# 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. Environmenta l 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 bei ng 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 Caroli na 27711 (email: pekar.zachary@epa.gov).

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

U.S. Environmental Protection Agency (E PA) 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 th ose of the authors and do not necessarily

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

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

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

authors and not the instituti ons providing the raw data.

ACKNOWLEDGEMENTS

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

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

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

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

(Epidemiological – Epi - Study Specific Information On PM 2.5 

# ), 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

# Table of Contents 1

2

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

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

List of Acronyms/Abbreviations...............................................................................................vi ii 5

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

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

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

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

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

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

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

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

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

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

2.4 Alternative Suites of PM 2.5 

# Standards Evalutated .................................................... 2-9 16

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

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

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

3.1.2 Calculating PM 2.5 

# -Related Health Effects Incidence ...................................... 3-7 20

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Functions.......................................................................................................3-32 36

# February 2010 Draft – Do Not Quote or Cite ii 3.4 Baseline Health Effects Incidence Data .................................................................3-47 1

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

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

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

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

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

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

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

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

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

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

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

3.5.4.2 Sensitivity Analyses for Short-Te rm Exposure-Related Mortality and 13

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

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

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

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

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

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

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

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

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

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

4.3.1 Sensitivity Analysis Results to Iden tify Potentially Important Sources of 24

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

4.3.1.1 Single-factor Sensit ivity Analysis..........................................................4-35 26

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

4.3.2 Additional Set of Reasonable Risk Es timates to Inform Consideration of 28

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

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

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

4.5 Consideration of Design Values and Patterns of PM 2.5 

# Monitoring Data in 32

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

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

4.5.2 Patterns in PM 2.5 

# Monitoring Data................................................................4-71 35

# February 2010 Draft – Do Not Quote or Cite iii 5 National-Scale Assessment of Lo ng-Term Mortality Related to PM 2.5 

# 1

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

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

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

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

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

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

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

6 Integrative Discussion of Urba n Case Study Analysis of PM 2.5 

# -related Risks.........6-1 9

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

6.2 Interpreation of Urba n Study Area Results ..............................................................6-4 11

6.2.1 Simulation of Just Meeting the Current Suite of PM 2.5 

# Standards..................6-7 12

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

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

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

6.3 National Perspective on PM 2.5 

# -related Risks.......................................................... 6-16 16

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

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

19

Appendix A. Air Quality Assessment 20

Appendix B. Hybrid (Non-Propor tional) Rollback Approach 21

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

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

Appendix E. Risk Estimates (Core Analysis) 24

Appendix F. Sensitivity Analysis Results 25

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

PM 2.5 

# Exposure 27

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

(PM 10-2.5 

# ) 29

# February 2010 Draft – Do Not Quote or Cite iv 

# List of Tables 1

2

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

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

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

Table 3-3. EPA Design Values for Annual and \24-hour PM 2.5 

# Standards for the Period 2005- 6

2007.* ...................................................................................................................... 3-1 6 7

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

Table 3-5. Locations, Health Endpoints, and Short-Term Exposure Studies Included in the PM 2.5 

# 9

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

Table 3-6. Locations, Health Endpoints, and Long-Term Exposure Studies Included in the PM 2.5 

# 11

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Associated with Long- and Shor t-Term Exposure to Ambient PM 2.5 

# Concentrations 28

that Just Meet the Current Standards, Based on Adjusting 2007 PM 2.5 

# Concentrations. 29

1,2



# ............................................................................................................................... . 4-6 30

Table 4-2 Estimated Percent of Total Annual In cidence of Selected Mo rtality and Morbidity 31

Endpoints Associated with Long- and Short-Term Exposure to Ambient PM 2.5 

# 32

Concentrations that Just Meet the Cu rrent Standards, Based on Adjusting 2007 PM 2.5 

# 33

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

Table 4-3 Overview of Se nsitivity Analysis Results ...............................................................4-31 35

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

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

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

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

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

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

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

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

standard levels .........................................................................................................4-70 44

# February 2010 Draft – Do Not Quote or Cite v Table 5-1 Estimated PM 2.5 

# -related premature mortality associat ed with incremental air 1

quality differences between 2005 ambient mean PM 2.5 

# levels and lowest measured 2

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

# percentile 3

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

5

6

# February 2010 Draft – Do Not Quote or Cite vi 

# List of Figures 1

2

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

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

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

Figure 3-4 15 urban study areas in cluded in the risk assessment (i ncluding seven PM regions 6

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

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

and recent conditions relativ e to the current standard s) (Note: inset shows PM 2.5 

# 9

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

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

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

relative to the current standa rds) (Note: inset shows PM 2.5 

# related incidence and 13

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

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

relative to the current standa rds) (Note: inset shows PM 2.5 

# related incidence and 16

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

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

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

inset shows PM 2.5 

# related incidence and percent of total inci dence for CV under the 20

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

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

conditions relative to the current standards) (Note: inset shows PM 2.5 

# related 23

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

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

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

(alternative standards relative to the cu rrent standards) (Note: inset shows PM 2.5 

# 27

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

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

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

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

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

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

Figure 4-9 Comparison of distributi ons for key elements of the ri sk equation: total population. 4- 34

57 35

Figure 4-10 Comparison of dist ributions for key elements of the risk equation: 98 th 

# percentile 24- 36

hour average PM 2.5 

# .................................................................................................. 4-58 37

Figure 4-11Comparison of distributions for key elem ents of the risk equa tion: all use mortality 38

rate. 4-59 39

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

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

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

risk from PM 2.5 

# : long term average July temperature. ............................................4-61 43

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

risk from PM 2.5 

# : percent of population 65 and older..............................................4-62 45

# February 2010 Draft – Do Not Quote or Cite vii Figure 4-15Comparison of distributi ons for selected variables expected to influence the relative 1

risk from PM 2.5 

# : per capita annual personal income. .............................................4-63 2

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

risk from PM 2.5 

# : per capita annual personal income. .............................................4-64 4

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Figure 5-2 2005 fused surface baseline PM 2.5 

# concentrations .................................................... 5-4 19

Figure 5-3 Percentage of premature mortality attributable to PM 2.5 

# exposure at various 2005 20

annual average PM 2.5 

# levels* .................................................................................... 5-7 21

Figure 5-4 Cumulative distribut ion of county-level percen tage of total mortality attributable to 22

PM 2.5 

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

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

25

# February 2010 Draft – Do Not Quote or Cite viii 

# List of Acronyms/Abbreviations 1

2

A/C Air conditioning 3

ACS American Cancer Society 4

Act Clean Air Act 5

AMI Acute Myocardial Infarction 6

AQS EPA's Air Quality System 7

β Slope coefficient 8

BenMAP Benefits Mapping Analysis Program 9

BMI Body Mass Index 10

BRFSS Behavioral Risk F actor Surveillance System 11

CASAC Clean Air Scientific Advisory Committee 12

CAA Clean Air Act 13

CBSA Core-based Statistical Area 14

CDC Centers for Disease Control 15

CDF Cumulative Distribution Function 16

CFR Code of Federal Regulations 17

CHD Coronary Heart Disease 18

CMAQ Community Multiscale Air Quality 19

CO Carbon Monoxide 20

COPD Chronic Obstructive Pulmonary Disease 21

CPD Cardio-pulmonary Disease 22

C-R Concentration-response 23

CSA Consolidated Statistical Area 24

CV Cardiovascular 25

CVD Cardiovascular Disease 26

df Degrees of freedom 27

ED Emergency Department 28

ER Emergency Room 29

EPA United States Environmental Protection Agency 30

FACA Federal Advisory Committee Act 31

FIPS Federal Information Processing System 32

GAM Generalized additive model 33

GEOS-CHEM Goddard Earth Observing System-Chemical Model 34

GLMs Generalized linear model 35

# February 2010 Draft – Do Not Quote or Cite ix HA Hospital Admissions 1

HCUP Healthcare Cost and Utilization Project 2

HEI Health Effects Institute 3

HS High School 4

ICD International Classification of Diseases 5

IHD ischemic heart disease 6

INF Influence of uncertainty on risk estimates 7

IRP Integrated Review Plan 8

ISA Integrated Science Assessment Document 9

KB Knowledge Base 10

km Kilometer 11

K-S Kolmogorov-Smirnov 12

LML Lowest Measured Level 13

MCAPS Medicare Air Pollution Study 14

MSA Metropolitan Statistical Area 15

NA Not Applicable 16

NAAQS National Ambient Air Quality Standards 17

NCEA National Center for Environmental Assessment 18

NEI National Emissions Inventory 19

NCHS National Center for Health Statistics 20

NMMAPS National Morbidity, Mo rtality, and Air Pollution Study 21

NOx Nitrogen oxides 22

O 3 

# Ozone 23

OAQPS Office of Air Qual ity Planning and Standards 24

PA Policy Assessment Document 25

PM Particulate Matter 26

PM X 

# The legal definition for PM X 

# , as defined in the Code of Federal 27

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

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

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

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

Depending on the specific penetra tion curve specified, particles 32

larger than X μ m aerodynamic diameter are collected with an 33

efficiently than decreases rapidly for particles larger than X while 34

the collection efficiency for particles smaller than X increases 35

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

PM 10 

# Particles with a 50% upper cut-point of 10± 0.5 μ m aerodynamic 37

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

Federal Regulations. 39

# February 2010 Draft – Do Not Quote or Cite x 1

PM 2.5 

# Particles with a 50% upper cut-point of 2.5 μ m aerodynamic 2

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

Federal Regulations. 4

PM 10-2.5 

# Particles with a 50% upper cut-point of 10 μ m aerodynamic 5

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

diameter. 7

PRB Policy-Relevant Background 8

RA Risk Assessment Document 9

RR Relative risk 10

REA Risk and Exposure Assessment 11

SAB Science Advisory Board 12

SEDD State Emergency Department Databases 13

SID State Inpatient Database 14

SO 2 

# Sulfur Dioxide 15

SO

x 

# Sulfur Oxides 16

SES Socio-economic Status 17

TRIM Total Risk Integrated Methodology 18

TRIM.Risk Total Risk Integrated Me thodology - Risk Assessment component 19

UFP Ultrafine Particles 20

USDA U.S. Department of Agriculture 21

VNA Voronoi Neighbor Averaging 22

WHI Women's Health Initiative 23

WHO World Health Organization 24

ZCA Zip Code Area 25

# February 2010 1-1 Draft – Do Not Quote or Cite 

# 1 INTRODUCTION 1

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

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

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

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

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

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

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

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

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

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

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

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

appropriate. The Act also requires that an inde pendent scientific revi ew committee advise the 14

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

Scientific Advisory Committee (CASAC). 1 

# 16

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

exposures to fine and co arse particles using PM 2.5 

# and PM 10 

# , as indicators, respectively (71 FR 18

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

in 2006 EPA revised the level of the 24-hour PM 2.5 

# standard to 35 μ g/m 3 

# (calculated as a 3-year 20

average of the 98 th 

# percentile of 24-hour concentrations at each population-oriented monitor), 21

retained the leve l of the annual PM 2.5 

# annual standard at 15 μ g/m 3 

# (calculated as the 3-year 22

average of the weighted annual mean PM 2.5 

# concentrations from single or multiple community23

oriented monitors), and revi sed the form of the annual PM 2.5 

# standard by narrowing the 24

constraints on the optional use of spatial averaging. 2 

# With regard to the primary and secondary 25

standards for PM 10 

# , EPA retained the 24-hour PM 10 

# standard at 150 μ g/m 3 

# (not to be exceeded 26

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 committ ee. CASAC provides advice,

information and recommendations on the scientific and technical aspects of air qua lity 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/WebCommi tteesSubcommittees/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 more than once per year on average over 3 year s) and revoked the annual standard because 1

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

ambient levels of coarse partic les and health or welfare effect s. These standards were based 3

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

various adverse health endpoints. Secondary standards for PM 2.5 

# and PM 10 

# were revised to be 5

identical to the primary standards. 6

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

# The review process 7

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

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

Ambient Air Quality Standards for Particulate Matter(IRP; EPA, 2008a), outlined the science10

policy questions that frame this review, the proc ess and schedule for the re view, and descriptions 11

of the purpose, contents, and approach for deve loping the other key documents for this review. 4 

# 12

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

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

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

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

on reported concentration-respons e (C-R) relationships for PM-re lated morbidity and mortality 17

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

# 18

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

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

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

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

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

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

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

2010. 26

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

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

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

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

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 r eceived from CASAC and the public on th e 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 to rulemaking. The PA is intended to help “b ridge the gap” between th e Agency’s scientific 1

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

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

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

CASAC’s advice to the Agency and recommenda tions on any new standa rds or revisions to 5

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

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

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

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

1.1 BACKGROUND 10

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

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

to ambient PM 2.5 

# and PM 10-2.5 

# in a number of urban study areas selected to illustrate the public 13

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

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

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

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

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

drafts of the risk assessment. 19

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

included estimates of risks of mo rtality (total non-accidental, car diovascular, and respiratory), 21

morbidity (hospital admissions for cardiovascul ar and respiratory cau ses), and respiratory 22

symptoms (not requiring hospitali zation) associated with recent short-term (daily) ambient PM 2.5 

# 23

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

exposure to PM 2.5 

# in nine urban study areas. The quantitat ive risk assessment included estimates 25

of: (1) risks of mortality, morbidity, and sy mptoms associated with recent ambient PM 2.5 

# levels; 26

(2) risk reductions and remaini ng risks associated with just meeting the existing suite of PM 2.5 

# 27

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

meeting various alternative PM 2.5 

# standards. 29

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

was much more limited than the analyses conducted for fine particles. The PM 10-2.5 

# risk 31

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

endpoints related to short-term exposure to PM 10-2.5 

# : hospital admissions for cardiovascular and 33

respiratory causes and respiratory sympto ms. While one of the goals of the PM 10-2.5 

# risk 34

assessment was to provide estimates of the ri sk reductions associated with just meeting 35

# February 2010 1-4 Draft – Do Not Quote or Cite alternative PM 10-2.5 

# standards, OAQPS staff concluded that the nature and magnitude of the 1

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

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

p. 5-69). 4

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

recommendations to the Administra tor supporting revisions of the PM 2.5 

# primary standards. 6

These recommendations placed substantial relian ce on the results of the quantitative risk 7

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

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

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

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

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

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

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

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

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

comprehensive in scope than the previous asse ssment conducted for the review completed in 17

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

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

20

Such uncertainties generally related to a l ack of clear understanding of a number of 21

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

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

determined not to exist; issues related to sel ection of appropriate sta tistical models for the 24

analysis of the epidemiologic data; the ro le of potentially confounding and modifying 25

factors in the concentration -response relationships; issues related to simulating how PM 2.5 

# 26

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

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

would be differential reductions in the many components within PM 2.5 

# and, if so, whether 29

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

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

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

mix of fine particles generally present in the ambient air. (7 2 FR 61168, October 17, 33

2006). 34

35

As a result, the Administrator viewed that th e quantitative risk asse ssment provided supporting 36

evidence for the conclusion that there was a need to revise the PM 2.5 

# primary standards, but he 37

judged that the assessment did not provide an ap propriate basis to dete rmine the level of the 38

standards (72 FR 61168, October 17, 2006). 39

# February 2010 1-5 Draft – Do Not Quote or Cite In a letter to the EPA Administrator follo wing the issuance of the final rule, CASAC 1

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

was concerned that the Agency “did not accept our finding that the annual PM 2.5 

# standard was 3

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

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

EPA’s decision on the primary PM 2.5 

# standard, CASAC stated, “While there is uncertainty 6

associated with the risk assessment for the PM 2.5 

# standard, this very uncertainty suggests a need 7

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

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

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

primary PM NAAQS: (1) selecting the level of the annual primary PM 2.5 

# standard, (2) retaining 11

PM 10 

# as the indicator for coarse particles and retaining the level and form of the 24-hour PM 10 

# 12

standard, and (3) revoking the PM 10 

# annual standard. On judici al review, the D.C. Circuit 13

remanded the annual primary PM 2.5 

# NAAQS to EPA because the Agency failed to adequately 14

explain why the standard provi ded the requisite protection fr om both short- and long-term 15

exposures to fine particles in cluding protection for at-risk po pulations. The court upheld the 16

Agency’s use of the quantitative risk assessm ent to inform the decision to revise the PM 2.5 

# 17

standards but not to inform the selection of level. 6 

# The court also upheld the decision to retain 18

the 24-hour PM 10 

# standard and revoke the annual PM 10 

# standard. American Farm Bureau 19

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

1.2 CURRENT RISK ASSESSMENT: GOA LS AND PLANNE D APPROACH 21

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

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

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

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

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

including, where data are available, considera tion of impacts on at-ris k populations; (b) to 27

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

estimates to more clearly differentiate among altern ative suites of standard s, including potential 29

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

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

6 

## One petition for review addressed the issue of setting the secondary PM 2.5 

## standards identical to the primary

standards. On judicial review, the court remanded the secondary PM 2.5 

## 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 addition, this assessment includes nationwide estima tes of the potential magnitude of premature 1

mortality associated with long-term e xposure to recent levels of ambient PM 2.5 

# to more broadly 2

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

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

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

goals. 6

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

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

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

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

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

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

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

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

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

of policy options. 16

1.3 ORGANIZATION OF DOCUMENT 17

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

of the scope of the quantitative risk assessmen t, including a summary of the previous risk 19

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

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

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

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

case studies included in this analysis and the ap proaches used in addr essing variability and 24

uncertainty (Appendices A, B, a nd C provide supplemental inform ation regarding the data and 25

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

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

of the representativeness of re levant risk-related factors (A ppendix D provides supplemental 28

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

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

national-scale assessment of PM 2.5 

# -related long-term mortality risk s associated with recent air 31

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

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

assessments that draws on the results of the urba n area case studies, the uncertainty/variability 34

# February 2010 1-7 Draft – Do Not Quote or Cite characterization, and the national- scale analyses to inform our quantitative characterization of 1

PM-related risks to public health. 2

# February 2010 Draft – Do Not Quote or Cite 2-1 

# 2 SCOPE 1

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

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

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

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

recognized in that analysis. 6

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

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

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

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

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

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

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

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

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

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

included initial plans for quantitativ e risk and exposure assessments. 17

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

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

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

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

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

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

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

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

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

the second draft ISA). 7 

# Based on consideration of CASA C (Samet, 2009) and public comments 27

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

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

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

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

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

7 

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

prepared by the CASAC PM Panel.

# February 2010 Draft – Do Not Quote or Cite 2-2 analysis. Section 2.2 provides a summary of the in itial design of the risk assessment as outlined 1

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

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

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

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

PM 2.5 

# 24-hour and annual standards. 6

2.1 OVERVIEW OF RISK ASSESSMENT FROM LAST REVIEW 7

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

PM 2.5 

# -related risk and a much more limited treatment of PM 10-2.5 

# -related risk. That assessment 9

included estimates of risks of mo rtality (total non-accidental, car diovascular, and respiratory), 10

morbidity (hospital admissions for cardiovascul ar and respiratory cau ses), and respiratory 11

symptoms (not requiring hospita lization) associated with sh ort-term (24-hour) exposure to 12

ambient PM 2.5 

# and risks of total, card iopulmonary, and lung cancer mortality associated with 13

long-term exposure to PM 2.5 

# in selected urban areas. Nine ur ban areas were selected across the 14

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

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

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

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

PM 2.5 

# risk estimates. Such uncertainties generall y related to a number of important factors, 19

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

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

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

factors in the C-R rela tionships; (d) methods for simulating how daily PM 2.5 

# ambient 23

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

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

the mix of ambient PM 2.5



# . 26

While some of these uncertainti es were addressed quantitativel y in the form of estimated 27

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

inputs were not reflected in these confidence rang es, but rather were addr essed through separate 29

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

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

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

a surrogate for actual ambient PM 2.5 

# exposures. The assessment in cluded a series of base case 33

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

potential population thresholds. Other uncertainties were addr essed in various sensitivity 35

# February 2010 Draft – Do Not Quote or Cite 2-3 analyses (e.g., the use of single- versus multi-po llutant models, use of single versus multi-city 1

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

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

These same sources of uncertainty and variabil ity were also applicab le to the quantitative 4

risk assessment conducted for PM 10-2.5 

# in the last review. However, the scope of the risk 5

assessment for PM 10-2.5 

# was much more limited than that for PM 2.5 

# reflecting the much more 6

limited body of epidemiological evidence and air quality information available for PM 10-2.5. 

# The 7

PM 10-2.5 

# risk assessment included risk estimates for just three urban areas for two categories of 8

health endpoints related to short-term exposure to PM 10-2.5 

# : hospital admissions for 9

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

PM 10-2.5 

# risk assessment was to provide estimates of the risk reductions associated with just 11

meeting alternative PM 10-2.5 

# standards, EPA staff concluded that the nature and magnitude of the 12

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

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

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

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

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

2.2 ORIGINAL ASSESSMENT PLAN 18

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

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

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

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

are briefly summarized below. 23

2.2.1 Risk Assessment 24

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

Methods Plan, included: 26

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

exposure to PM 2.5 

# with a much more limited assessment of PM 10-2.5 

# . Regarding PM 28

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

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

at this time. 31

 Selection of health effects categories (PM 2.5 

# ): We planned to focus primarily on 32

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

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

which evidence supports a judgment that there is a suggestive causal relationship 35

# February 2010 Draft – Do Not Quote or Cite 2-4 (e.g., reproductive, developmental outcomes), if sufficient information was available 1

to develop meaningful risk estimates for these additional categories. 2

 Selection of health effect categories (PM 10-2.5 

# ) : We planned to build on the limited 3

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

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

with short-term exposure to warrant analysis. 6

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

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

consideration of a number of factors (e.g., ava ilability of loca tion-specific C-R 9

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

risk-related attributes, covera ge for areas with more vulne rable populations). We also 11

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

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

code level analysis of long-term PM 2. 

# -exposure related mortality was presented in 14

Krewski et al., 2009). 15

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

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

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

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

historical patterns of reduc tions in urban areas, if these result in support for non20

proportional reductions across monitors and/or (b) model-based (e.g., CMAQ) 21

rollback designed to more realistically re flect patterns of PM reductions across 22

monitors in an urban area. 23

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

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

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

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

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

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

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

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

studies. 32

 Shape of the functional form of the risk model : We planned to emphasize non33

threshold C-R functions in the risk asse ssment model, based on the first draft ISA 34

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

mortality effects associated with either long-term or short-term PM 2.5 

# ambient 36

concentrations. 8 

# We also stated that we may cons ider population thresholds as part of 37

the sensitivity analysis. 38

8 

## In discussing short-term exposure mortality studies, the first draft ISA (U.S. EPA, 200 9a) 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  Modeling of risk down to PRB versus lowest measured level (LML) : We planned 1

to model risk down to LML for estimati ng risk associated with long-term PM 2.5 

# 2

exposures and down to PRB for estimating risks associated with short-term PM 2.5 

# 3

exposures. 4

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

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

covers key sources of variabili ty related to PM risk. Fo r uncertainty, we planned to 7

include a qualitative discussion of key s ources of uncertainty and provide ratings 8

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

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

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

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

estimates generated for the urban study areas. 13

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

assessment of mortality associated with long-term exposure to recent ambient PM 2.5 

# 15

levels. 16

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

analysis to evaluate the representativene ss of the selected urban study areas against 18

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

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

for a given attribute. 21

2.2.2 Population Exposure Analysis 22

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

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

planned analysis would have focused on PM 2.5 

# and have involved a subset of the urban study 25

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

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

available epidemiological studies. 28

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

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

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

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

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

potentially identifying sources of exposure measurement error associated with the 34

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

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

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

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

associated with designing and conducting such an assessment, we decided to continue methods 39

# February 2010 Draft – Do Not Quote or Cite 2-6 development work rather than attempt to comple te a preliminary population exposure analysis as 1

part of this review. Development of th e population exposure anal ysis methodology is ongoing, 2

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

review. 4

2.3 CURRENT SCOPE AND KEY DESIGN ELEMENTS 5

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

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

from the originally planned approach. 8

 PM size fractions : This quantitative risk assessme nt characterizes risk associated 9

with PM 2.5 

# -related exposures only. With regard to PM 10-2.5 

# , we have concluded that 10

continued limitations in data available for characterizing PM 10-2.5 

# exposure and risk 11

would introduce significant uncertainty into a PM 10-2.5 

# risk assessment such that the 12

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

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

last PM NAAQS risk assessment for not using the PM 10-2.5 

# risk estimates in 15

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

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

enhancements to the PM 10-2.5 

# monitoring network would fu ndamentally address these 18

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

the PM 10-2.5 

# monitoring network continue to ex ist such that a quantitative risk 20

assessment for PM 10-2.5 

# is not supported at this time (a more in-depth discussion of the 21

rationale behind the decision not to conduct a quantitative risk assessment for PM 10-2.5 

# 22

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

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

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

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

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

be presented in the draft PA.. 28

 Selection of health effects categories (PM 2.5 

# ): A multi-factor decision framework 29

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

assessment for PM 2.5 

# (section 3.3.1). This set of e ndpoints is consistent with those 31

outlined in the Scope and Methods Plan for PM 2.5 

# (i.e., all of the selected endpoints 32

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

relationship with PM 2.5 

# exposure), although selecting endpoints limited to these two 34

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

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

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

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

available to support selecti on of C-R functions for speci fic endpoints within these 39

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

CASAC members expressed diffe ring views as to the appropriateness of including 41

these categories. 42

# February 2010 Draft – Do Not Quote or Cite 2-7  Selection of urban study areas : We have included 15 urban study areas in the risk 1

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

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

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

(c) preference for locations with relatively elevated 24-hour and/or annual PM 2.5 

# 5

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

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

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

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

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

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

epidemiology studies using more highly-re fined exposure analys is (e.g., the study of 12

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

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

exposure in our discussion of uncertainty re lated to long-term mo rtality, since they 15

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

estimates used to m odel risks (section 3.5.3). 17

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

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

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

revised methods ensure that monitors c ontributing to a compos ite calculation in a 21

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

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

potentially leading to estima tes that were biased high. 24

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

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

first draft RA (and in the last risk assessment) to simulate PM 2.5 

# ambient levels that 27

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

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

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

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

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

area (section 3.2.3). 33

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

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

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

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

associated with both short-term and long-term PM 2.5 

# exposures, we have focused on 38

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

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

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

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

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

several single city studies to provide coverage for addi tional health effect endpoints 44

# February 2010 Draft – Do Not Quote or Cite 2-8 associated with short-term PM 2.5 

# exposures (e.g., emergency department visits). 1

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

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

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

particular includes rigorous examination of different model forms for estimating 5

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

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

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

below in section 3.3.3. 9

 Characterization of unc ertainty and variability : Our approach to characterizing 10

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

Characterizing and Communi cating Uncertainty In Exposure Assessment (WHO, 12

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

with long-term exposure to recent ambient PM 2.5 

# levels at the national scale, which 28

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

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

additional health endpoints or additional ai r quality scenarios that simulate just 31

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

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

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

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

conducted an analysis to evaluate the repr esentativeness of the selected urban study 36

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

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

the distribution for a given at tribute (section 4.4). 39

 Consideration of patterns in design values and ambient PM 2.5 

# monitoring data 40

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

examination of how 24-hour and annual design values, together with patterns in PM 2.5 

# 42

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

occur upon just meeting the current or alterna tive suites of standard s. This analysis 44

# February 2010 Draft – Do Not Quote or Cite 2-9 has resulted in a better unde rstanding of the factors behi nd specific patterns of risk 1

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

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

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

context. 5

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

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

PM NAAQS, we have added a new chapter 6: Integrative Discussion of PM 2.5 

# - 8

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

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

analysis of uncertainty, analyses of repres entativeness and patterns of design values, 11

and the national-scale mortality analysis. 12

2.4 ALTERNATIVE SUITES OF PM 2.5 

# STANDARDS EVALUTATED 13

In developing estimates of risks associated with just meeting alternative suites of PM 2.5 

# 14

standards, we selected alternative levels for the annual and 24-hour PM 2.5 

# standards during the 15

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

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

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

(24-hour and annual) and forms for the current suite of standards. 9 

# We note that all of the basic 19

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

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

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

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

In selecting alternative leve ls for the annual and 24-hour PM 2.5 

# standards for the purpose 24

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

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

PM 2.5 

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

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

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

compared to single-city studies including: (1) multi-city studies reflect ambient PM 2.5 

# levels and 30

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

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

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

9 

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

whether an area attains the standa rd. The form of the 24-hour PM 2.5 

## standard is the 98 th 

## percentile of the distribution

of 24-hour PM 2.5 

## concentrations at each population-oriented monitor within an area, averaged over 3 years. The

form of the annual PM 2.5 

## 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 Specifically, regarding alterna tive levels for the annual PM 2.5 

# standard to be evaluated in 1

this risk assessment, we first considered long-term average PM 2.5 

# concentrations associated with 2

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

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

mortality and long-term PM 2.5 

# exposure becomes more precise and consistently positive in 5

locations with mean PM 2.5 

# concentrations of 13.5 μg/m 3 

# and above. (EPA, 2009a, section 6

2.3.1.2). The second draft ISA also concluded that the strongest evidence for cardiovascular7

related effects related to long-term PM 2.5 

# exposures has been reported in large, multi-city U.S.- 8

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

reports associations between PM 2.5 

# and cardiovascular effects among post-menopausal women 10

with a mean annual average PM 2.5 

# concentration of 13.5 μg/m 3 

# (EPA, 2009a, section 2.3.1.2). In 11

addition, we evaluated long-term average PM 2.5 

# concentrations in short-term exposure studies 12

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

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

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

locations with mean PM 2.5 

# concentrations of 13 μg/m 3 

# and above (EPA, 2009a, section 2.3.1; 16

also see Figure 2-1). 10 

# 17

Based on the available epidemiological eviden ce indicating effects associated with a 18

range of annual averaged PM 2.5 

# concentrations, as br iefly described above, we selected levels of 19

12 and 13 μg/m 3 

# as the alternative annual standard levels to be evaluated in the quantitative risk 20

assessment. We have added 14 μg/m 3 

# to the set of annual levels evaluated in this second draft 21

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

of 15 μg/m 3 

# and the lowest level evaluated. 23

In identifying alternativ e levels for the 24-hour PM 2.5 

# standard to be evaluated in this risk 24

assessment, we considered the ambient PM 2.5 

# levels associated with mortality and morbidity 25

effects as reported in key s hort-term epidemiological studies. We focused on the 98 th 

# percentile 26

PM 2.5 

# ambient levels reported in two multi-city st udies that provided C-R functions used in the 27

core risk assessment, Zanobetti and Schwartz (2 009) and Bell et al. (2008). The focus on the 28

98 th 

# percentile of the 24-hour PM 2.5 

# concentrations observed in th e epidemiological studies is 29

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

current form of the 24-hour PM 2.5 

# standard. 31

10 

## We note that the association between long-term mean ambient PM 2.5 

## 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 PM 2.5 

## levels (in the underlying study counties) and the associated long-term mean PM 2.5 

## levels (i.e., the association between mean PM 2.5 

## 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 PM 2.5 

## levels).

# February 2010 Draft – Do Not Quote or Cite 2-11 The second draft ISA presented 98 th 

# percentile 24-hour PM 2.5 

# values for each of the 112 1

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

2009a, Figure 6-22). We evaluated th e trend in these county-level 98 th 

# percentile 24-hour PM 2.5 

# 3

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

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

significant effect estimates (i.e., higher 98 th 

# percentile PM 2.5 

# levels were consistently associated 6

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

lowest 98 th 

# percentile PM 2.5 

# level associated with the set of counties for which a statistically 8

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

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

observed mixed results with no clear correlation between 98 th 

# percentile air qua lity levels and 11

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

# 12

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

distribution as representative of a reasonably precautionary appro ach for identifying alternative 14

levels for consideration in the risk assessment. The 10 th 

# and 25 th 

# percentiles values were 25.5 15

and 29.8 μg/m 3 

# , respectively (Zanobetti, 2009) . We note that the overall 98 th 

# percentile value 16

across the entire set of urban areas analyzed in Zanobetti and Schwartz. (2009) was 34.3 μg/m 3 

# 17

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

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

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

distribution of 98 th 

# percentile values across the entire dataset resulted in identifying 10 th 

# and 25 th 

# 21

percentile values of about 24.4 and 29.3 μg/m 3 

# , respectively. We not e that the overall 98 th 

# 22

percentile value across the entire set of counties analyzed in Bell et al. (2008)) was 34.2 μg/m 3 

# 23

(EPA, 2009a, Table 6-11; Bell, 2009). 24

Based on the available epidemiological eviden ce indicating effects associated with a 25

range of 98 th 

# percentile 24-hour PM 2.5 

# concentrations, as briefly described above, we selected 26

levels of 25 and 30 μg/m 3 

# as the alternative 24-hour standard levels to be evaluated in this 27

quantitative risk assessment. 28

Once alternative levels were identified for the annual and 24- hour PM standards, we then 29

identified specific combinations of these standard levels to be considered in evaluating suites of 30

alternative standards in the risk assessment. In selecting the pairing of annual and 24-hour 31

standard levels, we considered which standard was likely to be controll ing across the set of 15 32

urban study areas (either the annua l or 24-hour standard will be th e “controlling standard” at a 33

# February 2010 Draft – Do Not Quote or Cite 2-12 given location, depending on the design valu e associated with that location). 11 

# For this risk 1

assessment, the goal was to select combinations of annual and 24-hour levels that would result in 2

a mixture of behavior in terms of which standa rds would control across the various urban study 3

areas. For example, with the 12/35 combinati on (i.e., an annual standard level of 12 μg/m 3 

# and a 4

24-hour standard level of 35 μg/m 3 

# ), the annual level of 12 μg/m 3 

# is the controlling standard for 5

all 15 urban study areas, while with the 12/25 comb ination, the annual standard is the controlling 6

standard at some locations and th e 24-hour standard is the control ling standard at other locations. 7

Consideration of these f actors resulted in a set of five alte rnative combinations of annual and 24- 8

hour standards being identified for in clusion in the risk assessment. 9

The full set of air quality scenarios included in the risk assessment, including the recent 10

conditions air quality scenario and current standard s scenario along with the five alternative sets 11

of standards are as follows: 12

 Recent conditions (risk estimates based on ambient PM 2.5 

# monitoring data for the 13

analysis period – 2005 to 2007) 14

 Current PM 2.5 

# NAAQS: annual 15 μg/m 3 

# ; 24-hour 35 μg/m 3 

# 15

 Alternative PM 2.5 

# standards: annual 14 μg/m 3 

# ; 24-hour 35 μg/m 3 

# 16

 Alternative PM 2.5 

# standards: annual 13 μg/m 3 

# ; 24-hour 35 μg/m 3 

# 17

 Alternative PM 2.5 

# standards: annual 12 μg/m 3 

# ; 24-hour 35 μg/m 3 

# 18

 Alternative PM 2.5 

# standards: annual 13 μg/m 3 

# ; 24-hour 30 μg/m 3 

# 19

 Alternative PM 2.5 

# standards: annual 12 μg/m 3 

# ; 24-hour 25 μg/m 3



# . 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 a dditional detail on the issue of controlling standards.

# February 2010 Draft – Do Not Quote or Cite 3-1 

# 3 URBAN CASE STUDY ANALYSIS METHODS 1

This chapter provides an overview of the met hods used in the risk assessment. Section 2

3.1 discusses the basic structure of the risk a ssessment, identifying the modeling elements and 3

related sources of input data needed for the analysis. S ection 3.2 discusses air quality 4

considerations. Section 3.3 disc usses the selection of health endpoints, urban study areas and C5

R functions from key epidemiological studies us ed in modeling those endpoints. Section 3.4 6

discusses baseline h ealth effects incidence ra tes. Finally, section 3.5 describes how uncertainty 7

and variability are addressed in the risk assessment. 8

3.1 GENERAL APPROACH 9

3.1.1 Basic Structure of the Risk Assessment 10

The general approach used in both the prior and the curren t PM risk assessment relies 11

upon C-R functions which have been estimated in epidemiological studies. Since these studies 12

estimate C-R functions using ambient air quality data from fixed-site, population-oriented 13

monitors, the appropriate appli cation of these functions in a PM risk assessment similarly 14

requires the use of ambient air quality data at fixed-site, population-oriented monitors. 15

The general PM health risk model, illustrat ed in Figure 3-1, combines information about 16

PM air quality for specific urban areas with C-R functions derived from epidemiological studies, 17

baseline health incidence data for specific hea lth endpoints, and populatio n estimates to derive 18

estimates of the annual incidence of specified health effects attribut able to ambient PM 19

concentrations under different air quality scenar ios. This assessment was implemented within 20

TRIM.Risk, the component of EPA’s Total Risk Integrated Methodolog y (TRIM) model that 21

estimates human health risks. 12 

# 22

The analyses conducted for this review focuse d on estimating risks associated with recent 23

PM 2.5 

# air quality and estimating cha nges in these risks associated with air quality simulated to 24

reflect just meeting the current suite of PM 2.5 

# ambient standards, as well as any additional 25

reductions in incidence estimated to occu r upon just meeting alternative suites of PM 2.5 

# 26

standards. 27

Consistent with past risk assessments fo r NAAQS reviews, this risk assessment is 28

intended to estimate risks attributable to anthrop ogenic sources and activitie s only. Therefore, for 29

all health endpoints as sociated with short- term exposure to PM 2.5 

# , the risk assessment considers 30

only the incidence of health effects associated with PM 2.5 

# concentrations in excess 31

12 

## For more detailed information about TRIM.Risk, see: http://www.epa.gov /ttn/fera/trim_risk.htmlFebruary 2010 Draft – Do Not Quote or Cite3-2Figure 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 PM 2.5 

# , however, the lowest measured levels (LMLs)

reported in the epidemiological studies were s ubstantially above PRB. Thus, estimating risk

down to PRB would have required substantial ex trapolation 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 un certainty related to this extrapolation into

this analysis. To provide consistency for the diffe rent C-R functions selected from the long-term

exposure studies, and, in particul ar, to avoid the choice of LML unduly influencing the results of

the risk assessment, we selected a single LML – 5.8 μg/m 3 

# from the later exposure period

evaluated in Krewski et al. (2009) -- to be used in estimating ri sks associated with long-term

PM 2.5 

# exposures.

For each health effect that has been associated with PM 2.5 

# , the risk assessment may be

viewed as assessing the incidence of th e health effect associated with PM 2.5 

# concentrations under

a given air quality scenario (e.g., a scenario in which PM 2.5 

# concentrations just meet a specified

suite of standards) above PRB or the LML . Equi valently, the risk assessment may be viewed as

assessing the change in incidence of each hea lth effect associated with a change in PM 2.5



# concentrations from some higher level (e.g., PM 2.5 

# 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 PM 2.5 

# concentrations in an assessment location, the

following analysis inputs are necessary:

 Air quality information including: (1) PM 2.5 

# air quality data from one or more recent

years from population-oriented monitors in the assessment locati on, (2) estimates of

PM 2.5 

# PRB concentrations appropriate to this location, and (3) a method for adjusting the

air quality data to reflect pa tterns of air quality changes to simulate just meeting the

current or alternative suite of PM 2.5 

# standards. (These air quality inputs are discussed in

more detail in section 3.2).

 C-R function(s) which provide an estimate of th e relationship between the health

endpoint of interest and PM 2.5 

# concentrations (preferabl y derived in the assessment

location, although functions estimated in other locations can be used at the cost of

increased uncertainty -- s ee section 3.5.3). For PM 2.5 

# , C-R functions ar e available from

epidemiological studies that assessed PM 2.5 

# -related health effects a ssociated with either

short- or long-term exposures. (Section 3.1.2 describes the role of C-R functions in

estimating health risks associated with PM 2.5 

# ).

 Baseline health effects incidence rate and population . The baseline incidence rate

provides an estimate of the incidence rate (num ber 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 popula tion) in the assessment location

corresponding to recent ambient PM 2.5 

# 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 inciden ce 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 re lated to the baseline incide nce rate and population data

inputs to the risk assessment).February 2010 Draft – Do Not Quote or Cite3-5Figure 3-2. Flow diagram of risk assessment for short-term exposure studies.February 2010 Draft – Do Not Quote or Cite3-6Figure 3-3. Flow diagram of risk assessment for long-term exposure studies.

# February 2010 Draft – Do Not Quote or Cite 3-7 The risk assessment was carried out using th ree years of recent ai r quality data from 1

2005, 2006, and 2007 (see section 3.2.1). We matche d the population data used in the risk 2

assessment to the year of the air quality data. For example, when we used 2005 air quality data, 3

we used 2005 population estimates. It was not possi ble to obtain the necessa ry data to calculate 4

baseline incidence rates separately for each of the three years for each of the risk assessment 5

locations, therefore, we calculated these rates for a single year, under th e assumption that these 6

rates are unlikely to have ch anged significantly from 2005 to 2007. The calculation of baseline 7

incidence rates is described in detail in section 3.4. 8

For this risk assessment, we developed a co re (primary) set of risk results based on the 9

application of modeling element choices (e.g., C-R functions, lag periods) that we believe have 10

the greatest overall support in the literature (herea fter referred to as the “core” results). While it 11

is not possible at this time to assign quantitative levels of confidence to these core risk estimates, 12

we do believe these estimates are generally ba sed on inputs having high er overall levels of 13

confidence relative to risk estimates that could have been generated usi ng other inputs identified 14

in the literature. 15

In addition, as discussed above in section 2.1 and later in section 3.5, we have also used 16

single-element and multi-element sensitivity analysis techniques to generate a set of reasonable 17

alternative risk estimates based on the applicati on of alternative modeli ng element choices that, 18

while not having as much support in the literature as t hose used in the core analysis, do still 19

represent plausible inputs. The re sults of these sensitivity analyses allow us to gain insights into 20

which sources of uncertainty and variability may ha ve the greatest impact on risk estimates when 21

acting alone, or in combination with other source s of uncertainty. The sensitivity analysis-based 22

risk estimates also provide us w ith an additional set of reasonable risk results that allow us to 23

place the results of the core analysis in contex t with regard to uncertainty. A number of 24

modeling elements were used in differentiating co re analyses from sensitivity analyses (e.g., C-R 25

function shape, alternative effect estimates, alte rnative lag structures, di fferent methods used to 26

rollback air quality to simulate attainment to curr ent or alternative standard levels, application of 27

PRB versus LML). Specific choices made in relation to individual modeling elements in 28

differentiating the core analysis from sensitivity analyses are described, as appropriate, in the 29

sections that follow, which cover specific aspe cts of the risk assessment design. The potential 30

utility of the sensitivity analysis-based risk es timates in informing consideration of uncertainty 31

and variability in the core result s is discussed in section 4.5.2. 32

3.1.2 Calculating PM 2.5 

# -Related Health Effects Incidence 33

The C-R functions used in the risk assessment are empirically estimated relations 34

between average ambient concentrations of PM 2.5 

# and the health endpoints of interest (e.g., 35

# February 2010 Draft – Do Not Quote or Cite 3-8 mortality or hospital admissions reported by epidem iological studies for sp ecific locations). This 1

section describes the basic method used to estimat e changes in the incidenc e of a health endpoint 2

associated with changes in PM 2.5 

# , using a “generic” C-R function of the most common functional 3

form. 4

Although some epidemiological studies have estimated linear C-R functions and some 5

have estimated logistic functions, most of th e studies used a method referred to as “Poisson 6

regression” to estimate exponentia l (or log-linear) C-R functions in which the natural logarithm 7

of the health endpoint is a linear function of PM 2.5 

# : 8

9

x 

# Be y 



#  (1) 10

11

where x is the ambient PM 2.5 

# level, y is the incidence of the hea lth endpoint of interest at 12

PM 2.5 

# level x

, β is the coefficient of ambient PM 2.5 

# concentration, and B is the incidence at x =0, 13

i.e., when there is no ambient PM 2.5 

# . The relationship between a specified ambient PM 2.5 

# level, 14

x 0 

# , for example, and the incidence of a given health endpoint associ ated with that level (denoted 15

as y 0 

# ) is then 16

17

0

0 x 

# Be y 



#  (2) 18

19

Because the log-linear form of a C-R func tion (equation (1)) is by far the most common 20

form, we use this form to illustrate th e “health impact function” used in the PM 2.5 

# risk 21

assessment. 22

If we let 

# x 0 

# denote the baseline (upper) PM 2.5 

# level, and 

# x 1 

# denote the lower PM 2.5 

# level, 23

and 

# y 0 

# and 

# y 1 

# denote the corresponding inci dences of the health effect, we can derive the 24

following relationship between the change in 

# x

, Δ 

# x = ( 

# x 0 

# - x 1 

# ), and the corresponding change in 

# y

, 25

Δ 

# y , from equation (1). 13 

# 26

  

## yyy y e x



##   



## ( ) [ ].

01 0 

## 1  

# (3) 27

28

Alternatively, the difference in health effects incidence can be calculated indirectly using 29

relative risk. Relative risk (RR) is a measure commonly used by epidemiologists to characterize 30

the comparative health effects a ssociated with a particular air quality comparison. The risk of 31

mortality at ambient PM 2.5 

# level 

# x 0 

# relative to the risk of mortality at ambient PM 2.5 

# level 

# x 1 

# , for 32

13 

## If Δ x

< 0 – i.e., if Δ x

= ( x 1 

## - x 0 

## ) – then the relationship between Δ x

and Δ y can be shown to be

] 1 [ ) (

0 0 1 

##  

    x 

## e y y y y  

## . If Δ x < 0, Δ y will similarly be negative. However, the magnitude of Δ y will be the

same whether Δ x

> 0 or Δ x

< 0 – i.e., the absolute value of Δ y does not depend on which equation is used.

# February 2010 Draft – Do Not Quote or Cite 3-9 example, may be characterized by the ratio of th e two mortality rates: the mortality rate among 1

individuals when the ambient PM 2.5 

# level is 

# x 0 

# and the mortality rate among (otherwise identical) 2

individuals when the ambient PM 2.5 

# level is 

# x 1 

# . This is the RR for mortality associated with the 3

difference between the two ambient PM 2.5 

# levels, 

# x 0 

# and 

# x 1 

# . Given a C-R function of the form 4

shown in equation (1) and a part icular difference in ambient PM 2.5 

# levels, Δ 

# x , the RR associated 5

with that difference in ambient PM 2.5 

# , denoted as RR Δ x 

# , is equal to e βΔ x 

# . The difference in health 6

effects incidence, Δ 

# y , corresponding to a given difference in ambient PM 2.5 

# levels, Δ 

# x , can then 7

be calculated based on this RR Δ x 

# as: 8

9

)] / 1 ( 1 [ ) (

0 1 0 x 

# RR y y y y  

#  

   . (4) 10

11

Equations (3) and (4) are simply alternative wa ys of expressing the relationship between 12

a given difference in ambient PM 2.5 

# levels, Δ 

# x

> 0, and the correspondi ng difference in health 13

effects incidence, Δ 

# y . These health impact equations are the key equations that combine air 14

quality information, C-R function information, and baseline health effects incidence information 15

to estimate ambient PM 2.5 

# health risk. 16

3.1.2.1 Short-term vs. Long-term Exposure 17

Concentration-response (C-R ) functions that use as input annual average PM 2.5 

# levels (or 18

some function of these, such as the average over a period of seve ral years) relate these to the 19

annual incidence of the health e ndpoint – i.e., in such studies 

# x in equation (1) above is the 20

average PM 2.5 

# concentration over a period of one or more years, meant to represent long-term 21

exposure, and 

# y is the annual incidence of the health effect associ ated with that long-term 22

exposure. 23

Concentration-response (C-R) functions that use as input 24-hour average PM 2.5 

# levels (or 24

some function of these, such as the average ove r one or more days) rela te these to the daily 25

incidence of the health endpoi nt – i.e., in such studies 

# x in equation (1) above is the average 26

PM 2.5 

# concentration over a period of one or a few days (short-term exposure), and 

# y is the daily 27

incidence of the health effect associat ed with that short-term exposure. 28

There are several variants of the short-term (daily) C-R function. Some C-R functions 29

were estimated by using moving averages of ambient PM 2.5 

# to predict daily health effects 30

incidence. Such a function might, for example, re late the incidence of th e health effect on day 

# t 31

to the average of PM 2.5 

# concentrations on days 

# t and ( 

# t -1). Some C-R func tions consider the 32

relationship between daily in cidence and daily average PM 2.5 

# lagged a certain number of days. 33

For example, a study might estimate the C-R relationship between mortality on day 

# t and average 34

PM 2.5 

# on a prior day ( 

# t -1). A few studies have estimated di stributed lag models, in which health 35

# February 2010 Draft – Do Not Quote or Cite 3-10 effect incidence is a function of PM 2.5 

# concentrations on several prior days – that is, the incidence 1

of the health endpoint on day 

# t is a function of the PM 2.5 

# concentration on day 

# t , day ( 

# t -1), day ( 

# t

2

2), and so forth. Such models can be reconfigur ed so that the sum of the coefficients of the 3

different PM 2.5 

# lags in the model can be used to predict the changes in incidence on several days. 4

For example, corresponding to a change in PM on day 

# t in a distributed lag model with 0-day, 1- 5

day, and 2- day lags considered, the sum of the co efficients of the 0-day, 1-day, and 2-day lagged 6

PM 2.5 

# concentrations can be used to predic t the sum of incidence changes on days 

# t , ( 

# t +1) and 7

( 

# t +2). 8

Most daily time-series epidemiological stud ies estimated C-R functions in which the PM9

related incidence on a given day depends only on same-day PM concentration(i.e. lag 0), the 10

previous-day PM concentration (i.e . lag 1), or some variant of t hose, such as a two-day average 11

concentration (e.g. lag 0-1). Such models necessa rily assume that the longer pattern of PM 12

levels preceding the PM concentration on a given day does not affect mortality or morbidity on 13

that day. To the extent that PM-related mortalit y on a given day is affected by PM concentrations 14

over a longer period of time, then these models would be mis-specified, and this mis15

specification would affect the predictions of daily incidence based on the model. 16

The extent to which time-seri es studies using single-day PM 2.5 

# concentrations may under 17

or over-estimate the relationship between short-term PM 2.5 

# exposure and risk of mortality is 18

unknown. However, there is some evidence, based on analyses of PM 10 

# data, that mortality or 19

morbidity on a given day is influenced by prior PM exposures up to more than a month before 20

the date of death (Schwartz, 2000). The extent to which short-term exposure studies (including 21

those that consider distributed lags) may not capture the full impact of long-term exposures to 22

PM 2.5 

# is similarly not adequately understood, although the current evidence (e.g., Krewski et al., 23

2009; Krewski et al., 2000) suggests that there is a subs tantial impact of long-term exposures on 24

health effects that is not picked up in the short-term exposure studies. 25

3.1.2.2 Calculating Annual Incidence 26

The risk assessment estimated health effects in cidence, and changes in incidence, on an 27

annual basis, for 2005, 2006, and 2007. For mortalit y, both short-term and long-term exposure 28

studies have reported estimated C-R functions. As noted above, most short-term exposure C-R 29

functions estimated by daily time-series epidemiol ogical studies relate daily mortality to same30

day PM 2.5 

# concentration or previous-day PM 2.5 

# concentration (or some variant of those). 31

To estimate the daily health impacts of 24-hour average ambient PM 2.5 

# levels above PRB, 32

C-R functions from short-term exposure studies we re used together with estimated changes in 33

24-hour ambient PM 2.5 

# concentrations to calculate the daily changes in the incidence of the 34

# February 2010 Draft – Do Not Quote or Cite 3-11 health endpoint. After daily ch anges in health effects were ca lculated, an annual change was 1

calculated by summing the daily changes. 2

The mortality associated with long-term expos ure is likely to include mortality related to 3

short-term exposures as well as mortality re lated to longer-term exposures. As discussed 4

previously, estimates of daily mort ality based on the time-series studi es also are likely influenced 5

by prior PM exposures. Therefore, the estimated annual incidences of mo rtality calculated based 6

on the short- and long-term exposure studies are not likely to be completely independent and 7

should not be added together. While we can ch aracterize the statistica l uncertainty surrounding 8

the estimated PM 2.5 

# coefficient in a reported C-R function, there are other sources of uncertainty 9

associated with the C-R functions used in the risk assessment that are addressed via sensitivity 10

analyses and/or qualitativel y discussed in section 3.5.3. 11

3.2 AIR QUALITY INPUTS 12

3.2.1 Characterizing Recent Conditions 13

As noted earlier, a major input to the PM 2.5 

# risk assessment is ambient PM 2.5 

# air quality 14

data for each assessment location. Twenty-four hour PM 2.5 

# air quality data for 2005, 2006, and 15

2007 were obtained for each of the urban study areas from monitors in EPA’s Air Quality 16

System (AQS). To characterize PM 2.5 

# air quality in each risk a ssessment location as accurately 17

as possible, we used only those mo nitors that were located within the county or counties that 18

were analyzed in the epidemiological studies used to select C-R functions. In a few cases, an 19

urban area was delineated differently by two or mo re epidemiological studies used in the risk 20

assessment. For example, Birmingham, AL was defined as Blount, Jeffers on, Shelby, St. Clair, 21

and Walker Counties in one study and as only Jeff erson County in another study. In such cases, 22

we matched our delineation of the urban study area to that used in each study, resulting in two or 23

more different delineations of the urban st udy area and identified them as, for example, 24

Birmingham 1 and Birmingham 2. The counties and the number of air quality monitors included 25

within each urban area are given in Table 3-1. 26

In order to be consistent with the approach generally used in the epidemiological studies 27

that estimated PM 2.5 

# C-R functions, the average ambient PM 2.5 

# concentration on each day for 28

which measured data were available was deemed most appropriate for use in the risk assessment 29

(i.e., we created a composite monitor average). C onsistent with the approach used in the prior 30

PM risk assessment, a composite monitor data set was created for each assessment location 31

based on a composite of all monitors located within each urban study area. For this risk 32

assessment, we have used an approach for creat ing composite monitors (s ee description below) 33

that reflects equal weighting of monitors in computing both 24-hour and annual composite 34

monitor values. (This reflects a change from th e approach used in the first draft RA which 35

# February 2010 Draft – Do Not Quote or Cite 3-12 weighted monitors by sampling frequency – an approach which could result in bias being 1

introduced into the analysis.) 2

To calculate daily averages at the composite monitor for a location, we first checked the 3

number of observations at each monitor at th at location. If a monitor had fewer than 11 4

observations in a quarter of the year (three mont hs, the first quarter bei ng January, February, and 5

March), we left the days in that quarter without observations as missing. If a monitor had at least 6

11 observations in a quarter, we filled in the missi ng days at that monitor in that quarter as 7

follows: For each series of seven or fewer c onsecutive days with missing values, we took the 8

average of the closest day with a reported value before the missing days and the closest day with 9

a reported value after the missing days, and we assigne d that average to all da ys in the series of 10

missing days. If a series of consecutive missing days was greater than seven, we did not fill 11

them in. After the missing days at monitors had been filled in as described, we calculated the 12

composite monitor value for a given day as the aver age of values across all monitors for that day. 13

If there were any days for which the composite monitor value was missing, we filled them in 14

with 7-day moving averages (i.e., an average of the 3 days before and the 3 days after the 15

missing day). Given the approach for interpolat ing missing days at individual monitors (just 16

described), the incidence of missi ng days at composite monitors was very low. The numbers of 17

monitors in the risk assessment locations are given in Table 3-1. 18

To calculate annual averages at the composite monitor for a location, we first checked the 19

number of observations in each quarter of each y ear at each monitor at th e location. If a monitor 20

had fewer than 11 observations in a quarter of th e year, we set the quarterly average at that 21

monitor to “missing.” If the monitor had at l east 11 observations in a qua rter, we calculated the 22

quarterly average at the monitor as the average of the reported observations at the monitor in that 23

quarter. For each quarter of the year, we th en calculated the composite monitor quarterly 24

average as the average of the monitor-specific quarterly averages. The annual average at the 25

composite monitor was then calculated as the av erage of the four composite monitor quarterly 26

averages. 14 

# 27

28

14 

## Pittsburgh was treated somewhat differently from the othe r 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 Table 3-1. Numbers of Monitors in Risk Assessment Locations From Which Composite 1

Monitor Values Were Calculated* 2

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

* Calculation of composite monitor values is described in the text above. 

# 3

** Barrow, Bartow, Carroll, Cherokee, Clayton, Cobb, Cowe ta, DeKalb, Douglas, Fayette, Forsyth, Fulton, Gwinett, 

# 4

Henry, Newton, Paulding, Pickens, Rockdale, Spalding, and Walton. 

# 5

*** The sets of monitors for New York (Manhattan) have 1-in-3 day sampling, with sampling schedules synced 

# 6

across monitors. This means that for the three year simulation period, roughly 2/3 of the days (i.e., 731) had no 

# 7

monitor coverage for the New York urban study area, resulting in a need to interpolate estimates for these days (for 

# 8

the composite monitor) using the approach described above. Similarly, with Tacoma, the single monitor at that 

# 9

location also has 1 in 3 day sampling, resulting again, in 2/3 of the days not having data with interpolation being 

# 10

used to derive estimates for those days (for the composite monitor). 

# 11

12

Appendix A summarizes the PM 2.5 

# air quality data that we re used in each of the 13

assessment locations, including quarterly and annu al counts, quarterly and annual averages, and 14

the 98 th 

# percentile of the daily (24-hour) averages. 15

3.2.2 Estimating Policy Relevant Background 16

Policy-relevant background estimates used in the risk assessment model (see Table 3-2 17

below) were obtained from the ISA (Table 3- 23, final ISA, EPA, 2009d). These values were 18

generated based on a combination of Community Multiscale Air Quality model (CMAQ) and 19

Goddard Earth Observing System (GEOS)-Chem modeling as desc ribed in the draft ISA (see 20

section 3.7.1.2). Annual values presented in Tabl e 3-2 were used in modeling health endpoints 21

associated with long-term exposure (in those sensitivity analysis scenarios where risk was 22

modeled down to PRB – see section 3.5.4). For h ealth endpoints associat ed with short-term 23

exposure (which involved modeling down to PRB, exclusively), quarterly values presented in 24

Table 3-2 were used to represent the appropria te block of days within a simulated year. 25

# February 2010 Draft – Do Not Quote or Cite 3-14 Table 3-2 Regional Policy-Relevant Ba ckground Estimates Used in the Risk 1

Assessment. 2

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

3.2.3 Simulating Air Quality to Just Me et Current and Alternative Standards 4

This section describes the methodologi es used to simulate ambient PM 2.5 

# levels in an area 5

that would just meet specified PM 2.5 

# standards. The form of the current PM 2.5 

# standards requires 6

that the 3-year average (r ounded to the nearest 0.1 μg/m 3 

# ) of the annual means from each single 7

monitor or the average of multiple monitors must be at or below the level of the annual standard 8

and the 3-year average (rounded to the nearest 1 μg/m 3 

# ) of the ninety-eighth percentile values at 9

each monitor cannot exceed the level of the 24-hour standard. In determining attainment of the 10

annual average standard, an area may choose to use either the spatially av eraged concentrations 11

across all population-oriented mon itors, subject to meeting certai n criteria detailed in Part 50, 12

Appendix N, of the CFR, or it may use the highest 3-year average based on individual monitors. 13

The most realistic simulation of just meeting both the annual and the 24-hour PM 2.5 

# standards in 14

a location would require changi ng the distributio n of 24-hour PM 2.5 

# concentrations at each 15

monitor separately, based on the specific mix of local and regional controls impacting that 16

particular location. This would require extensive analysis and a ssumptions about the nature of 17

future control strategies that is beyond the scope of quantitative risk assessments done as part of 18

the review of the NAAQS. 15 

# 19

In the last PM risk assessment, just meeting the current or alternative PM 2.5 

# standards was 20

simulated by changing 24-hour PM 2.5 

# concentrations at a “composite monitor,” which 21

represented the average of the m onitors in a location. In the cu rrent PM risk assessment, just 22

meeting the current or alternative PM 2.5 

# standards was simulated by changing 24-hour PM 2.5 

# 23

concentrations at each monitor separately. This change was made because the current PM risk 24

assessment considers three alternative approaches to simulating PM 2.5 

# concentrations that just 25

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 meet a given suite of standards (i.e., proportiona l, hybrid and peak-shaving – see below), and two 1

of these methods (hybrid and peak-shaving) i nvolve making monitor-speci fic changes of 24-hour 2

PM 2.5 

# concentrations to simulate ju st meeting standards. All three of these methods start with 3

monitor-specific series of PM 2.5 

# concentrations in which missing days have been filled in as 4

described above. 5

In simulating ambient PM 2.5 

# levels that would just meet cu rrent and alternative suites of 6

standards, we have applied the following approa ches to rolling back air quality levels: (a) 7



# proportional rollback, in which the same proportional adjustme nt is applied to all monitors in a 8

study area, has traditionally been used in the NAAQS risk assessments since it generally reflects 9

historical patterns in how air qua lity has changed over time, (b) 

# hybrid rollback , which involves 10

an initial localized reduction to bring higher m onitors down to the rang e of their neighbors, 11

followed by proportional reduction, if needed, to just meet a given suite of standards; and (c) 12



# peak-shaving rollback, in where each monitor that exceeds the 24-hour standard is simulated to 13

just meet the 24-hour standard through propor tional reduction of its annual 24-hour PM 2.5 

# 14

distribution (with no impact on monitors that are meeting the 24-hour standard). The 15

proportional rollback approach is applied to each of the 15 urban study areas, while the other two 16

rollback approaches are applied to a subset of areas as appropriat e (e.g., the peak-shaving 17

approach is only used for thos e study areas where the 24-hour st andard is both controlling and 18

being exceeded by one or more monitors). 19

The proportional rollback approach was used in generating the core risk estimates in light 20

of its use in past risk assessments, while the other two rollback approaches (hybrid and peak 21

shaving) were considered in sensitivity analyses to characterize potential variability in the way 22

urban areas may respond to suites of current or alternative standa rds. As described below, the 23

proportional rollback reflects a regional pattern of ambient PM 2.5 

# reduction, the hybrid approach 24

reflects a combination of local and regional patterns in ambient PM 2.5 

# reduction, and the peak 25

shaving approach reflects a lo calized pattern of ambient PM 2.5 

# reduction. We have not ascribed 26

greater confidence to the propor tional approach, since we have no basis for predicting which 27

approach would likely be most reflectiv e of future patterns of ambient PM 2.5 

# reductions in each 28

study area. 29

3.2.3.1 Proportional Rollback Method 30

The proportional approach, which reflects a re gional pattern of reductions in ambient 31

PM 2.5 

# concentrations, was used in previous PM 2.5 

# risk assessments. Th is approach involves 32

proportional adjustments to monitor levels, in which PM 2.5 

# concentrations are reduced (“rolled 33

back”) by the same percentage each day. When this approach is used, it does not matter whether 34

(1) PM 2.5 

# concentrations are first rolled back by the same percentage each day at each monitor, 35

# February 2010 Draft – Do Not Quote or Cite 3-16 and then the composite monitor values are calcula ted from these monitor-specific values or (2) 1

first the composite monitor values are calculate d and then these are ro lled back by the same 2

percentage each day – the re sults will be the same. 3

The percent reduction of 24-hour PM 2.5 

# concentrations in the proportional rollback 4

approach (and in the second step of the hybrid rollback approach, described below) at each 5

monitor each day to simulate just meeting current and alternative set of standard levels is 6

determined by the PM 2.5 

# annual and 24-hour design values. The annual design value (in μg/m 3 

# ) 7

was calculated as follows: 8



#  At each monitor, the annual average PM 2.5 

# concentration was calculated for each of the 9

years 2005, 2006, and 2007, and these three annual average concentrations were then 10

averaged. 11



#  The maximum of these monitor-specific 3-year averages of annual averages is the annual 12

design value, denoted 

# dv annual 

# ; 13

The 24-hour design value (in μg/m 3 

# ) was similarly calculated as follows: 14



#  At each monitor, the 98 th 

# percentile 24-hour PM 2.5 

# concentration was calculated for each 15

of the years 2005, 2006, and 2007, and these three 98 th 

# percentile concentrations were 16

then averaged. 17



#  The maximum of these monitor-specific 3-year averages of 98 th 

# percentile concentrations 18

is the 24-hour design value, denoted 

# dv daily 98 

# (note, we will refer to the 98 th 

# percentile 19

design value as the 24-hour design value throughout the rest of the document). 20

The annual and 24-hour design values used in assessing the current and alternative 21

standards for PM 2.5 

# are given in Table 3-3. Note that m onitors that were closed in 2005 (and 22

therefore, did not include monitoring data for the majority of the three year simulation period), or 23

which were missing an entire year’s worth of mo nitoring data during any of the three simulation 24

years (2005, 2006 or 2007) were excluded from c onsideration as design value monitors, although 25

these monitors were still used to construct comp osite monitors for purposes of estimating risks. 26

27

Table 3-3. EPA Design Values for Annual and \24-hour PM 2.5 

# Standards for the Period 28

2005-2007.* 29

Location Annual

(μg/m 3 

## ) 24-hour

(μg/m 3 

## )

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/m 3 

## ) 24-hour

(μg/m 3 

## )

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

*The calculation of design values is explained in the text above. 

# 1

2

The percent reduction required to meet a st andard (annual or 24-hour) was determined by 3

comparing the design value for that standard with the level of the standard. Because pollution 4

abatement methods are applied larg ely to anthropogenic sources of PM 2.5 

# , rollbacks were applied 5

only to PM 2.5 

# above estimated PRB levels. The percent reduction was determined by the 6

controlling standard. For example, suppose both annual and 24-hour PM 2.5 

# standards are being 7

simulated. Suppose 

# p a 

# is the percent reduction required to ju st meet the annual standard (i.e., the 8

percent reduction of daily PM 2.5 

# above background necessary to get the annual design value 9

down to the current or alterna tive annual standard). Suppose 

# p d 

# is the percent reduction required 10

to just meet the 24-hour standard (i .e., the percent reduction of daily PM 2.5 

# above background 11

necessary to get the 24-hour PM 2.5 

# design value down to the 24-hour standard). If 

# p d 

# is greater 12

than 

# p a 

# , then all 24-hour average PM 2.5 

# concentrations above background are reduced by 

# p d 

# 13

percent. If 

# p a 

# is greater than 

# p d 

# , then all 24-hour average PM 2.5 

# concentrations are reduced by 

# p a 

# 14

percent. The method of rollbacks to meet a set of annual and 24-hour PM 2.5 

# standards is 15

summarized as follows: 16

1. The percent by which the above- 

# PRB portion of all daily PM 2.5 

# concentrations (at the 17

composite monitor) would have to be reduced to just meet the annual standard (denoted 18



# std

a 

# ) is 19



# ) ) (

1

avg annual avg a

a 

# PRB dv PRB

std

p

 

 

, 20

# February 2010 Draft – Do Not Quote or Cite 3-18 1

where PRB

avg 

# is the average of the daily PRB concentrations. 16 

# 2

3

4

2. The percent by which the above- PRB portion of all 24-hour PM 2.5 

# concentrations (at the 5

composite monitor) would have to be reduced to just meet the curren t or alternative 24- 6

hour standard (denoted std d98 

# ) is: 7

8

) ) (

1

98 98

98

avg daily avg

d

d 

# PRB dv PRB

std

p

 

  9

10

Let p max 

# = maximum of (maximum of p a 

# and p d98 

# ) and zero. 17 

# 11

12

13

3. Then if PM

o 

# denotes the original PM value on a gi ven day (at the composite monitor), the 14

rolled back PM value on that day, denoted PM rb 

# , is: 15

16

PM rb 

# = PRB +( PM

o 

# − PRB )*(1

− p max 

# ). 17

18

Results of the simulations done in each ur ban study area using th e proportional rollback 19

approach, as well as the hybrid and peak shaving approaches discussed below, are presented in 20

Appendix F, Tables F-49 and F-50. For each urban study area and suite of standards, two sets of 21

values are presented in each table based on app lication of each rollback approach including: (a) 22

the maximum monitor-specific three-year (2005- 2007) annual average (i.e., “Max. M-S” in both 23

tables) and (b) the composite monitor value for 2007 (i.e., “2007 CM” in both tables). The first 24

estimate (Max M-S) allows us to see how the de sign value changes in just meeting each suite of 25

standards based on application of the different rollback methods, while the second estimate 26

(2007 CM) is the surrogate for long-term exposur e-related mortality, as described below in 27

section 3.5.4. The tables differ in terms of the information presented in the last set of columns, 28

with Table F-49 showing the percent reducti on in the composite monitor values given 29

application of a particular roll back approach (allows comparison of the pattern of risk reduction 30

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 neces sary 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 across standard levels generated using each rollback approach), and Table F-50 showing the 1

percent difference in the composite monitor valu es in comparing the hybrid and peak shaving 2

results to that obtained with the proportional rollb ack approach for a given standard level (allows 3

comparison of residual risk estimates generated using the different rollback approaches for each 4

standard level). The information in the last set of columns in each table is considered below in 5

the sensitivity analys is (section 3.5.4). 6

3.2.3.2 Hybrid Rollback Method 

# 7

The hybrid rollback approach reflects a combin ation of first localized and then regional 8

patterns of reductions in ambient PM 2.5 

# concentrations. In co mparison to the proportional 9

rollback approach, this approach has two steps: (1) first PM 2.5 

# concentrations are reduced at a 10

specific monitor location within an urban study area and then addi tional monitors within that 11

urban study area are adjusted to a lesser extent (with the magnitude of adjustment based on a 12

distance-decay function); then (2) a pr oportional rollback of the adjusted PM 2.5 

# concentrations at 13

all of the different monitors is carried out, as described in Section 3.2.3.1 above. Because the 14

initial step reflecting localized controls is non -proportional, this needs to be completed on the 15

monitor datasets (associated with a particular st udy area) prior to constr uction of the composite 16

monitor. However, once those non-proportional reduc tions have been implemented, a composite 17

monitor can then be construc ted (as described earlier) and the second step of conducting 18

proportional adjustment to simulate the curren t or alternative suites of standards can be 19

calculated for the composite monitor. New desi gn values are calculated for the hybrid rollback 20

approach based on the PM 2.5 

# concentrations that have been adju sted in the first step of the two21

step process. 18 

# The hybrid approach is describe d in more details in Appendix B. 22

3.2.3.3 Peak Shaving Rollback Method 

# 23

The peak shaving approach reflects local ized patterns of reduction in ambient PM 2.5 

# 24

concentrations and has only been applied in cases where the 24-hour standard is controlling (i.e., 25

the percent rollback necessary to meet the daily standard is greater than the percent rollback 26

necessary to meet the annual standard in that lo cation). This approach was used to calculate 27

annual averages for 2005, 2006, and 2007 at composite monitors for comparison with the 28

composite monitor annual averages calculate d using the proportional and hybrid rollback 29

18 

## As with the composite monitor values representing rece nt 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 remain ing 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 fo r “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 approaches. Because of time constraints, we did not calculate health risks with the application of 1

the peak shaving rollback approach. Because the C-R functions used in the risk assessment are 2

almost linear, a comparison of annual averages at composite monitors us ing the three different 3

approaches for simulating just meeting altern ative standards provide s a good surrogate for 4

estimates of health risks when a lternative standards are just met (see Section 3.5.4 for additional 5

detail on the composite monitor-based comparison of the three rollback strategies completed as 6

part of the sensitivity analysis). 7

As with the proportional and hybrid rollback approaches, the peak shaving approach for 8

calculating annual averages at composite monito rs starts with monitor-specific quarterly 9

averages that have been calcula ted as described above in Secti on 3.2.1. In contrast to the 10

proportional and hybrid rollback approaches, the peak shaving method uses monitor-specific 11

design values. For each monitor, we compared the monitor-specific 24-hour design value to the 12

level of the 24-hour standard and calculated the percent rollback necessary to reduce the 13

concentration at each monitor to the standard level (using a formula th at is analogous to the 14

proportional rollback formula given above in Sec tion 3.2.3.1). We then rolled back each 15

quarterly average at the monitor by this percen t rollback. We calculated the average quarterly 16

average across all monitors in th e location, for each quarter. Finally, we calculated the annual 17

average at the composite monitor under the sta ndard by averaging the four quarterly averages 18

calculated on the previous step. 19 

# 19

3.3 SELECTION OF MODEL INPUTS 

# 20

3.3.1 Health Endpoints 

# 21

The selection of health effect endpoints reflec ts consideration for a number of factors. 22

The specific set of factors considered in select ing health effects endpoints to model in this 23

assessment included: 24

 The overall weight of evidence from the co llective body of epidemiological, controlled 25

human exposure, and toxicologi cal studies and the determina tion made in the final ISA 26

regarding the strength of th e causal relationship between PM 2.5 

# and the more general 27

health effect category; 28

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 Pittsburg h 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”), monito r-specific quarterly averages in that

attainment area were rolled back by the percent rollback ne cessary to just meet the an nual standard there. Once

monitors in “Pittsburgh-1” and “Pittsburgh-2” were rolle d 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  The extent to which particular health effect endpoints within these broader health effect 1

categories are considered significant from a public health standpoint; 2

 The availability of well-conducted epidemiol ogical studies providing C-R functions for 3

specific health effect endpoints; 4

 The availability of sufficient air quality monito ring data in areas that were evaluated in 5

the epidemiological studies; 6

 The availability of baseline incidence da ta to support population risk (incidence) 7

modeling; and 8

 The anticipated value of developing quantitat ive risk estimates for the health effect 9

endpoint(s) to inform decision-making in the context of the PM NAAQS review. 10

11

In selecting the set of hea lth effect endpoint categories (and associated endpoints and 12

related at-risk populations ) to include in the PM 2.5 

# risk assessment, we considered the health 13

effects evidence presented in the final ISA (EPA, 2009d), as well as CASAC (Samet, 2009a) and 14

public comments received on the Scope and Methods Plan and CASAC (Samet, 2009b) and 15

public comments received on the first draft RA. In reviewing the final ISA in relation to PM 2.5 

# , 16

we focused on the following sections: (a) section 2.3.1.1 (Effects of Short-Term Exposure to 17

PM 2.5 

# ), (b) section 2.3.1.2 (Effect s of Long-Term Exposure to PM 2.5 

# ), (c) section 2.3.2 18

(Integration of PM 2.5 

# Health Effects), and (d) subsections in Chapter 6 and 7 of the final ISA 19

providing summaries of causal determination (f or both morbidity and mortality endpoints) 20

related to short-term and long-te rm exposure, respectively. We also considered information in 21

the ISA on at-risk populations, which identified the life stages of child ren and older adults, 22

people with pre-existing cardiov ascular and respiratory diseas es, and people with lower 23

socioeconomic status as populations at incr eased risk for PM-rela ted health effects. 24

Based on the evidence presented in the ISA and application of the above criteria, we 25

identified the following health effects endpoi nts for inclusion in the risk assessment: 26

Health effects associated with short-term PM 2.5 

# exposure : 27

 Mortality (causal relationship) 28

o 

# non-accidental, 29

o 

# cardiovascular-related 30

o 

# respiratory-related, 31

 Cardiovascular effects (causal relationship) 32

o 

# cardiovascular-related hospital admissions 33

 Respiratory effects (lik ely causal relationship) 34

o 

# respiratory-related hospital admissions 35

# February 2010 Draft – Do Not Quote or Cite 3-22 o 

# asthma-related emergency department visits 1

Health effects associated with long-term PM 2.5 

# exposure: 2

 Mortality (causal relationship) 3

o 

# all-cause 4

o 

# ischemic heart disease (IHD)-related 5

o 

# cardiopulmonary-related 6

o 

# lung cancer 7

While we selected specific hea lth effect endpoints that were all within broad health effect 8

categories classified in the ISA as having a “cau sal” or “likely causal” association with PM 2.5 

# 9

exposure, our selection is a based on applying the multi-factor approach described above. 10

The evidence available for th ese selected health effect endpoints generally focused on 11

the entire population, although some information was available that allowed us to consider 12

differences in estimated risk for the at-risk populations of older adul ts and people with pre13

existing cardiovascular and respir atory diseases. While evidence ofeffects in other important at14

risk populations, including childre n and people with lower socio economic status, was not judged 15

to be sufficient to support quantit ative risk assessment, this eviden ce will be part of the evidence16

based considerations to be discussed in the policy assessm ent document currently being 17

developed. 18

3.3.2 Selection and Delineation of Urban Study Areas 

# 19

This section describes the approach used in selecting the 15 urban study areas included in 20

this risk assessment (see Table 3-3 for a listing of the urban study areas). This approach builds 21

upon and expands the approach for selecting urba n study areas from the prior risk assessment 22

(EPA, 2005, section 3.2, p. 37). 23

Criteria used in the prior risk assessment and updated in this analysis include: 24

 Availability of sufficient air quality data 

# : Sufficient air quality data was 25

identified as having at least 11 observati ons per quarter for a one year period and 26

at least 122 observations per year. We assessed prospective study areas by 27

insuring that there was at least one PM 2.5 

# monitor within the boundaries of the 28

prospective study area that met these comp leteness criteria for the period 2005 to 29

2007 with additional preference given to locations with more than one PM 2.5 

# 30

monitor meeting completeness criter ia, since this provided a better 31

characterization of ambient air le vels for that urban location. 32

33

 Inclusion in epidemiology study : 

# Coverage of the location within one of the key 34

epidemiology studies included in the risk assessment (at or close to the location 35

where at least one C-R function for one of the recommended health endpoints has 36

been estimated by a study satisfying the selection criteria used in the risk 37

assessment). In this review, because the current risk assessment primarily utilizes 38

# February 2010 Draft – Do Not Quote or Cite 3-23 multi-city studies to evaluate ri sk for short-term and long-term PM 2.5 

# exposures 1

(whereas the prior risk assessment us ed city-specific studies in modeling 2

endpoints associated with short-term expos ures), this criterion no longer applies 3

for most prospective areas. 4

5

 Availability of city-speci fic baseline incidence data : 

# Regarding sufficiency of 6

baseline health effects incidence data, an ongoing effort by EPA to collect county7

level hospital and emergency department admissions data from states to support 8

this risk assessment (see section 3.5) ha s resulted in enhanced health effects 9

baseline incidence data, largely addressing this criterion (i.e., most urban areas in 10

the U.S. now have coverage with the updated baseline health effects incidence 11

data). 12

13

Two additional factors considered in sele cting locations to model in the current 14

assessment included: 15

 Potential for risk reductions us ing alternative standard levels 

# : Specifically, 16

we focused on those urban areas with PM 2.5 

# monitoring levels suggesting the 17

potential for risk reduction under alternat ive (24-hour or annual) standards under 18

consideration, particularly focusing on ur ban locations with at least one monitor 19

having an annual average above 12 μg/m 3 

# and/or a 24-hour value above 25 μg/m 3



# . 20

Furthermore, locations with ambient PM 2.5 

# level significantly higher than these 21

levels were favored (with several urba n study areas selected having both annual 22

and 24-hour design values exceeding the current standards – Table 3-4). 23

24

 Regional representation: 

# The second criterion we a dded for study area selection 25

focused on providing coverage for factors be lieved to play a role in influencing 26

risk heterogeneity at the national-level (e.g., PM 2.5 

# source characteristics and 27

composition, demographics, SES status, ai r conditioner use). Building on the 7 28

regions originally identified in th e 1996 PM Criteria Document (EPA, 1996, 29

section 6.4) (i.e., PM regions), we evalua ted several urban locations from each of 30

these PM regions with the goal to iden tify one or more candidate urban study 31

areas in each region. Ultimately, consid eration of the crite ria described here 32

resulted in an urban study area not being identified for one of the PM regions (the 33

Upper Midwest), however, the remaining si x PM regions each included at least 34

one urban study areas evaluated in the ri sk assessment. While the PM regions 35

were originally defined focusing primarily on differences in PM composition, size 36

and seasonality, by selecting urban study ar eas from regions across the continental 37

U.S., we recognize the potential for coveri ng regional differences in other factors 38

related to risk heterogeneity as well (e.g., demographics, SES). The 39

representativeness analysis (section 4.4) specifically a ssesses the degree to which 40

the 15 urban study areas provide coverage for national tre nds in key risk-related 41

factors such as those listed here. 42

43

Based on consideration of the above criteria, 15 study areas were selected for inclusion in 44

this risk assessment. Table 3-4 presents the 15 urban study areas including (a) whether the urban 45

# February 2010 Draft – Do Not Quote or Cite 3-24 study area was included in the prior risk assessmen t, (b) which PM region the urban study area is 1

located in, and (c) the 24-hour and annual design values using 2005- 2007 air quality data. Figure 2

3-4 identifies each of the 15 urban study areas in relation to the 7 regions used to guide the 3

selection of the urban study areas. 4

5

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

# 6

Urban study

area State Modeled in last

NAAQS review PM

region* Annual design

value (μg/m 3 

## ) 24-hour design

value (μg/m 3 

## )

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

* SE (Southeast), IM (industrial Midwest), SCA (South ern California), NE (Northeas t), NW (Northwest), SW 

# 7

(Southwest) (See, EPA, 1996, section 6.4 for description of these regions). 

# 8

9

10

# February 2010 Draft – Do Not Quote or Cite 3-25 Northwest

Southeast Southwest Northeast Upper Midwest Industrial Midwest

S

o u

t h

e

r n

C

a

l i f o

r n

i a Dallas Fresno Tacoma

Atlanta Detroit

Houston Phoenix New York

Baltimore St. Louis

Birmingham Los Angeles Salt Lake City Pittsburgh Philadelphia



# 1

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

# 2

regions used to guide selection of study areas). 

# 3

4

Once the 15 urban study areas were selected, th e next step was to identify th e spatial 5

template to use in defining each study area (i.e ., the geographical area associated with each study 6

area that would be used in id entifying which counties and PM 2.5 

# monitors were associated with a 7

particular study area). For 12 of the 15 urban stu dy areas, we either used a combined statistical 8

area (CSA) as the basis for the spatial template, or if that was not available, we used a core-based 9

statistical area (CBSA). The th ree remaining urban study areas were special cases and were 10

handled as follows: 11

 Baltimore: 

# We used counties in the Baltimore CBSA only and did not consider the 12

larger Baltimore-DC CSA since we felt it un likely that the entire larger CSA would 13

behave similarly with regard to PM 2.5 

# emissions reduction strategies; 14

 Philadelphia 

# : We used the Philadelphia CSA, but excluded Berks County (Reading), 15

and 16

 Tacoma 

# : we only used Pierce County (since we felt it unlikely that efforts to reduce 17

emissions at the “elevated” monitor in Pierce County, would significantly impact 18

monitors in Seattle). 19

# February 2010 Draft – Do Not Quote or Cite 3-26 As noted above, in a few instances, two or mo re epidemiological studies used different 1

geographic boundaries for determining which populati ons were included in their studies. For 2

example, in one study conducted in Birmingha m, AL populations from Blount, Jefferson, 3

Shelby, St. Clair, and Walker Counties were included, while anot her study included the 4

population residing in only Jefferson County. In such cases, we matched our delineation of the 5

urban area to that of each study, re sulting in two or more different de lineations of the urban area. 6

As we discuss below, two of the studies on which we rely for our core analysis – 7

Zanobetti and Schwartz (2009) and Bell et al. (20 08) – are multi-location studies. Zanobetti and 8

Schwartz (2009) specified the county or countie s included in each of the urban areas they 9

included in their analysis. Bell et al. (2008), however, did not focus on urban areas, but instead 10

focused on counties with populations above a spec ified threshold number. To limit the number 11

of different “versions” of a risk assessment lo cation, wherever possible we specified the counties 12

in a risk assessment location for Bell et al. ( 2008) to match the set specified for Zanobetti and 13

Schwartz (2009). This was possible in those cases in which Zanobett i and Schwartz (2009) 14

identified an urban area as a si ngle county, and that county was also included in Bell et al. 15

(2008). This was the case for several of the ri sk assessment locations. In some cases, however, 16

Zanobetti and Schwartz (2009) used a multi-county delineation of an urban area where at least 17

one of the counties was not among those included in Bell et al. (2008). In those cases, we had to 18

delineate two definitions of the urban area – one corresponding to Zanobetti and Schwartz (2009) 19

and the other corresponding to Bell et al. (2008) . This was the case for Atlanta, Birmingham, 20

and St. Louis. In both Atlanta and New York , other delineations by other studies forced 21

additional delineation of these urban ar eas, as shown in Table 3-1 above. 22

Finally, we applied the studies of mortality associated with long-term exposure to PM 2.5 

# 23

to the urban areas as defined by the short-term exposure mortality study, Zanobetti and Schwartz 24

(2009), to enable meaningful comparisons betwee n estimates of premature morality associated 25

with short-term and long-term exposure to PM 2.5



# . 26

3.3.3 Selection of Epidemiological Studies a nd Concentration-response (C-R) Functions 

# 27

within those Studies 

# 28

As discussed above, we included in the PM 2.5 

# risk assessment only those health effect 29

endpoint categories (and specific hea lth effects) that met the set ofcriteria reflected in the multi30

factor approach we developed for selecting health effect endpoints (see section 3.3.1). One of 31

these factors was the strength of eviden ce supporting a causal association between PM 2.5 

# 32

exposure and the endpoint of interest. Thus, in ca ses where the majority of the available studies 33

did not report a statistically si gnificant relationship, the effect endpoint was not included. Once 34

it had been determined that a health endpoint would be included in the analysis, however, 35

inclusion of a study on that health endpoint wa s not based on statistical significance alone, but 36

# February 2010 Draft – Do Not Quote or Cite 3-27 considered other factors (e.g., overall design of the study including degree of control for 1

confounders, method used to characterize exposure to PM 2.5 

# within the risk assessment). 2

A significant change since the previous PM ri sk assessment is the addition to the relevant 3

epidemiological literature of several multi-city studies. This type of study has several 4

advantages over single-city studies. First, multi-c ity studies use the same study design in each of 5

the cities included in the study, so that city-specific results ar e readily comparable. Second, 6

when they are estimating a single C-R function ba sed on several cities, multi-city studies also 7

tend to have more statistical pow er and provide effect estimates with relatively greater precision 8

than single city studies due to larger sample sizes, reducing th e uncertainty around the estimated 9

coefficient. Moreover, in a multi-city study the stat istical power to detect an effect in any given 10

city can be supplemented by drawin g statistical power from data across all the ci ties included in 11

the study (or all the cities in the same region) to adjust city -specific estimates towards the mean 12

across all cities included in the an alysis (or in the same region). This is particularly useful in 13

those instances, where a city has relatively less da ta resulting in a larger standard error for the 14

effect estimate. In this situat ion, the information on the C-R re lationship in all the other cities 15

included in a multi-city study can be used to help inform an assessment of the C-R relationship 16

in the city in question. Finally, multi-city studies tend to avoid the often-noted problem of 17

publication bias that single-city studies confront (in which studies with statistically insignificant 18

or negative results are less lik ely to get published than those w ith positive and/or statistically 19

significant results). 20

For this risk assessment, we selected what we considered to be the best study to assess 21

the C-R relationship between PM 2.5 

# and a given health endpoint, and we included other studies 22

for that health endpoint only if they were j udged to contribute something above and beyond what 23

we could learn from the primary study selected. 24

A primary study for a given health endpoint ha d to satisfy the study se lection criteria that 25

we have used in past PM (and other) risk assessments. In particular: 26

 It had to be a published, peer -reviewed study that has been evaluated in the PM ISA and 27

judged adequate by EPA staff for purposes of in clusion in this risk assessment based on 28

that evaluation. 29

 It had to directly measure, rather than estimate, PM 2.5 

# on a reasonable proportion of the 30

days in the study. 31

# February 2010 Draft – Do Not Quote or Cite 3-28 It had to either not rely on Generalize d Additive Models (GAMs) using the S-Plus 1

software to estimate C-R functions or to approp riately have re-estimated these functions using 2

revised methods. 20 

# 3

Because of the advantages noted above, we selected multi-city studies as our primary 4

studies for assessing the risks of premature non-accidental, cardiovascular, and respiratory 5

mortality (Zanobetti and Schwartz, 2009) and card iovascular and respirat ory hospital admissions 6

(Bell et al., 2008) associated w ith short-term exposure to PM 2.5 

# in our core analysis. In each of 7

these studies, the 15 urban areas selected for th e PM risk assessment were among the locations 8

included in their analysis. These two multi-city studies are based on more recent air quality and 9

health effects incidence data for short-term exposure-related mortal ity and morbidity and 10

therefore represent the best stud ies to use in deriving C-R functi ons for this risk assessment. 11

Dominici et al. (2007) was consid ered as an alternative study in identifying C-R functions for 12

modeling short-term exposure-rela ted mortality, however its study period and the underlying air 13

quality data and disease incidence data (1987-2000) are not as current as that of Zanobetti and 14

Schwartz et al., 2009 (study pe riod of 2001-2005), and therefore, we decided to focus on 15

Zanobetti and Schwartz et al. ( 2009) as the source of C-R func tions for modeling short-term 16

exposure-related mortality. 17

Studies often report more than one estimated C-R function for the same location and 18

health endpoint. Sometimes models including diffe rent sets of co-pollutants are estimated in a 19

study; sometimes different lag structures are used . Sometimes different modeling approaches are 20

used to fit weather and temporal variables in the model. Once a study has been selected, the next 21

step is to select one or more C-R func tions from among those reported in the study. 22

Zanobetti and Schwartz (2009) divided the Un ited States into six regions, based on the 23

Köppen climate classification (Kottek 2006; Kottek et al. 2006)(http://koeppen24

eiger.vuwien.ac.at/). 21 

# They estimated the coefficient of PM 2.5 

# in single-pollutant log-linear 25

models using Poisson regression for each of 112 citi es, as well as in two-pollutant models with 26

coarse PM. They estimated annual models (w hich assume that the relationship between 27

mortality and PM 2.5 

# is the same through the year), as well as four seasonal models per location. 28

They then used a random effects meta-analysis to combine the city-specific results (Berkey et al. 29

20 

## The GAM S-Plus problem was discovered prior to the re cent 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 w ith 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 1998). Pooling of city-specific results was done at the national level as well as at the regional 1

level, and separately for each season as well as for the annual functions. 2

With respect to the multi-city study for shor t-term exposure mortality, at the request of 3

EPA, the authors produced Empirical Bayes “shrunke n” city-specific estimates, adjusted towards 4

the appropriate regional mean, using the approach de scribed in Le Tertre et al. (2005). This was 5

done for the annual estimates as well as for each season-specific estimate. 22 

# The annual city6

specific “shrunken” estimates were used in our core analysis. 23 

# The seasonal estimates were 7

used in a sensitivity analysis. City-specific es timates have the advantage of relying on city8

specific data; however, as noted above, such estim ates can have large sta ndard errors (and thus 9

be unreliable); “shrinking” city-specific estimates towards the regional mean estimate is a more 10

efficient use of the data. 24 

# Such “shrinking” can be thought of as combining the advantages of a 11

single-city study (in which the estimation of a city -specific coefficient is not influenced by data 12

from other locations) with the advantages of a multi-city study (in which there is much greater 13

statistical power to detect small effects). 14

In Zanobetti and Sc hwartz (2009) all PM 2.5 

# models used the same lag structure (i.e., an 15

average of same-day and the previous day’s PM 2.5 

# ). The study did, however, examine both 16

single-pollutant and two-pollutant models (with co arse PM). We selected the single-pollutant 17

models, in part to avoid collinearity problems, and in part to be c onsistent with most of the other 18

studies used in the risk assessment, which were single-pollutant studies. 19

Bell et al. (2008) estimated log-linear m odels relating short-term exposure to PM 2.5 

# and 20

hospital admissions for cardiovas cular and respiratory illnes ses among people 65 and older, 21

using a 2-stage Bayesian hierarch ical model, for each of 202 countie s in the United States. They 22

reported both annual and season-specific results, nationally and regionally (for four regions: 23

Northeast, Southeast, Northwest, and Southwest), but not at the local (city-specific) level. All 24

cardiovascular hospital admissions models we re single-pollutant, 0-day lag models; for 25

respiratory hospital admissions, both single-pollutant 0-day mode ls and single-pollutant 2-day 26

models were estimated. We used the regional, annual C-R functions in our core analysis 27

(identifying the appropriate region for each of our 15 risk assessment locations). 25 

# For 28

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 func tions 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 interv als around those estimates. To produ ce correct confiden ce 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 “shrunk en” 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 counti es 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 respiratory hospital admissions (for the core anal ysis), we selected the 2-day lag models, based 1

on evidence that for respiratory effects the str ongest associations with PM exposure may be 2

associated with longer lag periods (on the order of 2 days or more). 26 

# We used the regional 3

season-specific functions in a sensitivity analysis. 4

We identified two studies that estimated C- R relationships between short-term exposure 5

to PM 2.5 

# and emergency department (ED) visits for cardiovascular and/or re spiratory illnesses. 6

(There were no multi-city studies for this catego ry of health endpoint.) Tolbert et al. (2007) 7

examined both cardiovascular and re spiratory ED visits in Atlanta, GA, using single-pollutant 8

log-linear models with a 3- day moving average (0-day, 1- day, and 2-day lags) of PM 2.5 

# . Ito et al. 9

(2007) estimated the relationship be tween short-term exposure to PM 2.5 

# and ED visits for asthma 10

in New York City (Manhattan). They estimated two single-pollutant models, one for the whole 11

year and one for the period from April through A ugust; in addition, they estimated several two12

pollutant models for the period from April throug h August. We selected the single-pollutant 13

model for the whole year for the core analysis, and we explored the impacts of using the annual 14

versus the April-through-August model, as well as the single- versus multi-pollutant models in 15

sensitivity analyses. 16

For the purpose of conducting a sensitivity anal ysis to show the impact of different lag 17

structures, different modeling approaches, and si ngle- versus two-pollutant models on estimates 18

of the risks of premature morta lity and hospital admissions associated with short-term exposure 19

to PM 2.5 

# , we selected Moolgavkar (2003). This study reported results for premature non20

accidental, cardiovascular, and respiratory mort ality and for cardiovascular and respiratory 21

hospital admissions associated w ith short-term exposures to PM 2.5 

# in Los Angeles, using several 22

different lag structures and several different approaches to modeling the effects of weather and 23

temporal variables. 24

In modeling premature mortality asso ciated with long-term exposure to PM 2.5 

# in our core 25

analysis, we selected Krewski et al. (2009) as our primary study. This st udy is an extension of 26

the ACS prospective cohort study (Pope et al., 2002) , used in the previous PM risk assessment,. 27

The Krewski et al., 2009 study (and the underlying ACS dataset) has a number of advantages 28

which informed our selection of this study as the basis for C-R functi ons used in the core 29

analysis, including: (a) extended air quality analysis incorporating data from 1989 to 2000 30

(extending the period of observati on to eighteen years: 1982-2000), which increases the power of 31

the study and allows the study auth ors to examine the important issue of exposure time windows, 32

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 (b) rigorous examination of a range of model form s and effect estimates, including consideration 1

for such factors as spatial autocorrelation in sp ecifying response functions, (c) coverage for a 2

range of ecological variables (soc ial, economic and demographic) which allows for consideration 3

for whether these confound or modi fy the relationship between PM 2.5 

# exposure and mortality, (d) 4

inclusion of a related analysis (focusing on Los Angeles), which allowed for consideration of 5

spatial gradients in PM 2.5 

# and whether they effect respons e models (by addressing effect 6

modification, for example) and (e) large overall dataset with over 1.2 million individuals and 156 7

MSAs. To provide coverage for one of the othe r larger datasets used in prospective cohort 8

analyses of long-term mortality (the six-cites dataset), we sel ected the Krewski et al. (2000) 9

study to provide C-R functions that were used in the sensitivity an alysis completed for this risk 10

assessment. 11

A number of other studies were considered as candidates for use in modeling long-term 12

exposure-related mortality in this analysis. Fo r purposes of transparency, we have included a 13

brief summary here of our rationale for not sel ecting a number of the more high-profile studies 14

for use in the core analysis. The Laden et al. (2006) study (which focused on the six-cities 15

dataset) was not selected because it used visibility data to estimate ambient PM 2.5 

# levels. The 16

Goss et al. (2004) study (based on th e cystic fibrosis data), while addressing an at-risk population 17

of concern, was not selected because of a lack of baseline incidence data for this population 18

which prevents quantitative modeling of mortalit y incidence. The Mill er et al. (2007) study 19

(focusing on the Women’s Health Initiative datase t) while providing coverage for a population of 20

particular interest, was not used, again due to an absence of baseline inci dence data (which is 21

particularly important for this population whic h is typically health ier than the general 22

population). And finally, the Eftim et al. ( 2008) study (focusing on th e Medicare population) 23

was not included because this study did not include representative confounder control for 24

smoking, which introduces uncertainty into C-R functions obtained from the study. 25

Krewski et al. (2009) (the st udy selected as the basis for C- R functions used in the core 26

analysis) considered mortality from all causes, as well as cardiopulmonary mortality, mortality 27

from ischemic heart disease, and lung cancer mortality. The study presents a variety of C-R 28

functions, in an effort to show how the result s vary with various changes to the method/model 29

used. It was not readily appare nt from review of the HEI repo rt, that the aut hors of the study 30

recommended any one of these as clearly superi or to the others. Therefore, we corresponded 31

with the authors of the Krewski et al. (2009) study to obtai n additional clarification regarding 32

specific aspects of the study and associated results as presented in the HEI report (Krewski et al., 33

2009). In response to the our question of wh ether the study authors had a preference for a 34

particular model (in the context of using that mo del and its hazard ratio(s) in risk assessment), 35

the authors stated that they had “refrained from expressing a prefer ence among the results for 36

# February 2010 Draft – Do Not Quote or Cite 3-32 their use in quantitative risk assessment,” preferring to “explore several plausible statistical 1

models that we have fit to the available data.” However, the authors go on to state that “...if one 2

had to choose a model for use in practical applic ations involved in air quality management, one 3

could argue that a rando m effects model (which accounts for a pparent spatial au tocorrelation in 4

the data) might be preferable. A model that in cluded ecological covariates, which has the effect 5

of reducing the residual variation in mortality, might also be of interest. If forced to pick a single 6

model for risk assessment applications in air quality management, our random effects model with 7

ecological covariates might be selected” (Krewski, 2009). 8

In addition to these statements from the st udy authors regarding the model form to use, 9

EPA staff also considered the results of an analysis presented in the study examining the 10

importance of exposure time windows in deriving C- R functions. This analysis suggested that 11

models developed using both expos ure time windows considered in the analysis (1979-1983 and 12

1999-2000) were equally effective at repr esenting the relationship between PM 2.5 

# exposure and 13

long-term exposure-related mortality. Therefore, we concluded that C-R functions used in the 14

core analysis should include functions fitted to both exposure time windows. However, the study 15

does not provide random effects models with ecological covariates for both exposure time 16

windows (this form of model is only provided w ith a fit to the latter exposure window). 17

Therefore, for the core analysis, we decided to use the Cox proportional hazard model with 44 18

individual and 7 ecological variables fitted to both exposure time windows (note, that if the 19

Krewski et al. (2009) study had provided a random effects model with ecol ogical covariates (for 20

both PM monitoring periods – 1979-1983 and 1999-2000), then we would have used those 21

models in our core analysis). 22

In specifying effect estimates for each se t of models, the relative risks for a 10 μg/m 3 

# 23

change in PM 2.5 

# were back-calculated from Table 33 of Krewski et al. (2009). We selected 24

several additional C-R functions fr om Krewski et al. (2009) to use in sensitivity analyses carried 25

out in two risk assessment locations (Los Angeles and Philadelphi a), including the random 26

effects form (section 3.5.4), as de scribed below. In addition, as mentioned earlier, we used C-R 27

functions obtained from Krewski et al. (2000) [reanalysis of the Six Cities Study] in the 28

sensitivity analysis. 29

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

# 30

A summary of the selected health endpoints, ur ban areas, and epidemiological studies used 31

in the risk assessment is given below in Ta bles 3-5 and 3-6 for s hort-term and long-term 32

exposure studies, respectively. A more detailed overview of the locati ons, health endpoints, 33

studies, and C-R functions included in the core analysis is given in Table 3-7. An overview of 34

# February 2010 Draft – Do Not Quote or Cite 3-33 the locations, health endpoints, studies, and C- R functions included in sensitivity analyses is 1

given in Table 3-8. 2February 2010 Draft – Do Not Quote or Cite3-34Table 3-5. Locations, Health Endpoints, andShort-Term Exposure Studies Included in the PM2.5RiskAssessment*Premature MortalityHospital AdmissionsED VisitsUrban AreaNon-AccidentalCardiovascularRespiratoryCardiovascularRespiratoryCardiovascularRespiratoryAtlanta, GATolbert et al.(2007)Tolbert et al.(2007)Baltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXZanobetti andSchwartz (2009)Zanobetti andSchwartz (2009)Zanobetti andSchwartz (2009)Bell et al. (2008)Bell et al. (2008)Los Angeles, CAMoolgavkar(2003)Moolgavkar(2003)Moolgavkar(2003)New York, NYIto et al. (2007)Philadelphia, PAPhoenix, AZPittsburgh, PASalt Lake City,UTSt. Louis, MOTacoma, WAZanobetti andSchwartz (2009)Zanobetti andSchwartz (2009)Zanobetti andSchwartz (2009)Bell et al. (2008)Bell et al. (2008)*Studies in italics are used only in sensitivity analyses.February 2010 Draft – Do Not Quote or Cite3-35Table 3-6. Locations, Health Endpoints, andLong-Term Exposure Studies Included in the PM2.5RiskAssessment*Premature MortalityUrban AreaAll-CauseCardiopulmonaryIschemic Heart DiseaseLung CancerAtlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXNew York, NYPhoenix, AZPittsburgh, PASalt Lake City, UTSt. Louis, MOTacoma, WAKrewski et al. (2009) [extensionof the ACS study]Krewski et al. (2009) [extensionof the ACS study]Krewski et al. (2009) [extensionof the ACS study]Los Angeles, CAPhiladelphia, PAKrewski et al. (2009) [extensionof the ACS study]Krewski et al. (2000) [reanalysisof the Six Cities Study]Krewski et al. (2009) [extensionof the ACS study]Krewski et al. (2000) [reanalysisof the Six Cities Study]Krewski et al. (2009) [extensionof the ACS study]Krewski et al. (2009) [extensionof the ACS study]Krewski et al. (2000) [reanalysisof the Six Cities Study]*Studies in italics are used only in sensitivity analyses.February 2010 Draft – Do Not Quote or Cite3-36Table 3-7. Summary of Locations, Health Endpoints, Studies and Concentration-Response Functions Included inthe Core Analysis.*RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureZanobetti and Schwartz (2009)1Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009)1Short-term exposure cardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009)1Short-term exposure respiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)2Long-term exposure all-cause mortality NAKrewski et al. (2009)2Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009)2Long-term exposure ischemicheart disease mortality NACobb, De Kalb, Fulton,GwinnettKrewski et al. (2009)2Long-term exposure lung cancer mortality NABell et al. (2008)3Short-term exposure HA (unscheduled), cardiovascular 0-day lagCobb, DeKalb, Fulton,Bell et al. (2008)3Short-term exposure HA (unscheduled), respiratory 2-day lagTolbert et al. (2007) Short-term exposure Emergency room (ED) visits,cardiovascularAvg. of 0-,1-day, and2-day lagsAtlantaBarrow, Bartow,Carroll, Cherokee,Clayton, Cobb, Coweta,DeKalb, Douglas,Fayette, Forsyth,Fulton, Gwinett, Henry,Newton, Paulding,Pickens, Rockdale,Spalding, WaltonTolbert et al. (2007) Short-term exposure Emergency room (ED) visits,respiratoryAvg. of 0-,1-day, and2-day lagsZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsBaltimore Baltimore city,Baltimore countyZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsFebruary 2010 Draft – Do Not Quote or Cite3-37RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NABlount, Jefferson,Shelby, St. Clair,WalkerKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagBirminghamJeffersonBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsDallas DallasKrewski et al. (2009) Long-termexposure all-cause mortality NAFebruary 2010 Draft – Do Not Quote or Cite3-38RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagDetroit WayneBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAFresno FresnoKrewski et al. (2009) Long-term exposure lung cancer mortality NAFebruary 2010 Draft – Do Not Quote or Cite3-39RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureBell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagHouston HarrisBell et al. (2008) Short-term exposure HA (unscheduled), respiratory2-day lagZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagLos Angeles Los AngelesBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagFebruary 2010 Draft – Do Not Quote or Cite3-40RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagKings, New York City(Manhattan), Queens,Richmond, BronxBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagNew YorkNew York City(Manhattan)Ito et al. (2007) Short-term exposure Emergency room (ED) visits,asthmaAvg. of 0-day and1-day lagsZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagPhiladelphia PhiladelphiaBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagPhoenix Maricopa Zanobetti and Schwartz(2009) Short-term exposure non-accidental mortality Avg. of 0-day and 1-day lagsFebruary 2010 Draft – Do Not Quote or Cite3-41RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagPittsburgh AlleghenyBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NASalt Lake City Salt LakeKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAFebruary 2010 Draft – Do Not Quote or Cite3-42RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAJeffferson, Madison(IL), St. Louis, St.Louis city, St. Clair (IL)Krewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagSt. LouisMadison (IL), St. Louis,St. Louis city, St. Clair(IL)Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009) Long-termexposure all-cause mortality NAKrewski et al. (2009) Long-term exposure cardiopulmonary mortality NAKrewski et al. (2009) Long-term exposure ischemic heart disease mortality NAKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008) Short-term exposure HA (unscheduled), cardiovascular 0-day lagTacoma PierceBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lag*All C-R functions in the core analysis are single-pollutant, log-linear models; all are for a full year. The exposure metricfor all short-term exposure C-Rfunctions is the 24-hour average; the exposure metric for all long-term exposure C-R functions is the annual average.1This is a multi-city study; city-specific estimates “shrunken”towards the mean across all cities in a region were supplied toEPA (Zanobetti, 2009).2Two C-R functions were used for the core analysis – one corresponding to the earlier exposure period, from 1979 – 1983, and the other corresponding to thelater exposure period, from 1999 – 2000. Both C-R functions were based on follow-up of the cohort through 2000. Both used the standard Cox proportionalFebruary 2010 Draft – Do Not Quote or Cite3-43hazards model, with 44 individual and 7 ecologic covariates. The relative risks for a 10 μg/m3change in PM2.5from which the PM2.5coefficients were backcalculated were taken from Table33 of Krewski et al. (2009).3This study estimated four regional C-R functions – for the Northeast, Southeast, Northwest, and Southwest – for each health endpoint. For each riskassessment location, we used the regional C-R function for the region containing the risk assessment location. The designationof counties to each of thesefour regions can be found athttp://www.biostat.jhsph.edu/MCAPS/estimates-full.html.February 2010 Draft – Do Not Quote or Cite3-44Table 3-8. Summary of Locations, Health Endpoints, Studies and Concentration-Response Functions Included inSensitivity Analyses.Sensitivity AnalysisStudy/C-R FunctionHealth Endpoint**Risk AssessmentLocation(s)Single-Factor Sensitivity Analyses:Impact of using different model choices – fixed effectslog-linear vs. random effects log-linear vs. randomeffects log-log C-R function*random effects log-linear:Krewski et al. (2009) [Table 9,"Autocorrelation at MSA andZCA levels" group - "MSA &Diff" row]random effects log-log:Krewski et al. (2009) [Table 11,"MSA and DIFF" rows]All-cause, cardiopulmonary, ischemicheart disease, and lung cancer mortalityassociated with long-term exposureLos Angeles andPhiladelphiaImpact of using copollutant models in modeling longterm exposure-related mortalityKrewski et al., 2000 (reanalysisof ACS) – provides 2-pollutantmodels combining PM2.5withCO, NO2, O3or SO2.All-cause mortality associated withlong-term exposureLos Angeles andPhiladelphiaImpact of estimating risks down to PRB rather thandown to LMLKrewski et al. (2009) – C-Rfunctions for each of twoexposure periodsLong-term exposure all-causemortality All 15 urban areasImpact of C-R function from alternative long-termexposure studyKrewski et al. (2000) [reanalysisof the Harvard Six Cities study]All-cause, cardiovascular, respiratory,lung cancer mortality associated withlong-term exposureLos Angeles andPhiladelphiaImpact of using alternative hybrid rollback approach(note, that as discussed insection 3.2.3, in addition tothe hybrid rollback approach, we have also included apeak-shaving rollback approachas an alternative to theproportional rollback approach).27Krewski et al. (2009) All-causemortality associated withlong-term exposureBaltimore, Birmingham,Detroit, Los Angeles, NewYork, Pittsburgh, and St.LouisImpact of using season-specific C-R functions (vs. anannual C-R function)Zanobetti and Schwartz (2009) –seasonal functions vs. annualfunctionNon-accidental mortality,cardiovascular mortality, respiratorymortality associated with short-termAll 15 urban areas27However, as noted in section 3.2.3 and in section 3.5.4, quantitative risk estimates were not generated using the peak-shavingapproach andinstead, composite monitor values (acting assurrogates for long-term exposure-relatedrisk) were used as the basis for the sensitivity analysis involving thepeak-shaving rollback approach.February 2010 Draft – Do Not Quote or Cite3-45Sensitivity AnalysisStudy/C-R FunctionHealth Endpoint**Risk AssessmentLocation(s)exposureImpact of using season-specific C-R functions (vs. anannual C-R function)Bell et al. (2008) – seasonalfunctions vs. annual functionHA (unscheduled), cardiovascular andrespiratory,associated with short-termexposureAll 15 urban areasImpact of using an annual C-R function (applied to thewhole year) vs. a seasonal function for April throughAugust (applied only to that period) (using a singlepollutant model).Ito et al. (2007) Asthma ED visits New YorkImpact of model selection (e.g., log-linear GAM with 30df; log-linear GAM with 100 df; and log-linear GLMwith 100 df)Moolgavkar (2003) Non-accidental and cardiovascularmortality; and cardiovascular andCOPD+ HA associatedwith short-termexposureLos AngelesImpact of lag structure (0-day, 1-day, 2-day) Moolgavkar (2003) Non-accidental and cardiovascular andCOPD+ HA associatedwith short-termexposureLos AngelesImpact of single- vs. multi-pollutant models (PM2.5with CO)Moolgavkar (2003) Non-accidental and cardiovascularmortality; and cardiovascular andCOPD+ HA associatedwith short-termexposureLos AngelesImpact of using alternativehybrid rollback approach Zanobetti and Schwartz (2009) Non-accidental mortality associatedwith short-term exposureBaltimore, Birmingham,Detroit, Los Angeles, NewYork, Pittsburgh, and St.LouisImpact of lag structure (0-day, 1-day, 2-day) Bell et al., 2008 Cardiovascular and respiratory hospitaladmissions associated with short-termexposureLos Angeles andPhiladelphiaMulti-Factor Sensitivity Analyses:Impact of using a fixed effects log-linear vs. a randomeffects log-log model, estimating incidence down to thelowest measured level (LML) in the study vs. down toPRB, and using a proportional vs. hybrid rollback toestimate incidence associatedwith long-term exposureto PM2.5concentrations that just meet the currentstandardsAll-cause and ischemic heart diseasemortality associated with long-termexposureLos Angeles andPhiladelphiaImpact of using season-specific vs. all-year C-Rfunctions and proportional vs. hybrid rollbacks toestimate incidence associated with short-term exposureto PM2.5concentrations that just meet the currentZanobetti and Schwartz (2009) Non-accidental mortality associatedwith short-term exposureBaltimore, Birmingham,Detroit, Los Angeles, NewYork, Pittsburgh, and St.LouisFebruary 2010 Draft – Do Not Quote or Cite3-46Sensitivity AnalysisStudy/C-R FunctionHealth Endpoint**Risk AssessmentLocation(s)standards*This “single-factor” sensitivity analysis is actually two factors – first the change from a fixed effects log-linear model toa random effects log linear model,and then the change from a random effects log-linear model to arandom effects log-log model. These were combined into a single sensitivity analysisbecause Krewski et al. (2009) did not present the results of a fixed effects log-log model (to compare to the core analysis fixed effects log-linear model).**”HA” = hospital admissions, “ED” = emergency departmentvisits, “COPD+” = chronic obstructive pulmonary disease.

# February 2010 Draft – Do Not Quote or Cite 3-47 3.4 BASELINE HEALTH EFFE CTS INCIDENCE DATA 

# 1

As noted in section 3.2.1 above, the form of C-R function most commonly used in 2

epidemiological studies on PM, shown in equation (1), is log-linear. To estimate the change in 3

incidence of a health endpoint asso ciated with a given change in PM 2.5 

# concentrations using this 4

form of C-R function requires the baseline incide nce (often calculated as the baseline incidence 5

rate times the population) of the health endpoint, that is, the number of cases per unit time (e.g., 6

per year) in the location before a change in PM 2.5 

# air quality (denoted y 0 

# in equations 3 and 4). 7

Incidence rates express the occurrence of a disease or event (e.g., asthma episode, death, 8

hospital admission) in a specific pe riod of time, usually per year. Ra tes are expressed either as a 9

value per population group (e.g., the number of cases in Philadelphia County) or a value per 10

number of people (e.g., the number of cases per 10,000 residents in Philadelphia County), and 11

may be age- and sex-specific. Incidence rates vary among geographic areas due to differences in 12

population characteristics (e.g., ag e distribution) and f actors promoting illness (e.g., smoking, air 13

pollution levels). 14

3.4.1 Data Sources 

# 15

3.4.1.1 Mortality 

# 16

We obtained individual-level mortality data for 2006 for the whole United States from 17

the Centers for Disease Control (CDC), National Ce nter for Health Statistics (NCHS). The data 18

are compressed into a CD-ROM, which contains death information for each decedent, including 19

residence county FIPS, age at de ath, month of death, and und erlying causes (ICD-10 codes). 20

The detailed mortality data allow us to generate cause-specific d eath counts at the county level 21

for selected age groups. Below we describe how we generated the county-level death counts. 22

3.4.1.2 Hospital Admission and Emergency Department Visits 

# 23

For hospital admissions (HA) and emergency department (ED) visits, there are multiple 24

data sources: 25

 Healthcare Cost and Utilization Pr oject (HCUP) Central Distributor 

# . HCUP is a 26

family of health care databases developed through a Federal-Stat e-Industry partnership 27

and sponsored by the Agency for Healthcare Research and Quality (AHRQ). The HCUP 28

databases are based on the data collection effo rts of data organizati ons in participating 29

states. We used two HCUP databases: the State Inpatient Database (SID) and the State 30

Emergency Department Database (SEDD) respectively. SID/SEDD include detailed 31

HA/ED information for each discharge, including patient county FIPS, age, admission 32

type (e.g., emergent, urgent), admission/discharge season, a nd principle diagnosis (ICD-9 33

codes). The HCUP databases can be purchas ed from the HCUP Central Distributor, 34

although not all participant st ates release the data to the Central Distributor. 35

# February 2010 Draft – Do Not Quote or Cite 3-48  HCUP State Partners 

# . For those HCUP participating stat es that don’t release their data 1

to the Central Distributor, we contacted the HCUP state partners to obtain the HA and/or 2

ED data. 3

 Communication with the author(s) of selected epidemiological studies 

# . The ED data 4

for Atlanta in 2004 were sent to EPA by one of the authors of Tolbert et al. (2007). 5

Table 3-9 shows the states for which we obtained data from the HCUP Central 6

Distributor and the HCUP State Pa rtners. The data are at the di scharge level if not otherwise 7

noted, and the data year is 2007 for all the states in the table. The column “PM RA Location” 8

indicates the selected risk assessment location( s) where the incidence rate is applied. 9

The necessary baseline incidence data we re not available for Atlanta, Birmingham, 10

Philadelphia, Pittsburgh and St. Louis. Theref ore, for each of these five risk assessment 11

locations EPA instead used the baseline incide nce rate for a designated surrogate location. 12

Surrogate locations were chosen if they were deemed to be sufficiently similar to the urban area 13

whose baseline incidence data were not available. Surrogate locations are noted in Table 3-9. 14

15

Table 3-9. Sources of Hospital Admission s (HA) and Emergency Department (ED) 

# 16

Visit Data. 

# 17

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 *NA denotes “not available, or not available with all variab les required for our analysis. If data were not available 

# 1

from the HCUP Central Distributor, we contacted the HCUP State Partner. 

