybrid: IHD: 9.3%, All cause: 

2.4% 

Peak shaving: IHD: 10.8%, 

All cause: 2.8% 

Philadelphia: 

IHD: 14.3% 

All cause: 3.9% 

Multi-element sensitivity analysis results 

Percent of total incidence

for IHD and all cause 

mortality (current suite of 

standards): 

Los Angeles: 

IHD: 6.1 to 7.7% 

All cause: 1.6 to 2.0%

Philadelphia: 

IHD: 10.5 to 13.2% 

All cause: 2.8 to 3.6% 

(note, two core estimates 

are presented for each 

combination of urban 

study area and mortality 

endpoint category 

reflecting use of C-R 

functions derived using 

different periods of 

ambient data from 

Krewski et al., 2009 – see 

section 3.3.3) 

(F) Random effects 

log-log & hybrid 

non-proportional 

rollback 

Los Angeles: 

IHD: +149% 

All cause: +211 

Philadelphia: 

NA2

Los Angeles: 

IHD: 19.2% 

All cause: 6.2% 

Philadelphia: 

NA2

1 4 Percent of total incidence that is PM2.5- related (note, the set of estimates for each entry reflect adjustment to the 

5 two core estimates generated for IHD and all-cause mortality) 2 6 hybrid not run for Philadelphia, so multi-element sensitivity analysis not completed 

 

Note, that the risk estimates for SO2 are presented as open circles in Figures 4-6 and 4-7, to signify that they have 

lower confidence and are de-emphasized relative to the other alternative risk estimates presented. 
February 2010 Draft – Do Not Quote or Cite 4-46

3 1 the two pollutant model for PM2.5 and CO and PM2.5 and O3 had the same sensitivity result, so both models are 

2 referenced here with the same impact on mortality estimates. 

4 3 Sensitivity analysis based on comparison of alternative model formulations to the core risk estimates based on the 

4 C-R function derived using 1999-2001 ambient monitoring data (see section 3.5.4). 

5 Figure 4-7 Comparison of core risk estimates with reasonable alternative set of 

6 risk estimates for Los Angeles and Philadelphia (IHD mortality). 

5 10 15 20 25

Los Angeles

Philadelphia

Percent total incidence attributable to PM2.5 (IHD mortality)

- core risk estimate

- alternative reasonable risk estimate (letter 

matches simulation in Table 4-2)

KEY:

7 

8 Figure 4-8 Comparison of core risk estimates with reasonable alternative set of 

9 risk estimates for Los Angeles and Philadelphia (all cause mortality). 

1 2 4 6 8

Los Angeles

Philadelphia

Percent total incidence attributable to PM2.5 (all-cause mortality)

- core risk estimate

- alternative reasonable risk estimate (letter 

matches simulation in Table 4-2)

KEY:

3 5 7

- alternative reasonable risk estimate for copollutant modelin including PM2.5 and 

SO2 (de-emphasized relative to other alternative risk estimates - see text)

0.5

10 
February 2010 Draft – Do Not Quote or Cite 4-47

1 

2 Review of the set of risk estimates presented in Table 4-4 and displayed in Figures 4-7 

3 and 4-8 results in a number of observations regarding uncertainty associated with the core risk 

4 estimates: 

5  Consideration for uncertainty and variability in the core risk estimates results in a 

6 notable spread in risk estimates: Given the factors considered in generating the 

7 alternative set of reasonable risk estimates, there appears to be a factor of 3 to 4 spread in 

8 risk estimates if we consider the lowest (core) estimates generated and the highest 

9 alternative risk estimates generated. This observation holds for both urban study areas 

10 considered, as well as for the two mortality endpoint categories. As noted earlier in this 

11 section, we have de-emphasized risk estimates generated using the copollutant model 

12 involving PM2.5 and SO2 due to concerns with collinearity between the two pollutants and 

13 the potential that SO2 represents risk attributable to secondarily formed PM2.5. 

14  Uncertainty set of risk estimates generated to supplement the core risk estimates are 

15 skewed towards higher risk: It appears that, given the factors considered in generating 

16 the alternative set of reasonable risk estimates, consideration of uncertainty could result 

17 in higher (more elevated) risk estimates, compared with the core risk estimates. In other 

18 words, most if not all of the alternative model specifications we considered resulted in 

19 risks that are higher than our core estimates. 

20  Sensitivity analysis is limited in its scope (potentially important sources of uncertainty 

21 not considered): As noted earlier, the sensitivity analysis did not consider a number of 

22 potentially important sources of uncertainty, some of which were addressed as part of the 

23 qualitative analysis of uncertainty (see Table 3-13). For example, information is not 

24 available to consider compositional differences in PM2.5 and the potential for 

25 differentiation of effects estimates. Further, not considering more refined patterns of 

26 intra-urban exposure to PM2.5 in deriving effects estimates could result in under27 estimation of risk. 

28 It is important to reiterate that this set of alternative realizations presented in Table 4-4 

29 and depicted in Figures 4-6 and 4-7, does not represent an uncertainty distribution. Therefore, 

30 we can not assign percentiles to the individual data points presented and (importantly), we do not 

31 draw any conclusions based on any clustering of the alternative risk estimates seen in Figures 4-6 

32 and 4-7. Further, we do not know whether any of the higher-end estimates generated actually 

33 represent true bounding risk estimates given overall uncertainty associated with the core risk 

34 estimates. Despite these key caveats, having a set of risk estimates reflecting the impact of 

35 modeling element uncertainties does provide information that helps to inform our 

36 characterization of uncertainty related to the core risk estimates. 
February 2010 Draft – Do Not Quote or Cite 4-48

1 4.4 EVALUATING THE REPRESENTATIVENESS OF THE URBAN STUDY AREAS 

2 IN THE NATIONAL CONTEXT 

3 The goal in selecting the 15 urban study areas included in this risk assessment was two4 fold: (a) to choose urban locations with relatively elevated ambient PM levels (in order to 

5 evaluate risk for locations likely to experience some degree of risk reduction under alternative 

6 standards) and (b) to include a range of urban areas reflecting heterogeneity in other PM risk7 related attributes across the country. To further support interpretation of risk estimates generated 

8 in this analysis, we are assessing the degree to which urban study areas represent the range of 

9 key PM2.5 risk-related attributes that spatially vary across the nation. We have partially 

10 addressed this issue by selecting urban study areas that provide coverage for different PM 

11 regions of the country (see section 3.3.2). In addition, we are considering how well the selected 

12 urban areas represent the overall U.S. for a set of spatially-distributed PM2.5 risk related variables 

13 (e.g., PM2.5 composition, weather, demographics including SES, baseline health incidence rates). 

14 This analysis will help to inform how well the urban study areas reflect national-level variability 

15 in these key PM risk-related variables. Based on generally available data (e.g. from the 2000 

16 Census, Centers for Disease Control (CDC), or other sources), distributions for risk-related 

17 variables across U.S. counties and for the specific counties represented in the urban study areas 

18 are generated. The specific values of these variables for the selected urban study areas are then 

19 plotted on these distributions, and an evaluation is conducted of how representative the selected 

20 study areas are with respect to these individual variables, relative to the national distributions. 

21 Estimates of risk (either relative or absolute, e.g. number of cases) within our risk 

22 assessment framework are based on four elements: population, baseline incidence rates, air 

23 quality, and the coefficient relating air quality and the health outcome (i.e., the PM2.5 effect 

24 estimates). Each of these elements can contribute to heterogeneity in risk across urban locations, 

25 and each is variable across locations. In addition, there may be additional identifiable factors 

26 that contribute to the variability of the four elements across locations. In this assessment, we 

27 examine the representativeness of the selected urban area locations for the four main elements, 

28 and also provide additional assessment of factors that have been identified as influential in 

29 determining the magnitude of the C-R function across locations. 

30 The specific choice of variables which may affect the PM2.5 effect estimates for which we 

31 will examine urban study area representativeness is informed by an assessment of the 

32 epidemiology literature. We particularly focused on meta-analyses and multi-city studies which 

33 identified variables that influence heterogeneity in PM2.5 effect estimates, and exposure studies 

34 which explored determinants of differences in personal exposures to ambient PM2.5. While 

35 personal exposure is not incorporated directly into PM epidemiology studies, differences in the 

36 PM2.5 effect estimates between cities clearly is impacted by differing levels of exposure and 
February 2010 Draft – Do Not Quote or Cite 4-49

1 differences in exposure are clearly related to a number of exposure determinants. Broadly 

2 speaking, determinants of PM2.5 effect estimates can be grouped into three areas: demographics, 

3 baseline health conditions, and climate and air quality. Based on a review of these studies, we 

4 identified the following variables within each group as potentially determining the PM2.5 effect 

5 estimates: 

6  Demographics: education (see Zeka et al, 2006; Ostro et al, 2006), age and gender (see 

7 Zeka et al, 2006), population density (see Zeka et al, 2005), unemployment rates (see Bell 

8 and Dominici, 2008), race (see Bell and Dominici, 2008), public transportation use (see 

9 Bell and Dominici, 2008), 

10  Baseline health conditions: disease prevalence (diabetes – Bateson and Schwartz, 2004; 

11 Ostro et al, 2006; Zeka et al, 2006; pneumonia – Zeka et al, 2006; stroke – Zeka et al, 

12 2006; heart and lung disease – Bateson and Schwartz, 2004; acute myocardial infarction 

13 – Bateson and Schwartz, 2004). 

 Climate and air quality: PM2.5 levels (average, 98th 14 percentiles, and numbers of days over 

the level of the 24-hour standard, e.g. 35 μg/m3 15 ), co-pollutant levels, PM composition 

16 (see Bell et al, 2009; Dominici et al, 2007; Samet, 2008; Tolbert, 2007), temperatures 

(temp) (days above 90 degrees, variance of summer temp, mean summer temp, 98th 17 

18 percentile temp, mean winter temp -- see Roberts, 2004; Medina-Ramon et al, 2006; Zeka 

19 et al., 2005), air conditioning prevalence (see Zanobetti and Schwartz, 2009; Franklin et 

20 al, 2007; Medina-Ramon et al, 2006), ventilation (see Sarnat et al, 2006), percent of 

21 primary PM from traffic (see Zeka et al., 2005), 

22 Based on these identified potential risk determinants, we identified possible datasets that 

23 could be used to generate nationally representative distributions for each parameter. We were 

24 not able to identify readily available national datasets for all variables. In these cases, if we were 

25 able to identify a broad enough dataset covering a large enough portion of the U.S., we used that 

26 dataset to generate the parameter distribution. In addition, we were not able to find exact 

27 matches for all of the variables identified through our review of the literature. In cases where an 

28 exact match was not available, we identified proxy variables to serve as surrogates. For each 

29 parameter, we report the source of the dataset, its degree of coverage, and whether it is a direct 

30 measure of the parameter or a proxy measure. The target variables and sources for the data are 

31 provided in Table 4-2. Summary statistics for the most relevant variables are provided in Table 

32 D-3. 
February 2010 Draft – Do Not Quote or Cite 4-50

1 Table 4-5 Data Sources for PM NAAQS Risk Assessment Risk Distribution 

2 Analysis. 

Potential Risk 

Determinant Metric Year Source 

Degree of 

National 

Coverage 

Demographics 

Age Median Age 2005 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Age Percent over 65 2005 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Age Percent under 15 2005 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Education Population with less 

than HS diploma 2000 

USDA/ERS, 

http://www.ers.usda.gov/Data/Edu

cation/ 

All counties 

Unemployment Percent unemployed 2005 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Income Per Capita Personal 

Income 2005 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Race Percent nonwhite 2006 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Population Total population 2008 

Cumulative Estimates of Resident 

Population Change for the United 

States, States, Counties, Puerto 

Rico, and Puerto Rico Municipios: 

April 1, 2000 to July 1, 2008, 

Source: Population Division, U.S. 

Census Bureau 

All counties 

Population density Population/square mile 2008 

Cumulative Estimates of Resident 

Population Change for the United 

States, States, Counties, Puerto 

Rico, and Puerto Rico Municipios: 

April 1, 2000 to July 1, 2008, 

Source: Population Division, U.S. 

Census Bureau 

All counties 

Urbanicity ERS Classification 

Code 2003 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Climate and Air Quality 

PM2.5 Levels PM2.5 Levels -- 

Monitored Ann Mean 2007 AQS 617 Monitored 

counties 

PM2.5 Levels PM2.5 Levels -- 

Monitored 98th %ile 2007 AQS 617 Monitored counties 

PM2.5 Levels Average MCAPS MCAPS website 204 counties 204 MCAPS 

counties 
February 2010 Draft – Do Not Quote or Cite 4-51

Potential Risk 

Determinant Metric Year Source 

Degree of 

National 

Coverage 

PM2.5 Levels % days exceeding 35 

μg/m3 MCAPS website 204 counties 204 MCAPS 

counties 

Copollutant Levels Ozone AQS 725 Monitored 

counties 

Roadway 

emissions/Exposure 

% of primary emissions 

from traffic 1999 NEI All counties 

Temperature Annual Average MCAPS website 204 counties 204 MCAPS 

counties 

Temperature Mean July Temp 1941-

1970 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Relative Humidity Mean July RH 1941-

1970 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Ventilation Air conditioning 

prevalence 2005 

American Housing Survey, with 

additional processing as in Reid et 

al (2009) 

83 urban areas 

Baseline Health Conditions

Baseline Mortality All Cause CDC Wonder 1999-2005 All counties 

Baseline Mortality Non Accidental CDC Wonder 1999-2006 All counties 

Baseline Mortality Cardiovascular CDC Wonder 1999-2007 All counties 

Baseline Mortality Respiratory CDC Wonder 1999-2008 All counties 

Baseline Morbidity AMI prevalence 2007 BRFSS MSA estimates 184 BRFSS 

MSA 

Baseline Morbidity Diabetes Prevalence 2007 BRFSS MSA estimates 184 BRFSS 

MSA 

Baseline Morbidity Pneumonia Prevalence 

Baseline Morbidity Stroke Prevalence 2007 BRFSS MSA estimates 184 BRFSS 

MSA 

Baseline Morbidity CHD Prevalence 2007 BRFSS MSA estimates 184 BRFSS 

MSA 

Baseline Morbidity COPD Prevalence 

Obesity BMI 2007 BRFSS MSA estimates 184 BRFSS 

MSA 

Level of exercise vigorous activity 20 

minutes 2007 BRFSS MSA estimates 184 BRFSS MSA 

Level of exercise 

moderate activity 30 

minutes or vigorous 

activity 20 minutes 

2007 BRFSS MSA estimates 184 BRFSS 

MSA 

Respiratory Risk 

Factors Current Asthma 2007 BRFSS MSA estimates 184 BRFSS MSA 

Smoking Ever Smoked 2007 BRFSS MSA estimates 184 BRFSS 

MSA 

C-R Estimates 

Mortality Risk All Cause 2009 Zanobetti and Schwartz (2009) 212 

cities 212 cities 

Mortality Risk Respiratory 2009 Zanobetti and Schwartz (2009) 212 

cities 212 cities 

Mortality Risk Cardiovascular 2009 Zanobetti and Schwartz (2009) 212 

cities 212 cities 
February 2010 Draft – Do Not 

4-52

1 Table 4-6 Summary Statistics for Selected PM Risk Attributes. 

Average Standard Deviation Maximum Minimum 

Risk Attributes 

Urban study 

areas 

U.S. 

counties 

Urban study 

areas 

U.S. 

counties 

Urban study 

areas 

U.S. 

counties 

Urban study 

areas 

co

Demographics Population 1,410,331 97,020 1,870,237 312,348 9,862,049 9,862,049 57,441 Population Density (Pop/sq mile) 7,212 258 14,960 1,757 71,758 71,758 87 Median Age (years) 35.5 38.6 2.6 4.4 41.5 55.3 30.2 % Age 65 Plus 11.3 14.9 2.6 4.1 17.2 34.7 5.8 Unemployment rate (%) 5.4 5.4 1.5 1.8 9.0 20.9 2.7 % with Less than High School Diploma 21.8 22.6 7.7 8.8 37.7 65.3 11.2 Income ($2005) 35691 27367 12605 6604 93377 93377 23492 5Air conditioning prevalence (%) 85.8 83.3 13.3 21.5 99.4 100.0 58.6 % Non-white 29.5 13.0 18.2 16.2 68.3 95.3 2.7 Health Conditions Prevalence of CHD (%) 3.9 4.3 0.9 1.3 5.2 8.7 1.8 Prevalence of Obesity (%) 26.4 26.0 3.0 4.1 32.7 35.7 22.2 Prevalence of Stroke (%) 2.7 2.7 0.8 1.0 4.1 6.5 1.1 Prevalence of Smoking (ever) (%) 18.4 19.6 3.1 4.0 23.1 34.4 14.2 Prevalence of Exercise (20 minutes) (%) 28.4 28.0 3.6 4.8 33.9 44.1 20.5 All Cause Mortality (per 100,000 population) 833.7 1022.3 241.1 258.6 1342.9 2064.2 402.5 1Non-accidental Mortality (per 100,000 population) 774.1 950.6 227.3 249.6 1242.0 1958.4 361.6 1Cardiovascular Mortality (per 100,000 population) 317.5 392.1 100.6 121.0 535.7 970.4 122.4 Respiratory Mortality (per 100,000 population) 70.8 97.3 23.0 32.3 130.3 351.0 34.8 Air Quality and Climate AQ - PM25 Annual Mean (μg/m3) 15.1 11.7 2.2 3.1 19.6 22.5 9.7 AQ - PM25 98th %ile 24-hour Average (μg/m3) 38.7 30.7 11.6 9.3 79.2 81.1 26.8 AQ - O3 4th High Maximum 8-hour Average (ppm) 0.087 0.077 0.009 0.010 0.105 0.126 0.064 0% Mobile Source PM Emissions 34.0 44.4 11.2 21.9 56.6 97.6 13.7 
February 2010 Draft – Do Not 

4-53

Average Standard Deviation Maximum Minimum 

Risk Attributes 

Urban study 

areas 

U.S. 

counties 

Urban study 

areas 

U.S. 

counties 

Urban study 

areas 

U.S. 

counties 

Urban study 

areas 

co

July Temperature Long Term Average (°F) 78.1 75.9 4.5 5.4 91.2 93.7 64.8 July Relative Humidity Long Term Average (°F) 58.2 56.2 14.0 14.6 70.0 80.0 19.0 C-R Estimates All Cause Mortality PM2.5 Risk Estimate 0.000971 0.000974 0.000340 0.000216 0.001349 0.001508 0.000159 -0.0Respiratory Mortality PM2.5 Risk Estimate 0.001606 0.001670 0.000419 0.000305 0.002157 0.002221 0.000931 -0.0Cardiovascular Mortality PM2.5 Risk Estimate 0.001013 0.000842 0.000586 0.000324 0.001958 0.001958 -0.000180 -0.0
February 2010 Draft – Do Not Quote or Cite 4-54

1 Formal comparisons of parameter distributions for the set of urban study areas and the 

2 national parameter distributions are conducted using standard statistical tests, e.g. the 

3 Kolmogorov-Smirnov non-parametric test for equality of distributions. In addition, visual 

4 comparisons are made using cumulative distribution functions, and boxplots. 

5 The formal Kolmogorov-Smirnov test results are provided in Table 4-4. The K-S tests 

6 the hypotheses that two distributions are not significantly different. A high p-value indicates a 

7 failure to reject the null hypotheses that the case-study and national distributions are the same. 

8 We used a rejection criterion of p≤0.05, which is a standard rejection criteria. It should be noted 

9 that the K-S test provides a good overall measure of fit, but will not provide a test of how well 

10 specific percentiles of the distributions are matched. As such, the K-S test results will not be 

11 sufficient to determine whether the urban study areas adequately capture the tails of the 

12 distributions of specific risk related variables. Additional visual analyses are used to assess 

13 representativeness for the tails of the distributions. Overall, the K-S test results show that for 

14 many of the important risk variables such as population, air quality, age, and baseline mortality 

15 rates, the urban study areas are not representative of the distributions of these variables for the 

16 U.S. as a whole. However, for some important potential risk determinants, such as prevalence of 

17 underlying hear and lung diseases, the case study areas are representative of the national 

18 distributions. However, for these specific variables, the national distribution is represented 

19 primarily by large urban areas, so it is more accurate in these cases to suggest that the urban 

20 study areas are representative of the overall distribution across urban areas. 

21 Figures 4-14 through 4-17 show for the four critical risk function elements (population, 

22 air quality, baseline incidence, and the PM2.5 effect estimate) the cumulative distribution 

23 functions plotted for the nation, as well as for the urban study areas. These four figures focus on 

24 critical variables representing each type of risk determinant, e.g. we focus on all-cause mortality 

25 rates, but we also have conducted analyses for cardiovascular and respiratory mortality 

26 separately. The complete set of analyses is provided in Appendix D. The vertical black lines in 

27 each graph show the values of the variables for the individual urban study areas. These figures 

28 show that the selected urban study areas represent the upper percentiles of the distributions of 

29 population and air quality, while not representing lower population locations with lower 24-hour 

30 PM2.5 levels. This is consistent with the objectives of our case study selection process, e.g. we 

31 are characterizing risk in areas that are likely to be experiencing excess risk due to PM levels 

32 above alternative standards. The urban case study locations represent the full distribution of 

33 PM2.5 risk coefficients, but do not capture the upper end of the distribution of baseline all-cause 

34 mortality. The interpretation of this is that the case study risk estimates may not capture the 

35 additional risk that may exist in locations that have the highest baseline mortality rates. 
February 2010 Draft – Do Not Quote or Cite 4-55

1 Figures 4-18 through 4-21 shows for several selected potential risk attributes the CDF 

2 plotted for the nation as well as for the urban study areas. These potential risk attributes do not 

3 directly enter the risk equations, but have been identified in the literature as potentially affecting 

4 the magnitude of the PM2.5 C-R functions reported in the epidemiological literature. The 

5 selected urban study areas do not capture the higher end percentiles of several risk 

6 characteristics, including populations over 65, income, and baseline cardiovascular disease 

7 prevalence. Comparison graphs for other risk attributes are provided in Appendix D. 

8 Summarizing the analyses of the other risk attributes, we conclude that the urban study areas 

9 provide adequate coverage across population, population density, annual and 24-hour PM2.5 

10 levels, ozone co-pollutant levels, temperature and relative humidity, unemployment rates, 

11 percent non-white population, asthma prevalence, obesity prevalence, stroke prevalence, exercise 

12 prevalence, and less than high school education. We also conclude that while the urban study 

13 areas cover a wide portion of the distributions, they do not provide coverage for the upper end of 

the distributions of age (all case study locations are below the 85th 14 %ile), % of population 65 and 

older (below 85th 15 %ile), percent of primary PM emissions from mobile sources (below 8oth 

%ile), prevalence of angina/coronary heart disease (below 85th 16 %ile), prevalence of diabetes 

(below 85th %ile), prevalence of heart attack (below 80th 17 %ile), prevalence of smoking (below 

85th %ile), all-cause mortality rates (below 90th %ile), cardiovascular mortality rates (below 90th 18 

%ile) and respiratory mortality rates (below 90th 19 %ile). In addition, all of the case study 

locations were above the 25th 20 percentile of the distribution of personal income. 

21 Based on the above analyses, we can draw several inferences regarding the 

22 representativeness of the urban case studies. First, the case studies represent urban areas that are 

23 among the most populated and most densely population in the U.S. Second, they represent areas 

with relatively higher levels of annual mean and 24-hour 98th 24 percentile PM2.5. Third, they 

25 capture well the range of effect estimates represented in the Zanobetti and Schwartz (2009) 

26 study. These three factors would suggest that the urban study areas should capture well overall 

27 risk for the nation, with a potential for better characterization of the high end of the risk 

28 distribution. However, there are several other factors that suggest that the urban study areas may 

29 not be representing areas that may have a high risk per microgram of PM2.5. The analysis 

30 suggests that the urban study areas are not capturing areas with the highest baseline mortality 

31 risks, nor those with the oldest populations. These areas may have higher risks per microgram of 

32 PM2.5, and thus the high end of the risk distribution may not be captured, although the impact on 

33 characterization of overall PM risk may not be as large, for the following reasons. 

34 It should be noted that several of the factors with underrepresented tails, including age 

35 and baseline mortality (R=0.81) are spatially correlated, so that certain counties which have high 

36 proportions of older adults also have high baseline mortality and high prevalence of underlying 
February 2010 Draft – Do Not Quote or Cite 4-56

1 chronic health conditions. Because of this, omission of certain urban areas with higher 

2 percentages of older populations, for example, cities in Florida, may lead to underrepresentation 

3 of high risk populations. However, with the exception of areas in Florida, most locations with 

4 high percentages of older populations have low overall populations, less than 50,000 people in a 

5 county. And even in Florida, the counties with the highest PM2.5 levels do not have a high 

6 percent of older populations. This suggests that while the risk per exposed person per microgram 

7 of PM2.5 may be higher in these locations, the overall risk to the population is likely to be within 

8 the range of risks represented by the urban case study locations. 

9 Table 4-7 Results of Kolomogrov-Smirnoff Tests for Equality Between National 

10 and Urban Study Area Distributions for Selected National Risk 

11 Characteristic Variables 

12 (null hypothesis is no difference between the distributions) 

Risk Attributes Reject H0? p-value 

Demographics 

Population Y 0.0001 

Population Density (Pop/sq mile) Y 0.0001 

Median Age Y 0.0001 

% Age 65 Plus Y 0.0001 

Unemployment rate N 0.5850 

% with Less than High School Diploma N 0.8535 

Income Y 0.0001 

Air Conditioning Prevalence (%) N 0.9592 

% Non-white Y 0.0001 

Health Conditions 

Prevalence of CHD N 0.7705 

Prevalence of Obesity N 0.9180 

Prevalence of Stroke N 0.7064 

Prevalence of Smoking (ever) N 0.5748 

Prevalence of Exercise (20 minutes) N 0.7649 

All Cause Mortality Y 0.0001 

Non-accidental Mortality Y 0.0002 

Cardiovascular Mortality Y 0.0060 

Respiratory Mortality Y 0.0001 

Air Quality and Climate 

AQ - PM25 Annual Mean Y 0.0001 

AQ - PM25 98th %ile 24-hour Average Y 0.0001 

AQ - PM25 % of days above 35 μg/m3 Y 0.0248 

AQ - O3 4th High Maximum 8-hour 

Average Y 0.0003 

% Mobile Source PM Emissions Y 0.0133 
February 2010 Draft – Do Not Quote or Cite 4-57

Risk Attributes Reject H0? p-value 

July Temperature Long Term Average Y 0.0003 

July Relative Humidity Long Term 

Average N 0.0614 

C-R Estimates 

All Cause Mortality PM2.5 Risk N 0.1585 

Respiratory Mortality PM2.5 Risk N 0.2864 

Cardiovascular Mortality PM2.5 Risk N 0.1161 

1 

2 

3 Figure 4-9 Comparison of distributions for key elements of the risk equation: 

4 total population. 

Comparison of Urban Case Study Area Population with U.S. Distribution of 

Population (all U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 1000 10000 100000 1000000 10000000

Population

% of Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all above the 65th Percentile 

of county populations

5 
February 2010 Draft – Do Not Quote or Cite 4-58

1 Figure 4-10 Comparison of distributions for key elements of the risk equation: 

 98th 2 percentile 24-hour average PM2.5

Comparison of Urban Case Study Area 98th %ile PM2.5 with U.S. Distribution of 

98th %ile PM2.5 

(617 U.S. Counties with PM2.5 Monitors)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90

98th Percentile Daily PM2.5

% of Monitored Counties

All Counties CDF Case Study Counties CDF Case Study Counties 3 
February 2010 Draft – Do Not Quote or Cite 4-59

1 Figure 4-11 Comparison of distributions for key elements of the risk equation: all 

2 use mortality rate.

Comparison of Urban Case Study All Cause Mortality Rate to U.S. Distribution of All 

Cause Mortality Rate

(3143 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 300 500 700 900 1100 1300 1500 1700 1900 2100

All Cause Mortality per 100,000 Population

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 90th percentile 

of county all cause mortality

3 
February 2010 Draft – Do Not Quote or Cite 4-60

1 Figure 4-12 Comparison of distributions for key elements of the risk equation: 

2 Mortality risk effect estimate from Zanobetti and Schwartz (2008). 

Comparison of Urban Case Study PM All-cause Mortality Risk (β) to 

U.S. Distribution of PM All-cause Mortality Risk

(212 U.S. Urban Areas)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014 0.0016

Bayesian Shrunken PM All Cause Mortality Risk Coefficient (β)

% of Z&S Urban Areas

All Z&S Urban Areas CDF Case Study Urban Area CDF Case Study Urban Areas 3 

4 

5 

6 

7 
February 2010 Draft – Do Not Quote or Cite 4-61

1 Figure 4-13 Comparison of distributions for selected variables expected to 

2 influence the relative risk from PM2.5: long term average July 

3 temperature. 

Comparison of Urban Case Study Area Long Term Average July Temperature to 

U.S. Distribution of Long Term Average July Temperature

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

50 60 70 80 90 100

July 30 Year Average Temperature, 1941-1970

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties 4 

5 
February 2010 Draft – Do Not Quote or Cite 4-62

1 Figure 4-14 Comparison of distributions for selected variables expected to 

2 influence the relative risk from PM2.5: percent of population 65 and 

3 older. 

Comparison of Urban Case Study Area % 65 and Older to U.S. Distribution of % 65 

and Older

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25 30 35

% of Population 65 and Older, 2005

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 75th percentile 

of county % of population 65 

and older

4 
February 2010 Draft – Do Not Quote or Cite 4-63

1 Figure 4-15 Comparison of distributions for selected variables expected to 

2 influence the relative risk from PM2.5: per capita annual personal 

3 income. 

Comparison of Urban Case Study Area Per Capita Personal Income to U.S. 

Distribution of Per Capita Personal Income

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

$10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000 $90,000

Per Capita Personal Income, 2005

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all above the 25th percentile 

of county per capita income

4 

5 
February 2010 Draft – Do Not Quote or Cite 4-64

1 Figure 4-16 Comparison of distributions for selected variables expected to 

2 influence the relative risk from PM2.5: per capita annual personal 

3 income. 

Comparison of Urban Case Study Area Angina/CHD Prevalence to U.S. Distribution 

of Angina/CHD Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

123456789

% Prevalence of Angina or CHD, BRFSS, 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Urban case study areas are 

all below the 85th percentile 

of MSA angina or CHD 

prevalence

4 

5 

6 4.5 CONSIDERATION OF DESIGN VALUES AND PATTERNS OF PM2.5

7 MONITORING DATA IN INTREPRETING CORE RISK ESTIMATES 

8 The degree of risk reduction associated with the current and alternative suites of 

9 standards at a particular urban study area depends to a great extent on the degree of reduction in 

10 PM2.5 concentrations simulated for that location. This in turn depends on the interplay between 

11 the 24-hour and annual design values and the monitoring data used to characterize ambient PM2.5

12 concentrations, since these factors determine the composite annual average and composite 24-

13 hour PM2.5 profiles used in modeling long-term and short-term exposure related risk for that 

14 study area. Because of the role that design values and underlying patterns in PM2.5 monitoring 

15 data play in determining the degree of risk reductions, these factors can be used in helping to 

16 interpret risk estimates generated for the 15 urban study areas under the various standard levels 

17 considered in this risk assessment. Further, it is possible to consider, more broadly, patterns of 

18 design values across urban areas in the U.S. and contrast these with patterns seen for the 15 
February 2010 Draft – Do Not Quote or Cite 4-65

1 urban study areas to help to place risk estimates for the 15 urban study areas in a broader national 

2 context. 

3 This section discusses consideration of patterns of design values (section 4.5.1) and 

4 underlying ambient monitoring PM2.5 data (section 4.5.2) for the 15 urban study areas in the 

5 context of helping to interpret risk estimates. Each of these discussions begins by describing the 

6 methods used in each analysis and concludes with a set of key observations. 

7 4.5.1 Design Values 

8 The set of design values for an urban study area determines whether the 24-hour or 

9 annual standard will be controlling as well as the degree of reduction in ambient PM2.5

10 concentrations associated with a particular suite of standards. Therefore, by plotting the 

11 relationship between 24-hour and annual design values for each of the 15 urban study areas, we 

12 can obtain a quick visual perspective on (a) which study areas will experience reductions in risk 

13 for a particular suite of standards, (b) whether the 24-hour or annual standard will control, and 

14 (c) the general magnitude of risk reduction. The last observations result from comparing the 

15 controlling standard level with the matching design value, which will determine the fractional 

16 reduction in PM2.5 levels at monitors exceeding the standard level (for peak shaving rollback), or 

17 more broadly across all monitors (for proportional rollback). 

18 Figures 4-17 through 4-19 present scatter plots of 24-hour and annual design values for a 

19 combination of the 15 urban study areas (red stars) and the broader set of larger urban areas in 

20 the U.S. (green circles). In addition to depicting the set of design values for these urban areas, 

21 each figure also includes a set of superimposed lines representing the current suite of standards 

22 (Figure 4-17) and three of the alternative suites of standards considered in the risk assessment 

23 (12/35 – Figure 4-18, and 12/25 – Figure 4-19). In each figure, the horizontal line represents the 

24 24-hour standard level, while the vertical line represents the annual standard level. The line that 

25 intercepts the origin (i.e., the “35/15 line” in Figure 4-17) represents the point of demarcation 

26 between those study areas where the 24-hour standard controls (to the left of the intercept line) 

27 and those study area where the annual standard level controls (to the right of the intercept line). 

28 By superimposing these lines related to the current standard level on the scatter plot, we have 

29 created five zones within each figure including: 

30  Zone A: 24-hour design values exceeding the 24-hour standard level, but annual design 

31 values below the annual standard level (i.e., 24-hour standard is controlling). Urban study 

32 areas in this zone are predicted to experience risk reduction with the degree of reduction 

33 reflecting the degree to which the 24-hour design value exceeds the 24-hour standard level. 

34 For example, in Figure 4-17 (depicting the current suite of standards), Tacoma and Salt Lake 

35 City fall in this zone, along with 20-30 additional urban areas in the U.S. 
February 2010 Draft – Do Not Quote or Cite 4-66

1  Zone B: 24-hour design values and annual design values exceed 24-hour and annual 

2 standard levels, respectively, and the 24-hour standard is controlling. We have further 

3 transected this zone into B1 and B2, with the former representing those urban areas with 

4 notably high 24-hour design values (Fresno, Los Angeles in Figure 4-17) and B2 those with 

5 lower, although still controlling, 24-hour design values (Baltimore, New York, Detroit, 

6 Philadelphia, St. Louis in Figure 4-17). Those urban areas in B1 have exceptionally peaky 

7 PM2.5 distributions relative to urban areas in B2 (i.e., relatively high 24-hour design values 

8 and lower annual average design values). 

9  Zone C: 24-hour design values and annual design values exceed 24-hour and annual 

10 standard levels, respectively, and the annual standard is controlling. Atlanta, Birmingham 

11 and Houston fall into this zone and represent a relatively small number of urban areas in the 

12 U.S.. 

13  Zone D: annual design values exceed the annual standard level, but 24-hour design values 

14 are below the 24-hour standard level (i.e., annual standard is controlling). Houston is the only 

15 urban study area falling into this zone for the current standard level, along with a small 

16 number of additional urban areas in the U.S.. 

17  Zone E: both the 24-hour and annual design values are below their respective standard levels 

18 (i.e., this is the only zone where urban areas would not be expected to experience risk 

19 reductions under the suite of standards being considered). The majority of urban areas in the 

20 U.S. depicted in these scatter plots fall into Zone E in Figure 4-17. 

21 

22 The five zones presented above are useful in interpreting the risk results generated for the 

23 current suite of standards (for the 15 urban study areas). Specifically, as noted above, they allow 

24 us to (a) quickly identify which of the 15 urban study areas experience risk reductions under the 

25 current standard level, (b) determine whether those reductions are due primarily to a controlling 

26 24-hour or annual standard and (c) to see how well our set of urban study areas provide coverage 

27 for the broader set of urban areas in the U.S.. 

28 In addition to presenting Figures 4-17 through 4-19 as a means for supporting the 

29 interpretation of risk estimates generated for the 15 urban study areas (based on consideration for 

30 patterns in design values), we have also included Table 4-8 for this purpose. Table 4-8 presents 

31 the annual and 24-hour design values for each urban study area and also identifies which 

32 standard is controlling for a given suite of standards. For example, we see that in Atlanta (which 

has design values of 16.2 μg/m3

 and 35 μg/m3 33 , annual and 24-hour, respectively), the annual 

34 standard controls for the current suite of standards (15/35) as well as the first 4 alternative suites 

35 of standards considered (14/35, 13/35, 12/35 and 13/30). However, the 24-hour standard controls 

36 for the final suite of standards considered (12/25). This matches with information presented in 

37 Figures 4-17 through 4-19 (e.g., Figure 4-17 shows that the Atlanta is just inside of zone C, 

38 suggesting that it meets the 24-hour standard, but not the annual standard. 

39 
February 2010 Draft – Do Not Quote or Cite 4-67

1 

2 Figure 4-17 Design values in 15 urban study areas and broader set of U.S. urban 

3 areas relative to the current suite of standards (15/35) 

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 

0

5

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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

35/15 line 

A B

C

D

E

0

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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

35/15 line 

A B

C

D

E

B1

B2

4 

5 

6 

7 

8 

9 
February 2010 Draft – Do Not Quote or Cite 4-68

1 

2 Figure 4-18 Design values in 15 urban study areas and broader set of U.S. urban 

3 areas relative to the 12/35 alternative suite of standards 

A B

C

D

E

0

5

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15

20

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55

60

65

70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

35/12 line 

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 

B1

B2

A B

C

D

E

0

5

10

15

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40

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55

60

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70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

35/12 line 

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 

B1

B2

4 

5 

6 

7 

8 

9 
February 2010 Draft – Do Not Quote or Cite 4-69

1 

2 Figure 4-19 Design values in 15 urban study areas and broader set of U.S. urban 

3 areas relative to the 12/25 alternative suite of standards) 

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

25/12 line 

A B

C

D

E

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

25/12 line 

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

25/12 line 

A B

C

D

E

B1

B2

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

25/12 line 

A B

C

D

E

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

25/12 line 

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

25/12 line 

A B

C

D

E

B1

B2

4 

5 

6 

7 

8 

9 
February 2010 Draft – Do Not Quote or Cite 4-70

1 

2 Table 4-8 Identification of controlling standard (24-hour or annual) for 

3 alternative suites of standard levels 

Combination of annual and 24-hour design values* 

Design Value

Current 

standard levels 

Alternative annual standard 

levels 

Combinations of 

alternative 24-hour

and annual standard 

levels 

Urban study area Annual 24-Hr 15/35 14/35 13/35 12/35 13/30 12/25 

 Atlanta, GA 16.2 35 A A A A A 24hr 

 Baltimore, MD 15.6 37 24hr A A A 24hr 24hr 

 Birmingham, AL 18.7 44 A A A A A 24hr 

 Dallas, TX 12.8 26 - - - A A A 

 Detroit, MI 17.2 43 24hr A A A 24hr 24hr 

 Fresno, CA 17.4 63 24hr 24hr 24hr 24hr 24hr 24hr 

 Houston, TX 15.8 31 A A A A A A 

 Los Angeles, CA 19.6 55 24hr 24hr 24hr A 24hr 24hr 

 New York, NY 15.9 42 24hr 24hr A A 24hr 24hr 

 Philadelphia, PA 15.0 38 24hr 24hr A A 24hr 24hr 

 Phoenix, AZ 12.6 32 - - - A A 24hr 

 Pittsburgh, PA 5 19.8 60 24hr 24hr 24hr 24hr 24hr 24hr 

 Salt Lake City, 

UT 

11.6 55 24hr 24hr 24hr 24hr 24hr 24hr 

 St. Louis, MO 16.5 39 24hr A A A 24hr 24hr 

 Tacoma, WA 10.2 43 24hr 24hr 24hr 24hr 24hr 24hr 

4 * “24hr” denotes that the 24-hour standard is controlling. “A” denotes that the annual standard is 

5 controlling 

6 

7 Based on consideration of the zones defined in Figures 4-17 through 4-19, we can make 

8 the following observations regarding potential patterns of risk reduction across urban study areas 

9 in the U.S., given the current and alternative suites of standards considered. Further, we can 

10 characterize the degree to which the 15 urban study areas provide coverage for these groupings 

11 of U.S. urban study areas: 

12  For the current suite of standards (see Figure 4-17), Based on 2005-2007 air quality data, 

13 most urban areas in the country meet the current standards based on 2005-2007 air quality 

14 data (zone E). A smaller but still notable number meet the current annual standard but do not 

15 meet the current 23hr standard (Zone A). A similar number of areas do not meet either 

16 current standard (zones B and C). Only a few areas do not meet the current annual standard, 

17 but do meet the current 24hr standard (zone D). Of the 15 urban study areas included in the 

18 risk assessment most fall into zones that do not meet either standard (zones B and C) 

19 although some study areas are in each of the other zones. 

20  Alternative suites of standards involving reduction of the annual standard levels (see Figure 

21 4-18) Based on 2005-2007 air quality data, as shown in Figure 4-18, reduction in the annual 

standard level down to 12 μg/m3 22 results in a significant increase in the number of areas that 
February 2010 Draft – Do Not Quote or Cite 4-71

1 do not meet the annual standard (zones C and D). And of those areas, roughly similar 

2 numbers of urban areas do meet the 24hr standard as do not meet the 24hr standard 

3 (comparing numbers of urban areas in B and C to the number in zone D). 

4  Alternative suite of standards involving reductions in both annual and 24-hour levels (see 

5 Figure F-19): Based on 2005-2007 air quality data, a large fraction of urban areas are 

6 predicted not to meet the 24hr standard (zones A, B and C). Furthermore, the majority of 

7 these have the 24hr controlling (zone A and B). We also note that there are virtually no urban 

8 areas that exceed the annual standard while not meeting the 24hr standard (zone C). Of the 

9 15 urban study areas, most do not meet either the 24hr or annual standards, while the 24hr is 

10 controlling in most (zone B). 

11 

12 4.5.2 Patterns in PM2.5 Monitoring Data 

13 As noted earlier, patterns in PM2.5 monitoring data for each of the 15 urban study areas 

14 can be used (together with consideration of design values as described in section 4.5.1) to 

15 support interpretation of risk estimates generated for current and alternative standard levels. This 

16 is particularly true when considering the impact of using different rollback methods in 

17 supporting risk characterization for current and alternative standard levels, as discussed below. 

18 To facilitate consideration of patterns in PM2.5 monitoring data across the 15 urban study 

19 areas, we have developed Figures 4-20 and 4-21. Each of these figures presents 24-hour and 

20 annual design values (blue and green dots, respectively) for each PM2.5 monitor within each 

21 study area. The figures also flag the highest design values for each study area (red and brown 

stars for the annual and 24-hour standard levels, respectively).58 22 Each figure has been scaled to 

23 represent a particular suite of standards, with Figure 4-20 scaled to represent the current suite of 

standards (15/35) and Figure F-21 scaled to represent the 12/25 alternative suite of standards.59 24 

25 In addition, the figures allow identification of whether a study area had the highest design value 

26 (for the 24-hour and annual averaging periods) occurring at the same or at different monitors. 

27 This factor can influence the degree to which simulation of a controlling 24hr standard level, 

28 given application of peak shaving, results in reduction in annual-average PM2.5 levels for that 

29 study area. If an area has both 24hr and annual design values occurring at the same monitor, then 

30 application of peak shaving to reduce the controlling 24hr standard will also bring down the 

 58 Note, that it is the highest viable study-area level design values (represented as stars in the diagram) that were 

used as the basis for determining the degree of rollback needed to simulate a particular standard level in the risk 

assessment. 

59 For example, in Figure 4-20, the left y-axis, which represents the annual standard level extends from the 15/35 

line up to a maximum of 30, with this representing a factor of two spread in the annual design value (i.e., from the 

current 15 up to 30). Similarly, the right hand y-axis represents the 24-hour standard level with the 15/35 line 

extending from 35 to a maximum of 70 (again a factor of 2 above the current standard of 35). This allows 24-hour 

and annual standard levels for a given study area to be compared directly in terms of how far they are above (or 

below) the 15/35 line in order to determine which standard is controlling (i.e., the standard which is higher on the 

plot). 
February 2010 Draft – Do Not Quote or Cite 4-72

1 annual design value (i.e., the annual-average PM2.5 level for that study area is likely to be 

2 reduced to a greater extent). By contrast, if 24hr and annual design values are located at different 

3 monitors, then peak shaving focused on reduction of the 24hr design value monitor will 

4 potentially not impact the annual design value (i.e., there will be a smaller impact on the annualaverage PM2.5 level for that study area).60 5 

6 To gain a better understanding of the information provided in Figures 4-20 and 4-21, we 

7 will provide a walkthrough for one of the urban study areas, highlighting key attributes related to 

8 24-hour and annual design values. With Los Angeles (in Figure 4-20) we see that the study area 

9 has a relatively wide spread in 24-hour and annual design values across the monitors (i.e., it has a 

10 relatively peaky PM2.5 distribution), with 24-hour values ranging from ~15 to ~55 and annual 

11 design values ranging from ~7 to ~19 (exact values are presented in Appendix A). In addition, 

12 we see that the 24-hour standard is clearly controlling, given how much farther the highest viable 

13 24-hour design value is from the 15/35 line compared with the highest annual design value. In 

14 addition, we can compare these trends in 24-hour and annual design values for Los Angeles to 

15 those for the other urban study area and see that generally, Los Angeles (a) has some of the 

16 widest spreads in both 24-hour and annual design values (i.e., it has one of the more peaky 

17 PM2.5 distributions across monitors) and (b) has one of the highest 24-hour design value of the 15 

18 urban study areas (i.e., it will require more rollback in simulating just meeting the current suite 

19 of standards compared with most of the other study areas). The attributes described above match 

20 well with urban areas falling into zone B1 in Figure 4-17 (i.e., the zone where urban areas do not 

21 meet both the current 24-hour and annual standards, and where the 24-hour standard is 

22 controlling). 

 60 When a star in either Figure 4-20 or 4-21 (signifying the highest design value for that study area) is placed over a 

point estimate, then the highest design value (for both 24-hour and annual levels) occurs at different monitors. This 

is the case, for example, with Phoenix, while Los Angeles represents a location where the highest 24-hour and 

annual design values occur at the same monitor. 
February 2010 4-73 Draft – 

1 Figure 4-20 Annual and 24-hour design values (for individual monitors and at the study-area leve2 study areas (with the presentation of values scaled to reflect current standard of 15/3

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1012141618202224262830

Atlanta

Baltimore

Birmingham

Dallas

Detroit

Fresno

Houston

LosAngeles

NewYork

Philadelphia

Phoenix

Pittsburgh

P

itt-Liberty

SaltLake

St.Louis

Tacoma

Annual Design Value (μg/m

3

)

 Annual DVs

 24-hour DVs

Stars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs

02468

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Atlanta

Baltimore

Birmingham

Dallas

Detroit

Fresno

Houston

LosAngeles

NewYork

Philadelphia

Phoenix

Pittsburgh

P

itt-Liberty

SaltLake

St.Louis

Tacoma

Annual Design Value (μg/m

3

)

 Annual DVs

 24-hour DVs

Stars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs

15/35 standard level line

02468

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Atlanta

Baltimore

Birmingham

Dallas

Detroit

Fresno

Houston

LosAngeles

NewYork

Philadelphia

Phoenix

Pittsburgh

P

itt-Liberty

SaltLake

St.Louis

Tacoma

Annual Design Value (μg/m

3

)

 Annual DVs

 24-hour DVs

Stars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs

02468

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Atlanta

Baltimore

Birmingham

Dallas

Detroit

Fresno

Houston

LosAngeles

NewYork

Philadelphia

Phoenix

Pittsburgh

P

itt-Liberty

SaltLake

St.Louis

Tacoma

Annual Design Value (μg/m

3

)

 Annual DVs

 24-hour DVs

Stars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs

15/35 standard level line

3 
February 2010 4-74 Draft – 

1 Figure 4-21 Annual and 24-hour design values (for individual monitors and at the study-area leve2 study areas (with the presentation of values scaled to reflect current standard of 12/2

02468

1012141618202224262830323436

Atlanta

Baltimore

Birmingham

Dallas

Detroit

Fresno

Houston

LosAngeles

NewYork

Philadelphia

Phoenix

Pittsburgh

P

itt-Liberty

SaltLake

St.Louis

Tacoma

Annual Design Value (μg/m

3

)

 Annual DVs

 24-hour DVs

Stars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs

02468

1012141618202224262830323436

Atlanta

Baltimore

Birmingham

Dallas

Detroit

Fresno

Houston

LosAngeles

NewYork

Philadelphia

Phoenix

Pittsburgh

P

itt-Liberty

SaltLake

St.Louis

Tacoma

Annual Design Value (μg/m

3

)

 Annual DVs

 24-hour DVs

Stars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs

12/25 standard level line

02468

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Atlanta

Baltimore

Birmingham

Dallas

Detroit

Fresno

Houston

LosAngeles

NewYork

Philadelphia

Phoenix

Pittsburgh

P

itt-Liberty

SaltLake

St.Louis

Tacoma

Annual Design Value (μg/m

3

)

 Annual DVs

 24-hour DVs

Stars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs

02468

1012141618202224262830323436

Atlanta

Baltimore

Birmingham

Dallas

Detroit

Fresno

Houston

LosAngeles

NewYork

Philadelphia

Phoenix

Pittsburgh

P

itt-Liberty

SaltLake

St.Louis

Tacoma

Annual Design Value (μg/m

3

)

 Annual DVs

 24-hour DVs

Stars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs

12/25 standard level line

3 
February 2010 Draft – Do Not Quote or Cite 4-75

1 The sensitivity analysis examining uncertainty related to conducting rollback 

2 demonstrated that for some of the study areas (e.g., Los Angeles and Salt Lake City) use of the 

3 peak shaving rollback method reflecting application of more localized controls resulted in 

4 composite monitor values that differed notably from values generated when the proportional 

rollback approach was used.61 5 In contrast, many of the other urban study areas displayed little 

6 difference in composite monitor values based on application of proportional or peak shaving 

7 rollback methods. 

8 Design value information provided in Figures 4-20 and 4-21 provide explanations for 

9 these sensitivity analysis results. For Los Angeles (which had composite monitor values 40% 

10 higher when using the peak shaving rollback method compared with the proportional approach – 

11 see Section 4.3.1.1), the 24-hour standard is controlling. This can be seen by noting that the 

12 maximum 24-hour design value is significantly further away from the 15/35 line in Figure 4-20 

13 compared with the maximum annual design value. In addition, these two maximum design 

values do not occur at the same monitor.62 14 This means that when the proportional rollback 

15 method is used, a relatively large fractional reduction is uniformly applied to all monitors, 

16 resulting in a new (adjusted) composite monitor value that has been reduced to a relatively large 

17 extent. However, if peak shaving rollback is used, then only those monitors with 24-hour design 

18 values exceeding the current 24-hour standard level are adjusted and only by the fraction 

required to get each 24-hour design value down to the current 24-hour standard level.63 19 This 

20 means that in an overall sense, there is less adjustment to PM2.5 levels, such that with peak 

21 shaving we will see higher composite monitor annual averages than with proportional rollback. 

22 In the case of Salt Lake City (which also has significantly higher composite monitor 

23 annual averages with peak shaving than with proportional rollback), while the highest 24-hour 

24 and annual design values occur at the same monitor, which means that even with peak shaving, 

25 the monitor with the highest annual averages will be adjusted downward substantially, because 

26 the annual design values for monitors are closer to each other, the impact of peak shaving on the 

27 composite annual average is smaller. Specifically, while some of the monitors with 24-hour 

28 design values above the current 24-hour standard level will have their annual averages adjusted 

 61 Recall that differences in composite monitor estimates represent surrogates for differences in long-term exposurerelated mortality - long-see section 4.3.1.1. 

62 In figures 4-20 and 4-21, when the max viable 24-hour and annual design values occur at the same monitor, this is 

signified by showing the red stars for the max viable standard level superimposed over a green dot. 

63 With the peak shaving approach, many of the monitors will not have their PM2.5 levels adjusted because their 24-

hour levels do not exceed the current standard. Furthermore, because Los Angeles has its max 24-hour and annual 

standard levels occurring at different monitors, the max adjustment applied (that associated with the highest 24-hour 

monitor) will not be applied to the monitor having the highest annual design value, resulting in a lower overall 

impact to the composite annual average, compared with proportional rollback. 
February 2010 Draft – Do Not Quote or Cite 4-76

1 down, there is a fraction of the monitors (with 24-hour design values below the current standard) 

2 that will not be adjusted under peak shaving. 

3 These two examples illustrate different conditions under which the type of rollback 

4 applied can have a significant impact on the degree of public health protection assessed for a 

5 particular standard level. By contrast, conditions at some of the other urban study areas result in 

6 little difference in risk from application of different rollback methods (i.e., simulation of more 

7 regional versus local control strategies). Specifically, if an urban location has 24-hour and annual 

8 design values at each monitor that display little variation, we expect to see less impact on risk 

9 from varying the type of rollback method used. Examples that fall into this latter category 

10 include Atlanta, Dallas, and St. Louis (see Figure 4-20).
February 2010 Draft – Do Not Quote or Cite 5-1

1 5 NATIONAL-SCALE ASSESSMENT OF LONG-TERM MORTALITY 

2 RELATED TO PM2.5 EXPOSURE 

3 5.1 OVERVIEW 

4 In this section we present the estimated nationwide premature mortality resulting from 

5 recent exposures to ambient PM2.5. The goal of this assessment is twofold: (1) estimate the 

6 incidence of premature mortality within the U.S. related to long-term PM2.5 exposure; and (2) 

7 identify where the subset of counties assessed in the urban case study areas analysis fall along 

the distribution of national county-level risk.64 8 To perform this assessment we use 2005 PM2.5

9 fused air quality estimates from the Community Model for Air Quality (CMAQ) (Byun and 

10 Schere, 2006) in conjunction with the environmental Benefits Mapping and Analysis Program 

11 (BenMAP, Abt Associates Inc, 2008) to estimate long-term PM2.5-related premature mortality 

12 nationwide. 

13 To address the first goal of the assessment, we estimate excess PM2.5-related long-term 

14 mortality by applying two estimates of all-cause mortality risk found in the Krewski et al. (2009) 

15 PM2.5 mortality extended analysis of the American Cancer Society (ACS) cohort, and an estimate 

16 of all-cause mortality risk found in the Laden et al. (2006) PM2.5 mortality extended analysis of 

17 the Six-Cities cohort. We estimate that total PM2.5-related premature mortality ranges from 

63,000 (39,000—87,000) (95th 18 percentile confidence interval) and 88,000 (49,000—130,000), 

19 respectively; in each case we estimated deaths per year down to the lowest measured levels 

20 (LMLs) in each epidemiological study. 

21 In addressing the second goal of this assessment, we observe that the subset of 31 

22 counties for the 15 urban study areas considered in the urban case study fall toward the upper 

23 end of the national distribution. Specifically, all of the 31 counties were above the median of the 

national risk distribution and 23 of the 31 fell within the upper 5th 24 percentile of the national 

25 distribution. Therefore, according to this analysis, we appear to be capturing high-end 

26 percentiles of the national risk distribution with the set of urban case study areas we are 

27 evaluating in the PM2.5 NAAQS risk assessment. 

28 We had considered expanding the national-scale mortality to include additional health 

29 endpoints (related to short-term PM2.5 exposure) or additional air quality scenarios that simulate 

30 just meeting the current and alternative suites of standards. However, as noted in section 2.3, we 

 

64 We do not directly compare the estimated county-level risks generated in the urban case study 

assessment and the county-level risks generated in the national-scale analysis. Rather, we identify where the 31 

counties modeled for urban case study fell along the national risk distribution. This assessment revealed whether the 

baseline PM2.5 mortality risks in the 31counties modeled in the urban case study areas represented more typical or 

higher-end risk relative to the national risk distribution. 
February 2010 Draft – Do Not Quote or Cite 5-2

1 continue to conclude that any expansion of this assessment, is beyond the scope of what is 

2 needed or can reasonably be done within the time and resources available for this review. Here 

3 we provide additional discussion of the rationale for our decision not to expand the scope of the 

4 national-scale analysis. 

5 The goal of the national-scale analysis is two-fold: to provide perspective on the 

6 magnitude of PM2.5 health impacts on a national-scale and to help to place the risk estimates 

7 generated for the urban study areas in a national context. The analysis as currently implemented 

8 achieves the first goal by providing estimates of long-term exposure-related all-cause mortality 

9 under recent conditions. While simulation of risk for the current and alternative standard levels 

10 would provide additional perspective on the magnitude of national-scale risk, that assessment 

11 would be resource-intensive and subject to considerably uncertainty if it were conducted using 

12 air quality simulation methods similar to those used in the urban study area analysis (i.e., 

13 application of a combination of rollback methods that reflects both local and regional patterns in 

14 ambient PM2.5 reductions implemented at the monitor-level). A particular area of uncertainty 

15 (and technical complexity) related to air quality simulation would be addressing the interplay 

16 between regional-scale reductions in ambient PM2.5 in adjacent urbanized areas. In the urban 

17 study area analysis, each location is treated independently with regard to simulating ambient 

18 PM2.5 under alternative suites of standards. However, if we were to expand the national analysis 

19 to include alternative standards, then simulation of rollbacks in ambient PM2.5 levels would 

20 necessarily have to address this contiguity issue between adjacent urban areas and even between 

21 suburban areas and adjacent urbanized areas in the context of simulating monitor rollback. 

22 In addition, because long-term exposure-related mortality dominates PM2.5 in terms of 

23 total incidence, providing coverage for this endpoint category ensures that the majority of PM2.5-

24 related mortality incidence is reflected in the analysis, without including short-term exposure25 related mortality. 

26 The national-scale mortality analysis, as currently implemented, also achieves its second 

27 goal: to help place risk estimates for the urban study areas in a national context. Because the 

28 national-scale analysis focuses on the long-term exposure-related mortality, which is the primary 

29 driver for PM2.5-related health impacts, the analysis allows us to assess how the urban study 

30 areas "fall" across a national distribution of risk for this key health endpoint category (see 

31 discussion below). This then allows us to characterize the degree to which the set of urban study 

32 areas provides coverage for areas of the country likely to experience relatively elevated levels of 

33 PM2.5-related health impacts. 
February 2010 Draft – Do Not Quote or Cite 5-3

1 5.2 METHODS 

2 This assessment combines information regarding estimated PM2.5 air quality levels, 

3 population projections, baseline mortality rates, and mortality risk coefficients to estimate PM2.5-

4 related premature mortality. Figure 5-1 below provides a conceptual diagram, detailing each of 

5 the key steps involved in performing this BenMAP-based health impact assessment. Appendix 

6 G contains additional information regarding the data inputs to this analysis. 

7 

8 Figure 5-1 Conceptual diagram of data inputs and outputs for national long-term 

9 mortality risk assessment 

11 

13 

14 5.2.1 Population Estimates 

15 The starting point for estimating the size and demographics of the potentially exposed 

16 population is the 2000 census-block level population, which BenMAP aggregates up to the same 

17 grid resolution as the air quality model. Using county-level growth factors based on economic 

18 projections (Woods and Poole Inc., 2001), BenMAP projects this 2000 population to the analysis 

19 year of 2005; we selected this population year because it matches both the year in which the 

20 emissions inventory was developed for the air quality modeling and the year to which the 

21 baseline mortality rates were projected (see below). 

22 5.2.2 Population Exposure 

23 Having first estimated the size and geographic distribution of the potentially exposed 

24 population, BenMAP then matches these population projections with estimates of the ambient 

25 levels of PM2.5. In contrast to the urban study areas analysis, the national-scale analysis 
February 2010 Draft – Do Not Quote or Cite 5-4

1 employed a data fusion approach, which joined 2005 monitored PM2.5 concentrations with 2005 

2 CMAQ-modeled air quality levels using the Voronoi Neighbor Averaging (VNA) technique 

3 (Abt, 2003). CMAQ was run at a horizontal grid resolution of 12km for the east and 36km in the 

4 west using 2005 estimated emission levels and meteorology. More information on this model 

5 run can be found in Appendix G of this document. Figure 5-2 shows the geographic distribution 

6 of baseline annual mean PM2.5 concentrations across the continental U.S. The maximum 

predicted value within the U.S. is 31 μg/m3

, the mean PM2.5 value is 8.7 μg/m3 7 , median is 8.8 

μg/m3

 and the 95th percentile value is about 14 μg/m3 8 . 

9 

11 Figure 5-2 2005 fused surface baseline PM2.5 concentrations 

12 

13 This assessment applies PM2.5 mortality risk coefficients drawn from long-term cohort 

14 studies which estimate changes in risk based on annual mean changes in PM2.5 concentration. 

15 For this reason, EPA used the CMAQ model to estimate annual mean concentrations at each grid 

16 cell. These grid-level annual average concentrations were then input to BenMAP. 
February 2010 Draft – Do Not Quote or Cite 5-5

1 5.2.3 Premature Mortality Estimates 

2 In this assessment of PM2.5-related premature mortality we considered risk estimates 

3 drawn from studies based on two prospective cohorts. The first study is the recently published 

4 Krewski et al. (2009) extended reanalysis of the ACS cohort. To remain consistent with the 

5 urban study areas analysis, we applied the two log-linear all-cause mortality risk coefficients 

6 based on the 1979-1983 and the 1999-2000 time periods that control for 44 individual and 7 

7 ecologic covariates. We also applied a log-linear all-cause mortality risk coefficient drawn from 

8 the extended analysis of the Six Cities cohort as reported by Laden et al. (2006). When 

9 estimating premature mortality using these functions we considered air quality levels down to the 

lowest measured levels (LML) in each study; for the Krewski et al. (2009) study this is 5.8 μg/m3 10 

and for the Laden et al. (2006) study this is 10 μg/m3 11 . In general, we place a higher degree of 

12 confidence in health impacts estimated at air quality levels at or above the LML because the 

13 portion of the concentration-response curve below this point is extrapolated beyond the observed 

14 data. We also estimated health impacts down to Policy Relevant Background (PRB) levels 

15 (EPA, 2008). The final ISA presents estimates of annual mean PRB for each of 7 Health Effects 

Institute PM regions; this value ranges from 0.62 μg/m3 in the southwest to 1.72 μg/m3 16 in the 

17 southeast. 

18 BenMAP contains baseline age-, cause- and county-specific mortality rates drawn from 

19 the CDC-WONDER. Current baseline mortality estimates are an average of a three year period 

20 from 1996-1998. EPA is in the process of updating these rates with 2006-2008 data; a sensitivity 

21 analysis suggests that the results reported here are largely insensitive to the use of more current 

22 mortality rates. 

23 5.3 RESULTS 

24 Table 5-1 and figures 5-3 through 5-4 below summarize the results of the national-scale 

25 analysis. Table 5-1 summarizes the total PM2.5-related premature mortality associated with 

26 modeled 2005 PM2.5 levels. 

27 Estimated PM2.5 -Related Premature Mortality Associated with Incremental Air Quality 

28 Differences Between 2005 Ambient Mean PM2.5 Levels and LML from the Epidemiology 

29 Studies or PRB (90th percentile confidence interval) 

30 

31 
February 2010 Draft – Do Not Quote or Cite 5-6

1 Table 5-1 Estimated PM2.5-related premature mortality associated with 

2 incremental air quality differences between 2005 ambient mean 

3 PM2.5 levels and lowest measured level from the epidemiology studies 

 or policy relevant background (90th 4 percentile confidence interval) 

Estimates Based on Krewski et al. (2009) 

Air Quality 

Level 

‘79-’83 estimate 

(90th percentile 

confidence interval) 

‘99-’00 estimate 

(90th percentile 

confidence interval) 

Estimates Based on 

Laden et al. (2006) 

(90th percentile 

confidence interval) 

10 μg/m3 (LML 

for Laden et al., 

2006) 

26,000 

(16,000—36,000) 

33,000 

(22,000—44,000) 

88,000

(49,000—130,000)

5.8 μg/m3 (LML 

for Krewski et 

al., 2009) 

63,000

(39,000—87,000)

80,000

(54,000—110,000)

210,000 

(120,000—300,000) 

Policy-Relevant 

Background 110,000 

(68,000—150,000) 

140,000 

(94,000—180,000) 

360,000 

(200,000—500,000) 

Bold indicates that the minimum air quality level used to calculate this estimate corresponds to the 

lowest measured level identified in the epidemiological study

5 

6 In this table, the bold figures indicate the estimate that corresponds with the LML 

7 identified in the epidemiological study. The bold estimates in the column Krewski et al. (2009) 

8 were calculated using the same risk coefficients as the urban case study analysis. We place a 

9 greater emphasis on those results calculated using the LML reported in the epidemiological 

studies.65 10 Figure 3 illustrates the percentage of baseline mortality attributable to PM2.5 exposure 

11 in each of the grid cells according to the 2005 PM2.5 air quality levels, using the Krewski et al. 

12 (2009) estimate based on 1999-2000 air quality levels. 

13 

14 

15 

16 

17 

 65 Note, that as stated in Section 4.3.2, modeling of risk down to PRB is subject to considerable uncertainty. While 

there is no evidence for a threshold (which conceptually supports estimation of risk below LML), we do not have 

information characterizing the nature of the C-R function for long-term mortality below the LML and consequently 

estimates of mortality based on incremental exposure below LML (and down to PRB) is subject to greater 

uncertainty. 
February 2010 Draft – Do Not Quote or Cite 5-7

1 Figure 5-3 Percentage of premature mortality attributable to PM2.5 exposure at various 

2 2005 annual average PM2.5 levels* 

3 

4 This figure illustrates the number of deaths attributable to PM2.5 according to the baseline 

level of ambient average PM2.5 levels down to 5.8 μg/m3 5 (the LML for the Krewski et al. (2009) 

6 analysis). Each of four box plots characterizes the range of premature mortality attributable to 

7 PM2.5 according to the baseline level of annual mean PM2.5 levels in that model grid cell. Note 

that while the lower whisker of the box plots for the baseline air quality values of 5.8 μg/m3 8 to 10 

μg/m3 9 appear to extend to zero, the minimum value is greater than zero. The number above each 

10 box plot indicates the number of grid cells summarized by that plot. 

11 Figure 5-4 displays the cumulative distribution of total mortality attributable to PM2.5

12 exposure at the county level developed as part of the national-scale analysis. The location of the 

13 31 counties included in the urban case study analysis is then superimposed on top of the 

14 cumulative distribution. 

15 

16 

17 

18 

19 
February 2010 Draft – Do Not Quote or Cite 5-8

1 Figure 5-4 Cumulative distribution of county-level percentage of total 

2 mortality attributable to PM2.5 for the U.S. with markers 

3 identifying where along that distribution the urban case study area 

5 analysis fall* 

6 

7 Counties considered in the urban scale analysis that are located toward the lower end of 

8 the distribution of all counties nationwide include Maricopa County, Arizona and Salt Lake City, 

9 Utah. Counties assessed in the urban scale analysis that are located toward the upper end of the 

10 distribution of all counties include Jefferson County, Alabama and Los Angeles County, 

11 California. The results of this analysis indicate that most of the 31 counties included in the urban 

12 case study counties fall toward the upper end of the national risk distribution and that 23 of these 

counties fall within the upper 5th 13 percentile of the risk distribution—suggesting that the PM2.5

14 mortality risk estimates included in the urban case study analysis generally represent the upper 

15 end of urban area mortality risks within the nation.
February 2010 Draft – Do Not Quote or Cite 6-1

1 6 INTEGRATIVE DISCUSSION OF URBAN CASE STUDY ANALYSIS 

2 OF PM2.5-RELATED RISKS 

3 This chapter provides an integrative discussion of the risk-related analyses presented 

4 throughout this second draft RA, including the PM2.5-related risk estimates generated for the set 

5 of urban study areas and the related uncertainty and sensitivity analyses, the representativeness 

6 analyses, and the national-scale long-term exposure PM2.5 mortality assessment. The goal of this 

7 integrative discussion is to inform our understanding of important policy-relevant risk-based 

8 questions, including: (a) what is the magnitude of risk likely to remain if the urban study areas 

9 were just meeting the current suite of PM2.5 standards, and what level of confidence do we have 

10 in those estimates?; (b) what is the degree and nature of risk reduction likely to be associated 

11 with just meeting the alternative suites of annual and 24-hour PM2.5 standards considered in this 

12 risk assessment, and what roles do the annual and 24-hour standards play in bringing about such 

13 reductions?; and c) what is the distribution of risks associated with recent PM2.5 air quality in 

14 areas across the U.S., and how representative are the risk results for the urban study areas from a 

15 national perspective? 

16 In addressing the risk-based questions listed above, we have placed primary focus on risk 

17 associated with long-term exposure to PM2.5. This choice reflects the fact that long-term 

18 exposure to PM2.5 has been shown in this and previous quantitative risk assessments to produce 

19 substantially larger mortality risk (in terms of overall incidence and percent of total mortality) 

20 compared with short-term PM2.5 exposure. Because of the emphasis placed on long-term PM2.5

21 exposure-related mortality risk, the risk assessment has been designed to generate robust 

22 estimates for this risk category, including comprehensive analysis of uncertainty. For the 

23 assessment of mortality and morbidity risks related to short-term PM2.5 exposure, the assessment 

24 of uncertainty and its impact on risk estimates has been more limited. 

25 In characterizing risks associated with both long- and short-term exposure to PM2.5

26 throughout this document, we have included those health endpoints for which sufficient 

27 information was available to generate quantitative risk estimates with a reasonable degree of 

28 confidence. It is important to emphasize that beyond the health endpoints evaluated 

29 quantitatively in this risk assessment, there is an array of additional health endpoints potentially 

30 associated with PM2.5 that will be discussed as part of the evidence-based considerations 

31 presented in the policy assessment now being prepared.. 

32 The following discussion begins with a summary of analytical approaches used in this 

33 quantitative risk assessment, emphasizing the degree of confidence we have in the data, models, 

34 and assumptions we have used in developing our core risk estimates and in the results of our 

35 sensitivity analyses (section 6.1). We then summarize our core risk results for the urban study 
February 2010 Draft – Do Not Quote or Cite 6-2

1 areas, and the confidence we have in those results in light of our uncertainty and variability 

2 analyses, and provide insights into how those results inform the policy-relevant considerations 

3 described above (section 6.2). Next we place these results into a national perspective (section 

4 6.3). In so doing, we provide insights into how well the set of urban study areas represent the 

5 broader set of urban areas in the U.S. likely to experience increased risk from PM2.5. We also 

6 integrate the results from the urban study areas with the national-scale mortality assessment to 

7 provide insights into the degree to which the PM2.5-related risks estimated in the urban study 

8 areas are likely to be characteristic of risks in the broader U.S. population. Finally, in section 

9 6.4, we highlight key points that address the policy-relevant questions that began this chapter. 

10 6.1 KEY ANALYTICAL ELEMENTS IN THIS RISK ASSESSMENT 

11 This quantitative risk assessment has been designed to generate estimates of risk for a set 

12 of urban study areas likely to represent those urban areas in the U.S. experiencing higher PM2.5-

13 related risk due to elevated PM2.5 levels and/or other attributes related to PM2.5 risk (e.g., 

14 meteorology, baseline health effects incidence rates, differences in PM2.5 emissions sources and 

composition).66 15 In addition, the risk assessment is designed to produce robust risk estimates that 

16 reflect consideration of the latest research into PM2.5-related exposure and risk. To achieve these 

17 goals, a deliberative process has been used in specifying each of the analytical elements 

18 comprising the risk model, including selection of urban study areas as well as specification of 

19 other inputs such as C-R functions. This deliberative process involved rigorous review of 

20 available literature addressing both PM2.5 exposure and risk combined with the application of a 

21 formal set of criteria to guide development of each of the key analytical elements in the risk 

22 assessment. In addition, the risk assessment design reflects consideration of CASAC and public 

23 comments on the initial risk assessment plan, as well as the first draft risk assessment. The 

24 application of this deliberative process increases overall confidence in the risk estimates by 

25 insuring that the estimates are based on the best available science and data characterizing PM2.5

26 exposure and risk, and that they reflect consideration of input from experts on PM exposure and 

27 risk through CASAC and public reviews. 

28 The approach used in specifying several of the key analytical elements used in the risk 

29 assessment is highlighted below for purposes of illustrating the systematic approach used in 

30 developing the model. 

 66 As discussed in section 3.3.2, the seven PM regions were designed to capture regional differences in factors 

potentially related to PM risk. By providing coverage for these regions with the set of urban study areas selected, we 

have provided some degree of coverage for regional differences in attributes potentially related to PM risk. In 

addition, the representativeness analysis discussed in section 4.4 also allowed us to assess the degree to which the 

set of urban study areas captured key patterns in PM risk-related attributes across urban areas in the U.S.. 
February 2010 Draft – Do Not Quote or Cite 6-3

1  Selection of the 15 urban study areas included consideration of (a) whether a city of county 

2 had been included in multi-city epidemiology studies used in specifying C-R functions used 

3 in the core risk estimates, (b) providing coverage for urban areas with relatively high annual 

4 and 24-hour design values, and (c) providing coverage for the seven PM regions which 

5 reflect differences in key PM risk-related attributes (e.g., meteorology, demographic 

6 attributes, PM sources and composition). See section 3.3.2 for additional detail on selection 

7 of study areas. 

8  Simulation of ambient PM2.5 levels under current and alternative standard levels included 

9 application of the proportional rollback approach used in previous risk assessments, which 

10 generally represents regional patterns of reductions in ambient PM2.5 concentrations. 

11 Recognizing that simulating regional patterns in ambient PM2.5 reductions alone does not 

12 capture the potential variability in future patterns of reductions that may occur, we also 

13 considered alternative rollback approaches, including hybrid and peak shaving approaches. 

14 Both of these approaches simulate more localized patterns of ambient PM2.5 reductions 

15 combined with additional regional patterns of reductions in ambient PM2.5. Including these 

16 three rollback approaches allowed us to assess the degree to which differences in the spatial 

17 pattern of ambient PM2.5 reductions resulting from simulations of just meeting current and 

18 alternative suites of PM2.5 standards can impact risk profiles. 

19  Selection of health endpoints reflected consideration of the degree of support in the literature 

20 for a causal relationship between PM2.5 exposure and the health effect of interest as assessed 

21 in the ISA, together with consideration of the health significance of the endpoint. In addition, 

22 we considered whether sufficient information existed in the literature to develop C-R 

23 functions and whether we could obtain the baseline incidence data necessary to generate risk 

24 estimates with a reasonable degree of confidence (see section 3.3.1). 

25  The selection of epidemiological studies and specification of C-R functions for use in 

26 modeling risk for these endpoints involved a rigorous review of existing literature based on 

27 application of criteria we identified for specifying robust C-R functions. These criteria took 

28 into account both study design as well as the potential scope of the C-R functions that could 

29 be drawn from the studies (e.g., geographic coverage, demographic groups covered and 

30 health endpoints involved). We outlined our rationale for the set of epidemiology studies we 

31 selected and the choices made in specifying C-R functions, and we discussed our rationale 

32 for not including other potential studies and/or forms of C-R functions in the risk assessment. 

33 The systematic approach described above resulted in a core risk model which included 

34 those model inputs that in our judgment have the greatest degree of support in the literature. 

35 These core risk estimates are emphasized in addressing the policy-related questions outlined 

36 above. To provide a more comprehensive assessment of risk for the urban study areas, we have 

37 included an assessment of uncertainty and variability and their impact on the core risk estimates 

38 as part of this analysis. This assessment of uncertainty and variability includes both qualitative 

39 and quantitative elements, the latter taking the form of single- and multi-factor sensitivity 
February 2010 Draft – Do Not Quote or Cite 6-4

analysis.67 1 The goal of these assessments was to evaluate the robustness of the core risk 

2 estimates given identified sources of uncertainty and variability. Inclusion of the qualitative 

3 analysis of uncertainty, in additional to the sensitivity analyses, helped insure that a more 

4 complete list of potentially important sources of uncertainty was considered in the risk 

5 assessment and not only those sources for which it is possible to conduct a sensitivity analysis. 

6 The assessment of uncertainty and variability completed for this analysis is more 

7 comprehensive than had been done for previous risk assessments. This reflects, in part, the 

8 development of methods by EPA staff to address potentially important sources of variability and 

9 uncertainty. For example, to more fully explore potential variability in the patterns of reductions 

10 in ambient PM2.5 that may occur upon just meeting the current and alternative suites of standards, 

11 we incorporated as part of the sensitivity analysis two additional rollback approaches (hybrid and 

12 peak shaving) in addition to the proportional rollback used in the core analyses. In addition, 

13 recently published literature has allowed us to more rigorously examine the impact of uncertainty 

14 related to specifying C-R functions for long-term exposure-related mortality (i.e., the Krewski et 

15 al., 2009 study which provided extensive analysis of alternative model specifications for 

mortality which could be readily incorporated into our sensitivity analysis).68 16 

17 In addition to enhanced sensitivity analyses, we also included a number of national-scale 

18 assessments that had not been done in past risk assessments (i.e., the representativeness analysis 

19 and national-scale assessment of long-term mortality). These national-scale assessments allowed 

20 us to more fully consider the degree to which the selected urban study areas are representative of 

21 the broader set of urban areas within the U.S., thereby allowing us to place risk estimates for the 

22 urban study areas in the broader national context. 

