# Quantitative Health Risk Assessment for

Particulate Matter

# DISCLAIMER

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

and Standards, U.S. Environmental Pr otection Agency. Any opinions, findings,

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

the views of the EPA. Questi ons 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 Acknowledgeme nts below) have been provided to the

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

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

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

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

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

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

authors and not the instituti ons providing the raw data.

ACKNOWLEDGEMENTS

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

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

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

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

Study Area)), C (Epidemiological Study Specifi c 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...............................................................................................vi ii

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 PM 2.5 

# 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 PM 2.5 

# -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 Studi es and Concentration-response (C-R)

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

3.3.4 Summary of Selected Health Endpoi nts, 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-Te rm Exposure-Related Mortality and

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

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

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

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

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

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

Alternative Suites of Sta ndards (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 Meetin g Alternative Suites of Standards........ 4-22

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

4.3.1 Sensitivity Analysis Results to Iden tify Potentially Important Sources of

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

4.3.1.1 Single-factor Sensit ivity Analysis.......................................................... 4-36

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

4.3.2 Additional Set of Reasonable Risk Es timates 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.................................................................................................................... 4-4 9

# iii 4.4.1 Analysis Based on Consideration ofNational 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 PM 2.5 

# Monitoring Data in

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

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

4.5.2 Patterns in PM 2.5 

# Monitoring Data................................................................ 4-77

5 Integrative Discussion of Urban Case Study Analysis of PM 2.5 

# -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 Va riability 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 Un certainty 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 PM 2.5



# Concentrations in Relation to the Da taset 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 Mee ting the Current Suite of PM 2.5 

# Standards... 5-10

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

Risk Remaining upon Just Meeti ng the Alternative Suite of PM 2.5



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

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

Risk Remaining upon Just Meeting Co mbinations of A lternative Annual

and 24-Hour PM 2.5 

# 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 S upporting Air Quality Characterization

Appendix C Epidemiological Study Specific Information for PM Risk Assessment

Appendix D Supplement to Representativ eness 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 As sessment of Long-Term Mortality Related to

PM 2.5 

# Exposure

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

(PM 10-2.5 

# )

Appendix I Analysis Comparing Distribu tion of Short-Term Exposure-Related

Cardiovascular Mortality Inciden ce to the Distribution of Daily PM 2.5 

# Levels tor

Detroit and New York

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

Alternative Annual St andard of 10 μg/m 3



# Appendix K: Maps of the Fifteen Urban St udy 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 PM 2.5 

# Standards for the Period 2005-

2007 ....................................................................................................................... 3-1 9

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 S hort-Term Exposure Studies Included in the PM 2.5



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

Table 3-7 Locations, Health Endpoints, and Long-Term Exposure Studies Included in the PM 2.5



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

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

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

Table 3-9 Summary of Locations , Health Endpoints, Studie s 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 fo r PM Risk Assessment Locations.............................. 3-57

Table 3-12 Baseline Mortality Rates (Deaths per 100,000 Relevant Popul ation 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 An alysis 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 Shor t-Term Exposure to Ambient PM 2.5 

# Concentrations

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

# Concentrations

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

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

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



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



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

Table 4-3 Overview of Se nsitivity Analysis Results ............................................................... 4-32

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

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

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

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

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

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

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

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

# vi 

# List of Figures

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

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

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

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

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

Figure 4-1 Percent reduction in long-term exposure-related mo rtality 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-t erm exposure-related mortality risk ........................ 4-10

Figure 4-4 Percent reduction in short-term e xposure-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 fo r key elements of the risk equation:

98 th 

# percentile 24-hour average PM 2.5 

# ............................................................... 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 fo r key elements of the risk equation:

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

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

relative risk from PM 2.5 

# : long term average July temperature.......................... 4-63

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

relative risk from PM 2.5 

# : percent of population 65 and older. .......................... 4-64

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

relative risk from PM 2.5 

# : per capita annual personal income............................ 4-65

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

relative risk from PM 2.5 

# : per capita annual personal income............................ 4-66

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

to PM 2.5 

# for the U.S. with markers identif ying 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 F actor 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, Mo rtality, and Air Pollution Study

NOx Nitrogen oxides

O 3 

# Ozone

OAQPS Office of Air Qual ity Planning and Standards

PA Policy Assessment Document

PM Particulate Matter

PM X 

# The legal definition for PM X 

# , as defined in the Code of Federal

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

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

particles with aerodynamic diameter of X are removed by the inlet

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

Depending on the specific penetra tion curve specified, particles

larger than X μ m 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.

PM 10 

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

diameter and a penetration curve as specified in the CFR.

PM 2.5 

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

diameter and a penetration cu rve as specified in the CFR

PM 10-2.5 

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

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

SO 2 

# Sulfur Dioxide

SO

x 

# Sulfur Oxides

SES Socio-economic Status

TRIM Total Risk Integrated Methodology

TRIM.Risk Total Risk Integrated Me thodology - 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 pollu tants that may reasonably be anticipated to

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

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

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

kind and extent of identifiable effects on public hea lth 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 pe riodic reviews of the air quality criteria and

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

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

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

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



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

exposures to fine and co arse particles using PM 2.5 

# and PM 10 

# , as indicators, respectively (71 FR

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

NAAQS review completed in 2006, EPA revised the level of the 24-hour PM 2.5 

# standard to 35

μ g/m 3 

# (calculated as a 3-year average of the 98 th 

# percentile of 24-hour concentrations at each

population-oriented monitor), reta ined the level of the annual PM 2.5 

# annual standard at 15 μ g/m 3



# (calculated as the 3-year averag e of the weighted annual mean PM 2.5 

# concentrations from single

or multiple community-oriented monitors), and revised the form of the annual PM 2.5 

# 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 qua lity criteria and NAAQS under

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

Advisory Committee Act (FACA). See

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

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

2 

# In the revisions to the PM NAAQS finalized in 200 6, EPA tightened the constraints on the spatial

averaging criteria by further limiting the conditions und er 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 PM 10 

# standard at 150

μ g/m 3 

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

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

exposure to current ambient levels of coarse pa rticles 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 PM 2.5 

# and PM 10 

# 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 in form 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 th is review, the process

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

developing the other key doc uments for this review. 5 

# In December 2009, EPA completed the

process of assessing the latest av ailable 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 evaluati on of the scientific evidence on the health

effects of PM, including information on expos ure, physiological mechanisms by which PM

might adversely impact human health, an evalua tion of the toxicologica l 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, in cluding consideration of eff ects 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 pr ocess 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 comm ents 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 th e effects of PM on pub lic 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 a nd Standards (OAQPS) has developed this

quantitative health risk assessm ent (RA) describing the quantita tive assessments of PM-related

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

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

associated with the quantitative analyses perf ormed. 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) a nd on the first and sec ond 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 manageme nt 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 require d of the Administrator in determining whether

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

also intended to facilitate elic itation of CASAC’s advice to th e Agency and recommendations on

any new standards or revisions to existing standards as may be a ppropriate, 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 teleco nference being planned

for late March. Proposed and final rulemaki ng 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 PM 2.5 

# and PM 10-2.5 

# 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 quantita tive risk assessment t ook into consideration

CASAC advice (Hopke, 2004; Henderson, 2005) and pub lic 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 mo rtality as well as morbidity effects including

hospital admissions for cardiovas cular and respiratory causes a nd respiratory symptoms (not

requiring hospitalization) associated with recent short-term (daily) ambient PM 2.5 

# concentrations.

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

mortality associated with long-term PM 2.5 

# exposures. The quantitative risk assessment included

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

PM 2.5 

# levels; (2) risk reductions and remaining risk s associated with just meeting the existing

suite of PM 2.5 

# NAAQS (1997 standards); and (3) risk reduc tions and remaining risks associated

with just meeting various alternative PM 2.5 

# 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 PM 10-2.5 

# risk

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

endpoints related to short-term PM 10-2.5 

# exposures: hospital admissions for cardiovascular and

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

# risk

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

alternative PM 10-2.5 

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

uncertainties and concerns associated with th is portion of the quantitative risk assessment

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

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

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

recommendations to the Administra tor supporting revisions of the PM 2.5 

# primary standards.

These recommendations placed substantial relian ce 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), CA SAC requested reconsideration of the

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

recommendations which included placing greater we ight 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 Administra tor 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 asse ssment conducted for the review completed in

1997. However, as presented in the final rulema king 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 l ack of clear understanding of a number of

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

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

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

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

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

# 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 PM 2.5 

# 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. (7 2 FR 61168, October 17,

2006).

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

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

# primary standards, but he

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

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

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

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

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

# standard was

not protective of human health and did not follow our recomme ndation 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 PM 2.5 

# standard, CASAC stated, “While there is uncertainty

associated with the risk assessment for the PM 2.5 

# 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 addre ssed the following issues with regard to the

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

# standard and (2)

retaining PM 10 

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

form of the 24-hour PM 10 

# standard, and revoking the PM 10 

# annual standard. On judicial review,

the D.C. Circuit remanded the annual primary PM 2.5 

# NAAQS to EPA because the Agency failed

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

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

upheld the Agency’s use of the qua ntitative risk assessment to info rm the decision to revise the

PM 2.5 

# standards but not to inform the selection of level. The court also upheld the decision to

# 1-6 retain the 24-hour PM 10 

# standard and revoke the annual PM 10 

# standard. 7 

# American Farm

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

1.2 CURRENT RISK ASSESSMENT: GOA LS AND PLANNE D 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. Thes e 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 standard s considered in selected urban study areas,

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

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

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

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

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

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

mortality associated with long-te rm exposure to recent ambient PM 2.5 

# 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 ur ban 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 approa ch 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 re lated 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 othe r 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 eviden ce-based considerations

that inform staff’s assessment of policy options.

7 

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

# standards identical to the

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

# NAAQS to EPA because the

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

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

# 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 assessmen t, including a summary of the previous risk

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

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

Chapter 3 describes the analyti cal 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 anal ysis and the approaches used in addressing variability and

uncertainty (Appendices A, B, a nd C provide supplemental inform ation 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 re levant risk-related factors (A ppendix D provides supplemental

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

sensitivity analysis results, re spectively). 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 in form the interpretation of the risk estimates generated in the

urban case study analyses.

# 2-1 

# 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 N AAQS review, EPA invited outside experts,

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

statistics, risk/exposure analysis, atmospheric sc ience) 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 av ailable 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 w ith CASAC on the draft

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

final IRP incorporated comments from CASA C (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 asse ssments (US EPA, 2008a, chapter 5).

As a next step in the design of these qua ntitative 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 a nd 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 qua ntitative 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 draf t 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 pub lic 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 wor k, we have prepared this final RA.

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

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

previous PM NAAQS review in section 2.1, including key limitati ons 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 pla nned approach reflecting consideration of CASAC

and public comments and additi onal 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 PM 2.5 

# standards.

2.1 OVERVIEW OF RISK ASSESSMENT FROM LAST REVIEW

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

assessment of PM 2.5 

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

# -related risk.

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

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

respiratory symptoms (not requ iring hospitalization) associated with short-term (24-hour) PM 2.5



# exposures and estimates of risks of total, card iopulmonary, and lung cancer mortality associated

with long-term PM 2.5 

# 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 uncer tainty in the resulting

PM 2.5 

# risk estimates. Such uncertainties generall y 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 potential ly confounding and modifying factors in the C-R

relationships; (d) the methods for simulating how daily PM 2.5 

# ambient concentrations would

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

for differences in the relative toxicity of the components within the mix of ambient PM 2.5



# .

# 2-3 While some of these uncertainti es were addressed quantitativel y in the form of estimated

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

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

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

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

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

surrogate for actual ambient PM 2.5 

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

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

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

analyses (e.g., the use of single- versus multi-po llutant 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 variabil ity were also applicab le to the quantitative

risk assessment conducted for PM 10-2.5 

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

assessment for PM 10-2.5 

# was much more limited than that for PM 2.5 

# reflecting the much more

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

# The

PM 10-2.5 

# risk assessment included risk estimates for just three urban study areas for two

categories of health endpoints rela ted to short-term exposure to PM 10-2.5 

# : hospital admissions for

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

PM 10-2.5 

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

meeting alternative PM 10-2.5 

# 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 pl anned 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 pla nned 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 PM 2.5 

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

# . Based on

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

insufficient data to support a quantitative ri sk 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 quantit ative risk assessmen t for specific PM

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

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

data to support such analyses..

 Selection of health effect categories (PM 2.5 

# ): We planned to focus primarily on

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

causal relationship with PM 2.5 

# exposures. We also pla nned to consider including

additional categories for which evidence is suggestive of causal relationship with

PM 2.5 

# exposures (e.g., reproductive and deve lopmental outcomes), if sufficient

information was available to develop meaningful risk estimates for these additional

categories.

 Selection of health effect categories (PM 10-2.5 

# ) : We planned to build on the limited

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

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

relationship with short-term PM 10-2.5 

# exposures, where sufficient information was

available to develop mean ingful risk estimates.

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

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

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

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

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

also discussed the possibility of including mo re 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 PM 2.5 

# -exposure related mortality

presented in Krewski et al., 2009).

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

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

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

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

historical patterns of reducti ons in urban areas, if thes e supported consideration for

non-proportional reductions acro ss monitors within a specif ic urban area and/or (b)

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

more realistically reflect patterns of PM reduc tions 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 co mbination of the globa l-scale circulation

model, GEOS-Chem, with the regional scal e 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 st udies to provide C-R functions : Recognizing

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

in both multi- and single-city epidemiological studies using both multi- and singlepollutant models, where available. We pla nned 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 asse ssment model, based on the first draft ISA

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

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

# ambient

concentrations. 9 

# We also stated that we may cons ider 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 estimati ng risk associated with long-term PM 2.5 

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



# 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 variabili ty related to PM risk. Fo r uncertainty, we planned to

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

(low, medium and high) in term s 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 uncertain ty 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 PM 2.5



# levels.

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

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

national distributions for key PM 2.5 

# 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 studi es, 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 descri bed a population exposure analysis based on

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

EPA, 2009b, chapter 4). The planne d analysis focused on evaluating PM 2.5 

# 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 analys is, with the goal of completing the analysis

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

need to more clearly define the intended purpos e of the analysis, includi ng specific ways in

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

(e.g., potentially identifying s ources 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 assessm ent results would be used (S amet, 2009a), as well as the

complexities associated with designing and c onducting 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 elemen ts 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 pres ented in the Scope and Methods Plan and summarized in section

2.2.1.

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

with PM 2.5 

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

# , we conclude that

continued limitations in data available for characterizing PM 10-2.5 

# exposure and risk

would introduce significant uncertainty into a PM 10-2.5 

# quantitative risk assessment

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

conclusions regarding either th e adequacy of the current PM 10 

# standard or alternative

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

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

# 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 PM 10-2.5 

# air quality monitoring data fundamentally addressed these

# 2-7 limitations and provided sufficient inform ation to support the development of

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

effects data base and the current PM 10-2.5 

# monitoring network continue to exist and

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

assessment for PM 10-2.5 

# at this time (a more in-depth discussion of the rationale

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

# is

presented in Appendix H). Furthermore, cons istent 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 component s or for ultrafine particles (UFPs), at

this time. We note, however, that the evid ence 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 (PM 2.5 

# ): A multi-factor decision framework

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

assessment for PM 2.5 

# (section 3.3.1). In evalua ting the currently available

epidemiological evidence within the contex t of the framework, the endpoints focused

on in the quantitative risk assessment fo cused on total, cardiopulmonary, and lung

cancer mortality associated with long-term PM 2.5 

# exposures mortal ity (total nonaccidental, cardiovascular, and respirator y), morbidity (hospital admissions for

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

hospitalization) associated with short-term (24-hour) PM 2.5 

# exposures. The selection

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

Methods Plan for PM 2.5 

# and included specific endpoints from health effect categories

classified in the ISA as having a causal or likely causal relationship with PM 2.5 

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

assessment including outcomes within health effect categories cl assified as having

evidence suggestive of a casual relationship with PM 2.5 

# (e.g., reproductive and

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

analysis for several reasons including limited available information to support the

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

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

considered in this quantitative risk assessm ent are limited to health effect categories

classified as having a causal or likely causal relationship with PM 2.5 

# exposures, this

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

sole determining factor. In addition, CASA C 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 evalua ted in the last PM risk assessment; (b)

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

locations with relatively el evated 24-hour and/or annual PM 2.5 

# monitored levels so

that the assessment can provide potential in sights into the degree of risk reduction

associated with just meeting the current a nd alternative suites of standards; and (d)

# 2-8 preference for including locations from differe nt 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 speci alized analysis of risk based on

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

Los Angeles, CA involving zip code -level analysis of long-term PM 2.5 

# -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 estim ating long-term morta lity risk, since they

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

estimates used to m odel risks (section 3.5.3).

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

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

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

The revised methods ensure that ambient m easurements from different monitors in a

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

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

in the first draft RA, which effectivel y weighted ambient measurements from

monitors based on sampling frequency, poten tially 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 co ncentrations using different

approaches. We first use a proportional ro llback 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 PM 2.5 

# ambient concentrations

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

developed and applied two alternative appr oaches (hybrid and locally focused) to

improve our understanding of the uncertainty associated with th is aspect of the

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

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

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

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

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

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

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

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

# exposures, we focus on selecting

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

studies provide more defensible effect es timates. 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 cove rage for additional health effect endpoints

associated with short-term PM 2.5 

# exposures (e.g., emergency depa rtment (ED) visits).

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

# 2-9 the American Cancer Society (ACS) data set (Krewski et al., 2009). This study

expands upon previous publications pres enting 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 fo r selecting C-R functions from specific

epidemiological studies to use in the asse ssment, 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 ri sk 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 asse ss sources of uncertainty and variability,

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

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

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

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

supplement the “core” risk estimates 10 

# in characterizing the potential magnitude of

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

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

two analyses to evaluate the representative ness of the selected urban study areas for

more broadly characterizing national ri sks. First, we considered key PM 2.5 

# riskrelated attributes to determine whether the selected urban study areas are nationally

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

attribute (section 4.4.1). Second, we analyzed estimates of morta lity 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 PM 2.5 

# –related risk (section 4.4.2). 11



#  Consideration of patterns in design values and ambient PM 2.5 

# monitoring data

across urban areas 12 

# : We examine how 24-hour and annual design values, together

with patterns in PM 2.5 

# monitoring data within an area, can influence the degree of risk

reduction estimated to occur upon simulating just meeting the curr ent or alternative

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

specific patterns of risk reduc tion. We also compare patter ns 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 an d 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 res ults and key observations: To enhance the utility of the

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

the PM 2.5 

# 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 repres entativeness, and patterns of design values

across the U.S (chapter 5).

2.4 ALTERNATIVE SUITES OF PM 2.5 

# STANDARDS EVALUATED

The scope of this quantitativ e risk assessment focuses on co nsideration of alternative

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

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

the existing suite of PM 2.5 

# standards. 13 

# The four basic elements of the NAAQS: indicator 14



# , averaging time, form, and level, which togeth er serve to define each standard, must be

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

standards. 15



# With regard to selecti ng alternative levels fo r the annual and 24-hour PM 2.5 

# standards for

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

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

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

final ISA and the ongoing development of the fi rst draft PA, we revisi ted the selection of

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

evaluated, as discussed below.

In evaluating the ambient air quality concentr ations associated with health effects in

epidemiological studies of long- and short-term exposure to PM 2.5 

# 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 PM 2.5 

# 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 th e 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 PM 2.5 

# standard is the 98 th



# percentile of the distribution of 24-hour PM 2.5 

# concentrations at each population-oriented monitor within an area,

averaged over 3 years. The cu rrent form of the annual PM 2.5 

# standard is an annual arith metic 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 standard s are discussed in the Policy Assessment (PA).

# 2-11 suffer from potential omission of negative analys es 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 a lternative levels for the annual PM 2.5 

# standard to be

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

# concentrations

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

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

increased risk of mortality and long-term PM 2.5 

# exposure becomes more precise and consistently

positive in locations with mean PM 2.5 

# concentrations of 13.5 μg/m 3 

# and above (US EPA, 2009a,

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

cardiovascular-related effect s related to long-term PM 2.5 

# exposures has been reported in large,

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

Initiative (WHI) Study, reports associations between PM 2.5 

# and cardiovascular effects among

post-menopausal women with a mean annual average PM 2.5 

# concentration of 13.5 μg/m 3 

# (US

EPA, 2009a, section 2.3.1.2). In addition, we evaluated long-term average PM 2.5 

# concentrations

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

reported in the second draft ISA, both cardiovas cular 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 PM 2.5 

# concentrations of 13 μg/m 3 

# and above

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



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

range of annual averaged PM 2.5 

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

12 and 13 μg/m 3 

# 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/m 3 

# to provide fuller coverage for the range

of values between the current a nnual standard level of 15 μg/m 3 

# and the lowest alternative level

evaluated. Subsequent to the release of the 2 nd 

# draft RA, we further expanded the range of

alternative annual standard levels evaluated to include a level of 10 μg/m 3 

# , consistent with

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

uncertainty associated with simulating ambient PM 2.5 

# levels for urban study areas that would just

16 

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

# levels and statistically-significant

health effects reported in short-term exposure studies would be dependent on the specific relationship between dayto-day variation in the 24-hour PM 2.5 

# levels (in the underlying study counti es) and the associated long-term mean

PM 2.5 

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

# levels and short-term health effects, would not hold for

counties with notably different relationships between sh ort-term day-to-day variation and longer-term mean PM 2.5 

# levels).

# 2-12 meet this lower standard level and consequently, the greater uncer tainty in the associated risk

estimates, relative to the higher alternative annual sta ndard levels evaluated.

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

# standard to be evaluated in this risk

assessment, we considered the ambient PM 2.5 

# levels associated with mortality and morbidity

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

# percentile

PM 2.5 

# ambient concentrations reporte d in two multi-city studies th at provided C-R functions used

in the core risk assessment, Zanobetti and Schw artz (2009) and Bell et al. (2008). The focus on

the 98 th 

# percentile of the 24-hour PM 2.5 

# concentrations observed in the epidemiological studies is

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

current form of the 24-hour PM 2.5 

# standard.

The second draft ISA presented 98 th 

# percentile 24-hour PM 2.5 

# values for each of the 112

urban areas included in the Zanobetti and Schwar tz (2009) short-term mortality study (US EPA,

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

# percentile 24-hour PM 2.5



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

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

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

# percentile PM 2.5 

# levels were consistently associated

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

lowest 98 th 

# percentile PM 2.5 

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

significant effect estimates was observed as the ba sis for selecting an alte rnative standard level

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

observed mixed results with no ap parent correlation between 98 th 

# percentile air quality levels and

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



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

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

levels for consideration in the risk assessment. The overall 98 th 

# percentile value across the entire

set of urban areas analyzed was 34.3 μg/m 3 

# (US EPA, 2009a, Figure 2-1; Zanobetti and

Schwartz, 2009). The 10 th 

# and 25 th 

# percentiles values were 25.5 and 29.8 μg/m 3 

# , respectively

(Zanobetti, 2009). We also completed a similar an alysis of the county-level ambient air quality

data for the 202 counties associated with the Be ll et al. (2008) study (Bell, 2009). The overall

98 th 

# percentile value across the en tire set of counties analyzed in Bell et al. (2008)) was 34.2

μg/m 3 

# (US EPA, 2009a, Table 6-11; Bell, 2009). The 10 th 

# and 25 th 

# percentile values were 24.4

and 29.3 μg/m 3 

# , respectively (Bell, 2009). Based on the available epidemiological evidence

indicating effects associat ed with a range of 98 th 

# percentile 24-hour PM 2.5 

# concentrations, as

briefly described above, we sele cted levels of 25 and 30 μg/m 3 

# 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 PM 2.5 

# standards, we

then identified specific combinations of these st andard levels to be c onsidered in evaluating

suites of alternative stan dards in the risk assessment. In selecting the pairing of annual and 24-

hour standard levels, we considered which standa rd was likely to be cont rolling across the set of

15 urban study areas. Either the annual or 24-ho ur standard will be the “controlling standard” at

a given location, depending on the design va lue 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 sta ndard would control acros s the various urban study

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

# and a

24-hour standard level of 35 μg/m 3 

# ), the annual level of 12 μg/m 3 

# is the controlling standard for

all 15 urban study areas. Altern atively, with the 12/25 combina tion, the annual standard is the

controlling standard at some loca tions 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 identifie d for inclusion in the risk assessment.

The air quality scenarios included in the ri sk assessment for which we felt sufficient

information was available to generate risk es timates with a reasonable degree of confidence,

include the recent conditions air qua lity 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 PM 2.5 

# monitoring data for the

analysis period – 2005 to 2007)

 Current suite of PM 2.5 

# NAAQS: annual 15 μg/m 3 

# ; 24-hour 35 μg/m 3



#  Alternative suite of PM 2.5 

# standards: annual 14 μg/m 3 

# ; 24-hour 35 μg/m 3



#  Alternative suite of PM 2.5 

# standards: annual 13 μg/m 3 

# ; 24-hour 35 μg/m 3



#  Alternative suite of PM 2.5 

# standards: annual 12 μg/m 3 

# ; 24-hour 35 μg/m 3



#  Alternative suite of PM 2.5 

# standards: annual 13 μg/m 3 

# ; 24-hour 30 μg/m 3



#  Alternative suite of PM 2.5 

# standards: annual 12 μg/m 3 

# ; 24-hour 25 μg/m 3



# Because of the increased uncertainty associated with estimates of risk generated for the

alternative annual sta ndard level of 10 μg/m 3 

# , 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/m 3 

# 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 fo r additional detail on the issue of controlling standards.

# 2-14  Alternative suite of PM 2.5 

# standards: annual 10 μg/m 3 

# ; 24-hour 35 μg/m 3



#  Alternative suite of PM 2.5 

# standards: annual 10 μg/m 3 

# ; 24-hour 25 μg/m 3

# 3-1 

# 3 URBAN CASE STUDY ANALYSIS METHODS

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

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

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

considerations. Section 3.3 disc usses the selection of health endpoints, urban study areas and CR functions from key epidemiological studies us ed in modeling those endpoints. Section 3.4

discusses baseline h ealth effects incidence ra tes. 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 curren t 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 appli cation 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, illustrat ed 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 hea lth endpoints, and populatio n estimates to derive

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

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

Total Risk Integrated Methodol ogy – 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 focuse d on estimating risks associated with recent

PM 2.5 

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

reflect just meeting the current suite of PM 2.5 

# ambient standards, as well as any additional

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



# standards.

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

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

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

# , the risk assessment considers

only the incidence of health effects associated with PM 2.5 

# concentrations in excess

18 

# For more detailed information about TRIM.Risk, see: http://www.epa.gov /ttn/fera/trim_risk.html3-2Figure 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 PM 2.5 

# , however, the lowest measured levels (LMLs)

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

down to PRB would have required substantial ex trapolation of the estimated C-R functions

below the range of the data on which they were estimated. Therefore, we estimated risk only

down to the LML to avoid introducing additional un certainty related to this extrapolation into

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

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

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

# from the later exposure period

evaluated in Krewski et al. (2009) -- to be used in estimating ri sks associated with long-term

PM 2.5 

# exposures.

For each health effect that has been associated with PM 2.5 

# , the risk assessment may be

viewed as assessing the incidence of th e health effect associated with PM 2.5 

# concentrations under

a given air quality scenario (e.g., a scenario in which PM 2.5 

# concentrations just meet a specified

suite of standards) above PRB or the LML . Equi valently, the risk assessment may be viewed as

assessing the change in incidence of each hea lth effect associated with a change in PM 2.5



# concentrations from some higher level (e.g., PM 2.5 

# concentrations that just meet a specified suite

of standards) to specified lower levels (PRB levels or the LML).

The risk assessment procedures described in more detail below are diagramed in Figure

3-2 for analyses based on short-term exposure studies and in Figure 3- 3 for analyses based on

long-term exposure studies. To estimate the change in incidence of a given health effect

resulting from a given change in ambient PM 2.5 

# concentrations in an assessment location, the

following analysis inputs are necessary:

 Air quality information including: (1) PM 2.5 

# air quality data from one or more recent

years from population-oriented monitors in the assessment locati on, (2) estimates of

PM 2.5 

# PRB concentrations appropriate to this location, and (3) a method for adjusting the

air quality data to reflect pa tterns of air quality changes to simulate just meeting the

current or alternative suite of PM 2.5 

# standards. (These air quality inputs are discussed in

more detail in section 3.2).

 C-R function(s) which provide an estimate of th e relationship between the health

endpoint of interest and PM 2.5 

# concentrations (preferabl y derived in the assessment

location, although functions estimated in other locations can be used at the cost of

increased uncertainty -- s ee section 3.5.3). For PM 2.5 

# , C-R functions ar e available from

epidemiological studies that assessed PM 2.5 

# -related health effects a ssociated with either

short- or long-term exposures. (Section 3.1.2 describes the role of C-R functions in

estimating health risks associated with PM 2.5 

# ).

 Baseline health effects incidence rate and population . The baseline incidence rate

provides an estimate of the incidence rate (num ber of cases of the health effect per year,

# 3-4 usually per 10,000 or 100,000 general popula tion) in the assessment location

corresponding to recent ambient PM 2.5 

# levels in that location. To derive the total baseline

incidence per year, this rate must be multiplied by the corresponding population number

(e.g., if the baseline inciden ce rate is number of cases per year per 100,000 population, it

must be multiplied by the number of 100,000s in the population). (Section 3.4

summarizes considerations re lated to the baseline incide nce rate and population data

inputs to the risk assessment).3-5Figure 3-2. Flow diagram of risk assessment for short-term exposure studies.3-6Figure 3-3. Flow diagram of risk assessment for long-term exposure studies.

# 3-7 The risk assessment was carried out using th ree years of recent ai r quality data from

2005, 2006, and 2007 (see section 3.2.1). We matche d 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 possi ble to obtain the necessa ry 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 th e assumption that these

rates are unlikely to have ch anged 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 co re (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 (herea fter 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 ba sed on inputs having high er overall levels of

confidence relative to risk estimates that could have been generated usi ng 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 applicati on of alternative modeli ng element choices that,

while not having as much support in the literature as t hose used in the core analysis, do still

represent plausible inputs. The re sults of these sensitivity analyses allow us to gain insights into

which sources of uncertainty and variability may ha ve the greatest impact on risk estimates when

acting alone, or in combination with other source s of uncertainty. The sensitivity analysis-based

risk estimates also provide us w ith an additional set of reasonable risk results that allow us to

place the results of the core analysis in contex t with regard to uncertainty. A number of

modeling elements were used in differentiating co re analyses from sensitivity analyses (e.g., C-R

function shape, alternative effect estimates, alte rnative lag structures, di fferent methods used to

rollback air quality to simulate attainment to curr ent 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 aspe cts of the risk assessment design. The potential

utility of the sensitivity analysis-based risk es timates in informing consideration of uncertainty

and variability in the core result s is discussed in section 4.3.2.

3.1.2 Calculating PM 2.5 

# -Related Health Effects Incidence

The C-R functions used in the risk assessment are empirically estimated relations

between average ambient concentrations of PM 2.5 

# and the health endpoints of interest (e.g.,

# 3-8 mortality or hospital admissions reported by epidem iological studies for sp ecific locations). This

section describes the basic method used to estimat e changes in the incidenc e of a health endpoint

associated with changes in PM 2.5 

# , 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 th e studies used a method referred to as “Poisson

regression” to estimate exponentia l (or log-linear) C-R functions in which the natural logarithm

of the health endpoint is a linear function of PM 2.5 

# :

x 

# Bey 



#  (1)

where x is the ambient PM 2.5 

# level, y is the incidence of the hea lth endpoint of interest at

PM 2.5 

# level x

, β is the coefficient of ambient PM 2.5 

# concentration, and B is the incidence at x =0,

i.e., when there is no ambient PM 2.5 

# . The relationship between a specified ambient PM 2.5 

# level,

x 0 

# , for example, and the incidence of a given health endpoint associ ated with that level (denoted

as y 0 

# ) is then

0



# 0 x 

# Bey 



#  (2)

Because the log-linear form of a C-R func tion (equation (1)) is by far the most common

form, we use this form to illustrate th e “health impact function” used in the PM 2.5 

# risk

assessment.

If we let 

# x 0 

# denote the baseline (upper) PM 2.5 

# level, and 

# x 1 

# denote the lower PM 2.5 

# level,

and 

# y 0 

# and 

# y 1 

# denote the corresponding inci dences of the health effect, we can derive the

following relationship between the change in 

# x

, Δ 

# x = ( 

# x 0 

# - x 1 

# ), and the corresponding change in 

# y

,

Δ 

# y , 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 a ssociated with a particular air quality comparison. The risk of

19 

# If Δ x

< 0 – i.e., if Δ x

= ( x 1 

# - x 0 

# ) – then the relationship between Δ x

and Δ y can be shown to be

]1[)(

001 

#   x 

# eyyyy  

# . If Δ x < 0, Δ y will similarly be negative. However, the magnitude of Δy will be the

same whether Δ x

> 0 or Δ x

< 0 – i.e., the absolute value of Δy does not depend on which equation is used.

# 3-9 mortality at ambient PM 2.5 

# level 

# x 0 

# relative to the risk of mortality at ambient PM 2.5 

# level 

# x 1 

# , for

example, may be characterized by the ratio of th e two mortality rates: the mortality rate among

individuals when the ambient PM 2.5 

# level is 

# x 0 

# and the mortality rate among (otherwise identical)

individuals when the ambient PM 2.5 

# level is 

# x 1 

# . This is the RR for mortality associated with the

difference between the two ambient PM 2.5 

# levels, 

# x 0 

# and 

# x 1 

# . Given a C-R function of the form

shown in equation (1) and a part icular difference in ambient PM 2.5 

# levels, Δ 

# x , the RR associated

with that difference in ambient PM 2.5 

# , denoted as RR Δ x 

# , is equal to e βΔ x 

# . The difference in health

effects incidence, Δ 

# y , corresponding to a given difference in ambient PM 2.5 

# levels, Δ 

# x , can then

be calculated based on this RR Δ x 

# as:

)]/1(1[)(

010 x 

# RR yyyy  

#  

   . (4)

Equations (3) and (4) are simply alternative wa ys of expressing the relationship between

a given difference in ambient PM 2.5 

# levels, Δ 

# x

> 0, and the correspondi ng difference in health

effects incidence, Δ 

# y . 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 PM 2.5 

# health risk.

3.1.2.1 Short-term vs. Long-term Exposure

Concentration-response (C-R ) functions that use as input annual average PM 2.5 

# levels (or

some function of these, such as the average over a period of seve ral years) relate these to the

annual incidence of the health e ndpoint – i.e., in such studies 

# x in equation (1) above is the

average PM 2.5 

# 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 associ ated with that long-term

exposure.

Concentration-response (C-R) functions that use as input 24-hour average PM 2.5 

# levels (or

some function of these, such as the average ove r one or more days) rela te these to the daily

incidence of the health endpoi nt – i.e., in such studies 

# x in equation (1) above is the average

PM 2.5 

# concentration over a period of one or a few days (short-term exposure), and 

# y is the daily

incidence of the health effect associat ed 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 PM 2.5 

# to predict daily health effects

incidence. Such a function might, for example, re late the incidence of th e health effect on day 

# t

to the average of PM 2.5 

# concentrations on days 

# t and ( 

# t -1). Some C-R func tions consider the

relationship between daily in cidence and daily average PM 2.5 

# 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 PM 2.5 

# on a prior day ( 

# t -1). A few studies have estimated di stributed lag models, in which health

effect incidence is a function of PM 2.5 

# concentrations on several prior days – that is, the incidence

of the health endpoint on day 

# t is a function of the PM 2.5 

# concentration on day 

# t , day ( 

# t -1), day ( 

# t

2), and so forth. Such models can be reconfigur ed so that the sum of the coefficients of the

different PM 2.5 

# 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 co efficients of the 0-day, 1-day, and 2-day lagged

PM 2.5 

# concentrations can be used to predic t 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 t hose, such as a two-day average

concentration (e.g. lag 0-1). Such models necessa rily 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 mortalit y 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-seri es studies using single-day PM 2.5 

# concentrations may under

or over-estimate the relationship between short-term PM 2.5 

# exposure and risk of mortality is

unknown. However, there is some evidence, based on analyses of PM 10 

# 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

PM 2.5 

# is similarly not adequately understood, although the current evidence (e.g., Krewski et al.,

2009; Krewski et al., 2000) suggests that there is a subs tantial 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 in cidence, and changes in incidence, on an

annual basis, for 2005, 2006, and 2007. For mortalit y, 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 epidemiol ogical studies relate da ily mortality to sameday PM 2.5 

# concentration or previous-day PM 2.5 

# concentration (or some variant of those).

To estimate the daily health impacts of 24-hour average ambient PM 2.5 

# levels above PRB,

C-R functions from short-term exposure studies we re used together with estimated changes in

24-hour ambient PM 2.5 

# concentrations to calculate the daily changes in the incidence of the

# 3-11 health endpoint. After daily ch anges in health effects were ca lculated, 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 ye ar) was driven by days with

24-hour average PM 2.5 

# levels nearer to the annual averag e value for the study area rather than

days with relatively higher PM 2.5 

# levels falling in the tail of the annual 24-hour PM 2.5



# distribution. 20 

# This finding reflects the fact that the num ber of deaths associat ed with short-term

exposure to PM 2.5 

# depends both on the number of days at a given concentration and on the

concentration itself. Because the urban ar eas considered in the 1996 RA had 24-hour PM 2.5



# 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 th e distribution, rather than the small number of days out at the tail. This

analysis was updated for the la st review completed in 2006. 21



# As part of the current analysis, we have upda ted the analysis origin ally presented in the

1996 PM NAAQS RA to reflect the ai r quality data and mortality estimates due to short-term

exposure generated as part of th e current review. For this upda ted analysis, we have focused on

Detroit and New York and on cardiovascular mort ality 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/m 3 

# for Detroit and 13.8 μg/m 3 

# for New York – see Appe ndix A, Table A-5 and A-9,

respectively), rather than days comprising the up per tail of each distri bution. In other words,

days with relatively elevated PM 2.5 

# levels contribute a relatively small fraction of short-term

exposure-related cardiovascular mortality incidence at each study area.

The mortality associated with long-term exposur e is likely to include mortality related to

short-term exposures as well as mortality re lated to longer-term exposures. As discussed

previously, estimates of daily mort ality based on the time-series studi es also are likely influenced

by prior PM exposures. Therefore, the estimated annual incidences of mo rtality 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 ch aracterize the statistica l 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 an alysis because this endpoint category (including both cardiovascular-related

mortality and morbidity) has the greatest degree of support in the literature.