# 2

3

3.4.1.3 Populations 

# 4

To calculate baseline incidence rate, in additi on to the health baseline incidence data we 5

also need the corresponding population. We obtained population data from the U.S. Census 6

Bureau ( http://www.census.gov/popest/counties/asrh/ ). These data, released on May 14, 2009, 7

are the population estimates of the resident populations by selected age groups and sex for 8

counties in each U.S. state from 2000 to 2008. We used 2007 populations for calculating most 9

incidence rates except for the ED visit rate in At lanta. Because the ED visit data obtained from 10

the authors of Tolbert et al. (2007) are for 2004, we used 2004 population estimates for the 20- 11

county Metropolitan area used in the Tolbert et al. study for the Atlanta ar ea to calculate the ED 12

incidence rates to be applied when using that st udy in the risk assessment; we then applied the 13

2004 rates to the 2007 population, assuming the ED incidence rates in Atlanta did not change 14

significantly from 2004 to 2007. The sizes of the popul ations in the assessme nt locations that are 15

relevant are shown below in Table 3-10. 16February 2010 Draft – Do Not Quote or Cite3-50Table 3-10. Relevant Population Sizes for PM Risk Assessment Locations.Population (Year 2006 and 2007)*All AgesAges$30Ages$65CityCounties200620072006200720062007Atlanta, GA - 1Cobb, De Kalb, Fulton, Gwinnett3,126,000 3,198,000 1,817,000 1,865,000 236,000 245,000Atlanta, GA - 2 Cobb, De Kalb, Fulton2,376,000, 2,421,000 1,400,000 1,433,000 191,000 198,000Atlanta, GA - 320-County MSA**4,975,000 5,123,000 2,831,000 2,918,000 391,000 408,000Baltimore, MDBaltimore city, Baltimore county1,429,000 1,426,000 849,000 848,000 190,000 189,000Birmingham, AL - 1 Blount, Jefferson, Shelby, St. Clair,Walker1,037,000 1,044,000 619,000 625,000 131,000 133,000Birmingham, AL - 2 Jefferson660,000 659,000 397,000 397,000 88,000 88,000Dallas, TXDallas2,338,000 2,367,000 1,285,000 1,308,000 195,000 199,000Detroit, MIWayne2,012,000 1,985,000 1,176,000 1,168,000 236,000 234,000Fresno, CA Fresno886,000 899,000 444,000 452,000 86,000 87,000Houston, TXHarris3,876,000 3,936,000 2,097,000 2,139,000 299,000 307,000Los Angeles, CALos Angeles9,881,000 9,879,000 5,544,000 5,579,000 1,011,000 1,030,000New York, NY - 1 Kings, New York City (Manhattan),Queens, Richmond, Bronx8,251,000 8,275,000 4,940,000 4,975,000 1,004,000 1,013,000New York, NY - 2New York city (Manhattan)1,613,000 1,621,000 1,061,000 1,074,000 201,000 204,000Philadelphia, PAPhiladelphia833,000 1,450,000 833,000 833,000 189,000 187,000Phoenix, AZMaricopa3,779,000 3,880,000 2,103,000 2,167,000 417,000 432,000Pittsburgh, PAAllegheny1,225,000 1,219,000 790,000 786,000 208,000 206,000Salt Lake City, UTSalt Lake991,000 1,010,000 504,000 517,000 83,000 86,000St. Louis, MO - 1Jefferson, Madison (IL), St. Louis, St.Louis city, St. Clair (IL)2,093,000 2,091,000 1,259,000 1,261,000 274,000 275,000February 2010 Draft – Do Not Quote or Cite3-51Population (Year 2006 and 2007)*All AgesAges$30Ages$65CityCounties200620072006200720062007St. Louis, MO - 2 Madison (IL), St. Louis, St. Louis city,St. Clair (IL)1,879,000 1,875,000 1,134,000 1,134,000 253,000 252,000Tacoma, WA Pierce764,000 773,000 437,000 444,000 79,000 81,000* Not all populations listed in the table were used for calculating the incidence rates. As noted above, the population yearneeds to match the year of the healthdata and the population age group needs to match what is used in the epidemiological studies. In addition, 2004 population (all ages) is used for ED visits inAtlanta-3, which is 4,663,946. Populations inthis 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, Newton, Paulding, Pickens,Rockdale, Spalding, and Walton.

# February 2010 Draft – Do Not Quote or Cite 3-52 3.4.2 Calculation of Baseline Incidence Rates 

# 1

To calculate a baseline incidence rate to be used with a C-R function from a given study, 2

we matched the counties, age groupings, and ICD c odes used in that study. For example, Bell et 3

al. (2008) designated Dallas, TX as Dallas Count y and estimated a C-R function for ICD-9 codes 4

490–492, 464–466, and 480–487 (respiratory HA) among ages 65 and up; we therefore selected 5

only those HA records that had corresponding IC D codes for ages 65 and up in Dallas County 6

and also selected the pop ulation for the same age group in the same county. The incidence rate 7

is simply the ratio of the selected HA count to the population. The same procedure was used to 8

calculate baseline incidence rates for all of the risk assessment locations. 28 

# 9

If a C-R function was estimated for a specifi c season, we selected only those HA records 10

within that season. The season definitions are: winter (December, January, and February), spring 11

(March, April, and May), summer (June, July, an d August) and fall (September, October, and 12

November). Note that the HA data for some states didn’t include in formation about admission 13

season but only discharge season or discharg e quarter. The admission season was then 14

approximated using discharge season or discharge quarter. 29 

# 15

Some studies (e.g., Bell et al., 2008) look at the unscheduled HAs only, so we excluded 16

scheduled admissions from the analyses to ma tch the study. A HA is unscheduled if the 17

admission type is emergency or urgent. 18

The baseline mortality rates are given in Ta ble 3-11. The baseline HA and ED visit rates 19

are given in Table 3-12. 20

28 

## For Atlanta, Birmingham, Ph iladelphia, 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.February 2010 Draft – Do Not Quote or Cite3-53Table 3-11. Baseline Mortality Rates (Deaths per 100,000 Relevant Population per Year) for 2006 for PM Risk AssessmentLocations.*Type of Mortality (ICD-10 or ICD-9 Codes)CityAge GroupAll-CauseNon-accidental(A00-R99)Cardiovascular(I01-I59)Respiratory(J00-J99)Cardiopulmonary(401-440, 460-519)IschemicHeartDisease(410-414)LungCancer(162)COPD(490-496)Atlanta, GA - 1All ages --- 480 120 41 --- --- --- ---Atlanta, GA - 1≥30 860 --- --- --- 330 89 51 ---Atlanta, GA - 2--- --- --- --- --- --- --- --- ---Atlanta, GA - 3--- --- --- --- --- --- --- --- ---Baltimore, MDAll ages --- 950 270 85 --- --- --- ---Baltimore, MD≥30 1,700 --- --- --- 690 300 110 ---Birmingham, AL - 1All ages --- 920 260 85 --- --- --- ---Birmingham, AL - 1≥30 1,600 --- --- --- 680 190 104 ---Birmingham, AL - 2--- --- --- --- --- --- --- --- ---Dallas, TXAll ages --- 540 150 48 --- --- --- ---Dallas, TX≥30 1,020 --- --- --- 420 170 66 ---Detroit, MIAll ages --- 850 300 67 --- --- --- ---Detroit, MI≥30 1,500 --- --- --- 700 360 107 ---Fresno, CAAll ages --- 620 190 67 --- --- --- ---Fresno, CA≥30 1,300 --- --- --- 590 260 66 ---Houston, TXAll ages --- 480 130 37 --- --- --- ---Houston, TX≥30 920 --- --- --- 370 150 57 ---Los Angeles, CAAll ages --- 560 190 57 --- --- --- 29Los Angeles, CA≥30 1,030 --- --- --- 510 250 55 ---New York, NY - 1All ages --- 630 270 52 --- --- --- ---February 2010 Draft – Do Not Quote or Cite3-54Type of Mortality (ICD-10 or ICD-9 Codes)CityAge GroupAll-CauseNon-accidental(A00-R99)Cardiovascular(I01-I59)Respiratory(J00-J99)Cardiopulmonary(401-440, 460-519)IschemicHeartDisease(410-414)LungCancer(162)COPD(490-496)New York, NY - 1≥30 1,0800 --- --- --- 580 380 56 ---New York, NY - 2--- --- --- --- --- --- --- --- ---Philadelphia, PAAll ages --- 970 280 83 --- --- --- ---Philadelphia, PA≥30 1,700 --- --- --- 720 300 120 ---Phoenix, AZAll ages --- 600 160 67 --- --- --- ---Phoenix, AZ≥30 1,100 --- --- --- 470 220 68 ---Pittsburgh, PAAll ages --- 1,090 330 96 --- --- --- ---Pittsburgh, PA≥30 1,800 --- --- --- 770 350 120 ---Salt Lake City, UTAll ages --- 480 110 45 --- --- --- ---Salt Lake City, UT≥30 980 --- --- --- 350 101 37 ---St. Louis, MO - 1All ages --- 870 270 83 --- --- --- ---St. Louis, MO - 1≥30 1,500 --- --- --- 680 320 106 ---St. Louis, MO - 2--- --- --- --- --- --- --- --- ---Tacoma, WAAll ages --- 660 190 66 --- --- --- ---Tacoma, WA≥30 1,200 --- --- --- 510 240 88 ---NationalAll ages 810 750 220 76 340 140 53 42National≥30 1,300 1,300 370 130 580 240 90 71* Figures in this table are rounded to a two-integer level of precision.February 2010 Draft – Do Not Quote or Cite3-55Table 3-12. Baseline Hospital Admission(HA) and Emergency Department (ED) Rates (Admissions/Visits per 100,000Relevant 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 becauseTolbert et al. (2007) provided 2004 ED visit data in a 20-county delineation of Atlanta. However, the 2004 rates wereapplied to the appropriate year population in the risk assessment.Health Endpoints (ICD-9 Codes)CityAge GroupHA, 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, respiratory(460–465, 466.1, 466.11,466.19, 477, 480–486, 491-493, 496, 786.07, 786.09)ED visits,asthma(493)Atlanta,GA - 1--- --- --- --- --- --- --- ---Atlanta,GA - 2≥65 --- 5,700 --- 2,020 --- --- ---Atlanta,GA - 3All ages --- --- --- --- 690** 2600** ---Baltimore,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,CAAll ages --- --- 223 --- --- --- ---Los Angeles,CA≥65 5,500 5,500 --- 2,000 --- --- ---New York,NY - 1≥65 --- 6,400 --- 2,030 --- --- ---New York,NY - 2All ages --- --- --- --- --- --- 1,100Philadelphia,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 3.5 ADDRESSING UNCERTAINTY AND VARIABILITY 

# 1

3.5.1 Overview 

# 2

An important component of a population health risk assessment is the characterization of 3

both uncertainty and variability. Variability refers to the heterogeneity of a variable of interest 4

within a population or across different populatio ns. For example, populations in different 5

regions of the country may have different beha vior and activity patterns (e.g., air conditioning 6

use, time spent indoors) that aff ect their exposure to ambient PM and thus the population health 7

response. The composition of populations in differe nt regions of the country may vary in ways 8

that can affect the population response to exposur e to PM – e.g., two populations exposed to the 9

same levels of PM might respond differently if one population is older than the other. In 10

addition, the composition of the PM to which di fferent populations are exposed may differ, with 11

different levels of toxicity and thus different population responses . Variability is inherent and 12

cannot be reduced through further research. Refinements in the design of a population risk 13

assessment are often focused on more completely characterizing variability in key factors 14

affecting population risk – e.g., factors affecti ng population exposure or re sponse – in order to 15

produce risk estimates whose distribution adequa tely characterizes the distribution in the 16

underlying population(s). 17

Uncertainty refers to the lack of knowledge regard ing the actual values of inputs to an 18

analysis. Models are typically us ed in analyses, and there is uncer tainty about the true values of 19

the parameters of the model (parameter uncertain ty) – e.g., the value of the coefficient for PM 2.5 

# 20

in a C-R function. There is also uncertainty a bout the extent to which the model is an accurate 21

representation of the underlyi ng physical systems or relati onships being modeled (model 22

uncertainty) – e.g., the shapes of C-R functions. In addition, there may be some uncertainty 23

surrounding other inputs to an anal ysis due to possible measurement error—e.g., the values of 24

daily PM 2.5 

# concentrations in a risk assessment locati on, or the value of the baseline incidence 25

rate for a health effect in a popul ation. In any risk assessment, uncertainty is, ideally, reduced to 26

the maximum extent possible through improve d measurement of key variables and ongoing 27

model refinement. However, significant uncertain ty often remains, and emphasis is then placed 28

on characterizing the nature of that uncertain ty and its impact on risk estimates. The 29

characterization of uncertainty can be both qualitative and, if a sufficient knowledgebase is 30

available, quantitative. 31

The selection of urba n study areas for the PM 2.5 

# risk assessment was designed to cover 32

the range of PM 2.5 

# -related risk experienced by the U.S. popula tion and, in general, to adequately 33

reflect the inherent variability in those fact ors affecting the public health impact of PM 2.5 

# 34

exposure. Sources of variability reflected in the risk assessmen t design are discussed in section 35

# February 2010 Draft – Do Not Quote or Cite 3-57 3.5.2, along with a discussion of those sources of variability whic h are not fully reflected in the 1

risk assessment and consequently introduce uncertainty into the analysis. 2

The characterization of uncertainty associated with risk assessment is often addressed in 3

the regulatory context using a tiered approach in which progr essively more sophisticated 4

methods are used to evaluate and characterize sources of uncertainty depending on the overall 5

complexity of the risk assessm ent (WHO, 2008). Guidance documents developed by EPA for 6

assessing air toxics-related risk and Superfund Site risks (USE PA, 2004b and 2001, respectively) 7

as well as recent guidance from the World H ealth Organization (WHO, 2008) specify multi8

tiered approaches for addressing uncertainty. 9

The WHO guidance presents a four-tiered appr oach, where the decision to proceed to the 10

next tier is based on the outcome of the previous tier’s assessment. The four tiers described in the 11

WHO guidance include: 12

 Tier 0 

# – recommended for routine screening asse ssments, uses default uncertainty factors 13

(rather than developing site-specific uncertainty characterizations); 14

 Tier 1 

# – the lowest level of site-specific uncerta inty characterization, involves qualitative 15

characterization of sources of uncertainty (e .g., a qualitative assessment of the general 16

magnitude and direction of th e effect on risk results); 17

 Tier 2 

# – site-specific deterministic quantitative analysis involving sensitivity analysis, 18

interval-based assessment, and possibl y probability bound (high- and low-end) 19

assessment; and 20

 Tier 3 

# – uses probabilistic methods to characteri ze the effects on risk estimates of sources 21

of uncertainty, individually and combined. 22

With this four-tiered approach, the WHO fr amework provides a means for systematically 23

linking the characterization of uncer tainty to the sophist ication of the underlying risk assessment. 24

Ultimately, the decision as to which tier of un certainty characterization to include in a risk 25

assessment will depend both on the overall soph istication of the risk assessment and the 26

availability of information for characterizing th e various sources of uncertainty. EPA staff has 27

used the WHO guidance as a framework for deve loping the approach used for characterizing 28

uncertainty in this risk assessment. 29

The overall analysis in the PM NAAQS risk assessment is relatively complex, thereby 30

warranting consideration of a full probabilistic (W HO Tier 3) uncertainty analysis. However, 31

limitations in available information prevent this le vel of analysis from being completed at this 32

time. In particular, the incorporation of uncerta inty related to key elements of C-R functions 33

(e.g., competing lag structures, alternative functi onal forms, etc.) into a full probabilistic WHO 34

Tier 3 analysis would require that probabilities be assigned to each competing specification of a 35

given model element (with each probability reflec ting a subjective assessmen t of the probability 36

# February 2010 Draft – Do Not Quote or Cite 3-58 that the given specification is th e “correct” description of reality). However, for many model 1

elements there is insufficient information on which to base these probabilities. One approach that 2

has been taken in such cases is expert elicitat ion; however, this approach is resource- and time3

intensive and consequently, it was not feasible to use this technique in the current PM NAAQS 4

review to support a WHO Tier 3 analysis. 30 

# 5

For most elements of this risk assessme nt, rather than conducting a full probabilistic 6

uncertainty analysis, we have included qualita tive discussions of the potential impact of 7

uncertainty on risk results (WHO Tier1) and/or completed sensitivity analyses assessing the 8

potential impact of sources of un certainty on risk results (WHO Ti er 2). Note, however, that in 9

conducting sensitivity analyses, we have used bot h single- and multi-factor approaches (to look 10

at the individual and combined im pacts of sources of uncertainty on risk estimates). Also, as 11

discussed below in section 3.5.4, in conducting sensitivity analyses, we used only those 12

alternative specifications for input parameters or modeling approach es that were deemed to have 13

scientific support in the literature (and so represen t alternative reasonable input parameter values 14

or modeling options). This means that the alte rnative risk results generated in the sensitivity 15

analyses represent reasonable risk estimates that can be used to provide a c ontext, with regard to 16

uncertainty, within which to assess the set of co re (base case) risk resu lts (see section 4.5.3). 17

The sensitivity analysis also includes coverage for potential variability in the pattern of 18

reductions in ambient PM 2.5 

# concentrations associated with simulations of just meeting the 19

current and alternative suites of standards. Specifically, as discussed above in section 3.2.3, we 20

have included three alternative rollback methods (proportional, hybrid and peak shaving) to 21

provide coverage for variability in this potentially important factor influencing risk estimates. 22

In addition to the qualitative and quantitativ e treatment of uncertainty and variability 23

which are described here, we have also complete d two additional analyses intended to place the 24

risk results generated for the 15 urban study areas in a broader nati onal context. The first is a 25

representativeness analysis (described in section 4.4) which evaluates the set of urban study areas 26

against national-distributions of key PM risk-related attributes (with the goal of determining the 27

degree to which the study areas are representative of national trends in these parameters). The 28

second is a national-scale assessment of long-term mortality related to PM 2.5 

# exposures 29

(discussed in chapter 5). In addition to provi ding an estimate of the national impact of PM 2.5 

# on 30

long-term mortality, this analysis also evaluates whether the set of 15 urban study areas generally 31

represents the broader distribution of risk across the U.S ., or a more focused portion of the 32

national risk distribution (e.g., the higher-end). 33

30 

## Note, that while a full probabilistic uncertainty analysis wa s not completed for this risk assessment, we were able

to use confidence intervals as sociated with effects estimates (obtained from epidemiological st udies) to incorporate

statistical uncertainty associated w ith sample size considerations in the presentation of risk estimates.

# February 2010 Draft – Do Not Quote or Cite 3-59 The remainder of this section is organized as follows. Key sources of variability which 1

are reflected in the design of the risk assessmen t, along with sources excluded from the design, 2

are discussed in section 3.5.2. A qualitative di scussion of key sources of uncertainty associated 3

with the risk assessment (including the potential direction, magnitude and degree of confidence 4

associated with our understanding of the sour ce of uncertainty – the knowledge base) is 5

presented in section 3.5.3. The methods and re sults of the single- and multi-factor sensitivity 6

analyses completed for the risk assessment are pr esented in section 3.5.4. An overall summary 7

of the methods used to address uncertainty and variability for the 15 urban study areas (including 8

the two assessments intended to place the urban study areas in a broader national context) is 9

presented in section 3.5.5. 10

3.5.2 Treatment of Key Sources Of Variability 

# 11

The risk assessment was designed to cover th e key sources of variability related to 12

population exposure and exposure response, to the extent supporte d by available data. 31 

# 13

However, as with all risk assessments, there are sources of variability wh ich have not been fully 14

reflected in the design of the risk assessment a nd consequently introduce a degree of uncertainty 15

into the risk estimates. While different sour ces of variability were captured in the risk 16

assessment, it was generally not possible to separa te out the impact of each factor on population 17

risk estimates, since many of the sources of vari ability are reflected co llectively in a specific 18

aspect of the risk model. For example, inclusi on of urban study areas fr om different PM regions 19

likely provides some degree of coverage for a variety of factors associated with PM 2.5 

# risk (e.g., 20

air conditioner use, PM 2.5 

# composition, differences in population commuting and exercise 21

patterns, weather). However, the model is not sufficiently precise or disaggregated to allow the 22

individual impacts of any one of these sources of variability on the risk estimates to be 23

characterized. 24

Key sources of potential variability that are lik ely to affect population risks are discussed 25

below, including the degree to which they are (or are not) fully captured in the design of the risk 26

assessment: 27

31 

## The term “key sources of variability” refers to those sour ces 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 di fferent elements of those earlier sensitivity analyses) can

influence risk estimates, as well as in formation 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 sta ff in identifying key sources of variability to address both

in the risk assessment and in the qualitative discu ssion of variability presented in this section.

# February 2010 Draft – Do Not Quote or Cite 3-60  PM 2.5 

# composition 

# : While information was not av ailable to support modeling risk 1

associated with different components of PM 2.5 

# , the assessment did use effect estimates 2

(for a number of the short-term exposure-re lated health endpoint s) differentiated by 3

region of the country, or differentiated for specific urban locations (sections 3.3.3 and 4

3.3.4). While many factors may contribute to differences in effect estimates (for the 5

same health endpoint) across different lo cations, compositional differences in PM 2.5 

# 6

may be partially responsible. Therefore, while the analysis did not explicitly address 7

compositional differences in generating risk estimates, potential differences in PM 2.5 

# 8

composition may be reflected in those effect estimates that are differentiated by region 9

and/or urban study area. The effect estimates for mortal ity associated with long-term 10

exposure to PM 2.5 

# are not regionally differentiated a nd instead, a single national-scale 11

estimate is used. This means that any differe nces in risks of mortality associated with 12

long-term exposure to PM 2.5 

# that are linked to differences in PM 2.5 

# composition (or to 13

any other differences across regions or loca tions) would not be discernable, since a 14

single national-scale risk estimate is generate d for each mortality category. In addition 15

to using region- or location-specific effect es timates for health effects associated with 16

short-term exposures, the selection of urban areas to include in the risk assessment was 17

designed in part to ensure th at areas in different regions of the country, with different 18

PM 2.5 

# composition, were included. 19

 Intra-urban variability in ambient PM 2.5 

# levels 

# : Several recent st udies (e.g., Jerrett 20

et al., 2005) have addressed th e issue of heterogeneity of PM concentrations within 21

urban areas and its pote ntial impact on the estimation of premature mortality associated 22

with long-term exposure to PM 2.5 

# . Most recently, the HEI R eanalysis II (Krewski et 23

al., 2009), focusing on the ACS dataset, discu sses epidemiological analyses completed 24

for Los Angeles and New York City which included more highly-refined (zip code 25

level) characterizations of spatial gradient s in population exposure within each urban 26

area based on land-use regression methods and/or kriging. While both analyses 27

provide insights into the issue of intra-urban heterogeneity in PM 2.5 

# concentrations and 28

its potential implications for epidemiology- based health assessments, due to the time 29

and resource necessary to integrate them into the risk assessment, we were not able to 30

incorporate these studies quantitatively. The implications of these studies for 31

interpretation of long-term mortality C- R functions and potential exposure error 32

associated with those functions is discussed below in section 3.5.3. 33

 Variability in the patterns of ambient PM 2.5 

# reduction as urban areas: 

# In 34

simulating just meeting the current or alte rnative suites of sta ndards, there can be 35

considerable variability in the patterns of ambient PM 2.5 

# reductions that result from 36

different simulation approaches (i.e., they can be more localized, more regional, or 37

some combination thereof). To address this issue in the risk assessment, we have 38

included three rollback approaches as part of the sensitivity analysis including: 39

proportional (reflecting regi onal patterns of reduction), hybrid (reflecting a 40

combination of localized and regional pa tterns of reduction), and peak shaving 41

(reflecting localized patterns of reduction) (see section 3.2.3 for additional detail on 42

these rollback methods and section 3.5.4 for a descrip tion of how this factor is 43

addressed in the sensitivity analysis). 44

# February 2010 Draft – Do Not Quote or Cite 3-61  Copollutant concentrations 

# : Inclusion of copollutant models in short-term exposure1

related time series studies has produced mi xed results in terms of the degree of 2

attenuation of the PM 2.5 

# signal that results from inclusi on of other pollutants (see final 3

PM ISA, sections 6.2.10.9 and 6.3.8.5). Th e PM ISA (section 6.2.10.9) suggests that 4

these inconsistent findings associated with controlling for gaseous pollutants are likely 5

due to differences in the correlation stru cture among pollutants as well as differing 6

degrees of exposure measurement error relate d to the copollutants. Further, the PM 7

ISA (section 2.1.3) notes that correlations between PM and copollutants (including CO, 8

O 3 

# , SO 2 

# and NO 2 

# ) can vary both seasonally and spatially . Therefore, it is possible that 9

the degree of attenuation of PM 2.5 

# -related risk by copollutant s may differ across study 10

areas. However, because the multi-city studies used in the core risk assessment 11

(Zanobetti and Schwartz., 2009; Bell et al ., 2008; and Krewski et al., 2009) provide 12

single pollutant models, our analysis does not directly address the issue of copollutant 13

confounding (see section 3.5.3 for additional di scussion of uncertainty introduced into 14

the analysis as a result of not including copo llutant models in the core risk assessment). 15

We did explore the issue of copollutant m odeling in the context of modeling long-term 16

exposure-related mortality as part of the sensitivity analysis (section 3.5.4). In 17

addition, the potential impact of copo llutant confounding on short-term exposure18

related mortality and morbidity was explored in the Moolgavkar et al., 2003 study, as 19

discussed below in section 4.3.1.1 (although th ey have limited applic ability to the core 20

risk estimates generated in this RA). 21

 Demographics and socioeconomic-st atus (SES)-related factors: 

# Variability in 22

population density particularly in re lation to elevated levels of PM 2.5 

# has the potential 23

to influence population risk. In addition, othe r aspects of demographi cs such as age of 24

housing stock (which can influence rates of air conditioner use thereby impacting rates 25

of infiltration of PM indoors) can impact exposure and therefore risk (discussed in PM 26

ISA – sections 2.2.1 and 2.3.2). While risk modeling completed for this analysis is 27

based on concentrations measured at central -site monitors used as surrogates for 28

population exposure and does not explicitly c onsider more detailed patterns of PM 29

exposure by different subpopul ations, potential differe nces in exposure to PM 2.5 

# 30

reflecting demographic and SES -related factors is covered to some degree by the use of 31

urban study area-differentiated effects esti mates (for short-term exposure-related 32

mortality) and regionally-diffe rentiated effects estimates (in the case of short-term 33

exposure-related morbidity). In the case of long-term exposure-related mortality, while 34

the modeling for this group of endpoints does not utilize location-specific or 35

regionally-differentiated effects estimates, the national-scale effects estimates that are 36

used do reflect differences in exposure a nd health response across urban study areas 37

(which will reflect, to some extent, differences in demographics and SES-related 38

factors to the extent that these fact ors influence the relationship between PM 2.5 

# 39

exposure and mortality response, as detect ed by the underling cohort studies). 40

 Behavior affecting exposure to PM 2.5 

# : We have incorporated, where available, 41

region- and/or city-specific e ffect estimates in order to capture behavioral differences 42

across locations that could a ffect population exposures to PM 2.5 

# (e.g., time spent 43

outdoors, air conditioning use). However, while these location-specific effect 44

estimates may be capturing differences in be havior, they may also be capturing other 45

# February 2010 Draft – Do Not Quote or Cite 3-62 differences (e.g., differences in the composition of PM 2.5 

# to which populations are 1

exposed). As noted above, it was not possi ble to separate out the impact of these 2

different factors, which may vary across lo cations and populations, on effect estimates. 3

 Baseline incidence of disease 

# : We collected baseline health effects incidence data (for 4

mortality and morbidity endpoints) from a number of different sources (see section 5

3.4). Often the data were available at the county-level, providing a relatively high 6

degree of spatial refinement in characterizi ng baseline incidence gi ven the overall level 7

of spatial refinement reflected in the risk assessment as a whole. Otherwise, for urban 8

study areas without county-level data, either (a) a surroga te urban study area (with its 9

baseline incidence rates) was used, or (b) less refined state-level incidence rate data 10

were used. 11

 Longer-term temporal variability in ambient PM 2.5 

# levels 

# (reflecting meteorological 12

trends, as well as future changes in the mix of PM 2.5 

# sources and regulations impacting 13

PM 2.5 

# ): Risk estimates for the PM 2.5 

# NAAQS review have been generated using recent 14

years of air quality data. In other words, efforts have not been made to simulate 15

potential future changes in either the c oncentrations or composition of ambient PM 2.5 

# 16

in the risk assessment locations based on possible changes in economic activity, 17

demographics or meteorology. Actual risk le vels potentially experienced in the future 18

as a result of implementing alternative standa rd levels may differ from those presented 19

in this report due, in part, to potential ch anges in these factors related to ambient PM 2.5



# . 20

3.5.3 Qualitative Assessment of Uncertainty 

# 21

As noted in section 3.5.1, we have based the design of the uncertainty analysis carried out 22

for this risk assessment on the framework outlined in the WHO guidance document (WHO, 23

2008). That guidance calls for the completion of a Tier 1 qualitative uncertainty analysis, 24

provided the initial Tier 0 screeni ng analysis suggests there is con cern that uncertainty associated 25

with the analysis is sufficient to significantly impact risk results (i.e ., to potentially affect 26

decision making based on those risk results). Given previous sens itivity analyses completed for 27

prior PM NAAQS reviews, which have shown various sources of uncertainty to have a 28

potentially significant impact on risk results, we believe that there is ju stification for conducting 29

a Tier 1 analysis. In fact, as argued earlier, give n the complexity of the overall risk assessment, a 30

full Tier 3 uncertainty analysis is warrant ed for consideration under the WHO guidelines 31

(although as discussed later, limitatio ns in available data preclude completion of this level of 32

more-refined uncertainty analysis at this time). 33

For the qualitative uncertainty analysis, we have described each source of uncertainty and 34

qualitatively assessed its potenti al impact (including both the ma gnitude and direction of the 35

impact) on risk results, as specified in the W HO guidance. As shown in Table 3-13, for each 36

source of uncertainty, we have (a) provided a descri ption, (b) estimated the direction of influence 37

( over, under, both, or unknown ) and magnitude ( low, medium, high) of the potential impact of 38

each source of uncertainty on the risk estimat es, (c) assessed the degree of uncertainty ( low, 39

# February 2010 Draft – Do Not Quote or Cite 3-63 medium, or high ) associated with the knowledge-base (i.e., assessed how well we understand 1

each source of uncertainty), and (d) provided comments further clarifying the qualitative 2

assessment presented. Table 3-13 includes all ke y sources of uncertainty identified for the PM 2.5 

# 3

NAAQS risk assessment. A subset of these sources has been included in th e Tier 2 quantitative 4

assessment discussed in section 3.5.4. 5

The categories used in describing the potential magnitude of impact for specific sources 6

of uncertainty on risk estimates (i.e., low, medi um, or high) reflect EPA staff consensus on the 7

degree to which a particular source could produc e a sufficient impact on risk estimates to 8

influence the interpretation of those estimates in the context of the PM NAAQS review. 32 

# 9

Sources classified as having a “low” impact woul d not be expected to impact the interpretation 10

of risk estimates in the context of the PM NAAQS review; sources classified as having a 11

“medium” impact have the potential to change the interpretation; and sources classified as “high” 12

are likely to influence the interp retation of risk in the context of the PM NAAQS review (if those 13

sources of uncertainty are reduced or more fully characterized). Because this classification of 14

the potential magnitude of impact of sources of uncertainty is qualitative and not informed 15

directly by any type of analytical results, it is not possible to place a quan titative level of impact 16

on each of the categories. 33 

# Therefore, the results of the qual itative analysis of uncertainty have 17

limited utility in informing consideration of overall confidence in the core risk estimates and, 18

instead, serve primarily as a means for guiding fu ture research to reduce uncertainty related to 19

PM 2.5 

# risk assessment. 20

As with the qualitative discussion of sources of variability included in the last section, the 21

characterization and relative ra nking of sources of uncertainty addressed here is based on 22

consideration by EPA staff of information provi ded in previous PM NAAQS risk assessments 23

(particularly sensitivity analyses), the results of the sensitivity analyses completed for the current 24

PM NAAQS risk assessment and information provide d in the final PM ISA as well as earlier PM 25

Criteria Documents. Where appropriate, in Table 3-13, we have included references to specific 26

sources of information consider ed in arriving at a ranking and classification for a particular 27

source of uncertainty. 28

32 

## For example, if a particular source of uncertainty were more fully charact erized (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 leve l 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 uncer tainty analysis are similar to the categories used in

categorizing the results of the single- and multi-factor sensit ivity analyses completed for this analysis (section 4.3).

However, in the context of the sensitiv ity analysis results, because we do ha ve 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 Not Quote or Cite3-64Table 3-13. Summary of Qualitative Uncertainty Analysisof Key Modeling Elementsin the PM NAAQS Risk1Assessment.2Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)A.Characterizingambient PM2.5levels for studypopulationsusing theexistingambientmonitoringnetworkIf the set of monitors used in aparticular urban study area tocharacterize populationexposure as part of an ongoingrisk assessment do not matchthe ambient monitoring dataused in the originalepidemiological study, thenuncertainty can be introducedinto the risk estimates.BothLow-mediumLow-mediumKB and INF: In modeling risk, we focus on those counties thatwere included in the epidemiological studies supplying theunderlying C-R functions. This means that, particularly for thoseendpoints modeled using C-R functions obtained from more recentstudies, there is likely a close association between the monitoringnetwork used in the risk assessment and the network used in thestudy supplying the C-R function(s). Note, however, that in thoseinstances where the networks are different (e.g., when olderstudies are used, resulting in an increased potential for networks tohave changed), uncertainty may be introduced into the riskassessment and it is challenging to evaluate the nature andmagnitude of the impact that that uncertainty would have on riskestimates, given the complex interplay of factors associated withmismatched monitoring networks (i.e., differences in the set ofmonitors used in modeling risk and those used in the underlyingepidemiological study).B.Characterizingpolicy-relevantbackground(PRB)For this analysis, we have usedmodeling to estimate PRBlevels for each urban studyarea. Depending on the natureof errors reflected in thatmodeling, uncertainty (in bothdirections) may be introducedinto the analysis.Both Low LowINF: Given that the risk assessment focuses primarily on thereduction in risk associated withmoving from the current NAAQSto alternative standard levels, the impact of uncertainty in PRBlevels on the risk estimates is expected to be low. In addition, forlong-term exposure related mortality, we have based the coreanalysis on modeling risk down to LML rather than PRB, whichreduces the significance of the PRB issue in the context ofmodeling long-term exposure-related mortality.C.Characterizingintra-urbanpopulationexposure in thecontext ofepidemiologystudies linkingExposure misclassificationwithin communities that isassociated with the use ofgeneralized populationmonitors (which may missimportant patterns of exposurewithin urban study areas)introduces uncertainty into theUnder(generally)MediumhighHighKB and INF: Recent analyses inLos Angeles and New York Citybased on ACS data (as reported in Krewski et al., 2009)demonstrate the relatively significant effect that this source ofuncertainty can have on effect estimates (and therefore on riskresults). These analyses also illustrate the complexity and sitespecific nature of this source of uncertainty. The results of the LosAngeles analysis suggest that exposure error may result in effectsestimates that are biased low and therefore result in theFebruary 2010 Draft – Do Not Quote or Cite3-65Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)PM2.5tospecific healtheffectseffect estimates obtained fromepidemiology studies.underestimation of risk. Specifically in relation to the zip-codelevel analysis based on ACS data conducted in Los Angeles(Jerrett et al., 2005), the final ISAstates that, “This [the refinedexposure analysis reported in the Jerrett study] resulted in bothimproved exposure assessment and an increased focus on localsources of fine particle pollution.Significant associations betweenPM2.5and mortality from all causes and cardiopulmonary diseaseswere reported with the magnitude of the relative risks beinggreater than those reported in previous assessments. In general,the associations for PM2.5and mortality using these two methods[kriging and land-use regression] for exposure assessment weresimilar, though the use of land use regression resulted insomewhat smaller hazard ratios and tighter confidence intervals(see Table 7-9). This indicates that city-to-city confounding wasnot the cause of the associationsfound in the earlier ACS Cohortstudies. This provides evidence that reducing exposure error canresult in stronger associations between PM2.5and mortality thangenerally observed in broader studies having less exposure detail”(final ISA, section 7.6.3, p. 7-90).D. Statistical fitof the C-RfunctionsExposure measurement errorcombined with other factors(e.g., size of the effect itself,sample size, control forconfounders) can effect theoverall level of confidenceassociated with the fitting ofstatistical effect-responsemodels in epidemiologicalstudies.BothLowmedium(long-termhealthendpoints)Medium(short-termhealthendpoints)MediumINF: Long-term mortality studiesbenefit from (a) having largersample sizes (given that large national datasets are typically usedin deriving national-scale models), (b) the fact that the form of themodels used appears to be subject to relatively low uncertainty(see next row below) and (c) our not attempting to derive locationspecific effects estimates (but instead, relying on national-scaleestimates). These factors combine toproduce effects estimates thattend to be statistically robust (as reflected in results presented inKrewski et al., 2009). In addition, while concerns remainregarding exposure misclassification and potential confounding,generally we do not believe that the effects estimates areconsistently biased in a particulardirection. In the case of shortterm mortality and morbidity health endpoints, there is greateruncertainty associated with the fit of models given the smallersample sizes often involved, difficulty in identifying theetiologically relevant time period for short-term PM exposure, andthe fact that models tend to be fitted to individual counties orFebruary 2010 Draft – Do Not Quote or Cite3-66Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)urban areas (which introduces the potential for varying degrees ofconfounding and effects modification across the locations). Incontrast to the long-term mortality studies, the short-termmortality and morbidity endpoints occasionally have effectsestimates that are not statistically significant. Note, however thatfor this risk assessment, in modeling both short-term mortality andmorbidity endpoints, we are not relying on location-specificmodels. In the case of short-term mortality, we are using cityspecific effects estimates derivedusing Bayesian techniques (thesecombine national-scale models with local-scale models) (personalcommunication with Zanobetti, 2009). For short-term morbidity,we are using regional effects estimates (Bell et al., 2008). In bothcases, while effects estimates are at times non-statisticallysignificant, these models do benefit from larger sample sizescompared to city-specific models.E. Shape of theC-R functionsUncertainty in predicting theshape of the C-R function,particularly in the lowerexposure regions which areoften the focus in PM NAAQSregulatory reviews.Both Medium Low-mediumINF: Regarding long-term mortality, the ISA suggests that a loglinear non-threshold model is best supported in the literature formodeling both short-term and long-term health endpoints.Although consideration for alternative model forms (Krewski etal., 2009) does suggest that different models can impact riskestimates to a certain extent, generally this appears to be amoderate source of overall uncertainty. Particularly if, as is thecase in this risk assessment, we are not extrapolating below thelowest measured levels found in the underlying epidemiologicalstudies. With regard to long-term mortality, the final ISAconcludes that, “In addition to examining the concentrationresponse relationship between short-term exposure to PM andmortality, Schwartz et al. (2008, 156963) conducted an analysis ofthe shape of the concentration-response relationship associatedwith long-term exposure to PM.Using a variety of statisticalmethods, the concentration-response curve was found to beindistinguishable from linear, and,therefore, little evidence wasobserved to suggest that a threshold exists in the associationbetween long-term exposure to PM2.5and the risk of death(Section 7.6).” (section 2.4.3, p.2-26). Regarding short-termmorbidity, the final ISA states that, “Overall, the studies evaluatedFebruary 2010 Draft – Do Not Quote or Cite3-67Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)further support the use of a no-threshold log-linear model, butadditional issues such as the influence of heterogeneity inestimates between cities, and the effect of seasonal and regionaldifferences in PM on the concentration-response relationship stillrequire further investigation.”(section 2.4.3, p. 2-25).F. Addressingco-pollutantsThe inclusion or exclusion ofco-pollutants which mayconfound, or in other ways,affect the PM effect, introducesuncertainty into the analysis.BothLow-mediumMediumINF: With regard to long-term health endpoints, the final ISAstates that, “Given similar sources for multiple pollutants (e.g.,traffic), disentangling the health responses of co-pollutants is achallenge in the study of ambient air pollution.” (ISA, section7.5.1, p. 7-57). The final ISA also notes that in some instances,consideration of copollutants can have a significant impact on riskestimates. For example, the more refined study of mortality in LAas reported in Krewski et al., 2009 suggested that inclusion ofozone in the model along with PM2.5results in statistically nonsignificant results for long-cancermortality, while IHD-associatedmortality remained statistically significant (Krewski et al., 2009 –Table 23). With regard to short-term mortality and morbidity, thefinal ISA generally concludes that observed associations are fairlyrobust to the inclusion of copollutants in the predictive models(see ISA, sections 6.3.8, 6.3.9,and 6.3.10). The mixed impact ofconsidering multi-pollutant models in assessing PM2.5-associatedrisk for short-term and long-term exposure related endpoints, leadsus to conclude that the potential impact of this source ofuncertainty is low-medium (depending on the specific endpointsunder consideration). The epidemiological studies used as thebasis for selecting C-R functionsfor the core risk assessment didnot include multi-pollutant models (with the exception of PM10-2.5and PM2.5combined models in Zanobetti and Schwartz, 2009).However, we have included copollutant models in the sensitivityanalysis (see Section 4.3).G. Potentialvariation ineffectsestimatesreflectingcompositionalThe composition of PM candiffer across study areasreflecting underlyingdifferences in primary andsecondary PM2.5sources (bothnatural and anthropogenic). IfBothMedium-HighMedium-HighKB and INF: Epidemiology studies examining regionaldifferences in PM2.5-related health effects have found differencesin the magnitude of those effects (see sections 2.3.1.1 and 2.3.2 inthe draft ISA). While these may be the result of factors other thancomposition (e.g., different degrees of exposure misclassification),composition remains one potential explanatory factor. For short-February 2010 Draft – Do Not Quote or Cite3-68Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)differences forPMthese compositional differencesin fact translate into significantdifferences in public healthimpact (per unit concentrationin ambient air) for PM2.5thensignificant uncertainty may beintroduced into riskassessments if thesecompositional differences arenot explicitly addressed.term exposure morbidity and mortality effects, the inclusion ofcity-specific and/or regional-specific effect estimates in the riskassessment may well reflect differences in PM composition and,thus consideration of differencesin risk due to city-specificdifferences in composition may already be incorporated in the riskestimates for these endpoints to some extent.H. Specifyinglag structure(short-termexposurestudies)Different lags may havevarying degrees of associationwith a particular healthendpoint and it may be difficultto clearly identify a specific lagas producing the majority of aPM-related effect (recently,distributed lags have beenrecommended since they allowfor a distribution of the impactacross multiple days of PMexposure prior to the healthoutcome). A lack of clarityregarding the specific lag(s)associated with a particularhealth endpoint addsuncertainty into risk estimatesgenerated for that endpoint.Both Medium MediumKB and INF: With regard to lag periods, the ISA states, “Anattempt has been made to identify whether certain lag periods aremore strongly associated withspecific health outcomes. Theepidemiologic evidence evaluated in the 2004 PM AQCDsupported the use of lags of 0-1 days for cardiovascular effects andlonger moving averages or distributed lags for respiratory diseases(U.S. EPA, 2004a). However, currently, little consensus exists asto the most appropriate a priorilag times to use when examiningmorbidity and mortality outcomes.” (final ISA, section 2.4.2, p. 2-24). This suggests that uncertainty remains concerning theidentification of appropriate lags, and thus the etiologicallyrelevant time period for exposure to PM for specific healthendpoints.I.Transferabilityof C-Rfunctions fromstudy locationsto urban studyarea locationsThe use of effects estimatesbased on data collected in aparticular location(s) as part ofthe underlying epidemiologicalstudy in different locations (thefocus of the risk assessment)introduces uncertainty into theanalysis.BothMedium (forlong-termexposuremortality)Notapplicable(for shortMedium (forlong-termexposuremortality)Low (for short-term exposuremortality)INF: This issue has been ameliorated to a great extent in this riskassessment since we are now using multi-city studies for keyshort-term endpoints with effects estimates generally beingapplied only to urban study areas matching locations used in theunderlying epidemiological study. In the case of long-termexposure mortality studies, theseare designed to capture a moregeneralized national signal and therefore, concerns over thetransferability of functions between locations is of greater concern.February 2010 Draft – Do Not Quote or Cite3-69Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)termexposurehealth effectriskestimates)J. Use ofsingle-cityversus multicity studies inthe derivationof C-RfunctionsOften both single-city andmulti-city studies are available(for a given health effectendpoint) for the derivation ofC-R functions. Each of thesestudy designs has advantagesand disadvantages whichshould be considered in thecontext of assessinguncertainty in a risk assessment(Note, that generally this issueapplies more to the modelingof short-term exposure-relatedendpoints then to the modelingof long-term exposure relatedendpoints, since the latter istypically based on multi-cityprospective cohort studies).Both Medium HighKB: Because many health endpoints have been evaluated usingboth single-city and multi-city studies, we have a relative largeselection of single city studies and a few large multi-city studies toconsider in examining this issue.INF: For reasons presented in section 3.3.3, we have decided tofocus on multi-city studies as a source of C-R functions for thecore risk assessment, reflecting advantages that these studies offer(e.g., they tend to have more statistical power and provide effectestimates with relatively greater precision than single city studiesdue to larger sample sizes, reducing the uncertainty around theestimated coefficient, and reducingpublication bias). While thechoice of multi-city studies is well-supported, this decision doesintroduce uncertainty since single city studies can provide a widerrange of C-R functions (and associated effects estimates)reflecting greater variation in study design, differences incomposition, human behavior, and copollutants, and differences inthe input datasets used (e.g., ambient air monitors and diseasebaseline incidence data). Even if there is greater confidence in CR functions obtained from multi-city studies, overall uncertainty inthose C-R functions may be reflected to some extent in the rangeof C-R functions seen across single-city studies.K. Impact ofhistorical airquality onestimates ofhealth riskfrom long-termPM2.5exposuresLong-term studies of mortalitysuggest that different timeperiods of PM exposure canproduce significantly differenteffects estimates, raising theissue of uncertainty in relationto determining which exposurewindow is most stronglyassociated with mortality.Both Medium MediumINF: The latest HEI Reanalysis II study (HEI, 2009) which lookedat exposure windows (1979-1983 and 1999-2000) for long-termexposure in relation to mortality, did not draw any conclusions asto which window was more strongly associated with mortality.However, the study did suggest that moderately different effectsestimates are associatedwith the different exposure periods (withthe more recent period having larger estimates). Overall, theevidence for determining the window over which the mortalityeffects of long-term pollution exposures occur suggests a latencyFebruary 2010 Draft – Do Not Quote or Cite3-70Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)period of up to five years, with the strongest results observed inthe first few years after intervention(final ISA, section 7.6.4. p. 7-95).L.Characterizingbaselineincidence ratesUncertainty can be introducedinto the characterization ofbaseline incidence in a numberof different ways (e.g., error inreporting incidence for specificendpoints, mismatch betweenthe spatial scale in which thebaseline data were capturedand the level of the riskassessment).BothLow-mediumLowINF: The degree of influence of this source of uncertainty on therisk estimates likely varies with the health endpoint category underconsideration. There is no reason to believe that there are anysystematic biases in estimates of the baseline incidence data. Theinfluence on risk estimates thatare expressed as incremental riskreductions between alternative standards should be relativelyunaffected by this source of uncertainty.KB: The county level baseline incidence and population estimatesat the county level were obtained from data bases where therelative degree of uncertainty is low.* Refers to the degree of uncertainty associated with our understanding of the phenomenon, in the contextof assessing and characterizing its uncertainty1(specifically in the context of modeling PM risk)2

# February 2010 Draft – Do Not Quote or Cite 3-71 The results presented in Table 3-13 consider only the potential impact of each source of 1

uncertainty when acting in isolation to impact core risk estimates. However, it is likely that a 2

number of these sources of uncertainty could act in concert to impact risk estimates and 3

furthermore, that these combined effects could be more than additive in certain circumstances. 4

EPA staff has identified several combinations of sources of uncertainty addressed in Table 3-13 5

that should be highlighted due to their potential to produce significant impacts on core risk 6

estimates when acting in concert. These are briefly described below: 7

 Uncertainty source D (statistical fit of th e C-R functions), Source E (shape of the 

# 8

C-R functions), Source F (addressing copo llutants), and Source J (use of single

# 9

city versus multi-city studies in the derivation of C-R functions): 

# Consideration of 10

uncertainty associated with the shape of C-R functions needs to be considered in light 11

of overall confidence (uncertainty) associated with a particular model. A number of 12

factors contribute to an interpretation of c onfidence in a model including: statistical fit 13

of the model, degree to wh ich potential confounding by copollu tants is considered, and 14

other aspects of study design including single- versus multi-city study design. While 15

choice of a particular model (e.g., threshold model, or log-log model) may produce a 16

significant impact on risk estimates relative to alternative model forms, the overall 17

scientific support for that pa rticular model form (informe d by consideration of the 18

factors listed above) is an important consider ation in assessing overa ll uncertainty both 19

from a qualitative and quantitative standpoint. 20

In addition, there is the potential for sources of uncertainty discussed in Table 3-13 to 21

interact with sources of variability covered in sect ion 3.5.2 in impacting core risk estimates. One 22

such interaction is discussed below: 23

 Uncertainty source A (characterizing ambient PM 2.5 

# levels for study populations 

# 24

using the existing ambient monitoring network) and variability related to the 

# 25

pattern of ambient PM 2.5 

# reductions at urban study areas (see section 3.5.2): 

# The 26

estimation of a composite monitor value to use in modeling risk for a study area under 27

an alternative suite of standards is de pendent both on the specification of the 28

monitoring network and the approach used in adjusting the concentrations for the 29

monitors in that network (i.e., the rollback approach used to simulate the pattern of 30

ambient PM 2.5 

# reductions associated with just mee ting the current or alternative suites 31

of standards). As we have seen in modeling risk for Pittsburgh, refinements in the 32

approach used to simulate air quality just meeting alternative suites of standards (in the 33

case of Pittsburgh transitioni ng from a single study area to two distinct study areas 34

each with different design values and se parate assessments of rollback) produced 35

significant differences in composite monitor values for the study area. Therefore, both 36

of these factors (the definition of the m onitoring network and ro llback approach) can 37

work in concert to impact ambient PM 2.5 

# levels and hence risk estimates. 38

39

# February 2010 Draft – Do Not Quote or Cite 3-72 3.5.4 Single and Multi-Factor Sensitivity Analyses 

# 1

We quantitatively examined the impact of severa l inputs to the risk assessment in a series 2

of single-factor sensitivity analys es summarized above in Table 3- 8. A number of these sources 3

of uncertainty were also examined in-concert to assess their combined impact on core risk 4

estimates through the multi-factor sensitivity analysis. In addi tion, the sensitivity analysis 5

considered variability in the pattern of reductions in ambient PM 2.5 

# associated with just meeting 6

the current and alternative suites of standards (i.e ., consideration of variability in the simulation 7

of rollback). This section focuses on providing additional detail on the sources of alternative 8

model specifications and input datase ts used in the sensitivity analys is (as alternativ e to the core 9

modeling approach). 10

Rather than present results for each sensitivity analysis for all of the air quality scenarios 11

considered in the core analysis, we selected a single air quality scenario – PM 2.5 

# concentrations 12

that just meet the current standards – to use fo r the sensitivity analyses. The one exception to 13

this was the sensitivity analyses examining the impact of alternative ap proaches to simulating 14

just meeting alternative standards (the hybrid and peak-shaving rollback methods). 34 

# 15

In discussing the approach used in conducting the sensitivity analysis, we focus first on 16

methods used in assessing long-term exposure re lated health endpoints followed by the methods 17

used in assessing short-term exposure related hea lth endpoints. We then discuss multi-factor 18

sensitivity analyses completed for both shor t-term and long-term exposure-related health 19

endpoints. Note, that the results of the sensi tivity analyses (including both single- and multi20

factor analyses) are presented a nd discussed in section 4.3. 21

3.5.4.1 Sensitivity Analyses for Long-Term Exposure-Related Mortality 

# 22

Because Krewski et al. (2009) presented result s based on alternative model specifications 23

only for the later exposure period (1999 – 2000), our sensitivity analyses focusing on the 24

estimates of health effects incidence a ssociated with long-term exposure to PM 2.5 

# similarly used 25

the C-R functions based on this later exposure peri od. Krewski et al. (2009) considered several 26

alternative modeling approaches to estimate the relationship between mortality (both all cause 27

and cause-specific) and long-term exposure to PM 2.5 

# , providing us the opportunity to examine 28

the impact of alternative modeling approaches on the estimate of mortality risk associated with 29

long-term exposure. In particular, we examined the impact of using a random effects log-linear 30

model and of using a random effects log-log model 35 

# (rather than the standard fixed effects log31

linear model used in the core analysis) to estimate the risks of all cause mortality, 32

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 fu ll 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 PM 2.5



## .

# February 2010 Draft – Do Not Quote or Cite 3-73 cardiopulmonary mortality, ischemic heart disease mortality, and lung cancer mortality 1

associated with long-term exposur e in Los Angeles and Philadelphia. 36 

# The coefficient of PM 2.5 

# 2

in the random effects log-linear model was back- calculated from the relative risk reported in 3

Table 9 (“Autocorrelation at MSA and ZCA leve ls” group – “MSA & DIFF” row) of Krewski et 4

al. (2009). The coefficient of PM 2.5 

# in the random effects log-l og model was back-calculated 5

from the relative risks reported in Table 11 (“MSA and DIFF” ro ws) of Krewski et al. (2009). 6

As noted above, for all health endpoints a ssociated with long-term exposure to PM 2.5 

# we 7

estimated risk associated with PM 2.5 

# concentrations above 5.8 μg/m 3 

# (the LML for the later 8

exposure period used in Krewski et al., 2009). In a sensitivity anal ysis we examined the impact 9

of that limitation by comparing those mortality ri sk estimates to the mortality risk estimates 10

obtained when we estimated risk associated with PM 2.5 

# concentrations above estimated PRB 11

levels. This sensitivity analysis was carried out for all cause mo rtality in all 15 risk assessment 12

urban areas. 13

In addition, we compared the impact of usi ng the primary C-R functions used in the risk 14

assessment, taken from Table 33 of Krewski et al. (2009), versus C-R functions for mortality 15

associated with long-term exposure reported in another study, Krewski et al. (2000), which was 16

based on a reanalysis of the Harvard Six Cities Study. The C-R functions estimated in Krewski 17

et al. (2000) from the Harvard Six Cities cohort were estimated for ages 25 and up, while the C18

R functions estimated in Krewski et al. (2009) from the ACS cohort were for ages 30 and up. 19

For purposes of consistency in the comparison, however, we applied the C-R functions from 20

Krewski et al. (2000) to ages 30 and up (and used the baseline in cidence rates for that age group 21

as well). 37 

# This sensitivity analysis was carried out for all cause mortality, cardiopulmonary 22

mortality, and lung cancer mortality in Los Angeles and Philadelphia. 23

We also considered the impact of using multi-pollutant models in estimating long-term 24

exposure-related mortality. Specifically, we obt ained 2-pollutant models (considering CO, NO 2 

# , 25

O 3 

# or SO 2 

# together with PM 2.5 

# ) from Krewski et al., 2000, which is an earlier reanalysis of the 26

ACS dataset and used them in generating alternative estimates of all-cause mortality to contrast 27

with the core estimates genera ted using Krewski et al., 2009. 28

For all of the sensitivity analyses involvi ng alternative C-R functions, in addition to 29

calculating the incidence of the health effect when an alternative approach is taken, we 30

36 

## As noted in Table 3-8, we combined both of these altern ative modeling approaches in a single sensitivity analysis.

In changing from a fixed effects log-lin ear 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 rand om 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 calculated the percent difference in estimates from the core analysis resulting from the change in 1

analysis input. So for example, when we calculated the incidence of all cause mortality 2

associated with long-term exposure to PM 2.5 

# using a random effects log- log model (instead of the 3

fixed effects log-linear model used in the core analysis), we calculated the percent difference in 4

the result as (incidence estimated using a random effects log-log model - incidence estimated 5

using a fixed effects log-linear model)/( incide nce estimated using a fixed effects log-linear 6

model). 7

Finally, we also examined the issue of variab ility in estimating the pattern of reductions 8

in ambient PM 2.5 

# levels under the curren t and alternative standard levels (i.e., conducting 9

rollback). For the first draft RA, we considered th e impact of using a hybri d rollback approach in 10

addition to the proportional rollback approach wh ich has been more traditionally used in PM 11

NAAQS risk assessment (this sensitivity analysis was implemented including the generation of 12

quantitative risk estimates for a full suite of l ong-term exposure-related mortality categories). 13

For this second draft, as disc ussed above in sections 2.6, and 3.2.3, we have included 14

consideration of a peak shavi ng rollback approach in addition to the hybrid as non-proportional 15

methods to contrast with proportional rollback. As discussed in Section 3.2.3, for the second 16

draft risk assessment, rather than generating qua ntitative risk estimates, we have calculated 17

composite monitor estimates using the different rollback methods (proportional, hybrid and peak 18

shaving). The composite monitor values are surroga tes for long-term exposure-related mortality. 19

Therefore, by comparing composite monitor valu es generated for the same study area/standard 20

level combination (using different rollback met hods), we can obtain insi ghts into the potential 21

impact of the rollback method used on long-te rm exposure-related mortality. Specifically, for 22

this sensitivity analysis, we compared composite monitor values in two ways: 23

 Potential difference in composite monitor va lues at the current or alternative standard 24

level (for the same study area) given app lication of alternat ive rollback methods: We 25

compared the absolute magnitude of composite monitors values produced using different 26

rollback methods for the same study area/stand ard level combination to provide insights 27

into differences in the magnitude of residual risk for a given suite of standards in a study 28

area using different rollback me thods (Appendix F, Table F-50). 38 

# For example, in Table 29

F-50, for Los Angeles, we see that for the cu rrent standard suite of standards, use of 30

proportional rollback and peak shaving rollback methods results in composite monitor 31

values of 9.5 μg/m 3 

# and 12.0 μg/m 3 

# , respectively, with the peak shaving value being 40% 32

higher than the value derived using proporti onal rollback. Given that the composite 33

monitor values are surrogates for long-term e xposure-related mortality, we conclude that 34

for this combination of urban study area and su ite of standards, use of the peak shaving 35

rollback method could produce PM 2.5 

# -attributable long-term mort ality risk estimates that 36

are approximately 40% higher than us e of the proportional rollback method. 37

38 

## This calculation reflects the fact that we model lo ng-term exposure-related mortality down to LML.

# February 2010 Draft – Do Not Quote or Cite 3-75  Potential difference in the pattern of re duction in composite monitor values across 1

alternative standards: We compared differences in the percent reduction in composite 2

monitor values across altern ative suites of standards for the same study area using 3

different rollback methods to provide insigh ts into differences in incremental risk 4

reduction resulting from the use of different roll back approaches (Appendix F, Table F5

49). 39 

# For example, in Table F-49, for Baltimore, we see that the proportional rollback 6

and hybrid rollback approaches resulted in composite monitor values for the 13/35 7

alternative suite of standards of 11.6 μg/m 3 

# and 11.8 μg/m 3 

# , respectively, with these 8

translating into a percent reduction (compa red with their respec tive values under the 9

current suite of standards) of 21% and 16% , respectively. Given that the composite 10

monitor values are surrogates for long-term e xposure-related mortality, we conclude that 11

use of the two rollback methods (in the case of Baltimore for these two suites of 12

standards) does not appear to produce notab ly different patterns of risk reduction (in 13

terms of percent reduction), although residual risk could differ using the two approaches. 14

The peak-shaving and hybrid rollback appro aches were not applie d to all study areas, 15

since they are primarily applicable in certain situations. 40 

# The sensitivity analysis results 16

described above (presented in Appendix F, Tabl es F-49 and F-50) form the basis for summary 17

information related to rollback appr oaches presented in Table 4-3. 18

In addition to the above insights regarding potential impacts on residual risk and the 19

degree of risk reduction across standard levels, inclusion of multiple rollback approaches also 20

allowed us to more fully examine the degree to which alternative 24-hour standards can produce 21

reductions in annual-average PM 2.5 

# concentrations, thereby produc ing reductions in long-term 22

exposure-related mortality. As discussed below in section 6.2, alternative 24-hour standards, 23

when controlling, can result in reductions in annual average PM 2.5 

# concentrations, particularly if 24

proportional rollback is used. In this case, the assu mption of more region al patterns of PM 2.5 

# 25

reduction in reducing PM 2.5 

# concentrations to just meet alte rnative 24-hour standards results in 26

an equivalent magnitude of reduction in the a nnual average. However, in simulating more 27

localized patterns of PM 2.5 

# reductions to just meet alte rnative 24-hour standards, the PM 2.5 

# 28

reductions can be more limited to the monitor(s ) (and areas) exceeding the 24-hour standard, and 29

other monitors may not be effected, resulting in a smaller impact on the annual average. 30

Inclusion of rollback approaches reflecti ng more localized patterns of ambient PM 2.5 

# reduction 31

(i.e., the hybrid and particularly the peak shaving methods) allo ws us to assess the degree to 32

which alternative 24-hour standards (when controlling) produce appreciable reductions in 33

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 consideratio n of a hybrid local/regional attainment strategy (i.e.,

application of the hybrid rollback) (i.e., Baltimore, Birmin gham, Detroit, Los Angeles, New York, St. Louis). In the

case of the peak sharing approach, only those locations wh ere 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 annual-average PM 2.5 

# concentrations and cons equently in long-term e xposure-related mortality. 1

This issue is revisited in discus sing the results of the sensitivity analysis (section 4.3.1.1) and in 2

the integrative discussion of the core risk estimates (section 6.2). 3

3.5.4.2 Sensitivity Analyses for Short-Ter m Exposure-Related Mortality and 

# 4

Morbidity 

# 5

The scope of the sensitivity analysis comple ted for short-term exposure-related mortality 6

and morbidity is more limited than that completed for long-term exposure-related mortality. 7

This reflects, in part, the much greater magnitude of long-term exposure-related mortality. An 8

additional factor is that while there has been co nsiderable research in the area of short-term 9

exposure-related mortality and morbidity which shed s light on uncertainty in such factors as C-R 10

function specification, this information is not directly applicable in a sensitivity analysis. In 11

order to complete a quantitative sensitivity analysis, we need alternative C-R function 12

specifications that produce risk estimates that can be directly compared to the core risk estimates. 13

Ideally, this is done by identifying alternative model forms in the epidemiological study used in 14

the core risk model. However, in the case of short-term exposur e-related mortality, the studies 15

providing our core risk models (Zanobetti and Schwartz et al ., 2009 and Bell et al., 2008), only 16

provide limited alternative model specifications, as described below. Further, alternative 17

epidemiological studies, such as Moolgavkar et al., 2003, while providing useful insights into 18

which factors can impact risk estimates (e.g ., lag, multipollutant forms), cannot generate 19

alternative risk estimates that can be readily compared with the core risk estimates given 20

differences in the underlying study desi gns and datasets employed. 21

The primary studies selected to assess mortality risk and risk of hospitalization associated 22

with short-term exposure to PM 2.5 

# (Zanobetti and Schwartz, 2009, and Bell et al., 2008, 23

respectively) both provided all-ye ar C-R functions as well as seas on-specific C-R functions. We 24

examined the impact of using season-specifi c functions by applying these functions to each 25

season, as defined by the study authors, 41 

# and summing the estimated season-specific incidences 26

of mortality and hospitalizations. We compared these estimates to the estimates obtained by 27

applying the corresponding all- year C-R functions to a ye ar of air quality data. 42 

# This sensitivity 28

analysis was carried out for all 15 of the risk assessment urban areas. 29

41 

## Both studies defined each season as three months, be ginning with winter defined as December, January, and

February. In applying a season-speci fic 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 su mmed to produce an all-year estimate of incidence.

However, the 2.5 th 

## and 97.5 th 

## percentile season-specific estimates cannot be summed. To calculate the 2.5 th 

## and

97.5 th 

## percentile estimates of all-year incidence from th e season-specific estimates would require the variancecovariance matrix of the season-specifi c coefficient estimators, which was not available. Theref ore our comparison

of all-year estimates based on summed season-specific estimates versus estima tes 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 In addition, Ito et al. (20 07) estimated an annual C-R f unction as well as a seasonal 1

function for April through August for asthma ED visits in New York City. We compared the 2

results of applying the annual C-R function to a whole year of air quality data to the results of 3

applying the seasonal function to only those mo nths (April through August) for which it was 4

estimated. 5

Moolgavkar (2003) estimated C- R functions for several heal th endpoints – non-accidental 6

and cardiovascular mortality; a nd cardiovascular and respiratory HAs – associated with short7

term exposures to PM 2.5 

# in Los Angeles using different lag structures, different modeling 8

approaches to incorporating weather and tempor al variables, and single-pollutant versus multi9

pollutant models. This study thus provided an op portunity to show the impact of lag structure, 10

modeling approach, and single- vs. multi-pollutant models, individually, for several health 11

endpoints associated with short-term exposures , although it is difficult to generalize to other 12

locations since the study was only co nducted in a single urban area. As noted earlier, differences 13

in study design and the underlying datasets used prevent the results base d on application of 14

models from Moolgavkar et al., 2003 from being co mpared directly to the core risk estimates. 15

Finally, as with estimates of long-term exposur e-related mortality, we also considered the 16

impact of variability related to simulating ambient PM 2.5 

# levels under the suite of current 17

standard levels (i.e., variability in conducting rollback) on estima tes of non-accidental mortality 18

associated with short-term exposures to PM 2.5 

# (using Zanobetti and Schwartz, 2009). However, 19

in this case, we only considered the hybrid m odel (consideration of peak shaving focused on the 20

impact on long-term exposure-related mortality) . We note however, that sensitivity analysis 21

findings based on consideration for peak shavi ng generally will hold fo r short-term exposure22

related mortality and morbidity since both categorie s of health endpoints are also driven primary 23

by annual-average PM 2.5 

# levels (see section 6.2). . 24

In all cases except the ED visits sensitivity analysis, in addition to calculating the 25

incidence of the health effect wh en an alternative approach is ta ken, we calculated the percent 26

difference in estimates from the core analysis resulting from the change in analysis input. 43 

# 27

3.5.4.3 Multi-factor Sensitivity Analyses 

# 28

Each single-element sensitivity analysis shows how the estimates of PM 2.5 

# –related health 29

effects incidence change as we change a single el ement of the analysis (such as the form of the 30

C-R function or the way we simulate just meeting a set of standards). Because each of the 31

alternative modeling choices is cons idered to be a reasonable choi ce, the results of these single- 32

43 

## We did not calculate percent different for the ED vi sits 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 element sensitivity analyses provide a set of reas onable alternative estimates that may similarly 1

be considered plausible (see section 4.3). The re sults of the single-element sensitivity analysis 2

are presented and discus sed in section 4.3.1. 3

The single-element sensitivity analyses provide insight into which sources of uncertainty 4

may have the greatest impact on risk estimates when acting alone. However, there are several 5

sources of uncertainty in estimating PM 2.5 

# –related health effects. To provide a more complete 6

picture of the uncertainty surrounding estimates of PM 2.5 

# –related health effects incidence – and 7

to expand the set of reasonable alternative estimates – we next carried out multi-element 8

sensitivity analyses. The results of the multi-f actor sensitivity analysis are presented and 9

discussed in section 4.3.1.2. 10

The choice of uncertain analysis elements to include in the multi-element sensitivity 11

analyses was guided by the single-element sensitivity analyses. In particular, we selected those 12

modeling choices that had the greatest impacts on th e estimates of health effects incidence in the 13

single-element sensitivity analyses to provide insight into the scope of possible estimates that, 14

while perhaps not based on our first choice of analysis elements, are nevertheless plausible 15

alternative estimates. 16

We identified three analysis elem ents that substantially affect ed the estimates of mortality 17

associated with long-term exposure to PM 2.5 

# -- the model choice (fixed effects log linear vs. 18

random effects log-log), whether effect s are estimated a ssociated with PM 2.5 

# concentrations 19

down to the LML in the study (5.8 μg/m 3 

# ) or down to PRB, and wh ether a proportional or a 20

hybrid rollback is used to simulate PM 2.5 

# concentrations that just meet a given set of standards. 21

This resulted in 2 x 2 x 2 = 8 different estimates of mortality, all of which could be considered 22

plausible, based on the fact that the underlyi ng model choices are all considered reasonable. 23

We identified two analysis elements that subs tantially affected the estimates of mortality 24

associated with short-term exposure to PM 2.5 

# – whether season-specific or all-year C-R functions 25

were used and whether a proportion al or a hybrid rollback approach was used to simulate just 26

meeting the current and alternativ e standards. 27

3.5.5 Summary of Approach to Addressing Variability and Uncertainty 

# 28

The characterization of uncertainty and variab ility associated with the risk assessment 29

includes a number of elements, which have been discussed in detail above. These include: 30

 Identification of key sources of variability associated with PM 2.5 

# -related population 31

exposure and hazard response and the degree to which they are captured in the risk 32

assessment (see section 3.5.2). When impor tant sources of vari ability in exposure 33

and/or hazard response are not reflected in a risk assessment, significant uncertainty 34

can be introduced into the risk estimates that are generated. While not explicitly 35

referenced in the WHO guidance, this assessm ent (focused on coverage for key sources 36

# February 2010 Draft – Do Not Quote or Cite 3-79 of variability) could be considered part of a Tier 1 analysis (i.e., the qualitative 1

characterization of sources of uncertainty). 2

 Qualitative assessment of uncertainty, including both an assessment of the magnitude 3

of potential impact of each s ource on risk estimates (along w ith the potential direction 4

of that impact) as well as an assessment of overall confidence associated with our 5

understanding of that source of uncertainty (see section 3.5.3). This represents a WHO 6

Tier 1 analysis. 7

 Single-factor sensitivity analysis intended to evaluate the impact of individual sources 8

of uncertainty and variability on risk estimates (see secti on 3.5.4). The goal of this 9

assessment is to evaluate the relative impor tance of these sources of uncertainty and 10

variability in impacting core risk estimates. The single-factor sensitivity analysis 11

represents a WHO Tier 2 anal ysis. In conducting these as sessments, we have used 12

alternative representations of modeling elements that have support in the literature to 13

ensure that the risk estimates that are gene rated represent reasonabl e alternate estimates 14

that can supplement the core risk estimates ge nerated in the analysis (see section 4.5.3). 15

 Multi-factor sensitivity analysis intended to assess the combined impact of multiple 16

sources of uncertainty and variability on risk estimate s (see section 3.5.4). By 17

considering the combined effect of multiple sources of uncertainty and variability, this 18

analysis has the potential to identify any non-linearities wh ich can magnify the impact 19

of uncertainty and variability on risk estimates, especially if several non-linear factors 20

act in concert. This also represents a WHO Tier 2 analysis. As with the single-factor 21

sensitivity analysis results, these risk estimates are also generated using modeling 22

inputs which have support in the literature and consequently, they also represent 23

reasonable alternate estimates that supplem ent the core risk estimates (see section 24

4.5.2). 25

As noted above, since information was not av ailable to characteri ze overall levels of 26

confidence in alternative model inputs, the uncer tainty characterization co mpleted for this risk 27

assessment did not include a full probabilistic as sessment of uncertainty and its impact on core 28

risk estimates (i.e., a WHO Tier 3 analysis was not completed). Further, the risk estimates 29

generated using the single- and multi-factor sens itivity analyses do not represent uncertainty 30

distributions, but rather additional plausible poin t estimates of risk (i.e., we do not know whether 31

they represent risk estimates near the upper or lower bounds of a true but undefined uncertainty 32

distribution and we do not know the actual population pe rcentiles that they represent). The 33

appropriate use for these reasonable alternate ri sk estimates in informing consideration of 34

uncertainty in the core risk estimat es is discussed in section 4.5.3. 35

In addition to the specific analyses disc ussed above, we have also completed two 36

additional analyses intended to place the 15 urban study areas in a broader national context with 37

regard to risk. These include a representative ness analysis which evaluates the way the 15 urban 38

study areas compare to national distributions for key PM-related risk attr ibutes (discussed in 39

section 4.4). We have also completed a national- scale assessment of long-term mortality related 40

# February 2010 Draft – Do Not Quote or Cite 3-80 to PM 2.5 

# exposures (chapter 5), which, in addition to providing an estimate of the national impact 1

of PM 2.5 

# on long-term mortality, also evaluates whet her the set of 15 urban study areas generally 2

represents the broader distribution of risk across the U.S ., or a more focused portion of the 3

national risk distribution (e.g., the higher-end). 4

A third set of analyses that has been added to this second draft RA focuses on evaluating 5

patterns in the design values (including both 24-hour and annual) and underlying PM 2.5 

# 6

monitoring data for the 15 urban stu dy areas (see Section 4.5). The goa l of this analysis is to use 7

this information to enhance our understanding of patterns in risk reduction seen under both the 8

current and alternative suites of standards across the urban study areas. The interplay of design 9

values and underlying PM 2.5 

# monitoring data play a key role in determining whether a location 10

will experience risk reductions when just meeting any given suite of standards is simulated and, 11

if so, the magnitude of those reduc tion. As part of this analysis , we contrast patterns in design 12

values for the 15 urban study areas with patterns seen more broadly acro ss urban areas in the 13

U.S. with the goal of placing the urban study areas in a national cont ext with regard to this key 14

factor influencing risk. 15

# February 2010 Draft – Do Not Quote or Cite 4-1 

# 4 URBAN CASE STUDY RESULTS 1

For this risk assessment, we have developed a core set of risk estimates supplemented by 2

an alternative set of risk results generated using single-factor and multi-factor sensitivity 3

analysis. The core set of risk estimates was de veloped using model inputs that staff judge to 4

have a greater degree of support in the literature relative to inputs used in th e sensitivity analyses 5

(the rationale for selection of specific epidemio logical studies and associ ated C-R functions for 6

the core analysis is discussed above in section 3. 3.3). This chapter presents and discusses the 7

core set of risk estimates genera ted for the urban case study area, a nd also discusses the results of 8

the sensitivity analyses which serve to augment the core risk estimates. The results of the 9

sensitivity analyses allow us to evaluate and rank the potential impact of key sources of 10

uncertainty on the core risk estimates. In a ddition, because the sensitivity analyses were 11

conducted using alternative modeling inputs having some degree of support in the literature, the 12

results of the sensitivity analysis also represent a set of reasonable alternatives to the core set of 13

risk estimates that can be used to inform charact erization of uncertainty in the core results (see 14

section 4.3 below). 15

As discussed above in section 2.2 and 3.2, th is risk assessment includes consideration of 16

the following air quality scenarios: 17

 Recent conditions: based on PM 2.5 

# concentrations characte rized through monitoring for 18

the period 2005-2007 at each ur ban case study location; 19

 Current NAAQS: based on rolling back PM 2.5 

# concentrations to just meet the current 20

suite of standards in each urban st udy area (annual standard of 15 μg/m 3 

# and a 24-hour 21

standard of 35 μg/m 3 

# , denoted 15/35); 22

 Alternative NAAQS: based on rolling back PM 2.5 

# concentrations to just meet alternative 23

suites of standards in each urban study area: 24

o 

# annual standard of 14 μg/m 3 

# and a 24-hour standard of 35 μg/m 3 

# (denoted 13/35); 25

o 

# annual standard of 13 μg/m 3 

# and a 24-hour standard of 35 μg/m 3 

# (denoted 13/35); 26

o 

# annual standard of 12 μg/m 3 

# and a 24-hour standard of 35 μg/m 3 

# (denoted 12/35); 27

o 

# annual standard of 13 μg/m 3 

# and a 24-hour standard of 30 μg/m 3 

# (denoted 13/30); 28

o 

# annual standard of 12 μg/m 3 

# and a 24-hour standard of 25 μg/m 3 

# (denoted 12/25). 29

In simulating both current and alternative suites of standards, for the core analysis, we 30

used a proportional roll-back a pproach (see section 3.2.3), while a hybrid roll-back approach 31

reflecting the potential for local source control was used for a subset of urban study areas as part 32

of the sensitivity analysis conducted for this asse ssment (see section 3.2.3). In addition, we have 33

considered the peak-shaving approach as a fu rther alternative to proportional rollback in 34

# February 2010 Draft – Do Not Quote or Cite 4-2 simulating just meeting the current and alternative suites of standards. While we did not generate 1

risk estimates based on application of the peak -shaving approach, we di d generate composite 2

monitor-based annual average PM 2.5 

# levels which allow us to assess how long-term exposure3

related risk could vary if th is alternative roll-b ack method was used (see Section 4.3). 4

As described in section 2.1 and 3.3.2, we asse ssed risk for 15 urban study areas chosen to 5

provide coverage for the diversity of urban settings across the U.S. that reflect areas with 6

elevated annual and/or daily PM 2.5 

# concentrations. At a minimum, all areas selected had recent 7

air quality levels at or above the lowest annual and/or 24-hour st andards analyzed. In addition, 8

our goal was to select areas reflecting the heterogeneity in PM risk-related attributes such as 9

sources, composition, demographi cs, and population behavior. 10

Risk estimates were generated for the follo wing health effects e ndpoints: (a) long-term 11

exposure-related mortality (all- cause, cardiopulmonary disease-related (CPD), ischemic heart 12

disease-related (IHD) and lung cancer-related), (b) s hort-term exposure-related mortality (non13

accidental, cardiovascular dise ase-related (CVD), respiratory), and (c) short-term exposure14

related morbidity (hospital admissions (HA) fo r CVD and respiratory illness and emergency 15

department (ED) visits). Risk estimates are pr esented separately for each of these 15 study areas, 16

although in certain circumstances, ri sk estimates may be restricted to a subset of these locations 17

if, for example, an endpoint is modeled usi ng a C-R function derived from an epidemiological 18

study that was conducted only in a subset of the urban areas. For the co re analysis, long-term 19

exposure mortality risk was modeled down to lo west measured level (LML), because the LML 20

was higher than estimated PRB and because there is substantial uncertainty as to the shape of the 21

concentration-response (C-R) f unction at concentrations below the LML. For long-term 22

exposure mortality a sensitivity analysis was conducted that estimated risk down to policy23

relevant background (PRB). In contrast, all shor t-term exposure health effects endpoints were 24

modeled down to PRB, since this was higher than the LML across all studies and for purposes of 25

NAAQS decision making, EPA is focused on risks associated with PM 2.5 

# levels that are due to 26

anthropogenic sources that can be controlled by U.S. regulations (o r through international 27

agreements with neighboring countries). 28

In modeling long-term exposure mortality, for th e core analysis, we have based estimates 29

on the latest reanalysis of the American Cancer Society (ACS) dataset, with two sets of risk 30

estimates being generated; one using a C-R function derived by fitting PM 2.5 

# monitoring data 31

from 1979-1983 and a second set based on fitting PM 2.5 

# monitoring data from 1999-2000 32

(Krewski et al., 2009) (see secti on 3.3.3). In presenting core risk estimates for long-term 33

mortality, both sets of estim ates are given equal weight. 34

In modeling short-term exposure mortality and morbidity for the core analysis, we have 35

used the latest multi-city studi es (Zanobetti and Schwartz, 2009; Bell et al., 2008) (see section 36

# February 2010 Draft – Do Not Quote or Cite 4-3 3.3.3). In the case of short-term exposure mort ality, we obtained and used city-specific effects 1

estimates derived using empirical Bayes met hods from the study authors (Zanobetti, 2009). 2

Multi-city studies were favored for the core an alysis, since these studies are not subject to 3

publication bias and because they reflect a diverse set of locations with regard to the observed 4

relationship between short-term PM 2.5 

# exposure and health affect response in the population. 5

Additional detail on the specific C-R functions and related modeli ng elements such as effects 6

estimates and lag periods used in the core analysis relative to the sensitivity analysis are 7

presented above in sections 3.3 and 3.4 and calle d out where appropriate be low as specific risk 8

estimates are discussed. 9

The pattern of mortality inci dence across the urban study areas is markedly different for 10

short-term exposure-related mortality compared with long-term exposure-related mortality 11

reflecting a number of factor s including: (a) differences in patterns of daily PM 2.5 

# levels versus 12

annual average values across the urban study areas and (b) the fact that urban study area-specific 13

effect estimates are used in modeling short-term exposure-related mortality, while a single effect 14

estimate is used for all study areas for long-term exposure-related mortality (for a particular 15

mortality category). Further, effect estimates for short-term exposure-related mortality can be 16

notably small for some study areas (e.g., the effect estimates for non-accidental mortality for Los 17

Angeles is significantly smaller than effect estimates for the ot her study areas, thereby 18

accounting for the relatively small total inciden ce estimate for this study area – see Appendix C, 19

Table C-1). 20

Because the recent conditions air quality scen ario spans three years (2005-2007), risk 21

estimates are generated for each of these years, reflecting the underlying air quality data for a 22

particular year. Risk metrics generated for the above health effects endpoints include: 23

 Annual incidence of the endpoint due to PM 2.5 

# exposure ( annual incidence ) 

# : 24

Generated for the population associated with a given urban study area (for a given 25

simulation year), in most cases, these risk estimates include both a point estimate as well 26

as a 95 th 

# percentile confidence interval, the latter reflecting sampling error as 27

characterized in the underly ing epidemiological study. 28

 Percent of total annual incidence fo r the health endpoint due to PM 2.5 

# exposure 

# 29

( percent of total incidence attributable to PM 2.5 

# ): 

# Again, generated for the population 30

associated with a given urban study area (and si mulation year), this metric characterizes 31

the fraction of total incidence that is associated with PM 2.5 

# exposure. As with the 32

underlying PM-related incidence estimates, this risk metric also typically includes a 95 th 

# 33

percentile confidence interval reflecting sa mpling error associated with the effects 34

estimate. Compared with the annual in cidence metric which reflects underlying 35

population size for each study area, this risk metric has the advantage of not being 36

dependent on the size of the underlying populat ion, thereby allowing direct comparison 37

of the potential impact of PM 2.5 

# for the health effect endpoi nt of interest across urban 38

study area locations. For this reason, in disc ussing risk estimates in this section, the 39

# February 2010 Draft – Do Not Quote or Cite 4-4 percent of total incidence attributable to PM 2.5 

# risk metric is given greater emphasis than 1

the absolute measure of annual incidence attributable to PM 2.5



# . 2

 Percent reduction in PM 2.5 

# -related health effect incide nce for an alternative set of 

# 3

standards or the recent conditions scen ario, relative to the current standards 

# 4

( percent change from the current set of standards ) 

# : Also estimated separately for each 5

urban study area and simulation year, this metric characte rizes the degree of risk 6

reduction (for alternative standard levels) or increased risk (for the recent conditions 7

scenario) relative to the current NAAQS. For th is metric, a negative value represents an 8

increase in risk (this is the case for the r ecent conditions scenario, where risks are higher 9

than those associated with just meeting the cu rrent suite of standards). This metric is 10

positive, or zero, for alternative suites of standards since they either produce no risk 11

reduction (if ambient air levels under recent conditions are already at or below that 12

alternative standard levels), or a positive risk reduction for alternative standards resulting 13

in a reductions in ambient PM 2.5 

# concentrations. Because this metric is incremental, it 14

was not possible to generate the 95 th 

# percentile confidence intervals included with the 15

other two “absolute” risk metrics described above. As with the previous risk metric, this 16

metric is not dependent on the underlying popul ation size and theref ore, allows direct 17

comparison across urban study areas. 18

In addition to presenting the central-tendenc y (highest confidence) estimates for each of 19

these metrics, we also include 95 th 

# percentile confidence interv als, reflecting statistical 20

uncertainty surrounding the estimated coefficients in the reported C-R functions used in deriving 21

the risk estimates (note, that th ese confidence intervals only capture this statistical fit uncertainty 22

– other sources of uncertainty in cluding shape and form of the f unction, are addressed separately 23

as part of the sensitivity analys is – see Section 4.3.1.1 and the qua litative analysis of uncertainty 24

– see Section 3.5.3). 25

Detailed tables presenting estimates for thes e risk metrics for the complete set of air 26

quality scenarios (for all 15 ur ban study areas) are included in Appendix E and referenced as 27

needed in the discussion of risk estimates pr esented in the following sections. To support the 28

discussion of risk estimates presen ted in this chapter, we have in cluded a subset of tables and 29

summary figures including: 30

 Tables summarizing risk for the current standard levels 

# : Two tables are included 31

which summarize both long-term and short-te rm exposure-related risk for the 15 urban 32

study areas associated with just meeting the current suite of standa rds. Both tables 33

include a subset of the hea lth endpoints believed to have the greatest su pport in the 34

literature including IHD mortalit y for long-term exposure, ca rdiovascular mortality and 35

hospital admissions for short-term exposure. Table 4-1 presen ts total incidence 36

attributable to PM 2.5 

# exposure for the endpoints and Tabl e 4-2 presents percent of total 37

incidence attributable to PM 2.5 

# exposure for these endpoints. Together, these tables 38

inform consideration for the magnitude of public health impact (relat ed to both long-term 39

and short-term exposure to PM 2.5 

# ) associated with just meeting the current suite of 40

standards in the 15 urban study areas. 41

# February 2010 Draft – Do Not Quote or Cite 4-5  Figures illustrating the percent reduction in long-term and short-term exposure

# 1

related risk for the alternative standard le vels relative to the current standard (as 

# 2

well as increases in risk under recent condi tions relative to the current standard) 

# : 3

Figures 4-1 and 4-4 provide a snapshot of tr ends in risk reduction for long-term exposure4

related risk (Figure 4-1) and short-term exposure-relate d risk (Figure 4-4) across 5

alternative standard levels relative to the ri sk under the current standard. These figures 6

include plots for each of the 15 urban study areas, thereby allowing trends in risk 7

reduction across standard levels (and urban study areas) to be a ssessed simultaneously. 44 

# 8

Each of these figures is presented in a dditional detail by splitting each into (a) 9

comparison of the recent conditions risk ag ainst the current standard level and (b) 10

comparison of risk under alternative standard level against the current standard, in order 11

to allow a more detailed look at patterns in risk reduction for individual urban study areas 12

(splitting Figures 4-1 and 4-4 in this fashion allows greater resolution in tracing the linear 13

risk plots for each study area). Specifically , Figures 4-2 and 4-3 provide these higher14

resolution plots for long-term exposure-rela ted risk and Figures 4-5 and 4-6 provide 15

higher-resolution plots for shor t-term exposure related risk. 16

Although risk estimates were genera ted for all three simulation years, in this chapter core risk 17

estimates primarily from 2007 are presented and discussed for both the recent conditions air 18

quality scenario and just meeting current and alternative suites of standards. This reflects the 19

observation that in generally 2007 re presents a reasonable central y ear (in terms of the magnitude 20

of risk generated for the three simulated years), when considering results for all modeled health 21

effect endpoints across the 15 study areas. In addition, 2007 is the most recent year of the three 22

simulated. We note, however, that while we do focus on 2007 in presenting and discussing risk 23

estimates, we include an assessment of general tr ends across the three simulation years to gain 24

perspective on year-to- year variation in PM 2.5 

# -related risk estimates as assessed here. 25

26

27

28

29

30

31

32

33

44 

## Note, that importantly, patterns of risk reduction across sta ndard 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-caus e, 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 Table 4-1. Estimated Annual Incidence of Se lected Mortality and Morbidity Endpoints 

# 1

Associated with Long- and Short-Term Exposure to Ambient PM 2.5 

# 2

Concentrations that Just Meet the Cu rrent Standards, Based on Adjusting 

# 3

2007 PM 2.5 

# 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 PM2.5 3 Incidence of

Cardiovascular

Mortality Associated

with Short-term

Exposure to PM2.5 4 Incidence of

Cardiovascular

Hospitalizations

A ssociated with Short

term Exposure to

PM2.5 5

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 statisti cal

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

al. (2008). Location-specific C-R function estimates were not available from this study. 

# 5

6

7

8

9

10

11

12

13

14

# February 2010 Draft – Do Not Quote or Cite 4-7 Table 4-2 Estimated Percent of Total Annual Incidence of Selected Mortality and 

# 1

Morbidity Endpoints Associated with Long- and Short-Term Exposure to 

# 2

Ambient PM 2.5 

# Concentrations that Just Mee t the Current Standards, Based 

# 3

on Adjusting 2007 PM 2.5 

# Concentrations. 1,2 

# 4

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 PM2.5 3 Percent of Incidence

of Cardiovascular

Mortality Associated

with Short-term

Exposure to PM2.5 4 Percent of Incidence

of Cardiovascular

Hospital Admissions

A ssociated with Short

term Exposure to

PM2.5 5

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 un certainty

surroundin g 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

al. (2008). Location-specific C-R function estimates were not available from this study. 

# 5February 2010Draft – Do Not Quote or Cite4-8Figure 4-1 Percent reductionin long-term exposure-related mortality risk (alternative standards and recent conditions relative to the current standards)1(Note: inset shows PM2.5related incidence and percent of total incidence for IHD mortality under the current suite of standards)2Atlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXLos Angeles, CANew York, NYPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTSt. Louis, MOTacoma, WA-50%-30%-10%10%30%50%70%90%2007 airquality15/35** 14/35 13/35 12/35 13/30 12/25Recent Air Quality, Current Standard and Alternative Standards

Long-Term Exposure-Related Risk: Percent Reduction from Current StandardAtlanta, 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%)34*Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (and 95% CI) and the5estimate of percent of total incidence (and 95% CI) under the current standards.6**The current standards consist of an annual standard of 15 μg/m3and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a daily standard7(m) are denoted n/m in this figure.8February 2010Draft – Do Not Quote or Cite4-9Figure 4-2 Percent reduction in long-term exposure-related mortality risk(recent conditions relative to the current standards) (Note: inset shows1PM2.5related incidence and percent of total incidence for IHD mortality under the current suite of standards)2Atlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXLos Angeles, CANew York, NYPhiladelphia, PAPittsburgh, PASalt Lake City, UTSt. Louis, MOTacoma, WAPhoenix, 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 StandardAtlanta, 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%)34*Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (and 95% CI) and the5estimate of percent of total incidence (and 95% CI) under the current standards.6**The current standards consist of an annual standard of 15 μg/m3and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a daily standard7(m) are denoted n/m in this figure.8February 2010Draft – Do Not Quote or Cite4-10Figure 4-3 Percent reduction in long-term exposure-related mortality risk(alternative standards relative to the current standards) (Note: inset shows1PM2.5related incidence and percent of total incidence for IHD mortality under the current suite of standards)2Atlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXLos Angeles, CANew York, NYPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTSt. Louis, MOTacoma, WA0%20%40%60%15/35** 14/35 13/35 12/35 13/30 12/25Current and Alternative Standards

Long-Term Exposure-Related Risk: Percent Reduction from Current StandardAtlanta, 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*Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (and 95% CI) and the4estimate of percent of total incidence (and 95% CI) under the current standards.5**The current standards consist of an annual standard of 15 μg/m3and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a daily standard6(m) are denoted n/m in this figure.7***The percent reductions for Salt Lake City and Tacoma at the 12/25 standard are 100% and 93%, respectively.89February 2010Draft – Do Not Quote or Cite4-11Figure 4-4 Percent reduction inshort-term exposure-related mortality and morbidity risk(alternative standards and recent conditions relative to the1current standards) (Note: inset shows PM2.5related incidence and percent of total incidence for CV under the current suite of standards)2Atlanta, GABaltimore, MDBirmingham,ALDallas, TXDetroit, MIFresno, CAHouston, TXLos Angeles,CANew York,NYPhiladelphia,PAPhoenix, AZPittsburgh,PASalt Lake City, UTSt. Louis, MOTacoma,WA-50%-40%-30%-20%-10%0%10%20%30%2007 airquality15/35** 14/35 13/35 12/35 13/30 12/25Recent Air Quality, Current Standard and Alternative Standards

Short-Term Exposure-Related Risk: Percent Reduction from Current StandardAtlanta, 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%)`34*Based on Zanobetti and Schwartz(2009). The legend contains,for each urban area, the incidence estimate (and 95% CI) and theestimate of percent of total5incidence (and 95% CI) under the current standards.6**The current standards consist of an annual standard of 15 μg/m3and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a daily standard7(m) are denoted n/m in this figure.8*** The percent reductions from 2007 air quality to the current standard for Salt Lake City and Fresno are -58% and -81%, respectively.9February 2010Draft – Do Not Quote or Cite4-12Figure 4-5 Percent reduction inshort-term exposure-related mortality and morbidity risk(recent conditions relative to the current standards) (Note:1inset shows PM2.5related incidence and percent of total incidence for CV under the current suite of standards)2Atlanta, GABaltimore, MDBirmingham, ALFresno, CAHouston, TXLos Angeles,CANew York,NYPhiladelphia,PAPhoenix, AZPittsburgh, PASalt Lake City, UTSt. Louis, MODallas, TXDetroit, MITacoma, WA-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 StandardAtlanta, 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%)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%)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% - 1.8%)3*Based on Zanobetti and Schwartz(2009). The legend contains,for each urban area, the incidence estimate (and 95% CI) and theestimate of percent of total4incidence (and 95% CI) under the current standards.5**The current standards consist of an annual standard of 15 μg/m3and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a daily standard6(m) are denoted n/m in this figure.7February 2010Draft – Do Not Quote or Cite4-13Figure 4-6 Percent reduction inshort-term exposure-related mortality and morbidity risk(alternative standards relative to the current standards)1(Note: inset shows PM2.5related incidence and percent of total incidence for CV under the current suite of standards)2Atlanta, GABaltimore,MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXLos Angeles,CANew York,NYPhiladelphia,PAPhoenix, AZPittsburgh, PASalt Lake City, UTSt. Louis, MOTacoma,WA0%5%10%15%20%25%30%35%15/35** 14/35 13/35 12/35 13/30 12/25Recent Air Quality, Current Standard and Alternative Standards

Short-Term Exposure-Related Risk: Percent Reduction from Current StandardAtlanta, 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*Based on Zanobetti and Schwartz(2009). The legend contains,for each urban area, the incidence estimate (and 95% CI) and theestimate of percent of total4incidence (and 95% CI) under the current standards.5**The current standards consist of an annual standard of 15 μg/m3and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a daily standard6(m) are denoted n/m in this figure.7

# February 2010 Draft – Draft Do Not Quote or Cite 4-14 As noted above, the risk assessment includes risk estimates for a range of short-term and 1

long-term exposure-related health effect endpoint s. To focus the discussion of these risk 2

estimates, we have selected a subset of the h ealth endpoints as examples to help illustrate 3

patterns in the risk estimates that might be of in terest from a policy standpoint. Specifically, we 4

have focused on those endpoints that the ISA id entifies as having the greatest support in the 5

literature (i.e., endpoints relate d to cardiovascular effects, including both mortality and 6

morbidity). The subset of health effect endpoint s selected as illustrative examples for this 7

overview include: IHD-related mortality (for lo ng-term exposure) and CV-related mortality and 8

HA (for short-term exposure). While the disc ussion does focus on these cardiovascular-related 9

endpoints, we do address other endpoints modeled in the risk assessment to a limited extent. The 10

full set of risk estimates generated is presen ted in the detailed tables in Appendix E. 11

For a subset of the urban case studies (e.g., Dallas and Phoenix), incremental reductions 12

across alternative standards are initially very lo w (or even zero) reflectin g the fact that recent 13

ambient PM 2.5 

# concentrations for these study areas are well below the current annual standard 14

levels. For these study areas, meaningful reducti ons in risk may not be seen until relatively 15

lower alternative standards are assessed (and resu lts in the percent reduction from the current set 16

of standards tables and figures may be zero for se veral of the less stringent, alternative sets of 17

standards). The pattern of risk reductions across alternative sta ndard levels for a given urban 18

study area is an important factor that is discussed in the integra tive discussion in Chapter 6. To 19

set up that later discussion, in summarizing ri sk estimates below, we provide observations 20

regarding trends in risk estimat es across alternative suites of standards (for a given urban study 21

area). 22

For a number of the urban study areas, confiden ce intervals (and in some instances, point 23

estimates) for short-term mortality and morbidity incidence and related risk metrics include 24

values that fall below zero. Population inci dence estimates with negative lower-confidence 25

bounds (or point estimates) do not imply that additional exposure to PM 2.5 

# has a beneficial effect, 26

but only that the estimated PM 2.5 

# effect estimate in the C-R function was not statistically 27

significantly different from zero. In the case ofshort-term exposure mortality, where study area28

specific effects estimates were used (see secti on 3.4), several of the urban locations have non29

statistically significant effects estimates; these result in incide nce estimates with non-positive 30

lower bounds and/or best estimates (e.g., Birm ingham, Detroit, and Los Angeles for non31

accidental mortality). In the case of short-te rm morbidity (e.g., HAs), where regional effects 32

estimates were used, one of the regional coeffici ents (for the southeast) is not statistically 33

significant, producing incidence estim ates including negative values in the confid ence interval 34

for urban study areas falling with in that region (e.g., Atlanta, Dallas, and Houston, for CV35

related HAs). Lack of statistical significance could mean that there is no relationship between 36

# February 2010 Draft – Draft Do Not Quote or Cite 4-15 PM 2.5 

# and the health endpoint or it could mean that there was not sufficient statistical power to 1

detect a relationship that actually exists. In the case of PM 2.5 

# and both short-term exposure 2

mortality and morbidity, recognizing that the ISA has concluded that there is either a causal or 3

likely causal relationship between short-term PM 2.5 

# exposure and these health effects (see section 4

3.3.1), we believe it is reasonable to assume that instances where effects estimates are not5

statistically significant are likely to reflect insuffi cient sample size, rather than the absence of an 6

actual association. We note, however, that (a s discussed in section 3.6.3) many factors can 7

potentially result in variations in the magnitude of effect estimates. In addition to sample size, 8

these include: source and co mpositional differences for PM 2.5 

# , exposure error associated with the 9

use of ambient monitors as a surrogate for actual exposure, and differences in population 10

susceptibility and vulnerability. 11

An important theme in discussing risk asso ciated with both current and alternative 12

standard levels is the linkage between the natu re and magnitude of risk reductions seen for a 13

particular study area (for a partic ular suite of 24-hour and annual standards) and the specific mix 14

of 24-hour and annual design values associated w ith that study area. Because design values 15

determine the degree to which the PM 2.5 

# monitors in a study area are adjusted in simulating 16

attainment of both current and alte rnative standard levels, they play a central role in determining 17

the degree of risk reduction associated with a pa rticular suite of standa rd levels. Given the 18

importance of design values in determining ri sk reduction under both cu rrent and alternative 19

standard levels, we have examined patterns in design values (specifi cally the relationship 20

between 24-hour and annual design values) acro ss the 15 urban study areas, as a means for 21

enhancing our interpretation of patterns in risk reductions for the standard levels modeled. In 22

addition, we have contrasted the patterns of design values for the 15 urban study areas with 23

patterns of design values for the br oader set of urban areas in the U.S.; this supporting efforts to 24

place risk estimates for the urban study areas in a broader national context. This exploration of 25

design values is discussed in section 4.5. 26

An additional factor to consider in examin ing patterns in risk estimates is the overall 27

spread in PM 2.5 

# measurements across monitors at a particular urban study area, including 28

distributions of both 24-hour dist ributions and annual averages. This factor works in concert 29

with the patterns in design values mentioned ea rlier in determining the degree of risk reduction 30

associated with a particular suite of standard leve ls. In addition, the spread in monitor values for 31

a particular urban study area can also determine the degree to which alte rnative rollback methods 32

(proportional, hybrid and peak shaving) produce di fferences in risk estimates for a given study 33

area. Consequently, in concer t with examining patterns in de sign values (see above) we have 34

also explored patterns in PM 2.5 

# monitoring data for the 15 urban study areas in an effort to better 35

# February 2010 Draft – Draft Do Not Quote or Cite 4-16 understand how application of diffe rent rollback methods results in differing impacts on core risk 1

estimates. This topic is discussed in section 4.5. 2

The remainder of this section is organized as follows. Core modeling results for the 3

recent conditions air quality scenario are presented in section 4.1. Core modeling results for just 4

meeting the current NAAQS and just meeting alte rnative NAAQS are presented in section 4.2. 5

The results of the sensitivity analysis (incl uding single-factor and multi-factor results) are 6

presented in section 4.3. The results of a re presentativeness analysis involving comparison of 7

counties associated with the 15 urban study area locations against the na tional distribution of 8

counties with regard to a set of PM-risk related at tributes are presented in section 4.4. Section 9

4.5 discusses the consideration of design values in interpreting ri sk estimates generated for the 10

15 urban study areas and helping to place them in a broader national context (section 5.4.1), as 11

well as consideration for the patterns in ambient PM 2.5 

# data within study areas as a factor 12

influencing patterns of risk es timates (section 4.5.2). Chap ter 6 provides an integrative 13

discussion of the results of the core risk asse ssment for the 15 urban study areas informed by 14

consideration of: (a) the single- and multi-factors sensitivity analysis, (b) the qualitative analysis 15

of sources of variability and uncertainty, (c) the representativeness analysis (d) the national-scale 16

mortality analysis (presented in chapter 5), and (e) the role of design va lues (and patterns in 17

ambient PM 2.5 

# monitoring data) in influencing overall pa tterns of risk estimates across alternative 18

suites of standards. 19

4.1 ASSESSMENT OF HEALTH RISK ASSOCIATED WITH RECENT CONDITIONS 

# 20

(CORE ANALYSIS) 

# 21

This section discusses core risk estimates generated for the recent conditions air quality 22

scenario, focusing on the 2007 simulation year. Speci fically, it provides a se t of key observations 23

regarding core risk estimates generated for the recent conditions air quality scenario. Note, that 24

while the focus of this section is on identifying key risk-related observations potentially relevant 25

to the current review of the PM NAAQS, additional review of the risk estimates provided in 26

Appendix E is likely to result in additional observations that mi ght be relevant to the PM 27

NAQQS review (EPA staff will continue to review those results as they work on completing the 28

summary of the RA presented in the first draft PA). 29

In discussing results for the recent conditi ons air quality scenario, we have focused on 30

absolute risk (either above PRB or LML, depending on the health effect endpoi nt). This reflects 31

the fact that this air quality scenario represents recent conditions within the urban study areas and 32

therefore, does not lend itself to an incremental assessment. The section is organized by health 33

endpoint category, with results discussed in th e following order: long-te rm exposure mortality, 34

# February 2010 Draft – Draft Do Not Quote or Cite 4-17 short-term exposure mortality and short-term exposure morbidity. 45 

# In summarizing estimates 1

for each endpoint category, we fist focus on the ce ntral-tendency risk estimat es (these are what is 2

discussed in each of the bullets focusing on a part icular endpoint category). A discussion of the 3

broader risk range reflect ing consideration for 95 th 

# % confidence interval risk estimates is 4

presented as a separate bullet towards the end of the discussion. Key observations include: 5

 Long-term exposure-related mortality: 

# Total incidence of PM 2.5 

# -related all-cause mortality 6

ranges from 50-60 (Salt Lake City) to 2,380- 3,000 (New York) (Appendix E, Table E-21 and 7

E-30), with this range reflecti ng not only differences in base line incidence across urban study 8

areas, but also the size of study populations wh ich vary considerably across the study areas. 9

The percent of total incidence of IHD -related mortality attributable to PM 2.5 

# ranges from 6.3- 10

8.0% (Tacoma) to 17.7-22.2% (Fresno) (Appe ndix E, Table E-24 and E-33). Total PM 2.5 

# - 11

attriutable incidence for all-cause mortality and cardiopulmonary mortality is larger than IHD 12

(for a given study area under re cent conditions), while total PM 2.5 

# -attributable incidence for 13

lung-cancer mortality is lower than for IHD. However, the percent of to tal incidence 14

attributable to PM 2.5 

# exposure is larger for IHD-related mortality than for any of the other 15

mortality categories modeled (Append ix E, Tables E-24 and E-33). 16

 Short-term exposure-related mortality: 

# Total incidence of PM 2.5 

# -related mortality for 17

short-term exposure (for all categories modeled) is substantially smaller than estimates for 18

long-term exposure-related mortality. Estimat es for CV mortality for short-term exposure 19

ranges from 14 (Salt Lake City) to 570 (New York) (Appendix E, Table E-84). The percent 20

of total non-accidental mort ality attributable to PM 2.5 

# ranges from 0.9% (Tacoma) to 2.5% 21

(New York). (Appendix E, Table E-87 ). Percent of total incidence attributable to PM 2.5 

# 22

exposure is generally lower fo r total non-accidental mortality (compared with CV), ranging 23

from 0.2% (Los Angeles) to 1.8% (Baltimo re) (Appendix E, Table E-78). Estimates for 24

respiratory mortality are usually higher than for CV mortality, ranging from 0.9% (Dallas) to 25

2.8% (Fresno and New York) (Appendix E, Table E-96). Of the 15 urban study areas 26

modeled for CV mortality, 12 locations had negative lower bound estimates of incidence 27

(and two of these head negative point estimates) , reflecting use of non-st atistically significant 28

effects estimates (see section 4.0 for additi onal discussion). The number of study areas 29

modeled with non-statistically si gnificant effects estimates was lower for the other two short30

term exposure-related mortality endpoints. 31

 Short-term exposure-related morbidity (hos pital admissions for respiratory and 

# 32

cardiovascular illness): 

# Total incidence of PM 2.5 

# -related cardiovascular HA range from 15 33

(Salt Lake City) to 910 (New York City) and are significantly larger than estimates of 34

respiratory HA attributable to PM 2.5 

# exposure (Appendix E, Ta bles E102 and E-111). 35

Similarly, the percent of total cardiovascular HA attributable to PM 2.5 

# is larger than estimates 36

for respiratory HA and ranges from 0.28% (Dalla s) to 1.6% (Pittsburgh) (Appendix E, Table 37

E-105). In this case, the pattern of risk acro ss urban study areas reflects both differences in 38

45 

## Note, that as discussed earlier, for long-term exposure-re lated mortality, two risk estimates are provided for each

urban study area, reflecting application of the two C-R fu nctions used in modeling each mortality endpoint in the

core analysis - i.e., C-R function derived using 1979-1983 PM 2.5 

## 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 underlying baseline incidence for these e ndpoints as well as th e use of regionally1

differentiated effect estimates obtained from Bell et al., 2008 (see Appendix C, Table C-1). 2

Of the 15 urban study areas modeled for cardi ovascular-related HAs, five locations had 3

negative lower bound estimates of incidence, reflecting use of non-statistically significant 4

effects estimates (see section 4. 0 for additional discussion). 5

 Patterns of recent conditions risk across the three simulation years: 

# A comparison of 6

IHD mortality incidence estimates (based on the C-R function derived using 1979-1982 7

monitoring data) across the three years (see Appendix E, Tables E-22 through E-24) shows 8

that, while 2007 does produce incidence estimate s that fall between those estimated for 2005 9

and 2006 for some urban areas (e.g., Tacoma, St . Louis, LA), results for 2007 can be the 10

highest of the three years (e.g., Fresno) or the lowest (e.g., Baltimore) for some locations. 11

Generally, results for the same urban study ar ea across the three year s are fairly similar 12

(results for Birmingham vary by less than 7% ac ross the years), although they can vary by as 13

much as 30% or more in some locations (see results for Tacoma in 2005 and 2006). All of 14

this temporal variation results from year -to-year variation in the annual average PM 2.5 

# levels 15

for the study areas (see Appendix A). This is because other candidate input parameters, 16

which could also involve temporal variability (e.g., demographics and baseline incidence 17

rates) were not modeled with year-specific valu es, but rather using one representative year 18

(see section 3.4.1.3 and 3.5 for demographics an d baseline health effects incidence rates, 19

respectively). In terms of s hort-term exposure-related morbidity and mortality endpoints, the 20

pattern is similar to that desc ribed above for long-term mortalit y, with risk estimates for 2007 21

generally falling between t hose generated for 2005 and 2006 (in terms of magnitude), 22

although the magnitude of variations across th e three simulation years for a given health 23

endpoint/case study combination was notably lo wer for the short-term exposure-related 24

endpoints than for the long-term endpoints. Fo r example, with CV mortality, one of the 25

urban study area with the greate st variation across the thr ee years (New York) had a 15% 26

difference in PM 2.5 

# –related risk across the three y ears (see Appendix E, Tables E-82 through 27

E-84). This compares with a spread of 30% for some of the urban study areas modeled for 28

long-term exposure-related IHD mortality – see above. As with the long -term mortality risk 29

metrics, all of this temporal variation results from year-to-year variation in the daily PM 2.5 

# 30

levels for the study areas (see Appendix A), gi ven that other candidate input parameters, 31

which could have temporal variability (e.g., dem ographics and baseline incidence rates) were 32

not modeled with year-specific values, but rather using one re presentative year. 33

 Consideration for the 95 th 

# percentile confidence interval risk estimates in assessing 

# 34

uncertainty related to the statistic fit of effect estimates: 

# As noted above, all of the risk 35

metrics generated for this analysis include 95 th 

# percentiles, reflecting uncertainty in the 36

statistical fit of the underlying effect estimates in the C-R f unctions. These results suggest 37

that this source of uncertainty can be notable. In the case of recent conditions risk estimates, 38

for long-term mortality, while the central tende ncy risk estimate for all-cause (long term 39

exposure-related) mortality incidence in New York range from 2,380-3,000, the 95 th 

# 40

percentile confidence in terval for this estimates is 1,960 to 3,500 (Appendix E, Table E-21 41

and E-30). In this case, this source of uncertain ty results in estimates that are ~18% lower to 42

~17% higher than the central tendency estimate range. Using the criteria we applied in 43

assessing the results of the sensitivity analys is, these would translate as having a “small” 44

impact on the core risk estimate (see Section 4.3. 1). The impact of statistical fit uncertainty 45

# February 2010 Draft – Draft Do Not Quote or Cite 4-19 on the IHD-related long-term exposure-related mo rtality results (see Appendix E, Tables E1

24 and E-33) are similar in magnitude to those se en for all-cause mortality and also results in 2

a classification of this uncertainty having a “small” impact on core risk estimates. For short3

term exposure-related mortality and morbidity, the impact of statistical f it (as reflected in the 4

95 th 

# percentile CI risk estimate ranges) is great er than for long-term mortality. For example 5

with CV-related mortality, the central tendenc y estimate for New York is 570 cases, while 6

the 95 th 

# percentile CI is 332 to 902 (i.e., ~40% lo wer and ~40% higher th an the core central7

tendency estimates). This translates into a “moderate” impact by this source of uncertainty 8

on core risk estimates using the classification scheme developed for the sensitivity analysis. 9

This suggests that uncertainty related to the stat istical fig of effect estimates used in risk 10

characterization has twice as grea ter an impact on short-term mo rtality as long-term mortality 11

risk estimates. 12

4.2 ASSESSMENT OF HEALTH RISK ASSO CIATED WITH JUST MEETING THE 

# 13

CURRENT AND ALTERNATIVE SUITES OF STANDARDS (CORE ANALYSIS) 

# 14

This section discusses core risk estimates ge nerated for just meeti ng the current suite of 15

standards and alternative suite s of standards, focusing on the 2007 simulation year (although 16

general trends in observations acros s the three simulated years are discussed to a limited extent). 17

In discussing risk estimates for the current a nd alternative suites of standards, we include 18

discussion of risk metric which characterize both incremental reductions in risk (across standard 19

levels) as well as absolute risk for a particular standard level. In presenting these two categories 20

of risk metric, we recognize that there is greater uncertainty in estimates of absolute risk relative 21

to estimates of incremental risk. This reflects the fact that we have greater confidence in the 22

ability of the risk models to differentiate risk between sets of standards, since this requires the 23

models to estimate risk for ambient air PM 2.5 

# levels likely near or with in the range of ambient air 24

quality data used in the underlyi ng epidemiology studies. By contra st, estimates of absolute risk 25

(for a given air quality scenario) require the models to perf orm at the lower boundary of ambient 26

air PM 2.5 

# levels reflected in the studies (i.e., dow n to the LML reflected in the long-term 27

exposure mortality epidemiology studies or down to PRB levels in the short-term exposure 28

morbidity and mortality studies). There is greater overall uncertainty in risk estimates generated 29

based on the contribution to risk of e xposures at these lower ambient air PM 2.5 

# levels. While 30

there is greater uncertainty associated with esti mates of absolute risk, these estimates are of 31

potential use in informing consideration of the magnitude of risk (and therefore public health 32

impact) for a particular standard level. The ove rall level of confidence associated with different 33

risk metrics (and implications for informing thei r use in the context of the PM NAAQS review) 34

is discussed in Chapter 6. 35

This section discusses risk estimates genera ted for the current standard level first, 36

followed by discussion of risk estimates associated the set of alternative standard levels assessed. 37

Each of these discussions is further organized by health endpoint category, with results discussed 38

# February 2010 Draft – Draft Do Not Quote or Cite 4-20 in the following order: long-term exposure mort ality, short-term exposure mortality and short1

term exposure morbidity. Observat ions presented in the previous section regarding the statistical 2

significance of effects estimates used in genera ting risk estimates and their implications for 3

interpretation of those risk estimates also hold for estimates presented in this section. 4

Consequently, observations regarding risk resu lts with confidence intervals including negative 5

estimates are not presented here and the reader is referred back to the earlier discussion in section 6

4.1. 7

We note that the lower magnitude of risk re ductions (in terms of percent change in PM 2.5 

# - 8

attributable risk) generally seen for short-term exposure-related endpoints relative to long-term 9

exposure-related endpoints primar ily reflects the fact that PM 2.5 

# -attributable risk is modeled 10

down to PRB for short-term, but only down to LML for long-term. This means that an 11

incremental change (reduction) in long-term ri sk will be a larger fraction of overall risk 12

compared with short-term risk and hence, th e magnitude of risk reductions for long-term 13

exposure-related risk is notably larg er compared with short-term risk 14

An important factor to consider in interpre ting the risk estimates for both the current set 15

of standards and sets of altern ative standards is whether the annual or 24-hour standard for a 16

given pairing of standards is c ontrolling for a particular area. 46 

# This factor can have a significant 17

impact on the pattern of risk reductions predicted for a given location under the simulation of just 18

meeting a specific set of standards. In additi on, the approach used to simulate ambient PM 2.5 

# 19

levels under current and alternative standard le vels (i.e., use of proportional, hybrid, or peak 20

shaving) can significantly impact the magnitude risk reduction seen acro ss standard levels 21

(particularly the degree to which a particular st andard produces notable reductions in long-term 22

exposure-related mortality). 47 

# The potential for different rollback strategies (reflecting 23

potentially different combinations of local and/or regional contro ls) to impact patterns of risk 24

reduction is not discussed here, but rather reserved for discussi on as part of the sensitivity 25

analysis (section 4.3) and the inte grative chapter (chapter 6). 26

An overview of which urban st udy areas are predicted to have risk reductions under the 27

current and alternative suites of standards included in the risk assessment is presented below 28

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 mo re localized control strategies have the

potential to reduce PM 2.5 

## levels at monitors exceeding the daily standa rd, while leaving other monitors (which may

have elevated annual-average PM 2.5 

## 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 valu es 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 (Appendix E contains tables presen ting the full set of detailed core risk estimates generated for 1

the current and alternat ive suites of standards). 2

4.2.1 Core Risk Estimates for Just Meeti ng the Current Suite of Standards 

# 3

This section summarizes risk estimates gene rated for the 15 urban study areas based on 4

simulating just meeting the current suite of stan dards (including the magnit ude of risk reductions 5

relative to recent conditions, where applicable). 6

 Long-term exposure-related mortality: 

# Total incidence of PM 2.5 

# -related IHD mortality 7

ranges from 15-20 (Salt Lake City) to 1,760-2,220 (New York) (Table 4-1). The percent of 8

total incidence of IHD mortality attributable to PM 2.5 

# ranges from 3.7-4.7% (Tacoma) to 9

13.2-16.7% (Atlanta) (Table 4-2). These levels of IHD mortality risk attributable to PM 2.5 

# 10

exposure reflect reductions in ri sk relative to recent conditi on ranging from 8.7% (Houston) 11

to 68.6% (Salt Lake City). Two of the urban study areas (Dallas and Phoenix) do not exhibit 12

reductions in risk in simulati ng just meeting the current suit e of standards since these two 13

locations meet the current suite of standards based on recent air quality data. As referenced 14

above for the recent conditions scenario, total PM 2.5 

# -attriutable incidence for all-cause 15

mortality and cardiopulmonary mo rtality is larger than IHD (for a given study area under 16

recent conditions), while total PM 2.5 

# -attributable incidence for l ung-cancer mortality is lower 17

than for IHD. However, the percent of total incidence attributable to PM 2.5 

# exposure is larger 18

for IHD-related mortality than for any of the other mortality categories modeled (Appendix 19

E, Tables E-24 and E-33). 20

 Short-term exposure-related mortality: 

# As with the recent c onditions analysis, total 21

incidence of PM 2.5 

# -related mortality for short-term expos ure is substantially smaller than 22

estimates for long-term exposure-related mortal ity. Estimates for CV mortality for short23

term exposure ranges from 9 (Sa lt Lake City) to 470 (New York) (Table 4- 1). The percent of 24

CV mortality attributable to PM 2.5 

# ranges from 0.7% (Fresno) to 2.1% (Philadelphia and New 25

York). (Table 4-2). The level of risk reduc tion (comparing risk under the current standard 26

with risk under recent conditi ons) is generally lower for s hort-term exposure-related CV 27

mortality compared with long-term exposure- related all-cause mort ality and ranges from 28

5.5% (Baltimore) to 36.9% (Los Angeles). As mentioned for long-term exposure-related 29

risk, both Phoenix and Dallas did not exhibit a ny risk reduction since these two locations 30

meet the current suite of standa rds based on recent air quality da ta. Percent of total incidence 31

attributable to PM 2.5 

# exposure is generally lower for total non-accidental mortality (compared 32

with CV), ranging from 0.1% (Los Angeles) to 1.7% (Baltimore) (Appendix E, Table E-78). 33

Estimates for respiratory mortality are usua lly higher than for CV, ranging from 0.9% 34

