Quantitative Health Risk Assessment for 

Particulate Matter 
DISCLAIMER

This document has been prepared by staff from the Office of Air Quality Planning 

and Standards, U.S. Environmental Protection Agency. Any opinions, findings, 

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

the views of the EPA. Questions related to this document should be addressed to Dr. 

Zachary Pekar, U.S. Environmental Protection Agency, Office of Air Quality Planning 

and Standards, C504-06, Research Triangle Park, North Carolina 27711 (email: 

pekar.zachary@epa.gov). 

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

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

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

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

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

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

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

authors and not the institutions providing the raw data. 

ACKNOWLEDGEMENTS

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

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

made contributions include: Chapter 3 (Urban Case Study Methods), and Appendices A 

(Air Quality Assessment (Summary of Individual and Composite Monitor Data by Urban 

Study Area)), C (Epidemiological Study Specific Information for PM Risk Assessment), 

E (Risk Analysis - Core Analysis), and F (Sensitivity Analysis Results). 

 

 
 EPA-452/R-10-005 

June 2010 

Quantitative Health Risk Assessment for 

Particulate Matter 

US Environmental Protection Agency 

Office of Air and Radiation 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, North Carolina 27711 
This page left intentionally blank.
i

Table of Contents 

List of Tables ................................................................................................................................. v 

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

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

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

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

1.2 Current Risk Assessment: Goals and Planned Approach......................................... 1-6 

1.3 Organization of Document ....................................................................................... 1-7 

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

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

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

2.2.1 Risk Assessment ............................................................................................. 2-4 

2.2.2 Population Exposure Analysis ........................................................................ 2-6 

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

2.4 Alternative Suites of PM2.5 Standards Evaluated ................................................... 2-10 

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

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

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

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

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

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

3.2 Air Quality Inputs................................................................................................... 3-12 

3.2.1 Characterizing Recent Conditions ................................................................ 3-12 

3.2.2 Estimating Policy Relevant Background ...................................................... 3-15 

3.2.3 Simulating Air Quality to Just Meet Current and Alternative Standards ..... 3-16 

3.2.3.1 Proportional Rollback Method............................................................... 3-18 

3.2.3.2 Hybrid Rollback Method ....................................................................... 3-20 

3.2.3.3 Locally focused Rollback Method......................................................... 3-22 

3.2.3.4 Presentation of Results for the Three Rollback Methods (with 

example calculation) .............................................................................. 3-23 

3.3 Selection of Model Inputs....................................................................................... 3-28 

3.3.1 Health Endpoints........................................................................................... 3-28 

3.3.2 Selection and Delineation of Urban Study Areas ......................................... 3-29 
ii

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

Functions within Those Studies .................................................................... 3-34 

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

Functions....................................................................................................... 3-40 

3.4 Baseline Health Effects Incidence Data ................................................................. 3-54 

3.4.1 Data Sources ................................................................................................. 3-54 

3.4.1.1 Mortality ................................................................................................ 3-54 

3.4.1.2 Hospital Admission and Emergency Department Visits ....................... 3-54 

3.4.1.3 Populations............................................................................................. 3-56 

3.4.2 Calculation of Baseline Incidence Rates....................................................... 3-59 

3.5 Addressing Uncertainty and Variability ................................................................. 3-63 

3.5.1 Overview....................................................................................................... 3-63 

3.5.2 Treatment of Key Sources of Variability...................................................... 3-66 

3.5.3 Qualitative Assessment of Uncertainty......................................................... 3-69 

3.5.4 Single and Multi-Factor Sensitivity Analyses .............................................. 3-80 

3.5.4.1 Sensitivity Analyses for Long-Term Exposure-Related Mortality........ 3-80 

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

Morbidity ............................................................................................... 3-84 

3.5.4.3 Multi-factor Sensitivity Analyses .......................................................... 3-86 

3.5.5 Summary of Approach to Addressing Variability and Uncertainty.............. 3-87 

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

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

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

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

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

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

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

4.3.1 Sensitivity Analysis Results to Identify Potentially Important Sources of 

Uncertainty and Variability........................................................................... 4-29 

4.3.1.1 Single-factor Sensitivity Analysis.......................................................... 4-36 

4.3.1.2 Multi-factor Sensitivity Analysis Results .............................................. 4-43 

4.3.2 Additional Set of Reasonable Risk Estimates to Inform Consideration of 

Uncertainty in Core Risk Estimates .............................................................. 4-45 

4.4 Evaluating the Representativeness of the Urban Study Areas in the National 

 Context.................................................................................................................... 4-49 
iii

4.4.1 Analysis Based on Consideration of National Distributions of RiskRelated Attributes ......................................................................................... 4-50 

4.4.2 Analysis Based on Consideration of National Distribution of PM-Related 

Mortality Risk ............................................................................................... 4-66 

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

Intrepreting Core Risk Estimates............................................................................ 4-68 

4.5.1 Design Values ............................................................................................... 4-68 

4.5.2 Patterns in PM2.5 Monitoring Data................................................................ 4-77 

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

5.1 Overall Confidence in the Risk Assessment............................................................. 5-1 

5.1.1 Use of a Deliberative Process in Designing the Risk Model .......................... 5-2 

5.1.2 Integration of Key Sources of Variability into the RA Design....................... 5-3 

5.1.3 Representativeness of the Urban Study Areas ................................................ 5-5 

5.1.4 Impact of Important Sources of Uncertainty on Core Risk Estimates............ 5-6 

5.1.5 Consideration of Alternative Reasonable Risk Estimates............................... 5-7 

5.1.6 Consideration of Composite Monitor Annual Average PM2.5

Concentrations in Relation to the Dataset Used in Deriving C-R Functions 

for Long-Term Exposure-Related Mortality................................................... 5-8 

5.2 Key Observations Related to the Urban Study Area Results ................................... 5-9 

5.2.1 Nature and Magnitude of Long-Term and Short-Term Exposure-Related 

Risk Remaining upon Just Meeting the Current Suite of PM2.5 Standards... 5-10 

5.2.2 Nature and Magnitude of Long-term and Short-Term Exposure-Related 

Risk Remaining upon Just Meeting the Alternative Suite of PM2.5

Standards....................................................................................................... 5-12 

5.2.3 Nature and Magnitude of Long-Term and Short-Term Exposure-Related 

Risk Remaining upon Just Meeting Combinations of Alternative Annual 

and 24-Hour PM2.5 Standards........................................................................ 5-14 

5.3 Summary of Key Observations............................................................................... 5-16 

6 References...............................................................................................................................6-1 

Appendix A Air Quality Assessment (Summary of Individual and Composite Monitor Data by 

Urban Study Area) 

Appendix B Additional Information Supporting Air Quality Characterization 

Appendix C Epidemiological Study Specific Information for PM Risk Assessment 

Appendix D Supplement to Representativeness of the 15 Urban Study Areas 

Appendix E Risk Estimates (Core Analysis)
iv

Appendix F Sensitivity Analysis Results 

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

PM2.5 Exposure 

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

 (PM10-2.5) 

Appendix I Analysis Comparing Distribution of Short-Term Exposure-Related 

Cardiovascular Mortality Incidence to the Distribution of Daily PM2.5 Levels tor 

Detroit and New York 

Appendix J: Provisional Risk Estimates and Additional Results of Simulation Involving the 

Alternative Annual Standard of 10 μg/m3

Appendix K: Maps of the Fifteen Urban Study Areas Evaluated in the Risk Assessment 
v

List of Tables 

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

Monitor Values Were Calculated ............................................................................ 3-12 

Table 3-2 Regional Policy-Relevant Background Estimates Used in the Risk Assessment. .. 3-16 

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

2007 ....................................................................................................................... 3-19 

Table 3-4. Application of the Three Rollback Methods in Simulating Current and Alternative 

Standard Levels for the 15 Urban Study Areas....................................................... 3-25 

Table 3-5 Urban Study Areas Selected for the Risk Assessment ............................................ 3-31 

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

Risk Assessment...................................................................................................... 3-41 

Table 3-7 Locations, Health Endpoints, and Long-Term Exposure Studies Included in the PM2.5

Risk Assessment*.................................................................................................... 3-42 

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

Functions Included in the Core Analysis. ............................................................... 3-43 

Table 3-9 Summary of Locations, Health Endpoints, Studies and Concentration-Response 

Functions Included in Sensitivity Analyses. ........................................................... 3-51 

Table 3-10 Sources of Hospital Admissions (HA) and Emergency Department (ED) 

 Visit Data................................................................................................................. 3-56 

Table 3-11 Relevant Population Sizes for PM Risk Assessment Locations.............................. 3-57 

Table 3-12 Baseline Mortality Rates (Deaths per 100,000 Relevant Population per Year) for 

2006 for PM Risk Assessment Locations............................................................... 3-60 

Table 3-13 Baseline Hospital Admission (HA) and Emergency Department (ED) Rates 

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

Assessment Locations. ............................................................................................ 3-62 

Table 3-14 Summary of Qualitative Uncertainty Analysis of Key Modeling Elements in the PM 

NAAQS Risk Assessment....................................................................................... 3-72 

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

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

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

 ........................................................................................................................ 4-6

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

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

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

Concentrations. ......................................................................................................... 4-7 

Table 4-3 Overview of Sensitivity Analysis Results ............................................................... 4-32 

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

estimates .................................................................................................................. 4-46

Table 4-5 Data Sources for PM NAAQS Risk Assessment Risk Distribution Analysis......... 4-52 

Table 4-6 Summary Statistics for Selected PM Risk Attributes.............................................. 4-54 

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

Study Area Distributions for Selected National Risk Characteristic Variables ...... 4-58 

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

standard levels ......................................................................................................... 4-74 
vi

List of Figures 

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

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

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

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

 used to guide selection of study areas). ............................................................. 3-32 

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

 standards and recent conditions relative to the current suite ofstandards)........... 4-8 

Figure 4-2 Percent reduction in long-term exposure-related mortality risk 

 (recent conditions relative to the current standards)............................................ 4-9 

Figure 4-3 Percent reduction in long-term exposure-related mortality risk ........................ 4-10 

Figure 4-4 Percent reduction in short-term exposure-related mortality and morbidity risk 4-11 

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

(recent conditions relative to the current standards).......................................... 4-12 

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

(alternative standards relative to the current standards) .................................... 4-13 

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

for Los Angeles and Philadelphia (IHD mortality)............................................ 4-47 

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

for Los Angeles and Philadelphia (all cause mortality)..................................... 4-48 

Figure 4-9 Comparison of distributions for key elements of the risk equation: 

 total population. ................................................................................................. 4-59 

Figure 4-10 Comparison of distributions for key elements of the risk equation: 

 98th percentile 24-hour average PM2.5 ............................................................... 4-60 

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

mortality rate...................................................................................................... 4-61 

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

 Mortality risk effect estimate from Zanobetti and Schwartz (2008).................. 4-62 

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

relative risk from PM2.5: long term average July temperature.......................... 4-63 

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

relative risk from PM2.5: percent of population 65 and older. .......................... 4-64 

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

relative risk from PM2.5: per capita annual personal income............................ 4-65 

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

relative risk from PM2.5: per capita annual personal income............................ 4-66 

Figure 4-17 Cumulative distribution of county-level percentage of total mortality attributable 

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

urban case study area analysis fall..................................................................... 4-67 

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

to the current suite of standards (15/35) ............................................................ 4-71 
vii

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

to the 12/35 alternative suite of standards.......................................................... 4-72 

Figure 4-20 Design values in 15 urban study areas and broader set of U.S. urban areas relative 

to the 12/25 alternative suite of standards.......................................................... 4-73 

Figure 4-21 Design values in 15 urban study areas and broader set of U.S. urban areas relative 

to the current standard (with regional differentiation)....................................... 4-76 

Figure 4-22 Annual and 24-hour design values (for individual monitors and at the study-area 

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

reflect current standard of 15/35)....................................................................... 4-79 

Figure 4-23 Annual and 24-hour design values (for individual monitors and at the study-area 

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

reflect current standard of 12/25)....................................................................... 4-80 
viii

List of Acronyms/Abbreviations 

ACS American Cancer Society 

AQS EPA's Air Quality System 

β Slope coefficient 

BenMAP Benefits Mapping Analysis Program 

BRFSS Behavioral Risk Factor Surveillance System 

 CASAC Clean Air Scientific Advisory Committee 

CAA Clean Air Act 

CBSA Core-based Statistical Area 

CDC Centers for Disease Control 

CDF Cumulative Distribution Function 

CFR Code of Federal Regulations 

CHD Coronary Heart Disease 

CMAQ Community Multiscale Air Quality 

CO Carbon Monoxide 

COPD Chronic Obstructive Pulmonary Disease 

CPD Cardio-pulmonary Disease 

C-R Concentration-response 

CSA Combined Statistical Area 

CV Cardiovascular 

CVD Cardiovascular Disease 

df Degrees of freedom 

ED Emergency Department 

EPA United States Environmental Protection Agency 

FACA Federal Advisory Committee Act 

FIPS Federal Information Processing System 

 GAM Generalized additive model 

GLM Generalized linear model 

HA Hospital Admissions 

HCUP Healthcare Cost and Utilization Project 

HEI Health Effects Institute 

HS High School 

ICD International Classification of Diseases 

IHD ischemic heart disease 

INF Influence of uncertainty on risk estimates 
ix

IRP Integrated Review Plan 

 ISA Integrated Science Assessment Document 

KB Knowledge Base 

K-S Kolmogorov-Smirnov 

LML Lowest Measured Level 

MCAPS Medicare Air Pollution Study 

MSA Metropolitan Statistical Area 

NA Not Applicable 

NAAQS National Ambient Air Quality Standards 

NCEA National Center for Environmental Assessment 

NEI National Emissions Inventory 

NCHS National Center for Health Statistics 

NMMAPS National Morbidity, Mortality, and Air Pollution Study 

 NOx Nitrogen oxides 

O3 Ozone 

OAQPS Office of Air Quality Planning and Standards 

PA Policy Assessment Document 

PM Particulate Matter 

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

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

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

particles with aerodynamic diameter of X are removed by the inlet 

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

Depending on the specific penetration curve specified, particles 

larger than X μm aerodynamic diameter are collected with an 

efficiently than decreases rapidly for particles larger than X while 

the collection efficiency for particles smaller than X increases 

rapidly with decreasing size until 100 % efficiency is reached. 

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

 diameter and a penetration curve as specified in the CFR. 

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

diameter and a penetration curve as specified in the CFR 

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

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

diameter. 

PRB Policy-Relevant Background 

 RA Risk Assessment 

RR Relative risk 
x

REA Risk and Exposure Assessment 

SAB Science Advisory Board 

SEDD State Emergency Department Databases 

SID State Inpatient Database 

SO2 Sulfur Dioxide 

SOx Sulfur Oxides 

SES Socio-economic Status 

TRIM Total Risk Integrated Methodology 

TRIM.Risk Total Risk Integrated Methodology - Risk Assessment component 

USDA U.S. Department of Agriculture 

WHI Women's Health Initiative 

WHO World Health Organization 

ZCA Zip Code Area 
 1-1 

1 INTRODUCTION 

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

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

and 109 of the Clean Air Act (CAA) govern the establishment and periodic review of the 

NAAQS. These standards are established for pollutants that may reasonably be anticipated to 

endanger public health and welfare, and whose presence in the ambient air results from 

numerous or diverse mobile or stationary sources. The NAAQS are to be based on air quality 

criteria, which are to accurately reflect the latest scientific knowledge useful in indicating the 

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

presence of the pollutant in ambient air. The EPA Administrator is to promulgate and 

periodically review, at five-year intervals, “primary” (health-based) and “secondary” (welfarebased) NAAQS for such pollutants. Based on periodic reviews of the air quality criteria and 

standards, the Administrator is to make revisions in the criteria and standards, and promulgate 

any new standards, as may be appropriate. The Act also requires that an independent scientific 

review committee advise the Administrator as part of this NAAQS review process, a function 

performed by the Clean Air Scientific Advisory Committee (CASAC).1

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

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

61144, October 17, 2006). With regard to the primary standards for fine particles, in last PM 

NAAQS review completed in 2006, EPA revised the level of the 24-hour PM2.5 standard to 35 

μg/m3 (calculated as a 3-year average of the 98th percentile of 24-hour concentrations at each 

population-oriented monitor), retained the level of the annual PM2.5 annual standard at 15 μg/m3 

(calculated as the 3-year average of the weighted annual mean PM2.5 concentrations from single 

or multiple community-oriented monitors), and revised the form of the annual PM2.5 standard by 

narrowing the constraints on the optional use of spatial averaging.2

 With regard to the primary 

 

1

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

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

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

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

Advisory Committee Act (FACA). See 

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

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

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

averaging criteria by further limiting the conditions under which some areas may average measurements from 

multiple community-oriented monitors to determine compliance (see 71 FR 61165 to 61167, October 17, 2006) . 
 1-2 

standards for coarse particles, in the last review, EPA retained the 24-hour PM10 standard at 150 

μg/m3 (not to be exceeded more than once per year on average over 3 years) and revoked the 

annual standard because available evidence generally did not suggest a link between long-term 

exposure to current ambient levels of coarse particles and health or welfare effects (71 FR 61177 

to 61203). Decisions related to the primary PM standards were based primarily on a large body 

of epidemiological evidence relating ambient PM concentrations to various adverse health 

endpoints. In 2006, secondary standards for PM2.5 and PM10 were revised to be identical to the 

primary standards (71 FR 61203 to 61210). 

The EPA initiated the current review of the PM NAAQS on June 28, 2007 with a call for 

information from the public (72 FR 35462).3 The NAAQS review process includes four key 

phases: planning, science assessment, risk/exposure assessment, and policy 

assessment/rulemaking.4 A workshop was held on July 11 through 13, 2007 (72 FR 34003) to 

discuss policy-relevant scientific and technical information to inform EPA’s planning for the PM 

NAAQS review. Following the workshop, EPA developed a planning document, the Integrated 

Review Plan for the National Ambient Air Quality Standards for Particulate Matter (IRP; US 

EPA, 2008a), which outlined the key policy-relevant issues that frame this review, the process 

and schedule for the review, and descriptions of the purpose, contents, and approach for 

developing the other key documents for this review.5 In December 2009, EPA completed the 

process of assessing the latest available policy-relevant scientific information to inform the 

review of the PM standards. This assessment the Integrated Science Assessment for Particulate 

Matter (ISA; US EPA, 2009a), includes an evaluation of the scientific evidence on the health 

effects of PM, including information on exposure, physiological mechanisms by which PM 

might adversely impact human health, an evaluation of the toxicological and controlled human 

exposure study evidence, and an evaluation of the epidemiological evidence including 

information on reported concentration-response (C-R) relationships for PM-related morbidity 

and mortality associations, including consideration of effects on susceptible populations.6

 

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

 For more information on the NAAQS review process see http://www.epa.gov/ttn/naaqs/review.html. 5

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

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

from senior Agency managers. 6

 The ISA also evaluates scientific evidence for the effects of PM on public welfare which EPA will 

consider in its review of the suite of secondary PM NAAQS. Building upon the visibility effects evidence presented 

in the ISA, OAQPS has also developed a second REA titled Particulate Matter Urban-Focused Visibility 

Assessment (US EPA, 2010b). 
 1-3 

The EPA’s Office of Air Quality Planning and Standards (OAQPS) has developed this 

quantitative health risk assessment (RA) describing the quantitative assessments of PM-related 

risks to public health to support the review of the primary PM standards. This document is a 

concise presentation of the scope, methods, key results, observations, and related uncertainties 

associated with the quantitative analyses performed. The RA builds upon the health effects 

evidence presented and assessed in the ISA, as well as CASAC advice (Samet, 2009a, b; Samet, 

2010) and public comments on a scope and methods planning document for the RA (here after, 

“Scope and Methods Plan”, US EPA, 2009b) and on the first and second draft RA documents 

(US EPA, 2009e; US EPA, 2010a). 

The ISA and RA will inform the policy assessment and rulemaking steps that will lead to 

final decisions on the primary PM NAAQS. The policy assessment is described in Policy 

Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards

(hereafter, “PA”), which include staff analysis of the scientific basis for alternative policy 

options for consideration by senior EPA management prior to rulemaking. The PA integrates 

and interprets information from the ISA and the RA to frame policy options for consideration by 

the Administrator. The PA is intended to link the Agency’s scientific and technical assessments, 

presented in the ISA and RA, to judgments required of the Administrator in determining whether 

it is appropriate to retain or revise the current suite of PM standards. Development of the PA is 

also intended to facilitate elicitation of CASAC’s advice to the Agency and recommendations on 

any new standards or revisions to existing standards as may be appropriate, as provided for in the 

Clean Air Act (CAA). The second draft PA is planned for release around the end of June 2010 

for review by the CASAC PM Panel and the public during a public teleconference being planned 

for late March. Proposed and final rulemaking notices are now scheduled for November 2010 

and July 2011, respectively. 

1.1 BACKGROUND 

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

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

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

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

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

the public, with CASAC concluding that the general assessment methodology and framework 

were appropriate (Hopke, 2002). The final quantitative risk assessment took into consideration 

CASAC advice (Hopke, 2004; Henderson, 2005) and public comments on two drafts of the risk 

assessment. 
 1-4 

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

focused on nine urban study areas and included estimated risks of total non-accidental, 

cardiovascular-related, and respiratory-related mortality as well as morbidity effects including 

hospital admissions for cardiovascular and respiratory causes and respiratory symptoms (not 

requiring hospitalization) associated with recent short-term (daily) ambient PM2.5 concentrations. 

This assessment also included estimated risks of total, cardiopulmonary, and lung cancer 

mortality associated with long-term PM2.5 exposures. The quantitative risk assessment included 

estimates of: (1) risks of mortality, morbidity, and symptoms associated with recent ambient 

PM2.5 levels; (2) risk reductions and remaining risks associated with just meeting the existing 

suite of PM2.5 NAAQS (1997 standards); and (3) risk reductions and remaining risks associated 

with just meeting various alternative PM2.5 standards. 

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

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

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

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

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

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

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

uncertainties and concerns associated with this portion of the quantitative risk assessment 

weighed against use of these risk estimates as a basis for recommending specific standard levels 

(U.S. EPA, 2005, p. 5-69).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

significant uncertainties associated with the risk estimates for fine particles. More specifically, 
 1-5 

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

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

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

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

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

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

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

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

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

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

Administrator recognized that for purposes of developing quantitative risk estimates, 

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

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

2006). 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

retaining PM10 as the indicator of a standard for thoracic coarse particles, retaining the level and 

form of the 24-hour PM10 standard, and revoking the PM10 annual standard. On judicial review, 

the D.C. Circuit remanded the annual primary PM2.5 NAAQS to EPA because the Agency failed 

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

long-term exposures to fine particles including protection for at-risk populations. The court 

upheld the Agency’s use of the quantitative risk assessment to inform the decision to revise the 

PM2.5 standards but not to inform the selection of level. The court also upheld the decision to 
 1-6 

retain the 24-hour PM10 standard and revoke the annual PM10 standard. 7 American Farm 

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

1.2 CURRENT RISK ASSESSMENT: GOALS AND PLANNED APPROACH 

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

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

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

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

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

including, where data are available, consideration of impacts on susceptible populations; (b) to 

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

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

impacts on various susceptible populations; and (c) to gain insights into the distribution of risks 

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

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

mortality associated with long-term exposure to recent ambient PM2.5 concentrations to more 

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

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

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

these goals. 

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

in the last PM risk assessment and focuses on improving the characterization of the overall 

confidence in the risk estimates, including related uncertainties, by incorporating a number of 

enhancements, in terms of both the methods and data used in the analyses. This assessment 

considers a variety of health endpoints for which, in staff’s judgment, there is adequate 

information to develop quantitative risk estimates that can meaningfully inform the review of the 

primary PM NAAQS. Evidence of relationships between PM and other health endpoints for 

which, in staff’s judgment, there currently is insufficient information to develop meaningful 

quantitative risk estimates are discussed in the PA as part of the evidence-based considerations 

that inform staff’s assessment of policy options. 

 

7

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

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

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

American Farm Bureau Federation v. EPA, 559 F. 3d 512, (D.C. Cir. 2009). 
 1-7 

1.3 ORGANIZATION OF DOCUMENT 

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

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

assessment, the original planned approach and the key design elements reflected in the final 

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

Chapter 3 describes the analytical approach, methods, and data used in conducting the risk 

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

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

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

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

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

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

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

sensitivity analysis results, respectively). Chapter 5 provides an integrative discussion of the 

various risk estimates generated in the analyses drawing on the results of the urban area case 

studies, the uncertainty/variability characterization, the assessment of the representativeness of 

the urban study areas in a national context, and the patterns in design values and air quality 

monitoring data considered to inform the interpretation of the risk estimates generated in the 

urban case study analyses.
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2 SCOPE 

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

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

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

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

recognized in that analysis. 

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

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

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

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

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

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

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

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

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

exposure assessment. Based in part on the workshop discussions, EPA developed a draft IRP 

(US EPA, 2007) outlining the schedule, process, and key policy-relevant questions that would 

frame this review. On November 30, 2007, EPA held a consultation with CASAC on the draft 

IRP (72 FR 63177, November 8, 2007), which included opportunity for public comment. The 

final IRP incorporated comments from CASAC (Henderson, 2008) and the public on the draft 

plan as well as input from senior Agency managers. The IRP included initial plans for 

quantitative risk and exposure assessments (US EPA, 2008a, chapter 5). 

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

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

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

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

on April 1-2, 2009 (74 FR 11580, March 18, 2009). Based on consideration of CASAC (Samet, 

2009a) and public comments on the Scope and Methods Plan and information in the first draft 

ISA, we modified the scope and design of the quantitative risk assessment and completed initial 

analyses that were presented in a first draft RA (US EPA, 2009e). The CASAC met on October 

5-6, 2009 to review the first draft RA (74 FR 46586, September 10, 2009).8

 Based on 

 

8

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

comment letter prepared by the CASAC PM Panel. 
2-2

consideration of CASAC (Samet, 2009b) and public comments on the first draft RA, together 

with ongoing refinement of elements of the risk assessment approach informed by the second 

draft ISA, we prepared a second draft RA (US EPA, 2010a). The CASAC PM Panel met again 

on March 10-11, 2010 to review the second draft RA (75 FR 8062, February 23, 2010). Based on 

consideration of CASAC comments (Samet, 2010) together with public comments on the second 

draft RA and ongoing, methods development work, we have prepared this final RA. 

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

chapter first provides a brief overview of the quantitative risk assessment completed for the 

previous PM NAAQS review in section 2.1, including key limitations and uncertainties 

associated with that analysis. Section 2.2 provides a summary of the initial design of the risk 

assessment as outlined in the Scope and Methods Plan. Section 2.3 provides an overview of key 

design elements reflected in this final risk assessment focusing on those aspects of the final 

approach which differ from the originally planned approach reflecting consideration of CASAC 

and public comments and additional EPA analyses. Section 2.4 provides a summary of the 

various air quality scenarios evaluated in this assessment, including recent air quality conditions 

and simulations of just meeting the current and alternative suites of PM2.5 standards. 

2.1 OVERVIEW OF RISK ASSESSMENT FROM LAST REVIEW 

The quantitative risk assessment conducted in the last review included a broad 

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

That assessment included estimates of risks of mortality (total non-accidental, cardiovascular, 

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

respiratory symptoms (not requiring hospitalization) associated with short-term (24-hour) PM2.5

exposures and estimates of risks of total, cardiopulmonary, and lung cancer mortality associated 

with long-term PM2.5 exposures in selected urban areas. Nine urban study areas were evaluated 

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

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

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

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

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

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

population threshold exists; (b) the selection of appropriate statistical models for the analysis of 

epidemiological data; (c) the role of potentially confounding and modifying factors in the C-R 

relationships; (d) the methods for simulating how daily PM2.5 ambient concentrations would 

likely change in any given area upon meeting a particular suite of standards; and (e) the potential 

for differences in the relative toxicity of the components within the mix of ambient PM2.5. 
2-3

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

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

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

sensitivity analyses or characterized qualitatively (US EPA, 2005, chapter 4; Abt Associates, 

2005). The C-R relationships used in the quantitative risk assessment were based on findings 

from epidemiological studies that relied on fixed-site, population oriented, ambient monitors as a 

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

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

potential population thresholds. Other uncertainties were addressed in various sensitivity 

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

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

risk estimates in some or all of the selected urban study areas. 

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

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

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

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

PM10-2.5 risk assessment included risk estimates for just three urban study areas for two 

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

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

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

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

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

use of these risk estimates as a basis for recommending a range of standard levels for 

consideration (US EPA, 2005, see p. 5-69). These uncertainties and concerns were summarized 

in the proposed rulemaking notice (FR 71 2662, January 17, 2006) and discussed more fully in 

the Staff Paper (US EPA, 2005, chapter 4) and associated technical support document (Abt 

Associates Inc., 2005). 

2.2 ORIGINAL ASSESSMENT PLAN 

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

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

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

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

are briefly summarized below. 
2-4

2.2.1 Risk Assessment 

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

Methods Plan, included: 

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

exposure to PM2.5 with a much more limited assessment of PM10-2.5. Based on 

information presented and assessed in the first draft ISA, we concluded that there was 

insufficient data to support a quantitative risk assessment for other size fractions (e.g., 

ultrafine particles). 

 PM components/sources/environments: We considered the extent to which 

evidence was available to support a quantitative risk assessment for specific PM 

components, sources, and/or environments. Based on review of the evidence 

presented and assessed in the first draft ISA, we concluded that there was insufficient 

data to support such analyses.. 

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

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

causal relationship with PM2.5 exposures. We also planned to consider including 

additional categories for which evidence is suggestive of causal relationship with 

PM2.5 exposures (e.g., reproductive and developmental outcomes), if sufficient 

information was available to develop meaningful risk estimates for these additional 

categories. 

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

quantitative risk assessment conducted in the last review (US EPA, 2005) with a 

focus on health effect categories for which the evidence is suggestive of a causal 

relationship with short-term PM10-2.5 exposures, where sufficient information was 

available to develop meaningful risk estimates. 

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

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

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

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

risk-related attributes, coverage for areas with more susceptible populations). We 

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

where more detailed exposure studies had been completed (e.g., Los Angeles, CA, 

based on a zip code level analysis of long-term PM2.5-exposure related mortality 

presented in Krewski et al., 2009). 

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

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

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

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

historical patterns of reductions in urban areas, if these supported consideration for 

non-proportional reductions across monitors within a specific urban area and/or (b) 

model-based (e.g., Community Multiscale Air Quality [CMAQ]) rollback designed to 

more realistically reflect patterns of PM reductions across monitors in an urban area. 
2-5

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

results of air quality modeling based on a combination of the global-scale circulation 

model, GEOS-Chem, with the regional scale air quality model, CMAQ, as presented 

in the first draft ISA, rather than empirical data to characterize PRB levels for use in 

the risk assessment model. 

 Selection of epidemiological studies to provide C-R functions: Recognizing 

advantages of different study designs, we planned to include C-R functions identified 

in both multi- and single-city epidemiological studies using both multi- and singlepollutant models, where available. We planned to place greater weight on the use of 

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

studies. 

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

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

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

concentrations.9

 We also stated that we may consider population thresholds as part of 

the sensitivity analysis. 

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

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

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

exposures. 

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

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

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

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

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

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

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

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

estimates generated for the urban study areas. 

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

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

levels. 

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

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

national distributions for key PM2.5 risk-related attributes to determine whether they 

are nationally representative or more focused on a particular portion of the 

distribution for a given attribute. 

 

9

 In discussing short-term exposure mortality studies, the first draft ISA (U.S. EPA, 2008a) indicated 

support for non-threshold, log-linear models, while acknowledging that the possible influence of exposure error and 

heterogeneity of shapes across cities remains to be resolved. 
2-6

2.2.2 Population Exposure Analysis 

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

micro-environmental exposure modeling using the Air Pollution Exposure Model (APEX) (US 

EPA, 2009b, chapter 4). The planned analysis focused on evaluating PM2.5 exposures in a subset 

of the urban study areas included in the quantitative risk assessment to provide insights to inform 

the interpretation of the available epidemiological studies. 

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

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

as part of the current PM NAAQS review. However, this additional design work highlighted the 

need to more clearly define the intended purpose of the analysis, including specific ways in 

which the results would be used to interpret the estimates generated from the risk assessment 

(e.g., potentially identifying sources of exposure measurement error associated with the 

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

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

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

defining how the exposure assessment results would be used (Samet, 2009a), as well as the 

complexities associated with designing and conducting such an assessment, we decided to 

continue methods development work rather than attempt to complete a preliminary population 

exposure analysis as part of this review. Development of the population exposure analysis 

methodology is ongoing, and we anticipate that such an assessment could be conducted as part of 

the next PM NAAQS review. 

2.3 CURRENT SCOPE AND KEY DESIGN ELEMENTS 

An overview of the scope and key design elements that are the basis for the final RA are 

presented below, focusing on those aspects of the risk assessment approach which differ from the 

originally planned approach presented in the Scope and Methods Plan and summarized in section 

2.2.1. 

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

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

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

would introduce significant uncertainty into a PM10-2.5 quantitative risk assessment 

such that the risk estimates generated would be of limited utility for informing 

conclusions regarding either the adequacy of the current PM10 standard or alternative 

standards for consideration. This conclusion was reached by first reviewing the set of 

limitations cited in the last PM NAAQS risk assessment for not using the PM10-2.5 risk 

estimates in recommending specific standard levels. We then considered whether the 

currently available health effects data assessed and presented in the ISA and the 

currently available PM10-2.5 air quality monitoring data fundamentally addressed these 
2-7

limitations and provided sufficient information to support the development of 

meaningful risk estimates. We conclude that significant limitations in both the health 

effects data base and the current PM10-2.5 monitoring network continue to exist and 

that the currently available information do not support conducting a quantitative risk 

assessment for PM10-2.5 at this time (a more in-depth discussion of the rationale 

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

presented in Appendix H). Furthermore, consistent with the Scope and Methods Plan, 

we conclude that the currently available data are too limited to support a quantitative 

risk assessment for any specific PM components or for ultrafine particles (UFPs), at 

this time. We note, however, that the evidence for health effects associated with 

thoracic coarse particles, PM components, and UFPs are presented and assessed in the 

ISA and will be discussed as part of the evidence-based considerations presented in 

the PA. 

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

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

assessment for PM2.5 (section 3.3.1). In evaluating the currently available 

epidemiological evidence within the context of the framework, the endpoints focused 

on in the quantitative risk assessment focused on total, cardiopulmonary, and lung 

cancer mortality associated with long-term PM2.5 exposures mortality (total nonaccidental, cardiovascular, and respiratory), morbidity (hospital admissions for 

cardiovascular and respiratory causes), and respiratory symptoms (not requiring 

hospitalization) associated with short-term (24-hour) PM2.5 exposures. The selection 

of this set of endpoints is consistent with those endpoints outlined in the Scope and 

Methods Plan for PM2.5 and included specific endpoints from health effect categories 

classified in the ISA as having a causal or likely causal relationship with PM2.5

exposures. We considered a broader range of endpoints for this quantitative risk 

assessment including outcomes within health effect categories classified as having 

evidence suggestive of a casual relationship with PM2.5 (e.g., reproductive and 

developmental effects). These endpoints were not selected for inclusion in this 

analysis for several reasons including limited available information to support the 

selection of C-R functions for specific endpoints within these health effect categories 

and lack of available baseline incidence data. While the final health endpoints 

considered in this quantitative risk assessment are limited to health effect categories 

classified as having a causal or likely causal relationship with PM2.5 exposures, this 

result was a consequence of applying our multi-factor decision framework and not the 

sole determining factor. In addition, CASAC members expressed differing views as 

to the appropriateness of including health effect categories classified as having 

evidence suggestive of a causal relationship. 

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

assessment. The selection of these areas is based on a number of criteria including: 

(a) consideration of urban study areas evaluated in the last PM risk assessment; (b) 

consideration of locations evaluated in key epidemiological studies; (c) preference for 

locations with relatively elevated 24-hour and/or annual PM2.5 monitored levels so 

that the assessment can provide potential insights into the degree of risk reduction 

associated with just meeting the current and alternative suites of standards; and (d) 
2-8

preference for including locations from different regions across the country, reflecting 

potential differences in PM sources, composition, and potentially other factors which 

might impact PM-related risk (section 3.3.2). Due in part to time and resource 

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

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

Los Angeles, CA involving zip code-level analysis of long-term PM2.5-exposure 

related mortality as presented in Krewski et al., 2009). We have included 

consideration of studies with more refined surrogate measures of exposure in our 

discussion of uncertainties related to estimating long-term mortality risk, since they 

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

estimates used to model risks (section 3.5.3). 

 Method used to develop composite monitor values: We revised the methods used 

to derive composite monitor values for both the annual and 24-hour air quality 

distributions based upon ongoing EPA methods development efforts (section 3.2.1). 

The revised methods ensure that ambient measurements from different monitors in a 

particular urban study area used to calculate a composite monitor value for that urban 

study area are given equal weight. This approach is in contrast to the approach used 

in the first draft RA, which effectively weighted ambient measurements from 

monitors based on sampling frequency, potentially leading to composite monitor 

estimates that were biased high. 

 Simulation of air quality concentrations that just meet current or alternative 

suites of standards: We simulate air quality concentrations using different 

approaches. We first use a proportional rollback approach as discussed in the Scope 

and Methods Plan and applied in the first draft RA as well as the risk assessment 

conducted for the last PM NAAQS review to simulate PM2.5 ambient concentrations 

that would “just meet” the current and alternative suites of standards. We also 

developed and applied two alternative approaches (hybrid and locally focused) to 

improve our understanding of the uncertainty associated with this aspect of the 

assessment (section 3.2.3). In addition, we refined our rollback approach for the 

Pittsburgh study area, using a dual-zone approach to take into account monitor 

locations and the related topography in that area (section 3.2.3). 

 Characterization of PRB: Consistent with the originally planned approach, we use 

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

modeling as presented and discussed in the ISA (section 3.2.2). 

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

associated with both short-term and long-term PM2.5 exposures, we focus on selecting 

C-R functions from larger multi-city studies based on our conclusion that these 

studies provide more defensible effect estimates. In modeling short-term exposurerelated mortality and morbidity, we obtained more spatially-refined effect estimates at 

the city- and regional-levels, respectively (i.e., effect estimates based on application 

of Bayesian methods). We also included C-R functions selected from several singlecity epidemiological studies to provide coverage for additional health effect endpoints 

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

Modeling of long-term exposure-related mortality focused on the latest reanalysis of 
2-9

the American Cancer Society (ACS) dataset (Krewski et al., 2009). This study 

expands upon previous publications presenting evaluations of the ACS long-term 

cohort study and, in particular, includes rigorous examination of different model 

forms for estimating effect estimates as well as updated and expanded datasets on 

incidence and exposure. Our rationale for selecting C-R functions from specific 

epidemiological studies to use in the assessment, as well as our rationale for not 

selecting C-R functions from alternative epidemiological studies, is discussed below 

in section 3.3.3. 

 Characterization of uncertainty and variability: To characterize uncertainty and 

variability, we follow guidance developed by the World Health Organization (WHO) 

which presents a four-tiered approach for characterizing uncertainty (and to a lesser 

extent variability) in the context of a risk assessment (WHO, 2008). This guidance 

includes tiers ranging from qualitative characterization of uncertainty (Tier 1) to use 

of full-probabilistic Monte Carlo-based simulation (Tier 3). Sensitivity analysis 

methods, which are used in the RA to assess sources of uncertainty and variability, 

represent a Tier 2 approach. The application of single- and multi-factor sensitivity 

analysis methods in the RA serves two purposes: (a) to characterize the potential 

magnitude of impact that a source(s) of uncertainty and/or variability can have on risk 

estimates and (b) to provide an additional set of reasonable risk estimates to 

supplement the “core” risk estimates10 in characterizing the potential magnitude of 

uncertainty in the risk estimates (section 3.5.1 and 3.5.4). 

 Representativeness of the selected urban study areas: As planned, we conducted 

two analyses to evaluate the representativeness of the selected urban study areas for 

more broadly characterizing national risks. First, we considered key PM2.5 riskrelated attributes to determine whether the selected urban study areas are nationally 

representative or more focused on a particular portion of the distribution for a given 

attribute (section 4.4.1). Second, we analyzed estimates of mortality associated with 

recent long-term ambient concentrations to assess the extent to which the 31 counties 

comprising the 15 urban study areas captured locations within the U.S. likely to 

experience the highest PM2.5–related risk (section 4.4.2).11

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

across urban areas12: We examine how 24-hour and annual design values, together 

with patterns in PM2.5 monitoring data within an area, can influence the degree of risk 

reduction estimated to occur upon simulating just meeting the current or alternative 

suites of standards. This analysis improves our understanding of the factors related to 

specific patterns of risk reduction. We also compare patterns of design values for the 

15 urban study areas with patterns of design values across a broader set of urban areas 

 

10 The “core” risk estimates produced in this assessment refer to those generated using the combination of 

modeling elements and input datasets in which we had the highest confidence relative to other modeling choices 11 The National-Scale Mortality analysis planned and discussed in the Scope and Methods Plan and 

presented in the first and second draft RA provided the county-level mortality estimates used in this 

representativeness analysis (see Appendix G). 12 See section 3.2.3.1 for additional detail on derivation of 24-hour and annual design values. 
2-10

in the U.S. for which adequate air quality data are available in order to place core risk 

estimates generated for the set of urban study areas in a broader national context. 

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

risk estimates generated for the 15 urban study areas to inform the current review of 

the PM2.5 NAAQS, we integrate the core risk estimates generated for the 15 urban 

study areas with key observations from the sensitivity analyses and the qualitative 

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

across the U.S (chapter 5). 

2.4 ALTERNATIVE SUITES OF PM2.5 STANDARDS EVALUATED 

The scope of this quantitative risk assessment focuses on consideration of alternative 

standard levels only. Simulation of just meeting alternative standard levels is considered in this 

assessment in conjunction with the current averaging times (24-hour and annual) and forms of 

the existing suite of PM2.5 standards.13 The four basic elements of the NAAQS: indicator14, 

averaging time, form, and level, which together serve to define each standard, must be 

considered collectively in evaluating the health protection afforded by the primary PM 

standards.15 

With regard to selecting alternative levels for the annual and 24-hour PM2.5 standards for 

evaluation in the quantitative risk assessment, we made initial selections during the development 

of the first draft RA based upon information available to us at that time as presented and assessed 

in the second draft ISA. In the process of finalizing the risk assessment in consideration of the 

final ISA and the ongoing development of the first draft PA, we revisited the selection of 

alternative levels and reached the conclusion that it was appropriate to expand the rang of levels 

evaluated, as discussed below. 

In evaluating the ambient air quality concentrations associated with health effects in 

epidemiological studies of long- and short-term exposure to PM2.5 we placed greatest weight on 

information from multi-city studies. These studies have a number of advantages compared to 

single-city studies including: (1) multi-city studies reflect ambient PM2.5 concentrations and 

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

 

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

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

percentile of the distribution of 24-hour PM2.5 concentrations at each population-oriented monitor within an area, 

averaged over 3 years. The current form of the annual PM2.5 standard is an annual arithmetic mean, averaged over 3 

years, from single or multiple community-oriented monitors. 14 The “indicator” of a standard defines the chemical species or mixture that is to be measured in 

determining whether an area attains the standard. 15 All of the basic elements of the standards are discussed in the Policy Assessment (PA). 
2-11

suffer from potential omission of negative analyses due to ‘publication bias’” (US EPA, 2004a, 

p. 8-30); and (3) multi-city studies generally have higher statistical power. 

With regard to selection of alternative levels for the annual PM2.5 standard to be 

evaluated in this risk assessment, we first considered long-term average PM2.5 concentrations 

associated with health effects observed in long-term epidemiological studies, as summarized in 

Figure 2-2 of the second draft ISA. The second draft ISA concluded that the association between 

increased risk of mortality and long-term PM2.5 exposure becomes more precise and consistently 

positive in locations with mean PM2.5 concentrations of 13.5 μg/m3 and above (US EPA, 2009a, 

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

cardiovascular-related effects related to long-term PM2.5 exposures has been reported in large, 

multi-city U.S.-based studies and, specifically, one of these studies, the Women’s Health 

Initiative (WHI) Study, reports associations between PM2.5 and cardiovascular effects among 

post-menopausal women with a mean annual average PM2.5 concentration of 13.5 μg/m3 (US 

EPA, 2009a, section 2.3.1.2). In addition, we evaluated long-term average PM2.5 concentrations 

in short-term exposure studies that reported statistically significant effects. More specifically, as 

reported in the second draft ISA, both cardiovascular and respiratory morbidity effects (e.g., 

emergency department visits, hospital admissions) have been observed and become more precise 

and consistently positive in locations with mean PM2.5 concentrations of 13 μg/m3

 and above 

(US EPA, 2009a, section 2.3.1; also see Figure 2-1).16 

Based on the available epidemiological evidence indicating effects associated with a 

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

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

assessment. Following CASAC and public review of the first draft RA, we expanded the range 

of alternative annual standard levels to include14 μg/m3 to provide fuller coverage for the range 

of values between the current annual standard level of 15 μg/m3 and the lowest alternative level 

evaluated. Subsequent to the release of the 2nd draft RA, we further expanded the range of 

alternative annual standard levels evaluated to include a level of 10 μg/m3

, consistent with 

considerations presented in the first draft PA. In so doing, we recognized the increased 

uncertainty associated with simulating ambient PM2.5 levels for urban study areas that would just 

 

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

health effects reported in short-term exposure studies would be dependent on the specific relationship between dayto-day variation in the 24-hour PM2.5 levels (in the underlying study counties) and the associated long-term mean 

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

counties with notably different relationships between short-term day-to-day variation and longer-term mean PM2.5

levels). 
2-12

meet this lower standard level and consequently, the greater uncertainty in the associated risk 

estimates, relative to the higher alternative annual standard levels evaluated. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

distribution as representative of a reasonably precautionary approach for identifying alternative 

levels for consideration in the risk assessment. The overall 98th percentile value across the entire 

set of urban areas analyzed was 34.3 μg/m3 (US EPA, 2009a, Figure 2-1; Zanobetti and 

Schwartz, 2009). The 10th and 25th percentiles values were 25.5 and 29.8 μg/m3

, respectively 

(Zanobetti, 2009). We also completed a similar analysis of the county-level ambient air quality 

data for the 202 counties associated with the Bell et al. (2008) study (Bell, 2009). The overall 

98th percentile value across the entire set of counties analyzed in Bell et al. (2008)) was 34.2 

μg/m3

 (US EPA, 2009a, Table 6-11; Bell, 2009). The 10th and 25th percentile values were 24.4 

and 29.3 μg/m3, respectively (Bell, 2009). Based on the available epidemiological evidence 

indicating effects associated with a range of 98th percentile 24-hour PM2.5 concentrations, as 

briefly described above, we selected levels of 25 and 30 μg/m3 as the alternative 24-hour 

standard levels to be evaluated in this quantitative risk assessment. 
2-13

 Once alternative levels were identified for the annual and 24-hour PM2.5 standards, we 

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

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

hour standard levels, we considered which standard was likely to be controlling across the set of 

15 urban study areas. Either the annual or 24-hour standard will be the “controlling standard” at 

a given location, depending on the design value associated with that location.17 For this risk 

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

a mixture of behavior in terms of which standard would control across the various urban study 

areas. For example, with the 12/35 combination (i.e., an annual standard level of 12 μg/m3 and a 

24-hour standard level of 35 μg/m3), the annual level of 12 μg/m3 is the controlling standard for 

all 15 urban study areas. Alternatively, with the 12/25 combination, the annual standard is the 

controlling standard at some locations and the 24-hour standard is the controlling standard at 

other locations. Consideration of these factors resulted in a set of five alternative suites of 

annual and 24-hour standards being identified for inclusion in the risk assessment. 

The air quality scenarios included in the risk assessment for which we felt sufficient 

information was available to generate risk estimates with a reasonable degree of confidence, 

include the recent conditions air quality scenario and simulations of just meeting the current suite 

of standards and five alternative suites of standards as follows:

 Recent conditions (risk estimates based on ambient PM2.5 monitoring data for the 

analysis period – 2005 to 2007) 

 Current suite of PM2.5 NAAQS: annual 15 μg/m3; 24-hour 35 μg/m3 

 Alternative suite of PM2.5 standards: annual 14 μg/m3; 24-hour 35 μg/m3

 Alternative suite of PM2.5 standards: annual 13 μg/m3; 24-hour 35 μg/m3

 Alternative suite of PM2.5 standards: annual 12 μg/m3

; 24-hour 35 μg/m3

 Alternative suite of PM2.5 standards: annual 13 μg/m3

; 24-hour 30 μg/m3

 Alternative suite of PM2.5 standards: annual 12 μg/m3

; 24-hour 25 μg/m3

Because of the increased uncertainty associated with estimates of risk generated for the 

alternative annual standard level of 10 μg/m3, we did not incorporate discussion of these 

estimates into the discussion of risk estimates presented in Chapter 4; instead, these risk 

estimates are presented separately in Appendix J. The two suites of alternative standards based 

on simulation of an alternative annual standard level of 10 μg/m3

 include: 

 

17 The controlling standard is the standard which requires the greatest percentage reduction to get the design 

value monitor to meet that standard - see section 3.3.3 for additional detail on the issue of controlling standards. 
2-14

 Alternative suite of PM2.5 standards: annual 10 μg/m3

; 24-hour 35 μg/m3

 Alternative suite of PM2.5 standards: annual 10 μg/m3

; 24-hour 25 μg/m3
3-1

3 URBAN CASE STUDY ANALYSIS METHODS 

This chapter provides an overview of the methods used in the risk assessment. Section 

3.1 discusses the basic structure of the risk assessment, identifying the modeling elements and 

related sources of input data needed for the analysis. Section 3.2 discusses air quality 

considerations. Section 3.3 discusses the selection of health endpoints, urban study areas and CR functions from key epidemiological studies used in modeling those endpoints. Section 3.4 

discusses baseline health effects incidence rates. Finally, section 3.5 describes how uncertainty 

and variability are addressed in the risk assessment. 

3.1 GENERAL APPROACH 

3.1.1 Basic Structure of the Risk Assessment 

The general approach used in both the prior and the current PM risk assessment relies 

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

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

monitors, the appropriate application of these functions in a PM risk assessment similarly 

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

The general PM health risk model, illustrated in Figure 3-1, combines information about 

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

baseline health incidence data for specific health endpoints, and population estimates to derive 

estimates of the annual incidence of specified health effects attributable to ambient PM 

concentrations under different air quality scenarios. This assessment was implemented within 

Total Risk Integrated Methodology – Risk Assessment component (TRIM.Risk), the component 

of EPA’s Total Risk Integrated Methodology (TRIM) model that estimates human health risks.18 

The analyses conducted for this review focused on estimating risks associated with recent 

PM2.5 air quality and estimating changes in these risks associated with air quality simulated to 

reflect just meeting the current suite of PM2.5 ambient standards, as well as any additional 

reductions in incidence estimated to occur upon just meeting alternative suites of PM2.5

standards. 

Consistent with past risk assessments for NAAQS reviews, this risk assessment is 

intended to estimate risks attributable to anthropogenic sources and activities only. Therefore, for 

all health endpoints associated with short-term exposure to PM2.5, the risk assessment considers 

only the incidence of health effects associated with PM2.5 concentrations in excess

 

18 For more detailed information about TRIM.Risk, see: http://www.epa.gov/ttn/fera/trim_risk.html 
3-2

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

of policy relevant background (PRB) levels. In the studies estimating a relationship between 

mortality and long-term exposure to PM2.5, however, the lowest measured levels (LMLs) 

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

down to PRB would have required substantial extrapolation of the estimated C-R functions 

below the range of the data on which they were estimated. Therefore, we estimated risk only 

down to the LML to avoid introducing additional uncertainty related to this extrapolation into 

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

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

the risk assessment, we selected a single LML – 5.8 μg/m3 from the later exposure period 

evaluated in Krewski et al. (2009) -- to be used in estimating risks associated with long-term 

PM2.5 exposures. 

For each health effect that has been associated with PM2.5, the risk assessment may be 

viewed as assessing the incidence of the health effect associated with PM2.5 concentrations under 

a given air quality scenario (e.g., a scenario in which PM2.5 concentrations just meet a specified 

suite of standards) above PRB or the LML . Equivalently, the risk assessment may be viewed as 

assessing the change in incidence of each health effect associated with a change in PM2.5

concentrations from some higher level (e.g., PM2.5 concentrations that just meet a specified suite 

of standards) to specified lower levels (PRB levels or the LML). 

 The risk assessment procedures described in more detail below are diagramed in Figure 

3-2 for analyses based on short-term exposure studies and in Figure 3-3 for analyses based on 

long-term exposure studies. To estimate the change in incidence of a given health effect 

resulting from a given change in ambient PM2.5 concentrations in an assessment location, the 

following analysis inputs are necessary: 

 Air quality information including: (1) PM2.5 air quality data from one or more recent 

years from population-oriented monitors in the assessment location, (2) estimates of 

PM2.5 PRB concentrations appropriate to this location, and (3) a method for adjusting the 

air quality data to reflect patterns of air quality changes to simulate just meeting the 

current or alternative suite of PM2.5 standards. (These air quality inputs are discussed in 

more detail in section 3.2). 

 C-R function(s) which provide an estimate of the relationship between the health 

endpoint of interest and PM2.5 concentrations (preferably derived in the assessment 

location, although functions estimated in other locations can be used at the cost of 

increased uncertainty -- see section 3.5.3). For PM2.5, C-R functions are available from 

epidemiological studies that assessed PM2.5-related health effects associated with either 

short- or long-term exposures. (Section 3.1.2 describes the role of C-R functions in 

estimating health risks associated with PM2.5). 

 Baseline health effects incidence rate and population. The baseline incidence rate 

provides an estimate of the incidence rate (number of cases of the health effect per year, 
 3-4

usually per 10,000 or 100,000 general population) in the assessment location 

corresponding to recent ambient PM2.5 levels in that location. To derive the total baseline 

incidence per year, this rate must be multiplied by the corresponding population number 

(e.g., if the baseline incidence rate is number of cases per year per 100,000 population, it 

must be multiplied by the number of 100,000s in the population). (Section 3.4 

summarizes considerations related to the baseline incidence rate and population data 

inputs to the risk assessment). 
3-5

Figure 3-2. Flow diagram of risk assessment for short-term exposure studies. 
3-6

Figure 3-3. Flow diagram of risk assessment for long-term exposure studies. 
 3-7

 The risk assessment was carried out using three years of recent air quality data from 

2005, 2006, and 2007 (see section 3.2.1). We matched the population data used in the risk 

assessment to the year of the air quality data. For example, when we used 2005 air quality data, 

we used 2005 population estimates. It was not possible to obtain the necessary data to calculate 

baseline incidence rates separately for each of the three years for each of the risk assessment 

locations, therefore, we calculated these rates for a single year, under the assumption that these 

rates are unlikely to have changed significantly from 2005 to 2007. The calculation of baseline 

incidence rates is described in detail in section 3.4. 

 For this risk assessment, we developed a core (primary) set of risk results based on the 

application of modeling element choices (e.g., C-R functions, lag periods) that we believe have 

the greatest overall support in the literature (hereafter referred to as the “core” results). While it 

is not possible at this time to assign quantitative levels of confidence to these core risk estimates, 

we do believe these estimates are generally based on inputs having higher overall levels of 

confidence relative to risk estimates that could have been generated using other inputs identified 

in the literature. 

 In addition, as discussed above in section 2.1 and later in section 3.5, we have also used 

single-element and multi-element sensitivity analysis techniques to generate a set of reasonable 

alternative risk estimates based on the application of alternative modeling element choices that, 

while not having as much support in the literature as those used in the core analysis, do still 

represent plausible inputs. The results of these sensitivity analyses allow us to gain insights into 

which sources of uncertainty and variability may have the greatest impact on risk estimates when 

acting alone, or in combination with other sources of uncertainty. The sensitivity analysis-based 

risk estimates also provide us with an additional set of reasonable risk results that allow us to 

place the results of the core analysis in context with regard to uncertainty. A number of 

modeling elements were used in differentiating core analyses from sensitivity analyses (e.g., C-R 

function shape, alternative effect estimates, alternative lag structures, different methods used to 

rollback air quality to simulate attainment to current or alternative standard levels, application of 

PRB versus LML). Specific choices made in relation to individual modeling elements in 

differentiating the core analysis from sensitivity analyses are described, as appropriate, in the 

sections that follow, which cover specific aspects of the risk assessment design. The potential 

utility of the sensitivity analysis-based risk estimates in informing consideration of uncertainty 

and variability in the core results is discussed in section 4.3.2. 

3.1.2 Calculating PM2.5-Related Health Effects Incidence 

The C-R functions used in the risk assessment are empirically estimated relations 

between average ambient concentrations of PM2.5 and the health endpoints of interest (e.g., 
 3-8

mortality or hospital admissions reported by epidemiological studies for specific locations). This 

section describes the basic method used to estimate changes in the incidence of a health endpoint 

associated with changes in PM2.5, using a “generic” C-R function of the most common functional 

form. 

 Although some epidemiological studies have estimated linear C-R functions and some 

have estimated logistic functions, most of the studies used a method referred to as “Poisson 

regression” to estimate exponential (or log-linear) C-R functions in which the natural logarithm 

of the health endpoint is a linear function of PM2.5: 

 x Bey   (1) 

where x is the ambient PM2.5 level, y is the incidence of the health endpoint of interest at 

PM2.5 level x, β is the coefficient of ambient PM2.5 concentration, and B is the incidence at x=0, 

i.e., when there is no ambient PM2.5. The relationship between a specified ambient PM2.5 level, 

x0, for example, and the incidence of a given health endpoint associated with that level (denoted 

as y0) is then 

 0

0

x Bey   (2) 

Because the log-linear form of a C-R function (equation (1)) is by far the most common 

form, we use this form to illustrate the “health impact function” used in the PM2.5 risk 

assessment. 

 If we let x0 denote the baseline (upper) PM2.5 level, and x1 denote the lower PM2.5 level, 

and y0 and y1 denote the corresponding incidences of the health effect, we can derive the 

following relationship between the change in x, Δx= (x0- x1), and the corresponding change in y, 

Δy, from equation (1).19

  

yyy y e x    ( ) [ ]. 01 0 1  (3) 

Alternatively, the difference in health effects incidence can be calculated indirectly using 

relative risk. Relative risk (RR) is a measure commonly used by epidemiologists to characterize 

the comparative health effects associated with a particular air quality comparison. The risk of 

 

19 If Δx < 0 – i.e., if Δx = (x1- x0) – then the relationship between Δx and Δy can be shown to be 

]1[)( 001  x eyyyy  . If Δx < 0, Δy will similarly be negative. However, the magnitude of Δy will be the 

same whether Δx > 0 or Δx < 0 – i.e., the absolute value of Δy does not depend on which equation is used. 
 3-9

mortality at ambient PM2.5 level x0 relative to the risk of mortality at ambient PM2.5 level x1, for 

example, may be characterized by the ratio of the two mortality rates: the mortality rate among 

individuals when the ambient PM2.5 level is x0 and the mortality rate among (otherwise identical) 

individuals when the ambient PM2.5 level is x1. This is the RR for mortality associated with the 

difference between the two ambient PM2.5 levels, x0 and x1. Given a C-R function of the form 

shown in equation (1) and a particular difference in ambient PM2.5 levels, Δx, the RR associated 

with that difference in ambient PM2.5, denoted as RRΔx, is equal to eβΔx. The difference in health 

effects incidence, Δy, corresponding to a given difference in ambient PM2.5 levels, Δx, can then 

be calculated based on this RRΔx as: 

)]/1(1[)( 010 RR x yyyy       . (4) 

Equations (3) and (4) are simply alternative ways of expressing the relationship between 

a given difference in ambient PM2.5 levels, Δx > 0, and the corresponding difference in health 

effects incidence, Δy. These health impact equations are the key equations that combine air 

quality information, C-R function information, and baseline health effects incidence information 

to estimate ambient PM2.5 health risk. 

3.1.2.1 Short-term vs. Long-term Exposure 

Concentration-response (C-R) functions that use as input annual average PM2.5 levels (or 

some function of these, such as the average over a period of several years) relate these to the 

annual incidence of the health endpoint – i.e., in such studies x in equation (1) above is the 

average PM2.5 concentration over a period of one or more years, meant to represent long-term 

exposure, and y is the annual incidence of the health effect associated with that long-term 

exposure. 

 Concentration-response (C-R) functions that use as input 24-hour average PM2.5 levels (or 

some function of these, such as the average over one or more days) relate these to the daily 

incidence of the health endpoint – i.e., in such studies x in equation (1) above is the average 

PM2.5 concentration over a period of one or a few days (short-term exposure), and y is the daily 

incidence of the health effect associated with that short-term exposure. 

There are several variants of the short-term (daily) C-R function. Some C-R functions 

were estimated by using moving averages of ambient PM2.5 to predict daily health effects 

incidence. Such a function might, for example, relate the incidence of the health effect on day t

to the average of PM2.5 concentrations on days t and (t-1). Some C-R functions consider the 

relationship between daily incidence and daily average PM2.5 lagged a certain number of days. 

For example, a study might estimate the C-R relationship between mortality on day t and average 
 3-10

PM2.5 on a prior day (t-1). A few studies have estimated distributed lag models, in which health 

effect incidence is a function of PM2.5 concentrations on several prior days – that is, the incidence 

of the health endpoint on day t is a function of the PM2.5 concentration on day t, day (t-1), day (t2), and so forth. Such models can be reconfigured so that the sum of the coefficients of the 

different PM2.5 lags in the model can be used to predict the changes in incidence on several days. 

For example, corresponding to a change in PM on day t in a distributed lag model with 0-day, 1-

day, and 2- day lags considered, the sum of the coefficients of the 0-day, 1-day, and 2-day lagged 

PM2.5 concentrations can be used to predict the sum of incidence changes on days t, (t+1) and 

(t+2). 

 Most daily time-series epidemiological studies estimated C-R functions in which the PMrelated incidence on a given day depends only on same-day PM concentration(i.e. lag 0), the 

previous-day PM concentration (i.e. lag 1), or some variant of those, such as a two-day average 

concentration (e.g. lag 0-1). Such models necessarily assume that the longer pattern of PM 

levels preceding the PM concentration on a given day does not affect mortality or morbidity on 

that day. To the extent that PM-related mortality on a given day is affected by PM concentrations 

over a longer period of time, then these models would be mis-specified, and this misspecification would affect the predictions of daily incidence based on the model. 

 The extent to which time-series studies using single-day PM2.5 concentrations may under 

or over-estimate the relationship between short-term PM2.5 exposure and risk of mortality is 

unknown. However, there is some evidence, based on analyses of PM10 data, that mortality or 

morbidity on a given day is influenced by prior PM exposures up to more than a month before 

the date of death (Schwartz, 2000). The extent to which short-term exposure studies (including 

those that consider distributed lags) may not capture the full impact of long-term exposures to 

PM2.5 is similarly not adequately understood, although the current evidence (e.g., Krewski et al., 

2009; Krewski et al., 2000) suggests that there is a substantial impact of long-term exposures on 

health effects that is not picked up in the short-term exposure studies. 

3.1.2.2 Calculating Annual Incidence 

The risk assessment estimated health effects incidence, and changes in incidence, on an 

annual basis, for 2005, 2006, and 2007. For mortality, both short-term and long-term exposure 

studies have reported estimated C-R functions. As noted above, most short-term exposure C-R 

functions estimated by daily time-series epidemiological studies relate daily mortality to sameday PM2.5 concentration or previous-day PM2.5 concentration (or some variant of those). 

 To estimate the daily health impacts of 24-hour average ambient PM2.5 levels above PRB, 

C-R functions from short-term exposure studies were used together with estimated changes in 

24-hour ambient PM2.5 concentrations to calculate the daily changes in the incidence of the 
 3-11

health endpoint. After daily changes in health effects were calculated, an annual change was 

calculated by summing the daily changes. 

As part of the PM NAAQS RA completed in 1996, we demonstrated that the majority of 

mortality incidence due to short-term exposure (aggregated over a year) was driven by days with 

24-hour average PM2.5 levels nearer to the annual average value for the study area rather than 

days with relatively higher PM2.5 levels falling in the tail of the annual 24-hour PM2.5

distribution.20 This finding reflects the fact that the number of deaths associated with short-term 

exposure to PM2.5 depends both on the number of days at a given concentration and on the 

concentration itself. Because the urban areas considered in the 1996 RA had 24-hour PM2.5

distributions that were closer to normal or log-normal in form (i.e., not uniform), overall 

incidence of short-term exposure-related mortality was driven by the relatively large number of 

days near the center of the distribution, rather than the small number of days out at the tail. This 

analysis was updated for the last review completed in 2006.21 

As part of the current analysis, we have updated the analysis originally presented in the 

1996 PM NAAQS RA to reflect the air quality data and mortality estimates due to short-term 

exposure generated as part of the current review. For this updated analysis, we have focused on 

Detroit and New York and on cardiovascular mortality as the short-term exposure mortalityrelated health effect category.22 The results of this analysis corroborate the findings of the earlier 

analysis from 1996 (see Figures I-1 and I-2 in Appendix I). Specifically, these figures 

demonstrate that for these two urban study areas, a large fraction of short-term exposure-related 

cardiovascular mortality incidence is associated with days around the mean of each distribution 

(13.9 μg/m3 for Detroit and 13.8 μg/m3

 for New York – see Appendix A, Table A-5 and A-9, 

respectively), rather than days comprising the upper tail of each distribution. In other words, 

days with relatively elevated PM2.5 levels contribute a relatively small fraction of short-term 

exposure-related cardiovascular mortality incidence at each study area. 

The mortality associated with long-term exposure is likely to include mortality related to 

short-term exposures as well as mortality related to longer-term exposures. As discussed 

previously, estimates of daily mortality based on the time-series studies also are likely influenced 

by prior PM exposures. Therefore, the estimated annual incidences of mortality calculated based 

on the short- and long-term exposure studies are not likely to be completely independent and 

should not be added together. While we can characterize the statistical uncertainty surrounding 

 

20 See Exhibit 7.6 on p. 79 of the 1996 PM NAAQS RA (Abt Associates Inc., 1996). 

21 See Figure 4-10 on p. 4-68 of the 2005 PM Staff Paper (US EPA, 2005). 

22 As discussed in the introduction to Chapter 4, we have focused on cardiovascular-related endpoints in 

summarizing risk estimates for this analysis because this endpoint category (including both cardiovascular-related 

mortality and morbidity) has the greatest degree of support in the literature. 
 3-12

the estimated PM2.5 coefficient in a reported C-R function, there are other sources of uncertainty 

associated with the C-R functions used in the risk assessment that are addressed via sensitivity 

analyses and/or qualitatively discussed in section 3.5.3. 

3.2 AIR QUALITY INPUTS 

3.2.1 Characterizing Recent Conditions 

Twenty-four hour PM2.5 air quality data for 2005, 2006, and 2007 were obtained for each 

of the urban study areas from monitors in EPA’s Air Quality System (AQS).23 To characterize 

PM2.5 air quality in each risk assessment location as accurately as possible, we used only those 

monitors that were located within the county or counties that were analyzed in the 

epidemiological studies used to select C-R functions. In a few cases, an urban area was 

delineated differently by two or more epidemiological studies used in the risk assessment. For 

example, Birmingham, AL was defined as Blount, Jefferson, Shelby, St. Clair, and Walker 

Counties in one study and as only Jefferson County in another study. In such cases, we matched 

our delineation of the urban study area to that used in each study, resulting in two or more 

different delineations of the urban study area and identified them as, for example, Birmingham 1 

and Birmingham 2. The counties and the number of air quality monitors included within each 

urban area are given in Table 3-1. 

 

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

Monitor Values Were Calculated* 

Risk Assessment 

Location Counties Number of Monitors 

Atlanta, GA - 1 Cobb, De Kalb, Fulton, Gwinnett 8 

Atlanta, GA - 2 Cobb, De Kalb, Fulton 7 

Atlanta, GA - 3 20-County MSA** 10 

Baltimore, MD Baltimore city, Baltimore county 8 

Birmingham, AL – 1 Blount, Jefferson, Shelby, St. Clair, Walker 10 

Birmingham, AL – 2 Jefferson 8 

Dallas, TX Dallas 6 

Detroit, MI Wayne 9 

Fresno, CA Fresno 3 

Houston, TX Harris 6 

Los Angeles, CA Los Angeles 10 

New York, NY – 1*** Kings, New York City (Manhattan), Queens, Richmond, Bronx 12 

Philadelphia, PA Philadelphia 7 

 

23 The specific sets of air quality monitoring data for each of the urban study areas are available in the 

docket (Docket ID#: EPA-HQ-OAR-2007-0492) and have been posted at: http://www.epa.gov/ttn/analysis/pm.htm. 
 3-13

Risk Assessment 

Location Counties Number of Monitors 

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. 

** Barrow, Bartow, Carroll, Cherokee, Clayton, Cobb, Coweta, DeKalb, Douglas, Fayette, Forsyth, Fulton, Gwinett, 

Henry, Newton, Paulding, Pickens, Rockdale, Spalding, and Walton. 

*** The sets of monitors for New York (Manhattan) have 1-in-3 day sampling, with sampling schedules synced 

across monitors. This means that for the three year simulation period, roughly 2/3 of the days (i.e., 731) had no 

monitor coverage for the New York urban study area, resulting in a need to interpolate estimates for these days (for 

the composite monitor) using the approach described above. 

**** For Tacoma, the single monitor at that location also has 1 in 3 day sampling, resulting again, in 2/3 of the days 

not having data with interpolation being used to derive estimates for those days (for the composite monitor). 

In order to be consistent with the approach generally used in the epidemiological studies 

that estimated PM2.5 C-R functions, rather than working directly with individual monitor values 

in estimating risk, we first derived composite monitor estimates (i.e., a composite of individual 

monitor values for a given study area) and then used those to represent population exposure in 

the risk assessment. Two types of composite monitor values were derived including annual 

estimates (used in modeling long-term exposure-related mortality) and distributions of 24-hour 

average levels (used in modeling short-term exposure-related mortality and morbidity). The 

procedure for deriving each of these types of composite monitor estimates is described below. 

The approach for creating composite monitors used in this risk assessment reflects the goal of 

providing equal weighting of monitors in computing both 24-hour and annual composite monitor 

values.24 

Composite monitor distributions of 24-hour estimates

To develop composite monitor distributions of 24-hour estimates reflecting equal 

weighting of the underlying monitor datasets, we completed an initial step of interpolation to fill 

in missing measurements at the individual monitors. We then calculated the average for a 

particular day across the contributing monitors to develop a 24-hour PM distribution reflecting 

equal weighting of the monitors. The specific step-wise procedure involved: 

 

24 This reflects a change from the approach used in the first draft RA which weighted monitors by sampling 

frequency – an approach which could result in bias being introduced into the analysis, in those instances where 

monitors with higher measurements were sampled more frequently. 
 3-14

1. Interpolate missing days at individual monitors: If a monitor had fewer than 11 

measurements in a calendar quarter, we left those days “as is” and did not conduct any 

interpolation. However, for those quarters with 11 or more measurements, we did interpolate 

missing values using the following procedure. For the day(s) (up to a string of seven in a 

row) without data, we took the average of the nearest day with measured data above and 

below the missing day(s) and assigned that interpolated value to that day(s). Note, however, 

that if the string of missing days was longer than seven, we did not interpolate and left those 

days blank.25 

2. Derive distribution of 24-hour measurements for the composite monitor: For each day in the 

simulation year, the composite monitor value is calculated as the average of the daily 

estimates across monitors in that study area. If a monitor is missing a measurement on a 

particular day (after interpolation described in Step 1), it is not factored into the composite 

estimate for that day. If after completing this step, any composite monitor days are missing 

values (reflecting the instance where the underlying monitors in that study area were all 

missing estimates for a specific day, even after interpolation described in step 1), then a 

composite monitor value is interpolated for that day using a 7-day moving average (i.e., the 

average of the 3 composite daily estimates before that day, the day on record, and the three 

after it) to represent that missing composite 24-hour estimate. 

Composite monitor annual average estimates

Composite monitor annual averages were calculated using an approach that did not 

require interpolation of missing days at individual monitors (i.e., Step 1 above). Instead, we used 

quarterly averages at each monitor within a study area as the basis for deriving composite annual 

average estimates. The specific step-wise procedure involved: 

1. Calculate quarterly averages at each monitor in the study area: For a given monitor, if a 

quarter has less than 11 measurements, we classify that quarter as “missing” and do not use it 

in computing the quarterly estimate. For the remaining quarters across the monitors with 11 

 

25 There are a variety of approaches that can be used to interpolate missing data as part of creating 

composite monitor 24-hour distributions. While the approach used in this analysis relies on data from the specific 

monitor for which interpolation is being conducted, alternative approaches can utilize trend data from across all of 

the monitors in a given study area. The presence of a variety of interpolation approaches from which to choose does 

represent a source of uncertainty impacting the interpolation process and consequently the risk assessment. To 

further examine this source of uncertainty, we have completed a sensitivity analysis for a single location 

(Birmingham), in which we apply both the interpolation technique used in the risk assessment, as well as an 

alternative approach that utilizes trend data from all of the Birmingham monitors to interpolate missing data at 

specific monitors (this sensitivity analysis is fully described in Appendix B, section B1). The results of this 

sensitivity analysis suggest that, in the context of Birmingham, these different approaches for interpolating missing 

monitoring data did not produce substantially different results. Furthermore, the small differences that were seen in 

the 24-hour PM2.5 distributions generated using these two interpolation approaches, would translate into negligible 

differences in short-term exposure-related risk estimates (which utilize the 24-hour PM2.5 distributions involved in 

the interpolation). Therefore, we conclude, based on the results of this sensitivity analysis, that uncertainty related 

to interpolation of missing data represents a relatively small source of uncertainty in the overall analysis. 
 3-15

or more measurements, we calculate a quarterly estimate for each quarter/monitor 

combination as the average of the existing values (i.e., no interpolation of daily estimates at 

individual monitors was completed). 

2. Calculate quarterly averages at the composite monitor: For each quarter of the year, we then 

calculated the composite monitor quarterly average as the average of the monitor-specific 

quarterly averages for that study area. 

3. Calculate the annual average for the composite monitor: We then averaged the four 

quarterly-average estimates (generated in Step 2) to produce an annual average for the 

composite monitor.26

 

Appendix A summarizes the PM2.5 air quality data that were used in each of the 

assessment locations, including quarterly and annual counts, quarterly and annual averages, and 

the 98th percentile of the daily (24-hour) averages for individual monitors. Appendix A also 

provides summary information on the composite monitor annual average estimates and 98th

percentile values generated for each study area. 

3.2.2 Estimating Policy Relevant Background 

Policy-relevant background estimates used in the risk assessment model (see Table 3-2 

below) were obtained from the ISA (Table 3-23, final ISA, US EPA, 2009d). These values were 

generated based on a combination of Community Multiscale Air Quality model (CMAQ) and 

Goddard Earth Observing System (GEOS)-Chem modeling as described in the draft ISA (see 

section 3.7.1.2). Annual values presented in Table 3-2 were used in modeling health endpoints 

associated with long-term exposure (in those sensitivity analysis scenarios where risk was 

modeled down to PRB – see section 3.5.4). For health endpoints associated with short-term 

exposure (which involved modeling down to PRB, exclusively), quarterly values presented in 

Table 3-2 were used to represent the appropriate block of days within a simulated year. 

 

26 Pittsburgh was treated somewhat differently from the other locations because there are effectively two 

attainment areas in Pittsburgh – one containing ten of the monitors we’re using in the risk assessment (“Pittsburgh1”), 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). 
 3-16

Table 3-2 Regional Policy-Relevant Background Estimates Used in the Risk 

Assessment. 

U.S. Region Annual 

JanuaryMarch April-June JulySeptember OctoberDecember 

Northeast 0.74 0.85 0.78 0.67 0.68 

Southeast 1.72 2.43 1.41 1.41 1.64 

Industrial Midwest 0.86 0.89 0.89 0.94 0.73 

Upper Midwest 0.84 0.79 0.93 0.99 0.66 

Southwest 0.62 0.61 0.76 0.70 0.40 

Northwest 1.01 0.48 0.81 1.42 1.32 

Southern California 0.84 0.54 0.92 1.21 0.67 

3.2.3 Simulating Air Quality to Just Meet Current and Alternative Standards 

This section describes the methodologies used to simulate ambient PM2.5 levels in an area 

that would just meet specified PM2.5 standards. The form of the current PM2.5 standards requires 

that the 3-year average (rounded to the nearest 0.1 μg/m3

) of the annual means from each single 

monitor or the average of multiple monitors must be at or below the level of the annual standard 

and the 3-year average (rounded to the nearest 1 μg/m3) of the ninety-eighth percentile values at 

each monitor cannot exceed the level of the 24-hour standard. In determining attainment of the 

annual average standard, an area may choose to use either the spatially averaged concentrations 

across all population-oriented monitors, subject to meeting certain criteria detailed in Part 50, 

Appendix N, of the Code of Federal Regulations (CFR), or it may use the highest 3-year average 

based on individual monitors. The most realistic simulation of just meeting both the annual and 

the 24-hour PM2.5 standards in a location would require changing the distribution of 24-hour 

PM2.5 concentrations at each monitor separately, based on the specific mix of local and regional 

controls impacting that particular location. This would require extensive analysis and 

assumptions about the nature of future control strategies that is beyond the scope of quantitative 

risk assessments done as part of the review of the NAAQS.27

In the last PM risk assessment, just meeting the current or alternative PM2.5 standards was 

simulated using a single rollback approach (the proportional) which reflected a uniform regional 

pattern of reduction in ambient PM2.5 levels across monitors. For this analysis, we have included 

two additional rollback approaches to provide greater coverage for variability associated with 

this key aspect of simulating risk for both the current and alternative standard levels. These two 

 

27 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. 
 3-17

approaches include the hybrid and the locally focused rollback methods. The hybrid rollback

approach involves a combination of an initial step of a more localized reduction in ambient PM2.5 

levels at source-oriented monitors followed by a regional pattern of reduction across all monitors 

in a study area. The locally focused rollback approach involves a focused reduction of levels 

only at those monitors exceeding the daily standard level under consideration. While the 

proportional rollback approach is applied to all 15 urban study areas included in the analysis, 

both the hybrid and locally focused rollback approaches are applied to a subset of the study areas 

meeting specific criteria as outlined below. Each of the three rollback methods is described 

below including a description of the step-wise procedure used in implementing each approach. 

In the last PM risk assessment, just meeting the current or alternative PM2.5 standards was 

simulated by changing 24-hour PM2.5 concentrations at a “composite monitor,” which 

represented the average of the monitors in a location. In the current PM risk assessment, 

depending on the type of rollback method used, just meeting the current or alternative PM2.5

standards was simulated by changing 24-hour PM2.5 concentrations at the composite monitor (for 

the proportional rollback approach) or by adjusting values at each monitor separately prior to 

generating composite-monitor estimates (for the hybrid and locally focused rollback methods). 

This change was made because the hybrid and locally focused rollback methods involve nonuniform degrees of reduction in ambient PM2.5 measurements for the monitors in a given study 

area, as contrasted with the proportional approach which involves the same percent reduction 

across all monitors in a study area. 

The proportional rollback approach was used in generating the core risk estimates in light 

of its use in past risk assessments, while the other two rollback approaches (hybrid and locally 

focused) have been included as part of the sensitivity analyses to characterize potential 

variability in the way urban areas may respond to suites of current or alternative standards. In 

considering the three rollback methods collectively, the proportional and locally focused 

methods represent approaches more likely to capture “bounding” behavior related to the spatial 

pattern of future reductions in ambient PM2.5 levels. By contrast, the hybrid approach can be 

interpreted as reflecting a more plausible or representative rollback strategy in principle, since it 

(a) reflects consideration for site-specific information regarding larger PM sources and their 

potential impact on source-oriented monitors and (b) combines elements of more locally focused 

and regionally-focused patterns of reduction.28 However, it is important to note that the hybrid 

 

28 CASAC in providing comments on the 2nd External Review Draft RA placed greater confidence in the 

hybrid rollback method relative to the other two methods, identifying both the proportional and locally focused (then 

referred to as locally focused) – this edit is wrong-- as representing potential bounding approaches and therefore 

warranting less focus than the hybrid (REFERENCE) 
 3-18

approach as implemented is only one of a variety of potential hybrid strategies for considering 

site-specific data in simulation spatial patterns of ambient PM2.5 reduction. Because there are a 

variety of hybrid rollback strategies that could be considered and because of challenges in 

assessing the reasonableness of different strategies in predicting spatial patterns of ambient PM2.5 

reductions, we conclude that there is substantial uncertainty associated with predicting rollback 

in relation to modeling risk for both the current and alternative standard level. Consequently, 

relative importance is placed on the sensitivity analysis examining the issue of rollback, which is 

based on application of the three rollback strategies described here. While we describe how the 

different rollback methods are implemented in this section, the impact of using different rollback 

methods on core risk estimates is discussed as part of the sensitivity analysis presented in section 

3.5.4.1. 

3.2.3.1 Proportional Rollback Method 

The proportional approach involves reducing PM2.5 concentrations by the same 

percentage across all monitors in a study area, thereby reflecting a more regional pattern of 

ambient PM2.5 reduction. When this approach is used, it does not matter whether (1) PM2.5

concentrations are first rolled back by the same percentage each day at each monitor, and then 

the composite monitor values are calculated from these monitor-specific values or (2) first the 

composite monitor values are calculated and then these are rolled back by the same percentage 

each day – the results will be the same. Therefore, to streamline the analytical process, the 

proportional rollback method was applied directly to the composite monitor estimates. The stepwise procedure used in conducting proportional rollback is described below: 

1. Calculate annual and 24-hour design values for the study area under consideration: The 

degree of reduction required to simulate attainment of a specific suite of standard levels is 

determined by comparing the design values (described here) against the specific standard 

level being considered. Therefore, the first step is to calculate 24-hour and annual design 

values for the study area.29 The annual design value (in μg/m3

) was calculated as follows: 

 At each monitor, the annual average PM2.5 concentration was calculated for each of 

the years 2005, 2006, and 2007, and these three annual average concentrations were 

then averaged. 

 The maximum of these monitor-specific 3-year averages of annual averages at a 

particular study area is the annual design value, denoted dvannual; 

The 24-hour design value (in μg/m3) was similarly calculated as follows: 

 

29 Note, that as discussed later in section 3.1.3.2, the second phase of the hybrid rollback method involving 

proportional reduction also uses the design values for a study area to determine the degree of rollback required. 
 3-19

 At each monitor, the 98th percentile 24-hour PM2.5 concentration was calculated for 

each of the years 2005, 2006, and 2007, and these three 98th percentile concentrations 

were then averaged. 

 The maximum of these monitor-specific 3-year averages of 98th percentile 

concentrations at a particular study area is the 24-hour design value, denoted dvdaily 98

(note, we will refer to the 98th percentile design value as the 24-hour design value 

throughout the rest of the document). 

The annual and 24-hour design values used in assessing the current and alternative 

standards for PM2.5 are given in Table 3-3. Note that monitors that were closed in 2005 (and 

therefore, did not include monitoring data for the majority of the three year simulation period), or 

which were missing an entire year’s worth of monitoring data during any of the three simulation 

years (2005, 2006 or 2007) were excluded from consideration as design value monitors, although 

these monitors were still used to construct composite monitors for purposes of estimating risks. 

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

2005-2007.* 

Location 

Annual 

 (μg/m3) 

24-hour 

(μg/m3) 

Atlanta 16.2 35 

Baltimore 15.6 37 

Birmingham 18.7 44 

Dallas 12.8 26 

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 
 3-20

*The calculation of design values is explained in the text above. 

2. Calculate the percent reduction required to simulate attainment of the 24-hour and annual 

standard levels under consideration: The degree of reduction required to simulate attainment 

of a particular standard level is calculated by comparing the standard level under 

consideration with its matching design value to determine the percent reduction required to 

bring the composite monitor values down to match the standard level. Because pollution 

abatement methods are applied largely to anthropogenic sources of PM2.5, rollbacks are 

applied only to PM2.5 above estimated PRB levels and consequently, in determining the 

percent reduction, we only consider that portion of both the design value and standard level 

above PRB. The specific equations used to estimate the percent reduction required to 

simulate attainment (with consideration of PRB) are presented as part of a detailed example 

of the three rollbacks (as applied to Detroit) in Appendix B, section B3. 

3. Determine which standard is controlling for the study area under consideration: The percent 

reduction required to simulate attainment of the 24-hour and annual standard levels 

(calculated in Step 2) are compared and the larger of the two values is identified as the 

controlling standard. Simulated attainment of the controlling standard, by definition, results 

in more than sufficient reduction in composite monitor values to produce simulated 

attainment of the other (non-controlling) standard level. 

4. Apply proportional reduction to the composite monitor values: The percent reduction for the 

controlling standard identified in Step 3 above is applied (a) to each 24-hour estimates at the 

composite monitor to generate an adjusted 24-hour PM2.5 composite monitor distribution to 

be used in modeling short-term exposure-related risk and (b) directly to the annual average at 

the composite monitor to generate an adjusted estimate that can be used in estimating longterm exposure-related mortality. 

3.2.3.2 Hybrid Rollback Method 

 The hybrid rollback approach reflects a combination of a more localized pattern of 

rollback focused on source-oriented monitors with relatively elevated ambient PM2.5 levels, 

followed by a more generalized regional pattern of rollback across all monitors in the study area 

to simulate attainment. The first localized reduction involves reducing levels at the selected 

source-oriented monitor(s) such that they match the level of the nearest non-source oriented 

monitor(s). This initial localized reduction includes a distance-decay impact on other monitors in 

the study area (i.e., monitors further from the source-oriented monitor experience a decreasing 

fraction of the reduction experienced by the targeted source-oriented monitor(s)). The second 

more generalized regional rollback is implemented using the proportional approach described 

above in section 3.2.3.1. However, for the hybrid rollback, the percent reduction is determined 

based on consideration of design values calculated after the initial localized reduction phase, 

rather than design values based on recent conditions as is the case with proportional rollback 
 3-21

approach described in 3.2.3.1. Because the hybrid rollback approach requires that specific 

criteria be met for a study area to be a candidate (i.e., presence of source-oriented monitors with 

clearly identifiable PM sources near-by and elevated PM2.5 levels relative to other monitors in the 

study area), this approach was only applied to a subset of the 15 urban study areas that met these 

criteria including: Baltimore, Birmingham, Detroit, Los Angeles, New York, and St. Louis.30 

The step-wise procedure used to implement the hybrid approach is described below (additional 

detail on sample calculations associated with the steps presented below can be found in 

Appendix B, section B3 as part of the sample calculations provided for the three rollback 

methods). 

1. Identify candidate urban study areas: The subset of the 15 urban study areas with high 

design values (exceeding the current suite of standards) where those design value monitors 

were in close proximity to large sources of PM2.5 were identified as candidate locations. 

2. Localized reduction of PM levels at that source-oriented monitor to meet levels at nearby 

non-source oriented monitors: PM levels at the source-oriented monitor identified in Step 1 

were reduced such that their design value (after adjustment) matched the design value for the 

non-source oriented monitor with the highest design value located close by. This process was 

repeated for all source-oriented monitors identified in step 1. This reduction focused on the 

design value that was controlling for the source-oriented monitor (i.e., for that specific 

monitor, the design value – annual or 24-hour – requiring the greatest percent reduction to 

meet the standard). A proportional reduction of all 24-hour estimates at the source-oriented 

monitor was conducted to have the design value for this monitor match that for the nearest 

non-source oriented monitor. 

3. Simulate the impact of the localized reduction of PM levels at the source-oriented monitor on 

levels at other monitors in the study area: While the localized reduction described in Step 2 

does primarily impact the source-oriented monitor(s) that is the focus of that reduction, we 

do consider the potential for that reduction to impact other monitors in the study area, albeit 

with reduced impact the further you move away from the targeted source-oriented monitor. 

Specifically, those monitors within one kilometer of the source-oriented monitor were 

assigned the same proportional percent reduction as the source-oriented monitor. However, 

monitors more than a kilometer away experience a distance-decay effect equal to the percent 

rollback multiplied by the inverse of the distance in kilometers between the monitors (e.g., a 

 

30 As with the composite monitor values representing recent air quality, “rolled back” composite monitor 

values in Pittsburgh, for both the proportional rollback and the hybrid rollback methods, were calculated based on 

the division of monitors into the 10 in “Pittsburgh-1” and the remaining 2 in “Pittsburgh-2” (see footnote in Section 

3.2.1). Daily and annual composite monitor values in “Pittsburgh-1” and “Pittsburgh-2” were rolled back as 

described in Section 3.2.3.1; rolled back composite monitor values for Pittsburgh were calculated as weighted 

averages of the rolled back composite monitor values for “Pittsburgh-1” and “Pittsburgh-2”, where the weights were 

the proportion of the monitors in each (i.e., 10/12 and 2/12). 
 3-22

monitor 10 kilometers away from the source-oriented monitor, would have 1/10th of the 

percent-reduction applied to that source-oriented monitor). 

4. Conduct proportional rollback to simulate PM levels meeting the suite of standards under 

consideration given the initial localized reduction focused on source-oriented monitors: A 

regional proportional rollback identical to that described in section 3.1.3.1 is now conducted 

except that in this case, we are starting with PM2.5 levels reflecting the initial localized 

reduction described above in Steps 1-3 above. Note, that just as with the proportional 

rollback approach described in section 3.2.3.1, we begin by first calculating composite 

monitor annual average estimates and composite monitor distributions of 24-hour levels 

(based on the adjusted PM levels at individual-monitors) and then apply proportional 

rollback directly to those composite monitor values. 

 

Additional detail on the hybrid approach, as applied specifically to the Detroit study area, is 

presented in Appendix B (sections B2 and B3), including identification of the source-oriented 

monitors targeted for focused reduction in the first step of the rollback process. 

3.2.3.3 Locally focused Rollback Method 

The locally focused rollback approach reflects a local pattern of reduction in ambient 

PM2.5 concentrations focused exclusively on those monitors within urban study areas assessed to 

exceed the 24-hour standard under consideration. As such, this approach is only considered for 

the subset of the 15 urban study areas where the 24-hour standard is controlling and there is no 

adjustment to monitors besides those exceeding the 24-hour standard (i.e., no distance-decay 

effect as implemented in the hybrid approach). This approach was applied to a subset of the 15 

urban study areas meeting the above criteria, including: Baltimore, Detroit, Fresno, Los Angeles, 

New York, Philadelphia, Pittsburgh, Salt Lake City, St. Louis and Tacoma. The step-wise 

procedure used to implement the hybrid approach is described below (as with the other two 

rollback approaches, equations and sample calculations are provided in Appendix B, section B3). 

1. Identify candidates for locally focused rollback: Identify the subset of the 15 urban study 

areas where the 24-hour standard is controlling. These locations will, by definition, have 

monitors with design values exceeding the current standard and consequently are candidates 

for locally focused rollback. 

2. Determine the degree of reduction required (at each monitor exceeding the 24-hour 

standard) to bring that study area into simulated attainment: For each monitor with a 24-

hour design value exceeding the standard under consideration, compare that design value to 

the standard under consideration to determine the degree of reduction required to bring that 

monitor into simulated attainment (i.e., the percent rollback). As with the proportional 

rollback described in section 3.1.3.1, calculation of the percent rollback takes into 

consideration PRB and is based on comparing only those portions of design values and 

standard levels above PRB. 
 3-23

3. Rollback individual monitors to meet the 24-hour standard level: Based on the percent 

rollback values calculated for individual monitors within each study area in Step 2, adjust the 

distribution of 24-hour PM2.5 levels at individual monitors such that their adjusted design 

values now meet the 24-hour standard under consideration. 

4. Calculate composite monitor 24-hour distributions and annual averages for each study area:

Using the adjusted 24-hour distributions created in Step 3, calculate composite monitor 24-

hour distributions and annual averages for each study area using the approach outlined in 

section 3.1.1.31 

3.2.3.4 Presentation of Results for the Three Rollback Methods (with example 

calculation) 

The results of applying the three rollback methods in simulating attainment of the current 

and alternative suites of standard levels are presented in Table 3-4 (as noted above, the hybrid 

and locally focused methods are only applied to a subset of the study areas). In summarizing the 

composite monitor values generated using the three rollback methods, we have included two 

types of annual averages: (a) the maximum monitor-specific three-year (2005-2007) annual 

average (i.e., “Max. M-S” in both tables) and (b) the composite monitor value for 2007 (i.e., 

“2007 CM” in both tables). The first estimate (Max M-S) allows us to see how the design value 

changes in just meeting each suite of standards based on application of the different rollback 

methods, while the second estimate (2007 CM) is the surrogate for long-term exposure-related 

mortality, as described below in section 3.5.4. As is expected, the Max M-S value is consistently 

larger than the CM value for a given combination of urban study area, rollback method and 

standard level simulated. In reviewing the results presented in Table 3-4, we see that both the 

hybrid and locally focused rollback methods generate larger Max M-S and CM values than the 

proportional approach, with the locally focused approach generally resulting in the highest 

values of the three rollback methods. This is expected, since both the hybrid and locally focused 

rollback methods target a subset of monitors, thereby leaving more of the monitor-signal at a 

given study area “unadjusted” compared with the proportional rollback method. The locally 

focused rollback method, since it targets only those monitors exceeding the 24-hour standard 

with no impact on other non-exceedence monitors, would be expected to have the highest 

 

31 As with the proportional and hybrid rollback methods, rolled back composite monitor values in 

Pittsburgh using the locally focused method were calculated based on the division of monitors into the 10 in 

“Pittsburgh-1” and the remaining 2 in “Pittsburgh-2” (as explained in the footnote in Section 3.2.3.2). However, 

unlike in the other locations, if the annual standard was controlling in one of the Pittsburgh attainment areas (i.e., in 

“Pittsburgh-1” or “Pittsburgh-2”), monitor-specific quarterly averages in that attainment area were rolled back by 

the percent rollback necessary to just meet the annual standard there. Once monitors in “Pittsburgh-1” and 

“Pittsburgh-2” were rolled back, the procedure to calculate annual composite monitor values in Pittsburgh was the 

same as in the other risk assessment locations. 
 3-24

remaining annual average composite monitor estimates of the three methods and this is generally 

borne out in the values presented in Table 3-4. 

To enhance transparency, we have included in Appendix B, section B3, a more detailed 

example calculation of the three rollback methods as applied to a single urban study area 

(Detroit), showing the step-wise procedure applied to individual monitors as appropriate, 

including equations used, input values and sample calculations. 
3-25

Table 3-4. Application of the Three Rollback Methods in Simulating Current and Alternative S

the 15 Urban Study Areas (including resulting maximum monitor-specific and compovalues) 

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Ma

Average at Composite Monitor (2007CM) (in μg/

 Design Value 

Recent Air 

Quality 

(2007) 15/35 2 14/35 13/35 12/35 

Risk 

Assessment 

Location 1

Rollback 

Method Annual 

24-

Hr 2007 CM 

Max. 

M-S 

2007 

CM 

Max. 

M-S 

2007 

CM 

Max. 

M-S 

2007 

CM 

Max. 

M-S 

2007 

CM 

Max

M-S 

Proportional 15.0 14.2 14.0 13.3 13.0 12.3 12.0 11.4 13.Hybrid 3 Atlanta, GA --- --- --- --- --- --- --- --- --- 

Locally focused 

16.2 35.0 15.3 

--- --- --- --- --- --- --- --- --- 

Proportional 14.8 13.1 14.0 12.5 13.0 11.6 12.0 10.7 12.

Baltimore, MD Hybrid 14.3 13.0 14.0 12.7 13.0 11.8 12.0 10.9 12.

Locally focused 

15.6 37.0 13.9 

15.2 13.6 --- --- --- --- --- --- 13.

Proportional 15.0 12.7 14.0 11.8 13.0 11.0 12.0 10.2 13.

Birmingham, AL Hybrid 15.0 14.2 14.0 13.2 13.0 12.3 12.0 11.4 13.

Locally focused 

18.7 44.0 15.7 

--- --- --- --- --- --- --- --- --- 

Proportional 12.8 11.4 12.8 11.4 12.8 11.4 12.0 10.7 12.

Dallas, TX Hybrid --- --- --- --- --- --- --- --- --- 

Locally focused 

12.8 26.0 11.4 

--- --- --- --- --- --- --- --- --- 

Proportional 14.1 11.4 14.0 11.4 13.0 10.6 12.0 9.8 12.

Detroit, MI Hybrid 13.2 11.7 13.2 11.7 13.0 11.5 12.0 10.6 11.

Locally focused 

17.2 43.0 13.9 

14.1 12.6 --- --- --- --- --- --- 12.

Proportional 9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 8.6 

Fresno, CA Hybrid --- --- --- --- --- --- --- --- --- 

Locally focused 

17.4 63.0 17.4 

10.1 10.3 10.1 10.3 10.1 10.3 10.1 10.3 8.8 

Proportional 15.0 12.5 14.0 11.7 13.0 10.9 12.0 10.1 13.

 Houston, TX Hybrid --- --- --- --- --- --- --- --- --- 

Locally focused 

15.8 31.0 13.2 

--- --- --- --- --- --- --- --- --- 

Los Angeles, Proportional 19.6 55.0 14.6 12.7 9.5 12.7 9.5 12.7 9.5 12.0 9.0 10.
3-26

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Ma

Average at Composite Monitor (2007CM) (in μg/

 Design Value 

Recent Air 

Quality 

(2007) 15/35 2 14/35 13/35 12/35 

Risk 

Assessment 

Location 1

Rollback 

Method Annual 

24-

Hr 2007 CM 

Max. 

M-S 

2007 

CM 

Max. 

M-S 

2007 

CM 

Max. 

M-S 

2007 

CM 

Max. 

M-S 

2007 

CM 

Max

M-S 

CA Hybrid 13.3 10.5 13.3 10.5 13.0 10.3 12.0 9.5 11.

Locally focused 13.9 12.1 13.9 12.1 13.9 12.1 --- --- 12.

Proportional 13.3 11.6 13.3 11.6 13.0 11.3 12.0 10.4 11.

New York, NY Hybrid 13.6 11.8 13.6 11.8 13.0 11.3 12.0 10.4 11.

Locally focused 

15.9 42.0 13.8 

14.3 13.3 14.3 13.3 --- --- --- --- 12.

Proportional 13.9 12.3 13.9 12.3 13.0 11.6 12.0 10.7 11.Hybrid --- --- --- --- --- --- --- --- --- 

Philadelphia, PA 

Locally focused 

15.0 38.0 13.4 

15.5 13.0 15.5 13.0 --- --- --- --- 14.

Proportional 12.6 9.9 12.6 9.9 12.6 9.9 12.0 9.4 11.

Phoenix, AZ Hybrid --- --- --- --- --- --- --- --- --- 

Locally focused 

12.6 32.0 9.9 

--- --- --- --- --- --- --- --- 12.

Proportional 13.3 11.6 13.3 11.6 12.8 11.2 11.8 10.5 11.

Pittsburgh, PA Hybrid --- --- --- --- --- --- --- --- --- 

5

Locally focused 

19.8 60.0 14.9 

15.6 13.2 15.6 13.2 15.3 11.8 15.3 11.2 15.

Proportional 7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 6.7 Hybrid --- --- --- --- --- --- --- --- --- 

Salt Lake City, UT 

Locally focused 

11.6 55.0 11.4 

10.8 9.7 10.8 9.7 10.8 9.7 10.8 9.7 10.

Proportional 14.9 12.9 14.0 12.1 13.0 11.3 12.0 10.4 12.

St. Louis, MO Hybrid 15.0 13.5 14.0 12.6 13.0 11.7 12.0 10.8 13.

Locally focused 

16.5 39.0 14.3 

16.5 14.1 --- --- --- --- --- --- 14.

Proportional 8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 7.4 

Tacoma, WA Hybrid --- --- --- --- --- --- --- --- --- 

Locally focused 

10.2 43.0 9.7 

8.5 8.0 8.5 8.0 8.5 8.0 8.5 8.0 7.4 

1For some locations (e.g., Atlanta) more than one "version" (group of counties) was used in the risk assessment. In this table only the ve

mortality associated with short-term exposure to PM2.5 (Zanobetti and Schwartz, 2009) is included. 

2 The current primary PM2.5 standards include an annual standard set at 15 μg/m3 and a daily standard set at 35 μg/m3.
3-27

3 The hybrid rollback method was applied to only a subset of the risk assessment locations. The "---" for a given location indicates that t

was not applied to that location. 

4 The locally focused method was applied to a location-standard combination only if the daily standard was controlling in that location. T

standard combination indicates that, for that set of annual and daily standards in that location, the annual standard was controll

focused method was not applied. 

5 The proportional. rollback and locally focused methods were applied to Pittsburgh differently from the way they were applied in the othe

details. 

6 Percent reduction in composite monitor value with consideration of LML of 5.8 μg/m3 (note: composite monitor value denoted as CMV)

 %reduction = (CMVcurrent standard - CMValternative standard)/(CMVcurrernt standard-LML). 
 3-28

3.3 SELECTION OF MODEL INPUTS 

3.3.1 Health Endpoints 

The selection of health effect endpoints reflects consideration of a number of factors. The 

specific set of factors considered in selecting health effects endpoints to model in this assessment 

included: 

 The overall weight of evidence from the collective body of epidemiological, controlled 

human exposure, and toxicological studies and the determination made in the final ISA 

regarding the strength of the causal relationship between PM2.5 and the more general 

health effect category; 

 The extent to which particular health effect endpoints within these broader health effect 

categories are considered significant from a public health standpoint; 

 The availability of well-conducted epidemiological studies providing C-R functions for 

specific health effect endpoints; 

 The availability of sufficient air quality monitoring data in areas that were evaluated in 

the epidemiological studies; 

 The availability of baseline incidence data to support population risk (incidence) 

modeling; and 

 The anticipated value of developing quantitative risk estimates for the health effect 

endpoint(s) to inform decision-making in the context of the PM NAAQS review. 

In selecting the set of health effect endpoint categories (and associated endpoints and 

related susceptible populations) to include in the PM2.5 risk assessment, we considered the health 

effects evidence presented in the final ISA (US EPA, 2009d), as well as CASAC (Samet, 2009a) 

and public comments received on the Scope and Methods Plan and CASAC (Samet, 2009b) and 

public comments received on the first draft RA. In reviewing the final ISA in relation to PM2.5, 

we focused on the following sections: (a) section 2.3.1.1 (Effects of Short-Term Exposure to 

PM2.5), (b) section 2.3.1.2 (Effects of Long-Term Exposure to PM2.5), (c) section 2.3.2 

(Integration of PM2.5 Health Effects), and (d) subsections in Chapter 6 and 7 of the final ISA 

providing summaries of causal determination (for both morbidity and mortality endpoints) 

related to short-term and long-term exposure, respectively. We also considered information in 

the ISA on susceptible populations, which identified the life stages of children and older adults, 

people with pre-existing cardiovascular and respiratory diseases, and people with lower 

socioeconomic status as populations at increased risk for PM-related health effects. 

Based on the evidence presented in the ISA and application of the above criteria, we 

identified the following health effects endpoints for inclusion in the risk assessment: 
 3-29

Health effects associated with short-term PM2.5 exposure: 

 Mortality (causal relationship) 

o non-accidental, 

o cardiovascular-related 

o respiratory-related, 

 Cardiovascular effects (causal relationship) 

o cardiovascular-related hospital admissions 

 Respiratory effects (likely causal relationship) 

o respiratory-related hospital admissions 

o asthma-related emergency department visits 

Health effects associated with long-term PM2.5 exposure:

 Mortality (causal relationship) 

o all-cause 

o ischemic heart disease (IHD)-related 

o cardiopulmonary-related 

o lung cancer 

While we selected specific health effect endpoints that were all within broad health effect 

categories classified in the ISA as having a “causal” or “likely causal” association with PM2.5

exposure, our selection is a based on applying the multi-factor approach described above. 

 The evidence available for these selected health effect endpoints generally focused on 

the entire population, although some information was available that allowed us to consider 

differences in estimated risk for the susceptible populations of older adults and people with preexisting cardiovascular and respiratory diseases. While evidence of effects in other important 

susceptible populations, including children and people with lower socioeconomic status, was not 

judged to be sufficient to support quantitative risk assessment, this evidence will be part of the 

evidence-based considerations to be discussed in the PA currently being developed. 

3.3.2 Selection and Delineation of Urban Study Areas 

This section describes the approach used in selecting the 15 urban study areas included in 

this risk assessment (see Table 3-4 for a listing of the urban study areas). This approach builds 

upon and expands the approach for selecting urban study areas from the prior risk assessment 

(US EPA, 2005, section 3.2, p. 37). 

Criteria used in the prior risk assessment and updated in this analysis include: 

 Availability of sufficient air quality data: Sufficient air quality data was 

identified as having at least 11 observations per quarter for a one year period and 
 3-30

at least 122 observations per year. We assessed prospective study areas by 

insuring that there was at least one PM2.5 monitor within the boundaries of the 

prospective study area that met these completeness criteria for the period 2005 to 

2007 with additional preference given to locations with more than one PM2.5 

monitor meeting completeness criteria, since this provided a better 

characterization of ambient air levels for that urban location. 

 Inclusion in epidemiology study: Coverage of the location within one of the key 

epidemiology studies included in the risk assessment (at or close to the location 

where at least one C-R function for one of the recommended health endpoints has 

been estimated by a study satisfying the selection criteria used in the risk 

assessment). In this review, because the current risk assessment primarily utilizes 

multi-city studies to evaluate risk for short-term and long-term PM2.5 exposures 

(whereas the prior risk assessment used city-specific studies in modeling 

endpoints associated with short-term exposures), this criterion no longer applies 

for most prospective areas. 

 Availability of city-specific baseline incidence data: Regarding sufficiency of 

baseline health effects incidence data, an ongoing effort by EPA to collect countylevel hospital and emergency department admissions data from states to support 

this risk assessment (see section 3.4) has resulted in enhanced health effects 

baseline incidence data, largely addressing this criterion (i.e., most urban areas in 

the U.S. now have coverage with the updated baseline health effects incidence 

data). 

Two additional factors considered in selecting locations to model in the current 

assessment included: 

 Potential for risk reductions using alternative standard levels: We focused on 

those urban areas with PM2.5 monitoring levels suggesting the potential for risk 

reduction under the alternative (24-hour or annual) standards being considered 

(i.e., urban locations with at least one monitor having an annual average above 12 

μg/m3 and/or a 24-hour value above 25 μg/m3). Furthermore, locations with 

ambient PM2.5 level significantly higher than these levels were favored (with 

several urban study areas selected having both annual and 24-hour design values 

exceeding the current standards – Table 3-4). 

 Regional representation: The second criterion we added for study area selection 

focused on providing coverage for factors believed to play a role in influencing 

risk heterogeneity at the national-level (e.g., PM2.5 source characteristics and 

composition, demographics, socio-economic status (SES) status, air conditioner 

use). Building on the 7 regions originally identified in the 1996 PM Criteria 

Document (US EPA, 1996, section 6.4) (i.e., PM regions), we considered several 

urban locations from each of these PM regions with the goal to identify one or 

more candidate urban study areas in each region. Ultimately, application of the 

criteria described here resulted in one of the PM regions (the Upper Midwest) not 
 3-31

being covered by an urban study area. However, the remaining six PM regions 

each have at least one urban study areas evaluated in the risk assessment. While 

the PM regions were originally defined focusing primarily on differences in PM 

composition, size and seasonality, by selecting urban study areas from regions 

across the continental U.S., we recognize the potential for covering regional 

differences in other factors related to risk heterogeneity as well (e.g., 

demographics, SES). The representativeness analysis (section 4.4) specifically 

assesses the degree to which the 15 urban study areas provide coverage for 

national trends in key risk-related factors such as those listed here. 

Based on application of the above criteria, 15 study areas were selected for inclusion in 

this risk assessment (see Table 3-4). In addition to identifying the 15 urban study areas, Table 3-

4 also provides additional information including: (a) whether the urban study area was included 

in the prior risk assessment, (b) which PM region the urban study area is located in, and (c) the 

24-hour and annual design values using 2005-2007 air quality data. Figure 3-4 identifies each of 

the 15 urban study areas in relation to the 7 PM regions used to guide the selection of the urban 

study areas. 

Table 3-5 Urban Study Areas Selected for the Risk Assessment. 

Urban study 

area 

State 

Modeled in last 

NAAQS review 

PM 

region* 

Annual design 

value (μg/m3) 

24-hour design 

value (μg/m3) 

Atlanta GA SE 16.2 35 

Baltimore MD NE 15.6 37 

Birmingham AL SE 18.7 44 

Dallas TX SE 12.8 26 

Detroit MI X IM 17.2 43 

Fresno CA SCA 17.4 63 

Houston TX SE 15.8 31 

LA CA X SCA 19.6 55 

New York NY NE 15.9 42 

Philadelphia PA X NE 15.0 38 

Phoenix AZ X SW 12.6 32 

Pittsburgh PA X IM 19.8 60 

Salt Lake City UT NW 11.6 55 

St. Louis MO X IM 16.5 39 

Tacoma WA X NW 10.2 43 

* SE (Southeast), IM (industrial Midwest), SCA (Southern California), NE (Northeast), NW (Northwest), SW 

(Southwest) (See S EPA, 1996, section 6.4 for description of these regions).
 3-32

Northwest

Southwest Southeast

Northeast

Upper Midwest Industrial Midwest

Southern California

Dallas

Fresno

Tacoma

Atlanta

Detroit

Houston

Phoenix

New York

Baltimore St. Louis

Birmingham Los Angeles

Salt Lake City Pittsburgh Philadelphia

 

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

 regions used to guide selection of study areas). 

Once the 15 urban study areas were selected, the next step was to identify the spatial 

template to use in defining each study area (i.e., the geographical area associated with each study 

area that would be used in identifying which counties and PM2.5 monitors were associated with a 

particular study area). For 12 of the 15 urban study areas, we either used a combined statistical 

area (CSA) as the basis for the spatial template, or if that was not available, we used a core-based 

statistical area (CBSA). The three remaining urban study areas were special cases and were 

handled as follows: 

 Baltimore: Used counties in the Baltimore CBSA only and did not consider the larger 

Baltimore-DC CSA since we felt it unlikely that the entire larger CSA would behave 

similarly with regard to PM2.5 emissions reduction strategies; 

 Philadelphia: Used the Philadelphia CSA, but excluded Berks County (Reading;: 
 3-33

 Tacoma: Used only Pierce County (since we felt it unlikely that efforts to reduce 

emissions at the “elevated” monitor in Pierce County, would significantly impact 

monitors in Seattle). 

Appendix K provides maps for each of the 15 urban study areas showing: (a) annual and 

daily (i.e., 24-hour) design values (DV) for each PM2.5 monitor in each study area with DV 

values based on monitoring data from 2005-2007, (b) sources of PM2.5 greater than 50 tons/year 

(c) depiction of the highway network within each study area, and (d) counties comprising each 

urban study area, together with the CSA or CBSA boundaries depending on location. These 

maps allow the reader to visually consider the interplay between both local and more regional 

sources of ambient PM2.5 and patterns of long-term (annual) and shorter-term (24-hour) design 

values across monitors for a particular study area. 

As noted earlier, in a few instances, two or more epidemiological studies used different 

geographic boundaries for determining which populations were included in their studies. For 

example, in one study conducted in Birmingham, AL populations from Blount, Jefferson, 

Shelby, St. Clair, and Walker Counties were included, while another study included the 

population residing in only Jefferson County. In such cases, we matched our delineation of the 

urban area to that of each study, resulting in two or more different delineations of the urban area. 

 As we discuss below, two of the studies on which we rely for our core analysis – 

Zanobetti and Schwartz (2009) and Bell et al. (2008) – are multi-location studies. Zanobetti and 

Schwartz (2009) specified the county or counties included in each of the urban areas they 

included in their analysis. Bell et al. (2008), however, did not focus on urban areas, but instead 

focused on counties with populations above a specified threshold number. To limit the number 

of different “versions” of a risk assessment location, wherever possible we specified the counties 

in a risk assessment location for Bell et al. (2008) to match the set specified for Zanobetti and 

Schwartz (2009). This was possible in those cases in which Zanobetti and Schwartz (2009) 

identified an urban area as a single county, and that county was also included in Bell et al. 

(2008). This was the case for several of the risk assessment locations. In some cases, however, 

Zanobetti and Schwartz (2009) used a multi-county delineation of an urban area where at least 

one of the counties was not among those included in Bell et al. (2008). In those cases, we had to 

delineate two definitions of the urban area – one corresponding to Zanobetti and Schwartz (2009) 

and the other corresponding to Bell et al. (2008). This was the case for Atlanta, Birmingham, 

and St. Louis. In both Atlanta and New York, other delineations by other studies forced 

additional delineation of these urban areas, as shown in Table 3-1 above. 

 Finally, we applied the studies of mortality associated with long-term exposure to PM2.5

to the urban areas as defined by the short-term exposure mortality study, Zanobetti and Schwartz 
 3-34

(2009), to enable meaningful comparisons between estimates of premature morality associated 

with short-term and long-term exposure to PM2.5. 

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

within Those Studies 

 As discussed above, we included in the PM2.5 risk assessment only those health effect 

endpoint categories (and specific health effects) that met the set of criteria reflected in the multifactor approach we developed for selecting health effect endpoints (see section 3.3.1). One of 

these factors was the strength of evidence supporting a causal association between PM2.5

exposure and the endpoint of interest. Thus, in cases where the majority of the available studies 

did not report a statistically significant relationship, the effect endpoint was not included. Once 

it had been determined that a health endpoint would be included in the analysis, however, 

inclusion of a study on that health endpoint was not based on statistical significance alone, but 

considered other factors (e.g., overall design of the study including degree of control for 

confounders, method used to characterize exposure to PM2.5 within the risk assessment). 

 A significant change since the previous PM risk assessment is the addition to the relevant 

epidemiological literature of several multi-city studies. This type of study has several 

advantages over single-city studies. First, multi-city studies use the same study design in each of 

the cities included in the study, so that city-specific results are readily comparable. Second, 

when they are estimating a single C-R function based on several cities, multi-city studies also 

tend to have more statistical power and provide effect estimates with relatively greater precision 

than single city studies due to larger sample sizes, reducing the uncertainty around the estimated 

coefficient. Moreover, in a multi-city study the statistical power to detect an effect in any given 

city can be supplemented by drawing statistical power from data across all the cities included in 

the study (or all the cities in the same region) to adjust city-specific estimates towards the mean 

across all cities included in the analysis (or in the same region). This is particularly useful in 

those instances, where a city has relatively less data resulting in a larger standard error for the 

effect estimate. In this situation, the information on the C-R relationship in all the other cities 

included in a multi-city study can be used to help inform an assessment of the C-R relationship 

in the city in question. Finally, multi-city studies tend to avoid the often-noted problem of 

publication bias that single-city studies confront (in which studies with statistically insignificant 

or negative results are less likely to get published than those with positive and/or statistically 

significant results). 

 For this risk assessment, we selected what we considered to be the best study to assess 

the C-R relationship between PM2.5 and a given health endpoint, and we included other studies 

for that health endpoint only if they were judged to contribute something above and beyond what 

we could learn from the primary study selected. 
 3-35

 A primary study for a given health endpoint had to satisfy the study selection criteria that 

we have used in past PM (and other) risk assessments. In particular: 

 It had to be a published, peer-reviewed study that has been evaluated in the PM ISA and 

judged adequate by EPA staff for purposes of inclusion in this risk assessment based on 

that evaluation. 

 It had to directly measure, rather than estimate, PM2.5 on a reasonable proportion of the 

days in the study. 

 It had to either not rely on Generalized Additive Models (GAMs) using the S-Plus 

software to estimate C-R functions or to appropriately have re-estimated these functions using 

revised methods.32

 Because of the advantages noted above, we selected multi-city studies as our primary 

studies for assessing the risks of premature non-accidental, cardiovascular, and respiratory 

mortality (Zanobetti and Schwartz, 2009) and cardiovascular and respiratory hospital admissions 

(Bell et al., 2008) associated with short-term exposure to PM2.5 in our core analysis. In each of 

these studies, the 15 urban areas selected for the PM risk assessment were among the locations 

included in their analysis. These two multi-city studies are based on more recent air quality and 

health effects incidence data for short-term exposure-related mortality and morbidity and 

therefore represent the best studies to use in deriving C-R functions for this risk assessment. 

Dominici et al. (2007) was considered as an alternative study in identifying C-R functions for 

modeling short-term exposure-related mortality, however its study period and the underlying air 

quality data and disease incidence data (1987-2000) are not as current as that of Zanobetti and 

Schwartz et al., 2009 (study period of 2001-2005), and therefore, we decided to focus on 

Zanobetti and Schwartz et al. (2009) as the source of C-R functions for modeling short-term 

exposure-related mortality. 

 Studies often report more than one estimated C-R function for the same location and 

health endpoint. Models can include different sets of co-pollutants, different lag structures, and 

different forms to accommodate weather and temporal variables. Once a study has been 

selected, the next step is to select one or more C-R functions from among those reported in the 

study. 

 Zanobetti and Schwartz (2009) divided the United States into six regions, based on the 

Köppen climate classification (Kottek 2006; Kottek et al. 2006)(http://koeppen-

 

32 The GAM S-Plus problem was discovered prior to the recent final PM risk assessment carried out as part 

of the PM NAAQS review completed in 2006. It is discussed in the 2004 PM Criteria Document (US EPA, 2004a), 

PM Staff Paper (US EPA, 2005c), and PM Health Risk Assessment Technical Support Document (Abt Associates, 

2005). 
 3-36

eiger.vuwien.ac.at/).33 They estimated the coefficient of PM2.5 in single-pollutant log-linear 

models using Poisson regression for each of 112 cities, as well as in two-pollutant models with 

coarse PM. They estimated annual models (which assume that the relationship between 

mortality and PM2.5 is the same through the year), as well as four seasonal models per location. 

They then used a random effects meta-analysis to combine the city-specific results (Berkey et al. 

1998). Pooling of city-specific results was done at the national level as well as at the regional 

level, and separately for each season as well as for the annual functions. 

 With respect to the multi-city study for short-term exposure mortality, at the request of 

EPA, the authors produced Empirical Bayes “shrunken” city-specific estimates, adjusted towards 

the appropriate regional mean, using the approach described in Le Tertre et al. (2005). This was 

done for the annual estimates as well as for each season-specific estimate.34 The annual cityspecific “shrunken” estimates were used in our core analysis.35 The seasonal estimates were 

used in a sensitivity analysis. City-specific estimates have the advantage of relying on cityspecific data; however, as noted above, such estimates can have large standard errors (and thus 

be unreliable); “shrinking” city-specific estimates towards the regional mean estimate is a more 

efficient use of the data.36 Such “shrinking” can be thought of as combining the advantages of a 

single-city study (in which the estimation of a city-specific coefficient is not influenced by data 

from other locations) with the advantages of a multi-city study (in which there is much greater 

statistical power to detect small effects). 

 In Zanobetti and Schwartz (2009) all PM2.5 models used the same lag structure (i.e., an 

average of same-day and the previous day’s PM2.5). The study did, however, examine both 

single-pollutant and two-pollutant models (with coarse PM). We selected the single-pollutant 

 

33 Zanobetti and Schwartz delineate regions as follows: “region 1: humid subtropical climates and 

maritime temperate climates (Cfa, Cfb), which includes FL, LA TX, GA, AL, MS, AR, OK, KS, MO, TN, SC, NC, 

VA, WV, KY; region 2: warm summer continental climates (Dfb), including ND, MN, WI, MI, PA, NY, CT, RI, 

MA, VT, NH, ME; region 3: hot summer continental climates (Dfa) with SD, NE, IA, IL, IN, OH; region 4: dry 

climates (BSk) (NM, AZ, NV); region 5: dry climates together with continental climate (Dfc, BSk) with MT, ID, 

WY, UT, CO; region 6: Mediterranean climates which includes CA, OR, WA (Csa, Csb)” (p. 10). 34 These city-specific “shrunken” estimates were provided to EPA (see Zanobetti, 2009) . 35 One reason we selected the annual functions over the season-specific functions for the core analysis is 

that, while we can sum the season-specific mortality estimates across the four seasons, we cannot do the same for 

the upper and lower bounds of 95% confidence intervals around those estimates. To produce correct confidence 

bounds around annual mortality estimates based on seasonal functions, we would need the covariance matrix of the 

season-specific estimates, separately for each location, which we do not have. 36 Each "shrunken" city-specific estimate is a weighted average of the regional mean estimate and the cityspecific estimate, where the weight on the city-specific estimate is proportional to the inverse of the standard error, 

and the weight assigned to the regional mean estimate is proportional to the inverse of a measure of between-city 

variability. If there is a lot of "true" variability between city-specific estimates, the regional mean will receive 

relatively less weight in the averaging, compared to a case where there is not a lot of "true" variability. Conversely, 

if there is substantial variance in the city-specific estimate, it will receive less weight in the averaging compared to a 

case where the city-specific estimate has low variance.. 
 3-37

models, in part to avoid collinearity problems, and in part to be consistent with most of the other 

studies used in the risk assessment, which were single-pollutant studies. 

 Bell et al. (2008) estimated log-linear models relating short-term exposure to PM2.5 and 

hospital admissions for cardiovascular and respiratory illnesses among people 65 and older, 

using a 2-stage Bayesian hierarchical model, for each of 202 counties in the United States. They 

reported both annual and season-specific results, nationally and regionally (for four regions: 

Northeast, Southeast, Northwest, and Southwest), but not at the local (city-specific) level. All 

cardiovascular hospital admissions models were single-pollutant, 0-day lag models; for 

respiratory hospital admissions, both single-pollutant 0-day models and single-pollutant 2-day 

models were estimated. We used the regional, annual C-R functions in our core analysis 

(identifying the appropriate region for each of our 15 risk assessment locations).37 For 

respiratory hospital admissions (for the core analysis), we selected the 2-day lag models, based 

on evidence that for respiratory effects the strongest associations with PM exposure may be 

associated with longer lag periods (on the order of 2 days or more).38 We used the regional 

season-specific functions in a sensitivity analysis. 

 We identified two studies that estimated C-R relationships between short-term exposure 

to PM2.5 and emergency department (ED) visits for cardiovascular and/or respiratory illnesses. 

(There were no multi-city studies for this category of health endpoint.) Tolbert et al. (2007) 

examined both cardiovascular and respiratory ED visits in Atlanta, GA, using single-pollutant 

log-linear models with a 3-day moving average (0-day, 1-day, and 2-day lags) of PM2.5. Ito et al. 

(2007) estimated the relationship between short-term exposure to PM2.5 and ED visits for asthma 

in New York City. They estimated two single-pollutant models, one for the whole year and one 

for the period from April through August; in addition, they estimated several two-pollutant 

models for the period from April through August. We selected the single-pollutant model for the 

whole year for the core analysis, and we explored the impacts of using the annual versus the 

April-through-August model, as well as the single- versus multi-pollutant models in sensitivity 

analyses. 

 For the purpose of conducting a sensitivity analysis to show the impact of different lag 

structures, different modeling approaches, and single- versus two-pollutant models on estimates 

of the risks of premature mortality and hospital admissions associated with short-term exposure 

 

37 The region into which each of the 202 counties in Bell et al. (2008) falls is given at: 

http://www.biostat.jhsph.edu/MCAPS/estimates-full.html. 38 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.” (U.S. EPA, 2009d, section 2.4.2.2). 
 3-38

to PM2.5, we selected Moolgavkar (2003). This study reported results for premature nonaccidental, cardiovascular, and respiratory mortality and for cardiovascular and respiratory 

hospital admissions associated with short-term exposures to PM2.5 in Los Angeles, using several 

different lag structures and several different approaches to modeling the effects of weather and 

temporal variables. 

 In modeling premature mortality associated with long-term exposure to PM2.5 in our core 

analysis, we selected Krewski et al. (2009) as our primary study. This study is an extension of 

the ACS prospective cohort study (Pope et al., 2002), used in the previous PM risk assessment,. 

The Krewski et al., 2009 study (and the underlying ACS dataset) has a number of advantages 

which informed our selection of this study as the basis for C-R functions used in the core 

analysis, including: (a) extended air quality analysis incorporating data from 1989 to 2000 

(extending the period of observation to eighteen years: 1982-2000), which increases the power of 

the study and allows the study authors to examine the important issue of exposure time windows, 

(b) rigorous examination of a range of model forms and effect estimates, including consideration 

of such factors as spatial autocorrelation in specifying response functions, (c) coverage for a 

range of ecological variables (social, economic and demographic) which allows for consideration 

of whether these confound or modify the relationship between PM2.5 exposure and mortality, (d) 

inclusion of a related analysis (focusing on Los Angeles), which allowed for consideration of 

spatial gradients in PM2.5 and whether they effect response models (by addressing effect 

modification, for example) and (e) large overall dataset with over 1.2 million individuals and 156 

MSAs. To provide coverage for one of the other larger datasets used in prospective cohort 

analyses of long-term mortality (the six-cites dataset), we selected the Krewski et al. (2000) 

study to provide C-R functions that were used in the sensitivity analysis completed for this risk 

assessment. 

 A number of other studies were considered as candidates for use in modeling long-term 

exposure-related mortality in this analysis. For purposes of transparency, we have included a 

brief summary here of our rationale for not selecting a number of the more high-profile studies 

for use in the core analysis. The Laden et al. (2006) study (which focused on the six-cities 

dataset) was not selected because it used visibility data to estimate ambient PM2.5 levels. The 

Goss et al. (2004) study (based on the cystic fibrosis data), while addressing an at-risk population 

of concern, was not selected because of a lack of baseline incidence data for this population 

which prevents quantitative modeling of mortality incidence. The Miller et al. (2007) study 

(focusing on the Women’s Health Initiative dataset) while providing coverage for a population of 

particular interest, was not used, again due to an absence of baseline incidence data (which is 

particularly important for this population which is typically healthier than the general 

population). And finally, the Eftim et al. (2008) study (focusing on the Medicare population) 
 3-39

was not included because this study did not include representative confounder control for 

smoking, which introduces uncertainty into C-R functions obtained from the study. 

 Krewski et al. (2009) (the study selected as the basis for C-R functions used in the core 

analysis) considered mortality from all causes, as well as cardiopulmonary mortality, mortality 

from ischemic heart disease, and lung cancer mortality. The study presents a variety of C-R 

functions, in an effort to show how the results vary with various changes to the method/model 

used. It was not readily apparent from review of the Health Effects Institute (HEI) report, that 

the authors of the study recommended any one of these as clearly superior to the others. 

Therefore, we corresponded with the authors to obtain additional clarification regarding specific 

aspects of the study and associated results as presented in the HEI report (Krewski et al., 2009). 

In response to the our question of whether the study authors had a preference for a particular 

model in the context of using that model and its hazard ratio(s) in risk assessment, the authors 

stated that they had “refrained from expressing a preference among the results for their use in 

quantitative risk assessment,” preferring to “explore several plausible statistical models that we 

have fit to the available data.” However, the authors went on to state that “...if one had to choose 

a model for use in practical applications involved in air quality management, one could argue 

that a random effects model (which accounts for apparent spatial autocorrelation in the data) 

might be preferable. A model that included ecological covariates, which has the effect of 

reducing the residual variation in mortality, might also be of interest. If forced to pick a single 

model for risk assessment applications in air quality management, our random effects model with 

ecological covariates might be selected” (Krewski, 2009). 

 In addition to these statements from the study authors regarding the model form to use, 

EPA staff also considered the results of an analysis presented in the study examining the 

importance of exposure time windows in deriving C-R functions. This analysis suggested that 

models developed using both exposure time windows considered in the analysis (1979-1983 and 

1999-2000) were equally effective at representing the relationship between PM2.5 exposure and 

long-term exposure-related mortality. Therefore, we concluded that C-R functions used in the 

core analysis should include functions fitted to both exposure time windows. However, the study 

does not provide random effects models with ecological covariates for both exposure time 

windows (this form of model is only provided with a fit to the latter exposure window). 

Therefore, for the core analysis, we decided to use the Cox proportional hazard model with 44 

individual and 7 ecological variables fitted to both exposure time windows.39 

 

39 Note, however, that if the Krewski et al. (2009) study had provided a random effects model with 

ecological covariates (for both PM monitoring periods – 1979-1983 and 1999-2000), then we would have used those 

models in our core analysis. 
 3-40

 In specifying effect estimates for each set of models, the relative risks for a 10 μg/m3

change in PM2.5 were back-calculated from Table 33 of Krewski et al. (2009). We selected 

several additional C-R functions from Krewski et al. (2009) to use in sensitivity analyses carried 

out in two risk assessment locations (Los Angeles and Philadelphia), including the random 

effects form (section 3.5.4), as described below. In addition, as mentioned earlier, we used C-R 

functions obtained from Krewski et al. (2000) [reanalysis of the Six Cities Study] in the 

sensitivity analysis. 

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

A summary of the selected health endpoints, urban areas, and epidemiological studies used 

in the risk assessment is given below in Tables 3-5 and 3-6 for short-term and long-term 

exposure studies, respectively. A more detailed overview of the locations, health endpoints, 

studies, and C-R functions included in the core analysis is given in Table 3-7. An overview of 

the locations, health endpoints, studies, and C-R functions included in sensitivity analyses is 

given in Table 3-8. 
3-41

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

Assessment* 

Premature Mortality Hospital Admissions 

Urban Area Non-Accidental Cardiovascular Respiratory Cardiovascular Respiratory CardiovaAtlanta, GA Tolbert 

(2007

Baltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TX

Zanobetti and 

Schwartz (2009)

Zanobetti and 

Schwartz (2009)

Zanobetti and 

Schwartz (2009)

Bell et al. (2008) Bell et al. (2008) 

Los Angeles, CA

Moolgavkar 

(2003)

Moolgavkar 

(2003)

Moolgavkar 

(2003)

New York, NY Philadelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTSt. Louis, MOTacoma, WA

Zanobetti and 

Schwartz (2009) 

Zanobetti and 

Schwartz (2009) 

Zanobetti and 

Schwartz (2009) 

Bell et al. (2008) Bell et al. (2008) 

*Studies in italics are used only in sensitivity analyses. 
3-42

Table 3-7. Locations, Health Endpoints, and Long-Term Exposure Studies Included in the PM

Assessment* 

Premature Mortality Urban Area

All-Cause Cardiopulmonary Ischemic Heart Disease 

Atlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXNew York, NY Phoenix, AZ Pittsburgh, PA Salt Lake City, UT St. Louis, MO Tacoma, WA 

Krewski et al. (2009) [extension 

of the ACS study] 

Krewski et al. (2009) [extension 

of the ACS study] 

Kre

Los Angeles, CA Philadelphia, PA

Krewski et al. (2009) [extension 

of the ACS study] 

Krewski et al. (2000) [reanalysis 

of the Six Cities Study]

Krewski et al. (2009) [extension 

of the ACS study] 

Krewski et al. (2000) [reanalysis 

of the Six Cities Study]

Krewski et al. (2009) [extension 

of the ACS study] 

Kre

Kre

*Studies in italics are used only in sensitivity analyses. 
3-43

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

the Core Analysis.* 

Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Zanobetti and Schwartz (2009)1 Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) 1 Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) 1 Short-term exposure respiratory mortality Krewski et al. (2009) 2 Long-term exposure all-cause mortality Krewski et al. (2009) 2 Long-term exposure cardiopulmonary mortality Krewski et al. (2009) 2 Long-term exposure ischemic heart disease mortalit

Cobb, De Kalb, Fulton, Gwinnett 

Krewski et al. (2009) 2 Long-term exposure lung cancer mortality 

Bell et al. (2008)3 Cobb, DeKalb, Fulton, Short-term exposure HA (unscheduled), cardiovasc

Bell et al. (2008)3 Short-term exposure HA (unscheduled), respiratory 

Tolbert et al. (2007) Short-term exposure emergency department (ED) v

cardiovascular 

Atlanta 

Barrow, Bartow, Carroll, Cherokee, Clayton, Cobb, Coweta, DeKalb, Douglas, Fayette, Forsyth, Fulton, Gwinett, Henry, Newton, Paulding, Pickens, Rockdale, Spalding, Walton 

Tolbert et al. (2007) Short-term exposure emergency department (ED) v

respiratory 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality 

Baltimore Baltimore city, 

Baltimore county 

Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality 
3-44

Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortaliKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovascBell et al. (2008) Short-term exposure HA (unscheduled), respiratory Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortali

Blount, Jefferson, Shelby, St. Clair, Walker 

Krewski et al. (2009) Long-term exposure lung cancer mortality 

Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Birmingham 

Jefferson 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality 

Dallas Dallas 

Krewski et al. (2009) Long-term exposure all-cause mortality 
3-45

Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortaliKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovascBell et al. (2008) Short-term exposure HA (unscheduled), respiratory Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortaliKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Detroit Wayne 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortali

Fresno Fresno 

Krewski et al. (2009) Long-term exposure lung cancer mortality 
3-46

Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovascBell et al. (2008) Short-term exposure HA (unscheduled), respiratory Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortaliKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Houston Harris 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortaliKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Los Angeles Los Angeles 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 
3-47

Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortaliKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Kings, New York City (Manhattan), Queens, Richmond, Bronx 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

New York 

Kings, New York City (Manhattan), Queens, Richmond, Bronx 

Ito et al. (2007) Short-term exposure emergency department (ED) v

asthma 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortaliKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Philadelphia Philadelphia 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 
3-48

Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Zanobetti and Schwartz (2009) Short-term exposure non-accidental mortality Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortaliKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Phoenix Maricopa 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortaliKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Pittsburgh Allegheny 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality 

Salt Lake City Salt Lake 

Krewski et al. (2009) Long-term exposure all-cause mortality 
3-49

Risk 

Assessment 

Location

Counties Study/C-R Function Health Endpoint 

Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortaliKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovascBell et al. (2008) Short-term exposure HA (unscheduled), respiratory Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortali

Jeffferson, Madison (IL), St. Louis, St. Louis city, St. Clair (IL)

Krewski et al. (2009) Long-term exposure lung cancer mortality 

Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

St. Louis 

Madison (IL), St. Louis, St. Louis city, St. Clair (IL) Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

Zanobetti and Schwartz (2009) Short-term exposure cardiovascular mortality Zanobetti and Schwartz (2009) Short-term exposure respiratory mortality Krewski et al. (2009) Long-term exposure all-cause mortality Krewski et al. (2009) Long-term exposure cardiopulmonary mortality Krewski et al. (2009) Long-term exposure ischemic heart disease mortaliKrewski et al. (2009) Long-term exposure lung cancer mortality Bell et al. (2008) Short-term exposure HA (unscheduled), cardiovasc

Tacoma Pierce 

Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 

*All C-R functions in the core analysis are single-pollutant, log-linear models; all are for a full year. The exposure metric for all shorfunctions is the 24-hour average; the exposure metric for all long-term exposure C-R functions is the annual average. 
3-50

1 This is a multi-city study; city-specific estimates “shrunken” towards the mean across all cities in a region were supplied to EPA (Za2 Two C-R functions were used for the core analysis – one corresponding to the earlier exposure period, from 1979 – 1983, and the ot

later exposure period, from 1999 – 2000. Both C-R functions were based on follow-up of the cohort through 2000. Both used the sthazards model, with 44 individual and 7 ecologic covariates. The relative risks for a 10 μg/m3 change in PM2.5 from which the PM2.5 

calculated were taken from Table 33 of Krewski et al. (2009). 

3 This study estimated four regional C-R functions – for the Northeast, Southeast, Northwest, and Southwest – for each health endpoi

assessment location, we used the regional C-R function for the region containing the risk assessment location. The designation of coufour regions can be found at http://www.biostat.jhsph.edu/MCAPS/estimates-full.html . 
3-51

Table 3-9. Summary of Locations, Health Endpoints, Studies and Concentration-Response Fu

Sensitivity Analyses. 

Sensitivity Analysis Study/C-R Function Health Endpoint** 

Single-Factor Sensitivity Analyses: Impact of using different model choices – fixed effects log-linear vs. random effects log-linear vs. random effects log-log C-R function* 

random effects log-linear: Krewski et al. (2009) [Table 9, "Autocorrelation at MSA and ZCA levels" group - "MSA & Diff" row] random effects log-log: Krewski et al. (2009) [Table 11, "MSA and DIFF" rows] 

All-cause, cardiopulmonary, ischemic heart disease, and lung cancer mortaliassociated with long-term exposure 

Impact of using copollutant models in modeling longterm exposure-related mortality Krewski et al., 2000 (reanalysis of ACS) – provides 2-pollutant 

models combining PM2.5 with 

CO, NO2 

, O3 or SO2. 

All-cause mortality associated with long-term exposure 

Impact of estimating risks down to PRB rather than down to LML 

Krewski et al. (2009) – C-R functions for each of two exposure periods 

Long-term exposure all-cause mortali

Impact of C-R function from alternative long-term exposure study 

Krewski et al. (2000) [reanalysis of the Harvard Six Cities study] 

All-cause, cardiovascular, respiratorylung cancer mortality associated with long-term exposure 

Impact of using alternative hybrid rollback approach (note, that as discussed in section 3.2.3, in addition to the hybrid rollback approach, we have also included a locally focused rollback approach as an alternative to the proportional rollback approach).40

Krewski et al. (2009) All-cause mortality associated with 

long-term exposure 

40 However, as noted in section 3.2.3 and in section 3.5.4, quantitative risk estimates were not generated using the locally foc

instead, composite monitor values (acting as surrogates for long-term exposure-related risk) were used as the basis for the sensitivity 

locally focused rollback approach. 
3-52

Sensitivity Analysis Study/C-R Function Health Endpoint** 

Impact of using season-specific C-R functions (vs. an annual C-R function) 

Zanobetti and Schwartz (2009) – seasonal functions vs. annual function 

Non-accidental mortality, cardiovascular mortality, respiratory mortality associated with short-term exposure 

Impact of using season-specific C-R functions (vs. an annual C-R function) 

Bell et al. (2008) – seasonal functions vs. annual function 

HA (unscheduled), cardiovascular and respiratory, associated with short-term 

exposure

Impact of using an annual C-R function (applied to the whole year) vs. a seasonal function for April through August (applied only to that period) (using a single pollutant model). 