# 3-12 the estimated PM 2.5 

# 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 qualitativel y discussed in section 3.5.3.

3.2 AIR QUALITY INPUTS

3.2.1 Characterizing Recent Conditions

Twenty-four hour PM 2.5 

# 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

PM 2.5 

# 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 f unctions. In a few cases, an urban area was

delineated differently by two or more epidemiologi cal studies used in the risk assessment. For

example, Birmingham, AL was defined as Bl ount, 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, resul ting 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, Cowe ta, 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 PM 2.5 

# C-R functions, rather than working di rectly with individual monitor values

in estimating risk, we first derived composite mo nitor 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-re lated mortality) and distributions of 24-hour

average levels (used in modeli ng short-term exposure-related mortality and morbidity). The

procedure for deriving each of these types of co mposite 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 distribut ions of 24-hour estimates

To develop composite monitor distributi ons of 24-hour estimates reflecting equal

weighting of the underlying monitor datasets, we comp leted an initial step of interpolation to fill

in missing measurements at the individual mon itors. We then calculated the average for a

particular day across the contri buting 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 th e 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 measuremen ts 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 thos e quarters with 11 or more me asurements, 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 wi th measured data above and

below the missing day(s) and assigned that interp olated 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 interpolat ion 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 underl ying 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 th at 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 missi ng composite 24-hour estimate.

Composite monitor annual average estimates

Composite monitor annual averages were cal culated 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 st udy area as the basis for deriving composite annual

average estimates. The specific step-wise procedure involved:

1. 

# Calculate quarterly averages at ea ch monitor in the study area: For a given monitor, if a

quarter has less than 11 measuremen ts, 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 variet y of interpolation approaches from which to choose does

represent a source of uncertainty impacting the interpola tion 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 descri bed in Appendix B, section B1). The results of this

sensitivity analysis suggest that, in the context of Birmi ngham, 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 PM 2.5 

# distributions generated using these two interpolation approaches, would translate into negligible

differences in short-term exposure-related risk estimates (which utilize the 24-hour PM 2.5 

# distributions involved in

the interpolation). Therefore, we conclude, based on the re sults of this sensitivity analysis, that uncertainty related

to interpolation of missing data represents a relatively smal l 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 aver age 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 St ep 2) to produce an annual average for the

composite monitor. 26



# Appendix A summarizes the PM 2.5 

# air quality data that we re used in each of the

assessment locations, including quarterly and annu al counts, quarterly and annual averages, and

the 98 th 

# percentile of the da ily (24-hour) averages for individual monitors. Appendix A also

provides summary information on the composite monitor annual average estimates and 98 th



# 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, fi nal 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 desc ribed in the draft ISA (see

section 3.7.1.2). Annual values presented in Tabl e 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 h ealth endpoints associat ed with short-term

exposure (which involved modeling down to PRB, exclusively), quarterly values presented in

Table 3-2 were used to represent the appropria te block of days within a simulated year.

26 

# Pittsburgh was treated somewhat differently from th e other locations because there are effectively two

attainment areas in Pittsburgh – one containing ten of th e 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 Pittsburg h as weighted averages of the composite monitor values

for “Pittsburgh-1” and “Pittsburgh-2”, wher e 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 Ba ckground 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 Me et Current and Alternative Standards

This section describes the methodologi es used to simulate ambient PM 2.5 

# levels in an area

that would just meet specified PM 2.5 

# standards. The form of the current PM 2.5 

# standards requires

that the 3-year average (r ounded to the nearest 0.1 μg/m 3 

# ) 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/m 3 

# ) 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 av eraged concentrations

across all population-oriented mon itors, subject to meeting certai n 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 realisti c simulation of just meeting both the annual and

the 24-hour PM 2.5 

# standards in a location would require changing the distribution of 24-hour

PM 2.5 

# 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 st rategies 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 PM 2.5 

# standards was

simulated using a single rollback approach (the 

# proportional ) which reflected a uniform regional

pattern of reduction in ambient PM 2.5 

# levels across monitors. For this analysis, we have included

two additional rollback approaches to provide gr eater coverage for variab ility associated with

this key aspect of simulating ri sk for both the current and altern ative 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 PM 2.5



# levels at source-oriented monito rs 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 st andard level under consideration. While the

proportional rollback approach is ap plied to all 15 urban study ar eas 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 PM 2.5 

# standards was

simulated by changing 24-hour PM 2.5 

# 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 PM 2.5



# standards was simulated by changing 24-hour PM 2.5 

# 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 hybr id and locally focused rollback methods).

This change was made because the hybrid a nd locally focused rollback methods involve nonuniform degrees of reduction in ambient PM 2.5 

# measurements for the monitors in a given study

area, as contrasted with the proportional appr oach 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 ot her 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 cap ture “bounding” behavior related to the spatial

pattern of future reductions in ambient PM 2.5 

# levels. By contrast, the hybrid approach can be

interpreted as reflecting a more pl ausible or representative rollback strategy in principle, since it

(a) reflects consideration for site-specific info rmation regarding larger PM sources and their

potential impact on source-oriented monitors and (b) combines elem ents 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 2 nd 

# External Review Draft RA pl aced 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 variet y of potential hybrid strategies for considering

site-specific data in simulation spatial patterns of ambient PM 2.5 

# reduction. Because there are a

variety of hybrid rollback strategies that coul d be considered and be cause of challenges in

assessing the reasonablene ss of different strategies in pred icting spatial patterns of ambient PM 2.5



# reductions, we conclude that ther e is substantial uncertainty asso ciated with predicting rollback

in relation to modeling risk for both the current and alternative standard level. Consequently,

relative importance is placed on the sensitivity an alysis examining the issue of rollback, which is

based on application of the three rollback strategi es described here. While we describe how the

different rollback methods are implemented in this section, the impact of us ing different rollback

methods on core risk estimates is discussed as part of the sensitiv ity analysis presented in section

3.5.4.1.

3.2.3.1 Proportional Rollback Method

The proportional approach involves reducing PM 2.5 

# concentrations by the same

percentage across all monitors in a study area, thereby reflecting a more regional pattern of

ambient PM 2.5 

# reduction. When this approach is us ed, it does not matter whether (1) PM 2.5



# 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. Therefor e, to streamline the analytical process, the

proportional rollback method was applied directly to the composite monitor estimates. The stepwise procedure used in conducting propor tional rollback is described below:

1. 

# Calculate annual and 24-hour design values for the study area under consideration: The

degree of reduction required to si mulate attainment of a specific suite of standard levels is

determined by comparing the design values (des cribed here) against the specific standard

level being considered. Therefore, the first st ep is to calculate 24- hour and annual design

values for the study area. 29 

# The annual design value (in μg/m 3 

# ) was calculated as follows:



#  At each monitor, the annual average PM 2.5 

# 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-ye ar averages of annual averages at a

particular study area is the annual design value, denoted 

# dv annual 

# ;

The 24-hour design value (in μg/m 3 

# ) 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 98 th 

# percentile 24-hour PM 2.5 

# concentration was calculated for

each of the years 2005, 2006, and 2007, and these three 98 th 

# percentile concentrations

were then averaged.



#  The maximum of these monitor-specific 3-year averages of 98 th 

# percentile

concentrations at a particular study ar ea is the 24-hour design value, denoted 

# dv daily 98 

# (note, we will refer to the 98 th 

# 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 PM 2.5 

# are given in Table 3-3. Note that m onitors 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 mo nitoring data during any of the three simulation

years (2005, 2006 or 2007) were excluded from c onsideration as design value monitors, although

these monitors were still used to construct comp osite monitors for purposes of estimating risks.

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

# Standards for the Period

2005-2007.*

Location Annual

(μg/m 3 

# ) 24-hour

(μg/m 3 

# )

Atlanta 16.2 35

Baltimore 15.6 37

Birmingham 18.7 44

Dallas 12.8 26

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 require d to simulate attainment

of a particular standard level is calcu lated 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 larg ely to anthropogenic sources of PM 2.5 

# , rollbacks are

applied only to PM 2.5 

# above estimated PRB levels and c onsequently, in determining the

percent reduction, we only cons ider that portion of both the de sign 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 De troit) in Appendix B, section B3.

3. 

# Determine which standard is controlling fo r 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 la rger of the two values is identified as the

controlling standard. Simulated at tainment of the controlling st andard, by definition, results

in more than sufficient reduction in com posite monitor values to produce simulated

attainment of the other (non-cont rolling) 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 PM 2.5 

# 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 adjustedestimate that can be used in estimating longterm exposure-related mortality.

3.2.3.2 Hybrid Rollback Method

The hybrid rollback approach reflects a co mbination of a more localized pattern of

rollback focused on source-orie nted monitors with rela tively elevated ambient PM 2.5 

# levels,

followed by a more generalized regional pattern of rollback across all monitors in the study area

to simulate attainment. The first localized re duction involves reducing le vels at the selected

source-oriented monitor(s) such that they matc h the level of the nearest non-source oriented

monitor(s). This initial localized reduction includes a distan ce-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 targ eted source-oriented monitor(s)). The second

more generalized regional rollb ack is implemented using the proportional approach described

above in section 3.2.3.1. However, for the hybrid ro llback, the percent reduction is determined

based on consideration of design values calculated after the in itial localized reduction phase,

rather than design values based on recent cond itions 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 PM 2.5 

# levels relative to other monitors in the

study area), this approach was onl y applied to a subset of the 15 urban study areas that met these

criteria including: Baltimore, Birmingham, Detroit, Los Ange les, 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 pr esented below can be found in

Appendix B, section B3 as part of the samp le 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 PM 2.5 

# were identified as candidate locations.

2. 

# Localized reduction of PM levels at that sour ce-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 (aft er adjustment) matched the design value for the

non-source oriented monitor with the highest de sign value located clos e by. This process was

repeated for all source-oriented monitors iden tified in step 1. This reduction focused on the

design value that was controlling for the sour ce-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 s ource-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 monito r(s) that is the focus of that reduction, we

do consider the potential for that reduction to im pact 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 kilo meter 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 distan ce 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 pr oportional 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 m onitor values for Pittsburgh were calculated as weighted

averages of the rolled back composite monitor values fo r “Pittsburgh-1” and “Pittsburgh-2”, where the weights were

the proportion of the monitors in each (i.e., 10/12 and 2/12).

# 3-22 monitor 10 kilometers away from the s ource-oriented monitor, would have 1/10 th 

# 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 id entical to that described in section 3.1.3.1 is now conducted

except that in this case , we are starting with PM 2.5 

# 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 compos ite monitor distributions of 24-hour levels

(based on the adjusted PM levels at indi vidual-monitors) and then apply proportional

rollback directly to those composite monitor values.

Additional detail on the hybrid approach, as app lied 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 refl ects a local pattern of reduction in ambient

PM 2.5 

# 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 exceedi ng the 24-hour standard (i.e., no distance-decay

effect as implemented in the hybrid approach). This approach wa s 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 calcula tions 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 control ling. 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 ar ea into simulated attainment : For each monitor with a 24-

hour design value exceeding the standard under c onsideration, compare that design value to

the standard under consideration to determine th e degree of reduction requ ired to bring that

monitor into simulated attainme nt (i.e., the percent rollback ). As with the proportional

rollback described in secti on 3.1.3.1, calculation of the perc ent rollback takes into

consideration PRB and is based on comparing on ly 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 PM 2.5 

# levels at individual monitors su ch that their adjusted design

values now meet the 24-hour standard under consideration.

4. 

# Calculate composite monitor 24-hour distributions and annual a verages for each study area:

Using the adjusted 24-hour dist ributions 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 met hods 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) a nd (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 standard s 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 combin ation of urban study area, rollback method and

standard level simulated. In reviewing the result s presented in Table 3-4, we see that both the

hybrid and locally focused rollback methods gene rate 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 ex pected, since both the hybrid and locally focused

rollback methods target a subset of monitors, th ereby 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 me thod were calculated based on the divi sion 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 cont rolling 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 met hods as applied to a single urban study area

(Detroit), showing the step-wise procedure appl ied to individual monitors as appropriate,

including equations used, input va lues and sample calculations.3-25Table 3-4. Application of the Three Rollback Methods in Simulating Current and Alternative Standard Levels forthe 15 Urban Study Areas (including resulting maximum monitor-specific and composite monitor PM2.5values)Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 AnnualAverage at Composite Monitor (2007CM) (inμg/m3)Design ValueRecent AirQuality(2007)15/35214/3513/3512/3513/3012/25RiskAssessmentLocation1RollbackMethodAnnual24-Hr2007 CMMax.M-S2007CMMax.M-S2007CMMax.M-S2007CMMax.M-S2007CMMax.M-S2007CMMax.M-S2007CMProportional15.0 14.2 14.0 13.3 13.0 12.3 12.0 11.4 13.0 12.3 11.8 11.2Hybrid3--- --- --- --- --- --- --- --- --- --- --- ---Atlanta, GALocally focused16.2 35.0 15.3--- --- --- --- --- --- --- --- --- --- 14 11.76Proportional14.8 13.1 14.0 12.5 13.0 11.6 12.0 10.7 12.7 11.3 10.7 9.5Hybrid14.3 13.0 14.0 12.7 13.0 11.8 12.0 10.9 12.3 11.2 10.3 9.4Baltimore, MDLocally focused15.6 37.0 13.915.2 13.6 --- --- --- --- --- --- 13.1 12.0 11.0 10.0Proportional15.0 12.7 14.0 11.8 13.0 11.0 12.0 10.2 13.0 11.0 11.1 9.4Hybrid15.0 14.2 14.0 13.2 13.0 12.3 12.0 11.4 13.0 12.3 11.3 10.7Birmingham, ALLocally focused18.7 44.0 15.7--- --- --- --- --- --- --- --- --- --- 12.3 11.4Proportional12.8 11.4 12.8 11.4 12.8 11.4 12.0 10.7 12.8 11.4 12.0 10.7Hybrid--- --- --- --- --- --- --- --- --- --- --- ---Dallas, TXLocally focused12.8 26.0 11.4--- --- --- --- --- --- --- --- --- --- --- ---Proportional14.1 11.4 14.0 11.4 13.0 10.6 12.0 9.8 12.2 9.9 10.2 8.3Hybrid13.2 11.7 13.2 11.7 13.0 11.5 12.0 10.6 11.4 10.1 9.6 8.5Detroit, MILocally focused17.2 43.0 13.914.1 12.6 --- --- --- --- --- --- 12.2 11.0 10.2 9.2Proportional9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 8.6 8.6 7.3 7.3Hybrid--- --- --- --- --- --- --- --- --- --- --- ---Fresno, CALocally focused17.4 63.0 17.410.1 10.3 10.1 10.3 10.1 10.3 10.1 10.3 8.8 8.9 7.4 7.5Proportional15.0 12.5 14.0 11.7 13.0 10.9 12.0 10.1 13.0 10.9 12.0 10.1Hybrid--- --- --- --- --- --- --- --- --- --- --- ---Houston, TXLocally focused15.8 31.0 13.2--- --- --- --- --- --- --- --- --- --- --- ---Los Angeles,Proportional19.6 55.0 14.612.7 9.5 12.7 9.5 12.7 9.5 12.0 9.0 10.9 8.2 9.2 7.03-26Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 AnnualAverage at Composite Monitor (2007CM) (inμg/m3)Design ValueRecent AirQuality(2007)15/35214/3513/3512/3513/3012/25RiskAssessmentLocation1RollbackMethodAnnual24-Hr2007 CMMax.M-S2007CMMax.M-S2007CMMax.M-S2007CMMax.M-S2007CMMax.M-S2007CMMax.M-S2007CMCAHybrid 13.3 10.5 13.3 10.5 13.0 10.3 12.0 9.5 11.5 9.1 9.6 7.7Locally focused 13.9 12.1 13.9 12.1 13.9 12.1 --- --- 12.0 10.6 10.1 9.1Proportional13.3 11.6 13.3 11.6 13.0 11.3 12.0 10.4 11.5 10.0 9.7 8.4Hybrid13.6 11.8 13.6 11.8 13.0 11.3 12.0 10.4 11.7 10.2 9.8 8.5New York, NYLocally focused15.9 42.0 13.814.3 13.3 14.3 13.3 --- --- --- --- 12.3 11.6 10.3 9.8Proportional13.9 12.3 13.9 12.3 13.0 11.6 12.0 10.7 11.9 10.7 10.0 9.0Hybrid--- --- --- --- --- --- --- --- --- --- --- ---Philadelphia,PALocally focused15.0 38.0 13.415.5 13.0 15.5 13.0 --- --- --- --- 14.1 11.3 11.8 9.5Proportional12.6 9.9 12.6 9.9 12.6 9.9 12.0 9.4 11.8 9.3 9.9 7.8Hybrid--- --- --- --- --- --- --- --- --- --- --- ---Phoenix, AZLocally focused12.6 32.0 9.9--- --- --- --- --- --- --- --- 12.2 9.7 10.2 9.0Proportional13.3 11.6 13.3 11.6 12.8 11.2 11.8 10.5 11.5 10.0 9.7 8.4Hybrid--- --- --- --- --- --- --- --- --- --- --- ---Pittsburgh, PA5Locally focused19.8 60.0 14.915.6 13.2 15.6 13.2 15.3 11.8 15.3 11.2 15.6 11.4 13.9 9.6Proportional7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 6.7 6.6 5.7 5.6Hybrid--- --- --- --- --- --- --- --- --- --- --- ---Salt Lake City,UTLocally focused11.6 55.0 11.410.8 9.7 10.8 9.7 10.8 9.7 10.8 9.7 10.8 8.8 9.1 7.7Proportional14.9 12.9 14.0 12.1 13.0 11.3 12.0 10.4 12.8 11.1 10.8 9.3Hybrid15.0 13.5 14.0 12.6 13.0 11.7 12.0 10.8 13.0 11.7 11.0 9.9St. Louis, MOLocally focused16.5 39.0 14.316.5 14.1 --- --- --- --- --- --- 14.2 12.4 11.9 10.4Proportional8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 7.4 7.0 6.3 6.0Hybrid--- --- --- --- --- --- --- --- --- --- --- ---Tacoma, WALocally focused10.2 43.0 9.78.5 8.0 8.5 8.0 8.5 8.0 8.5 8.0 7.4 7.0 6.3 6.01For some locations (e.g., Atlanta)more than one "version" (group of counties) was used in the risk assessment. In this tableonly the version that was used formortality associated with short-term exposure to PM2.5(Zanobetti and Schwartz, 2009) is included.2The current primary PM2.5standards include an annual standard set at 15μg/m3and a daily standard set at 35μg/m3.3-273The hybrid rollback method was applied toonly a subset of the risk assessment locations. The "---" for a given location indicates that the hybrid rollback methodwas not applied to that location.4The locally focused method was applied to a location-standard combination only if the daily standard was controlling in that location. The "--" for a given locationstandard combination indicates that, for that set of annual and daily standards in that location,the annual standard was controlling and so the locallyfocused method was not applied.5The proportional. rollback and locally focused methods were applied to Pittsburgh differently from the way they were applied in the other locations. See text fordetails.6Percent reduction in composite monitorvalue with consideration of LML of 5.8μg/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 endpoi nts to model in this assessment

included:



#  The overall weight of evidence from the co llective body of epidemiological, controlled

human exposure, and toxicologi cal studies and the determina tion made in the final ISA

regarding the strength of th e causal relationship between PM 2.5 

# 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 monito ring data in areas that were evaluated in

the epidemiological studies;



#  The availability of baseline incidence da ta to support population risk (incidence)

modeling; and



#  The anticipated value of developing quantitat ive 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 hea lth effect endpoint categories (and associated endpoints and

related susceptible populati ons) to include in the PM 2.5 

# 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 a nd Methods Plan and CASAC (Samet, 2009b) and

public comments received on the first draft RA. In reviewing the final ISA in relation to PM 2.5 

# , we focused on the following sections: (a) sec tion 2.3.1.1 (Effects of Short-Term Exposure to

PM 2.5 

# ), (b) section 2.3.1.2 (Effects of Long-Term Exposure to PM 2.5 

# ), (c) section 2.3.2

(Integration of PM 2.5 

# Health Effects), and (d) subsections in Chapter 6 and 7 of the final ISA

providing summaries of causal determination (f or both morbidity and mortality endpoints)

related to short-term and long-term exposure, re spectively. We also cons idered information in

the ISA on susceptible populations, which identified the life stages of children and older adults,

people with pre-existing cardiov ascular and respiratory diseas es, and people with lower

socioeconomic status as populations at incr eased risk for PM-rela ted health effects.

Based on the evidence presented in the ISA and application of the above criteria, we

identified the following health effects endpoi nts for inclusion in the risk assessment:

# 3-29 Health effects associated with short-term PM 2.5 

# exposure :



#  Mortality (causal relationship)

o non-accidental,

o cardiovascular-related

o respiratory-related,



#  Cardiovascular effects (causal relationship)

o cardiovascular-related hospital admissions



#  Respiratory effects (lik ely causal relationship)

o respiratory-related hospital admissions

o asthma-related emergency department visits

Health effects associat ed with long-term PM 2.5 

# exposure:



#  Mortality (causal relationship)

o all-cause

o ischemic heart disease (IHD)-related

o cardiopulmonary-related

o lung cancer

While we selected specific hea lth effect endpoints that were all within broad health effect

categories classified in the ISA as having a “cau sal” or “likely causal” association with PM 2.5



# exposure, our selection is a based on applying the multi-factor approach described above.

The evidence available for th ese 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 respir atory 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 disc ussed 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 urba n 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 observati ons 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 PM 2.5 

# monitor within the boundaries of the

prospective study area that met these comp leteness criteria for the period 2005 to

2007 with additional preference given to locations with more than one PM 2.5 

# monitor meeting completeness criter ia, since this provided a better

characterization of ambient air le vels 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 PM 2.5 

# exposures

(whereas the prior risk assessment us ed city-specific studies in modeling

endpoints associated with short-term expos ures), this criterion no longer applies

for most prospective areas.



#  Availability of city-speci fic 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) ha s 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 sele cting locations to model in the current

assessment included:



#  Potential for risk reductions us ing alternative standard levels : We focused on

those urban areas with PM 2.5 

# monitoring levels suggesting the potential for risk

reduction under the alternative (24-hour or annual) standard s being considered

(i.e., urban locations with at least one monitor having an annual average above 12

μg/m 3 

# and/or a 24-hour value above 25 μg/m 3 

# ). Furthermore, locations with

ambient PM 2.5 

# level significantly higher than these levels were favored (with

several urban study areas selected havi ng both annual and 24-hour design values

exceeding the current standards – Table 3-4).



#  Regional representation: The second criterion we a dded for study area selection

focused on providing coverage for factors be lieved to play a role in influencing

risk heterogeneity at the national-level (e.g., PM 2.5 

# source characteristics and

composition, demographics, socio-economic status (SES) status, air conditioner

use). Building on the 7 regions origina lly 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 re gions with the goal to identify one or

more candidate urban study areas in e ach region. Ultimately, application of the

criteria described here resu lted in one of the PM regions (the Upper Midwest) not

# 3-31 being covered by an urban study area. Ho wever, the remaining six PM regions

each have at least one urban study areas ev aluated in the risk assessment. While

the PM regions were originally defined focusing primarily on differences in PM

composition, size and seasonality, by sele cting urban study areas from regions

across the continental U.S., we recogni ze the potential for covering regional

differences in other factors related to risk heterogeneity as well (e.g.,

demographics, SES). The representativen ess analysis (secti on 4.4) specifically

assesses the degree to which the 15 ur ban study areas provide coverage for

national trends in key risk-related fa ctors 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 additi on to identifying the 15 urban study areas, Table 3-

4 also provides additional information includin g: (a) whether the urban study area was included

in the prior risk assessment, (b) which PM regi on 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 re gions 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/m 3 

# ) 24-hour design

value (μg/m 3 

# )

Atlanta GA SE 16.2 35

Baltimore MD NE 15.6 37

Birmingham AL SE 18.7 44

Dallas TX SE 12.8 26

Detroit MI X IM 17.2 43

Fresno CA SCA 17.4 63

Houston TX SE 15.8 31

LA CA X SCA 19.6 55

New York NY NE 15.9 42

Philadelphia PA X NE 15.0 38

Phoenix AZ X SW 12.6 32

Pittsburgh PA X IM 19.8 60

Salt Lake City UT NW 11.6 55

St. Louis MO X IM 16.5 39

Tacoma WA X NW 10.2 43

* SE (Southeast), IM (industrial Midwest), SCA (South ern California), NE (Northeas t), NW (Northwest), SW

(Southwest) (See S EPA, 1996, section 6.4 for description of these regions).

# 3-32 Northwest

Southeast Southwest Northeast Upper Midwest Industrial Midwest

S

o u

t h

e

r n

C

a

l i f o

r n

i a 

# Dallas Fresno Tacoma

Atlanta Detroit

Houston Phoenix New York

Baltimore St. Louis

Birmingham Los Angeles Salt Lake City Pittsburgh Philadelphia



# 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 th e spatial

template to use in defining each study area (i.e., the geographical area associated with each study

area that would be used in id entifying which counties and PM 2.5 

# monitors were associated with a

particular study area). For 12 of the 15 urban stu dy 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 th ree 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 th at the entire larger CSA would behave

similarly with regard to PM 2.5 

# emissions reduction strategies;



#  Philadelphia : Used the Philadelphia CSA, but excluded Berks County (Reading;:

# 3-33 

#  Tacoma : Used only Pierce County (since we fe lt 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 PM 2.5 

# monitor in each study area with DV

values based on monitoring data from 2005-2007, (b) sources of PM 2.5 

# 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 dependi ng on location. These

maps allow the reader to visually consider th e interplay between both local and more regional

sources of ambient PM 2.5 

# 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 populati ons were included in their studies. For

example, in one study conducted in Birmi ngham, AL populations from Blount, Jefferson,

Shelby, St. Clair, and Walker Counties were included, while anothe r study included the

population residing in only Jefferson County. In su ch cases, we matched our delineation of the

urban area to that of each study, resu lting in two or more different de lineations 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. (20 08) – are multi-location studies. Zanobetti and

Schwartz (2009) specified the county or countie s 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 spec ified threshold number. To limit the number

of different “versions” of a risk assessment lo cation, 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 Zanobett i and Schwartz (2009)

identified an urban area as a si ngle county, and that county was also included in Bell et al.

(2008). This was the case for several of the ri sk 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 t hose 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 ar eas, as shown in Table 3-1 above.

Finally, we applied the studies of mortality associated with long-term exposure to PM 2.5



# to the urban areas as defined by the short-term exposure mortality study, Zanobetti and Schwartz

# 3-34 (2009), to enable meaningful comparisons betwee n estimates of premature morality associated

with short-term and long-term exposure to PM 2.5



# .

3.3.3 Selection of Epidemiological Studies a nd Concentration-response (C-R) Functions

within Those Studies

As discussed above, we included in the PM 2.5 

# risk assessment only those health effect

endpoint categories (and specific hea lth effects) that met the set ofcriteria reflected in the multifactor approach we developed fo r selecting health effect endpoint s (see section 3.3.1). One of

these factors was the strength of eviden ce supporting a causal association between PM 2.5



# exposure and the endpoint of interest. Thus, in ca ses where the majority of the available studies

did not report a statistically si gnificant 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 PM 2.5 

# 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-c ity studies use the same study design in each of

the cities included in the study, so that city-spe cific 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 pow er and provide effect estimates with relatively greater precision

than single city studies due to larger sample sizes, reducing th e uncertainty around the estimated

coefficient. Moreover, in a multi-city study the stat istical power to detect an effect in any given

city can be supplemented by drawin g statistical power from data across all the ci ties 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 an alysis (or in the same region). This is particularly useful in

those instances, where a city has relatively less da ta resulting in a larger standard error for the

effect estimate. In this situation, the informa tion 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 st udies 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 lik ely to get published than those w ith 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 PM 2.5 

# and a given health endpoint, and we included other studies

for that health endpoint only if they were j udged 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 ha d to satisfy the study se lection 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 in clusion in this risk assessment based on

that evaluation.



#  It had to directly measure, rather than estimate, PM 2.5 

# on a reasonable proportion of the

days in the study.

It had to either not rely on Generalize d Additive Models (GAMs) using the S-Plus

software to estimate C-R functions or to approp riately 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, cardiovasc ular, and respiratory

mortality (Zanobetti and Schwartz, 2009) and card iovascular and respirat ory hospital admissions

(Bell et al., 2008) associated w ith short-term exposure to PM 2.5 

# in our core analysis. In each of

these studies, the 15 urban areas selected for th e 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 mortal ity and morbidity and

therefore represent the best stud ies to use in deriving C-R functi ons for this risk assessment.

Dominici et al. (2007) was consid ered as an alternative study in identifying C-R functions for

modeling short-term exposure-rela ted mortality, however its study period and the underlying air

quality data and disease inciden ce data (1987-2000) are not as cu rrent as that of Zanobetti and

Schwartz et al., 2009 (study pe riod of 2001-2005), and therefore, we decided to focus on

Zanobetti and Schwartz et al. ( 2009) as the source of C-R func tions 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 incl ude different sets of co-pollutant s, different lag structures, and

different forms to accommodate weather and te mporal 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 Un ited 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 th e 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 PM 2.5 

# in single-pollutant log-linear

models using Poisson regression for each of 112 citi es, as well as in two-pollutant models with

coarse PM. They estimated annual models (w hich assume that the relationship between

mortality and PM 2.5 

# 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 shor t-term exposure mortalit y, at the request of

EPA, the authors produced Empirical Bayes “shrunke n” city-specific estimates, adjusted towards

the appropriate regional mean, using the approach de scribed 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 es timates have the advantage of relying on cityspecific data; however, as noted above, such estim ates can have large sta ndard 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 Sc hwartz (2009) all PM 2.5 

# models used the same lag structure (i.e., an

average of same-day and the previous day’s PM 2.5 

# ). The study did, however, examine both

single-pollutant and two-pollutant models (with co arse PM). We selected the single-pollutant

33 

# Zanobetti and Schwartz delineate regions as follo ws: “region 1: humid subtropical climates and

maritime temperate climates (Cfa, Cfb), which include s FL, LA TX, GA, AL, MS, AR, OK, KS, MO, TN, SC, NC,

VA, WV, KY; region 2: warm summer continental climat es (Dfb), including ND, MN, WI, MI, PA, NY, CT, RI,

MA, VT, NH, ME; region 3: hot summer continental climat es (Dfa) with SD, NE, IA, IL, IN, OH; region 4: dry

climates (BSk) (NM, AZ, NV); region 5: dry climates together with continen tal 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 th e annual functions over the season-speci fic functions for the core analysis is

that, while we can sum the season-speci fic mortality estimates across the four seasons, we cannot do the same for

the upper and lower bounds of 95% conf idence intervals around th ose 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 estima te is a weighted average of the re gional mean estimate and the cityspecific estimate, where the weight on th e 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 varian ce 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 c onsistent with most of the other

studies used in the risk assessment, which were single-pollutant studies.

Bell et al. (2008) estimated log-linear m odels relating short-term exposure to PM 2.5 

# and

hospital admissions for cardiovas cular and respiratory illnes ses among people 65 and older,

using a 2-stage Bayesian hierarch ical model, for each of 202 countie s in the United States. They

reported both annual and season-spec ific results, nationa lly and regionally (for four regions:

Northeast, Southeast, Northwest, and Southwest), but not at the local (city-specific) level. All

cardiovascular hospital admissions models we re single-pollutant, 0-day lag models; for

respiratory hospital admissions, both single-pollutant 0-day mode ls 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 anal ysis), we selected the 2-day lag models, based

on evidence that for respiratory effects the str ongest 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 PM 2.5 

# and emergency department (ED) visits for cardiovascular and/or re spiratory illnesses.

(There were no multi-city studies for this category of health endpoint.) Tolbert et al. (2007)

examined both cardiovascular and re spiratory ED visits in Atlant a, GA, using single-pollutant

log-linear models with a 3-day moving average (0-day, 1-day, and 2-day lags) of PM 2.5 

# . Ito et al.

(2007) estimated the relationship be tween short-term exposure to PM 2.5 

# and ED visits for asthma

in New York City. They estimated two single-po llutant models, one for the whole year and one

for the period from April through August; in a ddition, 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 usi ng 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 anal ysis to show the impact of different lag

structures, different modeling approaches, and si ngle- versus two-pollutant models on estimates

of the risks of premature morta lity and hospital admissions associated with short-term exposure

37 

# The region into which each of the 202 counti es in Bell et al. (2008) falls is given at:

http://www.biostat.jhsph.edu/MCAPS/estimates-full.html

. 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 PM 2.5 

# , we selected Moolgavkar (2003). This study reported results for premature nonaccidental, cardiovascular, and respiratory mort ality and for cardiovascular and respiratory

hospital admissions associated w ith short-term exposures to PM 2.5 

# 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 asso ciated with long-term exposure to PM 2.5 

# in our core

analysis, we selected Krewski et al. (2009) as our primary study. This st udy 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 functi ons used in the core

analysis, including: (a) extended air quality analysis incorporating data from 1989 to 2000

(extending the period of observati on to eighteen years: 1982-2000), which increases the power of

the study and allows the study auth ors to examine the important issue of exposure time windows,

(b) rigorous examination of a range of model form s and effect estimates, including consideration

of such factors as spatial autocorrelation in sp ecifying response functions, (c) coverage for a

range of ecological variables (soc ial, economic and demographic) which allows for consideration

of whether these confound or modify the relationship between PM 2.5 

# exposure and mortality, (d)

inclusion of a related analysis (focusing on Los Angeles), whic h allowed for consideration of

spatial gradients in PM 2.5 

# and whether they effect respons e 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 othe r larger datasets used in prospective cohort

analyses of long-term mortality (the six-cites dataset), we sel ected the Krewski et al. (2000)

study to provide C-R functions that were used in the sensitivity an alysis 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. Fo r purposes of transparency, we have included a

brief summary here of our rationale for not sel ecting 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 PM 2.5 

# levels. The

Goss et al. (2004) study (based on th e 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 mortalit y incidence. The Mill er et al. (2007) study

(focusing on the Women’s Health Initiative datase t) while providing coverage for a population of

particular interest, was not used, again due to an absence of baseline inci dence data (which is

particularly important for this population whic h is typically health ier than the general

population). And finally, the Eftim et al. ( 2008) study (focusing on th e 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 st udy selected as the basis for C- R functions used in the core

analysis) considered mortality from all causes, as well as cardiopulmona ry mortality, mortality

from ischemic heart disease, and lung cancer mortality. The study pres ents a variety of C-R

functions, in an effort to show how the result s vary with various changes to the method/model

used. It was not readily apparent from review of the Health Eff ects 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 clarifica tion regarding specific

aspects of the study and associated results as presented in the HE I 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 expressi ng a preference among the re sults for their use in

quantitative risk assessment,” preferring to “explor e 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 invol ved in air quality management, one could argue

that a random effects model (whi ch accounts for apparent spatial autocorrelation in the data)

might be preferable. A model that included eco logical covariates, whic h has the effect of

reducing the residual variation in mo rtality, 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 st udy 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 expos ure time windows considered in the analysis (1979-1983 and

1999-2000) were equally effective at repr esenting the relationship between PM 2.5 

# 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 w ith 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 se t of models, the relative risks for a 10 μg/m 3