(Dallas) to 2.6% (Baltimore) (Appendix E, Ta ble E-96). As noted above, of the 15 urban 35

study areas modeled for CV mortality, 12 locat ions had negative lower bound estimates of 36

incidence (and two of these had negative point estimates), refl ecting use of non-statistically 37

significant effects estimates (see section 4.0 for additional discussion). 38

 Short-term exposure-related morbidity (hos pital admissions for respiratory and 

# 39

cardiovascular illness): 

# Total incidence of PM 2.5 

# -related cardiovascular HA range from 9 40

(Salt Lake City) to 750 (New York City) and are significantly larger than estimates of 41

respiratory HA attributable to PM 2.5 

# exposure (Appendix E, Ta bles E102 and E-111). 42

Similarly, the percent of total cardiovascular HA attributable to PM 2.5 

# is larger than estimates 43

# February 2010 Draft – Draft Do Not Quote or Cite 4-22 for respiratory HA and ranges from 0.28% (Dalla s) to 1.33% (Baltimore). As noted above, 1

the pattern of risk across urba n study areas reflects both differences in underlying baseline 2

incidence for these endpoints as well as the us e of regionally-differentiated effect estimates 3

obtained from Bell et al., 2008 (see Appendix C, Table C-1). The level of risk reduction 4

(comparing risk under the current standard with risk under recen t conditions) for both 5

respiratory and cardiovascular hospital admissi ons ranges from 5.5% (Baltimore) to 44.8% 6

(Fresno), again with Phoenix and Dallas not exhibiting any risk reduction since these two 7

locations meet the current suit e of standards based on recent ai r quality data. As noted above, 8

of the 15 urban study areas modeled for cardi ovascular-related HAs, five locations had 9

negative lower bound estimates of incidence, reflecting use of non-statistically significant 10

effects estimates (see section 4. 0 for additional discussion). 11

 Patterns of recent conditions risk across the three simulation years: 

# Observations made 12

earlier regarding patterns of risk across the three simulation years for the recent conditions 13

simulations generally hold for th e current standard level analys is. In other words, (a) 2007 14

generally represents risks in be tween the other two years in terms of magnitude, (b) there are 15

exceptions where 2007 had the highest risks an d lowest risk (depe nding on study area and 16

endpoint), and (c) generally, long-term exposure -related mortality endpoints showed greater 17

cross year variation then the short-term expos ure-related endpoints (with the magnitude of 18

this variation similar to what is reported above for the recent conditions simulation). 19

 Consideration for the 95 th 

# percentile confidence interval risk estimates in assessing 

# 20

uncertainty related to the statistic fit of effect estimates: 

# Uncertainty related to the 21

statistical fit of effect estimates has the same magnitude of effect in modeling risk under the 22

current standard as it did under recent conditions (i.e., an impact of about +/-18% on the core 23

risk estimates, translating into a “small” impact based on classification used in the sensitivity 24

analysis) (see (Appendix E, Table E-21 and E-30 for risk estimates used to reach this 25

conclusion). The impact of this source of uncertainty on short-term exposure-related CV 26

morality was similar (although slig htly larger) compared with what was seen with risk 27

estimates generated for the recent conditions ai r quality scenarios (i.e., 48% lower to 42% 28

higher than the core risk estimate – see estimate s in Appendix E, Table E-84). This results in 29

a classification of “moderate” for this source of uncertainty and its impact on short-term 30

exposure-related mortality, based on the classifi cation scheme developed for the sensitivity 31

analysis. 32

4.2.2 Core Risk Estimates for Just Meeting Alternative Suites of Standards 

# 33

This section summarizes risk estimates ge nerated for the 15 urban study areas when 34

ambient PM 2.5 

# levels under the alternative standard levels are simulated. As noted in section 4.2, 35

this discussion focuses on the magnitude of incr emental risk reductions for individual standard 36

levels relative to the current standard, given that overall confidence in incremental risk metrics is 37

considered higher than estimates of absolute risk for a given standa rd level. Note, however, that 38

we do provide limited discussion of absolu te risk levels attributable to PM 2.5 

# exposure for 39

alternative standard levels, with the provision that these be interpreted in the context of their 40

greater levels of uncertainty. In discussing risk estimates for the alternative standard levels, we 41

focus first on patterns of risk reduction across alternative annual levels (i.e., 14/35, 13/35 and 42

# February 2010 Draft – Draft Do Not Quote or Cite 4-23 12/35) and then discuss patterns across a combination of alternative 24 hour and annual 1

standards (i.e., 13/30 and 12/25). 2

As noted in Section 4.1, although reductions in absolute incidence will differ for health 3

effect endpoints associated with a particular averaging period across alternative suites of 4

standards for a given urban study area, the pattern s of reduction in terms of percent change in 5

PM 2.5 

# -attributable risk are very similar for a given urban study area across health endpoints. This 6

reflects the fact that the C-R functions used in the core analysis are close to linear across the 7

range of ambient PM 2.5 

# levels considered in this analysis, and consequently the main factor 8

producing percent reductions in risk across alternative standards is the reduction in the air quality 9

metric for a given study area (i.e ., reductions in annual average PM 2.5 

# concentrations or 10

reductions in the distribution of 24-hour estimate s for a year). Consequently, in discussing 11

incremental risk reduction in terms of percent ch ange relative to the current suite of standards, 12

we speak more generally in terms of the category of annual-average risk or 2-4hour average 13

risk , with the assumption that these observations hold for individual health effects endpoints 14

assessed for each averaging period. These obser vations regarding patte rns of percent risk 15

reduction for the two averaging periods are refl ected in Figures 4-1 through 4-6 which are 16

referenced in the discussion below. 17

Alternative annual standard le vels (14/35, 13/35, and 12/35) 48 

# 18

 Percent reductions in long-term exposure-related mortality: 

# Reductions in all long-term 19

exposure-related mortality categories were mo re limited under the 14/35 alternative standard, 20

with only 5 of the 15 urban study areas demons trating notable reducti ons ranging from 9% 21

(Baltimore) to 12% (Houston and Birmingham) (see Figure 4-3 and Appendix E, Table E-9). 22

Reducing the annual standard level to 13 μg/m 3 

# (i.e., the 13/35 alternative suite of standards) 23

produced a notable increase in th e number of locations (9 of the 15) with risk reductions 24

relative to the current standard ranging from 5% (New York) to 24% (Houston and 25

Birmingham). The lowest annua l standard evaluated (12 μg/m 3 

# as reflected in the 12/35 26

alternative suite of standards) resulted in additional study areas ( now 12 of the 15 study 27

areas) experiencing risk reducti ons with percentage risk re ductions now ranging from 11% 28

(Phoenix) to 26% (Houston and Baltimore). Note , that even in the 12/35 case, three of the 29

urban study areas (Tacoma, Fresno and Salt Lake City) did not experience any decreases in 30

risk, although risk reductions were seen for th ese three study areas when alternative 24-hour 31

standards were considered – see below. The specific pattern of ri sk reduction (including 32

importantly, the magnitude of risk reduction as well as residual risk associated with a 33

48 

## The three alternative annual standards considered in the risk assessment (12, 13 and 14 μg/m 3 

## ) were each paired

with the current 24-hour standard of 35 μg/m 3 

## 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/m 3 

## 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 particular standard level) reflects whether daily or annual standard leve ls were controlling – 1

see discussion below regarding patterns of risk reduction. 2

 Percent reduction in short-term exposure- related mortality and morbidity: 

# The pattern 3

of reductions in the percent of risk attributable to PM 2.5 

# for mortality and morbidity 4

associated with short-term exposure is similar to that described above for long-term mortality 5

(see Figures 4-4 through 4-6). Specifically, th e same five urban study areas (Atlanta, 6

Baltimore, Birmingham, Houston and St. Louis) had notable risk reductions under the full set 7

of alternative annual sta ndards, with the degree of risk reduction for PM 2.5 

# -related 8

cardiovascular mortality for the lowest alterna tive annual standard level (12/35) compared to 9

the current standard level, ranging from 20% (St. Louis) to 23% (Birmingham) (see Figure 4- 10

4 and 4-6 and Appendix E, Table E-90). A nu mber of the other study areas did not exhibit 11

notable risk reductions until the lowest altern ative annual standard was considered (i.e., 12

Detroit, Los Angeles, New York, Philadelphia, Pittsburgh), with the degree of reduction in 13

risk for the lowest alternative suite of standa rds (12/35) compared with the current standards 14

ranging from 5% (Phoenix) to 16% (Detroit) (see Figure 4-4 and 4-6 and Appendix E, Table 15

E-90). As with long-term exposure-related mortality, a number of additional study areas 16

(Fresno, Salt Lake City, Tacoma) did not exhib it any notable risk reduction under the set of 17

alternative annual standards c onsidered and only experienced risk reductions when the 24- 18

hour standard level was reduced. Because the sa me air quality metric (a nnual distributions of 19

24-hour PM 2.5 

# concentrations) is used in generati ng short-term exposure-related mortality 20

and morbidity endpoints, patterns of risk reducti on are similar for both sets of endpoints (see 21

Figures 4-4 through 4-6. Speci fically, the same groups of urban study areas experience the 22

same magnitude of risk reductions (in terms of percent changes in PM 2.5 

# -related risk relative 23

to the current standard level) across the altern ative standard levels for short-term exposure24

related morbidity (HAs). The specific pattern of risk reduction refl ects whether daily or 25

annual standard levels are controlling – see discussion below regarding patterns of risk 26

reduction. 27

 Pattern of risk reduction linked to design values 

# : The patterns of ri sk reduction across the 28

15 urban study areas for the set of alternative an nual standard levels c onsidered here depends 29

on whether the alternative annual (12, 13 or 14 μg/m 3 

# ) or the current 24-h our standard of 35 30

μg/m 3 

# is controlling. The approach used to simulate just meeting alternative 24-hour 31

standards (i.e., proportional, hybrid , or peak shaving) can have an impact on the magnitude 32

of risk reduction, although it does not influence whether the annual or 24-hour design value 33

was controlling for a given alternative suite of standards (see sensitivit y analysis discussion 34

in 4.3 and the integrative discussion in Chapter 6). The pattern in risk reduction seen across 35

the 15 urban study areas (given the set of alte rnative annual standards considered) can be 36

divided into three categories: (a ) all of the alternative annual st andard levels are controlling, 37

resulting in notable risk reductions for all of the annual standard levels considered 38

(Birmingham, Atlanta, Houston), (b) alternative annual standards only control at lower levels 39

(i.e., 13/35 and/or 12/35) and cons equently notable risk reducti ons are only seen at the lower 40

or lowest annual standard level( s) considered (Dallas, Los Angeles, New York, Philadelphia, 41

Phoenix, Pittsburgh), and (c) none of the alternat ive annual standard levels is controlling and 42

therefore there is no estimated risk reduction for the alternative annual standard levels 43

considered (Salt Lake City, Tacoma, Fresno). 44

# February 2010 Draft – Draft Do Not Quote or Cite 4-25  Absolute levels of PM 2.5 

# -attributable risk under altern ative annual standards: As 

# 1

discussed above, we have grea ter confidence in estimating incremental reductions in risk 2

between the current and alternat ive suites of standards, then the estimation of absolute 3

incidence under a given suite of standards. N onetheless, we provide a summary of that risk 4

metric here for long-term and short-term expos ure-related mortality and short-term exposure5

related morbidity endpoints: 6

o 

# Long-term exposure-related mortality : The four study areas displaying the greatest 7

degree of reduction across th e alternative annual standa rds (Atlanta, Baltimore, 8

Birmingham and Houston) have PM 2.5 

# -related IHD mortality estimates (under the 9

lowest alternative annual standard of 12/35) ranging from 85-110 (Birmingham) to 10

220-280 (Houston) (see Appendix E, Tabl e E-21 and E-30). The two urban study 11

areas with the greatest degree of PM 2.5 

# -related risk in absolute terms (Los Angeles 12

and New York) do not exhibit significant re ductions in risk un til the lowest annual 13

standard level of 12/35 is considered, with PM 2.5 

# -related IHD mortality estimated at 14

750-950 and 1,420-1,800, respectively under that alternative standard (see Appendix 15

E, Table E-21 and E-30). 16

o 

# Short-term exposure-related mortality : The four study areas displaying the greatest 17

degree of reduction across th e alternative annual standa rds (Atlanta, Baltimore, 18

Birmingham and Houston), have PM 2.5 

# -related CV mortality estimates (under the 19

lowest alternative standard of 12/35) rangi ng from 25 (Atlanta) to 50 (Baltimore) (see 20

Appendix E, Table E-84). We note that Birm ingham has an incidence estimate of -1, 21

reflecting application of a non- statistically significant eff ect estimate in modeling this 22

endpoint (see section 4.1). The urban st udy area with the grea test degree of PM 2.5 

# - 23

related risk in absolute terms (New York) does not exhibit significant reductions in 24

risk until the lowest annual standard level of 12/35 is considered with PM 2.5 

# -related 25

CV mortality estimated at 420 under that a lternative standard level (see Appendix E, 26

Table E-84). 27

o 

# Short-term exposure-related morbidity : The four study areas displaying the greatest 28

degree of reduction across the alternative annual standard levels (Atlanta, Baltimore, 29

Birmingham and Houston), have PM 2.5 

# -related cardiovascular HA (under the lowest 30

alternative standard of 12 /35) ranging from 12 (Birmingham) to 170 (Baltimore) (see 31

Appendix E, Table E-102). The two urban study areas with the greatest degree of 32

PM 2.5 

# -related risk in absolute terms (Los Angeles and New York) do not exhibit 33

significant reductions in risk until the lowe st annual standard level of 12/35 is 34

considered with PM 2.5 

# -related all-cause mortality estimated at 240 and 670, 35

respectively under that alte rnative standard level (see Appendix E, Table E-102). 36

 Patterns of recent conditions risk across the three simulation years: 

# Observations made 37

above regarding patterns of risk across the three simulation years for the recent conditions 38

and current standards simulations generally hold for the alternat ive standards analysis. In 39

other words, (a) 2007 generally represents risk s between the other two years in terms of 40

magnitude, (b) there are exceptions where 2007 had the highest risks and lowest risk 41

(depending on study area and endpoint), and (c) generally, long-te rm exposure-related 42

mortality endpoints showed greater cross-year variation then the short-term exposure-related 43

endpoints in terms of both absolute PM 2.5 

# risk for a particular alte rnative suite of standards, 44

as well as incremental risk reductions rela tive to the current suite of standards. 45

# February 2010 Draft – Draft Do Not Quote or Cite 4-26  Consideration of the 95 th 

# percentile confidence interval risk estimates in assessing 

# 1

uncertainty related to the statistic fit of effect estimates: 

# Continuing the pattern seen with 2

the current standard level, uncertainty related to the statistical fit of effect estimates has the 3

same magnitude of effect in modeling risk unde r alternative standards involving reduction of 4

the annual level as it did under re cent conditions (i.e., an impact of about +/-18% on the core 5

risk estimates, translating into a “small” impact based on classification used in the sensitivity 6

analysis) (see Appendix E, Table E-21 and E- 30 for risk estimates used to reach this 7

conclusion). Similarly, the pattern of imp act this source of uncertainty on short-term 8

exposure-related CV morality con tinues to be similar compared with what was seen for risk 9

estimates generated for the recent conditions air quality scenarios (i.e., 42% lower to 10

42%higher than the core risk estimate – see es timates in Appendix E, Table E-84). This 11

continues to result in a cla ssification of “moderate” for this source of uncer tainty based on 12

the classification scheme develope d for the sensitivity analysis. 13

Combinations of alternativ e 24-hour and annual standa rd levels (13/30, 12/25) 14

 Percent reductions in long-term exposure-related mortality: 

# The combination of suites 15

of alternative 2-hour and annual standards produced notable reductions in long-term 16

exposure-related mortality for 14 of the 15 ur ban study areas, with the lower combination 17

(12/25) producing a notable reduction in risk relative to the first combination of 13/30. The 18

only study area that did not exhi bit a reduction in risk under the first combination (13/30) 19

was Dallas, reflecting the fact that its 24- hour and annual design values are below 30 μg/m 3 

# 20

and 13 μg/m 3 

# , respectively (and consequently, th e 13/30 did not produce a reduction in 21

ambient air PM 2.5 

# , or a resulting reduction in risk). Reductions in long-term exposure-related 22

mortality (across all endpoints) under the 13 /30 combination ranged fr om 14% (Phoenix) to 23

55% (Salt Lake City), while reductions for th e 12/25 combination ranged from 12% (Dallas) 24

to ~100% (Salt Lake City) (see Figure 4-1 and 4-3 and Appendix E, Table E-27). The 25

reduction for Salt Lake City reflects a very high 24-hour design value which, when reduced 26

to meet the 24-hour standard of 25 μg/m 3 

# produced a very large reduction in the annual 27

design value (given application of the proporti onal adjustment to simu late rollback), such 28

that the value was very close to 5.8 μg/m 3 

# (the LML below which long-term exposure-related 29

mortality is not estimated). The specific patt ern of risk reduction re flects whether the 24-hour 30

or annual standard was controlling – see di scussion below regardi ng patterns of risk 31

reduction. 32

 Percent reduction in short-term exposure- related mortality and morbidity: 

# The pattern 33

of reductions in the percent of risk attributable to PM 2.5 

# for mortality and morbidity 34

associated with short-term exposure is similar to that described above for long-term mortality 35

in terms of the ordering of site s, however the magnitude of risk reduction (in terms of percent 36

change in PM 2.5 

# -related risk) is lower for short-te rm exposure-related health endpoints 37

compared with long-term exposure-related mortality (see Figures 4-4 through 4-6). 38

Specifically, 14 of the 15 urban study areas (D allas being the exception), had notable risk 39

reductions under both the 13/30 a nd 12/35 alternative suites of standards (Dallas only was 40

estimated to have reductions in risk under the lower 12/25 co mbination - see Figure 4-4 and 41

4-6 and Appendix E, Table E-108) . Reductions in short-term exposure-related mortality and 42

morbidity (across all endpoints) under the 13/30 combination ranged from 6% (Phoenix) to 43

15% (Salt Lake City), while reductions for th e 12/25 combination ranged from 7% (Dallas) 44

to 30% (Birmingham). 45

# February 2010 Draft – Draft Do Not Quote or Cite 4-27  Pattern of risk reduction linked to design values 

# : As with the set of alternative annual 1

standards discussed in the prev ious section, the pattern of risk reduction seen for the two 2

combinations of alternative 24-hour and annua l standards described here depends on which 3

standard is controlling. In addition, the magnitude of the re duction in risk reflects (a) the 4

magnitude of the difference between the cont rolling design value a nd the standard level 5

(which determines the degree of reduction in ambient air PM 2.5 

# levels) and (b) the method 6

used to simulate ambient PM 2.5 

# levels under alternativ e suites of standard s (i.e., proportional, 7

hybrid or peak shaving). For this set of altern ative suites of standa rds, 10 of the 15 study 8

areas had the alternative 24-hour standard controlling under the 13/30 case and that number 9

was increased to 12 out of the 15 study areas wi th the 12/25 case (Table 3-5). As expected, 10

those study areas with the greate st reduction in risk (in terms of percent reduction compared 11

with the current suite of standards) under the 12/25 case had a cont rolling 24-hour standard 12

(e.g., Tacoma, Salt Lake City, Los Angeles and Fresno - see Figure 4-4 and 4-6 and 13

Appendix E, Table E-90). 14

 Absolute levels of PM 2.5 

# -attributable risk under alternat ive suites of annual and 24- 

# 15

hour standards: 

# As with the alternativ e annual standards, belo w we provide a brief 16

overview of the magnitude of PM 2.5 

# -attributable risk (i.e., absolu te risk) associated with the 17

two alternative suites of annual and 24-hour standards: 18

o 

# Long-term exposure-related mortality : The four study areas displaying the greatest 19

degree of reduction across thes e two alternative suites of standards (Tacoma, St. 20

Louis, Los Angeles and Fresno), have PM 2.5 

# -related IHD mortality estimates (under 21

the 12/25 case) ranging from 3-4 (Tacoma ) to 290-360 (Los Angeles) (see Appendix 22

E, Table E-21 and E-30). The other urba n study area with the greatest degree of 23

PM 2.5 

# -related risk in absolute terms besides New York (New York) has PM 2.5 

# -related 24

all-cause mortality estimated at 820-1,040 under the 12/25 case. 25

o 

# Short-term exposure-related mortality : eleven of the 15 study areas had percent 26

reductions in risk for the 12/25 case (rela tive to the current standards) of 27

approximately 29% (the other study areas had lower percent re ductions). Of the 28

locations with ~29% reductions in risk, PM 2.5 

# -attributable CV mortality for the 12/25 29

case ranged from 6 (Salt Lake City) to 340 (New York) (see Appendix E, Table E30

84). New York City also represents the st udy area with the greates t residual risk for 31

short-term exposure-related mortality under the 12/25 case. 32

o 

# Short-term exposure-related morbidity Of the 11 urban study areas with ~29% 33

reduction in risk (for the 12/25 case relative to the current standards), the incidence of 34

PM 2.5 

# -attributable cardiovascular HA emissions ranges from 7 (Salt Lake City) to 530 35

(New York) (see Appendix E, Table E-102). New York City also represents the 36

study area with the greatest residual risk for short-term exposure-related morbidity 37

under the 12/25 case. 38

 Consideration for the 95 th 

# percentile confidence interval risk estimates in assessing 

# 39

uncertainty related to the statistic fit of effect estimates: 

# As with the alternative standards 40

considering lower annual levels, risk estimates generated for the two standards considering 41

lower annual and 24-hour levels also suggest that uncertainty rela ted to the statistical fit of 42

effect estimates will have a greater impact on short-term exposure-related mortality (+/- 43

~40%) compared with long-term exposure-rela ted mortality (+/- ~18%) (see Appendix E, 44

# February 2010 Draft – Draft Do Not Quote or Cite 4-28 Tables E-84 and E-21 plus Table E- 30, respectively). Again, this results in a classification of 1

this source of uncertainty as having a “low er” impact for long-term exposure-related 2

mortality and a “moderate” impact on short-term exposure-related mortality. 3

4.3 SENSITIVITY ANALYSIS RESULTS 

# 4

As noted in section 3.6.4 and section 4.1, th e sensitivity analysis was conducted in order 5

to gain insights into which of the identified so urces of uncertainty and variability in the risk 6

assessment model may have significant impact s on risk estimates. A second goal of the 7

sensitivity analysis was to genera te an additional set of reasonab le risk estimates to supplement 8

the core set of risk estimates to inform staff' s characterization of uncertainty and variability 9

associated with those core estimates. 10

The first goal can be achieved by considering the magnitude of the impact of individual 11

modeling elements based on results from the sens itivity analysis and identifying those elements 12

which have the greatest impact on the core risk estimates. Use of the sensitivity analysis results 13

in this context is addressed in section 4.3.1. Use of the results of the sensitivity analysis as an 14

additional set of reasonable risk estimates to augm ent the core risk estimates in considering the 15

impact of uncertainty and variability in the co re risk model is discussed in section 4.3.2. 16

In conducting the sensitivity analysis we modeled 2 of the 15 urban study areas 17

(Philadelphia and Los Angeles - representing east and west coast urban areas, respectively) for 18

most simulations. For some modeling elements (e.g., the hybrid and peak shaving alternative 19

rollback approaches) we included a larger number of urban study areas that were applicable to 20

the topic being assessed. In conducting the sens itivity analysis, we have also focused on long21

term exposure mortality and to a lesser extent on short-term exposure mortality and morbidity. 22

Although the sensitivity analys is simulations were completed for all three simulation 23

years (as reported in Appendix F), we have fo cused on results for 2007 in this presentation for 24

comparability with the core results discussed in sections 4.1 and 4.2. 25

4.3.1 Sensitivity Analysis Results to Identify Potentially Important Sources of Uncertainty 

# 26

and Variability 

# 27

The results of the sensitivity analysis are summ arized in Table 4-3 (detailed results tables 28

are presented in Appendix F). In presenting the results of the sensitivity analysis, we have 29

compared the risk estimates for the particular simulation to the core set of risk estimates 30

generated for the same health effect endpoint/u rban study area combination. Specifically, we 31

have calculated a percent difference between the sensitivity analysis result and the associated 32

core risk estimate to compare the results of th e sensitivity analysis across the different modeling 33

elements that were considered. These percent difference results are emphasized in Table 4-1 and 34

in the discussion presented below. 35

# February 2010 Draft – Draft Do Not Quote or Cite 4-29 In discussing the results of the sensitivity analysis, we have deve loped four descriptive 1

categories, based on the general magnitude of th e percent difference estimate generated for a 2

particular modeling element: 3

 Modeling elements estimated to have percent differences of 20% or smaller (i.e., they 4

produced risk estimates that differed from the core risk estimates by no more than 5

20%) are classified as having a small 

# contribution to uncertainty in the core risk 6

estimates. 7

 Modeling elements estimated to have percent difference estimates in the range of 20 to 8

50% are classified as having a moderate 

# contribution to uncertainty in the core risk 9

estimates. 10

 Modeling elements estimated to have percent difference estimates in the range of 50 to 11

100% are classified as having a moderate-large 

# contribution to uncertainty in the core 12

risk estimates. 13

 Modeling elements estimated to have percent difference results >100% are classified as 14

having a large 

# contribution to uncertainty in the core risk estimates. 15

The sensitivity analysis based on Moolgavka r’s (2003) study in Los Angeles addressing 16

model specifications for both short-term mortal ity and morbidity (e.g., model selection, lag 17

structure and co-pollutant models) are discussed together as a group. This reflects the fact that 18

the Moolgavkar-based simulations were based on the same underlying dataset and focused on 19

Los Angeles. Furthermore, the discussion of th e Moolgavkar-based sensitivity analysis results 20

presented below, as well as the summary of results presented in Table 4-1, focus on the 21

difference in the spread of risk results across the Moolgavkar-based model specifications (for a 22

particular endpoint), rather than the percent difference results based on co mparison against the 23

core result that are emphasized with the other sensitivity analyses. 49 

# 24

The sensitivity analysis examining the imp act of alternative rollback approaches for 25

simulating ambient PM 2.5 

# concentrations in urban study areas under both the current and 26

alternative suites of standards al so deserves additional discussion before presenting the results. 27

For the first draft RA, we consid ered the impact of using a hybrid rollback approach in addition 28

to the proportional rollback approach which has been traditionally used in PM NAAQS risk 29

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 un certainty 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 consider ed 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 assessments. For this second draft, as discusse d in sections 2.6, 3.2.3 and 3.5.4, we have 1

included consideration of a peak shaving rollback approach in addition to the hybrid as non2

proportional methods to contrast with proportional rollback. 50 

# 3

As discussed in Section 3.2.3, for the sec ond draft risk assessment, we have calculated 4

composite monitor estimates based on proportional rollback and hybrid and/or peak shaving, 5

where appropriate. The composite monitor valu es are surrogates for long-term exposure-related 6

mortality. 51 

# Therefore, by comparing composite mon itor values generated for the same study 7

area/suite of standard s (using different rollback methods), we can obtain insights into the 8

potential impact of the rollback method used on long-term exposure-related mortality (see 9

Section 3.5.4 for additional discussion of how the composite monitor values generated using the 10

different rollback methods are used in the sensitiv ity analysis). These sensitivity analysis results 11

based on consideration for composite monitor va lues generated using the different rollback 12

methods (which are presented in detail in Append ix F, Tables F-49 and F-50) form the basis for 13

summary information related to rollback presented in Table 4-3. Due to the complexity of the 14

sensitivity analysis conducted ex amining the issue of rollback, the discussion of results from that 15

particular analysis presented in section 4.3.1. 1 is more detailed than for the other factors 16

considered as part of the sensitivity analysis. 17

In discussing the results of the sensitivity an alysis, results of the single-factor simulations 18

are presented first (section 4.3.1.1), followed by the results of the multi-factor simulations 19

(section 4.3.1.2). Within these categories, result s are further organized by health effect endpoint 20

with results for long-term exposure mortality di scussed first and then short-term exposure 21

mortality, followed by short-term exposure morbid ity. An overall conclusion regarding which of 22

the factors included in the sensitivity analysis represent potentially significant sources of 23

uncertainty and variability impacting the core riskestimates is presented at the end of each sub24

section. 25

50 

## The peak shaving approach involves proportional reduction in 24-hour PM 2.5 

## levels only at those urban study

areas where the 24-hour standard is controlling (and only at those specific monitors w ith 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 PM 2.5 

## monitors in a study area. It

is this air quality metric that is used in calculating l ong-term exposure-related morta lity. Given that the same C-R

function is used across all study areas, differences in l ong-term mortality across study areas (and/or across standard

levels) reflect to a great extent underl ying differences in the composite mon itor 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 PM 2.5 

## exposure across study areas and/or standard levels (see Section

3.5.4).February 2010Draft – Do Not Quote or Cite4-31Table 4-3 Overview of Sensitivity Analysis Results1Sensitivity Analysis1Health Endpoint and RiskAssessment LocationSummary of Results(percent difference in riskestimate relativeto the core estimate)Appendix FTables withDetailedResults (for2007)Single-Factor Sensitivity Analyses (long-term exposure mortality):Impact of using different model choices:fixed effects log-linear (the core) vs. random effectslog-linearC-R functionAll-cause, CPD, IHDLos Angeles andPhiladelphiaRandom effects log-linear C-R model:all-cause: +23%IHD: +12%Table F-3Impact of using different model choices:fixed effects log-linear (the core) vs. random effectslog-logC-R functionAll-cause, CPD, IHDLos Angeles andPhiladelphiaRandom effects log-log C-R model:All-cause: +123 to +159%CPD: +50 to +74%IHD: +80 to +111%Lung Cancer: +67 to +94%Table F-3Impact of using different model choices:Single vs. multi-pollutant modelsAll-causeLos Angeles andPhiladelphiaModel with CO: +45%Model with NO2: +73%Model with O3: +45%Model with SO2: -74%F-43Impact of estimating risks down to PRB rather thandown to LML (the core)All causeAll 15 urban study areasAll-cause: +47 to +273%Table F-6Impact of using alternative C-R function fromanother long-term exposure mortality studyAll-cause, CPD, lungcancerLos Angeles, PhiladelphiaAll-cause: +119 to +121%CPD: +29 to +30%Lung cancer: +29 to +30%Table F-9Impact of using alternative hybrid rollback approachreflecting more localized patterns of ambient PM2.5reduction (evaluated across current and alternativestandard levels) – based on the composite monitoranalysis described in Section 3.5.4 considering bothhybrid and peak shaving approaches as alternativesto proportional rollbackSurrogate for long-termmortality (compositemonitor-based analysis)All study areas exceptDallas had either hybridand/or peak shavingapplied as an alternativeTrend in incremental risk reduction(alternative standard level compared tocurrent standard): rollback method did notappear to have a significant impact on thismetric (those urban study areas withdifferent trends in reduction did notdemonstrate a consistent pattern related toTables F-49and F-50February 2010Draft – Do Not Quote or Cite4-32Sensitivity Analysis1Health Endpoint and RiskAssessment LocationSummary of Results(percent difference in riskestimate relativeto the core estimate)Appendix FTables withDetailedResults (for2007)rollback method to theproportionalthe type of hybrid method used)Absolute risk for a given standard level:use of alternative rollback methods didappear to impact estimation of PM2.5riskremaining for a given standard level: <1%to >+50%Has implications for degree to which 24-hour standard levels produce reductionsin annual-average PM2.5levels (andconsequently on long-term and short-termexposure-related risk). Results suggestthat use of peek shaving rollback methodcan result in smaller degree of reductionin annual-average values compared withproportional rollback, (see discussion intext – 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 incidenceestimates, particularlyfor the locations with higher percent changeestimates, is very low, raising concerns overthe stability of these sensitivity analysisresults)Table F-15Table F-18Table F-21Impact of using alternative hybrid rollback approachreflecting a combination of more localized andregional patterns of ambient PM2.5reduction (note,this analysis is based exclusively on the hybridrollback – the composite monitor analysis describedNon-accidental mortalityBaltimore, Birmingham,Detroit, Los Angeles,New York and St. LouisResults for all seven urban study areas(across the current and alternativestandard levels) do not exceed +17%,with most <+10%.Table F-36February 2010Draft – Do Not Quote or Cite4-33Sensitivity Analysis1Health Endpoint and RiskAssessment LocationSummary of Results(percent difference in riskestimate relativeto the core estimate)Appendix FTables withDetailedResults (for2007)above pertains only to long-term mortality-relatedrisk)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), CVand respiratoryAll 15 urban study areasHA (CV): -105 to +9%HA (respiratory): -54 to +74%(Note, overall incidenceestimates, particularlyfor the locations with higher percent changeestimates, is very low, raising concerns overthe stability of these sensitivity analysisresults)Table F-24Table F-27Impact of using an annual C-R function (applied tothe whole year) vs. a seasonal function for Aprilthrough August (applied only to that period) (using asingle pollutant model)Asthma ED visitsNew YorkNA (although incidence estimates weregenerated for this simulation, “percentdifference from the core” were not generatedsince the alternate simulation focused on asubset of the year).Table F-30Impact of considering models with different lagsHA (CV and respiratoryLA and New YorkNA (although incidence estimates weregenerated for this simulation, “percentdifference from the core” were not generatedsince the lag-differentiated C-R functions usedare not regionally-differentiated, andtherefore, do not allow a focusedconsideration of thelag factor alone inimpacting risk estimates)Table F-48Single-Factor Sensitivity Analysis (short-term exposure mortality and morbidity in LA based on Moolgavkar, 2003 study model options) (Note, resultspresented here reflect spread in risk estimates across Moolgavkar-based model specifications and not percent difference from core risk estimates,unless so stated – see text)Impact of model selection (e.g., log-linear GAMwith 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%Table F-33February 2010Draft – Do Not Quote or Cite4-34Sensitivity Analysis1Health Endpoint and RiskAssessment LocationSummary of Results(percent difference in riskestimate relativeto the core estimate)Appendix FTables withDetailedResults (for2007)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%Table F-33Impact of single- vs. multi-pollutant models (PM2.5with CO)Mortality (CV); HA (CV)Los AngelesCV mortality: +106%CV HA: +140%Table F-33Multi-Factor Sensitivity Analyses (long-term mortality):Impact of using a fixed effects log-linear vs. arandom effects log-log model, estimating incidencedown to the lowest measured level (LML) in thestudy vs. down to PRB, and using a proportional vs.hybrid rollback to estimateincidence associated withlong-term exposure to PM2.5concentrations that justmeet the current standards (note consideration ofrollback in the multi-factor analysis did notincorporate the hybrid-based rollback approach)All-cause, IHD long-termmortalityLos Angeles andPhiladelphiaAll-cause: +27 to +1,089%IHD: +256to +673%F-39Multi-Factor Sensitivity Analyses (short--term mortality):Impact of using season-specific vs. all-year C-Rfunctions and proportional vs. hybrid rollbacks toestimate incidence associated with short-termexposure to PM2.5concentrations that just meet thecurrent standardsNon-accidentalBaltimore, Birmingham,Detroit, Los Angeles,New York and St. LouisNon-accidental (four seasons + hybrid): -116 to +179%F-421Unless otherwise noted, sensitivity analysis results are based onthe scenario reflecting just meeting the current suite of PM2.5standards.12This metric is the percent spread in risk estimates across the Moolgavkar-based model specifications (not the percent difference estimates – see text discussion2above).3

# February 2010 Draft – Do Not Quote or Cite 4-35 4.3.1.1 Single-factor Sensitivity Analysis 

# 1

This section presents the resu lts of the single-factor sensi tivity analysis, which involved 2

consideration of alternate model inputs on the core risk estimates, when those alternate inputs are 3

considered one at a time (consideration of the combined effect of several model inputs being 4

varied is covered by the multi-factor sensitivity analysis discussed in section 4.3.1.2). The 5

results of the single-factor sens itivity analysis are characterized qualitatively using the four6

category approach described above (i.e., low, mode rate, moderate-large and large, with each of 7

these representing a defined range of percent difference from the core risk estimates). 8

Long-term exposure mortality 9

This section summarizes the results of the sensitivity analysis focused on long-term 10

exposure-related mortality e ndpoints (see Table 4-1 for the specific modeling elements 11

considered in the sensitivity analysis). 12

 Impact of using different model choices for C-R function - fixed effects log-linear (the core 13

approach) vs. random effects log-linea r or random effects log-log models : This simulation 14

considered two alternative C-R model forms obtained from Krewski et al., 2009 for modeling 15

all-cause, CPD, IHD and lung cancer mortality , including (a) random effects log-linear 16

model and (b) a random effects log-log model (n ote, the core effect estimate was derived 17

using a fixed effects log-linear model obtained from Krewswki et al., 2009). The simulation 18

also considered the use of multi-pollutant models that control for CO, NO 2 

# , O 3 

# or SO 2 

# . The 19

results of the simulation suggest that the use of a random effects log-linear model, rather than 20

the core fixed effects model, has a relatively small effect on risk estimates, increasing them 21

by 12 to 23% across the mortality categories and urban study areas modeled (Appendix F, 22

Table F-3). However, use of a random effect s log-log model has a larger impact on risk 23

estimates, increasing them by 50 to 159% (Appendix F, Table F-3). The greater impact of 24

the log-log model results from this function having an incrementally steeper slope at lower 25

PM levels, which quickly increases incidence es timates compared with the core log-linear 26

model (whose slope has a much more gradual incremental increase in slope at lower PM 27

levels). The use of multi-pollutant models th at control for co-polluta nts was shown to have 28

moderate-large impact on risk es timates, with control for CO, NO 2 

# , or O 3 

# resulting in 29

increased PM 2.5 

# -attributable risk estimates, while control for SO 2 

# resulted in a moderate-large 30

decrease in estimated PM 2.5 

# risk. 52 

# 31

 Impact of estimating risks down to PRB rather than down to LML : This simulation 32

compared long-term exposure mortality incide nce associated with modeling risk down to 33

PRB (which varies by region – see section 3.2. 1) with the core approach of modeling down 34

to LML (5.8 μg/m 3 

# for long-term mortality – see section 3.1). This simulation involved all 35

15 urban study areas, given that PRB is strati fied by region and theref ore, results of the 36

52 

## Sensitivity analysis results generated using the copollutant model involving PM 2.5 

## and SO 2 

## have been deemphasized since it is likely that control for SO 2 

## may be capturing a portion of PM 2.5 

## -attributable risk related to the

secondary formation of sulfate, which is a component of the PM 2.5 

## mixture (i.e., the two pollutants are often highly

correlated).

# February 2010 Draft – Do Not Quote or Cite 4-36 simulation could differ significantly across the 15 urban study areas , or at least across the six 1

PM regions represented by those study areas. The results of this simulation suggest that 2

modeling risk down to PRB could have a mode rate to large impact on long-term exposure 3

mortality incidence, with estimates ranging fr om 47 to 273% higher than the core estimates 4

(for matching urban locations) (Appendix F, Ta ble F-6). Note, however, that risk metrics 5

based on considering the incremen tal reduction in risk (incidence) between two alternative 6

suites of standards would not be impacted by this source of un certainty, since it only affects 7

estimates of absolute risk. 8

 Impact of C-R function from alternati ve long-term exposure mortality study : This simulation 9

considered use of alternative C- R functions (and effect estimates ) based on the reanalysis of 10

the Six Cities study (Krewski et al., 2000). The results suggest that use of the alternative C-R 11

function could have a moderate to moderate-lar ge effect on CPD mortal ity (+45 to +74%), a 12

large effect on all-cause mortalit y (+123 to +159%), a moderate-large to large effect on IHD 13

mortality (+80 to +111%) and a moderate-lar ge effect on lung cancer mortality (+67 to 14

+94%) (Appendix F, Table F-9). The results of this simulation suggest that (at least with 15

regard to application of C-R functions obtai ned from the Six Cities study) the potential 16

impact of functions from alternative studies on long-term exposure mo rtality depends on the 17

mortality category being consider ed. In this analysis, use of the alternative C-R functions 18

was shown to have a significant impact on all of the long-term mortality categories 19

considered. 20

 Impact of using alternative ro llback approaches (hybrid and peak shaving) to simulate just 21

meeting the current and alte rnative suites of standards . This sensitivity analysis assessed the 22

impact of estimating risk for the current and alternative sets of standards using two 23

alternatives to the proportiona l rollback strategy: (a) the hybrid rollb ack approach that 24

reflects an initial localiz ed pattern of ambient PM 2.5 

# reduction (resulting in non-proportional 25

rollbacks of monitored PM 2.5 

# concentrations) with a sec ond phase of more regional 26

reductions in ambient PM 2.5 

# levels (based on proportional ad justments) and (b) peak shaving 27

which represents a primarily local pattern of reductions in ambient PM 2.5 

# (see Section 3.5.4 28

for additional discussion of how these alternativ e rollback methods were integrated into the 29

sensitivity analysis). We note that the core analysis utilized proporti onal rollback exclusively 30

in simulating conditions for the current and altern ative sets of standards, with this approach 31

representing a regional pattern of ambient PM 2.5 

# reduction. A number of observations can be 32

drawn from this sensitivity analysis including: 33

o 

# Impact on estimates of PM 2.5 

# -related risk remaining after simulation of just 34

meeting a given suite of standards : The sensitivity analysis results suggest that 35

the use of alternative rollback methods can have a notable impact on estimates of 36

the PM 2.5 

# -attributable risk remaining after simu lation of a given suite of standards 37

(see Appendix F, Table F-50 and discussion in section 3.5.4). Generally, use of 38

the hybrid approach had a small to moderate impact on absolute PM 2.5 

# - 39

attributable risk estimates, compared with the core approach of using proportional 40

rollback. By contrast, use of the peak shaving approach had a moderate to 41

moderate-large impact on absolute PM 2.5 

# -attributable risk estimates. For example, 42

Los Angeles had composite monitor values for the current suite of standards and 43

several of the alternative suites of standa rds that were 40 to 60% greater when the 44

peak shaving rollback method was used, compared with the proportional rollback 45

# February 2010 Draft – Do Not Quote or Cite 4-37 method (see Appendix F, Table F-50). By contrast, composite monitor values 1

generated using hybrid rollback for Lo s Angeles, were between 13 and 38% 2

higher than the proportional rollback methods. 3

o 

# Impact on degree of reduction across alternative suites of standards: When the 4

same rollback methods is used to simula te both the current and any alternative 5

suite of standards, the pattern of risk reduction across alternative standards is 6

generally similar regardless of the rollback approaches used (see Table F-49, in 7

Appendix F). However, if one looks at meeting the current suite of standards with 8

application of the peak-shaving approach , followed by application of proportional 9

rollback to simulate alternative suites of standards, we can see notable differences 10

in the pattern of risk reduction. This is particularly true for areas with peaky PM 2.5 

# 11

distributions (i.e., areas w ith relatively high 24-hour design values and lower 12

annual average design values). For exampl e, with Los Angeles, which represents 13

a study area with a relatively peaky PM 2.5 

# distribution, applic ation of proportional 14

rollback in simulating both the current su ite of standards and the alternative 15

annual standard of 12 μg/m 3 

# results in a 13% reduction in long-term exposure16

related mortality (see Figure 4-3 and Ta ble E-27 in Appendix E). By contrast, 17

application of peak shaving in simulati ng the current suite of standard levels 18

followed by proportional reduction in simu lating the same alternative annual 19

standard results in an estimated 48% reduction in long-term exposure-related 20

mortality. 53 

# 21

Based on the simulations discussed above c overing potential sources of uncertainty and 22

variability impacting long-term mortality, we co nclude that the following factors contribute 23

potentially large sources of uncertainty to the core risk estimates: (a) use of alternative form of 24

the C-R function, specifically use of a random-e ffects log-log model form obtained from the 25

updated ACS study (Krewski et al., 2009) (b) use of an alternative C-R function with effects 26

estimates obtained from the reanalysis of th e Six Cities study (Krewski et al. 2000), and (c) 27

estimation of risk down to PRB. 54 

# Other factors considered in the sensitivity analysis had 28

smaller impacts on core risk estimates. 29

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 PM 2.5 

## level. By contrast, application of proportional ro llback 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-a verages with consideration fo r the fact that long-term

exposure-related mortality is only calculated down to LML.

54 

## Use of peak-shaving as an alterna tive method for simulating ambient PM 2.5 

## concentrations for alternative

standards had a moderate-large impact on risk estimates.

# February 2010 Draft – Do Not Quote or Cite 4-38 Short-term exposure mortality 1

This section summarizes the results of the sensitivity analysis focused on short-term 2

exposure-related mortality e ndpoints (see Table 5-1 for the specific modeling elements 3

considered in the sensitivity analysis). 4

 Impact of using season-specific C-R f unctions (vs. an annual C-R function) 

# : This 5

simulation considered the impact on short-term exposure mortality risk of using seasonally6

differentiated effects estimates rather than the core approach of using a single C-R function 7

for the whole year (note, that the seasonal m odels were based on the same study as the model 8

used in the core analysis – Zanobetti and Sc hwartz, 2009). The results of the simulation 9

suggest that this source of un certainty can have a wide range of effects across urban study 10

areas (including not only variati on in the magnitude of effect, but also in the direction). 11

Percent changes compared with th e core risk estimate were la rge, ranging from -116% (Los 12

Angeles) to +179% (Birmingham) (these re sults are for non-accident al mortality – see 13

Appendix F, Table F-15). We note that these tw o locations also have relatively low overall 14

incidence estimates, which does raise concerns over the degree of stability in the sensitivity 15

analysis estimates. Furthermore, for 9 of the 15 urban study ar eas (for non-accidental 16

morality), percent changes from the core were small, with absolute values of 12% or less 17

(Appendix F, Tables F-15). The results for CV and respiratory mortal ity also demonstrate 18

considerable variation across locations, but are generally smaller than results cited above for 19

non-accidental, with one excepti on. Birmingham is estimated to have short-term CV 20

mortality that is +500% higher using seasonal effects estimates compared with the core 21

results (We note, however, that this endpoint ca tegory also has very small incidence, again 22

raising concerns over th e stability of the sensitivity anal ysis results) (see Table F-18). The 23

results for respiratory-related mortality also de monstrate considerable variability with results 24

that could suggest a moderate to large impact(i.e., -48 to +162% - see Appendix F, Table F25

21). We note, however, that small incidence estimates again raise concerns regarding the 26

stability of these percent difference results. 27

 Impact of using alternativ e hybrid rollback approach 

# : This simulation evaluates the 28

potential impact of using the hybrid (non-proportional) approach for simulating just meeting 29

current and alternative sets of standards, as an alternative to the proportional approach used 30

in the core analysis. 55 

# The results of this simulation (as contrasted with the impact of using 31

the hybrid approach on long-term exposure mortal ity) suggest that use of the hybrid rollback 32

approach has relatively little effect on short-te rm mortality risk (e.g., percentage differences 33

relative to the core risk estimates were in th e low single digits for mo st locations, with one 34

location having a difference of +17% - see Appendix F, Table F-36). 35

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 esti mates. 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 im pact from application of

alternative rollback methods.

# February 2010 Draft – Do Not Quote or Cite 4-39 The sensitivity analysis results discusse d above, result in a number of overall 1

observations regarding sources of uncertainty po tentially impacting short-term exposure morality 2

endpoints. The results of using the seasonally-differentiated effect estimates in modeling short3

term exposure mortality appear to generally have a relatively small impact (e.g., <15%) in most 4

study areas. For some study areas, the impact doe s appear to be much larger, with results 5

including both substantial negative and positive percent differences from the core estimates. 6

However, in all of these cases, the total incidence estimates involved are very small, raising 7

concerns over the stability of the risk estimates generated as part of this particular sensitivity 8

analysis (in many of these inst ances, the estimates include ne gative lower bounds, reflecting the 9

use of non-statistically significant effects estimates). For these reasons, the results of this 10

sensitivity analysis, while initia lly appearing to be notable in terms of magnitude in some study 11

areas, need to be interpreted with care. At this point, we are uncertain as to how important this 12

source of uncertainty is in the context of shor t-term exposure mortality estimation. Regarding 13

the use of the alternative hybr id (non-proportional) approach for simulating conditions under 14

alternative standard levels, the re sults suggest that this factor ha s a modest impact on short-term 15

exposure mortality (significantly less impact th an with the use of the hybrid approach in 16

estimating long-term exposure mortality). With the exception of factor s examined using the 17

Moolgavkar et al., (2003) study in Los Angeles (s ee section 4.3.1.4), it would appear that the 18

factors examined here do not have a large imp act on risk estimates generated for short-term 19

exposure mortality. However, we note that the overa ll scope of the sensitivity analysis completed 20

for short-term exposure-related mortality and morb idity is far more limited than that completed 21

for long-term exposure-related mortality. 22

Short-term exposure morbidity 23

This section summarizes the results of the sensitivity analysis focused on short-term 24

exposure-related morbidity endpoints (see Tabl e 5-1 for the specific modeling elements 25

considered in the sensitivity analysis). The results of individual sensitivity analysis simulations 26

are presented below, with overall observati ons presented at the end of the section. 27

 Impact of using season-specific C-R f unctions (vs. an annual C-R function): 

# This 28

simulation considered the impact on short- term exposure morbidity (HAs) of using 29

seasonally-differentiated effects estimates rather than the core approach of using a single C-R 30

function for the whole year (we note that the seasonal models were obtained from the same 31

study as the model used in the core analysis – Bell et al, 2008). The re sults of the simulation 32

suggest that, as with short-term exposure mortalit y this source of uncertainty can have a wide 33

range of impacts on the risk estimates across urban study areas (includi ng not only variation 34

in the magnitude of risk, but also in the dire ction) depending on the spec ific health endpoint 35

examined. We note, however, that the magnitude of impact appears to be less for short-term 36

morbidity than for short-term mortality. Pe rcent changes for most of the 15 urban study 37

# February 2010 Draft – Do Not Quote or Cite 4-40 areas were small for CV HAs (generally less than a 20% difference in either direction, 1

although there was a large impact for Tacoma (-105%)) (see Appendix F, Table F-24). This 2

source of uncertainty has a moderate to modera te-large impact for respiratory-related HAs 3

with most locations having greater than a 54% to 74% absolute effect (see Appendix F, Table 4

F-27). 5

 Impact of using a seasonal function for Ap ril through August (applied only to that 

# 6

period) in modeling asthma-related ED vi sits in New York, relative to the core 

# 7

approach of using a single annual effect estima te (and applying that to the whole year): 

# 8

This sensitivity analysis involved the approach of using a season-speci fic estimate to model 9

incidence for the period April th rough August (obtained from It o et al., 2007). Because this 10

sensitivity analysis estimate covers a period shorter than a year, we have not directly 11

compared it with the annual estimate generated fo r this endpoint in the core risk assessment 12

(i.e., we have not generated percent difference estimates as is done with other sensitivity 13

analysis simulations). However, the results of this sensitivity analysis do suggest that the use 14

of seasonally-differentiated estimates in modeling this endpoint can impact risk. 15

 Impact of considering models with different lags: 

# To examine the impact of lag on 16

modeling of short-term exposure-related morbid ity, we used a range of effects estimates 17

obtained from Bell et al., 2008 based on applicat ion of different lags, including 0-, 1- and 2- 18

day lags, (for both respiratory and cardi ovascular-related morbidity). Because lag19

differentiated effects estimates were only av ailable as national-averages and were not 20

regionally-differentiated, we coul d not directly compare the resu lts using different lag models 21

to the results generated for the core analysis (i .e., the sensitivity analys is results would have 22

mixed both the lag effect and the effect of re gional differentiation, th ereby preventing clear 23

assessment of the importance of either fact or considered in is olation). However, 24

consideration of the magnitude of the risk estim ates generated using different lag models, for 25

the same endpoint at the same urban study ar e, suggests that choi ce of lag does effect 26

estimates of short-term exposure-relate d morbidity (see Appendix F, Table F-48). 27

Given the results of the set of simulations completed for short-term exposure morbidity, 28

both of which focused on the use of seasonally-di fferentiated effects estimates, it would appear 29

that this factor does not have a substantial imp act on risk estimates. The analysis considering 30

different lag models does suggest that this factor could have a notable impact on risk estimates 31

and should be carefully considered when specifyi ng C-R functions to use in the risk assessment. 32

Additional factors poten tially impacting short-term exposure morbidity are addressed below in 33

relation to the sens itivity analysis based on al ternative models from Moolgavkar et al. (2003). As 34

noted earlier, the scope of the sensitivity an alysis completed for short-term exposure-related 35

morbidity is limited. 36

Short-term exposure-related mortality and morbidity (Moolgavkar et al., 2003 study-based 37

analysis) 38

As noted earlier in the introduction to section 4. 3, the results of sensitivity analysis based 39

on Moolgavkar et al., (2003) incl ude percent difference estimates based on considering the range 40

# February 2010 Draft – Do Not Quote or Cite 4-41 of risk estimates generated using alternative mo del specifications from this study for a given 1

health endpoint and it is these re sults that are discussed below. 2

 Impact of model selection (e.g., log-linear GAM with 30df, log-linear GAM with 100df, 

# 3

and log-linear GLM with 100df) on estimati ng short-term exposure mortality and 

# 4

morbidity 

# : Application of models obtained from Moolgavkar et al., (2003) with various 5

formulations related to model selection (deg rees of freedom, GLM vs. GAM) to the Los 6

Angeles urban case study location results in a range of short-term exposure mortality 7

estimates (for non-accidental and CV) that differ by 80% and 49%, respectively (see 8

Appendix F, Table F-33). In the case of s hort-term exposure morbidity (specifically, CV9

related HAs), incidence estimates differ by 36% (see Appendix F, Table F-33). These results 10

suggest that these elements of model specifica tion represent a moderate source of uncertainty 11

in estimating short-term mortality and morbidity. 12

 Impact of lag structure (0-day through 5- day) on estimating short-term exposure 

# 13

mortality: 

# Consideration of the rang e of risk estimates for non-accidental mortality 14

generated using different lag structures (a nd associated effect estimates) provided in 15

Moolgavkar et al., (2003), suggest that this factor could have a moderate impact on risk (in 16

the range of 55% when comparing the lowest and highest positive incidence estimates 17

generated). (see Appendix F, Table F-33). 18

 Impact of considering multi-pollutant mo dels on estimating short-term exposure 

# 19

mortality and morbidity 

# : The results of the Moolgavkar-based simulations (when 20

considering the spread in risk estimates specifically across t 

# hese simulations) suggest that the 21

multi-pollutant versus single-pollutant model issue (i.e., including CO in addition to PM 2.5 

# ), 22

could have a large impact on the estimation ofshort-term exposure mortality (106% for all23

cause) and morbidity (140% for CV-related HAs). 24

Overall observations regarding key sources of uncertainty impacting short-term exposure 25

mortality and morbidity risk estimates (based on the Moolgavkar et al ., 2003 study) include the 26

following. The spread in risk estimates generated across the Moolgavkar-based model 27

specifications suggests that factor related to specifying the C-R model may have a moderate to 28

large impact. More specifically, variation in the lag structure has a moderate impact on risk and 29

use of single versus multi-pollutant models coul d have a potentially large impact on risk. Note, 30

however, that as discussed earlier, the relevance of these sensitivity analysis results to the 31

interpretation of core risk estimates is not clear and may be relatively low (see Section 4.3.1). 32

4.3.1.2 Multi-factor Sensitivity Analysis Results 

# 33

The results of the multi-factor sensitivity analyses are intended to support both goals of 34

the sensitivity analysis: (a) identify which fact ors (now in combinati on), appear to have a 35

significant impact on estimation of the core esti mates and (b) to derive a set of reasonable 36

alternative risk estimates for us e in considering uncertainty and variability associated with the 37

core risk estimates. Regarding the latter appl ication, because these multi-factor simulations 38

combine multiple factors reflecting uncertainty and variability together in generating alternative 39

# February 2010 Draft – Do Not Quote or Cite 4-42 risk estimates, they are likely to produce the highes t sensitivity analysis results. Therefore, it is 1

particularly important to consider the reasona bleness of the results of these multi-factor 2

simulations, to insure that only credible estimates are included in the set of reasonable alternative 3

risk estimates. Consequently, we emphasize consideration for the reasonableness of these multi4

factor simulations in the di scussion presented below. 5

Long-term exposure mortality 6

This section summarizes the results of the sensitivity analysis focused on long-term 7

exposure-related mortality e ndpoints (see Table 4-1 for the specific modeling elements 8

considered in the sensitivity analysis). 9

 Impact of using log-linear vs. log-log C-R model with fixed or random effects, 

# 10

estimating incidence down to the LML vs. PRB, and using proportional vs. hybrid 

# 11

rollback to estimate long -term exposure mortality 

# : This multi-factor sensitivity 12

analysis focused on a number of model de sign choices related to modeling long-term 13

exposure mortality (all-cause and IHD). Modeling elements reflected in the 14

simulations included: (a) model form (l og-linear vs log-log and random vs fixed 15

effects), (b) modeling risk down to PRB (vs LML), and (c) use of an alternative hybrid 16

rollback approach (vs proportional rollback) to simulate just meeting the current and 17

alternative sets of standards. Various pe rmutations of these design elements choices 18

(relative to the elements selected for the core analysis) were considered. Percent 19

difference estimates (for all-cause mortality) ranged from 27% (for a model estimating 20

risk down to PRB and use of the hybrid ro llback approach) to 1,089% (for a model 21

with random effects log-log model, risk es timated down to PRB, and use of the hybrid 22

rollback approach). 23

We believe that application of a log-log model with random effects is a reasonable 24

alternative to the core model (fixed-effects log-linear model) , based on our review of the 25

discussion in Krewski et al. ( 2009). Similarly, the use of a hybrid rollback approach involving 26

non-proportional adjustment where ther e is the potential for greater us e of local control strategies 27

to address local-sources is a reasonable alternative to solely using a proportional rollback 28

approach in all study areas. Therefore, we belie ve that the combinations of modeling elements 29

including these alternative choices are reasonable. However, there is more concern in predicting 30

risk down to PRB. This is not because there is evidence for a th reshold, but rather because we 31

do not have data to support charac terization of the nature of the C-R function in the vicinity of 32

PRB. Specifically, there is increasing uncertainty in predicting the nature of the C-R function as 33

you move below the LML. So, while we believe it is reasonable conceptually to estimate risk 34

down to PRB, the quantitative process of doing this requires use of a function with very high 35

uncertainty. Therefore, we conc luded that those alternative risk estimates generated using risk 36

estimated down to PRB should not be used in creating the reasonable a lternative set of risk 37

estimates in considering uncertainty associ ated with the core risk estimates. 38

# February 2010 Draft – Do Not Quote or Cite 4-43 A key limitation of the multi-factor sensitivity an alysis is that the approach used did not 1

allow us to consider the peak-shaving rollb ack method in concert with the other modeling 2

elements described above. This means that the combined impact of peak shaving (which has a 3

greater impact than the hybrid rollback method) with other model specifications is not 4

characterized. However, as part of the integrative discussion in Chapter 6, we will consider the 5

results of the single-factor sens itivity analysis examining rollb ack (with its consideration for 6

peak shaving) along with the multi-factor se nsitivity analysis results described here. 7

Short-term exposure mortality 8

This section summarizes the results of the sensitivity analysis focused on short-term 9

exposure-related mortality e ndpoints (see Table 4-1 for the specific modeling elements 10

considered in the sensitivity analysis). 11

 Impact of using season-specific vs. annua l effect estimates and proportional vs. 

# 12

hybrid rollback approaches in modeling short -term exposure mortality 

# : This 13

multi-factor sensitivity analysis focused on a number of model design choices related 14

to modeling short-term mortality (non-acciden tal). Modeling elements included in this 15

sensitivity analysis were use of seasonal vs . annual effects estimates and use of hybrid 16

vs proportional rollback to simulate just meeting current and alternative standard 17

levels. Percent difference estimates (for non-accidental mortality) across the 7 urban 18

study areas included in the simulation ranged from -109% (LA) to +119% 19

(Birmingham) (see Appendix F, Table F-42). However, we note that the total 20

incidence estimates associated with these hi gher-impact locations were relatively low, 21

again raising the concern for the stability in re lative differences with the core estimates. 22

Because of the more limited scope of the mu lti-factor sensitivity analysis completed for 23

short-term exposure-related mortality, we have conc luded that these results should not be used as 24

an additional set of reasonable risk estimates to inform consideration of uncertainty associated 25

with this category of risk estimates. 26

4.3.2 Additional Set of Reasonable Risk Es timates to Inform Consideration of 

# 27

Uncertainty in Core Risk Estimates 

# 28

This section discusses the use of the output of the sensitivit y analysis completed as part 29

of this risk assessment as an addi tional set of reasonable risk estim ates to inform consideration of 30

uncertainty associated with the core risk es timates. Specifically, in the case of long-term 31

exposure-related mortality endpoints, staff has c oncluded that the result s of the sensitivity 32

analysis represent a reasonable se t of alternate risk estimates that fall within an overall set of 33

plausible risk estimates surrounding the core estimates. 56 

# 34

56 

## As noted in section 4.3.2 and in the integrative discussi on in Section 6.4, while staff believes that the sensitivity

analysis does provide insights into th e potential impact of certain sources ofuncertainty 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 While not representing a formal uncertainty di stribution, the output of the sensitivity 1

analysis, when combined with the core risk estim ates, represent a set of plausible risk estimates, 2

which reflect consideration for uncertainty in various elements of the risk assessment model. 3

Therefore, while the discussion of risk estimates in the context of assessing the degree of risk 4

reduction associated with suites of alternative standards (see Chap ter 6) does focus on the core 5

risk estimates since these are judged to have the greatest overall confidence, the output of the 6

sensitivity analysis can be us ed to provide additional perspe ctive on the potential range of 7

uncertainty around the core estimates. Note however, that we do not know the confidence 8

interval captured by this uncertainty set, or th e specific percentiles of the risk distribution are 9

represented by points wi thin that set. 10

As noted earlier, the quantitative single- and multi-factors sensitivity analyses generated 11

an additional set of risk estimates for a subset of the urban study areas, air quality scenarios and 12

health endpoints included in the core risk anal ysis (i.e., Los Angeles an d Philadelphia assessed 13

for the current standard level). However, the part of the sensitivity analysis focusing on 14

alternative methods for simulating ambient PM 2.5 

# levels (i.e., rollback), did consider a larger 15

number of study areas and air quali ty scenarios. In presenting the alternative sets of reasonable 16

risk estimates, we focus on Los Angeles and Ph iladelphia for many of the modeling elements, 17

although we expand the discussion in the context of discussing results related to conducting 18

rollback.. 19

In using the additional set of reasonable risk results to augment the core risk estimates, 20

we begin by presenting both the core and altern ative sets of estimates for Los Angeles and 21

Philadelphia in Table 4-4. Then, in Figures 4-7 and 4-8, we pres ent graphical display of the full 22

uncertainty set comprising the core plus additiona l reasonable risk estimates for Los Angeles and 23

Philadelphia, differentiated by mortality category (Figure 4-7 present results for IHD and Figure 24

4-8 presents results for all cause mortality). Th is section concludes with a set of observations 25

resulting from consideration of information depict ed in Table 4-4 and Figures 4-7 and 4-8 in the 26

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. Sp ecifically, we consider estimates based on modeling risk down to PRB to be

less reasonable than the other scenarios included in the se nsitivity analysis, since there is substantial uncertainty

associated with the C-R function shape below the LML. In addition, as discussed in S ection 4.3.1.1 risk estimates

generated using the copollutant model involving PM 2.5 

## and SO 2 

## have been de-emphasized since it is likely that

control for SO 2 

## may be capturing a portion of PM 2.5 

## -attributable risk related to the secondary formation of sulfate.

# February 2010 Draft – Do Not Quote or Cite 4-45 Table 4-4 Derivation of a set of reas onable alternative risk estimates to 

# 1

supplement the core risk estima tes (Los Angeles and Philadelphia, 

# 2

current standards, for long-term IHD mortality). 

# 3

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

estimates 1



### 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 – NO 2



### Vs O 3 

### /CO) 3 

### Los Angeles and Philadelphia:

All cause: +45 to +74%

(O 3 

### /CO and NO 2 

### , respectively)

and -74% for SO 2 

### Los Angeles and

All cause: 2.9% and 3.5%

(for O 3 

### /CO and NO 2 

### , respectively), 0.52% (SO 2 

### )

Philadelphia:

All cause: 5.2% and 6.3% (for

O 3 

### /CO and NO 2 

### , respectively), 0.94% (SO 2 

### )

(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:

NA 2 

### Los Angeles:

IHD: 19.2%

All cause: 6.2%

Philadelphia:

NA 2

1 

## Percent of total incidence that is PM 2.5 

## - related (note, the set of estimates fo r each entry reflect adjustment to the 

# 4

two core estimates generated fo r IHD and all-cause mortality) 

# 5

2 

## hybrid not run for Philadelphia, so multi-element sensitivity analysis not completed 

# 6

Note, that the risk estimates for SO 2 

## 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 

## the two pollutant model for PM 2.5 

## and CO and PM 2.5 

## and O 3 

## had the same sensitivity result, so both models are 

# 1

referenced here with the same impact on mortality estimates. 

# 2

4 

## Sensitivity analysis based on comparison of alternative model formulations to the core risk estimates based on the 

# 3

C-R function derived using 1999-2001 ambient monitoring data (see section 3.5.4). 

# 4

Figure 4-7 Comparison of core risk esti mates with reasonable alternative set of 

# 5

risk estimates for Los Angele s and Philadelphia (IHD mortality). 

# 6

5 10 15 20 25 Los Angeles

Philadelphia

Percent total incidence attributable to PM 2.5 

### (IHD mortality)

- core risk estimate

- alternative reasonable risk estimate (letter

matches simulation in Table 4-2) 

### KEY:



# 7

Figure 4-8 Comparison of core risk estima tes with reasonable al ternative set of 

# 8

risk estimates for Los Angeles and Philadelphia (all cause mortality). 

# 9

1 2 4 6 8 Los Angeles

Philadelphia

Percent total incidence attributable to PM 2.5 

### (all-cause mortality)

- core risk estimate

- alternative reasonable risk estimate (letter

matches simulation in Table 4-2) 

### KEY: 7 5 3

- alternative reasonable risk estimate for copollutant modelin including PM

2.5 

### and

SO

2 

### (de-emphasized relative to other alternative risk estimates - see text) 

### 0.5



# 10

# February 2010 Draft – Do Not Quote or Cite 4-47 1

Review of the set of risk estimates presente d in Table 4-4 and displayed in Figures 4-7 2

and 4-8 results in a number of obs ervations regarding uncertainty a ssociated with the core risk 3

estimates: 4

 Consideration for uncertainty and variability in the core risk es timates results in a 5

notable spread in risk estimates : Given the factors considered in generating the 6

alternative set of reasonable risk estimates, there appears to be a factor of 3 to 4 spread in 7

risk estimates if we consider the lowest (core) estimates gene rated and the highest 8

alternative risk estimates ge nerated. This observation ho lds for both urban study areas 9

considered, as well as for the two mortality endpoint categories. As noted earlier in this 10

section, we have de-emphasized risk estim ates generated using the copollutant model 11

involving PM 2.5 

# and SO 2 

# due to concerns with collinearity between the two pollutants and 12

the potential that SO 2 

# represents risk attributab le to secondarily formed PM 2.5



# . 13

 Uncertainty set of risk estimates generated to supplement the core risk estimates are 14

skewed towards higher risk : It appears that, given the factors considered in generating 15

the alternative set of reasonable risk estimates, consideration of uncertainty could result 16

in higher (more elevated) risk estimates, comp ared with the core risk estimates. In other 17

words, most if not all of the alternative m odel specifications we considered resulted in 18

risks that are higher than our core estimates. 19

 Sensitivity analysis is limited in its scope (potentially important sources of uncertainty 20

not considered) : As noted earlier, the sensitivity anal ysis did not consider a number of 21

potentially important sources of uncertainty, some of which we re addressed as part of the 22

qualitative analysis of uncertainty (see Ta ble 3-13). For example, information is not 23

available to consider com positional differences in PM 2.5 

# and the potential for 24

differentiation of effects estimates. Further, not considering more refined patterns of 25

intra-urban exposure to PM 2.5 

# in deriving effects estim ates could result in under26

estimation of risk. 27

It is important to reiterate that this set of alternative realizations presented in Table 4-4 28

and depicted in Figures 4-6 and 4-7, does not re present an uncertainty di stribution. Therefore, 29

we can not assign percentiles to the individual data points presente d and (importantly), we do not 30

draw any conclusions based on any clustering of the alternative risk estimates seen in Figures 4-6 31

and 4-7. Further, we do not know whether a ny of the higher-end estimates generated actually 32

represent true bounding risk estimates given overall uncertainty associated with the core risk 33

estimates. Despite these key caveats, having a se t of risk estimates reflecting the impact of 34

modeling element uncertainties does provide information that helps to inform our 35

characterization of uncertainty rela ted to the core risk estimates. 36

# February 2010 Draft – Do Not Quote or Cite 4-48 4.4 EVALUATING THE REPRESENTATIVE NESS OF THE URBAN STUDY AREAS 

# 1

IN THE NATIONAL CONTEXT 

# 2

The goal in selecting the 15 urban study areas included in this risk assessment was two3

fold: (a) to choose urban locati ons with relatively elevated am bient PM levels (in order to 4

evaluate risk for locations likely to experien ce some degree of risk reduction under alternative 5

standards) and (b) to include a range of urban areas reflecting he terogeneity in other PM risk6

related attributes ac ross the country. To further support inte rpretation of risk estimates generated 7

in this analysis, we are assessing the degree to which urban study areas represent the range of 8

key PM 2.5 

# risk-related attributes that spatially va ry across the nation. We have partially 9

addressed this issue by selecting urban study ar eas that provide covera ge for different PM 10

regions of the country (see section 3.3.2). In ad dition, we are considering how well the selected 11

urban areas represent the overall U.S. for a set of spatially-distributed PM 2.5 

# risk related variables 12

(e.g., PM 2.5 

# composition, weather, demogr aphics including SES, baseline health incidence rates). 13

This analysis will help to inform how well the urban study areas reflect na tional-level variability 14

in these key PM risk-related variables. Base d on generally available data (e.g. from the 2000 15

Census, Centers for Disease Cont rol (CDC), or other sources), distributions for risk-related 16

variables across U.S. counties and for the specific counties represented in the urban study areas 17

are generated. The specific values of these vari ables for the selected ur ban study areas are then 18

plotted on these distributions, and an evaluation is conducted of how representative the selected 19

study areas are with respect to thes e individual variables, relative to the national distributions. 20

Estimates of risk (either relative or absolu te, e.g. number of cases) within our risk 21

assessment framework are based on four elements : population, baseline incidence rates, air 22

quality, and the coefficient relating air qua lity and the health outcome (i.e., the PM 2.5 

# effect 23

estimates). Each of these elements can contribut e to heterogeneity in risk across urban locations, 24

and each is variable across locations. In additio n, there may be additional identifiable factors 25

that contribute to the variability of the four el ements across locations. In this assessment, we 26

examine the representativeness of the selected ur ban area locations for the four main elements, 27

and also provide additional assessm ent of factors that have been identified as influential in 28

determining the magnitude of th e C-R function across locations. 29

The specific choice of variables which may affect the PM 2.5 

# effect estimates for which we 30

will examine urban study area representativeness is informed by an assessment of the 31

epidemiology literature. We particularly focu sed on meta-analyses and multi-city studies which 32

identified variables that influence heterogeneity in PM 2.5 

# effect estimates, and exposure studies 33

which explored determinants of differen ces in personal exposures to ambient PM 2.5 

# . While 34

personal exposure is not incorpor ated directly into PM epidemiology studies, differences in the 35

PM 2.5 

# effect estimates between ci ties clearly is impacted by di ffering levels of exposure and 36

# February 2010 Draft – Do Not Quote or Cite 4-49 differences in exposure are clearly related to a number of exposure determinants. Broadly 1

speaking, determinants of PM 2.5 

# effect estimates can be grouped into three areas: demographics, 2

baseline health conditions, and climate and air quality. Based on a review of these studies, we 3

identified the following variables within each group as potentially determining the PM 2.5 

# effect 4

estimates: 5

 Demographics: education (see Zeka et al , 2006; Ostro et al, 2006) , age and gender (see 6

Zeka et al, 2006), population density (see Zeka et al, 2005), unemploy ment rates (see Bell 7

and Dominici, 2008), race (see Bell and Dominici, 2008), pub lic transportation use (see 8

Bell and Dominici, 2008), 9

 Baseline health conditions: disease prevalence (diabetes – Bateson and Schwartz, 2004; 10

Ostro et al, 2006; Zeka et al , 2006; pneumonia – Zeka et al, 2006; stroke – Zeka et al, 11

2006; heart and lung disease – Bateson and Sc hwartz, 2004; acute myocardial infarction 12

– Bateson and Schwartz, 2004). 13

 Climate and air quality: PM 2.5 

# levels (average, 98 th 

# percentiles, and numbers of days over 14

the level of the 24-hour standard, e.g. 35 μg/m 3 

# ), co-pollutant levels, PM composition 15

(see Bell et al, 2009; Domini ci et al, 2007; Samet, 2008; Tolbert, 2007), temperatures 16

(temp) (days above 90 degrees, variance of summer temp, mean summer temp, 98 th 

# 17

percentile temp, mean winter temp -- see R oberts, 2004; Medina-Ramon et al, 2006; Zeka 18

et al., 2005), air condit ioning prevalence (see Zanobetti a nd Schwartz, 2009; Franklin et 19

al, 2007; Medina-Ramon et al, 2006), ventil ation (see Sarnat et al, 2006), percent of 20

primary PM from traffi c (see Zeka et al., 2005), 21

Based on these identified potential risk determinants, we identified possible datasets that 22

could be used to generate nationally representati ve distributions for each parameter. We were 23

not able to identify readily availabl e national datasets for all variables. In these cases, if we were 24

able to identify a broad enough dataset covering a large enough portion of the U.S., we used that 25

dataset to generate the parameter distribution. In addition, we were not able to find exact 26

matches for all of the variables id entified through our re view of the literature. In cases where an 27

exact match was not available, we identified prox y variables to serve as surrogates. For each 28

parameter, we report the source of the dataset, its degree of coverage, and whether it is a direct 29

measure of the parameter or a proxy measure. Th e target variables and s ources for the data are 30

provided in Table 4-2. Summary statistics for the most releva nt variables are provided in Table 31

D-3. 32

# February 2010 Draft – Do Not Quote or Cite 4-50 Table 4-5 Data Sources for PM NAAQS Ri sk Assessment Risk Distribution 

# 1

Analysis. 

# 2

Potential Risk

Determinant Metric Year Source Degree of

National

Coverage

Demographic s

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

PM 2.5 

## Levels PM 2.5 

## Levels --

Monitored Ann Mean 2007 AQS 617 Monitored

counties

PM 2.5 

## Levels PM 2.5 

## Levels --

Monitored 98th %ile 2007 AQS 617 Monitored

counties

PM 2.5 

## 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

PM 2.5 

## Levels % days exceeding 35

μg/m 3 

## 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 Housin g 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 prevalen ce 2007 BRFSS MSA estimates 184 BRFSS

MSA

Baseline Morbidity Diabetes Prev alence 2007 BRFSS MSA estimates 184 BRFSS

MSA

Baseline Morbidity Pneumonia Prevalence

Baseline Morbidity Stroke Prev alence 2007 BRFSS MSA estimates 184 BRFSS

MSA

Baseline Morbidity CHD Preval ence 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 citiesFebruary 2010 Draft – Do Not Quote or Cite4-52Table 4-6 Summary Statistics forSelected PM Risk Attributes.1AverageStandard DeviationMaximumMinimumSample SizeRisk AttributesUrban studyareasU.S.countiesUrban studyareasU.S.countiesUrban studyareasU.S.countiesUrban studyareasU.S.countiesUrban studyareas(number ofcounties)U.S.(numberofcounties)DemographicsPopulation 1,410,331 97,020 1,870,237 312,348 9,862,049 9,862,049 57,441 42 31 3143Population Density (Pop/sq mile)7,212 258 14,960 1,757 71,758 71,758 87 0 31 3143Median Age (years) 35.5 38.6 2.6 4.4 41.5 55.3 30.2 20.1 31 3141% Age 65 Plus 11.3 14.9 2.6 4.1 17.2 34.7 5.8 2.3 31 3141Unemployment rate (%) 5.4 5.4 1.5 1.8 9.0 20.9 2.7 1.9 31 3133% with Less than High School Diploma 21.8 22.6 7.7 8.8 37.7 65.3 11.2 3.0 31 3141Income ($2005) 35691 27367 12605 6604 93377 93377 23492 5148 31 3086Air conditioning prevalence (%) 85.8 83.3 13.3 21.5 99.4 100.0 58.6 9.9 10 70% Non-white 29.5 13.0 18.2 16.2 68.3 95.3 2.7 0.0 31 3141Health ConditionsPrevalence of CHD (%) 3.9 4.3 0.9 1.3 5.2 8.7 1.8 1.8 14 184Prevalence of Obesity (%) 26.4 26.0 3.0 4.1 32.7 35.7 22.2 14.0 14 182Prevalence of Stroke (%) 2.7 2.7 0.8 1.0 4.1 6.5 1.1 0.7 14 184Prevalence of Smoking (ever) (%) 18.4 19.6 3.1 4.0 23.1 34.4 14.2 6.5 14 184Prevalence of Exercise (20 minutes) (%) 28.4 28.0 3.6 4.8 33.9 44.1 20.5 15.4 14 183All Cause Mortality (per 100,000 population)833.7 1022.3 241.1 258.6 1342.9 2064.2 402.5 176.8 31 3142Non-accidental Mortality (per100,000 population)774.1 950.6 227.3 249.6 1242.0 1958.4 361.6 117.7 31 3142Cardiovascular Mortality (per 100,000 population)317.5 392.1 100.6 121.0 535.7 970.4 122.4 37.5 31 3142Respiratory Mortality (per 100,000 population) 70.8 97.3 23.0 32.3 130.3 351.0 34.8 13.3 31 3136Air Quality and ClimateAQ - PM25 Annual Mean (μg/m3) 15.1 11.7 2.2 3.1 19.6 22.5 9.7 3.4 29 617AQ - PM25 98th %ile 24-hour Average (μg/m3) 38.7 30.7 11.6 9.3 79.2 81.1 26.8 9.1 29 617AQ - O34th High Maximum 8-hour Average (ppm) 0.087 0.077 0.009 0.010 0.105 0.126 0.064 0.033 27 725% Mobile Source PM Emissions 34.0 44.4 11.2 21.9 56.6 97.6 13.7 0.3 31 3141February 2010 Draft – Do Not Quote or Cite4-53AverageStandard DeviationMaximumMinimumSample SizeRisk AttributesUrban studyareasU.S.countiesUrban studyareasU.S.countiesUrban studyareasU.S.countiesUrban studyareasU.S.countiesUrban studyareas(number ofcounties)U.S.(numberofcounties)July Temperature Long Term Average (°F) 78.1 75.9 4.5 5.4 91.2 93.7 64.8 55.5 31 3104July Relative Humidity Long Term Average (°F) 58.2 56.2 14.0 14.6 70.0 80.0 19.0 14.0 31 3104C-R EstimatesAll Cause Mortality PM2.5Risk Estimate 0.000971 0.000974 0.000340 0.000216 0.001349 0.001508 0.000159 -0.000099 15 112Respiratory Mortality PM2.5Risk Estimate 0.001606 0.001670 0.000419 0.000305 0.002157 0.002221 0.000931 -0.000346 15 112Cardiovascular Mortality PM2.5Risk Estimate 0.001013 0.000842 0.000586 0.000324 0.001958 0.001958 -0.000180 -0.000180 15 112

# February 2010 Draft – Do Not Quote or Cite 4-54 Formal comparisons of parameter distributio ns for the set of ur ban study areas and the 1

national parameter distributions are conducted us ing standard statisti cal tests, e.g. the 2

Kolmogorov-Smirnov non-parametric test for equali ty of distributions. In addition, visual 3

comparisons are made using cumulative distribution functions, and boxplots. 4

The formal Kolmogorov-Smirnov test results ar e provided in Table 4- 4. The K-S tests 5

the hypotheses that two distributions are not significantly different. A high p-value indicates a 6

failure to reject the null hypothe ses that the case-study and nationa l distributions are the same. 7

We used a rejection criterion of p ≤ 0.05, which is a standard rejecti on criteria. It should be noted 8

that the K-S test provides a good overall measure of fit, but will not provide a test of how well 9

specific percentiles of the distributions are matched. As such, the K-S test results will not be 10

sufficient to determine whether the urban study areas adequately captu re the tails of the 11

distributions of specific risk related variables. Additional visual analyses are used to assess 12

representativeness for the tails of the distributions. Overall, the K-S test results show that for 13

many of the important risk variable s such as population, air qualit y, age, and baseline mortality 14

rates, the urban study areas are not representative of the distributions of these variables for the 15

U.S. as a whole. However, for some important pot ential risk determinants, such as prevalence of 16

underlying hear and lung diseas es, the case study areas are repr esentative of the national 17

distributions. However, for these specific vari ables, the national distribution is represented 18

primarily by large urban areas, so it is more accu rate in these cases to suggest that the urban 19

study areas are representative of the ove rall distribution across urban areas. 20

Figures 4-14 through 4-17 show for the four critical risk function elements (population, 21

air quality, baseline incidence, and the PM 2.5 

# effect estimate) the cumulative distribution 22

functions plotted for the nation, as well as for th e urban study areas. These four figures focus on 23

critical variables representing each type of risk determinant, e.g. we focus on all-cause mortality 24

rates, but we also have c onducted analyses for cardiovascul ar and respiratory mortality 25

separately. The complete set of analyses is provi ded in Appendix D. The vertical black lines in 26

each graph show the values of the variables fo r the individual urban study areas. These figures 27

show that the selected urban study areas represen t the upper percentiles of the distributions of 28

population and air quality, while not representing lower populati on locations with lower 24-hour 29

PM 2.5 

# levels. This is consistent with the objectives of our case study selection process, e.g. we 30

are characterizing risk in areas that are likely to be experiencing excess risk due to PM levels 31

above alternative standards. The urban case st udy locations represent the full distribution of 32

PM 2.5 

# risk coefficients, but do not ca pture the upper end of the dist ribution of baseline all-cause 33

mortality. The interpretation of this is that the case study ri sk estimates may not capture the 34

additional risk that may exist in locations that have the highest baseline mortality rates. 35

# February 2010 Draft – Do Not Quote or Cite 4-55 Figures 4-18 through 4-21 shows for several selected potential risk attributes the CDF 1

plotted for the nation as well as for the urban study areas. These potential risk attributes do not 2

directly enter the risk equations, but have been identified in the li terature as potentially affecting 3

the magnitude of the PM 2.5 

# C-R functions reported in the epidemiological literature. The 4

selected urban study areas do not capture the higher end percentile s of several risk 5

characteristics, including populations over 65, income, and baseline cardiovascular disease 6

prevalence. Comparison graphs for other risk attributes are provided in Appendix D. 7

Summarizing the analyses of the other risk attr ibutes, we conclude that the urban study areas 8

provide adequate coverage across population, population density, annual and 24-hour PM 2.5 

# 9

levels, ozone co-pollutant levels, temperature and relative humidity, unemployment rates, 10

percent non-white population, asthma prevalence, obe sity prevalence, stroke prevalence, exercise 11

prevalence, and less than high school education. We also conclude that while the urban study 12

areas cover a wide portion of the distributions, they do not provide coverage for the upper end of 13

the distributions of age (all case study locations are below the 85 th 

# %ile), % of population 65 and 14

older (below 85 th 

# %ile), percent of primary PM emissi ons from mobile sources (below 8oth 15

%ile), prevalence of angina/coronary heart disease (below 85 th 

# %ile), prevalence of diabetes 16

(below 85 th 

# %ile), prevalence of heart attack (below 80 th 

# %ile), prevalence of smoking (below 17

85 th 

# %ile), all-cause mortality rates (below 90 th 

# %ile), cardiovascular mortality rates (below 90 th 

# 18

%ile) and respiratory mortality rates (below 90 th 

# %ile). In addition, all of the case study 19

locations were above the 25 th 

# percentile of the distribution of personal income. 20

Based on the above analyses, we can dr aw several inferenc es regarding the 21

representativeness of the urban case studies. First, the case st udies represent urban areas that are 22

among the most populated and most densely populati on in the U.S. Second, they represent areas 23

with relatively higher levels of annual mean and 24-hour 98 th 

# percentile PM 2.5 

# . Third, they 24

capture well the range of effect estimates re presented in the Zanobetti and Schwartz (2009) 25

study. These three factors would suggest that the urban study areas shou ld capture well overall 26

risk for the nation, with a potential for better characterization of th e high end of the risk 27

distribution. However, there are several other factors that suggest that the urban study areas may 28

not be representing areas that may ha ve a high risk per microgram of PM 2.5 

# . The analysis 29

suggests that the urban study areas are not capturi ng areas with the highest baseline mortality 30

risks, nor those with th e oldest populations. These areas may have higher risks per microgram of 31

PM 2.5 

# , and thus the high end of the risk distribu tion may not be captured, although the impact on 32

characterization of overall PM risk may not be as large, for the following reasons. 33

It should be noted that several of the fact ors with underrepresented tails, including age 34

and baseline mortality (R=0.81) are spatially correlated, so that certain counties which have high 35

proportions of older adults also have high baseline mortality and high prevalence of underlying 36

# February 2010 Draft – Do Not Quote or Cite 4-56 chronic health conditions. Because of this, omission of certain urban areas with higher 1

percentages of older populations, fo r example, cities in Florida, may lead to underrepresentation 2

of high risk populations. However, with the excep tion of areas in Florida, most locations with 3

high percentages of older populations have low overall populations, less than 50,000 people in a 4

county. And even in Florida, the counties with the highest PM 2.5 

# levels do not have a high 5

percent of older populations. This suggests that while the risk per exposed person per microgram 6

of PM 2.5 

# may be higher in these locati ons, the overall risk to the popul ation is likely to be within 7

the range of risks represented by the urban case study locations. 8

Table 4-7 Results of Kolomogrov-Smirnof f Tests for Equality Between National 

# 9

and Urban Study Area Distributio ns for Selected National Risk 

# 10

Characteristic Variables 

# 11

(null hypothesis is no diff erence between the distributions) 

# 12

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/m 3 

## 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 PM 2.5 

## Risk N 0.1585

Respiratory Mortality PM 2.5 

## Risk N 0.2864

Cardiovascular Mortality PM 2.5 

## Risk N 0.1161



# 1

2

Figure 4-9 Comparison of distributions for key elements of the risk equation: 

# 3

total population. 

# 4

Comparison of Urban Case Study Area Po pulation 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 Figure 4-10 Comparison of distributions fo r key elements of the risk equation: 

# 1

98 th 

# percentile 24-hour average PM 2.5 

# 2

Comparison of Urban Case St udy Area 98th %ile PM2.5 wi th 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 Figure 4-11 Comparison of distributions for key elements of the risk equation: all 

# 1

use mortality rate. 

# 2

Comparison of Urban Case Study All Cause Mo rtality 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 Figure 4-12 Comparison of distributions fo r key elements of th e risk equation: 

# 1

Mortality risk effect estimate from Zanobetti and Schwartz (2008). 

# 2

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 Figure 4-13 Comparison of distributions for selected variables expected to 

# 1

influence the relative risk from PM 2.5 

# : long term average July 

# 2

temperature. 

# 3

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 Figure 4-14 Comparison of distributions for selected variables expected to 

# 1

influence the relative risk from PM 2.5 

# : percent of population 65 and 

# 2

older. 

# 3

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 Figure 4-15 Comparison of distributions for selected variables expected to 

# 1

influence the relative risk from PM 2.5 

# : per capita annual personal 

# 2

income. 

# 3

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 Figure 4-16 Comparison of distributions for selected variables expected to 

# 1

influence the relative risk from PM 2.5 

# : per capita annual personal 

# 2

income. 

# 3

Comparison of Urban Case Study Area Angi na/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

4.5 CONSIDERATION OF DESIGN VALUES AND PATTERNS OF PM 2.5 

# 6

MONITORING DATA IN INTREPR ETING CORE RISK ESTIMATES 

# 7

The degree of risk reduction associated w ith the current and alternative suites of 8

standards at a particular urban study area depends to a great exte nt on the degree of reduction in 9

PM 2.5 

# concentrations simulated for that location. Th is in turn depends on the interplay between 10

the 24-hour and annual design values and the mon itoring data used to characterize ambient PM 2.5 

# 11

concentrations, since these fact ors determine the composite annual average and composite 24- 12

hour PM 2.5 

# profiles used in modeling long-term and short-term exposure related risk for that 13

study area. Because of the role that de sign values and underlying patterns in PM 2.5 

# monitoring 14

data play in determining the de gree of risk reductions, these fact ors can be used in helping to 15

interpret risk estimates genera ted for the 15 urban study areas unde r the various standard levels 16

considered in this risk assessment. Further, it is possible to consider, more broadly, patterns of 17

design values across urban areas in the U.S. and contrast these with patterns seen for the 15 18

# February 2010 Draft – Do Not Quote or Cite 4-65 urban study areas to help to plac e risk estimates for the 15 urban study areas in a broader national 1

context. 2

This section discusses consid eration of patterns of design values (section 4.5.1) and 3

underlying ambient monitoring PM 2.5 

# data (section 4.5.2) for the 15 urban study areas in the 4

context of helping to interpret risk estimates. Ea ch of these discussions begins by describing the 5

methods used in each analysis and conc ludes with a set of key observations. 6

4.5.1 Design Values 

# 7

The set of design values for an urban st udy area determines whether the 24-hour or 8

annual standard will be controlling as we ll as the degree of re duction in ambient PM 2.5 

# 9

concentrations associated with a particular suite of standards. Th erefore, by plotting the 10

relationship between 24-hour and annual design values for each of the 15 urban study areas, we 11

can obtain a quick visual perspective on (a) whic h study areas will experien ce reductions in risk 12

for a particular suite of standards, (b) whether the 24-hour or annual standa rd will control, and 13

(c) the general magnitude of risk reduction. The last observations re sult from comparing the 14

controlling standard level with the matching design value, which will determine the fractional 15

reduction in PM 2.5 

# levels at monitors exceedi ng the standard level (for p eak shaving rollback), or 16

more broadly across all monitors (for proportional rollback). 17

Figures 4-17 through 4-19 present scatter pl ots of 24-hour and annual design values for a 18

combination of the 15 urban study areas (red stars) and the broader set of larger urban areas in 19

the U.S. (green circles). In addition to depicti ng the set of design values for these urban areas, 20

each figure also includes a set of superimposed lin es representing the current suite of standards 21

(Figure 4-17) and three of the alternative suites of standards considered in the risk assessment 22

(12/35 – Figure 4-18, and 12/25 – Figure 4-19). In each figure, the horizonta l line represents the 23

24-hour standard level, while the vertical line represents the annua l standard level. The line that 24

intercepts the origin (i.e., the “35/15 line” in Fi gure 4-17) represents th e point of demarcation 25

between those study areas where the 24-hour standard controls (to the left of the intercept line) 26

and those study area where the annual standard level controls (to th e right of the intercept line). 27

By superimposing these lines related to the curre nt standard level on the scatter plot, we have 28

created five zones within each figure including: 29

 Zone A: 24-hour design values exceeding th e 24-hour standard level, but annual design 30

values below the annual standard level (i.e., 24-hour standa rd is controlling ). Urban study 31

areas in this zone are predicted to experi ence risk reduction with the degree of reduction 32

reflecting the degree to which th e 24-hour design value exceeds the 24-hour standard level. 33

For example, in Figure 4-17 (depicting the current suite of standards), Tacoma and Salt Lake 34

City fall in this zone, along with 20- 30 additional urban areas in the U.S. 35

# February 2010 Draft – Do Not Quote or Cite 4-66  Zone B: 24-hour design values and annual design values exceed 24-hour and annual 1

standard levels, respectively, and the 24-hour standard is cont rolling. We have further 2

transected this zone into B1 and B2, with the former representing those urban areas with 3

notably high 24-hour design values (Fresno, Lo s Angeles in Figure 4-17) and B2 those with 4

lower, although still control ling, 24-hour design values (B altimore, New York, Detroit, 5

Philadelphia, St. Louis in Figure 4-17). Those urban areas in B1 have exceptionally peaky 6

PM 2.5 

# distributions relative to ur ban areas in B2 (i.e., relative ly high 24-hour design values 7

and lower annual average design values). 8

 Zone C: 24-hour design values and annual design values exceed 24-hour and annual 9

standard levels, respectively, and the annual standard is controlling. Atlanta, Birmingham 10

and Houston fall into this zone and represent a relatively small number of urban areas in the 11

U.S.. 12

 Zone D: annual design values exceed the annua l standard level, but 24-hour design values 13

are below the 24-hour standard level (i .e., annual standard is controlling ). Houston is the only 14

urban study area falling into this zone for th e current standard level, along with a small 15

number of additional urban areas in the U.S.. 16

 Zone E: both the 24-hour and a nnual design values are below thei r respective standard levels 17

(i.e., this is the only zone where urban areas would not be expected to experience risk 18

reductions under the suite of standards being cons idered). The majority of urban areas in the 19

U.S. depicted in these scatter plot s fall into Zone E in Figure 4-17. 20

21

The five zones presented above are useful in interpreting the risk re sults generated for the 22

current suite of standards (for the 15 urban study areas). Specifi cally, as noted above, they allow 23

us to (a) quickly identify which of the 15 urba n study areas experience risk reductions under the 24

current standard level, (b) determine whether those reductions are due primarily to a controlling 25

24-hour or annual standard and (c ) to see how well our set of ur ban study areas provide coverage 26

for the broader set of ur ban areas in the U.S.. 27

In addition to presenting Figures 4-17 through 4-19 as a means for supporting the 28

interpretation of risk estimates generated for th e 15 urban study areas (bas ed on consideration for 29

patterns in design values), we have also included Table 4-8 for this purpose. Table 4-8 presents 30

the annual and 24-hour design values for each urban study area and also identifies which 31

standard is controlling for a given suite of standards. For example, we see that in Atlanta (which 32

has design values of 16.2 μg/m 3 

# and 35 μg/m 3 

# , annual and 24-hour, re spectively), the annual 33

standard controls for the current suite of standard s (15/35) as well as the first 4 alternative suites 34

of standards considered (14/35, 13/35, 12/35 and 13/30). However, the 24-hour standard controls 35

for the final suite of standards considered (12/25). This matches with information presented in 36

Figures 4-17 through 4-19 (e.g., Figure 4-17 shows that the Atlanta is just inside of zone C, 37

suggesting that it meets the 24-hour st andard, but not the annual standard. 38

39

# February 2010 Draft – Do Not Quote or Cite 4-67 1

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

# 2

areas relative to the cu rrent suite of standards (15/35) 

# 3

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 0 5 10 15 20 25 30 35 4 0 4 5 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

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3) 

### 35/15 line  

# A B

C

D

E

0 5 10 15 20 25 30 35 4 0 4 5 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

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )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

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

# 2

areas relative to the 12/35 alternative suite of standards 

# 3



# A B

C

D

E

0 5 10 15 2 0 2 5 30 35 4 0 4 5 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

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )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 2 0 2 5 30 35 4 0 4 5 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

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )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

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

# 2

areas relative to the 12/25 alt ernative suite of standards) 

# 3

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 0 5 10 15 2 0 2 5 30 35 4 0 4 5 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

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3)



## 25/12 line  

# A B

C

D

E

0 5 10 15 2 0 2 5 30 35 4 0 4 5 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

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3)



## 25/12 line 

0 5 10 15 2 0 2 5 30 35 4 0 4 5 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

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )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 2 0 2 5 30 35 4 0 4 5 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

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3)



## 25/12 line  

# A B

C

D

E

0 5 10 15 2 0 2 5 30 35 4 0 4 5 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

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3)



## 25/12 line 

0 5 10 15 2 0 2 5 30 35 4 0 4 5 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

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )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

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

# 2

alternative suites of standard levels 

# 3

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

* “24hr” denotes that the 24-hour standard is controlling. “A” denotes that the annual standard is 

# 4

controlling 

# 5

6

Based on consideration of the zones defined in Figures 4-17 through 4-19, we can make 7

the following observations regarding potential pa tterns of risk reduction across urban study areas 8

in the U.S., given the current and alternative suit es of standards considered. Further, we can 9

characterize the degree to which the 15 urban st udy areas provide coverage for these groupings 10

of U.S. urban study areas: 11

 For the current suite of standards (see Figure 4-17), Based on 2005-2007 air quality data, 12

most urban areas in the country meet the current standards base d on 2005-2007 air quality 13

data (zone E). A smaller but still notable numbe r meet the current annual standard but do not 14

meet the current 23hr standard (Zone A). A similar number of areas do not meet either 15

current standard (zones B and C). Only a few areas do not meet the current annual standard, 16

but do meet the current 24hr standard (zone D) . Of the 15 urban study areas included in the 17

risk assessment most fall into zones that do not meet either standard (zones B and C) 18

although some study areas are in each of the other zones. 19

 Alternative suites of standards involv ing reduction of the annual standard levels (see Figure 20

4-18) Based on 2005-2007 air quality data, as sh own in Figure 4-18, reduction in the annual 21

standard level down to 12 μg/m 3 

# results in a significant increase in the number of areas that 22

# February 2010 Draft – Do Not Quote or Cite 4-71 do not meet the annual standard (zones C a nd D). And of those areas, roughly similar 1

numbers of urban areas do m eet the 24hr standard as do not meet the 24hr standard 2

(comparing numbers of urban areas in B and C to the number in zone D). 3

 Alternative suite of standards involving reductions in both annual and 24-hour levels (see 4

Figure F-19): Based on 2005-2007 air quality data, a la rge fraction of urban areas are 5

predicted not to meet the 24hr standard (zones A, B and C). Furthermore, the majority of 6

these have the 24hr controlling (zone A and B). We also note that ther e are virtually no urban 7

areas that exceed the annual standard while not meeting the 24hr standard (zone C). Of the 8

15 urban study areas, most do not meet either the 24hr or annual standards, while the 24hr is 9

controlling in most (zone B). 10

11

4.5.2 Patterns in PM 2.5 

# Monitoring Data 

# 12

As noted earlier, patterns in PM 2.5 

# monitoring data for each of the 15 urban study areas 13

can be used (together with cons ideration of design values as described in section 4.5.1) to 14

support interpretation of ri sk estimates generated for current an d alternative standard levels. This 15

is particularly true when considering the im pact of using different rollback methods in 16

supporting risk characterization for current and alternative standard levels, as discussed below. 17

To facilitate consideration of patterns in PM 2.5 

# monitoring data across the 15 urban study 18

areas, we have developed Figures 4-20 and 4-21. Each of these figures presents 24-hour and 19

annual design values (blue and green dots, respectively) for each PM 2.5 

# monitor within each 20

study area. The figures also fl ag the highest design values for each study area (red and brown 21

stars for the annual and 24-hour standard levels, respectively). 58 

# Each figure has been scaled to 22

represent a particular suite of standards, with Fi gure 4-20 scaled to represent the current suite of 23

standards (15/35) and Figure F-21 scaled to represent the 12/25 alternative suite of standards. 59 

# 24

In addition, the figures allow identification of whether a study area had the highest design value 25

(for the 24-hour and annual averaging periods) o ccurring at the same or at different monitors. 26

This factor can influence the degree to which simulation of a controlling 24hr standard level, 27

given application of peak shaving, re sults in reduction in annual-average PM 2.5 

# levels for that 28

study area. If an area has both 24hr and annual design values occurring at the same monitor, then 29

application of peak shaving to reduce the contro lling 24hr standard will also bring down the 30

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 annual design value (i.e., the annual-average PM 2.5 

# level for that study area is likely to be 1

reduced to a greater extent). By contrast, if 24hr and annual design values are located at different 2

monitors, then peak shaving focused on re duction of the 24hr design value monitor will 3

potentially not impact the annual design value (i.e ., there will be a smaller impact on the annual4

average PM 2.5 

# level for that study area). 60 

# 5

To gain a better understanding of the information provided in Figures 4-20 and 4-21, we 6

will provide a walkthrough for one of the urban st udy areas, highlighting key attributes related to 7

24-hour and annual design values. With Los Angele s (in Figure 4-20) we see that the study area 8

has a relatively wide spread in 24-hour and annu al design values across the monitors (i.e., it has a 9

relatively peaky PM 2.5 

# distribution), with 24- hour values ranging from ~15 to ~55 and annual 10

design values ranging from ~7 to ~19 (exact values are presented in Appendix A). In addition, 11

we see that the 24-hour standard is clearly controlling, given how much farther the highest viable 12

24-hour design value is from the 15/35 line compared with the highest annua l design value. In 13

addition, we can compare these trends in 24-hour and annual design values for Los Angeles to 14

those for the other urban study area and see that generally, Los Angeles (a) has some of the 15

widest spreads in both 24-hour and annual design values (i.e., it has one of the more peaky 16

PM 2.5 

# distributions across monitors) and (b) has one of the highest 24-hour design value of the 15 17

urban study areas (i.e., it will requi re more rollback in simulating just meeting the current suite 18

of standards compared with most of the other st udy areas). The attributes described above match 19

well with urban areas falling into zone B1 in Fi gure 4-17 (i.e., the zone where urban areas do not 20

meet both the current 24-hour and annual sta ndards, and where the 24-hour standard is 21

controlling). 22

60 

## When a star in either Figure 4-20 or 4-21 (signifying the highest design va lue 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 Phoe nix, while Los Angeles represents a location where the highest 24-hour and

annual design values occu r at the same monitor.February 2010Draft – Do Not Quote or Cite4-73Figure 4-20 Annual and 24-hour design values (for individual monitors and at the study-area level) for the 15 urban1study areas (with the presentation of values scaled to reflect current standard of 15/35)2024681012141618202224262830AtlantaBaltimoreBirminghamDallasDetroitFresnoHoustonLosAngelesNewYorkPhiladelphiaPhoenixPittsburghPitt-LibertySaltLakeSt.LouisTacoma0510152025303540455055606570

Annual Design Value (μg/m

3 ) 24-hour Design Value (μg/m

3 )

 Annual DVs  24-hour DVsStars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs024681012141618202224262830AtlantaBaltimoreBirminghamDallasDetroitFresnoHoustonLosAngelesNewYorkPhiladelphiaPhoenixPittsburghPitt-LibertySaltLakeSt.LouisTacoma0510152025303540455055606570

Annual Design Value (μg/m

3 ) 24-hour Design Value (μg/m

3 )

 Annual DVs  24-hour DVsStars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs15/35 standard level line024681012141618202224262830AtlantaBaltimoreBirminghamDallasDetroitFresnoHoustonLosAngelesNewYorkPhiladelphiaPhoenixPittsburghPitt-LibertySaltLakeSt.LouisTacoma0510152025303540455055606570

Annual Design Value (μg/m

3 ) 24-hour Design Value (μg/m

3 )

 Annual DVs  24-hour DVsStars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs024681012141618202224262830AtlantaBaltimoreBirminghamDallasDetroitFresnoHoustonLosAngelesNewYorkPhiladelphiaPhoenixPittsburghPitt-LibertySaltLakeSt.LouisTacoma0510152025303540455055606570

Annual Design Value (μg/m

3 ) 24-hour Design Value (μg/m

3 )

 Annual DVs  24-hour DVsStars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs15/35 standard level line3February 2010Draft – Do Not Quote or Cite4-74Figure 4-21 Annual and 24-hour design values (for individual monitors and at the study-area level) for the 15 urban1study areas (with the presentation of values scaled to reflect current standard of 12/25)2024681012141618202224262830323436AtlantaBaltimoreBirminghamDallasDetroitFresnoHoustonLosAngelesNewYorkPhiladelphiaPhoenixPittsburghPitt-LibertySaltLakeSt.LouisTacoma051015202530354045505560657075

Annual Design Value (μg/m

3 ) 24-hour Design Value (μg/m

3 )

 Annual DVs

 24-hour DVsStars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs024681012141618202224262830323436AtlantaBaltimoreBirminghamDallasDetroitFresnoHoustonLosAngelesNewYorkPhiladelphiaPhoenixPittsburghPitt-LibertySaltLakeSt.LouisTacoma051015202530354045505560657075

Annual Design Value (μg/m

3 ) 24-hour Design Value (μg/m

3 )

 Annual DVs

 24-hour DVsStars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs12/25 standard level line024681012141618202224262830323436AtlantaBaltimoreBirminghamDallasDetroitFresnoHoustonLosAngelesNewYorkPhiladelphiaPhoenixPittsburghPitt-LibertySaltLakeSt.LouisTacoma051015202530354045505560657075

Annual Design Value (μg/m

3 ) 24-hour Design Value (μg/m

3 )

 Annual DVs

 24-hour DVsStars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs024681012141618202224262830323436AtlantaBaltimoreBirminghamDallasDetroitFresnoHoustonLosAngelesNewYorkPhiladelphiaPhoenixPittsburghPitt-LibertySaltLakeSt.LouisTacoma051015202530354045505560657075

Annual Design Value (μg/m

3 ) 24-hour Design Value (μg/m

3 )

 Annual DVs

 24-hour DVsStars () = Area DV site Dots () = Other valid site DVs Triangles () = Invalid site DVs12/25 standard level line3

# February 2010 Draft – Do Not Quote or Cite 4-75 The sensitivity analysis examining uncer tainty related to conducting rollback 1

demonstrated that for some of the study areas (e .g., Los Angeles and Salt Lake City) use of the 2

peak shaving rollback method reflecting applica tion of more localized controls resulted in 3

composite monitor values that differed notably from values generated when the proportional 4

rollback approach was used. 61 

# In contrast, many of the other urban study areas displayed little 5

difference in composite monitor values based on application of proportio nal or peak shaving 6

rollback methods. 7

Design value information provided in Figures 4-20 and 4-21 provide explanations for 8

these sensitivity analysis results. For Los A ngeles (which had composite monitor values 40% 9

higher when using the peak shaving rollback me thod compared with the proportional approach – 10

see Section 4.3.1.1), the 24-hour standard is co ntrolling. This can be seen by noting that the 11

maximum 24-hour design value is significantly further away from the 15/35 line in Figure 4-20 12

compared with the maximum annual design valu e. In addition, these two maximum design 13

values do not occur at the same monitor. 62 

# This means that when the proportional rollback 14

method is used, a relatively large fractional re duction is uniformly applied to all monitors, 15

resulting in a new (adjusted) composite monitor va lue that has been reduced to a relatively large 16

extent. However, if peak shaving rollback is us ed, then only those monitors with 24-hour design 17

values exceeding the current 24- hour standard level are adju sted and only by the fraction 18

required to get each 24-hour design value down to the current 24-hour standard level. 63 

# This 19

means that in an overall sense, there is less adjustment to PM 2.5 

# levels, such that with peak 20

shaving we will see higher composite monitor a nnual averages than with proportional rollback. 21

In the case of Salt Lake City (which also has significantly higher composite monitor 22

annual averages with peak shaving than with proportional rollback), while the highest 24-hour 23

and annual design values occur at the same monito r, which means that even with peak shaving, 24

the monitor with the highest annual averages will be adjusted downward substantially, because 25

the annual design values for monitors are closer to each other, the impact of peak shaving on the 26

composite annual average is smaller. Specifica lly, while some of the monitors with 24-hour 27

design values above the current 24- hour standard level will have their annual averages adjusted 28

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 PM 2.5 

## 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 down, there is a fraction of the monitors (with 24-hour design va lues below the current standard) 1

that will not be adjusted under peak shaving. 2

These two examples illustrate different c onditions under which the type of rollback 3

applied can have a significant impact on the degree of public h ealth protection assessed for a 4

particular standard level. By c ontrast, conditions at some of th e other urban study areas result in 5

little difference in risk from application of diffe rent rollback methods (i.e., simulation of more 6

regional versus local control strategies). Specifi cally, if an urban location has 24-hour and annual 7

design values at each monitor that display little variation, we expect to see less impact on risk 8

from varying the type of rollback method used. Ex amples that fall into this latter category 9

include Atlanta, Dallas, and St. Louis (see Figure 4-20). 10

# February 2010 Draft – Do Not Quote or Cite 5-1 

# 5 NATIONAL-SCALE ASSESSMENT OF LONG-TER M MORTALITY 1



# RELATED TO PM 2.5 

# EXPOSURE 2

5.1 OVERVIEW 

# 3

In this section we present the estimated nationwide premature mortality resulting from 4

recent exposures to ambient PM 2.5. 

# The goal of this assessment is twofold: (1) estimate the 5

incidence of premature mortality within the U.S. related to long-term PM 2.5 

# exposure; and (2) 6

identify where the subset of counties assessed in the urban case study areas analysis fall along 7

the distribution of national county-level risk. 64 

# To perform this assessment we use 2005 PM 2.5 

# 8

fused air quality estimates fr om the Community Model for Air Quality (CMAQ) (Byun and 9

Schere, 2006) in conjunction with the envir onmental Benefits Mapping and Analysis Program 10

(BenMAP, Abt Associates Inc, 2008) to estimate long-term PM 2.5 

# -related premature mortality 11

nationwide. 12

To address the first goal of the assessment, we estimate excess PM 2.5 

# -related long-term 13

mortality by applying two estimates of all-cause mortal ity risk found in the Krewski et al. (2009) 14

PM 2.5 

# mortality extended analysis of the American Cancer Society (ACS) cohort, and an estimate 15

of all-cause mortality risk f ound in the Laden et al. (2006) PM 2.5 

# mortality extended analysis of 16

the Six-Cities cohort. We estimate that total PM 2.5 

# -related premature mortality ranges from 17

63,000 (39,000—87,000) (95 th 

# percentile confidence inte rval) and 88,000 (49,000—130,000), 18

respectively; in each case we estimated deaths per year down to the lowest measured levels 19

(LMLs) in each epidemiological study. 20

In addressing the second goal of this asse ssment, we observe that the subset of 31 21

counties for the 15 urban study areas considered in the urban case study fall toward the upper 22

end of the national distribution. Specifically, a ll of the 31 counties were above the median of the 23

national risk distribution and 23 of the 31 fell within the upper 5 th 

# percentile of the national 24

distribution. Therefore, according to this analysis, we appear to be capturing high-end 25

percentiles of the national risk distribution w ith the set of urban case study areas we are 26

evaluating in the PM 2.5 

# NAAQS risk assessment. 27

We had considered expanding th e national-scale mortality to include additional health 28

endpoints (related to short-term PM 2.5 

# exposure) or additional air qua lity scenarios that simulate 29

just meeting the current and alternative suites of standards. However, as noted in section 2.3, we 30

64 

## We do not directly compare th e estimated county-level risks ge nerated in the urban case study

assessment and the county-level risks generated in the na tional-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 PM 2.5 

## mortality risks in the 31counties modeled in the urban case study areas repr esented more typical or

higher-end risk relative to the national risk distribution.