23 6.2 INTERPREATION OF URBAN STUDY AREA RESULTS 

24 This section describes the core risk estimates generated for the 15 urban study areas, 

25 focusing on the policy-relevant questions outlined above. An important factor to consider in 

26 interpreting these results is that the magnitude of both long- and short-term exposure-related risk 

27 depends primarily on annual-average PM2.5 concentrations. Furthermore, reductions in both 

28 categories of risk, as we consider simulating just meeting alternative suites of standards, also 

29 depend on changes in annual-average PM2.5 concentrations. 

 67 As discussed in section 4.1, available information did not support a full probabilistic analysis of uncertainty and 

variability in the risk model and consequently, a combination of single- and multi-factor sensitivity analyses was 

used to assess the potential impact of these factors on core risk estimates. 68 Given increased emphasis placed in this analysis on long-term exposure-related mortality, the uncertainty analyses 

completed for this health endpoint category are somewhat more comprehensive than those conducted for short-term 

exposure-related mortality and morbidity, which to some extent reflects limitations in study data available for 

addressing uncertainty in the later category. 
February 2010 Draft – Do Not Quote or Cite 6-5

1 The role of annual-average ambient PM2.5 concentrations in driving long-term exposure2 related risk is intuitive given that this risk category is modeled using the annual-average air 

quality metric.69 3 The fact that short-term exposure-related risk is also driven by changes in long4 term average PM2.5 concentrations is less intuitive, since changes in average daily PM2.5

concentrations are used to estimate changes in risk for this category.70 5 Analyses in previous PM 

6 NAAQS risk assessments have shown that short-term exposure-related risks are not primarily 

7 driven by the small number of days with PM2.5 concentrations in the upper tail of the air quality 

8 distribution, but rather by the large number of days with PM2.5 concentrations at and around the 

9 mean of the distribution. Consequently, consideration of changes in annual-average PM2.5

10 concentrations will explain to a large extent changes in short-term exposure-related risk. 

11 Therefore, in interpreting patterns of long-term exposure-related risk, and the similar patterns we 

12 observe in short-term exposure-related risk, we focus primarily on how simulating just meeting 

13 specific suites of PM2.5 standards impacts the annual-average PM2.5 concentration for the study 

14 areas. 

15 In the case of simulating just meeting the current and alternative annual standards, this is 

16 straight forward, since the simulation produces a direct change in the annual-average PM2.5

17 concentration. However, simulating just meeting the current and alternative 24-hour standards 

18 has a less direct effect on annual average PM2.5 concentrations across study areas, which depends 

19 on a number of factors, including: (a) the type of rollback used to simulate just meeting the 

20 current or alternative standards, (b) the combination of 24-hour and annual design values in each 

21 study area (Table 4-8), and (c) the pattern of PM2.5 monitoring data across each study area. If 

22 proportional rollback is used, the annual-average PM2.5 concentrations will be reduced by the 

23 same percentage as was needed to lower the 24-hour design value to the level of the controlling 

24 24-hour standard. However, our sensitivity analysis examining alternative rollback methods 

25 showed that application of a peak shaving rollback approach (reflecting more localized patterns 

26 of PM2.5 reductions) can, under certain circumstances, produce notably smaller changes to annual 

27 average concentrations, which in turn, translate into smaller changes in both long-term and short28 term exposure-related risks. Specifically, for those urban study areas where a peak shaving 

 69 As noted in section 3.2.1, estimates of long-term exposure-related mortality are actually based on an average 

annual PM2.5 level across monitors in a study area (i.e., the composite monitor annual-average). 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 maximum monitor annual-average for a 

study area (i.e., the annual design value) determines the percent reduction in PM2.5 levels required to attain a 

particular standard. Both types of air quality estimates are provided in Tables F-49 and F-50 in Appendix F and 

both are referenced in this discussion of core risk estimates, as appropriate. 

70 Estimates of short-term exposure-related mortality and morbidity are based on composite monitor daily PM2.5. 

concentrations. However, similar to the case with long-term exposure-related mortality, it is the maximum monitor 

98th percentile 24-hour concentration (the 24-hour design value) that will determine the degree of reduction required 

to meet a given 24-hour standard. 
February 2010 Draft – Do Not Quote or Cite 6-6

1 rollback approach was applied to a PM2.5 distribution that was more “peaky” in nature (i.e., 

2 relatively high 24-hour design values and lower annual average design values), the resulting 

3 change in annual-average PM2.5 concentrations was notably smaller than when proportional 

rollback was used.71 4 We note also that an additional factor introducing variation in risk across 

5 urban study areas is the relationship between the annual-average PM2.5 concentrations at the 

6 maximum monitor and the composite monitor, which varies across study areas. For this reason, 

7 two study areas that are simulated to just meet the same annual standard (and consequently will 

8 have the same adjusted maximum monitor annual-average PM2.5 concentration) can have notably 

9 different composite monitor values. 

10 In discussing the core risk estimates below, we focus on cardiovascular-related endpoints 

11 given the greater overall degree of confidence assigned to this category in the ISA relative to 

12 other health effect categories (e.g., respiratory-related effects). This means that for long-term 

13 exposure-related risk, we focus our discussion on IHD-related mortality; the related categories 

14 for short-term exposure-related risk include CV-related mortality and morbidity (the latter in the 

15 form of HA related to CV symptoms). 

16 Finally, we note that the set of urban study areas selected for this assessment reflect the 

17 profile of urban areas in the U.S. with regard to the mix of annual and 24-hour design values. As 

18 illustrated in Figure 4-18, only a few urban areas have controlling annual standard levels 

19 exceeding the current standard level (i.e., fall into zones C or D in Figure 4-18). Therefore, there 

20 are not a large number of areas that will experience risk reductions due to simulation of the 

21 current annual standard alone. By contrast, there are a lot more urban areas in the U.S. in which 

22 the 24-hour standard is controlling and the 24-hour design value exceeds the level of the current 

23 standard (i.e., fall into zones A and B in Figure 4-18). Therefore, more of the urban study areas 

24 available for analysis are likely to see risk reductions under the current suite of standards driven 

25 by simulation of the 24-hour standard. Recognition of the profile of urban areas in the U.S. with 

26 regard to the interplay between the 24-hour and annual design values is important in fully 

27 understanding the core risk estimates summarized below and how those risk estimates can be 

28 interpreted in the national context. 

29 The discussion below is organized as follows. First, we present observations regarding 

30 core risk estimates generated for the current suite of standards. We then present observations 

 71 The results of the sensitivity analysis examining the hybrid rollback approach, which represents a combination of 

an initial localized pattern of ambient PM2.5 reduction, followed by a more regional pattern of reduction, showed this 

approach not to vary substantially from the proportional approach in terms of its impact on annual-average PM2.5

concentrations and consequently risk (i.e., the peak shaving rollback method was found to result in more substantial 

differences in annual-average PM2.5 concentrations and consequently risk, relative to the proportional) (see section 

4.3.1.1). Therefore, in discussing the results of the sensitivity analysis examining rollback, we focus here on 

contrasting results for the proportional approach with those for peak shaving. 
February 2010 Draft – Do Not Quote or Cite 6-7

related to simulation of alternative annual standards at levels of 14, 13, and 12 μg/m3 1 in 

conjunction with the current 24-hour standard (35 μg/m3 2 ). Finally, we discuss simulation of 

3 alternative suites of standards involving combinations of alternative annual and 24-hour levels 

(i.e., an annual standard of 13 μg/m3

 paired with a 24-hour standard of 30 μg/m3 4 (denoted as the 

13/30 suite of standards); an annual standard of 12 μg/m3 5 paired with a 24-hour standard of 25 

μg/m3 6 (denoted as the 12/25 suite of standards). 

7 6.2.1 Simulation of Just Meeting the Current Suite of PM2.5 Standards 

8 In characterizing PM2.5-related risks likely to remain upon just meeting the current PM2.5

9 annual and 24-hour standards in the 15 areas included in this assessment, we focus on the 13 

10 areas that would not meet the current standards based on recent (2005-2007) air quality. These 

11 13 areas have annual and/or 24-hour design values that are above the levels of the current 

standards (Table 4-8).72 12 Based on the core risk estimates for these areas presented above in 

13 section 4.2.1, we make the following general observation regarding the magnitude of risk 

14 remaining upon simulation (using proportional rollback) of just meeting the current suite of 

15 standards:

16  Long-term exposure-related mortality: Total incidence of long-term exposure-related IHD 

17 mortality attributable to PM2.5 ranges from 15-20 deaths per year (Salt Lake City) to 1,760-

18 2,220 deaths per year (New York) (Table 4-1). This translates into a percent of total 

19 mortality incidence attributable to PM2.5 ranging from 3.7-4.7% (Tacoma) to 13.2-16.7% 

20 (Atlanta) (Table 4-2). 

21 Variability in incidence estimates is obviously driven in large part by differences in the 

22 population in each study area, as well as by other factors such as differences in baseline 

23 incidence rates and in exposure patterns. Substantially less variability would be expected in 

24 estimates of the percent of total mortality attributable to PM2.5 when each area is simulated to 

25 just meet the current suite if standards, since this risk metric should normalize for population and 

26 baseline incidence rates. Nonetheless, we see appreciable variability across study areas for this 

27 risk metric as well. 

28 In considering the source of this variability, we recognize that, as noted above, the 

29 magnitude of long-term PM2.5 exposure-related mortality estimated to remain upon just meeting 

30 the current suite of standards depends directly on the annual-average PM2.5 concentrations that 

31 result from the simulated changes in air quality patterns. In the case of the three urban study 

32 areas out of the 13 experiencing risk reductions in which the annual standard is controlling 

33 (Atlanta, Birmingham, and Houston), simulation of the current suite of standards results in 

virtually the same annual-average PM2.5 concentration (~15 μg/m3 34 ) and, consequently, estimates 

 72 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. 
February 2010 Draft – Do Not Quote or Cite 6-8

1 of the percent of IHD-related mortality attributable to PM2.5 for these study areas is similar 

(Table 4-2).73 2 

3 However, the remaining 10 study areas in which the 24-hour standard is controlling 

4 display substantially greater variability in this risk metric when the proportional rollback 

5 approach is applied for the core analysis. This results because the simulation of just meeting the 

6 current 24-hour standard produces varying impacts on annual-average PM2.5 concentrations. For 

7 example, the urban study area with the highest estimated risk remaining upon just meeting the 

8 current suite of standards (Baltimore, with 11.7 to 14.7% of total mortality incidence attributable 

9 to PM2.5 - Table 4-2) has annual and 24-hour design values very close to the current suite of 

10 standard levels (Table 4-8). Therefore, simulating just meeting the current 24-hour standard does 

not much change the annual-average PM2.5 concentration, which is fairly close to 15 μg/m3 11 , and 

12 therefore, long-term exposure-related IHD mortality (as a percent of total incidence) is reduced 

13 only by a very small amount below that estimated for recent air quality. In contrast, Salt Lake 

14 City, which has one of the lowest estimates of the percent of total mortality incidence attributable 

15 to PM2.5 upon just meeting the current suite of standards (2.9 to 3.7% of total incidence – Table 

4-2), has a relatively low annual design value (11.6 μg/m3 16 ) and a relatively high 24-hour design 

value (55 μg/m3 17 ) (Table 4-8). Therefore, simulating just meeting the current 24-hour standard 

18 results in a substantial change in the annual average, using proportional rollback, since the same 

19 fractional reduction required to get the 24-our design value to meet the current standard (i.e., a 

35% reduction) is applied to the annual design value of 11.6 μg/m3 20 , resulting in an annual 

average of 7.7 μg/m3 21 . These two examples illustrate the varying impact that the 24-hour 

22 standard, if controlling, can have on annual-average PM2.5 concentrations and consequently on 

23 the magnitude of long-term (and short-term) PM2.5 exposure-related mortality associated with 

just meeting the current suite of standards.74 24 

25 As discussed above, the sensitivity analysis examining alternative rollback approaches 

26 showed that in instances where PM2.5 distributions are relatively peaky, application of peak 

27 shaving (reflecting more localized patterns of ambient PM2.5 reductions) can result in a 

28 controlling 24-hour standard having a substantially smaller impact on annual-average PM2.5

29 concentrations. Sensitivity analysis results for the examples referenced above (Baltimore and 

30 Salt Lake City) illustrate this issue related to application of alternative rollback methods. In the 

31 case of Baltimore, which has a less peaky PM2.5 distribution (in that its 24-hour and annual 

 73 Although, as noted earlier, composite monitor annual-averages will display differences across urban study areas, 

even in those cases where the maximum monitor annual-average has been adjusted to meet the same annual standard 

(see Table F-49 in Appendix F). 

74 As noted above, variation in the relationship between the maximum monitor annual-average and the composite 

monitor annual-average across study areas adds an additional degree of variability to the estimated long-term 

exposure-related mortality seen across the 10 study areas. 
February 2010 Draft – Do Not Quote or Cite 6-9

1 design values are both fairly close to the current suite of standard levels), application of peak 

2 shaving in simulating just meeting the current suite of standards resulted in an annual average 

only slightly higher than that simulated using proportional rollback (i.e., 15.2 μg/m3 3 compared 

with 14.8 μg/m3 4 – Table F-49). This means that long-term exposure-related IHD mortality for 

5 Baltimore would be relatively similar if either proportional or peak shaving rollback approaches 

6 were applied. In contrast, application of peak shaving in Salt Lake City resulted in annual7 average concentrations substantially higher than those simulated by proportional rollback (i.e., 

10.8 μg/m3

 compared with 7.7 μg/m3 8 , respectively – Table F-49). Therefore, for this study area, 

9 use of peak shaving rollback would result in estimates of IHD mortality risk that are larger than 

10 with proportional rollback (i.e., >50% higher than with proportional rollback – Table F-49). 

11 These examples further illustrate that variability in the pattern of estimated reductions in ambient 

12 PM2.5 concentrations based on simulation of just meeting the current suite of standards can result 

13 in quite different percentage reductions in long-term PM2.5 exposure-related mortality. 

14 Additional sensitivity analyses considering sources of uncertainty impacting the core risk 

15 estimates focused on specification of the C-R function for long-term PM2.5 exposure-related 

16 mortality. This analysis suggested that most of the alternative model specifications supported by 

17 available literature would produce risk estimates that were higher (by up to a factor of 2 to 3) 

18 than the core risk estimates. These findings would apply both to estimates of PM2.5-attributable 

19 IHD mortality incidence, as well as to estimates of the percent of total IHD mortality incidence 

20 attributable to PM2.5 exposure. 

21 Taken together, the sensitivity analyses completed for this risk assessment, including 

22 those considering variability in rollback methods as well as uncertainty in the form of C-R 

23 functions, suggest that the set of alternative risk model specifications that we identified generally 

24 produced risk estimates that are higher than the core risk estimates. Furthermore, our decision to 

25 model risk down to the LML (rather than to lower PRB levels) for long-term PM2.5 exposure26 related mortality, despite the lack of evidence for a threshold, results in lower estimates of risk 

27 that would have resulted from modeling risk down to PRB. These considerations increase our 

28 overall confidence that we did not over-state risks with the core risk estimates. 

29 In considering the results of the quantitative sensitivity analyses summarized above, we 

30 note that the qualitative analysis of uncertainty did identify areas of ongoing research which 

31 could impact risk estimates, including: (a) more refined characterization of intra-urban variability 

32 in ambient PM2.5 concentrations and the resulting impact on risk characterization and (b) 

33 consideration of specific components within the mix of PM2.5, including regional differences in 

34 composition, and potential implications for risk characterization. These considerations introduce 

35 further uncertainty into the overall risk assessment, although we do not believe that these 
February 2010 Draft – Do Not Quote or Cite 6-10

1 additional sources of uncertainty are likely to alter the fundamental observations resulting from 

2 the core risk assessment of the current suite of standards. 

3 6.2.2 Simulation of Just Meeting Alternative Annual Standards 

4 In characterizing PM2.5-related risks associated with simulation of the alternative annual 

5 standards, we estimate both the magnitude of risk reductions (relative to risk remaining upon just 

6 meeting the current suite of standards) as well as the magnitude of risk remaining upon just 

7 meeting the alternative standards. In discussing these risks, we focus on the set of urban study 

8 areas experiencing risk reductions under each alternative annual standard. 

9 Based on the risk estimates for these areas presented in section 4.2.2 and in Appendix E, 

10 we make the following general observations regarding the magnitude of risk remaining upon 

11 simulation (using proportional rollback) of just meeting the alternative annual standards (in 

12 combination with the current 24-hour standard):

13  Patterns of risk reduction across alternative annual standard levels: There is a consistent 

14 pattern of increasing risk reduction with decreasing alternative annual standard levels, both in 

15 terms of the number of study areas experiencing risk reductions and the magnitude of those 

16 reductions. Specifically, 5 of the 15 urban study areas experience risk reductions under the 

alternative annual standard level of 14 μg/m3 17 , with percent reductions in PM2.5-attributable 

18 long-term exposure-related mortality ranging from 9% (Baltimore) to 12% (Houston) (Figure 

4-3 and Table E-27 in Appendix E). For an annual standard level of 12 μg/m3 19 , 12 of the 15 

20 urban study areas experience risk reductions, with percent reductions ranging from 11% 

21 (Phoenix) to 35% (Houston and Birmingham) (Figure 4-3 and Table E-27 in Appendix E). 

22  Estimates of long-term PM2.5 exposure-related mortality remaining upon just meeting 

alternative annual standards: For an annual standard level of 14 μg/m3 23 , the percent of total 

24 incidence of IHD mortality attributable to PM2.5 in the 5 urban study areas experiencing risk 

25 reductions ranges from 9-11.3% (Detroit) to 11.8-14.9% (Atlanta) (Tables E-24 and E-33 in 

Appendix E). For an annual standard of 12 μg/m3 26 , estimated risk remaining in the 12 urban 

27 study areas experiencing risk reductions ranges from 6-7.6% (Phoenix) to 9-11.4% (Atlanta) 

28 in terms of PM2.5-attributable long-term exposure-related mortality (Tables E-24 and E-33 in 

29 Appendix E). 

30 While there is a consistent pattern of risk reduction across the alternative annual standards 

31 with lower standard levels resulting in more urban study areas experiencing increasingly larger 

32 risk reductions, there is considerable variability in the magnitude of these reductions across study 

33 areas for a given alternative annual standard level (e.g., as noted above, for the alternative annual 

standard level of 12 μg/m3 34 , risk reduction ranges from 11% for Phoenix to 35% for Houston). 

35 This variability in risk reflects differing degrees of reduction in annual-average concentrations 

36 across the study areas. These differences in annual-averages result in part because the study 

37 areas begin with varying annual-average PM2.5 concentrations after simulating just meeting the 

38 current suite of standards (see section 6.2.1). Therefore, even if study areas have similar 
February 2010 Draft – Do Not Quote or Cite 6-11

1 “ending” annual average PM2.5 concentrations after simulation of just meeting the a given 

2 alternative annual standard, because the starting point in the calculation (the annual-average 

3 PM2.5 concentrations upon just meeting the current suite of standards) can be variable, the overall 

4 reduction in annual-average PM2.5 concentrations across the standards can also be variable. This 

5 translates into variation in reductions in long-term exposure-related risk upon just meeting 

alternative annual standard levels across the study areas.75 6 

7 The sensitivity analysis involving application of peak shaving rollback reveals that the 

8 pattern of reductions in ambient PM2.5 concentrations upon just meeting the current suite of 

9 standards can impact the magnitude of additional risk reductions estimated for just meeting 

10 alternative (lower) annual standard levels. Specifically, for those study areas with more peaky 

11 PM2.5 distributions, application of peak shaving rollback will result in higher annual-average 

12 PM2.5 levels remaining upon just meeting the current suite of standards. If proportional rollback 

13 is then used to simulate just meeting alternative annual standard levels, a greater degree of 

14 reduction in annual-average PM2.5 concentrations will result, since the “starting point” for the 

15 calculation (annual-average PM2.5 levels upon just meeting the current suite of standards) will be 

16 higher. 

17 For example, with Los Angeles, which represents a study area with a relatively peaky PM2.5

18 distribution, application of proportional rollback in simulating both the current suite of standards 

and the alternative annual standard of 12 μg/m3 19 results in a 13% reduction in long-term 

20 exposure-related mortality (see Figure 4-3 and Table E-27 in Appendix E - this calculations 

21 represents the approach used in the core risk assessment model, since proportional rollback was 

22 used in simulating both suites of standards). In contrast, application of peak shaving in 

23 simulating the current suite of standards followed by proportional reduction in simulating the 

alternative annual standard of 12 μg/m3 24 results in an estimated 48% reduction in long-term 

exposure-related mortality.76 25 This example illustrates that application of peak shaving in 

26 simulating just meeting the current suite of standards for urban areas such as Los Angeles which 

27 have relatively peaky PM2.5 distributions can substantially increase the magnitude of risk 

28 reduction simulated for an alternative (lower) annual standard level. 

 75 We note that additional variation in the risk estimates, in terms of both risk reduction across standard levels and 

residual risk for each of the alternative annual standard levels, results from differences across study areas in the 

relationship between the maximum monitor annual-averages values used in estimating percent reductions under an 

alternative standard and the composite monitor annual-average values used in estimating long-term exposure-related 

risk. 

76 These risk reductions reflecting application of peak-shaving in simulating the current suite of standards are based 

on comparison of composite monitor annual-averages presented in Table F-49 in Appendix F. In generating this 

surrogate for reduction in long-term exposure-related mortality between the two standard levels, we compared 

composite monitor annual-averages taking into account that long-term exposure-related mortality is only calculated 

down to the LML. 
February 2010 Draft – Do Not Quote or Cite 6-12

1 Observations made above in the context of the current suite of standards regarding 

2 uncertainty and its impact on risk estimates apply in this context as well. Specifically, given the 

3 results of the sensitivity analysis examining the form of the C-R functions for long-term 

4 exposure-related mortality, combined with only modeling risk down to the LML, we have 

5 increased confidence that we have not overstated either the magnitude of risk reductions across 

6 alternative standard levels, or the magnitude of risk remaining for a given standard level. 

7 6.2.3 Simulation of Just Meeting Alternative Suites of Annual and 24-hour Standards 

8 The two suites of standards involving alternative annual and alternative 24-hour 

9 standards can be used to consider the impact on risk of reducing the 24-hour standard. 

10 Specifically, by comparing risks estimated for the 13/30 and 13/35 suites of standards, we can 

consider a reduction of 5 μg/m3 11 in the 24-hour standard. Similarly if we compare the 12/25 and 

12/35 suites of standards we can consider a 10 μg/m3 12 reduction. In both cases, the reduction in 

the 24-hour standard level is associated with a fixed annual standard level (i.e., 13 and 12 μg/m3 13 , 

14 respectively). These two comparisons of suites of alternative standards form the basis for the 

15 discussion presented below. As with the alternative annual standard levels, we address both the 

16 magnitude of risk reductions as well as the magnitude of risk remaining upon just meeting the 

17 alternative suites of standards. In discussing these risks, we also continue to focus on the set of 

18 urban study areas experiencing risk reductions under each alternative suite of standards. 

19 Based on the risk estimates for these areas presented in section 4.2.2 and in Appendix E, 

20 we make the following general observations regarding the magnitude of risk remaining upon 

21 simulation (using proportional rollback) of these alternative suites of standards: 

22  Patterns of reduction in long-term exposure-related mortality across alternative standards: 

23 Comparing risks associated with just meeting the 13/35 and 13/30 suites of alternative 

24 standards, we see considerable variation in the magnitude of risk reduction across urban 

25 study areas. For example, St Louis, under with the 13/35 suite of alternative standards has 

26 IHD mortality risk attributable to PM2.5 reduced by 22% relative to risk under the current 

27 suite of standards. Very little additional risk reduction (24%) is estimated under the 13/30 

28 alternative suite of standards. In contrast, with Salt Lake City, we estimate that the 13/35 

29 suite of alternative standards will produce no risk reduction relative to the current suite of 

30 standards, while the 13/30 suite would produce a 55% reduction in IHD mortality risk 

31 relative to risk under the current standard level (see Figure 4-3 and Table E-27 in Appendix 

32 E). The additional risk reduction provided by an alternative 24-hour standard is even more 

33 pronounced in comparing the 12/25 and 12/35 alternative suites of standards. In this case we 

34 see that for nine of the study areas (Detroit, Fresno, Los Angeles, New York, Philadelphia, 

35 Phoenix, Pittsburgh, Salt Lake City and Tacoma) the 12/25 suite of alternative standards 

36 produced estimated reductions in risk (relative to risk associated with just meeting the current 

37 suite of standards) that are twice as large as for the 12/35 suite of alternative standards (see 

38 Figure 4-3 and Table E-27 in Appendix E). 

39  Estimates of long-term exposure-related mortality remaining upon just meeting the 
February 2010 Draft – Do Not Quote or Cite 6-13

1 alternative 24-hour standards: There is appreciable variation in the estimated magnitude of 

2 risk remaining upon simulation of the 13/30 suite of alternative standards. For example, the 

3 percent of total IHD mortality incidence attributable to PM2.5 (again, for urban study areas 

4 experiencing risk reductions) ranges from 2-2.5% (for Tacoma) to 8.9-11.3% (for Baltimore) 

5 (see Tables E-24 and E-33, in Appendix E). There continues to be variation in the levels of 

6 residual risk under the 12/25 alternative suite of standards with estimates ranging from 0.3-

7 4.7% (for Tacoma) to 8.8-11.1% (for Atlanta) (see Tables E-24 and E-33, in Appendix E). 

8 The observations presented above again highlight variability both in the magnitude of 

9 risk reduction as well as in the residual risk estimated from the simulation of just meeting 

10 alternative 24-hour standards. This reflects the fact that, as noted above, alternative 24-hour 

11 standards can produce different degrees of reduction in the annual-average PM2.5 concentrations, 

12 depending on the relationship between 24-hour and annual design values at a particular location. 

13 For example, the fact that Salt Lake City is predicted to have a 55% reduction in long-term 

14 exposure-related mortality risk with the 13/30 suite of alternative standards (compared with risk 

15 under the current suite of standards), reflects the peaky nature of its PM2.5 distribution. 

16 Specifically, simulating just meeting the 24-hour standard using proportional rollback will 

17 produce a substantial reduction in the annual-average PM2.5 concentrations (i.e., from a recent 

conditions annual-average of 11.6 μg/m3

, to 7.7 μg/m3 18 under the current suite of standards, to 6.7 

μg/m3 19 with the 13/30 suite of alternative standards – see Table F-49 in Appendix F). In 

20 contrast, with St Louis, which does not experience as substantial a risk reduction under the 13/30 

21 suite of alternative standards, there is a far less peaky PM2.5 distribution (i.e., the annual and 24-

22 hour design values are relatively closer to each other – see Table F-49 in Appendix F). 

Therefore, simulation of the alternative 24-hour standard level of 30 μg/m3 23 does not have as 

24 substantial an effect on annual-average concentrations (i.e., from a recent conditions annualaverage of 16.5 μg/m3

, to 14.9 μg/m3 under the current suite of standards, to 12.8 μg/m3 25 under 

26 the 13/30 suite of alternative standards). 

27 It is possible to stratify the set of urban study areas based on patterns of risk reduction 

28 estimated under the alternative 24-hour standards. In this discussion, we focus on risk estimates 

29 generated for the 12/25 suite of alternative standards, focusing on how risks under this scenario 

compare with risks under the current suite of standards.77 30 The stratification of the study areas 

31 based on the magnitude of risk reduction highlights factors responsible for these differences 

32 across study areas. For example, when the 24-hour standard is controlling (in simulating the 

33 12/25 suite of alternative standards) and the PM2.5 distribution is relatively peaky, there is a 

34 greater potential for the annual-average PM2.5 concentrations to be reduced more in simulating 

just meeting the alternative 24-hour standard (in some instances, well below 12 μg/m3 35 ) resulting 

 77 Further, in considering risk reduction, we are comparing risk under the alternative suites of standards to risk under 

the current suite of standards based solely on application of proportional rollback. 
February 2010 Draft – Do Not Quote or Cite 6-14

1 in larger estimated risk reductions. In fact, we see that the urban study areas having the largest 

2 risk reductions have annual-average PM2.5 concentrations simulated under the 12/25 suite of 

standards (using proportional rollback) well below 12 μg/m3 3 , with some locations ranging down 

to ~6 μg/m3 4 . 

5 We identified four strata in considering patterns of risk reduction across the 15 urban study 

6 areas under the 12/25 suite of alternative standards (all of the percent reductions presented are in 

7 terms of long-term exposure-related IHD mortality). 

8  ~100% reduction in risk: Those study areas where the 24-hour standard was controlling 

9 and where the resulting annual-average PM2.5 concentrations (under the 12/25 suite of 

standards) were ~ 6 μg/m3 10 . Because annual-average concentrations for these study areas 

are at or below the LML for long-term exposure-related mortality (5.8 μg/m3 11 ), little to no 

12 risk is predicted under the alternative suite of standards, resulting in a near 100% 

13 reduction in risk relative to the current suite of standards. These study areas have the 

14 most peaky PM2.5 distributions of the 15 urban study areas (i.e., relatively high 24-hour 

15 design values and lower annual average design values) and include study areas Tacoma 

and Salt Lake City.78 16 

17  ~70% reduction in risk: Those study areas where the 24-hour standard is controlling and 

18 where the resulting annual-average PM2.5 levels (under the 12/25 suite of standards) were 

~7-9 μg/m3 19 . These study areas also have relatively peaky PM2.5 distributions and include 

Los Angeles and Fresno.79 20 

21  ~50-60% reduction in risk: Those study areas where the 24-hour standard is controlling 

22 and where the resulting annual-average PM2.5 levels (under the 12/25 suite of standards) 

were ~9-11 μg/m3 23 . These study areas have less peaky PM2.5 distributions (24-hour 

24 standard still controls, but there is not as great a disparity with the annual design values) 

25 and include the majority of the study areas (Detroit, NYC, Philadelphia, Pitts, St Louis, 

Baltimore, Birmingham, and Phoenix).80 26 

27  ~35-45% reduction in risk: This category includes some study areas where the 24-hour 

28 standard controls and some where the annual standard controls. Annual average PM2.5

concentrations under the 12/25 suite of standards are generally in the 12 μg/m3 29 range. 

30 These study areas have relatively less peaky PM2.5 distributions and include Atlanta and 

 78 These study areas fall in zone A in Figure 4-20, which represents the largest grouping of urban areas in the U.S. 

predicted to be exceeding this alternative suite of standards (12/25). However, we note that Tacoma and Salt Lake 

City have some of the most peaky PM2.5 distributions of the urban areas in this zone and therefore are likely to 

experience greater risk reductions than most of the urban areas in zone A. 

79 Los Angeles and Fresno fall in zone B and specifically, subarea B1, in Figure 4-20 (subarea B1 represents those 

study areas that exceed the 12/25 suite of alternative standards and that also have a greater degree of peakiness in 

their PM2.5 distributions relative to other urban areas in zone B – see section 4.5.1). Consequently, these study areas 

are likely to experience greater risk reductions relative to other urban areas in zone B. 80 These eight study areas fall in zone B in Figure 4-20 and specifically, subarea B2, which includes a relatively 

large fraction of those urban areas in the U.S. predicted to exceed the 12/25 suite of alternative standards. Urban 

areas in subarea B2 have less peaky PM2.5 distributions compared to areas in subarea B1. 
February 2010 Draft – Do Not Quote or Cite 6-15

Houston.81, 82 1 

2 Observations made earlier regarding the impact of variability in simulating changes in 

3 PM2.5 distributions using different rollback approaches, and its impact on the degree of risk 

4 reduction, also hold here. Specifically, in those instances where PM2.5 distributions are more 

5 peaky, application of peak shaving rollback would result in smaller reductions in annual-average 

6 PM2.5 concentrations and consequently, smaller reductions in estimates of long-term exposure7 related mortality. For example, with Salt Lake City, which has a peaky PM2.5 distribution, under 

8 the 12/25 suite of standards application of proportional rollback results in an annual average 

PM2.5 concentration of 5.7 μg/m3 9 , while application of peak shaving results in an estimate of 8.9 

μg/m3 10 . In contrast, simulation of the 12/25 suite of standards for Baltimore, which has a less 

peaky PM2.5 distribution, results in an annual average PM2.5 concentration of 10.7 μg/m3 11 for 

proportional rollback compared to 10.8 μg/m3 12 with peak shaving (see Table F-49 in Appendix F). 

13 A key observation made above in relation to the current suite of standards, that is even 

14 more relevant in considering the results discussed here, is that simulated annual-average PM2.5

15 concentrations upon just meeting alternative suites of standards for many of the urban study 

areas are considerably lower than 12 μg/m3 16 . For example, with the current suite of standards, 

17 Fresno and Salt Lake City are simulated to have annual average PM2.5 concentrations of 9.9 and 

7.7 μg/m3 18 , respectively, which are in turn reflected in the risk estimates generated (see Table F19 49, in Appendix F). Annual average concentrations in these study areas are even lower under the 

20 alternative suites of standards with lower 24-hour standard levels. For example, under the 13/30 

suite of standards, simulated annual average concentrations range down to 6.7 μg/m3 21 (Salt Lake 

22 City), with a number of urban study areas having annual-average concentrations simulated in the 

range of 7 to 11 μg/m3 23 (Fresno, Los Angeles, and Tacoma). Under the 12/25 suite of standards, 

simulated annual-average concentrations are even lower, ranging down to 5.7 μg/m3 24 (Salt Lake 

25 City). These very low annual-average PM2.5 concentrations reflect lower annual design values to 

26 begin with as well as relatively peaky PM2.5 distributions, which means that simulation of the 24-

27 hour standard (when controlling) will produce appreciable impacts on the annual average 

28 concentration. 

29 The results discussed above show that simulating just meeting alternative 24-hour 

standard levels in the range of 25 to 30 μg/m3 30 can produce substantial reductions in estimated 

 81 Atlanta and Houston fall into zones B and C in Figure 4-20, and specifically portions of those zones including 

urban areas with less peaky PM2.5 distributions. 82 We note that Dallas has a substantially smaller estimate of risk reduction (~13%) compared with the other 14 

urban study areas. The relatively low risk reduction for this location reflects the fact that Dallas has annual and 24-

hour design values (12.8 and 26 μg/m3

, respectively) that are well below the current suite of standards and only just 

exceed the 12/25 suite of standards. Therefore, the estimated risk reduction under this suite of standards is expected 

to be very low. Dallas just barely falls into Zone C in Figure 4-19. 
February 2010 Draft – Do Not Quote or Cite 6-16

1 risk, beyond that produced by simulations of just meeting lower annual standard level down to 

12 μg/m3

 (combined with a 24-hour standard of 35 μg/m3 2 ). This results from the simulations 

3 producing substantially lower annual-average PM2.5 concentrations, which drive reductions in 

4 both long-term and short-term exposure-related risk. The results also show that there can be 

5 considerable variability across study areas in the degree to which alternative 24-hour standard 

6 levels produce reductions in annual average PM2.5 concentrations and, consequently, reductions 

7 in risk. This variability is seen to depend largely on the peakiness of the PM2.5 distribution in an 

8 area and on the rollback approach used to simulate just meeting the current and alternative suites 

9 of standards. These results suggest that while lowering the 24-hour standard can be used to 

10 reduce annual-average PM2.5 concentrations, and thus to reduce estimated risk, the results are 

11 likely to be highly variable across urban areas. This analysis also suggests that more consistent 

12 annual-average PM2.5 concentrations, and thus more consistent reductions in estimated risk, 

would result from simulating just meeting alternative annual standards at levels below 12 μg/m3 13 

14 which was the lowest annual standard level considered in this assessment. In general, 

15 considering suites of standards in which the annual standard is the controlling standard would be 

16 expected to provide more consistent reductions in annual-average PM2.5 concentrations, thereby, 

17 providing more uniform public health protection across urban areas. 

18 Observations made earlier regarding overall confidence in the estimates of long-term 

19 exposure-related mortality also hold for these estimates (i.e., the sensitivity analysis results 

20 combined with the fact that we modeled risk down to LML result in our concluding that it is 

21 unlikely we have overstated either the degree of risk reduction or the degree of residual risk). 

22 6.3 NATIONAL PERSPECTIVE ON PM2.5-RELATED RISKS 

23 This section places the core risk estimates in the broader national-context by considering 

24 the degree to which the 15 urban study areas are representative of larger urban areas within the 

25 U.S., particularly areas likely to experience elevated risk related to PM exposure. As such, it 

26 draws on information presented in several sections of the risk assessment including: (a) the 

27 representativeness analysis discussed in section 4.4, (b) consideration of patterns of design 

28 values for the 15 urban study areas as contrasted with the broader set of larger urban areas within 

29 the US (section 4.5.1), and (c) the national-scale mortality analysis discussed in Chapter 5. 

30  The representativeness analysis presented in section 4.4, compared attributes of the 15 urban 

31 study eras (assessed at the county-level) against national distributions for the same attributes. 

32 The analysis suggests that the 15 urban study areas represent areas in the U.S. that are among 

the most densely populated, have relatively higher levels of annual and 24-hour 98th 33 

34 percentile PM2.5 concentrations, and capture well the range of effect estimates represented by 

35 the Zanobetti and Schwartz (2009) study. Together, these factors suggest that the urban 

36 study areas should capture well the overall distribution of risk for the nation, with the 
February 2010 Draft – Do Not Quote or Cite 6-17

potential for better characterization of the high end of that distribution.83 1 

2  Consideration of the mix of design values across the 15 urban study areas as contrasted with 

3 design values for the broader set of urban study areas in the U.S. suggests that the 15 urban 

4 study areas do a good job of capturing the key groupings of urban areas in the U.S. likely to 

5 experience elevated risk due to PM (i.e., we have coverage for each of the zones containing 

6 urban study areas likely to experience risk reductions under the suites of alternative standard 

7 levels considered – see section 4.5.1). Furthermore, this analysis suggested that we have also 

8 included study areas likely to experience relatively greater degrees of PM2.5-related risk, 

9 considering the pattern of design values across urban areas in the U.S.. 

10  Consideration of where the 15 urban study areas fell along the distribution of U.S. counties 

11 included in the national-scale mortality analysis further suggests that we have captured 

12 counties likely to experience elevated PM2.5-related risk. As part of the national-scale 

13 mortality analysis (see Chapter 5), we created a cumulative distribution of the percentage of 

mortality attributable to PM2.5 based on the county-level estimates for the U.S.84 14 We then 

15 identified where along this cumulative distribution the 31 counties comprising our 15 urban 

16 study areas fell. This analysis suggests that our urban study areas capture the upper end of 

17 the tail with regard to PM2.5-attributable risk, with 23 of these counties falling within the 

upper 5th 18 percentile of the distribution. These findings support the assertion based on the 

19 other analyses described above that the urban study areas are likely to capture risk at urban 

20 areas experiencing relatively elevated levels of PM2.5-attributable mortality. 

21 Our overall assessment of the representativeness of the 15 urban study areas in the 

22 national context, based on the three analyses summarized above, is that our study areas do a good 

23 job of representing urban areas in the U.S. experiencing elevated levels of risk related to ambient 

24 PM2.5 exposure. The results of the national-scale mortality analysis also suggest that, while our 

25 15 urban study areas do provide coverage for urban areas in the U.S. experiencing elevated 

26 levels of PM2.5-related risk, there are many additional areas (counties) not modeled in the risk 

27 assessment that experience elevated PM2.5-related risk. In other words, it should not be 

28 construed that significant PM2.5-related risk is limited only to the urban study areas included in 

29 the risk assessment. 

 83 This 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 lower PM2.5 levels (e.g., urban areas in Florida) 

and consequently failure to include these areas in the set of urban study areas is unlikely to bias the risk estimates in 

terms of excluding high PM2.5-risk locations. 84 Note that by using this risk metric, we avoid influence by difference in overall population size (as would be the 

case with raw incidence) and focus on a unitized estimate of PM2.5-related mortality which reflects differences in (a) 

baseline mortality incidence, and (b) the annual PM2.5 levels average for each county. 
February 2010 Draft – Do Not Quote or Cite 6-18

1 6.4 KEY OBSERVATIONS 

2 Key observations from this quantitative risk assessment for PM2.5, with emphasis on the 

3 observations made above in this chapter, are outlined below. These observations are organized 

4 around the three policy-relevant questions outlined at the beginning of this chapter. 

5 

6 (1) What is the magnitude of risk likely to remain if the urban study areas were just meeting the 

current suite of PM2.5 standards (an annual standard of 15 μg/m3 7 and a 24-hour standard of 35 

μg/m3 8 ), and what level of confidence do we have in those estimates? 

9  Upon simulation of just meeting the current suite of standards, the core analysis estimates 

10 that the urban study areas would have IHD-related mortality attributable to long-term PM2.5

11 exposure ranging from <100 to approximately 2,000 cases per year, with this variability 

12 reflecting to a great extent differences in the size of study area populations. These estimates 

13 represent from 4 to 17% of all IHD-related mortality in a given year for the urban study 

14 areas, which is a measure of risk that takes into account differences in population size and 

15 baseline mortality rates. Estimates were also developed for other long-term exposure-related 

16 mortality endpoints, including all-cause, cardiopulmonary-related, and lung cancer mortality. 

17  Generally comparable estimates of CV-related mortality attributable to short-term PM2.5

18 exposure are substantially lower than for long-term exposure-related IHD mortality. The 

19 core analysis estimates that the urban study areas would have CV-related mortality 

20 attributable to short-term PM2.5 exposure ranging from approximately 10 to 470 cases per 

21 year. Estimates were also developed for other short-term exposure-related endpoints, 

22 including non-accidental and respiratory-related mortality, CV- and respiratory-related 

23 hospital admissions, and asthma-related emergency department visits. 

24  A broader array of health effects has also been associated with PM2.5 exposures, including in 

25 particular effects on children, such as reproductive and developmental effects. While 

26 information was too limited to consider these effects in this quantitative risk assessment, such 

27 effects are appropriately considered based on the related evidence in the broader 

28 characterization of risks to be discussed in a separate Policy Assessment document. 

29  Given the quantitative and qualitative assessments of uncertainty and variability that we have 

30 completed as part of our quantitative risk assessment, we believe that it is unlikely that we 

31 have over-stated the degree of risk remaining upon simulation of just meeting the current 

32 suite of standards. While this conclusion applies to all quantitative estimates of risk, it applies 

33 most strongly for long-term PM2.5 exposure-related mortality for which more extensive 

34 uncertainty and variability assessment has been done. 

35  Estimated risks remaining upon just meeting the current suite of standards vary substantially 

36 across study areas, even when considering risks normalized for differences in population size 

37 and baseline incidence rates. This variability in estimated risks is a consequence of the 

38 substantial variability in the annual-average PM2.5 concentrations across study areas that 

39 result from simulating just meeting the current standards. This is important because annual40 average concentrations are highly correlated with both long-term and short-term exposure41 related risk. This variability in annual-average PM2.5 concentrations occurs especially in 
February 2010 Draft – Do Not Quote or Cite 6-19

those study areas in which the 24-hour standard is the “controlling” standard.85 1 In such 

2 areas, the variability across study areas in estimated risks is largest when regional patterns of 

3 reductions in PM2.5 concentrations are simulated (using proportional rollback, as was done in 

4 the core analyses), with less variability when more localized patterns of PM2.5 reductions are 

5 simulated (using peak shaving rollback, as was done in a sensitivity analysis). When 

6 simulations are done using peak shaving rollback, estimated risks remaining upon just 

7 meeting the current suite of standards can be appreciably larger than those estimated in the 

8 core analysis. 

9  In simulating just meeting the current suite of standards, the resulting annual-average PM2.5

concentrations range from about 15 μg/m3 10 (for those study areas in which the annual 

standard was controlling) down to as low as about 8 μg/m3 11 (for those study areas in which 

the 24-hour standard was controlling or the annual average was well below 15 μg/m3 12 based 

13 on recent air quality). Thus, estimates of risk remaining upon just meeting the current 

14 standards are, in many cases, reflective of annual average PM2.5 concentrations that are well 

15 below the level of the current annual standard. 

16  The 15 urban study areas included in this risk assessment are generally characteristic of 

17 urban areas across the U.S. that do not meet the current suite of standards. Of those urban 

18 areas in the U.S. that do not meet the current suite of standards (based on 2005-2007 air 

19 quality data), the 24-hour standard is controlling in most such areas – a pattern that is 

20 reflected in the urban study areas included in this assessment. Two areas are included in this 

21 assessment that meet the current suite of standards (reflective of the majority of urban areas 

22 in the U.S.), although these two areas fail to meet some of the alternative suites of standards 

23 considered in this assessment. 

24 

25 (2) What is the degree and nature of risk reduction likely to be associated with just meeting the 

26 alternative suites of annual and 24-hour PM2.5 standards considered in this risk assessment, and 

27 what roles do the annual and 24-hour standards play in bringing about such reductions? 

28  Upon simulation of just meeting the alternative annual standard levels considered (14, 13, 

and 12 μg/m3 29 ) in conjunction with the current 24-hour standard (denoted as 14/35, 13/35 and 

30 12/35 suites of standards), the core analysis estimates reductions in long-term exposure31 related mortality for 12 of the 15 urban study areas, with the degree of risk reduction 

32 increasing incrementally across the alternative standard levels (both in terms of the number 

33 of study areas experiencing risk reduction and the magnitude of those reductions). For the 

alternative annual standard level of 12 μg/m3 34 (in conjunction with the current 24-hour 

35 standard), the core analysis estimates that these study areas have reductions in risk (relative 

36 to risk remaining upon just meeting the current suite of standards) ranging from about 11 to 

37 35%. For some of those areas in which the 24-hour standard is controlling, larger risk 

38 reductions would have been estimated in this case (12/35 suite of standards) if peak shaving 

39 rollback had been used to simulate just meeting the current suite of standards. This result 

40 would be expected since the magnitude of risk remaining upon just meeting the current suite 

41 of standards would have been higher than that estimated based on the proportional rollback 

 85 The controlling standard is the standard (either 24-hour or annual) that requires the largest percent reduction in the 

related design value to just meet that standard. 
February 2010 Draft – Do Not Quote or Cite 6-20

1 used in the core analysis. Therefore, while we are going down to the same level of risk 

2 (under the 12/35 suite of standards), we are starting with a higher level of risk from the 

3 current standard. 

4  Upon just meeting the alternative suites of standards that included lower levels of both the 

5 annual and 24-hour standards (denoted as13/30 and 12/25 suites of standards), the core 

6 analysis estimates that the lower 24-hour standard levels produce additional risk reductions 

7 beyond the reductions estimated for the lower annual standard levels alone. In the case of the 

8 12/25 suite of standards, estimated risk reductions compared with reductions for the annual 

standard alone (12 μg/m3 9 ), were roughly twice as large in many of the study areas, although 

10 in a few areas risk reductions were much higher (ranging up to ~100%) and in a few other 

11 areas, there was little to no risk reduction. These results show that lower 24-hour standards 

12 can have an appreciable and highly variable impact on long-term exposure-related mortality, 

13 particularly when just meeting the lower standards is simulated using a more regional pattern 

14 of PM2.5 reductions (i.e., the proportional rollback used in the core analysis). However, the 

15 magnitude of risk reductions estimated for the lower 24-hour standards was reduced when 

16 simulations using a more localized pattern of PM2.5 reductions (i.e., the peak shaving rollback 

17 used in a sensitivity analysis). 

18  The results of simulating alternative suites of standards including lower levels of both annual 

19 and 24hr standards suggest that while lowering the 24-hour standard can be used to reduce 

20 annual-average PM2.5 concentrations, and thus to reduce estimated risk, the results are likely 

21 to be highly variable across urban areas. More consistent annual-average PM2.5

22 concentrations across study areas, and thus more consistent reductions in estimated risk, 

23 would result from simulating just meeting a specific alternative annual standard level. In 

24 general, considering suites of standards in which the annual standard is the controlling 

25 standard would be expected to provide more consistent reductions in annual-average PM2.5

26 concentrations, thereby, providing more uniform public health protection across urban areas. 

27  In simulating just meeting the alternative suites of standards, especially those with lower 24-

28 hour standard levels, the resulting annual-average PM2.5 concentrations are substantially 

lower than the lowest annual standard level considered in the analysis (12 μg/m3 29 ). For 

30 example, under the 12/25 suite of standards, estimated annual-average PM2.5 concentrations 

ranged down to approximately 6 μg/m3 31 , with eight urban study areas having annual average 

PM2.5 levels in the 8-11 μg/m3 32 range. 

33  Addressing overall confidence in risk estimates generated for just meeting the alternative 

34 suites of standards, as with the current suite of standards, we conclude based on our 

35 quantitative and qualitative analysis of uncertainty and variability that we have likely not 

36 over-stated risk reductions or levels of residual risk estimated for just meeting these 

37 alternative suites of standards. 

38 

39 (3) What is the distribution of risks associated with recent PM2.5 air quality in areas across the 

40 U.S., and how representative are the risks estimated for the urban study areas from a national 

41 perspective? 

42  Based on recent air quality from 2005 to 2007, we estimate that within the continental U.S., 

43 total PM2.5-related premature mortality ranges from 63,000 and 88,000 per year. Further, we 
February 2010 Draft – Do Not Quote or Cite 6-21

1 estimate that the percent of total mortality attributable to PM2.5 long-term exposure ranges 

2 from approximately 3 to 9% in about half of the counties in the U.S., with a, range from 

3 approximately 0 to 3% in the other half of counties. 

4  Efforts to place the 15 urban study areas and the core risk estimates generated for those areas 

5 into a broader national context suggest that these study areas likely capture well the full set of 

6 urban areas in the U.S. likely to experience relatively higher PM2.5-related risk. 

7  It is important to recognize that there are many additional areas besides those included in the 

8 risk assessment that experience elevated PM2.5-related risk of similar magnitude to the risks 

9 estimated for the urban study areas included in this assessment. 

10 
February 2010 Draft – Do Not Quote or Cite 7-1

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A-1

APPENDIX A: AIR QUALITY ASSESSMENT 
A-2

1 Appendix A. Air Quality Assessment 

2 

3 This Appendix describes the PM data for the 15 urban study areas evaluated in the risk 

4 assessment, including summaries of PM2.5 monitoring data associated with each study area as 

5 well as the composite monitor estimates generated for each study area based on that monitoring 

6 data (see section 3.2 for additional detail regarding selection of monitors and derivation of 

7 composite monitor values). 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 
A-3

Table A-1. Air Quality Data for Atlanta

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q

130630091 (3) 27 30 25 30 112 12.63 16.83 21.22 15.9

130670003 (1,2,3) 27 30 29 29 115 13.75 17.39 18.57 15.6130670004 (1,2,3) 30 28 26 27 111 12.98 17.17 18.03 13.9130890002 (1,2,3) 82 84 81 88 335 12.72 15.72 18.81 14.5130892001 (1,2,3) 80 75 67 85 307 12.84 15.10 20.44 14.8131210032 (1,2,3) 84 89 76 80 329 13.64 16.00 19.43 14.3131210039 (1,2,3) 27 30 23 29 109 15.03 18.35 17.97 16.5131210048 (1,2,3) 0 0 0 0 0 --- --- --- -

131350002 (1,3) 13 14 12 14 53 14.35 14.62 20.39 15.1

132230003 (3) 28 29 26 26 109 11.41 15.52 18.62 12.9Composite monitor for Atlanta - 1 90 91 92 92 365 13.62 16.34 19.09 15.0Composite monitor for Atlanta - 2 90 91 92 92 365 13.49 16.62 18.87 14.9Composite monitor for Atlanta - 3 90 91 92 92 365 13.26 16.30 19.27 14.8130630091 (3) 29 29 31 30 119 12.94 17.91 21.32 14.4130670003 (1,2,3) 28 29 31 30 118 12.22 17.88 21.52 14.2130670004 (1,2,3) 28 29 27 28 112 12.09 17.75 21.04 12.3130890002 (1,2,3) 85 86 81 81 333 12.25 16.09 19.86 13.4130892001 (1,2,3) 86 84 77 81 328 11.94 15.75 18.31 12.1131210032 (1,2,3) 88 86 84 90 348 12.46 15.99 19.28 13.7131210039 (1,2,3) 29 28 26 0 83 15.12 19.15 20.88 -131210048 (1,2,3) 0 0 2 30 32 --- --- 15.25 15.0

131350002 (1,3) 12 14 13 15 54 15.21 18.98 20.31 12.9

132230003 (3) 29 27 31 29 116 10.91 15.20 18.90 10.7Composite monitor for Atlanta - 1 90 91 92 92 365 13.04 17.37 20.17 13.4Composite monitor for Atlanta - 2 90 91 92 92 365 12.68 17.10 20.15 13.4Composite monitor for Atlanta - 3 90 91 92 92 365 12.79 17.19 20.16 13.2

Monitor

2006

2005

Quarterly Counts Quarterly Averages (ug/m3) Annual 

Total
A-4

Table A-1 cont'd. Air Quality Data for Atlanta

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

130630091 (3) 29 30 30 29 118 13.87 16.51 18.83 13.02 

130670003 (1,2,3) 29 30 29 29 117 13.49 17.03 19.49 13.41 130670004 (1,2,3) 26 27 30 30 113 12.50 17.47 18.77 11.39 130890002 (1,2,3) 85 83 90 85 343 12.78 15.54 19.38 12.15 130892001 (1,2,3) 69 79 76 75 299 12.48 17.11 20.04 12.38 131210032 (1,2,3) 87 88 91 85 351 12.99 17.95 19.64 13.08 131210039 (1,2,3) 0 0 0 0 0 --- --- --- --- 

131210048 (1,2,3) 28 28 31 28 115 13.45 18.97 18.24 12.83 

131350002 (1,3) 27 27 29 29 112 13.05 14.03 17.97 11.68 132230003 (3) 29 30 29 30 118 12.21 17.12 18.95 10.64 Composite monitor for Atlanta - 1 90 91 92 92 365 12.96 16.87 19.08 12.42 Composite monitor for Atlanta - 2 90 91 92 92 365 12.95 17.35 19.26 12.54 Composite monitor for Atlanta - 3 90 91 92 92 365 12.98 16.86 19.03 12.29 Note 1: Different definitions of Atlanta include different monitors. The number(s) shown in the parenthesis next to the monitor indicates the 

included. For example, monitor 130630091 is used in Atlanta - 3 only while 130670003 is used for all definitions of Atlanta.Note 2: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled i

2007

Monitor

Quarterly Counts Quarterly Averages (ug/m3) Annual 

Total
A-5

Table A-2. Air Quality Data for Baltimore

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

240051007 30 28 27 27 112 14.78 11.86 20.66 12.34 240053001 75 80 85 92 332 16.09 12.60 18.27 13.44 245100006 28 31 27 28 114 15.76 12.47 20.18 11.67 245100007 27 27 30 30 114 16.09 12.50 20.05 13.00 245100008 24 30 30 29 113 18.85 14.16 20.99 14.80 245100035 79 75 78 70 302 17.58 13.59 20.24 14.12 245100040 79 81 90 76 326 18.47 14.68 19.40 13.42 245100049 26 30 25 27 108 17.72 13.19 20.62 12.77 Composite Monitor for Baltimore 90 91 92 92 365 16.91 13.13 20.05 13.19 240051007 29 29 28 30 116 12.03 11.37 15.73 11.09 240053001 90 85 90 92 357 12.81 11.79 18.51 13.90 245100006 27 30 27 30 114 13.20 11.62 16.24 11.61 245100007 30 29 29 31 119 12.64 11.59 15.19 12.03 245100008 30 28 31 30 119 14.80 13.34 16.88 12.97 245100035 74 90 83 82 329 13.31 12.57 19.27 14.14 245100040 85 86 87 86 344 13.83 12.58 18.64 14.73 245100049 0 0 0 0 0 --- --- --- --- Composite Monitor for Baltimore 90 91 92 92 365 13.23 12.12 17.21 12.92 240051007 29 29 31 30 119 12.09 13.54 15.53 12.04 240053001 74 87 83 89 333 12.53 12.95 16.93 13.70 245100006 30 29 31 27 117 12.10 12.83 16.28 11.16 245100007 29 30 30 28 117 12.07 13.20 15.84 12.44 245100008 30 30 31 27 118 13.53 14.68 16.90 14.79 245100035 79 85 74 76 314 12.11 14.03 17.23 13.23 245100040 82 85 89 76 332 13.42 13.66 16.32 13.35 245100049 0 0 0 0 0 --- --- --- --- Composite Monitor for Baltimore 90 91 92 92 365 12.55 13.55 16.43 12.96 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-6

Table A-3. Air Quality Data for Birmingham

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

10730023* 90 90 89 92 361 14.35 20.49 26.42 17.27 10731005* 30 31 29 31 121 11.62 16.70 22.61 14.33 10731009* 30 31 29 31 121 9.82 16.12 20.26 11.87 10731010* 15 15 15 16 61 11.71 16.91 22.77 15.51 10732003* 88 90 91 91 360 14.49 18.48 23.75 15.03 10732006* 30 30 30 31 121 11.53 16.46 21.11 13.79 10735002* 30 31 30 31 122 10.84 16.33 21.08 12.61 10735003* 30 30 30 31 121 10.60 16.42 21.94 12.74 11170006 30 31 30 28 119 11.23 15.67 19.60 12.92 11270002 27 31 28 30 116 10.37 15.31 18.86 12.17 Composite Monitor for Birmingham - 1 90 91 92 92 365 11.66 16.89 21.84 13.82 Composite Monitor for Birmingham - 2 90 91 92 92 365 11.87 17.24 22.49 14.14 10730023* 89 91 92 92 364 13.61 20.57 22.35 17.02 10731005* 30 30 31 31 122 10.51 18.84 19.59 13.38 10731009* 30 29 30 30 119 8.81 17.16 17.78 10.02 10731010* 15 15 15 16 61 11.57 18.63 18.71 12.37 10732003* 89 90 90 92 361 14.41 20.48 21.62 15.67 10732006* 30 30 31 31 122 10.76 18.08 20.02 12.33 10735002* 30 30 31 31 122 9.87 17.15 19.61 10.60 10735003* 29 30 30 30 119 10.37 17.42 18.84 11.31 11170006 30 30 31 31 122 9.95 16.37 18.38 11.65 11270002 29 30 30 29 118 9.85 17.49 17.38 11.83 Composite Monitor for Birmingham - 1 90 91 92 92 365 10.97 18.22 19.43 12.62 Composite Monitor for Birmingham - 2 90 91 92 92 365 11.24 18.54 19.82 12.84 

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-7

Table A-3 cont'd. Air Quality Data for Birmingham

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

10731010* 15 15 15 15 60 14.53 18.69 19.31 13.63 10732003* 89 90 89 90 358 15.40 21.38 19.18 12.42 10732006* 30 30 31 30 121 12.24 19.29 18.53 10.93 10735002* 30 28 31 30 119 12.15 19.16 18.41 10.40 10735003* 29 30 31 30 120 11.79 18.99 17.83 10.38 11170006 29 30 31 30 120 12.97 18.27 17.52 10.84 11270002 28 29 31 29 117 11.97 17.81 17.72 10.95 Composite Monitor for Birmingham - 1 90 91 92 92 365 12.99 19.62 18.58 11.60 Composite Monitor for Birmingham - 2 90 91 92 92 365 13.12 20.02 18.82 11.78 Note 1: The monitors marked with * are used for Birmingham - 2. All monitors shown in this table are used for Birmingham - 1.Note 2: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in

2007

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-8

Table A-4. Air Quality Data for Dallas

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

481130035 30 31 20 0 81 11.78 15.16 13.90 --- 481130050 15 30 27 31 103 11.95 15.01 15.64 12.47 481130057 27 21 22 0 70 12.00 16.07 14.41 --- 481130069 78 88 90 91 347 11.07 13.80 14.03 11.11 481130087 27 31 30 30 118 9.87 13.32 13.45 10.18 481133004 88 89 61 0 238 10.86 13.58 12.82 --- Composite Monitor for Dallas 90 91 92 92 365 11.26 14.49 14.04 11.25 481130035 0 0 0 0 0 --- --- --- --- 481130050 28 30 31 31 120 10.99 12.53 12.98 10.68 481130057 0 0 0 0 0 --- --- --- --- 481130069 84 90 92 90 356 9.97 12.15 11.73 9.26 481130087 30 30 30 28 118 9.22 11.66 10.89 8.45 481133004 0 0 0 0 0 --- --- --- --- Composite Monitor for Dallas 90 91 92 92 365 10.06 12.11 11.87 9.46 481130035 0 0 0 0 0 --- --- --- --- 481130050 29 28 30 0 87 11.54 11.76 15.42 --- 481130057 0 0 0 0 0 --- --- --- --- 481130069 88 91 91 79 349 10.13 10.91 13.78 10.14 481130087 28 21 29 30 108 9.96 11.16 12.70 9.30 481133004 0 0 0 0 0 --- --- --- --- Composite Monitor for Dallas 90 91 92 92 365 10.54 11.27 13.97 9.72 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled 

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-9

Table A-5. Air Quality Data for Detroit

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

261630001 88 87 89 86 350 18.45 13.87 17.15 14.38 261630015 27 27 30 30 114 20.20 14.73 18.73 15.18 261630016 87 79 84 88 338 18.92 14.78 16.62 13.70 261630019 28 31 29 29 117 19.82 14.48 17.43 14.20 261630025 26 28 30 30 114 17.86 11.74 17.45 12.68 261630033 28 31 28 28 115 21.50 16.57 18.22 17.90 261630036 29 28 29 27 113 16.96 14.92 18.58 15.19 261630038 28 25 22 0 75 16.98 14.60 17.66 --- 261630039 0 0 7 28 35 --- --- 18.20 14.25 Composite Monitor for Detroit 90 91 92 92 365 18.84 14.46 17.73 14.69 261630001 82 85 88 90 345 13.66 11.89 13.68 13.65 261630015 29 26 28 31 114 16.98 12.26 14.93 14.56 261630016 79 14 13 17 123 13.04 11.58 12.58 14.97 261630019 30 15 14 16 75 15.20 10.39 11.78 13.46 261630025 27 14 15 17 73 13.49 11.23 10.01 12.70 261630033 28 29 27 31 115 18.79 12.85 15.56 17.30 261630036 29 26 29 29 113 15.10 10.95 13.69 11.94 261630038 0 29 27 28 84 --- 11.10 14.34 11.98 261630039 29 30 31 30 120 14.78 11.71 14.20 11.84 Composite Monitor for Detroit 90 91 92 92 365 15.13 11.55 13.42 13.60 261630001 86 89 87 92 354 12.92 10.28 14.00 14.08 261630015 28 30 27 29 114 15.15 13.06 15.12 14.82 261630016 26 26 30 29 111 13.98 12.12 14.74 14.61 261630019 30 28 31 27 116 13.20 11.16 14.36 13.31 261630025 26 30 31 27 114 12.23 10.59 13.76 14.42 261630033 29 29 29 27 114 18.84 15.20 16.02 17.49 261630036 29 28 30 29 116 13.75 11.96 14.60 13.47 261630038 27 27 28 30 112 13.63 12.85 15.35 14.23 261630039 29 30 30 28 117 13.83 12.98 14.65 13.86 Composite Monitor for Detroit 90 91 92 92 365 14.17 12.24 14.73 14.48 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled 

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-10

Table A-6. Air Quality Data for Fresno

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

60190008 85 78 89 91 343 19.53 7.19 11.42 28.65 60195001 30 15 15 22 82 17.11 7.55 10.78 29.95 60195025 30 15 13 31 89 20.24 8.29 11.24 27.92 Composite Monitor for Fresno 90 91 92 92 365 18.96 7.68 11.14 28.84 60190008 89 87 87 85 348 21.82 9.10 12.39 23.85 60195001 30 15 14 29 88 18.38 9.47 12.99 24.96 60195025 30 15 12 31 88 20.13 9.81 13.66 26.87 Composite Monitor for Fresno 90 91 92 92 365 20.11 9.46 13.01 25.22 60190008 87 90 88 91 356 27.61 8.32 10.70 28.71 60195001 29 13 14 27 83 23.70 7.16 9.91 24.91 60195025 29 14 15 30 88 24.91 8.73 9.65 24.10 Composite Monitor for Fresno 90 91 92 92 365 25.41 8.07 10.09 25.90 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in

20062007

A

2005

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-11

Table A-7. Air Quality Data for Houston

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

482010024 26 31 22 15 94 11.77 14.39 17.17 11.83 482010026 23 31 20 0 74 10.47 13.10 14.47 --- 482010055 25 28 19 0 72 9.12 12.31 12.97 --- 482010058 20 28 23 26 97 11.95 12.99 14.40 12.19 482011034 10 15 10 0 35 11.79 15.36 14.49 --- 482011035 84 68 78 87 317 13.09 16.59 18.41 15.47 Composite Monitor for Houston 90 91 92 92 365 11.28 14.12 15.48 13.16 482010024 15 13 13 13 54 10.92 11.66 15.97 12.58 482010026 0 0 0 0 0 --- --- --- --- 482010055 0 0 0 0 0 --- --- --- --- 482010058 26 29 29 29 113 9.74 12.34 9.04 9.82 482011034 0 0 0 0 0 --- --- --- --- 482011035 85 87 88 88 348 13.98 18.15 17.38 14.48 Composite Monitor for Houston 90 91 92 92 365 11.55 14.05 14.13 12.29 482010024 15 14 13 0 42 11.01 12.82 14.64 --- 482010026 0 0 0 0 0 --- --- --- --- 482010055 0 0 0 0 0 --- --- --- --- 482010058 26 30 30 30 116 9.40 10.96 11.84 11.75 482011034 0 0 0 0 0 --- --- --- --- 482011035 87 91 91 82 351 14.42 17.02 16.62 14.50 Composite Monitor for Houston 90 91 92 92 365 11.61 13.60 14.36 13.13 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-12

Table A-8. Air Quality Data for Los Angeles

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

60370002 65 78 87 62 292 11.37 13.97 20.71 21.78 60371002 29 25 30 22 106 17.01 13.75 18.55 21.95 60371103 90 84 87 89 350 15.26 13.78 19.62 22.48 60371201 25 29 28 22 104 12.27 11.97 15.01 16.18 60371301 29 26 28 31 114 16.68 13.28 18.15 21.75 60371602 29 9 9 29 76 16.90 11.63 17.13 22.31 60372005 30 26 26 31 113 12.98 12.95 17.15 17.28 60374002 87 82 88 67 324 13.39 11.54 16.21 22.56 60374004 90 84 87 83 344 12.64 10.83 15.63 19.59 60379033 28 30 27 18 103 8.18 8.27 9.96 9.00 Composite Monitor for Los Angeles 90 91 92 92 365 13.67 12.26 16.78 19.49 60370002 66 73 84 55 278 12.62 16.17 16.95 15.87 60371002 25 24 30 25 104 15.33 18.34 15.87 16.66 60371103 89 82 85 74 330 14.49 14.69 16.34 16.80 60371201 20 27 28 17 92 11.19 14.21 12.95 13.00 60371301 28 28 27 24 107 17.62 14.76 15.11 19.26 60371602 29 28 31 28 116 16.82 13.92 17.19 18.57 60372005 29 27 28 29 113 12.85 14.64 13.46 12.51 60374002 73 81 73 63 290 15.19 12.27 13.53 15.57 60374004 89 86 79 66 320 14.35 11.99 14.21 17.22 60379033 15 15 14 14 58 6.13 7.27 8.36 8.00 Composite Monitor for Los Angeles 90 91 92 92 365 13.66 13.83 14.40 15.35 

 

A

(

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-13

Table A-8 cont'd. Air Quality Data for Los Angeles

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

60370002 64 77 74 77 292 13.57 17.11 14.68 17.47 60371002 23 26 27 22 98 13.64 15.96 15.36 22.47 60371103 67 83 90 84 324 16.25 16.05 14.62 20.19 60371201 22 26 28 19 95 9.50 13.24 12.55 17.72 60371301 25 27 29 25 106 16.98 14.05 13.00 19.99 60371602 27 27 21 26 101 16.75 14.01 15.18 20.45 60372005 28 23 30 27 108 12.62 15.60 14.02 15.24 60374002 76 86 88 82 332 15.45 12.42 11.50 19.04 60374004 65 81 90 90 326 13.84 12.26 11.30 17.31 60379033 15 15 15 15 60 6.73 7.67 9.00 8.67 Composite Monitor for Los Angeles 90 91 92 92 365 13.53 13.84 13.12 17.85 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled 

2007

 

A

(

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-14

Table A-9. Air Quality Data for New York

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

360050080 28 31 29 27 115 18.59 14.78 18.42 15.68 360050083 30 31 30 31 122 13.77 12.21 16.90 12.71 360050110 90 91 91 91 363 14.93 12.17 15.38 12.30 360470122 28 30 28 27 113 16.04 13.74 17.31 14.13 360610056* 30 31 30 31 122 18.44 15.51 19.16 15.17 360610062* 27 31 30 31 119 17.14 13.84 18.34 13.54 360610079* 30 31 30 31 122 14.60 13.12 17.03 12.56 360610128* 25 31 30 31 117 17.74 14.11 18.37 15.21 360610134* 0 0 0 0 0 --- --- --- --- 360810124 89 79 62 74 304 13.02 10.44 15.21 10.84 360850055 28 25 28 27 108 14.92 12.49 17.81 12.91 360850067 24 28 28 30 110 12.60 10.75 16.17 10.41 Composite Monitor for New York City - 1 90 91 92 92 365 15.62 13.02 17.28 13.22 Composite Monitor for New York City - 2 90 91 92 92 365 16.98 14.15 18.22 14.12 360050080 29 30 27 29 115 16.57 13.17 13.95 11.88 360050083 30 30 29 29 118 13.44 11.06 13.34 10.33 360050110 86 91 84 86 347 13.10 11.15 14.49 11.40 360470122 28 30 29 25 112 15.00 12.49 14.75 9.00 360610056* 30 30 27 30 117 16.61 14.03 14.41 12.59 360610062* 30 28 28 27 113 14.33 13.00 13.82 9.86 360610079* 30 30 31 29 120 14.12 12.08 13.32 10.59 360610128* 26 30 29 29 114 15.79 13.07 14.39 12.64 360610134* 0 0 0 0 0 --- --- --- --- 360810124 69 86 84 76 315 11.17 10.67 13.68 10.91 360850055 25 27 29 29 110 12.27 12.07 14.06 10.56 360850067 30 26 31 29 116 10.01 10.49 12.60 8.54 Composite Monitor for New York City - 1 90 91 92 92 365 13.86 12.12 13.89 10.75 Composite Monitor for New York City - 2 90 91 92 92 365 15.21 13.04 13.99 11.42 

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-15

Table A-9 cont'd. Air Quality Data for New York

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

360050080 30 30 30 29 119 17.45 13.49 16.20 15.43 360050083 30 30 30 29 119 14.14 11.72 13.91 12.87 360050110 89 84 85 91 349 12.90 11.64 14.22 12.31 360470122 29 30 28 30 117 13.67 12.82 15.92 13.00 360610056* 30 27 31 30 118 18.43 14.73 15.99 15.29 360610062* 27 0 0 0 27 15.84 --- --- --- 360610079* 30 30 31 30 121 14.11 12.48 14.92 12.89 360610128* 30 30 29 21 110 19.10 13.83 14.63 14.76 360610134* 3 30 31 30 94 8.53 14.12 16.43 14.08 360810124 74 86 80 92 332 11.34 10.66 12.30 11.35 360850055 30 28 31 30 119 13.04 12.37 14.55 11.91 360850067 27 30 26 26 109 10.60 10.49 14.29 10.54 Composite Monitor for New York City - 1 90 91 92 92 365 14.60 12.58 14.85 13.13 Composite Monitor for New York City - 2 90 91 92 92 365 16.87 13.79 15.49 14.25 Note 1: The monitors marked with * are used for New York City - 2. All monitors in the table are used for New York City - 1.Note 2: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled 

2007

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-16

Table A-10. Air Quality Data for Philadelphia

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

421010003 0 0 0 62 62 --- --- --- 14.35 421010004 55 61 78 74 268 13.23 13.06 17.26 13.28 421010020 19 0 0 0 19 15.51 --- --- --- 421010024 37 54 67 71 229 12.68 10.76 16.26 12.02 421010047 19 28 26 12 85 16.99 12.04 18.91 12.31 421010057 0 0 0 0 0 --- --- --- --- 421010136 86 89 29 33 237 13.57 11.40 19.06 12.91 Composite Monitor for Philadelphia 90 91 92 92 365 14.40 11.81 17.87 12.97 421010003 85 26 0 0 111 12.21 8.74 --- --- 421010004 81 70 53 84 288 12.74 11.85 17.23 12.41 421010020 0 0 0 0 0 --- --- --- --- 421010024 34 70 71 80 255 11.52 10.56 16.17 11.34 421010047 40 67 45 47 199 14.44 14.57 18.04 15.04 421010057 0 0 0 0 0 --- --- --- --- 421010136 47 50 79 73 249 11.97 12.06 16.29 12.25 Composite Monitor for Philadelphia 90 91 92 92 365 12.58 11.55 16.93 12.76 421010003 0 0 0 0 0 --- --- --- --- 421010004 87 71 86 90 334 13.61 13.19 15.15 12.96 421010020 0 0 0 0 0 --- --- --- --- 421010024 87 58 86 90 321 12.05 12.76 14.88 11.73 421010047 71 59 90 92 312 14.49 13.05 16.33 13.43 421010057 0 0 18 90 108 --- --- 10.96 13.13 421010136 75 65 72 82 294 12.60 13.38 14.36 12.99 Composite Monitor for Philadelphia 90 91 92 92 365 13.19 13.09 14.33 12.85 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-17

Table A-11. Air Quality Data for Phoenix

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

40130019 32 32 30 31 125 11.04 10.78 11.11 18.37 40131003 0 22 30 29 81 --- 8.77 8.26 9.72 40134003 29 31 27 31 118 10.94 13.04 10.40 16.98 40137020 0 30 29 31 90 --- 8.08 7.72 9.46 40139997 29 31 30 31 121 9.04 8.69 7.58 13.56 Composite Monitor for Phoenix 90 91 92 92 365 10.34 9.87 9.01 13.62 40130019 30 30 31 31 122 14.17 13.58 8.07 17.82 40131003 26 28 31 31 116 8.87 9.52 8.92 11.33 40134003 28 28 31 29 116 13.53 10.34 9.31 17.58 40137020 29 30 31 30 120 8.09 7.98 7.14 9.12 40139997 29 29 30 30 118 10.74 8.66 7.46 14.04 Composite Monitor for Phoenix 90 91 92 92 365 11.08 10.01 8.18 13.98 40130019 32 30 31 30 123 10.26 8.85 8.63 15.42 40131003 29 28 30 30 117 7.66 10.45 9.50 11.27 40134003 30 29 30 29 118 10.54 11.76 11.32 15.45 40137020 30 30 31 20 111 5.85 7.81 7.35 8.21 40139997 30 29 32 30 121 8.85 8.12 8.21 12.75 Composite Monitor for Phoenix 90 91 92 92 365 8.63 9.40 9.00 12.62 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-18

Table A-12. Air Quality Data for Pittsburgh

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

420030008 89 90 92 89 360 13.80 15.29 20.72 13.40 420030021 28 27 30 27 112 12.91 14.99 22.00 11.49 420030064 88 90 92 86 356 16.28 22.26 25.94 21.10 420030067 26 28 29 27 110 12.32 13.95 20.35 10.26 420030093 13 11 12 13 49 10.66 13.83 23.66 9.63 420030095 14 13 14 15 56 12.79 14.49 21.55 9.83 420030116 23 29 28 26 106 13.82 16.42 21.68 12.66 420030133 14 13 13 9 49 13.54 12.62 20.51 9.51 420031008 30 29 30 29 118 12.79 15.60 21.90 13.52 420031301 29 29 29 26 113 14.39 16.86 23.90 13.37 420033007 15 13 14 15 57 14.13 14.25 24.36 12.71 420039002 13 13 14 15 55 12.95 14.01 21.32 11.25 Composite Monitor for Pittsburgh 90 91 92 92 365 13.37 15.38 22.32 12.58 420030008 85 89 91 92 357 11.60 13.28 20.19 12.54 420030021 0 0 0 0 0 --- --- --- --- 420030064 85 90 87 89 351 14.86 17.89 22.78 20.97 420030067 23 26 28 21 98 9.61 9.52 16.39 9.06 420030093 14 6 13 13 46 10.37 9.85 16.38 9.41 420030095 13 13 13 14 53 10.02 10.97 18.22 10.31 420030116 0 0 0 0 0 --- --- --- --- 420030133 0 0 0 0 0 --- --- --- --- 420031008 27 23 28 25 103 11.87 14.30 18.32 11.63 420031301 26 28 29 29 112 12.56 14.55 19.89 13.11 420033007 15 15 14 15 59 12.93 13.51 19.16 12.36 420039002 0 0 0 0 0 --- --- --- --- Composite Monitor for Pittsburgh 90 91 92 92 365 11.49 13.05 18.69 11.95 

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-19

Table A-12. Air Quality Data for Pittsburgh

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

420030008 85 86 86 89 346 11.80 14.72 20.30 12.74 420030021 0 0 0 0 0 --- --- --- --- 420030064 88 90 91 90 359 14.16 18.64 25.16 17.57 420030067 19 25 28 26 98 10.28 13.40 19.46 10.73 420030093 15 12 14 14 55 9.67 10.50 19.35 12.57 420030095 14 13 15 14 56 10.96 9.89 20.79 12.90 420030116 0 0 0 0 0 --- --- --- --- 420030133 0 0 0 0 0 --- --- --- --- 420031008 27 27 30 27 111 12.79 14.55 19.68 13.23 420031301 28 27 31 26 112 14.02 15.18 21.90 15.16 420033007 14 14 14 13 55 12.36 13.03 21.19 13.85 420039002 0 0 0 0 0 --- --- --- --- Composite Monitor for Pittsburgh 90 91 92 92 365 11.87 13.51 20.74 13.36 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-20

Table A-13. Air Quality Data for Salt Lake City

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

490350003 30 29 30 31 120 14.16 6.58 8.98 14.49 490350012 82 89 85 85 341 16.73 9.59 12.68 17.24 490351001 29 30 28 30 117 11.85 5.47 8.61 11.35 490353006 88 90 90 85 353 13.95 6.27 9.56 14.17 490353007 28 27 29 28 112 13.64 7.40 10.57 16.36 490353008 30 31 24 31 116 9.90 6.03 7.76 7.45 490353010 0 0 0 0 0 --- --- --- --- Composite Monitor for Salt Lake City 90 91 92 92 365 13.37 6.89 9.69 13.51 490350003 28 28 29 30 115 10.76 6.98 9.41 13.58 490350012 76 87 82 90 335 11.80 11.22 14.19 14.91 490351001 27 28 29 27 111 7.95 5.65 8.65 9.29 490353006 88 90 90 88 356 10.59 7.21 8.54 12.37 490353007 30 30 31 29 120 10.11 7.18 11.56 13.61 490353008 29 26 30 30 115 6.14 6.85 9.26 7.09 490353010 0 0 0 0 0 --- --- --- --- Composite Monitor for Salt Lake City 90 91 92 92 365 9.56 7.51 10.27 11.81 490350003 30 30 29 28 117 18.12 6.97 10.99 13.89 490350012 80 86 0 0 166 20.84 11.45 --- --- 490351001 24 30 31 26 111 11.42 6.44 10.08 9.71 490353006 89 85 78 89 341 18.17 6.11 9.42 12.05 490353007 29 29 29 31 118 17.72 7.17 11.53 13.42 490353008 23 28 28 30 109 10.03 6.06 9.66 7.09 490353010 0 80 83 92 255 --- 7.68 11.62 13.00 Composite Monitor for Salt Lake City 90 91 92 92 365 16.05 7.41 10.55 11.53 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-21

Table A-14. Air Quality Data for St. Louis

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

171190023* 28 28 29 29 114 18.01 19.10 21.49 16.95 171190024* 0 0 0 0 0 --- --- --- --- 171191007* 26 31 29 30 116 18.40 16.49 21.47 16.27 171192009* 12 12 13 12 49 14.94 16.35 20.82 11.98 171193007* 29 31 27 29 116 16.42 15.20 19.99 12.49 171630010* 13 15 14 15 57 17.31 16.81 19.97 14.47 171634001* 30 30 29 28 117 17.86 14.17 17.20 14.69 290990012 90 87 90 91 358 15.22 14.69 19.26 12.42 291890004* 29 29 28 31 117 16.01 12.64 17.80 11.87 291892003* 57 30 29 31 147 16.73 14.15 18.44 12.65 295100007* 88 88 83 81 340 16.99 14.67 18.92 12.87 295100085* 90 86 78 88 342 16.78 14.46 19.67 13.33 295100086* 84 26 30 29 169 15.11 14.34 18.43 13.14 295100087* 90 87 82 81 340 17.02 14.80 18.74 12.94 295100093* 0 0 0 0 0 --- --- --- --- Composite Monitor for St Louis - 1 90 91 92 92 365 16.68 15.22 19.40 13.54 Composite Monitor for St Louis - 2 90 91 92 92 365 16.80 15.27 19.41 13.64 171190023* 30 26 31 29 116 15.21 17.34 19.40 12.11 171190024* 0 0 0 0 0 --- --- --- --- 171191007* 27 24 24 27 102 14.95 16.12 20.18 14.05 171192009* 15 15 14 16 60 12.59 13.35 13.49 12.92 171193007* 28 30 31 31 120 13.08 12.00 16.47 10.87 171630010* 12 14 15 14 55 14.18 13.75 15.72 14.48 171634001* 28 28 31 29 116 13.43 12.87 15.20 12.00 290990012 82 81 91 89 343 11.62 11.79 15.46 11.49 291890004* 30 29 0 0 59 10.56 10.49 --- --- 291892003* 29 29 28 26 112 11.36 10.69 13.87 11.00 295100007* 78 88 91 90 347 12.27 11.82 15.89 12.51 295100085* 86 77 84 92 339 13.04 12.46 15.26 12.68 295100086* 30 30 31 29 120 11.94 11.55 15.48 10.90 295100087* 85 90 86 91 352 12.92 12.32 16.17 13.18 295100093* 0 0 0 0 0 --- --- --- --- Composite Monitor for St Louis - 1 90 91 92 92 365 12.86 12.81 16.05 12.35 Composite Monitor for St Louis - 2 90 91 92 92 365 12.96 12.90 16.10 12.43 

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-22

Table A-14 cont'd. Air Quality Data for St. Louis

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

171190023* 0 0 0 0 0 --- --- --- --- 171190024* 0 0 6 29 35 --- --- 15.07 14.94 171191007* 29 27 29 26 111 14.28 15.31 17.61 13.23 171192009* 15 12 14 13 54 14.31 16.02 15.66 13.51 171193007* 29 28 26 30 113 12.42 14.84 17.39 12.32 171630010* 13 13 14 14 54 14.94 17.65 15.94 13.79 171634001* 26 30 31 29 116 13.35 13.95 14.83 10.90 290990012 82 81 90 86 339 11.94 14.44 16.23 12.13 291890004* 0 0 0 0 0 --- --- --- --- 291892003* 89 90 91 90 360 11.63 12.96 15.25 12.49 295100007* 88 91 91 92 362 12.56 14.50 16.13 12.97 295100085* 90 88 89 90 357 12.59 13.79 16.09 13.30 295100086* 27 30 0 0 57 11.79 14.50 --- --- 295100087* 90 86 92 86 354 13.24 14.43 16.61 13.10 295100093* 0 0 24 29 53 --- --- 17.26 13.82 Composite Monitor for St Louis - 1 90 91 92 92 365 13.00 14.76 16.27 13.04 Composite Monitor for St Louis - 2 90 91 92 92 365 13.11 14.79 16.28 13.13 Note 1: The monitors marked with * are used for St Louis - 2. All monitors shown in the table are used for St Louis - 1.Note 2: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-23

Table A-15. Air Quality Data for Tacoma

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

530530029 29 30 30 31 120 16.46 5.34 7.13 17.07 Composite Monitor for Tacoma 90 91 92 92 365 16.46 5.34 7.13 17.07 530530029 30 30 31 26 117 8.92 5.89 7.45 15.93 Composite Monitor for Tacoma 90 91 92 92 365 8.92 5.89 7.45 15.93 530530029 29 28 31 29 117 13.76 5.94 5.23 13.76 Composite Monitor for Tacoma 90 91 92 92 365 13.76 5.94 5.23 13.76 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
B-1

APPENDIX B: HYBRID (NON-PROPORTIONAL) AND PEAK 

SHAVING ROLLBACK APPROACHES 
B-2

1 Appendix B. Methodologies for Rolling Back PM2.5 Concentrations Due to Local Source 

2 Impacts (hybrid non-proportional and peak shaving approaches) 

3 

4 During the last review of the Particulate Matter National Ambient Air Quality Standards 

5 (NAAQS), a technique was employed to simulate fine particulate concentrations under a series 

6 of attainment scenarios to determine the risk associated with each. The “rolling back” of the 

7 concentrations consisted of simply using a proportional rollback calculation where every 

8 measured concentration value was multiplied by a constant to obtain a set of concentrations 

9 which would meet alternative standard levels. This technique was reviewed by the Clean Air 

10 Scientific Advisory Committee (CASAC) and was considered to be a satisfactory way to 

11 simulate alternative PM2.5 distributions. The rolled back values, however, constituted only a 

12 regional reduction in PM concentrations without accounting in any way for emission reductions 

13 at local point sources. 