# change in PM 2.5 

# were back-calculated from Table 33 of Krewski et al. (2009). We selected

several additional C-R functions fr om Krewski et al. (2009) to use in sensitivity analyses carried

out in two risk assessment locations (Los Angeles and Philadelphi a), including the random

effects form (section 3.5.4), as de scribed 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, ur ban areas, and epidemiological studies used

in the risk assessment is given below in Tabl es 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 give n in Table 3-7. An overview of

the locations, health endpoints, studies, and C- R functions included in sensitivity analyses is

given in Table 3-8.3-41Table 3-6 Locations, Health Endpoints, andShort-Term Exposure Studies Included in the PM2.5RiskAssessment*Premature MortalityHospital AdmissionsED VisitsUrban AreaNon-AccidentalCardiovascularRespiratoryCardiovascularRespiratoryCardiovascularRespiratoryAtlanta, GATolbert et al.(2007)Tolbert et al.(2007)Baltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXZanobetti andSchwartz (2009)Zanobetti andSchwartz (2009)Zanobetti andSchwartz (2009)Bell et al. (2008)Bell et al. (2008)Los Angeles, CAMoolgavkar(2003)Moolgavkar(2003)Moolgavkar(2003)New York, NYIto et al. (2007)Philadelphia, PAPhoenix, AZPittsburgh, PASalt Lake City,UTSt. Louis, MOTacoma, WAZanobetti andSchwartz (2009)Zanobetti andSchwartz (2009)Zanobetti andSchwartz (2009)Bell et al. (2008)Bell et al. (2008)*Studies in italics are used only in sensitivity analyses.3-42Table 3-7. Locations, Health Endpoints, andLong-Term Exposure Studies Included in the PM2.5RiskAssessment*Premature MortalityUrban AreaAll-CauseCardiopulmonaryIschemic Heart DiseaseLung CancerAtlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXNew York, NYPhoenix, AZPittsburgh, PASalt Lake City, UTSt. Louis, MOTacoma, WAKrewski et al. (2009) [extensionof the ACS study]Krewski et al. (2009) [extensionof the ACS study]Krewski et al. (2009) [extensionof the ACS study]Los Angeles, CAPhiladelphia, PAKrewski et al. (2009) [extensionof the ACS study]Krewski et al. (2000) [reanalysisof the Six Cities Study]Krewski et al. (2009) [extensionof the ACS study]Krewski et al. (2000) [reanalysisof the Six Cities Study]Krewski et al. (2009) [extensionof the ACS study]Krewski et al. (2009) [extensionof the ACS study]Krewski et al. (2000) [reanalysisof the Six Cities Study]*Studies in italics are used only in sensitivity analyses.3-43Table 3-8 Summary of Locations, Health Endpoints, Studies and Concentration-Response Functions Included inthe Core Analysis.*RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureZanobetti and Schwartz (2009)1Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009)1Short-term exposure cardiovascular mortalityAvg. of 0-day and 1-day lagsZanobetti and Schwartz (2009)1Short-term exposure respiratory mortalityAvg. of 0-day and 1-day lagsKrewski et al. (2009)2Long-term exposure all-cause mortalityNAKrewski et al. (2009)2Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)2Long-term exposure ischemicheart disease mortalityNACobb, De Kalb, Fulton,GwinnettKrewski et al. (2009)2Long-term exposure lung cancer mortalityNABell et al. (2008)3Short-term exposure HA (unscheduled), cardiovascular 0-day lagCobb, DeKalb, Fulton,Bell et al. (2008)3Short-term exposure HA (unscheduled), respiratory2-day lagTolbert et al. (2007)Short-term exposure emergency department (ED) visits,cardiovascularAvg. of 0-,1-day, and2-day lagsAtlantaBarrow, Bartow,Carroll, Cherokee,Clayton, Cobb, Coweta,DeKalb, Douglas,Fayette, Forsyth,Fulton, Gwinett, Henry,Newton, Paulding,Pickens, Rockdale,Spalding, WaltonTolbert et al. (2007) Short-term exposure emergency department (ED) visits,respiratoryAvg. of 0-,1-day, and2-day lagsZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsBaltimore Baltimore city,Baltimore countyZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lags3-44RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)Long-termexposure all-cause mortalityNAKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAKrewski et al. (2009)Long-term exposure lung cancer mortalityNABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagBell et al. (2008)Short-term exposure HA (unscheduled), respiratory2-day lagZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)Long-termexposure all-cause mortalityNAKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNABlount, Jefferson,Shelby, St. Clair,WalkerKrewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagBirminghamJeffersonBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsDallas DallasKrewski et al. (2009) Long-termexposure all-cause mortality NA3-45RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAKrewski et al. (2009)Long-term exposure lung cancer mortalityNABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagBell et al. (2008)Short-term exposure HA (unscheduled), respiratory2-day lagZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)Long-termexposure all-cause mortalityNAKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAKrewski et al. (2009)Long-term exposure lung cancer mortalityNABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagDetroit WayneBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)Long-termexposure all-cause mortalityNAKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAFresno FresnoKrewski et al. (2009) Long-term exposure lung cancer mortality NA3-46RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureBell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagBell et al. (2008)Short-term exposure HA (unscheduled), respiratory2-day lagZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)Long-termexposure all-cause mortalityNAKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAKrewski et al. (2009)Long-term exposure lung cancer mortalityNABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagHouston HarrisBell et al. (2008) Short-term exposure HA (unscheduled), respiratory2-day lagZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)Long-termexposure all-cause mortalityNAKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAKrewski et al. (2009)Long-term exposure lung cancer mortalityNABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagLos Angeles Los AngelesBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lag3-47RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)Long-termexposure all-cause mortalityNAKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAKrewski et al. (2009)Long-term exposure lung cancer mortalityNABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagKings, New York City(Manhattan), Queens,Richmond, BronxBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagNew YorkKings, New York City(Manhattan), Queens,Richmond, BronxIto et al. (2007) Short-term exposure emergency department (ED) visits,asthmaAvg. of 0-day and1-day lagsZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)Long-termexposure all-cause mortalityNAKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAKrewski et al. (2009)Long-term exposure lung cancer mortalityNABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagPhiladelphia PhiladelphiaBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lag3-48RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureZanobetti and Schwartz (2009) Short-term exposure non-accidentalmortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)Long-termexposure all-cause mortalityNAKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAKrewski et al. (2009)Long-term exposure lung cancer mortalityNABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagPhoenix MaricopaBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)Long-termexposure all-cause mortalityNAKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAKrewski et al. (2009)Long-term exposure lung cancer mortalityNABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagPittsburgh AlleghenyBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsSalt Lake City Salt LakeKrewski et al. (2009)Long-termexposure all-cause mortalityNA3-49RiskAssessmentLocationCountiesStudy/C-R FunctionHealth EndpointLag StructureKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAKrewski et al. (2009)Long-term exposure lung cancer mortalityNABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagBell et al. (2008)Short-term exposure HA (unscheduled), respiratory2-day lagZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1-day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)Long-termexposure all-cause mortalityNAKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAJeffferson, Madison(IL), St. Louis, St.Louis city, St. Clair (IL)Krewski et al. (2009) Long-term exposure lung cancer mortality NABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagSt. LouisMadison (IL), St. Louis,St. Louis city, St. Clair(IL)Bell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lagZanobetti and Schwartz (2009) Short-term exposurecardiovascular mortality Avg. of 0-day and 1day lagsZanobetti and Schwartz (2009) Short-term exposurerespiratory mortality Avg. of 0-day and 1-day lagsKrewski et al. (2009)Long-termexposure all-cause mortalityNAKrewski et al. (2009)Long-term exposure cardiopulmonary mortalityNAKrewski et al. (2009)Long-term exposure ischemic heart disease mortalityNAKrewski et al. (2009)Long-term exposure lung cancer mortalityNABell et al. (2008)Short-term exposure HA (unscheduled), cardiovascular 0-day lagTacoma PierceBell et al. (2008) Short-term exposure HA (unscheduled), respiratory 2-day lag*All C-R functions in the core analysis are single-pollutant, log-linear models; all are for a full year. The exposure metricfor all short-term exposure C-Rfunctions is the 24-hour average; the exposure metric for all long-term exposure C-R functions is the annual average.3-501This is a multi-city study; city-specific estimates “shrunken”towards the mean across all cities in a region were supplied toEPA (Zanobetti, 2009).2Two C-R functions were used for the core analysis – one corresponding to the earlier exposure period, from 1979 – 1983, and the other corresponding to thelater exposure period, from 1999 – 2000. Both C-R functions were based on follow-up of the cohort through 2000. Both used the standard Cox proportionalhazards model, with 44 individual and 7 ecologic covariates. The relative risks for a 10 μg/m3change in PM2.5from which the PM2.5coefficients were backcalculated were taken from Table33 of Krewski et al. (2009).3This study estimated four regional C-R functions – for the Northeast, Southeast, Northwest, and Southwest – for each health endpoint. For each riskassessment location, we used the regional C-R function for the region containing the risk assessment location. The designationof counties to each of thesefour regions can be found athttp://www.biostat.jhsph.edu/MCAPS/estimates-full.html.3-51Table 3-9. Summary of Locations, Health Endpoints, Studies and Concentration-Response Functions Included inSensitivity Analyses.Sensitivity AnalysisStudy/C-R FunctionHealth Endpoint**Risk AssessmentLocation(s)Single-Factor Sensitivity Analyses:Impact of using different model choices – fixed effectslog-linear vs. random effects log-linear vs. randomeffects log-log C-R function*random effects log-linear:Krewski et al. (2009) [Table 9,"Autocorrelation at MSA andZCA levels" group - "MSA &Diff" row]random effects log-log:Krewski et al. (2009) [Table 11,"MSA and DIFF" rows]All-cause, cardiopulmonary, ischemicheart disease, and lung cancer mortalityassociated with long-term exposureLos Angeles andPhiladelphiaImpact of using copollutant models in modeling longterm exposure-related mortalityKrewski et al., 2000 (reanalysisof ACS) – provides 2-pollutantmodels combining PM2.5withCO, NO2, O3or SO2.All-cause mortality associated withlong-term exposureLos Angeles andPhiladelphiaImpact of estimating risks down to PRB rather thandown to LMLKrewski et al. (2009) – C-Rfunctions for each of twoexposure periodsLong-term exposure all-causemortality All 15 urban areasImpact of C-R function from alternative long-termexposure studyKrewski et al. (2000) [reanalysisof the Harvard Six Cities study]All-cause, cardiovascular, respiratory,lung cancer mortality associated withlong-term exposureLos Angeles andPhiladelphiaImpact of using alternative hybrid rollback approach(note, that as discussed insection 3.2.3, in addition tothe hybrid rollback approach, we have also included alocally focused rollback approachas an alternative to theproportional rollback approach).40Krewski et al. (2009) All-causemortality associated withlong-term exposureBaltimore, Birmingham,Detroit, Los Angeles, NewYork, Pittsburgh, and St.Louis40However, as noted in section 3.2.3 and in section 3.5.4, quantitative risk estimates were not generated using the locally focused approach andinstead, composite monitor values (acting assurrogates for long-term exposure-relatedrisk) were used as the basis for the sensitivity analysis involving thelocally focused rollback approach.3-52Sensitivity AnalysisStudy/C-R FunctionHealth Endpoint**Risk AssessmentLocation(s)Impact of using season-specific C-R functions (vs. anannual C-R function)Zanobetti and Schwartz (2009) –seasonal functions vs. annualfunctionNon-accidental mortality,cardiovascular mortality, respiratorymortality associated with short-termexposureAll 15 urban areasImpact of using season-specific C-R functions (vs. anannual C-R function)Bell et al. (2008) – seasonalfunctions vs. annual functionHA (unscheduled), cardiovascular andrespiratory,associated with short-termexposureAll 15 urban areasImpact of using an annual C-R function (applied to thewhole year) vs. a seasonal function for April throughAugust (applied only to that period) (using a singlepollutant model).Ito et al. (2007) Asthma ED visits New YorkImpact of model selection (e.g., log-linear GAM with 30df; log-linear GAM with 100 df; and log-linear GLMwith 100 df)Moolgavkar (2003) Non-accidental and cardiovascularmortality; and cardiovascular andCOPD+ HA associatedwith short-termexposureLos AngelesImpact of lag structure (0-day, 1-day, 2-day) Moolgavkar (2003) Non-accidental and cardiovascular andCOPD+ HA associatedwith short-termexposureLos AngelesImpact of single- vs. multi-pollutant models (PM2.5with CO)Moolgavkar (2003) Non-accidental and cardiovascularmortality; and cardiovascular andCOPD+ HA associatedwith short-termexposureLos AngelesImpact of using alternativehybrid rollback approach Zanobetti and Schwartz (2009) Non-accidental mortality associatedwith short-term exposureBaltimore, Birmingham,Detroit, Los Angeles, NewYork, Pittsburgh, and St.LouisImpact of lag structure (0-day, 1-day, 2-day)Bell et al., 2008Cardiovascular and respiratory hospitaladmissions associated with short-termexposureLos Angeles andPhiladelphiaMulti-Factor Sensitivity Analyses:Impact of using a fixed effects log-linear vs. a randomeffects log-log model, estimating incidence down to thelowest measured level (LML) in the study vs. down toPRB, and using a proportional vs. hybrid rollback toestimate incidence associatedwith long-term exposureto PM2.5concentrations that just meet the currentstandardsAll-cause and ischemic heart diseasemortality associated with long-termexposureLos Angeles andPhiladelphiaImpact of using season-specific vs. all-year C-R Zanobetti and Schwartz (2009) Non-accidental mortality associated Baltimore, Birmingham,3-53Sensitivity AnalysisStudy/C-R FunctionHealth Endpoint**Risk AssessmentLocation(s)functions and proportional vs. hybrid rollbacks toestimate incidence associated with short-term exposureto PM2.5concentrations that just meet the currentstandardswith short-term exposure Detroit, Los Angeles, NewYork, Pittsburgh, and St.Louis*This “single-factor” sensitivity analysis is actually two factors – first the change from a fixed effects log-linear model toa random effects log linear model,and then the change from a random effects log-linear model to arandom effects log-log model. These were combined into a single sensitivity analysisbecause Krewski et al. (2009) did not present the results of a fixed effects log-log model (to compare to the core analysis fixed effects log-linear model).**”HA” = hospital admissions, “ED” = emergency department visits, “COPD+” = chronic obstructive pulmonary disease.

# 3-54 3.4 BASELINE HEALTH EFFE CTS 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 asso ciated with a given change in PM 2.5 

# concentrations using this

form of C-R function requires the baseline incide nce (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 PM 2.5 

# air quality (denoted 

# y 0 

# 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 pe riod of time, usually per year. Ra tes are expressed either as a

value per population group (e.g., th e number of cases in Philadelphia County) or a value per

number of people (e.g., the number of cases per 10,000 residents in Phil adelphia County), and

may be age- and sex-specific. Incidence rates vary among geographic areas due to differences in

population characteristics (e.g., ag e distribution) and factors pr omoting 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 Ce nter 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 (IC D)-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 ge nerated 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 Pr oject (HCUP) Central Distributor . HCUP is a

family of health care databases developed through a Federal-Stat e-Industry partnership

and sponsored by the Agency for Healthcare Research and Quality (AHRQ). The HCUP

databases are based on the data collection effo rts of data organizati ons 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 purchas ed from the HCUP Central Distributor,

although not all participant st ates release the data to the Central Distributor.

# 3-55 

#  HCUP State Partners . For those HCUP participating stat es 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 Pa rtners. The data are at the di scharge 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 locatio n(s) where the incidence rate is applied.

The necessary baseline incidence data we re not available for Atlanta, Birmingham,

Philadelphia, Pittsburgh and St. Louis. Theref ore, for each of these five risk assessment

locations EPA instead used the baseline inci dence rate for a designa ted surrogate location.

Surrogate locations were chosen if they were deemed to be sufficiently similar to the urban area

whose baseline incidence data were not availabl e. Surrogate locations are noted in Table 3-9.

# 3-56 Table 3-10 Sources of Hospital Admission s (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 variab les 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 additi on to the health baseline incidence data we

also need the corresponding popul ation. 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 At lanta. Because the ED visit data obtained from

the authors of Tolbert et al. (2007) are fo r 2004, we used 2004 population estimates for the 20-

county Metropolitan area used in the Tolbert et al. study for the Atlanta ar ea to calculate the ED

incidence rates to be applied when using that st udy 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 popul ations in the assessme nt locations that are

relevant are shown below in Table 3-10.3-57Table 3-11. Relevant Population Sizes for PM Risk Assessment Locations.Population (Year 2006 and 2007)*All AgesAges30Ages65CityCounties200620072006200720062007Atlanta, GA - 1Cobb, De Kalb, Fulton, Gwinnett3,126,000 3,198,000 1,817,000 1,865,000 236,000 245,000Atlanta, GA - 2 Cobb, De Kalb, Fulton2,376,000, 2,421,000 1,400,000 1,433,000 191,000 198,000Atlanta, GA - 320-County MSA**4,975,000 5,123,000 2,831,000 2,918,000 391,000 408,000Baltimore, MDBaltimore city, Baltimore county1,429,000 1,426,000 849,000 848,000 190,000 189,000Birmingham, AL - 1 Blount, Jefferson, Shelby, St. Clair,Walker1,037,000 1,044,000 619,000 625,000 131,000 133,000Birmingham, AL - 2 Jefferson660,000 659,000 397,000 397,000 88,000 88,000Dallas, TXDallas2,338,000 2,367,000 1,285,000 1,308,000 195,000 199,000Detroit, MIWayne2,012,000 1,985,000 1,176,000 1,168,000 236,000 234,000Fresno, CA Fresno886,000 899,000 444,000 452,000 86,000 87,000Houston, TXHarris3,876,000 3,936,000 2,097,000 2,139,000 299,000 307,000Los Angeles, CALos Angeles9,881,000 9,879,000 5,544,000 5,579,000 1,011,000 1,030,000New York, NY - 1 Kings, New York City (Manhattan),Queens, Richmond, Bronx8,251,000 8,275,000 4,940,000 4,975,000 1,004,000 1,013,000New York, NY - 2New York city (Manhattan)1,613,000 1,621,000 1,061,000 1,074,000 201,000 204,000Philadelphia, PAPhiladelphia833,000 1,450,000 833,000 833,000 189,000 187,000Phoenix, AZMaricopa3,779,000 3,880,000 2,103,000 2,167,000 417,000 432,000Pittsburgh, PAAllegheny1,225,000 1,219,000 790,000 786,000 208,000 206,000Salt Lake City, UTSalt Lake991,000 1,010,000 504,000 517,000 83,000 86,000St. Louis, MO - 1Jefferson, Madison (IL), St. Louis, St.Louis city, St. Clair (IL)2,093,000 2,091,000 1,259,000 1,261,000 274,000 275,0003-58Population (Year 2006 and 2007)*All AgesAges30Ages65CityCounties200620072006200720062007St. Louis, MO - 2 Madison (IL), St. Louis, St. Louis city,St. Clair (IL)1,879,000 1,875,000 1,134,000 1,134,000 253,000 252,000Tacoma, WA Pierce764,000 773,000 437,000 444,000 79,000 81,000* Not all populations listed in the table were used for calculating the incidence rates. As noted above, the population year needs to match the year of the healthdata and the population age group needs to match what is used in the epidemiological studies. In addition, 2004 population (all ages) is used for ED visits inAtlanta-3, which is 4,663,946. Populations inthis table are rounded to the nearest 1,000.** The 20 counties are Barrow, Bartow, Carroll, Cherokee, Clayton, Cobb, Coweta, DeKalb, Douglas, Fayette,Forsyth, Fulton, Gwinett, Henry, Newton, Paulding, Pickens,Rockdale, Spalding, and Walton.

# 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 c odes used in that study. For example, Bell et

al. (2008) designated Dallas, TX as Dallas Count y and estimated a C-R fu nction 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 IC D codes for ages 65 and up in Dallas County

and also selected the pop ulation 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 speci fic 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, an d August) and fall (September, October, and

November). Note that the HA data for some states didn’t include in formation about admission

season but only discharge season or discharg e 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 unschedul ed hospital admissions (Has)

only, so we excluded scheduled admissions from the analyses to match the study. A HA is

unscheduled if the admission t ype is emergency or urgent.

The baseline mortality rates are given in Ta ble 3-11. The baseline HA and ED visit rates

are given in Table 3-12.

41 

# For Atlanta, Birmingham, Philadel phia, 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.3-60Table 3-4. Baseline Mortality Rates (Deaths per 100,000 Relevant Population per Year) for 2006 for PM Risk AssessmentLocations.*Type of Mortality (ICD-10 or ICD-9 Codes)CityAge GroupAll-CauseNon-accidental(A00-R99)Cardiovascular(I01-I59)Respiratory(J00-J99)Cardiopulmonary(401-440, 460-519)IschemicHeartDisease(410-414)LungCancer(162)COPD(490-496)Atlanta, GA - 1All ages --- 480 120 41 --- --- --- ---Atlanta, GA - 1≥30 860 NA NA NA 330 89 51 NAAtlanta, GA - 2NA NA NA NA NA NA NA NA NAAtlanta, GA - 3NA NA NA NA NA NA NA NA NABaltimore, MDAll ages NA 950 270 85 NA NA NA NABaltimore, MD≥30 1,700 NA NA NA 690 300 110 NABirmingham, AL - 1All ages NA 920 260 85 NA NA NA NABirmingham, AL - 1≥30 1,600 NA NA NA 680 190 104 NABirmingham, AL - 2NA NA NA NA NA NA NA NA NADallas, TXAll ages NA 540 150 48 NA NA NA NADallas, TX≥30 1,020 NA NA NA 420 170 66 NADetroit, MIAll ages NA 850 300 67 NA NA NA NADetroit, MI≥30 1,500 NA NA NA 700 360 107 NAFresno, CAAll ages NA 620 190 67 NA NA NA NAFresno, CA≥30 1,300 NA NA NA 590 260 66 NAHouston, TXAll ages NA 480 130 37 NA NA NA NAHouston, TX≥30 920 NA NA NA 370 150 57 NALos Angeles, CAAll ages NA 560 190 57 NA NA NA 29Los Angeles, CA≥30 1,030 NA NA NA 510 250 55 NANew York, NY - 1All ages NA 630 270 52 NA NA NA NA3-61Type of Mortality (ICD-10 or ICD-9 Codes)CityAge GroupAll-CauseNon-accidental(A00-R99)Cardiovascular(I01-I59)Respiratory(J00-J99)Cardiopulmonary(401-440, 460-519)IschemicHeartDisease(410-414)LungCancer(162)COPD(490-496)New York, NY - 1≥30 1,0800 NA NA NA 580 380 56 NANew York, NY - 2NA NA NA NA NA NA NA NA NAPhiladelphia, PAAll ages NA 970 280 83 NA NA NA NAPhiladelphia, PA≥30 1,700 NA NA NA 720 300 120 NAPhoenix, AZAll ages NA 600 160 67 NA NA NA NAPhoenix, AZ≥30 1,100 NA NA NA 470 220 68 NAPittsburgh, PAAll ages NA 1,090 330 96 NA NA NA NAPittsburgh, PA≥30 1,800 NA NA NA 770 350 120 NASalt Lake City, UTAll ages NA 480 110 45 NA NA NA NASalt Lake City, UT≥30 980 NA NA NA 350 101 37 NASt. Louis, MO - 1All ages NA 870 270 83 NA NA NA NASt. Louis, MO - 1≥30 1,500 NA NA NA 680 320 106 NASt. Louis, MO - 2NA NA NA NA NA NA NA NA NATacoma, WAAll ages NA 660 190 66 NA NA NA NATacoma, WA≥30 1,200 NA NA NA 510 240 88 NANationalAll ages 810 750 220 76 340 140 53 42National≥30 1,300 1,300 370 130 580 240 90 71* Figures in this table are rounded to a two-integer level of precision. NA refers to healthendpoint categories that are notrelevant for this particular countylevel study area definition (i.e., the epidemiology study and associated effect estimate reflected in this specification county-level of the study area did not include thisparticular endpoint category and consequently a baseline incidence value is not shown).3-62Table 3-5. Baseline Hospital Admission (HA) and Emergency Department (ED) Rates (Admissions/Visits per 100,000 RelevantPopulation 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 visitdata in a 20-county delineation of Atlanta. However, the 2004 rates wereapplied to the appropriate year population in the risk assessment.Health Endpoints (ICD-9 Codes)CityAge GroupHA, cardiovascular (390-429)HA (unscheduled),cardiovascular(426–429, 430–438,410–414, 440–449)HA, COPD(490-496)HA (unscheduled),respiratory (490–492,464–466, 480–487)ED visits,cardiovascular (410–414, 427, 428, 433–437, 440, 443–445,451–453)ED visits, respiratory(460–465, 466.1, 466.11,466.19, 477, 480–486, 491-493, 496, 786.07, 786.09)ED visits,asthma(493)Atlanta,GA - 1NA NA NA NA NA NA NA NAAtlanta,GA - 2≥65 NA 5,700 NA 2,020 NA NA NAAtlanta,GA - 3All ages NA NA NA NA 690** 2600** NABaltimore,MD≥65 NA 8,600 NA 2,600 NA NA NABirmingham,AL - 1NA NA NA NA NA NA NA NABirmingham,AL - 2≥65 NA 5,700 NA 2,020 NA NA NADallas,TX≥65 NA 5,000 NA 2,000 NA NA NADetroit,MI≥65 NA 8,800 NA 3,000 NA NA NAFresno,CA≥65 NA 5,600 NA 2,100 NA NA NAHouston,TX≥65 NA 5,900 NA 2,200 NA NA NALos Angeles,CAAll ages NA NA 223 NA NA NA NALos Angeles,CA≥65 5,500 5,500 NA 2,000 NA NA NANew York,NY - 1≥65 NA 6,400 NA 2,030 NA NA NANew York,NY - 2All ages NA NA NA NA NA NA 1,100Philadelphia,PA≥65 NA 8,600 NA 2,600 NA NA NAPhoenix,AZ≥65 NA 5,020 NA 1,600 NA NA NAPittsburgh,PA≥65 NA 6,100 NA 1,900 NA NA NASalt Lake City,UT≥65 NA 3,030 NA 1,200 NA NA NASt. Louis,MO - 1NA NA NA NA NA NA NA NASt. Louis,MO - 2≥65 NA 5,600 NA 2,600 NA NA NATacoma,WA≥65 NA 4,500 NA 1,600 NA NA 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 populatio ns. For example, populations in different

regions of the country may have different beha vior and activity patterns (e.g., air conditioning

use, time spent indoors) that aff ect their exposure to ambient PM and thus the population health

response. The composition of populations in differe nt regions of the country may vary in ways

that can affect the population response to expos ure 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 diff erent 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., f actors affecting population exposur e or response – in order to

produce risk estimates whose distribution adequa tely characterizes the distribution in the

underlying population(s).



# Uncertainty refers to the lack of knowledge regard ing the actual values of inputs to an

analysis. Models are typically used in analyses, and there is uncertainty a bout the true values of

the parameters of the model (parameter uncerta inty) – e.g., the value of the coefficient for PM 2.5



# in a C-R function. There is also uncertainty abou t the extent to which the model is an accurate

representation of the underlying physical systems or relati onships being modeled (model

uncertainty) – e.g., the shapes of C-R functions. In addition, there may be some uncertainty

surrounding other inputs to an anal ysis due to possible measurement error—e.g., the values of

daily PM 2.5 

# concentrations in a risk assessment locati on, or the value of the baseline incidence

rate for a health e ffect in a population. 43 

# In any risk assessment, uncertainty is, ideally, reduced

to the maximum extent possible through improv ed measurement of key variables and ongoing

model refinement. However, sign ificant uncertainty often remains, and emphasis is then placed

on characterizing the nature of that uncertain ty 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 PM 2.5 

# risk assessment was designed to cover

the range of PM 2.5 

# -related risk experienced by the U.S. popul ation and, in general, to adequately

reflect the inherent variability in those fact ors affecting the public health impact of PM 2.5



# exposure. Sources of variability reflected in the risk assessmen t design are discussed in section

3.5.2, along with a discussion of thos e 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 progr essively 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 ri sk and Superfund Site risks (U SEPA, 2004b and 2001, respectively)

as well as recent guidance from the World H ealth 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 asse ssments, uses default uncertainty factors

(rather than developing site-specifi c uncertainty characterizations);



#  Tier 1 – the lowest level of site-specific uncer tainty characterizati on, 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 possibl y probability bound (high- and low-end)

assessment; and



#  Tier 3 – uses probabilistic methods to characteri ze the effects on risk estimates of sources

of uncertainty, indivi dually and combined.

With this four-tiered approach, the WHO fr amework provides a means for systematically

linking the characterization of uncer tainty to the sophist ication of the underlyi ng risk assessment.

Ultimately, the decision as to which tier of uncer tainty characterization to include in a risk

assessment will depend both on the overall soph istication of the risk assessment and the

availability of information for characterizing th e various sources of uncertainty. EPA staff has

used the WHO guidance as a framework for deve loping 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 warrant ing consideration of a full probabilistic (WHO

Tier 3) uncertainty analysis. Ho wever, limitations in available information prevent this level of

# 3-65 analysis from being completed at this time. In particular, the incorporati on of uncertainty related

to key elements of C-R functions (e.g., competi ng lag structures, alternative functional forms,

etc.) into a full probabilistic WHO Tier 3 analysis would require that prob abilities be assigned to

each competing specification of a given mode l element (with each probability reflecting a

subjective assessment of the probability that the given specification is th e “correct” description

of reality). However, for many model elements there is insufficient information on which to

base these probabilities. One approach that has be en taken in such cases is expert elicitation;

however, this approach is resource- and time-inte nsive and consequently, it was not feasible to

use this technique in the cu rrent PM NAAQS review to s upport a WHO Tier 3 analysis. 44



# For most elements of this risk assessme nt, rather than conducting a full probabilistic

uncertainty analysis, we have included qualita tive discussions of the potential impact of

uncertainty on risk results (WHO Tier1) and/or completed sensitivity analyses assessing the

potential impact of sources of un certainty on risk results (WHO Ti er 2). Note, however, that in

conducting sensitivity analyses, we have used bot h single- and multi-factor approaches (to look

at the individual and combined im pacts 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 approach es that were deemed to have

scientific support in the literature (and so represen t alternative reasonable input parameter values

or modeling options). This means that the alte rnative risk results generated in the sensitivity

analyses represent reasonable risk estimates that canbe used to provide a c ontext, 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 PM 2.5 

# 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 distri butions for key PM risk-related attributes

to determine whether they are nationally represen tative or more focused on a particular portion

of the distribution for a given at tribute (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 e ffects estimates (obtained from epidemiological studies) to

incorporate statistical uncertainty associ ated with sample size considerations in the presentation of risk estimates.

# 3-66 comprising our 15 urban study area s fall along a distribution of na tional 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 PM 2.5 

# exposure.

The remainder of this section is organized as follows. Key sources of variability which

are reflected in the design of the risk assessm ent, along with sources excluded from the design,

are discussed in section 3.5.2. A qua litative discussion of key sour ces of uncertainty associated

with the risk assessment (including the potential direction, magnitude an d degree of confidence

associated with our understanding of the sour ce of uncertainty – the knowledge base) is

presented in section 3.5.3. The methods and resu lts 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 th e key sources of variability related to

population exposure and exposure response, to the extent supporte d 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 a nd consequently introduce a degree of uncertainty

into the risk estimates. While different sour ces of variability were captured in the risk

assessment, it was generally not possible to sepa rate out the impact of each factor on population

risk estimates, since many of the sources of vari ability are reflected co llectively in a specific

aspect of the risk model. For example, inclusi on of urban study areas fr om different PM regions

likely provides some degree of coverage for a variety of factors associated with PM 2.5 

# risk (e.g.,

air conditioner use, PM 2.5 

# composition, differences in population commuting and exercise

patterns, weather). However, the model is not su fficiently 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 identifica tion of sources of variability

as “key” reflects consideration for se nsitivity analyses conducted for previous PM NAAQS risk assessments, which

have provided insights into which sources of variability (re flected in different elements of those earlier sensitivity

analyses) can influence risk estimates, as well as informati on 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 consider ed by staff in identifying key sources of variability to

address both in the risk assessment and in the qualitativ e 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 lik ely 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:



#  PM 2.5 

# composition : While information was not av ailable to support modeling risk

associated with different components of PM 2.5 

# , the assessment did use effect estimates

(for a number of the short-term exposure-re lated health endpoints) differentiated by

region of the country, or di fferentiated for specific urba n 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 lo cations, compositional differences in PM 2.5 

# may be partially responsible. Therefore, while the analysis did not explicitly address

compositional differences in generating risk estimates, potential differences in PM 2.5 

# composition may be reflected in those effect estimates that are differentiated by region

and/or urban study area. The effect estimates for mortalit y associated with long-term

exposure to PM 2.5 

# are not regionally differentiated and instead, a single national-scale

estimate is used. This means that any differe nces in risks of mortality associated with

long-term exposure to PM 2.5 

# that are linked to differences in PM 2.5 

# composition (or to

any other differences across regions or loca tions) would not be discernable, since a

single national-scale risk estimate is genera ted for each mortality category. In addition

to using region- or location-specific effect es timates 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 th at areas in different regions of the country, with different

PM 2.5 

# composition, were included.



#  Intra-urban variability in ambient PM 2.5 

# levels : Several recent studies (e.g., Jerrett

et al., 2005) have addressed th e issue of heterogeneity of PM concentrations within

urban areas and its pote ntial impact on the estimation of premature mortality associated

with long-term exposure to PM 2.5 

# . Most recently, the HEI R eanalysis II (Krewski et

al., 2009), focusing on the ACS dataset, discu sses epidemiological analyses completed

for Los Angeles and New York City which included more highly-refined (zip code

level) characterizations of spatial gradient s in population exposure within each urban

area based on land-use regression methods and/or kriging. Wh ile both analyses

provide insights into the issue of intra-urban heterogeneity in PM 2.5 

# 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 PM 2.5 

# reduction as urban areas: In

simulating just meeting the current or alte rnative suites of sta ndards, there can be

considerable variability in the patterns of ambient PM 2.5 

# 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 regi onal patterns of reduction), hybrid (reflecting a

combination of localized and regional pa tterns 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 descrip tion of how this factor is

addressed in the sens itivity analysis).



#  Copollutant concentrations : Inclusion of copollutant models in short-term exposurerelated time series studies has produced mi xed results in terms of the degree of

attenuation of the PM 2.5 

# signal that results from inclusi on 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 stru cture among pollutants as well as differing

degrees of exposure measurement error relate d to the copollutants. Further, the PM

ISA (section 2.1.3) notes that correlations be tween PM and copollutants (including CO,

O 3 

# , SO 2 

# and NO 2 

# ) can vary both seasonally and spatiall y. Therefore, it is possible that

the degree of attenuation of PM 2.5 

# -related risk by copollutant s 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 disc ussion of uncertainty introduced into

the analysis as a result of not including copo llutant models in the core risk assessment).

We did explore the issue of copollutant m odeling 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 copollu tant 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-st atus (SES)-related factors: Variability in

population density particularly in re lation to elevated levels of PM 2.5 

# has the potential

to influence population risk. In addition, othe r aspects of demographi cs 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 c onsider more detailed patterns of PM

exposure by different populations, pote ntial differences in exposure to PM 2.5 

# reflecting

demographic and SES-related factors is c overed to some degree by the use of urban

study area-differentiated effects estimates (f or short-term exposure-related mortality)

and regionally-differentiated effects estim ates (in the case of short-term exposurerelated morbidity). In th e 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 nationa l-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 de mographics and SES-related factors to the

# 3-69 extent that these factors infl uence the relationship between PM 2.5 

# exposure and

mortality response, as detected by the underlying cohort studies).



#  Behavior affecting exposure to PM 2.5 

# : We have incorporated, where available,

region- and/or city-specific e ffect estimates in order to capture behavioral differences

across locations that could a ffect population exposures to PM 2.5 

# (e.g., time spent

outdoors, air conditioning use). However, while these location-specific effect

estimates may be capturing differences in be havior, they may also be capturing other

differences (e.g., differences in the composition of PM 2.5 

# to which populations are

exposed). As noted above, it was not possi ble to separate out the impact of these

different factors, which may vary across lo cations 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 th e county-level, providing a relatively high

degree of spatial refinement in characterizing baseline inci dence 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 PM 2.5 

# levels (reflecting meteorological

trends, as well as future changes in the mix of PM 2.5 

# sources and regulations impacting

PM 2.5 

# ): Risk estimates for the PM 2.5 

# 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 c oncentrations or composition of ambient PM 2.5



# in the risk assessment locations based on possible changes in economic activity,

demographics or meteorology. Actual risk le vels potentially experienced in the future

as a result of implementing alternative standa rd levels may differ from those presented

in this report due, in part, to potential changes in these factors related to ambient PM 2.5



# .

3.5.3 Qualitative Assessment of Uncertainty

As noted in section 3.5.1, we have based the de sign 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 screeni ng analysis suggests there is con cern 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 sens itivity 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 ju stification for conducting

a Tier 1 analysis. In fact, as ar gued earlier, given the complexity of the overall risk assessment, a

full Tier 3 uncertainty analysis is warrant ed for consideration under the WHO guidelines

(although as discussed later, limitatio ns 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 (inc luding both the magnitude and direction of the

impact) on risk results, as sp ecified in the WHO guidance. 46 

# As shown in Table 3-13, for each

source of uncertainty, we have (a) provided a desc ription, (b) estimat ed 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 estimat es, (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 PM 2.5



# 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) re flect EPA staff consensus on the

degree to which a particular source could produc e 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 woul d not be expected to im pact 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 in terpretation of risk in the c ontext 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 s ection, 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 ri sk assessments, together with information provided in

the final PM ISA and comments provided by CASAC on the an alytical plan for the RA, as well as the first draft RA.