# February 2010 Draft – Do Not Quote or Cite 5-2 continue to conclude that any expansion of this assessment, is beyond the scope of what is 1

needed or can reasonably be done within the time and resources available for this review. Here 2

we provide additional discussion of the rationale for our decision not to expand the scope of the 3

national-scale analysis. 4

The goal of the national-scale analysis is two-fold: to provide perspective on the 5

magnitude of PM 2.5 

# health impacts on a national-scale and to help to place the risk estimates 6

generated for the urban study areas in a national c ontext. The analysis as currently implemented 7

achieves the first goal by providi ng estimates of long-term exposur e-related all-cause mortality 8

under recent conditions. While simula tion of risk for the current a nd alternative standard levels 9

would provide additional perspective on the magn itude of national-scale risk, that assessment 10

would be resource-intensive and subject to considerably uncerta inty if it were conducted using 11

air quality simulation methods si milar to those used in the urban study area analysis (i.e., 12

application of a combination of rollback methods th at reflects both local a nd regional patterns in 13

ambient PM 2.5 

# reductions implemented at the monitor-level). A particular area of uncertainty 14

(and technical complexity) related to air quality simulation would be a ddressing the interplay 15

between regional-scale reductions in ambient PM 2.5 

# in adjacent urbanized areas. In the urban 16

study area analysis, each location is treated inde pendently with regard to simulating ambient 17

PM 2.5 

# under alternative suites of standards. However, if we were to expand the national analysis 18

to include alternative standards, then simulation of rollbacks in ambient PM 2.5 

# levels would 19

necessarily have to address this contiguity issue between adjacen t urban areas and even between 20

suburban areas and adjacent urbanized areas in th e context of simulating monitor rollback. 21

In addition, because long-term expos ure-related mortality dominates PM 2.5 

# in terms of 22

total incidence, providing coverage for this e ndpoint category ensures th at the majority of PM 2.5 

# - 23

related mortality incidence is reflected in the analysis, without includ ing short-term exposure24

related mortality. 25

The national-scale mortality analysis, as curr ently implemented, also achieves its second 26

goal: to help place risk estimates for the urban study areas in a national context. Because the 27

national-scale analysis focuses on the long-term e xposure-related mortality, which is the primary 28

driver for PM 2.5 

# -related health impacts, the analysis allows us to assess how the urban study 29

areas "fall" across a national di stribution of risk for this ke y health endpoint category (see 30

discussion below). This then allows us to char acterize the degree to whic h the set of urban study 31

areas provides coverage for areas of the country likely to experience relatively elevated levels of 32

PM 2.5 

# -related health impacts. 33

# February 2010 Draft – Do Not Quote or Cite 5-3 5.2 METHODS 

# 1

This assessment combines information regarding estimated PM 2.5 

# air quality levels, 2

population projections, baseline mort ality rates, and mortality risk coefficients to estimate PM 2.5 

# - 3

related premature mortality. Figure 5-1 below pr ovides a conceptual diagram, detailing each of 4

the key steps involved in performing this BenM AP-based health impact assessment. Appendix 5

G contains additional information regarding the data inputs to this analysis. 6

7

Figure 5-1 Conceptual diagram of data i nputs and outputs for national long-term 

# 8

mortality risk assessment 

# 9

11

13

5.2.1 Population Estimates 

# 14

The starting point for estimating the size a nd demographics of the potentially exposed 15

population is the 2000 census-block level population, which BenMAP aggregates up to the same 16

grid resolution as the air quali ty model. Using county-level growth factors based on economic 17

projections (Woods and Poole Inc ., 2001), BenMAP projects this 2000 population to the analysis 18

year of 2005; we selected this population year because it matches both the year in which the 19

emissions inventory was developed for the air quality modeling and the year to which the 20

baseline mortality rates were projected (see below). 21

5.2.2 Population Exposure 

# 22

Having first estimated the size and geographi c distribution of th e potentially exposed 23

population, BenMAP then matches these population projections with estimates of the ambient 24

levels of PM 2.5 

# . In contrast to the urban study areas analysis, the national-scale analysis 25

# February 2010 Draft – Do Not Quote or Cite 5-4 employed a data fusion approach, which joined 2005 monitored PM 2.5 

# concentrations with 2005 1

CMAQ-modeled air quality levels using the Voronoi Neighbor Averag ing (VNA) technique 2

(Abt, 2003). CMAQ was run at a ho rizontal grid resolution of 12k m for the east and 36km in the 3

west using 2005 estimated emission levels and meteorology. More information on this model 4

run can be found in Appendix G of this document. Figure 5-2 shows the geographic distribution 5

of baseline annual mean PM 2.5 

# concentrations across the continental U.S. The maximum 6

predicted value within the U.S. is 31 μg/m 3 

# , the mean PM 2.5 

# value is 8.7 μg/m 3



# , median is 8.8 7

μg/m 3 

# and the 95 th 

# percentile valu e is about 14 μg/m 3



# . 8

9

Figure 5-2 2005 fused surface baseline PM 2.5 

# concentrations 

# 11

12

This assessment applies PM 2.5 

# mortality risk coefficients drawn from long-term cohort 13

studies which estimate changes in risk based on annual mean changes in PM 2.5 

# concentration. 14

For this reason, EPA used the CMAQ model to estimate annual mean concentrations at each grid 15

cell. These grid-level annual average con centrations were then input to BenMAP. 16

# February 2010 Draft – Do Not Quote or Cite 5-5 5.2.3 Premature Mortality Estimates 

# 1

In this assessment of PM 2.5 

# -related premature mortality we considered risk estimates 2

drawn from studies based on two prospective coho rts. The first study is the recently published 3

Krewski et al. (2009) extended reanalysis of the ACS cohort. To remain consistent with the 4

urban study areas analysis, we a pplied the two log-linear all-cause mortality risk coefficients 5

based on the 1979-1983 and the 1999-2000 time periods that control for 44 individual and 7 6

ecologic covariates. We also applied a log-linear all-cause mortality risk coefficient drawn from 7

the extended analysis of the Six Cities cohor t as reported by Laden et al. (2006). When 8

estimating premature mortality using these functions we considered air quality levels down to the 9

lowest measured levels (LML) in each study; for the Krewski et al. (2009) study this is 5.8 μg/m 3 

# 10

and for the Laden et al. ( 2006) study this is 10 μg/m 3 

# . In general, we place a higher degree of 11

confidence in health impacts estimated at air quality levels at or a bove the LML because the 12

portion of the concentration-res ponse curve below this point is extrapolated beyond the observed 13

data. We also estimated health impacts dow n to Policy Relevant Background (PRB) levels 14

(EPA, 2008). The final ISA presents estimates of annual mean PRB for each of 7 Health Effects 15

Institute PM regions; this va lue ranges from 0.62 μg/m 3 

# in the southwest to 1.72 μg/m 3 

# in the 16

southeast. 17

BenMAP contains baseline age-, cause- and county-specific mortal ity rates drawn from 18

the CDC-WONDER. Current baseline mortality es timates are an average of a three year period 19

from 1996-1998. EPA is in the process of updating these rates with 2006-2008 data; a sensitivity 20

analysis suggests that the results reported here are largely insensitive to the use of more current 21

mortality rates. 22

5.3 RESULTS 

# 23

Table 5-1 and figures 5-3 through 5-4 below su mmarize the results of the national-scale 24

analysis. Table 5-1 summarizes the total PM 2.5 

# -related premature mortality associated with 25

modeled 2005 PM 2.5 

# levels. 26

Estimated PM 2.5 

# -Related Premature Mortality Associ ated with Incremental Air Quality 27

Differences Between 2005 Ambient Mean PM 2.5 

# Levels and LML from the Epidemiology 28

Studies or PRB (90th percen tile confidence interval) 29

30

31

# February 2010 Draft – Do Not Quote or Cite 5-6 Table 5-1 Estimated PM 2.5 

# -related premature mortalit y associated with 

# 1

incremental air quality differen ces between 2005 ambient mean 

# 2

PM 2.5 

# levels and lowest measured leve l from the epidemiology studies 

# 3

or policy relevant background (90 th 

# percentile confidence interval) 

# 4

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/m 3 

## (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/m 3 

## (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

In this table, the bold figures indicate the estimate that corre sponds with the LML 6

identified in the epidemiologica l study. The bold estimates in the column Krewski et al. (2009) 7

were calculated using the same risk coefficien ts as the urban case study analysis. We place a 8

greater emphasis on those results calculated using the LML reported in the epidemiological 9

studies. 65 

# Figure 3 illustrates the percentage of baseline mortality attributable to PM 2.5 

# exposure 10

in each of the grid cells according to the 2005 PM 2.5 

# air quality levels, using the Krewski et al. 11

(2009) estimate based on 1999- 2000 air quality levels. 12

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 (w hich 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 Figure 5-3 Percentage of prematu re mortality attributable to PM 2.5 

# exposure at various 

# 1

2005 annual average PM 2.5 

# levels* 

# 2

3

This figure illustrates the number of deaths attributable to PM 2.5 

# according to the baseline 4

level of ambient average PM 2.5 

# levels down to 5.8 μg/m 3 

# (the LML for the Krewski et al. (2009) 5

analysis). Each of four box plots characterizes the range of premature mo rtality attributable to 6

PM 2.5 

# according to the baseline level of annual mean PM 2.5 

# levels in that model grid cell. Note 7

that while the lower whisker of the box plots fo r the baseline air quality values of 5.8 μg/m 3 

# to 10 8

μg/m 3 

# appear to extend to zero, the minimum value is greater than zero. The number above each 9

box plot indicates the number of gr id cells summarized by that plot. 10

Figure 5-4 displays the cumulative distribution of total mortality attributable to PM 2.5 

# 11

exposure at the county level developed as part of the national-scale analys is. The location of the 12

31 counties included in the urban case study anal ysis is then superimposed on top of the 13

cumulative distribution. 14

15

16

17

18

19

# February 2010 Draft – Do Not Quote or Cite 5-8 Figure 5-4 Cumulative distribution of county-level percentage of total 

# 1

mortality attributable to PM 2.5 

# for the U.S. with markers 

# 2

identifying where along that distribut ion the urban case study area 

# 3

analysis fall* 

# 5

6

Counties considered in the urban scale analysis that are located towa rd the lower end of 7

the distribution of all counties nationwide include Maricopa County, Arizona and Salt Lake City, 8

Utah. Counties assessed in the urban scale analysis that are lo cated toward the upper end of the 9

distribution of all counties include Jeffers on County, Alabama and Los Angeles County, 10

California. The results of this analysis indicate that most of the 31 counties included in the urban 11

case study counties fall toward the upper end of th e national risk distribution and that 23 of these 12

counties fall within the upper 5 th 

# percentile of the risk distribution—s uggesting that the PM 2.5 

# 13

mortality risk estimates included in the urban case study analysis generally represent the upper 14

end of urban area mortalit y risks within the nation. 15

# February 2010 Draft – Do Not Quote or Cite 6-1 

# 6 INTEGRATIVE DISCUSSION OF URBAN CASE STUDY ANALYSIS 1



# OF PM 2.5 

# -RELATED RISKS 2

This chapter provides an integrative discussi on of the risk-related analyses presented 3

throughout this second draft RA, including the PM 2.5 

# -related risk estimates generated for the set 4

of urban study areas and the rela ted uncertainty and sensitivity analyses, the representativeness 5

analyses, and the national-s cale long-term exposure PM 2.5 

# mortality assessment. The goal of this 6

integrative discussion is to inform our understa nding of important polic y-relevant risk-based 7

questions, including: (a) wh at is the magnitude of risk likely to remain if the urban study areas 8

were just meeting the current suite of PM 2.5 

# standards, and what level of confidence do we have 9

in those estimates?; (b) what is the degree and na ture of risk reduction lik ely to be associated 10

with just meeting the alternativ e suites of annual and 24-hour PM 2.5 

# standards considered in this 11

risk assessment, and what roles do the annual an d 24-hour standards play in bringing about such 12

reductions?; and c) what is the distribu tion of risks associated with recent PM 2.5 

# air quality in 13

areas across the U.S., and how representative are th e risk results for the urban study areas from a 14

national perspective? 15

In addressing the risk-based questions listed above, we have placed primary focus on risk 16

associated with long-term exposure to PM 2.5 

# . This choice reflects th e fact that long-term 17

exposure to PM 2.5 

# has been shown in this and previous quantitative risk assessments to produce 18

substantially larger mortality risk (in terms of overall incidence and percent of total mortality) 19

compared with short-term PM 2.5 

# exposure. Because of the emphasis placed on long-term PM 2.5 

# 20

exposure-related mortality risk, the risk asse ssment has been designed to generate robust 21

estimates for this risk category, including co mprehensive analysis of uncertainty. For the 22

assessment of mortality and morbidity risks related to short-term PM 2.5 

# exposure, the assessment 23

of uncertainty and its impact on risk estimates has been more limited. 24

In characterizing risks associated with both long- and short-term exposure to PM 2.5 

# 25

throughout this document, we have included th ose health endpoints for which sufficient 26

information was available to generate quantitat ive risk estimates with a reasonable degree of 27

confidence. It is important to emphasize that beyond the health endpoints evaluated 28

quantitatively in this risk assessm ent, there is an array of add itional health endpoints potentially 29

associated with PM 2.5 

# that will be discussed as part of the evidence-based considerations 30

presented in the policy assessment now being prepared.. 31

The following discussion begins with a summar y of analytical approaches used in this 32

quantitative risk assessment, emphasizing the degr ee of confidence we have in the data, models, 33

and assumptions we have used in developing our core risk estimates and in the results of our 34

sensitivity analyses (section 6.1). We then su mmarize our core risk re sults for the urban study 35

# February 2010 Draft – Do Not Quote or Cite 6-2 areas, and the confidence we have in those resu lts in light of our uncertainty and variability 1

analyses, and provide insights into how those results inform the policy-relevant considerations 2

described above (section 6.2). Next we place th ese results into a national perspective (section 3

6.3). In so doing, we provide insights into how well the set of urban st udy areas represent the 4

broader set of urban areas in the U.S. lik ely to experience increased risk from PM 2.5 

# . We also 5

integrate the results from the urban study areas wi th the national-scale mo rtality assessment to 6

provide insights into the degree to which the PM 2.5 

# -related risks estimated in the urban study 7

areas are likely to be characteris tic of risks in the broader U.S. population. Finally, in section 8

6.4, we highlight key points that address the polic y-relevant questions that began this chapter. 9

6.1 KEY ANALYTICAL ELEMENTS IN THIS RISK ASSESSMENT 

# 10

This quantitative risk assessment has been designed to generate estimates of risk for a set 11

of urban study areas likely to represent those urban areas in the U.S. experiencing higher PM 2.5 

# - 12

related risk due to elevated PM 2.5 

# levels and/or other attributes related to PM 2.5 

# risk (e.g., 13

meteorology, baseline health effects incidence rates, differences in PM 2.5 

# emissions sources and 14

composition). 66 

# In addition, the risk assessment is desi gned to produce robust risk estimates that 15

reflect consideration of th e latest research into PM 2.5 

# -related exposure and ri sk. To achieve these 16

goals, a deliberative process has been used in specifying each of the analytical elements 17

comprising the risk model, including selection of urban study areas as well as specification of 18

other inputs such as C-R functi ons. This deliberative proce ss involved rigorous review of 19

available literature addressing both PM 2.5 

# exposure and risk combined with the application of a 20

formal set of criteria to guide development of each of the key analytical elements in the risk 21

assessment. In addition, the risk assessment design reflects consideration of CASAC and public 22

comments on the initial risk assessment plan, as well as the first draft risk assessment. The 23

application of this deliberative process increases overall confidence in the risk estimates by 24

insuring that the estimates are based on the best available science and data characterizing PM 2.5 

# 25

exposure and risk, and that they reflect consideration of input from experts on PM exposure and 26

risk through CASAC and public reviews. 27

The approach used in specifying several of the key analytical elements used in the risk 28

assessment is highlighted below for purposes of illustrating the systematic approach used in 29

developing the model. 30

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 th ese 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 ri sk-related attributes acros s urban areas in the U.S..

# February 2010 Draft – Do Not Quote or Cite 6-3  Selection of the 15 urban study areas included consideration of (a ) whether a city of county 1

had been included in multi-city epidemiology st udies used in specifying C-R functions used 2

in the core risk estimates, (b) providing coverage for urban ar eas with relatively high annual 3

and 24-hour design values, and (c) providing co verage for the seven PM regions which 4

reflect differences in key PM risk-related attributes (e.g., meteorology, demographic 5

attributes, PM sources and composition). See se ction 3.3.2 for additional detail on selection 6

of study areas. 7

 Simulation of ambient PM 2.5 

# levels under current and alternative standard levels included 8

application of the proportional ro llback approach used in previous risk assessments, which 9

generally represents regional patt erns of reductions in ambient PM 2.5 

# concentrations. 10

Recognizing that simulating re gional patterns in ambient PM 2.5 

# reductions alone does not 11

capture the potential variability in future pa tterns of reductions that may occur, we also 12

considered alternative rollback approaches, including hybrid and peak shaving approaches. 13

Both of these approaches simulate more localized patterns of ambient PM 2.5 

# reductions 14

combined with additional regional patterns of reductions in ambient PM 2.5. 

# Including these 15

three rollback approaches allowed us to assess the degree to which differences in the spatial 16

pattern of ambient PM 2.5 

# reductions resulting from simulatio ns of just meeting current and 17

alternative suites of PM 2.5 

# standards can impact risk profiles. 18

 Selection of health endpoints reflected consideration of the de gree of support in the literature 19

for a causal relationship between PM 2.5 

# exposure and the health eff ect of interest as assessed 20

in the ISA, together with consideration of the health significance of the endpoint. In addition, 21

we considered whether sufficient informati on existed in the liter ature to develop C-R 22

functions and whether we could obtain the baselin e incidence data necessary to generate risk 23

estimates with a reasonable degree of confidence (see section 3.3.1). 24

 The selection of epidemiological stud ies and specification of C-R functions for use in 25

modeling risk for these endpoints involved a ri gorous review of existing literature based on 26

application of criteria we identified for sp ecifying robust C-R functions. These criteria took 27

into account both study design as well as the pot ential scope of the C-R functions that could 28

be drawn from the studies (e.g., geographi c coverage, demographic groups covered and 29

health endpoints involved). We outlined our ra tionale for the set of epidemiology studies we 30

selected and the choices made in specifying C-R functions, and we discussed our rationale 31

for not including other potential studies and/or fo rms of C-R functions in the risk assessment. 32

The systematic approach described above resu lted in a core risk model which included 33

those model inputs that in our judgment have the greatest degree of support in the literature. 34

These core risk estimates are emphasized in addressing the policy-related questions outlined 35

above. To provide a more comprehensive assessme nt of risk for the urban study areas, we have 36

included an assessment of uncertain ty and variability and their imp act on the core risk estimates 37

as part of this analysis. This assessment of unc ertainty and variability includes both qualitative 38

and quantitative elements, the latter taking the form of single- and multi-factor sensitivity 39

# February 2010 Draft – Do Not Quote or Cite 6-4 analysis. 67 

# The goal of these assessments was to evaluate the robustness of the core risk 1

estimates given identified sources of uncertainty and variability. Incl usion of the qualitative 2

analysis of uncertainty, in additional to the se nsitivity analyses, helped insure that a more 3

complete list of potentially important sources of uncertainty was considered in the risk 4

assessment and not only those sources for which it is possible to conduct a sensitivity analysis. 5

The assessment of uncertainty and variability completed for this analysis is more 6

comprehensive than had been done for previous ri sk assessments. This reflects, in part, the 7

development of methods by EPA staff to address pot entially important sources of variability and 8

uncertainty. For example, to more fully explore potential variability in the patterns of reductions 9

in ambient PM 2.5 

# that may occur upon just meeting the curr ent and alternative suites of standards, 10

we incorporated as part of the sensitivity analys is two additional rollback approaches (hybrid and 11

peak shaving) in addition to the proportional rollb ack used in the core analyses. In addition, 12

recently published literature has allowed us to more rigorously examine the impact of uncertainty 13

related to specifying C-R functi ons for long-term exposure-related mortality (i.e., the Krewski et 14

al., 2009 study which provided extensive analysis of alternative mode l specifications for 15

mortality which could be readily incor porated into our sensitivity analysis). 68 

# 16

In addition to enhanced sensitivity analyses , we also included a num ber of national-scale 17

assessments that had not been done in past risk assessments (i.e., the representativeness analysis 18

and national-scale assessment of long-term mortality). These na tional-scale assessments allowed 19

us to more fully consider the degree to which th e selected urban study area s are representative of 20

the broader set of urban areas within the U.S., ther eby allowing us to place risk estimates for the 21

urban study areas in the br oader national context. 22

6.2 INTERPREATION OF URBAN STUDY AREA RESULTS 

# 23

This section describes the core risk estim ates generated for the 15 urban study areas, 24

focusing on the policy-relevant questions outlined above. An important factor to consider in 25

interpreting these results is that the magnitude of both long- and short-te rm exposure-related risk 26

depends primarily on annual-average PM 2.5 

# concentrations. Furthe rmore, reductions in both 27

categories of risk, as we consider simulating just meeting alternative suites of standards, also 28

depend on changes in annual-average PM 2.5 

# concentrations. 29

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-te rm 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 The role of annual-average ambient PM 2.5 

# concentrations in dr iving long-term exposure1

related risk is intuitive given that this risk category is modeled using the annual-average air 2

quality metric. 69 

# The fact that short-term exposure-relate d risk is also driven by changes in long3

term average PM 2.5 

# concentrations is less intuitive, since changes in average daily PM 2.5 

# 4

concentrations are used to estimate changes in risk for this category. 70 

# Analyses in previous PM 5

NAAQS risk assessments have shown that short- term exposure-related risks are not primarily 6

driven by the small number of days with PM 2.5 

# concentrations in the upper tail of the air quality 7

distribution, but rather by the large number of days with PM 2.5 

# concentrations at and around the 8

mean of the distribution. Consequently, c onsideration of changes in annual-average PM 2.5 

# 9

concentrations will explain to a large extent changes in short-term exposure-related risk. 10

Therefore, in interpretin g patterns of long-term exposure-relate d risk, and the similar patterns we 11

observe in short-term exposure-related risk, we focus primarily on how simulating just meeting 12

specific suites of PM 2.5 

# standards impacts the annual-average PM 2.5 

# concentration for the study 13

areas. 14

In the case of simulating just meeting the curr ent and alternative annual standards, this is 15

straight forward, since the simulation produces a direct change in the annual-average PM 2.5 

# 16

concentration. However, simulating just mee ting the current and alternative 24-hour standards 17

has a less direct effect on annual average PM 2.5 

# concentrations across study areas, which depends 18

on a number of factors, including: (a) the type of rollback used to simulate just meeting the 19

current or alternative standards, (b) the combination of 24-hour and annual design values in each 20

study area (Table 4-8), and (c) the pattern of PM 2.5 

# monitoring data across each study area. If 21

proportional rollback is us ed, the annual-average PM 2.5 

# concentrations will be reduced by the 22

same percentage as was needed to lower the 24-h our design value to the level of the controlling 23

24-hour standard. However, our sensitivity analysis examining alternative rollback methods 24

showed that application of a p eak shaving rollback approach (refl ecting more localized patterns 25

of PM 2.5 

# reductions) can, under certain circumstances, produce notably smalle r changes to annual 26

average concentrations, which in turn, translate into smaller changes in both long-term and short27

term exposure-related risks. Specifically, for those urban study areas where a peak shaving 28

69 

## As noted in section 3.2.1, estimates of long-term exposure-related mortality are actually based on an average

annual PM 2.5 

## 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 PM 2.5 

## 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 PM 2.5



## . concentrations. However, similar to the case with long-ter m exposure-related mortality, it is the maximum monitor

98 th 

## percentile 24-hour concentration (the 24-hour design va lue) 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 rollback approach was applied to a PM 2.5 

# distribution that was more “peaky” in nature (i.e., 1

relatively high 24-hour design values and lower annual average design values), the resulting 2

change in annual-average PM 2.5 

# concentrations was notably smaller than when proportional 3

rollback was used. 71 

# We note also that an additional fact or introducing varia tion in risk across 4

urban study areas is the relations hip between the annual-average PM 2.5 

# concentrations at the 5

maximum monitor and the composite monitor, whic h varies across study areas. For this reason, 6

two study areas that are simulated to just meet the same annual standard (and consequently will 7

have the same adjusted maximum monitor annual-average PM 2.5 

# concentration) can have notably 8

different composite monitor values. 9

In discussing the core risk estimates below, we focus on cardiovascular-related endpoints 10

given the greater overall degree of confidence assigned to this category in the ISA relative to 11

other health effect categories (e.g., respiratory -related effects). This means that for long-term 12

exposure-related risk, we focus our discussion on IHD-related mortality; the related categories 13

for short-term exposure-related risk include CV-rel ated mortality and morbid ity (the latter in the 14

form of HA related to CV symptoms). 15

Finally, we note that the set of urban study ar eas selected for this assessment reflect the 16

profile of urban areas in the U.S. with regard to the mix of an nual and 24-hour design values. As 17

illustrated in Figure 4-18, only a few urban areas have controlling a nnual standard levels 18

exceeding the current standard leve l (i.e., fall into zones C or D in Figure 4-18). Therefore, there 19

are not a large number of areas that will experi ence risk reductions due to simulation of the 20

current annual standard alone. By contrast, there are a lot more urban areas in the U.S. in which 21

the 24-hour standard is controlli ng and the 24-hour design value ex ceeds the level of the current 22

standard (i.e., fall into zones A and B in Figure 4-18). Therefore, more of the urban study areas 23

available for analysis are likely to see risk reduc tions under the current suit e of standards driven 24

by simulation of the 24-hour standard. Recognition of the profile of urban ar eas in the U.S. with 25

regard to the interplay between the 24-hour and annual design va lues is important in fully 26

understanding the core risk estimates summarize d below and how those risk estimates can be 27

interpreted in the national context. 28

The discussion below is organized as follows. First, we present observations regarding 29

core risk estimates generated for the current suit e of standards. We then present observations 30

71 

## The results of the sensitivity analysis examining the hybr id rollback approach, which represents a combination of

an initial localized pattern of ambient PM 2.5 

## 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 PM 2.5 

## concentrations and consequently risk (i.e., the peak shaving rollback method was found to result in more substantial

differences in annual-average PM 2.5 

## concentrations and consequently risk, relative to the proportional) (see section

4.3.1.1). Therefore, in discussing the results of the se nsitivity analysis examining rollback, we focus here on

contrasting results for the proportional appr oach with those for peak shaving.

# February 2010 Draft – Do Not Quote or Cite 6-7 related to simulation of alte rnative annual standards at le vels of 14, 13, and 12 μg/m 3 

# in 1

conjunction with the curren t 24-hour standard (35 μg/m 3 

# ). Finally, we discuss simulation of 2

alternative suites of standards involving combinations of alte rnative annual and 24-hour levels 3

(i.e., an annual standard of 13 μg/m 3 

# paired with a 24-hour standard of 30 μg/m 3 

# (denoted as the 4

13/30 suite of standards); an annual standard of 12 μg/m 3 

# paired with a 24-hour standard of 25 5

μg/m 3 

# (denoted as the 12/25 suite of standards). 6

6.2.1 Simulation of Just Meeting the Current Suite of PM 2.5 

# Standards 

# 7

In characterizing PM 2.5 

# -related risks likely to remain upon just meeting the current PM 2.5 

# 8

annual and 24-hour standards in the 15 areas in cluded in this assessment, we focus on the 13 9

areas that would not meet the current standards based on recent (2005-2007) air quality. These 10

13 areas have annual and/or 24-hou r design values that are above the levels of the current 11

standards (Table 4-8). 72 

# Based on the core risk estimates for these areas presented above in 12

section 4.2.1, we make the following general observation regarding the magnitude of risk 13

remaining upon simulation (using proportional rollback ) of just meeting the current suite of 14

standards: 15

 Long-term exposure-related mortality : Total incidence of long-term exposure-related IHD 16

mortality attributable to PM 2.5 

# ranges from 15-20 deaths per y ear (Salt Lake City) to 1,760- 17

2,220 deaths per year (New York) (Table 4-1). This translates into a percent of total 18

mortality incidence attributable to PM 2.5 

# ranging from 3.7-4.7% (Tacoma) to 13.2-16.7% 19

(Atlanta) (Table 4-2). 20

Variability in incidence estimates is obviously driven in large part by differences in the 21

population in each study area, as well as by ot her factors such as differences in baseline 22

incidence rates and in exposure pa tterns. Substantially less vari ability would be expected in 23

estimates of the percent of total mortality attributable to PM 2.5 

# when each area is simulated to 24

just meet the current suite if standards, since this risk metric should normalize for population and 25

baseline incidence rates. Nonetheless, we see appreciable variab ility across study areas for this 26

risk metric as well. 27

In considering the source of this variab ility, we recognize that, as noted above, the 28

magnitude of long-term PM 2.5 

# exposure-related mortality estima ted to remain upon just meeting 29

the current suite of standards depend s directly on the annual-average PM 2.5 

# concentrations that 30

result from the simulated changes in air quality patterns. In the case of the three urban study 31

areas out of the 13 experiencing risk reductions in which the annual standard is controlling 32

(Atlanta, Birmingham, and Houston), simulation of the current suite of standards results in 33

virtually the same annual-average PM 2.5 

# concentration (~15 μg/m 3 

# ) and, consequently, estimates 34

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 of the percent of IHD-related mortality attributable to PM 2.5 

# for these study areas is similar 1

(Table 4-2). 73 

# 2

However, the remaining 10 study areas in wh ich the 24-hour standard is controlling 3

display substantially greater variability in this risk metric when the proportional rollback 4

approach is applied for the core analysis. This results because the simulation of just meeting the 5

current 24-hour standard produces varying impacts on annual-average PM 2.5 

# concentrations. For 6

example, the urban study area with the highest estimated risk remaining upon just meeting the 7

current suite of standards (Baltimore, with 11.7 to 14.7% of total mortality incidence attributable 8

to PM 2.5 

# - Table 4-2) has annual and 24-hour design va lues very close to the current suite of 9

standard levels (Table 4-8). Th erefore, simulating just meeting the current 24-hour standard does 10

not much change the annual-average PM 2.5 

# concentration, which is fairly close to 15 μg/m 3 

# , and 11

therefore, long-term exposure-related IHD mortality (as a percent of total incidence) is reduced 12

only by a very small amount below that estimated for recent air quality. In contrast, Salt Lake 13

City, which has one of the lowest estimates of the percent of total mortality incidence attributable 14

to PM 2.5 

# upon just meeting the current suite of standa rds (2.9 to 3.7% of total incidence – Table 15

4-2), has a relatively low annual design value (11.6 μg/m 3 

# ) and a relatively high 24-hour design 16

value (55 μg/m 3 

# ) (Table 4-8). Therefore, simulating ju st meeting the current 24-hour standard 17

results in a substantial change in the annual average, using pr oportional rollback, since the same 18

fractional reduction required to ge t the 24-our design value to meet the current standard (i.e., a 19

35% reduction) is applied to the annual design value of 11.6 μg/m 3 

# , resulting in an annual 20

average of 7.7 μg/m 3 

# . These two examples illustrate th e varying impact that the 24-hour 21

standard, if controlling, can have on annual-average PM 2.5 

# concentrations and consequently on 22

the magnitude of long-term (and short-term) PM 2.5 

# exposure-related mortality associated with 23

just meeting the current suite of standards. 74 

# 24

As discussed above, the sensitivity analysis examining alternative rollback approaches 25

showed that in instances where PM 2.5 

# distributions are relatively peaky, application of peak 26

shaving (reflecting more localized patterns of ambient PM 2.5 

# reductions) can result in a 27

controlling 24-hour standard ha ving a substantially smaller impact on annual-average PM 2.5 

# 28

concentrations. Sensitivity analysis results for the examples referenced above (Baltimore and 29

Salt Lake City) illustrate this i ssue related to application of alte rnative rollback methods. In the 30

case of Baltimore, which has a less peaky PM 2.5 

# distribution (in that its 24-hour and annual 31

73 

## Although, as noted earlier, composite monitor annual-a verages 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 add itional 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 design values are both fairly close to the current suite of standard levels), application of peak 1

shaving in simulating just meeting the current su ite of standards resulted in an annual average 2

only slightly higher than that simulated us ing proportional rollback (i.e., 15.2 μg/m 3 

# compared 3

with 14.8 μg/m 3 

# – Table F-49). This means that long- term exposure-related IHD mortality for 4

Baltimore would be relatively similar if either proportional or peak shaving rollback approaches 5

were applied. In contrast, appl ication of peak shaving in Salt Lake City resulted in annual6

average concentrations substant ially higher than those simulate d by proportional rollback (i.e., 7

10.8 μg/m 3 

# compared with 7.7 μg/m 3 

# , respectively – Table F-49). Th erefore, for this study area, 8

use of peak shaving rollback would result in esti mates of IHD mortality risk that are larger than 9

with proportional rollback (i.e., >50% higher than with propor tional rollback – Table F-49). 10

These examples further illustrate that variability in the pattern of estimated reductions in ambient 11

PM 2.5 

# concentrations based on simulation of just m eeting the current suite of standards can result 12

in quite different percentage reductions in long-term PM 2.5 

# exposure-related mortality. 13

Additional sensitivity analyses considering sour ces of uncertainty impacting the core risk 14

estimates focused on specification of the C-R function for long-term PM 2.5 

# exposure-related 15

mortality. This analysis suggest ed that most of the alternativ e model specifications supported by 16

available literature would produce risk estimates th at were higher (by up to a factor of 2 to 3) 17

than the core risk estimates. These fi ndings would apply both to estimates of PM 2.5 

# -attributable 18

IHD mortality incidence, as well as to estimates of the percent of total IHD mortality incidence 19

attributable to PM 2.5 

# exposure. 20

Taken together, the sensitivity analyses completed for this risk assessment, including 21

those considering variability in rollback methods as well as uncertainty in the form of C-R 22

functions, suggest that the set of alternative risk model specifications that we identified generally 23

produced risk estimates that are higher than the co re risk estimates. Furthermore, our decision to 24

model risk down to the LML (rather th an to lower PRB levels) for long-term PM 2.5 

# exposure25

related mortality, despite the lack of evidence for a threshold, results in lower estimates of risk 26

that would have resulted from modeling risk down to PRB. These considerations increase our 27

overall confidence that we did not over-sta te risks with the core risk estimates. 28

In considering the results of the quantitativ e sensitivity analyses summarized above, we 29

note that the qualitative analysis of uncertain ty did identify areas of ongoing research which 30

could impact risk estimates, including: (a) more refined characterization of intra-urban variability 31

in ambient PM 2.5 

# concentrations and the resulting im pact on risk characterization and (b) 32

consideration of specific com ponents within the mix of PM 2.5 

# , including regional differences in 33

composition, and potential implications for risk characterization. These considerations introduce 34

further uncertainty into the overall risk asse ssment, although we do not believe that these 35

# February 2010 Draft – Do Not Quote or Cite 6-10 additional sources of uncertainty are likely to alter the fundamental observations resulting from 1

the core risk assessment of the current suite of standards. 2

6.2.2 Simulation of Just Meeting Alternative Annual Standards 

# 3

In characterizing PM 2.5 

# -related risks associated with si mulation of the a lternative annual 4

standards, we estimate both the magnitude of risk reductions (relative to risk remaining upon just 5

meeting the current suite of standards) as we ll as the magnitude of risk remaining upon just 6

meeting the alternative standards. In discussi ng these risks, we focus on the set of urban study 7

areas experiencing risk reductions unde r each alternative annual standard. 8

Based on the risk estimates for these areas pr esented in section 4.2.2 and in Appendix E, 9

we make the following general observations re garding the magnitude of risk remaining upon 10

simulation (using proportional rollback) of just meeting the alternativ e annual standards (in 11

combination with the cu rrent 24-hour standard): 12

 Patterns of risk reduction across alternative annual standard levels: There is a consistent 13

pattern of increasing ri sk reduction with decreas ing alternative annual st andard levels, both in 14

terms of the number of study ar eas experiencing risk reduction s and the magnitude of those 15

reductions. Specifically, 5 of the 15 urban study areas experience risk reductions under the 16

alternative annual sta ndard level of 14 μg/m 3 

# , with percent reductions in PM 2.5 

# -attributable 17

long-term exposure-related mortality ranging fro m 9% (Baltimore) to 12% (Houston) (Figure 18

4-3 and Table E-27 in Appendix E). For an annual standard level of 12 μg/m 3 

# , 12 of the 15 19

urban study areas experience risk reductions, with percent reductions ranging from 11% 20

(Phoenix) to 35% (Houston and Birmingham) (F igure 4-3 and Table E- 27 in Appendix E). 21

 Estimates of long-term PM 2.5 

# exposure-related mortality remaining upon just meeting 22

alternative annual standards : For an annual standard level of 14 μg/m 3 

# , the percent of total 23

incidence of IHD mortality attributable to PM 2.5 

# in the 5 urban study areas experiencing risk 24

reductions ranges from 9-11.3% (Detroit) to 11.8-14.9% (Atlanta) (T ables E-24 and E-33 in 25

Appendix E). For an annual standard of 12 μg/m 3 

# , estimated risk remaining in the 12 urban 26

study areas experiencing risk reductions ranges from 6-7.6% (Phoenix) to 9-11.4% (Atlanta) 27

in terms of PM 2.5 

# -attributable long-term exposure-relate d mortality (Tables E-24 and E-33 in 28

Appendix E). 29

While there is a consistent pattern of risk reduction across the altern ative annual standards 30

with lower standard levels resu lting in more urban study areas e xperiencing increasingly larger 31

risk reductions, there is consid erable variability in the magnit ude of these reductions across study 32

areas for a given alternative annual standard leve l (e.g., as noted above, for the alternative annual 33

standard level of 12 μg/m 3 

# , risk reduction ranges from 11% fo r Phoenix to 35% for Houston). 34

This variability in risk reflect s differing degrees of reduction in annual-average concentrations 35

across the study areas. These differences in annu al-averages result in part because the study 36

areas begin with varying annual-average PM 2.5 

# concentrations after simulating just meeting the 37

current suite of standards (see section 6.2.1). Therefore, even if study areas have similar 38

# February 2010 Draft – Do Not Quote or Cite 6-11 “ending” annual average PM 2.5 

# concentrations after simulatio n of just meeting the a given 1

alternative annual standa rd, because the starting point in th e calculation (the annual-average 2

PM 2.5 

# concentrations upon just meeti ng the current suite of standard s) can be variable, the overall 3

reduction in annual-average PM 2.5 

# concentrations across the standard s can also be variable. This 4

translates into variation in reductions in long-term exposure- related risk upon just meeting 5

alternative annual standard le vels across the study areas. 75 

# 6

The sensitivity analysis invol ving application of peak shav ing rollback rev eals that the 7

pattern of reductions in ambient PM 2.5 

# concentrations upon just m eeting the current suite of 8

standards can impact the magnit ude of additional risk reductions estimated for just meeting 9

alternative (lower) annual standard levels. Specifically, for th ose study areas with more peaky 10

PM 2.5 

# distributions, application of peak shaving rollback will result in higher annual-average 11

PM 2.5 

# levels remaining upon just meeti ng the current suite of standards . If proportional rollback 12

is then used to simulate just mee ting alternative annua l standard levels, a greater degree of 13

reduction in annual-average PM 2.5 

# concentrations will result, si nce the “starting point” for the 14

calculation (annual-average PM 2.5 

# levels upon just meeting the curr ent suite of standards) will be 15

higher. 16

For example, with Los Angeles, which repr esents a study area with a relatively peaky PM 2.5 

# 17

distribution, application of propor tional rollback in simu lating both the current suite of standards 18

and the alternative annua l standard of 12 μg/m 3 

# results in a 13% re duction in long-term 19

exposure-related mortality (see Figure 4-3 and Table E-27 in Appendix E - this calculations 20

represents the approach used in the core risk assessment model, since proportional rollback was 21

used in simulating both suites of standards). In contrast, application of peak shaving in 22

simulating the current suite of standards fo llowed by proportional reduction in simulating the 23

alternative annual st andard of 12 μg/m 3 

# results in an estimated 48% reduction in long-term 24

exposure-related mortality. 76 

# This example illustrates that application of peak shaving in 25

simulating just meeting the current suite of stan dards for urban areas such as Los Angeles which 26

have relatively peaky PM 2.5 

# distributions can substantially increase the magnitude of risk 27

reduction simulated for an alternativ e (lower) annual standard level. 28

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 alterna tive annual standard levels, results from differences across study areas in the

relationship between the maximum monitor annua l-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 mort ality 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 Observations made above in the context of the current suite of standards regarding 1

uncertainty and its impact on risk estimates apply in this context as well. Specifically, given the 2

results of the sensitiv ity analysis examining the form of the C-R functions for long-term 3

exposure-related mortality, combined with onl y modeling risk down to the LML, we have 4

increased confidence that we have not overstated either the magnitude of risk reductions across 5

alternative standard levels, or the magnitude of risk remaining for a given standard level. 6

6.2.3 Simulation of Just Meeting Alternativ e Suites of Annual and 24-hour Standards 

# 7

The two suites of standards involving a lternative annual and alternative 24-hour 8

standards can be used to consider the imp act on risk of reducing the 24-hour standard. 9

Specifically, by comparing risks estimated for th e 13/30 and 13/35 suites of standards, we can 10

consider a reduction of 5 μg/m 3 

# in the 24-hour standard. Simila rly if we compare the 12/25 and 11

12/35 suites of standards we can consider a 10 μg/m 3 

# reduction. In both cases, the reduction in 12

the 24-hour standard level is a ssociated with a fixed annual standard level (i.e., 13 and 12 μg/m 3



# , 13

respectively). These two comparisons of suites of alternative standards form the basis for the 14

discussion presented below. As with the altern ative annual standard levels, we address both the 15

magnitude of risk reductions as well as the ma gnitude of risk remaining upon just meeting the 16

alternative suites of standards. In discussing thes e risks, we also continue to focus on the set of 17

urban study areas experiencing risk reductions un der each alternative suite of standards. 18

Based on the risk estimates for these areas pr esented in section 4.2.2 and in Appendix E, 19

we make the following general observations re garding the magnitude of risk remaining upon 20

simulation (using proportional rollback) of these alternative su ites of standards: 21

 Patterns of reduction in long-term exposure-rela ted mortality across al ternative standards: 22

Comparing risks associated with just meeting the 13/35 and 13/30 suites of alternative 23

standards, we see considerable variation in the magnitude of risk reduction across urban 24

study areas. For example, St Louis, under with the 13/35 suite of alte rnative standards has 25

IHD mortality risk attributable to PM 2.5 

# reduced by 22% relative to risk under the current 26

suite of standards. Very lit tle additional risk reduction ( 24%) is estimated under the 13/30 27

alternative suite of standards. In contrast, with Salt Lake City, we estimate that the 13/35 28

suite of alternative standards will produce no ri sk reduction relative to the current suite of 29

standards, while the 13/30 suite would pr oduce a 55% reduction in IHD mortality risk 30

relative to risk under the curre nt standard level (see Figure 4-3 and Table E-27 in Appendix 31

E). The additional risk reduction provided by an alternative 24-hour sta ndard is even more 32

pronounced in comparing the 12/25 and 12/35 alternativ e suites of standards. In this case we 33

see that for nine of the study areas (Detroit, Fresno, Los Angeles, New York, Philadelphia, 34

Phoenix, Pittsburgh, Salt Lake City and Taco ma) the 12/25 suite of alternative standards 35

produced estimated reductions in ri sk (relative to risk associated with just meeting the current 36

suite of standards) that are tw ice as large as for the 12/35 suit e of alternative standards (see 37

Figure 4-3 and Table E-27 in Appendix E). 38

 Estimates of long-term exposure-related mortality remaining upon just meeting the 39

# February 2010 Draft – Do Not Quote or Cite 6-13 alternative 24-hour standards : There is appreciable variation in the estimated magnitude of 1

risk remaining upon simulation of the 13/30 suite of alternative standards. For example, the 2

percent of total IHD mortality incidence attributable to PM 2.5 

# (again, for urban study areas 3

experiencing risk reductions) ranges from 2-2.5% (for Tacoma) to 8.9-11.3% (for Baltimore) 4

(see Tables E-24 and E-33, in Appendix E). There continues to be varia tion in the levels of 5

residual risk under the 12/25 alternative suite of standards with estimates ranging from 0.3- 6

4.7% (for Tacoma) to 8.8-11.1% (for Atlant a) (see Tables E-24 and E-33, in Appendix E). 7

The observations presented above again highlight va riability both in the magnitude of 8

risk reduction as well as in the residual risk estimated from the simulation of just meeting 9

alternative 24-hour standards. This reflects th e fact that, as noted a bove, alternative 24-hour 10

standards can produce different degrees of reduction in the annual-average PM 2.5 

# concentrations, 11

depending on the relationship between 24-hour and a nnual design values at a particular location. 12

For example, the fact that Salt Lake City is predicted to have a 55% reduction in long-term 13

exposure-related mortality risk with the 13/30 suit e of alternative standard s (compared with risk 14

under the current suite of standards), reflects the peaky nature of its PM 2.5 

# distribution. 15

Specifically, simulating just meeting the 24-hour standard using proportional rollback will 16

produce a substantial reducti on in the annual-average PM 2.5 

# concentrations (i.e., from a recent 17

conditions annual-average of 11.6 μg/m 3 

# , to 7.7 μg/m 3 

# under the current suite of standards, to 6.7 18

μg/m 3 

# with the 13/30 suite of alternative sta ndards – see Table F-49 in Appendix F). In 19

contrast, with St Louis, which does not experience as substantial a risk re duction under the 13/30 20

suite of alternative standards, there is a far less peaky PM 2.5 

# distribution (i.e., the annual and 24- 21

hour design values are relatively closer to each other – see Table F-49 in Appendix F). 22

Therefore, simulation of the alterna tive 24-hour standard level of 30 μg/m 3 

# does not have as 23

substantial an effect on annual-average concen trations (i.e., from a recent conditions annual24

average of 16.5 μg/m 3 

# , to 14.9 μg/m 3 

# under the current suite of standards, to 12.8 μg/m 3 

# under 25

the 13/30 suite of alternative standards). 26

It is possible to stratify the set of urban study areas based on pattern s of risk reduction 27

estimated under the alternative 24-hour standards. In this discussion, we focus on risk estimates 28

generated for the 12/25 suite of alternative standards, focusing on how risks under this scenario 29

compare with risks under the current suite of standards. 77 

# The stratificati on of the study areas 30

based on the magnitude of risk reduction highlig hts factors responsible for these differences 31

across study areas. For example, when the 24-ho ur standard is controlling (in simulating the 32

12/25 suite of alternativ e standards) and the PM 2.5 

# distribution is relatively peaky, there is a 33

greater potential for the annual-average PM 2.5 

# concentrations to be reduced more in simulating 34

just meeting the alternative 24-hour standa rd (in some instances, well below 12 μg/m 3 

# ) resulting 35

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 in larger estimated risk reductions. In fact, we see that the urban study areas having the largest 1

risk reductions have annual-average PM 2.5 

# concentrations simulated under the 12/25 suite of 2

standards (using proportional rollback) well below 12 μg/m 3 

# , with some locations ranging down 3

to ~6 μg/m 3



# . 4

We identified four strata in considering patterns of risk reduction across the 15 urban study 5

areas under the 12/25 suite of alternative standards (all of the percent reductions presented are in 6

terms of long-term exposur e-related IHD mortality). 7

 ~100% reduction in risk : Those study areas where the 24-hour standard was controlling 8

and where the resulting annual-average PM 2.5 

# concentrations (unde r the 12/25 suite of 9

standards) were ~ 6 μg/m 3 

# . Because annual-average concentrations for these study areas 10

are at or below the LML for long-term exposure-related mortality (5.8 μg/m 3 

# ), little to no 11

risk is predicted under the alternative suit e of standards, resulting in a near 100% 12

reduction in risk relative to the current suit e of standards. These study areas have the 13

most peaky PM 2.5 

# distributions of the 15 urban st udy areas (i.e., relatively high 24-hour 14

design values and lower annual average desi gn values) and include study areas Tacoma 15

and Salt Lake City. 78 

# 16

 ~70% reduction in risk : Those study areas where the 24-hour standard is controlling and 17

where the resulting annual-average PM 2.5 

# levels (under the 12/25 su ite of standards) were 18

~7-9 μg/m 3 

# . These study areas also have relatively peaky PM 2.5 

# distributions and include 19

Los Angeles and Fresno. 79 

# 20

 ~50-60% reduction in risk : Those study areas where the 24- hour standard is controlling 21

and where the resulting annual-average PM 2.5 

# levels (under the 12/25 suite of standards) 22

were ~9-11 μg/m 3 

# . These study areas have less peaky PM 2.5 

# distributions (24-hour 23

standard still controls, but there is not as great a disparity with the annual design values) 24

and include the majority of the study areas (Detroit, NYC, Ph iladelphia, Pitts, St Louis, 25

Baltimore, Birmingham, and Phoenix). 80 

# 26

 ~35-45% reduction in risk : This category includes so me study areas where the 24-hour 27

standard controls and some where the a nnual standard controls. Annual average PM 2.5 

# 28

concentrations under the 12/25 suite of standards are generally in the 12 μg/m 3 

# range. 29

These study areas have re latively less peaky PM 2.5 

# distributions and include Atlanta and 30

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 PM 2.5 

## 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 PM 2.5 

## 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 th e U.S. predicted to exceed the 12/25 su ite of alternative standards. Urban

areas in subarea B2 have less peaky PM 2.5 

## distributions compared to areas in subarea B1.

# February 2010 Draft – Do Not Quote or Cite 6-15 Houston. 81, 82 

# 1

Observations made earlier regarding the imp act of variability in simulating changes in 2

PM 2.5 

# distributions using different rollback approaches, and its impact on the degree of risk 3

reduction, also hold here. Specifi cally, in those instances where PM 2.5 

# distributions are more 4

peaky, application of peak shaving rollback woul d result in smaller reductions in annual-average 5

PM 2.5 

# concentrations and consequently, smaller re ductions in estimates of long-term exposure6

related mortality. For example, with Salt Lake City, which has a peaky PM 2.5 

# distribution, under 7

the 12/25 suite of standards appl ication of proportional rollback results in an annual average 8

PM 2.5 

# concentration of 5.7 μg/m 3



# , while application of peak shavi ng results in an estimate of 8.9 9

μg/m 3 

# . In contrast, simulation of the 12/25 suite of standards for Baltimore, which has a less 10

peaky PM 2.5 

# distribution, results in an annual average PM 2.5 

# concentration of 10.7 μg/m 3 

# for 11

proportional rollback compared to 10.8 μg/m 3 

# with peak shaving (see Table F-49 in Appendix F). 12

A key observation made above in relation to the current suite of standards, that is even 13

more relevant in considering the results discussed here, is that simulated annual-average PM 2.5 

# 14

concentrations upon just meeting alternative suites of standa rds for many of the urban study 15

areas are considerably lower than 12 μg/m 3 

# . For example, with the current suite of standards, 16

Fresno and Salt Lake City are simu lated to have annual average PM 2.5 

# concentrations of 9.9 and 17

7.7 μg/m 3 

# , respectively, which are in turn reflected in the risk estimates generated (see Table F18

49, in Appendix F). Annual average concentrati ons in these study areas are even lower under the 19

alternative suites of standards with lower 24-hour standard leve ls. For example, under the 13/30 20

suite of standards, simula ted annual average concentra tions range down to 6.7 μg/m 3 

# (Salt Lake 21

City), with a number of urban study areas having annual-average c oncentrations simulated in the 22

range of 7 to 11 μg/m 3 

# (Fresno, Los Angeles, and Tacoma). Under the 12/25 suite of standards, 23

simulated annual-average concentrations are even lower, ranging down to 5.7 μg/m 3 

# (Salt Lake 24

City). These very low annual-average PM 2.5 

# concentrations reflect lo wer annual design values to 25

begin with as well as relatively peaky PM 2.5 

# distributions, which means that simulation of the 24- 26

hour standard (when controlling) will produ ce appreciable impacts on the annual average 27

concentration. 28

The results discussed above show that si mulating just meeti ng alternative 24-hour 29

standard levels in the range of 25 to 30 μg/m 3 

# can produce substantial re ductions in estimated 30

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 PM 2.5 

## 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/m 3 

## , respectively) that are well below the cu rrent suite of standa rds and only just

exceed the 12/25 suite of standards. Th erefore, the estimated risk reduction unde r 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 risk, beyond that produced by simulations of just meeting lower annual standard level down to 1

12 μg/m 3 

# (combined with a 24-hour standard of 35 μg/m 3 

# ). This results from the simulations 2

producing substantially lower annual-average PM 2.5 

# concentrations, which drive reductions in 3

both long-term and short-term exposure-related risk . The results also show that there can be 4

considerable variability across study areas in the degree to whic h alternative 24-hour standard 5

levels produce reductions in annual average PM 2.5 

# concentrations and, consequently, reductions 6

in risk. This variability is seen to depend largely on the peakiness of the PM 2.5 

# distribution in an 7

area and on the rollback approach used to simulate just meeting the current and alternative suites 8

of standards. These results suggest that while lowering the 24-hour standard can be used to 9

reduce annual-average PM 2.5 

# concentrations, and thus to reduce estimated risk, the results are 10

likely to be highly variable across urban areas. This analysis also suggests that more consistent 11

annual-average PM 2.5 

# concentrations, and thus more cons istent reductions in estimated risk, 12

would result from simulating just meeting altern ative annual standards at levels below 12 μg/m 3 

# 13

which was the lowest annual standard level considered in this assessment. In general, 14

considering suites of standards in which the annual standard is the controlling standard would be 15

expected to provide more consiste nt reductions in annual-average PM 2.5 

# concentrations, thereby, 16

providing more uniform public health protection across urban areas. 17

Observations made earlier regarding overall confidence in the estimates of long-term 18

exposure-related mortality also hold for these es timates (i.e., the sensi tivity analysis results 19

combined with the fact that we modeled risk down to LML result in our concluding that it is 20

unlikely we have overstated either the degree of risk re duction or the degree of residual risk). 21

6.3 NATIONAL PERSPECTIVE ON PM 2.5 

# -RELATED RISKS 

# 22

This section places the core risk estimates in the broader national-context by considering 23

the degree to which the 15 urban st udy areas are representative of larger urban areas within the 24

U.S., particularly areas likely to experience elev ated risk related to PM exposure. As such, it 25

draws on information presented in several sections of the risk assessment including: (a) the 26

representativeness analysis discussed in secti on 4.4, (b) considerati on of patterns of design 27

values for the 15 urban study areas as contrasted with the broader se t of larger urban areas within 28

the US (section 4.5.1), and (c) the national-scale mortality anal ysis discussed in Chapter 5. 29

 The representativeness analysis presented in section 4.4, comp ared attributes of the 15 urban 30

study eras (assessed at the county-le vel) against national distributi ons for the same attributes. 31

The analysis suggests that the 15 urban study areas represent areas in the U.S. that are among 32

the most densely populated, have relativel y higher levels of annual and 24-hour 98 th 

# 33

percentile PM 2.5 

# concentrations, and capture well the ra nge of effect estimates represented by 34

the Zanobetti and Schwartz (2009) study. Toge ther, these factors suggest that the urban 35

study areas should capture well the overall dist ribution of risk for the nation, with the 36

# February 2010 Draft – Do Not Quote or Cite 6-17 potential for better characterization of the high end of that distribution. 83 

# 1

 Consideration of the mi x of design values across the 15 urban study areas as contrasted with 2

design values for the broader set of urban study areas in the U.S. suggests that the 15 urban 3

study areas do a good job of capturing the key groupi ngs of urban areas in the U.S. likely to 4

experience elevated risk due to PM (i.e., we have coverage for each of the zones containing 5

urban study areas likely to experi ence risk reductions under the su ites of altern ative standard 6

levels considered – see section 4.5.1). Furthermor e, this analysis suggested that we have also 7

included study areas likely to experien ce relatively greater degrees of PM 2.5 

# -related risk, 8

considering the pattern of design valu es across urban areas in the U.S.. 9

 Consideration of where the 15 urban study area s fell along the distribut ion of U.S. counties 10

included in the national-scale mortality analysis further suggests that we have captured 11

counties likely to experience elevated PM 2.5 

# -related risk

. 

# As part of the national-scale 12

mortality analysis (see Chapter 5), we created a cumulative distribution of the percentage of 13

mortality attributable to PM 2.5 

# based on the county-level estimates for the U.S. 84 

# We then 14

identified where along this cumulative distri bution the 31 counties co mprising our 15 urban 15

study areas fell. This analysis suggests that our urban study areas cap ture the upper end of 16

the tail with regard to PM 2.5 

# -attributable risk, with 23 of these counties falling within the 17

upper 5 th 

# percentile of the distribu tion. These findings support the assertion based on the 18

other analyses described above that the urban st udy areas are likely to capture risk at urban 19

areas experiencing relatively elevated levels of PM 2.5- 

# attributable mortality. 20

Our overall assessment of the representati veness of the 15 urban study areas in the 21

national context, based on the three analyses summarized above, is that our study areas do a good 22

job of representing urban areas in the U.S. experiencing elevated leve ls of risk related to ambient 23

PM 2.5 

# exposure. The results of the national-scale mortality analysis also suggest that, while our 24

15 urban study areas do provide coverage for ur ban areas in the U.S. experiencing elevated 25

levels of PM 2.5 

# -related risk, there are many additional ar eas (counties) not modeled in the risk 26

assessment that experience elevated PM 2.5 

# -related risk. In other words, it should not be 27

construed that significant PM 2.5 

# -related risk is limited only to the urban study areas included in 28

the risk assessment. 29

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 PM 2.5 

## -related mortality estimates). However, some of

the areas with the highest values for th ese attributes have relatively lower PM 2.5 

## 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 PM 2.5 

## -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 PM 2.5 

## -related mortality which reflects differences in (a)

baseline mortality incidence, and (b) the annual PM 2.5 

## levels average for each county.

# February 2010 Draft – Do Not Quote or Cite 6-18 6.4 KEY OBSERVATIONS 

# 1

Key observations from this quantitative risk assessment for PM 2.5 

# , with emphasis on the 2

observations made above in this chapter, are out lined below. These observations are organized 3

around the three policy-relevant questions outlined at the beginning of this chapter. 4

5

(1) What is the magnitude of risk likely to remain if the urban study areas were just meeting the 6

current suite of PM 2.5 

# standards (an annual standard of 15 μg/m 3 

# and a 24-hour standard of 35 7

μg/m 3 

# ), and what level of confidence do we have in those estimates? 8

 Upon simulation of just meeting the current suit e of standards, the core analysis estimates 9

that the urban study areas would have I HD-related mortality attributable to long-term PM 2.5 

# 10

exposure ranging from <100 to approximately 2, 000 cases per year, with this variability 11

reflecting to a great extent differences in th e size of study area populati ons. These estimates 12

represent from 4 to 17% of all IHD-related mortality in a given year for the urban study 13

areas, which is a measure of risk that takes into account differences in population size and 14

baseline mortality rates. Estimates were al so developed for other long-term exposure-related 15

mortality endpoints, including all-cause, cardi opulmonary-related, and lung cancer mortality. 16

 Generally comparable estimates of CV -related mortality attributable to short-term PM 2.5 

# 17

exposure are substantially lower than for l ong-term exposure-related IHD mortality. The 18

core analysis estimates that the urban study areas would have CV-related mortality 19

attributable to short-term PM 2.5 

# exposure ranging from appr oximately 10 to 470 cases per 20

year. Estimates were also developed for other short-term exposure-related endpoints, 21

including non-accidental and re spiratory-related mortality, CV- and respiratory-related 22

hospital admissions, and asthma-related emergency department visits. 23

 A broader array of health effects has also been associated with PM 2.5 

# exposures, including in 24

particular effects on children, such as reproductive and developmental effects. While 25

information was too limited to consider these effects in this quantitative risk assessment, such 26

effects are appropriately c onsidered based on the relate d evidence in the broader 27

characterization of risks to be discussed in a separate Policy Assessment document. 28

 Given the quantitative and qualitative assessments of uncertainty and vari ability that we have 29

completed as part of our quantitative risk asse ssment, we believe that it is unlikely that we 30

have over-stated the degree of risk remaini ng upon simulation of just meeting the current 31

suite of standards. While this conclusion applies to all quantitative estimates of risk, it applies 32

most strongly for long-term PM 2.5 

# exposure-related mortality for which more extensive 33

uncertainty and variability assessment has been done. 34

 Estimated risks remaining upon just meeting the cu rrent suite of standards vary substantially 35

across study areas, even when c onsidering risks normalized fo r differences in population size 36

and baseline incidence rates. This variability in estimated risks is a consequence of the 37

substantial variability in the annual-average PM 2.5 

# concentrations across study areas that 38

result from simulating just meeting the current standards. This is important because annual39

average concentrations are highly correlatedwith both long-term and short-term exposure40

related risk. This variability in annual-average PM 2.5 

# concentrations occurs especially in 41

# February 2010 Draft – Do Not Quote or Cite 6-19 those study areas in which the 24-hour st andard is the “controlling” standard. 85 

# In such 1

areas, the variability across study ar eas in estimated risks is larg est when regional patterns of 2

reductions in PM 2.5 

# concentrations are simulated (using proportional rollback, as was done in 3

the core analyses), with less variabil ity when more localized patterns of PM 2.5 

# reductions are 4

simulated (using peak shaving rollback, as was done in a sensitivity analysis). When 5

simulations are done using peak shaving ro llback, estimated risks remaining upon just 6

meeting the current suite of standards can be a ppreciably larger than those estimated in the 7

core analysis. 8

 In simulating just meeting the current suite of standards, the resulting annual-average PM 2.5 

# 9

concentrations range from about 15 μg/m 3 

# (for those study areas in which the annual 10

standard was controlling) dow n to as low as about 8 μg/m 3 

# (for those study areas in which 11

the 24-hour standard was controlling or the annual average was well below 15 μg/m 3 

# based 12

on recent air quality). Thus, estimates of risk remaining upon just meeting the current 13

standards are, in many cases, reflective of annual average PM 2.5 

# concentrations that are well 14

below the level of the current annual standard. 15

 The 15 urban study areas included in this risk assessment are generally characteristic of 16

urban areas across the U.S. that do not meet the current suite of standards. Of those urban 17

areas in the U.S. that do not meet the curre nt suite of standards (based on 2005-2007 air 18

quality data), the 24-hour standa rd is controlling in most such areas – a pattern that is 19

reflected in the urban study areas included in th is assessment. Two areas are included in this 20

assessment that meet the current suite of standards (reflective of the majority of urban areas 21

in the U.S.), although these two areas fail to meet some of the alternat ive suites of standards 22

considered in this assessment. 23

24

(2) What is the degree and nature of risk reduction likely to be associated with just meeting the 25

alternative suites of annual and 24-hour PM 2.5 

# standards considered in this risk assessment, and 26

what roles do the annual and 24-hour standards play in bringing about such reductions? 27

 Upon simulation of just meeting the alternative annual standard levels considered (14, 13, 28

and 12 μg/m 3 

# ) in conjunction with the current 24-hour standard (denoted as 14/35, 13/35 and 29

12/35 suites of standards), the core analysis estimates reductions in long-term exposure30

related mortality for 12 of the 15 urban study areas, with the degree of risk reduction 31

increasing incrementally across th e alternative standard levels (both in terms of the number 32

of study areas experiencing risk reduction and the magnitude of those reductions). For the 33

alternative annual sta ndard level of 12 μg/m 3 

# (in conjunction with the current 24-hour 34

standard), the core analysis es timates that these study areas have reductions in risk (relative 35

to risk remaining upon just meeting the current suite of standards) ranging from about 11 to 36

35%. For some of those areas in which the 24- hour standard is controlling, larger risk 37

reductions would have been estimated in this ca se (12/35 suite of standards) if peak shaving 38

rollback had been used to simulate just meeti ng the current suite of standards. This result 39

would be expected since the magnitude of risk remaining upon just meeting the current suite 40

of standards would have been higher than that estimated based on the proportional rollback 41

85 

## The controlling standard is the standard (either 24-hour or annual) that requires the largest percent reduction in the

related design value to ju st meet that standard.

# February 2010 Draft – Do Not Quote or Cite 6-20 used in the core analysis. Therefore, while we are going down to the same level of risk 1

(under the 12/35 suite of standa rds), we are starting with a hi gher level of risk from the 2

current standard. 3

 Upon just meeting the alternative suites of standards that included lower levels of both the 4

annual and 24-hour standards (denoted as13/30 and 12/25 su ites of standards), the core 5

analysis estimates that the lower 24-hour standa rd levels produce addi tional risk reductions 6

beyond the reductions estimated for the lower annual standard levels alone. In the case of the 7

12/25 suite of standards, estimat ed risk reductions compared with reductions for the annual 8

standard alone (12 μg/m 3 

# ), were roughly twice as large in many of the study areas, although 9

in a few areas risk reductions were much hi gher (ranging up to ~100%) and in a few other 10

areas, there was little to no risk reduction. These results show that lower 24-hour standards 11

can have an appreciable and hi ghly variable impact on long-term exposure-related mortality, 12

particularly when just meeting the lower standa rds is simulated using a more regional pattern 13

of PM 2.5 

# reductions (i.e., the proporti onal rollback used in the core analysis). However, the 14

magnitude of risk reductions estimated for the lower 24-hour standa rds was reduced when 15

simulations using a more localized pattern of PM 2.5 

# reductions (i.e., the peak shaving rollback 16

used in a sensitivity analysis). 17

 The results of simulating alternative suites of standards including lower levels of both annual 18

and 24hr standards suggest that while lowering the 24-hour standard can be used to reduce 19

annual-average PM 2.5 

# concentrations, and thus to reduce estimated risk, the results are likely 20

to be highly variable across urban ar eas. More consistent annual-average PM 2.5 

# 21

concentrations across study areas, and thus mo re consistent reductions in estimated risk, 22

would result from simulating just meeting a speci fic alternative annual st andard level. In 23

general, considering suites of standards in which the annual standard is the controlling 24

standard would be expected to provide more consistent reductions in annual-average PM 2.5 

# 25

concentrations, thereby, providing more uniform public health protecti on across urban areas. 26

 In simulating just meeting the al ternative suites of standards, especially those with lower 24- 27

hour standard levels, the resulting annual-average PM 2.5 

# concentrations are substantially 28

lower than the lowest annual standard le vel considered in the analysis (12 μg/m 3 

# ). For 29

example, under the 12/25 suite of st andards, estimated annual-average PM 2.5 

# concentrations 30

ranged down to approximately 6 μg/m 3 

# , with eight urban study areas having annual average 31

PM 2.5 

# levels in the 8-11 μg/m 3 

# range. 32

 Addressing overall confidence in risk estimates generated for just meeting the alternative 33

suites of standards, as with the current su ite of standards, we conclude based on our 34

quantitative and qualitative analys is of uncertainty and variabili ty that we have likely not 35

over-stated risk reductions or levels of residual risk estimated for just meeting these 36

alternative suites of standards. 37

38

(3) What is the distribution of ri sks associated with recent PM 2.5 

# air quality in areas across the 39

U.S., and how representative are the risks estimated for the ur ban study areas from a national 40

perspective? 41

 Based on recent air quality from 2005 to 2007, we estimate that within the continental U.S., 42

total PM 2.5 

# -related premature mortality ranges from 63,000 and 88,000 per year. Further, we 43

# February 2010 Draft – Do Not Quote or Cite 6-21 estimate that the percent of total mortality attributable to PM 2.5 

# long-term exposure ranges 1

from approximately 3 to 9% in about half of the counties in the U.S., with a, range from 2

approximately 0 to 3% in the other half of counties. 3

 Efforts to place the 15 urban study areas and the co re risk estimates generated for those areas 4

into a broader national context suggest that thes e study areas likely capt ure well the full set of 5

urban areas in the U.S. likely to experience relatively higher PM 2.5 

# -related risk. 6

 It is important to recognize that there are ma ny additional areas besides those included in the 7

risk assessment that experience elevated PM2.5 -related risk of similar magnitude to the risks 8

estimated for the urban study areas included in this assessment. 9

10

# February 2010 Draft – Do Not Quote or Cite 7-1 

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Abt Associates Inc. (2003), “Environmental Benefits Mapping and Analysis Program (BenMAP), User’s Manual. 

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Available on the Internet at <http ://www.epa.gov/ttn/ecas /benmodels.html>. 

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4

Abt Associates Inc. ( 2005). Particulate Matter Health Risk Assessmen t for Selected Urban Areas. Prepared for 

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

. 

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# A-1 

# APPENDIX A: AIR QUALITY ASSESSMENT

# A-2 Appendix A. Air Quality Assessment 1

2

This Appendix describes the PM data for th e 15 urban study areas ev aluated in the risk 3

assessment, including summaries of PM 2.5 

# monitoring data associated with each study area as 4

well as the composite monitor estimates genera ted for each study area based on that monitoring 5

data (see section 3.2 for additional detail regard ing selection of monito rs and derivation of 6

composite monitor values). 7

8

9

10

11

12

13

14

15

16

17

18A-3Table A-1. Air Quality Data for AtlantaQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4130630091(3)27 30 25 30 112 12.63 16.83 21.22 15.92 16.65 36.09130670003(1,2,3)27 30 29 29 115 13.75 17.39 18.57 15.62 16.33 34.94130670004(1,2,3)30 28 26 27 111 12.98 17.17 18.03 13.98 15.54 30.28130890002(1,2,3)82 84 81 88 335 12.72 15.72 18.81 14.56 15.45 32.82130892001(1,2,3)80 75 67 85 307 12.84 15.10 20.44 14.83 15.80 36.72131210032(1,2,3)84 89 76 80 329 13.64 16.00 19.43 14.38 15.86 33.40131210039(1,2,3)27 30 23 29 109 15.03 18.35 17.97 16.56 16.98 30.29131210048(1,2,3)0 0 0 0 0 --- --- --- --- --- ---131350002(1,3)13 14 12 14 53 14.35 14.62 20.39 15.16 16.13 31.66132230003(3)28 29 26 26 109 11.41 15.52 18.62 12.99 14.63 34.52Composite monitor for Atlanta - 1 90 91 92 92 365 13.62 16.34 19.09 15.01 16.01 31.03Composite monitor for Atlanta - 2 90 91 92 92 365 13.49 16.62 18.87 14.99 15.99 31.52Composite monitor for Atlanta - 3 90 91 92 92 365 13.26 16.30 19.27 14.89 15.93 31.06130630091(3)29 29 31 30 119 12.94 17.91 21.32 14.49 16.67 30.84130670003(1,2,3)28 29 31 30 118 12.22 17.88 21.52 14.20 16.46 32.66130670004(1,2,3)28 29 27 28 112 12.09 17.75 21.04 12.39 15.82 33.34130890002(1,2,3)85 86 81 81 333 12.25 16.09 19.86 13.43 15.41 31.65130892001(1,2,3)86 84 77 81 328 11.94 15.75 18.31 12.18 14.54 28.89131210032(1,2,3)88 86 84 90 348 12.46 15.99 19.28 13.74 15.37 31.44131210039(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 15.00 --- ---131350002(1,3)12 14 13 15 54 15.21 18.98 20.31 12.93 16.86 30.64132230003(3)29 27 31 29 116 10.91 15.20 18.90 10.77 13.95 32.28Composite monitor for Atlanta - 1 90 91 92 92 365 13.04 17.37 20.17 13.41 16.00 27.34Composite monitor for Atlanta - 2 90 91 92 92 365 12.68 17.10 20.15 13.49 15.86 27.89Composite monitor for Atlanta - 3 90 91 92 92 365 12.79 17.19 20.16 13.24 15.84 26.82Monitor20062005Quarterly CountsQuarterly Averages (ug/m3)AnnualTotalAnnualAverage(ug/m3)98thPercentile(ug/m3)A-4Table A-1 cont'd. Air Quality Data for AtlantaQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4130630091(3)29 30 30 29 118 13.87 16.51 18.83 13.02 15.56 36.04130670003(1,2,3)29 30 29 29 117 13.49 17.03 19.49 13.41 15.85 35.51130670004(1,2,3)26 27 30 30 113 12.50 17.47 18.77 11.39 15.03 33.54130890002(1,2,3)85 83 90 85 343 12.78 15.54 19.38 12.15 14.96 34.22130892001(1,2,3)69 79 76 75 299 12.48 17.11 20.04 12.38 15.50 37.42131210032(1,2,3)87 88 91 85 351 12.99 17.95 19.64 13.08 15.91 35.10131210039(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 15.87 37.52131350002(1,3)27 27 29 29 112 13.05 14.03 17.97 11.68 14.18 30.19132230003(3)29 30 29 30 118 12.21 17.12 18.95 10.64 14.73 33.82Composite monitor for Atlanta - 1 90 91 92 92 365 12.96 16.87 19.08 12.42 15.33 31.82Composite monitor for Atlanta - 2 90 91 92 92 365 12.95 17.35 19.26 12.54 15.52 31.35Composite monitor for Atlanta - 3 90 91 92 92 365 12.98 16.86 19.03 12.29 15.29 30.59Note 1: Different definitions of Atlanta include different monitors. The number(s) shown in the parenthesis next to the monitorindicates the location(s) in which it isincluded. 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 in.2007MonitorQuarterly CountsQuarterly Averages (ug/m3)AnnualTotalAnnualAverage(ug/m3)98thPercentile(ug/m3)A-5Table A-2. Air Quality Data for BaltimoreQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4240051007 30 28 27 27 112 14.78 11.86 20.66 12.34 14.91 33.76240053001 75 80 85 92 332 16.09 12.60 18.27 13.44 15.10 35.77245100006 28 31 27 28 114 15.76 12.47 20.18 11.67 15.02 33.17245100007 27 27 30 30 114 16.09 12.50 20.05 13.00 15.41 35.27245100008 24 30 30 29 113 18.85 14.16 20.99 14.80 17.20 39.16245100035 79 75 78 70 302 17.58 13.59 20.24 14.12 16.38 37.49245100040 79 81 90 76 326 18.47 14.68 19.40 13.42 16.49 39.45245100049 26 30 25 27 108 17.72 13.19 20.62 12.77 16.07 36.43Composite Monitor for Baltimore 90 91 92 92 365 16.91 13.13 20.05 13.19 15.82 32.98240051007 29 29 28 30 116 12.03 11.37 15.73 11.09 12.55 32.06240053001 90 85 90 92 357 12.81 11.79 18.51 13.90 14.25 34.25245100006 27 30 27 30 114 13.20 11.62 16.24 11.61 13.17 32.67245100007 30 29 29 31 119 12.64 11.59 15.19 12.03 12.86 32.27245100008 30 28 31 30 119 14.80 13.34 16.88 12.97 14.50 35.21245100035 74 90 83 82 329 13.31 12.57 19.27 14.14 14.82 36.74245100040 85 86 87 86 344 13.83 12.58 18.64 14.73 14.94 35.932451000490 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Baltimore 90 91 92 92 365 13.23 12.12 17.21 12.92 13.87 31.34240051007 29 29 31 30 119 12.09 13.54 15.53 12.04 13.30 31.46240053001 74 87 83 89 333 12.53 12.95 16.93 13.70 14.03 34.01245100006 30 29 31 27 117 12.10 12.83 16.28 11.16 13.09 31.55245100007 29 30 30 28 117 12.07 13.20 15.84 12.44 13.39 33.31245100008 30 30 31 27 118 13.53 14.68 16.90 14.79 14.97 35.25245100035 79 85 74 76 314 12.11 14.03 17.23 13.23 14.15 33.77245100040 82 85 89 76 332 13.42 13.66 16.32 13.35 14.19 34.392451000490 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Baltimore 90 91 92 92 365 12.55 13.55 16.43 12.96 13.87 28.41Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-6Table A-3. Air Quality Data for BirminghamQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q410730023* 90 90 89 92 361 14.35 20.49 26.42 17.27 19.63 49.6810731005* 30 31 29 31 121 11.62 16.70 22.61 14.33 16.32 35.0610731009* 30 31 29 31 121 9.82 16.12 20.26 11.87 14.52 37.6810731010* 15 15 15 16 61 11.71 16.91 22.77 15.51 16.73 36.4610732003* 88 90 91 91 360 14.49 18.48 23.75 15.03 17.94 44.4110732006* 30 30 30 31 121 11.53 16.46 21.11 13.79 15.72 33.9810735002* 30 31 30 31 122 10.84 16.33 21.08 12.61 15.21 36.2310735003* 30 30 30 31 121 10.60 16.42 21.94 12.74 15.43 39.2011170006 30 31 30 28 119 11.23 15.67 19.60 12.92 14.85 32.8611270002 27 31 28 30 116 10.37 15.31 18.86 12.17 14.18 33.17Composite Monitor forBirmingham - 190 91 92 92 365 11.66 16.89 21.84 13.82 16.05 35.47Composite Monitor forBirmingham - 290 91 92 92 365 11.87 17.24 22.49 14.14 16.44 36.2710730023* 89 91 92 92 364 13.61 20.57 22.35 17.02 18.39 39.5510731005* 30 30 31 31 122 10.51 18.84 19.59 13.38 15.58 33.1410731009* 30 29 30 30 119 8.81 17.16 17.78 10.02 13.44 31.6910731010* 15 15 15 16 61 11.57 18.63 18.71 12.37 15.32 32.2810732003* 89 90 90 92 361 14.41 20.48 21.62 15.67 18.05 40.1810732006* 30 30 31 31 122 10.76 18.08 20.02 12.33 15.30 31.6910735002* 30 30 31 31 122 9.87 17.15 19.61 10.60 14.31 33.1610735003* 29 30 30 30 119 10.37 17.42 18.84 11.31 14.48 33.2211170006 30 30 31 31 122 9.95 16.37 18.38 11.65 14.09 29.7911270002 29 30 30 29 118 9.85 17.49 17.38 11.83 14.14 34.53Composite Monitor forBirmingham - 190 91 92 92 365 10.97 18.22 19.43 12.62 15.31 30.49Composite Monitor forBirmingham - 290 91 92 92 365 11.24 18.54 19.82 12.84 15.61 30.91AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-7Table A-3 cont'd. Air Quality Data for BirminghamQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q410731010* 15 15 15 15 60 14.53 18.69 19.31 13.63 16.54 37.9210732003* 89 90 89 90 358 15.40 21.38 19.18 12.42 17.10 44.0210732006* 30 30 31 30 121 12.24 19.29 18.53 10.93 15.25 39.9210735002* 30 28 31 30 119 12.15 19.16 18.41 10.40 15.03 37.9010735003* 29 30 31 30 120 11.79 18.99 17.83 10.38 14.75 38.5611170006 29 30 31 30 120 12.97 18.27 17.52 10.84 14.90 38.5211270002 28 29 31 29 117 11.97 17.81 17.72 10.95 14.61 34.91Composite Monitor forBirmingham - 190 91 92 92 365 12.99 19.62 18.58 11.60 15.70 37.65Composite Monitor forBirmingham - 290 91 92 92 365 13.12 20.02 18.82 11.78 15.93 38.40Note 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.AnnualAverage(ug/m3)98thPercentile(ug/m3)2007MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-8Table A-4. Air Quality Data for DallasQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4481130035 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 13.77 28.55481130057 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 12.50 27.44481130087 27 31 30 30 118 9.87 13.32 13.45 10.18 11.70 24.55481133004 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 12.76 26.93481130035 0 0 0 0 0 --- --- --- --- --- ---481130050 28 30 31 31 120 10.99 12.53 12.98 10.68 11.79 22.16481130057 0 0 0 0 0 --- --- --- --- --- ---481130069 84 90 92 90 356 9.97 12.15 11.73 9.26 10.78 21.99481130087 30 30 30 28 118 9.22 11.66 10.89 8.45 10.05 19.55481133004 0 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Dallas 90 91 92 92 365 10.06 12.11 11.87 9.46 10.88 19.22481130035 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 11.24 23.24481130087 28 21 29 30 108 9.96 11.16 12.70 9.30 10.78 20.03481133004 0 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Dallas 90 91 92 92 365 10.54 11.27 13.97 9.72 11.38 21.87Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-9Table A-5. Air Quality Data for DetroitQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4261630001 88 87 89 86 350 18.45 13.87 17.15 14.38 15.96 42.31261630015 27 27 30 30 114 20.20 14.73 18.73 15.18 17.21 48.27261630016 87 79 84 88 338 18.92 14.78 16.62 13.70 16.01 47.80261630019 28 31 29 29 117 19.82 14.48 17.43 14.20 16.48 51.37261630025 26 28 30 30 114 17.86 11.74 17.45 12.68 14.94 39.50261630033 28 31 28 28 115 21.50 16.57 18.22 17.90 18.55 48.69261630036 29 28 29 27 113 16.96 14.92 18.58 15.19 16.41 46.22261630038 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 16.43 44.06261630001 82 85 88 90 345 13.66 11.89 13.68 13.65 13.22 32.82261630015 29 26 28 31 114 16.98 12.26 14.93 14.56 14.68 35.89261630016 79 14 13 17 123 13.04 11.58 12.58 14.97 13.04 35.49261630019 30 15 14 16 75 15.20 10.39 11.78 13.46 12.71 35.67261630025 27 14 15 17 73 13.49 11.23 10.01 12.70 11.86 30.00261630033 28 29 27 31 115 18.79 12.85 15.56 17.30 16.13 42.43261630036 29 26 29 29 113 15.10 10.95 13.69 11.94 12.92 32.91261630038 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 13.13 32.32Composite Monitor for Detroit 90 91 92 92 365 15.13 11.55 13.42 13.60 13.42 28.34261630001 86 89 87 92 354 12.92 10.28 14.00 14.08 12.82 31.19261630015 28 30 27 29 114 15.15 13.06 15.12 14.82 14.54 32.73261630016 26 26 30 29 111 13.98 12.12 14.74 14.61 13.86 33.72261630019 30 28 31 27 116 13.20 11.16 14.36 13.31 13.01 31.09261630025 26 30 31 27 114 12.23 10.59 13.76 14.42 12.75 32.49261630033 29 29 29 27 114 18.84 15.20 16.02 17.49 16.89 36.60261630036 29 28 30 29 116 13.75 11.96 14.60 13.47 13.45 28.48261630038 27 27 28 30 112 13.63 12.85 15.35 14.23 14.01 33.38261630039 29 30 30 28 117 13.83 12.98 14.65 13.86 13.83 33.97Composite Monitor for Detroit 90 91 92 92 365 14.17 12.24 14.73 14.48 13.91 27.66Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-10Table A-6. Air Quality Data for FresnoQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q460190008 85 78 89 91 343 19.53 7.19 11.42 28.65 16.70 67.6460195001 30 15 15 22 82 17.11 7.55 10.78 29.95 16.35 64.5660195025 30 15 13 31 89 20.24 8.29 11.24 27.92 16.92 71.90Composite Monitor for Fresno 90 91 92 92 365 18.96 7.68 11.14 28.84 16.65 63.2660190008 89 87 87 85 348 21.82 9.10 12.39 23.85 16.79 50.0660195001 30 15 14 29 88 18.38 9.47 12.99 24.96 16.45 53.6960195025 30 15 12 31 88 20.13 9.81 13.66 26.87 17.62 57.60Composite Monitor for Fresno 90 91 92 92 365 20.11 9.46 13.01 25.22 16.95 47.4660190008 87 90 88 91 356 27.61 8.32 10.70 28.71 18.84 66.9560195001 29 13 14 27 83 23.70 7.16 9.91 24.91 16.42 61.0160195025 29 14 15 30 88 24.91 8.73 9.65 24.10 16.85 57.53Composite Monitor for Fresno 90 91 92 92 365 25.41 8.07 10.09 25.90 17.37 57.42Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.20062007AnnualAverage(ug/m3)98thPercentile(ug/m3)2005MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-11Table A-7. Air Quality Data for HoustonQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4482010024 26 31 22 15 94 11.77 14.39 17.17 11.83 13.79 26.00482010026 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 12.88 24.61482011034 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 15.89 30.10Composite Monitor for Houston 90 91 92 92 365 11.28 14.12 15.48 13.16 13.51 25.12482010024 15 13 13 13 54 10.92 11.66 15.97 12.58 12.78 23.804820100260 0 0 0 0 --- --- --- --- --- ---4820100550 0 0 0 0 --- --- --- --- --- ---482010058 26 29 29 29 113 9.74 12.34 9.04 9.82 10.24 21.934820110340 0 0 0 0 --- --- --- --- --- ---482011035 85 87 88 88 348 13.98 18.15 17.38 14.48 16.00 32.01Composite Monitor for Houston 90 91 92 92 365 11.55 14.05 14.13 12.29 13.01 23.67482010024 15 14 13 0 42 11.01 12.82 14.64 --- --- ---4820100260 0 0 0 0 --- --- --- --- --- ---4820100550 0 0 0 0 --- --- --- --- --- ---482010058 26 30 30 30 116 9.40 10.96 11.84 11.75 10.99 25.484820110340 0 0 0 0 --- --- --- --- --- ---482011035 87 91 91 82 351 14.42 17.02 16.62 14.50 15.64 32.00Composite Monitor for Houston 90 91 92 92 365 11.61 13.60 14.36 13.13 13.18 23.26Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-12Table A-8. Air Quality Data for Los AngelesQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q460370002 65 78 87 62 292 11.37 13.97 20.71 21.78 16.96 51.5660371002 29 25 30 22 106 17.01 13.75 18.55 21.95 17.82 50.4760371103 90 84 87 89 350 15.26 13.78 19.62 22.48 17.79 52.9160371201 25 29 28 22 104 12.27 11.97 15.01 16.18 13.86 35.6960371301 29 26 28 31 114 16.68 13.28 18.15 21.75 17.46 47.1860371602 29 9 9 29 76 16.90 11.63 17.13 22.31 16.99 52.6560372005 30 26 26 31 113 12.98 12.95 17.15 17.28 15.09 42.7160374002 87 82 88 67 324 13.39 11.54 16.21 22.56 15.93 40.1160374004 90 84 87 83 344 12.64 10.83 15.63 19.59 14.67 37.4460379033 28 30 27 18 103 8.18 8.27 9.96 9.00 8.85 15.96Composite Monitor for LosAngeles 90 91 92 92 365 13.67 12.26 16.78 19.49 15.55 38.7560370002 66 73 84 55 278 12.62 16.17 16.95 15.87 15.40 36.8360371002 25 24 30 25 104 15.33 18.34 15.87 16.66 16.55 43.2160371103 89 82 85 74 330 14.49 14.69 16.34 16.80 15.58 38.5560371201 20 27 28 17 92 11.19 14.21 12.95 13.00 12.84 30.4260371301 28 28 27 24 107 17.62 14.76 15.11 19.26 16.69 43.9860371602 29 28 31 28 116 16.82 13.92 17.19 18.57 16.63 42.3460372005 29 27 28 29 113 12.85 14.64 13.46 12.51 13.37 31.9560374002 73 81 73 63 290 15.19 12.27 13.53 15.57 14.14 33.8960374004 89 86 79 66 320 14.35 11.99 14.21 17.22 14.44 34.1760379033 15 15 14 14 58 6.13 7.27 8.36 8.00 7.44 12.86Composite Monitor for LosAngeles90 91 92 92 365 13.66 13.83 14.40 15.35 14.31 29.93AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-13Table A-8 cont'd. Air Quality Data for Los AngelesQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q460370002 64 77 74 77 292 13.57 17.11 14.68 17.47 15.71 48.7160371002 23 26 27 22 98 13.64 15.96 15.36 22.47 16.86 45.3260371103 67 83 90 84 324 16.25 16.05 14.62 20.19 16.78 49.4160371201 22 26 28 19 95 9.50 13.24 12.55 17.72 13.25 28.9060371301 25 27 29 25 106 16.98 14.05 13.00 19.99 16.00 45.2260371602 27 27 21 26 101 16.75 14.01 15.18 20.45 16.60 49.4060372005 28 23 30 27 108 12.62 15.60 14.02 15.24 14.37 43.6260374002 76 86 88 82 332 15.45 12.42 11.50 19.04 14.60 39.9660374004 65 81 90 90 326 13.84 12.26 11.30 17.31 13.68 33.2560379033 15 15 15 15 60 6.73 7.67 9.00 8.67 8.02 19.28Composite Monitor for LosAngeles90 91 92 92 365 13.53 13.84 13.12 17.85 14.59 35.51Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-14Table A-9. Air Quality Data for New YorkQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4360050080 28 31 29 27 115 18.59 14.78 18.42 15.68 16.87 37.50360050083 30 31 30 31 122 13.77 12.21 16.90 12.71 13.90 36.05360050110 90 91 91 91 363 14.93 12.17 15.38 12.30 13.69 36.58360470122 28 30 28 27 113 16.04 13.74 17.31 14.13 15.31 35.94360610056* 30 31 30 31 122 18.44 15.51 19.16 15.17 17.07 39.93360610062* 27 31 30 31 119 17.14 13.84 18.34 13.54 15.71 38.96360610079* 30 31 30 31 122 14.60 13.12 17.03 12.56 14.33 36.18360610128* 25 31 30 31 117 17.74 14.11 18.37 15.21 16.36 37.66360610134* 0 0 0 0 0 --- --- --- --- --- ---360810124 89 79 62 74 304 13.02 10.44 15.21 10.84 12.38 34.28360850055 28 25 28 27 108 14.92 12.49 17.81 12.91 14.53 33.37360850067 24 28 28 30 110 12.60 10.75 16.17 10.41 12.48 33.00Composite Monitor for NewYork City - 1 90 91 92 92 365 15.62 13.02 17.28 13.22 14.78 31.19Composite Monitor for NewYork City - 2 90 91 92 92 365 16.98 14.15 18.22 14.12 15.87 32.81360050080 29 30 27 29 115 16.57 13.17 13.95 11.88 13.89 38.89360050083 30 30 29 29 118 13.44 11.06 13.34 10.33 12.04 34.80360050110 86 91 84 86 347 13.10 11.15 14.49 11.40 12.53 36.51360470122 28 30 29 25 112 15.00 12.49 14.75 9.00 12.81 37.06360610056* 30 30 27 30 117 16.61 14.03 14.41 12.59 14.41 40.60360610062* 30 28 28 27 113 14.33 13.00 13.82 9.86 12.75 35.73360610079* 30 30 31 29 120 14.12 12.08 13.32 10.59 12.53 36.92360610128* 26 30 29 29 114 15.79 13.07 14.39 12.64 13.97 37.84360610134* 0 0 0 0 0 --- --- --- --- --- ---360810124 69 86 84 76 315 11.17 10.67 13.68 10.91 11.61 33.10360850055 25 27 29 29 110 12.27 12.07 14.06 10.56 12.24 35.89360850067 30 26 31 29 116 10.01 10.49 12.60 8.54 10.41 31.85Composite Monitor for NewYork City - 1 90 91 92 92 365 13.86 12.12 13.89 10.75 12.65 30.36Composite Monitor for NewYork City - 290 91 92 92 365 15.21 13.04 13.99 11.42 13.42 33.78AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-15Table A-9 cont'd. Air Quality Data for New YorkQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4360050080 30 30 30 29 119 17.45 13.49 16.20 15.43 15.64 36.16360050083 30 30 30 29 119 14.14 11.72 13.91 12.87 13.16 32.50360050110 89 84 85 91 349 12.90 11.64 14.22 12.31 12.77 33.92360470122 29 30 28 30 117 13.67 12.82 15.92 13.00 13.85 33.38360610056* 30 27 31 30 118 18.43 14.73 15.99 15.29 16.11 36.12360610062* 27 0 0 0 27 15.84 --- --- --- --- ---360610079* 30 30 31 30 121 14.11 12.48 14.92 12.89 13.60 33.86360610128* 30 30 29 21 110 19.10 13.83 14.63 14.76 15.58 37.01360610134* 3 30 31 30 94 8.53 14.12 16.43 14.08 13.29 33.66360810124 74 86 80 92 332 11.34 10.66 12.30 11.35 11.41 30.81360850055 30 28 31 30 119 13.04 12.37 14.55 11.91 12.97 31.58360850067 27 30 26 26 109 10.60 10.49 14.29 10.54 11.48 28.56Composite Monitor for NewYork City - 190 91 92 92 365 14.60 12.58 14.85 13.13 13.79 29.12Composite Monitor for NewYork City - 290 91 92 92 365 16.87 13.79 15.49 14.25 15.10 30.12Note 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 in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-16Table A-10. Air Quality Data for PhiladelphiaQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4421010003 0 0 0 62 62 --- --- --- 14.35 --- ---421010004 55 61 78 74 268 13.23 13.06 17.26 13.28 14.21 35.83421010020 19 0 0 0 19 15.51 --- --- --- --- ---421010024 37 54 67 71 229 12.68 10.76 16.26 12.02 12.93 34.57421010047 19 28 26 12 85 16.99 12.04 18.91 12.31 15.06 37.70421010057 0 0 0 0 0 --- --- --- --- --- ---421010136 86 89 29 33 237 13.57 11.40 19.06 12.91 14.23 31.13Composite Monitor for Philadelphia 90 91 92 92 365 14.40 11.81 17.87 12.97 14.26 32.12421010003 85 26 0 0 111 12.21 8.74 --- --- --- ---421010004 81 70 53 84 288 12.74 11.85 17.23 12.41 13.56 38.08421010020 0 0 0 0 0 --- --- --- --- --- ---421010024 34 70 71 80 255 11.52 10.56 16.17 11.34 12.40 34.60421010047 40 67 45 47 199 14.44 14.57 18.04 15.04 15.52 35.91421010057 0 0 0 0 0 --- --- --- --- --- ---421010136 47 50 79 73 249 11.97 12.06 16.29 12.25 13.14 36.36Composite Monitor for Philadelphia 90 91 92 92 365 12.58 11.55 16.93 12.76 13.46 33.46421010003 0 0 0 0 0 --- --- --- --- --- ---421010004 87 71 86 90 334 13.61 13.19 15.15 12.96 13.73 34.61421010020 0 0 0 0 0 --- --- --- --- --- ---421010024 87 58 86 90 321 12.05 12.76 14.88 11.73 12.85 33.42421010047 71 59 90 92 312 14.49 13.05 16.33 13.43 14.32 35.07421010057 0 0 18 90 108 --- --- 10.96 13.13 --- ---421010136 75 65 72 82 294 12.60 13.38 14.36 12.99 13.33 31.53Composite Monitor for Philadelphia 90 91 92 92 365 13.19 13.09 14.33 12.85 13.37 32.44Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-17Table A-11. Air Quality Data for PhoenixQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q440130019 32 32 30 31 125 11.04 10.78 11.11 18.37 12.83 39.8840131003 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 12.84 34.7340137020 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 9.72 27.48Composite Monitor for Phoenix 90 91 92 92 365 10.34 9.87 9.01 13.62 10.71 26.0340130019 30 30 31 31 122 14.17 13.58 8.07 17.82 13.41 28.5140131003 26 28 31 31 116 8.87 9.52 8.92 11.33 9.66 20.0740134003 28 28 31 29 116 13.53 10.34 9.31 17.58 12.69 28.3840137020 29 30 31 30 120 8.09 7.98 7.14 9.12 8.08 15.3540139997 29 29 30 30 118 10.74 8.66 7.46 14.04 10.22 24.29Composite Monitor for Phoenix 90 91 92 92 365 11.08 10.01 8.18 13.98 10.81 26.8440130019 32 30 31 30 123 10.26 8.85 8.63 15.42 10.79 26.6340131003 29 28 30 30 117 7.66 10.45 9.50 11.27 9.72 18.2040134003 30 29 30 29 118 10.54 11.76 11.32 15.45 12.27 27.3340137020 30 30 31 20 111 5.85 7.81 7.35 8.21 7.31 13.4440139997 30 29 32 30 121 8.85 8.12 8.21 12.75 9.48 22.02Composite Monitor for Phoenix 90 91 92 92 365 8.63 9.40 9.00 12.62 9.91 18.70Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-18Table A-12. Air Quality Data for PittsburghQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4420030008 89 90 92 89 360 13.80 15.29 20.72 13.40 15.80 42.23420030021 28 27 30 27 112 12.91 14.99 22.00 11.49 15.35 35.01420030064 88 90 92 86 356 16.28 22.26 25.94 21.10 21.40 69.46420030067 26 28 29 27 110 12.32 13.95 20.35 10.26 14.22 33.87420030093 13 11 12 13 49 10.66 13.83 23.66 9.63 14.44 41.68420030095 14 13 14 15 56 12.79 14.49 21.55 9.83 14.67 36.09420030116 23 29 28 26 106 13.82 16.42 21.68 12.66 16.15 38.72420030133 14 13 13 9 49 13.54 12.62 20.51 9.51 14.04 27.32420031008 30 29 30 29 118 12.79 15.60 21.90 13.52 15.95 40.11420031301 29 29 29 26 113 14.39 16.86 23.90 13.37 17.13 38.22420033007 15 13 14 15 57 14.13 14.25 24.36 12.71 16.36 30.68420039002 13 13 14 15 55 12.95 14.01 21.32 11.25 14.88 37.93Composite Monitor for Pittsburgh 90 91 92 92 365 13.37 15.38 22.32 12.58 15.91 41.92420030008 85 89 91 92 357 11.60 13.28 20.19 12.54 14.40 37.44420030021 0 0 0 0 0 --- --- --- --- --- ---420030064 85 90 87 89 351 14.86 17.89 22.78 20.97 19.13 55.70420030067 23 26 28 21 98 9.61 9.52 16.39 9.06 11.14 28.04420030093 14 6 13 13 46 10.37 9.85 16.38 9.41 11.50 29.46420030095 13 13 13 14 53 10.02 10.97 18.22 10.31 12.38 36.70420030116 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 14.03 37.54420031301 26 28 29 29 112 12.56 14.55 19.89 13.11 15.03 37.73420033007 15 15 14 15 59 12.93 13.51 19.16 12.36 14.49 34.73420039002 0 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Pittsburgh 90 91 92 92 365 11.49 13.05 18.69 11.95 13.79 33.16AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-19Table A-12. Air Quality Data for PittsburghQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4420030008 85 86 86 89 346 11.80 14.72 20.30 12.74 14.89 39.35420030021 0 0 0 0 0 --- --- --- --- --- ---420030064 88 90 91 90 359 14.16 18.64 25.16 17.57 18.88 54.67420030067 19 25 28 26 98 10.28 13.40 19.46 10.73 13.47 40.80420030093 15 12 14 14 55 9.67 10.50 19.35 12.57 13.02 32.56420030095 14 13 15 14 56 10.96 9.89 20.79 12.90 13.64 32.40420030116 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 15.06 39.60420031301 28 27 31 26 112 14.02 15.18 21.90 15.16 16.56 43.57420033007 14 14 14 13 55 12.36 13.03 21.19 13.85 15.11 34.74420039002 0 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Pittsburgh 90 91 92 92 365 11.87 13.51 20.74 13.36 14.87 36.08Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-20Table A-13. Air Quality Data for Salt Lake CityQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4490350003 30 29 30 31 120 14.16 6.58 8.98 14.49 11.06 41.66490350012 82 89 85 85 341 16.73 9.59 12.68 17.24 14.06 43.36490351001 29 30 28 30 117 11.85 5.47 8.61 11.35 9.32 36.25490353006 88 90 90 85 353 13.95 6.27 9.56 14.17 10.99 43.23490353007 28 27 29 28 112 13.64 7.40 10.57 16.36 11.99 39.37490353008 30 31 24 31 116 9.90 6.03 7.76 7.45 7.79 26.61490353010 0 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Salt Lake City 90 91 92 92 365 13.37 6.89 9.69 13.51 10.87 36.45490350003 28 28 29 30 115 10.76 6.98 9.41 13.58 10.18 38.67490350012 76 87 82 90 335 11.80 11.22 14.19 14.91 13.03 37.93490351001 27 28 29 27 111 7.95 5.65 8.65 9.29 7.88 27.72490353006 88 90 90 88 356 10.59 7.21 8.54 12.37 9.68 37.54490353007 30 30 31 29 120 10.11 7.18 11.56 13.61 10.61 35.69490353008 29 26 30 30 115 6.14 6.85 9.26 7.09 7.33 21.97490353010 0 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Salt Lake City 90 91 92 92 365 9.56 7.51 10.27 11.81 9.79 29.80490350003 30 30 29 28 117 18.12 6.97 10.99 13.89 12.49 55.65490350012 80 86 0 0 166 20.84 11.45 --- --- --- ---490351001 24 30 31 26 111 11.42 6.44 10.08 9.71 9.41 29.84490353006 89 85 78 89 341 18.17 6.11 9.42 12.05 11.44 54.28490353007 29 29 29 31 118 17.72 7.17 11.53 13.42 12.46 50.13490353008 23 28 28 30 109 10.03 6.06 9.66 7.09 8.21 23.02490353010 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 11.39 49.06Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-21Table A-14. Air Quality Data for St. LouisQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4171190023* 28 28 29 29 114 18.01 19.10 21.49 16.95 18.89 41.17171190024* 00 0 0 0 --- --- --- --- --- ---171191007* 26 31 29 30 116 18.40 16.49 21.47 16.27 18.16 43.68171192009* 12 12 13 12 49 14.94 16.35 20.82 11.98 16.02 39.63171193007* 29 31 27 29 116 16.42 15.20 19.99 12.49 16.02 41.08171630010* 13 15 14 15 57 17.31 16.81 19.97 14.47 17.14 39.59171634001* 30 30 29 28 117 17.86 14.17 17.20 14.69 15.98 37.61290990012 90 87 90 91 358 15.22 14.69 19.26 12.42 15.40 39.86291890004* 29 29 28 31 117 16.01 12.64 17.80 11.87 14.58 37.57291892003* 57 30 29 31 147 16.73 14.15 18.44 12.65 15.49 40.00295100007* 88 88 83 81 340 16.99 14.67 18.92 12.87 15.86 38.44295100085* 90 86 78 88 342 16.78 14.46 19.67 13.33 16.06 39.81295100086* 84 26 30 29 169 15.11 14.34 18.43 13.14 15.26 39.57295100087* 90 87 82 81 340 17.02 14.80 18.74 12.94 15.88 40.80295100093* 00 0 0 0 --- --- --- --- --- ---Composite Monitor for St Louis - 1 90 91 92 92 365 16.68 15.22 19.40 13.54 16.21 37.87Composite Monitor for St Louis - 2 90 91 92 92 365 16.80 15.27 19.41 13.64 16.28 37.78171190023* 30 26 31 29 116 15.21 17.34 19.40 12.11 16.02 32.81171190024* 00 0 0 0 --- --- --- --- --- ---171191007* 27 24 24 27 102 14.95 16.12 20.18 14.05 16.32 36.24171192009* 15 15 14 16 60 12.59 13.35 13.49 12.92 13.08 27.28171193007* 28 30 31 31 120 13.08 12.00 16.47 10.87 13.11 27.54171630010* 12 14 15 14 55 14.18 13.75 15.72 14.48 14.53 29.18171634001* 28 28 31 29 116 13.43 12.87 15.20 12.00 13.38 27.92290990012 82 81 91 89 343 11.62 11.79 15.46 11.49 12.59 30.20291890004* 30 29 0 0 59 10.56 10.49 --- --- --- ---291892003* 29 29 28 26 112 11.36 10.69 13.87 11.00 11.73 27.61295100007* 78 88 91 90 347 12.27 11.82 15.89 12.51 13.12 29.39295100085* 86 77 84 92 339 13.04 12.46 15.26 12.68 13.36 28.52295100086* 30 30 31 29 120 11.94 11.55 15.48 10.90 12.47 30.46295100087* 85 90 86 91 352 12.92 12.32 16.17 13.18 13.65 29.60295100093* 00 0 0 0 --- --- --- --- --- ---Composite Monitor for St Louis - 1 90 91 92 92 365 12.86 12.81 16.05 12.35 13.52 25.08Composite Monitor for St Louis - 2 90 91 92 92 365 12.96 12.90 16.10 12.43 13.60 24.78AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-22Table A-14 cont'd. Air Quality Data for St. LouisQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4171190023* 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 15.11 35.86171192009* 15 12 14 13 54 14.31 16.02 15.66 13.51 14.88 34.98171193007* 29 28 26 30 113 12.42 14.84 17.39 12.32 14.24 34.45171630010* 13 13 14 14 54 14.94 17.65 15.94 13.79 15.58 33.08171634001* 26 30 31 29 116 13.35 13.95 14.83 10.90 13.26 32.27290990012 82 81 90 86 339 11.94 14.44 16.23 12.13 13.68 31.92291890004* 0 0 0 0 0 --- --- --- --- --- ---291892003* 89 90 91 90 360 11.63 12.96 15.25 12.49 13.09 30.28295100007* 88 91 91 92 362 12.56 14.50 16.13 12.97 14.04 31.61295100085* 90 88 89 90 357 12.59 13.79 16.09 13.30 13.94 32.06295100086* 27 30 0 0 57 11.79 14.50 --- --- --- ---295100087* 90 86 92 86 354 13.24 14.43 16.61 13.10 14.34 33.72295100093* 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 14.27 31.51Composite Monitor for St Louis - 2 90 91 92 92 365 13.11 14.79 16.28 13.13 14.33 31.52Note 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 missingvalues have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-23Table A-15. Air Quality Data for TacomaQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4530530029 29 30 30 31 120 16.46 5.34 7.13 17.07 11.50 40.42Composite Monitor for Tacoma 90 91 92 92 365 16.46 5.34 7.13 17.07 11.50 39.61530530029 30 30 31 26 117 8.92 5.89 7.45 15.93 9.55 39.82Composite Monitor for Tacoma 90 91 92 92 365 8.92 5.89 7.45 15.93 9.55 37.05530530029 29 28 31 29 117 13.76 5.94 5.23 13.76 9.67 45.11Composite Monitor for Tacoma 90 91 92 92 365 13.76 5.94 5.23 13.76 9.67 41.26Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)

# B-1 

# APPENDIX B: HYBRID (NON -PROPORTIONAL) AND PEAK

SHAVING ROLLBACK APPROACHES

# B-2 Appendix B. Methodologies for Rolling Back PM 2.5 

# Concentrations Due to Local Source 1

Impacts (hybrid non-proportional and peak shaving approaches) 2

3

During the last review of the Particulate Ma tter National Ambient Air Quality Standards 4

(NAAQS), a technique was employed to simulate fine particulate concentrations under a series 5

of attainment scenarios to determine the risk a ssociated with each. The “rolling back” of the 6

concentrations consisted of simply using a proportional rollback calculation where every 7

measured concentration value was multiplied by a constant to obtain a set of concentrations 8

which would meet alternative standard levels. This technique was reviewed by the Clean Air 9

Scientific Advisory Committee (CASAC) and was considered to be a satisfactory way to 10

simulate alternative PM 2.5 

# distributions. The rolled back values, however, constituted only a 11

regional reduction in PM concen trations without accounting in a ny way for emission reductions 12

at local point sources. 13

14

The Hybrid Non-Proportional Approach 15

16

For the current review, an alternative rollb ack approach reflecting the combined effects 17

of both local and regional reduction strategies was considered (this alternative approach is 18

referred to as the hybrid non-proportional approach in the risk assessment). In addition to 19

utilizing a traditional proportional rollback to represent the regional PM reductions, a distance20

weighted rollback was conducted on a subset of the 15 study areas which contain source-oriented 21

monitors measuring concentrations higher than th ose observed at other sites within a particular 22

area. 1 

# 23

Unique sites with high design values exceedi ng the NAAQS were further investigated to 24

determine if they were in close proximity to a large source of PM 2.5 

# (Figure B-1). The presence 25

of possible source-oriented sites in each area wa s visually determined using satellite photographs 26

provided by Google Earth. Areas where source-orie nted adjustments were made include Detroit 27

MI, Pittsburgh PA, St. Louis MO-IL, Baltimor e MD, New York NY, Los Angeles CA and 28

Birmingham AL. 29

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

Figure B-1. Example of a mo nitor, in Dearborn MI, located near a large source of 6

emissions 7

8

For those sites that were within proximity to a large emitter, the site’s measured 9

concentrations were reduced using a proporti onal rollback depending on the magnitude of the 10

reduction needed to either the highest 24-hour or annual design value of a non-source oriented 11

site within the area whose design va lues were close to those of the source oriented site (Figure B12

2). 13

14

# B-4 1

Figure B-2. Plot of the 24-ho ur versus the annual average PM 2.5 

# design values for 2

individual sites in Detroit MI 3

4

5

The fractional reduction made to the site near the point source was then weighted by the 6

inverse distance in kilometers between the source -oriented site and all of the other individual 7

sites in the area to determine their fractional reduc tions in relation to the s ource-oriented site. If 8

more than one source-oriented site was reduced, a distance-weighted average fractional reduction 9

was calculated and implemented across the non-source- oriented sites. Sites within one kilometer 10

of the source oriented site received the same amount of reduction as the source oriented site. An 11

example of the effect of this reduction techniqu e for Detroit is presented in Table B-1. For 12

Detroit, adjustments were based on the differenc e between the two sites’ annual design values. 13

14

# B-5 Table B-1. Comparison of the original a nd adjusted design values for Detroit, MI 1

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

Reduction of the concentrations of the source-oriented site reduced either the 24-hour or 4

annual design value of the site to either th e maximum non-source-orient ed site’s 24-hour or 5

annual design value. This did not necessarily mean that the adjusted values at the source6

oriented site met either the 24-hour or annual st andard after th e reduction. Since the adjusted 7

design values were calculated using the same data handling rules as contained within 40 CFR 8

Part 50 Appendix N, truncation or rounding of the adjusted concentrations could sometimes give 9

adjusted design values at the source-oriented site that were not exactly the same value as the 10

original design value at the reference site. However, they were usually within 1 ug/m 3 

# for the 11

24-hour standard and a few tenths of a microgr am per cubic meter for the annual standard. 12

13

The Peak Shaving Approach 14

15

The peak shaving approach was used to calculate annual averages for 2005, 2006, and 16

2007 at composite monitors for comparison with the composite monitor annual averages 17

# B-6 calculated using the proportional and hybrid rollback approaches. Because of time constraints, 1

we did not calculate health risk s when alternative standards are just met using the peak shaving 2

approach. However, because the C-R functions us ed in the risk assessment are almost linear, a 3

comparison of annual averages at composite monitors using th e three different methods for 4

simulating just attaining standards should gi ve a good idea of the corresponding estimates of 5

health risks when altern ative standards are just met (see S ection 3.5.4 for additional detail on the 6

composite monitor-based comparison of the three ro llback strategies completed as part of the 7

sensitivity analysis). 8

We applied the peak shaving method only in th ose cases in which the daily standard in a 9

location is controlling (i.e., the pe rcent rollback necessary to meet the daily standard is greater 10

than the percent rollback necessary to meet the annual standard in that location). Like the 11

proportional and hybrid rollback methods, the pe ak shaving method for calculating annual 12

averages at composite monitors starts with mon itor-specific quarterly averages that have been 13

calculated as described in Section 3.2.1. 14

In contrast to the proportional and hybrid rollback approaches, the peak shaving method 15

uses monitor-specific design values. For each m onitor, we compared the monitor-specific daily 16

design value to the daily standard and calcula ted the percent rollback necessary to get each 17

monitor above the 24hr standard le vel into attainment (using a form ula that is analogous to the 18

proportional rollback formula given in Section 3. 2.3.1). We then rolled back each quarterly 19

average at the monitor by this percent rollbac k. We calculated the average quarterly average 20

across all monitors in the locati on, for each quarter. Finally, we calculated the annual average at 21

the composite monitor under the standard by averag ing the four quarterly averages calculated on 22

the previous step. See Section 3.2.2 for more detail. 23

The results of the peak shaving analysis ar e presented, along with results based on the 24

hybrid and proportional rollback approaches, as pa rt of the sensitivity analysis results (see 25