Ito et al. (2007) Asthma ED visits 

Impact of model selection (e.g., log-linear GAM with 30 df; log-linear GAM with 100 df; and log-linear GLM with 100 df) 

Moolgavkar (2003) Non-accidental and cardiovascular 

mortality; and cardiovascular and COPD+ HA associated with short-terexposure 

Impact of lag structure (0-day, 1-day, 2-day) Moolgavkar (2003) Non-accidental and cardiovascular an

COPD+ HA associated with short-terexposure 

Impact of single- vs. multi-pollutant models (PM2.5

with CO) 

Moolgavkar (2003) Non-accidental and cardiovascular 

mortality; and cardiovascular and COPD+ HA associated with short-terexposure 

Impact of using alternative hybrid rollback approach Zanobetti and Schwartz (2009) Non-accidental mortality associated 

with short-term exposure 

Impact of lag structure (0-day, 1-day, 2-day) Bell et al., 2008 Cardiovascular and respiratory hospit

admissions associated with short-term exposure 

Multi-Factor Sensitivity Analyses: Impact of using a fixed effects log-linear vs. a random effects log-log model, estimating incidence down to the lowest measured level (LML) in the study vs. down to PRB, and using a proportional vs. hybrid rollback to estimate incidence associated with long-term exposure to PM2.5 concentrations that just meet the current 

standards 

 All-cause and ischemic heart disease 

mortality associated with long-term exposure 

Impact of using season-specific vs. all-year C-R Zanobetti and Schwartz (2009) Non-accidental mortality associated 
3-53

Sensitivity Analysis Study/C-R Function Health Endpoint** 

functions and proportional vs. hybrid rollbacks to estimate incidence associated with short-term exposure to PM2.5 concentrations that just meet the current 

standards 

with short-term exposure 

*This “single-factor” sensitivity analysis is actually two factors – first the change from a fixed effects log-linear model to a random ef

and then the change from a random effects log-linear model to a random effects log-log model. These were combined into a single sebecause Krewski et al. (2009) did not present the results of a fixed effects log-log model (to compare to the core analysis fixed effects 

**”HA” = hospital admissions, “ED” = emergency department visits, “COPD+” = chronic obstructive pulmona
 3-54

3.4 BASELINE HEALTH EFFECTS INCIDENCE DATA 

As noted in section 3.1.2 above, the form of C-R function most commonly used in 

epidemiological studies on PM, shown in equation (1), is log-linear. To estimate the change in 

incidence of a health endpoint associated with a given change in PM2.5 concentrations using this 

form of C-R function requires the baseline incidence (often calculated as the baseline incidence 

rate times the population) of the health endpoint, that is, the number of cases per unit time (e.g., 

per year) in the location before a change in PM2.5 air quality (denoted y0 in equations 2, 3 and 4). 

 Incidence rates express the occurrence of a disease or event (e.g., asthma episode, death, 

hospital admission) in a specific period of time, usually per year. Rates are expressed either as a 

value per population group (e.g., the number of cases in Philadelphia County) or a value per 

number of people (e.g., the number of cases per 10,000 residents in Philadelphia County), and 

may be age- and sex-specific. Incidence rates vary among geographic areas due to differences in 

population characteristics (e.g., age distribution) and factors promoting illness (e.g., smoking, air 

pollution levels). 

3.4.1 Data Sources 

3.4.1.1 Mortality 

We obtained individual-level mortality data for 2006 for the whole United States from 

the Centers for Disease Control (CDC), National Center for Health Statistics (NCHS). The data 

are compressed into a CD-ROM, which contains death information for each decedent, including 

residence county Federal Information Processing System (FIPS), age at death, month of death, 

and underlying causes (International Classification of Diseases (ICD)-10 codes). The detailed 

mortality data allow us to generate cause-specific death counts at the county level for selected 

age groups. Below we describe how we generated the county-level death counts. 

3.4.1.2 Hospital Admission and Emergency Department Visits 

For hospital admissions (HA) and emergency department (ED) visits, there are multiple 

data sources: 

 Healthcare Cost and Utilization Project (HCUP) Central Distributor. HCUP is a 

family of health care databases developed through a Federal-State-Industry partnership 

and sponsored by the Agency for Healthcare Research and Quality (AHRQ). The HCUP 

databases are based on the data collection efforts of data organizations in participating 

states. We used two HCUP databases: the State Inpatient Database (SID) and the State 

Emergency Department Database (SEDD) respectively. SID/SEDD include detailed 

HA/ED information for each discharge, including patient county FIPS, age, admission 

type (e.g., emergent, urgent), admission/discharge season, and principle diagnosis (ICD-9 

codes). The HCUP databases can be purchased from the HCUP Central Distributor, 

although not all participant states release the data to the Central Distributor. 
 3-55

 HCUP State Partners. For those HCUP participating states that don’t release their data 

to the Central Distributor, we contacted the HCUP state partners to obtain the HA and/or 

ED data. 

 Communication with the author(s) of selected epidemiological studies. The ED data 

for Atlanta in 2004 were sent to EPA by one of the authors of Tolbert et al. (2007). 

Table 3-9 shows the states for which we obtained data from the HCUP Central 

Distributor and the HCUP State Partners. The data are at the discharge level if not otherwise 

noted, and the data year is 2007 for all the states in the table. The column “PM RA Location” 

indicates the selected risk assessment location(s) where the incidence rate is applied. 

 The necessary baseline incidence data were not available for Atlanta, Birmingham, 

Philadelphia, Pittsburgh and St. Louis. Therefore, for each of these five risk assessment 

locations EPA instead used the baseline incidence rate for a designated surrogate location. 

Surrogate locations were chosen if they were deemed to be sufficiently similar to the urban area 

whose baseline incidence data were not available. Surrogate locations are noted in Table 3-9. 

 
 3-56

Table 3-10 Sources of Hospital Admissions (HA) and Emergency Department (ED) 

Visit Data. 

States 

HCUP 

Central 

Distributor 

HCUP State 

Partner 

PM RA 

Location Notes 

Arizona HA data -- Phoenix 

California NA* HA data Fresno, Los 

Angeles 

 Due to privacy concerns, CA state 

agency provided county level data. 

Illinois NA HA data St. Louis 

1. Due to privacy concerns, IL state 

agency provided county level data. 

2. Two IL counties (Madison and St. 

Clair) serve as the surrogate for the 

St. Louis metropolitan region. 

Maryland HA data -- Baltimore, 

Philadelphia 

Baltimore serves as the surrogate for 

Philadelphia. 

Michigan HA data -- Detroit 

New York NA HA and ED data New York, 

Pittsburgh 

 Buffalo, NY serves as the surrogate 

for Pittsburgh. 

North Carolina HA data -- Atlanta and 

Birmingham 

Charlotte, NC serves as the surrogate 

for both Atlanta and Birmingham. 

Texas NA HA data Dallas, 

Houston 

Utah HA data -- Salt Lake 

City 

Washington HA data -- Tacoma 

*NA denotes “not available, or not available with all variables required for our analysis. If data were not available 

from the HCUP Central Distributor, we contacted the HCUP State Partner. 

3.4.1.3 Populations 

To calculate baseline incidence rate, in addition to the health baseline incidence data we 

also need the corresponding population. We obtained population data from the U.S. Census 

Bureau (http://www.census.gov/popest/counties/asrh/). These data, released on May 14, 2009, 

are the population estimates of the resident populations by selected age groups and sex for 

counties in each U.S. state from 2000 to 2008. We used 2007 populations for calculating most 

incidence rates except for the ED visit rate in Atlanta. Because the ED visit data obtained from 

the authors of Tolbert et al. (2007) are for 2004, we used 2004 population estimates for the 20-

county Metropolitan area used in the Tolbert et al. study for the Atlanta area to calculate the ED 

incidence rates to be applied when using that study in the risk assessment; we then applied the 

2004 rates to the 2007 population, assuming the ED incidence rates in Atlanta did not change 

significantly from 2004 to 2007. The sizes of the populations in the assessment locations that are 

relevant are shown below in Table 3-10. 
3-57

Table 3-11. Relevant Population Sizes for PM Risk Assessment Locations. 

Population (Year 2006 and 2007)* 

City Counties All Ages Ages 30 

2006 2007 2006 2007 

Atlanta, GA - 1 Cobb, De Kalb, Fulton, Gwinnett 3,126,000 3,198,000 1,817,000 1,865,000 23Atlanta, GA - 2 Cobb, De Kalb, Fulton 2,376,000, 2,421,000 1,400,000 1,433,000 19Atlanta, GA - 3 20-County MSA** 4,975,000 5,123,000 2,831,000 2,918,000 39Baltimore, MD Baltimore city, Baltimore county 1,429,000 1,426,000 849,000 848,000 19Birmingham, AL - 1 Blount, Jefferson, Shelby, St. Clair, 

Walker 1,037,000 1,044,000 619,000 625,000 13

Birmingham, AL - 2 Jefferson 660,000 659,000 397,000 397,000 8Dallas, TX Dallas 2,338,000 2,367,000 1,285,000 1,308,000 19Detroit, MI Wayne 2,012,000 1,985,000 1,176,000 1,168,000 23Fresno, CA Fresno 886,000 899,000 444,000 452,000 8Houston, TX Harris 3,876,000 3,936,000 2,097,000 2,139,000 29Los Angeles, CA Los Angeles 9,881,000 9,879,000 5,544,000 5,579,000 1,0New York, NY - 1 Kings, New York City (Manhattan), 

Queens, Richmond, Bronx 8,251,000 8,275,000 4,940,000 4,975,000 1,0

New York, NY - 2 New York city (Manhattan) 1,613,000 1,621,000 1,061,000 1,074,000 20Philadelphia, PA Philadelphia 833,000 1,450,000 833,000 833,000 18Phoenix, AZ Maricopa 3,779,000 3,880,000 2,103,000 2,167,000 41Pittsburgh, PA Allegheny 1,225,000 1,219,000 790,000 786,000 20Salt Lake City, UT Salt Lake 991,000 1,010,000 504,000 517,000 8St. Louis, MO - 1 Jefferson, Madison (IL), St. Louis, St. 

Louis city, St. Clair (IL) 2,093,000 2,091,000 1,259,000 1,261,000 27
3-58

Population (Year 2006 and 2007)* 

City Counties All Ages Ages 30 

2006 2007 2006 2007 

St. Louis, MO - 2 Madison (IL), St. Louis, St. Louis city, 

St. Clair (IL) 1,879,000 1,875,000 1,134,000 1,134,000 25

Tacoma, WA Pierce 764,000 773,000 437,000 444,000 7

* Not all populations listed in the table were used for calculating the incidence rates. As noted above, the population year needs to madata and the population age group needs to match what is used in the epidemiological studies. In addition, 2004 population (all ages) is Atlanta-3, which is 4,663,946. Populations in this table are rounded to the nearest 1,000. ** The 20 counties are Barrow, Bartow, Carroll, Cherokee, Clayton, Cobb, Coweta, DeKalb, Douglas, Fayette, Forsyth, Fulton, Gwinett, Henry, New

Rockdale, Spalding, and Walton.
 3-59

3.4.2 Calculation of Baseline Incidence Rates 

To calculate a baseline incidence rate to be used with a C-R function from a given study, 

we matched the counties, age groupings, and ICD codes used in that study. For example, Bell et 

al. (2008) designated Dallas, TX as Dallas County and estimated a C-R function for ICD-9 codes 

490–492, 464–466, and 480–487 (respiratory HA) among ages 65 and up; we therefore selected 

only those HA records that had corresponding ICD codes for ages 65 and up in Dallas County 

and also selected the population for the same age group in the same county. The incidence rate 

is simply the ratio of the selected HA count to the population. The same procedure was used to 

calculate baseline incidence rates for all of the risk assessment locations.41

 If a C-R function was estimated for a specific season, we selected only those HA records 

within that season. The season definitions are: winter (December, January, and February), spring 

(March, April, and May), summer (June, July, and August) and fall (September, October, and 

November). Note that the HA data for some states didn’t include information about admission 

season but only discharge season or discharge quarter. The admission season was then 

approximated using discharge season or discharge quarter.42 

 Some studies (e.g., Bell et al., 2008) look at the unscheduled hospital admissions (Has) 

only, so we excluded scheduled admissions from the analyses to match the study. A HA is 

unscheduled if the admission type is emergency or urgent. 

The baseline mortality rates are given in Table 3-11. The baseline HA and ED visit rates 

are given in Table 3-12. 

 

41 For Atlanta, Birmingham, Philadelphia, Pittsburgh and St. Louis, the HA data are not available. We 

calculated the hospital admission rates for the surrogate cities. These cities are listed in Table 3-7. 42 Based on communication with the HCUP state partner in Texas, patients are normally admitted and 

discharged in the same season. 
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Table 3-4. Baseline Mortality Rates (Deaths per 100,000 Relevant Population per Year) for 2006 for PM R

 Locations.* 

Type of Mortality (ICD-10 or ICD-9 Codes) 

City Age Group All-Cause 

Non-accidental 

(A00-R99) 

Cardiovascular 

(I01-I59) 

Respiratory 

(J00-J99) 

Cardiopulmonary 

(401-440, 460-

519) 

Ischemic 

Heart 

Disease 

(410-414) 

Atlanta, GA - 1 All ages --- 480 120 41 --- --- Atlanta, GA - 1 ≥ 30 860 NA NA NA 330 89 Atlanta, GA - 2 NA NA NA NA NA NA NA Atlanta, GA - 3 NA NA NA NA NA NA NA Baltimore, MD All ages NA 950 270 85 NA NA Baltimore, MD ≥ 30 1,700 NA NA NA 690 300 Birmingham, AL - 1 All ages NA 920 260 85 NA NA Birmingham, AL - 1 ≥ 30 1,600 NA NA NA 680 190 Birmingham, AL - 2 NA NA NA NA NA NA NA Dallas, TX All ages NA 540 150 48 NA NA Dallas, TX ≥ 30 1,020 NA NA NA 420 170 Detroit, MI All ages NA 850 300 67 NA NA Detroit, MI ≥ 30 1,500 NA NA NA 700 360 Fresno, CA All ages NA 620 190 67 NA NA Fresno, CA ≥ 30 1,300 NA NA NA 590 260 Houston, TX All ages NA 480 130 37 NA NA Houston, TX ≥ 30 920 NA NA NA 370 150 Los Angeles, CA All ages NA 560 190 57 NA NA Los Angeles, CA ≥ 30 1,030 NA NA NA 510 250 New York, NY - 1 All ages NA 630 270 52 NA NA 
3-61

Type of Mortality (ICD-10 or ICD-9 Codes) 

City Age Group All-Cause 

Non-accidental 

(A00-R99) 

Cardiovascular 

(I01-I59) 

Respiratory 

(J00-J99) 

Cardiopulmonary 

(401-440, 460-

519) 

Ischemic 

Heart 

Disease 

(410-414) 

New York, NY - 1 ≥ 30 1,0800 NA NA NA 580 380 New York, NY - 2 NA NA NA NA NA NA NA Philadelphia, PA All ages NA 970 280 83 NA NA Philadelphia, PA ≥ 30 1,700 NA NA NA 720 300 Phoenix, AZ All ages NA 600 160 67 NA NA Phoenix, AZ ≥ 30 1,100 NA NA NA 470 220 Pittsburgh, PA All ages NA 1,090 330 96 NA NA Pittsburgh, PA ≥ 30 1,800 NA NA NA 770 350 Salt Lake City, UT All ages NA 480 110 45 NA NA Salt Lake City, UT ≥ 30 980 NA NA NA 350 101 St. Louis, MO - 1 All ages NA 870 270 83 NA NA St. Louis, MO - 1 ≥ 30 1,500 NA NA NA 680 320 St. Louis, MO - 2 NA NA NA NA NA NA NA Tacoma, WA All ages NA 660 190 66 NA NA Tacoma, WA ≥ 30 1,200 NA NA NA 510 240 National All ages 810 750 220 76 340 140 National ≥ 30 1,300 1,300 370 130 580 240 

* Figures in this table are rounded to a two-integer level of precision. NA refers to health endpoint categories that are not relevan

level study area definition (i.e., the epidemiology study and associated effect estimate reflected in this specification county-level of the studparticular endpoint category and consequently a baseline incidence value is not shown). 
3-62

Table 3-5. Baseline Hospital Admission (HA) and Emergency Department (ED) Rates (Admissions/Visits 

Population per Year) for 2007 for PM Risk Assessment Locations.* 

* Figures in this table are rounded to a two-integer level of precision. NA - see footnote to Table 3-11. **These are 2004 incidence rates because Tolbert et al. (2007) provided 2004 ED visit data in a 20-county delineation of Atlanta. Howev

applied to the appropriate year population in the risk assessment. 

Health Endpoints (ICD-9 Codes) 

City Age Group 

HA, cardiovascular (390-

429) 

HA (unscheduled), 

cardiovascular(426

–429, 430–438, 

410–414, 440–449) 

HA, COPD 

(490-496) 

HA (unscheduled), 

respiratory (490–492, 

464–466, 480–487) 

ED visits, 

cardiovascular (410–

414, 427, 428, 433–

437, 440, 443–445, 

451–453) 

ED visits

(460–465, 

466.19, 477

493, 496, 7

Atlanta, GA - 1 NA NA NA NA NA NA Atlanta, GA - 2 ≥ 65 NA 5,700 NA 2,020 NA Atlanta, GA - 3 All ages NA NA NA NA 690** 26Baltimore, MD ≥ 65 NA 8,600 NA 2,600 NA Birmingham, AL - 1 NA NA NA NA NA NA Birmingham, AL - 2 ≥ 65 NA 5,700 NA 2,020 NA Dallas, TX ≥ 65 NA 5,000 NA 2,000 NA Detroit, MI ≥ 65 NA 8,800 NA 3,000 NA Fresno, CA ≥ 65 NA 5,600 NA 2,100 NA Houston, TX ≥ 65 NA 5,900 NA 2,200 NA Los Angeles, CA All ages NA NA 223 NA NA Los Angeles, CA ≥ 65 5,500 5,500 NA 2,000 NA New York, NY - 1 ≥ 65 NA 6,400 NA 2,030 NA New York, NY - 2 All ages NA NA NA NA NA Philadelphia, PA ≥ 65 NA 8,600 NA 2,600 NA Phoenix, AZ ≥ 65 NA 5,020 NA 1,600 NA Pittsburgh, PA ≥ 65 NA 6,100 NA 1,900 NA Salt Lake City, UT ≥ 65 NA 3,030 NA 1,200 NA St. Louis, MO - 1 NA NA NA NA NA NA St. Louis, MO - 2 ≥ 65 NA 5,600 NA 2,600 NA Tacoma, WA ≥ 65 NA 4,500 NA 1,600 NA 
 3-63

3.5 ADDRESSING UNCERTAINTY AND VARIABILITY 

3.5.1 Overview 

 An important component of a population health risk assessment is the characterization of 

both uncertainty and variability. Variability refers to the heterogeneity of a variable of interest 

within a population or across different populations. For example, populations in different 

regions of the country may have different behavior and activity patterns (e.g., air conditioning 

use, time spent indoors) that affect their exposure to ambient PM and thus the population health 

response. The composition of populations in different regions of the country may vary in ways 

that can affect the population response to exposure to PM – e.g., two populations exposed to the 

same levels of PM might respond differently if one population is older than the other. In 

addition, the composition of the PM to which different populations are exposed may differ, with 

different levels of toxicity and thus different population responses. Variability is inherent and 

cannot be reduced through further research. Refinements in the design of a population risk 

assessment are often focused on more completely characterizing variability in key factors 

affecting population risk – e.g., factors affecting population exposure or response – in order to 

produce risk estimates whose distribution adequately characterizes the distribution in the 

underlying population(s). 

Uncertainty refers to the lack of knowledge regarding the actual values of inputs to an 

analysis. Models are typically used in analyses, and there is uncertainty about the true values of 

the parameters of the model (parameter uncertainty) – e.g., the value of the coefficient for PM2.5

in a C-R function. There is also uncertainty about the extent to which the model is an accurate 

representation of the underlying physical systems or relationships being modeled (model 

uncertainty) – e.g., the shapes of C-R functions. In addition, there may be some uncertainty 

surrounding other inputs to an analysis due to possible measurement error—e.g., the values of 

daily PM2.5 concentrations in a risk assessment location, or the value of the baseline incidence 

rate for a health effect in a population.43 In any risk assessment, uncertainty is, ideally, reduced 

to the maximum extent possible through improved measurement of key variables and ongoing 

model refinement. However, significant uncertainty often remains, and emphasis is then placed 

on characterizing the nature of that uncertainty and its impact on risk estimates. The 

characterization of uncertainty can be both qualitative and, if a sufficient knowledgebase is 

available, quantitative. 

 

43 It is also important to point out that failure to characterize variability in an input used in modeling can 

also introduce uncertainty into the analysis. This reflects the important link between uncertainty and variability with 

the effort to accurately characterize variability in key model inputs actually reflecting an effort to reduce uncertainty. 
 3-64

 The selection of urban study areas for the PM2.5 risk assessment was designed to cover 

the range of PM2.5-related risk experienced by the U.S. population and, in general, to adequately 

reflect the inherent variability in those factors affecting the public health impact of PM2.5

exposure. Sources of variability reflected in the risk assessment design are discussed in section 

3.5.2, along with a discussion of those sources of variability which are not fully reflected in the 

risk assessment and consequently introduce uncertainty into the analysis. 

 The characterization of uncertainty associated with risk assessment is often addressed in 

the regulatory context using a tiered approach in which progressively more sophisticated 

methods are used to evaluate and characterize sources of uncertainty depending on the overall 

complexity of the risk assessment (WHO, 2008). Guidance documents developed by EPA for 

assessing air toxics-related risk and Superfund Site risks (USEPA, 2004b and 2001, respectively) 

as well as recent guidance from the World Health Organization (WHO, 2008) specify multitiered approaches for addressing uncertainty. 

 The WHO guidance presents a four-tiered approach, where the decision to proceed to the 

next tier is based on the outcome of the previous tier’s assessment. The four tiers described in the 

WHO guidance include: 

 Tier 0 – recommended for routine screening assessments, uses default uncertainty factors 

(rather than developing site-specific uncertainty characterizations); 

 Tier 1 – the lowest level of site-specific uncertainty characterization, involves qualitative 

characterization of sources of uncertainty (e.g., a qualitative assessment of the general 

magnitude and direction of the effect on risk results); 

 Tier 2 – site-specific deterministic quantitative analysis involving sensitivity analysis, 

interval-based assessment, and possibly probability bound (high- and low-end) 

assessment; and 

 Tier 3 – uses probabilistic methods to characterize the effects on risk estimates of sources 

of uncertainty, individually and combined. 

With this four-tiered approach, the WHO framework provides a means for systematically 

linking the characterization of uncertainty to the sophistication of the underlying risk assessment. 

Ultimately, the decision as to which tier of uncertainty characterization to include in a risk 

assessment will depend both on the overall sophistication of the risk assessment and the 

availability of information for characterizing the various sources of uncertainty. EPA staff has 

used the WHO guidance as a framework for developing the approach used for characterizing 

uncertainty in this risk assessment. 

 The overall analysis in the PM National Ambient Air Quality Standard (NAAQS) risk 

assessment is relatively complex, thereby warranting consideration of a full probabilistic (WHO 

Tier 3) uncertainty analysis. However, limitations in available information prevent this level of 
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analysis from being completed at this time. In particular, the incorporation of uncertainty related 

to key elements of C-R functions (e.g., competing lag structures, alternative functional forms, 

etc.) into a full probabilistic WHO Tier 3 analysis would require that probabilities be assigned to 

each competing specification of a given model element (with each probability reflecting a 

subjective assessment of the probability that the given specification is the “correct” description 

of reality). However, for many model elements there is insufficient information on which to 

base these probabilities. One approach that has been taken in such cases is expert elicitation; 

however, this approach is resource- and time-intensive and consequently, it was not feasible to 

use this technique in the current PM NAAQS review to support a WHO Tier 3 analysis.44 

 For most elements of this risk assessment, rather than conducting a full probabilistic 

uncertainty analysis, we have included qualitative discussions of the potential impact of 

uncertainty on risk results (WHO Tier1) and/or completed sensitivity analyses assessing the 

potential impact of sources of uncertainty on risk results (WHO Tier 2). Note, however, that in 

conducting sensitivity analyses, we have used both single- and multi-factor approaches (to look 

at the individual and combined impacts of sources of uncertainty on risk estimates). Also, as 

discussed below in section 3.5.4, in conducting sensitivity analyses, we used only those 

alternative specifications for input parameters or modeling approaches that were deemed to have 

scientific support in the literature (and so represent alternative reasonable input parameter values 

or modeling options). This means that the alternative risk results generated in the sensitivity 

analyses represent reasonable risk estimates that can be used to provide a context, with regard to 

uncertainty, within which to assess the set of core (base case) risk results (see section 4.5.3). 

The sensitivity analysis also includes coverage for potential variability in the pattern of 

reductions in ambient PM2.5 concentrations associated with simulations of just meeting the 

current and alternative suites of standards. Specifically, as discussed above in section 3.2.3, we 

have included three alternative rollback methods (proportional, hybrid and locally focused) to 

provide coverage for variability in this potentially important factor influencing risk estimates. 

In addition to the qualitative and quantitative treatment of uncertainty and variability 

which are described here, we have also completed an analysis to evaluate the representativeness 

of the selected urban study areas against national distributions for key PM risk-related attributes 

to determine whether they are nationally representative or more focused on a particular portion 

of the distribution for a given attribute (section 4.4.1). In addition, we have completed a second 

analysis addressing the representativeness issue, which identified where the subset of 31 counties 

 

44 Note, that while a full probabilistic uncertainty analysis was not completed for this risk assessment, we 

were able to use confidence intervals associated with effects estimates (obtained from epidemiological studies) to 

incorporate statistical uncertainty associated with sample size considerations in the presentation of risk estimates. 
 3-66

comprising our 15 urban study areas fall along a distribution of national county-level long-term 

exposure-related mortality risk (section 4.4.2). This analysis allowed us to assess the degree of 

which the 15 urban study areas capture locations within the U.S. likely to experience elevated 

levels of risk related to PM2.5 exposure. 

The remainder of this section is organized as follows. Key sources of variability which 

are reflected in the design of the risk assessment, along with sources excluded from the design, 

are discussed in section 3.5.2. A qualitative discussion of key sources of uncertainty associated 

with the risk assessment (including the potential direction, magnitude and degree of confidence 

associated with our understanding of the source of uncertainty – the knowledge base) is 

presented in section 3.5.3. The methods and results of the single- and multi-factor sensitivity 

analyses completed for the risk assessment are presented in section 3.5.4. An overall summary 

of the methods used to address uncertainty and variability for the 15 urban study areas (including 

the two assessments intended to place the urban study areas in a broader national context) is 

presented in section 3.5.5. 

3.5.2 Treatment of Key Sources of Variability 

The risk assessment was designed to cover the key sources of variability related to 

population exposure and exposure response, to the extent supported by available data.45

However, as with all risk assessments, there are sources of variability which have not been fully 

reflected in the design of the risk assessment and consequently introduce a degree of uncertainty 

into the risk estimates. While different sources of variability were captured in the risk 

assessment, it was generally not possible to separate out the impact of each factor on population 

risk estimates, since many of the sources of variability are reflected collectively in a specific 

aspect of the risk model. For example, inclusion of urban study areas from different PM regions 

likely provides some degree of coverage for a variety of factors associated with PM2.5 risk (e.g., 

air conditioner use, PM2.5 composition, differences in population commuting and exercise 

patterns, weather). However, the model is not sufficiently precise or disaggregated to allow the 

 

45 The term “key sources of variability” refers to those sources that the EPA staff believes have the 

potential to play an important role in impacting population incidence estimates generated for this risk assessment. 

Specifically, EPA staff has concluded that these sources of variability, if fully addressed and integrated into the 

analysis, could result in adjustments to the core risk estimates which might be relevant from the standpoint of 

interpreting the risk estimates in the context of the PM NAAQS review. The identification of sources of variability 

as “key” reflects consideration for sensitivity analyses conducted for previous PM NAAQS risk assessments, which 

have provided insights into which sources of variability (reflected in different elements of those earlier sensitivity 

analyses) can influence risk estimates, as well as information presented in the final PM ISA. For example, chapter 2 

of the final PM ISA addresses such issues as: ambient PM variability and correlations (section 2.1.1), trends and 

temporal variability (section 2.1.2), correlations between pollutants (section 2.1.4), and source contributions to PM 

(section 2.1.6). These discussions were carefully considered by staff in identifying key sources of variability to 

address both in the risk assessment and in the qualitative discussion of variability presented in this section. 
 3-67

individual impacts of any one of these sources of variability on the risk estimates to be 

characterized. 

Key sources of potential variability that are likely to affect population risks are discussed 

below, including the degree to which they are (or are not) fully captured in the design of the risk 

assessment: 

 PM2.5 composition: While information was not available to support modeling risk 

associated with different components of PM2.5, the assessment did use effect estimates 

(for a number of the short-term exposure-related health endpoints) differentiated by 

region of the country, or differentiated for specific urban locations (sections 3.3.3 and 

3.3.4). While many factors may contribute to differences in effect estimates (for the 

same health endpoint) across different locations, compositional differences in PM2.5

may be partially responsible. Therefore, while the analysis did not explicitly address 

compositional differences in generating risk estimates, potential differences in PM2.5

composition may be reflected in those effect estimates that are differentiated by region 

and/or urban study area. The effect estimates for mortality associated with long-term 

exposure to PM2.5 are not regionally differentiated and instead, a single national-scale 

estimate is used. This means that any differences in risks of mortality associated with 

long-term exposure to PM2.5 that are linked to differences in PM2.5 composition (or to 

any other differences across regions or locations) would not be discernable, since a 

single national-scale risk estimate is generated for each mortality category. In addition 

to using region- or location-specific effect estimates for health effects associated with 

short-term exposures, the selection of urban areas to include in the risk assessment was 

designed in part to ensure that areas in different regions of the country, with different 

PM2.5 composition, were included. 

 Intra-urban variability in ambient PM2.5 levels: Several recent studies (e.g., Jerrett 

et al., 2005) have addressed the issue of heterogeneity of PM concentrations within 

urban areas and its potential impact on the estimation of premature mortality associated 

with long-term exposure to PM2.5. Most recently, the HEI Reanalysis II (Krewski et 

al., 2009), focusing on the ACS dataset, discusses epidemiological analyses completed 

for Los Angeles and New York City which included more highly-refined (zip code 

level) characterizations of spatial gradients in population exposure within each urban 

area based on land-use regression methods and/or kriging. While both analyses 

provide insights into the issue of intra-urban heterogeneity in PM2.5 concentrations and 

its potential implications for epidemiology-based health assessments, due to the time 

and resources necessary to integrate them into the risk assessment, we were not able to 

incorporate these studies quantitatively. The implications of these studies for 

interpretation of long-term mortality C-R functions and potential exposure error 

associated with those functions is discussed below in section 3.5.3. 

 Variability in the patterns of ambient PM2.5 reduction as urban areas: In 

simulating just meeting the current or alternative suites of standards, there can be 

considerable variability in the patterns of ambient PM2.5 reductions that result from 

different simulation approaches (i.e., they can be more localized, more regional, or 

some combination thereof). To address this issue in the risk assessment, we have 
 3-68

included three rollback approaches as part of the sensitivity analysis including: 

proportional (reflecting regional patterns of reduction), hybrid (reflecting a 

combination of localized and regional patterns of reduction), and locally focused 

(reflecting localized patterns of reduction) (see section 3.2.3 for additional detail on 

these rollback methods and section 3.5.4 for a description of how this factor is 

addressed in the sensitivity analysis). 

 Copollutant concentrations: Inclusion of copollutant models in short-term exposurerelated time series studies has produced mixed results in terms of the degree of 

attenuation of the PM2.5 signal that results from inclusion of other pollutants (see final 

PM ISA, sections 6.2.10.9 and 6.3.8.5). The PM ISA (section 6.2.10.9) suggests that 

these inconsistent findings associated with controlling for gaseous pollutants are likely 

due to differences in the correlation structure among pollutants as well as differing 

degrees of exposure measurement error related to the copollutants. Further, the PM 

ISA (section 2.1.3) notes that correlations between PM and copollutants (including CO, 

O3, SO2 and NO2) can vary both seasonally and spatially. Therefore, it is possible that 

the degree of attenuation of PM2.5-related risk by copollutants may differ across study 

areas. However, because the multi-city studies used in the core risk assessment 

(Zanobetti and Schwartz., 2009; Bell et al., 2008; and Krewski et al., 2009) provide 

single pollutant models, our analysis does not directly address the issue of copollutant 

confounding (see section 3.5.3 for additional discussion of uncertainty introduced into 

the analysis as a result of not including copollutant models in the core risk assessment). 

We did explore the issue of copollutant modeling in the context of modeling long-term 

exposure-related mortality as part of the sensitivity analysis (section 3.5.4). In 

addition, the potential impact of copollutant confounding on short-term exposurerelated mortality and morbidity was explored in the Moolgavkar et al., 2003 study, as 

discussed below in section 4.3.1.1 (although they have limited applicability to the core 

risk estimates generated in this RA). 

 Demographics and socioeconomic-status (SES)-related factors: Variability in 

population density particularly in relation to elevated levels of PM2.5 has the potential 

to influence population risk. In addition, other aspects of demographics such as age of 

housing stock (which can influence rates of air conditioner use thereby impacting rates 

of infiltration of PM indoors) can impact exposure and therefore risk (discussed in PM 

ISA – sections 2.2.1 and 2.3.2). While risk modeling completed for this analysis is 

based on concentrations measured at central-site monitors used as surrogates for 

population exposure and does not explicitly consider more detailed patterns of PM 

exposure by different populations, potential differences in exposure to PM2.5 reflecting 

demographic and SES-related factors is covered to some degree by the use of urban 

study area-differentiated effects estimates (for short-term exposure-related mortality) 

and regionally-differentiated effects estimates (in the case of short-term exposurerelated morbidity). In the case of long-term exposure-related mortality, while the 

modeling for this group of endpoints does not utilize location-specific or regionallydifferentiated effects estimates, the national-scale effects estimates that are used do 

reflect differences in exposure and health response across urban study areas (which will 

reflect, to some extent, differences in demographics and SES-related factors to the 
 3-69

extent that these factors influence the relationship between PM2.5 exposure and 

mortality response, as detected by the underlying cohort studies). 

 Behavior affecting exposure to PM2.5: We have incorporated, where available, 

region- and/or city-specific effect estimates in order to capture behavioral differences 

across locations that could affect population exposures to PM2.5 (e.g., time spent 

outdoors, air conditioning use). However, while these location-specific effect 

estimates may be capturing differences in behavior, they may also be capturing other 

differences (e.g., differences in the composition of PM2.5 to which populations are 

exposed). As noted above, it was not possible to separate out the impact of these 

different factors, which may vary across locations and populations, on effect estimates. 

 Baseline incidence of disease: We collected baseline health effects incidence data (for 

mortality and morbidity endpoints) from a number of different sources (see section 

3.4). Often the data were available at the county-level, providing a relatively high 

degree of spatial refinement in characterizing baseline incidence given the overall level 

of spatial refinement reflected in the risk assessment as a whole. Otherwise, for urban 

study areas without county-level data, either (a) a surrogate urban study area (with its 

baseline incidence rates) was used, or (b) less refined state-level incidence rate data 

were used. 

 Longer-term temporal variability in ambient PM2.5 levels (reflecting meteorological 

trends, as well as future changes in the mix of PM2.5 sources and regulations impacting 

PM2.5): Risk estimates for the PM2.5 NAAQS review have been generated using recent 

years of air quality data. In other words, efforts have not been made to simulate 

potential future changes in either the concentrations or composition of ambient PM2.5

in the risk assessment locations based on possible changes in economic activity, 

demographics or meteorology. Actual risk levels potentially experienced in the future 

as a result of implementing alternative standard levels may differ from those presented 

in this report due, in part, to potential changes in these factors related to ambient PM2.5. 

3.5.3 Qualitative Assessment of Uncertainty 

As noted in section 3.5.1, we have based the design of the uncertainty analysis carried out 

for this risk assessment on the framework outlined in the WHO guidance document (WHO, 

2008). That guidance calls for the completion of a Tier 1 qualitative uncertainty analysis, 

provided the initial Tier 0 screening analysis suggests there is concern that uncertainty associated 

with the analysis is sufficient to significantly impact risk results (i.e., to potentially affect 

decision making based on those risk results). Given previous sensitivity analyses completed for 

prior PM NAAQS reviews, which have shown various sources of uncertainty to have a 

potentially significant impact on risk results, we believe that there is justification for conducting 

a Tier 1 analysis. In fact, as argued earlier, given the complexity of the overall risk assessment, a 

full Tier 3 uncertainty analysis is warranted for consideration under the WHO guidelines 

(although as discussed later, limitations in available data preclude completion of this level of 

more-refined uncertainty analysis at this time). 
 3-70

 For the qualitative uncertainty analysis, we have described each key source of uncertainty 

and qualitatively assessed its potential impact (including both the magnitude and direction of the 

impact) on risk results, as specified in the WHO guidance. 46 As shown in Table 3-13, for each 

source of uncertainty, we have (a) provided a description, (b) estimated the direction of influence 

(over, under, both, or unknown) and magnitude (low, medium, high) of the potential impact of 

each source of uncertainty on the risk estimates, (c) assessed the degree of uncertainty (low, 

medium, or high) associated with the knowledge-base (i.e., assessed how well we understand 

each source of uncertainty), and (d) provided comments further clarifying the qualitative 

assessment presented. Table 3-13 includes all key sources of uncertainty identified for the PM2.5

NAAQS risk assessment. A subset of these sources has been included in the Tier 2 quantitative 

assessment discussed in section 3.5.4. 

The categories used in describing the potential magnitude of impact for specific sources 

of uncertainty on risk estimates (i.e., low, medium, or high) reflect EPA staff consensus on the 

degree to which a particular source could produce a sufficient impact on risk estimates to 

influence the interpretation of those estimates in the context of the PM NAAQS review.47

Sources classified as having a “low” impact would not be expected to impact the interpretation 

of risk estimates in the context of the PM NAAQS review; sources classified as having a 

“medium” impact have the potential to change the interpretation; and sources classified as “high” 

are likely to influence the interpretation of risk in the context of the PM NAAQS review. 

Because this classification of the potential magnitude of impact of sources of uncertainty is 

qualitative and not informed directly by any type of analytical results, it is not possible to place a 

quantitative level of impact on each of the categories. 48 Therefore, the results of the qualitative 

analysis of uncertainty have limited utility in informing consideration of overall confidence in 

 

46 Similar to our discussion of variability in the last section, the term “key sources of uncertainty” refers to 

those sources that the EPA staff believes have the potential to play an important role in impacting population 

incidence estimates generated for this risk assessment (i.e., these sources of uncertainty, if fully addressed could 

result in adjustments to the core risk estimates which might impact the interpretation of those risk estimates in the 

context of the PM NAAQS review). These key sources of uncertainty have been identified through consideration 

for sensitivity analyses conducted for previous PM NAAQS risk assessments, together with information provided in 

the final PM ISA and comments provided by CASAC on the analytical plan for the RA, as well as the first draft RA. 47 For example, if a particular source of uncertainty were more fully characterized (or if that source was 

resolved, potentially reducing bias in a core risk estimate), could the estimate of incremental risk reduction in going 

from the current to an alternative standard level change sufficiently to produce a different conclusion regarding the 

magnitude of that risk reduction in the context of the PM NAAQS review? 48 Thematically, the categories used in the qualitative uncertainty analysis are similar to the categories used 

in categorizing the results of the single- and multi-factor sensitivity analyses completed for this analysis (section 

4.3). However, in the context of the sensitivity analysis results, because we do have quantitative estimates of the 

impact of individual modeling elements, it is possible to categorize the modeling elements included in the sensitivity 

analysis based on magnitude of impact on risk estimates. This is not possible for the qualitative uncertainty analysis 

described in this section. 
 3-71

the core risk estimates and, instead, serve primarily as a means for guiding future research to 

reduce uncertainty related to PM2.5 risk assessment. 

As with the qualitative discussion of sources of variability included in the last section, the 

characterization and relative ranking of sources of uncertainty addressed here is based on 

consideration by EPA staff of information provided in previous PM NAAQS risk assessments 

(particularly past sensitivity analyses), the results of the sensitivity analyses completed for the 

current PM NAAQS risk assessment and information provided in the final PM ISA as well as 

earlier PM Criteria Documents. Where appropriate, in Table 3-13, we have included references 

to specific sources of information considered in arriving at a ranking and classification for a 

particular source of uncertainty. 
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Table 3-6. Summary of Qualitative Uncertainty Analysis of Key Modeling Elements in the P

 Assessment. 

Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

A. Characterizing ambient PM2.5

levels for study populations using the existing ambient monitoring network 

If the set of monitors used in a particular urban study area to characterize population exposure as part of an ongoing risk assessment do not match the ambient monitoring data used in the original epidemiological study, then uncertainty can be introduced into the risk estimates. 

Both Low- medium Low-medium 

KB and INF: In modeling risk, we were included in the epidemiologicunderlying C-R functions. This meendpoints modeled using C-R funcrecent studies, there is likely a closmonitoring network used in the risk used in the study supplying the C-R that in those instances where the ne

when older studies are used, resultinetworks to have changed), uncertarisk assessment and it is challenginmagnitude of the impact that that uestimates, given the complex interpmismatched monitoring networks (monitors used in modeling risk and epidemiological study). 

B. Characterizing policy-relevant background (PRB) 

For this analysis, we have used modeling to estimate PRB levels for each urban study area. Depending on the nature of errors reflected in that modeling, uncertainty (in both directions) may be introduced into the analysis. 

Both Low Low 

INF: Given that the risk assessmenreduction in risk associated with mto alternative standard levels, the imlevels on the risk estimates is expelong-term exposure related mortalianalysis on modeling risk down to reduces the significance of the PRB modeling long-term exposure-relat

C. Characterizing intra-urban population exposure in the context of epidemiology studies linking 

Exposure misclassification within communities that is associated with the use of generalized population monitors (which may miss important patterns of exposure within urban study areas) introduces uncertainty into the 

Under 

(generally) 

Mediumhigh Medium 

KB and INF: Recent analyses in Lbased on ACS data (as reported in demonstrate the relatively significauncertainty can have on effect estimresults). These analyses also illustspecific nature of this source of unLos Angeles analysis suggest that eeffects estimates that are biased low 
3-73

Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

PM2.5 to 

specific health effects 

effect estimates obtained from epidemiology studies. 

underestimation of risk. Specificallevel analysis based on ACS data c(Jerrett et al., 2005), the final ISA exposure analysis reported in the Jimproved exposure assessment and sources of fine particle pollution. Sbetween PM2.5 and mortality from 

diseases were reported with the mabeing greater than those reported in general, the associations for PM2.5 

methods [kriging and land-use regrassessment were similar, though thresulted in somewhat smaller hazarintervals (see Table 7-9). This indconfounding was not the cause of tearlier ACS Cohort studies. This pexposure error can result in stronge

and mortality than generally observless exposure detail” (final ISA, se

D. Statistical fit of the C-R functions 

Exposure measurement error combined with other factors (e.g., size of the effect itself, sample size, control for confounders) can effect the overall level of confidence associated with the fitting of statistical effect-response models in epidemiological studies. 

Both 

 Lowmedium 

(long-term health endpoints) 

 Medium 

(short-term health endpoints) 

Medium 

INF: Long-term mortality studies bsample sizes (given that large natioin deriving national-scale models), models used appears to be subject (see next row below) and (c) our nspecific effects estimates (but insteestimates). These factors combine that tend to be statistically robust (in Krewski et al., 2009). In additioregarding exposure misclassificatiogenerally we do not believe that thconsistently biased in a particular dterm mortality and morbidity healtuncertainty associated with the fit osample sizes often involved, difficuetiologically relevant time period fthe fact that models tend to be fitte
3-74

Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

urban areas (which introduces the pconfounding and effects modificaticontrast to the long-term mortality mortality and morbidity endpoints estimates that are not statistically sfor this risk assessment, in modelinmorbidity endpoints, we are not relmodels. In the case of short-term mspecific effects estimates derived u(these combine national-scale mod(personal communication with Zanmorbidity, we are using regional ef2008). In both cases, while effects statistically significant, these modesample sizes compared to city-spec

E. Representativeness of the population used in the epidemiological study 

If a population was used in the epidemiological study that is not representative of the general (urban) population, then the effect estimate that results may not be optimal for the population being modeled in the risk assessment (i.e., risks may be biased high or low). The issue of representativeness would ideally focus on factors directly related to PM risk (including effect modifiers). 

Both  Low- Medium Low-Medium 

KB: often we will have information study that allows us to identify potstudy population and the general (ufor example, to SES factors such aHowever, it can be more difficult tinto quantitative estimates of potenINF: In the case of the ACS dataselong-term exposure-related mortaliconcerns have been raised that the SES status relative to the U.S. poputhis to result in effect estimates tha(educational status is one factor thaU.S. EPA, 2004a). 

E. Shape of the C-R functions 

Uncertainty in predicting the shape of the C-R function, particularly in the lower exposure regions which are often the focus in PM NAAQS regulatory reviews. 

Both Medium Low-medium 

INF: Regarding long-term mortalitlinear non-threshold model is best modeling both short-term and longAlthough consideration of alternati2009) does suggest that different mto a certain extent, generally this apof overall uncertainty. Particularly 
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Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

assessment, we are not extrapolatinlevels found in the underlying epidregard to long-term mortality, the faddition to examining the concentrbetween short-term exposure to PM (2008, 156963) conducted an analyconcentration-response relationship exposure to PM. Using a variety of concentration-response curve was from linear, and, therefore, little evthat a threshold exists in the associbetween long-term exposure to PM

(Section 7.6).” (section 2.4.3, p. 2-morbidity, the final ISA states thatfurther support the use of a no-threadditional issues such as the influeestimates between cities, and the efdifferences in PM on the concentrarequire further investigation.” (sect

F. Addressing co-pollutants 

The inclusion or exclusion of co-pollutants which may confound, or in other ways, affect the PM effect, introduces uncertainty into the analysis. 

Both Low- medium Medium 

INF: With regard to long-term heastates that, “Given similar sources traffic), disentangling the health rechallenge in the study of ambient a7.5.1, p. 7-57). The final ISA also consideration of copollutants can hestimates. For example, the more ras reported in Krewski et al., 2009 ozone in the model along with PM

significant results for lung-cancer mmortality remained statistically sigTable 23). With regard to short-terfinal ISA generally concludes that robust to the inclusion of copolluta(see ISA, sections 6.3.8, 6.3.9, and considering multi-pollutant models 

risk for short-term and long-term e
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Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

leads us to conclude that the potentuncertainty is low-medium (dependunder consideration). The epidemibasis for selecting C-R functions fonot include multi-pollutant models 

and PM2.5 combined models in Zan

However, we have included copollanalysis (see Section 4.3). 

G. Potential variation in effects estimates reflecting compositional differences for PM 

The composition of PM can differ across study areas reflecting underlying differences in primary and secondary PM2.5 sources (both 

natural and anthropogenic). If these compositional differences in fact translate into significant differences in public health impact (per unit concentration in ambient air) for PM2.5 then 

significant uncertainty may be introduced into risk assessments if these compositional differences are not explicitly addressed. 

Both Medium- High Medium-High 

KB and INF: Epidemiology studiedifferences in PM2.5-related health 

in the magnitude of those effects (sthe draft ISA). While these may be composition (e.g., different degreecomposition remains one potential term exposure morbidity and mortacity-specific and/or regional-specifassessment may well reflect differethus consideration of differences in differences in composition may alrestimates for these endpoints to som

H. Specifying lag structure (short-term exposure studies) 

Different lags may have varying degrees of association with a particular health endpoint and it may be difficult to clearly identify a specific lag as producing the majority of a PM-related effect (recently, distributed lags have been recommended since they allow for a distribution of the impact across multiple days of PM exposure prior to the health 

Both Medium Medium 

KB and INF: With regard to lag peattempt has been made to identify wmore strongly associated with specepidemiologic evidence evaluated supported the use of lags of 0-1 dayand longer moving averages or disdiseases (U.S. EPA, 2004a). Howeexists as to the most appropriate a pexamining morbidity and mortality 2.4.2, p. 2-24). This suggests that uthe identification of appropriate lagrelevant time period for exposure t
3-77

Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

outcome). A lack of clarity regarding the specific lag(s) associated with a particular health endpoint adds uncertainty into risk estimates generated for that endpoint. 

endpoints. 

I. Transferability of C-R functions from study locations to urban study area locations 

The use of effects estimates based on data collected in a particular location(s) as part of the underlying epidemiological study in different locations (the focus of the risk assessment) introduces uncertainty into the analysis. 

Both 

Medium (for 

long-term 

exposure 

mortality) 

Not 

applicable 

(for shortterm 

exposure 

health effect 

risk 

estimates) 

Medium (for 

long-term 

exposure 

mortality) 

Low (for shortterm exposure 

mortality) 

INF: This issue has been amelioratassessment since we are now using short-term endpoints with effects eapplied only to urban study areas munderlying epidemiological study. exposure mortality studies, these argeneralized national signal and thetransferability of functions between concern. 

J. Use of single-city versus multicity studies in the derivation of C-R functions 

Often both single-city and multi-city studies are available (for a given health effect endpoint) for the derivation of C-R functions. Each of these study designs has advantages and disadvantages which should be considered in the context of assessing uncertainty in a risk assessment (Note, that generally this issue applies more to the modeling of short-term exposure-related endpoints then to the modeling of long-term exposure related endpoints, since the latter is 

Both Medium High 

KB: Because many health endpoinboth single-city and multi-city studselection of single city studies and consider in examining this issue. INF: For reasons presented in sectifocus on multi-city studies as a soucore risk assessment, reflecting adv(e.g., they tend to have more statistestimates with relatively greater prdue to larger sample sizes, reducinestimated coefficient, and reducing choice of multi-city studies is wellintroduce uncertainty since single crange of C-R functions (and associreflecting greater variation in study composition, human behavior, and 
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Potential influence of 

uncertainty on risk 

estimates 

Source Description Direction Magnitude 

KnowledgeBase 

uncertainty* 

Comm

(KB: knowledge base, INF: infl

estima

typically based on multi-city prospective cohort studies). 

the input datasets used (e.g., ambiebaseline incidence data). Even if thR functions obtained from multi-ciin those C-R functions may be reflrange of C-R functions seen across 

K. Impact of historical air quality on estimates of health risk from long-term PM2.5

exposures 

Long-term studies of mortality suggest that different time periods of PM exposure can produce significantly different effects estimates, raising the issue of uncertainty in relation to determining which exposure window is most strongly associated with mortality. 

Both Medium Medium 

INF: The latest HEI Reanalysis II slooked at exposure windows (1979term exposure in relation to mortalconclusions as to which window wwith mortality. However, the study different effects estimates are assocexposure periods (with the more reestimates). Overall, the evidence fover which the mortality effects of occur suggests a latency period of strongest results observed in the fir(final ISA, section 7.6.4. p. 7-95). 

L. Characterizing baseline incidence rates 

Uncertainty can be introduced into the characterization of baseline incidence in a number of different ways (e.g., error in reporting incidence for specific endpoints, mismatch between the spatial scale in which the baseline data were captured and the level of the risk assessment). 

Both Low- medium Low 

INF: The degree of influence of thirisk estimates likely varies with the under consideration. There is no reany systematic biases in estimates The influence on risk estimates tharisk reductions between alternative unaffected by this source of uncertKB: The county level baseline incat the county level were obtained frelative degree of uncertainty is low

* Refers to the degree of uncertainty associated with our understanding of the phenomenon, in the context of assessing and charact(specifically in the context of modeling PM risk) 
3-79

The results presented in Table 3-13 consider only the potential impact of each source of 

uncertainty when acting in isolation to impact core risk estimates. However, it is likely that a 

number of these sources of uncertainty could act in concert to impact risk estimates and 

furthermore, that these combined effects could be more than additive in certain circumstances. 

EPA staff has identified several combinations of sources of uncertainty addressed in Table 3-13 

that should be highlighted due to their potential to produce significant impacts on core risk 

estimates when acting in concert. These are briefly described below: 

 Uncertainty source D (statistical fit of the C-R functions), Source E (shape of the 

C-R functions), Source F (addressing copollutants), and Source J (use of singlecity versus multi-city studies in the derivation of C-R functions): Consideration of 

uncertainty associated with the shape of C-R functions needs to be considered in light 

of overall confidence (uncertainty) associated with a particular model. A number of 

factors contribute to an interpretation of confidence in a model including: statistical fit 

of the model, degree to which potential confounding by copollutants is considered, and 

other aspects of study design including single- versus multi-city study design. While 

choice of a particular model (e.g., threshold model, or log-log model) may produce a 

significant impact on risk estimates relative to alternative model forms, the overall 

scientific support for that particular model form (informed by consideration of the 

factors listed above) is an important consideration in assessing overall uncertainty both 

from a qualitative and quantitative standpoint. 

In addition, there is the potential for sources of uncertainty discussed in Table 3-13 to 

interact with sources of variability covered in section 3.5.2 in impacting core risk estimates. One 

such interaction is discussed below: 

 Uncertainty source A (characterizing ambient PM2.5 levels for study populations 

using the existing ambient monitoring network) and variability related to the 

pattern of ambient PM2.5 reductions at urban study areas (see section 3.5.2): The 

estimation of a composite monitor value to use in modeling risk for a study area under 

an alternative suite of standards is dependent both on the specification of the 

monitoring network and the approach used in adjusting the concentrations for the 

monitors in that network (i.e., the rollback approach used to simulate the pattern of 

ambient PM2.5 reductions associated with just meeting the current or alternative suites 

of standards). As we have seen in modeling risk for Pittsburgh, refinements in the 

approach used to simulate air quality just meeting alternative suites of standards (in the 

case of Pittsburgh transitioning from a single study area to two distinct study areas 

each with different design values and separate assessments of rollback) produced 

significant differences in composite monitor values for the study area. Therefore, both 

of these factors (the definition of the monitoring network and rollback approach) can 

work in concert to impact ambient PM2.5 levels and hence risk estimates. 
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3.5.4 Single and Multi-Factor Sensitivity Analyses 

We quantitatively examined the impact of several inputs to the risk assessment in a series 

of single-factor sensitivity analyses summarized above in Table 3-8. A number of these sources 

of uncertainty were also examined in-concert to assess their combined impact on core risk 

estimates through the multi-factor sensitivity analysis. In addition, the sensitivity analysis 

considered variability in the pattern of reductions in ambient PM2.5 associated with just meeting 

the current and alternative suites of standards (i.e., consideration of variability in the simulation 

of rollback). This section focuses on providing additional detail on the sources of alternative 

model specifications and input datasets used in the sensitivity analysis (as alternative to the core 

modeling approach). 

Rather than present results for each sensitivity analysis for all of the air quality scenarios 

considered in the core analysis, we selected a single air quality scenario – PM2.5 concentrations 

that just meet the current standards – to use for the sensitivity analyses. The one exception to 

this was the sensitivity analyses examining the impact of alternative approaches to simulating 

just meeting alternative standards (the hybrid and locally focused rollback methods).49 

In discussing the approach used in conducting the sensitivity analysis, we focus first on 

methods used in assessing long-term exposure related health endpoints followed by the methods 

used in assessing short-term exposure related health endpoints. We then discuss multi-factor 

sensitivity analyses completed for both short-term and long-term exposure-related health 

endpoints. Note, that the results of the sensitivity analyses (including both single- and multifactor analyses) are presented and discussed in section 4.3. 

3.5.4.1 Sensitivity Analyses for Long-Term Exposure-Related Mortality 

Because Krewski et al. (2009) presented results based on alternative model specifications 

only for the later exposure period (1999 – 2000), our sensitivity analyses focusing on the 

estimates of health effects incidence associated with long-term exposure to PM2.5 similarly used 

the C-R functions based on this later exposure period. Krewski et al. (2009) considered several 

alternative modeling approaches to estimate the relationship between mortality (both all cause 

and cause-specific) and long-term exposure to PM2.5, providing us the opportunity to examine 

the impact of alternative modeling approaches on the estimate of mortality risk associated with 

long-term exposure. In particular, we examined the impact of using a random effects log-linear 

 

49 Sensitivity analyses focusing on the hybrid and locally focused rollback approach (relative to the 

proportional rollback approach used in the core analysis) involved the full set of alternative standard levels, in order 

to assess potential differences in risk across the range of standard levels. 
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model and of using a random effects log-log model50 (rather than the standard fixed effects loglinear model used in the core analysis) to estimate the risks of all cause mortality, 

cardiopulmonary mortality, ischemic heart disease mortality, and lung cancer mortality 

associated with long-term exposure in Los Angeles and Philadelphia.51 The coefficient of PM2.5

in the random effects log-linear model was back-calculated from the relative risk reported in 

Table 9 (“Autocorrelation at MSA and ZCA levels” group – “MSA & DIFF” row) of Krewski et 

al. (2009). The coefficient of PM2.5 in the random effects log-log model was back-calculated 

from the relative risks reported in Table 11 (“MSA and DIFF” rows) of Krewski et al. (2009). 

As noted above, for all health endpoints associated with long-term exposure to PM2.5 we 

estimated risk associated with PM2.5 concentrations above 5.8 μg/m3 (the LML for the later 

exposure period used in Krewski et al., 2009). In a sensitivity analysis we examined the impact 

of that limitation by comparing those mortality risk estimates to the mortality risk estimates 

obtained when we estimated risk associated with PM2.5 concentrations above estimated PRB 

levels. This sensitivity analysis was carried out for all cause mortality in all 15 risk assessment 

urban areas. 

In addition, we compared the impact of using the primary C-R functions used in the risk 

assessment, taken from Table 33 of Krewski et al. (2009), versus C-R functions for mortality 

associated with long-term exposure reported in another study, Krewski et al. (2000), which was 

based on a reanalysis of the Harvard Six Cities Study. The C-R functions estimated in Krewski 

et al. (2000) from the Harvard Six Cities cohort were estimated for ages 25 and up, while the CR functions estimated in Krewski et al. (2009) from the ACS cohort were for ages 30 and up. 

For purposes of consistency in the comparison, however, we applied the C-R functions from 

Krewski et al. (2000) to ages 30 and up (and used the baseline incidence rates for that age group 

as well).52 This sensitivity analysis was carried out for all cause mortality, cardiopulmonary 

mortality, and lung cancer mortality in Los Angeles and Philadelphia. 

We also considered the impact of using multi-pollutant models in estimating long-term 

exposure-related mortality. Specifically, we obtained 2-pollutant models (considering CO, NO2 , 

 

50 In the log-log model, the natural logarithm of mortality is a linear function of the natural logarithm of 

PM2.5. 51As noted in Table 3-8, we combined both of these alternative modeling approaches in a single sensitivity 

analysis. In changing from a fixed effects log-linear model to a random effects log-log model, two changes are 

actually being made – the change from a fixed effects log-linear model to a random effects log-linear model, and the 

change from a random effects log-linear model to a random effects log-log model. However, because Krewski et al. 

(2009) did not present results for a fixed effects log-log model, it was not possible to compare the impact of making 

the single change from a fixed effects log-linear model (our core analysis selection) to a fixed effects log-log model. 

We thus instead present a two-stage sensitivity analysis incorporating both of the changes. 52 The baseline incidence rates for ages 25 and up and ages 30 and up are likely to be very similar. 
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O3 or SO2 together with PM2.5) from Krewski et al., 2000, which is an earlier reanalysis of the 

ACS dataset and used them in generating alternative estimates of all-cause mortality to contrast 

with the core estimates generated using Krewski et al., 2009. 

For all of the sensitivity analyses involving alternative C-R functions, in addition to 

calculating the incidence of the health effect when an alternative approach is taken, we 

calculated the percent difference in estimates from the core analysis resulting from the change in 

analysis input. So for example, when we calculated the incidence of all cause mortality 

associated with long-term exposure to PM2.5 using a random effects log-log model (instead of the 

fixed effects log-linear model used in the core analysis), we calculated the percent difference in 

the result as (incidence estimated using a random effects log-log model - incidence estimated 

using a fixed effects log-linear model)/( incidence estimated using a fixed effects log-linear 

model). 

Finally, we also examined the issue of variability in estimating the pattern of reductions 

in ambient PM2.5 levels under the current and alternative standard levels (i.e., conducting 

rollback). For the first draft RA, we considered the impact of using a hybrid rollback approach in 

addition to the proportional rollback approach which has been more traditionally used in PM 

NAAQS risk assessment (this sensitivity analysis was implemented including the generation of 

quantitative risk estimates for a full suite of long-term exposure-related mortality categories). 

For the final RA, as discussed above in sections 2.3, and 3.2.3.1, we have included consideration 

of a locally focused rollback approach in addition to the hybrid as non-proportional methods to 

contrast with proportional rollback. As discussed in Section 3.2.3.1, for the final risk 

assessment, rather than generating quantitative risk estimates, we have calculated composite 

monitor estimates using the different rollback methods (proportional, hybrid and locally 

focused). The composite monitor values are surrogates for long-term exposure-related mortality. 

Therefore, by comparing composite monitor values generated for the same study area/standard 

level combination (using different rollback methods), we can obtain insights into the potential 

impact of the rollback method used on long-term exposure-related mortality. Specifically, for 

this sensitivity analysis, we compared composite monitor values in two ways: 

 Potential difference in composite monitor values at the current or alternative standard 

level (for the same study area) given application of alternative rollback methods: We 

compared the absolute magnitude of composite monitors values produced using different 

rollback methods for the same study area/standard level combination to provide insights 

into differences in the magnitude of residual risk for a given suite of standards in a study 

area using different rollback methods (Appendix F, Table F-50).53 For example, in Table 

 

53 This calculation reflects the fact that we model long-term exposure-related mortality down to LML. 
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F-50, for Los Angeles, we see that for the current standard suite of standards, use of 

proportional rollback and locally focused rollback methods results in composite monitor 

values of 9.5 μg/m3 and 12.0 μg/m3, respectively, with the locally focused value being 

40% higher than the value derived using proportional rollback. Given that the composite 

monitor values are surrogates for long-term exposure-related mortality, we conclude that 

for this combination of urban study area and suite of standards, use of the locally focused 

rollback method could produce PM2.5-attributable long-term mortality risk estimates that 

are approximately 40% higher than use of the proportional rollback method. 

 Potential difference in the pattern of reduction in composite monitor values across 

alternative standards: We compared differences in the percent reduction in composite 

monitor values across alternative suites of standards for the same study area using 

different rollback methods to provide insights into differences in incremental risk 

reduction resulting from the use of different rollback approaches (Appendix F, Table F49).54 For example, in Table F-49, for Baltimore, we see that the proportional rollback 

and hybrid rollback approaches resulted in composite monitor values for the 13/35 

alternative suite of standards of 11.6 μg/m3

 and 11.8 μg/m3, respectively, with these 

translating into a percent reduction (compared with their respective values under the 

current suite of standards) of 21% and 16%, respectively. Given that the composite 

monitor values are surrogates for long-term exposure-related mortality, we conclude that 

use of the two rollback methods (in the case of Baltimore for these two suites of 

standards) does not appear to produce notably different patterns of risk reduction (in 

terms of percent reduction), although residual risk could differ using the two approaches. 

The locally focused and hybrid rollback approaches were not applied to all study areas, 

since they are primarily applicable in certain situations.55 The sensitivity analysis results 

described above (presented in Appendix F, Tables F-49 and F-50) form the basis for summary 

information related to rollback approaches presented in Table 4-3. 

In addition to the above insights regarding potential impacts on residual risk and the 

degree of risk reduction across standard levels, inclusion of multiple rollback approaches also 

allowed us to more fully examine the degree to which alternative 24-hour standards can produce 

reductions in annual average PM2.5 concentrations, thereby producing reductions in long-term 

exposure-related mortality. As discussed below in section 5.2.3, alternative 24-hour standards, 

when controlling, can result in reductions in annual average PM2.5 concentrations, particularly if 

proportional rollback is used. In this case, the assumption of more regional patterns of PM2.5

reduction in reducing PM2.5 concentrations to just meet alternative 24-hour standards results in 

 

54 We note that this analysis also reflects calculation of long-term exposure-related mortality down to LML. 

55 For the hybrid rollback approach, only select study areas had the mix of local sources in proximity to 

monitor with elevated levels necessary to support consideration of a hybrid local/regional attainment strategy (i.e., 

application of the hybrid rollback) (i.e., Baltimore, Birmingham, Detroit, Los Angeles, New York, St. Louis). In the 

case of the peak sharing approach, only those locations where the 24-hour standard was controlling were considered 

for this sensitivity analysis (i.e., Atlanta, Baltimore, Birmingham, Detroit, Fresno, Los Angeles, New York, 

Philadelphia, Phoenix, Pittsburgh, St. Louis, Tacoma). 
3-84

an equivalent magnitude of reduction in the annual average. However, in simulating more 

localized patterns of PM2.5 reductions to just meet alternative 24-hour standards, the PM2.5

reductions can be more limited to the monitor(s) (and areas) exceeding the 24-hour standard, and 

other monitors may not be effected, resulting in a smaller impact on the annual average. 

Inclusion of rollback approaches reflecting more localized patterns of ambient PM2.5 reduction 

(i.e., the hybrid and particularly the locally focused methods) allows us to assess the degree to 

which alternative 24-hour standards (when controlling) produce appreciable reductions in annual 

average PM2.5 concentrations and consequently in long-term exposure-related mortality. This 

issue is revisited in discussing the results of the sensitivity analysis (section 4.3.1.1) and in the 

integrative discussion of the core risk estimates (section 5.2). 

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

Morbidity 

The scope of the sensitivity analysis completed for short-term exposure-related mortality 

and morbidity is more limited than that completed for long-term exposure-related mortality. 

This reflects, in part, the much greater magnitude of long-term exposure-related mortality. An 

additional factor is that while there has been considerable research in the area of short-term 

exposure-related mortality and morbidity which sheds light on uncertainty in such factors as C-R 

function specification, this information is not directly applicable in a sensitivity analysis. In 

order to complete a quantitative sensitivity analysis, we need alternative C-R function 

specifications that produce risk estimates that can be directly compared to the core risk estimates. 

Ideally, this is done by identifying alternative model forms in the epidemiological study used in 

the core risk model. However, in the case of short-term exposure-related mortality, the studies 

providing our core risk models (Zanobetti and Schwartz et al., 2009 and Bell et al., 2008), only 

provide limited alternative model specifications, as described below. Further, alternative 

epidemiological studies, such as Moolgavkar et al., 2003, while providing useful insights into 

which factors can impact risk estimates (e.g., lag, multi-pollutant forms), cannot generate 

alternative risk estimates that can be readily compared with the core risk estimates given 

differences in the underlying study designs and datasets employed. 

The primary studies selected to assess mortality risk and risk of hospitalization associated 

with short-term exposure to PM2.5 (Zanobetti and Schwartz, 2009, and Bell et al., 2008, 

respectively) both provided all-year C-R functions as well as season-specific C-R functions. We 

examined the impact of using season-specific functions by applying these functions to each 
3-85

season, as defined by the study authors,56 and summing the estimated season-specific incidences 

of mortality and hospitalizations. We compared these estimates to the estimates obtained by 

applying the corresponding all-year C-R functions to a year of air quality data.57 This sensitivity 

analysis was carried out for all 15 of the risk assessment urban areas. 

In addition, Ito et al. (2007) estimated an annual C-R function as well as a seasonal 

function for April through August for asthma ED visits in New York City. We compared the 

results of applying the annual C-R function to a whole year of air quality data to the results of 

applying the seasonal function to only those months (April through August) for which it was 

estimated. 

Moolgavkar (2003) estimated C-R functions for several health endpoints – non-accidental 

and cardiovascular mortality; and cardiovascular and respiratory HAs – associated with shortterm exposures to PM2.5 in Los Angeles using different lag structures, different modeling 

approaches to incorporating weather and temporal variables, and single-pollutant versus multipollutant models. This study thus provided an opportunity to show the impact of lag structure, 

modeling approach, and single- vs. multi-pollutant models, individually, for several health 

endpoints associated with short-term exposures, although it is difficult to generalize to other 

locations since the study was only conducted in a single urban area. As noted earlier, differences 

in study design and the underlying datasets used prevent the results based on application of 

models from Moolgavkar et al., 2003 from being compared directly to the core risk estimates. 

Finally, as with estimates of long-term exposure-related mortality, we also considered the 

impact of variability related to simulating ambient PM2.5 levels under the suite of current 

standard levels (i.e., variability in conducting rollback) on estimates of non-accidental mortality 

associated with short-term exposures to PM2.5 (using Zanobetti and Schwartz, 2009). However, 

in this case, we only considered the hybrid model (consideration of locally focused on the impact 

on long-term exposure-related mortality). We note however, that sensitivity analysis findings 

based on consideration of locally focused generally will hold for short-term exposure-related 

mortality and morbidity since both categories of health endpoints are also driven primary by 

annual average PM2.5 levels (see section 6.2). . 

 

56 Both studies defined each season as three months, beginning with winter defined as December, January, 

and February. In applying a season-specific function to a year of air quality data, we chose to keep a calendar year 

together, so that, for example, winter 2005 was defined as December 2005, January 2005, and February 2005. 57 The mean season-specific incidence estimates can be summed to produce an all-year estimate of 

incidence. However, the 2.5th and 97.5th percentile season-specific estimates cannot be summed. To calculate the 

2.5th and 97.5th percentile estimates of all-year incidence from the season-specific estimates would require the 

variance-covariance matrix of the season-specific coefficient estimators, which was not available. Therefore our 

comparison of all-year estimates based on summed season-specific estimates versus estimates based on an all-year 

C-R function was carried out only using the mean estimates. 
3-86

In all cases except the ED visits sensitivity analysis, in addition to calculating the 

incidence of the health effect when an alternative approach is taken, we calculated the percent 

difference in estimates from the core analysis resulting from the change in analysis input.58

3.5.4.3 Multi-factor Sensitivity Analyses 

Each single-element sensitivity analysis shows how the estimates of PM2.5–related health 

effects incidence change as we change a single element of the analysis (such as the form of the 

C-R function or the way we simulate just meeting a set of standards). Because each of the 

alternative modeling choices is considered to be a reasonable choice, the results of these singleelement sensitivity analyses provide a set of reasonable alternative estimates that may similarly 

be considered plausible (see section 4.3). The results of the single-element sensitivity analysis 

are presented and discussed in section 4.3.1.1. 

The single-element sensitivity analyses provide insight into which sources of uncertainty 

may have the greatest impact on risk estimates when acting alone. However, there are several 

sources of uncertainty in estimating PM2.5–related health effects. To provide a more complete 

picture of the uncertainty surrounding estimates of PM2.5–related health effects incidence – and 

to expand the set of reasonable alternative estimates – we next carried out multi-element 

sensitivity analyses. The results of the multi-factor sensitivity analysis are presented and 

discussed in section 4.3.1.2. 

 The choice of uncertain analysis elements to include in the multi-element sensitivity 

analyses was guided by the single-element sensitivity analyses. In particular, we selected those 

modeling choices that had the greatest impacts on the estimates of health effects incidence in the 

single-element sensitivity analyses to provide insight into the scope of possible estimates that, 

while perhaps not based on our first choice of analysis elements, are nevertheless plausible 

alternative estimates. 

We identified three analysis elements that substantially affected the estimates of mortality 

associated with long-term exposure to PM2.5 -- the model choice (fixed effects log linear vs. 

random effects log-log), whether effects are estimated associated with PM2.5 concentrations 

down to the LML in the study (5.8 μg/m3) or down to PRB, and whether a proportional or a 

hybrid rollback is used to simulate PM2.5 concentrations that just meet a given set of standards. 

This resulted in 2 x 2 x 2 = 8 different estimates of mortality, all of which could be considered 

plausible, based on the fact that the underlying model choices are all considered reasonable. 

 

58 We did not calculate percent different for the ED visits sensitivity analysis because the two different C-R 

functions (all-year in the core analysis vs. April through August in the sensitivity analysis) are also being applied to 

different portions of the year (all year vs. April through August), so it is something of an “apple to oranges” 

comparison. 
3-87

We identified two analysis elements that substantially affected the estimates of mortality 

associated with short-term exposure to PM2.5 – whether season-specific or all-year C-R functions 

were used and whether a proportional or a hybrid rollback approach was used to simulate just 

meeting the current and alternative standards. 

3.5.5 Summary of Approach to Addressing Variability and Uncertainty 

The characterization of uncertainty and variability associated with the risk assessment 

includes a number of elements, which have been discussed in detail above. These include: 

 Identification of key sources of variability associated with PM2.5-related population 

exposure and hazard response and the degree to which they are captured in the risk 

assessment (see section 3.5.2). When important sources of variability in exposure 

and/or hazard response are not reflected in a risk assessment, significant uncertainty 

can be introduced into the risk estimates that are generated. While not explicitly 

referenced in the WHO guidance, this assessment (focused on coverage for key sources 

of variability) could be considered part of a Tier 1 analysis (i.e., the qualitative 

characterization of sources of uncertainty). 

 Qualitative assessment of uncertainty, including both an assessment of the magnitude 

of potential impact of each source on risk estimates (along with the potential direction 

of that impact) as well as an assessment of overall confidence associated with our 

understanding of that source of uncertainty (see section 3.5.3). This represents a WHO 

Tier 1 analysis. 

 Single-factor sensitivity analysis intended to evaluate the impact of individual sources 

of uncertainty and variability on risk estimates (see section 3.5.4). The goal of this 

assessment is to evaluate the relative importance of these sources of uncertainty and 

variability in impacting core risk estimates. The single-factor sensitivity analysis 

represents a WHO Tier 2 analysis. In conducting these assessments, we have used 

alternative representations of modeling elements that have support in the literature to 

ensure that the risk estimates that are generated represent reasonable alternate estimates 

that can supplement the core risk estimates generated in the analysis (see section 4.3). 

 Multi-factor sensitivity analysis intended to assess the combined impact of multiple 

sources of uncertainty and variability on risk estimates (see section 3.5.4). By 

considering the combined effect of multiple sources of uncertainty and variability, this 

analysis has the potential to identify any non-linearities which can magnify the impact 

of uncertainty and variability on risk estimates, especially if several non-linear factors 

act in concert. This also represents a WHO Tier 2 analysis. As with the single-factor 

sensitivity analysis results, these risk estimates are also generated using modeling 

inputs which have support in the literature and consequently, they also represent 

reasonable alternate estimates that supplement the core risk estimates (see section 

4.3.2). 

As noted above, since information was not available to characterize overall levels of 

confidence in alternative model inputs, the uncertainty characterization completed for this risk 

assessment did not include a full probabilistic assessment of uncertainty and its impact on core 
3-88

risk estimates (i.e., a WHO Tier 3 analysis was not completed). Further, the risk estimates 

generated using the single- and multi-factor sensitivity analyses do not represent uncertainty 

distributions, but rather additional plausible point estimates of risk (i.e., we do not know whether 

they represent risk estimates near the upper or lower bounds of a true but undefined uncertainty 

distribution and we do not know the actual population percentiles that they represent). The 

appropriate use for these reasonable alternate risk estimates in informing consideration of 

uncertainty in the core risk estimates is discussed in section 4.3.2. 

In addition to the qualitative and quantitative treatment of uncertainty and variability 

which are described here, we have also completed an analysis to evaluate the representativeness 

of the selected urban study areas against national distributions for key PM risk-related attributes 

to determine whether they are nationally representative or more focused on a particular portion 

of the distribution for a given attribute (section 4.4.1). In addition, we have completed a second 

analysis addressing the representativeness issue, which identified where the subset of 31 counties 

comprising our 15 urban study areas fall along a distribution of national county-level long-term 

exposure-related mortality risk (section 4.4.2). This analysis allowed us to assess the degree of 

which the 15 urban study areas capture locations within the U.S. likely to experience elevated 

levels of risk related to PM2.5 exposure. 

A third set of analyses that has been added to this final RA focuses on evaluating patterns 

in the design values (including both 24-hour and annual) and underlying PM2.5 monitoring data 

for the 15 urban study areas (see Section 4.5). The goal of this analysis is to use this information 

to enhance our understanding of patterns in risk reduction seen under both the current and 

alternative suites of standards across the urban study areas. The interplay of design values and 

underlying PM2.5 monitoring data play a key role in determining whether a location will 

experience risk reductions when just meeting any given suite of standards is simulated and, if so, 

the magnitude of those reduction. As part of this analysis, we contrast patterns in design values 

for the 15 urban study areas with patterns seen more broadly across urban areas in the U.S. with 

the goal of placing the urban study areas in a national context with regard to this key factor 

influencing risk.
4-1

4 URBAN CASE STUDY RESULTS 

For this risk assessment, we have developed a core set of risk estimates supplemented by 

an alternative set of risk results generated using single-factor and multi-factor sensitivity 

analysis. The core set of risk estimates was developed using model inputs that staff judge to 

have a greater degree of support in the literature relative to inputs used in the sensitivity analyses 

(the rationale for selection of specific epidemiological studies and associated C-R functions for 

the core analysis is discussed above in section 3.3.3). This chapter presents and discusses the 

core set of risk estimates generated for the urban case study area, and also discusses the results of 

the sensitivity analyses which serve to augment the core risk estimates. The results of the 

sensitivity analyses allow us to evaluate and rank the potential impact of key sources of 

uncertainty on the core risk estimates. In addition, because the sensitivity analyses were 

conducted using alternative modeling inputs having some degree of support in the literature, the 

results of the sensitivity analysis also represent a set of reasonable alternatives to the core set of 

risk estimates that can be used to inform characterization of uncertainty in the core results (see 

section 4.3.2 below). 

As discussed above in section 2.4 and 3.2, this risk assessment includes consideration of 

the following air quality scenarios: 

 Recent conditions: based on PM2.5 concentrations characterized through monitoring for 

the period 2005-2007 at each urban case study location; 

 Current NAAQS: based on rolling back PM2.5 concentrations to just meet the current 

suite of standards in each urban study area (annual standard of 15 μg/m3 and a 24-hour 

standard of 35 μg/m3

, denoted 15/35); 

 Alternative NAAQS: based on rolling back PM2.5 concentrations to just meet alternative 

suites of standards in each urban study area: 

o annual standard of 14 μg/m3

 and a 24-hour standard of 35 μg/m3 (denoted 13/35); 

o annual standard of 13 μg/m3 and a 24-hour standard of 35 μg/m3 (denoted 13/35); 

o annual standard of 12 μg/m3

 and a 24-hour standard of 35 μg/m3 (denoted 12/35); 

o annual standard of 13 μg/m3 and a 24-hour standard of 30 μg/m3 (denoted 13/30); 

o annual standard of 12 μg/m3 and a 24-hour standard of 25 μg/m3 (denoted 12/25). 

We have also estimated risk for an alternative annual standard level of 10 μg/m3

 (paired 

with 24-hour standard levels of 35 μg/m3 and 25 μg/m3). However, as discussed in section 2.4, 

because of increased uncertainty associated with these risk estimates relative to estimates 

generated for the other alternative annual standard levels evaluated in the RA, estimates based on 

the alternative annual standard level of 10 μg/m3 are not discussed in this chapter and instead are 
4-2

presented in Appendix J and only briefly addressed as part of the integrative discussion presented 

in Chapter 5. 

In simulating both current and alternative suites of standards, for the core analysis, we 

used a proportional roll-back approach (see section 3.2.3), while a hybrid roll-back approach 

reflecting the potential for local source control was used for a subset of urban study areas as part 

of the sensitivity analysis conducted for this assessment (see section 3.2.3). In addition, we have 

considered the locally focused approach as a further alternative to proportional rollback in 

simulating just meeting the current and alternative suites of standards. While we did not generate 

risk estimates based on application of the locally focused approach, we did generate composite 

monitor-based annual average PM2.5 levels which allow us to assess how long-term exposurerelated risk could vary if this alternative roll-back method was used (see Section 4.3). 

As described in section 2.3 and 3.3.2, we assessed risk for 15 urban study areas chosen to 

provide coverage for the diversity of urban settings across the U.S. that reflect areas with 

elevated annual and/or daily PM2.5 concentrations. At a minimum, all areas selected had recent 

air quality levels at or above the lowest annual and/or 24-hour standards analyzed. In addition, 

our goal was to select areas reflecting the heterogeneity in PM risk-related attributes such as 

sources, composition, demographics, and population behavior. 

Risk estimates were generated for the following health effects endpoints: (a) long-term 

exposure-related mortality (all-cause, cardiopulmonary disease-related (CPD), ischemic heart 

disease-related (IHD) and lung cancer-related), (b) short-term exposure-related mortality (nonaccidental, cardiovascular disease (CVD), respiratory), and (c) short-term exposure-related 

morbidity (hospital admissions (HA) for CVD and respiratory illness and emergency department 

(ED) visits). Risk estimates are presented separately for each of these 15 study areas, although in 

certain circumstances, risk estimates may be restricted to a subset of these locations if, for 

example, an endpoint is modeled using a concentration-response (C-R) function derived from an 

epidemiological study that was conducted only in a subset of the urban areas. For the core 

analysis, long-term exposure mortality risk was modeled down to lowest measured level (LML), 

because the LML was higher than estimated PRB and because there is substantial uncertainty as 

to the shape of the concentration-response (C-R) function at concentrations below the LML. For 

long-term exposure mortality a sensitivity analysis was conducted that estimated risk down to 

policy-relevant background (PRB). In contrast, all short-term exposure health effects endpoints 

were modeled down to PRB, since this was higher than the LML across all studies and for 

purposes of NAAQS decision making, EPA is focused on risks associated with PM2.5 levels that 

are due to anthropogenic sources that can be controlled by U.S. regulations (or through 

international agreements with neighboring countries). 
4-3

In modeling long-term exposure mortality, for the core analysis, we have based estimates 

on the latest reanalysis of the American Cancer Society (ACS) dataset, with two sets of risk 

estimates being generated; one using a C-R function derived by fitting PM2.5 monitoring data 

from 1979-1983 and a second set based on fitting PM2.5 monitoring data from 1999-2000 

(Krewski et al., 2009) (see section 3.3.3). In presenting core risk estimates for long-term 

mortality, both sets of estimates are given equal weight. 

In modeling short-term exposure mortality and morbidity for the core analysis, we have 

used the latest multi-city studies (Zanobetti and Schwartz, 2009; Bell et al., 2008) (see section 

3.3.3). In the case of short-term exposure mortality, we obtained and used city-specific effects 

estimates derived using empirical Bayes methods from the study authors (Zanobetti, 2009). 

Multi-city studies were favored for the core analysis, since these studies are not subject to 

publication bias and because they reflect a diverse set of locations with regard to the observed 

relationship between short-term PM2.5 exposure and health affect response in the population. 

Additional detail on the specific C-R functions and related modeling elements such as effects 

estimates and lag periods used in the core analysis relative to the sensitivity analysis are 

presented above in sections 3.3 and 3.4 and called out where appropriate below as specific risk 

estimates are discussed. 

The pattern of mortality incidence across the urban study areas is markedly different for 

short-term exposure-related mortality compared with long-term exposure-related mortality 

reflecting a number of factors including: (a) differences in patterns of daily PM2.5 levels versus 

annual average values across the urban study areas and (b) the fact that urban study area-specific 

effect estimates are used in modeling short-term exposure-related mortality, while a single effect 

estimate is used for all study areas for long-term exposure-related mortality (for a particular 

mortality category). Further, effect estimates for short-term exposure-related mortality can be 

notably small for some study areas (e.g., the effect estimates for non-accidental mortality for Los 

Angeles is significantly smaller than effect estimates for the other study areas, thereby 

accounting for the relatively small total incidence estimate for this study area – see Appendix C, 

Table C-1). 

Because the recent conditions air quality scenario spans three years (2005-2007), risk 

estimates are generated for each of these years, reflecting the underlying air quality data for a 

particular year. Risk metrics generated for the above health effects endpoints include: 

 Annual incidence of the endpoint due to PM2.5 exposure (annual incidence): 

Generated for the population associated with a given urban study area (for a given 

simulation year), in most cases, these risk estimates include both a point estimate as well 

as a 95th percentile confidence interval, the latter reflecting sampling error as 

characterized in the underlying epidemiological study. 
4-4

 Percent of total annual incidence for the health endpoint due to PM2.5 exposure 

(percent of total incidence attributable to PM2.5): Again, generated for the population 

associated with a given urban study area (and simulation year), this metric characterizes 

the fraction of total incidence that is associated with PM2.5 exposure. As with the 

underlying PM-related incidence estimates, this risk metric also typically includes a 95th

percentile confidence interval reflecting sampling error associated with the effects 

estimate. Compared with the annual incidence metric which reflects underlying 

population size for each study area, this risk metric has the advantage of not being 

dependent on the size of the underlying population, thereby allowing direct comparison 

of the potential impact of PM2.5 for the health effect endpoint of interest across urban 

study area locations. For this reason, in discussing risk estimates in this section, the 

percent of total incidence attributable to PM2.5 risk metric is given somewhat greater 

emphasis than the absolute measure of annual incidence attributable to PM2.5. 

 Percent reduction in PM2.5-related health effect incidence for an alternative set of 

standards or the recent conditions scenario, relative to the current standards 

(percent change from the current set of standards): Also estimated separately for each 

urban study area and simulation year, this metric characterizes the degree of risk 

reduction (for alternative standard levels) or increased risk (for the recent conditions 

scenario) relative to the current NAAQS. For this metric, a negative value represents an 

increase in risk (this is the case for the recent conditions scenario, where risks are higher 

than those associated with just meeting the current suite of standards). This metric is 

positive, or zero, for alternative suites of standards since they either produce no risk 

reduction (if ambient air levels under recent conditions are already at or below that 

alternative standard levels), or a positive risk reduction for alternative standards resulting 

in a reductions in ambient PM2.5 concentrations. Because this metric is incremental, it 

was not possible to generate the 95th percentile confidence intervals included with the 

other two “absolute” risk metrics described above. As with the previous risk metric, this 

metric is not dependent on the underlying population size and therefore, allows direct 

comparison across urban study areas. 

In addition to presenting the central-tendency (highest confidence) estimates for each of 

these metrics, we also include 95th percentile confidence intervals, reflecting statistical 

uncertainty surrounding the estimated coefficients in the reported C-R functions used in deriving 

the risk estimates. Note, that these confidence intervals only capture this statistical fit uncertainty 

– other sources of uncertainty including shape and form of the function, are addressed separately 

as part of the sensitivity analysis (see Section 4.3.1) and the qualitative analysis of uncertainty 

(see Section 3.5.3). 

Detailed tables presenting estimates for these risk metrics for the complete set of air 

quality scenarios (for all 15 urban study areas) are included in Appendix E and referenced as 

needed in the discussion of risk estimates presented in the following sections. To support the 

discussion of risk estimates presented in this chapter, we have included a subset of tables and 

summary figures including: 
4-5

 Tables summarizing risk for the current standard levels: Two tables are included 

which summarize both long-term and short-term exposure-related risk for the 15 urban 

study areas associated with just meeting the current suite of standards. Both tables 

include a subset of the health endpoints believed to have the greatest support in the 

literature including IHD mortality for long-term exposure, cardiovascular mortality and 

hospital admissions for short-term exposure. Table 4-1 presents total incidence 

attributable to PM2.5 exposure for the endpoints and Table 4-2 presents percent of total 

incidence attributable to PM2.5 exposure for these endpoints. Together, these tables 

inform consideration of the magnitude of public health impact (related to both long-term 

and short-term exposure to PM2.5) associated with just meeting the current suite of 

standards in the 15 urban study areas. 

 Figures illustrating the percent reduction in long-term and short-term exposurerelated risk for the alternative standard levels relative to the current standard (as 

well as increases in risk under recent conditions relative to the current standard): 

Figures 4-1 and 4-4 provide a snapshot of trends in risk reduction for long-term exposurerelated risk (Figure 4-1) and short-term exposure-related risk (Figure 4-4) across 

alternative standard levels relative to the risk under the current standard. These figures 

include plots for each of the 15 urban study areas, thereby allowing trends in risk 

reduction across standard levels (and urban study areas) to be assessed simultaneously.59 

Each of these figures is presented in additional detail by splitting each into (a) 

comparison of the recent conditions risk against the current standard level and (b) 

comparison of risk under alternative standard level against the current standard, in order 

to allow a more detailed look at patterns in risk reduction for individual urban study areas 

(splitting Figures 4-1 and 4-4 in this fashion allows greater resolution in tracing the linear 

risk plots for each study area). Specifically, Figures 4-2 and 4-3 provide these higherresolution plots for long-term exposure-related risk and Figures 4-5 and 4-6 provide 

higher-resolution plots for short-term exposure related risk. 

Although risk estimates were generated for all three simulation years, in this chapter core risk 

estimates primarily from 2007 are presented and discussed for both the recent conditions air 

quality scenario and just meeting current and alternative suites of standards. This reflects the 

observation that in generally 2007 represents a reasonable central year (in terms of the magnitude 

of risk generated for the three simulated years), when considering results for all modeled health 

effect endpoints across the 15 study areas. In addition, 2007 is the most recent year of the three 

simulated. We note, however, that while we do focus on 2007 in presenting and discussing risk 

 

59 Note, that importantly, patterns of risk reduction across standard levels (in terms of percent change 

relative to risk for the current standard level) are similar for all health endpoints modeled for a particular exposure 

duration (i.e., patterns of percent risk reduction will be similar for long-term exposure related all-cause, IHD and 

cardiopulmonary mortality). This reflects the fact that the C-R functions used in this risk assessment are close to 

linear across the range of ambient air levels evaluated. This allows us to present these figures plotting changes in 

risk more generally for short-term exposure-related endpoints and long-term exposure related endpoints without 

having to provide figures for each specific endpoint category. 
4-6

estimates, we include an assessment of general trends across the three simulation years to gain 

perspective on year-to-year variation in PM2.5-related risk estimates as assessed here. 

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

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

Concentrations that Just Meet the Current Standards, Based on Adjusting 

2007 PM2.5 Concentrations. 1,2

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)

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical 

uncertainty surrounding the PM coefficient.

3Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 1983 and from 1999-2000 respectively. Incidence is for 30+ 

year olds within each urban study area.

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. Incidence is for all-ages (i.e., all individuals) within each urban study area.

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. Incidence is for 65+ year olds within each urban study 

area.

 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.

 New York, NY

 Philadelphia, PA

 Phoenix, AZ

 Pittsburgh, PA

 Detroit, MI

 Fresno, CA

 Houston, TX

 Los Angeles, CA

 Atlanta, GA

 Baltimore, MD

 Birmingham, AL

 Dallas, TX

Risk Assessment 

Location

 Incidence of Ischemic Heart Disease 

Mortality Associated with Long-term 

Exposure to PM2.53

Incidence of 

Cardiovascular 

Mortality Associated 

with Short-term 

Exposure to PM2.54

Incidence of 

Cardiovascular 

Hospitalizations 

Associated with Shortterm Exposure to 

PM2.55
4-7

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

Morbidity Endpoints Associated with Long- and Short-Term Exposure to 

Ambient PM2.5 Concentrations that Just Meet the Current Standards, Based 

on Adjusting 2007 PM2.5 Concentrations. 1,2

Exposure Period: 

1979-1983

Exposure Period: 

1999-2000

13.2% 16.7% 0.8% 0.4%

(10.9% - 15.5%) (13.7% - 19.5%) (-0.8% - 2.4%) (-0.2% - 1%)

11.7% 14.7% 1.6% 1.3%

(9.6% - 13.7%) (12.1% - 17.3%) (-0.1% - 3.2%) (1% - 1.7%)

10.9% 13.8% 0% 0.3%

(8.9% - 12.9%) (11.3% - 16.2%) (-1.5% - 1.5%) (-0.2% - 0.9%)

9% 11.4% 0.8% 0.3%

(7.3% - 10.6%) (9.3% - 13.4%) (-0.5% - 2.2%) (-0.2% - 0.7%)

9.1% 11.5% 1% 1.1%

(7.4% - 10.7%) (9.4% - 13.5%) (-0.1% - 2.2%) (0.8% - 1.4%)

6.7% 8.5% 0.7% 0.5%

(5.5% - 8%) (7% - 10.1%) (-0.5% - 2%) (0% - 0.9%)

10.7% 13.6% 0.9% 0.3%

(8.8% - 12.6%) (11.1% - 16%) (-0.6% - 2.4%) (-0.2% - 0.8%)

6.1% 7.7% -0.2% 0.5%

(4.9% - 7.2%) (6.3% - 9.1%) (-0.7% - 0.4%) (0% - 0.9%)

9.3% 11.8% 2.1% 1.2%

(7.6% - 11%) (9.6% - 13.9%) (1.2% - 3%) (0.8% - 1.5%)

10.5% 13.2% 2.1% 1.3%

(8.6% - 12.3%) (10.8% - 15.6%) (0.5% - 3.6%) (0.9% - 1.6%)

6.7% 8.5% 1.3% 0.5%

(5.5% - 7.9%) (6.9% - 10.1%) (-0.1% - 2.7%) (0% - 1%)

9.3% 11.8% 1.1% 1.1%

(7.6% - 11%) (9.6% - 13.9%) (-0.2% - 2.3%) (0.8% - 1.4%)

2.9% 3.7% 0.8% 0.4%

(2.4% - 3.4%) (3% - 4.4%) (-0.2% - 1.7%) (0% - 0.7%)

11.2% 14.2% 1.9% 1.3%

(9.2% - 13.2%) (11.6% - 16.7%) (0.4% - 3.3%) (0.9% - 1.6%)

3.7% 4.7% 0.7% 0.5%

(3% - 4.4%) (3.8% - 5.6%) (-0.4% - 1.8%) (-1.3% - 2.3%)

Risk Assessment 

Location

Percent of Incidence of Ischemic Heart 

Disease Mortality Associated with Long-term 

Exposure to PM2.53

Percent of Incidence 

of Cardiovascular 

Mortality Associated 

with Short-term 

Exposure to PM2.54

Percent of Incidence 

of Cardiovascular 

Hospital Admissions 

Associated with Short

term Exposure to 

PM2.55

 Atlanta, GA

 Baltimore, MD

 Birmingham, AL

 Dallas, TX

 Detroit, MI

 Fresno, CA

 Houston, TX

 Los Angeles, CA

 New York, NY

 Philadelphia, PA

 Phoenix, AZ

 Pittsburgh, PA

 Salt Lake City, UT

 St. Louis, MO

 Tacoma, WA

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3.

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty 

surrounding the PM coefficient.

3Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 1983 and from 1999-2000 respectively

4Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been 

"shrunken" towards the appropriate regional means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. 

Zanobetti via email. 

5Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et 

al. (2008). Location-specific C-R function estimates were not available from this study.
4-8

Figure 4-1 Percent reduction in long-term exposure-related mortality risk (alternative standards and recent conditions relativ

(Note: inset shows PM2.5 related incidence and percent of total incidence for IHD mortality under the current suite of 

Detroit, MI 

 New York, NY

Philadelphia, PA Phoenix, AZ 

S

-50%-30%-10%

10%30%50%70%90%

2007 air

quality

15/35** 14/35 13/35 12/35 13/30 

Recent Air Quality, Current Standard and Alternative Standards

Long-Term Exposure-Related Risk: 

Percent Reduction from Current Standard

 Atlanta, GA 277 (227 - 324); 16.7% (13.7% - 19.5%) Baltimore, MD 374 (307 - 440); 14.7% (12.1% - 17.3%) Birmingham, AL 165 (135 - 194); 13.8% (11.3% - 16.2%) Dallas, TX 247 (202 - 291); 11.4% (9.3% - 13.4%) Detroit, MI 478 (390 - 563); 11.5% (9.4% - 13.5%) Fresno, CA 98 (80 - 116); 8.5% (7% - 10.1%) Houston, TX 434 (355 - 511); 13.6% (11.1% - 16%) Los Angeles, CA 1094 (890 - 1296); 7.7% (6.3% - 9.1%) New York, NY 2222 (1814 - 2620); 11.8% (9.6% - 13.9%) Philadelphia, PA 330 (270 - 389); 13.2% (10.8% - 15.6%) Phoenix, AZ 402 (327 - 476); 8.5% (6.9% - 10.1%) Pittsburgh, PA 324 (264 - 382); 11.8% (9.6% - 13.9%) Salt Lake City, UT 19 (16 - 23); 3.7% (3% - 4.4%) St. Louis, MO 563 (461 - 662); 14.2% (11.6% - 16.7%) Tacoma, WA 49 (40 - 58); 4.7% (3.8% - 5.6%)

*Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (estimate of percent of total incidence (and 95% CI) under the current standards. **The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3. Combinations of an annual standar

(m) are denoted n/m in this figure.
4-9

Figure 4-2 Percent reduction in long-term exposure-related mortality risk (recent conditions relative to the current standards

PM2.5 related incidence and percent of total incidence for IHD mortality under the current suite of standards) 

Atlanta, GA 

Baltimore, MD 

 Birmingham, AL

Dallas, TX 

 Detroit, MI

 Fresno, CA

Houston, TX 

Los Angeles, CA 

 New York, NY

Philadelphia, PA 

Pittsburgh, PA Salt Lake City, UT 

 St. Louis, MO

Tacoma, WA 

Phoenix, AZ 

-250%-200%-150%-100%

-50%

0%

2007 air quality 15/35**

Recent Air Quality and Current Standard

Long-Term Exposure-Related Risk: 

Percent Reduction from Current Standard Atlanta, GA 277 (227 - 324); 16.7% (13.7% - 19.5%) Baltimore, MD 374 (307 - 440); 14.7% (12.1% - 17.3%) Birmingham, AL 165 (135 - 194); 13.8% (11.3% - 16.2%) Dallas, TX 247 (202 - 291); 11.4% (9.3% - 13.4%) Detroit, MI 478 (390 - 563); 11.5% (9.4% - 13.5%) Fresno, CA 98 (80 - 116); 8.5% (7% - 10.1%) Houston, TX 434 (355 - 511); 13.6% (11.1% - 16%) Los Angeles, CA 1094 (890 - 1296); 7.7% (6.3% - 9.1%) New York, NY 2222 (1814 - 2620); 11.8% (9.6% - 13.9%

 Philadelphia, PA 330 (270 - 389); 13.2% (10.8% - 15.6% Pittsburgh, PA 324 (264 - 382); 11.8% (9.6% - 13.9%) Salt Lake City, UT 19 (16 - 23); 3.7% (3% - 4.4%) St. Louis, MO 563 (461 - 662); 14.2% (11.6% - 16.7%) Tacoma, WA 49 (40 - 58); 4.7% (3.8% - 5.6%) Phoenix, AZ 402 (327 - 476); 8.5% (6.9% - 10.1%)

*Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (

estimate of percent of total incidence (and 95% CI) under the current standards. **The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3. Combinations of an annual standar

(m) are denoted n/m in this figure. 
4-10

Figure 4-3 Percent reduction in long-term exposure-related mortality risk (alternative standards relative to the current stand

PM2.5 related incidence and percent of total incidence for IHD mortality under the current suite of standards)

 Detroit, MI

 New York, NY

 Philadelphia, PA

 Phoenix, AZ

 Salt Lake City, UT

Tacoma, W

0%

20%40%60%

15/35** 14/35 13/35 12/35 13/30 

Current and Alternative Standards

Long-Term Exposure-Related Risk: 

Percent Reduction from Current Standard

 Atlanta, GA 277 (227 - 324); 16.7% (13.7% - 19.5%) Baltimore, MD 374 (307 - 440); 14.7% (12.1% - 17.3%) Birmingham, AL 165 (135 - 194); 13.8% (11.3% - 16.2%) Dallas, TX 247 (202 - 291); 11.4% (9.3% - 13.4%) Detroit, MI 478 (390 - 563); 11.5% (9.4% - 13.5%) Fresno, CA 98 (80 - 116); 8.5% (7% - 10.1%) Houston, TX 434 (355 - 511); 13.6% (11.1% - 16%) Los Angeles, CA 1094 (890 - 1296); 7.7% (6.3% - 9.1%) New York, NY 2222 (1814 - 2620); 11.8% (9.6% - 13.9%) Philadelphia, PA 330 (270 - 389); 13.2% (10.8% - 15.6%) Phoenix, AZ 402 (327 - 476); 8.5% (6.9% - 10.1%) Pittsburgh, PA 324 (264 - 382); 11.8% (9.6% - 13.9%) Salt Lake City, UT 19 (16 - 23); 3.7% (3% - 4.4%) St. Louis, MO 563 (461 - 662); 14.2% (11.6% - 16.7%) Tacoma, WA 49 (40 - 58); 4.7% (3.8% - 5.6%)

*Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (estimate of percent of total incidence (and 95% CI) under the current standards. **The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3. Combinations of an annual standar

(m) are denoted n/m in this figure. ***The percent reductions for Salt Lake City and Tacoma at the 12/25 standard are 100% and 93%, respectively. 
4-11

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

current standards) (Note: inset shows PM2.5 related incidence and percent of total incidence for CV under the current 

 Atlanta, GA 

 Baltimore, MD

 Birmingham,

 AL

D

 Detroit, MI 

Fresno, CA 

 Houston, TX 

Los Angeles, 

CA 

 New York,

 NY

 Philadelphia,

PA

 Phoenix, AZ 

 Pittsburgh,

 PA

 Salt Lake City, UT 

 St. Louis, MO 

 Tacoma,

 WA

-50%-40%-30%-20%-10%

0%

10%20%30%

2007 air

quality

15/35** 14/35 13/35 12/35 13/30 1

Recent Air Quality, Current Standard and Alternative Standards

Short-Term Exposure-Related Risk: 

Percent Reduction from Current Standard Atlanta, GA 32 (-33 - 95); 0.8% (-0.8% - 2.4%) Baltimore, MD 62 (-4 - 126); 1.6% (-0.1% - 3.2%

 Birmingham, AL -1 (-42 - 40); 0% (-1.5% - 1.5%) Dallas, TX 29 (-19 - 76); 0.8% (-0.5% - 2.2%) Detroit, MI 60 (-8 - 127); 1% (-0.1% - 2.2%) Fresno, CA 12 (-9 - 33); 0.7% (-0.5% - 2%) Houston, TX 46 (-31 - 122); 0.9% (-0.6% - 2.4% Los Angeles, CA -30 (-132 - 72); -0.2% (-0.7% -  New York, NY 473 (276 - 668); 2.1% (1.2% - 3% Philadelphia, PA 84 (22 - 145); 2.1% (0.5% - 3.6 Phoenix, AZ 84 (-4 - 170); 1.3% (-0.1% - 2.7%) Pittsburgh, PA 43 (-9 - 93); 1.1% (-0.2% - 2.3%) Salt Lake City, UT 9 (-2 - 20); 0.8% (-0.2% - 1.7% St. Louis, MO 106 (24 - 187); 1.9% (0.4% - 3.3% Tacoma, WA 11 (-6 - 27); 0.7% (-0.4% - 1.8%)

`

*Based on Zanobetti and Schwartz (2009). The legend contains, for each urban area, the incidence estimate (and 95% CI) and the estim

incidence (and 95% CI) under the current standards. **The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3. Combinations of an annual standar

(m) are denoted n/m in this figure. *** The percent reductions from 2007 air quality to the current standard for Salt Lake City and Fresno are -58% and -81%, respectively
4-12

Figure 4-5 Percent reduction in short-term exposure-related mortality and morbidity risk (recent conditions relative to the 

inset shows PM2.5 related incidence and percent of total incidence for CV under the current suite of standards)

Atlanta, GA 

 Baltimore, MD

 Birmingham, AL

Fresno, CA 

Houston, TX 

Los Angeles, 

CA 

 New York,

 NY

 Philadelphia,PA

Phoenix, AZ 

 Pittsburgh, PA

Salt Lake City, UT 

St. Louis, MO 

Dallas, TX 

Tacoma, WA Detroit, MI 

-90%-80%-70%-60%-50%-40%-30%-20%-10%

0%

2007 air quality 15/35**

Recent Air Quality and Current Standard

Short-Term Exposure-Related Risk: 

Percent Reduction from Current Standard Atlanta, GA 32 (-33 - 95); 0.8% (-0.8% - 2.4

 Baltimore, MD 62 (-4 - 126); 1.6% (-0.1% -  Birmingham, AL -1 (-42 - 40); 0% (-1.5% -  Fresno, CA 12 (-9 - 33); 0.7% (-0.5% - 2% Houston, TX 46 (-31 - 122); 0.9% (-0.6% -  Los Angeles, CA -30 (-132 - 72); -0.2% (-0 New York, NY 473 (276 - 668); 2.1% (1.2%  Philadelphia, PA 84 (22 - 145); 2.1% (0.5%  Phoenix, AZ 84 (-4 - 170); 1.3% (-0.1% - 2 Pittsburgh, PA 43 (-9 - 93); 1.1% (-0.2% - 2 Salt Lake City, UT 9 (-2 - 20); 0.8% (-0.2%  St. Louis, MO 106 (24 - 187); 1.9% (0.4% -  Dallas, TX 29 (-19 - 76); 0.8% (-0.5% - 2.2 Detroit, MI 60 (-8 - 127); 1% (-0.1% - 2.2% Tacoma, WA 11 (-6 - 27); 0.7% (-0.4% - 1.

*Based on Zanobetti and Schwartz (2009). The legend contains, for each urban area, the incidence estimate (and 95% CI) and the estimincidence (and 95% CI) under the current standards. **The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3. Combinations of an annual standar

(m) are denoted n/m in this figure.
4-13

Figure 4-6 Percent reduction in short-term exposure-related mortality and morbidity risk (alternative standards relative to 

(Note: inset shows PM2.5 related incidence and percent of total incidence for CV under the current suite of standards)

 Baltimore,

 MD

 Birmingham, AL

 Detroit, MI 

 Fresno, CA 

 Houston, TX 

 New York,

 NY

 Philadelphia,

PA

 Pittsburgh, PA Phoenix, AZ 

Salt Lake City, UT 

St. Louis, MO 

 Tacoma,

 WA

0%5%

10%15%20%25%30%35%

15/35** 14/35 13/35 12/35 13/30 

Recent Air Quality, Current Standard and Alternative Standards

Short-Term Exposure-Related Risk: 

Percent Reduction from Current Standard

 Atlanta, GA 32 (-33 - 95); 0.8% (-0.8% - 2.4%) Baltimore, MD 62 (-4 - 126); 1.6% (-0.1% - 3.2%) Birmingham, AL -1 (-42 - 40); 0% (-1.5% - 1.5%) Dallas, TX 29 (-19 - 76); 0.8% (-0.5% - 2.2%) Detroit, MI 60 (-8 - 127); 1% (-0.1% - 2.2%) Fresno, CA 12 (-9 - 33); 0.7% (-0.5% - 2%) Houston, TX 46 (-31 - 122); 0.9% (-0.6% - 2.4%) Los Angeles, CA -30 (-132 - 72); -0.2% (-0.7% - 0.4%) New York, NY 473 (276 - 668); 2.1% (1.2% - 3%) Philadelphia, PA 84 (22 - 145); 2.1% (0.5% - 3.6%) Phoenix, AZ 84 (-4 - 170); 1.3% (-0.1% - 2.7%) Pittsburgh, PA 43 (-9 - 93); 1.1% (-0.2% - 2.3%) Salt Lake City, UT 9 (-2 - 20); 0.8% (-0.2% - 1.7%) St. Louis, MO 106 (24 - 187); 1.9% (0.4% - 3.3%) Tacoma, WA 11 (-6 - 27); 0.7% (-0.4% - 1.8%)

`

*Based on Zanobetti and Schwartz (2009). The legend contains, for each urban area, the incidence estimate (and 95% CI) and the estimincidence (and 95% CI) under the current standards. **The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3. Combinations of an annual standar

(m) are denoted n/m in this figure.
4-14

As noted above, the risk assessment includes risk estimates for a range of short-term and 

long-term exposure-related health effect endpoints. To focus the discussion of these risk 

estimates, we have selected a subset of the health endpoints as examples to help illustrate 

patterns in the risk estimates that might be of interest from a policy standpoint. Specifically, we 

have focused on those endpoints that the ISA identifies as having the greatest support in the 

literature (i.e., endpoints related to cardiovascular effects, including both mortality and 

morbidity). The subset of health effect endpoints selected as illustrative examples for this 

overview include: IHD-related mortality (for long-term exposure) and CV-related mortality and 

HA (for short-term exposure). While the discussion does focus on these cardiovascular-related 

endpoints, we do address other endpoints modeled in the risk assessment to a limited extent. The 

full set of risk estimates generated is presented in the detailed tables in Appendix E. 

For a subset of the urban case studies (e.g., Dallas and Phoenix), incremental reductions 

across alternative standards are initially very low (or even zero) reflecting the fact that recent 

ambient PM2.5 concentrations for these study areas are well below the current annual standard 

levels. For these study areas, meaningful reductions in risk may not be seen until relatively 

lower alternative standards are assessed (and results in the percent reduction from the current set 

of standards tables and figures may be zero for several of the less stringent, alternative sets of 

standards). The pattern of risk reductions across alternative standard levels for a given urban 

study area is an important factor that is discussed in the integrative discussion in Chapter 6. To 

set up that later discussion, in summarizing risk estimates below, we provide observations 

regarding trends in risk estimates across alternative suites of standards (for a given urban study 

area). 

For a number of the urban study areas, confidence intervals (and in some instances, point 

estimates) for short-term mortality and morbidity incidence and related risk metrics include 

values that fall below zero. Population incidence estimates with negative lower-confidence 

bounds (or point estimates) do not imply that additional exposure to PM2.5 has a beneficial effect, 

but only that the estimated PM2.5 effect estimate in the C-R function was not statistically 

significantly different from zero. In the case of short-term exposure mortality, where study areaspecific effects estimates were used (see section 3.4), several of the urban locations have nonstatistically significant effects estimates; these result in incidence estimates with non-positive 

lower bounds and/or best estimates (e.g., Birmingham, Detroit, and Los Angeles for nonaccidental mortality). In the case of short-term morbidity (e.g., HAs), where regional effects 

estimates were used, one of the regional coefficients (for the southeast) is not statistically 

significant, producing incidence estimates including negative values in the confidence interval 

for urban study areas falling within that region (e.g., Atlanta, Dallas, and Houston, for CVrelated HAs). Lack of statistical significance could mean that there is no relationship between 
4-15

PM2.5 and the health endpoint or it could mean that there was not sufficient statistical power to 

detect a relationship that actually exists. In the case of PM2.5 and both short-term exposure 

mortality and morbidity, recognizing that the ISA has concluded that there is either a causal or 

likely causal relationship between short-term PM2.5 exposure and these health effects (see section 

3.3.1), we believe it is reasonable to assume that instances where effects estimates are notstatistically significant are likely to reflect insufficient sample size, rather than the absence of an 

actual association. We note, however, that (as discussed in section 3.3.3, 3.5.2 and 3.5.3) many 

factors can potentially result in variations in the magnitude of effect estimates. In addition to 

sample size, these include: source and compositional differences for PM2.5, exposure error 

associated with the use of ambient monitors as a surrogate for actual exposure, and differences in 

population susceptibility and vulnerability. 

An important theme in discussing risk associated with both current and alternative 

standard levels is the linkage between the nature and magnitude of risk reductions seen for a 

particular study area (for a particular suite of 24-hour and annual standards) and the specific mix 

of 24-hour and annual design values associated with that study area. Because design values 

determine the degree to which the PM2.5 monitors in a study area are adjusted in simulating 

attainment of both current and alternative standard levels, they play a central role in determining 

the degree of risk reduction associated with a particular suite of standard levels. Given the 

importance of design values in determining risk reduction under both current and alternative 

standard levels, we have examined patterns in design values (specifically the relationship 

between 24-hour and annual design values) across the 15 urban study areas, as a means for 

enhancing our interpretation of patterns in risk reductions for the standard levels modeled. In 

addition, we have contrasted the patterns of design values for the 15 urban study areas with 

patterns of design values for the broader set of urban areas in the U.S.; this supporting efforts to 

place risk estimates for the urban study areas in a broader national context. This exploration of 

design values is discussed in section 4.5. 

An additional factor to consider in examining patterns in risk estimates is the overall 

spread in PM2.5 measurements across monitors at a particular urban study area, including 

distributions of both 24-hour and annual averages. This factor works in concert with the patterns 

in design values mentioned earlier in determining the degree of risk reduction associated with a 

particular suite of standard levels. In addition, the spread in monitor values for a particular urban 

study area can also determine the degree to which alternative rollback methods (proportional, 

hybrid and locally focused) produce differences in risk estimates for a given study area. 

Consequently, in concert with examining patterns in design values (see above) we have also 

explored patterns in PM2.5 monitoring data for the 15 urban study areas in an effort to better 
4-16

understand how application of different rollback methods results in differing impacts on core risk 

estimates. This topic is discussed in section 4.5. 

The remainder of this section is organized as follows. Core modeling results for the 

recent conditions air quality scenario are presented in section 4.1. Core modeling results for just 

meeting the current NAAQS and just meeting alternative NAAQS are presented in section 4.2. 

The results of the sensitivity analysis (including single-factor and multi-factor results) are 

presented in section 4.3. The results of a representativeness analysis involving comparison of 

counties associated with the 15 urban study area locations against the national distribution of 

counties with regard to a set of PM-risk related attributes are presented in section 4.4. Section 

4.5 discusses the consideration of design values in interpreting risk estimates generated for the 

15 urban study areas and helping to place them in a broader national context (section 4.5.1), as 

well as consideration of the patterns in ambient PM2.5 data within study areas as a factor 

influencing patterns of risk estimates (section 4.5.2). Chapter 5 provides an integrative 

discussion of the results of the core risk assessment for the 15 urban study areas informed by 

consideration of: (a) the single- and multi-factors sensitivity analysis, (b) the qualitative analysis 

of sources of variability and uncertainty, (c) the representativeness analyses, and (d) the role of 

design values (and patterns in ambient PM2.5 monitoring data) in influencing overall patterns of 

risk estimates across alternative suites of standards. 

4.1 ASSESSMENT OF HEALTH RISK ASSOCIATED WITH RECENT CONDITIONS 

(CORE ANALYSIS) 

This section discusses core risk estimates generated for the recent conditions air quality 

scenario, focusing on the 2007 simulation year. Specifically, it provides a set of key observations 

regarding core risk estimates generated for the recent conditions air quality scenario. Note, that 

while the focus of this section is on identifying key risk-related observations potentially relevant 

to the current review of the PM NAAQS, additional review of the risk estimates provided in 

Appendix E is likely to result in additional observations that might be relevant to the PM 

NAQQS review (EPA staff will continue to review those results as they work on completing the 

summary of the RA presented in the PA). 

 In discussing results for the recent conditions air quality scenario, we have focused on 

absolute risk (either above PRB or LML, depending on the health effect endpoint). This reflects 

the fact that this air quality scenario represents recent conditions within the urban study areas and 

therefore, does not lend itself to an incremental assessment. The section is organized by health 

endpoint category, with results discussed in the following order: long-term exposure mortality, 
4-17

short-term exposure mortality and short-term exposure morbidity.60 In summarizing estimates 

for each endpoint category, we first focus on the central-tendency risk estimates (these are what 

is discussed in each of the bullets focusing on a particular endpoint category). A discussion of 

the broader risk range reflecting consideration of 95th% confidence interval risk estimates is 

presented as a separate bullet towards the end of the discussion. Key observations include: 

 Long-term exposure-related mortality: Total incidence of PM2.5-related all-cause mortality 

ranges from 50-60 (Salt Lake City) to 2,380-3,000 (New York) (Appendix E, Table E-21 and 

E-30), with this range reflecting not only differences in baseline incidence across urban study 

areas, but also the size of study populations which vary considerably across the study areas. 

The percent of total incidence of IHD-related mortality attributable to PM2.5 ranges from 6.3-

8.0% (Tacoma) to 17.7-22.2% (Fresno) (Appendix E, Table E-24 and E-33). Total PM2.5-

attriutable incidence for all-cause mortality and cardiopulmonary mortality is larger than IHD 

(for a given study area under recent conditions), while total PM2.5-attributable incidence for 

lung-cancer mortality is lower than for IHD. However, the percent of total incidence 

attributable to PM2.5 exposure is larger for IHD-related mortality than for any of the other 

mortality categories modeled (Appendix E, Tables E-24 and E-33). 

 Short-term exposure-related mortality: Total incidence of PM2.5-related mortality for 

short-term exposure (for all categories modeled) is substantially smaller than estimates for 

long-term exposure-related mortality. Estimates for CV mortality for short-term exposure 

ranges from 14 (Salt Lake City) to 570 (New York) (Appendix E, Table E-84). The percent 

of total non-accidental mortality attributable to PM2.5 ranges from 0.9% (Tacoma) to 2.5% 

(New York). (Appendix E, Table E-87). Percent of total incidence attributable to PM2.5

exposure is generally lower for total non-accidental mortality (compared with CV), ranging 

from 0.2% (Los Angeles) to 1.8% (Baltimore) (Appendix E, Table E-78). Estimates for 

respiratory mortality are usually higher than for CV mortality, ranging from 0.9% (Dallas) to 

2.8% (Fresno and New York) (Appendix E, Table E-96). Of the 15 urban study areas 

modeled for CV mortality, 12 locations had negative lower bound estimates of incidence 

(and two of these head negative point estimates), reflecting use of non-statistically significant 

effects estimates (see section 4.0 for additional discussion). The number of study areas 

modeled with non-statistically significant effects estimates was lower for the other two shortterm exposure-related mortality endpoints. 

 Short-term exposure-related morbidity (hospital admissions for respiratory and 

cardiovascular illness): Total incidence of PM2.5-related cardiovascular HA range from 15 

(Salt Lake City) to 910 (New York City) and are significantly larger than estimates of 

respiratory HA attributable to PM2.5 exposure (Appendix E, Tables E102 and E-111). 

Similarly, the percent of total cardiovascular HA attributable to PM2.5 is larger than estimates 

for respiratory HA and ranges from 0.28% (Dallas) to 1.6% (Pittsburgh) (Appendix E, Table 

 

60 Note, that as discussed earlier, for long-term exposure-related mortality, two risk estimates are provided 

for each urban study area, reflecting application of the two C-R functions used in modeling each mortality endpoint 

in the core analysis - i.e., C-R function derived using 1979-1983 PM2.5 monitoring data and the C-R function derived 

using 1999-2000 data, with the latter function having the larger effect estimates and therefore, producing higher risk 

estimates. 
4-18

E-105). In this case, the pattern of risk across urban study areas reflects both differences in 

underlying baseline incidence for these endpoints as well as the use of regionallydifferentiated effect estimates obtained from Bell et al., 2008 (see Appendix C, Table C-1). 

Of the 15 urban study areas modeled for cardiovascular-related HAs, five locations had 

negative lower bound estimates of incidence, reflecting use of non-statistically significant 

effects estimates (see section 4.0 for additional discussion). 

 Patterns of recent conditions risk across the three simulation years: A comparison of 

IHD mortality incidence estimates (based on the C-R function derived using 1979-1982 

monitoring data) across the three years (see Appendix E, Tables E-22 through E-24) shows 

that, while 2007 does produce incidence estimates that fall between those estimated for 2005 

and 2006 for some urban areas (e.g., Tacoma, St. Louis, LA), results for 2007 can be the 

highest of the three years (e.g., Fresno) or the lowest (e.g., Baltimore) for some locations. 

Generally, results for the same urban study area across the three years are fairly similar 

(results for Birmingham vary by less than 7% across the years), although they can vary by as 

much as 30% or more in some locations (see results for Tacoma in 2005 and 2006). All of 

this temporal variation results from year-to-year variation in the annual average PM2.5 levels 

for the study areas (see Appendix A). This is because other candidate input parameters, 

which could also involve temporal variability (e.g., demographics and baseline incidence 

rates) were not modeled with year-specific values, but rather using one representative year 

(see section 3.4.1.3 and 3.4.2 for demographics and baseline health effects incidence rates, 

respectively). In terms of short-term exposure-related morbidity and mortality endpoints, the 

pattern is similar to that described above for long-term mortality, with risk estimates for 2007 

generally falling between those generated for 2005 and 2006 (in terms of magnitude), 

although the magnitude of variations across the three simulation years for a given health 

endpoint/case study combination was notably lower for the short-term exposure-related 

endpoints than for the long-term endpoints. For example, with CV mortality, one of the 

urban study areas with the greatest variation across the three years (New York) had a 15% 

difference in PM2.5 –related risk across the three years (see Appendix E, Tables E-82 through 

E-84). This compares with a spread of 30% for some of the urban study areas modeled for 

long-term exposure-related IHD mortality – see above. As with the long-term mortality risk 

metrics, all of this temporal variation results from year-to-year variation in the daily PM2.5

levels for the study areas (see Appendix A), given that other candidate input parameters, 

which could have temporal variability (e.g., demographics and baseline incidence rates) were 

not modeled with year-specific values, but rather using one representative year. 

 Consideration of the 95th percentile confidence interval risk estimates in assessing 

uncertainty related to the statistic fit of effect estimates: As noted above, all of the risk 

metrics generated for this analysis include 95th percentiles, reflecting uncertainty in the 

statistical fit of the underlying effect estimates in the C-R functions. These results suggest 

that this source of uncertainty can be notable. In the case of recent conditions risk estimates, 

for long-term mortality, while the central tendency risk estimate for all-cause (long term 

exposure-related) mortality incidence in New York range from 2,380-3,000, the 95th

percentile confidence interval for this estimates is 1,960 to 3,500 (Appendix E, Table E-21 

and E-30). In this case, this source of uncertainty results in estimates that are ~18% lower to 

~17% higher than the central tendency estimate range. Using the criteria we applied in 

assessing the results of the sensitivity analysis, these would translate as having a “small” 
4-19

impact on the core risk estimate (see Section 4.3.1). The impact of statistical fit uncertainty 

on the IHD-related long-term exposure-related mortality results (see Appendix E, Tables E24 and E-33) are similar in magnitude to those seen for all-cause mortality and also results in 

a classification of this uncertainty having a “small” impact on core risk estimates. For shortterm exposure-related mortality and morbidity, the impact of statistical fit (as reflected in the 

95th percentile CI risk estimate ranges) is greater than for long-term mortality. For example 

with CV-related mortality, the central tendency estimate for New York is 570 cases, while 

the 95th percentile CI is 332 to 902 (i.e., ~40% lower and ~40% higher than the core centraltendency estimates). This translates into a “moderate” impact by this source of uncertainty 

on core risk estimates using the classification scheme developed for the sensitivity analysis. 

This suggests that uncertainty related to the statistical fig of effect estimates used in risk 

characterization has twice as great an impact on short-term mortality as long-term mortality 

risk estimates. 

4.2 ASSESSMENT OF HEALTH RISK ASSOCIATED WITH JUST MEETING THE 

CURRENT AND ALTERNATIVE SUITES OF STANDARDS (CORE ANALYSIS) 

This section discusses core risk estimates generated for just meeting the current suite of 

standards and alternative suites of standards, focusing on the 2007 simulation year (although 

general trends in observations across the three simulated years are discussed to a limited extent). 

In discussing risk estimates for the current and alternative suites of standards, we include 

discussion of risk metrics which characterize both incremental reductions in risk (across standard 

levels) as well as absolute risk for a particular standard level. In presenting these two categories 

of risk metric, we recognize that there is greater uncertainty in estimates of absolute risk relative 

to estimates of incremental risk. This reflects the fact that we have greater confidence in the 

ability of the risk models to differentiate risk between sets of standards, since this requires the 

models to estimate risk for ambient air PM2.5 levels likely near or within the range of ambient air 

quality data used in the underlying epidemiology studies. By contrast, estimates of absolute risk 

(for a given air quality scenario) require the models to perform at the lower boundary of ambient 

air PM2.5 levels reflected in the studies (i.e., down to the LML reflected in the long-term 

exposure mortality epidemiology studies or down to PRB levels in the short-term exposure 

morbidity and mortality studies). There is greater overall uncertainty in risk estimates generated 

based on the contribution to risk of exposures at these lower ambient air PM2.5 levels. While 

there is greater uncertainty associated with estimates of absolute risk, these estimates are of 

potential use in informing consideration of the magnitude of risk (and therefore public health 

impact) for a particular standard level. The overall level of confidence associated with different 

risk metrics (and implications for informing their use in the context of the PM NAAQS review) 

is discussed in Chapter 5. 

This section discusses risk estimates generated for the current standard levels first, 

followed by discussion of risk estimates associated the set of alternative standard levels assessed. 
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Each of these discussions is further organized by health endpoint category, with results discussed 

in the following order: long-term exposure mortality, short-term exposure mortality and shortterm exposure morbidity. Observations presented in the previous section regarding the statistical 

significance of effect estimates used in generating risk estimates and their implications for 

interpretation of those risk estimates also hold for estimates presented in this section. 

Consequently, observations regarding risk results with confidence intervals including negative 

estimates are not presented here and the reader is referred back to the earlier discussion in section 

4.1. 

We note that the lower magnitude of risk reductions (in terms of percent change in PM2.5-

attributable risk) generally seen for short-term exposure-related endpoints relative to long-term 

exposure-related endpoints primarily reflects the fact that PM2.5-attributable risk is modeled 

down to PRB for short-term, but only down to LML for long-term. This means that an 

incremental change (reduction) in long-term risk will be a larger fraction of overall risk 

compared with short-term risk and hence, the magnitude of risk reductions for long-term 

exposure-related risk is notably larger compared with short-term risk 

An important factor to consider in interpreting the risk estimates for both the current set 

of standards and sets of alternative standards is whether the annual or 24-hour standard for a 

given pairing of standards is controlling for a particular area.61 This factor can have a significant 

impact on the pattern of risk reductions predicted for a given location under the simulation of just 

meeting a specific set of standards. In addition, the approach used to simulate ambient PM2.5

levels under current and alternative standard levels (i.e., use of proportional, hybrid, or locally 

focused) can significantly impact the magnitude risk reduction seen across standard levels 

(particularly the degree to which a particular standard produces notable reductions in long-term 

exposure-related mortality).62 The potential for different rollback strategies (reflecting 

potentially different combinations of local and/or regional controls) to impact patterns of risk 

reduction is not discussed here, but rather reserved for discussion as part of the sensitivity 

analysis (section 4.3) and the integrative chapter (chapter 5). 

 

61 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. 62 Approaches such as hybrid rollback or locally focused which simulate more localized control strategies 

have the potential to reduce PM2.5 levels at monitors exceeding the daily standard, while leaving other monitors 

(which may have elevated annual average PM2.5 levels) relatively or totally unadjusted. This can result in the 24-

hour standard not providing coverage for the annual standard, even when the 24-hour standard is controlling (i.e., 

additional reduction focused on monitors with high annual design values may be required to attain the annual) - see 

discussion in Section 4.3 and Chapter 6. 
4-21

An overview of which urban study areas are predicted to have risk reductions under the 

current and alternative suites of standards included in the risk assessment is presented below 

(Appendix E contains tables presenting the full set of detailed core risk estimates generated for 

the current and alternative suites of standards). 

4.2.1 Core Risk Estimates for Just Meeting the Current Suite of Standards 

This section summarizes risk estimates generated for the 15 urban study areas based on 

simulating just meeting the current suite of standards (including the magnitude of risk reductions 

relative to recent conditions, where applicable). 

 Long-term exposure-related mortality: Total incidence of PM2.5-related IHD mortality 

ranges from 15-20 (Salt Lake City) to 1,760-2,220 (New York) (Table 4-1). The percent of 

total incidence of IHD mortality attributable to PM2.5 ranges from 3.7-4.7% (Tacoma) to 

13.2-16.7% (Atlanta) (Table 4-2). These levels of IHD mortality risk attributable to PM2.5

exposure reflect reductions in risk relative to recent condition ranging from 8.7% (Houston) 

to 68.6% (Salt Lake City). Two of the urban study areas (Dallas and Phoenix) do not exhibit 

reductions in risk in simulating just meeting the current suite of standards since these two 

locations meet the current suite of standards based on recent air quality data. As referenced 

above for the recent conditions scenario, total PM2.5-attriutable incidence for all-cause 

mortality and cardiopulmonary mortality is larger than IHD (for a given study area under 

recent conditions), while total PM2.5-attributable incidence for lung-cancer mortality is lower 

than for IHD. However, the percent of total incidence attributable to PM2.5 exposure is larger 

for IHD-related mortality than for any of the other mortality categories modeled (Appendix 

E, Tables E-24 and E-33). 

 Short-term exposure-related mortality: As with the recent conditions analysis, total 

incidence of PM2.5-related mortality for short-term exposure is substantially smaller than 

estimates for long-term exposure-related mortality. Estimates for CV mortality for shortterm exposure ranges from 9 (Salt Lake City) to 470 (New York) (Table 4-1). The percent of 

CV mortality attributable to PM2.5 ranges from 0.7% (Fresno) to 2.1% (Philadelphia and New 

York). (Table 4-2). The level of risk reduction (comparing risk under the current standard 

with risk under recent conditions) is generally lower for short-term exposure-related CV 

mortality compared with long-term exposure-related all-cause mortality and ranges from 

5.5% (Baltimore) to 36.9% (Los Angeles). As mentioned for long-term exposure-related 

risk, both Phoenix and Dallas did not exhibit any risk reduction since these two locations 

meet the current suite of standards based on recent air quality data. Percent of total incidence

attributable to PM2.5 exposure is generally lower for total non-accidental mortality (compared 

with CV), ranging from 0.1% (Los Angeles) to 1.7% (Baltimore) (Appendix E, Table E-78). 

Estimates for respiratory mortality are usually higher than for CV, ranging from 0.9% 

(Dallas) to 2.6% (Baltimore) (Appendix E, Table E-96). As noted above, of the 15 urban 

study areas modeled for CV mortality, 12 locations had negative lower bound estimates of 

incidence (and two of these had negative point estimates), reflecting use of non-statistically 

significant effects estimates (see section 4.0 for additional discussion). 

 Short-term exposure-related morbidity (hospital admissions for respiratory and 

cardiovascular illness): Total incidence of PM2.5-related cardiovascular HA range from 9 
4-22

(Salt Lake City) to 750 (New York City) and are significantly larger than estimates of 

respiratory HA attributable to PM2.5 exposure (Appendix E, Tables E102 and E-111). 

Similarly, the percent of total cardiovascular HA attributable to PM2.5 is larger than estimates 

for respiratory HA and ranges from 0.28% (Dallas) to 1.33% (Baltimore). As noted above, 

the pattern of risk across urban study areas reflects both differences in underlying baseline 

incidence for these endpoints as well as the use of regionally-differentiated effect estimates 

obtained from Bell et al., 2008 (see Appendix C, Table C-1). The level of risk reduction 

(comparing risk under the current standard with risk under recent conditions) for both 

respiratory and cardiovascular hospital admissions ranges from 5.5% (Baltimore) to 44.8% 

(Fresno), again with Phoenix and Dallas not exhibiting any risk reduction since these two 

locations meet the current suite of standards based on recent air quality data. As noted above, 

of the 15 urban study areas modeled for cardiovascular-related HAs, five locations had 

negative lower bound estimates of incidence, reflecting use of non-statistically significant 

effects estimates (see section 4.0 for additional discussion). 

 Patterns of recent conditions risk across the three simulation years: Observations made 

earlier regarding patterns of risk across the three simulation years for the recent conditions 

simulations generally hold for the current standard level analysis. In other words, (a) 2007 

generally represents risks in between the other two years in terms of magnitude, (b) there are 

exceptions where 2007 had the highest risks and lowest risk (depending on study area and 

endpoint), and (c) generally, long-term exposure-related mortality endpoints showed greater 

cross year variation then the short-term exposure-related endpoints (with the magnitude of 

this variation similar to what is reported above for the recent conditions simulation). 

 Consideration of the 95th percentile confidence interval risk estimates in assessing 

uncertainty related to the statistic fit of effect estimates: Uncertainty related to the 

statistical fit of effect estimates has the same magnitude of effect in modeling risk under the 

current standard as it did under recent conditions (i.e., an impact of about +/-18% on the core 

risk estimates, translating into a “small” impact based on classification used in the sensitivity 

analysis) (see section 4.3.1 for a description of the classification scheme for sources of 

uncertainty and Appendix E, Table E-21 and E-30 for risk estimates used to reach this 

conclusion). The impact of this source of uncertainty on short-term exposure-related CV 

morality was similar (although slightly larger) compared with what was seen with risk 

estimates generated for the recent conditions air quality scenarios (i.e., 48% lower to 42% 

higher than the core risk estimate – see estimates in Appendix E, Table E-84). This results in 

a classification of “moderate” for this source of uncertainty and its impact on short-term 

exposure-related mortality, based on the classification scheme developed for the sensitivity 

analysis. 

4.2.2 Core Risk Estimates for Just Meeting Alternative Suites of Standards 

This section summarizes risk estimates generated for the 15 urban study areas when 

ambient PM2.5 levels under the alternative standard levels are simulated. As noted in section 4.2, 

this discussion focuses on the magnitude of incremental risk reductions for individual standard 

levels relative to the current standard, given that overall confidence in incremental risk metrics is 

considered higher than estimates of absolute risk for a given standard level. Note, however, that 

we do provide limited discussion of absolute risk levels attributable to PM2.5 exposure for 
4-23

alternative standard levels, with the provision that these be interpreted in the context of their 

greater levels of uncertainty. In discussing risk estimates for the alternative standard levels, we 

focus first on patterns of risk reduction across alternative annual levels (i.e., 14/35, 13/35 and 

12/35) and then discuss patterns across a combination of alternative 24 hour and annual 

standards (i.e., 13/30 and 12/25).

As noted in Section 4.1, although reductions in absolute incidence will differ for health 

effect endpoints associated with a particular averaging period across alternative suites of 

standards for a given urban study area, the patterns of reduction in terms of percent change in 

PM2.5-attributable risk are very similar for a given urban study area across health endpoints. This 

reflects the fact that the C-R functions used in the core analysis are close to linear across the 

range of ambient PM2.5 levels considered in this analysis, and consequently the main factor 

producing percent reductions in risk across alternative standards is the reduction in the air quality 

metric for a given study area (i.e., reductions in annual average PM2.5 concentrations or 

reductions in the distribution of 24-hour estimates for a year). Consequently, in discussing 

incremental risk reduction in terms of percent change relative to the current suite of standards, 

we speak more generally in terms of the category of annual average risk or 2-4hour average 

risk, with the assumption that these observations hold for individual health effects endpoints 

assessed for each averaging period. These observations regarding patterns of percent risk 

reduction for the two averaging periods are reflected in Figures 4-1 through 4-6 which are 

referenced in the discussion below. 

Alternative annual standard levels (14/35, 13/35, and 12/35) 63

 Percent reductions in long-term exposure-related mortality: Reductions in all long-term 

exposure-related mortality categories were more limited under the 14/35 alternative standard, 

with only 5 of the 15 urban study areas demonstrating notable reductions ranging from 9% 

(Baltimore) to 12% (Houston and Birmingham) (see Figure 4-3 and Appendix E, Table E-9). 

Reducing the annual standard level to 13 μg/m3

 (i.e., the 13/35 alternative suite of standards) 

produced a notable increase in the number of locations (9 of the 15) with risk reductions 

relative to the current standard ranging from 5% (New York) to 24% (Houston and 

Birmingham). The lowest annual standard evaluated (12 μg/m3 as reflected in the 12/35 

alternative suite of standards) resulted in additional study areas (now 12 of the 15 study 

areas) experiencing risk reductions with percentage risk reductions now ranging from 11% 

 

63 The three alternative annual standards considered in the risk assessment (12, 13 and 14 μg/m3 ) were each 

paired with the current 24-hour standard of 35 μg/m3 for purposes of generating risk estimates. A separate set of 

alternative suites of standards (i.e., 13/30 and 12/25) were also considered – see next section below. In discussing 

risk estimates associated with the alternative annual standards, each alternative annual standard level was paired 

with the current 24-hour standard of 35 μg/m3 in determining which standard level was controlling and, 

consequently, whether the alternative annual standard would produce any notable reductions in risk.
4-24

(Phoenix) to 36% (Houston and Baltimore). Note, that even in the 12/35 case, three of the 

urban study areas (Tacoma, Fresno and Salt Lake City) did not experience any decreases in 

risk, although risk reductions were seen for these three study areas when alternative 24-hour 

standards were considered – see below. The specific pattern of risk reduction (including 

importantly, the magnitude of risk reduction as well as residual risk associated with a 

particular standard level) reflects whether daily or annual standard levels were controlling – 

see discussion below regarding patterns of risk reduction. 