14 

15 The Hybrid Non-Proportional Approach 

16 

17 For the current review, an alternative rollback approach reflecting the combined effects 

18 of both local and regional reduction strategies was considered (this alternative approach is 

19 referred to as the hybrid non-proportional approach in the risk assessment). In addition to 

20 utilizing a traditional proportional rollback to represent the regional PM reductions, a distance21 weighted rollback was conducted on a subset of the 15 study areas which contain source-oriented 

22 monitors measuring concentrations higher than those observed at other sites within a particular 

area.1 23 

24 Unique sites with high design values exceeding the NAAQS were further investigated to 

25 determine if they were in close proximity to a large source of PM2.5 (Figure B-1). The presence 

26 of possible source-oriented sites in each area was visually determined using satellite photographs 

27 provided by Google Earth. Areas where source-oriented adjustments were made include Detroit 

28 MI, Pittsburgh PA, St. Louis MO-IL, Baltimore MD, New York NY, Los Angeles CA and 

29 Birmingham AL. 

30 

31 

32 

33 

 

1

 In the risk assessment, as outlined in Section 3.1, the proportional rollback approach was used in 

generating the core risk estimates, while the hybrid non-proportional approach described here, was considered as 

part of the sensitivity analysis. 
B-3

1 

2 

3 

4 

5 

6 Figure B-1. Example of a monitor, in Dearborn MI, located near a large source of 

7 emissions 

8 

9 For those sites that were within proximity to a large emitter, the site’s measured 

10 concentrations were reduced using a proportional rollback depending on the magnitude of the 

11 reduction needed to either the highest 24-hour or annual design value of a non-source oriented 

12 site within the area whose design values were close to those of the source oriented site (Figure B13 2). 

14 
B-4

1 

2 Figure B-2. Plot of the 24-hour versus the annual average PM2.5 design values for 

3 individual sites in Detroit MI 

4 

5 

6 The fractional reduction made to the site near the point source was then weighted by the 

7 inverse distance in kilometers between the source-oriented site and all of the other individual 

8 sites in the area to determine their fractional reductions in relation to the source-oriented site. If 

9 more than one source-oriented site was reduced, a distance-weighted average fractional reduction 

10 was calculated and implemented across the non-source-oriented sites. Sites within one kilometer 

11 of the source oriented site received the same amount of reduction as the source oriented site. An 

12 example of the effect of this reduction technique for Detroit is presented in Table B-1. For 

13 Detroit, adjustments were based on the difference between the two sites’ annual design values. 

14 
B-5

1 Table B-1. Comparison of the original and adjusted design values for Detroit, MI 

Site ID 

Original Annual 

Design Value 

(2005-2007) 

Adjusted 

Annual Design 

Value (2005-

2007) 

Original 24-hour 

Design Value 

(2005-2007) 

Adjusted 24-hour 

Design Value 

(2005-2007) 

260490021 11.6 11.5 29 29 

260990009 12.5 12.4 35 35 

261150005 13.8 13.7 38 38 

261250001 13.6 13.5 40 40 

261470005 13.2 13.1 41 40 

261610005 13.2 13.1 39 39 

261610008 13.7 13.6 39 39 

261630001 14 13.9 36 36 

261630015 15.5 15.2 40 39 

261630016 14.3 14.2 41 41 

261630019 14.1 14 40 40 

261630025 13.2 13.1 34 34 

261630033 17.2 15.4 43 39 

261630036 14.3 14.2 36 36 

261630038 14.3 14.1 40 39 

261630039 14.4 14.3 37 37 

 

Site in blue represents source-oriented site 

Site in red represents reference site used for reduction 

2 

3 

4 Reduction of the concentrations of the source-oriented site reduced either the 24-hour or 

5 annual design value of the site to either the maximum non-source-oriented site’s 24-hour or 

6 annual design value. This did not necessarily mean that the adjusted values at the source7 oriented site met either the 24-hour or annual standard after the reduction. Since the adjusted 

8 design values were calculated using the same data handling rules as contained within 40 CFR 

9 Part 50 Appendix N, truncation or rounding of the adjusted concentrations could sometimes give 

10 adjusted design values at the source-oriented site that were not exactly the same value as the 

original design value at the reference site. However, they were usually within 1 ug/m3 11 for the 

12 24-hour standard and a few tenths of a microgram per cubic meter for the annual standard. 

13 

14 The Peak Shaving Approach 

15 

16 The peak shaving approach was used to calculate annual averages for 2005, 2006, and 

17 2007 at composite monitors for comparison with the composite monitor annual averages 
B-6

1 calculated using the proportional and hybrid rollback approaches. Because of time constraints, 

2 we did not calculate health risks when alternative standards are just met using the peak shaving 

3 approach. However, because the C-R functions used in the risk assessment are almost linear, a 

4 comparison of annual averages at composite monitors using the three different methods for 

5 simulating just attaining standards should give a good idea of the corresponding estimates of 

6 health risks when alternative standards are just met (see Section 3.5.4 for additional detail on the 

7 composite monitor-based comparison of the three rollback strategies completed as part of the 

8 sensitivity analysis). 

9 We applied the peak shaving method only in those cases in which the daily standard in a 

10 location is controlling (i.e., the percent rollback necessary to meet the daily standard is greater 

11 than the percent rollback necessary to meet the annual standard in that location). Like the 

12 proportional and hybrid rollback methods, the peak shaving method for calculating annual 

13 averages at composite monitors starts with monitor-specific quarterly averages that have been 

14 calculated as described in Section 3.2.1. 

15 In contrast to the proportional and hybrid rollback approaches, the peak shaving method 

16 uses monitor-specific design values. For each monitor, we compared the monitor-specific daily 

17 design value to the daily standard and calculated the percent rollback necessary to get each 

18 monitor above the 24hr standard level into attainment (using a formula that is analogous to the 

19 proportional rollback formula given in Section 3.2.3.1). We then rolled back each quarterly 

20 average at the monitor by this percent rollback. We calculated the average quarterly average 

21 across all monitors in the location, for each quarter. Finally, we calculated the annual average at 

22 the composite monitor under the standard by averaging the four quarterly averages calculated on 

23 the previous step. See Section 3.2.2 for more detail. 

24 The results of the peak shaving analysis are presented, along with results based on the 

25 hybrid and proportional rollback approaches, as part of the sensitivity analysis results (see 

26 Appendix F, Tables F-49 and F-50). 
C-1

 

APPENDIX C: EPI STUDY SPECIFIC INFORMATION ON 

PM2.5
C-2

1 Appendix C. Epidemiology Study-Specific Information for PM2.5 Risk Assessment 

2 

3 This Appendix provides detailed summary information for the epidemiological studies 

4 used to obtain the concentration-response (C-R) functions used in the risk assessment. For 

5 additional details on selection of epidemiological studies and specification of the C-R functions, 

6 see section 3.3.3. 

7 
C-3

Table C-1. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

Mortality, all-cause All 

Mortality, 

cardiopulmonary 401-440, 460-519 

Mortality, ischemic 

heart disease 410-414 

Mortality, lung cancer 162 

Mortality, all-cause All 

Mortality, 

cardiopulmonary 401-440, 460-519 

Mortality, ischemic 

heart disease 410-414 

Mortality, lung cancer 162 

Mortality, all-cause All 

Mortality, ischemic 

heart disease 410-414 

Mortality, all-cause All 

Mortality, 

cardiopulmonary 401-440, 460-519 

Mortality, ischemic 

heart disease 410-414 

Mortality, lung cancer 162 

Mortality, all-cause All Six U.S. Cities 

Mortality, 

cardiopulmonary 400-440, 485-495 

Mortality, lung cancer 162 

Krewski et al. (2009) - exposure period 1979-1983Krewski et al. (2009) - exposure period 1999-2000Krewski et al. (2000) [reanalysis of Six Cities Study]

none

log-linear National

National

30+ none n/a annual mean30+ log-linear none annual mean30+ log-linear (random effects) none n/a annual mean

n/a

annual mean

25+ log-linear none n/a annual mean30+ log-log n/a

 Health Effects Associated with Long-Term Exposure to PM 2.5 :
C-4

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

Northeast 

Northwest 

Southeast 

Southwest 

Northeast 

Northwest 

Southeast 

Southwest 

Ito et al. (2007) ER visits, asthma 493 all ages log-linear none avg of 0- and 1-day 24-hr avg. New York 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

Los Angeles

 Health Effects Associated with Short-Term Exposure to PM 2.5 :

0-day2-day

24-hr avg.24-hr avg.

65+65+

log-linearlog-linear

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

426–427, 428, 

430–438; 410–414, 

429; 440–449

490–492; 464–466, 

480–487

nonenone

24-hr avg.

none

CO

none

CO

0 day1 day

390-429 all ages

HA (unscheduled), 

respiratory

Bell et al. (2008)

Mortality, 

cardiovascular

HA (unscheduled), 

cardiovascular
C-5

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

log-linear, GAM 

(stringent), 30 df 

log-linear, GLM, 30 

df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GLM, 30 

df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

0 day 1 day 2 day 3 day 4 day 5 day 

Mortality, nonaccidental <800 all ages

log-linear, GAM 

(stringent), 30 df

nonenone

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Los Angeles

24-hr avg.24-hr avg.

CO 1 day 24-hr avg.

1 day0 day

none 24-hr avg.
C-6

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

CO 1 day 24-hr avg.

24-hr avg.

none 1 day 24-hr avg.none 0 day 24-hr avg.

CO 0 day 24-hr avg.

none 0 day 24-hr avg.

Los AngelesLos Angeles

490-496

none 1 day

390-429 65+

all ages

Moolgavkar (2003) [reanalysis of Moolgavkar (2000b)]Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

HA, cardiovascular

Mortality, respiratory 

(COPD+)
C-7

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

0 day 1 day 2 day 3 day 

ER visits, 

cardiovascular

410–414, 427, 428, 

433–437, 440, 

443–445, 451–453

all ages log-linear none

avg of 0-,1-

day, and 2-

day

24-hr avg. Atlanta 

ER visits, respiratory

493, 786.07, 786.09; 

491, 492, and 496; 

460–465, 460.0, and 

477; 480–486; 466.1, 

466.11, and 466.19

all ages log-linear none

avg of 0-,1-

day, and 2-

day

24-hr avg. Atlanta 

HA, respiratory 

(COPD+)

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]Tolbert et al. (2007) 

490-496 all ages

log-linear, GAM 

(stringent), 100 df

Los Angeles

none 1 day 24-hr avg.none 0 day 24-hr avg.

NO2 24-hr avg.

none 2 day 24-hr avg.
C-8

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

Atlanta 

Baltimore 

Birmingham 

Dallas 

Detroit 

Fresno 

Houston 

Los Angeles 

New York 

Philadelphia 

Phoenix 

Pittsburgh 

Salt Lake City 

St. Louis 

Tacoma 

24-hr avg. avg of 0- and 1-day I01-I59 none all ages log-linear Mortality, cardiovascular Zanobetti and Schwartz (2009)
C-9

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

Atlanta 

Baltimore 

Birmingham 

Dallas 

Detroit 

Fresno 

Houston 

Los Angeles 

New York 

Philadelphia 

Phoenix 

Pittsburgh 

Salt Lake City 

St. Louis 

Tacoma 

Zanobetti and Schwartz (2009)

Mortality, nonaccidental A00-R99 24-hr avg. all ages avg of 0- and 1-day log-linear none
C-10

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

Atlanta 

Baltimore 

Birmingham 

Dallas 

Detroit 

Fresno 

Houston 

Los Angeles 

New York 

Philadelphia 

Phoenix 

Pittsburgh 

Salt Lake City 

St. Louis 

Tacoma 

24-hr avg. avg of 0- and 1-day J00-J99 all ages log-linear none Zanobetti and Schwartz (2009) Mortality, respiratory
C-11

Table C-2. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

Winter 0.0Spring 0.0Summer 0.0Fall 0.0Winter 0.0Spring -0.0Summer -0.0Fall -0.0Winter 0.0Spring 0.0Summer -0.0Fall 0.0Winter 0.0Spring 0.0Summer -0.0Fall 0.0Winter 0.0Spring 0.0Summer 0.0Fall 0.0Winter -0.0Spring 0.0Summer 0.0Fall -0.0Winter 0.0Spring 0.0Summer -0.0Fall 0.0Winter 0.0Spring 0.0Summer 0.0Fall 0.0

none New York April-August 0.0

O3 New York April-August 0.0

NO2 New York April-August 0.0

CO New York April-August 0.0

SO2 New York April-August 0.0

Bell et al. (2008)

Ito et al. (2007) ER visits, asthma

HA (unscheduled), 

respiratory

HA (unscheduled), 

cardiovascular

493

490–492; 464–466, 

480–487

426–427, 428, 

430–438; 

410–414, 429; 

440–449

none

65+65+

0-day2-day

avg of 0- 

and 1-day all ages

NortheastNorthwestSoutheastSouthwestNortheastNorthwestSoutheastSouthwest
C-12

Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

Atlanta Winter 0.0Atlanta Spring 0.0Atlanta Summer 0.0Atlanta Fall -0.0Baltimore Winter 0.0Baltimore Spring 0.0Baltimore Summer 0.0Baltimore Fall 0.0Birmingham Winter 0.0Birmingham Spring 0.0Birmingham Summer -0.0Birmingham Fall -0.0Dallas Winter 0.0Dallas Spring 0.0Dallas Summer 0.0Dallas Fall -0.0Detroit Winter -0.0Detroit Spring 0.0Detroit Summer 0.0Detroit Fall 0.0Fresno Winter -0.0Fresno Spring 0.0Fresno Summer 0.0Fresno Fall 0.0Houston Winter 0.0Houston Spring 0.0Houston Summer 0.0Houston Fall 0.0Los Angeles Winter -0.0Los Angeles Spring 0.0Los Angeles Summer -0.0Los Angeles Fall 0.0

all ages avg of 0- and 1-day Zanobetti and Schwartz (2009) Mortality, short-term cardiovascular I01-I59 none
C-13

Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

New York Winter 0.0New York Spring 0.0New York Summer 0.0New York Fall 0.0Philadelphia Winter 0.0Philadelphia Spring 0.0Philadelphia Summer 0.0Philadelphia Fall 0.0Phoenix Winter 

Phoenix Spring Phoenix Summer Phoenix Fall Pittsburgh Winter 0.0Pittsburgh Spring 0.0Pittsburgh Summer 0.0Pittsburgh Fall 0.0Salt Lake City Winter Salt Lake City Spring Salt Lake City Summer Salt Lake City Fall St. Louis Winter -0.0St. Louis Spring 0.0St. Louis Summer 0.0St. Louis Fall 0.0Tacoma Winter 0.0Tacoma Spring 0.0Tacoma Summer 0.0Tacoma Fall 0.0

Mortality, short-term 

cardiovascular none

Zanobetti and 

Schwartz (2009) I01-I59 all ages avg of 0- and 1-day
C-14

Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

Atlanta Winter 0.0Atlanta Spring 0.0Atlanta Summer 0.0Atlanta Fall 0.0Baltimore Winter 0.0Baltimore Spring 0.0Baltimore Summer 0.0Baltimore Fall 0.0Birmingham Winter 0.0Birmingham Spring 0.0Birmingham Summer 0.0Birmingham Fall 0.0Dallas Winter 0.0Dallas Spring 0.0Dallas Summer 0.0Dallas Fall 0.0Detroit Winter -0.0Detroit Spring 0.0Detroit Summer 0.0Detroit Fall 0.0Fresno Winter 0.0Fresno Spring 0.0Fresno Summer 0.0Fresno Fall 0.0Houston Winter 0.0Houston Spring 0.0Houston Summer 0.0Houston Fall 0.0Los Angeles Winter 0.0Los Angeles Spring 0.0Los Angeles Summer -0.0Los Angeles Fall -0.0

avg of 0- 

and 1-day

Zanobetti and 

Schwartz (2009)

Mortality, short-term nonaccidental A00-R99 all ages none
C-15

Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

New York Winter 0.0New York Spring 0.0New York Summer 0.0New York Fall 0.0Philadelphia Winter 0.0Philadelphia Spring 0.0Philadelphia Summer 0.0Philadelphia Fall 0.0Phoenix Winter Phoenix Spring Phoenix Summer Phoenix Fall Pittsburgh Winter 0.0Pittsburgh Spring 0.0Pittsburgh Summer 0.0Pittsburgh Fall 0.0Salt Lake City Winter 0.0Salt Lake City Spring 0.0Salt Lake City Summer 0.0Salt Lake City Fall 0.0St. Louis Winter 0.0St. Louis Spring 0.0St. Louis Summer 0.0St. Louis Fall 0.0Tacoma Winter 0.0Tacoma Spring 0.0Tacoma Summer 0.0Tacoma Fall 0.0

A00-R99 Zanobetti and Schwartz (2009) Mortality, short-term non- accidental all ages none avg of 0- and 1-day
C-16

Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

Atlanta Winter 0.0Atlanta Spring 0.0Atlanta Summer 0.0Atlanta Fall 0.0Baltimore Winter 0.0Baltimore Spring 0.0Baltimore Summer 0.0Baltimore Fall 0.0Birmingham Winter 0.0Birmingham Spring 0.0Birmingham Summer -0.0Birmingham Fall 0.0Dallas Winter 0.0Dallas Spring 0.0Dallas Summer 0.0Dallas Fall 0.0Detroit Winter 0.0Detroit Spring 0.0Detroit Summer 0.0Detroit Fall 0.0Fresno Winter -0.0Fresno Spring 0.0Fresno Summer 0.0Fresno Fall 0.0Houston Winter 0.0Houston Spring 0.0Houston Summer 0.0Houston Fall 0.0Los Angeles Winter 0.0Los Angeles Spring 0.0Los Angeles Summer -0.0Los Angeles Fall -0.0

all ages none avg of 0- and 1-day Zanobetti and Schwartz (2009) Mortality, short-term respiratory J00-J99
C-17

Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

New York Winter 0.0New York Spring 0.0New York Summer 0.0New York Fall 0.0Philadelphia Winter 0.0Philadelphia Spring 0.0Philadelphia Summer 0.0Philadelphia Fall 0.0Phoenix Winter 0.0Phoenix Spring 0.0Phoenix Summer 0.0Phoenix Fall 0.0Pittsburgh Winter 0.0Pittsburgh Spring 0.0Pittsburgh Summer 0.0Pittsburgh Fall 0.0Salt Lake City Winter 0.0Salt Lake City Spring 0.0Salt Lake City Summer -0.0Salt Lake City Fall -0.0St. Louis Winter 0.0St. Louis Spring 0.0St. Louis Summer 0.0St. Louis Fall 0.0Tacoma Winter 0.0Tacoma Spring 0.0Tacoma Summer 0.0Tacoma Fall 0.0

J00-J99 none

1 --- indicates that results were not available.

Zanobetti and 

Schwartz (2009)

Mortality, short-term 

respiratory

avg of 0- 

and 1-day all ages
D-1

APPENDIX D: SUPPLEMENT TO THE REPRESENTATIVENESS 

ANALYSIS OF THE 15 URBAN STUDY AREAS 
D-2

1 Appendix D. Supplement to the Representativeness Analysis of the 15 Urban Study Areas 

2 (additional graphical comparisons of distributions for key contributors to PM2.5 risk ) 

3 

4 Following the analysis discussed in Section 4.4, this appendix provides graphical 

5 comparisons of the empirical distributions of components of the risk function, and additional 

6 variables that have been identified as potentially influencing the risk associated with PM 

7 exposures. 

8 In each graph, the orange line represents the empirical cumulative distribution function 

9 (CDF) for the complete set of data available for the variable. In some cases, this may encompass 

10 all counties in the U.S., while in others it may be based on a subset of the U.S., usually for large 

11 urban areas. The green line in each graph represents the empirical cumulative distribution 

12 function for the variable based only on the data available for the set of urban case study 

13 locations. The black squares at the bottom of each graph represents the specific value of the 

14 variable for one of the case study locations, with the line showing where that value intersects the 

15 two empirical CDFs.
D-3

Comparison of Urban Case Study Area Population with U.S. Distribution of 

Population (all U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 1000 10000 100000 1000000 10000000

Population

% of Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all above the 65th Percentile 

of county populations

D.1 Elements of the Risk Equation 

Figure D-1. Comparison of Distributions for Key Elements of the Risk Equation: 

 Total Population 
D-4

Comparison of Urban Case Study Area % Under 15 to U.S. Distribution of % Under 

15

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

10 15 20 25 30 35

% of Population Under Age 15, 2005

% of U.S. Counties

All Counties CDF Case Study Counties Case Study Counties

Figure D-2. Comparison of Distributions for Key Elements of the Risk Equation: 

 Percent of Population Under 15 Years of Age 
D-5

Comparison of Urban Case Study Area % 65 and Older to U.S. Distribution of % 65 

and Older

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25 30 35

% of Population 65 and Older, 2005

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 75th percentile 

of county % of population 65 

and older

Figure D-3. Comparison of Distributions for Key Elements of the Risk Equation: 

 Percent of Population 65 Years of Age and Older 
D-6

Comparison of Urban Case Study Area % 85 and Older to U.S. Distribution of % 85 

and Older

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

12345678

% of Population 85 and Older, 2005

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 85th percentile 

of county % of population 85 

and older

Figure D-4. Comparison of Distributions for Key Elements of the Risk Equation: 

 Percent of Population 85 Years of Age and Older 
D-7

Comparison of Urban Case Study Area Annual Mean PM2.5 with U.S. 

Distribution of Annual Mean PM2.5 (617 U.S. Counties with PM2.5 Monitors)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25

Annual Mean PM2.5

% of Monitored Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-5. Comparison of Distributions for Key Elements of the Risk Equation: Annual 

 Mean PM2.5
D-8

Comparison of Urban Case Study Area 98th %ile PM2.5 with U.S. Distribution of 

98th %ile PM2.5 

(617 U.S. Counties with PM2.5 Monitors)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90

98th Percentile Daily PM2.5

% of Monitored Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-6. Comparison of Distributions for Key Elements of the Risk Equation: 98th 

 %ile Daily Average PM2.5
D-9

Comparison of Urban Case Study Area % of Days with PM2.5>35 ug/m3 to U.S. 

Distribution of % of Days with PM2.5>35 ug/m3

(204 U.S. Counties in MCAPS Study)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25

% of Days Above 35 ug/m3

% of MCAPS Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-7. Comparison of Distributions for Key Elements of the Risk Equation: % of 

 Days with PM2.5 > 35 μg/m3
D-10

Comparison of Urban Case Study All Cause Mortality Rate to U.S. Distribution of All 

Cause Mortality Rate

(3143 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 300 500 700 900 1100 1300 1500 1700 1900 2100

All Cause Mortality per 100,000 Population

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 90th percentile 

of county all cause mortality

Figure D-8. Comparison of Distributions for Key Elements of the Risk Equation: All 

 Cause Mortality Rate 
D-11

Comparison of Urban Case Study Non-accidental Mortality Rate to U.S. Distribution of 

Non-accidental Mortality Rate

(3143 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

200 400 600 800 1000 1200 1400 1600 1800 2000

Non-accidental Mortality per 100,000 Population

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 90th percentile 

of county non-accidental 

mortality

Figure D-9. Comparison of Distributions for Key Elements of the Risk Equation: Non- 

 Accidental Mortality Rate 
D-12

Comparison of Urban Case Study Cardiovascular Mortality Rate to U.S. Distribution of 

Cardiovascular Mortality Rate

(3143 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

50 150 250 350 450 550 650 750 850 950

Cardiovascular Mortality per 100,000 Population

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 90th percentile 

of county cardiovascular 

mortality

Figure D-10. Comparison of Distributions for Key Elements of the Risk Equation: 

 Cardiovascular Mortality Rate 
D-13

Comparison of Urban Case Study Respiratory Mortality Rate to U.S. Distribution of 

Respiratory Mortality Rate

(3143 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

10 60 110 160 210 260

Respiratory Mortality per 100,000 Population

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 90th percentile 

of county respiratory 

mortality

Figure D-11. Comparison of Distributions for Key Elements of the Risk Equation: 

 Respiratory Mortality Rate 
D-14

Comparison of Urban Case Study PM All-cause Mortality Risk (β) to 

U.S. Distribution of PM All-cause Mortality Risk

(212 U.S. Urban Areas)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014 0.0016

Bayesian Shrunken PM All Cause Mortality Risk Coefficient (β)

% of Z&S Urban Areas

All Z&S Urban Areas CDF Case Study Urban Area CDF Case Study Urban Areas

Figure D-12. Comparison of Distributions for Key Elements of the Risk Equation: All 

 Cause Mortality Risk Effect Estimate from Zanobetti and Schwartz (2008) 
D-15

Comparison of Urban Case Study PM Cardiovascular Mortality Risk (β) to U.S. 

Distribution of PM Cardiovascular Mortality Risk

(212 U.S. Urban Areas)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014 0.0016 0.0018 0.002

Bayesian Shrunken PM Cardiovascular Mortality Risk Coefficient (β)

% of Z&S Urban Areas

All Z&S Urban Areas CDF Case Study Urban Area CDF Case Study Urban Areas

Figure D-13. Comparison of Distributions for Key Elements of the Risk Equation: 

Cardiovascular Mortality Risk Effect Estimate from Zanobetti and Schwartz 

(2008) 
D-16

Comparison of Urban Case Study PM Respiratory Mortality Risk (β) to U.S. 

Distribution of PM Respiratory Mortality Risk

(212 U.S. Urban Areas)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.0005 0.001 0.0015 0.002

Bayesian Shrunken PM Respiratory Mortality Risk Coefficient (β)

% of Z&S Urban Areas

All Z&S Urban Areas CDF Case Study Urban Area CDF Case Study Urban Areas

Figure D-14. Comparison of Distributions for Key Elements of the Risk Equation: 

Respiratory Mortality Risk Effect Estimate from Zanobetti and Schwartz 

(2008) 
D-17

Comparison of Urban Case Study Area Population Density with U.S. Distribution of 

Population Density (all U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 10 100 1000 10000 100000

Population Density (persons per sq mile)

% of Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all above the 65th Percentile 

of county population density

D.2. Variables Expected to Influence the Relative Risk from PM2.5

 D.2.1. Demographic Variables 

Figure D-15. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Population Density 
D-18

Comparison of Urban Case Study Area Unemployment Rate to U.S. Distribution of 

Unemployment Rates

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 2 4 6 8 10 12 14 16 18 20

Unemployment Rate (%)

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-16. Comparison of Distributions for Selected Variables Expected to 

 Influence the Relative Risk from PM2.5: Unemployment Rate 
D-19

Comparison of Urban Case Study % Less than High School Education to U.S. 

Distribution of % Less than High School Education

(3146 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

3 13 23 33 43 53 63

% of Population with Less than a High School Education, 2000

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-17. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: % with Less than a High School Education 

 
D-20

Comparison of Urban Case Study Area Per Capita Personal Income to U.S. 

Distribution of Per Capita Personal Income

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

$10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000 $90,000

Per Capita Personal Income, 2005

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all above the 25th percentile 

of county per capita income

Figure D-18. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Per Capita Personal Income 
D-21

Comparison of Urban Case Study Air Conditioning Prevalence to U.S. Distribution of 

Air Conditioning Prevalence

(70 U.S. Urban Areas)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

% Air Conditioning Prevalence

% of Urban Areas

All Urban Areas CDF Case Study Urban Areas CDF Case Study Urban Areas

Figure D-19. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Air Conditioning Prevalence 
D-22

Comparison of Urban Case Study Air Conditioning Prevalence to U.S. Distribution of 

Air Conditioning Prevalence

(70 U.S. Urban Areas)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

% Air Conditioning Prevalence

% of Urban Areas

All Urban Areas CDF Case Study Urban Areas CDF Case Study Urban Areas

Figure D-20. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: % Non-White Population 
D-23

Comparison of Urban Case Study Area Angina/CHD Prevalence to U.S. Distribution 

of Angina/CHD Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

123456789

% Prevalence of Angina or CHD, BRFSS, 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Urban case study areas are 

all below the 85th percentile 

of MSA angina or CHD 

prevalence

D.2.2. Health Conditions 

Figure D-21. Comparison of Distributions for Selected Variables Expected to 

 Influence the Relative Risk from PM2.5: Angina/Coronary Heart 

 Disease Prevalence 
D-24

Comparison of Urban Case Study Area Current Asthma Prevalence to U.S. 

Distribution of Current Asthma Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

3 5 7 9 11 13

% Current Prevalence of Asthma, BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Figure D-22. Comparison of Distributions for Selected Variables Expected to 

 Influence the Relative Risk from PM2.5: Asthma Prevalence 
D-25

Comparison of Urban Case Study Area Diabetes Prevalence to U.S. Distribution of 

Diabetes Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2 4 6 8 10 12 14 16

% Prevalence of Diabetes, BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Urban case study areas are 

all below the 85th percentile 

of MSA diabetes prevalence

Figure D-23. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Diabetes Prevalence 
D-26

Comparison of Urban Case Study Area Heart Attack Prevalence to U.S. Distribution of 

Heart Attack Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2 3 4 5 6 7 8 9 10 11

% Prevalence of Heart Attacks, BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Urban case study areas are 

all below the 80th percentile 

of MSA heart attack 

prevalence

Figure D-24. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Heart Attack Prevalence 
D-27

Comparison of Urban Case Study Area Obesity Prevalence to U.S. Distribution of 

Obesity Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

14 19 24 29 34

% Prevalence of Obesity, BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Figure D-25. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Obesity Prevalence 
D-28

Comparison of Urban Case Study Area Stroke Prevalence to U.S. Distribution of 

Stroke Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0123456

% Prevalence of Stroke, BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Figure D-26. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Stroke Prevalence 
D-29

Comparison of Urban Case Study Area Smoking Prevalence to U.S. Distribution of 

Smoking Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

6 11 16 21 26 31

% Prevalence of Smoking (ever), BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Urban case study areas are 

all below the 85th percentile 

of MSA smoking prevalence

Figure D-27. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Smoking Prevalence 
D-30

Comparison of Urban Case Study Area Exercise Prevalence to U.S. Distribution of 

Exercise Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

15 20 25 30 35 40 45

% Prevalence of Exercise (Adults with 20+ minutes of vigorous physical activity 3 or 

more days per week), BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Figure D-28. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Exercise Prevalence 
D-31

Comparison of Urban Case Study Area 4th Highest Daily 8-Hour Ozone with U.S. 

Distribution of 4th Highest Daily 8-Hour Ozone 

(725 U.S. Counties with Ozone Monitors)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.02 0.04 0.06 0.08 0.1 0.12

4th Highest Daily 8-Hour Maximum

% of Monitored Counties

All Counties CDF Case Study Counties CDF Case Study Counties

D.2.3. Air Quality and Climate Variables 

Figure D-29. Comparison of Distributions for Selected Variables Expected to 

 Influence the Relative Risk from PM2.5: 4th Highest Daily Max 8- 

 hour Average 
D-32

Comparison of Urban Case Study Mobile Source Carbon to U.S. Distribution of Mobile 

Source Carbon

(3142 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Mobile Source Proportion of Total Organic and Elemental Carbon Emissions

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 80th percentile 

of county mobile source 

proportion of EC/OC 

emissions

Figure D-30. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: % Mobile Source Direct PM2.5 Emissions 

 
D-33

Comparison of Urban Case Study Area Long Term Average July Temperature to 

U.S. Distribution of Long Term Average July Temperature

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

50 60 70 80 90 100

July 30 Year Average Temperature, 1941-1970

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-31. Comparison of Distributions for Selected Variables Expected to 

 Influence the Relative Risk from PM2.5: July Temperature Long 

 Term Average 
D-34

Comparison of Urban Case Study Area Long Term Average July Relative Humidity to 

U.S. Distribution of Long Term Average July Relative Humidity

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90

July 30 Year Average Relative Humidity, 1941-1970

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-32. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: July Relative Humidity Long Term Average 

 
E-1

APPENDIX E: RISK ESTIMATES (CORE ANALYSIS) 
E-1

1 Appendix E. Risk Estimates (core analysis) 

2 

3 This Appendix provides detailed risk estimates generated for the core analysis for the 15 

4 urban study areas. The tables cover all of the air quality scenarios modeled, including recent 

5 conditions, the current standard, and alternative standard levels. For additional detail on the types 

6 of risk metrics (and figures summarizing key metrics) presented in this Appendix, see section 

7 4.0. 

8 
E-2

Table E-1. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5 Co

 Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjustin

 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 1

649 575 513 451 389 4

(421 - 873) (373 - 774) (333 - 692) (292 - 608) (252 - 525) (292 

597 548 502 442 382 4

(387 - 803) (355 - 738) (325 - 676) (286 - 596) (247 - 516) (276 

424 297 262 227 192 

(275 - 571) (192 - 400) (170 - 354) (147 - 307) (124 - 260) (147 

379 379 379 379 336 

(245 - 511) (245 - 511) (245 - 511) (245 - 511) (218 - 454) (245 

798 580 573 502 431 4

(518 - 1073) (376 - 782) (371 - 772) (325 - 677) (279 - 581) (286 

254 89 89 89 89 

(165 - 342) (57 - 120) (57 - 120) (57 - 120) (57 - 120) (38 

609 557 491 426 360 4

(394 - 820) (360 - 751) (318 - 663) (275 - 575) (233 - 486) (275 

2333 1045 1045 1045 919 

(1514 - 3141) (676 - 1413) (676 - 1413) (676 - 1413) (593 - 1242) (464 

2000 1477 1477 1410 1205 1

(1297 - 2693) (956 - 1992) (956 - 1992) (912 - 1902) (779 - 1627) (711 

521 455 455 406 348 

(338 - 703) (295 - 614) (295 - 614) (263 - 548) (225 - 470) (223 

483 483 483 483 433 4

(312 - 652) (312 - 652) (312 - 652) (312 - 652) (280 - 586) (271 

593 387 387 363 318 

(385 - 798) (251 - 523) (251 - 523) (235 - 490) (206 - 430) (187 

102 29 29 29 29 

(66 - 138) (19 - 39) (19 - 39) (19 - 39) (19 - 39) (7 

826 700 634 557 480 

(536 - 1111) (454 - 943) (411 - 855) (361 - 752) (310 - 648) (351 

123 80 80 80 80 

(80 - 166) (52 - 109) (52 - 109) (52 - 109) (52 - 109) (34 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Stan

n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009). Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-3

Table E-2. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5 Con

 Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjustin

 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

668 592 528 464 400 464 

(434 - 899) (384 - 797) (342 - 712) (301 - 626) (259 - 540) (301 - 6

483 441 400 347 295 333 

(313 - 651) (285 - 594) (259 - 539) (225 - 469) (191 - 398) (216 - 4

399 276 243 209 176 209 

(259 - 536) (179 - 373) (157 - 328) (135 - 283) (114 - 238) (135 - 2

284 284 284 284 247 284 

(183 - 383) (183 - 383) (183 - 383) (183 - 383) (160 - 334) (183 - 3

576 398 392 334 276 285 

(373 - 776) (257 - 537) (253 - 529) (216 - 451) (178 - 373) (184 - 3

265 94 94 94 94 63 

(172 - 356) (61 - 127) (61 - 127) (61 - 127) (61 - 127) (41 - 

589 537 472 407 342 407 

(381 - 794) (348 - 725) (306 - 638) (263 - 550) (221 - 462) (263 - 5

2054 863 863 863 745 561 

(1332 - 2767) (557 - 1166) (557 - 1166) (557 - 1166) (481 - 1008) (362 - 

1548 1096 1096 1038 861 771 

(1002 - 2087) (708 - 1481) (708 - 1481) (671 - 1403) (556 - 1164) (498 - 1

471 409 409 363 308 305 

(305 - 636) (265 - 552) (265 - 552) (235 - 490) (199 - 417) (197 - 4

512 512 512 512 460 446 

(331 - 691) (331 - 691) (331 - 691) (331 - 691) (297 - 622) (288 - 6

468 290 290 270 231 205 

(303 - 631) (187 - 392) (187 - 392) (174 - 364) (149 - 312) (133 - 2

84 16 16 16 16 0 

(54 - 113) (10 - 21) (10 - 21) (10 - 21) (10 - 21) (0 - 

618 514 458 394 329 382 

(401 - 834) (332 - 693) (296 - 619) (255 - 532) (213 - 445) (247 - 5

83 47 47 47 47 25 

(54 - 112) (30 - 64) (30 - 64) (30 - 64) (30 - 64) (16 - 

 Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in a 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-4

Table E-3. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5 Con

 Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 

 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

642 567 505 442 379 4

(416 - 864) (368 - 764) (327 - 680) (286 - 596) (245 - 512) (286 

482 440 399 347 294 3

(313 - 650) (285 - 593) (258 - 538) (224 - 468) (190 - 398) (215 

418 291 257 222 187 2

(271 - 563) (189 - 393) (166 - 347) (144 - 300) (121 - 253) (144 

317 317 317 317 278 3

(205 - 428) (205 - 428) (205 - 428) (205 - 428) (180 - 375) (205 

607 424 418 358 299 3

(393 - 818) (274 - 572) (270 - 564) (232 - 484) (193 - 404) (199 

279 101 101 101 101 

(181 - 375) (65 - 137) (65 - 137) (65 - 137) (65 - 137) (44 

615 561 494 427 359 4

(398 - 829) (363 - 757) (320 - 667) (276 - 577) (232 - 486) (276 

2134 911 911 911 791 6

(1384 - 2874) (588 - 1232) (588 - 1232) (588 - 1232) (511 - 1070) (388 

1812 1316 1316 1253 1058 9

(1174 - 2443) (852 - 1777) (852 - 1777) (810 - 1692) (684 - 1430) (620 

466 405 405 359 304 3

(302 - 629) (262 - 546) (262 - 546) (232 - 484) (197 - 411) (195 

433 433 433 433 385 3

(280 - 586) (280 - 586) (280 - 586) (280 - 586) (248 - 520) (240 

527 339 339 316 274 2

(342 - 710) (219 - 457) (219 - 457) (205 - 427) (177 - 371) (160 

120 38 38 38 38 

(78 - 162) (24 - 51) (24 - 51) (24 - 51) (24 - 51) (11 

679 568 509 441 373 4

(440 - 915) (368 - 766) (330 - 687) (285 - 596) (241 - 504) (277 

87 50 50 50 50 

(56 - 118) (32 - 68) (32 - 68) (32 - 68) (32 - 68) (17 

 Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Stan

n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-5

Table E-4. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to A Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

4.3% 3.8% 3.4% 3% 2.6% 3

(2.8% - 5.8%) (2.5% - 5.1%) (2.2% - 4.6%) (1.9% - 4%) (1.7% - 3.5%) (1.9% 

4.2% 3.9% 3.6% 3.1% 2.7% 3

(2.7% - 5.7%) (2.5% - 5.2%) (2.3% - 4.8%) (2% - 4.2%) (1.8% - 3.7%) (2% 

4.3% 3% 2.7% 2.3% 2% 2

(2.8% - 5.8%) (2% - 4.1%) (1.7% - 3.6%) (1.5% - 3.1%) (1.3% - 2.6%) (1.5% 

3% 3% 3% 3% 2.6% 3

(1.9% - 4%) (1.9% - 4%) (1.9% - 4%) (1.9% - 4%) (1.7% - 3.5%) (1.9% 

4.5% 3.3% 3.2% 2.8% 2.4% 2

(2.9% - 6%) (2.1% - 4.4%) (2.1% - 4.3%) (1.8% - 3.8%) (1.6% - 3.3%) (1.6% 

4.6% 1.6% 1.6% 1.6% 1.6% 1

(3% - 6.1%) (1% - 2.2%) (1% - 2.2%) (1% - 2.2%) (1% - 2.2%) (0.7% 

3.3% 3% 2.6% 2.3% 1.9% 2

(2.1% - 4.4%) (1.9% - 4%) (1.7% - 3.6%) (1.5% - 3.1%) (1.2% - 2.6%) (1.5% 

4.1% 1.8% 1.8% 1.8% 1.6% 1

(2.7% - 5.5%) (1.2% - 2.5%) (1.2% - 2.5%) (1.2% - 2.5%) (1% - 2.2%) (0.8% 

3.8% 2.8% 2.8% 2.7% 2.3% 2

(2.5% - 5.1%) (1.8% - 3.8%) (1.8% - 3.8%) (1.7% - 3.6%) (1.5% - 3.1%) (1.3% 

3.6% 3.1% 3.1% 2.8% 2.4% 2

(2.3% - 4.8%) (2% - 4.2%) (2% - 4.2%) (1.8% - 3.8%) (1.5% - 3.2%) (1.5% 

2.1% 2.1% 2.1% 2.1% 1.9% 1

(1.4% - 2.8%) (1.4% - 2.8%) (1.4% - 2.8%) (1.4% - 2.8%) (1.2% - 2.5%) (1.2% 

4.3% 2.8% 2.8% 2.6% 2.3% 2

(2.8% - 5.7%) (1.8% - 3.8%) (1.8% - 3.8%) (1.7% - 3.5%) (1.5% - 3.1%) (1.3% 

2.2% 0.6% 0.6% 0.6% 0.6% 0

(1.4% - 2.9%) (0.4% - 0.8%) (0.4% - 0.8%) (0.4% - 0.8%) (0.4% - 0.8%) (0.1% 

4.4% 3.7% 3.4% 3% 2.5% 2

(2.8% - 5.9%) (2.4% - 5%) (2.2% - 4.5%) (1.9% - 4%) (1.6% - 3.4%) (1.9% 

2.4% 1.6% 1.6% 1.6% 1.6% 1

(1.6% - 3.3%) (1% - 2.1%) (1% - 2.1%) (1% - 2.1%) (1% - 2.1%) (0.7% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Conc

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standa

Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-6

Table E-5. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Am Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Sta

 Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

4.3% 3.8% 3.4% 3% 2.6% 3% 

(2.8% - 5.8%) (2.5% - 5.1%) (2.2% - 4.6%) (1.9% - 4%) (1.7% - 3.5%) (1.9% - 

3.4% 3.1% 2.8% 2.5% 2.1% 2.4% 

(2.2% - 4.6%) (2% - 4.2%) (1.8% - 3.8%) (1.6% - 3.3%) (1.3% - 2.8%) (1.5% - 

4% 2.8% 2.4% 2.1% 1.8% 2.1% 

(2.6% - 5.4%) (1.8% - 3.8%) (1.6% - 3.3%) (1.4% - 2.8%) (1.1% - 2.4%) (1.4% - 

2.2% 2.2% 2.2% 2.2% 1.9% 2.2% 

(1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.2% - 2.5%) (1.4% - 

3.2% 2.2% 2.2% 1.9% 1.5% 1.6% 

(2.1% - 4.4%) (1.4% - 3%) (1.4% - 3%) (1.2% - 2.5%) (1% - 2.1%) (1% - 2

4.7% 1.7% 1.7% 1.7% 1.7% 1.1% 

(3% - 6.3%) (1.1% - 2.2%) (1.1% - 2.2%) (1.1% - 2.2%) (1.1% - 2.2%) (0.7% - 

3.1% 2.8% 2.5% 2.1% 1.8% 2.1% 

(2% - 4.1%) (1.8% - 3.8%) (1.6% - 3.3%) (1.4% - 2.9%) (1.1% - 2.4%) (1.4% - 

3.6% 1.5% 1.5% 1.5% 1.3% 1% 

(2.3% - 4.8%) (1% - 2%) (1% - 2%) (1% - 2%) (0.8% - 1.8%) (0.6% - 

2.9% 2.1% 2.1% 2% 1.6% 1.4% 

(1.9% - 3.9%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.6%) (1% - 2.2%) (0.9% - 

3.2% 2.8% 2.8% 2.5% 2.1% 2.1% 

(2.1% - 4.4%) (1.8% - 3.8%) (1.8% - 3.8%) (1.6% - 3.4%) (1.4% - 2.9%) (1.4% - 

2.1% 2.1% 2.1% 2.1% 1.9% 1.9% 

(1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.2% - 2.6%) (1.2% - 

3.4% 2.1% 2.1% 1.9% 1.7% 1.5% 

(2.2% - 4.6%) (1.4% - 2.8%) (1.4% - 2.8%) (1.3% - 2.6%) (1.1% - 2.3%) (1% - 

1.7% 0.3% 0.3% 0.3% 0.3% 0% 

(1.1% - 2.3%) (0.2% - 0.4%) (0.2% - 0.4%) (0.2% - 0.4%) (0.2% - 0.4%) (0% - 

#DIV/0! 2.7% 2.4% 2.1% 1.7% 2% #DIV/0! (1.8% - 3.7%) (1.6% - 3.3%) (1.3% - 2.8%) (1.1% - 2.4%) (1.3% - 

3.3% 0.9% 0.9% 0.9% 0.9% 0.5% 

(2.1% - 4.4%) (0.6% - 1.2%) (0.6% - 1.2%) (0.6% - 1.2%) (0.6% - 1.2%) (0.3% - 

Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-7

Table E-6. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Am Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Sta

 Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

4% 3.6% 3.2% 2.8% 2.4% 2.8

(2.6% - 5.4%) (2.3% - 4.8%) (2% - 4.3%) (1.8% - 3.7%) (1.5% - 3.2%) (1.8% - 

3.4% 3.1% 2.8% 2.5% 2.1% 2.4

(2.2% - 4.6%) (2% - 4.2%) (1.8% - 3.8%) (1.6% - 3.3%) (1.3% - 2.8%) (1.5% - 

4.2% 2.9% 2.6% 2.2% 1.9% 2.2

(2.7% - 5.6%) (1.9% - 3.9%) (1.7% - 3.5%) (1.4% - 3%) (1.2% - 2.5%) (1.4% - 

2.4% 2.4% 2.4% 2.4% 2.1% 2.4

(1.5% - 3.2%) (1.5% - 3.2%) (1.5% - 3.2%) (1.5% - 3.2%) (1.3% - 2.8%) (1.5% - 

3.4% 2.4% 2.4% 2% 1.7% 1.7

(2.2% - 4.6%) (1.5% - 3.2%) (1.5% - 3.2%) (1.3% - 2.7%) (1.1% - 2.3%) (1.1% - 

4.9% 1.8% 1.8% 1.8% 1.8% 1.2

(3.2% - 6.5%) (1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (0.8% - 

3.1% 2.9% 2.5% 2.2% 1.8% 2.2

(2% - 4.2%) (1.8% - 3.9%) (1.6% - 3.4%) (1.4% - 2.9%) (1.2% - 2.5%) (1.4% - 

3.7% 1.6% 1.6% 1.6% 1.4% 1% 

(2.4% - 5%) (1% - 2.1%) (1% - 2.1%) (1% - 2.1%) (0.9% - 1.9%) (0.7% - 

3.4% 2.5% 2.5% 2.3% 2% 1.8

(2.2% - 4.6%) (1.6% - 3.3%) (1.6% - 3.3%) (1.5% - 3.2%) (1.3% - 2.7%) (1.2% - 

3.2% 2.8% 2.8% 2.5% 2.1% 2.1

(2.1% - 4.3%) (1.8% - 3.8%) (1.8% - 3.8%) (1.6% - 3.3%) (1.4% - 2.8%) (1.3% - 

1.8% 1.8% 1.8% 1.8% 1.6% 1.5

(1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (1% - 2.1%) (1% - 

3.8% 2.5% 2.5% 2.3% 2% 1.8

(2.5% - 5.2%) (1.6% - 3.3%) (1.6% - 3.3%) (1.5% - 3.1%) (1.3% - 2.7%) (1.2% - 

2.4% 0.7% 0.7% 0.7% 0.7% 0.3

(1.5% - 3.2%) (0.5% - 1%) (0.5% - 1%) (0.5% - 1%) (0.5% - 1%) (0.2% - 

3.6% 3% 2.7% 2.3% 2% 2.3

(2.3% - 4.8%) (1.9% - 4%) (1.7% - 3.6%) (1.5% - 3.1%) (1.3% - 2.7%) (1.5% - 

1.7% 1% 1% 1% 1% 0.5

(1.1% - 2.2%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.3% - 

Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Conce

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-8

Table E-7. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated  Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on K

 (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-13% 0% 11% 22% 32% 22% 

(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 2

-9% 0% 8% 19% 30% 22% 

(-9% - -9%) (0% - 0%) (8% - 9%) (19% - 19%) (30% - 30%) (22% - 2

-43% 0% 12% 23% 35% 23% 

(-43% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 35%) (23% - 2

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0

-38% 0% 1% 13% 26% 24% 

(-37% - -38%) (0% - 0%) (1% - 1%) (13% - 14%) (26% - 26%) (24% - 2

-187% 0% 0% 0% 0% 34% 

(-185% - -188%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (34% - 3

-9% 0% 12% 24% 35% 24% 

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 35%) (23% - 2

-123% 0% 0% 0% 12% 31% 

(-122% - -124%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 3

-35% 0% 0% 5% 18% 26% 

(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (18% - 18%) (25% - 2

-14% 0% 0% 11% 24% 24% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 2

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 1

-53% 0% 0% 6% 18% 25% 

(-53% - -54%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (25% - 2

-255% 0% 0% 0% 0% 64% 

(-254% - -256%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 6

-18% 0% 9% 20% 31% 23% 

(-18% - -18%) (0% - 0%) (9% - 9%) (20% - 21%) (31% - 32%) (22% - 2

-53% 0% 0% 0% 0% 34% 

(-53% - -54%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (34% - 3

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009). 

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with 

PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alterna

(m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-9

Table E-8. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated  Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on 

 (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 22% 32% 22% 

(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 2

-10% 0% 9% 21% 33% 24% 

(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 2

-44% 0% 12% 24% 36% 24% 

(-44% - -45%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 2

0% 0% 0% 0% 13% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 0

-45% 0% 1% 16% 31% 28% 

(-45% - -45%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 31%) (28% - 2

-182% 0% 0% 0% 0% 33% 

(-180% - -184%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 3

-10% 0% 12% 24% 36% 24% 

(-10% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 2

-138% 0% 0% 0% 14% 35% 

(-137% - -139%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 14%) (35% - 3

-41% 0% 0% 5% 21% 30% 

(-41% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (30% - 3

-15% 0% 0% 11% 25% 25% 

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (25% - 25%) (25% - 2

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 1

-61% 0% 0% 7% 20% 29% 

(-61% - -62%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 2

-438% 0% 0% 0% 0% 100% 

(-437% - -440%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 1

-20% 0% 11% 23% 36% 26% 

(-20% - -21%) (0% - 0%) (11% - 11%) (23% - 23%) (36% - 36%) (26% - 2

-76% 0% 0% 0% 0% 48% 

(-76% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (48% - 4

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009). 

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with 

PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alterna

(m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-10

Table E-9. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated  Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on 

 (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 22% 33% 22% 

(-13% - -13%) (0% - 0%) (11% - 11%) (22% - 22%) (33% - 33%) (22% - 2

-10% 0% 9% 21% 33% 24% 

(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 2

-44% 0% 12% 24% 36% 24% 

(-43% - -44%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 2

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0

-43% 0% 1% 15% 30% 27% 

(-43% - -44%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 30%) (27% - 2

-176% 0% 0% 0% 0% 32% 

(-175% - -178%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 3

-10% 0% 12% 24% 36% 24% 

(-9% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 2

-134% 0% 0% 0% 13% 34% 

(-133% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 3

-38% 0% 0% 5% 20% 27% 

(-37% - -38%) (0% - 0%) (0% - 0%) (5% - 5%) (20% - 20%) (27% - 2

-15% 0% 0% 11% 25% 26% 

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (25% - 25%) (26% - 2

0% 0% 0% 0% 11% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 1

-56% 0% 0% 7% 19% 27% 

(-55% - -56%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 19%) (27% - 2

-218% 0% 0% 0% 0% 55% 

(-217% - -219%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 5

-20% 0% 10% 22% 34% 25% 

(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 22%) (34% - 34%) (25% - 2

-74% 0% 0% 0% 0% 46% 

(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 4

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).  Dallas, TX Fresno, CA Los Angeles, CA3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with 

PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alterna

(m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-11

Table E-10. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5 Co

 Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjustin

 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

830 736 657 578 499 57

(531 - 1123) (470 - 997) (420 - 891) (369 - 785) (318 - 677) (369 - 

763 702 643 566 490 54

(488 - 1033) (448 - 950) (410 - 871) (361 - 768) (312 - 665) (348 - 

543 380 336 292 247 29

(347 - 734) (243 - 516) (214 - 457) (186 - 397) (157 - 336) (186 - 

486 486 486 486 431 48

(310 - 659) (310 - 659) (310 - 659) (310 - 659) (275 - 586) (310 - 

1021 743 734 643 552 56

(653 - 1380) (474 - 1008) (468 - 996) (410 - 874) (352 - 751) (361 - 

325 114 114 114 114 75 

(208 - 439) (72 - 155) (72 - 155) (72 - 155) (72 - 155) (48 - 

780 713 630 546 462 54

(498 - 1058) (455 - 968) (401 - 856) (348 - 743) (294 - 629) (348 - 

2986 1342 1342 1342 1180 92

(1910 - 4042) (854 - 1827) (854 - 1827) (854 - 1827) (750 - 1607) (587 - 

2560 1893 1893 1808 1546 141

(1636 - 3468) (1207 - 2571) (1207 - 2571) (1152 - 2455) (984 - 2101) (898 - 

668 584 584 521 447 44

(427 - 905) (372 - 792) (372 - 792) (332 - 707) (285 - 607) (282 - 

620 620 620 620 557 53

(394 - 843) (394 - 843) (394 - 843) (394 - 843) (354 - 757) (343 - 

759 497 497 466 409 37

(485 - 1026) (317 - 674) (317 - 674) (297 - 633) (260 - 555) (236 - 

132 37 37 37 37 13 

(84 - 179) (24 - 51) (24 - 51) (24 - 51) (24 - 51) (8 - 

1056 897 813 714 616 69

(676 - 1429) (573 - 1215) (519 - 1102) (456 - 970) (392 - 836) (443 - 

158 103 103 103 103 69 

(101 - 215) (66 - 141) (66 - 141) (66 - 141) (66 - 141) (44 - 

 Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in a 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-12

Table E-11. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5 Co

 Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjustin

 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

854 758 677 595 513 59

(547 - 1156) (484 - 1026) (432 - 918) (380 - 808) (327 - 697) (380 - 

619 565 513 446 378 42

(395 - 839) (360 - 766) (327 - 696) (284 - 606) (241 - 515) (272 - 

510 354 312 269 226 26

(326 - 691) (226 - 481) (198 - 423) (171 - 365) (144 - 307) (171 - 

364 364 364 364 317 36

(232 - 495) (232 - 495) (232 - 495) (232 - 495) (202 - 432) (232 - 

737 510 503 429 355 36

(471 - 1000) (325 - 694) (320 - 684) (273 - 584) (225 - 483) (233 - 

338 121 121 121 121 81 

(217 - 457) (77 - 164) (77 - 164) (77 - 164) (77 - 164) (51 - 

755 689 606 523 439 52

(481 - 1024) (439 - 935) (386 - 823) (333 - 711) (279 - 598) (333 - 

2631 1108 1108 1108 958 72

(1680 - 3565) (704 - 1509) (704 - 1509) (704 - 1509) (608 - 1305) (457 - 

1984 1407 1407 1333 1106 99

(1265 - 2693) (895 - 1913) (895 - 1913) (848 - 1813) (703 - 1506) (629 - 

604 525 525 466 396 39

(386 - 819) (335 - 713) (335 - 713) (297 - 633) (252 - 538) (249 - 

657 657 657 657 591 57

(418 - 893) (418 - 893) (418 - 893) (418 - 893) (376 - 803) (364 - 

599 372 372 346 297 26

(383 - 813) (237 - 506) (237 - 506) (220 - 471) (189 - 404) (168 - 

107 20 20 20 20 0 

(68 - 146) (13 - 27) (13 - 27) (13 - 27) (13 - 27) (0 - 

792 659 588 506 423 49

(506 - 1075) (420 - 894) (374 - 799) (322 - 688) (269 - 575) (312 - 

107 61 61 61 61 32 

(68 - 145) (38 - 83) (38 - 83) (38 - 83) (38 - 83) (20 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in a 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-13

Table E-12. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5 C

 Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusti

 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

821 726 647 567 486 5

(525 - 1112) (464 - 984) (413 - 877) (361 - 769) (310 - 661) (361 

618 564 512 445 378 4

(394 - 838) (360 - 765) (326 - 695) (283 - 605) (240 - 514) (272 

535 374 330 285 241 2

(342 - 724) (238 - 507) (210 - 448) (182 - 388) (153 - 327) (182 

407 407 407 407 356 4

(259 - 553) (259 - 553) (259 - 553) (259 - 553) (227 - 485) (259 

778 544 536 460 384 3

(496 - 1054) (346 - 739) (341 - 729) (293 - 626) (244 - 522) (252 

357 130 130 130 130 

(228 - 482) (82 - 177) (82 - 177) (82 - 177) (82 - 177) (56 

788 719 634 548 461 5

(502 - 1069) (459 - 977) (404 - 861) (349 - 745) (293 - 628) (349 

2732 1170 1170 1170 1016 7

(1746 - 3702) (744 - 1593) (744 - 1593) (744 - 1593) (645 - 1384) (490 

2322 1689 1689 1607 1359 1

(1482 - 3148) (1076 - 2295) (1076 - 2295) (1023 - 2185) (864 - 1848) (783 

598 519 519 460 391 3

(381 - 811) (331 - 704) (331 - 704) (293 - 625) (249 - 531) (246 

556 556 556 556 494 4

(354 - 757) (354 - 757) (354 - 757) (354 - 757) (314 - 673) (303 

675 434 434 406 352 3

(431 - 914) (277 - 590) (277 - 590) (258 - 552) (224 - 479) (202 

154 48 48 48 48 

(98 - 209) (31 - 66) (31 - 66) (31 - 66) (31 - 66) (14 

869 728 653 566 478 5

(555 - 1178) (464 - 988) (416 - 887) (360 - 769) (304 - 651) (350 

112 64 64 64 64 

(71 - 152) (41 - 88) (41 - 88) (41 - 88) (41 - 88) (22 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Stand

n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-14

Table E-13. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to A Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

5.5% 4.9% 4.4% 3.8% 3.3% 3

(3.5% - 7.4%) (3.1% - 6.6%) (2.8% - 5.9%) (2.4% - 5.2%) (2.1% - 4.5%) (2.4% 

5.4% 5% 4.5% 4% 3.5% 3

(3.5% - 7.3%) (3.2% - 6.7%) (2.9% - 6.2%) (2.6% - 5.4%) (2.2% - 4.7%) (2.5% 

5.5% 3.9% 3.4% 3% 2.5% 3

(3.5% - 7.5%) (2.5% - 5.3%) (2.2% - 4.7%) (1.9% - 4%) (1.6% - 3.4%) (1.9% 

3.8% 3.8% 3.8% 3.8% 3.4% 3

(2.4% - 5.1%) (2.4% - 5.1%) (2.4% - 5.1%) (2.4% - 5.1%) (2.1% - 4.6%) (2.4% 

5.7% 4.2% 4.1% 3.6% 3.1% 3

(3.7% - 7.7%) (2.7% - 5.6%) (2.6% - 5.6%) (2.3% - 4.9%) (2% - 4.2%) (2% 

5.8% 2% 2% 2% 2% 1

(3.7% - 7.9%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (0.9% 

4.2% 3.8% 3.4% 2.9% 2.5% 2

(2.7% - 5.7%) (2.4% - 5.2%) (2.2% - 4.6%) (1.9% - 4%) (1.6% - 3.4%) (1.9% 

5.3% 2.4% 2.4% 2.4% 2.1% 1

(3.4% - 7.1%) (1.5% - 3.2%) (1.5% - 3.2%) (1.5% - 3.2%) (1.3% - 2.8%) (1% 

4.9% 3.6% 3.6% 3.4% 2.9% 2

(3.1% - 6.6%) (2.3% - 4.9%) (2.3% - 4.9%) (2.2% - 4.7%) (1.9% - 4%) (1.7% 

4.6% 4% 4% 3.6% 3.1% 3

(2.9% - 6.2%) (2.6% - 5.4%) (2.6% - 5.4%) (2.3% - 4.9%) (2% - 4.2%) (1.9% 

2.7% 2.7% 2.7% 2.7% 2.4% 2

(1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.5% - 3.3%) (1.5% 

5.5% 3.6% 3.6% 3.3% 2.9% 2

(3.5% - 7.4%) (2.3% - 4.8%) (2.3% - 4.8%) (2.1% - 4.5%) (1.9% - 4%) (1.7% 

2.8% 0.8% 0.8% 0.8% 0.8% 0

(1.8% - 3.8%) (0.5% - 1.1%) (0.5% - 1.1%) (0.5% - 1.1%) (0.5% - 1.1%) (0.2% 

5.6% 4.8% 4.3% 3.8% 3.3% 3

(3.6% - 7.6%) (3% - 6.5%) (2.8% - 5.9%) (2.4% - 5.1%) (2.1% - 4.4%) (2.4% 

3.1% 2% 2% 2% 2% 1

(2% - 4.2%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (0.9% 

Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Conc

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standa

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-15

Table E-14. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to A Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

5.5% 4.9% 4.4% 3.8% 3.3% 3

(3.5% - 7.4%) (3.1% - 6.6%) (2.8% - 5.9%) (2.4% - 5.2%) (2.1% - 4.5%) (2.4% 

4.4% 4% 3.6% 3.1% 2.7% 3

(2.8% - 5.9%) (2.5% - 5.4%) (2.3% - 4.9%) (2% - 4.3%) (1.7% - 3.6%) (1.9% 

5.1% 3.6% 3.1% 2.7% 2.3% 2

(3.3% - 7%) (2.3% - 4.8%) (2% - 4.3%) (1.7% - 3.7%) (1.4% - 3.1%) (1.7% 

2.8% 2.8% 2.8% 2.8% 2.4% 2

(1.8% - 3.8%) (1.8% - 3.8%) (1.8% - 3.8%) (1.8% - 3.8%) (1.5% - 3.3%) (1.8% 

4.1% 2.9% 2.8% 2.4% 2% 2

(2.6% - 5.6%) (1.8% - 3.9%) (1.8% - 3.8%) (1.5% - 3.3%) (1.3% - 2.7%) (1.3% 

6% 2.1% 2.1% 2.1% 2.1% 1

(3.8% - 8.1%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (0.9% 

3.9% 3.6% 3.1% 2.7% 2.3% 2

(2.5% - 5.3%) (2.3% - 4.9%) (2% - 4.3%) (1.7% - 3.7%) (1.4% - 3.1%) (1.7% 

4.6% 1.9% 1.9% 1.9% 1.7% 1

(2.9% - 6.2%) (1.2% - 2.6%) (1.2% - 2.6%) (1.2% - 2.6%) (1.1% - 2.3%) (0.8% 

3.7% 2.6% 2.6% 2.5% 2.1% 1

(2.4% - 5.1%) (1.7% - 3.6%) (1.7% - 3.6%) (1.6% - 3.4%) (1.3% - 2.8%) (1.2% 

4.2% 3.6% 3.6% 3.2% 2.7% 2

(2.7% - 5.6%) (2.3% - 4.9%) (2.3% - 4.9%) (2% - 4.4%) (1.7% - 3.7%) (1.7% 

2.7% 2.7% 2.7% 2.7% 2.5% 2

(1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.6% - 3.4%) (1.5% 

4.3% 2.7% 2.7% 2.5% 2.1% 1

(2.8% - 5.9%) (1.7% - 3.7%) (1.7% - 3.7%) (1.6% - 3.4%) (1.4% - 2.9%) (1.2% 

2.2% 0.4% 0.4% 0.4% 0.4% 0

(1.4% - 3%) (0.3% - 0.6%) (0.3% - 0.6%) (0.3% - 0.6%) (0.3% - 0.6%) (0% 

4.2% 3.5% 3.1% 2.7% 2.2% 2

(2.7% - 5.7%) (2.2% - 4.7%) (2% - 4.2%) (1.7% - 3.6%) (1.4% - 3%) (1.6% 

2.1% 1.2% 1.2% 1.2% 1.2% 0

(1.3% - 2.8%) (0.7% - 1.6%) (0.7% - 1.6%) (0.7% - 1.6%) (0.7% - 1.6%) (0.4% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Conc

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standa

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-16

Table E-15. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to A Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

5.1% 4.6% 4.1% 3.6% 3% 3.6

(3.3% - 7%) (2.9% - 6.2%) (2.6% - 5.5%) (2.3% - 4.8%) (1.9% - 4.1%) (2.3% - 

4.4% 4% 3.6% 3.2% 2.7% 3% 

(2.8% - 5.9%) (2.5% - 5.4%) (2.3% - 4.9%) (2% - 4.3%) (1.7% - 3.6%) (1.9% - 

5.3% 3.7% 3.3% 2.8% 2.4% 2.8

(3.4% - 7.2%) (2.4% - 5.1%) (2.1% - 4.5%) (1.8% - 3.9%) (1.5% - 3.3%) (1.8% - 

3% 3% 3% 3% 2.7% 3% 

(1.9% - 4.1%) (1.9% - 4.1%) (1.9% - 4.1%) (1.9% - 4.1%) (1.7% - 3.6%) (1.9% - 

4.4% 3.1% 3% 2.6% 2.2% 2.2

(2.8% - 6%) (2% - 4.2%) (1.9% - 4.1%) (1.7% - 3.5%) (1.4% - 3%) (1.4% 

6.2% 2.3% 2.3% 2.3% 2.3% 1.5

(4% - 8.4%) (1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1% - 2

4% 3.7% 3.2% 2.8% 2.3% 2.8

(2.6% - 5.4%) (2.3% - 5%) (2.1% - 4.4%) (1.8% - 3.8%) (1.5% - 3.2%) (1.8% - 

4.8% 2% 2% 2% 1.8% 1.3

(3% - 6.4%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (1.1% - 2.4%) (0.9% - 

4.3% 3.2% 3.2% 3% 2.5% 2.3

(2.8% - 5.9%) (2% - 4.3%) (2% - 4.3%) (1.9% - 4.1%) (1.6% - 3.4%) (1.5% - 

4.1% 3.6% 3.6% 3.2% 2.7% 2.7

(2.6% - 5.6%) (2.3% - 4.8%) (2.3% - 4.8%) (2% - 4.3%) (1.7% - 3.7%) (1.7% - 

2.3% 2.3% 2.3% 2.3% 2% 1.9

(1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1.3% - 2.7%) (1.2% - 

4.9% 3.2% 3.2% 2.9% 2.6% 2.3

(3.1% - 6.6%) (2% - 4.3%) (2% - 4.3%) (1.9% - 4%) (1.6% - 3.5%) (1.5% - 

3% 1% 1% 1% 1% 0.4

(1.9% - 4.1%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.3% - 

4.6% 3.8% 3.4% 3% 2.5% 2.9

(2.9% - 6.2%) (2.4% - 5.2%) (2.2% - 4.7%) (1.9% - 4.1%) (1.6% - 3.4%) (1.8% - 

2.1% 1.2% 1.2% 1.2% 1.2% 0.7

(1.3% - 2.9%) (0.8% - 1.7%) (0.8% - 1.7%) (0.8% - 1.7%) (0.8% - 1.7%) (0.4% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Conce

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-17

Table E-16. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associate Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based o

 (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 21% 32% 21% 

(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 32%) (21% - 

-9% 0% 8% 19% 30% 22% 

(-9% - -9%) (0% - 0%) (8% - 8%) (19% - 19%) (30% - 30%) (22% - 

-43% 0% 12% 23% 35% 23% 

(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 23%) (35% - 35%) (23% - 

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 

-37% 0% 1% 13% 26% 24% 

(-37% - -38%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (24% - 

-185% 0% 0% 0% 0% 34% 

(-183% - -187%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (34% - 

-9% 0% 12% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 35%) (23% - 

-122% 0% 0% 0% 12% 31% 

(-121% - -124%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 

-35% 0% 0% 5% 18% 25% 

(-35% - -36%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 

-14% 0% 0% 11% 23% 24% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-53% 0% 0% 6% 18% 25% 

(-52% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (25% - 

-254% 0% 0% 0% 0% 64% 

(-253% - -256%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 

-18% 0% 9% 20% 31% 22% 

(-18% - -18%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (22% - 

-53% 0% 0% 0% 0% 34% 

(-53% - -53%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated wit

PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Altern

(m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-18

Table E-17. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associate Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based o