Appendix F, Tables F-49 and F-50). 26

# C-1 

# APPENDIX C: EPI STUDY SP ECIFIC INFORMATION ON

PM 2.5

# C-2 Appendix C. Epidemiology Study -Specific Information for PM 2.5 

# Risk Assessment 1

2

This Appendix provides detailed summary in formation for the epidemiological studies 3

used to obtain the concentrati on-response (C-R) functions used in the risk assessment. For 4

additional details on selection of epidemiological studies and specification of the C-R functions, 5

see section 3.3.3. 6

7C-3Table C-1. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundMortality, all-cause All0.00431 0.00276 0.00583Mortality,cardiopulmonary401-440, 460-5190.00898 0.00677 0.01115Mortality, ischemicheart disease410-4140.01689 0.01363 0.02005Mortality, lung cancer 1620.00880 0.00325 0.01432Mortality, all-cause All0.00554 0.00354 0.00760Mortality,cardiopulmonary401-440, 460-5190.01293 0.01007 0.01587Mortality, ischemicheart disease410-4140.02167 0.01748 0.02585Mortality, lung cancer 1620.01293 0.00554 0.02029Mortality, all-cause All0.00686 0.00315 0.01053Mortality, ischemicheart disease410-4140.02437 0.01450 0.03429Mortality, all-cause All0.10966 0.06758 0.15306Mortality,cardiopulmonary401-440, 460-5190.17225 0.11261 0.23161Mortality, ischemicheart disease410-4140.35942 0.24629 0.47210Mortality, lung cancer 1620.19284 0.09861 0.28797Mortality, all-cause AllSix U.S. Cities 0.00414 0.00414 0.02071Mortality,cardiopulmonary400-440, 485-4950.00561 0.00561 0.02789Mortality, lung cancer 162-0.01133 -0.01133 0.04525Krewski et al.(2009) - exposureperiod 1979-1983Krewski et al.(2009) - exposureperiod 1999-2000Krewski et al.(2000) [reanalysisof Six Cities Study]nonelog-linear NationalNational30+ none n/a annual mean30+ log-linear none annual mean30+log-linear (randomeffects)none n/a annual meann/aannual mean25+ log-linear none n/a annual mean30+ log-log n/aHealth Effects Associatedwith Long-Term Exposure to PM2.5:C-4Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundNortheast 0.00107 0.00079 0.00136Northwest 0.00074 -0.00176 0.00324Southeast 0.00029 -0.00019 0.00077Southwest 0.00053 0.00000 0.00104Northeast 0.00028 -0.00017 0.00072Northwest 0.00019 -0.00255 0.00294Southeast 0.00035 -0.00044 0.00113Southwest 0.00094 0.00022 0.00166Ito et al. (2007) ER visits, asthma 493 all ages log-linear noneavg of 0-and 1-day24-hr avg. New York 0.00453 0.00286 0.00621log-linear, GAM(stringent), 30 df0.00099 0.00010 0.00188log-linear, GAM(stringent), 100 df0.00097 0.00014 0.00180log-linear, GLM,100 df0.00097 -0.00002 0.00196log-linear, GAM(stringent), 100 df0.00178 0.00075 0.00281log-linear, GLM,100 df0.00188 0.00067 0.00309log-linear, GAM(stringent), 30 df0.00103 0.00015 0.00191log-linear, GAM(stringent), 100 df0.00080 -0.00003 0.00163log-linear, GLM,100 df0.00069 -0.00032 0.00170log-linear, GAM(stringent), 100 df0.00091 -0.00013 0.00195log-linear, GLM,100 df0.00091 -0.00035 0.00217Los AngelesHealth Effects Associated with Short-Term Exposure to PM2.5:0-day2-day24-hr avg.24-hr avg.65+65+log-linearlog-linearMoolgavkar (2003)[reanalysis ofMoolgavkar(2000a)]426–427, 428,430–438; 410–414,429; 440–449490–492; 464–466,480–487nonenone24-hr avg.noneCOnoneCO0 day1 dayall ages390-429HA (unscheduled),respiratoryBell et al. (2008)Mortality,cardiovascularHA (unscheduled),cardiovascularC-5Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundlog-linear, GAM(stringent), 30 df0.00054 -0.00007 0.00115log-linear, GLM, 30df0.00040 -0.00034 0.00114log-linear, GAM(stringent), 100 df0.00032 -0.00023 0.00087log-linear, GLM,100 df0.00030 -0.00043 0.00103log-linear, GAM(stringent), 30 df0.00059 0.00000 0.00118log-linear, GLM, 30df0.00055 -0.00017 0.00127log-linear, GAM(stringent), 100 df0.00010 -0.00046 0.00066log-linear, GLM,100 df-0.00001 -0.00099 0.00097log-linear, GAM(stringent), 30 df-0.00053 -0.00131 0.00025log-linear, GAM(stringent), 100 df-0.00033 -0.00105 0.00039log-linear, GLM,100 df-0.00033 -0.00117 0.000510 day 0.00054 -0.00007 0.001151 day 0.00059 0.00000 0.001182 day 0.00038 -0.00019 0.000953 day -0.00015 -0.00073 0.000434 day -0.00009 -0.00064 0.000465 day -0.00056 -0.00115 0.00003Mortality, nonaccidentalall ages<800log-linear, GAM(stringent), 30 dfnonenoneMoolgavkar (2003)[reanalysis ofMoolgavkar(2000a)]Los Angeles24-hr avg.24-hr avg.CO 1 day 24-hr avg.1 day0 day24-hr avg.noneC-6Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundlog-linear, GAM(stringent), 30 df-0.00056 -0.00300 0.00188log-linear, GAM(stringent), 100 df-0.00142 -0.00380 0.00096log-linear, GLM,100 df-0.00121 -0.00407 0.00165log-linear, GAM(stringent), 30 df0.00038 -0.00210 0.00286log-linear, GAM(stringent), 100 df0.00086 -0.00158 0.00330log-linear, GLM,100 df0.00020 -0.00282 0.00322log-linear, GAM(stringent), 30 df0.00158 0.00091 0.00225log-linear, GAM(stringent), 100 df0.00116 0.00050 0.00182log-linear, GLM,100 df0.00126 0.00045 0.00207log-linear, GAM(stringent), 100 df0.00039 -0.00044 0.00122log-linear, GLM,100 df0.00058 -0.00041 0.00157log-linear, GAM(stringent), 30 df0.00139 0.00069 0.00209log-linear, GAM(stringent), 100 df0.00113 0.00046 0.00180log-linear, GLM,100 df0.00120 0.00038 0.00202log-linear, GAM(stringent), 100 df0.00024 -0.00065 0.00113log-linear, GLM,100 df0.00027 -0.00075 0.00129CO 1 day 24-hr avg.24-hr avg.24-hr avg.1 daynonenone 0 day 24-hr avg.CO 0 day 24-hr avg.none 0 day 24-hr avg.Los AngelesLos Angeles490-4961 daynone390-429 65+all agesMoolgavkar (2003)[reanalysis ofMoolgavkar(2000b)]Moolgavkar (2003)[reanalysis ofMoolgavkar(2000a)]HA, cardiovascularMortality, respiratory(COPD+)C-7Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundlog-linear, GAM(stringent), 30 df0.00167 0.00068 0.00266log-linear, GAM(stringent), 100 df0.00138 0.00052 0.00224log-linear, GLM,100 df0.00149 0.00041 0.00257log-linear, GAM(stringent), 30 df0.00119 0.00022 0.00216log-linear, GAM(stringent), 100 df0.00075 -0.00011 0.00161log-linear, GLM,100 df0.00077 -0.00027 0.00181log-linear, GAM(stringent), 30 df0.00185 0.00082 0.00288log-linear, GAM(stringent), 100 df0.00114 0.00021 0.00207log-linear, GLM,100 df0.00103 -0.00012 0.002180 day 0.00042 -0.00091 0.001751 day -0.00004 -0.00161 0.001532 day 0.00035 -0.00102 0.001723 day -0.00109 -0.00238 0.00020ER visits,cardiovascular410–414, 427, 428,433–437, 440,443–445, 451–453all ages log-linear noneavg of 0-,1-day, and 2-day24-hr avg. Atlanta 0.00046 -0.00064 0.00154ER visits, respiratory493, 786.07, 786.09;491, 492, and 496;460–465, 460.0, and477; 480–486; 466.1,466.11, and 466.19all ages log-linear noneavg of 0-,1-day, and 2-day24-hr avg. Atlanta 0.00046 -0.00046 0.00136HA, respiratory(COPD+)Moolgavkar (2003)[reanalysis ofMoolgavkar(2000c)]Tolbert et al.(2007)490-496 all ageslog-linear, GAM(stringent), 100 dfLos Angelesnone 1 day 24-hr avg.none 0 day 24-hr avg.NO2 24-hr avg.none 2 day 24-hr avg.C-8Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundAtlanta 0.00066 -0.00066 0.00198Baltimore 0.00128 -0.00009 0.00265Birmingham -0.00002 -0.00140 0.00135Dallas 0.00086 -0.00056 0.00228Detroit 0.00097 -0.00012 0.00205Fresno 0.00082 -0.00056 0.00219Houston 0.00084 -0.00056 0.00223Los Angeles -0.00018 -0.00080 0.00044New York 0.00196 0.00114 0.00278Philadelphia 0.00179 0.00046 0.00313Phoenix 0.00142 -0.00006 0.00291Pittsburgh 0.00102 -0.00020 0.00225Salt Lake City 0.00117 -0.00027 0.00260St. Louis 0.00158 0.00035 0.00282Tacoma 0.00104 -0.00055 0.0026224-hr avg.avg of 0-and 1-dayall agesI01-I59 nonelog-linearMortality,cardiovascularZanobetti andSchwartz (2009)C-9Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundAtlanta 0.00094 0.00018 0.00170Baltimore 0.00135 0.00054 0.00215Birmingham 0.00032 -0.00050 0.00115Dallas 0.00112 0.00027 0.00198Detroit 0.00068 -0.00012 0.00147Fresno 0.00096 0.00014 0.00178Houston 0.00104 0.00021 0.00188Los Angeles 0.00016 -0.00023 0.00055New York 0.00132 0.00077 0.00186Philadelphia 0.00126 0.00046 0.00206Phoenix 0.00110 0.00018 0.00202Pittsburgh 0.00104 0.00030 0.00177Salt Lake City 0.00105 0.00021 0.00188St. Louis 0.00105 0.00030 0.00180Tacoma 0.00117 0.00020 0.00214Zanobetti andSchwartz (2009)Mortality, nonaccidentalall agesA00-R99 24-hr avg.avg of 0-and 1-daynonelog-linearC-10Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundAtlanta 0.00121 -0.00048 0.00290Baltimore 0.00211 0.00039 0.00384Birmingham 0.00096 -0.00076 0.00268Dallas 0.00093 -0.00084 0.00270Detroit 0.00169 0.00008 0.00330Fresno 0.00175 0.00006 0.00344Houston 0.00211 0.00033 0.00388Los Angeles 0.00112 0.00011 0.00213New York 0.00216 0.00075 0.00356Philadelphia 0.00157 -0.00015 0.00329Phoenix 0.00194 0.00015 0.00374Pittsburgh 0.00149 -0.00014 0.00313Salt Lake City 0.00194 0.00024 0.00364St. Louis 0.00132 -0.00034 0.00298Tacoma 0.00179 -0.00005 0.0036324-hr avg.avg of 0-and 1-daynonelog-linearall agesJ00-J99Zanobetti andSchwartz (2009)Mortality, respiratoryC-11Table C-2. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundWinter 0.00199 0.00138 0.00260Spring 0.00095 0.00032 0.00157Summer 0.00055 0.00008 0.00101Fall 0.00102 0.00048 0.00157Winter 0.00085 -0.00420 0.00589Spring -0.00007 -0.01324 0.01309Summer -0.00156 -0.01651 0.01337Fall -0.00067 -0.00721 0.00587Winter 0.00105 -0.00007 0.00219Spring 0.00075 -0.00026 0.00176Summer -0.00067 -0.00161 0.00026Fall 0.00017 -0.00072 0.00106Winter 0.00076 -0.00025 0.00177Spring 0.00176 -0.00087 0.00441Summer -0.00121 -0.00502 0.00262Fall 0.00030 -0.00098 0.00158Winter 0.00079 -0.00021 0.00178Spring 0.00004 -0.00088 0.00097Summer 0.00077 -0.00001 0.00155Fall 0.00012 -0.00082 0.00106Winter -0.00006 -0.00674 0.00663Spring 0.00226 -0.01539 0.01991Summer 0.00074 -0.02074 0.02220Fall -0.00074 -0.01062 0.00915Winter 0.00040 -0.00146 0.00224Spring 0.00075 -0.00082 0.00231Summer -0.00052 -0.00209 0.00105Fall 0.00014 -0.00130 0.00158Winter 0.00119 -0.00010 0.00249Spring 0.00104 -0.00220 0.00430Summer 0.00238 -0.00264 0.00741Fall 0.00097 -0.00137 0.00330none New York April-August 0.00759 0.00486 0.01032O3 New York April-August 0.00602 0.00322 0.00883NO2 New York April-August 0.00334 0.00029 0.00640CO New York April-August 0.00647 0.00356 0.00939SO2 New York April-August 0.00469 0.00163 0.00775Bell et al. (2008)Ito et al. (2007) ER visits, asthmaHA (unscheduled),respiratoryHA (unscheduled),cardiovascular493490–492;464–466,480–487426–427, 428,430–438;410–414, 429;440–449none65+65+0-day2-dayavg of 0-and 1-dayall agesNortheastNorthwestSoutheastSouthwestNortheastNorthwestSoutheastSouthwestC-12Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundAtlanta Winter 0.00135 -0.00193 0.00462Atlanta Spring 0.00076 -0.00273 0.00425Atlanta Summer 0.00062 -0.00222 0.00347Atlanta Fall -0.00018 -0.00293 0.00257Baltimore Winter 0.00104 -0.00196 0.00405Baltimore Spring 0.00085 -0.00269 0.00438Baltimore Summer 0.00067 -0.00251 0.00384Baltimore Fall 0.00296 -0.00017 0.00609Birmingham Winter 0.00080 -0.00283 0.00443Birmingham Spring 0.00016 -0.00333 0.00365Birmingham Summer -0.00004 -0.00301 0.00293Birmingham Fall -0.00189 -0.00485 0.00106Dallas Winter 0.00120 -0.00214 0.00454Dallas Spring 0.00125 -0.00222 0.00472Dallas Summer 0.00115 -0.00223 0.00453Dallas Fall -0.00022 -0.00349 0.00306Detroit Winter -0.00006 -0.00203 0.00191Detroit Spring 0.00166 -0.00045 0.00378Detroit Summer 0.00136 -0.00099 0.00371Detroit Fall 0.00226 -0.00001 0.00452Fresno Winter -0.00033 -0.00201 0.00135Fresno Spring 0.00050 -0.00138 0.00238Fresno Summer 0.00019 -0.00173 0.00211Fresno Fall 0.00071 -0.00105 0.00248Houston Winter 0.00070 -0.00285 0.00425Houston Spring 0.00013 -0.00347 0.00373Houston Summer 0.00183 -0.00142 0.00509Houston Fall 0.00046 -0.00246 0.00337Los Angeles Winter -0.00014 -0.00109 0.00080Los Angeles Spring 0.00007 -0.00113 0.00127Los Angeles Summer -0.00106 -0.00253 0.00042Los Angeles Fall 0.00000 -0.00099 0.00099all agesavg of 0-and 1-dayZanobetti andSchwartz (2009)Mortality, short-termcardiovascularI01-I59 noneC-13Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundNew York Winter 0.00204 0.00048 0.00360New York Spring 0.00231 0.00050 0.00412New York Summer 0.00202 0.00038 0.00366New York Fall 0.00205 0.00047 0.00363Philadelphia Winter 0.00214 -0.00042 0.00470Philadelphia Spring 0.00153 -0.00135 0.00441Philadelphia Summer 0.00178 -0.00082 0.00438Philadelphia Fall 0.00300 0.00044 0.00555Phoenix Winter ---1--- ---Phoenix Spring --- --- ---Phoenix Summer --- --- ---Phoenix Fall --- --- ---Pittsburgh Winter 0.00150 -0.00102 0.00401Pittsburgh Spring 0.00284 0.00026 0.00543Pittsburgh Summer 0.00085 -0.00148 0.00318Pittsburgh Fall 0.00047 -0.00185 0.00279Salt Lake City Winter --- --- ---Salt Lake City Spring --- --- ---Salt Lake City Summer --- --- ---Salt Lake City Fall --- --- ---St. Louis Winter -0.00013 -0.00297 0.00270St. Louis Spring 0.00278 -0.00013 0.00568St. Louis Summer 0.00188 -0.00084 0.00459St. Louis Fall 0.00253 -0.00022 0.00527Tacoma Winter 0.00006 -0.00182 0.00193Tacoma Spring 0.00020 -0.00173 0.00212Tacoma Summer 0.00025 -0.00168 0.00219Tacoma Fall 0.00053 -0.00136 0.00242Mortality, short-termcardiovascularnoneZanobetti andSchwartz (2009)I01-I59 all agesavg of 0-and 1-dayC-14Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundAtlanta Winter 0.00133 0.00020 0.00246Atlanta Spring 0.00123 0.00007 0.00238Atlanta Summer 0.00078 -0.00027 0.00184Atlanta Fall 0.00069 -0.00035 0.00172Baltimore Winter 0.00126 0.00016 0.00236Baltimore Spring 0.00119 0.00002 0.00236Baltimore Summer 0.00100 -0.00011 0.00212Baltimore Fall 0.00129 0.00017 0.00240Birmingham Winter 0.00097 -0.00022 0.00216Birmingham Spring 0.00105 -0.00012 0.00222Birmingham Summer 0.00049 -0.00061 0.00160Birmingham Fall 0.00035 -0.00074 0.00144Dallas Winter 0.00099 -0.00017 0.00215Dallas Spring 0.00090 -0.00027 0.00208Dallas Summer 0.00106 -0.00008 0.00221Dallas Fall 0.00132 0.00018 0.00247Detroit Winter -0.00009 -0.00125 0.00107Detroit Spring 0.00174 0.00043 0.00304Detroit Summer 0.00090 -0.00053 0.00233Detroit Fall 0.00072 -0.00066 0.00210Fresno Winter 0.00002 -0.00159 0.00163Fresno Spring 0.00225 -0.00021 0.00471Fresno Summer 0.00054 -0.00217 0.00325Fresno Fall 0.00088 -0.00090 0.00266Houston Winter 0.00106 -0.00011 0.00223Houston Spring 0.00129 0.00010 0.00248Houston Summer 0.00092 -0.00023 0.00207Houston Fall 0.00092 -0.00015 0.00199Los Angeles Winter 0.00012 -0.00059 0.00083Los Angeles Spring 0.00059 -0.00031 0.00149Los Angeles Summer -0.00084 -0.00208 0.00039Los Angeles Fall -0.00002 -0.00067 0.00064avg of 0-and 1-dayZanobetti andSchwartz (2009)Mortality, short-term nonaccidentalA00-R99 all ages noneC-15Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundNew York Winter 0.00168 0.00061 0.00275New York Spring 0.00123 0.00001 0.00245New York Summer 0.00074 -0.00029 0.00177New York Fall 0.00181 0.00078 0.00285Philadelphia Winter 0.00195 0.00041 0.00350Philadelphia Spring 0.00078 -0.00090 0.00247Philadelphia Summer 0.00064 -0.00089 0.00217Philadelphia Fall 0.00200 0.00050 0.00350Phoenix Winter --- --- ---Phoenix Spring --- --- ---Phoenix Summer --- --- ---Phoenix Fall --- --- ---Pittsburgh Winter 0.00135 -0.00013 0.00283Pittsburgh Spring 0.00193 0.00034 0.00352Pittsburgh Summer 0.00090 -0.00047 0.00227Pittsburgh Fall 0.00062 -0.00073 0.00197Salt Lake City Winter 0.00113 -0.00013 0.00240Salt Lake City Spring 0.00152 -0.00047 0.00352Salt Lake City Summer 0.00106 -0.00095 0.00308Salt Lake City Fall 0.00131 -0.00051 0.00314St. Louis Winter 0.00054 -0.00055 0.00164St. Louis Spring 0.00136 0.00025 0.00247St. Louis Summer 0.00097 -0.00009 0.00203St. Louis Fall 0.00129 0.00022 0.00236Tacoma Winter 0.00006 -0.00236 0.00249Tacoma Spring 0.00154 -0.00123 0.00431Tacoma Summer 0.00088 -0.00203 0.00378Tacoma Fall 0.00145 -0.00099 0.00389A00-R99Zanobetti andSchwartz (2009)Mortality, short-term non-accidentalall ages noneavg of 0-and 1-dayC-16Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundAtlanta Winter 0.00093 -0.00144 0.00329Atlanta Spring 0.00035 -0.00205 0.00275Atlanta Summer 0.00077 -0.00155 0.00310Atlanta Fall 0.00096 -0.00134 0.00325Baltimore Winter 0.00107 -0.00127 0.00340Baltimore Spring 0.00144 -0.00097 0.00384Baltimore Summer 0.00116 -0.00120 0.00353Baltimore Fall 0.00103 -0.00134 0.00340Birmingham Winter 0.00043 -0.00197 0.00282Birmingham Spring 0.00079 -0.00160 0.00318Birmingham Summer -0.00018 -0.00252 0.00217Birmingham Fall 0.00145 -0.00087 0.00377Dallas Winter 0.00040 -0.00198 0.00278Dallas Spring 0.00106 -0.00135 0.00347Dallas Summer 0.00060 -0.00180 0.00300Dallas Fall 0.00038 -0.00202 0.00278Detroit Winter 0.00104 -0.00128 0.00335Detroit Spring 0.00226 -0.00015 0.00467Detroit Summer 0.00253 0.00009 0.00498Detroit Fall 0.00247 0.00001 0.00492Fresno Winter -0.00022 -0.00423 0.00380Fresno Spring 0.00496 -0.00093 0.01085Fresno Summer 0.00263 -0.00375 0.00900Fresno Fall 0.00099 -0.00383 0.00580Houston Winter 0.00138 -0.00102 0.00377Houston Spring 0.00129 -0.00114 0.00372Houston Summer 0.00100 -0.00140 0.00341Houston Fall 0.00092 -0.00143 0.00327Los Angeles Winter 0.00165 -0.00016 0.00345Los Angeles Spring 0.00237 -0.00018 0.00493Los Angeles Summer -0.00134 -0.00500 0.00233Los Angeles Fall -0.00003 -0.00190 0.00183all ages noneavg of 0-and 1-dayZanobetti andSchwartz (2009)Mortality, short-termrespiratoryJ00-J99C-17Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundNew York Winter 0.00334 0.00122 0.00547New York Spring 0.00172 -0.00058 0.00403New York Summer 0.00157 -0.00066 0.00381New York Fall 0.00235 0.00013 0.00457Philadelphia Winter 0.00217 -0.00030 0.00463Philadelphia Spring 0.00219 -0.00033 0.00471Philadelphia Summer 0.00182 -0.00068 0.00432Philadelphia Fall 0.00186 -0.00062 0.00435Phoenix Winter 0.00251 -0.00253 0.00755Phoenix Spring 0.00538 -0.00140 0.01215Phoenix Summer 0.00577 -0.00083 0.01238Phoenix Fall 0.00887 0.00285 0.01489Pittsburgh Winter 0.00134 -0.00110 0.00377Pittsburgh Spring 0.00223 -0.00024 0.00470Pittsburgh Summer 0.00188 -0.00052 0.00428Pittsburgh Fall 0.00231 -0.00009 0.00472Salt Lake City Winter 0.00301 -0.00088 0.00690Salt Lake City Spring 0.00438 -0.00459 0.01336Salt Lake City Summer -0.00353 -0.01304 0.00598Salt Lake City Fall -0.00138 -0.00915 0.00639St. Louis Winter 0.00019 -0.00212 0.00250St. Louis Spring 0.00123 -0.00112 0.00357St. Louis Summer 0.00060 -0.00171 0.00292St. Louis Fall 0.00127 -0.00106 0.00360Tacoma Winter 0.00011 -0.00563 0.00585Tacoma Spring 0.00287 -0.00349 0.00924Tacoma Summer 0.00190 -0.00467 0.00848Tacoma Fall 0.00138 -0.00458 0.00733J00-J99 none1 --- indicates that results were not available.Zanobetti andSchwartz (2009)Mortality, short-termrespiratoryavg of 0-and 1-dayall ages

# D-1 

# APPENDIX D: SUPPLEMENT TO THE REPRESENTATIVENESS

ANALYSIS OF THE 15 URBAN STUDY AREAS

# D-2 Appendix D. Supplement to the Representativeness Analysis of the 15 Urban Study Areas 1

(additional graphical comparisons of di stributions for key contributors to PM 2.5 

# risk ) 2

3

Following the analysis discussed in Sec tion 4.4, this appendix provides graphical 4

comparisons of the empirical distributions of components of the risk function, and additional 5

variables that have been identif ied as potentially influencing the risk associated with PM 6

exposures. 7

In each graph, the orange line represents th e empirical cumulative distribution function 8

(CDF) for the complete set of data available for the variable. In some cases, this may encompass 9

all counties in the U.S., while in others it may be based on a subset of the U.S., usually for large 10

urban areas. The green line in each graph repr esents the empirical cumulative distribution 11

function for the variable based only on the da ta available for the se t of urban case study 12

locations. The black squares at the bottom of each graph represents the specific value of the 13

variable for one of the case study locations, with the line showing where that value intersects the 14

two empirical CDFs. 15

# D-3 Comparison of Urban Case Study Area Po pulation 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 fo r 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 fo r 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 fo r 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 PM 2.5

# D-8 Comparison of Urban Case St udy Area 98th %ile PM2.5 wi th 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 fo r Key Elements of the Risk Equation: 98 th



# %ile Daily Average PM 2.5

# 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 PM 2.5 

# > 35 μg/m 3

# D-10 Comparison of Urban Case Study All Cause Mo rtality 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 fo r 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 fo r 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 Estima te from Zanobetti and Schwartz (2008)

# D-15 Comparison of Urban Case Study PM Cardiovascular Mortality Risk ( β ) to U.S.

Distribution of PM Card iovascular 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 Es timate 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 Populat ion 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 PM 2.5



# D.2.1. Demographic Variables

Figure D-15. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : Population Density

# D-18 Comparison of Urban Case Study Area Unempl oyment 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 PM 2.5 

# : 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 PM 2.5 

# : % 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 PM 2.5 

# : Per Capita Personal Income

# D-21 Comparison of Urban Case Study Air Conditi oning 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 PM 2.5 

# : Air Conditioning Prevalence

# D-22 Comparison of Urban Case Study Air Conditi oning 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 PM 2.5 

# : % Non-White Population

# D-23 Comparison of Urban Case Study Area Angi na/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 PM 2.5 

# : 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 PM 2.5 

# : Asthma Prevalence

# D-25 Comparison of Urban Case Study Area Diabet es 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 PM 2.5 

# : Diabetes Prevalence

# D-26 Comparison of Urban Case Study Area Heart At tack 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 PM 2.5 

# : Heart Attack Prevalence

# D-27 Comparison of Urban Case Study Area Obesit y 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 PM 2.5 

# : 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 PM 2.5 

# : Stroke Prevalence

# D-29 Comparison of Urban Case Study Area Smoki ng 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 PM 2.5 

# : Smoking Prevalence

# D-30 Comparison of Urban Case Study Area Exerci se 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 PM 2.5 

# : 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 PM 2.5 

# : 4 th 

# Highest Daily Max 8-

hour Average

# D-32 Comparison of Urban Case Study Mobile Sour ce 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 Tota l 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 PM 2.5 

# : % Mobile Source Direct PM 2.5 

# 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 PM 2.5 

# : 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 PM 2.5 

# : July Relative Humidity Long Term Average

# E-1 

# APPENDIX E: RISK ESTI MATES (CORE ANALYSIS)

# E-1 Appendix E. Risk Estimates (core analysis) 1

2

This Appendix provides detailed risk estimates generated for the core analysis for the 15 3

urban study areas. The tables cove r all of the air quality scenar ios modeled, including recent 4

conditions, the current standard, and alternative st andard levels. For additional detail on the types 5

of risk metrics (and figures summarizing key me trics) presented in th is Appendix, see section 6