 Percent reduction in short-term exposure-related mortality and morbidity: The pattern 

of reductions in the percent of risk attributable to PM2.5 for mortality and morbidity 

associated with short-term exposure is similar to that described above for long-term mortality 

(see Figures 4-4 through 4-6). Specifically, the same five urban study areas (Atlanta, 

Baltimore, Birmingham, Houston and St. Louis) had notable risk reductions under the full set 

of alternative annual standards, with the degree of risk reduction for PM2.5-related 

cardiovascular mortality for the lowest alternative annual standard level (12/35) compared to 

the current standard level, ranging from 20% (St. Louis) to 23% (Birmingham) (see Figure 4-

4 and 4-6 and Appendix E, Table E-90). A number of the other study areas did not exhibit 

notable risk reductions until the lowest alternative annual standard was considered (i.e., 

Detroit, Los Angeles, New York, Philadelphia, Pittsburgh), with the degree of reduction in 

risk for the lowest alternative suite of standards (12/35) compared with the current standards 

ranging from 5% (Phoenix) to 16% (Detroit) (see Figure 4-4 and 4-6 and Appendix E, Table 

E-90). As with long-term exposure-related mortality, a number of additional study areas 

(Fresno, Salt Lake City, Tacoma) did not exhibit any notable risk reduction under the set of 

alternative annual standards considered and only experienced risk reductions when the 24-

hour standard level was reduced. Because the same air quality metric (annual distributions of 

24-hour PM2.5 concentrations) is used in generating short-term exposure-related mortality 

and morbidity endpoints, patterns of risk reduction are similar for both sets of endpoints (see 

Figures 4-4 through 4-6). Specifically, the same groups of urban study areas experience the 

same magnitude of risk reductions (in terms of percent changes in PM2.5-related risk relative 

to the current standard level) across the alternative standard levels for short-term exposurerelated morbidity (HAs). The specific pattern of risk reduction reflects whether daily or 

annual standard levels are controlling – see discussion below regarding patterns of risk 

reduction. 

 Pattern of risk reduction linked to design values: The patterns of risk reduction across the 

15 urban study areas for the set of alternative annual standard levels considered here depends 

on whether the alternative annual (12, 13 or 14 μg/m3

) or the current 24-hour standard of 35 

μg/m3 is controlling. The approach used to simulate just meeting alternative 24-hour 

standards (i.e., proportional, hybrid, or locally focused) can have an impact on the magnitude 

of risk reduction, although it does not influence whether the annual or 24-hour design value 

was controlling for a given alternative suite of standards (see sensitivity analysis discussion 

in 4.3 and the integrative discussion in Chapter 5). The pattern in risk reduction seen across 

the 15 urban study areas (given the set of alternative annual standards considered) can be 

divided into three categories: (a) all of the alternative annual standard levels are controlling, 

resulting in notable risk reductions for all of the annual standard levels considered 

(Birmingham, Atlanta, Houston), (b) alternative annual standards only control at lower levels 

(i.e., 13/35 and/or 12/35) and consequently notable risk reductions are only seen at the lower 
4-25

or lowest annual standard level(s) considered (Dallas, Los Angeles, New York, Philadelphia, 

Phoenix, Pittsburgh), and (c) none of the alternative annual standard levels is controlling and 

therefore there is no estimated risk reduction for the alternative annual standard levels 

considered (Salt Lake City, Tacoma, Fresno). 

 Absolute levels of PM2.5-attributable risk under alternative annual standards: As

discussed above, we have greater confidence in estimating incremental reductions in risk 

between the current and alternative suites of standards, then the estimation of absolute 

incidence under a given suite of standards. Nonetheless, we provide a summary of that risk 

metric here for long-term and short-term exposure-related mortality and short-term exposurerelated morbidity endpoints: 

o Long-term exposure-related mortality: The four study areas displaying the greatest 

degree of reduction across the alternative annual standards (Atlanta, Baltimore, 

Birmingham and Houston) have PM2.5-related IHD mortality estimates (under the 

lowest alternative annual standard of 12/35) ranging from 85-110 (Birmingham) to 

220-280 (Houston) (see Appendix E, Table E-21 and E-30). The two urban study 

areas with the greatest degree of PM2.5-related risk in absolute terms (Los Angeles 

and New York) do not exhibit significant reductions in risk until the lowest annual 

standard level of 12/35 is considered, with PM2.5-related IHD mortality estimated at 

750-950 and 1,420-1,800, respectively under that alternative standard (see Appendix 

E, Table E-21 and E-30). 

o Short-term exposure-related mortality: The four study areas displaying the greatest 

degree of reduction across the alternative annual standards (Atlanta, Baltimore, 

Birmingham and Houston), have PM2.5-related CV mortality estimates (under the 

lowest alternative standard of 12/35) ranging from 25 (Atlanta) to 50 (Baltimore) (see 

Appendix E, Table E-84). We note that Birmingham has an incidence estimate of -1, 

reflecting application of a non-statistically significant effect estimate in modeling this 

endpoint (see section 4.1). The urban study area with the greatest degree of PM2.5-

related risk in absolute terms (New York) does not exhibit significant reductions in 

risk until the lowest annual standard level of 12/35 is considered with PM2.5-related 

CV mortality estimated at 420 under that alternative standard level (see Appendix E, 

Table E-84). 

o Short-term exposure-related morbidity: The four study areas displaying the greatest 

degree of reduction across the alternative annual standard levels (Atlanta, Baltimore, 

Birmingham and Houston), have PM2.5-related cardiovascular HA (under the lowest 

alternative standard of 12/35) ranging from 12 (Birmingham) to 170 (Baltimore) (see 

Appendix E, Table E-102). The two urban study areas with the greatest degree of 

PM2.5-related risk in absolute terms (Los Angeles and New York) do not exhibit 

significant reductions in risk until the lowest annual standard level of 12/35 is 

considered with PM2.5-related all-cause mortality estimated at 240 and 670, 

respectively under that alternative standard level (see Appendix E, Table E-102). 

 Patterns of recent conditions risk across the three simulation years: Observations made 

above regarding patterns of risk across the three simulation years for the recent conditions 

and current standards simulations generally hold for the alternative standards analysis. In 

other words, (a) 2007 generally represents risks between the other two years in terms of 
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magnitude, (b) there are exceptions where 2007 had the highest risks and lowest risk 

(depending on study area and endpoint), and (c) generally, long-term exposure-related 

mortality endpoints showed greater cross-year variation then the short-term exposure-related 

endpoints in terms of both absolute PM2.5 risk for a particular alternative suite of standards, 

as well as incremental risk reductions relative to the current suite of standards. 

 Consideration of the 95th percentile confidence interval risk estimates in assessing 

uncertainty related to the statistic fit of effect estimates: Continuing the pattern seen with 

the current standard level, uncertainty related to the statistical fit of effect estimates has the 

same magnitude of effect in modeling risk under alternative standards involving reduction of 

the annual level as it did under recent conditions (i.e., an impact of about +/-18% on the core 

risk estimates, translating into a “small” impact based on classification used in the sensitivity 

analysis) (see Appendix E, Table E-21 and E-30 for risk estimates used to reach this 

conclusion). Similarly, the pattern of impact this source of uncertainty on short-term 

exposure-related CV morality continues to be similar compared with what was seen for risk 

estimates generated for the recent conditions air quality scenarios (i.e., 42% lower to 42% 

higher than the core risk estimate – see estimates in Appendix E, Table E-84). This 

continues to result in a classification of “moderate” for this source of uncertainty based on 

the classification scheme developed for the sensitivity analysis. 

Combinations of alternative 24-hour and annual standard levels (13/30, 12/25)

 Percent reductions in long-term exposure-related mortality: The combination of suites 

of alternative 2-hour and annual standards produced notable reductions in long-term 

exposure-related mortality for 14 of the 15 urban study areas, with the lower combination 

(12/25) producing a notable reduction in risk relative to the first combination of 13/30. The 

only study area that did not exhibit a reduction in risk under the first combination (13/30) 

was Dallas, reflecting the fact that its 24-hour and annual design values are below 30 μg/m3

and 13 μg/m3, respectively (and consequently, the 13/30 did not produce a reduction in 

ambient air PM2.5, or a resulting reduction in risk). Reductions in long-term exposure-related 

mortality (across all endpoints) under the 13/30 combination ranged from 14% (Phoenix) to 

55% (Salt Lake City), while reductions for the 12/25 combination ranged from 12% (Dallas) 

to ~100% (Salt Lake City) (see Figure 4-1 and 4-3 and Appendix E, Table E-27). The 

reduction for Salt Lake City reflects a very high 24-hour design value which, when reduced 

to meet the 24-hour standard of 25 μg/m3 produced a very large reduction in the annual 

design value (given application of proportional adjustment to simulate rollback), such that 

the value was very close to 5.8 μg/m3

 (the LML below which long-term exposure-related 

mortality is not estimated). The specific pattern of risk reduction reflects whether the 24-hour 

or annual standard was controlling – see discussion below regarding patterns of risk 

reduction. 

 Percent reduction in short-term exposure-related mortality and morbidity: The pattern 

of reductions in the percent of risk attributable to PM2.5 for mortality and morbidity 

associated with short-term exposure is similar to that described above for long-term mortality 

in terms of the ordering of sites, however the magnitude of risk reduction (in terms of percent 

change in PM2.5-related risk) is lower for short-term exposure-related health endpoints 

compared with long-term exposure-related mortality (see Figures 4-4 through 4-6). 

Specifically, 14 of the 15 urban study areas (Dallas being the exception), had notable risk 

reductions under both the 13/30 and 12/35 alternative suites of standards (Dallas only was 
4-27

estimated to have reductions in risk under the lower 12/25 combination - see Figure 4-4 and 

4-6 and Appendix E, Table E-108). Reductions in short-term exposure-related mortality and 

morbidity (across all endpoints) under the 13/30 combination ranged from 6% (Phoenix) to 

15% (Salt Lake City), while reductions for the 12/25 combination ranged from 7% (Dallas) 

to 30% (Birmingham). 

 Pattern of risk reduction linked to design values: As with the set of alternative annual 

standards discussed in the previous section, the pattern of risk reduction seen for the two 

combinations of alternative 24-hour and annual standards described here depends on which 

standard is controlling. In addition, the magnitude of the reduction in risk reflects (a) the 

magnitude of the difference between the controlling design value and the standard level 

(which determines the degree of reduction in ambient air PM2.5 levels) and (b) the method 

used to simulate ambient PM2.5 levels under alternative suites of standards (i.e., proportional, 

hybrid or locally focused rollback). For this set of alternative suites of standards, 10 of the 15 

study areas had the alternative 24-hour standard controlling under the 13/30 case and that 

number was increased to 12 out of the 15 study areas with the 12/25 case (Table 3-5). As 

expected, those study areas with the greatest reduction in risk (in terms of percent reduction 

compared with the current suite of standards) under the 12/25 case had a controlling 24-hour 

standard (e.g., Tacoma, Salt Lake City, Los Angeles and Fresno - see Figure 4-4 and 4-6 and 

Appendix E, Table E-90). 

 Absolute levels of PM2.5-attributable risk under alternative suites of annual and 24-

hour standards: As with the alternative annual standards, below we provide a brief 

overview of the magnitude of PM2.5-attributable risk (i.e., absolute risk) associated with the 

two alternative suites of annual and 24-hour standards: 

o Long-term exposure-related mortality: The four study areas displaying the greatest 

degree of reduction across these two alternative suites of standards (Tacoma, St. 

Louis, Los Angeles and Fresno), have PM2.5-related IHD mortality estimates (under 

the 12/25 case) ranging from 3-4 (Tacoma) to 290-360 (Los Angeles) (see Appendix 

E, Table E-21 and E-30). The other urban study area with the greatest degree of 

PM2.5-related risk in absolute terms besides New York (New York) has PM2.5-related 

all-cause mortality estimated at 820-1,040 under the 12/25 case. 

o Short-term exposure-related mortality: eleven of the 15 study areas had percent 

reductions in risk for the 12/25 case (relative to the current standards) of 

approximately 29% (the other study areas had lower percent reductions). Of the 

locations with ~29% reductions in risk, PM2.5-attributable CV mortality for the 12/25 

case ranged from 6 (Salt Lake City) to 340 (New York) (see Appendix E, Table E84). New York City also represents the study area with the greatest residual risk for 

short-term exposure-related mortality under the 12/25 case. 

o Short-term exposure-related morbidity Of the 11 urban study areas with ~29% 

reduction in risk (for the 12/25 case relative to the current standards), the incidence of 

PM2.5-attributable cardiovascular HA emissions ranges from 7 (Salt Lake City) to 530 

(New York) (see Appendix E, Table E-102). New York City also represents the 

study area with the greatest residual risk for short-term exposure-related morbidity 

under the 12/25 case. 
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 Consideration of the 95th percentile confidence interval risk estimates in assessing 

uncertainty related to the statistic fit of effect estimates: As with the alternative standards 

considering lower annual levels, risk estimates generated for the two standards considering 

lower annual and 24-hour levels also suggest that uncertainty related to the statistical fit of 

effect estimates will have a greater impact on short-term exposure-related mortality (+/- 

~40%) compared with long-term exposure-related mortality (+/- ~18%) (see Appendix E, 

Tables E-84 and E-21 plus Table E-30, respectively). Again, this results in a classification of 

this source of uncertainty as having a “lower” impact for long-term exposure-related 

mortality and a “moderate” impact on short-term exposure-related mortality. 

4.3 SENSITIVITY ANALYSIS RESULTS 

As noted in section 3.5.4 and section 4.0, the sensitivity analysis was conducted in order 

to gain insights into which of the identified sources of uncertainty and variability in the risk 

assessment model may have significant impacts on risk estimates. A second goal of the 

sensitivity analysis was to generate an additional set of reasonable risk estimates to supplement 

the core set of risk estimates to inform staff's characterization of uncertainty and variability 

associated with those core estimates. 

The first goal can be achieved by considering the magnitude of the impact of individual 

modeling elements based on results from the sensitivity analysis and identifying those elements 

which have the greatest impact on the core risk estimates. Use of the sensitivity analysis results 

in this context (i.e., identify those elements that contribute the most to sensitivity in the risk 

estimates) is addressed in section 4.3.1. Use of the results of the sensitivity analysis as an 

additional set of reasonable risk estimates to augment the core risk estimates in considering the 

impact of uncertainty and variability in the core risk model is discussed in section 4.3.2. 

In conducting the sensitivity analysis we modeled 2 of the 15 urban study areas 

(Philadelphia and Los Angeles - representing east and west coast urban areas, respectively) for 

most simulations.64 For some modeling elements (e.g., the hybrid and locally focused alternative 

rollback approaches) we included a larger number of urban study areas that were applicable to 

the topic being assessed. In conducting the sensitivity analysis, we have also focused on longterm exposure mortality and to a lesser extent on short-term exposure mortality and morbidity. 

Although the sensitivity analysis simulations were completed for all three simulation 

years (as reported in Appendix F), we have focused on results for 2007 in this presentation for 

comparability with the core results discussed in sections 4.1 and 4.2. 

 

64 These urban study areas were chosen generally to provide coverage for locations with recognized 

differences in factors associated with PM-related risk (e.g., meteorology, mix of local and regional PM sources, 

demographics), however a rigorous selection framework was not used. It should be noted that for some elements of 

the sensitivity analysis (e.g., consideration for alternative rollback methods) a larger number of the urban areas were 

included in the sensitivity analysis. 
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4.3.1 Sensitivity Analysis Results to Identify Potentially Important Sources of Uncertainty 

and Variability 

The results of the sensitivity analysis are summarized in Table 4-3 (detailed results tables 

are presented in Appendix F). In presenting the results of the sensitivity analysis, we have 

compared the risk estimates for the particular simulation to the core set of risk estimates 

generated for the same health effect endpoint/urban study area combination. Specifically, we 

have calculated a percent difference between the sensitivity analysis result and the associated 

core risk estimate to compare the results of the sensitivity analysis across the different modeling 

elements that were considered. This metric is used because it is not influenced by locationspecific differences in such factors as population size and baseline incidence rates and therefore, 

supports ready comparison of modeling elements (in terms of their impact on core risk estimates) 

across the urban locations assessed. These percent difference results are emphasized in Table 4-3 

and in the discussion presented below. Note that by contrast, the alternative risk estimates 

discussed below in section 4.3.2 focus on absolute risk, since the emphasis with that analysis is 

on assessing the potential spread in (absolute) risk that results from considering alternative 

modeling element specifications from those used in the core analysis. 

In discussing the results of the sensitivity analysis, we have developed four descriptive 

categories, based on the general magnitude of the percent difference estimate generated for a 

particular modeling element: 

 Modeling elements estimated to have percent differences of 20% or smaller (i.e., they 

produced risk estimates that differed from the core risk estimates by no more than 

20%) are classified as having a small contribution to uncertainty in the core risk 

estimates. 

 Modeling elements estimated to have percent difference estimates in the range of 20 to 

50% are classified as having a moderate contribution to uncertainty in the core risk 

estimates. 

 Modeling elements estimated to have percent difference estimates in the range of 50 to 

100% are classified as having a moderate-large contribution to uncertainty in the core 

risk estimates. 

 Modeling elements estimated to have percent difference results >100% are classified as 

having a large contribution to uncertainty in the core risk estimates. 

The sensitivity analysis based on Moolgavkar’s (2003) study in Los Angeles addressing 

model specifications for both short-term mortality and morbidity (e.g., model selection, lag 

structure and co-pollutant models) are discussed together as a group. This reflects the fact that 

the Moolgavkar-based simulations were based on the same underlying dataset and focused on 

Los Angeles. Furthermore, the discussion of the Moolgavkar-based sensitivity analysis results 

presented below, as well as the summary of results presented in Table 4-3, focus on the 
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difference in the spread of risk results across the Moolgavkar-based model specifications (for a 

particular endpoint), rather than the percent difference results based on comparison against the 

core result that are emphasized with the other sensitivity analyses.65 

The sensitivity analysis examining the impact of alternative rollback approaches for 

simulating ambient PM2.5 concentrations in urban study areas under both the current and 

alternative suites of standards also deserves additional discussion before presenting the results. 

For the first draft RA, we considered the impact of using a hybrid rollback approach in addition 

to the proportional rollback approach which has been traditionally used in PM NAAQS risk 

assessments. For this second draft, as discussed in sections 2.3, 3.2.3 and 3.5.4, we have 

included consideration of a locally focused rollback approach in addition to the hybrid as nonproportional methods to contrast with proportional rollback.66 

As discussed in Section 3.2.3, for the second draft risk assessment, we have calculated 

composite monitor estimates based on proportional rollback and hybrid and/or locally focused, 

where appropriate. The composite monitor values are surrogates for long-term exposure-related 

mortality.67 Therefore, by comparing composite monitor values generated for the same study 

area/suite of standards (using different rollback methods), we can obtain insights into the 

potential impact of the rollback method used on long-term exposure-related mortality (see 

Section 3.5.4 for additional discussion of how the composite monitor values generated using the 

different rollback methods are used in the sensitivity analysis). These sensitivity analysis results 

based on consideration of composite monitor values generated using the different rollback 

methods (which are presented in detail in Appendix F, Tables F-49 and F-50) form the basis for 

 

65 Comparison of the Moolgavkar-based risk estimates with the core risk estimates consistently produce 

percent difference estimates that range to levels well above +100%, resulting in a general conclusion, based on this 

metric, that all of the factors considered in the Moolgavkar-based sensitivity analysis are large contributors to 

uncertainty in the core risk estimates. However, there is significant uncertainty in assuming that the behavior of the 

Moolgavkar-based risk models (reflecting consideration for alternate design elements) would be representative of 

how models derived from either of the key short-term studies considered in this risk assessment (Zanobetti and 

Schwartz., 2009 and Bell et al., 2008) would respond to variations in design. Therefore, while sensitivity analysis 

results based on comparing Moolgavkar-based risk estimates against the core risk estimates are included in the 

detailed sensitivity analysis results tables presented in Appendix F (see Tables F-31 through F-33), we do not 

discuss these results here due to the degree of uncertainty associated with them. 66 The locally focused approach involves proportional reduction in 24-hour PM2.5 levels only at those urban 

study areas where the 24-hour standard is controlling (and only at those specific monitors with design values 

exceeding that 24-hour standard level) – see Section 3.2.3 for additional detail. 67 The composite monitor is essentially the mean of the annual averages across the PM2.5 monitors in a 

study area. It is this air quality metric that is used in calculating long-term exposure-related mortality. Given that the 

same C-R function is used across all study areas, differences in long-term mortality across study areas (and/or across 

standard levels) reflect to a great extent underlying differences in the composite monitor values. Therefore, 

comparison of composite monitors (in terms of percent difference for example) can provide insights into potential 

percent differences in long-term mortality related to PM2.5 exposure across study areas and/or standard levels (see 

Section 3.5.4). 
4-31

summary information related to rollback presented in Table 4-3. Due to the complexity of the 

sensitivity analysis conducted examining the issue of rollback, the discussion of results from that 

particular analysis presented in section 4.3.1.1 is more detailed than for the other factors 

considered as part of the sensitivity analysis. 

In discussing the results of the sensitivity analysis, results of the single-factor simulations 

are presented first (section 4.3.1.1), followed by the results of the multi-factor simulations 

(section 4.3.1.2). Within these categories, results are further organized by health effect endpoint 

with results for long-term exposure mortality discussed first and then short-term exposure 

mortality, followed by short-term exposure morbidity. An overall conclusion regarding which of 

the factors included in the sensitivity analysis represent potentially significant sources of 

uncertainty and variability impacting the core risk estimates is presented at the end of each subsection. 
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Table 4-3 Overview of Sensitivity Analysis Results 

Sensitivity Analysis1

Health Endpoint and Risk 

Assessment Location 

Summary of Results 

(percent difference in risk estimate rela

to the core estimate)

Single-Factor Sensitivity Analyses (long-term exposure mortality): Impact of using different model choices: fixed effects log-linear (the core) vs. random effects log-linear C-R function 

 All-cause, CPD, IHD  Los Angeles and 

Philadelphia 

Random effects log-linear C-R model:  all-cause: +23% 

 IHD: +12% 

Impact of using different model choices: fixed effects log-linear (the core) vs. random effects log-log C-R function 

 All-cause, CPD, IHD  Los Angeles and 

Philadelphia 

Random effects log-log C-R model:  All-cause: +123 to +159% 

 CPD: +50 to +74% 

 IHD: +80 to +111% 

 Lung Cancer: +67 to +94% 

Impact of using different model choices: Single vs. multi-pollutant models 

 All-cause  Los Angeles and 

Philadelphia 

 Model with CO: +45% 

 Model with NO2: +73% 

 Model with O3: +45% 

 Model with SO2: -74% 

Impact of estimating risks down to PRB rather than down to LML (the core) 

 All cause  All 15 urban study areas 

 All-cause: +47 to +273% 

Impact of using alternative C-R function from another long-term exposure mortality study 

 All-cause, CPD, lung 

cancer 

 Los Angeles, Philadelphia 

 All-cause: +119 to +121% 

 CPD: +29 to +30% 

 Lung cancer: +29 to +30% 

Impact of using alternative hybrid rollback approach reflecting more localized patterns of ambient PM2.5

reduction (evaluated across current and alternative standard levels) – based on the composite monitor analysis described in Section 3.5.4 considering both hybrid and locally focused approaches as alternatives to proportional rollback 

 Surrogate for long-term 

mortality (composite monitor-based analysis) 

 All study areas except 

Dallas had either hybrid and/or locally focused applied as an alternative 

 Trend in incremental risk reduction 

(alternative standard level compared tcurrent standard): rollback method diappear to have a significant impact on metric (those urban study areas with different trends in reduction did not demonstrate a consistent pattern relat
4-33

Sensitivity Analysis1

Health Endpoint and Risk 

Assessment Location 

Summary of Results 

(percent difference in risk estimate rela

to the core estimate)

rollback method to the proportional 

the type of hybrid method used) 

 Absolute risk for a given standard lev

use of alternative rollback methods diappear to impact estimation of PM2.5 

remaining for a given standard level: to >+50% 

 Has implications for degree to which 

hour standard levels produce reductioin annual average PM2.5 levels (and 

consequently on long-term and short-exposure-related risk). Results suggesthat use of locally focused rollback method can result in smaller degree oreduction in annual average values compared with proportional rollback, (see discussion in text – section 4.3.1

Single-Factor Sensitivity Analyses (short--term exposure mortality):Impact of using season-specific C-R functions (vs. an annual C-R function) 

 Non-accidental mortality, 

CV, respiratory 

 All 15 urban study areas 

 Non-accidental: -116 to +179% 

 CV: -82 to +500%  Respiratory: -48 to +162% 

(Note, overall incidence estimates, particufor the locations with higher percent chanestimates, is very low, raising concerns ovthe stability of these sensitivity analysis results) 

Impact of using alternative hybrid rollback approach reflecting a combination of more localized and regional patterns of ambient PM2.5 reduction (note, 

this analysis is based exclusively on the hybrid rollback – the composite monitor analysis described 

 Non-accidental mortality  Baltimore, Birmingham, 

Detroit, Los Angeles, New York and St. Louis 

 Results for all seven urban study area

(across the current and alternative standard levels) do not exceed +17%, with most <+10%. 
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Sensitivity Analysis1

Health Endpoint and Risk 

Assessment Location 

Summary of Results 

(percent difference in risk estimate rela

to the core estimate)

above pertains only to long-term mortality-related risk) Single-Factor Sensitivity Analyses (short-term morbidity: hospital admissions (HA) and ED visits):Impact of using season-specific C-R functions (vs. an annual C-R function) 

 HA (unscheduled), CV 

and respiratory 

 All 15 urban study areas 

 HA (CV): -105 to +9% 

 HA (respiratory): -54 to +74% 

(Note, overall incidence estimates, particufor the locations with higher percent chanestimates, is very low, raising concerns ovthe stability of these sensitivity analysis results) 

Impact of using an annual C-R function (applied to the whole year) vs. a seasonal function for April through August (applied only to that period) (using a single pollutant model) 

 Asthma ED visits  New York 

NA (although incidence estimates were generated for this simulation, “percent difference from the core” were not generasince the alternate simulation focused on a subset of the year). 

Impact of considering models with different lags  HA (CV and respiratory 

 LA and New York 

NA (although incidence estimates were generated for this simulation, “percent difference from the core” were not generasince the lag-differentiated C-R functions are not regionally-differentiated, and therefore, do not allow a focused consideration of the lag factor alone in impacting risk estimates) 

Single-Factor Sensitivity Analysis (short-term exposure mortality and morbidity in LA based on Moolgavkar, 2003 study model o

presented here reflect spread in risk estimates across Moolgavkar-based model specifications and not percent difference from cunless so stated – see text) 

Impact of model selection (e.g., log-linear GAM with 30 df; log-linear GAM with 100 df; and loglinear GLM with 100 df) 

 Mortality (non-accidental, 

CV); HA (CV) 

 Los Angeles 

 Non-accidental mortality: +80% 

 CV mortality: +49  CV HA: +36% 
4-35

Sensitivity Analysis1

Health Endpoint and Risk 

Assessment Location 

Summary of Results 

(percent difference in risk estimate rela

to the core estimate)

Impact of lag structure (0-day, 1-day, 2-day, 3-day, 4-day, 5-day) 

 Mortality (non-accidental)  Los Angeles 

 Non-accidental mortality: +55% 

Impact of single- vs. multi-pollutant models (PM2.5 with CO) 

 Mortality (CV); HA (CV)  Los Angeles 

 CV mortality: +106% 

 CV HA: +140% 

Multi-Factor Sensitivity Analyses (long-term mortality): Impact of using a fixed effects log-linear vs. a random effects log-log model, estimating incidence down to the lowest measured level (LML) in the study vs. down to PRB, and using a proportional vs. hybrid rollback to estimate incidence associated with long-term exposure to PM2.5 concentrations that just 

meet the current standards (note consideration of rollback in the multi-factor analysis did not incorporate the hybrid-based rollback approach) 

 All-cause, IHD long-term 

mortality 

 Los Angeles and 

Philadelphia 

 All-cause: +27 to +1,089%  IHD: +26 to +673% 

Multi-Factor Sensitivity Analyses (short--term mortality):Impact of using season-specific vs. all-year C-R functions and proportional vs. hybrid rollbacks to estimate incidence associated with short-term exposure to PM2.5 concentrations that just meet the 

current standards 

 Non-accidental 

 Baltimore, Birmingham, 

Detroit, Los Angeles, New York and St. Louis 

 Non-accidental (four seasons + hybrid

-116 to +179% 

1 Unless otherwise noted, sensitivity analysis results are based on the scenario reflecting just meeting the current suite of PM2.5 standard2 This metric is the percent spread in risk estimates across the Moolgavkar-based model specifications (not the percent difference estim

above). 
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4.3.1.1 Single-factor Sensitivity Analysis 

This section presents the results of the single-factor sensitivity analysis, which involved 

consideration of alternate model inputs on the core risk estimates, when those alternate inputs are 

considered one at a time (consideration of the combined effect of several model inputs being 

varied is covered by the multi-factor sensitivity analysis discussed in section 4.3.1.2). The 

results of the single-factor sensitivity analysis are characterized qualitatively using the fourcategory approach described above (i.e., low, moderate, moderate-large and large, with each of 

these representing a defined range of percent difference from the core risk estimates). 

Long-term exposure mortality

This section summarizes the results of the sensitivity analysis focused on long-term 

exposure-related mortality endpoints (see Table 4-3 for the specific modeling elements 

considered in the sensitivity analysis). 

 Impact of using different model choices for C-R function - fixed effects log-linear (the core 

approach) vs. random effects log-linear or random effects log-log models: This simulation 

considered two alternative C-R model forms obtained from Krewski et al., 2009 for modeling 

all-cause, CPD, IHD and lung cancer mortality, including (a) random effects log-linear 

model and (b) a random effects log-log model (note, the core effect estimate was derived 

using a fixed effects log-linear model obtained from Krewswki et al., 2009). The simulation 

also considered the use of multi-pollutant models that control for CO, NO2, O3 or SO2. The 

results of the simulation suggest that the use of a random effects log-linear model, rather than 

the core fixed effects model, has a relatively small effect on risk estimates, increasing them 

by 12 to 23% across the mortality categories and urban study areas modeled (Appendix F, 

Table F-3). However, use of a random effects log-log model has a larger impact on risk 

estimates, increasing them by 50 to 159% (Appendix F, Table F-3). The greater impact of 

the log-log model results from this function having an incrementally steeper slope at lower 

PM levels, which quickly increases incidence estimates compared with the core log-linear 

model (whose slope has a much more gradual incremental increase in slope at lower PM 

levels). The use of multi-pollutant models that control for co-pollutants was shown to have 

moderate-large impact on risk estimates, with control for CO, NO2, or O3 resulting in 

increased PM2.5-attributable risk estimates, while control for SO2 resulted in a moderate-large 

decrease in estimated PM2.5 risk.68

 Impact of estimating risks down to PRB rather than down to LML: This simulation 

compared long-term exposure mortality incidence associated with modeling risk down to 

PRB (which varies by region – see section 3.2.1) with the core approach of modeling down 

to LML (5.8 μg/m3 for long-term mortality – see section 3.1). This simulation involved all 

 

68 Sensitivity analysis results generated using the copollutant model involving PM2.5 and SO2 have been deemphasized since it is likely that control for SO2 may be capturing a portion of PM2.5 -attributable risk related to the 

secondary formation of sulfate, which is a component of the PM2.5 mixture (i.e., the two pollutants are often highly 

correlated). 
4-37

15 urban study areas, given that PRB is stratified by region and therefore, results of the 

simulation could differ significantly across the 15 urban study areas, or at least across the six 

PM regions represented by those study areas. The results of this simulation suggest that 

modeling risk down to PRB could have a moderate to large impact on long-term exposure 

mortality incidence, with estimates ranging from 47 to 273% higher than the core estimates 

(for matching urban locations) (Appendix F, Table F-6). Note, however, that risk metrics 

based on considering the incremental reduction in risk (incidence) between two alternative 

suites of standards would not be impacted by this source of uncertainty, since it only affects 

estimates of absolute risk. 

 Impact of C-R function from alternative long-term exposure mortality study: This simulation 

considered use of alternative C-R functions (and effect estimates) based on the reanalysis of 

the Six Cities study (Krewski et al., 2000). The results suggest that use of the alternative C-R 

function could have a moderate to moderate-large effect on CPD mortality (+45 to +74%), a 

large effect on all-cause mortality (+123 to +159%), a moderate-large to large effect on IHD 

mortality (+80 to +111%) and a moderate-large effect on lung cancer mortality (+67 to 

+94%) (Appendix F, Table F-9). The results of this simulation suggest that (at least with 

regard to application of C-R functions obtained from the Six Cities study) the potential 

impact of functions from alternative studies on long-term exposure mortality depends on the 

mortality category being considered. In this analysis, use of the alternative C-R functions 

was shown to have a significant impact on all of the long-term mortality categories 

considered. 

 Impact of using alternative rollback approaches (hybrid and locally focused) to simulate just 

meeting the current and alternative suites of standards. This sensitivity analysis assessed the 

impact of estimating risk for the current and alternative sets of standards using two 

alternatives to the proportional rollback strategy: (a) the hybrid rollback approach that 

reflects an initial localized pattern of ambient PM2.5 reduction (resulting in non-proportional 

rollbacks of monitored PM2.5 concentrations) with a second phase of more regional 

reductions in ambient PM2.5 levels (based on proportional adjustments) and (b) locally 

focused which represents a primarily local pattern of reductions in ambient PM2.5 (see 

Section 3.5.4 for additional discussion of how these alternative rollback methods were 

integrated into the sensitivity analysis). We note that the core analysis utilized proportional 

rollback exclusively in simulating conditions for the current and alternative sets of standards, 

with this approach representing a regional pattern of ambient PM2.5 reduction. A number of 

observations can be drawn from this sensitivity analysis including: 

o Impact on estimates of PM2.5-related risk remaining after simulation of just 

meeting a given suite of standards: The sensitivity analysis results suggest that 

the use of alternative rollback methods can have a notable impact on estimates of 

the PM2.5-attributable risk remaining after simulation of a given suite of standards 

(see Appendix F, Table F-50 and discussion in section 3.5.4). Generally, use of 

the hybrid approach had a small to moderate impact on absolute PM2.5-

attributable risk estimates, compared with the core approach of using proportional 

rollback. By contrast, use of the locally focused approach had a moderate to 

moderate-large impact on absolute PM2.5-attributable risk estimates. For example, 

Los Angeles had composite monitor values for the current suite of standards and 

several of the alternative suites of standards that were 40 to 60% greater when the 
4-38

locally focused rollback method was used, compared with the proportional 

rollback method (see Appendix F, Table F-50). By contrast, composite monitor 

values generated using hybrid rollback for Los Angeles, were between 13 and 

38% higher than the proportional rollback methods. These results suggest that 

more localized spatial patterns of reduction in PM2.5 in response to alternative 

standard levels, as reflected in application of the hybrid and even to a greater 

extent, the locally focused rollback approach, can result in a larger fraction of risk 

remaining after simulating those standards. By contrast, more regional spatial 

patterns of PM2.5 reduction, as reflected in the proportional rollback approach 

used in the core analysis, results in a greater reduction in risk with less risk 

remaining upon simulation of the alternative standard. These results point to the 

potentially important role played by the nature of the spatial pattern of PM2.5 

reduction (i.e., rollback) in determining the magnitude of public health protection 

potentially provided by alternative standard levels. 

o Impact on degree of reduction across alternative suites of standards: When the 

same rollback methods is used to simulate both the current and any alternative 

suite of standards, the pattern of risk reduction across alternative standards is 

generally similar regardless of the rollback approaches used (see Table F-49, in 

Appendix F). However, if one looks at meeting the current suite of standards with 

application of the locally focused approach, followed by application of 

proportional rollback to simulate alternative suites of standards, we can see 

notable differences in the pattern of risk reduction. This is particularly true for 

areas with peaky PM2.5 distributions (i.e., areas with relatively high 24-hour 

design values and lower annual average design values). For example, with Los 

Angeles, which represents a study area with a relatively peaky PM2.5 distribution, 

application of proportional rollback in simulating both the current suite of 

standards and the alternative annual standard of 12 μg/m3 results in a 13% 

reduction in long-term exposure-related mortality (see Figure 4-3 and Table E-27 

in Appendix E). By contrast, application of the locally focused approach in 

simulating the current suite of standard levels followed by proportional reduction 

in simulating the same alternative annual standard results in an estimated 48% 

reduction in long-term exposure-related mortality.69 These results highlight the 

point made in the previous bullet, that the nature of the spatial pattern of PM2.5 

reduction in response to an alternative standard level can impact the magnitude of 

risk reduction and consequently, the magnitude of risk predicted to remain upon 

 

69 The difference in risk reductions based on application of different rollback methods in simulating the 

current suite of standards reflects the fact that locally focused 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 annual average PM2.5 level. By contrast, application of proportional rollback will produce a larger degree of 

rollback in the composite monitor annual average (i.e., a level equal to that needed to get the 24hr design value to 

meet the 24hr standard). We also note that the risk reductions cited here reflecting application of locally focused in 

simulating the current suite of standards are based on comparison of composite monitor annual averages presented 

in Table F-49 in Appendix F. In generating this surrogate for reduction in long-term exposure-related mortality 

between the two standard levels, we compared composite monitor annual averages with consideration for the fact 

that long-term exposure-related mortality is only calculated down to LML. 
4-39

simulation of that standard. These results illustrate further that the type of rollback 

used in simulating the current standard level can impact the magnitude of risk 

reduction predicted for an alternative (lower) standard level. Particularly in urban 

locations with peaky PM2.5 distributions, application of more localized patterns of 

PM2.5 reduction (for the current standard), followed by a more regional pattern of 

PM2.5 reduction for an alternative standard level can result in a larger estimate of 

risk reduction for that alternative standard. 

Based on the simulations discussed above covering potential sources of uncertainty and 

variability impacting long-term mortality, we conclude that the following factors contribute 

potentially large sources of uncertainty to the core risk estimates: (a) use of alternative form of 

the C-R function, specifically use of a random-effects log-log model form obtained from the 

updated ACS study (Krewski et al., 2009) (b) use of an alternative C-R function with effects 

estimates obtained from the reanalysis of the Six Cities study (Krewski et al. 2000), and (c) 

estimation of risk down to PRB.70 Other factors considered in the sensitivity analysis had 

smaller impacts on core risk estimates. 

Short-term exposure mortality

This section summarizes the results of the sensitivity analysis focused on short-term 

exposure-related mortality endpoints (see Table 4-3 for the specific modeling elements 

considered in the sensitivity analysis). 

 Impact of using season-specific C-R functions (vs. an annual C-R function): This 

simulation considered the impact on short-term exposure mortality risk of using seasonallydifferentiated effects estimates rather than the core approach of using a single C-R function 

for the whole year (note, that the seasonal models were based on the same study as the model 

used in the core analysis – Zanobetti and Schwartz, 2009). The results of the simulation 

suggest that this source of uncertainty can have a wide range of effects across urban study 

areas (including not only variation in the magnitude of effect, but also in the direction). 

Percent changes compared with the core risk estimate were large, ranging from -116% (Los 

Angeles) to +179% (Birmingham) (these results are for non-accidental mortality – see 

Appendix F, Table F-15). We note that these two locations also have relatively low overall 

incidence estimates, which does raise concerns over the degree of stability in the sensitivity 

analysis estimates. Furthermore, for 9 of the 15 urban study areas (for non-accidental 

morality), percent changes from the core were small, with absolute values of 12% or less 

(Appendix F, Tables F-15). The results for CV and respiratory mortality also demonstrate 

considerable variation across locations, but are generally smaller than results cited above for 

non-accidental, with one exception. Birmingham is estimated to have short-term CV 

mortality that is +500% higher using seasonal effects estimates compared with the core 

results (we note, however, that this endpoint category also has very small incidence, again 

 

70 Use of locally focused rollback as an alternative method for simulating ambient PM2.5 concentrations for 

alternative standards had a moderate-large impact on risk estimates. 
4-40

raising concerns over the stability of the sensitivity analysis results - see Table F-18). The 

results for respiratory-related mortality also demonstrate considerable variability with results 

that could suggest a moderate to large impact (i.e., -48 to +162% - see Appendix F, Table F21). We note, however, that small incidence estimates again raise concerns regarding the 

stability of these percent difference results. 

 Impact of using alternative hybrid rollback approach: This simulation evaluates the 

potential impact of using the hybrid approach for simulating just meeting current and 

alternative sets of standards, as an alternative to the proportional approach used in the core 

analysis.71 The results of this simulation (as contrasted with the impact of using the hybrid 

approach on long-term exposure mortality) suggest that use of the hybrid rollback approach 

has relatively little effect on short-term mortality risk (e.g., percentage differences relative to 

the core risk estimates were in the low single digits for most locations, with one location 

having a difference of +17% - see Appendix F, Table F-36). 

The sensitivity analysis results discussed above, result in a number of overall 

observations regarding sources of uncertainty potentially impacting short-term exposure morality 

endpoints. The results of using the seasonally-differentiated effect estimates in modeling shortterm exposure mortality appear to generally have a relatively small impact (e.g., <15%) in most 

study areas. For some study areas, the impact does appear to be much larger, with results 

including both substantial negative and positive percent differences from the core estimates. 

However, in all of these cases, the total incidence estimates involved are very small, raising 

concerns over the stability of the risk estimates generated as part of this particular sensitivity 

analysis (in many of these instances, the estimates include negative lower bounds, reflecting the 

use of non-statistically significant effects estimates). For these reasons, the results of this 

sensitivity analysis, while initially appearing to be notable in terms of magnitude in some study 

areas, need to be interpreted with care. At this point, we are uncertain as to how important this 

source of uncertainty is in the context of short-term exposure mortality estimation. Regarding 

the use of the alternative hybrid (non-proportional) approach for simulating conditions under 

alternative standard levels, the results suggest that this factor has a modest impact on short-term 

exposure mortality (significantly less impact than with the use of the hybrid approach in 

estimating long-term exposure mortality). With the exception of factors examined using the 

Moolgavkar et al., (2003) study in Los Angeles (see below), it would appear that the factors 

examined here do not have a large impact on risk estimates generated for short-term exposure 

 

71 Note, that the locally focused rollback method was only assessed in the context of the composite monitor 

values used in generating long-term exposure-related mortality estimates. Consequently, consideration of the locally 

focused rollback method is only assessed in terms of its impact on long-term risk and not short-term exposurerelated mortality. Note, however, that the impact of using locally focused versus proportional rollback on short-term 

exposure-related risk is expected to be smaller than the impact on long-term exposure-related risk, since the latter is 

linked to composite annual averages which are expected to experience the greatest impact from application of 

alternative rollback methods. 
4-41

mortality. However, we note that the overall scope of the sensitivity analysis completed for 

short-term exposure-related mortality and morbidity is far more limited than that completed for 

long-term exposure-related mortality. 

Short-term exposure morbidity

This section summarizes the results of the sensitivity analysis focused on short-term 

exposure-related morbidity endpoints (see Table 4-3 for the specific modeling elements 

considered in the sensitivity analysis). The results of individual sensitivity analysis simulations 

are presented below, with overall observations presented at the end of the section. 

 Impact of using season-specific C-R functions (vs. an annual C-R function): This 

simulation considered the impact on short-term exposure morbidity (HAs) of using 

seasonally-differentiated effects estimates rather than the core approach of using a single C-R 

function for the whole year (we note that the seasonal models were obtained from the same 

study as the model used in the core analysis – Bell et al, 2008). The results of the simulation 

suggest that, as with short-term exposure mortality this source of uncertainty can have a wide 

range of impacts on the risk estimates across urban study areas (including not only variation 

in the magnitude of risk, but also in the direction) depending on the specific health endpoint 

examined. We note, however, that the magnitude of impact appears to be less for short-term 

morbidity than for short-term mortality. Percent changes for most of the 15 urban study 

areas were small for CV HAs (generally less than a 20% difference in either direction, 

although there was a large impact for Tacoma (-105%), see Appendix F, Table F-24). This 

source of uncertainty has a moderate to moderate-large impact for respiratory-related HAs 

with most locations having greater than a 54% to 74% absolute effect (see Appendix F, Table 

F-27). 

 Impact of using a seasonal function for April through August (applied only to that 

period) in modeling asthma-related ED visits in New York, relative to the core 

approach of using a single annual effect estimate (and applying that to the whole year): 

This sensitivity analysis involved the approach of using a season-specific estimate to model 

incidence for the period April through August (obtained from Ito et al., 2007). Because this 

sensitivity analysis estimate covers a period shorter than a year, we have not directly 

compared it with the annual estimate generated for this endpoint in the core risk assessment 

(i.e., we have not generated percent difference estimates as is done with other sensitivity 

analysis simulations). However, the results of this sensitivity analysis do suggest that the use 

of seasonally-differentiated estimates in modeling this endpoint can impact risk.

 Impact of considering models with different lags: To examine the impact of lag on 

modeling of short-term exposure-related morbidity, we used a range of effects estimates 

obtained from Bell et al., 2008 based on application of different lags, including 0-, 1- and 2-

day lags, (for both respiratory and cardiovascular-related morbidity). Because lagdifferentiated effects estimates were only available as national-averages and were not 

regionally-differentiated, we could not directly compare the results using different lag models 

to the results generated for the core analysis (i.e., the sensitivity analysis results would have 

mixed both the lag effect and the effect of regional differentiation, thereby preventing clear 

assessment of the importance of either factor considered in isolation). However, 
4-42

consideration of the magnitude of the risk estimates generated using different lag models, for 

the same endpoint at the same urban study are, suggests that choice of lag does effect 

estimates of short-term exposure-related morbidity (see Appendix F, Table F-48).

Given the results of the set of simulations completed for short-term exposure morbidity, 

both of which focused on the use of seasonally-differentiated effects estimates, it would appear 

that this factor does not have a substantial impact on risk estimates. The analysis considering 

different lag models does suggest that this factor could have a notable impact on risk estimates 

and should be carefully considered when specifying C-R functions to use in the risk assessment. 

Additional factors potentially impacting short-term exposure morbidity are addressed below in 

relation to the sensitivity analysis based on alternative models from Moolgavkar et al. (2003). As 

noted earlier, the scope of the sensitivity analysis completed for short-term exposure-related 

morbidity is limited. 

Short-term exposure-related mortality and morbidity (Moolgavkar et al., 2003 study-based 

analysis)

As noted earlier in the introduction to section 4.3, the results of sensitivity analysis based 

on Moolgavkar et al., (2003) include percent difference estimates based on considering the range 

of risk estimates generated using alternative model specifications from this study for a given 

health endpoint and it is these results that are discussed below. 

 Impact of model selection (e.g., log-linear GAM with 30df, log-linear GAM with 100df, 

and log-linear GLM with 100df) on estimating short-term exposure mortality and 

morbidity: Application of models obtained from Moolgavkar et al., (2003) with various 

formulations related to model selection (degrees of freedom, GLM vs. GAM) to the Los 

Angeles urban case study location results in a range of short-term exposure mortality 

estimates (for non-accidental and CV) that differ by 80% and 49%, respectively (see 

Appendix F, Table F-33). In the case of short-term exposure morbidity (specifically, CVrelated HAs), incidence estimates differ by 36% (see Appendix F, Table F-33). These results 

suggest that these elements of model specification represent a moderate source of uncertainty 

in estimating short-term mortality and morbidity. 

 Impact of lag structure (0-day through 5-day) on estimating short-term exposure 

mortality: Consideration of the range of risk estimates for non-accidental mortality 

generated using different lag structures (and associated effect estimates) provided in 

Moolgavkar et al., (2003), suggest that this factor could have a moderate impact on risk (in 

the range of 55% when comparing the lowest and highest positive incidence estimates 

generated). (see Appendix F, Table F-33). 

 Impact of considering multi-pollutant models on estimating short-term exposure 

mortality and morbidity: The results of the Moolgavkar-based simulations (when 

considering the spread in risk estimates specifically across these simulations) suggest that the 

multi-pollutant versus single-pollutant model issue (i.e., including CO in addition to PM2.5), 

could have a large impact on the estimation of short-term exposure mortality (106% for allcause) and morbidity (140% for CV-related HAs). 
4-43

Overall observations regarding key sources of uncertainty impacting short-term exposure 

mortality and morbidity risk estimates (based on the Moolgavkar et al., 2003 study) include the 

following. The spread in risk estimates generated across the Moolgavkar-based model 

specifications suggests that factor related to specifying the C-R model may have a moderate to 

large impact. More specifically, variation in the lag structure has a moderate impact on risk and 

use of single versus multi-pollutant models could have a potentially large impact on risk. Note, 

however, that as discussed earlier, the relevance of these sensitivity analysis results to the 

interpretation of core risk estimates is not clear and may be relatively low (see Section 4.3.1). 

4.3.1.2 Multi-factor Sensitivity Analysis Results 

The results of the multi-factor sensitivity analyses are intended to support both goals of 

the sensitivity analysis: (a) identify which factors (now in combination), appear to have a 

significant impact on estimation of the core estimates and (b) to derive a set of reasonable 

alternative risk estimates for use in considering uncertainty and variability associated with the 

core risk estimates. Regarding the latter application, because these multi-factor simulations 

combine multiple factors reflecting uncertainty and variability together in generating alternative 

risk estimates, they are likely to produce the highest sensitivity analysis results. Therefore, it is 

particularly important to consider the reasonableness of the results of these multi-factor 

simulations, to insure that only credible estimates are included in the set of reasonable alternative 

risk estimates. Consequently, we emphasize consideration of the reasonableness of these multifactor simulations in the discussion presented below. 

Long-term exposure mortality

This section summarizes the results of the sensitivity analysis focused on long-term 

exposure-related mortality endpoints (see Table 4-3 for the specific modeling elements 

considered in the sensitivity analysis). 

 Impact of using log-linear vs. log-log C-R model with fixed or random effects, 

estimating incidence down to the LML vs. PRB, and using proportional vs. hybrid 

rollback to estimate long-term exposure mortality: This multi-factor sensitivity 

analysis focused on a number of model design choices related to modeling long-term 

exposure mortality (all-cause and IHD). Modeling elements reflected in the 

simulations included: (a) model form (log-linear vs log-log and random vs fixed 

effects), (b) modeling risk down to PRB (vs LML), and (c) use of an alternative hybrid 

rollback approach (vs proportional rollback) to simulate just meeting the current and 

alternative sets of standards. Various permutations of these design elements choices 

(relative to the elements selected for the core analysis) were considered. Percent 

difference estimates (for all-cause mortality) ranged from 27% (for a model estimating 

risk down to PRB and use of the hybrid rollback approach) to 1,089% (for a model 

with random effects log-log model, risk estimated down to PRB, and use of the hybrid 

rollback approach). 
4-44

We believe that application of a log-log model with random effects is a reasonable 

alternative to the core model (fixed-effects log-linear model), based on our review of the 

discussion in Krewski et al. (2009). Similarly, the use of a hybrid rollback approach involving 

non-proportional adjustment where there is the potential for greater use of local control strategies 

to address local-sources is a reasonable alternative to solely using a proportional rollback 

approach in all study areas. Therefore, we believe that the combinations of modeling elements 

including these alternative choices are reasonable. However, there is more concern in predicting 

risk down to PRB. This is not because there is evidence for a threshold, but rather because we 

do not have data to support characterization of the nature of the C-R function in the vicinity of 

PRB. Specifically, there is increasing uncertainty in predicting the nature of the C-R function as 

you move below the LML. So, while we believe it is reasonable conceptually to estimate risk 

down to PRB, the quantitative process of doing this requires use of a function with very high 

uncertainty. Therefore, we concluded that those alternative risk estimates generated using risk 

estimated down to PRB should not be used in creating the reasonable alternative set of risk 

estimates in considering uncertainty associated with the core risk estimates. 

A key limitation of the multi-factor sensitivity analysis is that the approach used did not 

allow us to consider the locally focused rollback method in concert with the other modeling 

elements described above. This means that the combined impact of locally focused (which has a 

greater impact than the hybrid rollback method) with other model specifications was not 

characterized. However, as part of the integrative discussion in Chapter 5, we do consider the 

results of the single-factor sensitivity analysis examining rollback (with its consideration of 

locally focused) along with the multi-factor sensitivity analysis results described here. 

Short-term exposure mortality

This section summarizes the results of the sensitivity analysis focused on short-term 

exposure-related mortality endpoints (see Table 4-3 for the specific modeling elements 

considered in the sensitivity analysis). 

 Impact of using season-specific vs. annual effect estimates and proportional vs. 

hybrid rollback approaches in modeling short -term exposure mortality: This 

multi-factor sensitivity analysis focused on a number of model design choices related 

to modeling short-term mortality (non-accidental). Modeling elements included in this 

sensitivity analysis were use of seasonal vs. annual effects estimates and use of hybrid 

vs proportional rollback to simulate just meeting current and alternative standard 

levels. Percent difference estimates (for non-accidental mortality) across the 7 urban 

study areas included in the simulation ranged from -116% (LA) to +179% 

(Birmingham) (see Appendix F, Table F-42). However, we note that the total 

incidence estimates associated with these higher-impact locations were relatively low, 

again raising the concern for the stability in relative differences with the core estimates. 
4-45

Because of the more limited scope of the multi-factor sensitivity analysis completed for 

short-term exposure-related mortality, we have concluded that these results should not be used as 

an additional set of reasonable risk estimates to inform consideration of uncertainty associated 

with this category of risk estimates. 

4.3.2 Additional Set of Reasonable Risk Estimates to Inform Consideration of 

Uncertainty in Core Risk Estimates 

This section discusses the use of the output of the sensitivity analysis completed as part 

of this risk assessment as an additional set of reasonable risk estimates to inform consideration of 

uncertainty associated with the core risk estimates. Specifically, in the case of long-term 

exposure-related mortality endpoints, staff has concluded that the results of the sensitivity 

analysis represent a reasonable set of alternate risk estimates that fall within an overall set of 

plausible risk estimates surrounding the core estimates.72 

While not representing a formal uncertainty distribution, the output of the sensitivity 

analysis, when combined with the core risk estimates, represent a set of plausible risk estimates, 

which reflect consideration of uncertainty in various elements of the risk assessment model. 

Therefore, while the discussion of risk estimates in the context of assessing the degree of risk 

reduction associated with suites of alternative standards (see Section 5.2) does focus on the core 

risk estimates since these are judged to have the greatest overall confidence, the output of the 

sensitivity analysis can be used to provide additional perspective on the potential range of 

uncertainty around the core estimates. Note however, that we do not know the confidence 

interval captured by this uncertainty set, or the specific percentiles of the risk distribution are 

represented by points within that set. 

As noted earlier, the quantitative single- and multi-factors sensitivity analyses generated 

an additional set of risk estimates for a subset of the urban study areas, air quality scenarios and 

health endpoints included in the core risk analysis (i.e., Los Angeles and Philadelphia assessed 

for the current standard level). However, the part of the sensitivity analysis focusing on 

alternative methods for simulating ambient PM2.5 levels (i.e., rollback), did consider a larger 

number of study areas and air quality scenarios. In presenting the alternative sets of reasonable 

risk estimates, we focus on Los Angeles and Philadelphia for many of the modeling elements, 

 

72 While staff believes that the sensitivity analysis does provide insights into the potential impact of certain 

sources of uncertainty on short-term exposure-related mortality and morbidity risk, the sensitivity analysis 

conducted for short-term exposure-related endpoints 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. 
4-46

although we expand the discussion in the context of discussing results related to conducting 

rollback. 

In using the additional set of reasonable risk results to augment the core risk estimates, 

we begin by presenting both the core and alternative sets of estimates for Los Angeles and 

Philadelphia in Table 4-4. Then, in Figures 4-7 and 4-8, we present graphical display of the full 

uncertainty set comprising the core plus additional reasonable risk estimates for Los Angeles and 

Philadelphia, differentiated by mortality category (Figure 4-7 present results for IHD and Figure 

4-8 presents results for all cause mortality). This section concludes with a set of observations 

resulting from consideration of information depicted in Table 4-4 and Figures 4-7 and 4-8 in the 

context of interpreting uncertainty in the core risk estimates.73 

Table 4-4 Derivation of a set of reasonable alternative risk estimates to 

 supplement the core risk estimates (Los Angeles and Philadelphia, 

 current standards, for long-term IHD mortality). 

Sensitivity analysis 

Core risk estimate 

Description of 

simulation 

Results 

(percent difference: 

sensitivity analysis versus 

core estimate)4

Adjusted set of risk estimate 

to supplement core risk 

estimates1 

Single-element sensitivity analysis results 

(A) Impact of using 

different model 

choices: random 

effects log-linear 

model 

Los Angeles and Philadelphia: 

IHD: +12%; 

All cause: +23% 

Los Angeles and 

IHD: 8.6%, All cause: 2.5% 

Philadelphia: 

IHD: 14.8%, All cause: 4.4% 

(B) Impact of using 

different model 

choices: random 

effects log-log model 

Los Angeles: 

IHD: +111%; All cause: +159 

Philadelphia: 

IHD: +80%; All cause: +123% 

Los Angeles and 

IHD: 16.2%, All cause: 5.2% 

Philadelphia: 

IHD: 23.8%, All cause: 8.0% 

(C) Impact of using 

different model 

choices (single vs. 

multi-pollutant – NO2 

Vs O3/CO)3

Los Angeles and Philadelphia: 

All cause: +45 to +74% 

(O3/CO and NO2 , respectively) 

and -74% for SO2 

Los Angeles and 

All cause: 2.9% and 3.5% 

(for O3/CO and NO2 , 

respectively), 0.52% (SO2) 

Philadelphia: 

All cause: 5.2% and 6.3% (for 

O3/CO and NO2 , 

respectively), 0.94% (SO2) 

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 (D) Impact of C-R Los Angeles: Los Angeles: 

 

73 We have excluded several of the sensitivity analysis results in defining the set of alternative reasonable 

risk estimates. Specifically, we consider estimates based on modeling risk down to PRB to be less reasonable than 

the other scenarios included in the sensitivity analysis, since there is substantial uncertainty associated with the C-R 

function shape below the LML. In addition, as discussed in Section 4.3.1.1 risk estimates generated using the 

copollutant model involving PM2.5 and SO2 have been de-emphasized since it is likely that control for SO2 may be 

capturing a portion of PM2.5-attributable risk related to the secondary formation of sulfate. Note, that the risk 

estimates for SO2 are presented as open circles in Figure 4-8, to signify that they have lower confidence and are deemphasized relative to the other alternative risk estimates presented. 
4-47

Sensitivity analysis 

Core risk estimate 

Description of 

simulation 

Results 

(percent difference: 

sensitivity analysis versus 

core estimate)4

Adjusted set of risk estimate 

to supplement core risk 

estimates1 

function from 

alternative long-term 

exposure study 

(Krewski et al., 2000) 

All cause: +121% 

Philadelphia: 

All cause: +119% 

All cause: 4.4% 

Philadelphia: 

All cause: 7.9% 

(E) Impact of using 

alternative roll-back 

approach (hybrid and 

locally focused) to 

simulate just meeting 

alternative standards 

Los Angeles: 

Both all cause & IHD: +21 to 

+40% (hybrid and locally 

focused, respectively) 

Philadelphia: 

Both all cause & IHD: +8% 

(locally focused only) 

Los Angeles and 

Hybrid: IHD: 9.3%, All cause: 

2.4% 

Locally focused: IHD: 10.8%, 

All cause: 2.8% 

Philadelphia: 

IHD: 14.3% 

All cause: 3.9% 

Multi-element sensitivity analysis results 

ambient data from 

Krewski et al., 2009 – see 

section 3.3.3) 

(F) Random effects 

log-log & hybrid 

non-proportional 

rollback 

Los Angeles: 

IHD: +149% 

All cause: +211 

Philadelphia: 

NA2

Los Angeles: 

IHD: 19.2% 

All cause: 6.2% 

Philadelphia: 

NA2

1

 Percent of total incidence that is PM2.5- related (note, the set of estimates for each entry reflect adjustment to the 

two core estimates generated for IHD and all-cause mortality) 2

 hybrid not run for Philadelphia, so multi-element sensitivity analysis not completed 

3

 the two pollutant model for PM2.5 and CO and PM2.5 and O3 had the same sensitivity result, so both models are 

referenced here with the same impact on mortality estimates. 

4

 Sensitivity analysis based on comparison of alternative model formulations to the core risk estimates based on the 

C-R function derived using 1999-2001 ambient monitoring data (see section 3.5.4). 

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

 risk estimates for Los Angeles and Philadelphia (IHD mortality). 

Percent total incidence attributable to PM2.5 (IHD mortality)

Los Angeles

Philadelphia

0 5 10 15 20 25

KEY:

- core risk estimate

- alternative reasonable risk estimate (from 

single- and multi-element sensitivity analyses)

Percent total incidence attributable to PM2.5 (IHD mortality)

Los Angeles

Philadelphia

0 5 10 15 20 25 0 5 10 15 20 25

KEY:

- core risk estimate

- alternative reasonable risk estimate (from 

single- and multi-element sensitivity analyses)
4-48

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

 risk estimates for Los Angeles and Philadelphia (all cause mortality). 

Percent total incidence attributable to PM2.5 (all cause mortality)

Los Angeles

Philadelphia

2 3 4 6 8

KEY:

- core risk estimate

- alternative reasonable risk estimate (from single- and multielement sensitivity analyses)

- alternative reasonable risk estimate for copollutant modeling 

including PM2.5 and SO2 (de-emphasized relative to other alternative 

risk estimates – see text)

0.5 1 5 7

Percent total incidence attributable to PM2.5 (all cause mortality)

Los Angeles

Philadelphia

2 3 4 6 8

KEY:

- core risk estimate

- alternative reasonable risk estimate (from single- and multielement sensitivity analyses)

- alternative reasonable risk estimate for copollutant modeling 

including PM2.5 and SO2 (de-emphasized relative to other alternative 

risk estimates – see text)

0.5 1 5 7

Review of the set of risk estimates presented in Table 4-4 and displayed in Figures 4-7 

and 4-8 results in a number of observations regarding uncertainty associated with the core risk 

estimates: 

 Consideration of uncertainty and variability in the core risk estimates results in a notable 

spread in risk estimates: Given the factors considered in generating the alternative set of 

reasonable risk estimates, there appears to be a factor of 2 to 3 spread in risk estimates if 

we consider the lowest (core) estimates generated and the highest alternative risk 

estimates generated. This observation holds for both urban study areas considered, as 

well as for the two mortality endpoint categories. As noted earlier in this section, we have 

de-emphasized risk estimates generated using the copollutant model involving PM2.5 and 

SO2 due to concerns with collinearity between the two pollutants and the potential that 

SO2 represents risk attributable to secondarily formed PM2.5. 

 Uncertainty set of risk estimates generated to supplement the core risk estimates are 

skewed towards higher risk: It appears that, given the factors considered in generating 

the alternative set of reasonable risk estimates, consideration of uncertainty could result 

in higher (more elevated) risk estimates, compared with the core risk estimates. In other 

words, most if not all of the alternative model specifications we considered resulted in 

risks that are higher than our core estimates. 
4-49

 Sensitivity analysis is limited in its scope (potentially important sources of uncertainty 

not considered): As noted earlier, the sensitivity analysis did not consider a number of 

potentially important sources of uncertainty, some of which were addressed as part of the 

qualitative analysis of uncertainty (see Table 3-13). For example, information is not 

available to consider compositional differences in PM2.5 and the potential for 

differentiation of effects estimates. Further, not considering more refined patterns of 

intra-urban exposure to PM2.5 in deriving effects estimates could result in underestimation of risk. 

It is important to reiterate that this set of alternative realizations presented in Table 4-4 

and depicted in Figures 4-7 and 4-8, does not represent an uncertainty distribution. Therefore, 

we can not assign percentiles to the individual data points presented and (importantly), we do not 

draw any conclusions based on any clustering of the alternative risk estimates seen in Figures 4-7 

and 4-8. Further, we do not know whether any of the higher-end estimates generated actually 

represent true bounding risk estimates given overall uncertainty associated with the core risk 

estimates. Despite these key caveats, having a set of risk estimates reflecting the impact of 

modeling element uncertainties does provide information that helps to inform our 

characterization of uncertainty related to the core risk estimates. 

4.4 EVALUATING THE REPRESENTATIVENESS OF THE URBAN STUDY AREAS 

IN THE NATIONAL CONTEXT 

The goal in selecting the 15 urban study areas included in this risk assessment was twofold: (a) to choose urban locations with relatively elevated ambient PM levels (in order to 

evaluate risk for locations likely to experience some degree of risk reduction under alternative 

standards) and (b) to include a range of urban areas reflecting heterogeneity in other PM riskrelated attributes across the country. To further support interpretation of risk estimates generated 

in this analysis, we included two analyses that assess the representativeness of the 15 urban study 

areas in the national context. First, we assessed the degree to which urban study areas represent 

the range of key PM2.5 risk-related attributes that spatially vary across the nation. We have 

partially addressed this issue by selecting urban study areas that provide coverage for different 

PM regions of the country (see section 3.3.2). In addition, we have evaluated how well the 

selected urban areas represent the overall U.S. for a set of spatially-distributed PM2.5 risk related 

variables (e.g., PM2.5 composition, weather, demographics including SES, baseline health 

incidence rates). This analysis, which is discussed in section 4.4.1, helps inform how well the 

urban study areas reflect national-level variability in these key PM risk-related variables. The 

second representativeness analysis, which is discussed in section 4.4.2, identified where the 

subset 31 counties comprising our 15 urban study areas fall along the distribution of national 

county-level long-term exposure-related mortality risk. This analysis allowed us to assess the 

degree of which the 15 urban study areas capture locations within the U.S. likely to experience 
4-50

elevated levels of risk related to PM2.5 exposure. To complete this second representativeness 

analysis, we completed a national-scale county-level analysis of long-term exposure-related 

mortality risk. 

4.4.1 Analysis Based on Consideration of National Distributions of Risk-Related 

Attributes 

As noted above, the first representativeness analysis evaluated how well the urban study 

areas reflect national-level variability in a series of PM risk-related variables.74 That analysis 

was conducted as follows. Based on generally available data (e.g. from the 2000 Census, Centers 

for Disease Control (CDC), or other sources), distributions for risk-related variables across U.S. 

counties and for the specific counties represented in the urban study areas were generated. The 

specific values of these variables for the selected urban study areas were then plotted on these 

distributions, and an evaluation was conducted of how representative the selected study areas are 

with respect to these individual variables, relative to the national distributions. 

Estimates of risk (either relative or absolute, e.g. number of cases) within our risk 

assessment framework are based on four elements: population, baseline incidence rates, air 

quality, and the coefficient relating air quality and the health outcome (i.e., the PM2.5 effect 

estimates). Each of these elements can contribute to heterogeneity in risk across urban locations, 

and each is variable across locations. In addition, there may be additional identifiable factors 

that contribute to the variability of the four elements across locations. In this assessment, we 

examine the representativeness of the selected urban area locations for the four main elements, 

and also provide additional assessment of factors that have been identified as influential in 

determining the magnitude of the C-R function across locations. 

The specific choice of variables which may affect the PM2.5 effect estimates for which we 

will examine urban study area representativeness is informed by an assessment of the 

epidemiology literature. We particularly focused on meta-analyses and multi-city studies which 

identified variables that influence heterogeneity in PM2.5 effect estimates, and exposure studies 

which explored determinants of differences in personal exposures to ambient PM2.5. While 

personal exposure is not incorporated directly into PM epidemiology studies, differences in the 

PM2.5 effect estimates between cities clearly is impacted by differing levels of exposure and 

differences in exposure are clearly related to a number of exposure determinants. Broadly 

 

74 In selecting these variables, we focused on variables that play a direct role in determining the relative 

magnitude of PM-attributable risk, including potential effect modifiers. We did not focus on confounders, as these 

were not primary factors we considered in selecting case study areas, and are not expected to impact the 

representativeness of our risk estimates. As such, we excluded consideration of variables such as SO2, which are 

potential confounders, but have not been identified as effect modifiers in the literature. 
4-51

speaking, determinants of the PM2.5 effect estimates used in risk assessment can be grouped into 

three areas: demographics, baseline health conditions, and climate and air quality. Based on a 

review of these studies, we identified the following variables within each group as potentially 

determining the PM2.5 effect estimates: 

 Demographics: education (see Zeka et al, 2006; Ostro et al, 2006), age and gender (see 

Zeka et al, 2006), population density (see Zeka et al, 2005), unemployment rates (see Bell 

and Dominici, 2008), race (see Bell and Dominici, 2008), public transportation use (see 

Bell and Dominici, 2008), 

 Baseline health conditions: disease prevalence (diabetes – Bateson and Schwartz, 2004; 

Ostro et al, 2006; Zeka et al, 2006; pneumonia – Zeka et al, 2006; stroke – Zeka et al, 

2006; heart and lung disease – Bateson and Schwartz, 2004; acute myocardial infarction 

– Bateson and Schwartz, 2004). 

 Climate and air quality: PM2.5 levels (average, 98th percentiles, and numbers of days over 

the level of the 24-hour standard, e.g. 35 μg/m3

), co-pollutant levels, PM composition 

(see Bell et al, 2009; Dominici et al, 2007; Samet, 2008; Tolbert, 2007), temperatures 

(temp) (days above 90 degrees, variance of summer temp, mean summer temp, 98th

percentile temp, mean winter temp -- see Roberts, 2004; Medina-Ramon et al, 2006; Zeka 

et al., 2005), air conditioning prevalence (see Zanobetti and Schwartz, 2009; Franklin et 

al, 2007; Medina-Ramon et al, 2006), ventilation (see Sarnat et al, 2006), percent of 

primary PM from traffic (see Zeka et al., 2005), 

Based on these identified potential risk determinants, we identified possible datasets that 

could be used to generate nationally representative distributions for each parameter. We were 

not able to identify readily available national datasets for all variables. In these cases, if we were 

able to identify a broad enough dataset covering a large enough portion of the U.S., we used that 

dataset to generate the parameter distribution. In addition, we were not able to find exact 

matches for all of the variables identified through our review of the literature. In cases where an 

exact match was not available, we identified proxy variables to serve as surrogates. For each 

parameter, we report the source of the dataset, its degree of coverage, and whether it is a direct 

measure of the parameter or a proxy measure. The target variables and sources for the data are 

provided in Table 4-5. Summary statistics for the most relevant variables are provided in Table 

4-6. 
4-52

Table 4-5 Data Sources for PM NAAQS Risk Assessment Risk Distribution 

Analysis 

Potential Risk 

Determinant Metric Year Source 

Degree of 

National 

Coverage 

Demographics 

Age Median Age 2005 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Age Percent over 65 2005 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Age Percent under 15 2005 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Education Population with less 

than HS diploma 2000 

USDA/ERS, 

http://www.ers.usda.gov/Data/Edu

cation/ 

All counties 

Unemployment Percent unemployed 2005 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Income Per Capita Personal 

Income 2005 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Race Percent nonwhite 2006 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Population Total population 2008 

Cumulative Estimates of Resident 

Population Change for the United 

States, States, Counties, Puerto 

Rico, and Puerto Rico Municipios: 

April 1, 2000 to July 1, 2008, 

Source: Population Division, U.S. 

Census Bureau 

All counties 

Population density Population/square mile 2008 

Cumulative Estimates of Resident 

Population Change for the United 

States, States, Counties, Puerto 

Rico, and Puerto Rico Municipios: 

April 1, 2000 to July 1, 2008, 

Source: Population Division, U.S. 

Census Bureau 

All counties 

Urbanicity ERS Classification 

Code 2003 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Climate and Air Quality 

PM2.5 Levels PM2.5 Levels -- 

Monitored Ann Mean 2007 AQS 617 Monitored counties 

PM2.5 Levels PM2.5 Levels -- 

Monitored 98th %ile 2007 AQS 617 Monitored counties 

PM2.5 Levels Average MCAPS MCAPS website 204 counties 204 MCAPS 

counties 
4-53

Potential Risk 

Determinant Metric Year Source 

Degree of 

National 

Coverage 

PM2.5 Levels % days exceeding 35 

μg/m3 MCAPS website 204 counties 204 MCAPS 

counties 

Copollutant Levels Ozone AQS 725 Monitored 

counties 

Roadway 

emissions/Exposure 

% of primary emissions 

from traffic 1999 NEI All counties 

Temperature Annual Average MCAPS website 204 counties 204 MCAPS 

counties 

Temperature Mean July Temp 1941-

1970 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Relative Humidity Mean July RH 1941-

1970 

County Characteristics, 2000-2007 

Inter-university Consortium for 

Political and Social Research 

All counties 

Ventilation Air conditioning 

prevalence 2005 

American Housing Survey, with 

additional processing as in Reid et 

al (2009) 

83 urban areas 

Baseline Health Conditions

Baseline Mortality All Cause CDC Wonder 1999-2005 All counties 

Baseline Mortality Non Accidental CDC Wonder 1999-2006 All counties 

Baseline Mortality Cardiovascular CDC Wonder 1999-2007 All counties 

Baseline Mortality Respiratory CDC Wonder 1999-2008 All counties 

Baseline Morbidity AMI prevalence 2007 BRFSS MSA estimates 184 BRFSS 

MSA 

Baseline Morbidity Diabetes Prevalence 2007 BRFSS MSA estimates 184 BRFSS 

MSA 

Baseline Morbidity Pneumonia Prevalence 

Baseline Morbidity Stroke Prevalence 2007 BRFSS MSA estimates 184 BRFSS 

MSA 

Baseline Morbidity CHD Prevalence 2007 BRFSS MSA estimates 184 BRFSS 

MSA 

Baseline Morbidity COPD Prevalence 

Obesity BMI 2007 BRFSS MSA estimates 184 BRFSS 

MSA 

Level of exercise vigorous activity 20 

minutes 2007 BRFSS MSA estimates 184 BRFSS MSA 

Level of exercise 

moderate activity 30 

minutes or vigorous 

activity 20 minutes 

2007 BRFSS MSA estimates 184 BRFSS 

MSA 

Respiratory Risk 

Factors Current Asthma 2007 BRFSS MSA estimates 184 BRFSS MSA 

Smoking Ever Smoked 2007 BRFSS MSA estimates 184 BRFSS 

MSA 

C-R Estimates 

Mortality Risk All Cause 2009 Zanobetti and Schwartz (2009) 212 

cities 212 cities 

Mortality Risk Respiratory 2009 Zanobetti and Schwartz (2009) 212 

cities 212 cities 

Mortality Risk Cardiovascular 2009 Zanobetti and Schwartz (2009) 212 

cities 212 cities 
4-54

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

Average Standard Deviation Maximum Minimum 

Risk Attributes 

Urban study 

areas 

U.S. 

counties 

Urban study 

areas 

U.S. 

counties 

Urban study 

areas 

U.S. 

counties 

Urban study 

areas 

co

Demographics Population 1,410,331 97,020 1,870,237 312,348 9,862,049 9,862,049 57,441 Population Density (Pop/sq mile) 7,212 258 14,960 1,757 71,758 71,758 87 Median Age (years) 35.5 38.6 2.6 4.4 41.5 55.3 30.2 % Age 65 Plus 11.3 14.9 2.6 4.1 17.2 34.7 5.8 Unemployment rate (%) 5.4 5.4 1.5 1.8 9.0 20.9 2.7 % with Less than High School Diploma 21.8 22.6 7.7 8.8 37.7 65.3 11.2 Income ($2005) 35691 27367 12605 6604 93377 93377 23492 5Air conditioning prevalence (%) 85.8 83.3 13.3 21.5 99.4 100.0 58.6 % Non-white 29.5 13.0 18.2 16.2 68.3 95.3 2.7 Health Conditions Prevalence of CHD (%) 3.9 4.3 0.9 1.3 5.2 8.7 1.8 Prevalence of Obesity (%) 26.4 26.0 3.0 4.1 32.7 35.7 22.2 Prevalence of Stroke (%) 2.7 2.7 0.8 1.0 4.1 6.5 1.1 Prevalence of Smoking (ever) (%) 18.4 19.6 3.1 4.0 23.1 34.4 14.2 Prevalence of Exercise (20 minutes) (%) 28.4 28.0 3.6 4.8 33.9 44.1 20.5 All Cause Mortality (per 100,000 population) 833.7 1022.3 241.1 258.6 1342.9 2064.2 402.5 1Non-accidental Mortality (per 100,000 population) 774.1 950.6 227.3 249.6 1242.0 1958.4 361.6 1Cardiovascular Mortality (per 100,000 population) 317.5 392.1 100.6 121.0 535.7 970.4 122.4 Respiratory Mortality (per 100,000 population) 70.8 97.3 23.0 32.3 130.3 351.0 34.8 Air Quality and Climate AQ - PM25 Annual Mean (μg/m3) 15.1 11.7 2.2 3.1 19.6 22.5 9.7 AQ - PM25 98th %ile 24-hour Average (μg/m3) 38.7 30.7 11.6 9.3 79.2 81.1 26.8 AQ - O3 4th High Maximum 8-hour Average (ppm) 0.087 0.077 0.009 0.010 0.105 0.126 0.064 0% Mobile Source PM Emissions 34.0 44.4 11.2 21.9 56.6 97.6 13.7 
4-55

Average Standard Deviation Maximum Minimum 

Risk Attributes 

Urban study 

areas 

U.S. 

counties 

Urban study 

areas 

U.S. 

counties 

Urban study 

areas 

U.S. 

counties 

Urban study 

areas 

co

July Temperature Long Term Average (°F) 78.1 75.9 4.5 5.4 91.2 93.7 64.8 July Relative Humidity Long Term Average (°F) 58.2 56.2 14.0 14.6 70.0 80.0 19.0 C-R Estimates All Cause Mortality PM2.5 Risk Estimate 0.000971 0.000974 0.000340 0.000216 0.001349 0.001508 0.000159 -0.0Respiratory Mortality PM2.5 Risk Estimate 0.001606 0.001670 0.000419 0.000305 0.002157 0.002221 0.000931 -0.0Cardiovascular Mortality PM2.5 Risk Estimate 0.001013 0.000842 0.000586 0.000324 0.001958 0.001958 -0.000180 -0.0
4-56

Formal comparisons of parameter distributions for the set of urban study areas and the 

national parameter distributions are conducted using standard statistical tests, e.g. the 

Kolmogorov-Smirnov non-parametric test for equality of distributions. In addition, visual 

comparisons are made using cumulative distribution functions, and box plots. 

The formal Kolmogorov-Smirnov (K-S) test results are provided in Table 4-7. The K-S 

tests the hypotheses that two distributions are not significantly different. A high p-value 

indicates a failure to reject the null hypotheses that the case-study and national distributions are 

the same. We used a rejection criterion of p≤0.05, which is a standard rejection criterion. It 

should be noted that the K-S test provides a good overall measure of fit, but will not provide a 

test of how well specific percentiles of the distributions are matched. As such, the K-S test 

results will not be sufficient to determine whether the urban study areas adequately capture the 

tails of the distributions of specific risk related variables. Additional visual analyses are used to 

assess representativeness for the tails of the distributions. Overall, the K-S test results show that 

for many of the important risk variables such as population, air quality, age, and baseline 

mortality rates, the urban study areas are not representative of the distributions of these variables 

for the U.S. as a whole. However, for some important potential risk determinants, such as 

prevalence of underlying hear and lung diseases, the case study areas are representative of the 

national distributions. However, for these specific variables, the national distribution is 

represented primarily by large urban areas, so it is more accurate in these cases to suggest that 

the urban study areas are representative of the overall distribution across urban areas. 

Figures 4-9 through 4-12 show for the four critical risk function elements (population, air 

quality, baseline incidence, and the PM2.5 effect estimate) the cumulative distribution functions 

plotted for the nation, as well as for the urban study areas. These four figures focus on critical 

variables representing each type of risk determinant, e.g. we focus on all-cause mortality rates, 

but we also have conducted analyses for cardiovascular and respiratory mortality separately. The 

complete set of analyses is provided in Appendix D. The vertical black lines in each graph show 

the values of the variables for the individual urban study areas. These figures show that the 

selected urban study areas represent the upper percentiles of the distributions of population and 

air quality, while not representing lower population locations with lower 24-hour PM2.5 levels. 

This is consistent with the objectives of our case study selection process, e.g. we are 

characterizing risk in areas that are likely to be experiencing excess risk due to PM levels above 

alternative standards. The urban case study locations represent the full distribution of PM2.5 risk 

coefficients, but do not capture the upper end of the distribution of baseline all-cause mortality. 

The interpretation of this is that the case study risk estimates may not capture the additional risk 

that may exist in locations that have the highest baseline mortality rates. 
4-57

Figures 4-13 through 4-16 shows for several selected potential risk attributes the 

cumulative distribution function (CDF) plotted for the nation as well as for the urban study areas. 

These potential risk attributes do not directly enter the risk equations, but have been identified in 

the literature as potentially affecting the magnitude of the PM2.5 C-R functions reported in the 

epidemiological literature. The selected urban study areas do not capture the higher end 

percentiles of several risk characteristics, including populations over 65, income, and baseline 

cardiovascular disease prevalence. Comparison graphs for other risk attributes are provided in 

Appendix D. Summarizing the analyses of the other risk attributes, we conclude that the urban 

study areas provide adequate coverage across population, population density, annual and 24-hour 

PM2.5 levels, ozone co-pollutant levels, temperature and relative humidity, unemployment rates, 

percent non-white population, asthma prevalence, obesity prevalence, stroke prevalence, exercise 

prevalence, and less than high school education. We also conclude that while the urban study 

areas cover a wide portion of the distributions, they do not provide coverage for the upper end of 

the distributions of age (all case study locations are below the 85th percentile), % of population 

65 and older (below 85th percentile), percent of primary PM emissions from mobile sources 

(below 8oth percentile), prevalence of angina/coronary heart disease (below 85th percentile), 

prevalence of diabetes (below 85th percentile), prevalence of heart attack (below 80th percentile), 

prevalence of smoking (below 85th percentile), all-cause mortality rates (below 90th %ile), 

cardiovascular mortality rates (below 90th percentile) and respiratory mortality rates (below 90th

percentile). In addition, all of the case study locations were above the 25th percentile of the 

distribution of personal income. 

Based on the above analyses, we can draw several inferences regarding the 

representativeness of the urban case studies. First, the case studies represent urban areas that are 

among the most populated and most densely population in the U.S. Second, they represent areas 

with relatively higher levels of annual mean and 24-hour 98th percentile PM2.5. Third, they 

capture well the range of effect estimates represented in the Zanobetti and Schwartz (2009) 

study. These three factors would suggest that the urban study areas should capture well overall 

risk for the nation, with a potential for better characterization of the high end of the risk 

distribution. However, there are several other factors that suggest that the urban study areas may 

not be representing areas that may have a high risk per microgram of PM2.5. The analysis 

suggests that the urban study areas are not capturing areas with the highest baseline mortality 

risks, nor those with the oldest populations. These areas may have higher risks per microgram of 

PM2.5, and thus the high end of the risk distribution may not be captured, although the impact on 

characterization of overall PM risk may not be as large, for the following reasons. 

It should be noted that several of the factors with underrepresented tails, including age 

and baseline mortality (R=0.81) are spatially correlated, so that certain counties which have high 
4-58

proportions of older adults also have high baseline mortality and high prevalence of underlying 

chronic health conditions. Because of this, omission of certain urban areas with higher 

percentages of older populations, for example, cities in Florida, may lead to underrepresentation 

of high risk populations. However, with the exception of areas in Florida, most locations with 

high percentages of older populations have low overall populations, less than 50,000 people in a 

county. And even in Florida, the counties with the highest PM2.5 levels do not have a high 

percent of older populations. This suggests that while the risk per exposed person per microgram 

of PM2.5 may be higher in these locations, the overall risk to the population is likely to be within 

the range of risks represented by the urban case study locations. 

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

 and Urban Study Area Distributions for Selected National Risk 

 Characteristic Variables 

 (null hypothesis is no difference between the distributions) 

Risk Attributes Reject H0? p-value 

Demographics 

Population Y 0.0001 

Population Density (Pop/sq mile) Y 0.0001 

Median Age Y 0.0001 

% Age 65 Plus Y 0.0001 

Unemployment rate N 0.5850 

% with Less than High School Diploma N 0.8535 

Income Y 0.0001 

Air Conditioning Prevalence (%) N 0.9592 

% Non-white Y 0.0001 

Health Conditions 

Prevalence of CHD N 0.7705 

Prevalence of Obesity N 0.9180 

Prevalence of Stroke N 0.7064 

Prevalence of Smoking (ever) N 0.5748 

Prevalence of Exercise (20 minutes) N 0.7649 

All Cause Mortality Y 0.0001 

Non-accidental Mortality Y 0.0002 

Cardiovascular Mortality Y 0.0060 

Respiratory Mortality Y 0.0001 

Air Quality and Climate 

AQ - PM25 Annual Mean Y 0.0001 

AQ - PM25 98th %ile 24-hour Average Y 0.0001 

AQ - PM25 % of days above 35 μg/m3 Y 0.0248 

AQ - O3 4th High Maximum 8-hour 

Average Y 0.0003 
4-59

Risk Attributes Reject H0? p-value 

% Mobile Source PM Emissions Y 0.0133 

July Temperature Long Term Average Y 0.0003 

July Relative Humidity Long Term 

Average N 0.0614 

C-R Estimates 

All Cause Mortality PM2.5 Risk N 0.1585 

Respiratory Mortality PM2.5 Risk N 0.2864 

Cardiovascular Mortality PM2.5 Risk N 0.1161 

 

Figure 4-9 Comparison of distributions for key elements of the risk equation: 

 total population. 

Comparison of Urban Case Study Area Population with U.S. Distribution of 

Population (all U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 1000 10000 100000 1000000 10000000

Population

% of Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all above the 65th Percentile 

of county populations
4-60

Figure 4-10 Comparison of distributions for key elements of the risk equation: 

 98th percentile 24-hour average PM2.5

Comparison of Urban Case Study Area 98th %ile PM2.5 with U.S. Distribution of 

98th %ile PM2.5 

(617 U.S. Counties with PM2.5 Monitors)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90

98th Percentile Daily PM2.5

% of Monitored Counties

All Counties CDF Case Study Counties CDF Case Study Counties
4-61

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

 use mortality rate.

Comparison of Urban Case Study All Cause Mortality Rate to U.S. Distribution of All 

Cause Mortality Rate

(3143 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 300 500 700 900 1100 1300 1500 1700 1900 2100

All Cause Mortality per 100,000 Population

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 90th percentile 

of county all cause mortality
4-62

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

 Mortality risk effect estimate from Zanobetti and Schwartz (2008). 

Comparison of Urban Case Study PM All-cause Mortality Risk (β) to 

U.S. Distribution of PM All-cause Mortality Risk

(212 U.S. Urban Areas)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014 0.0016

Bayesian Shrunken PM All Cause Mortality Risk Coefficient (β)

% of Z&S Urban Areas

All Z&S Urban Areas CDF Case Study Urban Area CDF Case Study Urban Areas
4-63

Figure 4-13 Comparison of distributions for selected variables expected to 

 influence the relative risk from PM2.5: long term average July 

 temperature. 

Comparison of Urban Case Study Area Long Term Average July Temperature to 

U.S. Distribution of Long Term Average July Temperature

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

50 60 70 80 90 100

July 30 Year Average Temperature, 1941-1970

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties
4-64

Figure 4-14 Comparison of distributions for selected variables expected to 

 influence the relative risk from PM2.5: percent of population 65 and 

 older. 

Comparison of Urban Case Study Area % 65 and Older to U.S. Distribution of % 65 

and Older

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25 30 35

% of Population 65 and Older, 2005

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 75th percentile 

of county % of population 65 

and older
4-65

Figure 4-15 Comparison of distributions for selected variables expected to 

 influence the relative risk from PM2.5: per capita annual personal 

 income. 

Comparison of Urban Case Study Area Per Capita Personal Income to U.S. 

Distribution of Per Capita Personal Income

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

$10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000 $90,000

Per Capita Personal Income, 2005

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all above the 25th percentile 

of county per capita income
4-66

Figure 4-16 Comparison of distributions for selected variables expected to 

 influence the relative risk from PM2.5: per capita annual personal 

 income. 

4.4.2 Analysis Based on Consideration of National Distribution of PM-Related Mortality 

Risk 

In this section we discuss the second representativeness analysis which identified where 

the subset of 31 counties comprising the 15 urban study areas fall along a distribution of 

estimated national-scale mortality risk. The national-scale mortality analysis which underpins 

this representativeness analysis used 2005 PM2.5 fused air quality estimates from the Community 

Model for Air Quality (CMAQ) (Byun and Schere, 2006) in conjunction with the environmental 

Benefits Mapping and Analysis Program (BenMAP, Abt Associates Inc, 2008) to estimate longterm PM2.5-related premature mortality nationwide at the county-level. In relating the 31 counties 

comprising the 15 urban study areas to the national county-level distribution of risk, we did not 

directly compare the 31 county-level risks and the county-level risks generated in the nationalscale analysis. Rather, we identify where the 31 counties modeled for urban case study fell along 

the national risk distribution. This assessment revealed whether the baseline PM2.5 mortality 

Comparison of Urban Case Study Area Angina/CHD Prevalence to U.S. 

Distribution of Angina/CHD Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9

% Prevalence of Angina or CHD, BRFSS, 2007

% of BRFSS MSA

All BRFSS MSA CDFCase Study County MSA CDFCase Study County MSA

Urban case study areas are 

all below the 85th percentile 

of MSA angina or CHD 

prevalence
4-67

risks in the 31 counties modeled in the urban case study areas represented more typical or higherend risk relative to the national risk distribution. 

The results of this representativeness analysis are presented graphically in Figure 4-17, 

which displays the cumulative distribution of total mortality attributable to PM2.5 exposure at the 

county level developed as part of the national-scale analysis. The location of the 31 counties 

included in the urban case study analysis is then superimposed on top of the cumulative 

distribution. 

Figure 4-17 Cumulative distribution of county-level percentage of total 

 mortality attributable to PM2.5 for the U.S. with markers 

 identifying where along that distribution the urban case study area 

 analysis fall* 

The results of this analysis, as depicted in Figure 4-17, indicate that most of the 31 

counties comprising the 15 urban study areas fall toward the upper end of the national risk 

distribution and that 23 of these counties fall within the upper 5th percentile of the risk 

distribution -- suggesting that the PM2.5 mortality risk estimates included in the urban case study 

analysis generally represent the upper end of urban area mortality risks within the nation. 

Additional details on this second representativeness analysis, together with discussion of the 

national-scale mortality assessment underpinning the analysis, are presented in Appendix G. 
4-68

4.5 CONSIDERATION OF DESIGN VALUES AND PATTERNS OF PM2.5

MONITORING DATA IN INTREPRETING CORE RISK ESTIMATES 

The degree of risk reduction associated with the current and alternative suites of 

standards at a particular urban study area depends to a great extent on the degree of reduction in 

PM2.5 concentrations simulated for that location. This in turn depends on the interplay between 

the 24-hour and annual design values and the monitoring data used to characterize ambient PM2.5

concentrations, since these factors determine the composite annual average and composite 24-

hour PM2.5 profiles used in modeling long-term and short-term exposure related risk for that 

study area.75 Because of the role that design values and underlying patterns in PM2.5 monitoring 

data play in determining the degree of risk reductions, these factors can be used in helping to 

interpret risk estimates generated for the 15 urban study areas under the various standard levels 

considered in this risk assessment. Further, it is possible to consider, more broadly, patterns of 

design values across urban areas in the U.S. and contrast these with patterns seen for the 15 

urban study areas to help to place risk estimates for the 15 urban study areas in a broader national 

context. 

This section discusses consideration of patterns of design values (section 4.5.1) and 

underlying ambient monitoring PM2.5 data (section 4.5.2) for the 15 urban study areas in the 

context of helping to interpret risk estimates. Each of these discussions begins by describing the 

methods used in each analysis and concludes with a set of key observations. 

4.5.1 Design Values 

The set of design values for an urban study area determines whether the 24-hour or 

annual standard will be controlling as well as the degree of reduction in ambient PM2.5

concentrations associated with a particular suite of standards. Therefore, by plotting the 

relationship between 24-hour and annual design values for each of the 15 urban study areas, we 

can obtain a quick visual perspective on (a) which study areas will experience reductions in risk 

for a particular suite of standards, (b) whether the 24-hour or annual standard will control, and 

(c) the general magnitude of risk reduction. The last observations result from comparing the 

controlling standard level with the matching design value, which will determine the fractional 

reduction in PM2.5 levels at monitors exceeding the standard level (for locally focused rollback), 

or more broadly across all monitors (for proportional rollback). 

Figures 4-18 through 4-20 present scatter plots of 24-hour and annual design values for a 

combination of the 15 urban study areas (red stars) and the broader set of larger urban areas in 

the U.S. (green circles). In addition to depicting the set of design values for these urban areas, 

 

75 See section 3.2.3.1 for additional detail on derivation of 24-hour and annual design values. 
4-69

each figure also includes a set of superimposed lines representing the current suite of standards 

(Figure 4-18) and three of the alternative suites of standards considered in the risk assessment 

(12/35 – Figure 4-19, and 12/25 – Figure 4-20). In each figure, the horizontal line represents the 

24-hour standard level, while the vertical line represents the annual standard level. The line that 

intercepts the origin (i.e., the “35/15 line” in Figure 4-18) represents the point of demarcation 

between those study areas where the 24-hour standard controls (to the left of the intercept line) 

and those study area where the annual standard level controls (to the right of the intercept line). 

By superimposing these lines related to the current standard level on the scatter plot, we have 

created five zones within each figure including: 

 Zone A: 24-hour design values exceeding the 24-hour standard level, but annual design 

values below the annual standard level (i.e., 24-hour standard is controlling). Urban study 

areas in this zone are predicted to experience risk reduction with the degree of reduction 

reflecting the degree to which the 24-hour design value exceeds the 24-hour standard level. 

For example, in Figure 4-18 (depicting the current suite of standards), Tacoma and Salt Lake 

City fall in this zone, along with 20-30 additional urban areas in the U.S. 

 Zone B: 24-hour design values and annual design values exceed 24-hour and annual 

standard levels, respectively, and the 24-hour standard is controlling. We have further 

transected this zone into B1 and B2, with the former representing those urban areas with 

notably high 24-hour design values (Fresno, Los Angeles in Figure 4-18) and B2 those with 

lower, although still controlling, 24-hour design values (Pittsburgh, New York, and Detroit in 

Figure 4-18). Those urban areas in B1 have exceptionally peaky PM2.5 distributions relative 

to urban areas in B2 (i.e., relatively high 24-hour design values and lower annual average 

design values). 

 Zone C: 24-hour design values and annual design values exceed 24-hour and annual 

standard levels, respectively, and the annual standard is controlling. Atlanta, Birmingham 

and Houston fall into this zone and represent a relatively small number of urban areas in the 

U.S.. 

 Zone D: annual design values exceed the annual standard level, but 24-hour design values 

are below the 24-hour standard level (i.e., annual standard is controlling). Houston is the only 

urban study area falling into this zone for the current standard level, along with a small 

number of additional urban areas in the U.S.. 

 Zone E: both the 24-hour and annual design values are below their respective standard levels 

(i.e., this is the only zone where urban areas would not be expected to experience risk 

reductions under the suite of standards being considered). The majority of urban areas in the 

U.S. depicted in these scatter plots fall into Zone E in Figure 4-18. 

The five zones presented above are useful in interpreting the risk results generated for the 

current suite of standards (for the 15 urban study areas). Specifically, as noted above, they allow 

us to (a) quickly identify which of the 15 urban study areas experience risk reductions under the 

current standard level, (b) determine whether those reductions are due primarily to a controlling 
4-70

24-hour or annual standard and (c) to see how well our set of urban study areas provide coverage 

for the broader set of urban areas in the U.S.. We do note that three of the 15 urban study areas 

(Baltimore, St. Louis and Birmingham) fall on or near lines demarcating zones B and C depicted 

in Figures 4-18 and consequently can not be definitively assigned to either zone. While these 

three urban study areas are assessed not to be in attainment of either the current annual or 24-

hour standard level, neither of these standards is definitively controlling for these urban study 

areas. 

In addition to presenting Figures 4-18 through 4-20 as a means for supporting the 

interpretation of risk estimates generated for the 15 urban study areas (based on consideration of 

patterns in design values), we have also included Table 4-8 for this purpose. Table 4-8 presents 

the annual and 24-hour design values for each urban study area and also identifies which 

standard is controlling for a given suite of standards. For example, we see that in Atlanta (which 

has design values of 16.2 μg/m3

 and 35 μg/m3, annual and 24-hour, respectively), the annual 

standard controls for the current suite of standards (15/35) as well as the first 4 alternative suites 

of standards considered (14/35, 13/35, 12/35 and 13/30). However, the 24-hour standard controls 

for the final suite of standards considered (12/25). This matches with information presented in 

Figures 4-18 through 4-20 (e.g., Figure 4-18 shows that the Atlanta is just inside of zone C, 

suggesting that it meets the 24-hour standard, but not the annual standard). 
4-71

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

 areas relative to the current suite of standards (15/35) 

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 

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 Baltimore

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Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

35/15 line 

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Houston

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35/15 line 

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4-72

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

 areas relative to the 12/35 alternative suite of standards 

A B

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Fresno

Houston

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35/12 line 

Key:

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- MSA’s within the U.S. 

B1

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Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

35/12 line 

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 

B1

B2
4-73

Figure 4-20 Design values in 15 urban study areas and broader set of U.S. urban 

 areas relative to the 12/25 alternative suite of standards 

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 

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 Baltimore

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Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

25/12 line 

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Atlanta

Birmingham

 Baltimore

LA

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Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

25/12 line 

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Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

25/12 line 

A B

C

D

E

B1

B2

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 

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Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

25/12 line 

A B

C

D

E

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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

25/12 line 

0

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30

35

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45

50

55

60

65

70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Atlanta

Birmingham

 Baltimore

LA

NY

SLC

Pittsburgh

Pitt-Liiberty

Phoenix 

Dallas

Detroit Tacoma

Philadelphia St. Louis

Fresno

Houston

Annual Design Value (μg/m3)

24-hour Design Value (μg/m3)

25/12 line 

A B

C

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B1

B2
4-74

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

 alternative suites of standard levels 

Combination of annual and 24-hour design values* 

Design Value

Current 

standard levels 

Alternative annual standard 

levels 

Combinations of 

alternative 24-hour

and annual standard 

levels 

Urban study area Annual 24-Hr 15/35 14/35 13/35 12/35 13/30 12/25 

 Atlanta, GA 16.2 35 A A A A A 24hr 

 Baltimore, MD 15.6 37 24hr A A A 24hr 24hr 

 Birmingham, AL 18.7 44 A A A A A 24hr 

 Dallas, TX 12.8 26 - - - A A A 

 Detroit, MI 17.2 43 24hr A A A 24hr 24hr 

 Fresno, CA 17.4 63 24hr 24hr 24hr 24hr 24hr 24hr 

 Houston, TX 15.8 31 A A A A A A 

 Los Angeles, CA 19.6 55 24hr 24hr 24hr A 24hr 24hr 

 New York, NY 15.9 42 24hr 24hr A A 24hr 24hr 

 Philadelphia, PA 15.0 38 24hr 24hr A A 24hr 24hr 

 Phoenix, AZ 12.6 32 - - - A A 24hr 

 Pittsburgh, PA 5 19.8 60 24hr 24hr 24hr 24hr 24hr 24hr 

 Salt Lake City, 

UT 

11.6 55 24hr 24hr 24hr 24hr 24hr 24hr 

 St. Louis, MO 16.5 39 24hr A A A 24hr 24hr 

 Tacoma, WA 10.2 43 24hr 24hr 24hr 24hr 24hr 24hr 

* “24hr” denotes that the 24-hour standard is controlling. “A” denotes that the annual standard is 

controlling 

Based on consideration of the zones defined in Figures 4-18 through 4-20, we can make 

the following observations regarding potential patterns of risk reduction across urban study areas 

in the U.S., given the current and alternative suites of standards considered. Further, we can 

characterize the degree to which the 15 urban study areas provide coverage for these groupings 

of U.S. urban study areas: 

 For the current suite of standards (see Figure 4-18), Based on 2005-2007 air quality data, 

most urban areas in the country meet the current standards based on 2005-2007 air quality 

data (zone E). A smaller but still notable number meet the current annual standard but do not 

meet the current 24-hour standard (Zone A). A similar number of areas do not meet either 

current standard (zones B and C). Only a few areas do not meet the current annual standard, 

but do meet the current 24-hour standard (zone D). Of the 15 urban study areas included in 

the risk assessment most fall into zones that do not meet either standard (zones B and C) 

although some study areas are in each of the other zones. 

 Alternative suites of standards involving reduction of the annual standard levels (see Figure 

4-19) Based on 2005-2007 air quality data, as shown in Figure 4-19, reduction in the annual 

standard level down to 12 μg/m3

 results in a significant increase in the number of areas that 

do not meet the annual standard (zones C and D). And of those areas, roughly similar 
4-75

numbers of urban areas do meet the 24hr standard as do not meet the 24hr standard 

(comparing numbers of urban areas in B and C to the number in zone D). 

 Alternative suite of standards involving reductions in both annual and 24-hour levels (see 

Figure 4-20): Based on 2005-2007 air quality data, a large fraction of urban areas are 

predicted not to meet the 24hr standard (zones A, B and C). Furthermore, the majority of 

these have the 24hr controlling (zone A and B). We also note that there are virtually no urban 

areas that exceed the annual standard while meeting the 24hr standard (zone C). Of the 15 

urban study areas, most do not meet either the 24hr or annual standards, while the 24hr is 

controlling in most (zone B). 

An additional factor to consider in examining the relationship between annual and 24-

hour design values for the set of larger urban areas in the U.S. is regionality (i.e., do we see 

regional differences in the relationship between annual and 24-hour design values across the 15 

urban study areas and the broader set of urban areas in the U.S?) To examine this issue, we color 

coded the urban areas depicted in Figure 4-18 by PM region to produce a new scatter diagram 

that allows us to look for regional patterns in the mix of annual and 24-hour design values across 

the urban study areas (see Figure 4-21 – note that as with Figure 4-18, Figure 4-21 provides a 

scatter diagram referenced on the current suite of standard levels: 15/35). Visual inspection of 

Figure 4-21 suggests that, while urban locations in southern California and the Northwest tend to 

dominate locations with relatively elevated 24-hour design values (i.e., urban locations with 

higher 24-hour design values in zones A and B), the picture is less clear with regard to regional 

patterns in other zones of the scatter diagram. When we look at portions of zones A and B closer 

to the current 24-hour standard line, or in zones C, D or E, we do not see patterns of urban study 

areas differentiated by PM region. 
4-76

Figure 4-21 Design values in 15 urban study areas and broader set of U.S. urban 

 areas relative to the current standard (with regional differentiation) 

 
4-77

4.5.2 Patterns in PM2.5 Monitoring Data 

As noted earlier, patterns in PM2.5 monitoring data for each of the 15 urban study areas 

can be used (together with consideration of design values as described in section 4.5.1) to 

support interpretation of risk estimates generated for current and alternative standard levels. This 

is particularly true when considering the impact of using different rollback methods in 

supporting risk characterization for current and alternative standard levels, as discussed below. 

To facilitate consideration of patterns in PM2.5 monitoring data across the 15 urban study 

areas, we have developed Figures 4-22and 4-23. Each of these figures presents 24-hour and 

annual design values (blue and green dots, respectively) for each PM2.5 monitor within each 

study area. The figures also flag the highest design values for each study area (red and brown 

stars for the annual and 24-hour standard levels, respectively).76 Each figure has been scaled to 

represent a particular suite of standards, with Figure 4-22 scaled to represent the current suite of 

standards (15/35) and Figure 4-23 scaled to represent the 12/25 alternative suite of standards.77 

In addition, the figures allow identification of whether a study area had the highest design value 

(for the 24-hour and annual averaging periods) occurring at the same or at different monitors. 

This factor can influence the degree to which simulation of a controlling 24-hour standard level, 

given application of the locally focused rollback approach, results in reduction in annual average 

PM2.5 levels for that study area. If an area has both 24-hour and annual design values occurring at 

the same monitor, then application of locally focused rollback to reduce the controlling 24-hour 

standard will also bring down the annual design value (i.e., the annual average PM2.5 level for 

that study area is likely to be reduced to a greater extent). By contrast, if 24-hour and annual 

design values are located at different monitors, then locally focused rollback focused on 

reduction of the 24-hour design value monitor will potentially not impact the annual design value 

(i.e., there will be a smaller impact on the annual average PM2.5 level for that study area).78 

 

76 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. 77 For example, in Figure 4-22, 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). 78 When a star in either Figure 4-22 or 4-23 (signifying the highest design value for that study area) is 

placed over a point estimate, then the highest design value (for both 24-hour and annual levels) occurs at different 

monitors. This is the case, for example, with Phoenix, while Los Angeles represents a location where the highest 24-

hour and annual design values occur at the same monitor. 
4-78

To gain a better understanding of the information provided in Figures 4-22 and 4-23, we 

will provide a walkthrough for one of the urban study areas, highlighting key attributes related to 

24-hour and annual design values. With Los Angeles (in Figure 4-22) we see that the study area 

has a relatively wide spread in 24-hour and annual design values across the monitors (i.e., it has a 

relatively peaky PM2.5 distribution), with 24-hour values ranging from ~15 to ~55 and annual 

design values ranging from ~7 to ~19 (exact values are presented in Appendix A). In addition, 

we see that the 24-hour standard is clearly controlling, given how much farther the highest viable 

24-hour design value is from the 15/35 line compared with the highest annual design value. In 

addition, we can compare these trends in 24-hour and annual design values for Los Angeles to 

those for the other urban study area and see that generally, Los Angeles (a) has some of the 

widest spreads in both 24-hour and annual design values (i.e., it has one of the more peaky 

PM2.5 distributions across monitors) and (b) has one of the highest 24-hour design value of the 15 

urban study areas (i.e., it will require more rollback in simulating just meeting the current suite 

of standards compared with most of the other study areas). The attributes described above match 

well with urban areas falling into zone B1 in Figure 4-18 (i.e., the zone where urban areas do not 

meet both the current 24-hour and annual standards, and where the 24-hour standard is 

controlling). 
4-79

Figure 4-22 Annual and 24-hour design values (for individual monitors and at the 

study-area level) for the 15 urban study areas (with the presentation 

of values scaled to reflect current standard of 15/35) 

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Key: stars () = area DV for the site (red for annual and brown for 24-hour); dots () = other valid site DVs (green 

for annual and blue for 24-hour); triangles () = invalid site DVs

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for annual and blue for 24-hour); triangles () = invalid site DVs
4-80

Figure 4-23 Annual and 24-hour design values (for individual monitors and at the 

study-area level) for the 15 urban study areas (with the presentation 

of values scaled to reflect current standard of 12/25) 

 

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Key: stars () = area DV for the site (red for annual and brown for 24-hour); dots () = other valid site DVs (green 

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for annual and blue for 24-hour); triangles () = invalid site DVs
4-81

The sensitivity analysis examining uncertainty related to conducting rollback 

demonstrated that for some of the study areas (e.g., Los Angeles and Salt Lake City) use of the 

locally focused rollback method reflecting application of more localized controls resulted in 

composite monitor values that differed notably from values generated when the proportional 

rollback approach was used.79 In contrast, many of the other urban study areas displayed little 

difference in composite monitor values based on application of proportional or locally focused 

rollback methods. 

Design value information provided in Figures 4-22 and 4-23 provides explanations for 

these sensitivity analysis results. For Los Angeles (which had composite monitor values 40% 

higher when using the locally focused rollback method compared with the proportional approach 

– see Section 4.3.1.1), the 24-hour standard is controlling. This can be seen by noting that the 

maximum 24-hour design value is significantly further away from the 15/35 line in Figure 4-22 

compared with the maximum annual design value. In addition, these two maximum design 

values do not occur at the same monitor.80 This means that when the proportional rollback 

method is used, a relatively large fractional reduction is uniformly applied to all monitors, 

resulting in a new (adjusted) composite monitor value that has been reduced to a relatively large 

extent. However, if locally focused rollback is used, then only those monitors with 24-hour 

design values exceeding the current 24-hour standard level are adjusted and only by the fraction 

required to get each 24-hour design value down to the current 24-hour standard level.81 This 

means that in an overall sense, there is less adjustment to PM2.5 levels, such that with locally 

focused we will see higher composite monitor annual averages than with proportional rollback. 

In the case of Salt Lake City (which also has significantly higher composite monitor 

annual averages with locally focused than with proportional rollback), while the highest 24-hour 

and annual design values occur at the same monitor, which means that even with locally focused, 

the monitor with the highest annual averages will be adjusted downward substantially, because 

the annual design values for monitors are closer to each other, the impact of locally focused on 

the composite annual average is smaller. Specifically, while some of the monitors with 24-hour 

design values above the current 24-hour standard level will have their annual averages adjusted 

 

79 Recall that differences in composite monitor estimates represent surrogates for differences in long-term 

exposure-related mortality - long-see section 4.3.1.1. 80 In figures 4-22 and 4-23, 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. 81 With the locally focused approach, many of the monitors will not have their PM2.5 levels adjusted 

because their 24-hour levels do not exceed the current standard. Furthermore, because Los Angeles has its max 24-

hour and annual standard levels occurring at different monitors, the max adjustment applied (that associated with the 

highest 24-hour monitor) will not be applied to the monitor having the highest annual design value, resulting in a 

lower overall impact to the composite annual average, compared with proportional rollback. 
4-82

down, there is a fraction of the monitors (with 24-hour design values below the current standard) 

that will not be adjusted with application of locally focused rollback. 

These two examples illustrate different conditions under which the type of rollback 

applied can have a significant impact on the degree of public health protection assessed for a 

particular standard level. By contrast, conditions at some of the other urban study areas result in 

little difference in risk from application of different rollback methods (i.e., simulation of more 

regional versus local control strategies). Specifically, if an urban location has 24-hour and annual 

design values at each monitor that display little variation, we expect to see less impact on risk 

from varying the type of rollback method used. Examples that fall into this latter category 

include Atlanta, Dallas, and St. Louis (see Figure 4-22). 
5-1

5 INTEGRATIVE DISCUSSION OF URBAN CASE STUDY ANALYSIS 

OF PM2.5-RELATED RISKS 

This chapter provides an integrative discussion of the risk-related analyses presented 

throughout this final RA, including (a) the core PM2.5-related risk estimates generated for the set 

of urban study areas (sections 4.1 and 4.2), (b) the related uncertainty and sensitivity analyses, 

including additional set of reasonable risk estimates generated to supplement the core analysis 

(sections 3.5.4, and 4.3), (c) assessment of the representativeness of the urban study areas in the 

national context (section 4.4), and (d) consideration of patterns in design values and air quality 

monitoring data to inform interpretation of risk estimates generated for the urban study areas 

(section 4.5). The goal of this integrative discussion is to lay out information in such a way as to 

inform consideration of the policy-relevant risk-related questions which are considered in the 

PA. 

We begin by discussing the overall level of confidence associated with estimates of risk 

presented in the RA (section 5.1). We then discuss key observations about the nature and 

magnitude of long-term and short-term exposure-related risks estimated for the air quality 

scenarios considered in the RA, including: (a) the current suite of PM2.5 standards (section 5.2.1), 

(b) alternative annual standard levels paired with the current 24-hour standard level (section 

5.2.2) and (c) combinations of alternative annual and 24-hour standard levels (section 5.2.3). As 

part of these discussions we consider the role played by 24-hour and annual standards in 

influencing the nature and magnitude of estimated risk reductions across the 15 urban study 

areas. At the end of this discussion, we summarize our key observations (section 5.3). 

This integrative discussion focuses on those health endpoints for which quantitative risk 

estimates were generated as part of the RA. However, additional health endpoints for which risks 

could not be quantified, but that are of potential concern, are considered in the PA. The PA also 

considers the extent to which health endpoints considered in the RA, as well as the additional 

endpoints considered in the PA, are of importance from a public health perspective. 

5.1 OVERALL CONFIDENCE IN THE RISK ASSESSMENT 

This quantitative risk assessment has been designed to generate estimates of risk for a set 

of urban study areas likely to represent those urban areas in the U.S. experiencing higher PM2.5-

related risk due to elevated PM2.5 concentrations and/or other attributes related to PM2.5 risk 

(e.g., meteorology, baseline health effects incidence rates, differences in PM2.5 emissions sources 

and composition). The RA includes design elements intended to increase overall confidence in 

the risk estimates generated for the 15 urban study areas including: (a) use of a deliberative 

process in specifying components of the risk model that reflects consideration of the latest 
5-2

research on PM2.5 exposure and risk, (b) integration of key sources of variability into the design 

and interpretation of results from the analysis, (c) assessment of the degree to which the urban 

study areas included in the RA are representative of areas in the U.S. experiencing higher PM2.5-

related risk, and (d) identification and assessment of the impact of important sources of 

uncertainty on core risk estimates. In addition to these design elements, we also completed two 

additional analyses which examine potential bias and overall confidence in the risk estimates. 

The first of these analyses explored potential bias in the core risk estimates by considering a set 

of alternative reasonable risk estimates generated as part of a sensitivity analysis. The second 

analysis compared the annual average PM2.5 concentrations simulated under both current and 

alternative standard levels with the air quality distribution used in deriving the C-R functions 

applied in modeling mortality risk. Greater confidence is associated with risk estimates based on 

annual average PM2.5 concentrations that are within the region of the air quality distribution used 

in deriving the C-R functions where the bulk of the data reside (i.e., within one standard 

deviation (SD) around the mean). Each of the design elements listed above together with the two 

additional analyses is discussed below. 

5.1.1 Use of a Deliberative Process in Designing the Risk Model 

To increase overall confidence in the RA, a deliberative process has been used in 

specifying each of the analytical elements comprising the risk model, including selection of 

urban study areas as well as specification of other inputs such as C-R functions. This deliberative 

process involved rigorous review of available literature addressing both PM2.5 exposure and risk 

combined with the application of a formal set of criteria to guide development of each of the key 

analytical elements in the risk assessment. In addition, the risk assessment design reflects 

consideration of CASAC and public comments on the initial risk assessment plan and the first 

draft risk assessment. The application of this deliberative process increases overall confidence in 

the risk estimates by insuring that the estimates are based on the best available science and data 

characterizing PM2.5 exposure and risk, and that they reflect consideration of input from experts 

on PM exposure and risk through CASAC and public reviews. 

The approach used in specifying several of the key analytical elements used in the risk 

assessment is highlighted below for purposes of illustrating the systematic approach used in 

developing the model. 

 Selection of the 15 urban study areas included consideration of (a) whether a city of county 

had been included in multi-city epidemiology studies used in specifying C-R functions used 

in the core risk estimates, (b) providing coverage for urban areas with relatively high annual 

and 24-hour design values, and (c) providing coverage for the seven PM regions which 

reflect differences in key PM risk-related attributes (e.g., meteorology, demographic 
5-3

attributes, PM sources and composition). See section 3.3.2 for additional detail on selection 

of study areas. 

 Simulation of ambient PM2.5 concentrations under current and alternative standard levels

included the proportional rollback approach used in past risk assessments, which reflects a 

regional pattern of ambient PM2.5 reductions. To more fully reflect potential variability in 

the future pattern of ambient PM2.5 reductions, we added two alternative rollback approaches 

(hybrid and locally focused), both of which simulate more localized patterns of ambient 

PM2.5 reductions combined with additional regional patterns of reduction (see section 3.2.3). 

 Selection of health endpoints reflected consideration of the degree of support in the literature 

for a causal relationship between PM2.5 exposure and the health effect of interest as assessed 

in the ISA, together with consideration of the health significance of the endpoint. In addition, 

we considered whether sufficient information existed in the literature to develop C-R 

functions and whether we could obtain the baseline incidence data necessary to generate risk 

estimates with a reasonable degree of confidence for a particular endpoint (see section 3.3.1). 

 The selection of epidemiological studies and specification of C-R functions for use in 

modeling risk involved a rigorous review of existing literature based on application of 

criteria we identified for specifying robust C-R functions. These criteria took into account 

both study design as well as the potential scope of the C-R functions that could be drawn 

from the studies (e.g., geographic coverage, demographic groups covered and health 

endpoints involved). We outlined our rationale for the set of epidemiology studies we 

selected and the choices made in specifying C-R functions, and we discussed our rationale 

for not including other potential studies and/or forms of C-R functions in the risk assessment 

(see section 3.3.3). 

The systematic approach described above resulted in a core risk model which included 

those model inputs that in our judgment have the greatest degree of support in the literature. 

These core risk estimates are emphasized in addressing the policy-related questions considered in 

the PA. To provide a more comprehensive assessment of risk for the urban study areas, we have 

included an assessment of uncertainty and variability and their impact on the core risk estimates 

as part of this analysis, as discussed below. 

5.1.2 Integration of Key Sources of Variability into the RA Design 

The RA has been designed to provide coverage for key sources of variability which can 

impact the nature and magnitude of risks associated with current and alternative standard levels 

across the urban study areas. Several of these sources of variability contribute to differences in 

risk across urban study areas, but do not directly affect the degree of risk reduction associated 

with the simulation of a particular standard levels (i.e., their impact on risk is constant across the 

air quality scenarios evaluated for a given study area). These sources of variability include 

differences in PM2.5 sources and composition and differences in baseline incidence rates, 

demographics and population behavior related to PM2.5 exposure and risk across urban areas. 
5-4

Coverage for these sources of variability is provided through inclusion of study areas drawn from 

different PM regions in the U.S, since these factors tend to demonstrate regional differences. 

In contrast, two additional sources of variability not only introduce variability into risk 

estimates across study areas, but also play an important role in determining the magnitude of risk 

reductions upon simulation of current and alternative standard levels. These sources of 

variability are (a) the degree of “peakiness” in monitored PM2.5 concentrations across urban 

study areas 82 and (b) variability in the spatial patterns of ambient PM2.5 reduction resulting from 

simulation of current or alternative standard levels. Variability in the peakiness of monitored 

PM2.5 concentrations is covered in the RA by including urban study areas from different PM 

regions, while variability in the spatial pattern of ambient PM2.5 reduction resulting from 

simulation of just meeting current or alternative standard levels is addressed by including 

multiple rollback methods in the RA. 

As discussed in sections 4.3.1.1 and 4.5, the interplay of 24-hour and annual design 

values in a given study area (reflecting the peakiness of ambient PM2.5 concentrations in that 

study area) also plays an important role in determining the magnitude of projected risk 

reductions under current and alternative standard levels. For example, those study areas with 

relative peaky PM2.5 distributions and where the 24-hour standard is controlling can be especially 

sensitive to the type of rollback approach used, with the proportional approach resulting in 

notably greater risk reduction compared with the locally focused approach. Rigorous 

consideration of these factors (i.e., the interplay of the rollback method used with patterns of 24-

hour and annual design values for a given study area), allowed us to obtain a better 

understanding of the nature and pattern of risk reductions and risk remaining following 

simulation of just meeting both current and alternative standard levels across the urban study 

areas. This in turn increased our overall confidence in the RA, since we could better explain 

complex patterns of risk reduction seen for some of the study areas. 

As discussed in section 4.3.1.1, the nature of the spatial pattern of reductions in ambient 

PM2.5 concentrations resulting from simulation of just meeting current or alternative standard 

levels, as reflected in application of different rollback methods, can have a substantial impact on 

the magnitude of risk reductions estimated for those standard levels. If a more generalized 

regional pattern of reduction is assumed (as reflected in the proportional rollback approach), all 

monitors in the study area are adjusted by the same proportion and there is a relatively greater 

 

82 The term “peakiness” as used here refers to air quality distributions across urban areas that have high 

peak-to-mean ratios relative to distributions in other urban study areas in the U.S. Here, the peak concentration is 

represented by the 24-hour design value, while the mean concentration is represented by the annual average design 

value for a particular monitor. 
5-5

impact on risk. In contrast, if only those monitors exceeding the 24-hour standard are subjected 

to an initial stage of reduction to bring the concentrations down to match those at adjacent 

monitors (as reflected in the hybrid and to an even greater extent in the locally focused 

approaches), there will be a relatively smaller reduction in risk. 

5.1.3 Representativeness of the Urban Study Areas 

The assessment of the degree to which the 15 urban study areas are representative of 

areas within the U.S. likely to experience elevated PM2.5-related risk draws on information 

presented in several sections of the RA including: (a) the analysis based on consideration of 

national distributions of risk-related attributes (section 4.4.1), (b) analysis based on evaluating 

where 31 counties comprising the 15 urban study areas fall along a national distribution of PMrelated mortality risk (section 4.4.2) and (c) consideration of patterns of design values for the 15 

urban study areas as contrasted with the broader set of urban areas within the U.S. (section 

4.5.1). Key observations from these representativeness analyses are presented below. 

 Comparison of attributes of the 15 urban study areas (assessed at the county-level) against 

national distributions for the same attributes (section 4.4.1) suggests that the 15 urban study 

areas represent areas in the U.S. that are among the most densely populated, have relatively 

higher levels of annual and 24-hour 98th percentile PM2.5 concentrations, and capture well the 

range of effect estimates represented by the Zanobetti and Schwartz (2009) study. Together, 

these factors suggest that the urban study areas should likely reflect the distribution of risk 

for the nation, with the potential for better characterization of the high end of that 

distribution.83 

 Analysis of where the 15 urban study areas fall along the distribution of U.S. counties 

included in the national-scale mortality analysis completed as part of the RA further suggests 

that we have captured counties likely to experience elevated PM2.5-related risk (see section 

4.4.2). Specifically, this analysis suggests that our urban study areas capture the upper end of 

the tail with regard to PM2.5-attributable risk, with 23 of these counties falling within the 

upper 5th percentile of the distribution. These findings support the assertion based on the 

other analyses described above that the urban study areas are likely to capture risk at urban 

areas experiencing relatively elevated levels of PM2.5-attributable mortality. 

 Consideration of the mix of design values across the 15 urban study areas as contrasted with 

design values for the broader set of urban study areas in the U.S. suggests that (a) the 15 

urban study areas do a good job of capturing the key groupings of urban areas in the U.S. 

 

83 This representativeness analysis also showed that the urban study areas do not capture areas with the 

highest baseline morality risks or the oldest populations (both of which can result in higher PM2.5-related mortality 

estimates). However, some of the areas with the highest values for these attributes have relatively lower PM2.5

concentrations (e.g., urban areas in Florida) and consequently failure to include these areas in the set of urban study 

areas is unlikely to exclude high PM2.5-risk locations. 
5-6

likely to experience elevated risk due to PM (i.e., they provide coverage for the zones 

containing urban study areas likely to experience risk reductions under the suites of 

alternative standard levels considered – see section 4.5.1) and (b) we have included study 

areas likely to experience relatively greater degrees of PM2.5-related risk, based on 

consideration of the pattern of design values across urban areas in the U.S.. In addition, for 

the current suite of standard levels, the 24-hour standard is controlling for most of our 15 

urban study areas, reflecting the pattern seen in the U.S. for urban areas assessed not to be 

meeting the current suite of standards (based on 2005-2007 air quality data - see section 

4.5.1). 

Our overall assessment of the representativeness of the 15 urban study areas in the 

national context, based on the three analyses summarized above, is that these areas are 

representative of urban areas in the U.S. experiencing elevated levels of risk related to ambient 

PM2.5 exposure. 

5.1.4 Impact of Important Sources of Uncertainty on Core Risk Estimates 

As part of the RA, we completed both a qualitative analysis of uncertainty (section 3.5.3) 

as well as a quantitative sensitivity analysis (section 3.5.4 and 4.3), both designed to identify 

those sources of uncertainty having a potentially important impact on core risk estimates.84 Key 

observations from these analyses are presented below. 

 The qualitative analysis of uncertainty identified the following sources of uncertainty as 

potentially having a medium to high impact on core risk estimates (see Table 3-13 for 

additional detail): (a) characterizing intra-urban population exposure in the context of 

epidemiology studies linking PM2.5 to specific health effects, (b) statistical fit of the C-R 

functions (short-term exposure-related health endpoints), (c) shape of the C-R functions, (d) 

specifying lag structure (short-term exposure studies), (e) transferability of C-R functions 

from study locations to urban study area locations (long-term exposure-related health 

endpoints), (f) use of single-city versus multi-city studies in the derivation of C-R functions, 

(g) impact of historical air quality on estimates of health risk from long-term PM2.5 exposures 

and (h) potential variation in effects estimates reflecting compositional differences for PM. 

 The quantitative single-factor sensitivity analysis identified the following sources of 

uncertainty as having a moderate to large impact on core risk estimates (see section 4.3.1 for 

additional detail): 

o Long-term exposure-related mortality: (a) different C-R function model choices (e.g., 

fixed versus random effects, log-linear versus log-log, single- versus multi-pollutant), 

(b) modeling risk down to PRB rather than LML, (c) impact of using C-R functions 

 

84 The sensitivity analysis also produced an additional set of reasonable risk estimates that augments the 

core risk estimates in considering the impact of uncertainty and variability in the core risk model (this application of 

the sensitivity analysis is discussed below in section 5.1.5). 
5-7

from different epidemiological studies (e.g., ACS versus six cities), and (d) nature of 

the spatial pattern of ambient PM2.5 reductions (i.e., rollback method used). 

o Short-term exposure-related mortality and morbidity: (a) use of seasonallydifferentiated versus annual-based C-R functions, and (b) different models, lag 

structures and single-versus multi-pollutant model forms (these results based on 

Moolgavkar, 2003 study which is not as directly applicable in the context of our RA – 

see section 4.3.1.1). 

 The quantitative multi-factor sensitivity analysis applied both to long-term exposure-related 

mortality and short-term exposure-related mortality and morbidity showed that a number of 

sources of uncertainty could work in concert to produce notably larger impacts on core risk 

estimates (see section 4.3.1.2). 

The qualitative analysis of uncertainty and quantitative sensitivity analyses described 

above provided us with a comprehensive understanding of which sources of uncertainty were 

likely to have a significant impact on the core risk estimates. This information proved useful in 

interpreting core risk estimates and increases our overall confidence in the analysis. 

5.1.5 Consideration of Alternative Reasonable Risk Estimates 

As noted above, the quantitative sensitivity analysis produced an additional set of 

reasonable risk estimates that augments the core risk estimates in considering the impact of 

uncertainty and variability in the core risk model. Most of the alternative model specifications 

supported by available literature produced risk estimates that are higher (by up to a factor of 2 to 

3) than the core risk estimates. This is not unexpected, since the epidemiological study used in 

obtaining the C-R functions for estimating long-term exposure-related mortality in the core 

analysis (Krewski et al., 2009) is based on the ACS dataset which does not provide 

representative coverage for lower-SES segments of the general population that are at greater risk 

from PM exposure. Because of this, there is the potential that effect estimates, and consequently 

risk estimates based on the ACS dataset are biased low, relative to risks estimated for the general 

population (which would include the lower SES-population) and especially relative to risks that 

might be estimated for the lower-SES population. In contrast, the alternative epidemiological 

study considered in the sensitivity analysis for modeling long-term exposure-related mortality 

(i.e., Krewski et al., 2000, based on the Six Cities dataset) provides better coverage for lower 

SES individuals and has a higher effect estimate and consequently generates higher risk 

estimates. 

While use of C-R functions from Krewski et al., 2009 does introduce potential for low 

bias in the core RA, because of the other strengths associated with this study (e.g., larger number 

of cities, inclusion of two time periods which allows us to consider different exposure windows 

and analysis of a wide range of C-R function models), the risk assessment team concluded that 
5-8

C-R functions obtained from this study had the greatest overall support and should be used in the 

core risk model. However, consideration of the alternative set of reasonable risk estimates does 

provide several observations relevant to the interpretation of the core risk estimates including: (a) 

the core estimates are unlikely to under-estimate risk and (b) the degree of potential bias in the 

core risk estimates could range up to at least a factor of 2-3 higher. 85 

5.1.6 Consideration of composite monitor annual-average PM2.5 concentrations in 

relation to the dataset used in deriving C-R functions for long-term exposurerelated mortality 

In considering the overall confidence in the core risk estimates, we have compared the 

PM2.5 concentrations simulated under both current and alternative standard levels across the 

urban study areas to the distribution of PM2.5 concentrations used in deriving the C-R functions 

used for long-term exposure-related mortality (as presented in Kreswki et al., 2009). Specifically, 

this assessment compares the composite monitor annual average PM2.5 concentrations used in 

modeling long-term exposure-related mortality risk in the core analysis to the distribution of 

annual-average PM2.5 concentrations from the 1999-2000 ACS exposure period.86 Generally, 

when composite monitor annual average values are within one SD of the mean of the ACS 

dataset (i.e., in the range of 11 μg/m3 or above), we have relatively high confidence in those risk 

estimates, since they are based on PM2.5 concentrations that roughly match those used in deriving 

the C-R functions. However, as composite monitor annual average PM2.5 concentrations extend 

below this range, our confidence in the risk estimates decreases, with our confidence being 

significantly reduced when composite monitor annual average values reach or extend below the 

LML of the ACS dataset (i.e., 5.8 μg/m3

). 

 

85 We note that these findings regarding potential bias in the core risk estimates were based on modeling 

PM2.5-attributable IHD and all-cause mortality associated with long-term PM2.5 exposure for the current suite of 

standards. However, we would expect these observations regarding overall confidence in the core risk estimates to 

hold for other long-term exposure-related mortality endpoints modeled in the RA for both the alternative annual and 

24-hour standard levels considered. Furthermore, given increased emphasis placed in this analysis on long-term 

exposure-related mortality, the uncertainty analyses completed for this health endpoint category are 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. 86 As discussed in sections 3.3.3 and 4.0, each category of long-term exposure-related mortality is 

estimated using separate C-R functions derived form the 1979-1983 and 1999-2000 ACS monitoring periods. For 

purposes of comparing composite monitor annual-average PM2.5 levels to these ACS datasets used in deriving the CR functions, we focus on the later monitoring period (1999-2000), since ambient PM2.5 levels from this period more 

closely match those associated with the study areas in our simulation under recent conditions. The 1999-2000 ACS 

monitoring period has a mean PM2.5 level of 14 μg/m3 , a SD of 3.0 μg/m3 and an LML of 5.8 μg/m3 (see Table 1 in 

Krewski et al., 2009). 
5-9

5.2 KEY OBSERVATIONS RELATED TO THE URBAN STUDY AREA RESULTS 

This section provides key observations from the simulation of risks under current and 

alternative suites of standards for the set of 15 urban study areas. These observations are focused 

on providing information relevant to addressing the key policy-relevant risk-related questions 

that are considered in the PA. 

In presenting these observations, we focus on cardiovascular-related endpoints given the 

greater overall degree of confidence assigned to this category in the ISA relative to other health 

effect categories (e.g., respiratory-related effects). This means that for long-term exposurerelated risk, we focus our discussion on IHD-related mortality and for short-term exposurerelated risk we focus on CV-related mortality and morbidity (the latter in the form of HAs related 

to CV symptoms). Although not discussed here, risk estimates were also generated for additional 

health effect categories including all-cause, cardiopulmonary, and lung cancer mortality (for 

long-term exposure) and non-accidental- and respiratory-related mortality and respiratory 

effects-related HAs and asthma-related emergency department visits (for short-term exposure). 

It is also important to note that a broader array of health effects beyond those modeled in the RA 

has also been associated with PM2.5 exposure, including reproductive and developmental effects. 

While information was too limited to consider these effects in this quantitative RA, such effects 

are appropriately considered based on the related evidence in the broader characterization of 

risks to be discussed in the PA. 

The role of annual average ambient PM2.5 concentrations in driving long-term exposurerelated risk is intuitive given that this risk category is modeled using the annual average air 

quality metric.87 The fact that changes in the annual average air quality metric can also impact 

short-term exposure-related risk is less intuitive, since changes in risk for this category are 

modeled using average daily PM2.5 concentrations and not annual averages.88 However, as 

discussed in section 3.1.2.2, because the 24-hour PM2.5 distributions tend to be approximately 

normal or log-normal in form, overall incidence of short-term exposure-related mortality tends to 

be driven by the relatively large number of days near the center of the distribution, rather than 

 

87 As noted in section 3.2.1, estimates of long-term exposure-related mortality are actually based on an 

average annual PM2.5 level across monitors in a study area (i.e., the composite monitor annual average). Therefore, 

in considering changes in long-term exposure-related mortality, it is most appropriate to compare composite monitor 

estimates generated for a study area under each suite of standards. The maximum monitor annual average for a 

study area (i.e., the annual design value) determines the percent reduction in PM2.5 levels required to attain a 

particular standard. Both types of air quality estimates are provided in Table 3-4 and both are referenced in this 

discussion of core risk estimates, as appropriate. 88 Estimates of short-term exposure-related mortality and morbidity are based on composite monitor daily 

PM2.5 concentrations. However, similar to the case with long-term exposure-related mortality, it is the maximum 

monitor 98th percentile 24-hour concentration (the 24-hour design value) that will determine the degree of reduction 

required to meet a given 24-hour standard. 
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the small number of days out at the tail. Therefore, a shift in the annual average, which will most 

directly focus on the bulk of the days near the center of the 24-hour average PM2.5 distribution, 

can have a significant effect on short-term exposure-related risk aggregated over a year. This 

means that in order to assess the impact of alternative 24-hour standard levels on short-term 

exposure-related mortality, it is most appropriate to first consider how those alternative 24-hour 

standards impact the composite monitor annual average PM2.5 concentration for a study area, 

since this will ultimately determine the magnitude of reductions in both long-term and short-term 

exposure-related mortality, as well as short-term exposure-related morbidity. 

5.2.1 Nature and Magnitude of Long-Term and Short-Term Exposure-Related Risk 

Remaining upon Just Meeting the Current Suite of PM2.5 Standards 

In considering PM2.5-related risks likely to remain upon just meeting the current PM2.5

annual and 24-hour standards in the 15 areas included in this assessment, we focus on the 13 

areas that would not meet the current standards based on recent (2005-2007) air quality. These 

13 areas have annual and/or 24-hour design values that are above the levels of the current 

standards (Table 4-8).89 Based on the core risk estimates for these areas presented in section 

4.2.1, we make the following key observation regarding the magnitude of risk remaining upon 

simulation (using proportional rollback) of just meeting the current suite of standards: 

 Long-term exposure-related mortality risk remaining: The core analysis estimates that the 

urban study areas would have IHD-related mortality attributable to long-term PM2.5 exposure 

ranging from <100 to approximately 2,000 cases per year, with this variability reflecting to a 

great extent differences in the size of study area populations. These estimates represent from 

4 to 17% of all IHD-related mortality in a given year for the urban study areas, which is a 

measure of risk that takes into account differences in population size and baseline mortality 

rates. 

 Short-term exposure-related mortality and morbidity risk remaining: The core analysis has 

short-term exposure-related CV-related mortality across the urban study areas ranging from 

<10 to 500 cases per year. These estimates represent from ~1 to 2% of total CV-related 

mortality in a given year for the urban study areas. In terms of morbidity risk, CV-related 

HA range from ~10 to 800 cases per year across the study areas, with this translating into 

<1% of total CV-related HA incidence. 

Although short-term and long-term exposure-related mortality have similar patterns (in 

terms of the subset of urban study areas experiencing risk reductions for a particular standard 

level), the magnitude of risk remaining is substantially lower for short-term exposure-related 

mortality. These findings are expected, since, as noted in the introduction to section 5.2, changes 

 

89 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. 
5-11

in annual average PM2.5 concentrations are expected to drive both short-term and long-term 

exposure-related risk, resulting in similar overall patterns in risk reduction for both categories of 

endpoint (in terms of the subset of study areas experiencing risk reductions). Note, however, that 

variability in the effect estimates used in modeling short-term exposure-related health endpoints 

across study areas will introduce additional variation into the pattern of risk reduction across 

study areas. 

 Substantial variability across study areas in the magnitude of risk remaining: Estimated risks 

remaining upon just meeting the current suite of standards vary substantially across study 

areas, even when considering risks normalized for differences in population size and baseline 

incidence rates. This variability in estimated risks is a consequence of the substantial 

variability in the annual average PM2.5 concentrations across study areas that result from 

simulating just meeting the current standards. This is important because, as noted in the 

introduction to this section, annual average concentrations are highly correlated with both 

long-term and short-term exposure-related risk. This variability in annual average PM2.5

concentrations occurs especially in those study areas in which the 24-hour standard is the 

“controlling” standard. In such areas, the variability across study areas in estimated risks is 

largest when regional patterns of reductions in PM2.5 concentrations are simulated (using 

proportional rollback, as was done in the core analyses), with less variability when more 

localized patterns of PM2.5 reductions are simulated (using locally focused rollback, as was 

done in a sensitivity analysis). When simulations are done using locally focused rollback, 

estimated risks remaining upon just meeting the current suite of standards can be appreciably 

larger than those estimated in the core analysis. 

 Simulation of risk involves annual average PM2.5 concentrations well below the current 

standard for some study areas: In simulating just meeting the current suite of standards, the 

resulting composite monitor annual average PM2.5 concentrations range from about 15 μg/m3

(for those study areas in which the annual standard was controlling) down to as low as about 

8 μg/m3 (for those study areas in which the 24-hour standard was controlling or the annual 

average was well below 15 μg/m3

based on recent air quality). As discussed above in section 

5.1.6. as the composite monitor annual average PM2.5 concentrations used in generating risk 

estimates extend below 11.0 μg/m3

 (one SD below the mean for the 1999-2000 ACS 

monitoring period) we have increasingly less confidence in those risk estimates, with 

confidence decreasing significantly as composite monitor concentrations approach the LML 

for the ACS dataset (5.8 μg/m3

). We make the observation that all four of the urban study 

areas with composite monitor annual average PM2.5 concentrations below 11μg/m3 under 

simulated attainment of the current suite of standards have the 24-hour standard controlling 

(see Table 3-4). This illustrates the point that for the 15 urban study areas assessed, 

typically, it is the locations where the 24-hour standard is controlling that are simulated to 

have the lowest composite monitor annual average PM2.5 concentrations. While these 

locations often are estimated to have the greatest risk reductions, there is also reduced 

confidence associated with these risk estimates. 
5-12

5.2.2 Nature and Magnitude of Long-term and Short-Term Exposure-Related Risk 

Remaining upon Just Meeting the Alternative Suite of PM2.5 Standards 

In characterizing PM2.5-related risks associated with simulation of the alternative annual 

standards (14/35, 13/35 and 12/35), we estimate both the magnitude of risk reductions (relative 

to risk remaining upon just meeting the current suite of standards) as well as the magnitude of 

risk remaining upon just meeting the alternative standards. In discussing these risks, we focus on 

the set of urban study areas experiencing risk reductions under each alternative annual standard. 