47 

# For example, if a particular source of uncertainty were more fully characteri zed (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 un certainty 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, inst ead, serve primarily as a means for guiding future research to

reduce uncertainty related to PM 2.5 

# risk assessment.

As with the qualitative discussion of sources of variability included in the last section, the

characterization and relative ra nking of sources of uncertainty addressed here is based on

consideration by EPA staff of information provi ded 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 informa tion 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.3-72Table 3-6. Summary of Qualitative Uncertainty Analysisof Key Modeling Elementsin the PM NAAQS RiskAssessment.Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)A.Characterizingambient PM2.5levels for studypopulationsusing theexistingambientmonitoringnetworkIf the set of monitors used in aparticular urban study area tocharacterize populationexposure as part of an ongoingrisk assessment do not matchthe ambient monitoring dataused in the originalepidemiological study, thenuncertainty can be introducedinto the risk estimates.BothLow-mediumLow-mediumKB and INF: In modeling risk, we focus on those counties thatwere included in the epidemiological studies supplying theunderlying C-R functions. This means that, particularly for thoseendpoints modeled using C-R functions obtained from morerecent studies, there is likely a close association between themonitoring network used in the risk assessment and the networkused in the study supplying the C-R function(s). Note, however,that in those instances where the networks are different (e.g.,when older studies are used, resulting in an increased potential fornetworks to have changed), uncertainty may be introduced into therisk assessment and it is challenging to evaluate the nature andmagnitude of the impact that that uncertainty would have on riskestimates, given the complex interplay of factors associated withmismatched monitoring networks (i.e., differences in the set ofmonitors used in modeling risk and those used in the underlyingepidemiological study).B.Characterizingpolicy-relevantbackground(PRB)For this analysis, we have usedmodeling to estimate PRBlevels for each urban studyarea. Depending on the natureof errors reflected in thatmodeling, uncertainty (in bothdirections) may be introducedinto the analysis.Both Low LowINF: Given that the risk assessment focuses primarily on thereduction in risk associated withmoving from the current NAAQSto alternative standard levels, the impact of uncertainty in PRBlevels on the risk estimates is expected to be low. In addition, forlong-term exposure related mortality, we have based the coreanalysis on modeling risk down to LML rather than PRB, whichreduces the significance of the PRB issue in the context ofmodeling long-term exposure-related mortality.C.Characterizingintra-urbanpopulationexposure in thecontext ofepidemiologystudies linkingExposure misclassificationwithin communities that isassociated with the use ofgeneralized populationmonitors (which may missimportant patterns of exposurewithin urban study areas)introduces uncertainty into theUnder(generally)MediumhighMediumKB and INF: Recent analyses inLos Angeles and New York Citybased on ACS data (as reported in Krewski et al., 2009)demonstrate the relatively significant effect that this source ofuncertainty can have on effect estimates (and therefore on riskresults). These analyses also illustrate the complexity and sitespecific nature of this source of uncertainty. The results of theLos Angeles analysis suggest that exposure error may result ineffects estimates that are biasedlow and therefore result in the3-73Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)PM2.5tospecific healtheffectseffect estimates obtained fromepidemiology studies.underestimation of risk. Specifically in relation to the zip-codelevel analysis based on ACS data conducted in Los Angeles(Jerrett et al., 2005), the final ISAstates that, “This [the refinedexposure analysis reported in the Jerrett study] resulted in bothimproved exposure assessment and an increased focus on localsources of fine particle pollution. Significant associationsbetween PM2.5and mortality from all causes and cardiopulmonarydiseases were reported with the magnitude of the relative risksbeing greater than those reported in previous assessments. Ingeneral, the associations for PM2.5and mortality using these twomethods [kriging and land-use regression] for exposureassessment were similar, though the use of land use regressionresulted in somewhat smaller hazard ratios and tighter confidenceintervals (see Table 7-9). Thisindicates that city-to-cityconfounding was not the cause of the associations found in theearlier ACS Cohort studies. This provides evidence that reducingexposure error can result in stronger associations between PM2.5and mortality than generally observed in broader studies havingless exposure detail” (final ISA, section 7.6.3, p. 7-90).D. Statistical fitof the C-RfunctionsExposure measurement errorcombined with other factors(e.g., size of the effect itself,sample size, control forconfounders) can effect theoverall level of confidenceassociated with the fitting ofstatistical effect-responsemodels in epidemiologicalstudies.BothLowmedium(long-termhealthendpoints)Medium(short-termhealthendpoints)MediumINF: Long-term mortality studiesbenefit from (a) having largersample sizes (given that large national datasets are typically usedin deriving national-scale models), (b) the fact that the form of themodels used appears to be subject to relatively low uncertainty(see next row below) and (c) our not attempting to derive locationspecific effects estimates (but instead, relying on national-scaleestimates). These factors combine to produce effects estimatesthat tend to be statistically robust(as reflected in results presentedin Krewski et al., 2009). In addition, while concerns remainregarding exposure misclassification and potential confounding,generally we do not believe that the effects estimates areconsistently biased in a particulardirection. In the case of shortterm mortality and morbidity health endpoints, there is greateruncertainty associated with the fit of models given the smallersample sizes often involved, difficulty in identifying theetiologically relevant time period for short-term PM exposure, andthe fact that models tend to be fitted to individual counties or3-74Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)urban areas (which introduces the potential for varying degrees ofconfounding and effects modification across the locations). Incontrast to the long-term mortality studies, the short-termmortality and morbidity endpoints occasionally have effectsestimates that are not statistically significant. Note, however thatfor this risk assessment, in modeling both short-term mortality andmorbidity endpoints, we are not relying on location-specificmodels. In the case of short-term mortality, we are using cityspecific effects estimates derived using Bayesian techniques(these combine national-scale models with local-scale models)(personal communication with Zanobetti, 2009). For short-termmorbidity, we are using regionaleffects estimates (Bell et al.,2008). In both cases, while effects estimates are at times nonstatistically significant, these models do benefit from largersample sizes compared to city-specific models.E. Representativeness of thepopulation usedin theepidemiologicalstudyIf a population was used in theepidemiological study that isnot representative of thegeneral (urban) population,then the effect estimate thatresults may not be optimal forthe population being modeledin the risk assessment (i.e.,risks may be biased high orlow). The issue ofrepresentativeness wouldideally focus on factors directlyrelated to PM risk (includingeffect modifiers).BothLow-MediumLow-MediumKB: often we will have information from the epidemiologicalstudy that allows us to identify potential differences between thestudy population and the general (urban) U.S. population related,for example, to SES factors such as income or education.However, it can be more difficult to translate these differencesinto quantitative estimates of potential bias in effect estimates.INF: In the case of the ACS dataset underlying our modeling oflong-term exposure-related mortality in the core analysis,concerns have been raised that the study population has a higherSES status relative to the U.S. population. With the potential forthis to result in effect estimates that are biased downward(educational status is one factor that has been cited – see p. 8-118,U.S. EPA, 2004a).E. Shape of theC-R functionsUncertainty in predicting theshape of the C-R function,particularly in the lowerexposure regions which areoften the focus in PM NAAQSregulatory reviews.Both Medium Low-mediumINF: Regarding long-term mortality, the ISA suggests that a loglinear non-threshold model is best supported in the literature formodeling both short-term and long-term health endpoints.Although consideration of alternative model forms (Krewski et al.,2009) does suggest thatdifferent models can impact risk estimatesto a certain extent, generally this appears to be a moderate sourceof overall uncertainty. Particularlyif, as is the case in this risk3-75Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)assessment, we are not extrapolating below the lowest measuredlevels found in the underlying epidemiological studies. Withregard to long-term mortality,the final ISA concludes that, “Inaddition to examining the concentration-response relationshipbetween short-term exposure to PM and mortality, Schwartz et al.(2008, 156963) conducted an analysis of the shape of theconcentration-response relationship associated with long-termexposure to PM. Using a variety of statistical methods, theconcentration-response curve was found to be indistinguishablefrom linear, and, therefore, little evidence was observed to suggestthat a threshold exists in the associationbetween long-term exposure to PM2.5and the risk of death(Section 7.6).” (section 2.4.3, p.2-26). Regarding short-termmorbidity, the final ISA states that, “Overall, the studies evaluatedfurther support the use of a no-threshold log-linear model, butadditional issues such as the influence of heterogeneity inestimates between cities, and the effect of seasonal and regionaldifferences in PM on the concentration-response relationship stillrequire further investigation.”(section 2.4.3, p. 2-25).F. Addressingco-pollutantsThe inclusion or exclusion ofco-pollutants which mayconfound, or in other ways,affect the PM effect, introducesuncertainty into the analysis.BothLow-mediumMediumINF: With regard to long-term health endpoints, the final ISAstates that, “Given similar sources for multiple pollutants (e.g.,traffic), disentangling the health responses of co-pollutants is achallenge in the study of ambient air pollution.” (ISA, section7.5.1, p. 7-57). The final ISA also notes that in some instances,consideration of copollutants can have a significant impact on riskestimates. For example, the more refined study of mortality in LAas reported in Krewski et al., 2009 suggested that inclusion ofozone in the model along with PM2.5results in statistically nonsignificant results for lung-cancermortality, while IHD-associatedmortality remained statistically significant (Krewski et al., 2009 –Table 23). With regard to short-term mortality and morbidity, thefinal ISA generally concludes that observed associations are fairlyrobust to the inclusion of copollutants in the predictive models(see ISA, sections 6.3.8, 6.3.9,and 6.3.10). The mixed impact ofconsidering multi-pollutant models in assessing PM2.5-associatedrisk for short-term and long-term exposure related endpoints,3-76Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)leads us to conclude that the potential impact of this source ofuncertainty is low-medium (depending on the specific endpointsunder consideration). The epidemiological studies used as thebasis for selecting C-R functionsfor the core risk assessment didnot include multi-pollutant models (with the exception of PM10-2.5and PM2.5combined models in Zanobetti and Schwartz, 2009).However, we have included copollutant models in the sensitivityanalysis (see Section 4.3).G. Potentialvariation ineffectsestimatesreflectingcompositionaldifferences forPMThe composition of PM candiffer across study areasreflecting underlyingdifferences in primary andsecondary PM2.5sources (bothnatural and anthropogenic). Ifthese compositional differencesin fact translate into significantdifferences in public healthimpact (per unit concentrationin ambient air) for PM2.5thensignificant uncertainty may beintroduced into riskassessments if thesecompositional differences arenot explicitly addressed.BothMedium-HighMedium-HighKB and INF: Epidemiology studies examining regionaldifferences in PM2.5-related health effects have found differencesin the magnitude of those effects (see sections 2.3.1.1 and 2.3.2 inthe draft ISA). While these may be the result of factors other thancomposition (e.g., different degrees of exposure misclassification),composition remains one potential explanatory factor. For shortterm exposure morbidity and mortality effects, the inclusion ofcity-specific and/or regional-specific effect estimates in the riskassessment may well reflect differences in PM composition and,thus consideration of differencesin risk due to city-specificdifferences in composition may already be incorporated in the riskestimates for these endpoints to some extent.H. Specifyinglag structure(short-termexposurestudies)Different lags may havevarying degrees of associationwith a particular healthendpoint and it may be difficultto clearly identify a specific lagas producing the majority of aPM-related effect (recently,distributed lags have beenrecommended since they allowfor a distribution of the impactacross multiple days of PMexposure prior to the healthBoth Medium MediumKB and INF: With regard to lag periods, the ISA states, “Anattempt has been made to identify whether certain lag periods aremore strongly associated withspecific health outcomes. Theepidemiologic evidence evaluated in the 2004 PM AQCDsupported the use of lags of 0-1 days for cardiovascular effectsand longer moving averages or distributed lags for respiratorydiseases (U.S. EPA, 2004a). However, currently, little consensusexists as to the most appropriate a priori lag times to use whenexamining morbidity and mortality outcomes.” (final ISA, section2.4.2, p. 2-24). This suggests that uncertainty remains concerningthe identification of appropriate lags, and thus the etiologicallyrelevant time period for exposure to PM for specific health3-77Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)outcome). A lack of clarityregarding the specific lag(s)associated with a particularhealth endpoint addsuncertainty into risk estimatesgenerated for that endpoint.endpoints.I.Transferabilityof C-Rfunctions fromstudy locationsto urban studyarea locationsThe use of effects estimatesbased on data collected in aparticular location(s) as part ofthe underlying epidemiologicalstudy in different locations (thefocus of the risk assessment)introduces uncertainty into theanalysis.BothMedium (forlong-termexposuremortality)Notapplicable(for shorttermexposurehealth effectriskestimates)Medium (forlong-termexposuremortality)Low (for shortterm exposuremortality)INF: This issue has been ameliorated to a great extent in this riskassessment since we are now using multi-city studies for keyshort-term endpoints with effects estimates generally beingapplied only to urban study areas matching locations used in theunderlying epidemiological study. In the case of long-termexposure mortality studies, theseare designed to capture a moregeneralized national signal and therefore, concerns over thetransferability of functions between locations is of greaterconcern.J. Use ofsingle-cityversus multicity studies inthe derivationof C-RfunctionsOften both single-city andmulti-city studies are available(for a given health effectendpoint) for the derivation ofC-R functions. Each of thesestudy designs has advantagesand disadvantages whichshould be considered in thecontext of assessinguncertainty in a risk assessment(Note, that generally this issueapplies more to the modelingof short-term exposure-relatedendpoints then to the modelingof long-term exposure relatedendpoints, since the latter isBoth Medium HighKB: Because many health endpoints have been evaluated usingboth single-city and multi-city studies, we have a relative largeselection of single city studies and a few large multi-city studies toconsider in examining this issue.INF: For reasons presented in section 3.3.3, we have decided tofocus on multi-city studies as a source of C-R functions for thecore risk assessment, reflecting advantages that these studies offer(e.g., they tend to have more statistical power and provide effectestimates with relatively greater precision than single city studiesdue to larger sample sizes, reducing the uncertainty around theestimated coefficient, and reducingpublication bias). While thechoice of multi-city studies is well-supported, this decision doesintroduce uncertainty since single city studies can provide a widerrange of C-R functions (and associated effects estimates)reflecting greater variation in study design, differences incomposition, human behavior, and copollutants, and differences in3-78Potential influence ofuncertainty on riskestimatesSourceDescriptionDirectionMagnitudeKnowledgeBaseuncertainty*Comments(KB: knowledge base, INF: influence of uncertainty on riskestimates)typically based on multi-cityprospective cohort studies).the input datasets used (e.g., ambient air monitors and diseasebaseline incidence data). Even if there is greater confidence in CR functions obtained from multi-city studies, overall uncertaintyin those C-R functions may be reflected to some extent in therange of C-R functions seen across single-city studies.K. Impact ofhistorical airquality onestimates ofhealth risk fromlong-termPM2.5exposuresLong-term studies of mortalitysuggest that different timeperiods of PM exposure canproduce significantly differenteffects estimates, raising theissue of uncertainty in relationto determining which exposurewindow is most stronglyassociated with mortality.Both Medium MediumINF: The latest HEI Reanalysis II study (Krewski, 2009) whichlooked at exposure windows (1979-1983 and 1999-2000) for longterm exposure in relation to mortality, did not draw anyconclusions as to which window was more strongly associatedwith mortality. However, the study did suggest that moderatelydifferent effects estimates areassociated with the differentexposure periods (with the more recent period having largerestimates). Overall, the evidence for determining the windowover which the mortality effects of long-term pollution exposuresoccur suggests a latency period of up to five years, with thestrongest results observed in the first few years after intervention(final ISA, section 7.6.4. p. 7-95).L.Characterizingbaselineincidence ratesUncertainty can be introducedinto the characterization ofbaseline incidence in a numberof different ways (e.g., error inreporting incidence for specificendpoints, mismatch betweenthe spatial scale in which thebaseline data were capturedand the level of the riskassessment).BothLow-mediumLowINF: The degree of influence of this source of uncertainty on therisk estimates likely varies with the health endpoint categoryunder consideration. There is noreason to believe that there areany systematic biases in estimatesof the baseline incidence data.The influence on risk estimates that are expressed as incrementalrisk reductions between alternative standards should be relativelyunaffected by this source of uncertainty.KB: The county level baseline incidence and population estimatesat the county level were obtained from data bases where therelative degree of uncertainty is low.* Refers to the degree of uncertainty associated with our understanding of the phenomenon, in the contextof assessing and characterizing its uncertainty(specifically in the context of modeling PM risk)

# 3-79 The results presented in Table 3-13 consider on ly 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 th e C-R functions), Source E (shape of the

C-R functions), Source F (addressing copo llutants), 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 c onfidence in a model including: statistical fit

of the model, degree to wh ich potential confounding by copollu tants 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 pa rticular model form (informe d by consideration of the

factors listed above) is an important consider ation in assessing overa ll uncertainty both

from a qualitative and quantitative standpoint.

In addition, there is the potenti al 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 PM 2.5 

# levels for study populations

using the existing ambient monitoring network) and variability related to the

pattern of ambient PM 2.5 

# 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 de pendent 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 PM 2.5 

# reductions associated with just mee ting 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 transitioni ng from a single study area to two distinct study areas

each with different design values and se parate assessments of rollback) produced

significant differences in composite monitor values for the study area. Therefore, both

of these factors (the definition of the m onitoring network and ro llback approach) can

work in concert to impact ambient PM 2.5 

# levels and hence risk estimates.

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

We quantitatively examined the impact of severa l inputs to the risk assessment in a series

of single-factor sensitivity anal yses 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 addi tion, the sensitivity analysis

considered variability in the pa ttern of reductions in ambient PM 2.5 

# 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 datase ts used in the sensitivity analys is (as alternativ e 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 – PM 2.5 

# concentrations

that just meet the current standards – to use fo r 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 hybr id 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 re lated health endpoints followed by the methods

used in assessing short-term exposure related hea lth endpoints. We then discuss multi-factor

sensitivity analyses completed for both shor t-term and long-term exposure-related health

endpoints. Note, that the results of the sensi tivity analyses (including both single- and multifactor analyses) are presented and discussed in section 4.3.

3.5.4.1 Sensitivity Analyses for Long-T erm Exposure-Related Mortality

Because Krewski et al. (2009) presented result s based on alternative model specifications

only for the later exposure period (1999 – 2000), our sensitivity analyses focusing on the

estimates of health effects incidence a ssociated with long-term exposure to PM 2.5 

# similarly used

the C-R functions based on this later exposure peri od. 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 PM 2.5 

# , 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.

# 3-81 model and of using a random effects log-log model 50 

# (rather than the stan dard fixed effects loglinear model used in the core analysis) to estimate the risks of all cause mortality,

cardiopulmonary mortality, ischemic heart di sease mortality, and lung cancer mortality

associated with long-term exposur e in Los Angeles and Philadelphia. 51 

# The coefficient of PM 2.5



# in the random effects log-linear model was back-calculated from the relative risk reported in

Table 9 (“Autocorrelation at MSA and ZCA leve ls” group – “MSA & DIFF” row) of Krewski et

al. (2009). The coefficient of PM 2.5 

# in the random effects log-l og model was back-calculated

from the relative risks reported in Table 11 (“MSA and DIFF” ro ws) of Krewski et al. (2009).

As noted above, for all health endpoints a ssociated with long-term exposure to PM 2.5 

# we

estimated risk associated with PM 2.5 

# concentrations above 5.8 μg/m 3 

# (the LML for the later

exposure period used in Krewski et al., 2009). In a sensitivity anal ysis we examined the impact

of that limitation by comparing those mortality ri sk estimates to the mortality risk estimates

obtained when we estimated risk associated with PM 2.5 

# concentrations above estimated PRB

levels. This sensitivity analysis was carried out for all cause mo rtality 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 in cidence 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 mu lti-pollutant models in estimating long-term

exposure-related mortality. Specifically, we obt ained 2-pollutant models (considering CO, NO 2 

# ,

50 

# In the log-log model, the natural logarithm of mortality is a linear function of the natural logarithm of

PM 2.5



# . 51 

# As 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 e ffects 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 lo g-linear model (our core analysis selecti on) 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.

# 3-82 O 3 

# or SO 2 

# together with PM 2.5 

# ) 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 genera ted using Krewski et al., 2009.

For all of the sensitivity analyses involvi ng alternative C-R func tions, 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 PM 2.5 

# 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 variab ility in estimating the pattern of reductions

in ambient PM 2.5 

# levels under the curren t and alternative standard levels (i.e., conducting

rollback). For the first draft RA, we considered th e impact of using a hybrid rollback approach in

addition to the proportional rollback approach wh ich 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 l ong-term exposure-related mortality categories).

For the final RA, as discussed above in secti ons 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 ro llback. 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 di fferent rollback methods (proportional, hybrid and locally

focused). The composite monitor values are surr ogates for long-term exposure-related mortality.

Therefore, by comparing composite monitor values generated for the same study area/standard

level combination (using different rollback met hods), we can obtain insi ghts into the potential

impact of the rollback method used on long-te rm exposure-related mortality. Specifically, for

this sensitivity analysis, we compared composite monitor values in two ways:



#  Potential difference in composite monitor va lues at the current or alternative standard

level (for the same study area) given app lication of alternat ive rollback methods: We

compared the absolute magnitude of composite monitors values produced using different

rollback methods for the same study area/stand ard 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 me thods (Appendix F, Table F-50). 53 

# For example, in Table

53 

# This calculation reflects the fact that we model lo ng-term exposure-related mortality down to LML.

# 3-83 F-50, for Los Angeles, we see that for the cu rrent standard suite of standards, use of

proportional rollback and locally focused rollba ck methods results in composite monitor

values of 9.5 μg/m 3 

# and 12.0 μg/m 3 

# , respectively, with the locally focused value being

40% higher than the value derived using propor tional rollback. Given that the composite

monitor values are surrogates for long-term e xposure-related mortalit y, we conclude that

for this combination of urban study area and suite of standards, use of the locally focused

rollback method could produce PM 2.5 

# -attributable long-term mort ality risk estimates that

are approximately 40% higher than us e of the proportional rollback method.



#  Potential difference in the pattern of re duction in composite monitor values across

alternative standards: We compared differences in th e percent reduction in composite

monitor values across altern ative suites of standards for the same study area using

different rollback methods to provide insigh ts into differences in incremental risk

reduction resulting from the use of different roll back 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/m 3 

# and 11.8 μg/m 3 

# , respectively, with these

translating into a percent reduction (compa red with their respec tive values under the

current suite of standards) of 21% and 16%, respectively. Given that the composite

monitor values are surrogates for long-term e xposure-related mortalit y, 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 notab ly 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 appro aches 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, Tabl es F-49 and F-50) form the basis for summary

information related to rollback appr oaches 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 PM 2.5 

# concentrations, thereby produc ing reductions in long-term

exposure-related mortality. As discussed below in section 5.2.3, alternat ive 24-hour standards,

when controlling, can result in re ductions in annual average PM 2.5 

# concentrations, particularly if

proportional rollback is used. In this case, th e assumption of more regional patterns of PM 2.5



# reduction in reducing PM 2.5 

# concentrations to just meet alte rnative 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 considera tion of a hybrid local/regional attainment strategy (i.e.,

application of the hybrid rollback) (i.e., Baltimore, Birmin gham, Detroit, Los Angeles, New York, St. Louis). In the

case of the peak sharing approach, only those locations wh ere the 24-hour standard was controlling were considered

for this sensitivity analysis (i.e., Atlanta, Baltimore, Birmingham, Detroit, Fresno, Los Angeles, New York,

Philadelphia, Phoenix, Pittsburgh, St. Louis, Tacoma).

# 3-84 an equivalent magnitude of reduction in the a nnual average. However, in simulating more

localized patterns of PM 2.5 

# reductions to just meet alte rnative 24-hour standards, the PM 2.5



# 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 reflecti ng more localized patterns of ambient PM 2.5 

# 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 PM 2.5 

# concentrations and consequently in l ong-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 comple ted 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 magnit ude of long-term exposure-related mortality. An

additional factor is that while there has been considerable res earch in the area of short-term

exposure-related mortality and morbidity which shed s light on uncertainty in such factors as C-R

function specification, this information is not dir ectly 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 exposur e-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 PM 2.5 

# (Zanobetti and Schwartz, 2009, and Bell et al., 2008,

respectively) both provided all-ye ar C-R functions as well as seas on-specific C-R functions. We

examined the impact of using season-specifi c 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 ye ar 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 mo nths (April through August) for which it was

estimated.

Moolgavkar (2003) estimated C- R functions for several heal th endpoints – non-accidental

and cardiovascular mortality; a nd cardiovascular and respiratory HAs – associated with shortterm exposures to PM 2.5 

# 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 op portunity 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 co nducted in a single urban area. As noted earlier, differences

in study design and the underlying datasets used prevent the results base d on application of

models from Moolgavkar et al., 2003 from being co mpared directly to the core risk estimates.

Finally, as with estimates of long-term exposur e-related mortality, we also considered the

impact of variability related to simulating ambient PM 2.5 

# levels under the suite of current

standard levels (i.e., variability in conducting rollback) on estima tes of non-accidental mortality

associated with short-term exposures to PM 2.5 

# (using Zanobetti and Schwartz, 2009). However,

in this case, we only considered the hybrid mode l (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 fo r short-term exposure-related

mortality and morbidity since both categories of health endpoints are also driven primary by

annual average PM 2.5 

# 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-sp ecific 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 2.5 th 

# and 97.5 th 

# percentile season-specific estimates cannot be summed. To calculate the

2.5 th 

# and 97.5 th 

# percentile estimates of all-year incidence from the season-specific estimates would require the

variance-covariance matrix of the season-specific coeffici ent 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 wh en 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 PM 2.5 

# –related health

effects incidence change as we change a single el ement of the analysis (such as the form of the

C-R function or the way we simulate just meetin g 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 reas onable alternative estimates that may similarly

be considered plausible (see section 4.3). The re sults 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 PM 2.5 

# –related health effects. To provide a more complete

picture of the uncertainty surrounding estimates of PM 2.5 

# –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-f actor 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 particul ar, we selected those

modeling choices that had the greatest impacts on th e 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 elem ents that substantially affect ed the estimates of mortality

associated with long-term exposure to PM 2.5 

# -- the model choice (fix ed effects log linear vs.

random effects log-log), whether effect s are estimated associated with PM 2.5 

# concentrations

down to the LML in the study (5.8 μg/m 3 

# ) or down to PRB, and whether a proportional or a

hybrid rollback is used to simulate PM 2.5 

# 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 underlyi ng model choices are all considered reasonable.

58 

# We did not calculate percent different for the ED vi sits sensitivity analysis because the two different C-R

functions (all-year in the core analysis vs. April through August in the sensitivity analysis) are also being applied to

different portions of the year (all year vs. April through August), so it is something of an “apple to oranges”

comparison.

# 3-87 We identified two analysis elements that subs tantially affected the estimates of mortality

associated with short-term exposure to PM 2.5 

# – whether season-specific or all-year C-R functions

were used and whether a proportion al 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 variab ility 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 PM 2.5 

# -related population

exposure and hazard response and the degree to which they are captured in the risk

assessment (see section 3.5.2). When importa nt sources of vari ability in exposure

and/or hazard response are not reflected in a risk assessment, significant uncertainty

can be introduced into the risk estimate s that are generated. While not explicitly

referenced in the WHO guidan ce, 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, includ ing both an assessment of the magnitude

of potential impact of each sour ce on risk estimates (along w ith 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 analys is intended to evaluate the impact of individual sources

of uncertainty and variability on risk estimates (see secti on 3.5.4). The goal of this

assessment is to evaluate the relative impor tance of these sources of uncertainty and

variability in impacting core risk estimate s. The single-factor sensitivity analysis

represents a WHO Tier 2 anal ysis. In conducting these as sessments, we have used

alternative representations of modeling elements that have support in the literature to

ensure that the risk estimates that are gene rated represent reasonabl e alternate estimates

that can supplement the core risk estimates generated in the analys is (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 supplem ent the core risk estimates (see section

4.3.2).

As noted above, since information was not av ailable to characteri ze overall levels of

confidence in alternative model inputs, the uncer tainty characterization co mpleted for this risk

assessment did not include a full probabilistic as sessment 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 sens itivity analyses do not represent uncertainty

distributions, but rather additional plausible poin t 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 pe rcentiles that they represent). The

appropriate use for these reasonable alternate ri sk estimates in informing consideration of

uncertainty in the core risk estim ates is discusse d 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 distri butions for key PM risk-related attributes

to determine whether they are nationally represen tative or more focused on a particular portion

of the distribution for a given at tribute (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 area s fall along a distribution of na tional 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 PM 2.5 

# exposure.

A third set of analyses that ha s been added to this final RA focuses on evaluating patterns

in the design values (including both 24-hour and annual) and underlying PM 2.5 

# 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 unde r both the current and

alternative suites of standards ac ross the urban study areas. The interplay of design values and

underlying PM 2.5 

# monitoring data play a key role in determining whether a location will

experience risk reductions when ju st meeting any given suite of st andards 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 mo re broadly across urban ar eas in the U.S. with

the goal of placing the urban study areas in a nati onal 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 de veloped using model inputs that staff judge to

have a greater degree of support in the literature relative to inputs used in th e sensitivity analyses

(the rationale for selection of specific epidemio logical studies and associ ated 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 genera ted for the urban case study area, a nd 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 charact erization 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 PM 2.5 

# concentrations characte rized through monitoring for

the period 2005-2007 at each ur ban case study location;



#  Current NAAQS: based on rolling back PM 2.5 

# concentrations to just meet the current

suite of standards in each urban st udy area (annual standard of 15 μg/m 3 

# and a 24-hour

standard of 35 μg/m 3 

# , denoted 15/35);



#  Alternative NAAQS: based on rolling back PM 2.5 

# concentrations to just meet alternative

suites of standards in each urban study area:

o annual standard of 14 μg/m 3 

# and a 24-hour standard of 35 μg/m 3 

# (denoted 13/35);

o annual standard of 13 μg/m 3 

# and a 24-hour standard of 35 μg/m 3 

# (denoted 13/35);

o annual standard of 12 μg/m 3 

# and a 24-hour standard of 35 μg/m 3 

# (denoted 12/35);

o annual standard of 13 μg/m 3 

# and a 24-hour standard of 30 μg/m 3 

# (denoted 13/30);

o annual standard of 12 μg/m 3 

# and a 24-hour standard of 25 μg/m 3 

# (denoted 12/25).

We have also estimated risk for an alte rnative annual standard level of 10 μg/m 3 

# (paired

with 24-hour standard levels of 35 μg/m 3 

# and 25 μg/m 3 

# ). However, as discussed in section 2.4,

because of increased uncertainty associated w ith these risk estimates relative to estimates

generated for the other alternativ e annual standard levels evaluate d in the RA, estimates based on

the alternative annual standard level of 10 μg/m 3 

# are not discussed in this chapter and instead are

# 4-2 presented in Appendix J and only br iefly addressed as part of the integrative discussion presented

in Chapter 5.

In simulating both current and alternative suites of standards, for th e core analysis, we

used a proportional roll-back approach (see se ction 3.2.3), while a hybrid roll-back approach

reflecting the potential for local source control was used for a subs et of urban study areas as part

of the sensitivity analysis conducted for this asse ssment (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 PM 2.5 

# levels which allow us to assess how long-term exposurerelated risk could vary if th is alternative roll-b ack 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 PM 2.5 

# concentrations. At a minimum, all areas selected had recent

air quality levels at or above the lowest annual and/or 24-hour st andards analyzed. In addition,

our goal was to select areas reflecting the hetero geneity in PM risk-related attributes such as

sources, composition, demographics , and population behavior.

Risk estimates were generated for the follo wing health effects e ndpoints: (a) long-term

exposure-related mortality (all- cause, cardiopulmonary disease-related (CPD), ischemic heart

disease-related (IHD) and lung cancer-related), (b) s hort-term exposure-related mortality (nonaccidental, cardiovascular disease (CVD), respir atory), and (c) short- term exposure-related

morbidity (hospital admissions (HA) for CVD an d respiratory illness and emergency department

(ED) visits). Risk estimates are presented sepa rately for each of thes e 15 study areas, although in

certain circumstances, risk estimates may be rest ricted to a subset of these locations if, for

example, an endpoint is modeled using a concentr ation-response (C-R) function derived from an

epidemiological study that was c onducted 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 a nd because there is subs tantial uncertainty as

to the shape of the concentrati on-response (C-R) function at concen trations below the LML. For

long-term exposure mortality a sensitivity analys is 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 hi gher than the LML across all studies and for

purposes of NAAQS decision making, EPA is focused on risks associated with PM 2.5 

# 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 th e 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 PM 2.5 

# monitoring data

from 1979-1983 and a second set based on fitting PM 2.5 

# monitoring data from 1999-2000

(Krewski et al., 2009) (see secti on 3.3.3). In presenting core risk estimates for long-term

mortality, both sets of estimat es are given equal weight.

In modeling short-term exposure mortality and morbidity for the core analysis, we have

used the latest multi-city studi es (Zanobetti and Schwartz, 2009; Bell et al., 2008) (see section

3.3.3). In the case of short-term exposure mortal ity, we obtained and used city-specific effects

estimates derived using empirical Bayes met hods from the study authors (Zanobetti, 2009).

Multi-city studies were favored for the core an alysis, 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 PM 2.5 

# exposure and health affect response in the population.

Additional detail on the specific C-R functions and related modeli ng 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 inci dence 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 fact ors including: (a) differenc es in patterns of daily PM 2.5 

# 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 ot her study areas, thereby

accounting for the relatively small total inciden ce estimate for this study area – see Appendix C,

Table C-1).

Because the recent conditions air quality scen ario 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 PM 2.5 

# 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 95 th 

# percentile confidence interval, the latter reflecting sampling error as

characterized in the underly ing epidemiological study.

# 4-4 

#  Percent of total annual incidence fo r the health endpoint due to PM 2.5 

# exposure

( percent of total incidence attributable to PM 2.5 

# ): Again, generated for the population

associated with a given urban study area (and si mulation year), this metric characterizes

the fraction of total incidence that is associated with PM 2.5 

# exposure. As with the

underlying PM-related incidence estimates, this risk metric also typically includes a 95 th



# percentile confidence interval reflecting sa mpling error associated with the effects

estimate. Compared with the annual in cidence 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 popul ation, thereby allowing direct comparison

of the potential impact of PM 2.5 

# for the health effect endpoi nt of interest across urban

study area locations. For this reason, in disc ussing risk estimates in this section, the



# percent of total incidence attributable to PM 2.5 

# risk metric is given somewhat greater

emphasis than the absolute measure of 

# annual incidence attributable to PM 2.5



# .



#  Percent reduction in PM 2.5 

# -related health effect incide nce for an alternative set of

standards or the recent conditions scen ario, 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 me tric characterizes the degree of risk

reduction (for alternative sta ndard levels) or increased risk (for the recent conditions

scenario) relative to the current NAAQS. For th is metric, a negative value represents an

increase in risk (this is the case for the r ecent conditions scenario, where risks are higher

than those associated with just meeting the cu rrent suite of standards). This metric is

positive, or zero, for alternative suites of st andards 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 PM 2.5 

# concentrations. Because this metric is incremental, it

was not possible to generate the 95 th 

# 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 popul ation size and theref ore, allows direct

comparison across urban study areas.

In addition to presenting the central-tendency (highest confidence) es timates for each of

these metrics, we also include 95 th 

# 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 inte rvals only capture this statistical fit uncertainty

– other sources of uncertainty including shape an d form of the function, are addressed separately

as part of the sensitiv ity analysis (see Section 4.3.1) and the qualitative analysis of uncertainty

(see Section 3.5.3).

Detailed tables presenting estimates for thes e risk metrics for the complete set of air

quality scenarios (for all 15 ur ban study areas) are included in Appendix E and referenced as

needed in the discussion of risk estimates pr esented in the following sections. To support the

discussion of risk estimates presen ted in this chapter, we have in cluded 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-te rm exposure-related risk for the 15 urban

study areas associated with just meeting the current suite of standa rds. Both tables

include a subset of the hea lth endpoints believed to have the greatest su pport in the

literature including IHD mortalit y for long-term exposure, ca rdiovascular mortality and

hospital admissions for short-term exposure. Table 4-1 presen ts total incidence

attributable to PM 2.5 

# exposure for the endpoints and Table 4-2 presents percent of total

incidence attributable to PM 2.5 

# 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 PM 2.5 

# ) 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 le vels 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) a nd short-term exposure-relate d risk (Figure 4-4) across

alternative standard levels relative to the ri sk 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 a ssessed simultaneously. 59



# Each of these figures is presented in a dditional detail by splitting each into (a)

comparison of the recent conditions risk ag ainst 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). Specificall y, Figures 4-2 and 4-3 provide these higherresolution plots for long-term exposure-rela ted 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 thr ee simulation years, in th is 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 re presents a reasonable central y ear (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 mo st 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 ac ross 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 pe rcent risk reduction will be similar for lo ng-term exposure relate d 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. Th is allows us to present these figures plotting changes in

risk more generally for short-term exposure-related e ndpoints 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 tr ends across the three simulation years to gain

perspective on year-to- year variation in PM 2.5 

# -related risk estimates as assessed here.

Table 4-1. Estimated Annual Incidence of Se lected Mortality and Morbidity Endpoints

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



# Concentrations that Just Meet the Cu rrent Standards, Based on Adjusting

2007 PM 2.5 

# 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 statisti cal

uncertainty surrounding the PM coefficient.

3Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 1983 and from 1999-2000 respectively. Incidence is f or 30+

year olds within each urban study area.

4Based on location-specific single pollutant concentration-res ponse function estimates from Z anobetti and Schwartz (2009) that have been

"shrunken" towards the appropriate regional means. "Shrunken" coe fficient 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 regi onal concentration-response function estimates reported in Table 2 of Bell et

al. (2008). Location-specific C-R function estimates were not av ailable from this study. Incidenc e 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 PM2.5 3 

# Incidence of

Cardiovascular

Mortality Associated

with Short-term

Exposure to PM2.5 4 

# Incidence of

Cardiovascular

Hospitalizations

A

ssociated with Short

term Exposure to

PM2.5 5

# 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 PM 2.5 

# Concentrations that Just Mee t the Current Standards, Based

on Adjusting 2007 PM 2.5 

# 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 PM2.5 3 

# Percent of Incidence

of Cardiovascular

Mortality Associated

with Short-term

Exposure to PM2.5 4 

# Percent of Incidence

of Cardiovascular

Hospital Admissions

A

ssociated with Short

term Exposure to

PM2.5 5



# Atlanta, GA

Baltimore, MD

Birmingham, AL

Dallas, TX

Detroit, MI

Fresno, CA

Houston, TX

Los Angeles, CA

New York, NY

Philadelphia, PA

Phoenix, AZ

Pittsburgh, PA

Salt Lake City, UT

St. Louis, MO

Tacoma, WA

1The current primary PM2.5 standards include an annual standard set at 15 ug/m3 and a daily standard set at 35 ug/m3.

2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical un certainty

surroundin g the PM coefficient.

3Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1979 - 1983 and from 1999-2000 respectively

4Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been

"shrunken" towards the appropriate regional means. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A.

Zanobetti via email.

5Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table 2 of Bell et

al. (2008). Location-specific C-R function estimates were not available from this study.4-8Figure 4-1 Percent reductionin long-term exposure-related mortality risk (alternative standards and recent conditions relative to the current standards)(Note: inset shows PM2.5related incidence and percent of total incidence for IHD mortality under the current suite of standards)Atlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXLos Angeles, CANew York, NYPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTSt. Louis, MOTacoma, WA-50%-30%-10%10%30%50%70%90%2007 airquality15/35** 14/35 13/35 12/35 13/30 12/25Recent Air Quality, Current Standard and Alternative Standards

Long-Term Exposure-Related Risk: Percent Reduction from Current StandardAtlanta, GA 277 (227 - 324); 16.7% (13.7% - 19.5%)Baltimore, MD 374 (307 - 440); 14.7% (12.1% - 17.3%)Birmingham, AL 165 (135 - 194); 13.8% (11.3% - 16.2%)Dallas, TX 247 (202 - 291); 11.4% (9.3% - 13.4%)Detroit, MI 478 (390 - 563); 11.5% (9.4% - 13.5%)Fresno, CA 98 (80 - 116); 8.5% (7% - 10.1%)Houston, TX 434 (355 - 511); 13.6% (11.1% - 16%)Los Angeles, CA 1094 (890 - 1296); 7.7% (6.3% - 9.1%)New York, NY 2222 (1814 - 2620); 11.8% (9.6% - 13.9%)Philadelphia, PA 330 (270 - 389); 13.2% (10.8% - 15.6%)Phoenix, AZ 402 (327 - 476); 8.5% (6.9% - 10.1%)Pittsburgh, PA 324 (264 - 382); 11.8% (9.6% - 13.9%)Salt Lake City, UT 19 (16 - 23); 3.7% (3% - 4.4%)St. Louis, MO 563 (461 - 662); 14.2% (11.6% - 16.7%)Tacoma, WA 49 (40 - 58); 4.7% (3.8% - 5.6%)*Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (and 95% CI) and theestimate of percent of total incidence (and 95% CI) under the current standards.**The current standards consist of an annual standard of 15 μg/m3and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a daily standard(m) are denoted n/m in this figure.4-9Figure 4-2 Percent reduction in long-term exposure-related mortality risk(recent conditions relative to the current standards) (Note: inset showsPM2.5related incidence and percent of total incidence for IHD mortality under the current suite of standards)Atlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXLos Angeles, CANew York, NYPhiladelphia, PAPittsburgh, PASalt Lake City, UTSt. Louis, MOTacoma, WAPhoenix, AZ-250%-200%-150%-100%-50%0%2007 air quality 15/35**Recent Air Quality and Current Standard

Long-Term Exposure-Related Risk: Percent Reduction from Current StandardAtlanta, GA 277 (227 - 324); 16.7% (13.7% - 19.5%)Baltimore, MD 374 (307 - 440); 14.7% (12.1% - 17.3%)Birmingham, AL 165 (135 - 194); 13.8% (11.3% - 16.2%)Dallas, TX 247 (202 - 291); 11.4% (9.3% - 13.4%)Detroit, MI 478 (390 - 563); 11.5% (9.4% - 13.5%)Fresno, CA 98 (80 - 116); 8.5% (7% - 10.1%)Houston, TX 434 (355 - 511); 13.6% (11.1% - 16%)Los Angeles, CA 1094 (890 - 1296); 7.7% (6.3% - 9.1%)New York, NY 2222 (1814 - 2620); 11.8% (9.6% - 13.9%)Philadelphia, PA 330 (270 - 389); 13.2% (10.8% - 15.6%)Pittsburgh, PA 324 (264 - 382); 11.8% (9.6% - 13.9%)Salt Lake City, UT 19 (16 - 23); 3.7% (3% - 4.4%)St. Louis, MO 563 (461 - 662); 14.2% (11.6% - 16.7%)Tacoma, WA 49 (40 - 58); 4.7% (3.8% - 5.6%)Phoenix, AZ 402 (327 - 476); 8.5% (6.9% - 10.1%)*Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (and 95% CI) and theestimate of percent of total incidence (and 95% CI) under the current standards.**The current standards consist of an annual standard of 15 μg/m3and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a daily standard(m) are denoted n/m in this figure.4-10Figure 4-3 Percent reduction in long-term exposure-related mortality risk(alternative standards relative to the current standards) (Note: inset showsPM2.5related incidence and percent of total incidence for IHD mortality under the current suite of standards)Atlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXLos Angeles, CANew York, NYPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTSt. Louis, MOTacoma, WA0%20%40%60%15/35** 14/35 13/35 12/35 13/30 12/25Current and Alternative Standards

Long-Term Exposure-Related Risk: Percent Reduction from Current StandardAtlanta, GA 277 (227 - 324); 16.7% (13.7% - 19.5%)Baltimore, MD 374 (307 - 440); 14.7% (12.1% - 17.3%)Birmingham, AL 165 (135 - 194); 13.8% (11.3% - 16.2%)Dallas, TX 247 (202 - 291); 11.4% (9.3% - 13.4%)Detroit, MI 478 (390 - 563); 11.5% (9.4% - 13.5%)Fresno, CA 98 (80 - 116); 8.5% (7% - 10.1%)Houston, TX 434 (355 - 511); 13.6% (11.1% - 16%)Los Angeles, CA 1094 (890 - 1296); 7.7% (6.3% - 9.1%)New York, NY 2222 (1814 - 2620); 11.8% (9.6% - 13.9%)Philadelphia, PA 330 (270 - 389); 13.2% (10.8% - 15.6%)Phoenix, AZ 402 (327 - 476); 8.5% (6.9% - 10.1%)Pittsburgh, PA 324 (264 - 382); 11.8% (9.6% - 13.9%)Salt Lake City, UT 19 (16 - 23); 3.7% (3% - 4.4%)St. Louis, MO 563 (461 - 662); 14.2% (11.6% - 16.7%)Tacoma, WA 49 (40 - 58); 4.7% (3.8% - 5.6%)*Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (and 95% CI) and theestimate of percent of total incidence (and 95% CI) under the current standards.**The current standards consist of an annual standard of 15 μg/m3and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a daily standard(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.4-11Figure 4-4 Percent reduction inshort-term exposure-related mortality and morbidity risk(alternative standards and recent conditions relative to thecurrent standards) (Note: inset shows PM2.5related incidence and percent of total incidence for CV under the current suite of standards)Atlanta, GABaltimore, MDBirmingham,ALDallas, TXDetroit, MIFresno, CAHouston, TXLos Angeles,CANew York,NYPhiladelphia,PAPhoenix, AZPittsburgh,PASalt Lake City, UTSt. Louis, MOTacoma,WA-50%-40%-30%-20%-10%0%10%20%30%2007 airquality15/35** 14/35 13/35 12/35 13/30 12/25Recent Air Quality, Current Standard and Alternative Standards

Short-Term Exposure-Related Risk: Percent Reduction from Current StandardAtlanta, GA 32 (-33 - 95); 0.8% (-0.8% - 2.4%)Baltimore, MD 62 (-4 - 126); 1.6% (-0.1% - 3.2%)Birmingham, AL -1 (-42 - 40); 0% (-1.5% - 1.5%)Dallas, TX 29 (-19 - 76); 0.8% (-0.5% - 2.2%)Detroit, MI 60 (-8 - 127); 1% (-0.1% - 2.2%)Fresno, CA 12 (-9 - 33); 0.7% (-0.5% - 2%)Houston, TX 46 (-31 - 122); 0.9% (-0.6% - 2.4%)Los Angeles, CA -30 (-132 - 72); -0.2% (-0.7% - 0.4%)New York, NY 473 (276 - 668); 2.1% (1.2% - 3%)Philadelphia, PA 84 (22 - 145); 2.1% (0.5% - 3.6%)Phoenix, AZ 84 (-4 - 170); 1.3% (-0.1% - 2.7%)Pittsburgh, PA 43 (-9 - 93); 1.1% (-0.2% - 2.3%)Salt Lake City, UT 9 (-2 - 20); 0.8% (-0.2% - 1.7%)St. Louis, MO 106 (24 - 187); 1.9% (0.4% - 3.3%)Tacoma, WA 11 (-6 - 27); 0.7% (-0.4% - 1.8%)`*Based on Zanobetti and Schwartz(2009). The legend contains,for each urban area, the incidence estimate (and 95% CI) and theestimate of percent of totalincidence (and 95% CI) under the current standards.**The current standards consist of an annual standard of 15 μg/m3and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a daily standard(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.4-12Figure 4-5 Percent reduction inshort-term exposure-related mortality and morbidity risk(recent conditions relative to the current standards) (Note:inset shows PM2.5related incidence and percent of total incidence for CV under the current suite of standards)Atlanta, GABaltimore, MDBirmingham, ALFresno, CAHouston, TXLos Angeles,CANew York,NYPhiladelphia,PAPhoenix, AZPittsburgh, PASalt Lake City, UTSt. Louis, MODallas, TXDetroit, MITacoma, WA-90%-80%-70%-60%-50%-40%-30%-20%-10%0%2007 air quality 15/35**Recent Air Quality and Current Standard

Short-Term Exposure-Related Risk: Percent Reduction from Current StandardAtlanta, GA 32 (-33 - 95); 0.8% (-0.8% - 2.4%)Baltimore, MD 62 (-4 - 126); 1.6% (-0.1% - 3.2%)Birmingham, AL -1 (-42 - 40); 0% (-1.5% - 1.5%)Fresno, CA 12 (-9 - 33); 0.7% (-0.5% - 2%)Houston, TX 46 (-31 - 122); 0.9% (-0.6% - 2.4%)Los Angeles, CA -30 (-132 - 72); -0.2% (-0.7% - 0.4%)New York, NY 473 (276 - 668); 2.1% (1.2% - 3%)Philadelphia, PA 84 (22 - 145); 2.1% (0.5% - 3.6%)Phoenix, AZ 84 (-4 - 170); 1.3% (-0.1% - 2.7%)Pittsburgh, PA 43 (-9 - 93); 1.1% (-0.2% - 2.3%)Salt Lake City, UT 9 (-2 - 20); 0.8% (-0.2% - 1.7%)St. Louis, MO 106 (24 - 187); 1.9% (0.4% - 3.3%)Dallas, TX 29 (-19 - 76); 0.8% (-0.5% - 2.2%)Detroit, MI 60 (-8 - 127); 1% (-0.1% - 2.2%)Tacoma, WA 11 (-6 - 27); 0.7% (-0.4% - 1.8%)*Based on Zanobetti and Schwartz(2009). The legend contains,for each urban area, the incidence estimate (and 95% CI) and theestimate of percent of totalincidence (and 95% CI) under the current standards.**The current standards consist of an annual standard of 15 μg/m3and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a daily standard(m) are denoted n/m in this figure.4-13Figure 4-6 Percent reduction inshort-term exposure-related mortality and morbidity risk(alternative standards relative to the current standards)(Note: inset shows PM2.5related incidence and percent of total incidence for CV under the current suite of standards)Atlanta, GABaltimore,MDBirmingham, ALDallas, TXDetroit, MIFresno, CAHouston, TXLos Angeles,CANew York,NYPhiladelphia,PAPhoenix, AZPittsburgh, PASalt Lake City, UTSt. Louis, MOTacoma,WA0%5%10%15%20%25%30%35%15/35**14/3513/3512/3513/3012/25Recent Air Quality, Current Standard and Alternative Standards

Short-Term Exposure-Related Risk: Percent Reduction from Current StandardAtlanta, GA 32 (-33 - 95); 0.8% (-0.8% - 2.4%)Baltimore, MD 62 (-4 - 126); 1.6% (-0.1% - 3.2%)Birmingham, AL -1 (-42 - 40); 0% (-1.5% - 1.5%)Dallas, TX 29 (-19 - 76); 0.8% (-0.5% - 2.2%)Detroit, MI 60 (-8 - 127); 1% (-0.1% - 2.2%)Fresno, CA 12 (-9 - 33); 0.7% (-0.5% - 2%)Houston, TX 46 (-31 - 122); 0.9% (-0.6% - 2.4%)Los Angeles, CA -30 (-132 - 72); -0.2% (-0.7% - 0.4%)New York, NY 473 (276 - 668); 2.1% (1.2% - 3%)Philadelphia, PA 84 (22 - 145); 2.1% (0.5% - 3.6%)Phoenix, AZ 84 (-4 - 170); 1.3% (-0.1% - 2.7%)Pittsburgh, PA 43 (-9 - 93); 1.1% (-0.2% - 2.3%)Salt Lake City, UT 9 (-2 - 20); 0.8% (-0.2% - 1.7%)St. Louis, MO 106 (24 - 187); 1.9% (0.4% - 3.3%)Tacoma, WA 11 (-6 - 27); 0.7% (-0.4% - 1.8%)`*Based on Zanobetti and Schwartz(2009). The legend contains,for each urban area, the incidence estimate (and 95% CI) and theestimate of percent of totalincidence (and 95% CI) under the current standards.**The current standards consist of an annual standard of 15 μg/m3and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a daily standard(m) are denoted n/m in this figure.

# 4-14 As noted above, the risk assessment includes ri sk estimates for a range of short-term and

long-term exposure-related health effect endpoint s. To focus the discussion of these risk

estimates, we have selected a subset of the h ealth endpoints as examples to help illustrate

patterns in the risk estimates that might be of in terest from a policy standpoint. Specifically, we

have focused on those endpoints that the ISA id entifies as having the greatest support in the

literature (i.e., endpoints relate d to cardiovascular effects, including both mortality and

morbidity). The subset of health effect endpoint s 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 disc ussion 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 presen ted in the detailed tables in Appendix E.

For a subset of the urban case studies (e.g., Da llas and Phoenix), incremental reductions

across alternative standards are initially very lo w (or even zero) reflectin g the fact that recent

ambient PM 2.5 

# concentrations for these study areas are well below the current annual standard

levels. For these study areas, meaningful reducti ons in risk may not be seen until relatively

lower alternative standards are assessed (and resu lts in the percent reduction from the current set

of standards tables and figures may be zero for se veral of the less stringent , alternative sets of

standards). The pattern of risk reductions across alternative sta ndard levels for a given urban

study area is an important factor that is discussed in the integra tive discussion in Chapter 6. To

set up that later discussion, in summarizing ri sk estimates below, we provide observations

regarding trends in risk estimat es 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 inci dence estimates with negative lower-confidence

bounds (or point estimates) do not imply that additional exposure to PM 2.5 

# has a beneficial effect,

but only that the estimated PM 2.5 

# effect estimate in the C-R function was not statistically

significantly different from zero. In the case ofshort-term exposure mortality, where study areaspecific effects estimates were used (see secti on 3.4), several of the urban locations have nonstatistically significant effects estimates; these result in incide nce estimates with non-positive

lower bounds and/or best estimates (e.g., Birm ingham, Detroit, and Los Angeles for nonaccidental mortality). In the case of short-te rm morbidity (e.g., HAs), where regional effects

estimates were used, one of the regional coeffici ents (for the southeast) is not statistically

significant, producing incidence estim ates including negative values in the confid ence interval

for urban study areas falling with in 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 PM 2.5 

# 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 PM 2.5 

# and both short-term exposure

mortality and morbidity, recognizing that the ISA ha s concluded that there is either a causal or

likely causal relationship between short-term PM 2.5 

# exposure and these health effects (see section

3.3.1), we believe it is reasonable to assume th at instances where effects estimates are notstatistically significant are likely to reflect insuffi cient 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 th e magnitude of effect estimates. In addition to

sample size, these include: source and compositional differences for PM 2.5 

# , 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 asso ciated with both current and alternative

standard levels is the linkage between the natu re and magnitude of risk reductions seen for a

particular study area (for a partic ular suite of 24-hour and annual standards) and the specific mix

of 24-hour and annual design values associated w ith that study area. Because design values

determine the degree to which the PM 2.5 

# monitors in a study area are adjusted in simulating

attainment of both current and alte rnative standard levels, they play a central role in determining

the degree of risk reduction associated with a pa rticular suite of standard levels. Given the

importance of design values in determining risk reduction under both cu rrent and alternative

standard levels, we have examined patterns in design values (specifi cally the relationship

between 24-hour and annual design values) acro ss 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 de sign values for the 15 urban study areas with

patterns of design values for the br oader 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 examin ing patterns in risk estimates is the overall

spread in PM 2.5 

# measurements across monitors at a particular urban study area, including

distributions of both 24-hour and annual averages. This factor work s 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 mon itor 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 patter ns in design values (see above) we have also

explored patterns in PM 2.5 

# monitoring data for the 15 urban study areas in an effort to better

# 4-16 understand how application of diffe rent 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 m odeling 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 al ternative NAAQS are presented in section 4.2.

The results of the sensitivity analysis (incl uding single-factor and multi-factor results) are

presented in section 4.3. The results of a repr esentativeness analysis involving comparison of

counties associated with the 15 urban study area locations against the na tional 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 ri sk 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 th e patterns in ambient PM 2.5 

# data within study areas as a factor

influencing patterns of risk estimates (section 4.5.2). Chap ter 5 provides an integrative

discussion of the results of the core risk asse ssment 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 PM 2.5 

# monitoring data) in influe ncing 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. Speci fically, it provides a se t of key observations

regarding core risk estimates generated for the recent conditions ai r 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 mi ght 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 pr esented in the PA).

In discussing results for th e recent conditions air quality scenario, we have focused on

absolute risk (either above PRB or LML, depending on the health effect endpoi nt). 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 th e following order: long-te rm 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 pa rticular endpoint category). A discussion of

the broader risk range refl ecting consideration of 95 th 

# % 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 PM 2.5 

# -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 reflecti ng not only differences in base line incidence across urban study

areas, but also the size of study populations whic h vary considerably across the study areas.

The percent of total incidence of IHD -related mortality attributable to PM 2.5 

# ranges from 6.3-

8.0% (Tacoma) to 17.7-22.2% (Fresno) (Append ix E, Table E-24 and E-33). Total PM 2.5 

# - attriutable incidence for all-cause mortality and cardiopulmonary mortality is larger than IHD

(for a given study area under re cent conditions), while total PM 2.5 

# -attributable incidence for

lung-cancer mortality is lower than for IHD. However, the percent of total incidence

attributable to PM 2.5 

# exposure is larger for IHD-related mortality than for any of the other

mortality categories modeled (Append ix E, Tables E-24 and E-33).



#  Short-term exposure-related mortality: Total incidence of PM 2.5 

# -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 mort ality attributable to PM 2.5 

# ranges from 0.9% (Tacoma) to 2.5%

(New York). (Appendix E, Table E-87 ). Percent of total incidence attributable to PM 2.5 

# exposure is generally lower fo r 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 usuall y higher than for CV mortalit y, ranging from 0.9% (Dallas) to

2.8% (Fresno and New York) (Appendix E, Ta ble E-96). Of the 15 urban study areas

modeled for CV mortality, 12 lo cations had negative lower bound estimates of incidence

(and two of these head negative point estimates) , reflecting use of non-st atistically significant

effects estimates (see section 4.0 for additiona l discussion). The number of study areas

modeled with non-statistically si gnificant effects estimates was lower for the other two shortterm exposure-related mortality endpoints.



#  Short-term exposure-related morbidity (hos pital admissions for respiratory and

cardiovascular illness): Total incidence of PM 2.5 

# -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 PM 2.5 

# exposure (Appendix E, Ta bles E102 and E-111).

Similarly, the percent of total cardiovascular HA attributable to PM 2.5 

# is larger than estimates

for respiratory HA and ranges from 0.28% (Dalla s) to 1.6% (Pittsburgh) (Appendix E, Table

60 

# Note, that as discussed earlier, for long-term exposu re-related mortality, two risk estimates are provided

for each urban study area, refl ecting 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 PM 2.5 

# monitoring data and the C-R function derived

using 1999-2000 data, with the latter function having the larger effect estimates and therefore, producing higher risk

estimates.

# 4-18 E-105). In this case, the pattern of risk acro ss urban study areas reflects both differences in

underlying baseline incidence for these e ndpoints as well as th e 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 cardi ovascular-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 thr ee years (see Appendix E, Tables E-22 through E-24) shows

that, while 2007 does produce incidence estimate s 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 year s are fairly similar

(results for Birmingham vary by less than 7% ac ross 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 PM 2.5 

# 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 valu es, but rather using one representative year

(see section 3.4.1.3 and 3.4.2 for demographics and ba seline health effects incidence rates,

respectively). In terms of s hort-term exposure-related morbid ity and mortality endpoints, the

pattern is similar to that described above for long-term mortality, with risk estimates for 2007

generally falling between t hose generated for 2005 and 2006 (in terms of magnitude),

although the magnitude of variations across th e three simulation years for a given health

endpoint/case study combination was notably lo wer for the short-term exposure-related

endpoints than for the long-term endpoints. Fo r example, with CV mortality, one of the

urban study areas with the greate st variation across the thr ee years (New York) had a 15%

difference in PM 2.5 

# –related risk across the three y ears (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 PM 2.5 

# levels for the study areas (see Appendix A), gi ven that other candidate input parameters,

which could have temporal variability (e.g., dem ographics and baseline incidence rates) were

not modeled with year-specific values, but rather using one re presentative year.



#  Consideration of the 95 th 

# 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 95 th 

# percentiles, reflecting uncertainty in the

statistical fit of the underlying effect estimates in the C-R f unctions. These results suggest

that this source of uncertainty can be notable. In the case of recent cond itions risk estimates,

for long-term mortality, while the central tendency risk esti mate for all-cause (long term

exposure-related) mortality incidence in New York range from 2,380-3,000, the 95 th



# percentile confidence in terval for this estimates is 1,960 to 3,500 (Appendix E, Table E-21

and E-30). In this case, this source of uncertain ty 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 se nsitivity analysis, these would translate as having a “small”

# 4-19 impact on the core risk estimate (see Section 4.3.1) . The impact of sta tistical fit uncertainty

on the IHD-related long-term exposure-related mo rtality results (see Appendix E, Tables E24 and E-33) are similar in magnitude to those se en for all-cause mortality and also results in

a classification of this uncertainty having a “sma ll” impact on core risk estimates. For shortterm exposure-related mortality and morbidity, the impact of statistical f it (as reflected in the

95 th 

# percentile CI risk estimate ranges) is great er than for long-term mortality. For example

with CV-related mortality, the central tendenc y estimate for New York is 570 cases, while

the 95 th 

# percentile CI is 332 to 902 (i.e., ~40% lo wer and ~40% higher th an 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 stat istical 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 ASSO CIATED WITH JUST MEETING THE

CURRENT AND ALTERNATIVE SUITES OF STANDARDS (CORE ANALYSIS)

This section discusses core risk estimates ge nerated for just meeti ng the current suite of

standards and alternative suite s of standards, focusing on the 2007 simulation year (although

general trends in observations acros s the three simulated years are discussed to a limited extent).

In discussing risk estimates for the current a nd 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 th e fact that we have gr eater confidence in the

ability of the risk models to differentiate risk be tween sets of standards, since this requires the

models to estimate risk for ambient air PM 2.5 

# levels likely near or with in the range of ambient air

quality data used in the underlyi ng epidemiology studies. By contra st, estimates of absolute risk

(for a given air quality scenario) require the models to perf orm at the lower boundary of ambient

air PM 2.5 

# levels reflected in the st udies (i.e., down to the LML re flected 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 e xposures at these lower ambient air PM 2.5 

# levels. While

there is greater uncertainty associated with esti mates 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 ove rall level of confidence associated with different

risk metrics (and implications for informing thei r use in the context of the PM NAAQS review)

is discussed in Chapter 5.

This section discusses risk estimates genera ted for the current standard levels first,

followed by discussion of risk estimates associated the set of alternative st andard levels assessed.

# 4-20 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. Observat ions presented in the previous section regarding the statistical

significance of effect estimates used in genera ting risk estimates and their implications for

interpretation of those risk estimates also hold for estimates presented in this section.

Consequently, observations regarding risk resu lts with confidence inte rvals 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 re ductions (in terms of percent change in PM 2.5 

# -

attributable risk) generally seen for short-term exposure-related endpoints relative to long-term

exposure-related endpoints primar ily reflects the fact that PM 2.5 

# -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 ri sk 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 larg er compared with short-term risk

An important factor to consid er in interpreting the risk estimates for both the current set

of standards and sets of altern ative standards is whether the annual or 24-hour standard for a

given pairing of standards is c ontrolling 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 addi tion, the approach used to simulate ambient PM 2.5



# levels under current and alternative standard leve ls (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 st andard 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 contro ls) to impact patterns of risk

reduction is not discussed here, but rather reserved for discussi on as part of the sensitivity

analysis (section 4.3) and the inte grative 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 st udy 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 hybr id rollback or locally focused which simu late more localized control strategies

have the potential to reduce PM 2.5 

# levels at monitors exceeding the daily standard, while leaving other monitors

(which may have elevated annual average PM 2.5 

# levels) relatively or totally unadjusted. This can result in the 24-

hour standard not providing coverage for the annual standard, even when the 24-hour standard is controlling (i.e.,

additional reduction focused on monitors with high annual de sign values may be required to attain the annual) - see

discussion in Section 4.3 and Chapter 6.

# 4-21 An overview of which urban st udy 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 presen ting the full set of detailed core risk estimates generated for

the current and alternat ive suites of standards).

4.2.1 Core Risk Estimates for Just Meeting the Current Suite of Standards

This section summarizes risk estimates gene rated for the 15 urban study areas based on

simulating just meeting the current suite of stan dards (including the magnit ude of risk reductions

relative to recent conditions, where applicable).



#  Long-term exposure-related mortality: Total incidence of PM 2.5 

# -related IHD mortality

ranges from 15-20 (Salt Lake City) to 1,760-2,220 (N ew York) (Table 4-1). The percent of

total incidence of IHD mortality attributable to PM 2.5 

# ranges from 3.7-4.7% (Tacoma) to

13.2-16.7% (Atlanta) (Table 4-2). These levels of IHD mortality risk attributable to PM 2.5 

# 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 simulati ng just meeting the current suit e 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 PM 2.5 

# -attriutable incidence for all-cause

mortality and cardiopulmonary mo rtality is larger than IHD (for a given study area under

recent conditions), while total PM 2.5 

# -attributable incidence for l ung-cancer mortality is lower

than for IHD. However, the percent of total incidence attributable to PM 2.5 

# 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 PM 2.5 

# -related mortality for short-term expos ure is substantially smaller than

estimates for long-term exposure-related mortal ity. Estimates for CV mortality for shortterm exposure ranges from 9 (Sa lt Lake City) to 470 (New York) (Table 4-1). The percent of

CV mortality attributable to PM 2.5 

# ranges from 0.7% (Fresno) to 2.1% (Philadelphia and New

York). (Table 4-2). The level of risk reduc tion (comparing risk under the current standard

with risk under recent conditi ons) is generally lower for s hort-term exposure-related CV

mortality compared with long-term exposure- related all-cause mort ality 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 a ny risk reduction since these two locations

meet the current suite of standa rds based on recent air quality da ta. Percent of total incidence

attributable to PM 2.5 

# 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 usua lly higher than for CV, ranging from 0.9%

(Dallas) to 2.6% (Baltimore) (Appendix E, Ta ble E-96). As noted above, of the 15 urban

study areas modeled for CV mortality, 12 locatio ns had negative lower bound estimates of

incidence (and two of these had negative point estimates), refl ecting use of non-statistically

significant effects estimates (see se ction 4.0 for additional discussion).



#  Short-term exposure-related morbidity (hos pital admissions for respiratory and

cardiovascular illness): Total incidence of PM 2.5 

# -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 PM 2.5 

# exposure (Appendix E, Ta bles E102 and E-111).

Similarly, the percent of total cardiovascular HA attributable to PM 2.5 

# is larger than estimates

for respiratory HA and ranges from 0.28% (Dalla s) to 1.33% (Baltimore). As noted above,

the pattern of risk across urba n study areas reflects both differences in underlying baseline

incidence for these endpoints as well as the us e of regionally-differen tiated 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 recen t conditions) for both

respiratory and cardiovascular hospital admissi ons 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 suit e of standards based on recent ai r quality data. As noted above,

of the 15 urban study areas modeled for cardi ovascular-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 th e current standard level analys is. In other words, (a) 2007

generally represents risks in be tween the other two years in te rms of magnitude, (b) there are

exceptions where 2007 had the highest risks an d lowest risk (depe nding 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 endp oints (with the magnitude of

this variation similar to what is reported above for the recent conditions simulation).



#  Consideration of the 95 th 

# 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 sc heme 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 slig htly larger) compared with what was seen with risk

estimates generated for the recent conditions ai r quality scenarios (i.e., 48% lower to 42%

higher than the core risk estimate – see estimate s 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 classifi cation scheme developed for the sensitivity

analysis.

4.2.2 Core Risk Estimates for Just Meeting Alternative Suites of Standards

This section summarizes risk estimates ge nerated for the 15 urban study areas when

ambient PM 2.5 

# levels under the alternative standard le vels are simulated. As noted in section 4.2,

this discussion focuses on the magnitude of incr emental 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 standa rd level. Note, however, that

we do provide limited discussion of absolu te risk levels attributable to PM 2.5 

# exposure for

# 4-23 alternative standard levels, with the provision that these be inte rpreted 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 alte rnative 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 pattern s of reduction in terms of percent change in

PM 2.5 

# -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 PM 2.5 

# 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 PM 2.5 

# concentrations or

reductions in the distribution of 24-hour estimate s for a year). Consequently, in discussing

incremental risk reduction in terms of percent ch ange relative to the curre nt 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 obser vations regarding patte rns of percent risk

reduction for the two averaging periods are refl ected 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 mo re limited under the 14/35 alternative standard,

with only 5 of the 15 urban study areas demons trating notable reducti ons 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/m 3 

# (i.e., the 13/35 alternat ive suite of standards)

produced a notable increase in th e 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/m 3 

# as reflected in the 12/35

alternative suite of standards) resulted in additional study areas ( now 12 of the 15 study

areas) experiencing risk reducti ons with percentage risk re ductions now ranging from 11%

63 

# The three alternative annual standards considered in the risk assessment (12, 13 and 14 μg/m 3 

# ) were each

paired with the current 24-hour standard of 35 μg/m 3 

# for purposes of generating risk estimates. A separate set of

alternative suites of standards (i.e., 13/30 and 12/25) were also considered – see next section below. In discussing

risk estimates associated with the alternative annual standards , each alternative annual standard level was paired

with the current 24-hour standard of 35 μg/m 3 

# in determining which standard level was controlling and,

consequently, whether the alternative annual standard would produce any notable reductions in risk.

# 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 th ese three study areas when alternative 24-hour

standards were considered – see below. The specific pattern of ri sk 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 PM 2.5 

# 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, th e same five urban study areas (Atlanta,

Baltimore, Birmingham, Houston and St. Louis) had notable risk reductions under the full set

of alternative annual sta ndards, with the degree of risk reduction for PM 2.5 

# -related

cardiovascular mortality for the lowest alterna tive 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 nu mber of the other study areas did not exhibit

notable risk reductions until the lowest altern ative 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 standa rds (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 exhib it any notable risk reduction under the set of

alternative annual standards c onsidered and only experienced risk reductions when the 24-

hour standard level was reduced. Because the sa me air quality metric (a nnual distributions of

24-hour PM 2.5 

# concentrations) is used in generati ng 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). Speci fically, the same groups of urban study areas experience the

same magnitude of risk reductions (in terms of percent changes in PM 2.5 

# -related risk relative

to the current standard level) across the altern ative 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 le vels considered here depends

on whether the alternative annual (12, 13 or 14 μg/m 3 

# ) or the current 24-h our standard of 35

μg/m 3 

# is controlling. The approach used to simulate just meeting alternative 24-hour

standards (i.e., proportional, hybri d, or locally focused) can have an impact on the magnitude

of risk reduction, although it doe s 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 alte rnative annual standards considered) can be

divided into three categories: (a ) all of the alternative annual st andard 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 cons equently notable risk reducti ons are only seen at the lower

# 4-25 or lowest annual standard level( s) considered (Dallas, Los A ngeles, New York, Philadelphia,

Phoenix, Pittsburgh), and (c) none of the alternat ive 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 PM 2.5 

# -attributable risk under altern ative annual standards: As

discussed above, we have grea ter confidence in estimating incremental reductions in risk

between the current and alternat ive suites of standards, then the estimation of absolute

incidence under a given suite of standards. N onetheless, 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 th e alternative annual standa rds (Atlanta, Baltimore,

Birmingham and Houston) have PM 2.5 

# -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, Tabl e E-21 and E-30). The two urban study

areas with the greatest degree of PM 2.5 

# -related risk in absolute terms (Los Angeles

and New York) do not exhibit significant re ductions in risk un til the lowest annual

standard level of 12/35 is considered, with PM 2.5 

# -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 th e alternative annual standa rds (Atlanta, Baltimore,

Birmingham and Houston), have PM 2.5 

# -related CV mortality estimates (under the

lowest alternative standard of 12/35) rangi ng from 25 (Atlanta) to 50 (Baltimore) (see

Appendix E, Table E-84). We note that Birm ingham has an incidence estimate of -1,

reflecting application of a non- statistically significant eff ect estimate in modeling this

endpoint (see section 4.1). The urban st udy area with the grea test degree of PM 2.5 

# - 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 PM 2.5 

# -related

CV mortality estimated at 420 under that a lternative 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 PM 2.5 

# -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

PM 2.5 

# -related risk in absolute terms (Los Angeles and New York) do not exhibit

significant reductions in risk until the lowe st annual standard level of 12/35 is

considered with PM 2.5 

# -related all-cause mortality estimated at 240 and 670,

respectively under that alte rnative 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 th ree simulation years for the recent conditions

and current standards simulations generally hold for the alternative standards analysis. In

other words, (a) 2007 generally represents risk s between the other two years in terms of

# 4-26 magnitude, (b) there are exceptions where 2007 had the highest risks and lowest risk

(depending on study area and endpoint), and (c) generally, long-te rm exposure-related

mortality endpoints showed greater cross-year variation then th e short-term exposure-related

endpoints in terms of both absolute PM 2.5 

# risk for a particular alte rnative suite of standards,

as well as incremental risk reductions rela tive to the current suite of standards.



#  Consideration of the 95 th 

# 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 unde r alternative standards involving reduction of

the annual level as it did under re cent 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 con tinues to be similar compared with what was seen for risk

estimates generated for the recent conditions ai r quality scenarios (i.e., 42% lower to 42%

higher than the core risk estimate – see es timates in Appendix E, Table E-84). This

continues to result in a cla ssification of “moderate” for this source of uncer tainty based on

the classification scheme develope d for the sensitivity analysis.

Combinations of alterna tive 24-hour and annual sta ndard 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 ur ban study areas, with the lower combination

(12/25) producing a notable reduction in risk relative to the first co mbination of 13/30. The

only study area that did not exhi bit 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/m 3



# and 13 μg/m 3 

# , respectively (and consequently, th e 13/30 did not produce a reduction in

ambient air PM 2.5 

# , or a resulting reduction in risk). Reductions in long-term exposure-related

mortality (across all endpoints) under the 13 /30 combination ranged fr om 14% (Phoenix) to

55% (Salt Lake City), while reductions for th e 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/m 3 

# 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/m 3 

# (the LML below which long-term exposure-related

mortality is not estimated). The specific patt ern of risk reduction re flects whether the 24-hour

or annual standard was controlling – see di scussion below regardi ng 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 PM 2.5 

# 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 site s, however the magnitude of risk reduction (in terms of percent

change in PM 2.5 

# -related risk) is lower for short-te rm 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 (D allas being the exception), had notable risk

reductions under both the 13/30 a nd 12/35 alternative suites of standards (Dallas only was

# 4-27 estimated to have reductions in risk under the lower 12/25 co mbination - 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 th e 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 prev ious section, the pattern of ri sk reduction seen for the two

combinations of alternative 24-hour and annual standards described here depends on which

standard is controlling. In a ddition, the magnitude of the redu ction in risk reflects (a) the

magnitude of the difference between the cont rolling design value a nd the standard level

(which determines the degree of reduction in ambient air PM 2.5 

# levels) and (b) the method

used to simulate ambient PM 2.5 

# levels under alternativ e suites of standard s (i.e., proportional,

hybrid or locally focused rollback). For this set of alte rnative suites of st andards, 10 of the 15

study areas had the alternative 24-hour standa rd 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 standard s) 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 PM 2.5 

# -attributable risk under alternat ive suites of annual and 24-

hour standards: As with the alternativ e annual standards, belo w we provide a brief

overview of the magnitude of PM 2.5 

# -attributable risk (i.e., absolu te 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 thes e two alternative suites of standards (Tacoma, St.

Louis, Los Angeles and Fresno), have PM 2.5 

# -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 urba n study area with the greatest degree of

PM 2.5 

# -related risk in absolute terms besides New York (New York) has PM 2.5 

# -related

all-cause mortality estimated at 820-1,040 under the 12/25 case.

o 

# Short-term exposure-related mortality : eleven of the 15 st udy areas had percent

reductions in risk for the 12/25 case (rela tive to the current standards) of

approximately 29% (the othe r study areas had lower per cent reductions). Of the

locations with ~29% reductions in risk, PM 2.5 

# -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

PM 2.5 

# -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.

# 4-28 

#  Consideration of the 95 th 

# 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 rela ted to the statistical fit of

effect estimates will have a greater impact on short-term exposure-related mortality (+/-

~40%) compared with long-term exposure-rela ted mortality (+/- ~18%) (see Appendix E,

Tables E-84 and E-21 plus Table E-30, respectivel y). Again, this results in a classification of

this source of uncertainty as having a “low er” 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 impact s on risk estimates. A second goal of the

sensitivity analysis was to generate an additiona l 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 sens itivity analysis and identifying those elements

which have the greatest impact on the core risk es timates. 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 secti on 4.3.1. Use of the results of th e sensitivity analysis as an

additional set of reasonable risk estimates to augm ent the core risk estimates in considering the

impact of uncertainty and vari ability in the core risk mode l 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 sens itivity analysis, we have also focused on longterm exposure mortality and to a lesser extent on short-term exposure mortality and morbidity.

Although the sensitivity analys is simulations were completed for all three simulation

years (as reported in Appendix F), we have fo cused 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 fo r alternative rollback methods) a larg er number of the urban areas were

included in the sensitivity analysis.

# 4-29 4.3.1 Sensitivity Analysis Results to Identify Po tentially Important Sources of Uncertainty

and Variability

The results of the sensitivity analysis are summ arized 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/u rban 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 th e 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 inciden ce 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 th at by contrast, the alternative risk estimates

discussed below in section 4.3.2 focus on absolute ri sk, since the emphasis with that analysis is

on assessing the potential spread in (absolute) risk that result s from considering alternative

modeling element specifications from t hose 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 th e 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 uncertain ty 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 uncertain ty 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 Moolgavka r’s (2003) study in Los Angeles addressing

model specifications for both short-term mort ality and morbidity (e.g., model selection, lag

structure and co-pollutant models ) are discussed together as a gr oup. 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 th e Moolgavkar-based sensitivity analysis results

presented below, as well as the summary of results presented in Table 4-3, focus on the

# 4-30 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 co mparison against the

core result that are emphasized with the other sensitivity analyses. 65



# The sensitivity analysis examining the imp act of alternative rollback approaches for

simulating ambient PM 2.5 

# concentrations in urban study areas under both the current and

alternative suites of standards al so deserves additional discussion before presenting the results.

For the first draft RA, we consid ered 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 di scussed in sections 2.3, 3.2.3 and 3.5.4, we have

included consideration of a loca lly 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 dr aft risk assessment, we have calculated

composite monitor estimates based on proportional rollback and hybrid and/or locally focused,

where appropriate. The composite monitor valu es are surrogates for long-term exposure-related

mortality. 67 

# Therefore, by comparing composite mon itor values generated for the same study

area/suite of standard s (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 sensitiv ity analysis). These sensitivity analysis results

based on consideration of composite monitor va lues generated using the different rollback

methods (which are presented in detail in Append ix 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 Moolga vkar-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 fo r alternate design elements) would be representative of

how models derived from either of the key short-term st udies considered in this risk assessment (Zanobetti and

Schwartz., 2009 and Bell et al., 2008) would respond to vari ations in design. Therefore, while sensitivity analysis

results based on comparing Moolgavkar-based risk estima tes 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 degr ee of uncertainty asso ciated with them.

66 

# The locally focused approach involves proportional reduction in 24-hour PM 2.5 

# levels only at those urban

study areas where the 24-hour standard is controlling (and only at those specific monitors 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 PM 2.5 

# 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 PM 2.5 

# exposure across study areas and/or standard levels (see

Section 3.5.4).