4.0. 7

8E-2Table E-1. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a RecentYear (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25649 575 513 451 389 451 379(421 - 873) (373 - 774) (333 - 692) (292 - 608) (252 - 525) (292 - 608) (245 - 512)597 548 502 442 382 426 303(387 - 803) (355 - 738) (325 - 676) (286 - 596) (247 - 516) (276 - 574) (196 - 409)424 297 262 227 192 227 160(275 - 571) (192 - 400) (170 - 354) (147 - 307) (124 - 260) (147 - 307) (103 - 216)379 379 379 379 336 379 336(245 - 511) (245 - 511) (245 - 511) (245 - 511) (218 - 454) (245 - 511) (218 - 454)798 580 573 502 431 442 303(518 - 1073) (376 - 782) (371 - 772) (325 - 677) (279 - 581) (286 - 597) (196 - 410)254 89 89 89 89 59 28(165 - 342) (57 - 120) (57 - 120) (57 - 120) (57 - 120) (38 - 79) (18 - 38)609 557 491 426 360 426 360(394 - 820) (360 - 751) (318 - 663) (275 - 575) (233 - 486) (275 - 575) (233 - 486)2333 1045 1045 1045 919 719 390(1514 - 3141) (676 - 1413) (676 - 1413) (676 - 1413) (593 - 1242) (464 - 972) (252 - 528)2000 1477 1477 1410 1205 1100 721(1297 - 2693) (956 - 1992) (956 - 1992) (912 - 1902) (779 - 1627) (711 - 1486) (465 - 975)521 455 455 406 348 345 233(338 - 703) (295 - 614) (295 - 614) (263 - 548) (225 - 470) (223 - 465) (151 - 315)483 483 483 483 433 420 263(312 - 652) (312 - 652) (312 - 652) (312 - 652) (280 - 586) (271 - 568) (170 - 356)593 387 387 363 318 289 190(385 - 798) (251 - 523) (251 - 523) (235 - 490) (206 - 430) (187 - 390) (122 - 257)102 29 29 29 29 10 0(66 - 138) (19 - 39) (19 - 39) (19 - 39) (19 - 39) (7 - 14) (0 - 0)826 700 634 557 480 543 383(536 - 1111) (454 - 943) (411 - 855) (361 - 752) (310 - 648) (351 - 732) (248 - 518)123 80 80 80 80 53 26(80 - 166) (52 - 109) (52 - 109) (52 - 109) (52 - 109) (34 - 72) (17 - 36)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-3Table E-2. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a RecentYear (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25668 592 528 464 400 464 390(434 - 899) (384 - 797) (342 - 712) (301 - 626) (259 - 540) (301 - 626) (253 - 527)483 441 400 347 295 333 225(313 - 651) (285 - 594) (259 - 539) (225 - 469) (191 - 398) (216 - 450) (145 - 304)399 276 243 209 176 209 144(259 - 536) (179 - 373) (157 - 328) (135 - 283) (114 - 238) (135 - 283) (93 - 195)284 284 284 284 247 284 247(183 - 383) (183 - 383) (183 - 383) (183 - 383) (160 - 334) (183 - 383) (160 - 334)576 398 392 334 276 285 172(373 - 776) (257 - 537) (253 - 529) (216 - 451) (178 - 373) (184 - 386) (111 - 233)265 94 94 94 94 63 32(172 - 356) (61 - 127) (61 - 127) (61 - 127) (61 - 127) (41 - 85) (20 - 43)589 537 472 407 342 407 342(381 - 794) (348 - 725) (306 - 638) (263 - 550) (221 - 462) (263 - 550) (221 - 462)2054 863 863 863 745 561 257(1332 - 2767) (557 - 1166) (557 - 1166) (557 - 1166) (481 - 1008) (362 - 759) (166 - 348)1548 1096 1096 1038 861 771 444(1002 - 2087) (708 - 1481) (708 - 1481) (671 - 1403) (556 - 1164) (498 - 1043) (286 - 601)471 409 409 363 308 305 200(305 - 636) (265 - 552) (265 - 552) (235 - 490) (199 - 417) (197 - 412) (129 - 271)512 512 512 512 460 446 281(331 - 691) (331 - 691) (331 - 691) (331 - 691) (297 - 622) (288 - 603) (181 - 380)468 290 290 270 231 205 120(303 - 631) (187 - 392) (187 - 392) (174 - 364) (149 - 312) (133 - 278) (78 - 163)84 16 16 16 16 0 0(54 - 113) (10 - 21) (10 - 21) (10 - 21) (10 - 21) (0 - 0) (0 - 0)618 514 458 394 329 382 249(401 - 834) (332 - 693) (296 - 619) (255 - 532) (213 - 445) (247 - 515) (160 - 336)83 47 47 47 47 25 2(54 - 112) (30 - 64) (30 - 64) (30 - 64) (30 - 64) (16 - 33) (1 - 3)Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-4Table E-3. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a RecentYear (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski etal. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25642 567 505 442 379 442 370(416 - 864) (368 - 764) (327 - 680) (286 - 596) (245 - 512) (286 - 596) (239 - 499)482 440 399 347 294 333 225(313 - 650) (285 - 593) (258 - 538) (224 - 468) (190 - 398) (215 - 449) (145 - 304)418 291 257 222 187 222 155(271 - 563) (189 - 393) (166 - 347) (144 - 300) (121 - 253) (144 - 300) (100 - 209)317 317 317 317 278 317 278(205 - 428) (205 - 428) (205 - 428) (205 - 428) (180 - 375) (205 - 428) (180 - 375)607 424 418 358 299 308 192(393 - 818) (274 - 572) (270 - 564) (232 - 484) (193 - 404) (199 - 417) (124 - 260)279 101 101 101 101 69 36(181 - 375) (65 - 137) (65 - 137) (65 - 137) (65 - 137) (44 - 93) (23 - 49)615 561 494 427 359 427 359(398 - 829) (363 - 757) (320 - 667) (276 - 577) (232 - 486) (276 - 577) (232 - 486)2134 911 911 911 791 601 289(1384 - 2874) (588 - 1232) (588 - 1232) (588 - 1232) (511 - 1070) (388 - 813) (187 - 392)1812 1316 1316 1253 1058 959 600(1174 - 2443) (852 - 1777) (852 - 1777) (810 - 1692) (684 - 1430) (620 - 1296) (387 - 811)466 405 405 359 304 301 197(302 - 629) (262 - 546) (262 - 546) (232 - 484) (197 - 411) (195 - 407) (127 - 266)433 433 433 433 385 371 216(280 - 586) (280 - 586) (280 - 586) (280 - 586) (248 - 520) (240 - 502) (139 - 292)527 339 339 316 274 247 156(342 - 710) (219 - 457) (219 - 457) (205 - 427) (177 - 371) (160 - 334) (100 - 211)120 38 38 38 38 17 0(78 - 162) (24 - 51) (24 - 51) (24 - 51) (24 - 51) (11 - 23) (0 - 0)679 568 509 441 373 428 287(440 - 915) (368 - 766) (330 - 687) (285 - 596) (241 - 504) (277 - 578) (186 - 389)87 50 50 50 50 27 4(56 - 118) (32 - 68) (32 - 68) (32 - 68) (32 - 68) (17 - 36) (2 - 5)Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest whole number.Numbers in parentheses are 95% confidenceor credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-5Table E-4. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/254.3% 3.8% 3.4% 3% 2.6% 3% 2.5%(2.8% - 5.8%) (2.5% - 5.1%) (2.2% - 4.6%) (1.9% - 4%) (1.7% - 3.5%) (1.9% - 4%) (1.6% - 3.4%)4.2% 3.9% 3.6% 3.1% 2.7% 3% 2.1%(2.7% - 5.7%) (2.5% - 5.2%) (2.3% - 4.8%) (2% - 4.2%) (1.8% - 3.7%) (2% - 4.1%) (1.4% - 2.9%)4.3% 3% 2.7% 2.3% 2% 2.3% 1.6%(2.8% - 5.8%) (2% - 4.1%) (1.7% - 3.6%) (1.5% - 3.1%) (1.3% - 2.6%) (1.5% - 3.1%) (1.1% - 2.2%)3% 3% 3% 3% 2.6% 3% 2.6%(1.9% - 4%) (1.9% - 4%) (1.9% - 4%) (1.9% - 4%) (1.7% - 3.5%) (1.9% - 4%) (1.7% - 3.5%)4.5% 3.3% 3.2% 2.8% 2.4% 2.5% 1.7%(2.9% - 6%) (2.1% - 4.4%) (2.1% - 4.3%) (1.8% - 3.8%) (1.6% - 3.3%) (1.6% - 3.3%) (1.1% - 2.3%)4.6% 1.6% 1.6% 1.6% 1.6% 1.1% 0.5%(3% - 6.1%) (1% - 2.2%) (1% - 2.2%) (1% - 2.2%) (1% - 2.2%) (0.7% - 1.4%) (0.3% - 0.7%)3.3% 3% 2.6% 2.3% 1.9% 2.3% 1.9%(2.1% - 4.4%) (1.9% - 4%) (1.7% - 3.6%) (1.5% - 3.1%) (1.2% - 2.6%) (1.5% - 3.1%) (1.2% - 2.6%)4.1% 1.8% 1.8% 1.8% 1.6% 1.3% 0.7%(2.7% - 5.5%) (1.2% - 2.5%) (1.2% - 2.5%) (1.2% - 2.5%) (1% - 2.2%) (0.8% - 1.7%) (0.4% - 0.9%)3.8% 2.8% 2.8% 2.7% 2.3% 2.1% 1.4%(2.5% - 5.1%) (1.8% - 3.8%) (1.8% - 3.8%) (1.7% - 3.6%) (1.5% - 3.1%) (1.3% - 2.8%) (0.9% - 1.8%)3.6% 3.1% 3.1% 2.8% 2.4% 2.4% 1.6%(2.3% - 4.8%) (2% - 4.2%) (2% - 4.2%) (1.8% - 3.8%) (1.5% - 3.2%) (1.5% - 3.2%) (1% - 2.2%)2.1% 2.1% 2.1% 2.1% 1.9% 1.8% 1.1%(1.4% - 2.8%) (1.4% - 2.8%) (1.4% - 2.8%) (1.4% - 2.8%) (1.2% - 2.5%) (1.2% - 2.5%) (0.7% - 1.5%)4.3% 2.8% 2.8% 2.6% 2.3% 2.1% 1.4%(2.8% - 5.7%) (1.8% - 3.8%) (1.8% - 3.8%) (1.7% - 3.5%) (1.5% - 3.1%) (1.3% - 2.8%) (0.9% - 1.8%)2.2% 0.6% 0.6% 0.6% 0.6% 0.2% 0%(1.4% - 2.9%) (0.4% - 0.8%) (0.4% - 0.8%) (0.4% - 0.8%) (0.4% - 0.8%) (0.1% - 0.3%) (0% - 0%)4.4% 3.7% 3.4% 3% 2.5% 2.9% 2%(2.8% - 5.9%) (2.4% - 5%) (2.2% - 4.5%) (1.9% - 4%) (1.6% - 3.4%) (1.9% - 3.9%) (1.3% - 2.7%)2.4% 1.6% 1.6% 1.6% 1.6% 1.1% 0.5%(1.6% - 3.3%) (1% - 2.1%) (1% - 2.1%) (1% - 2.1%) (1% - 2.1%) (0.7% - 1.4%) (0.3% - 0.7%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-6Table E-5. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/254.3% 3.8% 3.4% 3% 2.6% 3% 2.5%(2.8% - 5.8%) (2.5% - 5.1%) (2.2% - 4.6%) (1.9% - 4%) (1.7% - 3.5%) (1.9% - 4%) (1.6% - 3.4%)3.4% 3.1% 2.8% 2.5% 2.1% 2.4% 1.6%(2.2% - 4.6%) (2% - 4.2%) (1.8% - 3.8%) (1.6% - 3.3%) (1.3% - 2.8%) (1.5% - 3.2%) (1% - 2.2%)4% 2.8% 2.4% 2.1% 1.8% 2.1% 1.5%(2.6% - 5.4%) (1.8% - 3.8%) (1.6% - 3.3%) (1.4% - 2.8%) (1.1% - 2.4%) (1.4% - 2.8%) (0.9% - 2%)2.2% 2.2% 2.2% 2.2% 1.9% 2.2% 1.9%(1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.2% - 2.5%) (1.4% - 2.9%) (1.2% - 2.5%)3.2% 2.2% 2.2% 1.9% 1.5% 1.6% 1%(2.1% - 4.4%) (1.4% - 3%) (1.4% - 3%) (1.2% - 2.5%) (1% - 2.1%) (1% - 2.2%) (0.6% - 1.3%)4.7% 1.7% 1.7% 1.7% 1.7% 1.1% 0.6%(3% - 6.3%) (1.1% - 2.2%) (1.1% - 2.2%) (1.1% - 2.2%) (1.1% - 2.2%) (0.7% - 1.5%) (0.4% - 0.8%)3.1% 2.8% 2.5% 2.1% 1.8% 2.1% 1.8%(2% - 4.1%) (1.8% - 3.8%) (1.6% - 3.3%) (1.4% - 2.9%) (1.1% - 2.4%) (1.4% - 2.9%) (1.1% - 2.4%)3.6% 1.5% 1.5% 1.5% 1.3% 1% 0.5%(2.3% - 4.8%) (1% - 2%) (1% - 2%) (1% - 2%) (0.8% - 1.8%) (0.6% - 1.3%) (0.3% - 0.6%)2.9% 2.1% 2.1% 2% 1.6% 1.4% 0.8%(1.9% - 3.9%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.6%) (1% - 2.2%) (0.9% - 2%) (0.5% - 1.1%)3.2% 2.8% 2.8% 2.5% 2.1% 2.1% 1.4%(2.1% - 4.4%) (1.8% - 3.8%) (1.8% - 3.8%) (1.6% - 3.4%) (1.4% - 2.9%) (1.4% - 2.8%) (0.9% - 1.9%)2.1% 2.1% 2.1% 2.1% 1.9% 1.9% 1.2%(1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.2% - 2.6%) (1.2% - 2.5%) (0.8% - 1.6%)3.4% 2.1% 2.1% 1.9% 1.7% 1.5% 0.9%(2.2% - 4.6%) (1.4% - 2.8%) (1.4% - 2.8%) (1.3% - 2.6%) (1.1% - 2.3%) (1% - 2%) (0.6% - 1.2%)1.7% 0.3% 0.3% 0.3% 0.3% 0% 0%(1.1% - 2.3%) (0.2% - 0.4%) (0.2% - 0.4%) (0.2% - 0.4%) (0.2% - 0.4%) (0% - 0%) (0% - 0%)#DIV/0! 2.7% 2.4% 2.1% 1.7% 2% 1.3%#DIV/0! (1.8% - 3.7%) (1.6% - 3.3%) (1.3% - 2.8%) (1.1% - 2.4%) (1.3% - 2.7%) (0.8% - 1.8%)3.3% 0.9% 0.9% 0.9% 0.9% 0.5% 0%(2.1% - 4.4%) (0.6% - 1.2%) (0.6% - 1.2%) (0.6% - 1.2%) (0.6% - 1.2%) (0.3% - 0.6%) (0% - 0.1%)Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted 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 coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-7Table E-6. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/254% 3.6% 3.2% 2.8% 2.4% 2.8% 2.3%(2.6% - 5.4%) (2.3% - 4.8%) (2% - 4.3%) (1.8% - 3.7%) (1.5% - 3.2%) (1.8% - 3.7%) (1.5% - 3.1%)3.4% 3.1% 2.8% 2.5% 2.1% 2.4% 1.6%(2.2% - 4.6%) (2% - 4.2%) (1.8% - 3.8%) (1.6% - 3.3%) (1.3% - 2.8%) (1.5% - 3.2%) (1% - 2.2%)4.2% 2.9% 2.6% 2.2% 1.9% 2.2% 1.5%(2.7% - 5.6%) (1.9% - 3.9%) (1.7% - 3.5%) (1.4% - 3%) (1.2% - 2.5%) (1.4% - 3%) (1% - 2.1%)2.4% 2.4% 2.4% 2.4% 2.1% 2.4% 2.1%(1.5% - 3.2%) (1.5% - 3.2%) (1.5% - 3.2%) (1.5% - 3.2%) (1.3% - 2.8%) (1.5% - 3.2%) (1.3% - 2.8%)3.4% 2.4% 2.4% 2% 1.7% 1.7% 1.1%(2.2% - 4.6%) (1.5% - 3.2%) (1.5% - 3.2%) (1.3% - 2.7%) (1.1% - 2.3%) (1.1% - 2.4%) (0.7% - 1.5%)4.9% 1.8% 1.8% 1.8% 1.8% 1.2% 0.6%(3.2% - 6.5%) (1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (0.8% - 1.6%) (0.4% - 0.9%)3.1% 2.9% 2.5% 2.2% 1.8% 2.2% 1.8%(2% - 4.2%) (1.8% - 3.9%) (1.6% - 3.4%) (1.4% - 2.9%) (1.2% - 2.5%) (1.4% - 2.9%) (1.2% - 2.5%)3.7% 1.6% 1.6% 1.6% 1.4% 1% 0.5%(2.4% - 5%) (1% - 2.1%) (1% - 2.1%) (1% - 2.1%) (0.9% - 1.9%) (0.7% - 1.4%) (0.3% - 0.7%)3.4% 2.5% 2.5% 2.3% 2% 1.8% 1.1%(2.2% - 4.6%) (1.6% - 3.3%) (1.6% - 3.3%) (1.5% - 3.2%) (1.3% - 2.7%) (1.2% - 2.4%) (0.7% - 1.5%)3.2% 2.8% 2.8% 2.5% 2.1% 2.1% 1.4%(2.1% - 4.3%) (1.8% - 3.8%) (1.8% - 3.8%) (1.6% - 3.3%) (1.4% - 2.8%) (1.3% - 2.8%) (0.9% - 1.8%)1.8% 1.8% 1.8% 1.8% 1.6% 1.5% 0.9%(1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (1% - 2.1%) (1% - 2%) (0.6% - 1.2%)3.8% 2.5% 2.5% 2.3% 2% 1.8% 1.1%(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% - 1.5%)2.4% 0.7% 0.7% 0.7% 0.7% 0.3% 0%(1.5% - 3.2%) (0.5% - 1%) (0.5% - 1%) (0.5% - 1%) (0.5% - 1%) (0.2% - 0.5%) (0% - 0%)3.6% 3% 2.7% 2.3% 2% 2.3% 1.5%(2.3% - 4.8%) (1.9% - 4%) (1.7% - 3.6%) (1.5% - 3.1%) (1.3% - 2.7%) (1.5% - 3.1%) (1% - 2.1%)1.7% 1% 1% 1% 1% 0.5% 0.1%(1.1% - 2.2%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.3% - 0.7%) (0% - 0.1%)Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-8Table E-7. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated with Long-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-13% 0% 11% 22% 32% 22% 34%(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 22%) (34% - 34%)-9% 0% 8% 19% 30% 22% 45%(-9% - -9%) (0% - 0%) (8% - 9%) (19% - 19%) (30% - 30%) (22% - 22%) (45% - 45%)-43% 0% 12% 23% 35% 23% 46%(-43% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 35%) (23% - 24%) (46% - 46%)0% 0% 0% 0% 11% 0% 11%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-38% 0% 1% 13% 26% 24% 48%(-37% - -38%) (0% - 0%) (1% - 1%) (13% - 14%) (26% - 26%) (24% - 24%) (48% - 48%)-187% 0% 0% 0% 0% 34% 68%(-185% - -188%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (34% - 34%) (68% - 68%)-9% 0% 12% 24% 35% 24% 35%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 35%) (23% - 24%) (35% - 35%)-123% 0% 0% 0% 12% 31% 63%(-122% - -124%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 31%) (63% - 63%)-35% 0% 0% 5% 18% 26% 51%(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (18% - 18%) (25% - 26%) (51% - 51%)-14% 0% 0% 11% 24% 24% 49%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 24%) (49% - 49%)0% 0% 0% 0% 10% 13% 46%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 46%)-53% 0% 0% 6% 18% 25% 51%(-53% - -54%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (25% - 26%) (51% - 51%)-255% 0% 0% 0% 0% 64% 100%(-254% - -256%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 64%) (100% - 100%)-18% 0% 9% 20% 31% 23% 45%(-18% - -18%) (0% - 0%) (9% - 9%) (20% - 21%) (31% - 32%) (22% - 23%) (45% - 45%)-53% 0% 0% 0% 0% 34% 67%(-53% - -54%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (34% - 34%) (67% - 67%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with Long-Term Exposure toPM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily(m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-9Table E-8. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated with Long-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-13% 0% 11% 22% 32% 22% 34%(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 22%) (34% - 34%)-10% 0% 9% 21% 33% 24% 49%(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 24%) (49% - 49%)-44% 0% 12% 24% 36% 24% 48%(-44% - -45%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 24%) (48% - 48%)0% 0% 0% 0% 13% 0% 13%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 0%) (13% - 13%)-45% 0% 1% 16% 31% 28% 57%(-45% - -45%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 31%) (28% - 28%) (57% - 57%)-182% 0% 0% 0% 0% 33% 66%(-180% - -184%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (66% - 66%)-10% 0% 12% 24% 36% 24% 36%(-10% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 24%) (36% - 36%)-138% 0% 0% 0% 14% 35% 70%(-137% - -139%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 14%) (35% - 35%) (70% - 70%)-41% 0% 0% 5% 21% 30% 59%(-41% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (30% - 30%) (59% - 60%)-15% 0% 0% 11% 25% 25% 51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (25% - 25%) (25% - 26%) (51% - 51%)0% 0% 0% 0% 10% 13% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 45%)-61% 0% 0% 7% 20% 29% 58%(-61% - -62%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 29%) (58% - 59%)-438% 0% 0% 0% 0% 100% 100%(-437% - -440%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%)-20% 0% 11% 23% 36% 26% 52%(-20% - -21%) (0% - 0%) (11% - 11%) (23% - 23%) (36% - 36%) (26% - 26%) (51% - 52%)-76% 0% 0% 0% 0% 48% 96%(-76% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (48% - 48%) (96% - 96%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with Long-Term Exposure toPM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily(m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-10Table E-9. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated with Long-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-13% 0% 11% 22% 33% 22% 35%(-13% - -13%) (0% - 0%) (11% - 11%) (22%- 22%) (33% - 33%) (22% - 22%) (35% - 35%)-10% 0% 9% 21% 33% 24% 49%(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 24%) (49% - 49%)-44% 0% 12% 24% 36% 24% 47%(-43% - -44%) (0% - 0%) (12% - 12%) (24%- 24%) (36% - 36%) (24% - 24%) (47% - 47%)0% 0% 0% 0% 12% 0% 12%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-43% 0% 1% 15% 30% 27% 55%(-43% - -44%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 30%) (27% - 27%) (55% - 55%)-176% 0% 0% 0% 0% 32% 64%(-175% - -178%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (32% - 32%) (64% - 64%)-10% 0% 12% 24% 36% 24% 36%(-9% - -10%) (0% - 0%) (12%- 12%) (24% - 24%) (36% - 36%) (24% - 24%) (36% - 36%)-134% 0% 0% 0% 13% 34% 68%(-133% - -135%) (0% - 0%) (0% - 0%) (0%- 0%) (13% - 13%) (34% - 34%) (68% - 68%)-38% 0% 0% 5% 20% 27% 54%(-37% - -38%) (0% - 0%) (0% - 0%) (5% - 5%) (20% - 20%) (27% - 27%) (54% - 55%)-15% 0% 0% 11% 25% 26% 51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (25% - 25%) (26% - 26%) (51% - 52%)0% 0% 0% 0% 11% 14% 50%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (50% - 50%)-56% 0% 0% 7% 19% 27% 54%(-55% - -56%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 19%) (27% - 27%) (54% - 54%)-218% 0% 0% 0% 0% 55% 100%(-217% - -219%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (55% - 55%) (100% - 100%)-20% 0% 10% 22% 34% 25% 49%(-19% - -20%) (0% - 0%) (10% - 10%) (22%- 22%) (34% - 34%) (25% - 25%) (49% - 50%)-74% 0% 0% 0% 0% 46% 93%(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with Long-Term Exposure toPM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily(m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-11Table E-10. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a RecentYear (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25830 736 657 578 499 578 487(531 - 1123) (470 - 997) (420 - 891) (369 - 785) (318 - 677) (369 - 785) (310 - 661)763 702 643 566 490 546 388(488 - 1033) (448 - 950) (410 - 871) (361 - 768) (312 - 665) (348 - 741) (247 - 528)543 380 336 292 247 292 205(347 - 734) (243 - 516) (214 - 457) (186 - 397) (157 - 336) (186 - 397) (130 - 280)486 486 486 486 431 486 431(310 - 659) (310 - 659) (310 - 659) (310 - 659) (275 - 586) (310 - 659) (275 - 586)1021 743 734 643 552 567 389(653 - 1380) (474 - 1008) (468 - 996) (410 - 874) (352 - 751) (361 - 770) (247 - 530)325 114 114 114 114 75 36(208 - 439) (72 - 155) (72 - 155) (72 - 155) (72 - 155) (48 - 103) (23 - 50)780 713 630 546 462 546 462(498 - 1058) (455 - 968) (401 - 856) (348 - 743) (294 - 629) (348 - 743) (294 - 629)2986 1342 1342 1342 1180 924 502(1910 - 4042) (854 - 1827) (854 - 1827) (854 - 1827) (750 - 1607) (587 - 1258) (318 - 684)2560 1893 1893 1808 1546 1412 926(1636 - 3468) (1207 - 2571) (1207 - 2571) (1152 - 2455) (984 - 2101) (898 - 1920) (588 - 1261)668 584 584 521 447 442 299(427 - 905) (372 - 792) (372 - 792) (332 - 707) (285 - 607) (282 - 601) (190 - 408)620 620 620 620 557 539 338(394 - 843) (394 - 843) (394 - 843) (394 - 843) (354 - 757) (343 - 734) (214 - 460)759 497 497 466 409 371 244(485 - 1026) (317 - 674) (317 - 674) (297 - 633) (260 - 555) (236 - 504) (155 - 332)132 37 37 37 37 13 0(84 - 179) (24 - 51) (24 - 51) (24 - 51) (24 - 51) (8 - 18) (0 - 0)1056 897 813 714 616 696 492(676 - 1429) (573 - 1215) (519 - 1102) (456 - 970) (392 - 836) (443 - 944) (313 - 669)158 103 103 103 103 69 34(101 - 215) (66 - 141) (66 - 141) (66 - 141) (66 - 141) (44 - 94) (21 - 46)Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-12Table E-11. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a RecentYear (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25854 758 677 595 513 595 501(547 - 1156) (484 - 1026) (432 - 918) (380 - 808) (327 - 697) (380 - 808) (319 - 681)619 565 513 446 378 428 289(395 - 839) (360 - 766) (327 - 696) (284 - 606) (241 - 515) (272 - 581) (184 - 394)510 354 312 269 226 269 186(326 - 691) (226 - 481) (198 - 423) (171 - 365) (144 - 307) (171 - 365) (118 - 253)364 364 364 364 317 364 317(232 - 495) (232 - 495) (232 - 495) (232 - 495) (202 - 432) (232 - 495) (202 - 432)737 510 503 429 355 366 222(471 - 1000) (325 - 694) (320 - 684) (273 - 584) (225 - 483) (233 - 499) (141 - 302)338 121 121 121 121 81 41(217 - 457) (77 - 164) (77 - 164) (77 - 164) (77 - 164) (51 - 110) (26 - 55)755 689 606 523 439 523 439(481 - 1024) (439 - 935) (386 - 823) (333 - 711) (279 - 598) (333 - 711) (279 - 598)2631 1108 1108 1108 958 721 331(1680 - 3565) (704 - 1509) (704 - 1509) (704 - 1509) (608 - 1305) (457 - 983) (210 - 451)1984 1407 1407 1333 1106 990 571(1265 - 2693) (895 - 1913) (895 - 1913) (848 - 1813) (703 - 1506) (629 - 1349) (362 - 779)604 525 525 466 396 392 257(386 - 819) (335 - 713) (335 - 713) (297 - 633) (252 - 538) (249 - 533) (163 - 350)657 657 657 657 591 572 361(418 - 893) (418 - 893) (418 - 893) (418 - 893) (376 - 803) (364 - 779) (229 - 492)599 372 372 346 297 264 155(383 - 813) (237 - 506) (237 - 506) (220 - 471) (189 - 404) (168 - 359) (98 - 211)107 20 20 20 20 0 0(68 - 146) (13 - 27) (13 - 27) (13 - 27) (13 - 27) (0 - 0) (0 - 0)792 659 588 506 423 490 319(506 - 1075) (420 - 894) (374 - 799) (322 - 688) (269 - 575) (312 - 666) (203 - 435)107 61 61 61 61 32 3(68 - 145) (38 - 83) (38 - 83) (38 - 83) (38 - 83) (20 - 43) (2 - 3)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-13Table E-12. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a RecentYear (2007) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25821 726 647 567 486 567 474(525 - 1112) (464 - 984) (413 - 877) (361 - 769) (310 - 661) (361 - 769) (302 - 644)618 564 512 445 378 427 289(394 - 838) (360 - 765) (326 - 695) (283 - 605) (240 - 514) (272 - 580) (184 - 393)535 374 330 285 241 285 199(342 - 724) (238 - 507) (210 - 448) (182 - 388) (153 - 327) (182 - 388) (126 - 271)407 407 407 407 356 407 356(259 - 553) (259 - 553) (259 - 553) (259 - 553) (227 - 485) (259 - 553) (227 - 485)778 544 536 460 384 396 247(496 - 1054) (346 - 739) (341 - 729) (293 - 626) (244 - 522) (252 - 539) (157 - 336)357 130 130 130 130 88 46(228 - 482) (82 - 177) (82 - 177) (82 - 177) (82 - 177) (56 - 120) (29 - 63)788 719 634 548 461 548 461(502 - 1069) (459 - 977) (404 - 861) (349 - 745) (293 - 628) (349 - 745) (293 - 628)2732 1170 1170 1170 1016 773 372(1746 - 3702) (744 - 1593) (744 - 1593) (744 - 1593) (645 - 1384) (490 - 1053) (236 - 508)2322 1689 1689 1607 1359 1232 771(1482 - 3148) (1076 - 2295) (1076 - 2295) (1023 - 2185) (864 - 1848) (783 - 1676) (489 - 1051)598 519 519 460 391 386 253(381 - 811) (331 - 704) (331 - 704) (293 - 625) (249 - 531) (246 - 525) (161 - 344)556 556 556 556 494 477 278(354 - 757) (354 - 757) (354 - 757) (354 - 757) (314 - 673) (303 - 650) (176 - 379)675 434 434 406 352 318 200(431 - 914) (277 - 590) (277 - 590) (258 - 552) (224 - 479) (202 - 432) (127 - 273)154 48 48 48 48 22 0(98 - 209) (31 - 66) (31 - 66) (31 - 66) (31 - 66) (14 - 30) (0 - 0)869 728 653 566 478 549 369(555 - 1178) (464 - 988) (416 - 887) (360 - 769) (304 - 651) (350 - 747) (235 - 503)112 64 64 64 64 35 5(71 - 152) (41 - 88) (41 - 88) (41 - 88) (41 - 88) (22 - 47) (3 - 6)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-14Table E-13. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/255.5% 4.9% 4.4% 3.8% 3.3% 3.8% 3.2%(3.5% - 7.4%) (3.1% - 6.6%) (2.8% - 5.9%) (2.4% - 5.2%) (2.1% - 4.5%) (2.4% - 5.2%) (2.1% - 4.4%)5.4% 5% 4.5% 4% 3.5% 3.9% 2.8%(3.5% - 7.3%) (3.2% - 6.7%) (2.9% - 6.2%) (2.6% - 5.4%) (2.2% - 4.7%) (2.5% - 5.2%) (1.8% - 3.7%)5.5% 3.9% 3.4% 3% 2.5% 3% 2.1%(3.5% - 7.5%) (2.5% - 5.3%) (2.2% - 4.7%) (1.9% - 4%) (1.6% - 3.4%) (1.9% - 4%) (1.3% - 2.8%)3.8% 3.8% 3.8% 3.8% 3.4% 3.8% 3.4%(2.4% - 5.1%) (2.4% - 5.1%) (2.4% - 5.1%) (2.4% - 5.1%) (2.1% - 4.6%) (2.4% - 5.1%) (2.1% - 4.6%)5.7% 4.2% 4.1% 3.6% 3.1% 3.2% 2.2%(3.7% - 7.7%) (2.7% - 5.6%) (2.6% - 5.6%) (2.3% - 4.9%) (2% - 4.2%) (2% - 4.3%) (1.4% - 3%)5.8% 2% 2% 2% 2% 1.4% 0.7%(3.7% - 7.9%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (0.9% - 1.8%) (0.4% - 0.9%)4.2% 3.8% 3.4% 2.9% 2.5% 2.9% 2.5%(2.7% - 5.7%) (2.4% - 5.2%) (2.2% - 4.6%) (1.9% - 4%) (1.6% - 3.4%) (1.9% - 4%) (1.6% - 3.4%)5.3% 2.4% 2.4% 2.4% 2.1% 1.6% 0.9%(3.4% - 7.1%) (1.5% - 3.2%) (1.5% - 3.2%) (1.5% - 3.2%) (1.3% - 2.8%) (1% - 2.2%) (0.6% - 1.2%)4.9% 3.6% 3.6% 3.4% 2.9% 2.7% 1.8%(3.1% - 6.6%) (2.3% - 4.9%) (2.3% - 4.9%) (2.2% - 4.7%) (1.9% - 4%) (1.7% - 3.6%) (1.1% - 2.4%)4.6% 4% 4% 3.6% 3.1% 3% 2.1%(2.9% - 6.2%) (2.6% - 5.4%) (2.6% - 5.4%) (2.3% - 4.9%) (2% - 4.2%) (1.9% - 4.1%) (1.3% - 2.8%)2.7% 2.7% 2.7% 2.7% 2.4% 2.3% 1.5%(1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.5% - 3.3%) (1.5% - 3.2%) (0.9% - 2%)5.5% 3.6% 3.6% 3.3% 2.9% 2.7% 1.8%(3.5% - 7.4%) (2.3% - 4.8%) (2.3% - 4.8%) (2.1% - 4.5%) (1.9% - 4%) (1.7% - 3.6%) (1.1% - 2.4%)2.8% 0.8% 0.8% 0.8% 0.8% 0.3% 0%(1.8% - 3.8%) (0.5% - 1.1%) (0.5% - 1.1%) (0.5% - 1.1%) (0.5% - 1.1%) (0.2% - 0.4%) (0% - 0%)5.6% 4.8% 4.3% 3.8% 3.3% 3.7% 2.6%(3.6% - 7.6%) (3% - 6.5%) (2.8% - 5.9%) (2.4% - 5.1%) (2.1% - 4.4%) (2.4% - 5%) (1.7% - 3.6%)3.1% 2% 2% 2% 2% 1.3% 0.7%(2% - 4.2%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (0.9% - 1.8%) (0.4% - 0.9%)Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-15Table E-14. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/255.5% 4.9% 4.4% 3.8% 3.3% 3.8% 3.2%(3.5% - 7.4%) (3.1% - 6.6%) (2.8% - 5.9%) (2.4% - 5.2%) (2.1% - 4.5%) (2.4% - 5.2%) (2.1% - 4.4%)4.4% 4% 3.6% 3.1% 2.7% 3% 2%(2.8% - 5.9%) (2.5% - 5.4%) (2.3% - 4.9%) (2% - 4.3%) (1.7% - 3.6%) (1.9% - 4.1%) (1.3% - 2.8%)5.1% 3.6% 3.1% 2.7% 2.3% 2.7% 1.9%(3.3% - 7%) (2.3% - 4.8%) (2% - 4.3%) (1.7% - 3.7%) (1.4% - 3.1%) (1.7% - 3.7%) (1.2% - 2.5%)2.8% 2.8% 2.8% 2.8% 2.4% 2.8% 2.4%(1.8% - 3.8%) (1.8% - 3.8%) (1.8% - 3.8%) (1.8% - 3.8%) (1.5% - 3.3%) (1.8% - 3.8%) (1.5% - 3.3%)4.1% 2.9% 2.8% 2.4% 2% 2.1% 1.2%(2.6% - 5.6%) (1.8% - 3.9%) (1.8% - 3.8%) (1.5% - 3.3%) (1.3% - 2.7%) (1.3% - 2.8%) (0.8% - 1.7%)6% 2.1% 2.1% 2.1% 2.1% 1.4% 0.7%(3.8% - 8.1%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (0.9% - 1.9%) (0.5% - 1%)3.9% 3.6% 3.1% 2.7% 2.3% 2.7% 2.3%(2.5% - 5.3%) (2.3% - 4.9%) (2% - 4.3%) (1.7% - 3.7%) (1.4% - 3.1%) (1.7% - 3.7%) (1.4% - 3.1%)4.6% 1.9% 1.9% 1.9% 1.7% 1.3% 0.6%(2.9% - 6.2%) (1.2% - 2.6%) (1.2% - 2.6%) (1.2% - 2.6%) (1.1% - 2.3%) (0.8% - 1.7%) (0.4% - 0.8%)3.7% 2.6% 2.6% 2.5% 2.1% 1.9% 1.1%(2.4% - 5.1%) (1.7% - 3.6%) (1.7% - 3.6%) (1.6% - 3.4%) (1.3% - 2.8%) (1.2% - 2.5%) (0.7% - 1.5%)4.2% 3.6% 3.6% 3.2% 2.7% 2.7% 1.8%(2.7% - 5.6%) (2.3% - 4.9%) (2.3% - 4.9%) (2% - 4.4%) (1.7% - 3.7%) (1.7% - 3.7%) (1.1% - 2.4%)2.7% 2.7% 2.7% 2.7% 2.5% 2.4% 1.5%(1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.6% - 3.4%) (1.5% - 3.3%) (1% - 2.1%)4.3% 2.7% 2.7% 2.5% 2.1% 1.9% 1.1%(2.8% - 5.9%) (1.7% - 3.7%) (1.7% - 3.7%) (1.6% - 3.4%) (1.4% - 2.9%) (1.2% - 2.6%) (0.7% - 1.5%)2.2% 0.4% 0.4% 0.4% 0.4% 0% 0%(1.4% - 3%) (0.3% - 0.6%) (0.3% - 0.6%) (0.3% - 0.6%) (0.3% - 0.6%) (0% - 0%) (0% - 0%)4.2% 3.5% 3.1% 2.7% 2.2% 2.6% 1.7%(2.7% - 5.7%) (2.2% - 4.7%) (2% - 4.2%) (1.7% - 3.6%) (1.4% - 3%) (1.6% - 3.5%) (1.1% - 2.3%)2.1% 1.2% 1.2% 1.2% 1.2% 0.6% 0%(1.3% - 2.8%) (0.7% - 1.6%) (0.7% - 1.6%) (0.7% - 1.6%) (0.7% - 1.6%) (0.4% - 0.8%) (0% - 0.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-16Table E-15. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/255.1% 4.6% 4.1% 3.6% 3% 3.6% 3%(3.3% - 7%) (2.9% - 6.2%) (2.6% - 5.5%) (2.3% - 4.8%) (1.9% - 4.1%) (2.3% - 4.8%) (1.9% - 4%)4.4% 4% 3.6% 3.2% 2.7% 3% 2%(2.8% - 5.9%) (2.5% - 5.4%) (2.3% - 4.9%) (2% - 4.3%) (1.7% - 3.6%) (1.9% - 4.1%) (1.3% - 2.8%)5.3% 3.7% 3.3% 2.8% 2.4% 2.8% 2%(3.4% - 7.2%) (2.4% - 5.1%) (2.1% - 4.5%) (1.8% - 3.9%) (1.5% - 3.3%) (1.8% - 3.9%) (1.3% - 2.7%)3% 3% 3% 3% 2.7% 3% 2.7%(1.9% - 4.1%) (1.9% - 4.1%) (1.9% - 4.1%) (1.9% - 4.1%) (1.7% - 3.6%) (1.9% - 4.1%) (1.7% - 3.6%)4.4% 3.1% 3% 2.6% 2.2% 2.2% 1.4%(2.8% - 6%) (2% - 4.2%) (1.9% - 4.1%) (1.7% - 3.5%) (1.4% - 3%) (1.4% - 3%) (0.9% - 1.9%)6.2% 2.3% 2.3% 2.3% 2.3% 1.5% 0.8%(4% - 8.4%) (1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1% - 2.1%) (0.5% - 1.1%)4% 3.7% 3.2% 2.8% 2.3% 2.8% 2.3%(2.6% - 5.4%) (2.3% - 5%) (2.1% - 4.4%) (1.8% - 3.8%) (1.5% - 3.2%) (1.8% - 3.8%) (1.5% - 3.2%)4.8% 2% 2% 2% 1.8% 1.3% 0.6%(3% - 6.4%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (1.1% - 2.4%) (0.9% - 1.8%) (0.4% - 0.9%)4.3% 3.2% 3.2% 3% 2.5% 2.3% 1.4%(2.8% - 5.9%) (2% - 4.3%) (2% - 4.3%) (1.9% - 4.1%) (1.6% - 3.4%) (1.5% - 3.1%) (0.9% - 2%)4.1% 3.6% 3.6% 3.2% 2.7% 2.7% 1.7%(2.6% - 5.6%) (2.3% - 4.8%) (2.3% - 4.8%) (2% - 4.3%) (1.7% - 3.7%) (1.7% - 3.6%) (1.1% - 2.4%)2.3% 2.3% 2.3% 2.3% 2% 1.9% 1.1%(1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1.3% - 2.7%) (1.2% - 2.6%) (0.7% - 1.5%)4.9% 3.2% 3.2% 2.9% 2.6% 2.3% 1.5%(3.1% - 6.6%) (2% - 4.3%) (2% - 4.3%) (1.9% - 4%) (1.6% - 3.5%) (1.5% - 3.1%) (0.9% - 2%)3% 1% 1% 1% 1% 0.4% 0%(1.9% - 4.1%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.3% - 0.6%) (0% - 0%)4.6% 3.8% 3.4% 3% 2.5% 2.9% 1.9%(2.9% - 6.2%) (2.4% - 5.2%) (2.2% - 4.7%) (1.9% - 4.1%) (1.6% - 3.4%) (1.8% - 3.9%) (1.2% - 2.7%)2.1% 1.2% 1.2% 1.2% 1.2% 0.7% 0.1%(1.3% - 2.9%) (0.8% - 1.7%) (0.8% - 1.7%) (0.8% - 1.7%) (0.8% - 1.7%) (0.4% - 0.9%) (0.1% - 0.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence orcredible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-17Table E-16. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated with Long-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-13% 0% 11% 21% 32% 21% 34%(-13% - -13%) (0% - 0%) (11% - 11%) (21% -22%) (32% - 32%) (21% - 22%) (34% - 34%)-9% 0% 8% 19% 30% 22% 45%(-9% - -9%) (0% - 0%) (8% - 8%) (19% - 19%) (30% - 30%) (22% - 22%) (44% - 45%)-43% 0% 12% 23% 35% 23% 46%(-42% - -43%) (0% - 0%) (12% - 12%) (23% -23%) (35% - 35%) (23% - 23%) (46% - 46%)0% 0% 0% 0% 11% 0% 11%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-37% 0% 1% 13% 26% 24% 48%(-37% - -38%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (24% - 24%) (47% - 48%)-185% 0% 0% 0% 0% 34% 68%(-183% - -187%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (34% - 34%) (68% - 68%)-9% 0% 12% 23% 35% 23% 35%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 35%) (23% - 24%) (35% - 35%)-122% 0% 0% 0% 12% 31% 63%(-121% - -124%) (0% - 0%) (0% - 0%) (0%- 0%) (12% - 12%) (31% - 31%) (63% - 63%)-35% 0% 0% 5% 18% 25% 51%(-35% - -36%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 26%) (51% - 51%)-14% 0% 0% 11% 23% 24% 49%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 24%) (49% - 49%)0% 0% 0% 0% 10% 13% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 46%)-53% 0% 0% 6% 18% 25% 51%(-52% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (25% - 25%) (51% - 51%)-254% 0% 0% 0% 0% 64% 100%(-253% - -256%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (64% - 64%) (100% - 100%)-18% 0% 9% 20% 31% 22% 45%(-18% - -18%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (22% - 23%) (45% - 45%)-53% 0% 0% 0% 0% 34% 67%(-53% - -53%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (67% - 67%)Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with Long-Term Exposure toPM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily(m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-18Table E-17. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated with Long-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-13% 0% 11% 21% 32% 21% 34%(-13% - -13%) (0% - 0%) (11% - 11%) (21% -22%) (32% - 32%) (21% - 22%) (34% - 34%)-10% 0% 9% 21% 33% 24% 49%(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 24%) (49% - 49%)-44% 0% 12% 24% 36% 24% 48%(-44% - -44%) (0% - 0%) (12% - 12%) (24% -24%) (36% - 36%) (24% - 24%) (47% - 48%)0% 0% 0% 0% 13% 0% 13%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 0%) (13% - 13%)-45% 0% 1% 16% 30% 28% 57%(-44% - -45%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 31%) (28% - 28%) (56% - 57%)-181% 0% 0% 0% 0% 33% 66%(-179% - -183%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (33% - 33%) (66% - 66%)-10% 0% 12% 24% 36% 24% 36%(-10% - -10%) (0% - 0%) (12% - 12%) (24% -24%) (36% - 36%) (24% - 24%) (36% - 36%)-137% 0% 0% 0% 14% 35% 70%(-136% - -139%) (0% - 0%) (0% - 0%) (0%- 0%) (14% - 14%) (35% - 35%) (70% - 70%)-41% 0% 0% 5% 21% 30% 59%(-41% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 30%) (59% - 60%)-15% 0% 0% 11% 25% 25% 51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 26%) (51% - 51%)0% 0% 0% 0% 10% 13% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 45%)-61% 0% 0% 7% 20% 29% 58%(-61% - -62%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 29%) (58% - 58%)-437% 0% 0% 0% 0% 100% 100%(-435% - -439%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (100% - 100%) (100% - 100%)-20% 0% 11% 23% 36% 26% 51%(-20% - -20%) (0% - 0%) (11% - 11%) (23% -23%) (36% - 36%) (26% - 26%) (51% - 52%)-76% 0% 0% 0% 0% 48% 96%(-76% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (48% - 48%) (96% - 96%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with Long-Term Exposure toPM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily(m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-19Table E-18. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated with Long-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-13% 0% 11% 22% 33% 22% 35%(-13% - -13%) (0% - 0%) (11% - 11%) (22% -22%) (33% - 33%) (22% - 22%) (35% - 35%)-10% 0% 9% 21% 33% 24% 49%(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 24%) (49% - 49%)-43% 0% 12% 24% 36% 24% 47%(-43% - -44%) (0% - 0%) (12% - 12%) (24% -24%) (35% - 36%) (24% - 24%) (47% - 47%)0% 0% 0% 0% 12% 0% 12%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-43% 0% 1% 15% 29% 27% 55%(-43% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 30%) (27% - 27%) (54% - 55%)-175% 0% 0% 0% 0% 32% 64%(-173% - -177%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (32% - 32%) (64% - 64%)-9% 0% 12% 24% 36% 24% 36%(-9% - -10%) (0% - 0%) (12% - 12%) (24% -24%) (36% - 36%) (24% - 24%) (36% - 36%)-134% 0% 0% 0% 13% 34% 68%(-132% - -135%) (0% - 0%) (0% - 0%) (0%- 0%) (13% - 13%) (34% - 34%) (68% - 68%)-37% 0% 0% 5% 20% 27% 54%(-37% - -38%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 20%) (27% - 27%) (54% - 55%)-15% 0% 0% 11% 25% 26% 51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (25% - 25%) (25% - 26%) (51% - 51%)0% 0% 0% 0% 11% 14% 50%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (50% - 50%)-55% 0% 0% 7% 19% 27% 54%(-55% - -56%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 19%) (27% - 27%) (54% - 54%)-217% 0% 0% 0% 0% 55% 100%(-216% - -218%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (55% - 55%) (100% - 100%)-19% 0% 10% 22% 34% 25% 49%(-19% - -20%) (0% - 0%) (10% - 10%) (22% -22%) (34% - 34%) (24% - 25%) (49% - 49%)-74% 0% 0% 0% 0% 46% 93%(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with Long-Term Exposure toPM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily(m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-20Table E-19. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25249 222 199 176 153 176 149(205 - 291) (182 - 260) (163 - 234) (144 - 207) (125 - 180) (144 - 207) (122 - 176)396 366 337 298 259 288 207(326 - 464) (301 - 429) (276 - 395) (244 - 351) (212 - 306) (236 - 339) (169 - 245)186 133 118 103 87 103 73(153 - 218) (109 - 156) (96 - 139) (84 - 121) (71 - 103) (84 - 121) (59 - 86)231 231 231 231 206 231 206(189 - 272) (189 - 272) (189 - 272) (189 - 272) (169 - 243) (189 - 272) (169 - 243)689 509 504 444 383 393 272(567 - 806) (418 - 599) (413 - 592) (363 - 523) (313 - 452) (321 - 463) (222 - 322)187 68 68 68 68 45 22(154 - 219) (56 - 81) (56 - 81) (56 - 81) (56 - 81) (37 - 54) (18 - 26)370 340 302 263 223 263 223(304 - 435) (278 - 400) (247 - 356) (215 - 310) (182 - 264) (215 - 310) (182 - 264)2124 984 984 984 867 682 373(1746 - 2489) (802 - 1163) (802 - 1163) (802 - 1163) (707 - 1026) (555 - 808) (303 - 443)2614 1959 1959 1874 1610 1475 976(2147 - 3068) (1603 - 2307) (1603 - 2307) (1533 - 2208) (1315 - 1900) (1204 - 1742) (795 - 1156)333 293 293 263 226 224 153(273 - 391) (240 - 345) (240 - 345) (215 - 309) (185 - 267) (183 - 264) (125 - 181)351 351 351 351 316 307 194(286 - 414) (286 - 414) (286 - 414) (286 - 414) (258 - 374) (250 - 362) (158 - 230)436 291 291 274 241 219 146(359 - 511) (238 - 343) (238 - 343) (224 - 323) (197 - 285) (179 - 259) (119 - 172)40 12 12 12 12 4 0(33 - 47) (9 - 14) (9 - 14) (9 - 14) (9 - 14) (3 - 5) (0 - 0)636 544 496 438 379 427 305(523 - 744) (447 - 639) (406 - 583) (359 - 516) (310 - 447) (350 - 503) (249 - 361)93 61 61 61 61 41 20(76 - 109) (50 - 72) (50 - 72) (50 - 72) (50 - 72) (33 - 48) (16 - 24)Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-21Table E-20. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2006) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25256 229 205 181 157 181 154(211 - 300) (188 - 268) (168 - 241) (149 - 214) (129 - 185) (149 - 214) (126 - 181)325 297 271 237 202 228 155(266 - 382) (244 - 350) (222 - 319) (194 - 279) (165 - 239) (186 - 268) (127 - 184)176 124 110 95 80 95 66(144 - 206) (101 - 146) (90 - 129) (77 - 112) (65 - 95) (77 - 112) (54 - 78)175 175 175 175 153 175 153(143 - 207) (143 - 207) (143 - 207) (143 - 207) (125 - 181) (143 - 207) (125 - 181)506 355 350 300 249 257 157(415 - 595) (290 - 418) (286 - 413) (244 - 354) (203 - 294) (209 - 304) (127 - 186)194 72 72 72 72 49 25(160 - 227) (59 - 85) (59 - 85) (59 - 85) (59 - 85) (40 - 58) (20 - 29)359 329 291 252 213 252 213(294 - 423) (269 - 388) (238 - 343) (206 - 298) (173 - 252) (206 - 298) (173 - 252)1884 815 815 815 707 534 247(1546 - 2212) (664 - 965) (664 - 965) (664 - 965) (575 - 837) (434 - 633) (200 - 293)2050 1470 1470 1394 1163 1043 606(1678 - 2413) (1200 - 1736) (1200 - 1736) (1138 - 1648) (947 - 1375) (850 - 1235) (492 - 719)303 264 264 236 201 199 132(248 - 356) (216 - 311) (216 - 311) (193 - 278) (164 - 238) (163 - 235) (107 - 156)372 372 372 372 335 325 207(303 - 439) (303 - 439) (303 - 439) (303 - 439) (273 - 396) (265 - 384) (168 - 245)349 220 220 205 177 157 93(286 - 410) (180 - 260) (180 - 260) (167 - 242) (144 - 209) (128 - 186) (76 - 110)33 6 6 6 6 0 0(27 - 39) (5 - 7) (5 - 7) (5 - 7) (5 - 7) (0 - 0) (0 - 0)484 405 363 314 263 304 200(397 - 569) (331 - 477) (297 - 428) (256 - 370) (215 - 311) (248 - 359) (163 - 237)63 36 36 36 36 19 2(51 - 75) (29 - 43) (29 - 43) (29 - 43) (29 - 43) (15 - 23) (1 - 2)Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:3The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-22Table E-21. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25247 220 197 173 149 173 146(203 - 290) (180 - 258) (161 - 231) (142 - 204) (122 - 176) (142 - 204) (119 - 172)324 297 271 236 202 227 155(266 - 381) (243 - 349) (221 - 319) (193 - 279) (165 - 238) (186 - 268) (126 - 184)184 131 116 101 85 101 71(151 - 216) (107 - 154) (95 - 136) (82 - 119) (70 - 101) (82 - 119) (58 - 84)195 195 195 195 172 195 172(159 - 230) (159 - 230) (159 - 230) (159 - 230) (140 - 203) (159 - 230) (140 - 203)532 377 372 321 269 277 174(436 - 625) (308 - 445) (304 - 439) (262 - 379) (219 - 318) (226 - 327) (142 - 206)204 77 77 77 77 53 28(169 - 239) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (43 - 63) (23 - 33)375 344 304 264 223 264 223(307 - 441) (281 - 405) (249 - 358) (215 - 312) (182 - 264) (215 - 312) (182 - 264)1953 860 860 860 749 572 278(1604 - 2293) (701 - 1018) (701 - 1018) (701 - 1018) (610 - 887) (465 - 678) (225 - 330)2384 1755 1755 1673 1421 1292 815(1955 - 2802) (1435 - 2070) (1435 - 2070) (1367 - 1974) (1160 - 1679) (1053 - 1527) (663 - 966)300 261 261 233 199 197 130(245 - 352) (214 - 308) (214 - 308) (190 - 275) (162 - 235) (160 - 232) (106 - 154)317 317 317 317 282 272 160(258 - 374) (258 - 374) (258 - 374) (258 - 374) (230 - 333) (222 - 322) (130 - 189)390 256 256 239 209 189 120(321 - 458) (209 - 302) (209 - 302) (196 - 283) (170 - 246) (154 - 223) (98 - 142)47 15 15 15 15 7 0(38 - 55) (12 - 18) (12 - 18) (12 - 18) (12 - 18) (6 - 8) (0 - 0)529 446 402 350 297 340 231(434 - 621) (365 - 525) (329 - 474) (286 - 413) (243 - 351) (278 - 401) (188 - 273)66 38 38 38 38 21 3(54 - 78) (31 - 46) (31 - 46) (31 - 46) (31 - 46) (17 - 25) (2 - 3)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidenceor credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-23Table E-22. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2515.8% 14.1% 12.7% 11.2% 9.7% 11.2% 9.5%(13% - 18.6%) (11.6% - 16.6%) (10.4% - 14.9%) (9.2%- 13.2%) (8% - 11.5%) (9.2% - 13.2%) (7.8% - 11.2%)15.6% 14.4% 13.2% 11.7% 10.2% 11.3% 8.1%(12.8% - 18.2%) (11.8% - 16.9%) (10.9% - 15.5%) (9.6% - 13.8%) (8.3% - 12%) (9.3% - 13.3%) (6.7% - 9.6%)15.9% 11.3% 10.1% 8.8% 7.5% 8.8% 6.2%(13.1% - 18.6%) (9.3% - 13.4%) (8.2% - 11.9%) (7.2%- 10.4%) (6.1% - 8.8%) (7.2% - 10.4%) (5.1% - 7.4%)11.1% 11.1% 11.1% 11.1% 9.9% 11.1% 9.9%(9.1% - 13.1%) (9.1% - 13.1%) (9.1% - 13.1%) (9.1% - 13.1%) (8.1% - 11.7%) (9.1% - 13.1%) (8.1% - 11.7%)16.4% 12.2% 12% 10.6% 9.1% 9.4% 6.5%(13.5% - 19.2%) (10% - 14.3%) (9.8% - 14.1%) (8.7% - 12.5%) (7.5% - 10.8%) (7.7% - 11.1%) (5.3% - 7.7%)16.8% 6.1% 6.1% 6.1% 6.1% 4.1% 2%(13.8% - 19.6%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (3.3% - 4.8%) (1.6% - 2.4%)12.2% 11.2% 9.9% 8.7% 7.4% 8.7% 7.4%(10% - 14.4%) (9.2% - 13.2%) (8.1% - 11.7%) (7.1% - 10.2%) (6% - 8.7%) (7.1% - 10.2%) (6% - 8.7%)15.2% 7% 7% 7% 6.2% 4.9% 2.7%(12.5% - 17.8%) (5.7% - 8.3%) (5.7% - 8.3%) (5.7% - 8.3%) (5.1% - 7.3%) (4% - 5.8%) (2.2% - 3.2%)14.1% 10.6% 10.6% 10.1% 8.7% 7.9% 5.3%(11.6% - 16.5%) (8.6% - 12.4%) (8.6% - 12.4%) (8.3% - 11.9%) (7.1% - 10.2%) (6.5% - 9.4%) (4.3% - 6.2%)13.3% 11.7% 11.7% 10.5% 9.1% 9% 6.1%(10.9% - 15.6%) (9.6% - 13.8%) (9.6% - 13.8%) (8.6% - 12.4%) (7.4% - 10.7%) (7.3% - 10.6%) (5% - 7.3%)8% 8% 8% 8% 7.2% 7% 4.4%(6.5% - 9.4%) (6.5% - 9.4%) (6.5% - 9.4%) (6.5% -9.4%) (5.9% - 8.5%) (5.7% - 8.2%) (3.6% - 5.2%)15.7% 10.5% 10.5% 9.9% 8.7% 7.9% 5.2%(12.9% - 18.4%) (8.6% - 12.3%) (8.6% - 12.3%) (8.1% - 11.6%) (7.1% - 10.2%) (6.4% - 9.3%) (4.3% - 6.2%)8.2% 2.4% 2.4% 2.4% 2.4% 0.8% 0%(6.7% - 9.7%) (1.9% - 2.8%) (1.9% - 2.8%) (1.9%- 2.8%) (1.9% - 2.8%) (0.7% - 1%) (0% - 0%)16.1% 13.8% 12.6% 11.1% 9.6% 10.8% 7.7%(13.3% - 18.9%) (11.3% - 16.2%) (10.3% - 14.8%) (9.1% - 13.1%) (7.9% - 11.4%) (8.9% - 12.8%) (6.3% - 9.2%)9.2% 6.1% 6.1% 6.1% 6.1% 4.1% 2%(7.5% - 10.8%)(4.9% - 7.2%)(4.9% - 7.2%)(4.9% - 7.2%)(4.9% - 7.2%)(3.3% - 4.8%)(1.6% - 2.4%)Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-24Table E-23. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2515.8% 14.1% 12.7% 11.2% 9.7% 11.2% 9.5%(13% - 18.5%) (11.6% - 16.6%) (10.4% - 14.9%) (9.2%- 13.2%) (8% - 11.5%) (9.2% - 13.2%) (7.8% - 11.2%)12.7% 11.7% 10.6% 9.3% 7.9% 8.9% 6.1%(10.5% - 15%) (9.6% - 13.7%) (8.7% - 12.5%) (7.6%- 11%) (6.5% - 9.4%) (7.3% - 10.5%) (5% - 7.2%)14.8% 10.5% 9.3% 8% 6.8% 8% 5.6%(12.2% - 17.4%) (8.6% - 12.3%) (7.6% - 10.9%) (6.5% - 9.5%) (5.5% - 8%) (6.5% - 9.5%) (4.5% - 6.6%)8.2% 8.2% 8.2% 8.2% 7.2% 8.2% 7.2%(6.7% - 9.7%) (6.7% - 9.7%) (6.7% - 9.7%) (6.7% -9.7%) (5.9% - 8.5%) (6.7% - 9.7%) (5.9% - 8.5%)12.1% 8.5% 8.4% 7.2% 5.9% 6.1% 3.7%(9.9% - 14.2%) (6.9% - 10%) (6.8% - 9.9%) (5.8% - 8.5%) (4.8% - 7%) (5% - 7.3%) (3% - 4.4%)17.2% 6.4% 6.4% 6.4% 6.4% 4.3% 2.2%(14.1% - 20.1%) (5.2% - 7.5%) (5.2% - 7.5%) (5.2%- 7.5%) (5.2% - 7.5%) (3.5% - 5.1%) (1.8% - 2.6%)11.5% 10.5% 9.3% 8% 6.8% 8% 6.8%(9.4% - 13.5%) (8.6% - 12.4%) (7.6% - 10.9%) (6.6% - 9.5%) (5.5% - 8%) (6.6% - 9.5%) (5.5% - 8%)13.4% 5.8% 5.8% 5.8% 5% 3.8% 1.8%(11% - 15.7%) (4.7% - 6.9%) (4.7% - 6.9%) (4.7% -6.9%) (4.1% - 5.9%) (3.1% - 4.5%) (1.4% - 2.1%)10.9% 7.8% 7.8% 7.4% 6.2% 5.6% 3.2%(9% - 12.9%) (6.4% - 9.3%) (6.4% - 9.3%) (6.1% -8.8%) (5.1% - 7.3%) (4.5% - 6.6%) (2.6% - 3.8%)12.1% 10.6% 10.6% 9.4% 8.1% 8% 5.3%(9.9% - 14.3%) (8.7% - 12.5%) (8.7% - 12.5%) (7.7% -11.1%) (6.6% - 9.5%) (6.5% - 9.4%) (4.3% - 6.3%)8.1% 8.1% 8.1% 8.1% 7.3% 7.1% 4.5%(6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6%- 9.6%) (6% - 8.7%) (5.8% - 8.4%) (3.7% - 5.4%)12.6% 8% 8% 7.4% 6.4% 5.7% 3.4%(10.4% - 14.8%) (6.5% - 9.4%) (6.5% - 9.4%) (6.1% - 8.8%) (5.2% - 7.6%) (4.6% - 6.7%) (2.7% - 4%)6.5% 1.2% 1.2% 1.2% 1.2% 0% 0%(5.3% - 7.7%) (1% - 1.5%) (1% - 1.5%) (1% - 1.5%) (1% - 1.5%) (0% - 0%) (0% - 0%)12.2% 10.2% 9.2% 7.9% 6.7% 7.7% 5.1%(10% - 14.4%) (8.4% - 12.1%) (7.5% - 10.8%) (6.5% - 9.4%) (5.4% - 7.9%) (6.3% - 9.1%) (4.1% - 6%)6.1% 3.5% 3.5% 3.5% 3.5% 1.8% 0.1%(5% - 7.3%)(2.9% - 4.2%)(2.9% - 4.2%)(2.9% - 4.2%)(2.9% - 4.2%)(1.5% - 2.2%)(0.1% - 0.2%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-25Table E-24. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2514.9% 13.2% 11.8% 10.4% 9% 10.4% 8.8%(12.2% - 17.4%) (10.9% - 15.5%) (9.7% - 13.9%) (8.5%- 12.3%) (7.4% - 10.6%) (8.5% - 12.3%) (7.2% - 10.4%)12.7% 11.7% 10.6% 9.3% 7.9% 8.9% 6.1%(10.5% - 15%) (9.6% - 13.7%) (8.7% - 12.5%) (7.6%- 11%) (6.5% - 9.4%) (7.3% - 10.5%) (5% - 7.2%)15.4% 10.9% 9.7% 8.4% 7.1% 8.4% 5.9%(12.7% - 18%) (8.9% - 12.9%) (7.9% - 11.4%) (6.9%- 9.9%) (5.8% - 8.4%) (6.9% - 9.9%) (4.8% - 7%)9% 9% 9% 9% 7.9% 9% 7.9%(7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (7.3% -10.6%) (6.5% - 9.3%) (7.3% - 10.6%) (6.5% - 9.3%)12.8% 9.1% 9% 7.7% 6.5% 6.7% 4.2%(10.5% - 15%) (7.4% - 10.7%) (7.3% - 10.6%) (6.3%- 9.1%) (5.3% - 7.6%) (5.4% - 7.9%) (3.4% - 5%)17.7% 6.7% 6.7% 6.7% 6.7% 4.6% 2.4%(14.6% - 20.7%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (3.7% - 5.5%) (2% - 2.9%)11.7% 10.7% 9.5% 8.2% 7% 8.2% 7%(9.6% - 13.8%) (8.8% - 12.6%) (7.8% - 11.2%) (6.7% -9.7%) (5.7% - 8.3%) (6.7% - 9.7%) (5.7% - 8.3%)13.8% 6.1% 6.1% 6.1% 5.3% 4% 2%(11.3% - 16.2%) (4.9% - 7.2%) (4.9% - 7.2%) (4.9%- 7.2%) (4.3% - 6.3%) (3.3% - 4.8%) (1.6% - 2.3%)12.6% 9.3% 9.3% 8.9% 7.5% 6.8% 4.3%(10.4% - 14.8%) (7.6% - 11%) (7.6% - 11%) (7.2% - 10.5%) (6.1% - 8.9%) (5.6% - 8.1%) (3.5% - 5.1%)12% 10.5% 10.5% 9.3% 8% 7.9% 5.2%(9.8% - 14.1%) (8.6% - 12.3%) (8.6% - 12.3%) (7.6% - 11%) (6.5% - 9.4%) (6.4% - 9.3%) (4.2% - 6.2%)6.7% 6.7% 6.7% 6.7% 6% 5.8% 3.4%(5.5% - 7.9%) (5.5% - 7.9%) (5.5% - 7.9%) (5.5%- 7.9%) (4.9% - 7.1%) (4.7% - 6.8%) (2.8% - 4%)14.2% 9.3% 9.3% 8.7% 7.6% 6.9% 4.4%(11.7% - 16.7%) (7.6% - 11%) (7.6% - 11%) (7.1% -10.3%) (6.2% - 9%) (5.6% - 8.1%) (3.5% - 5.2%)9% 2.9% 2.9% 2.9% 2.9% 1.3% 0%(7.4% - 10.6%) (2.4% - 3.4%) (2.4% - 3.4%) (2.4%- 3.4%) (2.4% - 3.4%) (1.1% - 1.6%) (0% - 0%)13.3% 11.2% 10.1% 8.8% 7.5% 8.6% 5.8%(10.9% - 15.7%) (9.2% - 13.2%) (8.3% - 11.9%) (7.2% - 10.4%) (6.1% - 8.9%) (7% - 10.1%) (4.7% - 6.9%)6.3% 3.7% 3.7% 3.7% 3.7% 2% 0.3%(5.2% - 7.5%)(3% - 4.4%)(3% - 4.4%)(3% - 4.4%)(3% - 4.4%)(1.6% - 2.4%)(0.2% - 0.3%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-26Table E-25. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-12% 0% 10% 21% 31% 21% 33%(-12% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (31% - 31%) (20% - 21%) (32% - 33%)-8% 0% 8% 18% 29% 21% 43%(-8% - -8%) (0% - 0%) (8% - 8%) (18% - 19%) (29% - 29%) (21% - 22%) (43% - 44%)-40% 0% 11% 23% 34% 23% 45%(-39% - -41%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 34%) (22% - 23%) (45% - 45%)0% 0% 0% 0% 11% 0% 11%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-35% 0% 1% 13% 25% 23% 47%(-35% - -36%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 25%) (23% - 23%) (46% - 47%)-174% 0% 0% 0% 0% 33% 68%(-171% - -177%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (67% - 68%)-9% 0% 11% 23% 34% 23% 34%(-9% - -9%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (23% - 23%) (34% - 35%)-116% 0% 0% 0% 12% 31% 62%(-114% - -118%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 31%) (62% - 62%)-33% 0% 0% 4% 18% 25% 50%(-33% - -34%) (0% - 0%) (0% - 0%) (4% - 4%) (18% - 18%) (25% - 25%) (50% - 50%)-14% 0% 0% 10% 23% 23% 48%(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 10%) (22% - 23%) (23% - 24%) (47% - 48%)0% 0% 0% 0% 10% 13% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 45%)-50% 0% 0% 6% 17% 25% 50%(-49% - -51%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 17%) (24% - 25%) (50% - 50%)-247% 0% 0% 0% 0% 64% 100%(-245% - -249%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 64%) (100% - 100%)-17% 0% 9% 20% 30% 22% 44%(-16% - -17%) (0% - 0%) (9% - 9%) (19% - 20%) (30% - 31%) (21% - 22%) (44% - 44%)-51% 0% 0% 0% 0% 33% 67%(-51% - -52%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (67% - 67%)Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-27Table E-26. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-12% 0% 10% 21% 31% 21% 33%(-12% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (31% - 31%) (20% - 21%) (32% - 33%)-9% 0% 9% 20% 32% 24% 48%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 32%) (23% - 24%) (47% - 48%)-42% 0% 12% 23% 35% 23% 47%(-41% - -42%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (46% - 47%)0% 0% 0% 0% 13% 0% 13%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (0% - 0%) (12% - 13%)-43% 0% 1% 16% 30% 28% 56%(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 16%) (30% - 30%) (27% - 28%) (56% - 56%)-170% 0% 0% 0% 0% 33% 66%(-167% - -173%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 33%) (66% - 66%)-9% 0% 12% 23% 35% 23% 35%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-131% 0% 0% 0% 13% 35% 70%(-129% - -133%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 35%) (70% - 70%)-39% 0% 0% 5% 21% 29% 59%(-39% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 29%) (59% - 59%)-14% 0% 0% 11% 24% 25% 50%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (24% - 25%) (50% - 50%)0% 0% 0% 0% 10% 12% 44%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 13%) (44% - 45%)-58% 0% 0% 7% 20% 28% 58%(-58% - -59%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (28% - 29%) (58% - 58%)-427% 0% 0% 0% 0% 100% 100%(-425% - -430%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%)-19% 0% 10% 23% 35% 25% 51%(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 23%) (35% - 35%) (25% - 25%) (50% - 51%)-74% 0% 0% 0% 0% 47% 96%(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 48%) (96% - 96%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-28Table E-27. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-12% 0% 10% 21% 32% 21% 34%(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 21%) (32% - 32%) (21% - 21%) (33% - 34%)-9% 0% 9% 20% 32% 24% 48%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 32%) (23% - 24%) (47% - 48%)-41% 0% 11% 23% 35% 23% 46%(-40% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 23%) (46% - 46%)0% 0% 0% 0% 12% 0% 12%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-41% 0% 1% 15% 29% 27% 54%(-41% - -42%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (26% - 27%) (54% - 54%)-164% 0% 0% 0% 0% 31% 64%(-161% - -167%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 32%) (64% - 64%)-9% 0% 12% 23% 35% 23% 35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 23%) (35% - 35%)-127% 0% 0% 0% 13% 34% 68%(-125% - -129%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (33% - 34%) (68% - 68%)-36% 0% 0% 5% 19% 26% 54%(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (26% - 27%) (53% - 54%)-15% 0% 0% 11% 24% 25% 50%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% - 25%) (50% - 51%)0% 0% 0% 0% 11% 14% 50%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (49% - 50%)-53% 0% 0% 6% 18% 26% 53%(-52% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 19%) (26% - 26%) (53% - 53%)-210% 0% 0% 0% 0% 55% 100%(-209% - -212%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 55%) (100% - 100%)-19% 0% 10% 22% 33% 24% 48%(-18% - -19%) (0% - 0%) (10% - 10%) (21% - 22%) (33% - 34%) (24% - 24%) (48% - 49%)-72% 0% 0% 0% 0% 46% 93%(-71% - -72%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-29Table E-28. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski etal. (2009), Using Ambient PM2.5 from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25312 279 251 222 193 222 189(257 - 364) (229 - 327) (206 - 295) (182 - 262) (158 - 228) (182 - 262) (154 - 223)497 460 423 376 328 363 263(409 - 581) (378 - 538) (348 - 497) (308 - 442) (268 - 386) (298 - 427) (214 - 310)233 168 149 130 111 130 93(192 - 273) (137 - 197) (122 - 176) (106 - 154) (90 - 131) (106 - 154) (75 - 110)292 292 292 292 261 292 261(239 - 344) (239 - 344) (239 - 344) (239 - 344) (213 - 307) (239 - 344) (213 - 307)862 642 635 561 485 497 346(711 - 1007) (526 - 754) (520 - 746) (459 - 660) (396 - 572) (406 - 586) (282 - 410)234 87 87 87 87 58 28(193 - 273) (71 - 103) (71 - 103) (71 - 103) (71 - 103) (47 - 69) (23 - 34)467 429 382 333 284 333 284(383 - 548) (351 - 505) (312 - 449) (272 - 393) (231 - 335) (272 - 393) (231 - 335)2664 1249 1249 1249 1103 869 477(2192 - 3117) (1017 - 1477) (1017 - 1477) (1017 - 1477) (897 - 1306) (705 - 1030) (386 - 567)3285 2475 2475 2369 2040 1871 1243(2700 - 3849) (2024 - 2914) (2024 - 2914) (1936 - 2790) (1665 - 2408) (1525 - 2210) (1010 - 1474)419 369 369 332 287 284 195(344 - 492) (303 - 434) (303 - 434) (271 - 391) (234 - 338) (232 - 335) (159 - 231)445 445 445 445 401 389 247(363 - 526) (363 - 526) (363 - 526) (363 - 526) (327 - 475) (317 - 461) (200 - 293)547 368 368 346 305 278 185(450 - 639) (301 - 433) (301 - 433) (283 - 408) (249 - 361) (227 - 329) (151 - 220)51 15 15 15 15 5 0(42 - 60) (12 - 18) (12 - 18) (12 - 18) (12 - 18) (4 - 6) (0 - 0)796 684 624 553 480 539 388(656 - 930) (562 - 802) (512 - 733) (453 - 650) (392 - 566) (441 - 635) (316 - 458)117 78 78 78 78 52 26(96 - 138) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (42 - 62) (21 - 31)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest whole number.Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-30Table E-29. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25321 287 258 229 199 229 194(264 - 375) (236 - 336) (212 - 303) (187 - 269) (163 - 235) (187 - 269) (159 - 229)409 375 342 300 256 288 198(335 - 480) (307 - 441) (280 - 403) (245 - 353) (209 - 303) (235 - 340) (161 - 234)221 157 139 120 102 120 84(181 - 258) (128 - 184) (113 - 164) (98 - 142) (83 - 120) (98 - 142) (68 - 100)222 222 222 222 195 222 195(181 - 262) (181 - 262) (181 - 262) (181 - 262) (158 - 230) (181 - 262) (158 - 230)638 449 443 380 316 327 200(523 - 749) (367 - 530) (361 - 523) (310 - 450) (257 - 375) (266 - 387) (162 - 237)243 92 92 92 92 62 32(201 - 284) (75 - 108) (75 - 108) (75 - 108) (75 - 108) (50 - 74) (26 - 38)453 416 368 320 270 320 270(371 - 533) (340 - 490) (301 - 434) (261 - 378) (220 - 320) (261 - 378) (220 - 320)2370 1038 1038 1038 901 682 316(1945 - 2779) (843 - 1229) (843 - 1229) (843 - 1229) (731 - 1068) (553 - 809) (255 - 376)2588 1865 1865 1770 1478 1328 774(2118 - 3046) (1520 - 2203) (1520 - 2203) (1442 - 2092) (1202 - 1750) (1079 - 1573) (627 - 920)381 334 334 298 255 253 168(313 - 448) (273 - 393) (273 - 393) (244 - 352) (208 - 302) (206 - 298) (137 - 199)471 471 471 471 426 413 264(384 - 557) (384 - 557) (384 - 557) (384 - 557) (347 - 503) (336 - 488) (214 - 313)439 279 279 260 225 200 119(360 - 516) (228 - 330) (228 - 330) (212 - 308) (183 - 266) (163 - 237) (96 - 141)42 8 8 8 8 0 0(34 - 50) (6 - 10) (6 - 10) (6 - 10) (6 - 10) (0 - 0) (0 - 0)610 512 460 398 335 386 255(500 - 716) (419 - 603) (375 - 542) (324 - 470) (272 - 396) (314 - 456) (207 - 302)80 46 46 46 46 24 2(65 - 95) (37 - 55) (37 - 55) (37 - 55) (37 - 55) (20 - 29) (2 - 2)Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-31Table E-30. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25310 277 248 219 189 219 185(255 - 363) (227 - 324) (203 - 291) (179 - 258) (154 - 223) (179 - 258) (151 - 218)408 374 342 299 256 288 197(335 - 479) (307 - 440) (280 - 402) (244 - 353) (209 - 302) (235 - 339) (160 - 234)231 165 146 128 108 128 90(190 - 270) (135 - 194) (120 - 173) (104 - 151) (88 - 128) (104 - 151) (73 - 107)247 247 247 247 218 247 218(202 - 291) (202 - 291) (202 - 291) (202 - 291) (178 - 257) (202 - 291) (178 - 257)670 478 471 407 341 352 222(549 - 786) (390 - 563) (385 - 556) (332 - 481) (278 - 404) (286 - 416) (180 - 264)255 98 98 98 98 68 36(211 - 298) (80 - 116) (80 - 116) (80 - 116) (80 - 116) (55 - 80) (29 - 43)473 434 385 335 284 335 284(387 - 556) (355 - 511) (314 - 453) (273 - 395) (231 - 335) (273 - 395) (231 - 335)2456 1094 1094 1094 954 730 355(2017 - 2879) (890 - 1296) (890 - 1296) (890 - 1296) (775 - 1131) (592 - 866) (287 - 423)3003 2222 2222 2120 1804 1641 1040(2462 - 3525) (1814 - 2620) (1814 - 2620) (1730 - 2501) (1469 - 2132) (1336 - 1941) (843 - 1234)378 330 330 295 252 249 165(309 - 444) (270 - 389) (270 - 389) (241 - 347) (205 - 298) (203 - 295) (134 - 196)402 402 402 402 359 347 204(327 - 476) (327 - 476) (327 - 476) (327 - 476) (291 - 425) (282 - 410) (165 - 242)490 324 324 303 265 240 153(403 - 574) (264 - 382) (264 - 382) (248 - 358) (216 - 313) (195 - 284) (124 - 181)59 19 19 19 19 9 0(48 - 70) (16 - 23) (16 - 23) (16 - 23) (16 - 23) (7 - 10) (0 - 0)665 563 508 443 377 431 294(546 - 780) (461 - 662) (415 - 599) (362 - 523) (307 - 446) (351 - 509) (239 - 348)84 49 49 49 49 27 4(68 - 99) (40 - 58) (40 - 58) (40 - 58) (40 - 58) (21 - 32) (3 - 4)Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-32Table E-31. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 2001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2519.9% 17.8% 16% 14.2% 12.3% 14.2% 12%(16.4% - 23.2%) (14.6% - 20.8%) (13.1% - 18.8%) (11.6%- 16.7%) (10.1% - 14.5%) (11.6% - 16.7%) (9.8% - 14.2%)19.5% 18.1% 16.6% 14.8% 12.9% 14.3% 10.3%(16.1% - 22.8%) (14.8% - 21.2%) (13.7% - 19.5%) (12.1%- 17.4%) (10.5% - 15.2%) (11.7% - 16.8%) (8.4% - 12.2%)19.9% 14.3% 12.7% 11.1% 9.5% 11.1% 7.9%(16.4% - 23.3%) (11.7% - 16.8%) (10.4% - 15%) (9.1% - 13.1%) (7.7% - 11.2%) (9.1% - 13.1%) (6.4% - 9.4%)14% 14% 14% 14% 12.5% 14% 12.5%(11.5% - 16.5%) (11.5% - 16.5%) (11.5% - 16.5%) (11.5%- 16.5%) (10.2% - 14.7%) (11.5% - 16.5%) (10.2% - 14.7%)20.6% 15.3% 15.1% 13.4% 11.6% 11.9% 8.3%(17% - 24%) (12.6% - 18%) (12.4% - 17.8%) (10.9%- 15.7%) (9.4% - 13.6%) (9.7% - 14%) (6.7% - 9.8%)21% 7.8% 7.8% 7.8% 7.8% 5.2% 2.5%(17.3% - 24.5%) (6.3% - 9.2%) (6.3% - 9.2%) (6.3%- 9.2%) (6.3% - 9.2%) (4.2% - 6.2%) (2.1% - 3%)15.4% 14.2% 12.6% 11% 9.4% 11% 9.4%(12.6% - 18.1%) (11.6% - 16.6%) (10.3% - 14.8%) (9% - 13%) (7.6% - 11.1%) (9% - 13%) (7.6% - 11.1%)19% 8.9% 8.9% 8.9% 7.9% 6.2% 3.4%(15.7% - 22.3%) (7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (6.4% - 9.3%) (5% - 7.4%) (2.8% - 4.1%)17.7% 13.3% 13.3% 12.8% 11% 10.1% 6.7%(14.5% - 20.7%) (10.9% - 15.7%) (10.9% - 15.7%) (10.4% - 15%) (9% - 13%) (8.2% - 11.9%) (5.4% - 7.9%)16.8% 14.8% 14.8% 13.3% 11.5% 11.4% 7.8%(13.8% - 19.7%) (12.1% - 17.4%) (12.1% - 17.4%) (10.8% - 15.6%) (9.4% - 13.5%) (9.3% - 13.4%) (6.3% - 9.2%)10.1% 10.1% 10.1% 10.1% 9.1% 8.8% 5.6%(8.2% - 11.9%) (8.2% - 11.9%) (8.2% - 11.9%) (8.2% - 11.9%) (7.4% - 10.8%) (7.2% - 10.5%) (4.5% - 6.6%)19.7% 13.2% 13.2% 12.5% 11% 10% 6.7%(16.2% - 23%) (10.8% - 15.6%) (10.8% - 15.6%) (10.2% - 14.7%) (9% - 13%) (8.2% - 11.8%) (5.4% - 7.9%)10.4% 3% 3% 3% 3% 1.1% 0%(8.5% - 12.3%) (2.4% - 3.6%) (2.4% - 3.6%) (2.4% - 3.6%) (2.4% - 3.6%) (0.9% - 1.3%) (0% - 0%)20.2% 17.4% 15.8% 14% 12.2% 13.7% 9.8%(16.6% - 23.6%) (14.3% - 20.4%) (13% - 18.6%) (11.5% - 16.5%) (10% - 14.4%) (11.2% - 16.1%) (8% - 11.6%)11.6% 7.7% 7.7% 7.7% 7.7% 5.2% 2.6%(9.5% - 13.7%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% -9.1%) (6.3% - 9.1%) (4.2% - 6.1%) (2.1% - 3.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-33Table E-32. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 2001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2519.8% 17.7% 16% 14.2% 12.3% 14.2% 12%(16.3% - 23.2%) (14.6% - 20.8%) (13.1% - 18.7%) (11.6% - 16.6%) (10% - 14.5%) (11.6% - 16.6%) (9.8% - 14.2%)16% 14.7% 13.4% 11.8% 10.1% 11.3% 7.8%(13.2% - 18.8%) (12.1% - 17.3%) (11% - 15.8%) (9.6% - 13.9%) (8.2% - 11.9%) (9.2% - 13.3%) (6.3% - 9.2%)18.6% 13.2% 11.7% 10.2% 8.6% 10.2% 7.1%(15.3% - 21.8%) (10.8% - 15.6%) (9.6% - 13.8%) (8.3% - 12%) (7% - 10.2%) (8.3% - 12%) (5.8% - 8.4%)10.4% 10.4% 10.4% 10.4% 9.1% 10.4% 9.1%(8.5% - 12.3%) (8.5% - 12.3%) (8.5% - 12.3%) (8.5% - 12.3%) (7.4% - 10.8%) (8.5% - 12.3%) (7.4% - 10.8%)15.2% 10.7% 10.6% 9.1% 7.6% 7.8% 4.8%(12.5% - 17.9%) (8.8% - 12.7%) (8.6% - 12.5%) (7.4% - 10.7%) (6.1% - 8.9%) (6.3% - 9.2%) (3.9% - 5.7%)21.5% 8.1% 8.1% 8.1% 8.1% 5.5% 2.8%(17.7% - 25.1%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6%- 9.6%) (6.6% - 9.6%) (4.4%- 6.5%) (2.3% - 3.3%)14.5% 13.3% 11.7% 10.2% 8.6% 10.2% 8.6%(11.8% - 17%) (10.8% - 15.6%) (9.6% - 13.8%) (8.3% - 12%) (7% - 10.2%) (8.3% - 12%) (7% - 10.2%)16.8% 7.4% 7.4% 7.4% 6.4% 4.8% 2.2%(13.8% - 19.7%) (6% - 8.7%) (6% - 8.7%) (6% - 8.7%) (5.2% - 7.6%) (3.9% - 5.8%) (1.8% - 2.7%)13.8% 9.9% 9.9% 9.4% 7.9% 7.1% 4.1%(11.3% - 16.2%) (8.1% - 11.7%) (8.1% - 11.7%) (7.7% - 11.2%) (6.4% - 9.3%) (5.8% - 8.4%) (3.3% - 4.9%)15.3% 13.4% 13.4% 11.9% 10.2% 10.1% 6.7%(12.5% - 18%) (10.9% - 15.8%) (10.9% - 15.8%) (9.8% - 14.1%) (8.3%- 12.1%) (8.3% - 12%) (5.5% - 8%)10.3% 10.3% 10.3% 10.3% 9.3% 9% 5.8%(8.4% - 12.2%) (8.4% - 12.2%) (8.4% - 12.2%) (8.4% - 12.2%) (7.6% - 11%) (7.3% - 10.7%) (4.7% - 6.8%)15.9% 10.1% 10.1% 9.4% 8.1% 7.3% 4.3%(13% - 18.7%) (8.2% - 11.9%) (8.2% - 11.9%) (7.7% - 11.1%) (6.6% - 9.6%) (5.9% - 8.6%) (3.5% - 5.1%)8.3% 1.6% 1.6% 1.6% 1.6% 0% 0%(6.7% - 9.8%) (1.3% - 1.9%) (1.3% - 1.9%) (1.3%- 1.9%) (1.3% - 1.9%) (0% - 0%) (0% - 0%)15.4% 12.9% 11.6% 10% 8.5% 9.7% 6.4%(12.6% - 18.1%) (10.6% - 15.2%) (9.5% - 13.7%) (8.2% - 11.9%) (6.9% - 10%) (7.9% - 11.5%) (5.2% - 7.6%)7.8% 4.5% 4.5% 4.5% 4.5% 2.4% 0.2%(6.3% - 9.2%) (3.6% - 5.3%) (3.6% - 5.3%) (3.6% -5.3%) (3.6% - 5.3%) (1.9% - 2.8%) (0.2% - 0.2%)Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-34Table E-33. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2518.7% 16.7% 14.9% 13.2% 11.4% 13.2% 11.1%(15.4% - 21.8%) (13.7% - 19.5%) (12.2% - 17.6%) (10.8% - 15.5%) (9.3% - 13.4%) (10.8% - 15.5%) (9.1% - 13.1%)16.1% 14.7% 13.4% 11.8% 10.1% 11.3% 7.8%(13.2% - 18.8%) (12.1% - 17.3%) (11% - 15.8%) (9.6% - 13.9%) (8.2% - 11.9%) (9.2% - 13.3%) (6.3% - 9.2%)19.3% 13.8% 12.2% 10.7% 9.1% 10.7% 7.5%(15.9% - 22.6%) (11.3% - 16.2%) (10% - 14.4%) (8.7% - 12.6%) (7.4% - 10.7%) (8.7% - 12.6%) (6.1% - 8.9%)11.4% 11.4% 11.4% 11.4% 10% 11.4% 10%(9.3% - 13.4%) (9.3% - 13.4%) (9.3% - 13.4%) (9.3% - 13.4%) (8.2% - 11.9%) (9.3% - 13.4%) (8.2% - 11.9%)16.1% 11.5% 11.3% 9.8% 8.2% 8.5% 5.3%(13.2% - 18.9%) (9.4% - 13.5%) (9.3% - 13.4%) (8% - 11.6%) (6.7% - 9.7%) (6.9% - 10%) (4.3% - 6.3%)22.2% 8.5% 8.5% 8.5% 8.5% 5.9% 3.1%(18.3% - 25.9%) (7% - 10.1%) (7% - 10.1%) (7% - 10.1%) (7% - 10.1%) (4.8% - 7%) (2.5% - 3.7%)14.8% 13.6% 12% 10.5% 8.9% 10.5% 8.9%(12.1% - 17.4%) (11.1% - 16%) (9.8% - 14.2%) (8.5% - 12.3%) (7.2% - 10.5%) (8.5% - 12.3%) (7.2% - 10.5%)17.3% 7.7% 7.7% 7.7% 6.7% 5.2% 2.5%(14.2% - 20.3%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% - 9.1%) (5.5% - 8%) (4.2% - 6.1%) (2% - 3%)15.9% 11.8% 11.8% 11.2% 9.6% 8.7% 5.5%(13% - 18.7%) (9.6% - 13.9%) (9.6% - 13.9%) (9.2% - 13.2%) (7.8% - 11.3%) (7.1% - 10.3%) (4.5% - 6.5%)15.1% 13.2% 13.2% 11.8% 10.1% 10% 6.6%(12.4% - 17.8%) (10.8% - 15.6%) (10.8% - 15.6%) (9.6% - 13.9%) (8.2% - 11.9%) (8.1% - 11.8%) (5.4% - 7.8%)8.5% 8.5% 8.5% 8.5% 7.6% 7.3% 4.3%(6.9% - 10.1%) (6.9% - 10.1%) (6.9% - 10.1%) (6.9%- 10.1%) (6.2% - 9%) (6% - 8.7%) (3.5% - 5.1%)17.8% 11.8% 11.8% 11% 9.6% 8.7% 5.6%(14.7% - 20.9%) (9.6% - 13.9%) (9.6% - 13.9%) (9% - 13%) (7.8% - 11.4%) (7.1% - 10.3%) (4.5% - 6.6%)11.4% 3.7% 3.7% 3.7% 3.7% 1.7% 0%(9.3% - 13.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (1.4% - 2%) (0% - 0%)16.8% 14.2% 12.8% 11.2% 9.5% 10.9% 7.4%(13.8% - 19.7%) (11.6% - 16.7%) (10.5% - 15.1%) (9.1%- 13.2%) (7.7% - 11.2%) (8.9% - 12.8%) (6% - 8.8%)8% 4.7% 4.7% 4.7% 4.7% 2.5% 0.3%(6.5% - 9.5%) (3.8% - 5.6%) (3.8% - 5.6%) (3.8%- 5.6%) (3.8% - 5.6%) (2.1% - 3%) (0.3% - 0.4%)Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-35Table E-34. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-12% 0% 10% 20% 31% 20% 32%(-11% - -12%) (0% - 0%) (10% - 10%) (20% -21%) (30% - 31%) (20% - 21%) (32% - 33%)-8% 0% 8% 18% 29% 21% 43%(-8% - -8%) (0% - 0%) (8% - 8%) (18% - 18%) (28% - 29%) (21% - 21%) (42% - 43%)-39% 0% 11% 22% 34% 22% 45%(-38% - -40%) (0% - 0%) (11% - 11%) (22% -23%) (33% - 34%) (22% - 23%) (44% - 45%)0% 0% 0% 0% 11% 0% 11%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-34% 0% 1% 13% 24% 23% 46%(-34% - -35%) (0% - 0%) (1% - 1%) (12% - 13%) (24% - 25%) (22% - 23%) (46% - 46%)-170% 0% 0% 0% 0% 33% 67%(-166% - -174%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (33% - 33%) (67% - 67%)-9% 0% 11% 22% 34% 22% 34%(-9% - -9%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 34%) (22% - 23%) (34% - 34%)-113% 0% 0% 0% 12% 30% 62%(-111% - -116%) (0% - 0%) (0% - 0%) (0%- 0%) (12% - 12%) (30% - 31%) (62% - 62%)-33% 0% 0% 4% 18% 24% 50%(-32% - -33%) (0% - 0%) (0% - 0%) (4% - 4%) (17% - 18%) (24% - 25%) (49% - 50%)-13% 0% 0% 10% 22% 23% 47%(-13% - -14%) (0% - 0%) (0% - 0%) (10% - 10%) (22% - 23%) (23% - 23%) (47% - 48%)0% 0% 0% 0% 10% 12% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 13%) (44% - 45%)-49% 0% 0% 6% 17% 24% 50%(-48% - -50%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 17%) (24% - 25%) (49% - 50%)-244% 0% 0% 0% 0% 64% 100%(-242% - -247%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (64% - 64%) (100% - 100%)-16% 0% 9% 19% 30% 21% 43%(-16% - -17%) (0% - 0%) (9% - 9%) (19% - 19%) (29% - 30%) (21% - 22%) (43% - 44%)-51% 0% 0% 0% 0% 33% 67%(-50% - -51%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (66% - 67%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-36Table E-35. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-12% 0% 10% 20% 31% 20% 32%(-11% - -12%) (0% - 0%) (10% - 10%) (20% -21%) (30% - 31%) (20% - 21%) (32% - 33%)-9% 0% 9% 20% 32% 23% 47%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (23% - 23%) (47% - 48%)-41% 0% 11% 23% 35% 23% 46%(-40% - -42%) (0% - 0%) (11% - 12%) (23% -23%) (35% - 35%) (23% - 23%) (46% - 47%)0% 0% 0% 0% 12% 0% 12%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (0% - 0%) (12% - 13%)-42% 0% 1% 15% 30% 27% 56%(-41% - -43%) (0% - 0%) (1% - 1%) (15% - 16%) (29% - 30%) (27% - 28%) (55% - 56%)-165% 0% 0% 0% 0% 32% 66%(-162% - -169%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (32% - 33%) (65% - 66%)-9% 0% 11% 23% 35% 23% 35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 23%) (35% - 35%)-128% 0% 0% 0% 13% 34% 70%(-126% - -131%) (0% - 0%) (0% - 0%) (0%- 0%) (13% - 13%) (34% - 34%) (69% - 70%)-39% 0% 0% 5% 21% 29% 58%(-38% - -39%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 29%) (58% - 59%)-14% 0% 0% 11% 24% 24% 50%(-14% - -14%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 25%) (49% - 50%)0% 0% 0% 0% 10% 12% 44%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 12%) (44% - 44%)-57% 0% 0% 7% 20% 28% 57%(-56% - -58%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 20%) (28% - 28%) (57% - 58%)-423% 0% 0% 0% 0% 100% 100%(-420% - -427%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (100% - 100%) (100% - 100%)-19% 0% 10% 22% 35% 25% 50%(-19% - -19%) (0% - 0%) (10% - 10%) (22% -23%) (34% - 35%) (24% - 25%) (50% - 51%)-74% 0% 0% 0% 0% 47% 96%(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 47%) (96% - 96%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-37Table E-36. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/25-12% 0% 10% 21% 32% 21% 33%(-12% - -12%) (0% - 0%) (10% - 10%) (21% -21%) (31% - 32%) (21% - 21%) (33% - 34%)-9% 0% 9% 20% 32% 23% 47%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (23% - 23%) (47% - 48%)-40% 0% 11% 23% 34% 23% 45%(-39% - -41%) (0% - 0%) (11% - 11%) (22% -23%) (34% - 35%) (22% - 23%) (45% - 46%)0% 0% 0% 0% 12% 0% 12%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-40% 0% 1% 15% 28% 26% 53%(-40% - -41%) (0% - 0%) (1% - 1%) (15% - 15%) (28% - 29%) (26% - 27%) (53% - 54%)-159% 0% 0% 0% 0% 31% 63%(-156% - -163%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (31% - 31%) (63% - 64%)-9% 0% 11% 23% 35% 23% 35%(-9% - -9%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (23% - 23%) (34% - 35%)-124% 0% 0% 0% 13% 33% 68%(-122% - -127%) (0% - 0%) (0% - 0%) (0%- 0%) (13% - 13%) (33% - 33%) (67% - 68%)-35% 0% 0% 5% 19% 26% 53%(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (26% - 26%) (53% - 54%)-14% 0% 0% 11% 24% 25% 50%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 25%) (50% - 50%)0% 0% 0% 0% 11% 14% 49%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (49% - 50%)-51% 0% 0% 6% 18% 26% 53%(-51% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (26% - 26%) (52% - 53%)-208% 0% 0% 0% 0% 55% 100%(-205% - -210%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (55% - 55%) (100% - 100%)-18% 0% 10% 21% 33% 23% 48%(-18% - -18%) (0% - 0%) (10% - 10%) (21% -22%) (33% - 33%) (23% - 24%) (47% - 48%)-71% 0% 0% 0% 0% 46% 93%(-71% - -72%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-38Table E-37. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2005) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25512 455 407 359 310 359 302(391 - 630) (347 - 561) (310 - 502) (273 - 443) (236 - 383) (273 - 443) (230 - 374)507 467 428 378 327 364 260(387 - 624) (356 - 575) (326 - 528) (288 - 466) (249 - 405) (278 - 450) (198 - 322)365 258 228 198 168 198 140(279 - 450) (196 - 318) (174 - 282) (151 - 245) (128 - 208) (151 - 245) (106 - 173)321 321 321 321 285 321 285(244 - 396) (244 - 396) (244 - 396) (244 - 396) (217 - 352) (244 - 396) (217 - 352)748 547 540 474 408 419 288(572 - 920) (417 - 675) (412 - 667) (361 - 586) (310 - 505) (318 - 518) (219 - 357)240 85 85 85 85 56 27(184 - 295) (65 - 105) (65 - 105) (65 - 105) (65 - 105) (43 - 70) (21 - 34)499 457 404 351 297 351 297(380 - 616) (348 - 564) (307 - 499) (267 - 434) (226 - 368) (267 - 434) (226 - 368)2357 1069 1069 1069 941 737 401(1800 - 2902) (812 - 1324) (812 - 1324) (812 - 1324) (714 - 1166) (559 - 915) (304 - 499)2205 1637 1637 1564 1339 1224 805(1683 - 2717) (1246 - 2022) (1246 - 2022) (1190 - 1933) (1018 - 1657) (930 - 1515) (611 - 998)439 384 384 343 295 292 198(335 - 541) (293 - 474) (293 - 474) (261 - 424) (224 - 365) (222 - 361) (151 - 246)406 406 406 406 365 354 222(309 - 503) (309 - 503) (309 - 503) (309 - 503) (278 - 453) (269 - 439) (169 - 276)529 349 349 328 287 261 172(405 - 652) (265 - 431) (265 - 431) (249 - 405) (219 - 356) (198 - 323) (131 - 213)76 22 22 22 22 8 0(58 - 94) (16 - 27) (16 - 27) (16 - 27) (16 - 27) (6 - 10) (0 - 0)758 646 586 516 445 503 357(580 - 933) (493 - 796) (447 - 723) (393 - 637) (339 - 550) (383 - 621) (271 - 442)110 72 72 72 72 48 24(84 - 136) (55 - 89) (55 - 89) (55 - 89) (55 - 89) (36 - 60) (18 - 29)Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-39Table E-38. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2006PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25527 469 419 369 319 369 311(403 - 649) (358 - 577) (320 - 517) (281 - 456) (243 - 394) (281 - 456) (237 - 385)412 377 342 298 253 286 194(314 - 509) (287 - 465) (261 - 423) (227 - 369) (193 - 314) (218 - 354) (147 - 241)344 240 211 183 154 183 127(263 - 424) (183 - 297) (161 - 261) (139 - 226) (117 - 190) (139 - 226) (96 - 157)241 241 241 241 210 241 210(183 - 298) (183 - 298) (183 - 298) (183 - 298) (160 - 260) (183 - 298) (160 - 260)543 377 372 317 263 272 165(414 - 670) (287 - 466) (283 - 460) (241 - 393) (199 - 326) (206 - 336) (125 - 204)250 90 90 90 90 60 30(191 - 307) (68 - 112) (68 - 112) (68 - 112) (68 - 112) (46 - 75) (23 - 38)483 441 389 336 283 336 283(368 - 597) (336 - 545) (296 - 481) (255 - 416) (215 - 350) (255 - 416) (215 - 350)2081 884 884 884 765 576 265(1587 - 2566) (671 - 1095) (671 - 1095) (671 - 1095) (580 - 948) (436 - 715) (200 - 329)1715 1220 1220 1156 961 861 497(1306 - 2118) (927 - 1510) (927 - 1510) (878 - 1431) (729 - 1191) (653 - 1067) (377 - 618)398 346 346 307 262 259 171(303 - 491) (263 - 427) (263 - 427) (234 - 380) (199 - 324) (197 - 320) (129 - 211)431 431 431 431 388 376 238(327 - 533) (327 - 533) (327 - 533) (327 - 533) (294 - 480) (286 - 466) (180 - 295)420 262 262 244 209 186 110(320 - 518) (199 - 324) (199 - 324) (185 - 302) (159 - 259) (141 - 231) (83 - 136)62 12 12 12 12 0 0(47 - 77) (9 - 15) (9 - 15) (9 - 15) (9 - 15) (0 - 0) (0 - 0)572 476 426 366 307 355 232(436 - 705) (362 - 588) (324 - 526) (278 - 453) (233 - 380) (270 - 440) (176 - 288)74 42 42 42 42 22 2(56 - 92) (32 - 53) (32 - 53) (32 - 53) (32 - 53) (17 - 28) (1 - 2)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-40Table E-39. Estimated Annual Incidence of CardiopulmonaryMortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2007) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25507 449 401 352 302 352 295(387 - 625) (343 - 554) (305 - 495) (268 - 435) (230 - 374) (268 - 435) (224 - 365)412 376 342 298 253 286 194(314 - 508) (286 - 464) (260 - 422) (226 - 368) (192 - 313) (217 - 353) (147 - 240)361 253 224 194 164 194 136(276 - 444) (193 - 313) (170 - 276) (147 - 240) (124 - 203) (147 - 240) (103 - 168)269 269 269 269 236 269 236(205 - 333) (205 - 333) (205 - 333) (205 - 333) (179 - 292) (205 - 333) (179 - 292)572 402 396 340 284 293 183(436 - 705) (305 - 497) (301 - 490) (259 - 421) (216 - 352) (223 - 363) (139 - 227)263 97 97 97 97 66 35(201 - 323) (74 - 120) (74 - 120) (74 - 120) (74 - 120) (50 - 82) (26 - 43)504 461 407 352 297 352 297(384 - 622) (351 - 569) (309 - 503) (268 - 436) (225 - 368) (268 - 436) (225 - 368)2160 933 933 933 811 617 298(1648 - 2663) (708 - 1156) (708 - 1156) (708 - 1156) (615 - 1006) (468 - 766) (226 - 370)2003 1462 1462 1392 1179 1069 671(1527 - 2470) (1112 - 1808) (1112 - 1808) (1059 - 1722) (895 - 1459) (812 - 1324) (508 - 832)393 342 342 304 258 255 168(300 - 485) (260 - 423) (260 - 423) (231 - 375) (196 - 320) (194 - 316) (127 - 208)366 366 366 366 325 314 183(278 - 453) (278 - 453) (278 - 453) (278 - 453) (247 - 403) (238 - 389) (139 - 227)472 305 305 286 248 224 141(360 - 581) (232 - 378) (232 - 378) (217 - 353) (188 - 307) (170 - 277) (107 - 175)89 28 28 28 28 13 0(68 - 110) (21 - 35) (21 - 35) (21 - 35) (21 - 35) (10 - 16) (0 - 0)626 526 472 410 347 398 268(478 - 772) (400 - 649) (359 - 584) (312 - 507) (264 - 430) (302 - 492) (203 - 332)78 45 45 45 45 24 3(59 - 97) (34 - 56) (34 - 56) (34 - 56) (34 - 56) (18 - 30) (2 - 4)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidenceor credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-41Table E-40. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/258.8% 7.8% 7% 6.1% 5.3% 6.1% 5.2%(6.7% - 10.8%) (5.9% - 9.6%) (5.3% - 8.6%) (4.7% - 7.6%) (4% - 6.6%) (4.7% - 7.6%) (3.9% - 6.4%)8.6% 7.9% 7.3% 6.4% 5.6% 6.2% 4.4%(6.6% - 10.6%) (6% - 9.8%) (5.5% - 9%) (4.9% - 7.9%) (4.2% - 6.9%) (4.7% - 7.6%) (3.4% - 5.5%)8.8% 6.2% 5.5% 4.8% 4% 4.8% 3.4%(6.7% - 10.8%) (4.7% - 7.7%) (4.2% - 6.8%) (3.6% - 5.9%) (3.1% - 5%) (3.6% - 5.9%) (2.6% - 4.2%)6.1% 6.1% 6.1% 6.1% 5.4% 6.1% 5.4%(4.6% - 7.5%) (4.6% - 7.5%) (4.6% - 7.5%) (4.6% - 7.5%) (4.1% - 6.7%) (4.6% - 7.5%) (4.1% - 6.7%)9.1% 6.7% 6.6% 5.8% 5% 5.1% 3.5%(7% - 11.2%) (5.1% - 8.2%) (5% - 8.1%) (4.4% - 7.1%) (3.8% - 6.1%) (3.9% - 6.3%) (2.7% - 4.3%)9.3% 3.3% 3.3% 3.3% 3.3% 2.2% 1.1%(7.1% - 11.4%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (1.7% - 2.7%) (0.8% - 1.3%)6.7% 6.1% 5.4% 4.7% 4% 4.7% 4%(5.1% - 8.3%) (4.7% - 7.6%) (4.1% - 6.7%) (3.6% - 5.8%) (3% - 4.9%) (3.6% - 5.8%) (3% - 4.9%)8.4% 3.8% 3.8% 3.8% 3.3% 2.6% 1.4%(6.4% - 10.3%) (2.9% - 4.7%) (2.9% - 4.7%) (2.9% - 4.7%) (2.5% - 4.1%) (2% - 3.3%) (1.1% - 1.8%)7.8% 5.8% 5.8% 5.5% 4.7% 4.3% 2.8%(5.9% - 9.6%) (4.4% - 7.1%) (4.4% - 7.1%) (4.2% - 6.8%) (3.6% - 5.8%) (3.3% - 5.3%) (2.1% - 3.5%)7.3% 6.4% 6.4% 5.7% 4.9% 4.9% 3.3%(5.6% - 9%) (4.9% - 7.9%) (4.9% - 7.9%) (4.4% - 7.1%) (3.7% - 6.1%) (3.7% - 6%) (2.5% - 4.1%)4.3% 4.3% 4.3% 4.3% 3.9% 3.8% 2.4%(3.3% - 5.3%) (3.3% - 5.3%) (3.3% - 5.3%) (3.3% - 5.3%) (2.9% - 4.8%) (2.9% - 4.7%) (1.8% - 2.9%)8.7% 5.7% 5.7% 5.4% 4.7% 4.3% 2.8%(6.6% - 10.7%) (4.4% - 7.1%) (4.4% - 7.1%) (4.1% - 6.6%) (3.6% - 5.8%) (3.3% - 5.3%) (2.1% - 3.5%)4.5% 1.3% 1.3% 1.3% 1.3% 0.5% 0%(3.4% - 5.5%) (1% - 1.6%) (1% - 1.6%) (1%- 1.6%) (1% - 1.6%) (0.3% - 0.6%) (0% - 0%)8.9% 7.6% 6.9% 6.1% 5.2% 5.9% 4.2%(6.8% - 11%) (5.8% - 9.4%) (5.3% - 8.5%) (4.6% - 7.5%) (4% - 6.5%) (4.5% - 7.3%) (3.2% - 5.2%)5% 3.3% 3.3% 3.3% 3.3% 2.2% 1.1%(3.8% - 6.2%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (1.7% - 2.7%) (0.8% - 1.3%)Percent of Total Incidence of CardiopulmonaryMortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-42Table E-41. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/258.8% 7.8% 7% 6.1% 5.3% 6.1% 5.2%(6.7% - 10.8%) (5.9% - 9.6%) (5.3% - 8.6%) (4.7% - 7.6%) (4% - 6.5%) (4.7% - 7.6%) (3.9% - 6.4%)7% 6.4% 5.8% 5.1% 4.3% 4.9% 3.3%(5.3% - 8.6%) (4.9% - 7.9%) (4.4% - 7.2%) (3.8% - 6.2%) (3.3% - 5.3%) (3.7% - 6%) (2.5% - 4.1%)8.2% 5.7% 5% 4.3% 3.7% 4.3% 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%) (2.3% - 3.7%)4.5% 4.5% 4.5% 4.5% 3.9% 4.5% 3.9%(3.4% - 5.5%) (3.4% - 5.5%) (3.4% - 5.5%) (3.4% - 5.5%) (3% - 4.8%) (3.4% - 5.5%) (3% - 4.8%)6.6% 4.6% 4.5% 3.9% 3.2% 3.3% 2%(5% - 8.2%) (3.5% - 5.7%) (3.4% - 5.6%) (2.9% - 4.8%) (2.4% - 4%) (2.5% - 4.1%) (1.5% - 2.5%)9.5% 3.4% 3.4% 3.4% 3.4% 2.3% 1.2%(7.3% - 11.7%) (2.6% - 4.3%) (2.6% - 4.3%) (2.6% - 4.3%) (2.6% - 4.3%) (1.7% - 2.9%) (0.9% - 1.4%)6.3% 5.7% 5% 4.4% 3.7% 4.4% 3.7%(4.8% - 7.7%) (4.4% - 7.1%) (3.8% - 6.2%) (3.3% - 5.4%) (2.8% - 4.5%) (3.3% - 5.4%) (2.8% - 4.5%)7.4% 3.1% 3.1% 3.1% 2.7% 2% 0.9%(5.6% - 9.1%) (2.4% - 3.9%) (2.4% - 3.9%) (2.4% - 3.9%) (2.1% - 3.4%) (1.5% - 2.5%) (0.7% - 1.2%)6% 4.2% 4.2% 4% 3.3% 3% 1.7%(4.5% - 7.4%) (3.2% - 5.3%) (3.2% - 5.3%) (3.1% - 5%) (2.5% - 4.1%) (2.3% - 3.7%) (1.3% - 2.2%)6.6% 5.8% 5.8% 5.1% 4.4% 4.3% 2.9%(5.1% - 8.2%) (4.4% - 7.1%) (4.4% - 7.1%) (3.9% - 6.4%) (3.3% - 5.4%) (3.3% - 5.4%) (2.2% - 3.5%)4.4% 4.4% 4.4% 4.4% 4% 3.8% 2.4%(3.3% - 5.4%) (3.3% - 5.4%) (3.3% - 5.4%) (3.3% - 5.4%) (3% - 4.9%) (2.9% - 4.8%) (1.8% - 3%)6.9% 4.3% 4.3% 4% 3.5% 3.1% 1.8%(5.3% - 8.5%) (3.3% - 5.4%) (3.3% - 5.4%) (3.1% - 5%) (2.6% - 4.3%) (2.3% - 3.8%) (1.4% - 2.2%)3.5% 0.7% 0.7% 0.7% 0.7% 0% 0%(2.7% - 4.4%) (0.5% - 0.8%) (0.5% - 0.8%) (0.5% - 0.8%) (0.5% - 0.8%) (0% - 0%) (0% - 0%)6.7% 5.6% 5% 4.3% 3.6% 4.2% 2.7%(5.1% - 8.3%) (4.2% - 6.9%) (3.8% - 6.2%) (3.3% - 5.3%) (2.7% - 4.5%) (3.2% - 5.1%) (2.1% - 3.4%)3.3% 1.9% 1.9% 1.9% 1.9% 1% 0.1%(2.5% - 4.1%) (1.4% - 2.3%) (1.4% - 2.3%) (1.4% - 2.3%) (1.4% - 2.3%) (0.7% - 1.2%) (0.1% - 0.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of CardiopulmonaryMortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-43Table E-42. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/258.2% 7.3% 6.5% 5.7% 4.9% 5.7% 4.8%(6.3% - 10.1%) (5.5% - 9%) (4.9% - 8%) (4.3% - 7%) (3.7% - 6.1%) (4.3% - 7%) (3.6% - 5.9%)7% 6.4% 5.8% 5.1% 4.3% 4.9% 3.3%(5.3% - 8.6%) (4.9% - 7.9%) (4.4% - 7.2%) (3.8% - 6.3%) (3.3% - 5.3%) (3.7% - 6%) (2.5% - 4.1%)8.5% 6% 5.3% 4.6% 3.9% 4.6% 3.2%(6.5% - 10.5%) (4.5% - 7.4%) (4% - 6.5%) (3.5% - 5.7%) (2.9% - 4.8%) (3.5% - 5.7%) (2.4% - 4%)4.9% 4.9% 4.9% 4.9% 4.3% 4.9% 4.3%(3.7% - 6%) (3.7% - 6%) (3.7% - 6%) (3.7% - 6%) (3.3% - 5.3%) (3.7% - 6%) (3.3% - 5.3%)7% 4.9% 4.9% 4.2% 3.5% 3.6% 2.3%(5.4% - 8.7%) (3.8% - 6.1%) (3.7% - 6%) (3.2% - 5.2%) (2.6% - 4.3%) (2.7% - 4.5%) (1.7% - 2.8%)9.9% 3.6% 3.6% 3.6% 3.6% 2.5% 1.3%(7.6% - 12.1%) (2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (1.9% - 3.1%) (1% - 1.6%)6.4% 5.9% 5.2% 4.5% 3.8% 4.5% 3.8%(4.9% - 7.9%) (4.5% - 7.2%) (3.9% - 6.4%) (3.4% - 5.5%) (2.9% - 4.7%) (3.4% - 5.5%) (2.9% - 4.7%)7.6% 3.3% 3.3% 3.3% 2.8% 2.2% 1%(5.8% - 9.4%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (2.2% - 3.5%) (1.6% - 2.7%) (0.8% - 1.3%)6.9% 5.1% 5.1% 4.8% 4.1% 3.7% 2.3%(5.3% - 8.5%) (3.8% - 6.3%) (3.8% - 6.3%) (3.7% - 6%) (3.1% - 5%) (2.8% - 4.6%) (1.8% - 2.9%)6.6% 5.7% 5.7% 5.1% 4.3% 4.3% 2.8%(5% - 8.1%) (4.3% - 7.1%) (4.3% - 7.1%) (3.9% - 6.3%) (3.3% - 5.3%) (3.2% - 5.3%) (2.1% - 3.5%)3.6% 3.6% 3.6% 3.6% 3.2% 3.1% 1.8%(2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (2.4% - 4%) (2.4% - 3.9%) (1.4% - 2.3%)7.8% 5.1% 5.1% 4.7% 4.1% 3.7% 2.3%(6% - 9.6%) (3.8% - 6.3%) (3.8% - 6.3%) (3.6% - 5.9%) (3.1% - 5.1%) (2.8% - 4.6%) (1.8% - 2.9%)4.9% 1.6% 1.6% 1.6% 1.6% 0.7% 0%(3.7% - 6.1%) (1.2% - 1.9%) (1.2% - 1.9%) (1.2% - 1.9%) (1.2% - 1.9%) (0.5% - 0.9%) (0% - 0%)7.3% 6.1% 5.5% 4.8% 4.1% 4.7% 3.1%(5.6% - 9%) (4.7% - 7.6%) (4.2% - 6.8%) (3.6% - 5.9%) (3.1% - 5%) (3.5% - 5.8%) (2.4% - 3.9%)3.4% 2% 2% 2% 2% 1.1% 0.1%(2.6% - 4.2%) (1.5% - 2.5%) (1.5% - 2.5%) (1.5% - 2.5%) (1.5% - 2.5%) (0.8% - 1.3%) (0.1% - 0.2%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of CardiopulmonaryMortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-44Table E-43. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-13% 0% 11% 21% 32% 21% 34%(-12% - -13%) (0% - 0%) (10% - 11%) (21% -21%) (32% - 32%) (21% - 21%) (33% - 34%)-9% 0% 8% 19% 30% 22% 44%(-9% - -9%) (0% - 0%) (8% - 8%) (19% - 19%) (30% - 30%) (22% - 22%) (44% - 44%)-42% 0% 12% 23% 35% 23% 46%(-41% - -42%) (0% - 0%) (11% - 12%) (23% -23%) (35% - 35%) (23% - 23%) (46% - 46%)0% 0% 0% 0% 11% 0% 11%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-37% 0% 1% 13% 25% 23% 47%(-36% - -37%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (23% - 24%) (47% - 47%)-182% 0% 0% 0% 0% 34% 68%(-180% - -184%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (34% - 34%) (68% - 68%)-9% 0% 12% 23% 35% 23% 35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 23%) (35% - 35%)-120% 0% 0% 0% 12% 31% 62%(-119% - -122%) (0% - 0%) (0% - 0%) (0%- 0%) (12% - 12%) (31% - 31%) (62% - 63%)-35% 0% 0% 4% 18% 25% 51%(-34% - -35%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 25%) (51% - 51%)-14% 0% 0% 11% 23% 24% 48%(-14% - -14%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 23%) (24% - 24%) (48% - 49%)0% 0% 0% 0% 10% 13% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 45%)-52% 0% 0% 6% 18% 25% 51%(-51% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (25% - 25%) (50% - 51%)-252% 0% 0% 0% 0% 64% 100%(-251% - -254%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (64% - 64%) (100% - 100%)-17% 0% 9% 20% 31% 22% 45%(-17% - -18%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 31%) (22% - 22%) (45% - 45%)-53% 0% 0% 0% 0% 33% 67%(-52% - -53%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (67% - 67%)Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-45Table E-44. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-12% 0% 10% 21% 32% 21% 33%(-12% - -12%) (0% - 0%) (10% - 11%) (21% - 21%) (31% - 32%) (21% - 21%) (33% - 33%)-9% 0% 9% 21% 32% 24% 48%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (24% - 24%) (48% - 48%)-43% 0% 12% 24% 36% 24% 47%(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (47% - 47%)0% 0% 0% 0% 13% 0% 13%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 0%) (13% - 13%)-43% 0% 1% 16% 30% 28% 56%(-43% - -44%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 30%) (28% - 28%) (56% - 56%)-173% 0% 0% 0% 0% 33% 66%(-171% - -176%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (66% - 66%)-9% 0% 12% 24% 36% 24% 36%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-133% 0% 0% 0% 13% 35% 70%(-132% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (35% - 35%) (70% - 70%)-40% 0% 0% 5% 21% 29% 59%(-40% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 29%) (59% - 59%)-15% 0% 0% 11% 24% 25% 50%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% - 25%) (50% - 51%)0% 0% 0% 0% 10% 13% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (44% - 45%)-59% 0% 0% 7% 20% 29% 58%(-59% - -60%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 29%) (58% - 58%)-431% 0% 0% 0% 0% 100% 100%(-428% - -433%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%)-20% 0% 10% 23% 35% 25% 51%(-20% - -20%) (0% - 0%) (10% - 11%) (23% - 23%) (35% - 35%) (25% - 25%) (51% - 51%)-75% 0% 0% 0% 0% 48% 96%(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 48%) (96% - 96%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundingthe PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-46Table E-45. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-13% 0% 11% 22% 33% 22% 34%(-13% - -13%) (0% - 0%) (11% - 11%) (22% - 22%) (33% - 33%) (22% - 22%) (34% - 35%)-10% 0% 9% 21% 33% 24% 48%(-9% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 24%) (48% - 49%)-43% 0% 12% 23% 35% 23% 46%(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (46% - 47%)0% 0% 0% 0% 12% 0% 12%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-42% 0% 1% 15% 29% 27% 54%(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (27% - 27%) (54% - 55%)-171% 0% 0% 0% 0% 32% 64%(-169% - -174%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (32% - 32%) (64% - 64%)-9% 0% 12% 24% 36% 24% 36%(-9% - -9%) (0% - 0%) (12% - 12%) (24% - 24%) (35% - 36%) (24% - 24%) (35% - 36%)-132% 0% 0% 0% 13% 34% 68%(-130% - -133%) (0% - 0%) (0% - 0%) (0%- 0%) (13% - 13%) (34% - 34%) (68% - 68%)-37% 0% 0% 5% 19% 27% 54%(-37% - -37%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (27% - 27%) (54% - 54%)-15% 0% 0% 11% 24% 25% 51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 25%) (51% - 51%)0% 0% 0% 0% 11% 14% 50%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (50% - 50%)-55% 0% 0% 6% 19% 27% 54%(-54% - -55%) (0% - 0%) (0% - 0%) (6% - 7%) (19% - 19%) (27% - 27%) (54% - 54%)-215% 0% 0% 0% 0% 55% 100%(-214% - -216%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (55% - 55%) (100% - 100%)-19% 0% 10% 22% 34% 24% 49%(-19% - -19%) (0% - 0%) (10% - 10%) (22% - 22%) (34% - 34%) (24% - 24%) (49% - 49%)-73% 0% 0% 0% 0% 46% 93%(-73% - -73%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-47Table E-46. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations ina Recent Year (2005) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations: Estimates Based on Krewski etal. (2009), Using Ambient PM2.5 from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25722 643 577 509 441 509 430(569 - 872) (506 - 778) (453 - 698) (399 - 617) (345 - 535) (399 - 617) (337 - 522)715 660 606 536 465 517 371(563 - 863) (518 - 797) (476 - 733) (420 - 649) (364 - 564) (405 - 627) (290 - 451)516 366 324 282 240 282 200(406 - 622) (287 - 443) (254 - 393) (221 - 343) (187 - 291) (221 - 343) (156 - 243)455 455 455 455 405 455 405(357 - 552) (357 - 552) (357 - 552) (357 - 552) (317 - 492) (357 - 552) (317 - 492)1054 775 766 674 581 596 412(830 - 1271) (608 - 939) (601 - 928) (528 - 817) (454 - 705) (466 - 723) (321 - 501)338 122 122 122 122 81 39(266 - 408) (95 - 148) (95 - 148) (95 - 148) (95 - 148) (63 - 99) (31 - 48)707 649 574 500 424 500 424(555 - 856) (508 - 786) (450 - 697) (391 - 607) (331 - 516) (391 - 607) (331 - 516)3328 1526 1526 1526 1344 1055 576(2618 - 4019) (1191 - 1856) (1191 - 1856) (1191 - 1856) (1048 - 1636) (822 - 1286) (448 - 703)3117 2326 2326 2223 1907 1745 1151(2450 - 3768) (1821 - 2820) (1821 - 2820) (1740 - 2697) (1491 - 2317) (1363 - 2121) (897 - 1403)621 545 545 488 420 416 283(488 - 752) (427 - 660) (427 - 660) (382 - 592) (328 - 510) (325 - 505) (221 - 345)579 579 579 579 521 506 318(453 - 704) (453 - 704) (453 - 704) (453 - 704) (407 - 634) (395 - 615) (248 - 388)747 495 495 466 409 372 246(588 - 902) (388 - 601) (388 - 601) (364 - 565) (320 - 497) (291 - 452) (192 - 300)109 31 31 31 31 11 0(85 - 132) (24 - 38) (24 - 38) (24 - 38) (24 - 38) (9 - 14) (0 - 0)1069 913 830 732 633 714 509(842 - 1290) (718 - 1104) (651 - 1005) (574 - 888) (496 - 769) (559 - 865) (397 - 618)156 103 103 103 103 69 34(122 - 190) (80 - 125) (80 - 125) (80 - 125) (80 - 125) (54 - 84) (26 - 42)Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-48Table E-47. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations ina Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25744 662 594 524 454 524 443(586 - 898) (521 - 801) (466 - 719) (411 - 635) (355 - 550) (411 - 635) (346 - 538)584 534 486 424 361 407 277(459 - 707) (419 - 647) (381 - 590) (332 - 515) (282 - 439) (319 - 495) (216 - 338)486 341 301 260 219 260 181(382 - 587) (267 - 414) (235 - 365) (203 - 316) (171 - 267) (203 - 316) (141 - 220)344 344 344 344 300 344 300(268 - 417) (268 - 417) (268 - 417) (268 - 417) (234 - 365) (268 - 417) (234 - 365)770 537 530 453 375 388 236(604 - 932) (420 - 652) (414 - 643) (354 - 551) (293 - 457) (303 - 472) (184 - 288)352 129 129 129 129 87 44(277 - 424) (100 - 157) (100 - 157) (100 - 157) (100 - 157) (67 - 106) (34 - 53)686 627 553 479 404 479 404(537 - 831) (491 - 761) (433 - 672) (374 - 582) (315 - 491) (374 - 582) (315 - 491)2945 1263 1263 1263 1094 825 380(2313 - 3562) (985 - 1538) (985 - 1538) (985 - 1538) (852 - 1333) (642 - 1007) (296 - 465)2435 1739 1739 1649 1373 1231 713(1907 - 2951) (1358 - 2114) (1358 - 2114) (1288 - 2005) (1071 - 1671) (960 - 1499) (555 - 870)564 491 491 437 373 369 244(442 - 683) (385 - 596) (385 - 596) (342 - 531) (291 - 453) (288 - 449) (190 - 297)614 614 614 614 553 536 340(480 - 746) (480 - 746) (480 - 746) (480 - 746) (432 - 673) (419 - 653) (265 - 415)595 373 373 348 299 266 157(467 - 720) (292 - 454) (292 - 454) (271 - 423) (233 - 364) (208 - 324) (122 - 192)89 17 17 17 17 0 0(69 - 108) (13 - 21) (13 - 21) (13 - 21) (13 - 21) (0 - 0) (0 - 0)810 677 606 522 438 506 332(636 - 981) (530 - 821) (474 - 735) (408 - 635) (342 - 533) (395 - 616) (259 - 405)106 61 61 61 61 32 3(83 - 129) (47 - 74) (47 - 74) (47 - 74) (47 - 74) (25 - 39) (2 - 3)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-49Table E-48. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations ina Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25717 636 568 500 430 500 420(563 - 865) (500 - 770) (446 - 689) (391 - 606) (337 - 523) (391 - 606) (328 - 510)583 533 485 423 361 407 277(458 - 706) (418 - 646) (380 - 589) (331 - 514) (282 - 438) (318 - 494) (216 - 337)509 359 318 276 234 276 194(401 - 615) (282 - 436) (249 - 386) (216 - 335) (182 - 284) (216 - 335) (151 - 236)383 383 383 383 337 383 337(299 - 465) (299 - 465) (299 - 465) (299 - 465) (263 - 409) (299 - 465) (263 - 409)810 572 564 485 406 419 262(636 - 980) (447 - 694) (441 - 685) (379 - 590) (317 - 494) (327 - 509) (204 - 320)370 138 138 138 138 95 50(292 - 446) (108 - 168) (108 - 168) (108 - 168) (108 - 168) (74 - 115) (39 - 61)715 655 579 501 424 501 424(561 - 866) (513 - 794) (453 - 702) (392 - 609) (331 - 515) (392 - 609) (331 - 515)3056 1333 1333 1333 1160 884 428(2401 - 3695) (1040 - 1623) (1040 - 1623) (1040 - 1623) (904 - 1413) (688 - 1079) (333 - 523)2837 2080 2080 1982 1681 1526 960(2227 - 3434) (1627 - 2526) (1627 - 2526) (1550 - 2408) (1313 - 2044) (1191 - 1857) (748 - 1171)558 486 486 432 368 364 240(437 - 675) (381 - 589) (381 - 589) (338 - 525) (288 - 447) (284 - 443) (187 - 292)522 522 522 522 464 448 262(407 - 635) (407 - 635) (407 - 635) (407 - 635) (362 - 565) (350 - 546) (204 - 320)667 434 434 407 354 320 202(524 - 806) (340 - 527) (340 - 527) (318 - 494) (276 - 430) (249 - 389) (158 - 247)127 41 41 41 41 18 0(99 - 154) (32 - 50) (32 - 50) (32 - 50) (32 - 50) (14 - 22) (0 - 0)887 746 671 584 495 567 383(696 - 1072) (585 - 904) (526 - 814) (456 - 709) (386 - 602) (443 - 688) (299 - 467)111 65 65 65 65 35 5(87 - 135) (50 - 79) (50 - 79) (50 - 79) (50 - 79) (27 - 43) (4 - 6)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted 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, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-50Table E-49. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2512.4% 11% 9.9% 8.7% 7.5% 8.7% 7.4%(9.7% - 14.9%) (8.7% - 13.3%) (7.7% - 11.9%) (6.8% - 10.6%) (5.9% - 9.2%) (6.8% - 10.6%) (5.8% - 8.9%)12.2% 11.2% 10.3% 9.1% 7.9% 8.8% 6.3%(9.6% - 14.7%) (8.8% - 13.5%) (8.1% - 12.4%) (7.1% - 11%) (6.2% - 9.6%) (6.9% - 10.6%) (4.9% - 7.7%)12.4% 8.8% 7.8% 6.8% 5.8% 6.8% 4.8%(9.8% - 15%) (6.9% - 10.7%) (6.1% - 9.5%) (5.3% - 8.3%) (4.5% - 7%) (5.3% - 8.3%) (3.8% - 5.9%)8.6% 8.6% 8.6% 8.6% 7.7% 8.6% 7.7%(6.7% - 10.4%) (6.7% - 10.4%) (6.7% - 10.4%) (6.7% - 10.4%) (6% - 9.3%) (6.7% - 10.4%) (6% - 9.3%)12.8% 9.4% 9.3% 8.2% 7.1% 7.3% 5%(10.1% - 15.5%) (7.4% - 11.4%) (7.3% - 11.3%) (6.4% - 10%) (5.5% - 8.6%) (5.7% - 8.8%) (3.9% - 6.1%)13.1% 4.7% 4.7% 4.7% 4.7% 3.1% 1.5%(10.3% - 15.8%) (3.7% - 5.7%) (3.7% - 5.7%) (3.7% - 5.7%) (3.7% - 5.7%) (2.4% - 3.8%) (1.2% - 1.9%)9.5% 8.7% 7.7% 6.7% 5.7% 6.7% 5.7%(7.4% - 11.5%) (6.8% - 10.5%) (6% - 9.4%) (5.2% - 8.1%) (4.4% - 6.9%) (5.2% - 8.1%) (4.4% - 6.9%)11.8% 5.4% 5.4% 5.4% 4.8% 3.8% 2%(9.3% - 14.3%) (4.2% - 6.6%) (4.2% - 6.6%) (4.2% - 6.6%) (3.7% - 5.8%) (2.9% - 4.6%) (1.6% - 2.5%)11% 8.2% 8.2% 7.8% 6.7% 6.1% 4%(8.6% - 13.3%) (6.4% - 9.9%) (6.4% - 9.9%) (6.1% - 9.5%) (5.2% - 8.1%) (4.8% - 7.5%) (3.2% - 4.9%)10.4% 9.1% 9.1% 8.1% 7% 6.9% 4.7%(8.1% - 12.5%) (7.1% - 11%) (7.1% - 11%) (6.4% - 9.9%) (5.5% - 8.5%) (5.4% - 8.4%) (3.7% - 5.8%)6.2% 6.2% 6.2% 6.2% 5.5% 5.4% 3.4%(4.8% - 7.5%) (4.8% - 7.5%) (4.8% - 7.5%) (4.8% - 7.5%) (4.3% - 6.7%) (4.2% - 6.5%) (2.6% - 4.1%)12.3% 8.1% 8.1% 7.6% 6.7% 6.1% 4%(9.6% - 14.8%) (6.4% - 9.9%) (6.4% - 9.9%) (6% - 9.3%) (5.2% - 8.2%) (4.8% - 7.4%) (3.1% - 4.9%)6.3% 1.8% 1.8% 1.8% 1.8% 0.6% 0%(5% - 7.7%) (1.4% - 2.2%) (1.4% - 2.2%) (1.4% - 2.2%) (1.4% - 2.2%) (0.5% - 0.8%) (0% - 0%)12.6% 10.8% 9.8% 8.6% 7.5% 8.4% 6%(9.9% - 15.2%) (8.4% - 13%) (7.7% - 11.8%) (6.8% - 10.5%) (5.8% - 9%) (6.6% - 10.2%) (4.7% - 7.3%)7.1% 4.7% 4.7% 4.7% 4.7% 3.1% 1.5%(5.6% - 8.6%) (3.6% - 5.7%) (3.6% - 5.7%) (3.6% - 5.7%) (3.6% - 5.7%) (2.4% - 3.8%) (1.2% - 1.9%)Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-51Table E-50. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2512.4% 11% 9.9% 8.7% 7.5% 8.7% 7.4%(9.7% - 14.9%) (8.6% - 13.3%) (7.7% - 11.9%) (6.8% - 10.5%) (5.9% - 9.1%) (6.8% - 10.5%) (5.8% - 8.9%)9.9% 9.1% 8.2% 7.2% 6.1% 6.9% 4.7%(7.8% - 12%) (7.1% - 11%) (6.5% - 10%) (5.6% - 8.7%) (4.8% - 7.4%) (5.4% - 8.4%) (3.7% - 5.7%)11.6% 8.1% 7.2% 6.2% 5.2% 6.2% 4.3%(9.1% - 14%) (6.4% - 9.8%) (5.6% - 8.7%) (4.8% - 7.5%) (4.1% - 6.4%) (4.8% - 7.5%) (3.4% - 5.2%)6.4% 6.4% 6.4% 6.4% 5.5% 6.4% 5.5%(5% - 7.7%) (5% - 7.7%) (5% - 7.7%) (5% - 7.7%) (4.3% - 6.7%) (5% - 7.7%) (4.3% - 6.7%)9.4% 6.5% 6.5% 5.5% 4.6% 4.7% 2.9%(7.4% - 11.4%) (5.1% - 8%) (5% - 7.8%) (4.3% - 6.7%) (3.6% - 5.6%) (3.7% - 5.8%) (2.2% - 3.5%)13.4% 4.9% 4.9% 4.9% 4.9% 3.3% 1.7%(10.6% - 16.2%) (3.8% - 6%) (3.8% - 6%) (3.8% - 6%) (3.8% - 6%) (2.6% - 4%) (1.3% - 2%)8.9% 8.1% 7.2% 6.2% 5.2% 6.2% 5.2%(7% - 10.8%) (6.4% - 9.9%) (5.6% - 8.7%) (4.9% - 7.6%) (4.1% - 6.4%) (4.9% - 7.6%) (4.1% - 6.4%)10.4% 4.5% 4.5% 4.5% 3.9% 2.9% 1.3%(8.2% - 12.6%) (3.5% - 5.4%) (3.5% - 5.4%) (3.5% - 5.4%) (3% - 4.7%) (2.3% - 3.6%) (1% - 1.6%)8.5% 6.1% 6.1% 5.7% 4.8% 4.3% 2.5%(6.6% - 10.3%) (4.7% - 7.4%) (4.7% - 7.4%) (4.5% - 7%) (3.7% - 5.8%) (3.3% - 5.2%) (1.9% - 3%)9.4% 8.2% 8.2% 7.3% 6.2% 6.2% 4.1%(7.4% - 11.4%) (6.4% - 10%) (6.4% - 10%) (5.7% - 8.9%) (4.9% - 7.6%) (4.8% - 7.5%) (3.2% - 5%)6.3% 6.3% 6.3% 6.3% 5.7% 5.5% 3.5%(4.9% - 7.6%) (4.9% - 7.6%) (4.9% - 7.6%) (4.9% - 7.6%) (4.4% - 6.9%) (4.3% - 6.7%) (2.7% - 4.2%)9.8% 6.2% 6.2% 5.7% 4.9% 4.4% 2.6%(7.7% - 11.9%) (4.8% - 7.5%) (4.8% - 7.5%) (4.5% - 7%) (3.8% - 6%) (3.4% - 5.4%) (2% - 3.2%)5% 0.9% 0.9% 0.9% 0.9% 0% 0%(3.9% - 6.1%) (0.7% - 1.2%) (0.7% - 1.2%) (0.7% - 1.2%) (0.7% - 1.2%) (0% - 0%) (0% - 0%)9.5% 7.9% 7.1% 6.1% 5.1% 5.9% 3.9%(7.4% - 11.5%) (6.2% - 9.6%) (5.5% - 8.6%) (4.8% - 7.4%) (4% - 6.2%) (4.6% - 7.2%) (3% - 4.7%)4.7% 2.7% 2.7% 2.7% 2.7% 1.4% 0.1%(3.7% - 5.8%) (2.1% - 3.3%) (2.1% - 3.3%) (2.1% - 3.3%) (2.1% - 3.3%) (1.1% - 1.7%) (0.1% - 0.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence orcredible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-52Table E-51. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 2001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2511.6% 10.3% 9.2% 8.1% 7% 8.1% 6.8%(9.1% - 14%) (8.1% - 12.5%) (7.2% - 11.1%) (6.3% - 9.8%) (5.4% - 8.5%) (6.3% - 9.8%) (5.3% - 8.3%)9.9% 9.1% 8.2% 7.2% 6.1% 6.9% 4.7%(7.8% - 12%) (7.1% - 11%) (6.5% - 10%) (5.6% - 8.7%) (4.8% - 7.5%) (5.4% - 8.4%) (3.7% - 5.7%)12% 8.5% 7.5% 6.5% 5.5% 6.5% 4.6%(9.5% - 14.5%) (6.6% - 10.3%) (5.9% - 9.1%) (5.1% - 7.9%) (4.3% - 6.7%) (5.1% - 7.9%) (3.6% - 5.6%)7% 7% 7% 7% 6.1% 7% 6.1%(5.4% - 8.4%) (5.4% - 8.4%) (5.4% - 8.4%) (5.4% - 8.4%) (4.8% - 7.4%) (5.4% - 8.4%) (4.8% - 7.4%)9.9% 7% 6.9% 6% 5% 5.1% 3.2%(7.8% - 12%) (5.5% - 8.5%) (5.4% - 8.4%) (4.7% - 7.2%) (3.9% - 6.1%) (4% - 6.3%) (2.5% - 3.9%)13.9% 5.2% 5.2% 5.2% 5.2% 3.5% 1.9%(11% - 16.7%) (4.1% - 6.3%) (4.1% - 6.3%) (4.1% - 6.3%) (4.1% - 6.3%) (2.8% - 4.3%) (1.5% - 2.3%)9.1% 8.3% 7.4% 6.4% 5.4% 6.4% 5.4%(7.1% - 11%) (6.5% - 10.1%) (5.8% - 8.9%) (5% - 7.8%) (4.2% - 6.6%) (5% - 7.8%) (4.2% - 6.6%)10.7% 4.7% 4.7% 4.7% 4.1% 3.1% 1.5%(8.4% - 13%) (3.7% - 5.7%) (3.7% - 5.7%) (3.7% - 5.7%) (3.2% - 5%) (2.4% - 3.8%) (1.2% - 1.8%)9.8% 7.2% 7.2% 6.9% 5.8% 5.3% 3.3%(7.7% - 11.9%) (5.6% - 8.7%) (5.6% - 8.7%) (5.4% - 8.3%) (4.5% - 7.1%) (4.1% - 6.4%) (2.6% - 4.1%)9.3% 8.1% 8.1% 7.2% 6.2% 6.1% 4%(7.3% - 11.3%) (6.4% - 9.8%) (6.4% - 9.8%) (5.6% - 8.8%) (4.8% - 7.5%) (4.8% - 7.4%) (3.1% - 4.9%)5.2% 5.2% 5.2% 5.2% 4.6% 4.4% 2.6%(4% - 6.3%) (4% - 6.3%) (4% - 6.3%) (4% - 6.3%) (3.6% - 5.6%) (3.5% - 5.4%) (2% - 3.2%)11.1% 7.2% 7.2% 6.7% 5.9% 5.3% 3.4%(8.7% - 13.4%) (5.6% - 8.7%) (5.6% - 8.7%) (5.3% - 8.2%) (4.6% - 7.1%) (4.1% - 6.4%) (2.6% - 4.1%)7% 2.2% 2.2% 2.2% 2.2% 1% 0%(5.4% - 8.5%) (1.7% - 2.7%) (1.7% - 2.7%) (1.7% - 2.7%) (1.7% - 2.7%) (0.8% - 1.2%) (0% - 0%)10.4% 8.7% 7.9% 6.8% 5.8% 6.6% 4.5%(8.1% - 12.5%) (6.8% - 10.6%) (6.1% - 9.5%) (5.3% - 8.3%) (4.5% - 7%) (5.2% - 8.1%) (3.5% - 5.5%)4.9% 2.8% 2.8% 2.8% 2.8% 1.5% 0.2%(3.8% - 5.9%) (2.2% - 3.5%) (2.2% - 3.5%) (2.2% - 3.5%) (2.2% - 3.5%) (1.2% - 1.9%) (0.2% - 0.2%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of CardiopulmonaryMortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-53Table E-52. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-12% 0% 10% 21% 32% 21% 33%(-12% - -12%) (0% - 0%) (10% - 10%) (21% - 21%) (31% - 32%) (21% - 21%) (33% - 33%)-8% 0% 8% 19% 29% 22% 44%(-8% - -9%) (0% - 0%) (8% - 8%) (19% - 19%) (29% - 30%) (21% - 22%) (43% - 44%)-41% 0% 11% 23% 34% 23% 45%(-40% - -42%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (23% - 23%) (45% - 46%)0% 0% 0% 0% 11% 0% 11%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-36% 0% 1% 13% 25% 23% 47%(-35% - -37%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 25%) (23% - 23%) (47% - 47%)-178% 0% 0% 0% 0% 34% 68%(-175% - -181%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (68% - 68%)-9% 0% 11% 23% 35% 23% 35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 23%) (34% - 35%)-118% 0% 0% 0% 12% 31% 62%(-116% - -120%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 31%) (62% - 62%)-34% 0% 0% 4% 18% 25% 50%(-34% - -34%) (0% - 0%) (0% - 0%) (4% - 4%) (18% - 18%) (25% - 25%) (50% - 51%)-14% 0% 0% 10% 23% 24% 48%(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 11%) (23% - 23%) (24% - 24%) (48% - 48%)0% 0% 0% 0% 10% 13% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 45%)-51% 0% 0% 6% 17% 25% 50%(-50% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (25% - 25%) (50% - 51%)-250% 0% 0% 0% 0% 64% 100%(-248% - -251%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 64%) (100% - 100%)-17% 0% 9% 20% 31% 22% 44%(-17% - -17%) (0% - 0%) (9% - 9%) (20% - 20%) (30% - 31%) (22% - 22%) (44% - 45%)-52% 0% 0% 0% 0% 33% 67%(-52% - -52%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (67% - 67%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-54Table E-53. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-12% 0% 10% 21% 32% 21% 33%(-12% - -12%) (0% - 0%) (10% - 11%) (21% - 21%) (31% - 32%) (21% - 21%) (33% - 33%)-9% 0% 9% 21% 32% 24% 48%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (24% - 24%) (48% - 48%)-43% 0% 12% 24% 36% 24% 47%(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (47% - 47%)0% 0% 0% 0% 13% 0% 13%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 0%) (13% - 13%)-43% 0% 1% 16% 30% 28% 56%(-43% - -44%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 30%) (28% - 28%) (56% - 56%)-173% 0% 0% 0% 0% 33% 66%(-171% - -176%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (66% - 66%)-9% 0% 12% 24% 36% 24% 36%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-133% 0% 0% 0% 13% 35% 70%(-132% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (35% - 35%) (70% - 70%)-40% 0% 0% 5% 21% 29% 59%(-40% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 29%) (59% - 59%)-15% 0% 0% 11% 24% 25% 50%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% - 25%) (50% - 51%)0% 0% 0% 0% 10% 13% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (44% - 45%)-59% 0% 0% 7% 20% 29% 58%(-59% - -60%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 29%) (58% - 58%)-431% 0% 0% 0% 0% 100% 100%(-428% - -433%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%)-20% 0% 10% 23% 35% 25% 51%(-20% - -20%) (0% - 0%) (10% - 11%) (23% - 23%) (35% - 35%) (25% - 25%) (51% - 51%)-75% 0% 0% 0% 0% 48% 96%(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 48%) (96% - 96%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-55Table E-54. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-13% 0% 11% 21% 32% 21% 34%(-12% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 22%) (34% - 34%)-9% 0% 9% 21% 32% 24% 48%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (24% - 24%) (48% - 48%)-42% 0% 12% 23% 35% 23% 46%(-41% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 23%) (46% - 46%)0% 0% 0% 0% 12% 0% 12%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-42% 0% 1% 15% 29% 27% 54%(-41% - -42%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (27% - 27%) (54% - 54%)-168% 0% 0% 0% 0% 32% 64%(-165% - -170%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 32%) (64% - 64%)-9% 0% 12% 23% 35% 23% 35%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-129% 0% 0% 0% 13% 34% 68%(-128% - -131%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 34%) (68% - 68%)-36% 0% 0% 5% 19% 27% 54%(-36% - -37%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (26% - 27%) (54% - 54%)-15% 0% 0% 11% 24% 25% 51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% - 25%) (50% - 51%)0% 0% 0% 0% 11% 14% 50%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (50% - 50%)-54% 0% 0% 6% 19% 26% 53%(-53% - -54%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 19%) (26% - 27%) (53% - 54%)-213% 0% 0% 0% 0% 55% 100%(-211% - -215%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 55%) (100% - 100%)-19% 0% 10% 22% 34% 24% 49%(-19% - -19%) (0% - 0%) (10% - 10%) (22% - 22%) (33% - 34%) (24% - 24%) (48% - 49%)-72% 0% 0% 0% 0% 46% 93%(-72% - -73%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-56Table E-55. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2577 68 61 54 46 54 45(29 - 122) (26 - 108) (23 - 97) (20 - 86) (17 - 74) (20 - 86) (17 - 73)81 74 68 60 52 58 42(31 - 128) (28 - 118) (26 - 109) (23 - 96) (20 - 84) (22 - 93) (16 - 67)55 39 34 30 25 30 21(21 - 87) (15 - 62) (13 - 55) (11 - 48) (10 - 41) (11 - 48) (8 - 34)50 50 50 50 44 50 44(19 - 79) (19 - 79) (19 - 79) (19 - 79) (17 - 71) (19 - 79) (17 - 71)112 82 81 71 61 63 43(43 - 178) (31 - 131) (31 - 129) (27 - 114) (23 - 98) (24 - 101) (16 - 70)26 9 9 9 9 6 3(10 - 41) (3 - 15) (3 - 15) (3 - 15) (3 - 15) (2 - 10) (1 - 5)76 70 62 54 46 54 46(29 - 122) (26 - 112) (23 - 99) (20 - 86) (17 - 73) (20 - 86) (17 - 73)248 112 112 112 99 78 42(94 - 393) (42 - 181) (42 - 181) (42 - 181) (37 - 160) (29 - 125) (16 - 68)208 155 155 148 126 116 76(79 - 331) (58 - 247) (58 - 247) (56 - 236) (48 - 203) (43 - 186) (28 - 123)70 61 61 55 47 46 32(26 - 111) (23 - 98) (23 - 98) (21 - 87) (18 - 75) (17 - 75) (12 - 51)58 58 58 58 53 51 32(22 - 94) (22 - 94) (22 - 94) (22 - 94) (20 - 85) (19 - 82) (12 - 52)80 53 53 50 44 40 26(31 - 127) (20 - 84) (20 - 84) (19 - 79) (16 - 70) (15 - 64) (10 - 42)8 222210(3 - 13) (1 - 4) (1 - 4) (1 - 4) (1 - 4) (0 - 1) (0 - 0)116 99 90 79 68 77 54(44 - 184) (37 - 157) (34 - 143) (30 - 126) (26 - 109) (29 - 123) (20 - 88)19 12 12 12 12 8 4(7 - 30) (5 - 20) (5 - 20) (5 - 20) (5 - 20) (3 - 13) (1 - 6)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-57Table E-56. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2579 70 63 55 48 55 47(30 - 125) (27 - 112) (24 - 100) (21 - 88) (18 - 77) (21 - 88) (18 - 75)66 60 55 48 40 46 31(25 - 105) (23 - 96) (21 - 87) (18 - 76) (15 - 65) (17 - 73) (12 - 50)52 36 32 28 23 28 19(20 - 82) (14 - 58) (12 - 51) (10 - 44) (9 - 37) (10 - 44) (7 - 31)37 37 37 37 33 37 33(14 - 60) (14 - 60) (14 - 60) (14 - 60) (12 - 52) (14 - 60) (12 - 52)82 57 56 48 39 41 25(31 - 130) (21 - 91) (21 - 90) (18 - 77) (15 - 64) (15 - 66) (9 - 40)27 10 10 10 10 7 3(10 - 43) (4 - 16) (4 - 16) (4 - 16) (4 - 16) (2 - 11) (1 - 5)74 68 60 52 43 52 43(28 - 118) (26 - 108) (22 - 96) (19 - 83) (16 - 70) (19 - 83) (16 - 70)219 93 93 93 80 61 28(83 - 348) (35 - 150) (35 - 150) (35 - 150) (30 - 130) (23 - 98) (10 - 45)162 115 115 109 91 81 47(61 - 259) (43 - 185) (43 - 185) (41 - 176) (34 - 146) (30 - 131) (17 - 76)63 55 55 49 42 41 27(24 - 101) (21 - 88) (21 - 88) (18 - 78) (16 - 67) (15 - 66) (10 - 44)62 62 62 62 56 54 34(23 - 100) (23 - 100) (23 - 100) (23 - 100) (21 - 90) (20 - 87) (13 - 55)64 40 40 37 32 28 17(24 - 101) (15 - 64) (15 - 64) (14 - 59) (12 - 51) (11 - 45) (6 - 27)6 111100(2 - 10) (0 - 2) (0 - 2) (0 - 2) (0 - 2) (0 - 0) (0 - 0)87 73 65 56 47 54 35(33 - 139) (27 - 116) (24 - 104) (21 - 90) (18 - 75) (20 - 87) (13 - 57)13 7 7 7 7 4 0(5 - 20) (3 - 12) (3 - 12) (3 - 12) (3 - 12) (1 - 6) (0 - 0)Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-58Table E-57. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2576 67 60 53 45 53 44(29 - 121) (26 - 107) (23 - 96) (20 - 84) (17 - 73) (20 - 84) (17 - 71)66 60 55 47 40 46 31(25 - 105) (23 - 96) (21 - 87) (18 - 76) (15 - 65) (17 - 73) (12 - 50)54 38 34 29 25 29 20(21 - 86) (14 - 61) (13 - 54) (11 - 47) (9 - 40) (11 - 47) (8 - 33)42 42 42 42 37 42 37(16 - 67) (16 - 67) (16 - 67) (16 - 67) (14 - 59) (16 - 67) (14 - 59)86 60 59 51 43 44 28(33 - 137) (23 - 97) (22 - 95) (19 - 82) (16 - 69) (16 - 71) (10 - 44)29 11 11 11 11 7 4(11 - 45) (4 - 17) (4 - 17) (4 - 17) (4 - 17) (3 - 12) (1 - 6)77 71 62 54 46 54 46(29 - 123) (27 - 113) (23 - 100) (20 - 87) (17 - 73) (20 - 87) (17 - 73)227 98 98 98 85 65 31(86 - 361) (37 - 158) (37 - 158) (37 - 158) (32 - 138) (24 - 105) (12 - 51)189 138 138 131 111 101 63(72 - 301) (52 - 221) (52 - 221) (49 - 211) (42 - 179) (38 - 163) (24 - 102)63 54 54 48 41 41 27(24 - 100) (21 - 87) (21 - 87) (18 - 77) (15 - 66) (15 - 65) (10 - 43)53 53 53 53 47 45 26(20 - 85) (20 - 85) (20 - 85) (20 - 85) (17 - 75) (17 - 73) (10 - 43)71 46 46 43 38 34 21(27 - 114) (17 - 74) (17 - 74) (16 - 69) (14 - 60) (13 - 55) (8 - 35)9 333310(3 - 15) (1 - 5) (1 - 5) (1 - 5) (1 - 5) (0 - 2) (0 - 0)96 80 72 63 53 61 41(36 - 152) (30 - 128) (27 - 116) (24 - 100) (20 - 85) (23 - 98) (15 - 66)13 8 8 8 8 4 1(5 - 21) (3 - 12) (3 - 12) (3 - 12) (3 - 12) (2 - 7) (0 - 1)Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credibleintervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annualstandard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-59Table E-58. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to AmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/258.6% 7.6% 6.8% 6% 5.2% 6% 5.1%(3.3% - 13.6%) (2.9% - 12.1%) (2.6% - 10.9%) (2.3% - 9.6%) (2% - 8.3%) (2.3% - 9.6%) (1.9% - 8.1%)8.4% 7.8% 7.1% 6.3% 5.5% 6.1% 4.3%(3.2% - 13.4%) (3% - 12.3%) (2.7% - 11.3%) (2.4% - 10%) (2.1% - 8.7%) (2.3% - 9.7%) (1.6% - 7%)8.6% 6.1% 5.4% 4.7% 4% 4.7% 3.3%(3.3% - 13.7%) (2.3% - 9.7%) (2% - 8.6%) (1.8% - 7.5%) (1.5% - 6.4%) (1.8% - 7.5%) (1.2% - 5.3%)5.9% 5.9% 5.9% 5.9% 5.3% 5.9% 5.3%(2.2% - 9.5%) (2.2% - 9.5%) (2.2% - 9.5%) (2.2%- 9.5%) (2% - 8.5%) (2.2% - 9.5%) (2% - 8.5%)8.9% 6.5% 6.5% 5.7% 4.9% 5% 3.4%(3.4% - 14.1%) (2.5% - 10.4%) (2.4% - 10.3%) (2.1% - 9.1%) (1.8% - 7.8%) (1.9% - 8%) (1.3% - 5.5%)9.1% 3.2% 3.2% 3.2% 3.2% 2.1% 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%) (0.4% - 1.7%)6.6% 6% 5.3% 4.6% 3.9% 4.6% 3.9%(2.5% - 10.5%) (2.3% - 9.6%) (2% - 8.5%) (1.7% - 7.4%) (1.5% - 6.3%) (1.7% - 7.4%) (1.5% - 6.3%)8.2% 3.7% 3.7% 3.7% 3.3% 2.6% 1.4%(3.1% - 13%) (1.4% - 6%) (1.4% - 6%) (1.4% - 6%) (1.2% - 5.3%) (1% - 4.2%) (0.5% - 2.3%)7.6% 5.6% 5.6% 5.4% 4.6% 4.2% 2.8%(2.9% - 12.1%) (2.1% - 9%) (2.1% - 9%) (2% - 8.6%) (1.7% - 7.4%) (1.6% - 6.8%) (1% - 4.5%)7.2% 6.3% 6.3% 5.6% 4.8% 4.8% 3.2%(2.7% - 11.4%) (2.4% - 10%) (2.4% - 10%) (2.1% - 9%) (1.8% - 7.7%) (1.8% - 7.7%) (1.2% - 5.2%)4.2% 4.2% 4.2% 4.2% 3.8% 3.7% 2.3%(1.6% - 6.8%) (1.6% - 6.8%) (1.6% - 6.8%) (1.6% -6.8%) (1.4% - 6.1%) (1.4% - 5.9%) (0.9% - 3.7%)8.5% 5.6% 5.6% 5.3% 4.6% 4.2% 2.8%(3.2% - 13.5%) (2.1% - 9%) (2.1% - 9%) (2% - 8.4%) (1.7% - 7.4%) (1.6% - 6.7%) (1% - 4.5%)4.4% 1.2% 1.2% 1.2% 1.2% 0.4% 0%(1.6% - 7%) (0.5% - 2%) (0.5% - 2%) (0.5% - 2%) (0.5% - 2%) (0.2% - 0.7%) (0% - 0%)8.8% 7.5% 6.8% 6% 5.1% 5.8% 4.1%(3.3% - 13.9%) (2.8% - 11.9%) (2.6% - 10.8%) (2.3% - 9.5%) (1.9% - 8.2%) (2.2% - 9.3%) (1.5% - 6.6%)4.9% 3.2% 3.2% 3.2% 3.2% 2.1% 1.1%(1.8% - 7.8%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% -5.2%) (1.2% - 5.2%) (0.8% - 3.5%) (0.4% - 1.7%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-60Table E-59. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposureto Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Currentand Alternative Standards, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009),Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/258.6% 7.6% 6.8% 6% 5.2% 6% 5.1%(3.3% - 13.6%) (2.9% - 12.1%) (2.6% - 10.9%) (2.3% - 9.6%) (2% - 8.3%) (2.3% - 9.6%) (1.9% - 8.1%)6.9% 6.3% 5.7% 5% 4.2% 4.8% 3.2%(2.6% - 10.9%) (2.4% - 10%) (2.1% - 9.1%) (1.9% - 7.9%) (1.6% - 6.8%) (1.8% - 7.6%) (1.2% - 5.2%)8% 5.6% 4.9% 4.3% 3.6% 4.3% 3%(3.1% - 12.7%) (2.1% - 9%) (1.9% - 7.9%) (1.6% - 6.8%) (1.3% - 5.8%) (1.6% - 6.8%) (1.1% - 4.8%)4.4% 4.4% 4.4% 4.4% 3.8% 4.4% 3.8%(1.6% - 7%) (1.6% - 7%) (1.6% - 7%) (1.6% - 7%) (1.4% - 6.1%) (1.6% - 7%) (1.4% - 6.1%)6.5% 4.5% 4.4% 3.8% 3.1% 3.2% 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%) (0.7% - 3.2%)9.3% 3.4% 3.4% 3.4% 3.4% 2.3% 1.1%(3.6% - 14.8%) (1.3% - 5.4%) (1.3% - 5.4%) (1.3% - 5.4%) (1.3% - 5.4%) (0.8% - 3.7%) (0.4% - 1.9%)6.1% 5.6% 4.9% 4.3% 3.6% 4.3% 3.6%(2.3% - 9.8%) (2.1% - 9%) (1.9% - 7.9%) (1.6% -6.9%) (1.3% - 5.8%) (1.6% - 6.9%) (1.3% - 5.8%)7.2% 3.1% 3.1% 3.1% 2.6% 2% 0.9%(2.7% - 11.5%) (1.1% - 4.9%) (1.1% - 4.9%) (1.1% - 4.9%) (1% - 4.3%) (0.7% - 3.2%) (0.3% - 1.5%)5.9% 4.2% 4.2% 3.9% 3.3% 2.9% 1.7%(2.2% - 9.4%) (1.6% - 6.7%) (1.6% - 6.7%) (1.5% -6.4%) (1.2% - 5.3%) (1.1% - 4.7%) (0.6% - 2.8%)6.5% 5.7% 5.7% 5% 4.3% 4.2% 2.8%(2.5% - 10.4%) (2.1% - 9.1%) (2.1% - 9.1%) (1.9% - 8.1%) (1.6% - 6.9%) (1.6% - 6.8%) (1% - 4.5%)4.3% 4.3% 4.3% 4.3% 3.9% 3.8% 2.4%(1.6% - 6.9%) (1.6% - 6.9%) (1.6% - 6.9%) (1.6% -6.9%) (1.5% - 6.3%) (1.4% - 6.1%) (0.9% - 3.9%)6.8% 4.2% 4.2% 3.9% 3.4% 3% 1.8%(2.6% - 10.8%) (1.6% - 6.8%) (1.6% - 6.8%) (1.5% - 6.3%) (1.3% - 5.5%) (1.1% - 4.9%) (0.7% - 2.9%)3.4% 0.6% 0.6% 0.6% 0.6% 0% 0%(1.3% - 5.6%) (0.2% - 1%) (0.2% - 1%) (0.2%- 1%) (0.2% - 1%) (0% - 0%) (0% - 0%)6.6% 5.5% 4.9% 4.2% 3.5% 4.1% 2.7%(2.5% - 10.5%) (2.1% - 8.8%) (1.8% - 7.8%) (1.6% - 6.8%) (1.3% - 5.7%) (1.5% - 6.6%) (1% - 4.3%)3.2% 1.9% 1.9% 1.9% 1.9% 1% 0.1%(1.2% - 5.2%) (0.7% - 3%) (0.7% - 3%) (0.7%- 3%) (0.7% - 3%) (0.4% - 1.6%) (0% - 0.1%)Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-61Table E-60. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to AmbientPM2.5 Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from1979 - 19831Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/258% 7.1% 6.4% 5.6% 4.8% 5.6% 4.7%(3.1% - 12.8%) (2.7% - 11.4%) (2.4% - 10.1%) (2.1% - 8.9%) (1.8% - 7.7%) (2.1% - 8.9%) (1.8% - 7.5%)6.9% 6.3% 5.7% 5% 4.2% 4.8% 3.2%(2.6% - 10.9%) (2.4% - 10%) (2.1% - 9.1%) (1.9% - 7.9%) (1.6% - 6.8%) (1.8% - 7.6%) (1.2% - 5.2%)8.3% 5.9% 5.2% 4.5% 3.8% 4.5% 3.1%(3.2% - 13.2%) (2.2% - 9.4%) (1.9% - 8.3%) (1.7% - 7.2%) (1.4% - 6.1%) (1.7% - 7.2%) (1.2% - 5.1%)4.8% 4.8% 4.8% 4.8% 4.2% 4.8% 4.2%(1.8% - 7.7%) (1.8% - 7.7%) (1.8% - 7.7%) (1.8% -7.7%) (1.6% - 6.8%) (1.8% - 7.7%) (1.6% - 6.8%)6.9% 4.8% 4.8% 4.1% 3.4% 3.5% 2.2%(2.6% - 11%) (1.8% - 7.8%) (1.8% - 7.7%) (1.5% -6.6%) (1.3% - 5.5%) (1.3% - 5.7%) (0.8% - 3.6%)9.7% 3.6% 3.6% 3.6% 3.6% 2.4% 1.3%(3.7% - 15.3%) (1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (0.9% - 3.9%) (0.5% - 2.1%)6.3% 5.7% 5.1% 4.4% 3.7% 4.4% 3.7%(2.4% - 10%) (2.2% - 9.2%) (1.9% - 8.1%) (1.6%- 7%) (1.4% - 6%) (1.6% - 7%) (1.4% - 6%)7.4% 3.2% 3.2% 3.2% 2.8% 2.1% 1%(2.8% - 11.8%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (1% - 4.5%) (0.8% - 3.4%) (0.4% - 1.7%)6.8% 5% 5% 4.7% 4% 3.6% 2.3%(2.6% - 10.8%) (1.9% - 7.9%) (1.9% - 7.9%) (1.8% - 7.6%) (1.5% - 6.4%) (1.4% - 5.8%) (0.8% - 3.7%)6.4% 5.6% 5.6% 5% 4.2% 4.2% 2.7%(2.4% - 10.3%) (2.1% - 9%) (2.1% - 9%) (1.9% - 8%) (1.6% - 6.8%) (1.6% - 6.7%) (1% - 4.4%)3.6% 3.6% 3.6% 3.6% 3.2% 3.1% 1.8%(1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (1.3% -5.7%) (1.2% - 5.1%) (1.1% - 4.9%) (0.7% - 2.9%)7.7% 5% 5% 4.6% 4% 3.6% 2.3%(2.9% - 12.2%) (1.9% - 8%) (1.9% - 8%) (1.7% - 7.4%) (1.5% - 6.5%) (1.4% - 5.9%) (0.9% - 3.7%)4.8% 1.5% 1.5% 1.5% 1.5% 0.7% 0%(1.8% - 7.7%) (0.6% - 2.5%) (0.6% - 2.5%) (0.6%- 2.5%) (0.6% - 2.5%) (0.3% - 1.1%) (0% - 0%)7.2% 6% 5.4% 4.7% 4% 4.6% 3.1%(2.7% - 11.4%) (2.3% - 9.6%) (2% - 8.7%) (1.8% - 7.5%) (1.5% - 6.4%) (1.7% - 7.3%) (1.2% - 5%)3.3% 1.9% 1.9% 1.9% 1.9% 1% 0.1%(1.3% - 5.4%) (0.7% - 3.1%) (0.7% - 3.1%) (0.7% -3.1%) (0.7% - 3.1%) (0.4% - 1.7%) (0.1% - 0.2%)Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-62Table E-61. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: EstimatesBased onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/3531435 13/35 12/35 13/30 12/25-13% 0% 11% 21% 32% 21% 34%(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 22%) (31% - 32%) (21% - 22%) (33% - 34%)-9% 0% 8% 19% 30% 22% 44%(-8% - -9%) (0% - 0%) (8% - 9%) (19% - 19%) (29% - 30%) (21% - 22%) (44% - 45%)-42% 0% 12% 23% 35% 23% 46%(-41% - -43%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 23%) (45% - 46%)0% 0% 0% 0% 11% 0% 11%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-37% 0% 1% 13% 25% 23% 47%(-36% - -38%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (23% - 24%) (47% - 48%)-182% 0% 0% 0% 0% 34% 68%(-177% - -188%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (68% - 68%)-9% 0% 12% 23% 35% 23% 35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 24%) (35% - 35%) (23% - 24%) (35% - 35%)-121% 0% 0% 0% 12% 31% 62%(-117% - -124%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 31%) (62% - 63%)-35% 0% 0% 4% 18% 25% 51%(-34% - -36%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 26%) (50% - 51%)-14% 0% 0% 11% 23% 24% 48%(-14% - -15%) (0% - 0%) (0% - 0%) (10% - 11%) (23% - 24%) (24% - 24%) (48% - 49%)0% 0% 0% 0% 10% 13% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 46%)-52% 0% 0% 6% 18% 25% 51%(-50% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (25% - 25%) (50% - 51%)-252% 0% 0% 0% 0% 64% 100%(-249% - -256%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 64%) (100% - 100%)-17% 0% 9% 20% 31% 22% 45%(-17% - -18%) (0% - 0%) (9% - 9%) (20% - 21%) (31% - 32%) (22% - 23%) (44% - 45%)-53% 0% 0% 0% 0% 33% 67%(-52% - -54%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (67% - 67%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidenceof Lung Cancer Mortality Associated with Long-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-63Table E-62. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/3531435 13/35 12/35 13/30 12/25-13% 0% 11% 21% 32% 21% 34%(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 22%) (31% - 32%) (21% - 22%) (33% - 34%)-10% 0% 9% 21% 33% 24% 48%(-9% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (32% - 33%) (24% - 24%) (48% - 49%)-43% 0% 12% 24% 36% 24% 47%(-42% - -45%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 24%) (47% - 48%)0% 0% 0% 0% 13% 0% 13%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 0%) (13% - 13%)-44% 0% 1% 16% 30% 28% 56%(-43% - -45%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 31%) (28% - 28%) (56% - 57%)-177% 0% 0% 0% 0% 33% 66%(-172% - -183%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (66% - 66%)-9% 0% 12% 24% 36% 24% 36%(-9% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 24%) (36% - 36%)-136% 0% 0% 0% 13% 35% 70%(-132% - -139%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 14%) (35% - 35%) (70% - 70%)-41% 0% 0% 5% 21% 29% 59%(-40% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 30%) (59% - 60%)-15% 0% 0% 11% 24% 25% 51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 26%) (50% - 51%)0% 0% 0% 0% 10% 13% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (44% - 45%)-60% 0% 0% 7% 20% 29% 58%(-59% - -62%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 29%) (58% - 59%)-434% 0% 0% 0% 0% 100% 100%(-430% - -439%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%)-20% 0% 11% 23% 36% 25% 51%(-20% - -21%) (0% - 0%) (10% - 11%) (23% - 23%) (35% - 36%) (25% - 26%) (51% - 52%)-75% 0% 0% 0% 0% 48% 96%(-75% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (48% - 48%) (96% - 96%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidenceof Lung Cancer Mortality Associated with Long-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-64Table E-63. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/3531435 13/35 12/35 13/30 12/25-13% 0% 11% 22% 33% 22% 34%(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 22%) (34% - 35%)-10% 0% 9% 21% 33% 24% 48%(-9% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (32% - 33%) (24% - 24%) (48% - 49%)-43% 0% 12% 23% 35% 23% 46%(-41% - -44%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (46% - 47%)0% 0% 0% 0% 12% 0% 12%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-42% 0% 1% 15% 29% 27% 54%(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 30%) (27% - 27%) (54% - 55%)-172% 0% 0% 0% 0% 32% 64%(-166% - -177%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 32%) (64% - 64%)-9% 0% 12% 24% 36% 24% 36%(-9% - -10%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-132% 0% 0% 0% 13% 34% 68%(-128% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 34%) (68% - 68%)-37% 0% 0% 5% 19% 27% 54%(-36% - -38%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 20%) (27% - 27%) (54% - 55%)-15% 0% 0% 11% 24% 25% 51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 26%) (51% - 51%)0% 0% 0% 0% 11% 14% 50%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (50% - 50%)-55% 0% 0% 6% 19% 27% 54%(-53% - -56%) (0% - 0%) (0% - 0%) (6% - 7%) (19% - 19%) (26% - 27%) (53% - 54%)-215% 0% 0% 0% 0% 55% 100%(-212% - -219%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 55%) (100% - 100%)-19% 0% 10% 22% 34% 24% 49%(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 22%) (34% - 34%) (24% - 25%) (49% - 49%)-73% 0% 0% 0% 0% 46% 93%(-72% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidenceof Lung Cancer Mortality Associated with Long-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-65Table E-64. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in aRecent Year (2005) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2005PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25110 98 88 78 67 78 66(49 - 167) (44 - 149) (39 - 134) (34 - 119) (30 - 103) (34 - 119) (29 - 101)116 107 99 87 76 84 60(52 - 176) (48 - 163) (44 - 150) (38 - 133) (33 - 116) (37 - 129) (26 - 93)79 56 50 43 37 43 31(35 - 120) (25 - 86) (22 - 77) (19 - 67) (16 - 57) (19 - 67) (13 - 48)72 72 72 72 64 72 64(32 - 110) (32 - 110) (32 - 110) (32 - 110) (28 - 98) (32 - 110) (28 - 98)161 119 117 103 89 91 63(72 - 244) (52 - 181) (52 - 179) (45 - 158) (39 - 137) (40 - 140) (27 - 98)38 14 14 14 14 9 4(17 - 57) (6 - 21) (6 - 21) (6 - 21) (6 - 21) (4 - 14) (2 - 7)111 101 90 78 66 78 66(49 - 169) (45 - 155) (39 - 138) (34 - 120) (29 - 102) (34 - 120) (29 - 102)357 164 164 164 144 113 62(159 - 541) (71 - 253) (71 - 253) (71 - 253) (63 - 223) (49 - 176) (27 - 96)300 224 224 214 184 168 111(133 - 457) (99 - 343) (99 - 343) (94 - 329) (80 - 283) (73 - 259) (48 - 172)101 88 88 79 68 67 46(45 - 154) (39 - 135) (39 - 135) (35 - 121) (30 - 105) (30 - 104) (20 - 71)85 85 85 85 76 74 47(37 - 131) (37 - 131) (37 - 131) (37 - 131) (33 - 118) (32 - 115) (20 - 73)115 76 76 72 63 57 38(51 - 175) (34 - 117) (34 - 117) (32 - 110) (28 - 97) (25 - 89) (17 - 59)11 3 3 3 3 1 0(5 - 18) (1 - 5) (1 - 5) (1 - 5) (1 - 5) (1 - 2) (0 - 0)167 142 129 114 99 111 79(74 - 252) (63 - 217) (57 - 197) (50 - 175) (43 - 152) (49 - 170) (35 - 122)27 18 18 18 18 12 6(12 - 41) (8 - 27) (8 - 27) (8 - 27) (8 - 27) (5 - 18) (3 - 9)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-66Table E-65. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in aRecent Year (2006) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations: Estimates Based on Krewskiet al. (2009), Using Ambient PM2.5 from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25114 101 91 80 69 80 68(51 - 172) (45 - 154) (40 - 138) (35 - 123) (30 - 106) (35 - 123) (30 - 104)95 87 79 69 59 66 45(42 - 145) (38 - 133) (35 - 121) (30 - 106) (26 - 91) (29 - 102) (20 - 70)75 52 46 40 34 40 28(33 - 113) (23 - 80) (20 - 71) (18 - 62) (15 - 52) (18 - 62) (12 - 43)54 54 54 54 47 54 47(24 - 84) (24 - 84) (24 - 84) (24 - 84) (21 - 73) (24 - 84) (21 - 73)118 82 81 69 58 59 36(52 - 180) (36 - 127) (35 - 125) (30 - 107) (25 - 89) (26 - 92) (16 - 56)39 14 14 14 14 10 5(18 - 59) (6 - 22) (6 - 22) (6 - 22) (6 - 22) (4 - 15) (2 - 8)107 98 87 75 63 75 63(47 - 164) (43 - 150) (38 - 133) (33 - 116) (28 - 98) (33 - 116) (28 - 98)316 135 135 135 117 89 41(140 - 481) (59 - 210) (59 - 210) (59 - 210) (51 - 182) (38 - 138) (18 - 64)234 167 167 159 132 119 69(103 - 359) (73 - 258) (73 - 258) (69 - 245) (58 - 205) (52 - 184) (30 - 107)91 80 80 71 61 60 40(40 - 140) (35 - 122) (35 - 122) (31 - 109) (26 - 93) (26 - 92) (17 - 61)90 90 90 90 81 79 50(39 - 139) (39 - 139) (39 - 139) (39 - 139) (35 - 125) (34 - 122) (22 - 78)92 58 58 54 46 41 24(41 - 140) (25 - 89) (25 - 89) (23 - 83) (20 - 71) (18 - 64) (10 - 38)9 222200(4 - 14) (1 - 3) (1 - 3) (1 - 3) (1 - 3) (0 - 0) (0 - 0)126 105 94 81 68 79 52(56 - 193) (46 - 162) (41 - 145) (36 - 126) (30 - 106) (34 - 122) (22 - 80)18 10 10 10 10 5 0(8 - 28) (5 - 16) (5 - 16) (5 - 16) (5 - 16) (2 - 9) (0 - 1)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Baltimore, MDDetroit, MIHouston, TXSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UT3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAIncidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:E-67Table E-66. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in aRecent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25109 97 87 76 66 76 64(49 - 166) (43 - 148) (38 - 133) (34 - 117) (29 - 101) (34 - 117) (28 - 99)95 87 79 69 59 66 45(42 - 145) (38 - 133) (35 - 121) (30 - 106) (26 - 91) (29 - 102) (20 - 70)78 55 49 42 36 42 30(35 - 119) (24 - 85) (21 - 75) (19 - 65) (16 - 56) (19 - 65) (13 - 46)60 60 60 60 53 60 53(27 - 93) (27 - 93) (27 - 93) (27 - 93) (23 - 82) (27 - 93) (23 - 82)124 88 86 74 62 64 40(55 - 189) (38 - 135) (38 - 133) (32 - 115) (27 - 96) (28 - 99) (17 - 63)41 15 15 15 15 11 6(18 - 62) (7 - 24) (7 - 24) (7 - 24) (7 - 24) (5 - 16) (2 - 9)112 102 90 78 66 78 66(49 - 171) (45 - 157) (40 - 139) (34 - 121) (29 - 102) (34 - 121) (29 - 102)328 143 143 143 124 95 46(145 - 499) (62 - 222) (62 - 222) (62 - 222) (54 - 193) (41 - 148) (20 - 72)273 200 200 191 162 147 92(121 - 417) (88 - 308) (88 - 308) (84 - 294) (71 - 250) (64 - 227) (40 - 144)91 79 79 70 60 59 39(40 - 138) (35 - 121) (35 - 121) (31 - 108) (26 - 92) (26 - 91) (17 - 60)77 77 77 77 68 66 38(33 - 118) (33 - 118) (33 - 118) (33 - 118) (30 - 106) (29 - 102) (17 - 60)103 67 67 63 55 49 31(46 - 157) (29 - 103) (29 - 103) (28 - 97) (24 - 84) (22 - 76) (14 - 49)13 4 4 4 4 2 0(6 - 21) (2 - 7) (2 - 7) (2 - 7) (2 - 7) (1 - 3) (0 - 0)138 116 105 91 77 88 60(61 - 210) (51 - 178) (46 - 161) (40 - 140) (34 - 119) (39 - 136) (26 - 93)19 11 11 11 11 6 1(8 - 30) (5 - 17) (5 - 17) (5 - 17) (5 - 17) (3 - 9) (0 - 1)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Baltimore, MDDetroit, MIHouston, TXSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UT3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAIncidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:E-68Table E-67. Estimated Percent of Total Annual Incidence of LungCancer Mortality Associated withLong-Term Exposure to AmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2512.4% 11% 9.9% 8.7% 7.5% 8.7% 7.4%(5.5% - 18.7%) (4.9% - 16.7%) (4.4% - 15.1%) (3.8% -13.3%) (3.3% - 11.6%) (3.8%- 13.3%) (3.2% - 11.3%)12.2% 11.2% 10.3% 9.1% 7.9% 8.8% 6.3%(5.4% - 18.4%) (5% - 17%) (4.6% - 15.7%) (4% - 13.9%) (3.5% - 12.1%) (3.9% - 13.4%) (2.8% - 9.7%)12.4% 8.8% 7.8% 6.8% 5.8% 6.8% 4.8%(5.5% - 18.8%) (3.9% - 13.5%) (3.4% - 12%) (3% - 10.5%) (2.5% - 8.9%) (3% - 10.5%) (2.1% - 7.4%)8.6% 8.6% 8.6% 8.6% 7.7% 8.6% 7.7%(3.8% - 13.2%) (3.8% - 13.2%) (3.8% - 13.2%) (3.8% -13.2%) (3.4% - 11.8%) (3.8%- 13.2%) (3.4% - 11.8%)12.8% 9.4% 9.3% 8.2% 7.1% 7.3% 5%(5.7% - 19.4%) (4.2% - 14.4%) (4.1% - 14.3%) (3.6% -12.6%) (3.1% - 10.9%) (3.2% - 11.2%) (2.2% - 7.8%)13.1% 4.7% 4.7% 4.7% 4.7% 3.1% 1.5%(5.8% - 19.8%) (2.1% - 7.3%) (2.1% - 7.3%) (2.1% -7.3%) (2.1% - 7.3%) (1.4% - 4.9%) (0.7% - 2.4%)9.5% 8.7% 7.7% 6.7% 5.7% 6.7% 5.7%(4.2% - 14.5%) (3.8% - 13.3%) (3.4% - 11.8%) (2.9% -10.3%) (2.5% - 8.8%) (2.9% - 10.3%) (2.5% - 8.8%)11.8% 5.4% 5.4% 5.4% 4.8% 3.8% 2%(5.3% - 18%) (2.4% - 8.4%) (2.4% - 8.4%) (2.4% -8.4%) (2.1% - 7.4%) (1.6% - 5.8%) (0.9% - 3.2%)11% 8.2% 8.2% 7.8% 6.7% 6.1% 4%(4.9% - 16.7%) (3.6% - 12.5%) (3.6% - 12.5%) (3.4% - 12%) (2.9% - 10.3%) (2.7% - 9.5%) (1.8% - 6.3%)10.4% 9.1% 9.1% 8.1% 7% 6.9% 4.7%(4.6% - 15.8%) (4% - 13.9%) (4% - 13.9%) (3.6% - 12.5%) (3.1% - 10.8%) (3% - 10.7%) (2.1% - 7.3%)6.2% 6.2% 6.2% 6.2% 5.5% 5.4% 3.4%(2.7% - 9.5%) (2.7% - 9.5%) (2.7% - 9.5%) (2.7% -9.5%) (2.4% - 8.6%) (2.3% - 8.3%) (1.5% - 5.3%)12.3% 8.1% 8.1% 7.6% 6.7% 6.1% 4%(5.5% - 18.6%) (3.6% - 12.5%) (3.6% - 12.5%) (3.4% -11.7%) (2.9% - 10.3%) (2.7% - 9.4%) (1.8% - 6.3%)6.3% 1.8% 1.8% 1.8% 1.8% 0.6% 0%(2.8% - 9.8%) (0.8% - 2.8%) (0.8% - 2.8%) (0.8% - 2.8%) (0.8% - 2.8%) (0.3% - 1%) (0% - 0%)12.6% 10.8% 9.8% 8.6% 7.5% 8.4% 6%(5.6% - 19.1%) (4.8% - 16.4%) (4.3% - 14.9%) (3.8% -13.2%) (3.3% - 11.5%) (3.7% - 12.9%) (2.6% - 9.2%)7.1% 4.7% 4.7% 4.7% 4.7% 3.1% 1.5%(3.1% - 10.9%) (2% - 7.2%) (2% - 7.2%) (2% - 7.2%) (2% - 7.2%) (1.4% - 4.9%) (0.7% - 2.4%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-69Table E-68. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to AmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2512.4% 11% 9.9% 8.7% 7.5% 8.7% 7.4%(5.5% - 18.7%) (4.9% - 16.7%) (4.4% - 15%) (3.8% - 13.3%) (3.3% - 11.6%) (3.8% - 13.3%) (3.2% - 11.3%)9.9% 9.1% 8.2% 7.2% 6.1% 6.9% 4.7%(4.4% - 15.1%) (4% - 13.9%) (3.6% - 12.6%) (3.2% - 11.1%) (2.7% - 9.5%) (3% - 10.6%) (2% - 7.3%)11.6% 8.1% 7.2% 6.2% 5.2% 6.2% 4.3%(5.1% - 17.6%) (3.6% - 12.4%) (3.1% - 11%) (2.7% - 9.6%) (2.3% - 8.1%) (2.7% - 9.6%) (1.9% - 6.7%)6.4% 6.4% 6.4% 6.4% 5.5% 6.4% 5.5%(2.8% - 9.8%) (2.8% - 9.8%) (2.8% - 9.8%) (2.8% -9.8%) (2.4% - 8.6%) (2.8% - 9.8%) (2.4% - 8.6%)9.4% 6.5% 6.5% 5.5% 4.6% 4.7% 2.9%(4.1% - 14.3%) (2.9% - 10.1%) (2.8% - 10%) (2.4% - 8.5%) (2% - 7.1%) (2.1% - 7.3%) (1.2% - 4.5%)13.4% 4.9% 4.9% 4.9% 4.9% 3.3% 1.7%(6% - 20.3%) (2.1% - 7.6%) (2.1% - 7.6%) (2.1% -7.6%) (2.1% - 7.6%) (1.4% - 5.1%) (0.7% - 2.6%)8.9% 8.1% 7.2% 6.2% 5.2% 6.2% 5.2%(3.9% - 13.6%) (3.6% - 12.5%) (3.1% - 11%) (2.7% - 9.6%) (2.3% - 8.1%) (2.7% - 9.6%) (2.3% - 8.1%)10.4% 4.5% 4.5% 4.5% 3.9% 2.9% 1.3%(4.6% - 15.9%) (1.9% - 6.9%) (1.9% - 6.9%) (1.9%- 6.9%) (1.7% - 6%) (1.3% - 4.5%) (0.6% - 2.1%)8.5% 6.1% 6.1% 5.7% 4.8% 4.3% 2.5%(3.7% - 13%) (2.6% - 9.3%) (2.6% - 9.3%) (2.5% -8.9%) (2.1% - 7.4%) (1.9% - 6.7%) (1.1% - 3.9%)9.4% 8.2% 8.2% 7.3% 6.2% 6.2% 4.1%(4.2% - 14.4%) (3.6% - 12.6%) (3.6% - 12.6%) (3.2% - 11.2%) (2.7% - 9.6%) (2.7% - 9.5%) (1.8% - 6.3%)6.3% 6.3% 6.3% 6.3% 5.7% 5.5% 3.5%(2.7% - 9.7%) (2.7% - 9.7%) (2.7% - 9.7%) (2.7% -9.7%) (2.5% - 8.7%) (2.4% - 8.5%) (1.5% - 5.4%)9.8% 6.2% 6.2% 5.7% 4.9% 4.4% 2.6%(4.3% - 15%) (2.7% - 9.5%) (2.7% - 9.5%) (2.5% - 8.9%) (2.1% - 7.6%) (1.9% - 6.8%) (1.1% - 4%)5% 0.9% 0.9% 0.9% 0.9% 0% 0%(2.2% - 7.8%) (0.4% - 1.5%) (0.4% - 1.5%) (0.4% - 1.5%) (0.4% - 1.5%) (0% - 0%) (0% - 0%)9.5% 7.9% 7.1% 6.1% 5.1% 5.9% 3.9%(4.2% - 14.5%) (3.5% - 12.2%) (3.1% - 10.9%) (2.7% - 9.4%) (2.2% - 7.9%) (2.6% - 9.2%) (1.7% - 6%)4.7% 2.7% 2.7% 2.7% 2.7% 1.4% 0.1%(2.1% - 7.3%)(1.2% - 4.2%)(1.2% - 4.2%)(1.2% - 4.2%)(1.2% - 4.2%)(0.6% - 2.2%)(0% - 0.2%)Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-70Table E-69. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to AmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2511.6% 10.3% 9.2% 8.1% 7% 8.1% 6.8%(5.2% - 17.6%) (4.6% - 15.7%) (4.1% - 14.1%) (3.6% - 12.4%) (3.1% - 10.7%) (3.6% - 12.4%) (3% - 10.5%)9.9% 9.1% 8.2% 7.2% 6.1% 6.9% 4.7%(4.4% - 15.1%) (4% - 13.9%) (3.6% - 12.6%) (3.2% - 11.1%) (2.7% - 9.5%) (3% - 10.6%) (2% - 7.3%)12% 8.5% 7.5% 6.5% 5.5% 6.5% 4.6%(5.3% - 18.2%) (3.7% - 13%) (3.3% - 11.5%) (2.8% - 10%) (2.4% - 8.5%) (2.8% - 10%) (2% - 7.1%)7% 7% 7% 7% 6.1% 7% 6.1%(3% - 10.7%) (3% - 10.7%) (3% - 10.7%) (3% - 10.7%) (2.7% - 9.4%) (3% - 10.7%) (2.7% - 9.4%)9.9% 7% 6.9% 6% 5% 5.1% 3.2%(4.4% - 15.2%) (3.1% - 10.8%) (3% - 10.7%) (2.6% - 9.2%) (2.2% - 7.7%) (2.2% - 8%) (1.4% - 5%)13.9% 5.2% 5.2% 5.2% 5.2% 3.5% 1.9%(6.2% - 20.9%) (2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (1.5% - 5.5%) (0.8% - 2.9%)9.1% 8.3% 7.4% 6.4% 5.4% 6.4% 5.4%(4% - 13.9%) (3.7% - 12.8%) (3.2% - 11.3%) (2.8% - 9.8%) (2.4% - 8.3%) (2.8% - 9.8%) (2.4% - 8.3%)10.7% 4.7% 4.7% 4.7% 4.1% 3.1% 1.5%(4.8% - 16.3%) (2% - 7.3%) (2% - 7.3%) (2% - 7.3%) (1.8% - 6.3%) (1.3% - 4.8%) (0.6% - 2.4%)9.8% 7.2% 7.2% 6.9% 5.8% 5.3% 3.3%(4.3% - 15%) (3.2% - 11.1%) (3.2% - 11.1%) (3% - 10.6%) (2.5% - 9%) (2.3% - 8.2%) (1.4% - 5.2%)9.3% 8.1% 8.1% 7.2% 6.2% 6.1% 4%(4.1% - 14.2%) (3.6% - 12.5%) (3.6% - 12.5%) (3.2% - 11.1%) (2.7% - 9.5%) (2.7% - 9.4%) (1.7% - 6.2%)5.2% 5.2% 5.2% 5.2% 4.6% 4.4% 2.6%(2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (2% - 7.1%) (1.9% - 6.9%) (1.1% - 4.1%)11.1% 7.2% 7.2% 6.7% 5.9% 5.3% 3.4%(4.9% - 16.8%) (3.2% - 11.1%) (3.2% - 11.1%) (3% - 10.4%) (2.6% - 9.1%) (2.3% - 8.2%) (1.5% - 5.2%)7% 2.2% 2.2% 2.2% 2.2% 1% 0%(3.1% - 10.7%) (1% - 3.5%) (1% - 3.5%) (1% - 3.5%) (1% - 3.5%) (0.4% - 1.6%) (0% - 0%)10.4% 8.7% 7.9% 6.8% 5.8% 6.6% 4.5%(4.6% - 15.8%) (3.8% - 13.4%) (3.5% - 12.1%) (3% - 10.5%) (2.5% - 8.9%) (2.9% - 10.2%) (2% - 6.9%)4.9% 2.8% 2.8% 2.8% 2.8% 1.5% 0.2%(2.1% - 7.6%) (1.2% - 4.4%) (1.2% - 4.4%) (1.2% - 4.4%) (1.2% - 4.4%) (0.7% - 2.4%) (0.1% - 0.3%)Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-71Table E-70. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations -- Estimates Basedon Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-12% 0% 10% 21% 32% 21% 33%(-12% - -13%) (0% - 0%) (10% - 11%) (20% - 21%) (31% - 32%) (20% - 21%) (32% - 34%)-8% 0% 8% 19% 29% 22% 44%(-8% - -9%) (0% - 0%) (8% - 8%) (18% - 19%) (29% - 30%) (21% - 22%) (43% - 45%)-41% 0% 11% 23% 34% 23% 45%(-39% - -43%) (0% - 0%) (11% - 12%) (22% - 23%) (34% - 35%) (22% - 23%) (45% - 46%)0% 0% 0% 0% 11% 0% 11%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-36% 0% 1% 13% 25% 23% 47%(-35% - -37%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (23% - 24%) (46% - 48%)-178% 0% 0% 0% 0% 34% 68%(-171% - -185%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (67% - 68%)-9% 0% 11% 23% 35% 23% 35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 23%) (34% - 35%)-118% 0% 0% 0% 12% 31% 62%(-114% - -122%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 31%) (62% - 63%)-34% 0% 0% 4% 18% 25% 50%(-33% - -35%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 25%) (50% - 51%)-14% 0% 0% 10% 23% 24% 48%(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 11%) (22% - 23%) (23% - 24%) (47% - 49%)0% 0% 0% 0% 10% 13% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 45%)-51% 0% 0% 6% 17% 25% 50%(-49% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (24% - 25%) (50% - 51%)-250% 0% 0% 0% 0% 64% 100%(-245% - -254%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 64%) (100% - 100%)-17% 0% 9% 20% 31% 22% 44%(-16% - -18%) (0% - 0%) (9% - 9%) (19% - 20%) (30% - 31%) (21% - 22%) (44% - 45%)-52% 0% 0% 0% 0% 33% 67%(-51% - -53%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (67% - 67%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidenceof Lung Cancer Mortality Associated with Long-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-72Table E-71. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations -- Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-12% 0% 10% 21% 32% 21% 33%(-12% - -13%) (0% - 0%) (10% - 11%) (20% - 21%) (31% - 32%) (20% - 21%) (32% - 34%)-9% 0% 9% 21% 32% 24% 48%(-9% - -10%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (23% - 24%) (47% - 49%)-43% 0% 12% 24% 36% 24% 47%(-41% - -44%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (46% - 48%)0% 0% 0% 0% 13% 0% 13%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (0% - 0%) (12% - 13%)-43% 0% 1% 16% 30% 28% 56%(-42% - -45%) (0% - 0%) (1% - 1%) (15% - 16%) (30% - 30%) (27% - 28%) (56% - 57%)-173% 0% 0% 0% 0% 33% 66%(-167% - -181%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 33%) (66% - 66%)-9% 0% 12% 24% 36% 24% 36%(-9% - -10%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-133% 0% 0% 0% 13% 35% 70%(-129% - -137%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 14%) (34% - 35%) (70% - 70%)-40% 0% 0% 5% 21% 29% 59%(-39% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 30%) (59% - 59%)-15% 0% 0% 11% 24% 25% 50%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (24% - 25%) (50% - 51%)0% 0% 0% 0% 10% 13% 45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 13%) (44% - 45%)-59% 0% 0% 7% 20% 29% 58%(-58% - -61%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (28% - 29%) (57% - 58%)-431% 0% 0% 0% 0% 100% 100%(-425% - -437%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%)-20% 0% 10% 23% 35% 25% 51%(-19% - -20%) (0% - 0%) (10% - 11%) (22% - 23%) (35% - 36%) (25% - 26%) (50% - 51%)-75% 0% 0% 0% 0% 48% 96%(-74% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 48%) (96% - 96%)Percent Reduction from the Current Standards: Annual Incidenceof Lung Cancer Mortality Associated with Long-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-73Table E-72. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations -- Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-13% 0% 11% 21% 32% 21% 34%(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 22%) (32% - 33%) (21% - 22%) (33% - 35%)-9% 0% 9% 21% 32% 24% 48%(-9% - -10%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (23% - 24%) (47% - 49%)-42% 0% 12% 23% 35% 23% 46%(-40% - -43%) (0% - 0%) (11% - 12%) (23% - 24%) (34% - 36%) (23% - 24%) (45% - 47%)0% 0% 0% 0% 12% 0% 12%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-42% 0% 1% 15% 29% 27% 54%(-41% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (26% - 27%) (54% - 55%)-168% 0% 0% 0% 0% 32% 64%(-161% - -175%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 32%) (64% - 64%)-9% 0% 12% 23% 35% 23% 35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-129% 0% 0% 0% 13% 34% 68%(-125% - -134%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (33% - 34%) (68% - 68%)-36% 0% 0% 5% 19% 27% 54%(-35% - -37%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 20%) (26% - 27%) (53% - 54%)-15% 0% 0% 11% 24% 25% 51%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 26%) (50% - 51%)0% 0% 0% 0% 11% 14% 50%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (49% - 50%)-54% 0% 0% 6% 19% 26% 53%(-52% - -55%) (0% - 0%) (0% - 0%) (6% - 7%) (18% - 19%) (26% - 27%) (53% - 54%)-213% 0% 0% 0% 0% 55% 100%(-209% - -217%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 55%) (100% - 100%)-19% 0% 10% 22% 34% 24% 49%(-18% - -19%) (0% - 0%) (10% - 10%) (21% - 22%) (33% - 34%) (24% - 25%) (48% - 49%)-72% 0% 0% 0% 0% 46% 93%(-71% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)Percent Reduction from the Current Standards: Annual Incidenceof Lung Cancer Mortality Associated with Long-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-74Table E-73. Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25193 177 164 151 137 151 135(37 - 347) (34 - 319) (31 - 295) (29 - 272) (26 - 248) (29 - 272) (26 - 244)271 256 242 224 206 219 182(110 - 430) (104 - 406) (98 - 384) (91 - 356) (83 - 327) (89 - 348) (74 - 289)44 34 32 29 27 29 24(-68 - 154) (-53 - 121) (-49 - 112) (-45 - 103) (-41 - 94) (-45 - 103) (-38 - 85)156 156 156 156 145 156 145(37 - 273) (37 - 273) (37 - 273) (37 - 273) (35 - 253) (37 - 273) (35 - 253)181 147 146 135 124 125 104(-32 - 390) (-26 - 317) (-26 - 315) (-24 - 291) (-22 - 267) (-22 - 271) (-18 - 225)79 44 44 44 44 37 31(11 - 145) (6 - 81) (6 - 81) (6 - 81) (6 - 81) (5 - 69) (4 - 57)227 214 198 182 166 182 166(46 - 405) (44 - 383) (40 - 354) (37 - 326) (34 - 297) (37 - 326) (34 - 297)129 81 81 81 77 69 58(-185 - 441) (-117 - 278) (-117 - 278) (-117 - 278) (-110 - 263) (-100 - 238) (-82 - 197)939 781 781 761 700 668 555(552 - 1323) (459 - 1102) (459 - 1102) (447 - 1073) (411 - 987) (392 - 943) (325 - 783)234 216 216 202 185 184 153(86 - 380) (79 - 350) (79 - 350) (74 - 328) (68 - 301) (68 - 300) (56 - 249)242 242 242 242 230 227 188(40 - 442) (40 - 442) (40 - 442) (40 - 442) (38 - 420) (38 - 414) (31 - 344)224 159 159 155 147 136 112(66 - 380) (47 - 270) (47 - 270) (45 - 263) (43 - 249) (40 - 231) (33 - 191)48 30 30 30 30 26 21(10 - 85) (6 - 54) (6 - 54) (6 - 54) (6 - 54) (5 - 46) (4 - 38)290 260 244 226 207 222 184(84 - 494) (75 - 443) (71 - 416) (65 - 385) (60 - 354) (64 - 379) (53 - 315)59 48 48 48 48 41 34(10 - 107) (8 - 87) (8 - 87) (8 - 87) (8 - 87) (7 - 74) (6 - 62)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standarderrors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-75Table E-74. Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25196 180 166 153 139 153 137(37 - 353) (34 - 324) (32 - 300) (29 - 276) (26 - 251) (29 - 276) (26 - 248)237 224 212 196 180 192 159(96 - 376) (91 - 356) (86 - 336) (79 - 311) (73 - 286) (78 - 305) (64 - 253)42 33 30 28 26 28 23(-66 - 148) (-51 - 116) (-47 - 108) (-44 - 99) (-40 - 90) (-44 - 99) (-36 - 82)130 130 130 130 121 130 121(31 - 228) (31 - 228) (31 - 228) (31 - 228) (29 - 212) (31 - 228) (29 - 212)145 118 117 108 99 101 83(-25 - 314) (-21 - 255) (-20 - 253) (-19 - 234) (-17 - 215) (-18 - 218) (-15 - 181)84 47 47 47 47 40 33(12 - 155) (7 - 86) (7 - 86) (7 - 86) (7 - 86) (6 - 74) (5 - 61)221 208 193 177 162 177 162(45 - 395) (42 - 373) (39 - 345) (36 - 317) (33 - 289) (36 - 317) (33 - 289)119 75 75 75 71 64 53(-171 - 407) (-108 - 257) (-108 - 257) (-108 - 257) (-101 - 242) (-92 - 219) (-76 - 182)807 671 671 654 601 574 476(474 - 1137) (394 - 946) (394 - 946) (383 - 922) (352 - 847) (336 - 809) (279 - 672)222 204 204 191 175 174 145(82 - 359) (75 - 331) (75 - 331) (70 - 310) (65 - 285) (64 - 283) (53 - 235)254 254 254 254 241 238 198(42 - 463) (42 - 463) (42 - 463) (42 - 463) (40 - 440) (39 - 434) (33 - 361)194 136 136 133 126 116 96(57 - 329) (40 - 232) (40 - 232) (39 - 226) (37 - 215) (34 - 198) (28 - 164)44 27 27 27 27 23 19(9 - 78) (6 - 49) (6 - 49) (6 - 49) (6 - 49) (5 - 42) (4 - 35)240 215 202 187 171 184 152(69 - 409) (62 - 367) (58 - 345) (54 - 319) (49 - 293) (53 - 314) (44 - 260)50 40 40 40 40 34 28(9 - 90) (7 - 73) (7 - 73) (7 - 73) (7 - 73) (6 - 62) (5 - 52)Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UT3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.E-76Table E-75. Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25193 177 164 151 137 151 135(37 - 348) (34 - 319) (31 - 296) (29 - 272) (26 - 248) (29 - 272) (26 - 244)240 227 214 198 182 194 161(97 - 380) (92 - 360) (87 - 340) (80 - 315) (74 - 289) (79 - 308) (65 - 256)44 34 32 29 26 29 24(-68 - 154) (-53 - 120) (-49 - 111) (-45 - 102) (-41 - 93) (-45 - 102) (-37 - 85)139 139 139 139 129 139 129(33 - 243) (33 - 243) (33 - 243) (33 - 243) (31 - 225) (33 - 243) (31 - 225)150 121 120 111 102 104 86(-26 - 323) (-21 - 262) (-21 - 261) (-19 - 241) (-18 - 221) (-18 - 224) (-15 - 186)87 48 48 48 48 41 34(12 - 160) (7 - 89) (7 - 89) (7 - 89) (7 - 89) (6 - 76) (5 - 63)224 212 196 180 164 180 164(46 - 401) (43 - 378) (40 - 350) (37 - 322) (33 - 294) (37 - 322) (33 - 294)121 77 77 77 72 65 54(-174 - 415) (-110 - 262) (-110 - 262) (-110 - 262) (-104 - 247) (-94 - 224) (-78 - 186)882 734 734 715 657 627 521(518 - 1243) (431 - 1035) (431 - 1035) (419 - 1008) (385 - 927) (368 - 885) (305 - 735)226 208 208 195 179 178 148(83 - 367) (77 - 338) (77 - 338) (72 - 316) (66 - 291) (66 - 289) (54 - 240)242 242 242 242 230 227 188(40 - 442) (40 - 442) (40 - 442) (40 - 442) (38 - 420) (38 - 414) (31 - 344)204 143 143 140 133 122 102(60 - 346) (42 - 244) (42 - 244) (41 - 237) (39 - 226) (36 - 208) (30 - 173)54 34 34 34 34 29 24(11 - 96) (7 - 61) (7 - 61) (7 - 61) (7 - 61) (6 - 52) (5 - 43)251 225 211 195 179 192 160(73 - 428) (65 - 384) (61 - 360) (56 - 333) (52 - 306) (55 - 328) (46 - 272)52 42 42 42 42 36 30(9 - 94) (7 - 76) (7 - 76) (7 - 76) (7 - 76) (6 - 65) (5 - 54)Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UT3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.E-77Table E-76. Estimated Percent of Total Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/251.3% 1.2% 1.1% 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% - 1.9%) (0.2% - 1.7%)2% 1.9% 1.8% 1.7% 1.5% 1.6% 1.3%(0.8% - 3.2%) (0.8% - 3%) (0.7% - 2.8%) (0.7% - 2.6%) (0.6% - 2.4%) (0.7% - 2.6%) (0.5% - 2.1%)0.5% 0.4% 0.3% 0.3% 0.3% 0.3% 0.3%(-0.7% - 1.6%) (-0.6% - 1.3%) (-0.5% - 1.2%) (-0.5% - 1.1%) (-0.4% - 1%) (-0.5% - 1.1%) (-0.4% - 0.9%)1.2% 1.2% 1.2% 1.2% 1.2% 1.2% 1.2%(0.3% - 2.2%) (0.3% - 2.2%) (0.3% - 2.2%) (0.3% - 2.2%) (0.3% - 2%) (0.3% - 2.2%) (0.3% - 2%)1% 0.8% 0.8% 0.8% 0.7% 0.7% 0.6%(-0.2% - 2.3%) (-0.1% - 1.8%) (-0.1% - 1.8%) (-0.1% - 1.7%) (-0.1% - 1.5%) (-0.1% - 1.6%) (-0.1% - 1.3%)1.5% 0.8% 0.8% 0.8% 0.8% 0.7% 0.6%(0.2% - 2.7%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.3%) (0.1% - 1.1%)1.3% 1.2% 1.1% 1% 0.9% 1% 0.9%(0.3% - 2.3%) (0.2% - 2.1%) (0.2% - 2%) (0.2% - 1.8%) (0.2% - 1.7%) (0.2% - 1.8%) (0.2% - 1.7%)0.2% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1%(-0.3% - 0.8%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.4%) (-0.1% - 0.4%)1.8% 1.5% 1.5% 1.5% 1.4% 1.3% 1.1%(1.1% - 2.6%) (0.9% - 2.1%) (0.9% - 2.1%) (0.9% - 2.1%) (0.8% - 1.9%) (0.8% - 1.8%) (0.6% - 1.5%)1.7% 1.5% 1.5% 1.4% 1.3% 1.3% 1.1%(0.6% - 2.7%) (0.6% - 2.5%) (0.6% - 2.5%) (0.5% - 2.3%) (0.5% - 2.1%) (0.5% - 2.1%) (0.4% - 1.8%)1.1% 1.1% 1.1% 1.1% 1.1% 1% 0.9%(0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.9%) (0.1% - 1.6%)1.7% 1.2% 1.2% 1.1% 1.1% 1% 0.8%(0.5% - 2.8%) (0.3% - 2%) (0.3% - 2%) (0.3% - 1.9%) (0.3% - 1.8%) (0.3% - 1.7%) (0.2% - 1.4%)1% 0.6% 0.6% 0.6% 0.6% 0.6% 0.5%(0.2% - 1.8%) (0.1% - 1.2%) (0.1% - 1.2%) (0.1% - 1.2%) (0.1% - 1.2%) (0.1% - 1%) (0.1% - 0.8%)1.6% 1.4% 1.3% 1.2% 1.1% 1.2% 1%(0.5% - 2.7%) (0.4% - 2.4%) (0.4% - 2.3%) (0.4% - 2.1%) (0.3% - 1.9%) (0.4% - 2.1%) (0.3% - 1.7%)1.2% 1% 1% 1% 1% 0.8% 0.7%(0.2% - 2.2%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.1% - 1.5%) (0.1% - 1.3%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based 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.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-78Table E-77. Estimated Percent of Total Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/251.3% 1.2% 1.1% 1% 0.9% 1% 0.9%(0.3% - 2.4%) (0.2% - 2.2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% - 1.9%) (0.2% - 1.7%)1.7% 1.6% 1.6% 1.4% 1.3% 1.4% 1.2%(0.7% - 2.8%) (0.7% - 2.6%) (0.6% - 2.5%) (0.6% - 2.3%) (0.5% - 2.1%) (0.6% - 2.2%) (0.5% - 1.9%)0.4% 0.3% 0.3% 0.3% 0.3% 0.3% 0.2%(-0.7% - 1.6%) (-0.5% - 1.2%) (-0.5% - 1.1%) (-0.5% - 1%) (-0.4% - 0.9%) (-0.5% - 1%) (-0.4% - 0.9%)1% 1% 1% 1% 0.9% 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% - 1.8%) (0.2% - 1.7%)0.8% 0.7% 0.7% 0.6% 0.6% 0.6% 0.5%(-0.1% - 1.8%) (-0.1% - 1.5%) (-0.1% - 1.5%) (-0.1% - 1.4%) (-0.1% - 1.3%) (-0.1% - 1.3%) (-0.1% - 1.1%)1.5% 0.8% 0.8% 0.8% 0.8% 0.7% 0.6%(0.2% - 2.8%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.3%) (0.1% - 1.1%)1.2% 1.1% 1% 1% 0.9% 1% 0.9%(0.2% - 2.1%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% - 1.6%) (0.2% - 1.7%) (0.2% - 1.6%)0.2% 0.1% 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% - 0.4%) (-0.1% - 0.3%)1.6% 1.3% 1.3% 1.3% 1.2% 1.1% 0.9%(0.9% - 2.2%) (0.8% - 1.8%) (0.8% - 1.8%) (0.7% - 1.8%) (0.7% - 1.6%) (0.6% - 1.6%) (0.5% - 1.3%)1.6% 1.5% 1.5% 1.4% 1.2% 1.2% 1%(0.6% - 2.6%) (0.5% - 2.4%) (0.5% - 2.4%) (0.5% - 2.2%) (0.5% - 2%) (0.5% - 2%) (0.4% - 1.7%)1.1% 1.1% 1.1% 1.1% 1.1% 1.1% 0.9%(0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.9%) (0.1% - 1.6%)1.4% 1% 1% 1% 0.9% 0.9% 0.7%(0.4% - 2.5%) (0.3% - 1.7%) (0.3% - 1.7%) (0.3% - 1.7%) (0.3% - 1.6%) (0.3% - 1.5%) (0.2% - 1.2%)0.9% 0.6% 0.6% 0.6% 0.6% 0.5% 0.4%(0.2% - 1.6%) (0.1% - 1%) (0.1% - 1%) (0.1% - 1%) (0.1% - 1%) (0.1% - 0.9%) (0.1% - 0.7%)1.3% 1.2% 1.1% 1% 0.9% 1% 0.8%(0.4% - 2.2%) (0.3% - 2%) (0.3% - 1.9%) (0.3% - 1.7%) (0.3% - 1.6%) (0.3% - 1.7%) (0.2% - 1.4%)1% 0.8% 0.8% 0.8% 0.8% 0.7% 0.6%(0.2% - 1.8%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.2%) (0.1% - 1%)Percent of Total Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based 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.Dallas, TXFresno, CALos Angeles, CAE-79Table E-78. Estimated Percent of Total Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/251.3% 1.2% 1.1% 1% 0.9% 1% 0.9%(0.2% - 2.3%) (0.2% - 2.1%) (0.2% - 1.9%) (0.2% - 1.8%) (0.2% - 1.6%) (0.2% - 1.8%) (0.2% - 1.6%)1.8% 1.7% 1.6% 1.5% 1.3% 1.4% 1.2%(0.7% - 2.8%) (0.7% - 2.6%) (0.6% - 2.5%) (0.6% - 2.3%) (0.5% - 2.1%) (0.6% - 2.3%) (0.5% - 1.9%)0.5% 0.4% 0.3% 0.3% 0.3% 0.3% 0.2%(-0.7% - 1.6%) (-0.6% - 1.2%) (-0.5% - 1.2%) (-0.5% - 1.1%) (-0.4% - 1%) (-0.5% - 1.1%) (-0.4% - 0.9%)1.1% 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% - 1.9%) (0.2% - 1.8%)0.9% 0.7% 0.7% 0.7% 0.6% 0.6% 0.5%(-0.2% - 1.9%) (-0.1% - 1.6%) (-0.1% - 1.5%) (-0.1% - 1.4%) (-0.1% - 1.3%) (-0.1% - 1.3%) (-0.1% - 1.1%)1.6% 0.9% 0.9% 0.9% 0.9% 0.7% 0.6%(0.2% - 2.9%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.4%) (0.1% - 1.1%)1.2% 1.1% 1% 1% 0.9% 1% 0.9%(0.2% - 2.1%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% - 1.6%) (0.2% - 1.7%) (0.2% - 1.6%)0.2% 0.1% 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% - 0.4%) (-0.1% - 0.3%)1.7% 1.4% 1.4% 1.4% 1.3% 1.2% 1%(1% - 2.4%) (0.8% - 2%) (0.8% - 2%) (0.8% - 1.9%) (0.7% - 1.8%) (0.7% - 1.7%) (0.6% - 1.4%)1.6% 1.5% 1.5% 1.4% 1.3% 1.3% 1.1%(0.6% - 2.6%) (0.5% - 2.4%) (0.5% - 2.4%) (0.5% - 2.3%) (0.5% - 2.1%) (0.5% - 2.1%) (0.4% - 1.7%)1% 1% 1% 1% 1% 1% 0.8%(0.2% - 1.9%) (0.2% - 1.9%) (0.2% - 1.9%) (0.2% - 1.9%) (0.2% - 1.8%) (0.2% - 1.8%) (0.1% - 1.5%)1.5% 1.1% 1.1% 1% 1% 0.9% 0.8%(0.4% - 2.6%) (0.3% - 1.8%) (0.3% - 1.8%) (0.3% - 1.8%) (0.3% - 1.7%) (0.3% - 1.6%) (0.2% - 1.3%)1.1% 0.7% 0.7% 0.7% 0.7% 0.6% 0.5%(0.2% - 2%) (0.1% - 1.3%) (0.1% - 1.3%) (0.1% - 1.3%) (0.1% - 1.3%) (0.1% - 1.1%) (0.1% - 0.9%)1.4% 1.2% 1.2% 1.1% 1% 1.1% 0.9%(0.4% - 2.4%) (0.4% - 2.1%) (0.3% - 2%) (0.3% - 1.8%) (0.3% - 1.7%) (0.3% - 1.8%) (0.3% - 1.5%)1% 0.8% 0.8% 0.8% 0.8% 0.7% 0.6%(0.2% - 1.9%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.3%) (0.1% - 1.1%)Percent of Total Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence orcredible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-responsefunction 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.Dallas, TXFresno, CALos Angeles, CAE-80Table E-79. Percent Reduction from the Current Standards: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25Atlanta, GA-9% 0% 7% 15% 22% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (23% - 24%)Baltimore, MD-6% 0% 5% 13% 20% 14% 29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)Birmingham, AL-28% 0% 8% 15% 23% 15% 30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)Dallas, TX0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)Detroit, MI-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15%) (29% - 29%)Fresno, CA-81% 0% 0% 0% 0% 15% 29%(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)Houston, TX-6% 0% 7% 15% 22% 15% 22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)Los Angeles, CA-58% 0% 0% 0% 6% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)New York, NY-20% 0% 0% 3% 10% 14% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15%) (29% - 29%)Philadelphia, PA-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)Phoenix, AZ0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)Pittsburgh, PA-41% 0% 0% 3% 8% 15% 29%(-41% - -41%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (14% - 15%) (29% - 29%)Salt Lake City, UT-58% 0% 0% 0% 0% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)St. Louis, MO-12% 0% 6% 13% 20% 15% 29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)Tacoma, WA-23% 0% 0% 0% 0% 15% 29%(-23% - -23%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 coefficient.1Based 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.Risk AssessmentLocationEstimated Percent Reduction From the Current Standards to Several Alternative Standards in Non-Accidental MortalityAssociated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet theCurrent and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:E-81Table E-80. Percent Reduction fromthe Current Standards: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25Atlanta, GA-9% 0% 7% 15% 22% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (23% - 24%)Baltimore, MD-6% 0% 6% 13% 20% 14% 29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)Birmingham, AL-28% 0% 8% 15% 23% 15% 30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)Dallas, TX0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)Detroit, MI-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)Fresno, CA-81% 0% 0% 0% 0% 15% 29%(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)Houston, TX-6% 0% 7% 15% 22% 15% 22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)Los Angeles, CA-58% 0% 0% 0% 6% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)New York, NY-20% 0% 0% 3% 10% 14% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%) (29% - 29%)Philadelphia, PA-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)Phoenix, AZ0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)Pittsburgh, PA-42% 0% 0% 3% 7% 15% 29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%) (29% - 29%)Salt Lake City, UT-58% 0% 0% 0% 0% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)St. Louis, MO-12% 0% 6% 13% 20% 15% 29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)Tacoma, WA-23% 0% 0% 0% 0% 15% 29%(-23% - -23%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Risk AssessmentLocationEstimated Percent Reduction From the Current Standards to Several Alternative Standards in Non-Accidental MortalityAssociated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet theCurrent and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:E-82Table E-81. Percent Reduction from the Current Standards: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25Atlanta, GA-9% 0% 7% 15% 22% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (23% - 24%)Baltimore, MD-6% 0% 6% 13% 20% 14% 29%(-6% - -6%) (0% - 0%) (5% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)Birmingham, AL-28% 0% 8% 15% 23% 15% 30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)Dallas, TX0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)Detroit, MI-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)Fresno, CA-81% 0% 0% 0% 0% 15% 29%(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)Houston, TX-6% 0% 7% 15% 22% 15% 22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)Los Angeles, CA-58% 0% 0% 0% 6% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)New York, NY-20% 0% 0% 3% 10% 14% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%) (29% - 29%)Philadelphia, PA-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)Phoenix, AZ0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)Pittsburgh, PA-42% 0% 0% 3% 7% 15% 29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%) (29% - 29%)Salt Lake City, UT-58% 0% 0% 0% 0% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)St. Louis, MO-12% 0% 6% 13% 20% 15% 29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)Tacoma, WA-23% 0% 0% 0% 0% 15% 29%(-23% - -23%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Risk AssessmentLocationEstimated Percent Reduction From the Current Standards to Several Alternative Standards in Non-Accidental MortalityAssociated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet theCurrent and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:E-83Table E-82. Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2535 32 30 27 25 27 24(-36 - 104) (-33 - 95) (-30 - 88) (-28 - 81) (-25 - 74) (-28 - 81) (-25 - 73)74 70 66 61 56 60 50(-5 - 151) (-5 - 143) (-4 - 135) (-4 - 125) (-4 - 115) (-4 - 122) (-3 - 102)-1 -1 -1 -1 -1 -1 0(-55 - 52) (-43 - 40) (-39 - 37) (-36 - 34) (-33 - 31) (-36 - 34) (-30 - 29)32 32 32 32 30 32 30(-21 - 85) (-21 - 85) (-21 - 85) (-21 - 85) (-20 - 79) (-21 - 85) (-20 - 79)89 73 72 67 61 62 51(-11 - 188) (-9 - 153) (-9 - 152) (-8 - 140) (-8 - 129) (-8 - 131) (-6 - 109)20 11 11 11 11 10 8(-14 - 54) (-8 - 30) (-8 - 30) (-8 - 30) (-8 - 30) (-7 - 26) (-6 - 21)50 47 43 40 36 40 36(-34 - 131) (-32 - 124) (-29 - 114) (-27 - 105) (-25 - 96) (-27 - 105) (-25 - 96)-50 -31 -31 -31 -30 -27 -22(-223 - 121) (-140 - 76) (-140 - 76) (-140 - 76) (-132 - 72) (-119 - 65) (-99 - 54)605 504 504 491 451 431 358(353 - 853) (294 - 711) (294 - 711) (286 - 693) (263 - 637) (251 - 609) (208 - 506)94 87 87 81 75 74 62(25 - 163) (23 - 150) (23 - 150) (21 - 140) (19 - 129) (19 - 129) (16 - 107)84 84 84 84 80 79 65(-4 - 170) (-4 - 170) (-4 - 170) (-4 - 170) (-3 - 161) (-3 - 159) (-3 - 132)67 47 47 46 44 41 34(-13 - 145) (-9 - 103) (-9 - 103) (-9 - 101) (-9 - 96) (-8 - 88) (-7 - 73)13 8 8 8 8 7 6(-3 - 28) (-2 - 18) (-2 - 18) (-2 - 18) (-2 - 18) (-2 - 15) (-1 - 12)136 122 115 106 98 105 87(30 - 240) (27 - 215) (26 - 203) (24 - 187) (22 - 172) (23 - 185) (19 - 153)15 12 12 12 12 11 9(-8 - 38) (-7 - 31) (-7 - 31) (-7 - 31) (-7 - 31) (-6 - 27) (-5 - 22)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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.Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIRisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-84Table E-83. Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2535 32 30 28 25 28 25(-36 - 105) (-33 - 97) (-31 - 90) (-28 - 82) (-26 - 75) (-28 - 82) (-25 - 74)65 61 58 53 49 52 43(-4 - 132) (-4 - 125) (-4 - 118) (-4 - 109) (-3 - 101) (-4 - 107) (-3 - 89)-1 -1 -1 -1 0 -1 0(-52 - 50) (-41 - 39) (-38 - 36) (-35 - 33) (-32 - 30) (-35 - 33) (-29 - 27)27 27 27 27 25 27 25(-18 - 71) (-18 - 71) (-18 - 71) (-18 - 71) (-16 - 66) (-18 - 71) (-16 - 66)72 58 58 54 49 50 41(-9 - 152) (-7 - 123) (-7 - 122) (-7 - 113) (-6 - 104) (-6 - 105) (-5 - 87)22 12 12 12 12 10 9(-15 - 58) (-8 - 32) (-8 - 32) (-8 - 32) (-8 - 32) (-7 - 27) (-6 - 23)48 45 42 39 35 39 35(-33 - 128) (-31 - 120) (-29 - 112) (-26 - 103) (-24 - 94) (-26 - 103) (-24 - 94)-46 -29 -29 -29 -27 -25 -20(-205 - 112) (-129 - 70) (-129 - 70) (-129 - 70) (-122 - 66) (-110 - 60) (-91 - 50)519 432 432 421 387 370 307(303 - 733) (252 - 611) (252 - 611) (246 - 595) (226 - 548) (216 - 523) (179 - 435)89 82 82 77 71 70 58(23 - 154) (21 - 142) (21 - 142) (20 - 133) (18 - 122) (18 - 122) (15 - 101)88 88 88 88 84 82 69(-4 - 178) (-4 - 178) (-4 - 178) (-4 - 178) (-4 - 169) (-4 - 167) (-3 - 139)58 41 41 40 38 35 29(-12 - 126) (-8 - 89) (-8 - 89) (-8 - 86) (-8 - 82) (-7 - 76) (-6 - 63)11 7 7 7 7 6 5(-3 - 25) (-2 - 16) (-2 - 16) (-2 - 16) (-2 - 16) (-1 - 14) (-1 - 11)113 101 95 88 81 87 72(25 - 199) (23 - 179) (21 - 168) (20 - 155) (18 - 143) (19 - 153) (16 - 127)13 10 10 10 10 9 7(-7 - 32) (-6 - 26) (-6 - 26) (-6 - 26) (-6 - 26) (-5 - 22) (-4 - 19)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXRisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-85Table E-84. Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2535 32 30 27 25 27 24(-36 - 104) (-33 - 95) (-30 - 88) (-28 - 81) (-25 - 74) (-28 - 81) (-25 - 73)65 62 58 54 50 53 44(-4 - 133) (-4 - 126) (-4 - 119) (-4 - 111) (-3 - 102) (-4 - 108) (-3 - 90)-1 -1 -1 -1 -1 -1 0(-54 - 51) (-42 - 40) (-39 - 37) (-36 - 34) (-33 - 31) (-36 - 34) (-30 - 28)29 29 29 29 27 29 27(-19 - 76) (-19 - 76) (-19 - 76) (-19 - 76) (-17 - 70) (-19 - 76) (-17 - 70)74 60 60 55 51 51 43(-9 - 156) (-8 - 127) (-7 - 126) (-7 - 116) (-6 - 107) (-6 - 108) (-5 - 90)23 12 12 12 12 11 9(-16 - 59) (-9 - 33) (-9 - 33) (-9 - 33) (-9 - 33) (-7 - 28) (-6 - 24)49 46 43 39 36 39 36(-33 - 130) (-31 - 122) (-29 - 113) (-27 - 104) (-24 - 95) (-27 - 104) (-24 - 95)-47 -30 -30 -30 -28 -25 -21(-209 - 114) (-132 - 72) (-132 - 72) (-132 - 72) (-124 - 68) (-112 - 61) (-93 - 51)568 473 473 461 424 405 336(332 - 802) (276 - 668) (276 - 668) (269 - 651) (247 - 599) (236 - 572) (196 - 476)91 84 84 79 72 72 60(24 - 157) (22 - 145) (22 - 145) (20 - 136) (19 - 125) (19 - 124) (15 - 103)84 84 84 84 80 79 65(-4 - 170) (-4 - 170) (-4 - 170) (-4 - 170) (-3 - 162) (-3 - 159) (-3 - 133)61 43 43 42 40 37 30(-12 - 132) (-9 - 93) (-9 - 93) (-8 - 91) (-8 - 87) (-7 - 80) (-6 - 66)14 9 9 9 9 8 6(-3 - 31) (-2 - 20) (-2 - 20) (-2 - 20) (-2 - 20) (-2 - 17) (-1 - 14)118 106 99 92 84 91 75(26 - 208) (24 - 187) (22 - 176) (20 - 162) (19 - 149) (20 - 160) (17 - 133)14 11 11 11 11 9 8(-7 - 34) (-6 - 27) (-6 - 27) (-6 - 27) (-6 - 27) (-5 - 23) (-4 - 19)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXRisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-86Table E-85. Estimated Percent of Total Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/250.9% 0.9% 0.8% 0.7% 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% - 2.2%) (-0.7% - 2%)1.9% 1.8% 1.7% 1.6% 1.4% 1.5% 1.3%(-0.1% - 3.9%) (-0.1% - 3.7%) (-0.1% - 3.5%) (-0.1% - 3.2%) (-0.1% - 3%) (-0.1% - 3.1%) (-0.1% - 2.6%)0% 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% - 1.3%) (-1.1% - 1.1%)1% 1% 1% 1% 0.9% 1% 0.9%(-0.6% - 2.5%) (-0.6% - 2.5%) (-0.6% - 2.5%) (-0.6% - 2.5%) (-0.6% - 2.3%) (-0.6% - 2.5%) (-0.6% - 2.3%)1.5% 1.2% 1.2% 1.1% 1% 1% 0.9%(-0.2% - 3.1%) (-0.2% - 2.5%) (-0.2% - 2.5%) (-0.1% - 2.3%) (-0.1% - 2.1%) (-0.1% - 2.2%) (-0.1% - 1.8%)1.2% 0.7% 0.7% 0.7% 0.7% 0.6% 0.5%(-0.9% - 3.3%) (-0.5% - 1.8%) (-0.5% - 1.8%) (-0.5% - 1.8%) (-0.5% - 1.8%) (-0.4% - 1.6%) (-0.3% - 1.3%)1% 1% 0.9% 0.8% 0.7% 0.8% 0.7%(-0.7% - 2.7%) (-0.7% - 2.5%) (-0.6% - 2.3%) (-0.6% - 2.2%) (-0.5% - 2%) (-0.6% - 2.2%) (-0.5% - 2%)-0.3% -0.2% -0.2% -0.2% -0.2% -0.1% -0.1%(-1.2% - 0.6%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.6% - 0.3%) (-0.5% - 0.3%)2.7% 2.2% 2.2% 2.2% 2% 1.9% 1.6%(1.6% - 3.8%) (1.3% - 3.2%) (1.3% - 3.2%) (1.3% - 3.1%) (1.2% - 2.8%) (1.1% - 2.7%) (0.9% - 2.3%)2.3% 2.2% 2.2% 2% 1.9% 1.8% 1.5%(0.6% - 4%) (0.6% - 3.7%) (0.6% - 3.7%) (0.5% - 3.5%) (0.5% - 3.2%) (0.5% - 3.2%) (0.4% - 2.7%)1.4% 1.4% 1.4% 1.4% 1.4% 1.3% 1.1%(-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.7%) (-0.1% - 2.7%) (0% - 2.3%)1.6% 1.2% 1.2% 1.1% 1.1% 1% 0.8%(-0.3% - 3.6%) (-0.2% - 2.5%) (-0.2% - 2.5%) (-0.2% - 2.5%) (-0.2% - 2.3%) (-0.2% - 2.2%) (-0.2% - 1.8%)1.1% 0.7% 0.7% 0.7% 0.7% 0.6% 0.5%(-0.3% - 2.5%) (-0.2% - 1.6%) (-0.2% - 1.6%) (-0.2% - 1.6%) (-0.2% - 1.6%) (-0.1% - 1.4%) (-0.1% - 1.1%)2.4% 2.2% 2% 1.9% 1.7% 1.8% 1.5%(0.5% - 4.2%) (0.5% - 3.8%) (0.5% - 3.6%) (0.4% - 3.3%) (0.4% - 3%) (0.4% - 3.2%) (0.3% - 2.7%)1.1% 0.9% 0.9% 0.9% 0.9% 0.7% 0.6%(-0.6% - 2.7%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.4% - 1.8%) (-0.3% - 1.5%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based 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 errorswere sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-87Table E-86. Estimated Percent of Total Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/250.9% 0.8% 0.8% 0.7% 0.7% 0.7% 0.6%(-0.9% - 2.8%) (-0.9% - 2.5%) (-0.8% - 2.3%) (-0.7% - 2.2%) (-0.7% - 2%) (-0.7% - 2.2%) (-0.7% - 1.9%)1.7% 1.6% 1.5% 1.4% 1.3% 1.3% 1.1%(-0.1% - 3.4%) (-0.1% - 3.2%) (-0.1% - 3%) (-0.1% - 2.8%) (-0.1% - 2.6%) (-0.1% - 2.7%) (-0.1% - 2.3%)0% 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% - 1.2%) (-1.1% - 1%)0.8% 0.8% 0.8% 0.8% 0.7% 0.8% 0.7%(-0.5% - 2.1%) (-0.5% - 2.1%) (-0.5% - 2.1%) (-0.5% - 2.1%) (-0.5% - 1.9%) (-0.5% - 2.1%) (-0.5% - 1.9%)1.2% 1% 1% 0.9% 0.8% 0.8% 0.7%(-0.2% - 2.5%) (-0.1% - 2.1%) (-0.1% - 2.1%) (-0.1% - 1.9%) (-0.1% - 1.7%) (-0.1% - 1.8%) (-0.1% - 1.5%)1.3% 0.7% 0.7% 0.7% 0.7% 0.6% 0.5%(-0.9% - 3.5%) (-0.5% - 1.9%) (-0.5% - 1.9%) (-0.5% - 1.9%) (-0.5% - 1.9%) (-0.4% - 1.6%) (-0.4% - 1.4%)1% 0.9% 0.8% 0.8% 0.7% 0.8% 0.7%(-0.6% - 2.5%) (-0.6% - 2.4%) (-0.6% - 2.2%) (-0.5% - 2%) (-0.5% - 1.9%) (-0.5% - 2%) (-0.5% - 1.9%)-0.2% -0.2% -0.2% -0.2% -0.1% -0.1% -0.1%(-1.1% - 0.6%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.6% - 0.4%) (-0.6% - 0.3%) (-0.5% - 0.3%)2.3% 1.9% 1.9% 1.9% 1.7% 1.6% 1.4%(1.3% - 3.3%) (1.1% - 2.7%) (1.1% - 2.7%) (1.1% - 2.6%) (1% - 2.4%) (1% - 2.3%) (0.8% - 1.9%)2.2% 2.1% 2.1% 1.9% 1.8% 1.8% 1.5%(0.6% - 3.8%) (0.5% - 3.5%) (0.5% - 3.5%) (0.5% - 3.3%) (0.5% - 3.1%) (0.5% - 3%) (0.4% - 2.5%)1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.1%(-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.8%) (-0.1% - 2.7%) (0% - 2.3%)1.4% 1% 1% 1% 0.9% 0.9% 0.7%(-0.3% - 3.1%) (-0.2% - 2.2%) (-0.2% - 2.2%) (-0.2% - 2.1%) (-0.2% - 2%) (-0.2% - 1.9%) (-0.1% - 1.6%)1% 0.6% 0.6% 0.6% 0.6% 0.5% 0.5%(-0.2% - 2.2%) (-0.1% - 1.4%) (-0.1% - 1.4%) (-0.1% - 1.4%) (-0.1% - 1.4%) (-0.1% - 1.2%) (-0.1% - 1%)2% 1.8% 1.7% 1.5% 1.4% 1.5% 1.3%(0.4% - 3.5%) (0.4% - 3.1%) (0.4% - 3%) (0.3% - 2.7%) (0.3% - 2.5%) (0.3% - 2.7%) (0.3% - 2.2%)0.9% 0.7% 0.7% 0.7% 0.7% 0.6% 0.5%(-0.5% - 2.2%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.3% - 1.5%) (-0.3% - 1.3%)Percent of Total Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence orcredible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutantconcentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards theappropriate regional means."Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-88Table E-87. Estimated Percent of Total Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/250.9% 0.8% 0.8% 0.7% 0.6% 0.7% 0.6%(-0.9% - 2.7%) (-0.8% - 2.4%) (-0.8% - 2.3%) (-0.7% - 2.1%) (-0.6% - 1.9%) (-0.7% - 2.1%) (-0.6% - 1.9%)1.7% 1.6% 1.5% 1.4% 1.3% 1.4% 1.1%(-0.1% - 3.4%) (-0.1% - 3.2%) (-0.1% - 3.1%) (-0.1% - 2.8%) (-0.1% - 2.6%) (-0.1% - 2.8%) (-0.1% - 2.3%)0% 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% - 1.2%) (-1.1% - 1%)0.8% 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% - 2.2%) (-0.5% - 2%)1.3% 1% 1% 0.9% 0.9% 0.9% 0.7%(-0.2% - 2.7%) (-0.1% - 2.2%) (-0.1% - 2.1%) (-0.1% - 2%) (-0.1% - 1.8%) (-0.1% - 1.8%) (-0.1% - 1.5%)1.3% 0.7% 0.7% 0.7% 0.7% 0.6% 0.5%(-0.9% - 3.5%) (-0.5% - 2%) (-0.5% - 2%) (-0.5% - 2%) (-0.5% - 2%) (-0.4% - 1.7%) (-0.4% - 1.4%)1% 0.9% 0.8% 0.8% 0.7% 0.8% 0.7%(-0.6% - 2.5%) (-0.6% - 2.4%) (-0.6% - 2.2%) (-0.5% - 2%) (-0.5% - 1.9%) (-0.5% - 2%) (-0.5% - 1.9%)-0.2% -0.2% -0.2% -0.2% -0.1% -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% - 0.3%) (-0.5% - 0.3%)2.5% 2.1% 2.1% 2% 1.9% 1.8% 1.5%(1.5% - 3.5%) (1.2% - 3%) (1.2% - 3%) (1.2% - 2.9%) (1.1% - 2.6%) (1% - 2.5%) (0.9% - 2.1%)2.3% 2.1% 2.1% 2% 1.8% 1.8% 1.5%(0.6% - 3.9%) (0.5% - 3.6%) (0.5%- 3.6%) (0.5% - 3.4%) (0.5% - 3.1%) (0.5% - 3.1%) (0.4% - 2.6%)1.3% 1.3% 1.3% 1.3% 1.3% 1.3% 1%(-0.1% - 2.7%) (-0.1% - 2.7%) (-0.1% - 2.7%) (-0.1% - 2.7%) (-0.1% - 2.6%) (-0.1% - 2.5%) (0% - 2.1%)1.5% 1.1% 1.1% 1% 1% 0.9% 0.8%(-0.3% - 3.3%) (-0.2% - 2.3%) (-0.2% - 2.3%) (-0.2% - 2.3%) (-0.2% - 2.1%) (-0.2% - 2%) (-0.1% - 1.6%)1.2% 0.8% 0.8% 0.8% 0.8% 0.7% 0.6%(-0.3% - 2.7%) (-0.2% - 1.7%) (-0.2% - 1.7%) (-0.2% - 1.7%) (-0.2% - 1.7%) (-0.2% - 1.5%) (-0.1% - 1.2%)2.1% 1.9% 1.7% 1.6% 1.5% 1.6% 1.3%(0.5% - 3.7%) (0.4% - 3.3%) (0.4%- 3.1%) (0.4% - 2.9%) (0.3% - 2.6%) (0.4% - 2.8%) (0.3% - 2.3%)0.9% 0.7% 0.7% 0.7% 0.7% 0.6% 0.5%(-0.5% - 2.3%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.3% - 1.6%) (-0.3% - 1.3%)Percent of Total Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination 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 coefficient.1Based 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.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-89Table E-88. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-9% 0% 7% 15% 23% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (23% - 24%)-6% 0% 6% 13% 20% 14% 29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 15%) (29% - 29%)-28% 0% 8% 15% 23% 15% 30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15%) (29% - 29%)-81% 0% 0% 0% 0% 15% 29%(-80% - -83%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6% 0% 7% 15% 22% 15% 22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)-59% 0% 0% 0% 6% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20% 0% 0% 3% 10% 14% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 14%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-41% 0% 0% 3% 8% 15% 29%(-41% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (14% - 15%) (29% - 29%)-58% 0% 0% 0% 0% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12% 0% 6% 13% 20% 14% 29%(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-23% 0% 0% 0% 0% 15% 29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 30%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutantconcentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional means."Shrunken" coefficient estimates andtheir standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Cardiovascular Mortality Associated with Short-TermExposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual(n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-90Table E-89. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular MortalityAssociated with Short-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-9% 0% 7% 15% 23% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (23% - 24%)-6% 0% 6% 13% 20% 14% 29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% -13%) (19% - 20%) (14% - 15%) (29% - 29%)-28% 0% 8% 15% 23% 15% 30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15%) (29% - 29%)-81% 0% 0% 0% 0% 15% 29%(-80% - -83%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6% 0% 7% 15% 23% 15% 23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (22% - 23%)-59% 0% 0% 0% 6% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20% 0% 0% 3% 10% 14% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% -6%) (14% - 14%) (14% - 15%) (29% - 29%)0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-42% 0% 0% 3% 7% 15% 29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 15%) (29% - 29%)-58% 0% 0% 0% 0% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12% 0% 6% 13% 20% 15% 29%(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-23% 0% 0% 0% 0% 15% 29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Mortality Associated with Short-TermExposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meetthe Current and Alternative Annual(n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-91Table E-90. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-9% 0% 7% 15% 23% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (23% - 24%)-6% 0% 6% 13% 20% 14% 29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 15%) (29% - 29%)-28% 0% 8% 15% 23% 15% 30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% -0%) (7% - 7%) (0% - 0%) (7% - 7%)-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% -8%) (16% - 16%) (14% - 15%) (29% - 29%)-81% 0% 0% 0% 0% 15% 29%(-79% - -83%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6% 0% 7% 15% 23% 15% 23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)-59% 0% 0% 0% 6% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0%- 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20% 0% 0% 3% 10% 14% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% -3%) (10% - 10%) (14% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-42% 0% 0% 3% 7% 15% 29%(-42% - -43%) (0% - 0%) (0% - 0%) (3%- 3%) (7% - 7%) (14% - 15%) (29% - 29%)-58% 0% 0% 0% 0% 15% 29%(-57% - -59%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (14% - 15%) (29% - 29%)-12% 0% 6% 13% 20% 15% 29%(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-23% 0% 0% 0% 0% 15% 29%(-23% - -24%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (14% - 15%) (29% - 30%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based 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.Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidenceof Cardiovascular Mortality Associated with Short-TermExposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual(n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-92Table E-91. Estimated Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in aRecent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2521 20 18 17 15 17 15(-9 - 51) (-8 - 47) (-7 - 43) (-7 - 40) (-6 - 36) (-7 - 40) (-6 - 36)38 36 34 31 29 30 25(7 - 67) (7 - 64) (6 - 60) (6 - 56) (5 - 51) (6 - 55) (5 - 45)12 9 9 8 7 8 7(-10 - 33) (-7 - 26) (-7 - 24) (-6 - 22) (-6 - 20) (-6 - 22) (-5 - 18)11 11 11 11 10 11 10(-10 - 32) (-10 - 32) (-10 - 32) (-10 - 32) (-10 - 30) (-10 - 32) (-10 - 30)35 28 28 26 24 24 20(2 - 67) (1 - 55) (1 - 54) (1 - 50) (1 - 46) (1 - 47) (1 - 39)15 9 9 9 9 7 6(0 - 30) (0 - 17) (0 - 17) (0 - 17) (0 - 17) (0 - 14) (0 - 12)36 34 31 29 26 29 26(6 - 65) (5 - 61) (5 - 57) (5 - 52) (4 - 48) (5 - 52) (4 - 48)90 57 57 57 54 49 41(9 - 171) (6 - 108) (6 - 108) (6 - 108) (5 - 102) (5 - 93) (4 - 77)128 106 106 104 95 91 76(45 - 208) (37 - 174) (37 - 174) (37 - 169) (34 - 156) (32 - 149) (27 - 124)25 23 23 22 20 20 16(-2 - 52) (-2 - 48) (-2 - 48) (-2 - 45) (-2 - 41) (-2 - 41) (-2 - 34)47 47 47 47 45 44 37(4 - 90) (4 - 90) (4 - 90) (4 - 90) (4 - 85) (4 - 84) (3 - 70)28 20 20 20 19 17 14(-3 - 58) (-2 - 42) (-2 - 42) (-2 - 40) (-2 - 38) (-2 - 36) (-1 - 30)8 555544(1 - 15) (1 - 10) (1 - 10) (1 - 10) (1 - 10) (1 - 8) (0 - 7)35 31 29 27 25 27 22(-9 - 78) (-8 - 70) (-8 - 65) (-7 - 61) (-7 - 56) (-7 - 60) (-6 - 50)9 777765(0 - 18) (0 - 15) (0 - 15) (0 - 15) (0 - 15) (0 - 13) (0 - 10)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-93Table E-92. Estimated Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in aRecent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2522 20 18 17 16 17 15(-9 - 51) (-8 - 47) (-7 - 44) (-7 - 40) (-6 - 37) (-7 - 40) (-6 - 36)33 31 29 27 25 27 22(6 - 59) (6 - 56) (5 - 53) (5 - 49) (5 - 45) (5 - 48) (4 - 40)11 9 8 8 7 8 6(-9 - 31) (-7 - 25) (-7 - 23) (-6 - 21) (-6 - 19) (-6 - 21) (-5 - 17)9 999999(-9 - 27) (-9 - 27) (-9 - 27) (-9 - 27) (-8 - 25) (-9 - 27) (-8 - 25)28 23 23 21 19 20 16(1 - 54) (1 - 44) (1 - 44) (1 - 41) (1 - 37) (1 - 38) (1 - 31)16 9 9 9 9 8 6(1 - 32) (0 - 18) (0 - 18) (0 - 18) (0 - 18) (0 - 15) (0 - 13)35 33 30 28 25 28 25(6 - 63) (5 - 60) (5 - 55) (4 - 51) (4 - 46) (4 - 51) (4 - 46)84 53 53 53 50 45 37(8 - 158) (5 - 100) (5 - 100) (5 - 100) (5 - 95) (4 - 86) (4 - 71)110 91 91 89 82 78 65(39 - 179) (32 - 149) (32 - 149) (31 - 146) (29 - 134) (27 - 128) (23 - 107)24 22 22 20 19 19 15(-2 - 49) (-2 - 45) (-2 - 45) (-2 - 42) (-2 - 39) (-2 - 39) (-1 - 32)50 50 50 50 47 46 39(4 - 94) (4 - 94) (4 - 94) (4 - 94) (4 - 90) (4 - 88) (3 - 74)24 17 17 17 16 15 12(-2 - 51) (-2 - 36) (-2 - 36) (-2 - 35) (-2 - 33) (-1 - 31) (-1 - 25)8 555543(1 - 14) (1 - 9) (1 - 9) (1 - 9) (1 - 9) (1 - 8) (0 - 6)29 26 24 22 21 22 18(-8 - 64) (-7 - 58) (-6 - 54) (-6 - 50) (-5 - 46) (-6 - 49) (-5 - 41)8 666654(0 - 15) (0 - 12) (0 - 12) (0 - 12) (0 - 12) (0 - 11) (0 - 9)Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UT3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.E-94Table E-93. Estimated Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in aRecent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2521 20 18 17 15 17 15(-9 - 51) (-8 - 47) (-7 - 43) (-7 - 40) (-6 - 36) (-7 - 40) (-6 - 36)33 31 30 28 25 27 22(6 - 60) (6 - 56) (6 - 53) (5 - 49) (5 - 45) (5 - 48) (4 - 40)12 9 9 8 7 8 7(-10 - 32) (-7 - 25) (-7 - 24) (-6 - 22) (-6 - 20) (-6 - 22) (-5 - 18)10 10 10 10 9 10 9(-9 - 29) (-9 - 29) (-9 - 29) (-9 - 29) (-8 - 27) (-9 - 29) (-8 - 27)29 24 23 22 20 20 17(1 - 56) (1 - 45) (1 - 45) (1 - 42) (1 - 38) (1 - 39) (1 - 32)17 9 9 9 9 8 7(1 - 33) (0 - 18) (0 - 18) (0 - 18) (0 - 18) (0 - 16) (0 - 13)35 33 31 28 26 28 26(6 - 64) (5 - 61) (5 - 56) (5 - 52) (4 - 47) (5 - 52) (4 - 47)85 54 54 54 51 46 38(8 - 161) (5 - 102) (5 - 102) (5 - 102) (5 - 96) (4 - 87) (4 - 73)120 100 100 97 89 85 71(42 - 196) (35 - 163) (35 - 163) (34 - 159) (31 - 147) (30 - 140) (25 - 117)24 22 22 21 19 19 16(-2 - 50) (-2 - 46) (-2 - 46) (-2 - 43) (-2 - 40) (-2 - 39) (-1 - 33)47 47 47 47 45 44 37(4 - 90) (4 - 90) (4 - 90) (4 - 90) (4 - 85) (4 - 84) (3 - 70)26 18 18 18 17 15 13(-3 - 53) (-2 - 38) (-2 - 38) (-2 - 37) (-2 - 35) (-2 - 32) (-1 - 27)9 666654(1 - 17) (1 - 11) (1 - 11) (1 - 11) (1 - 11) (1 - 9) (1 - 8)30 27 25 23 22 23 19(-8 - 67) (-7 - 60) (-7 - 57) (-6 - 53) (-6 - 48) (-6 - 52) (-5 - 43)8 666655(0 - 16) (0 - 13) (0 - 13) (0 - 13) (0 - 13) (0 - 11) (0 - 9)Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UT3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.E-95Table E-94. Estimated Percent of Total Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/251.7% 1.6% 1.5% 1.3% 1.2% 1.3% 1.2%(-0.7% - 4.1%) (-0.6% - 3.7%) (-0.6% - 3.5%) (-0.5% - 3.2%) (-0.5% - 2.9%) (-0.5% - 3.2%) (-0.5% - 2.9%)3.1% 2.9% 2.8% 2.6% 2.4% 2.5% 2.1%(0.6% - 5.6%) (0.5% - 5.3%) (0.5% - 5%) (0.5% - 4.6%) (0.4% - 4.2%) (0.5% - 4.5%) (0.4% - 3.8%)1.4% 1.1% 1% 0.9% 0.8% 0.9% 0.8%(-1.1% - 3.7%) (-0.9% - 2.9%) (-0.8% - 2.7%) (-0.7% - 2.5%) (-0.7% - 2.3%) (-0.7% - 2.5%) (-0.6% - 2.1%)1% 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%) (-0.9% - 2.7%)2.6% 2.1% 2.1% 1.9% 1.8% 1.8% 1.5%(0.1% - 5%) (0.1% - 4.1%) (0.1% - 4%) (0.1% - 3.7%) (0.1% - 3.4%) (0.1% - 3.5%) (0.1% - 2.9%)2.6% 1.5% 1.5% 1.5% 1.5% 1.2% 1%(0.1% - 5.1%) (0% - 2.8%) (0% - 2.8%) (0% - 2.8%) (0% - 2.8%) (0% - 2.4%) (0% - 2%)2.5% 2.4% 2.2% 2% 1.9% 2% 1.9%(0.4% - 4.6%) (0.4% - 4.4%) (0.4% - 4%) (0.3% - 3.7%) (0.3% - 3.4%) (0.3% - 3.7%) (0.3% - 3.4%)1.6% 1% 1% 1% 1% 0.9% 0.7%(0.2% - 3.1%) (0.1% - 1.9%) (0.1% - 1.9%) (0.1% - 1.9%) (0.1% - 1.8%) (0.1% - 1.7%) (0.1% - 1.4%)3% 2.5% 2.5% 2.4% 2.2% 2.1% 1.8%(1% - 4.8%) (0.9% - 4%) (0.9% - 4%) (0.8% - 3.9%) (0.8% - 3.6%) (0.7% - 3.5%) (0.6% - 2.9%)2.1% 1.9% 1.9% 1.8% 1.6% 1.6% 1.3%(-0.2% - 4.3%) (-0.2% - 3.9%) (-0.2% - 3.9%) (-0.2% - 3.7%) (-0.2% - 3.4%) (-0.2% - 3.4%) (-0.1% - 2.8%)1.9% 1.9% 1.9% 1.9% 1.8% 1.8% 1.5%(0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 2.9%)2.4% 1.7% 1.7% 1.6% 1.6% 1.4% 1.2%(-0.2% - 4.9%) (-0.2% - 3.5%) (-0.2% - 3.5%) (-0.2% - 3.4%) (-0.2% - 3.2%) (-0.1% - 3%) (-0.1% - 2.5%)1.9% 1.2% 1.2% 1.2% 1.2% 1% 0.8%(0.2% - 3.5%) (0.1% - 2.2%) (0.1% - 2.2%) (0.1% - 2.2%) (0.1% - 2.2%) (0.1% - 1.9%) (0.1% - 1.6%)2% 1.8% 1.7% 1.6% 1.4% 1.5% 1.3%(-0.5% - 4.5%) (-0.5% - 4%) (-0.4% - 3.8%) (-0.4% - 3.5%) (-0.4% - 3.2%) (-0.4% - 3.4%) (-0.3% - 2.9%)1.8% 1.5% 1.5% 1.5% 1.5% 1.3% 1.1%(0% - 3.6%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 2.5%) (0% - 2.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-96Table E-95. Estimated Percent of Total Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/251.7% 1.6% 1.4% 1.3% 1.2% 1.3% 1.2%(-0.7% - 4%) (-0.6% - 3.7%) (-0.6% - 3.4%) (-0.5% - 3.1%) (-0.5% - 2.9%) (-0.5% - 3.1%) (-0.5% - 2.8%)2.7% 2.6% 2.4% 2.2% 2.1% 2.2% 1.8%(0.5% - 4.9%) (0.5% - 4.6%) (0.4% - 4.3%) (0.4% - 4%) (0.4% - 3.7%) (0.4% - 3.9%) (0.3% - 3.3%)1.3% 1% 0.9% 0.9% 0.8% 0.9% 0.7%(-1% - 3.6%) (-0.8% - 2.8%) (-0.8% - 2.6%) (-0.7% - 2.4%) (-0.6% - 2.2%) (-0.7% - 2.4%) (-0.6% - 2%)0.8% 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% - 2.4%) (-0.7% - 2.3%)2.1% 1.7% 1.7% 1.6% 1.4% 1.5% 1.2%(0.1% - 4.1%) (0.1% - 3.3%) (0.1% - 3.3%) (0.1% - 3%) (0.1% - 2.8%) (0.1% - 2.8%) (0.1% - 2.3%)2.8% 1.5% 1.5% 1.5% 1.5% 1.3% 1.1%(0.1% - 5.3%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 2.6%) (0% - 2.1%)2.4% 2.3% 2.1% 1.9% 1.8% 1.9% 1.8%(0.4% - 4.4%) (0.4% - 4.1%) (0.3% - 3.8%) (0.3% - 3.5%) (0.3% - 3.2%) (0.3% - 3.5%) (0.3% - 3.2%)1.5% 0.9% 0.9% 0.9% 0.9% 0.8% 0.7%(0.1% - 2.8%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.7%) (0.1% - 1.5%) (0.1% - 1.3%)2.5% 2.1% 2.1% 2.1% 1.9% 1.8% 1.5%(0.9% - 4.1%) (0.7% - 3.5%) (0.7% - 3.5%) (0.7% - 3.4%) (0.7% - 3.1%) (0.6% - 3%) (0.5% - 2.5%)2% 1.8% 1.8% 1.7% 1.6% 1.5% 1.3%(-0.2% - 4%) (-0.2% - 3.7%) (-0.2% - 3.7%) (-0.2% - 3.5%) (-0.1% - 3.2%) (-0.1% - 3.2%) (-0.1% - 2.7%)2% 2% 2% 2% 1.9% 1.8% 1.5%(0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.1% - 3.6%) (0.1% - 3.5%) (0.1% - 2.9%)2.1% 1.5% 1.5% 1.4% 1.4% 1.2% 1%(-0.2% - 4.3%) (-0.1% - 3%) (-0.1% - 3%) (-0.1% - 3%) (-0.1% - 2.8%) (-0.1% - 2.6%) (-0.1% - 2.2%)1.7% 1.1% 1.1% 1.1% 1.1% 0.9% 0.8%(0.2% - 3.1%) (0.1% - 2%) (0.1% - 2%) (0.1% - 2%) (0.1% - 2%) (0.1% - 1.7%) (0.1% - 1.4%)1.7% 1.5% 1.4% 1.3% 1.2% 1.3% 1.1%(-0.4% - 3.7%) (-0.4% - 3.3%) (-0.4% - 3.1%) (-0.3% - 2.9%) (-0.3% - 2.7%) (-0.3% - 2.8%) (-0.3% - 2.4%)1.5% 1.2% 1.2% 1.2% 1.2% 1% 0.9%(0% - 3%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.1%) (0% - 1.7%)Percent of Total Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-97Table E-96. Estimated Percent of Total Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/251.6% 1.5% 1.4% 1.3% 1.2% 1.3% 1.2%(-0.7% - 3.9%) (-0.6% - 3.6%) (-0.6% - 3.3%) (-0.5% - 3%) (-0.5% - 2.8%) (-0.5% - 3%) (-0.5% - 2.7%)2.7% 2.6% 2.5% 2.3% 2.1% 2.2% 1.8%(0.5% - 4.9%) (0.5% - 4.7%) (0.5% - 4.4%) (0.4% - 4.1%) (0.4% - 3.8%) (0.4% - 4%) (0.3% - 3.3%)1.3% 1% 1% 0.9% 0.8% 0.9% 0.7%(-1.1% - 3.7%) (-0.8% - 2.9%) (-0.8% - 2.7%) (-0.7% - 2.5%) (-0.7% - 2.2%) (-0.7% - 2.5%) (-0.6% - 2%)0.9% 0.9% 0.9% 0.9% 0.8% 0.9% 0.8%(-0.8% - 2.6%) (-0.8% - 2.6%) (-0.8% - 2.6%) (-0.8% - 2.6%) (-0.8% - 2.4%) (-0.8% - 2.6%) (-0.8% - 2.4%)2.2% 1.8% 1.8% 1.6% 1.5% 1.5% 1.3%(0.1% - 4.2%) (0.1% - 3.4%) (0.1% - 3.4%) (0.1% - 3.2%) (0.1% - 2.9%) (0.1% - 2.9%) (0.1% - 2.4%)2.8% 1.6% 1.6% 1.6% 1.6% 1.3% 1.1%(0.1% - 5.4%) (0.1% - 3%) (0.1% - 3%) (0.1% - 3%) (0.1% - 3%) (0% - 2.6%) (0% - 2.2%)2.4% 2.3% 2.1% 1.9% 1.8% 1.9% 1.8%(0.4% - 4.4%) (0.4% - 4.1%) (0.3% - 3.8%) (0.3% - 3.5%) (0.3% - 3.2%) (0.3% - 3.5%) (0.3% - 3.2%)1.5% 1% 1% 1% 0.9% 0.8% 0.7%(0.1% - 2.9%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.7%) (0.1% - 1.6%) (0.1% - 1.3%)2.8% 2.3% 2.3% 2.2% 2.1% 2% 1.6%(1% - 4.5%) (0.8% - 3.8%) (0.8% - 3.8%) (0.8% - 3.7%) (0.7% - 3.4%) (0.7% - 3.2%) (0.6% - 2.7%)2% 1.8% 1.8% 1.7% 1.6% 1.6% 1.3%(-0.2% - 4.1%) (-0.2% - 3.8%) (-0.2% - 3.8%) (-0.2% - 3.6%) (-0.1% - 3.3%) (-0.1% - 3.3%) (-0.1% - 2.7%)1.8% 1.8% 1.8% 1.8% 1.7% 1.7% 1.4%(0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 3.3%) (0.1% - 3.3%) (0.1% - 2.7%)2.2% 1.5% 1.5% 1.5% 1.4% 1.3% 1.1%(-0.2% - 4.5%) (-0.2% - 3.2%) (-0.2% - 3.2%) (-0.1% - 3.1%) (-0.1% - 3%) (-0.1% - 2.7%) (-0.1% - 2.3%)2% 1.3% 1.3% 1.3% 1.3% 1.1% 0.9%(0.3% - 3.8%) (0.2% - 2.4%) (0.2% - 2.4%) (0.2% - 2.4%) (0.2% - 2.4%) (0.1% - 2%) (0.1% - 1.7%)1.7% 1.6% 1.5% 1.3% 1.2% 1.3% 1.1%(-0.5% - 3.9%) (-0.4% - 3.5%) (-0.4% - 3.3%) (-0.4% - 3%) (-0.3% - 2.8%) (-0.3% - 3%) (-0.3% - 2.5%)1.6% 1.3% 1.3% 1.3% 1.3% 1.1% 0.9%(0% - 3.1%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.2%) (0% - 1.8%)Percent of Total Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-98Table E-97. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Mortality Associated withShort-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-9% 0% 7% 15% 22% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (23% - 24%)-6% 0% 5% 12% 20% 14% 29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% -13%) (19% - 20%) (14% - 15%) (29% - 29%)-28% 0% 7% 15% 22% 15% 29%(-27% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23% 0% 1% 8% 16% 14% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (15% - 16%) (14% - 15%) (29% - 29%)-80% 0% 0% 0% 0% 14% 29%(-78% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6% 0% 7% 15% 22% 15% 22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (22% - 23%)-58% 0% 0% 0% 6% 15% 29%(-57% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 15%) (29% - 29%)-20% 0% 0% 3% 10% 14% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% -7%) (14% - 14%) (14% - 15%) (29% - 29%)0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-41% 0% 0% 3% 8% 15% 29%(-40% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (14% - 15%) (29% - 29%)-58% 0% 0% 0% 0% 15% 29%(-57% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-12% 0% 6% 13% 20% 15% 29%(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-23% 0% 0% 0% 0% 15% 29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidenceof Respiratory Mortality Associated with Short-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-99Table E-98. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Mortality Associated withShort-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-9% 0% 7% 15% 22% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (23% - 24%)-6% 0% 5% 12% 20% 14% 29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% -13%) (19% - 20%) (14% - 15%) (29% - 29%)-28% 0% 7% 15% 22% 15% 29%(-27% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15%) (29% - 29%)-80% 0% 0% 0% 0% 14% 29%(-79% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6% 0% 7% 15% 22% 15% 22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (22% - 23%)-58% 0% 0% 0% 6% 15% 29%(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20% 0% 0% 3% 10% 14% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% -7%) (14% - 14%) (14% - 15%) (29% - 29%)0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-42% 0% 0% 3% 7% 15% 29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 15%) (29% - 29%)-58% 0% 0% 0% 0% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12% 0% 6% 13% 20% 15% 29%(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-23% 0% 0% 0% 0% 15% 29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)Percent Reduction from the Current Standards: Annual Incidenceof Respiratory Mortality Associated with Short-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-100Table E-99. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Mortality Associated withShort-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-9% 0% 7% 15% 22% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (23% - 24%)-6% 0% 5% 13% 20% 14% 29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% -13%) (19% - 20%) (14% - 15%) (29% - 29%)-28% 0% 7% 15% 22% 15% 29%(-27% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15%) (29% - 29%)-80% 0% 0% 0% 0% 14% 29%(-78% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6% 0% 7% 15% 22% 15% 22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (22% - 23%)-58% 0% 0% 0% 6% 15% 29%(-57% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 15%) (29% - 29%)-20% 0% 0% 3% 10% 14% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% -7%) (14% - 14%) (14% - 15%) (29% - 29%)0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-42% 0% 0% 3% 7% 15% 29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 15%) (29% - 29%)-58% 0% 0% 0% 0% 15% 29%(-57% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-12% 0% 6% 13% 20% 15% 29%(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-23% 0% 0% 0% 0% 15% 29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)Percent Reduction from the Current Standards: Annual Incidenceof Respiratory Mortality Associated with Short-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-101Table E-100. Estimated Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2543 40 37 34 31 34 30(-28 - 115) (-26 - 105) (-24 - 98) (-22 - 90) (-20 - 82) (-22 - 90) (-20 - 81)262 247 234 216 199 212 176(192 - 331) (182 - 313) (172 - 295) (159 - 273) (146 - 251) (155 - 267) (129 - 222)21 17 15 14 13 14 12(-14 - 56) (-11 - 44) (-10 - 41) (-9 - 37) (-8 - 34) (-9 - 37) (-8 - 31)31 31 31 31 28 31 28(-20 - 81) (-20 - 81) (-20 - 81) (-20 - 81) (-19 - 75) (-20 - 81) (-19 - 75)345 280 278 257 236 239 198(253 - 435) (206 - 354) (204 - 351) (189 - 325) (173 - 298) (176 - 302) (146 - 251)38 21 21 21 21 18 15(0 - 75) (0 - 41) (0 - 41) (0 - 41) (0 - 41) (0 - 35) (0 - 29)60 56 52 48 44 48 44(-39 - 158) (-37 - 149) (-34 - 138) (-31 - 127) (-29 - 115) (-31 - 127) (-29 - 115)418 264 264 264 249 225 187(5 - 827) (3 - 523) (3 - 523) (3 - 523) (3 - 494) (3 - 447) (2 - 371)952 792 792 772 709 677 562(700 - 1204) (582 - 1002) (582 - 1002) (567 - 976) (521 - 897) (497 - 857) (413 - 711)233 214 214 200 184 183 152(171 - 294) (157 - 271) (157 - 271) (147 - 253) (135 - 233) (134 - 232) (112 - 192)108 108 108 108 102 101 84(1 - 213) (1 - 213) (1 - 213) (1 - 213) (1 - 203) (1 - 200) (1 - 166)222 157 157 153 145 134 111(163 - 280) (115 - 199) (115 - 199) (112 - 193) (106 - 183) (98 - 170) (82 - 141)13 8 8 8 8 7 6(0 - 25) (0 - 16) (0 - 16) (0 - 16) (0 - 16) (0 - 13) (0 - 11)231 207 195 180 165 177 147(170 - 293) (152 - 262) (143 - 246) (132 - 228) (121 - 209) (130 - 224) (108 - 186)26 21 21 21 21 18 15(-65 - 113) (-52 - 92) (-52 - 92) (-52 - 92) (-52 - 92) (-44 - 79) (-37 - 65)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table2 of Bell et al. (2008). Location-specific C-R functionestimates were not available from this study.Dallas, TXFresno, CALos Angeles, CATotal Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrationsin a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards(Standard Combination Denoted n/m)2:Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXSt. Louis, MOE-102Table E-101. Estimated Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2544 41 38 35 31 35 31(-29 - 117) (-27 - 108) (-25 - 99) (-23 - 91) (-21 - 83) (-23 - 91) (-20 - 82)227 214 203 187 172 183 152(167 - 287) (157 - 271) (149 - 256) (138 - 237) (126 - 218) (135 - 232) (112 - 192)20 16 15 13 12 13 11(-13 - 53) (-10 - 42) (-10 - 38) (-9 - 35) (-8 - 32) (-9 - 35) (-7 - 29)26 26 26 26 24 26 24(-17 - 68) (-17 - 68) (-17 - 68) (-17 - 68) (-16 - 63) (-17 - 68) (-16 - 63)278 225 224 207 190 192 160(204 - 351) (165 - 285) (164 - 283) (152 - 261) (139 - 240) (141 - 243) (117 - 202)40 22 22 22 22 19 16(0 - 80) (0 - 44) (0 - 44) (0 - 44) (0 - 44) (0 - 38) (0 - 31)58 55 51 47 43 47 43(-38 - 154) (-36 - 145) (-33 - 134) (-31 - 123) (-28 - 113) (-31 - 123) (-28 - 113)392 248 248 248 234 211 175(5 - 776) (3 - 491) (3 - 491) (3 - 491) (3 - 463) (3 - 419) (2 - 348)822 684 684 666 612 585 485(604 - 1040) (502 - 865) (502 - 865) (489 - 843) (449 - 774) (429 - 740) (356 - 614)218 201 201 188 173 172 142(160 - 276) (147 - 254) (147 - 254) (138 - 237) (127 - 218) (126 - 217) (105 - 180)113 113 113 113 107 106 88(1 - 224) (1 - 224) (1 - 224) (1 - 224) (1 - 212) (1 - 209) (1 - 174)190 134 134 130 124 114 95(140 - 240) (98 - 169) (98 - 169) (96 - 165) (91 - 157) (84 - 144) (69 - 120)12 7 7 7 7 6 5(0 - 23) (0 - 15) (0 - 15) (0 - 15) (0 - 15) (0 - 12) (0 - 10)191 171 160 148 136 146 121(140 - 241) (126 - 216) (118 - 203) (109 - 188) (100 - 172) (107 - 185) (89 - 153)22 18 18 18 18 15 13(-54 - 95) (-44 - 78) (-44 - 78) (-44 - 78) (-44 - 78) (-37 - 66) (-31 - 55)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table2 of Bell et al. (2008). Location-specific C-R functionestimates were not available from this study.Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrationsin a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards(Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-103Table E-102. Estimated Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations1Recent AmbientPM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2545 41 38 35 32 35 31(-29 - 119) (-27 - 109) (-25 - 101) (-23 - 92) (-21 - 84) (-23 - 92) (-21 - 83)229 216 204 189 174 185 153(168 - 289) (159 - 273) (150 - 258) (139 - 239) (127 - 220) (136 - 234) (113 - 194)21 16 15 14 12 14 11(-14 - 54) (-11 - 43) (-10 - 39) (-9 - 36) (-8 - 33) (-9 - 36) (-7 - 30)28 28 28 28 26 28 26(-18 - 73) (-18 - 73) (-18 - 73) (-18 - 73) (-17 - 68) (-18 - 73) (-17 - 68)288 233 232 214 197 199 165(211 - 364) (171 - 295) (170 - 293) (157 - 271) (144 - 249) (146 - 252) (121 - 209)42 23 23 23 23 20 16(1 - 83) (0 - 46) (0 - 46) (0 - 46) (0 - 46) (0 - 39) (0 - 32)60 56 52 48 44 48 44(-39 - 158) (-37 - 149) (-34 - 138) (-31 - 127) (-29 - 116) (-31 - 127) (-29 - 116)408 258 258 258 243 220 182(5 - 807) (3 - 511) (3 - 511) (3 - 511) (3 - 482) (3 - 436) (2 - 362)905 752 752 733 673 643 534(665 - 1144) (552 - 951) (552 - 951) (538 - 927) (494 - 852) (472 - 814) (392 - 676)221 203 203 190 175 174 144(162 - 279) (149 - 257) (149 - 257) (140 - 240) (128 - 221) (128 - 220) (106 - 183)108 108 108 108 103 102 84(1 - 215) (1 - 215) (1 - 215) (1 - 215) (1 - 204) (1 - 201) (1 - 167)199 140 140 136 129 119 99(146 - 251) (103 - 177) (103 - 177) (100 - 172) (95 - 164) (88 - 151) (73 - 125)15 9 9 9 9 8 7(0 - 29) (0 - 18) (0 - 18) (0 - 18) (0 - 18) (0 - 16) (0 - 13)199 178 167 155 142 152 126(146 - 251) (131 - 225) (123 - 212) (114 - 196) (104 - 180) (112 - 193) (93 - 160)23 19 19 19 19 16 13(-57 - 101) (-46 - 82) (-46 - 82) (-46 - 82) (-46 - 82) (-39 - 70) (-33 - 58)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table2 of Bell et al. (2008). Location-specific C-R functionestimates were not available from this study.Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrationsin a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards(Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-104Table E-103. Estimated Percent of Total Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-TermExposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/250.41% 0.4% 0.35% 0.32% 0.29% 0.32% 0.29%(-0.27% - 1.09%) (-0.2% - 1%) (-0.23% - 0.93%) (-0.21% - 0.85%) (-0.19% - 0.78%) (-0.21% - 0.85%) (-0.19% - 0.77%)1.59% 1.5% 1.42% 1.32% 1.21% 1.29% 1.07%(1.17% - 2.01%) (1.1% - 1.9%) (1.05% - 1.8%) (0.97% - 1.67%) (0.89% - 1.53%) (0.95% - 1.63%) (0.79% - 1.35%)0.42% 0.3% 0.31% 0.28% 0.26% 0.28% 0.23%(-0.28% - 1.12%) (-0.2% - 0.9%) (-0.2% - 0.81%) (-0.18% - 0.75%) (-0.17% - 0.68%) (-0.18% - 0.75%) (-0.15% - 0.62%)0.32% 0.3% 0.32% 0.32% 0.3% 0.32% 0.3%(-0.21% - 0.85%) (-0.2% - 0.9%) (-0.21% - 0.85%) (-0.21% - 0.85%) (-0.2% - 0.79%) (-0.21% - 0.85%) (-0.2% - 0.79%)1.65% 1.3% 1.33% 1.23% 1.13% 1.15% 0.95%(1.22% - 2.09%) (1% - 1.7%) (0.98% - 1.68%) (0.91% - 1.56%) (0.83% - 1.43%) (0.84% - 1.45%) (0.7% - 1.2%)0.81% 0.4% 0.44% 0.44% 0.44% 0.38% 0.31%(0.01% - 1.59%) (0% - 0.9%) (0.01% - 0.88%) (0.01% - 0.88%) (0.01% - 0.88%) (0% - 0.75%) (0% - 0.62%)0.35% 0.3% 0.31% 0.28% 0.26% 0.28% 0.26%(-0.23% - 0.93%) (-0.2% - 0.9%) (-0.2% - 0.82%) (-0.19% - 0.75%) (-0.17% - 0.68%) (-0.19% - 0.75%) (-0.17% - 0.68%)0.77% 0.5% 0.49% 0.49% 0.46% 0.41% 0.34%(0.01% - 1.52%) (0% - 1%) (0.01% - 0.96%) (0.01% - 0.96%) (0.01% - 0.91%) (0% - 0.82%) (0% - 0.68%)1.49% 1.2% 1.24% 1.21% 1.11% 1.06% 0.88%(1.09% - 1.88%) (0.9% - 1.6%) (0.91% - 1.57%) (0.89% - 1.53%) (0.81% - 1.4%) (0.78% - 1.34%) (0.65% - 1.11%)1.41% 1.3% 1.3% 1.22% 1.12% 1.11% 0.92%(1.04% - 1.79%) (1% - 1.6%) (0.96% - 1.64%) (0.89% - 1.54%) (0.82% - 1.41%) (0.82% - 1.41%) (0.68% - 1.17%)0.53% 0.5% 0.53% 0.53% 0.51% 0.5% 0.41%(0.01% - 1.05%) (0% - 1.1%) (0.01% - 1.05%) (0.01% -1.05%) (0.01% - 1%) (0.01% - 0.99%) (0% - 0.82%)1.72% 1.2% 1.22% 1.19% 1.13% 1.04% 0.86%(1.26% - 2.17%) (0.9% - 1.5%) (0.89% - 1.54%) (0.87%- 1.5%) (0.83% - 1.42%) (0.76% - 1.32%) (0.63% - 1.09%)0.52% 0.3% 0.33% 0.33% 0.33% 0.28% 0.23%(0.01% - 1.03%) (0% - 0.7%) (0% - 0.65%) (0% -0.65%) (0% - 0.65%) (0% - 0.56%) (0% - 0.46%)1.64% 1.5% 1.38% 1.28% 1.17% 1.26% 1.04%(1.21% - 2.08%) (1.1% - 1.9%) (1.02% - 1.75%) (0.94% - 1.62%) (0.86% - 1.48%) (0.92% - 1.59%) (0.77% - 1.32%)0.76% 0.6% 0.62% 0.62% 0.62% 0.53% 0.44%(-1.86% - 3.26%) (-1.5% - 2.7%) (-1.5% - 2.65%) (-1.5% - 2.65%) (-1.5% - 2.65%) (-1.28% - 2.27%) (-1.05% - 1.89%)2Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table2 of Bell et al. (2008). Location-specific C-R functionestimates were not available from this study.Dallas, TXFresno, CALos Angeles, CAPercent of Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)StandardSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-105Table E-104. Estimated Percent of Total Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-TermExposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/250.41% 0.4% 0.35% 0.32% 0.29% 0.32% 0.29%(-0.27% - 1.08%) (-0.2% - 1%) (-0.23% - 0.92%) (-0.21% - 0.84%) (-0.19% - 0.77%) (-0.21% - 0.84%) (-0.19% - 0.76%)1.39% 1.3% 1.24% 1.15% 1.05% 1.12% 0.93%(1.02% - 1.75%) (1% - 1.7%) (0.91% - 1.57%) (0.84% - 1.45%) (0.77% - 1.33%) (0.82% - 1.42%) (0.68% - 1.18%)0.4% 0.3% 0.29% 0.27% 0.24% 0.27% 0.22%(-0.26% - 1.06%) (-0.2% - 0.8%) (-0.19% - 0.77%) (-0.17% - 0.71%) (-0.16% - 0.64%) (-0.17% - 0.71%) (-0.14% - 0.59%)0.27% 0.3% 0.27% 0.27% 0.25% 0.27% 0.25%(-0.17% - 0.7%) (-0.2% - 0.7%) (-0.17% - 0.7%) (-0.17% - 0.7%) (-0.16% - 0.65%) (-0.17% - 0.7%) (-0.16% - 0.65%)1.34% 1.1% 1.08% 1% 0.92% 0.93% 0.77%(0.98% - 1.69%) (0.8% - 1.4%) (0.79% - 1.37%) (0.73% - 1.26%) (0.67% - 1.16%) (0.68% - 1.18%) (0.57% - 0.97%)0.85% 0.5% 0.47% 0.47% 0.47% 0.4% 0.33%(0.01% - 1.68%) (0% - 0.9%) (0.01% - 0.93%) (0.01% - 0.93%) (0.01% - 0.93%) (0% - 0.79%) (0% - 0.66%)0.33% 0.3% 0.29% 0.27% 0.24% 0.27% 0.24%(-0.22% - 0.88%) (-0.2% - 0.8%) (-0.19% - 0.77%) (-0.17% - 0.71%) (-0.16% - 0.64%) (-0.17% - 0.71%) (-0.16% - 0.64%)0.71% 0.4% 0.45% 0.45% 0.42% 0.38% 0.32%(0.01% - 1.41%) (0% - 0.9%) (0.01% - 0.89%) (0.01% - 0.89%) (0.01% - 0.84%) (0% - 0.76%) (0% - 0.63%)1.27% 1.1% 1.06% 1.03% 0.95% 0.9% 0.75%(0.93% - 1.61%) (0.8% - 1.3%) (0.78% - 1.34%) (0.76% - 1.3%) (0.7% - 1.2%) (0.66% - 1.14%) (0.55% - 0.95%)1.34% 1.2% 1.24% 1.16% 1.06% 1.06% 0.88%(0.99% - 1.7%) (0.9% - 1.6%) (0.91% - 1.56%) (0.85% - 1.46%) (0.78% - 1.34%) (0.78% - 1.34%) (0.64% - 1.11%)0.54% 0.5% 0.54% 0.54% 0.51% 0.5% 0.42%(0.01% - 1.07%) (0% - 1.1%) (0.01% - 1.07%) (0.01% - 1.07%) (0.01% - 1.02%) (0.01% - 1%) (0.01% - 0.83%)1.5% 1.1% 1.05% 1.03% 0.98% 0.9% 0.75%(1.1% - 1.89%) (0.8% - 1.3%) (0.77% - 1.33%) (0.75% - 1.3%) (0.72% - 1.23%) (0.66% - 1.14%) (0.55% - 0.94%)0.46% 0.3% 0.29% 0.29% 0.29% 0.25% 0.21%(0.01% - 0.92%) (0% - 0.6%) (0% - 0.58%) (0% -0.58%) (0% - 0.58%) (0% - 0.49%) (0% - 0.41%)1.36% 1.2% 1.14% 1.06% 0.97% 1.04% 0.86%(1% - 1.72%) (0.9% - 1.5%) (0.84% - 1.45%) (0.78% - 1.34%) (0.71% - 1.23%) (0.76% - 1.32%) (0.63% - 1.09%)0.63% 0.5% 0.51% 0.51% 0.51% 0.43% 0.36%(-1.53% - 2.69%) (-1.2% - 2.2%) (-1.23% - 2.19%) (-1.23% - 2.19%) (-1.23% - 2.19%) (-1.05% - 1.87%) (-0.87% - 1.56%)2Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table2 of Bell et al. (2008). Location-specific C-R functionestimates were not available from this study.Dallas, TXFresno, CALos Angeles, CAPercent of Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)StandardSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-106Table E-105. Estimated Percent of Total Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-TermExposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/250.4% 0.4% 0.34% 0.31% 0.28% 0.31% 0.28%(-0.26% - 1.06%) (-0.2% - 1%) (-0.22% - 0.9%) (-0.2% - 0.83%) (-0.19% - 0.75%) (-0.2% - 0.83%) (-0.18% - 0.74%)1.41% 1.3% 1.26% 1.16% 1.07% 1.14% 0.94%(1.03% - 1.78%) (1% - 1.7%) (0.92% - 1.59%) (0.85% - 1.47%) (0.78% - 1.35%) (0.83% - 1.44%) (0.69% - 1.19%)0.41% 0.3% 0.3% 0.27% 0.25% 0.27% 0.23%(-0.27% - 1.09%) (-0.2% - 0.9%) (-0.19% - 0.79%) (-0.18% - 0.72%) (-0.16% - 0.66%) (-0.18% - 0.72%) (-0.15% - 0.6%)0.28% 0.3% 0.28% 0.28% 0.26% 0.28% 0.26%(-0.18% - 0.74%) (-0.2% - 0.7%) (-0.18% - 0.74%) (-0.18% - 0.74%) (-0.17% - 0.68%) (-0.18% - 0.74%) (-0.17% - 0.68%)1.4% 1.1% 1.13% 1.04% 0.96% 0.97% 0.8%(1.03% - 1.77%) (0.8% - 1.4%) (0.83% - 1.42%) (0.76% - 1.32%) (0.7% - 1.21%) (0.71% - 1.23%) (0.59% - 1.02%)0.86% 0.5% 0.48% 0.48% 0.48% 0.41% 0.34%(0.01% - 1.7%) (0% - 0.9%) (0.01% - 0.94%) (0.01% - 0.94%) (0.01% - 0.94%) (0% - 0.81%) (0% - 0.67%)0.33% 0.3% 0.29% 0.27% 0.24% 0.27% 0.24%(-0.22% - 0.88%) (-0.2% - 0.8%) (-0.19% - 0.77%) (-0.17% - 0.71%) (-0.16% - 0.64%) (-0.17% - 0.71%) (-0.16% - 0.64%)0.72% 0.5% 0.46% 0.46% 0.43% 0.39% 0.32%(0.01% - 1.43%) (0% - 0.9%) (0.01% - 0.91%) (0.01% - 0.91%) (0.01% - 0.86%) (0% - 0.78%) (0% - 0.64%)1.39% 1.2% 1.15% 1.12% 1.03% 0.99% 0.82%(1.02% - 1.75%) (0.8% - 1.5%) (0.85% - 1.46%) (0.83% - 1.42%) (0.76% - 1.31%) (0.72% - 1.25%) (0.6% - 1.04%)1.38% 1.3% 1.27% 1.18% 1.09% 1.08% 0.9%(1.01% - 1.74%) (0.9% - 1.6%) (0.93% - 1.6%) (0.87% - 1.5%) (0.8% - 1.38%) (0.79% - 1.37%) (0.66% - 1.14%)0.5% 0.5% 0.5% 0.5% 0.47% 0.47% 0.39%(0.01% - 0.99%) (0% - 1%) (0.01% - 0.99%) (0.01% - 0.99%) (0.01% - 0.94%) (0.01% - 0.93%) (0% - 0.77%)1.58% 1.1% 1.11% 1.08% 1.03% 0.95% 0.79%(1.16% - 2%) (0.8% - 1.4%) (0.82% - 1.41%) (0.8% - 1.37%) (0.76% - 1.3%) (0.7% - 1.2%) (0.58% - 1%)0.56% 0.4% 0.36% 0.36% 0.36% 0.3% 0.25%(0.01% - 1.11%) (0% - 0.7%) (0% - 0.7%) (0% - 0.7%) (0% - 0.7%) (0% - 0.6%) (0% - 0.5%)1.42% 1.3% 1.19% 1.1% 1.01% 1.09% 0.9%(1.04% - 1.79%) (0.9% - 1.6%) (0.88% - 1.51%) (0.81% - 1.4%) (0.74% - 1.28%) (0.8% - 1.37%) (0.66% - 1.14%)0.65% 0.5% 0.52% 0.52% 0.52% 0.45% 0.37%(-1.58% - 2.77%) (-1.3% - 2.3%) (-1.28% - 2.26%) (-1.28% - 2.26%) (-1.28% - 2.26%) (-1.09% - 1.93%) (-0.9% - 1.6%)2Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table2 of Bell et al. (2008). Location-specific C-R functionestimates were not available from this study.Dallas, TXFresno, CALos Angeles, CAPercent of Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)StandardSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-107Table E-106. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Hospital Admissions Associated withShort-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-9% 0% 8% 15% 23% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (24% - 24%)-6% 0% 6% 13% 20% 14% 29%(-6% - -6%) (0% - 0%) (5% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-28% 0% 8% 15% 23% 15% 30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)-81% 0% 0% 0% 0% 15% 29%(-81% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-6% 0% 8% 15% 23% 15% 23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% - 15%) (23% - 23%)-58% 0% 0% 0% 6% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20% 0% 0% 3% 10% 14% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-41% 0% 0% 3% 8% 15% 29%(-41% - -41%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (15% - 15%) (29% - 29%)-59% 0% 0% 0% 0% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12% 0% 6% 13% 20% 15% 29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-23% 0% 0% 0% 0% 15% 29%(-23% - -24%)(0% - 0%)(0% - 0%)(0% - 0%)(0% - 0%)(14% - 15%)(29% - 30%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidenceof Cardiovascular Hospital Admissions Associated with ShortTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundingthe PM coefficient.1Incidence estimates were calculated usingthe appropriate regional concentration-responsefunction estimates reported in Table2 of Bell et al. (2008). Location-specific C-R function estimateswere not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-108Table E-107. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Hospital Admissions Associated withShort-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-9% 0% 8% 15% 23% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (24% - 24%)-6% 0% 6% 13% 20% 14% 29%(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-28% 0% 8% 15% 23% 15% 30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% - 15%) (29% - 30%)0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)-81% 0% 0% 0% 0% 15% 29%(-81% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-6% 0% 8% 15% 23% 15% 23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% - 15%) (23% - 23%)-58% 0% 0% 0% 6% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20% 0% 0% 3% 10% 15% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-42% 0% 0% 3% 7% 15% 29%(-42% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%) (29% - 29%)-59% 0% 0% 0% 0% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12% 0% 6% 13% 20% 15% 29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-23% 0% 0% 0% 0% 15% 29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 30%)Percent Reduction from the Current Standards: Annual Incidenceof Cardiovascular Hospital Admissions Associated with ShortTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundingthe PM coefficient.1Incidence estimates were calculated usingthe appropriate regional concentration-responsefunction estimates reported in Table2 of Bell et al. (2008). Location-specific C-R function estimateswere not available from this study.Dallas, TXFresno, CALos Angeles, CAE-109Table E-108. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Hospital Admissions Associated withShort-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-9% 0% 8% 15% 23% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (24% - 24%)-6% 0% 6% 13% 20% 14% 29%(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-28% 0% 8% 15% 23% 15% 30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)-81% 0% 0% 0% 0% 15% 29%(-81% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-6% 0% 8% 15% 23% 15% 23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% - 15%) (23% - 23%)-58% 0% 0% 0% 6% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20% 0% 0% 3% 10% 15% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-42% 0% 0% 3% 7% 15% 29%(-42% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%) (29% - 29%)-59% 0% 0% 0% 0% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12% 0% 6% 13% 20% 15% 29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-23% 0% 0% 0% 0% 15% 29%(-23% - -24%)(0% - 0%)(0% - 0%)(0% - 0%)(0% - 0%)(14% - 15%)(29% - 30%)Percent Reduction from the Current Standards: Annual Incidenceof Cardiovascular Hospital Admissions Associated with ShortTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundingthe PM coefficient.1Incidence estimates were calculated usingthe appropriate regional concentration-responsefunction estimates reported in Table2 of Bell et al. (2008). Location-specific C-R function estimateswere not available from this study.Dallas, TXFresno, CALos Angeles, CAE-110Table E-109. Estimated Annual Incidence ofHospital Admissions for Respiratory Illness Associated with Short-Term Exposure toAmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2519 17 16 15 13 15 13(-23 - 60) (-22 - 55) (-20 - 51) (-18 - 47) (-17 - 43) (-18 - 47) (-16 - 42)21 20 19 17 16 17 14(-12 - 54) (-12 - 51) (-11 - 48) (-10 - 45) (-9 - 41) (-10 - 44) (-8 - 36)9 776665(-11 - 29) (-9 - 23) (-8 - 21) (-8 - 20) (-7 - 18) (-8 - 20) (-6 - 16)15 15 15 15 14 15 14(-18 - 47) (-18 - 47) (-18 - 47) (-18 - 47) (-17 - 44) (-18 - 47) (-17 - 44)31 25 25 23 21 21 18(-18 - 79) (-15 - 64) (-15 - 64) (-13 - 59) (-12 - 54) (-13 - 55) (-10 - 46)25 14 14 14 14 12 10(6 - 44) (3 - 25) (3 - 25) (3 - 25) (3 - 25) (3 - 21) (2 - 17)27 25 23 21 19 21 19(-34 - 86) (-32 - 81) (-29 - 75) (-27 - 69) (-24 - 63) (-27 - 69) (-24 - 63)269 170 170 170 161 145 121(63 - 473) (40 - 300) (40 - 300) (40 - 300) (37 - 283) (34 - 256) (28 - 213)79 65 65 64 58 56 46(-46 - 203) (-38 - 169) (-38 - 169) (-37 - 164) (-34 - 151) (-33 - 144) (-27 - 120)19 17 17 16 15 15 12(-11 - 48) (-10 - 44) (-10 - 44) (-9 - 41) (-9 - 38) (-9 - 38) (-7 - 31)61 61 61 61 58 57 47(14 - 107) (14 - 107) (14 - 107) (14 - 107) (14 - 102) (13 - 101) (11 - 84)18 13 13 12 12 11 9(-11 - 47) (-8 - 33) (-8 - 33) (-7 - 32) (-7 - 30) (-6 - 28) (-5 - 23)9 666654(2 - 16) (1 - 10) (1 - 10) (1 - 10) (1 - 10) (1 - 9) (1 - 7)28 25 23 22 20 21 18(-16 - 72) (-15 - 64) (-14 - 60) (-13 - 56) (-12 - 51) (-13 - 55) (-10 - 46)2 222221(-34 - 37) (-27 - 30) (-27 - 30) (-27 - 30) (-27 - 30) (-23 - 26) (-19 - 21)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTDetroit, MIHouston, TXNew York, NYSt. Louis, MOIncidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Incidence estimates were calculated using the appropriate regional concentration-response function estimates from models with a2-day lag for respiratory hospital admissions reported inTable 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDE-111Table E-110. Estimated Annual Incidence ofHospital Admissions for Respiratory Illness Associated with Short-Term Exposure toAmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2519 17 16 15 13 15 13(-24 - 61) (-22 - 56) (-20 - 52) (-19 - 48) (-17 - 44) (-19 - 48) (-17 - 43)18 17 16 15 14 15 12(-11 - 47) (-10 - 44) (-10 - 42) (-9 - 39) (-8 - 36) (-9 - 38) (-7 - 31)9 766565(-11 - 28) (-8 - 22) (-8 - 20) (-7 - 19) (-7 - 17) (-7 - 19) (-6 - 15)12 12 12 12 11 12 11(-15 - 40) (-15 - 40) (-15 - 40) (-15 - 40) (-14 - 37) (-15 - 40) (-14 - 37)25 20 20 18 17 17 14(-15 - 64) (-12 - 52) (-12 - 51) (-11 - 47) (-10 - 44) (-10 - 44) (-8 - 37)27 15 15 15 15 13 11(6 - 47) (3 - 26) (3 - 26) (3 - 26) (3 - 26) (3 - 22) (2 - 19)26 25 23 21 19 21 19(-33 - 84) (-31 - 79) (-29 - 73) (-26 - 68) (-24 - 62) (-26 - 68) (-24 - 62)253 160 160 160 151 136 113(59 - 444) (37 - 281) (37 - 281) (37 - 281) (35 - 265) (32 - 240) (26 - 199)68 56 56 55 50 48 40(-40 - 175) (-33 - 145) (-33 - 145) (-32 - 142) (-30 - 130) (-28 - 124) (-24 - 103)17 16 16 15 14 14 11(-10 - 45) (-9 - 41) (-9 - 41) (-9 - 39) (-8 - 36) (-8 - 35) (-7 - 29)64 64 64 64 61 60 50(15 - 112) (15 - 112) (15 - 112) (15 - 112) (14 - 107) (14 - 105) (12 - 87)16 11 11 11 10 9 8(-9 - 40) (-6 - 28) (-6 - 28) (-6 - 27) (-6 - 26) (-5 - 24) (-5 - 20)8 555554(2 - 15) (1 - 9) (1 - 9) (1 - 9) (1 - 9) (1 - 8) (1 - 7)23 21 19 18 16 18 15(-14 - 59) (-12 - 53) (-11 - 50) (-10 - 46) (-10 - 42) (-10 - 45) (-9 - 38)2 222211(-28 - 31) (-23 - 25) (-23 - 25) (-23 - 25) (-23 - 25) (-19 - 22) (-16 - 18)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:1Incidence estimates were calculated using the appropriate regional concentration-response function estimates from models with a2-day lag for respiratory hospital admissions reported inTable 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDE-112Table E-111. Estimated Annual Incidence ofHospital Admissions for Respiratory Illness Associated with Short-Term Exposure toAmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/2519 18 16 15 14 15 13(-24 - 62) (-22 - 57) (-21 - 53) (-19 - 48) (-17 - 44) (-19 - 48) (-17 - 44)18 17 16 15 14 15 12(-11 - 47) (-10 - 45) (-10 - 42) (-9 - 39) (-8 - 36) (-9 - 38) (-7 - 32)9 766565(-11 - 29) (-9 - 22) (-8 - 21) (-7 - 19) (-7 - 17) (-7 - 19) (-6 - 16)13 13 13 13 12 13 12(-17 - 43) (-17 - 43) (-17 - 43) (-17 - 43) (-15 - 40) (-17 - 43) (-15 - 40)26 21 21 19 18 18 15(-15 - 66) (-12 - 54) (-12 - 53) (-11 - 49) (-10 - 45) (-10 - 46) (-9 - 38)28 15 15 15 15 13 11(7 - 49) (4 - 27) (4 - 27) (4 - 27) (4 - 27) (3 - 23) (3 - 19)27 25 23 21 20 21 20(-34 - 87) (-32 - 82) (-29 - 76) (-27 - 69) (-25 - 63) (-27 - 69) (-25 - 63)263 166 166 166 157 142 118(61 - 461) (39 - 293) (39 - 293) (39 - 293) (37 - 276) (33 - 250) (27 - 207)75 62 62 60 56 53 44(-44 - 193) (-37 - 160) (-37 - 160) (-36 - 156) (-33 - 143) (-31 - 137) (-26 - 113)18 16 16 15 14 14 12(-10 - 46) (-10 - 42) (-10 - 42) (-9 - 39) (-8 - 36) (-8 - 36) (-7 - 30)61 61 61 61 58 57 48(14 - 108) (14 - 108) (14 - 108) (14 - 108) (14 - 103) (13 - 101) (11 - 84)16 11 11 11 11 10 8(-10 - 42) (-7 - 29) (-7 - 29) (-7 - 29) (-6 - 27) (-6 - 25) (-5 - 21)11 7 7 7 7 6 5(2 - 19) (2 - 12) (2 - 12) (2 - 12) (2 - 12) (1 - 10) (1 - 8)24 21 20 19 17 18 15(-14 - 62) (-13 - 55) (-12 - 52) (-11 - 48) (-10 - 44) (-11 - 47) (-9 - 39)2 222221(-30 - 33) (-24 - 27) (-24 - 27) (-24 - 27) (-24 - 27) (-21 - 23) (-17 - 19)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:1Incidence estimates were calculated using the appropriate regional concentration-response function estimates from models with a2-day lag for respiratory hospital admissions reported inTable 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDE-113Table E-112. Estimated Percent of Total Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure toAmbient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and AlternativeStandards, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/250.5% 0.46% 0.42% 0.39% 0.35% 0.39% 0.35%(-0.63% - 1.61%) (-0.58% - 1.48%) (-0.53% - 1.37%) (-0.49%- 1.26%) (-0.45% - 1.15%) (-0.49% - 1.26%) (-0.44% - 1.13%)0.42% 0.39% 0.37% 0.34% 0.32% 0.34% 0.28%(-0.25% - 1.08%) (-0.23% - 1.02%) (-0.22% - 0.96%) (-0.2% - 0.89%)(-0.19% - 0.82%) (-0.2%- 0.87%) (-0.16% - 0.72%)0.51% 0.4% 0.37% 0.34% 0.31% 0.34% 0.28%(-0.65% - 1.65%) (-0.51% - 1.3%) (-0.47% - 1.2%) (-0.43%- 1.1%) (-0.39% - 1.01%) (-0.43% - 1.1%) (-0.36% - 0.92%)0.39% 0.39% 0.39% 0.39% 0.36% 0.39% 0.36%(-0.49% - 1.26%) (-0.49% - 1.26%) (-0.49% - 1.26%) (-0.49%- 1.26%) (-0.45% - 1.17%) (-0.49% - 1.26%) (-0.45% - 1.17%)0.43% 0.35% 0.35% 0.32% 0.3% 0.3% 0.25%(-0.26% - 1.12%) (-0.21% - 0.91%) (-0.21% - 0.9%) (-0.19% - 0.83%) (-0.17% - 0.76%) (-0.18% - 0.77%) (-0.15% - 0.64%)1.42% 0.78% 0.78% 0.78% 0.78% 0.67% 0.56%(0.33% - 2.49%) (0.18% - 1.38%) (0.18% - 1.38%) (0.18% - 1.38%) (0.18% - 1.38%) (0.16% - 1.18%) (0.13% - 0.98%)0.43% 0.4% 0.37% 0.34% 0.31% 0.34% 0.31%(-0.54% - 1.38%) (-0.51% - 1.3%) (-0.47% - 1.2%) (-0.43%- 1.11%) (-0.39% - 1.01%) (-0.43% - 1.11%) (-0.39% - 1.01%)1.36% 0.86% 0.86% 0.86% 0.81% 0.73% 0.61%(0.32% - 2.38%) (0.2% - 1.51%) (0.2% - 1.51%) (0.2% -1.51%) (0.19% - 1.42%) (0.17% - 1.29%) (0.14% - 1.07%)0.39% 0.32% 0.32% 0.32% 0.29% 0.28% 0.23%(-0.23% - 1.01%) (-0.19% - 0.84%) (-0.19% - 0.84%) (-0.19%- 0.81%) (-0.17% - 0.75%) (-0.16% - 0.71%) (-0.14% - 0.59%)0.37% 0.34% 0.34% 0.32% 0.29% 0.29% 0.24%(-0.22% - 0.95%) (-0.2% - 0.88%) (-0.2% - 0.88%) (-0.19% -0.82%) (-0.17% - 0.75%) (-0.17% - 0.75%) (-0.14% - 0.62%)0.94% 0.94% 0.94% 0.94% 0.89% 0.88% 0.73%(0.22% - 1.65%) (0.22% - 1.65%) (0.22% - 1.65%) (0.22% - 1.65%) (0.21% - 1.57%) (0.21% - 1.55%) (0.17% - 1.29%)0.45% 0.32% 0.32% 0.31% 0.29% 0.27% 0.23%(-0.27% - 1.16%) (-0.19% - 0.82%) (-0.19% - 0.82%) (-0.18% - 0.8%)(-0.17% - 0.76%) (-0.16%- 0.7%) (-0.13% - 0.58%)0.92% 0.58% 0.58% 0.58% 0.58% 0.49% 0.41%(0.21% - 1.61%) (0.14% - 1.02%) (0.14% - 1.02%) (0.14% - 1.02%) (0.14% - 1.02%) (0.12% - 0.87%) (0.1% - 0.72%)0.43% 0.39% 0.36% 0.33% 0.31% 0.33% 0.27%(-0.25% - 1.11%) (-0.23% - 0.99%) (-0.21% - 0.93%) (-0.2% - 0.86%)(-0.18% - 0.79%) (-0.19%- 0.85%) (-0.16% - 0.7%)0.2% 0.16% 0.16% 0.16% 0.16% 0.14% 0.11%(-2.72% - 2.96%) (-2.19% - 2.41%) (-2.19% - 2.41%) (-2.19%- 2.41%) (-2.19% - 2.41%) (-1.87% - 2.06%) (-1.54% - 1.71%)2Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)Standards (Standard Combination Denoted n/m)2:1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lagfor respiratory hospital admissions reported in Table 2 ofBell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-114Table E-113. Estimated Percent of Total Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure toAmbient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and AlternativeStandards, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/250.49% 0.45% 0.42% 0.38% 0.35% 0.38% 0.34%(-0.62% - 1.59%) (-0.57% - 1.46%) (-0.53% - 1.35%) (-0.48% - 1.24%) (-0.44% - 1.13%) (-0.48% - 1.24%) (-0.43% - 1.12%)0.36% 0.34% 0.32% 0.3% 0.28% 0.29% 0.24%(-0.21% - 0.94%) (-0.2% - 0.89%) (-0.19% - 0.84%) (-0.18% - 0.77%) (-0.16% - 0.71%) (-0.17% - 0.76%) (-0.14% - 0.63%)0.48% 0.38% 0.35% 0.32% 0.29% 0.32% 0.27%(-0.61% - 1.57%) (-0.48% - 1.23%) (-0.44% - 1.14%) (-0.41% - 1.04%) (-0.37% - 0.95%) (-0.41% - 1.04%) (-0.34% - 0.87%)0.32% 0.32% 0.32% 0.32% 0.3% 0.32% 0.3%(-0.4% - 1.04%) (-0.4% - 1.04%) (-0.4% - 1.04%) (-0.4% - 1.04%) (-0.37% - 0.96%) (-0.4% - 1.04%) (-0.37% - 0.96%)0.35% 0.28% 0.28% 0.26% 0.24% 0.24% 0.2%(-0.21% - 0.9%) (-0.17% - 0.73%) (-0.17% - 0.73%) (-0.15% - 0.67%) (-0.14% - 0.62%) (-0.14% - 0.63%) (-0.12% - 0.52%)1.49% 0.83% 0.83% 0.83% 0.83% 0.71% 0.58%(0.35% - 2.62%) (0.19% - 1.45%) (0.19% - 1.45%) (0.19% - 1.45%) (0.19% - 1.45%) (0.16% - 1.24%) (0.14% - 1.03%)0.4% 0.38% 0.35% 0.32% 0.29% 0.32% 0.29%(-0.51% - 1.3%) (-0.48% - 1.23%) (-0.44% - 1.13%) (-0.4% - 1.04%) (-0.37% - 0.95%) (-0.4% - 1.04%) (-0.37% - 0.95%)1.25% 0.79% 0.79% 0.79% 0.75% 0.68% 0.56%(0.29% - 2.2%) (0.18% - 1.4%) (0.18% - 1.4%) (0.18% - 1.4%) (0.17% - 1.32%) (0.16% - 1.19%) (0.13% - 0.99%)0.33% 0.28% 0.28% 0.27% 0.25% 0.24% 0.2%(-0.2% - 0.86%) (-0.16% - 0.71%) (-0.16% - 0.71%) (-0.16% - 0.7%) (-0.15% - 0.64%) (-0.14% - 0.61%) (-0.12% - 0.51%)0.35% 0.32% 0.32% 0.3% 0.28% 0.28% 0.23%(-0.21% - 0.91%) (-0.19% - 0.83%) (-0.19% - 0.83%) (-0.18% - 0.78%) (-0.16% - 0.72%) (-0.16% - 0.71%) (-0.14% - 0.59%)0.95% 0.95% 0.95% 0.95% 0.9% 0.89% 0.74%(0.22% - 1.67%) (0.22% - 1.67%) (0.22% - 1.67%) (0.22% - 1.67%) (0.21% - 1.59%) (0.21% - 1.57%) (0.17% - 1.3%)0.39% 0.28% 0.28% 0.27% 0.26% 0.24% 0.19%(-0.23% - 1.01%) (-0.16% - 0.71%) (-0.16% - 0.71%) (-0.16% - 0.69%) (-0.15% - 0.66%) (-0.14% - 0.61%) (-0.11% - 0.5%)0.82% 0.51% 0.51% 0.51% 0.51% 0.44% 0.36%(0.19% - 1.43%) (0.12% - 0.91%) (0.12% - 0.91%) (0.12% - 0.91%) (0.12% - 0.91%) (0.1% - 0.77%) (0.08% - 0.64%)0.36% 0.32% 0.3% 0.28% 0.25% 0.27% 0.23%(-0.21% - 0.92%) (-0.19% - 0.82%) (-0.18% - 0.77%) (-0.16% - 0.71%) (-0.15% - 0.65%) (-0.16% - 0.7%) (-0.13% - 0.58%)0.16% 0.13% 0.13% 0.13% 0.13% 0.11% 0.09%(-2.23% - 2.45%) (-1.8% - 1.99%) (-1.8% - 1.99%) (-1.8% - 1.99%) (-1.8% - 1.99%) (-1.53% - 1.7%) (-1.26% - 1.41%)2Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)Standards (Standard Combination Denoted n/m)2:1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lagfor respiratory hospital admissions reported in Table 2 ofBell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-115Table E-114. Estimated Percent of Total Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure toAmbient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and AlternativeStandards, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/250.48% 0.44% 0.41% 0.38% 0.34% 0.38% 0.34%(-0.61% - 1.56%) (-0.56% - 1.43%) (-0.52% - 1.32%) (-0.47% - 1.22%) (-0.43% - 1.11%) (-0.47% - 1.22%) (-0.43% - 1.09%)0.37% 0.35% 0.33% 0.3% 0.28% 0.3% 0.25%(-0.22% - 0.95%) (-0.2% - 0.9%) (-0.19% - 0.85%) (-0.18% - 0.78%) (-0.16% - 0.72%) (-0.18% - 0.77%) (-0.15% - 0.64%)0.5% 0.39% 0.36% 0.33% 0.3% 0.33% 0.27%(-0.63% - 1.6%) (-0.49% - 1.26%) (-0.45% - 1.16%) (-0.42% - 1.07%) (-0.38% - 0.97%) (-0.42% - 1.07%) (-0.34% - 0.89%)0.34% 0.34% 0.34% 0.34% 0.31% 0.34% 0.31%(-0.42% - 1.09%) (-0.42% - 1.09%) (-0.42% - 1.09%) (-0.42% - 1.09%) (-0.39% - 1.01%) (-0.42% - 1.09%) (-0.39% - 1.01%)0.37% 0.3% 0.29% 0.27% 0.25% 0.25% 0.21%(-0.22% - 0.94%) (-0.17% - 0.77%) (-0.17% - 0.76%) (-0.16% - 0.7%) (-0.15% - 0.64%) (-0.15% - 0.65%) (-0.12% - 0.54%)1.52% 0.84% 0.84% 0.84% 0.84% 0.72% 0.6%(0.36% - 2.66%) (0.2% - 1.48%) (0.2% - 1.48%) (0.2% - 1.48%) (0.2% - 1.48%) (0.17% - 1.26%) (0.14% - 1.05%)0.4% 0.38% 0.35% 0.32% 0.29% 0.32% 0.29%(-0.51% - 1.3%) (-0.48% - 1.23%) (-0.44% - 1.13%) (-0.41% - 1.04%) (-0.37% - 0.95%) (-0.41% - 1.04%) (-0.37% - 0.95%)1.28% 0.81% 0.81% 0.81% 0.76% 0.69% 0.57%(0.3% - 2.25%) (0.19% - 1.42%) (0.19% - 1.42%) (0.19% - 1.42%) (0.18% - 1.34%) (0.16% - 1.22%) (0.13% - 1.01%)0.36% 0.3% 0.3% 0.29% 0.27% 0.26% 0.21%(-0.21% - 0.94%) (-0.18% - 0.78%) (-0.18% - 0.78%) (-0.17% - 0.76%) (-0.16% - 0.7%) (-0.15% - 0.67%) (-0.13% - 0.55%)0.36% 0.33% 0.33% 0.31% 0.28% 0.28% 0.23%(-0.21% - 0.93%) (-0.2% - 0.85%) (-0.2% - 0.85%) (-0.18% - 0.8%) (-0.17% - 0.73%) (-0.17% - 0.73%) (-0.14% - 0.61%)0.88% 0.88% 0.88% 0.88% 0.84% 0.83% 0.69%(0.21% - 1.55%) (0.21% - 1.55%) (0.21% - 1.55%) (0.21%- 1.55%) (0.2% - 1.48%) (0.19% - 1.46%) (0.16% - 1.21%)0.42% 0.29% 0.29% 0.28% 0.27% 0.25% 0.21%(-0.24% - 1.07%) (-0.17% - 0.75%) (-0.17% - 0.75%) (-0.17% - 0.73%) (-0.16% - 0.7%) (-0.15% - 0.64%) (-0.12% - 0.53%)0.99% 0.63% 0.63% 0.63% 0.63% 0.54% 0.44%(0.23% - 1.74%) (0.15% - 1.1%) (0.15% - 1.1%) (0.15% - 1.1%) (0.15% - 1.1%) (0.12% - 0.94%) (0.1% - 0.78%)0.37% 0.33% 0.31% 0.29% 0.26% 0.28% 0.24%(-0.22% - 0.96%) (-0.2% - 0.86%) (-0.18% - 0.81%) (-0.17% - 0.74%) (-0.16% - 0.68%) (-0.17% - 0.73%) (-0.14% - 0.61%)0.17% 0.14% 0.14% 0.14% 0.14% 0.12% 0.1%(-2.31% - 2.52%) (-1.86% - 2.05%) (-1.86% - 2.05%) (-1.86% - 2.05%) (-1.86% - 2.05%) (-1.59% - 1.76%) (-1.31% - 1.46%)2Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)Standards (Standard Combination Denoted n/m)2:1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lagfor respiratory hospital admissions reported in Table 2 ofBell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-116Table E-115. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Hospital Admissions Associated withShort-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-9% 0% 8% 15% 23% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (24% - 24%)-6% 0% 6% 13% 20% 15% 29%(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-28% 0% 8% 15% 23% 15% 30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)-81% 0% 0% 0% 0% 15% 29%(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6% 0% 8% 15% 23% 15% 23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)-58% 0% 0% 0% 6% 15% 29%(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20% 0% 0% 3% 11% 15% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (11% - 11%) (15% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 15% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (15% - 15%) (29% - 29%)0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-41% 0% 0% 3% 8% 15% 29%(-41% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (15% - 15%) (29% - 29%)-58% 0% 0% 0% 0% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12% 0% 6% 13% 20% 15% 29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-23% 0% 0% 0% 0% 15% 29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 30%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lagfor respiratory hospital admissions reported in Table 2 ofBell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidenceof Respiratory Hospital Admissions Associated with ShortTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-117Table E-116. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Hospital Admissions Associated withShort-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-9% 0% 8% 15% 23% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (24% - 24%)-6% 0% 6% 13% 20% 15% 29%(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-28% 0% 8% 15% 23% 15% 30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)-81% 0% 0% 0% 0% 15% 29%(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-6% 0% 8% 15% 23% 15% 23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)-58% 0% 0% 0% 6% 15% 29%(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20% 0% 0% 3% 11% 15% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (11% - 11%) (15% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 15% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (15% - 15%) (29% - 29%)0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-43% 0% 0% 3% 7% 15% 29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%) (29% - 29%)-58% 0% 0% 0% 0% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12% 0% 6% 13% 20% 15% 29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-23% 0% 0% 0% 0% 15% 29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 30%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lagfor respiratory hospital admissions reported in Table 2 ofBell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Respiratory Hospital Admissions Associated with ShortTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-118Table E-117. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Hospital Admissions Associated withShort-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/35 13/35 12/35 13/30 12/25-9% 0% 8% 15% 23% 15% 24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (24% - 24%)-6% 0% 6% 13% 20% 15% 29%(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-28% 0% 8% 15% 23% 15% 30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0% 0% 0% 0% 7% 0% 7%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23% 0% 1% 8% 16% 15% 29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)-81% 0% 0% 0% 0% 15% 29%(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6% 0% 8% 15% 23% 15% 23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)-58% 0% 0% 0% 6% 15% 29%(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20% 0% 0% 3% 11% 15% 29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (11% - 11%) (15% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 15% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (15% - 15%) (29% - 29%)0% 0% 0% 0% 5% 6% 22%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-43% 0% 0% 3% 7% 15% 29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%) (29% - 29%)-58% 0% 0% 0% 0% 15% 29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12% 0% 6% 13% 20% 15% 29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-23% 0% 0% 0% 0% 15% 29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 30%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lagfor respiratory hospital admissions reported in Table 2 ofBell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidenceof Respiratory Hospital Admissions Associated with ShortTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-119Table E-118. Estimated AnnualIncidence of Emergency Room (ER) Visits Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2005) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/25216 198 183 169 154 169 151(-304 - 727) (-279 - 668) (-258 - 618) (-237 - 568) (-216 - 518) (-237 - 568) (-212 - 511)814 746 690 634 578 634 570(-816 - 2419) (-748 - 2220) (-691 - 2055) (-635 - 1889) (-578 - 1723) (-635 - 1889) (-570 - 1698)5235 4375 4375 4265 3927 3754 3127(3346 - 7071) (2790 - 5923) (2790 - 5923) (2719 - 5776) (2501 - 5323) (2390 - 5091) (1987 - 4248)Tolbert et al. (2007)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 the PM coefficient.StudyTolbert et al. (2007)Ito et al. (2007)Location ER Visit for:Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards(Standard Combination Denoted n/m):New York, NY AsthmaAtlanta, GACardiovascularillnessAtlanta, GARespiratoryillnessE-120Table E-119. Estimated Annual Incidenceof Emergency Room (ER) Visits Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2006PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/25220 202 187 172 157 172 154(-310 - 741) (-284 - 681) (-263 - 630) (-241 - 579) (-220 - 528) (-241 - 579) (-216 - 521)829 761 704 647 589 647 581(-831 - 2465) (-762 - 2263) (-705 - 2094) (-647 - 1925) (-590 - 1756) (-647 - 1925) (-581 - 1730)4506 3764 3764 3669 3377 3228 2688(2876 - 6095) (2397 - 5102) (2397 - 5102) (2336 - 4974) (2149 - 4582) (2053 - 4382) (1707 - 3654)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 surrounding the PM coefficient.StudyTolbert et al. (2007)Ito et al. (2007)Location ER Visit for:Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m):New York, NY AsthmaAtlanta, GACardiovascularillnessAtlanta, GARespiratoryillnessE-121Table E-120. Estimated Annual Incidence ofEmergency Room (ER) Visits Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2007) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/25219 201 186 171 156 171 154(-308 - 738) (-283 - 677) (-261 - 627) (-240 - 576) (-219 - 526) (-240 - 576) (-215 - 518)825 757 700 643 586 643 578(-827 - 2453) (-758 - 2251) (-701 - 2084) (-644 - 1915) (-587 - 1747) (-644 - 1915) (-578 - 1721)4926 4115 4115 4011 3692 3529 2939(3145 - 6660) (2622 - 5575) (2622 - 5575) (2555 - 5436) (2350 - 5008) (2245 - 4790) (1867 - 3995)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 surrounding the PM coefficient.StudyTolbert et al. (2007)Ito et al. (2007)Location ER Visit for:Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards(Standard Combination Denoted n/m):New York, NY AsthmaAtlanta, GACardiovascularillnessAtlanta, GARespiratoryillnessE-122Table E-121. Estimated Percent of Total Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exposure toAmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/250.6% 0.6% 0.5% 0.5% 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%) (-0.7% - 1.7%) (-0.6% - 1.5%)0.6% 0.6% 0.5% 0.5% 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%) (-0.5% - 1.5%) (-0.5% - 1.4%)6.1% 5.1% 5.1% 5% 4.6% 4.4% 3.6%(3.9% - 8.2%) (3.3% - 6.9%) (3.3% - 6.9%) (3.2% - 6.7%) (2.9% - 6.2%) (2.8% - 5.9%) (2.3% - 5%)AsthmaAtlanta, GACardiovascularillnessAtlanta, GARespiratoryillnessPercent of Total Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)Standards (Standard Combination Denoted n/m):Tolbert et al. (2007)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 coefficient.StudyTolbert et al. (2007)Ito et al. (2007)Location ER Visit for:New York, NYE-123Table E-122. Estimated Percent of Total Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exposure toAmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/250.6% 0.6% 0.5% 0.5% 0.5% 0.5% 0.4%(-0.9% - 2.1%) (-0.8% - 2%) (-0.8% - 1.8%) (-0.7% - 1.7%) (-0.6% - 1.5%) (-0.7% - 1.7%) (-0.6% - 1.5%)0.6% 0.6% 0.5% 0.5% 0.5% 0.5% 0.4%(-0.6% - 1.9%) (-0.6% - 1.7%) (-0.5% - 1.6%) (-0.5% - 1.5%) (-0.5% - 1.4%) (-0.5% - 1.5%) (-0.4% - 1.3%)5.2% 4.4% 4.4% 4.3% 3.9% 3.7% 3.1%(3.3% - 7.1%) (2.8% - 5.9%) (2.8% - 5.9%) (2.7% - 5.8%) (2.5% - 5.3%) (2.4% - 5.1%) (2% - 4.2%)AsthmaAtlanta, GACardiovascularillnessAtlanta, GARespiratoryillnessPercent of Total Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)Standards (Standard Combination Denoted n/m):Tolbert et al. (2007)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 coefficient.StudyTolbert et al. (2007)Ito et al. (2007)Location ER Visit for:New York, NYE-124Table E-123. Estimated Percent of Total Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exposure toAmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/250.6% 0.6% 0.5% 0.5% 0.4% 0.5% 0.4%(-0.9% - 2.1%) (-0.8% - 1.9%) (-0.7% - 1.8%) (-0.7% - 1.6%) (-0.6% - 1.5%) (-0.7% - 1.6%) (-0.6% - 1.5%)0.6% 0.6% 0.5% 0.5% 0.4% 0.5% 0.4%(-0.6% - 1.8%) (-0.6% - 1.7%) (-0.5% - 1.6%) (-0.5% - 1.4%) (-0.4% - 1.3%) (-0.5% - 1.4%) (-0.4% - 1.3%)5.7% 4.8% 4.8% 4.6% 4.3% 4.1% 3.4%(3.6% - 7.7%) (3% - 6.5%) (3% - 6.5%) (3% - 6.3%) (2.7% - 5.8%) (2.6% - 5.5%) (2.2% - 4.6%)AsthmaAtlanta, GACardiovascularillnessAtlanta, GARespiratoryillnessPercent of Total Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)Standards (Standard Combination Denoted n/m):Tolbert et al. (2007)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Percents rounded to the nearest tenth. Numbers in parenthesesare 95% confidence or credibleintervals based on statistical uncertainty surrounding the PM coefficient.StudyTolbert et al. (2007)Ito et al. (2007)Location ER Visit for:New York, NY