Key policy-relevant observations associated with these risk estimates include: 

 Reductions in long-term exposure-related mortality risk: Upon simulation of just meeting the 

alternative annual standard levels considered (14, 13, and 12 μg/m3) in conjunction with the 

current 24-hour standard (denoted as 14/35, 13/35 and 12/35 suites of standards), the core 

analysis estimates reductions in long-term exposure-related mortality for 12 of the 15 urban 

study areas, with the degree of risk reduction increasing incrementally across the alternative 

standard levels (both in terms of the number of study areas experiencing risk reduction and 

the magnitude of those reductions). For the alternative annual standard level of 12 μg/m3

 (in 

conjunction with the current 24-hour standard), the core analysis estimates that these study 

areas have reductions in risk (relative to risk remaining upon just meeting the current suite of 

standards) ranging from about 11 to 35%. 

For some of those areas in which the 24-hour standard is controlling, larger risk 

reductions would have been estimated in this case (12/35 suite of standards) if locally focused 

rollback had been used to simulate just meeting the current suite of standards. This result would 

be expected since the magnitude of risk remaining upon just meeting the current suite of 

standards would have been higher than that estimated based on the proportional rollback used in 

the core analysis. Therefore, while we would have gone down to the same level of risk (under the 

12/35 suite of standards), we would have started with a higher level of simulated risk from the 

current standard. 

 Long-term exposure-related mortality risk remaining: For an annual standard level of 14 

μg/m3

, the percent of total incidence of long-term exposure-related IHD mortality 

attributable to PM2.5 (i.e., risk remaining) in the 5 urban study areas experiencing risk 

reductions ranges from 9-15%. For an annual standard of 12 μg/m3

, estimated risk remaining 

in the 12 urban study areas experiencing risk reductions ranges from 6-11% in terms of 

PM2.5-attributable long-term exposure-related mortality. This translates into between 90 and 

300 cases per year attributable to long-term PM2.5 exposure for those study areas 

experiencing the greatest reductions in risk under the lowest alternative annual standard 

simulated. 

 Simulation of risks for an alternative standard level below 12 μg/m3: Simulation of risks for 

an alternative annual standard of 10 μg/m3

 suggests that additional risk reductions could be 

expected with alternative annual standards below 12 μg/m3

. However, we recognize that 

there is potentially greater uncertainty associated with these risk estimates compared with 

estimates generated for the higher alternative annual standards considered in the RA, since 
5-13

these estimates require simulation of relatively greater reductions in ambient PM2.5 

concentrations. As lower ambient PM2.5 concentrations are simulated (i.e., ambient 

concentrations further from recent conditions), potential variability in such factors as the 

spatial pattern of ambient PM2.5 reductions (rollback) increases, thereby introducing greater 

uncertainty into the simulation of composite monitor annual average PM2.5 concentrations 

and consequently risk estimates. 

 Short-term exposure-related mortality and morbidity risk: For the alternative annual standard 

level of 12 μg/m3 (in conjunction with the current 24-hour standard), the core analysis 

estimates that reductions in both short-term exposure-related CV mortality and morbidity risk 

ranged from 5 to 23%.90 In terms of risk remaining upon simulation of 12 μg/m3 (in 

conjunction with the current 24-hour standard), the urban study areas with the greatest 

percent reduction have CV-related mortality estimates ranging from 25 to 50 deaths per year. 

 Substantial variability in magnitude of risk reduction across urban study areas: While there 

is a consistent pattern of risk reduction across the alternative annual standards with lower 

standard levels resulting in more urban study areas experiencing increasingly larger risk 

reductions, there is considerable variability in the magnitude of these reductions across study 

areas for a given alternative annual standard level (e.g., as noted above, for the alternative 

annual standard level of 12 μg/m3

, risk reduction ranges from 11% to 35% for the study areas 

experiencing risk reductions). This variability in risk reflects differing degrees of reduction 

in annual average concentrations across the study areas. These differences in annual 

averages result in part because the study areas begin with varying annual average PM2.5

concentrations after simulating just meeting the current suite of standards. Therefore, even if 

study areas have similar “ending” annual average PM2.5 concentrations after simulation of 

just meeting the a given alternative annual standard, because the starting point in the 

calculation (the annual average PM2.5 concentrations upon just meeting the current suite of 

standards) can be variable, the overall reduction in annual average PM2.5 concentrations 

across the standards can also be variable. This translates into variation in reductions in longterm exposure-related risk upon just meeting alternative annual standard levels across the 

study areas. 

 The nature of the spatial pattern in PM2.5 reductions (reflected in the rollback method used) 

can impact the magnitude of risk reductions: The sensitivity analysis involving application of 

locally focused rollback reveals that the pattern of reductions in ambient PM2.5

concentrations upon just meeting the current suite of standards can impact the magnitude of 

additional risk reductions estimated for just meeting alternative (lower) annual standard 

levels. Specifically, for those study areas with more peaky PM2.5 distributions, application of 

locally focused rollback will result in higher annual average PM2.5 concentrations remaining 

upon just meeting the current suite of standards. If proportional rollback is then used to 

simulate just meeting alternative annual standard levels, a greater degree of reduction in 

composite monitor annual average PM2.5 concentrations will result, since the “starting point” 

for the calculation (annual average PM2.5 concentrations upon just meeting the current suite 

 

90 Because the same air quality metric (annual distributions of 24-hour PM2.5 concentrations) is used in 

generating short-term exposure-related mortality and morbidity endpoints, patterns of risk reduction (as a percent of 

risk under the current suite of standards) are similar for both sets of endpoints (see section 4.2.2). 
5-14

of standards) will be higher. These findings highlight the important roll played by variability 

in the spatial pattern of ambient PM2.5 concentrations in influencing the magnitude of risk 

reductions under alternative annual standard levels. 

 Based on consideration of the composite monitor annual average PM2.5 concentrations 

involved in estimating long-term exposure-related mortality, we have varying levels of 

confidence in risk estimates generated for the three alternative annual standard levels 

considered: With the exception of one study area, those study areas estimated to have risk 

reductions under the alternative annual standards of 14 and 13 μg/m3 have simulated 

composite monitor annual average PM2.5 concentrations ranging from just below 10.6 to over 

13.3 μg/m3 (see Table 3-4). In other words, these composite monitor annual average PM2.5

concentrations generally fall well within the range of ambient PM2.5 data used in fitting the 

C-R functions used (i.e., within one SD of the mean PM2.5 concentration from 1999-2000 

ACS dataset). The urban study areas estimated to have risk reductions under the lower 

alternative annual standard level of 12 μg/m3 have lower composite monitor annual average 

values ranging from 9.0 to over 11.4 μg/m3

. These values extend to below one SD of the 

mean of the ACS dataset and therefore, we have somewhat lower confidence in these risk 

estimates, relative to those generated for the two higher alternative annual standards. By 

contrast, urban study areas estimated to have risk reductions under the alternative standard 

level of 10 μg/m3

 (paired with the current 24-hour standard) have simulated composite 

monitor annual estimates ranging from 7.6 to 8.9 μg/m3

 (see Table J-19). These 

concentrations are towards the lower end of the range of ACS data used in fitting the C-R 

functions (in some cases approaching the LML) and therefore, we conclude that we have 

significantly less confidence in these risk estimates, compared with those for the higher 

alternative annual standards assessed. 

5.2.3 Nature and Magnitude of Long-Term and Short-Term Exposure-Related Risk 

Remaining upon Just Meeting Combinations of Alternative Annual and 24-Hour 

PM2.5 Standards 

In characterizing PM2.5-related risks associated with simulation of the alternative annual 

standards combined with alternative 24-hour standards (13/30 and 12/25), we estimate both the 

magnitude of risk reductions (relative to risk remaining upon just meeting the current suite of 

standards) as well as the magnitude of risk remaining upon just meeting the alternative standards. 

In discussing these risks, we focus on the set of urban study areas experiencing risk reductions 

under each alternative annual standard. 

 Additional reduction in long-term exposure-related risk provided by considering alternative 

24-hour standards combined with alternative annual standards: In the case of the 12/25 suite 

of standards, estimated reductions in long-term exposure-related mortality risk compared 

with reductions for the annual standard alone (12 μg/m3

), were roughly twice as large in 

many of the study areas, although in a few areas risk reductions were much higher (ranging 

up to ~100%) and in a few other areas, there was little to no risk reduction. 
5-15

These results show that lower 24-hour standards can have an appreciable and highly 

variable impact on long-term exposure-related mortality, particularly when just meeting the 

lower standards is simulated using a more regional pattern of PM2.5 reductions (i.e., the 

proportional rollback used in the core analysis). However, the magnitude of risk reductions 

estimated for the lower 24-hour standards was reduced when simulations using a more localized 

pattern of PM2.5 reductions (i.e., the locally focused rollback used in the sensitivity analysis). 

 Based on consideration of the composite monitor annual average PM2.5 concentrations 

involved in estimating long-term exposure-related mortality, we have lower degrees of 

confidence in risk estimates generated for the two alternative 24-hour standard levels 

considered (30 and 25 μg/m3

): Of the 11 urban study areas estimated to have risk reductions 

under the alternative 24-hour standard of 30 μg/m3 (with the 24-hour standard controlling – 

see Table 3-4), composite monitor annual average PM2.5 concentrations range from 6.6 to 

11.3 μg/m3

 with most of the urban study areas having concentrations in the 8 to 10 μg/m3

range. These concentrations extend into the lower range of PM2.5 data used in the ACS study 

to fit the C-R functions and therefore, we have somewhat lower confidence in these 

estimates. When we consider composite monitor concentrations for urban study areas 

assessed to have risk reductions under the alternative 24-hour standard level of 25 μg/m3

(again, where the 24-hour standard is controlling), we see composite monitor annual average 

PM2.5 levels ranging from 5.6 to 11.2 μg/m3 with most study areas have concentrations in the 

range of 7 to 9 μg/m3. Because this range extends well into the lower range of ACS data 

used in fitting the C-R functions (in some cases extending below the LML), we have 

significantly lower confidence in these risk estimates. 

 Increased variability associated with simulating composite monitor annual average PM2.5

concentrations for the lowest 24-hour standard considered (when it is controlling): We note 

that risk estimates generated for the subset of urban study areas where the alternative 24-hour 

standards of 30 and 25 μg/m3 are controlling are subject to additional variability related to 

simulating the spatial pattern of ambient PM2.5 concentrations under these alternative levels 

(i.e., application of rollback methods). In those scenarios where the alternative 24-hour 

standard is controlling, our sensitivity analyses showed the application of alternative rollback 

methods (particularly the proportional versus locally focused) produce substantially different 

composite monitor annual average PM2.5 concentrations, which translate into differences in 

estimated risk. Based on our sensitivity analysis results, this source of variability (rollback) 

does not have as great an impact in those instances where alternative annual standard levels 

are controlling. 

 Alternative 24-hour standard levels provide inconsistent degree of risk reduction: The results 

of simulating alternative suites of standards including a combination of alternative annual 

and 24-hour standard levels suggest that the alternative 24-hour standard can produce 

additional estimated risk reduction beyond that provided by the alternative annual standard 

alone. However, the degree of estimated risk reduction provided by the alternative 24-hour 

standard is highly variable, as illustrated by the considerable spread in the percentage 

reductions in long-term exposure-related IHD mortality risk seen across study areas when 

comparing risk under the 12/25 suite of alternative standard levels to risk under the current 

standard (see section 4.2.2). More consistent reductions in estimated risk and consequently 
5-16

degrees of public health protection are estimated to result from simulating just meeting the 

alternative annual standard levels considered. 

5.3 SUMMARY OF KEY OBSERVATIONS 

Key policy-relevant observations drawn from discussions presented in sections 5.1 and 

5.2 are summarized below. 

 The use of a deliberative process in specifying models and inputs used in the RA together 

with consideration of key sources of variability and uncertainty in designing the modeling 

approach increases our overall confidence in the risk estimates that are generated. In 

addition, based on the consideration of both the qualitative and quantitative assessments of 

uncertainty completed as part of the analysis, we believe it unlikely that the RA as 

implemented has over-stated risk, particularly for long-term PM2.5 exposure-related 

mortality. In fact, the core risk estimates for this category of health effect endpoint may well 

be biased low based on consideration of alternative model specifications evaluated in the 

sensitivity analysis. 

 Based on the results of several analyses examining the representativeness of the 15 urban 

study areas, we believe that the RA provides coverage for urban areas in the U.S. likely to 

experience elevated risk due to ambient PM2.5 exposure, consistent with the original goal set 

out for the RA. 

 Simulation of just meeting the current suite of standards (15/35) suggests that long-term 

exposure-related IHD mortality attributable to PM2.5 exposure in the urban study areas 

included in this assessment could range from <100 to 2,000 cases per year across the study 

areas, which translates into a range from 4-17% of total IHD-related mortality incidence. 

Short-term exposure-related CV mortality risk is lower by up to an order of magnitude. 

 Simulation of just meeting the alternative annual standard levels evaluated (12, 13, and 14 

μg/m3), combined with the current 24-hour standard (35 μg/m3), resulted in estimated risk 

reductions for most of the urban study areas, with the degree of risk reduction increasing 

incrementally across the alternative standard levels (both in terms of the number of study 

areas experiencing risk reduction and the magnitude of those reductions). Estimated 

reductions in long-term exposure-related IHD mortality (relative to the risk remaining upon 

just meeting the current suite of standards) ranged from 11 to 35% across the study areas for 

the lowest alternative annual standard considered (12 μg/m3

). Additional, but more uncertain 

reductions in long-term exposure-related mortality risk were estimated with the simulation of 

an alternative annual standard level of 10 μg/m3

. 

 In general, we have the most confidence in risk estimates based on PM2.5 concentrations near 

the mean PM2.5 levels in the underlying epidemiological studies providing the C-R functions. 

As PM2.5 concentrations decrease from these mean levels, we have decreasing confidence in 

the risk estimates. Risk estimates for the alternative annul standard levels of 14 and 13 μg/m3

are based on PM2.5 concentrations that are generally within one SD of the mean of the ACS 

data from which the C-R functions are derived. Consequently we have a relatively high 

degree of confidence in these risk estimates. Risk estimates for the alternative annual 

standard level of 12 μg/m3

 are based on PM2.5 concentrations that begin to extend into the 

lower range of data used in fitting the C-R functions and consequently our confidence in 
5-17

these estimates is somewhat lower. Risk estimates for the alternative standard level of 10 

μg/m3 are based on PM2.5 concentrations that extend well into the lower range of the ACS 

data and consequently, we have significantly less confidence in these risk estimates 

compared with estimates generated for the other alternative annual standard levels 

considered. 

 Simulation of just meeting combinations of alternative annual and 24-hour standard levels

(13/30 and 12/25) resulted in estimates of additional risk reductions in some study areas 

compared with the alternative annual standards alone, particularly for the 12/25 combination, 

where estimated reductions were roughly twice as large in many of the study areas, with a 

few areas experiencing substantially higher estimated risk reductions (ranging up to ~100% 

of estimated risk). 

 Risk estimates for the alternative 24-hour standard level of 30 μg/m3 are based on composite 

monitor annual average PM2.5 concentrations that span a wide range extending from within 

one to below two SDs of the mean of the ACS data used in fitting the long-term exposurerelated mortality C-R functions. We have somewhat lower confidence in these estimates 

relative to risk estimates based on annual mean concentrations more consistently in the range 

of one SD of the mean of the ACS data. In contrast, risk estimates for the alternative 24-hour 

standard level of 25 μg/m3

 are based on lower composite monitor annual average PM2.5

concentrations that extend well into the lower range of the ACS data (in some cases 

extending down to the LML) and therefore, we have significantly less confidence in these 

risk estimates. 

 Risk estimates generated for the alternative 24-hour standard levels are subject to substantial 

variability related to the spatial pattern of ambient PM2.5 concentrations (i.e., rollback) 

assumed in simulating these standard levels. Application of more localized patterns of 

ambient PM2.5 reduction (locally-focused rollback) versus more regional patterns of 

reduction (proportional rollback), can produce significantly different degrees of risk 

reduction. This variability in risk reduction associated with application of different rollback 

methods was not as pronounced with simulation of the alternative annual standard levels 

considered. 

 While the alternative 24-hour standard levels considered (when controlling) did result in 

additional estimated risk reductions beyond those estimated for alternative annual standards 

alone, these additional estimated reductions are highly variable, in part due to different 

rollback approaches. Conversely, alternative annual standard levels, when controlling, 

resulted in more consistent risk reductions across urban study areas, thereby potentially 

providing a more consistent degree of public health protection. 

. 
6-1

6 REFERENCES 

Abt Associates Inc. (1996). A Particulate Matter Risk Assessment for Philadelphia and Los Angeles. Prepared for 

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

Park, NC. July 1996. Available electronically on the internet at: 

http://www.epa.gov/ttnnaaqs/standards/pm/data/jly3amd.pdf

Abt Associates Inc. (2003), “Environmental Benefits Mapping and Analysis Program (BenMAP), User’s Manual. 

Available on the Internet at <http://www.epa.gov/ttn/ecas/benmodels.html>. 

Abt Associates Inc. (2005). Particulate Matter Health Risk Assessment for Selected Urban Areas. Prepared for 

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

Park, NC. June 2005. Available electronically on the internet at: 

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

Abt Associates Inc. (2008). Environmental Benefits Mapping and Analysis Program (Version 3.0). Bethesda, MD. 

Prepared for Environmental Protection Agency, Office of Air Quality Planning and Standards, Air Benefits 

and Costs Group. Research Triangle Park, NC. 

Bateson, T.F.; Schwartz, J. (2004). Who is sensitive to the effects of particulate air pollution on mortality? A casecrossover analysis of effect modifiers. Epidemiology. 15(2):143-9. 

Bell, M.L.; Ebisu, K.; Peng, R.D.; Walker, J.; Samet, JM; Zeger, S.L.; Dominici, F. (2008). Seasonal and regional 

short-term effects of fine particles on hospital admissions in 202 US counties, 1999-2005. Am J 

Epidemiol. 168(11):1301-1310. 

Bell, M.L.; Dominici, F. (2008). Effect modification by community characteristics on the short-term effects of ozone 

exposure and mortality in 98 US communities. Am J Epidemiol. 167(8):986-97. 

Bell (2009). Personal communication via e-mail between Dr. Michelle Bell and Beth Hasset-Sipple, June 26th, 2009 

(e-mail with attachments placed in docket #EPA-HQ-OAR-2007-0492). 

Bell M.L.; Ebisu K.; Peng R.D.; Samet J.M.; Dominici, F. (2009). Hospital admissions and chemical composition of 

fine particle air pollution. Am J Respir Crit Care Med. 179(12):1115-20. 

Berkey, C.S.; Hoaglin, D.C.; Antczak-Bouckoms, A.; Mosteller, F.; Colditz, G.A. (1998). Meta-analysis of multiple 

outcomes by regression with random effects. Stat Med. 17(22):2537-2550. 

Byun, D., and K.L. Schere, (2006). Review of the Governing Equations, Computational Algorithms, and Other 

Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Applied 

Mechanics Reviews, 59, 51-77. 

Dominici, F.; Peng, R.D.; Ebisu, K.; Zeger, S.L.; Samet, J.M.; Bell, M.L. (2007). Does the effect of PM10 on 

mortality depend on PM nickel and vanadium content? A reanalysis of the NMMAPS data. Environ Health 

Perspect. 115(12):1701-3. 

Eftim SE, Samet JM, Janes H, McDermott A, Dominici F. (2008). Fine Particulate Matter and Mortality: A 

Comparison of the Six Cities and American Cancer Society Cohorts With a Medicare Cohort. 

Epidemiology, 19: 209- 216. 

Franklin, M.; Zeka, A.; Schwartz, J. (2007). Association between PM2.5 and all-cause and specific-cause motrality in 

27 US communities. J Expo Sci Environ Epidemiol. 17:279-287. 
6-2

Goss CH: Newsom SA, Schildcrout JS, Sheppard L, Kaufman JD. (2004). Effect of ambient air pollution on 

pulmonary exacerbations and lung function in cystic fibrosis. Am J Respir Crit Care Med, 169: 816-821. 

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

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

Air Scientific Advisory Committee. June 6, 2005. Available electronically on the internet at: 

http://www.epa.gov/sab/pdf/casac-05-007.pdf. 

Henderson, R. (2006). Clean Air Scientific Advisory Committee Recommendations Concerning the Proposed 

National Ambient Air Quality Standards for Particulate Matter. March 21, 2006. Available electronically 

on the internet at: http://www.epa.gov/sab/pdf/casac-ltr-06-002.pdf

Henderson, R; Poirot, R.L.; Cowling, E.; Speizer, F.; Crapo, J.D.; Zielinska, B.; Miller, F.J. (2006). Clean Air 

Scientific Advisory Committee Recommendations Concerning the Final National Ambient Air Quality 

Standards for Particulate Matter. September 29, 2006. Available electronically on the internet at: 

http://www.epa.gov/sab/pdf/casac-ltr-06-003.pdf. 

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

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

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

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

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

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

Hopke, P. (2002). Letter from Dr. Phil Hopke, Chair, Clean Air Scientific Advisory Committee (CASAC) 

Particulate Matter Review Panel, to Honorable Christine Todd Whitman, Administrator, U.S. EPA. Final 

advisory review report by the CASAC Particulate Matter Review Panel on the proposed particulate matter 

risk assessment. EPA-SAB-CASAC-ADV-02- 002. May 23. Available electronically on the internet 

at:http://www.epa.gov/sab/pdf/casacadv02002.pdf. 

Hopke, P. (2004). Letter from Dr. Phil Hopke, Clean Air Scientific Advisory Committee 

(CASAC) Particulate Matter (PM) Review Panel, to Honorable Michael O. Leavitt. CASAC PM Review Panel’s 

Ongoing Peer Review of the Agency’s Fourth External Review Draft of Air Quality Criteria for Particulate 

Matter (June 2003); and Peer Review of the Review of the National Ambient Air Quality Standards for 

Particulate Matter: Policy Assessment of Scientific and Technical Information (OAQPS Staff Paper - First 

Draft) (August 2003) and a Related Draft Technical Report, Particulate Matter Health Risk Assessment for 

Selected Urban Areas (Draft Report) (August 2003). EPA-SAB-CASAC-04-004. February 18. Available 

electronically on the internet at: http://www.epa.gov/sab/pdf. 

Ito, K.; Thurston, G.; Silverman, R.A. (2007). Characterization of PM2.5 gaseous pollutants and meteorological 

interactions in the context of time-series health effects models. J Expo Sci Environ Epidemiol. 17: 45-60. 

Jerrett. M.; Burnett, R.T.; Ma, R.; Pope, C.A. III; Krewski, D.; Newbold, K.B.; Thurston, G.; Shi, Y.; Finkelstein, 

N.; Calle, E.; Thun, M.J. (2005). Spatial analysis of air pollution and mortality in Los Angeles.

Epidemiology. 6(6): 1-10. 

Kottek (2006) – use below 

Kottek, M.; Grieser, J.; Beck, C.; Rudolf, B.; Rubel, F. (2006). World map of the Köppen-Geiger climate 

classification updated. Meteorol Zeit. 15(3): 259-263. 

Kottek, M. (2008). World Map of the Köppen-Geiger climate classification updated.Available: http://koeppengeiger.vu-wien.ac.at/ [accessed 12/5/2008].
6-3

Krewski (2009). Letter from Dr. Daniel Krewski to HEI’s Dr. Kate Adams (dated July July 7, 2009) regarding 

“EPA queries regarding HEI Report 140”. Dr. Adams then forwarded the letter on July 10, 2009 to EPA’s 

Beth Hassett-Sipple. (letter placed in docket #EPA-HQ-OAR-2007-0492). 

Krewski, D.; Burnett, R.T.; Goldberg, M.S.; Hoover, K.; Siemiatycki, J.; Jerrett, M.; Abrahamowicz, M.; White, 

W.H. (2000). Reanalysis of the Harvard Six Cities study and the American Cancer Society study of 

particulate air matter pollution and mortality. HEI Research Report, Health Effects Institute, Boston, MA. 

Krewski, D.; Jerrett, M.; Burnett, R.T.; Ma, R.; Hughes, E.; Shi, Y.; Turner, M.C.; Pope, C.A. III; Thurston, G.; 

Calle, E.E.; Thun, M.J. (2009). Extended follow-up and spatial analysis of the American Cancer Society 

study linking particulate air pollution and mortality. HEI Research Report, 140, Health Effects Institute, 

Boston, MA. 

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. (2006). Reduction in Fine Particulate Air Pollution and 

Mortality. American Journal of Respiratory and Critical Care Medicine 173:667-672 

Le Terte, A.; Schwartz, J.; Touloumi, G. (2005). Empirical bayes and adjusted estimates approach to estimating the 

relation of mortality to exposure of PM10. Risk Anal. 25(3):711-718. 

Medina-Ramón, M.; Zanobetti, A.; Schwartz, J. (2006). The effect of ozone and PM10 on hospital admissions for 

pneumonia and chronic obstructive pulmonary disease: a national multicity study. Am J Epidemiol. 

163(6):579-88. 

Miller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan JH, Anderson GL, Kaufman JD. (2007). Long-term 

exposure to air pollution and incidence of cardiovascular events in women. N Engl J Med, 356: 447-458. 

Moolgavkar, S. (2003). Air pollution and daily deaths and hospital admissions in Los Angeles and Cook counties. 

pp. 183-198. In: Health Effects Institute Special Report, Revised Analyses of Time-Series Studies of Air 

Pollution and Health. Health Effects Institute, Cambridge, MA. 

Ostro, B.; Broadwin, R.; Green, S.; Feng, W.Y.; Lipsett, M. (2006). Fine particulate air pollution and mortality in 

nine California counties: results from CALFINE. Environ Health Perspect. 114(1):29-33. 

Pope, C.A. III ; Burnett, R.T. ; Thun, M.J. ; Calle, E.E. ; Krewski, D. ; Ito, K. ; Thurston G.D. (2002). Lung cancer, 

cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA. 287(9):1132-

1141. 

Reid, C.E. ., O’Neill, M.S., Gronlund, C. Brines, S.J., Brown, D.G., Diez-Roux, A.V., Schwartz. J. (2009) Mapping 

Community Determinants of Heat Vulnerability. Environmental Health Perspectives. doi: 

10.1289/ehp.0900683 (available at http://dx.doi.org/) Online 11 June 2009Roberts, S. (2004). Interactions 

between particulate air pollution and temperature in air pollution mortality time series studies. Environ Res. 

96(3):328-37. 

Samet, J.M. (2008). Air pollution risk estimates: determinants of heterogeneity. J Toxicol Environ Health A. 71(9-

10):578-82. 

Samet, 2009a. Letter from Dr. Jonathan M. Samet, Chair Clean Air Scientific Advisory Committee, to Lisa P. 

Jackson, Administrator U.S. Environmental Protection Agency regarding “Consultation on EPA’s 

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

Exposure Assessment.” May, 21, 2009. (letter placed in docket #EPA-HQ-OAR-2007-0492). 

Samet JM; Rappold A; Graff D; Cascio WE; Berntsen JH; Huang YC; Herbst M; Bassett M; Montilla T; Hazucha 

MJ; Bromberg PA; Devlin RB. (2009b). Concentrated ambient ultrafine particle exposure induces cardiac 

changes in young healthy volunteers. Am J Respir Crit Care Med, 179: 1034-1042. 
6-4

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

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

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

http://yosemite.epa.gov/sab/sabproduct.nsf/BC4F6E77B6385155852577070002F09F/$File/EPA-CASAC10-008-unsigned.pdf 

Sarnat, S.E.; Coull, B.A.; Schwartz, J.; Gold, D.R.; Suh, H.H. (2006). Factors affecting the association between 

ambient concentrations and personal exposures to particles and gases. Environ Health Perspect. 

114(5):649-54. 

Schwartz, J. (2000). The distributed lag between air pollution and daily deaths. Epidemiology 11(3):320-326. 

Schwartz J, Coull B, Laden F, Ryan L (2008). The effect of dose and timing of dose on the association between 

airborne particles and survival. Environ Health Perspect, 116: 64-69. 

Tolbert, P.E. (2007). Invited commentary: heterogeneity of particulate matter health risks. Am J Epidemiol. 

166(8):889-91; discussion 892-3. 

Tolbert, P.E.; Klein, M.; Peel, J.L.; Sarnat, S.E.; Sarnat, J.A. (2007). Multipollutant Modeling Issues in a Study of 

Ambient Air Quality and Emergency Department Visits in Atlanta. J Expo Sci Environ Epidemiol. 17:S29-

S35. 

US Environmental Protection Agency. (2004). Air Quality Criteria for Particulate Matter. National Center for 

Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 27711; report no. EPA/600/P-99/002aF and EPA/600/P-99/002bF. October 

2004. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_cd.html

U.S. Environmental Protection Agency. (2005). Review of the National Ambient Air Quality Standards for 

Particulate Matter: Policy Assessment of Scientific and Technical Information - OAQPS Staff Paper, 

Office of Air Quality Planning and Standards, Research Triangle Park, NC, EPA-452/R-05-005. Available: 

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

U.S. Environmental Protection Agency. (2007). Draft Integrated Review Plan for the National Ambient Air Quality 

Standards for Particulate Matter. U.S. Environmental Protection Agency, Research Triangle Park, NC, EPA 

452/P-08-006. 

U.S. Environmental Protection Agency. (2008a). Integrated Review Plan for the National Ambient Air Quality 

Standards for Particulate Matter. U.S. Environmental Protection Agency, Research Triangle Park, NC, EPA 

452/R-08/004. 

U.S. Environmental Protection Agency. (2008b). Integrated Science Assessment for Particulate Matter: First 

External Review Draft. U.S. Environmental Protection Agency, Research Triangle Park, NC, EPA/600/R08/139 and 139A. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html. 

U.S. Environmental Protection Agency. (2009a). Integrated Science Assessment for Particulate Matter: Second 

External Review Draft. U.S. Environmental Protection Agency, Research Triangle Park, NC, EPA 600/R08/139B. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html. 

U.S. Environmental Protection Agency. (2009b). Particulate Matter National Ambient Air Quality Standards: Scope 

and Methods Plan for Health Risk and Exposure Assessment. U.S. Environmental Protection Agency, 

Research Triangle Park, NC, EPA-452/P-09-002. 

U.S. Environmental Protection Agency. (2009c). Particulate Matter Urban-Focused Visibility Assessment – External 

Review Draft. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 
6-5

Research Triangle Park, NC. EPA-452/P-09/005. September 2009. Available: 

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

U.S. Environmental Protection Agency. (2009d). Integrated Science Assessment for Particulate Matter: Final. U.S. 

Environmental Protection Agency, Research Triangle Park, NC, EPA/600/R-08/139F. Available 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546 . 

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

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

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

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

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

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

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

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

US EPA (2010b). Quantitative Health Risk Assessment for Particulate Matter – Second External Review Draft 

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

Triangle Park, NC. EPA-452/P-10-001. February 2010. Available:. 

http://www.epa.gov/ttn/naaqs/standards/pm/data/20100209RA2ndExternalReviewDraft.pdf

World Health Organization. (2008). Part 1: Guidance Document on Characterizing and Communicating Uncertainty 

in Exposure Assessment, Harmonization Project Document No. 6. Published under joint sponsorship of the 

World Health Organization, the International Labour Organization and the United Nations Environment 

Programme. WHO Press, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland 

(tel.: +41 22 791 2476). 

Zanobetti (2009). Shrunken estimates for PM2.5. Personal Communication, June 1, 2009. (e-mail with attachments 

placed in docket #EPA-HQ-OAR-2007-0492). 

Zanobetti, A.; Schwartz, J. (2009). The effect of fine and coarse particulate air pollution on mortality: A National 

Analysis. Environ Health Perspect. 117(6): 898-903. 

Zeka, A.; Zanobetti, A.; Schwartz, J. (2005). Short term effects of particulate matter on cause specific mortality: 

effects of lags and modification by city characteristics. Occup Environ Med. 62(10):718-25. 

Zeka, A.; Zanobetti, A.; Schwartz, J. (2006). Individual-level modifiers of the effects of particulate matter on daily 

mortality. American Journal of Epidemiology. 163(9): 849-859. 
A-1

APPENDIX A: AIR QUALITY ASSESSMENT (SUMMARY OF 

INDIVDIUAL AND COMPOSITE MONITOR DATA BY URBAN 

STUDY AREA) 
A-2

Appendix A. Air Quality Assessment 

This Appendix describes the PM data for the 15 urban study areas evaluated in the risk 

assessment, including summaries of PM2.5 monitoring data associated with each study area as 

well as the composite monitor estimates generated for each study area based on that monitoring 

data (see section 3.2 for additional detail regarding selection of monitors and derivation of 

composite monitor values). 
A-3

Table A-1. Air Quality Data for Atlanta

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q

130630091 (3) 27 30 25 30 112 12.63 16.83 21.22 15.9

130670003 (1,2,3) 27 30 29 29 115 13.75 17.39 18.57 15.6130670004 (1,2,3) 30 28 26 27 111 12.98 17.17 18.03 13.9130890002 (1,2,3) 82 84 81 88 335 12.72 15.72 18.81 14.5130892001 (1,2,3) 80 75 67 85 307 12.84 15.10 20.44 14.8131210032 (1,2,3) 84 89 76 80 329 13.64 16.00 19.43 14.3131210039 (1,2,3) 27 30 23 29 109 15.03 18.35 17.97 16.5131210048 (1,2,3) 0 0 0 0 0 --- --- --- -

131350002 (1,3) 13 14 12 14 53 14.35 14.62 20.39 15.1

132230003 (3) 28 29 26 26 109 11.41 15.52 18.62 12.9Composite monitor for Atlanta - 1 90 91 92 92 365 13.62 16.34 19.09 15.0Composite monitor for Atlanta - 2 90 91 92 92 365 13.49 16.62 18.87 14.9Composite monitor for Atlanta - 3 90 91 92 92 365 13.26 16.30 19.27 14.8130630091 (3) 29 29 31 30 119 12.94 17.91 21.32 14.4130670003 (1,2,3) 28 29 31 30 118 12.22 17.88 21.52 14.2130670004 (1,2,3) 28 29 27 28 112 12.09 17.75 21.04 12.3130890002 (1,2,3) 85 86 81 81 333 12.25 16.09 19.86 13.4130892001 (1,2,3) 86 84 77 81 328 11.94 15.75 18.31 12.1131210032 (1,2,3) 88 86 84 90 348 12.46 15.99 19.28 13.7131210039 (1,2,3) 29 28 26 0 83 15.12 19.15 20.88 -131210048 (1,2,3) 0 0 2 30 32 --- --- 15.25 15.0

131350002 (1,3) 12 14 13 15 54 15.21 18.98 20.31 12.9

132230003 (3) 29 27 31 29 116 10.91 15.20 18.90 10.7Composite monitor for Atlanta - 1 90 91 92 92 365 13.04 17.37 20.17 13.4Composite monitor for Atlanta - 2 90 91 92 92 365 12.68 17.10 20.15 13.4Composite monitor for Atlanta - 3 90 91 92 92 365 12.79 17.19 20.16 13.2

Monitor

2006

2005

Quarterly Counts Quarterly Averages (ug/m3) Annual 

Total
A-4

Table A-1 cont'd. Air Quality Data for Atlanta

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

130630091 (3) 29 30 30 29 118 13.87 16.51 18.83 13.02 

130670003 (1,2,3) 29 30 29 29 117 13.49 17.03 19.49 13.41 130670004 (1,2,3) 26 27 30 30 113 12.50 17.47 18.77 11.39 130890002 (1,2,3) 85 83 90 85 343 12.78 15.54 19.38 12.15 130892001 (1,2,3) 69 79 76 75 299 12.48 17.11 20.04 12.38 131210032 (1,2,3) 87 88 91 85 351 12.99 17.95 19.64 13.08 131210039 (1,2,3) 0 0 0 0 0 --- --- --- --- 

131210048 (1,2,3) 28 28 31 28 115 13.45 18.97 18.24 12.83 

131350002 (1,3) 27 27 29 29 112 13.05 14.03 17.97 11.68 132230003 (3) 29 30 29 30 118 12.21 17.12 18.95 10.64 Composite monitor for Atlanta - 1 90 91 92 92 365 12.96 16.87 19.08 12.42 Composite monitor for Atlanta - 2 90 91 92 92 365 12.95 17.35 19.26 12.54 Composite monitor for Atlanta - 3 90 91 92 92 365 12.98 16.86 19.03 12.29 Note 1: Different definitions of Atlanta include different monitors. The number(s) shown in the parenthesis next to the monitor indicates the 

included. For example, monitor 130630091 is used in Atlanta - 3 only while 130670003 is used for all definitions of Atlanta.Note 2: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled i

2007

Monitor

Quarterly Counts Quarterly Averages (ug/m3) Annual 

Total
A-5

Table A-2. Air Quality Data for Baltimore

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

240051007 30 28 27 27 112 14.78 11.86 20.66 12.34 240053001 75 80 85 92 332 16.09 12.60 18.27 13.44 245100006 28 31 27 28 114 15.76 12.47 20.18 11.67 245100007 27 27 30 30 114 16.09 12.50 20.05 13.00 245100008 24 30 30 29 113 18.85 14.16 20.99 14.80 245100035 79 75 78 70 302 17.58 13.59 20.24 14.12 245100040 79 81 90 76 326 18.47 14.68 19.40 13.42 245100049 26 30 25 27 108 17.72 13.19 20.62 12.77 Composite Monitor for Baltimore 90 91 92 92 365 16.91 13.13 20.05 13.19 240051007 29 29 28 30 116 12.03 11.37 15.73 11.09 240053001 90 85 90 92 357 12.81 11.79 18.51 13.90 245100006 27 30 27 30 114 13.20 11.62 16.24 11.61 245100007 30 29 29 31 119 12.64 11.59 15.19 12.03 245100008 30 28 31 30 119 14.80 13.34 16.88 12.97 245100035 74 90 83 82 329 13.31 12.57 19.27 14.14 245100040 85 86 87 86 344 13.83 12.58 18.64 14.73 245100049 0 0 0 0 0 --- --- --- --- Composite Monitor for Baltimore 90 91 92 92 365 13.23 12.12 17.21 12.92 240051007 29 29 31 30 119 12.09 13.54 15.53 12.04 240053001 74 87 83 89 333 12.53 12.95 16.93 13.70 245100006 30 29 31 27 117 12.10 12.83 16.28 11.16 245100007 29 30 30 28 117 12.07 13.20 15.84 12.44 245100008 30 30 31 27 118 13.53 14.68 16.90 14.79 245100035 79 85 74 76 314 12.11 14.03 17.23 13.23 245100040 82 85 89 76 332 13.42 13.66 16.32 13.35 245100049 0 0 0 0 0 --- --- --- --- Composite Monitor for Baltimore 90 91 92 92 365 12.55 13.55 16.43 12.96 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-6

Table A-3. Air Quality Data for Birmingham

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

10730023* 90 90 89 92 361 14.35 20.49 26.42 17.27 10731005* 30 31 29 31 121 11.62 16.70 22.61 14.33 10731009* 30 31 29 31 121 9.82 16.12 20.26 11.87 10731010* 15 15 15 16 61 11.71 16.91 22.77 15.51 10732003* 88 90 91 91 360 14.49 18.48 23.75 15.03 10732006* 30 30 30 31 121 11.53 16.46 21.11 13.79 10735002* 30 31 30 31 122 10.84 16.33 21.08 12.61 10735003* 30 30 30 31 121 10.60 16.42 21.94 12.74 11170006 30 31 30 28 119 11.23 15.67 19.60 12.92 11270002 27 31 28 30 116 10.37 15.31 18.86 12.17 Composite Monitor for Birmingham - 1 90 91 92 92 365 11.66 16.89 21.84 13.82 Composite Monitor for Birmingham - 2 90 91 92 92 365 11.87 17.24 22.49 14.14 10730023* 89 91 92 92 364 13.61 20.57 22.35 17.02 10731005* 30 30 31 31 122 10.51 18.84 19.59 13.38 10731009* 30 29 30 30 119 8.81 17.16 17.78 10.02 10731010* 15 15 15 16 61 11.57 18.63 18.71 12.37 10732003* 89 90 90 92 361 14.41 20.48 21.62 15.67 10732006* 30 30 31 31 122 10.76 18.08 20.02 12.33 10735002* 30 30 31 31 122 9.87 17.15 19.61 10.60 10735003* 29 30 30 30 119 10.37 17.42 18.84 11.31 11170006 30 30 31 31 122 9.95 16.37 18.38 11.65 11270002 29 30 30 29 118 9.85 17.49 17.38 11.83 Composite Monitor for Birmingham - 1 90 91 92 92 365 10.97 18.22 19.43 12.62 Composite Monitor for Birmingham - 2 90 91 92 92 365 11.24 18.54 19.82 12.84 

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-7

Table A-3 cont'd. Air Quality Data for Birmingham

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

10731010* 15 15 15 15 60 14.53 18.69 19.31 13.63 10732003* 89 90 89 90 358 15.40 21.38 19.18 12.42 10732006* 30 30 31 30 121 12.24 19.29 18.53 10.93 10735002* 30 28 31 30 119 12.15 19.16 18.41 10.40 10735003* 29 30 31 30 120 11.79 18.99 17.83 10.38 11170006 29 30 31 30 120 12.97 18.27 17.52 10.84 11270002 28 29 31 29 117 11.97 17.81 17.72 10.95 Composite Monitor for Birmingham - 1 90 91 92 92 365 12.99 19.62 18.58 11.60 Composite Monitor for Birmingham - 2 90 91 92 92 365 13.12 20.02 18.82 11.78 Note 1: The monitors marked with * are used for Birmingham - 2. All monitors shown in this table are used for Birmingham - 1.Note 2: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in

2007

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-8

Table A-4. Air Quality Data for Dallas

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

481130035 30 31 20 0 81 11.78 15.16 13.90 --- 481130050 15 30 27 31 103 11.95 15.01 15.64 12.47 481130057 27 21 22 0 70 12.00 16.07 14.41 --- 481130069 78 88 90 91 347 11.07 13.80 14.03 11.11 481130087 27 31 30 30 118 9.87 13.32 13.45 10.18 481133004 88 89 61 0 238 10.86 13.58 12.82 --- Composite Monitor for Dallas 90 91 92 92 365 11.26 14.49 14.04 11.25 481130035 0 0 0 0 0 --- --- --- --- 481130050 28 30 31 31 120 10.99 12.53 12.98 10.68 481130057 0 0 0 0 0 --- --- --- --- 481130069 84 90 92 90 356 9.97 12.15 11.73 9.26 481130087 30 30 30 28 118 9.22 11.66 10.89 8.45 481133004 0 0 0 0 0 --- --- --- --- Composite Monitor for Dallas 90 91 92 92 365 10.06 12.11 11.87 9.46 481130035 0 0 0 0 0 --- --- --- --- 481130050 29 28 30 0 87 11.54 11.76 15.42 --- 481130057 0 0 0 0 0 --- --- --- --- 481130069 88 91 91 79 349 10.13 10.91 13.78 10.14 481130087 28 21 29 30 108 9.96 11.16 12.70 9.30 481133004 0 0 0 0 0 --- --- --- --- Composite Monitor for Dallas 90 91 92 92 365 10.54 11.27 13.97 9.72 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled 

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-9

Table A-5. Air Quality Data for Detroit

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

261630001 88 87 89 86 350 18.45 13.87 17.15 14.38 261630015 27 27 30 30 114 20.20 14.73 18.73 15.18 261630016 87 79 84 88 338 18.92 14.78 16.62 13.70 261630019 28 31 29 29 117 19.82 14.48 17.43 14.20 261630025 26 28 30 30 114 17.86 11.74 17.45 12.68 261630033 28 31 28 28 115 21.50 16.57 18.22 17.90 261630036 29 28 29 27 113 16.96 14.92 18.58 15.19 261630038 28 25 22 0 75 16.98 14.60 17.66 --- 261630039 0 0 7 28 35 --- --- 18.20 14.25 Composite Monitor for Detroit 90 91 92 92 365 18.84 14.46 17.73 14.69 261630001 82 85 88 90 345 13.66 11.89 13.68 13.65 261630015 29 26 28 31 114 16.98 12.26 14.93 14.56 261630016 79 14 13 17 123 13.04 11.58 12.58 14.97 261630019 30 15 14 16 75 15.20 10.39 11.78 13.46 261630025 27 14 15 17 73 13.49 11.23 10.01 12.70 261630033 28 29 27 31 115 18.79 12.85 15.56 17.30 261630036 29 26 29 29 113 15.10 10.95 13.69 11.94 261630038 0 29 27 28 84 --- 11.10 14.34 11.98 261630039 29 30 31 30 120 14.78 11.71 14.20 11.84 Composite Monitor for Detroit 90 91 92 92 365 15.13 11.55 13.42 13.60 261630001 86 89 87 92 354 12.92 10.28 14.00 14.08 261630015 28 30 27 29 114 15.15 13.06 15.12 14.82 261630016 26 26 30 29 111 13.98 12.12 14.74 14.61 261630019 30 28 31 27 116 13.20 11.16 14.36 13.31 261630025 26 30 31 27 114 12.23 10.59 13.76 14.42 261630033 29 29 29 27 114 18.84 15.20 16.02 17.49 261630036 29 28 30 29 116 13.75 11.96 14.60 13.47 261630038 27 27 28 30 112 13.63 12.85 15.35 14.23 261630039 29 30 30 28 117 13.83 12.98 14.65 13.86 Composite Monitor for Detroit 90 91 92 92 365 14.17 12.24 14.73 14.48 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled 

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-10

Table A-6. Air Quality Data for Fresno

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

60190008 85 78 89 91 343 19.53 7.19 11.42 28.65 60195001 30 15 15 22 82 17.11 7.55 10.78 29.95 60195025 30 15 13 31 89 20.24 8.29 11.24 27.92 Composite Monitor for Fresno 90 91 92 92 365 18.96 7.68 11.14 28.84 60190008 89 87 87 85 348 21.82 9.10 12.39 23.85 60195001 30 15 14 29 88 18.38 9.47 12.99 24.96 60195025 30 15 12 31 88 20.13 9.81 13.66 26.87 Composite Monitor for Fresno 90 91 92 92 365 20.11 9.46 13.01 25.22 60190008 87 90 88 91 356 27.61 8.32 10.70 28.71 60195001 29 13 14 27 83 23.70 7.16 9.91 24.91 60195025 29 14 15 30 88 24.91 8.73 9.65 24.10 Composite Monitor for Fresno 90 91 92 92 365 25.41 8.07 10.09 25.90 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in

20062007

2005

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-11

Table A-7. Air Quality Data for Houston

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

482010024 26 31 22 15 94 11.77 14.39 17.17 11.83 482010026 23 31 20 0 74 10.47 13.10 14.47 --- 482010055 25 28 19 0 72 9.12 12.31 12.97 --- 482010058 20 28 23 26 97 11.95 12.99 14.40 12.19 482011034 10 15 10 0 35 11.79 15.36 14.49 --- 482011035 84 68 78 87 317 13.09 16.59 18.41 15.47 Composite Monitor for Houston 90 91 92 92 365 11.28 14.12 15.48 13.16 482010024 15 13 13 13 54 10.92 11.66 15.97 12.58 482010026 0 0 0 0 0 --- --- --- --- 482010055 0 0 0 0 0 --- --- --- --- 482010058 26 29 29 29 113 9.74 12.34 9.04 9.82 482011034 0 0 0 0 0 --- --- --- --- 482011035 85 87 88 88 348 13.98 18.15 17.38 14.48 Composite Monitor for Houston 90 91 92 92 365 11.55 14.05 14.13 12.29 482010024 15 14 13 0 42 11.01 12.82 14.64 --- 482010026 0 0 0 0 0 --- --- --- --- 482010055 0 0 0 0 0 --- --- --- --- 482010058 26 30 30 30 116 9.40 10.96 11.84 11.75 482011034 0 0 0 0 0 --- --- --- --- 482011035 87 91 91 82 351 14.42 17.02 16.62 14.50 Composite Monitor for Houston 90 91 92 92 365 11.61 13.60 14.36 13.13 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-12

Table A-8. Air Quality Data for Los Angeles

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

60370002 65 78 87 62 292 11.37 13.97 20.71 21.78 60371002 29 25 30 22 106 17.01 13.75 18.55 21.95 60371103 90 84 87 89 350 15.26 13.78 19.62 22.48 60371201 25 29 28 22 104 12.27 11.97 15.01 16.18 60371301 29 26 28 31 114 16.68 13.28 18.15 21.75 60371602 29 9 9 29 76 16.90 11.63 17.13 22.31 60372005 30 26 26 31 113 12.98 12.95 17.15 17.28 60374002 87 82 88 67 324 13.39 11.54 16.21 22.56 60374004 90 84 87 83 344 12.64 10.83 15.63 19.59 60379033 28 30 27 18 103 8.18 8.27 9.96 9.00 Composite Monitor for Los Angeles 90 91 92 92 365 13.67 12.26 16.78 19.49 60370002 66 73 84 55 278 12.62 16.17 16.95 15.87 60371002 25 24 30 25 104 15.33 18.34 15.87 16.66 60371103 89 82 85 74 330 14.49 14.69 16.34 16.80 60371201 20 27 28 17 92 11.19 14.21 12.95 13.00 60371301 28 28 27 24 107 17.62 14.76 15.11 19.26 60371602 29 28 31 28 116 16.82 13.92 17.19 18.57 60372005 29 27 28 29 113 12.85 14.64 13.46 12.51 60374002 73 81 73 63 290 15.19 12.27 13.53 15.57 60374004 89 86 79 66 320 14.35 11.99 14.21 17.22 60379033 15 15 14 14 58 6.13 7.27 8.36 8.00 Composite Monitor for Los Angeles 90 91 92 92 365 13.66 13.83 14.40 15.35 

 

A

(

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-13

Table A-8 cont'd. Air Quality Data for Los Angeles

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

60370002 64 77 74 77 292 13.57 17.11 14.68 17.47 60371002 23 26 27 22 98 13.64 15.96 15.36 22.47 60371103 67 83 90 84 324 16.25 16.05 14.62 20.19 60371201 22 26 28 19 95 9.50 13.24 12.55 17.72 60371301 25 27 29 25 106 16.98 14.05 13.00 19.99 60371602 27 27 21 26 101 16.75 14.01 15.18 20.45 60372005 28 23 30 27 108 12.62 15.60 14.02 15.24 60374002 76 86 88 82 332 15.45 12.42 11.50 19.04 60374004 65 81 90 90 326 13.84 12.26 11.30 17.31 60379033 15 15 15 15 60 6.73 7.67 9.00 8.67 Composite Monitor for Los Angeles 90 91 92 92 365 13.53 13.84 13.12 17.85 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled 

2007

 

A

(

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-14

Table A-9. Air Quality Data for New York

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

360050080 28 31 29 27 115 18.59 14.78 18.42 15.68 360050083 30 31 30 31 122 13.77 12.21 16.90 12.71 360050110 90 91 91 91 363 14.93 12.17 15.38 12.30 360470122 28 30 28 27 113 16.04 13.74 17.31 14.13 360610056* 30 31 30 31 122 18.44 15.51 19.16 15.17 360610062* 27 31 30 31 119 17.14 13.84 18.34 13.54 360610079* 30 31 30 31 122 14.60 13.12 17.03 12.56 360610128* 25 31 30 31 117 17.74 14.11 18.37 15.21 360610134* 0 0 0 0 0 --- --- --- --- 360810124 89 79 62 74 304 13.02 10.44 15.21 10.84 360850055 28 25 28 27 108 14.92 12.49 17.81 12.91 360850067 24 28 28 30 110 12.60 10.75 16.17 10.41 Composite Monitor for New York City - 1 90 91 92 92 365 15.62 13.02 17.28 13.22 Composite Monitor for New York City - 2 90 91 92 92 365 16.98 14.15 18.22 14.12 360050080 29 30 27 29 115 16.57 13.17 13.95 11.88 360050083 30 30 29 29 118 13.44 11.06 13.34 10.33 360050110 86 91 84 86 347 13.10 11.15 14.49 11.40 360470122 28 30 29 25 112 15.00 12.49 14.75 9.00 360610056* 30 30 27 30 117 16.61 14.03 14.41 12.59 360610062* 30 28 28 27 113 14.33 13.00 13.82 9.86 360610079* 30 30 31 29 120 14.12 12.08 13.32 10.59 360610128* 26 30 29 29 114 15.79 13.07 14.39 12.64 360610134* 0 0 0 0 0 --- --- --- --- 360810124 69 86 84 76 315 11.17 10.67 13.68 10.91 360850055 25 27 29 29 110 12.27 12.07 14.06 10.56 360850067 30 26 31 29 116 10.01 10.49 12.60 8.54 Composite Monitor for New York City - 1 90 91 92 92 365 13.86 12.12 13.89 10.75 Composite Monitor for New York City - 2 90 91 92 92 365 15.21 13.04 13.99 11.42 

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-15

Table A-9 cont'd. Air Quality Data for New York

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

360050080 30 30 30 29 119 17.45 13.49 16.20 15.43 360050083 30 30 30 29 119 14.14 11.72 13.91 12.87 360050110 89 84 85 91 349 12.90 11.64 14.22 12.31 360470122 29 30 28 30 117 13.67 12.82 15.92 13.00 360610056* 30 27 31 30 118 18.43 14.73 15.99 15.29 360610062* 27 0 0 0 27 15.84 --- --- --- 360610079* 30 30 31 30 121 14.11 12.48 14.92 12.89 360610128* 30 30 29 21 110 19.10 13.83 14.63 14.76 360610134* 3 30 31 30 94 8.53 14.12 16.43 14.08 360810124 74 86 80 92 332 11.34 10.66 12.30 11.35 360850055 30 28 31 30 119 13.04 12.37 14.55 11.91 360850067 27 30 26 26 109 10.60 10.49 14.29 10.54 Composite Monitor for New York City - 1 90 91 92 92 365 14.60 12.58 14.85 13.13 Composite Monitor for New York City - 2 90 91 92 92 365 16.87 13.79 15.49 14.25 Note 1: The monitors marked with * are used for New York City - 2. All monitors in the table are used for New York City - 1.Note 2: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled 

2007

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-16

Table A-10. Air Quality Data for Philadelphia

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

421010003 0 0 0 62 62 --- --- --- 14.35 421010004 55 61 78 74 268 13.23 13.06 17.26 13.28 421010020 19 0 0 0 19 15.51 --- --- --- 421010024 37 54 67 71 229 12.68 10.76 16.26 12.02 421010047 19 28 26 12 85 16.99 12.04 18.91 12.31 421010057 0 0 0 0 0 --- --- --- --- 421010136 86 89 29 33 237 13.57 11.40 19.06 12.91 Composite Monitor for Philadelphia 90 91 92 92 365 14.40 11.81 17.87 12.97 421010003 85 26 0 0 111 12.21 8.74 --- --- 421010004 81 70 53 84 288 12.74 11.85 17.23 12.41 421010020 0 0 0 0 0 --- --- --- --- 421010024 34 70 71 80 255 11.52 10.56 16.17 11.34 421010047 40 67 45 47 199 14.44 14.57 18.04 15.04 421010057 0 0 0 0 0 --- --- --- --- 421010136 47 50 79 73 249 11.97 12.06 16.29 12.25 Composite Monitor for Philadelphia 90 91 92 92 365 12.58 11.55 16.93 12.76 421010003 0 0 0 0 0 --- --- --- --- 421010004 87 71 86 90 334 13.61 13.19 15.15 12.96 421010020 0 0 0 0 0 --- --- --- --- 421010024 87 58 86 90 321 12.05 12.76 14.88 11.73 421010047 71 59 90 92 312 14.49 13.05 16.33 13.43 421010057 0 0 18 90 108 --- --- 10.96 13.13 421010136 75 65 72 82 294 12.60 13.38 14.36 12.99 Composite Monitor for Philadelphia 90 91 92 92 365 13.19 13.09 14.33 12.85 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-17

Table A-11. Air Quality Data for Phoenix

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

40130019 32 32 30 31 125 11.04 10.78 11.11 18.37 40131003 0 22 30 29 81 --- 8.77 8.26 9.72 40134003 29 31 27 31 118 10.94 13.04 10.40 16.98 40137020 0 30 29 31 90 --- 8.08 7.72 9.46 40139997 29 31 30 31 121 9.04 8.69 7.58 13.56 Composite Monitor for Phoenix 90 91 92 92 365 10.34 9.87 9.01 13.62 40130019 30 30 31 31 122 14.17 13.58 8.07 17.82 40131003 26 28 31 31 116 8.87 9.52 8.92 11.33 40134003 28 28 31 29 116 13.53 10.34 9.31 17.58 40137020 29 30 31 30 120 8.09 7.98 7.14 9.12 40139997 29 29 30 30 118 10.74 8.66 7.46 14.04 Composite Monitor for Phoenix 90 91 92 92 365 11.08 10.01 8.18 13.98 40130019 32 30 31 30 123 10.26 8.85 8.63 15.42 40131003 29 28 30 30 117 7.66 10.45 9.50 11.27 40134003 30 29 30 29 118 10.54 11.76 11.32 15.45 40137020 30 30 31 20 111 5.85 7.81 7.35 8.21 40139997 30 29 32 30 121 8.85 8.12 8.21 12.75 Composite Monitor for Phoenix 90 91 92 92 365 8.63 9.40 9.00 12.62 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-18

Table A-12. Air Quality Data for Pittsburgh

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

420030008 89 90 92 89 360 13.80 15.29 20.72 13.40 420030021 28 27 30 27 112 12.91 14.99 22.00 11.49 420030064 88 90 92 86 356 16.28 22.26 25.94 21.10 420030067 26 28 29 27 110 12.32 13.95 20.35 10.26 420030093 13 11 12 13 49 10.66 13.83 23.66 9.63 420030095 14 13 14 15 56 12.79 14.49 21.55 9.83 420030116 23 29 28 26 106 13.82 16.42 21.68 12.66 420030133 14 13 13 9 49 13.54 12.62 20.51 9.51 420031008 30 29 30 29 118 12.79 15.60 21.90 13.52 420031301 29 29 29 26 113 14.39 16.86 23.90 13.37 420033007 15 13 14 15 57 14.13 14.25 24.36 12.71 420039002 13 13 14 15 55 12.95 14.01 21.32 11.25 Composite Monitor for Pittsburgh 90 91 92 92 365 13.37 15.38 22.32 12.58 420030008 85 89 91 92 357 11.60 13.28 20.19 12.54 420030021 0 0 0 0 0 --- --- --- --- 

420030064 85 90 87 89 351 14.86 17.89 22.78 20.97 420030067 23 26 28 21 98 9.61 9.52 16.39 9.06 420030093 14 6 13 13 46 10.37 9.85 16.38 9.41 420030095 13 13 13 14 53 10.02 10.97 18.22 10.31 420030116 0 0 0 0 0 --- --- --- --- 

420030133 0 0 0 0 0 --- --- --- --- 

420031008 27 23 28 25 103 11.87 14.30 18.32 11.63 420031301 26 28 29 29 112 12.56 14.55 19.89 13.11 420033007 15 15 14 15 59 12.93 13.51 19.16 12.36 420039002 0 0 0 0 0 --- --- --- --- 

Composite Monitor for Pittsburgh 90 91 92 92 365 11.49 13.05 18.69 11.95 

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-19

Table A-12. Air Quality Data for Pittsburgh

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

420030008 85 86 86 89 346 11.80 14.72 20.30 12.74 420030021 0 0 0 0 0 --- --- --- --- 420030064 88 90 91 90 359 14.16 18.64 25.16 17.57 420030067 19 25 28 26 98 10.28 13.40 19.46 10.73 420030093 15 12 14 14 55 9.67 10.50 19.35 12.57 420030095 14 13 15 14 56 10.96 9.89 20.79 12.90 420030116 0 0 0 0 0 --- --- --- --- 420030133 0 0 0 0 0 --- --- --- --- 420031008 27 27 30 27 111 12.79 14.55 19.68 13.23 420031301 28 27 31 26 112 14.02 15.18 21.90 15.16 420033007 14 14 14 13 55 12.36 13.03 21.19 13.85 420039002 0 0 0 0 0 --- --- --- --- Composite Monitor for Pittsburgh 90 91 92 92 365 11.87 13.51 20.74 13.36 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-20

Table A-13. Air Quality Data for Salt Lake City

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

490350003 30 29 30 31 120 14.16 6.58 8.98 14.49 490350012 82 89 85 85 341 16.73 9.59 12.68 17.24 490351001 29 30 28 30 117 11.85 5.47 8.61 11.35 490353006 88 90 90 85 353 13.95 6.27 9.56 14.17 490353007 28 27 29 28 112 13.64 7.40 10.57 16.36 490353008 30 31 24 31 116 9.90 6.03 7.76 7.45 490353010 0 0 0 0 0 --- --- --- --- Composite Monitor for Salt Lake City 90 91 92 92 365 13.37 6.89 9.69 13.51 490350003 28 28 29 30 115 10.76 6.98 9.41 13.58 490350012 76 87 82 90 335 11.80 11.22 14.19 14.91 490351001 27 28 29 27 111 7.95 5.65 8.65 9.29 490353006 88 90 90 88 356 10.59 7.21 8.54 12.37 490353007 30 30 31 29 120 10.11 7.18 11.56 13.61 490353008 29 26 30 30 115 6.14 6.85 9.26 7.09 490353010 0 0 0 0 0 --- --- --- --- Composite Monitor for Salt Lake City 90 91 92 92 365 9.56 7.51 10.27 11.81 490350003 30 30 29 28 117 18.12 6.97 10.99 13.89 490350012 80 86 0 0 166 20.84 11.45 --- --- 490351001 24 30 31 26 111 11.42 6.44 10.08 9.71 490353006 89 85 78 89 341 18.17 6.11 9.42 12.05 490353007 29 29 29 31 118 17.72 7.17 11.53 13.42 490353008 23 28 28 30 109 10.03 6.06 9.66 7.09 490353010 0 80 83 92 255 --- 7.68 11.62 13.00 Composite Monitor for Salt Lake City 90 91 92 92 365 16.05 7.41 10.55 11.53 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-21

Table A-14. Air Quality Data for St. Louis

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

171190023* 28 28 29 29 114 18.01 19.10 21.49 16.95 171190024* 0 0 0 0 0 --- --- --- --- 171191007* 26 31 29 30 116 18.40 16.49 21.47 16.27 171192009* 12 12 13 12 49 14.94 16.35 20.82 11.98 171193007* 29 31 27 29 116 16.42 15.20 19.99 12.49 171630010* 13 15 14 15 57 17.31 16.81 19.97 14.47 171634001* 30 30 29 28 117 17.86 14.17 17.20 14.69 290990012 90 87 90 91 358 15.22 14.69 19.26 12.42 291890004* 29 29 28 31 117 16.01 12.64 17.80 11.87 291892003* 57 30 29 31 147 16.73 14.15 18.44 12.65 295100007* 88 88 83 81 340 16.99 14.67 18.92 12.87 295100085* 90 86 78 88 342 16.78 14.46 19.67 13.33 295100086* 84 26 30 29 169 15.11 14.34 18.43 13.14 295100087* 90 87 82 81 340 17.02 14.80 18.74 12.94 295100093* 0 0 0 0 0 --- --- --- --- Composite Monitor for St Louis - 1 90 91 92 92 365 16.68 15.22 19.40 13.54 Composite Monitor for St Louis - 2 90 91 92 92 365 16.80 15.27 19.41 13.64 171190023* 30 26 31 29 116 15.21 17.34 19.40 12.11 171190024* 0 0 0 0 0 --- --- --- --- 171191007* 27 24 24 27 102 14.95 16.12 20.18 14.05 171192009* 15 15 14 16 60 12.59 13.35 13.49 12.92 171193007* 28 30 31 31 120 13.08 12.00 16.47 10.87 171630010* 12 14 15 14 55 14.18 13.75 15.72 14.48 171634001* 28 28 31 29 116 13.43 12.87 15.20 12.00 290990012 82 81 91 89 343 11.62 11.79 15.46 11.49 291890004* 30 29 0 0 59 10.56 10.49 --- --- 291892003* 29 29 28 26 112 11.36 10.69 13.87 11.00 295100007* 78 88 91 90 347 12.27 11.82 15.89 12.51 295100085* 86 77 84 92 339 13.04 12.46 15.26 12.68 295100086* 30 30 31 29 120 11.94 11.55 15.48 10.90 295100087* 85 90 86 91 352 12.92 12.32 16.17 13.18 295100093* 0 0 0 0 0 --- --- --- --- Composite Monitor for St Louis - 1 90 91 92 92 365 12.86 12.81 16.05 12.35 Composite Monitor for St Louis - 2 90 91 92 92 365 12.96 12.90 16.10 12.43 

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-22

Table A-14 cont'd. Air Quality Data for St. Louis

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

171190023* 0 0 0 0 0 --- --- --- --- 

171190024* 0 0 6 29 35 --- --- 15.07 14.94 

171191007* 29 27 29 26 111 14.28 15.31 17.61 13.23 171192009* 15 12 14 13 54 14.31 16.02 15.66 13.51 171193007* 29 28 26 30 113 12.42 14.84 17.39 12.32 171630010* 13 13 14 14 54 14.94 17.65 15.94 13.79 171634001* 26 30 31 29 116 13.35 13.95 14.83 10.90 290990012 82 81 90 86 339 11.94 14.44 16.23 12.13 291890004* 0 0 0 0 0 --- --- --- --- 

291892003* 89 90 91 90 360 11.63 12.96 15.25 12.49 295100007* 88 91 91 92 362 12.56 14.50 16.13 12.97 295100085* 90 88 89 90 357 12.59 13.79 16.09 13.30 295100086* 27 30 0 0 57 11.79 14.50 --- --- 

295100087* 90 86 92 86 354 13.24 14.43 16.61 13.10 295100093* 0 0 24 29 53 --- --- 17.26 13.82 

Composite Monitor for St Louis - 1 90 91 92 92 365 13.00 14.76 16.27 13.04 Composite Monitor for St Louis - 2 90 91 92 92 365 13.11 14.79 16.28 13.13 Note 1: The monitors marked with * are used for St Louis - 2. All monitors shown in the table are used for St Louis - 1.Note 2: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
A-23

Table A-15. Air Quality Data for Tacoma

 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

530530029 29 30 30 31 120 16.46 5.34 7.13 17.07 Composite Monitor for Tacoma 90 91 92 92 365 16.46 5.34 7.13 17.07 530530029 30 30 31 26 117 8.92 5.89 7.45 15.93 Composite Monitor for Tacoma 90 91 92 92 365 8.92 5.89 7.45 15.93 530530029 29 28 31 29 117 13.76 5.94 5.23 13.76 Composite Monitor for Tacoma 90 91 92 92 365 13.76 5.94 5.23 13.76 Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.

2007

20052006

Monitor

Quarterly Counts Annual 

Total

Quarterly Averages (ug/m3)
B-1

APPENDIX B: ADDITIONAL INFORMATION SUPPORTING AIR 

QUALITY CHARACTERIZATION 
B-2

Appendix B. Additional Information Supporting Air Quality Characterization 

This appendix provides information supporting air quality characterization completed as 

part of the risk assessment including both the characterization of recent conditions (as addressed 

section 3.2.1) and the simulation of air quality to just meet current and alternative standards (as 

addressed in section 3.2.3). Specifically, section B1 considers an alternative approach for 

interpolating missing data as part of constructing distributions of 24-hour PM2.5 estimates at 

composite monitors, section B2 provides additional detail on the hybrid rollback method (one of 

the three rollback methods used in the risk assessment) and section B3 provides example 

calculations of the three rollback methods applied to one of the urban study areas (Detroit). 

B1. SENSITIVITY ANALYSIS ADDRESSING THE INTERPOLATION OF MISSING 

DATA COMPLETED IN DEVELOPING COMPOSITE MONITOR 24-HOUR 

PM2.5 DISTRIBUTIONS 

As noted in section 3.2.1, there are a variety of possible approaches for interpolating 

missing 24-hour monitoring data as part of generating composite monitor 24-hour PM2.5 

distributions for the study areas. For the risk assessment, we have used an approach that relied 

on other measurements at the specific monitor where the interpolation was being conducted (i.e., 

the nearest measurements before and after the point of needed interpolation – see section 3.2.1 

for additional details on this approach). However, as noted by CASAC, there are other 

interpolation approaches available, some of which make use of monitoring trend data across the 

entire set of PM2.5 monitors associated with a study area, rather than relying on data only from 

the monitor undergoing the interpolation. In addition, these alternative interpolation methods can 

address another limitation of the method used in the risk assessment – the restriction of 

interpolation to periods shorter than 8 days which excludes interpolation for 1 in 6 day monitors 

missing data. 

The availability of alternative interpolation methods highlights the potential uncertainty 

associated with this component of the risk assessment. To further examine this source of 

uncertainty, we have completed a sensitivity analysis based on the application of an alternative 

interpolation approach based on centering. The centering approach uses variance-trend data from 

all of the monitors in a study area (with emphasis on locations with measurements on the day 

being interpolated) as the basis for interpolating missing daily measurements. This sensitivity 

analysis has been implemented for Birmingham (specifically the Birmingham 1 grouping of 

counties and associated monitors). The alternate approach used the following steps for each of 

the simulation years 2005, 2006, and 2007: 
B-3

1) The annual mean of the actual data from each individual site was determined, using a 

seasonally weighted approach that is also the basis for the calculation of official design 

values for the current annual NAAQS. All samples within Jan-March, April-June, JulySeptember, and October-December were averaged. Then these four quarterly means were 

averaged to get the annual mean for the calendar year. 

2) The annual averages for the individual sites were averaged, with equal weight, to give the 

composite monitor annual average. 

3) The annual average for a site was subtracted from each daily value for the site. The residuals 

represent the deviation of the site concentration from the site annual average on a given day. 

4) For each day, all available site residuals were averaged across sites with equal weight. For 

most days, the only available residuals were from the two sites that sampled every day. On 

every third day, up to 10 residuals were available. There was only a single day in 2005 for 

which no site reported a concentration so no average of residuals could be calculated. 

5) On each day, the concentration of the composite monitor was taken to be the sum of the 

result from the second step and the result from the fourth step. No composite monitor 

concentration was calculated for the single day in 2005 mentioned in step 4. 

It was observed that when the weighted annual mean of the resulting estimates of daily 

24-hour concentrations for the composite monitor from step 5 were compared to the result of 

step 2, slight differences exist. The absolute value of the differences was less than 0.1 μg/m3

 in 

2005 and 2006 and less than 0.2 μg/m3

 in 2007. 

In comparing same-day estimates of 24-hour concentrations for the composite monitor as 

estimated by the method used in the core risk estimates versus the alternative method described 

here, differences were larger (as would be expected when comparing 24-hour estimates versus 

annual-average estimates) but occurred in both directions. Figure B-1 is a scatter plot of the two 

sets of daily estimates. 
B-4

Figure B-1. Comparison of Composite Monitor 24-hour PM2.5 Distributions (2007) 

Generated Using the Two Interpolation Methods 

Comparison of Two Approaches for Estimating 

Birmingham 1 Composite Monitor

0

10

20

30

40

50

60

0 10 20 30 40 50 60

Estimate from Main Analysis Method

Estimate from Alternative Method

 

To investigate how the differences in same-day concentration estimates illustrated in 

Figure B-1 would affect estimates of risk (specifically short-term exposure-related risk) 

aggregated across individual years and across all three years, we used the 24-hour PM2.5 

concentration above PRB as a surrogate for risk, since the incidences of (short-term exposurerelated) health endpoints related to 24-hour concentrations are nearly proportional to this metric. 

We averaged this across all days in a year, and then across the three years. Averaging rather than 

summation was used because the completely missing day in 2005 in the alternate approach 

prevents a valid comparison of the sum across all available days. Table B-1 shows the results of 

this comparison. 
B-5

Table B-1. Comparison of Surrogate for Short-Term Exposure-Related Risk (see text) 

Generated Using the Two Interpolation Methods 

Interpolation Method

Simulation Year Main method used in RA

Alternate (centering‐based)

method

2005 14.33 14.27

2006 13.61 13.58

2007 14.00 13.80

2005‐2007 13.98 13.89

The results presented in Table B-1 suggest that, while conceptually the two interpolation 

methods considered in the sensitivity analysis differ significantly, the impact of switching 

between these two methods on short-term exposure-related risk is negligible. While these 

findings need to be considered in the context of the sensitivity analysis as conducted (i.e., based 

on considering a single alternative interpolation method as applied to one of the 15 urban study 

areas), they do reduce concerns that this source of uncertainty significantly impacts short-term 

exposure-related risk. 

B.2 ADDITIONAL DETAIL ON THE HYBRID ROLLBACK APPROACH 

This section provides additional detail on one of the three methods (the hybrid method) 

used to simulate ambient PM2.5 levels under both current and alternative standard levels. For 

additional detail on the other two methods (proportional and locally focused rollback) used in the 

risk assessment to simulate standard levels as well as an overview of how the rollback methods 

are applied in the context of assessing risk, see section 3.2.3. 

The hybrid approach reflects the combined effects of both local and regional reduction 

strategies. As such, in addition to utilizing a traditional proportional rollback to represent the 

regional PM reductions, the hybrid approach also includes a distance-weighted rollback was 

conducted on a subset of the 15 study areas which contain source-oriented monitors measuring 

concentrations higher than those observed at other sites within a particular area.1

Unique sites with high design values exceeding the NAAQS were further investigated to 

determine if they were in close proximity to a large source of PM2.5 (Figure B-2). The presence 

of possible source-oriented sites in each area was visually determined using satellite photographs 

 

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 approach described here, was considered as part of the 

sensitivity analysis along with the locally focused rollback approach described in section 3.2.3.3. 
B-6

provided by Google Earth. Areas where source-oriented adjustments were made include Detroit 

MI, Pittsburgh PA, St. Louis MO-IL, Baltimore MD, New York NY, Los Angeles CA and 

Birmingham AL. 

Figure B-2. Example of a monitor, in Dearborn MI, located near a large source of 

emissions 

For those sites that were within proximity to a large emitter, the site’s measured 

concentrations were reduced using a proportional rollback depending on the magnitude of the 

reduction needed to either the highest 24-hour or annual design value of a non-source oriented 

site within the area whose design values were close to those of the source oriented site (Figure B3). 
B-7

Figure B-3. Plot of the 24-hour versus the annual average PM2.5 design values for 

 individual sites in Detroit MI 

The fractional reduction made to the site near the point source was then weighted by the 

inverse distance in kilometers between the source-oriented site and all of the other individual 

sites in the area to determine their fractional reductions in relation to the source-oriented site. If 

more than one source-oriented site was reduced, a distance-weighted average fractional reduction 

was calculated and implemented across the non-source-oriented sites. Sites within one kilometer 

of the source oriented site received the same amount of reduction as the source oriented site. An 

example of the effect of this reduction technique for Detroit is presented in Table B-2. For 

Detroit, adjustments were based on the difference between the two sites’ annual design values. 
B-8

Table B-2. Comparison of the original and adjusted design values for Detroit, MI 

Site ID 

Original 

Annual Design 

Value (2005-

2007) 

Adjusted 

Annual Design 

Value (2005-

2007) 

Original 24-

hour Design 

Value (2005-

2007) 

Adjusted 24-

hour Design 

Value (2005-

2007) 

260490021 11.6 11.5 29 29 

260990009 12.5 12.4 35 35 

261150005 13.8 13.7 38 38 

261250001 13.6 13.5 40 40 

261470005 13.2 13.1 41 40 

261610005 13.2 13.1 39 39 

261610008 13.7 13.6 39 39 

261630001 14 13.9 36 36 

261630015 15.5 15.2 40 39 

261630016 14.3 14.2 41 41 

261630019 14.1 14 40 40 

261630025 13.2 13.1 34 34 

261630033 17.2 15.4 43 39 

261630036 14.3 14.2 36 36 

261630038 14.3 14.1 40 39 

261630039 14.4 14.3 37 37 

 

Site in blue represents source-oriented site 

Site in red represents reference site used for reduction 

Reduction of the concentrations of the source-oriented site reduced either the 24-hour or 

annual design value of the site to either the maximum non-source-oriented site’s 24-hour or 

annual design value. This did not necessarily mean that the adjusted values at the sourceoriented site met either the 24-hour or annual standard after the reduction. Since the adjusted 

design values were calculated using the same data handling rules as contained within 40 CFR 

Part 50 Appendix N, truncation or rounding of the adjusted concentrations could sometimes give 

adjusted design values at the source-oriented site that were not exactly the same value as the 

original design value at the reference site. However, they were usually within 1 ug/m3

 for the 

24-hour standard and a few tenths of a microgram per cubic meter for the annual standard. 

Air quality datasets adjusted using the hybrid rollback approach for the subset of urban 

study areas where this approach was used are available in the docket (Docket ID#: EPA-HQOAR-2007-0492) and have been posted at: http://www.epa.gov/ttn/analysis/pm.htm. 
B-9

B.3 EXAMPLE CALCULATION OF THREE ROLLBACK METHODS AS APPLIED 

TO THE DETROIT URBAN STUDY AREA 

This section provides a sample calculation of the three rollback methods (hybrid, 

proportional, and locally focused) as applied to simulating attainment of the current suite of 

standards for Detroit. This sample calculation is intended to illustrate how each rollback method 

is applied, including equations (and associated intermediate calculations) as appropriate. 

Several details regarding the calculation need to be clarified to ensure that the context for 

the sample calculations is well understood before they are reviewed. The current 24-hour 

standard level is controlling for this study area and consequently for the sample calculations, 

which means that simulated attainment of the 24-hour standard will determine the degree of 

reduction in ambient PM2.5 levels needed to simulate attainment of this suite of standards. In 

presenting composite monitor values generated using each rollback method, we have focused on 

annual-averages rather than the 24-hour PM2.5 distributions. This decision reflects the fact that 

composite monitor annual-averages have been shown to drive both short-term and long-term 

exposure related risk estimates and consequently are a better metric to consider in comparing and 

contrasting the rollback methods than are 24-hour PM2.5 distributions or percentiles summarizing 

those distributions. 

The calculations using the three rollback methods are presented in Table B-3, which 

shows how values at individual monitors are adjusted in applying rollback methods, when 

appropriate, together with the composite monitor values that result.2 The table is organized by 

rollback method, with the values related to the proportional method presented first followed by 

the hybrid and the locally focused method. The first block of columns track monitor-specific 

values across the three rollback methods with the last column presenting composite monitorrelated values. 

 

2

 As discussed in sections 3.2.3.1 through 3.2.3.3, the rollback methods differ as to whether they involve 

direct adjustment at individual monitors (hybrid and locally focused methods) or involve adjustment only at the 

composite monitors (proportional). 
B-10

Table B-3. Application of Three Rollback Methods (proportional, hybrid and locally 

focused) in Simulating Composite Monitor Annual-Average PM2.5 Levels for the 

Current Suite of Standards (Detroit, 2007) 

Row 

ID

Monitor ID:

261630001261630015261630016261630019261630025261630033261630036261630038261630039

Quarterly 

and Annual 

Averages at 

Composite 

Monitor

P1 Proportional Rollback Method

P2

P3 2007 Q1 Avg. 12.92 15.15 13.98 13.20 12.23 18.84 13.75 13.63 13.83 14.17

P4 2007 Q2 Avg. 10.28 13.06 12.12 11.16 10.59 15.20 11.96 12.85 12.98 12.24

P5 2007 Q3 Avg. 14.00 15.12 14.74 14.36 13.76 16.02 14.60 15.35 14.65 14.73

P6 2007 Q4 Avg. 14.08 14.82 14.61 13.31 14.42 17.49 13.47 14.23 13.86 14.48

P7 2007 Ann. Avg. 12.82 14.54 13.86 13.01 12.75 16.89 13.45 14.01 13.83 13.91

P8 11.43

H1

H2

H3 2007 Q1 Avg. 12.82 14.87 13.86 13.10 12.13 16.93 13.65 13.44 13.71 13.83

H4 2007 Q2 Avg. 10.18 12.81 12.00 11.06 10.49 13.64 11.86 12.67 12.85 11.95

H5 2007 Q3 Avg. 13.90 14.84 14.61 14.26 13.66 14.39 14.50 15.15 14.52 14.42

H6 2007 Q4 Avg. 13.98 14.56 14.49 13.21 14.32 15.70 13.37 14.04 13.74 14.16

H7 2007 Ann. Avg. 12.72 14.27 13.74 12.91 12.65 15.16 13.35 13.82 13.70 13.59

H8 11.69

LF1

LF2 Monitor-specific 36 40 41 40 34 43 36 40 37

LF3 Monitor-specific 2.8% 12.8% 14.9% 12.8% 0.0% 19.0% 2.8% 12.8% 5.5%

LF4 2007 Q1 Avg. 12.57 13.32 12.02 11.63 12.23 15.43 13.38 12.00 13.12 12.86

LF5 2007 Q2 Avg. 10.02 11.50 10.44 9.85 10.59 12.47 11.64 11.32 12.31 11.13

LF6 2007 Q3 Avg. 13.63 13.30 12.67 12.63 13.76 13.15 14.21 13.50 13.89 13.41

LF7 2007 Q4 Avg. 13.71 13.04 12.56 11.72 14.42 14.33 13.11 12.52 13.14 13.17

LF8 2007 Ann. Avg. 12.48 12.79 11.92 11.46 12.75 13.84 13.09 12.33 13.11 12.64

Locally-Focused Rollback Method

Recent (2007) Air Quality

Composite monitor annual average after proportional rollback

Quarterly and Annual Averages After Proportional Rollback

Hybrid Rollback Method

Quarterly and Annual Averages After Initial Adjustment of Recent Air Quality:

Quarterly and Annual Averages

A step-wise explanation of the values presented in Table B-3 is presented below. Row 

identifiers (P# for proportional, H# for hybrid and LF# for locally focused) are included in the 

table to facilitate this discussion. In presenting the step-wise explanations we provide only 

sufficient explanation of the conceptual approach underpinning each rollback method to insure 

that Table B-3 and the step-wise calculations can be understood. The reader is referred back to 

relevant sections of the document for a more complete discussion of the conceptual basis for 

each rollback method. 

Proportional rollback 
B-11

The proportional rollback method is applied at the composite monitor levels, which are 

themselves based on quarterly-average estimates at individual monitors (see section 3.2.3.1). 

Therefore, we begin the step-wise calculation for the proportional method by presenting the 

approach used to calculate the composite monitor quarterly- and annual-averages (set of “A” 

bullets below). We then present how design values are calculated (set of “B” bullets below). We 

then show how the design value is used to implement the proportional rollback at the composite 

monitor (set of “C” bullets below). 

A. Calculating the composite monitor annual average based on recent air quality 

1) Calculate quarterly averages for those monitors with at least 11 observations 

in the quarter (middle columns in rows P3-P6). 

2) Calculate the quarterly average at the composite monitor as the average of 

these monitor-specific quarterly averages (last column in rows P3-P6). 

3) Calculate the annual average at the composite monitor as the average of these 

4 quarterly averages (last column in row P7). This is the composite monitor 

annual-average under recent conditions, prior to proportional rollback. 

B. Calculating design values 

1) At each monitor, calculate the annual average PM2.5 concentration for each of 

the 3 years (2005, 2006, 2007). Average these. 

2) The maximum of these monitor-specific 3-year averages of annual averages is 

the annual design value. In Detroit this was 17.2 μg/m3, at monitor 

261630033 (see Table A-5 in Appendix A). 

3) At each monitor, calculate the 98th percentile 24-hour PM2.5 concentration for 

each of the 3 years (2005, 2006, 2007). Average these. 

4) The maximum of these monitor-specific 3-year averages of 98th percentile 

concentrations is the 24-hour design value. In Detroit, this was 43 μg/m3

 at 

monitor 261630033 (see Table A-5 in Appendix A). 

 

C. Calculating the composite monitor annual average when the 15/35 standard is just 

met, using the proportional rollback

1) Calculate the percent rollback needed to meet both the daily and the annual 

standard: 

 Inputs for the proportional rollback in Detroit: 

o Annual Design Value = 17.2 

o Daily Design Value = 43 

o Annual Average PRB = 0.86 

 Percent rollback to just meet the annual standard of 15 is calculated as: 

1 - (annual std. - PRB)/(annual design value - PRB) = 

1 - (15 - 0.86)/(17.2 - 0.86) = 13.5% 
B-12

 Percent rollback to just meet the daily standard of 35 is calculated as: 

1 - (daily std. - PRB)/(daily design value - PRB) = 

1 - (35 - 0.86)/(43 - 0.86) = 19.0% 

 

 Percent rollback to just meet both standards = maximum of 13.5% and 

19% = 19% (this determines which standard is controlling) 

2) Rolled back annual average at composite monitor = PRB + (annual avg at 

composite monitor using recent air quality - PRB)*(1 - 0.19) (see row P8) 

Hybrid rollback

As discussed in section 3.2.3.2, the hybrid approach involves a two-phase adjustment 

process with the first phase involving targeted reduction of source-oriented monitors to bring 

their levels down to that of near-by non-source-oriented monitors and a second proportional 

rollback phase to reach simulated attainment of the standard for the study area. In presenting the 

step-wise description of the hybrid rollback approach (and the sample calculation in Table B-3), 

we illustrate the first phase of adjustment by presenting quarterly values at individual monitors 

after the first phase of adjustment has occurred (rows H3-H6). The resulting composite monitor 

annual average is shown in the last column of row H7. Then, the second phase of proportional 

reduction results in the final adjusted composite monitor annual-average (last column in row 

H8). The step-wise procedure is presented in the set of “D” bullets below. 

 

D. Calculating the composite monitor annual average when the 15/35 standard is just 

met, using the hybrid rollback 

1) Adjust daily concentrations at individual monitors on a first step (see rows 

H3-H6); 

2) Calculate an annual average at the composite monitor from those adjusted 

values, using the same procedure used in Section A above (see last column in 

row H7). 

3) Roll back this intermediate annual average composite monitor value using the 

same procedure used for the proportional rollback, but with design values 

based on the adjusted data (see last column row H8). 

 Inputs for the hybrid rollback in Detroit: 

o Annual Design Value = 15.4 

o Daily Design Value = 41 

o Annual Average PRB = 0.86 

 Percent rollback to just meet the annual standard of 15 = 2.8%; percent 

rollback to just meet the daily standard of 35 = 14.9% 

 Percent rollback to just meet both standards = 14.9% 
B-13

4) Rolled back annual average at composite monitor = PRB + (intermediate 

annual avg at composite monitor - PRB)*(1 - 0.149) (see last column row 

H8). 

Locally focused rollback

The locally focused rollback method involves targeted rollback of those monitors 

exceeding the 24-hour standard under consideration. As discussed in section 2.3.2.3, this 

rollback approach is only applied for study areas where the 24-hour standard is controlling and 

therefore, typically, will result in simulated attainment of the annual-standard along with the 24-

hour standard. The illustration of this rollback approach presented in Table B-3 presents the 

adjusted quarterly values by monitor (rows LF4-LF7) together with the percent reductions 

reflected in those adjusted monitor-specific levels (row LF3). The composite monitor annualaverage that results from this targeted monitor-level reduction is presented in the last column of 

row LF8. It is interesting to point out that in the case of Detroit, all of the monitors had some 

degree of targeted reduction to achieve simulated attainment of the 24-hour standard, although 

the spread in magnitude of percent reduction was substantial (see values in row LF3). The stepwise procedure used in implementing the hybrid approach is presented in the set of “E” bullets 

below. 

E. Calculating the composite monitor annual average when the 15/35 standard is just 

met, using the locally focused rollback 

1) Identify monitor-specific design values (for daily standard -- this procedure 

was applied only when daily standard is controlling) (row LF2) 

2) Calculate monitor-specific percent rollbacks (row LF3) 

3) Roll back each quarterly average at each monitor, using the monitor-specific 

percents for rollback (rows LF4-LF7): 

 

Rolled back quarterly average at monitor = PRB + (quarterly average at 

monitor - PRB)*(1 - monitor-specific percent rollback) 

 

4) Calculate the rolled back quarterly average at the composite monitor as the 

average of these monitor-specific rolled back quarterly averages (last column 

in rows LF4-LF7). 

Calculate the rolled back annual average at the composite monitor as the average of these 

4 rolled back quarterly averages (last column in row LF8). 
C-1

 

APPENDIX C: EPI STUDY SPECIFIC INFORMATION ON PM2.5
C-2

Appendix C. Epidemiology Study-Specific Information for PM2.5 Risk Assessment 

This Appendix provides detailed summary information for the epidemiological studies 

used to obtain the concentration-response (C-R) functions used in the risk assessment. For 

additional details on selection of epidemiological studies and specification of the C-R functions, 

see section 3.3.3. 
C-3

Table C-1. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

Mortality, all-cause All 

Mortality, 

cardiopulmonary 401-440, 460-519 

Mortality, ischemic 

heart disease 410-414 

Mortality, lung cancer 162 

Mortality, all-cause All 

Mortality, 

cardiopulmonary 401-440, 460-519 

Mortality, ischemic 

heart disease 410-414 

Mortality, lung cancer 162 

Mortality, all-cause All 

Mortality, ischemic 

heart disease 410-414 

Mortality, all-cause All 

Mortality, 

cardiopulmonary 401-440, 460-519 

Mortality, ischemic 

heart disease 410-414 

Mortality, lung cancer 162 

Mortality, all-cause All Six U.S. Cities 

Mortality, 

cardiopulmonary 400-440, 485-495 

Mortality, lung cancer 162 

Krewski et al. (2009) - exposure period 1979-1983Krewski et al. (2009) - exposure period 1999-2000Krewski et al. (2000) [reanalysis of Six Cities Study]

none

log-linear National

National

30+ none n/a annual mean

30+ log-linear none annual mean30+ log-linear (random effects) none n/a annual mean

n/a

annual mean

25+ log-linear none n/a annual mean30+ log-log n/a

 Health Effects Associated with Long-Term Exposure to PM 2.5 :
C-4

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

Northeast 

Northwest 

Southeast 

Southwest 

Northeast 

Northwest 

Southeast 

Southwest 

Ito et al. (2007) ER visits, asthma 493 all ages log-linear none avg of 0- and 1-day 24-hr avg. New York 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

Los Angeles

 Health Effects Associated with Short-Term Exposure to PM 2.5 :

0-day2-day

24-hr avg.24-hr avg.

65+65+

log-linearlog-linear

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

426–427, 428, 

430–438; 410–414, 

429; 440–449

490–492; 464–466, 

480–487

nonenone

24-hr avg.

none

CO

none

CO

0 day1 day

390-429 all ages

HA (unscheduled), 

respiratory

Bell et al. (2008)

Mortality, 

cardiovascular

HA (unscheduled), 

cardiovascular
C-5

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

log-linear, GAM 

(stringent), 30 df 

log-linear, GLM, 30 

df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GLM, 30 

df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

0 day 1 day 2 day 3 day 4 day 5 day 

Mortality, nonaccidental <800 all ages

log-linear, GAM 

(stringent), 30 df

nonenone

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Los Angeles

24-hr avg.24-hr avg.

CO 1 day 24-hr avg.

1 day0 day

none 24-hr avg.
C-6

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

CO 1 day 24-hr avg.

24-hr avg.

none 1 day 24-hr avg.none 0 day 24-hr avg.

CO 0 day 24-hr avg.

none 0 day 24-hr avg.

Los AngelesLos Angeles

490-496

none 1 day

390-429 65+

all ages

Moolgavkar (2003) [reanalysis of Moolgavkar (2000b)]Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

HA, cardiovascular

Mortality, respiratory 

(COPD+)
C-7

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

log-linear, GAM 

(stringent), 30 df 

log-linear, GAM 

(stringent), 100 df 

log-linear, GLM, 

100 df 

0 day 1 day 2 day 3 day 

ER visits, 

cardiovascular

410–414, 427, 428, 

433–437, 440, 

443–445, 451–453

all ages log-linear none

avg of 0-,1-

day, and 2-

day

24-hr avg. Atlanta 

ER visits, respiratory

493, 786.07, 786.09; 

491, 492, and 496; 

460–465, 460.0, and 

477; 480–486; 466.1, 

466.11, and 466.19

all ages log-linear none

avg of 0-,1-

day, and 2-

day

24-hr avg. Atlanta 

HA, respiratory 

(COPD+)

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]Tolbert et al. (2007) 

490-496 all ages

log-linear, GAM 

(stringent), 100 df

Los Angeles

none 1 day 24-hr avg.none 0 day 24-hr avg.

NO2 24-hr avg.

none 2 day 24-hr avg.
C-8

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

Atlanta 

Baltimore 

Birmingham 

Dallas 

Detroit 

Fresno 

Houston 

Los Angeles 

New York 

Philadelphia 

Phoenix 

Pittsburgh 

Salt Lake City 

St. Louis 

Tacoma 

24-hr avg. avg of 0- and 1-day I01-I59 none all ages log-linear Mortality, cardiovascular Zanobetti and Schwartz (2009)
C-9

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

Atlanta 

Baltimore 

Birmingham 

Dallas 

Detroit 

Fresno 

Houston 

Los Angeles 

New York 

Philadelphia 

Phoenix 

Pittsburgh 

Salt Lake City 

St. Louis 

Tacoma 

Zanobetti and Schwartz (2009)

Mortality, nonaccidental A00-R99 24-hr avg. all ages avg of 0- and 1-day log-linear none
C-10

Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: All-Year Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Model Other Pollutants in Model Lag Metric Region Covered 

Atlanta 

Baltimore 

Birmingham 

Dallas 

Detroit 

Fresno 

Houston 

Los Angeles 

New York 

Philadelphia 

Phoenix 

Pittsburgh 

Salt Lake City 

St. Louis 

Tacoma 

24-hr avg. avg of 0- and 1-day J00-J99 all ages log-linear none Zanobetti and Schwartz (2009) Mortality, respiratory
C-11

Table C-2. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

Winter 0.0Spring 0.0Summer 0.0Fall 0.0Winter 0.0Spring -0.0Summer -0.0Fall -0.0Winter 0.0Spring 0.0Summer -0.0Fall 0.0Winter 0.0Spring 0.0Summer -0.0Fall 0.0Winter 0.0Spring 0.0Summer 0.0Fall 0.0Winter -0.0Spring 0.0Summer 0.0Fall -0.0Winter 0.0Spring 0.0Summer -0.0Fall 0.0Winter 0.0Spring 0.0Summer 0.0Fall 0.0

none New York April-August 0.0

O3 New York April-August 0.0

NO2 New York April-August 0.0

CO New York April-August 0.0

SO2 New York April-August 0.0

Bell et al. (2008)

Ito et al. (2007) ER visits, asthma

HA (unscheduled), 

respiratory

HA (unscheduled), 

cardiovascular

493

490–492; 464–466, 

480–487

426–427, 428, 

430–438; 

410–414, 429; 

440–449

none

65+65+

0-day2-day

avg of 0- 

and 1-day all ages

NortheastNorthwestSoutheastSouthwestNortheastNorthwestSoutheastSouthwest
C-12

Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

Atlanta Winter 0.0Atlanta Spring 0.0Atlanta Summer 0.0Atlanta Fall -0.0Baltimore Winter 0.0Baltimore Spring 0.0Baltimore Summer 0.0Baltimore Fall 0.0Birmingham Winter 0.0Birmingham Spring 0.0Birmingham Summer -0.0Birmingham Fall -0.0Dallas Winter 0.0Dallas Spring 0.0Dallas Summer 0.0

Dallas Fall -0.0

Detroit Winter -0.0Detroit Spring 0.0Detroit Summer 0.0Detroit Fall 0.0Fresno Winter -0.0Fresno Spring 0.0Fresno Summer 0.0Fresno Fall 0.0Houston Winter 0.0Houston Spring 0.0Houston Summer 0.0Houston Fall 0.0Los Angeles Winter -0.0Los Angeles Spring 0.0Los Angeles Summer -0.0Los Angeles Fall 0.0

all ages avg of 0- and 1-day Zanobetti and Schwartz (2009) Mortality, short-term cardiovascular I01-I59 none
C-13

Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

New York Winter 0.0New York Spring 0.0New York Summer 0.0New York Fall 0.0Philadelphia Winter 0.0Philadelphia Spring 0.0Philadelphia Summer 0.0Philadelphia Fall 0.0Phoenix Winter 

Phoenix Spring Phoenix Summer 

Phoenix Fall 

Pittsburgh Winter 0.0Pittsburgh Spring 0.0Pittsburgh Summer 0.0Pittsburgh Fall 0.0Salt Lake City Winter 

Salt Lake City Spring Salt Lake City Summer 

Salt Lake City Fall 

St. Louis Winter -0.0St. Louis Spring 0.0St. Louis Summer 0.0St. Louis Fall 0.0Tacoma Winter 0.0Tacoma Spring 0.0Tacoma Summer 0.0Tacoma Fall 0.0

Mortality, short-term 

cardiovascular none

Zanobetti and 

Schwartz (2009) I01-I59 all ages avg of 0- and 1-day
C-14

Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

Atlanta Winter 0.0Atlanta Spring 0.0Atlanta Summer 0.0Atlanta Fall 0.0Baltimore Winter 0.0Baltimore Spring 0.0Baltimore Summer 0.0Baltimore Fall 0.0Birmingham Winter 0.0Birmingham Spring 0.0Birmingham Summer 0.0Birmingham Fall 0.0Dallas Winter 0.0Dallas Spring 0.0Dallas Summer 0.0

Dallas Fall 0.0Detroit Winter -0.0Detroit Spring 0.0Detroit Summer 0.0Detroit Fall 0.0Fresno Winter 0.0Fresno Spring 0.0Fresno Summer 0.0Fresno Fall 0.0Houston Winter 0.0Houston Spring 0.0Houston Summer 0.0Houston Fall 0.0Los Angeles Winter 0.0Los Angeles Spring 0.0Los Angeles Summer -0.0Los Angeles Fall -0.0

avg of 0- 

and 1-day

Zanobetti and 

Schwartz (2009)

Mortality, short-term nonaccidental A00-R99 all ages none
C-15

Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

New York Winter 0.0New York Spring 0.0New York Summer 0.0New York Fall 0.0Philadelphia Winter 0.0Philadelphia Spring 0.0Philadelphia Summer 0.0Philadelphia Fall 0.0Phoenix Winter Phoenix Spring Phoenix Summer 

Phoenix Fall 

Pittsburgh Winter 0.0Pittsburgh Spring 0.0Pittsburgh Summer 0.0Pittsburgh Fall 0.0Salt Lake City Winter 0.0Salt Lake City Spring 0.0Salt Lake City Summer 0.0Salt Lake City Fall 0.0St. Louis Winter 0.0St. Louis Spring 0.0St. Louis Summer 0.0St. Louis Fall 0.0Tacoma Winter 0.0Tacoma Spring 0.0Tacoma Summer 0.0

Tacoma Fall 0.0

A00-R99 Zanobetti and Schwartz (2009) Mortality, short-term non- accidental all ages none avg of 0- and 1-day
C-16

Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

Atlanta Winter 0.0Atlanta Spring 0.0Atlanta Summer 0.0Atlanta Fall 0.0Baltimore Winter 0.0Baltimore Spring 0.0Baltimore Summer 0.0Baltimore Fall 0.0Birmingham Winter 0.0Birmingham Spring 0.0Birmingham Summer -0.0Birmingham Fall 0.0Dallas Winter 0.0Dallas Spring 0.0Dallas Summer 0.0

Dallas Fall 0.0Detroit Winter 0.0Detroit Spring 0.0Detroit Summer 0.0Detroit Fall 0.0Fresno Winter -0.0Fresno Spring 0.0Fresno Summer 0.0Fresno Fall 0.0Houston Winter 0.0Houston Spring 0.0Houston Summer 0.0Houston Fall 0.0Los Angeles Winter 0.0Los Angeles Spring 0.0Los Angeles Summer -0.0Los Angeles Fall -0.0

all ages none avg of 0- and 1-day Zanobetti and Schwartz (2009) Mortality, short-term respiratory J00-J99
C-17

Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5 Risk Assessment: Season-Specific Functions

Study Health Endpoint ICD-9 or 10 Codes Ages Covered Other Pollutants in Model Lag Region Covered Season Covered Coeffic

New York Winter 0.0New York Spring 0.0New York Summer 0.0New York Fall 0.0Philadelphia Winter 0.0Philadelphia Spring 0.0Philadelphia Summer 0.0Philadelphia Fall 0.0Phoenix Winter 0.0Phoenix Spring 0.0Phoenix Summer 0.0Phoenix Fall 0.0Pittsburgh Winter 0.0Pittsburgh Spring 0.0Pittsburgh Summer 0.0Pittsburgh Fall 0.0Salt Lake City Winter 0.0Salt Lake City Spring 0.0Salt Lake City Summer -0.0Salt Lake City Fall -0.0St. Louis Winter 0.0St. Louis Spring 0.0St. Louis Summer 0.0St. Louis Fall 0.0Tacoma Winter 0.0Tacoma Spring 0.0Tacoma Summer 0.0Tacoma Fall 0.0

J00-J99 none

1 --- indicates that results were not available.

Zanobetti and 

Schwartz (2009)

Mortality, short-term 

respiratory

avg of 0- 

and 1-day all ages
D-1

APPENDIX D: SUPPLEMENT TO THE REPRESENTATIVENESS 

ANALYSIS OF THE 15 URBAN STUDY AREAS 
D-2

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

(additional graphical comparisons of distributions for key contributors to PM2.5 risk ) 

Following the analysis discussed in Section 4.4.1, this appendix provides graphical 

comparisons of the empirical distributions of components of the risk function, and additional 

variables that have been identified as potentially influencing the risk associated with PM 

exposures. 

In each graph, the orange line represents the empirical cumulative distribution function 

(CDF) for the complete set of data available for the variable. In some cases, this may encompass 

all counties in the U.S., while in others it may be based on a subset of the U.S., usually for large 

urban areas. The green line in each graph represents the empirical cumulative distribution 

function for the variable based only on the data available for the set of urban case study 

locations. The black squares at the bottom of each graph represents the specific value of the 

variable for one of the case study locations, with the line showing where that value intersects the 

two empirical CDFs.
D-3

Comparison of Urban Case Study Area Population with U.S. Distribution of 

Population (all U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 1000 10000 100000 1000000 10000000

Population

% of Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all above the 65th Percentile 

of county populations

D.1 Elements of the Risk Equation 

Figure D-1. Comparison of Distributions for Key Elements of the Risk Equation: 

 Total Population 
D-4

Comparison of Urban Case Study Area % Under 15 to U.S. Distribution of % Under 

15

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

10 15 20 25 30 35

% of Population Under Age 15, 2005

% of U.S. Counties

All Counties CDF Case Study Counties Case Study Counties

Figure D-2. Comparison of Distributions for Key Elements of the Risk Equation: 

 Percent of Population Under 15 Years of Age 
D-5

Comparison of Urban Case Study Area % 65 and Older to U.S. Distribution of % 65 

and Older

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25 30 35

% of Population 65 and Older, 2005

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 75th percentile 

of county % of population 65 

and older

Figure D-3. Comparison of Distributions for Key Elements of the Risk Equation: 

 Percent of Population 65 Years of Age and Older 
D-6

Comparison of Urban Case Study Area % 85 and Older to U.S. Distribution of % 85 

and Older

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

12345678

% of Population 85 and Older, 2005

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 85th percentile 

of county % of population 85 

and older

Figure D-4. Comparison of Distributions for Key Elements of the Risk Equation: 

 Percent of Population 85 Years of Age and Older 
D-7

Comparison of Urban Case Study Area Annual Mean PM2.5 with U.S. 

Distribution of Annual Mean PM2.5 (617 U.S. Counties with PM2.5 Monitors)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25

Annual Mean PM2.5

% of Monitored Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-5. Comparison of Distributions for Key Elements of the Risk Equation: Annual 

 Mean PM2.5
D-8

Comparison of Urban Case Study Area 98th %ile PM2.5 with U.S. Distribution of 

98th %ile PM2.5 

(617 U.S. Counties with PM2.5 Monitors)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90

98th Percentile Daily PM2.5

% of Monitored Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-6. Comparison of Distributions for Key Elements of the Risk Equation: 98th 

 %ile Daily Average PM2.5
D-9

Comparison of Urban Case Study Area % of Days with PM2.5>35 ug/m3 to U.S. 

Distribution of % of Days with PM2.5>35 ug/m3

(204 U.S. Counties in MCAPS Study)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25

% of Days Above 35 ug/m3

% of MCAPS Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-7. Comparison of Distributions for Key Elements of the Risk Equation: % of 

 Days with PM2.5 > 35 μg/m3
D-10

Comparison of Urban Case Study All Cause Mortality Rate to U.S. Distribution of All 

Cause Mortality Rate

(3143 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 300 500 700 900 1100 1300 1500 1700 1900 2100

All Cause Mortality per 100,000 Population

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 90th percentile 

of county all cause mortality

Figure D-8. Comparison of Distributions for Key Elements of the Risk Equation: All 

 Cause Mortality Rate 
D-11

Comparison of Urban Case Study Non-accidental Mortality Rate to U.S. Distribution of 

Non-accidental Mortality Rate

(3143 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

200 400 600 800 1000 1200 1400 1600 1800 2000

Non-accidental Mortality per 100,000 Population

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 90th percentile 

of county non-accidental 

mortality

Figure D-9. Comparison of Distributions for Key Elements of the Risk Equation: Non- 

 Accidental Mortality Rate 
D-12

Comparison of Urban Case Study Cardiovascular Mortality Rate to U.S. Distribution of 

Cardiovascular Mortality Rate

(3143 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

50 150 250 350 450 550 650 750 850 950

Cardiovascular Mortality per 100,000 Population

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 90th percentile 

of county cardiovascular 

mortality

Figure D-10. Comparison of Distributions for Key Elements of the Risk Equation: 

 Cardiovascular Mortality Rate 
D-13

Comparison of Urban Case Study Respiratory Mortality Rate to U.S. Distribution of 

Respiratory Mortality Rate

(3143 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

10 60 110 160 210 260

Respiratory Mortality per 100,000 Population

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 90th percentile 

of county respiratory 

mortality

Figure D-11. Comparison of Distributions for Key Elements of the Risk Equation: 

 Respiratory Mortality Rate 
D-14

Comparison of Urban Case Study PM All-cause Mortality Risk (β) to 

U.S. Distribution of PM All-cause Mortality Risk

(212 U.S. Urban Areas)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014 0.0016

Bayesian Shrunken PM All Cause Mortality Risk Coefficient (β)

% of Z&S Urban Areas

All Z&S Urban Areas CDF Case Study Urban Area CDF Case Study Urban Areas

Figure D-12. Comparison of Distributions for Key Elements of the Risk Equation: All 

 Cause Mortality Risk Effect Estimate from Zanobetti and Schwartz (2008) 
D-15

Comparison of Urban Case Study PM Cardiovascular Mortality Risk (β) to U.S. 

Distribution of PM Cardiovascular Mortality Risk

(212 U.S. Urban Areas)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014 0.0016 0.0018 0.002

Bayesian Shrunken PM Cardiovascular Mortality Risk Coefficient (β)

% of Z&S Urban Areas

All Z&S Urban Areas CDF Case Study Urban Area CDF Case Study Urban Areas

Figure D-13. Comparison of Distributions for Key Elements of the Risk Equation: 

Cardiovascular Mortality Risk Effect Estimate from Zanobetti and Schwartz 

(2008) 
D-16

Comparison of Urban Case Study PM Respiratory Mortality Risk (β) to U.S. 

Distribution of PM Respiratory Mortality Risk

(212 U.S. Urban Areas)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.0005 0.001 0.0015 0.002

Bayesian Shrunken PM Respiratory Mortality Risk Coefficient (β)

% of Z&S Urban Areas

All Z&S Urban Areas CDF Case Study Urban Area CDF Case Study Urban Areas

Figure D-14. Comparison of Distributions for Key Elements of the Risk Equation: 

Respiratory Mortality Risk Effect Estimate from Zanobetti and Schwartz 

(2008) 
D-17

Comparison of Urban Case Study Area Population Density with U.S. Distribution of 

Population Density (all U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 10 100 1000 10000 100000

Population Density (persons per sq mile)

% of Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all above the 65th Percentile 

of county population density

D.2. Variables Expected to Influence the Relative Risk from PM2.5

 D.2.1. Demographic Variables 

Figure D-15. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Population Density 
D-18

Comparison of Urban Case Study Area Unemployment Rate to U.S. Distribution of 

Unemployment Rates

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 2 4 6 8 10 12 14 16 18 20

Unemployment Rate (%)

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-16. Comparison of Distributions for Selected Variables Expected to 

 Influence the Relative Risk from PM2.5: Unemployment Rate 
D-19

Comparison of Urban Case Study % Less than High School Education to U.S. 

Distribution of % Less than High School Education

(3146 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

3 13 23 33 43 53 63

% of Population with Less than a High School Education, 2000

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-17. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: % with Less than a High School Education 

 
D-20

Comparison of Urban Case Study Area Per Capita Personal Income to U.S. 

Distribution of Per Capita Personal Income

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

$10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000 $90,000

Per Capita Personal Income, 2005

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all above the 25th percentile 

of county per capita income

Figure D-18. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Per Capita Personal Income 
D-21

Comparison of Urban Case Study Air Conditioning Prevalence to U.S. Distribution of 

Air Conditioning Prevalence

(70 U.S. Urban Areas)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

% Air Conditioning Prevalence

% of Urban Areas

All Urban Areas CDF Case Study Urban Areas CDF Case Study Urban Areas

Figure D-19. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Air Conditioning Prevalence 
D-22

Comparison of Urban Case Study Air Conditioning Prevalence to U.S. Distribution of 

Air Conditioning Prevalence

(70 U.S. Urban Areas)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.2 0.4 0.6 0.8 1

% Air Conditioning Prevalence

% of Urban Areas

All Urban Areas CDF Case Study Urban Areas CDF Case Study Urban Areas

Figure D-20. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: % Non-White Population 
D-23

Comparison of Urban Case Study Area Angina/CHD Prevalence to U.S. Distribution 

of Angina/CHD Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

123456789

% Prevalence of Angina or CHD, BRFSS, 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Urban case study areas are 

all below the 85th percentile 

of MSA angina or CHD 

prevalence

D.2.2. Health Conditions 

Figure D-21. Comparison of Distributions for Selected Variables Expected to 

 Influence the Relative Risk from PM2.5: Angina/Coronary Heart 

 Disease Prevalence 
D-24

Comparison of Urban Case Study Area Current Asthma Prevalence to U.S. 

Distribution of Current Asthma Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

3 5 7 9 11 13

% Current Prevalence of Asthma, BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Figure D-22. Comparison of Distributions for Selected Variables Expected to 

 Influence the Relative Risk from PM2.5: Asthma Prevalence 
D-25

Comparison of Urban Case Study Area Diabetes Prevalence to U.S. Distribution of 

Diabetes Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2 4 6 8 10 12 14 16

% Prevalence of Diabetes, BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Urban case study areas are 

all below the 85th percentile 

of MSA diabetes prevalence

Figure D-23. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Diabetes Prevalence 
D-26

Comparison of Urban Case Study Area Heart Attack Prevalence to U.S. Distribution of 

Heart Attack Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2 3 4 5 6 7 8 9 10 11

% Prevalence of Heart Attacks, BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Urban case study areas are 

all below the 80th percentile 

of MSA heart attack 

prevalence

Figure D-24. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Heart Attack Prevalence 
D-27

Comparison of Urban Case Study Area Obesity Prevalence to U.S. Distribution of 

Obesity Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

14 19 24 29 34

% Prevalence of Obesity, BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Figure D-25. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Obesity Prevalence 
D-28

Comparison of Urban Case Study Area Stroke Prevalence to U.S. Distribution of 

Stroke Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0123456

% Prevalence of Stroke, BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Figure D-26. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Stroke Prevalence 
D-29

Comparison of Urban Case Study Area Smoking Prevalence to U.S. Distribution of 

Smoking Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

6 11 16 21 26 31

% Prevalence of Smoking (ever), BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Urban case study areas are 

all below the 85th percentile 

of MSA smoking prevalence

Figure D-27. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Smoking Prevalence 
D-30

Comparison of Urban Case Study Area Exercise Prevalence to U.S. Distribution of 

Exercise Prevalence

(183 U.S. MSA)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

15 20 25 30 35 40 45

% Prevalence of Exercise (Adults with 20+ minutes of vigorous physical activity 3 or 

more days per week), BRFSS 2007

% of BRFSS MSA

All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA

Figure D-28. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: Exercise Prevalence 
D-31

Comparison of Urban Case Study Area 4th Highest Daily 8-Hour Ozone with U.S. 

Distribution of 4th Highest Daily 8-Hour Ozone 

(725 U.S. Counties with Ozone Monitors)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.02 0.04 0.06 0.08 0.1 0.12

4th Highest Daily 8-Hour Maximum

% of Monitored Counties

All Counties CDF Case Study Counties CDF Case Study Counties

D.2.3. Air Quality and Climate Variables 

Figure D-29. Comparison of Distributions for Selected Variables Expected to 

 Influence the Relative Risk from PM2.5: 4th Highest Daily Max 8- 

 hour Average 
D-32

Comparison of Urban Case Study Mobile Source Carbon to U.S. Distribution of Mobile 

Source Carbon

(3142 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Mobile Source Proportion of Total Organic and Elemental Carbon Emissions

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Urban case study areas are 

all below the 80th percentile 

of county mobile source 

proportion of EC/OC 

emissions

Figure D-30. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: % Mobile Source Direct PM2.5 Emissions 

 
D-33

Comparison of Urban Case Study Area Long Term Average July Temperature to 

U.S. Distribution of Long Term Average July Temperature

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

50 60 70 80 90 100

July 30 Year Average Temperature, 1941-1970

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-31. Comparison of Distributions for Selected Variables Expected to 

 Influence the Relative Risk from PM2.5: July Temperature Long 

 Term Average 
D-34

Comparison of Urban Case Study Area Long Term Average July Relative Humidity to 

U.S. Distribution of Long Term Average July Relative Humidity

(3141 U.S. Counties)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90

July 30 Year Average Relative Humidity, 1941-1970

% of U.S. Counties

All Counties CDF Case Study Counties CDF Case Study Counties

Figure D-32. Comparison of Distributions for Selected Variables Expected to Influence the 

 Relative Risk from PM2.5: July Relative Humidity Long Term Average 

 
E-1

APPENDIX E: RISK ESTIMATES (CORE ANALYSIS) 
E-1

Appendix E. Risk Estimates (core analysis) 

This Appendix provides detailed risk estimates generated for the core analysis for the 15 

urban study areas. The tables cover all of the air quality scenarios modeled, including recent 

conditions, the current standard, and alternative standard levels. For additional detail on the types 

of risk metrics (and figures summarizing key metrics) presented in this Appendix, see section 

4.0. 
E-2

Table E-1. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5 Co

 Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjustin

 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 1

649 575 513 451 389 4

(421 - 873) (373 - 774) (333 - 692) (292 - 608) (252 - 525) (292 

597 548 502 442 382 4

(387 - 803) (355 - 738) (325 - 676) (286 - 596) (247 - 516) (276 

424 297 262 227 192 2

(275 - 571) (192 - 400) (170 - 354) (147 - 307) (124 - 260) (147 

379 379 379 379 336 

(245 - 511) (245 - 511) (245 - 511) (245 - 511) (218 - 454) (245 

798 580 573 502 431 4

(518 - 1073) (376 - 782) (371 - 772) (325 - 677) (279 - 581) (286 

254 89 89 89 89 

(165 - 342) (57 - 120) (57 - 120) (57 - 120) (57 - 120) (38 

609 557 491 426 360 4

(394 - 820) (360 - 751) (318 - 663) (275 - 575) (233 - 486) (275 

2333 1045 1045 1045 919 

(1514 - 3141) (676 - 1413) (676 - 1413) (676 - 1413) (593 - 1242) (464 

2000 1477 1477 1410 1205 1

(1297 - 2693) (956 - 1992) (956 - 1992) (912 - 1902) (779 - 1627) (711 

521 455 455 406 348 3

(338 - 703) (295 - 614) (295 - 614) (263 - 548) (225 - 470) (223 

483 483 483 483 433 4

(312 - 652) (312 - 652) (312 - 652) (312 - 652) (280 - 586) (271 

593 387 387 363 318 2

(385 - 798) (251 - 523) (251 - 523) (235 - 490) (206 - 430) (187 

102 29 29 29 29 

(66 - 138) (19 - 39) (19 - 39) (19 - 39) (19 - 39) (7 

826 700 634 557 480 5

(536 - 1111) (454 - 943) (411 - 855) (361 - 752) (310 - 648) (351 

123 80 80 80 80 

(80 - 166) (52 - 109) (52 - 109) (52 - 109) (52 - 109) (34 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Stan

n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009). Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-3

Table E-2. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5 Con

 Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjustin

 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

668 592 528 464 400 464 

(434 - 899) (384 - 797) (342 - 712) (301 - 626) (259 - 540) (301 - 6

483 441 400 347 295 333 

(313 - 651) (285 - 594) (259 - 539) (225 - 469) (191 - 398) (216 - 4

399 276 243 209 176 209 

(259 - 536) (179 - 373) (157 - 328) (135 - 283) (114 - 238) (135 - 2

284 284 284 284 247 284 

(183 - 383) (183 - 383) (183 - 383) (183 - 383) (160 - 334) (183 - 

576 398 392 334 276 285 

(373 - 776) (257 - 537) (253 - 529) (216 - 451) (178 - 373) (184 - 

265 94 94 94 94 63 

(172 - 356) (61 - 127) (61 - 127) (61 - 127) (61 - 127) (41 - 

589 537 472 407 342 407 

(381 - 794) (348 - 725) (306 - 638) (263 - 550) (221 - 462) (263 - 

2054 863 863 863 745 561 

(1332 - 2767) (557 - 1166) (557 - 1166) (557 - 1166) (481 - 1008) (362 - 

1548 1096 1096 1038 861 771 

(1002 - 2087) (708 - 1481) (708 - 1481) (671 - 1403) (556 - 1164) (498 - 1

471 409 409 363 308 305 

(305 - 636) (265 - 552) (265 - 552) (235 - 490) (199 - 417) (197 - 4

512 512 512 512 460 446 

(331 - 691) (331 - 691) (331 - 691) (331 - 691) (297 - 622) (288 - 

468 290 290 270 231 205 

(303 - 631) (187 - 392) (187 - 392) (174 - 364) (149 - 312) (133 - 

84 16 16 16 16 0 

(54 - 113) (10 - 21) (10 - 21) (10 - 21) (10 - 21) (0 - 

618 514 458 394 329 382 

(401 - 834) (332 - 693) (296 - 619) (255 - 532) (213 - 445) (247 - 

83 47 47 47 47 25 

(54 - 112) (30 - 64) (30 - 64) (30 - 64) (30 - 64) (16 - 

 Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in a 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-4

Table E-3. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5 Con

 Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 

 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

642 567 505 442 379 4

(416 - 864) (368 - 764) (327 - 680) (286 - 596) (245 - 512) (286 

482 440 399 347 294 3

(313 - 650) (285 - 593) (258 - 538) (224 - 468) (190 - 398) (215 

418 291 257 222 187 2

(271 - 563) (189 - 393) (166 - 347) (144 - 300) (121 - 253) (144 

317 317 317 317 278 3

(205 - 428) (205 - 428) (205 - 428) (205 - 428) (180 - 375) (205 

607 424 418 358 299 3

(393 - 818) (274 - 572) (270 - 564) (232 - 484) (193 - 404) (199 

279 101 101 101 101 

(181 - 375) (65 - 137) (65 - 137) (65 - 137) (65 - 137) (44 

615 561 494 427 359 4

(398 - 829) (363 - 757) (320 - 667) (276 - 577) (232 - 486) (276 

2134 911 911 911 791 6

(1384 - 2874) (588 - 1232) (588 - 1232) (588 - 1232) (511 - 1070) (388 

1812 1316 1316 1253 1058 9

(1174 - 2443) (852 - 1777) (852 - 1777) (810 - 1692) (684 - 1430) (620 

466 405 405 359 304 3

(302 - 629) (262 - 546) (262 - 546) (232 - 484) (197 - 411) (195 

433 433 433 433 385 3

(280 - 586) (280 - 586) (280 - 586) (280 - 586) (248 - 520) (240 

527 339 339 316 274 2

(342 - 710) (219 - 457) (219 - 457) (205 - 427) (177 - 371) (160 

120 38 38 38 38 

(78 - 162) (24 - 51) (24 - 51) (24 - 51) (24 - 51) (11 

679 568 509 441 373 4

(440 - 915) (368 - 766) (330 - 687) (285 - 596) (241 - 504) (277 

87 50 50 50 50 

(56 - 118) (32 - 68) (32 - 68) (32 - 68) (32 - 68) (17 

 Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Stan

n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-5

Table E-4. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to A Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

4.3% 3.8% 3.4% 3% 2.6% 3

(2.8% - 5.8%) (2.5% - 5.1%) (2.2% - 4.6%) (1.9% - 4%) (1.7% - 3.5%) (1.9% 

4.2% 3.9% 3.6% 3.1% 2.7% 3

(2.7% - 5.7%) (2.5% - 5.2%) (2.3% - 4.8%) (2% - 4.2%) (1.8% - 3.7%) (2% 

4.3% 3% 2.7% 2.3% 2% 2

(2.8% - 5.8%) (2% - 4.1%) (1.7% - 3.6%) (1.5% - 3.1%) (1.3% - 2.6%) (1.5% 

3% 3% 3% 3% 2.6% 3

(1.9% - 4%) (1.9% - 4%) (1.9% - 4%) (1.9% - 4%) (1.7% - 3.5%) (1.9% 

4.5% 3.3% 3.2% 2.8% 2.4% 2

(2.9% - 6%) (2.1% - 4.4%) (2.1% - 4.3%) (1.8% - 3.8%) (1.6% - 3.3%) (1.6% 

4.6% 1.6% 1.6% 1.6% 1.6% 1

(3% - 6.1%) (1% - 2.2%) (1% - 2.2%) (1% - 2.2%) (1% - 2.2%) (0.7% 

3.3% 3% 2.6% 2.3% 1.9% 2

(2.1% - 4.4%) (1.9% - 4%) (1.7% - 3.6%) (1.5% - 3.1%) (1.2% - 2.6%) (1.5% 

4.1% 1.8% 1.8% 1.8% 1.6% 1

(2.7% - 5.5%) (1.2% - 2.5%) (1.2% - 2.5%) (1.2% - 2.5%) (1% - 2.2%) (0.8% 

3.8% 2.8% 2.8% 2.7% 2.3% 2

(2.5% - 5.1%) (1.8% - 3.8%) (1.8% - 3.8%) (1.7% - 3.6%) (1.5% - 3.1%) (1.3% 

3.6% 3.1% 3.1% 2.8% 2.4% 2

(2.3% - 4.8%) (2% - 4.2%) (2% - 4.2%) (1.8% - 3.8%) (1.5% - 3.2%) (1.5% 

2.1% 2.1% 2.1% 2.1% 1.9% 1

(1.4% - 2.8%) (1.4% - 2.8%) (1.4% - 2.8%) (1.4% - 2.8%) (1.2% - 2.5%) (1.2% 

4.3% 2.8% 2.8% 2.6% 2.3% 2

(2.8% - 5.7%) (1.8% - 3.8%) (1.8% - 3.8%) (1.7% - 3.5%) (1.5% - 3.1%) (1.3% 

2.2% 0.6% 0.6% 0.6% 0.6% 0

(1.4% - 2.9%) (0.4% - 0.8%) (0.4% - 0.8%) (0.4% - 0.8%) (0.4% - 0.8%) (0.1% 

4.4% 3.7% 3.4% 3% 2.5% 2

(2.8% - 5.9%) (2.4% - 5%) (2.2% - 4.5%) (1.9% - 4%) (1.6% - 3.4%) (1.9% 

2.4% 1.6% 1.6% 1.6% 1.6% 1

(1.6% - 3.3%) (1% - 2.1%) (1% - 2.1%) (1% - 2.1%) (1% - 2.1%) (0.7% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Conc

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standa

Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-6

Table E-5. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Am Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Sta

 Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

4.3% 3.8% 3.4% 3% 2.6% 3% 

(2.8% - 5.8%) (2.5% - 5.1%) (2.2% - 4.6%) (1.9% - 4%) (1.7% - 3.5%) (1.9% - 

3.4% 3.1% 2.8% 2.5% 2.1% 2.4% 

(2.2% - 4.6%) (2% - 4.2%) (1.8% - 3.8%) (1.6% - 3.3%) (1.3% - 2.8%) (1.5% - 

4% 2.8% 2.4% 2.1% 1.8% 2.1% 

(2.6% - 5.4%) (1.8% - 3.8%) (1.6% - 3.3%) (1.4% - 2.8%) (1.1% - 2.4%) (1.4% - 

2.2% 2.2% 2.2% 2.2% 1.9% 2.2% 

(1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.2% - 2.5%) (1.4% - 

3.2% 2.2% 2.2% 1.9% 1.5% 1.6% 

(2.1% - 4.4%) (1.4% - 3%) (1.4% - 3%) (1.2% - 2.5%) (1% - 2.1%) (1% - 2

4.7% 1.7% 1.7% 1.7% 1.7% 1.1% 

(3% - 6.3%) (1.1% - 2.2%) (1.1% - 2.2%) (1.1% - 2.2%) (1.1% - 2.2%) (0.7% - 

3.1% 2.8% 2.5% 2.1% 1.8% 2.1% 

(2% - 4.1%) (1.8% - 3.8%) (1.6% - 3.3%) (1.4% - 2.9%) (1.1% - 2.4%) (1.4% - 

3.6% 1.5% 1.5% 1.5% 1.3% 1% 

(2.3% - 4.8%) (1% - 2%) (1% - 2%) (1% - 2%) (0.8% - 1.8%) (0.6% - 

2.9% 2.1% 2.1% 2% 1.6% 1.4% 

(1.9% - 3.9%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.6%) (1% - 2.2%) (0.9% - 

3.2% 2.8% 2.8% 2.5% 2.1% 2.1% 

(2.1% - 4.4%) (1.8% - 3.8%) (1.8% - 3.8%) (1.6% - 3.4%) (1.4% - 2.9%) (1.4% - 

2.1% 2.1% 2.1% 2.1% 1.9% 1.9% 

(1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.2% - 2.6%) (1.2% - 

3.4% 2.1% 2.1% 1.9% 1.7% 1.5% 

(2.2% - 4.6%) (1.4% - 2.8%) (1.4% - 2.8%) (1.3% - 2.6%) (1.1% - 2.3%) (1% - 

1.7% 0.3% 0.3% 0.3% 0.3% 0% 

(1.1% - 2.3%) (0.2% - 0.4%) (0.2% - 0.4%) (0.2% - 0.4%) (0.2% - 0.4%) (0% - 

#DIV/0! 2.7% 2.4% 2.1% 1.7% 2% 

#DIV/0! (1.8% - 3.7%) (1.6% - 3.3%) (1.3% - 2.8%) (1.1% - 2.4%) (1.3% - 

3.3% 0.9% 0.9% 0.9% 0.9% 0.5% 

(2.1% - 4.4%) (0.6% - 1.2%) (0.6% - 1.2%) (0.6% - 1.2%) (0.6% - 1.2%) (0.3% - 

Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-7

Table E-6. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Am Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Sta

 Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

4% 3.6% 3.2% 2.8% 2.4% 2.8

(2.6% - 5.4%) (2.3% - 4.8%) (2% - 4.3%) (1.8% - 3.7%) (1.5% - 3.2%) (1.8% - 

3.4% 3.1% 2.8% 2.5% 2.1% 2.4

(2.2% - 4.6%) (2% - 4.2%) (1.8% - 3.8%) (1.6% - 3.3%) (1.3% - 2.8%) (1.5% - 

4.2% 2.9% 2.6% 2.2% 1.9% 2.2

(2.7% - 5.6%) (1.9% - 3.9%) (1.7% - 3.5%) (1.4% - 3%) (1.2% - 2.5%) (1.4% - 

2.4% 2.4% 2.4% 2.4% 2.1% 2.4

(1.5% - 3.2%) (1.5% - 3.2%) (1.5% - 3.2%) (1.5% - 3.2%) (1.3% - 2.8%) (1.5% - 

3.4% 2.4% 2.4% 2% 1.7% 1.7% 

(2.2% - 4.6%) (1.5% - 3.2%) (1.5% - 3.2%) (1.3% - 2.7%) (1.1% - 2.3%) (1.1% - 

4.9% 1.8% 1.8% 1.8% 1.8% 1.2% 

(3.2% - 6.5%) (1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (0.8% - 

3.1% 2.9% 2.5% 2.2% 1.8% 2.2% 

(2% - 4.2%) (1.8% - 3.9%) (1.6% - 3.4%) (1.4% - 2.9%) (1.2% - 2.5%) (1.4% - 

3.7% 1.6% 1.6% 1.6% 1.4% 1% 

(2.4% - 5%) (1% - 2.1%) (1% - 2.1%) (1% - 2.1%) (0.9% - 1.9%) (0.7% - 

3.4% 2.5% 2.5% 2.3% 2% 1.8% 

(2.2% - 4.6%) (1.6% - 3.3%) (1.6% - 3.3%) (1.5% - 3.2%) (1.3% - 2.7%) (1.2% - 

3.2% 2.8% 2.8% 2.5% 2.1% 2.1% 

(2.1% - 4.3%) (1.8% - 3.8%) (1.8% - 3.8%) (1.6% - 3.3%) (1.4% - 2.8%) (1.3% - 

1.8% 1.8% 1.8% 1.8% 1.6% 1.5% 

(1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (1% - 2.1%) (1% - 

3.8% 2.5% 2.5% 2.3% 2% 1.8% 

(2.5% - 5.2%) (1.6% - 3.3%) (1.6% - 3.3%) (1.5% - 3.1%) (1.3% - 2.7%) (1.2% - 

2.4% 0.7% 0.7% 0.7% 0.7% 0.3% 

(1.5% - 3.2%) (0.5% - 1%) (0.5% - 1%) (0.5% - 1%) (0.5% - 1%) (0.2% - 

3.6% 3% 2.7% 2.3% 2% 2.3% 

(2.3% - 4.8%) (1.9% - 4%) (1.7% - 3.6%) (1.5% - 3.1%) (1.3% - 2.7%) (1.5% - 

1.7% 1% 1% 1% 1% 0.5% 

(1.1% - 2.2%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.3% - 

Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Conce

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-8

Table E-7. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated  Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on K

 (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-13% 0% 11% 22% 32% 22% 

(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 2

-9% 0% 8% 19% 30% 22% 

(-9% - -9%) (0% - 0%) (8% - 9%) (19% - 19%) (30% - 30%) (22% - 2

-43% 0% 12% 23% 35% 23% 

(-43% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 35%) (23% - 2

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0

-38% 0% 1% 13% 26% 24% 

(-37% - -38%) (0% - 0%) (1% - 1%) (13% - 14%) (26% - 26%) (24% - 2

-187% 0% 0% 0% 0% 34% 

(-185% - -188%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (34% - 3

-9% 0% 12% 24% 35% 24% 

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 35%) (23% - 2

-123% 0% 0% 0% 12% 31% 

(-122% - -124%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 3

-35% 0% 0% 5% 18% 26% 

(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (18% - 18%) (25% - 2

-14% 0% 0% 11% 24% 24% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 2

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 1

-53% 0% 0% 6% 18% 25% 

(-53% - -54%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (25% - 2

-255% 0% 0% 0% 0% 64% 

(-254% - -256%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 6

-18% 0% 9% 20% 31% 23% 

(-18% - -18%) (0% - 0%) (9% - 9%) (20% - 21%) (31% - 32%) (22% - 2

-53% 0% 0% 0% 0% 34% 

(-53% - -54%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (34% - 3

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009). 

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with 

PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alterna

(m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-9

Table E-8. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated  Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on 

 (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 22% 32% 22% 

(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 2

-10% 0% 9% 21% 33% 24% 

(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 2

-44% 0% 12% 24% 36% 24% 

(-44% - -45%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 2

0% 0% 0% 0% 13% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 0

-45% 0% 1% 16% 31% 28% 

(-45% - -45%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 31%) (28% - 2

-182% 0% 0% 0% 0% 33% 

(-180% - -184%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 3

-10% 0% 12% 24% 36% 24% 

(-10% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 2

-138% 0% 0% 0% 14% 35% 

(-137% - -139%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 14%) (35% - 3

-41% 0% 0% 5% 21% 30% 

(-41% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (30% - 3

-15% 0% 0% 11% 25% 25% 

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (25% - 25%) (25% - 2

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 1

-61% 0% 0% 7% 20% 29% 

(-61% - -62%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 2

-438% 0% 0% 0% 0% 100% 

(-437% - -440%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 1

-20% 0% 11% 23% 36% 26% 

(-20% - -21%) (0% - 0%) (11% - 11%) (23% - 23%) (36% - 36%) (26% - 2

-76% 0% 0% 0% 0% 48% 

(-76% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (48% - 4

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009). 