 (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 21% 32% 21% 

(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 32%) (21% - 

-10% 0% 9% 21% 33% 24% 

(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 

-44% 0% 12% 24% 36% 24% 

(-44% - -44%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 

0% 0% 0% 0% 13% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 

-45% 0% 1% 16% 30% 28% 

(-44% - -45%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 31%) (28% - 

-181% 0% 0% 0% 0% 33% 

(-179% - -183%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

-10% 0% 12% 24% 36% 24% 

(-10% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 

-137% 0% 0% 0% 14% 35% 

(-136% - -139%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 14%) (35% - 

-41% 0% 0% 5% 21% 30% 

(-41% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 

-15% 0% 0% 11% 25% 25% 

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-61% 0% 0% 7% 20% 29% 

(-61% - -62%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 

-437% 0% 0% 0% 0% 100

(-435% - -439%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 

-20% 0% 11% 23% 36% 26% 

(-20% - -20%) (0% - 0%) (11% - 11%) (23% - 23%) (36% - 36%) (26% - 

-76% 0% 0% 0% 0% 48% 

(-76% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (48% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated wit

PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Altern

(m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-19

Table E-18. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associate Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based o

 (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 22% 33% 22% 

(-13% - -13%) (0% - 0%) (11% - 11%) (22% - 22%) (33% - 33%) (22% - 

-10% 0% 9% 21% 33% 24% 

(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 

-43% 0% 12% 24% 36% 24% 

(-43% - -44%) (0% - 0%) (12% - 12%) (24% - 24%) (35% - 36%) (24% - 

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 

-43% 0% 1% 15% 29% 27% 

(-43% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 30%) (27% - 

-175% 0% 0% 0% 0% 32% 

(-173% - -177%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 

-9% 0% 12% 24% 36% 24% 

(-9% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 

-134% 0% 0% 0% 13% 34% 

(-132% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 

-37% 0% 0% 5% 20% 27% 

(-37% - -38%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 20%) (27% - 

-15% 0% 0% 11% 25% 26% 

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (25% - 25%) (25% - 

0% 0% 0% 0% 11% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 

-55% 0% 0% 7% 19% 27% 

(-55% - -56%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 19%) (27% - 

-217% 0% 0% 0% 0% 55% 

(-216% - -218%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 

-19% 0% 10% 22% 34% 25% 

(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 22%) (34% - 34%) (24% - 

-74% 0% 0% 0% 0% 46% 

(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 

Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated wit

PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Altern

(m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-20

Table E-19. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb

 in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

249 222 199 176 153 17

(205 - 291) (182 - 260) (163 - 234) (144 - 207) (125 - 180) (144 - 

396 366 337 298 259 28

(326 - 464) (301 - 429) (276 - 395) (244 - 351) (212 - 306) (236 - 

186 133 118 103 87 10

(153 - 218) (109 - 156) (96 - 139) (84 - 121) (71 - 103) (84 - 

231 231 231 231 206 23

(189 - 272) (189 - 272) (189 - 272) (189 - 272) (169 - 243) (189 - 

689 509 504 444 383 39

(567 - 806) (418 - 599) (413 - 592) (363 - 523) (313 - 452) (321 - 

187 68 68 68 68 45 

(154 - 219) (56 - 81) (56 - 81) (56 - 81) (56 - 81) (37 - 

370 340 302 263 223 26

(304 - 435) (278 - 400) (247 - 356) (215 - 310) (182 - 264) (215 - 

2124 984 984 984 867 68

(1746 - 2489) (802 - 1163) (802 - 1163) (802 - 1163) (707 - 1026) (555 - 

2614 1959 1959 1874 1610 147

(2147 - 3068) (1603 - 2307) (1603 - 2307) (1533 - 2208) (1315 - 1900) (1204 - 

333 293 293 263 226 22

(273 - 391) (240 - 345) (240 - 345) (215 - 309) (185 - 267) (183 - 

351 351 351 351 316 30

(286 - 414) (286 - 414) (286 - 414) (286 - 414) (258 - 374) (250 - 

436 291 291 274 241 21

(359 - 511) (238 - 343) (238 - 343) (224 - 323) (197 - 285) (179 - 

40 12 12 12 12 4 

(33 - 47) (9 - 14) (9 - 14) (9 - 14) (9 - 14) (3 - 

636 544 496 438 379 42

(523 - 744) (447 - 639) (406 - 583) (359 - 516) (310 - 447) (350 - 

93 61 61 61 61 41 

(76 - 109) (50 - 72) (50 - 72) (50 - 72) (50 - 72) (33 - 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-21

Table E-20. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Base

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

256 229 205 181 157 18

(211 - 300) (188 - 268) (168 - 241) (149 - 214) (129 - 185) (149 - 

325 297 271 237 202 22

(266 - 382) (244 - 350) (222 - 319) (194 - 279) (165 - 239) (186 - 

176 124 110 95 80 95 

(144 - 206) (101 - 146) (90 - 129) (77 - 112) (65 - 95) (77 - 1

175 175 175 175 153 17

(143 - 207) (143 - 207) (143 - 207) (143 - 207) (125 - 181) (143 - 

506 355 350 300 249 25

(415 - 595) (290 - 418) (286 - 413) (244 - 354) (203 - 294) (209 - 

194 72 72 72 72 49 

(160 - 227) (59 - 85) (59 - 85) (59 - 85) (59 - 85) (40 - 

359 329 291 252 213 252 

(294 - 423) (269 - 388) (238 - 343) (206 - 298) (173 - 252) (206 - 

1884 815 815 815 707 534 

(1546 - 2212) (664 - 965) (664 - 965) (664 - 965) (575 - 837) (434 - 

2050 1470 1470 1394 1163 104

(1678 - 2413) (1200 - 1736) (1200 - 1736) (1138 - 1648) (947 - 1375) (850 - 1

303 264 264 236 201 19

(248 - 356) (216 - 311) (216 - 311) (193 - 278) (164 - 238) (163 - 

372 372 372 372 335 32

(303 - 439) (303 - 439) (303 - 439) (303 - 439) (273 - 396) (265 - 

349 220 220 205 177 15

(286 - 410) (180 - 260) (180 - 260) (167 - 242) (144 - 209) (128 - 

33 6 6 6 6 0 

(27 - 39) (5 - 7) (5 - 7) (5 - 7) (5 - 7) (0 - 

484 405 363 314 263 304 

(397 - 569) (331 - 477) (297 - 428) (256 - 370) (215 - 311) (248 - 

63 36 36 36 36 19 

(51 - 75) (29 - 43) (29 - 43) (29 - 43) (29 - 43) (15 - 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2

:

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-22

Table E-21. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb

 in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

247 220 197 173 149 17

(203 - 290) (180 - 258) (161 - 231) (142 - 204) (122 - 176) (142 - 

324 297 271 236 202 22

(266 - 381) (243 - 349) (221 - 319) (193 - 279) (165 - 238) (186 - 

184 131 116 101 85 10

(151 - 216) (107 - 154) (95 - 136) (82 - 119) (70 - 101) (82 - 1

195 195 195 195 172 19

(159 - 230) (159 - 230) (159 - 230) (159 - 230) (140 - 203) (159 - 

532 377 372 321 269 27

(436 - 625) (308 - 445) (304 - 439) (262 - 379) (219 - 318) (226 - 

204 77 77 77 77 53 

(169 - 239) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (43 - 

375 344 304 264 223 264 

(307 - 441) (281 - 405) (249 - 358) (215 - 312) (182 - 264) (215 - 

1953 860 860 860 749 57

(1604 - 2293) (701 - 1018) (701 - 1018) (701 - 1018) (610 - 887) (465 - 

2384 1755 1755 1673 1421 129

(1955 - 2802) (1435 - 2070) (1435 - 2070) (1367 - 1974) (1160 - 1679) (1053 - 

300 261 261 233 199 19

(245 - 352) (214 - 308) (214 - 308) (190 - 275) (162 - 235) (160 - 

317 317 317 317 282 27

(258 - 374) (258 - 374) (258 - 374) (258 - 374) (230 - 333) (222 - 

390 256 256 239 209 18

(321 - 458) (209 - 302) (209 - 302) (196 - 283) (170 - 246) (154 - 

47 15 15 15 15 7 

(38 - 55) (12 - 18) (12 - 18) (12 - 18) (12 - 18) (6 - 

529 446 402 350 297 34

(434 - 621) (365 - 525) (329 - 474) (286 - 413) (243 - 351) (278 - 

66 38 38 38 38 21 

(54 - 78) (31 - 46) (31 - 46) (31 - 46) (31 - 46) (17 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-23

Table E-22. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Ex PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternati

 Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

15.8% 14.1% 12.7% 11.2% 9.7% 11.2% 

(13% - 18.6%) (11.6% - 16.6%) (10.4% - 14.9%) (9.2% - 13.2%) (8% - 11.5%) (9.2% - 13.2

15.6% 14.4% 13.2% 11.7% 10.2% 11.3% 

(12.8% - 18.2%) (11.8% - 16.9%) (10.9% - 15.5%) (9.6% - 13.8%) (8.3% - 12%) (9.3% - 13.3

15.9% 11.3% 10.1% 8.8% 7.5% 8.8% 

(13.1% - 18.6%) (9.3% - 13.4%) (8.2% - 11.9%) (7.2% - 10.4%) (6.1% - 8.8%) (7.2% - 10.4

11.1% 11.1% 11.1% 11.1% 9.9% 11.1% 

(9.1% - 13.1%) (9.1% - 13.1%) (9.1% - 13.1%) (9.1% - 13.1%) (8.1% - 11.7%) (9.1% - 13.1

16.4% 12.2% 12% 10.6% 9.1% 9.4% 

(13.5% - 19.2%) (10% - 14.3%) (9.8% - 14.1%) (8.7% - 12.5%) (7.5% - 10.8%) (7.7% - 11.1

16.8% 6.1% 6.1% 6.1% 6.1% 4.1% 

(13.8% - 19.6%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (3.3% - 4.8

12.2% 11.2% 9.9% 8.7% 7.4% 8.7% 

(10% - 14.4%) (9.2% - 13.2%) (8.1% - 11.7%) (7.1% - 10.2%) (6% - 8.7%) (7.1% - 10.2

15.2% 7% 7% 7% 6.2% 4.9% 

(12.5% - 17.8%) (5.7% - 8.3%) (5.7% - 8.3%) (5.7% - 8.3%) (5.1% - 7.3%) (4% - 5.8%

14.1% 10.6% 10.6% 10.1% 8.7% 7.9% 

(11.6% - 16.5%) (8.6% - 12.4%) (8.6% - 12.4%) (8.3% - 11.9%) (7.1% - 10.2%) (6.5% - 9.4

13.3% 11.7% 11.7% 10.5% 9.1% 9% 

(10.9% - 15.6%) (9.6% - 13.8%) (9.6% - 13.8%) (8.6% - 12.4%) (7.4% - 10.7%) (7.3% - 10.6

8% 8% 8% 8% 7.2% 7% 

(6.5% - 9.4%) (6.5% - 9.4%) (6.5% - 9.4%) (6.5% - 9.4%) (5.9% - 8.5%) (5.7% - 8.2

15.7% 10.5% 10.5% 9.9% 8.7% 7.9% 

(12.9% - 18.4%) (8.6% - 12.3%) (8.6% - 12.3%) (8.1% - 11.6%) (7.1% - 10.2%) (6.4% - 9.3

8.2% 2.4% 2.4% 2.4% 2.4% 0.8% 

(6.7% - 9.7%) (1.9% - 2.8%) (1.9% - 2.8%) (1.9% - 2.8%) (1.9% - 2.8%) (0.7% - 1%

16.1% 13.8% 12.6% 11.1% 9.6% 10.8% 

(13.3% - 18.9%) (11.3% - 16.2%) (10.3% - 14.8%) (9.1% - 13.1%) (7.9% - 11.4%) (8.9% - 12.8

9.2% 6.1% 6.1% 6.1% 6.1% 4.1% 

(7.5% - 10.8%) (4.9% - 7.2%) (4.9% - 7.2%) (4.9% - 7.2%) (4.9% - 7.2%) (3.3% - 4.8

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coe1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-24

Table E-23. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Ex PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternati

 Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

15.8% 14.1% 12.7% 11.2% 9.7% 11.2% 

(13% - 18.5%) (11.6% - 16.6%) (10.4% - 14.9%) (9.2% - 13.2%) (8% - 11.5%) (9.2% - 13.2

12.7% 11.7% 10.6% 9.3% 7.9% 8.9% 

(10.5% - 15%) (9.6% - 13.7%) (8.7% - 12.5%) (7.6% - 11%) (6.5% - 9.4%) (7.3% - 10.5

14.8% 10.5% 9.3% 8% 6.8% 8% 

(12.2% - 17.4%) (8.6% - 12.3%) (7.6% - 10.9%) (6.5% - 9.5%) (5.5% - 8%) (6.5% - 9.5

8.2% 8.2% 8.2% 8.2% 7.2% 8.2% 

(6.7% - 9.7%) (6.7% - 9.7%) (6.7% - 9.7%) (6.7% - 9.7%) (5.9% - 8.5%) (6.7% - 9.7

12.1% 8.5% 8.4% 7.2% 5.9% 6.1% 

(9.9% - 14.2%) (6.9% - 10%) (6.8% - 9.9%) (5.8% - 8.5%) (4.8% - 7%) (5% - 7.3%

17.2% 6.4% 6.4% 6.4% 6.4% 4.3% 

(14.1% - 20.1%) (5.2% - 7.5%) (5.2% - 7.5%) (5.2% - 7.5%) (5.2% - 7.5%) (3.5% - 5.1

11.5% 10.5% 9.3% 8% 6.8% 8% 

(9.4% - 13.5%) (8.6% - 12.4%) (7.6% - 10.9%) (6.6% - 9.5%) (5.5% - 8%) (6.6% - 9.5

13.4% 5.8% 5.8% 5.8% 5% 3.8% 

(11% - 15.7%) (4.7% - 6.9%) (4.7% - 6.9%) (4.7% - 6.9%) (4.1% - 5.9%) (3.1% - 4.5

10.9% 7.8% 7.8% 7.4% 6.2% 5.6% 

(9% - 12.9%) (6.4% - 9.3%) (6.4% - 9.3%) (6.1% - 8.8%) (5.1% - 7.3%) (4.5% - 6.6

12.1% 10.6% 10.6% 9.4% 8.1% 8% 

(9.9% - 14.3%) (8.7% - 12.5%) (8.7% - 12.5%) (7.7% - 11.1%) (6.6% - 9.5%) (6.5% - 9.4

8.1% 8.1% 8.1% 8.1% 7.3% 7.1% 

(6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (6% - 8.7%) (5.8% - 8.4

12.6% 8% 8% 7.4% 6.4% 5.7% 

(10.4% - 14.8%) (6.5% - 9.4%) (6.5% - 9.4%) (6.1% - 8.8%) (5.2% - 7.6%) (4.6% - 6.7

6.5% 1.2% 1.2% 1.2% 1.2% 0% 

(5.3% - 7.7%) (1% - 1.5%) (1% - 1.5%) (1% - 1.5%) (1% - 1.5%) (0% - 0%

12.2% 10.2% 9.2% 7.9% 6.7% 7.7% 

(10% - 14.4%) (8.4% - 12.1%) (7.5% - 10.8%) (6.5% - 9.4%) (5.4% - 7.9%) (6.3% - 9.1

6.1% 3.5% 3.5% 3.5% 3.5% 1.8% 

(5% - 7.3%) (2.9% - 4.2%) (2.9% - 4.2%) (2.9% - 4.2%) (2.9% - 4.2%) (1.5% - 2.2

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coe1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-25

Table E-24. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Ex PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternati

 Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

14.9% 13.2% 11.8% 10.4% 9% 10.4% 

(12.2% - 17.4%) (10.9% - 15.5%) (9.7% - 13.9%) (8.5% - 12.3%) (7.4% - 10.6%) (8.5% - 12.3

12.7% 11.7% 10.6% 9.3% 7.9% 8.9% 

(10.5% - 15%) (9.6% - 13.7%) (8.7% - 12.5%) (7.6% - 11%) (6.5% - 9.4%) (7.3% - 10.5

15.4% 10.9% 9.7% 8.4% 7.1% 8.4% 

(12.7% - 18%) (8.9% - 12.9%) (7.9% - 11.4%) (6.9% - 9.9%) (5.8% - 8.4%) (6.9% - 9.9

9% 9% 9% 9% 7.9% 9% 

(7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (6.5% - 9.3%) (7.3% - 10.6

12.8% 9.1% 9% 7.7% 6.5% 6.7% 

(10.5% - 15%) (7.4% - 10.7%) (7.3% - 10.6%) (6.3% - 9.1%) (5.3% - 7.6%) (5.4% - 7.9

17.7% 6.7% 6.7% 6.7% 6.7% 4.6% 

(14.6% - 20.7%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (3.7% - 5.5

11.7% 10.7% 9.5% 8.2% 7% 8.2% 

(9.6% - 13.8%) (8.8% - 12.6%) (7.8% - 11.2%) (6.7% - 9.7%) (5.7% - 8.3%) (6.7% - 9.7

13.8% 6.1% 6.1% 6.1% 5.3% 4% 

(11.3% - 16.2%) (4.9% - 7.2%) (4.9% - 7.2%) (4.9% - 7.2%) (4.3% - 6.3%) (3.3% - 4.8

12.6% 9.3% 9.3% 8.9% 7.5% 6.8% 

(10.4% - 14.8%) (7.6% - 11%) (7.6% - 11%) (7.2% - 10.5%) (6.1% - 8.9%) (5.6% - 8.1

12% 10.5% 10.5% 9.3% 8% 7.9% 

(9.8% - 14.1%) (8.6% - 12.3%) (8.6% - 12.3%) (7.6% - 11%) (6.5% - 9.4%) (6.4% - 9.3

6.7% 6.7% 6.7% 6.7% 6% 5.8% 

(5.5% - 7.9%) (5.5% - 7.9%) (5.5% - 7.9%) (5.5% - 7.9%) (4.9% - 7.1%) (4.7% - 6.8

14.2% 9.3% 9.3% 8.7% 7.6% 6.9% 

(11.7% - 16.7%) (7.6% - 11%) (7.6% - 11%) (7.1% - 10.3%) (6.2% - 9%) (5.6% - 8.1

9% 2.9% 2.9% 2.9% 2.9% 1.3% 

(7.4% - 10.6%) (2.4% - 3.4%) (2.4% - 3.4%) (2.4% - 3.4%) (2.4% - 3.4%) (1.1% - 1.6

13.3% 11.2% 10.1% 8.8% 7.5% 8.6% 

(10.9% - 15.7%) (9.2% - 13.2%) (8.3% - 11.9%) (7.2% - 10.4%) (6.1% - 8.9%) (7% - 10.1%

6.3% 3.7% 3.7% 3.7% 3.7% 2% 

(5.2% - 7.5%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (1.6% - 2.4

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coe1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-26

Table E-25. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Morta Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 21% 31% 21% 

(-12% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (31% - 31%) (20% - 

-8% 0% 8% 18% 29% 21% 

(-8% - -8%) (0% - 0%) (8% - 8%) (18% - 19%) (29% - 29%) (21% - 

-40% 0% 11% 23% 34% 23% 

(-39% - -41%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 34%) (22% - 

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 

-35% 0% 1% 13% 25% 23% 

(-35% - -36%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 25%) (23% - 

-174% 0% 0% 0% 0% 33% 

(-171% - -177%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

-9% 0% 11% 23% 34% 23% 

(-9% - -9%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (23% - 

-116% 0% 0% 0% 12% 31% 

(-114% - -118%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 

-33% 0% 0% 4% 18% 25% 

(-33% - -34%) (0% - 0%) (0% - 0%) (4% - 4%) (18% - 18%) (25% - 

-14% 0% 0% 10% 23% 23% 

(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 10%) (22% - 23%) (23% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-50% 0% 0% 6% 17% 25% 

(-49% - -51%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 17%) (24% - 

-247% 0% 0% 0% 0% 64% 

(-245% - -249%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 

-17% 0% 9% 20% 30% 22% 

(-16% - -17%) (0% - 0%) (9% - 9%) (19% - 20%) (30% - 31%) (21% - 

-51% 0% 0% 0% 0% 33% 

(-51% - -52%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-27

Table E-26. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Morta Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 21% 31% 21% 

(-12% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (31% - 31%) (20% - 

-9% 0% 9% 20% 32% 24% 

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 32%) (23% - 

-42% 0% 12% 23% 35% 23% 

(-41% - -42%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 

0% 0% 0% 0% 13% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (0% - 

-43% 0% 1% 16% 30% 28% 

(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 16%) (30% - 30%) (27% - 

-170% 0% 0% 0% 0% 33% 

(-167% - -173%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 

-9% 0% 12% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 

-131% 0% 0% 0% 13% 35% 

(-129% - -133%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 

-39% 0% 0% 5% 21% 29% 

(-39% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 

-14% 0% 0% 11% 24% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (24% - 

0% 0% 0% 0% 10% 12% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 

-58% 0% 0% 7% 20% 28% 

(-58% - -59%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (28% - 

-427% 0% 0% 0% 0% 100

(-425% - -430%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 

-19% 0% 10% 23% 35% 25% 

(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 23%) (35% - 35%) (25% - 

-74% 0% 0% 0% 0% 47% 

(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-28

Table E-27. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Morta Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 21%) (32% - 32%) (21% - 

-9% 0% 9% 20% 32% 24% 

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 32%) (23% - 

-41% 0% 11% 23% 35% 23% 

(-40% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 

-41% 0% 1% 15% 29% 27% 

(-41% - -42%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (26% - 

-164% 0% 0% 0% 0% 31% 

(-161% - -167%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 

-9% 0% 12% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 

-127% 0% 0% 0% 13% 34% 

(-125% - -129%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (33% - 

-36% 0% 0% 5% 19% 26% 

(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (26% - 

-15% 0% 0% 11% 24% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% - 

0% 0% 0% 0% 11% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 

-53% 0% 0% 6% 18% 26% 

(-52% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 19%) (26% - 

-210% 0% 0% 0% 0% 55% 

(-209% - -212%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 

-19% 0% 10% 22% 33% 24% 

(-18% - -19%) (0% - 0%) (10% - 10%) (21% - 22%) (33% - 34%) (24% - 

-72% 0% 0% 0% 0% 46% 

(-71% - -72%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-29

Table E-28. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

312 279 251 222 193 222 

(257 - 364) (229 - 327) (206 - 295) (182 - 262) (158 - 228) (182 - 

497 460 423 376 328 36

(409 - 581) (378 - 538) (348 - 497) (308 - 442) (268 - 386) (298 - 

233 168 149 130 111 13

(192 - 273) (137 - 197) (122 - 176) (106 - 154) (90 - 131) (106 - 

292 292 292 292 261 292 

(239 - 344) (239 - 344) (239 - 344) (239 - 344) (213 - 307) (239 - 

862 642 635 561 485 49

(711 - 1007) (526 - 754) (520 - 746) (459 - 660) (396 - 572) (406 - 

234 87 87 87 87 58 

(193 - 273) (71 - 103) (71 - 103) (71 - 103) (71 - 103) (47 - 

467 429 382 333 284 33

(383 - 548) (351 - 505) (312 - 449) (272 - 393) (231 - 335) (272 - 

2664 1249 1249 1249 1103 86

(2192 - 3117) (1017 - 1477) (1017 - 1477) (1017 - 1477) (897 - 1306) (705 - 1

3285 2475 2475 2369 2040 187

(2700 - 3849) (2024 - 2914) (2024 - 2914) (1936 - 2790) (1665 - 2408) (1525 - 

419 369 369 332 287 284 

(344 - 492) (303 - 434) (303 - 434) (271 - 391) (234 - 338) (232 - 

445 445 445 445 401 38

(363 - 526) (363 - 526) (363 - 526) (363 - 526) (327 - 475) (317 - 

547 368 368 346 305 27

(450 - 639) (301 - 433) (301 - 433) (283 - 408) (249 - 361) (227 - 

51 15 15 15 15 5 

(42 - 60) (12 - 18) (12 - 18) (12 - 18) (12 - 18) (4 - 

796 684 624 553 480 53

(656 - 930) (562 - 802) (512 - 733) (453 - 650) (392 - 566) (441 - 

117 78 78 78 78 52 

(96 - 138) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (42 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-30

Table E-29. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

321 287 258 229 199 22

(264 - 375) (236 - 336) (212 - 303) (187 - 269) (163 - 235) (187 - 

409 375 342 300 256 28

(335 - 480) (307 - 441) (280 - 403) (245 - 353) (209 - 303) (235 - 

221 157 139 120 102 12

(181 - 258) (128 - 184) (113 - 164) (98 - 142) (83 - 120) (98 - 

222 222 222 222 195 22

(181 - 262) (181 - 262) (181 - 262) (181 - 262) (158 - 230) (181 - 

638 449 443 380 316 32

(523 - 749) (367 - 530) (361 - 523) (310 - 450) (257 - 375) (266 - 

243 92 92 92 92 62 

(201 - 284) (75 - 108) (75 - 108) (75 - 108) (75 - 108) (50 - 

453 416 368 320 270 32

(371 - 533) (340 - 490) (301 - 434) (261 - 378) (220 - 320) (261 - 

2370 1038 1038 1038 901 68

(1945 - 2779) (843 - 1229) (843 - 1229) (843 - 1229) (731 - 1068) (553 - 

2588 1865 1865 1770 1478 132

(2118 - 3046) (1520 - 2203) (1520 - 2203) (1442 - 2092) (1202 - 1750) (1079 - 

381 334 334 298 255 25

(313 - 448) (273 - 393) (273 - 393) (244 - 352) (208 - 302) (206 - 

471 471 471 471 426 41

(384 - 557) (384 - 557) (384 - 557) (384 - 557) (347 - 503) (336 - 

439 279 279 260 225 20

(360 - 516) (228 - 330) (228 - 330) (212 - 308) (183 - 266) (163 - 

42 8 8 8 8 0 

(34 - 50) (6 - 10) (6 - 10) (6 - 10) (6 - 10) (0 - 

610 512 460 398 335 38

(500 - 716) (419 - 603) (375 - 542) (324 - 470) (272 - 396) (314 - 

80 46 46 46 46 24 

(65 - 95) (37 - 55) (37 - 55) (37 - 55) (37 - 55) (20 - 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-31

Table E-30. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

310 277 248 219 189 21

(255 - 363) (227 - 324) (203 - 291) (179 - 258) (154 - 223) (179 - 

408 374 342 299 256 28

(335 - 479) (307 - 440) (280 - 402) (244 - 353) (209 - 302) (235 - 

231 165 146 128 108 12

(190 - 270) (135 - 194) (120 - 173) (104 - 151) (88 - 128) (104 - 

247 247 247 247 218 24

(202 - 291) (202 - 291) (202 - 291) (202 - 291) (178 - 257) (202 - 

670 478 471 407 341 35

(549 - 786) (390 - 563) (385 - 556) (332 - 481) (278 - 404) (286 - 

255 98 98 98 98 68 

(211 - 298) (80 - 116) (80 - 116) (80 - 116) (80 - 116) (55 - 

473 434 385 335 284 33

(387 - 556) (355 - 511) (314 - 453) (273 - 395) (231 - 335) (273 - 

2456 1094 1094 1094 954 73

(2017 - 2879) (890 - 1296) (890 - 1296) (890 - 1296) (775 - 1131) (592 - 

3003 2222 2222 2120 1804 164

(2462 - 3525) (1814 - 2620) (1814 - 2620) (1730 - 2501) (1469 - 2132) (1336 - 

378 330 330 295 252 24

(309 - 444) (270 - 389) (270 - 389) (241 - 347) (205 - 298) (203 - 

402 402 402 402 359 34

(327 - 476) (327 - 476) (327 - 476) (327 - 476) (291 - 425) (282 - 

490 324 324 303 265 24

(403 - 574) (264 - 382) (264 - 382) (248 - 358) (216 - 313) (195 - 

59 19 19 19 19 9 

(48 - 70) (16 - 23) (16 - 23) (16 - 23) (16 - 23) (7 - 

665 563 508 443 377 43

(546 - 780) (461 - 662) (415 - 599) (362 - 523) (307 - 446) (351 - 

84 49 49 49 49 27 

(68 - 99) (40 - 58) (40 - 58) (40 - 58) (40 - 58) (21 - 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-32

Table E-31. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term  PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Altern

 Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

19.9% 17.8% 16% 14.2% 12.3% 14.2

(16.4% - 23.2%) (14.6% - 20.8%) (13.1% - 18.8%) (11.6% - 16.7%) (10.1% - 14.5%) (11.6% - 

19.5% 18.1% 16.6% 14.8% 12.9% 14.3

(16.1% - 22.8%) (14.8% - 21.2%) (13.7% - 19.5%) (12.1% - 17.4%) (10.5% - 15.2%) (11.7% - 

19.9% 14.3% 12.7% 11.1% 9.5% 11.1

(16.4% - 23.3%) (11.7% - 16.8%) (10.4% - 15%) (9.1% - 13.1%) (7.7% - 11.2%) (9.1% - 1

14% 14% 14% 14% 12.5% 14% 

(11.5% - 16.5%) (11.5% - 16.5%) (11.5% - 16.5%) (11.5% - 16.5%) (10.2% - 14.7%) (11.5% - 

20.6% 15.3% 15.1% 13.4% 11.6% 11.9

(17% - 24%) (12.6% - 18%) (12.4% - 17.8%) (10.9% - 15.7%) (9.4% - 13.6%) (9.7% - 

21% 7.8% 7.8% 7.8% 7.8% 5.2% 

(17.3% - 24.5%) (6.3% - 9.2%) (6.3% - 9.2%) (6.3% - 9.2%) (6.3% - 9.2%) (4.2% - 

15.4% 14.2% 12.6% 11% 9.4% 11% 

(12.6% - 18.1%) (11.6% - 16.6%) (10.3% - 14.8%) (9% - 13%) (7.6% - 11.1%) (9% - 1

19% 8.9% 8.9% 8.9% 7.9% 6.2% 

(15.7% - 22.3%) (7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (6.4% - 9.3%) (5% - 7

17.7% 13.3% 13.3% 12.8% 11% 10.1

(14.5% - 20.7%) (10.9% - 15.7%) (10.9% - 15.7%) (10.4% - 15%) (9% - 13%) (8.2% - 1

16.8% 14.8% 14.8% 13.3% 11.5% 11.4

(13.8% - 19.7%) (12.1% - 17.4%) (12.1% - 17.4%) (10.8% - 15.6%) (9.4% - 13.5%) (9.3% - 1

10.1% 10.1% 10.1% 10.1% 9.1% 8.8% 

(8.2% - 11.9%) (8.2% - 11.9%) (8.2% - 11.9%) (8.2% - 11.9%) (7.4% - 10.8%) (7.2% - 1

19.7% 13.2% 13.2% 12.5% 11% 10% 

(16.2% - 23%) (10.8% - 15.6%) (10.8% - 15.6%) (10.2% - 14.7%) (9% - 13%) (8.2% - 1

10.4% 3% 3% 3% 3% 1.1% 

(8.5% - 12.3%) (2.4% - 3.6%) (2.4% - 3.6%) (2.4% - 3.6%) (2.4% - 3.6%) (0.9% - 

20.2% 17.4% 15.8% 14% 12.2% 13.7

(16.6% - 23.6%) (14.3% - 20.4%) (13% - 18.6%) (11.5% - 16.5%) (10% - 14.4%) (11.2% - 

11.6% 7.7% 7.7% 7.7% 7.7% 5.2% 

(9.5% - 13.7%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% - 9.1%) (4.2% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to 

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily 

Combination Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-33

Table E-32. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term  PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Altern

 Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

19.8% 17.7% 16% 14.2% 12.3% 14.2

(16.3% - 23.2%) (14.6% - 20.8%) (13.1% - 18.7%) (11.6% - 16.6%) (10% - 14.5%) (11.6% - 

16% 14.7% 13.4% 11.8% 10.1% 11.3

(13.2% - 18.8%) (12.1% - 17.3%) (11% - 15.8%) (9.6% - 13.9%) (8.2% - 11.9%) (9.2% - 1

18.6% 13.2% 11.7% 10.2% 8.6% 10.2

(15.3% - 21.8%) (10.8% - 15.6%) (9.6% - 13.8%) (8.3% - 12%) (7% - 10.2%) (8.3% - 

10.4% 10.4% 10.4% 10.4% 9.1% 10.4

(8.5% - 12.3%) (8.5% - 12.3%) (8.5% - 12.3%) (8.5% - 12.3%) (7.4% - 10.8%) (8.5% - 1

15.2% 10.7% 10.6% 9.1% 7.6% 7.8% 

(12.5% - 17.9%) (8.8% - 12.7%) (8.6% - 12.5%) (7.4% - 10.7%) (6.1% - 8.9%) (6.3% - 

21.5% 8.1% 8.1% 8.1% 8.1% 5.5% 

(17.7% - 25.1%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (4.4% - 

14.5% 13.3% 11.7% 10.2% 8.6% 10.2

(11.8% - 17%) (10.8% - 15.6%) (9.6% - 13.8%) (8.3% - 12%) (7% - 10.2%) (8.3% - 

16.8% 7.4% 7.4% 7.4% 6.4% 4.8% 

(13.8% - 19.7%) (6% - 8.7%) (6% - 8.7%) (6% - 8.7%) (5.2% - 7.6%) (3.9% - 

13.8% 9.9% 9.9% 9.4% 7.9% 7.1% 

(11.3% - 16.2%) (8.1% - 11.7%) (8.1% - 11.7%) (7.7% - 11.2%) (6.4% - 9.3%) (5.8% - 

15.3% 13.4% 13.4% 11.9% 10.2% 10.1

(12.5% - 18%) (10.9% - 15.8%) (10.9% - 15.8%) (9.8% - 14.1%) (8.3% - 12.1%) (8.3% - 

10.3% 10.3% 10.3% 10.3% 9.3% 9% 

(8.4% - 12.2%) (8.4% - 12.2%) (8.4% - 12.2%) (8.4% - 12.2%) (7.6% - 11%) (7.3% - 1

15.9% 10.1% 10.1% 9.4% 8.1% 7.3% 

(13% - 18.7%) (8.2% - 11.9%) (8.2% - 11.9%) (7.7% - 11.1%) (6.6% - 9.6%) (5.9% - 

8.3% 1.6% 1.6% 1.6% 1.6% 0% 

(6.7% - 9.8%) (1.3% - 1.9%) (1.3% - 1.9%) (1.3% - 1.9%) (1.3% - 1.9%) (0% - 

15.4% 12.9% 11.6% 10% 8.5% 9.7% 

(12.6% - 18.1%) (10.6% - 15.2%) (9.5% - 13.7%) (8.2% - 11.9%) (6.9% - 10%) (7.9% - 1

7.8% 4.5% 4.5% 4.5% 4.5% 2.4% 

(6.3% - 9.2%) (3.6% - 5.3%) (3.6% - 5.3%) (3.6% - 5.3%) (3.6% - 5.3%) (1.9% - 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to 

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily 

Combination Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-34

Table E-33. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term  PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Altern

 Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

18.7% 16.7% 14.9% 13.2% 11.4% 13.2

(15.4% - 21.8%) (13.7% - 19.5%) (12.2% - 17.6%) (10.8% - 15.5%) (9.3% - 13.4%) (10.8% - 

16.1% 14.7% 13.4% 11.8% 10.1% 11.3

(13.2% - 18.8%) (12.1% - 17.3%) (11% - 15.8%) (9.6% - 13.9%) (8.2% - 11.9%) (9.2% - 1

19.3% 13.8% 12.2% 10.7% 9.1% 10.7

(15.9% - 22.6%) (11.3% - 16.2%) (10% - 14.4%) (8.7% - 12.6%) (7.4% - 10.7%) (8.7% - 1

11.4% 11.4% 11.4% 11.4% 10% 11.4

(9.3% - 13.4%) (9.3% - 13.4%) (9.3% - 13.4%) (9.3% - 13.4%) (8.2% - 11.9%) (9.3% - 1

16.1% 11.5% 11.3% 9.8% 8.2% 8.5% 

(13.2% - 18.9%) (9.4% - 13.5%) (9.3% - 13.4%) (8% - 11.6%) (6.7% - 9.7%) (6.9% - 

22.2% 8.5% 8.5% 8.5% 8.5% 5.9% 

(18.3% - 25.9%) (7% - 10.1%) (7% - 10.1%) (7% - 10.1%) (7% - 10.1%) (4.8% - 

14.8% 13.6% 12% 10.5% 8.9% 10.5

(12.1% - 17.4%) (11.1% - 16%) (9.8% - 14.2%) (8.5% - 12.3%) (7.2% - 10.5%) (8.5% - 1

17.3% 7.7% 7.7% 7.7% 6.7% 5.2% 

(14.2% - 20.3%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% - 9.1%) (5.5% - 8%) (4.2% - 

15.9% 11.8% 11.8% 11.2% 9.6% 8.7% 

(13% - 18.7%) (9.6% - 13.9%) (9.6% - 13.9%) (9.2% - 13.2%) (7.8% - 11.3%) (7.1% - 1

15.1% 13.2% 13.2% 11.8% 10.1% 10% 

(12.4% - 17.8%) (10.8% - 15.6%) (10.8% - 15.6%) (9.6% - 13.9%) (8.2% - 11.9%) (8.1% - 1

8.5% 8.5% 8.5% 8.5% 7.6% 7.3% 

(6.9% - 10.1%) (6.9% - 10.1%) (6.9% - 10.1%) (6.9% - 10.1%) (6.2% - 9%) (6% - 8

17.8% 11.8% 11.8% 11% 9.6% 8.7% 

(14.7% - 20.9%) (9.6% - 13.9%) (9.6% - 13.9%) (9% - 13%) (7.8% - 11.4%) (7.1% - 1

11.4% 3.7% 3.7% 3.7% 3.7% 1.7% 

(9.3% - 13.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (1.4% - 

16.8% 14.2% 12.8% 11.2% 9.5% 10.9

(13.8% - 19.7%) (11.6% - 16.7%) (10.5% - 15.1%) (9.1% - 13.2%) (7.7% - 11.2%) (8.9% - 1

8% 4.7% 4.7% 4.7% 4.7% 2.5% 

(6.5% - 9.5%) (3.8% - 5.6%) (3.8% - 5.6%) (3.8% - 5.6%) (3.8% - 5.6%) (2.1% - 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to 

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily 

Combination Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-35

Table E-34. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Morta with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: E

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 20% 31% 20% 

(-11% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (30% - 31%) (20% - 

-8% 0% 8% 18% 29% 21% 

(-8% - -8%) (0% - 0%) (8% - 8%) (18% - 18%) (28% - 29%) (21% - 

-39% 0% 11% 22% 34% 22% 

(-38% - -40%) (0% - 0%) (11% - 11%) (22% - 23%) (33% - 34%) (22% - 

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 

-34% 0% 1% 13% 24% 23% 

(-34% - -35%) (0% - 0%) (1% - 1%) (12% - 13%) (24% - 25%) (22% - 

-170% 0% 0% 0% 0% 33% 

(-166% - -174%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

-9% 0% 11% 22% 34% 22% 

(-9% - -9%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 34%) (22% - 

-113% 0% 0% 0% 12% 30% 

(-111% - -116%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (30% - 

-33% 0% 0% 4% 18% 24% 

(-32% - -33%) (0% - 0%) (0% - 0%) (4% - 4%) (17% - 18%) (24% - 

-13% 0% 0% 10% 22% 23% 

(-13% - -14%) (0% - 0%) (0% - 0%) (10% - 10%) (22% - 23%) (23% - 

0% 0% 0% 0% 10% 12% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 

-49% 0% 0% 6% 17% 24% 

(-48% - -50%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 17%) (24% - 

-244% 0% 0% 0% 0% 64% 

(-242% - -247%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 

-16% 0% 9% 19% 30% 21% 

(-16% - -17%) (0% - 0%) (9% - 9%) (19% - 19%) (29% - 30%) (21% - 

-51% 0% 0% 0% 0% 33% 

(-50% - -51%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-36

Table E-35. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Morta with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: E

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 20% 31% 20% 

(-11% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (30% - 31%) (20% - 

-9% 0% 9% 20% 32% 23% 

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (23% - 

-41% 0% 11% 23% 35% 23% 

(-40% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (0% - 

-42% 0% 1% 15% 30% 27% 

(-41% - -43%) (0% - 0%) (1% - 1%) (15% - 16%) (29% - 30%) (27% - 

-165% 0% 0% 0% 0% 32% 

(-162% - -169%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 

-9% 0% 11% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 

-128% 0% 0% 0% 13% 34% 

(-126% - -131%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 

-39% 0% 0% 5% 21% 29% 

(-38% - -39%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 

-14% 0% 0% 11% 24% 24% 

(-14% - -14%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 

0% 0% 0% 0% 10% 12% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 

-57% 0% 0% 7% 20% 28% 

(-56% - -58%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 20%) (28% - 

-423% 0% 0% 0% 0% 100

(-420% - -427%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 

-19% 0% 10% 22% 35% 25% 

(-19% - -19%) (0% - 0%) (10% - 10%) (22% - 23%) (34% - 35%) (24% - 

-74% 0% 0% 0% 0% 47% 

(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-37

Table E-36. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Morta with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: E

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 13/3

-12% 0% 10% 21% 32% 21% 

(-12% - -12%) (0% - 0%) (10% - 10%) (21% - 21%) (31% - 32%) (21% - 

-9% 0% 9% 20% 32% 23% 

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (23% - 

-40% 0% 11% 23% 34% 23% 

(-39% - -41%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 35%) (22% - 

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 

-40% 0% 1% 15% 28% 26% 

(-40% - -41%) (0% - 0%) (1% - 1%) (15% - 15%) (28% - 29%) (26% - 

-159% 0% 0% 0% 0% 31% 

(-156% - -163%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 

-9% 0% 11% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (23% - 

-124% 0% 0% 0% 13% 33% 

(-122% - -127%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (33% - 

-35% 0% 0% 5% 19% 26% 

(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (26% - 

-14% 0% 0% 11% 24% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 

0% 0% 0% 0% 11% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 

-51% 0% 0% 6% 18% 26% 

(-51% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (26% - 

-208% 0% 0% 0% 0% 55% 

(-205% - -210%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 

-18% 0% 10% 21% 33% 23% 

(-18% - -18%) (0% - 0%) (10% - 10%) (21% - 22%) (33% - 33%) (23% - 

-71% 0% 0% 0% 0% 46% 

(-71% - -72%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-38

Table E-37. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient P

 in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

512 455 407 359 310 35

(391 - 630) (347 - 561) (310 - 502) (273 - 443) (236 - 383) (273 - 

507 467 428 378 327 36

(387 - 624) (356 - 575) (326 - 528) (288 - 466) (249 - 405) (278 - 

365 258 228 198 168 19

(279 - 450) (196 - 318) (174 - 282) (151 - 245) (128 - 208) (151 - 

321 321 321 321 285 32

(244 - 396) (244 - 396) (244 - 396) (244 - 396) (217 - 352) (244 - 

748 547 540 474 408 41

(572 - 920) (417 - 675) (412 - 667) (361 - 586) (310 - 505) (318 - 

240 85 85 85 85 56 

(184 - 295) (65 - 105) (65 - 105) (65 - 105) (65 - 105) (43 - 

499 457 404 351 297 35

(380 - 616) (348 - 564) (307 - 499) (267 - 434) (226 - 368) (267 - 

2357 1069 1069 1069 941 73

(1800 - 2902) (812 - 1324) (812 - 1324) (812 - 1324) (714 - 1166) (559 - 

2205 1637 1637 1564 1339 122

(1683 - 2717) (1246 - 2022) (1246 - 2022) (1190 - 1933) (1018 - 1657) (930 - 

439 384 384 343 295 29

(335 - 541) (293 - 474) (293 - 474) (261 - 424) (224 - 365) (222 - 

406 406 406 406 365 35

(309 - 503) (309 - 503) (309 - 503) (309 - 503) (278 - 453) (269 - 

529 349 349 328 287 26

(405 - 652) (265 - 431) (265 - 431) (249 - 405) (219 - 356) (198 - 

76 22 22 22 22 8 

(58 - 94) (16 - 27) (16 - 27) (16 - 27) (16 - 27) (6 - 

758 646 586 516 445 50

(580 - 933) (493 - 796) (447 - 723) (393 - 637) (339 - 550) (383 - 

110 72 72 72 72 48 

(84 - 136) (55 - 89) (55 - 89) (55 - 89) (55 - 89) (36 - 

 Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5 Concentratio

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards 

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-39

Table E-38. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient P

 in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

527 469 419 369 319 36

(403 - 649) (358 - 577) (320 - 517) (281 - 456) (243 - 394) (281 - 

412 377 342 298 253 28

(314 - 509) (287 - 465) (261 - 423) (227 - 369) (193 - 314) (218 - 

344 240 211 183 154 18

(263 - 424) (183 - 297) (161 - 261) (139 - 226) (117 - 190) (139 - 

241 241 241 241 210 24

(183 - 298) (183 - 298) (183 - 298) (183 - 298) (160 - 260) (183 - 

543 377 372 317 263 27

(414 - 670) (287 - 466) (283 - 460) (241 - 393) (199 - 326) (206 - 

250 90 90 90 90 60 

(191 - 307) (68 - 112) (68 - 112) (68 - 112) (68 - 112) (46 - 

483 441 389 336 283 33

(368 - 597) (336 - 545) (296 - 481) (255 - 416) (215 - 350) (255 - 

2081 884 884 884 765 57

(1587 - 2566) (671 - 1095) (671 - 1095) (671 - 1095) (580 - 948) (436 - 

1715 1220 1220 1156 961 86

(1306 - 2118) (927 - 1510) (927 - 1510) (878 - 1431) (729 - 1191) (653 - 

398 346 346 307 262 25

(303 - 491) (263 - 427) (263 - 427) (234 - 380) (199 - 324) (197 - 

431 431 431 431 388 37

(327 - 533) (327 - 533) (327 - 533) (327 - 533) (294 - 480) (286 - 

420 262 262 244 209 18

(320 - 518) (199 - 324) (199 - 324) (185 - 302) (159 - 259) (141 - 

62 12 12 12 12 0 

(47 - 77) (9 - 15) (9 - 15) (9 - 15) (9 - 15) (0 - 

572 476 426 366 307 35

(436 - 705) (362 - 588) (324 - 526) (278 - 453) (233 - 380) (270 - 

74 42 42 42 42 22 

(56 - 92) (32 - 53) (32 - 53) (32 - 53) (32 - 53) (17 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5 Concentratio

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards 

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-40

Table E-39. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient P

 in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

507 449 401 352 302 35

(387 - 625) (343 - 554) (305 - 495) (268 - 435) (230 - 374) (268 - 

412 376 342 298 253 28

(314 - 508) (286 - 464) (260 - 422) (226 - 368) (192 - 313) (217 - 

361 253 224 194 164 194 

(276 - 444) (193 - 313) (170 - 276) (147 - 240) (124 - 203) (147 - 

269 269 269 269 236 26

(205 - 333) (205 - 333) (205 - 333) (205 - 333) (179 - 292) (205 - 

572 402 396 340 284 29

(436 - 705) (305 - 497) (301 - 490) (259 - 421) (216 - 352) (223 - 

263 97 97 97 97 66 

(201 - 323) (74 - 120) (74 - 120) (74 - 120) (74 - 120) (50 - 

504 461 407 352 297 35

(384 - 622) (351 - 569) (309 - 503) (268 - 436) (225 - 368) (268 - 

2160 933 933 933 811 61

(1648 - 2663) (708 - 1156) (708 - 1156) (708 - 1156) (615 - 1006) (468 - 

2003 1462 1462 1392 1179 106

(1527 - 2470) (1112 - 1808) (1112 - 1808) (1059 - 1722) (895 - 1459) (812 - 1

393 342 342 304 258 25

(300 - 485) (260 - 423) (260 - 423) (231 - 375) (196 - 320) (194 - 

366 366 366 366 325 314 

(278 - 453) (278 - 453) (278 - 453) (278 - 453) (247 - 403) (238 - 

472 305 305 286 248 224 

(360 - 581) (232 - 378) (232 - 378) (217 - 353) (188 - 307) (170 - 

89 28 28 28 28 13 

(68 - 110) (21 - 35) (21 - 35) (21 - 35) (21 - 35) (10 - 

626 526 472 410 347 39

(478 - 772) (400 - 649) (359 - 584) (312 - 507) (264 - 430) (302 - 

78 45 45 45 45 24 

(59 - 97) (34 - 56) (34 - 56) (34 - 56) (34 - 56) (18 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5 Concentratio

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards 

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-41

Table E-40. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Expos Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

8.8% 7.8% 7% 6.1% 5.3% 6.1% 

(6.7% - 10.8%) (5.9% - 9.6%) (5.3% - 8.6%) (4.7% - 7.6%) (4% - 6.6%) (4.7% - 7.

8.6% 7.9% 7.3% 6.4% 5.6% 6.2% 

(6.6% - 10.6%) (6% - 9.8%) (5.5% - 9%) (4.9% - 7.9%) (4.2% - 6.9%) (4.7% - 7.

8.8% 6.2% 5.5% 4.8% 4% 4.8% 

(6.7% - 10.8%) (4.7% - 7.7%) (4.2% - 6.8%) (3.6% - 5.9%) (3.1% - 5%) (3.6% - 5.

6.1% 6.1% 6.1% 6.1% 5.4% 6.1% 

(4.6% - 7.5%) (4.6% - 7.5%) (4.6% - 7.5%) (4.6% - 7.5%) (4.1% - 6.7%) (4.6% - 7.

9.1% 6.7% 6.6% 5.8% 5% 5.1% 

(7% - 11.2%) (5.1% - 8.2%) (5% - 8.1%) (4.4% - 7.1%) (3.8% - 6.1%) (3.9% - 6.

9.3% 3.3% 3.3% 3.3% 3.3% 2.2% 

(7.1% - 11.4%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (1.7% - 2.

6.7% 6.1% 5.4% 4.7% 4% 4.7% 

(5.1% - 8.3%) (4.7% - 7.6%) (4.1% - 6.7%) (3.6% - 5.8%) (3% - 4.9%) (3.6% - 5.

8.4% 3.8% 3.8% 3.8% 3.3% 2.6% 

(6.4% - 10.3%) (2.9% - 4.7%) (2.9% - 4.7%) (2.9% - 4.7%) (2.5% - 4.1%) (2% - 3.3

7.8% 5.8% 5.8% 5.5% 4.7% 4.3% 

(5.9% - 9.6%) (4.4% - 7.1%) (4.4% - 7.1%) (4.2% - 6.8%) (3.6% - 5.8%) (3.3% - 5.

7.3% 6.4% 6.4% 5.7% 4.9% 4.9% 

(5.6% - 9%) (4.9% - 7.9%) (4.9% - 7.9%) (4.4% - 7.1%) (3.7% - 6.1%) (3.7% - 6

4.3% 4.3% 4.3% 4.3% 3.9% 3.8% 

(3.3% - 5.3%) (3.3% - 5.3%) (3.3% - 5.3%) (3.3% - 5.3%) (2.9% - 4.8%) (2.9% - 4.

8.7% 5.7% 5.7% 5.4% 4.7% 4.3% 

(6.6% - 10.7%) (4.4% - 7.1%) (4.4% - 7.1%) (4.1% - 6.6%) (3.6% - 5.8%) (3.3% - 5.

4.5% 1.3% 1.3% 1.3% 1.3% 0.5% 

(3.4% - 5.5%) (1% - 1.6%) (1% - 1.6%) (1% - 1.6%) (1% - 1.6%) (0.3% - 0.

8.9% 7.6% 6.9% 6.1% 5.2% 5.9% 

(6.8% - 11%) (5.8% - 9.4%) (5.3% - 8.5%) (4.6% - 7.5%) (4% - 6.5%) (4.5% - 7.

5% 3.3% 3.3% 3.3% 3.3% 2.2% 

(3.8% - 6.2%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (1.7% - 2.

 Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-42

Table E-41. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Expos Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

8.8% 7.8% 7% 6.1% 5.3% 6.1% 

(6.7% - 10.8%) (5.9% - 9.6%) (5.3% - 8.6%) (4.7% - 7.6%) (4% - 6.5%) (4.7% - 7.

7% 6.4% 5.8% 5.1% 4.3% 4.9% 

(5.3% - 8.6%) (4.9% - 7.9%) (4.4% - 7.2%) (3.8% - 6.2%) (3.3% - 5.3%) (3.7% - 6

8.2% 5.7% 5% 4.3% 3.7% 4.3% 

(6.3% - 10.1%) (4.3% - 7.1%) (3.8% - 6.2%) (3.3% - 5.4%) (2.8% - 4.5%) (3.3% - 5.

4.5% 4.5% 4.5% 4.5% 3.9% 4.5% 

(3.4% - 5.5%) (3.4% - 5.5%) (3.4% - 5.5%) (3.4% - 5.5%) (3% - 4.8%) (3.4% - 5.

6.6% 4.6% 4.5% 3.9% 3.2% 3.3% 

(5% - 8.2%) (3.5% - 5.7%) (3.4% - 5.6%) (2.9% - 4.8%) (2.4% - 4%) (2.5% - 4.

9.5% 3.4% 3.4% 3.4% 3.4% 2.3% 

(7.3% - 11.7%) (2.6% - 4.3%) (2.6% - 4.3%) (2.6% - 4.3%) (2.6% - 4.3%) (1.7% - 2.

6.3% 5.7% 5% 4.4% 3.7% 4.4% 

(4.8% - 7.7%) (4.4% - 7.1%) (3.8% - 6.2%) (3.3% - 5.4%) (2.8% - 4.5%) (3.3% - 5.

7.4% 3.1% 3.1% 3.1% 2.7% 2% 

(5.6% - 9.1%) (2.4% - 3.9%) (2.4% - 3.9%) (2.4% - 3.9%) (2.1% - 3.4%) (1.5% - 2.

6% 4.2% 4.2% 4% 3.3% 3% 

(4.5% - 7.4%) (3.2% - 5.3%) (3.2% - 5.3%) (3.1% - 5%) (2.5% - 4.1%) (2.3% - 3.

6.6% 5.8% 5.8% 5.1% 4.4% 4.3% 

(5.1% - 8.2%) (4.4% - 7.1%) (4.4% - 7.1%) (3.9% - 6.4%) (3.3% - 5.4%) (3.3% - 5.

4.4% 4.4% 4.4% 4.4% 4% 3.8% 

(3.3% - 5.4%) (3.3% - 5.4%) (3.3% - 5.4%) (3.3% - 5.4%) (3% - 4.9%) (2.9% - 4.

6.9% 4.3% 4.3% 4% 3.5% 3.1% 

(5.3% - 8.5%) (3.3% - 5.4%) (3.3% - 5.4%) (3.1% - 5%) (2.6% - 4.3%) (2.3% - 3.

3.5% 0.7% 0.7% 0.7% 0.7% 0% 

(2.7% - 4.4%) (0.5% - 0.8%) (0.5% - 0.8%) (0.5% - 0.8%) (0.5% - 0.8%) (0% - 0%

6.7% 5.6% 5% 4.3% 3.6% 4.2% 

(5.1% - 8.3%) (4.2% - 6.9%) (3.8% - 6.2%) (3.3% - 5.3%) (2.7% - 4.5%) (3.2% - 5.

3.3% 1.9% 1.9% 1.9% 1.9% 1% 

(2.5% - 4.1%) (1.4% - 2.3%) (1.4% - 2.3%) (1.4% - 2.3%) (1.4% - 2.3%) (0.7% - 1.

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-43

Table E-42. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Expos Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

8.2% 7.3% 6.5% 5.7% 4.9% 5.7% 

(6.3% - 10.1%) (5.5% - 9%) (4.9% - 8%) (4.3% - 7%) (3.7% - 6.1%) (4.3% - 7

7% 6.4% 5.8% 5.1% 4.3% 4.9% 

(5.3% - 8.6%) (4.9% - 7.9%) (4.4% - 7.2%) (3.8% - 6.3%) (3.3% - 5.3%) (3.7% - 6

8.5% 6% 5.3% 4.6% 3.9% 4.6% 

(6.5% - 10.5%) (4.5% - 7.4%) (4% - 6.5%) (3.5% - 5.7%) (2.9% - 4.8%) (3.5% - 5.

4.9% 4.9% 4.9% 4.9% 4.3% 4.9% 

(3.7% - 6%) (3.7% - 6%) (3.7% - 6%) (3.7% - 6%) (3.3% - 5.3%) (3.7% - 6

7% 4.9% 4.9% 4.2% 3.5% 3.6% 

(5.4% - 8.7%) (3.8% - 6.1%) (3.7% - 6%) (3.2% - 5.2%) (2.6% - 4.3%) (2.7% - 4.

9.9% 3.6% 3.6% 3.6% 3.6% 2.5% 

(7.6% - 12.1%) (2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (1.9% - 3.

6.4% 5.9% 5.2% 4.5% 3.8% 4.5% 

(4.9% - 7.9%) (4.5% - 7.2%) (3.9% - 6.4%) (3.4% - 5.5%) (2.9% - 4.7%) (3.4% - 5.

7.6% 3.3% 3.3% 3.3% 2.8% 2.2% 

(5.8% - 9.4%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (2.2% - 3.5%) (1.6% - 2.

6.9% 5.1% 5.1% 4.8% 4.1% 3.7% 

(5.3% - 8.5%) (3.8% - 6.3%) (3.8% - 6.3%) (3.7% - 6%) (3.1% - 5%) (2.8% - 4.

6.6% 5.7% 5.7% 5.1% 4.3% 4.3% 

(5% - 8.1%) (4.3% - 7.1%) (4.3% - 7.1%) (3.9% - 6.3%) (3.3% - 5.3%) (3.2% - 5.

3.6% 3.6% 3.6% 3.6% 3.2% 3.1% 

(2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (2.4% - 4%) (2.4% - 3.

7.8% 5.1% 5.1% 4.7% 4.1% 3.7% 

(6% - 9.6%) (3.8% - 6.3%) (3.8% - 6.3%) (3.6% - 5.9%) (3.1% - 5.1%) (2.8% - 4.

4.9% 1.6% 1.6% 1.6% 1.6% 0.7% 

(3.7% - 6.1%) (1.2% - 1.9%) (1.2% - 1.9%) (1.2% - 1.9%) (1.2% - 1.9%) (0.5% - 0.

7.3% 6.1% 5.5% 4.8% 4.1% 4.7% 

(5.6% - 9%) (4.7% - 7.6%) (4.2% - 6.8%) (3.6% - 5.9%) (3.1% - 5%) (3.5% - 5.

3.4% 2% 2% 2% 2% 1.1% 

(2.6% - 4.2%) (1.5% - 2.5%) (1.5% - 2.5%) (1.5% - 2.5%) (1.5% - 2.5%) (0.8% - 1.

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-44

Table E-43. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mo with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: E

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 21%) (32% - 32%) (21% - 

-9% 0% 8% 19% 30% 22% 

(-9% - -9%) (0% - 0%) (8% - 8%) (19% - 19%) (30% - 30%) (22% - 

-42% 0% 12% 23% 35% 23% 

(-41% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 

-37% 0% 1% 13% 25% 23% 

(-36% - -37%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (23% - 

-182% 0% 0% 0% 0% 34% 

(-180% - -184%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (34% - 

-9% 0% 12% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 

-120% 0% 0% 0% 12% 31% 

(-119% - -122%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 

-35% 0% 0% 4% 18% 25% 

(-34% - -35%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 

-14% 0% 0% 11% 23% 24% 

(-14% - -14%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 23%) (24% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-52% 0% 0% 6% 18% 25% 

(-51% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (25% - 

-252% 0% 0% 0% 0% 64% 

(-251% - -254%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 

-17% 0% 9% 20% 31% 22% 

(-17% - -18%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 31%) (22% - 

-53% 0% 0% 0% 0% 33% 

(-52% - -53%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortali

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-45

Table E-44. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mor with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: E

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

-12% 0% 10% 21% 32% 2

(-12% - -12%) (0% - 0%) (10% - 11%) (21% - 21%) (31% - 32%) (21% 

-9% 0% 9% 21% 32% 2

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (24% 

-43% 0% 12% 24% 36% 2

(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% 

0% 0% 0% 0% 13% 0

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% 

-43% 0% 1% 16% 30% 2

(-43% - -44%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 30%) (28% 

-173% 0% 0% 0% 0% 3

(-171% - -176%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% 

-9% 0% 12% 24% 36% 2

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% 

-133% 0% 0% 0% 13% 3

(-132% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (35% 

-40% 0% 0% 5% 21% 2

(-40% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% 

-15% 0% 0% 11% 24% 2

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% 

0% 0% 0% 0% 10% 1

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% 

-59% 0% 0% 7% 20% 2

(-59% - -60%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% 

-431% 0% 0% 0% 0% 10

(-428% - -433%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% 

-20% 0% 10% 23% 35% 2

(-20% - -20%) (0% - 0%) (10% - 11%) (23% - 23%) (35% - 35%) (25% 

-75% 0% 0% 0% 0% 4

(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mort

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-46

Table E-45. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mor with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Es

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 1

-13% 0% 11% 22% 33% 2

(-13% - -13%) (0% - 0%) (11% - 11%) (22% - 22%) (33% - 33%) (22% 

-10% 0% 9% 21% 33% 2

(-9% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% 

-43% 0% 12% 23% 35% 2

(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% 

0% 0% 0% 0% 12% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% 

-42% 0% 1% 15% 29% 2

(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (27% 

-171% 0% 0% 0% 0% 3

(-169% - -174%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% 

-9% 0% 12% 24% 36% 2

(-9% - -9%) (0% - 0%) (12% - 12%) (24% - 24%) (35% - 36%) (24% 

-132% 0% 0% 0% 13% 3

(-130% - -133%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% 

-37% 0% 0% 5% 19% 2

(-37% - -37%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (27% 

-15% 0% 0% 11% 24% 2

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% 

0% 0% 0% 0% 11% 1

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% 

-55% 0% 0% 6% 19% 2

(-54% - -55%) (0% - 0%) (0% - 0%) (6% - 7%) (19% - 19%) (27% 

-215% 0% 0% 0% 0% 5

(-214% - -216%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% 

-19% 0% 10% 22% 34% 2

(-19% - -19%) (0% - 0%) (10% - 10%) (22% - 22%) (34% - 34%) (24% 

-73% 0% 0% 0% 0% 4

(-73% - -73%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mort

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-47

Table E-46. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient 

 a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

722 643 577 509 441 50

(569 - 872) (506 - 778) (453 - 698) (399 - 617) (345 - 535) (399 - 

715 660 606 536 465 51

(563 - 863) (518 - 797) (476 - 733) (420 - 649) (364 - 564) (405 - 

516 366 324 282 240 28

(406 - 622) (287 - 443) (254 - 393) (221 - 343) (187 - 291) (221 - 

455 455 455 455 405 45

(357 - 552) (357 - 552) (357 - 552) (357 - 552) (317 - 492) (357 - 

1054 775 766 674 581 59

(830 - 1271) (608 - 939) (601 - 928) (528 - 817) (454 - 705) (466 - 

338 122 122 122 122 8

(266 - 408) (95 - 148) (95 - 148) (95 - 148) (95 - 148) (63 - 

707 649 574 500 424 50

(555 - 856) (508 - 786) (450 - 697) (391 - 607) (331 - 516) (391 - 

3328 1526 1526 1526 1344 10

(2618 - 4019) (1191 - 1856) (1191 - 1856) (1191 - 1856) (1048 - 1636) (822 - 

3117 2326 2326 2223 1907 17

(2450 - 3768) (1821 - 2820) (1821 - 2820) (1740 - 2697) (1491 - 2317) (1363 - 

621 545 545 488 420 41

(488 - 752) (427 - 660) (427 - 660) (382 - 592) (328 - 510) (325 - 

579 579 579 579 521 50

(453 - 704) (453 - 704) (453 - 704) (453 - 704) (407 - 634) (395 - 

747 495 495 466 409 37

(588 - 902) (388 - 601) (388 - 601) (364 - 565) (320 - 497) (291 - 

109 31 31 31 31 1

(85 - 132) (24 - 38) (24 - 38) (24 - 38) (24 - 38) (9 - 

1069 913 830 732 633 71

(842 - 1290) (718 - 1104) (651 - 1005) (574 - 888) (496 - 769) (559 - 

156 103 103 103 103 6

(122 - 190) (80 - 125) (80 - 125) (80 - 125) (80 - 125) (54 - 

 Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5 Concentratio

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards 

Denoted n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-48

Table E-47. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient P

 a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

744 662 594 524 454 52

(586 - 898) (521 - 801) (466 - 719) (411 - 635) (355 - 550) (411 - 

584 534 486 424 361 40

(459 - 707) (419 - 647) (381 - 590) (332 - 515) (282 - 439) (319 - 

486 341 301 260 219 26

(382 - 587) (267 - 414) (235 - 365) (203 - 316) (171 - 267) (203 - 

344 344 344 344 300 34

(268 - 417) (268 - 417) (268 - 417) (268 - 417) (234 - 365) (268 - 

770 537 530 453 375 38

(604 - 932) (420 - 652) (414 - 643) (354 - 551) (293 - 457) (303 - 

352 129 129 129 129 87 

(277 - 424) (100 - 157) (100 - 157) (100 - 157) (100 - 157) (67 - 

686 627 553 479 404 47

(537 - 831) (491 - 761) (433 - 672) (374 - 582) (315 - 491) (374 - 

2945 1263 1263 1263 1094 82

(2313 - 3562) (985 - 1538) (985 - 1538) (985 - 1538) (852 - 1333) (642 - 

2435 1739 1739 1649 1373 123

(1907 - 2951) (1358 - 2114) (1358 - 2114) (1288 - 2005) (1071 - 1671) (960 - 

564 491 491 437 373 36

(442 - 683) (385 - 596) (385 - 596) (342 - 531) (291 - 453) (288 - 

614 614 614 614 553 53

(480 - 746) (480 - 746) (480 - 746) (480 - 746) (432 - 673) (419 - 

595 373 373 348 299 26

(467 - 720) (292 - 454) (292 - 454) (271 - 423) (233 - 364) (208 - 

89 17 17 17 17 0 

(69 - 108) (13 - 21) (13 - 21) (13 - 21) (13 - 21) (0 - 

810 677 606 522 438 50

(636 - 981) (530 - 821) (474 - 735) (408 - 635) (342 - 533) (395 - 

106 61 61 61 61 32 

(83 - 129) (47 - 74) (47 - 74) (47 - 74) (47 - 74) (25 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5 Concentratio

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards 

Denoted n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-49

Table E-48. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient 

 a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based o

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

717 636 568 500 430 5

(563 - 865) (500 - 770) (446 - 689) (391 - 606) (337 - 523) (391 

583 533 485 423 361 4

(458 - 706) (418 - 646) (380 - 589) (331 - 514) (282 - 438) (318 

509 359 318 276 234 2

(401 - 615) (282 - 436) (249 - 386) (216 - 335) (182 - 284) (216 

383 383 383 383 337 3

(299 - 465) (299 - 465) (299 - 465) (299 - 465) (263 - 409) (299 

810 572 564 485 406 4

(636 - 980) (447 - 694) (441 - 685) (379 - 590) (317 - 494) (327 

370 138 138 138 138 

(292 - 446) (108 - 168) (108 - 168) (108 - 168) (108 - 168) (74 

715 655 579 501 424 5

(561 - 866) (513 - 794) (453 - 702) (392 - 609) (331 - 515) (392 

3056 1333 1333 1333 1160 8

(2401 - 3695) (1040 - 1623) (1040 - 1623) (1040 - 1623) (904 - 1413) (688 

2837 2080 2080 1982 1681 1

(2227 - 3434) (1627 - 2526) (1627 - 2526) (1550 - 2408) (1313 - 2044) (1191 

558 486 486 432 368 3

(437 - 675) (381 - 589) (381 - 589) (338 - 525) (288 - 447) (284 

522 522 522 522 464 4

(407 - 635) (407 - 635) (407 - 635) (407 - 635) (362 - 565) (350 

667 434 434 407 354 3

(524 - 806) (340 - 527) (340 - 527) (318 - 494) (276 - 430) (249 

127 41 41 41 41 

(99 - 154) (32 - 50) (32 - 50) (32 - 50) (32 - 50) (14 

887 746 671 584 495 5

(696 - 1072) (585 - 904) (526 - 814) (456 - 709) (386 - 602) (443 

111 65 65 65 65 

(87 - 135) (50 - 79) (50 - 79) (50 - 79) (50 - 79) (27 

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5 Concentrat

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2

:

1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-50

Table E-49. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Expos Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

12.4% 11% 9.9% 8.7% 7.5% 8.7

(9.7% - 14.9%) (8.7% - 13.3%) (7.7% - 11.9%) (6.8% - 10.6%) (5.9% - 9.2%) (6.8% - 

12.2% 11.2% 10.3% 9.1% 7.9% 8.8

(9.6% - 14.7%) (8.8% - 13.5%) (8.1% - 12.4%) (7.1% - 11%) (6.2% - 9.6%) (6.9% - 

12.4% 8.8% 7.8% 6.8% 5.8% 6.8

(9.8% - 15%) (6.9% - 10.7%) (6.1% - 9.5%) (5.3% - 8.3%) (4.5% - 7%) (5.3% - 

8.6% 8.6% 8.6% 8.6% 7.7% 8.6

(6.7% - 10.4%) (6.7% - 10.4%) (6.7% - 10.4%) (6.7% - 10.4%) (6% - 9.3%) (6.7% - 

12.8% 9.4% 9.3% 8.2% 7.1% 7.3

(10.1% - 15.5%) (7.4% - 11.4%) (7.3% - 11.3%) (6.4% - 10%) (5.5% - 8.6%) (5.7% - 

13.1% 4.7% 4.7% 4.7% 4.7% 3.1

(10.3% - 15.8%) (3.7% - 5.7%) (3.7% - 5.7%) (3.7% - 5.7%) (3.7% - 5.7%) (2.4% - 

9.5% 8.7% 7.7% 6.7% 5.7% 6.7

(7.4% - 11.5%) (6.8% - 10.5%) (6% - 9.4%) (5.2% - 8.1%) (4.4% - 6.9%) (5.2% - 

11.8% 5.4% 5.4% 5.4% 4.8% 3.8

(9.3% - 14.3%) (4.2% - 6.6%) (4.2% - 6.6%) (4.2% - 6.6%) (3.7% - 5.8%) (2.9% - 

11% 8.2% 8.2% 7.8% 6.7% 6.1

(8.6% - 13.3%) (6.4% - 9.9%) (6.4% - 9.9%) (6.1% - 9.5%) (5.2% - 8.1%) (4.8% - 

10.4% 9.1% 9.1% 8.1% 7% 6.9

(8.1% - 12.5%) (7.1% - 11%) (7.1% - 11%) (6.4% - 9.9%) (5.5% - 8.5%) (5.4% - 

6.2% 6.2% 6.2% 6.2% 5.5% 5.4

(4.8% - 7.5%) (4.8% - 7.5%) (4.8% - 7.5%) (4.8% - 7.5%) (4.3% - 6.7%) (4.2% - 

12.3% 8.1% 8.1% 7.6% 6.7% 6.1

(9.6% - 14.8%) (6.4% - 9.9%) (6.4% - 9.9%) (6% - 9.3%) (5.2% - 8.2%) (4.8% - 

6.3% 1.8% 1.8% 1.8% 1.8% 0.6

(5% - 7.7%) (1.4% - 2.2%) (1.4% - 2.2%) (1.4% - 2.2%) (1.4% - 2.2%) (0.5% - 

12.6% 10.8% 9.8% 8.6% 7.5% 8.4

(9.9% - 15.2%) (8.4% - 13%) (7.7% - 11.8%) (6.8% - 10.5%) (5.8% - 9%) (6.6% - 

7.1% 4.7% 4.7% 4.7% 4.7% 3.1

(5.6% - 8.6%) (3.6% - 5.7%) (3.6% - 5.7%) (3.6% - 5.7%) (3.6% - 5.7%) (2.4% - 

 Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to P

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily 

Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-51

Table E-50. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Expos Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

12.4% 11% 9.9% 8.7% 7.5% 8.7

(9.7% - 14.9%) (8.6% - 13.3%) (7.7% - 11.9%) (6.8% - 10.5%) (5.9% - 9.1%) (6.8% - 

9.9% 9.1% 8.2% 7.2% 6.1% 6.9

(7.8% - 12%) (7.1% - 11%) (6.5% - 10%) (5.6% - 8.7%) (4.8% - 7.4%) (5.4% - 

11.6% 8.1% 7.2% 6.2% 5.2% 6.2

(9.1% - 14%) (6.4% - 9.8%) (5.6% - 8.7%) (4.8% - 7.5%) (4.1% - 6.4%) (4.8% - 

6.4% 6.4% 6.4% 6.4% 5.5% 6.4

(5% - 7.7%) (5% - 7.7%) (5% - 7.7%) (5% - 7.7%) (4.3% - 6.7%) (5% - 7

9.4% 6.5% 6.5% 5.5% 4.6% 4.7

(7.4% - 11.4%) (5.1% - 8%) (5% - 7.8%) (4.3% - 6.7%) (3.6% - 5.6%) (3.7% - 

13.4% 4.9% 4.9% 4.9% 4.9% 3.3

(10.6% - 16.2%) (3.8% - 6%) (3.8% - 6%) (3.8% - 6%) (3.8% - 6%) (2.6% 

8.9% 8.1% 7.2% 6.2% 5.2% 6.2

(7% - 10.8%) (6.4% - 9.9%) (5.6% - 8.7%) (4.9% - 7.6%) (4.1% - 6.4%) (4.9% - 

10.4% 4.5% 4.5% 4.5% 3.9% 2.9

(8.2% - 12.6%) (3.5% - 5.4%) (3.5% - 5.4%) (3.5% - 5.4%) (3% - 4.7%) (2.3% - 

8.5% 6.1% 6.1% 5.7% 4.8% 4.3

(6.6% - 10.3%) (4.7% - 7.4%) (4.7% - 7.4%) (4.5% - 7%) (3.7% - 5.8%) (3.3% - 

9.4% 8.2% 8.2% 7.3% 6.2% 6.2

(7.4% - 11.4%) (6.4% - 10%) (6.4% - 10%) (5.7% - 8.9%) (4.9% - 7.6%) (4.8% - 

6.3% 6.3% 6.3% 6.3% 5.7% 5.5

(4.9% - 7.6%) (4.9% - 7.6%) (4.9% - 7.6%) (4.9% - 7.6%) (4.4% - 6.9%) (4.3% - 

9.8% 6.2% 6.2% 5.7% 4.9% 4.4

(7.7% - 11.9%) (4.8% - 7.5%) (4.8% - 7.5%) (4.5% - 7%) (3.8% - 6%) (3.4% - 

5% 0.9% 0.9% 0.9% 0.9% 0% 

(3.9% - 6.1%) (0.7% - 1.2%) (0.7% - 1.2%) (0.7% - 1.2%) (0.7% - 1.2%) (0% - 

9.5% 7.9% 7.1% 6.1% 5.1% 5.9

(7.4% - 11.5%) (6.2% - 9.6%) (5.5% - 8.6%) (4.8% - 7.4%) (4% - 6.2%) (4.6% - 

4.7% 2.7% 2.7% 2.7% 2.7% 1.4

(3.7% - 5.8%) (2.1% - 3.3%) (2.1% - 3.3%) (2.1% - 3.3%) (2.1% - 3.3%) (1.1% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to P

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily 

Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-52

Table E-51. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Expos Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

11.6% 10.3% 9.2% 8.1% 7% 8.1% 

(9.1% - 14%) (8.1% - 12.5%) (7.2% - 11.1%) (6.3% - 9.8%) (5.4% - 8.5%) (6.3% - 9.

9.9% 9.1% 8.2% 7.2% 6.1% 6.9% 

(7.8% - 12%) (7.1% - 11%) (6.5% - 10%) (5.6% - 8.7%) (4.8% - 7.5%) (5.4% - 8.

12% 8.5% 7.5% 6.5% 5.5% 6.5% 

(9.5% - 14.5%) (6.6% - 10.3%) (5.9% - 9.1%) (5.1% - 7.9%) (4.3% - 6.7%) (5.1% - 7.

7% 7% 7% 7% 6.1% 7% 

(5.4% - 8.4%) (5.4% - 8.4%) (5.4% - 8.4%) (5.4% - 8.4%) (4.8% - 7.4%) (5.4% - 8.

9.9% 7% 6.9% 6% 5% 5.1% 

(7.8% - 12%) (5.5% - 8.5%) (5.4% - 8.4%) (4.7% - 7.2%) (3.9% - 6.1%) (4% - 6.3

13.9% 5.2% 5.2% 5.2% 5.2% 3.5% 

(11% - 16.7%) (4.1% - 6.3%) (4.1% - 6.3%) (4.1% - 6.3%) (4.1% - 6.3%) (2.8% - 4.

9.1% 8.3% 7.4% 6.4% 5.4% 6.4% 

(7.1% - 11%) (6.5% - 10.1%) (5.8% - 8.9%) (5% - 7.8%) (4.2% - 6.6%) (5% - 7.8

10.7% 4.7% 4.7% 4.7% 4.1% 3.1% 

(8.4% - 13%) (3.7% - 5.7%) (3.7% - 5.7%) (3.7% - 5.7%) (3.2% - 5%) (2.4% - 3.