# 4-31 summary information related to rollback presente d in Table 4-3. Due to the complexity of the

sensitivity analysis conducted ex amining 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 result s of the sensitivity analysis, result s of the single-factor simulations

are presented first (section 4.3.1.1), followed by th e 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 morbidit y. An overall conclusion regarding which of

the factors included in the sens itivity analysis represent pote ntially significant sources of

uncertainty and variability impacting the core riskestimates is presented at the end of each subsection.4-32Table 4-3 Overview of Sensitivity Analysis ResultsSensitivity Analysis1Health Endpoint and RiskAssessment LocationSummary of Results(percent difference in riskestimate relativeto the core estimate)Appendix FTables withDetailedResults (for2007)Single-Factor Sensitivity Analyses (long-term exposure mortality):Impact of using different model choices:fixed effects log-linear (the core) vs. random effectslog-linearC-R functionAll-cause, CPD, IHDLos Angeles andPhiladelphiaRandom effects log-linear C-R model:all-cause: +23%IHD: +12%Table F-3Impact of using different model choices:fixed effects log-linear (the core) vs. random effectslog-logC-R functionAll-cause, CPD, IHDLos Angeles andPhiladelphiaRandom effects log-log C-R model:All-cause: +123 to +159%CPD: +50 to +74%IHD: +80 to +111%Lung Cancer: +67 to +94%Table F-3Impact of using different model choices:Single vs. multi-pollutant modelsAll-causeLos Angeles andPhiladelphiaModel with CO: +45%Model with NO2: +73%Model with O3: +45%Model with SO2: -74%F-43Impact of estimating risks down to PRB rather thandown to LML (the core)All causeAll 15 urban study areasAll-cause: +47 to +273%Table F-6Impact of using alternative C-R function fromanother long-term exposure mortality studyAll-cause, CPD, lungcancerLos Angeles, PhiladelphiaAll-cause: +119 to +121%CPD: +29 to +30%Lung cancer: +29 to +30%Table F-9Impact of using alternative hybrid rollback approachreflecting more localized patterns of ambient PM2.5reduction (evaluated across current and alternativestandard levels) – based on the composite monitoranalysis described in Section 3.5.4 considering bothhybrid and locally focused approaches as alternativesto proportional rollbackSurrogate for long-termmortality (compositemonitor-based analysis)All study areas exceptDallas had either hybridand/or locally focusedapplied as an alternativeTrend in incremental risk reduction(alternative standard level compared tocurrent standard): rollback method did notappear to have a significant impact on thismetric (those urban study areas withdifferent trends in reduction did notdemonstrate a consistent pattern related toTables F-49and F-504-33Sensitivity Analysis1Health Endpoint and RiskAssessment LocationSummary of Results(percent difference in riskestimate relativeto the core estimate)Appendix FTables withDetailedResults (for2007)rollback method to theproportionalthe type of hybrid method used)Absolute risk for a given standard level:use of alternative rollback methods didappear to impact estimation of PM2.5riskremaining for a given standard level: <1%to >+50%Has implications for degree to which 24-hour standard levels produce reductionsin annual average PM2.5levels (andconsequently on long-term and short-termexposure-related risk). Results suggestthat use of locally focused rollbackmethod can result in smaller degree ofreduction in annual average valuescompared with proportional rollback,(see discussion in text – section 4.3.1.1)Single-Factor Sensitivity Analyses (short--term exposure mortality):Impact of using season-specific C-R functions (vs.an annual C-R function)Non-accidental mortality,CV, respiratoryAll 15 urban study areasNon-accidental: -116 to +179%CV: -82 to +500%Respiratory: -48 to +162%(Note, overall incidenceestimates, particularlyfor the locations with higher percent changeestimates, is very low, raising concerns overthe stability of these sensitivity analysisresults)Table F-15Table F-18Table F-21Impact of using alternative hybrid rollback approachreflecting a combination of more localized andregional patterns of ambient PM2.5reduction (note,this analysis is based exclusively on the hybridrollback – the composite monitor analysis describedNon-accidental mortalityBaltimore, Birmingham,Detroit, Los Angeles,New York and St. LouisResults for all seven urban study areas(across the current and alternativestandard levels) do not exceed +17%,with most <+10%.Table F-364-34Sensitivity Analysis1Health Endpoint and RiskAssessment LocationSummary of Results(percent difference in riskestimate relativeto the core estimate)Appendix FTables withDetailedResults (for2007)above pertains only to long-term mortality-relatedrisk)Single-Factor Sensitivity Analyses (short-term morbidity: hospital admissions (HA) and ED visits):Impact of using season-specific C-R functions (vs.an annual C-R function)HA (unscheduled), CVand respiratoryAll 15 urban study areasHA (CV): -105 to +9%HA (respiratory): -54 to +74%(Note, overall incidenceestimates, particularlyfor the locations with higher percent changeestimates, is very low, raising concerns overthe stability of these sensitivity analysisresults)Table F-24Table F-27Impact of using an annual C-R function (applied tothe whole year) vs. a seasonal function for Aprilthrough August (applied only to that period) (using asingle pollutant model)Asthma ED visitsNew YorkNA (although incidence estimates weregenerated for this simulation, “percentdifference from the core” were not generatedsince the alternate simulation focused on asubset of the year).Table F-30Impact of considering models with different lagsHA (CV and respiratoryLA and New YorkNA (although incidence estimates weregenerated for this simulation, “percentdifference from the core” were not generatedsince the lag-differentiated C-R functions usedare not regionally-differentiated, andtherefore, do not allow a focusedconsideration of thelag factor alone inimpacting risk estimates)Table F-48Single-Factor Sensitivity Analysis (short-term exposure mortality and morbidity in LA based on Moolgavkar, 2003 study model options) (Note, resultspresented here reflect spread in risk estimates across Moolgavkar-based model specifications and not percent difference from core risk estimates,unless so stated – see text)Impact of model selection (e.g., log-linear GAMwith 30 df; log-linear GAM with 100 df; and loglinear GLM with 100 df)Mortality (non-accidental,CV); HA (CV)Los AngelesNon-accidental mortality: +80%CV mortality: +49CV HA: +36%Table F-334-35Sensitivity Analysis1Health Endpoint and RiskAssessment LocationSummary of Results(percent difference in riskestimate relativeto the core estimate)Appendix FTables withDetailedResults (for2007)Impact of lag structure (0-day, 1-day, 2-day, 3-day,4-day, 5-day)Mortality (non-accidental)Los AngelesNon-accidental mortality: +55%Table F-33Impact of single- vs. multi-pollutant models (PM2.5with CO)Mortality (CV); HA (CV)Los AngelesCV mortality: +106%CV HA: +140%Table F-33Multi-Factor Sensitivity Analyses (long-term mortality):Impact of using a fixed effects log-linear vs. arandom effects log-log model, estimating incidencedown to the lowest measured level (LML) in thestudy vs. down to PRB, and using a proportional vs.hybrid rollback to estimateincidence associated withlong-term exposure to PM2.5concentrations that justmeet the current standards (note consideration ofrollback in the multi-factor analysis did notincorporate the hybrid-based rollback approach)All-cause, IHD long-termmortalityLos Angeles andPhiladelphiaAll-cause: +27 to +1,089%IHD: +26 to +673%F-39Multi-Factor Sensitivity Analyses (short--term mortality):Impact of using season-specific vs. all-year C-Rfunctions and proportional vs. hybrid rollbacks toestimate incidence associated with short-termexposure to PM2.5concentrations that just meet thecurrent standardsNon-accidentalBaltimore, Birmingham,Detroit, Los Angeles,New York and St. LouisNon-accidental (four seasons + hybrid):-116 to +179%F-421Unless otherwise noted, sensitivity analysis results are based onthe scenario reflecting just meeting the current suite of PM2.5standards.2This metric is the percent spread in risk estimates across the Moolgavkar-based model specifications (not the percent difference estimates – see text discussionabove).

# 4-36 4.3.1.1 Single-factor Sensitivity Analysis

This section presents the resu lts of the single-factor sensi tivity 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 sensitiv ity analysis discussed in section 4.3.1.2). The

results of the single-factor sensitivity analysis are characterize d 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 Tabl e 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 mortalit y, including (a) random effects log-linear

model and (b) a random effects log-log model (n ote, 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, NO 2 

# , O 3 

# or SO 2 

# . 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 effect s 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 ha ving an incrementally steeper slope at lower

PM levels, which quickly increases incidence es timates 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 th at control for co-polluta nts was shown to have

moderate-large impact on risk es timates, with control for CO, NO 2 

# , or O 3 

# resulting in

increased PM 2.5 

# -attributable risk estimates, while control for SO 2 

# resulted in a moderate-large

decrease in estimated PM 2.5 

# risk. 68



#  Impact of estimating risks down to PRB rather than down to LML : This simulation

compared long-term exposure mortality incide nce 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/m 3 

# for long-term mortality – see section 3.1). This simulation involved all

68 

# Sensitivity analysis results generated using the copollutant model involving PM 2.5 

# and SO 2 

# have been deemphasized since it is likely that control for SO 2 

# may be capturing a portion of PM 2.5 

# -attributable risk related to the

secondary formation of sulfate, which is a component of the PM 2.5 

# mixture (i.e., the two pollutants are often highly

correlated).

# 4-37 15 urban study areas, given that PRB is strati fied by region and theref ore, 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 mode rate to large impact on long-term exposure

mortality incidence, with estimates ranging fr om 47 to 273% higher than the core estimates

(for matching urban locations) (Appendix F, Ta ble F-6). Note, however, that risk metrics

based on considering the incremen tal reduction in risk (incidence) between two alternative

suites of standards would not be impacted by this source of un certainty, since it only affects

estimates of absolute risk.



#  Impact of C-R function from alternati ve 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-lar ge effect on CPD mortality (+45 to +74%), a

large effect on all-cause mortalit y (+123 to +159%), a moderate-large to large effect on IHD

mortality (+80 to +111%) and a moderate-lar ge 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 obtai ned from the Six Cities study) the potential

impact of functions from alternative studies on long-term exposure mo rtality depends on the

mortality category being consider ed. 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 roll back approaches (hybrid and locally focused) to simulate just

meeting the current and alte rnative suites of standards . This sensitivity analysis assessed the

impact of estimating risk for the current a nd alternative sets of standards using two

alternatives to the proportiona l rollback strategy: (a) the hybrid rollb ack approach that

reflects an initial localiz ed pattern of ambient PM 2.5 

# reduction (resulting in non-proportional

rollbacks of monitored PM 2.5 

# concentrations) with a sec ond phase of more regional

reductions in ambient PM 2.5 

# levels (based on proportional adjustments) and (b) locally

focused which represents a primarily lo cal pattern of reductions in ambient PM 2.5 

# (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 anal ysis utilized proportional

rollback exclusively in simulating conditions for th e current and alternativ e sets of standards,

with this approach representing a regional pattern of ambient PM 2.5 

# reduction. A number of

observations can be drawn from this sensitivity analysis including:

o 

# Impact on estimates of PM 2.5 

# -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 PM 2.5 

# -attributable risk remaining after simu lation of a given suite of standards

(see Appendix F, Table F-50 and discussi on in section 3.5.4). Generally, use of

the hybrid approach had a small to moderate impact on absolute PM 2.5 

# - 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 PM 2.5 

# -attributable risk estimates. For example,

Los Angeles had composite monitor values for the current suite of standards and

several of the alternative su ites 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 PM 2.5 

# in response to alternative

standard levels, as reflected in applica tion of the hybrid and even to a greater

extent, the locally focused rollback approac h, can result in a larger fraction of risk

remaining after simulating those standard s. By contrast, more regional spatial

patterns of PM 2.5 

# 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 alternativ e standard. These results point to the

potentially important role played by th e nature of the spatial pattern of PM 2.5 

# reduction (i.e., rollback) in determining th e 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 simula te both the current and any alternative

suite of standards, the pa ttern of risk reduction acro ss alternative standards is

generally similar regardless of the rollb ack 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 alternat ive suites of standards, we can see

notable differences in the pattern of risk reduction. This is pa rticularly true for

areas with peaky PM 2.5 

# distributions (i.e ., areas with relatively high 24-hour

design values and lower annual average de sign values). For example, with Los

Angeles, which represents a st udy area with a relatively peaky PM 2.5 

# distribution,

application of proportional rollback in simulating bot h the current suite of

standards and the alternative annual standard of 12 μg/m 3 

# results in a 13%

reduction in long-term exposure-related mo rtality (see Figure 4-3 and Table E-27

in Appendix E). By contrast, applicati on 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 PM 2.5 

# reduction in response to an alternative sta ndard level can impact the magnitude of

risk reduction and consequently, the magn itude 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, w ill produce a smaller degree of reduction in the composite

monitor annual average PM 2.5 

# level. By contrast, application of propor tional 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 comp arison of composite monitor annual averages presented

in Table F-49 in Appendix F. In generating this surr ogate 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 il lustrate further that the type of rollback

used in simulating the current standard level can impact the magnitude of risk

reduction predicted for an alternative (lowe r) standard level. Particularly in urban

locations with peaky PM 2.5 

# distributions, application of more localized patterns of

PM 2.5 

# reduction (for the current standard), followed by a more regional pattern of

PM 2.5 

# reduction for an alternativ e standard level can result in a larger estimate of

risk reduction for that alternative standard.

Based on the simulations discussed above c overing potential sources of uncertainty and

variability impacting long-term mortality, we co nclude 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-e ffects 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 th e 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 Tabl e 4-3 for the specific modeling elements

considered in the sensitivity analysis).



#  Impact of using season-specific C-R f unctions (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 m odels were based on the same study as the model

used in the core analysis – Zanobetti and Sc hwartz, 2009). The results of the simulation

suggest that this source of un certainty can have a wide range of effects across urban study

areas (including not only variati on in the magnitude of effect, but also in the direction).

Percent changes compared with the core risk es timate were large, ranging from -116% (Los

Angeles) to +179% (Birmingham) (these re sults are for non-accident al mortality – see

Appendix F, Table F-15). We note that these tw o 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 ar eas (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 mortal ity also demonstrate

considerable variation across locations, but are generally smaller than results cited above for

non-accidental, with one excep tion. 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 ca tegory also has very small incidence, again

70 

# Use of locally focused rollback as an alternative method for simulating ambient PM 2.5 

# concentrations for

alternative standards had a moderate-large impact on risk estimates.

# 4-40 raising concerns over th e stability of the sensitivity anal ysis results - see Table F-18). The

results for respiratory-related mortality also de monstrate 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 alternativ e hybrid rollback approach : This simulation evaluates the

potential impact of using th e hybrid approach for simulating just meeting current and

alternative sets of standards, as an alternativ e to the proportional appr oach used in the core

analysis. 71 

# The results of this simulation (as contra sted with the impact of using the hybrid

approach on long-term exposure mortality) sugge st that use of the hybrid rollback approach

has relatively little effect on short-term mortality risk (e.g., percentage di fferences 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 discusse d above, result in a number of overall

observations regarding sources of uncertainty po tentially 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 doe s 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 inst ances, the estimates include ne gative lower bounds, reflecting the

use of non-statistically significant effects estimates). For these reasons, the results of this

sensitivity analysis, while initia lly 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 shor t-term exposure mortality estimation. Regarding

the use of the alternative hybr id (non-proportional) approach for simulating conditions under

alternative standard levels, the re sults suggest that this factor ha s a modest impact on short-term

exposure mortality (significantly less impact th an 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 (s ee below), it would app ear 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 morta lity 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 im pact 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 Tabl e 4-3 for the specific modeling elements

considered in the sensitivity analysis). The resu lts of individual sensitiv ity analysis simulations

are presented below, with overall observat ions presented at the end of the section.



#  Impact of using season-specific C-R f unctions (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 re sults of the simulation

suggest that, as with short-term exposure mortalit y this source of uncertainty can have a wide

range of impacts on the risk estimates across urban study areas (includi ng not only variation

in the magnitude of risk, but also in the dire ction) depending on the spec ific health endpoint

examined. We note, however, that the magnitude of impact appears to be less for short-term

morbidity than for short-term mortality. Pe rcent 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 modera te-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 Ap ril through August (applied only to that

period) in modeling asthma-related ED vi sits in New York, relative to the core

approach of using a single annual effect estima te (and applying that to the whole year):

This sensitivity analysis involved the approach of using a season-speci fic estimate to model

incidence for the period April th rough August (obtained from It o 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 fo r 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 morbid ity, we used a range of effects estimates

obtained from Bell et al., 2008 based on applicat ion of different lags, including 0-, 1- and 2-

day lags, (for both respiratory and cardi ovascular-related morbidity). Because lagdifferentiated effects estimates were only av ailable as national-averages and were not

regionally-differentiated, we coul d not directly compare the resu lts using different lag models

to the results generated for the core analysis (i .e., the sensitivity analys is results would have

mixed both the lag effect and the effect of regional differentiation, th ereby preventing clear

assessment of the importance of either fact or considered in is olation). 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 ar e, suggests that choi ce of lag does effect

estimates of short-term exposure-relate d 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-di fferentiated effects estimates, it would appear

that this factor does not have a substantial imp act 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 poten tially impacting short-term exposure morbidity are addressed below in

relation to the sens itivity analysis based on al ternative models from Moolgavkar et al. (2003). As

noted earlier, the scope of the sensitivity anal ysis 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) incl ude percent difference estimates based on considering the range

of risk estimates generated using alternative mo del specifications from this study for a given

health endpoint and it is these re sults 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 estimati ng short-term exposure mortality and

morbidity : Application of models obtained from Moolgavkar et al., (2003) with various

formulations related to mode l selection (degrees of freed om, 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 s hort-term exposure morbid ity (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 m oderate 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 (a nd 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 mo dels on estimating short-term exposure

mortality and morbidity : The results of the Moolgavkar-based simulations (when

considering the spread in risk estimates specifically across t hese simulations) suggest that the

multi-pollutant versus single-pollutant model issue (i.e., including CO in addition to PM 2.5 

# ),

could have a large impact on the estimation ofshort-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 coul d 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 cl ear 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 fact ors (now in combinati on), appear to have a

significant impact on estimation of the core estima tes and (b) to derive a set of reasonable

alternative risk estimates for us e in considering uncertainty and variability associated with the

core risk estimates. Regard ing 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 reasona bleness 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 di scussion presented below.

Long-term exposure mortality

This section summarizes the results of the sensitivity analysis focused on long-term

exposure-related mortality endpoints (see Tabl e 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 de sign choices related to modeling long-term

exposure mortality (all-cause and IHD). Modeling elements reflected in the

simulations included: (a) model form (l og-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 ro llback approach) to 1,089% (for a model

with random effects log-log model, risk es timated 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 hybr id rollback approach involving

non-proportional adjustment where ther e is the potential for greater us e 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 belie ve 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 charac terization 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 conc luded that those alternative risk estimates generated using risk

estimated down to PRB should not be used in creating the reasonable a lternative set of risk

estimates in considering uncertainty associ ated with the core risk estimates.

A key limitation of the multi-factor sensitivity an alysis is that the approach used did not

allow us to consider the locally focused roll back method in concert with the other modeling

elements described above. This means that the co mbined 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 integrat ive discussion in Chapter 5, we do consider the

results of the single-factor sens itivity analysis examining rollb ack (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 Tabl e 4-3 for the specific modeling elements

considered in the sensitivity analysis).



#  Impact of using season-specific vs. annua l 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-acciden tal). 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 hi gher-impact locations were relatively low,

again raising the concern for the stability in re lative differences with the core estimates.

# 4-45 Because of the more limited scope of the mu lti-factor sensitivity analysis completed for

short-term exposure-related mortality, we have conc luded 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 Es timates to Inform Consideration of

Uncertainty in Core Risk Estimates

This section discusses the use of the output of the sensitivit y analysis completed as part

of this risk assessment as an addi tional set of reasonable risk estimates to inform consideration of

uncertainty associated with the core risk es timates. Specifically, in the case of long-term

exposure-related mortality endpoints, staff has c oncluded that the result s of the sensitivity

analysis represent a reasonable se t 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 va rious 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 confid ence, the output of the

sensitivity analysis can be us ed to provide additional perspe ctive 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 th e specific percentiles of the risk distribution are

represented by points wi thin 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 anal ysis (i.e., Los Angeles an d Philadelphia assessed

for the current standard level). However, the part of the sensitivity analysis focusing on

alternative methods for simulating ambient PM 2.5 

# levels (i.e., rollback), did consider a larger

number of study areas and air quali ty scenarios. In presenting the alternative sets of reasonable

risk estimates, we focus on Los Angeles and Ph iladelphia 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 th at 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 altern ative sets of estimates for Los Angeles and

Philadelphia in Table 4-4. Then, in Figures 4-7 and 4-8, we presen t graphical display of the full

uncertainty set comprising the core plus additiona l 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). Th is 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 reas onable alternative risk estimates to

supplement the core risk estima tes (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

estimates 1



# Single-element sensitivity analysis results

(A) Impact of using

different model

choices: random

effects log-linear

model Los Angeles and Philadelphia:

IHD: +12%;

All cause: +23% Los Angeles and

IHD: 8.6%, All cause: 2.5%

Philadelphia:

IHD: 14.8%, All cause: 4.4%

(B) Impact of using

different model

choices: random

effects log-log model Los Angeles:

IHD: +111%; All cause: +159

Philadelphia:

IHD: +80%; All cause: +123% Los Angeles and

IHD: 16.2%, All cause: 5.2%

Philadelphia:

IHD: 23.8%, All cause: 8.0%

(C) Impact of using

different model

choices (single vs.

multi-pollutant – NO 2



# Vs O 3 

# /CO) 3 

# Los Angeles and Philadelphia:

All cause: +45 to +74%

(O 3 

# /CO and NO 2 

# , respectively)

and -74% for SO 2 

# Los Angeles and

All cause: 2.9% and 3.5%

(for O 3 

# /CO and NO 2 

# , respectively), 0.52% (SO 2 

# )

Philadelphia:

All cause: 5.2% and 6.3% (for

O 3 

# /CO and NO 2 

# , respectively), 0.94% (SO 2 

# ) 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 re sults 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, sinc e there is substantial uncertain ty 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 PM 2.5 

# and SO 2 

# have been de-emphasized since it is likely that control for SO 2 

# may be

capturing a portion of PM 2.5 

# -attributable risk related to the secondary formation of sulfate. Note, that the risk

estimates for SO 2 

# are presented as open circles in Figure 4-8, to si gnify that they have lowe r confidence and are deemphasized relative to the other alte rnative 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

estimates 1



# 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:

NA 2 

# Los Angeles:

IHD: 19.2%

All cause: 6.2%

Philadelphia:

NA 2



# 1 

# Percent of total incidence that is PM 2.5 

# - related (note, the set of estimates fo r each entry reflect adjustment to the

two core estimates generated fo r IHD and all-cause mortality)

2 

# hybrid not run for Philadelphia, so multi-element sensitivity analysis not completed

3 

# the two pollutant model for PM 2.5 

# and CO and PM 2.5 

# and O 3 

# had the same sensitivity result, so both models are

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 esti mates with reasonable alternative set of

risk estimates for Los Angele s and Philadelphia (IHD mortality).

Percent total incidence attributable to PM

2.5 

# (IHD mortality ) Los Angeles

Philadelphia

0 10 5 15 20 25

KEY:

- core risk estimate

- alternative reasonable risk estimate (from

single- and multi-element sensitivity analyses) 

# Percent total incidence attributable to PM

2.5 

# (IHD mortality ) Los Angeles

Philadelphia

0 10 5 15 20 25 0 10 5 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 estimate s with reasonable alternative set of

risk estimates for Los Angeles a nd Philadelphia (all cause mortality).

Percent total incidence attributable to PM

2.5 

# (all cause mortality ) Los Angeles

Philadelphia

3 2 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 PM

2.5 

# and SO

2 

# (de-emphasized relative to other alternative

risk estimates – see text) 

# 0.5 1 7 5

Percent total incidence attributable to PM

2.5 

# (all cause mortality ) Los Angeles

Philadelphia

3 2 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 PM

2.5 

# and SO

2 

# (de-emphasized relative to other alternative

risk estimates – see text) 

# 0.5 1 7 5



# Review of the set of risk estimates presente d in Table 4-4 and displayed in Figures 4-7

and 4-8 results in a number of obs ervations regarding uncertainty a ssociated 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 PM 2.5 

# and

SO 2 

# due to concerns with collinearity between the two pollutants a nd the potential that

SO 2 

# represents risk attributab le to secondarily formed PM 2.5



# .



#  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 estimat es, consideration of un certainty could result

in higher (more elevated) risk estimates, comp ared with the core risk estimates. In other

words, most if not all of the alternative m odel 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 anal ysis 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 Ta ble 3-13). For example, information is not

available to consider com positional differences in PM 2.5 

# and the potential for

differentiation of effects estimates. Further, not considering more refined patterns of

intra-urban exposure to PM 2.5 

# in deriving effects estim ates 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 re present an uncertainty di stribution. Therefore,

we can not assign percentiles to the individual data points presente d 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 se t of risk estimates reflecting the impact of

modeling element uncertainties does provide information that helps to inform our

characterization of uncertainty rela ted to the core risk estimates.

4.4 EVALUATING THE REPRESENTATIVE NESS 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 locati ons with relatively elevated am bient PM levels (in order to

evaluate risk for locations likely to experien ce some degree of risk reduction under alternative

standards) and (b) to include a range of urban areas reflecting he terogeneity in other PM riskrelated attributes ac ross the country. To further support inte rpretation of risk estimates generated

in this analysis, we included two analyses that assess the representativen ess of the 15 urban study

areas in the national context. First, we assess ed the degree to which ur ban study areas represent

the range of key PM 2.5 

# risk-related attributes that spatia lly 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 PM 2.5 

# risk related

variables (e.g., PM 2.5 

# composition, weather, de mographics 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-le vel 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 al ong the distribution of national

county-level long-term exposure-related mortality risk. This analysis al lowed 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 PM 2.5 

# exposure. To complete th is 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 Na tional 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 ava ilable data (e.g. from the 2000 Census, Centers

for Disease Control (CDC), or ot her sources), distributions for ri sk-related variables across U.S.

counties and for the specific counties represente d in the urban study area s were generated. The

specific values of these variable s for the selected urban study ar eas were then plotted on these

distributions, and an evaluation was conducted of how representative the se lected study areas are

with respect to these individual variables, relative to the national distributions.

Estimates of risk (either relative or absolu te, 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 qua lity and the health outcome (i.e., the PM 2.5 

# effect

estimates). Each of these elements can contribut e to heterogeneity in ri sk across urban locations,

and each is variable across locations. In additio n, there may be additional identifiable factors

that contribute to the variability of the four el ements across locations. In this assessment, we

examine the representativeness of the selected ur ban area locations for th e four main elements,

and also provide additional assessm ent of factors that have been identified as influential in

determining the magnitude of th e C-R function across locations.

The specific choice of variables which may affect the PM 2.5 

# effect estimates for which we

will examine urban study area representativeness is informed by an assessment of the

epidemiology literature. We particularly focu sed on meta-analyses and multi-city studies which

identified variables that influence heterogeneity in PM 2.5 

# effect estimates, and exposure studies

which explored determinants of differen ces in personal exposures to ambient PM 2.5 

# . While

personal exposure is not incorpor ated directly into PM epidemiology studies, differences in the

PM 2.5 

# effect estimates between ci ties clearly is impacted by di ffering 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 SO 2 

# , which are

potential confounders, but have not been identif ied as effect modifiers in the literature.

# 4-51 speaking, determinants of the PM 2.5 

# effect estimates used in risk assessment can be grouped into

three areas: demographics, baseline health c onditions, and climate and air quality. Based on a

review of these studies, we identified the following variables within each group as potentially

determining the PM 2.5 

# 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), unemploy ment rates (see Bell

and Dominici, 2008), race (see Bell and Dominici, 2008), pub lic 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: PM 2.5 

# levels (average, 98 th 

# percentiles, and numbers of days over

the level of the 24-hour standard, e.g. 35 μg/m 3 

# ), co-pollutant levels, PM composition

(see Bell et al, 2009; Domini ci et al, 2007; Samet, 2008; Tolbert, 2007), temperatures

(temp) (days above 90 degrees, variance of summer temp, mean summer temp, 98 th



# percentile temp, mean winter temp -- see Roberts, 2004; Medina-Ramon et al, 2006; Zeka

et al., 2005), air condit ioning prevalence (see Zanobetti a nd Schwartz, 2009; Franklin et

al, 2007; Medina-Ramon et al, 2006), ventilation (see Sarnat et al, 2006), percent of

primary PM from traffi c (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 availabl e 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 id entified through our re view of the literature. In cases where an

exact match was not available, we identified prox y 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. Th e target variables and s ources for the data are

provided in Table 4-5. Su mmary statistics for the most releva nt variables are provided in Table

4-6.

# 4-52 Table 4-5 Data Sources for PM NAAQS Ri sk Assessment Risk Distribution

Analysis

Potential Risk

Determinant Metric Year Source Degree of

National

Coverage

Demographic s

Age Median Age 2005 County Characteristics, 2000-2007

Inter-university Consortium for

Political and Social Research All counties

Age Percent over 65 2005 County Characteristics, 2000-2007

Inter-university Consortium for

Political and Social Research All counties

Age Percent under 15 2005 County Characteristics, 2000-2007

Inter-university Consortium for

Political and Social Research All counties

Education Population with less

than HS diploma 2000 USDA/ERS,

http://www.ers.usda.gov/Data/Edu

cation/ All counties

Unemployment Percent unemployed 2005 County Characteristics, 2000-2007

Inter-university Consortium for

Political and Social Research All counties

Income Per Capita Personal

Income 2005 County Characteristics, 2000-2007

Inter-university Consortium for

Political and Social Research All counties

Race Percent nonwhite 2006 County Characteristics, 2000-2007

Inter-university Consortium for

Political and Social Research All counties

Population Total population 2008 Cumulative Estimates of Resident

Population Change for the United

States, States, Counties, Puerto

Rico, and Puerto Rico Municipios:

April 1, 2000 to July 1, 2008,

Source: Population Division, U.S.

Census Bureau All counties

Population density Population/square mile 2008 Cumulative Estimates of Resident

Population Change for the United

States, States, Counties, Puerto

Rico, and Puerto Rico Municipios:

April 1, 2000 to July 1, 2008,

Source: Population Division, U.S.

Census Bureau All counties

Urbanicity ERS Classification

Code 2003 County Characteristics, 2000-2007

Inter-university Consortium for

Political and Social Research All counties

Climate and Air Quality

PM 2.5 

# Levels PM 2.5 

# Levels --

Monitored Ann Mean 2007 AQS 617 Monitored

counties

PM 2.5 

# Levels PM 2.5 

# Levels --

Monitored 98th %ile 2007 AQS 617 Monitored

counties

PM 2.5 

# Levels Average MCAPS MCAPS website 204 counties 204 MCAPS

counties

# 4-53 Potential Risk

Determinant Metric Year Source Degree of

National

Coverage

PM 2.5 

# Levels % days exceeding 35

μg/m 3 

# MCAPS website 204 counties 204 MCAPS

counties

Copollutant Levels Ozone AQS 725 Monitored

counties

Roadway

emissions/Exposure % of primary emissions

from traffic 1999 NEI All counties

Temperature Annual Average MCAPS website 204 counties 204 MCAPS

counties

Temperature Mean July Temp 1941-

1970 County Characteristics, 2000-2007

Inter-university Consortium for

Political and Social Research All counties

Relative Humidity Mean July RH 1941-

1970 County Characteristics, 2000-2007

Inter-university Consortium for

Political and Social Research All counties

Ventilation Air conditioning

prevalence 2005 American Housin g Survey, with

additional processing as in Reid et

al (2009) 83 urban areas

Baseline Health Conditions

Baseline Mortality All Cause CDC Wonder 1999-2005 All counties

Baseline Mortality Non Accidental CDC Wonder 1999-2006 All counties

Baseline Mortality Cardiovascular CDC Wonder 1999-2007 All counties

Baseline Mortality Respiratory CDC Wonder 1999-2008 All counties

Baseline Morbidity AMI prevalen ce 2007 BRFSS MSA estimates 184 BRFSS

MSA

Baseline Morbidity Diabetes Prev alence 2007 BRFSS MSA estimates 184 BRFSS

MSA

Baseline Morbidity Pneumonia Prevalence

Baseline Morbidity Stroke Prev alence 2007 BRFSS MSA estimates 184 BRFSS

MSA

Baseline Morbidity CHD Preval ence 2007 BRFSS MSA estimates 184 BRFSS

MSA

Baseline Morbidity COPD Prevalence

Obesity BMI 2007 BRFSS MSA estimates 184 BRFSS

MSA

Level of exercise vigorous activity 20

minutes 2007 BRFSS MSA estimates 184 BRFSS

MSA

Level of exercise moderate activity 30

minutes or vigorous

activity 20 minutes 2007 BRFSS MSA estimates 184 BRFSS

MSA

Respiratory Risk

Factors Current Asthma 2007 BRFSS MSA estimates 184 BRFSS

MSA

Smoking Ever Smoked 2007 BRFSS MSA estimates 184 BRFSS

MSA

C-R Estimates

Mortality Risk All Cause 2009 Zanobetti and Schwartz (2009) 212

cities 212 cities

Mortality Risk Respiratory 2009 Zanobetti and Schwartz (2009) 212

cities 212 cities

Mortality Risk Cardiovascular 2009 Zanobetti and Schwartz (2009) 212

cities 212 cities4-54Table 4-6 Summary Statistics forSelected PM Risk Attributes.AverageStandard DeviationMaximumMinimumSample SizeRisk AttributesUrban studyareasU.S.countiesUrban studyareasU.S.countiesUrban studyareasU.S.countiesUrban studyareasU.S.countiesUrban studyareas(number ofcounties)U.S.(numberofcounties)DemographicsPopulation1,410,331 97,020 1,870,237 312,348 9,862,049 9,862,049 57,441 42 31 3143Population Density (Pop/sq mile)7,212 258 14,960 1,757 71,758 71,758 87 0 31 3143Median Age (years)35.5 38.6 2.6 4.4 41.5 55.3 30.2 20.1 31 3141% Age 65 Plus11.3 14.9 2.64.117.2 34.75.82.3313141Unemploymentrate(%)5.4 5.4 1.5 1.8 9.0 20.9 2.7 1.9 31 3133% with Less than High School Diploma21.8 22.6 7.78.837.7 65.3 11.23.0313141Income ($2005)35691 27367 12605 6604 93377 93377 23492 5148 31 3086Air conditioning prevalence (%)85.8 83.3 13.3 21.5 99.4 100.0 58.69.91070% Non-white29.5 13.0 18.2 16.2 68.3 95.3 2.7 0.0 31 3141Health ConditionsPrevalenceofCHD(%)3.9 4.3 0.9 1.3 5.2 8.7 1.8 1.8 14 184Prevalence of Obesity (%)26.4 26.0 3.04.132.7 35.7 22.2 14.014182PrevalenceofStroke(%)2.7 2.7 0.8 1.0 4.1 6.5 1.1 0.7 14 184Prevalence of Smoking (ever) (%)18.4 19.6 3.14.023.1 34.4 14.26.514184Prevalence of Exercise (20 minutes) (%)28.4 28.0 3.64.833.9 44.1 20.5 15.414183All Cause Mortality (per 100,000 population)833.7 1022.3 241.1 258.6 1342.9 2064.2 402.5 176.8 31 3142Non-accidental Mortality (per100,000 population)774.1 950.6 227.3 249.6 1242.0 1958.4 361.6 117.7 31 3142Cardiovascular Mortality (per 100,000 population)317.5 392.1 100.6 121.0 535.7 970.4 122.4 37.5 31 3142Respiratory Mortality (per 100,000 population)70.8 97.3 23.0 32.3 130.3 351.0 34.8 13.3313136Air Quality and ClimateAQ - PM25 Annual Mean (μg/m3)15.1 11.7 2.2 3.1 19.6 22.5 9.7 3.4 29 617AQ - PM25 98th %ile 24-hour Average (μg/m3) 38.7 30.7 11.6 9.3 79.2 81.1 26.8 9.1 29 617AQ - O34th High Maximum 8-hour Average (ppm) 0.087 0.077 0.009 0.010 0.105 0.126 0.064 0.033 27 725% Mobile Source PM Emissions34.0 44.4 11.2 21.9 56.6 97.6 13.7 0.3 31 31414-55AverageStandard DeviationMaximumMinimumSample SizeRisk AttributesUrban studyareasU.S.countiesUrban studyareasU.S.countiesUrban studyareasU.S.countiesUrban studyareasU.S.countiesUrban studyareas(number ofcounties)U.S.(numberofcounties)July Temperature Long Term Average (°F)78.1 75.9 4.55.491.2 93.7 64.8 55.5313104July Relative Humidity Long Term Average (°F) 58.2 56.2 14.0 14.6 70.0 80.0 19.0 14.0313104C-R EstimatesAll Cause Mortality PM2.5Risk Estimate 0.000971 0.000974 0.000340 0.000216 0.001349 0.001508 0.000159 -0.000099 15 112Respiratory Mortality PM2.5Risk Estimate 0.001606 0.001670 0.000419 0.000305 0.002157 0.002221 0.000931 -0.000346 15 112Cardiovascular Mortality PM2.5Risk Estimate 0.001013 0.000842 0.000586 0.000324 0.001958 0.001958 -0.000180 -0.000180 15 112

# 4-56 Formal comparisons of parameter distributio ns for the set of ur ban study areas and the

national parameter distributions are conducted using standard statis tical tests, e.g. the

Kolmogorov-Smirnov non-parametric test for equali ty of distributions. In addition, visual

comparisons are made using cumulative distribution functi ons, and box plots.

The formal Kolmogorov-Smirnov (K-S) test resu lts are provided in Table 4-7. The K-S

tests the hypotheses that two distributions are not significan tly different. A high p-value

indicates a failure to reject th e null hypotheses that the case-study and nationa l 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 percen tiles of the distributions are ma tched. As such, the K-S test

results will not be sufficient to determine whether the urban st udy areas adequately capture the

tails of the distributions of specific risk related va riables. Additional visual analyses are used to

assess representativeness for the tails of the distribu tions. Overall, the K-S test results show that

for many of the important risk variables such as population, air qualit y, age, and baseline

mortality rates, the urban study ar eas 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 a nd lung diseases, the case study areas are repr esentative of the

national distributions. However, for these spec ific 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 distributi on across urban areas.

Figures 4-9 through 4-12 show for the four cr itical risk function el ements (population, air

quality, baseline incidence, and the PM 2.5 

# effect estimate) the cumulative distribution functions

plotted for the nation, as well as for the urban stu dy 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 analys es for cardiovascular and respirat ory mortality separately. The

complete set of analyses is provi ded in Appendix D. The vertical black lines in each graph show

the values of the variables for the individual ur ban study areas. These figures show that the

selected urban study areas repres ent the upper percentile s of the distributions of population and

air quality, while not representing lower population locations with lower 24-hour PM 2.5 

# levels.

This is consistent with th e objectives of our case study se lection process, e.g. we are

characterizing risk in areas that are likely to be experiencing exce ss risk due to PM levels above

alternative standards. The urban case study locations represent the full distribution of PM 2.5 

# risk

coefficients, but do not capture th e upper end of the distribution of baseline all-cause mortality.

The interpretation of this is that the case study risk estimates ma y 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 severa l selected potential risk attributes the

cumulative distribution function (CDF) plotted for th e nation as well as for the urban study areas.