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with 

PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alterna

(m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-10

Table E-9. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated  Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on 

 (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 22% 33% 22% 

(-13% - -13%) (0% - 0%) (11% - 11%) (22% - 22%) (33% - 33%) (22% - 2

-10% 0% 9% 21% 33% 24% 

(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 2

-44% 0% 12% 24% 36% 24% 

(-43% - -44%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 2

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0

-43% 0% 1% 15% 30% 27% 

(-43% - -44%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 30%) (27% - 2

-176% 0% 0% 0% 0% 32% 

(-175% - -178%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 3

-10% 0% 12% 24% 36% 24% 

(-9% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 2

-134% 0% 0% 0% 13% 34% 

(-133% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 3

-38% 0% 0% 5% 20% 27% 

(-37% - -38%) (0% - 0%) (0% - 0%) (5% - 5%) (20% - 20%) (27% - 2

-15% 0% 0% 11% 25% 26% 

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (25% - 25%) (26% - 2

0% 0% 0% 0% 11% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 1

-56% 0% 0% 7% 19% 27% 

(-55% - -56%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 19%) (27% - 2

-218% 0% 0% 0% 0% 55% 

(-217% - -219%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 5

-20% 0% 10% 22% 34% 25% 

(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 22%) (34% - 34%) (25% - 2

-74% 0% 0% 0% 0% 46% 

(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 4

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).  Dallas, TX Fresno, CA Los Angeles, CA3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with 

PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alterna

(m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-11

Table E-10. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5 Co

 Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjustin

 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

830 736 657 578 499 57

(531 - 1123) (470 - 997) (420 - 891) (369 - 785) (318 - 677) (369 - 

763 702 643 566 490 54

(488 - 1033) (448 - 950) (410 - 871) (361 - 768) (312 - 665) (348 - 

543 380 336 292 247 29

(347 - 734) (243 - 516) (214 - 457) (186 - 397) (157 - 336) (186 - 

486 486 486 486 431 48

(310 - 659) (310 - 659) (310 - 659) (310 - 659) (275 - 586) (310 - 

1021 743 734 643 552 56

(653 - 1380) (474 - 1008) (468 - 996) (410 - 874) (352 - 751) (361 - 

325 114 114 114 114 75 

(208 - 439) (72 - 155) (72 - 155) (72 - 155) (72 - 155) (48 - 

780 713 630 546 462 54

(498 - 1058) (455 - 968) (401 - 856) (348 - 743) (294 - 629) (348 - 

2986 1342 1342 1342 1180 92

(1910 - 4042) (854 - 1827) (854 - 1827) (854 - 1827) (750 - 1607) (587 - 

2560 1893 1893 1808 1546 141

(1636 - 3468) (1207 - 2571) (1207 - 2571) (1152 - 2455) (984 - 2101) (898 - 

668 584 584 521 447 44

(427 - 905) (372 - 792) (372 - 792) (332 - 707) (285 - 607) (282 - 

620 620 620 620 557 53

(394 - 843) (394 - 843) (394 - 843) (394 - 843) (354 - 757) (343 - 

759 497 497 466 409 37

(485 - 1026) (317 - 674) (317 - 674) (297 - 633) (260 - 555) (236 - 

132 37 37 37 37 13 

(84 - 179) (24 - 51) (24 - 51) (24 - 51) (24 - 51) (8 - 

1056 897 813 714 616 69

(676 - 1429) (573 - 1215) (519 - 1102) (456 - 970) (392 - 836) (443 - 

158 103 103 103 103 69 

(101 - 215) (66 - 141) (66 - 141) (66 - 141) (66 - 141) (44 - 

 Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in a 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-12

Table E-11. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5 Co

 Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjustin

 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

854 758 677 595 513 59

(547 - 1156) (484 - 1026) (432 - 918) (380 - 808) (327 - 697) (380 - 

619 565 513 446 378 42

(395 - 839) (360 - 766) (327 - 696) (284 - 606) (241 - 515) (272 - 

510 354 312 269 226 26

(326 - 691) (226 - 481) (198 - 423) (171 - 365) (144 - 307) (171 - 

364 364 364 364 317 36

(232 - 495) (232 - 495) (232 - 495) (232 - 495) (202 - 432) (232 - 

737 510 503 429 355 36

(471 - 1000) (325 - 694) (320 - 684) (273 - 584) (225 - 483) (233 - 

338 121 121 121 121 81 

(217 - 457) (77 - 164) (77 - 164) (77 - 164) (77 - 164) (51 - 

755 689 606 523 439 52

(481 - 1024) (439 - 935) (386 - 823) (333 - 711) (279 - 598) (333 - 

2631 1108 1108 1108 958 72

(1680 - 3565) (704 - 1509) (704 - 1509) (704 - 1509) (608 - 1305) (457 - 

1984 1407 1407 1333 1106 99

(1265 - 2693) (895 - 1913) (895 - 1913) (848 - 1813) (703 - 1506) (629 - 

604 525 525 466 396 39

(386 - 819) (335 - 713) (335 - 713) (297 - 633) (252 - 538) (249 - 

657 657 657 657 591 57

(418 - 893) (418 - 893) (418 - 893) (418 - 893) (376 - 803) (364 - 

599 372 372 346 297 26

(383 - 813) (237 - 506) (237 - 506) (220 - 471) (189 - 404) (168 - 

107 20 20 20 20 0 

(68 - 146) (13 - 27) (13 - 27) (13 - 27) (13 - 27) (0 - 

792 659 588 506 423 49

(506 - 1075) (420 - 894) (374 - 799) (322 - 688) (269 - 575) (312 - 

107 61 61 61 61 32 

(68 - 145) (38 - 83) (38 - 83) (38 - 83) (38 - 83) (20 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in a 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-13

Table E-12. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5 C

 Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusti

 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

821 726 647 567 486 5

(525 - 1112) (464 - 984) (413 - 877) (361 - 769) (310 - 661) (361 

618 564 512 445 378 4

(394 - 838) (360 - 765) (326 - 695) (283 - 605) (240 - 514) (272 

535 374 330 285 241 2

(342 - 724) (238 - 507) (210 - 448) (182 - 388) (153 - 327) (182 

407 407 407 407 356 4

(259 - 553) (259 - 553) (259 - 553) (259 - 553) (227 - 485) (259 

778 544 536 460 384 3

(496 - 1054) (346 - 739) (341 - 729) (293 - 626) (244 - 522) (252 

357 130 130 130 130 

(228 - 482) (82 - 177) (82 - 177) (82 - 177) (82 - 177) (56 

788 719 634 548 461 5

(502 - 1069) (459 - 977) (404 - 861) (349 - 745) (293 - 628) (349 

2732 1170 1170 1170 1016 7

(1746 - 3702) (744 - 1593) (744 - 1593) (744 - 1593) (645 - 1384) (490 

2322 1689 1689 1607 1359 1

(1482 - 3148) (1076 - 2295) (1076 - 2295) (1023 - 2185) (864 - 1848) (783 

598 519 519 460 391 3

(381 - 811) (331 - 704) (331 - 704) (293 - 625) (249 - 531) (246 

556 556 556 556 494 4

(354 - 757) (354 - 757) (354 - 757) (354 - 757) (314 - 673) (303 

675 434 434 406 352 3

(431 - 914) (277 - 590) (277 - 590) (258 - 552) (224 - 479) (202 

154 48 48 48 48 

(98 - 209) (31 - 66) (31 - 66) (31 - 66) (31 - 66) (14 

869 728 653 566 478 5

(555 - 1178) (464 - 988) (416 - 887) (360 - 769) (304 - 651) (350 

112 64 64 64 64 

(71 - 152) (41 - 88) (41 - 88) (41 - 88) (41 - 88) (22 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Stand

n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-14

Table E-13. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to A Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

5.5% 4.9% 4.4% 3.8% 3.3% 3

(3.5% - 7.4%) (3.1% - 6.6%) (2.8% - 5.9%) (2.4% - 5.2%) (2.1% - 4.5%) (2.4% 

5.4% 5% 4.5% 4% 3.5% 3

(3.5% - 7.3%) (3.2% - 6.7%) (2.9% - 6.2%) (2.6% - 5.4%) (2.2% - 4.7%) (2.5% 

5.5% 3.9% 3.4% 3% 2.5% 3

(3.5% - 7.5%) (2.5% - 5.3%) (2.2% - 4.7%) (1.9% - 4%) (1.6% - 3.4%) (1.9% 

3.8% 3.8% 3.8% 3.8% 3.4% 3

(2.4% - 5.1%) (2.4% - 5.1%) (2.4% - 5.1%) (2.4% - 5.1%) (2.1% - 4.6%) (2.4% 

5.7% 4.2% 4.1% 3.6% 3.1% 3

(3.7% - 7.7%) (2.7% - 5.6%) (2.6% - 5.6%) (2.3% - 4.9%) (2% - 4.2%) (2% 

5.8% 2% 2% 2% 2% 1

(3.7% - 7.9%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (0.9% 

4.2% 3.8% 3.4% 2.9% 2.5% 2

(2.7% - 5.7%) (2.4% - 5.2%) (2.2% - 4.6%) (1.9% - 4%) (1.6% - 3.4%) (1.9% 

5.3% 2.4% 2.4% 2.4% 2.1% 1

(3.4% - 7.1%) (1.5% - 3.2%) (1.5% - 3.2%) (1.5% - 3.2%) (1.3% - 2.8%) (1% 

4.9% 3.6% 3.6% 3.4% 2.9% 2

(3.1% - 6.6%) (2.3% - 4.9%) (2.3% - 4.9%) (2.2% - 4.7%) (1.9% - 4%) (1.7% 

4.6% 4% 4% 3.6% 3.1% 3

(2.9% - 6.2%) (2.6% - 5.4%) (2.6% - 5.4%) (2.3% - 4.9%) (2% - 4.2%) (1.9% 

2.7% 2.7% 2.7% 2.7% 2.4% 2

(1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.5% - 3.3%) (1.5% 

5.5% 3.6% 3.6% 3.3% 2.9% 2

(3.5% - 7.4%) (2.3% - 4.8%) (2.3% - 4.8%) (2.1% - 4.5%) (1.9% - 4%) (1.7% 

2.8% 0.8% 0.8% 0.8% 0.8% 0

(1.8% - 3.8%) (0.5% - 1.1%) (0.5% - 1.1%) (0.5% - 1.1%) (0.5% - 1.1%) (0.2% 

5.6% 4.8% 4.3% 3.8% 3.3% 3

(3.6% - 7.6%) (3% - 6.5%) (2.8% - 5.9%) (2.4% - 5.1%) (2.1% - 4.4%) (2.4% 

3.1% 2% 2% 2% 2% 1

(2% - 4.2%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (0.9% 

Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Conc

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standa

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-15

Table E-14. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to A Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

5.5% 4.9% 4.4% 3.8% 3.3% 3

(3.5% - 7.4%) (3.1% - 6.6%) (2.8% - 5.9%) (2.4% - 5.2%) (2.1% - 4.5%) (2.4% 

4.4% 4% 3.6% 3.1% 2.7% 3

(2.8% - 5.9%) (2.5% - 5.4%) (2.3% - 4.9%) (2% - 4.3%) (1.7% - 3.6%) (1.9% 

5.1% 3.6% 3.1% 2.7% 2.3% 2

(3.3% - 7%) (2.3% - 4.8%) (2% - 4.3%) (1.7% - 3.7%) (1.4% - 3.1%) (1.7% 

2.8% 2.8% 2.8% 2.8% 2.4% 2

(1.8% - 3.8%) (1.8% - 3.8%) (1.8% - 3.8%) (1.8% - 3.8%) (1.5% - 3.3%) (1.8% 

4.1% 2.9% 2.8% 2.4% 2% 2

(2.6% - 5.6%) (1.8% - 3.9%) (1.8% - 3.8%) (1.5% - 3.3%) (1.3% - 2.7%) (1.3% 

6% 2.1% 2.1% 2.1% 2.1% 1

(3.8% - 8.1%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (0.9% 

3.9% 3.6% 3.1% 2.7% 2.3% 2

(2.5% - 5.3%) (2.3% - 4.9%) (2% - 4.3%) (1.7% - 3.7%) (1.4% - 3.1%) (1.7% 

4.6% 1.9% 1.9% 1.9% 1.7% 1

(2.9% - 6.2%) (1.2% - 2.6%) (1.2% - 2.6%) (1.2% - 2.6%) (1.1% - 2.3%) (0.8% 

3.7% 2.6% 2.6% 2.5% 2.1% 1

(2.4% - 5.1%) (1.7% - 3.6%) (1.7% - 3.6%) (1.6% - 3.4%) (1.3% - 2.8%) (1.2% 

4.2% 3.6% 3.6% 3.2% 2.7% 2

(2.7% - 5.6%) (2.3% - 4.9%) (2.3% - 4.9%) (2% - 4.4%) (1.7% - 3.7%) (1.7% 

2.7% 2.7% 2.7% 2.7% 2.5% 2

(1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.6% - 3.4%) (1.5% 

4.3% 2.7% 2.7% 2.5% 2.1% 1

(2.8% - 5.9%) (1.7% - 3.7%) (1.7% - 3.7%) (1.6% - 3.4%) (1.4% - 2.9%) (1.2% 

2.2% 0.4% 0.4% 0.4% 0.4% 0

(1.4% - 3%) (0.3% - 0.6%) (0.3% - 0.6%) (0.3% - 0.6%) (0.3% - 0.6%) (0% 

4.2% 3.5% 3.1% 2.7% 2.2% 2

(2.7% - 5.7%) (2.2% - 4.7%) (2% - 4.2%) (1.7% - 3.6%) (1.4% - 3%) (1.6% 

2.1% 1.2% 1.2% 1.2% 1.2% 0

(1.3% - 2.8%) (0.7% - 1.6%) (0.7% - 1.6%) (0.7% - 1.6%) (0.7% - 1.6%) (0.4% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Conc

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standa

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-16

Table E-15. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to A Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

5.1% 4.6% 4.1% 3.6% 3% 3.6

(3.3% - 7%) (2.9% - 6.2%) (2.6% - 5.5%) (2.3% - 4.8%) (1.9% - 4.1%) (2.3% - 

4.4% 4% 3.6% 3.2% 2.7% 3% 

(2.8% - 5.9%) (2.5% - 5.4%) (2.3% - 4.9%) (2% - 4.3%) (1.7% - 3.6%) (1.9% - 

5.3% 3.7% 3.3% 2.8% 2.4% 2.8

(3.4% - 7.2%) (2.4% - 5.1%) (2.1% - 4.5%) (1.8% - 3.9%) (1.5% - 3.3%) (1.8% - 

3% 3% 3% 3% 2.7% 3% 

(1.9% - 4.1%) (1.9% - 4.1%) (1.9% - 4.1%) (1.9% - 4.1%) (1.7% - 3.6%) (1.9% - 

4.4% 3.1% 3% 2.6% 2.2% 2.2

(2.8% - 6%) (2% - 4.2%) (1.9% - 4.1%) (1.7% - 3.5%) (1.4% - 3%) (1.4% 

6.2% 2.3% 2.3% 2.3% 2.3% 1.5

(4% - 8.4%) (1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1% - 2

4% 3.7% 3.2% 2.8% 2.3% 2.8

(2.6% - 5.4%) (2.3% - 5%) (2.1% - 4.4%) (1.8% - 3.8%) (1.5% - 3.2%) (1.8% - 

4.8% 2% 2% 2% 1.8% 1.3

(3% - 6.4%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (1.1% - 2.4%) (0.9% - 

4.3% 3.2% 3.2% 3% 2.5% 2.3

(2.8% - 5.9%) (2% - 4.3%) (2% - 4.3%) (1.9% - 4.1%) (1.6% - 3.4%) (1.5% - 

4.1% 3.6% 3.6% 3.2% 2.7% 2.7

(2.6% - 5.6%) (2.3% - 4.8%) (2.3% - 4.8%) (2% - 4.3%) (1.7% - 3.7%) (1.7% - 

2.3% 2.3% 2.3% 2.3% 2% 1.9

(1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1.3% - 2.7%) (1.2% - 

4.9% 3.2% 3.2% 2.9% 2.6% 2.3

(3.1% - 6.6%) (2% - 4.3%) (2% - 4.3%) (1.9% - 4%) (1.6% - 3.5%) (1.5% - 

3% 1% 1% 1% 1% 0.4

(1.9% - 4.1%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.3% - 

4.6% 3.8% 3.4% 3% 2.5% 2.9

(2.9% - 6.2%) (2.4% - 5.2%) (2.2% - 4.7%) (1.9% - 4.1%) (1.6% - 3.4%) (1.8% - 

2.1% 1.2% 1.2% 1.2% 1.2% 0.7

(1.3% - 2.9%) (0.8% - 1.7%) (0.8% - 1.7%) (0.8% - 1.7%) (0.8% - 1.7%) (0.4% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5 Conce

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-17

Table E-16. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associate Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based o

 (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 21% 32% 21% 

(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 32%) (21% - 

-9% 0% 8% 19% 30% 22% 

(-9% - -9%) (0% - 0%) (8% - 8%) (19% - 19%) (30% - 30%) (22% - 

-43% 0% 12% 23% 35% 23% 

(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 23%) (35% - 35%) (23% - 

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 

-37% 0% 1% 13% 26% 24% 

(-37% - -38%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (24% - 

-185% 0% 0% 0% 0% 34% 

(-183% - -187%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (34% - 

-9% 0% 12% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 35%) (23% - 

-122% 0% 0% 0% 12% 31% 

(-121% - -124%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 

-35% 0% 0% 5% 18% 25% 

(-35% - -36%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 

-14% 0% 0% 11% 23% 24% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-53% 0% 0% 6% 18% 25% 

(-52% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (25% - 

-254% 0% 0% 0% 0% 64% 

(-253% - -256%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 

-18% 0% 9% 20% 31% 22% 

(-18% - -18%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (22% - 

-53% 0% 0% 0% 0% 34% 

(-53% - -53%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated wit

PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Altern

(m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-18

Table E-17. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associate Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based o

 (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 21% 32% 21% 

(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 32%) (21% - 

-10% 0% 9% 21% 33% 24% 

(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 

-44% 0% 12% 24% 36% 24% 

(-44% - -44%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 

0% 0% 0% 0% 13% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 

-45% 0% 1% 16% 30% 28% 

(-44% - -45%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 31%) (28% - 

-181% 0% 0% 0% 0% 33% 

(-179% - -183%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

-10% 0% 12% 24% 36% 24% 

(-10% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 

-137% 0% 0% 0% 14% 35% 

(-136% - -139%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 14%) (35% - 

-41% 0% 0% 5% 21% 30% 

(-41% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 

-15% 0% 0% 11% 25% 25% 

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-61% 0% 0% 7% 20% 29% 

(-61% - -62%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 

-437% 0% 0% 0% 0% 100

(-435% - -439%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 

-20% 0% 11% 23% 36% 26% 

(-20% - -20%) (0% - 0%) (11% - 11%) (23% - 23%) (36% - 36%) (26% - 

-76% 0% 0% 0% 0% 48% 

(-76% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (48% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated wit

PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Altern

(m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-19

Table E-18. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associate Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based o

 (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 22% 33% 22% 

(-13% - -13%) (0% - 0%) (11% - 11%) (22% - 22%) (33% - 33%) (22% - 

-10% 0% 9% 21% 33% 24% 

(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 

-43% 0% 12% 24% 36% 24% 

(-43% - -44%) (0% - 0%) (12% - 12%) (24% - 24%) (35% - 36%) (24% - 

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 

-43% 0% 1% 15% 29% 27% 

(-43% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 30%) (27% - 

-175% 0% 0% 0% 0% 32% 

(-173% - -177%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 

-9% 0% 12% 24% 36% 24% 

(-9% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 

-134% 0% 0% 0% 13% 34% 

(-132% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 

-37% 0% 0% 5% 20% 27% 

(-37% - -38%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 20%) (27% - 

-15% 0% 0% 11% 25% 26% 

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (25% - 25%) (25% - 

0% 0% 0% 0% 11% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 

-55% 0% 0% 7% 19% 27% 

(-55% - -56%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 19%) (27% - 

-217% 0% 0% 0% 0% 55% 

(-216% - -218%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 

-19% 0% 10% 22% 34% 25% 

(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 22%) (34% - 34%) (24% - 

-74% 0% 0% 0% 0% 46% 

(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 

Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated wit

PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Altern

(m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-20

Table E-19. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb

 in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

249 222 199 176 153 17

(205 - 291) (182 - 260) (163 - 234) (144 - 207) (125 - 180) (144 - 

396 366 337 298 259 28

(326 - 464) (301 - 429) (276 - 395) (244 - 351) (212 - 306) (236 - 

186 133 118 103 87 10

(153 - 218) (109 - 156) (96 - 139) (84 - 121) (71 - 103) (84 - 

231 231 231 231 206 23

(189 - 272) (189 - 272) (189 - 272) (189 - 272) (169 - 243) (189 - 

689 509 504 444 383 39

(567 - 806) (418 - 599) (413 - 592) (363 - 523) (313 - 452) (321 - 

187 68 68 68 68 45 

(154 - 219) (56 - 81) (56 - 81) (56 - 81) (56 - 81) (37 - 

370 340 302 263 223 26

(304 - 435) (278 - 400) (247 - 356) (215 - 310) (182 - 264) (215 - 

2124 984 984 984 867 68

(1746 - 2489) (802 - 1163) (802 - 1163) (802 - 1163) (707 - 1026) (555 - 

2614 1959 1959 1874 1610 147

(2147 - 3068) (1603 - 2307) (1603 - 2307) (1533 - 2208) (1315 - 1900) (1204 - 

333 293 293 263 226 22

(273 - 391) (240 - 345) (240 - 345) (215 - 309) (185 - 267) (183 - 

351 351 351 351 316 30

(286 - 414) (286 - 414) (286 - 414) (286 - 414) (258 - 374) (250 - 

436 291 291 274 241 21

(359 - 511) (238 - 343) (238 - 343) (224 - 323) (197 - 285) (179 - 

40 12 12 12 12 4 

(33 - 47) (9 - 14) (9 - 14) (9 - 14) (9 - 14) (3 - 

636 544 496 438 379 42

(523 - 744) (447 - 639) (406 - 583) (359 - 516) (310 - 447) (350 - 

93 61 61 61 61 41 

(76 - 109) (50 - 72) (50 - 72) (50 - 72) (50 - 72) (33 - 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-21

Table E-20. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Base

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

256 229 205 181 157 18

(211 - 300) (188 - 268) (168 - 241) (149 - 214) (129 - 185) (149 - 

325 297 271 237 202 22

(266 - 382) (244 - 350) (222 - 319) (194 - 279) (165 - 239) (186 - 

176 124 110 95 80 95 

(144 - 206) (101 - 146) (90 - 129) (77 - 112) (65 - 95) (77 - 1

175 175 175 175 153 17

(143 - 207) (143 - 207) (143 - 207) (143 - 207) (125 - 181) (143 - 

506 355 350 300 249 25

(415 - 595) (290 - 418) (286 - 413) (244 - 354) (203 - 294) (209 - 

194 72 72 72 72 49 

(160 - 227) (59 - 85) (59 - 85) (59 - 85) (59 - 85) (40 - 

359 329 291 252 213 25

(294 - 423) (269 - 388) (238 - 343) (206 - 298) (173 - 252) (206 - 

1884 815 815 815 707 534 

(1546 - 2212) (664 - 965) (664 - 965) (664 - 965) (575 - 837) (434 - 

2050 1470 1470 1394 1163 104

(1678 - 2413) (1200 - 1736) (1200 - 1736) (1138 - 1648) (947 - 1375) (850 - 1

303 264 264 236 201 19

(248 - 356) (216 - 311) (216 - 311) (193 - 278) (164 - 238) (163 - 

372 372 372 372 335 32

(303 - 439) (303 - 439) (303 - 439) (303 - 439) (273 - 396) (265 - 

349 220 220 205 177 15

(286 - 410) (180 - 260) (180 - 260) (167 - 242) (144 - 209) (128 - 

3366660

(27 - 39) (5 - 7) (5 - 7) (5 - 7) (5 - 7) (0 - 

484 405 363 314 263 304 

(397 - 569) (331 - 477) (297 - 428) (256 - 370) (215 - 311) (248 - 

63 36 36 36 36 19 

(51 - 75) (29 - 43) (29 - 43) (29 - 43) (29 - 43) (15 - 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2

:

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-22

Table E-21. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb

 in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

247 220 197 173 149 17

(203 - 290) (180 - 258) (161 - 231) (142 - 204) (122 - 176) (142 - 

324 297 271 236 202 22

(266 - 381) (243 - 349) (221 - 319) (193 - 279) (165 - 238) (186 - 

184 131 116 101 85 10

(151 - 216) (107 - 154) (95 - 136) (82 - 119) (70 - 101) (82 - 1

195 195 195 195 172 19

(159 - 230) (159 - 230) (159 - 230) (159 - 230) (140 - 203) (159 - 

532 377 372 321 269 27

(436 - 625) (308 - 445) (304 - 439) (262 - 379) (219 - 318) (226 - 

204 77 77 77 77 53 

(169 - 239) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (43 - 

375 344 304 264 223 26

(307 - 441) (281 - 405) (249 - 358) (215 - 312) (182 - 264) (215 - 

1953 860 860 860 749 57

(1604 - 2293) (701 - 1018) (701 - 1018) (701 - 1018) (610 - 887) (465 - 

2384 1755 1755 1673 1421 129

(1955 - 2802) (1435 - 2070) (1435 - 2070) (1367 - 1974) (1160 - 1679) (1053 - 

300 261 261 233 199 19

(245 - 352) (214 - 308) (214 - 308) (190 - 275) (162 - 235) (160 - 

317 317 317 317 282 27

(258 - 374) (258 - 374) (258 - 374) (258 - 374) (230 - 333) (222 - 

390 256 256 239 209 18

(321 - 458) (209 - 302) (209 - 302) (196 - 283) (170 - 246) (154 - 

47 15 15 15 15 7 

(38 - 55) (12 - 18) (12 - 18) (12 - 18) (12 - 18) (6 - 

529 446 402 350 297 34

(434 - 621) (365 - 525) (329 - 474) (286 - 413) (243 - 351) (278 - 

66 38 38 38 38 21 

(54 - 78) (31 - 46) (31 - 46) (31 - 46) (31 - 46) (17 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-23

Table E-22. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Ex PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternati

 Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

15.8% 14.1% 12.7% 11.2% 9.7% 11.2% 

(13% - 18.6%) (11.6% - 16.6%) (10.4% - 14.9%) (9.2% - 13.2%) (8% - 11.5%) (9.2% - 13.2

15.6% 14.4% 13.2% 11.7% 10.2% 11.3% 

(12.8% - 18.2%) (11.8% - 16.9%) (10.9% - 15.5%) (9.6% - 13.8%) (8.3% - 12%) (9.3% - 13.3

15.9% 11.3% 10.1% 8.8% 7.5% 8.8% 

(13.1% - 18.6%) (9.3% - 13.4%) (8.2% - 11.9%) (7.2% - 10.4%) (6.1% - 8.8%) (7.2% - 10.4

11.1% 11.1% 11.1% 11.1% 9.9% 11.1% 

(9.1% - 13.1%) (9.1% - 13.1%) (9.1% - 13.1%) (9.1% - 13.1%) (8.1% - 11.7%) (9.1% - 13.1

16.4% 12.2% 12% 10.6% 9.1% 9.4% 

(13.5% - 19.2%) (10% - 14.3%) (9.8% - 14.1%) (8.7% - 12.5%) (7.5% - 10.8%) (7.7% - 11.1

16.8% 6.1% 6.1% 6.1% 6.1% 4.1% 

(13.8% - 19.6%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (3.3% - 4.8

12.2% 11.2% 9.9% 8.7% 7.4% 8.7% 

(10% - 14.4%) (9.2% - 13.2%) (8.1% - 11.7%) (7.1% - 10.2%) (6% - 8.7%) (7.1% - 10.2

15.2% 7% 7% 7% 6.2% 4.9% 

(12.5% - 17.8%) (5.7% - 8.3%) (5.7% - 8.3%) (5.7% - 8.3%) (5.1% - 7.3%) (4% - 5.8%

14.1% 10.6% 10.6% 10.1% 8.7% 7.9% 

(11.6% - 16.5%) (8.6% - 12.4%) (8.6% - 12.4%) (8.3% - 11.9%) (7.1% - 10.2%) (6.5% - 9.4

13.3% 11.7% 11.7% 10.5% 9.1% 9% 

(10.9% - 15.6%) (9.6% - 13.8%) (9.6% - 13.8%) (8.6% - 12.4%) (7.4% - 10.7%) (7.3% - 10.6

8% 8% 8% 8% 7.2% 7% 

(6.5% - 9.4%) (6.5% - 9.4%) (6.5% - 9.4%) (6.5% - 9.4%) (5.9% - 8.5%) (5.7% - 8.2

15.7% 10.5% 10.5% 9.9% 8.7% 7.9% 

(12.9% - 18.4%) (8.6% - 12.3%) (8.6% - 12.3%) (8.1% - 11.6%) (7.1% - 10.2%) (6.4% - 9.3

8.2% 2.4% 2.4% 2.4% 2.4% 0.8% 

(6.7% - 9.7%) (1.9% - 2.8%) (1.9% - 2.8%) (1.9% - 2.8%) (1.9% - 2.8%) (0.7% - 1%

16.1% 13.8% 12.6% 11.1% 9.6% 10.8% 

(13.3% - 18.9%) (11.3% - 16.2%) (10.3% - 14.8%) (9.1% - 13.1%) (7.9% - 11.4%) (8.9% - 12.8

9.2% 6.1% 6.1% 6.1% 6.1% 4.1% 

(7.5% - 10.8%) (4.9% - 7.2%) (4.9% - 7.2%) (4.9% - 7.2%) (4.9% - 7.2%) (3.3% - 4.8

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coe1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-24

Table E-23. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Ex PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternati

 Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

15.8% 14.1% 12.7% 11.2% 9.7% 11.2% 

(13% - 18.5%) (11.6% - 16.6%) (10.4% - 14.9%) (9.2% - 13.2%) (8% - 11.5%) (9.2% - 13.2

12.7% 11.7% 10.6% 9.3% 7.9% 8.9% 

(10.5% - 15%) (9.6% - 13.7%) (8.7% - 12.5%) (7.6% - 11%) (6.5% - 9.4%) (7.3% - 10.5

14.8% 10.5% 9.3% 8% 6.8% 8% 

(12.2% - 17.4%) (8.6% - 12.3%) (7.6% - 10.9%) (6.5% - 9.5%) (5.5% - 8%) (6.5% - 9.5

8.2% 8.2% 8.2% 8.2% 7.2% 8.2% 

(6.7% - 9.7%) (6.7% - 9.7%) (6.7% - 9.7%) (6.7% - 9.7%) (5.9% - 8.5%) (6.7% - 9.7

12.1% 8.5% 8.4% 7.2% 5.9% 6.1% 

(9.9% - 14.2%) (6.9% - 10%) (6.8% - 9.9%) (5.8% - 8.5%) (4.8% - 7%) (5% - 7.3%

17.2% 6.4% 6.4% 6.4% 6.4% 4.3% 

(14.1% - 20.1%) (5.2% - 7.5%) (5.2% - 7.5%) (5.2% - 7.5%) (5.2% - 7.5%) (3.5% - 5.1

11.5% 10.5% 9.3% 8% 6.8% 8% 

(9.4% - 13.5%) (8.6% - 12.4%) (7.6% - 10.9%) (6.6% - 9.5%) (5.5% - 8%) (6.6% - 9.5

13.4% 5.8% 5.8% 5.8% 5% 3.8% 

(11% - 15.7%) (4.7% - 6.9%) (4.7% - 6.9%) (4.7% - 6.9%) (4.1% - 5.9%) (3.1% - 4.5

10.9% 7.8% 7.8% 7.4% 6.2% 5.6% 

(9% - 12.9%) (6.4% - 9.3%) (6.4% - 9.3%) (6.1% - 8.8%) (5.1% - 7.3%) (4.5% - 6.6

12.1% 10.6% 10.6% 9.4% 8.1% 8% 

(9.9% - 14.3%) (8.7% - 12.5%) (8.7% - 12.5%) (7.7% - 11.1%) (6.6% - 9.5%) (6.5% - 9.4

8.1% 8.1% 8.1% 8.1% 7.3% 7.1% 

(6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (6% - 8.7%) (5.8% - 8.4

12.6% 8% 8% 7.4% 6.4% 5.7% 

(10.4% - 14.8%) (6.5% - 9.4%) (6.5% - 9.4%) (6.1% - 8.8%) (5.2% - 7.6%) (4.6% - 6.7

6.5% 1.2% 1.2% 1.2% 1.2% 0% 

(5.3% - 7.7%) (1% - 1.5%) (1% - 1.5%) (1% - 1.5%) (1% - 1.5%) (0% - 0%

12.2% 10.2% 9.2% 7.9% 6.7% 7.7% 

(10% - 14.4%) (8.4% - 12.1%) (7.5% - 10.8%) (6.5% - 9.4%) (5.4% - 7.9%) (6.3% - 9.1

6.1% 3.5% 3.5% 3.5% 3.5% 1.8% 

(5% - 7.3%) (2.9% - 4.2%) (2.9% - 4.2%) (2.9% - 4.2%) (2.9% - 4.2%) (1.5% - 2.2

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coe1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-25

Table E-24. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Ex PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternati

 Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

14.9% 13.2% 11.8% 10.4% 9% 10.4% 

(12.2% - 17.4%) (10.9% - 15.5%) (9.7% - 13.9%) (8.5% - 12.3%) (7.4% - 10.6%) (8.5% - 12.3

12.7% 11.7% 10.6% 9.3% 7.9% 8.9% 

(10.5% - 15%) (9.6% - 13.7%) (8.7% - 12.5%) (7.6% - 11%) (6.5% - 9.4%) (7.3% - 10.5

15.4% 10.9% 9.7% 8.4% 7.1% 8.4% 

(12.7% - 18%) (8.9% - 12.9%) (7.9% - 11.4%) (6.9% - 9.9%) (5.8% - 8.4%) (6.9% - 9.9

9% 9% 9% 9% 7.9% 9% 

(7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (6.5% - 9.3%) (7.3% - 10.6

12.8% 9.1% 9% 7.7% 6.5% 6.7% 

(10.5% - 15%) (7.4% - 10.7%) (7.3% - 10.6%) (6.3% - 9.1%) (5.3% - 7.6%) (5.4% - 7.9

17.7% 6.7% 6.7% 6.7% 6.7% 4.6% 

(14.6% - 20.7%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (3.7% - 5.5

11.7% 10.7% 9.5% 8.2% 7% 8.2% 

(9.6% - 13.8%) (8.8% - 12.6%) (7.8% - 11.2%) (6.7% - 9.7%) (5.7% - 8.3%) (6.7% - 9.7

13.8% 6.1% 6.1% 6.1% 5.3% 4% 

(11.3% - 16.2%) (4.9% - 7.2%) (4.9% - 7.2%) (4.9% - 7.2%) (4.3% - 6.3%) (3.3% - 4.8

12.6% 9.3% 9.3% 8.9% 7.5% 6.8% 

(10.4% - 14.8%) (7.6% - 11%) (7.6% - 11%) (7.2% - 10.5%) (6.1% - 8.9%) (5.6% - 8.1

12% 10.5% 10.5% 9.3% 8% 7.9% 

(9.8% - 14.1%) (8.6% - 12.3%) (8.6% - 12.3%) (7.6% - 11%) (6.5% - 9.4%) (6.4% - 9.3

6.7% 6.7% 6.7% 6.7% 6% 5.8% 

(5.5% - 7.9%) (5.5% - 7.9%) (5.5% - 7.9%) (5.5% - 7.9%) (4.9% - 7.1%) (4.7% - 6.8

14.2% 9.3% 9.3% 8.7% 7.6% 6.9% 

(11.7% - 16.7%) (7.6% - 11%) (7.6% - 11%) (7.1% - 10.3%) (6.2% - 9%) (5.6% - 8.1

9% 2.9% 2.9% 2.9% 2.9% 1.3% 

(7.4% - 10.6%) (2.4% - 3.4%) (2.4% - 3.4%) (2.4% - 3.4%) (2.4% - 3.4%) (1.1% - 1.6

13.3% 11.2% 10.1% 8.8% 7.5% 8.6% 

(10.9% - 15.7%) (9.2% - 13.2%) (8.3% - 11.9%) (7.2% - 10.4%) (6.1% - 8.9%) (7% - 10.1%

6.3% 3.7% 3.7% 3.7% 3.7% 2% 

(5.2% - 7.5%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (1.6% - 2.4

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coe1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-26

Table E-25. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Morta Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 21% 31% 21% 

(-12% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (31% - 31%) (20% - 

-8% 0% 8% 18% 29% 21% 

(-8% - -8%) (0% - 0%) (8% - 8%) (18% - 19%) (29% - 29%) (21% - 

-40% 0% 11% 23% 34% 23% 

(-39% - -41%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 34%) (22% - 

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 

-35% 0% 1% 13% 25% 23% 

(-35% - -36%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 25%) (23% - 

-174% 0% 0% 0% 0% 33% 

(-171% - -177%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

-9% 0% 11% 23% 34% 23% 

(-9% - -9%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (23% - 

-116% 0% 0% 0% 12% 31% 

(-114% - -118%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 

-33% 0% 0% 4% 18% 25% 

(-33% - -34%) (0% - 0%) (0% - 0%) (4% - 4%) (18% - 18%) (25% - 

-14% 0% 0% 10% 23% 23% 

(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 10%) (22% - 23%) (23% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-50% 0% 0% 6% 17% 25% 

(-49% - -51%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 17%) (24% - 

-247% 0% 0% 0% 0% 64% 

(-245% - -249%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 

-17% 0% 9% 20% 30% 22% 

(-16% - -17%) (0% - 0%) (9% - 9%) (19% - 20%) (30% - 31%) (21% - 

-51% 0% 0% 0% 0% 33% 

(-51% - -52%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-27

Table E-26. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Morta Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 21% 31% 21% 

(-12% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (31% - 31%) (20% - 

-9% 0% 9% 20% 32% 24% 

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 32%) (23% - 

-42% 0% 12% 23% 35% 23% 

(-41% - -42%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 

0% 0% 0% 0% 13% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (0% - 

-43% 0% 1% 16% 30% 28% 

(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 16%) (30% - 30%) (27% - 

-170% 0% 0% 0% 0% 33% 

(-167% - -173%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 

-9% 0% 12% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 

-131% 0% 0% 0% 13% 35% 

(-129% - -133%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 

-39% 0% 0% 5% 21% 29% 

(-39% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 

-14% 0% 0% 11% 24% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (24% - 

0% 0% 0% 0% 10% 12% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 

-58% 0% 0% 7% 20% 28% 

(-58% - -59%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (28% - 

-427% 0% 0% 0% 0% 100

(-425% - -430%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 

-19% 0% 10% 23% 35% 25% 

(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 23%) (35% - 35%) (25% - 

-74% 0% 0% 0% 0% 47% 

(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-28

Table E-27. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Morta Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 21%) (32% - 32%) (21% - 

-9% 0% 9% 20% 32% 24% 

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 32%) (23% - 

-41% 0% 11% 23% 35% 23% 

(-40% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 

-41% 0% 1% 15% 29% 27% 

(-41% - -42%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (26% - 

-164% 0% 0% 0% 0% 31% 

(-161% - -167%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 

-9% 0% 12% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 

-127% 0% 0% 0% 13% 34% 

(-125% - -129%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (33% - 

-36% 0% 0% 5% 19% 26% 

(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (26% - 

-15% 0% 0% 11% 24% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% - 

0% 0% 0% 0% 11% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 

-53% 0% 0% 6% 18% 26% 

(-52% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 19%) (26% - 

-210% 0% 0% 0% 0% 55% 

(-209% - -212%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 

-19% 0% 10% 22% 33% 24% 

(-18% - -19%) (0% - 0%) (10% - 10%) (21% - 22%) (33% - 34%) (24% - 

-72% 0% 0% 0% 0% 46% 

(-71% - -72%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-29

Table E-28. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

312 279 251 222 193 222 

(257 - 364) (229 - 327) (206 - 295) (182 - 262) (158 - 228) (182 - 

497 460 423 376 328 36

(409 - 581) (378 - 538) (348 - 497) (308 - 442) (268 - 386) (298 - 

233 168 149 130 111 13

(192 - 273) (137 - 197) (122 - 176) (106 - 154) (90 - 131) (106 - 

292 292 292 292 261 292 

(239 - 344) (239 - 344) (239 - 344) (239 - 344) (213 - 307) (239 - 

862 642 635 561 485 49

(711 - 1007) (526 - 754) (520 - 746) (459 - 660) (396 - 572) (406 - 

234 87 87 87 87 58 

(193 - 273) (71 - 103) (71 - 103) (71 - 103) (71 - 103) (47 - 

467 429 382 333 284 33

(383 - 548) (351 - 505) (312 - 449) (272 - 393) (231 - 335) (272 - 

2664 1249 1249 1249 1103 86

(2192 - 3117) (1017 - 1477) (1017 - 1477) (1017 - 1477) (897 - 1306) (705 - 1

3285 2475 2475 2369 2040 187

(2700 - 3849) (2024 - 2914) (2024 - 2914) (1936 - 2790) (1665 - 2408) (1525 - 

419 369 369 332 287 284 

(344 - 492) (303 - 434) (303 - 434) (271 - 391) (234 - 338) (232 - 

445 445 445 445 401 38

(363 - 526) (363 - 526) (363 - 526) (363 - 526) (327 - 475) (317 - 

547 368 368 346 305 27

(450 - 639) (301 - 433) (301 - 433) (283 - 408) (249 - 361) (227 - 

51 15 15 15 15 5 

(42 - 60) (12 - 18) (12 - 18) (12 - 18) (12 - 18) (4 - 

796 684 624 553 480 53

(656 - 930) (562 - 802) (512 - 733) (453 - 650) (392 - 566) (441 - 

117 78 78 78 78 52 

(96 - 138) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (42 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-30

Table E-29. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

321 287 258 229 199 22

(264 - 375) (236 - 336) (212 - 303) (187 - 269) (163 - 235) (187 - 

409 375 342 300 256 28

(335 - 480) (307 - 441) (280 - 403) (245 - 353) (209 - 303) (235 - 

221 157 139 120 102 12

(181 - 258) (128 - 184) (113 - 164) (98 - 142) (83 - 120) (98 - 

222 222 222 222 195 22

(181 - 262) (181 - 262) (181 - 262) (181 - 262) (158 - 230) (181 - 

638 449 443 380 316 32

(523 - 749) (367 - 530) (361 - 523) (310 - 450) (257 - 375) (266 - 

243 92 92 92 92 62 

(201 - 284) (75 - 108) (75 - 108) (75 - 108) (75 - 108) (50 - 

453 416 368 320 270 32

(371 - 533) (340 - 490) (301 - 434) (261 - 378) (220 - 320) (261 - 

2370 1038 1038 1038 901 68

(1945 - 2779) (843 - 1229) (843 - 1229) (843 - 1229) (731 - 1068) (553 - 

2588 1865 1865 1770 1478 132

(2118 - 3046) (1520 - 2203) (1520 - 2203) (1442 - 2092) (1202 - 1750) (1079 - 

381 334 334 298 255 25

(313 - 448) (273 - 393) (273 - 393) (244 - 352) (208 - 302) (206 - 

471 471 471 471 426 41

(384 - 557) (384 - 557) (384 - 557) (384 - 557) (347 - 503) (336 - 

439 279 279 260 225 20

(360 - 516) (228 - 330) (228 - 330) (212 - 308) (183 - 266) (163 - 

4288880

(34 - 50) (6 - 10) (6 - 10) (6 - 10) (6 - 10) (0 - 

610 512 460 398 335 38

(500 - 716) (419 - 603) (375 - 542) (324 - 470) (272 - 396) (314 - 

80 46 46 46 46 24 

(65 - 95) (37 - 55) (37 - 55) (37 - 55) (37 - 55) (20 - 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-31

Table E-30. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

310 277 248 219 189 21

(255 - 363) (227 - 324) (203 - 291) (179 - 258) (154 - 223) (179 - 

408 374 342 299 256 28

(335 - 479) (307 - 440) (280 - 402) (244 - 353) (209 - 302) (235 - 

231 165 146 128 108 12

(190 - 270) (135 - 194) (120 - 173) (104 - 151) (88 - 128) (104 - 

247 247 247 247 218 24

(202 - 291) (202 - 291) (202 - 291) (202 - 291) (178 - 257) (202 - 

670 478 471 407 341 35

(549 - 786) (390 - 563) (385 - 556) (332 - 481) (278 - 404) (286 - 

255 98 98 98 98 68 

(211 - 298) (80 - 116) (80 - 116) (80 - 116) (80 - 116) (55 - 

473 434 385 335 284 33

(387 - 556) (355 - 511) (314 - 453) (273 - 395) (231 - 335) (273 - 

2456 1094 1094 1094 954 73

(2017 - 2879) (890 - 1296) (890 - 1296) (890 - 1296) (775 - 1131) (592 - 

3003 2222 2222 2120 1804 164

(2462 - 3525) (1814 - 2620) (1814 - 2620) (1730 - 2501) (1469 - 2132) (1336 - 

378 330 330 295 252 24

(309 - 444) (270 - 389) (270 - 389) (241 - 347) (205 - 298) (203 - 

402 402 402 402 359 34

(327 - 476) (327 - 476) (327 - 476) (327 - 476) (291 - 425) (282 - 

490 324 324 303 265 24

(403 - 574) (264 - 382) (264 - 382) (248 - 358) (216 - 313) (195 - 

59 19 19 19 19 9 

(48 - 70) (16 - 23) (16 - 23) (16 - 23) (16 - 23) (7 - 

665 563 508 443 377 43

(546 - 780) (461 - 662) (415 - 599) (362 - 523) (307 - 446) (351 - 

84 49 49 49 49 27 

(68 - 99) (40 - 58) (40 - 58) (40 - 58) (40 - 58) (21 - 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concen

and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-32

Table E-31. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term  PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Altern

 Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

19.9% 17.8% 16% 14.2% 12.3% 14.2

(16.4% - 23.2%) (14.6% - 20.8%) (13.1% - 18.8%) (11.6% - 16.7%) (10.1% - 14.5%) (11.6% - 

19.5% 18.1% 16.6% 14.8% 12.9% 14.3

(16.1% - 22.8%) (14.8% - 21.2%) (13.7% - 19.5%) (12.1% - 17.4%) (10.5% - 15.2%) (11.7% - 

19.9% 14.3% 12.7% 11.1% 9.5% 11.1

(16.4% - 23.3%) (11.7% - 16.8%) (10.4% - 15%) (9.1% - 13.1%) (7.7% - 11.2%) (9.1% - 1

14% 14% 14% 14% 12.5% 14% 

(11.5% - 16.5%) (11.5% - 16.5%) (11.5% - 16.5%) (11.5% - 16.5%) (10.2% - 14.7%) (11.5% - 

20.6% 15.3% 15.1% 13.4% 11.6% 11.9

(17% - 24%) (12.6% - 18%) (12.4% - 17.8%) (10.9% - 15.7%) (9.4% - 13.6%) (9.7% - 

21% 7.8% 7.8% 7.8% 7.8% 5.2% 

(17.3% - 24.5%) (6.3% - 9.2%) (6.3% - 9.2%) (6.3% - 9.2%) (6.3% - 9.2%) (4.2% - 

15.4% 14.2% 12.6% 11% 9.4% 11% 

(12.6% - 18.1%) (11.6% - 16.6%) (10.3% - 14.8%) (9% - 13%) (7.6% - 11.1%) (9% - 1

19% 8.9% 8.9% 8.9% 7.9% 6.2% 

(15.7% - 22.3%) (7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (6.4% - 9.3%) (5% - 7

17.7% 13.3% 13.3% 12.8% 11% 10.1

(14.5% - 20.7%) (10.9% - 15.7%) (10.9% - 15.7%) (10.4% - 15%) (9% - 13%) (8.2% - 1

16.8% 14.8% 14.8% 13.3% 11.5% 11.4

(13.8% - 19.7%) (12.1% - 17.4%) (12.1% - 17.4%) (10.8% - 15.6%) (9.4% - 13.5%) (9.3% - 1

10.1% 10.1% 10.1% 10.1% 9.1% 8.8% 

(8.2% - 11.9%) (8.2% - 11.9%) (8.2% - 11.9%) (8.2% - 11.9%) (7.4% - 10.8%) (7.2% - 1

19.7% 13.2% 13.2% 12.5% 11% 10% 

(16.2% - 23%) (10.8% - 15.6%) (10.8% - 15.6%) (10.2% - 14.7%) (9% - 13%) (8.2% - 1

10.4% 3% 3% 3% 3% 1.1% 

(8.5% - 12.3%) (2.4% - 3.6%) (2.4% - 3.6%) (2.4% - 3.6%) (2.4% - 3.6%) (0.9% - 

20.2% 17.4% 15.8% 14% 12.2% 13.7

(16.6% - 23.6%) (14.3% - 20.4%) (13% - 18.6%) (11.5% - 16.5%) (10% - 14.4%) (11.2% - 

11.6% 7.7% 7.7% 7.7% 7.7% 5.2% 

(9.5% - 13.7%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% - 9.1%) (4.2% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to 

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily 

Combination Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-33

Table E-32. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term  PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Altern

 Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

19.8% 17.7% 16% 14.2% 12.3% 14.2

(16.3% - 23.2%) (14.6% - 20.8%) (13.1% - 18.7%) (11.6% - 16.6%) (10% - 14.5%) (11.6% - 

16% 14.7% 13.4% 11.8% 10.1% 11.3

(13.2% - 18.8%) (12.1% - 17.3%) (11% - 15.8%) (9.6% - 13.9%) (8.2% - 11.9%) (9.2% - 1

18.6% 13.2% 11.7% 10.2% 8.6% 10.2

(15.3% - 21.8%) (10.8% - 15.6%) (9.6% - 13.8%) (8.3% - 12%) (7% - 10.2%) (8.3% - 

10.4% 10.4% 10.4% 10.4% 9.1% 10.4

(8.5% - 12.3%) (8.5% - 12.3%) (8.5% - 12.3%) (8.5% - 12.3%) (7.4% - 10.8%) (8.5% - 1

15.2% 10.7% 10.6% 9.1% 7.6% 7.8% 

(12.5% - 17.9%) (8.8% - 12.7%) (8.6% - 12.5%) (7.4% - 10.7%) (6.1% - 8.9%) (6.3% - 

21.5% 8.1% 8.1% 8.1% 8.1% 5.5% 

(17.7% - 25.1%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (4.4% - 

14.5% 13.3% 11.7% 10.2% 8.6% 10.2

(11.8% - 17%) (10.8% - 15.6%) (9.6% - 13.8%) (8.3% - 12%) (7% - 10.2%) (8.3% - 

16.8% 7.4% 7.4% 7.4% 6.4% 4.8% 

(13.8% - 19.7%) (6% - 8.7%) (6% - 8.7%) (6% - 8.7%) (5.2% - 7.6%) (3.9% - 

13.8% 9.9% 9.9% 9.4% 7.9% 7.1% 

(11.3% - 16.2%) (8.1% - 11.7%) (8.1% - 11.7%) (7.7% - 11.2%) (6.4% - 9.3%) (5.8% - 

15.3% 13.4% 13.4% 11.9% 10.2% 10.1

(12.5% - 18%) (10.9% - 15.8%) (10.9% - 15.8%) (9.8% - 14.1%) (8.3% - 12.1%) (8.3% - 

10.3% 10.3% 10.3% 10.3% 9.3% 9% 

(8.4% - 12.2%) (8.4% - 12.2%) (8.4% - 12.2%) (8.4% - 12.2%) (7.6% - 11%) (7.3% - 1

15.9% 10.1% 10.1% 9.4% 8.1% 7.3% 

(13% - 18.7%) (8.2% - 11.9%) (8.2% - 11.9%) (7.7% - 11.1%) (6.6% - 9.6%) (5.9% - 

8.3% 1.6% 1.6% 1.6% 1.6% 0% 

(6.7% - 9.8%) (1.3% - 1.9%) (1.3% - 1.9%) (1.3% - 1.9%) (1.3% - 1.9%) (0% - 

15.4% 12.9% 11.6% 10% 8.5% 9.7% 

(12.6% - 18.1%) (10.6% - 15.2%) (9.5% - 13.7%) (8.2% - 11.9%) (6.9% - 10%) (7.9% - 1

7.8% 4.5% 4.5% 4.5% 4.5% 2.4% 

(6.3% - 9.2%) (3.6% - 5.3%) (3.6% - 5.3%) (3.6% - 5.3%) (3.6% - 5.3%) (1.9% - 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to 

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily 

Combination Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-34

Table E-33. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term  PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Altern

 Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

18.7% 16.7% 14.9% 13.2% 11.4% 13.2

(15.4% - 21.8%) (13.7% - 19.5%) (12.2% - 17.6%) (10.8% - 15.5%) (9.3% - 13.4%) (10.8% - 

16.1% 14.7% 13.4% 11.8% 10.1% 11.3

(13.2% - 18.8%) (12.1% - 17.3%) (11% - 15.8%) (9.6% - 13.9%) (8.2% - 11.9%) (9.2% - 1

19.3% 13.8% 12.2% 10.7% 9.1% 10.7

(15.9% - 22.6%) (11.3% - 16.2%) (10% - 14.4%) (8.7% - 12.6%) (7.4% - 10.7%) (8.7% - 1

11.4% 11.4% 11.4% 11.4% 10% 11.4

(9.3% - 13.4%) (9.3% - 13.4%) (9.3% - 13.4%) (9.3% - 13.4%) (8.2% - 11.9%) (9.3% - 1

16.1% 11.5% 11.3% 9.8% 8.2% 8.5% 

(13.2% - 18.9%) (9.4% - 13.5%) (9.3% - 13.4%) (8% - 11.6%) (6.7% - 9.7%) (6.9% - 

22.2% 8.5% 8.5% 8.5% 8.5% 5.9% 

(18.3% - 25.9%) (7% - 10.1%) (7% - 10.1%) (7% - 10.1%) (7% - 10.1%) (4.8% - 

14.8% 13.6% 12% 10.5% 8.9% 10.5

(12.1% - 17.4%) (11.1% - 16%) (9.8% - 14.2%) (8.5% - 12.3%) (7.2% - 10.5%) (8.5% - 1

17.3% 7.7% 7.7% 7.7% 6.7% 5.2% 

(14.2% - 20.3%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% - 9.1%) (5.5% - 8%) (4.2% - 

15.9% 11.8% 11.8% 11.2% 9.6% 8.7% 

(13% - 18.7%) (9.6% - 13.9%) (9.6% - 13.9%) (9.2% - 13.2%) (7.8% - 11.3%) (7.1% - 1

15.1% 13.2% 13.2% 11.8% 10.1% 10% 

(12.4% - 17.8%) (10.8% - 15.6%) (10.8% - 15.6%) (9.6% - 13.9%) (8.2% - 11.9%) (8.1% - 1

8.5% 8.5% 8.5% 8.5% 7.6% 7.3% 

(6.9% - 10.1%) (6.9% - 10.1%) (6.9% - 10.1%) (6.9% - 10.1%) (6.2% - 9%) (6% - 8

17.8% 11.8% 11.8% 11% 9.6% 8.7% 

(14.7% - 20.9%) (9.6% - 13.9%) (9.6% - 13.9%) (9% - 13%) (7.8% - 11.4%) (7.1% - 1

11.4% 3.7% 3.7% 3.7% 3.7% 1.7% 

(9.3% - 13.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (1.4% - 

16.8% 14.2% 12.8% 11.2% 9.5% 10.9

(13.8% - 19.7%) (11.6% - 16.7%) (10.5% - 15.1%) (9.1% - 13.2%) (7.7% - 11.2%) (8.9% - 1

8% 4.7% 4.7% 4.7% 4.7% 2.5% 

(6.5% - 9.5%) (3.8% - 5.6%) (3.8% - 5.6%) (3.8% - 5.6%) (3.8% - 5.6%) (2.1% - 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to 

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily 

Combination Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-35

Table E-34. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Morta with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: E

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 20% 31% 20% 

(-11% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (30% - 31%) (20% - 

-8% 0% 8% 18% 29% 21% 

(-8% - -8%) (0% - 0%) (8% - 8%) (18% - 18%) (28% - 29%) (21% - 

-39% 0% 11% 22% 34% 22% 

(-38% - -40%) (0% - 0%) (11% - 11%) (22% - 23%) (33% - 34%) (22% - 

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 

-34% 0% 1% 13% 24% 23% 

(-34% - -35%) (0% - 0%) (1% - 1%) (12% - 13%) (24% - 25%) (22% - 

-170% 0% 0% 0% 0% 33% 

(-166% - -174%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

-9% 0% 11% 22% 34% 22% 

(-9% - -9%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 34%) (22% - 

-113% 0% 0% 0% 12% 30% 

(-111% - -116%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (30% - 

-33% 0% 0% 4% 18% 24% 

(-32% - -33%) (0% - 0%) (0% - 0%) (4% - 4%) (17% - 18%) (24% - 

-13% 0% 0% 10% 22% 23% 

(-13% - -14%) (0% - 0%) (0% - 0%) (10% - 10%) (22% - 23%) (23% - 

0% 0% 0% 0% 10% 12% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 

-49% 0% 0% 6% 17% 24% 

(-48% - -50%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 17%) (24% - 

-244% 0% 0% 0% 0% 64% 

(-242% - -247%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 

-16% 0% 9% 19% 30% 21% 

(-16% - -17%) (0% - 0%) (9% - 9%) (19% - 19%) (29% - 30%) (21% - 

-51% 0% 0% 0% 0% 33% 

(-50% - -51%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-36

Table E-35. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Morta with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: E

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 20% 31% 20% 

(-11% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (30% - 31%) (20% - 

-9% 0% 9% 20% 32% 23% 

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (23% - 

-41% 0% 11% 23% 35% 23% 

(-40% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (0% - 

-42% 0% 1% 15% 30% 27% 

(-41% - -43%) (0% - 0%) (1% - 1%) (15% - 16%) (29% - 30%) (27% - 

-165% 0% 0% 0% 0% 32% 

(-162% - -169%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 

-9% 0% 11% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 

-128% 0% 0% 0% 13% 34% 

(-126% - -131%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 

-39% 0% 0% 5% 21% 29% 

(-38% - -39%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 

-14% 0% 0% 11% 24% 24% 

(-14% - -14%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 

0% 0% 0% 0% 10% 12% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 

-57% 0% 0% 7% 20% 28% 

(-56% - -58%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 20%) (28% - 

-423% 0% 0% 0% 0% 100

(-420% - -427%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 

-19% 0% 10% 22% 35% 25% 

(-19% - -19%) (0% - 0%) (10% - 10%) (22% - 23%) (34% - 35%) (24% - 

-74% 0% 0% 0% 0% 47% 

(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-37

Table E-36. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Morta with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: E

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 13/3

-12% 0% 10% 21% 32% 21% 

(-12% - -12%) (0% - 0%) (10% - 10%) (21% - 21%) (31% - 32%) (21% - 

-9% 0% 9% 20% 32% 23% 

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (23% - 

-40% 0% 11% 23% 34% 23% 

(-39% - -41%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 35%) (22% - 

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 

-40% 0% 1% 15% 28% 26% 

(-40% - -41%) (0% - 0%) (1% - 1%) (15% - 15%) (28% - 29%) (26% - 

-159% 0% 0% 0% 0% 31% 

(-156% - -163%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 

-9% 0% 11% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (23% - 

-124% 0% 0% 0% 13% 33% 

(-122% - -127%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (33% - 

-35% 0% 0% 5% 19% 26% 

(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (26% - 

-14% 0% 0% 11% 24% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 

0% 0% 0% 0% 11% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 

-51% 0% 0% 6% 18% 26% 

(-51% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (26% - 

-208% 0% 0% 0% 0% 55% 

(-205% - -210%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 

-18% 0% 10% 21% 33% 23% 

(-18% - -18%) (0% - 0%) (10% - 10%) (21% - 22%) (33% - 33%) (23% - 

-71% 0% 0% 0% 0% 46% 

(-71% - -72%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-38

Table E-37. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient P

 in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

512 455 407 359 310 35

(391 - 630) (347 - 561) (310 - 502) (273 - 443) (236 - 383) (273 - 

507 467 428 378 327 36

(387 - 624) (356 - 575) (326 - 528) (288 - 466) (249 - 405) (278 - 

365 258 228 198 168 19

(279 - 450) (196 - 318) (174 - 282) (151 - 245) (128 - 208) (151 - 

321 321 321 321 285 32

(244 - 396) (244 - 396) (244 - 396) (244 - 396) (217 - 352) (244 - 

748 547 540 474 408 41

(572 - 920) (417 - 675) (412 - 667) (361 - 586) (310 - 505) (318 - 

240 85 85 85 85 56 

(184 - 295) (65 - 105) (65 - 105) (65 - 105) (65 - 105) (43 - 

499 457 404 351 297 35

(380 - 616) (348 - 564) (307 - 499) (267 - 434) (226 - 368) (267 - 

2357 1069 1069 1069 941 73

(1800 - 2902) (812 - 1324) (812 - 1324) (812 - 1324) (714 - 1166) (559 - 

2205 1637 1637 1564 1339 122

(1683 - 2717) (1246 - 2022) (1246 - 2022) (1190 - 1933) (1018 - 1657) (930 - 

439 384 384 343 295 29

(335 - 541) (293 - 474) (293 - 474) (261 - 424) (224 - 365) (222 - 

406 406 406 406 365 35

(309 - 503) (309 - 503) (309 - 503) (309 - 503) (278 - 453) (269 - 

529 349 349 328 287 26

(405 - 652) (265 - 431) (265 - 431) (249 - 405) (219 - 356) (198 - 

76 22 22 22 22 8 

(58 - 94) (16 - 27) (16 - 27) (16 - 27) (16 - 27) (6 - 

758 646 586 516 445 50

(580 - 933) (493 - 796) (447 - 723) (393 - 637) (339 - 550) (383 - 

110 72 72 72 72 48 

(84 - 136) (55 - 89) (55 - 89) (55 - 89) (55 - 89) (36 - 

 Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5 Concentratio

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards 

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-39

Table E-38. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient P

 in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

527 469 419 369 319 36

(403 - 649) (358 - 577) (320 - 517) (281 - 456) (243 - 394) (281 - 

412 377 342 298 253 28

(314 - 509) (287 - 465) (261 - 423) (227 - 369) (193 - 314) (218 - 

344 240 211 183 154 18

(263 - 424) (183 - 297) (161 - 261) (139 - 226) (117 - 190) (139 - 

241 241 241 241 210 24

(183 - 298) (183 - 298) (183 - 298) (183 - 298) (160 - 260) (183 - 

543 377 372 317 263 27

(414 - 670) (287 - 466) (283 - 460) (241 - 393) (199 - 326) (206 - 

250 90 90 90 90 60 

(191 - 307) (68 - 112) (68 - 112) (68 - 112) (68 - 112) (46 - 

483 441 389 336 283 33

(368 - 597) (336 - 545) (296 - 481) (255 - 416) (215 - 350) (255 - 

2081 884 884 884 765 57

(1587 - 2566) (671 - 1095) (671 - 1095) (671 - 1095) (580 - 948) (436 - 

1715 1220 1220 1156 961 86

(1306 - 2118) (927 - 1510) (927 - 1510) (878 - 1431) (729 - 1191) (653 - 

398 346 346 307 262 25

(303 - 491) (263 - 427) (263 - 427) (234 - 380) (199 - 324) (197 - 

431 431 431 431 388 37

(327 - 533) (327 - 533) (327 - 533) (327 - 533) (294 - 480) (286 - 

420 262 262 244 209 18

(320 - 518) (199 - 324) (199 - 324) (185 - 302) (159 - 259) (141 - 

62 12 12 12 12 0 

(47 - 77) (9 - 15) (9 - 15) (9 - 15) (9 - 15) (0 - 

572 476 426 366 307 35

(436 - 705) (362 - 588) (324 - 526) (278 - 453) (233 - 380) (270 - 

74 42 42 42 42 22 

(56 - 92) (32 - 53) (32 - 53) (32 - 53) (32 - 53) (17 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5 Concentratio

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards 

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-40

Table E-39. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient P

 in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

507 449 401 352 302 35

(387 - 625) (343 - 554) (305 - 495) (268 - 435) (230 - 374) (268 - 

412 376 342 298 253 28

(314 - 508) (286 - 464) (260 - 422) (226 - 368) (192 - 313) (217 - 

361 253 224 194 164 19

(276 - 444) (193 - 313) (170 - 276) (147 - 240) (124 - 203) (147 - 

269 269 269 269 236 26

(205 - 333) (205 - 333) (205 - 333) (205 - 333) (179 - 292) (205 - 

572 402 396 340 284 29

(436 - 705) (305 - 497) (301 - 490) (259 - 421) (216 - 352) (223 - 

263 97 97 97 97 66 

(201 - 323) (74 - 120) (74 - 120) (74 - 120) (74 - 120) (50 - 

504 461 407 352 297 35

(384 - 622) (351 - 569) (309 - 503) (268 - 436) (225 - 368) (268 - 

2160 933 933 933 811 61

(1648 - 2663) (708 - 1156) (708 - 1156) (708 - 1156) (615 - 1006) (468 - 

2003 1462 1462 1392 1179 106

(1527 - 2470) (1112 - 1808) (1112 - 1808) (1059 - 1722) (895 - 1459) (812 - 1

393 342 342 304 258 25

(300 - 485) (260 - 423) (260 - 423) (231 - 375) (196 - 320) (194 - 

366 366 366 366 325 31

(278 - 453) (278 - 453) (278 - 453) (278 - 453) (247 - 403) (238 - 

472 305 305 286 248 22

(360 - 581) (232 - 378) (232 - 378) (217 - 353) (188 - 307) (170 - 

89 28 28 28 28 13 

(68 - 110) (21 - 35) (21 - 35) (21 - 35) (21 - 35) (10 - 

626 526 472 410 347 39

(478 - 772) (400 - 649) (359 - 584) (312 - 507) (264 - 430) (302 - 

78 45 45 45 45 24 

(59 - 97) (34 - 56) (34 - 56) (34 - 56) (34 - 56) (18 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5 Concentratio

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards 

Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-41

Table E-40. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Expos Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

8.8% 7.8% 7% 6.1% 5.3% 6.1% 

(6.7% - 10.8%) (5.9% - 9.6%) (5.3% - 8.6%) (4.7% - 7.6%) (4% - 6.6%) (4.7% - 7.

8.6% 7.9% 7.3% 6.4% 5.6% 6.2% 

(6.6% - 10.6%) (6% - 9.8%) (5.5% - 9%) (4.9% - 7.9%) (4.2% - 6.9%) (4.7% - 7.

8.8% 6.2% 5.5% 4.8% 4% 4.8% 

(6.7% - 10.8%) (4.7% - 7.7%) (4.2% - 6.8%) (3.6% - 5.9%) (3.1% - 5%) (3.6% - 5.

6.1% 6.1% 6.1% 6.1% 5.4% 6.1% 

(4.6% - 7.5%) (4.6% - 7.5%) (4.6% - 7.5%) (4.6% - 7.5%) (4.1% - 6.7%) (4.6% - 7.

9.1% 6.7% 6.6% 5.8% 5% 5.1% 

(7% - 11.2%) (5.1% - 8.2%) (5% - 8.1%) (4.4% - 7.1%) (3.8% - 6.1%) (3.9% - 6.

9.3% 3.3% 3.3% 3.3% 3.3% 2.2% 

(7.1% - 11.4%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (1.7% - 2.

6.7% 6.1% 5.4% 4.7% 4% 4.7% 

(5.1% - 8.3%) (4.7% - 7.6%) (4.1% - 6.7%) (3.6% - 5.8%) (3% - 4.9%) (3.6% - 5.

8.4% 3.8% 3.8% 3.8% 3.3% 2.6% 

(6.4% - 10.3%) (2.9% - 4.7%) (2.9% - 4.7%) (2.9% - 4.7%) (2.5% - 4.1%) (2% - 3.3

7.8% 5.8% 5.8% 5.5% 4.7% 4.3% 

(5.9% - 9.6%) (4.4% - 7.1%) (4.4% - 7.1%) (4.2% - 6.8%) (3.6% - 5.8%) (3.3% - 5.

7.3% 6.4% 6.4% 5.7% 4.9% 4.9% 

(5.6% - 9%) (4.9% - 7.9%) (4.9% - 7.9%) (4.4% - 7.1%) (3.7% - 6.1%) (3.7% - 6

4.3% 4.3% 4.3% 4.3% 3.9% 3.8% 

(3.3% - 5.3%) (3.3% - 5.3%) (3.3% - 5.3%) (3.3% - 5.3%) (2.9% - 4.8%) (2.9% - 4.

8.7% 5.7% 5.7% 5.4% 4.7% 4.3% 

(6.6% - 10.7%) (4.4% - 7.1%) (4.4% - 7.1%) (4.1% - 6.6%) (3.6% - 5.8%) (3.3% - 5.

4.5% 1.3% 1.3% 1.3% 1.3% 0.5% 

(3.4% - 5.5%) (1% - 1.6%) (1% - 1.6%) (1% - 1.6%) (1% - 1.6%) (0.3% - 0.

8.9% 7.6% 6.9% 6.1% 5.2% 5.9% 

(6.8% - 11%) (5.8% - 9.4%) (5.3% - 8.5%) (4.6% - 7.5%) (4% - 6.5%) (4.5% - 7.

5% 3.3% 3.3% 3.3% 3.3% 2.2% 

(3.8% - 6.2%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (1.7% - 2.

 Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-42

Table E-41. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Expos Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

8.8% 7.8% 7% 6.1% 5.3% 6.1% 

(6.7% - 10.8%) (5.9% - 9.6%) (5.3% - 8.6%) (4.7% - 7.6%) (4% - 6.5%) (4.7% - 7.

7% 6.4% 5.8% 5.1% 4.3% 4.9% 

(5.3% - 8.6%) (4.9% - 7.9%) (4.4% - 7.2%) (3.8% - 6.2%) (3.3% - 5.3%) (3.7% - 6

8.2% 5.7% 5% 4.3% 3.7% 4.3% 

(6.3% - 10.1%) (4.3% - 7.1%) (3.8% - 6.2%) (3.3% - 5.4%) (2.8% - 4.5%) (3.3% - 5.

4.5% 4.5% 4.5% 4.5% 3.9% 4.5% 

(3.4% - 5.5%) (3.4% - 5.5%) (3.4% - 5.5%) (3.4% - 5.5%) (3% - 4.8%) (3.4% - 5.

6.6% 4.6% 4.5% 3.9% 3.2% 3.3% 

(5% - 8.2%) (3.5% - 5.7%) (3.4% - 5.6%) (2.9% - 4.8%) (2.4% - 4%) (2.5% - 4.

9.5% 3.4% 3.4% 3.4% 3.4% 2.3% 

(7.3% - 11.7%) (2.6% - 4.3%) (2.6% - 4.3%) (2.6% - 4.3%) (2.6% - 4.3%) (1.7% - 2.

6.3% 5.7% 5% 4.4% 3.7% 4.4% 

(4.8% - 7.7%) (4.4% - 7.1%) (3.8% - 6.2%) (3.3% - 5.4%) (2.8% - 4.5%) (3.3% - 5.

7.4% 3.1% 3.1% 3.1% 2.7% 2% 

(5.6% - 9.1%) (2.4% - 3.9%) (2.4% - 3.9%) (2.4% - 3.9%) (2.1% - 3.4%) (1.5% - 2.

6% 4.2% 4.2% 4% 3.3% 3% 

(4.5% - 7.4%) (3.2% - 5.3%) (3.2% - 5.3%) (3.1% - 5%) (2.5% - 4.1%) (2.3% - 3.

6.6% 5.8% 5.8% 5.1% 4.4% 4.3% 

(5.1% - 8.2%) (4.4% - 7.1%) (4.4% - 7.1%) (3.9% - 6.4%) (3.3% - 5.4%) (3.3% - 5.

4.4% 4.4% 4.4% 4.4% 4% 3.8% 

(3.3% - 5.4%) (3.3% - 5.4%) (3.3% - 5.4%) (3.3% - 5.4%) (3% - 4.9%) (2.9% - 4.

6.9% 4.3% 4.3% 4% 3.5% 3.1% 

(5.3% - 8.5%) (3.3% - 5.4%) (3.3% - 5.4%) (3.1% - 5%) (2.6% - 4.3%) (2.3% - 3.

3.5% 0.7% 0.7% 0.7% 0.7% 0% 

(2.7% - 4.4%) (0.5% - 0.8%) (0.5% - 0.8%) (0.5% - 0.8%) (0.5% - 0.8%) (0% - 0%

6.7% 5.6% 5% 4.3% 3.6% 4.2% 

(5.1% - 8.3%) (4.2% - 6.9%) (3.8% - 6.2%) (3.3% - 5.3%) (2.7% - 4.5%) (3.2% - 5.

3.3% 1.9% 1.9% 1.9% 1.9% 1% 

(2.5% - 4.1%) (1.4% - 2.3%) (1.4% - 2.3%) (1.4% - 2.3%) (1.4% - 2.3%) (0.7% - 1.

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-43

Table E-42. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Expos Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

8.2% 7.3% 6.5% 5.7% 4.9% 5.7% 

(6.3% - 10.1%) (5.5% - 9%) (4.9% - 8%) (4.3% - 7%) (3.7% - 6.1%) (4.3% - 7

7% 6.4% 5.8% 5.1% 4.3% 4.9% 

(5.3% - 8.6%) (4.9% - 7.9%) (4.4% - 7.2%) (3.8% - 6.3%) (3.3% - 5.3%) (3.7% - 6

8.5% 6% 5.3% 4.6% 3.9% 4.6% 

(6.5% - 10.5%) (4.5% - 7.4%) (4% - 6.5%) (3.5% - 5.7%) (2.9% - 4.8%) (3.5% - 5.

4.9% 4.9% 4.9% 4.9% 4.3% 4.9% 

(3.7% - 6%) (3.7% - 6%) (3.7% - 6%) (3.7% - 6%) (3.3% - 5.3%) (3.7% - 6

7% 4.9% 4.9% 4.2% 3.5% 3.6% 

(5.4% - 8.7%) (3.8% - 6.1%) (3.7% - 6%) (3.2% - 5.2%) (2.6% - 4.3%) (2.7% - 4.

9.9% 3.6% 3.6% 3.6% 3.6% 2.5% 

(7.6% - 12.1%) (2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (1.9% - 3.

6.4% 5.9% 5.2% 4.5% 3.8% 4.5% 

(4.9% - 7.9%) (4.5% - 7.2%) (3.9% - 6.4%) (3.4% - 5.5%) (2.9% - 4.7%) (3.4% - 5.

7.6% 3.3% 3.3% 3.3% 2.8% 2.2% 

(5.8% - 9.4%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (2.2% - 3.5%) (1.6% - 2.

6.9% 5.1% 5.1% 4.8% 4.1% 3.7% 

(5.3% - 8.5%) (3.8% - 6.3%) (3.8% - 6.3%) (3.7% - 6%) (3.1% - 5%) (2.8% - 4.

6.6% 5.7% 5.7% 5.1% 4.3% 4.3% 

(5% - 8.1%) (4.3% - 7.1%) (4.3% - 7.1%) (3.9% - 6.3%) (3.3% - 5.3%) (3.2% - 5.

3.6% 3.6% 3.6% 3.6% 3.2% 3.1% 

(2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (2.4% - 4%) (2.4% - 3.

7.8% 5.1% 5.1% 4.7% 4.1% 3.7% 

(6% - 9.6%) (3.8% - 6.3%) (3.8% - 6.3%) (3.6% - 5.9%) (3.1% - 5.1%) (2.8% - 4.

4.9% 1.6% 1.6% 1.6% 1.6% 0.7% 

(3.7% - 6.1%) (1.2% - 1.9%) (1.2% - 1.9%) (1.2% - 1.9%) (1.2% - 1.9%) (0.5% - 0.

7.3% 6.1% 5.5% 4.8% 4.1% 4.7% 

(5.6% - 9%) (4.7% - 7.6%) (4.2% - 6.8%) (3.6% - 5.9%) (3.1% - 5%) (3.5% - 5.

3.4% 2% 2% 2% 2% 1.1% 

(2.6% - 4.2%) (1.5% - 2.5%) (1.5% - 2.5%) (1.5% - 2.5%) (1.5% - 2.5%) (0.8% - 1.

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-44

Table E-43. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mo with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: E

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 21%) (32% - 32%) (21% - 

-9% 0% 8% 19% 30% 22% 

(-9% - -9%) (0% - 0%) (8% - 8%) (19% - 19%) (30% - 30%) (22% - 

-42% 0% 12% 23% 35% 23% 

(-41% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 

-37% 0% 1% 13% 25% 23% 

(-36% - -37%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (23% - 

-182% 0% 0% 0% 0% 34% 

(-180% - -184%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (34% - 

-9% 0% 12% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 

-120% 0% 0% 0% 12% 31% 

(-119% - -122%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 

-35% 0% 0% 4% 18% 25% 

(-34% - -35%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 

-14% 0% 0% 11% 23% 24% 

(-14% - -14%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 23%) (24% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-52% 0% 0% 6% 18% 25% 

(-51% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (25% - 

-252% 0% 0% 0% 0% 64% 

(-251% - -254%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 

-17% 0% 9% 20% 31% 22% 

(-17% - -18%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 31%) (22% - 

-53% 0% 0% 0% 0% 33% 

(-52% - -53%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortali

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-45

Table E-44. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mor with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: E

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

-12% 0% 10% 21% 32% 2

(-12% - -12%) (0% - 0%) (10% - 11%) (21% - 21%) (31% - 32%) (21% 

-9% 0% 9% 21% 32% 2

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (24% 

-43% 0% 12% 24% 36% 2

(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% 

0% 0% 0% 0% 13% 0

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% 

-43% 0% 1% 16% 30% 2

(-43% - -44%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 30%) (28% 

-173% 0% 0% 0% 0% 3

(-171% - -176%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% 

-9% 0% 12% 24% 36% 2

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% 

-133% 0% 0% 0% 13% 3

(-132% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (35% 

-40% 0% 0% 5% 21% 2

(-40% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% 

-15% 0% 0% 11% 24% 2

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% 

0% 0% 0% 0% 10% 1

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% 

-59% 0% 0% 7% 20% 2

(-59% - -60%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% 

-431% 0% 0% 0% 0% 10

(-428% - -433%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% 

-20% 0% 10% 23% 35% 2

(-20% - -20%) (0% - 0%) (10% - 11%) (23% - 23%) (35% - 35%) (25% 

-75% 0% 0% 0% 0% 4

(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mort

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-46

Table E-45. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mor with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Es

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 1

-13% 0% 11% 22% 33% 2

(-13% - -13%) (0% - 0%) (11% - 11%) (22% - 22%) (33% - 33%) (22% 

-10% 0% 9% 21% 33% 2

(-9% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% 

-43% 0% 12% 23% 35% 2

(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% 

0% 0% 0% 0% 12% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% 

-42% 0% 1% 15% 29% 2

(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (27% 

-171% 0% 0% 0% 0% 3

(-169% - -174%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% 

-9% 0% 12% 24% 36% 2

(-9% - -9%) (0% - 0%) (12% - 12%) (24% - 24%) (35% - 36%) (24% 

-132% 0% 0% 0% 13% 3

(-130% - -133%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% 

-37% 0% 0% 5% 19% 2

(-37% - -37%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (27% 

-15% 0% 0% 11% 24% 2

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% 

0% 0% 0% 0% 11% 1

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% 

-55% 0% 0% 6% 19% 2

(-54% - -55%) (0% - 0%) (0% - 0%) (6% - 7%) (19% - 19%) (27% 

-215% 0% 0% 0% 0% 5

(-214% - -216%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% 

-19% 0% 10% 22% 34% 2

(-19% - -19%) (0% - 0%) (10% - 10%) (22% - 22%) (34% - 34%) (24% 

-73% 0% 0% 0% 0% 4

(-73% - -73%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mort

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-47

Table E-46. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient 

 a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

722 643 577 509 441 50

(569 - 872) (506 - 778) (453 - 698) (399 - 617) (345 - 535) (399 - 

715 660 606 536 465 51

(563 - 863) (518 - 797) (476 - 733) (420 - 649) (364 - 564) (405 - 

516 366 324 282 240 28

(406 - 622) (287 - 443) (254 - 393) (221 - 343) (187 - 291) (221 - 

455 455 455 455 405 45

(357 - 552) (357 - 552) (357 - 552) (357 - 552) (317 - 492) (357 - 

1054 775 766 674 581 59

(830 - 1271) (608 - 939) (601 - 928) (528 - 817) (454 - 705) (466 - 

338 122 122 122 122 8

(266 - 408) (95 - 148) (95 - 148) (95 - 148) (95 - 148) (63 - 

707 649 574 500 424 50

(555 - 856) (508 - 786) (450 - 697) (391 - 607) (331 - 516) (391 - 

3328 1526 1526 1526 1344 10

(2618 - 4019) (1191 - 1856) (1191 - 1856) (1191 - 1856) (1048 - 1636) (822 - 

3117 2326 2326 2223 1907 17

(2450 - 3768) (1821 - 2820) (1821 - 2820) (1740 - 2697) (1491 - 2317) (1363 - 

621 545 545 488 420 41

(488 - 752) (427 - 660) (427 - 660) (382 - 592) (328 - 510) (325 - 

579 579 579 579 521 50

(453 - 704) (453 - 704) (453 - 704) (453 - 704) (407 - 634) (395 - 

747 495 495 466 409 37

(588 - 902) (388 - 601) (388 - 601) (364 - 565) (320 - 497) (291 - 

109 31 31 31 31 1

(85 - 132) (24 - 38) (24 - 38) (24 - 38) (24 - 38) (9 - 

1069 913 830 732 633 71

(842 - 1290) (718 - 1104) (651 - 1005) (574 - 888) (496 - 769) (559 - 

156 103 103 103 103 6

(122 - 190) (80 - 125) (80 - 125) (80 - 125) (80 - 125) (54 - 

 Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5 Concentratio

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards 

Denoted n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-48

Table E-47. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient P

 a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

744 662 594 524 454 52

(586 - 898) (521 - 801) (466 - 719) (411 - 635) (355 - 550) (411 - 

584 534 486 424 361 40

(459 - 707) (419 - 647) (381 - 590) (332 - 515) (282 - 439) (319 - 

486 341 301 260 219 26

(382 - 587) (267 - 414) (235 - 365) (203 - 316) (171 - 267) (203 - 

344 344 344 344 300 34

(268 - 417) (268 - 417) (268 - 417) (268 - 417) (234 - 365) (268 - 

770 537 530 453 375 38

(604 - 932) (420 - 652) (414 - 643) (354 - 551) (293 - 457) (303 - 

352 129 129 129 129 87 

(277 - 424) (100 - 157) (100 - 157) (100 - 157) (100 - 157) (67 - 

686 627 553 479 404 47

(537 - 831) (491 - 761) (433 - 672) (374 - 582) (315 - 491) (374 - 

2945 1263 1263 1263 1094 82

(2313 - 3562) (985 - 1538) (985 - 1538) (985 - 1538) (852 - 1333) (642 - 

2435 1739 1739 1649 1373 123

(1907 - 2951) (1358 - 2114) (1358 - 2114) (1288 - 2005) (1071 - 1671) (960 - 

564 491 491 437 373 36

(442 - 683) (385 - 596) (385 - 596) (342 - 531) (291 - 453) (288 - 

614 614 614 614 553 53

(480 - 746) (480 - 746) (480 - 746) (480 - 746) (432 - 673) (419 - 

595 373 373 348 299 26

(467 - 720) (292 - 454) (292 - 454) (271 - 423) (233 - 364) (208 - 

89 17 17 17 17 0 

(69 - 108) (13 - 21) (13 - 21) (13 - 21) (13 - 21) (0 - 

810 677 606 522 438 50

(636 - 981) (530 - 821) (474 - 735) (408 - 635) (342 - 533) (395 - 

106 61 61 61 61 32 

(83 - 129) (47 - 74) (47 - 74) (47 - 74) (47 - 74) (25 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5 Concentratio

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards 

Denoted n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-49

Table E-48. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient 

 a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based o

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

717 636 568 500 430 5

(563 - 865) (500 - 770) (446 - 689) (391 - 606) (337 - 523) (391 

583 533 485 423 361 4

(458 - 706) (418 - 646) (380 - 589) (331 - 514) (282 - 438) (318 

509 359 318 276 234 2

(401 - 615) (282 - 436) (249 - 386) (216 - 335) (182 - 284) (216 

383 383 383 383 337 3

(299 - 465) (299 - 465) (299 - 465) (299 - 465) (263 - 409) (299 

810 572 564 485 406 4

(636 - 980) (447 - 694) (441 - 685) (379 - 590) (317 - 494) (327 

370 138 138 138 138 

(292 - 446) (108 - 168) (108 - 168) (108 - 168) (108 - 168) (74 

715 655 579 501 424 5

(561 - 866) (513 - 794) (453 - 702) (392 - 609) (331 - 515) (392 

3056 1333 1333 1333 1160 8

(2401 - 3695) (1040 - 1623) (1040 - 1623) (1040 - 1623) (904 - 1413) (688 

2837 2080 2080 1982 1681 1

(2227 - 3434) (1627 - 2526) (1627 - 2526) (1550 - 2408) (1313 - 2044) (1191 

558 486 486 432 368 3

(437 - 675) (381 - 589) (381 - 589) (338 - 525) (288 - 447) (284 

522 522 522 522 464 4

(407 - 635) (407 - 635) (407 - 635) (407 - 635) (362 - 565) (350 

667 434 434 407 354 3

(524 - 806) (340 - 527) (340 - 527) (318 - 494) (276 - 430) (249 

127 41 41 41 41 

(99 - 154) (32 - 50) (32 - 50) (32 - 50) (32 - 50) (14 

887 746 671 584 495 5

(696 - 1072) (585 - 904) (526 - 814) (456 - 709) (386 - 602) (443 

111 65 65 65 65 

(87 - 135) (50 - 79) (50 - 79) (50 - 79) (50 - 79) (27 

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5 Concentrat

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standard

Denoted n/m)2

:

1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-50

Table E-49. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Expos Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

12.4% 11% 9.9% 8.7% 7.5% 8.7

(9.7% - 14.9%) (8.7% - 13.3%) (7.7% - 11.9%) (6.8% - 10.6%) (5.9% - 9.2%) (6.8% - 

12.2% 11.2% 10.3% 9.1% 7.9% 8.8

(9.6% - 14.7%) (8.8% - 13.5%) (8.1% - 12.4%) (7.1% - 11%) (6.2% - 9.6%) (6.9% - 

12.4% 8.8% 7.8% 6.8% 5.8% 6.8

(9.8% - 15%) (6.9% - 10.7%) (6.1% - 9.5%) (5.3% - 8.3%) (4.5% - 7%) (5.3% - 

8.6% 8.6% 8.6% 8.6% 7.7% 8.6

(6.7% - 10.4%) (6.7% - 10.4%) (6.7% - 10.4%) (6.7% - 10.4%) (6% - 9.3%) (6.7% - 

12.8% 9.4% 9.3% 8.2% 7.1% 7.3

(10.1% - 15.5%) (7.4% - 11.4%) (7.3% - 11.3%) (6.4% - 10%) (5.5% - 8.6%) (5.7% - 

13.1% 4.7% 4.7% 4.7% 4.7% 3.1

(10.3% - 15.8%) (3.7% - 5.7%) (3.7% - 5.7%) (3.7% - 5.7%) (3.7% - 5.7%) (2.4% - 

9.5% 8.7% 7.7% 6.7% 5.7% 6.7

(7.4% - 11.5%) (6.8% - 10.5%) (6% - 9.4%) (5.2% - 8.1%) (4.4% - 6.9%) (5.2% - 

11.8% 5.4% 5.4% 5.4% 4.8% 3.8

(9.3% - 14.3%) (4.2% - 6.6%) (4.2% - 6.6%) (4.2% - 6.6%) (3.7% - 5.8%) (2.9% - 

11% 8.2% 8.2% 7.8% 6.7% 6.1

(8.6% - 13.3%) (6.4% - 9.9%) (6.4% - 9.9%) (6.1% - 9.5%) (5.2% - 8.1%) (4.8% - 

10.4% 9.1% 9.1% 8.1% 7% 6.9

(8.1% - 12.5%) (7.1% - 11%) (7.1% - 11%) (6.4% - 9.9%) (5.5% - 8.5%) (5.4% - 

6.2% 6.2% 6.2% 6.2% 5.5% 5.4

(4.8% - 7.5%) (4.8% - 7.5%) (4.8% - 7.5%) (4.8% - 7.5%) (4.3% - 6.7%) (4.2% - 

12.3% 8.1% 8.1% 7.6% 6.7% 6.1

(9.6% - 14.8%) (6.4% - 9.9%) (6.4% - 9.9%) (6% - 9.3%) (5.2% - 8.2%) (4.8% - 

6.3% 1.8% 1.8% 1.8% 1.8% 0.6

(5% - 7.7%) (1.4% - 2.2%) (1.4% - 2.2%) (1.4% - 2.2%) (1.4% - 2.2%) (0.5% - 

12.6% 10.8% 9.8% 8.6% 7.5% 8.4

(9.9% - 15.2%) (8.4% - 13%) (7.7% - 11.8%) (6.8% - 10.5%) (5.8% - 9%) (6.6% - 

7.1% 4.7% 4.7% 4.7% 4.7% 3.1

(5.6% - 8.6%) (3.6% - 5.7%) (3.6% - 5.7%) (3.6% - 5.7%) (3.6% - 5.7%) (2.4% - 

 Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to P

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily 

Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-51

Table E-50. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Expos Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

12.4% 11% 9.9% 8.7% 7.5% 8.7

(9.7% - 14.9%) (8.6% - 13.3%) (7.7% - 11.9%) (6.8% - 10.5%) (5.9% - 9.1%) (6.8% - 

9.9% 9.1% 8.2% 7.2% 6.1% 6.9

(7.8% - 12%) (7.1% - 11%) (6.5% - 10%) (5.6% - 8.7%) (4.8% - 7.4%) (5.4% - 

11.6% 8.1% 7.2% 6.2% 5.2% 6.2

(9.1% - 14%) (6.4% - 9.8%) (5.6% - 8.7%) (4.8% - 7.5%) (4.1% - 6.4%) (4.8% - 

6.4% 6.4% 6.4% 6.4% 5.5% 6.4

(5% - 7.7%) (5% - 7.7%) (5% - 7.7%) (5% - 7.7%) (4.3% - 6.7%) (5% - 7

9.4% 6.5% 6.5% 5.5% 4.6% 4.7

(7.4% - 11.4%) (5.1% - 8%) (5% - 7.8%) (4.3% - 6.7%) (3.6% - 5.6%) (3.7% - 

13.4% 4.9% 4.9% 4.9% 4.9% 3.3

(10.6% - 16.2%) (3.8% - 6%) (3.8% - 6%) (3.8% - 6%) (3.8% - 6%) (2.6% 

8.9% 8.1% 7.2% 6.2% 5.2% 6.2

(7% - 10.8%) (6.4% - 9.9%) (5.6% - 8.7%) (4.9% - 7.6%) (4.1% - 6.4%) (4.9% - 

10.4% 4.5% 4.5% 4.5% 3.9% 2.9

(8.2% - 12.6%) (3.5% - 5.4%) (3.5% - 5.4%) (3.5% - 5.4%) (3% - 4.7%) (2.3% - 

8.5% 6.1% 6.1% 5.7% 4.8% 4.3

(6.6% - 10.3%) (4.7% - 7.4%) (4.7% - 7.4%) (4.5% - 7%) (3.7% - 5.8%) (3.3% - 

9.4% 8.2% 8.2% 7.3% 6.2% 6.2

(7.4% - 11.4%) (6.4% - 10%) (6.4% - 10%) (5.7% - 8.9%) (4.9% - 7.6%) (4.8% - 

6.3% 6.3% 6.3% 6.3% 5.7% 5.5

(4.9% - 7.6%) (4.9% - 7.6%) (4.9% - 7.6%) (4.9% - 7.6%) (4.4% - 6.9%) (4.3% - 

9.8% 6.2% 6.2% 5.7% 4.9% 4.4

(7.7% - 11.9%) (4.8% - 7.5%) (4.8% - 7.5%) (4.5% - 7%) (3.8% - 6%) (3.4% - 

5% 0.9% 0.9% 0.9% 0.9% 0% 

(3.9% - 6.1%) (0.7% - 1.2%) (0.7% - 1.2%) (0.7% - 1.2%) (0.7% - 1.2%) (0% - 

9.5% 7.9% 7.1% 6.1% 5.1% 5.9

(7.4% - 11.5%) (6.2% - 9.6%) (5.5% - 8.6%) (4.8% - 7.4%) (4% - 6.2%) (4.6% - 

4.7% 2.7% 2.7% 2.7% 2.7% 1.4

(3.7% - 5.8%) (2.1% - 3.3%) (2.1% - 3.3%) (2.1% - 3.3%) (2.1% - 3.3%) (1.1% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to P

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily 

Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-52

Table E-51. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Expos Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

11.6% 10.3% 9.2% 8.1% 7% 8.1% 

(9.1% - 14%) (8.1% - 12.5%) (7.2% - 11.1%) (6.3% - 9.8%) (5.4% - 8.5%) (6.3% - 9.

9.9% 9.1% 8.2% 7.2% 6.1% 6.9% 

(7.8% - 12%) (7.1% - 11%) (6.5% - 10%) (5.6% - 8.7%) (4.8% - 7.5%) (5.4% - 8.

12% 8.5% 7.5% 6.5% 5.5% 6.5% 

(9.5% - 14.5%) (6.6% - 10.3%) (5.9% - 9.1%) (5.1% - 7.9%) (4.3% - 6.7%) (5.1% - 7.

7% 7% 7% 7% 6.1% 7% 

(5.4% - 8.4%) (5.4% - 8.4%) (5.4% - 8.4%) (5.4% - 8.4%) (4.8% - 7.4%) (5.4% - 8.

9.9% 7% 6.9% 6% 5% 5.1% 

(7.8% - 12%) (5.5% - 8.5%) (5.4% - 8.4%) (4.7% - 7.2%) (3.9% - 6.1%) (4% - 6.3

13.9% 5.2% 5.2% 5.2% 5.2% 3.5% 

(11% - 16.7%) (4.1% - 6.3%) (4.1% - 6.3%) (4.1% - 6.3%) (4.1% - 6.3%) (2.8% - 4.

9.1% 8.3% 7.4% 6.4% 5.4% 6.4% 

(7.1% - 11%) (6.5% - 10.1%) (5.8% - 8.9%) (5% - 7.8%) (4.2% - 6.6%) (5% - 7.8

10.7% 4.7% 4.7% 4.7% 4.1% 3.1% 

(8.4% - 13%) (3.7% - 5.7%) (3.7% - 5.7%) (3.7% - 5.7%) (3.2% - 5%) (2.4% - 3.

9.8% 7.2% 7.2% 6.9% 5.8% 5.3% 

(7.7% - 11.9%) (5.6% - 8.7%) (5.6% - 8.7%) (5.4% - 8.3%) (4.5% - 7.1%) (4.1% - 6.

9.3% 8.1% 8.1% 7.2% 6.2% 6.1% 

(7.3% - 11.3%) (6.4% - 9.8%) (6.4% - 9.8%) (5.6% - 8.8%) (4.8% - 7.5%) (4.8% - 7.

5.2% 5.2% 5.2% 5.2% 4.6% 4.4% 

(4% - 6.3%) (4% - 6.3%) (4% - 6.3%) (4% - 6.3%) (3.6% - 5.6%) (3.5% - 5.

11.1% 7.2% 7.2% 6.7% 5.9% 5.3% 

(8.7% - 13.4%) (5.6% - 8.7%) (5.6% - 8.7%) (5.3% - 8.2%) (4.6% - 7.1%) (4.1% - 6.

7% 2.2% 2.2% 2.2% 2.2% 1% 

(5.4% - 8.5%) (1.7% - 2.7%) (1.7% - 2.7%) (1.7% - 2.7%) (1.7% - 2.7%) (0.8% - 1.

10.4% 8.7% 7.9% 6.8% 5.8% 6.6% 

(8.1% - 12.5%) (6.8% - 10.6%) (6.1% - 9.5%) (5.3% - 8.3%) (4.5% - 7%) (5.2% - 8.

4.9% 2.8% 2.8% 2.8% 2.8% 1.5% 

(3.8% - 5.9%) (2.2% - 3.5%) (2.2% - 3.5%) (2.2% - 3.5%) (2.2% - 3.5%) (1.2% - 1.

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-53

Table E-52. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mo Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: Estima

 (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 

-12% 0% 10% 21% 32% 

(-12% - -12%) (0% - 0%) (10% - 10%) (21% - 21%) (31% - 32%) (21

-8% 0% 8% 19% 29% 

(-8% - -9%) (0% - 0%) (8% - 8%) (19% - 19%) (29% - 30%) (21

-41% 0% 11% 23% 34% 

(-40% - -42%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (23

0% 0% 0% 0% 11% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0

-36% 0% 1% 13% 25% 

(-35% - -37%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 25%) (23

-178% 0% 0% 0% 0% 

(-175% - -181%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33

-9% 0% 11% 23% 35% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23

-118% 0% 0% 0% 12% 

(-116% - -120%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31

-34% 0% 0% 4% 18% 

(-34% - -34%) (0% - 0%) (0% - 0%) (4% - 4%) (18% - 18%) (25

-14% 0% 0% 10% 23% 

(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 11%) (23% - 23%) (24

0% 0% 0% 0% 10% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13

-51% 0% 0% 6% 17% 

(-50% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (25

-250% 0% 0% 0% 0% 

(-248% - -251%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64

-17% 0% 9% 20% 31% 

(-17% - -17%) (0% - 0%) (9% - 9%) (20% - 20%) (30% - 31%) (22

-52% 0% 0% 0% 0% 

(-52% - -52%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mo

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-54

Table E-53. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mor Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: Estima

 (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

-12% 0% 10% 21% 32% 2

(-12% - -12%) (0% - 0%) (10% - 11%) (21% - 21%) (31% - 32%) (21% 

-9% 0% 9% 21% 32% 2

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (24% 

-43% 0% 12% 24% 36% 2

(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% 

0% 0% 0% 0% 13% 0

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% 

-43% 0% 1% 16% 30% 2

(-43% - -44%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 30%) (28% 

-173% 0% 0% 0% 0% 3

(-171% - -176%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% 

-9% 0% 12% 24% 36% 2

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% 

-133% 0% 0% 0% 13% 3

(-132% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (35% 

-40% 0% 0% 5% 21% 2

(-40% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% 

-15% 0% 0% 11% 24% 2

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% 

0% 0% 0% 0% 10% 1

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% 

-59% 0% 0% 7% 20% 2

(-59% - -60%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% 

-431% 0% 0% 0% 0% 10

(-428% - -433%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% 

-20% 0% 10% 23% 35% 2

(-20% - -20%) (0% - 0%) (10% - 11%) (23% - 23%) (35% - 35%) (25% 

-75% 0% 0% 0% 0% 4

(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mort

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-55

Table E-54. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mor Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Estima

 (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

-13% 0% 11% 21% 32% 2

(-12% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% 

-9% 0% 9% 21% 32% 2

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (24% 

-42% 0% 12% 23% 35% 2

(-41% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% 

0% 0% 0% 0% 12% 0

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% 

-42% 0% 1% 15% 29% 2

(-41% - -42%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (27% 

-168% 0% 0% 0% 0% 3

(-165% - -170%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% 

-9% 0% 12% 23% 35% 2

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% 

-129% 0% 0% 0% 13% 3

(-128% - -131%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% 

-36% 0% 0% 5% 19% 2

(-36% - -37%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (26% 

-15% 0% 0% 11% 24% 2

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% 

0% 0% 0% 0% 11% 1

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% 

-54% 0% 0% 6% 19% 2

(-53% - -54%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 19%) (26% 

-213% 0% 0% 0% 0% 5

(-211% - -215%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% 

-19% 0% 10% 22% 34% 2

(-19% - -19%) (0% - 0%) (10% - 10%) (22% - 22%) (33% - 34%) (24% 

-72% 0% 0% 0% 0% 4

(-72% - -73%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mort

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-56

Table E-55. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5  Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

77 68 61 54 46 54 

(29 - 122) (26 - 108) (23 - 97) (20 - 86) (17 - 74) (20 - 

81 74 68 60 52 58 

(31 - 128) (28 - 118) (26 - 109) (23 - 96) (20 - 84) (22 - 

55 39 34 30 25 30 

(21 - 87) (15 - 62) (13 - 55) (11 - 48) (10 - 41) (11 - 

50 50 50 50 44 50 

(19 - 79) (19 - 79) (19 - 79) (19 - 79) (17 - 71) (19 - 

112 82 81 71 61 63 

(43 - 178) (31 - 131) (31 - 129) (27 - 114) (23 - 98) (24 - 

2699996

(10 - 41) (3 - 15) (3 - 15) (3 - 15) (3 - 15) (2 - 

76 70 62 54 46 54 

(29 - 122) (26 - 112) (23 - 99) (20 - 86) (17 - 73) (20 - 

248 112 112 112 99 78 

(94 - 393) (42 - 181) (42 - 181) (42 - 181) (37 - 160) (29 - 

208 155 155 148 126 11

(79 - 331) (58 - 247) (58 - 247) (56 - 236) (48 - 203) (43 - 

70 61 61 55 47 46 

(26 - 111) (23 - 98) (23 - 98) (21 - 87) (18 - 75) (17 - 

58 58 58 58 53 51 

(22 - 94) (22 - 94) (22 - 94) (22 - 94) (20 - 85) (19 - 

80 53 53 50 44 40 

(31 - 127) (20 - 84) (20 - 84) (19 - 79) (16 - 70) (15 - 

8 22221

(3 - 13) (1 - 4) (1 - 4) (1 - 4) (1 - 4) (0 - 

116 99 90 79 68 77 

(44 - 184) (37 - 157) (34 - 143) (30 - 126) (26 - 109) (29 - 

19 12 12 12 12 8 

(7 - 30) (5 - 20) (5 - 20) (5 - 20) (5 - 20) (3 - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-57

Table E-56. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2. Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

79 70 63 55 48 5

(30 - 125) (27 - 112) (24 - 100) (21 - 88) (18 - 77) (21 

66 60 55 48 40 4

(25 - 105) (23 - 96) (21 - 87) (18 - 76) (15 - 65) (17 

52 36 32 28 23 2

(20 - 82) (14 - 58) (12 - 51) (10 - 44) (9 - 37) (10 

37 37 37 37 33 3

(14 - 60) (14 - 60) (14 - 60) (14 - 60) (12 - 52) (14 

82 57 56 48 39 4

(31 - 130) (21 - 91) (21 - 90) (18 - 77) (15 - 64) (15 

27 10 10 10 10 

(10 - 43) (4 - 16) (4 - 16) (4 - 16) (4 - 16) (2 

74 68 60 52 43 5

(28 - 118) (26 - 108) (22 - 96) (19 - 83) (16 - 70) (19 

219 93 93 93 80 6

(83 - 348) (35 - 150) (35 - 150) (35 - 150) (30 - 130) (23 

162 115 115 109 91 8

(61 - 259) (43 - 185) (43 - 185) (41 - 176) (34 - 146) (30 

63 55 55 49 42 4

(24 - 101) (21 - 88) (21 - 88) (18 - 78) (16 - 67) (15 

62 62 62 62 56 5

(23 - 100) (23 - 100) (23 - 100) (23 - 100) (21 - 90) (20 

64 40 40 37 32 2

(24 - 101) (15 - 64) (15 - 64) (14 - 59) (12 - 51) (11 

6 1111

(2 - 10) (0 - 2) (0 - 2) (0 - 2) (0 - 2) (0 

87 73 65 56 47 5

(33 - 139) (27 - 116) (24 - 104) (21 - 90) (18 - 75) (20 

137777

(5 - 20) (3 - 12) (3 - 12) (3 - 12) (3 - 12) (1 

 Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Concentrations i

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Stand

n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-58

Table E-57. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5  Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

76 67 60 53 45 53 

(29 - 121) (26 - 107) (23 - 96) (20 - 84) (17 - 73) (20 - 

66 60 55 47 40 46 

(25 - 105) (23 - 96) (21 - 87) (18 - 76) (15 - 65) (17 - 

54 38 34 29 25 29 

(21 - 86) (14 - 61) (13 - 54) (11 - 47) (9 - 40) (11 - 

42 42 42 42 37 42 

(16 - 67) (16 - 67) (16 - 67) (16 - 67) (14 - 59) (16 - 

86 60 59 51 43 44 

(33 - 137) (23 - 97) (22 - 95) (19 - 82) (16 - 69) (16 - 

29 11 11 11 11 7 

(11 - 45) (4 - 17) (4 - 17) (4 - 17) (4 - 17) (3 - 

77 71 62 54 46 54 

(29 - 123) (27 - 113) (23 - 100) (20 - 87) (17 - 73) (20 - 

227 98 98 98 85 65 

(86 - 361) (37 - 158) (37 - 158) (37 - 158) (32 - 138) (24 - 

189 138 138 131 111 10

(72 - 301) (52 - 221) (52 - 221) (49 - 211) (42 - 179) (38 - 

63 54 54 48 41 41 

(24 - 100) (21 - 87) (21 - 87) (18 - 77) (15 - 66) (15 - 

53 53 53 53 47 45 

(20 - 85) (20 - 85) (20 - 85) (20 - 85) (17 - 75) (17 - 

71 46 46 43 38 34 

(27 - 114) (17 - 74) (17 - 74) (16 - 69) (14 - 60) (13 - 

9 33331

(3 - 15) (1 - 5) (1 - 5) (1 - 5) (1 - 5) (0 - 

96 80 72 63 53 61 

(36 - 152) (30 - 128) (27 - 116) (24 - 100) (20 - 85) (23 - 

1388884

(5 - 21) (3 - 12) (3 - 12) (3 - 12) (3 - 12) (2 - 

 Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2:

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-59

Table E-58. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to  PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternat

 Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 f

 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

8.6% 7.6% 6.8% 6% 5.2% 6% 

(3.3% - 13.6%) (2.9% - 12.1%) (2.6% - 10.9%) (2.3% - 9.6%) (2% - 8.3%) (2.3% - 9.6

8.4% 7.8% 7.1% 6.3% 5.5% 6.1% 

(3.2% - 13.4%) (3% - 12.3%) (2.7% - 11.3%) (2.4% - 10%) (2.1% - 8.7%) (2.3% - 9.7

8.6% 6.1% 5.4% 4.7% 4% 4.7% 

(3.3% - 13.7%) (2.3% - 9.7%) (2% - 8.6%) (1.8% - 7.5%) (1.5% - 6.4%) (1.8% - 7.5

5.9% 5.9% 5.9% 5.9% 5.3% 5.9% 

(2.2% - 9.5%) (2.2% - 9.5%) (2.2% - 9.5%) (2.2% - 9.5%) (2% - 8.5%) (2.2% - 9.5

8.9% 6.5% 6.5% 5.7% 4.9% 5% 

(3.4% - 14.1%) (2.5% - 10.4%) (2.4% - 10.3%) (2.1% - 9.1%) (1.8% - 7.8%) (1.9% - 8

9.1% 3.2% 3.2% 3.2% 3.2% 2.1% 

(3.5% - 14.4%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (0.8% - 3.5

6.6% 6% 5.3% 4.6% 3.9% 4.6% 

(2.5% - 10.5%) (2.3% - 9.6%) (2% - 8.5%) (1.7% - 7.4%) (1.5% - 6.3%) (1.7% - 7.4

8.2% 3.7% 3.7% 3.7% 3.3% 2.6% 

(3.1% - 13%) (1.4% - 6%) (1.4% - 6%) (1.4% - 6%) (1.2% - 5.3%) (1% - 4.2

7.6% 5.6% 5.6% 5.4% 4.6% 4.2% 

(2.9% - 12.1%) (2.1% - 9%) (2.1% - 9%) (2% - 8.6%) (1.7% - 7.4%) (1.6% - 6.8

7.2% 6.3% 6.3% 5.6% 4.8% 4.8% 

(2.7% - 11.4%) (2.4% - 10%) (2.4% - 10%) (2.1% - 9%) (1.8% - 7.7%) (1.8% - 7.7

4.2% 4.2% 4.2% 4.2% 3.8% 3.7% 

(1.6% - 6.8%) (1.6% - 6.8%) (1.6% - 6.8%) (1.6% - 6.8%) (1.4% - 6.1%) (1.4% - 5.9

8.5% 5.6% 5.6% 5.3% 4.6% 4.2% 

(3.2% - 13.5%) (2.1% - 9%) (2.1% - 9%) (2% - 8.4%) (1.7% - 7.4%) (1.6% - 6.7

4.4% 1.2% 1.2% 1.2% 1.2% 0.4% 

(1.6% - 7%) (0.5% - 2%) (0.5% - 2%) (0.5% - 2%) (0.5% - 2%) (0.2% - 0.7

8.8% 7.5% 6.8% 6% 5.1% 5.8% 

(3.3% - 13.9%) (2.8% - 11.9%) (2.6% - 10.8%) (2.3% - 9.5%) (1.9% - 8.2%) (2.2% - 9.3

4.9% 3.2% 3.2% 3.2% 3.2% 2.1% 

(1.8% - 7.8%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (0.8% - 3.5

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Conc

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM co1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-60

Table E-59. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure  to Ambient PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current 

 and Alternative Standards, Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (20

 Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

8.6% 7.6% 6.8% 6% 5.2% 6% 

(3.3% - 13.6%) (2.9% - 12.1%) (2.6% - 10.9%) (2.3% - 9.6%) (2% - 8.3%) (2.3% - 9.6

6.9% 6.3% 5.7% 5% 4.2% 4.8% 

(2.6% - 10.9%) (2.4% - 10%) (2.1% - 9.1%) (1.9% - 7.9%) (1.6% - 6.8%) (1.8% - 7.6

8% 5.6% 4.9% 4.3% 3.6% 4.3% 

(3.1% - 12.7%) (2.1% - 9%) (1.9% - 7.9%) (1.6% - 6.8%) (1.3% - 5.8%) (1.6% - 6.8

4.4% 4.4% 4.4% 4.4% 3.8% 4.4% 

(1.6% - 7%) (1.6% - 7%) (1.6% - 7%) (1.6% - 7%) (1.4% - 6.1%) (1.6% - 7

6.5% 4.5% 4.4% 3.8% 3.1% 3.2% 

(2.5% - 10.4%) (1.7% - 7.2%) (1.7% - 7.1%) (1.4% - 6.1%) (1.2% - 5.1%) (1.2% - 5.2

9.3% 3.4% 3.4% 3.4% 3.4% 2.3% 

(3.6% - 14.8%) (1.3% - 5.4%) (1.3% - 5.4%) (1.3% - 5.4%) (1.3% - 5.4%) (0.8% - 3.7

6.1% 5.6% 4.9% 4.3% 3.6% 4.3% 

(2.3% - 9.8%) (2.1% - 9%) (1.9% - 7.9%) (1.6% - 6.9%) (1.3% - 5.8%) (1.6% - 6.9

7.2% 3.1% 3.1% 3.1% 2.6% 2% 

(2.7% - 11.5%) (1.1% - 4.9%) (1.1% - 4.9%) (1.1% - 4.9%) (1% - 4.3%) (0.7% - 3.2

5.9% 4.2% 4.2% 3.9% 3.3% 2.9% 

(2.2% - 9.4%) (1.6% - 6.7%) (1.6% - 6.7%) (1.5% - 6.4%) (1.2% - 5.3%) (1.1% - 4.7

6.5% 5.7% 5.7% 5% 4.3% 4.2% 

(2.5% - 10.4%) (2.1% - 9.1%) (2.1% - 9.1%) (1.9% - 8.1%) (1.6% - 6.9%) (1.6% - 6.8

4.3% 4.3% 4.3% 4.3% 3.9% 3.8% 

(1.6% - 6.9%) (1.6% - 6.9%) (1.6% - 6.9%) (1.6% - 6.9%) (1.5% - 6.3%) (1.4% - 6.

6.8% 4.2% 4.2% 3.9% 3.4% 3% 

(2.6% - 10.8%) (1.6% - 6.8%) (1.6% - 6.8%) (1.5% - 6.3%) (1.3% - 5.5%) (1.1% - 4.9

3.4% 0.6% 0.6% 0.6% 0.6% 0% 

(1.3% - 5.6%) (0.2% - 1%) (0.2% - 1%) (0.2% - 1%) (0.2% - 1%) (0% - 0%

6.6% 5.5% 4.9% 4.2% 3.5% 4.1% 

(2.5% - 10.5%) (2.1% - 8.8%) (1.8% - 7.8%) (1.6% - 6.8%) (1.3% - 5.7%) (1.5% - 6.6

3.2% 1.9% 1.9% 1.9% 1.9% 1% 

(1.2% - 5.2%) (0.7% - 3%) (0.7% - 3%) (0.7% - 3%) (0.7% - 3%) (0.4% - 1.6

Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Conc

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM co1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-61

Table E-60. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure t PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alterna

 Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 f

 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

8% 7.1% 6.4% 5.6% 4.8% 5.6% 

(3.1% - 12.8%) (2.7% - 11.4%) (2.4% - 10.1%) (2.1% - 8.9%) (1.8% - 7.7%) (2.1% - 8.9

6.9% 6.3% 5.7% 5% 4.2% 4.8% 

(2.6% - 10.9%) (2.4% - 10%) (2.1% - 9.1%) (1.9% - 7.9%) (1.6% - 6.8%) (1.8% - 7.6

8.3% 5.9% 5.2% 4.5% 3.8% 4.5% 

(3.2% - 13.2%) (2.2% - 9.4%) (1.9% - 8.3%) (1.7% - 7.2%) (1.4% - 6.1%) (1.7% - 7.2

4.8% 4.8% 4.8% 4.8% 4.2% 4.8% 

(1.8% - 7.7%) (1.8% - 7.7%) (1.8% - 7.7%) (1.8% - 7.7%) (1.6% - 6.8%) (1.8% - 7.7

6.9% 4.8% 4.8% 4.1% 3.4% 3.5% 

(2.6% - 11%) (1.8% - 7.8%) (1.8% - 7.7%) (1.5% - 6.6%) (1.3% - 5.5%) (1.3% - 5.7

9.7% 3.6% 3.6% 3.6% 3.6% 2.4% 

(3.7% - 15.3%) (1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (0.9% - 3.9

6.3% 5.7% 5.1% 4.4% 3.7% 4.4% 

(2.4% - 10%) (2.2% - 9.2%) (1.9% - 8.1%) (1.6% - 7%) (1.4% - 6%) (1.6% - 7

7.4% 3.2% 3.2% 3.2% 2.8% 2.1% 

(2.8% - 11.8%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (1% - 4.5%) (0.8% - 3.4

6.8% 5% 5% 4.7% 4% 3.6% 

(2.6% - 10.8%) (1.9% - 7.9%) (1.9% - 7.9%) (1.8% - 7.6%) (1.5% - 6.4%) (1.4% - 5.8

6.4% 5.6% 5.6% 5% 4.2% 4.2% 

(2.4% - 10.3%) (2.1% - 9%) (2.1% - 9%) (1.9% - 8%) (1.6% - 6.8%) (1.6% - 6.7

3.6% 3.6% 3.6% 3.6% 3.2% 3.1% 

(1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (1.2% - 5.1%) (1.1% - 4.9

7.7% 5% 5% 4.6% 4% 3.6% 

(2.9% - 12.2%) (1.9% - 8%) (1.9% - 8%) (1.7% - 7.4%) (1.5% - 6.5%) (1.4% - 5.9

4.8% 1.5% 1.5% 1.5% 1.5% 0.7% 

(1.8% - 7.7%) (0.6% - 2.5%) (0.6% - 2.5%) (0.6% - 2.5%) (0.6% - 2.5%) (0.3% - 1.

7.2% 6% 5.4% 4.7% 4% 4.6% 

(2.7% - 11.4%) (2.3% - 9.6%) (2% - 8.7%) (1.8% - 7.5%) (1.5% - 6.4%) (1.7% - 7.3

3.3% 1.9% 1.9% 1.9% 1.9% 1% 

(1.3% - 5.4%) (0.7% - 3.1%) (0.7% - 3.1%) (0.7% - 3.1%) (0.7% - 3.1%) (0.4% - 1.7

Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Conc

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM co1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-62

Table E-61. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Assoc Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 1435 13/35 12/35 13/3

-13% 0% 11% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 22%) (31% - 32%) (21% - 

-9% 0% 8% 19% 30% 22% 

(-8% - -9%) (0% - 0%) (8% - 9%) (19% - 19%) (29% - 30%) (21% - 

-42% 0% 12% 23% 35% 23% 

(-41% - -43%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 

-37% 0% 1% 13% 25% 23% 

(-36% - -38%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (23% - 

-182% 0% 0% 0% 0% 34% 

(-177% - -188%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

-9% 0% 12% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 24%) (35% - 35%) (23% - 

-121% 0% 0% 0% 12% 31% 

(-117% - -124%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 

-35% 0% 0% 4% 18% 25% 

(-34% - -36%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 

-14% 0% 0% 11% 23% 24% 

(-14% - -15%) (0% - 0%) (0% - 0%) (10% - 11%) (23% - 24%) (24% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-52% 0% 0% 6% 18% 25% 

(-50% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (25% - 

-252% 0% 0% 0% 0% 64% 

(-249% - -256%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 

-17% 0% 9% 20% 31% 22% 

(-17% - -18%) (0% - 0%) (9% - 9%) (20% - 21%) (31% - 32%) (22% - 

-53% 0% 0% 0% 0% 33% 

(-52% - -54%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated 

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Al

Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-63

Table E-62. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Assoc Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 1435 13/35 12/35 13/3

-13% 0% 11% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 22%) (31% - 32%) (21% - 

-10% 0% 9% 21% 33% 24% 

(-9% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (32% - 33%) (24% - 

-43% 0% 12% 24% 36% 24% 

(-42% - -45%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 

0% 0% 0% 0% 13% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 

-44% 0% 1% 16% 30% 28% 

(-43% - -45%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 31%) (28% - 

-177% 0% 0% 0% 0% 33% 

(-172% - -183%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

-9% 0% 12% 24% 36% 24% 

(-9% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 

-136% 0% 0% 0% 13% 35% 

(-132% - -139%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 14%) (35% - 

-41% 0% 0% 5% 21% 29% 

(-40% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 

-15% 0% 0% 11% 24% 25% 

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-60% 0% 0% 7% 20% 29% 

(-59% - -62%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 

-434% 0% 0% 0% 0% 100

(-430% - -439%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 

-20% 0% 11% 23% 36% 25% 

(-20% - -21%) (0% - 0%) (10% - 11%) (23% - 23%) (35% - 36%) (25% - 

-75% 0% 0% 0% 0% 48% 

(-75% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (48% - 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated 

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Al

Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-64

Table E-63. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Assoc Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 1435 13/35 12/35 13/3

-13% 0% 11% 22% 33% 22% 

(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 

-10% 0% 9% 21% 33% 24% 

(-9% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (32% - 33%) (24% - 

-43% 0% 12% 23% 35% 23% 

(-41% - -44%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 

-42% 0% 1% 15% 29% 27% 

(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 30%) (27% - 

-172% 0% 0% 0% 0% 32% 

(-166% - -177%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 

-9% 0% 12% 24% 36% 24% 

(-9% - -10%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 

-132% 0% 0% 0% 13% 34% 

(-128% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 

-37% 0% 0% 5% 19% 27% 

(-36% - -38%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 20%) (27% - 

-15% 0% 0% 11% 24% 25% 

(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 

0% 0% 0% 0% 11% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 

-55% 0% 0% 6% 19% 27% 

(-53% - -56%) (0% - 0%) (0% - 0%) (6% - 7%) (19% - 19%) (26% - 

-215% 0% 0% 0% 0% 55% 

(-212% - -219%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 

-19% 0% 10% 22% 34% 24% 

(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 22%) (34% - 34%) (24% - 

-73% 0% 0% 0% 0% 46% 

(-72% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated 

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Al

Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-65

Table E-64. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.

 Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 1

110 98 88 78 67 

(49 - 167) (44 - 149) (39 - 134) (34 - 119) (30 - 103) (34 

116 107 99 87 76 

(52 - 176) (48 - 163) (44 - 150) (38 - 133) (33 - 116) (37 

79 56 50 43 37 

(35 - 120) (25 - 86) (22 - 77) (19 - 67) (16 - 57) (19 

72 72 72 72 64 

(32 - 110) (32 - 110) (32 - 110) (32 - 110) (28 - 98) (32 

161 119 117 103 89 

(72 - 244) (52 - 181) (52 - 179) (45 - 158) (39 - 137) (40 

38 14 14 14 14 

(17 - 57) (6 - 21) (6 - 21) (6 - 21) (6 - 21) (4 

111 101 90 78 66 

(49 - 169) (45 - 155) (39 - 138) (34 - 120) (29 - 102) (34 

357 164 164 164 144 1

(159 - 541) (71 - 253) (71 - 253) (71 - 253) (63 - 223) (49 

300 224 224 214 184 1

(133 - 457) (99 - 343) (99 - 343) (94 - 329) (80 - 283) (73 

101 88 88 79 68 

(45 - 154) (39 - 135) (39 - 135) (35 - 121) (30 - 105) (30 

85 85 85 85 76 

(37 - 131) (37 - 131) (37 - 131) (37 - 131) (33 - 118) (32 

115 76 76 72 63 

(51 - 175) (34 - 117) (34 - 117) (32 - 110) (28 - 97) (25 

113333

(5 - 18) (1 - 5) (1 - 5) (1 - 5) (1 - 5) (1 

167 142 129 114 99 1

(74 - 252) (63 - 217) (57 - 197) (50 - 175) (43 - 152) (49 

27 18 18 18 18 

(12 - 41) (8 - 27) (8 - 27) (8 - 27) (8 - 27) (5 

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Concentrations 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Stan

n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-66

Table E-65. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5 

 Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on 

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

114 101 91 80 69 8

(51 - 172) (45 - 154) (40 - 138) (35 - 123) (30 - 106) (35 

95 87 79 69 59 6

(42 - 145) (38 - 133) (35 - 121) (30 - 106) (26 - 91) (29 

75 52 46 40 34 4

(33 - 113) (23 - 80) (20 - 71) (18 - 62) (15 - 52) (18 

54 54 54 54 47 5

(24 - 84) (24 - 84) (24 - 84) (24 - 84) (21 - 73) (24 

118 82 81 69 58 5

(52 - 180) (36 - 127) (35 - 125) (30 - 107) (25 - 89) (26 

39 14 14 14 14 

(18 - 59) (6 - 22) (6 - 22) (6 - 22) (6 - 22) (4 

107 98 87 75 63 7

(47 - 164) (43 - 150) (38 - 133) (33 - 116) (28 - 98) (33 

316 135 135 135 117 8

(140 - 481) (59 - 210) (59 - 210) (59 - 210) (51 - 182) (38 

234 167 167 159 132 1

(103 - 359) (73 - 258) (73 - 258) (69 - 245) (58 - 205) (52 

91 80 80 71 61 6

(40 - 140) (35 - 122) (35 - 122) (31 - 109) (26 - 93) (26 

90 90 90 90 81 7

(39 - 139) (39 - 139) (39 - 139) (39 - 139) (35 - 125) (34 

92 58 58 54 46 4

(41 - 140) (25 - 89) (25 - 89) (23 - 83) (20 - 71) (18 

9 2222

(4 - 14) (1 - 3) (1 - 3) (1 - 3) (1 - 3) (0 

126 105 94 81 68 7

(56 - 193) (46 - 162) (41 - 145) (36 - 126) (30 - 106) (34 

18 10 10 10 10 

(8 - 28) (5 - 16) (5 - 16) (5 - 16) (5 - 16) (2 

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin

 Baltimore, MD Detroit, MI Houston, TX St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

 Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Concentrations i

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Stand

n/m)2:
E-67

Table E-66. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5 

 Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on A

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

109 97 87 76 66 76 

(49 - 166) (43 - 148) (38 - 133) (34 - 117) (29 - 101) (34 - 

95 87 79 69 59 66 

(42 - 145) (38 - 133) (35 - 121) (30 - 106) (26 - 91) (29 - 

78 55 49 42 36 42 

(35 - 119) (24 - 85) (21 - 75) (19 - 65) (16 - 56) (19 - 

60 60 60 60 53 60 

(27 - 93) (27 - 93) (27 - 93) (27 - 93) (23 - 82) (27 - 

124 88 86 74 62 64 

(55 - 189) (38 - 135) (38 - 133) (32 - 115) (27 - 96) (28 - 

41 15 15 15 15 11 

(18 - 62) (7 - 24) (7 - 24) (7 - 24) (7 - 24) (5 - 

112 102 90 78 66 78 

(49 - 171) (45 - 157) (40 - 139) (34 - 121) (29 - 102) (34 - 

328 143 143 143 124 95 

(145 - 499) (62 - 222) (62 - 222) (62 - 222) (54 - 193) (41 - 

273 200 200 191 162 14

(121 - 417) (88 - 308) (88 - 308) (84 - 294) (71 - 250) (64 - 2

91 79 79 70 60 59 

(40 - 138) (35 - 121) (35 - 121) (31 - 108) (26 - 92) (26 - 

77 77 77 77 68 66 

(33 - 118) (33 - 118) (33 - 118) (33 - 118) (30 - 106) (29 - 

103 67 67 63 55 49 

(46 - 157) (29 - 103) (29 - 103) (28 - 97) (24 - 84) (22 - 

1344442

(6 - 21) (2 - 7) (2 - 7) (2 - 7) (2 - 7) (1 - 

138 116 105 91 77 88 

(61 - 210) (51 - 178) (46 - 161) (40 - 140) (34 - 119) (39 - 

19 11 11 11 11 6 

(8 - 30) (5 - 17) (5 - 17) (5 - 17) (5 - 17) (3 - 

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Baltimore, MD Detroit, MI Houston, TX St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

 Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2:
E-68

Table E-67. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to 

 PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alterna

 Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 

 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

12.4% 11% 9.9% 8.7% 7.5% 8.7% 

(5.5% - 18.7%) (4.9% - 16.7%) (4.4% - 15.1%) (3.8% - 13.3%) (3.3% - 11.6%) (3.8% - 13

12.2% 11.2% 10.3% 9.1% 7.9% 8.8% 

(5.4% - 18.4%) (5% - 17%) (4.6% - 15.7%) (4% - 13.9%) (3.5% - 12.1%) (3.9% - 13

12.4% 8.8% 7.8% 6.8% 5.8% 6.8% 

(5.5% - 18.8%) (3.9% - 13.5%) (3.4% - 12%) (3% - 10.5%) (2.5% - 8.9%) (3% - 10.

8.6% 8.6% 8.6% 8.6% 7.7% 8.6% 

(3.8% - 13.2%) (3.8% - 13.2%) (3.8% - 13.2%) (3.8% - 13.2%) (3.4% - 11.8%) (3.8% - 13

12.8% 9.4% 9.3% 8.2% 7.1% 7.3% 

(5.7% - 19.4%) (4.2% - 14.4%) (4.1% - 14.3%) (3.6% - 12.6%) (3.1% - 10.9%) (3.2% - 11

13.1% 4.7% 4.7% 4.7% 4.7% 3.1% 

(5.8% - 19.8%) (2.1% - 7.3%) (2.1% - 7.3%) (2.1% - 7.3%) (2.1% - 7.3%) (1.4% - 4.

9.5% 8.7% 7.7% 6.7% 5.7% 6.7% 

(4.2% - 14.5%) (3.8% - 13.3%) (3.4% - 11.8%) (2.9% - 10.3%) (2.5% - 8.8%) (2.9% - 10

11.8% 5.4% 5.4% 5.4% 4.8% 3.8% 

(5.3% - 18%) (2.4% - 8.4%) (2.4% - 8.4%) (2.4% - 8.4%) (2.1% - 7.4%) (1.6% - 5

11% 8.2% 8.2% 7.8% 6.7% 6.1% 

(4.9% - 16.7%) (3.6% - 12.5%) (3.6% - 12.5%) (3.4% - 12%) (2.9% - 10.3%) (2.7% - 9

10.4% 9.1% 9.1% 8.1% 7% 6.9% 

(4.6% - 15.8%) (4% - 13.9%) (4% - 13.9%) (3.6% - 12.5%) (3.1% - 10.8%) (3% - 10.

6.2% 6.2% 6.2% 6.2% 5.5% 5.4% 

(2.7% - 9.5%) (2.7% - 9.5%) (2.7% - 9.5%) (2.7% - 9.5%) (2.4% - 8.6%) (2.3% - 8.

12.3% 8.1% 8.1% 7.6% 6.7% 6.1% 

(5.5% - 18.6%) (3.6% - 12.5%) (3.6% - 12.5%) (3.4% - 11.7%) (2.9% - 10.3%) (2.7% - 9.

6.3% 1.8% 1.8% 1.8% 1.8% 0.6% 

(2.8% - 9.8%) (0.8% - 2.8%) (0.8% - 2.8%) (0.8% - 2.8%) (0.8% - 2.8%) (0.3% - 1

12.6% 10.8% 9.8% 8.6% 7.5% 8.4% 

(5.6% - 19.1%) (4.8% - 16.4%) (4.3% - 14.9%) (3.8% - 13.2%) (3.3% - 11.5%) (3.7% - 12

7.1% 4.7% 4.7% 4.7% 4.7% 3.1% 

(3.1% - 10.9%) (2% - 7.2%) (2% - 7.2%) (2% - 7.2%) (2% - 7.2%) (1.4% - 4.

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Con

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-69

Table E-68. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to  PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternati

 Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 fr

 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

12.4% 11% 9.9% 8.7% 7.5% 8.7% 

(5.5% - 18.7%) (4.9% - 16.7%) (4.4% - 15%) (3.8% - 13.3%) (3.3% - 11.6%) (3.8% - 13

9.9% 9.1% 8.2% 7.2% 6.1% 6.9% 

(4.4% - 15.1%) (4% - 13.9%) (3.6% - 12.6%) (3.2% - 11.1%) (2.7% - 9.5%) (3% - 10.

11.6% 8.1% 7.2% 6.2% 5.2% 6.2% 

(5.1% - 17.6%) (3.6% - 12.4%) (3.1% - 11%) (2.7% - 9.6%) (2.3% - 8.1%) (2.7% - 9

6.4% 6.4% 6.4% 6.4% 5.5% 6.4% 

(2.8% - 9.8%) (2.8% - 9.8%) (2.8% - 9.8%) (2.8% - 9.8%) (2.4% - 8.6%) (2.8% - 9

9.4% 6.5% 6.5% 5.5% 4.6% 4.7% 

(4.1% - 14.3%) (2.9% - 10.1%) (2.8% - 10%) (2.4% - 8.5%) (2% - 7.1%) (2.1% - 7

13.4% 4.9% 4.9% 4.9% 4.9% 3.3% 

(6% - 20.3%) (2.1% - 7.6%) (2.1% - 7.6%) (2.1% - 7.6%) (2.1% - 7.6%) (1.4% - 5

8.9% 8.1% 7.2% 6.2% 5.2% 6.2% 

(3.9% - 13.6%) (3.6% - 12.5%) (3.1% - 11%) (2.7% - 9.6%) (2.3% - 8.1%) (2.7% - 9

10.4% 4.5% 4.5% 4.5% 3.9% 2.9% 

(4.6% - 15.9%) (1.9% - 6.9%) (1.9% - 6.9%) (1.9% - 6.9%) (1.7% - 6%) (1.3% - 4

8.5% 6.1% 6.1% 5.7% 4.8% 4.3% 

(3.7% - 13%) (2.6% - 9.3%) (2.6% - 9.3%) (2.5% - 8.9%) (2.1% - 7.4%) (1.9% - 6

9.4% 8.2% 8.2% 7.3% 6.2% 6.2% 

(4.2% - 14.4%) (3.6% - 12.6%) (3.6% - 12.6%) (3.2% - 11.2%) (2.7% - 9.6%) (2.7% - 9

6.3% 6.3% 6.3% 6.3% 5.7% 5.5% 

(2.7% - 9.7%) (2.7% - 9.7%) (2.7% - 9.7%) (2.7% - 9.7%) (2.5% - 8.7%) (2.4% - 8

9.8% 6.2% 6.2% 5.7% 4.9% 4.4% 

(4.3% - 15%) (2.7% - 9.5%) (2.7% - 9.5%) (2.5% - 8.9%) (2.1% - 7.6%) (1.9% - 6

5% 0.9% 0.9% 0.9% 0.9% 0% 

(2.2% - 7.8%) (0.4% - 1.5%) (0.4% - 1.5%) (0.4% - 1.5%) (0.4% - 1.5%) (0% - 0

9.5% 7.9% 7.1% 6.1% 5.1% 5.9% 

(4.2% - 14.5%) (3.5% - 12.2%) (3.1% - 10.9%) (2.7% - 9.4%) (2.2% - 7.9%) (2.6% - 9

4.7% 2.7% 2.7% 2.7% 2.7% 1.4% 

(2.1% - 7.3%) (1.2% - 4.2%) (1.2% - 4.2%) (1.2% - 4.2%) (1.2% - 4.2%) (0.6% - 2

Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Con

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coe1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-70

Table E-69. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to  PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternat

 Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 f

 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

11.6% 10.3% 9.2% 8.1% 7% 8.1

(5.2% - 17.6%) (4.6% - 15.7%) (4.1% - 14.1%) (3.6% - 12.4%) (3.1% - 10.7%) (3.6% - 

9.9% 9.1% 8.2% 7.2% 6.1% 6.9

(4.4% - 15.1%) (4% - 13.9%) (3.6% - 12.6%) (3.2% - 11.1%) (2.7% - 9.5%) (3% - 1

12% 8.5% 7.5% 6.5% 5.5% 6.5

(5.3% - 18.2%) (3.7% - 13%) (3.3% - 11.5%) (2.8% - 10%) (2.4% - 8.5%) (2.8% - 

7% 7% 7% 7% 6.1% 7% 

(3% - 10.7%) (3% - 10.7%) (3% - 10.7%) (3% - 10.7%) (2.7% - 9.4%) (3% - 1

9.9% 7% 6.9% 6% 5% 5.1

(4.4% - 15.2%) (3.1% - 10.8%) (3% - 10.7%) (2.6% - 9.2%) (2.2% - 7.7%) (2.2% 

13.9% 5.2% 5.2% 5.2% 5.2% 3.5

(6.2% - 20.9%) (2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (1.5% - 

9.1% 8.3% 7.4% 6.4% 5.4% 6.4

(4% - 13.9%) (3.7% - 12.8%) (3.2% - 11.3%) (2.8% - 9.8%) (2.4% - 8.3%) (2.8% - 

10.7% 4.7% 4.7% 4.7% 4.1% 3.1

(4.8% - 16.3%) (2% - 7.3%) (2% - 7.3%) (2% - 7.3%) (1.8% - 6.3%) (1.3% - 

9.8% 7.2% 7.2% 6.9% 5.8% 5.3

(4.3% - 15%) (3.2% - 11.1%) (3.2% - 11.1%) (3% - 10.6%) (2.5% - 9%) (2.3% - 

9.3% 8.1% 8.1% 7.2% 6.2% 6.1

(4.1% - 14.2%) (3.6% - 12.5%) (3.6% - 12.5%) (3.2% - 11.1%) (2.7% - 9.5%) (2.7% - 

5.2% 5.2% 5.2% 5.2% 4.6% 4.4

(2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (2% - 7.1%) (1.9% - 

11.1% 7.2% 7.2% 6.7% 5.9% 5.3

(4.9% - 16.8%) (3.2% - 11.1%) (3.2% - 11.1%) (3% - 10.4%) (2.6% - 9.1%) (2.3% - 

7% 2.2% 2.2% 2.2% 2.2% 1% 

(3.1% - 10.7%) (1% - 3.5%) (1% - 3.5%) (1% - 3.5%) (1% - 3.5%) (0.4% - 

10.4% 8.7% 7.9% 6.8% 5.8% 6.6

(4.6% - 15.8%) (3.8% - 13.4%) (3.5% - 12.1%) (3% - 10.5%) (2.5% - 8.9%) (2.9% - 

4.9% 2.8% 2.8% 2.8% 2.8% 1.5

(2.1% - 7.6%) (1.2% - 4.4%) (1.2% - 4.4%) (1.2% - 4.4%) (1.2% - 4.4%) (0.7% - 

Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5 Co

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) 

Combination Denoted n/m)2

:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM co1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-71

Table E-70. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Assoc Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations -- Estim

 on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (20% - 21%) (31% - 32%) (20% - 

-8% 0% 8% 19% 29% 22% 

(-8% - -9%) (0% - 0%) (8% - 8%) (18% - 19%) (29% - 30%) (21% - 

-41% 0% 11% 23% 34% 23% 

(-39% - -43%) (0% - 0%) (11% - 12%) (22% - 23%) (34% - 35%) (22% - 

0% 0% 0% 0% 11% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 

-36% 0% 1% 13% 25% 23% 

(-35% - -37%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (23% - 

-178% 0% 0% 0% 0% 34% 

(-171% - -185%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

-9% 0% 11% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 

-118% 0% 0% 0% 12% 31% 

(-114% - -122%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 

-34% 0% 0% 4% 18% 25% 

(-33% - -35%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 

-14% 0% 0% 10% 23% 24% 

(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 11%) (22% - 23%) (23% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 

-51% 0% 0% 6% 17% 25% 

(-49% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (24% - 

-250% 0% 0% 0% 0% 64% 

(-245% - -254%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 

-17% 0% 9% 20% 31% 22% 

(-16% - -18%) (0% - 0%) (9% - 9%) (19% - 20%) (30% - 31%) (21% - 

-52% 0% 0% 0% 0% 33% 

(-51% - -53%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated 

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Al

Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-72

Table E-71. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Assoc Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations -- Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-12% 0% 10% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (20% - 21%) (31% - 32%) (20% - 

-9% 0% 9% 21% 32% 24% 

(-9% - -10%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (23% - 

-43% 0% 12% 24% 36% 24% 

(-41% - -44%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 

0% 0% 0% 0% 13% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (0% - 

-43% 0% 1% 16% 30% 28% 

(-42% - -45%) (0% - 0%) (1% - 1%) (15% - 16%) (30% - 30%) (27% - 

-173% 0% 0% 0% 0% 33% 

(-167% - -181%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 

-9% 0% 12% 24% 36% 24% 

(-9% - -10%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 

-133% 0% 0% 0% 13% 35% 

(-129% - -137%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 14%) (34% - 

-40% 0% 0% 5% 21% 29% 

(-39% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 

-15% 0% 0% 11% 24% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (24% - 

0% 0% 0% 0% 10% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 

-59% 0% 0% 7% 20% 29% 

(-58% - -61%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (28% - 

-431% 0% 0% 0% 0% 100

(-425% - -437%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 

-20% 0% 10% 23% 35% 25% 

(-19% - -20%) (0% - 0%) (10% - 11%) (22% - 23%) (35% - 36%) (25% - 

-75% 0% 0% 0% 0% 48% 

(-74% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 

Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated 

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Al

Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-73

Table E-72. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Assoc Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations -- Estim

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-13% 0% 11% 21% 32% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 22%) (32% - 33%) (21% - 

-9% 0% 9% 21% 32% 24% 

(-9% - -10%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (23% - 

-42% 0% 12% 23% 35% 23% 

(-40% - -43%) (0% - 0%) (11% - 12%) (23% - 24%) (34% - 36%) (23% - 

0% 0% 0% 0% 12% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 

-42% 0% 1% 15% 29% 27% 

(-41% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (26% - 

-168% 0% 0% 0% 0% 32% 

(-161% - -175%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 

-9% 0% 12% 23% 35% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 24%) (35% - 36%) (23% - 

-129% 0% 0% 0% 13% 34% 

(-125% - -134%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (33% - 

-36% 0% 0% 5% 19% 27% 

(-35% - -37%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 20%) (26% - 

-15% 0% 0% 11% 24% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 

0% 0% 0% 0% 11% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 

-54% 0% 0% 6% 19% 26% 

(-52% - -55%) (0% - 0%) (0% - 0%) (6% - 7%) (18% - 19%) (26% - 

-213% 0% 0% 0% 0% 55% 

(-209% - -217%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 

-19% 0% 10% 22% 34% 24% 

(-18% - -19%) (0% - 0%) (10% - 10%) (21% - 22%) (33% - 34%) (24% - 

-72% 0% 0% 0% 0% 46% 

(-71% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 

Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated 

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Al

Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).