9.8% 7.2% 7.2% 6.9% 5.8% 5.3% 

(7.7% - 11.9%) (5.6% - 8.7%) (5.6% - 8.7%) (5.4% - 8.3%) (4.5% - 7.1%) (4.1% - 6.

9.3% 8.1% 8.1% 7.2% 6.2% 6.1% 

(7.3% - 11.3%) (6.4% - 9.8%) (6.4% - 9.8%) (5.6% - 8.8%) (4.8% - 7.5%) (4.8% - 7.

5.2% 5.2% 5.2% 5.2% 4.6% 4.4% 

(4% - 6.3%) (4% - 6.3%) (4% - 6.3%) (4% - 6.3%) (3.6% - 5.6%) (3.5% - 5.

11.1% 7.2% 7.2% 6.7% 5.9% 5.3% 

(8.7% - 13.4%) (5.6% - 8.7%) (5.6% - 8.7%) (5.3% - 8.2%) (4.6% - 7.1%) (4.1% - 6.

7% 2.2% 2.2% 2.2% 2.2% 1% 

(5.4% - 8.5%) (1.7% - 2.7%) (1.7% - 2.7%) (1.7% - 2.7%) (1.7% - 2.7%) (0.8% - 1.

10.4% 8.7% 7.9% 6.8% 5.8% 6.6% 

(8.1% - 12.5%) (6.8% - 10.6%) (6.1% - 9.5%) (5.3% - 8.3%) (4.5% - 7%) (5.2% - 8.

4.9% 2.8% 2.8% 2.8% 2.8% 1.5% 

(3.8% - 5.9%) (2.2% - 3.5%) (2.2% - 3.5%) (2.2% - 3.5%) (2.2% - 3.5%) (1.2% - 1.

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-53

Table E-52. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mo Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: Estima

 (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 

-12% 0% 10% 21% 32% 

(-12% - -12%) (0% - 0%) (10% - 10%) (21% - 21%) (31% - 32%) (21

-8% 0% 8% 19% 29% 

(-8% - -9%) (0% - 0%) (8% - 8%) (19% - 19%) (29% - 30%) (21

-41% 0% 11% 23% 34% 

(-40% - -42%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (23

0% 0% 0% 0% 11% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0

-36% 0% 1% 13% 25% 

(-35% - -37%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 25%) (23

-178% 0% 0% 0% 0% 

(-175% - -181%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33

-9% 0% 11% 23% 35% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23

-118% 0% 0% 0% 12% 

(-116% - -120%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31

-34% 0% 0% 4% 18% 

(-34% - -34%) (0% - 0%) (0% - 0%) (4% - 4%) (18% - 18%) (25

-14% 0% 0% 10% 23% 

(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 11%) (23% - 23%) (24

0% 0% 0% 0% 10% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13

-51% 0% 0% 6% 17% 

(-50% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (25

-250% 0% 0% 0% 0% 

(-248% - -251%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64

-17% 0% 9% 20% 31% 

(-17% - -17%) (0% - 0%) (9% - 9%) (20% - 20%) (30% - 31%) (22

-52% 0% 0% 0% 0% 

(-52% - -52%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mo

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-54

Table E-53. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mor Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: Estima

 (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

-12% 0% 10% 21% 32% 2

(-12% - -12%) (0% - 0%) (10% - 11%) (21% - 21%) (31% - 32%) (21% 

-9% 0% 9% 21% 32% 2

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (24% 

-43% 0% 12% 24% 36% 2

(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% 

0% 0% 0% 0% 13% 0

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% 

-43% 0% 1% 16% 30% 2

(-43% - -44%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 30%) (28% 

-173% 0% 0% 0% 0% 3

(-171% - -176%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% 

-9% 0% 12% 24% 36% 2

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% 

-133% 0% 0% 0% 13% 3

(-132% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (35% 

-40% 0% 0% 5% 21% 2

(-40% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% 

-15% 0% 0% 11% 24% 2

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% 

0% 0% 0% 0% 10% 1

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% 

-59% 0% 0% 7% 20% 2

(-59% - -60%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% 

-431% 0% 0% 0% 0% 10

(-428% - -433%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% 

-20% 0% 10% 23% 35% 2

(-20% - -20%) (0% - 0%) (10% - 11%) (23% - 23%) (35% - 35%) (25% 

-75% 0% 0% 0% 0% 4

(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mort

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-55

Table E-54. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mor Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Estima

 (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

-13% 0% 11% 21% 32% 2

(-12% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% 

-9% 0% 9% 21% 32% 2

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (24% 

-42% 0% 12% 23% 35% 2

(-41% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% 

0% 0% 0% 0% 12% 0

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% 

-42% 0% 1% 15% 29% 2

(-41% - -42%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (27% 

-168% 0% 0% 0% 0% 3

(-165% - -170%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% 

-9% 0% 12% 23% 35% 2

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% 

-129% 0% 0% 0% 13% 3

(-128% - -131%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% 

-36% 0% 0% 5% 19% 2

(-36% - -37%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (26% 

-15% 0% 0% 11% 24% 2

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% 

0% 0% 0% 0% 11% 1

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% 

-54% 0% 0% 6% 19% 2

(-53% - -54%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 19%) (26% 

-213% 0% 0% 0% 0% 5

(-211% - -215%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% 

-19% 0% 10% 22% 34% 2

(-19% - -19%) (0% - 0%) (10% - 10%) (22% - 22%) (33% - 34%) (24% 

-72% 0% 0% 0% 0% 4

(-72% - -73%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mort

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-56

Table E-55. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5  Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

77 68 61 54 46 54 

(29 - 122) (26 - 108) (23 - 97) (20 - 86) (17 - 74) (20 - 

81 74 68 60 52 58 

(31 - 128) (28 - 118) (26 - 109) (23 - 96) (20 - 84) (22 - 

55 39 34 30 25 30 

(21 - 87) (15 - 62) (13 - 55) (11 - 48) (10 - 41) (11 - 

50 50 50 50 44 50 

(19 - 79) (19 - 79) (19 - 79) (19 - 79) (17 - 71) (19 - 

112 82 81 71 61 63 

(43 - 178) (31 - 131) (31 - 129) (27 - 114) (23 - 98) (24 - 

26 9 9 9 9 6 

(10 - 41) (3 - 15) (3 - 15) (3 - 15) (3 - 15) (2 - 

76 70 62 54 46 54 

(29 - 122) (26 - 112) (23 - 99) (20 - 86) (17 - 73) (20 - 

248 112 112 112 99 78 

(94 - 393) (42 - 181) (42 - 181) (42 - 181) (37 - 160) (29 - 

208 155 155 148 126 11

(79 - 331) (58 - 247) (58 - 247) (56 - 236) (48 - 203) (43 - 

70 61 61 55 47 46 

(26 - 111) (23 - 98) (23 - 98) (21 - 87) (18 - 75) (17 - 

58 58 58 58 53 51 

(22 - 94) (22 - 94) (22 - 94) (22 - 94) (20 - 85) (19 - 

80 53 53 50 44 40 

(31 - 127) (20 - 84) (20 - 84) (19 - 79) (16 - 70) (15 - 

8 22221

(3 - 13) (1 - 4) (1 - 4) (1 - 4) (1 - 4) (0 - 

116 99 90 79 68 77 

(44 - 184) (37 - 157) (34 - 143) (30 - 126) (26 - 109) (29 - 

19 12 12 12 12 8 

(7 - 30) (5 - 20) (5 - 20) (5 - 20) (5 - 20) (3 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-57

Table E-56. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2. Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

79 70 63 55 48 5

(30 - 125) (27 - 112) (24 - 100) (21 - 88) (18 - 77) (21 

66 60 55 48 40 4

(25 - 105) (23 - 96) (21 - 87) (18 - 76) (15 - 65) (17 

52 36 32 28 23 2

(20 - 82) (14 - 58) (12 - 51) (10 - 44) (9 - 37) (10 

37 37 37 37 33 3

(14 - 60) (14 - 60) (14 - 60) (14 - 60) (12 - 52) (14 

82 57 56 48 39 4

(31 - 130) (21 - 91) (21 - 90) (18 - 77) (15 - 64) (15 

27 10 10 10 10 

(10 - 43) (4 - 16) (4 - 16) (4 - 16) (4 - 16) (2 

74 68 60 52 43 5

(28 - 118) (26 - 108) (22 - 96) (19 - 83) (16 - 70) (19 

219 93 93 93 80 6

(83 - 348) (35 - 150) (35 - 150) (35 - 150) (30 - 130) (23 

162 115 115 109 91 8

(61 - 259) (43 - 185) (43 - 185) (41 - 176) (34 - 146) (30 

63 55 55 49 42 4

(24 - 101) (21 - 88) (21 - 88) (18 - 78) (16 - 67) (15 

62 62 62 62 56 5

(23 - 100) (23 - 100) (23 - 100) (23 - 100) (21 - 90) (20 

64 40 40 37 32 2

(24 - 101) (15 - 64) (15 - 64) (14 - 59) (12 - 51) (11 

6 1111

(2 - 10) (0 - 2) (0 - 2) (0 - 2) (0 - 2) (0 

87 73 65 56 47 5

(33 - 139) (27 - 116) (24 - 104) (21 - 90) (18 - 75) (20 

13 7 7 7 7 

(5 - 20) (3 - 12) (3 - 12) (3 - 12) (3 - 12) (1 

 Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Concentrations i

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Stand

n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-58

Table E-57. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5  Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

76 67 60 53 45 53 

(29 - 121) (26 - 107) (23 - 96) (20 - 84) (17 - 73) (20 - 

66 60 55 47 40 46 

(25 - 105) (23 - 96) (21 - 87) (18 - 76) (15 - 65) (17 - 

54 38 34 29 25 29 

(21 - 86) (14 - 61) (13 - 54) (11 - 47) (9 - 40) (11 - 

42 42 42 42 37 42 

(16 - 67) (16 - 67) (16 - 67) (16 - 67) (14 - 59) (16 - 

86 60 59 51 43 44 

(33 - 137) (23 - 97) (22 - 95) (19 - 82) (16 - 69) (16 - 

29 11 11 11 11 7 

(11 - 45) (4 - 17) (4 - 17) (4 - 17) (4 - 17) (3 - 

77 71 62 54 46 54 

(29 - 123) (27 - 113) (23 - 100) (20 - 87) (17 - 73) (20 - 

227 98 98 98 85 65 

(86 - 361) (37 - 158) (37 - 158) (37 - 158) (32 - 138) (24 - 

189 138 138 131 111 10

(72 - 301) (52 - 221) (52 - 221) (49 - 211) (42 - 179) (38 - 

63 54 54 48 41 41 

(24 - 100) (21 - 87) (21 - 87) (18 - 77) (15 - 66) (15 - 

53 53 53 53 47 45 

(20 - 85) (20 - 85) (20 - 85) (20 - 85) (17 - 75) (17 - 

71 46 46 43 38 34 

(27 - 114) (17 - 74) (17 - 74) (16 - 69) (14 - 60) (13 - 

9 33331

(3 - 15) (1 - 5) (1 - 5) (1 - 5) (1 - 5) (0 - 

96 80 72 63 53 61 

(36 - 152) (30 - 128) (27 - 116) (24 - 100) (20 - 85) (23 - 

13 8 8 8 8 4 

(5 - 21) (3 - 12) (3 - 12) (3 - 12) (3 - 12) (2 - 

 Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-59

Table E-58. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to  PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternat

 Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 f

 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

8.6% 7.6% 6.8% 6% 5.2% 6% 

(3.3% - 13.6%) (2.9% - 12.1%) (2.6% - 10.9%) (2.3% - 9.6%) (2% - 8.3%) (2.3% - 9.6

8.4% 7.8% 7.1% 6.3% 5.5% 6.1% 

(3.2% - 13.4%) (3% - 12.3%) (2.7% - 11.3%) (2.4% - 10%) (2.1% - 8.7%) (2.3% - 9.7

8.6% 6.1% 5.4% 4.7% 4% 4.7% 

(3.3% - 13.7%) (2.3% - 9.7%) (2% - 8.6%) (1.8% - 7.5%) (1.5% - 6.4%) (1.8% - 7.5

5.9% 5.9% 5.9% 5.9% 5.3% 5.9% 

(2.2% - 9.5%) (2.2% - 9.5%) (2.2% - 9.5%) (2.2% - 9.5%) (2% - 8.5%) (2.2% - 9.5

8.9% 6.5% 6.5% 5.7% 4.9% 5% 

(3.4% - 14.1%) (2.5% - 10.4%) (2.4% - 10.3%) (2.1% - 9.1%) (1.8% - 7.8%) (1.9% - 8

9.1% 3.2% 3.2% 3.2% 3.2% 2.1% 

(3.5% - 14.4%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (0.8% - 3.5

6.6% 6% 5.3% 4.6% 3.9% 4.6% 

(2.5% - 10.5%) (2.3% - 9.6%) (2% - 8.5%) (1.7% - 7.4%) (1.5% - 6.3%) (1.7% - 7.4

8.2% 3.7% 3.7% 3.7% 3.3% 2.6% 

(3.1% - 13%) (1.4% - 6%) (1.4% - 6%) (1.4% - 6%) (1.2% - 5.3%) (1% - 4.2

7.6% 5.6% 5.6% 5.4% 4.6% 4.2% 

(2.9% - 12.1%) (2.1% - 9%) (2.1% - 9%) (2% - 8.6%) (1.7% - 7.4%) (1.6% - 6.8

7.2% 6.3% 6.3% 5.6% 4.8% 4.8% 

(2.7% - 11.4%) (2.4% - 10%) (2.4% - 10%) (2.1% - 9%) (1.8% - 7.7%) (1.8% - 7.7

4.2% 4.2% 4.2% 4.2% 3.8% 3.7% 

(1.6% - 6.8%) (1.6% - 6.8%) (1.6% - 6.8%) (1.6% - 6.8%) (1.4% - 6.1%) (1.4% - 5.9

8.5% 5.6% 5.6% 5.3% 4.6% 4.2% 

(3.2% - 13.5%) (2.1% - 9%) (2.1% - 9%) (2% - 8.4%) (1.7% - 7.4%) (1.6% - 6.7

4.4% 1.2% 1.2% 1.2% 1.2% 0.4% 

(1.6% - 7%) (0.5% - 2%) (0.5% - 2%) (0.5% - 2%) (0.5% - 2%) (0.2% - 0.7

8.8% 7.5% 6.8% 6% 5.1% 5.8% 

(3.3% - 13.9%) (2.8% - 11.9%) (2.6% - 10.8%) (2.3% - 9.5%) (1.9% - 8.2%) (2.2% - 9.3

4.9% 3.2% 3.2% 3.2% 3.2% 2.1% 

(1.8% - 7.8%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (0.8% - 3.5

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Conc

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM co1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-60

Table E-59. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure  to Ambient PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current 

 and Alternative Standards, Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (20

 Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

8.6% 7.6% 6.8% 6% 5.2% 6% 

(3.3% - 13.6%) (2.9% - 12.1%) (2.6% - 10.9%) (2.3% - 9.6%) (2% - 8.3%) (2.3% - 9.6

6.9% 6.3% 5.7% 5% 4.2% 4.8% 

(2.6% - 10.9%) (2.4% - 10%) (2.1% - 9.1%) (1.9% - 7.9%) (1.6% - 6.8%) (1.8% - 7.6

8% 5.6% 4.9% 4.3% 3.6% 4.3% 

(3.1% - 12.7%) (2.1% - 9%) (1.9% - 7.9%) (1.6% - 6.8%) (1.3% - 5.8%) (1.6% - 6.8

4.4% 4.4% 4.4% 4.4% 3.8% 4.4% 

(1.6% - 7%) (1.6% - 7%) (1.6% - 7%) (1.6% - 7%) (1.4% - 6.1%) (1.6% - 7

6.5% 4.5% 4.4% 3.8% 3.1% 3.2% 

(2.5% - 10.4%) (1.7% - 7.2%) (1.7% - 7.1%) (1.4% - 6.1%) (1.2% - 5.1%) (1.2% - 5.2

9.3% 3.4% 3.4% 3.4% 3.4% 2.3% 

(3.6% - 14.8%) (1.3% - 5.4%) (1.3% - 5.4%) (1.3% - 5.4%) (1.3% - 5.4%) (0.8% - 3.7

6.1% 5.6% 4.9% 4.3% 3.6% 4.3% 

(2.3% - 9.8%) (2.1% - 9%) (1.9% - 7.9%) (1.6% - 6.9%) (1.3% - 5.8%) (1.6% - 6.9

7.2% 3.1% 3.1% 3.1% 2.6% 2% 

(2.7% - 11.5%) (1.1% - 4.9%) (1.1% - 4.9%) (1.1% - 4.9%) (1% - 4.3%) (0.7% - 3.2

5.9% 4.2% 4.2% 3.9% 3.3% 2.9% 

(2.2% - 9.4%) (1.6% - 6.7%) (1.6% - 6.7%) (1.5% - 6.4%) (1.2% - 5.3%) (1.1% - 4.7

6.5% 5.7% 5.7% 5% 4.3% 4.2% 

(2.5% - 10.4%) (2.1% - 9.1%) (2.1% - 9.1%) (1.9% - 8.1%) (1.6% - 6.9%) (1.6% - 6.8

4.3% 4.3% 4.3% 4.3% 3.9% 3.8% 

(1.6% - 6.9%) (1.6% - 6.9%) (1.6% - 6.9%) (1.6% - 6.9%) (1.5% - 6.3%) (1.4% - 6.

6.8% 4.2% 4.2% 3.9% 3.4% 3% 

(2.6% - 10.8%) (1.6% - 6.8%) (1.6% - 6.8%) (1.5% - 6.3%) (1.3% - 5.5%) (1.1% - 4.9

3.4% 0.6% 0.6% 0.6% 0.6% 0% 

(1.3% - 5.6%) (0.2% - 1%) (0.2% - 1%) (0.2% - 1%) (0.2% - 1%) (0% - 0%

6.6% 5.5% 4.9% 4.2% 3.5% 4.1% 

(2.5% - 10.5%) (2.1% - 8.8%) (1.8% - 7.8%) (1.6% - 6.8%) (1.3% - 5.7%) (1.5% - 6.6

3.2% 1.9% 1.9% 1.9% 1.9% 1% 

(1.2% - 5.2%) (0.7% - 3%) (0.7% - 3%) (0.7% - 3%) (0.7% - 3%) (0.4% - 1.6

Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Conc

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM co1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-61

Table E-60. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure t PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alterna

 Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 f

 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

8% 7.1% 6.4% 5.6% 4.8% 5.6% 

(3.1% - 12.8%) (2.7% - 11.4%) (2.4% - 10.1%) (2.1% - 8.9%) (1.8% - 7.7%) (2.1% - 8.9

6.9% 6.3% 5.7% 5% 4.2% 4.8% 

(2.6% - 10.9%) (2.4% - 10%) (2.1% - 9.1%) (1.9% - 7.9%) (1.6% - 6.8%) (1.8% - 7.6

8.3% 5.9% 5.2% 4.5% 3.8% 4.5% 

(3.2% - 13.2%) (2.2% - 9.4%) (1.9% - 8.3%) (1.7% - 7.2%) (1.4% - 6.1%) (1.7% - 7.2

4.8% 4.8% 4.8% 4.8% 4.2% 4.8% 

(1.8% - 7.7%) (1.8% - 7.7%) (1.8% - 7.7%) (1.8% - 7.7%) (1.6% - 6.8%) (1.8% - 7.7

6.9% 4.8% 4.8% 4.1% 3.4% 3.5% 

(2.6% - 11%) (1.8% - 7.8%) (1.8% - 7.7%) (1.5% - 6.6%) (1.3% - 5.5%) (1.3% - 5.7

9.7% 3.6% 3.6% 3.6% 3.6% 2.4% 

(3.7% - 15.3%) (1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (0.9% - 3.9

6.3% 5.7% 5.1% 4.4% 3.7% 4.4% 

(2.4% - 10%) (2.2% - 9.2%) (1.9% - 8.1%) (1.6% - 7%) (1.4% - 6%) (1.6% - 7

7.4% 3.2% 3.2% 3.2% 2.8% 2.1% 

(2.8% - 11.8%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (1% - 4.5%) (0.8% - 3.4

6.8% 5% 5% 4.7% 4% 3.6% 

(2.6% - 10.8%) (1.9% - 7.9%) (1.9% - 7.9%) (1.8% - 7.6%) (1.5% - 6.4%) (1.4% - 5.8

6.4% 5.6% 5.6% 5% 4.2% 4.2% 

(2.4% - 10.3%) (2.1% - 9%) (2.1% - 9%) (1.9% - 8%) (1.6% - 6.8%) (1.6% - 6.7

3.6% 3.6% 3.6% 3.6% 3.2% 3.1% 

(1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (1.2% - 5.1%) (1.1% - 4.9

7.7% 5% 5% 4.6% 4% 3.6% 

(2.9% - 12.2%) (1.9% - 8%) (1.9% - 8%) (1.7% - 7.4%) (1.5% - 6.5%) (1.4% - 5.9

4.8% 1.5% 1.5% 1.5% 1.5% 0.7% 

(1.8% - 7.7%) (0.6% - 2.5%) (0.6% - 2.5%) (0.6% - 2.5%) (0.6% - 2.5%) (0.3% - 1.

7.2% 6% 5.4% 4.7% 4% 4.6% 

(2.7% - 11.4%) (2.3% - 9.6%) (2% - 8.7%) (1.8% - 7.5%) (1.5% - 6.4%) (1.7% - 7.3

3.3% 1.9% 1.9% 1.9% 1.9% 1% 

(1.3% - 5.4%) (0.7% - 3.1%) (0.7% - 3.1%) (0.7% - 3.1%) (0.7% - 3.1%) (0.4% - 1.7

Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Conc

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM co1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-62

Table E-61. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Assoc Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 1435 13/35 12/35 13/3

-13% 0% 11% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 22%) (31% - 32%) (21% - 

-9% 0% 8% 19% 30% 22% 

(-8% - -9%) (0% - 0%) (8% - 9%) (19% - 19%) (29% - 30%) (21% - 

-42% 0% 12% 23% 35% 23% 

(-41% - -43%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 

-37% 0% 1% 13% 25% 23% 

(-36% - -38%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (23% - 

-182% 0% 0% 0% 0% 34% 

(-177% - -188%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

-9% 0% 12% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 24%) (35% - 35%) (23% - 

-121% 0% 0% 0% 12% 31% 

(-117% - -124%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 

-35% 0% 0% 4% 18% 25% 

(-34% - -36%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 

-14% 0% 0% 11% 23% 24% 

(-14% - -15%) (0% - 0%) (0% - 0%) (10% - 11%) (23% - 24%) (24% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-52% 0% 0% 6% 18% 25% 

(-50% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (25% - 

-252% 0% 0% 0% 0% 64% 

(-249% - -256%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 

-17% 0% 9% 20% 31% 22% 

(-17% - -18%) (0% - 0%) (9% - 9%) (20% - 21%) (31% - 32%) (22% - 

-53% 0% 0% 0% 0% 33% 

(-52% - -54%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated 

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Al

Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-63

Table E-62. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Assoc Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 1435 13/35 12/35 13/3

-13% 0% 11% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 22%) (31% - 32%) (21% - 

-10% 0% 9% 21% 33% 24% 

(-9% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (32% - 33%) (24% - 

-43% 0% 12% 24% 36% 24% 

(-42% - -45%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 

0% 0% 0% 0% 13% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 

-44% 0% 1% 16% 30% 28% 

(-43% - -45%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 31%) (28% - 

-177% 0% 0% 0% 0% 33% 

(-172% - -183%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

-9% 0% 12% 24% 36% 24% 

(-9% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 

-136% 0% 0% 0% 13% 35% 

(-132% - -139%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 14%) (35% - 

-41% 0% 0% 5% 21% 29% 

(-40% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 

-15% 0% 0% 11% 24% 25% 

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-60% 0% 0% 7% 20% 29% 

(-59% - -62%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 

-434% 0% 0% 0% 0% 100

(-430% - -439%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 

-20% 0% 11% 23% 36% 25% 

(-20% - -21%) (0% - 0%) (10% - 11%) (23% - 23%) (35% - 36%) (25% - 

-75% 0% 0% 0% 0% 48% 

(-75% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (48% - 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated 

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Al

Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-64

Table E-63. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Assoc Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 1435 13/35 12/35 13/3

-13% 0% 11% 22% 33% 22% 

(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 

-10% 0% 9% 21% 33% 24% 

(-9% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (32% - 33%) (24% - 

-43% 0% 12% 23% 35% 23% 

(-41% - -44%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 

-42% 0% 1% 15% 29% 27% 

(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 30%) (27% - 

-172% 0% 0% 0% 0% 32% 

(-166% - -177%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 

-9% 0% 12% 24% 36% 24% 

(-9% - -10%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 

-132% 0% 0% 0% 13% 34% 

(-128% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 

-37% 0% 0% 5% 19% 27% 

(-36% - -38%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 20%) (27% - 

-15% 0% 0% 11% 24% 25% 

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 

0% 0% 0% 0% 11% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 

-55% 0% 0% 6% 19% 27% 

(-53% - -56%) (0% - 0%) (0% - 0%) (6% - 7%) (19% - 19%) (26% - 

-215% 0% 0% 0% 0% 55% 

(-212% - -219%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 

-19% 0% 10% 22% 34% 24% 

(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 22%) (34% - 34%) (24% - 

-73% 0% 0% 0% 0% 46% 

(-72% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated 

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Al

Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-65

Table E-64. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.

 Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

110 98 88 78 67 

(49 - 167) (44 - 149) (39 - 134) (34 - 119) (30 - 103) (34 

116 107 99 87 76 

(52 - 176) (48 - 163) (44 - 150) (38 - 133) (33 - 116) (37 

79 56 50 43 37 

(35 - 120) (25 - 86) (22 - 77) (19 - 67) (16 - 57) (19 

72 72 72 72 64 

(32 - 110) (32 - 110) (32 - 110) (32 - 110) (28 - 98) (32 

161 119 117 103 89 

(72 - 244) (52 - 181) (52 - 179) (45 - 158) (39 - 137) (40 

38 14 14 14 14 

(17 - 57) (6 - 21) (6 - 21) (6 - 21) (6 - 21) (4 

111 101 90 78 66 

(49 - 169) (45 - 155) (39 - 138) (34 - 120) (29 - 102) (34 

357 164 164 164 144 1

(159 - 541) (71 - 253) (71 - 253) (71 - 253) (63 - 223) (49 

300 224 224 214 184 1

(133 - 457) (99 - 343) (99 - 343) (94 - 329) (80 - 283) (73 

101 88 88 79 68 

(45 - 154) (39 - 135) (39 - 135) (35 - 121) (30 - 105) (30 

85 85 85 85 76 

(37 - 131) (37 - 131) (37 - 131) (37 - 131) (33 - 118) (32 

115 76 76 72 63 

(51 - 175) (34 - 117) (34 - 117) (32 - 110) (28 - 97) (25 

11 3 3 3 3 

(5 - 18) (1 - 5) (1 - 5) (1 - 5) (1 - 5) (1 

167 142 129 114 99 1

(74 - 252) (63 - 217) (57 - 197) (50 - 175) (43 - 152) (49 

27 18 18 18 18 

(12 - 41) (8 - 27) (8 - 27) (8 - 27) (8 - 27) (5 

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Concentrations 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Stan

n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-66

Table E-65. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5 

 Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

114 101 91 80 69 8

(51 - 172) (45 - 154) (40 - 138) (35 - 123) (30 - 106) (35 

95 87 79 69 59 6

(42 - 145) (38 - 133) (35 - 121) (30 - 106) (26 - 91) (29 

75 52 46 40 34 4

(33 - 113) (23 - 80) (20 - 71) (18 - 62) (15 - 52) (18 

54 54 54 54 47 5

(24 - 84) (24 - 84) (24 - 84) (24 - 84) (21 - 73) (24 

118 82 81 69 58 5

(52 - 180) (36 - 127) (35 - 125) (30 - 107) (25 - 89) (26 

39 14 14 14 14 

(18 - 59) (6 - 22) (6 - 22) (6 - 22) (6 - 22) (4 

107 98 87 75 63 7

(47 - 164) (43 - 150) (38 - 133) (33 - 116) (28 - 98) (33 

316 135 135 135 117 8

(140 - 481) (59 - 210) (59 - 210) (59 - 210) (51 - 182) (38 

234 167 167 159 132 1

(103 - 359) (73 - 258) (73 - 258) (69 - 245) (58 - 205) (52 

91 80 80 71 61 6

(40 - 140) (35 - 122) (35 - 122) (31 - 109) (26 - 93) (26 

90 90 90 90 81 7

(39 - 139) (39 - 139) (39 - 139) (39 - 139) (35 - 125) (34 

92 58 58 54 46 4

(41 - 140) (25 - 89) (25 - 89) (23 - 83) (20 - 71) (18 

9 2222

(4 - 14) (1 - 3) (1 - 3) (1 - 3) (1 - 3) (0 

126 105 94 81 68 7

(56 - 193) (46 - 162) (41 - 145) (36 - 126) (30 - 106) (34 

18 10 10 10 10 

(8 - 28) (5 - 16) (5 - 16) (5 - 16) (5 - 16) (2 

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin

 Baltimore, MD Detroit, MI Houston, TX St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

 Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Concentrations i

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Stand

n/m)2:
E-67

Table E-66. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5 

 Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on A

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

109 97 87 76 66 76 

(49 - 166) (43 - 148) (38 - 133) (34 - 117) (29 - 101) (34 - 

95 87 79 69 59 66 

(42 - 145) (38 - 133) (35 - 121) (30 - 106) (26 - 91) (29 - 

78 55 49 42 36 42 

(35 - 119) (24 - 85) (21 - 75) (19 - 65) (16 - 56) (19 - 

60 60 60 60 53 60 

(27 - 93) (27 - 93) (27 - 93) (27 - 93) (23 - 82) (27 - 

124 88 86 74 62 64 

(55 - 189) (38 - 135) (38 - 133) (32 - 115) (27 - 96) (28 - 

41 15 15 15 15 11 

(18 - 62) (7 - 24) (7 - 24) (7 - 24) (7 - 24) (5 - 

112 102 90 78 66 78 

(49 - 171) (45 - 157) (40 - 139) (34 - 121) (29 - 102) (34 - 

328 143 143 143 124 95 

(145 - 499) (62 - 222) (62 - 222) (62 - 222) (54 - 193) (41 - 

273 200 200 191 162 14

(121 - 417) (88 - 308) (88 - 308) (84 - 294) (71 - 250) (64 - 2

91 79 79 70 60 59 

(40 - 138) (35 - 121) (35 - 121) (31 - 108) (26 - 92) (26 - 

77 77 77 77 68 66 

(33 - 118) (33 - 118) (33 - 118) (33 - 118) (30 - 106) (29 - 

103 67 67 63 55 49 

(46 - 157) (29 - 103) (29 - 103) (28 - 97) (24 - 84) (22 - 

13 4 4 4 4 2 

(6 - 21) (2 - 7) (2 - 7) (2 - 7) (2 - 7) (1 - 

138 116 105 91 77 88 

(61 - 210) (51 - 178) (46 - 161) (40 - 140) (34 - 119) (39 - 

19 11 11 11 11 6 

(8 - 30) (5 - 17) (5 - 17) (5 - 17) (5 - 17) (3 - 

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Baltimore, MD Detroit, MI Houston, TX St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

 Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2:
E-68

Table E-67. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to  PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alterna

 Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

12.4% 11% 9.9% 8.7% 7.5% 8.7% 

(5.5% - 18.7%) (4.9% - 16.7%) (4.4% - 15.1%) (3.8% - 13.3%) (3.3% - 11.6%) (3.8% - 13

12.2% 11.2% 10.3% 9.1% 7.9% 8.8% 

(5.4% - 18.4%) (5% - 17%) (4.6% - 15.7%) (4% - 13.9%) (3.5% - 12.1%) (3.9% - 13

12.4% 8.8% 7.8% 6.8% 5.8% 6.8% 

(5.5% - 18.8%) (3.9% - 13.5%) (3.4% - 12%) (3% - 10.5%) (2.5% - 8.9%) (3% - 10.

8.6% 8.6% 8.6% 8.6% 7.7% 8.6% 

(3.8% - 13.2%) (3.8% - 13.2%) (3.8% - 13.2%) (3.8% - 13.2%) (3.4% - 11.8%) (3.8% - 13

12.8% 9.4% 9.3% 8.2% 7.1% 7.3% 

(5.7% - 19.4%) (4.2% - 14.4%) (4.1% - 14.3%) (3.6% - 12.6%) (3.1% - 10.9%) (3.2% - 11

13.1% 4.7% 4.7% 4.7% 4.7% 3.1% 

(5.8% - 19.8%) (2.1% - 7.3%) (2.1% - 7.3%) (2.1% - 7.3%) (2.1% - 7.3%) (1.4% - 4.

9.5% 8.7% 7.7% 6.7% 5.7% 6.7% 

(4.2% - 14.5%) (3.8% - 13.3%) (3.4% - 11.8%) (2.9% - 10.3%) (2.5% - 8.8%) (2.9% - 10

11.8% 5.4% 5.4% 5.4% 4.8% 3.8% 

(5.3% - 18%) (2.4% - 8.4%) (2.4% - 8.4%) (2.4% - 8.4%) (2.1% - 7.4%) (1.6% - 5.

11% 8.2% 8.2% 7.8% 6.7% 6.1% 

(4.9% - 16.7%) (3.6% - 12.5%) (3.6% - 12.5%) (3.4% - 12%) (2.9% - 10.3%) (2.7% - 9.

10.4% 9.1% 9.1% 8.1% 7% 6.9% 

(4.6% - 15.8%) (4% - 13.9%) (4% - 13.9%) (3.6% - 12.5%) (3.1% - 10.8%) (3% - 10.

6.2% 6.2% 6.2% 6.2% 5.5% 5.4% 

(2.7% - 9.5%) (2.7% - 9.5%) (2.7% - 9.5%) (2.7% - 9.5%) (2.4% - 8.6%) (2.3% - 8.

12.3% 8.1% 8.1% 7.6% 6.7% 6.1% 

(5.5% - 18.6%) (3.6% - 12.5%) (3.6% - 12.5%) (3.4% - 11.7%) (2.9% - 10.3%) (2.7% - 9.

6.3% 1.8% 1.8% 1.8% 1.8% 0.6% 

(2.8% - 9.8%) (0.8% - 2.8%) (0.8% - 2.8%) (0.8% - 2.8%) (0.8% - 2.8%) (0.3% - 1

12.6% 10.8% 9.8% 8.6% 7.5% 8.4% 

(5.6% - 19.1%) (4.8% - 16.4%) (4.3% - 14.9%) (3.8% - 13.2%) (3.3% - 11.5%) (3.7% - 12

7.1% 4.7% 4.7% 4.7% 4.7% 3.1% 

(3.1% - 10.9%) (2% - 7.2%) (2% - 7.2%) (2% - 7.2%) (2% - 7.2%) (1.4% - 4.

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Con

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-69

Table E-68. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to  PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternati

 Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 fr

 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

12.4% 11% 9.9% 8.7% 7.5% 8.7% 

(5.5% - 18.7%) (4.9% - 16.7%) (4.4% - 15%) (3.8% - 13.3%) (3.3% - 11.6%) (3.8% - 13

9.9% 9.1% 8.2% 7.2% 6.1% 6.9% 

(4.4% - 15.1%) (4% - 13.9%) (3.6% - 12.6%) (3.2% - 11.1%) (2.7% - 9.5%) (3% - 10.

11.6% 8.1% 7.2% 6.2% 5.2% 6.2% 

(5.1% - 17.6%) (3.6% - 12.4%) (3.1% - 11%) (2.7% - 9.6%) (2.3% - 8.1%) (2.7% - 9

6.4% 6.4% 6.4% 6.4% 5.5% 6.4% 

(2.8% - 9.8%) (2.8% - 9.8%) (2.8% - 9.8%) (2.8% - 9.8%) (2.4% - 8.6%) (2.8% - 9

9.4% 6.5% 6.5% 5.5% 4.6% 4.7% 

(4.1% - 14.3%) (2.9% - 10.1%) (2.8% - 10%) (2.4% - 8.5%) (2% - 7.1%) (2.1% - 7

13.4% 4.9% 4.9% 4.9% 4.9% 3.3% 

(6% - 20.3%) (2.1% - 7.6%) (2.1% - 7.6%) (2.1% - 7.6%) (2.1% - 7.6%) (1.4% - 5

8.9% 8.1% 7.2% 6.2% 5.2% 6.2% 

(3.9% - 13.6%) (3.6% - 12.5%) (3.1% - 11%) (2.7% - 9.6%) (2.3% - 8.1%) (2.7% - 9

10.4% 4.5% 4.5% 4.5% 3.9% 2.9% 

(4.6% - 15.9%) (1.9% - 6.9%) (1.9% - 6.9%) (1.9% - 6.9%) (1.7% - 6%) (1.3% - 4

8.5% 6.1% 6.1% 5.7% 4.8% 4.3% 

(3.7% - 13%) (2.6% - 9.3%) (2.6% - 9.3%) (2.5% - 8.9%) (2.1% - 7.4%) (1.9% - 6

9.4% 8.2% 8.2% 7.3% 6.2% 6.2% 

(4.2% - 14.4%) (3.6% - 12.6%) (3.6% - 12.6%) (3.2% - 11.2%) (2.7% - 9.6%) (2.7% - 9

6.3% 6.3% 6.3% 6.3% 5.7% 5.5% 

(2.7% - 9.7%) (2.7% - 9.7%) (2.7% - 9.7%) (2.7% - 9.7%) (2.5% - 8.7%) (2.4% - 8

9.8% 6.2% 6.2% 5.7% 4.9% 4.4% 

(4.3% - 15%) (2.7% - 9.5%) (2.7% - 9.5%) (2.5% - 8.9%) (2.1% - 7.6%) (1.9% - 6

5% 0.9% 0.9% 0.9% 0.9% 0% 

(2.2% - 7.8%) (0.4% - 1.5%) (0.4% - 1.5%) (0.4% - 1.5%) (0.4% - 1.5%) (0% - 0

9.5% 7.9% 7.1% 6.1% 5.1% 5.9% 

(4.2% - 14.5%) (3.5% - 12.2%) (3.1% - 10.9%) (2.7% - 9.4%) (2.2% - 7.9%) (2.6% - 9

4.7% 2.7% 2.7% 2.7% 2.7% 1.4% 

(2.1% - 7.3%) (1.2% - 4.2%) (1.2% - 4.2%) (1.2% - 4.2%) (1.2% - 4.2%) (0.6% - 2

Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Con

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coe1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-70

Table E-69. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to  PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternat

 Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 f

 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

11.6% 10.3% 9.2% 8.1% 7% 8.1

(5.2% - 17.6%) (4.6% - 15.7%) (4.1% - 14.1%) (3.6% - 12.4%) (3.1% - 10.7%) (3.6% - 

9.9% 9.1% 8.2% 7.2% 6.1% 6.9

(4.4% - 15.1%) (4% - 13.9%) (3.6% - 12.6%) (3.2% - 11.1%) (2.7% - 9.5%) (3% - 1

12% 8.5% 7.5% 6.5% 5.5% 6.5

(5.3% - 18.2%) (3.7% - 13%) (3.3% - 11.5%) (2.8% - 10%) (2.4% - 8.5%) (2.8% - 

7% 7% 7% 7% 6.1% 7% 

(3% - 10.7%) (3% - 10.7%) (3% - 10.7%) (3% - 10.7%) (2.7% - 9.4%) (3% - 1

9.9% 7% 6.9% 6% 5% 5.1

(4.4% - 15.2%) (3.1% - 10.8%) (3% - 10.7%) (2.6% - 9.2%) (2.2% - 7.7%) (2.2% 

13.9% 5.2% 5.2% 5.2% 5.2% 3.5

(6.2% - 20.9%) (2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (1.5% - 

9.1% 8.3% 7.4% 6.4% 5.4% 6.4

(4% - 13.9%) (3.7% - 12.8%) (3.2% - 11.3%) (2.8% - 9.8%) (2.4% - 8.3%) (2.8% - 

10.7% 4.7% 4.7% 4.7% 4.1% 3.1

(4.8% - 16.3%) (2% - 7.3%) (2% - 7.3%) (2% - 7.3%) (1.8% - 6.3%) (1.3% - 

9.8% 7.2% 7.2% 6.9% 5.8% 5.3

(4.3% - 15%) (3.2% - 11.1%) (3.2% - 11.1%) (3% - 10.6%) (2.5% - 9%) (2.3% - 

9.3% 8.1% 8.1% 7.2% 6.2% 6.1

(4.1% - 14.2%) (3.6% - 12.5%) (3.6% - 12.5%) (3.2% - 11.1%) (2.7% - 9.5%) (2.7% - 

5.2% 5.2% 5.2% 5.2% 4.6% 4.4

(2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (2% - 7.1%) (1.9% - 

11.1% 7.2% 7.2% 6.7% 5.9% 5.3

(4.9% - 16.8%) (3.2% - 11.1%) (3.2% - 11.1%) (3% - 10.4%) (2.6% - 9.1%) (2.3% - 

7% 2.2% 2.2% 2.2% 2.2% 1% 

(3.1% - 10.7%) (1% - 3.5%) (1% - 3.5%) (1% - 3.5%) (1% - 3.5%) (0.4% - 

10.4% 8.7% 7.9% 6.8% 5.8% 6.6

(4.6% - 15.8%) (3.8% - 13.4%) (3.5% - 12.1%) (3% - 10.5%) (2.5% - 8.9%) (2.9% - 

4.9% 2.8% 2.8% 2.8% 2.8% 1.5

(2.1% - 7.6%) (1.2% - 4.4%) (1.2% - 4.4%) (1.2% - 4.4%) (1.2% - 4.4%) (0.7% - 

Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Co

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) 

Combination Denoted n/m)2

:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM co1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-71

Table E-70. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Assoc Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations -- Estim

 on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (20% - 21%) (31% - 32%) (20% - 

-8% 0% 8% 19% 29% 22% 

(-8% - -9%) (0% - 0%) (8% - 8%) (18% - 19%) (29% - 30%) (21% - 

-41% 0% 11% 23% 34% 23% 

(-39% - -43%) (0% - 0%) (11% - 12%) (22% - 23%) (34% - 35%) (22% - 

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 

-36% 0% 1% 13% 25% 23% 

(-35% - -37%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (23% - 

-178% 0% 0% 0% 0% 34% 

(-171% - -185%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

-9% 0% 11% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 

-118% 0% 0% 0% 12% 31% 

(-114% - -122%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 

-34% 0% 0% 4% 18% 25% 

(-33% - -35%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 

-14% 0% 0% 10% 23% 24% 

(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 11%) (22% - 23%) (23% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-51% 0% 0% 6% 17% 25% 

(-49% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (24% - 

-250% 0% 0% 0% 0% 64% 

(-245% - -254%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 

-17% 0% 9% 20% 31% 22% 

(-16% - -18%) (0% - 0%) (9% - 9%) (19% - 20%) (30% - 31%) (21% - 

-52% 0% 0% 0% 0% 33% 

(-51% - -53%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated 

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Al

Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-72

Table E-71. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Assoc Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations -- Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (20% - 21%) (31% - 32%) (20% - 

-9% 0% 9% 21% 32% 24% 

(-9% - -10%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (23% - 

-43% 0% 12% 24% 36% 24% 

(-41% - -44%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 

0% 0% 0% 0% 13% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (0% - 

-43% 0% 1% 16% 30% 28% 

(-42% - -45%) (0% - 0%) (1% - 1%) (15% - 16%) (30% - 30%) (27% - 

-173% 0% 0% 0% 0% 33% 

(-167% - -181%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 

-9% 0% 12% 24% 36% 24% 

(-9% - -10%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 

-133% 0% 0% 0% 13% 35% 

(-129% - -137%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 14%) (34% - 

-40% 0% 0% 5% 21% 29% 

(-39% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 

-15% 0% 0% 11% 24% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (24% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 

-59% 0% 0% 7% 20% 29% 

(-58% - -61%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (28% - 

-431% 0% 0% 0% 0% 100

(-425% - -437%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 

-20% 0% 10% 23% 35% 25% 

(-19% - -20%) (0% - 0%) (10% - 11%) (22% - 23%) (35% - 36%) (25% - 

-75% 0% 0% 0% 0% 48% 

(-74% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 

Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated 

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Al

Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-73

Table E-72. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Assoc Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations -- Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 22%) (32% - 33%) (21% - 

-9% 0% 9% 21% 32% 24% 

(-9% - -10%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (23% - 

-42% 0% 12% 23% 35% 23% 

(-40% - -43%) (0% - 0%) (11% - 12%) (23% - 24%) (34% - 36%) (23% - 

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 

-42% 0% 1% 15% 29% 27% 

(-41% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (26% - 

-168% 0% 0% 0% 0% 32% 

(-161% - -175%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 

-9% 0% 12% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 24%) (35% - 36%) (23% - 

-129% 0% 0% 0% 13% 34% 

(-125% - -134%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (33% - 

-36% 0% 0% 5% 19% 27% 

(-35% - -37%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 20%) (26% - 

-15% 0% 0% 11% 24% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 

0% 0% 0% 0% 11% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 

-54% 0% 0% 6% 19% 26% 

(-52% - -55%) (0% - 0%) (0% - 0%) (6% - 7%) (18% - 19%) (26% - 

-213% 0% 0% 0% 0% 55% 

(-209% - -217%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 

-19% 0% 10% 22% 34% 24% 

(-18% - -19%) (0% - 0%) (10% - 10%) (21% - 22%) (33% - 34%) (24% - 

-72% 0% 0% 0% 0% 46% 

(-71% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 

Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated 

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Al

Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-74

Table E-73. Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM

 in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

193 177 164 151 137 151 

(37 - 347) (34 - 319) (31 - 295) (29 - 272) (26 - 248) (29 - 2

271 256 242 224 206 219 

(110 - 430) (104 - 406) (98 - 384) (91 - 356) (83 - 327) (89 - 3

44 34 32 29 27 29 

(-68 - 154) (-53 - 121) (-49 - 112) (-45 - 103) (-41 - 94) (-45 - 1

156 156 156 156 145 156 

(37 - 273) (37 - 273) (37 - 273) (37 - 273) (35 - 253) (37 - 2

181 147 146 135 124 125 

(-32 - 390) (-26 - 317) (-26 - 315) (-24 - 291) (-22 - 267) (-22 - 2

79 44 44 44 44 37 

(11 - 145) (6 - 81) (6 - 81) (6 - 81) (6 - 81) (5 - 6

227 214 198 182 166 182 

(46 - 405) (44 - 383) (40 - 354) (37 - 326) (34 - 297) (37 - 3

129 81 81 81 77 69 

(-185 - 441) (-117 - 278) (-117 - 278) (-117 - 278) (-110 - 263) (-100 - 

939 781 781 761 700 668 

(552 - 1323) (459 - 1102) (459 - 1102) (447 - 1073) (411 - 987) (392 - 

234 216 216 202 185 184 

(86 - 380) (79 - 350) (79 - 350) (74 - 328) (68 - 301) (68 - 3

242 242 242 242 230 227 

(40 - 442) (40 - 442) (40 - 442) (40 - 442) (38 - 420) (38 - 4

224 159 159 155 147 136 

(66 - 380) (47 - 270) (47 - 270) (45 - 263) (43 - 249) (40 - 2

48 30 30 30 30 26 

(10 - 85) (6 - 54) (6 - 54) (6 - 54) (6 - 54) (5 - 4

290 260 244 226 207 222 

(84 - 494) (75 - 443) (71 - 416) (65 - 385) (60 - 354) (64 - 3

59 48 48 48 48 41 

(10 - 107) (8 - 87) (8 - 87) (8 - 87) (8 - 87) (7 - 7

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2

:

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-75

Table E-74. Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM

 in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

196 180 166 153 139 153 

(37 - 353) (34 - 324) (32 - 300) (29 - 276) (26 - 251) (29 - 27

237 224 212 196 180 192 

(96 - 376) (91 - 356) (86 - 336) (79 - 311) (73 - 286) (78 - 30

42 33 30 28 26 28 

(-66 - 148) (-51 - 116) (-47 - 108) (-44 - 99) (-40 - 90) (-44 - 9

130 130 130 130 121 130 

(31 - 228) (31 - 228) (31 - 228) (31 - 228) (29 - 212) (31 - 22

145 118 117 108 99 101 

(-25 - 314) (-21 - 255) (-20 - 253) (-19 - 234) (-17 - 215) (-18 - 21

84 47 47 47 47 40 

(12 - 155) (7 - 86) (7 - 86) (7 - 86) (7 - 86) (6 - 74

221 208 193 177 162 177 

(45 - 395) (42 - 373) (39 - 345) (36 - 317) (33 - 289) (36 - 31

119 75 75 75 71 64 

(-171 - 407) (-108 - 257) (-108 - 257) (-108 - 257) (-101 - 242) (-92 - 21

807 671 671 654 601 574 

(474 - 1137) (394 - 946) (394 - 946) (383 - 922) (352 - 847) (336 - 80

222 204 204 191 175 174 

(82 - 359) (75 - 331) (75 - 331) (70 - 310) (65 - 285) (64 - 28

254 254 254 254 241 238 

(42 - 463) (42 - 463) (42 - 463) (42 - 463) (40 - 440) (39 - 43

194 136 136 133 126 116 

(57 - 329) (40 - 232) (40 - 232) (39 - 226) (37 - 215) (34 - 19

44 27 27 27 27 23 

(9 - 78) (6 - 49) (6 - 49) (6 - 49) (6 - 49) (5 - 42

240 215 202 187 171 184 

(69 - 409) (62 - 367) (58 - 345) (54 - 319) (49 - 293) (53 - 31

50 40 40 40 40 34 

(9 - 90) (7 - 73) (7 - 73) (7 - 73) (7 - 73) (6 - 62

Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
E-76

Table E-75. Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM

 in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

193 177 164 151 137 151 

(37 - 348) (34 - 319) (31 - 296) (29 - 272) (26 - 248) (29 - 27

240 227 214 198 182 194 

(97 - 380) (92 - 360) (87 - 340) (80 - 315) (74 - 289) (79 - 30

44 34 32 29 26 29 

(-68 - 154) (-53 - 120) (-49 - 111) (-45 - 102) (-41 - 93) (-45 - 10

139 139 139 139 129 139 

(33 - 243) (33 - 243) (33 - 243) (33 - 243) (31 - 225) (33 - 24

150 121 120 111 102 104 

(-26 - 323) (-21 - 262) (-21 - 261) (-19 - 241) (-18 - 221) (-18 - 22

87 48 48 48 48 41 

(12 - 160) (7 - 89) (7 - 89) (7 - 89) (7 - 89) (6 - 76

224 212 196 180 164 180 

(46 - 401) (43 - 378) (40 - 350) (37 - 322) (33 - 294) (37 - 32

121 77 77 77 72 65 

(-174 - 415) (-110 - 262) (-110 - 262) (-110 - 262) (-104 - 247) (-94 - 22

882 734 734 715 657 627 

(518 - 1243) (431 - 1035) (431 - 1035) (419 - 1008) (385 - 927) (368 - 88

226 208 208 195 179 178 

(83 - 367) (77 - 338) (77 - 338) (72 - 316) (66 - 291) (66 - 28

242 242 242 242 230 227 

(40 - 442) (40 - 442) (40 - 442) (40 - 442) (38 - 420) (38 - 41

204 143 143 140 133 122 

(60 - 346) (42 - 244) (42 - 244) (41 - 237) (39 - 226) (36 - 20

54 34 34 34 34 29 

(11 - 96) (7 - 61) (7 - 61) (7 - 61) (7 - 61) (6 - 52

251 225 211 195 179 192 

(73 - 428) (65 - 384) (61 - 360) (56 - 333) (52 - 306) (55 - 32

52 42 42 42 42 36 

(9 - 94) (7 - 76) (7 - 76) (7 - 76) (7 - 76) (6 - 65

Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
E-77

Table E-76. Estimated Percent of Total Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposu Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

1.3% 1.2% 1.1% 1% 1% 

(0.3% - 2.4%) (0.2% - 2.2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% 

2% 1.9% 1.8% 1.7% 1.5% 1

(0.8% - 3.2%) (0.8% - 3%) (0.7% - 2.8%) (0.7% - 2.6%) (0.6% - 2.4%) (0.7% 

0.5% 0.4% 0.3% 0.3% 0.3% 0

(-0.7% - 1.6%) (-0.6% - 1.3%) (-0.5% - 1.2%) (-0.5% - 1.1%) (-0.4% - 1%) (-0.5% 

1.2% 1.2% 1.2% 1.2% 1.2% 1

(0.3% - 2.2%) (0.3% - 2.2%) (0.3% - 2.2%) (0.3% - 2.2%) (0.3% - 2%) (0.3% 

1% 0.8% 0.8% 0.8% 0.7% 0

(-0.2% - 2.3%) (-0.1% - 1.8%) (-0.1% - 1.8%) (-0.1% - 1.7%) (-0.1% - 1.5%) (-0.1% 

1.5% 0.8% 0.8% 0.8% 0.8% 0

(0.2% - 2.7%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% 

1.3% 1.2% 1.1% 1% 0.9% 

(0.3% - 2.3%) (0.2% - 2.1%) (0.2% - 2%) (0.2% - 1.8%) (0.2% - 1.7%) (0.2% 

0.2% 0.1% 0.1% 0.1% 0.1% 0

(-0.3% - 0.8%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% 

1.8% 1.5% 1.5% 1.5% 1.4% 1

(1.1% - 2.6%) (0.9% - 2.1%) (0.9% - 2.1%) (0.9% - 2.1%) (0.8% - 1.9%) (0.8% 

1.7% 1.5% 1.5% 1.4% 1.3% 1

(0.6% - 2.7%) (0.6% - 2.5%) (0.6% - 2.5%) (0.5% - 2.3%) (0.5% - 2.1%) (0.5% 

1.1% 1.1% 1.1% 1.1% 1.1% 

(0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% 

1.7% 1.2% 1.2% 1.1% 1.1% 

(0.5% - 2.8%) (0.3% - 2%) (0.3% - 2%) (0.3% - 1.9%) (0.3% - 1.8%) (0.3% 

1% 0.6% 0.6% 0.6% 0.6% 0

(0.2% - 1.8%) (0.1% - 1.2%) (0.1% - 1.2%) (0.1% - 1.2%) (0.1% - 1.2%) (0.1% 

1.6% 1.4% 1.3% 1.2% 1.1% 1

(0.5% - 2.7%) (0.4% - 2.4%) (0.4% - 2.3%) (0.4% - 2.1%) (0.3% - 1.9%) (0.4% 

1.2% 1% 1% 1% 1% 0

(0.2% - 2.2%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.1% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-78

Table E-77. Estimated Percent of Total Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposu Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

1.3% 1.2% 1.1% 1% 0.9% 

(0.3% - 2.4%) (0.2% - 2.2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% 

1.7% 1.6% 1.6% 1.4% 1.3% 1

(0.7% - 2.8%) (0.7% - 2.6%) (0.6% - 2.5%) (0.6% - 2.3%) (0.5% - 2.1%) (0.6% 

0.4% 0.3% 0.3% 0.3% 0.3% 0

(-0.7% - 1.6%) (-0.5% - 1.2%) (-0.5% - 1.1%) (-0.5% - 1%) (-0.4% - 0.9%) (-0.5% 

1% 1% 1% 1% 0.9% 

(0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.7%) (0.2% 

0.8% 0.7% 0.7% 0.6% 0.6% 0

(-0.1% - 1.8%) (-0.1% - 1.5%) (-0.1% - 1.5%) (-0.1% - 1.4%) (-0.1% - 1.3%) (-0.1% 

1.5% 0.8% 0.8% 0.8% 0.8% 0

(0.2% - 2.8%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% 

1.2% 1.1% 1% 1% 0.9% 

(0.2% - 2.1%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% - 1.6%) (0.2% 

0.2% 0.1% 0.1% 0.1% 0.1% 0

(-0.3% - 0.7%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.4%) (-0.2% 

1.6% 1.3% 1.3% 1.3% 1.2% 1

(0.9% - 2.2%) (0.8% - 1.8%) (0.8% - 1.8%) (0.7% - 1.8%) (0.7% - 1.6%) (0.6% 

1.6% 1.5% 1.5% 1.4% 1.2% 1

(0.6% - 2.6%) (0.5% - 2.4%) (0.5% - 2.4%) (0.5% - 2.2%) (0.5% - 2%) (0.5% 

1.1% 1.1% 1.1% 1.1% 1.1% 1

(0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% 

1.4% 1% 1% 1% 0.9% 0

(0.4% - 2.5%) (0.3% - 1.7%) (0.3% - 1.7%) (0.3% - 1.7%) (0.3% - 1.6%) (0.3% 

0.9% 0.6% 0.6% 0.6% 0.6% 0

(0.2% - 1.6%) (0.1% - 1%) (0.1% - 1%) (0.1% - 1%) (0.1% - 1%) (0.1% 

1.3% 1.2% 1.1% 1% 0.9% 

(0.4% - 2.2%) (0.3% - 2%) (0.3% - 1.9%) (0.3% - 1.7%) (0.3% - 1.6%) (0.3% 

1% 0.8% 0.8% 0.8% 0.8% 0

(0.2% - 1.8%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% 

Percent of Total Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-79

Table E-78. Estimated Percent of Total Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposur Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

1.3% 1.2% 1.1% 1% 0.9% 1% 

(0.2% - 2.3%) (0.2% - 2.1%) (0.2% - 1.9%) (0.2% - 1.8%) (0.2% - 1.6%) (0.2% - 

1.8% 1.7% 1.6% 1.5% 1.3% 1.4

(0.7% - 2.8%) (0.7% - 2.6%) (0.6% - 2.5%) (0.6% - 2.3%) (0.5% - 2.1%) (0.6% - 

0.5% 0.4% 0.3% 0.3% 0.3% 0.3

(-0.7% - 1.6%) (-0.6% - 1.2%) (-0.5% - 1.2%) (-0.5% - 1.1%) (-0.4% - 1%) (-0.5% - 

1.1% 1.1% 1.1% 1.1% 1% 1.1

(0.3% - 1.9%) (0.3% - 1.9%) (0.3% - 1.9%) (0.3% - 1.9%) (0.2% - 1.8%) (0.3% - 

0.9% 0.7% 0.7% 0.7% 0.6% 0.6

(-0.2% - 1.9%) (-0.1% - 1.6%) (-0.1% - 1.5%) (-0.1% - 1.4%) (-0.1% - 1.3%) (-0.1% - 

1.6% 0.9% 0.9% 0.9% 0.9% 0.7

(0.2% - 2.9%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 

1.2% 1.1% 1% 1% 0.9% 1% 

(0.2% - 2.1%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% - 1.6%) (0.2% - 

0.2% 0.1% 0.1% 0.1% 0.1% 0.1

(-0.3% - 0.7%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.4%) (-0.2% - 

1.7% 1.4% 1.4% 1.4% 1.3% 1.2

(1% - 2.4%) (0.8% - 2%) (0.8% - 2%) (0.8% - 1.9%) (0.7% - 1.8%) (0.7% - 

1.6% 1.5% 1.5% 1.4% 1.3% 1.3

(0.6% - 2.6%) (0.5% - 2.4%) (0.5% - 2.4%) (0.5% - 2.3%) (0.5% - 2.1%) (0.5% - 

1% 1% 1% 1% 1% 1% 

(0.2% - 1.9%) (0.2% - 1.9%) (0.2% - 1.9%) (0.2% - 1.9%) (0.2% - 1.8%) (0.2% - 

1.5% 1.1% 1.1% 1% 1% 0.9

(0.4% - 2.6%) (0.3% - 1.8%) (0.3% - 1.8%) (0.3% - 1.8%) (0.3% - 1.7%) (0.3% - 

1.1% 0.7% 0.7% 0.7% 0.7% 0.6

(0.2% - 2%) (0.1% - 1.3%) (0.1% - 1.3%) (0.1% - 1.3%) (0.1% - 1.3%) (0.1% - 

1.4% 1.2% 1.2% 1.1% 1% 1.1

(0.4% - 2.4%) (0.4% - 2.1%) (0.3% - 2%) (0.3% - 1.8%) (0.3% - 1.7%) (0.3% - 

1% 0.8% 0.8% 0.8% 0.8% 0.7

(0.2% - 1.9%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 

Percent of Total Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 C

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) 

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-80

Table E-79. Percent Reduction from the Current Standards: Estimated Annual Incidence of Non-Accidental Mortality Ass Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

 Atlanta, GA -9% 0% 7% 15% 22% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

 Baltimore, MD -6% 0% 5% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (20% - 20%) (14% - 

 Birmingham, AL -28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

 Dallas, TX 0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 

 Detroit, MI -23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 

 Fresno, CA -81% 0% 0% 0% 0% 15% 

(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

 Houston, TX -6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

 Los Angeles, CA -58% 0% 0% 0% 6% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 

 New York, NY -20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 

 Philadelphia, PA -9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 

 Phoenix, AZ 0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 

 Pittsburgh, PA -41% 0% 0% 3% 8% 15% 

(-41% - -41%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (14% - 

 Salt Lake City, UT -58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 

 St. Louis, MO -12% 0% 6% 13% 20% 15% 

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 

 Tacoma, WA -23% 0% 0% 0% 0% 15% 

(-23% - -23%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.

Risk Assessment 

Location

Estimated Percent Reduction From the Current Standards to Several Alternative Standards in Non

Associated with Short-Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentra

Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Den
E-81

Table E-80. Percent Reduction from the Current Standards: Estimated Annual Incidence of Non-Accidental Mortality Assoc

 Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

 Atlanta, GA -9% 0% 7% 15% 22% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%

 Baltimore, MD -6% 0% 6% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (20% - 20%) (14% - 15%

 Birmingham, AL -28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%

 Dallas, TX 0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

 Detroit, MI -23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%

 Fresno, CA -81% 0% 0% 0% 0% 15% 

(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%

 Houston, TX -6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%

 Los Angeles, CA -58% 0% 0% 0% 6% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%

 New York, NY -20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%

 Philadelphia, PA -9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%

 Phoenix, AZ 0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

 Pittsburgh, PA -42% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%

 Salt Lake City, UT -58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%

 St. Louis, MO -12% 0% 6% 13% 20% 15% 

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%

 Tacoma, WA -23% 0% 0% 0% 0% 15% 

(-23% - -23%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards th

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.

Risk Assessment 

Location

Estimated Percent Reduction From the Current Standards to Several Alternative Standards in Non-Ac

Associated with Short-Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentratio

Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denote
E-82

Table E-81. Percent Reduction from the Current Standards: Estimated Annual Incidence of Non-Accidental Mortality Associ Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

 Atlanta, GA -9% 0% 7% 15% 22% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%

 Baltimore, MD -6% 0% 6% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (13% - 13%) (20% - 20%) (14% - 15%

 Birmingham, AL -28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%

 Dallas, TX 0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) 

 Detroit, MI -23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%

 Fresno, CA -81% 0% 0% 0% 0% 15% 

(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%

 Houston, TX -6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%

 Los Angeles, CA -58% 0% 0% 0% 6% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%

 New York, NY -20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%

 Philadelphia, PA -9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%

 Phoenix, AZ 0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) 

 Pittsburgh, PA -42% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%

 Salt Lake City, UT -58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%

 St. Louis, MO -12% 0% 6% 13% 20% 15% 

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%

 Tacoma, WA -23% 0% 0% 0% 0% 15% 

(-23% - -23%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the 

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.

Risk Assessment 

Location

Estimated Percent Reduction From the Current Standards to Several Alternative Standards in Non-Ac

Associated with Short-Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentration

Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denote
E-83

Table E-82. Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM

 in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

35 32 30 27 25 27 

(-36 - 104) (-33 - 95) (-30 - 88) (-28 - 81) (-25 - 74) (-28 - 8

74 70 66 61 56 60 

(-5 - 151) (-5 - 143) (-4 - 135) (-4 - 125) (-4 - 115) (-4 - 12

-1 -1 -1 -1 -1 -1 

(-55 - 52) (-43 - 40) (-39 - 37) (-36 - 34) (-33 - 31) (-36 - 3

32 32 32 32 30 32 

(-21 - 85) (-21 - 85) (-21 - 85) (-21 - 85) (-20 - 79) (-21 - 8

89 73 72 67 61 62 

(-11 - 188) (-9 - 153) (-9 - 152) (-8 - 140) (-8 - 129) (-8 - 13

20 11 11 11 11 10 

(-14 - 54) (-8 - 30) (-8 - 30) (-8 - 30) (-8 - 30) (-7 - 26

50 47 43 40 36 40 

(-34 - 131) (-32 - 124) (-29 - 114) (-27 - 105) (-25 - 96) (-27 - 10

-50 -31 -31 -31 -30 -27 

(-223 - 121) (-140 - 76) (-140 - 76) (-140 - 76) (-132 - 72) (-119 - 6

605 504 504 491 451 431 

(353 - 853) (294 - 711) (294 - 711) (286 - 693) (263 - 637) (251 - 60

94 87 87 81 75 74 

(25 - 163) (23 - 150) (23 - 150) (21 - 140) (19 - 129) (19 - 12

84 84 84 84 80 79 

(-4 - 170) (-4 - 170) (-4 - 170) (-4 - 170) (-3 - 161) (-3 - 15

67 47 47 46 44 41 

(-13 - 145) (-9 - 103) (-9 - 103) (-9 - 101) (-9 - 96) (-8 - 88

13 8 8 8 8 7 

(-3 - 28) (-2 - 18) (-2 - 18) (-2 - 18) (-2 - 18) (-2 - 15

136 122 115 106 98 105 

(30 - 240) (27 - 215) (26 - 203) (24 - 187) (22 - 172) (23 - 18

15 12 12 12 12 11 

(-8 - 38) (-7 - 31) (-7 - 31) (-7 - 31) (-7 - 31) (-6 - 27

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-84

Table E-83. Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM

 in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

35 32 30 28 25 28 

(-36 - 105) (-33 - 97) (-31 - 90) (-28 - 82) (-26 - 75) (-28 - 8

65 61 58 53 49 52 

(-4 - 132) (-4 - 125) (-4 - 118) (-4 - 109) (-3 - 101) (-4 - 10

-1 -1 -1 -1 0 -1 

(-52 - 50) (-41 - 39) (-38 - 36) (-35 - 33) (-32 - 30) (-35 - 3

27 27 27 27 25 27 

(-18 - 71) (-18 - 71) (-18 - 71) (-18 - 71) (-16 - 66) (-18 - 7

72 58 58 54 49 50 

(-9 - 152) (-7 - 123) (-7 - 122) (-7 - 113) (-6 - 104) (-6 - 10

22 12 12 12 12 10 

(-15 - 58) (-8 - 32) (-8 - 32) (-8 - 32) (-8 - 32) (-7 - 27

48 45 42 39 35 39 

(-33 - 128) (-31 - 120) (-29 - 112) (-26 - 103) (-24 - 94) (-26 - 10

-46 -29 -29 -29 -27 -25 

(-205 - 112) (-129 - 70) (-129 - 70) (-129 - 70) (-122 - 66) (-110 - 6

519 432 432 421 387 370 

(303 - 733) (252 - 611) (252 - 611) (246 - 595) (226 - 548) (216 - 52

89 82 82 77 71 70 

(23 - 154) (21 - 142) (21 - 142) (20 - 133) (18 - 122) (18 - 12

88 88 88 88 84 82 

(-4 - 178) (-4 - 178) (-4 - 178) (-4 - 178) (-4 - 169) (-4 - 16

58 41 41 40 38 35 

(-12 - 126) (-8 - 89) (-8 - 89) (-8 - 86) (-8 - 82) (-7 - 76

11 7 7 7 7 6 

(-3 - 25) (-2 - 16) (-2 - 16) (-2 - 16) (-2 - 16) (-1 - 14

113 101 95 88 81 87 

(25 - 199) (23 - 179) (21 - 168) (20 - 155) (18 - 143) (19 - 15

13 10 10 10 10 9 

(-7 - 32) (-6 - 26) (-6 - 26) (-6 - 26) (-6 - 26) (-5 - 22

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-85

Table E-84. Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM

 in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

35 32 30 27 25 27 

(-36 - 104) (-33 - 95) (-30 - 88) (-28 - 81) (-25 - 74) (-28 - 8

65 62 58 54 50 53 

(-4 - 133) (-4 - 126) (-4 - 119) (-4 - 111) (-3 - 102) (-4 - 10

-1 -1 -1 -1 -1 -1 

(-54 - 51) (-42 - 40) (-39 - 37) (-36 - 34) (-33 - 31) (-36 - 3

29 29 29 29 27 29 

(-19 - 76) (-19 - 76) (-19 - 76) (-19 - 76) (-17 - 70) (-19 - 7

74 60 60 55 51 51 

(-9 - 156) (-8 - 127) (-7 - 126) (-7 - 116) (-6 - 107) (-6 - 10

23 12 12 12 12 11 

(-16 - 59) (-9 - 33) (-9 - 33) (-9 - 33) (-9 - 33) (-7 - 28

49 46 43 39 36 39 

(-33 - 130) (-31 - 122) (-29 - 113) (-27 - 104) (-24 - 95) (-27 - 10

-47 -30 -30 -30 -28 -25 

(-209 - 114) (-132 - 72) (-132 - 72) (-132 - 72) (-124 - 68) (-112 - 6

568 473 473 461 424 405 

(332 - 802) (276 - 668) (276 - 668) (269 - 651) (247 - 599) (236 - 57

91 84 84 79 72 72 

(24 - 157) (22 - 145) (22 - 145) (20 - 136) (19 - 125) (19 - 12

84 84 84 84 80 79 

(-4 - 170) (-4 - 170) (-4 - 170) (-4 - 170) (-3 - 162) (-3 - 15

61 43 43 42 40 37 

(-12 - 132) (-9 - 93) (-9 - 93) (-8 - 91) (-8 - 87) (-7 - 80

14 9 9 9 9 8 

(-3 - 31) (-2 - 20) (-2 - 20) (-2 - 20) (-2 - 20) (-2 - 17

118 106 99 92 84 91 

(26 - 208) (24 - 187) (22 - 176) (20 - 162) (19 - 149) (20 - 16

14 11 11 11 11 9 

(-7 - 34) (-6 - 27) (-6 - 27) (-6 - 27) (-6 - 27) (-5 - 23

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-86

Table E-85. Estimated Percent of Total Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposur Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

0.9% 0.9% 0.8% 0.7% 0.7% 0.7

(-1% - 2.8%) (-0.9% - 2.6%) (-0.8% - 2.4%) (-0.7% - 2.2%) (-0.7% - 2%) (-0.7% - 

1.9% 1.8% 1.7% 1.6% 1.4% 1.5

(-0.1% - 3.9%) (-0.1% - 3.7%) (-0.1% - 3.5%) (-0.1% - 3.2%) (-0.1% - 3%) (-0.1% - 

0% 0% 0% 0% 0% 0% 

(-2% - 1.9%) (-1.6% - 1.5%) (-1.5% - 1.4%) (-1.3% - 1.3%) (-1.2% - 1.2%) (-1.3% - 

1% 1% 1% 1% 0.9% 1% 

(-0.6% - 2.5%) (-0.6% - 2.5%) (-0.6% - 2.5%) (-0.6% - 2.5%) (-0.6% - 2.3%) (-0.6% - 

1.5% 1.2% 1.2% 1.1% 1% 1% 

(-0.2% - 3.1%) (-0.2% - 2.5%) (-0.2% - 2.5%) (-0.1% - 2.3%) (-0.1% - 2.1%) (-0.1% - 

1.2% 0.7% 0.7% 0.7% 0.7% 0.6

(-0.9% - 3.3%) (-0.5% - 1.8%) (-0.5% - 1.8%) (-0.5% - 1.8%) (-0.5% - 1.8%) (-0.4% - 

1% 1% 0.9% 0.8% 0.7% 0.8

(-0.7% - 2.7%) (-0.7% - 2.5%) (-0.6% - 2.3%) (-0.6% - 2.2%) (-0.5% - 2%) (-0.6% - 

-0.3% -0.2% -0.2% -0.2% -0.2% -0.1

(-1.2% - 0.6%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.6% - 

2.7% 2.2% 2.2% 2.2% 2% 1.9

(1.6% - 3.8%) (1.3% - 3.2%) (1.3% - 3.2%) (1.3% - 3.1%) (1.2% - 2.8%) (1.1% - 

2.3% 2.2% 2.2% 2% 1.9% 1.8

(0.6% - 4%) (0.6% - 3.7%) (0.6% - 3.7%) (0.5% - 3.5%) (0.5% - 3.2%) (0.5% - 

1.4% 1.4% 1.4% 1.4% 1.4% 1.3

(-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.7%) (-0.1% - 

1.6% 1.2% 1.2% 1.1% 1.1% 1% 

(-0.3% - 3.6%) (-0.2% - 2.5%) (-0.2% - 2.5%) (-0.2% - 2.5%) (-0.2% - 2.3%) (-0.2% - 

1.1% 0.7% 0.7% 0.7% 0.7% 0.6

(-0.3% - 2.5%) (-0.2% - 1.6%) (-0.2% - 1.6%) (-0.2% - 1.6%) (-0.2% - 1.6%) (-0.1% - 

2.4% 2.2% 2% 1.9% 1.7% 1.8

(0.5% - 4.2%) (0.5% - 3.8%) (0.5% - 3.6%) (0.4% - 3.3%) (0.4% - 3%) (0.4% - 

1.1% 0.9% 0.9% 0.9% 0.9% 0.7

(-0.6% - 2.7%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.4% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 C

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) 

Combination Denoted n/m)2

:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-87

Table E-86. Estimated Percent of Total Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposur Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 Adjusting 2006 PM2.5 Concentrations1 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

0.9% 0.8% 0.8% 0.7% 0.7% 0.7

(-0.9% - 2.8%) (-0.9% - 2.5%) (-0.8% - 2.3%) (-0.7% - 2.2%) (-0.7% - 2%) (-0.7% 

1.7% 1.6% 1.5% 1.4% 1.3% 1.3

(-0.1% - 3.4%) (-0.1% - 3.2%) (-0.1% - 3%) (-0.1% - 2.8%) (-0.1% - 2.6%) (-0.1% 

0% 0% 0% 0% 0% 0

(-1.9% - 1.8%) (-1.5% - 1.4%) (-1.4% - 1.3%) (-1.3% - 1.2%) (-1.2% - 1.1%) (-1.3% 

0.8% 0.8% 0.8% 0.8% 0.7% 0.8

(-0.5% - 2.1%) (-0.5% - 2.1%) (-0.5% - 2.1%) (-0.5% - 2.1%) (-0.5% - 1.9%) (-0.5% 

1.2% 1% 1% 0.9% 0.8% 0.8

(-0.2% - 2.5%) (-0.1% - 2.1%) (-0.1% - 2.1%) (-0.1% - 1.9%) (-0.1% - 1.7%) (-0.1% 

1.3% 0.7% 0.7% 0.7% 0.7% 0.6

(-0.9% - 3.5%) (-0.5% - 1.9%) (-0.5% - 1.9%) (-0.5% - 1.9%) (-0.5% - 1.9%) (-0.4% 

1% 0.9% 0.8% 0.8% 0.7% 0.8

(-0.6% - 2.5%) (-0.6% - 2.4%) (-0.6% - 2.2%) (-0.5% - 2%) (-0.5% - 1.9%) (-0.5% 

-0.2% -0.2% -0.2% -0.2% -0.1% -0.