These potential risk attributes do not directly enter the ri sk equations, but have been identified in

the literature as potentially a ffecting the magnitude of the PM 2.5 

# C-R functions reported in the

epidemiological literature. The selected ur ban study areas do not capture the higher end

percentiles of several risk ch aracteristics, includ ing populations over 65, income, and baseline

cardiovascular disease prevalence. Comparison gr aphs for other risk attr ibutes are provided in

Appendix D. Summarizing the analyses of the other risk attributes, we conclude that the urban

study areas provide adequate c overage across population, population densit y, annual and 24-hour

PM 2.5 

# levels, ozone co-pollutant levels, temperat ure and relative humidity, unemployment rates,

percent non-white population, asthma prevalence, obes ity prevalence, stroke prevalence, exercise

prevalence, and less than high sc hool education. We al so 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 85 th 

# percentile), % of population

65 and older (below 85 th 

# percentile), percent of primary PM emissions from mobile sources

(below 8oth percentile), prevalence of a ngina/coronary heart disease (below 85 th 

# percentile),

prevalence of diabetes (below 85 th 

# percentile), prevalence of heart attack (below 80 th 

# percentile),

prevalence of smoking (below 85 th 

# percentile), all-cause mortality rates (below 90 th 

# %ile),

cardiovascular mortality rates (below 90 th 

# percentile) and respiratory mortality rates (below 90 th



# percentile). In addition, all of the case study locations were above the 25 th 

# percentile of the

distribution of personal income.

Based on the above analyses, we can dr aw several inferenc es regarding the

representativeness of the urban case studies. First, the case st udies represent urba n areas that are

among the most populated and most densely populati on in the U.S. Second, they represent areas

with relatively higher levels of annual mean and 24-hour 98 th 

# percentile PM 2.5 

# . Third, they

capture well the range of effect estimates re presented in the Zanobetti and Schwartz (2009)

study. These three factors would suggest that th e urban study areas should capture well overall

risk for the nation, with a potential for better characterization of th e 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 ha ve a high risk per microgram of PM 2.5 

# . The analysis

suggests that the urban study areas are not capturi ng areas with the highest baseline mortality

risks, nor those with th e oldest populations. These areas may have higher risks per microgram of

PM 2.5 

# , and thus the high end of the risk distribu tion 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 fact ors with underrepresented tails, including age

and baseline mortality (R=0.81) ar e 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, fo r example, cities in Florida, may lead to underrepresentation

of high risk populations. However, with the excep tion of areas in Florida, most locations with

high percentages of older populations have lo w overall populations, less than 50,000 people in a

county. And even in Florida, the counties with the highest PM 2.5 

# levels do not have a high

percent of older populations. This suggests that while the risk per exposed person per microgram

of PM 2.5 

# may be higher in these locati ons, 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-Smirnof f Tests for Equality Between National

and Urban Study Area Distributio ns for Selected National Risk

Characteristic Variables

(null hypothesis is no diff erence 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/m 3 

# 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 PM 2.5 

# Risk N 0.1585

Respiratory Mortality PM 2.5 

# Risk N 0.2864

Cardiovascular Mortality PM 2.5 

# Risk N 0.1161



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

total population.

Comparison of Urban Case Study Area Po pulation with U.S. Distribution of

Population (all U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

100 1000 10000 100000 1000000 10000000

Population% of Counties



# All Counties CDF Case Study Counties CDF Case Study Counties Urban case study areas are

all above the 65th Percentile

of county populations

# 4-60 Figure 4-10 Comparison of distributions fo r key elements of th e risk equation:

98 th 

# percentile 24-hour average PM 2.5



# Comparison of Urban Case St udy Area 98th %ile PM2.5 wi th U.S. Dist ribution of

98th %ile PM2.5

(617 U.S. Counties wi th 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 fo r selected variables expected to

influence the relative risk from PM 2.5 

# : 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 Averag e 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 fo r selected variables expected to

influence the relative risk from PM 2.5 

# : 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 fo r selected variables expected to

influence the relative risk from PM 2.5 

# : per capita annual personal

income.

Comparison of Urban Case Study Area Pe r 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 fo r selected variables expected to

influence the relative risk from PM 2.5 

# : per capita annual personal

income.

4.4.2 Analysis Based on Consideration of Nation al Distribution of PM -Related Mortality

Risk

In this section we discuss the second repres entativeness analysis which identified where

the subset of 31 counties comprising the 15 urban study areas fall al ong a distribution of

estimated national-scale mortality risk. The natio nal-scale mortality analysis which underpins

this representativene ss analysis used 2005 PM 2.5 

# 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 PM 2.5 

# -related premature mortality nationwide at the county-level. In relating the 31 counties

comprising the 15 urban study areas to the national county-level distributi on of risk, we did not

directly compare the 31 county-le vel risks and the county-level ri sks generated in the nationalscale analysis. Rather, we identify where the 31 counties modeled for ur ban case study fell along

the national risk distribution. This asse ssment revealed whether the baseline PM 2.5 

# mortality Comparison of Urban Case Study Ar ea 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 CDF Case Study County MSA CDF Case Study County MSA Urban case study areas are

all below the 85th percentile

of MSA angina or CHD

prevalence

# 4-67 risks in the 31 counties modeled in the urban case study areas repr esented more typical or higherend risk relative to the na tional risk distribution.

The results of this representativeness analys is are presented graphi cally in Figure 4-17,

which displays the cumulative distribution of total mortality attributable to PM 2.5 

# exposure at the

county level developed as part of the national- scale analysis. The loca tion 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 PM 2.5 

# for the U.S. with markers

identifying where along that distri bution 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 thes e counties fall within the upper 5th percentile of the risk

distribution -- suggesting that the PM 2.5 

# mortality risk estimates incl uded in the urban case study

analysis generally represent the upper end of urban area mortality ri sks within the nation.

Additional details on this second representativeness anal ysis, together with discussion of the

national-scale mortality assessment underpinning th e analysis, are presented in Appendix G.

# 4-68 4.5 CONSIDERATION OF DESIGN VALUES AND PATTERNS OF PM 2.5



# MONITORING DATA IN INTREPR ETING 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

PM 2.5 

# concentrations simulated for that location. Th is in turn depends on the interplay between

the 24-hour and annual design values and the mon itoring data used to characterize ambient PM 2.5



# concentrations, since these fact ors determine the composite annual average and composite 24-

hour PM 2.5 

# profiles used in modeling long-term and s hort-term exposure related risk for that

study area. 75 

# Because of the role that design values and underlying patterns in PM 2.5 

# monitoring

data play in determining the degree of risk reduc tions, these factors can be used in helping to

interpret risk estimates genera ted 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 plac e risk estimates for the 15 urban study areas in a broader national

context.

This section discusses cons ideration of patterns of de sign values (section 4.5.1) and

underlying ambient monitoring PM 2.5 

# data (section 4.5.2) for the 15 urban study areas in the

context of helping to interpret risk estimates. Ea ch of these discussions begins by describing the

methods used in each analysis and conc ludes with a set of key observations.

4.5.1 Design Values

The set of design values for an urban st udy area determines whether the 24-hour or

annual standard will be controlling as we ll as the degree of re duction in ambient PM 2.5



# concentrations associated with a particular suite of standards. Therefore, by plotting the

relationship between 24-hour and an nual design values for each of the 15 urban study areas, we

can obtain a quick visual perspective on (a) whic h study areas will experien ce reductions in risk

for a particular suite of standards, (b) whether the 24-hour or annual standa rd will control, and

(c) the general magnitude of risk reduction. Th e last observations resu lt from comparing the

controlling standard level with the matching design value, which will determine the fractional

reduction in PM 2.5 

# levels at monitors exceeding the standa rd level (for locally focused rollback),

or more broadly across all monito rs (for proportional rollback).

Figures 4-18 through 4-20 present scatter pl ots 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 depicti ng 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 lin es 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 horizonta l line represents the

24-hour standard level, while the vertical line represents the annua l standard level. The line that

intercepts the origin (i.e., the “35/15 line” in Fi gure 4-18) represents th e 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 th e right of the intercept line).

By superimposing these lines related to the curre nt standard level on the scatter plot, we have

created five zones within each figure including:



#  Zone A: 24-hour design values exceeding th e 24-hour standard level, but annual design

values below the annual standard level (i.e., 24-hour standa rd is controlling ). Urban study

areas in this zone are predicted to experi ence risk reduction with the degree of reduction

reflecting the degree to which th e 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 co ntrolling. 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 controlli ng, 24-hour design values (Pitts burgh, New York, and Detroit in

Figure 4-18). Those urban areas in B1 have exceptionally peaky PM 2.5 

# 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 annua l 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 th e current standard level, along with a small

number of additional urban areas in the U.S..



#  Zone E: both the 24-hour and a nnual design values are below thei r 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 cons idered). The majority of urban areas in the

U.S. depicted in these scatter pl ots fall into Zone E in Figure 4-18.

The five zones presented above are useful in interpreting the risk re sults generated for the

current suite of standards (for the 15 urban study areas). Specifi cally, as noted above, they allow

us to (a) quickly identify which of the 15 urba n study areas experience risk reductions under the

current standard level, (b) determine whether th ose reductions are due primarily to a controlling

# 4-70 24-hour or annual standard and (c ) to see how well our set of ur ban study areas provide coverage

for the broader set of urban area s 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 standard s 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 th e 15 urban study areas (bas ed 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/m 3 

# and 35 μg/m 3 

# , annual and 24-hour, re spectively), 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 th at the Atlanta is just inside of zone C,

suggesting that it meets the 24-hour sta ndard, 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 cu rrent suite of standards (15/35)

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 0 5 10 15 20 25 30 35 4 0 4 5 50 55 60 65 70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 

# Atlanta Birmingham

 Baltimore LA

NY SLC

Pittsburgh Pitt-Liiberty

Phoenix 

Dallas Detroit Tacoma

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3) 

# 35/15 line  

# A B

C

D

E

0 5 10 15 20 25 30 35 4 0 4 5 50 55 60 65 70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 

# Atlanta Birmingham

 Baltimore LA

NY SLC

Pittsburgh Pitt-Liiberty

Phoenix 

Dallas Detroit Tacoma

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3) 

# 35/15 line  

# A B

C

D

E 

# B1

B2

# 4-72 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

C

D

E

0 5 10 15 2 0 2 5 30 35 4 0 4 5 50 55 60 65 70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 

# Atlanta Birmingham

 Baltimore LA

NY SLC

Pittsburgh Pitt-Liiberty

Phoenix 

Dallas Detroit Tacoma

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3) 

# 35/12 line 

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 

# B1

B2 A B

C

D

E

0 5 10 15 2 0 2 5 30 35 4 0 4 5 50 55 60 65 70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 

# Atlanta Birmingham

 Baltimore LA

NY SLC

Pittsburgh Pitt-Liiberty

Phoenix 

Dallas Detroit Tacoma

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3) 

# 35/12 line 

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 

# B1

B2

# 4-73 Figure 4-20 Design values in 15 urban study areas and broader set of U.S. urban

areas relative to the 12/25 al ternative suite of standards

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 0 5 10 15 2 0 2 5 30 35 4 0 4 5 50 55 60 65 70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 

# Atlanta Birmingham

 Baltimore LA

NY SLC

Pittsburgh Pitt-Liiberty

Phoenix 

Dallas Detroit Tacoma

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3)



# 25/12 line  

# A B

C

D

E

0 5 10 15 2 0 2 5 30 35 4 0 4 5 50 55 60 65 70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 

# Atlanta Birmingham

 Baltimore LA

NY SLC

Pittsburgh Pitt-Liiberty

Phoenix 

Dallas Detroit Tacoma

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3)



# 25/12 line 

0 5 10 15 2 0 2 5 30 35 4 0 4 5 50 55 60 65 70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 

# Atlanta Birmingham

 Baltimore LA

NY SLC

Pittsburgh Pitt-Liiberty

Phoenix 

Dallas Detroit Tacoma

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3)



# 25/12 line  

# A B

C

D

E 

# B1

B2

Key:

- urban study area included in risk assessment

- MSA’s within the U.S. 0 5 10 15 2 0 2 5 30 35 4 0 4 5 50 55 60 65 70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 

# Atlanta Birmingham

 Baltimore LA

NY SLC

Pittsburgh Pitt-Liiberty

Phoenix 

Dallas Detroit Tacoma

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3)



# 25/12 line  

# A B

C

D

E

0 5 10 15 2 0 2 5 30 35 4 0 4 5 50 55 60 65 70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 

# Atlanta Birmingham

 Baltimore LA

NY SLC

Pittsburgh Pitt-Liiberty

Phoenix 

Dallas Detroit Tacoma

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3)



# 25/12 line 

0 5 10 15 2 0 2 5 30 35 4 0 4 5 50 55 60 65 70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 

# Atlanta Birmingham

 Baltimore LA

NY SLC

Pittsburgh Pitt-Liiberty

Phoenix 

Dallas Detroit Tacoma

St. Louis Philadelphia Fresno

Houston



# Annual Design Value (μg/m 3 

# )24-hour Design Value (μg/m3)



# 25/12 line  

# A B

C

D

E 

# B1

B2

# 4-74 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 pa tterns of risk reduction across urban study areas

in the U.S., given the current a nd alternative suites of standard s considered. Further, we can

characterize the degree to which the 15 urban st udy 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 base d on 2005-2007 air quality

data (zone E). A smaller but still notable numbe r meet the current annual standard but do not

meet the current 24-hour standard (Zone A). A si milar 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 (zon e 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 involv ing reduction of the annual standard levels (see Figure

4-19) Based on 2005-2007 air quality data, as sh own in Figure 4-19, reduction in the annual

standard level down to 12 μg/m 3 

# results in a significant increase in the number of areas that

do not meet the annual standard (zones C a nd D). And of those areas, roughly similar

# 4-75 numbers of urban areas do m eet 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 standar ds involving reductions in both annual and 24-hour levels (see

Figure 4-20): Based on 2005-2007 air quality data, a la rge 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 ther e 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 standard s, while the 24hr is

controlling in most (zone B).

An additional factor to consider in exam ining 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 se t 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-ho ur 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 ur ban 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), th e picture is less clear w ith regard to regional

patterns in other zones of the scatter diagram. Wh en 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 stan dard (with regional differentiation )

# 4-77 4.5.2 Patterns in PM 2.5 

# Monitoring Data

As noted earlier, patterns in PM 2.5 

# monitoring data for each of the 15 urban study areas

can be used (together with cons ideration of design values as described in section 4.5.1) to

support interpretation of ri sk estimates generated for current an d alternative standard levels. This

is particularly true when considering the im pact of using different rollback methods in

supporting risk characterization for current and alternative standard levels, as discussed below.

To facilitate consideration of patterns in PM 2.5 

# 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 PM 2.5 

# monitor within each

study area. The figures also fl ag the highest design values fo r 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 Fi gure 4-22 scaled to repres ent 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 id entification of whethe r a study area had the highest design value

(for the 24-hour and annual averaging periods) o ccurring at the same or at different monitors.

This factor can influence the degree to which si mulation of a controlling 24-hour standard level,

given application of the locally focused rollback approach, results in reduction in annual average

PM 2.5 

# levels for that study area. If an area has both 24-hour and a nnual design values occurring at

the same monitor, then applica tion of locally focused rollback to reduce the controlling 24-hour

standard will also bring down the annua l design value (i.e., the annual average PM 2.5 

# level for

that study area is likely to be reduced to a grea ter extent). By contrast, if 24-hour and annual

design values are located at di fferent 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 PM 2.5 

# level for that study area). 78

76 

# Note, that it is the highest viable study-area level desi gn 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-h our and annual levels) occurs at different

monitors. This is the case, for example, with Phoenix, while Los Angeles repr esents 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 st udy areas, highlighting key attributes related to

24-hour and annual design values. With Los Angele s (in Figure 4-22) we see that the study area

has a relatively wide spread in 24-hour and annu al design values across the monitors (i.e., it has a

relatively peaky PM 2.5 

# distribution), with 24-hour values ranging from ~15 to ~55 and annual

design values ranging from ~7 to ~19 (exact valu es 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 annua l 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

PM 2.5 

# distributions across monitors) and (b) has one of the highest 24-hour design value of the 15

urban study areas (i.e., it will requi re more rollback in simulating just meeting the current suite

of standards compared with most of the other st udy areas). The attributes described above match

well with urban areas falling into zone B1 in Fi gure 4-18 (i.e., the zone where urban areas do not

meet both the current 24-hour and annual sta ndards, and where the 24-hour standard is

controlling).

# 4-79 Figure 4-22 Annual and 24-hour design value s (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)

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

A

t

l

a

n

t

a B

a l

t

i

m

o

r

e B

i

r

m i

n

g h

a

m D

a l

l

a

s D

e

t

r

o

i

t F

r

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# 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70Annual Design Value (μg/m3)24-hour Design Value (μg/m3)Annual DVs24-hour DVs



# 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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# 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70Annual Design Value (μg/m3)24-hour Design Value (μg/m3)Annual DVs24-hour DVs



# 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

# 4-80 Figure 4-23 Annual and 24-hour design value s (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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# 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75Annual Design Value (μg/m3)24-hour Design Value (μg/m3)Annual DVs24-hour DVs



# 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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# 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75Annual Design Value (μg/m3)24-hour Design Value (μg/m3)Annual DVs24-hour DVs



# 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

# 4-81 The sensitivity analysis examining uncer tainty 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 applica tion 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 proportio nal 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 A ngeles (which had composite monitor values 40%

higher when using the locally fo cused rollback method compared w ith the proportional approach

– see Section 4.3.1.1), the 24-hour sta ndard 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 valu e. 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 re duction is uniformly applied to all monitors,

resulting in a new (adjusted) composite monitor va lue that has been reduced to a relatively large

extent. However, if locally fo cused rollback is used, then only those monitors with 24-hour

design values exceeding the current 24-hour standa rd 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 PM 2.5 

# 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 monito r, 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 sm aller. 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 estima tes 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 PM 2.5 

# levels adjusted

because their 24-hour levels do not exceed the current st andard. 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 monito r having the highest annual design value, resulting in a

lower overall impact to the composite annual av erage, compared with proportional rollback.

# 4-82 down, there is a fraction of the m onitors (with 24-hour design valu es below the current standard)

that will not be adjusted with appl ication 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 h ealth protection assessed for a

particular standard level. By c ontrast, conditions at some of th e other urban study areas result in

little difference in risk from application of diffe rent rollback methods (i.e., simulation of more

regional versus local control strategies). Specifi cally, if an urban location has 24-hour and annual

design values at each monitor that display little variation, we e xpect to see less impact on risk

from varying the type of rollback method used. Ex amples 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 PM 2.5 

# -RELATED RISKS

This chapter provides an integrative discussi on of the risk-related analyses presented

throughout this final RA, including (a) the core PM 2.5 

# -related risk estimates generated for the set

of urban study areas (sections 4.1 and 4.2), (b) the rela ted uncertainty and sensitivity analyses,

including additional set of reasonable risk estima tes generated to supplement the core analysis

(sections 3.5.4, and 4.3), (c) assessm ent of the representativeness of the urban study areas in the

national context (section 4.4), and (d) considerati on 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 integr ative 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 conf idence 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 exposur e-related risks estimated for the air quality

scenarios considered in the RA, including: (a) the current suite of PM 2.5 

# 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 a lternative annual and 24-hour sta ndard 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 estim ated risk reductions across the 15 urban study

areas. At the end of this di scussion, 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. Ho wever, additional health endpoints for which risks

could not be quantified, but that are of potential concern, are consid ered in the PA. The PA also

considers the extent to which health endpoints c onsidered in the RA, as well as the additional

endpoints considered in the PA, are of importanc e from a public health perspective.

5.1 OVERALL CONFIDENCE IN THE RISK ASSESSMENT

This quantitative risk assessment has been design ed to generate estimates of risk for a set

of urban study areas likely to represent those urban areas in the U.S. experiencing higher PM 2.5 

# -

related risk due to elevated PM 2.5 

# concentrations and/or othe r attributes related to PM 2.5 

# risk

(e.g., meteorology, baseline health effect s incidence rates, differences in PM 2.5 

# emissions sources

and composition). The RA includes design elements intended to increase overall confidence in

the risk estimates generated for the 15 urban st udy areas including: (a) use of a deliberative

process in specifying components of the risk mode l that reflects consideration of the latest

# 5-2 research on PM 2.5 

# 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 ar e representative of areas in the U.S. experiencing higher PM 2.5 

# -

related risk, and (d) identification and assessm ent 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 estim ates by considering a set

of alternative reasonable risk estimates generated as part of a sensitivity analysis. The second

analysis compared the annual average PM 2.5 

# concentrations simula ted 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 confid ence is associated with risk estimates based on

annual average PM 2.5 

# concentrations that are within the regi on 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 co mprising the risk model, including selection of

urban study areas as well as speci fication of other inputs such as C-R functions. This deliberative

process involved rigorous review of available literature addressing both PM 2.5 

# 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 th e initial risk assessment plan and the first

draft risk assessment. The appl ication of this deliberative pro cess increases overall confidence in

the risk estimates by insuring that the estimates are based on the best available science and data

characterizing PM 2.5 

# exposure and risk, and that they reflec t consideration of input from experts

on PM exposure and risk through CASAC and public reviews.

The approach used in specifying several of th e 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 st udies used in specifying C-R functions used

in the core risk estimates, (b) providing coverage for urban ar eas with relatively high annual

and 24-hour design values, and (c) providing co verage for the seven PM regions which

reflect differences in key PM risk-relat ed 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 PM 2.5 

# concentrations under current an d alternative standard levels

included the proportional rollback approach used in past risk assessments, which reflects a

regional pattern of ambient PM 2.5 

# reductions. To more fully re flect potential variability in

the future pattern of ambient PM 2.5 

# reductions, we added two alte rnative rollback approaches

(hybrid and locally focused), both of which si mulate more localized patterns of ambient

PM 2.5 

# reductions combined with additional region al patterns of reduc tion (see section 3.2.3)

.



#  Selection of health endpoints reflected consideration of the de gree of support in the literature

for a causal relationship between PM 2.5 

# exposure and the health eff ect of interest as assessed

in the ISA, together with cons ideration of the health significa nce of the endpoint. In addition,

we considered whether sufficient informati on existed in the liter ature to develop C-R

functions and whether we could obtain the baseline incidence data necessary to generate risk

estimates with a reasonable degr ee of confidence for a particul ar endpoint (see section 3.3.1).



#  The selection of epidemiological stud ies and specification of C-R functions for use in

modeling risk involved a rigorous review of existing literat ure based on application of

criteria we identified for specifying robust C- R functions. These crite ria 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 outline d 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 resu lted 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 addr essing the policy-related questions considered in

the PA. To provide a more comprehensive assessme nt of risk for the urban study areas, we have

included an assessment of uncertain ty and variability and their imp act 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 associat ed with current and alternative standard levels

across the urban study areas. Several of these sour ces 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 s ources of variability include

differences in PM 2.5 

# sources and composition and differen ces in baseline incidence rates,

demographics and population behavior related to PM 2.5 

# exposure and risk across urban areas.

# 5-4 Coverage for these sources of variability is pr ovided through inclusion of study areas drawn from

different PM regions in the U.S, since these f actors tend to demonstrate regional differences.

In contrast, two additional sources of variabil ity not only introduce variability into risk

estimates across study areas, but also play an importa nt role in determining the magnitude of risk

reductions upon simulation of current and altern ative standard levels. These sources of

variability are (a) the degree of “peakiness” in monitored PM 2.5 

# concentrations across urban

study areas 82 

# and (b) variability in the spatial patterns of ambient PM 2.5 

# reduction resulting from

simulation of current or alternative standard le vels. Variability in the peakiness of monitored

PM 2.5 

# concentrations is covered in the RA by including urban st udy areas from different PM

regions, while variability in th e spatial pattern of ambient PM 2.5 

# reduction resulting from

simulation of just meeting current or alternat ive 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 (refle cting the peakiness of ambient PM 2.5 

# 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 PM 2.5 

# distributions and where the 24-hour stan dard is controlling can be especially

sensitive to the type of rollback approach used, with the propo rtional approach resulting in

notably greater risk reduction compared w ith the locally focused approach. Rigorous

consideration of these f actors (i.e., the interplay of the rollback method us ed with patterns of 24-

hour and annual design values for a given st udy area), allowed us to obtain a better

understanding of the nature a nd pattern of risk reductions and risk remaining following

simulation of just meeting both current and alte rnative standard levels across the urban study

areas. This in turn increased our overall confid ence 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 patte rn of reductions in ambient

PM 2.5 

# concentrations resulting from simulation of just meeting curre nt 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 refl ected in the proportional rollback approach), all

monitors in the study area are adjusted by the sa me proportion and there is a relatively greater

82 

# The term “peakiness” as used here refers to air qu ality 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 concentra tion is represented by the annual average design

value for a particular monitor.

# 5-5 impact on risk. In contrast, if only those mon itors exceeding the 24-hour standard are subjected

to an initial stage of reducti on 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 relati vely 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 PM 2.5 

# -related risk draws on information

presented in several sections of the RA includi ng: (a) the analysis base d on consideration of

national distributions of risk-rel ated 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 patte rns 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 representa tiveness 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 98 th 

# percentile PM 2.5 

# concentrations, and capture well the

range of effect estimates repr esented by the Zanobett i 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 bette r 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 PM 2.5 

# -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 PM 2.5 

# -attributable risk, with 23 of these counties falling within the

upper 5th percentile of the di stribution. These findings suppo rt the assertion based on the

other analyses described above that the urban st udy areas are likely to capture risk at urban

areas experiencing relatively elevated levels of PM 2.5 

# -attributable mortality.



#  Consideration of the mi x of design values across the 15 urba n 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 PM 2.5 

# -related mortality

estimates). However, some of the areas with the highe st values for these attributes have relatively lower PM 2.5 

# 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 PM 2.5 

# -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 experi ence risk reductions under the suites of

alternative standard levels c onsidered – see section 4.5.1) and (b) we have included study

areas likely to experience re latively greater degrees of PM 2.5 

# -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 control ling for most of our 15

urban study areas, reflecting the pa ttern seen in the U.S. for ur ban 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 representati veness 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 leve ls of risk related to ambient

PM 2.5 

# exposure.

5.1.4 Impact of Important Sources of Uncertainty on Core Risk Estimates

As part of the RA, we completed both a quali tative analysis of uncer tainty (section 3.5.3)

as well as a quantitative sensitivity analysis (section 3.5.4 and 4.3), bot h designed to identify

those sources of uncertainty having a potential ly 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) charac terizing intra-urban population exposure in the context of

epidemiology studies linking PM 2.5 

# 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-te rm exposure studies), (e) tran sferability of C-R functions

from study locations to urban study area locat ions (long-term exposure-related health

endpoints), (f) use of single-city versus multi-c ity studies in the derivation of C-R functions,

(g) impact of historical air quality on es timates of health risk from long-term PM 2.5 

# exposures

and (h) potential variation in effects estimates reflecting compositional differences for PM.



#  The quantitative single-fact or sensitivity analysis identified the following sources of

uncertainty as having a moderate to large imp act on core risk estimat es (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, lo g-linear versus log-log, sing le- 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 disc ussed 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 PM 2.5 

# 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 mort ality 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 understa nding of which sources of uncertainty were

likely to have a significant impact on the core ri sk estimates. This information proved useful in

interpreting core risk estimates and increas es 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 cons idering the impact of

uncertainty and variability in the core risk mode l. Most of the alternative model specifications

supported by available literature pr oduced risk estimates that are hi gher (by up to a factor of 2 to

3) than the core risk estimates. This is not unexpected, since the epidem iological study used in

obtaining the C-R functions for estimating long-te rm 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 th at are at greater risk

from PM exposure. Because of this, there is the potential that effect es timates, and consequently

risk estimates based on the ACS dataset are biased low, relative to risks estimated for the general

population (which would include th e lower SES-population) and especi ally relative to risks that

might be estimated for the lower-SES population. In contrast, the alternative epidemiological

study considered in the sensitivity analysis fo r modeling long-term expos ure-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 estimat e 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 streng ths associated with this study (e.g., larger number

of cities, inclusion of two time periods which a llows us to consider different exposure windows

and analysis of a wide range of C-R function m odels), 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 PM 2.5 

# concentrations in

relation to the dataset used in derivi ng C-R functions for long-term exposurerelated mortality

In considering the overall confidence in the core risk estimates, we have compared the

PM 2.5 

# concentrations simulated und er both current and alternativ e standard levels across the

urban study areas to th e distribution of PM 2.5 

# concentrations used in deriving the C-R functions

used for long-term exposure-related mortality (a s presented in Kreswki et al., 2009). Specifically,

this assessment compares the composite monitor annual average PM 2.5 

# concentrations used in

modeling long-term exposure-related mortality risk in the core an alysis to the distribution of

annual-average PM 2.5 

# concentrations from th e 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/m 3 

# or above), we have relatively high confidence in those risk

estimates, since they are based on PM 2.5 

# concentrations that roughly match those used in deriving

the C-R functions. However, as composite monitor annual average PM 2.5 

# concentrations extend

below this range, our confidence in the risk es timates decreases, with our confidence being

significantly reduced when composite monitor a nnual average values reach or extend below the

LML of the ACS dataset (i.e., 5.8 μg/m 3 

# ).

85 

# We note that these findings regarding potential bias in the core risk estimates were based on modeling

PM 2.5 

# -attributable IHD and all-cause mortality associated with long-term PM 2.5 

# 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 comp leted 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 PM 2.5 

# levels to these ACS datasets used in deriving the CR functions, we focus on the later monitoring period (1999-2000), since ambient PM 2.5 

# 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 PM 2.5 

# level of 14 μg/m 3 

# , a SD of 3.0 μg/m 3 

# and an LML of 5.8 μg/m 3 

# (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 fo r 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 rela tive to other health

effect categories (e.g., respiratory-related effects). This mean s that for long-term exposurerelated risk, we focus our di scussion on IHD-related mortality and for short-term exposurerelated risk we focus on CV-related mortality and morbidity (the latt er in the form of HAs related

to CV symptoms). Although not disc ussed here, risk estimates were also generated for additional

health effect categories includi ng all-cause, cardiopulmonary, a nd lung cancer mortality (for

long-term exposure) and non-accidental- and re spiratory-related mortality and respiratory

effects-related HAs and asthma-related emergency de partment visits (for short-term exposure).

It is also important to note th at a broader array of health eff ects beyond those modeled in the RA

has also been associated with PM 2.5 

# 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 rela ted evidence in the broa der characterization of

risks to be discussed in the PA.

The role of annual average ambient PM 2.5 

# concentrations in dr iving 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, sin ce changes in risk for this category are

modeled using average daily PM 2.5 

# concentrations and not annual averages. 88 

# However, as

discussed in section 3.1.2.2, because the 24-hour PM 2.5 

# 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 PM 2.5 

# level across monitors in a study area (i.e., th e composite monitor annual average). Therefore,

in considering changes in long-term exposure-related mortality, it is most appropriate to compare composite monitor

estimates generated for a study area under each suite of standards. The maximum monitor annual average for a

study area (i.e., the annual design value) determines the percent reduction in PM 2.5 

# levels required to attain a

particular standard. Both types of air quality estimates ar e 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

PM 2.5 

# concentrations. However, similar to the case with long-term exposure-related mortality, it is the maximum

monitor 98 th 

# percentile 24-hour concentration (the 24-hour design value) that will determine the degree of reduction

required to meet a given 24-hour standard.

# 5-10 the small number of days out at the tail. Therefor e, a shift in the annual average, which will most

directly focus on the bulk of the days n ear the center of the 24-hour average PM 2.5 

# 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 al ternative 24-hour standard levels on short-term

exposure-related mortality, it is mo st appropriate to fi rst consider how those alternative 24-hour

standards impact the compos ite monitor annual average PM 2.5 

# 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 a nd Short-Term Exposure-Related Risk

Remaining upon Just Meeting the Current Suite of PM 2.5 

# Standards

In considering PM 2.5 

# -related risks likely to remain upon just meeting the current PM 2.5



# annual and 24-hour standards in the 15 areas in cluded in this assessment, we focus on the 13

areas that would not meet the current standard s based on recent (2005-2007) air quality. These

13 areas have annual and/or 24-hou r 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 rega rding the magnitude of risk remaining upon

simulation (using proportional rollback) of just meeting the current suit e 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 PM 2.5 

# 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 gi ven year for the urban study areas, which is a

measure of risk that takes into account diffe rences 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 morta lity across the urban study areas ranging from

<10 to 500 cases per year. These estimates repr esent from ~1 to 2% of total CV-related

mortality in a given year for the urban study ar eas. In terms of morb idity 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-rela ted mortality have similar patterns (in

terms of the subset of urban study areas experien cing risk reductions for a particular standard

level), the magnitude of risk remaining is subs tantially 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 PM 2.5 

# 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 estimat es used in modeling short-term exposure-related health endpoints

across study areas will introduce additional variati on 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 PM 2.5 

# concentrations across st udy areas that result from

simulating just meeting the current standards. This is important because, as noted in the

introduction to this section, annual average co ncentrations are highly correlated with both

long-term and short-term exposure-related ri sk. This variability in annual average PM 2.5 

# concentrations occurs especia lly in those study areas in whic h the 24-hour standard is the

“controlling” standard. In su ch areas, the variability across study areas in estimated risks is

largest when regional patter ns of reductions in PM 2.5 

# concentrations are simulated (using

proportional rollback, as was done in the core an alyses), with less variability when more

localized patterns of PM 2.5 

# reductions are simulated (using locally focused rollback, as was

done in a sensitivity analysis). When simula tions are done using locally focused rollback,

estimated risks remaining upon just meeting the cu rrent suite of standards can be appreciably

larger than those estimated in the core analysis.



#  Simulation of risk in volves annual average PM 2.5 

# concentrations well below the current

standard for some study areas: In simulating just meeting the cu rrent suite of standards, the

resulting composite mo nitor annual average PM 2.5 

# concentrations range from about 15 μg/m 3



# (for those study areas in which the annual standa rd was controlling) down to as low as about

8 μg/m 3 

# (for those study areas in which the 24-ho ur standard was controlling or the annual

average was well below 15 μg/m 3 

# based on recent air quality). As discussed above in section

5.1.6. as the composite monitor annual average PM 2.5 

# concentrations used in generating risk

estimates extend below 11.0 μg/m 3 

# (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/m 3 

# ). We make the observation that all four of the urban study

areas with composite m onitor annual average PM 2.5 

# concentrations below 11μg/m 3 

# under

simulated attainment of the current suite of standards have the 24-hour standard controlling

(see Table 3-4). This illust rates the point that for the 15 urban study areas assessed,

typically, it is the locat ions where the 24-hour standard is controlling that are simulated to

have the lowest composite monitor annual average PM 2.5 

# concentrations. While these

locations often are estimated to have the grea test risk reductions, there is also reduced

confidence associated with these risk estimates.

# 5-12 5.2.2 Nature and Magnitude of Long-term a nd Short-Term Exposure-Related Risk

Remaining upon Just Meeting th e Alternative Suite of PM 2.5 

# Standards

In characterizing PM 2.5 

# -related risks associated with si mulation of the a lternative 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 sta ndards. In discussing these risks, we focus on

the set of urban study areas experiencing risk redu ctions under each alternative annual standard.

Key policy-relevant observations associated with these risk estimates include:



#  Reductions in long-term expos ure-related mortality risk: Upon simulation of just meeting the

alternative annual standard leve ls considered (14, 13, and 12 μg/m 3 

# ) in conjunction with the

current 24-hour standard (de noted as 14/35, 13/35 and 12/35 su ites of standards), the core

analysis estimates reductions in long-term expos ure-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 term s of the number of study areas experiencing risk reduction and

the magnitude of those reductions). For the alternative annual stan dard level of 12 μg/m 3 

# (in

conjunction with the current 24-hour standard), the core analys is 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 standard s) if locally focused

rollback had been used to simulate just meeting th e current suite of standards. This result would

be expected since the magnitude of risk re maining upon just meeting the current suite of

standards would have been higher than that estimated based on th e 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 woul d have started with a higher leve l of simulated risk from the

current standard.



#  Long-term exposure-related mortality risk remaining: For an annual standard level of 14

μg/m 3 

# , the percent of total incidence of l ong-term exposure-related IHD mortality

attributable to PM 2.5 

# (i.e., risk remaining) in the 5 urban study areas experiencing risk

reductions ranges from 9-15%. For an annual standard of 12 μg/m 3 

# , estimated risk remaining

in the 12 urban study areas experiencing risk reductions ranges from 6-11% in terms of

PM 2.5 

# -attributable long-term exposure-related morta lity. This translates into between 90 and

300 cases per year attrib utable to long-term PM 2.5 

# exposure for those study areas

experiencing the greatest reductions in risk under the lowest altern ative annual standard

simulated.



#  Simulation of risks for an alte rnative standard level below 12 μg/m 3



# : Simulation of risks for

an alternative annual standard of 10 μg/m 3 

# suggests that additional risk reductions could be

expected with alternative annual standards below 12 μg/m 3 

# . However, we recognize that

there is potentially greater uncertainty associat ed with these risk es timates compared with

estimates generated for the higher alternative an nual standards considered in the RA, since

# 5-13 these estimates require simulation of re latively greater reductions in ambient PM 2.5



# concentrations. As lower ambient PM 2.5 

# concentrations are simulated (i.e., ambient

concentrations further from recent conditions), po tential variability in such factors as the

spatial pattern of ambient PM 2.5 

# reductions (rollback ) increases, thereby introducing greater

uncertainty into the simulation of composite monitor annual average PM 2.5 

# concentrations

and consequently risk estimates.



#  Short-term exposure-related mortality and morbidity risk : For the alternativ e annual standard

level of 12 μg/m 3 

# (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/m 3 

# (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 acro ss urban study areas: While there

is a consistent pattern of risk reduction acro ss 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 thes e 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/m 3 

# , 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 PM 2.5 

# concentrations after simulating just meeting the cu rrent suite of standards. Therefore, even if

study areas have similar “ending” annual average PM 2.5 

# concentrations after simulation of

just meeting the a given alte rnative annual standard, because the starting point in the

calculation (the annual average PM 2.5 

# concentrations upon just meeting the current suite of

standards) can be variable, the ov erall reduction in annual average PM 2.5 

# concentrations

across the standards can also be variable. This translates into variati on in reductions in longterm exposure-related risk upon just meeting a lternative annual standa rd levels across the

study areas.