 Dallas, TX Fresno, CA Los Angeles, CA
E-74

Table E-73. Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM

 in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

193 177 164 151 137 151 

(37 - 347) (34 - 319) (31 - 295) (29 - 272) (26 - 248) (29 - 2

271 256 242 224 206 219 

(110 - 430) (104 - 406) (98 - 384) (91 - 356) (83 - 327) (89 - 3

44 34 32 29 27 29 

(-68 - 154) (-53 - 121) (-49 - 112) (-45 - 103) (-41 - 94) (-45 - 1

156 156 156 156 145 156 

(37 - 273) (37 - 273) (37 - 273) (37 - 273) (35 - 253) (37 - 2

181 147 146 135 124 125 

(-32 - 390) (-26 - 317) (-26 - 315) (-24 - 291) (-22 - 267) (-22 - 2

79 44 44 44 44 37 

(11 - 145) (6 - 81) (6 - 81) (6 - 81) (6 - 81) (5 - 6

227 214 198 182 166 182 

(46 - 405) (44 - 383) (40 - 354) (37 - 326) (34 - 297) (37 - 3

129 81 81 81 77 69 

(-185 - 441) (-117 - 278) (-117 - 278) (-117 - 278) (-110 - 263) (-100 - 

939 781 781 761 700 668 

(552 - 1323) (459 - 1102) (459 - 1102) (447 - 1073) (411 - 987) (392 - 

234 216 216 202 185 184 

(86 - 380) (79 - 350) (79 - 350) (74 - 328) (68 - 301) (68 - 3

242 242 242 242 230 227 

(40 - 442) (40 - 442) (40 - 442) (40 - 442) (38 - 420) (38 - 4

224 159 159 155 147 136 

(66 - 380) (47 - 270) (47 - 270) (45 - 263) (43 - 249) (40 - 2

48 30 30 30 30 26 

(10 - 85) (6 - 54) (6 - 54) (6 - 54) (6 - 54) (5 - 4

290 260 244 226 207 222 

(84 - 494) (75 - 443) (71 - 416) (65 - 385) (60 - 354) (64 - 3

59 48 48 48 48 41 

(10 - 107) (8 - 87) (8 - 87) (8 - 87) (8 - 87) (7 - 7

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standa

n/m)2

:

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-75

Table E-74. Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM

 in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

196 180 166 153 139 153 

(37 - 353) (34 - 324) (32 - 300) (29 - 276) (26 - 251) (29 - 27

237 224 212 196 180 192 

(96 - 376) (91 - 356) (86 - 336) (79 - 311) (73 - 286) (78 - 30

42 33 30 28 26 28 

(-66 - 148) (-51 - 116) (-47 - 108) (-44 - 99) (-40 - 90) (-44 - 9

130 130 130 130 121 130 

(31 - 228) (31 - 228) (31 - 228) (31 - 228) (29 - 212) (31 - 22

145 118 117 108 99 101 

(-25 - 314) (-21 - 255) (-20 - 253) (-19 - 234) (-17 - 215) (-18 - 21

84 47 47 47 47 40 

(12 - 155) (7 - 86) (7 - 86) (7 - 86) (7 - 86) (6 - 74

221 208 193 177 162 177 

(45 - 395) (42 - 373) (39 - 345) (36 - 317) (33 - 289) (36 - 31

119 75 75 75 71 64 

(-171 - 407) (-108 - 257) (-108 - 257) (-108 - 257) (-101 - 242) (-92 - 21

807 671 671 654 601 574 

(474 - 1137) (394 - 946) (394 - 946) (383 - 922) (352 - 847) (336 - 80

222 204 204 191 175 174 

(82 - 359) (75 - 331) (75 - 331) (70 - 310) (65 - 285) (64 - 28

254 254 254 254 241 238 

(42 - 463) (42 - 463) (42 - 463) (42 - 463) (40 - 440) (39 - 43

194 136 136 133 126 116 

(57 - 329) (40 - 232) (40 - 232) (39 - 226) (37 - 215) (34 - 19

44 27 27 27 27 23 

(9 - 78) (6 - 49) (6 - 49) (6 - 49) (6 - 49) (5 - 42

240 215 202 187 171 184 

(69 - 409) (62 - 367) (58 - 345) (54 - 319) (49 - 293) (53 - 31

50 40 40 40 40 34 

(9 - 90) (7 - 73) (7 - 73) (7 - 73) (7 - 73) (6 - 62

Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
E-76

Table E-75. Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM

 in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

193 177 164 151 137 151 

(37 - 348) (34 - 319) (31 - 296) (29 - 272) (26 - 248) (29 - 27

240 227 214 198 182 194 

(97 - 380) (92 - 360) (87 - 340) (80 - 315) (74 - 289) (79 - 30

44 34 32 29 26 29 

(-68 - 154) (-53 - 120) (-49 - 111) (-45 - 102) (-41 - 93) (-45 - 10

139 139 139 139 129 139 

(33 - 243) (33 - 243) (33 - 243) (33 - 243) (31 - 225) (33 - 24

150 121 120 111 102 104 

(-26 - 323) (-21 - 262) (-21 - 261) (-19 - 241) (-18 - 221) (-18 - 22

87 48 48 48 48 41 

(12 - 160) (7 - 89) (7 - 89) (7 - 89) (7 - 89) (6 - 76

224 212 196 180 164 180 

(46 - 401) (43 - 378) (40 - 350) (37 - 322) (33 - 294) (37 - 32

121 77 77 77 72 65 

(-174 - 415) (-110 - 262) (-110 - 262) (-110 - 262) (-104 - 247) (-94 - 22

882 734 734 715 657 627 

(518 - 1243) (431 - 1035) (431 - 1035) (419 - 1008) (385 - 927) (368 - 88

226 208 208 195 179 178 

(83 - 367) (77 - 338) (77 - 338) (72 - 316) (66 - 291) (66 - 28

242 242 242 242 230 227 

(40 - 442) (40 - 442) (40 - 442) (40 - 442) (38 - 420) (38 - 41

204 143 143 140 133 122 

(60 - 346) (42 - 244) (42 - 244) (41 - 237) (39 - 226) (36 - 20

54 34 34 34 34 29 

(11 - 96) (7 - 61) (7 - 61) (7 - 61) (7 - 61) (6 - 52

251 225 211 195 179 192 

(73 - 428) (65 - 384) (61 - 360) (56 - 333) (52 - 306) (55 - 32

52 42 42 42 42 36 

(9 - 94) (7 - 76) (7 - 76) (7 - 76) (7 - 76) (6 - 65

Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
E-77

Table E-76. Estimated Percent of Total Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposu Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

1.3% 1.2% 1.1% 1% 1% 

(0.3% - 2.4%) (0.2% - 2.2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% 

2% 1.9% 1.8% 1.7% 1.5% 1

(0.8% - 3.2%) (0.8% - 3%) (0.7% - 2.8%) (0.7% - 2.6%) (0.6% - 2.4%) (0.7% 

0.5% 0.4% 0.3% 0.3% 0.3% 0

(-0.7% - 1.6%) (-0.6% - 1.3%) (-0.5% - 1.2%) (-0.5% - 1.1%) (-0.4% - 1%) (-0.5% 

1.2% 1.2% 1.2% 1.2% 1.2% 1

(0.3% - 2.2%) (0.3% - 2.2%) (0.3% - 2.2%) (0.3% - 2.2%) (0.3% - 2%) (0.3% 

1% 0.8% 0.8% 0.8% 0.7% 0

(-0.2% - 2.3%) (-0.1% - 1.8%) (-0.1% - 1.8%) (-0.1% - 1.7%) (-0.1% - 1.5%) (-0.1% 

1.5% 0.8% 0.8% 0.8% 0.8% 0

(0.2% - 2.7%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% 

1.3% 1.2% 1.1% 1% 0.9% 

(0.3% - 2.3%) (0.2% - 2.1%) (0.2% - 2%) (0.2% - 1.8%) (0.2% - 1.7%) (0.2% 

0.2% 0.1% 0.1% 0.1% 0.1% 0

(-0.3% - 0.8%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% 

1.8% 1.5% 1.5% 1.5% 1.4% 1

(1.1% - 2.6%) (0.9% - 2.1%) (0.9% - 2.1%) (0.9% - 2.1%) (0.8% - 1.9%) (0.8% 

1.7% 1.5% 1.5% 1.4% 1.3% 1

(0.6% - 2.7%) (0.6% - 2.5%) (0.6% - 2.5%) (0.5% - 2.3%) (0.5% - 2.1%) (0.5% 

1.1% 1.1% 1.1% 1.1% 1.1% 

(0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% 

1.7% 1.2% 1.2% 1.1% 1.1% 

(0.5% - 2.8%) (0.3% - 2%) (0.3% - 2%) (0.3% - 1.9%) (0.3% - 1.8%) (0.3% 

1% 0.6% 0.6% 0.6% 0.6% 0

(0.2% - 1.8%) (0.1% - 1.2%) (0.1% - 1.2%) (0.1% - 1.2%) (0.1% - 1.2%) (0.1% 

1.6% 1.4% 1.3% 1.2% 1.1% 1

(0.5% - 2.7%) (0.4% - 2.4%) (0.4% - 2.3%) (0.4% - 2.1%) (0.3% - 1.9%) (0.4% 

1.2% 1% 1% 1% 1% 0

(0.2% - 2.2%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.1% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-78

Table E-77. Estimated Percent of Total Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposu Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13

1.3% 1.2% 1.1% 1% 0.9% 

(0.3% - 2.4%) (0.2% - 2.2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% 

1.7% 1.6% 1.6% 1.4% 1.3% 1

(0.7% - 2.8%) (0.7% - 2.6%) (0.6% - 2.5%) (0.6% - 2.3%) (0.5% - 2.1%) (0.6% 

0.4% 0.3% 0.3% 0.3% 0.3% 0

(-0.7% - 1.6%) (-0.5% - 1.2%) (-0.5% - 1.1%) (-0.5% - 1%) (-0.4% - 0.9%) (-0.5% 

1% 1% 1% 1% 0.9% 

(0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.7%) (0.2% 

0.8% 0.7% 0.7% 0.6% 0.6% 0

(-0.1% - 1.8%) (-0.1% - 1.5%) (-0.1% - 1.5%) (-0.1% - 1.4%) (-0.1% - 1.3%) (-0.1% 

1.5% 0.8% 0.8% 0.8% 0.8% 0

(0.2% - 2.8%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% 

1.2% 1.1% 1% 1% 0.9% 

(0.2% - 2.1%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% - 1.6%) (0.2% 

0.2% 0.1% 0.1% 0.1% 0.1% 0

(-0.3% - 0.7%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.4%) (-0.2% 

1.6% 1.3% 1.3% 1.3% 1.2% 1

(0.9% - 2.2%) (0.8% - 1.8%) (0.8% - 1.8%) (0.7% - 1.8%) (0.7% - 1.6%) (0.6% 

1.6% 1.5% 1.5% 1.4% 1.2% 1

(0.6% - 2.6%) (0.5% - 2.4%) (0.5% - 2.4%) (0.5% - 2.2%) (0.5% - 2%) (0.5% 

1.1% 1.1% 1.1% 1.1% 1.1% 1

(0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% 

1.4% 1% 1% 1% 0.9% 0

(0.4% - 2.5%) (0.3% - 1.7%) (0.3% - 1.7%) (0.3% - 1.7%) (0.3% - 1.6%) (0.3% 

0.9% 0.6% 0.6% 0.6% 0.6% 0

(0.2% - 1.6%) (0.1% - 1%) (0.1% - 1%) (0.1% - 1%) (0.1% - 1%) (0.1% 

1.3% 1.2% 1.1% 1% 0.9% 

(0.4% - 2.2%) (0.3% - 2%) (0.3% - 1.9%) (0.3% - 1.7%) (0.3% - 1.6%) (0.3% 

1% 0.8% 0.8% 0.8% 0.8% 0

(0.2% - 1.8%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% 

Percent of Total Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-79

Table E-78. Estimated Percent of Total Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposur Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

1.3% 1.2% 1.1% 1% 0.9% 1% 

(0.2% - 2.3%) (0.2% - 2.1%) (0.2% - 1.9%) (0.2% - 1.8%) (0.2% - 1.6%) (0.2% - 

1.8% 1.7% 1.6% 1.5% 1.3% 1.4

(0.7% - 2.8%) (0.7% - 2.6%) (0.6% - 2.5%) (0.6% - 2.3%) (0.5% - 2.1%) (0.6% - 

0.5% 0.4% 0.3% 0.3% 0.3% 0.3

(-0.7% - 1.6%) (-0.6% - 1.2%) (-0.5% - 1.2%) (-0.5% - 1.1%) (-0.4% - 1%) (-0.5% - 

1.1% 1.1% 1.1% 1.1% 1% 1.1

(0.3% - 1.9%) (0.3% - 1.9%) (0.3% - 1.9%) (0.3% - 1.9%) (0.2% - 1.8%) (0.3% - 

0.9% 0.7% 0.7% 0.7% 0.6% 0.6

(-0.2% - 1.9%) (-0.1% - 1.6%) (-0.1% - 1.5%) (-0.1% - 1.4%) (-0.1% - 1.3%) (-0.1% - 

1.6% 0.9% 0.9% 0.9% 0.9% 0.7

(0.2% - 2.9%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 

1.2% 1.1% 1% 1% 0.9% 1% 

(0.2% - 2.1%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% - 1.6%) (0.2% - 

0.2% 0.1% 0.1% 0.1% 0.1% 0.1

(-0.3% - 0.7%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.4%) (-0.2% - 

1.7% 1.4% 1.4% 1.4% 1.3% 1.2

(1% - 2.4%) (0.8% - 2%) (0.8% - 2%) (0.8% - 1.9%) (0.7% - 1.8%) (0.7% - 

1.6% 1.5% 1.5% 1.4% 1.3% 1.3

(0.6% - 2.6%) (0.5% - 2.4%) (0.5% - 2.4%) (0.5% - 2.3%) (0.5% - 2.1%) (0.5% - 

1% 1% 1% 1% 1% 1% 

(0.2% - 1.9%) (0.2% - 1.9%) (0.2% - 1.9%) (0.2% - 1.9%) (0.2% - 1.8%) (0.2% - 

1.5% 1.1% 1.1% 1% 1% 0.9

(0.4% - 2.6%) (0.3% - 1.8%) (0.3% - 1.8%) (0.3% - 1.8%) (0.3% - 1.7%) (0.3% - 

1.1% 0.7% 0.7% 0.7% 0.7% 0.6

(0.2% - 2%) (0.1% - 1.3%) (0.1% - 1.3%) (0.1% - 1.3%) (0.1% - 1.3%) (0.1% - 

1.4% 1.2% 1.2% 1.1% 1% 1.1

(0.4% - 2.4%) (0.4% - 2.1%) (0.3% - 2%) (0.3% - 1.8%) (0.3% - 1.7%) (0.3% - 

1% 0.8% 0.8% 0.8% 0.8% 0.7

(0.2% - 1.9%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 

Percent of Total Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 C

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) 

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-80

Table E-79. Percent Reduction from the Current Standards: Estimated Annual Incidence of Non-Accidental Mortality Ass Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

 Atlanta, GA -9% 0% 7% 15% 22% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

 Baltimore, MD -6% 0% 5% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (20% - 20%) (14% - 

 Birmingham, AL -28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

 Dallas, TX 0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 

 Detroit, MI -23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 

 Fresno, CA -81% 0% 0% 0% 0% 15% 

(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

 Houston, TX -6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

 Los Angeles, CA -58% 0% 0% 0% 6% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 

 New York, NY -20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 

 Philadelphia, PA -9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 

 Phoenix, AZ 0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 

 Pittsburgh, PA -41% 0% 0% 3% 8% 15% 

(-41% - -41%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (14% - 

 Salt Lake City, UT -58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 

 St. Louis, MO -12% 0% 6% 13% 20% 15% 

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 

 Tacoma, WA -23% 0% 0% 0% 0% 15% 

(-23% - -23%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.

Risk Assessment 

Location

Estimated Percent Reduction From the Current Standards to Several Alternative Standards in Non

Associated with Short-Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentra

Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Den
E-81

Table E-80. Percent Reduction from the Current Standards: Estimated Annual Incidence of Non-Accidental Mortality Assoc Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

 Atlanta, GA -9% 0% 7% 15% 22% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%

 Baltimore, MD -6% 0% 6% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (20% - 20%) (14% - 15%

 Birmingham, AL -28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%

 Dallas, TX 0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

 Detroit, MI -23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%

 Fresno, CA -81% 0% 0% 0% 0% 15% 

(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%

 Houston, TX -6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%

 Los Angeles, CA -58% 0% 0% 0% 6% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%

 New York, NY -20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%

 Philadelphia, PA -9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%

 Phoenix, AZ 0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

 Pittsburgh, PA -42% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%

 Salt Lake City, UT -58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%

 St. Louis, MO -12% 0% 6% 13% 20% 15% 

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%

 Tacoma, WA -23% 0% 0% 0% 0% 15% 

(-23% - -23%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards th

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.

Risk Assessment 

Location

Estimated Percent Reduction From the Current Standards to Several Alternative Standards in Non-Ac

Associated with Short-Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentratio

Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denote
E-82

Table E-81. Percent Reduction from the Current Standards: Estimated Annual Incidence of Non-Accidental Mortality Associ Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

 Atlanta, GA -9% 0% 7% 15% 22% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%

 Baltimore, MD -6% 0% 6% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (13% - 13%) (20% - 20%) (14% - 15%

 Birmingham, AL -28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%

 Dallas, TX 0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) 

 Detroit, MI -23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%

 Fresno, CA -81% 0% 0% 0% 0% 15% 

(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%

 Houston, TX -6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%

 Los Angeles, CA -58% 0% 0% 0% 6% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%

 New York, NY -20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%

 Philadelphia, PA -9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%

 Phoenix, AZ 0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) 

 Pittsburgh, PA -42% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%

 Salt Lake City, UT -58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%

 St. Louis, MO -12% 0% 6% 13% 20% 15% 

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%

 Tacoma, WA -23% 0% 0% 0% 0% 15% 

(-23% - -23%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the 

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.

Risk Assessment 

Location

Estimated Percent Reduction From the Current Standards to Several Alternative Standards in Non-Ac

Associated with Short-Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentration

Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denote
E-83

Table E-82. Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM

 in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

35 32 30 27 25 27 

(-36 - 104) (-33 - 95) (-30 - 88) (-28 - 81) (-25 - 74) (-28 - 8

74 70 66 61 56 60 

(-5 - 151) (-5 - 143) (-4 - 135) (-4 - 125) (-4 - 115) (-4 - 12

-1 -1 -1 -1 -1 -1 

(-55 - 52) (-43 - 40) (-39 - 37) (-36 - 34) (-33 - 31) (-36 - 3

32 32 32 32 30 32 

(-21 - 85) (-21 - 85) (-21 - 85) (-21 - 85) (-20 - 79) (-21 - 8

89 73 72 67 61 62 

(-11 - 188) (-9 - 153) (-9 - 152) (-8 - 140) (-8 - 129) (-8 - 13

20 11 11 11 11 10 

(-14 - 54) (-8 - 30) (-8 - 30) (-8 - 30) (-8 - 30) (-7 - 26

50 47 43 40 36 40 

(-34 - 131) (-32 - 124) (-29 - 114) (-27 - 105) (-25 - 96) (-27 - 10

-50 -31 -31 -31 -30 -27 

(-223 - 121) (-140 - 76) (-140 - 76) (-140 - 76) (-132 - 72) (-119 - 6

605 504 504 491 451 431 

(353 - 853) (294 - 711) (294 - 711) (286 - 693) (263 - 637) (251 - 60

94 87 87 81 75 74 

(25 - 163) (23 - 150) (23 - 150) (21 - 140) (19 - 129) (19 - 12

84 84 84 84 80 79 

(-4 - 170) (-4 - 170) (-4 - 170) (-4 - 170) (-3 - 161) (-3 - 15

67 47 47 46 44 41 

(-13 - 145) (-9 - 103) (-9 - 103) (-9 - 101) (-9 - 96) (-8 - 88

1388887

(-3 - 28) (-2 - 18) (-2 - 18) (-2 - 18) (-2 - 18) (-2 - 15

136 122 115 106 98 105 

(30 - 240) (27 - 215) (26 - 203) (24 - 187) (22 - 172) (23 - 18

15 12 12 12 12 11 

(-8 - 38) (-7 - 31) (-7 - 31) (-7 - 31) (-7 - 31) (-6 - 27

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-84

Table E-83. Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM

 in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

35 32 30 28 25 28 

(-36 - 105) (-33 - 97) (-31 - 90) (-28 - 82) (-26 - 75) (-28 - 8

65 61 58 53 49 52 

(-4 - 132) (-4 - 125) (-4 - 118) (-4 - 109) (-3 - 101) (-4 - 10

-1 -1 -1 -1 0 -1 

(-52 - 50) (-41 - 39) (-38 - 36) (-35 - 33) (-32 - 30) (-35 - 3

27 27 27 27 25 27 

(-18 - 71) (-18 - 71) (-18 - 71) (-18 - 71) (-16 - 66) (-18 - 7

72 58 58 54 49 50 

(-9 - 152) (-7 - 123) (-7 - 122) (-7 - 113) (-6 - 104) (-6 - 10

22 12 12 12 12 10 

(-15 - 58) (-8 - 32) (-8 - 32) (-8 - 32) (-8 - 32) (-7 - 27

48 45 42 39 35 39 

(-33 - 128) (-31 - 120) (-29 - 112) (-26 - 103) (-24 - 94) (-26 - 10

-46 -29 -29 -29 -27 -25 

(-205 - 112) (-129 - 70) (-129 - 70) (-129 - 70) (-122 - 66) (-110 - 6

519 432 432 421 387 370 

(303 - 733) (252 - 611) (252 - 611) (246 - 595) (226 - 548) (216 - 52

89 82 82 77 71 70 

(23 - 154) (21 - 142) (21 - 142) (20 - 133) (18 - 122) (18 - 12

88 88 88 88 84 82 

(-4 - 178) (-4 - 178) (-4 - 178) (-4 - 178) (-4 - 169) (-4 - 16

58 41 41 40 38 35 

(-12 - 126) (-8 - 89) (-8 - 89) (-8 - 86) (-8 - 82) (-7 - 76

1177776

(-3 - 25) (-2 - 16) (-2 - 16) (-2 - 16) (-2 - 16) (-1 - 14

113 101 95 88 81 87 

(25 - 199) (23 - 179) (21 - 168) (20 - 155) (18 - 143) (19 - 15

13 10 10 10 10 9 

(-7 - 32) (-6 - 26) (-6 - 26) (-6 - 26) (-6 - 26) (-5 - 22

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-85

Table E-84. Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM

 in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

35 32 30 27 25 27 

(-36 - 104) (-33 - 95) (-30 - 88) (-28 - 81) (-25 - 74) (-28 - 8

65 62 58 54 50 53 

(-4 - 133) (-4 - 126) (-4 - 119) (-4 - 111) (-3 - 102) (-4 - 10

-1 -1 -1 -1 -1 -1 

(-54 - 51) (-42 - 40) (-39 - 37) (-36 - 34) (-33 - 31) (-36 - 3

29 29 29 29 27 29 

(-19 - 76) (-19 - 76) (-19 - 76) (-19 - 76) (-17 - 70) (-19 - 7

74 60 60 55 51 51 

(-9 - 156) (-8 - 127) (-7 - 126) (-7 - 116) (-6 - 107) (-6 - 10

23 12 12 12 12 11 

(-16 - 59) (-9 - 33) (-9 - 33) (-9 - 33) (-9 - 33) (-7 - 28

49 46 43 39 36 39 

(-33 - 130) (-31 - 122) (-29 - 113) (-27 - 104) (-24 - 95) (-27 - 10

-47 -30 -30 -30 -28 -25 

(-209 - 114) (-132 - 72) (-132 - 72) (-132 - 72) (-124 - 68) (-112 - 6

568 473 473 461 424 405 

(332 - 802) (276 - 668) (276 - 668) (269 - 651) (247 - 599) (236 - 57

91 84 84 79 72 72 

(24 - 157) (22 - 145) (22 - 145) (20 - 136) (19 - 125) (19 - 12

84 84 84 84 80 79 

(-4 - 170) (-4 - 170) (-4 - 170) (-4 - 170) (-3 - 162) (-3 - 15

61 43 43 42 40 37 

(-12 - 132) (-9 - 93) (-9 - 93) (-8 - 91) (-8 - 87) (-7 - 80

1499998

(-3 - 31) (-2 - 20) (-2 - 20) (-2 - 20) (-2 - 20) (-2 - 17

118 106 99 92 84 91 

(26 - 208) (24 - 187) (22 - 176) (20 - 162) (19 - 149) (20 - 16

14 11 11 11 11 9 

(-7 - 34) (-6 - 27) (-6 - 27) (-6 - 27) (-6 - 27) (-5 - 23

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-86

Table E-85. Estimated Percent of Total Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposur Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

0.9% 0.9% 0.8% 0.7% 0.7% 0.7

(-1% - 2.8%) (-0.9% - 2.6%) (-0.8% - 2.4%) (-0.7% - 2.2%) (-0.7% - 2%) (-0.7% - 

1.9% 1.8% 1.7% 1.6% 1.4% 1.5

(-0.1% - 3.9%) (-0.1% - 3.7%) (-0.1% - 3.5%) (-0.1% - 3.2%) (-0.1% - 3%) (-0.1% - 

0% 0% 0% 0% 0% 0% 

(-2% - 1.9%) (-1.6% - 1.5%) (-1.5% - 1.4%) (-1.3% - 1.3%) (-1.2% - 1.2%) (-1.3% - 

1% 1% 1% 1% 0.9% 1% 

(-0.6% - 2.5%) (-0.6% - 2.5%) (-0.6% - 2.5%) (-0.6% - 2.5%) (-0.6% - 2.3%) (-0.6% - 

1.5% 1.2% 1.2% 1.1% 1% 1% 

(-0.2% - 3.1%) (-0.2% - 2.5%) (-0.2% - 2.5%) (-0.1% - 2.3%) (-0.1% - 2.1%) (-0.1% - 

1.2% 0.7% 0.7% 0.7% 0.7% 0.6

(-0.9% - 3.3%) (-0.5% - 1.8%) (-0.5% - 1.8%) (-0.5% - 1.8%) (-0.5% - 1.8%) (-0.4% - 

1% 1% 0.9% 0.8% 0.7% 0.8

(-0.7% - 2.7%) (-0.7% - 2.5%) (-0.6% - 2.3%) (-0.6% - 2.2%) (-0.5% - 2%) (-0.6% - 

-0.3% -0.2% -0.2% -0.2% -0.2% -0.1

(-1.2% - 0.6%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.6% - 

2.7% 2.2% 2.2% 2.2% 2% 1.9

(1.6% - 3.8%) (1.3% - 3.2%) (1.3% - 3.2%) (1.3% - 3.1%) (1.2% - 2.8%) (1.1% - 

2.3% 2.2% 2.2% 2% 1.9% 1.8

(0.6% - 4%) (0.6% - 3.7%) (0.6% - 3.7%) (0.5% - 3.5%) (0.5% - 3.2%) (0.5% - 

1.4% 1.4% 1.4% 1.4% 1.4% 1.3

(-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.7%) (-0.1% - 

1.6% 1.2% 1.2% 1.1% 1.1% 1% 

(-0.3% - 3.6%) (-0.2% - 2.5%) (-0.2% - 2.5%) (-0.2% - 2.5%) (-0.2% - 2.3%) (-0.2% - 

1.1% 0.7% 0.7% 0.7% 0.7% 0.6

(-0.3% - 2.5%) (-0.2% - 1.6%) (-0.2% - 1.6%) (-0.2% - 1.6%) (-0.2% - 1.6%) (-0.1% - 

2.4% 2.2% 2% 1.9% 1.7% 1.8

(0.5% - 4.2%) (0.5% - 3.8%) (0.5% - 3.6%) (0.4% - 3.3%) (0.4% - 3%) (0.4% - 

1.1% 0.9% 0.9% 0.9% 0.9% 0.7

(-0.6% - 2.7%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.4% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 C

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) 

Combination Denoted n/m)2

:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-87

Table E-86. Estimated Percent of Total Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposur Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 Adjusting 2006 PM2.5 Concentrations1 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/

0.9% 0.8% 0.8% 0.7% 0.7% 0.7

(-0.9% - 2.8%) (-0.9% - 2.5%) (-0.8% - 2.3%) (-0.7% - 2.2%) (-0.7% - 2%) (-0.7% 

1.7% 1.6% 1.5% 1.4% 1.3% 1.3

(-0.1% - 3.4%) (-0.1% - 3.2%) (-0.1% - 3%) (-0.1% - 2.8%) (-0.1% - 2.6%) (-0.1% 

0% 0% 0% 0% 0% 0

(-1.9% - 1.8%) (-1.5% - 1.4%) (-1.4% - 1.3%) (-1.3% - 1.2%) (-1.2% - 1.1%) (-1.3% 

0.8% 0.8% 0.8% 0.8% 0.7% 0.8

(-0.5% - 2.1%) (-0.5% - 2.1%) (-0.5% - 2.1%) (-0.5% - 2.1%) (-0.5% - 1.9%) (-0.5% 

1.2% 1% 1% 0.9% 0.8% 0.8

(-0.2% - 2.5%) (-0.1% - 2.1%) (-0.1% - 2.1%) (-0.1% - 1.9%) (-0.1% - 1.7%) (-0.1% 

1.3% 0.7% 0.7% 0.7% 0.7% 0.6

(-0.9% - 3.5%) (-0.5% - 1.9%) (-0.5% - 1.9%) (-0.5% - 1.9%) (-0.5% - 1.9%) (-0.4% 

1% 0.9% 0.8% 0.8% 0.7% 0.8

(-0.6% - 2.5%) (-0.6% - 2.4%) (-0.6% - 2.2%) (-0.5% - 2%) (-0.5% - 1.9%) (-0.5% 

-0.2% -0.2% -0.2% -0.2% -0.1% -0.

(-1.1% - 0.6%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.6% - 0.4%) (-0.6% 

2.3% 1.9% 1.9% 1.9% 1.7% 1.6

(1.3% - 3.3%) (1.1% - 2.7%) (1.1% - 2.7%) (1.1% - 2.6%) (1% - 2.4%) (1% - 

2.2% 2.1% 2.1% 1.9% 1.8% 1.8

(0.6% - 3.8%) (0.5% - 3.5%) (0.5% - 3.5%) (0.5% - 3.3%) (0.5% - 3.1%) (0.5% 

1.4% 1.4% 1.4% 1.4% 1.4% 1.4

(-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.8%) (-0.1% 

1.4% 1% 1% 1% 0.9% 0.9

(-0.3% - 3.1%) (-0.2% - 2.2%) (-0.2% - 2.2%) (-0.2% - 2.1%) (-0.2% - 2%) (-0.2% 

1% 0.6% 0.6% 0.6% 0.6% 0.5

(-0.2% - 2.2%) (-0.1% - 1.4%) (-0.1% - 1.4%) (-0.1% - 1.4%) (-0.1% - 1.4%) (-0.1% 

2% 1.8% 1.7% 1.5% 1.4% 1.5

(0.4% - 3.5%) (0.4% - 3.1%) (0.4% - 3%) (0.3% - 2.7%) (0.3% - 2.5%) (0.3% 

0.9% 0.7% 0.7% 0.7% 0.7% 0.6

(-0.5% - 2.2%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.3% 

Percent of Total Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-88

Table E-87. Estimated Percent of Total Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure  Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Sta

 Adjusting 2007 PM2.5 Concentrations1 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

0.9% 0.8% 0.8% 0.7% 0.6% 0.7

(-0.9% - 2.7%) (-0.8% - 2.4%) (-0.8% - 2.3%) (-0.7% - 2.1%) (-0.6% - 1.9%) (-0.7% - 

1.7% 1.6% 1.5% 1.4% 1.3% 1.4

(-0.1% - 3.4%) (-0.1% - 3.2%) (-0.1% - 3.1%) (-0.1% - 2.8%) (-0.1% - 2.6%) (-0.1% - 

0% 0% 0% 0% 0% 0% 

(-2% - 1.9%) (-1.5% - 1.5%) (-1.4% - 1.4%) (-1.3% - 1.2%) (-1.2% - 1.1%) (-1.3% - 

0.8% 0.8% 0.8% 0.8% 0.8% 0.8

(-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2%) (-0.5% - 

1.3% 1% 1% 0.9% 0.9% 0.9

(-0.2% - 2.7%) (-0.1% - 2.2%) (-0.1% - 2.1%) (-0.1% - 2%) (-0.1% - 1.8%) (-0.1% - 

1.3% 0.7% 0.7% 0.7% 0.7% 0.6

(-0.9% - 3.5%) (-0.5% - 2%) (-0.5% - 2%) (-0.5% - 2%) (-0.5% - 2%) (-0.4% - 

1% 0.9% 0.8% 0.8% 0.7% 0.8

(-0.6% - 2.5%) (-0.6% - 2.4%) (-0.6% - 2.2%) (-0.5% - 2%) (-0.5% - 1.9%) (-0.5% 

-0.2% -0.2% -0.2% -0.2% -0.1% -0.1

(-1.1% - 0.6%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.6% - 

2.5% 2.1% 2.1% 2% 1.9% 1.8

(1.5% - 3.5%) (1.2% - 3%) (1.2% - 3%) (1.2% - 2.9%) (1.1% - 2.6%) (1% - 2

2.3% 2.1% 2.1% 2% 1.8% 1.8

(0.6% - 3.9%) (0.5% - 3.6%) (0.5% - 3.6%) (0.5% - 3.4%) (0.5% - 3.1%) (0.5% - 

1.3% 1.3% 1.3% 1.3% 1.3% 1.3

(-0.1% - 2.7%) (-0.1% - 2.7%) (-0.1% - 2.7%) (-0.1% - 2.7%) (-0.1% - 2.6%) (-0.1% - 

1.5% 1.1% 1.1% 1% 1% 0.9

(-0.3% - 3.3%) (-0.2% - 2.3%) (-0.2% - 2.3%) (-0.2% - 2.3%) (-0.2% - 2.1%) (-0.2% 

1.2% 0.8% 0.8% 0.8% 0.8% 0.7

(-0.3% - 2.7%) (-0.2% - 1.7%) (-0.2% - 1.7%) (-0.2% - 1.7%) (-0.2% - 1.7%) (-0.2% - 

2.1% 1.9% 1.7% 1.6% 1.5% 1.6

(0.5% - 3.7%) (0.4% - 3.3%) (0.4% - 3.1%) (0.4% - 2.9%) (0.3% - 2.6%) (0.4% - 

0.9% 0.7% 0.7% 0.7% 0.7% 0.6

(-0.5% - 2.3%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.3% - 

Percent of Total Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 C

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) 

Combination Denoted n/m)2

:

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM co1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-89

Table E-88. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Mortality Asso Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-9% 0% 7% 15% 23% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

-6% 0% 6% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 

-28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 

-81% 0% 0% 0% 0% 15% 

(-80% - -83%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

-6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

-59% 0% 0% 0% 6% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 

-20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 

-9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 

-41% 0% 0% 3% 8% 15% 

(-41% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (14% - 

-58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 

-12% 0% 6% 13% 20% 14% 

(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Mortality Assoc

Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Curre

(n) and Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-90

Table E-89. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Mortality Asso

 Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-9% 0% 7% 15% 23% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-6% 0% 6% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 15

-28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15

-81% 0% 0% 0% 0% 15% 

(-80% - -83%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-6% 0% 7% 15% 23% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-59% 0% 0% 0% 6% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15

-20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15

-9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

-42% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 15

-58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15

-12% 0% 6% 13% 20% 15% 

(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Mortality Associ

Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Curren

(n) and Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-91

Table E-90. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Mortality Ass Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-9% 0% 7% 15% 23% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

-6% 0% 6% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 

-28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 

-81% 0% 0% 0% 0% 15% 

(-79% - -83%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

-6% 0% 7% 15% 23% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

-59% 0% 0% 0% 6% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 

-20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 

-9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 

-42% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 

-58% 0% 0% 0% 0% 15% 

(-57% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

-12% 0% 6% 13% 20% 15% 

(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards 

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Mortality Asso

Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Curre

(n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-92

Table E-91. Estimated Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5 C

 Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on A

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

21 20 18 17 15 17 

(-9 - 51) (-8 - 47) (-7 - 43) (-7 - 40) (-6 - 36) (-7 - 40

38 36 34 31 29 30 

(7 - 67) (7 - 64) (6 - 60) (6 - 56) (5 - 51) (6 - 55

1299878

(-10 - 33) (-7 - 26) (-7 - 24) (-6 - 22) (-6 - 20) (-6 - 22

11 11 11 11 10 11 

(-10 - 32) (-10 - 32) (-10 - 32) (-10 - 32) (-10 - 30) (-10 - 3

35 28 28 26 24 24 

(2 - 67) (1 - 55) (1 - 54) (1 - 50) (1 - 46) (1 - 47

1599997

(0 - 30) (0 - 17) (0 - 17) (0 - 17) (0 - 17) (0 - 14

36 34 31 29 26 29 

(6 - 65) (5 - 61) (5 - 57) (5 - 52) (4 - 48) (5 - 52

90 57 57 57 54 49 

(9 - 171) (6 - 108) (6 - 108) (6 - 108) (5 - 102) (5 - 93

128 106 106 104 95 91 

(45 - 208) (37 - 174) (37 - 174) (37 - 169) (34 - 156) (32 - 14

25 23 23 22 20 20 

(-2 - 52) (-2 - 48) (-2 - 48) (-2 - 45) (-2 - 41) (-2 - 41

47 47 47 47 45 44 

(4 - 90) (4 - 90) (4 - 90) (4 - 90) (4 - 85) (4 - 84

28 20 20 20 19 17 

(-3 - 58) (-2 - 42) (-2 - 42) (-2 - 40) (-2 - 38) (-2 - 36

8 55554

(1 - 15) (1 - 10) (1 - 10) (1 - 10) (1 - 10) (1 - 8) 

35 31 29 27 25 27 

(-9 - 78) (-8 - 70) (-8 - 65) (-7 - 61) (-7 - 56) (-7 - 60

9 77776

(0 - 18) (0 - 15) (0 - 15) (0 - 15) (0 - 15) (0 - 13

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in a 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-93

Table E-92. Estimated Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5 C

 Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on A

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

22 20 18 17 16 17 

(-9 - 51) (-8 - 47) (-7 - 44) (-7 - 40) (-6 - 37) (-7 - 40

33 31 29 27 25 27 

(6 - 59) (6 - 56) (5 - 53) (5 - 49) (5 - 45) (5 - 48

1198878

(-9 - 31) (-7 - 25) (-7 - 23) (-6 - 21) (-6 - 19) (-6 - 21

9 99999

(-9 - 27) (-9 - 27) (-9 - 27) (-9 - 27) (-8 - 25) (-9 - 27

28 23 23 21 19 20 

(1 - 54) (1 - 44) (1 - 44) (1 - 41) (1 - 37) (1 - 38

1699998

(1 - 32) (0 - 18) (0 - 18) (0 - 18) (0 - 18) (0 - 15

35 33 30 28 25 28 

(6 - 63) (5 - 60) (5 - 55) (4 - 51) (4 - 46) (4 - 51

84 53 53 53 50 45 

(8 - 158) (5 - 100) (5 - 100) (5 - 100) (5 - 95) (4 - 86

110 91 91 89 82 78 

(39 - 179) (32 - 149) (32 - 149) (31 - 146) (29 - 134) (27 - 12

24 22 22 20 19 19 

(-2 - 49) (-2 - 45) (-2 - 45) (-2 - 42) (-2 - 39) (-2 - 39

50 50 50 50 47 46 

(4 - 94) (4 - 94) (4 - 94) (4 - 94) (4 - 90) (4 - 88

24 17 17 17 16 15 

(-2 - 51) (-2 - 36) (-2 - 36) (-2 - 35) (-2 - 33) (-1 - 31

8 55554

(1 - 14) (1 - 9) (1 - 9) (1 - 9) (1 - 9) (1 - 8) 

29 26 24 22 21 22 

(-8 - 64) (-7 - 58) (-6 - 54) (-6 - 50) (-5 - 46) (-6 - 49

8 66665

(0 - 15) (0 - 12) (0 - 12) (0 - 12) (0 - 12) (0 - 11

Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in a 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
E-94

Table E-93. Estimated Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5 C

 Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on A

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

21 20 18 17 15 17 

(-9 - 51) (-8 - 47) (-7 - 43) (-7 - 40) (-6 - 36) (-7 - 40

33 31 30 28 25 27 

(6 - 60) (6 - 56) (6 - 53) (5 - 49) (5 - 45) (5 - 48

1299878

(-10 - 32) (-7 - 25) (-7 - 24) (-6 - 22) (-6 - 20) (-6 - 22

10 10 10 10 9 10 

(-9 - 29) (-9 - 29) (-9 - 29) (-9 - 29) (-8 - 27) (-9 - 29

29 24 23 22 20 20 

(1 - 56) (1 - 45) (1 - 45) (1 - 42) (1 - 38) (1 - 39

1799998

(1 - 33) (0 - 18) (0 - 18) (0 - 18) (0 - 18) (0 - 16

35 33 31 28 26 28 

(6 - 64) (5 - 61) (5 - 56) (5 - 52) (4 - 47) (5 - 52

85 54 54 54 51 46 

(8 - 161) (5 - 102) (5 - 102) (5 - 102) (5 - 96) (4 - 87

120 100 100 97 89 85 

(42 - 196) (35 - 163) (35 - 163) (34 - 159) (31 - 147) (30 - 14

24 22 22 21 19 19 

(-2 - 50) (-2 - 46) (-2 - 46) (-2 - 43) (-2 - 40) (-2 - 39

47 47 47 47 45 44 

(4 - 90) (4 - 90) (4 - 90) (4 - 90) (4 - 85) (4 - 84

26 18 18 18 17 15 

(-3 - 53) (-2 - 38) (-2 - 38) (-2 - 37) (-2 - 35) (-2 - 32

9 66665

(1 - 17) (1 - 11) (1 - 11) (1 - 11) (1 - 11) (1 - 9) 

30 27 25 23 22 23 

(-8 - 67) (-7 - 60) (-7 - 57) (-6 - 53) (-6 - 48) (-6 - 52

8 66665

(0 - 16) (0 - 13) (0 - 13) (0 - 13) (0 - 13) (0 - 11

Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations in a 

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 

n/m)2

:

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
E-95

Table E-94. Estimated Percent of Total Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to  Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

1.7% 1.6% 1.5% 1.3% 1.2% 1.3% 

(-0.7% - 4.1%) (-0.6% - 3.7%) (-0.6% - 3.5%) (-0.5% - 3.2%) (-0.5% - 2.9%) (-0.5% - 3

3.1% 2.9% 2.8% 2.6% 2.4% 2.5% 

(0.6% - 5.6%) (0.5% - 5.3%) (0.5% - 5%) (0.5% - 4.6%) (0.4% - 4.2%) (0.5% - 4.

1.4% 1.1% 1% 0.9% 0.8% 0.9% 

(-1.1% - 3.7%) (-0.9% - 2.9%) (-0.8% - 2.7%) (-0.7% - 2.5%) (-0.7% - 2.3%) (-0.7% - 2

1% 1% 1% 1% 1% 1% 

(-0.9% - 3%) (-0.9% - 3%) (-0.9% - 3%) (-0.9% - 3%) (-0.9% - 2.7%) (-0.9% - 3

2.6% 2.1% 2.1% 1.9% 1.8% 1.8% 

(0.1% - 5%) (0.1% - 4.1%) (0.1% - 4%) (0.1% - 3.7%) (0.1% - 3.4%) (0.1% - 3.

2.6% 1.5% 1.5% 1.5% 1.5% 1.2% 

(0.1% - 5.1%) (0% - 2.8%) (0% - 2.8%) (0% - 2.8%) (0% - 2.8%) (0% - 2.4

2.5% 2.4% 2.2% 2% 1.9% 2% 

(0.4% - 4.6%) (0.4% - 4.4%) (0.4% - 4%) (0.3% - 3.7%) (0.3% - 3.4%) (0.3% - 3.

1.6% 1% 1% 1% 1% 0.9% 

(0.2% - 3.1%) (0.1% - 1.9%) (0.1% - 1.9%) (0.1% - 1.9%) (0.1% - 1.8%) (0.1% - 1.

3% 2.5% 2.5% 2.4% 2.2% 2.1% 

(1% - 4.8%) (0.9% - 4%) (0.9% - 4%) (0.8% - 3.9%) (0.8% - 3.6%) (0.7% - 3.

2.1% 1.9% 1.9% 1.8% 1.6% 1.6% 

(-0.2% - 4.3%) (-0.2% - 3.9%) (-0.2% - 3.9%) (-0.2% - 3.7%) (-0.2% - 3.4%) (-0.2% - 3

1.9% 1.9% 1.9% 1.9% 1.8% 1.8% 

(0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.1% - 3.5%) (0.1% - 3.

2.4% 1.7% 1.7% 1.6% 1.6% 1.4% 

(-0.2% - 4.9%) (-0.2% - 3.5%) (-0.2% - 3.5%) (-0.2% - 3.4%) (-0.2% - 3.2%) (-0.1% - 3

1.9% 1.2% 1.2% 1.2% 1.2% 1% 

(0.2% - 3.5%) (0.1% - 2.2%) (0.1% - 2.2%) (0.1% - 2.2%) (0.1% - 2.2%) (0.1% - 1.

2% 1.8% 1.7% 1.6% 1.4% 1.5% 

(-0.5% - 4.5%) (-0.5% - 4%) (-0.4% - 3.8%) (-0.4% - 3.5%) (-0.4% - 3.2%) (-0.4% - 3

1.8% 1.5% 1.5% 1.5% 1.5% 1.3% 

(0% - 3.6%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 2.5

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Conc

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-96

Table E-95. Estimated Percent of Total Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to  Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

1.7% 1.6% 1.4% 1.3% 1.2% 1.3% 

(-0.7% - 4%) (-0.6% - 3.7%) (-0.6% - 3.4%) (-0.5% - 3.1%) (-0.5% - 2.9%) (-0.5% - 3

2.7% 2.6% 2.4% 2.2% 2.1% 2.2% 

(0.5% - 4.9%) (0.5% - 4.6%) (0.4% - 4.3%) (0.4% - 4%) (0.4% - 3.7%) (0.4% - 3.

1.3% 1% 0.9% 0.9% 0.8% 0.9% 

(-1% - 3.6%) (-0.8% - 2.8%) (-0.8% - 2.6%) (-0.7% - 2.4%) (-0.6% - 2.2%) (-0.7% - 2

0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 

(-0.8% - 2.4%) (-0.8% - 2.4%) (-0.8% - 2.4%) (-0.8% - 2.4%) (-0.7% - 2.3%) (-0.8% - 2

2.1% 1.7% 1.7% 1.6% 1.4% 1.5% 

(0.1% - 4.1%) (0.1% - 3.3%) (0.1% - 3.3%) (0.1% - 3%) (0.1% - 2.8%) (0.1% - 2.

2.8% 1.5% 1.5% 1.5% 1.5% 1.3% 

(0.1% - 5.3%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 2.6

2.4% 2.3% 2.1% 1.9% 1.8% 1.9% 

(0.4% - 4.4%) (0.4% - 4.1%) (0.3% - 3.8%) (0.3% - 3.5%) (0.3% - 3.2%) (0.3% - 3.

1.5% 0.9% 0.9% 0.9% 0.9% 0.8% 

(0.1% - 2.8%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.7%) (0.1% - 1.

2.5% 2.1% 2.1% 2.1% 1.9% 1.8% 

(0.9% - 4.1%) (0.7% - 3.5%) (0.7% - 3.5%) (0.7% - 3.4%) (0.7% - 3.1%) (0.6% - 3

2% 1.8% 1.8% 1.7% 1.6% 1.5% 

(-0.2% - 4%) (-0.2% - 3.7%) (-0.2% - 3.7%) (-0.2% - 3.5%) (-0.1% - 3.2%) (-0.1% - 3

2% 2% 2% 2% 1.9% 1.8% 

(0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.1% - 3.6%) (0.1% - 3.

2.1% 1.5% 1.5% 1.4% 1.4% 1.2% 

(-0.2% - 4.3%) (-0.1% - 3%) (-0.1% - 3%) (-0.1% - 3%) (-0.1% - 2.8%) (-0.1% - 2

1.7% 1.1% 1.1% 1.1% 1.1% 0.9% 

(0.2% - 3.1%) (0.1% - 2%) (0.1% - 2%) (0.1% - 2%) (0.1% - 2%) (0.1% - 1.

1.7% 1.5% 1.4% 1.3% 1.2% 1.3% 

(-0.4% - 3.7%) (-0.4% - 3.3%) (-0.4% - 3.1%) (-0.3% - 2.9%) (-0.3% - 2.7%) (-0.3% - 2

1.5% 1.2% 1.2% 1.2% 1.2% 1% 

(0% - 3%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.1

Percent of Total Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Conc

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-97

Table E-96. Estimated Percent of Total Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to  Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative St

 Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

1.6% 1.5% 1.4% 1.3% 1.2% 1.3% 

(-0.7% - 3.9%) (-0.6% - 3.6%) (-0.6% - 3.3%) (-0.5% - 3%) (-0.5% - 2.8%) (-0.5% - 3

2.7% 2.6% 2.5% 2.3% 2.1% 2.2% 

(0.5% - 4.9%) (0.5% - 4.7%) (0.5% - 4.4%) (0.4% - 4.1%) (0.4% - 3.8%) (0.4% - 4

1.3% 1% 1% 0.9% 0.8% 0.9% 

(-1.1% - 3.7%) (-0.8% - 2.9%) (-0.8% - 2.7%) (-0.7% - 2.5%) (-0.7% - 2.2%) (-0.7% - 2

0.9% 0.9% 0.9% 0.9% 0.8% 0.9% 

(-0.8% - 2.6%) (-0.8% - 2.6%) (-0.8% - 2.6%) (-0.8% - 2.6%) (-0.8% - 2.4%) (-0.8% - 2

2.2% 1.8% 1.8% 1.6% 1.5% 1.5% 

(0.1% - 4.2%) (0.1% - 3.4%) (0.1% - 3.4%) (0.1% - 3.2%) (0.1% - 2.9%) (0.1% - 2.

2.8% 1.6% 1.6% 1.6% 1.6% 1.3% 

(0.1% - 5.4%) (0.1% - 3%) (0.1% - 3%) (0.1% - 3%) (0.1% - 3%) (0% - 2.6

2.4% 2.3% 2.1% 1.9% 1.8% 1.9% 

(0.4% - 4.4%) (0.4% - 4.1%) (0.3% - 3.8%) (0.3% - 3.5%) (0.3% - 3.2%) (0.3% - 3.

1.5% 1% 1% 1% 0.9% 0.8% 

(0.1% - 2.9%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.7%) (0.1% - 1.

2.8% 2.3% 2.3% 2.2% 2.1% 2% 

(1% - 4.5%) (0.8% - 3.8%) (0.8% - 3.8%) (0.8% - 3.7%) (0.7% - 3.4%) (0.7% - 3.

2% 1.8% 1.8% 1.7% 1.6% 1.6% 

(-0.2% - 4.1%) (-0.2% - 3.8%) (-0.2% - 3.8%) (-0.2% - 3.6%) (-0.1% - 3.3%) (-0.1% - 3

1.8% 1.8% 1.8% 1.8% 1.7% 1.7% 

(0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 3.3%) (0.1% - 3.

2.2% 1.5% 1.5% 1.5% 1.4% 1.3% 

(-0.2% - 4.5%) (-0.2% - 3.2%) (-0.2% - 3.2%) (-0.1% - 3.1%) (-0.1% - 3%) (-0.1% - 2

2% 1.3% 1.3% 1.3% 1.3% 1.1% 

(0.3% - 3.8%) (0.2% - 2.4%) (0.2% - 2.4%) (0.2% - 2.4%) (0.2% - 2.4%) (0.1% - 2

1.7% 1.6% 1.5% 1.3% 1.2% 1.3% 

(-0.5% - 3.9%) (-0.4% - 3.5%) (-0.4% - 3.3%) (-0.4% - 3%) (-0.3% - 2.8%) (-0.3% - 3

1.6% 1.3% 1.3% 1.3% 1.3% 1.1% 

(0% - 3.1%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.2

Percent of Total Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Conc

Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) S

Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-98

Table E-97. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Mortality Associat Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-9% 0% 7% 15% 22% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-6% 0% 5% 12% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 15

-28% 0% 7% 15% 22% 15% 

(-27% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

-23% 0% 1% 8% 16% 14% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (15% - 16%) (14% - 15

-80% 0% 0% 0% 0% 14% 

(-78% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-58% 0% 0% 0% 6% 15% 

(-57% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 15

-20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15

-9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (14% - 15

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

-41% 0% 0% 3% 8% 15% 

(-40% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (14% - 15

-58% 0% 0% 0% 0% 15% 

(-57% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-12% 0% 6% 13% 20% 15% 

(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Respiratory Mortality Associated wi

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alte

Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-99

Table E-98. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Mortality Associat Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-9% 0% 7% 15% 22% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-6% 0% 5% 12% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 15

-28% 0% 7% 15% 22% 15% 

(-27% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15

-80% 0% 0% 0% 0% 14% 

(-79% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-58% 0% 0% 0% 6% 15% 

(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15

-20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15

-9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (14% - 15

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

-42% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 15

-58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15

-12% 0% 6% 13% 20% 15% 

(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

Percent Reduction from the Current Standards: Annual Incidence of Respiratory Mortality Associated wi

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alte

Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-100

Table E-99. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Mortality Associat Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-9% 0% 7% 15% 22% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-6% 0% 5% 13% 20% 14% 

(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 15

-28% 0% 7% 15% 22% 15% 

(-27% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15

-80% 0% 0% 0% 0% 14% 

(-78% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-6% 0% 7% 15% 22% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-58% 0% 0% 0% 6% 15% 

(-57% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 15

-20% 0% 0% 3% 10% 14% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15

-9% 0% 0% 6% 14% 14% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (14% - 15

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

-42% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 15

-58% 0% 0% 0% 0% 15% 

(-57% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-12% 0% 6% 13% 20% 15% 

(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

Percent Reduction from the Current Standards: Annual Incidence of Respiratory Mortality Associated wi

to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alte

Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards t

means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. 

 Dallas, TX Fresno, CA Los Angeles, CA
E-101

Table E-100. Estimated Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term E PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alter

 Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

43 40 37 34 31 34 

(-28 - 115) (-26 - 105) (-24 - 98) (-22 - 90) (-20 - 82) (-22 - 

262 247 234 216 199 212 

(192 - 331) (182 - 313) (172 - 295) (159 - 273) (146 - 251) (155 - 2

21 17 15 14 13 14 

(-14 - 56) (-11 - 44) (-10 - 41) (-9 - 37) (-8 - 34) (-9 - 3

31 31 31 31 28 31 

(-20 - 81) (-20 - 81) (-20 - 81) (-20 - 81) (-19 - 75) (-20 - 

345 280 278 257 236 239 

(253 - 435) (206 - 354) (204 - 351) (189 - 325) (173 - 298) (176 - 

38 21 21 21 21 18 

(0 - 75) (0 - 41) (0 - 41) (0 - 41) (0 - 41) (0 - 3

60 56 52 48 44 48 

(-39 - 158) (-37 - 149) (-34 - 138) (-31 - 127) (-29 - 115) (-31 - 1

418 264 264 264 249 225 

(5 - 827) (3 - 523) (3 - 523) (3 - 523) (3 - 494) (3 - 44

952 792 792 772 709 677 

(700 - 1204) (582 - 1002) (582 - 1002) (567 - 976) (521 - 897) (497 - 

233 214 214 200 184 183 

(171 - 294) (157 - 271) (157 - 271) (147 - 253) (135 - 233) (134 - 2

108 108 108 108 102 101 

(1 - 213) (1 - 213) (1 - 213) (1 - 213) (1 - 203) (1 - 2

222 157 157 153 145 134 

(163 - 280) (115 - 199) (115 - 199) (112 - 193) (106 - 183) (98 - 1

1388887

(0 - 25) (0 - 16) (0 - 16) (0 - 16) (0 - 16) (0 - 1

231 207 195 180 165 177 

(170 - 293) (152 - 262) (143 - 246) (132 - 228) (121 - 209) (130 - 2

26 21 21 21 21 18 

(-65 - 113) (-52 - 92) (-52 - 92) (-52 - 92) (-52 - 92) (-44 - 

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locat

estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposur

in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) an

(Standard Combination Denoted n/m)

2

:

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX St. Louis, MO
E-102

Table E-101. Estimated Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term E PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alter

 Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

44 41 38 35 31 35 

(-29 - 117) (-27 - 108) (-25 - 99) (-23 - 91) (-21 - 83) (-23 - 

227 214 203 187 172 183 

(167 - 287) (157 - 271) (149 - 256) (138 - 237) (126 - 218) (135 - 2

20 16 15 13 12 13 

(-13 - 53) (-10 - 42) (-10 - 38) (-9 - 35) (-8 - 32) (-9 - 3

26 26 26 26 24 26 

(-17 - 68) (-17 - 68) (-17 - 68) (-17 - 68) (-16 - 63) (-17 - 

278 225 224 207 190 192 

(204 - 351) (165 - 285) (164 - 283) (152 - 261) (139 - 240) (141 - 2

40 22 22 22 22 19 

(0 - 80) (0 - 44) (0 - 44) (0 - 44) (0 - 44) (0 - 3

58 55 51 47 43 47 

(-38 - 154) (-36 - 145) (-33 - 134) (-31 - 123) (-28 - 113) (-31 - 1

392 248 248 248 234 211 

(5 - 776) (3 - 491) (3 - 491) (3 - 491) (3 - 463) (3 - 4

822 684 684 666 612 585 

(604 - 1040) (502 - 865) (502 - 865) (489 - 843) (449 - 774) (429 - 

218 201 201 188 173 172 

(160 - 276) (147 - 254) (147 - 254) (138 - 237) (127 - 218) (126 - 2

113 113 113 113 107 106 

(1 - 224) (1 - 224) (1 - 224) (1 - 224) (1 - 212) (1 - 2

190 134 134 130 124 114 

(140 - 240) (98 - 169) (98 - 169) (96 - 165) (91 - 157) (84 - 1

1277776

(0 - 23) (0 - 15) (0 - 15) (0 - 15) (0 - 15) (0 - 1

191 171 160 148 136 146 

(140 - 241) (126 - 216) (118 - 203) (109 - 188) (100 - 172) (107 - 

22 18 18 18 18 15 

(-54 - 95) (-44 - 78) (-44 - 78) (-44 - 78) (-44 - 78) (-37 - 

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locat

estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposur

in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) an

(Standard Combination Denoted n/m)

2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-103

Table E-102. Estimated Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term E PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alter

 Based on Adjusting 2007 PM2.5 Concentrations1

Recent Ambient 

PM2.5 

Concentrations

15/353 14/35 13/35 12/35 13/3

45 41 38 35 32 35 

(-29 - 119) (-27 - 109) (-25 - 101) (-23 - 92) (-21 - 84) (-23 - 

229 216 204 189 174 185 

(168 - 289) (159 - 273) (150 - 258) (139 - 239) (127 - 220) (136 - 2

21 16 15 14 12 14 

(-14 - 54) (-11 - 43) (-10 - 39) (-9 - 36) (-8 - 33) (-9 - 3

28 28 28 28 26 28 

(-18 - 73) (-18 - 73) (-18 - 73) (-18 - 73) (-17 - 68) (-18 - 

288 233 232 214 197 199 

(211 - 364) (171 - 295) (170 - 293) (157 - 271) (144 - 249) (146 - 2

42 23 23 23 23 20 

(1 - 83) (0 - 46) (0 - 46) (0 - 46) (0 - 46) (0 - 3

60 56 52 48 44 48 

(-39 - 158) (-37 - 149) (-34 - 138) (-31 - 127) (-29 - 116) (-31 - 1

408 258 258 258 243 220 

(5 - 807) (3 - 511) (3 - 511) (3 - 511) (3 - 482) (3 - 4

905 752 752 733 673 643 

(665 - 1144) (552 - 951) (552 - 951) (538 - 927) (494 - 852) (472 - 

221 203 203 190 175 174 

(162 - 279) (149 - 257) (149 - 257) (140 - 240) (128 - 221) (128 - 2

108 108 108 108 103 102 

(1 - 215) (1 - 215) (1 - 215) (1 - 215) (1 - 204) (1 - 2

199 140 140 136 129 119 

(146 - 251) (103 - 177) (103 - 177) (100 - 172) (95 - 164) (88 - 1

1599998

(0 - 29) (0 - 18) (0 - 18) (0 - 18) (0 - 18) (0 - 1

199 178 167 155 142 152 

(146 - 251) (131 - 225) (123 - 212) (114 - 196) (104 - 180) (112 - 

23 19 19 19 19 16 

(-57 - 101) (-46 - 82) (-46 - 82) (-46 - 82) (-46 - 82) (-39 - 

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locat

estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposur

in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) an

(Standard Combination Denoted n/m)

2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-104

Table E-103. Estimated Percent of Total Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with  Exposure to Ambient PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet th

 Alternative Standards, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

0.41% 0.4% 0.35% 0.32% 0.29% 0.32% 

(-0.27% - 1.09%) (-0.2% - 1%) (-0.23% - 0.93%) (-0.21% - 0.85%) (-0.19% - 0.78%) (-0.21% - 0

1.59% 1.5% 1.42% 1.32% 1.21% 1.29% 

(1.17% - 2.01%) (1.1% - 1.9%) (1.05% - 1.8%) (0.97% - 1.67%) (0.89% - 1.53%) (0.95% - 1.

0.42% 0.3% 0.31% 0.28% 0.26% 0.28% 

(-0.28% - 1.12%) (-0.2% - 0.9%) (-0.2% - 0.81%) (-0.18% - 0.75%) (-0.17% - 0.68%) (-0.18% - 0

0.32% 0.3% 0.32% 0.32% 0.3% 0.32% 

(-0.21% - 0.85%) (-0.2% - 0.9%) (-0.21% - 0.85%) (-0.21% - 0.85%) (-0.2% - 0.79%) (-0.21% - 0

1.65% 1.3% 1.33% 1.23% 1.13% 1.15% 

(1.22% - 2.09%) (1% - 1.7%) (0.98% - 1.68%) (0.91% - 1.56%) (0.83% - 1.43%) (0.84% - 1.

0.81% 0.4% 0.44% 0.44% 0.44% 0.38% 

(0.01% - 1.59%) (0% - 0.9%) (0.01% - 0.88%) (0.01% - 0.88%) (0.01% - 0.88%) (0% - 0.7

0.35% 0.3% 0.31% 0.28% 0.26% 0.28% 

(-0.23% - 0.93%) (-0.2% - 0.9%) (-0.2% - 0.82%) (-0.19% - 0.75%) (-0.17% - 0.68%) (-0.19% - 0

0.77% 0.5% 0.49% 0.49% 0.46% 0.41% 

(0.01% - 1.52%) (0% - 1%) (0.01% - 0.96%) (0.01% - 0.96%) (0.01% - 0.91%) (0% - 0.8

1.49% 1.2% 1.24% 1.21% 1.11% 1.06% 

(1.09% - 1.88%) (0.9% - 1.6%) (0.91% - 1.57%) (0.89% - 1.53%) (0.81% - 1.4%) (0.78% - 1.

1.41% 1.3% 1.3% 1.22% 1.12% 1.11% 

(1.04% - 1.79%) (1% - 1.6%) (0.96% - 1.64%) (0.89% - 1.54%) (0.82% - 1.41%) (0.82% - 1.

0.53% 0.5% 0.53% 0.53% 0.51% 0.5% 

(0.01% - 1.05%) (0% - 1.1%) (0.01% - 1.05%) (0.01% - 1.05%) (0.01% - 1%) (0.01% - 0.

1.72% 1.2% 1.22% 1.19% 1.13% 1.04% 

(1.26% - 2.17%) (0.9% - 1.5%) (0.89% - 1.54%) (0.87% - 1.5%) (0.83% - 1.42%) (0.76% - 1.

0.52% 0.3% 0.33% 0.33% 0.33% 0.28% 

(0.01% - 1.03%) (0% - 0.7%) (0% - 0.65%) (0% - 0.65%) (0% - 0.65%) (0% - 0.5

1.64% 1.5% 1.38% 1.28% 1.17% 1.26% 

(1.21% - 2.08%) (1.1% - 1.9%) (1.02% - 1.75%) (0.94% - 1.62%) (0.86% - 1.48%) (0.92% - 1.

0.76% 0.6% 0.62% 0.62% 0.62% 0.53% 

(-1.86% - 3.26%) (-1.5% - 2.7%) (-1.5% - 2.65%) (-1.5% - 2.65%) (-1.5% - 2.65%) (-1.28% - 2

2 Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locatio

estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Percent of Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Te

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative A

Standard

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-105

Table E-104. Estimated Percent of Total Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with  Exposure to Ambient PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet th

 Alternative Standards, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

0.41% 0.4% 0.35% 0.32% 0.29% 0.32% 

(-0.27% - 1.08%) (-0.2% - 1%) (-0.23% - 0.92%) (-0.21% - 0.84%) (-0.19% - 0.77%) (-0.21% - 0

1.39% 1.3% 1.24% 1.15% 1.05% 1.12% 

(1.02% - 1.75%) (1% - 1.7%) (0.91% - 1.57%) (0.84% - 1.45%) (0.77% - 1.33%) (0.82% - 1.

0.4% 0.3% 0.29% 0.27% 0.24% 0.27% 

(-0.26% - 1.06%) (-0.2% - 0.8%) (-0.19% - 0.77%) (-0.17% - 0.71%) (-0.16% - 0.64%) (-0.17% - 0

0.27% 0.3% 0.27% 0.27% 0.25% 0.27% 

(-0.17% - 0.7%) (-0.2% - 0.7%) (-0.17% - 0.7%) (-0.17% - 0.7%) (-0.16% - 0.65%) (-0.17% - 0

1.34% 1.1% 1.08% 1% 0.92% 0.93% 

(0.98% - 1.69%) (0.8% - 1.4%) (0.79% - 1.37%) (0.73% - 1.26%) (0.67% - 1.16%) (0.68% - 1.

0.85% 0.5% 0.47% 0.47% 0.47% 0.4% 

(0.01% - 1.68%) (0% - 0.9%) (0.01% - 0.93%) (0.01% - 0.93%) (0.01% - 0.93%) (0% - 0.7

0.33% 0.3% 0.29% 0.27% 0.24% 0.27% 

(-0.22% - 0.88%) (-0.2% - 0.8%) (-0.19% - 0.77%) (-0.17% - 0.71%) (-0.16% - 0.64%) (-0.17% - 0

0.71% 0.4% 0.45% 0.45% 0.42% 0.38% 

(0.01% - 1.41%) (0% - 0.9%) (0.01% - 0.89%) (0.01% - 0.89%) (0.01% - 0.84%) (0% - 0.7

1.27% 1.1% 1.06% 1.03% 0.95% 0.9% 

(0.93% - 1.61%) (0.8% - 1.3%) (0.78% - 1.34%) (0.76% - 1.3%) (0.7% - 1.2%) (0.66% - 1.

1.34% 1.2% 1.24% 1.16% 1.06% 1.06% 

(0.99% - 1.7%) (0.9% - 1.6%) (0.91% - 1.56%) (0.85% - 1.46%) (0.78% - 1.34%) (0.78% - 1.

0.54% 0.5% 0.54% 0.54% 0.51% 0.5% 

(0.01% - 1.07%) (0% - 1.1%) (0.01% - 1.07%) (0.01% - 1.07%) (0.01% - 1.02%) (0.01% - 

1.5% 1.1% 1.05% 1.03% 0.98% 0.9% 

(1.1% - 1.89%) (0.8% - 1.3%) (0.77% - 1.33%) (0.75% - 1.3%) (0.72% - 1.23%) (0.66% - 1.

0.46% 0.3% 0.29% 0.29% 0.29% 0.25% 

(0.01% - 0.92%) (0% - 0.6%) (0% - 0.58%) (0% - 0.58%) (0% - 0.58%) (0% - 0.4

1.36% 1.2% 1.14% 1.06% 0.97% 1.04% 

(1% - 1.72%) (0.9% - 1.5%) (0.84% - 1.45%) (0.78% - 1.34%) (0.71% - 1.23%) (0.76% - 1.

0.63% 0.5% 0.51% 0.51% 0.51% 0.43% 

(-1.53% - 2.69%) (-1.2% - 2.2%) (-1.23% - 2.19%) (-1.23% - 2.19%) (-1.23% - 2.19%) (-1.05% - 1

2 Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locatio

estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Percent of Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Te

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative A

Standard

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-106

Table E-105. Estimated Percent of Total Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with  Exposure to Ambient PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet th

 Alternative Standards, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

0.4% 0.4% 0.34% 0.31% 0.28% 0.31% 

(-0.26% - 1.06%) (-0.2% - 1%) (-0.22% - 0.9%) (-0.2% - 0.83%) (-0.19% - 0.75%) (-0.2% - 0.

1.41% 1.3% 1.26% 1.16% 1.07% 1.14% 

(1.03% - 1.78%) (1% - 1.7%) (0.92% - 1.59%) (0.85% - 1.47%) (0.78% - 1.35%) (0.83% - 1.

0.41% 0.3% 0.3% 0.27% 0.25% 0.27% 

(-0.27% - 1.09%) (-0.2% - 0.9%) (-0.19% - 0.79%) (-0.18% - 0.72%) (-0.16% - 0.66%) (-0.18% - 0

0.28% 0.3% 0.28% 0.28% 0.26% 0.28% 

(-0.18% - 0.74%) (-0.2% - 0.7%) (-0.18% - 0.74%) (-0.18% - 0.74%) (-0.17% - 0.68%) (-0.18% - 0

1.4% 1.1% 1.13% 1.04% 0.96% 0.97% 

(1.03% - 1.77%) (0.8% - 1.4%) (0.83% - 1.42%) (0.76% - 1.32%) (0.7% - 1.21%) (0.71% - 1.

0.86% 0.5% 0.48% 0.48% 0.48% 0.41% 

(0.01% - 1.7%) (0% - 0.9%) (0.01% - 0.94%) (0.01% - 0.94%) (0.01% - 0.94%) (0% - 0.8

0.33% 0.3% 0.29% 0.27% 0.24% 0.27% 

(-0.22% - 0.88%) (-0.2% - 0.8%) (-0.19% - 0.77%) (-0.17% - 0.71%) (-0.16% - 0.64%) (-0.17% - 0

0.72% 0.5% 0.46% 0.46% 0.43% 0.39% 

(0.01% - 1.43%) (0% - 0.9%) (0.01% - 0.91%) (0.01% - 0.91%) (0.01% - 0.86%) (0% - 0.7

1.39% 1.2% 1.15% 1.12% 1.03% 0.99% 

(1.02% - 1.75%) (0.8% - 1.5%) (0.85% - 1.46%) (0.83% - 1.42%) (0.76% - 1.31%) (0.72% - 1.

1.38% 1.3% 1.27% 1.18% 1.09% 1.08% 

(1.01% - 1.74%) (0.9% - 1.6%) (0.93% - 1.6%) (0.87% - 1.5%) (0.8% - 1.38%) (0.79% - 1.

0.5% 0.5% 0.5% 0.5% 0.47% 0.47% 

(0.01% - 0.99%) (0% - 1%) (0.01% - 0.99%) (0.01% - 0.99%) (0.01% - 0.94%) (0.01% - 0.

1.58% 1.1% 1.11% 1.08% 1.03% 0.95% 

(1.16% - 2%) (0.8% - 1.4%) (0.82% - 1.41%) (0.8% - 1.37%) (0.76% - 1.3%) (0.7% - 1.

0.56% 0.4% 0.36% 0.36% 0.36% 0.3% 

(0.01% - 1.11%) (0% - 0.7%) (0% - 0.7%) (0% - 0.7%) (0% - 0.7%) (0% - 0.6

1.42% 1.3% 1.19% 1.1% 1.01% 1.09% 

(1.04% - 1.79%) (0.9% - 1.6%) (0.88% - 1.51%) (0.81% - 1.4%) (0.74% - 1.28%) (0.8% - 1.3

0.65% 0.5% 0.52% 0.52% 0.52% 0.45% 

(-1.58% - 2.77%) (-1.3% - 2.3%) (-1.28% - 2.26%) (-1.28% - 2.26%) (-1.28% - 2.26%) (-1.09% - 1

2 Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locatio

estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Percent of Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Te

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative A

Standard

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-107

Table E-106. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Hospital Adm

 Short-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 1

-9% 0% 8% 15% 23% 1

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% 

-6% 0% 6% 13% 20% 1

(-6% - -6%) (0% - 0%) (5% - 6%) (13% - 13%) (20% - 20%) (14% 

-28% 0% 8% 15% 23% 1

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% 

0% 0% 0% 0% 7% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% 

-23% 0% 1% 8% 16% 1

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% 

-81% 0% 0% 0% 0% 1

(-81% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% 

-6% 0% 8% 15% 23% 1

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% 

-58% 0% 0% 0% 6% 1

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% 

-20% 0% 0% 3% 10% 1

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% 

-9% 0% 0% 6% 14% 1

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% 

0% 0% 0% 0% 5% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% 

-41% 0% 0% 3% 8% 1

(-41% - -41%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (15% 

-59% 0% 0% 0% 0% 1

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% 

-12% 0% 6% 13% 20% 1

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% 

-23% 0% 0% 0% 0% 1

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Hospital Admiss

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locatio

were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
E-108

Table E-107. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Hospital Adm

 Short-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 1

-9% 0% 8% 15% 23% 1

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% 

-6% 0% 6% 13% 20% 1

(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% 

-28% 0% 8% 15% 23% 1

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% 

0% 0% 0% 0% 7% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% 

-23% 0% 1% 8% 16% 1

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% 

-81% 0% 0% 0% 0% 1

(-81% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% 

-6% 0% 8% 15% 23% 1

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% 

-58% 0% 0% 0% 6% 1

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% 

-20% 0% 0% 3% 10% 1

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% 

-9% 0% 0% 6% 14% 1

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% 

0% 0% 0% 0% 5% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% 

-42% 0% 0% 3% 7% 1

(-42% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% 

-59% 0% 0% 0% 0% 1

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% 

-12% 0% 6% 13% 20% 1

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% 

-23% 0% 0% 0% 0% 1

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% 

Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Hospital Admiss

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locatio

were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA
E-109

Table E-108. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Hospital Adm

 Short-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 1

-9% 0% 8% 15% 23% 1

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% 

-6% 0% 6% 13% 20% 1

(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% 

-28% 0% 8% 15% 23% 1

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% 

0% 0% 0% 0% 7% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% 

-23% 0% 1% 8% 16% 1

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% 

-81% 0% 0% 0% 0% 1

(-81% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% 

-6% 0% 8% 15% 23% 1

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% 

-58% 0% 0% 0% 6% 1

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% 

-20% 0% 0% 3% 10% 1

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% 

-9% 0% 0% 6% 14% 1

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% 

0% 0% 0% 0% 5% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% 

-42% 0% 0% 3% 7% 1

(-42% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% 

-59% 0% 0% 0% 0% 1

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% 

-12% 0% 6% 13% 20% 1

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% 

-23% 0% 0% 0% 0% 1

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% 

Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Hospital Admiss

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet t

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et al. (2008). Locatio

were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA
E-110

Table E-109. Estimated Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Expos

 PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Altern

 Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

19 17 16 15 13 15 

(-23 - 60) (-22 - 55) (-20 - 51) (-18 - 47) (-17 - 43) (-18 - 4

21 20 19 17 16 17 

(-12 - 54) (-12 - 51) (-11 - 48) (-10 - 45) (-9 - 41) (-10 - 4

9 77666

(-11 - 29) (-9 - 23) (-8 - 21) (-8 - 20) (-7 - 18) (-8 - 20

15 15 15 15 14 15 

(-18 - 47) (-18 - 47) (-18 - 47) (-18 - 47) (-17 - 44) (-18 - 4

31 25 25 23 21 21 

(-18 - 79) (-15 - 64) (-15 - 64) (-13 - 59) (-12 - 54) (-13 - 5

25 14 14 14 14 12 

(6 - 44) (3 - 25) (3 - 25) (3 - 25) (3 - 25) (3 - 21

27 25 23 21 19 21 

(-34 - 86) (-32 - 81) (-29 - 75) (-27 - 69) (-24 - 63) (-27 - 6

269 170 170 170 161 145 

(63 - 473) (40 - 300) (40 - 300) (40 - 300) (37 - 283) (34 - 25

79 65 65 64 58 56 

(-46 - 203) (-38 - 169) (-38 - 169) (-37 - 164) (-34 - 151) (-33 - 14

19 17 17 16 15 15 

(-11 - 48) (-10 - 44) (-10 - 44) (-9 - 41) (-9 - 38) (-9 - 38

61 61 61 61 58 57 

(14 - 107) (14 - 107) (14 - 107) (14 - 107) (14 - 102) (13 - 10

18 13 13 12 12 11 

(-11 - 47) (-8 - 33) (-8 - 33) (-7 - 32) (-7 - 30) (-6 - 28

9 66665

(2 - 16) (1 - 10) (1 - 10) (1 - 10) (1 - 10) (1 - 9) 

28 25 23 22 20 21 

(-16 - 72) (-15 - 64) (-14 - 60) (-13 - 56) (-12 - 51) (-13 - 5

2 22222

(-34 - 37) (-27 - 30) (-27 - 30) (-27 - 30) (-27 - 30) (-23 - 2

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Detroit, MI Houston, TX New York, NY St. Louis, MO

Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Incidence estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hos

Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD
E-111

Table E-110. Estimated Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Expos

 PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Altern

 Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

19 17 16 15 13 15 

(-24 - 61) (-22 - 56) (-20 - 52) (-19 - 48) (-17 - 44) (-19 - 4

18 17 16 15 14 15 

(-11 - 47) (-10 - 44) (-10 - 42) (-9 - 39) (-8 - 36) (-9 - 38

9 76656

(-11 - 28) (-8 - 22) (-8 - 20) (-7 - 19) (-7 - 17) (-7 - 19

12 12 12 12 11 12 

(-15 - 40) (-15 - 40) (-15 - 40) (-15 - 40) (-14 - 37) (-15 - 4

25 20 20 18 17 17 

(-15 - 64) (-12 - 52) (-12 - 51) (-11 - 47) (-10 - 44) (-10 - 4

27 15 15 15 15 13 

(6 - 47) (3 - 26) (3 - 26) (3 - 26) (3 - 26) (3 - 22

26 25 23 21 19 21 

(-33 - 84) (-31 - 79) (-29 - 73) (-26 - 68) (-24 - 62) (-26 - 6

253 160 160 160 151 136 

(59 - 444) (37 - 281) (37 - 281) (37 - 281) (35 - 265) (32 - 24

68 56 56 55 50 48 

(-40 - 175) (-33 - 145) (-33 - 145) (-32 - 142) (-30 - 130) (-28 - 12

17 16 16 15 14 14 

(-10 - 45) (-9 - 41) (-9 - 41) (-9 - 39) (-8 - 36) (-8 - 35

64 64 64 64 61 60 

(15 - 112) (15 - 112) (15 - 112) (15 - 112) (14 - 107) (14 - 10

16 11 11 11 10 9 

(-9 - 40) (-6 - 28) (-6 - 28) (-6 - 27) (-6 - 26) (-5 - 24

8 55555

(2 - 15) (1 - 9) (1 - 9) (1 - 9) (1 - 9) (1 - 8) 

23 21 19 18 16 18 

(-14 - 59) (-12 - 53) (-11 - 50) (-10 - 46) (-10 - 42) (-10 - 4

2 22221

(-28 - 31) (-23 - 25) (-23 - 25) (-23 - 25) (-23 - 25) (-19 - 2

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hos

Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD
E-112

Table E-111. Estimated Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Expos

 PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Altern

 Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

19 18 16 15 14 15 

(-24 - 62) (-22 - 57) (-21 - 53) (-19 - 48) (-17 - 44) (-19 - 4

18 17 16 15 14 15 

(-11 - 47) (-10 - 45) (-10 - 42) (-9 - 39) (-8 - 36) (-9 - 38

9 76656

(-11 - 29) (-9 - 22) (-8 - 21) (-7 - 19) (-7 - 17) (-7 - 19

13 13 13 13 12 13 

(-17 - 43) (-17 - 43) (-17 - 43) (-17 - 43) (-15 - 40) (-17 - 4

26 21 21 19 18 18 

(-15 - 66) (-12 - 54) (-12 - 53) (-11 - 49) (-10 - 45) (-10 - 4

28 15 15 15 15 13 

(7 - 49) (4 - 27) (4 - 27) (4 - 27) (4 - 27) (3 - 23

27 25 23 21 20 21 

(-34 - 87) (-32 - 82) (-29 - 76) (-27 - 69) (-25 - 63) (-27 - 6

263 166 166 166 157 142 

(61 - 461) (39 - 293) (39 - 293) (39 - 293) (37 - 276) (33 - 25

75 62 62 60 56 53 

(-44 - 193) (-37 - 160) (-37 - 160) (-36 - 156) (-33 - 143) (-31 - 13

18 16 16 15 14 14 

(-10 - 46) (-10 - 42) (-10 - 42) (-9 - 39) (-8 - 36) (-8 - 36

61 61 61 61 58 57 

(14 - 108) (14 - 108) (14 - 108) (14 - 108) (14 - 103) (13 - 10

16 11 11 11 11 10 

(-10 - 42) (-7 - 29) (-7 - 29) (-7 - 29) (-6 - 27) (-6 - 25

1177776

(2 - 19) (2 - 12) (2 - 12) (2 - 12) (2 - 12) (1 - 10

24 21 20 19 17 18 

(-14 - 62) (-13 - 55) (-12 - 52) (-11 - 48) (-10 - 44) (-11 - 4

2 22222

(-30 - 33) (-24 - 27) (-24 - 27) (-24 - 27) (-24 - 27) (-21 - 2

2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

 Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Detroit, MI Houston, TX New York, NY St. Louis, MO

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m

Combination Denoted n/m)2

:

1Incidence estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hos

Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD
E-113

Table E-112. Estimated Percent of Total Annual Incidence of Hospital Admissions for Respiratory Illness Associated with S Ambient PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

0.5% 0.46% 0.42% 0.39% 0.35% 0.39

(-0.63% - 1.61%) (-0.58% - 1.48%) (-0.53% - 1.37%) (-0.49% - 1.26%) (-0.45% - 1.15%) (-0.49% - 

0.42% 0.39% 0.37% 0.34% 0.32% 0.34

(-0.25% - 1.08%) (-0.23% - 1.02%) (-0.22% - 0.96%) (-0.2% - 0.89%) (-0.19% - 0.82%) (-0.2% - 0

0.51% 0.4% 0.37% 0.34% 0.31% 0.34

(-0.65% - 1.65%) (-0.51% - 1.3%) (-0.47% - 1.2%) (-0.43% - 1.1%) (-0.39% - 1.01%) (-0.43% - 

0.39% 0.39% 0.39% 0.39% 0.36% 0.39

(-0.49% - 1.26%) (-0.49% - 1.26%) (-0.49% - 1.26%) (-0.49% - 1.26%) (-0.45% - 1.17%) (-0.49% - 

0.43% 0.35% 0.35% 0.32% 0.3% 0.3% 

(-0.26% - 1.12%) (-0.21% - 0.91%) (-0.21% - 0.9%) (-0.19% - 0.83%) (-0.17% - 0.76%) (-0.18% - 

1.42% 0.78% 0.78% 0.78% 0.78% 0.67

(0.33% - 2.49%) (0.18% - 1.38%) (0.18% - 1.38%) (0.18% - 1.38%) (0.18% - 1.38%) (0.16% - 

0.43% 0.4% 0.37% 0.34% 0.31% 0.34

(-0.54% - 1.38%) (-0.51% - 1.3%) (-0.47% - 1.2%) (-0.43% - 1.11%) (-0.39% - 1.01%) (-0.43% - 

1.36% 0.86% 0.86% 0.86% 0.81% 0.73

(0.32% - 2.38%) (0.2% - 1.51%) (0.2% - 1.51%) (0.2% - 1.51%) (0.19% - 1.42%) (0.17% - 

0.39% 0.32% 0.32% 0.32% 0.29% 0.28

(-0.23% - 1.01%) (-0.19% - 0.84%) (-0.19% - 0.84%) (-0.19% - 0.81%) (-0.17% - 0.75%) (-0.16% - 

0.37% 0.34% 0.34% 0.32% 0.29% 0.29

(-0.22% - 0.95%) (-0.2% - 0.88%) (-0.2% - 0.88%) (-0.19% - 0.82%) (-0.17% - 0.75%) (-0.17% - 

0.94% 0.94% 0.94% 0.94% 0.89% 0.88

(0.22% - 1.65%) (0.22% - 1.65%) (0.22% - 1.65%) (0.22% - 1.65%) (0.21% - 1.57%) (0.21% - 

0.45% 0.32% 0.32% 0.31% 0.29% 0.27

(-0.27% - 1.16%) (-0.19% - 0.82%) (-0.19% - 0.82%) (-0.18% - 0.8%) (-0.17% - 0.76%) (-0.16% - 

0.92% 0.58% 0.58% 0.58% 0.58% 0.49

(0.21% - 1.61%) (0.14% - 1.02%) (0.14% - 1.02%) (0.14% - 1.02%) (0.14% - 1.02%) (0.12% - 

0.43% 0.39% 0.36% 0.33% 0.31% 0.33

(-0.25% - 1.11%) (-0.23% - 0.99%) (-0.21% - 0.93%) (-0.2% - 0.86%) (-0.18% - 0.79%) (-0.19% - 

0.2% 0.16% 0.16% 0.16% 0.16% 0.14

(-2.72% - 2.96%) (-2.19% - 2.41%) (-2.19% - 2.41%) (-2.19% - 2.41%) (-2.19% - 2.41%) (-1.87% - 

2 Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding th3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Te

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative 

Standards (Standard Combination Denoted n/m)2

:

1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital adm

Bell et al. (2008). Location-specific C-R function estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-114

Table E-113. Estimated Percent of Total Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Sh Ambient PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current an

 Standards, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

0.49% 0.45% 0.42% 0.38% 0.35% 0.38% 

(-0.62% - 1.59%) (-0.57% - 1.46%) (-0.53% - 1.35%) (-0.48% - 1.24%) (-0.44% - 1.13%) (-0.48% - 1

0.36% 0.34% 0.32% 0.3% 0.28% 0.29% 

(-0.21% - 0.94%) (-0.2% - 0.89%) (-0.19% - 0.84%) (-0.18% - 0.77%) (-0.16% - 0.71%) (-0.17% - 0

0.48% 0.38% 0.35% 0.32% 0.29% 0.32% 

(-0.61% - 1.57%) (-0.48% - 1.23%) (-0.44% - 1.14%) (-0.41% - 1.04%) (-0.37% - 0.95%) (-0.41% - 1

0.32% 0.32% 0.32% 0.32% 0.3% 0.32% 

(-0.4% - 1.04%) (-0.4% - 1.04%) (-0.4% - 1.04%) (-0.4% - 1.04%) (-0.37% - 0.96%) (-0.4% - 1.

0.35% 0.28% 0.28% 0.26% 0.24% 0.24% 

(-0.21% - 0.9%) (-0.17% - 0.73%) (-0.17% - 0.73%) (-0.15% - 0.67%) (-0.14% - 0.62%) (-0.14% - 0

1.49% 0.83% 0.83% 0.83% 0.83% 0.71% 

(0.35% - 2.62%) (0.19% - 1.45%) (0.19% - 1.45%) (0.19% - 1.45%) (0.19% - 1.45%) (0.16% - 1.

0.4% 0.38% 0.35% 0.32% 0.29% 0.32% 

(-0.51% - 1.3%) (-0.48% - 1.23%) (-0.44% - 1.13%) (-0.4% - 1.04%) (-0.37% - 0.95%) (-0.4% - 1.

1.25% 0.79% 0.79% 0.79% 0.75% 0.68% 

(0.29% - 2.2%) (0.18% - 1.4%) (0.18% - 1.4%) (0.18% - 1.4%) (0.17% - 1.32%) (0.16% - 1.

0.33% 0.28% 0.28% 0.27% 0.25% 0.24% 

(-0.2% - 0.86%) (-0.16% - 0.71%) (-0.16% - 0.71%) (-0.16% - 0.7%) (-0.15% - 0.64%) (-0.14% - 0

0.35% 0.32% 0.32% 0.3% 0.28% 0.28% 

(-0.21% - 0.91%) (-0.19% - 0.83%) (-0.19% - 0.83%) (-0.18% - 0.78%) (-0.16% - 0.72%) (-0.16% - 0

0.95% 0.95% 0.95% 0.95% 0.9% 0.89% 

(0.22% - 1.67%) (0.22% - 1.67%) (0.22% - 1.67%) (0.22% - 1.67%) (0.21% - 1.59%) (0.21% - 1.

0.39% 0.28% 0.28% 0.27% 0.26% 0.24% 

(-0.23% - 1.01%) (-0.16% - 0.71%) (-0.16% - 0.71%) (-0.16% - 0.69%) (-0.15% - 0.66%) (-0.14% - 0

0.82% 0.51% 0.51% 0.51% 0.51% 0.44% 

(0.19% - 1.43%) (0.12% - 0.91%) (0.12% - 0.91%) (0.12% - 0.91%) (0.12% - 0.91%) (0.1% - 0.7

0.36% 0.32% 0.3% 0.28% 0.25% 0.27% 

(-0.21% - 0.92%) (-0.19% - 0.82%) (-0.18% - 0.77%) (-0.16% - 0.71%) (-0.15% - 0.65%) (-0.16% - 0

0.16% 0.13% 0.13% 0.13% 0.13% 0.11% 

(-2.23% - 2.45%) (-1.8% - 1.99%) (-1.8% - 1.99%) (-1.8% - 1.99%) (-1.8% - 1.99%) (-1.53% - 1

2 Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term 

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative A

Standards (Standard Combination Denoted n/m)2

:

1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital admis

Bell et al. (2008). Location-specific C-R function estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-115

Table E-114. Estimated Percent of Total Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Sh Ambient PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current an

 Standards, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

0.48% 0.44% 0.41% 0.38% 0.34% 0.38% 

(-0.61% - 1.56%) (-0.56% - 1.43%) (-0.52% - 1.32%) (-0.47% - 1.22%) (-0.43% - 1.11%) (-0.47% - 1

0.37% 0.35% 0.33% 0.3% 0.28% 0.3% 

(-0.22% - 0.95%) (-0.2% - 0.9%) (-0.19% - 0.85%) (-0.18% - 0.78%) (-0.16% - 0.72%) (-0.18% - 0

0.5% 0.39% 0.36% 0.33% 0.3% 0.33% 

(-0.63% - 1.6%) (-0.49% - 1.26%) (-0.45% - 1.16%) (-0.42% - 1.07%) (-0.38% - 0.97%) (-0.42% - 1

0.34% 0.34% 0.34% 0.34% 0.31% 0.34% 

(-0.42% - 1.09%) (-0.42% - 1.09%) (-0.42% - 1.09%) (-0.42% - 1.09%) (-0.39% - 1.01%) (-0.42% - 1

0.37% 0.3% 0.29% 0.27% 0.25% 0.25% 

(-0.22% - 0.94%) (-0.17% - 0.77%) (-0.17% - 0.76%) (-0.16% - 0.7%) (-0.15% - 0.64%) (-0.15% - 0

1.52% 0.84% 0.84% 0.84% 0.84% 0.72% 

(0.36% - 2.66%) (0.2% - 1.48%) (0.2% - 1.48%) (0.2% - 1.48%) (0.2% - 1.48%) (0.17% - 1.

0.4% 0.38% 0.35% 0.32% 0.29% 0.32% 

(-0.51% - 1.3%) (-0.48% - 1.23%) (-0.44% - 1.13%) (-0.41% - 1.04%) (-0.37% - 0.95%) (-0.41% - 1

1.28% 0.81% 0.81% 0.81% 0.76% 0.69% 

(0.3% - 2.25%) (0.19% - 1.42%) (0.19% - 1.42%) (0.19% - 1.42%) (0.18% - 1.34%) (0.16% - 1.

0.36% 0.3% 0.3% 0.29% 0.27% 0.26% 

(-0.21% - 0.94%) (-0.18% - 0.78%) (-0.18% - 0.78%) (-0.17% - 0.76%) (-0.16% - 0.7%) (-0.15% - 0

0.36% 0.33% 0.33% 0.31% 0.28% 0.28% 

(-0.21% - 0.93%) (-0.2% - 0.85%) (-0.2% - 0.85%) (-0.18% - 0.8%) (-0.17% - 0.73%) (-0.17% - 0

0.88% 0.88% 0.88% 0.88% 0.84% 0.83% 

(0.21% - 1.55%) (0.21% - 1.55%) (0.21% - 1.55%) (0.21% - 1.55%) (0.2% - 1.48%) (0.19% - 1.

0.42% 0.29% 0.29% 0.28% 0.27% 0.25% 

(-0.24% - 1.07%) (-0.17% - 0.75%) (-0.17% - 0.75%) (-0.17% - 0.73%) (-0.16% - 0.7%) (-0.15% - 0

0.99% 0.63% 0.63% 0.63% 0.63% 0.54% 

(0.23% - 1.74%) (0.15% - 1.1%) (0.15% - 1.1%) (0.15% - 1.1%) (0.15% - 1.1%) (0.12% - 0.

0.37% 0.33% 0.31% 0.29% 0.26% 0.28% 

(-0.22% - 0.96%) (-0.2% - 0.86%) (-0.18% - 0.81%) (-0.17% - 0.74%) (-0.16% - 0.68%) (-0.17% - 0

0.17% 0.14% 0.14% 0.14% 0.14% 0.12% 

(-2.31% - 2.52%) (-1.86% - 2.05%) (-1.86% - 2.05%) (-1.86% - 2.05%) (-1.86% - 2.05%) (-1.59% - 1

2 Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the 3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term 

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative A

Standards (Standard Combination Denoted n/m)2

:

1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital admis

Bell et al. (2008). Location-specific C-R function estimates were not available from this study.

 Dallas, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
E-116

Table E-115. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Hospital Admissio Short-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-9% 0% 8% 15% 23% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-6% 0% 6% 13% 20% 15% 

(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15

-28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15

-81% 0% 0% 0% 0% 15% 

(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-6% 0% 8% 15% 23% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-58% 0% 0% 0% 6% 15% 

(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15

-20% 0% 0% 3% 11% 15% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (11% - 11%) (15% - 15

-9% 0% 0% 6% 14% 15% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (15% - 15

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

-41% 0% 0% 3% 8% 15% 

(-41% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (15% - 15

-58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15

-12% 0% 6% 13% 20% 15% 

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital admis

Bell et al. (2008). Location-specific C-R function estimates were not available from this study. 

 Dallas, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Respiratory Hospital Admissions 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the C

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
E-117

Table E-116. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Hospital Admiss

 Short-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/3

-9% 0% 8% 15% 23% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

-6% 0% 6% 13% 20% 15% 

(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 

-28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 

-81% 0% 0% 0% 0% 15% 

(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 

-6% 0% 8% 15% 23% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 

-58% 0% 0% 0% 6% 15% 

(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 

-20% 0% 0% 3% 11% 15% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (11% - 11%) (15% - 

-9% 0% 0% 6% 14% 15% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (15% - 

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 

-43% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 

-58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 

-12% 0% 6% 13% 20% 15% 

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the P1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital adm

Bell et al. (2008). Location-specific C-R function estimates were not available from this study. 

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Respiratory Hospital Admissions 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the 

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2

:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-118

Table E-117. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Hospital Admissio Short-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 13/30 

-9% 0% 8% 15% 23% 15% 

(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-6% 0% 6% 13% 20% 15% 

(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15

-28% 0% 8% 15% 23% 15% 

(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

0% 0% 0% 0% 7% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%

-23% 0% 1% 8% 16% 15% 

(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15

-81% 0% 0% 0% 0% 15% 

(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

-6% 0% 8% 15% 23% 15% 

(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15

-58% 0% 0% 0% 6% 15% 

(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15

-20% 0% 0% 3% 11% 15% 

(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (11% - 11%) (15% - 15

-9% 0% 0% 6% 14% 15% 

(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (15% - 15

0% 0% 0% 0% 5% 6% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%

-43% 0% 0% 3% 7% 15% 

(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15

-58% 0% 0% 0% 0% 15% 

(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15

-12% 0% 6% 13% 20% 15% 

(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15

-23% 0% 0% 0% 0% 15% 

(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital admis

Bell et al. (2008). Location-specific C-R function estimates were not available from this study. 

 Dallas, TX Fresno, CA Los Angeles, CA

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Respiratory Hospital Admissions 

Term Exposure to PM2.5 Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the C

Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
E-119

Table E-118. Estimated Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exposure to Ambien

 in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Base

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

216 198 183 169 154 

(-304 - 727) (-279 - 668) (-258 - 618) (-237 - 568) (-216 - 518) 

814 746 690 634 578 

(-816 - 2419) (-748 - 2220) (-691 - 2055) (-635 - 1889) (-578 - 1723

5235 4375 4375 4265 3927 

(3346 - 7071) (2790 - 5923) (2790 - 5923) (2719 - 5776) (2501 - 5323

 Tolbert et al. (2007)

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 

Study

 Tolbert et al. (2007) Ito et al. (2007)

Location ER Visit for:

Incidence of ER Visits Associated with Short-Term Exposure to PM2.5 Conc

PM2.5 Concentrations that Just Meet the Current and Alternative Annual 

(Standard Combination Denoted n/m):

New York, NY Asthma

Atlanta, GA Cardiovascular illnessAtlanta, GA Respiratory illness
E-120

Table E-119. Estimated Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exposure to Ambien

 in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Base

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

220 202 187 172 157 

(-310 - 741) (-284 - 681) (-263 - 630) (-241 - 579) (-220 - 52

829 761 704 647 589 

(-831 - 2465) (-762 - 2263) (-705 - 2094) (-647 - 1925) (-590 - 17

4506 3764 3764 3669 3377 

(2876 - 6095) (2397 - 5102) (2397 - 5102) (2336 - 4974) (2149 - 45

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tolbert et al. (2007)

1Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin

Study

 Tolbert et al. (2007) Ito et al. (2007)

Location ER Visit for:

Incidence of ER Visits Associated with Short-Term Exposure to PM2.5 Con

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) a

Combination Denoted n/m):

New York, NY Asthma

Atlanta, GA Cardiovascular illnessAtlanta, GA Respiratory illness
E-121

Table E-120. Estimated Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exposure to Ambien

 in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Base

 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

219 201 186 171 156 

(-308 - 738) (-283 - 677) (-261 - 627) (-240 - 576) (-219 - 526) 

825 757 700 643 586 

(-827 - 2453) (-758 - 2251) (-701 - 2084) (-644 - 1915) (-587 - 1747) 

4926 4115 4115 4011 3692 

(3145 - 6660) (2622 - 5575) (2622 - 5575) (2555 - 5436) (2350 - 5008

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Tolbert et al. (2007)

1Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundin

Study

 Tolbert et al. (2007) Ito et al. (2007)

Location ER Visit for:

Incidence of ER Visits Associated with Short-Term Exposure to PM2.5 Conce

PM2.5 Concentrations that Just Meet the Current and Alternative Annual 

(Standard Combination Denoted n/m):

New York, NY Asthma

Atlanta, GA Cardiovascular illnessAtlanta, GA Respiratory illness
E-122

Table E-121. Estimated Percent of Total Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exp

 PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Altern

 on Adjusting 2005 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

0.6% 0.6% 0.5% 0.5% 0.5% 

(-0.9% - 2.2%) (-0.8% - 2%) (-0.8% - 1.9%) (-0.7% - 1.7%) (-0.6% - 1.6%

0.6% 0.6% 0.5% 0.5% 0.5% 

(-0.6% - 1.9%) (-0.6% - 1.8%) (-0.6% - 1.6%) (-0.5% - 1.5%) (-0.5% - 1.4%

6.1% 5.1% 5.1% 5% 4.6% 

(3.9% - 8.2%) (3.3% - 6.9%) (3.3% - 6.9%) (3.2% - 6.7%) (2.9% - 6.2%

Asthma

Atlanta, GA Cardiovascular illness

Atlanta, GA Respiratory illness

Percent of Total Incidence of ER Visits Associated with Short-Term Exposu

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alter

Standards (Standard Combination Denoted n/m

 Tolbert et al. (2007)

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM c

Study

 Tolbert et al. (2007) Ito et al. (2007)

Location ER Visit for:

New York, NY
E-123

Table E-122. Estimated Percent of Total Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Ex PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alter

 on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

0.6% 0.6% 0.5% 0.5% 0.5% 

(-0.9% - 2.1%) (-0.8% - 2%) (-0.8% - 1.8%) (-0.7% - 1.7%) (-0.6% - 1.

0.6% 0.6% 0.5% 0.5% 0.5% 

(-0.6% - 1.9%) (-0.6% - 1.7%) (-0.5% - 1.6%) (-0.5% - 1.5%) (-0.5% - 1.

5.2% 4.4% 4.4% 4.3% 3.9% 

(3.3% - 7.1%) (2.8% - 5.9%) (2.8% - 5.9%) (2.7% - 5.8%) (2.5% - 5.3

Asthma

Atlanta, GA Cardiovascular illnessAtlanta, GA Respiratory illness

Percent of Total Incidence of ER Visits Associated with Short-Term Expo

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alt

Standards (Standard Combination Denoted n

 Tolbert et al. (2007)

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 

Study

 Tolbert et al. (2007) Ito et al. (2007)

Location ER Visit for:

New York, NY
E-124

Table E-123. Estimated Percent of Total Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exp

 PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Altern

 on Adjusting 2007 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

0.6% 0.6% 0.5% 0.5% 0.4% 

(-0.9% - 2.1%) (-0.8% - 1.9%) (-0.7% - 1.8%) (-0.7% - 1.6%) (-0.6% - 1.

0.6% 0.6% 0.5% 0.5% 0.4% 

(-0.6% - 1.8%) (-0.6% - 1.7%) (-0.5% - 1.6%) (-0.5% - 1.4%) (-0.4% - 1.

5.7% 4.8% 4.8% 4.6% 4.3% 

(3.6% - 7.7%) (3% - 6.5%) (3% - 6.5%) (3% - 6.3%) (2.7% - 5.8

Asthma

Atlanta, GA Cardiovascular illnessAtlanta, GA Respiratory illness

Percent of Total Incidence of ER Visits Associated with Short-Term Expos

Recent Year and PM2.5 Concentrations that Just Meet the Current and Alte

Standards (Standard Combination Denoted n

 Tolbert et al. (2007)

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 

Study

 Tolbert et al. (2007) Ito et al. (2007)

Location ER Visit for:

New York, NY
F-1

APPENDIX F: SENSITIVITY ANALYSIS RESULTS 
F-2

Appendix F. Sensitivity Analysis Results 

This Appendix provides detailed results of the single- and multi-factor sensitivity 

analyses completed as part of this risk analysis. For additional detail on the sensitivity analysis 

results completed for this analysis, as well as the types of results generated, see section 4.3. 
F-3

Table F-1. Sensitivity Analysis: Impact of Using Different Model Choices to Estimate the Incidence of Mortality Associate Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concen

Standard Fixed 

Effects Log-Linear 

(Cox Proportional 

Hazard) Model3

Random Effects LogLinear Model 4 Random Effects Log- Log Model 5 Fixed Effects vs. Random Effects Log

Linear Models 

1342 1656 3360

(854 - 1827) (772 - 2527) (2075 - 4615)

1526 2569

(1191 - 1856) (1709 - 3400)

1249 1397 2535

(1017 - 1477) (847 - 1924) (1793 - 3232)

164 307

(71 - 253) (160 - 446)

584 719 1254

(372 - 792) (337 - 1090) (779 - 1713)

545 790

(427 - 660) (530 - 1038)

369 411 639

(303 - 434) (253 - 558) (458 - 803)

88 142

(39 - 135) (75 - 204)

6Calculated as (core analysis model estimate - alternative model estimate)/(core analysis model estimate.)

Health Endpoint

Incidence of Mortality Associated with Long-Term Exposure to 

PM2.5 Concentrations Using:2 Percent 

Los Angeles, CA

 All Cause Mortality 23% 

 Cardiopulmonary Mortality --- 7 ---  Ischemic Heart Disease Mortality 12%  Lung Cancer Mortality --- --- 

Philadelphia, PA

 All Cause Mortality 23% 

 Cardiopulmonary Mortality --- ---  Ischemic Heart Disease Mortality 11%  Lung Cancer Mortality --- --- 5Estimates based on Table 11, "MSA and DIFF" rows, in Krewski et al. (2009) -- exposure period from 1999 - 2000. 7Estimates for cardiopulmonary mortality and lung cancer mortality were not available for the random effects log-linear model.1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Mortality incidence was estimated for PM2.5 concentrations down to the lowest measured level in the study (5.8 ug/m3). Numbers rounded to the nearest w

parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

3Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in 4Estimates based on Table 9. Autocorrelation at MSA and ZCA levels; MSA & DIFF, in Krewski et al. (2009) -- exposure period from 1999 - 2000. 
F-4

Table F-2. Sensitivity Analysis: Impact of Using Different Model Choices to Estimate the Incidence of Mortality Associated  Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentr

Standard Fixed 

Effects Log-Linear 

(Cox Proportional 

Hazard) Model3

Random Effects LogLinear Model 4 Random Effects Log- Log Model 5 Fixed Effects vs. Random Effects Log

Linear Models 

1108 1368 2904

(704 - 1509) (637 - 2090) (1790 - 3995)

1263 2225

(985 - 1538) (1477 - 2953)

1038 1162 2212

(843 - 1229) (702 - 1605) (1558 - 2833)

135 266

(59 - 210) (138 - 388)

525 647 1166

(335 - 713) (303 - 982) (723 - 1595)

491 736

(385 - 596) (493 - 969)

334 372 598

(273 - 393) (228 - 507) (428 - 755)

80 133

(35 - 122) (70 - 191)

6Calculated as (core analysis model estimate - alternative model estimate)/(core analysis model estimate.)5Estimates based on Table 11, "MSA and DIFF" rows, in Krewski et al. (2009) -- exposure period from 1999 - 2000. 7Estimates for cardiopulmonary mortality and lung cancer mortality were not available for the random effects log-linear model.1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Mortality incidence was estimated for PM2.5 concentrations down to the lowest measured level in the study (5.8 ug/m3). Numbers rounded to the nearest who

parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

3Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Kre4Estimates based on Table 9. Autocorrelation at MSA and ZCA levels; MSA & DIFF, in Krewski et al. (2009) -- exposure period from 1999 - 2000.  Ischemic Heart Disease Mortality 11%  Lung Cancer Mortality --- ---  Cardiopulmonary Mortality --- --- 

Philadelphia, PA

 All Cause Mortality 23% 

 Ischemic Heart Disease Mortality 12%  Lung Cancer Mortality --- ---  All Cause Mortality 23%  Cardiopulmonary Mortality --- 7 --- 

Health Endpoint

Incidence of Mortality Associated with Long-Term Exposure to 

PM2.5 Concentrations Using:2 Percent 

Los Angeles, CA
F-5

Table F-3. Sensitivity Analysis: Impact of Using Different Model Choices to Estimate the Incidence of Mortality Associate Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concen

Standard Fixed 

Effects Log-Linear 

(Cox Proportional 

Hazard) Model3

Random Effects LogLinear Model 4 Random Effects Log- Log Model 5 Fixed Effects vs. Random Effects Log

Linear Models 

1170 1444 3034

(744 - 1593) (672 - 2206) (1871 - 4173)

1333 2324

(1040 - 1623) (1544 - 3082)

1094 1225 2306

(890 - 1296) (741 - 1691) (1626 - 2950)

143 278

(62 - 222) (145 - 405)

519 639 1157

(331 - 704) (299 - 971) (718 - 1583)

486 731

(381 - 589) (489 - 962)

330 368 594

(270 - 389) (226 - 502) (424 - 750)

79 132

(35 - 121) (69 - 190)

6Calculated as (core analysis model estimate - alternative model estimate)/(core analysis model estimate.) All Cause Mortality Cardiopulmonary Mortality Ischemic Heart Disease Mortality Lung Cancer Mortality5Estimates based on Table 11, "MSA and DIFF" rows, in Krewski et al. (2009) -- exposure period from 1999 - 2000. 3Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in 1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3.  All Cause Mortality Ischemic Heart Disease Mortality2Mortality incidence was estimated for PM2.5 concentrations down to the lowest measured level in the study (5.8 ug/m3). Numbers rounded to the nearest w

parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

 Lung Cancer Mortality

23%

Incidence of Mortality Associated with Long-Term Exposure to 

PM2.5 Concentrations Using:2

Los Angeles, CA

 Cardiopulmonary Mortality ---

Percent 

23%12%

Philadelphia, PA

--- 

Health Endpoint

---

4Estimates based on Table 9. Autocorrelation at MSA and ZCA levels; MSA & DIFF, in Krewski et al. (2009) -- exposure period from 1999 - 2000. 7Estimates for cardiopulmonary mortality and lung cancer mortality were not available for the random effects log-linear model.

--- 

7

---------

12%

---
F-6

Table F-4. Sensitivity Analysis: Impact of Limiting Estimated Annual Incidence of All-Cause Mortality 

 Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards to the Lowest Measured Level in the Study vs. to PRB, Based on Adjusting 2005 

 PM2.5 Concentrations1, 2

Lowest Measured Level in 

Study (5.8 ug/m3) Estimated PRB

736 1057

(470 - 997) (678 - 1426)

702 1073

(448 - 950) (689 - 1446)

380 592

(243 - 516) (379 - 800)

486 762

(310 - 659) (488 - 1030)

743 1205

(474 - 1008) (773 - 1626)

114 262

(72 - 155) (167 - 355)

713 1114

(455 - 968) (713 - 1506)

1342 2845

(854 - 1827) (1819 - 3853)

1893 3299

(1207 - 2571) (2113 - 4456)

584 971

(372 - 792) (622 - 1310)

620 1255

(394 - 843) (803 - 1698)

497 859

(317 - 674) (550 - 1161)

37 161

(24 - 51) (102 - 218)

897 1381

(573 - 1215) (887 - 1862)

103 234

(66 - 141) (149 - 317)

3

 Calculated as (mortality estimated down to PRB - mortality estimated down to LML)/(mortality estimated down to LML).

62%

130%

56%

112%

44%

53%

56%

 Dallas, TX 57%

 Fresno, CA

 Los Angeles, CA

Risk Assessment Location

 Atlanta, GA

 Birmingham, AL

 Houston, TX

 New York, NY

2

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

1

Estimates based on Table 33 in Krewski et al. (2009) -- exposure period from 1999 - 2000, follow-up through 2000, models with 44 individual 

and 7 ecological covariates. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals 

based on statistical uncertainty surrounding the PM coefficient. 

74%

66%

102%

73%

335%

54%

127%

Incidence of All Cause Mortality Associated with LongTerm Exposure to PM2.5 Concentrations Measured 

Down to: Percent Difference3

 St. Louis, MO

 Tacoma, WA

 Philadelphia, PA

 Phoenix, AZ

 Pittsburgh, PA

 Salt Lake City, UT

 Baltimore, MD

 Detroit, MI
F-7

Table F-5. Sensitivity Analysis: Impact of Limiting Estimated Annual Incidence of Ischemic Heart Disease 

 Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards to the Lowest Measured Level in the Study vs. to PRB, Based on Adjusting 2006 PM2.5 

 Concentrations1, 2 

Lowest Measured Level in 

Study (5.8 ug/m3

) Estimated PRB

287 400

(236 - 336) (331 - 465)

375 601

(307 - 441) (497 - 699)

157 244

(128 - 184) (201 - 285)

222 384

(181 - 262) (315 - 450)

449 829

(367 - 530) (683 - 969)

92 198

(75 - 108) (162 - 232)

416 646

(340 - 490) (533 - 755)

1038 2366

(843 - 1229) (1943 - 2775)

1865 3618

(1520 - 2203) (2979 - 4232)

334 559

(273 - 393) (461 - 651)

471 907

(384 - 557) (747 - 1061)

279 531

(228 - 330) (437 - 621)

8 57

(6 - 10) (47 - 67)

512 862

(419 - 603) (712 - 1006)

46 143

(37 - 55) (117 - 168)

3

 Calculated as (IHD mortality estimated down to PRB - IHD mortality estimated down to LML)/(IHD mortality estimated down to LML).

613%

68%

211%

94%

67%

93%

90%

85%

115%

55%

128%

 Tacoma, WA

 Philadelphia, PA

 Phoenix, AZ

 Pittsburgh, PA

 Salt Lake City, UT

2

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

1

Estimates based on Table 33 in Krewski et al. (2009) -- exposure period from 1999 - 2000, follow-up through 2000, models with 44 individual 

and 7 ecological covariates. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals 

based on statistical uncertainty surrounding the PM coefficient. 

 Dallas, TX

 Fresno, CA

 Los Angeles, CA

73%

 Detroit, MI

 Houston, TX

 New York, NY

 St. Louis, MO

Risk Assessment Location

 Atlanta, GA

 Birmingham, AL 55%

 Baltimore, MD

Incidence of Ischemic Heart Disease Mortality 

Associated with Long-Term Exposure to PM2.5 

Concentrations Measured Down to: Percent Difference3

39%

60%
F-8

Table F-6. Sensitivity Analysis: Impact of Limiting Estimated Annual Incidence of All-Cause Mortality 

 Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards to the Lowest Measured Level in the Study vs. to PRB, Based on Adjusting 2007 

 PM2.5 Concentrations1, 2

Lowest Measured Level in 

Study (5.8 ug/m3) Estimated PRB

726 1067

(464 - 984) (684 - 1440)

564 938

(360 - 765) (602 - 1267)

374 590

(238 - 507) (377 - 797)

407 696

(259 - 553) (445 - 942)

544 1007

(346 - 739) (644 - 1362)

130 282

(82 - 177) (180 - 382)

719 1143

(459 - 977) (732 - 1545)

1170 2697

(744 - 1593) (1723 - 3654)

1689 3124

(1076 - 2295) (2000 - 4224)

519 907

(331 - 704) (581 - 1225)

556 1240

(354 - 757) (792 - 1678)

434 795

(277 - 590) (509 - 1074)

48 179

(31 - 66) (114 - 244)

728 1220

(464 - 988) (782 - 1648)

64 201

(41 - 88) (128 - 272)

3

 Calculated as (mortality estimated down to PRB - mortality estimated down to LML)/(mortality estimated down to LML).

85%

117%

59%

131%

47%

66%

58%

 Dallas, TX 71%

 Fresno, CA

 Los Angeles, CA

Risk Assessment Location

 Atlanta, GA

 Birmingham, AL

 Houston, TX

 New York, NY

2

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

1

Estimates based on Table 33 in Krewski et al. (2009) -- exposure period from 1999 - 2000, follow-up through 2000, models with 44 individual 

and 7 ecological covariates. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals 

based on statistical uncertainty surrounding the PM coefficient. 

85%

75%

123%

83%

273%

68%

214%

Incidence of All Cause Mortality Associated with LongTerm Exposure to PM2.5 Concentrations Measured 

Down to: Percent Difference3

 St. Louis, MO

 Tacoma, WA

 Philadelphia, PA

 Phoenix, AZ

 Pittsburgh, PA

 Salt Lake City, UT

 Baltimore, MD

 Detroit, MI
F-9

Table F-7. Sensitivity Analysis: Impact of Using a Different Study to Estimate the Incidence of Mortality 

 Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2005 PM2.5 Concentrations1

Krewski et al. (2009)3 Krewski et al. (2000)4

1342 2965

(854 - 1827) (1005 - 4855)

1526 1981

(1191 - 1856) (693 - 3207)

164 212

(71 - 253) (-152 - 535)

584 1276

(372 - 792) (438 - 2064)

545 704

(427 - 660) (250 - 1121)

88 114

(39 - 135) (-85 - 276)

30%

29%

2

Mortality incidence was estimated for PM2.5 concentrations down to the lowest measured level in Krewski et al., 2009 (5.8 ug/m3). Numbers 

rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty 

5

Calculated as (Krewski et al. (2000) estimate - Krewski et al. (2009) estimate)/(Krewski et al. (2009) estimate).

4

Estimates based on Table 21b in Krewski et al. (2000) [reanalysis of Six Cities Study].

3

Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates 

(see Table 33 in Krewski et al., 2009). 

1

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

30%

Incidence of Mortality Associated with Long-Term 

Exposure to PM2.5 Concentrations Using:2

Percent Difference5

118%

29%

121%

Los Angeles, CA

 Cardiopulmonary Mortality

Philadelphia, PA

Health Endpoint

 All Cause Mortality

 All Cause Mortality

 Cardiopulmonary Mortality

 Lung Cancer Mortality

 Lung Cancer Mortality
F-10

Table F-8. Sensitivity Analysis: Impact of Using a Different Study to Estimate the Incidence of Mortality 

 Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2006 PM2.5 Concentrations1

Krewski et al. (2009)3 Krewski et al. (2000)4

1108 2454

(704 - 1509) (829 - 4031)

1263 1642

(985 - 1538) (572 - 2671)

135 176

(59 - 210) (-124 - 448)

525 1150

(335 - 713) (394 - 1866)

491 635

(385 - 596) (225 - 1016)

80 103

(35 - 122) (-76 - 251)

30%

30%

2

Mortality incidence was estimated for PM2.5 concentrations down to the lowest measured level in Krewski et al., 2009 (5.8 ug/m3). Numbers 

rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty 

5

Calculated as (Krewski et al. (2000) estimate - Krewski et al. (2009) estimate)/(Krewski et al. (2009) estimate).

4

Estimates based on Table 21b in Krewski et al. (2000) [reanalysis of Six Cities Study].

3

Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates 

(see Table 33 in Krewski et al., 2009). 

1

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

29%

Incidence of Mortality Associated with Long-Term 

Exposure to PM2.5 Concentrations Using:2

Percent Difference5

119%

29%

121%

Los Angeles, CA

 Cardiopulmonary Mortality

Philadelphia, PA

Health Endpoint

 All Cause Mortality

 All Cause Mortality

 Cardiopulmonary Mortality

 Lung Cancer Mortality

 Lung Cancer Mortality
F-11

Table F-9. Sensitivity Analysis: Impact of Using a Different Study to Estimate the Incidence of Mortality 

 Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2007 PM2.5 Concentrations1

Krewski et al. (2009)3 Krewski et al. (2000)4

1170 2590

(744 - 1593) (876 - 4252)

1333 1732

(1040 - 1623) (604 - 2815)

143 186

(62 - 222) (-131 - 472)

519 1137

(331 - 704) (389 - 1846)

486 628

(381 - 589) (223 - 1005)

79 102

(35 - 121) (-75 - 249)

30%

30%

2

Mortality incidence was estimated for PM2.5 concentrations down to the lowest measured level in Krewski et al., 2009 (5.8 ug/m3). Numbers 

rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty 

surrounding the PM coefficient.

5

Calculated as (Krewski et al. (2000) estimate - Krewski et al. (2009) estimate)/(Krewski et al. (2009) estimate).

4

Estimates based on Table 21b in Krewski et al. (2000) [reanalysis of Six Cities Study].

3

Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates 

(see Table 33 in Krewski et al., 2009). 

1

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

29%

Incidence of Mortality Associated with Long-Term 

Exposure to PM2.5 Concentrations Using:2

Percent Difference5

119%

29%

121%

Los Angeles, CA

 Cardiopulmonary Mortality

Philadelphia, PA

Health Endpoint

 All Cause Mortality

 All Cause Mortality

 Cardiopulmonary Mortality

 Lung Cancer Mortality

 Lung Cancer Mortality
F-12

Table F-10. Sensitivity Analysis: Estimated Annual Incidence of All Cause Mortality Associated with Long-Term Exposure to  Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005 PM2.5 Concentratio

 Comparison of Proportional and Hybrid Rollback Methods1

15/35 2 14/35 13/35 12/35 13

Proportional 702 643 566 490 5

(448 - 950) (410 - 871) (361 - 768) (312 - 665) (348 

Hybrid 691 667 589 511 5

(442 - 936) (426 - 904) (376 - 799) (326 - 694) (342 

Percent Difference 3 -2% 4% 4% 4% -2

Proportional 380 336 292 247 2

(243 - 516) (214 - 457) (186 - 397) (157 - 336) (186 

Hybrid 461 411 360 310 3

(294 - 624) (262 - 557) (230 - 489) (197 - 421) (230 

Percent Difference 21% 22% 23% 26% 23

Proportional 743 734 643 552 5

(474 - 1008) (468 - 996) (410 - 874) (352 - 751) (361 

Hybrid 773 773 750 651 5

(493 - 1048) (493 - 1048) (479 - 1018) (415 - 884) (378 

Percent Difference 4% 5% 17% 18% 5

Proportional 1342 1342 1342 1180 9

(854 - 1827) (854 - 1827) (854 - 1827) (750 - 1607) (587 - 

Hybrid 1675 1675 1599 1344 12

(1066 - 2276) (1066 - 2276) (1018 - 2175) (855 - 1830) (769 - 

Percent Difference 25% 25% 19% 14% 31

Proportional 1893 1893 1808 1546 14

(1207 - 2571) (1207 - 2571) (1152 - 2455) (984 - 2101) (898 - 

Hybrid 1950 1950 1806 1544 14

(1244 - 2648) (1244 - 2648) (1151 - 2452) (983 - 2099) (930 - 

Percent Difference 3% 3% 0% 0% 3

Proportional 897 813 714 616 6

(573 - 1215) (519 - 1102) (456 - 970) (392 - 836) (443 

Hybrid 956 855 754 652 7

(611 - 1294) (546 - 1159) (481 - 1022) (415 - 885) (481 - 

Percent Difference 7% 5% 6% 6% 8

3 Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5 Conce

Current and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard 

Type of Rollback

1Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewsk

the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

Risk Assessment 

Location

 St. Louis, MO

 Baltimore, MD

 Birmingham, AL

 Detroit, MI

 Los Angeles, CA

 New York, NY
F-13

Table F-11. Sensitivity Analysis: Estimated Annual Incidence of All Cause Mortality Associated with Long-Term Exposure to  Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006 PM2.5 Concentration

 Comparison of Proportional and Hybrid Rollback Methods1

15/35 2 14/35 13/35 12/35 13

Proportional 565 513 446 378 4

(360 - 766) (327 - 696) (284 - 606) (241 - 515) (272 

Hybrid 554 533 465 396 4

(354 - 752) (340 - 724) (296 - 632) (252 - 539) (267 

Percent Difference 3 -2% 4% 4% 5% -2

Proportional 354 312 269 226 2

(226 - 481) (198 - 423) (171 - 365) (144 - 307) (171 

Hybrid 430 382 334 286 3

(275 - 584) (244 - 519) (213 - 454) (182 - 388) (213 

Percent Difference 21% 22% 24% 27% 24

Proportional 510 503 429 355 3

(325 - 694) (320 - 684) (273 - 584) (225 - 483) (233 

Hybrid 534 534 515 434 3

(340 - 725) (340 - 725) (328 - 700) (276 - 591) (246 

Percent Difference 5% 6% 20% 22% 6

Proportional 1108 1108 1108 958 7

(704 - 1509) (704 - 1509) (704 - 1509) (608 - 1305) (457 

Hybrid 1414 1414 1344 1108 9

(899 - 1923) (899 - 1923) (855 - 1829) (704 - 1510) (625 - 

Percent Difference 28% 28% 21% 16% 36

Proportional 1407 1407 1333 1106 9

(895 - 1913) (895 - 1913) (848 - 1813) (703 - 1506) (629 - 

Hybrid 1453 1453 1327 1101 10

(924 - 1975) (924 - 1975) (844 - 1806) (700 - 1499) (654 - 

Percent Difference 3% 3% 0% 0% 4

Proportional 659 588 506 423 4

(420 - 894) (374 - 799) (322 - 688) (269 - 575) (312 

Hybrid 704 620 535 450 5

(449 - 956) (395 - 842) (341 - 727) (286 - 612) (341 

Percent Difference 7% 5% 6% 6% 9

3 Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).

 New York, NY

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5 Conce

Current and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard 

Type of Rollback

1Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewsk

the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

Risk Assessment 

Location

 Baltimore, MD

 Birmingham, AL

 St. Louis, MO

 Detroit, MI

 Los Angeles, CA
F-14

Table F-12. Sensitivity Analysis: Estimated Annual Incidence of All Cause Mortality Associated with Long-Term Exposure to  Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007 PM2.5 Concentration

 Comparison of Proportional and Hybrid Rollback Methods1

15/35 2 14/35 13/35 12/35 13

Proportional 564 512 445 378 4

(360 - 765) (326 - 695) (283 - 605) (240 - 514) (272 

Hybrid 553 532 464 395 4

(353 - 751) (339 - 722) (296 - 630) (252 - 537) (266 

Percent Difference 3 -2% 4% 4% 4% -2

Proportional 374 330 285 241 2

(238 - 507) (210 - 448) (182 - 388) (153 - 327) (182 

Hybrid 454 404 354 304 3

(290 - 615) (258 - 548) (226 - 481) (193 - 413) (226 

Percent Difference 21% 22% 24% 26% 24

Proportional 544 536 460 384 3

(346 - 739) (341 - 729) (293 - 626) (244 - 522) (252 

Hybrid 568 568 549 466 4

(362 - 772) (362 - 772) (350 - 747) (297 - 634) (265 

Percent Difference 4% 6% 19% 21% 5

Proportional 1170 1170 1170 1016 7

(744 - 1593) (744 - 1593) (744 - 1593) (645 - 1384) (490 - 

Hybrid 1484 1484 1413 1171 10

(944 - 2019) (944 - 2019) (899 - 1922) (744 - 1594) (662 - 

Percent Difference 27% 27% 21% 15% 35

Proportional 1689 1689 1607 1359 12

(1076 - 2295) (1076 - 2295) (1023 - 2185) (864 - 1848) (783 - 

Hybrid 1741 1741 1604 1355 12

(1109 - 2366) (1109 - 2366) (1021 - 2180) (862 - 1844) (812 - 

Percent Difference 3% 3% 0% 0% 4

Proportional 728 653 566 478 54

(464 - 988) (416 - 887) (360 - 769) (304 - 651) (350 

Hybrid 787 698 607 516 6

(503 - 1068) (445 - 947) (387 - 825) (329 - 702) (387 

Percent Difference 8% 7% 7% 8% 11

3 Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).

Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5 Conce

Current and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard 

Type of Rollback

1Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewsk

the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

Risk Assessment 

Location

 St. Louis, MO

 New York, NY

 Los Angeles, CA

 Detroit, MI

 Birmingham, AL

 Baltimore, MD

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
F-15

Table F-13. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Respo

 Function to Estimate the Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

 that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

55 53 43 33 184 177

(8 - 102) (3 - 101) (-15 - 99) (-17 - 83) --- 4 (34 - 319)

66 46 60 50 222 256

(9 - 122) (1 - 91) (-7 - 126) (7 - 92) --- (104 - 406)

18 25 17 10 70 34

(-4 - 41) (-3 - 51) (-21 - 55) (-21 - 40) --- (-53 - 121)

30 30 43 46 149 156

(-5 - 64) (-9 - 68) (-3 - 88) (6 - 84) --- (37 - 273)

-6 77 54 34 159 147

(-83 - 69) (19 - 134) (-32 - 137) (-31 - 98) --- (-26 - 317)

0 16 3 11 30 44

(-33 - 33) (-1 - 32) (-14 - 20) (-12 - 34) --- (6 - 81)

45 61 51 55 212 214

(-5 - 94) (5 - 116) (-13 - 113) (-9 - 117) --- (44 - 383)

17 66 -104 -2 -23 81

(-84 - 117) (-35 - 166) (-257 - 48) (-90 - 85) --- (-117 - 278)

279 159 136 206 780 781

(102 - 453) (1 - 315) (-55 - 323) (89 - 321) --- (459 - 1102)

93 28 34 65 220 216

(20 - 165) (-33 - 89) (-48 - 114) (16 - 112) --- (79 - 350)

--- --- --- --- --- 242--- --- --- --- --- (40 - 442)

43 65 44 23 175 159

(-4 - 90) (12 - 117) (-23 - 109) (-28 - 73) --- (47 - 270)

16 6 6 8 36 30

(-2 - 32) (-2 - 14) (-5 - 17) (-3 - 19) --- (6 - 54)

37 75 66 73 251 260

(-37 - 109) (14 - 136) (-6 - 136) (13 - 133) --- (75 - 443)

1 9 4 14 28 48

(-53 - 53) (-7 - 25) (-9 - 17) (-10 - 37) --- (8 - 87)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-spe

coefficient estimators was not available.

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional mea

Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

-

Pe

Diffe

1

 Dallas, TX Fresno, CA Houston, TX Detroit, MI

5 Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.

 St. Louis, MO

Estimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Philadelphia, PA Phoenix, AZ 5

 Pittsburgh, PA Tacoma, WA Salt Lake City, UT
F-16

Table F-14. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Respo Function to Estimate the Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

 that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

48 57 51 29 185 180

(7 - 89) (3 - 109) (-18 - 119) (-15 - 72) --- 4 (34 - 324)

54 41 52 46 193 224

(7 - 101) (1 - 81) (-6 - 109) (6 - 86) --- (91 - 356)

16 27 18 8 69 33

(-4 - 36) (-3 - 56) (-23 - 58) (-16 - 32) --- (-51 - 116)

24 28 36 34 122 130

(-4 - 52) (-8 - 64) (-3 - 75) (5 - 63) --- (31 - 228)

-5 77 39 26 137 118

(-64 - 54) (19 - 134) (-23 - 100) (-24 - 75) --- (-21 - 255)

1 14 4 12 31 47

(-36 - 36) (-1 - 30) (-16 - 24) (-12 - 35) --- (7 - 86)

40 68 51 48 207 208

(-4 - 84) (5 - 130) (-13 - 115) (-8 - 102) --- (42 - 373)

17 57 -97 -2 -25 75

(-86 - 120) (-30 - 143) (-239 - 45) (-78 - 74) --- (-108 - 257)

242 141 111 183 677 671

(89 - 394) (1 - 279) (-44 - 263) (79 - 286) --- (394 - 946)

79 26 33 70 208 204

(17 - 140) (-31 - 83) (-46 - 109) (18 - 121) --- (75 - 331)

--- --- --- --- --- 254--- --- --- --- --- (42 - 463)

39 58 40 17 154 136

(-4 - 81) (10 - 104) (-21 - 100) (-20 - 53) --- (40 - 232)

12 7 7 7 33 27

(-1 - 25) (-2 - 15) (-6 - 19) (-3 - 17) --- (6 - 49)

26 67 58 60 211 215

(-27 - 79) (12 - 120) (-5 - 120) (10 - 110) --- (62 - 367)

1 10 4 12 27 40

(-38 - 38) (-8 - 26) (-10 - 19) (-8 - 30) --- (7 - 73)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

-

P

Diff

 Dallas, TX Fresno, CA Houston, TX Detroit, MI

5 Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.

 St. Louis, MO

Estimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Philadelphia, PA Phoenix, AZ 5

 Pittsburgh, PA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate 

regional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA Salt Lake City, UT
F-17

Table F-15. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Respo Function to Estimate the Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

 that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year

48 70 53 30 201 177

(7 - 88) (4 - 134) (-18 - 122) (-16 - 76) --- 4 (34 - 319)

56 46 56 49 207 227

(7 - 104) (1 - 90) (-6 - 118) (7 - 91) --- (92 - 360)

20 44 21 10 95 34

(-5 - 45) (-5 - 92) (-27 - 67) (-21 - 39) --- (-53 - 120)

28 25 39 39 131 139

(-5 - 60) (-8 - 58) (-3 - 80) (5 - 73) --- (33 - 243)

-6 84 46 42 166 121

(-79 - 66) (21 - 145) (-28 - 119) (-39 - 121) --- (-21 - 262)

1 25 6 22 54 48

(-78 - 76) (-2 - 53) (-23 - 33) (-23 - 64) --- (7 - 89)

49 63 57 52 221 212

(-5 - 102) (5 - 120) (-14 - 127) (-9 - 112) --- (43 - 378)

23 112 -144 -3 -12 77

(-115 - 160) (-59 - 280) (-359 - 66) (-140 - 131) --- (-110 - 262)

319 177 150 241 887 734

(117 - 517) (1 - 350) (-60 - 355) (105 - 376) --- (431 - 1035)

88 32 34 78 232 208

(19 - 156) (-37 - 99) (-48 - 114) (20 - 134) --- (77 - 338)

--- --- --- --- --- 242--- --- --- --- --- (40 - 442)

54 84 57 30 225 143

(-5 - 113) (15 - 152) (-30 - 140) (-35 - 93) --- (42 - 244)

28 11 11 13 63 34

(-3 - 57) (-4 - 26) (-10 - 32) (-5 - 32) --- (7 - 61)

32 83 63 70 248 225

(-32 - 95) (15 - 150) (-6 - 130) (12 - 127) --- (65 - 384)

1 12 4 20 37 42

(-47 - 47) (-9 - 32) (-9 - 16) (-14 - 52) --- (7 - 76)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

-

-

1

5 Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.

 Philadelphia, PA Phoenix, AZ 5 Pittsburgh, PA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate 

regional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA St. Louis, MO

Estimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX

Pe

Diffe

 Fresno, CA Houston, TX Salt Lake City, UT Detroit, MI
F-18

Table F-16. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Resp Function to Estimate the Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 Concentratio

 that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year

14 9 9 -2 30 32

(-21 - 49) (-32 - 48) (-31 - 46) (-37 - 31) --- 4 (-33 - 95)

16 10 11 32 69 70

(-30 - 59) (-31 - 48) (-45 - 64) (-2 - 65) --- (-5 - 143)

4 1 0 -15 -10 -1

(-16 - 24) (-23 - 25) (-29 - 27) (-40 - 8) --- (-43 - 40)

10 11 13 -2 32 32

(-18 - 36) (-21 - 42) (-25 - 49) (-33 - 27) --- (-21 - 85)

-1 25 28 36 88 73

(-48 - 43) (-7 - 57) (-21 - 74) (0 - 72) --- (-9 - 153)

-2 1 0 3 2 11

(-13 - 8) (-3 - 5) (-3 - 4) (-4 - 9) --- (-8 - 30)

8 2 27 7 44 47

(-34 - 49) (-46 - 47) (-21 - 73) (-41 - 54) --- (-32 - 124)

-7 3 -43 0 -47 -31

(-54 - 39) (-45 - 49) (-105 - 17) (-43 - 43) --- (-140 - 76)

149 130 160 100 539 504

(35 - 261) (29 - 228) (30 - 286) (23 - 174) --- (294 - 711)

28 16 27 27 98 87

(-6 - 60) (-14 - 46) (-13 - 65) (4 - 50) --- (23 - 150)

--- --- --- --- --- 84--- --- --- --- --- (-4 - 170)

14 30 13 5 62 47

(-10 - 38) (3 - 56) (-23 - 47) (-20 - 29) --- (-9 - 103)

--- --- --- --- --- 8--- --- --- --- --- (-2 - 18)

-3 48 38 43 126 122

(-68 - 59) (-2 - 95) (-17 - 90) (-4 - 88) --- (27 - 215)

00022 12

(-12 - 13) (-3 - 4) (-2 - 3) (-4 - 7) --- (-7 - 31)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-s

coefficient estimators was not available.

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional m

Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficien

 Birmingham, AL  Dallas, TX  Pittsburgh, PA

Estimated Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 

Concentrations that Just Meet the Current Standards

 Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD

Di

 Detroit, MI

5 Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.

 Fresno, CA Houston, TX Salt Lake City, UT 5 St. Louis, MO Philadelphia, PA Phoenix, AZ 5 Tacoma, WA New York, NY
F-19

Table F-17. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentra

 Function to Estimate the Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 Con

 that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

13 9 10 -2 30 32

(-18 - 42) (-35 - 52) (-38 - 56) (-32 - 27) --- 4 (-33 - 97)

13 8 10 30 61 61

(-25 - 49) (-28 - 43) (-39 - 56) (-2 - 61) --- (-4 - 125)

4 1 0-12 -7 -1

(-14 - 21) (-25 - 27) (-31 - 29) (-31 - 7) --- (-41 - 39)

8 11 11 -1 29 27

(-14 - 29) (-19 - 40) (-21 - 41) (-24 - 21) --- (-18 - 71)

-1 25 20 28 72 58

(-37 - 34) (-7 - 57) (-15 - 55) (0 - 55) --- (-7 - 123)

-21 0 3 2 12

(-14 - 9) (-3 - 5) (-4 - 5) (-4 - 9) --- (-8 - 32)

7 227642 45

(-30 - 44) (-51 - 53) (-22 - 75) (-35 - 47) --- (-31 - 120

-7 2-410-46 -29

(-56 - 40) (-38 - 42) (-98 - 16) (-38 - 37) --- (-129 - 70

130 115 130 88 463 432

(31 - 227) (25 - 202) (25 - 233) (21 - 155) --- (252 - 611

24 15 26 30 95 82

(-5 - 51) (-13 - 42) (-12 - 62) (4 - 54) --- (21 - 142)

--- --- --- --- --- 88--- --- --- --- --- (-4 - 178)

13 27 12 4 56 41

(-9 - 35) (2 - 50) (-21 - 43) (-14 - 21) --- (-8 - 89)

--- --- --- --- --- 7

--- --- --- --- --- (-2 - 16)

-2 42 33 36 109 101

(-49 - 43) (-2 - 85) (-15 - 80) (-3 - 73) --- (23 - 179

00011 10

(-9 - 9) (-3 - 4) (-2 - 3) (-3 - 6) --- (-6 - 26)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

Estimated Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 

Concentrations that Just Meet the Current Standards

 Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD

5 Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.

 Fresno, CA Houston, TX Salt Lake City, UT 5 St. Louis, MO Philadelphia, PA Phoenix, AZ 5

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate 

regional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainsurrounding the PM coefficient. 