(-1.1% - 0.6%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.6% - 0.4%) (-0.6% 

2.3% 1.9% 1.9% 1.9% 1.7% 1.6

(1.3% - 3.3%) (1.1% - 2.7%) (1.1% - 2.7%) (1.1% - 2.6%) (1% - 2.4%) (1% - 

2.2% 2.1% 2.1% 1.9% 1.8% 1.8

(0.6% - 3.8%) (0.5% - 3.5%) (0.5% - 3.5%) (0.5% - 3.3%) (0.5% - 3.1%) (0.5% 

1.4% 1.4% 1.4% 1.4% 1.4% 1.4

(-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.8%) (-0.1% 

1.4% 1% 1% 1% 0.9% 0.9

(-0.3% - 3.1%) (-0.2% - 2.2%) (-0.2% - 2.2%) (-0.2% - 2.1%) (-0.2% - 2%) (-0.2% 

1% 0.6% 0.6% 0.6% 0.6% 0.5

(-0.2% - 2.2%) (-0.1% - 1.4%) (-0.1% - 1.4%) (-0.1% - 1.4%) (-0.1% - 1.4%) (-0.1% 

2% 1.8% 1.7% 1.5% 1.4% 1.5

(0.4% - 3.5%) (0.4% - 3.1%) (0.4% - 3%) (0.3% - 2.7%) (0.3% - 2.5%) (0.3% 

0.9% 0.7% 0.7% 0.7% 0.7% 0.6

(-0.5% - 2.2%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.3% 

Percent of Total Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-88

Table E-87. Estimated Percent of Total Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure  Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Sta

 Adjusting 2007 PM2.5 Concentrations1 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

0.9% 0.8% 0.8% 0.7% 0.6% 0.7

(-0.9% - 2.7%) (-0.8% - 2.4%) (-0.8% - 2.3%) (-0.7% - 2.1%) (-0.6% - 1.9%) (-0.7% - 

1.7% 1.6% 1.5% 1.4% 1.3% 1.4

(-0.1% - 3.4%) (-0.1% - 3.2%) (-0.1% - 3.1%) (-0.1% - 2.8%) (-0.1% - 2.6%) (-0.1% - 

0% 0% 0% 0% 0% 0% 

(-2% - 1.9%) (-1.5% - 1.5%) (-1.4% - 1.4%) (-1.3% - 1.2%) (-1.2% - 1.1%) (-1.3% - 

0.8% 0.8% 0.8% 0.8% 0.8% 0.8

(-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2%) (-0.5% - 

1.3% 1% 1% 0.9% 0.9% 0.9

(-0.2% - 2.7%) (-0.1% - 2.2%) (-0.1% - 2.1%) (-0.1% - 2%) (-0.1% - 1.8%) (-0.1% - 

1.3% 0.7% 0.7% 0.7% 0.7% 0.6

(-0.9% - 3.5%) (-0.5% - 2%) (-0.5% - 2%) (-0.5% - 2%) (-0.5% - 2%) (-0.4% - 

1% 0.9% 0.8% 0.8% 0.7% 0.8

(-0.6% - 2.5%) (-0.6% - 2.4%) (-0.6% - 2.2%) (-0.5% - 2%) (-0.5% - 1.9%) (-0.5% 

-0.2% -0.2% -0.2% -0.2% -0.1% -0.1

(-1.1% - 0.6%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.6% - 

2.5% 2.1% 2.1% 2% 1.9% 1.8

(1.5% - 3.5%) (1.2% - 3%) (1.2% - 3%) (1.2% - 2.9%) (1.1% - 2.6%) (1% - 2

2.3% 2.1% 2.1% 2% 1.8% 1.8

(0.6% - 3.9%) (0.5% - 3.6%) (0.5% - 3.6%) (0.5% - 3.4%) (0.5% - 3.1%) (0.5% - 

1.3% 1.3% 1.3% 1.3% 1.3% 1.3

(-0.1% - 2.7%) (-0.1% - 2.7%) (-0.1% - 2.7%) (-0.1% - 2.7%) (-0.1% - 2.6%) (-0.1% - 

1.5% 1.1% 1.1% 1% 1% 0.9

(-0.3% - 3.3%) (-0.2% - 2.3%) (-0.2% - 2.3%) (-0.2% - 2.3%) (-0.2% - 2.1%) (-0.2% 

1.2% 0.8% 0.8% 0.8% 0.8% 0.7

(-0.3% - 2.7%) (-0.2% - 1.7%) (-0.2% - 1.7%) (-0.2% - 1.7%) (-0.2% - 1.7%) (-0.2% - 

2.1% 1.9% 1.7% 1.6% 1.5% 1.6

(0.5% - 3.7%) (0.4% - 3.3%) (0.4% - 3.1%) (0.4% - 2.9%) (0.3% - 2.6%) (0.4% - 

0.9% 0.7% 0.7% 0.7% 0.7% 0.6

(-0.5% - 2.3%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.3% - 

Percent of Total Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 C

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) 

Combination Denoted n/m)2

:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM co1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-89

Table E-88. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Mortality Asso Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-9% 0% 7% 15% 23% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

-6% 0% 6% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 

-28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 

-81% 0% 0% 0% 0% 15% 

(-80% - -83%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

-6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

-59% 0% 0% 0% 6% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 

-20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 

-9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 

-41% 0% 0% 3% 8% 15% 

(-41% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (14% - 

-58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 

-12% 0% 6% 13% 20% 14% 

(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Mortality Assoc

Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Curre

(n) and Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-90

Table E-89. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Mortality Asso

 Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-9% 0% 7% 15% 23% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-6% 0% 6% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 15

-28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15

-81% 0% 0% 0% 0% 15% 

(-80% - -83%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-6% 0% 7% 15% 23% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-59% 0% 0% 0% 6% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15

-20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15

-9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

-42% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 15

-58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15

-12% 0% 6% 13% 20% 15% 

(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Mortality Associ

Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Curren

(n) and Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-91

Table E-90. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Mortality Ass Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-9% 0% 7% 15% 23% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

-6% 0% 6% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 

-28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 

-81% 0% 0% 0% 0% 15% 

(-79% - -83%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

-6% 0% 7% 15% 23% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

-59% 0% 0% 0% 6% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 

-20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 

-9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 

-42% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 

-58% 0% 0% 0% 0% 15% 

(-57% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

-12% 0% 6% 13% 20% 15% 

(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Mortality Asso

Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Curre

(n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-92

Table E-91. Estimated Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5 C

 Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on A

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

21 20 18 17 15 17 

(-9 - 51) (-8 - 47) (-7 - 43) (-7 - 40) (-6 - 36) (-7 - 40

38 36 34 31 29 30 

(7 - 67) (7 - 64) (6 - 60) (6 - 56) (5 - 51) (6 - 55

12 9 9 8 7 8 

(-10 - 33) (-7 - 26) (-7 - 24) (-6 - 22) (-6 - 20) (-6 - 22

11 11 11 11 10 11 

(-10 - 32) (-10 - 32) (-10 - 32) (-10 - 32) (-10 - 30) (-10 - 3

35 28 28 26 24 24 

(2 - 67) (1 - 55) (1 - 54) (1 - 50) (1 - 46) (1 - 47

15 9 9 9 9 7 

(0 - 30) (0 - 17) (0 - 17) (0 - 17) (0 - 17) (0 - 14

36 34 31 29 26 29 

(6 - 65) (5 - 61) (5 - 57) (5 - 52) (4 - 48) (5 - 52

90 57 57 57 54 49 

(9 - 171) (6 - 108) (6 - 108) (6 - 108) (5 - 102) (5 - 93

128 106 106 104 95 91 

(45 - 208) (37 - 174) (37 - 174) (37 - 169) (34 - 156) (32 - 14

25 23 23 22 20 20 

(-2 - 52) (-2 - 48) (-2 - 48) (-2 - 45) (-2 - 41) (-2 - 41

47 47 47 47 45 44 

(4 - 90) (4 - 90) (4 - 90) (4 - 90) (4 - 85) (4 - 84

28 20 20 20 19 17 

(-3 - 58) (-2 - 42) (-2 - 42) (-2 - 40) (-2 - 38) (-2 - 36

8 55554

(1 - 15) (1 - 10) (1 - 10) (1 - 10) (1 - 10) (1 - 8) 

35 31 29 27 25 27 

(-9 - 78) (-8 - 70) (-8 - 65) (-7 - 61) (-7 - 56) (-7 - 60

9 77776

(0 - 18) (0 - 15) (0 - 15) (0 - 15) (0 - 15) (0 - 13

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in a 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-93

Table E-92. Estimated Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5 C

 Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on A

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

22 20 18 17 16 17 

(-9 - 51) (-8 - 47) (-7 - 44) (-7 - 40) (-6 - 37) (-7 - 40

33 31 29 27 25 27 

(6 - 59) (6 - 56) (5 - 53) (5 - 49) (5 - 45) (5 - 48

11 9 8 8 7 8 

(-9 - 31) (-7 - 25) (-7 - 23) (-6 - 21) (-6 - 19) (-6 - 21

9 99999

(-9 - 27) (-9 - 27) (-9 - 27) (-9 - 27) (-8 - 25) (-9 - 27

28 23 23 21 19 20 

(1 - 54) (1 - 44) (1 - 44) (1 - 41) (1 - 37) (1 - 38

16 9 9 9 9 8 

(1 - 32) (0 - 18) (0 - 18) (0 - 18) (0 - 18) (0 - 15

35 33 30 28 25 28 

(6 - 63) (5 - 60) (5 - 55) (4 - 51) (4 - 46) (4 - 51

84 53 53 53 50 45 

(8 - 158) (5 - 100) (5 - 100) (5 - 100) (5 - 95) (4 - 86

110 91 91 89 82 78 

(39 - 179) (32 - 149) (32 - 149) (31 - 146) (29 - 134) (27 - 12

24 22 22 20 19 19 

(-2 - 49) (-2 - 45) (-2 - 45) (-2 - 42) (-2 - 39) (-2 - 39

50 50 50 50 47 46 

(4 - 94) (4 - 94) (4 - 94) (4 - 94) (4 - 90) (4 - 88

24 17 17 17 16 15 

(-2 - 51) (-2 - 36) (-2 - 36) (-2 - 35) (-2 - 33) (-1 - 31

8 55554

(1 - 14) (1 - 9) (1 - 9) (1 - 9) (1 - 9) (1 - 8) 

29 26 24 22 21 22 

(-8 - 64) (-7 - 58) (-6 - 54) (-6 - 50) (-5 - 46) (-6 - 49

8 66665

(0 - 15) (0 - 12) (0 - 12) (0 - 12) (0 - 12) (0 - 11

Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in a 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
E-94

Table E-93. Estimated Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5 C

 Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on A

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

21 20 18 17 15 17 

(-9 - 51) (-8 - 47) (-7 - 43) (-7 - 40) (-6 - 36) (-7 - 40

33 31 30 28 25 27 

(6 - 60) (6 - 56) (6 - 53) (5 - 49) (5 - 45) (5 - 48

12 9 9 8 7 8 

(-10 - 32) (-7 - 25) (-7 - 24) (-6 - 22) (-6 - 20) (-6 - 22

10 10 10 10 9 10 

(-9 - 29) (-9 - 29) (-9 - 29) (-9 - 29) (-8 - 27) (-9 - 29

29 24 23 22 20 20 

(1 - 56) (1 - 45) (1 - 45) (1 - 42) (1 - 38) (1 - 39

17 9 9 9 9 8 

(1 - 33) (0 - 18) (0 - 18) (0 - 18) (0 - 18) (0 - 16

35 33 31 28 26 28 

(6 - 64) (5 - 61) (5 - 56) (5 - 52) (4 - 47) (5 - 52

85 54 54 54 51 46 

(8 - 161) (5 - 102) (5 - 102) (5 - 102) (5 - 96) (4 - 87

120 100 100 97 89 85 

(42 - 196) (35 - 163) (35 - 163) (34 - 159) (31 - 147) (30 - 14

24 22 22 21 19 19 

(-2 - 50) (-2 - 46) (-2 - 46) (-2 - 43) (-2 - 40) (-2 - 39

47 47 47 47 45 44 

(4 - 90) (4 - 90) (4 - 90) (4 - 90) (4 - 85) (4 - 84

26 18 18 18 17 15 

(-3 - 53) (-2 - 38) (-2 - 38) (-2 - 37) (-2 - 35) (-2 - 32

9 66665

(1 - 17) (1 - 11) (1 - 11) (1 - 11) (1 - 11) (1 - 9) 

30 27 25 23 22 23 

(-8 - 67) (-7 - 60) (-7 - 57) (-6 - 53) (-6 - 48) (-6 - 52

8 66665

(0 - 16) (0 - 13) (0 - 13) (0 - 13) (0 - 13) (0 - 11

Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in a 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
E-95

Table E-94. Estimated Percent of Total Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to  Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

1.7% 1.6% 1.5% 1.3% 1.2% 1.3% 

(-0.7% - 4.1%) (-0.6% - 3.7%) (-0.6% - 3.5%) (-0.5% - 3.2%) (-0.5% - 2.9%) (-0.5% - 3

3.1% 2.9% 2.8% 2.6% 2.4% 2.5% 

(0.6% - 5.6%) (0.5% - 5.3%) (0.5% - 5%) (0.5% - 4.6%) (0.4% - 4.2%) (0.5% - 4.

1.4% 1.1% 1% 0.9% 0.8% 0.9% 

(-1.1% - 3.7%) (-0.9% - 2.9%) (-0.8% - 2.7%) (-0.7% - 2.5%) (-0.7% - 2.3%) (-0.7% - 2

1% 1% 1% 1% 1% 1% 

(-0.9% - 3%) (-0.9% - 3%) (-0.9% - 3%) (-0.9% - 3%) (-0.9% - 2.7%) (-0.9% - 3

2.6% 2.1% 2.1% 1.9% 1.8% 1.8% 

(0.1% - 5%) (0.1% - 4.1%) (0.1% - 4%) (0.1% - 3.7%) (0.1% - 3.4%) (0.1% - 3.

2.6% 1.5% 1.5% 1.5% 1.5% 1.2% 

(0.1% - 5.1%) (0% - 2.8%) (0% - 2.8%) (0% - 2.8%) (0% - 2.8%) (0% - 2.4

2.5% 2.4% 2.2% 2% 1.9% 2% 

(0.4% - 4.6%) (0.4% - 4.4%) (0.4% - 4%) (0.3% - 3.7%) (0.3% - 3.4%) (0.3% - 3.

1.6% 1% 1% 1% 1% 0.9% 

(0.2% - 3.1%) (0.1% - 1.9%) (0.1% - 1.9%) (0.1% - 1.9%) (0.1% - 1.8%) (0.1% - 1.

3% 2.5% 2.5% 2.4% 2.2% 2.1% 

(1% - 4.8%) (0.9% - 4%) (0.9% - 4%) (0.8% - 3.9%) (0.8% - 3.6%) (0.7% - 3.

2.1% 1.9% 1.9% 1.8% 1.6% 1.6% 

(-0.2% - 4.3%) (-0.2% - 3.9%) (-0.2% - 3.9%) (-0.2% - 3.7%) (-0.2% - 3.4%) (-0.2% - 3

1.9% 1.9% 1.9% 1.9% 1.8% 1.8% 

(0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.1% - 3.5%) (0.1% - 3.

2.4% 1.7% 1.7% 1.6% 1.6% 1.4% 

(-0.2% - 4.9%) (-0.2% - 3.5%) (-0.2% - 3.5%) (-0.2% - 3.4%) (-0.2% - 3.2%) (-0.1% - 3

1.9% 1.2% 1.2% 1.2% 1.2% 1% 

(0.2% - 3.5%) (0.1% - 2.2%) (0.1% - 2.2%) (0.1% - 2.2%) (0.1% - 2.2%) (0.1% - 1.

2% 1.8% 1.7% 1.6% 1.4% 1.5% 

(-0.5% - 4.5%) (-0.5% - 4%) (-0.4% - 3.8%) (-0.4% - 3.5%) (-0.4% - 3.2%) (-0.4% - 3

1.8% 1.5% 1.5% 1.5% 1.5% 1.3% 

(0% - 3.6%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 2.5

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Conc

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-96

Table E-95. Estimated Percent of Total Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to  Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

1.7% 1.6% 1.4% 1.3% 1.2% 1.3% 

(-0.7% - 4%) (-0.6% - 3.7%) (-0.6% - 3.4%) (-0.5% - 3.1%) (-0.5% - 2.9%) (-0.5% - 3

2.7% 2.6% 2.4% 2.2% 2.1% 2.2% 

(0.5% - 4.9%) (0.5% - 4.6%) (0.4% - 4.3%) (0.4% - 4%) (0.4% - 3.7%) (0.4% - 3.

1.3% 1% 0.9% 0.9% 0.8% 0.9% 

(-1% - 3.6%) (-0.8% - 2.8%) (-0.8% - 2.6%) (-0.7% - 2.4%) (-0.6% - 2.2%) (-0.7% - 2

0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 

(-0.8% - 2.4%) (-0.8% - 2.4%) (-0.8% - 2.4%) (-0.8% - 2.4%) (-0.7% - 2.3%) (-0.8% - 2

2.1% 1.7% 1.7% 1.6% 1.4% 1.5% 

(0.1% - 4.1%) (0.1% - 3.3%) (0.1% - 3.3%) (0.1% - 3%) (0.1% - 2.8%) (0.1% - 2.

2.8% 1.5% 1.5% 1.5% 1.5% 1.3% 

(0.1% - 5.3%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 2.6

2.4% 2.3% 2.1% 1.9% 1.8% 1.9% 

(0.4% - 4.4%) (0.4% - 4.1%) (0.3% - 3.8%) (0.3% - 3.5%) (0.3% - 3.2%) (0.3% - 3.

1.5% 0.9% 0.9% 0.9% 0.9% 0.8% 

(0.1% - 2.8%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.7%) (0.1% - 1.

2.5% 2.1% 2.1% 2.1% 1.9% 1.8% 

(0.9% - 4.1%) (0.7% - 3.5%) (0.7% - 3.5%) (0.7% - 3.4%) (0.7% - 3.1%) (0.6% - 3

2% 1.8% 1.8% 1.7% 1.6% 1.5% 

(-0.2% - 4%) (-0.2% - 3.7%) (-0.2% - 3.7%) (-0.2% - 3.5%) (-0.1% - 3.2%) (-0.1% - 3

2% 2% 2% 2% 1.9% 1.8% 

(0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.1% - 3.6%) (0.1% - 3.

2.1% 1.5% 1.5% 1.4% 1.4% 1.2% 

(-0.2% - 4.3%) (-0.1% - 3%) (-0.1% - 3%) (-0.1% - 3%) (-0.1% - 2.8%) (-0.1% - 2

1.7% 1.1% 1.1% 1.1% 1.1% 0.9% 

(0.2% - 3.1%) (0.1% - 2%) (0.1% - 2%) (0.1% - 2%) (0.1% - 2%) (0.1% - 1.

1.7% 1.5% 1.4% 1.3% 1.2% 1.3% 

(-0.4% - 3.7%) (-0.4% - 3.3%) (-0.4% - 3.1%) (-0.3% - 2.9%) (-0.3% - 2.7%) (-0.3% - 2

1.5% 1.2% 1.2% 1.2% 1.2% 1% 

(0% - 3%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.1

Percent of Total Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Conc

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-97

Table E-96. Estimated Percent of Total Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to  Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

1.6% 1.5% 1.4% 1.3% 1.2% 1.3% 

(-0.7% - 3.9%) (-0.6% - 3.6%) (-0.6% - 3.3%) (-0.5% - 3%) (-0.5% - 2.8%) (-0.5% - 3

2.7% 2.6% 2.5% 2.3% 2.1% 2.2% 

(0.5% - 4.9%) (0.5% - 4.7%) (0.5% - 4.4%) (0.4% - 4.1%) (0.4% - 3.8%) (0.4% - 4

1.3% 1% 1% 0.9% 0.8% 0.9% 

(-1.1% - 3.7%) (-0.8% - 2.9%) (-0.8% - 2.7%) (-0.7% - 2.5%) (-0.7% - 2.2%) (-0.7% - 2

0.9% 0.9% 0.9% 0.9% 0.8% 0.9% 

(-0.8% - 2.6%) (-0.8% - 2.6%) (-0.8% - 2.6%) (-0.8% - 2.6%) (-0.8% - 2.4%) (-0.8% - 2

2.2% 1.8% 1.8% 1.6% 1.5% 1.5% 

(0.1% - 4.2%) (0.1% - 3.4%) (0.1% - 3.4%) (0.1% - 3.2%) (0.1% - 2.9%) (0.1% - 2.

2.8% 1.6% 1.6% 1.6% 1.6% 1.3% 

(0.1% - 5.4%) (0.1% - 3%) (0.1% - 3%) (0.1% - 3%) (0.1% - 3%) (0% - 2.6

2.4% 2.3% 2.1% 1.9% 1.8% 1.9% 

(0.4% - 4.4%) (0.4% - 4.1%) (0.3% - 3.8%) (0.3% - 3.5%) (0.3% - 3.2%) (0.3% - 3.

1.5% 1% 1% 1% 0.9% 0.8% 

(0.1% - 2.9%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.7%) (0.1% - 1.

2.8% 2.3% 2.3% 2.2% 2.1% 2% 

(1% - 4.5%) (0.8% - 3.8%) (0.8% - 3.8%) (0.8% - 3.7%) (0.7% - 3.4%) (0.7% - 3.

2% 1.8% 1.8% 1.7% 1.6% 1.6% 

(-0.2% - 4.1%) (-0.2% - 3.8%) (-0.2% - 3.8%) (-0.2% - 3.6%) (-0.1% - 3.3%) (-0.1% - 3

1.8% 1.8% 1.8% 1.8% 1.7% 1.7% 

(0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 3.3%) (0.1% - 3.

2.2% 1.5% 1.5% 1.5% 1.4% 1.3% 

(-0.2% - 4.5%) (-0.2% - 3.2%) (-0.2% - 3.2%) (-0.1% - 3.1%) (-0.1% - 3%) (-0.1% - 2

2% 1.3% 1.3% 1.3% 1.3% 1.1% 

(0.3% - 3.8%) (0.2% - 2.4%) (0.2% - 2.4%) (0.2% - 2.4%) (0.2% - 2.4%) (0.1% - 2

1.7% 1.6% 1.5% 1.3% 1.2% 1.3% 

(-0.5% - 3.9%) (-0.4% - 3.5%) (-0.4% - 3.3%) (-0.4% - 3%) (-0.3% - 2.8%) (-0.3% - 3

1.6% 1.3% 1.3% 1.3% 1.3% 1.1% 

(0% - 3.1%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.2

Percent of Total Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Conc

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-98

Table E-97. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Mortality Associat Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-9% 0% 7% 15% 22% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-6% 0% 5% 12% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 15

-28% 0% 7% 15% 22% 15% 

(-27% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

-23% 0% 1% 8% 16% 14% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (15% - 16%) (14% - 15

-80% 0% 0% 0% 0% 14% 

(-78% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-58% 0% 0% 0% 6% 15% 

(-57% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 15

-20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15

-9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (14% - 15

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

-41% 0% 0% 3% 8% 15% 

(-40% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (14% - 15

-58% 0% 0% 0% 0% 15% 

(-57% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-12% 0% 6% 13% 20% 15% 

(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Respiratory Mortality Associated wi

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alte

Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-99

Table E-98. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Mortality Associat Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-9% 0% 7% 15% 22% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-6% 0% 5% 12% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 15

-28% 0% 7% 15% 22% 15% 

(-27% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15

-80% 0% 0% 0% 0% 14% 

(-79% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-58% 0% 0% 0% 6% 15% 

(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15

-20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15

-9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (14% - 15

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

-42% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 15

-58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15

-12% 0% 6% 13% 20% 15% 

(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

Percent Reduction from the Current Standards: Annual Incidence of Respiratory Mortality Associated wi

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alte

Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-100

Table E-99. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Mortality Associat Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-9% 0% 7% 15% 22% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-6% 0% 5% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 15

-28% 0% 7% 15% 22% 15% 

(-27% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15

-80% 0% 0% 0% 0% 14% 

(-78% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-58% 0% 0% 0% 6% 15% 

(-57% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 15

-20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15

-9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (14% - 15

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

-42% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 15

-58% 0% 0% 0% 0% 15% 

(-57% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-12% 0% 6% 13% 20% 15% 

(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

Percent Reduction from the Current Standards: Annual Incidence of Respiratory Mortality Associated wi

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alte

Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-101

Table E-100. Estimated Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term E PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alter

 Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

43 40 37 34 31 34 

(-28 - 115) (-26 - 105) (-24 - 98) (-22 - 90) (-20 - 82) (-22 - 

262 247 234 216 199 212 

(192 - 331) (182 - 313) (172 - 295) (159 - 273) (146 - 251) (155 - 2

21 17 15 14 13 14 

(-14 - 56) (-11 - 44) (-10 - 41) (-9 - 37) (-8 - 34) (-9 - 3

31 31 31 31 28 31 

(-20 - 81) (-20 - 81) (-20 - 81) (-20 - 81) (-19 - 75) (-20 - 

345 280 278 257 236 239 

(253 - 435) (206 - 354) (204 - 351) (189 - 325) (173 - 298) (176 - 3

38 21 21 21 21 18 

(0 - 75) (0 - 41) (0 - 41) (0 - 41) (0 - 41) (0 - 3

60 56 52 48 44 48 

(-39 - 158) (-37 - 149) (-34 - 138) (-31 - 127) (-29 - 115) (-31 - 1

418 264 264 264 249 225 

(5 - 827) (3 - 523) (3 - 523) (3 - 523) (3 - 494) (3 - 44

952 792 792 772 709 677 

(700 - 1204) (582 - 1002) (582 - 1002) (567 - 976) (521 - 897) (497 - 8

233 214 214 200 184 183 

(171 - 294) (157 - 271) (157 - 271) (147 - 253) (135 - 233) (134 - 2

108 108 108 108 102 101 

(1 - 213) (1 - 213) (1 - 213) (1 - 213) (1 - 203) (1 - 2

222 157 157 153 145 134 

(163 - 280) (115 - 199) (115 - 199) (112 - 193) (106 - 183) (98 - 1

13 8 8 8 8 7 

(0 - 25) (0 - 16) (0 - 16) (0 - 16) (0 - 16) (0 - 1

231 207 195 180 165 177 

(170 - 293) (152 - 262) (143 - 246) (132 - 228) (121 - 209) (130 - 2

26 21 21 21 21 18 

(-65 - 113) (-52 - 92) (-52 - 92) (-52 - 92) (-52 - 92) (-44 - 

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locat

estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposur

in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) an

(Standard Combination Denoted n/m)

2

:

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX St. Louis, MO
E-102

Table E-101. Estimated Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term E PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alter

 Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

44 41 38 35 31 35 

(-29 - 117) (-27 - 108) (-25 - 99) (-23 - 91) (-21 - 83) (-23 - 

227 214 203 187 172 183 

(167 - 287) (157 - 271) (149 - 256) (138 - 237) (126 - 218) (135 - 2

20 16 15 13 12 13 

(-13 - 53) (-10 - 42) (-10 - 38) (-9 - 35) (-8 - 32) (-9 - 3

26 26 26 26 24 26 

(-17 - 68) (-17 - 68) (-17 - 68) (-17 - 68) (-16 - 63) (-17 - 

278 225 224 207 190 192 

(204 - 351) (165 - 285) (164 - 283) (152 - 261) (139 - 240) (141 - 2

40 22 22 22 22 19 

(0 - 80) (0 - 44) (0 - 44) (0 - 44) (0 - 44) (0 - 3

58 55 51 47 43 47 

(-38 - 154) (-36 - 145) (-33 - 134) (-31 - 123) (-28 - 113) (-31 - 1

392 248 248 248 234 211 

(5 - 776) (3 - 491) (3 - 491) (3 - 491) (3 - 463) (3 - 4

822 684 684 666 612 585 

(604 - 1040) (502 - 865) (502 - 865) (489 - 843) (449 - 774) (429 - 7

218 201 201 188 173 172 

(160 - 276) (147 - 254) (147 - 254) (138 - 237) (127 - 218) (126 - 2

113 113 113 113 107 106 

(1 - 224) (1 - 224) (1 - 224) (1 - 224) (1 - 212) (1 - 2

190 134 134 130 124 114 

(140 - 240) (98 - 169) (98 - 169) (96 - 165) (91 - 157) (84 - 1

12 7 7 7 7 6 

(0 - 23) (0 - 15) (0 - 15) (0 - 15) (0 - 15) (0 - 1

191 171 160 148 136 146 

(140 - 241) (126 - 216) (118 - 203) (109 - 188) (100 - 172) (107 - 

22 18 18 18 18 15 

(-54 - 95) (-44 - 78) (-44 - 78) (-44 - 78) (-44 - 78) (-37 - 

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locat

estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposur

in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) an

(Standard Combination Denoted n/m)

2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-103

Table E-102. Estimated Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term E PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alter

 Based on Adjusting 2007 PM2.5 Concentrations1

Recent Ambient 

PM2.5 

Concentrations

15/353 14/35 13/35 12/35 13/3

45 41 38 35 32 35 

(-29 - 119) (-27 - 109) (-25 - 101) (-23 - 92) (-21 - 84) (-23 - 

229 216 204 189 174 185 

(168 - 289) (159 - 273) (150 - 258) (139 - 239) (127 - 220) (136 - 2

21 16 15 14 12 14 

(-14 - 54) (-11 - 43) (-10 - 39) (-9 - 36) (-8 - 33) (-9 - 3

28 28 28 28 26 28 

(-18 - 73) (-18 - 73) (-18 - 73) (-18 - 73) (-17 - 68) (-18 - 

288 233 232 214 197 199 

(211 - 364) (171 - 295) (170 - 293) (157 - 271) (144 - 249) (146 - 2

42 23 23 23 23 20 

(1 - 83) (0 - 46) (0 - 46) (0 - 46) (0 - 46) (0 - 3

60 56 52 48 44 48 

(-39 - 158) (-37 - 149) (-34 - 138) (-31 - 127) (-29 - 116) (-31 - 1

408 258 258 258 243 220 

(5 - 807) (3 - 511) (3 - 511) (3 - 511) (3 - 482) (3 - 4

905 752 752 733 673 643 

(665 - 1144) (552 - 951) (552 - 951) (538 - 927) (494 - 852) (472 - 8

221 203 203 190 175 174 

(162 - 279) (149 - 257) (149 - 257) (140 - 240) (128 - 221) (128 - 2

108 108 108 108 103 102 

(1 - 215) (1 - 215) (1 - 215) (1 - 215) (1 - 204) (1 - 2

199 140 140 136 129 119 

(146 - 251) (103 - 177) (103 - 177) (100 - 172) (95 - 164) (88 - 1

15 9 9 9 9 8 

(0 - 29) (0 - 18) (0 - 18) (0 - 18) (0 - 18) (0 - 1

199 178 167 155 142 152 

(146 - 251) (131 - 225) (123 - 212) (114 - 196) (104 - 180) (112 - 

23 19 19 19 19 16 

(-57 - 101) (-46 - 82) (-46 - 82) (-46 - 82) (-46 - 82) (-39 - 

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locat

estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposur

in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) an

(Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-104

Table E-103. Estimated Percent of Total Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with  Exposure to Ambient PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet th

 Alternative Standards, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

0.41% 0.4% 0.35% 0.32% 0.29% 0.32% 

(-0.27% - 1.09%) (-0.2% - 1%) (-0.23% - 0.93%) (-0.21% - 0.85%) (-0.19% - 0.78%) (-0.21% - 0

1.59% 1.5% 1.42% 1.32% 1.21% 1.29% 

(1.17% - 2.01%) (1.1% - 1.9%) (1.05% - 1.8%) (0.97% - 1.67%) (0.89% - 1.53%) (0.95% - 1.

0.42% 0.3% 0.31% 0.28% 0.26% 0.28% 

(-0.28% - 1.12%) (-0.2% - 0.9%) (-0.2% - 0.81%) (-0.18% - 0.75%) (-0.17% - 0.68%) (-0.18% - 0

0.32% 0.3% 0.32% 0.32% 0.3% 0.32% 

(-0.21% - 0.85%) (-0.2% - 0.9%) (-0.21% - 0.85%) (-0.21% - 0.85%) (-0.2% - 0.79%) (-0.21% - 0

1.65% 1.3% 1.33% 1.23% 1.13% 1.15% 

(1.22% - 2.09%) (1% - 1.7%) (0.98% - 1.68%) (0.91% - 1.56%) (0.83% - 1.43%) (0.84% - 1.

0.81% 0.4% 0.44% 0.44% 0.44% 0.38% 

(0.01% - 1.59%) (0% - 0.9%) (0.01% - 0.88%) (0.01% - 0.88%) (0.01% - 0.88%) (0% - 0.7

0.35% 0.3% 0.31% 0.28% 0.26% 0.28% 

(-0.23% - 0.93%) (-0.2% - 0.9%) (-0.2% - 0.82%) (-0.19% - 0.75%) (-0.17% - 0.68%) (-0.19% - 0

0.77% 0.5% 0.49% 0.49% 0.46% 0.41% 

(0.01% - 1.52%) (0% - 1%) (0.01% - 0.96%) (0.01% - 0.96%) (0.01% - 0.91%) (0% - 0.8

1.49% 1.2% 1.24% 1.21% 1.11% 1.06% 

(1.09% - 1.88%) (0.9% - 1.6%) (0.91% - 1.57%) (0.89% - 1.53%) (0.81% - 1.4%) (0.78% - 1.

1.41% 1.3% 1.3% 1.22% 1.12% 1.11% 

(1.04% - 1.79%) (1% - 1.6%) (0.96% - 1.64%) (0.89% - 1.54%) (0.82% - 1.41%) (0.82% - 1.

0.53% 0.5% 0.53% 0.53% 0.51% 0.5% 

(0.01% - 1.05%) (0% - 1.1%) (0.01% - 1.05%) (0.01% - 1.05%) (0.01% - 1%) (0.01% - 0.

1.72% 1.2% 1.22% 1.19% 1.13% 1.04% 

(1.26% - 2.17%) (0.9% - 1.5%) (0.89% - 1.54%) (0.87% - 1.5%) (0.83% - 1.42%) (0.76% - 1.

0.52% 0.3% 0.33% 0.33% 0.33% 0.28% 

(0.01% - 1.03%) (0% - 0.7%) (0% - 0.65%) (0% - 0.65%) (0% - 0.65%) (0% - 0.5

1.64% 1.5% 1.38% 1.28% 1.17% 1.26% 

(1.21% - 2.08%) (1.1% - 1.9%) (1.02% - 1.75%) (0.94% - 1.62%) (0.86% - 1.48%) (0.92% - 1.

0.76% 0.6% 0.62% 0.62% 0.62% 0.53% 

(-1.86% - 3.26%) (-1.5% - 2.7%) (-1.5% - 2.65%) (-1.5% - 2.65%) (-1.5% - 2.65%) (-1.28% - 2

2 Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locatio

estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Percent of Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Te

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative A

Standard

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-105

Table E-104. Estimated Percent of Total Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with  Exposure to Ambient PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet th

 Alternative Standards, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

0.41% 0.4% 0.35% 0.32% 0.29% 0.32% 

(-0.27% - 1.08%) (-0.2% - 1%) (-0.23% - 0.92%) (-0.21% - 0.84%) (-0.19% - 0.77%) (-0.21% - 0

1.39% 1.3% 1.24% 1.15% 1.05% 1.12% 

(1.02% - 1.75%) (1% - 1.7%) (0.91% - 1.57%) (0.84% - 1.45%) (0.77% - 1.33%) (0.82% - 1.

0.4% 0.3% 0.29% 0.27% 0.24% 0.27% 

(-0.26% - 1.06%) (-0.2% - 0.8%) (-0.19% - 0.77%) (-0.17% - 0.71%) (-0.16% - 0.64%) (-0.17% - 0

0.27% 0.3% 0.27% 0.27% 0.25% 0.27% 

(-0.17% - 0.7%) (-0.2% - 0.7%) (-0.17% - 0.7%) (-0.17% - 0.7%) (-0.16% - 0.65%) (-0.17% - 0

1.34% 1.1% 1.08% 1% 0.92% 0.93% 

(0.98% - 1.69%) (0.8% - 1.4%) (0.79% - 1.37%) (0.73% - 1.26%) (0.67% - 1.16%) (0.68% - 1.

0.85% 0.5% 0.47% 0.47% 0.47% 0.4% 

(0.01% - 1.68%) (0% - 0.9%) (0.01% - 0.93%) (0.01% - 0.93%) (0.01% - 0.93%) (0% - 0.7

0.33% 0.3% 0.29% 0.27% 0.24% 0.27% 

(-0.22% - 0.88%) (-0.2% - 0.8%) (-0.19% - 0.77%) (-0.17% - 0.71%) (-0.16% - 0.64%) (-0.17% - 0

0.71% 0.4% 0.45% 0.45% 0.42% 0.38% 

(0.01% - 1.41%) (0% - 0.9%) (0.01% - 0.89%) (0.01% - 0.89%) (0.01% - 0.84%) (0% - 0.7

1.27% 1.1% 1.06% 1.03% 0.95% 0.9% 

(0.93% - 1.61%) (0.8% - 1.3%) (0.78% - 1.34%) (0.76% - 1.3%) (0.7% - 1.2%) (0.66% - 1.

1.34% 1.2% 1.24% 1.16% 1.06% 1.06% 

(0.99% - 1.7%) (0.9% - 1.6%) (0.91% - 1.56%) (0.85% - 1.46%) (0.78% - 1.34%) (0.78% - 1.

0.54% 0.5% 0.54% 0.54% 0.51% 0.5% 

(0.01% - 1.07%) (0% - 1.1%) (0.01% - 1.07%) (0.01% - 1.07%) (0.01% - 1.02%) (0.01% - 

1.5% 1.1% 1.05% 1.03% 0.98% 0.9% 

(1.1% - 1.89%) (0.8% - 1.3%) (0.77% - 1.33%) (0.75% - 1.3%) (0.72% - 1.23%) (0.66% - 1.

0.46% 0.3% 0.29% 0.29% 0.29% 0.25% 

(0.01% - 0.92%) (0% - 0.6%) (0% - 0.58%) (0% - 0.58%) (0% - 0.58%) (0% - 0.4

1.36% 1.2% 1.14% 1.06% 0.97% 1.04% 

(1% - 1.72%) (0.9% - 1.5%) (0.84% - 1.45%) (0.78% - 1.34%) (0.71% - 1.23%) (0.76% - 1.

0.63% 0.5% 0.51% 0.51% 0.51% 0.43% 

(-1.53% - 2.69%) (-1.2% - 2.2%) (-1.23% - 2.19%) (-1.23% - 2.19%) (-1.23% - 2.19%) (-1.05% - 1

2 Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locatio

estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Percent of Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Te

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative A

Standard

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-106

Table E-105. Estimated Percent of Total Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with  Exposure to Ambient PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet th

 Alternative Standards, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

0.4% 0.4% 0.34% 0.31% 0.28% 0.31% 

(-0.26% - 1.06%) (-0.2% - 1%) (-0.22% - 0.9%) (-0.2% - 0.83%) (-0.19% - 0.75%) (-0.2% - 0.

1.41% 1.3% 1.26% 1.16% 1.07% 1.14% 

(1.03% - 1.78%) (1% - 1.7%) (0.92% - 1.59%) (0.85% - 1.47%) (0.78% - 1.35%) (0.83% - 1.

0.41% 0.3% 0.3% 0.27% 0.25% 0.27% 

(-0.27% - 1.09%) (-0.2% - 0.9%) (-0.19% - 0.79%) (-0.18% - 0.72%) (-0.16% - 0.66%) (-0.18% - 0

0.28% 0.3% 0.28% 0.28% 0.26% 0.28% 

(-0.18% - 0.74%) (-0.2% - 0.7%) (-0.18% - 0.74%) (-0.18% - 0.74%) (-0.17% - 0.68%) (-0.18% - 0

1.4% 1.1% 1.13% 1.04% 0.96% 0.97% 

(1.03% - 1.77%) (0.8% - 1.4%) (0.83% - 1.42%) (0.76% - 1.32%) (0.7% - 1.21%) (0.71% - 1.

0.86% 0.5% 0.48% 0.48% 0.48% 0.41% 

(0.01% - 1.7%) (0% - 0.9%) (0.01% - 0.94%) (0.01% - 0.94%) (0.01% - 0.94%) (0% - 0.8

0.33% 0.3% 0.29% 0.27% 0.24% 0.27% 

(-0.22% - 0.88%) (-0.2% - 0.8%) (-0.19% - 0.77%) (-0.17% - 0.71%) (-0.16% - 0.64%) (-0.17% - 0

0.72% 0.5% 0.46% 0.46% 0.43% 0.39% 

(0.01% - 1.43%) (0% - 0.9%) (0.01% - 0.91%) (0.01% - 0.91%) (0.01% - 0.86%) (0% - 0.7

1.39% 1.2% 1.15% 1.12% 1.03% 0.99% 

(1.02% - 1.75%) (0.8% - 1.5%) (0.85% - 1.46%) (0.83% - 1.42%) (0.76% - 1.31%) (0.72% - 1.

1.38% 1.3% 1.27% 1.18% 1.09% 1.08% 

(1.01% - 1.74%) (0.9% - 1.6%) (0.93% - 1.6%) (0.87% - 1.5%) (0.8% - 1.38%) (0.79% - 1.

0.5% 0.5% 0.5% 0.5% 0.47% 0.47% 

(0.01% - 0.99%) (0% - 1%) (0.01% - 0.99%) (0.01% - 0.99%) (0.01% - 0.94%) (0.01% - 0.

1.58% 1.1% 1.11% 1.08% 1.03% 0.95% 

(1.16% - 2%) (0.8% - 1.4%) (0.82% - 1.41%) (0.8% - 1.37%) (0.76% - 1.3%) (0.7% - 1.

0.56% 0.4% 0.36% 0.36% 0.36% 0.3% 

(0.01% - 1.11%) (0% - 0.7%) (0% - 0.7%) (0% - 0.7%) (0% - 0.7%) (0% - 0.6

1.42% 1.3% 1.19% 1.1% 1.01% 1.09% 

(1.04% - 1.79%) (0.9% - 1.6%) (0.88% - 1.51%) (0.81% - 1.4%) (0.74% - 1.28%) (0.8% - 1.3

0.65% 0.5% 0.52% 0.52% 0.52% 0.45% 

(-1.58% - 2.77%) (-1.3% - 2.3%) (-1.28% - 2.26%) (-1.28% - 2.26%) (-1.28% - 2.26%) (-1.09% - 1

2 Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locatio

estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Percent of Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Te

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative A

Standard

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-107

Table E-106. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Hospital Adm

 Short-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 1

-9% 0% 8% 15% 23% 1

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% 

-6% 0% 6% 13% 20% 1

(-6% - -6%) (0% - 0%) (5% - 6%) (13% - 13%) (20% - 20%) (14% 

-28% 0% 8% 15% 23% 1

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% 

0% 0% 0% 0% 7% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% 

-23% 0% 1% 8% 16% 1

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% 

-81% 0% 0% 0% 0% 1

(-81% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% 

-6% 0% 8% 15% 23% 1

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% 

-58% 0% 0% 0% 6% 1

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% 

-20% 0% 0% 3% 10% 1

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% 

-9% 0% 0% 6% 14% 1

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% 

0% 0% 0% 0% 5% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% 

-41% 0% 0% 3% 8% 1

(-41% - -41%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (15% 

-59% 0% 0% 0% 0% 1

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% 

-12% 0% 6% 13% 20% 1

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% 

-23% 0% 0% 0% 0% 1

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Hospital Admiss

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locatio

were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-108

Table E-107. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Hospital Adm

 Short-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 1

-9% 0% 8% 15% 23% 1

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% 

-6% 0% 6% 13% 20% 1

(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% 

-28% 0% 8% 15% 23% 1

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% 

0% 0% 0% 0% 7% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% 

-23% 0% 1% 8% 16% 1

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% 

-81% 0% 0% 0% 0% 1

(-81% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% 

-6% 0% 8% 15% 23% 1

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% 

-58% 0% 0% 0% 6% 1

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% 

-20% 0% 0% 3% 10% 1

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% 

-9% 0% 0% 6% 14% 1

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% 

0% 0% 0% 0% 5% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% 

-42% 0% 0% 3% 7% 1

(-42% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% 

-59% 0% 0% 0% 0% 1

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% 

-12% 0% 6% 13% 20% 1

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% 

-23% 0% 0% 0% 0% 1

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% 

Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Hospital Admiss

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locatio

were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA
E-109

Table E-108. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Hospital Adm

 Short-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 1

-9% 0% 8% 15% 23% 1

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% 

-6% 0% 6% 13% 20% 1

(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% 

-28% 0% 8% 15% 23% 1

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% 

0% 0% 0% 0% 7% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% 

-23% 0% 1% 8% 16% 1

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% 

-81% 0% 0% 0% 0% 1

(-81% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% 

-6% 0% 8% 15% 23% 1

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% 

-58% 0% 0% 0% 6% 1

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% 

-20% 0% 0% 3% 10% 1

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% 

-9% 0% 0% 6% 14% 1

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% 

0% 0% 0% 0% 5% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% 

-42% 0% 0% 3% 7% 1

(-42% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% 

-59% 0% 0% 0% 0% 1

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% 

-12% 0% 6% 13% 20% 1

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% 

-23% 0% 0% 0% 0% 1

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% 

Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Hospital Admiss

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locatio

were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA
E-110

Table E-109. Estimated Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Expos PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Altern

 Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

19 17 16 15 13 15 

(-23 - 60) (-22 - 55) (-20 - 51) (-18 - 47) (-17 - 43) (-18 - 4

21 20 19 17 16 17 

(-12 - 54) (-12 - 51) (-11 - 48) (-10 - 45) (-9 - 41) (-10 - 4

9 77666

(-11 - 29) (-9 - 23) (-8 - 21) (-8 - 20) (-7 - 18) (-8 - 20

15 15 15 15 14 15 

(-18 - 47) (-18 - 47) (-18 - 47) (-18 - 47) (-17 - 44) (-18 - 4

31 25 25 23 21 21 

(-18 - 79) (-15 - 64) (-15 - 64) (-13 - 59) (-12 - 54) (-13 - 5

25 14 14 14 14 12 

(6 - 44) (3 - 25) (3 - 25) (3 - 25) (3 - 25) (3 - 21

27 25 23 21 19 21 

(-34 - 86) (-32 - 81) (-29 - 75) (-27 - 69) (-24 - 63) (-27 - 6

269 170 170 170 161 145 

(63 - 473) (40 - 300) (40 - 300) (40 - 300) (37 - 283) (34 - 25

79 65 65 64 58 56 

(-46 - 203) (-38 - 169) (-38 - 169) (-37 - 164) (-34 - 151) (-33 - 14

19 17 17 16 15 15 

(-11 - 48) (-10 - 44) (-10 - 44) (-9 - 41) (-9 - 38) (-9 - 38

61 61 61 61 58 57 

(14 - 107) (14 - 107) (14 - 107) (14 - 107) (14 - 102) (13 - 10

18 13 13 12 12 11 

(-11 - 47) (-8 - 33) (-8 - 33) (-7 - 32) (-7 - 30) (-6 - 28

9 66665

(2 - 16) (1 - 10) (1 - 10) (1 - 10) (1 - 10) (1 - 9) 

28 25 23 22 20 21 

(-16 - 72) (-15 - 64) (-14 - 60) (-13 - 56) (-12 - 51) (-13 - 5

2 22222

(-34 - 37) (-27 - 30) (-27 - 30) (-27 - 30) (-27 - 30) (-23 - 2

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Detroit, MI Houston, TX New York, NY St. Louis, MO

Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Incidence estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hos

Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD
E-111

Table E-110. Estimated Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Expos PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Altern

 Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

19 17 16 15 13 15 

(-24 - 61) (-22 - 56) (-20 - 52) (-19 - 48) (-17 - 44) (-19 - 4

18 17 16 15 14 15 

(-11 - 47) (-10 - 44) (-10 - 42) (-9 - 39) (-8 - 36) (-9 - 38

9 76656

(-11 - 28) (-8 - 22) (-8 - 20) (-7 - 19) (-7 - 17) (-7 - 19

12 12 12 12 11 12 

(-15 - 40) (-15 - 40) (-15 - 40) (-15 - 40) (-14 - 37) (-15 - 4

25 20 20 18 17 17 

(-15 - 64) (-12 - 52) (-12 - 51) (-11 - 47) (-10 - 44) (-10 - 4

27 15 15 15 15 13 

(6 - 47) (3 - 26) (3 - 26) (3 - 26) (3 - 26) (3 - 22

26 25 23 21 19 21 

(-33 - 84) (-31 - 79) (-29 - 73) (-26 - 68) (-24 - 62) (-26 - 6

253 160 160 160 151 136 

(59 - 444) (37 - 281) (37 - 281) (37 - 281) (35 - 265) (32 - 24

68 56 56 55 50 48 

(-40 - 175) (-33 - 145) (-33 - 145) (-32 - 142) (-30 - 130) (-28 - 12

17 16 16 15 14 14 

(-10 - 45) (-9 - 41) (-9 - 41) (-9 - 39) (-8 - 36) (-8 - 35

64 64 64 64 61 60 

(15 - 112) (15 - 112) (15 - 112) (15 - 112) (14 - 107) (14 - 10

16 11 11 11 10 9 

(-9 - 40) (-6 - 28) (-6 - 28) (-6 - 27) (-6 - 26) (-5 - 24

8 55555

(2 - 15) (1 - 9) (1 - 9) (1 - 9) (1 - 9) (1 - 8) 

23 21 19 18 16 18 

(-14 - 59) (-12 - 53) (-11 - 50) (-10 - 46) (-10 - 42) (-10 - 4

2 22221

(-28 - 31) (-23 - 25) (-23 - 25) (-23 - 25) (-23 - 25) (-19 - 2

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hos

Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD
E-112

Table E-111. Estimated Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Expos PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Altern

 Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

19 18 16 15 14 15 

(-24 - 62) (-22 - 57) (-21 - 53) (-19 - 48) (-17 - 44) (-19 - 4

18 17 16 15 14 15 

(-11 - 47) (-10 - 45) (-10 - 42) (-9 - 39) (-8 - 36) (-9 - 38

9 76656

(-11 - 29) (-9 - 22) (-8 - 21) (-7 - 19) (-7 - 17) (-7 - 19

13 13 13 13 12 13 

(-17 - 43) (-17 - 43) (-17 - 43) (-17 - 43) (-15 - 40) (-17 - 4

26 21 21 19 18 18 

(-15 - 66) (-12 - 54) (-12 - 53) (-11 - 49) (-10 - 45) (-10 - 4

28 15 15 15 15 13 

(7 - 49) (4 - 27) (4 - 27) (4 - 27) (4 - 27) (3 - 23

27 25 23 21 20 21 

(-34 - 87) (-32 - 82) (-29 - 76) (-27 - 69) (-25 - 63) (-27 - 6

263 166 166 166 157 142 

(61 - 461) (39 - 293) (39 - 293) (39 - 293) (37 - 276) (33 - 25

75 62 62 60 56 53 

(-44 - 193) (-37 - 160) (-37 - 160) (-36 - 156) (-33 - 143) (-31 - 13

18 16 16 15 14 14 

(-10 - 46) (-10 - 42) (-10 - 42) (-9 - 39) (-8 - 36) (-8 - 36

61 61 61 61 58 57 

(14 - 108) (14 - 108) (14 - 108) (14 - 108) (14 - 103) (13 - 10

16 11 11 11 11 10 

(-10 - 42) (-7 - 29) (-7 - 29) (-7 - 29) (-6 - 27) (-6 - 25

11 7 7 7 7 6 

(2 - 19) (2 - 12) (2 - 12) (2 - 12) (2 - 12) (1 - 10

24 21 20 19 17 18 

(-14 - 62) (-13 - 55) (-12 - 52) (-11 - 48) (-10 - 44) (-11 - 4

2 22222

(-30 - 33) (-24 - 27) (-24 - 27) (-24 - 27) (-24 - 27) (-21 - 2

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hos

Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD
E-113

Table E-112. Estimated Percent of Total Annual Incidence of Hospital Admissions for Respiratory Illness Associated with S Ambient PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

0.5% 0.46% 0.42% 0.39% 0.35% 0.39

(-0.63% - 1.61%) (-0.58% - 1.48%) (-0.53% - 1.37%) (-0.49% - 1.26%) (-0.45% - 1.15%) (-0.49% - 

0.42% 0.39% 0.37% 0.34% 0.32% 0.34

(-0.25% - 1.08%) (-0.23% - 1.02%) (-0.22% - 0.96%) (-0.2% - 0.89%) (-0.19% - 0.82%) (-0.2% - 0

0.51% 0.4% 0.37% 0.34% 0.31% 0.34

(-0.65% - 1.65%) (-0.51% - 1.3%) (-0.47% - 1.2%) (-0.43% - 1.1%) (-0.39% - 1.01%) (-0.43% - 

0.39% 0.39% 0.39% 0.39% 0.36% 0.39

(-0.49% - 1.26%) (-0.49% - 1.26%) (-0.49% - 1.26%) (-0.49% - 1.26%) (-0.45% - 1.17%) (-0.49% - 

0.43% 0.35% 0.35% 0.32% 0.3% 0.3% 

(-0.26% - 1.12%) (-0.21% - 0.91%) (-0.21% - 0.9%) (-0.19% - 0.83%) (-0.17% - 0.76%) (-0.18% - 

1.42% 0.78% 0.78% 0.78% 0.78% 0.67

(0.33% - 2.49%) (0.18% - 1.38%) (0.18% - 1.38%) (0.18% - 1.38%) (0.18% - 1.38%) (0.16% - 

0.43% 0.4% 0.37% 0.34% 0.31% 0.34

(-0.54% - 1.38%) (-0.51% - 1.3%) (-0.47% - 1.2%) (-0.43% - 1.11%) (-0.39% - 1.01%) (-0.43% - 

1.36% 0.86% 0.86% 0.86% 0.81% 0.73

(0.32% - 2.38%) (0.2% - 1.51%) (0.2% - 1.51%) (0.2% - 1.51%) (0.19% - 1.42%) (0.17% - 

0.39% 0.32% 0.32% 0.32% 0.29% 0.28

(-0.23% - 1.01%) (-0.19% - 0.84%) (-0.19% - 0.84%) (-0.19% - 0.81%) (-0.17% - 0.75%) (-0.16% - 

0.37% 0.34% 0.34% 0.32% 0.29% 0.29

(-0.22% - 0.95%) (-0.2% - 0.88%) (-0.2% - 0.88%) (-0.19% - 0.82%) (-0.17% - 0.75%) (-0.17% - 

0.94% 0.94% 0.94% 0.94% 0.89% 0.88

(0.22% - 1.65%) (0.22% - 1.65%) (0.22% - 1.65%) (0.22% - 1.65%) (0.21% - 1.57%) (0.21% - 

0.45% 0.32% 0.32% 0.31% 0.29% 0.27

(-0.27% - 1.16%) (-0.19% - 0.82%) (-0.19% - 0.82%) (-0.18% - 0.8%) (-0.17% - 0.76%) (-0.16% - 

0.92% 0.58% 0.58% 0.58% 0.58% 0.49

(0.21% - 1.61%) (0.14% - 1.02%) (0.14% - 1.02%) (0.14% - 1.02%) (0.14% - 1.02%) (0.12% - 

0.43% 0.39% 0.36% 0.33% 0.31% 0.33

(-0.25% - 1.11%) (-0.23% - 0.99%) (-0.21% - 0.93%) (-0.2% - 0.86%) (-0.18% - 0.79%) (-0.19% - 

0.2% 0.16% 0.16% 0.16% 0.16% 0.14

(-2.72% - 2.96%) (-2.19% - 2.41%) (-2.19% - 2.41%) (-2.19% - 2.41%) (-2.19% - 2.41%) (-1.87% - 

2 Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding th3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Te

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative 

Standards (Standard Combination Denoted n/m)2

:

1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital adm

Bell et al. (2008). Location-specific C-R function estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-114

Table E-113. Estimated Percent of Total Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Sh Ambient PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current an

 Standards, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

0.49% 0.45% 0.42% 0.38% 0.35% 0.38% 

(-0.62% - 1.59%) (-0.57% - 1.46%) (-0.53% - 1.35%) (-0.48% - 1.24%) (-0.44% - 1.13%) (-0.48% - 1

0.36% 0.34% 0.32% 0.3% 0.28% 0.29% 

(-0.21% - 0.94%) (-0.2% - 0.89%) (-0.19% - 0.84%) (-0.18% - 0.77%) (-0.16% - 0.71%) (-0.17% - 0

0.48% 0.38% 0.35% 0.32% 0.29% 0.32% 

(-0.61% - 1.57%) (-0.48% - 1.23%) (-0.44% - 1.14%) (-0.41% - 1.04%) (-0.37% - 0.95%) (-0.41% - 1

0.32% 0.32% 0.32% 0.32% 0.3% 0.32% 

(-0.4% - 1.04%) (-0.4% - 1.04%) (-0.4% - 1.04%) (-0.4% - 1.04%) (-0.37% - 0.96%) (-0.4% - 1.

0.35% 0.28% 0.28% 0.26% 0.24% 0.24% 

(-0.21% - 0.9%) (-0.17% - 0.73%) (-0.17% - 0.73%) (-0.15% - 0.67%) (-0.14% - 0.62%) (-0.14% - 0

1.49% 0.83% 0.83% 0.83% 0.83% 0.71% 

(0.35% - 2.62%) (0.19% - 1.45%) (0.19% - 1.45%) (0.19% - 1.45%) (0.19% - 1.45%) (0.16% - 1.

0.4% 0.38% 0.35% 0.32% 0.29% 0.32% 

(-0.51% - 1.3%) (-0.48% - 1.23%) (-0.44% - 1.13%) (-0.4% - 1.04%) (-0.37% - 0.95%) (-0.4% - 1.

1.25% 0.79% 0.79% 0.79% 0.75% 0.68% 

(0.29% - 2.2%) (0.18% - 1.4%) (0.18% - 1.4%) (0.18% - 1.4%) (0.17% - 1.32%) (0.16% - 1.

0.33% 0.28% 0.28% 0.27% 0.25% 0.24% 

(-0.2% - 0.86%) (-0.16% - 0.71%) (-0.16% - 0.71%) (-0.16% - 0.7%) (-0.15% - 0.64%) (-0.14% - 0

0.35% 0.32% 0.32% 0.3% 0.28% 0.28% 

(-0.21% - 0.91%) (-0.19% - 0.83%) (-0.19% - 0.83%) (-0.18% - 0.78%) (-0.16% - 0.72%) (-0.16% - 0

0.95% 0.95% 0.95% 0.95% 0.9% 0.89% 

(0.22% - 1.67%) (0.22% - 1.67%) (0.22% - 1.67%) (0.22% - 1.67%) (0.21% - 1.59%) (0.21% - 1.

0.39% 0.28% 0.28% 0.27% 0.26% 0.24% 

(-0.23% - 1.01%) (-0.16% - 0.71%) (-0.16% - 0.71%) (-0.16% - 0.69%) (-0.15% - 0.66%) (-0.14% - 0

0.82% 0.51% 0.51% 0.51% 0.51% 0.44% 

(0.19% - 1.43%) (0.12% - 0.91%) (0.12% - 0.91%) (0.12% - 0.91%) (0.12% - 0.91%) (0.1% - 0.7

0.36% 0.32% 0.3% 0.28% 0.25% 0.27% 

(-0.21% - 0.92%) (-0.19% - 0.82%) (-0.18% - 0.77%) (-0.16% - 0.71%) (-0.15% - 0.65%) (-0.16% - 0

0.16% 0.13% 0.13% 0.13% 0.13% 0.11% 

(-2.23% - 2.45%) (-1.8% - 1.99%) (-1.8% - 1.99%) (-1.8% - 1.99%) (-1.8% - 1.99%) (-1.53% - 1

2 Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term 

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative A

Standards (Standard Combination Denoted n/m)2

:

1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital admis

Bell et al. (2008). Location-specific C-R function estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-115

Table E-114. Estimated Percent of Total Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Sh Ambient PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current an

 Standards, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

0.48% 0.44% 0.41% 0.38% 0.34% 0.38% 

(-0.61% - 1.56%) (-0.56% - 1.43%) (-0.52% - 1.32%) (-0.47% - 1.22%) (-0.43% - 1.11%) (-0.47% - 1

0.37% 0.35% 0.33% 0.3% 0.28% 0.3% 

(-0.22% - 0.95%) (-0.2% - 0.9%) (-0.19% - 0.85%) (-0.18% - 0.78%) (-0.16% - 0.72%) (-0.18% - 0

0.5% 0.39% 0.36% 0.33% 0.3% 0.33% 

(-0.63% - 1.6%) (-0.49% - 1.26%) (-0.45% - 1.16%) (-0.42% - 1.07%) (-0.38% - 0.97%) (-0.42% - 1

0.34% 0.34% 0.34% 0.34% 0.31% 0.34% 

(-0.42% - 1.09%) (-0.42% - 1.09%) (-0.42% - 1.09%) (-0.42% - 1.09%) (-0.39% - 1.01%) (-0.42% - 1

0.37% 0.3% 0.29% 0.27% 0.25% 0.25% 

(-0.22% - 0.94%) (-0.17% - 0.77%) (-0.17% - 0.76%) (-0.16% - 0.7%) (-0.15% - 0.64%) (-0.15% - 0

1.52% 0.84% 0.84% 0.84% 0.84% 0.72% 

(0.36% - 2.66%) (0.2% - 1.48%) (0.2% - 1.48%) (0.2% - 1.48%) (0.2% - 1.48%) (0.17% - 1.

0.4% 0.38% 0.35% 0.32% 0.29% 0.32% 

(-0.51% - 1.3%) (-0.48% - 1.23%) (-0.44% - 1.13%) (-0.41% - 1.04%) (-0.37% - 0.95%) (-0.41% - 1

1.28% 0.81% 0.81% 0.81% 0.76% 0.69% 

(0.3% - 2.25%) (0.19% - 1.42%) (0.19% - 1.42%) (0.19% - 1.42%) (0.18% - 1.34%) (0.16% - 1.

0.36% 0.3% 0.3% 0.29% 0.27% 0.26% 

(-0.21% - 0.94%) (-0.18% - 0.78%) (-0.18% - 0.78%) (-0.17% - 0.76%) (-0.16% - 0.7%) (-0.15% - 0

0.36% 0.33% 0.33% 0.31% 0.28% 0.28% 

(-0.21% - 0.93%) (-0.2% - 0.85%) (-0.2% - 0.85%) (-0.18% - 0.8%) (-0.17% - 0.73%) (-0.17% - 0

0.88% 0.88% 0.88% 0.88% 0.84% 0.83% 

(0.21% - 1.55%) (0.21% - 1.55%) (0.21% - 1.55%) (0.21% - 1.55%) (0.2% - 1.48%) (0.19% - 1.

0.42% 0.29% 0.29% 0.28% 0.27% 0.25% 

(-0.24% - 1.07%) (-0.17% - 0.75%) (-0.17% - 0.75%) (-0.17% - 0.73%) (-0.16% - 0.7%) (-0.15% - 0

0.99% 0.63% 0.63% 0.63% 0.63% 0.54% 

(0.23% - 1.74%) (0.15% - 1.1%) (0.15% - 1.1%) (0.15% - 1.1%) (0.15% - 1.1%) (0.12% - 0.

0.37% 0.33% 0.31% 0.29% 0.26% 0.28% 

(-0.22% - 0.96%) (-0.2% - 0.86%) (-0.18% - 0.81%) (-0.17% - 0.74%) (-0.16% - 0.68%) (-0.17% - 0

0.17% 0.14% 0.14% 0.14% 0.14% 0.12% 

(-2.31% - 2.52%) (-1.86% - 2.05%) (-1.86% - 2.05%) (-1.86% - 2.05%) (-1.86% - 2.05%) (-1.59% - 1

2 Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term 

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative A

Standards (Standard Combination Denoted n/m)2

:

1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital admis

Bell et al. (2008). Location-specific C-R function estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-116

Table E-115. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Hospital Admissio Short-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-9% 0% 8% 15% 23% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-6% 0% 6% 13% 20% 15% 

(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15

-28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15

-81% 0% 0% 0% 0% 15% 

(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-6% 0% 8% 15% 23% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-58% 0% 0% 0% 6% 15% 

(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15

-20% 0% 0% 3% 11% 15% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (11% - 11%) (15% - 15

-9% 0% 0% 6% 14% 15% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (15% - 15

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

-41% 0% 0% 3% 8% 15% 

(-41% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (15% - 15

-58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15

-12% 0% 6% 13% 20% 15% 

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital admis

Bell et al. (2008). Location-specific C-R function estimates were not available from this study. 

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Respiratory Hospital Admissions 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the C

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-117

Table E-116. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Hospital Admiss

 Short-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-9% 0% 8% 15% 23% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

-6% 0% 6% 13% 20% 15% 

(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 

-28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 

-81% 0% 0% 0% 0% 15% 

(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 

-6% 0% 8% 15% 23% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

-58% 0% 0% 0% 6% 15% 

(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 

-20% 0% 0% 3% 11% 15% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (11% - 11%) (15% - 

-9% 0% 0% 6% 14% 15% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (15% - 

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 

-43% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 

-58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 

-12% 0% 6% 13% 20% 15% 

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital adm

Bell et al. (2008). Location-specific C-R function estimates were not available from this study. 

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Respiratory Hospital Admissions 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-118

Table E-117. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Hospital Admissio Short-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-9% 0% 8% 15% 23% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-6% 0% 6% 13% 20% 15% 

(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15

-28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15

-81% 0% 0% 0% 0% 15% 

(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-6% 0% 8% 15% 23% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-58% 0% 0% 0% 6% 15% 

(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15

-20% 0% 0% 3% 11% 15% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (11% - 11%) (15% - 15

-9% 0% 0% 6% 14% 15% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (15% - 15

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

-43% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15

-58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15

-12% 0% 6% 13% 20% 15% 

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital admis

Bell et al. (2008). Location-specific C-R function estimates were not available from this study. 

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Respiratory Hospital Admissions 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the C

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-119

Table E-118. Estimated Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exposure to Ambien

 in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Base

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

216 198 183 169 154 

(-304 - 727) (-279 - 668) (-258 - 618) (-237 - 568) (-216 - 518) 

814 746 690 634 578 

(-816 - 2419) (-748 - 2220) (-691 - 2055) (-635 - 1889) (-578 - 1723

5235 4375 4375 4265 3927 

(3346 - 7071) (2790 - 5923) (2790 - 5923) (2719 - 5776) (2501 - 5323

 Tolbert et al. (2007)

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

Study

 Tolbert et al. (2007) Ito et al. (2007)

Location ER Visit for:

Incidence of ER Visits Associated with Short-Term Exposure to PM2.5 Conc

PM2.5 Concentrations that Just Meet the Current and Alternative Annual 

(Standard Combination Denoted n/m):

New York, NY Asthma

Atlanta, GA Cardiovascular illness

Atlanta, GA Respiratory illness
E-120

Table E-119. Estimated Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exposure to Ambien

 in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Base

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

220 202 187 172 157 

(-310 - 741) (-284 - 681) (-263 - 630) (-241 - 579) (-220 - 52

829 761 704 647 589 

(-831 - 2465) (-762 - 2263) (-705 - 2094) (-647 - 1925) (-590 - 17

4506 3764 3764 3669 3377 

(2876 - 6095) (2397 - 5102) (2397 - 5102) (2336 - 4974) (2149 - 45

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tolbert et al. (2007)

1Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin

Study

 Tolbert et al. (2007) Ito et al. (2007)

Location ER Visit for:

Incidence of ER Visits Associated with Short-Term Exposure to PM2.5 Con

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) a

Combination Denoted n/m):

New York, NY Asthma

Atlanta, GA Cardiovascular illnessAtlanta, GA Respiratory illness
E-121

Table E-120. Estimated Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exposure to Ambien

 in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Base

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

219 201 186 171 156 

(-308 - 738) (-283 - 677) (-261 - 627) (-240 - 576) (-219 - 526) 

825 757 700 643 586 

(-827 - 2453) (-758 - 2251) (-701 - 2084) (-644 - 1915) (-587 - 1747) 

4926 4115 4115 4011 3692 

(3145 - 6660) (2622 - 5575) (2622 - 5575) (2555 - 5436) (2350 - 5008

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tolbert et al. (2007)

1Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin

Study

 Tolbert et al. (2007) Ito et al. (2007)

Location ER Visit for:

Incidence of ER Visits Associated with Short-Term Exposure to PM2.5 Conce

PM2.5 Concentrations that Just Meet the Current and Alternative Annual 

(Standard Combination Denoted n/m):

New York, NY Asthma

Atlanta, GA Cardiovascular illness

Atlanta, GA Respiratory illness
E-122

Table E-121. Estimated Percent of Total Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exp

 PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Altern

 on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

0.6% 0.6% 0.5% 0.5% 0.5% 

(-0.9% - 2.2%) (-0.8% - 2%) (-0.8% - 1.9%) (-0.7% - 1.7%) (-0.6% - 1.6%

0.6% 0.6% 0.5% 0.5% 0.5% 

(-0.6% - 1.9%) (-0.6% - 1.8%) (-0.6% - 1.6%) (-0.5% - 1.5%) (-0.5% - 1.4%

6.1% 5.1% 5.1% 5% 4.6% 

(3.9% - 8.2%) (3.3% - 6.9%) (3.3% - 6.9%) (3.2% - 6.7%) (2.9% - 6.2%

Asthma

Atlanta, GA Cardiovascular illnessAtlanta, GA Respiratory illness

Percent of Total Incidence of ER Visits Associated with Short-Term Exposu

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alter

Standards (Standard Combination Denoted n/m

 Tolbert et al. (2007)

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c

Study

 Tolbert et al. (2007) Ito et al. (2007)

Location ER Visit for:

New York, NY
E-123

Table E-122. Estimated Percent of Total Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Ex PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alter

 on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

0.6% 0.6% 0.5% 0.5% 0.5% 

(-0.9% - 2.1%) (-0.8% - 2%) (-0.8% - 1.8%) (-0.7% - 1.7%) (-0.6% - 1.

0.6% 0.6% 0.5% 0.5% 0.5% 

(-0.6% - 1.9%) (-0.6% - 1.7%) (-0.5% - 1.6%) (-0.5% - 1.5%) (-0.5% - 1.

5.2% 4.4% 4.4% 4.3% 3.9% 

(3.3% - 7.1%) (2.8% - 5.9%) (2.8% - 5.9%) (2.7% - 5.8%) (2.5% - 5.3

Asthma

Atlanta, GA Cardiovascular illnessAtlanta, GA Respiratory illness

Percent of Total Incidence of ER Visits Associated with Short-Term Expo

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alt

Standards (Standard Combination Denoted n

 Tolbert et al. (2007)

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 

Study

 Tolbert et al. (2007) Ito et al. (2007)

Location ER Visit for:

New York, NY
E-124

Table E-123. Estimated Percent of Total Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exp

 PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Altern

 on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

0.6% 0.6% 0.5% 0.5% 0.4% 

(-0.9% - 2.1%) (-0.8% - 1.9%) (-0.7% - 1.8%) (-0.7% - 1.6%) (-0.6% - 1.

0.6% 0.6% 0.5% 0.5% 0.4% 

(-0.6% - 1.8%) (-0.6% - 1.7%) (-0.5% - 1.6%) (-0.5% - 1.4%) (-0.4% - 1.

5.7% 4.8% 4.8% 4.6% 4.3% 

(3.6% - 7.7%) (3% - 6.5%) (3% - 6.5%) (3% - 6.3%) (2.7% - 5.8

Asthma

Atlanta, GA Cardiovascular illnessAtlanta, GA Respiratory illness

Percent of Total Incidence of ER Visits Associated with Short-Term Expos

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alte

Standards (Standard Combination Denoted n

 Tolbert et al. (2007)

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 

Study

 Tolbert et al. (2007) Ito et al. (2007)

Location ER Visit for:

New York, NY
F-1

APPENDIX F: SENSITIVITY ANALYSIS RESULTS 
F-1

1 Appendix F. Sensitivity Analysis Results 

2 

3 This Appendix provides detailed results of the single- and multi-factor sensitivity 

4 analyses completed as part of this risk analysis. For additional detail on the sensitivity analysis 

5 results completed for this analysis, as well as the types of results generated, see section 4.3. 

6 
F-1

Table F-1. Sensitivity Analysis: Impact of Using Different Model Choices to Estimate the Incidence of Mortality Associate Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concen

Standard Fixed 

Effects Log-Linear 

(Cox Proportional 

Hazard) Model3

Random Effects LogLinear Model 4 Random Effects Log- Log Model 5 Fixed Effects vs. Random Effects Log

Linear Models 

1342 1656 3360

(854 - 1827) (772 - 2527) (2075 - 4615)

1526 2569

(1191 - 1856) (1709 - 3400)

1249 1397 2535

(1017 - 1477) (847 - 1924) (1793 - 3232)

164 307

(71 - 253) (160 - 446)

584 719 1254

(372 - 792) (337 - 1090) (779 - 1713)

545 790

(427 - 660) (530 - 1038)

369 411 639

(303 - 434) (253 - 558) (458 - 803)

88 142

(39 - 135) (75 - 204)

6Calculated as (core analysis model estimate - alternative model estimate)/(core analysis model estimate.)

Health Endpoint

Incidence of Mortality Associated with Long-Term Exposure to 

PM2.5 Concentrations Using:2 Percent 

Los Angeles, CA

 All Cause Mortality 23% 

 Cardiopulmonary Mortality --- 7 ---  Ischemic Heart Disease Mortality 12%  Lung Cancer Mortality --- --- 

Philadelphia, PA

 All Cause Mortality 23% 

 Cardiopulmonary Mortality --- ---  Ischemic Heart Disease Mortality 11%  Lung Cancer Mortality --- --- 5Estimates based on Table 11, "MSA and DIFF" rows, in Krewski et al. (2009) -- exposure period from 1999 - 2000. 7Estimates for cardiopulmonary mortality and lung cancer mortality were not available for the random effects log-linear model.1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Mortality incidence was estimated for PM2.5 concentrations down to the lowest measured level in the study (5.8 ug/m3). Numbers rounded to the nearest w

parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

3Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in 4Estimates based on Table 9. Autocorrelation at MSA and ZCA levels; MSA & DIFF, in Krewski et al. (2009) -- exposure period from 1999 - 2000. 
F-2

Table F-2. Sensitivity Analysis: Impact of Using Different Model Choices to Estimate the Incidence of Mortality Associated  Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentr

Standard Fixed 

Effects Log-Linear 

(Cox Proportional 

Hazard) Model3

Random Effects LogLinear Model 4 Random Effects Log- Log Model 5 Fixed Effects vs. Random Effects Log

Linear Models 

1108 1368 2904

(704 - 1509) (637 - 2090) (1790 - 3995)

1263 2225

(985 - 1538) (1477 - 2953)

1038 1162 2212

(843 - 1229) (702 - 1605) (1558 - 2833)

135 266

(59 - 210) (138 - 388)

525 647 1166

(335 - 713) (303 - 982) (723 - 1595)

491 736

(385 - 596) (493 - 969)

334 372 598

(273 - 393) (228 - 507) (428 - 755)

80 133

(35 - 122) (70 - 191)

6Calculated as (core analysis model estimate - alternative model estimate)/(core analysis model estimate.)5Estimates based on Table 11, "MSA and DIFF" rows, in Krewski et al. (2009) -- exposure period from 1999 - 2000. 7Estimates for cardiopulmonary mortality and lung cancer mortality were not available for the random effects log-linear model.1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Mortality incidence was estimated for PM2.5 concentrations down to the lowest measured level in the study (5.8 ug/m3). Numbers rounded to the nearest who

parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

3Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Kre4Estimates based on Table 9. Autocorrelation at MSA and ZCA levels; MSA & DIFF, in Krewski et al. (2009) -- exposure period from 1999 - 2000.  Ischemic Heart Disease Mortality 11%  Lung Cancer Mortality --- ---  Cardiopulmonary Mortality --- --- 

Philadelphia, PA

 All Cause Mortality 23% 

 Ischemic Heart Disease Mortality 12%  Lung Cancer Mortality --- ---  All Cause Mortality 23%  Cardiopulmonary Mortality --- 7 --- 

Health Endpoint

Incidence of Mortality Associated with Long-Term Exposure to 

PM2.5 Concentrations Using:2 Percent 

Los Angeles, CA
F-3

Table F-3. Sensitivity Analysis: Impact of Using Different Model Choices to Estimate the Incidence of Mortality Associate Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concen

Standard Fixed 

Effects Log-Linear 

(Cox Proportional 

Hazard) Model3

Random Effects LogLinear Model 4 Random Effects Log- Log Model 5 Fixed Effects vs. Random Effects Log

Linear Models 

1170 1444 3034

(744 - 1593) (672 - 2206) (1871 - 4173)

1333 2324

(1040 - 1623) (1544 - 3082)

1094 1225 2306

(890 - 1296) (741 - 1691) (1626 - 2950)

143 278

(62 - 222) (145 - 405)

519 639 1157

(331 - 704) (299 - 971) (718 - 1583)

486 731

(381 - 589) (489 - 962)

330 368 594

(270 - 389) (226 - 502) (424 - 750)

79 132

(35 - 121) (69 - 190)

6Calculated as (core analysis model estimate - alternative model estimate)/(core analysis model estimate.) All Cause Mortality Cardiopulmonary Mortality Ischemic Heart Disease Mortality Lung Cancer Mortality5Estimates based on Table 11, "MSA and DIFF" rows, in Krewski et al. (2009) -- exposure period from 1999 - 2000. 3Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in 1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3.  All Cause Mortality Ischemic Heart Disease Mortality2Mortality incidence was estimated for PM2.5 concentrations down to the lowest measured level in the study (5.8 ug/m3). Numbers rounded to the nearest w

parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

 Lung Cancer Mortality

23%

Incidence of Mortality Associated with Long-Term Exposure to 

PM2.5 Concentrations Using:2

Los Angeles, CA

 Cardiopulmonary Mortality ---

Percent 

23%12%

Philadelphia, PA

--- 

Health Endpoint

---

4Estimates based on Table 9. Autocorrelation at MSA and ZCA levels; MSA & DIFF, in Krewski et al. (2009) -- exposure period from 1999 - 2000. 7Estimates for cardiopulmonary mortality and lung cancer mortality were not available for the random effects log-linear model.