# F-1 

# APPENDIX F: SENSITIVITY ANALYSIS RESULTS

# F-1 Appendix F. Sensitivity Analysis Results 1

2

This Appendix provides detailed results of the single- and multi-factor sensitivity 3

analyses completed as part of this risk analysis . For additional detail on the sensitivity analysis 4

results completed for this analysis, as well as the types of results ge nerated, see section 4.3. 5

6F-1Table F-1. Sensitivity Analysis: Impact of Using Different Model Choices to Estimate the Incidence of Mortality Associated with Long-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1Standard FixedEffects Log-Linear(Cox ProportionalHazard)Model3Random Effects LogLinear Model4Random Effects Log-Log Model5Fixed Effects vs.Random Effects Log-Linear ModelsFixed Effects vs.Random Effects Log-Log Models1342 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 EndpointIncidence of Mortality Associated with Long-Term Exposure toPM2.5Concentrations Using:2Percent Difference6Los Angeles, CAAll Cause Mortality23% 150%Cardiopulmonary Mortality---7--- 68%Ischemic Heart Disease Mortality12% 103%Lung Cancer Mortality--- --- 87%Philadelphia, PAAll Cause Mortality23% 115%Cardiopulmonary Mortality--- --- 45%Ischemic Heart Disease Mortality11% 73%Lung Cancer Mortality--- --- 61%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 PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.2Mortality incidence was estimated for PM2.5concentrations down to the lowest measured level in the study (5.8 ug/m3). Numbers rounded to the nearest whole number. Numbers inparentheses 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 Krewski et al., 2009).4Estimates based on Table 9. Autocorrelation at MSA and ZCA levels; MSA & DIFF, in Krewski et al. (2009) -- exposure period from 1999 - 2000.F-2Table F-2. Sensitivity Analysis: Impact of Using Different Model Choices to Estimate the Incidence of Mortality Associated with Long-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1Standard FixedEffects Log-Linear(Cox ProportionalHazard)Model3Random Effects LogLinear Model4Random Effects Log-Log Model5Fixed Effects vs.Random Effects Log-Linear ModelsFixed Effects vs.Random Effects Log-Log Models1108 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 PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.2Mortality incidence was estimated for PM2.5concentrations down to the lowest measured level in the study (5.8 ug/m3). Numbers rounded to the nearest whole number. Numbers inparentheses 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 Krewski et al., 2009).4Estimates 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 Mortality11% 79%Lung Cancer Mortality--- --- 66%Cardiopulmonary Mortality--- --- 50%Philadelphia, PAAll Cause Mortality23% 122%Ischemic Heart Disease Mortality12% 113%Lung Cancer Mortality--- --- 97%All Cause Mortality23% 162%Cardiopulmonary Mortality---7--- 76%Health EndpointIncidence of Mortality Associated with Long-Term Exposure toPM2.5Concentrations Using:2Percent Difference6Los Angeles, CAF-3Table F-3. Sensitivity Analysis: Impact of Using Different Model Choices to Estimate the Incidence of Mortality Associated with Long-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5Concentrations1Standard FixedEffects Log-Linear(Cox ProportionalHazard)Model3Random Effects LogLinear Model4Random Effects Log-Log Model5Fixed Effects vs.Random Effects Log-Linear ModelsFixed Effects vs.Random Effects Log-Log Models1170 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 MortalityCardiopulmonary MortalityIschemic Heart Disease MortalityLung 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 Krewski et al., 2009).1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.All Cause MortalityIschemic Heart Disease Mortality2Mortality incidence was estimated for PM2.5concentrations down to the lowest measured level in the study (5.8 ug/m3). Numbers rounded to the nearest whole number. Numbers inparentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Lung Cancer Mortality80%67%23%Incidence of Mortality Associated with Long-Term Exposure toPM2.5Concentrations Using:2123%50%159%Los Angeles, CACardiopulmonary Mortality---Percent Difference623%12%Philadelphia, PA74%111%94%---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 PM 2.5 

## Concentrations that Just Meet the Current

Standards to the Lowest Measured Level in the Study vs. to PRB, Based on Adjusting 2005

PM 2.5 

## Concentrations 1, 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%

57% 

### Dallas, TX

Fresno, CA

Los Angeles, CA

Risk Assessment Location

Atlanta, GA

Birmingham, AL

Houston, TX

New York, NY

2 The current primary PM 2.5 standards include an annual standard set at 15 ug/m 3 and a daily standard set at 35 ug/m 3

. 1 Estimates based on Table 33 in Krewski et al. (2009) -- exposure period from 1999 - 2000, follow-up through 2000, models with 4 4 individual

and 7 ecological covariates. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credibl e 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 PM 2.5 

### Concentrations Measured

Down to: Percent Difference 3



### 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 As sociated with Long-Term Exposure to PM 2.5 

## Concentrations that Just Meet the Current

Standards to the Lowest Measu red Level in the Study vs. to PRB, Based on Adjusting 2006 PM 2.5



## Concentrations 1, 2



### Lowest Measured Level in

Study (5.8 ug/m 3 

### ) 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 mortalit y 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 PM 2.5 standards include an annual standard set at 15 ug/m 3 and a daily standard set at 35 ug/m 3

. 1 Estimates based on Table 33 in Krewski et al. (2009) -- exposure period from 1999 - 2000, follow-up through 2000, models with 4 4 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 PM 2.5



### Concentrations Measured Down to: Percent Difference 3



### 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 PM 2.5 

## Concentrations that Just Meet the Current

Standards to the Lowest Meas ured Level in the Study vs. to PRB, Based on Adjusting 2007

PM 2.5 

## Concentrations 1, 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%

71% 

### Dallas, TX

Fresno, CA

Los Angeles, CA

Risk Assessment Location

Atlanta, GA

Birmingham, AL

Houston, TX

New York, NY

2 The current primary PM 2.5 standards include an annual standard set at 15 ug/m 3 and a daily standard set at 35 ug/m 3

. 1 Estimates based on Table 33 in Krewski et al. (2009) -- exposure period from 1999 - 2000, follow-up through 2000, models with 4 4 individual

and 7 ecological covariates. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credibl e 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 PM 2.5 

### Concentrations Measured

Down to: Percent Difference 3



### 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 PM 2.5 

## Concentrations that Just Meet the Current

Standards, Based on Adjusting 2005 PM 2.5 

## Concentrations 1



### 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 PM 2.5 concentrations down to the lowest measured level in Krewski et al., 2009 (5.8 ug/m 3 ). Numbers

rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncer tainty

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 co variates

(see Table 33 in Krewski et al., 2009). 1 The current primary PM 2.5 standards include an annual standard set at 15 ug/m 3 and a daily standard set at 35 ug/m 3

. 

### 30% 

### Incidence of Mortality Associated with Long-Term

Exposure to PM 2.5 

### Concentrations Using: 2



### Percent Difference 5



### 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 PM 2.5 

## Concentrations that Just Meet the Current

Standards, Based on Adjusting 2006 PM 2.5 

## Concentrations 1



### 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 PM 2.5 concentrations down to the lowest measured level in Krewski et al., 2009 (5.8 ug/m 3 ). Numbers

rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncer tainty

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 co variates

(see Table 33 in Krewski et al., 2009). 1 The current primary PM 2.5 standards include an annual standard set at 15 ug/m 3 and a daily standard set at 35 ug/m 3

. 

### 29% 

### Incidence of Mortality Associated with Long-Term

Exposure to PM 2.5 

### Concentrations Using: 2



### Percent Difference 5



### 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 PM 2.5 

## Concentrations that Just Meet the Current

Standards, Based on Adjusting 2007 PM 2.5 

## Concentrations 1



### 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 PM 2.5 concentrations down to the lowest measured level in Krewski et al., 2009 (5.8 ug/m 3 ). Numbers

rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncer tainty

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 co variates

(see Table 33 in Krewski et al., 2009). 1 The current primary PM 2.5 standards include an annual standard set at 15 ug/m 3 and a daily standard set at 35 ug/m 3

. 