 Tacoma, WA New York, NY Pittsburgh, PA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for th

season-specific coefficient estimators was not available.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Birmingham, AL Dallas, TX Detroit, MI
F-20

Table F-18. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Respo Function to Estimate the Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 Concentration

 that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

11 11 10 -2 30 32

(-17 - 39) (-40 - 58) (-35 - 52) (-31 - 26) --- 4 (-33 - 95)

13 9 10 30 62 62

(-24 - 48) (-29 - 45) (-39 - 57) (-2 - 61) --- (-4 - 126)

4 2 0-12 -6 -1

(-14 - 21) (-33 - 35) (-28 - 26) (-31 - 6) --- (-42 - 40)

9 10 11 -2 28 29

(-16 - 34) (-18 - 36) (-23 - 44) (-28 - 24) --- (-19 - 76)

-1 22 19 36 76 60

(-37 - 34) (-6 - 50) (-14 - 52) (0 - 72) --- (-8 - 127)

-31 0 3 1 12

(-16 - 11) (-3 - 5) (-3 - 4) (-4 - 9) --- (-9 - 33)

8 229746 46

(-35 - 50) (-45 - 46) (-23 - 78) (-37 - 48) --- (-31 - 122)

-6 3-380-41 -30

(-47 - 34) (-48 - 53) (-92 - 15) (-42 - 42) --- (-132 - 72)

142 120 147 97 506 473

(34 - 248) (26 - 212) (28 - 262) (23 - 170) --- (276 - 668)

24 16 25 30 95 84

(-5 - 52) (-15 - 47) (-12 - 60) (5 - 55) --- (22 - 145)

--- --- --- --- --- 84

--- --- --- --- --- (-4 - 170)

13 27 12 4 56 43

(-9 - 34) (2 - 51) (-21 - 43) (-18 - 26) --- (-9 - 93)

--- --- --- --- --- 9

--- --- --- --- --- (-2 - 20)

-2 47 32 37 114 106

(-53 - 46) (-2 - 94) (-15 - 78) (-3 - 76) --- (24 - 187)

00022 11

(-9 - 9) (-3 - 4) (-2 - 2) (-5 - 8) --- (-6 - 27)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

 Phoenix, AZ 5 Pittsburgh, PA

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Birmingham, AL Dallas, TX Detroit, MI

5 Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

 Fresno, CA Houston, TX Salt Lake City, UT 5 St. Louis, MO Philadelphia, PA

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate 

regional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA New York, NY

Estimated Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5 

Concentrations that Just Meet the Current Standards

 Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD

P

Diff
F-21

Table F-19. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re Function to Estimate the Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

 that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

4 1 3 311 20

(-6 - 14) (-8 - 11) (-7 - 13) (-5 - 11) --- 4 (-8 - 47)

5 6 6 3 20 36

(-6 - 15) (-4 - 15) (-6 - 17) (-4 - 11) --- (7 - 64)

1 2 -1 3 5 9

(-4 - 5) (-4 - 7) (-8 - 7) (-2 - 9) --- (-7 - 26)

13217 11

(-6 - 9) (-4 - 10) (-6 - 9) (-6 - 7) --- (-10 - 32)

5 9 10 9 33 28

(-7 - 16) (-1 - 18) (0 - 19) (0 - 18) --- (1 - 55)

-1 4 1 1 5 9

(-11 - 9) (-1 - 9) (-2 - 4) (-6 - 8) --- (0 - 17)

5 5 4 4 18 34

(-4 - 14) (-4 - 13) (-5 - 13) (-7 - 15) --- (5 - 61)

27 27 -15 0 39 57

(-3 - 56) (-2 - 56) (-58 - 26) (-23 - 21) --- (6 - 108)

51 18 22 22 113 106

(19 - 82) (-6 - 41) (-10 - 53) (1 - 42) --- (37 - 174)

10 7 7 5 29 23

(-1 - 21) (-1 - 15) (-3 - 16) (-2 - 11) --- (-2 - 48)

27 30 21 41 119 47

(-29 - 79) (-8 - 66) (-3 - 45) (14 - 67) --- (4 - 90)

4 7 8 726 20

(-3 - 11) (-1 - 15) (-2 - 17) (0 - 14) --- (-2 - 42)

4 2 -2 -1 3 5

(-1 - 9) (-2 - 6) (-6 - 3) (-5 - 3) --- (1 - 10)

1 7 4 719 31

(-15 - 17) (-6 - 20) (-10 - 17) (-6 - 18) --- (-8 - 70)

02114 7

(-15 - 13) (-2 - 6) (-2 - 3) (-4 - 6) --- (0 - 15)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

 St. Louis, MO Salt Lake City, UT 

Estimated Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Philadelphia, PA Phoenix, AZ Pittsburgh, PA Tacoma, WA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the season

coefficient estimators was not available.

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional 

Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM co

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Birmingham, AL Dallas, TX Detroit, MI Houston, TX Fresno, CA

D
F-22

Table F-20. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re Function to Estimate the Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

 that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

3 2 4 312 20

(-5 - 12) (-9 - 12) (-8 - 16) (-4 - 10) --- 4 (-8 - 47)

4 5 5 317 31

(-5 - 13) (-3 - 13) (-5 - 15) (-4 - 10) --- (6 - 56)

1 2-13 5 9

(-3 - 5) (-4 - 8) (-9 - 7) (-2 - 7) --- (-7 - 25)

13217 9

(-5 - 7) (-4 - 10) (-5 - 8) (-4 - 6) --- (-9 - 27)

4 9 7 727 23

(-5 - 13) (-1 - 18) (0 - 14) (0 - 14) --- (1 - 44)

-14 1 1 5 9

(-11 - 10) (-1 - 9) (-2 - 5) (-6 - 8) --- (0 - 18)

5 5 4 418 33

(-4 - 13) (-5 - 15) (-5 - 13) (-6 - 13) --- (5 - 60)

28 24 -14 0 38 53

(-3 - 57) (-2 - 48) (-54 - 24) (-19 - 18) --- (5 - 100)

44 16 18 20 98 91

(16 - 72) (-5 - 37) (-8 - 43) (1 - 37) --- (32 - 149)

9 7 7 528 22

(-1 - 18) (-1 - 14) (-3 - 16) (-2 - 12) --- (-2 - 45)

31 30 22 41 124 50

(-33 - 90) (-8 - 65) (-3 - 46) (14 - 66) --- (4 - 94)

4 6 7 522 17

(-3 - 10) (-1 - 13) (-2 - 15) (0 - 10) --- (-2 - 36)

3 2-2 -1 2 5

(-1 - 7) (-2 - 6) (-7 - 3) (-5 - 3) --- (1 - 9)

1 6 3 515 26

(-10 - 12) (-6 - 18) (-9 - 15) (-5 - 15) --- (-7 - 58)

02114 6

(-11 - 10) (-2 - 6) (-2 - 4) (-3 - 5) --- (0 - 12)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

 Philadelphia, PA Phoenix, AZ Pittsburgh, PA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate 

regional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA Fresno, CA

Estimated Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX Detroit, MI Houston, TX Salt Lake City, UT St. Louis, MO
F-23

Table F-21. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re Function to Estimate the Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

 that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

3 2 4 312 20

(-5 - 11) (-10 - 13) (-8 - 15) (-4 - 9) --- 4 (-8 - 47)

4 5 5 317 31

(-5 - 12) (-4 - 14) (-5 - 15) (-4 - 10) --- (6 - 56)

1 2-13 5 9

(-3 - 4) (-5 - 10) (-8 - 7) (-2 - 7) --- (-7 - 25)

13217 10

(-6 - 8) (-3 - 9) (-5 - 8) (-5 - 6) --- (-9 - 29)

4 8 7 928 24

(-5 - 13) (-1 - 16) (0 - 14) (0 - 18) --- (1 - 45)

-14 1 1 5 9

(-14 - 11) (-1 - 8) (-2 - 4) (-6 - 8) --- (0 - 18)

5 4 4 417 33

(-4 - 15) (-4 - 13) (-6 - 14) (-6 - 13) --- (5 - 61)

23 29 -13 0 39 54

(-2 - 48) (-2 - 60) (-51 - 23) (-22 - 21) --- (5 - 102)

49 17 20 21 107 100

(18 - 79) (-6 - 38) (-9 - 48) (1 - 41) --- (35 - 163)

9 7 7 528 22

(-1 - 19) (-1 - 15) (-3 - 15) (-2 - 12) --- (-2 - 46)

24 29 25 45 123 47

(-24 - 68) (-8 - 63) (-4 - 51) (15 - 73) --- (4 - 90)

4 6 7 623 18

(-3 - 10) (-1 - 13) (-2 - 15) (0 - 13) --- (-2 - 38)

5 2 -2 -1 4 6

(-1 - 10) (-2 - 6) (-7 - 3) (-5 - 4) --- (1 - 11)

1 7 3 617 27

(-11 - 13) (-6 - 20) (-9 - 14) (-5 - 16) --- (-7 - 60)

02114 6

(-11 - 10) (-2 - 6) (-1 - 3) (-5 - 7) --- (0 - 13)

3 Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).

 St. Louis, MO 

D

Estimated Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5 Concentrations 

that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX Detroit, MI Houston, TX Fresno, CA Philadelphia, PA Phoenix, AZ Pittsburgh, PA

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate 

regional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty 

surrounding the PM coefficient. 

 Tacoma, WA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

 Salt Lake City, UT
F-24

Table F-22. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re

 Function to Estimate the Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure t

 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

29 24 -28 6 31 40

(-2 - 60) (-9 - 57) (-68 - 11) (-27 - 39) --- 4 (-26 - 105)

129 45 40 48 262 247

(90 - 168) (16 - 75) (6 - 74) (23 - 73) --- (182 - 313)

10 10 -13 3 10 17

(-1 - 21) (-3 - 23) (-32 - 5) (-12 - 17) --- (-11 - 44)

24 19 -21 5 27 31

(-2 - 50) (-7 - 45) (-50 - 8) (-19 - 28) --- (-20 - 81)

153 54 40 57 304 280

(107 - 198) (18 - 89) (6 - 74) (27 - 87) --- (206 - 354)

14 11 -7 3 21 21

(-5 - 31) (-6 - 27) (-28 - 14) (-11 - 17) --- (0 - 41)

44 34 -35 10 53 56

(-3 - 91) (-12 - 80) (-84 - 14) (-41 - 59) --- (-37 - 149)

104 194 -144 42 196 264

(-35 - 241) (-98 - 479) (-613 - 307) (-138 - 218) --- (3 - 523)

391 161 131 145 828 792

(273 - 509) (55 - 266) (19 - 241) (68 - 221) --- (582 - 1002)

118 39 35 39 231 214

(82 - 153) (13 - 64) (5 - 65) (18 - 59) --- (157 - 271)

58 81 -47 14 106 108

(-20 - 135) (-41 - 200) (-198 - 99) (-47 - 75) --- (1 - 213)

59 31 28 36 154 157

(41 - 77) (11 - 51) (4 - 51) (17 - 55) --- (115 - 199)

5 4-31 7 8

(-2 - 13) (-2 - 10) (-15 - 7) (-3 - 5) --- (0 - 16)

103 44 30 44 221 207

(72 - 134) (15 - 73) (4 - 55) (20 - 66) --- (152 - 262)

120-5 -5 2 21

(-65 - 82) (-61 - 55) (-56 - 42) (-53 - 40) --- (-52 - 92)

3 Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response function estimates reported in Table 2 of Bell et al. (200

specific C-R function estimates were not available from this study. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credibbased on statistical uncertainty surrounding the PM coefficient. 

 Fresno, CA Houston, TX Salt Lake City, UT Detroit, MI

Estimated Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure 

to PM2.5 Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX

 Philadelphia, PA Phoenix, AZ Pittsburgh, PA Tacoma, WA St. Louis, MO

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the season

coefficient estimators was not available.
F-25

Table F-23. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re Function to Estimate the Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure t Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

27 26 -34 6 25 41

(-2 - 55) (-9 - 60) (-81 - 13) (-24 - 35) --- 4 (-27 - 108)

106 40 35 44 225 214

(74 - 137) (14 - 66) (5 - 64) (21 - 68) --- (157 - 271)

9 10 -14 2 7 16

(-1 - 19) (-4 - 24) (-33 - 5) (-9 - 13) --- (-10 - 42)

19 18 -18 3 22 26

(-1 - 40) (-6 - 43) (-43 - 7) (-15 - 21) --- (-17 - 68)

119 54 29 44 246 225

(83 - 155) (19 - 90) (4 - 54) (20 - 66) --- (165 - 285)

15 10 -8 3 20 22

(-5 - 34) (-5 - 25) (-34 - 17) (-11 - 18) --- (0 - 44)

39 38 -36 8 49 55

(-3 - 81) (-14 - 90) (-86 - 14) (-36 - 52) --- (-36 - 145)

108 170 -137 37 178 248

(-37 - 252) (-86 - 419) (-580 - 291) (-121 - 192) --- (3 - 491)

342 143 107 129 721 684

(239 - 445) (49 - 237) (16 - 197) (61 - 198) --- (502 - 865)

99 35 33 41 208 201

(69 - 129) (12 - 59) (5 - 62) (19 - 63) --- (147 - 254)

66 80 -48 14 112 113

(-23 - 154) (-40 - 198) (-202 - 101) (-47 - 74) --- (1 - 224)

53 27 25 26 131 134

(37 - 69) (9 - 45) (4 - 46) (12 - 40) --- (98 - 169)

4 4 -4 1 5 7

(-1 - 10) (-2 - 11) (-17 - 9) (-3 - 4) --- (0 - 15)

74 39 26 36 175 171

(52 - 96) (13 - 64) (4 - 48) (17 - 54) --- (126 - 216)

9 0 -6 -4 -1 18

(-48 - 60) (-64 - 59) (-61 - 46) (-43 - 33) --- (-44 - 78)

3 Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).

 Fresno, CA Houston, TX Salt Lake City, UT Detroit, MI

Estimated Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure 

to PM2.5 Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX

 Philadelphia, PA Phoenix, AZ Pittsburgh, PA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response function estimates reported in Table 2 of Bell et al. 

(2008). Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% 

confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA St. Louis, MO
F-26

Table F-24. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re Function to Estimate the Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure t Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

24 30 -33 5 26 41

(-2 - 50) (-11 - 70) (-80 - 13) (-23 - 34) --- 4 (-27 - 109)

103 42 35 44 224 216

(72 - 134) (14 - 70) (5 - 65) (21 - 67) --- (159 - 273)

9 13 -12 2 12 16

(-1 - 18) (-5 - 31) (-30 - 5) (-9 - 13) --- (-11 - 43)

23 17 -19 4 25 28

(-2 - 47) (-6 - 39) (-46 - 7) (-17 - 25) --- (-18 - 73)

121 48 28 58 255 233

(84 - 157) (16 - 79) (4 - 52) (27 - 88) --- (171 - 295)

17 10 -6 3 24 23

(-6 - 40) (-5 - 25) (-26 - 13) (-11 - 18) --- (0 - 46)

46 34 -38 9 51 56

(-3 - 95) (-12 - 79) (-91 - 15) (-37 - 54) --- (-37 - 149)

93 215 -131 42 219 258

(-31 - 216) (-109 - 531) (-556 - 279) (-139 - 220) --- (3 - 511)

377 151 121 143 792 752

(263 - 490) (51 - 249) (18 - 223) (67 - 218) --- (552 - 951)

101 39 32 42 214 203

(71 - 131) (13 - 64) (5 - 59) (20 - 64) --- (149 - 257)

50 78 -54 16 90 108

(-17 - 117) (-39 - 193) (-230 - 115) (-52 - 83) --- (1 - 215)

51 28 25 32 136 140

(36 - 67) (9 - 46) (4 - 46) (15 - 49) --- (103 - 177)

6 5 -4 1 8 9

(-2 - 15) (-2 - 12) (-18 - 9) (-3 - 5) --- (0 - 18)

80 43 25 37 185 178

(56 - 104) (15 - 71) (4 - 47) (17 - 56) --- (131 - 225)

9 0 -4 -6 -1 19

(-48 - 60) (-64 - 59) (-43 - 33) (-63 - 47) --- (-46 - 82)

3 Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).

 Philadelphia, PA Phoenix, AZ Pittsburgh, PA

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response function estimates reported in Table 2 of Bell et al. 

(2008). Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

 St. Louis, MO

Estimated Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure 

to PM2.5 Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX

D

 Fresno, CA Houston, TX Salt Lake City, UT Detroit, MI
F-27

Table F-25. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Re Function to Estimate the Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

6 9-6110 17

(-21 - 31) (-10 - 28) (-25 - 12) (-13 - 15) --- 4 (-22 - 55)

18 1 14 2 35 20

(-5 - 40) (-14 - 15) (0 - 28) (-12 - 15) --- (-12 - 51)

2 4 -3 1 4 7

(-7 - 11) (-4 - 11) (-12 - 6) (-5 - 6) --- (-9 - 23)

4 7 -5 1 7 15

(-17 - 25) (-8 - 23) (-21 - 10) (-13 - 16) --- (-18 - 47)

24 1 17 2 44 25

(-6 - 53) (-18 - 20) (0 - 35) (-14 - 18) --- (-15 - 64)

10 3 4 3 20 14

(-1 - 20) (-6 - 11) (-4 - 11) (-5 - 11) --- (3 - 25)

7 12 -8 3 14 25

(-27 - 42) (-14 - 38) (-33 - 16) (-27 - 32) --- (-32 - 81)

71 45 86 42 244 170

(-6 - 148) (-97 - 183) (-98 - 261) (-60 - 140) --- (40 - 300)

57249 5113 65

(-15 - 129) (-52 - 56) (-1 - 99) (-33 - 42) --- (-38 - 169)

16112130 17

(-4 - 37) (-12 - 12) (0 - 25) (-9 - 12) --- (-10 - 44)

35 17 23 13 88 61

(-3 - 72) (-36 - 68) (-26 - 70) (-18 - 43) --- (14 - 107)

8 0 9 118 13

(-2 - 18) (-10 - 11) (0 - 19) (-9 - 12) --- (-8 - 33)

41218 6

(0 - 8) (-2 - 5) (-3 - 7) (-2 - 4) --- (1 - 10)

23115241 25

(-6 - 53) (-20 - 21) (0 - 29) (-15 - 19) --- (-15 - 64)

0 4 1-2 3 2

(-50 - 43) (-27 - 32) (-19 - 19) (-24 - 18) --- (-27 - 30)

3 Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).

 St. Louis, MO Salt Lake City, UT 

Estimated Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to 

PM2.5 Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Philadelphia, PA Phoenix, AZ Pittsburgh, PA Tacoma, WA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the season

coefficient estimators was not available.

1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response function estimates from models with a 2-day lag for res

hospital admissions reported in Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest w

Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Birmingham, AL Dallas, TX Detroit, MI Houston, TX Fresno, CA
F-28

Table F-26. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Respo Function to Estimate the Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

5 10 -71 9 17

(-19 - 28) (-11 - 30) (-30 - 14) (-11 - 13) --- 4 (-22 - 56)

15112230 17

(-4 - 33) (-12 - 13) (0 - 24) (-11 - 14) --- (-10 - 44)

2 4-30 3 7

(-6 - 10) (-4 - 12) (-12 - 6) (-4 - 5) --- (-8 - 22)

4 7-41 8 12

(-13 - 20) (-8 - 22) (-18 - 9) (-10 - 12) --- (-15 - 40)

18113234 20

(-5 - 41) (-19 - 20) (0 - 25) (-11 - 13) --- (-12 - 52)

11 3 5 3 22 15

(-1 - 22) (-5 - 10) (-5 - 14) (-5 - 12) --- (3 - 26)

7 14 -8316 25

(-24 - 37) (-15 - 42) (-34 - 17) (-24 - 28) --- (-31 - 79)

75 40 82 37 234 160

(-6 - 155) (-85 - 160) (-93 - 248) (-53 - 124) --- (37 - 281)

50240496 56

(-13 - 113) (-46 - 50) (-1 - 81) (-30 - 38) --- (-33 - 145)

14012127 16

(-4 - 31) (-11 - 12) (0 - 23) (-10 - 13) --- (-9 - 41)

40 17 24 13 94 64

(-3 - 82) (-35 - 67) (-27 - 72) (-18 - 43) --- (15 - 112)

7 0 8 116 11

(-2 - 16) (-9 - 10) (0 - 17) (-7 - 8) --- (-6 - 28)

31318 5

(0 - 6) (-3 - 5) (-3 - 8) (-1 - 3) --- (1 - 9)

17113233 21

(-5 - 38) (-17 - 18) (0 - 26) (-13 - 16) --- (-12 - 53)

0 4 1-1 4 2

(-37 - 31) (-29 - 34) (-21 - 20) (-20 - 15) --- (-23 - 25)

3 Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).

 Philadelphia, PA Phoenix, AZ Pittsburgh, PA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response function estimates from models with a 2-day lag for 

respiratory hospital admissions reported in Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA Fresno, CA

Estimated Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to 

PM2.5 Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX Detroit, MI Houston, TX Salt Lake City, UT St. Louis, MO

P

Dif
F-29

Table F-27. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-Resp Function to Estimate the Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations 1, 2

Winter Spring Summer Fall Sum of Four Seasons All Year 

5 11 -7110 18

(-17 - 26) (-13 - 35) (-29 - 14) (-11 - 13) --- 4 (-22 - 57)

14 1 12 2 29 17

(-4 - 32) (-13 - 14) (0 - 25) (-11 - 14) --- (-10 - 45)

2 5 -3 0 4 7

(-6 - 9) (-6 - 16) (-11 - 5) (-4 - 5) --- (-9 - 22)

4 6 -5 1 6 13

(-16 - 24) (-7 - 20) (-19 - 10) (-12 - 14) --- (-17 - 43)

19 1 12 2 34 21

(-5 - 42) (-16 - 18) (0 - 25) (-14 - 18) --- (-12 - 54)

13 2 3 4 22 15

(-1 - 26) (-5 - 10) (-4 - 11) (-5 - 12) --- (4 - 27)

8 12 -9 3 14 25

(-28 - 43) (-14 - 38) (-36 - 18) (-25 - 29) --- (-32 - 82)

64 50 78 42 234 166

(-5 - 133) (-108 - 203) (-89 - 239) (-61 - 142) --- (39 - 293)

55 2 46 5 108 62

(-15 - 124) (-48 - 52) (-1 - 91) (-33 - 42) --- (-37 - 160)

14 1 11 1 27 16

(-4 - 32) (-12 - 13) (0 - 22) (-10 - 13) --- (-10 - 42)

30 16 27 14 87 61

(-3 - 63) (-34 - 65) (-31 - 82) (-20 - 48) --- (14 - 108)

7 0 8 1 16 11

(-2 - 16) (-9 - 10) (0 - 17) (-8 - 10) --- (-7 - 29)

5 1 3 1 10 7

(0 - 9) (-3 - 5) (-3 - 9) (-2 - 4) --- (2 - 12)

18 1 12 2 33 21

(-5 - 41) (-19 - 21) (0 - 25) (-13 - 17) --- (-13 - 55)

0 4 1 -2 3 2

(-37 - 31) (-29 - 34) (-15 - 15) (-29 - 22) --- (-24 - 27)

3 Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).

 St. Louis, MO 

P

Dif

Estimated Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to 

PM2.5 Concentrations that Just Meet the Current Standards

 Los Angeles, CA New York, NY

Risk Assessment 

Location

 Atlanta, GA Baltimore, MD Birmingham, AL Dallas, TX Detroit, MI Houston, TX Fresno, CA Philadelphia, PA Phoenix, AZ Pittsburgh, PA

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response function estimates from models with a 2-day lag for 

respiratory hospital admissions reported in Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient. 

 Tacoma, WA

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the 

season-specific coefficient estimators was not available.

 Salt Lake City, UT
F-30

Table F-28. Sensitivity Analysis: Impact of Using an Annual Concentration-Response Function vs. a Seasonal Function (fo Applied Only to that Period to Estimate the Incidence of Emergency Room Visits for Asthma Associated with Sho

 Exposure to Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Al

 Standards in New York City, Based on Adjusting 2005 PM2.5 Concentrations1 

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

5235 4375 4375 4265 3927 

(3346 - 7071) (2790 - 5923) (2790 - 5923) (2719 - 5776) (2501 - 5323) 

3136 2634 2634 2569 2370 

(2058 - 4162) (1722 - 3509) (1722 - 3509) (1678 - 3425) (1546 - 3164) 

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on Ito et al. (2007). Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statis

coefficient.

Incidence of ER Visits Associated with Short-Term Exposure to PM2.5 Concentrat

Concentrations that Just Meet the Current and Alternative Annual (n) and Da

Combination Denoted n/m):

Annual C-R Function Applied to the Whole Year Seasonal C-R Function for April - August Applied Only to that Period:

Concentration-Response (C-R) Function 

and Period to Which Applied:
F-31

Table F-29. Sensitivity Analysis: Impact of Using an Annual Concentration-Response Function vs. a Seasonal Function (fo

 Applied Only to that Period to Estimate the Incidence of Emergency Room Visits for Asthma Associated with Shor to Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative S

 New York City, Based on Adjusting 2006 PM2.5 Concentrations1

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

4506 3764 3764 3669 3377 

(2876 - 6095) (2397 - 5102) (2397 - 5102) (2336 - 4974) (2149 - 4582) 

2732 2293 2293 2237 2063 

(1791 - 3631) (1497 - 3059) (1497 - 3059) (1460 - 2985) (1344 - 2757) 

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on Ito et al. (2007). Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statis

coefficient.

Incidence of ER Visits Associated with Short-Term Exposure to PM2.5 Concentrat

Concentrations that Just Meet the Current and Alternative Annual (n) and Dai

Combination Denoted n/m):

Annual C-R Function Applied to the Whole Year Seasonal C-R Function for April - August Applied Only to that Period:

Concentration-Response (C-R) Function 

and Period to Which Applied:
F-32

Table F-30. Sensitivity Analysis: Impact of Using an Annual Concentration-Response Function vs. a Seasonal Function (fo Applied Only to that Period to Estimate the Incidence of Emergency Room Visits for Asthma Associated with Sho

 Exposure to Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Al

 Standards in New York City, Based on Adjusting 2007 PM2.5 Concentrations1 

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 

4926 4115 4115 4011 3692 

(3145 - 6660) (2622 - 5575) (2622 - 5575) (2555 - 5436) (2350 - 5008) 

2908 2441 2441 2380 2195 

(1906 - 3864) (1593 - 3256) (1593 - 3256) (1553 - 3177) (1431 - 2934) 

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 1Based on Ito et al. (2007). Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statis

coefficient.

Incidence of ER Visits Associated with Short-Term Exposure to PM2.5 Concentrat

Concentrations that Just Meet the Current and Alternative Annual (n) and Da

Combination Denoted n/m):

Annual C-R Function Applied to the Whole Year Seasonal C-R Function for April - August Applied Only to that Period:

Concentration-Response (C-R) Function 

and Period to Which Applied:
F-33

Table F-31. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated wi Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations1Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 275 Max. positive est. = 81

(-35 - 584) 301 (-117 - 27

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 301 Min. positive est. =

(0 - 600) 194

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 2 day none 194

(-97 - 483) Percent diff. =

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 3 day none -77 55%

(-373 - 218)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 4 day none -46

(-329 - 235)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 5 day none -287

(-592 - 15)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 275 Max. positive est. = 81

(-35 - 584) 275 (-117 - 27

Mortality, short-term non-accidental log-linear, GLM, 30 df 0 day none 204 Min. positive est. =

(-174 - 579) 153

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 0 day none 163

(-115 - 441) Percent diff. =

Mortality, short-term non-accidental log-linear, GLM, 100 df 0 day none 153 80%

(-218 - 522)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 301 Max. positive est. = 81

(0 - 600) 301 (-117 - 27

Mortality, short-term non-accidental log-linear, GLM, 30 df 1 day none 281 Min. positive est. =

(-86 - 644) 51

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day none 51

(-236 - 336) Percent diff. =

Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day none -5 490%

(-509 - 494)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day CO -272 81

(-676 - 128) (-117 - 27

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day CO -169

(-540 - 198)

Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day CO -169

(-603 - 260)

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Health Effects Model

Incidenc

Estimate U

Core Anal

Model 

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Lag Structure:

Lag

Other 

Pollutants 

in Model

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day Lag
F-34

Table F-31 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations1Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 0 day none 171 Max. positive est. = -3

(17 - 324) 171 (-140 

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day none 168 Min. positive est. =

(24 - 310) 168

Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day none 168 Percent diff. =

(-4 - 337) 2%

Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 1 day none 178 Max. positive est. = -3

(26 - 328) 178 (-140 

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day none 139 Min. positive est. =

(-6 - 282) 120

Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day none 120 Percent diff. =

(-56 - 293) 48%

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 307 Max. positive est. = -3

(130 - 481) 324 (-140 

Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day CO 324 Min. positive est. =

(116 - 529) 158

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 158 Percent diff. =

(-22 - 335) 105%

Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day CO 158

(-60 - 372)

log-linear, GAM (stringent), 30 df 0 day none -15 --

(-80 - 49)

log-linear, GAM (stringent), 100 df 0 day none -37

(-102 - 25)

log-linear, GLM, 100 df 0 day none -32

(-109 - 43)

log-linear, GAM (stringent), 30 df 1 day none 10 Max. positive est. = -

(-56 - 74) 22

log-linear, GAM (stringent), 100 df 1 day none 22 Min. positive est. =

(-42 - 85) 5

log-linear, GLM, 100 df 1 day none 5 Percent diff. =

(-75 - 83) 340%

Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Health Effects

Respiratory Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod

Model

Mortality, short-term respiratory (COPD+)Respiratory Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day Lag

Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)

Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day Lag

Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant Model

Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)
F-35

Table F-31 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations1HA, cardiovascular log-linear, GAM (stringent), 30 df 0 day none 794 Max. positive est. = 3

(457 - 1128) 794 (-60 - 

HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day none 584 Min. positive est. =

(254 - 912) 584

HA, cardiovascular log-linear, GLM, 100 df 0 day none 634 Percent diff. =

(226 - 1038) 36%

HA, cardiovascular log-linear, GAM (stringent), 30 df 1 day none 699 Max. positive est. = 3

(347 - 1048) 699 (-60 - 

HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day none 569 Min. positive est. =

(234 - 902) 569

HA, cardiovascular log-linear, GLM, 100 df 1 day none 604 Percent diff. =

(194 - 1011) 23%

HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 197 Max. positive est. = 3

(-224 - 615) 293 (-60 - 

HA, cardiovascular log-linear, GLM, 100 df 0 day CO 293 Min. positive est. =

(-208 - 788) 122

HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 122 Percent diff. =

(-330 - 568) 140%

HA, cardiovascular log-linear, GLM, 100 df 1 day CO 137

(-381 - 648)

HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 0 day none 336 Max. positive est. = -

(138 - 531) 336

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day none 278 Min. positive est. =

(104 - 450) 278

HA, respiratory (COPD+) log-linear, GLM, 100 df 0 day none 300 Percent diff. =

(83 - 514) 21%

HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 1 day none 240 Max. positive est. = -

(45 - 432) 240

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day none 152 Min. positive est. =

(-22 - 324) 152

HA, respiratory (COPD+) log-linear, GLM, 100 df 1 day none 156 Percent diff. =

(-55 - 364) 58%

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod

Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Health Effects

Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day L

Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day LCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant ModelRespiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagRespiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag
F-36

Table F-31 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Concentrations1HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 2 day none 371 Max. positive est. = -

(166 - 574) 371

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day none 230 Min. positive est. =

(43 - 414) 208

HA, respiratory (COPD+) log-linear, GLM, 100 df 2 day none 208 Percent diff. =

(-24 - 436) 78%

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day NO2 85 Max. positive est. = -

(-185 - 351) 85

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day NO2 -8 Min. positive est. =

(-329 - 307) 71

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day NO2 71 Percent diff. =

(-209 - 346) 20%

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 3 day NO2 -223

(-491 - 41)

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod

Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Health Effects

Respiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 2-Day Lag

Respiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Lag Structure, with a Copollutan5Because "respiratory illness" was much more broadly defined in both Zanobetti and Schwartz (2009) and Bell et al. (2008) than in Moolgavkar (2003), a comparison between t

and the corresponding core analysis estimates is not shown.

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. Results are based on Moolgavkar (2003) [reanalysis of M

2000c)]. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 

2The core analysis estimates for non-accidental mortality and cardiovascular mortality associated with short-term exposure to PM2.5 are from Zanobetti and Schwartz (2009). 

cardiovascular hospital admissions associated with short-term exposure to PM2.5 are from Bell et al. (2008). 

3Calculated as (maximum positive estimate - minimum positive estimate)/(minimum positive estimate).4Calculated as (Moolgavkar (2003) estimate - core analysis estimate)/(core analysis estimate).
F-37

Table F-32. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated wi Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations1Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 254 Max. positive est. = 75

(-32 - 539) 278 (-108 - 25

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 278 Min. positive est. =

(0 - 554) 179

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 2 day none 179

(-89 - 445) Percent diff. =

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 3 day none -71 55%

(-344 - 201)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 4 day none -42

(-304 - 217)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 5 day none -265

(-546 - 14)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 254 Max. positive est. = 75

(-32 - 539) 254 (-108 - 25

Mortality, short-term non-accidental log-linear, GLM, 30 df 0 day none 188 Min. positive est. =

(-161 - 535) 141

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 0 day none 151

(-106 - 407) Percent diff. =

Mortality, short-term non-accidental log-linear, GLM, 100 df 0 day none 141 80%

(-201 - 482)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 278 Max. positive est. = 75

(0 - 554) 278 (-108 - 25

Mortality, short-term non-accidental log-linear, GLM, 30 df 1 day none 259 Min. positive est. =

(-80 - 595) 47

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day none 47

(-218 - 310) Percent diff. =

Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day none -5 491%

(-469 - 455)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day CO -251 75

(-623 - 118) (-108 - 25

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day CO -156

(-497 - 183)

Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day CO -156

(-555 - 240)

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day Lag

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant ModelNon-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Health Effects Model

Incidenc

Estimate U

Core Anal

Model 

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Lag Structure:

Lag

Other 

Pollutants 

in Model
F-38

Table F-32 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations1Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 0 day none 158 Max. positive est. = -2

(15 - 299) 158 (-129 

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day none 155 Min. positive est. =

(22 - 286) 155

Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day none 155 Percent diff. =

(-3 - 311) 2%

Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 1 day none 164 Max. positive est. = -2

(24 - 303) 164 (-129 

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day none 128 Min. positive est. =

(-5 - 260) 110

Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day none 110 Percent diff. =

(-51 - 270) 49%

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 283 Max. positive est. = -2

(120 - 444) 299 (-129 

Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day CO 299 Min. positive est. =

(107 - 489) 145

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 145 Percent diff. =

(-20 - 309) 106%

Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day CO 145

(-56 - 344)

log-linear, GAM (stringent), 30 df 0 day none -14 --

(-74 - 45)

log-linear, GAM (stringent), 100 df 0 day none -35

(-94 - 23)

log-linear, GLM, 100 df 0 day none -29

(-100 - 39)

log-linear, GAM (stringent), 30 df 1 day none 9 Max. positive est. = -

(-51 - 68) 21

log-linear, GAM (stringent), 100 df 1 day none 21 Min. positive est. =

(-39 - 78) 5

log-linear, GLM, 100 df 1 day none 5 Percent diff. =

(-69 - 76) 320%

Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)

Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant ModelCardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Incid

Estimat

Core A

Mod

Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day Lag

Health Effects

Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)Respiratory Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagRespiratory Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Mortality, short-term respiratory (COPD+)
F-39

Table F-32 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations1HA, cardiovascular log-linear, GAM (stringent), 30 df 0 day none 745 Max. positive est. = 24

(428 - 1060) 745 (3 - 

HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day none 548 Min. positive est. =

(238 - 856) 548

HA, cardiovascular log-linear, GLM, 100 df 0 day none 595 Percent diff. =

(212 - 975) 36%

HA, cardiovascular log-linear, GAM (stringent), 30 df 1 day none 656 Max. positive est. = 24

(326 - 984) 656 (3 - 4

HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day none 534 Min. positive est. =

(220 - 847) 534

HA, cardiovascular log-linear, GLM, 100 df 1 day none 567 Percent diff. =

(182 - 949) 23%

HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 185 Max. positive est. = 24

(-210 - 577) 275 (3 - 4

HA, cardiovascular log-linear, GLM, 100 df 0 day CO 275 Min. positive est. =

(-195 - 740) 114

HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 114 Percent diff. =

(-309 - 533) 141%

HA, cardiovascular log-linear, GLM, 100 df 1 day CO 128

(-357 - 608)

HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 0 day none 310 Max. positive est. = -

(127 - 491) 310

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day none 256 Min. positive est. =

(96 - 415) 256

HA, respiratory (COPD+) log-linear, GLM, 100 df 0 day none 277 Percent diff. =

(76 - 475) 21%

HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 1 day none 221 Max. positive est. = -

(42 - 399) 221

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day none 140 Min. positive est. =

(-21 - 299) 140

HA, respiratory (COPD+) log-linear, GLM, 100 df 1 day none 144 Percent diff. =

(-51 - 336) 58%

Respiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day LagCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LRespiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant ModelCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day L

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod

Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Health Effects
F-40

Table F-32 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Concentrations1HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 2 day none 343 Max. positive est. = -

(153 - 531) 343

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day none 212 Min. positive est. =

(40 - 383) 192

HA, respiratory (COPD+) log-linear, GLM, 100 df 2 day none 192 Percent diff. =

(-22 - 403) 79%

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day NO2 78 Max. positive est. = -

(-171 - 324) 78

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day NO2 -7 Min. positive est. =

(-303 - 284) 65

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day NO2 65 Percent diff. =

(-192 - 319) 20%

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 3 day NO2 -205

(-452 - 38)

5Because "respiratory illness" was much more broadly defined in both Zanobetti and Schwartz (2009) and Bell et al. (2008) than in Moolgavkar (2003), a comparison between t

and the corresponding core analysis estimates is not shown.

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. Results are based on Moolgavkar (2003) [reanalysis of M

2000c)].

2The core analysis estimates for non-accidental mortality and cardiovascular mortality associated with short-term exposure to PM2.5 are from Zanobetti and Schwartz (2009). 

cardiovascular hospital admissions associated with short-term exposure to PM2.5 are from Bell et al. (2008). Numbers rounded to the nearest whole number. Numbers in parencredible intervals based on statistical uncertainty surrounding the PM coefficient. 

3Calculated as (maximum positive estimate - minimum positive estimate)/(minimum positive estimate).4Calculated as (Moolgavkar (2003) estimate - core analysis estimate)/(core analysis estimate).Respiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Lag Structure, with a CopollutanRespiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 2-Day Lag

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod

Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Health Effects
F-41

Table F-33. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated wi Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations1Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 259 Max. positive est. = 77

(-33 - 550) 283 (-110 - 26

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 283 Min. positive est. =

(0 - 565) 183

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 2 day none 183

(-91 - 455) Percent diff. =

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 3 day none -72 55%

(-351 - 205)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 4 day none -43

(-310 - 222)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 5 day none -271

(-558 - 14)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none 259 Max. positive est. = 77

(-33 - 550) 259 (-110 - 26

Mortality, short-term non-accidental log-linear, GLM, 30 df 0 day none 192 Min. positive est. =

(-164 - 546) 144

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 0 day none 154

(-109 - 415) Percent diff. =

Mortality, short-term non-accidental log-linear, GLM, 100 df 0 day none 144 80%

(-206 - 492)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none 283 Max. positive est. = 77

(0 - 565) 283 (-110 - 26

Mortality, short-term non-accidental log-linear, GLM, 30 df 1 day none 264 Min. positive est. =

(-81 - 607) 48

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day none 48

(-222 - 317) Percent diff. =

Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day none -5 490%

(-480 - 465)

Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day CO -256 77

(-636 - 121) (-110 - 26

Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day CO -159

(-508 - 187)

Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day CO -159

(-567 - 245) 

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagNon-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Lag Structure:

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incidenc

Estimate U

Core Anal

Model 

Health Effects Model Lag

Other 

Pollutants 

in Model

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Non-Accidental Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant Model
F-42

Table F-33 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations1Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 0 day none 161 Max. positive est. = -3

(16 - 306) 161 (-132 

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day none 158 Min. positive est. =

(23 - 292) 158

Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day none 158 Percent diff. =

(-3 - 318) 2%

Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 1 day none 168 Max. positive est. = -3

(25 - 309) 168 (-132 

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day none 130 Min. positive est. =

(-6 - 265) 113

Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day none 113 Percent diff. =

(-52 - 276) 49%

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 289 Max. positive est. = -3

(123 - 453) 305 (-132 

Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day CO 305 Min. positive est. =

(109 - 498) 148

Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 148 Percent diff. =

(-21 - 316) 106%

Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day CO 148

(-57 - 351)

log-linear, GAM (stringent), 30 df 0 day none -14 --

(-75 - 46)

log-linear, GAM (stringent), 100 df 0 day none -35

(-96 - 24)

log-linear, GLM, 100 df 0 day none -30

(-103 - 40)

log-linear, GAM (stringent), 30 df 1 day none 9 Max. positive est. = -

(-52 - 70) 21

log-linear, GAM (stringent), 100 df 1 day none 21 Min. positive est. =

(-39 - 80) 5

log-linear, GLM, 100 df 1 day none 5 Percent diff. =

(-71 - 78) 320%

Respiratory Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)

Respiratory Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagMortality, short-term respiratory (COPD+)Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagMortality, short-term respiratory (COPD+)Mortality, short-term respiratory (COPD+)Cardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant ModelCardiovascular Mortality Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day Lag

Health Effects Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod
F-43

Table F-33 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations1 

HA, cardiovascular log-linear, GAM (stringent), 30 df 0 day none 775 Max. positive est. = 25

(446 - 1102) 775 (3 - 

HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day none 570 Min. positive est. =

(248 - 890) 570

HA, cardiovascular log-linear, GLM, 100 df 0 day none 619 Percent diff. =

(221 - 1014) 36%

HA, cardiovascular log-linear, GAM (stringent), 30 df 1 day none 682 Max. positive est. = 25

(339 - 1023) 682 (3 - 

HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day none 556 Min. positive est. =

(228 - 880) 556

HA, cardiovascular log-linear, GLM, 100 df 1 day none 590 Percent diff. =

(189 - 987) 23%

HA, cardiovascular log-linear, GAM (stringent), 100 df 0 day CO 193 Max. positive est. = 25

(-219 - 600) 286 (3 - 

HA, cardiovascular log-linear, GLM, 100 df 0 day CO 286 Min. positive est. =

(-203 - 769) 119

HA, cardiovascular log-linear, GAM (stringent), 100 df 1 day CO 119 Percent diff. =

(-321 - 554) 140%

HA, cardiovascular log-linear, GLM, 100 df 1 day CO 133

(-371 - 633)

HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 0 day none 316 Max. positive est. = --

(130 - 501) 316

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day none 262 Min. positive est. =

(98 - 424) 262

HA, respiratory (COPD+) log-linear, GLM, 100 df 0 day none 282 Percent diff. =

(78 - 485) 21%

HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 1 day none 226 Max. positive est. = --

(42 - 407) 226

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day none 143 Min. positive est. =

(-21 - 305) 143

HA, respiratory (COPD+) log-linear, GLM, 100 df 1 day none 146 Percent diff. =

(-52 - 343) 58%

Respiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day LagRespiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day LagCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of a Copollutant ModelCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 1-Day L

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod

Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Health Effects

Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 0-Day L
F-44

Table F-33 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Ass Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Concentrations1HA, respiratory (COPD+) log-linear, GAM (stringent), 30 df 2 day none 350 Max. positive est. = -

(156 - 541) 350

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day none 216 Min. positive est. =

(41 - 391) 196

HA, respiratory (COPD+) log-linear, GLM, 100 df 2 day none 196 Percent diff. =

(-22 - 411) 79%

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 0 day NO2 80 Max. positive est. = -

(-174 - 331) 80

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 1 day NO2 -8 Min. positive est. =

(-310 - 290) 67

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 2 day NO2 67 Percent diff. =

(-196 - 326) 19%

HA, respiratory (COPD+) log-linear, GAM (stringent), 100 df 3 day NO2 -209

(-462 - 39)

5Because "respiratory illness" was much more broadly defined in both Zanobetti and Schwartz (2009) and Bell et al. (2008) than in Moolgavkar (2003), a comparison between t

and the corresponding core analysis estimates is not shown.

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. Results are based on Moolgavkar (2003) [reanalysis of M

2000c)]. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 

2The core analysis estimates for non-accidental mortality and cardiovascular mortality associated with short-term exposure to PM2.5 are from Zanobetti and Schwartz (2009). 

cardiovascular hospital admissions associated with short-term exposure to PM2.5 are from Bell et al. (2008). 

3Calculated as (maximum positive estimate - minimum positive estimate)/(minimum positive estimate).4Calculated as (Moolgavkar (2003) estimate - core analysis estimate)/(core analysis estimate).Respiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Lag Structure, with a CopollutanRespiratory Hospital Admissions Associated with Short-Term Exposure to PM 2.5 -- Impact of Changing the Type of Model, with a 2-Day Lag

Health Effects Model Lag

Other 

Pollutants 

in Model

Incidence 

Associated with 

PM2.5 Above Policy 

Relevant 

Background

Range of Positive 

Estimates and 

Percent Difference 

Between Maximum 

and Minimum 2

Incid

Estimat

Core A

Mod
F-45

Table F-34. Sensitivity Analysis: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to  PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005 PM2.5 Concentration

 Comparison of Proportional and Hybrid Rollback Methods1

15/35 2 14/35 13/35 12/35 13/30 

Proportional 256 242 224 206 219 

(104 - 406) (98 - 384) (91 - 356) (83 - 327) (89 - 34

Hybrid 254 248 229 211 217 

(103 - 402) (101 - 393) (93 - 364) (86 - 335) (88 - 34

Percent Difference 3 -1% 2% 2% 2% -1% 

Proportional 34 32 29 27 29 

(-53 - 121) (-49 - 112) (-45 - 103) (-41 - 94) (-45 - 10

Hybrid 39 36 33 30 33 

(-61 - 137) (-56 - 127) (-52 - 117) (-47 - 107) (-52 - 1

Percent Difference 15% 13% 14% 11% 14% 

Proportional 147 146 135 124 125 

(-26 - 317) (-26 - 315) (-24 - 291) (-22 - 267) (-22 - 27

Hybrid 151 151 148 136 129 

(-26 - 325) (-26 - 325) (-26 - 319) (-24 - 293) (-23 - 27

Percent Difference 3% 3% 10% 10% 3% 

Proportional 81 81 81 77 69 

(-117 - 278) (-117 - 278) (-117 - 278) (-110 - 263) (-100 - 2

Hybrid 91 91 89 81 78 

(-130 - 311) (-130 - 311) (-127 - 304) (-117 - 279) (-111 - 2

Percent Difference 12% 12% 10% 5% 13% 

Proportional 781 781 761 700 668 

(459 - 1102) (459 - 1102) (447 - 1073) (411 - 987) (392 - 9

Hybrid 795 795 761 699 680 

(467 - 1121) (467 - 1121) (446 - 1073) (410 - 986) (399 - 9

Percent Difference 2% 2% 0% 0% 2% 

Proportional 260 244 226 207 222 

(75 - 443) (71 - 416) (65 - 385) (60 - 354) (64 - 37

Hybrid 271 252 233 214 233 

(78 - 462) (73 - 429) (67 - 397) (62 - 365) (67 - 39

Percent Difference 4% 3% 3% 3% 5% 

3 Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).

 St. Louis, MO

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Meet the Current and Alternative Combinations of Annual (n) and Daily (m) Standards 

Denoted n/m):

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropria

coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. Numbers are rounded to the nearest whole number. Numbers in parentheses a

intervals based on statistical uncertainty surrounding the PM coefficient. 

Risk Assessment 

Location

 Baltimore, MD

 Birmingham, AL

Type of Rollback

 Detroit, MI

 Los Angeles, CA

 New York, NY
F-46

Table F-35. Sensitivity Analysis: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to  Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006 PM2.5 Concentrations: 

 Comparison of Proportional and Hybrid Rollback Methods1

15/35 2 14/35 13/35 12/35 13/30 

Proportional 224 212 196 180 192 

(91 - 356) (86 - 336) (79 - 311) (73 - 286) (78 - 30

Hybrid 222 217 200 184 190 

(90 - 352) (88 - 344) (81 - 318) (75 - 293) (77 - 30

Percent Difference 3 -1% 2% 2% 2% -1% 

Proportional 33 30 28 26 28 

(-51 - 116) (-47 - 108) (-44 - 99) (-40 - 90) (-44 - 9

Hybrid 37 35 32 29 32 

(-58 - 132) (-54 - 122) (-49 - 112) (-45 - 102) (-49 - 11

Percent Difference 12% 17% 14% 12% 14% 

Proportional 118 117 108 99 101 

(-21 - 255) (-20 - 253) (-19 - 234) (-17 - 215) (-18 - 21

Hybrid 121 121 118 109 103 

(-21 - 261) (-21 - 261) (-21 - 256) (-19 - 235) (-18 - 22

Percent Difference 3% 3% 9% 10% 2% 

Proportional 75 75 75 71 64 

(-108 - 257) (-108 - 257) (-108 - 257) (-101 - 242) (-92 - 21

Hybrid 84 84 82 75 72 

(-120 - 287) (-120 - 287) (-117 - 280) (-108 - 257) (-102 - 2

Percent Difference 12% 12% 9% 6% 13% 

Proportional 671 671 654 601 574 

(394 - 946) (394 - 946) (383 - 922) (352 - 847) (336 - 80

Hybrid 682 682 652 599 583 

(400 - 961) (400 - 961) (383 - 920) (352 - 846) (342 - 82

Percent Difference 2% 2% 0% 0% 2% 

Proportional 215 202 187 171 184 

(62 - 367) (58 - 345) (54 - 319) (49 - 293) (53 - 31

Hybrid 224 208 192 176 192 

(64 - 381) (60 - 354) (55 - 328) (51 - 301) (55 - 32

Percent Difference 4% 3% 3% 3% 4% 

3 Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).

 New York, NY

 Baltimore, MD

 Birmingham, AL

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Meet the Current and Alternative Combinations of Annual (n) and Daily (m) Standards 

Denoted n/m):

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the approp

"Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. Numbers rounded to the nearest whole number. Numbers in parentcredible intervals based on statistical uncertainty surrounding the PM coefficient. 

Risk Assessment 

Location Type of Rollback

 St. Louis, MO

 Detroit, MI

 Los Angeles, CA
F-47

Table F-36. Sensitivity Analysis: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to P Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007 PM2.5 Concentrations: 

 Comparison of Proportional and Hybrid Rollback Methods1

15/35 2 14/35 13/35 12/35 13/30 

Proportional 227 214 198 182 194 

(92 - 360) (87 - 340) (80 - 315) (74 - 289) (79 - 30

Hybrid 224 219 203 186 192 

(91 - 356) (89 - 348) (82 - 322) (75 - 296) (78 - 30

Percent Difference 3 -1% 2% 3% 2% -1% 

Proportional 34 32 29 26 29 

(-53 - 120) (-49 - 111) (-45 - 102) (-41 - 93) (-45 - 1

Hybrid 39 36 33 30 33 

(-60 - 137) (-56 - 127) (-51 - 116) (-47 - 106) (-51 - 1

Percent Difference 15% 13% 14% 15% 14% 

Proportional 121 120 111 102 104 

(-21 - 262) (-21 - 261) (-19 - 241) (-18 - 221) (-18 - 2

Hybrid 124 124 122 112 106 

(-22 - 269) (-22 - 269) (-21 - 264) (-20 - 242) (-19 - 2

Percent Difference 2% 3% 10% 10% 2% 

Proportional 77 77 77 72 65 

(-110 - 262) (-110 - 262) (-110 - 262) (-104 - 247) (-94 - 2

Hybrid 86 86 83 77 73 

(-123 - 293) (-123 - 293) (-120 - 286) (-110 - 262) (-105 - 2

Percent Difference 12% 12% 8% 7% 12% 

Proportional 734 734 715 657 627 

(431 - 1035) (431 - 1035) (419 - 1008) (385 - 927) (368 - 8

Hybrid 746 746 714 656 638 

(438 - 1052) (438 - 1052) (419 - 1007) (385 - 926) (374 - 9

Percent Difference 2% 2% 0% 0% 2% 

Proportional 225 211 195 179 192 

(65 - 384) (61 - 360) (56 - 333) (52 - 306) (55 - 32

Hybrid 236 219 203 186 203 

(68 - 402) (63 - 374) (59 - 346) (54 - 318) (59 - 34

Percent Difference 5% 4% 4% 4% 6% 

3 Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).

 Los Angeles, CA

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 St. Louis, MO

 New York, NY

Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5 

Meet the Current and Alternative Combinations of Annual (n) and Daily (m) Standards 

Denoted n/m):

1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriat

coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% based on statistical uncertainty surrounding the PM coefficient. 

Risk Assessment 

Location Type of Rollback

 Detroit, MI

 Birmingham, AL

 Baltimore, MD
F-48

Table F-37. Multi-Factor Sensitivity Analysis: Impact of Using a Log-Linear vs. a Log-Log Model, Estimating Incidence Down to the Low

 Measured Level (LML) in the Study vs. PRB, and Using a Proportional vs. a Hybrid Rollback to Estimate the Incidence of All Ca

 and Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2005 PM2.5 Concentrations1

Modeling Choices:

Fixed Effects (FE) Log-linear vs. Random Effects (RE) log-log model FE Log-Linear FE Log-Linear FE Log-Linear FE Log-Linear RE Log-Log RE Log-Log RE Down to LML (5.8 ug/m3) vs. PRB LML LML PRB PRB LML LML 

Proportional vs. hybrid rollback Proportional Hybrid Proportional Hybrid Proportional Hybrid Pro

1342 1675 2845 3169 3360 3953 

(854 - 1827) (1066 - 2276) (1819 - 3853) (2027 - 4286) (2075 - 4615) (2446 - 5418) (87

Percent Difference: 3 --- 25% 112% 136% 150% 195% 

1249 1545 2548 2813 2535 2947 

(1017 - 1477) (1261 - 1824) (2095 - 2983) (2318 - 3288) (1793 - 3232) (2095 - 3738) (64

Percent Difference: --- 24% 104% 125% 103% 136% 

584 859 1254 

(372 - 792) (550 - 1161) (779 - 1713) (25

Percent Difference: --- --- 47% --- 115% --- 

369 591 639 

(303 - 434) (489 - 688) (458 - 803) (12

Percent Difference --- --- 60% --- 73% --- 

Incidence of Mortality Associated with Long-Term Exposure to PM2.5 Concentration

Los Angeles, CA

 All Cause Mortality Ischemic Heart Disease Mortality

2Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000. The fixed effects log-linear estimates are from Table 33, using models with 44 individual and 7 eco

effects log-log estimates are from Table 11, "MSA and DIFF" rows. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible inte

uncertainty surrounding the PM coefficient.

3 Percent differences are calculated relative to the model selections used in the core analysis (fixed effects log-linear model; LML, and proportional rollbacks). So, for example, th

all cause mortality in Los Angeles resulting from changing from the core analysis input selections to instead using (1) a fixed effects log-linear model, (2) PRB, and (3) hybrid rollb

136%. 

4 Philadelphia was not among the risk assessment urban areas for which hybrid rollbacks were calculated.

Philadelphia, PA

 All Cause Mortality Ischemic Heart Disease Mortality

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

---

--- ---

--- 

---

4

---
F-49

Table F-38. Multi-Factor Sensitivity Analysis: Impact of Using a Log-Linear vs. a Log-Log Model, Estimating Incidence Down to the Low

 Measured Level (LML) in the Study vs. PRB, and Using a Proportional vs. a Hybrid Rollback to Estimate the Incidence of All Ca

 and Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2006 PM2.5 Concentrations1

Modeling Choices:

Fixed Effects (FE) Log-linear vs. Random Effects (RE) log-log model FE Log-Linear FE Log-Linear FE Log-Linear FE Log-Linear RE Log-Log RE Log-Log RE Down to LML (5.8 ug/m3) vs. PRB LML LML PRB PRB LML LML 

Proportional vs. hybrid rollback Proportional Hybrid Proportional Hybrid Proportional Hybrid Pro

1108 1414 2627 2924 2904 3498 

(704 - 1509) (899 - 1923) (1678 - 3560) (1869 - 3959) (1790 - 3995) (2161 - 4803) (85

Percent Difference: 3 --- 28% 137% 164% 162% 216% 

1038 1314 2366 2614 2212 2633 

(843 - 1229) (1070 - 1553) (1943 - 2775) (2150 - 3060) (1558 - 2833) (1864 - 3354) (63

Percent Difference: --- 27% 128% 152% 113% 154% 

525 912 1166 

(335 - 713) (585 - 1233) (723 - 1595) (25

Percent Difference: --- --- 74% --- 122% --- 

334 559 598 

(273 - 393) (461 - 651) (428 - 755) (12

Percent Difference --- --- 67% --- 79% --- 

Incidence of Mortality Associated with Long-Term Exposure to PM2.5 Concentration

Los Angeles, CA

 All Cause Mortality Ischemic Heart Disease Mortality

Philadelphia, PA

 All Cause Mortality Ischemic Heart Disease Mortality

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

--- 

4

2Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000. The fixed effects log-linear estimates are from Table 33, using models with 44 individual and 7 eco

effects log-log estimates are from Table 11, "MSA and DIFF" rows. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible inte

uncertainty surrounding the PM coefficient.

3 Percent differences are calculated relative to the model selections used in the core analysis (fixed effects log-linear model; LML, and proportional rollbacks). So, for example, th

all cause mortality in Los Angeles resulting from changing from the core analysis input selections to instead using (1) a fixed effects log-linear model, (2) PRB, and (3) hybrid rollb

164%. 

4 Philadelphia was not among the risk assessment urban areas for which hybrid rollbacks were calculated.

------

---

--- ---
F-50

Table F-39. Multi-Factor Sensitivity Analysis: Impact of Using a Log-Linear vs. a Log-Log Model, Estimating Incidence Down to the Low

 Measured Level (LML) in the Study vs. PRB, and Using a Proportional vs. a Hybrid Rollback to Estimate the Incidence of All Ca

 and Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the Current 

 Standards, Based on Adjusting 2007 PM2.5 Concentrations1

Modeling Choices:

Fixed Effects (FE) Log-linear vs. Random Effects (RE) log-log model FE Log-Linear FE Log-Linear FE Log-Linear FE Log-Linear RE Log-Log RE Log-Log RE Down to LML (5.8 ug/m3) vs. PRB LML LML PRB PRB LML LML 

Proportional vs. hybrid rollback Proportional Hybrid Proportional Hybrid Proportional Hybrid Pro

1170 1484 2697 3003 3034 3633 

(744 - 1593) (944 - 2019) (1723 - 3654) (1920 - 4064) (1871 - 4173) (2245 - 4986) (86

Percent Difference: 3 --- 27% 131% 157% 159% 211% 

1094 1377 2426 2680 2306 2728 

(890 - 1296) (1122 - 1627) (1993 - 2845) (2205 - 3136) (1626 - 2950) (1933 - 3472) (63

Percent Difference: --- 26% 122% 145% 111% 149% 

519 --- 4 907 --- 1157 --- 

(331 - 704) --- (581 - 1225) --- (718 - 1583) --- (24

Percent Difference: --- --- 75% --- 123% --- 

330 --- 555 --- 594 --- 

(270 - 389) --- (459 - 647) --- (424 - 750) --- (12

Percent Difference --- --- 68% --- 80% --- 

Los Angeles, CA

Philadelphia, PA

3 Percent differences are calculated relative to the model selections used in the core analysis (fixed effects log-linear model; LML, and proportional rollbacks). So, for example, th

all cause mortality in Los Angeles resulting from changing from the core analysis input selections to instead using (1) a fixed effects log-linear model, (2) PRB, and (3) hybrid rollb

157%. 

Incidence of Mortality Associated with Long-Term Exposure to PM2.5 Concentration

 All Cause Mortality Ischemic Heart Disease Mortality

4 Philadelphia was not among the risk assessment urban areas for which hybrid rollbacks were calculated.2Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000. The fixed effects log-linear estimates are from Table 33, using models with 44 individual and 7 eco

effects log-log estimates are from Table 11, "MSA and DIFF" rows. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible inte

uncertainty surrounding the PM coefficient.

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 All Cause Mortality Ischemic Heart Disease Mortality
F-51

Table F-40. Sensitivity Analysis: Impact of Using Season-Specific vs. Annual Concentration-Response Functions and  Proportional vs. Hybrid Rollbacks to Estimate the Incidence of Non-Accidental Mortality Associated with Short- Term Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5 Conc

Modeling Choices:

Seasonal C-R Functions vs. an All-Year Function All Year All Year Sum of Four Seasons Sum o

Proportional vs. Hybrid Rollback Proportional Hybrid Proportional 

256 254 222 

(104 - 406) (103 - 402) --- 4 

Percent Difference 3 --- -1% -13% 

34 39 70 

(-53 - 121) (-61 - 137) --- 

Percent Difference --- 15% 106% 

147 151 159 

(-26 - 317) (-26 - 325) --- 

Percent Difference --- 3% 8% 

81 91 -23 

(-117 - 278) (-130 - 311) --- 

Percent Difference --- 12% -128% 

781 795 780 

(459 - 1102) (467 - 1121) --- 

Percent Difference --- 2% 0% 

159 163 175 

(47 - 270) (48 - 277) --- 

Percent Difference --- 3% 10% 

260 271 251 

(75 - 443) (78 - 462) --- 

Percent Difference --- 4% -3% 

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appro

means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty scoefficient. 

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 3 Percent differences are calculated relative to the model selections used in the core analysis (all-year C-R function and proportional rollback). So, for example, 

difference in estimated non-accidental mortality in Baltimore resulting from changing from the core analysis input selections to instead using the sum of four seaestimates and hybrid rollbacks is (192 - 225)/225 = -15%. 

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix 

specific coefficient estimators was not available.

 Pittsburgh, PA St. Louis, MO Los Angeles, CA New York, NY Birmingham, AL Detroit, MI

Estimated Incidence of Non-Accidental Mortality Associated with Short-Term Exp

Concentrations that Just Meet the Current Standards

 Baltimore, MD
F-52

Table F-41. Sensitivity Analysis: Impact of Using Season-Specific vs. Annual Concentration-Response Functions and  Proportional vs. Hybrid Rollbacks to Estimate the Incidence of Non-Accidental Mortality Associated with Short- Term Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5 Conc

Modeling Choices:

Seasonal C-R Functions vs. an All-Year Function All Year All Year Sum of Four Seasons Sum o

Proportional vs. Hybrid Rollback Proportional Hybrid Proportional 

224 222 193 

(91 - 356) (90 - 352) --- 4 

Percent Difference 3 --- -1% -14% 

33 37 69 

(-51 - 116) (-58 - 132) --- 

Percent Difference --- 12% 109% 

118 121 137 

(-21 - 255) (-21 - 261) --- 

Percent Difference --- 3% 16% 

75 84 -25 

(-108 - 257) (-120 - 287) --- 

Percent Difference --- 12% -133% 

671 682 677 

(394 - 946) (400 - 961) --- 

Percent Difference --- 2% 1% 

136 147 154 

(40 - 232) (43 - 249) --- 

Percent Difference --- 8% 13% 

215 224 211 

(62 - 367) (64 - 381) --- 

Percent Difference --- 4% -2% 

Estimated Incidence of Non-Accidental Mortality Associated with Short-Term Exp

Concentrations that Just Meet the Current Standards

 Baltimore, MD Birmingham, AL Detroit, MI Los Angeles, CA New York, NY Pittsburgh, PA St. Louis, MO

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appro

means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty su

coefficient. 

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 3 Percent differences are calculated relative to the model selections used in the core analysis (all-year C-R function and proportional rollback). So, for example, 

difference in estimated non-accidental mortality in Baltimore resulting from changing from the core analysis input selections to instead using the sum of four seaestimates and hybrid rollbacks is (192 - 225)/225 = -15%. 

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix 

specific coefficient estimators was not available.
F-53

Table F-42. Sensitivity Analysis: Impact of Using Season-Specific vs. Annual Concentration-Response Functions and  Proportional vs. Hybrid Rollbacks to Estimate the Incidence of Non-Accidental Mortality Associated with Short- Term Exposure to PM2.5 Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5 Conc

Modeling Choices:

Seasonal C-R Functions vs. an All-Year Function All Year All Year Sum of Four Seasons Sum o

Proportional vs. Hybrid Rollback Proportional Hybrid Proportional 

227 224 207 

(92 - 360) (91 - 356) --- 4 

Percent Difference 3 --- -1% -9% 

34 39 95 

(-53 - 120) (-60 - 137) --- 

Percent Difference --- 15% 179% 

121 124 166 

(-21 - 262) (-22 - 269) --- 

Percent Difference --- 2% 37% 

77 86 -12 

(-110 - 262) (-123 - 293) --- 

Percent Difference --- 12% -116% 

734 746 887 

(431 - 1035) (438 - 1052) --- 

Percent Difference --- 2% 21% 

143 147 225 

(42 - 244) (43 - 250) --- 

Percent Difference --- 3% 57% 

225 236 248 

(65 - 384) (68 - 402) --- 

Percent Difference --- 5% 10% 

 St. Louis, MO New York, NY Pittsburgh, PA Detroit, MI Los Angeles, CA Birmingham, AL

Estimated Incidence of Non-Accidental Mortality Associated with Short-Term 

PM2.5 Concentrations that Just Meet the Current Standards

 Baltimore, MD

1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appro

means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty scoefficient. 

2The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 3 Percent differences are calculated relative to the model selections used in the core analysis (all-year C-R function and proportional rollback). So, for example, 

difference in estimated non-accidental mortality in Baltimore resulting from changing from the core analysis input selections to instead using the sum of four seaestimates and hybrid rollbacks is (192 - 225)/225 = -15%. 

4 It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix 

specific coefficient estimators was not available.
F-54

Table F-43. Sensitivity Analysis: Impact of Copollutant Models in Estimating the Incidence of All Cause 

 Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the 

 Current Standards, Based on Adjusting 2005 PM2.5 Concentrations1,2

Copollutant in Model Incidence Percent Difference 3

1122

(580 - 1713)

1632

(945 - 2341)

1954

(1034 - 2782)

1632

(945 - 2341)

295

(-515 - 1209)

489

(253 - 743)

708

(412 - 1012)

847

(451 - 1199)

708

(412 - 1012)

129

(-227 - 526)

Los Angeles, CA

CO

Philadelphia, PA

None

45%

None

CO

-74%

0%

3 Calculated as (estimate with copollutant - estimate without copollutant)/(estimate without copollutant).

1

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

SO2 -74%

45%

74%

45%

2 Estimates based on Krewski et al. (2000) [reanalysis of the ACS study]. Mortality incidence was estimated for PM2.5 concentrations down to 

5.8 ug/m3 (the lowest measured level used for the analyses of long-term exposure). Numbers rounded to the nearest whole number. 

Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

73%

45%

NO2

O3

SO2

0%

NO2

O3
F-55

Table F-44. Sensitivity Analysis: Impact of Copollutant Models in Estimating the Incidence of All Cause 

 Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the 

 Current Standards, Based on Adjusting 2006 PM2.5 Concentrations1,2

Copollutant in Model Incidence Percent Difference 3

926

(478 - 1415)

1347

(780 - 1936)

1615

(853 - 2302)

1347

(780 - 1936)

243

(-424 - 998)

439

(228 - 669)

637

(370 - 911)

762

(405 - 1080)

637

(370 - 911)

116

(-203 - 473)

Los Angeles, CA

None 0%

CO 45%

NO2 74%

O3 45%

SO2 -74%

Philadelphia, PA

None 0%

CO 45%

NO2 74%

1

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

2 Estimates based on Krewski et al. (2000) [reanalysis of the ACS study]. Mortality incidence was estimated for PM2.5 concentrations down to 

5.8 ug/m3 (the lowest measured level used for the analyses of long-term exposure). Numbers rounded to the nearest whole number. 

Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

3 Calculated as (estimate with copollutant - estimate without copollutant)/(estimate without copollutant).

O3 45%

SO2 -74%
F-56

Table F-45. Sensitivity Analysis: Impact of Copollutant Models in Estimating the Incidence of All Cause 

 Mortality Associated with Long-Term Exposure to PM2.5 Concentrations that Just Meet the 

 Current Standards, Based on Adjusting 2007 PM2.5 Concentrations1,2

Copollutant in Model Incidence Percent Difference 3

978

(505 - 1494)

1423

(824 - 2043)

1705

(901 - 2429)

1423

(824 - 2043)

257

(-448 - 1054)

434

(225 - 661)

630

(366 - 901)

753

(400 - 1068)

630

(366 - 901)

115

(-201 - 468) 1

The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2 Estimates based on Krewski et al. (2000) [reanalysis of the ACS study]. Mortality incidence was estimated for PM2.5 concentrations down to 

5.8 ug/m3 (the lowest measured level used for the analyses of long-term exposure). Numbers rounded to the nearest whole number. 

Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.

3 Calculated as (estimate with copollutant - estimate without copollutant)/(estimate without copollutant).

O3 45%

SO2 -74%

CO 45%

NO2 74%

SO2 -74%

Philadelphia, PA

None 0%

NO2 74%

O3 46%

Los Angeles, CA

None 0%

CO 46%
F-57

Table F-46. Sensitivity Analysis: Impact of Different Lag Models on Estimated Annual Incidence of Hosp Admissions Associated with Short-Term Exposure to Ambient PM2.5 Concentrations that Just 

 Current Standards, Based on Adjusting 2005 PM2.5 Concentrations 1,2

0-Day Lag 1-Day Lag 2-Day Lag 0-Day Lag 1-Day Lag 2

397 35 30 409

(294 - 501) (-60 - 130) (-58 - 118) (-22 - 102) (-53 - 71) 

159 14 12 133

(118 - 200) (-24 - 52) (-23 - 47) (-7 - 34) (-18 - 24) 

Risk Assessment 

Location

Cardiovascular Hospital Admissions Respiratory Hospital Admissi

2 Incidence estimates were calculated using the national concentration-response function estimates reported in Table 1 of Bell et al. (20

specific C-R function estimates were not available from this study.

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3.  Los Angeles, CA Philadelphia, PA
F-58

Table F-47. Sensitivity Analysis: Impact of Different Lag Models on Estimated Annual Incidence of Hosp Admissions Associated with Short-Term Exposure to Ambient PM2.5 Concentrations that Just 

 Current Standards, Based on Adjusting 2006 PM2.5 Concentrations 1,2

0-Day Lag 1-Day Lag 2-Day Lag 0-Day Lag 1-Day Lag 2

373 33 28 38 9 

(276 - 470) (-56 - 122) (-54 - 110) (-21 - 96) (-50 - 67) 

149 13 11 13 3 

(110 - 188) (-23 - 49) (-22 - 44) (-7 - 32) (-17 - 22) 

 Los Angeles, CA Philadelphia, PA

2 Incidence estimates were calculated using the national concentration-response function estimates reported in Table 1 of Bell et al. (20

specific C-R function estimates were not available from this study.

Risk Assessment 

Location

Cardiovascular Hospital Admissions Respiratory Hospital Admissi

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
F-59

Table F-48. Sensitivity Analysis: Impact of Different Lag Models on Estimated Annual Incidence of Hosp Admissions Associated with Short-Term Exposure to Ambient PM2.5 Concentrations that Just 

 Current Standards, Based on Adjusting 2007 PM2.5 Concentrations 1,2

0-Day Lag 1-Day Lag 2-Day Lag 0-Day Lag 1-Day Lag 2

388 34 29 39 9 

(287 - 489) (-59 - 127) (-56 - 115) (-21 - 99) (-52 - 69) 

151 13 11 13 3 

(112 - 190) (-23 - 49) (-22 - 45) (-7 - 32) (-17 - 23) 

2 Incidence estimates were calculated using the national concentration-response function estimates reported in Table 1 of Bell et al. (20

specific C-R function estimates were not available from this study.

 Los Angeles, CA Philadelphia, PA

Risk Assessment 

Location

Cardiovascular Hospital Admissions Respiratory Hospital Admissi

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 
F-60

Annual 24-Hr 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM 14/35 

Proportional 15.0 14.2 14.0 13.3 13.0 12.3 12.0 11.4 13.0 12.3 11.8 11.2 11% 

Hybrid 3 --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused4 --- --- --- --- --- --- --- --- --- --- 14 11.76 --- Proportional 14.8 13.1 14.0 12.5 13.0 11.6 12.0 10.7 12.7 11.3 10.7 9.5 9% Hybrid 14.3 13.0 14.0 12.7 13.0 11.8 12.0 10.9 12.3 11.2 10.3 9.4 4% Locally Focused 15.2 13.6 --- --- --- --- --- --- 13.1 12.0 11.0 10.0 --- Proportional 15.0 12.7 14.0 11.8 13.0 11.0 12.0 10.2 13.0 11.0 11.1 9.4 12% Hybrid 15.0 14.2 14.0 13.2 13.0 12.3 12.0 11.4 13.0 12.3 11.3 10.7 11% Locally Focused --- --- --- --- --- --- --- --- --- --- 12.3 11.4 --- Proportional 12.8 11.4 12.8 11.4 12.8 11.4 12.0 10.7 12.8 11.4 12.0 10.7 0% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused --- --- --- --- --- --- --- --- --- --- --- --- --- Proportional 14.1 11.4 14.0 11.4 13.0 10.6 12.0 9.8 12.2 9.9 10.2 8.3 1% Hybrid 13.2 11.7 13.2 11.7 13.0 11.5 12.0 10.6 11.4 10.1 9.6 8.5 0% Locally Focused 14.1 12.6 --- --- --- --- --- --- 12.2 11.0 10.2 9.2 --- Proportional 9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 8.6 8.6 7.3 7.3 0% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused 10.1 10.3 10.1 10.3 10.1 10.3 10.1 10.3 8.8 8.9 7.4 7.5 0% Proportional 15.0 12.5 14.0 11.7 13.0 10.9 12.0 10.1 13.0 10.9 12.0 10.1 12% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused --- --- --- --- --- --- --- --- --- --- --- --- --- Proportional 12.7 9.5 12.7 9.5 12.7 9.5 12.0 9.0 10.9 8.2 9.2 7.0 0% Hybrid 13.3 10.5 13.3 10.5 13.0 10.3 12.0 9.5 11.5 9.1 9.6 7.7 0% Locally Focused 13.9 12.1 13.9 12.1 13.9 12.1 --- --- 12.0 10.6 10.1 9.1 0% Proportional 13.3 11.6 13.3 11.6 13.0 11.3 12.0 10.4 11.5 10.0 9.7 8.4 0% Hybrid 13.6 11.8 13.6 11.8 13.0 11.3 12.0 10.4 11.7 10.2 9.8 8.5 0% Locally Focused 14.3 13.3 14.3 13.3 --- --- --- --- 12.3 11.6 10.3 9.8 0% Proportional 13.9 12.3 13.9 12.3 13.0 11.6 12.0 10.7 11.9 10.7 10.0 9.0 0% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused 15.5 13.0 15.5 13.0 --- --- --- --- 14.1 11.3 11.8 9.5 0% Proportional 12.6 9.9 12.6 9.9 12.6 9.9 12.0 9.4 11.8 9.3 9.9 7.8 0% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused --- --- --- --- --- --- --- --- 12.2 9.7 10.2 9.0 --- 

Table F-49. Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with comparison of  percent reduction in surrogate for long-term mortality risk across rollback methods)

Perce

long-t

(alte

Risk Assessment 

Location 1 Rollback Method

 Design Value

Recent 

Air 

Quality 

(2007)

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 

Annual Average at Composite Monitor (2007CM) (in ug/m3)

15/35 2 14/35 13/35 12/35 13/30 12/25

 Atlanta, GA 15.3

 Baltimore, MD 15.6 37.0 13.9

16.2 35.0

 Birmingham, AL 15.7 Dallas, TX 12.8 26.0 11.4

18.7 44.0

 Detroit, MI 13.9 Fresno, CA 17.4 63.0 17.4

17.2 43.0

 Houston, TX 13.2 Los Angeles, CA 19.6 55.0 14.6

15.8 31.0

 New York, NY 13.8 Philadelphia, PA 15.0 38.0 13.4

15.9 42.0

 Phoenix, AZ 12.6 32.0 9.9
F-61

Annual 24-Hr 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM 14/35 

Proportional 13.3 11.6 13.3 11.6 12.8 11.2 11.8 10.5 11.5 10.0 9.7 8.4 0% 

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused 15.6 13.2 15.6 13.2 15.3 11.8 15.3 11.2 15.6 11.4 13.9 9.6 0% Proportional 7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 6.7 6.6 5.7 5.6 0% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused 10.8 9.7 10.8 9.7 10.8 9.7 10.8 9.7 10.8 8.8 9.1 7.7 0% Proportional 14.9 12.9 14.0 12.1 13.0 11.3 12.0 10.4 12.8 11.1 10.8 9.3 10% Hybrid 15.0 13.5 14.0 12.6 13.0 11.7 12.0 10.8 13.0 11.7 11.0 9.9 12% Locally Focused 16.5 14.1 --- --- --- --- --- --- 14.2 12.4 11.9 10.4 --- Proportional 8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 7.4 7.0 6.3 6.0 0% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused 8.5 8.0 8.5 8.0 8.5 8.0 8.5 8.0 7.4 7.0 6.3 6.0 0% 

Table F-49 (cont'd). Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with compa

 of percent reduction in surrogate for long-term mortality risk across rollback methods)

6 Percent reduction in composite monitor value with consideration for LML of 5.8 ug/m3 (note: composite monitor value denoted as CMV): %reduction = (CMVcurrent standard - C

standard-LML). Note, greyed cells identify instances where percent change differs by >10% across alternative rollback methods (for a given alternative standard level/study area 

1For some locations (e.g., Atlanta) more than one "version" (group of counties) was used in the risk assessment. In this table only the version that was used for mortality asso

to PM2.5 (Zanobetti and Schwartz, 2009) is included. 2 The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 3 The hybrid rollback method was applied to only a subset of the risk assessment locations. The "---" for a given location indicates that the hybrid rollback method was not ap

 Tacoma, WA 10.2 43.0 9.7

4 The locally focused method was applied to a location-standard combination only if the daily standard was controlling in that location. The "--" for a given location-standard co

set of annual and daily standards in that location, the annual standard was controlling and so the locally focused method was not applied. 5 The proportional rollback and locally focused methods were applied to Pittsburgh differently from the way they were applied in the other locations. See text for details. 

 Salt Lake City, UT 11.4

 St. Louis, MO 16.5 39.0 14.3

11.6 55.0

 12/35 12/25

 Pittsburgh, PA 5 19.8 60.0 14.9

Perce

long-te

(alter

Risk Assessment 

Location 1

Rollback 

Method

 Design Value

Recent 

Air 

Quality 

(2007)

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 

Annual Average at Composite Monitor (2007CM) (in ug/m3)

15/35 2 14/35 13/35 13/30
F-62

Annual 24-Hr 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM 15/35 14/35 

Proportional 15.0 14.2 14.0 13.3 13.0 12.3 12.0 11.4 13.0 12.3 11.8 11.2

Hybrid 3 --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused4 --- --- --- --- --- --- --- --- --- --- 14 11.76 --- --- Proportional 14.8 13.1 14.0 12.5 13.0 11.6 12.0 10.7 12.7 11.3 10.7 9.5

Hybrid 14.3 13.0 14.0 12.7 13.0 11.8 12.0 10.9 12.3 11.2 10.3 9.4 -2% 4% 

Locally Focused 15.2 13.6 --- --- --- --- --- --- 13.1 12.0 11.0 10.0 6% --- 

Proportional 15.0 12.7 14.0 11.8 13.0 11.0 12.0 10.2 13.0 11.0 11.1 9.4

Hybrid 15.0 14.2 14.0 13.2 13.0 12.3 12.0 11.4 13.0 12.3 11.3 10.7 18% 19% 

Locally Focused --- --- --- --- --- --- --- --- --- --- 12.3 11.4 --- --- 

Proportional 12.8 11.4 12.8 11.4 12.8 11.4 12.0 10.7 12.8 11.4 12.0 10.7

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused --- --- --- --- --- --- --- --- --- --- --- --- --- --- Proportional 14.1 11.4 14.0 11.4 13.0 10.6 12.0 9.8 12.2 9.9 10.2 8.3

Hybrid 13.2 11.7 13.2 11.7 13.0 11.5 12.0 10.6 11.4 10.1 9.6 8.5 4% 6% 

Locally Focused 14.1 12.6 --- --- --- --- --- --- 12.2 11.0 10.2 9.2 18% --- 

Proportional 9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 8.6 8.6 7.3 7.3

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused 10.1 10.3 10.1 10.3 10.1 10.3 10.1 10.3 8.8 8.9 7.4 7.5 8% 8% 

Proportional 15.0 12.5 14.0 11.7 13.0 10.9 12.0 10.1 13.0 10.9 12.0 10.1

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused --- --- --- --- --- --- --- --- --- --- --- --- --- --- Proportional 12.7 9.5 12.7 9.5 12.7 9.5 12.0 9.0 10.9 8.2 9.2 7.0

Hybrid 13.3 10.5 13.3 10.5 13.0 10.3 12.0 9.5 11.5 9.1 9.6 7.7 21% 21% 

Locally Focused 13.9 12.1 13.9 12.1 13.9 12.1 --- --- 12.0 10.6 10.1 9.1 41% 41% Proportional 13.3 11.6 13.3 11.6 13.0 11.3 12.0 10.4 11.5 10.0 9.7 8.4

Hybrid 13.6 11.8 13.6 11.8 13.0 11.3 12.0 10.4 11.7 10.2 9.8 8.5 3% 3% 

Locally Focused 14.3 13.3 14.3 13.3 --- --- --- --- 12.3 11.6 10.3 9.8 23% 23% 

Proportional 13.9 12.3 13.9 12.3 13.0 11.6 12.0 10.7 11.9 10.7 10.0 9.0

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused 15.5 13.0 15.5 13.0 --- --- --- --- 14.1 11.3 11.8 9.5 9% 9% 

Proportional 12.6 9.9 12.6 9.9 12.6 9.9 12.0 9.4 11.8 9.3 9.9 7.8

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused --- --- --- --- --- --- --- --- 12.2 9.7 10.2 9.0 --- --- Proportional 13.3 11.6 13.3 11.6 12.8 11.2 11.8 10.5 11.5 10.0 9.7 8.4

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused 15.6 13.2 15.6 13.2 15.3 11.8 15.3 11.2 15.6 11.4 13.9 9.6 22% 22% 

Table F-50. Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with percent difference in 

 surrogate for long-term exposure-related mortality across rollback methods) 

cells u

13/30 12/25

 Atlanta, GA 15.3

Risk 

Assessment 

Location 1

Rollback 

Method

 Design Value

Recent Air 

Quality 

(2007)

16.2 35.0

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 

Annual Average at Composite Monitor (2007CM) (in ug/m3)

15/35 2 14/35 13/35 12/35

 Baltimore, MD 15.6 37.0 13.9

 Birmingham, AL 18.7 44.0 15.7 Dallas, TX 11.4 Detroit, MI 17.2 43.0 13.9

12.8 26.0

 Fresno, CA 17.4 Houston, TX 15.8 31.0 13.2

17.4 63.0

 Los Angeles, CA 14.6 New York, NY 15.9 42.0 13.8

19.6 55.0

 Philadelphia, PA 13.4 Phoenix, AZ 9.9 Pittsburgh, PA 5 19.8 14.9

cells ucells ucells ucells ucells ucells ucells ucells u

Percent d

monitor val

compared w

difference 

cells ucells ucells u

60.0

12.6 32.015.0 38.0
F-63

Annual 24-Hr 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM 15/35 14/35 

Proportional 7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 6.7 6.6 5.7 5.6

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused 10.8 9.7 10.8 9.7 10.8 9.7 10.8 9.7 10.8 8.8 9.1 7.7 55% 55% 

Proportional 14.9 12.9 14.0 12.1 13.0 11.3 12.0 10.4 12.8 11.1 10.8 9.3

Hybrid 15.0 13.5 14.0 12.6 13.0 11.7 12.0 10.8 13.0 11.7 11.0 9.9 8% 6% 

Locally Focused 16.5 14.1 --- --- --- --- --- --- 14.2 12.4 11.9 10.4 15% --- 

Proportional 8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 7.4 7.0 6.3 6.0

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused 8.5 8.0 8.5 8.0 8.5 8.0 8.5 8.0 7.4 7.0 6.3 6.0 0% 0% 

Table F-50 (cont'd). Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with percent diffe

 surrogate for long-term exposure-related mortality across rollback methods) 

6 Percent reduction in composite monitor value (CMV) with consideration for LML of 5.8 ug/m3. Percent reduction = (CMVlocally focused or hybrid - CMVproportional)/(CMVlocally focused or hybr

identify instances where two values differ by >25% across alternative rollback methods (for a given alternative standard level/study area combination). 

1For some locations (e.g., Atlanta) more than one "version" (group of counties) was used in the risk assessment. In this table only the version that was used for mortality associated (Zanobetti and Schwartz, 2009) is included. 3 The hybrid rollback method was applied to only a subset of the risk assessment locations. The "---" for a given location indicates that the hybrid rollback method was not applied to 2 The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

13/30 12/25

Risk 

Assessment 

Location 1

Rollback 

Method

 Design Value

Recent Air 

Quality 

(2007)

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 

Annual Average at Composite Monitor (2007CM) (in ug/m3)

15/35 2 14/35 13/35 12/35

 Tacoma, WA 9.7

 Salt Lake City, UT 11.4 St. Louis, MO 14.3

11.6 55.0

4 The locally focused method was applied to a location-standard combination only if the daily standard was controlling in that location. The "--" for a given location-standard combina

annual and daily standards in that location, the annual standard was controlling and so the locally focused method was not applied. 

5 The proportional rollback and locally focused 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 

Percent d

monitor val

compared w

difference 

cells ucells ucells u

10.2 43.016.5 39.0
G-1

APPENDIX G: SUPPLEMEMNT TO THE NATIONAL-SCALE 

ASSESSMENT OF LONG-TERM MORTALITY RELATED TO PM2.5 

EXPOSURE
G-2

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

 Exposure (additional discussion of the analysis and technical detail regarding 

methods and inputs used) 

As mentioned in section 4.4, as part of assessing the representativeness of the 15 urban 

study areas in the national-context, we completed a national-scale assessment of long-term 

exposure-related mortality. This allowed us to assess where along the national-scale distribution 

the 31 counties comprising our 15 urban study areas fell and specifically, whether they provide 

coverage for areas in the country likely to experience relative elevated PM2.5-related long-term 

exposure-related mortality risk. This appendix provides additional information related to the 

national-scale assessment of long-term mortality that was completed as part of the risk analysis. 

It begins with an overview of the national-scale analysis (section G.1), including presentation of 

the national-scale mortality estimates that were generated as part of the analysis and a brief 

description of the technical approach used in conducting the analysis. Then, additional technical 

detail on air quality (CMAQ) modeling is presented in section G.2. Additional detail on the 

outputs of the analysis, including air quality, exposure and risk estimates (focusing on graphical 

presentation of results), is presented in section G.3. 

G.1 OVERVIEW OF NATIONAL-SCALE MORTALITY ANALYSIS 

In this section we present the estimated nationwide premature mortality resulting from 

recent exposures to ambient PM2.5. To perform this assessment we use 2005 PM2.5 fused air 

quality estimates from the Community Model for Air Quality (CMAQ) (Byun and Schere, 2006) 

in conjunction with the environmental Benefits Mapping and Analysis Program (BenMAP, Abt 

Associates Inc, 2008) to estimate long-term PM2.5-related premature mortality nationwide. We 

estimate excess PM2.5-related long-term mortality by applying two estimates of all-cause 

mortality risk found in the Krewski et al. (2009) PM2.5 mortality extended analysis of the 

American Cancer Society (ACS) cohort, and an estimate of all-cause mortality risk found in the 

Laden et al. (2006) PM2.5 mortality extended analysis of the Six-Cities cohort. We estimate that 

total PM2.5-related premature mortality ranges from 63,000 (39,000—87,000) (95th percentile 

confidence interval) and 88,000 (49,000—130,000), respectively; in each case we estimated 

deaths per year down to the lowest measured levels (LMLs) in each epidemiological study. 

We had considered expanding the national-scale mortality to include additional health 

endpoints (related to short-term PM2.5 exposure) or additional air quality scenarios that simulate 

just meeting the current and alternative suites of standards. However, we have concluded that 

any expansion of this assessment is beyond the scope of what is needed or can reasonably be 
G-3

done within the time and resources available for this review. The rationale for our decision not to 

expand the scope of the national-scale analysis is as follows. 

The goal of the national-scale analysis is two-fold: to provide perspective on the 

magnitude of PM2.5 health impacts on a national-scale and to help to place the risk estimates 

generated for the urban study areas in a national context. We note, however that the second goal 

(assessing the representativeness of the 15 urban study areas in the national context) plays a 

greater role in the context of the risk assessment completed as part of the PM NAAQS review. 

The analysis as currently implemented achieves the first goal by providing estimates of longterm exposure-related all-cause mortality under recent conditions. While simulation of risk for 

the current and alternative standard levels would provide additional perspective on the magnitude 

of national-scale risk, that assessment would be resource-intensive and subject to considerable 

uncertainty if it were conducted using air quality simulation methods similar to those used in the 

urban study area analysis (i.e., application of a combination of rollback methods that reflects 

both local and regional patterns in ambient PM2.5 reductions implemented at the monitor-level). 

A particular area of uncertainty (and technical complexity) related to air quality simulation 

would be addressing the interplay between regional-scale reductions in ambient PM2.5 in adjacent 

urbanized areas. In the urban study area analysis, each location is treated independently with 

regard to simulating ambient PM2.5 under alternative suites of standards. However, if we were to 

expand the national analysis to include alternative standards, then simulation of rollbacks in 

ambient PM2.5 levels would necessarily have to address this contiguity issue between adjacent 

urban areas and even between suburban areas and adjacent urbanized areas in the context of 

simulating monitor rollback. 

In addition, because long-term exposure-related mortality dominates PM2.5 in terms of 

total incidence, providing coverage for this endpoint category ensures that the majority of PM2.5-

related mortality incidence is reflected in the analysis, without including short-term exposurerelated mortality. 

The national-scale mortality analysis, as currently implemented, also achieves its second 

goal: to help place risk estimates for the urban study areas in a national context. Because the 

national-scale analysis focuses on the long-term exposure-related mortality, which is the primary 

driver for PM2.5-related health impacts, the analysis allows us to assess how the urban study 

areas "fall" across a national distribution of risk for this key health endpoint category (see section 

4.4.2 of the RA). This then allows us to characterize the degree to which the set of urban study 

areas provides coverage for areas of the country likely to experience relatively elevated levels of 

PM2.5-related health impacts. 

This assessment combines information regarding estimated PM2.5 air quality levels, 

population projections, baseline mortality rates, and mortality risk coefficients to estimate PM2.5-
G-4

related premature mortality. Figure G-1 below provides a conceptual diagram, detailing each of 

the key steps involved in performing this BenMAP-based health impact assessment. 

Figure G-1 Conceptual diagram of data inputs and outputs for national long-term 

 mortality risk assessment 

Population Estimates

The starting point for estimating the size and demographics of the potentially exposed 

population is the 2000 census-block level population, which BenMAP aggregates up to the same 

grid resolution as the air quality model. Using county-level growth factors based on economic 

projections (Woods and Poole Inc., 2001), BenMAP projects this 2000 population to the analysis 

year of 2005; we selected this population year because it matches both the year in which the 

emissions inventory was developed for the air quality modeling and the year to which the 

baseline mortality rates were projected (see below). 

Population Exposure

Having first estimated the size and geographic distribution of the potentially exposed 

population, BenMAP then matches these population projections with estimates of the ambient 

levels of PM2.5. In contrast to the urban study areas analysis, the national-scale analysis 

employed a data fusion approach, which joined 2005 monitored PM2.5 concentrations with 2005 

CMAQ-modeled air quality levels using the Voronoi Neighbor Averaging (VNA) technique 

(Abt, 2003). CMAQ was run at a horizontal grid resolution of 12km for the east and 36km in the 
G-5

west using 2005 estimated emission levels and meteorology. Figure G-2 shows the geographic 

distribution of baseline annual mean PM2.5 concentrations across the continental U.S. The 

maximum predicted value within the U.S. is 31 μg/m3, the mean PM2.5 value is 8.7 μg/m3,

median is 8.8 μg/m3 and the 95th percentile value is about 14 μg/m3. 

Figure G-2 2005 fused surface baseline PM2.5 concentrations 

This assessment applies PM2.5 mortality risk coefficients drawn from long-term cohort 

studies which estimate changes in risk based on annual mean changes in PM2.5 concentration. 

For this reason, EPA used the CMAQ model to estimate annual mean concentrations at each grid 

cell. These grid-level annual average concentrations were then input to BenMAP. 

Premature Mortality Estimates

In this assessment of PM2.5-related premature mortality we considered risk estimates 

drawn from studies based on two prospective cohorts. The first study is the recently published 
G-6

Krewski et al. (2009) extended reanalysis of the ACS cohort. To remain consistent with the 

urban study areas analysis, we applied the two log-linear all-cause mortality risk coefficients 

based on the 1979-1983 and the 1999-2000 time periods that control for 44 individual and 7 

ecologic covariates. We also applied a log-linear all-cause mortality risk coefficient drawn from 

the extended analysis of the Six Cities cohort as reported by Laden et al. (2006). When 

estimating premature mortality using these functions we considered air quality levels down to the 

lowest measured levels (LML) in each study; for the Krewski et al. (2009) study this is 5.8 μg/m3 

and for the Laden et al. (2006) study this is 10 μg/m3. In general, we place a higher degree of 

confidence in health impacts estimated at air quality levels at or above the LML because the 

portion of the concentration-response curve below this point is extrapolated beyond the observed 

data. We also estimated health impacts down to Policy Relevant Background (PRB) levels 

(EPA, 2008). The final ISA presents estimates of annual mean PRB for each of 7 Health Effects 

Institute PM regions; this value ranges from 0.62 μg/m3 in the southwest to 1.72 μg/m3 in the 

southeast. 

BenMAP contains baseline age-, cause- and county-specific mortality rates drawn from 

the CDC-WONDER. Current baseline mortality estimates are an average of a three year period 

from 1996-1998. EPA is in the process of updating these rates with 2006-2008 data; a sensitivity 

analysis suggests that the results reported here are largely insensitive to the use of more current 

mortality rates. 

Results

Table G-1 and figure G-3 below summarize the results of the national-scale mortality 

analysis. Table G-1 summarizes the total PM2.5-related premature mortality associated with 

modeled 2005 PM2.5 levels. 

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

 incremental air quality differences between 2005 ambient mean 

 PM2.5 levels and lowest measured level from the epidemiology studies 

 or policy relevant background (90th percentile confidence interval) 

Estimates Based on Krewski et al. (2009) 

Air Quality 

Level 

‘79-’83 estimate 

(90th percentile 

confidence interval) 

‘99-’00 estimate 

(90th percentile 

confidence interval) 

Estimates Based on 

Laden et al. (2006) 

(90th percentile 

confidence interval) 

10 μg/m3 (LML 

for Laden et al., 

2006) 

26,000 

(16,000—36,000) 

33,000 

(22,000—44,000) 

88,000

(49,000—130,000)
G-7

Estimates Based on Krewski et al. (2009) 

5.8 μg/m3 (LML 

for Krewski et 

al., 2009) 

63,000

(39,000—87,000)

80,000

(54,000—110,000)

210,000 

(120,000—300,000) 

Policy-Relevant 

Background 110,000 

(68,000—150,000) 

140,000 

(94,000—180,000) 

360,000 

(200,000—500,000) 

Bold indicates that the minimum air quality level used to calculate this estimate corresponds to the 

lowest measured level identified in the epidemiological study

In this table, the bold figures indicate the estimate that corresponds with the LML 

identified in the epidemiological study. The bold estimates in the column Krewski et al. (2009) 

were calculated using the same risk coefficients as the urban case study analysis. We place a 

greater emphasis on those results calculated using the LML reported in the epidemiological 

studies.3

 Figure G-3 illustrates the percentage of baseline mortality attributable to PM2.5

exposure in each of the grid cells according to the 2005 PM2.5 air quality levels, using the 

Krewski et al. (2009) estimate based on 1999-2000 air quality levels. 

 3

 Note, that as stated in Section 4.3.2, modeling of risk down to PRB is subject to considerable uncertainty. While 

there is no evidence for a threshold (which conceptually supports estimation of risk below LML), we do not have 

information characterizing the nature of the C-R function for long-term mortality below the LML and consequently 

estimates of mortality based on incremental exposure below LML (and down to PRB) is subject to greater 

uncertainty. 
G-8

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

2005 annual average PM2.5 levels* 

Figure G-3 illustrates the number of deaths attributable to PM2.5 according to the 

baseline level of ambient average PM2.5 levels down to 5.8 μg/m3 (the LML for the Krewski et 

al. (2009) analysis). Each of four box plots characterizes the range of premature mortality 

attributable to PM2.5 according to the baseline level of annual mean PM2.5 levels in that model 

grid cell. Note that while the lower whisker of the box plots for the baseline air quality values of 

5.8 μg/m3 to 10 μg/m3 appear to extend to zero, the minimum value is greater than zero. The 

number above each box plot indicates the number of grid cells summarized by that plot. 

We note that the results of the national-scale analysis used in assessing the 

representativeness of the 15 urban study areas in the national context have already been 

discussed in section 4.4 and are not discussed further here. 

G.2 ADDITIONAL TECHNICAL DETAIL ON AIR QUALITY MODELING 

The Community Model for Air Quality (CMAQ) model was used to estimate annual 

PM2.5 concentrations for the year 2005 for the continental US. These data were then combined 

with ambient monitored PM2.5 measurements to create “fused” spatial surfaces supplied to 

BenMAP.

CMAQ Model Application and Evaluation
G-9

CMAQ is a non-proprietary computer model that simulates the formation and fate of 

photochemical oxidants, including PM2.5 and ozone, for given input sets of meteorological 

conditions and emissions. This analysis employed a version of CMAQ based on the latest 

publicly released version (i.e. CMAQ version 4.74

). 

Model Domain and Grid Resolution 

The CMAQ modeling analyses were performed for two domains covering the continental 

United States, as shown in Figure G-4. These domains consist of a horizontal grid of 36 km 

covering the entire continental US and a finer-scale 12-km grid covering the Eastern U.S. The 

model extends vertically from the surface to 100 millibars (approximately 15 km) using a sigmapressure coordinate system. The 36-km grid was used to establish the incoming air quality 

concentrations along the boundaries of the 12-km grids. Table G-2 provides some basic 

geographic information regarding the CMAQ domains. The 36-km and both 12-km CMAQ 

modeling domains were modeled for the entire year of 2005. All 365 model days were used in 

the annual average levels of PM2.5. 

Table G-2. Geographic Information for Modeling Domains 

CMAQ Modeling Configuration 

National Grid 

Eastern U.S. 

Fine Grid 

Map Projection Lambert Conformal Projection 

Grid Resolution 36 km 12 km 

Coordinate Center 97 W, 40 N 

True Latitudes 33 and 45 N 

Dimensions 148 x 112 x 24 279 x 240 x 24 

Vertical Extent 24 Layers: Surface to 100 mb level 

 

4

CMAQ version 4.7 was released on December 1, 2008. It is available from the Community Modeling and 

Analysis System (CMAS) at: http://www.cmascenter.org. 
G-10

Figure G-4. Map of the CMAQ Modeling Domain (Note, the black outer box denotes the 

36-km national modeling domain; the red inner box is the 12-km Eastern U.S. fine 

grid). 

CMAQ Model Inputs

Emissions

The 2005 emissions inputs to CMAQ included five source sectors: a) Electric Generating 

Units (EGUs); b) Other Stationary Sources (Point and Nonpoint); c) Onroad and Nonroad 

Mobile Sources; d) Biogenic Emissions; and e) Fires. The fires portion of the inventory included 

emissions from wildfires and prescribed burning computed as hour-specific point sources. 

Electric Generating Units (EGUs) 
G-11

Annual emissions estimates for EGUs for all National Emissions Inventory (NEI) air 

pollutants for 2005 were developed using data reported to the USEPA’s Clean Air Marketing 

Division’s (CAMD) Acid Rain database. The Acid Rain database contains hourly emissions for 

SO2 and NOx emissions plus hourly heat input amounts. These three values are reported to the 

database by the largest electric generating facilities, usually based upon Continuous Emissions 

Monitors (CEMs). For all pollutants except the directly monitored SO2 and NOx, the ratio of the 

Acid Rain heat input for 2005 to the Acid Rain heat input for 2002 was used as the adjusting 

ratio to estimate the 2005 emissions. 

Other Stationary Sources (Point and Nonpoint) 

Emission estimates for other stationary sources including both point and nonpoint 

stationary sources were held constant at the level in Version 3 of the 2002 NEI. The only 

exception to this was that some information on plants that closed after 2002 was incorporated 

into the emissions modeled. Emissions for plants that closed were set to zero. U.S. EPA, 2008c 

provides complete documentation on the development of the 2002 NEI. 

Onroad and Nonroad Mobile Sources 

Emission estimates for all pollutants were developed using EPA’s National Mobile 

Inventory Model (NMIM), which uses MOBILE6 to calculate onroad emission factors. A full 

VMT database at the county, roadway type, and vehicle type level of detail was developed from 

Federal Highway Administration (FHWA) information. However, state and local agencies had 

the opportunity to provide model inputs (vehicle populations, fuel characteristics, VMT, etc) for 

2002 and 2005. If the state or local area submitted 2005 VMT estimates, these data were used. 

However, if the state or local area only provided 2002 VMT estimates that were incorporated in 

the 2002 NEI, the 2002 NEI VMT data were grown to 2005 using growth factors developed from 

the FHWA data, and these grown VMT data replaced the baseline FHWA-based VMT data. 

Otherwise, the FHWA-based VMT data were used. 

Emission estimates for NONROAD model engines were developed using EPA’s National 

Mobile Inventory Model (NMIM), which incorporates NONROAD2005. Where states provided 

alternate nonroad inputs, these data replaced EPA default inputs, as described above. For more 
G-12

information on how NMIM is run, refer to the 2005 NEI documentation posted at 

ftp://ftp.epa.gov/EmisInventory/2005_nei/mobile/2005_mobile_nei_version_2_report.pdf. 

Fires 

Fires in the 2005 emissions inventory were modeled with the same methodology as used 

for the 2002 NEI (U.S. EPA, 2008). However, as described in Raffuse et al., 2008, the wildland 

fire emission inventories for 2005 were produced using the BlueSky framework for the 

conterminous United States, which used the Satellite Mapping Automatic Reanalysis Tool for 

Fire Incident Reconciliation (SMARTFIRE) as the fire information source. SMARTFIRE is an 

algorithm and database system designed to reconcile these disparate fire information sources to 

produce daily fire location and size information (Sullivan et al., 2008). 

Biogenic Emissions 

Biogenic emissions were computed for CMAQ based on 2005 meteorology data using the 

BEIS3.13 model (Schwede, et. al, 2005) from the Sparse Matrix Operator Kernel Emissions 

(SMOKE). The BEIS3.13 model creates gridded, hourly, model-species emissions from 

vegetation and soils. It estimates CO, VOC, and NOX emissions for the U.S., Mexico, and 

Canada. The inputs to BEIS include: 

• temperature data at 10 meters which were obtained from the CMAQ 

meteorological input files, and 

• land-use data from the Biogenic Emissions Landuse Database, version 3 

(BELD3), which provides data on the 230 vegetation classes at 1 km resolution over most 

of North America. 

Meteorological Input Data: 

The gridded meteorological input data for the entire year of 2005 were derived from 

simulations of the Pennsylvania State University / National Center for Atmospheric Research 

Mesoscale Model. This model, commonly referred to as MM5, is a limited-area, nonhydrostatic, 

terrain-following system that solves for the full set of physical and thermodynamic equations 

which govern atmospheric motions (Grell et al., 1994). Meteorological model input fields were 
G-13

prepared separately for both of the domains shown in Figure G-4 using MM5 version 3.7.4. The 

MM5 simulations were run on the same map projection as CMAQ. 

Both meteorological model runs were configured similarly. The selections for key MM5 

physics options are shown below: 

 Pleim-Xiu PBL and land surface schemes 

 Kain-Fritsh 2 cumulus parameterization 

 Reisner 2 mixed phase moisture scheme 

 RRTM longwave radiation scheme 

 Dudhia shortwave radiation scheme 

Three dimensional analysis nudging for temperature and moisture was applied above the 

boundary layer only. Analysis nudging for the wind field was applied above and below the 

boundary layer. The 36 km domain nudging weighting factors were 3.0 x 104

 for wind fields and 

temperatures and 1.0 x 105 for moisture fields. The 12 km domain nudging weighting factors 

were 1.0 x 104 for wind fields and temperatures and 1.0 x 105 for moisture fields. 

All model runs were conducted in 5.5 day segments with 12 hours of overlap for spin-up 

purposes. Both domains contained 34 vertical layers with an approximately 38 m deep surface 

layer and a 100 millibar top. The MM5 and CMAQ vertical structures are shown in Table G-3 

and do not vary by horizontal grid resolution. 

Table G-3. Vertical Layer Structure for MM5 and CMAQ (heights are layer top). 

CMAQ 

Layers 

MM5 

Layers Sigma P 

Approximate 

Height (m)

Approximate 

Pressure (mb)

0 0 1 0 1000 

1 1 0.995 38 995 

2 2 0.99 77 991 

3 0.985 115 987 3 

4 0.98 154 982 

4 5 0.97 232 973 

5 6 0.96 310 964 

7 0.95 389 955 6 

8 0.94 469 946 

9 0.93 550 937 7 

10 0.92 631 928 

11 0.91 712 919 8 

12 0.9 794 910 
G-14

CMAQ 

Layers 

MM5 

Layers Sigma P 

Approximate 

Height (m)

Approximate 

Pressure (mb)

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 

The meteorological outputs from the MM5 sets were processed to create model-ready 

inputs for CMAQ using the Meteorology-Chemistry Interface Processor (MCIP), version 3.4, to 

derive the specific inputs to CMAQ. 

Before initiating the air quality simulations, it was important to identify the biases and 

errors associated with the meteorological modeling inputs. The 2005 MM5 model performance 

evaluations used an approach which included a combination of qualitative and quantitative 

analyses to assess the adequacy of the MM5 simulated fields. The qualitative aspects involved 

comparisons of the model-estimated synoptic patterns against observed patterns from historical 

weather chart archives. Additionally, the evaluations compared spatial patterns of monthly 

average rainfall and monthly maximum planetary boundary layer (PBL) heights. Qualitatively, 

the model fields closely matched the observed synoptic patterns, which is not unexpected given 

the use of nudging. The operational evaluation included statistical comparisons of 

model/observed pairs (e.g., mean normalized bias, mean normalized error, index of agreement, 

root mean square errors, etc.) for multiple meteorological parameters, including temperature, 

humidity, shortwave downward radiation, wind speed, and wind direction (Baker and Dolwick, 
G-15

2009a, Baker and Dolwick, 2009b). It was ultimately determined that the bias and error values 

associated with the 2005 meteorological data were generally within the range of past 

meteorological modeling results that have been used for air quality applications. 

Initial and Boundary Conditions 

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM model (Yantosca, 2004). 

The global GEOS-CHEM model simulates atmospheric chemical and physical processes driven 

by assimilated meteorological observations from the NASA’s Goddard Earth Observing System 

(GEOS). This model was run for 2002 with a grid resolution of 2.0 degrees x 2.5 degrees 

(latitude-longitude) and 24 vertical layers. The 2005 CMAQ 36km simulation used non-year 

specific GEOS-CHEM data, which was created by taking the median value for each month in 

each individual grid cell of the 2002 GEOS-CHEM data described above. The predictions were 

used to provide one-way dynamic boundary conditions and an initial concentration field for the 

CMAQ simulations. More information is available about the GEOS-CHEM model and other 

applications using this tool at: http://www-as.harvard.edu/chemistry/trop/geos. 

CMAQ Model Performance Evaluation

An operational model performance evaluation for PM2.5 and its related speciated 

components was conducted for 2005 using state/local monitoring sites data in order to estimate 

the ability of the CMAQ modeling system to replicate the concentrations for the 12-km Eastern 

domain and 36-km domain in the west. The principal evaluation statistics used to evaluate 

CMAQ performance included two bias metrics, normalized mean bias and fractional bias; and 

two error metrics, normalized mean error and fractional error. For the 12-km Eastern domain, 

performance evaluation statistics were computed for the entire domain as well as its subregions. 

For the 36-km domain, evaluation focuses on the parts of the US not covered by the 12-km 

Eastern domain by computing performance evaluation statistics for the states included in the 

Western Regional Air Partnership (WRAP). 

The PM2.5 evaluation focuses on PM2.5 total mass and its components, including sulfate 

(SO4), nitrate (NO3), total nitrate (TNO3 = NO3 + HNO3), ammonium (NH4), elemental carbon 

(EC), and organic carbon (OC). PM2.5 ambient measurements for 2005 were obtained from the 

following networks for model evaluation: Speciation Trends Network (STN), Interagency 

Monitoring of PROtected Visual Environments (IMPROVE), and Clean Air Status and Trends 

Network (CASTNET). For PM2.5 species that are measured by more than one network, we 
G-16

calculated separate sets of statistics for each network. Table G-4 provides annual model 

performance statistics for PM2.5 and its component species. Based on the bias and error values 

associated with the 2005 CMAQ-modeled PM2.5 concentration data, it was determined that the 

annual average PM2.5 data were generally within the range of past modeling results used for air 

quality applications and are applicable to be used for this national-scale current conditions 

analysis. 

Table G-4. CMAQ modeled performance evaluation statistics for PM2.5 for 2005. 

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 

Sulfate 

IMPROVE 

Central 2640 -27.9 38.0 -22.0 42.4 
G-17

CMAQ 2005 Annual 

No. of 

Obs. NMB (%) NME (%) FB (%) FE (%) 

36-km 

West 

WRAP 

9693 -5.2 45.2 9.6 47.6 

12-km EUS 3170 -16.5 22.9 -15.6 26.0 

Northeast 786 -11.7 20.5 -9.8 22.6 

Midwest 615 -13.6 21.4 -11.2 22.2 

Southeast 1099 -18.4 22.9 -19.6 25.7 

Central 300 -29.4 32.5 -30.3 36.1 

CASTNet 

36-km 

West 

WRAP 

1112 -12.6 34.5 -3.2 36.7 

12-km EUS 12186 20.1 67.8 -10.1 76.3 

Northeast 3248 28.7 70.2 -3.7 74.1 

Midwest 2495 20.2 61.0 9.2 63.0 

Southeast 3499 23.5 84.0 -25.0 87.2 

Central 1812 8.1 60.2 -5.9 72.4 

STN 

36-km 

West 

WRAP 

15,533 15.2 79.3 -15.6 85.9 

12-km EUS 10157 30.1 85.2 -32.5 99.1 

Northeast 2388 67.0 108.9 0.5 93.4 

Midwest 622 14.0 67.9 -24.1 88.9 

Southeast 1990 37.4 104.6 -46.2 105.9 

Central 2640 17.3 70.8 -19.3 89.6 

Nitrate 

IMPROVE 

36-km 

West 

WRAP 

17,452 33.1 99.1 -41.9 109.9 

12-km EUS 3170 24.6 39.7 17.8 38.0 

Northeast 786 36.5 43.0 30.3 40.6 

Midwest 615 23.3 36.5 23.9 33.2 

Southeast 1099 23.6 42.2 12.8 40.5 

Central 300 10.6 35.5 5.0 35.0 

Total Nitrate 

(NO3+HNO3) 

CASTNet 

36-km 

West 

WRAP 

4065 37.7 51.9 24.2 45.1 

12-km EUS 13317 1.8 41.9 8.3 45.6 

Northeast 3247 7.1 42.9 18.9 45.7 

Midwest 2495 7.1 40.5 16.4 41.4 

Southeast 3499 -2.1 40.5 2.9 43.3 

Ammonium STN 

Central 2944 -7.6 44.0 -4.0 51.4 
G-18

CMAQ 2005 Annual 

No. of 

Obs. NMB (%) NME (%) FB (%) FE (%) 

36-km 

West 

WRAP 

16,680 8.1 47.2 12.8 48.9 

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 

Organic Carbon 

IMPROVE 

Central 2622 -34.1 52.8 -52.7 68.1 
G-19

CMAQ 2005 Annual 

No. of 

Obs. NMB (%) NME (%) FB (%) FE (%) 

36-km 

West 

WRAP 

17,295 -22.5 57.5 -40.8 67.6 

“Fused” Spatial Surfaces

Spatial surfaces of the 2005 data were created by fusing CMAQ-modeled annual average 

PM2.5 concentrations with total PM2.5 data from STN, IMPROVE, and CASTNET monitoring 

sites for the two domains shown in Figure G-4. We used the EPA’s Model Attainment Test 

Software (MATS) (Abt, 2009) which employees the Voronoi Neighbor Averaging (VNA) 

interpolation technique (Abt, 2008). This technique identifies the set of monitors that are nearest 

to the center of each grid cell, and then takes an inverse distance squared weighted average of the 

monitor concentrations. The “fused” spatial fields are calculated by adjusting the interpolated 

ambient data (in each grid cell) up or down by a multiplicative factor calculated as the ratio of 

the modeled concentration at the grid cell divided by the modeled concentration at the nearest 

neighbor monitor locations (weighted by distance). 

To create the spatial surfaces for use in BenMAP, the 2005 CMAQ-modeled annual 

average PM2.5 concentrations were “fused” with 2005 total PM2.5 ambient monitoring data from 

STN, IMPROVE, and CASTNET sites. This was done for both the 36km national domain and 

the 12km eastern US domain. The spatial surface of annual average PM2.5 air quality 

concentrations produced by this technique is shown in Figure G-5 for the continental U.S. Where 

available, the 12km spatial surface was used to supply BenMAP with annual average PM2.5 

concentrations. In the western part of the U.S., annual average PM2.5 concentrations were 

supplied from the 36km domain. 
G-20

Figure G-5: 2005 Predicted Annual Mean PM2.5 Levels 

Advantages and Limitations

As compared to using monitored data alone, an advantage of using the CMAQ model 

output for comparing with health outcomes is that it has the potential to provide more complete 

spatial and temporal coverage. In addition, “fusing” the CMAQ data with ambient monitoring 

data allows for an improvement over non-fused fields (Timin et al., 2009). Doing so allows for a 

combination of the advantages of both sets of data: better spatial coverage and more accurate air 

quality estimates. Of course, the more accurate the model estimates of PM2.5, the better the 

performance of the “fused” spatial fields. Therefore, it is important to use model outputs that 

have adequate PM2.5 performance. As discussed above, we believe that the 2005 CMAQmodeled PM2.5 concentration data showed adequate model performance to be used for this 

national-scale current conditions analysis. 

As with any model estimate of air quality, there are limitations. For example, the 

emissions and meteorological data used in CMAQ can each have large uncertainties, in particular 
G-21

for unusual emission or meteorological events. There are also uncertainties associated with the 

chemical transformation and fate process algorithms used in air quality models. For these 

reasons, CMAQ predicts best on longer time scale bases (e.g., synoptic, monthly, and annual 

scales). These limitations have led us to use modeled air quality estimates in this analysis that 

are “fused” with measured ambient data and averaged over an annual scale. 

G.3 ADDITIONAL DETAIL ON ESTIMATES (AIR QUALITY, EXPOSURE AND 

RISK) 

Air Quality Estimates

Figures G-6 through G-9 below illustrate the spatial distribution of air quality impacts. 

Figure G-6 illustrates the modeled 2005 PM2.5 air quality levels across the U.S. Figures G-7 and 

G-8 display the PM2.5 air quality levels after being adjusted so that the maximum level is no 

higher than the LML reported in the Krewski et al. (2009) and Laden et al. (2006) studies. Figure 

G-9 displays the PRB by region of the county.
G-22

Figure G-6: 2005 Predicted Annual Mean PM2.5 Levels 
G-23

Figure G-7: 2005 Predicted Annual Mean PM2.5 Levels Adjusted for LML of the 

Krewski et al. (2009) study 
G-24

Figure G-8: 2005 Predicted Annual Mean PM2.5 Levels Adjusted for LML of the 

Laden et al. (2006) study 
G-25

Figure G-9: PRB by Geographic Area in the U.S. 

Figure G-10 displays the distribution of grid cells at different baseline PM2.5 air quality 

levels. Figures G-11 and G-13 displays the distribution of grid cells according to the incremental 

change in PM2.5 air quality for each of three scenarios: current conditions to 10 μg/m3, current 

conditions to 5.8 μg/m3 and current conditions to PRB. 
G-26

Figure G-10: The Number of Grid Cells at Each Level of PM2.5 Concentration in 

2005 Current Conditions Air Quality Modeling Run 
G-27

Figure G-11: The Number of CMAQ Grid Cells Experiencing an Incremental 

Change in Annual Mean PM2.5 (μg/m3) (Current Conditions – 10 

μg/m3) 
G-28

Figure G-12: The Number of CMAQ Grid Cells Experiencing an Incremental 

Change in Annual Mean PM2.5 (μg/m3) (Current Conditions – 5.8 

μg/m3) 
G-29

Figure G-13: The Number of CMAQ Grid Cells Experiencing an Incremental 

Change in Annual Mean PM2.5 (μg/m3) (Current Conditions – Policy 

Relevant Background) 

Figure G-14 displays the cumulative distribution of grid cells at each baseline 

concentration. Figures G-15 through G-17 display the cumulative distribution of grid cells 

experiencing an incremental air quality change. 
G-30

Figure G-14: Cumulative Distribution of Baseline PM2.5 Concentrations (μg/m3

) 
G-31

Figure G-15: Cumulative Distribution of PM2.5 (μg/m3

) Changes (Baseline – 10 

μg/m3

) 
G-32

Figure G-16: Cumulative Distribution of PM2.5 (μg/m3

) Changes (Baseline – 5.8 

μg/m3

) 
G-33

Figure G-14: Cumulative Distribution of PM2.5 (μg/m3

) (Baseline – Policy Relevant 

Background) 

Exposure Estimates

Below we provide additional details regarding the estimated exposure changes occurring 

as a result of each of the air quality changes assumed in each of the three health impact 

assessments: current conditions incremental to 10 μg/m3, 5.8 μg/m3 and PRB. Table G-5 

summarizes the population-weighted air quality change occurring among populations 30-99 (the 

age range considered in the ACS cohort) for each scenario. 

Population-weighted air quality change is the average per-person change in PM2.5. It is 

estimated by calculating the summation of the population in each grid cell multiplied against the 

change in annual mean PM2.5 concentration in that grid cell and then dividing by the total 

population. 
G-34

Table G-5. Estimated Change in Annual Mean Population-Weighted PM2.5 by 

Model Scenario 

Model scenario 

Population-weighted air quality change or 

baseline 

Current conditions to 10 μg/m3

 2.6 μg/m3

Current conditions to 5.8 μg/m3 6.3 μg/m3

Current conditions to PRB 11 μg/m3

Current conditions 12 μg/m3

 

Health Impact Estimates

Figure G-14 through G-16 illustrate the distribution of total mortality attributable to 

PM2.5 exposure for each of three scenarios: current conditions to 10 μg/m3, 5.8 μg/m3 and PRB. 
G-35

Figure G-14: The Percentage of Total Mortality Attributable to PM2.5 Exposure: 

Baseline – 10 μg/m3
G-36

Figure G-15: The Percentage of Total Mortality Attributable to PM2.5 Exposure: 

Baseline – 5.8 μg/m3
G-37

Figure G-16: The Percentage of Total Mortality Attributable to PM2.5 Exposure: 

Baseline – Policy Relevant Background 

Figures G-17 through G-19 illustrate the cumulative distribution of total mortality 

attributable to PM2.5 exposure for each of three scenarios: current conditions to 10 μg/m3, 5.8 

μg/m3 and PRB. 
G-38

Figure G-17: The Cumulative Distribution of the Percentage of Total Mortality 

Attributable to PM2.5 Exposure: Baseline – 10 μg/m3
G-39

Figure G-18: The Cumulative Distribution of the Percentage of Total Mortality 

Attributable to PM2.5 Exposure: Baseline – 5.8 μg/m3
G-40

Figure G-19: The Cumulative Distribution of the Percentage of Total Mortality 

Attributable to PM2.5 Exposure: Baseline – Policy Relevant Background 
H-1

APPENDIX H: CONSIDERATION OF RISK ASSOCIATED WITH 

EXPOSURE TO THORACIC COARESE PM (PM10-2.5)
H-2

H.1 OVERVIEW 

This appendix discusses the issue of assessing public health risk associated with exposure 

to thoracic coarse PM (PM10-2.5). As mentioned in Section 2.3, due to limitations in available 

monitoring data characterizing ambient levels of PM10-2.5 in prospective urban study areas, 

together with limitations in the epidemiological study data available for deriving C-R functions 

for this PM size fraction, EPA staff has concluded that uncertainties in characterizing risk for 

PM10-2.5 are potentially significant enough at this time to limit the utility of those estimates in 

informing the review of the PM coarse standard level. Therefore, we have not conducted a PM10-

2.5 risk assessment for this review; instead, we have included a summary of risk estimates for 

PM10-2.5 generated as part of the last PM NAAQS review completed in 2005.5

As part of our summarizing PM10-2.5 risk estimates from the last review below in section 

H.2, we have included a discussion of the limitations and uncertainties associated with those risk 

estimates which resulted in the decision by EPA not to use those risk estimates in recommending 

specific standard levels (USEPA, 2006 – Final Rule FR Notice, p. 61178). This discussion 

provides the basis for a more detailed discussion (in Section H.3) of our rationale for not 

conducting a PM10-2.5 risk assessment as part of the current review. Specifically, in Section H-3, 

we consider each of the limitations in the PM10-2.5 risk assessment from the last review and assess 

whether data available since the last review, including more recent ambient monitoring data and 

epidemiological study data, address these limitation. Our conclusion is that additional 

information on PM10-2.5 that has become available since the last review does not substantially 

reduce overall uncertainty associated with modeling risk for this PM size fraction, and 

consequently, we conclude that conducting a PM10-2.5 risk assessment is not supported at this 

time. 

H.2 SUMMARY OF PM10-2.5 RISK ESTIMATES GENERATED FOR THE 

PREVIOUS REVIEW 

This section provides a brief overview of the approach used in completing the PM10-2.5

risk assessment for the previous review and provides a summary of key observations resulting 

from that assessment. Additional details on the risk estimates can be found in the risk 

assessment report completed for the previous analysis (USEPA, 2005). 

The PM10-2.5 risk assessment completed for the previous review is similar in design to the 

PM2.5 risk assessment, although the scope is significantly more limited, reflecting the more 

 

5

 We note that inclusion in this appendix of a summary of the PM10-2.5 risk assessment completed for the 

previous review should not be construed as implying that overall conclusions regarding limitations and uncertainties 

in that risk assessment have changed. Conclusions reached in the last review, that PM10-2.5 risk estimates should not 

be used in recommending specific standard levels, still holds. Rather, we have included a summary of the PM10-2.5

risk assessment completed for the last review in the interest of completeness. 
H-3

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

PM10-2.5 risk assessment assessed risk for populations in three urban study areas (Detroit, Seattle 

and St. Louis), with a set of short-term exposure-related morbidity health endpoints being 

modeled, including: respiratory hospital admissions (for Detroit and Seattle), cardiovascular 

hospital admissions (for Detroit) and respiratory symptoms (for St. Louis). Selection of these 

three urban study areas reflected consideration of the locations included in epidemiological 

studies providing C-R functions, as well as availability of co-located PM10 and PM2.5 monitoring 

data used in deriving estimates of ambient PM10-2.5 levels for urban study areas. EPA staff noted 

in the last review that the locations used in the PM10-2.5 risk assessment were not representative 

of urban locations in the U.S. that experience the most significant elevated 24-hour PM10-2.5

ambient concentrations. Thus, observations regarding risk reductions associated with alternative 

standards in these three urban areas may not be fully relevant to the areas expected to have the 

greatest health risks associated with peak daily ambient PM10-2.5 concentrations. This is a key 

limitation impacting the PM10-2.5 risk assessment and remains a primary concern in conducting a 

PM10-2.5 risk assessment (see below). 

In summarizing PM10-2.5 risk estimates from the last review, we focus here on risk 

estimates generated for the recent conditions air quality scenario.6 In the risk assessment, risk 

estimates are provided for Detroit for several categories of cardiovascular and respiratory-related 

hospital admissions and show point estimates ranging from about 2 to 7% of cause-specific 

admissions being associated with “as is” short-term exposures to PM10-2.5. The point estimate for 

asthma hospital admissions associated with short-term PM10-2.5 exposures for Seattle, an area 

with lower PM10-2.5 ambient concentrations than either Detroit or St. Louis, is about 1%. Point 

estimates for lower respiratory symptoms and cough in St. Louis are about 12 and 15%, 

respectively. These estimates use estimated policy-relevant background as the cutpoint. 

The specific set of uncertainties that resulted in EPA staff concluding that the PM10-2.5

risk estimates should not be used in recommending specific standard levels include, but are not 

limited to, the following (see USEPA, 2005, PM SP, Section 5.4.4.2): 

 Concerns that the current PM10-2.5 levels measured at ambient monitoring sites during the 

study period for the risk assessment may be quite different from the levels used to 

characterize exposure in the original epidemiologic studies based on monitoring sites in 

different location, thus possibly over- or underestimating population risk levels; 

 Greater uncertainty about the reasonableness of the use of proportional rollback to 

simulate attainment of alternative PM10-2.5 daily standards in any urban area due to the 

 

6

 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

limited availability of PM10-2.5 air quality data over time (this uncertainty only being 

relevant to risk estimates generated for the alternative standard levels); 

 Concerns that the locations used in the risk assessment are not representative of urban 

areas in the U.S. that experience the most significant 24-hour peak PM10-2.5 

concentrations, and thus, observations about relative risk reductions associated with 

alternative standards may not be relevant to the areas expected to have the greatest health 

risks associated with elevated ambient PM10-2.5 levels; and 

 Concerns about the much smaller health effects database that supplies the C-R 

relationships used in the risk assessment, compared to that available for PM2.5, which 

limits our ability to evaluate the robustness of the risk estimates for the same health 

endpoints across different locations. 

H.3 RATIONALE FOR THE DECISION NOT TO CONDUCT A PM10-2.5 RISK 

ASSESSMENT AS PART OF THE CURRENT REVIEW 

 The decision not to conduct a PM10-2.5 risk assessment for the current review is 

based on consideration of key uncertainties identified in the last review and an assessment as to 

whether newly available information has significantly reduced those uncertainties. Each of the 

sources of uncertainty is addressed below: 

 Concerns that monitoring data that would be used in a PM10-2.5 risk assessment (i.e., for 

the period 2005-2007) would not match ambient monitoring data used in the underlying 

epidemiological studies providing C-R functions: While this is always a concern in 

conducting PM-related risk assessments, due to the potential for greater spatial 

heterogeneity in PM10-2.5 ambient levels (see final PM ISA, Sections 2.1.1.2 and 2.2.1, 

USEPA 2009b), the potential for discrepancies between the monitoring networks used in 

epidemiological studies providing C-R functions and the monitoring network used in the 

risk assessment introducing uncertainty is increased relative to PM2.5. That is, the 

potential for greater spatial variation in PM10-2.5 levels means that the particular mix of 

collocated monitors used in generating an exposure surrogate in epidemiological studies 

needs to be more closely matched to the monitoring network used in conducting the risk 

assessment if significant uncertainty is to be avoided. 

 Uncertainty in the prediction of ambient levels under current and alternative standard 

levels: This remains a significant factor introducing uncertainty into PM10-2.5 risk 

estimates generated for alternative standard levels, and continues to weigh against the use 

of these risk estimates in identifying alternative standard levels for consideration in this 

review. Not only is the monitoring network (i.e., co-located PM10 and PM2.5 monitors) 

available for characterizing PM10-2.5 levels in candidate urban study areas limited (see 

above), given the potential for greater spatial heterogeneity in PM10-2.5 levels (relative to 

PM2.5 levels), generating representative estimates of ambient air profiles for PM10-2.5

under alternative standard levels is substantially more challenging than for PM2.5. In 

particular, the use of proportional rollback as a means for conducting rollbacks would be 

subject to significant uncertainty given the greater potential for local-scale gradients in 

PM10-2.5 levels and the linkage of PM10-2.5 to local-scale sources. 
H-5

 Concerns that locations used in the risk assessment may not be representative of areas 

experiencing the most significant 24-hour peak PM10-2.5 concentrations (and 

consequently, may not capture locations with the highest risk): This concern still holds 

since the monitoring network available for characterizing PM10-2.5 levels in urban areas 

has not been significantly expanded (final PM ISA, Section 3.5.1.2, ). Specifically, the 

final PM ISA states that: “Given the limited number of co-located low-volume FRM 

PM10 and FRM PM2.5 monitors, only a very limited investigation into the intra-urban 

spatial variability of PM10-2.5 was possible using AQS data. Of the 15 cities under 

investigation, only six (Atlanta, Boston, Chicago, Denver, New York and Phoenix) 

contained data sufficient for calculating PM10-2.5 according to the data completeness and 

monitor specification requirements discussed earlier.” As noted in the previous risk 

assessment, these urban study areas may not capture locations with the highest peak 

levels of PM10-2.5 based on consideration of general patterns in PM10 and PM2.5 levels. 

 Concerns about the much smaller health effects database that supplies the C-R 

relationships (relative to PM2.5): While a number of epidemiological studies have been 

published since completion of the previous PM NAAQS review, including several large 

multi-city studies that inform consideration of the effects of short-term exposure to PM10-

2.5, limitations in the available studies still result in uncertainty in specifying C-R 

functions for PM10-2.5. For example, while Peng et al. (2008) and Zanobetti and Schwartz 

(2009) both provide affect estimates for short-term exposure-related mortality (with 

consideration of copollutant confounding by PM2.5), both have specific limitations that 

impact their use in risk assessment. For example, Zanobetti and Schwartz (2009) derives 

estimates of PM10-2.5 by subtracting county-level PM10 and PM2.5 levels, rather than using 

collocated monitors. Given the significant spatial gradients associated with PM10-2.5

relative to PM2.5, the use of this approach for assessing exposure introduces significant 

uncertainty (i.e., exposure measurement error). In the case of Peng et al. (2008), 

significant uncertainty results from the study not providing regional and/or seasonallydifferentiated effects estimates that control for PM2.5. Given the potential for regional 

differences in the composition of PM10-2.5 which could impact risk estimates, combined 

with the potential for PM2.5 to vary regionally as a confounder for the effect of PM10-2.5, 

EPA staff believes that C-R functions with control for PM2.5 would ideally be available at 

the regional level. . 

 When considered together, the limitations outlined above resulted in EPA staff 

concluding that a quantitative PM10-2.5 risk assessment would not significantly enhance the 

review of the NAAQS for coarse-fraction PM. Specifically, these limitations would likely result 

in sufficient uncertainty in the resulting risk estimates to significantly limit their utility in 

informing policy-related questions, including the assessment of whether the current standard is 

protective of public health and characterization of the degree of additional public health 

protection potentially afforded by alternative standards. Because of the decision not to conduct a 

quantitative PM10-2.5 risk assessment, these questions will draw more heavily on the results of the 

evidence-based analysis to be discussed in the Policy Assessment.
I-1

APPENDIX I: ANALYSIS COMPARING DISTRIBUTION OF SHORTTERM EXPOSURE-RELATED CARDIOVASCULAR MORTALITY 

INCIDENCE TO THE DISTIRBUTION OF DAILY PM2.5 LEVELS FOR 

DETROIT AND NEW YORK
6/30/2010 I-2

I.1 OVERVIEW 

As discussed in section 3.1.2.2, we have updated an earlier analysis from the 1996 PM 

NAAQS Risk Assessment which showed that short-term exposure-related mortality incidence 

was driven more by days with PM2.5 levels near the typical annual level, than by the days with 

higher PM2.5 levels that comprise the tail of the annual daily PM2.5 distribution. This analysis 

was completed using short-term exposure-related incidence estimates for cardiovascular 

mortality for the simulation year 2007 (recent conditions) for Detroit (Figure I-1) and New York 

(Figure I-2). The daily PM2.5 distributions are the composite monitor 24-hour annual distributions 

for 2007 for each urban study area. Methods used in developing these composite monitor 

distributions are discussed in section 3.2.1, while the methods used in generating the short-term 

mortality estimates are presented in section 3.1.2. 

As discussed in section 3.1.2.2, these figures clearly demonstrate that short-term 

mortality cardiovascular incidence for these two urban study areas is driven more by daily PM2.5 

levels nearer to the annual average levels, than by higher-end concentrations. 
I-3

Figure I-1: Comparison of Short-Term Exposure-Related Cardiovascular Mortality 

Against 24-hour PM2.5 Distribution (Detroit – recent conditions for 

2007 – total PM-attributable incidence: 74) 

0

20

40

60

80

100

120

4 8 12 16 20 24 28 32 36 40 44 48 52

Upper Bound of 4 ug/m3

 PM2.5 "Bins"

Frequency of Daily Average PM2.5 Concentrations

-10

0

10

20

30

40

50

60

Associated Cardiovascular Mortality (95% CI) 

Frequency of Daily Average PM2.5

Concentrations

Associated Cardiovascular Mortality
I-4

Figure I-2: Comparison of Short-Term Exposure-Related Cardiovascular Mortality 

Against 24-hour PM2.5 Distribution (New York – recent conditions for 

2007 - total PM-attributable incidence: 568) 

0

10

20

30

40

50

60

70

80

90

100

4 8 12 16 20 24 28 32 36 40 44 48 52

Upper Bound of 4 ug/m3

 PM2.5 "Bins"

Frequency of Daily Average PM2.5 Concentrations

0

50

100

150

200

250

Associated Cardiovascular Mortality (95% CI) 

Frequency of Daily Average PM2.5

Concentrations

Associated Cardiovascular Mortality
J-1

APPENDIX J: PROVISIONAL RISK ESTIMATES AND ADDITIONAL 

RESULTS OF SIMULATION INVOLVING THE ALTERNATIVE 

ANNUAL STANDARD OF 10 μg/m3
J-2

This appendix provides risk estimates generated for two sets of alternative standard levels 

involving an alternative annual standard of 10 μg/m3, including: (a) a pairing with the current 24-

hour standard (10/35) and (b) a pairing with the alternative 24-hour standard of 25 μg/m3

(10/25). As noted in section 2.4, these risk estimates are potentially subject to greater uncertainty 

than risk estimates generated for the other alternative annual standard levels considered in the 

RA due to the need to simulate ambient PM2.5 levels for urban study areas assuming attainment 

of this lower annual standard level. 

To facilitate comparison of estimates for these two additional sets of alternative standards 

against the other standards assessed as part of the formal risk assessment, we have repeated risk 

estimates for the additional sets of alternative standard levels, as well as recent conditions and 

the current set of standard levels (as appropriate). This does mean that the risk estimates 

presented in these tables repeat many of the same estimates presented in tables in Appendix E. 