--- 

7

---------

12%

---
F-4

Table F-4. Sensitivity Analysis: Impact of Limiting Estimated Annual Incidence of All-Cause Mortality 

 Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards to the Lowest Measured Level in the Study vs. to PRB, Based on Adjusting 2005 

 PM2.5 Concentrations1, 2

Lowest Measured Level in 

Study (5.8 ug/m3) Estimated PRB

736 1057

(470 - 997) (678 - 1426)

702 1073

(448 - 950) (689 - 1446)

380 592

(243 - 516) (379 - 800)

486 762

(310 - 659) (488 - 1030)

743 1205

(474 - 1008) (773 - 1626)

114 262

(72 - 155) (167 - 355)

713 1114

(455 - 968) (713 - 1506)

1342 2845

(854 - 1827) (1819 - 3853)

1893 3299

(1207 - 2571) (2113 - 4456)

584 971

(372 - 792) (622 - 1310)

620 1255

(394 - 843) (803 - 1698)

497 859

(317 - 674) (550 - 1161)

37 161

(24 - 51) (102 - 218)

897 1381

(573 - 1215) (887 - 1862)

103 234

(66 - 141) (149 - 317)

3

 Calculated as (mortality estimated down to PRB - mortality estimated down to LML)/(mortality estimated down to LML).

62%

130%

56%

112%

44%

53%

56%

 Dallas, TX 57%

 Fresno, CA

 Los Angeles, CA

Risk Assessment Location

 Atlanta, GA

 Birmingham, AL

 Houston, TX

 New York, NY

2

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

1

Estimates based on Table 33 in Krewski et al. (2009) -- exposure period from 1999 - 2000, follow-up through 2000, models with 44 individual 

and 7 ecological covariates. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals 

based on statistical uncertainty surrounding the PM coefficient. 

74%

66%

102%

73%

335%

54%

127%

Incidence of All Cause Mortality Associated with LongTerm Exposure to PM2.5 Concentrations Measured 

Down to: Percent Difference3

 St. Louis, MO

 Tacoma, WA

 Philadelphia, PA

 Phoenix, AZ

 Pittsburgh, PA

 Salt Lake City, UT

 Baltimore, MD

 Detroit, MI
F-5

Table F-5. Sensitivity Analysis: Impact of Limiting Estimated Annual Incidence of Ischemic Heart Disease 

 Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards to the Lowest Measured Level in the Study vs. to PRB, Based on Adjusting 2006 PM2.5 

 Concentrations1, 2 

Lowest Measured Level in 

Study (5.8 ug/m3

) Estimated PRB

287 400

(236 - 336) (331 - 465)

375 601

(307 - 441) (497 - 699)

157 244

(128 - 184) (201 - 285)

222 384

(181 - 262) (315 - 450)

449 829

(367 - 530) (683 - 969)

92 198

(75 - 108) (162 - 232)

416 646

(340 - 490) (533 - 755)

1038 2366

(843 - 1229) (1943 - 2775)

1865 3618

(1520 - 2203) (2979 - 4232)

334 559

(273 - 393) (461 - 651)

471 907

(384 - 557) (747 - 1061)

279 531

(228 - 330) (437 - 621)

8 57

(6 - 10) (47 - 67)

512 862

(419 - 603) (712 - 1006)

46 143

(37 - 55) (117 - 168)

3

 Calculated as (IHD mortality estimated down to PRB - IHD mortality estimated down to LML)/(IHD mortality estimated down to LML).

613%

68%

211%

94%

67%

93%

90%

85%

115%

55%

128%

 Tacoma, WA

 Philadelphia, PA

 Phoenix, AZ

 Pittsburgh, PA

 Salt Lake City, UT

2

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

1

Estimates based on Table 33 in Krewski et al. (2009) -- exposure period from 1999 - 2000, follow-up through 2000, models with 44 individual 

and 7 ecological covariates. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals 

based on statistical uncertainty surrounding the PM coefficient. 

 Dallas, TX

 Fresno, CA

 Los Angeles, CA

73%

 Detroit, MI

 Houston, TX

 New York, NY

 St. Louis, MO

Risk Assessment Location

 Atlanta, GA

 Birmingham, AL 55%

 Baltimore, MD

Incidence of Ischemic Heart Disease Mortality 

Associated with Long-Term Exposure to PM2.5 

Concentrations Measured Down to: Percent Difference3

39%

60%
F-6

Table F-6. Sensitivity Analysis: Impact of Limiting Estimated Annual Incidence of All-Cause Mortality 

 Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards to the Lowest Measured Level in the Study vs. to PRB, Based on Adjusting 2007 

 PM2.5 Concentrations1, 2

Lowest Measured Level in 

Study (5.8 ug/m3) Estimated PRB

726 1067

(464 - 984) (684 - 1440)

564 938

(360 - 765) (602 - 1267)

374 590

(238 - 507) (377 - 797)

407 696

(259 - 553) (445 - 942)

544 1007

(346 - 739) (644 - 1362)

130 282

(82 - 177) (180 - 382)

719 1143

(459 - 977) (732 - 1545)

1170 2697

(744 - 1593) (1723 - 3654)

1689 3124

(1076 - 2295) (2000 - 4224)

519 907

(331 - 704) (581 - 1225)

556 1240

(354 - 757) (792 - 1678)

434 795

(277 - 590) (509 - 1074)

48 179

(31 - 66) (114 - 244)

728 1220

(464 - 988) (782 - 1648)

64 201

(41 - 88) (128 - 272)

3

 Calculated as (mortality estimated down to PRB - mortality estimated down to LML)/(mortality estimated down to LML).

85%

117%

59%

131%

47%

66%

58%

 Dallas, TX 71%

 Fresno, CA

 Los Angeles, CA

Risk Assessment Location

 Atlanta, GA

 Birmingham, AL

 Houston, TX

 New York, NY

2

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

1

Estimates based on Table 33 in Krewski et al. (2009) -- exposure period from 1999 - 2000, follow-up through 2000, models with 44 individual 

and 7 ecological covariates. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals 

based on statistical uncertainty surrounding the PM coefficient. 

85%

75%

123%

83%

273%

68%

214%

Incidence of All Cause Mortality Associated with LongTerm Exposure to PM2.5 Concentrations Measured 

Down to: Percent Difference3

 St. Louis, MO

 Tacoma, WA

 Philadelphia, PA

 Phoenix, AZ

 Pittsburgh, PA

 Salt Lake City, UT

 Baltimore, MD

 Detroit, MI
F-7

Table F-7. Sensitivity Analysis: Impact of Using a Different Study to Estimate the Incidence of Mortality 

 Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2005 PM2.5 Concentrations1

Krewski et al. (2009)3 Krewski et al. (2000)4

1342 2965

(854 - 1827) (1005 - 4855)

1526 1981

(1191 - 1856) (693 - 3207)

164 212

(71 - 253) (-152 - 535)

584 1276

(372 - 792) (438 - 2064)

545 704

(427 - 660) (250 - 1121)

88 114

(39 - 135) (-85 - 276)

30%

29%

2

Mortality incidence was estimated for PM2.5 concentrations down to the lowest measured level in Krewski et al., 2009 (5.8 ug/m3). Numbers 

rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty 

5

Calculated as (Krewski et al. (2000) estimate - Krewski et al. (2009) estimate)/(Krewski et al. (2009) estimate).

4

Estimates based on Table 21b in Krewski et al. (2000) [reanalysis of Six Cities Study].

3

Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates 

(see Table 33 in Krewski et al., 2009). 

1

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

30%

Incidence of Mortality Associated with Long-Term 

Exposure to PM2.5 Concentrations Using:2

Percent Difference5

118%

29%

121%

Los Angeles, CA

 Cardiopulmonary Mortality

Philadelphia, PA

Health Endpoint

 All Cause Mortality

 All Cause Mortality

 Cardiopulmonary Mortality

 Lung Cancer Mortality

 Lung Cancer Mortality
F-8

Table F-8. Sensitivity Analysis: Impact of Using a Different Study to Estimate the Incidence of Mortality 

 Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2006 PM2.5 Concentrations1

Krewski et al. (2009)3 Krewski et al. (2000)4

1108 2454

(704 - 1509) (829 - 4031)

1263 1642

(985 - 1538) (572 - 2671)

135 176

(59 - 210) (-124 - 448)

525 1150

(335 - 713) (394 - 1866)

491 635

(385 - 596) (225 - 1016)

80 103

(35 - 122) (-76 - 251)

30%

30%

2

Mortality incidence was estimated for PM2.5 concentrations down to the lowest measured level in Krewski et al., 2009 (5.8 ug/m3). Numbers 

rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty 

5

Calculated as (Krewski et al. (2000) estimate - Krewski et al. (2009) estimate)/(Krewski et al. (2009) estimate).

4

Estimates based on Table 21b in Krewski et al. (2000) [reanalysis of Six Cities Study].

3

Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates 

(see Table 33 in Krewski et al., 2009). 

1

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

29%

Incidence of Mortality Associated with Long-Term 

Exposure to PM2.5 Concentrations Using:2

Percent Difference5

119%

29%

121%

Los Angeles, CA

 Cardiopulmonary Mortality

Philadelphia, PA

Health Endpoint

 All Cause Mortality

 All Cause Mortality

 Cardiopulmonary Mortality

 Lung Cancer Mortality

 Lung Cancer Mortality
F-9

Table F-9. Sensitivity Analysis: Impact of Using a Different Study to Estimate the Incidence of Mortality 

 Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2007 PM2.5 Concentrations1

Krewski et al. (2009)3 Krewski et al. (2000)4

1170 2590

(744 - 1593) (876 - 4252)

1333 1732

(1040 - 1623) (604 - 2815)

143 186

(62 - 222) (-131 - 472)

519 1137

(331 - 704) (389 - 1846)

486 628

(381 - 589) (223 - 1005)

79 102

(35 - 121) (-75 - 249)

30%

30%

2

Mortality incidence was estimated for PM2.5 concentrations down to the lowest measured level in Krewski et al., 2009 (5.8 ug/m3). Numbers 

rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty 

surrounding the PM coefficient.

5

Calculated as (Krewski et al. (2000) estimate - Krewski et al. (2009) estimate)/(Krewski et al. (2009) estimate).

4

Estimates based on Table 21b in Krewski et al. (2000) [reanalysis of Six Cities Study].

3

Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates 

(see Table 33 in Krewski et al., 2009). 

1

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

29%

Incidence of Mortality Associated with Long-Term 

Exposure to PM2.5 Concentrations Using:2

Percent Difference5

119%

29%

121%

Los Angeles, CA

 Cardiopulmonary Mortality

Philadelphia, PA

Health Endpoint

 All Cause Mortality

 All Cause Mortality

 Cardiopulmonary Mortality

 Lung Cancer Mortality

 Lung Cancer Mortality
F-10

Table F-10. Sensitivity Analysis: Estimated Annual Incidence of All Cause Mortality Associated with Long-Term Exposure to  Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005 PM2.5 Concentratio

 Comparison of Proportional and Hybrid Rollback Methods1

15/35 2 14/35 13/35 12/35 13

Proportional 702 643 566 490 5

(448 - 950) (410 - 871) (361 - 768) (312 - 665) (348 

Hybrid 691 667 589 511 5

(442 - 936) (426 - 904) (376 - 799) (326 - 694) (342 

Percent Difference 3 -2% 4% 4% 4% -2

Proportional 380 336 292 247 2

(243 - 516) (214 - 457) (186 - 397) (157 - 336) (186 

Hybrid 461 411 360 310 3

(294 - 624) (262 - 557) (230 - 489) (197 - 421) (230 

Percent Difference 21% 22% 23% 26% 23

Proportional 743 734 643 552 5

(474 - 1008) (468 - 996) (410 - 874) (352 - 751) (361 

Hybrid 773 773 750 651 5

(493 - 1048) (493 - 1048) (479 - 1018) (415 - 884) (378 

Percent Difference 4% 5% 17% 18% 5

Proportional 1342 1342 1342 1180 9

(854 - 1827) (854 - 1827) (854 - 1827) (750 - 1607) (587 - 

Hybrid 1675 1675 1599 1344 12

(1066 - 2276) (1066 - 2276) (1018 - 2175) (855 - 1830) (769 - 

Percent Difference 25% 25% 19% 14% 31

Proportional 1893 1893 1808 1546 14

(1207 - 2571) (1207 - 2571) (1152 - 2455) (984 - 2101) (898 - 

Hybrid 1950 1950 1806 1544 14

(1244 - 2648) (1244 - 2648) (1151 - 2452) (983 - 2099) (930 - 

Percent Difference 3% 3% 0% 0% 3

Proportional 897 813 714 616 6

(573 - 1215) (519 - 1102) (456 - 970) (392 - 836) (443 

Hybrid 956 855 754 652 7

(611 - 1294) (546 - 1159) (481 - 1022) (415 - 885) (481 - 

Percent Difference 7% 5% 6% 6% 8

3 Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5 Conce

Current and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard 

Type of Rollback

1Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewsk

the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

Risk Assessment 

Location

 St. Louis, MO

 Baltimore, MD

 Birmingham, AL

 Detroit, MI

 Los Angeles, CA

 New York, NY
F-11

Table F-11. Sensitivity Analysis: Estimated Annual Incidence of All Cause Mortality Associated with Long-Term Exposure to  Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006 PM2.5 Concentration

 Comparison of Proportional and Hybrid Rollback Methods1

15/35 2 14/35 13/35 12/35 13

Proportional 565 513 446 378 4

(360 - 766) (327 - 696) (284 - 606) (241 - 515) (272 

Hybrid 554 533 465 396 4

(354 - 752) (340 - 724) (296 - 632) (252 - 539) (267 

Percent Difference 3 -2% 4% 4% 5% -2

Proportional 354 312 269 226 2

(226 - 481) (198 - 423) (171 - 365) (144 - 307) (171 

Hybrid 430 382 334 286 3

(275 - 584) (244 - 519) (213 - 454) (182 - 388) (213 

Percent Difference 21% 22% 24% 27% 24

Proportional 510 503 429 355 3

(325 - 694) (320 - 684) (273 - 584) (225 - 483) (233 

Hybrid 534 534 515 434 3

(340 - 725) (340 - 725) (328 - 700) (276 - 591) (246 

Percent Difference 5% 6% 20% 22% 6

Proportional 1108 1108 1108 958 7

(704 - 1509) (704 - 1509) (704 - 1509) (608 - 1305) (457 

Hybrid 1414 1414 1344 1108 9

(899 - 1923) (899 - 1923) (855 - 1829) (704 - 1510) (625 - 

Percent Difference 28% 28% 21% 16% 36

Proportional 1407 1407 1333 1106 9

(895 - 1913) (895 - 1913) (848 - 1813) (703 - 1506) (629 - 

Hybrid 1453 1453 1327 1101 10

(924 - 1975) (924 - 1975) (844 - 1806) (700 - 1499) (654 - 

Percent Difference 3% 3% 0% 0% 4

Proportional 659 588 506 423 4

(420 - 894) (374 - 799) (322 - 688) (269 - 575) (312 

Hybrid 704 620 535 450 5

(449 - 956) (395 - 842) (341 - 727) (286 - 612) (341 

Percent Difference 7% 5% 6% 6% 9

3 Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).

 New York, NY

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5 Conce

Current and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard 

Type of Rollback

1Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewsk

the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

Risk Assessment 

Location

 Baltimore, MD

 Birmingham, AL

 St. Louis, MO

 Detroit, MI

 Los Angeles, CA
F-12

Table F-12. Sensitivity Analysis: Estimated Annual Incidence of All Cause Mortality Associated with Long-Term Exposure to  Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007 PM2.5 Concentration

 Comparison of Proportional and Hybrid Rollback Methods1

15/35 2 14/35 13/35 12/35 13

Proportional 564 512 445 378 4

(360 - 765) (326 - 695) (283 - 605) (240 - 514) (272 

Hybrid 553 532 464 395 4

(353 - 751) (339 - 722) (296 - 630) (252 - 537) (266 

Percent Difference 3 -2% 4% 4% 4% -2

Proportional 374 330 285 241 2

(238 - 507) (210 - 448) (182 - 388) (153 - 327) (182 

Hybrid 454 404 354 304 3

(290 - 615) (258 - 548) (226 - 481) (193 - 413) (226 

Percent Difference 21% 22% 24% 26% 24

Proportional 544 536 460 384 3

(346 - 739) (341 - 729) (293 - 626) (244 - 522) (252 

Hybrid 568 568 549 466 4

(362 - 772) (362 - 772) (350 - 747) (297 - 634) (265 

Percent Difference 4% 6% 19% 21% 5

Proportional 1170 1170 1170 1016 7

(744 - 1593) (744 - 1593) (744 - 1593) (645 - 1384) (490 - 

Hybrid 1484 1484 1413 1171 10

(944 - 2019) (944 - 2019) (899 - 1922) (744 - 1594) (662 - 

Percent Difference 27% 27% 21% 15% 35

Proportional 1689 1689 1607 1359 12

(1076 - 2295) (1076 - 2295) (1023 - 2185) (864 - 1848) (783 - 

Hybrid 1741 1741 1604 1355 12

(1109 - 2366) (1109 - 2366) (1021 - 2180) (862 - 1844) (812 - 

Percent Difference 3% 3% 0% 0% 4

Proportional 728 653 566 478 54

(464 - 988) (416 - 887) (360 - 769) (304 - 651) (350 

Hybrid 787 698 607 516 6

(503 - 1068) (445 - 947) (387 - 825) (329 - 702) (387 

Percent Difference 8% 7% 7% 8% 11

3 Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).

Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5 Conce

Current and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard 

Type of Rollback

1Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewsk

the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

Risk Assessment 

Location

 St. Louis, MO

 New York, NY

 Los Angeles, CA

 Detroit, MI

 Birmingham, AL

 Baltimore, MD

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
F-13

Table F-13. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Respo Function to Estimate the Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

 that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

55 53 43 33 184 177

(8 - 102) (3 - 101) (-15 - 99) (-17 - 83) --- 4 (34 - 319)

66 46 60 50 222 256

(9 - 122) (1 - 91) (-7 - 126) (7 - 92) --- (104 - 406)

18 25 17 10 70 34

(-4 - 41) (-3 - 51) (-21 - 55) (-21 - 40) --- (-53 - 121)

30 30 43 46 149 156

(-5 - 64) (-9 - 68) (-3 - 88) (6 - 84) --- (37 - 273)

-6 77 54 34 159 147

(-83 - 69) (19 - 134) (-32 - 137) (-31 - 98) --- (-26 - 317)

0 16 3 11 30 44

(-33 - 33) (-1 - 32) (-14 - 20) (-12 - 34) --- (6 - 81)

45 61 51 55 212 214

(-5 - 94) (5 - 116) (-13 - 113) (-9 - 117) --- (44 - 383)

17 66 -104 -2 -23 81

(-84 - 117) (-35 - 166) (-257 - 48) (-90 - 85) --- (-117 - 278)

279 159 136 206 780 781

(102 - 453) (1 - 315) (-55 - 323) (89 - 321) --- (459 - 1102)

93 28 34 65 220 216

(20 - 165) (-33 - 89) (-48 - 114) (16 - 112) --- (79 - 350)

--- --- --- --- --- 242--- --- --- --- --- (40 - 442)

43 65 44 23 175 159

(-4 - 90) (12 - 117) (-23 - 109) (-28 - 73) --- (47 - 270)

16 6 6 8 36 30

(-2 - 32) (-2 - 14) (-5 - 17) (-3 - 19) --- (6 - 54)

37 75 66 73 251 260

(-37 - 109) (14 - 136) (-6 - 136) (13 - 133) --- (75 - 443)

1 9 4 14 28 48

(-53 - 53) (-7 - 25) (-9 - 17) (-10 - 37) --- (8 - 87)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-spe

coefficient estimators was not available.

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional mea

Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

-

Pe

Diffe

1

 Dallas, TX Fresno, CA Houston, TX Detroit, MI

5 Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.

 St. Louis, MO

Estimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Philadelphia, PA Phoenix, AZ 5

 Pittsburgh, PA Tacoma, WA Salt Lake City, UT
F-14

Table F-14. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Respo Function to Estimate the Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

 that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

48 57 51 29 185 180

(7 - 89) (3 - 109) (-18 - 119) (-15 - 72) --- 4 (34 - 324)

54 41 52 46 193 224

(7 - 101) (1 - 81) (-6 - 109) (6 - 86) --- (91 - 356)

16 27 18 8 69 33

(-4 - 36) (-3 - 56) (-23 - 58) (-16 - 32) --- (-51 - 116)

24 28 36 34 122 130

(-4 - 52) (-8 - 64) (-3 - 75) (5 - 63) --- (31 - 228)

-5 77 39 26 137 118

(-64 - 54) (19 - 134) (-23 - 100) (-24 - 75) --- (-21 - 255)

1 14 4 12 31 47

(-36 - 36) (-1 - 30) (-16 - 24) (-12 - 35) --- (7 - 86)

40 68 51 48 207 208

(-4 - 84) (5 - 130) (-13 - 115) (-8 - 102) --- (42 - 373)

17 57 -97 -2 -25 75

(-86 - 120) (-30 - 143) (-239 - 45) (-78 - 74) --- (-108 - 257)

242 141 111 183 677 671

(89 - 394) (1 - 279) (-44 - 263) (79 - 286) --- (394 - 946)

79 26 33 70 208 204

(17 - 140) (-31 - 83) (-46 - 109) (18 - 121) --- (75 - 331)

--- --- --- --- --- 254--- --- --- --- --- (42 - 463)

39 58 40 17 154 136

(-4 - 81) (10 - 104) (-21 - 100) (-20 - 53) --- (40 - 232)

12 7 7 7 33 27

(-1 - 25) (-2 - 15) (-6 - 19) (-3 - 17) --- (6 - 49)

26 67 58 60 211 215

(-27 - 79) (12 - 120) (-5 - 120) (10 - 110) --- (62 - 367)

1 10 4 12 27 40

(-38 - 38) (-8 - 26) (-10 - 19) (-8 - 30) --- (7 - 73)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

-

P

Diff

 Dallas, TX Fresno, CA Houston, TX Detroit, MI

5 Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.

 St. Louis, MO

Estimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Philadelphia, PA Phoenix, AZ 5

 Pittsburgh, PA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate 

regional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA Salt Lake City, UT
F-15

Table F-15. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Respo

 Function to Estimate the Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

 that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year

48 70 53 30 201 177

(7 - 88) (4 - 134) (-18 - 122) (-16 - 76) --- 4 (34 - 319)

56 46 56 49 207 227

(7 - 104) (1 - 90) (-6 - 118) (7 - 91) --- (92 - 360)

20 44 21 10 95 34

(-5 - 45) (-5 - 92) (-27 - 67) (-21 - 39) --- (-53 - 120)

28 25 39 39 131 139

(-5 - 60) (-8 - 58) (-3 - 80) (5 - 73) --- (33 - 243)

-6 84 46 42 166 121

(-79 - 66) (21 - 145) (-28 - 119) (-39 - 121) --- (-21 - 262)

1 25 6 22 54 48

(-78 - 76) (-2 - 53) (-23 - 33) (-23 - 64) --- (7 - 89)

49 63 57 52 221 212

(-5 - 102) (5 - 120) (-14 - 127) (-9 - 112) --- (43 - 378)

23 112 -144 -3 -12 77

(-115 - 160) (-59 - 280) (-359 - 66) (-140 - 131) --- (-110 - 262)

319 177 150 241 887 734

(117 - 517) (1 - 350) (-60 - 355) (105 - 376) --- (431 - 1035)

88 32 34 78 232 208

(19 - 156) (-37 - 99) (-48 - 114) (20 - 134) --- (77 - 338)

--- --- --- --- --- 242--- --- --- --- --- (40 - 442)

54 84 57 30 225 143

(-5 - 113) (15 - 152) (-30 - 140) (-35 - 93) --- (42 - 244)

28 11 11 13 63 34

(-3 - 57) (-4 - 26) (-10 - 32) (-5 - 32) --- (7 - 61)

32 83 63 70 248 225

(-32 - 95) (15 - 150) (-6 - 130) (12 - 127) --- (65 - 384)

1 12 4 20 37 42

(-47 - 47) (-9 - 32) (-9 - 16) (-14 - 52) --- (7 - 76)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

-

-

1

5 Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.

 Philadelphia, PA Phoenix, AZ 5 Pittsburgh, PA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate 

regional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty 

surrounding the PM coefficient. 

 Tacoma, WA St. Louis, MO

Estimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX

Pe

Diffe

 Fresno, CA Houston, TX Salt Lake City, UT Detroit, MI
F-16

Table F-16. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Resp

 Function to Estimate the Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 Concentratio

 that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year

14 9 9 -2 30 32

(-21 - 49) (-32 - 48) (-31 - 46) (-37 - 31) --- 4 (-33 - 95)

16 10 11 32 69 70

(-30 - 59) (-31 - 48) (-45 - 64) (-2 - 65) --- (-5 - 143)

4 1 0 -15 -10 -1

(-16 - 24) (-23 - 25) (-29 - 27) (-40 - 8) --- (-43 - 40)

10 11 13 -2 32 32

(-18 - 36) (-21 - 42) (-25 - 49) (-33 - 27) --- (-21 - 85)

-1 25 28 36 88 73

(-48 - 43) (-7 - 57) (-21 - 74) (0 - 72) --- (-9 - 153)

-2 1 0 3 2 11

(-13 - 8) (-3 - 5) (-3 - 4) (-4 - 9) --- (-8 - 30)

8 2 27 7 44 47

(-34 - 49) (-46 - 47) (-21 - 73) (-41 - 54) --- (-32 - 124)

-7 3 -43 0 -47 -31

(-54 - 39) (-45 - 49) (-105 - 17) (-43 - 43) --- (-140 - 76)

149 130 160 100 539 504

(35 - 261) (29 - 228) (30 - 286) (23 - 174) --- (294 - 711)

28 16 27 27 98 87

(-6 - 60) (-14 - 46) (-13 - 65) (4 - 50) --- (23 - 150)

--- --- --- --- --- 84--- --- --- --- --- (-4 - 170)

14 30 13 5 62 47

(-10 - 38) (3 - 56) (-23 - 47) (-20 - 29) --- (-9 - 103)

--- --- --- --- --- 8--- --- --- --- --- (-2 - 18)

-3 48 38 43 126 122

(-68 - 59) (-2 - 95) (-17 - 90) (-4 - 88) --- (27 - 215)

00022 12

(-12 - 13) (-3 - 4) (-2 - 3) (-4 - 7) --- (-7 - 31)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-s

coefficient estimators was not available.

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional m

Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficien

 Birmingham, AL  Dallas, TX  Pittsburgh, PA

Estimated Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 

Concentrations that Just Meet the Current Standards

 Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD

Di

 Detroit, MI

5 Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.

 Fresno, CA Houston, TX Salt Lake City, UT 5 St. Louis, MO Philadelphia, PA Phoenix, AZ 5 Tacoma, WA New York, NY
F-17

Table F-17. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentra

 Function to Estimate the Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 Con

 that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

13 9 10 -2 30 32

(-18 - 42) (-35 - 52) (-38 - 56) (-32 - 27) --- 4 (-33 - 97)

13 8 10 30 61 61

(-25 - 49) (-28 - 43) (-39 - 56) (-2 - 61) --- (-4 - 125)

4 1 0 -12 -7 -1

(-14 - 21) (-25 - 27) (-31 - 29) (-31 - 7) --- (-41 - 39)

8 11 11 -1 29 27

(-14 - 29) (-19 - 40) (-21 - 41) (-24 - 21) --- (-18 - 71)

-1 25 20 28 72 58

(-37 - 34) (-7 - 57) (-15 - 55) (0 - 55) --- (-7 - 123)

-2 1 0 3 2 12

(-14 - 9) (-3 - 5) (-4 - 5) (-4 - 9) --- (-8 - 32)

7 2 27 6 42 45

(-30 - 44) (-51 - 53) (-22 - 75) (-35 - 47) --- (-31 - 120

-7 2 -41 0 -46 -29

(-56 - 40) (-38 - 42) (-98 - 16) (-38 - 37) --- (-129 - 70

130 115 130 88 463 432

(31 - 227) (25 - 202) (25 - 233) (21 - 155) --- (252 - 611

24 15 26 30 95 82

(-5 - 51) (-13 - 42) (-12 - 62) (4 - 54) --- (21 - 142)

--- --- --- --- --- 88--- --- --- --- --- (-4 - 178)

13 27 12 4 56 41

(-9 - 35) (2 - 50) (-21 - 43) (-14 - 21) --- (-8 - 89)

--- --- --- --- --- 7--- --- --- --- --- (-2 - 16)

-2 42 33 36 109 101

(-49 - 43) (-2 - 85) (-15 - 80) (-3 - 73) --- (23 - 179)

00011 10

(-9 - 9) (-3 - 4) (-2 - 3) (-3 - 6) --- (-6 - 26)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

Estimated Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 

Concentrations that Just Meet the Current Standards

 Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD

5 Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.

 Fresno, CA Houston, TX Salt Lake City, UT 5 St. Louis, MO Philadelphia, PA Phoenix, AZ 5

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate 

regional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertain

surrounding the PM coefficient. 

 Tacoma, WA New York, NY Pittsburgh, PA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for th

season-specific coefficient estimators was not available.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Birmingham, AL Dallas, TX Detroit, MI
F-18

Table F-18. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Respo Function to Estimate the Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 Concentration

 that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

11 11 10 -2 30 32

(-17 - 39) (-40 - 58) (-35 - 52) (-31 - 26) --- 4 (-33 - 95)

13 9 10 30 62 62

(-24 - 48) (-29 - 45) (-39 - 57) (-2 - 61) --- (-4 - 126)

4 2 0 -12 -6 -1

(-14 - 21) (-33 - 35) (-28 - 26) (-31 - 6) --- (-42 - 40)

9 10 11 -2 28 29

(-16 - 34) (-18 - 36) (-23 - 44) (-28 - 24) --- (-19 - 76)

-1 22 19 36 76 60

(-37 - 34) (-6 - 50) (-14 - 52) (0 - 72) --- (-8 - 127)

-3 1 0 3 1 12

(-16 - 11) (-3 - 5) (-3 - 4) (-4 - 9) --- (-9 - 33)

8 2 29 7 46 46

(-35 - 50) (-45 - 46) (-23 - 78) (-37 - 48) --- (-31 - 122)

-6 3 -38 0 -41 -30

(-47 - 34) (-48 - 53) (-92 - 15) (-42 - 42) --- (-132 - 72)

142 120 147 97 506 473

(34 - 248) (26 - 212) (28 - 262) (23 - 170) --- (276 - 668)

24 16 25 30 95 84

(-5 - 52) (-15 - 47) (-12 - 60) (5 - 55) --- (22 - 145)

--- --- --- --- --- 84--- --- --- --- --- (-4 - 170)

13 27 12 4 56 43

(-9 - 34) (2 - 51) (-21 - 43) (-18 - 26) --- (-9 - 93)

--- --- --- --- --- 9--- --- --- --- --- (-2 - 20)

-2 47 32 37 114 106

(-53 - 46) (-2 - 94) (-15 - 78) (-3 - 76) --- (24 - 187)

00022 11

(-9 - 9) (-3 - 4) (-2 - 2) (-5 - 8) --- (-6 - 27)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

 Phoenix, AZ 5 Pittsburgh, PA

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Birmingham, AL Dallas, TX Detroit, MI

5 Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

 Fresno, CA Houston, TX Salt Lake City, UT 5 St. Louis, MO Philadelphia, PA

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate 

regional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA New York, NY

Estimated Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 

Concentrations that Just Meet the Current Standards

 Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD

P

Diff
F-19

Table F-19. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re

 Function to Estimate the Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

 that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

4 1 3 3 11 20

(-6 - 14) (-8 - 11) (-7 - 13) (-5 - 11) --- 4 (-8 - 47)

5 6 6 3 20 36

(-6 - 15) (-4 - 15) (-6 - 17) (-4 - 11) --- (7 - 64)

1 2 -1 3 5 9

(-4 - 5) (-4 - 7) (-8 - 7) (-2 - 9) --- (-7 - 26)

13217 11

(-6 - 9) (-4 - 10) (-6 - 9) (-6 - 7) --- (-10 - 32)

5 9 10 9 33 28

(-7 - 16) (-1 - 18) (0 - 19) (0 - 18) --- (1 - 55)

-1 4 1 1 5 9

(-11 - 9) (-1 - 9) (-2 - 4) (-6 - 8) --- (0 - 17)

5 5 4 4 18 34

(-4 - 14) (-4 - 13) (-5 - 13) (-7 - 15) --- (5 - 61)

27 27 -15 0 39 57

(-3 - 56) (-2 - 56) (-58 - 26) (-23 - 21) --- (6 - 108)

51 18 22 22 113 106

(19 - 82) (-6 - 41) (-10 - 53) (1 - 42) --- (37 - 174)

10 7 7 5 29 23

(-1 - 21) (-1 - 15) (-3 - 16) (-2 - 11) --- (-2 - 48)

27 30 21 41 119 47

(-29 - 79) (-8 - 66) (-3 - 45) (14 - 67) --- (4 - 90)

4 7 8 7 26 20

(-3 - 11) (-1 - 15) (-2 - 17) (0 - 14) --- (-2 - 42)

4 2 -2 -1 3 5

(-1 - 9) (-2 - 6) (-6 - 3) (-5 - 3) --- (1 - 10)

1 7 4 7 19 31

(-15 - 17) (-6 - 20) (-10 - 17) (-6 - 18) --- (-8 - 70)

02114 7

(-15 - 13) (-2 - 6) (-2 - 3) (-4 - 6) --- (0 - 15)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

 St. Louis, MO Salt Lake City, UT 

Estimated Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Philadelphia, PA Phoenix, AZ Pittsburgh, PA Tacoma, WA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the season

coefficient estimators was not available.

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional 

Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM co

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Birmingham, AL Dallas, TX Detroit, MI Houston, TX Fresno, CA

D
F-20

Table F-20. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re Function to Estimate the Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

 that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

3 2 4 3 12 20

(-5 - 12) (-9 - 12) (-8 - 16) (-4 - 10) --- 4 (-8 - 47)

4 5 5 3 17 31

(-5 - 13) (-3 - 13) (-5 - 15) (-4 - 10) --- (6 - 56)

1 2 -1 3 5 9

(-3 - 5) (-4 - 8) (-9 - 7) (-2 - 7) --- (-7 - 25)

13217 9

(-5 - 7) (-4 - 10) (-5 - 8) (-4 - 6) --- (-9 - 27)

4 9 7 7 27 23

(-5 - 13) (-1 - 18) (0 - 14) (0 - 14) --- (1 - 44)

-1 4 1 1 5 9

(-11 - 10) (-1 - 9) (-2 - 5) (-6 - 8) --- (0 - 18)

5 5 4 4 18 33

(-4 - 13) (-5 - 15) (-5 - 13) (-6 - 13) --- (5 - 60)

28 24 -14 0 38 53

(-3 - 57) (-2 - 48) (-54 - 24) (-19 - 18) --- (5 - 100)

44 16 18 20 98 91

(16 - 72) (-5 - 37) (-8 - 43) (1 - 37) --- (32 - 149)

9 7 7 5 28 22

(-1 - 18) (-1 - 14) (-3 - 16) (-2 - 12) --- (-2 - 45)

31 30 22 41 124 50

(-33 - 90) (-8 - 65) (-3 - 46) (14 - 66) --- (4 - 94)

4 6 7 5 22 17

(-3 - 10) (-1 - 13) (-2 - 15) (0 - 10) --- (-2 - 36)

3 2 -2 -1 2 5

(-1 - 7) (-2 - 6) (-7 - 3) (-5 - 3) --- (1 - 9)

1 6 3 5 15 26

(-10 - 12) (-6 - 18) (-9 - 15) (-5 - 15) --- (-7 - 58)

02114 6

(-11 - 10) (-2 - 6) (-2 - 4) (-3 - 5) --- (0 - 12)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

 Philadelphia, PA Phoenix, AZ Pittsburgh, PA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate 

regional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA Fresno, CA

Estimated Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX Detroit, MI Houston, TX Salt Lake City, UT St. Louis, MO
F-21

Table F-21. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re Function to Estimate the Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

 that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

3 2 4 3 12 20

(-5 - 11) (-10 - 13) (-8 - 15) (-4 - 9) --- 4 (-8 - 47)

4 5 5 3 17 31

(-5 - 12) (-4 - 14) (-5 - 15) (-4 - 10) --- (6 - 56)

1 2 -1 3 5 9

(-3 - 4) (-5 - 10) (-8 - 7) (-2 - 7) --- (-7 - 25)

13217 10

(-6 - 8) (-3 - 9) (-5 - 8) (-5 - 6) --- (-9 - 29)

4 8 7 9 28 24

(-5 - 13) (-1 - 16) (0 - 14) (0 - 18) --- (1 - 45)

-1 4 1 1 5 9

(-14 - 11) (-1 - 8) (-2 - 4) (-6 - 8) --- (0 - 18)

5 4 4 4 17 33

(-4 - 15) (-4 - 13) (-6 - 14) (-6 - 13) --- (5 - 61)

23 29 -13 0 39 54

(-2 - 48) (-2 - 60) (-51 - 23) (-22 - 21) --- (5 - 102)

49 17 20 21 107 100

(18 - 79) (-6 - 38) (-9 - 48) (1 - 41) --- (35 - 163)

9 7 7 5 28 22

(-1 - 19) (-1 - 15) (-3 - 15) (-2 - 12) --- (-2 - 46)

24 29 25 45 123 47

(-24 - 68) (-8 - 63) (-4 - 51) (15 - 73) --- (4 - 90)

4 6 7 6 23 18

(-3 - 10) (-1 - 13) (-2 - 15) (0 - 13) --- (-2 - 38)

5 2 -2 -1 4 6

(-1 - 10) (-2 - 6) (-7 - 3) (-5 - 4) --- (1 - 11)

1 7 3 6 17 27

(-11 - 13) (-6 - 20) (-9 - 14) (-5 - 16) --- (-7 - 60)

02114 6

(-11 - 10) (-2 - 6) (-1 - 3) (-5 - 7) --- (0 - 13)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

 St. Louis, MO 

D

Estimated Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX Detroit, MI Houston, TX Fresno, CA Philadelphia, PA Phoenix, AZ Pittsburgh, PA

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate 

regional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

 Salt Lake City, UT
F-22

Table F-22. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re Function to Estimate the Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure t Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

29 24 -28 6 31 40

(-2 - 60) (-9 - 57) (-68 - 11) (-27 - 39) --- 4 (-26 - 105)

129 45 40 48 262 247

(90 - 168) (16 - 75) (6 - 74) (23 - 73) --- (182 - 313)

10 10 -13 3 10 17

(-1 - 21) (-3 - 23) (-32 - 5) (-12 - 17) --- (-11 - 44)

24 19 -21 5 27 31

(-2 - 50) (-7 - 45) (-50 - 8) (-19 - 28) --- (-20 - 81)

153 54 40 57 304 280

(107 - 198) (18 - 89) (6 - 74) (27 - 87) --- (206 - 354)

14 11 -7 3 21 21

(-5 - 31) (-6 - 27) (-28 - 14) (-11 - 17) --- (0 - 41)

44 34 -35 10 53 56

(-3 - 91) (-12 - 80) (-84 - 14) (-41 - 59) --- (-37 - 149)

104 194 -144 42 196 264

(-35 - 241) (-98 - 479) (-613 - 307) (-138 - 218) --- (3 - 523)

391 161 131 145 828 792

(273 - 509) (55 - 266) (19 - 241) (68 - 221) --- (582 - 1002)

118 39 35 39 231 214

(82 - 153) (13 - 64) (5 - 65) (18 - 59) --- (157 - 271)

58 81 -47 14 106 108

(-20 - 135) (-41 - 200) (-198 - 99) (-47 - 75) --- (1 - 213)

59 31 28 36 154 157

(41 - 77) (11 - 51) (4 - 51) (17 - 55) --- (115 - 199)

5 4 -3 1 7 8

(-2 - 13) (-2 - 10) (-15 - 7) (-3 - 5) --- (0 - 16)

103 44 30 44 221 207

(72 - 134) (15 - 73) (4 - 55) (20 - 66) --- (152 - 262)

12 0 -5 -5 2 21

(-65 - 82) (-61 - 55) (-56 - 42) (-53 - 40) --- (-52 - 92)

3 Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response function estimates reported in Table 2 of Bell et al. (200

specific C-R function estimates were not available from this study. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credib

based on statistical uncertainty surrounding the PM coefficient. 

 Fresno, CA Houston, TX Salt Lake City, UT Detroit, MI

Estimated Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure 

to PM2.5 Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX

 Philadelphia, PA Phoenix, AZ Pittsburgh, PA Tacoma, WA St. Louis, MO

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the season

coefficient estimators was not available.
F-23

Table F-23. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re Function to Estimate the Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure t Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

27 26 -34 6 25 41

(-2 - 55) (-9 - 60) (-81 - 13) (-24 - 35) --- 4 (-27 - 108)

106 40 35 44 225 214

(74 - 137) (14 - 66) (5 - 64) (21 - 68) --- (157 - 271)

9 10 -14 2 7 16

(-1 - 19) (-4 - 24) (-33 - 5) (-9 - 13) --- (-10 - 42)

19 18 -18 3 22 26

(-1 - 40) (-6 - 43) (-43 - 7) (-15 - 21) --- (-17 - 68)

119 54 29 44 246 225

(83 - 155) (19 - 90) (4 - 54) (20 - 66) --- (165 - 285)

15 10 -8 3 20 22

(-5 - 34) (-5 - 25) (-34 - 17) (-11 - 18) --- (0 - 44)

39 38 -36 8 49 55

(-3 - 81) (-14 - 90) (-86 - 14) (-36 - 52) --- (-36 - 145)

108 170 -137 37 178 248

(-37 - 252) (-86 - 419) (-580 - 291) (-121 - 192) --- (3 - 491)

342 143 107 129 721 684

(239 - 445) (49 - 237) (16 - 197) (61 - 198) --- (502 - 865)

99 35 33 41 208 201

(69 - 129) (12 - 59) (5 - 62) (19 - 63) --- (147 - 254)

66 80 -48 14 112 113

(-23 - 154) (-40 - 198) (-202 - 101) (-47 - 74) --- (1 - 224)

53 27 25 26 131 134

(37 - 69) (9 - 45) (4 - 46) (12 - 40) --- (98 - 169)

4 4 -4 1 5 7

(-1 - 10) (-2 - 11) (-17 - 9) (-3 - 4) --- (0 - 15)

74 39 26 36 175 171

(52 - 96) (13 - 64) (4 - 48) (17 - 54) --- (126 - 216)

9 0 -6 -4 -1 18

(-48 - 60) (-64 - 59) (-61 - 46) (-43 - 33) --- (-44 - 78)

3 Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).

 Fresno, CA Houston, TX Salt Lake City, UT Detroit, MI

Estimated Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure 

to PM2.5 Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX

 Philadelphia, PA Phoenix, AZ Pittsburgh, PA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response function estimates reported in Table 2 of Bell et al. 

(2008). Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% 

confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA St. Louis, MO
F-24

Table F-24. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re

 Function to Estimate the Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure t

 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

24 30 -33 5 26 41

(-2 - 50) (-11 - 70) (-80 - 13) (-23 - 34) --- 4 (-27 - 109)

103 42 35 44 224 216

(72 - 134) (14 - 70) (5 - 65) (21 - 67) --- (159 - 273)

9 13 -12 2 12 16

(-1 - 18) (-5 - 31) (-30 - 5) (-9 - 13) --- (-11 - 43)

23 17 -19 4 25 28

(-2 - 47) (-6 - 39) (-46 - 7) (-17 - 25) --- (-18 - 73)

121 48 28 58 255 233

(84 - 157) (16 - 79) (4 - 52) (27 - 88) --- (171 - 295)

17 10 -6 3 24 23

(-6 - 40) (-5 - 25) (-26 - 13) (-11 - 18) --- (0 - 46)

46 34 -38 9 51 56

(-3 - 95) (-12 - 79) (-91 - 15) (-37 - 54) --- (-37 - 149)

93 215 -131 42 219 258

(-31 - 216) (-109 - 531) (-556 - 279) (-139 - 220) --- (3 - 511)

377 151 121 143 792 752

(263 - 490) (51 - 249) (18 - 223) (67 - 218) --- (552 - 951)

101 39 32 42 214 203

(71 - 131) (13 - 64) (5 - 59) (20 - 64) --- (149 - 257)

50 78 -54 16 90 108

(-17 - 117) (-39 - 193) (-230 - 115) (-52 - 83) --- (1 - 215)

51 28 25 32 136 140

(36 - 67) (9 - 46) (4 - 46) (15 - 49) --- (103 - 177)

6 5 -4 1 8 9

(-2 - 15) (-2 - 12) (-18 - 9) (-3 - 5) --- (0 - 18)

80 43 25 37 185 178

(56 - 104) (15 - 71) (4 - 47) (17 - 56) --- (131 - 225)

9 0 -4 -6 -1 19

(-48 - 60) (-64 - 59) (-43 - 33) (-63 - 47) --- (-46 - 82)

3 Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).

 Philadelphia, PA Phoenix, AZ Pittsburgh, PA

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response function estimates reported in Table 2 of Bell et al. 

(2008). Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

 St. Louis, MO

Estimated Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure 

to PM2.5 Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX

D

 Fresno, CA Houston, TX Salt Lake City, UT Detroit, MI
F-25

Table F-25. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re Function to Estimate the Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

6 9 -6 1 10 17

(-21 - 31) (-10 - 28) (-25 - 12) (-13 - 15) --- 4 (-22 - 55)

18 1 14 2 35 20

(-5 - 40) (-14 - 15) (0 - 28) (-12 - 15) --- (-12 - 51)

2 4 -3 1 4 7

(-7 - 11) (-4 - 11) (-12 - 6) (-5 - 6) --- (-9 - 23)

4 7 -5 1 7 15

(-17 - 25) (-8 - 23) (-21 - 10) (-13 - 16) --- (-18 - 47)

24 1 17 2 44 25

(-6 - 53) (-18 - 20) (0 - 35) (-14 - 18) --- (-15 - 64)

10 3 4 3 20 14

(-1 - 20) (-6 - 11) (-4 - 11) (-5 - 11) --- (3 - 25)

7 12 -8 3 14 25

(-27 - 42) (-14 - 38) (-33 - 16) (-27 - 32) --- (-32 - 81)

71 45 86 42 244 170

(-6 - 148) (-97 - 183) (-98 - 261) (-60 - 140) --- (40 - 300)

57 2 49 5 113 65

(-15 - 129) (-52 - 56) (-1 - 99) (-33 - 42) --- (-38 - 169)

16 1 12 1 30 17

(-4 - 37) (-12 - 12) (0 - 25) (-9 - 12) --- (-10 - 44)

35 17 23 13 88 61

(-3 - 72) (-36 - 68) (-26 - 70) (-18 - 43) --- (14 - 107)

8 0 9 1 18 13

(-2 - 18) (-10 - 11) (0 - 19) (-9 - 12) --- (-8 - 33)

41218 6

(0 - 8) (-2 - 5) (-3 - 7) (-2 - 4) --- (1 - 10)

23 1 15 2 41 25

(-6 - 53) (-20 - 21) (0 - 29) (-15 - 19) --- (-15 - 64)

0 4 1 -2 3 2

(-50 - 43) (-27 - 32) (-19 - 19) (-24 - 18) --- (-27 - 30)

3 Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).

 St. Louis, MO Salt Lake City, UT 

Estimated Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to 

PM2.5 Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Philadelphia, PA Phoenix, AZ Pittsburgh, PA Tacoma, WA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the season

coefficient estimators was not available.

1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response function estimates from models with a 2-day lag for res

hospital admissions reported in Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest w

Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Birmingham, AL Dallas, TX Detroit, MI Houston, TX Fresno, CA
F-26

Table F-26. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Respo Function to Estimate the Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

5 10 -7 1 9 17

(-19 - 28) (-11 - 30) (-30 - 14) (-11 - 13) --- 4 (-22 - 56)

15 1 12 2 30 17

(-4 - 33) (-12 - 13) (0 - 24) (-11 - 14) --- (-10 - 44)

2 4 -3 0 3 7

(-6 - 10) (-4 - 12) (-12 - 6) (-4 - 5) --- (-8 - 22)

4 7 -4 1 8 12

(-13 - 20) (-8 - 22) (-18 - 9) (-10 - 12) --- (-15 - 40)

18 1 13 2 34 20

(-5 - 41) (-19 - 20) (0 - 25) (-11 - 13) --- (-12 - 52)

11 3 5 3 22 15

(-1 - 22) (-5 - 10) (-5 - 14) (-5 - 12) --- (3 - 26)

7 14 -8 3 16 25

(-24 - 37) (-15 - 42) (-34 - 17) (-24 - 28) --- (-31 - 79)

75 40 82 37 234 160

(-6 - 155) (-85 - 160) (-93 - 248) (-53 - 124) --- (37 - 281)

50 2 40 4 96 56

(-13 - 113) (-46 - 50) (-1 - 81) (-30 - 38) --- (-33 - 145)

14 0 12 1 27 16

(-4 - 31) (-11 - 12) (0 - 23) (-10 - 13) --- (-9 - 41)

40 17 24 13 94 64

(-3 - 82) (-35 - 67) (-27 - 72) (-18 - 43) --- (15 - 112)

7 0 8 1 16 11

(-2 - 16) (-9 - 10) (0 - 17) (-7 - 8) --- (-6 - 28)

31318 5

(0 - 6) (-3 - 5) (-3 - 8) (-1 - 3) --- (1 - 9)

17 1 13 2 33 21

(-5 - 38) (-17 - 18) (0 - 26) (-13 - 16) --- (-12 - 53)

0 4 1 -1 4 2

(-37 - 31) (-29 - 34) (-21 - 20) (-20 - 15) --- (-23 - 25)

3 Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).

 Philadelphia, PA Phoenix, AZ Pittsburgh, PA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response function estimates from models with a 2-day lag for 

respiratory hospital admissions reported in Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA Fresno, CA

Estimated Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to 

PM2.5 Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX Detroit, MI Houston, TX Salt Lake City, UT St. Louis, MO

P

Dif
F-27

Table F-27. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Resp Function to Estimate the Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

5 11 -7 1 10 18

(-17 - 26) (-13 - 35) (-29 - 14) (-11 - 13) --- 4 (-22 - 57)

14 1 12 2 29 17

(-4 - 32) (-13 - 14) (0 - 25) (-11 - 14) --- (-10 - 45)

2 5 -3 0 4 7

(-6 - 9) (-6 - 16) (-11 - 5) (-4 - 5) --- (-9 - 22)

4 6 -5 1 6 13

(-16 - 24) (-7 - 20) (-19 - 10) (-12 - 14) --- (-17 - 43)

19 1 12 2 34 21

(-5 - 42) (-16 - 18) (0 - 25) (-14 - 18) --- (-12 - 54)

13 2 3 4 22 15

(-1 - 26) (-5 - 10) (-4 - 11) (-5 - 12) --- (4 - 27)

8 12 -9 3 14 25

(-28 - 43) (-14 - 38) (-36 - 18) (-25 - 29) --- (-32 - 82)

64 50 78 42 234 166

(-5 - 133) (-108 - 203) (-89 - 239) (-61 - 142) --- (39 - 293)

55 2 46 5 108 62

(-15 - 124) (-48 - 52) (-1 - 91) (-33 - 42) --- (-37 - 160)

14 1 11 1 27 16

(-4 - 32) (-12 - 13) (0 - 22) (-10 - 13) --- (-10 - 42)

30 16 27 14 87 61

(-3 - 63) (-34 - 65) (-31 - 82) (-20 - 48) --- (14 - 108)

7 0 8 1 16 11

(-2 - 16) (-9 - 10) (0 - 17) (-8 - 10) --- (-7 - 29)

5 1 3 1 10 7

(0 - 9) (-3 - 5) (-3 - 9) (-2 - 4) --- (2 - 12)

18 1 12 2 33 21

(-5 - 41) (-19 - 21) (0 - 25) (-13 - 17) --- (-13 - 55)

0 4 1 -2 3 2

(-37 - 31) (-29 - 34) (-15 - 15) (-29 - 22) --- (-24 - 27)

3 Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).

 St. Louis, MO 

P

Dif

Estimated Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to 

PM2.5 Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX Detroit, MI Houston, TX Fresno, CA Philadelphia, PA Phoenix, AZ Pittsburgh, PA

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response function estimates from models with a 2-day lag for 

respiratory hospital admissions reported in Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

 Salt Lake City, UT
F-28

Table F-28. Sensitivity Analysis: Impact of Using an Annual Concentration-Response Function vs. a Seasonal Function  Applied Only to that Period to Estimate the Incidence of Emergency Room Visits for Asthma Associated with S Exposure to Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and 

 Standards in New York City, Based on Adjusting 2005 PM2.5 Concentrations1 

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

5235 4375 4375 4265 3927 

(3346 - 7071) (2790 - 5923) (2790 - 5923) (2719 - 5776) (2501 - 5323

3136 2634 2634 2569 2370 

(2058 - 4162) (1722 - 3509) (1722 - 3509) (1678 - 3425) (1546 - 3164

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on Ito et al. (2007). New York City in this study consisted only of Manhattan. Numbers rounded to the nearest whole number. Numbers in parenthe

intervals based on statistical uncertainty surrounding the PM coefficient.

Incidence of ER Visits Associated with Short-Term Exposure to PM2.5 Concent

Concentrations that Just Meet the Current and Alternative Annual (n) and 

Combination Denoted n/m):

Annual C-R Function Applied to the Whole Year Seasonal C-R Function for April - August Applied Only to that Period:

Concentration-Response (C-R) Function 

and Period to Which Applied:
F-29

Table F-29. Sensitivity Analysis: Impact of Using an Annual Concentration-Response Function vs. a Seasonal  Applied Only to that Period to Estimate the Incidence of Emergency Room Visits for Asthma Associat to Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and A

 New York City, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 

4506 3764 3764 3669 

(2876 - 6095) (2397 - 5102) (2397 - 5102) (2336 - 4974) (2

2732 2293 2293 2237 

(1791 - 3631) (1497 - 3059) (1497 - 3059) (1460 - 2985) (

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on Ito et al. (2007). New York City in this study consisted only of Manhattan. Numbers rounded to the nearest whole number. Numbers 

intervals based on statistical uncertainty surrounding the PM coefficient.

Incidence of ER Visits Associated with Short-Term Exposure to PM2.

Concentrations that Just Meet the Current and Alternative Annua

Combination Denoted n/m

Annual C-R Function Applied to the Whole Year Seasonal C-R Function for April - August Applied Only to that Period:

Concentration-Response (C-R) Function 

and Period to Which Applied:
F-30

Table F-30. Sensitivity Analysis: Impact of Using an Annual Concentration-Response Function vs. a Seasonal Function  Applied Only to that Period to Estimate the Incidence of Emergency Room Visits for Asthma Associated with S Exposure to Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and 

 Standards in New York City, Based on Adjusting 2007 PM2.5 Concentrations1 

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

4926 4115 4115 4011 3692 

(3145 - 6660) (2622 - 5575) (2622 - 5575) (2555 - 5436) (2350 - 5008

2908 2441 2441 2380 2195 

(1906 - 3864) (1593 - 3256) (1593 - 3256) (1553 - 3177) (1431 - 2934

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on Ito et al. (2007). New York City in this study consisted only of Manhattan. Numbers rounded to the nearest whole number. Numbers in parenthe

intervals based on statistical uncertainty surrounding the PM coefficient.

Incidence of ER Visits Associated with Short-Term Exposure to PM2.5 Concent

Concentrations that Just Meet the Current and Alternative Annual (n) and 

Combination Denoted n/m):

Annual C-R Function Applied to the Whole Year Seasonal C-R Function for April - August Applied Only to that Period:

Concentration-Response (C-R) Function 

and Period to Which Applied:
F-31

Table F-31. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated wi Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations1Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 275 Max. positive est. = 81

(-35 - 584) 301 (-117 - 27

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 301 Min. positive est. =

(0 - 600) 194

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 2 day none 194

(-97 - 483) Percent diff. =

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 3 day none -77 55%

(-373 - 218)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 4 day none -46

(-329 - 235)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 5 day none -287

(-592 - 15)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 275 Max. positive est. = 81

(-35 - 584) 275 (-117 - 27

Mortality, short-term non-accidental log-linear, GLM, 30 df 0 day none 204 Min. positive est. =

(-174 - 579) 153

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 0 day none 163

(-115 - 441) Percent diff. =

Mortality, short-term non-accidental log-linear, GLM, 100 df 0 day none 153 80%

(-218 - 522)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 301 Max. positive est. = 81

(0 - 600) 301 (-117 - 27

Mortality, short-term non-accidental log-linear, GLM, 30 df 1 day none 281 Min. positive est. =

(-86 - 644) 51

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day none 51

(-236 - 336) Percent diff. =

Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day none -5 490%

(-509 - 494)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day CO -272 81

(-676 - 128) (-117 - 27

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day CO -169

(-540 - 198)

Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day CO -169

(-603 - 260)

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Health Effects Model

Incidenc

Estimate U

Core Anal

Model 

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Lag Structure:

Lag

Other 

Pollutants 

in Model

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day Lag
F-32

Table F-31 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations1Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 0 day none 171 Max. positive est. = -3

(17 - 324) 171 (-140 

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day none 168 Min. positive est. =

(24 - 310) 168

Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day none 168 Percent diff. =

(-4 - 337) 2%

Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 1 day none 178 Max. positive est. = -3

(26 - 328) 178 (-140 

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day none 139 Min. positive est. =

(-6 - 282) 120

Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day none 120 Percent diff. =

(-56 - 293) 48%

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 307 Max. positive est. = -3

(130 - 481) 324 (-140 

Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day CO 324 Min. positive est. =

(116 - 529) 158

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 158 Percent diff. =

(-22 - 335) 105%

Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day CO 158

(-60 - 372)

log-linear, GAM (stringent), 30 df 0 day none -15 --

(-80 - 49)

log-linear, GAM (stringent), 100 df 0 day none -37

(-102 - 25)

log-linear, GLM, 100 df 0 day none -32

(-109 - 43)

log-linear, GAM (stringent), 30 df 1 day none 10 Max. positive est. = -

(-56 - 74) 22

log-linear, GAM (stringent), 100 df 1 day none 22 Min. positive est. =

(-42 - 85) 5

log-linear, GLM, 100 df 1 day none 5 Percent diff. =

(-75 - 83) 340%

Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Health Effects

Respiratory Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod

Model

Mortality, short-term respiratory (COPD+)Respiratory Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day Lag

Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)

Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day Lag

Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant Model

Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)
F-33

Table F-31 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations1HA, cardiovascular log-linear, GAM (stringent), 30 df 0 day none 794 Max. positive est. = 3

(457 - 1128) 794 (-60 - 

HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day none 584 Min. positive est. =

(254 - 912) 584

HA, cardiovascular log-linear, GLM, 100 df 0 day none 634 Percent diff. =

(226 - 1038) 36%

HA, cardiovascular log-linear, GAM (stringent), 30 df 1 day none 699 Max. positive est. = 3

(347 - 1048) 699 (-60 - 

HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day none 569 Min. positive est. =

(234 - 902) 569

HA, cardiovascular log-linear, GLM, 100 df 1 day none 604 Percent diff. =

(194 - 1011) 23%

HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 197 Max. positive est. = 3

(-224 - 615) 293 (-60 - 

HA, cardiovascular log-linear, GLM, 100 df 0 day CO 293 Min. positive est. =

(-208 - 788) 122

HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 122 Percent diff. =

(-330 - 568) 140%

HA, cardiovascular log-linear, GLM, 100 df 1 day CO 137

(-381 - 648)

HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 0 day none 336 Max. positive est. = -

(138 - 531) 336

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day none 278 Min. positive est. =

(104 - 450) 278

HA, respiratory (COPD+) log-linear, GLM, 100 df 0 day none 300 Percent diff. =

(83 - 514) 21%

HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 1 day none 240 Max. positive est. = -

(45 - 432) 240

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day none 152 Min. positive est. =

(-22 - 324) 152

HA, respiratory (COPD+) log-linear, GLM, 100 df 1 day none 156 Percent diff. =

(-55 - 364) 58%

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod

Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Health Effects

Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day L

Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day LCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant ModelRespiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagRespiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag
F-34

Table F-31 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations1HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 2 day none 371 Max. positive est. = -

(166 - 574) 371

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day none 230 Min. positive est. =

(43 - 414) 208

HA, respiratory (COPD+) log-linear, GLM, 100 df 2 day none 208 Percent diff. =

(-24 - 436) 78%

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day NO2 85 Max. positive est. = -

(-185 - 351) 85

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day NO2 -8 Min. positive est. =

(-329 - 307) 71

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day NO2 71 Percent diff. =

(-209 - 346) 20%

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 3 day NO2 -223

(-491 - 41)

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod

Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Health Effects

Respiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 2-Day Lag

Respiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Lag Structure, with a Copollutan5Because "respiratory illness" was much more broadly defined in both Zanobetti and Schwartz (2009) and Bell et al. (2008) than in Moolgavkar (2003), a comparison between t

and the corresponding core analysis estimates is not shown.

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. Results are based on Moolgavkar (2003) [reanalysis of M

2000c)]. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 

2The core analysis estimates for non-accidental mortality and cardiovascular mortality associated with short-term exposure to PM2.5 are from Zanobetti and Schwartz (2009). 

cardiovascular hospital admissions associated with short-term exposure to PM2.5 are from Bell et al. (2008). 

3Calculated as (maximum positive estimate - minimum positive estimate)/(minimum positive estimate).4Calculated as (Moolgavkar (2003) estimate - core analysis estimate)/(core analysis estimate).
F-35

Table F-32. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated wi Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations1Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 254 Max. positive est. = 75

(-32 - 539) 278 (-108 - 25

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 278 Min. positive est. =

(0 - 554) 179

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 2 day none 179

(-89 - 445) Percent diff. =

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 3 day none -71 55%

(-344 - 201)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 4 day none -42

(-304 - 217)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 5 day none -265

(-546 - 14)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 254 Max. positive est. = 75

(-32 - 539) 254 (-108 - 25

Mortality, short-term non-accidental log-linear, GLM, 30 df 0 day none 188 Min. positive est. =

(-161 - 535) 141

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 0 day none 151

(-106 - 407) Percent diff. =

Mortality, short-term non-accidental log-linear, GLM, 100 df 0 day none 141 80%

(-201 - 482)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 278 Max. positive est. = 75

(0 - 554) 278 (-108 - 25

Mortality, short-term non-accidental log-linear, GLM, 30 df 1 day none 259 Min. positive est. =

(-80 - 595) 47

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day none 47

(-218 - 310) Percent diff. =

Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day none -5 491%

(-469 - 455)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day CO -251 75

(-623 - 118) (-108 - 25

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day CO -156

(-497 - 183)

Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day CO -156

(-555 - 240)

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day Lag

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant ModelNon-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Health Effects Model

Incidenc

Estimate U

Core Anal

Model 

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Lag Structure:

Lag

Other 

Pollutants 

in Model
F-36

Table F-32 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations1Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 0 day none 158 Max. positive est. = -2

(15 - 299) 158 (-129 

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day none 155 Min. positive est. =

(22 - 286) 155

Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day none 155 Percent diff. =

(-3 - 311) 2%

Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 1 day none 164 Max. positive est. = -2

(24 - 303) 164 (-129 

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day none 128 Min. positive est. =

(-5 - 260) 110

Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day none 110 Percent diff. =

(-51 - 270) 49%

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 283 Max. positive est. = -2

(120 - 444) 299 (-129 

Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day CO 299 Min. positive est. =

(107 - 489) 145

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 145 Percent diff. =

(-20 - 309) 106%

Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day CO 145

(-56 - 344)

log-linear, GAM (stringent), 30 df 0 day none -14 --

(-74 - 45)

log-linear, GAM (stringent), 100 df 0 day none -35

(-94 - 23)

log-linear, GLM, 100 df 0 day none -29

(-100 - 39)

log-linear, GAM (stringent), 30 df 1 day none 9 Max. positive est. = -

(-51 - 68) 21

log-linear, GAM (stringent), 100 df 1 day none 21 Min. positive est. =

(-39 - 78) 5

log-linear, GLM, 100 df 1 day none 5 Percent diff. =

(-69 - 76) 320%

Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)

Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant ModelCardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Incid

Estimat

Core A

Mod

Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day Lag

Health Effects

Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)Respiratory Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagRespiratory Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Mortality, short-term respiratory (COPD+)
F-37

Table F-32 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations1HA, cardiovascular log-linear, GAM (stringent), 30 df 0 day none 745 Max. positive est. = 24

(428 - 1060) 745 (3 - 

HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day none 548 Min. positive est. =

(238 - 856) 548

HA, cardiovascular log-linear, GLM, 100 df 0 day none 595 Percent diff. =

(212 - 975) 36%

HA, cardiovascular log-linear, GAM (stringent), 30 df 1 day none 656 Max. positive est. = 24

(326 - 984) 656 (3 - 

HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day none 534 Min. positive est. =

(220 - 847) 534

HA, cardiovascular log-linear, GLM, 100 df 1 day none 567 Percent diff. =

(182 - 949) 23%

HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 185 Max. positive est. = 24

(-210 - 577) 275 (3 - 

HA, cardiovascular log-linear, GLM, 100 df 0 day CO 275 Min. positive est. =

(-195 - 740) 114

HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 114 Percent diff. =

(-309 - 533) 141%

HA, cardiovascular log-linear, GLM, 100 df 1 day CO 128

(-357 - 608)

HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 0 day none 310 Max. positive est. = -

(127 - 491) 310

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day none 256 Min. positive est. =

(96 - 415) 256

HA, respiratory (COPD+) log-linear, GLM, 100 df 0 day none 277 Percent diff. =

(76 - 475) 21%

HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 1 day none 221 Max. positive est. = -

(42 - 399) 221

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day none 140 Min. positive est. =

(-21 - 299) 140

HA, respiratory (COPD+) log-linear, GLM, 100 df 1 day none 144 Percent diff. =

(-51 - 336) 58%

Respiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day LagCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LRespiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant ModelCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day L

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod

Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Health Effects
F-38

Table F-32 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations1HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 2 day none 343 Max. positive est. = -

(153 - 531) 343

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day none 212 Min. positive est. =

(40 - 383) 192

HA, respiratory (COPD+) log-linear, GLM, 100 df 2 day none 192 Percent diff. =

(-22 - 403) 79%

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day NO2 78 Max. positive est. = -

(-171 - 324) 78

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day NO2 -7 Min. positive est. =

(-303 - 284) 65

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day NO2 65 Percent diff. =

(-192 - 319) 20%

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 3 day NO2 -205

(-452 - 38)

5Because "respiratory illness" was much more broadly defined in both Zanobetti and Schwartz (2009) and Bell et al. (2008) than in Moolgavkar (2003), a comparison between t

and the corresponding core analysis estimates is not shown.

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. Results are based on Moolgavkar (2003) [reanalysis of M

2000c)].

2The core analysis estimates for non-accidental mortality and cardiovascular mortality associated with short-term exposure to PM2.5 are from Zanobetti and Schwartz (2009). 

cardiovascular hospital admissions associated with short-term exposure to PM2.5 are from Bell et al. (2008). Numbers rounded to the nearest whole number. Numbers in parencredible intervals based on statistical uncertainty surrounding the PM coefficient. 

3Calculated as (maximum positive estimate - minimum positive estimate)/(minimum positive estimate).4Calculated as (Moolgavkar (2003) estimate - core analysis estimate)/(core analysis estimate).Respiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Lag Structure, with a CopollutanRespiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 2-Day Lag

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod

Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Health Effects
F-39

Table F-33. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated wi Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations1Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 259 Max. positive est. = 77

(-33 - 550) 283 (-110 - 26

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 283 Min. positive est. =

(0 - 565) 183

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 2 day none 183

(-91 - 455) Percent diff. =

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 3 day none -72 55%

(-351 - 205)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 4 day none -43

(-310 - 222)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 5 day none -271

(-558 - 14)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 259 Max. positive est. = 77

(-33 - 550) 259 (-110 - 26

Mortality, short-term non-accidental log-linear, GLM, 30 df 0 day none 192 Min. positive est. =

(-164 - 546) 144

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 0 day none 154

(-109 - 415) Percent diff. =

Mortality, short-term non-accidental log-linear, GLM, 100 df 0 day none 144 80%

(-206 - 492)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 283 Max. positive est. = 77

(0 - 565) 283 (-110 - 26

Mortality, short-term non-accidental log-linear, GLM, 30 df 1 day none 264 Min. positive est. =

(-81 - 607) 48

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day none 48

(-222 - 317) Percent diff. =

Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day none -5 490%

(-480 - 465)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day CO -256 77

(-636 - 121) (-110 - 26

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day CO -159

(-508 - 187)

Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day CO -159

(-567 - 245) 

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagNon-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Lag Structure:

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incidenc

Estimate U

Core Anal

Model 

Health Effects Model Lag

Other 

Pollutants 

in Model

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant Model
F-40

Table F-33 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations1Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 0 day none 161 Max. positive est. = -3

(16 - 306) 161 (-132 

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day none 158 Min. positive est. =

(23 - 292) 158

Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day none 158 Percent diff. =

(-3 - 318) 2%

Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 1 day none 168 Max. positive est. = -3

(25 - 309) 168 (-132 

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day none 130 Min. positive est. =

(-6 - 265) 113

Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day none 113 Percent diff. =

(-52 - 276) 49%

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 289 Max. positive est. = -3

(123 - 453) 305 (-132 

Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day CO 305 Min. positive est. =

(109 - 498) 148

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 148 Percent diff. =

(-21 - 316) 106%

Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day CO 148

(-57 - 351)

log-linear, GAM (stringent), 30 df 0 day none -14 --

(-75 - 46)

log-linear, GAM (stringent), 100 df 0 day none -35

(-96 - 24)

log-linear, GLM, 100 df 0 day none -30

(-103 - 40)

log-linear, GAM (stringent), 30 df 1 day none 9 Max. positive est. = -

(-52 - 70) 21

log-linear, GAM (stringent), 100 df 1 day none 21 Min. positive est. =

(-39 - 80) 5

log-linear, GLM, 100 df 1 day none 5 Percent diff. =

(-71 - 78) 320%

Respiratory Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)

Respiratory Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagMortality, short-term respiratory (COPD+)Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagMortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant ModelCardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Health Effects Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod
F-41

Table F-33 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass

 Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations1 

HA, cardiovascular log-linear, GAM (stringent), 30 df 0 day none 775 Max. positive est. = 25

(446 - 1102) 775 (3 - 

HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day none 570 Min. positive est. =

(248 - 890) 570

HA, cardiovascular log-linear, GLM, 100 df 0 day none 619 Percent diff. =

(221 - 1014) 36%

HA, cardiovascular log-linear, GAM (stringent), 30 df 1 day none 682 Max. positive est. = 25

(339 - 1023) 682 (3 - 

HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day none 556 Min. positive est. =

(228 - 880) 556

HA, cardiovascular log-linear, GLM, 100 df 1 day none 590 Percent diff. =

(189 - 987) 23%

HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 193 Max. positive est. = 25

(-219 - 600) 286 (3 - 

HA, cardiovascular log-linear, GLM, 100 df 0 day CO 286 Min. positive est. =

(-203 - 769) 119

HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 119 Percent diff. =

(-321 - 554) 140%

HA, cardiovascular log-linear, GLM, 100 df 1 day CO 133

(-371 - 633)

HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 0 day none 316 Max. positive est. = --

(130 - 501) 316

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day none 262 Min. positive est. =

(98 - 424) 262

HA, respiratory (COPD+) log-linear, GLM, 100 df 0 day none 282 Percent diff. =

(78 - 485) 21%

HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 1 day none 226 Max. positive est. = --

(42 - 407) 226

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day none 143 Min. positive est. =

(-21 - 305) 143

HA, respiratory (COPD+) log-linear, GLM, 100 df 1 day none 146 Percent diff. =

(-52 - 343) 58%

Respiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day LagRespiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant ModelCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day L

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod

Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Health Effects

Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day L
F-42

Table F-33 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations1HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 2 day none 350 Max. positive est. = -

(156 - 541) 350

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day none 216 Min. positive est. =

(41 - 391) 196

HA, respiratory (COPD+) log-linear, GLM, 100 df 2 day none 196 Percent diff. =

(-22 - 411) 79%

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day NO2 80 Max. positive est. = -

(-174 - 331) 80

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day NO2 -8 Min. positive est. =

(-310 - 290) 67

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day NO2 67 Percent diff. =

(-196 - 326) 19%

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 3 day NO2 -209

(-462 - 39)

5Because "respiratory illness" was much more broadly defined in both Zanobetti and Schwartz (2009) and Bell et al. (2008) than in Moolgavkar (2003), a comparison between t

and the corresponding core analysis estimates is not shown.

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. Results are based on Moolgavkar (2003) [reanalysis of M

2000c)]. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 

2The core analysis estimates for non-accidental mortality and cardiovascular mortality associated with short-term exposure to PM2.5 are from Zanobetti and Schwartz (2009). 

cardiovascular hospital admissions associated with short-term exposure to PM2.5 are from Bell et al. (2008). 

3Calculated as (maximum positive estimate - minimum positive estimate)/(minimum positive estimate).4Calculated as (Moolgavkar (2003) estimate - core analysis estimate)/(core analysis estimate).Respiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Lag Structure, with a CopollutanRespiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 2-Day Lag

Health Effects Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod
F-43

Table F-34. Sensitivity Analysis: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to  PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005 PM2.5 Concentration

 Comparison of Proportional and Hybrid Rollback Methods1

15/35 2 14/35 13/35 12/35 13/30 

Proportional 256 242 224 206 219 

(104 - 406) (98 - 384) (91 - 356) (83 - 327) (89 - 34

Hybrid 254 248 229 211 217 

(103 - 402) (101 - 393) (93 - 364) (86 - 335) (88 - 34

Percent Difference 3 -1% 2% 2% 2% -1% 

Proportional 34 32 29 27 29 

(-53 - 121) (-49 - 112) (-45 - 103) (-41 - 94) (-45 - 10

Hybrid 39 36 33 30 33 

(-61 - 137) (-56 - 127) (-52 - 117) (-47 - 107) (-52 - 1

Percent Difference 15% 13% 14% 11% 14% 

Proportional 147 146 135 124 125 

(-26 - 317) (-26 - 315) (-24 - 291) (-22 - 267) (-22 - 27

Hybrid 151 151 148 136 129 

(-26 - 325) (-26 - 325) (-26 - 319) (-24 - 293) (-23 - 27

Percent Difference 3% 3% 10% 10% 3% 

Proportional 81 81 81 77 69 

(-117 - 278) (-117 - 278) (-117 - 278) (-110 - 263) (-100 - 2

Hybrid 91 91 89 81 78 

(-130 - 311) (-130 - 311) (-127 - 304) (-117 - 279) (-111 - 2

Percent Difference 12% 12% 10% 5% 13% 

Proportional 781 781 761 700 668 

(459 - 1102) (459 - 1102) (447 - 1073) (411 - 987) (392 - 9

Hybrid 795 795 761 699 680 

(467 - 1121) (467 - 1121) (446 - 1073) (410 - 986) (399 - 9

Percent Difference 2% 2% 0% 0% 2% 

Proportional 260 244 226 207 222 

(75 - 443) (71 - 416) (65 - 385) (60 - 354) (64 - 37

Hybrid 271 252 233 214 233 

(78 - 462) (73 - 429) (67 - 397) (62 - 365) (67 - 39

Percent Difference 4% 3% 3% 3% 5% 

3 Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).

 St. Louis, MO

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Meet the Current and Alternative Combinations of Annual (n) and Daily (m) Standards 

Denoted n/m):

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropria

coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. Numbers are rounded to the nearest whole number. Numbers in parentheses a

intervals based on statistical uncertainty surrounding the PM coefficient. 

Risk Assessment 

Location

 Baltimore, MD

 Birmingham, AL

Type of Rollback

 Detroit, MI

 Los Angeles, CA

 New York, NY
F-44

Table F-35. Sensitivity Analysis: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to  Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006 PM2.5 Concentrations: 

 Comparison of Proportional and Hybrid Rollback Methods1

15/35 2 14/35 13/35 12/35 13/30 

Proportional 224 212 196 180 192 

(91 - 356) (86 - 336) (79 - 311) (73 - 286) (78 - 30

Hybrid 222 217 200 184 190 

(90 - 352) (88 - 344) (81 - 318) (75 - 293) (77 - 30

Percent Difference 3 -1% 2% 2% 2% -1% 

Proportional 33 30 28 26 28 

(-51 - 116) (-47 - 108) (-44 - 99) (-40 - 90) (-44 - 9

Hybrid 37 35 32 29 32 

(-58 - 132) (-54 - 122) (-49 - 112) (-45 - 102) (-49 - 11

Percent Difference 12% 17% 14% 12% 14% 

Proportional 118 117 108 99 101 

(-21 - 255) (-20 - 253) (-19 - 234) (-17 - 215) (-18 - 21

Hybrid 121 121 118 109 103 

(-21 - 261) (-21 - 261) (-21 - 256) (-19 - 235) (-18 - 22

Percent Difference 3% 3% 9% 10% 2% 

Proportional 75 75 75 71 64 

(-108 - 257) (-108 - 257) (-108 - 257) (-101 - 242) (-92 - 21

Hybrid 84 84 82 75 72 

(-120 - 287) (-120 - 287) (-117 - 280) (-108 - 257) (-102 - 2

Percent Difference 12% 12% 9% 6% 13% 

Proportional 671 671 654 601 574 

(394 - 946) (394 - 946) (383 - 922) (352 - 847) (336 - 80

Hybrid 682 682 652 599 583 

(400 - 961) (400 - 961) (383 - 920) (352 - 846) (342 - 82

Percent Difference 2% 2% 0% 0% 2% 

Proportional 215 202 187 171 184 

(62 - 367) (58 - 345) (54 - 319) (49 - 293) (53 - 31

Hybrid 224 208 192 176 192 

(64 - 381) (60 - 354) (55 - 328) (51 - 301) (55 - 32

Percent Difference 4% 3% 3% 3% 4% 

3 Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).