### 29% 

### Incidence of Mortality Associated with Long-Term

Exposure to PM 2.5 

### Concentrations Using: 2



### Percent Difference 5



### 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 MortalityF-10Table F-10. Sensitivity Analysis: Estimated Annual Incidence ofAll Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005 PM2.5Concentrations:Comparison of Proportional and Hybrid Rollback Methods115/35214/35 13/35 12/35 13/30 12/25Proportional 702 643 566 490 546 388(448 - 950) (410 - 871) (361 - 768) (312 - 665) (348 - 741) (247 - 528)Hybrid 691 667 589 511 537 381(442 - 936) (426 - 904) (376 - 799) (326 - 694) (342 - 729) (242 - 518)Percent Difference3-2% 4% 4% 4% -2% -2%Proportional 380 336 292 247 292 205(243 - 516) (214 - 457) (186 - 397) (157 - 336) (186 - 397) (130 - 280)Hybrid 461 411 360 310 360 274(294 - 624) (262 - 557) (230 - 489) (197 - 421) (230 - 489) (174 - 372)Percent Difference21% 22% 23% 26% 23% 34%Proportional 743 734 643 552 567 389(474 - 1008) (468 - 996) (410 - 874) (352 - 751) (361 - 770) (247 - 530)Hybrid 773 773 750 651 593 411(493 - 1048) (493 - 1048) (479 - 1018) (415 - 884) (378 - 805) (261 - 559)Percent Difference4% 5% 17% 18% 5% 6%Proportional 1342 1342 1342 1180 924 502(854 - 1827) (854 - 1827) (854 - 1827) (750 - 1607) (587 - 1258) (318 - 684)Hybrid 1675 1675 1599 1344 1209 740(1066 - 2276) (1066 - 2276) (1018 - 2175) (855 - 1830) (769 - 1647) (470 - 1010)Percent Difference25% 25% 19% 14% 31% 47%Proportional 1893 1893 1808 1546 1412 926(1207 - 2571) (1207 - 2571) (1152 - 2455) (984 - 2101) (898 - 1920) (588 - 1261)Hybrid 1950 1950 1806 1544 1461 967(1244 - 2648) (1244 - 2648) (1151 - 2452) (983 - 2099) (930 - 1987) (614 - 1317)Percent Difference3% 3% 0% 0% 3% 4%Proportional 897 813 714 616 696 492(573 - 1215) (519 - 1102) (456 - 970) (392 - 836) (443 - 944) (313 - 669)Hybrid 956 855 754 652 754 548(611 - 1294) (546 - 1159) (481 - 1022) (415 - 885) (481 - 1022) (349 - 745)Percent Difference7% 5% 6% 6% 8% 11%3Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet theCurrent and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m):Type of Rollback1Estimates Based on Krewski et al. (2009), exposure period from1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009). Numbers rounded tothe nearest whole number. Numbers in parentheses are 95% confidenceor credible intervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationSt. Louis, MOBaltimore, MDBirmingham, ALDetroit, MILos Angeles, CANew York, NYF-11Table F-11. Sensitivity Analysis: Estimated Annual Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006 PM2.5Concentrations:Comparisonof Proportional and Hybrid Rollback Methods115/35214/35 13/35 12/35 13/30 12/25Proportional 565 513 446 378 428 289(360 - 766) (327 - 696) (284 - 606) (241 - 515) (272 - 581) (184 - 394)Hybrid 554 533 465 396 419 282(354 - 752) (340 - 724) (296 - 632) (252 - 539) (267 - 569) (179 - 384)Percent Difference3-2% 4% 4% 5% -2% -2%Proportional 354 312 269 226 269 186(226 - 481) (198 - 423) (171 - 365) (144 - 307) (171 - 365) (118 - 253)Hybrid 430 382 334 286 334 251(275 - 584) (244 - 519) (213 - 454) (182 - 388) (213 - 454) (159 - 341)Percent Difference21% 22% 24% 27% 24% 35%Proportional 510 503 429 355 366 222(325 - 694) (320 - 684) (273 - 584) (225 - 483) (233 - 499) (141 - 302)Hybrid 534 534 515 434 387 238(340 - 725) (340 - 725) (328 - 700) (276 - 591) (246 - 526) (151 - 325)Percent Difference5% 6% 20% 22% 6% 7%Proportional 1108 1108 1108 958 721 331(704 - 1509) (704 - 1509) (704 - 1509) (608 - 1305) (457 - 983) (210 - 451)Hybrid 1414 1414 1344 1108 984 550(899 - 1923) (899 - 1923) (855 - 1829) (704 - 1510) (625 - 1340) (349 - 750)Percent Difference28% 28% 21% 16% 36% 66%Proportional 1407 1407 1333 1106 990 571(895 - 1913) (895 - 1913) (848 - 1813) (703 - 1506) (629 - 1349) (362 - 779)Hybrid 1453 1453 1327 1101 1030 604(924 - 1975) (924 - 1975) (844 - 1806) (700 - 1499) (654 - 1403) (383 - 823)Percent Difference3% 3% 0% 0% 4% 6%Proportional 659 588 506 423 490 319(420 - 894) (374 - 799) (322 - 688) (269 - 575) (312 - 666) (203 - 435)Hybrid 704 620 535 450 535 363(449 - 956) (395 - 842) (341 - 727) (286 - 612) (341 - 727) (231 - 495)Percent Difference7% 5% 6% 6% 9% 14%3Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).New York, NY2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet theCurrent and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m):Type of Rollback1Estimates Based on Krewski et al. (2009), exposure period from1999 - 2000, using models with 44individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009). Numbers rounded tothe nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationBaltimore, MDBirmingham, ALSt. Louis, MODetroit, MILos Angeles, CAF-12Table F-12. Sensitivity Analysis: Estimated Annual Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007 PM2.5Concentrations:Comparisonof Proportional and Hybrid Rollback Methods115/35214/35 13/35 12/35 13/30 12/25Proportional 564 512 445 378 427 289(360 - 765) (326 - 695) (283 - 605) (240 - 514) (272 - 580) (184 - 393)Hybrid 553 532 464 395 418 281(353 - 751) (339 - 722) (296 - 630) (252 - 537) (266 - 568) (179 - 383)Percent Difference3-2% 4% 4% 4% -2% -3%Proportional 374 330 285 241 285 199(238 - 507) (210 - 448) (182 - 388) (153 - 327) (182 - 388) (126 - 271)Hybrid 454 404 354 304 354 268(290 - 615) (258 - 548) (226 - 481) (193 - 413) (226 - 481) (170 - 364)Percent Difference21% 22% 24% 26% 24% 35%Proportional 544 536 460 384 396 247(346 - 739) (341 - 729) (293 - 626) (244 - 522) (252 - 539) (157 - 336)Hybrid 568 568 549 466 417 265(362 - 772) (362 - 772) (350 - 747) (297 - 634) (265 - 568) (168 - 361)Percent Difference4% 6% 19% 21% 5% 7%Proportional 1170 1170 1170 1016 773 372(744 - 1593) (744 - 1593) (744 - 1593) (645 - 1384) (490 - 1053) (236 - 508)Hybrid 1484 1484 1413 1171 1043 598(944 - 2019) (944 - 2019) (899 - 1922) (744 - 1594) (662 - 1420) (379 - 815)Percent Difference27% 27% 21% 15% 35% 61%Proportional 1689 1689 1607 1359 1232 771(1076 - 2295) (1076 - 2295) (1023 - 2185) (864 - 1848) (783 - 1676) (489 - 1051)Hybrid 1741 1741 1604 1355 1277 809(1109 - 2366) (1109 - 2366) (1021 - 2180) (862 - 1844) (812 - 1738) (513 - 1102)Percent Difference3% 3% 0% 0% 4% 5%Proportional 728 653 566 478 549 369(464 - 988) (416 - 887) (360 - 769) (304 - 651) (350 - 747) (235 - 503)Hybrid 787 698 607 516 607 424(503 - 1068) (445 - 947) (387 - 825) (329 - 702) (387 - 825) (269 - 577)Percent Difference8% 7% 7% 8% 11% 15%3Calculated 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 PM2.5Concentrations that Just Meet theCurrent and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m):Type of Rollback1Estimates Based on Krewski et al. (2009), exposure period from1999 - 2000, using models with 44individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009). Numbers rounded tothe nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationSt. Louis, MONew York, NYLos Angeles, CADetroit, MIBirmingham, ALBaltimore, MD2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.F-13Table F-13. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence ofNon-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year55 53 43 33 184177(8 - 102) (3 - 101) (-15 - 99) (-17 - 83)---4(34 - 319)66 46 60 50 222256(9 - 122) (1 - 91) (-7 - 126) (7 - 92) ---(104 - 406)18 25 17 10 7034(-4 - 41) (-3 - 51) (-21 - 55) (-21 - 40) ---(-53 - 121)30 30 43 46 149156(-5 - 64) (-9 - 68) (-3 - 88) (6 - 84) ---(37 - 273)-6 77 54 34 159147(-83 - 69) (19 - 134) (-32 - 137) (-31 - 98) ---(-26 - 317)0 16 3 11 3044(-33 - 33) (-1 - 32) (-14 - 20) (-12 - 34) ---(6 - 81)45 61 51 55 212214(-5 - 94) (5 - 116) (-13 - 113) (-9 - 117) ---(44 - 383)17 66 -104 -2 -2381(-84 - 117) (-35 - 166) (-257 - 48) (-90 - 85) ---(-117 - 278)279 159 136 206 780781(102 - 453) (1 - 315) (-55 - 323) (89 - 321) ---(459 - 1102)93 28 34 65 220216(20 - 165) (-33 - 89) (-48 - 114) (16 - 112) ---(79 - 350)--- --- --- --- ---242--- --- --- --- ---(40 - 442)43 65 44 23 175159(-4 - 90) (12 - 117) (-23 - 109) (-28 - 73) ---(47 - 270)16 6 6 8 3630(-2 - 32) (-2 - 14) (-5 - 17) (-3 - 19) ---(6 - 54)37 75 66 73 251260(-37 - 109) (14 - 136) (-6 - 136) (13 - 133) ---(75 - 443)1 9 4 14 2848(-53 - 53) (-7 - 25) (-9 - 17) (-10 - 37) ---(8 - 87)3Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).4It 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-specificcoefficient estimators was not available.10%20%-3%-42%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 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 PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.-128%0%2%----4%8%-32%-1%PercentDifference34%-13%106%Dallas, TXFresno, CAHouston, TXDetroit, MI5Season-specific coefficientestimates were not available from Zanobetti and Schwartz (2009) for this location.St. Louis, MOEstimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALPhiladelphia, PAPhoenix, AZ5Pittsburgh, PATacoma, WASalt Lake City, UTF-14Table F-14. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year48 57 51 29 185180(7 - 89) (3 - 109) (-18 - 119) (-15 - 72)---4(34 - 324)54 41 52 46 193224(7 - 101) (1 - 81) (-6 - 109) (6 - 86) ---(91 - 356)16 27 18 8 6933(-4 - 36) (-3 - 56) (-23 - 58) (-16 - 32) ---(-51 - 116)24 28 36 34 122130(-4 - 52) (-8 - 64) (-3 - 75) (5 - 63) ---(31 - 228)-5 77 39 26 137118(-64 - 54) (19 - 134) (-23 - 100) (-24 - 75) ---(-21 - 255)1 14 4 12 3147(-36 - 36) (-1 - 30) (-16 - 24) (-12 - 35) ---(7 - 86)40 68 51 48 207208(-4 - 84) (5 - 130) (-13 - 115) (-8 - 102) ---(42 - 373)17 57 -97 -2 -2575(-86 - 120) (-30 - 143) (-239 - 45) (-78 - 74) ---(-108 - 257)242 141 111 183 677671(89 - 394) (1 - 279) (-44 - 263) (79 - 286) ---(394 - 946)79 26 33 70 208204(17 - 140) (-31 - 83) (-46 - 109) (18 - 121) ---(75 - 331)--- --- --- --- ---254--- --- --- --- ---(42 - 463)39 58 40 17 154136(-4 - 81) (10 - 104) (-21 - 100) (-20 - 53) ---(40 - 232)12 7 7 7 3327(-1 - 25) (-2 - 15) (-6 - 19) (-3 - 17) ---(6 - 49)26 67 58 60 211215(-27 - 79) (12 - 120) (-5 - 120) (10 - 110) ---(62 - 367)1 10 4 12 2740(-38 - 38) (-8 - 26) (-10 - 19) (-8 - 30) ---(7 - 73)3Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).13%22%-2%-33%-133%1%2%----6%16%-34%0%PercentDifference33%-14%109%Dallas, TXFresno, CAHouston, TXDetroit, MI5Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.St. Louis, MOEstimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALPhiladelphia, PAPhoenix, AZ5Pittsburgh, PA4It 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 theseason-specific coefficient estimators was not available.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 appropriateregional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WASalt Lake City, UTF-15Table F-15. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting 2007 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year48 70 53 30 201177(7 - 88) (4 - 134) (-18 - 122) (-16 - 76)---4(34 - 319)56 46 56 49 207227(7 - 104) (1 - 90) (-6 - 118) (7 - 91) ---(92 - 360)20 44 21 10 9534(-5 - 45) (-5 - 92) (-27 - 67) (-21 - 39) ---(-53 - 120)28 25 39 39 131139(-5 - 60) (-8 - 58) (-3 - 80) (5 - 73) ---(33 - 243)-6 84 46 42 166121(-79 - 66) (21 - 145) (-28 - 119) (-39 - 121) ---(-21 - 262)1 25 6 22 5448(-78 - 76) (-2 - 53) (-23 - 33) (-23 - 64) ---(7 - 89)49 63 57 52 221212(-5 - 102) (5 - 120) (-14 - 127) (-9 - 112) ---(43 - 378)23 112 -144 -3 -1277(-115 - 160) (-59 - 280) (-359 - 66) (-140 - 131) ---(-110 - 262)319 177 150 241 887734(117 - 517) (1 - 350) (-60 - 355) (105 - 376) ---(431 - 1035)88 32 34 78 232208(19 - 156) (-37 - 99) (-48 - 114) (20 - 134) ---(77 - 338)--- --- --- --- ---242--- --- --- --- ---(40 - 442)54 84 57 30 225143(-5 - 113) (15 - 152) (-30 - 140) (-35 - 93) ---(42 - 244)28 11 11 13 6334(-3 - 57) (-4 - 26) (-10 - 32) (-5 - 32) ---(7 - 61)32 83 63 70 248225(-32 - 95) (15 - 150) (-6 - 130) (12 - 127) ---(65 - 384)1 12 4 20 3742(-47 - 47) (-9 - 32) (-9 - 16) (-14 - 52) ---(7 - 76)3Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).85%10%-12%21%12%---57%37%13%4%-116%14%-9%179%-6%5Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.Philadelphia, PAPhoenix, AZ5Pittsburgh, PA4It 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 theseason-specific coefficient estimators was not available.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 appropriateregional means. Numbers are rounded to the nearest whole number.Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WASt. Louis, MOEstimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXPercentDifference3Fresno, CAHouston, TXSalt Lake City, UTDetroit, MIF-16Table F-16. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction toEstimate the Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year14 9 9 -2 3032(-21 - 49) (-32 - 48) (-31 - 46) (-37 - 31)---4(-33 - 95)16 10 11 32 6970(-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 3232(-18 - 36) (-21 - 42) (-25 - 49) (-33 - 27) ---(-21 - 85)-1 25 28 36 8873(-48 - 43) (-7 - 57) (-21 - 74) (0 - 72) ---(-9 - 153)-2 1 0 3 211(-13 - 8) (-3 - 5) (-3 - 4) (-4 - 9) ---(-8 - 30)8 2 27 7 4447(-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 539504(35 - 261) (29 - 228) (30 - 286) (23 - 174) ---(294 - 711)28 16 27 27 9887(-6 - 60) (-14 - 46) (-13 - 65) (4 - 50) ---(23 - 150)--- --- --- --- ---84--- --- --- --- ---(-4 - 170)14 30 13 5 6247(-10 - 38) (3 - 56) (-23 - 47) (-20 - 29) ---(-9 - 103)--- --- --- --- ---8--- --- --- --- ---(-2 - 18)-3 48 38 43 126122(-68 - 59) (-2 - 95) (-17 - 90) (-4 - 88) ---(27 - 215)0002212(-12 - 13) (-3 - 4) (-2 - 3) (-4 - 7) ---(-7 - 31)3Calculated as (sum-of-4-seasons mortality -all-year mortality)/(all-year mortality).2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.4It was not possible to calculate the 2.5th and 97.5th percentileestimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-specificcoefficient estimators was not available.---3%-83%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 rounded to the nearest whole number.Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.7%13%---32%21%-82%-6%52%Birmingham, AL900%0%Dallas, TXPittsburgh, PAEstimated Incidence of Cardiovascular MortalityAssociated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CARisk AssessmentLocationAtlanta, GABaltimore, MDPercentDifference3-6%-1%Detroit, MI5Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.Fresno, CAHouston, TXSalt Lake City, UT5St. Louis, MOPhiladelphia, PAPhoenix, AZ5Tacoma, WANew York, NYF-17Table F-17. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year13 9 10 -2 3032(-18 - 42) (-35 - 52) (-38 - 56) (-32 - 27)---4(-33 - 97)13 8 10 30 6161(-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 2927(-14 - 29) (-19 - 40) (-21 - 41) (-24 - 21) ---(-18 - 71)-1 25 20 28 7258(-37 - 34) (-7 - 57) (-15 - 55) (0 - 55) ---(-7 - 123)-2 1 0 3 212(-14 - 9) (-3 - 5) (-4 - 5) (-4 - 9) ---(-8 - 32)7 2 27 6 4245(-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 463432(31 - 227) (25 - 202) (25 - 233) (21 - 155) ---(252 - 611)24 15 26 30 9582(-5 - 51) (-13 - 42) (-12 - 62) (4 - 54) ---(21 - 142)--- --- --- --- ---88--- --- --- --- ---(-4 - 178)13 27 12 4 5641(-9 - 35) (2 - 50) (-21 - 43) (-14 - 21) ---(-8 - 89)--- --- --- --- ---7--- --- --- --- ---(-2 - 16)-2 42 33 36 109101(-49 - 43) (-2 - 85) (-15 - 80) (-3 - 73) ---(23 - 179)0001110(-9 - 9) (-3 - 4) (-2 - 3) (-3 - 6) ---(-6 - 26)3Calculated as (sum-of-4-seasons mortality -all-year mortality)/(all-year mortality).59%7%16%-90%---37%---8%7%24%-83%-7%Estimated Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CARisk AssessmentLocationAtlanta, GABaltimore, MDPercentDifference3-6%0%600%5Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.Fresno, CAHouston, TXSalt Lake City, UT5St. Louis, MOPhiladelphia, PAPhoenix, AZ51Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriateregional means. Numbers are rounded to thenearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WANew York, NYPittsburgh, PA4It was not possible to calculate the 2.5th and 97.5th percentileestimates of the sum of the season-specific incidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Birmingham, ALDallas, TXDetroit, MIF-18Table F-18. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards,Based on Adjusting 2007 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year11 11 10 -2 3032(-17 - 39) (-40 - 58) (-35 - 52) (-31 - 26)---4(-33 - 95)13 9 10 30 6262(-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 2829(-16 - 34) (-18 - 36) (-23 - 44) (-28 - 24) ---(-19 - 76)-1 22 19 36 7660(-37 - 34) (-6 - 50) (-14 - 52) (0 - 72) ---(-8 - 127)-3 1 0 3 112(-16 - 11) (-3 - 5) (-3 - 4) (-4 - 9) ---(-9 - 33)8 2 29 7 4646(-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 506473(34 - 248) (26 - 212) (28 - 262) (23 - 170) ---(276 - 668)24 16 25 30 9584(-5 - 52) (-15 - 47) (-12 - 60) (5 - 55) ---(22 - 145)--- --- --- --- ---84--- --- --- --- ---(-4 - 170)13 27 12 4 5643(-9 - 34) (2 - 51) (-21 - 43) (-18 - 26) ---(-9 - 93)--- --- --- --- ---9--- --- --- --- ---(-2 - 20)-2 47 32 37 114106(-53 - 46) (-2 - 94) (-15 - 78) (-3 - 76) ---(24 - 187)0002211(-9 - 9) (-3 - 4) (-2 - 2) (-5 - 8) ---(-6 - 27)3Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).30%---8%-82%37%7%13%----3%27%-92%0%Phoenix, AZ5Pittsburgh, PA2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Birmingham, ALDallas, TXDetroit, MI5Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.4It 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 theseason-specific coefficient estimators was not available.Fresno, CAHouston, TXSalt Lake City, UT5St. Louis, MOPhiladelphia, PA1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriateregional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WANew York, NYEstimated Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CARisk AssessmentLocationAtlanta, GABaltimore, MDPercentDifference3-6%0%500%F-19Table F-19. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidenceof Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year4 1 3 3 1120(-6 - 14) (-8 - 11) (-7 - 13) (-5 - 11)---4(-8 - 47)5 6 6 3 2036(-6 - 15) (-4 - 15) (-6 - 17) (-4 - 11) ---(7 - 64)1 2 -1 3 59(-4 - 5) (-4 - 7) (-8 - 7) (-2 - 9) ---(-7 - 26)1321711(-6 - 9) (-4 - 10) (-6 - 9) (-6 - 7) ---(-10 - 32)5 9 10 9 3328(-7 - 16) (-1 - 18) (0 - 19) (0 - 18) ---(1 - 55)-1 4 1 1 59(-11 - 9) (-1 - 9) (-2 - 4) (-6 - 8) ---(0 - 17)5 5 4 4 1834(-4 - 14) (-4 - 13) (-5 - 13) (-7 - 15) ---(5 - 61)27 27 -15 0 3957(-3 - 56) (-2 - 56) (-58 - 26) (-23 - 21) ---(6 - 108)51 18 22 22 113106(19 - 82) (-6 - 41) (-10 - 53) (1 - 42) ---(37 - 174)10 7 7 5 2923(-1 - 21) (-1 - 15) (-3 - 16) (-2 - 11) ---(-2 - 48)27 30 21 41 11947(-29 - 79) (-8 - 66) (-3 - 45) (14 - 67) ---(4 - 90)4 7 8 7 2620(-3 - 11) (-1 - 15) (-2 - 17) (0 - 14) ---(-2 - 42)4 2 -2 -1 35(-1 - 9) (-2 - 6) (-6 - 3) (-5 - 3) ---(1 - 10)1 7 4 7 1931(-15 - 17) (-6 - 20) (-10 - 17) (-6 - 18) ---(-8 - 70)021147(-15 - 13) (-2 - 6) (-2 - 3) (-4 - 6) ---(0 - 15)3Calculated as (sum-of-4-seasons mortality -all-year mortality)/(all-year mortality).St. Louis, MO26%153%30%-40%Salt Lake City, UTEstimated Incidence of Respiratory MortalityAssociated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current StandardsLos Angeles, CANew York, NY-44%-47%-32%7%Risk AssessmentLocationAtlanta, GABaltimore, MDPhiladelphia, PAPhoenix, AZPittsburgh, PATacoma, WA4It was not possible to calculate the 2.5th and 97.5th percentileestimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-specificcoefficient estimators was not available.-39%-43%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.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Birmingham, ALDallas, TXDetroit, MIHouston, TXFresno, CA-44%-36%18%PercentDifference3-45%-44%F-20Table F-20. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting2006 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year3 2 4 3 1220(-5 - 12) (-9 - 12) (-8 - 16) (-4 - 10)---4(-8 - 47)4 5 5 3 1731(-5 - 13) (-3 - 13) (-5 - 15) (-4 - 10) ---(6 - 56)1 2 -1 3 59(-3 - 5) (-4 - 8) (-9 - 7) (-2 - 7) ---(-7 - 25)132179(-5 - 7) (-4 - 10) (-5 - 8) (-4 - 6) ---(-9 - 27)4 9 7 7 2723(-5 - 13) (-1 - 18) (0 - 14) (0 - 14) ---(1 - 44)-1 4 1 1 59(-11 - 10) (-1 - 9) (-2 - 5) (-6 - 8) ---(0 - 18)5 5 4 4 1833(-4 - 13) (-5 - 15) (-5 - 13) (-6 - 13) ---(5 - 60)28 24 -14 0 3853(-3 - 57) (-2 - 48) (-54 - 24) (-19 - 18) ---(5 - 100)44 16 18 20 9891(16 - 72) (-5 - 37) (-8 - 43) (1 - 37) ---(32 - 149)9 7 7 5 2822(-1 - 18) (-1 - 14) (-3 - 16) (-2 - 12) ---(-2 - 45)31 30 22 41 12450(-33 - 90) (-8 - 65) (-3 - 46) (14 - 66) ---(4 - 94)4 6 7 5 2217(-3 - 10) (-1 - 13) (-2 - 15) (0 - 10) ---(-2 - 36)3 2 -2 -1 25(-1 - 7) (-2 - 6) (-7 - 3) (-5 - 3) ---(1 - 9)1 6 3 5 1526(-10 - 12) (-6 - 18) (-9 - 15) (-5 - 15) ---(-7 - 58)021146(-11 - 10) (-2 - 6) (-2 - 4) (-3 - 5) ---(0 - 12)3Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).-42%-33%27%148%29%-60%-44%-45%-28%8%Philadelphia, PAPhoenix, AZPittsburgh, PA4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of theseason-specificincidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Based on season-specific and all-year location-specificcoefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriateregional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WAFresno, CAEstimated Incidence of Respiratory MortalityAssociated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIHouston, TXSalt Lake City, UTSt. Louis, MOPercentDifference3-40%-45%-44%-22%17%F-21Table F-21. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting2007 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year3 2 4 3 1220(-5 - 11) (-10 - 13) (-8 - 15) (-4 - 9)---4(-8 - 47)4 5 5 3 1731(-5 - 12) (-4 - 14) (-5 - 15) (-4 - 10) ---(6 - 56)1 2 -1 3 59(-3 - 4) (-5 - 10) (-8 - 7) (-2 - 7) ---(-7 - 25)1321710(-6 - 8) (-3 - 9) (-5 - 8) (-5 - 6) ---(-9 - 29)4 8 7 9 2824(-5 - 13) (-1 - 16) (0 - 14) (0 - 18) ---(1 - 45)-1 4 1 1 59(-14 - 11) (-1 - 8) (-2 - 4) (-6 - 8) ---(0 - 18)5 4 4 4 1733(-4 - 15) (-4 - 13) (-6 - 14) (-6 - 13) ---(5 - 61)23 29 -13 0 3954(-2 - 48) (-2 - 60) (-51 - 23) (-22 - 21) ---(5 - 102)49 17 20 21 107100(18 - 79) (-6 - 38) (-9 - 48) (1 - 41) ---(35 - 163)9 7 7 5 2822(-1 - 19) (-1 - 15) (-3 - 15) (-2 - 12) ---(-2 - 46)24 29 25 45 12347(-24 - 68) (-8 - 63) (-4 - 51) (15 - 73) ---(4 - 90)4 6 7 6 2318(-3 - 10) (-1 - 13) (-2 - 15) (0 - 13) ---(-2 - 38)5 2 -2 -1 46(-1 - 10) (-2 - 6) (-7 - 3) (-5 - 4) ---(1 - 11)1 7 3 6 1727(-11 - 13) (-6 - 20) (-9 - 14) (-5 - 16) ---(-7 - 60)021146(-11 - 10) (-2 - 6) (-1 - 3) (-5 - 7) ---(0 - 13)3Calculated as (sum-of-4-seasons mortality -all-year mortality)/(all-year mortality).-28%7%27%-33%162%28%-33%-37%St. Louis, MOPercentDifference3-40%-45%-44%-30%17%-44%-48%Estimated Incidence of Respiratory MortalityAssociated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIHouston, TXFresno, CAPhiladelphia, PAPhoenix, AZPittsburgh, PA2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Based on season-specific and all-year location-specific coefficient estimates fromZanobetti and Schwartz (2009) that have been"shrunken" towards the appropriateregional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WA4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of theseason-specific incidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.Salt Lake City, UTF-22Table F-22. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence ofHospital Admissions for Cardiovascular Illness Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year29 24 -28 6 3140(-2 - 60) (-9 - 57) (-68 - 11) (-27 - 39)---4(-26 - 105)129 45 40 48 262247(90 - 168) (16 - 75) (6 - 74) (23 - 73) ---(182 - 313)10 10 -13 3 1017(-1 - 21) (-3 - 23) (-32 - 5) (-12 - 17) ---(-11 - 44)24 19 -21 5 2731(-2 - 50) (-7 - 45) (-50 - 8) (-19 - 28) ---(-20 - 81)153 54 40 57 304280(107 - 198) (18 - 89) (6 - 74) (27 - 87) ---(206 - 354)14 11 -7 3 2121(-5 - 31) (-6 - 27) (-28 - 14) (-11 - 17) ---(0 - 41)44 34 -35 10 5356(-3 - 91) (-12 - 80) (-84 - 14) (-41 - 59) ---(-37 - 149)104 194 -144 42 196264(-35 - 241) (-98 - 479) (-613 - 307) (-138 - 218) ---(3 - 523)391 161 131 145 828792(273 - 509) (55 - 266) (19 - 241) (68 - 221) ---(582 - 1002)118 39 35 39 231214(82 - 153) (13 - 64) (5 - 65) (18 - 59) ---(157 - 271)58 81 -47 14 106108(-20 - 135) (-41 - 200) (-198 - 99) (-47 - 75) ---(1 - 213)59 31 28 36 154157(41 - 77) (11 - 51) (4 - 51) (17 - 55) ---(115 - 199)5 4 -3 1 78(-2 - 13) (-2 - 10) (-15 - 7) (-3 - 5) ---(0 - 16)103 44 30 44 221207(72 - 134) (15 - 73) (4 - 55) (20 - 66) ---(152 - 262)12 0 -5 -5 221(-65 - 82) (-61 - 55) (-56 - 42) (-53 - 40) ---(-52 - 92)3Calculated as (sum-of-4-seasons hospital admissions - all-year hospitaladmissions)/(all-yearhospital admissions).-90%1Incidence estimates were calculated usingthe appropriate season-specific or all-year regional concentration-response functionestimates reported in Table 2 of Bell et al. (2008). Locationspecific C-R function estimates were not available from thisstudy. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervalsbased on statistical uncertainty surrounding the PM coefficient.5%8%-2%-2%9%0%-5%-26%-23%6%-41%-13%Fresno, CAHouston, TXSalt Lake City, UTDetroit, MIEstimated Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposureto PM2.5Concentrations that JustMeet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXPercentDifference3Philadelphia, PAPhoenix, AZPittsburgh, PATacoma, WASt. Louis, MO2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.4It was not possible to calculate the 2.5th and 97.5th percentileestimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-specificcoefficient estimators was not available.-13%7%F-23Table F-23. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting2006 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year27 26 -34 6 2541(-2 - 55) (-9 - 60) (-81 - 13) (-24 - 35)---4(-27 - 108)106 40 35 44 225214(74 - 137) (14 - 66) (5 - 64) (21 - 68) ---(157 - 271)9 10 -14 2 716(-1 - 19) (-4 - 24) (-33 - 5) (-9 - 13) ---(-10 - 42)19 18 -18 3 2226(-1 - 40) (-6 - 43) (-43 - 7) (-15 - 21) ---(-17 - 68)119 54 29 44 246225(83 - 155) (19 - 90) (4 - 54) (20 - 66) ---(165 - 285)15 10 -8 3 2022(-5 - 34) (-5 - 25) (-34 - 17) (-11 - 18) ---(0 - 44)39 38 -36 8 4955(-3 - 81) (-14 - 90) (-86 - 14) (-36 - 52) ---(-36 - 145)108 170 -137 37 178248(-37 - 252) (-86 - 419) (-580 - 291) (-121 - 192) ---(3 - 491)342 143 107 129 721684(239 - 445) (49 - 237) (16 - 197) (61 - 198) ---(502 - 865)99 35 33 41 208201(69 - 129) (12 - 59) (5 - 62) (19 - 63) ---(147 - 254)66 80 -48 14 112113(-23 - 154) (-40 - 198) (-202 - 101) (-47 - 74) ---(1 - 224)53 27 25 26 131134(37 - 69) (9 - 45) (4 - 46) (12 - 40) ---(98 - 169)4 4 -4 1 57(-1 - 10) (-2 - 11) (-17 - 9) (-3 - 4) ---(0 - 15)74 39 26 36 175171(52 - 96) (13 - 64) (4 - 48) (17 - 54) ---(126 - 216)9 0 -6 -4 -118(-48 - 60) (-64 - 59) (-61 - 46) (-43 - 33) ---(-44 - 78)3Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).-29%2%-106%5%3%-1%-2%9%-9%-11%-28%-39%5%-56%-15%Fresno, CAHouston, TXSalt Lake City, UTDetroit, MIEstimated Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposureto PM2.5Concentrations that JustMeet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXPercentDifference3Philadelphia, PAPhoenix, AZPittsburgh, PA4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of theseason-specificincidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Incidence estimates were calculated usingthe appropriate season-specific or all-yearregional concentration-response functionestimates 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 wholenumber. Numbers in parentheses are 95%confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WASt. Louis, MOF-24Table F-24. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence ofHospital Admissions for Cardiovascular Illness Associated with Short-TermExposure to PM2.5Concentrations that JustMeet the Current Standards,Based on Adjusting 2007 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year24 30 -33 5 2641(-2 - 50) (-11 - 70) (-80 - 13) (-23 - 34)---4(-27 - 109)103 42 35 44 224216(72 - 134) (14 - 70) (5 - 65) (21 - 67) ---(159 - 273)9 13 -12 2 1216(-1 - 18) (-5 - 31) (-30 - 5) (-9 - 13) ---(-11 - 43)23 17 -19 4 2528(-2 - 47) (-6 - 39) (-46 - 7) (-17 - 25) ---(-18 - 73)121 48 28 58 255233(84 - 157) (16 - 79) (4 - 52) (27 - 88) ---(171 - 295)17 10 -6 3 2423(-6 - 40) (-5 - 25) (-26 - 13) (-11 - 18) ---(0 - 46)46 34 -38 9 5156(-3 - 95) (-12 - 79) (-91 - 15) (-37 - 54) ---(-37 - 149)93 215 -131 42 219258(-31 - 216) (-109 - 531) (-556 - 279) (-139 - 220) ---(3 - 511)377 151 121 143 792752(263 - 490) (51 - 249) (18 - 223) (67 - 218) ---(552 - 951)101 39 32 42 214203(71 - 131) (13 - 64) (5 - 59) (20 - 64) ---(149 - 257)50 78 -54 16 90108(-17 - 117) (-39 - 193) (-230 - 115) (-52 - 83) ---(1 - 215)51 28 25 32 136140(36 - 67) (9 - 46) (4 - 46) (15 - 49) ---(103 - 177)6 5 -4 1 89(-2 - 15) (-2 - 12) (-18 - 9) (-3 - 5) ---(0 - 18)80 43 25 37 185178(56 - 104) (15 - 71) (4 - 47) (17 - 56) ---(131 - 225)9 0 -4 -6 -119(-48 - 60) (-64 - 59) (-43 - 33) (-63 - 47) ---(-46 - 82)3Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).-11%4%-105%5%5%-17%-3%9%4%-9%-15%-37%4%-25%-11%Philadelphia, PAPhoenix, AZPittsburgh, PA2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Incidence estimates were calculated usingthe appropriate season-specific or all-year regional concentration-response functionestimates 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 wholenumber. Numbers in parentheses are 95%confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WA4It was not possible to calculate the 2.5th and 97.5th percentileestimates of the sum of the season-specific incidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.St. Louis, MOEstimated Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposureto PM2.5Concentrations that JustMeet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXPercentDifference3Fresno, CAHouston, TXSalt Lake City, UTDetroit, MIF-25Table F-25. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year6 9 -6 1 1017(-21 - 31) (-10 - 28) (-25 - 12) (-13 - 15)---4(-22 - 55)18 1 14 2 3520(-5 - 40) (-14 - 15) (0 - 28) (-12 - 15) ---(-12 - 51)2 4 -3 1 47(-7 - 11) (-4 - 11) (-12 - 6) (-5 - 6) ---(-9 - 23)4 7 -5 1 715(-17 - 25) (-8 - 23) (-21 - 10) (-13 - 16) ---(-18 - 47)24 1 17 2 4425(-6 - 53) (-18 - 20) (0 - 35) (-14 - 18) ---(-15 - 64)10 3 4 3 2014(-1 - 20) (-6 - 11) (-4 - 11) (-5 - 11) ---(3 - 25)7 12 -8 3 1425(-27 - 42) (-14 - 38) (-33 - 16) (-27 - 32) ---(-32 - 81)71 45 86 42 244170(-6 - 148) (-97 - 183) (-98 - 261) (-60 - 140) ---(40 - 300)57 2 49 5 11365(-15 - 129) (-52 - 56) (-1 - 99) (-33 - 42) ---(-38 - 169)16 1 12 1 3017(-4 - 37) (-12 - 12) (0 - 25) (-9 - 12) ---(-10 - 44)35 17 23 13 8861(-3 - 72) (-36 - 68) (-26 - 70) (-18 - 43) ---(14 - 107)8 0 9 1 1813(-2 - 18) (-10 - 11) (0 - 19) (-9 - 12) ---(-8 - 33)412186(0 - 8) (-2 - 5) (-3 - 7) (-2 - 4) ---(1 - 10)23 1 15 2 4125(-6 - 53) (-20 - 21) (0 - 29) (-15 - 19) ---(-15 - 64)0 4 1 -2 32(-50 - 43) (-27 - 32) (-19 - 19) (-24 - 18) ---(-27 - 30)3Calculated as (sum-of-4-seasons hospital admissions - all-year hospitaladmissions)/(all-yearhospital admissions).St. Louis, MO76%44%38%33%Salt Lake City, UTEstimated Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure toPM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CANew York, NY43%-44%44%74%Risk AssessmentLocationAtlanta, GABaltimore, MDPhiladelphia, PAPhoenix, AZPittsburgh, PATacoma, WA4It was not possible to calculate the 2.5th and 97.5th percentileestimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-specificcoefficient estimators was not available.64%50%1Incidence estimates were calculated usingthe appropriate season-specific or all-year regional concentration-response functionestimates from models with a 2-day lag for respiratoryhospital 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.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Birmingham, ALDallas, TXDetroit, MIHouston, TXFresno, CA-43%-53%76%PercentDifference3-41%75%F-26Table F-26. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations that JustMeet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year5 10 -7 1 917(-19 - 28) (-11 - 30) (-30 - 14) (-11 - 13)---4(-22 - 56)15 1 12 2 3017(-4 - 33) (-12 - 13) (0 - 24) (-11 - 14) ---(-10 - 44)2 4 -3 0 37(-6 - 10) (-4 - 12) (-12 - 6) (-4 - 5) ---(-8 - 22)4 7 -4 1 812(-13 - 20) (-8 - 22) (-18 - 9) (-10 - 12) ---(-15 - 40)18 1 13 2 3420(-5 - 41) (-19 - 20) (0 - 25) (-11 - 13) ---(-12 - 52)11 3 5 3 2215(-1 - 22) (-5 - 10) (-5 - 14) (-5 - 12) ---(3 - 26)7 14 -8 3 1625(-24 - 37) (-15 - 42) (-34 - 17) (-24 - 28) ---(-31 - 79)75 40 82 37 234160(-6 - 155) (-85 - 160) (-93 - 248) (-53 - 124) ---(37 - 281)50 2 40 4 9656(-13 - 113) (-46 - 50) (-1 - 81) (-30 - 38) ---(-33 - 145)14 0 12 1 2716(-4 - 31) (-11 - 12) (0 - 23) (-10 - 13) ---(-9 - 41)40 17 24 13 9464(-3 - 82) (-35 - 67) (-27 - 72) (-18 - 43) ---(15 - 112)7 0 8 1 1611(-2 - 16) (-9 - 10) (0 - 17) (-7 - 8) ---(-6 - 28)313185(0 - 6) (-3 - 5) (-3 - 8) (-1 - 3) ---(1 - 9)17 1 13 2 3321(-5 - 38) (-17 - 18) (0 - 26) (-13 - 16) ---(-12 - 53)0 4 1 -1 42(-37 - 31) (-29 - 34) (-21 - 20) (-20 - 15) ---(-23 - 25)3Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).57%100%69%47%45%60%47%-36%46%71%Philadelphia, PAPhoenix, AZPittsburgh, PA4It 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 theseason-specific coefficient estimators was not available.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response functionestimates from models with a 2-day lag forrespiratory 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 roundedto the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WAFresno, CAEstimated Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure toPM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIHouston, TXSalt Lake City, UTSt. Louis, MOPercentDifference3-47%76%-57%-33%70%F-27Table F-27. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence ofHospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations that JustMeet the Current Standards,Based on Adjusting 2007 PM2.5Concentrations1,2Winter Spring Summer FallSum of FourSeasonsAll Year5 11 -7 1 1018(-17 - 26) (-13 - 35) (-29 - 14) (-11 - 13)---4(-22 - 57)14 1 12 2 2917(-4 - 32) (-13 - 14) (0 - 25) (-11 - 14) ---(-10 - 45)2 5 -3 0 47(-6 - 9) (-6 - 16) (-11 - 5) (-4 - 5) ---(-9 - 22)4 6 -5 1 613(-16 - 24) (-7 - 20) (-19 - 10) (-12 - 14) ---(-17 - 43)19 1 12 2 3421(-5 - 42) (-16 - 18) (0 - 25) (-14 - 18) ---(-12 - 54)13 2 3 4 2215(-1 - 26) (-5 - 10) (-4 - 11) (-5 - 12) ---(4 - 27)8 12 -9 3 1425(-28 - 43) (-14 - 38) (-36 - 18) (-25 - 29) ---(-32 - 82)64 50 78 42 234166(-5 - 133) (-108 - 203) (-89 - 239) (-61 - 142) ---(39 - 293)55 2 46 5 10862(-15 - 124) (-48 - 52) (-1 - 91) (-33 - 42) ---(-37 - 160)14 1 11 1 2716(-4 - 32) (-12 - 13) (0 - 22) (-10 - 13) ---(-10 - 42)30 16 27 14 8761(-3 - 63) (-34 - 65) (-31 - 82) (-20 - 48) ---(14 - 108)7 0 8 1 1611(-2 - 16) (-9 - 10) (0 - 17) (-8 - 10) ---(-7 - 29)5 1 3 1 107(0 - 9) (-3 - 5) (-3 - 9) (-2 - 4) ---(2 - 12)18 1 12 2 3321(-5 - 41) (-19 - 21) (0 - 25) (-13 - 17) ---(-13 - 55)0 4 1 -2 32(-37 - 31) (-29 - 34) (-15 - 15) (-29 - 22) ---(-24 - 27)3Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).41%74%69%50%43%45%43%57%St. Louis, MOPercentDifference3-44%71%-43%-54%62%47%-44%Estimated Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure toPM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIHouston, TXFresno, CAPhiladelphia, PAPhoenix, AZPittsburgh, PA2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response functionestimates from models with a 2-day lag forrespiratory 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 roundedto the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WA4It 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 theseason-specific coefficient estimators was not available.Salt Lake City, UTF-28Table F-28. Sensitivity Analysis: Impact of Using an Annual Concentration-Response Function vs. a Seasonal Function (for April - August)Applied Only to that Period to Estimate the Incidence of Emergency Room Visits for Asthma Associated with Short-TermExposure to Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and AlternativeStandards inNew York City, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/255235 4375 4375 4265 3927 3754 3127(3346 - 7071) (2790 - 5923) (2790 - 5923) (2719 - 5776) (2501 - 5323) (2390 - 5091) (1987 - 4248)3136 2634 2634 2569 2370 2268 1896(2058 - 4162) (1722 - 3509) (1722 - 3509) (1678 - 3425) (1546 - 3164) (1478 - 3031) (1232 - 2541)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 parentheses are 95% confidence or credibleintervals based on statistical uncertainty surrounding the PM coefficient.Incidence of ER Visits Associatedwith Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m):Annual C-R Function Applied to the WholeYearSeasonal C-R Function for April - AugustApplied Only to that Period:Concentration-Response (C-R) Functionand Period to Which Applied:F-29Table F-29. Sensitivity Analysis: Impact of Using an Annual Concentration-Response Function vs. a Seasonal Function (for April - August)Applied Only to that Period to Estimate the Incidence of Emergency Room Visits for Asthma Associated with Short-Term Exposureto Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards inNew York City, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/254506 3764 3764 3669 3377 3228 2688(2876 - 6095) (2397 - 5102) (2397 - 5102) (2336 - 4974) (2149 - 4582) (2053 - 4382) (1707 - 3652732 2293 2293 2237 2063 1974 1649(1791 - 3631) (1497 - 3059) (1497 - 3059) (1460 - 2985) (1344 - 2757) (1285 - 2640) (1071 - 2212The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 parentheses are 95% confidence or credibleintervals based on statistical uncertainty surrounding the PM coefficient.Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PMConcentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m):Annual C-R Function Applied to the WholeYearSeasonal C-R Function for April - AugustApplied Only to that Period:Concentration-Response (C-R) Functionand Period to Which Applied:F-30Table F-30. Sensitivity Analysis: Impact of Using an Annual Concentration-Response Function vs. a Seasonal Function (for April - August)Applied Only to that Period to Estimate the Incidence of Emergency Room Visits for Asthma Associated with Short-TermExposure to Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and AlternativeStandards inNew York City, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/254926 4115 4115 4011 3692 3529 2939(3145 - 6660) (2622 - 5575) (2622 - 5575) (2555 - 5436) (2350 - 5008) (2245 - 4790) (1867 - 3995)2908 2441 2441 2380 2195 2101 1755(1906 - 3864) (1593 - 3256) (1593 - 3256) (1553 - 3177) (1431 - 2934) (1368 - 2810) (1140 - 2354)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and 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 parentheses are 95% confidence or credibleintervals based on statistical uncertainty surrounding the PM coefficient.Incidence of ER Visits Associatedwith Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m):Annual C-R Function Applied to the WholeYearSeasonal C-R Function for April - AugustApplied Only to that Period:Concentration-Response (C-R) Functionand Period to Which Applied:F-31Table F-31. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 275 Max. positive est. = 81(-35 - 584) 301 (-117 - 278)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 301 Min. positive est. =(0 - 600) 194Mortality, 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 -7755%(-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 - 278)Mortality, short-term non-accidental log-linear, GLM, 30 df 0 day none 204 Min. positive est. =(-174 - 579) 153Mortality, 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 15380%(-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 - 278)Mortality, short-term non-accidental log-linear, GLM, 30 df 1 day none 281 Min. positive est. =(-86 - 644) 51Mortality, 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 -5490%(-509 - 494)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day CO -272 81(-676 - 128) (-117 - 278)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 PM2.5-- Impact of Changing the Type of Model, with a 1-Day LagHealth Effects ModelIncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Lag Structure:240%LagOtherPollutantsin ModelNon-Accidental MortalityAssociated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model-436%-309%-309%IncidenceAssociated withPM2.5Above PolicyRelevantBackgroundRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2272%140%-195%-157%-454%Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag240%152%101%89%272%247%-37%-106%F-32Table F-31 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 0 day none 171 Max. positive est. = -31(17 - 324) 171 (-140 - 76)Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day none 168 Min. positive est. =(24 - 310) 168Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day none 168Percent diff. =(-4 - 337)2%Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 1 day none 178 Max. positive est. = -31(26 - 328) 178 (-140 - 76)Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day none 139 Min. positive est. =(-6 - 282) 120Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day none 120Percent diff. =(-56 - 293)48%Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 307 Max. positive est. = -31(130 - 481) 324 (-140 - 76)Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day CO 324 Min. positive est. =(116 - 529) 158Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 158Percent 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---5(-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) 22log-linear, GAM (stringent), 100 df 1 day none 22 Min. positive est. =(-42 - 85) 5log-linear, GLM, 100 df 1 day none 5Percent diff. =(-75 - 83)340%Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag120%Health EffectsRespiratory Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day LagRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4ModelMortality, short-term respiratory(COPD+)300%95%95%Respiratory Mortality Associated with Short-Term Exposure to PM2.5-- 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+)---LagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundCardiovascular Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag111%107%107%72%48%Cardiovascular MortalityAssociated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model279%---Mortality, short-term respiratory(COPD+)---Mortality, short-term respiratory(COPD+)F-33Table F-31 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1HA, cardiovascular log-linear, GAM (stringent), 30 df 0 day none 794 Max. positive est. = 35(457 - 1128) 794 (-60 - 130)HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day none 584 Min. positive est. =(254 - 912) 584HA, cardiovascular log-linear, GLM, 100 df 0 day none 634Percent diff. =(226 - 1038)36%HA, cardiovascular log-linear, GAM (stringent), 30 df 1 day none 699 Max. positive est. = 35(347 - 1048) 699 (-60 - 130)HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day none 569 Min. positive est. =(234 - 902) 569HA, cardiovascular log-linear, GLM, 100 df 1 day none 604Percent diff. =(194 - 1011)23%HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 197 Max. positive est. = 35(-224 - 615) 293 (-60 - 130)HA, cardiovascular log-linear, GLM, 100 df 0 day CO 293 Min. positive est. =(-208 - 788) 122HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 122Percent 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) 336HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day none 278 Min. positive est. =(104 - 450) 278HA, respiratory (COPD+) log-linear, GLM, 100 df 0 day none 300Percent diff. =(83 - 514)21%HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 1 day none 240 Max. positive est. = ---(45 - 432) 240HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day none 152 Min. positive est. =(-22 - 324) 152HA, respiratory (COPD+) log-linear, GLM, 100 df 1 day none 156Percent diff. =(-55 - 364)58%Range of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Model LagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundHealth EffectsCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag880%621%683%Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag763%602%646%Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model143%262%51%69%Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag---------Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag---------F-34Table F-31 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 2 day none 371 Max. positive est. = ---(166 - 574) 371HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day none 230 Min. positive est. =(43 - 414) 208HA, respiratory (COPD+) log-linear, GLM, 100 df 2 day none 208Percent diff. =(-24 - 436)78%HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day NO2 85 Max. positive est. = ---(-185 - 351) 85HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day NO2 -8 Min. positive est. =(-329 - 307) 71HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day NO2 71Percent diff. =(-209 - 346)20%HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 3 day NO2 -223(-491 - 41)Range of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Model LagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundHealth EffectsRespiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 2-Day Lag---------Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Lag Structure, with a Copollutant Model------------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 the Moolgavkar (2003) estimatesand the corresponding core analysis estimates is not shown.1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3. Results are based on Moolgavkar (2003) [reanalysis of Moolgavkar (2000a, 2000b, and2000c)]. 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 core analysis estimates for non-accidental mortality and cardiovascular mortality associated with short-term exposure to PM2.5 are from Zanobetti and Schwartz (2009). The core analysis estimates forcardiovascular 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-35Table F-32. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 254 Max. positive est. = 75(-32 - 539) 278 (-108 - 257)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 278 Min. positive est. =(0 - 554) 179Mortality, 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 -7155%(-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 - 257)Mortality, short-term non-accidental log-linear, GLM, 30 df 0 day none 188 Min. positive est. =(-161 - 535) 141Mortality, 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 14180%(-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 - 257)Mortality, short-term non-accidental log-linear, GLM, 30 df 1 day none 259 Min. positive est. =(-80 - 595) 47Mortality, 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 -5491%(-469 - 455)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day CO -251 75(-623 - 118) (-108 - 257)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)271%245%-37%-107%239%151%101%88%-195%-156%-453%Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day LagIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2271%139%Non-Accidental MortalityAssociated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model-435%-308%-308%Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day LagHealth Effects ModelIncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Lag Structure:239%LagOtherPollutantsin ModelF-36Table F-32 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 0 day none 158 Max. positive est. = -29(15 - 299) 158 (-129 - 70)Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day none 155 Min. positive est. =(22 - 286) 155Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day none 155Percent diff. =(-3 - 311)2%Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 1 day none 164 Max. positive est. = -29(24 - 303) 164 (-129 - 70)Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day none 128 Min. positive est. =(-5 - 260) 110Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day none 110Percent diff. =(-51 - 270)49%Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 283 Max. positive est. = -29(120 - 444) 299 (-129 - 70)Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day CO 299 Min. positive est. =(107 - 489) 145Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 145Percent 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---5(-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) 21log-linear, GAM (stringent), 100 df 1 day none 21 Min. positive est. =(-39 - 78) 5log-linear, GLM, 100 df 1 day none 5Percent diff. =(-69 - 76)320%---Mortality, short-term respiratory(COPD+)---Mortality, short-term respiratory(COPD+)47%Cardiovascular MortalityAssociated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model277%107%107%71%Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag119%IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag111%Health EffectsMortality, short-term respiratory(COPD+)---Mortality, short-term respiratory(COPD+)---Mortality, short-term respiratory(COPD+)---93%93%Respiratory Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag---Respiratory Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day LagModel LagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2Mortality, short-term respiratory(COPD+)299%F-37Table F-32 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1HA, cardiovascular log-linear, GAM (stringent), 30 df 0 day none 745 Max. positive est. = 248(428 - 1060) 745 (3 - 491)HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day none 548 Min. positive est. =(238 - 856) 548HA, cardiovascular log-linear, GLM, 100 df 0 day none 595Percent diff. =(212 - 975)36%HA, cardiovascular log-linear, GAM (stringent), 30 df 1 day none 656 Max. positive est. = 248(326 - 984) 656 (3 - 491)HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day none 534 Min. positive est. =(220 - 847) 534HA, cardiovascular log-linear, GLM, 100 df 1 day none 567Percent diff. =(182 - 949)23%HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 185 Max. positive est. = 248(-210 - 577) 275 (3 - 491)HA, cardiovascular log-linear, GLM, 100 df 0 day CO 275 Min. positive est. =(-195 - 740) 114HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 114Percent 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) 310HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day none 256 Min. positive est. =(96 - 415) 256HA, respiratory (COPD+) log-linear, GLM, 100 df 0 day none 277Percent diff. =(76 - 475)21%HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 1 day none 221 Max. positive est. = ---(42 - 399) 221HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day none 140 Min. positive est. =(-21 - 299) 140HA, respiratory (COPD+) log-linear, GLM, 100 df 1 day none 144Percent diff. =(-51 - 336)58%Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag---Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag693%---------------71%Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag147%267%52%775%612%656%Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model893%631%Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day LagRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Model LagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundHealth EffectsF-38Table F-32 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 2 day none 343 Max. positive est. = ---(153 - 531) 343HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day none 212 Min. positive est. =(40 - 383) 192HA, respiratory (COPD+) log-linear, GLM, 100 df 2 day none 192Percent diff. =(-22 - 403)79%HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day NO2 78 Max. positive est. = ---(-171 - 324) 78HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day NO2 -7 Min. positive est. =(-303 - 284) 65HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day NO2 65Percent 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 the Moolgavkar (2003) estimatesand the corresponding core analysis estimates is not shown.1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3. Results are based on Moolgavkar (2003) [reanalysis of Moolgavkar (2000a, 2000b, and2000c)].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). The core analysis estimates forcardiovascular hospital admissions associated with short-term exposure to PM2.5 are from Bell et al. (2008). Numbers rounded tothe nearest whole number. Numbers in parentheses are 95% confidence orcredible 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 PM2.5-- Impact of Changing the Lag Structure, with a Copollutant Model------------Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 2-Day LagRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Model LagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundHealth EffectsF-39Table F-33. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5Concentrations1Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 259 Max. positive est. = 77(-33 - 550) 283 (-110 - 262)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 283 Min. positive est. =(0 - 565) 183Mortality, 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 -7255%(-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 - 262)Mortality, short-term non-accidental log-linear, GLM, 30 df 0 day none 192 Min. positive est. =(-164 - 546) 144Mortality, 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 14480%(-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 - 262)Mortality, short-term non-accidental log-linear, GLM, 30 df 1 day none 264 Min. positive est. =(-81 - 607) 48Mortality, 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 -5490%(-480 - 465)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day CO -256 77(-636 - 121) (-110 - 262)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)-306%Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag236%149%100%87%Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Lag Structure:236%268%138%-194%-156%-452%IncidenceAssociated withPM2.5Above PolicyRelevantBackgroundRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Health Effects Model LagOtherPollutantsin ModelNon-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag268%243%-38%-106%Non-Accidental MortalityAssociated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model-432%-306%F-40Table F-33 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5Concentrations1Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 0 day none 161 Max. positive est. = -30(16 - 306) 161 (-132 - 72)Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day none 158 Min. positive est. =(23 - 292) 158Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day none 158Percent diff. =(-3 - 318)2%Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 1 day none 168 Max. positive est. = -30(25 - 309) 168 (-132 - 72)Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day none 130 Min. positive est. =(-6 - 265) 113Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day none 113Percent diff. =(-52 - 276)49%Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 289 Max. positive est. = -30(123 - 453) 305 (-132 - 72)Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day CO 305 Min. positive est. =(109 - 498) 148Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 148Percent 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---5(-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) 21log-linear, GAM (stringent), 100 df 1 day none 21 Min. positive est. =(-39 - 80) 5log-linear, GLM, 100 df 1 day none 5Percent diff. =(-71 - 78)320%Respiratory Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag---Mortality, short-term respiratory(COPD+)---Mortality, short-term respiratory(COPD+)---92%---Mortality, short-term respiratory(COPD+)---Respiratory Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day LagMortality, short-term respiratory(COPD+)---296%92%Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag109%105%105%PercentDifference(Compared toCore AnalysisModel)4Mortality, short-term respiratory(COPD+)Mortality, short-term respiratory(COPD+)47%Cardiovascular MortalityAssociated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model275%Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag118%69%Health Effects Model LagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3F-41Table F-33 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5Concentrations1HA, cardiovascular log-linear, GAM (stringent), 30 df 0 day none 775 Max. positive est. = 258(446 - 1102) 775(3 - 511)HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day none 570 Min. positive est. =(248 - 890) 570HA, cardiovascular log-linear, GLM, 100 df 0 day none 619Percent diff. =(221 - 1014)36%HA, cardiovascular log-linear, GAM (stringent), 30 df 1 day none 682 Max. positive est. = 258(339 - 1023) 682(3 - 511)HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day none 556 Min. positive est. =(228 - 880) 556HA, cardiovascular log-linear, GLM, 100 df 1 day none 590Percent diff. =(189 - 987)23%HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 193 Max. positive est. = 258(-219 - 600) 286(3 - 511)HA, cardiovascular log-linear, GLM, 100 df 0 day CO 286 Min. positive est. =(-203 - 769) 119HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 119Percent 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) 316HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day none 262 Min. positive est. =(98 - 424) 262HA, respiratory (COPD+) log-linear, GLM, 100 df 0 day none 282Percent diff. =(78 - 485)21%HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 1 day none 226 Max. positive est. = ---(42 - 407) 226HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day none 143 Min. positive est. =(-21 - 305) 143HA, respiratory (COPD+) log-linear, GLM, 100 df 1 day none 146Percent diff. =(-52 - 343)58%Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag---------Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag---------Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model151%271%55%73%Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag786%622%666%Range of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Model LagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundHealth EffectsCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag906%640%704%F-42Table F-33 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5Concentrations1HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 2 day none 350 Max. positive est. = ---(156 - 541) 350HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day none 216 Min. positive est. =(41 - 391) 196HA, respiratory (COPD+) log-linear, GLM, 100 df 2 day none 196Percent diff. =(-22 - 411)79%HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day NO2 80 Max. positive est. = ---(-174 - 331) 80HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day NO2 -8 Min. positive est. =(-310 - 290) 67HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day NO2 67Percent 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 the Moolgavkar (2003) estimatesand the corresponding core analysis estimates is not shown.1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3. Results are based on Moolgavkar (2003) [reanalysis of Moolgavkar (2000a, 2000b, and2000c)]. 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 core analysis estimates for non-accidental mortality and cardiovascular mortality associated with short-term exposure to PM2.5 are from Zanobetti and Schwartz (2009). The core analysis estimates forcardiovascular 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 PM2.5-- Impact of Changing the Lag Structure, with a Copollutant Model------------Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 2-Day Lag---------PercentDifference(Compared toCore AnalysisModel)4Health Effects Model LagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3F-43Table F-34. Sensitivity Analysis: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term ExposuretoPM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005 PM2.5Concentrations:Comparisonof Proportional and Hybrid Rollback Methods115/35214/35 13/35 12/35 13/30 12/25Proportional 256 242 224 206 219 182(104 - 406) (98 - 384) (91 - 356) (83 - 327) (89 - 348) (74 - 289)Hybrid 254 248 229 211 217 180(103 - 402) (101 - 393) (93 - 364) (86 - 335) (88 - 344) (73 - 286)Percent Difference3-1% 2% 2% 2% -1% -1%Proportional 34 32 29 27 29 24(-53 - 121) (-49 - 112) (-45 - 103) (-41 - 94) (-45 - 103) (-38 - 85)Hybrid 39 36 33 30 33 28(-61 - 137) (-56 - 127) (-52 - 117) (-47 - 107) (-52 - 117) (-44 - 99)Percent Difference15% 13% 14% 11% 14% 17%Proportional 147 146 135 124 125 104(-26 - 317) (-26 - 315) (-24 - 291) (-22 - 267) (-22 - 271) (-18 - 225)Hybrid 151 151 148 136 129 107(-26 - 325) (-26 - 325) (-26 - 319) (-24 - 293) (-23 - 278) (-19 - 231)Percent Difference3% 3% 10% 10% 3% 3%Proportional 81 81 81 77 69 58(-117 - 278) (-117 - 278) (-117 - 278) (-110 - 263) (-100 - 238) (-82 - 197)Hybrid 91 91 89 81 78 64(-130 - 311) (-130 - 311) (-127 - 304) (-117 - 279) (-111 - 266) (-92 - 220)Percent Difference12% 12% 10% 5% 13% 10%Proportional 781 781 761 700 668 555(459 - 1102) (459 - 1102) (447 - 1073) (411 - 987) (392 - 943) (325 - 783)Hybrid 795 795 761 699 680 564(467 - 1121) (467 - 1121) (446 - 1073) (410 - 986) (399 - 959) (331 - 797)Percent Difference2% 2% 0% 0% 2% 2%Proportional 260 244 226 207 222 184(75 - 443) (71 - 416) (65 - 385) (60 - 354) (64 - 379) (53 - 315)Hybrid 271 252 233 214 233 195(78 - 462) (73 - 429) (67 - 397) (62 - 365) (67 - 397) (56 - 332)Percent Difference4% 3% 3% 3% 5% 6%3Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).St. Louis, MO2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that JustMeet the Current and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m):1Based on location-specific single pollutant concentration-responsefunction 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. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credibleintervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationBaltimore, MDBirmingham, ALType of RollbackDetroit, MILos Angeles, CANew York, NYF-44Table F-35. Sensitivity Analysis: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposureto PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2006 PM2.5Concentrations:Comparisonof Proportional and Hybrid Rollback Methods115/35214/35 13/35 12/35 13/30 12/25Proportional 224 212 196 180 192 159(91 - 356) (86 - 336) (79 - 311) (73 - 286) (78 - 305) (64 - 253)Hybrid 222 217 200 184 190 157(90 - 352) (88 - 344) (81 - 318) (75 - 293) (77 - 301) (64 - 250)Percent Difference3-1% 2% 2% 2% -1% -1%Proportional 33 30 28 26 28 23(-51 - 116) (-47 - 108) (-44 - 99) (-40 - 90) (-44 - 99) (-36 - 82)Hybrid 37 35 32 29 32 27(-58 - 132) (-54 - 122) (-49 - 112) (-45 - 102) (-49 - 112) (-42 - 95)Percent Difference12% 17% 14% 12% 14% 17%Proportional 118 117 108 99 101 83(-21 - 255) (-20 - 253) (-19 - 234) (-17 - 215) (-18 - 218) (-15 - 181)Hybrid 121 121 118 109 103 85(-21 - 261) (-21 - 261) (-21 - 256) (-19 - 235) (-18 - 223) (-15 - 185)Percent Difference3% 3% 9% 10% 2% 2%Proportional 75 75 75 71 64 53(-108 - 257) (-108 - 257) (-108 - 257) (-101 - 242) (-92 - 219) (-76 - 182)Hybrid 84 84 82 75 72 59(-120 - 287) (-120 - 287) (-117 - 280) (-108 - 257) (-102 - 245) (-85 - 203)Percent Difference12% 12% 9% 6% 13% 11%Proportional 671 671 654 601 574 476(394 - 946) (394 - 946) (383 - 922) (352 - 847) (336 - 809) (279 - 672)Hybrid 682 682 652 599 583 484(400 - 961) (400 - 961) (383 - 920) (352 - 846) (342 - 822) (284 - 683)Percent Difference2% 2% 0% 0% 2% 2%Proportional 215 202 187 171 184 152(62 - 367) (58 - 345) (54 - 319) (49 - 293) (53 - 314) (44 - 260)Hybrid 224 208 192 176 192 160(64 - 381) (60 - 354) (55 - 328) (51 - 301) (55 - 328) (46 - 274)Percent Difference4% 3% 3% 3% 4% 5%3Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).New York, NYBaltimore, MDBirmingham, AL2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that JustMeet the Current and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m):1Based on location-specific single pollutant concentration-responsefunction 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. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence orcredible intervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationType of RollbackSt. Louis, MODetroit, MILos Angeles, CAF-45Table F-36. Sensitivity Analysis: Estimated Annual Incidence ofNon-Accidental Mortality Associated with Short-Term Exposureto PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007 PM2.5Concentrations:Comparison of Proportional and Hybrid Rollback Methods115/35214/35 13/35 12/35 13/30 12/25Proportional 227 214 198 182 194 161(92 - 360) (87 - 340) (80 - 315) (74 - 289) (79 - 308) (65 - 256)Hybrid 224 219 203 186 192 159(91 - 356) (89 - 348) (82 - 322) (75 - 296) (78 - 304) (64 - 253)Percent Difference3-1% 2% 3% 2% -1% -1%Proportional 34 32 29 26 29 24(-53 - 120) (-49 - 111) (-45 - 102) (-41 - 93) (-45 - 102) (-37 - 85)Hybrid 39 36 33 30 33 28(-60 - 137) (-56 - 127) (-51 - 116) (-47 - 106) (-51 - 116) (-43 - 99)Percent Difference15% 13% 14% 15% 14% 17%Proportional 121 120 111 102 104 86(-21 - 262) (-21 - 261) (-19 - 241) (-18 - 221) (-18 - 224) (-15 - 186)Hybrid 124 124 122 112 106 88(-22 - 269) (-22 - 269) (-21 - 264) (-20 - 242) (-19 - 230) (-15 - 191)Percent Difference2% 3% 10% 10% 2% 2%Proportional 77 77 77 72 65 54(-110 - 262) (-110 - 262) (-110 - 262) (-104 - 247) (-94 - 224) (-78 - 186)Hybrid 86 86 83 77 73 61(-123 - 293) (-123 - 293) (-120 - 286) (-110 - 262) (-105 - 250) (-87 - 207)Percent Difference12% 12% 8% 7% 12% 13%Proportional 734 734 715 657 627 521(431 - 1035) (431 - 1035) (419 - 1008) (385 - 927) (368 - 885) (305 - 735)Hybrid 746 746 714 656 638 530(438 - 1052) (438 - 1052) (419 - 1007) (385 - 926) (374 - 900) (310 - 748)Percent Difference2% 2% 0% 0% 2% 2%Proportional 225 211 195 179 192 160(65 - 384) (61 - 360) (56 - 333) (52 - 306) (55 - 328) (46 - 272)Hybrid 236 219 203 186 203 169(68 - 402) (63 - 374) (59 - 346) (54 - 318) (59 - 346) (49 - 289)Percent Difference5% 4% 4% 4% 6% 6%3Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).Los Angeles, CA2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.St. Louis, MONew York, NYIncidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that JustMeet the Current and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m):1Based on location-specific single pollutantconcentration-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. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervalsbased on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationType of RollbackDetroit, MIBirmingham, ALBaltimore, MDF-46Table F-37. Multi-Factor Sensitivity Analysis: Impact of Usinga Log-Linear vs. a Log-Log Model, Estimating Incidence Down tothe LowestMeasured Level (LML) in the Study vs. PRB,and Using a Proportional vs. a Hybrid Rollback to Estimate the Incidence of All Causeand Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet the CurrentStandards, Based on Adjusting 2005 PM2.5Concentrations1Modeling Choices:Fixed Effects (FE) Log-linear vs.Random Effects (RE) log-log modelFE Log-Linear FE Log-Linear FE Log-Linear FE Log-Linear RE Log-Log RE Log-Log RE Log-Log RE Log-LogDown to LML (5.8 ug/m3) vs. PRBLML LML PRB PRB LML LML PRB PRBProportional vs. hybrid rollback Proportional Hybrid Proportional Hybrid Proportional Hybrid Proportional Hybrid1342 1675 2845 3169 3360 3953 13557 14037(854 - 1827) (1066 - 2276)(1819 - 3853)(2027 - 4286) (2075 - 4615) (2446 - 5418) (8709 - 17917) (9035 - 18516)Percent Difference:3--- 25% 112% 136% 150% 195% 910% 946%1249 1545 2548 2813 2535 2947 8269 8475(1017 - 1477) (1261 - 1824)(2095 - 2983)(2318 - 3288) (1793 - 3232) (2095 - 3738) (6414 - 9670) (6602 - 9873)Percent Difference:--- 24% 104% 125% 103% 136% 562% 579%584 859 1254 3946(372 - 792)(550 - 1161)(779 - 1713) (2554 - 5176)Percent Difference:--- --- 47% --- 115% --- 576% ---369 591 639 1612(303 - 434)(489 - 688)(458 - 803) (1271 - 1859)Percent Difference--- --- 60% --- 73% --- 337% ---Incidence of Mortality Associated with Long-Term Exposure to PM2.5Concentrations Using:2Los Angeles, CAAll Cause MortalityIschemic Heart Disease Mortality2Estimates Based on Krewski et al. (2009),exposure period from 1999 - 2000. The fixedeffects log-linear estimates are from Table 33, using models with 44 individual and 7 ecological covariates; the randomeffects 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 intervals based on statisticaluncertainty surrounding the PM coefficient.3Percent differences are calculated relative to the model selections used inthe core analysis (fixed effects log-linear model;LML, and proportional rollbacks). So, for example, the percent difference in estimatedall cause mortality in Los Angeles resulting from changing from the core analysis inputselections to instead using (1) a fixedeffects log-linear model, (2) PRB, and(3) hybrid rollbacks is (3169 - 1342)/1342 =136%.4Philadelphia was not among the risk assessment urban areas for which hybrid rollbacks were calculated.Philadelphia, PAAll Cause MortalityIschemic Heart Disease Mortality1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.---------------------4---F-47Table F-38. Multi-Factor Sensitivity Analysis: Impact of Usinga Log-Linear vs. a Log-Log Model, Estimating Incidence Down tothe LowestMeasured Level (LML) in the Study vs. PRB,and Using a Proportional vs. a Hybrid Rollback to Estimate the Incidence of All Causeand Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet the CurrentStandards, Based on Adjusting 2006 PM2.5Concentrations1Modeling Choices:Fixed Effects (FE) Log-linear vs.Random Effects (RE) log-log modelFE Log-Linear FE Log-Linear FE Log-Linear FE Log-Linear RE Log-Log RE Log-Log RE Log-Log RE Log-LogDown to LML (5.8 ug/m3) vs. PRBLML LML PRB PRB LML LML PRB PRBProportional vs. hybrid rollback Proportional Hybrid Proportional Hybrid Proportional Hybrid Proportional Hybrid1108 1414 2627 2924 2904 3498 13255 13736(704 - 1509) (899 - 1923)(1678 - 3560)(1869 - 3959) (1790 - 3995) (2161 - 4803) (8501 - 17544) (8827 - 18146)Percent Difference:3--- 28% 137% 164% 162% 216% 1096% 1140%1038 1314 2366 2614 2212 2633 8151 8361(843 - 1229) (1070 - 1553)(1943 - 2775)(2150 - 3060) (1558 - 2833) (1864 - 3354) (6301 - 9561) (6491 - 9770)Percent Difference:--- 27% 128% 152% 113% 154% 685% 705%525 912 1166 3869(335 - 713)(585 - 1233)(723 - 1595) (2502 - 5082)Percent Difference:--- --- 74% --- 122% --- 637% ---334 559 598 1590(273 - 393)(461 - 651)(428 - 755) (1251 - 1837)Percent Difference--- --- 67% --- 79% --- 376% ---Incidence of Mortality Associated with Long-Term Exposure to PM2.5Concentrations Using:2Los Angeles, CAAll Cause MortalityIschemic Heart Disease MortalityPhiladelphia, PAAll Cause MortalityIschemic Heart Disease Mortality1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.---42Estimates Based on Krewski et al. (2009),exposure period from 1999 - 2000. The fixedeffects log-linear estimates are from Table 33, using models with 44 individual and 7 ecological covariates; the randomeffects 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 intervals based on statisticaluncertainty surrounding the PM coefficient.3Percent differences are calculated relative to the model selections used inthe core analysis (fixed effects log-linear model;LML, and proportional rollbacks). So, for example, the percent difference in estimatedall cause mortality in Los Angeles resulting from changing from the core analysis inputselections to instead using (1) a fixedeffects log-linear model, (2) PRB, and(3) hybrid rollbacks is (2924 - 1108)/1108 =164%.4Philadelphia was not among the risk assessment urban areas for which hybrid rollbacks were calculated.---------------------F-48Table F-39. Multi-Factor Sensitivity Analysis: Impact of Usinga Log-Linear vs. a Log-Log Model, Estimating Incidence Down tothe LowestMeasured Level (LML) in the Study vs. PRB,and Using a Proportional vs. a Hybrid Rollback to Estimate the Incidence of All Causeand Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet the CurrentStandards, Based on Adjusting 2007 PM2.5Concentrations1Modeling Choices:Fixed Effects (FE) Log-linear vs.Random Effects (RE) log-log modelFE Log-Linear FE Log-Linear FE Log-Linear FE Log-Linear RE Log-Log RE Log-Log RE Log-Log RE Log-LogDown to LML (5.8 ug/m3) vs. PRBLML LML PRB PRB LML LML PRB PRBProportional vs. hybrid rollback Proportional Hybrid Proportional Hybrid Proportional Hybrid Proportional Hybrid1170 1484 2697 3003 3034 3633 13430 13914(744 - 1593) (944 - 2019) (1723 - 3654) (1920 - 4064) (1871 - 4173) (2245 - 4986) (8616 - 17770) (8945 - 18375)Percent Difference:3--- 27% 131% 157% 159% 211% 1048% 1089%1094 1377 2426 2680 2306 2728 8243 8454(890 - 1296) (1122 - 1627)(1993 - 2845)(2205 - 3136) (1626 - 2950) (1933 - 3472) (6377 - 9662) (6568 - 9871)Percent Difference:--- 26% 122% 145% 111% 149% 653% 673%519---4907 --- 1157 --- 3864 ---(331 - 704)--- (581 - 1225) ---(718 - 1583)---(2498 - 5075)---Percent Difference:--- --- 75% --- 123% --- 645% ---330 --- 555 --- 594 --- 1589 ---(270 - 389)--- (459 - 647) ---(424 - 750)---(1249 - 1836)---Percent Difference--- --- 68% --- 80% --- 382% ---Los Angeles, CAPhiladelphia, PA3Percent differences are calculated relative to the model selections used inthe core analysis (fixed effects log-linear model;LML, and proportional rollbacks). So, for example, the percent difference in estimatedall cause mortality in Los Angeles resulting from changing from the core analysis inputselections to instead using (1) a fixedeffects log-linear model, (2) PRB, and(3) hybrid rollbacks is (3003 - 1170)/1170 =157%.Incidence of Mortality Associated with Long-Term Exposure to PM2.5Concentrations Using:2All Cause MortalityIschemic Heart Disease Mortality4Philadelphia 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 fixedeffects log-linear estimates are from Table 33, using models with 44 individualand 7 ecological covariates; the randomeffects 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 intervals based on statisticaluncertainty surrounding the PM coefficient.1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.All Cause MortalityIschemic Heart Disease MortalityF-49Table F-40. Sensitivity Analysis: Impact of Using Season-Specific vs. AnnualConcentration-Response Functions andProportional vs. Hybrid Rollbacks to Estimatethe Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting2005 PM2.5Concentrations1,2Modeling Choices:Seasonal C-R Functions vs. an All-YearFunctionAll Year All Year Sum of Four Seasons Sum of Four SeasonsProportional vs. Hybrid Rollback Proportional Hybrid Proportional Hybrid256 254 222 220(104 - 406) (103 - 402)---4---Percent Difference3--- -1% -13% -14%34 39 70 79(-53 - 121) (-61 - 137) --- ---Percent Difference--- 15% 106% 132%147 151 159 163(-26 - 317) (-26 - 325) --- ---Percent Difference--- 3% 8% 11%81 91 -23 -25(-117 - 278) (-130 - 311) --- ---Percent Difference--- 12% -128% -131%781 795 780 792(459 - 1102) (467 - 1121) --- ---Percent Difference--- 2% 0% 1%159 163 175 182(47 - 270) (48 - 277) --- ---Percent Difference--- 3% 10% 14%260 271 251 261(75 - 443) (78 - 462) --- ---Percent Difference--- 4% -3% 0%1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriate regionalmeans. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals basedon statistical uncertainty surrounding the PMcoefficient.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.3Percent differences are calculated relative to the model selections used in the core analysis (all-year C-R function and proportional rollback). So, for example, the percentdifference in estimated non-accidental mortality in Baltimore resulting from changing from the core analysis input selections to instead using the sum of four season-specific mortalityestimates and hybrid rollbacks is (192 - 225)/225 = -15%.4It 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 seasonspecific coefficient estimators was not available.Pittsburgh, PASt. Louis, MOLos Angeles, CANew York, NYBirmingham, ALDetroit, MIEstimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsBaltimore, MDF-50Table F-41. Sensitivity Analysis: Impact of Using Season-Specific vs. AnnualConcentration-Response Functions andProportional vs. Hybrid Rollbacks to Estimatethe Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting2006 PM2.5Concentrations1,2Modeling Choices:Seasonal C-R Functions vs. an All-YearFunctionAll Year All Year Sum of Four Seasons Sum of Four SeasonsProportional vs. Hybrid Rollback Proportional Hybrid Proportional Hybrid224 222 193 193(91 - 356) (90 - 352)---4---Percent Difference3--- -1% -14% -14%33 37 69 78(-51 - 116) (-58 - 132) --- ---Percent Difference--- 12% 109% 136%118 121 137 140(-21 - 255) (-21 - 261) --- ---Percent Difference--- 3% 16% 19%75 84 -25 -28(-108 - 257) (-120 - 287) --- ---Percent Difference--- 12% -133% -137%671 682 677 688(394 - 946) (400 - 961) --- ---Percent Difference--- 2% 1% 3%136 147 154 164(40 - 232) (43 - 249) --- ---Percent Difference--- 8% 13% 21%215 224 211 219(62 - 367) (64 - 381) --- ---Percent Difference--- 4% -2% 2%Estimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsBaltimore, MDBirmingham, ALDetroit, MILos Angeles, CANew York, NYPittsburgh, PASt. Louis, MO1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriate regionalmeans. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PMcoefficient.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.3Percent differences are calculated relative to the model selections used in the core analysis (all-year C-R function and proportional rollback). So, for example, the percentdifference in estimated non-accidental mortality in Baltimore resulting from changing from the core analysis input selections to instead using the sum of four season-specific mortalityestimates and hybrid rollbacks is (192 - 225)/225 = -15%.4It 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 seasonspecific coefficient estimators was not available.F-51Table F-42. Sensitivity Analysis: Impact of Using Season-Specific vs. AnnualConcentration-Response Functions andProportional vs. Hybrid Rollbacks to Estimatethe Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting2007 PM2.5Concentrations1,2Modeling Choices:Seasonal C-R Functions vs. an All-YearFunctionAll Year All Year Sum of Four Seasons Sum of Four SeasonsProportional vs. Hybrid Rollback Proportional Hybrid Proportional Hybrid227 224 207 194(92 - 360) (91 - 356)---4---Percent Difference3--- -1% -9% -15%34 39 95 86(-53 - 120) (-60 - 137) --- ---Percent Difference--- 15% 179% 153%121 124 166 137(-21 - 262) (-22 - 269) --- ---Percent Difference--- 2% 37% 13%77 86 -12 -8(-110 - 262) (-123 - 293) --- ---Percent Difference--- 12% -116% -110%734 746 887 750(431 - 1035) (438 - 1052) --- ---Percent Difference--- 2% 21% 2%143 147 225 162(42 - 244) (43 - 250) --- ---Percent Difference--- 3% 57% 13%225 236 248 232(65 - 384) (68 - 402) --- ---Percent Difference--- 5% 10% 3%St. Louis, MONew York, NYPittsburgh, PADetroit, MILos Angeles, CABirmingham, ALEstimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure toPM2.5Concentrations that Just Meet the Current StandardsBaltimore, MD1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriate regionalmeans. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals basedon statistical uncertainty surrounding the PMcoefficient.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.3Percent differences are calculated relative to the model selections used in the core analysis (all-year C-R function and proportional rollback). So, for example, the percentdifference in estimated non-accidental mortality in Baltimore resulting from changing from the core analysis input selections to instead using the sum of four season-specific mortalityestimates and hybrid rollbacks is (192 - 225)/225 = -15%.4It 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 seasonspecific 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 PM

2.5 

## Concentrations that Just Meet the

Current Standards, Based on Adjusting 2005 PM

2.5 

## Concentrations 1

, 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 PM 2.5 standards include an annual standard set at 15 ug/m 3 and a daily standard set at 35 ug/m 3

. 

### -74% 

### SO

2 

### 45%

74%

45%

2 Estimates based on Krewski et al. (2000) [reanalysis of the ACS study]. Mortality incidence was estimated for PM 2.5 concentrations down to

5.8 ug/m 3 (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% 

### NO 2



### O

3



### SO 2



### 0%



### NO 2



### O

3

# 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 PM

2.5 

## Concentrations that Just Meet the

Current Standards, Based on Adjusting 2006 PM

2.5 

## Concentrations 1

, 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%



### NO 2 

### 74%



### O

3 

### 45%



### SO 2 

### -74%



### Philadelphia, PA

None 0%



### CO 45%



### NO 2 

### 74%

1 The current primary PM 2.5 standards include an annual standard set at 15 ug/m 3 and a daily standard set at 35 ug/m 3

.

2 Estimates based on Krewski et al. (2000) [reanalysis of the ACS study]. Mortality incidence was estimated for PM 2.5 concentrations down to

5.8 ug/m 3 (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). 

### O

3 

### 45%



### SO

2 

### -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 PM

2.5 

## Concentrations that Just Meet the

Current Standards, Based on Adjusting 2007 PM

2.5 

## Concentrations 1

, 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 PM 2.5 standards include an annual standard set at 15 ug/m 3 and a daily standard set at 35 ug/m 3

.

2 Estimates based on Krewski et al. (2000) [reanalysis of the ACS study]. Mortality incidence was estimated for PM 2.5 concentrations down to

5.8 ug/m 3 (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). 

### O

3 

### 45%



### SO

2 

### -74% 

### CO 45%



### NO 2 

### 74% 

### SO 2 

### -74%



### Philadelphia, PA

None 0% 

### NO 2 

### 74%



### O

3 

### 46% 

### Los Angeles, CA

None 0%



### CO 46%F-55Table F-46. Sensitivity Analysis: Impact of Different Lag Models on Estimated Annual Incidence of HospitalAdmissions Associated with Short-Term Exposure to Ambient PM2.5Concentrations that Just Meet theCurrent Standards, Based on Adjusting 2005 PM2.5Concentrations1,20-Day Lag 1-Day Lag 2-Day Lag 0-Day Lag 1-Day Lag 2-Day Lag397 35 30 40 9 75(294 - 501) (-60 - 130) (-58 - 118) (-22 - 102) (-53 - 71) (16 - 133)159 14 12 13 3 25(118 - 200) (-24 - 52) (-23 - 47) (-7 - 34) (-18 - 24) (5 - 45)Risk AssessmentLocationCardiovascular Hospital Admissions Respiratory Hospital Admissions2Incidence estimates were calculated using the national concentration-response function estimates reported in Table 1 of Bell etal. (2008). Locationspecific C-R function estimates were not available from this study.1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Los Angeles, CAPhiladelphia, PAF-56Table F-47. Sensitivity Analysis: Impact of Different Lag Models on Estimated Annual Incidence of HospitalAdmissions Associated with Short-Term Exposure to Ambient PM2.5Concentrations that Just Meet theCurrent Standards, Based on Adjusting 2006 PM2.5Concentrations1,20-Day Lag 1-Day Lag 2-Day Lag 0-Day Lag 1-Day Lag 2-Day Lag373 33 28 38 9 70(276 - 470) (-56 - 122) (-54 - 110) (-21 - 96) (-50 - 67) (15 - 125)149 13 11 13 3 24(110 - 188) (-23 - 49) (-22 - 44) (-7 - 32) (-17 - 22) (5 - 42)Los Angeles, CAPhiladelphia, PA2Incidence estimates were calculated using the national concentration-response function estimates reported in Table 1 of Bell etal. (2008). Locationspecific C-R function estimates were not available from this study.Risk AssessmentLocationCardiovascular Hospital Admissions Respiratory Hospital Admissions1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.F-57Table F-48. Sensitivity Analysis: Impact of Different Lag Models on Estimated Annual Incidence of HospitalAdmissions Associated with Short-Term Exposure to Ambient PM2.5Concentrations that Just Meet theCurrent Standards, Based on Adjusting 2007 PM2.5Concentrations1,20-Day Lag 1-Day Lag 2-Day Lag 0-Day Lag 1-Day Lag 2-Day Lag388 34 29 39 9 73(287 - 489) (-59 - 127) (-56 - 115) (-21 - 99) (-52 - 69) (15 - 130)151 13 11 13 3 24(112 - 190) (-23 - 49) (-22 - 45) (-7 - 32) (-17 - 23) (5 - 43)2Incidence estimates were calculated using the national concentration-response function estimates reported in Table 1 of Bell etal. (2008). Locationspecific C-R function estimates were not available from this study.Los Angeles, CAPhiladelphia, PARisk AssessmentLocationCardiovascular Hospital Admissions Respiratory Hospital Admissions1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.F-58Table F-49Annual 24-Hr2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CM14/35 13/35 12/35 13/30 12/25Proportional15.0 14.2 14.0 13.3 13.0 12.3 12.0 11.4 13.0 12.3 11.8 11.2 11% 22% 34% 22% 35%Hybrid3--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Peak Shaving4--- --- --- --- --- --- --- --- --- --- 13.6 11.31 --- --- --- --- 35%Proportional14.8 13.1 14.0 12.5 13.0 11.6 12.0 10.7 12.7 11.3 10.7 9.5 9% 21% 33% 25% 49%Hybrid14.3 13.0 14.0 12.7 13.0 11.8 12.0 10.9 12.3 11.2 10.3 9.4 4% 16% 29% 25% 50%Peak Shaving15.2 13.6 --- --- --- --- --- --- 13.0 11.9 10.8 9.8 25% 49%Proportional15.0 12.7 14.0 11.8 13.0 11.0 12.0 10.2 13.0 11.0 11.1 9.4 12% 24% 36% 24% 47%Hybrid15.0 14.2 14.0 13.2 13.0 12.3 12.0 11.4 13.0 12.3 11.3 10.7 11% 22% 34% 22% 42%Peak Shaving--- --- --- --- --- --- --- --- --- --- 11.9 10.9 --- --- --- --- 47%Proportional12.8 11.4 12.8 11.4 12.8 11.4 12.0 10.7 12.8 11.4 12.0 10.7 196% 0% 13% 0% 13%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Peak Shaving--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Proportional14.1 11.4 14.0 11.4 13.0 10.6 12.0 9.8 12.2 9.9 10.2 8.3 1% 16% 30% 27% 55%Hybrid13.2 11.7 13.2 11.7 13.0 11.5 12.0 10.6 11.4 10.1 9.6 8.5 0% 3% 18% 27% 54%Peak Shaving13.9 12.6 --- --- --- --- --- --- 11.9 10.8 9.8 8.9 --- --- --- 27% 55%Proportional9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 8.6 8.6 7.3 7.3 0% 0% 0% 32% 64%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Peak Shaving9.8 9.9 9.8 9.9 9.8 9.9 9.8 9.9 8.4 8.5 6.9 7.0 0% 0% 0% 32% 64%Proportional15.0 12.5 14.0 11.7 13.0 10.9 12.0 10.1 13.0 10.9 12.0 10.1 12% 24% 36% 24% 36%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Peak Shaving--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Proportional12.7 9.5 12.7 9.5 12.7 9.5 12.0 9.0 10.9 8.2 9.2 7.0 0% 0% 13% 34% 68%Hybrid13.3 10.5 13.3 10.5 13.0 10.3 12.0 9.5 11.5 9.1 9.6 7.7 0% 5% 21% 30% 60%Peak Shaving13.9 12.0 13.9 12.0 13.9 12.0 --- --- 11.8 10.4 9.8 8.8 0%0%--- 34% 68%Proportional13.3 11.6 13.3 11.6 13.0 11.3 12.0 10.4 11.5 10.0 9.7 8.4 0% 5% 20% 27% 55%Hybrid13.6 11.8 13.6 11.8 13.0 11.3 12.0 10.4 11.7 10.2 9.8 8.5 0% 8% 22% 27% 54%Peak Shaving14.2 13.2 14.2 13.2 --- --- --- --- 12.1 11.5 10.1 9.5 0% --- --- 27% 55%Proportional13.9 12.3 13.9 12.3 13.0 11.6 12.0 10.7 11.9 10.7 10.0 9.0 0% 12% 25% 26% 52%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Peak Shaving15.5 12.9 15.5 12.9 --- --- --- --- 14.1 11.2 11.7 9.3 0% --- --- 26% 52%Proportional12.6 9.9 12.6 9.9 12.6 9.9 12.0 9.4 11.8 9.3 9.9 7.8 0% 0% 11% 14% 50%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Peak Shaving--- --- --- --- --- --- --- --- --- --- 10.1 8.9 --- --- --- --- 50%Phoenix, AZ9.912.6 32.0New York, NY13.8Philadelphia, PA13.415.0 38.015.9 42.0Houston, TX13.2Los Angeles, CA14.619.6 55.015.8 31.0Detroit, MI13.9Fresno, CA17.417.4 63.017.2 43.0Birmingham, AL15.7Dallas, TX11.412.8 26.018.7 44.0Atlanta, GA15.3Baltimore, MD13.915.6 37.016.2 35.0Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007Annual Average at Composite Monitor (2007CM) (in ug/m3)15/35214/35 13/35 12/35 13/30 12/25Maximum 3yr Monitor-Specific Average andAnnual Composite Monitor Value Given Different Rollback Methods (with comparison ofpercent reduction in surrogate for long-term mortality risk across rollback methods)Percent reduction in a surrogate forlong-term exposure-related mortality(alternative standard compared withcurrent standard)6Risk AssessmentLocation1RollbackMethodDesign ValueRecentAirQuality(2007)F-59Table F-49Annual 24-Hr2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CM14/35 13/35 12/35 13/30 12/25Proportional13.3 11.6 13.3 11.6 12.8 11.2 11.810.5 11.5 10.0 9.7 8.4 0% 7% 19% 27% 54%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Peak Shaving15.6 13.1 15.6 13.1 15.3 11.7 15.311.0 15.6 11.2 13.8 9.3 0% 20% 29% 26% 52%Proportional7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 6.7 6.6 5.7 5.6 0% 0% 0% 55% 110%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Peak Shaving10.8 9.5 10.8 9.5 10.8 9.5 10.8 9.5 10.8 8.6 8.9 7.4 0% 0% 0% 24% 58%Proportional14.9 12.9 14.0 12.1 13.0 11.3 12.010.4 12.8 11.1 10.8 9.3 10% 23% 35% 25% 50%Hybrid15.0 13.5 14.0 12.6 13.0 11.7 12.010.8 13.0 11.7 11.0 9.9 12% 23% 35% 23% 47%Peak Shaving16.5 14.1 --- --- --- --- --- --- 14.1 12.3 11.7 10.2 --- --- --- 25% 50%Proportional8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 7.4 7.0 6.3 6.0 0% 0% 0% 46% 93%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Peak Shaving8.3 7.8 8.3 7.8 8.3 7.8 8.3 7.8 7.1 6.7 5.9 5.5 0% 0% 0% 57% 114%(cont'd) Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with comparisonof percent reduction in surrogate for long-term mortality risk across rollback methods)6Percent reduction in composite monitor value (CMV) with consideration for LML of 5.8 ug/m3. Percent reduction = (CMVcurrent standard- CMValternative standard)/(CMVcurrernt standard-LML). Notethat greyed cells identify instances where percent change differs by >10% across alternative rollback methods (for a given alternative standard level/study area combination).Percent reduction in a surrogate forlong-term exposure-related mortality(alternative standard compared withcurrent standard)61For some locations (e.g., Atlanta) more than one "version" (group of counties) was used in the risk assessment. In this tableonly the version that was used for mortality associated withshort-term exposure to PM2.5(Zanobetti and Schwartz, 2009) is included.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.3The 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 applied to thatRisk AssessmentLocation1RollbackMethodDesign ValueRecentAirQuality(2007)Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007Annual Average at Composite Monitor (2007CM) (in ug/m3)15/35214/35 13/35 12/35 13/30 12/25Pittsburgh, PA514.919.8 60.0Salt Lake City, UT11.4St. Louis, MO14.316.5 39.011.6 55.0Tacoma, WA9.74The 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 combinationindicates 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.5The 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 3.2.3.3 for10.2 43.0F-60Table F-50.Annual 24-Hr 2007 CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CM15/35 14/35 13/35 12/35 13/30 12/25Proportional15.0 14.2 14.0 13.3 13.0 12.312.0 11.4 13.0 12.3 11.8 11.2Hybrid3--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Peak Shaving4--- --- --- --- --- --- --- --- --- --- 13.6 11.31--- --- --- --- --- 1%Proportional14.8 13.1 14.0 12.5 13.0 11.612.0 10.7 12.7 11.3 10.7 9.5Hybrid14.3 13.0 14.0 12.7 13.0 11.812.0 10.9 12.3 11.2 10.3 9.4-2% 4% 4% 4% -2% -3%Peak Shaving15.2 13.6 --- --- --- --- --- --- 13.0 11.9 10.8 9.86%--- --- --- 8% 7%Proportional15.0 12.7 14.0 11.8 13.0 11.012.0 10.2 13.0 11.0 11.1 9.4--- --- --- --- ---Hybrid15.0 14.2 14.0 13.2 13.0 12.312.0 11.4 13.0 12.3 11.3 10.718% 19% 20% 21% 20% 26%Peak Shaving--- --- --- --- --- --- --- --- --- --- 11.9 10.9------ --- --- --- 29%Proportional12.8 11.4 12.8 11.4 12.8 11.412.0 10.7 12.8 11.4 12.0 10.7Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Peak Shaving--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Proportional14.1 11.4 14.0 11.4 13.0 10.6 12.0 9.8 12.2 9.9 10.2 8.3Hybrid13.2 11.7 13.2 11.7 13.0 11.512.0 10.6 11.4 10.1 9.6 8.54% 6% 16% 18% 5% 7%Peak Shaving13.9 12.6 --- --- --- --- --- --- 11.9 10.8 9.8 8.917%--- --- --- 18% 19%Proportional9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 8.6 8.6 7.3 7.3Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Peak Shaving9.8 9.9 9.8 9.9 9.8 9.9 9.8 9.9 8.4 8.5 6.9 7.00%0% 0% 0% -5% -21%Proportional15.0 12.5 14.0 11.7 13.0 10.912.0 10.1 13.0 10.9 12.0 10.1Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Peak Shaving--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Proportional12.7 9.5 12.7 9.5 12.7 9.5 12.0 9.0 10.9 8.2 9.2 7.0Hybrid13.3 10.5 13.3 10.5 13.0 10.3 12.0 9.5 11.5 9.1 9.6 7.721% 21% 17% 13% 26% 38%Peak Shaving13.9 12.0 13.9 12.0 13.9 12.0 --- --- 11.8 10.4 9.8 8.840%40% 40% --- 47% 60%Proportional13.3 11.6 13.3 11.6 13.0 11.312.0 10.4 11.5 10.0 9.7 8.4Hybrid13.6 11.8 13.6 11.8 13.0 11.312.0 10.4 11.7 10.2 9.8 8.53% 3% 0% 0% 4% 5%Peak Shaving14.2 13.2 14.2 13.2 --- --- --- --- 12.1 11.5 10.1 9.522%22% --- --- 26% 30%Proportional13.9 12.3 13.9 12.3 13.0 11.612.0 10.7 11.9 10.7 10.0 9.0Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Peak Shaving15.5 12.9 15.5 12.9 --- --- --- --- 14.1 11.2 11.7 9.38%8% --- --- 10% 9%Proportional12.6 9.9 12.6 9.9 12.6 9.9 12.0 9.4 11.8 9.3 9.9 7.8Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Peak Shaving--- --- --- --- --- --- --- --- --- --- 10.1 8.9--- --- --- --- --- 35%Proportional13.3 11.6 13.3 11.6 12.8 11.211.8 10.5 11.5 10.0 9.7 8.4Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Peak Shaving15.6 13.1 15.6 13.1 15.3 11.715.3 11.0 15.6 11.2 13.8 9.321%21% 8% 10% 22% 24%19.8 60.0RiskAssessmentLocation1RollbackMethodDesign ValueRecent AirQuality(2007)Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007Annual Average at Composite Monitor (2007CM) (in ug/m3)15/35214/35 13/35 12/35 13/30 12/25Atlanta, GA15.3Baltimore, MD13.915.6 37.016.2 35.0Birmingham,AL15.7Dallas, TX11.412.8 26.018.7 44.0Detroit, MI13.9Fresno, CA17.417.4 63.017.2 43.0Houston, TX13.2Los Angeles,CA14.619.6 55.015.8 31.0New York, NY13.8Philadelphia,PA13.415.0 38.015.9 42.0Phoenix, AZ9.9Pittsburgh, PA514.912.6 32.0cells used as basis for calculationcells used as basis for calculationcells used as basis for calculationcells used as basis for calculationPercent difference between compositemonitor value with hybrid or peak shavingcompared with proportional(surrogate fordifference in long-term exposure-relatedmortality)6cells used as basis for calculationcells used as basis for calculationcells used as basis for calculationMaximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with percent difference insurrogate for long-term exposure-related mortality across rollback methods)cells used as basis for calculationcells used as basis for calculationcells used as basis for calculationcells used as basis for calculationF-61Table F-50.Annual 24-Hr 2007 CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CM15/35 14/35 13/35 12/35 13/30 12/25Proportional7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 6.7 6.6 5.7 5.6Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Peak Shaving10.8 9.5 10.8 9.5 10.8 9.5 10.8 9.5 10.8 8.6 8.9 7.453%53% 53% 53% 72% 111%Proportional14.9 12.9 14.0 12.1 13.0 11.3 12.0 10.4 12.8 11.1 10.8 9.3Hybrid15.0 13.5 14.0 12.6 13.0 11.7 12.0 10.8 13.0 11.7 11.0 9.98% 6% 7% 7% 10% 13%Peak Shaving16.5 14.1 --- --- --- --- --- --- 14.1 12.3 11.7 10.215%--- --- --- 18% 19%Proportional8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 7.4 7.0 6.3 6.0Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Peak Shaving8.3 7.8 8.3 7.8 8.3 7.8 8.3 7.8 7.1 6.7 5.9 5.5-9%-9% -9% -9% -36% 157%13/35 12/3511.6 55.013/30 12/25(cont'd) Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with percentdifference in surrogate for long-term exposure-related mortality across rollback methods)RiskAssessmentLocation1RollbackMethodDesign ValueRecent AirQuality(2007)Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007Annual Average at Composite Monitor (2007CM) (in ug/m3)15/35214/35Percent difference between compositemonitor value with hybrid or peak shavingcompared with proportional(surrogate fordifference in long-term exposure-relatedmortality)62The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.3The hybrid rollback method was applied to only a subset of therisk assessment locations. The "---" for a given location indicates that the hybrid rollback method was not applied to thatlocation.4The 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 combinationindicates 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, WA9.7Salt Lake City,UT11.4St. Louis, MO14.36Percent 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 hybrid-LML). Notethat greyed cells identify instances where two values differ by>25% across alternative rollback methods (for a given alternative standard level/study area combination).cells used as basis for calculationcells used as basis for calculationcells used as basis for calculation1For some locations (e.g., Atlanta) more than one "version" (group of counties) was used in the risk assessment. In this tableonly the version that was used for mortality associated withshort-term exposure to PM2.5(Zanobetti and Schwartz, 2009) is included.5The 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 3.2.3.3 fordetails.16.510.239.043.0

# G-1 

# APPENDIX G: SUPPLEMEMN T TO THE NATIONAL-SCALE

ASSESSMENT OF LONG-TERM MORTALITY RELATED TO PM 2.5



# EXPOSURE

# G-2 Appendix G. National-Scale Assessment of Long-Term Mortality Related to PM 2.5 

# 1

Exposure (additional technical detail reg arding inputs used in the analysis) 2

3

4

This technical appendix includes additional details regarding the inputs to the national5

scale current conditions health impact analysis . Below we present air quality modeling, exposure 6

and risk information. 7

8



# Air Quality Modeled Inputs 9

The Community Model for Air Quality (CM AQ) model was used to estimate annual 10

PM 2.5 

# concentrations for the year 2005 for the contin ental US. These data were then combined 11

with ambient monitored PM 2.5 

# measurements to create “fused ” spatial surfaces supplied to 12

BenMAP. 13

CMAQ Model Application and Evaluation 14

CMAQ is a non-proprietary computer model th at simulates the formation and fate of 15

photochemical oxidants, including PM 2.5 

# and ozone, for given input sets of meteorological 16

conditions and emissions. This analysis empl oyed a version of CMAQ based on the latest 17

publicly released version (i.e. CMAQ version 4.7 2 

# ). 18

Model Domain and Grid Resolution 19

The CMAQ modeling analyses we re performed for two domains covering the continental 20

United States, as shown in Figure G-1. These dom ains consist of a horizontal grid of 36 km 21

covering the entire continental US and a finer-s cale 12-km grid covering the Eastern U.S. The 22

model extends vertically from the surface to 100 millibars (approximately 15 km) using a sigma23

pressure coordinate system. The 36-km grid was used to establish the incoming air quality 24

concentrations along the boundaries of the 12-km grids. Tabl e G-1 provides some basic 25

geographic information regarding the CMAQ domains. The 36-km and both 12-km CMAQ 26

modeling domains were modeled for the entire ye ar of 2005. All 365 model days were used in 27

the annual average levels of PM 2.5



# . 28

29

2 

## CMAQ version 4.7 was released on D ecember 1, 2008. It is availabl e from the Community Modeling and

Analysis System (CMAS) a t: http://www.cmascenter.org.

# G-3 Table G-1. Geographic Information for Modeling Domains 1

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

Figure G-1. Map of the CMAQ Modeling Domain (Note, the black outer box denotes the 5

36-km national modeling domain; the red inne r box is the 12-km Eastern U.S. fine 6

grid). 7

8

# G-4 CMAQ Model Inputs 1

Emissions: 2

The 2005 emissions inputs to CMAQ included five source sectors: a) Electric Generating 3

Units (EGUs); b) Other Stationary Sources (P oint and Nonpoint); c) Onroad and Nonroad 4

Mobile Sources; d) Biogenic Emissions; and e) Fi res. The fires portion of the inventory included 5

emissions from wildfires and prescribed bur ning computed as hour-specific point sources. 6

Electric Generating Units (EGUs) 7

Annual emissions estimates for EGUs for a ll National Emissions Inventory (NEI) air 8

pollutants for 2005 were developed using data re ported to the USEPA’s Clean Air Marketing 9

Division’s (CAMD) Acid Rain database. The Acid Rain database contains hourly emissions for 10

SO2 and NOx emissions plus hourly heat input amount s. These three values are reported to the 11

database by the largest electri c generating facilities, usually based upon Continuous Emissions 12

Monitors (CEMs). For all pollutants except the di rectly monitored SO2 and NOx, the ratio of the 13

Acid Rain heat input for 2005 to the Acid Rain heat input for 2002 was used as the adjusting 14

ratio to estimate the 2005 emissions. 15

Other Stationary Source s (Point and Nonpoint) 16

Emission estimates for other stationary sources including both point and nonpoint 17

stationary sources were held constant at the level in Vers ion 3 of the 2002 NEI. The only 18

exception to this was that some information on pl ants that closed after 2002 was incorporated 19

into the emissions modeled. Emissions for plants that closed were set to zero. U.S. EPA, 2008c 20

provides complete documentation on the development of the 2002 NEI. 21

Onroad and Nonroad Mobile Sources 22

Emission estimates for all pollutants were developed using EPA’s National Mobile 23

Inventory Model (NMIM), which uses MOBILE6 to calculate onroad emission factors. A full 24

VMT database at the county, roadway type, and ve hicle type level of de tail was developed from 25

Federal Highway Administration (FHWA) informa tion. However, state and local agencies had 26

the opportunity to provide model inputs (vehicle popul ations, fuel characteristics, VMT, etc) for 27

2002 and 2005. If the state or lo cal area submitted 2005 VMT estimat es, these data were used. 28

However, if the state or local area only provided 2002 VMT estimates that were incorporated in 29

the 2002 NEI, the 2002 NEI VMT data were grown to 2005 using growth factors developed from 30

# G-5 the FHWA data, and these grown VMT data re placed the baseline FHWA-based VMT data. 1

Otherwise, the FHWA-based VMT data were used. 2

Emission estimates for NONROAD model engi nes were developed using EPA’s National 3

Mobile Inventory Model (NMIM), which in corporates NONROAD2005. Wh ere states provided 4

alternate nonroad inputs, these data replaced EPA default inputs, as described above. For more 5

information on how NMIM is run, refer to the 2005 NEI documentation posted at 6

ftp://ftp.epa.gov/EmisInventory/2005_nei/m obile/2005_mobile_nei_version_2_report.pdf. 7

Fires 8

Fires in the 2005 emissions inventory were modeled with the same methodology as used 9

for the 2002 NEI (U.S. EPA, 2008). However, as described in Raffuse et al., 2008, the wildland 10

fire emission inventories for 2005 were produ ced using the BlueSky framework for the 11

conterminous United States, which used the Sate llite Mapping Automatic Reanalysis Tool for 12

Fire Incident Reconciliation (SMARTFIRE) as th e fire information source. SMARTFIRE is an 13

algorithm and database system designed to reconcile these disparate fire information sources to 14

produce daily fire location and size information (Sullivan et al., 2008). 15

Biogenic Emissions 16

Biogenic emissions were computed for CMAQ based on 2005 meteorol ogy data using the 17

BEIS3.13 model (Schwede, et. al, 2005) from the Sparse Matrix Operator Kernel Emissions 18

(SMOKE). The BEIS3.13 model creates gridde d, hourly, model-species emissions from 19

vegetation and soils. It estimates CO, VOC, and NOX emissions for the U.S., Mexico, and 20

Canada. The inputs to BEIS include: 21

• temperature data at 10 meters which were obtained from the CMAQ 22

meteorological input files, and 23

• land-use data from the Biogenic Em issions Landuse Database, version 3 24

(BELD3), which provides data on the 230 vegeta tion classes at 1 km resolution over most 25

of North America. 26

Meteorological Input Data: 27

The gridded meteorological input data for the entire year of 2005 were derived from 28

simulations of the Pennsylvania State University / National Center for Atmospheric Research 29

Mesoscale Model. This model, commonly refe rred to as MM5, is a limited-area, nonhydrostatic, 30

terrain-following system that solves for the full set of physical and thermodynamic equations 31

# G-6 which govern atmospheric motions (Grell et al., 1994) . Meteorological mode l input fields were 1

prepared separately for both of the domains shown in Figure G-1 usi ng MM5 version 3.7.4. The 2

MM5 simulations were run on the same map projection as CMAQ. 3

Both meteorological model runs were configur ed similarly. The selections for key MM5 4

physics options are shown below: 5

 Pleim-Xiu PBL and land surface schemes 6

 Kain-Fritsh 2 cumulus parameterization 7

 Reisner 2 mixed phase moisture scheme 8

 RRTM longwave radiation scheme 9

 Dudhia shortwave radiation scheme 10

11

Three dimensional analysis nudging for temper ature and moisture was applied above the 12

boundary layer only. Analysis nudging for the wind field was applied above and below the 13

boundary layer. The 36 km domain nudging weighting factors were 3.0 x 10 4 

# for wind fields and 14

temperatures and 1.0 x 10 5 

# for moisture fields. The 12 km domain nudging weighting factors 15

were 1.0 x 10 4 

# for wind fields and te mperatures and 1.0 x 10 5 

# for moisture fields. 16

All model runs were conducted in 5.5 day se gments with 12 hours of overlap for spin-up 17

purposes. Both domains contained 34 vertical layers with an approximately 38 m deep surface 18

layer and a 100 millibar top. The MM5 and CMAQ ve rtical structures are shown in Table G-2 19

and do not vary by horizontal grid resolution. 20

21

22

23

Table G-2. Vertical Layer Structure for MM5 and CMAQ (heigh ts are layer top). 24

CMAQ

La y ers MM5

La y ers Sigma P Approximate

Hei g ht ( 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

La y ers MM5

La y ers Sigma P Approximate

Hei g ht ( 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

The meteorological outputs from the MM5 sets were processed to create model-ready 2

inputs for CMAQ using the Meteorology-Chemistry Interface Pro cessor (MCIP), version 3.4, to 3

derive the specific inputs to CMAQ. 4

Before initiating the air quality simulations, it was important to identify the biases and 5

errors associated with the meteorological m odeling inputs. The 2005 MM5 model performance 6

evaluations used an approach which include d a combination of qualitative and quantitative 7

analyses to assess the adequacy of the MM5 si mulated fields. The qua litative aspects involved 8

comparisons of the model-estimated synoptic patterns against obser ved patterns from historical 9

weather chart archives. Additionally, the eval uations compared spatial patterns of monthly 10

average rainfall and monthly maximum planetar y boundary layer (PBL) heights. Qualitatively, 11

the model fields closely matched the observed sy noptic patterns, which is not unexpected given 12

the use of nudging. The operational evaluati on included statistical comparisons of 13

model/observed pairs (e.g., mean normalized bias, mean normalized error, index of agreement, 14

root mean square errors, etc.) for multiple meteorological parameters, including temperature, 15

humidity, shortwave downward radiation, wind speed, and wind direction (Baker and Dolwick, 16

# G-8 2009a, Baker and Dolwick, 2009b). It was ultimately determined that the bias and error values 1

associated with the 2005 meteorological data were generally within the range of past 2

meteorological modeling results that have been used for air quality applications. 3

Initial and Boundary Conditions: 4

The lateral boundary and init ial species concentrations are provided by a three5

dimensional global atmospheric chemistry mode l, the GEOS-CHEM model (Yantosca, 2004). 6

The global GEOS-CHEM model simulates atmosphe ric chemical and physical processes driven 7

by assimilated meteorological observations from the NASA’s Goddard Earth Observing System 8

(GEOS). This model was run for 2002 with a grid resolution of 2.0 degrees x 2.5 degrees 9

(latitude-longitude) and 24 vertical layers. The 2005 CMAQ 36km simulation used non-year 10

specific GEOS-CHEM data, which was created by taking the median value for each month in 11

each individual grid cell of the 2002 GEOS-CHEM da ta described above. The predictions were 12

used to provide one-way dynamic boundary conditions and an initial concen tration field for the 13

CMAQ simulations. More information is avai lable about the GEOS-CHEM model and other 14

applications using this tool at: http ://www-as.harvard.edu/chemistry/trop/geos. 15

CMAQ Model Performance Evaluation 16

An operational model performance evaluation for PM 2.5 

# and its related speciated 17

components was conducted for 2005 using state/local monitoring sites data in order to estimate 18

the ability of the CMAQ modeling system to repl icate the concentrations for the 12-km Eastern 19

domain and 36-km domain in the west. The prin cipal evaluation statisti cs used to evaluate 20

CMAQ performance included two bias metrics, norm alized mean bias and fractional bias; and 21

two error metrics, normalized mean error and fr actional error. For the 12-km Eastern domain, 22

performance evaluation statistics were computed fo r the entire domain as well as its subregions. 23

For the 36-km domain, evaluation focuses on th e parts of the US not covered by the 12-km 24

Eastern domain by computing performance evaluati on statistics for the states included in the 25

Western Regional Air Partnership (WRAP). 26

The PM 2.5 

# evaluation focuses on PM 2.5 

# total mass and its com ponents, including sulfate 27

(SO4), nitrate (NO3), total nitrate (TNO3 = NO3 + HNO3), ammonium (NH4), elemental carbon 28

(EC), and organic carbon (OC). PM2.5 ambient measurements for 2005 were obtained from the 29

following networks for model evaluation: Speci ation Trends Network (STN), Interagency 30

Monitoring of PROtected Visual Environments (IMPROVE), and Clean Air Status and Trends 31

Network (CASTNET). For PM2.5 species that are measured by more than one network, we 32

calculated separate sets of statistics for each network. Ta ble G-3 provides annual model 33

performance statistics for PM 2.5 

# and its component species. Based on the bias and error values 34

# G-9 associated with the 2005 CMAQ-modeled PM 2.5 

# concentration data, it was determined that the 1

annual average PM 2.5 

# data were generally within the range of past modeling results used for air 2

quality applications and are app licable to be used for this national-scale current conditions 3

analysis. 4

5

Table G-3. CMAQ modeled performance ev aluation statistics for PM2.5 for 2005. 6

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

(NO 3 

## +HNO 3 

## ) 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

“Fused” Spatial Surfaces 3

Spatial surfaces of the 20 05 data were created by fusing CMAQ-modeled annual average 4

PM2.5 concentrations with total PM 2.5 

# data from STN, IMPROVE , and CASTNET monitoring 5

sites for the two domains shown in Figure 1. We used the EPA’s Model Attainment Test 6

Software (MATS) (Abt, 2009) which employ ees the Voronoi Nei ghbor Averaging (VNA) 7

interpolation technique (Abt, 2008). This technique identifies the set of m onitors that are nearest 8

to the center of each grid cell, and then takes an inverse distance squared weighted average of the 9

monitor concentrations. The “fused” spatial fiel ds are calculated by adjusting the interpolated 10

ambient data (in each grid cell) up or down by a multiplicative factor calculated as the ratio of 11

the modeled concentration at the grid cell divi ded by the modeled concentration at the nearest 12

neighbor monitor locations (w eighted by distance). 13

To create the spatial surfaces for use in BenMAP, the 2005 CMAQ-modeled annual 14

average PM 2.5 

# concentrations were “fused” with 2005 total PM 2.5 

# ambient monitoring data from 15

STN, IMPROVE, and CASTNET sites. This wa s done for both the 36km national domain and 16

the 12km eastern US domain. The spatial surface of annual aver age PM2.5 air quality 17

concentrations produced by this technique is show n in Figure G-2 for the continental U.S. Where 18

available, the 12km spatial surface was used to supply BenMAP with annual average PM 2.5 

# 19

concentrations. In the western part of the U.S., annual average PM 2.5 

# concentrations were 20

supplied from the 36km domain. 21

22

# G-13 Figure G-2: 2005 Predicted Annual Mean PM 2.5 

# Levels 1

2

3

4

Advantages and Limitations 5

As compared to using monitored data al one, an advantage of using the CMAQ model 6

output for comparing with health outcomes is that it has the potential to provide more complete 7

spatial and temporal coverage. In addition, “f using” the CMAQ data with ambient monitoring 8

data allows for an improvement over non-fused fields (Timin et al., 2009). Doing so allows for a 9

combination of the advantages of both sets of data : better spatial coverage and more accurate air 10

quality estimates. Of course, the more accurate the model estimates of PM 2.5 

# , the better the 11

performance of the “fused” spatial fields. Theref ore, it is important to use model outputs that 12

have adequate PM 2.5 

# performance. As discussed a bove, we believe that the 2005 CMAQ13

modeled PM 2.5 

# concentration data showed adequate m odel performance to be used for this 14

national-scale current conditi ons analysis. 15

As with any model estimate of air quality, there are limitations. For example, the 16

emissions and meteorological data used in CMAQ can each have large uncertai nties, in particular 17

# G-14 for unusual emission or meteorological events. Th ere are also uncertaintie s associated with the 1

chemical transformation and fate process algorithms used in air quality models. For these 2

reasons, CMAQ predicts best on longer time scale bases (e .g., synoptic, monthly, and annual 3

scales). These limitations have led us to use modeled air quality estimates in this analysis that 4

are “fused” with measured ambient data and averaged over an annual scale. 5

Air Quality Estimates 6

Figures G-3 through G-6 below il lustrate the spatial distribu tion of air quality impacts. 7

Figure 1 illustrates the modeled 2005 PM 2.5 

# air quality levels across the U.S. Figures 2 and 3 8

display the PM2.5 air quality levels after being adjusted so that the maximum level is no higher 9

than the LML reported in the Krewski et al. (200 9) and Laden et al. ( 2006) studies. Figure G-4 10

displays the PRB by region of the county. 11

# G-15 Figure G-3: 2005 Predicted Annual Mean PM 2.5 

# Levels

# G-16 Figure G-4: 2005 Predicted Annual Mean PM 2.5 

# Levels Adjusted for LML of the

Krewski et al. (2009) study

# G-17 Figure G-5: 2005 Predicted Annual Mean PM 2.5 

# 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 di stribution of grid cells at different baseline PM 2.5 

# air quality

levels. Figures G-8 through G-10 displays the distribution of grid ce lls according to the

incremental change in PM 2.5 

# air quality for each of three s cenarios: 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 PM 2.5 

# 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/m 3 

# ) (Current Conditions – 10

μg/m 3 

# )

# G-21 Figure G-9: The Number of CMAQ Grid Cells Experiencing an Incremental

Change in Annual Mean PM 2.5 

# (μg/m 3 

# ) (Current Conditions – 5.8

μg/m 3 

# )

# G-22 Figure G-10: The Number of CMAQ Grid Cells Experiencing an Incremental

Change in Annual Mean PM 2.5 

# (μg/m 3 

# ) (Current Conditions – Policy

Relevant Background)

Figure G-11 displays the cumulative distri bution 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 PM 2.5 

# Concentrations (μg/m 3 

# )

# G-24 Figure G-12: Cumulative Distribution of PM 2.5 

# (μg/m 3 

# ) Changes (Baseline – 10

μg/m 3 

# )

# G-25 Figure G-13: Cumulative Distribution of PM 2.5 

# (μg/m 3 

# ) Changes (Baseline – 5.8

μg/m 3 

# )

# G-26 Figure G-14: Cumulative Distribution of PM 2.5 

# (μg/m 3 

# ) (Baseline – Policy Relevant

Background)

Exposure Estimates

Below we provide additional details regardi ng 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 AC S cohort) for each scenario.

Population-weighted air quality change is the average per-person change in PM 2.5 

# . It is

estimated by calculating the summation of the popul ation in each grid cell multiplied against the

change in annual mean PM 2.5 

# concentration in that grid ce ll and then dividing by the total

population.

# G-27 Table G-4. Estimated Change in Annual Mean Population-Weighted PM 2.5 

# by

Model Scenario

Model scenario Population-weighted air quality change or

baseline

Current conditions to 10 μg/m 3 

# 2.6 μg/m 3



# Current conditions to 5.8 μg/m 3 

# 6.3 μg/m 3



# Current conditions to PRB 11 μg/m 3



# Current conditions 12 μg/m 3



# Health Impact Estimates

Figure G-15 through G-17 illustrate the distribut ion of total mortality attributable to

PM 2.5 

# exposure for each of three scenar ios: current conditions to 10 μg/m 3 

# , 5.8 μg/m 3 

# and PRB.

# G-28 Figure G-15: The Percentage of To tal Mortality Attributable to PM 2.5 

# Exposure:

Baseline – 10 μg/m 3

# G-29 Figure G-16: The Percentage of To tal Mortality Attributable to PM 2.5 

# Exposure:

Baseline – 5.8 μg/m 3

# G-30 Figure G-17: The Percentage of To tal Mortality Attributable to PM 2.5 

# Exposure:

Baseline – Policy Relevant Background

Figures G-18 through G-20 illustrate the cu mulative distribution of total mortality

attributable to PM 2.5 

# exposure for each of three scen arios: current conditions to 10 μg/m 3 

# , 5.8

μg/m 3 

# and PRB.

# G-31 Figure G-18: The Cumulative Distribution of the Percentage of Total Mortality

Attributable to PM 2.5 

# Exposure: Baseline – 10 μg/m 3

# G-32 Figure G-19: The Cumulative Distribution of the Percentage of Total Mortality

Attributable to PM2.5 Exposure: Baseline – 5.8 μg/m 3

# G-33 Figure G-20: The Cumulative Distribution of the Percentage of Total Mortality

Attributable to PM 2.5 

# Exposure: Baseline – Policy Relevant Background

# H-1 

# APPENDIX H: CONSIDERATION OF RISK ASSOCIATED WITH

EXPOSURE TO THORACI C COARESE PM (PM 10-2.5 

# )

# H-2 H.1 OVERVIEW

This appendix discusses the issue of assessing public he alth risk associated with

exposure to thoracic coarse PM (PM 10-2.5 

# ). As mentioned in Section 2.6, due to

limitations in available monitoring data characterizing ambient levels of PM 10-2.5 

# in

prospective urban study areas, together with limitations in the epidemiological study data

available for deriving C-R functions for this PM size fraction, EP A staff has concluded

that uncertainties in characterizing risk for PM 10-2.5 

# are potentially si gnificant 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 PM 10-2.5 

# risk assessment for this

review; instead, we have included a summary of risk estimates for PM 10-2.5 

# generated as

part of the last PM NAAQS review completed in 2005. 3



# As part of our summarizing PM 10-2.5 

# risk estimates from the last review below in

section H.2, we have included a discussion of the limitations and un certainties associated

with those risk estimates which resulted in the decision by EPA not to use those risk

estimates in recommending specific standa rd levels (USEPA, 2006 – Final Rule FR

Notice, p. 61178). This discus sion provides the basis for a mo re detailed discussion (in

Section H.3) of our ratio nale for not conducting a PM 10-2.5 

# risk assessment as part of the

current review. Specifically, in Section H-3, we consider each of the limitations in the

PM 10-2.5 

# risk assessment from the last review a nd assess whether data available since the

last review, including more recent ambient monitoring data and epidemiological study

data, address these limitation. Our conclusi on is that additional information on PM 10-2.5



# that has become available since the last re view does not substantially reduce overall

uncertainty associated with modeling risk fo r this PM size fraction, and consequently, we

conclude that conducting a PM 10-2.5 

# risk assessment is not supported at this time.

H.2 SUMMARY OF PM 10-2.5 

# RISK ESTIMATES GENERATED FOR

THE PREVIOUS REVIEW

This section provides a brief overview of the approach used in completing the

PM 10-2.5 

# risk assessment for the previous re view 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 PM 10-2.5 

## 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 change d. Conclusions reached in the last review, that PM 10-

2.5 

## risk estimates should not be used in recommending spec ific standard levels, still holds. Rather, we have

included a summary of the PM 10-2.5 

## risk assessment completed for the last review in the interest of

completeness.

# H-3 be found in the risk assessment report comp leted for the previous analysis (USEPA,

2005).

The PM 10-2.5 

# risk assessment completed for th e previous review is similar in

design to the PM 2.5 

# risk assessment, although the scope is significantly more limited,

reflecting the more limited body of epidemiol ogical evidence and ai r quality information

available for PM 10-2.5 

# . The PM 10-2.5 

# risk assessment assessed risk for populations in three

urban study areas (Detroit, Sea ttle and St. Louis), with a set of short-term exposurerelated morbidity health e ndpoints being modeled, includ ing: respiratory hospital

admissions (for Detroit and S eattle), cardiovascular hospital admissions (for Detroit) and

respiratory symptoms (for St. Louis). Selecti on of these three urban study areas reflected

consideration of the locati ons included in epidemiol ogical studies providing C-R

functions, as well as availability of co-located PM 10 

# and PM 2.5 

# monitoring data used in

deriving estimates of ambient PM 10-2.5 

# levels for urban study areas. EPA staff noted in

the last review that the locations used in the PM 10-2.5 

# risk assessment were not

representative of urban locations in the U.S. that experience the most significant elevated

24-hour PM 10-2.5 

# ambient concentrations. Thus, obs ervations regarding risk reductions

associated with alternative standards in thes e three urban areas may not be fully relevant

to the areas expected to have the greatest h ealth risks associated with peak daily ambient

PM 10-2.5 

# concentrations. This is a key limitation impacting the PM 10-2.5 

# risk assessment

and remains a primary concern in conducting a PM 10-2.5 

# risk assessment (see below).

In summarizing PM 10-2.5 

# 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 associat ed with “as is” shor t-term exposures to

PM 10-2.5 

# . The point estimate for asthma hospita l admissions associated with short-term

PM 10-2.5 

# exposures for Seattle, an area with lower PM 10-2.5 

# 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 bac kground as the cutpoint.

The specific set of uncertainties that resu lted in EPA staff concluding that the

PM 10-2.5 

# risk estimates should not be used in recommending specific standard levels

include, but are not limited to, the followi ng (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 PM 10-2.5 

# levels measured at ambient monitoring sites

during the study period for the risk assessm ent may be quite different from the

levels used to characterize exposure in the original epidemio logic studies based

on monitoring sites in different locati on, thus possibly over- or underestimating

population risk levels;

 Greater uncertainty about the reasonablene ss of the use of proportional rollback to

simulate attainment of alternative PM 10-2.5 

# daily standards in any urban area due

to the limited availability of PM 10-2.5 

# air quality data over time (this uncertainty

only being relevant to risk estimates genera ted 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 PM 10-2.5 

# concentrations, and thus, observations about relative risk reductions associated

with alternative standards may not be rele vant to the areas expected to have the

greatest health risks associat ed with elevated ambient PM 10-2.5 

# 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 PM 2.5 

# , 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 DECISI ON NOT TO CONDUCT A PM 10-2.5



# RISK ASSESSMENT AS PA RT OF THE CURRENT REVIEW

The decision not to conduct a PM 10-2.5 

# risk assessment for the current

review is based on consideration of key uncerta inties identified in the last review and an

assessment as to whether newly available in formation has significantly reduced those

uncertainties. Each of the sources of uncertainty is addressed below:

 Concerns that monitoring data that would be used in a PM 10-2.5 

# risk assessment

(i.e., for the period 2005-2007) would not match ambient monitoring data used in

the underlying epidemiological st udies 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 PM 10-2.5 

# ambient levels (see final PM ISA,

Sections 2.1.1.2 and 2.2.1, USEPA 2009b), the potential for discrepancies

between the monitoring netw orks used in epidemiol ogical studies providing C-R

functions and the monitoring network used in the risk assessment introducing

uncertainty is increased relative to PM 2.5 

# . That is, the potential for greater spatial

variation in PM 10-2.5 

# levels means that the particul ar mix of collocated monitors

used in generating an exposure surrogate in epidemiological studies needs to be

more closely matched to the monitori ng network used in conducting the risk

assessment if significant uncerta inty is to be avoided.

 Uncertainty in the predic tion of ambient levels unde r current and alternative

standard levels: This remains a significant fact or introducing uncertainty into

PM 10-2.5 

# risk estimates generated for alternat ive 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 PM 10 

# and PM 2.5 

# monitors) available for characterizing PM 10-2.5 

# levels

in candidate urban study areas limited (see above), given the pot ential for greater

spatial heterogeneity in PM 10-2.5 

# levels (relative to PM 2.5 

# levels), generating

representative estimates of ambient air profiles for PM 10-2.5 

# under alternative

standard levels is substantia lly more challenging than for PM 2.5 

# . In particular, the

use of proportional rollback as a means fo r conducting rollbacks would be subject

to significant uncertainty gi ven the greater potential for local-scale gradients in

PM 10-2.5 

# levels and the linkage of PM 10-2.5 

# to local-scale sources.

 Concerns that locations used in the risk assessment ma y not be representative of

areas experiencing the most significant 24-hour peak PM 10-2.5 

# concentrations

(and consequently, may not capture locations with the highest risk) : This concern

still holds since the monitoring network available for characterizing PM 10-2.5 

# levels in urban areas has not been signif icantly expanded (final PM ISA, Section

3.5.1.2, ). Specifically, the final PM ISA st ates that: “Given the limited number

of co-located low-volume FRM PM 10 

# and FRM PM 2.5 

# monitors, only a very

limited investigation into the intr a-urban spatial variability of PM 10-2.5 

# was

possible using AQS data. Of the 15 cities under investigation, only six (Atlanta,

Boston, Chicago, Denver, New York and P hoenix) contained data sufficient for

calculating PM 10-2.5 

# according to the data complete ness 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 PM 10-

2.5 

# based on consideration of general patterns in PM 10 

# and PM 2.5 

# 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 PM 10-2.5 

# , limitations in the available studies still result in

uncertainty in specifying C-R functions for PM 10-2.5 

# . For example, while Peng et

al. (2008) and Zanobetti and Schwartz ( 2009) both provide effect estimates for

short-term exposure-related mortality (with considerati on of copollutant

confounding by PM 2.5 

# ), both have specific limitations that impact their use in risk

assessment. For example, Zanobetti and Schwartz (2009) derives estimates of

PM 10-2.5 

# by subtracting county-level PM 10 

# and PM 2.5 

# levels, rather than using

collocated monitors. Given the significant spatial gradients associated with PM 10-

2.5 

# relative to PM 2.5 

# , the use of this approach fo r assessing exposure introduces

significant uncertainty (i.e., exposure meas urement 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 PM 2.5 

# . Given

the potential for regional differe nces in the composition of PM 10-2.5 

# which could

impact risk estimates, combined with the potential for PM 2.5 

# to vary regionally as

a confounder for the effect of PM 10-2.5 

# , EPA staff believes that C-R functions with

control for PM 2.5 

# would ideally be available at the regional level. .

When considered together, the limita tions outlined above resulted in EPA

staff concluding that a quantitative PM 10-2.5 

# risk assessment would not significantly

# H-6 enhance the review of the NAAQ S for coarse-fraction PM. Sp ecifically, these limitations

would likely result in sufficient uncertainty in the resulting risk es timates to significantly

limit their utility in informing policy-rela ted questions, including the assessment of

whether the current standard is protective of public health and characterization of the

degree of additional public health protection pot entially afforded by alternative standards.

Because of the decision not to conduct a quantitative PM 10-2.5 

# risk assessment, these

questions will draw more heavily on the result s 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