The organization of the tables in this appendix mirror those presented in Appendix E, although 

the tables presented here focus on long-term exposure-related health endpoints and do not 

present estimates for any of the short-term exposure-related health endpoints modeled in the 

formal risk assessment. The final tables presented in this appendix (Table J-19 and J-20) provide 

the same type of information related to application of the different rollback methods as is 

presented in Tables F-49 and F-50. 
J-3

Table J-1. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5 Concen

 in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 20

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

249 222 199 176 153 105 176 

(205 - 291) (182 - 260) (163 - 234) (144 - 207) (125 - 180) (85 - 124) (144 - 207) (

396 366 337 298 259 180 288 

(326 - 464) (301 - 429) (276 - 395) (244 - 351) (212 - 306) (147 - 212) (236 - 339) (

186 133 118 103 87 56 103 

(153 - 218) (109 - 156) (96 - 139) (84 - 121) (71 - 103) (46 - 66) (84 - 121) 

231 231 231 231 206 142 231 

(189 - 272) (189 - 272) (189 - 272) (189 - 272) (169 - 243) (116 - 168) (189 - 272) (

689 509 504 444 383 258 393 

(567 - 806) (418 - 599) (413 - 592) (363 - 523) (313 - 452) (211 - 306) (321 - 463) (2

187 68 68 68 68 68 45 

(154 - 219) (56 - 81) (56 - 81) (56 - 81) (56 - 81) (56 - 81) (37 - 54) 

370 340 302 263 223 143 263 

(304 - 435) (278 - 400) (247 - 356) (215 - 310) (182 - 264) (116 - 169) (215 - 310) (

2124 984 984 984 867 515 682 

(1746 - 2489) (802 - 1163) (802 - 1163) (802 - 1163) (707 - 1026) (419 - 611) (555 - 808) (3

2614 1959 1959 1874 1610 1071 1475 

(2147 - 3068) (1603 - 2307) (1603 - 2307) (1533 - 2208) (1315 - 1900) (873 - 1268) (1204 - 1742) (7

333 293 293 263 226 152 224 

(273 - 391) (240 - 345) (240 - 345) (215 - 309) (185 - 267) (124 - 180) (183 - 264) (

351 351 351 351 316 198 307 

(286 - 414) (286 - 414) (286 - 414) (286 - 414) (258 - 374) (161 - 235) (250 - 362) (

436 291 291 274 241 162 219 

(359 - 511) (238 - 343) (238 - 343) (224 - 323) (197 - 285) (132 - 191) (179 - 259) (

40 12 12 12 12 12 4 

(33 - 47) (9 - 14) (9 - 14) (9 - 14) (9 - 14) (9 - 14) (3 - 5) 

636 544 496 438 379 259 427 

(523 - 744) (447 - 639) (406 - 583) (359 - 516) (310 - 447) (211 - 307) (350 - 503) (2

93 61 61 61 61 61 41 

(76 - 109) (50 - 72) (50 - 72) (50 - 72) (50 - 72) (50 - 72) (33 - 48) 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in a R

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combina

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, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3.  Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
J-4

Table J-2. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5 Concen

 in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 20

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

256 229 205 181 157 108 181 

(211 - 300) (188 - 268) (168 - 241) (149 - 214) (129 - 185) (88 - 128) (149 - 214) (

325 297 271 237 202 131 228 

(266 - 382) (244 - 350) (222 - 319) (194 - 279) (165 - 239) (107 - 155) (186 - 268) (

176 124 110 95 80 50 95 

(144 - 206) (101 - 146) (90 - 129) (77 - 112) (65 - 95) (41 - 59) (77 - 112) 

175 175 175 175 153 97 175 

(143 - 207) (143 - 207) (143 - 207) (143 - 207) (125 - 181) (79 - 115) (143 - 207) (

506 355 350 300 249 145 257 

(415 - 595) (290 - 418) (286 - 413) (244 - 354) (203 - 294) (118 - 172) (209 - 304) (

194 72 72 72 72 72 49 

(160 - 227) (59 - 85) (59 - 85) (59 - 85) (59 - 85) (59 - 85) (40 - 58) 

359 329 291 252 213 133 252 

(294 - 423) (269 - 388) (238 - 343) (206 - 298) (173 - 252) (108 - 157) (206 - 298) (

1884 815 815 815 707 379 534 

(1546 - 2212) (664 - 965) (664 - 965) (664 - 965) (575 - 837) (308 - 450) (434 - 633) (2

2050 1470 1470 1394 1163 689 1043 

(1678 - 2413) (1200 - 1736) (1200 - 1736) (1138 - 1648) (947 - 1375) (560 - 817) (850 - 1235) (4

303 264 264 236 201 131 199 

(248 - 356) (216 - 311) (216 - 311) (193 - 278) (164 - 238) (107 - 155) (163 - 235) (

372 372 372 372 335 212 325 

(303 - 439) (303 - 439) (303 - 439) (303 - 439) (273 - 396) (172 - 251) (265 - 384) (

349 220 220 205 177 107 157 

(286 - 410) (180 - 260) (180 - 260) (167 - 242) (144 - 209) (87 - 127) (128 - 186) (

33 666660

(27 - 39) (5 - 7) (5 - 7) (5 - 7) (5 - 7) (5 - 7) (0 - 0) 

484 405 363 314 263 161 304 

(397 - 569) (331 - 477) (297 - 428) (256 - 370) (215 - 311) (131 - 191) (248 - 359) (1

63 36 36 36 36 36 19 

(51 - 75) (29 - 43) (29 - 43) (29 - 43) (29 - 43) (29 - 43) (15 - 23) 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in a R

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combina

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 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, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
J-5

Table J-3. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5 Concen

 in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 20

 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

247 220 197 173 149 101 173 

(203 - 290) (180 - 258) (161 - 231) (142 - 204) (122 - 176) (82 - 119) (142 - 204) (

324 297 271 236 202 131 227 

(266 - 381) (243 - 349) (221 - 319) (193 - 279) (165 - 238) (106 - 155) (186 - 268) (

184 131 116 101 85 54 101 

(151 - 216) (107 - 154) (95 - 136) (82 - 119) (70 - 101) (44 - 64) (82 - 119) 

195 195 195 195 172 112 195 

(159 - 230) (159 - 230) (159 - 230) (159 - 230) (140 - 203) (91 - 133) (159 - 230) (

532 377 372 321 269 162 277 

(436 - 625) (308 - 445) (304 - 439) (262 - 379) (219 - 318) (132 - 192) (226 - 327) (

204 77 77 77 77 77 53 

(169 - 239) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (43 - 63) 

375 344 304 264 223 140 264 

(307 - 441) (281 - 405) (249 - 358) (215 - 312) (182 - 264) (114 - 166) (215 - 312) (

1953 860 860 860 749 413 572 

(1604 - 2293) (701 - 1018) (701 - 1018) (701 - 1018) (610 - 887) (335 - 490) (465 - 678) (2

2384 1755 1755 1673 1421 906 1292 

(1955 - 2802) (1435 - 2070) (1435 - 2070) (1367 - 1974) (1160 - 1679) (737 - 1073) (1053 - 1527) (6

300 261 261 233 199 129 197 

(245 - 352) (214 - 308) (214 - 308) (190 - 275) (162 - 235) (105 - 153) (160 - 232) (

317 317 317 317 282 164 272 

(258 - 374) (258 - 374) (258 - 374) (258 - 374) (230 - 333) (133 - 195) (222 - 322) (

390 256 256 239 209 135 189 

(321 - 458) (209 - 302) (209 - 302) (196 - 283) (170 - 246) (109 - 159) (154 - 223) (

47 15 15 15 15 15 7 

(38 - 55) (12 - 18) (12 - 18) (12 - 18) (12 - 18) (12 - 18) (6 - 8) 

529 446 402 350 297 189 340 

(434 - 621) (365 - 525) (329 - 474) (286 - 413) (243 - 351) (154 - 224) (278 - 401) (

66 38 38 38 38 38 21 

(54 - 78) (31 - 46) (31 - 46) (31 - 46) (31 - 46) (31 - 46) (17 - 25) 

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX New York, NY3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in a R

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combina

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, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL
J-6

Table J-4. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambi PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, 

 Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 198

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

15.8% 14.1% 12.7% 11.2% 9.7% 6.7% 11.2% 

(13% - 18.6%) (11.6% - 16.6%) (10.4% - 14.9%) (9.2% - 13.2%) (8% - 11.5%) (5.4% - 7.9%) (9.2% - 13.2%) (7

15.6% 14.4% 13.2% 11.7% 10.2% 7.1% 11.3% 

(12.8% - 18.2%) (11.8% - 16.9%) (10.9% - 15.5%) (9.6% - 13.8%) (8.3% - 12%) (5.8% - 8.4%) (9.3% - 13.3%) (6

15.9% 11.3% 10.1% 8.8% 7.5% 4.8% 8.8% 

(13.1% - 18.6%) (9.3% - 13.4%) (8.2% - 11.9%) (7.2% - 10.4%) (6.1% - 8.8%) (3.9% - 5.7%) (7.2% - 10.4%) (5

11.1% 11.1% 11.1% 11.1% 9.9% 6.8% 11.1% 

(9.1% - 13.1%) (9.1% - 13.1%) (9.1% - 13.1%) (9.1% - 13.1%) (8.1% - 11.7%) (5.5% - 8%) (9.1% - 13.1%) (8

16.4% 12.2% 12% 10.6% 9.1% 6.2% 9.4% 

(13.5% - 19.2%) (10% - 14.3%) (9.8% - 14.1%) (8.7% - 12.5%) (7.5% - 10.8%) (5% - 7.3%) (7.7% - 11.1%) (5

16.8% 6.1% 6.1% 6.1% 6.1% 6.1% 4.1% 

(13.8% - 19.6%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (3.3% - 4.8%) (1

12.2% 11.2% 9.9% 8.7% 7.4% 4.7% 8.7% 

(10% - 14.4%) (9.2% - 13.2%) (8.1% - 11.7%) (7.1% - 10.2%) (6% - 8.7%) (3.8% - 5.6%) (7.1% - 10.2%) (

15.2% 7% 7% 7% 6.2% 3.7% 4.9% 

(12.5% - 17.8%) (5.7% - 8.3%) (5.7% - 8.3%) (5.7% - 8.3%) (5.1% - 7.3%) (3% - 4.4%) (4% - 5.8%) (2

14.1% 10.6% 10.6% 10.1% 8.7% 5.8% 7.9% 

(11.6% - 16.5%) (8.6% - 12.4%) (8.6% - 12.4%) (8.3% - 11.9%) (7.1% - 10.2%) (4.7% - 6.8%) (6.5% - 9.4%) (4

13.3% 11.7% 11.7% 10.5% 9.1% 6.1% 9% 

(10.9% - 15.6%) (9.6% - 13.8%) (9.6% - 13.8%) (8.6% - 12.4%) (7.4% - 10.7%) (5% - 7.2%) (7.3% - 10.6%) (

8% 8% 8% 8% 7.2% 4.5% 7% 

(6.5% - 9.4%) (6.5% - 9.4%) (6.5% - 9.4%) (6.5% - 9.4%) (5.9% - 8.5%) (3.7% - 5.3%) (5.7% - 8.2%) (3

15.7% 10.5% 10.5% 9.9% 8.7% 5.8% 7.9% 

(12.9% - 18.4%) (8.6% - 12.3%) (8.6% - 12.3%) (8.1% - 11.6%) (7.1% - 10.2%) (4.7% - 6.9%) (6.4% - 9.3%) (4

8.2% 2.4% 2.4% 2.4% 2.4% 2.4% 0.8% 

(6.7% - 9.7%) (1.9% - 2.8%) (1.9% - 2.8%) (1.9% - 2.8%) (1.9% - 2.8%) (1.9% - 2.8%) (0.7% - 1%) 

16.1% 13.8% 12.6% 11.1% 9.6% 6.6% 10.8% 

(13.3% - 18.9%) (11.3% - 16.2%) (10.3% - 14.8%) (9.1% - 13.1%) (7.9% - 11.4%) (5.4% - 7.8%) (8.9% - 12.8%) (6

9.2% 6.1% 6.1% 6.1% 6.1% 6.1% 4.1% 

(7.5% - 10.8%) (4.9% - 7.2%) (4.9% - 7.2%) (4.9% - 7.2%) (4.9% - 7.2%) (4.9% - 7.2%) (3.3% - 4.8%) (1

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentr

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Com

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 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, TX Fresno, CA Los Angeles, CA
J-7

Table J-5. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambi PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, 

 Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 198

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

15.8% 14.1% 12.7% 11.2% 9.7% 6.7% 11.2% 

(13% - 18.5%) (11.6% - 16.6%) (10.4% - 14.9%) (9.2% - 13.2%) (8% - 11.5%) (5.4% - 7.9%) (9.2% - 13.2%) (7.

12.7% 11.7% 10.6% 9.3% 7.9% 5.1% 8.9% 

(10.5% - 15%) (9.6% - 13.7%) (8.7% - 12.5%) (7.6% - 11%) (6.5% - 9.4%) (4.2% - 6.1%) (7.3% - 10.5%) (

14.8% 10.5% 9.3% 8% 6.8% 4.2% 8% 

(12.2% - 17.4%) (8.6% - 12.3%) (7.6% - 10.9%) (6.5% - 9.5%) (5.5% - 8%) (3.4% - 5%) (6.5% - 9.5%) (4

8.2% 8.2% 8.2% 8.2% 7.2% 4.6% 8.2% 

(6.7% - 9.7%) (6.7% - 9.7%) (6.7% - 9.7%) (6.7% - 9.7%) (5.9% - 8.5%) (3.7% - 5.4%) (6.7% - 9.7%) (5

12.1% 8.5% 8.4% 7.2% 5.9% 3.5% 6.1% 

(9.9% - 14.2%) (6.9% - 10%) (6.8% - 9.9%) (5.8% - 8.5%) (4.8% - 7%) (2.8% - 4.1%) (5% - 7.3%) (

17.2% 6.4% 6.4% 6.4% 6.4% 6.4% 4.3% 

(14.1% - 20.1%) (5.2% - 7.5%) (5.2% - 7.5%) (5.2% - 7.5%) (5.2% - 7.5%) (5.2% - 7.5%) (3.5% - 5.1%) (1

11.5% 10.5% 9.3% 8% 6.8% 4.2% 8% 

(9.4% - 13.5%) (8.6% - 12.4%) (7.6% - 10.9%) (6.6% - 9.5%) (5.5% - 8%) (3.4% - 5%) (6.6% - 9.5%) (

13.4% 5.8% 5.8% 5.8% 5% 2.7% 3.8% 

(11% - 15.7%) (4.7% - 6.9%) (4.7% - 6.9%) (4.7% - 6.9%) (4.1% - 5.9%) (2.2% - 3.2%) (3.1% - 4.5%) (1

10.9% 7.8% 7.8% 7.4% 6.2% 3.7% 5.6% 

(9% - 12.9%) (6.4% - 9.3%) (6.4% - 9.3%) (6.1% - 8.8%) (5.1% - 7.3%) (3% - 4.4%) (4.5% - 6.6%) (2

12.1% 10.6% 10.6% 9.4% 8.1% 5.3% 8% 

(9.9% - 14.3%) (8.7% - 12.5%) (8.7% - 12.5%) (7.7% - 11.1%) (6.6% - 9.5%) (4.3% - 6.2%) (6.5% - 9.4%) (4

8.1% 8.1% 8.1% 8.1% 7.3% 4.6% 7.1% 

(6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (6% - 8.7%) (3.8% - 5.5%) (5.8% - 8.4%) (3

12.6% 8% 8% 7.4% 6.4% 3.9% 5.7% 

(10.4% - 14.8%) (6.5% - 9.4%) (6.5% - 9.4%) (6.1% - 8.8%) (5.2% - 7.6%) (3.2% - 4.6%) (4.6% - 6.7%) (

6.5% 1.2% 1.2% 1.2% 1.2% 1.2% 0% 

(5.3% - 7.7%) (1% - 1.5%) (1% - 1.5%) (1% - 1.5%) (1% - 1.5%) (1% - 1.5%) (0% - 0%) 

12.2% 10.2% 9.2% 7.9% 6.7% 4.1% 7.7% 

(10% - 14.4%) (8.4% - 12.1%) (7.5% - 10.8%) (6.5% - 9.4%) (5.4% - 7.9%) (3.3% - 4.8%) (6.3% - 9.1%) (

6.1% 3.5% 3.5% 3.5% 3.5% 3.5% 1.8% 

(5% - 7.3%) (2.9% - 4.2%) (2.9% - 4.2%) (2.9% - 4.2%) (2.9% - 4.2%) (2.9% - 4.2%) (1.5% - 2.2%) (0

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentr

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Com

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 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, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
J-8

Table J-6. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambi PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, 

 Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 1983

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

14.9% 13.2% 11.8% 10.4% 9% 6.1% 10.4% 

(12.2% - 17.4%) (10.9% - 15.5%) (9.7% - 13.9%) (8.5% - 12.3%) (7.4% - 10.6%) (4.9% - 7.2%) (8.5% - 12.3%) (7.

12.7% 11.7% 10.6% 9.3% 7.9% 5.1% 8.9% 

(10.5% - 15%) (9.6% - 13.7%) (8.7% - 12.5%) (7.6% - 11%) (6.5% - 9.4%) (4.2% - 6.1%) (7.3% - 10.5%) (5

15.4% 10.9% 9.7% 8.4% 7.1% 4.5% 8.4% 

(12.7% - 18%) (8.9% - 12.9%) (7.9% - 11.4%) (6.9% - 9.9%) (5.8% - 8.4%) (3.7% - 5.4%) (6.9% - 9.9%) (4

9% 9% 9% 9% 7.9% 5.2% 9% 

(7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (6.5% - 9.3%) (4.2% - 6.1%) (7.3% - 10.6%) (6

12.8% 9.1% 9% 7.7% 6.5% 3.9% 6.7% 

(10.5% - 15%) (7.4% - 10.7%) (7.3% - 10.6%) (6.3% - 9.1%) (5.3% - 7.6%) (3.2% - 4.6%) (5.4% - 7.9%) (3

17.7% 6.7% 6.7% 6.7% 6.7% 6.7% 4.6% 

(14.6% - 20.7%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (3.7% - 5.5%) (2

11.7% 10.7% 9.5% 8.2% 7% 4.4% 8.2% 

(9.6% - 13.8%) (8.8% - 12.6%) (7.8% - 11.2%) (6.7% - 9.7%) (5.7% - 8.3%) (3.6% - 5.2%) (6.7% - 9.7%) (5

13.8% 6.1% 6.1% 6.1% 5.3% 2.9% 4% 

(11.3% - 16.2%) (4.9% - 7.2%) (4.9% - 7.2%) (4.9% - 7.2%) (4.3% - 6.3%) (2.4% - 3.5%) (3.3% - 4.8%) (1

12.6% 9.3% 9.3% 8.9% 7.5% 4.8% 6.8% 

(10.4% - 14.8%) (7.6% - 11%) (7.6% - 11%) (7.2% - 10.5%) (6.1% - 8.9%) (3.9% - 5.7%) (5.6% - 8.1%) (3

12% 10.5% 10.5% 9.3% 8% 5.2% 7.9% 

(9.8% - 14.1%) (8.6% - 12.3%) (8.6% - 12.3%) (7.6% - 11%) (6.5% - 9.4%) (4.2% - 6.1%) (6.4% - 9.3%) (4

6.7% 6.7% 6.7% 6.7% 6% 3.5% 5.8% 

(5.5% - 7.9%) (5.5% - 7.9%) (5.5% - 7.9%) (5.5% - 7.9%) (4.9% - 7.1%) (2.8% - 4.1%) (4.7% - 6.8%) (2

14.2% 9.3% 9.3% 8.7% 7.6% 4.9% 6.9% 

(11.7% - 16.7%) (7.6% - 11%) (7.6% - 11%) (7.1% - 10.3%) (6.2% - 9%) (4% - 5.8%) (5.6% - 8.1%) (3

9% 2.9% 2.9% 2.9% 2.9% 2.9% 1.3% 

(7.4% - 10.6%) (2.4% - 3.4%) (2.4% - 3.4%) (2.4% - 3.4%) (2.4% - 3.4%) (2.4% - 3.4%) (1.1% - 1.6%) 

13.3% 11.2% 10.1% 8.8% 7.5% 4.8% 8.6% 

(10.9% - 15.7%) (9.2% - 13.2%) (8.3% - 11.9%) (7.2% - 10.4%) (6.1% - 8.9%) (3.9% - 5.7%) (7% - 10.1%) (4

6.3% 3.7% 3.7% 3.7% 3.7% 3.7% 2% 

(5.2% - 7.5%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (1.6% - 2.4%) (0

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

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, TX Fresno, CA Los Angeles, CA3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentra

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Comb

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
J-9

Table J-7. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated wit Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on 

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

-12% 0% 10% 21% 31% 53% 21% 

(-12% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (31% - 31%) (52% - 53%) (20% - 21%) (3

-8% 0% 8% 18% 29% 51% 21% 

(-8% - -8%) (0% - 0%) (8% - 8%) (18% - 19%) (29% - 29%) (50% - 51%) (21% - 22%) (4

-40% 0% 11% 23% 34% 58% 23% 

(-39% - -41%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 34%) (57% - 58%) (22% - 23%) (4

0% 0% 0% 0% 11% 39% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (38% - 39%) (0% - 0%) (1

-35% 0% 1% 13% 25% 49% 23% 

(-35% - -36%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 25%) (49% - 50%) (23% - 23%) (4

-174% 0% 0% 0% 0% 0% 33% 

(-171% - -177%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (6

-9% 0% 11% 23% 34% 58% 23% 

(-9% - -9%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (58% - 58%) (23% - 23%) (3

-116% 0% 0% 0% 12% 48% 31% 

(-114% - -118%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (47% - 48%) (31% - 31%) (6

-33% 0% 0% 4% 18% 45% 25% 

(-33% - -34%) (0% - 0%) (0% - 0%) (4% - 4%) (18% - 18%) (45% - 46%) (25% - 25%) (5

-14% 0% 0% 10% 23% 48% 23% 

(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 10%) (22% - 23%) (48% - 48%) (23% - 24%) (4

0% 0% 0% 0% 10% 44% 13% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (43% - 44%) (13% - 13%) (4

-50% 0% 0% 6% 17% 44% 25% 

(-49% - -51%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 17%) (44% - 45%) (24% - 25%) (5

-247% 0% 0% 0% 0% 0% 64% 

(-245% - -249%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 64%) (10

-17% 0% 9% 20% 30% 52% 22% 

(-16% - -17%) (0% - 0%) (9% - 9%) (19% - 20%) (30% - 31%) (52% - 53%) (21% - 22%) (4

-51% 0% 0% 0% 0% 0% 33% 

(-51% - -52%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (6

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with Lo

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Da

Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 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, TX Fresno, CA Los Angeles, CA
J-10

Table J-8. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated wit Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on 

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

-12% 0% 10% 21% 31% 53% 21% 

(-12% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (31% - 31%) (52% - 53%) (20% - 21%) (3

-9% 0% 9% 20% 32% 56% 24% 

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 32%) (56% - 56%) (23% - 24%) (4

-42% 0% 12% 23% 35% 60% 23% 

(-41% - -42%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (60% - 60%) (23% - 24%) (4

0% 0% 0% 0% 13% 45% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (44% - 45%) (0% - 0%) (1

-43% 0% 1% 16% 30% 59% 28% 

(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 16%) (30% - 30%) (59% - 59%) (27% - 28%) (5

-170% 0% 0% 0% 0% 0% 33% 

(-167% - -173%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 33%) (6

-9% 0% 12% 23% 35% 60% 23% 

(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (59% - 60%) (23% - 24%) (3

-131% 0% 0% 0% 13% 54% 35% 

(-129% - -133%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (53% - 54%) (34% - 35%) (7

-39% 0% 0% 5% 21% 53% 29% 

(-39% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (53% - 53%) (29% - 29%) (5

-14% 0% 0% 11% 24% 50% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (50% - 51%) (24% - 25%) (5

0% 0% 0% 0% 10% 43% 12% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (43% - 43%) (12% - 13%) (4

-58% 0% 0% 7% 20% 51% 28% 

(-58% - -59%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (51% - 51%) (28% - 29%) (5

-427% 0% 0% 0% 0% 0% 100% 

(-425% - -430%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (10

-19% 0% 10% 23% 35% 60% 25% 

(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 23%) (35% - 35%) (60% - 61%) (25% - 25%) (5

-74% 0% 0% 0% 0% 0% 47% 

(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 48%) (9

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with Lo

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Da

Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 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, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX
J-11

Table J-9. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated wit Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on 

 Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 19831

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

-12% 0% 10% 21% 32% 54% 21% 

(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 21%) (32% - 32%) (54% - 55%) (21% - 21%) (3

-9% 0% 9% 20% 32% 56% 24% 

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 32%) (56% - 56%) (23% - 24%) (4

-41% 0% 11% 23% 35% 59% 23% 

(-40% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (58% - 59%) (23% - 23%) (4

0% 0% 0% 0% 12% 43% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (42% - 43%) (0% - 0%) (1

-41% 0% 1% 15% 29% 57% 27% 

(-41% - -42%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (57% - 57%) (26% - 27%) (5

-164% 0% 0% 0% 0% 0% 31% 

(-161% - -167%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 32%) (6

-9% 0% 12% 23% 35% 59% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (59% - 59%) (23% - 23%) (3

-127% 0% 0% 0% 13% 52% 34% 

(-125% - -129%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (52% - 52%) (33% - 34%) (6

-36% 0% 0% 5% 19% 48% 26% 

(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (48% - 49%) (26% - 27%) (5

-15% 0% 0% 11% 24% 51% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (50% - 51%) (25% - 25%) (5

0% 0% 0% 0% 11% 48% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (48% - 48%) (14% - 14%) (4

-53% 0% 0% 6% 18% 47% 26% 

(-52% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 19%) (47% - 48%) (26% - 26%) (5

-210% 0% 0% 0% 0% 0% 55% 

(-209% - -212%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 55%) (10

-19% 0% 10% 22% 33% 58% 24% 

(-18% - -19%) (0% - 0%) (10% - 10%) (21% - 22%) (33% - 34%) (57% - 58%) (24% - 24%) (4

-72% 0% 0% 0% 0% 0% 46% 

(-71% - -72%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (9

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 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, TX Fresno, CA Los Angeles, CA3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with Lo

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Da

Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
J-12

Table J-10. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5  Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

312 279 251 222 193 133 222 

(257 - 364) (229 - 327) (206 - 295) (182 - 262) (158 - 228) (108 - 158) (182 - 262) (

497 460 423 376 328 228 363 

(409 - 581) (378 - 538) (348 - 497) (308 - 442) (268 - 386) (186 - 270) (298 - 427) (

233 168 149 130 111 72 130 

(192 - 273) (137 - 197) (122 - 176) (106 - 154) (90 - 131) (58 - 85) (106 - 154) 

292 292 292 292 261 180 292 

(239 - 344) (239 - 344) (239 - 344) (239 - 344) (213 - 307) (147 - 213) (239 - 344) (

862 642 635 561 485 329 497 

(711 - 1007) (526 - 754) (520 - 746) (459 - 660) (396 - 572) (267 - 389) (406 - 586) (

234 87 87 87 87 87 58 

(193 - 273) (71 - 103) (71 - 103) (71 - 103) (71 - 103) (71 - 103) (47 - 69) 

467 429 382 333 284 182 333 

(383 - 548) (351 - 505) (312 - 449) (272 - 393) (231 - 335) (148 - 216) (272 - 393) (

2664 1249 1249 1249 1103 658 869 

(2192 - 3117) (1017 - 1477) (1017 - 1477) (1017 - 1477) (897 - 1306) (533 - 781) (705 - 1030) (

3285 2475 2475 2369 2040 1363 1871 

(2700 - 3849) (2024 - 2914) (2024 - 2914) (1936 - 2790) (1665 - 2408) (1108 - 1615) (1525 - 2210) (1

419 369 369 332 287 194 284 

(344 - 492) (303 - 434) (303 - 434) (271 - 391) (234 - 338) (158 - 229) (232 - 335) (

445 445 445 445 401 252 389 

(363 - 526) (363 - 526) (363 - 526) (363 - 526) (327 - 475) (205 - 300) (317 - 461) (

547 368 368 346 305 206 278 

(450 - 639) (301 - 433) (301 - 433) (283 - 408) (249 - 361) (167 - 244) (227 - 329) (

51 15 15 15 15 15 5 

(42 - 60) (12 - 18) (12 - 18) (12 - 18) (12 - 18) (12 - 18) (4 - 6) 

796 684 624 553 480 330 539 

(656 - 930) (562 - 802) (512 - 733) (453 - 650) (392 - 566) (268 - 390) (441 - 635) (

117 78 78 78 78 78 52 

(96 - 138) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (42 - 62) 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY Houston, TX

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, TX Fresno, CA Los Angeles, CA3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in a R

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combina

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI
J-13

Table J-11. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5  Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

321 287 258 229 199 137 229 

(264 - 375) (236 - 336) (212 - 303) (187 - 269) (163 - 235) (112 - 162) (187 - 269) (

409 375 342 300 256 167 288 

(335 - 480) (307 - 441) (280 - 403) (245 - 353) (209 - 303) (135 - 198) (235 - 340) (

221 157 139 120 102 64 120 

(181 - 258) (128 - 184) (113 - 164) (98 - 142) (83 - 120) (52 - 75) (98 - 142) 

222 222 222 222 195 124 222 

(181 - 262) (181 - 262) (181 - 262) (181 - 262) (158 - 230) (101 - 147) (181 - 262) (

638 449 443 380 316 185 327 

(523 - 749) (367 - 530) (361 - 523) (310 - 450) (257 - 375) (150 - 220) (266 - 387) (

243 92 92 92 92 92 62 

(201 - 284) (75 - 108) (75 - 108) (75 - 108) (75 - 108) (75 - 108) (50 - 74) 

453 416 368 320 270 169 320 

(371 - 533) (340 - 490) (301 - 434) (261 - 378) (220 - 320) (137 - 201) (261 - 378) (

2370 1038 1038 1038 901 484 682 

(1945 - 2779) (843 - 1229) (843 - 1229) (843 - 1229) (731 - 1068) (392 - 576) (553 - 809) (

2588 1865 1865 1770 1478 880 1328 

(2118 - 3046) (1520 - 2203) (1520 - 2203) (1442 - 2092) (1202 - 1750) (713 - 1045) (1079 - 1573) (

381 334 334 298 255 167 253 

(313 - 448) (273 - 393) (273 - 393) (244 - 352) (208 - 302) (136 - 198) (206 - 298) (

471 471 471 471 426 270 413 

(384 - 557) (384 - 557) (384 - 557) (384 - 557) (347 - 503) (219 - 320) (336 - 488) (

439 279 279 260 225 137 200 

(360 - 516) (228 - 330) (228 - 330) (212 - 308) (183 - 266) (111 - 163) (163 - 237) 

42 888880

(34 - 50) (6 - 10) (6 - 10) (6 - 10) (6 - 10) (6 - 10) (0 - 0) 

610 512 460 398 335 205 386 

(500 - 716) (419 - 603) (375 - 542) (324 - 470) (272 - 396) (167 - 244) (314 - 456) (

80 46 46 46 46 46 24 

(65 - 95) (37 - 55) (37 - 55) (37 - 55) (37 - 55) (37 - 55) (20 - 29) 

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in a R

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combina

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 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, TX Fresno, CA Los Angeles, CA
J-14

Table J-12. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5  Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, Based 

 on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

310 277 248 219 189 128 219 

(255 - 363) (227 - 324) (203 - 291) (179 - 258) (154 - 223) (104 - 152) (179 - 258) (

408 374 342 299 256 167 288 

(335 - 479) (307 - 440) (280 - 402) (244 - 353) (209 - 302) (135 - 197) (235 - 339) (

231 165 146 128 108 69 128 

(190 - 270) (135 - 194) (120 - 173) (104 - 151) (88 - 128) (56 - 82) (104 - 151) 

247 247 247 247 218 143 247 

(202 - 291) (202 - 291) (202 - 291) (202 - 291) (178 - 257) (116 - 169) (202 - 291) (

670 478 471 407 341 207 352 

(549 - 786) (390 - 563) (385 - 556) (332 - 481) (278 - 404) (168 - 246) (286 - 416) (

255 98 98 98 98 98 68 

(211 - 298) (80 - 116) (80 - 116) (80 - 116) (80 - 116) (80 - 116) (55 - 80) 

473 434 385 335 284 179 335 

(387 - 556) (355 - 511) (314 - 453) (273 - 395) (231 - 335) (145 - 212) (273 - 395) (

2456 1094 1094 1094 954 528 730 

(2017 - 2879) (890 - 1296) (890 - 1296) (890 - 1296) (775 - 1131) (427 - 627) (592 - 866) (

3003 2222 2222 2120 1804 1155 1641 

(2462 - 3525) (1814 - 2620) (1814 - 2620) (1730 - 2501) (1469 - 2132) (937 - 1369) (1336 - 1941) (8

378 330 330 295 252 164 249 

(309 - 444) (270 - 389) (270 - 389) (241 - 347) (205 - 298) (133 - 195) (203 - 295) (

402 402 402 402 359 209 347 

(327 - 476) (327 - 476) (327 - 476) (327 - 476) (291 - 425) (170 - 249) (282 - 410) (

490 324 324 303 265 171 240 

(403 - 574) (264 - 382) (264 - 382) (248 - 358) (216 - 313) (139 - 203) (195 - 284) (

59 19 19 19 19 19 9 

(48 - 70) (16 - 23) (16 - 23) (16 - 23) (16 - 23) (16 - 23) (7 - 10) 

665 563 508 443 377 241 431 

(546 - 780) (461 - 662) (415 - 599) (362 - 523) (307 - 446) (196 - 286) (351 - 509) (

84 49 49 49 49 49 27 

(68 - 99) (40 - 58) (40 - 58) (40 - 58) (40 - 58) (40 - 58) (21 - 32) 

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding 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, TX Fresno, CA Los Angeles, CA

 Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentrations in a R

Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combina

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
J-15

Table J-13. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb PM2.5 Concentrations in a Recent Year (2005) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, 

 Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 2001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

19.9% 17.8% 16% 14.2% 12.3% 8.5% 14.2% 

(16.4% - 23.2%) (14.6% - 20.8%) (13.1% - 18.8%) (11.6% - 16.7%) (10.1% - 14.5%) (6.9% - 10%) (11.6% - 16.7%) (9.

19.5% 18.1% 16.6% 14.8% 12.9% 9% 14.3% 

(16.1% - 22.8%) (14.8% - 21.2%) (13.7% - 19.5%) (12.1% - 17.4%) (10.5% - 15.2%) (7.3% - 10.6%) (11.7% - 16.8%) (8.

19.9% 14.3% 12.7% 11.1% 9.5% 6.1% 11.1% 

(16.4% - 23.3%) (11.7% - 16.8%) (10.4% - 15%) (9.1% - 13.1%) (7.7% - 11.2%) (5% - 7.2%) (9.1% - 13.1%) (6

14% 14% 14% 14% 12.5% 8.6% 14% 

(11.5% - 16.5%) (11.5% - 16.5%) (11.5% - 16.5%) (11.5% - 16.5%) (10.2% - 14.7%) (7% - 10.2%) (11.5% - 16.5%) (10

20.6% 15.3% 15.1% 13.4% 11.6% 7.8% 11.9% 

(17% - 24%) (12.6% - 18%) (12.4% - 17.8%) (10.9% - 15.7%) (9.4% - 13.6%) (6.4% - 9.3%) (9.7% - 14%) (6

21% 7.8% 7.8% 7.8% 7.8% 7.8% 5.2% 

(17.3% - 24.5%) (6.3% - 9.2%) (6.3% - 9.2%) (6.3% - 9.2%) (6.3% - 9.2%) (6.3% - 9.2%) (4.2% - 6.2%) (2

15.4% 14.2% 12.6% 11% 9.4% 6% 11% 

(12.6% - 18.1%) (11.6% - 16.6%) (10.3% - 14.8%) (9% - 13%) (7.6% - 11.1%) (4.9% - 7.1%) (9% - 13%) (7.

19% 8.9% 8.9% 8.9% 7.9% 4.7% 6.2% 

(15.7% - 22.3%) (7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (6.4% - 9.3%) (3.8% - 5.6%) (5% - 7.4%) (2

17.7% 13.3% 13.3% 12.8% 11% 7.3% 10.1% 

(14.5% - 20.7%) (10.9% - 15.7%) (10.9% - 15.7%) (10.4% - 15%) (9% - 13%) (6% - 8.7%) (8.2% - 11.9%) (5

16.8% 14.8% 14.8% 13.3% 11.5% 7.7% 11.4% 

(13.8% - 19.7%) (12.1% - 17.4%) (12.1% - 17.4%) (10.8% - 15.6%) (9.4% - 13.5%) (6.3% - 9.2%) (9.3% - 13.4%) (6

10.1% 10.1% 10.1% 10.1% 9.1% 5.7% 8.8% 

(8.2% - 11.9%) (8.2% - 11.9%) (8.2% - 11.9%) (8.2% - 11.9%) (7.4% - 10.8%) (4.6% - 6.8%) (7.2% - 10.5%) (4

19.7% 13.2% 13.2% 12.5% 11% 7.4% 10% 

(16.2% - 23%) (10.8% - 15.6%) (10.8% - 15.6%) (10.2% - 14.7%) (9% - 13%) (6% - 8.8%) (8.2% - 11.8%) (5

10.4% 3% 3% 3% 3% 3% 1.1% 

(8.5% - 12.3%) (2.4% - 3.6%) (2.4% - 3.6%) (2.4% - 3.6%) (2.4% - 3.6%) (2.4% - 3.6%) (0.9% - 1.3%) (

20.2% 17.4% 15.8% 14% 12.2% 8.4% 13.7% 

(16.6% - 23.6%) (14.3% - 20.4%) (13% - 18.6%) (11.5% - 16.5%) (10% - 14.4%) (6.8% - 9.9%) (11.2% - 16.1%) (8

11.6% 7.7% 7.7% 7.7% 7.7% 7.7% 5.2% 

(9.5% - 13.7%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% - 9.1%) (4.2% - 6.1%) (2

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentra

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Comb

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, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
J-16

Table J-14. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb PM2.5 Concentrations in a Recent Year (2006) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, 

 Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 2001 

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

19.8% 17.7% 16% 14.2% 12.3% 8.5% 14.2% 

(16.3% - 23.2%) (14.6% - 20.8%) (13.1% - 18.7%) (11.6% - 16.6%) (10% - 14.5%) (6.9% - 10%) (11.6% - 16.6%) (9.

16% 14.7% 13.4% 11.8% 10.1% 6.5% 11.3% 

(13.2% - 18.8%) (12.1% - 17.3%) (11% - 15.8%) (9.6% - 13.9%) (8.2% - 11.9%) (5.3% - 7.8%) (9.2% - 13.3%) (6

18.6% 13.2% 11.7% 10.2% 8.6% 5.4% 10.2% 

(15.3% - 21.8%) (10.8% - 15.6%) (9.6% - 13.8%) (8.3% - 12%) (7% - 10.2%) (4.4% - 6.4%) (8.3% - 12%) (5

10.4% 10.4% 10.4% 10.4% 9.1% 5.8% 10.4% 

(8.5% - 12.3%) (8.5% - 12.3%) (8.5% - 12.3%) (8.5% - 12.3%) (7.4% - 10.8%) (4.7% - 6.9%) (8.5% - 12.3%) (7.

15.2% 10.7% 10.6% 9.1% 7.6% 4.4% 7.8% 

(12.5% - 17.9%) (8.8% - 12.7%) (8.6% - 12.5%) (7.4% - 10.7%) (6.1% - 8.9%) (3.6% - 5.3%) (6.3% - 9.2%) (3

21.5% 8.1% 8.1% 8.1% 8.1% 8.1% 5.5% 

(17.7% - 25.1%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (4.4% - 6.5%) (2

14.5% 13.3% 11.7% 10.2% 8.6% 5.4% 10.2% 

(11.8% - 17%) (10.8% - 15.6%) (9.6% - 13.8%) (8.3% - 12%) (7% - 10.2%) (4.4% - 6.4%) (8.3% - 12%) (7

16.8% 7.4% 7.4% 7.4% 6.4% 3.4% 4.8% 

(13.8% - 19.7%) (6% - 8.7%) (6% - 8.7%) (6% - 8.7%) (5.2% - 7.6%) (2.8% - 4.1%) (3.9% - 5.8%) (1

13.8% 9.9% 9.9% 9.4% 7.9% 4.7% 7.1% 

(11.3% - 16.2%) (8.1% - 11.7%) (8.1% - 11.7%) (7.7% - 11.2%) (6.4% - 9.3%) (3.8% - 5.6%) (5.8% - 8.4%) (3

15.3% 13.4% 13.4% 11.9% 10.2% 6.7% 10.1% 

(12.5% - 18%) (10.9% - 15.8%) (10.9% - 15.8%) (9.8% - 14.1%) (8.3% - 12.1%) (5.4% - 7.9%) (8.3% - 12%) (5

10.3% 10.3% 10.3% 10.3% 9.3% 5.9% 9% 

(8.4% - 12.2%) (8.4% - 12.2%) (8.4% - 12.2%) (8.4% - 12.2%) (7.6% - 11%) (4.8% - 7%) (7.3% - 10.7%) (4

15.9% 10.1% 10.1% 9.4% 8.1% 5% 7.3% 

(13% - 18.7%) (8.2% - 11.9%) (8.2% - 11.9%) (7.7% - 11.1%) (6.6% - 9.6%) (4% - 5.9%) (5.9% - 8.6%) (3

8.3% 1.6% 1.6% 1.6% 1.6% 1.6% 0% 

(6.7% - 9.8%) (1.3% - 1.9%) (1.3% - 1.9%) (1.3% - 1.9%) (1.3% - 1.9%) (1.3% - 1.9%) (0% - 0%) (

15.4% 12.9% 11.6% 10% 8.5% 5.2% 9.7% 

(12.6% - 18.1%) (10.6% - 15.2%) (9.5% - 13.7%) (8.2% - 11.9%) (6.9% - 10%) (4.2% - 6.2%) (7.9% - 11.5%) (5

7.8% 4.5% 4.5% 4.5% 4.5% 4.5% 2.4% 

(6.3% - 9.2%) (3.6% - 5.3%) (3.6% - 5.3%) (3.6% - 5.3%) (3.6% - 5.3%) (3.6% - 5.3%) (1.9% - 2.8%) (0

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentra

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Comb

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, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD Detroit, MI Houston, TX New York, NY St. Louis, MO3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3.  Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT
J-17

Table J-15. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Amb PM2.5 Concentrations in a Recent Year (2007) and PM2.5 Concentrations that Just Meet the Current and Alternative Standards, 

 Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 2000

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

18.7% 16.7% 14.9% 13.2% 11.4% 7.7% 13.2% 

(15.4% - 21.8%) (13.7% - 19.5%) (12.2% - 17.6%) (10.8% - 15.5%) (9.3% - 13.4%) (6.3% - 9.1%) (10.8% - 15.5%) (9.

16.1% 14.7% 13.4% 11.8% 10.1% 6.5% 11.3% 

(13.2% - 18.8%) (12.1% - 17.3%) (11% - 15.8%) (9.6% - 13.9%) (8.2% - 11.9%) (5.3% - 7.8%) (9.2% - 13.3%) (6

19.3% 13.8% 12.2% 10.7% 9.1% 5.8% 10.7% 

(15.9% - 22.6%) (11.3% - 16.2%) (10% - 14.4%) (8.7% - 12.6%) (7.4% - 10.7%) (4.7% - 6.8%) (8.7% - 12.6%) (6

11.4% 11.4% 11.4% 11.4% 10% 6.6% 11.4% 

(9.3% - 13.4%) (9.3% - 13.4%) (9.3% - 13.4%) (9.3% - 13.4%) (8.2% - 11.9%) (5.3% - 7.8%) (9.3% - 13.4%) (8.

16.1% 11.5% 11.3% 9.8% 8.2% 5% 8.5% 

(13.2% - 18.9%) (9.4% - 13.5%) (9.3% - 13.4%) (8% - 11.6%) (6.7% - 9.7%) (4% - 5.9%) (6.9% - 10%) (4

22.2% 8.5% 8.5% 8.5% 8.5% 8.5% 5.9% 

(18.3% - 25.9%) (7% - 10.1%) (7% - 10.1%) (7% - 10.1%) (7% - 10.1%) (7% - 10.1%) (4.8% - 7%) (2

14.8% 13.6% 12% 10.5% 8.9% 5.6% 10.5% 

(12.1% - 17.4%) (11.1% - 16%) (9.8% - 14.2%) (8.5% - 12.3%) (7.2% - 10.5%) (4.5% - 6.6%) (8.5% - 12.3%) (7.

17.3% 7.7% 7.7% 7.7% 6.7% 3.7% 5.2% 

(14.2% - 20.3%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% - 9.1%) (5.5% - 8%) (3% - 4.4%) (4.2% - 6.1%) (

15.9% 11.8% 11.8% 11.2% 9.6% 6.1% 8.7% 

(13% - 18.7%) (9.6% - 13.9%) (9.6% - 13.9%) (9.2% - 13.2%) (7.8% - 11.3%) (5% - 7.3%) (7.1% - 10.3%) (4

15.1% 13.2% 13.2% 11.8% 10.1% 6.6% 10% 

(12.4% - 17.8%) (10.8% - 15.6%) (10.8% - 15.6%) (9.6% - 13.9%) (8.2% - 11.9%) (5.3% - 7.8%) (8.1% - 11.8%) (5

8.5% 8.5% 8.5% 8.5% 7.6% 4.4% 7.3% 

(6.9% - 10.1%) (6.9% - 10.1%) (6.9% - 10.1%) (6.9% - 10.1%) (6.2% - 9%) (3.6% - 5.3%) (6% - 8.7%) (3

17.8% 11.8% 11.8% 11% 9.6% 6.2% 8.7% 

(14.7% - 20.9%) (9.6% - 13.9%) (9.6% - 13.9%) (9% - 13%) (7.8% - 11.4%) (5.1% - 7.4%) (7.1% - 10.3%) (4

11.4% 3.7% 3.7% 3.7% 3.7% 3.7% 1.7% 

(9.3% - 13.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (1.4% - 2%) (

16.8% 14.2% 12.8% 11.2% 9.5% 6.1% 10.9% 

(13.8% - 19.7%) (11.6% - 16.7%) (10.5% - 15.1%) (9.1% - 13.2%) (7.7% - 11.2%) (4.9% - 7.2%) (8.9% - 12.8%) (6

8% 4.7% 4.7% 4.7% 4.7% 4.7% 2.5% 

(6.5% - 9.5%) (3.8% - 5.6%) (3.8% - 5.6%) (3.8% - 5.6%) (3.8% - 5.6%) (3.8% - 5.6%) (2.1% - 3%) (0

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3.  Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Detroit, MI Houston, TX New York, NY St. Louis, MO

Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5 Concentra

PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Comb

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, TX Fresno, CA Los Angeles, CA

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL Baltimore, MD
J-18

Table J-16. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated  with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2005 PM2.5 Concentrations: Estimates Based o

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

-12% 0% 10% 20% 31% 52% 20% 

(-11% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (30% - 31%) (59% - 42%) (20% - 21%) (3

-8% 0% 8% 18% 29% 50% 21% 

(-8% - -8%) (0% - 0%) (8% - 8%) (18% - 18%) (28% - 29%) (58% - 40%) (21% - 21%) (4

-39% 0% 11% 22% 34% 57% 22% 

(-38% - -40%) (0% - 0%) (11% - 11%) (22% - 23%) (33% - 34%) (64% - 48%) (22% - 23%) (4

0% 0% 0% 0% 11% 38% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (48% - 25%) (0% - 0%) (1

-34% 0% 1% 13% 24% 49% 23% 

(-34% - -35%) (0% - 0%) (1% - 1%) (12% - 13%) (24% - 25%) (56% - 38%) (22% - 23%) (4

-170% 0% 0% 0% 0% 0% 33% 

(-166% - -174%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (16% - -23%) (33% - 33%) (6

-9% 0% 11% 22% 34% 58% 22% 

(-9% - -9%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 34%) (64% - 48%) (22% - 23%) (3

-113% 0% 0% 0% 12% 47% 30% 

(-111% - -116%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (55% - 35%) (30% - 31%) (6

-33% 0% 0% 4% 18% 45% 24% 

(-32% - -33%) (0% - 0%) (0% - 0%) (4% - 4%) (17% - 18%) (53% - 33%) (24% - 25%) (4

-13% 0% 0% 10% 22% 48% 23% 

(-13% - -14%) (0% - 0%) (0% - 0%) (10% - 10%) (22% - 23%) (55% - 36%) (23% - 23%) (4

0% 0% 0% 0% 10% 43% 12% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (52% - 30%) (12% - 13%) (4

-49% 0% 0% 6% 17% 44% 24% 

(-48% - -50%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 17%) (52% - 32%) (24% - 25%) (4

-244% 0% 0% 0% 0% 0% 64% 

(-242% - -247%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (16% - -24%) (64% - 64%) (10

-16% 0% 9% 19% 30% 52% 21% 

(-16% - -17%) (0% - 0%) (9% - 9%) (19% - 19%) (29% - 30%) (59% - 41%) (21% - 22%) (4

-51% 0% 0% 0% 0% 0% 33% 

(-50% - -51%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (16% - -23%) (33% - 33%) (6

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 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, TX Fresno, CA Los Angeles, CA

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with Lo

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Da

Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX
J-19

Table J-17. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated  with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2006 PM2.5 Concentrations: Estimates Based o

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/353 14/35 13/35 12/35 10/35 13/30 

-12% 0% 10% 20% 31% 52% 20% 

(-11% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (30% - 31%) (52% - 53%) (20% - 21%) (3

-9% 0% 9% 20% 32% 56% 23% 

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (55% - 56%) (23% - 23%) (4

-41% 0% 11% 23% 35% 59% 23% 

(-40% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (59% - 60%) (23% - 23%) (4

0% 0% 0% 0% 12% 44% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (44% - 44%) (0% - 0%) (1

-42% 0% 1% 15% 30% 59% 27% 

(-41% - -43%) (0% - 0%) (1% - 1%) (15% - 16%) (29% - 30%) (59% - 59%) (27% - 28%) (5

-165% 0% 0% 0% 0% 0% 32% 

(-162% - -169%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 33%) (6

-9% 0% 11% 23% 35% 59% 23% 

(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (59% - 60%) (23% - 23%) (3

-128% 0% 0% 0% 13% 53% 34% 

(-126% - -131%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (53% - 54%) (34% - 34%) (6

-39% 0% 0% 5% 21% 53% 29% 

(-38% - -39%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (53% - 53%) (29% - 29%) (5

-14% 0% 0% 11% 24% 50% 24% 

(-14% - -14%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (50% - 50%) (24% - 25%) (4

0% 0% 0% 0% 10% 43% 12% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (43% - 43%) (12% - 12%) (4

-57% 0% 0% 7% 20% 51% 28% 

(-56% - -58%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 20%) (51% - 51%) (28% - 28%) (5

-423% 0% 0% 0% 0% 0% 100% 

(-420% - -427%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (10

-19% 0% 10% 22% 35% 60% 25% 

(-19% - -19%) (0% - 0%) (10% - 10%) (22% - 23%) (34% - 35%) (60% - 60%) (24% - 25%) (5

-74% 0% 0% 0% 0% 0% 47% 

(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 47%) (9

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with Lo

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Da

Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 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, TX Fresno, CA Los Angeles, CA
J-20

Table J-18. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated  with Long-Term Exposure to Ambient PM2.5 Concentrations, Based on Adjusting 2007 PM2.5 Concentrations: Estimates Based o

 Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001

Recent PM2.5 

Concentrations 15/352 14/35 13/35 12/35 10/35 13/30 

-12% 0% 10% 21% 32% 54% 21% 

(-12% - -12%) (0% - 0%) (10% - 10%) (21% - 21%) (31% - 32%) (53% - 54%) (21% - 21%) (3

-9% 0% 9% 20% 32% 56% 23% 

(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (55% - 56%) (23% - 23%) (4

-40% 0% 11% 23% 34% 58% 23% 

(-39% - -41%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 35%) (58% - 59%) (22% - 23%) (4

0% 0% 0% 0% 12% 42% 0% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (42% - 43%) (0% - 0%) (1

-40% 0% 1% 15% 28% 57% 26% 

(-40% - -41%) (0% - 0%) (1% - 1%) (15% - 15%) (28% - 29%) (56% - 57%) (26% - 27%) (5

-159% 0% 0% 0% 0% 0% 31% 

(-156% - -163%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 31%) (6

-9% 0% 11% 23% 35% 59% 23% 

(-9% - -9%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (58% - 59%) (23% - 23%) (3

-124% 0% 0% 0% 13% 52% 33% 

(-122% - -127%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (52% - 52%) (33% - 33%) (6

-35% 0% 0% 5% 19% 48% 26% 

(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (48% - 48%) (26% - 26%) (5

-14% 0% 0% 11% 24% 50% 25% 

(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (50% - 51%) (24% - 25%) (5

0% 0% 0% 0% 11% 48% 14% 

(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (48% - 48%) (14% - 14%) (4

-51% 0% 0% 6% 18% 47% 26% 

(-51% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (47% - 47%) (26% - 26%) (5

-208% 0% 0% 0% 0% 0% 55% 

(-205% - -210%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 55%) (10

-18% 0% 10% 21% 33% 57% 23% 

(-18% - -18%) (0% - 0%) (10% - 10%) (21% - 22%) (33% - 33%) (57% - 57%) (23% - 24%) (4

-71% 0% 0% 0% 0% 0% 46% 

(-71% - -72%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (9

Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with Lo

Concentrations in a Recent Year and PM2.5 Concentrations that Just Meet the Current and Alternative Annual (n) and Da

Combination Denoted n/m)2:

 St. Louis, MO Tacoma, WA Philadelphia, PA Phoenix, AZ Pittsburgh, PA Salt Lake City, UT Baltimore, MD Detroit, MI Houston, TX

3The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

Risk Assessment 

Location

 Atlanta, GA Birmingham, AL New York, NY

2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM 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, TX Fresno, CA Los Angeles, CA
J-21

Annual 24-Hr 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM 14/35 13/35 

Proportional 15.0 14.2 14.0 13.3 13.0 12.3 12.0 11.4 10.0 9.5 13.0 12.3 11.8 11.2 10.0 9.5 11% 22% 

Hybrid 3 --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused4 --- --- --- --- --- --- --- --- --- --- --- --- 14 11.76 --- --- --- --- Proportional 14.8 13.1 14.0 12.5 13.0 11.6 12.0 10.7 10.0 8.9 12.7 11.3 10.7 9.5 10.0 8.9 9% 21% Hybrid 14.3 13.0 14.0 12.7 13.0 11.8 12.0 10.9 10.0 9.1 12.3 11.2 10.3 9.4 10.0 9.1 4% 16% Locally Focused 15.2 13.6 --- --- --- --- --- --- --- --- 13.1 12.0 11.0 10.0 --- --- --- --- Proportional 15.0 12.7 14.0 11.8 13.0 11.0 12.0 10.2 10.0 8.5 13.0 11.0 11.1 9.4 10.0 8.5 12% 24% Hybrid 15.0 14.2 14.0 13.2 13.0 12.3 12.0 11.4 10.0 9.5 13.0 12.3 11.3 10.7 10.0 9.5 11% 22% Locally Focused --- --- --- --- --- --- --- --- --- --- --- --- 12.3 11.4 --- --- --- --- Proportional 12.8 11.4 12.8 11.4 12.8 11.4 12.0 10.7 10.0 8.9 12.8 11.4 12.0 10.7 10.0 8.9 0% 0% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Proportional 14.1 11.4 14.0 11.4 13.0 10.6 12.0 9.8 10.0 8.2 12.2 9.9 10.2 8.3 10.0 8.2 1% 16% Hybrid 13.2 11.7 13.2 11.7 13.0 11.5 12.0 10.6 10.0 8.9 11.4 10.1 9.6 8.5 9.6 8.5 0% 3% Locally Focused 14.1 12.6 --- --- --- --- --- --- --- --- 12.2 11.0 10.2 9.2 --- --- --- --- Proportional 9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 8.6 8.6 7.3 7.3 7.3 7.3 0% 0% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused 10.1 10.3 10.1 10.3 10.1 10.3 10.1 10.3 10.1 10.3 8.8 8.9 7.4 7.5 7.4 7.5 0% 0% Proportional 15.0 12.5 14.0 11.7 13.0 10.9 12.0 10.1 10.0 8.5 13.0 10.9 12.0 10.1 10.0 8.5 12% 24% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Proportional 12.7 9.5 12.7 9.5 12.7 9.5 12.0 9.0 10.0 7.6 10.9 8.2 9.2 7.0 9.2 7.0 0% 0% Hybrid 13.3 10.5 13.3 10.5 13.0 10.3 12.0 9.5 10.0 8.0 11.5 9.1 9.6 7.7 9.6 7.7 0% 5% Locally Focused 13.9 12.1 13.9 12.1 13.9 12.1 --- --- --- --- 12.0 10.6 10.1 9.1 10.1 9.1 0% 0% Proportional 13.3 11.6 13.3 11.6 13.0 11.3 12.0 10.4 10.0 8.7 11.5 10.0 9.7 8.4 9.7 8.4 0% 5% Hybrid 13.6 11.8 13.6 11.8 13.0 11.3 12.0 10.4 10.0 8.7 11.7 10.2 9.8 8.5 9.8 8.5 0% 8% Locally Focused 14.3 13.3 14.3 13.3 --- --- --- --- --- --- 12.3 11.6 10.3 9.8 10.3 9.8 0% --- Proportional 13.9 12.3 13.9 12.3 13.0 11.6 12.0 10.7 10.0 8.9 11.9 10.7 10.0 9.0 10.0 8.9 0% 12% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused 15.5 13.0 15.5 13.0 --- --- --- --- --- --- 14.1 11.3 11.8 9.5 --- --- 0% --- Proportional 12.6 9.9 12.6 9.9 12.6 9.9 12.0 9.4 10.0 7.9 11.8 9.3 9.9 7.8 9.9 7.8 0% 0% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused --- --- --- --- --- --- --- --- --- --- 12.2 9.7 10.2 9.0 10.2 9.0 --- --- 

Table J-19. Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with comparison of percent reduction in surrogate for lon

 across rollback methods)

Percent red

exposure-r

com

Risk Assessment 

Location 1 Rollback Method

 Design Value

Recent 

Air 

Quality 

(2007)

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 Annual Average at Composite 

Monitor (2007CM) (in ug/m3)

15/35 2 14/35 13/35 12/35 10/35 13/30 12/25 10/25

 Atlanta, GA 15.3

 Baltimore, MD 15.6 37.0 13.9

16.2 35.0

 Birmingham, AL 15.7 Dallas, TX 12.8 26.0 11.4

18.7 44.0

 Detroit, MI 13.9 Fresno, CA 17.4 63.0 17.4

17.2 43.0

 Houston, TX 13.2 Los Angeles, CA 19.6 55.0 14.6

15.8 31.0

 New York, NY 13.8 Philadelphia, PA 15.0 38.0 13.4

15.9 42.0

 Phoenix, AZ 12.6 32.0 9.9
J-22

Annual 24-Hr 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM 14/35 13/35 

Proportional 13.3 11.6 13.3 11.6 12.8 11.2 11.8 10.5 10.0 8.8 11.5 10.0 9.7 8.4 9.7 8.4 0% 7% 

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused 15.6 13.2 15.6 13.2 15.3 11.8 15.3 11.2 --- --- 15.6 11.4 13.9 9.6 --- --- 0% 18% Proportional 7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 6.7 6.6 5.7 5.6 5.7 5.6 0% 0% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused 10.8 9.7 10.8 9.7 10.8 9.7 10.8 9.7 10.8 9.7 10.8 8.8 9.1 7.7 9.1 7.7 0% 0% Proportional 14.9 12.9 14.0 12.1 13.0 11.3 12.0 10.4 10.0 8.7 12.8 11.1 10.8 9.3 10.0 8.7 10% 23% Hybrid 15.0 13.5 14.0 12.6 13.0 11.7 12.0 10.8 10.0 9.0 13.0 11.7 11.0 9.9 10.0 9.0 12% 23% Locally Focused 16.5 14.1 --- --- --- --- --- --- --- --- 14.2 12.4 11.9 10.4 --- --- --- --- Proportional 8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 7.4 7.0 6.3 6.0 6.3 6.0 0% 0% Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Locally Focused 8.5 8.0 8.5 8.0 8.5 8.0 8.5 8.0 8.5 8.0 7.4 7.0 6.3 6.0 6.3 6.0 0% 0% 

Table J-19 (cont'd). Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with comparison of percent reduction in surro

 across rollback methods)

6 Percent reduction in composite monitor value with consideration for LML of 5.8 ug/m3 (note: composite monitor value denoted as CMV): %reduction = (CMVcurrent standard - CMValternative standard)/(CMVcurrernt standard-LML

where percent change differs by >10% across alternative rollback methods (for a given alternative standard level/study area combination). 

1For some locations (e.g., Atlanta) more than one "version" (group of counties) was used in the risk assessment. In this table only the version that was used for mortality associated with short-term exposure to PM2.

included. 

2 The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 3 The hybrid rollback method was applied to only a subset of the risk assessment locations. The "---" for a given location indicates that the hybrid rollback method was not applied to that location.

 Tacoma, WA 10.2 43.0 9.7

4 The locally focused method was applied to a location-standard combination only if the daily standard was controlling in that location. The "--" for a given location-standard combination indicates that, for that set of a

location, the annual standard was controlling and so the locally focused method was not applied. 

5 The proportional rollback and locally focused methods were applied to Pittsburgh differently from the way they were applied in the other locations. See text for details. 

 Salt Lake City, UT 11.4

 St. Louis, MO 16.5 39.0 14.3

11.6 55.0

 Pittsburgh, PA 5 19.8 60.0 14.9

 12/35 13/30 12/25 10/25

Percent red

exposure-re

com

Risk Assessment 

Location 1

Rollback 

Method

 Design Value

Recent 

Air 

Quality 

(2007)

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 Annual Average at Composite 

Monitor (2007CM) (in ug/m3)

15/35 2 14/35 13/35 10/35
J-23

Annual 24-Hr 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM 15/35 14/35 13/35 

Proportional 15.0 14.2 14.0 13.3 13.0 12.3 12.0 11.4 10.0 9.5 13.0 12.3 11.8 11.2 10.0 9.5

Hybrid 3 --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused4 --- --- --- --- --- --- --- --- --- --- --- --- 14 11.76 --- --- --- --- --- Proportional 14.8 13.1 14.0 12.5 13.0 11.6 12.0 10.7 10.0 8.9 12.7 11.3 10.7 9.5 10.0 8.9

Hybrid 14.3 13.0 14.0 12.7 13.0 11.8 12.0 10.9 10.0 9.1 12.3 11.2 10.3 9.4 10.0 9.1 -2% 4% 4% 

Locally Focused 15.2 13.6 --- --- --- --- --- --- --- --- 13.1 12.0 11.0 10.0 --- --- 6% --- --- 

Proportional 15.0 12.7 14.0 11.8 13.0 11.0 12.0 10.2 10.0 8.5 13.0 11.0 11.1 9.4 10.0 8.5

Hybrid 15.0 14.2 14.0 13.2 13.0 12.3 12.0 11.4 10.0 9.5 13.0 12.3 11.3 10.7 10.0 9.5 18% 19% 20% 

Locally Focused --- --- --- --- --- --- --- --- --- --- --- --- 12.3 11.4 --- --- --- --- --- 

Proportional 12.8 11.4 12.8 11.4 12.8 11.4 12.0 10.7 10.0 8.9 12.8 11.4 12.0 10.7 10.0 8.9

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Proportional 14.1 11.4 14.0 11.4 13.0 10.6 12.0 9.8 10.0 8.2 12.2 9.9 10.2 8.3 10.0 8.2

Hybrid 13.2 11.7 13.2 11.7 13.0 11.5 12.0 10.6 10.0 8.9 11.4 10.1 9.6 8.5 9.6 8.5 4% 6% 16% 

Locally Focused 14.1 12.6 --- --- --- --- --- --- --- --- 12.2 11.0 10.2 9.2 --- --- 18% --- --- 

Proportional 9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 8.6 8.6 7.3 7.3 7.3 7.3

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused 10.1 10.3 10.1 10.3 10.1 10.3 10.1 10.3 10.1 10.3 8.8 8.9 7.4 7.5 7.4 7.5 8% 8% 8% 

Proportional 15.0 12.5 14.0 11.7 13.0 10.9 12.0 10.1 10.0 8.5 13.0 10.9 12.0 10.1 10.0 8.5

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- Proportional 12.7 9.5 12.7 9.5 12.7 9.5 12.0 9.0 10.0 7.6 10.9 8.2 9.2 7.0 9.2 7.0

Hybrid 13.3 10.5 13.3 10.5 13.0 10.3 12.0 9.5 10.0 8.0 11.5 9.1 9.6 7.7 9.6 7.7 21% 21% 17% 

Locally Focused 13.9 12.1 13.9 12.1 13.9 12.1 --- --- --- --- 12.0 10.6 10.1 9.1 10.1 9.1 41% 41% 41% Proportional 13.3 11.6 13.3 11.6 13.0 11.3 12.0 10.4 10.0 8.7 11.5 10.0 9.7 8.4 9.7 8.4

Hybrid 13.6 11.8 13.6 11.8 13.0 11.3 12.0 10.4 10.0 8.7 11.7 10.2 9.8 8.5 9.8 8.5 3% 3% 0% 

Locally Focused 14.3 13.3 14.3 13.3 --- --- --- --- --- --- 12.3 11.6 10.3 9.8 10.3 9.8 23% 23% --- 

Proportional 13.9 12.3 13.9 12.3 13.0 11.6 12.0 10.7 10.0 8.9 11.9 10.7 10.0 9.0 10.0 8.9

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused 15.5 13.0 15.5 13.0 --- --- --- --- --- --- 14.1 11.3 11.8 9.5 --- --- 9% 9% --- 

Proportional 12.6 9.9 12.6 9.9 12.6 9.9 12.0 9.4 10.0 7.9 11.8 9.3 9.9 7.8 9.9 7.8

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused --- --- --- --- --- --- --- --- --- --- 12.2 9.7 10.2 9.0 10.2 9.0 --- --- --- Proportional 13.3 11.6 13.3 11.6 12.8 11.2 11.8 10.5 10.0 8.8 11.5 10.0 9.7 8.4 9.7 8.4

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused 15.6 13.2 15.6 13.2 15.3 11.8 15.3 11.2 --- --- 15.6 11.4 13.9 9.6 --- --- 22% 22% 11% 

Table J-20. Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with percent difference in surrogate for long-term exposure-relat methods) 

 10/25

cells u

13/30 12/25

 Atlanta, GA 15.3

Risk 

Assessment 

Location 1

Rollback Method

 Design Value

Recent Air 

Quality 

(2007)

16.2 35.0

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 Annual Average at Composite 

Monitor (2007CM) (in ug/m3)

15/35 2 14/35 13/35 12/35 10/35

 Baltimore, MD 15.6 37.0 13.9

 Birmingham, AL 18.7 44.0 15.7 Dallas, TX 11.4 Detroit, MI 17.2 43.0 13.9

12.8 26.0

 Fresno, CA 17.4 Houston, TX 15.8 31.0 13.2

17.4 63.0

 Los Angeles, CA 14.6 New York, NY 15.9 42.0 13.8

19.6 55.0

 Philadelphia, PA 13.4 Phoenix, AZ 9.9 Pittsburgh, PA 5 19.8 14.9

cells ucells ucells ucells ucells ucells ucells ucells u

Percent difference 

hybrid or peak shavin

for difference in 

cells ucells ucells u

60.0

12.6 32.015.0 38.0
J-24

Annual 24-Hr 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM Max. M- S 2007 CM 15/35 14/35 13/35 

Proportional 7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 6.7 6.6 5.7 5.6 5.7 5.6

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused 10.8 9.7 10.8 9.7 10.8 9.7 10.8 9.7 10.8 9.7 10.8 8.8 9.1 7.7 9.1 7.7 55% 55% 55% 

Proportional 14.9 12.9 14.0 12.1 13.0 11.3 12.0 10.4 10.0 8.7 12.8 11.1 10.8 9.3 10.0 8.7

Hybrid 15.0 13.5 14.0 12.6 13.0 11.7 12.0 10.8 10.0 9.0 13.0 11.7 11.0 9.9 10.0 9.0 8% 6% 7% 

Locally Focused 16.5 14.1 --- --- --- --- --- --- --- --- 14.2 12.4 11.9 10.4 --- --- 15% --- --- 

Proportional 8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 7.4 7.0 6.3 6.0 6.3 6.0

Hybrid --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- 

Locally Focused 8.5 8.0 8.5 8.0 8.5 8.0 8.5 8.0 8.5 8.0 7.4 7.0 6.3 6.0 6.3 6.0 0% 0% 0% 

Table J-20 (cont'd). Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with percent difference in surrogate for long-term expos rollback methods) 

6 Percent reduction in composite monitor value (CMV) with consideration for LML of 5.8 ug/m3. Percent reduction = (CMVlocally focused or hybrid - CMVproportional)/(CMVlocally focused or hybrid-LML). Note that greyed cells identify 

across alternative rollback methods (for a given alternative standard level/study area combination). 

 10/25

1For some locations (e.g., Atlanta) more than one "version" (group of counties) was used in the risk assessment. In this table only the version that was used for mortality associated with short-term exposure to PM2.5 (Zanobet3 The hybrid rollback method was applied to only a subset of the risk assessment locations. The "---" for a given location indicates that the hybrid rollback method was not applied to that location.2 The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3. 

13/30 12/25

Risk 

Assessment 

Location 1

Rollback Method

 Design Value

Recent Air 

Quality 

(2007)

Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 Annual Average at Composite 

Monitor (2007CM) (in ug/m3)

15/35 2 14/35 13/35 12/35 10/35

 Tacoma, WA 9.7

 Salt Lake City, UT 11.4 St. Louis, MO 14.3

11.6 55.0

4 The locally focused method was applied to a location-standard combination only if the daily standard was controlling in that location. The "--" for a given location-standard combination indicates that, for that set of annual and 

standard was controlling and so the locally focused method was not applied. 

5 The proportional rollback and locally focused 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 for details. 

Percent difference 

hybrid or peak shavin

for difference in 

cells ucells ucells u

10.2 43.016.5 39.0
K-1

APPENDIX K: MAPS OF THE FIFTEEN URBAN STUDY AREAS 

EVALUATED IN THE RISK ASSESSMENT
K-2

This appendix provides maps for each of the 15 urban study areas included in the 

risk assessment. The spatial templates used in defining 13 of the 15 study areas were 

based either on the combined statistical area (CSA) or the core-based statistical area 

(CBSA), if the CSA was not available.7

 The three remaining urban study areas 

(Baltimore, Philadelphia and Tacoma) are special cases and were handled as described in 

section 3.3.2. The maps presented in this appendix provide several types of information 

including: (a) annual and daily (i.e., 24-hour) design values (DV) for each PM2.5 monitor 

in each study area with DV values based on monitoring data from 2005-2007, (b) sources 

of PM2.5 greater than 50 tons/year (c) major highways within each study area, and (d) 

counties comprising each urban study area, together with the CSA or CBSA boundary as 

relevant. 

 

7

 The spatial template defines the geographical area associated with each study area that would be 

used in identifying which counties and PM2.5 monitors were associated with a particular study area. 
K-3

Figure K-1: Atlanta Urban Study Area 
K-4

Figure K-2: Baltimore Urban Study Area 

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AQSite 240032002 AQSite 240032002 AQSite 240032002 AQSite 240032002 AQSite 240032002 AQSite 240032002 AQSite 240032002 AQSite 240032002 AQSite 240032002Annual DV 15.7 μg/m3 Annual DV 15.7 μg/m3 Annual DV 15.7 μg/m3 Annual DV 15.7 μg/m3 Annual DV 15.7 μg/m3 Annual DV 15.7 μg/m3 Annual DV 15.7 μg/m3 Annual DV 15.7 μg/m3 Annual DV 15.7 μg/m3Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3

AQSite 240031003 AQSite 240031003 AQSite 240031003 AQSite 240031003 AQSite 240031003 AQSite 240031003 AQSite 240031003 AQSite 240031003 AQSite 240031003Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3

AQSite 245100007 AQSite 245100007 AQSite 245100007 AQSite 245100007 AQSite 245100007 AQSite 245100007 AQSite 245100007 AQSite 245100007 AQSite 245100007Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3

AQSite 245100040 AQSite 245100040 AQSite 245100040 AQSite 245100040 AQSite 245100040 AQSite 245100040 AQSite 245100040 AQSite 245100040 AQSite 245100040Annual DV 15.2 μg/m3 Annual DV 15.2 μg/m3 Annual DV 15.2 μg/m3 Annual DV 15.2 μg/m3 Annual DV 15.2 μg/m3 Annual DV 15.2 μg/m3 Annual DV 15.2 μg/m3 Annual DV 15.2 μg/m3 Annual DV 15.2 μg/m3Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3

AQSite 245100035 AQSite 245100035 AQSite 245100035 AQSite 245100035 AQSite 245100035 AQSite 245100035 AQSite 245100035 AQSite 245100035 AQSite 245100035Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3

AQSite 245100049 AQSite 245100049 AQSite 245100049 AQSite 245100049 AQSite 245100049 AQSite 245100049 AQSite 245100049 AQSite 245100049 AQSite 245100049Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3

AQSite 240053001 AQSite 240053001 AQSite 240053001 AQSite 240053001 AQSite 240053001 AQSite 240053001 AQSite 240053001 AQSite 240053001 AQSite 240053001Annual DV 14.5 μg/m3 Annual DV 14.5 μg/m3 Annual DV 14.5 μg/m3 Annual DV 14.5 μg/m3 Annual DV 14.5 μg/m3 Annual DV 14.5 μg/m3 Annual DV 14.5 μg/m3 Annual DV 14.5 μg/m3 Annual DV 14.5 μg/m3Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3

AQSite 240251001 AQSite 240251001 AQSite 240251001 AQSite 240251001 AQSite 240251001 AQSite 240251001 AQSite 240251001 AQSite 240251001 AQSite 240251001Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3

AQSite 245100008 AQSite 245100008 AQSite 245100008 AQSite 245100008 AQSite 245100008 AQSite 245100008 AQSite 245100008 AQSite 245100008 AQSite 245100008Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3

AQSite 245100006 AQSite 245100006 AQSite 245100006 AQSite 245100006 AQSite 245100006 AQSite 245100006 AQSite 245100006 AQSite 245100006 AQSite 245100006Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3

AQSite 240030014 AQSite 240030014 AQSite 240030014 AQSite 240030014 AQSite 240030014 AQSite 240030014 AQSite 240030014 AQSite 240030014 AQSite 240030014Annual DV 12 μg/m3 Annual DV 12 μg/m3 Annual DV 12 μg/m3 Annual DV 12 μg/m3 Annual DV 12 μg/m3 Annual DV 12 μg/m3 Annual DV 12 μg/m3 Annual DV 12 μg/m3 Annual DV 12 μg/m3Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3

AQSite 240051007 AQSite 240051007 AQSite 240051007 AQSite 240051007 AQSite 240051007 AQSite 240051007 AQSite 240051007 AQSite 240051007 AQSite 240051007Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3

CONSTELLATION POWER SOURCE GENPrimary PM25 Annual Emission799.3 tpy

CONSTELLATION POWER SOURCE GENERAPrimary PM25 Annual Emission1660 tpy

CONSTELLATION POWER SOURCE GPrimary PM25 Annual Emission1968 tpy

TATE & LYLE NORTH APrimary PM25 Annual E88.4 tpy

RED STAR YEASTPrimary PM25 Annual Emission56.6 tpy UNITED ST

Primary PM132 tpy BETHLEHEM STEEL

Primary PM25 Annual Emission1326.2 tpy

GRACE - DAVISON CHPrimary PM25 Annual E87.2 tpy

Baltimore Baltimore Baltimore Baltimore Baltimore Baltimore Baltimore Baltimore Baltimore

Baltimore City Baltimore City Baltimore City Baltimore City Baltimore City Baltimore City Baltimore City Baltimore City Baltimore City

Anne Arundel Anne Arundel Anne Arundel Anne Arundel Anne Arundel Anne Arundel Anne Arundel Anne Arundel Anne Arundel

Carroll Carroll Carroll Carroll Carroll Carroll

Harford Harford Harford Harford Harford Harford

Howard Howard Howard Howard Howard Howard
K-5

Figure K-3: Birmingham Urban Study Area 

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AQSite 010735002 AQSite 010735002 AQSite 010735002 AQSite 010735002 AQSite 010735002 AQSite 010735002 AQSite 010735002 AQSite 010735002 AQSite 010735002Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3

AQSite 010731010 AQSite 010731010 AQSite 010731010 AQSite 010731010 AQSite 010731010 AQSite 010731010 AQSite 010731010 AQSite 010731010 AQSite 010731010Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3

AQSite 010730023 AQSite 010730023 AQSite 010730023 AQSite 010730023 AQSite 010730023 AQSite 010730023 AQSite 010730023 AQSite 010730023 AQSite 010730023Annual DV 18.7 μg/m3 Annual DV 18.7 μg/m3 Annual DV 18.7 μg/m3 Annual DV 18.7 μg/m3 Annual DV 18.7 μg/m3 Annual DV 18.7 μg/m3 Annual DV 18.7 μg/m3 Annual DV 18.7 μg/m3 Annual DV 18.7 μg/m3Daily DV 44 μg/m3 Daily DV 44 μg/m3 Daily DV 44 μg/m3 Daily DV 44 μg/m3 Daily DV 44 μg/m3 Daily DV 44 μg/m3 Daily DV 44 μg/m3 Daily DV 44 μg/m3 Daily DV 44 μg/m3

AQSite 010732006 AQSite 010732006 AQSite 010732006 AQSite 010732006 AQSite 010732006 AQSite 010732006 AQSite 010732006 AQSite 010732006 AQSite 010732006Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3 Annual DV 15.1 μg/m3Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3

AQSite 010732003 AQSite 010732003 AQSite 010732003 AQSite 010732003 AQSite 010732003 AQSite 010732003 AQSite 010732003 AQSite 010732003 AQSite 010732003Annual DV 17.5 μg/m3 Annual DV 17.5 μg/m3 Annual DV 17.5 μg/m3 Annual DV 17.5 μg/m3 Annual DV 17.5 μg/m3 Annual DV 17.5 μg/m3 Annual DV 17.5 μg/m3 Annual DV 17.5 μg/m3 Annual DV 17.5 μg/m3Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3

AQSite 010731005 AQSite 010731005 AQSite 010731005 AQSite 010731005 AQSite 010731005 AQSite 010731005 AQSite 010731005 AQSite 010731005 AQSite 010731005Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3 Annual DV 15.6 μg/m3Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3

AQSite 010731009 AQSite 010731009 AQSite 010731009 AQSite 010731009 AQSite 010731009 AQSite 010731009 AQSite 010731009 AQSite 010731009 AQSite 010731009Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3

AQSite 010735003 AQSite 010735003 AQSite 010735003 AQSite 010735003 AQSite 010735003 AQSite 010735003 AQSite 010735003 AQSite 010735003 AQSite 010735003Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3

AQSite 011270002 AQSite 011270002 AQSite 011270002 AQSite 011270002 AQSite 011270002 AQSite 011270002 AQSite 011270002 AQSite 011270002 AQSite 011270002Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3

National Cement CPrimary PM25 An129.6 tpy

DRUMMOND COMPANPrimary PM25 Annual E1222.2 tpy

LEHIGH CEMENT COMPANYPrimary PM25 Annual Emission215.2 tpy

SMPri134 

NUCPrim85.6 

U. S. PIPE & FOUNDRY COMPANY INPrimary PM25 Annual Emission356.8 tpy

SLOSS INDUSTRIES CORPORATION - COKPrimary PM25 Annual Emission478.2 tpy

SLOSS INDUSTRIES CORPORATION - MINEPrimary PM25 Annual Emission161.9 tpy

MCWANE CAST IPrimary PM25 Ann308.3 tpy

AMERICAN CAST IRON PIPE COMPANYPrimary PM25 Annual Emission436.9 tpy

UNITED STATES STEEL CORPORATION-FAPrimary PM25 Annual Emission187.2 tpy

UNITED STATES STEEL CORPOPrimary PM25 Annual Emission635.1 tpy

Alabama Power CompanyPrimary PM25 Annual Emission6804.7 tpy

LaFarge Building MaterialsPrimary PM25 Annual Emission

Southern Lime 344 tpyPrimary PM25 Annual Emission51.8 tpy

U. S. PIPE & FOUNDRY COMPANY,INC.(BESSEMER PLANT)Primary PM25 Annual Emission295.4 tpy

Alabama Power CompanyPrimary PM25 Annual Emission2738.8 tpy

ALABAMA POWER COMPANY (MILLER POWER PLANT)Primary PM25 Annual Emission1666.5 tpy

OAK GROVE RESOURCES, LLCPrimary PM25 Annual Emission169.1 tpy

Blount Blount Blount Blount Blount Blount

Jefferson Jefferson Jefferson Jefferson Jefferson Jefferson

St. Clair St. Clair St. Clair St. Clair St. Clair St. Clair St. Clair St. Clair St. Clair

Shelby Shelby Shelby Shelby Shelby Shelby

Walker Walker Walker Walker Walker

Bibb Bibb Bibb Bibb

Chilton Chilton Chilton Chilton Chilton Chilton Chilton

Cullman Cullman Cullman Cullman Cullman Cullman Cullman Cullman Cullman
K-6

Figure K-4: Dallas Urban Study Area 

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AQSite 484393006 AQSite 484393006 AQSite 484393006 AQSite 484393006 AQSite 484393006 AQSite 484393006 AQSite 484393006 AQSite 484393006 AQSite 484393006Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3

AQSite 484391006 AQSite 484391006 AQSite 484391006 AQSite 484391006 AQSite 484391006 AQSite 484391006 AQSite 484391006 AQSite 484391006 AQSite 484391006Annual DV 11.9 μg/m3 Annual DV 11.9 μg/m3 Annual DV 11.9 μg/m3 Annual DV 11.9 μg/m3 Annual DV 11.9 μg/m3 Annual DV 11.9 μg/m3 Annual DV 11.9 μg/m3 Annual DV 11.9 μg/m3 Annual DV 11.9 μg/m3Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3

AQSite 484391002 AQSite 484391002 AQSite 484391002 AQSite 484391002 AQSite 484391002 AQSite 484391002 AQSite 484391002 AQSite 484391002 AQSite 484391002Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3

AQSite 481390016 AQSite 481390016 AQSite 481390016 AQSite 481390016 AQSite 481390016 AQSite 481390016 AQSite 481390016 AQSite 481390016 AQSite 481390016Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3

AQSite 481390015 AQSite 481390015 AQSite 481390015 AQSite 481390015 AQSite 481390015 AQSite 481390015 AQSite 481390015 AQSite 481390015 AQSite 481390015Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3

AQSite 481130087 AQSite 481130087 AQSite 481130087 AQSite 481130087 AQSite 481130087 AQSite 481130087 AQSite 481130087 AQSite 481130087 AQSite 481130087Annual DV 10.8 μg/m3 Annual DV 10.8 μg/m3 Annual DV 10.8 μg/m3 Annual DV 10.8 μg/m3 Annual DV 10.8 μg/m3 Annual DV 10.8 μg/m3 Annual DV 10.8 μg/m3 Annual DV 10.8 μg/m3 Annual DV 10.8 μg/m3Daily DV 22 μg/m3 Daily DV 22 μg/m3 Daily DV 22 μg/m3 Daily DV 22 μg/m3 Daily DV 22 μg/m3 Daily DV 22 μg/m3 Daily DV 22 μg/m3 Daily DV 22 μg/m3 Daily DV 22 μg/m3

AQSite 481130050 AQSite 481130050 AQSite 481130050 AQSite 481130050 AQSite 481130050 AQSite 481130050 AQSite 481130050 AQSite 481130050 AQSite 481130050Annual DV 12.8 μg/m3 Annual DV 12.8 μg/m3 Annual DV 12.8 μg/m3 Annual DV 12.8 μg/m3 Annual DV 12.8 μg/m3 Annual DV 12.8 μg/m3 Annual DV 12.8 μg/m3 Annual DV 12.8 μg/m3 Annual DV 12.8 μg/m3Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3 Daily DV 26 μg/m3

AQSite 481130035 AQSite 481130035 AQSite 481130035 AQSite 481130035 AQSite 481130035 AQSite 481130035 AQSite 481130035 AQSite 481130035 AQSite 481130035Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3 Daily DV 27 μg/m3

AQSite 481130069 AQSite 481130069 AQSite 481130069 AQSite 481130069 AQSite 481130069 AQSite 481130069 AQSite 481130069 AQSite 481130069 AQSite 481130069Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3

AQSite 481130057 AQSite 481130057 AQSite 481130057 AQSite 481130057 AQSite 481130057 AQSite 481130057 AQSite 481130057 AQSite 481130057 AQSite 481130057Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3

NORTH TEXAS CEMENT CO.Primary PM25 Annual Emission349.4 tpy

HOLCIM (TEXAS) LPPrimary PM25 Annual Emission378.1 tpy

NORTH LAKE STEAM ELECTRIC STATIONPrimary PM25 Annual Emission58.2 tpy

HANDLEY STEAM ELEC. STA.Primary PM25 Annual Emission105.1 tpy

JOHNS MANVILLEPrimary PM25 Annual Emission308 tpy

PONDEROSA PINE ENERGYPrimary PM25 Annual Emission51.2 tpy

CHAPARRAL STEEL MIDLOTHIANPrimary PM25 Annual Emission129.9 tpy

WAXAHACHIE PLANTPrimary PM25 Annual Emission419.8 tpy

SAINT GOBAIN CONTAINERSPrimary PM25 Annual Emission61.6 tpy

G.S. ROOFING PRODUCTSPrimary PM25 Annual Emis61 tpy

DAL-TILE Primary P52.9 tpy

C.O. MFGPrimary P61.2 tpy

CP58

MIDLOTHIAN PLANTPrimary PM25 Annual Emission115.5 tpy

TEXAS LIME COMPANYPrimary PM25 Annual Emission65.3 tpy

Dallas

o Pinto

Parker

Hood

Johnson

Ellis

Henderson

Kaufman

Rockwall

Cooke Grayson

Fannin

Delt

Hunt

Collin

Denton

Wise

Tarrant
K-7

Figure K-5: Detroit Urban Study Area 

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AQSite 260990009 AQSite 260990009 AQSite 260990009 AQSite 260990009 AQSite 260990009 AQSite 260990009 AQSite 260990009 AQSite 260990009 AQSite 260990009Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3

AQSite 260490021 AQSite 260490021 AQSite 260490021 AQSite 260490021 AQSite 260490021 AQSite 260490021 AQSite 260490021 AQSite 260490021 AQSite 260490021Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3

AQSite 261150005 AQSite 261150005 AQSite 261150005 AQSite 261150005 AQSite 261150005 AQSite 261150005 AQSite 261150005 AQSite 261150005 AQSite 261150005Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3 Annual DV 13.8 μg/m3Daily DV 38 μg/m3 Dail Day DV 38 μg/m3 Dail Daily DV 38 μg/m3 Dailyy DV 38DV38 DV 38 μgμg/m3m /3

AQSite 261630001 AQSite 261630001 AQSite 261630001 AQSite 261630001 AQSite 261630001 AQSite 261630001 AQSite 261630001 AQSite 261630001 AQSite 261630001Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 

AQAQAQ

AQAQAQ

AQAQAQ

AnAnAn

AnAnAn

AnAnAn

DaDaDa

DaDaDa

DaDaDa

AAA

AAA

AAA

AAA

AAA

AAA

DDD

DDD

DDD

AQSite 261630016 AQSite 261630016 AQSite 261630016 AQSite 26163001

AQSite 26163001AQSite 26163001

AQSite 26163001AQSite 26163001AQSite 26163001

Annual DV 14.3 μAnnual DV 14.3 μAnnual DV 14.3 μ

Annual DV 14.3 μAnnual DV 14.3 μAnnual DV 14.3 μ

Annual DV 14.3 μAnnual DV 14.3 μAnnual DV 14.3 μ

Daily DV 41 μg/mDaily DV 41 μg/mDaily DV 41 μg/m

Daily DV 41 μg/mDaily DV 41 μg/mDaily DV 41 μg/m

Daily DV 41 μg/mDaily DV 41 μg/mDaily DV 41 μg/m

AQSite 261630039 AQSite 261630039 AQSite 261630039 AQSite 261630039 AQSite 261630039 AQSite 261630039 AQSite 261630039 AQSite 261630039 AQSite 261630039Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3

AQSite 261630019 AQSite 261630019 AQSite 261630019 AQSite 261630019 AQSite 261630019 AQSite 261630019 AQSite 261630019 AQSite 261630019 AQSite 261630019Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3 Annual DV 14.1 μg/m3Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3

AQSite 261250001 AQSite 261250001 AQSite 261250001 AQSite 261250001 AQSite 261250001 AQSite 261250001 AQSite 261250001 AQSite 261250001 AQSite 261250001Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3

AQSite 261610008 AQSite 261610008 AQSite 261610008 AQSite 261610008 AQSite 261610008 AQSite 261610008 AQSite 261610008 AQSite 261610008 AQSite 261610008Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3 Annual DV 13.7 μg/m3Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3

AQSite 261610005 AQSite 261610005 AQSite 261610005 AQSite 261610005 AQSite 261610005 AQSite 261610005 AQSite 261610005 AQSite 261610005 AQSite 261610005Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3

AQSite 261630025 AQSite 261630025 AQSite 261630025 AQSite 261630025 AQSite 261630025 AQSite 261630025 AQSite 261630025 AQSite 261630025 AQSite 261630025Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3

AQSite 261470005 AQSite 261470005 AQSite 261470005 AQSite 261470005 AQSite 261470005 AQSite 261470005 AQSite 261470005 AQSite 261470005 AQSite 261470005Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3

J.R. WHITING COPrimary PM25 Annual Emission1029.2 tpy

HOLCIM (US) INC.Primary PM25 Annual Emission656.5 tpy DETROIT E

Primary PM22814.8 tpy

ROCPrim52.5 

GUARDIAN INDUSTRIESPrimary PM25 Annual Emission124.9 tpy

DPr11

DETPrim378 

NATIONAL STEEL CORPPrimary PM25 Annual Emission1983 tpy

MARPrim144.6 

ROUGE STEEL COPrimary PM25 Annual Emission1335.9 tpy

SIX S INC PT 5Primary PM25 Annual Emission74.3 tpy

SIX S Primar64.9 tp

ST. CLAIR / BELLPrimary PM25 An1593.1 tpy

CARGPrima80.5 tp

DETROIT EDISON GREENWOOD ENERGY CEPrimary PM25 Annual Emission87.6 tpy

Monroe

Livingston

Wayne

Washtenaw

Macomb

Oakland

Genesee

St. Clair Lapeer
K-8

Figure K-6: Fresno Urban Study Area 

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 AQSite 060195025 AQSite 060195025 AQSite 060195025 AQSite 060195025 AQSite 060195025 AQSite 060195025 AQSite 060195025 AQSite 060195025 AQSite 060195025 Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3

Daily DV 61 μg/m3 Daily DV 61 μg/m3 Daily DV 61 μg/m3

AQSite 060190008 AQSite 060190008 AQSite 060190008 AQSite 060190008 AQSite 060190008 AQSite 060190008 AQSite 060190008 AQSite 060190008 AQSite 060190008Annual DV 17.4 μg/m3 Annual DV 17.4 μg/m3 Annual DV 17.4 μg/m3Daily DV 63 μg/m3 Daily DV 63 μg/m3 Daily DV 63 μg/m3

AQSite 060195001 AQSite 060195001 AQSite 060195001 AQSite 060195001 AQSite 060195001 AQSite 060195001 AQSite 060195001 AQSite 060195001 AQSite 060195001Annual DV 16.4 μg/m3 Annual DV 16.4 μg/m3 Annual DV 16.4 μg/m3 Annual DV 16.4 μg/m3 Annual DV 16.4 μg/m3 Annual DV 16.4 μg/m3Daily DV 58 μg/m3 Daily DV 58 μg/m3 Daily DV 58 μg/m3

SAINT-GOBAIN CONTAINERS, INCPrimary PM25 Annual Emission51.4 tpy

AES MENDOTA, L.P.Primary PM25 Annual Emission50.3 tpy

MADERA POWER, LLCPrimary PM25 Annual Emission77.8 tpy

Madera

Fresno
K-9

Figure K-7: Houston Urban Study Area 

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AQSite 482010055 AQSite 482010055 AQSite 482010055 AQSite 482010055 AQSite 482010055 AQSite 482010055 AQSite 482010055 AQSite 482010055 AQSite 482010055Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3

AQSite 482010058 AQSite 482010058 AQSite 482010058 AQSite 482010058 AQSite 482010058 AQSite 482010058Annual DV 11.4 μg/m3 Annual DV 11.4 μg/m3 Annual DV 11.4 μg/m3 Annual DV 11.4 μg/m3 Annual DV 11.4 μg/m3 Annual DV 11.4 μg/m3 Annual DV 11.4 μg/m3Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3

AQSite 482010026 AQSite 482010026 AQSite 482010026 AQSite 482010026Annual DV 12.7 μg/m3 Annual DV 12.7 μg/m3 Annual DV 12.7 μg/m3 Annual DV 12.7 μg/m3 Annual DV 12.7 μg/m3 Annual DV 12.7 μg/m3 Annual DV 12.7 μg/m3 Annual DV 12.7 μg/m3 Annual DV 12.7 μg/m3Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3

AQSite 483390078 AQSite 483390078 AQSite 483390078 AQSite 483390078 AQSite 483390078 AQSite 483390078 AQSite 483390078 AQSite 483390078 AQSite 483390078Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3

AQSite 482010024 AQSite 482010024 AQSite 482010024 AQSite 482010024 AQSite 482010024 AQSite 482010024 AQSite 482010024 AQSite 482010024 AQSite 482010024 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3Annual DV 13.1 μg/m3 Annual DV 13.1 μg/m3 Annual DV 13.1 μg/m3 Annual DV 13.1 μg/m3 Annual DV 13.1 μg/m3 Annual DV 13.1 μg/m3 Annual DV 13.1 μg/m3 Annual DV 13.1 μg/m3 Annual DV 13.1 μg/m3Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3

AQSite 482011034 AQSite 482011034 AQSite 482011034 AQSite 482011034 AQSite 482011034 AQSite 482011034 AQSite 482011034 AQSite 482011034 AQSite 482011034Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3 Annual DV 13.9 μg/m3Daily DV 28 μg/m3 Daily DV 28 μg/m3 Daily DV 28 μg/m3 Daily DV 28 μg/m3 Daily DV 28 μg/m3 Daily DV 28 μg/m3 Daily DV 28 μg/m3 Daily DV 28 μg/m3 Daily DV 28 μg/m3

AQSite 482011035 AQSite 482011035 AQSite 482011035 AQSite 482011035 AQSite 482011035 AQSite 482011035Annual DV 15.8 μg/m3 Annual DV 15.8 μg/m3 Annual DV 15.8 μg/m3 Annual DV 15.8 μg/m3 Annual DV 15.8 μg/m3 Annual DV 15.8 μg/m3 Annual DV 15.8 μg/m3 Annual DV 15.8 μg/m3 Annual DV 15.8 μg/m3Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3

AQSite 4AQSite 4AQSite 4

AQSite 4AQSite 4AQSite 4

AQSite 4AQSite 4AQSite 4

Annual DAnnual DAnnual D

Annual DAnnual DAnnual D

Annual DAnnual DAnnual D

Daily DV Daily DV Daily DV 

Daily DV Daily DV 

Daily DV 

CLEAR LAKE COGENERATIONPrimary PM25 Annual Emission116.7 tpy

KANEKA TEXAS CORPORATIONPrimary PM25 Annual Emission50.9 tpy

AKZO NOBEL CHEMICALS, INC.Primary PM25 Annual Emission66 tpy

HOUSTON REFINERYPrimary PM25 Annual Emission133.8 tpy

LYONDELL-CITGO REFININGPrimary PM25 Annual Emission177.9 tpy

CHANNEL ENERGY ENERGYPrimary PM25 Annual Emission80.2 tpy

CLEAR LAKE PLANTPrimary PM25 Annual Emission515.5 tpy

BP AMOCO CHEMICAL TEXAS CPrimary PM25 Annual Emission71.6 tpy

VINYL ACETATPrimary PM25 97.7 tpy

CHOCOLATE BAYOU PLNTPrimary PM25 Annual Emission90.2 tpy

TEXAS CITPrimary PM264.4 tpy TEXAS CI

Primary P178.7 tpy

TEXAS CPrimary P912.1 tpy

CEDAR Primary 164.2 tp

BAYTOPrima62.9 tp

SAN JACIPrimary PM73.1 tpy

DEER PAPrimary P194.3 tpy

AGRPrima80.7 

EXXPrim915

QUALITY ELECTRIC STEEL CASTING, INC.Primary PM25 Annual Emission56.6 tpy

CHANNELVIEW COMPLEXPrimary PM25 Annual Emission269.5 tpy

DEER PARPrimary PM233.1 tpy

TX PETROPrimary P119.1 tpy

CHANNELVIEW PLANTPrimary PM25 Annual Emission410.5 tpy

CHANNELVIEW COGENERGATION FACILITYPrimary PM25 Annual Emission212 tpy

LONGHORN GLASSPrimary PM25 Annual Emission72.2 tpy

KATY GAS PLANTPrimary PM25 Annual Emission174.9 tpy

PASADENA PLANTPrimary PM25 Annual Emission449.1 tpy

MAXWELL HOUSE DIVISIONPrimary PM25 Annual Emission53.6 tpy

PLANT BPrimary PM25 Annual Emission171.7 tpyFREEPORT SITEPrimary PM25 Annual Emission154 9 tpy

CHEPrim129.9 

MONT BPrimary 67.9 tpy

P H ROBINSON STATIONPrimary PM25 Annual Emission177.5 tpy

W A PARISH STATIONPrimary PM25 Annual Emission2186.7 tpy

SWEENY REFINERY PETROCHEMPrimary PM25 Annual Emission54.6 tpy

SWEENY COMPLEXPrimary PM25 Annual Emission218.2 tpy

LEWIS CREEK PLANTPrimary PM25 Annual Emission95.4 tpy

Fort Bend

Galveston

Brazoria

Chambers

Liberty

Montgomery

Waller

Austin

Harris
K-10

Figure K-8: Los Angeles Urban Study Area 

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AQSite 060710025 AQSite 060710025 AQSite 060710025 AQSite 060710025 AQSite 060710025 AQSite 060710025Annual DV 18.5 μg/m3 Annual DV 18.5 μg/m3 Annual DV 18.5 μg/m3 Annual DV 18.5 μg/m3 Annual DV 18.5 μg/m3 Annual DV 18.5 μg/m3Daily DV 47 μg/m3 Daily DV 47 μg/m3 Daily DV 47 μg/m3

AQSite 060370002 AQSite 060370002 AQSite 060370002 AQSite 060370002 AQSite 060370002 AQSite 060370002 AQSite 060370002 AQSite 060370002 AQSite 060370002Annual DV 16 μg/m3 Annual DV 16 μg/m3 Annual DV 16 μg/m3 Annual DV 16 μg/m3 Annual DV 16 μg/m3 Annual DV 16 μg/m3Daily DV 47 μg/m3 Daily DV 47 μg/m3 Daily DV 47 μg/m3 Daily DV 47 μg/m3 Daily DV 47 μg/m3 Daily DV 47 μg/m3

AQSite 060590007 AQSite 060590007 AQSite 060590007 AQSite 060590007 AQSite 060590007 AQSite 060590007Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3Dail Dail Dail Dail Dailyyyy DV 42 DV 42 DV 42 DV 42 DV 42V D DV 42 μgμgμgμg/m3 /m3 /m3 /m3 /m33 /m3

AQSite 0606580AQSite 0606580AQSite 0606580

AQSite 0606580AQSite 0606580AQSite 0606580

AQSite 060658AQSite 060658AQSite 060658

Annual DV 20.9 Annual DV 20.9 Annual DV 20.9 

Annual DV 20.9 Annual DV 20.9 Annual DV 20.9 

Daily DV 56 μg/Daily DV 56 μg/Daily DV 56 μg/

Daily DV 56 μg/Daily DV 56 μg/

Daily DV 56 μg/

AQSite 0606AQSite 0606AQSite 0606

AQSite 0606AQSite 0606AQSite 0606

Annual DV 1Annual DV 1Annual DV 1

Annual DV Annual DV Annual DV 

Daily DV 49 Daily DV 49 Daily DV 49 

AQSite 060658001 AQSite 060658001 AQSite 060658001 AQSite 060658001 AQSite 060658001 AQSite 060658001Annual DV 19.6 μgAnnual DV 19.6 μgAnnual DV 19.6 μg

Annual DV 19.6 μgAnnual DV 19.6 μgAnnual DV 19.6 μg

Daily DV 55 μg/m3 Daily DV 55 μg/m3 Daily DV 55 μg/m3

AQSite 060374004 AQSite 060374004 AQSite 060374004 AQSite 060374004 AQSite 060374004 AQSite 060374004Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3Daily DV 36 μg/m Daily DV 36 μg/m Dail Daily DV 36 μg/m Dailyy DV 36 36 DVμgμg/m3 /m333

AQSite 060374002 AQSite 060374002 AQSite 060374002 AQSite 060374002 AQSite 060374002 AQSite 060374002Annual DV 14.9 μg/m3 Annual DV 14.9 μg/m3 Annual DV 14.9 μg/m3 Annual DV 14.9 μg/m3 Annual DV 14.9 μg/m3 Annual DV 14.9 μg/m3Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3

AQSite 060371201 AQSite 060371201 AQSite 060371201 AQSite 060371201 AQSite 060371201 AQSite 060371201 AQSite 060371201 AQSite 060371201 AQSite 060371201Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3

AQSite 060371103 AQSite 060371103 AQSite 060371103 AQSite 060371103 AQSite 060371103 AQSite 060371103Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3Daily DV 48 μg/m3 Daily DV 48 μg/m3 Daily DV 48 μg/m3 Daily DV 48 μg/m3 Daily DV 48 μg/m3 Daily DV 48 μg/m3

AQSite 060371301 AQSite 060371301 AQSite 060371301 AQSite 060371301 AQSite 060371301 AQSite 060371301Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3Daily DV 46 μg/m3 Daily DV 46 μg/m3 Daily DV 46 μg/m3 Daily DV 46 μg/m3 Daily DV 46 μg/m3 Daily DV 46 μg/m3

AQSite 060712002 AQSite 060712002 AQSite 060712002 AQSite 060712002 AQSite 060712002 AQSite 060712002Annual DV 18.4 μg/m3 Annual DV 18.4 μg/m3 Annual DV 18.4 μg/m3 Annual DV 18.4 μg/m3 Annual DV 18.4 μg/m3 Annual DV 18.4 μg/m3Daily DV 52 μg/m3 Daily DV 52 μg/m3 Daily DV 52 μg/m3

AQSite 060372005 AQSite 060372005 AQSite 060372005 AQSite 060372005 AQSite 060372005 AQSite 060372005Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3 Annual DV 14.3 μg/m3Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3

AQSite 060371002 AQSite 060371002 AQSite 060371002 AQSite 060371002 AQSite 060371002 AQSite 060371002Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3 Annual DV 17.1 μg/m3Daily DV 48 μg/m3 Daily DV 48 μg/m3 Daily DV 48 μg/m3 Daily DV 48 μg/m3 Daily DV 48 μg/m3 Daily DV 48 μg/m3

AQSite 061112002 AQSite 061112002 AQSite 061112002 AQSite 061112002 AQSite 061112002 AQSite 061112002Annual DV 11.1 μg/m3 Annual DV 11.1 μg/m3 Annual DV 11.1 μg/m3 Annual DV 11.1 μg/m3 Annual DV 11.1 μg/m3 Annual DV 11.1 μg/m3Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3

AQSite 060592022 AQSite 060592022 AQSite 060592022 AQSite 060592022 AQSite 060592022 AQSite 060592022Annual DV 10.9 μg/m3 Annual DV 10.9 μg/m3 Annual DV 10.9 μg/m3 Annual DV 10.9 μg/m3 Annual DV 10.9 μg/m3 Annual DV 10.9 μg/m3Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3

AQSite 060710306 AQSite 060710306 AQSite 060710306 AQSite 060710306 AQSite 060710306 AQSite 060710306 AQSite 060710306 AQSite 060710306 AQSite 060710306Annual DV 9.9 μg/m3 Annual DV 9.9 μg/m3 Annual DV 9.9 μg/m3 Annual DV 9.9 μg/m3 Annual DV 9.9 μg/m3 Annual DV 9.9 μg/m3 Annual DV 9.9 μg/m3 Annual DV 9.9 μg/m3 Annual DV 9.9 μg/m3Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3

AQSite 061110007 AQSite 061110007 AQSite 061110007 AQSite 061110007 AQSite 061110007 AQSite 061110007Annual DV 10.3 μg/m3 Annual DV 10.3 μg/m3 Annual DV 10.3 μg/m3 Annual DV 10.3 μg/m3 Annual DV 10.3 μg/m3 Annual DV 10.3 μg/m3Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3

GRANITE CONS. INC/ LITTLEROCKPrimary PM25 Annual Emission88.6 tpy

SCE-ORMOND BEACH GEN STATIONPrimary PM25 Annual Emission77.7 tpy

LA CO., SANITATION DISTRICTPrimary PM25 Annual Emission70.3 tpy

TAP67

JOHNS MANVILLE CORPPrimary PM25 Annual Emission122.8 tpy

AES ALAMITOS, LLCPrimary PM25 Annual Emission243.4 tpy

CHEVRON U.S.A. INC.Primary PM25 Annual Emission223 tpy

EL SEGUNDO POWER, LLCPrimary PM25 Annual Emission108.7 tpy

LA CITY, DWP SCATTERGOOD GENERATING STAPrimary PM25 Annual Emission50.8 tpy

WEST COAST PRODUCTS LLCPrimary PM25 Annual Emission172.4 tpy

EQUILON ENTERPRISES, LLCPrimary PM25 Annual Emission89 tpy

ULTRAMAR INC (NSR USE ONLY)Primary PM25 Annual Emission140.5 tpy

TOSCO REFINING COMPANYPrimary PM25 Annual Emission140.5 tpy

AES REDONDO BEACH, LLCPrimary PM25 Annual Emission109 tpy

MOBIL OIL CORPPrimary PM25 Annual Emission119 tpy SAN CLEMENTE ISLAND RANGE

Primary PM25 Annual Emission57.9 tpy

MP2

AFG INDUPrimary P97.2 tpy

TXI RIVERSIDE CEMENT COMPANYPrimary PM25 Annual Emission312.2 tpy

ANTELOPE VALLEY AGGREGATE INCPrimary PM25 Annual Emission257.2 tpy

Ventura

San Bernardino

Orange Riverside

Los Angeles

AQSite 060371602 AQSite 060371602 AQSite 060371602 AQSite 060371602 AQSite 060371602 AQSite 060371602Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3 Annual DV 16.7 μg/m3Daily DV 49 μg/m3 Daily DV 49 μg/m3 Daily DV 49 μg/m3

AQSite 060379033 AQSite 060379033 AQSite 060379033 AQSite 060379033 AQSite 060379033 AQSite 060379033Annual DV 8.1 μg/m3 Annual DV 8.1 μg/m3 Annual DV 8.1 μg/m3 Annual DV 8.1 μg/m3 Annual DV 8.1 μg/m3 Annual DV 8.1 μg/m3Daily DV 17 μg/m3 Daily DV 17 μg/m3 Daily DV 17 μg/m3 Daily DV 17 μg/m3 Daily DV 17 μg/m3 Daily DV 17 μg/m3

AQSite 061110009 AQSite 061110009 AQSite 061110009 AQSite 061110009 AQSite 061110009 AQSite 061110009Annual DV 9.6 μg/m3 Annual DV 9.6 μg/m3 Annual DV 9.6 μg/m3 Annual DV 9.6 μg/m3 Annual DV 9.6 μg/m3 Annual DV 9.6 μg/m3

AQSite 061113001 AQSite 061113001 AQSite 061113001 AQSite 061113001 AQSite 061113001 AQSite 061113001 Daily DV 21 μg/m3 Daily DV 21 μg/m3 Daily DV 21 μg/m3Annual DV 10.3 μg/m3 Annual DV 10.3 μg/m3 Annual DV 10.3 μg/m3 Annual DV 10.3 μg/m3 Annual DV 10.3 μg/m3 Annual DV 10.3 μg/m3Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3
K-11

Figure K-9: New York Urban Study Area 

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AQSite 360610079 AQSite 360610079 AQSite 360610079 AQSite 360610079 AQSite 360610079 AQSite 360610079 AQSite 360610079 AQSite 360610079 AQSite 360610079Annual DV 13.5 μg/m3 Annual DV 13.5 μg/m3 Annual DV 13.5 μg/m3 Annual DV 13.5 μg/m3 Annual DV 13.5 μg/m3 Annual DV 13.5 μg/m3 Annual DV 13.5 μg/m3 Annual DV 13.5 μg/m3 Annual DV 13.5 μg/m3Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3

AQSite 360050110 AQSite 360050110 AQSite 360050110 AQSite 360050110 AQSite 360050110 AQSite 360050110 AQSite 360050110 AQSite 360050110 AQSite 360050110Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3

AQSite 360050083 AQSite 360050083 AQSite 360050083 AQSite 360050083 AQSite 360050083 AQSite 360050083 AQSite 360050083 AQSite 360050083 AQSite 360050083Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3

AQSite 360050080 AQSite 360050080 AQSite 360050080 AQSite 360050080 AQSite 360050080 AQSite 360050080 AQSite 360050080 AQSite 360050080 AQSite 360050080Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3

AQSite 340171003 AQSite 340171003 AQSite 340171003 AQSite 340171003 AQSite 340171003 AQSite 340171003 AQSite 340171003 AQSite 340171003 AQSite 340171003Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3

AQSite 360850067 AQSite 360850067 AQSite 360850067 AQSite 360850067 AQSite 360850067 AQSite 360850067Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3 Annual DV 11.5 μg/m3Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3

AQSite 360850055 AQSite 360850055 AQSite 360850055 AQSite 360850055 AQSite 360850055 AQSite 360850055 AQSite 360850055 AQSite 360850055 AQSite 360850055Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3Daily DV 34 μg/m3 Daily DV 34 μg/m3 Dail Daily DV 34 μg/m3 Dail Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Dailyyy DV 34 DV 34 DV34 μgμgμg/m3 /m /m3

AQSite 360470122 AQSite 360470122 AQSite 360470122 AQSite 360470122 AQSite 360470122 AQSite 360470122Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3 Annual DV 14 μg/m3Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3

AQSite 360610128 AQSite 360610128 AQSite 360610128 AQSite 360610128 AQSite 360610128 AQSite 360610128 AQSite 360610128 AQSite 360610128 AQSite 360610128Annual DV 15.3 μg/m3 Annual DV 15.3 μg/m3 Annual DV 15.3 μg/m3 Annual DV 15.3 μg/m3 Annual DV 15.3 μg/m3 Annual DV 15.3 μg/m3 Annual DV 15.3 μg/m3 Annual DV 15.3 μg/m3 Annual DV 15.3 μg/m3Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3

AQSite 360610134 AQSite 360610134 AQSite 360610134 AQSite 360610134 AQSite 360610134 AQSite 360610134 AQSite 360610134 AQSite 360610134 AQSite 360610134Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3Daily DV 37 μg/m3 Daily DV 37 μg/m3 Dail Daily DV 37 μg/m3 Dail Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Dailyyy DV 37 DV 37D 3 μgμgμg/m3m3 /m3

AQSite 360610062 AQSite 360610062 AQSite 360610062 AQSite 360610062 AQSite 360610062 AQSite 360610062 AQSite 360610062 AQSite 360610062 AQSite 360610062Annual DV 14.8 μg/m3 Annual DV 14.8 μg/m3 Annual DV 14.8 μg/m3 Annual DV 14.8 μg/m3 Annual DV 14.8 μg/m3 Annual DV 14.8 μg/m3 Annual DV 14.8 μg/m3 Annual DV 14.8 μg/m3 Annual DV 14.8 μg/m3Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3

AQSite 360610056 AQSite 360610056 AQSite 360610056 AQSite 360610056 AQSite 360610056 AQSite 360610056 AQSite 360610056 AQSite 360610056 AQSite 360610056Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3 Annual DV 15.9 μg/m3Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3

AQSite 340172002 AQSite 340172002 AQSite 340172002 AQSite 340172002 AQSite 340172002 AQSite 340172002 AQSite 340172002 AQSite 340172002 AQSite 340172002Annual DV 15.4 μg/m3 Annual DV 15.4 μg/m3 Annual DV 15.4 μg/m3 Annual DV 15.4 μg/m3 Annual DV 15.4 μg/m3 Annual DV 15.4 μg/m3 Annual DV 15.4 μg/m3 Annual DV 15.4 μg/m3 Annual DV 15.4 μg/m3Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3 Daily DV 41 μg/m3

AQSite AQSite 

AQSite AQSite 

AQSite AQSite 

AnnuaAnnuaAnnua

AnnuaAnnuaAnnua

AnnuaAnnuaAnnua

Daily DDaily DDaily D

Daily DDaily D

Daily D

AQSite 361191002 AQSite 361191002 AQSite 361191002 AQSite 361191002 AQSite 361191002 AQSite 361191002 AQSite 361191002 AQSite 361191002 AQSite 361191002Annual DV 11.7 μg/m3 Annual DV 11.7 μg/m3 Annual DV 11.7 μg/m3 Annual DV 11.7 μg/m3 Annual DV 11.7 μg/m3 Annual DV 11.7 μg/m3 Annual DV 11.7 μg/m3 Annual DV 11.7 μg/m3 Annual DV 11.7 μg/m3Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3

AA

AA

AA

AAA

AA

A

DDD

DD

D

AQSite 340030003 AQSite 340030003 AQSite 340030003 AQSite 340030003 AQSite 340030003 AQSite 340030003Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3 Annual DV 13.2 μg/m3Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3

AQSite 340390006 AQSite 340390006 AQSite 340390006 AQSite 340390006 AQSite 340390006 AQSite 340390006 AQSite 340390006 AQSite 340390006 AQSite 340390006Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3

AQSite 340130015 AQSite 340130015 AQSite 340130015 AQSite 340130015 AQSite 340130015 AQSite 340130015Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3

AQSite 340310005 AQSite 340310005 AQSite 340310005 AQSite 340310005Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3

AQSAQS

AQSAQS

AQSAQS

AnnuAnnuAnnu

AnnAnnAnn

AnnAnnAnn

Daily Daily Daily 

Daily Daily Daily 

Daily Daily Daily 

AQSite 340392003 AQSite 340392003 AQSite 340392003 AQSite 340392003 AQSite 340392003 AQSite 340392003Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3

AQSite 340390004 AQSite 340390004 AQSite 340390004 AQSite 340390004 AQSite 340390004 AQSite 340390004 AQSite 340390004 AQSite 340390004 AQSite 340390004Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3Daily DV 42 μg/m3 Daily DV 42 μg/m3 Daily DV 42 μg/m3 Daily DV 42 μg/m3 Daily DV 42 μg/m3 Daily DV 42 μg/m3 Daily DV 42 μg/m3 Daily DV 42 μg/m3 Daily DV 42 μg/m3

ASTORIA GENEPrimary PM25 A254.9 tpy

POLETTI POWER PROJECTPrimary PM25 Annual Emission158.5 tpy

STATEN ISLAND LANDFILLPrimary PM25 Annual Emission70.8 tpy

CON ED-74Primary PM83.7 tpy

RAVENSWOOD Primary PM25 A269.7 tpy

CON ED-WAPrimary PM250.2 tpy

CON ED-EAST RPrimary PM25 An96.8 tpy

CON EDISON - HUDSON AVE STATIONPrimary PM25 Annual Emission80.1 tpy

Hudson Generating StationPrimary PM25 Annual Emission1816.1 tpy

EF BARRPrimary P97 tpy

Bayway RefineryPrimary PM25 Annual Emission147.9 tpy

New York City New York City New York City New York City New York City New York City

(Manhattan) (Manhattan) (Manhattan)

Bronx

Richmond

Queens

Kings

AQSite 340270004 AQSite 340270004 AQSite 340270004 AQSite 340270004 AQSite 340270004 AQSite 340270004Annual DV 11.3 μg/m3 Annual DV 11.3 μg/m3 Annual DV 11.3 μg/m3 Annual DV 11.3 μg/m3 Annual DV 11.3 μg/m3 Annual DV 11.3 μg/m3 Annual DV 11.3 μg/m3 Annual DV 11.3 μg/m3 Annual DV 11.3 μg/m3Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3

NORTHPrimary 543.2 t
K-12

Figure K-10: Philadelphia Urban Study Area 

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AQSite 421010136 AQSite 421010136 AQSite 421010136 AQSite 421010136 AQSite 421010136 AQSite 421010136 AQSite 421010136 AQSite 421010136 AQSite 421010136Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3

AQSite 420450002 AQSite 420450002 AQSite 420450002 AQSite 420450002 AQSite 420450002 AQSite 420450002 AQSite 420450002 AQSite 420450002 AQSite 420450002Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3

AQSite 340155001 AQSite 340155001 AQSite 340155001 AQSite 340155001 AQSite 340155001 AQSite 340155001 AQSite 340155001 AQSite 340155001 AQSite 340155001Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3 Annual DV 12.2 μg/m3Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3

AQSite 421010057 AQSite 421010057 AQSite 421010057 AQSite 421010057 AQSite 421010057 AQSite 421010057 AQSite 421010057 AQSite 421010057 AQSite 421010057Annual DV 12 μg/mAnnual DV 12 μg/mAnnual DV 12 μg/m

Annual DV 12 μg/mAnnual DV 12 μg/mAnnual DV 12 μg/m

Annual DV 12 μg/Annual DV 12 μg/Annual DV 12 μg/

Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3

AQSite 421010003 AQSite 421010003 AQSite 421010003 AQSite 421010003 AQSite 421010003 AQSite 421010003 AQSite 421010003 AQSite 421010003 AQSite 421010003Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3 Annual DV 12.4 μg/m3Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3

AQSite 420910013 AQSite 420910013 AQSite 420910013 AQSite 420910013 AQSite 420910013 AQSite 420910013 AQSite 420910013 AQSite 420910013 AQSite 420910013Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3

AQSite 42101000AQSite 42101000AQSite 42101000

AQSite 42101000AQSite 42101000AQSite 42101000

AQSite 42101000AQSite 42101000AQSite 42101000

Annual DV 13.8 μAnnual DV 13.8 μAnnual DV 13.8 μ

Annual DV 13.8 μAnnual DV 13.8 μAnnual DV 13.8 μ

Annual DV 13.8 Annual DV 13.8 Annual DV 13.8 

Daily DV 37 μg/mDaily DV 37 μg/mDaily DV 37 μg/m

Daily DV 37 μg/mDaily DV 37 μg/mDaily DV 37 μg/m

Daily DV 37 μg/mDaily DV 37 μg/mDaily DV 37 μg/m

AQSite 421010020 AQSite 421010020 AQSite 421010020 AQSite 421010020 AQSite 421010020 AQSite 421010020 AQSite 421010020 AQSite 421010020 AQSite 421010020Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3

AQSite 100031003 AQSite 100031003 AQSite 100031003 AQSite 100031003 AQSite 100031003 AQSite 100031003 AQSite 100031003 AQSite 100031003 AQSite 100031003Annual DV 13.4 μg/m3 Annual DV 13.4 μg/m3 Annual DV 13.4 μg/m3 Annual DV 13.4 μg/m3 Annual DV 13.4 μg/m3 Annual DV 13.4 μg/m3 Annual DV 13.4 μg/m3 Annual DV 13.4 μg/m3 Annual DV 13.4 μg/m3Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3 Daily DV 33 μg/m3

AQSite 100032004 AQSite 100032004 AQSite 100032004 AQSite 100032004 AQSite 100032004 AQSite 100032004 AQSite 100032004 AQSite 100032004 AQSite 100032004Annual DV 14.7 μg/m3 Annual DV 14.7 μg/m3 Annual DV 14.7 μg/m3 Annual DV 14.7 μg/m3 Annual DV 14.7 μg/m3 Annual DV 14.7 μg/m3 Annual DV 14.7 μg/m3 Annual DV 14.7 μg/m3 Annual DV 14.7 μg/m3Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3

AQSite 4201700AQSite 4201700AQSite 4201700

AQSite 4201700AQSite 4201700AQSite 4201700

AQSite 4201700AQSite 4201700AQSite 4201700

Annual DV 13.2 Annual DV 13.2 Annual DV 13.2 

Annual DV 13.2 Annual DV 13.2 Annual DV 13.2 

Annual DV 13.2 Annual DV 13.2 Annual DV 13.2 

Daily DV 35 μg/Daily DV 35 μg/Daily DV 35 μg/

Daily DV 35 μg/Daily DV 35 μg/Daily DV 35 μg/

Daily DV 35 μg/Daily DV 35 μg/Daily DV 35 μg/

PQ CORP/CHESTERPrimary PM25 Annual Emission52.3 tpy

EXELON GENERATION CO/EDDYSTONEPrimary PM25 Annual Emission400.4 tpy

SUNOCO INC. (R&M)Primary PM25 Annual Emission212.1 tpy

COASTAL EAGLPrimary PM25 An165.5 tpy

EXELON GENERATION CO/CROMBY GENERATION STATIONPrimary PM25 Annual Emission160.6 tpy

SUNOCO CHEMICALS (FORMER ALLIED SIGNAL)Primary PM25 Annual Emission113.9 tpy

OCCIDENTAL CHEM CORP/POTTSTOWNPrimary PM25 Annual Emission64.2 tpy

DeePrim107

CARNEYS POINT GENERATING PLANTPrimary PM25 Annual Emission257.3 tpy

CONECTIV DELMARVA GENERATION-EDPrimary PM25 Annual Emission519 tpy

Logan GeneratinPrimary PM25 A145.5 tpy

SUNOCO INC (R&M)/MARCUS HOOK REFINERYPrimary PM25 Annual Emission240.9 tpy

CONOCOPHILLIPS CO/TRAINER REFPrimary PM25 Annual Emission89 tpy

KIMBERLY CLARPrimary PM25 Ann97.8 tpy

Delaware

Burlington

Bucks

Montgomery

Philadelphia

AQSite 420290100 AQSite 420290100 AQSite 420290100 AQSite 420290100 AQSite 420290100 AQSite 420290100 AQSite 420290100 AQSite 420290100 AQSite 420290100Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3 Annual DV 14.2 μg/m3Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3 Daily DV 37 μg/m3

AQSite 340210008 AQSite 340210008 AQSite 340210008 AQSite 340210008 AQSite 340210008 AQSite 340210008 AQSite 340210008 AQSite 340210008 AQSite 340210008Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3 Annual DV 12.5 μg/m3Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3 Daily DV 34 μg/m3
K-13

Figure K-11: Phoenix Urban Study Area 

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AQSite 040131003 AQSite 040131003 AQSite 040131003 AQSite 040131003 AQSite 040131003 AQSite 040131003 AQSite 040131003 AQSite 040131003 AQSite 040131003Annual DV 9.4 μg/Annual DV 9.4 μg/Annual DV 9.4 μg/

Annual DV 9.4 μgAnnual DV 9.4 μgAnnual DV 9.4 μg

Annual DV 9.4 μgAnnual DV 9.4 μgAnnual DV 9.4 μg

Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3 Daily DV 19 μg/m3

AQSite 040137020 AQSite 040137020 AQSite 040137020 AQSite 040137020 AQSite 040137020 AQSite 040137020 AQSite 040137020 AQSite 040137020 AQSite 040137020Annual DV 7.9 μg/m3 Annual DV 7.9 μg/m3 Annual DV 7.9 μg/m3 Annual DV 7.9 μg/m3 Annual DV 7.9 μg/m3 Annual DV 7.9 μg/m3 Annual DV 7.9 μg/m3 Annual DV 7.9 μg/m3 Annual DV 7.9 μg/m3Daily DV 15 μg/m3 Daily DV 15 μg/m3 Daily DV 15 μg/m3 Daily DV 15 μg/m3 Daily DV 15 μg/m3 Daily DV 15 μg/m3 Daily DV 15 μg/m3 Daily DV 15 μg/m3 Daily DV 15 μg/m3

AQSite 040210001 AQSite 040210001 AQSite 040210001 AQSite 040210001 AQSite 040210001 AQSite 040210001Annual DV 8.4 μg/m3 Annual DV 8.4 μg/m3 Annual DV 8.4 μg/m3 Annual DV 8.4 μg/m3 Annual DV 8.4 μg/m3 Annual DV 8.4 μg/m3 Annual DV 8.4 μg/m3 Annual DV 8.4 μg/m3 Annual DV 8.4 μg/m3Daily DV 18 μg/m3 Daily DV 18 μg/m3 Daily DV 18 μg/m3 Daily DV 18 μg/m3 Daily DV 18 μg/m3 Daily DV 18 μg/m3 Daily DV 18 μg/m3 Daily DV 18 μg/m3 Daily DV 18 μg/m3

AQSite 040213013 AQSite 040213013 AQSite 040213013 AQSite 040213013 AQSite 040213013 AQSite 040213013Annual DV 22.6 μg/m3 Annual DV 22.6 μg/m3 Annual DV 22.6 μg/m3 Annual DV 22.6 μg/m3 Annual DV 22.6 μg/m3 Annual DV 22.6 μg/m3 Annual DV 22.6 μg/m3 Annual DV 22.6 μg/m3 Annual DV 22.6 μg/m3Daily DV 51 μg/m3 Daily DV 51 μg/m3 Daily DV 51 μg/m3 Daily DV 51 μg/m3 Daily DV 51 μg/m3 Daily DV 51 μg/m3 Daily DV 51 μg/m3 Daily DV 51 μg/m3 Daily DV 51 μg/m3

AQSite 040134003 AQSite 040134003 AQSite 040134003 AQSite 040134003 AQSite 040134003 AQSite 040134003Annual DV 12.6 μg/m3 Annual DV 12.6 μg/m3 Annual DV 12.6 μg/m3 Annual DV 12.6 μg/m3 Annual DV 12.6 μg/m3 Annual DV 12.6 μg/m3 Annual DV 12.6 μg/m3 Annual DV 12.6 μg/m3 Annual DV 12.6 μg/m3Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3

AQSite 040130019 AQSite 040130019 AQSite 040130019 AQSite 040130019 AQSite 040130019 AQSite 040130019Annual DV 12.3 μg/m3 Annual DV 12.3 μg/m3 Annual DV 12.3 μg/m3 Annual DV 12.3 μg/m3 Annual DV 12.3 μg/m3 Annual DV 12.3 μg/m3 Annual DV 12.3 μg/m3 Annual DV 12.3 μg/m3 Annual DV 12.3 μg/m3Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3

AQSite 040139997 AQSite 040139997 AQSite 040139997 AQSite 040139997 AQSite 040139997 AQSite 040139997 AQSite 040139997 AQSite 040139997 AQSite 040139997Annual DV 9.8 μg/m3 Annual DV 9.8 μg/m3 Annual DV 9.8 μg/m3 Annual DV 9.8 μg/m3 Annual DV 9.8 μg/m3 Annual DV 9.8 μg/m3 Annual DV 9.8 μg/m3 Annual DV 9.8 μg/m3 Annual DV 9.8 μg/m3Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3 Daily DV 25 μg/m3

Asarco Primar992.8 t

Cactus Waste SyPrimary PM25 An64.5 tpy

Maricopa

Pinal

ASARPrima126.6 
K-14

Figure K-12: Pittsburgh Urban Study Area 

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AQSite 421255001 AQSite 421255001 AQSite 421255001 AQSite 421255001 AQSite 421255001 AQSite 421255001 AQSite 421255001 AQSite 421255001 AQSite 421255001Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3 Annual DV 13.3 μg/m3Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3

AQSAQSAQS

AQSAQSAQS

AQSAQSAQS

AnnAnnAnn

AnnAnnAnn

AnnAnnAnn

Daily Daily Daily 

Daily Daily Daily 

DailDailDail

AQSite 420030093 AQSite 420030093 AQSite 420030093 AQSite 420030093 AQSite 420030093 AQSite 420030093 AQSite 420030093 AQSite 420030093 AQSite 420030093Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3 Annual DV 13 μg/m3Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3

AQSite 420030095 AQSite 420030095 AQSite 420030095 AQSite 420030095 AQSite 420030095 AQSite 420030095 AQSite 420030095 AQSite 420030095 AQSite 420030095Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3 Annual DV 13.6 μg/m3Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3 Daily DV 36 μg/m3

AQSite 420030116 AQSite 420030116 AQSite 420030116 AQSite 420030116 AQSite 420030116 AQSite 420030116 AQSite 420030116 AQSite 420030116 AQSite 420030116Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3 Annual DV 16.1 μg/m3Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3 Daily DV 39 μg/m3

AQSite 420030067 AQSite 420030067 AQSite 420030067 AQSite 420030067 AQSite 420030067 AQSite 420030067 AQSite 420030067 AQSite 420030067 AQSite 420030067Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3 Annual DV 12.9 μg/m3Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 

AQSite 42003AQSite 42003AQSite 42003

AQSite 42003AQSite 42003AQSite 42003

AQSite 4200AQSite 4200AQSite 4200

Annual DV 1Annual DV 1Annual DV 1

Annual DV 1Annual DV 1Annual DV 1

Annual DV 1Annual DV 1Annual DV 1

Daily DV 39 μDaily DV 39 μDaily DV 39 μ

Daily DV 39 μDaily DV 39 μDaily DV 39 μ

Daily DV 39 Daily DV 39 Daily DV 39 

AQSite 420031008 AQSite 420031008 AQSite 420031008 AQSite 420031008 AQSite 420031008 AQSite 420031008 AQSite 420031008 AQSite 420031008 AQSite 420031008Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3 Annual DV 15 μg/m3Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3 Daily DV 40 μg/m3

AQSite 420030AQSite 420030AQSite 420030

AQSite 420030AQSite 420030AQSite 420030

AQSite 420030AQSite 420030AQSite 420030

Annual DV 19.8 Annual DV 19.8 Annual DV 19.8 

Annual DV 19.8 Annual DV 19.8 Annual DV 19.8 

Annual DV 19.Annual DV 19.Annual DV 19.

Daily DV 60 μgDaily DV 60 μgDaily DV 60 μg

Daily DV 60 μgDaily DV 60 μgDaily DV 60 μg

Daily DV 60 μgDaily DV 60 μgDaily DV 60 μg

AQSite 421250005 AQSite 421250005 AQSite 421250005 AQSite 421250005 AQSite 421250005 AQSite 421250005 AQSite 421250005 AQSite 421250005 AQSite 421250005Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3 Annual DV 15.5 μg/m3Daily DV 36 μg/m Daily DV 36 μg/m Daily DV 36 μg/m Daily DV 36 μg/m Daily DV 36 μg/m3333

AQSite 421250200 AQSite 421250200 AQSite 421250200 AQSite 421250200 AQSite 421250200 AQSite 421250200 AQSite 421250200 AQSite 421250200 AQSite 421250200Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3 Annual DV 14.6 μg/m3Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3 Daily DV 35 μg/m3

AQSite 420070014 AQSite 420070014 AQSite 420070014 AQSite 420070014 AQSite 420070014 AQSite 420070014 AQSite 420070014 AQSite 420070014 AQSite 420070014Annual DV 16.5 μg/m3 Annual DV 16.5 μg/m3 Annual DV 16.5 μg/m3 Annual DV 16.5 μg/m3 Annual DV 16.5 μg/m3 Annual DV 16.5 μg/m3 Annual DV 16.5 μg/m3 Annual DV 16.5 μg/m3 Annual DV 16.5 μg/m3Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3

WHEELING-PITTSBURGH STEEL CORPORATION - STEUBENVILPrimary PM25 Annual Emission251.5 tpy

USS - CLAIRTON WORKSPrimary PM25 Annual Emission394.3 tpy

ALLEGHENY ENERGY SPrimary PM25 Annual Em181.2 tpy

GLEPrim58.

ORION POWPrimary PM591.3 tpy

SHENANGO INC.Primary PM25 Annual Emission70.2 tpy

AES BEAVER VALLEY LLC/BEAVER VALLY COGENPrimary PM25 Annual Emission417.8 tpy

PA POWER CO/BRUCE MANSFIELD PLTPrimary PM25 Annual Emission1602.8 tpy

ALLEGHENY LUDLUM CORP - Primary PM25 Annual Emission271.4 tpy

US STEEL CORPORATION - IRVIN PLANTPrimary PM25 Annual Emission63.5 tpy

ORION POPrimary PM555.8 tpy

USS CORPORATIOPrimary PM25 Annu472.1 tpy

Brooke

Hancock

Washington

Allegheny

Westmoreland

Butler

Beaver

CARDINAL POWER PLANT (CARDINAL OPERATING COMPANY)Primary PM25 Annual Emission3581.4 tpy

W. H. SAMMIS PLANTPrimary PM25 Annual Emission6485.3 tpy

WHEELING-PITTSBURGH STEEL CORPORATIONPrimary PM25 Annual Emission223.7 tpy

WEIRTON STEEL CORPORATIONPrimary PM25 Annual Emission3610 tpy
K-15

Figure K-13 Salt Lake City Urban Study Area 

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AQSite 490450003 AQSite 490450003 AQSite 490450003 AQSite 490450003Annual DV 7.6 μg/m3 Annual DV 7.6 μg/m3 Annual DV 7.6 μg/m3 Annual DV 7.6 μg/m3 Annual DV 7.6 μg/m3 Annual DV 7.6 μg/m3 Annual DV 7.6 μg/m3Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3 Daily DV 31 μg/m3

AQSite 490353010 AQSite 490353010 AQSite 490353010Annual DV 10.5 μg/m3 Annual DV 10.5 μg/m3 Annual DV 10.5 μg/m3 Annual DV 10.5 μg/m3 Annual DV 10.5 μg/m3 Annual DV 10.5 μg/m3 Annual DV 10.5 μg/m3 Annual DV 10.5 μg/m3 Annual DV 10.5 μg/m3Daily DV 30 μg/m3 Daily DV 30 μg/m3 Daily DV 30 μg/m3 Daily DV 30 μg/m3 Daily DV 30 μg/m3 Daily DV 30 μg/m3 Daily DV 30 μg/m3 Daily DV 30 μg/m3 Daily DV 30 μg/m3

AQSite 490353006 AQSite 490353006 AQSite 490353006 AQSite 490353006 AQSite 490353006 AQSite 490353006Annual DV 10.7 μg/mAnnual DV 10.7 μg/mAnnual DV 10.7 μg/m

Annual DV 10.7 μg/mAnnual DV 10.7 μg/mAnnual DV 10.7 μg/m

Annual DV 10.7 μg/mAnnual DV 10.7 μg/mAnnual DV 10.7 μg/m

Daily DV 48 μg/m3 Daily DV 48 μg/m3 Daily DV 48 μg/m3

AQSite 490353007 AQSite 490353007 AQSite 490353007 AQSite 490353007 AQSite 490353007 AQSite 490353007 AQSite 490353007Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3 Annual DV 11.6 μg/m3Daily DV 42 μg/m3 Daily DV 42 μg/m3 Daily DV 42 μg/m3

AQSite 490350012 AQSite 490350012Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3 Annual DV 14.4 μg/m3Daily DV 55 μg/m3 Daily DV 55 μg/m3 Daily DV 55 μg/m3 Daily DV 55 μg/m3 Daily DV 55 μg/m3 Daily DV 55 μg/m3

AQSite 490350003 AQSite 490350003Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3 Annual DV 11.2 μg/m3Daily DV 47 μg/m3 Daily DV 47 μg/m3 Daily DV 47 μg/m3 Daily DV 47 μg/m3 Daily DV 47 μg/m3 Daily DV 47 μg/m3 Daily DV 47 μg/m3 Daily DV 47 μg/m3 Daily DV 47 μg/m3

AQSite 490351001 AQSite 490351001 AQSite 490351001Annual DV 8.9 μg/m3 Annual DV 8.9 μg/m3 Annual DV 8.9 μg/m3 Annual DV 8.9 μg/m3 Annual DV 8.9 μg/m3 Annual DV 8.9 μg/m3 Annual DV 8.9 μg/m3 Annual DV 8.9 μg/m3 Annual DV 8.9 μg/m3Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3 Daily DV 32 μg/m3

AQAQAQAnAnAn

AnAn

A

DaDaDa

Da

DDD

AQSite 490110004 AQSite 490110004 AQSite 490110004Annual DV 10 μg/m3 Annual DV 10 μg/m3 Annual DV 10 μg/m3 Annual DV 10 μg/m3 Annual DV 10 μg/m3 Annual DV 10 μg/m3 Annual DV 10 μg/m3 Annual DV 10 μg/m3 Annual DV 10 μg/m3Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3 Daily DV 38 μg/m3

AQSite 490353008 AQSite 490353008 AQSite 490353008 AQSite 490353008 AQSite 490353008 AQSite 490353008 AQSite 490353008 AQSite 490353008 AQSite 490353008Annual DV 7.8 μg/m3 Annual DV 7.8 μg/m3 Annual DV 7.8 μg/m3 Annual DV 7.8 μg/m3 Annual DV 7.8 μg/m3 Annual DV 7.8 μg/m3Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3 Daily DV 24 μg/m3

AQSite 4905710AQSite 4905710

AQSite 4905710AQSite 4905710

Annual DV 9.1 Annual DV 9.1 Annual DV 9.1 

Annual DV 9.1 

Annual DV 9.1 Annual DV 9.1 Annual DV 9.1 

Daily DV 35 μg/Daily DV 35 μg/Daily DV 35 μg/

Daily DV 35 μg/Daily DV 35 μg/Daily DV 35 μg/

Daily DV 35 μgDaily DV 35 μgDaily DV 35 μg

AQSite 490030003 AQSite 490030003Annual DV 8.2 μg/m3 Annual DV 8.2 μg/m3 Annual DV 8.2 μg/m3 Annual DV 8.2 μg/m3 Annual DV 8.2 μg/m3 Annual DV 8.2 μg/m3 Annual DV 8.2 μg/m3 Annual DV 8.2 μg/m3 Annual DV 8.2 μg/m3Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3 Daily DV 29 μg/m3

AQSite 490570AQSite 490570AQSite 490570

AQSite 490570

AQSite 490570AQSite 490570AQSite 490570

Annual DV 10.Annual DV 10.Annual DV 10.

Annual DV 10.Annual DV 10.Annual DV 10.

Annual DV 10Annual DV 10Annual DV 10

Daily DV 36 μgDaily DV 36 μgDaily DV 36 μg

Production PlantPrimary PM25 Annual Emission65.2 tpy

Smelter & RefineryPrimary PM25 Annual Emission152.2 tpy

Power Plt/ Lab/ Tailings ImpoundmentPrimary PM25 Annual Emission64.3 tpy

Salt Lake CPrimary PM70.7 tpy

Salt Lake Valley Landfill & Transfer StationPrimary PM25 Annual Emission61.1 tpy

Western Fiberglass - Salt Lake City PlantPrimary PM25 Annual Emission58.7 tpy

Rowley PlantPrimary PM25 Annual Emission219.5 tpy

Mine & Copperton ConcentratorPrimary PM25 Annual Emission312 tpy

U.S. Army-Dugway Proving GroundPrimary PM25 Annual Emission174.7 tpy

Box Elder Weber

Tooele

Salt Lake Salt Lake Salt Lake Salt Lake Salt Lake Salt Lake Salt Lake Salt Lake Salt Lake

Summit

Morgan

Wasatch

Utah
K-16

Figure K-14 St. Louis Urban Study Area
K-17

Figure K-15 Tacoma Urban Study Area

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AQSite 530530029 AQSite 530530029 AQSite 530530029 AQSite 530530029 AQSite 530530029 AQSite 530530029 AQSite 530530029 AQSite 530530029 AQSite 530530029Annual DV 10.2 μg/m3 Annual DV 10.2 μg/m3 Annual DV 10.2 μg/m3 Annual DV 10.2 μg/m3 Annual DV 10.2 μg/m3 Annual DV 10.2 μg/m3Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3 Daily DV 43 μg/m3

SIMPSON TIMBER COPrimary PM25 Annual Emission137 tpy

SIMPSON DOOR COPrimary PM25 Annual Emission57.1 tpy

WEYERHAEUSER CO.Primary PM25 Annual Emission113.8 tpy

GRAYS HARBOR PAPER LPPrimary PM25 Annual Emission174.6 tpy

Saint-Prima70.3 t

SIMPSON Primary P79.3 tpy

GraymonPrimary 53.5 tpy

Weyerhaeuser CompanyPrimary PM25 Annual Emission60.9 tpy

TransAlta Centralia GenerationPrimary PM25 Annual Emission2052.8 tpy

TransAlta Centralia Mining, LLCPrimary PM25 Annual Emission112.2 tpy

Snohomish Snohomish Snohomish Snohomish Snohomish Snohomish Snohomish

King King

Pierce Pierce Pierce Pierce

Jefferson

Grays Harbor

Pacific

Lewis

Mason

Thurston

 

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/R-10-005

June, 2010
 
 

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/R-10-005

June, 2010