 New York, NY

 Baltimore, MD

 Birmingham, AL

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Meet the Current and Alternative Combinations of Annual (n) and Daily (m) Standards 

Denoted n/m):

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the approp

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. Numbers rounded to the nearest whole number. Numbers in parentcredible intervals based on statistical uncertainty surrounding the PM coefficient. 

Risk Assessment 

Location Type of Rollback

 St. Louis, MO

 Detroit, MI

 Los Angeles, CA
F-45

Table F-36. Sensitivity Analysis: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to P Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007 PM2.5 Concentrations: 

 Comparison of Proportional and Hybrid Rollback Methods1

15/35 2 14/35 13/35 12/35 13/30 

Proportional 227 214 198 182 194 

(92 - 360) (87 - 340) (80 - 315) (74 - 289) (79 - 30

Hybrid 224 219 203 186 192 

(91 - 356) (89 - 348) (82 - 322) (75 - 296) (78 - 30

Percent Difference 3 -1% 2% 3% 2% -1% 

Proportional 34 32 29 26 29 

(-53 - 120) (-49 - 111) (-45 - 102) (-41 - 93) (-45 - 1

Hybrid 39 36 33 30 33 

(-60 - 137) (-56 - 127) (-51 - 116) (-47 - 106) (-51 - 1

Percent Difference 15% 13% 14% 15% 14% 

Proportional 121 120 111 102 104 

(-21 - 262) (-21 - 261) (-19 - 241) (-18 - 221) (-18 - 2

Hybrid 124 124 122 112 106 

(-22 - 269) (-22 - 269) (-21 - 264) (-20 - 242) (-19 - 2

Percent Difference 2% 3% 10% 10% 2% 

Proportional 77 77 77 72 65 

(-110 - 262) (-110 - 262) (-110 - 262) (-104 - 247) (-94 - 2

Hybrid 86 86 83 77 73 

(-123 - 293) (-123 - 293) (-120 - 286) (-110 - 262) (-105 - 2

Percent Difference 12% 12% 8% 7% 12% 

Proportional 734 734 715 657 627 

(431 - 1035) (431 - 1035) (419 - 1008) (385 - 927) (368 - 8

Hybrid 746 746 714 656 638 

(438 - 1052) (438 - 1052) (419 - 1007) (385 - 926) (374 - 9

Percent Difference 2% 2% 0% 0% 2% 

Proportional 225 211 195 179 192 

(65 - 384) (61 - 360) (56 - 333) (52 - 306) (55 - 32

Hybrid 236 219 203 186 203 

(68 - 402) (63 - 374) (59 - 346) (54 - 318) (59 - 34

Percent Difference 5% 4% 4% 4% 6% 

3 Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).

 Los Angeles, CA

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 St. Louis, MO

 New York, NY

Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Meet the Current and Alternative Combinations of Annual (n) and Daily (m) Standard

Denoted n/m):

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropria

coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% based on statistical uncertainty surrounding the PM coefficient. 

Risk Assessment 

Location Type of Rollback

 Detroit, MI

 Birmingham, AL

 Baltimore, MD
F-46

Table F-37. Multi-Factor Sensitivity Analysis: Impact of Using a Log-Linear vs. a Log-Log Model, Estimating Incidence Down to the Low Measured Level (LML) in the Study vs. PRB, and Using a Proportional vs. a Hybrid Rollback to Estimate the Incidence of All Ca

 and Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2005 PM2.5 Concentrations1

Modeling Choices:

Fixed Effects (FE) Log-linear vs. Random Effects (RE) log-log model FE Log-Linear FE Log-Linear FE Log-Linear FE Log-Linear RE Log-Log RE Log-Log RE 

Down to LML (5.8 ug/m3) vs. PRB LML LML PRB PRB LML LML 

Proportional vs. hybrid rollback Proportional Hybrid Proportional Hybrid Proportional Hybrid Pro

1342 1675 2845 3169 3360 3953 

(854 - 1827) (1066 - 2276) (1819 - 3853) (2027 - 4286) (2075 - 4615) (2446 - 5418) (87

Percent Difference: 3 --- 25% 112% 136% 150% 195% 

1249 1545 2548 2813 2535 2947 

(1017 - 1477) (1261 - 1824) (2095 - 2983) (2318 - 3288) (1793 - 3232) (2095 - 3738) (64

Percent Difference: --- 24% 104% 125% 103% 136% 

584 859 1254 

(372 - 792) (550 - 1161) (779 - 1713) (25

Percent Difference: --- --- 47% --- 115% --- 

369 591 639 

(303 - 434) (489 - 688) (458 - 803) (12

Percent Difference --- --- 60% --- 73% --- 

Incidence of Mortality Associated with Long-Term Exposure to PM2.5 Concentration

Los Angeles, CA

 All Cause Mortality Ischemic Heart Disease Mortality

2Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000. The fixed effects log-linear estimates are from Table 33, using models with 44 individual and 7 eco

effects log-log estimates are from Table 11, "MSA and DIFF" rows. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible inte

uncertainty surrounding the PM coefficient.

3 Percent differences are calculated relative to the model selections used in the core analysis (fixed effects log-linear model; LML, and proportional rollbacks). So, for example, th

all cause mortality in Los Angeles resulting from changing from the core analysis input selections to instead using (1) a fixed effects log-linear model, (2) PRB, and (3) hybrid rollb

136%. 

4 Philadelphia was not among the risk assessment urban areas for which hybrid rollbacks were calculated.

Philadelphia, PA

 All Cause Mortality Ischemic Heart Disease Mortality

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

---

--- ---

--- 

---

4

---
F-47

Table F-38. Multi-Factor Sensitivity Analysis: Impact of Using a Log-Linear vs. a Log-Log Model, Estimating Incidence Down to the Low Measured Level (LML) in the Study vs. PRB, and Using a Proportional vs. a Hybrid Rollback to Estimate the Incidence of All Ca

 and Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2006 PM2.5 Concentrations1

Modeling Choices:

Fixed Effects (FE) Log-linear vs. Random Effects (RE) log-log model FE Log-Linear FE Log-Linear FE Log-Linear FE Log-Linear RE Log-Log RE Log-Log RE 

Down to LML (5.8 ug/m3) vs. PRB LML LML PRB PRB LML LML 

Proportional vs. hybrid rollback Proportional Hybrid Proportional Hybrid Proportional Hybrid Pro

1108 1414 2627 2924 2904 3498 

(704 - 1509) (899 - 1923) (1678 - 3560) (1869 - 3959) (1790 - 3995) (2161 - 4803) (85

Percent Difference: 3 --- 28% 137% 164% 162% 216% 

1038 1314 2366 2614 2212 2633 

(843 - 1229) (1070 - 1553) (1943 - 2775) (2150 - 3060) (1558 - 2833) (1864 - 3354) (63

Percent Difference: --- 27% 128% 152% 113% 154% 

525 912 1166 

(335 - 713) (585 - 1233) (723 - 1595) (25

Percent Difference: --- --- 74% --- 122% --- 

334 559 598 

(273 - 393) (461 - 651) (428 - 755) (12

Percent Difference --- --- 67% --- 79% --- 

Incidence of Mortality Associated with Long-Term Exposure to PM2.5 Concentration

Los Angeles, CA

 All Cause Mortality Ischemic Heart Disease Mortality

Philadelphia, PA

 All Cause Mortality Ischemic Heart Disease Mortality

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

--- 

4

2Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000. The fixed effects log-linear estimates are from Table 33, using models with 44 individual and 7 eco

effects log-log estimates are from Table 11, "MSA and DIFF" rows. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible inte

uncertainty surrounding the PM coefficient.

3 Percent differences are calculated relative to the model selections used in the core analysis (fixed effects log-linear model; LML, and proportional rollbacks). So, for example, th

all cause mortality in Los Angeles resulting from changing from the core analysis input selections to instead using (1) a fixed effects log-linear model, (2) PRB, and (3) hybrid rollb

164%. 

4 Philadelphia was not among the risk assessment urban areas for which hybrid rollbacks were calculated.

------

---

--- ---
F-48

Table F-39. Multi-Factor Sensitivity Analysis: Impact of Using a Log-Linear vs. a Log-Log Model, Estimating Incidence Down to the Low Measured Level (LML) in the Study vs. PRB, and Using a Proportional vs. a Hybrid Rollback to Estimate the Incidence of All Ca

 and Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2007 PM2.5 Concentrations1

Modeling Choices:

Fixed Effects (FE) Log-linear vs. Random Effects (RE) log-log model FE Log-Linear FE Log-Linear FE Log-Linear FE Log-Linear RE Log-Log RE Log-Log RE 

Down to LML (5.8 ug/m3) vs. PRB LML LML PRB PRB LML LML 

Proportional vs. hybrid rollback Proportional Hybrid Proportional Hybrid Proportional Hybrid Pro

1170 1484 2697 3003 3034 3633 

(744 - 1593) (944 - 2019) (1723 - 3654) (1920 - 4064) (1871 - 4173) (2245 - 4986) (86

Percent Difference: 3 --- 27% 131% 157% 159% 211% 

1094 1377 2426 2680 2306 2728 

(890 - 1296) (1122 - 1627) (1993 - 2845) (2205 - 3136) (1626 - 2950) (1933 - 3472) (63

Percent Difference: --- 26% 122% 145% 111% 149% 

519 --- 4 907 --- 1157 --- 

(331 - 704) --- (581 - 1225) --- (718 - 1583) --- (24

Percent Difference: --- --- 75% --- 123% --- 

330 --- 555 --- 594 --- 

(270 - 389) --- (459 - 647) --- (424 - 750) --- (12

Percent Difference --- --- 68% --- 80% --- 

Los Angeles, CA

Philadelphia, PA

3 Percent differences are calculated relative to the model selections used in the core analysis (fixed effects log-linear model; LML, and proportional rollbacks). So, for example, th

all cause mortality in Los Angeles resulting from changing from the core analysis input selections to instead using (1) a fixed effects log-linear model, (2) PRB, and (3) hybrid rollb

157%. 

Incidence of Mortality Associated with Long-Term Exposure to PM2.5 Concentration

 All Cause Mortality Ischemic Heart Disease Mortality

4 Philadelphia was not among the risk assessment urban areas for which hybrid rollbacks were calculated.2Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000. The fixed effects log-linear estimates are from Table 33, using models with 44 individual and 7 ec

effects log-log estimates are from Table 11, "MSA and DIFF" rows. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible inteuncertainty surrounding the PM coefficient.

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 All Cause Mortality Ischemic Heart Disease Mortality
F-49

Table F-40. Sensitivity Analysis: Impact of Using Season-Specific vs. Annual Concentration-Response Functions and 

 Proportional vs. Hybrid Rollbacks to Estimate the Incidence of Non-Accidental Mortality Associated with Short- Term Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Conc

Modeling Choices:

Seasonal C-R Functions vs. an All-Year Function All Year All Year Sum of Four Seasons Sum oProportional vs. Hybrid Rollback Proportional Hybrid Proportional 

256 254 222 

(104 - 406) (103 - 402) --- 4 

Percent Difference 3 --- -1% -13% 

34 39 70 

(-53 - 121) (-61 - 137) --- 

Percent Difference --- 15% 106% 

147 151 159 

(-26 - 317) (-26 - 325) --- 

Percent Difference --- 3% 8% 

81 91 -23 

(-117 - 278) (-130 - 311) --- 

Percent Difference --- 12% -128% 

781 795 780 

(459 - 1102) (467 - 1121) --- 

Percent Difference --- 2% 0% 

159 163 175 

(47 - 270) (48 - 277) --- 

Percent Difference --- 3% 10% 

260 271 251 

(75 - 443) (78 - 462) --- 

Percent Difference --- 4% -3% 

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appro

means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty scoefficient. 

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 3 Percent differences are calculated relative to the model selections used in the core analysis (all-year C-R function and proportional rollback). So, for example, 

difference in estimated non-accidental mortality in Baltimore resulting from changing from the core analysis input selections to instead using the sum of four seaestimates and hybrid rollbacks is (192 - 225)/225 = -15%. 

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix 

specific coefficient estimators was not available.

 Pittsburgh, PA St. Louis, MO Los Angeles, CA New York, NY Birmingham, AL Detroit, MI

Estimated Incidence of Non-Accidental Mortality Associated with Short-Term Exp

Concentrations that Just Meet the Current Standards

 Baltimore, MD
F-50

Table F-41. Sensitivity Analysis: Impact of Using Season-Specific vs. Annual Concentration-Response Functions and 

 Proportional vs. Hybrid Rollbacks to Estimate the Incidence of Non-Accidental Mortality Associated with Short- Term Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Conc

Modeling Choices:

Seasonal C-R Functions vs. an All-Year Function All Year All Year Sum of Four Seasons Sum oProportional vs. Hybrid Rollback Proportional Hybrid Proportional 

224 222 193 

(91 - 356) (90 - 352) --- 4 

Percent Difference 3 --- -1% -14% 

33 37 69 

(-51 - 116) (-58 - 132) --- 

Percent Difference --- 12% 109% 

118 121 137 

(-21 - 255) (-21 - 261) --- 

Percent Difference --- 3% 16% 

75 84 -25 

(-108 - 257) (-120 - 287) --- 

Percent Difference --- 12% -133% 

671 682 677 

(394 - 946) (400 - 961) --- 

Percent Difference --- 2% 1% 

136 147 154 

(40 - 232) (43 - 249) --- 

Percent Difference --- 8% 13% 

215 224 211 

(62 - 367) (64 - 381) --- 

Percent Difference --- 4% -2% 

Estimated Incidence of Non-Accidental Mortality Associated with Short-Term Exp

Concentrations that Just Meet the Current Standards

 Baltimore, MD Birmingham, AL Detroit, MI Los Angeles, CA New York, NY Pittsburgh, PA St. Louis, MO

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appro

means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty sucoefficient. 

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 3 Percent differences are calculated relative to the model selections used in the core analysis (all-year C-R function and proportional rollback). So, for example, 

difference in estimated non-accidental mortality in Baltimore resulting from changing from the core analysis input selections to instead using the sum of four seaestimates and hybrid rollbacks is (192 - 225)/225 = -15%. 

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix 

specific coefficient estimators was not available.
F-51

Table F-42. Sensitivity Analysis: Impact of Using Season-Specific vs. Annual Concentration-Response Functions and 

 Proportional vs. Hybrid Rollbacks to Estimate the Incidence of Non-Accidental Mortality Associated with Short- Term Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Conc

Modeling Choices:

Seasonal C-R Functions vs. an All-Year Function All Year All Year Sum of Four Seasons Sum oProportional vs. Hybrid Rollback Proportional Hybrid Proportional 

227 224 207 

(92 - 360) (91 - 356) --- 4 

Percent Difference 3 --- -1% -9% 

34 39 95 

(-53 - 120) (-60 - 137) --- 

Percent Difference --- 15% 179% 

121 124 166 

(-21 - 262) (-22 - 269) --- 

Percent Difference --- 2% 37% 

77 86 -12 

(-110 - 262) (-123 - 293) --- 

Percent Difference --- 12% -116% 

734 746 887 

(431 - 1035) (438 - 1052) --- 

Percent Difference --- 2% 21% 

143 147 225 

(42 - 244) (43 - 250) --- 

Percent Difference --- 3% 57% 

225 236 248 

(65 - 384) (68 - 402) --- 

Percent Difference --- 5% 10% 

 St. Louis, MO New York, NY Pittsburgh, PA Detroit, MI Los Angeles, CA Birmingham, AL

Estimated Incidence of Non-Accidental Mortality Associated with Short-Term 

PM2.5 Concentrations that Just Meet the Current Standards

 Baltimore, MD

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appro

means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty scoefficient. 

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 3 Percent differences are calculated relative to the model selections used in the core analysis (all-year C-R function and proportional rollback). So, for example, 

difference in estimated non-accidental mortality in Baltimore resulting from changing from the core analysis input selections to instead using the sum of four seaestimates and hybrid rollbacks is (192 - 225)/225 = -15%. 

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix 

specific coefficient estimators was not available.
F-52

Table F-43. Sensitivity Analysis: Impact of Copollutant Models in Estimating the Incidence of All Cause 

 Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the 

 Current Standards, Based on Adjusting 2005 PM2.5 Concentrations1,2

Copollutant in Model Incidence Percent Difference 3

1122

(580 - 1713)

1632

(945 - 2341)

1954

(1034 - 2782)

1632

(945 - 2341)

295

(-515 - 1209)

489

(253 - 743)

708

(412 - 1012)

847

(451 - 1199)

708

(412 - 1012)

129

(-227 - 526)

Los Angeles, CA

CO

Philadelphia, PA

None

45%

None

CO

-74%

0%

3 Calculated as (estimate with copollutant - estimate without copollutant)/(estimate without copollutant).

1

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

SO2 -74%

45%

74%

45%

2 Estimates based on Krewski et al. (2000) [reanalysis of the ACS study]. Mortality incidence was estimated for PM2.5 concentrations down to 

5.8 ug/m3 (the lowest measured level used for the analyses of long-term exposure). Numbers rounded to the nearest whole number. 

Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

73%

45%

NO2

O3

SO2

0%

NO2

O3
F-53

Table F-44. Sensitivity Analysis: Impact of Copollutant Models in Estimating the Incidence of All Cause 

 Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the 

 Current Standards, Based on Adjusting 2006 PM2.5 Concentrations1,2

Copollutant in Model Incidence Percent Difference 3

926

(478 - 1415)

1347

(780 - 1936)

1615

(853 - 2302)

1347

(780 - 1936)

243

(-424 - 998)

439

(228 - 669)

637

(370 - 911)

762

(405 - 1080)

637

(370 - 911)

116

(-203 - 473)

Los Angeles, CA

None 0%

CO 45%

NO2 74%

O3 45%

SO2 -74%

Philadelphia, PA

None 0%

CO 45%

NO2 74%

1

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

2 Estimates based on Krewski et al. (2000) [reanalysis of the ACS study]. Mortality incidence was estimated for PM2.5 concentrations down to 

5.8 ug/m3 (the lowest measured level used for the analyses of long-term exposure). 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 Calculated as (estimate with copollutant - estimate without copollutant)/(estimate without copollutant).

O3 45%

SO2 -74%
F-54

Table F-45. Sensitivity Analysis: Impact of Copollutant Models in Estimating the Incidence of All Cause 

 Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the 

 Current Standards, Based on Adjusting 2007 PM2.5 Concentrations1,2

Copollutant in Model Incidence Percent Difference 3

978

(505 - 1494)

1423

(824 - 2043)

1705

(901 - 2429)

1423

(824 - 2043)

257

(-448 - 1054)

434

(225 - 661)

630

(366 - 901)

753

(400 - 1068)

630

(366 - 901)

115

(-201 - 468) 1

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2 Estimates based on Krewski et al. (2000) [reanalysis of the ACS study]. Mortality incidence was estimated for PM2.5 concentrations down to 

5.8 ug/m3 (the lowest measured level used for the analyses of long-term exposure). 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 Calculated as (estimate with copollutant - estimate without copollutant)/(estimate without copollutant).

O3 45%

SO2 -74%

CO 45%

NO2 74%

SO2 -74%

Philadelphia, PA

None 0%

NO2 74%

O3 46%

Los Angeles, CA

None 0%

CO 46%
F-55

Table F-46. Sensitivity Analysis: Impact of Different Lag Models on Estimated Annual Incidence of Hosp Admissions Associated with Short-Term Exposure to Ambient PM2.5 Concentrations that Just 

 Current Standards, Based on Adjusting 2005 PM2.5 Concentrations 1,2

0-Day Lag 1-Day Lag 2-Day Lag 0-Day Lag 1-Day Lag 2

397 35 30 40 9 

(294 - 501) (-60 - 130) (-58 - 118) (-22 - 102) (-53 - 71) 

159 14 12 13 3 

(118 - 200) (-24 - 52) (-23 - 47) (-7 - 34) (-18 - 24) 

Risk Assessment 

Location

Cardiovascular Hospital Admissions Respiratory Hospital Admissi

2 Incidence estimates were calculated using the national concentration-response function estimates reported in Table 1 of Bell et al. (20

specific C-R function estimates were not available from this study.

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3.  Los Angeles, CA Philadelphia, PA
F-56

Table F-47. Sensitivity Analysis: Impact of Different Lag Models on Estimated Annual Incidence of Hosp Admissions Associated with Short-Term Exposure to Ambient PM2.5 Concentrations that Just 

 Current Standards, Based on Adjusting 2006 PM2.5 Concentrations 1,2

0-Day Lag 1-Day Lag 2-Day Lag 0-Day Lag 1-Day Lag 2

373 33 28 38 9 

(276 - 470) (-56 - 122) (-54 - 110) (-21 - 96) (-50 - 67) 

149 13 11 13 3 

(110 - 188) (-23 - 49) (-22 - 44) (-7 - 32) (-17 - 22) 

 Los Angeles, CA Philadelphia, PA

2 Incidence estimates were calculated using the national concentration-response function estimates reported in Table 1 of Bell et al. (20

specific C-R function estimates were not available from this study.

Risk Assessment 

Location

Cardiovascular Hospital Admissions Respiratory Hospital Admissi

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
F-57

Table F-48. Sensitivity Analysis: Impact of Different Lag Models on Estimated Annual Incidence of Hosp Admissions Associated with Short-Term Exposure to Ambient PM2.5 Concentrations that Just 

 Current Standards, Based on Adjusting 2007 PM2.5 Concentrations 1,2

0-Day Lag 1-Day Lag 2-Day Lag 0-Day Lag 1-Day Lag 2

388 34 29 39 9 

(287 - 489) (-59 - 127) (-56 - 115) (-21 - 99) (-52 - 69) 

151 13 11 13 3 

(112 - 190) (-23 - 49) (-22 - 45) (-7 - 32) (-17 - 23) 

2 Incidence estimates were calculated using the national concentration-response function estimates reported in Table 1 of Bell et al. (20

specific C-R function estimates were not available from this study.

 Los Angeles, CA Philadelphia, PA

Risk Assessment 

Location

Cardiovascular Hospital Admissions Respiratory Hospital Admissi

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
F-58

Table F-49

Annual 24-Hr 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM 

Proportional 15.0 14.2 14.0 13.3 13.0 12.3 12.0 11.4 13.0 12.3 11.8 11.2 

Hybrid 3 --- --- --- --- --- --- --- --- --- --- --- --- Peak Shaving4 --- --- --- --- --- --- --- --- --- --- 13.6 11.31 Proportional 14.8 13.1 14.0 12.5 13.0 11.6 12.0 10.7 12.7 11.3 10.7 9.5 Hybrid 14.3 13.0 14.0 12.7 13.0 11.8 12.0 10.9 12.3 11.2 10.3 9.4 Peak Shaving 15.2 13.6 --- --- --- --- --- --- 13.0 11.9 10.8 9.8 Proportional 15.0 12.7 14.0 11.8 13.0 11.0 12.0 10.2 13.0 11.0 11.1 9.4 Hybrid 15.0 14.2 14.0 13.2 13.0 12.3 12.0 11.4 13.0 12.3 11.3 10.7 Peak Shaving --- --- --- --- --- --- --- --- --- --- 11.9 10.9 Proportional 12.8 11.4 12.8 11.4 12.8 11.4 12.0 10.7 12.8 11.4 12.0 10.7 Hybrid --- --- --- --- --- --- --- --- --- --- --- --- Peak Shaving --- --- --- --- --- --- --- --- --- --- --- --- Proportional 14.1 11.4 14.0 11.4 13.0 10.6 12.0 9.8 12.2 9.9 10.2 8.3 Hybrid 13.2 11.7 13.2 11.7 13.0 11.5 12.0 10.6 11.4 10.1 9.6 8.5 Peak Shaving 13.9 12.6 --- --- --- --- --- --- 11.9 10.8 9.8 8.9 Proportional 9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 8.6 8.6 7.3 7.3 Hybrid --- --- --- --- --- --- --- --- --- --- --- --- Peak Shaving 9.8 9.9 9.8 9.9 9.8 9.9 9.8 9.9 8.4 8.5 6.9 7.0 Proportional 15.0 12.5 14.0 11.7 13.0 10.9 12.0 10.1 13.0 10.9 12.0 10.1 Hybrid --- --- --- --- --- --- --- --- --- --- --- --- Peak Shaving --- --- --- --- --- --- --- --- --- --- --- --- Proportional 12.7 9.5 12.7 9.5 12.7 9.5 12.0 9.0 10.9 8.2 9.2 7.0 Hybrid 13.3 10.5 13.3 10.5 13.0 10.3 12.0 9.5 11.5 9.1 9.6 7.7 Peak Shaving 13.9 12.0 13.9 12.0 13.9 12.0 --- --- 11.8 10.4 9.8 8.8 Proportional 13.3 11.6 13.3 11.6 13.0 11.3 12.0 10.4 11.5 10.0 9.7 8.4 Hybrid 13.6 11.8 13.6 11.8 13.0 11.3 12.0 10.4 11.7 10.2 9.8 8.5 Peak Shaving 14.2 13.2 14.2 13.2 --- --- --- --- 12.1 11.5 10.1 9.5 Proportional 13.9 12.3 13.9 12.3 13.0 11.6 12.0 10.7 11.9 10.7 10.0 9.0 Hybrid --- --- --- --- --- --- --- --- --- --- --- --- Peak Shaving 15.5 12.9 15.5 12.9 --- --- --- --- 14.1 11.2 11.7 9.3 Proportional 12.6 9.9 12.6 9.9 12.6 9.9 12.0 9.4 11.8 9.3 9.9 7.8 Hybrid --- --- --- --- --- --- --- --- --- --- --- --- Peak Shaving --- --- --- --- --- --- --- --- --- --- 10.1 8.9 

 Phoenix, AZ 12.6 32.0 9.9 New York, NY 13.8 Philadelphia, PA 15.0 38.0 13.4

15.9 42.0

 Houston, TX 13.2 Los Angeles, CA 19.6 55.0 14.6

15.8 31.0

 Detroit, MI 13.9 Fresno, CA 17.4 63.0 17.4

17.2 43.0

 Birmingham, AL 15.7 Dallas, TX 12.8 26.0 11.4

18.7 44.0

 Atlanta, GA 15.3 Baltimore, MD 15.6 37.0 13.9

16.2 35.0

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 

Annual Average at Composite Monitor (2007CM) (in ug/m3)

15/35 2 14/35 13/35 12/35 13/30 12/25

 Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with copercent reduction in surrogate for long-term mortality risk across rollback methods)

Risk Assessment 

Location 1

Rollback 

Method

 Design Value

Recent 

Air 

Quality 

(2007)
F-59

Table F-49

Annual 24-Hr 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM 14

Proportional 13.3 11.6 13.3 11.6 12.8 11.2 11.8 10.5 11.5 10.0 9.7 8.4 0

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- -Peak Shaving 15.6 13.1 15.6 13.1 15.3 11.7 15.3 11.0 15.6 11.2 13.8 9.3 0Proportional 7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 6.7 6.6 5.7 5.6 0Hybrid --- --- --- --- --- --- --- --- --- --- --- --- -Peak Shaving 10.8 9.5 10.8 9.5 10.8 9.5 10.8 9.5 10.8 8.6 8.9 7.4 0Proportional 14.9 12.9 14.0 12.1 13.0 11.3 12.0 10.4 12.8 11.1 10.8 9.3 10Hybrid 15.0 13.5 14.0 12.6 13.0 11.7 12.0 10.8 13.0 11.7 11.0 9.9 12Peak Shaving 16.5 14.1 --- --- --- --- --- --- 14.1 12.3 11.7 10.2 -Proportional 8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 7.4 7.0 6.3 6.0 0Hybrid --- --- --- --- --- --- --- --- --- --- --- --- -Peak Shaving 8.3 7.8 8.3 7.8 8.3 7.8 8.3 7.8 7.1 6.7 5.9 5.5 0

 (cont'd) Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (of percent reduction in surrogate for long-term mortality risk across rollback methods)

6 Percent reduction in composite monitor value (CMV) with consideration for LML of 5.8 ug/m3. Percent reduction = (CMVcurrent standard - CMValternative standard)/(CMVcurrernt s

that greyed cells identify instances where percent change differs by >10% across alternative rollback methods (for a given alternative standard level/study area combina

Pe

lo

(

1For some locations (e.g., Atlanta) more than one "version" (group of counties) was used in the risk assessment. In this table only the version that was used for mortali

short-term exposure to PM2.5 (Zanobetti and Schwartz, 2009) is included. 

2 The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 3 The hybrid rollback method was applied to only a subset of the risk assessment locations. The "---" for a given location indicates that the hybrid rollback method was 

Risk Assessment 

Location 1

Rollback 

Method

 Design Value

Recent 

Air 

Quality 

(2007)

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 

Annual Average at Composite Monitor (2007CM) (in ug/m3)

15/35 2 14/35 13/35 12/35 13/30 12/25

 Pittsburgh, PA 5 19.8 60.0 14.9

 Salt Lake City, UT 11.4 St. Louis, MO 16.5 39.0 14.3

11.6 55.0

 Tacoma, WA 9.7

4 The peak shaving method was applied to a location-standard combination only if the daily standard was controlling in that location. The "--" for a given location-standa

indicates that, for that set of annual and daily standards in that location, the annual standard was controlling and so the peak shaving method was not applied. 

5 The proportional rollback and peak shaving methods were applied to Pittsburgh differently from the way they were applied in the other locations. See Sections 3.2.3.2 

10.2 43.0
F-60

Table F-50. 

Annual 24-Hr 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM 15/35 

Proportional 15.0 14.2 14.0 13.3 13.0 12.3 12.0 11.4 13.0 12.3 11.8 11.2

Hybrid 3 --- --- --- --- --- --- --- --- --- --- --- --- --- 

Peak Shaving4 --- --- --- --- --- --- --- --- --- --- 13.6 11.31 --- Proportional 14.8 13.1 14.0 12.5 13.0 11.6 12.0 10.7 12.7 11.3 10.7 9.5

Hybrid 14.3 13.0 14.0 12.7 13.0 11.8 12.0 10.9 12.3 11.2 10.3 9.4 -2% 

Peak Shaving 15.2 13.6 --- --- --- --- --- --- 13.0 11.9 10.8 9.8 6% 

Proportional 15.0 12.7 14.0 11.8 13.0 11.0 12.0 10.2 13.0 11.0 11.1 9.4 

Hybrid 15.0 14.2 14.0 13.2 13.0 12.3 12.0 11.4 13.0 12.3 11.3 10.7 18% 

Peak Shaving --- --- --- --- --- --- --- --- --- --- 11.9 10.9 --- 

Proportional 12.8 11.4 12.8 11.4 12.8 11.4 12.0 10.7 12.8 11.4 12.0 10.7

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- 

Peak Shaving --- --- --- --- --- --- --- --- --- --- --- --- --- Proportional 14.1 11.4 14.0 11.4 13.0 10.6 12.0 9.8 12.2 9.9 10.2 8.3

Hybrid 13.2 11.7 13.2 11.7 13.0 11.5 12.0 10.6 11.4 10.1 9.6 8.5 4% 

Peak Shaving 13.9 12.6 --- --- --- --- --- --- 11.9 10.8 9.8 8.9 17% 

Proportional 9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 8.6 8.6 7.3 7.3

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- 

Peak Shaving 9.8 9.9 9.8 9.9 9.8 9.9 9.8 9.9 8.4 8.5 6.9 7.0 0% 

Proportional 15.0 12.5 14.0 11.7 13.0 10.9 12.0 10.1 13.0 10.9 12.0 10.1

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- 

Peak Shaving --- --- --- --- --- --- --- --- --- --- --- --- --- Proportional 12.7 9.5 12.7 9.5 12.7 9.5 12.0 9.0 10.9 8.2 9.2 7.0

Hybrid 13.3 10.5 13.3 10.5 13.0 10.3 12.0 9.5 11.5 9.1 9.6 7.7 21% 

Peak Shaving 13.9 12.0 13.9 12.0 13.9 12.0 --- --- 11.8 10.4 9.8 8.8 40% Proportional 13.3 11.6 13.3 11.6 13.0 11.3 12.0 10.4 11.5 10.0 9.7 8.4

Hybrid 13.6 11.8 13.6 11.8 13.0 11.3 12.0 10.4 11.7 10.2 9.8 8.5 3% 

Peak Shaving 14.2 13.2 14.2 13.2 --- --- --- --- 12.1 11.5 10.1 9.5 22% 

Proportional 13.9 12.3 13.9 12.3 13.0 11.6 12.0 10.7 11.9 10.7 10.0 9.0

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- 

Peak Shaving 15.5 12.9 15.5 12.9 --- --- --- --- 14.1 11.2 11.7 9.3 8% 

Proportional 12.6 9.9 12.6 9.9 12.6 9.9 12.0 9.4 11.8 9.3 9.9 7.8

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- 

Peak Shaving --- --- --- --- --- --- --- --- --- --- 10.1 8.9 --- Proportional 13.3 11.6 13.3 11.6 12.8 11.2 11.8 10.5 11.5 10.0 9.7 8.4

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- 

Peak Shaving 15.6 13.1 15.6 13.1 15.3 11.7 15.3 11.0 15.6 11.2 13.8 9.3 21% 

19.8 60.0

Risk 

Assessment 

Location 1

Rollback 

Method

 Design Value

Recent Air 

Quality 

(2007)

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 

Annual Average at Composite Monitor (2007CM) (in ug/m3)

15/35 2 14/35 13/35 12/35 13/30 12/25

 Atlanta, GA 15.3

 Baltimore, MD 15.6 37.0 13.9

16.2 35.0

 Birmingham, AL 15.7 Dallas, TX 12.8 26.0 11.4

18.7 44.0

 Detroit, MI 13.9 Fresno, CA 17.4 63.0 17.4

17.2 43.0

 Houston, TX 13.2 Los Angeles, CA 19.6 55.0 14.6

15.8 31.0

 New York, NY 13.8 Philadelphia, PA 15.0 38.0 13.4

15.9 42.0

 Phoenix, AZ 9.9 Pittsburgh, PA 5 14.9

12.6 32.0

Pe

mon

com

dif

Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with percent difsurrogate for long-term exposure-related mortality across rollback methods)
F-61

Table F-50. 

Annual 24-Hr 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM 15/35 

Proportional 7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 6.7 6.6 5.7 5.6

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- 

Peak Shaving 10.8 9.5 10.8 9.5 10.8 9.5 10.8 9.5 10.8 8.6 8.9 7.4 53% Proportional 14.9 12.9 14.0 12.1 13.0 11.3 12.0 10.4 12.8 11.1 10.8 9.3

Hybrid 15.0 13.5 14.0 12.6 13.0 11.7 12.0 10.8 13.0 11.7 11.0 9.9 8% 

Peak Shaving 16.5 14.1 --- --- --- --- --- --- 14.1 12.3 11.7 10.2 15% 

Proportional 8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 7.4 7.0 6.3 6.0

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- 

Peak Shaving 8.3 7.8 8.3 7.8 8.3 7.8 8.3 7.8 7.1 6.7 5.9 5.5 -9% 

13/35 12/35

11.6 55.0

13/30 12/25

(cont'd) Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with pedifference in surrogate for long-term exposure-related mortality across rollback methods)

Risk 

Assessment 

Location 1

Rollback 

Method

 Design Value

Recent Air 

Quality 

(2007)

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 

Annual Average at Composite Monitor (2007CM) (in ug/m3)

15/35 2 14/35

Pe

mon

com

di

2 The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

3 The hybrid rollback method was applied to only a subset of the risk assessment locations. The "---" for a given location indicates that the hybrid rollback method was not alocation.4 The peak shaving method was applied to a location-standard combination only if the daily standard was controlling in that location. The "--" for a given location-standard c

indicates that, for that set of annual and daily standards in that location, the annual standard was controlling and so the peak shaving method was not applied. 

 Tacoma, WA 9.7

 Salt Lake City, UT 11.4 St. Louis, MO 14.3

6 Percent reduction in composite monitor value (CMV) with consideration for LML of 5.8 ug/m3. Percent reduction = (CMVpeak shaving or hybrid - CMVproportional)/(CMVpeak shaving or h

that greyed cells identify instances where two values differ by >25% across alternative rollback methods (for a given alternative standard level/study area combination). 

1For some locations (e.g., Atlanta) more than one "version" (group of counties) was used in the risk assessment. In this table only the version that was used for mortality as

short-term exposure to PM2.5 (Zanobetti and Schwartz, 2009) is included. 

5 The proportional rollback and peak shaving methods were applied to Pittsburgh differently from the way they were applied in the other locations. See Sections 3.2.3.2 and 

details. 

16.510.2

39.043.0
G-1

APPENDIX G: SUPPLEMEMNT TO THE NATIONAL-SCALE 

ASSESSMENT OF LONG-TERM MORTALITY RELATED TO PM2.5 

EXPOSURE
G-2

1 Appendix G. National-Scale Assessment of Long-Term Mortality Related to PM2.5

2 Exposure (additional technical detail regarding inputs used in the analysis) 

3 

4 

5 This technical appendix includes additional details regarding the inputs to the national6 scale current conditions health impact analysis. Below we present air quality modeling, exposure 

7 and risk information. 

8 

9 Air Quality Modeled Inputs 

10 The Community Model for Air Quality (CMAQ) model was used to estimate annual 

11 PM2.5 concentrations for the year 2005 for the continental US. These data were then combined 

12 with ambient monitored PM2.5 measurements to create “fused” spatial surfaces supplied to 

13 BenMAP.

14 CMAQ Model Application and Evaluation 

15 CMAQ is a non-proprietary computer model that simulates the formation and fate of 

16 photochemical oxidants, including PM2.5 and ozone, for given input sets of meteorological 

17 conditions and emissions. This analysis employed a version of CMAQ based on the latest 

publicly released version (i.e. CMAQ version 4.72 18 ). 

19 Model Domain and Grid Resolution 

20 The CMAQ modeling analyses were performed for two domains covering the continental 

21 United States, as shown in Figure G-1. These domains consist of a horizontal grid of 36 km 

22 covering the entire continental US and a finer-scale 12-km grid covering the Eastern U.S. The 

23 model extends vertically from the surface to 100 millibars (approximately 15 km) using a sigma24 pressure coordinate system. The 36-km grid was used to establish the incoming air quality 

25 concentrations along the boundaries of the 12-km grids. Table G-1 provides some basic 

26 geographic information regarding the CMAQ domains. The 36-km and both 12-km CMAQ 

27 modeling domains were modeled for the entire year of 2005. All 365 model days were used in 

28 the annual average levels of PM2.5. 

29 

 

2

CMAQ version 4.7 was released on December 1, 2008. It is available from the Community Modeling and 

Analysis System (CMAS) at: http://www.cmascenter.org. 
G-3

1 Table G-1. Geographic Information for Modeling Domains 

CMAQ Modeling Configuration 

National Grid Eastern U.S. Fine Grid 

Map Projection Lambert Conformal Projection 

Grid Resolution 36 km 12 km 

Coordinate Center 97 W, 40 N 

True Latitudes 33 and 45 N 

Dimensions 148 x 112 x 24 279 x 240 x 24 

Vertical Extent 24 Layers: Surface to 100 mb level 

2 

3 

4 

5 Figure G-1. Map of the CMAQ Modeling Domain (Note, the black outer box denotes the 

6 36-km national modeling domain; the red inner box is the 12-km Eastern U.S. fine 

7 grid). 

8 
G-4

1 CMAQ Model Inputs 

2 Emissions:

3 The 2005 emissions inputs to CMAQ included five source sectors: a) Electric Generating 

4 Units (EGUs); b) Other Stationary Sources (Point and Nonpoint); c) Onroad and Nonroad 

5 Mobile Sources; d) Biogenic Emissions; and e) Fires. The fires portion of the inventory included 

6 emissions from wildfires and prescribed burning computed as hour-specific point sources. 

7 Electric Generating Units (EGUs)

8 Annual emissions estimates for EGUs for all National Emissions Inventory (NEI) air 

9 pollutants for 2005 were developed using data reported to the USEPA’s Clean Air Marketing 

10 Division’s (CAMD) Acid Rain database. The Acid Rain database contains hourly emissions for 

11 SO2 and NOx emissions plus hourly heat input amounts. These three values are reported to the 

12 database by the largest electric generating facilities, usually based upon Continuous Emissions 

13 Monitors (CEMs). For all pollutants except the directly monitored SO2 and NOx, the ratio of the 

14 Acid Rain heat input for 2005 to the Acid Rain heat input for 2002 was used as the adjusting 

15 ratio to estimate the 2005 emissions. 

16 Other Stationary Sources (Point and Nonpoint)

17 Emission estimates for other stationary sources including both point and nonpoint 

18 stationary sources were held constant at the level in Version 3 of the 2002 NEI. The only 

19 exception to this was that some information on plants that closed after 2002 was incorporated 

20 into the emissions modeled. Emissions for plants that closed were set to zero. U.S. EPA, 2008c 

21 provides complete documentation on the development of the 2002 NEI. 

22 Onroad and Nonroad Mobile Sources

23 Emission estimates for all pollutants were developed using EPA’s National Mobile 

24 Inventory Model (NMIM), which uses MOBILE6 to calculate onroad emission factors. A full 

25 VMT database at the county, roadway type, and vehicle type level of detail was developed from 

26 Federal Highway Administration (FHWA) information. However, state and local agencies had 

27 the opportunity to provide model inputs (vehicle populations, fuel characteristics, VMT, etc) for 

28 2002 and 2005. If the state or local area submitted 2005 VMT estimates, these data were used. 

29 However, if the state or local area only provided 2002 VMT estimates that were incorporated in 

30 the 2002 NEI, the 2002 NEI VMT data were grown to 2005 using growth factors developed from 
G-5

1 the FHWA data, and these grown VMT data replaced the baseline FHWA-based VMT data. 

2 Otherwise, the FHWA-based VMT data were used. 

3 Emission estimates for NONROAD model engines were developed using EPA’s National 

4 Mobile Inventory Model (NMIM), which incorporates NONROAD2005. Where states provided 

5 alternate nonroad inputs, these data replaced EPA default inputs, as described above. For more 

6 information on how NMIM is run, refer to the 2005 NEI documentation posted at 

7 ftp://ftp.epa.gov/EmisInventory/2005_nei/mobile/2005_mobile_nei_version_2_report.pdf. 

8 Fires

9 Fires in the 2005 emissions inventory were modeled with the same methodology as used 

10 for the 2002 NEI (U.S. EPA, 2008). However, as described in Raffuse et al., 2008, the wildland 

11 fire emission inventories for 2005 were produced using the BlueSky framework for the 

12 conterminous United States, which used the Satellite Mapping Automatic Reanalysis Tool for 

13 Fire Incident Reconciliation (SMARTFIRE) as the fire information source. SMARTFIRE is an 

14 algorithm and database system designed to reconcile these disparate fire information sources to 

15 produce daily fire location and size information (Sullivan et al., 2008). 

16 Biogenic Emissions

17 Biogenic emissions were computed for CMAQ based on 2005 meteorology data using the 

18 BEIS3.13 model (Schwede, et. al, 2005) from the Sparse Matrix Operator Kernel Emissions 

19 (SMOKE). The BEIS3.13 model creates gridded, hourly, model-species emissions from 

20 vegetation and soils. It estimates CO, VOC, and NOX emissions for the U.S., Mexico, and 

21 Canada. The inputs to BEIS include: 

22 • temperature data at 10 meters which were obtained from the CMAQ 

23 meteorological input files, and 

24 • land-use data from the Biogenic Emissions Landuse Database, version 3 

25 (BELD3), which provides data on the 230 vegetation classes at 1 km resolution over most 

26 of North America. 

27 Meteorological Input Data: 

28 The gridded meteorological input data for the entire year of 2005 were derived from 

29 simulations of the Pennsylvania State University / National Center for Atmospheric Research 

30 Mesoscale Model. This model, commonly referred to as MM5, is a limited-area, nonhydrostatic, 

31 terrain-following system that solves for the full set of physical and thermodynamic equations 
G-6

1 which govern atmospheric motions (Grell et al., 1994). Meteorological model input fields were 

2 prepared separately for both of the domains shown in Figure G-1 using MM5 version 3.7.4. The 

3 MM5 simulations were run on the same map projection as CMAQ. 

4 Both meteorological model runs were configured similarly. The selections for key MM5 

5 physics options are shown below: 

6  Pleim-Xiu PBL and land surface schemes 

7  Kain-Fritsh 2 cumulus parameterization 

8  Reisner 2 mixed phase moisture scheme 

9  RRTM longwave radiation scheme 

10  Dudhia shortwave radiation scheme 

11 

12 Three dimensional analysis nudging for temperature and moisture was applied above the 

13 boundary layer only. Analysis nudging for the wind field was applied above and below the 

boundary layer. The 36 km domain nudging weighting factors were 3.0 x 104 14 for wind fields and 

temperatures and 1.0 x 105 15 for moisture fields. The 12 km domain nudging weighting factors 

were 1.0 x 104

 for wind fields and temperatures and 1.0 x 105 16 for moisture fields. 

17 All model runs were conducted in 5.5 day segments with 12 hours of overlap for spin-up 

18 purposes. Both domains contained 34 vertical layers with an approximately 38 m deep surface 

19 layer and a 100 millibar top. The MM5 and CMAQ vertical structures are shown in Table G-2 

20 and do not vary by horizontal grid resolution. 

21 

22 

23 

24 Table G-2. Vertical Layer Structure for MM5 and CMAQ (heights are layer top). 

CMAQ 

Layers 

MM5 

Layers Sigma P 

Approximate 

Height (m)

Approximate 

Pressure (mb)

0 0 1 0 1000 

1 1 0.995 38 995 

2 2 0.99 77 991 

3 0.985 115 987 3 

4 0.98 154 982 

4 5 0.97 232 973 

5 6 0.96 310 964 

7 0.95 389 955 6 

8 0.94 469 946 

9 0.93 550 937 7 

10 0.92 631 928 

8 11 0.91 712 919 
G-7

CMAQ 

Layers 

MM5 

Layers Sigma P 

Approximate 

Height (m)

Approximate 

Pressure (mb)

12 0.9 794 910 

9 13 0.88 961 892 

10 14 0.86 1,130 874 

11 15 0.84 1,303 856 

12 16 0.82 1,478 838 

13 17 0.8 1,657 820 

14 18 0.77 1,930 793 

15 19 0.74 2,212 766 

16 20 0.7 2,600 730 

17 21 0.65 3,108 685 

18 22 0.6 3,644 640 

23 0.55 4,212 595 19 

24 0.5 4,816 550 

25 0.45 5,461 505 20 

26 0.4 6,153 460 

27 0.35 6,903 415 21 

28 0.3 7,720 370 

29 0.25 8,621 325 22 

30 0.2 9,625 280 

31 0.15 10,764 235 23 

32 0.1 12,085 190 

33 0.05 13,670 145 24 

34 0 15,674 100 

1 

2 The meteorological outputs from the MM5 sets were processed to create model-ready 

3 inputs for CMAQ using the Meteorology-Chemistry Interface Processor (MCIP), version 3.4, to 

4 derive the specific inputs to CMAQ. 

5 Before initiating the air quality simulations, it was important to identify the biases and 

6 errors associated with the meteorological modeling inputs. The 2005 MM5 model performance 

7 evaluations used an approach which included a combination of qualitative and quantitative 

8 analyses to assess the adequacy of the MM5 simulated fields. The qualitative aspects involved 

9 comparisons of the model-estimated synoptic patterns against observed patterns from historical 

10 weather chart archives. Additionally, the evaluations compared spatial patterns of monthly 

11 average rainfall and monthly maximum planetary boundary layer (PBL) heights. Qualitatively, 

12 the model fields closely matched the observed synoptic patterns, which is not unexpected given 

13 the use of nudging. The operational evaluation included statistical comparisons of 

14 model/observed pairs (e.g., mean normalized bias, mean normalized error, index of agreement, 

15 root mean square errors, etc.) for multiple meteorological parameters, including temperature, 

16 humidity, shortwave downward radiation, wind speed, and wind direction (Baker and Dolwick, 
G-8

1 2009a, Baker and Dolwick, 2009b). It was ultimately determined that the bias and error values 

2 associated with the 2005 meteorological data were generally within the range of past 

3 meteorological modeling results that have been used for air quality applications. 

4 Initial and Boundary Conditions: 

5 The lateral boundary and initial species concentrations are provided by a three6 dimensional global atmospheric chemistry model, the GEOS-CHEM model (Yantosca, 2004). 

7 The global GEOS-CHEM model simulates atmospheric chemical and physical processes driven 

8 by assimilated meteorological observations from the NASA’s Goddard Earth Observing System 

9 (GEOS). This model was run for 2002 with a grid resolution of 2.0 degrees x 2.5 degrees 

10 (latitude-longitude) and 24 vertical layers. The 2005 CMAQ 36km simulation used non-year 

11 specific GEOS-CHEM data, which was created by taking the median value for each month in 

12 each individual grid cell of the 2002 GEOS-CHEM data described above. The predictions were 

13 used to provide one-way dynamic boundary conditions and an initial concentration field for the 

14 CMAQ simulations. More information is available about the GEOS-CHEM model and other 

15 applications using this tool at: http://www-as.harvard.edu/chemistry/trop/geos. 

16 CMAQ Model Performance Evaluation 

17 An operational model performance evaluation for PM2.5 and its related speciated 

18 components was conducted for 2005 using state/local monitoring sites data in order to estimate 

19 the ability of the CMAQ modeling system to replicate the concentrations for the 12-km Eastern 

20 domain and 36-km domain in the west. The principal evaluation statistics used to evaluate 

21 CMAQ performance included two bias metrics, normalized mean bias and fractional bias; and 

22 two error metrics, normalized mean error and fractional error. For the 12-km Eastern domain, 

23 performance evaluation statistics were computed for the entire domain as well as its subregions. 

24 For the 36-km domain, evaluation focuses on the parts of the US not covered by the 12-km 

25 Eastern domain by computing performance evaluation statistics for the states included in the 

26 Western Regional Air Partnership (WRAP). 

27 The PM2.5 evaluation focuses on PM2.5 total mass and its components, including sulfate 

28 (SO4), nitrate (NO3), total nitrate (TNO3 = NO3 + HNO3), ammonium (NH4), elemental carbon 

29 (EC), and organic carbon (OC). PM2.5 ambient measurements for 2005 were obtained from the 

30 following networks for model evaluation: Speciation Trends Network (STN), Interagency 

31 Monitoring of PROtected Visual Environments (IMPROVE), and Clean Air Status and Trends 

32 Network (CASTNET). For PM2.5 species that are measured by more than one network, we 

33 calculated separate sets of statistics for each network. Table G-3 provides annual model 

34 performance statistics for PM2.5 and its component species. Based on the bias and error values 
G-9

1 associated with the 2005 CMAQ-modeled PM2.5 concentration data, it was determined that the 

2 annual average PM2.5 data were generally within the range of past modeling results used for air 

3 quality applications and are applicable to be used for this national-scale current conditions 

4 analysis. 

5 

6 Table G-3. CMAQ modeled performance evaluation statistics for PM2.5 for 2005. 

7 

CMAQ 2005 Annual 

No. of 

Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 11622 -2.2 39.1 -4.7 40.3 

Northeast 2795 4.2 41.3 3.4 39.5 

Midwest 2318 4.3 35.2 5.0 34.1 

Southeast 2960 -13.0 37.5 -15.9 41.1 

Central 2523 -2.2 43.1 -8.4 45.6 

STN 

36-km 

West 

WRAP 

3082 -35.1 50.7 -40.3 57.4 

12-km EUS 10534 -9.4 44.3 -13.8 48.6 

Northeast 2464 5.3 48.6 2.3 46.2 

Midwest 668 -4.6 38.2 -7.3 40.8 

Southeast 1963 -20.8 42.8 -25.9 51.3 

Central 2768 -10.5 42.8 -12.9 47.7 

PM2.5 Total 

Mass 

IMPROVE 

36-km 

West 

WRAP 

10,122 -21.0 56.0 -24.4 57.6 

12-km EUS 13317 -17.1 34.0 -13.5 37.0 

Northeast 3247 -13.7 32.4 -9.4 34.3 

Midwest 2495 -10.9 33.9 -4.4 34.9 

Southeast 3499 -19.2 32.8 -16.8 35.8 

Central 2944 -25.7 38.7 -23.1 43.5 

STN 

36-km 

West 

WRAP 

3450 -21.9 46.4 -15.0 46.5 

12-km EUS 10164 -21.8 36.4 -13.2 41.1 

Northeast 2393 -14.6 35.5 -6.6 38.6 

Midwest 622 -19.0 34.5 -9.4 36.7 

Southeast 1990 -25.2 35.9 -22.3 41.1 

Central 2640 -27.9 38.0 -22.0 42.4 

Sulfate 

IMPROVE 

36-km 

West 

WRAP 

9693 -5.2 45.2 9.6 47.6 
G-10

CMAQ 2005 Annual 

No. of 

Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 3170 -16.5 22.9 -15.6 26.0 

Northeast 786 -11.7 20.5 -9.8 22.6 

Midwest 615 -13.6 21.4 -11.2 22.2 

Southeast 1099 -18.4 22.9 -19.6 25.7 

Central 300 -29.4 32.5 -30.3 36.1 

CASTNet 

36-km 

West 

WRAP 

1112 -12.6 34.5 -3.2 36.7 

12-km EUS 12186 20.1 67.8 -10.1 76.3 

Northeast 3248 28.7 70.2 -3.7 74.1 

Midwest 2495 20.2 61.0 9.2 63.0 

Southeast 3499 23.5 84.0 -25.0 87.2 

Central 1812 8.1 60.2 -5.9 72.4 

STN 

36-km 

West 

WRAP 

15,533 15.2 79.3 -15.6 85.9 

12-km EUS 10157 30.1 85.2 -32.5 99.1 

Northeast 2388 67.0 108.9 0.5 93.4 

Midwest 622 14.0 67.9 -24.1 88.9 

Southeast 1990 37.4 104.6 -46.2 105.9 

Central 2640 17.3 70.8 -19.3 89.6 

Nitrate 

IMPROVE 

36-km 

West 

WRAP 

17,452 33.1 99.1 -41.9 109.9 

12-km EUS 3170 24.6 39.7 17.8 38.0 

Northeast 786 36.5 43.0 30.3 40.6 

Midwest 615 23.3 36.5 23.9 33.2 

Southeast 1099 23.6 42.2 12.8 40.5 

Central 300 10.6 35.5 5.0 35.0 

Total Nitrate 

(NO3+HNO3) 

CASTNet 

36-km 

West 

WRAP 

4065 37.7 51.9 24.2 45.1 

12-km EUS 13317 1.8 41.9 8.3 45.6 

Northeast 3247 7.1 42.9 18.9 45.7 

Midwest 2495 7.1 40.5 16.4 41.4 

Southeast 3499 -2.1 40.5 2.9 43.3 

Central 2944 -7.6 44.0 -4.0 51.4 

Ammonium 

STN 

36-km 

West 

WRAP 

16,680 8.1 47.2 12.8 48.9 
G-11

CMAQ 2005 Annual 

No. of 

Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 3170 2.2 35.4 3.1 36.5 

Northeast 786 9.2 38.1 13.3 36.6 

Midwest 615 10.9 35.3 14.8 33.7 

Southeast 1099 -9.2 33.3 -9.7 37.6 

Central 300 1.5 36.9 3.0 40.2 

CASTNet 

36-km 

West 

WRAP 

4065 12.8 39.6 13.0 40.1 

12-km EUS 13460 19.7 63.5 11.9 53.9 

Northeast 3230 20.8 61.9 14.6 52.0 

Midwest 2502 7.3 46.1 10.8 44.9 

Southeast 3495 10.2 60.2 3.0 50.6 

Central 3107 47.6 88.2 23.0 64.9 

STN 

36-km 

West 

WRAP 

16,700 2.6 56.7 2.6 55.0 

12-km EUS 10244 -29.0 49.7 -39.1 61.3 

Northeast 2341 -17.8 49.2 -25.6 57.7 

Midwest 696 -26.7 41.9 -39.6 55.7 

Southeast 1995 -45.6 53.3 -58.5 69.8 

Central 2626 -22.9 49.2 -31.3 56.8 

Elemental 

Carbon 

IMPROVE 

36-km 

West 

WRAP 

17,289 -16.6 53.4 -23.4 60.2 

12-km EUS 12118 -36.5 53.6 -40.6 66.5 

Northeast 3083 -29.1 53.1 -27.6 64.2 

Midwest 2385 -42.5 52.6 -41.7 65.3 

Southeast 3442 -42.6 53.5 -55.6 70.2 

Central 2164 -30.6 57.7 -39.6 66.5 

STN 

36-km 

West 

WRAP 

15,397 -41.2 56.1 -45.7 69.2 

12-km EUS 10210 -34.7 53.7 -53.0 70.0 

Northeast 2336 -21.0 52.2 -29.2 58.4 

Midwest 696 -41.3 47.6 -55.7 63.6 

Southeast 1993 -40.4 53.7 -64.0 74.2 

Central 2622 -34.1 52.8 -52.7 68.1 

Organic Carbon 

IMPROVE 

36-km 

West 

WRAP 

17,295 -22.5 57.5 -40.8 67.6 
G-12

1 

2 

3 “Fused” Spatial Surfaces

4 Spatial surfaces of the 2005 data were created by fusing CMAQ-modeled annual average 

5 PM2.5 concentrations with total PM2.5 data from STN, IMPROVE, and CASTNET monitoring 

6 sites for the two domains shown in Figure 1. We used the EPA’s Model Attainment Test 

7 Software (MATS) (Abt, 2009) which employees the Voronoi Neighbor Averaging (VNA) 

8 interpolation technique (Abt, 2008). This technique identifies the set of monitors that are nearest 

9 to the center of each grid cell, and then takes an inverse distance squared weighted average of the 

10 monitor concentrations. The “fused” spatial fields are calculated by adjusting the interpolated 

11 ambient data (in each grid cell) up or down by a multiplicative factor calculated as the ratio of 

12 the modeled concentration at the grid cell divided by the modeled concentration at the nearest 

13 neighbor monitor locations (weighted by distance). 

14 To create the spatial surfaces for use in BenMAP, the 2005 CMAQ-modeled annual 

15 average PM2.5 concentrations were “fused” with 2005 total PM2.5 ambient monitoring data from 

16 STN, IMPROVE, and CASTNET sites. This was done for both the 36km national domain and 

17 the 12km eastern US domain. The spatial surface of annual average PM2.5 air quality 

18 concentrations produced by this technique is shown in Figure G-2 for the continental U.S. Where 

19 available, the 12km spatial surface was used to supply BenMAP with annual average PM2.5 

20 concentrations. In the western part of the U.S., annual average PM2.5 concentrations were 

21 supplied from the 36km domain. 

22 
G-13

1 Figure G-2: 2005 Predicted Annual Mean PM2.5 Levels 

2 

3 

4 

5 Advantages and Limitations

6 As compared to using monitored data alone, an advantage of using the CMAQ model 

7 output for comparing with health outcomes is that it has the potential to provide more complete 

8 spatial and temporal coverage. In addition, “fusing” the CMAQ data with ambient monitoring 

9 data allows for an improvement over non-fused fields (Timin et al., 2009). Doing so allows for a 

10 combination of the advantages of both sets of data: better spatial coverage and more accurate air 

11 quality estimates. Of course, the more accurate the model estimates of PM2.5, the better the 

12 performance of the “fused” spatial fields. Therefore, it is important to use model outputs that 

13 have adequate PM2.5 performance. As discussed above, we believe that the 2005 CMAQ14 modeled PM2.5 concentration data showed adequate model performance to be used for this 

15 national-scale current conditions analysis. 

16 As with any model estimate of air quality, there are limitations. For example, the 

17 emissions and meteorological data used in CMAQ can each have large uncertainties, in particular 
G-14

1 for unusual emission or meteorological events. There are also uncertainties associated with the 

2 chemical transformation and fate process algorithms used in air quality models. For these 

3 reasons, CMAQ predicts best on longer time scale bases (e.g., synoptic, monthly, and annual 

4 scales). These limitations have led us to use modeled air quality estimates in this analysis that 

5 are “fused” with measured ambient data and averaged over an annual scale. 

6 Air Quality Estimates

7 Figures G-3 through G-6 below illustrate the spatial distribution of air quality impacts. 

8 Figure 1 illustrates the modeled 2005 PM2.5 air quality levels across the U.S. Figures 2 and 3 

9 display the PM2.5 air quality levels after being adjusted so that the maximum level is no higher 

10 than the LML reported in the Krewski et al. (2009) and Laden et al. (2006) studies. Figure G-4 

11 displays the PRB by region of the county.
G-15

Figure G-3: 2005 Predicted Annual Mean PM2.5 Levels 
G-16

Figure G-4: 2005 Predicted Annual Mean PM2.5 Levels Adjusted for LML of the 

Krewski et al. (2009) study 
G-17

Figure G-5: 2005 Predicted Annual Mean PM2.5 Levels Adjusted for LML of the 

Laden et al. (2006) study 
G-18

Figure G-6: PRB by Geographic Area in the U.S. 

Figure G-7 displays the distribution of grid cells at different baseline PM2.5 air quality 

levels. Figures G-8 through G-10 displays the distribution of grid cells according to the 

incremental change in PM2.5 air quality for each of three scenarios: current conditions to 10 

μg/m3, current conditions to 5.8 μg/m3 and current conditions to PRB. 
G-19

Figure G-7: The Number of Grid Cells at Each Level of PM2.5 Concentration in 

2005 Current Conditions Air Quality Modeling Run 
G-20

Figure G-8: The Number of CMAQ Grid Cells Experiencing an Incremental 

Change in Annual Mean PM2.5 (μg/m3) (Current Conditions – 10 

μg/m3) 
G-21

Figure G-9: The Number of CMAQ Grid Cells Experiencing an Incremental 

Change in Annual Mean PM2.5 (μg/m3) (Current Conditions – 5.8 

μg/m3) 
G-22

Figure G-10: The Number of CMAQ Grid Cells Experiencing an Incremental 

Change in Annual Mean PM2.5 (μg/m3) (Current Conditions – Policy 

Relevant Background) 

Figure G-11 displays the cumulative distribution of grid cells at each baseline 

concentration. Figures G-12 through G-14 display the cumulative distribution of grid cells 

experiencing an incremental air quality change. 
G-23

Figure G-11: Cumulative Distribution of Baseline PM2.5 Concentrations (μg/m3

) 
G-24

Figure G-12: Cumulative Distribution of PM2.5 (μg/m3

) Changes (Baseline – 10 

μg/m3

) 
G-25

Figure G-13: Cumulative Distribution of PM2.5 (μg/m3

) Changes (Baseline – 5.8 

μg/m3

) 
G-26

Figure G-14: Cumulative Distribution of PM2.5 (μg/m3

) (Baseline – Policy Relevant 

Background) 

Exposure Estimates

Below we provide additional details regarding the estimated exposure changes occurring 

as a result of each of the air quality changes assumed in each of the three health impact 

assessments: current conditions incremental to 10 μg/m3, 5.8 μg/m3 and PRB. Table G-4 

summarizes the population-weighted air quality change occurring among populations 30-99 (the 

age range considered in the ACS cohort) for each scenario. 

Population-weighted air quality change is the average per-person change in PM2.5. It is 

estimated by calculating the summation of the population in each grid cell multiplied against the 

change in annual mean PM2.5 concentration in that grid cell and then dividing by the total 

population. 
G-27

Table G-4. Estimated Change in Annual Mean Population-Weighted PM2.5 by 

Model Scenario 

Model scenario 

Population-weighted air quality change or 

baseline 

Current conditions to 10 μg/m3

 2.6 μg/m3

Current conditions to 5.8 μg/m3 6.3 μg/m3

Current conditions to PRB 11 μg/m3

Current conditions 12 μg/m3

 

Health Impact Estimates

Figure G-15 through G-17 illustrate the distribution of total mortality attributable to 

PM2.5 exposure for each of three scenarios: current conditions to 10 μg/m3

, 5.8 μg/m3 and PRB. 
G-28

Figure G-15: The Percentage of Total Mortality Attributable to PM2.5 Exposure: 

Baseline – 10 μg/m3
G-29

Figure G-16: The Percentage of Total Mortality Attributable to PM2.5 Exposure: 

Baseline – 5.8 μg/m3
G-30

Figure G-17: The Percentage of Total Mortality Attributable to PM2.5 Exposure: 

Baseline – Policy Relevant Background 

Figures G-18 through G-20 illustrate the cumulative distribution of total mortality 

attributable to PM2.5 exposure for each of three scenarios: current conditions to 10 μg/m3, 5.8 

μg/m3 and PRB. 
G-31

Figure G-18: The Cumulative Distribution of the Percentage of Total Mortality 

Attributable to PM2.5 Exposure: Baseline – 10 μg/m3
G-32

Figure G-19: The Cumulative Distribution of the Percentage of Total Mortality 

Attributable to PM2.5 Exposure: Baseline – 5.8 μg/m3
G-33

Figure G-20: The Cumulative Distribution of the Percentage of Total Mortality 

Attributable to PM2.5 Exposure: Baseline – Policy Relevant Background 
H-1

APPENDIX H: CONSIDERATION OF RISK ASSOCIATED WITH 

EXPOSURE TO THORACIC COARESE PM (PM10-2.5)
H-2

H.1 OVERVIEW 

This appendix discusses the issue of assessing public health risk associated with 

exposure to thoracic coarse PM (PM10-2.5). As mentioned in Section 2.6, due to 

limitations in available monitoring data characterizing ambient levels of PM10-2.5 in 

prospective urban study areas, together with limitations in the epidemiological study data 

available for deriving C-R functions for this PM size fraction, EPA staff has concluded 

that uncertainties in characterizing risk for PM10-2.5 are potentially significant enough at 

this time to limit the utility of those estimates in informing the review of the PM coarse 

standard level. Therefore, we have not conducted a PM10-2.5 risk assessment for this 

review; instead, we have included a summary of risk estimates for PM10-2.5 generated as 

part of the last PM NAAQS review completed in 2005.3

As part of our summarizing PM10-2.5 risk estimates from the last review below in 

section H.2, we have included a discussion of the limitations and uncertainties associated 

with those risk estimates which resulted in the decision by EPA not to use those risk 

estimates in recommending specific standard levels (USEPA, 2006 – Final Rule FR 

Notice, p. 61178). This discussion provides the basis for a more detailed discussion (in 

Section H.3) of our rationale for not conducting a PM10-2.5 risk assessment as part of the 

current review. Specifically, in Section H-3, we consider each of the limitations in the 

PM10-2.5 risk assessment from the last review and assess whether data available since the 

last review, including more recent ambient monitoring data and epidemiological study 

data, address these limitation. Our conclusion is that additional information on PM10-2.5

that has become available since the last review does not substantially reduce overall 

uncertainty associated with modeling risk for this PM size fraction, and consequently, we 

conclude that conducting a PM10-2.5 risk assessment is not supported at this time. 

H.2 SUMMARY OF PM10-2.5 RISK ESTIMATES GENERATED FOR 

 THE PREVIOUS REVIEW 

This section provides a brief overview of the approach used in completing the 

PM10-2.5 risk assessment for the previous review and provides a summary of key 

observations resulting from that assessment. Additional details on the risk estimates can 

 

3

 We note that inclusion in this appendix of a summary of the PM10-2.5 risk assessment completed 

for the previous review should not be construed as implying that overall conclusions regarding limitations 

and uncertainties in that risk assessment have changed. Conclusions reached in the last review, that PM10-

2.5 risk estimates should not be used in recommending specific standard levels, still holds. Rather, we have 

included a summary of the PM10-2.5 risk assessment completed for the last review in the interest of 

completeness. 
H-3

be found in the risk assessment report completed for the previous analysis (USEPA, 

2005). 

The PM10-2.5 risk assessment completed for the previous review is similar in 

design to the PM2.5 risk assessment, although the scope is significantly more limited, 

reflecting the more limited body of epidemiological evidence and air quality information 

available for PM10-2.5. The PM10-2.5 risk assessment assessed risk for populations in three 

urban study areas (Detroit, Seattle and St. Louis), with a set of short-term exposurerelated morbidity health endpoints being modeled, including: respiratory hospital 

admissions (for Detroit and Seattle), cardiovascular hospital admissions (for Detroit) and 

respiratory symptoms (for St. Louis). Selection of these three urban study areas reflected 

consideration of the locations included in epidemiological studies providing C-R 

functions, as well as availability of co-located PM10 and PM2.5 monitoring data used in 

deriving estimates of ambient PM10-2.5 levels for urban study areas. EPA staff noted in 

the last review that the locations used in the PM10-2.5 risk assessment were not 

representative of urban locations in the U.S. that experience the most significant elevated 

24-hour PM10-2.5 ambient concentrations. Thus, observations regarding risk reductions 

associated with alternative standards in these three urban areas may not be fully relevant 

to the areas expected to have the greatest health risks associated with peak daily ambient 

PM10-2.5 concentrations. This is a key limitation impacting the PM10-2.5 risk assessment 

and remains a primary concern in conducting a PM10-2.5 risk assessment (see below). 

In summarizing PM10-2.5 risk estimates from the last review, we focus here on risk 

estimates generated for the recent conditions air quality scenario.4 In the risk assessment, 

risk estimates are provided for Detroit for several categories of cardiovascular and 

respiratory-related hospital admissions and show point estimates ranging from about 2 to 

7% of cause-specific admissions being associated with “as is” short-term exposures to 

PM10-2.5. The point estimate for asthma hospital admissions associated with short-term 

PM10-2.5 exposures for Seattle, an area with lower PM10-2.5 ambient concentrations than 

either Detroit or St. Louis, is about 1%. Point estimates for lower respiratory symptoms 

and cough in St. Louis are about 12 and 15%, respectively. These estimates use 

estimated policy-relevant background as the cutpoint. 

The specific set of uncertainties that resulted in EPA staff concluding that the 

PM10-2.5 risk estimates should not be used in recommending specific standard levels 

include, but are not limited to, the following (see USEPA, 2005, PM SP, Section 5.4.4.2): 

 

4

 We have chosen not to discuss risk estimates generated for alternate standard levels here since 

uncertainty in those estimates would be even higher than for recent conditions estimates. 
H-4

 Concerns that the current PM10-2.5 levels measured at ambient monitoring sites 

during the study period for the risk assessment may be quite different from the 

levels used to characterize exposure in the original epidemiologic studies based 

on monitoring sites in different location, thus possibly over- or underestimating 

population risk levels; 

 Greater uncertainty about the reasonableness of the use of proportional rollback to 

simulate attainment of alternative PM10-2.5 daily standards in any urban area due 

to the limited availability of PM10-2.5 air quality data over time (this uncertainty 

only being relevant to risk estimates generated for the alternative standard levels); 

 Concerns that the locations used in the risk assessment are not representative of 

urban areas in the U.S. that experience the most significant 24-hour peak PM10-2.5 

concentrations, and thus, observations about relative risk reductions associated 

with alternative standards may not be relevant to the areas expected to have the 

greatest health risks associated with elevated ambient PM10-2.5 levels; and 

 Concerns about the much smaller health effects database that supplies the C-R 

relationships used in the risk assessment, compared to that available for PM2.5, 

which limits our ability to evaluate the robustness of the risk estimates for the 

same health endpoints across different locations. 

H.3 RATIONALE FOR THE DECISION NOT TO CONDUCT A PM10-2.5

 RISK ASSESSMENT AS PART OF THE CURRENT REVIEW 

 The decision not to conduct a PM10-2.5 risk assessment for the current 

review is based on consideration of key uncertainties identified in the last review and an 

assessment as to whether newly available information has significantly reduced those 

uncertainties. Each of the sources of uncertainty is addressed below: 

 Concerns that monitoring data that would be used in a PM10-2.5 risk assessment 

(i.e., for the period 2005-2007) would not match ambient monitoring data used in 

the underlying epidemiological studies providing C-R functions: While this is 

always a concern in conducting PM-related risk assessments, due to the potential 

for greater spatial heterogeneity in PM10-2.5 ambient levels (see final PM ISA, 

Sections 2.1.1.2 and 2.2.1, USEPA 2009b), the potential for discrepancies 

between the monitoring networks used in epidemiological studies providing C-R 

functions and the monitoring network used in the risk assessment introducing 

uncertainty is increased relative to PM2.5. That is, the potential for greater spatial 

variation in PM10-2.5 levels means that the particular mix of collocated monitors 

used in generating an exposure surrogate in epidemiological studies needs to be 

more closely matched to the monitoring network used in conducting the risk 

assessment if significant uncertainty is to be avoided. 

 Uncertainty in the prediction of ambient levels under current and alternative 

standard levels: This remains a significant factor introducing uncertainty into 

PM10-2.5 risk estimates generated for alternative standard levels, and continues to 

weigh against the use of these risk estimates in identifying alternative standard 
H-5

levels for consideration in this review. Not only is the monitoring network (i.e., 

co-located PM10 and PM2.5 monitors) available for characterizing PM10-2.5 levels 

in candidate urban study areas limited (see above), given the potential for greater 

spatial heterogeneity in PM10-2.5 levels (relative to PM2.5 levels), generating 

representative estimates of ambient air profiles for PM10-2.5 under alternative 

standard levels is substantially more challenging than for PM2.5. In particular, the 

use of proportional rollback as a means for conducting rollbacks would be subject 

to significant uncertainty given the greater potential for local-scale gradients in 

PM10-2.5 levels and the linkage of PM10-2.5 to local-scale sources. 

 Concerns that locations used in the risk assessment may not be representative of 

areas experiencing the most significant 24-hour peak PM10-2.5 concentrations 

(and consequently, may not capture locations with the highest risk): This concern 

still holds since the monitoring network available for characterizing PM10-2.5

levels in urban areas has not been significantly expanded (final PM ISA, Section 

3.5.1.2, ). Specifically, the final PM ISA states that: “Given the limited number 

of co-located low-volume FRM PM10 and FRM PM2.5 monitors, only a very 

limited investigation into the intra-urban spatial variability of PM10-2.5 was 

possible using AQS data. Of the 15 cities under investigation, only six (Atlanta, 

Boston, Chicago, Denver, New York and Phoenix) contained data sufficient for 

calculating PM10-2.5 according to the data completeness and monitor specification 

requirements discussed earlier.” As noted in the previous risk assessment, these 

urban study areas may not capture locations with the highest peak levels of PM10-

2.5 based on consideration of general patterns in PM10 and PM2.5 levels. 

 Concerns about the much smaller health effects database that supplies the C-R 

relationships (relative to PM2.5): While a number of epidemiological studies 

have been published since completion of the previous PM NAAQS review, 

including several large multi-city studies that inform consideration of the effects 

of short-term exposure to PM10-2.5, limitations in the available studies still result in 

uncertainty in specifying C-R functions for PM10-2.5. For example, while Peng et 

al. (2008) and Zanobetti and Schwartz (2009) both provide effect estimates for 

short-term exposure-related mortality (with consideration of copollutant 

confounding by PM2.5), both have specific limitations that impact their use in risk 

assessment. For example, Zanobetti and Schwartz (2009) derives estimates of 

PM10-2.5 by subtracting county-level PM10 and PM2.5 levels, rather than using 

collocated monitors. Given the significant spatial gradients associated with PM10-

2.5 relative to PM2.5, the use of this approach for assessing exposure introduces 

significant uncertainty (i.e., exposure measurement error). In the case of Peng et 

al. (2008), significant uncertainty results from the study not providing regional 

and/or seasonally-differentiated effects estimates that control for PM2.5. Given 

the potential for regional differences in the composition of PM10-2.5 which could 

impact risk estimates, combined with the potential for PM2.5 to vary regionally as 

a confounder for the effect of PM10-2.5, EPA staff believes that C-R functions with 

control for PM2.5 would ideally be available at the regional level. . 

 When considered together, the limitations outlined above resulted in EPA 

staff concluding that a quantitative PM10-2.5 risk assessment would not significantly 
H-6

enhance the review of the NAAQS for coarse-fraction PM. Specifically, these limitations 

would likely result in sufficient uncertainty in the resulting risk estimates to significantly 

limit their utility in informing policy-related questions, including the assessment of 

whether the current standard is protective of public health and characterization of the 

degree of additional public health protection potentially afforded by alternative standards. 

Because of the decision not to conduct a quantitative PM10-2.5 risk assessment, these 

questions will draw more heavily on the results of the evidence-based analysis to be 

discussed in the Policy Assessment. 

 

United States 

Environmental Protection 

Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Effects Division 

Research Triangle Park, NC 

Publication No. 

EPA-452/P-10-001

February, 2010