#  The nature of the spatial pattern in PM 2.5 

# 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 PM 2.5 

# concentrations upon just meeting the current suit e of standards can impact the magnitude of

additional risk reductions estimated for just meeting alternative (l ower) annual standard

levels. Specifically, for those study areas with more peaky PM 2.5 

# distributions, application of

locally focused rollback will result in higher annual average PM 2.5 

# concentrations remaining

upon just meeting the current suite of standard s. If proportional rollback is then used to

simulate just meeting alternative annual sta ndard levels, a greater degree of reduction in

composite monitor annual average PM 2.5 

# concentrations will result, since the “starting point”

for the calculation (annual average PM 2.5 

# concentrations upon just meeting the current suite

90 

# Because the same air quality metric (annual distributions of 24-hour PM 2.5 

# concentrations) is used in

generating short-term exposure-related mortality and morbid ity 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 PM 2.5 

# concentrations in influencing the magnitude of risk

reductions under alternative annual standard levels.



#  Based on consideration of the co mposite monitor annual average PM 2.5 

# concentrations

involved in estimating long-term exposure-re lated mortality, we have varying levels of

confidence in risk estimates generated for the three alte rnative annual standard levels

considered: With the exception of one study area, thos e study areas estimated to have risk

reductions under the alternative a nnual standards of 14 and 13 μg/m 3 

# have simulated

composite monitor annual average PM 2.5 

# concentrations ranging fr om just below 10.6 to over

13.3 μg/m 3 

# (see Table 3-4). In other words, th ese composite monitor annual average PM 2.5 

# concentrations generally fall well within the range of ambient PM 2.5 

# data used in fitting the

C-R functions used (i.e., w ithin one SD of the mean PM 2.5 

# concentration from 1999-2000

ACS dataset). The urban study areas estimated to have risk reductions under the lower

alternative annual sta ndard level of 12 μg/m 3 

# have lower composite monitor annual average

values ranging from 9.0 to over 11.4 μg/m 3 

# . These values extend to below one SD of the

mean of the ACS dataset and therefore, we ha ve somewhat lower confidence in these risk

estimates, relative to those generated for the two higher altern ative annual standards. By

contrast, urban study areas estimated to have risk reductions under the alternative standard

level of 10 μg/m 3 

# (paired with the current 24-hour st andard) have simulated composite

monitor annual estimates ranging from 7.6 to 8.9 μg/m 3 

# (see Table J-19). These

concentrations are towards the lower end of th e 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 st andards assessed.

5.2.3 Nature and Magnitude of Long-Term a nd Short-Term Exposure-Related Risk

Remaining upon Just Meeting Combinations of Alternative Annual and 24-Hour

PM 2.5 

# Standards

In characterizing PM 2.5 

# -related risks associated with si mulation of the a lternative 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 mee ting the alternative standards.

In discussing these risks, we focus on the set of urban study areas expe riencing risk reductions

under each alternative annual standard.



#  Additional reduction in long-term exposure-related risk provided by considering alternative

24-hour standards combined w ith alternativ e annual standards : In the case of the 12/25 suite

of standards, estimated reductions in long-te rm exposure-related mortality risk compared

with reductions for the annua l standard alone (12 μg/m 3 

# ), 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, th ere was little to no risk reduction.

# 5-15 These results show that lower 24-hour sta ndards can have an appreciable and highly

variable impact on long-term exposure-related mo rtality, particularly when just meeting the

lower standards is simulated usi ng a more regional pattern of PM 2.5 

# 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 re duced when simulations using a more localized

pattern of PM 2.5 

# reductions (i.e., the locally focused rollback used in the sensit ivity analysis).



#  Based on consideration of the co mposite monitor annual average PM 2.5 

# concentrations

involved in estimating long-term exposure-re lated mortality, we have lower degrees of

confidence in risk estimates generated for the two altern ative 24-hour st andard levels

considered (30 and 25 μg/m 3 

# ): Of the 11 urban study areas estimat ed to have risk reductions

under the alternative 24-hour standard of 30 μg/m 3 

# (with the 24-hour standard controlling –

see Table 3-4), composite monitor annual average PM 2.5 

# concentrations range from 6.6 to

11.3 μg/m 3 

# with most of the urban study areas ha ving concentrations in the 8 to 10 μg/m 3



# range. These concentrations extend into the lower range of PM 2.5 

# 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 m onitor concentrations for urban study areas

assessed to have risk reductions under the al ternative 24-hour standard level of 25 μg/m 3



# (again, where the 24-hour standard is controllin g), we see composite monitor annual average

PM 2.5 

# levels ranging from 5.6 to 11.2 μg/m 3 

# with most study areas have concentrations in the

range of 7 to 9 μg/m 3 

# . 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 associat ed with simulating compos ite monitor annual average PM 2.5 

# concentrations for th e lowest 24-hour standard consider ed (when it is controlling): We note

that risk estimates generated for the subset of urban study areas wher e the alternative 24-hour

standards of 30 and 25 μg/m 3 

# are controlling are subject to additional variability related to

simulating the spatial pattern of ambient PM 2.5 

# concentrations under these alternative levels

(i.e., application of rollback methods). In those scenarios where th e alternative 24-hour

standard is controlling, our sens itivity analyses show ed the application of alternative rollback

methods (particularly the proportional versus lo cally focused) produce substantially different

composite monitor annual average PM 2.5 

# 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 l evels provide inconsistent degree of risk reduction: The results

of simulating alternative suites of standards in cluding a combination of alternative annual

and 24-hour standard levels s uggest 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 exposur e-related IHD mortality risk seen across study areas when

comparing risk under the 12/25 suite of alternativ e standard levels to risk under the current

standard (see section 4.2.2). More consistent reductions in es timated risk and consequently

# 5-16 degrees of public health protec tion 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 sour ces of variability and uncertain ty 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 analys is, we believe it unlikely that the RA as

implemented has over-stated risk, particularly for long-term PM 2.5 

# exposure-related

mortality. In fact, the core risk estimates for this category of health effect endpoint may well

be biased low based on consider ation of alternative model sp ecifications evaluated in the

sensitivity analysis.



#  Based on the results of several analyses exam ining the representativeness of the 15 urban

study areas, we believe that th e RA provides coverage for urban areas in the U.S. likely to

experience elevated risk due to ambient PM 2.5 

# 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 morta lity attributable to PM 2.5 

# 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 al ternative annual standard levels evaluated (12, 13, and 14

μg/m 3 

# ), combined with the current 24-hour standard (35 μg/m 3 

# ), resulted in estimated risk

reductions for most of the urban study areas, w ith the degree of risk reduction increasing

incrementally across the alternative standard le vels (both in terms of the number of study

areas experiencing risk reduction and the ma gnitude of those reductions). Estimated

reductions in long-term exposur e-related IHD morta lity (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/m 3 

# ). Additional, but more uncertain

reductions in long-term exposure- related mortality risk were estimated with the simulation of

an alternative annual st andard level of 10 μg/m 3



# .



#  In general, we have the most conf idence in risk estimates based on PM 2.5 

# concentrations near

the mean PM 2.5 

# levels in the underlying epidemiological studies providing the C-R functions.

As PM 2.5 

# concentrations decrease from these mean levels, we have decreasing confidence in

the risk estimates. Risk estimates for the alte rnative annul standard levels of 14 and 13 μg/m 3



# are based on PM 2.5 

# concentrations that are generally within one SD of the mean of the ACS

data from which the C-R functions are derive d. Consequently we have a relatively high

degree of confidence in these risk estimates. Risk estimates for the alternative annual

standard level of 12 μg/m 3 

# are based on PM 2.5 

# concentrations that begin to extend into the

lower range of data used in fitting the C-R functions and c onsequently our confidence in

# 5-17 these estimates is somewhat lower. Risk esti mates for the alternative standard level of 10

μg/m 3 

# are based on PM 2.5 

# concentrations that extend well into the lower range of the ACS

data and consequently, we have significan tly less confidence in these risk estimates

compared with estimates generated for the other alternative annua l 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 a dditional risk reductions in some study areas

compared with the alternative annual standards alone, particul arly for the 12/25 combination,

where estimated reductions were roughly twice as large in many of th e study areas, with a

few areas experiencing substant ially higher estimated risk re ductions (ranging up to ~100%

of estimated risk).



#  Risk estimates for the alternative 24-hour standard level of 30 μg/m 3 

# are based on composite

monitor annual average PM 2.5 

# concentrations that span a wi de range extending from within

one to below two SDs of the mean of the AC S data used in fitting the long-term exposurerelated mortality C-R functions. We have so mewhat lower confidence in these estimates

relative to risk estimates based on annual mean c oncentrations more consistently in the range

of one SD of the mean of the ACS data. In contrast, risk es timates for the alternative 24-hour

standard level of 25 μg/m 3 

# are based on lower composite monitor annual average PM 2.5



# concentrations that extend well into the lo wer 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-h our standard levels are subject to substantial

variability related to the spatial pattern of ambient PM 2.5 

# concentrations (i.e., rollback)

assumed in simulating these standard levels. Application of more localized patterns of

ambient PM 2.5 

# 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 app lication of different rollback

methods was not as pronounced with simulation of the alternative a nnual standard levels

considered.



#  While the alternative 24-hour standard levels considered (when contro lling) did result in

additional estimated risk reduc tions 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 area s, thereby potentially

providing a more consistent degree of public health protection.

.

# 6-1 

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# A-1 

# APPENDIX A: AIR QUALIT Y 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 th e 15 urban study areas ev aluated in the risk

assessment, including summaries of PM 2.5 

# monitoring data associated with each study area as

well as the composite monitor estimates genera ted for each study area based on that monitoring

data (see section 3.2 for additional detail regard ing selection of monito rs and derivation of

composite monitor values).A-3Table A-1. Air Quality Data for AtlantaQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4130630091(3)27 30 25 30 112 12.63 16.83 21.22 15.92 16.65 36.09130670003(1,2,3)27 30 29 29 115 13.75 17.39 18.57 15.62 16.33 34.94130670004(1,2,3)30 28 26 27 111 12.98 17.17 18.03 13.98 15.54 30.28130890002(1,2,3)82 84 81 88 335 12.72 15.72 18.81 14.56 15.45 32.82130892001(1,2,3)80 75 67 85 307 12.84 15.10 20.44 14.83 15.80 36.72131210032(1,2,3)84 89 76 80 329 13.64 16.00 19.43 14.38 15.86 33.40131210039(1,2,3)27 30 23 29 109 15.03 18.35 17.97 16.56 16.98 30.29131210048(1,2,3)0 0 0 0 0 --- --- --- --- --- ---131350002(1,3)13 14 12 14 53 14.35 14.62 20.39 15.16 16.13 31.66132230003(3)28 29 26 26 109 11.41 15.52 18.62 12.99 14.63 34.52Composite monitor for Atlanta - 1 90 91 92 92 365 13.62 16.34 19.09 15.01 16.01 31.03Composite monitor for Atlanta - 2 90 91 92 92 365 13.49 16.62 18.87 14.99 15.99 31.52Composite monitor for Atlanta - 3 90 91 92 92 365 13.26 16.30 19.27 14.89 15.93 31.06130630091(3)29 29 31 30 119 12.94 17.91 21.32 14.49 16.67 30.84130670003(1,2,3)28 29 31 30 118 12.22 17.88 21.52 14.20 16.46 32.66130670004(1,2,3)28 29 27 28 112 12.09 17.75 21.04 12.39 15.82 33.34130890002(1,2,3)85 86 81 81 333 12.25 16.09 19.86 13.43 15.41 31.65130892001(1,2,3)86 84 77 81 328 11.94 15.75 18.31 12.18 14.54 28.89131210032(1,2,3)88 86 84 90 348 12.46 15.99 19.28 13.74 15.37 31.44131210039(1,2,3)29 28 26 0 83 15.12 19.15 20.88 --- --- ---131210048(1,2,3)0 0 2 30 32 --- --- 15.25 15.00 --- ---131350002(1,3)12 14 13 15 54 15.21 18.98 20.31 12.93 16.86 30.64132230003(3)29 27 31 29 116 10.91 15.20 18.90 10.77 13.95 32.28Composite monitor for Atlanta - 1 90 91 92 92 365 13.04 17.37 20.17 13.41 16.00 27.34Composite monitor for Atlanta - 2 90 91 92 92 365 12.68 17.10 20.15 13.49 15.86 27.89Composite monitor for Atlanta - 3 90 91 92 92 365 12.79 17.19 20.16 13.24 15.84 26.82Monitor20062005Quarterly CountsQuarterly Averages (ug/m3)AnnualTotalAnnualAverage(ug/m3)98thPercentile(ug/m3)A-4Table A-1 cont'd. Air Quality Data for AtlantaQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4130630091(3)29 30 30 29 118 13.87 16.51 18.83 13.02 15.56 36.04130670003(1,2,3)29 30 29 29 117 13.49 17.03 19.49 13.41 15.85 35.51130670004(1,2,3)26 27 30 30 113 12.50 17.47 18.77 11.39 15.03 33.54130890002(1,2,3)85 83 90 85 343 12.78 15.54 19.38 12.15 14.96 34.22130892001(1,2,3)69 79 76 75 299 12.48 17.11 20.04 12.38 15.50 37.42131210032(1,2,3)87 88 91 85 351 12.99 17.95 19.64 13.08 15.91 35.10131210039(1,2,3)0 0 0 0 0 --- --- --- --- --- ---131210048(1,2,3)28 28 31 28 115 13.45 18.97 18.24 12.83 15.87 37.52131350002(1,3)27 27 29 29 112 13.05 14.03 17.97 11.68 14.18 30.19132230003(3)29 30 29 30 118 12.21 17.12 18.95 10.64 14.73 33.82Composite monitor for Atlanta - 1 90 91 92 92 365 12.96 16.87 19.08 12.42 15.33 31.82Composite monitor for Atlanta - 2 90 91 92 92 365 12.95 17.35 19.26 12.54 15.52 31.35Composite monitor for Atlanta - 3 90 91 92 92 365 12.98 16.86 19.03 12.29 15.29 30.59Note 1: Different definitions of Atlanta include different monitors. The number(s) shown in the parenthesis next to the monitorindicates the location(s) in which it isincluded. For example, monitor 130630091 is used in Atlanta - 3 only while 130670003 is used for all definitions of Atlanta.Note 2: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007MonitorQuarterly CountsQuarterly Averages (ug/m3)AnnualTotalAnnualAverage(ug/m3)98thPercentile(ug/m3)A-5Table A-2. Air Quality Data for BaltimoreQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4240051007 30 28 27 27 112 14.78 11.86 20.66 12.34 14.91 33.76240053001 75 80 85 92 332 16.09 12.60 18.27 13.44 15.10 35.77245100006 28 31 27 28 114 15.76 12.47 20.18 11.67 15.02 33.17245100007 27 27 30 30 114 16.09 12.50 20.05 13.00 15.41 35.27245100008 24 30 30 29 113 18.85 14.16 20.99 14.80 17.20 39.16245100035 79 75 78 70 302 17.58 13.59 20.24 14.12 16.38 37.49245100040 79 81 90 76 326 18.47 14.68 19.40 13.42 16.49 39.45245100049 26 30 25 27 108 17.72 13.19 20.62 12.77 16.07 36.43Composite Monitor for Baltimore 90 91 92 92 365 16.91 13.13 20.05 13.19 15.82 32.98240051007 29 29 28 30 116 12.03 11.37 15.73 11.09 12.55 32.06240053001 90 85 90 92 357 12.81 11.79 18.51 13.90 14.25 34.25245100006 27 30 27 30 114 13.20 11.62 16.24 11.61 13.17 32.67245100007 30 29 29 31 119 12.64 11.59 15.19 12.03 12.86 32.27245100008 30 28 31 30 119 14.80 13.34 16.88 12.97 14.50 35.21245100035 74 90 83 82 329 13.31 12.57 19.27 14.14 14.82 36.74245100040 85 86 87 86 344 13.83 12.58 18.64 14.73 14.94 35.932451000490 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Baltimore 90 91 92 92 365 13.23 12.12 17.21 12.92 13.87 31.34240051007 29 29 31 30 119 12.09 13.54 15.53 12.04 13.30 31.46240053001 74 87 83 89 333 12.53 12.95 16.93 13.70 14.03 34.01245100006 30 29 31 27 117 12.10 12.83 16.28 11.16 13.09 31.55245100007 29 30 30 28 117 12.07 13.20 15.84 12.44 13.39 33.31245100008 30 30 31 27 118 13.53 14.68 16.90 14.79 14.97 35.25245100035 79 85 74 76 314 12.11 14.03 17.23 13.23 14.15 33.77245100040 82 85 89 76 332 13.42 13.66 16.32 13.35 14.19 34.392451000490 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Baltimore 90 91 92 92 365 12.55 13.55 16.43 12.96 13.87 28.41Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-6Table A-3. Air Quality Data for BirminghamQ1 Q2 Q3 Q4Q1 Q2 Q3 Q410730023*90 90 89 92 361 14.35 20.49 26.42 17.27 19.63 49.6810731005*30 31 29 31 121 11.62 16.70 22.61 14.33 16.32 35.0610731009*30 31 29 31 121 9.82 16.12 20.26 11.87 14.52 37.6810731010*15 15 15 16 61 11.71 16.91 22.77 15.51 16.73 36.4610732003*88 90 91 91 360 14.49 18.48 23.75 15.03 17.94 44.4110732006*30 30 30 31 121 11.53 16.46 21.11 13.79 15.72 33.9810735002*30 31 30 31 122 10.84 16.33 21.08 12.61 15.21 36.2310735003*30 30 30 31 121 10.60 16.42 21.94 12.74 15.43 39.201117000630 31 30 28 119 11.23 15.67 19.60 12.92 14.85 32.861127000227 31 28 30 116 10.37 15.31 18.86 12.17 14.18 33.17Composite Monitor forBirmingham - 190 91 92 92 365 11.66 16.89 21.84 13.82 16.05 35.47Composite Monitor forBirmingham - 290 91 92 92 365 11.87 17.24 22.49 14.14 16.44 36.2710730023*89 91 92 92 364 13.61 20.57 22.35 17.02 18.39 39.5510731005*30 30 31 31 122 10.51 18.84 19.59 13.38 15.58 33.1410731009*30 29 30 30 119 8.81 17.16 17.78 10.02 13.44 31.6910731010*15 15 15 16 61 11.57 18.63 18.71 12.37 15.32 32.2810732003*89 90 90 92 361 14.41 20.48 21.62 15.67 18.05 40.1810732006*30 30 31 31 122 10.76 18.08 20.02 12.33 15.30 31.6910735002*30 30 31 31 122 9.87 17.15 19.61 10.60 14.31 33.1610735003*29 30 30 30 119 10.37 17.42 18.84 11.31 14.48 33.221117000630 30 31 31 122 9.95 16.37 18.38 11.65 14.09 29.791127000229 30 30 29 118 9.85 17.49 17.38 11.83 14.14 34.53Composite Monitor forBirmingham - 190 91 92 92 365 10.97 18.22 19.43 12.62 15.31 30.49Composite Monitor forBirmingham - 290 91 92 92 365 11.24 18.54 19.82 12.84 15.61 30.91AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-7Table A-3 cont'd. Air Quality Data for BirminghamQ1 Q2 Q3 Q4Q1 Q2 Q3 Q410731010*15 15 15 15 60 14.53 18.69 19.31 13.63 16.54 37.9210732003*89 90 89 90 358 15.40 21.38 19.18 12.42 17.10 44.0210732006*30 30 31 30 121 12.24 19.29 18.53 10.93 15.25 39.9210735002*30 28 31 30 119 12.15 19.16 18.41 10.40 15.03 37.9010735003*29 30 31 30 120 11.79 18.99 17.83 10.38 14.75 38.561117000629 30 31 30 120 12.97 18.27 17.52 10.84 14.90 38.521127000228 29 31 29 117 11.97 17.81 17.72 10.95 14.61 34.91Composite Monitor forBirmingham - 190 91 92 92 365 12.99 19.62 18.58 11.60 15.70 37.65Composite Monitor forBirmingham - 290 91 92 92 365 13.12 20.02 18.82 11.78 15.93 38.40Note 1: The monitors marked with * are used for Birmingham - 2. All monitors shown in this table are used for Birmingham - 1.Note 2: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.AnnualAverage(ug/m3)98thPercentile(ug/m3)2007MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-8Table A-4. Air Quality Data for DallasQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4481130035 30 31 20 0 81 11.78 15.16 13.90 --- --- ---481130050 15 30 27 31 103 11.95 15.01 15.64 12.47 13.77 28.55481130057 27 21 22 0 70 12.00 16.07 14.41 --- --- ---481130069 78 88 90 91 347 11.07 13.80 14.03 11.11 12.50 27.44481130087 27 31 30 30 118 9.87 13.32 13.45 10.18 11.70 24.55481133004 88 89 61 0 238 10.86 13.58 12.82 --- --- ---Composite Monitor for Dallas 90 91 92 92 365 11.26 14.49 14.04 11.25 12.76 26.93481130035 0 0 0 0 0 --- --- --- --- --- ---481130050 28 30 31 31 120 10.99 12.53 12.98 10.68 11.79 22.16481130057 0 0 0 0 0 --- --- --- --- --- ---481130069 84 90 92 90 356 9.97 12.15 11.73 9.26 10.78 21.99481130087 30 30 30 28 118 9.22 11.66 10.89 8.45 10.05 19.55481133004 0 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Dallas 90 91 92 92 365 10.06 12.11 11.87 9.46 10.88 19.22481130035 0 0 0 0 0 --- --- --- --- --- ---481130050 29 28 30 0 87 11.54 11.76 15.42 --- --- ---481130057 0 0 0 0 0 --- --- --- --- --- ---481130069 88 91 91 79 349 10.13 10.91 13.78 10.14 11.24 23.24481130087 28 21 29 30 108 9.96 11.16 12.70 9.30 10.78 20.03481133004 0 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Dallas 90 91 92 92 365 10.54 11.27 13.97 9.72 11.38 21.87Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-9Table A-5. Air Quality Data for DetroitQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4261630001 88 87 89 86 350 18.45 13.87 17.15 14.38 15.96 42.31261630015 27 27 30 30 114 20.20 14.73 18.73 15.18 17.21 48.27261630016 87 79 84 88 338 18.92 14.78 16.62 13.70 16.01 47.80261630019 28 31 29 29 117 19.82 14.48 17.43 14.20 16.48 51.37261630025 26 28 30 30 114 17.86 11.74 17.45 12.68 14.94 39.50261630033 28 31 28 28 115 21.50 16.57 18.22 17.90 18.55 48.69261630036 29 28 29 27 113 16.96 14.92 18.58 15.19 16.41 46.22261630038 28 25 22 0 75 16.98 14.60 17.66 --- --- ---261630039 0 0 7 28 35 --- --- 18.20 14.25 --- ---Composite Monitor for Detroit 90 91 92 92 365 18.84 14.46 17.73 14.69 16.43 44.06261630001 82 85 88 90 345 13.66 11.89 13.68 13.65 13.22 32.82261630015 29 26 28 31 114 16.98 12.26 14.93 14.56 14.68 35.89261630016 79 14 13 17 123 13.04 11.58 12.58 14.97 13.04 35.49261630019 30 15 14 16 75 15.20 10.39 11.78 13.46 12.71 35.67261630025 27 14 15 17 73 13.49 11.23 10.01 12.70 11.86 30.00261630033 28 29 27 31 115 18.79 12.85 15.56 17.30 16.13 42.43261630036 29 26 29 29 113 15.10 10.95 13.69 11.94 12.92 32.91261630038 0 29 27 28 84 --- 11.10 14.34 11.98 --- ---261630039 29 30 31 30 120 14.78 11.71 14.20 11.84 13.13 32.32Composite Monitor for Detroit 90 91 92 92 365 15.13 11.55 13.42 13.60 13.42 28.34261630001 86 89 87 92 354 12.92 10.28 14.00 14.08 12.82 31.19261630015 28 30 27 29 114 15.15 13.06 15.12 14.82 14.54 32.73261630016 26 26 30 29 111 13.98 12.12 14.74 14.61 13.86 33.72261630019 30 28 31 27 116 13.20 11.16 14.36 13.31 13.01 31.09261630025 26 30 31 27 114 12.23 10.59 13.76 14.42 12.75 32.49261630033 29 29 29 27 114 18.84 15.20 16.02 17.49 16.89 36.60261630036 29 28 30 29 116 13.75 11.96 14.60 13.47 13.45 28.48261630038 27 27 28 30 112 13.63 12.85 15.35 14.23 14.01 33.38261630039 29 30 30 28 117 13.83 12.98 14.65 13.86 13.83 33.97Composite Monitor for Detroit 90 91 92 92 365 14.17 12.24 14.73 14.48 13.91 27.66Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-10Table A-6. Air Quality Data for FresnoQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q460190008 85 78 89 91 343 19.53 7.19 11.42 28.65 16.70 67.6460195001 30 15 15 22 82 17.11 7.55 10.78 29.95 16.35 64.5660195025 30 15 13 31 89 20.24 8.29 11.24 27.92 16.92 71.90Composite Monitor for Fresno 90 91 92 92 365 18.96 7.68 11.14 28.84 16.65 63.2660190008 89 87 87 85 348 21.82 9.10 12.39 23.85 16.79 50.0660195001 30 15 14 29 88 18.38 9.47 12.99 24.96 16.45 53.6960195025 30 15 12 31 88 20.13 9.81 13.66 26.87 17.62 57.60Composite Monitor for Fresno 90 91 92 92 365 20.11 9.46 13.01 25.22 16.95 47.4660190008 87 90 88 91 356 27.61 8.32 10.70 28.71 18.84 66.9560195001 29 13 14 27 83 23.70 7.16 9.91 24.91 16.42 61.0160195025 29 14 15 30 88 24.91 8.73 9.65 24.10 16.85 57.53Composite Monitor for Fresno 90 91 92 92 365 25.41 8.07 10.09 25.90 17.37 57.42Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.20062007AnnualAverage(ug/m3)98thPercentile(ug/m3)2005MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-11Table A-7. Air Quality Data for HoustonQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4482010024 26 31 22 15 94 11.77 14.39 17.17 11.83 13.79 26.00482010026 23 31 20 0 74 10.47 13.10 14.47 --- --- ---482010055 25 28 19 0 72 9.12 12.31 12.97 --- --- ---482010058 20 28 23 26 97 11.95 12.99 14.40 12.19 12.88 24.61482011034 10 15 10 0 35 11.79 15.36 14.49 --- --- ---482011035 84 68 78 87 317 13.09 16.59 18.41 15.47 15.89 30.10Composite Monitor for Houston 90 91 92 92 365 11.28 14.12 15.48 13.16 13.51 25.12482010024 15 13 13 13 54 10.92 11.66 15.97 12.58 12.78 23.804820100260 0 0 0 0 --- --- --- --- --- ---4820100550 0 0 0 0 --- --- --- --- --- ---482010058 26 29 29 29 113 9.74 12.34 9.04 9.82 10.24 21.934820110340 0 0 0 0 --- --- --- --- --- ---482011035 85 87 88 88 348 13.98 18.15 17.38 14.48 16.00 32.01Composite Monitor for Houston 90 91 92 92 365 11.55 14.05 14.13 12.29 13.01 23.67482010024 15 14 13 0 42 11.01 12.82 14.64 --- --- ---4820100260 0 0 0 0 --- --- --- --- --- ---4820100550 0 0 0 0 --- --- --- --- --- ---482010058 26 30 30 30 116 9.40 10.96 11.84 11.75 10.99 25.484820110340 0 0 0 0 --- --- --- --- --- ---482011035 87 91 91 82 351 14.42 17.02 16.62 14.50 15.64 32.00Composite Monitor for Houston 90 91 92 92 365 11.61 13.60 14.36 13.13 13.18 23.26Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-12Table A-8. Air Quality Data for Los AngelesQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q460370002 65 78 87 62 292 11.37 13.97 20.71 21.78 16.96 51.5660371002 29 25 30 22 106 17.01 13.75 18.55 21.95 17.82 50.4760371103 90 84 87 89 350 15.26 13.78 19.62 22.48 17.79 52.9160371201 25 29 28 22 104 12.27 11.97 15.01 16.18 13.86 35.6960371301 29 26 28 31 114 16.68 13.28 18.15 21.75 17.46 47.1860371602 29 9 9 29 76 16.90 11.63 17.13 22.31 16.99 52.6560372005 30 26 26 31 113 12.98 12.95 17.15 17.28 15.09 42.7160374002 87 82 88 67 324 13.39 11.54 16.21 22.56 15.93 40.1160374004 90 84 87 83 344 12.64 10.83 15.63 19.59 14.67 37.4460379033 28 30 27 18 103 8.18 8.27 9.96 9.00 8.85 15.96Composite Monitor for LosAngeles 90 91 92 92 365 13.67 12.26 16.78 19.49 15.55 38.7560370002 66 73 84 55 278 12.62 16.17 16.95 15.87 15.40 36.8360371002 25 24 30 25 104 15.33 18.34 15.87 16.66 16.55 43.2160371103 89 82 85 74 330 14.49 14.69 16.34 16.80 15.58 38.5560371201 20 27 28 17 92 11.19 14.21 12.95 13.00 12.84 30.4260371301 28 28 27 24 107 17.62 14.76 15.11 19.26 16.69 43.9860371602 29 28 31 28 116 16.82 13.92 17.19 18.57 16.63 42.3460372005 29 27 28 29 113 12.85 14.64 13.46 12.51 13.37 31.9560374002 73 81 73 63 290 15.19 12.27 13.53 15.57 14.14 33.8960374004 89 86 79 66 320 14.35 11.99 14.21 17.22 14.44 34.1760379033 15 15 14 14 58 6.13 7.27 8.36 8.00 7.44 12.86Composite Monitor for LosAngeles90 91 92 92 365 13.66 13.83 14.40 15.35 14.31 29.93AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-13Table A-8 cont'd. Air Quality Data for Los AngelesQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q460370002 64 77 74 77 292 13.57 17.11 14.68 17.47 15.71 48.7160371002 23 26 27 22 98 13.64 15.96 15.36 22.47 16.86 45.3260371103 67 83 90 84 324 16.25 16.05 14.62 20.19 16.78 49.4160371201 22 26 28 19 95 9.50 13.24 12.55 17.72 13.25 28.9060371301 25 27 29 25 106 16.98 14.05 13.00 19.99 16.00 45.2260371602 27 27 21 26 101 16.75 14.01 15.18 20.45 16.60 49.4060372005 28 23 30 27 108 12.62 15.60 14.02 15.24 14.37 43.6260374002 76 86 88 82 332 15.45 12.42 11.50 19.04 14.60 39.9660374004 65 81 90 90 326 13.84 12.26 11.30 17.31 13.68 33.2560379033 15 15 15 15 60 6.73 7.67 9.00 8.67 8.02 19.28Composite Monitor for LosAngeles90 91 92 92 365 13.53 13.84 13.12 17.85 14.59 35.51Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-14Table A-9. Air Quality Data for New YorkQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4360050080 28 31 29 27 115 18.59 14.78 18.42 15.68 16.87 37.50360050083 30 31 30 31 122 13.77 12.21 16.90 12.71 13.90 36.05360050110 90 91 91 91 363 14.93 12.17 15.38 12.30 13.69 36.58360470122 28 30 28 27 113 16.04 13.74 17.31 14.13 15.31 35.94360610056* 30 31 30 31 122 18.44 15.51 19.16 15.17 17.07 39.93360610062* 27 31 30 31 119 17.14 13.84 18.34 13.54 15.71 38.96360610079* 30 31 30 31 122 14.60 13.12 17.03 12.56 14.33 36.18360610128* 25 31 30 31 117 17.74 14.11 18.37 15.21 16.36 37.66360610134* 0 0 0 0 0 --- --- --- --- --- ---360810124 89 79 62 74 304 13.02 10.44 15.21 10.84 12.38 34.28360850055 28 25 28 27 108 14.92 12.49 17.81 12.91 14.53 33.37360850067 24 28 28 30 110 12.60 10.75 16.17 10.41 12.48 33.00Composite Monitor for NewYork City - 1 90 91 92 92 365 15.62 13.02 17.28 13.22 14.78 31.19Composite Monitor for NewYork City - 2 90 91 92 92 365 16.98 14.15 18.22 14.12 15.87 32.81360050080 29 30 27 29 115 16.57 13.17 13.95 11.88 13.89 38.89360050083 30 30 29 29 118 13.44 11.06 13.34 10.33 12.04 34.80360050110 86 91 84 86 347 13.10 11.15 14.49 11.40 12.53 36.51360470122 28 30 29 25 112 15.00 12.49 14.75 9.00 12.81 37.06360610056* 30 30 27 30 117 16.61 14.03 14.41 12.59 14.41 40.60360610062* 30 28 28 27 113 14.33 13.00 13.82 9.86 12.75 35.73360610079* 30 30 31 29 120 14.12 12.08 13.32 10.59 12.53 36.92360610128* 26 30 29 29 114 15.79 13.07 14.39 12.64 13.97 37.84360610134* 0 0 0 0 0 --- --- --- --- --- ---360810124 69 86 84 76 315 11.17 10.67 13.68 10.91 11.61 33.10360850055 25 27 29 29 110 12.27 12.07 14.06 10.56 12.24 35.89360850067 30 26 31 29 116 10.01 10.49 12.60 8.54 10.41 31.85Composite Monitor for NewYork City - 1 90 91 92 92 365 13.86 12.12 13.89 10.75 12.65 30.36Composite Monitor for NewYork City - 290 91 92 92 365 15.21 13.04 13.99 11.42 13.42 33.78AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-15Table A-9 cont'd. Air Quality Data for New YorkQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4360050080 30 30 30 29 119 17.45 13.49 16.20 15.43 15.64 36.16360050083 30 30 30 29 119 14.14 11.72 13.91 12.87 13.16 32.50360050110 89 84 85 91 349 12.90 11.64 14.22 12.31 12.77 33.92360470122 29 30 28 30 117 13.67 12.82 15.92 13.00 13.85 33.38360610056* 30 27 31 30 118 18.43 14.73 15.99 15.29 16.11 36.12360610062* 27 0 0 0 27 15.84 --- --- --- --- ---360610079* 30 30 31 30 121 14.11 12.48 14.92 12.89 13.60 33.86360610128* 30 30 29 21 110 19.10 13.83 14.63 14.76 15.58 37.01360610134* 3 30 31 30 94 8.53 14.12 16.43 14.08 13.29 33.66360810124 74 86 80 92 332 11.34 10.66 12.30 11.35 11.41 30.81360850055 30 28 31 30 119 13.04 12.37 14.55 11.91 12.97 31.58360850067 27 30 26 26 109 10.60 10.49 14.29 10.54 11.48 28.56Composite Monitor for NewYork City - 190 91 92 92 365 14.60 12.58 14.85 13.13 13.79 29.12Composite Monitor for NewYork City - 290 91 92 92 365 16.87 13.79 15.49 14.25 15.10 30.12Note 1: The monitors marked with * are used for New York City- 2. All monitors in the table are used for New York City - 1.Note 2: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-16Table A-10. Air Quality Data for PhiladelphiaQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4421010003 0 0 0 62 62 --- --- --- 14.35 --- ---421010004 55 61 78 74 268 13.23 13.06 17.26 13.28 14.21 35.83421010020 19 0 0 0 19 15.51 --- --- --- --- ---421010024 37 54 67 71 229 12.68 10.76 16.26 12.02 12.93 34.57421010047 19 28 26 12 85 16.99 12.04 18.91 12.31 15.06 37.70421010057 0 0 0 0 0 --- --- --- --- --- ---421010136 86 89 29 33 237 13.57 11.40 19.06 12.91 14.23 31.13Composite Monitor for Philadelphia 90 91 92 92 365 14.40 11.81 17.87 12.97 14.26 32.12421010003 85 26 0 0 111 12.21 8.74 --- --- --- ---421010004 81 70 53 84 288 12.74 11.85 17.23 12.41 13.56 38.08421010020 0 0 0 0 0 --- --- --- --- --- ---421010024 34 70 71 80 255 11.52 10.56 16.17 11.34 12.40 34.60421010047 40 67 45 47 199 14.44 14.57 18.04 15.04 15.52 35.91421010057 0 0 0 0 0 --- --- --- --- --- ---421010136 47 50 79 73 249 11.97 12.06 16.29 12.25 13.14 36.36Composite Monitor for Philadelphia 90 91 92 92 365 12.58 11.55 16.93 12.76 13.46 33.46421010003 0 0 0 0 0 --- --- --- --- --- ---421010004 87 71 86 90 334 13.61 13.19 15.15 12.96 13.73 34.61421010020 0 0 0 0 0 --- --- --- --- --- ---421010024 87 58 86 90 321 12.05 12.76 14.88 11.73 12.85 33.42421010047 71 59 90 92 312 14.49 13.05 16.33 13.43 14.32 35.07421010057 0 0 18 90 108 --- --- 10.96 13.13 --- ---421010136 75 65 72 82 294 12.60 13.38 14.36 12.99 13.33 31.53Composite Monitor for Philadelphia 90 91 92 92 365 13.19 13.09 14.33 12.85 13.37 32.44Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-17Table A-11. Air Quality Data for PhoenixQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q440130019 32 32 30 31 125 11.04 10.78 11.11 18.37 12.83 39.8840131003 0 22 30 29 81 --- 8.77 8.26 9.72 --- ---40134003 29 31 27 31 118 10.94 13.04 10.40 16.98 12.84 34.7340137020 0 30 29 31 90 --- 8.08 7.72 9.46 --- ---40139997 29 31 30 31 121 9.04 8.69 7.58 13.56 9.72 27.48Composite Monitor for Phoenix 90 91 92 92 365 10.34 9.87 9.01 13.62 10.71 26.0340130019 30 30 31 31 122 14.17 13.58 8.07 17.82 13.41 28.5140131003 26 28 31 31 116 8.87 9.52 8.92 11.33 9.66 20.0740134003 28 28 31 29 116 13.53 10.34 9.31 17.58 12.69 28.3840137020 29 30 31 30 120 8.09 7.98 7.14 9.12 8.08 15.3540139997 29 29 30 30 118 10.74 8.66 7.46 14.04 10.22 24.29Composite Monitor for Phoenix 90 91 92 92 365 11.08 10.01 8.18 13.98 10.81 26.8440130019 32 30 31 30 123 10.26 8.85 8.63 15.42 10.79 26.6340131003 29 28 30 30 117 7.66 10.45 9.50 11.27 9.72 18.2040134003 30 29 30 29 118 10.54 11.76 11.32 15.45 12.27 27.3340137020 30 30 31 20 111 5.85 7.81 7.35 8.21 7.31 13.4440139997 30 29 32 30 121 8.85 8.12 8.21 12.75 9.48 22.02Composite Monitor for Phoenix 90 91 92 92 365 8.63 9.40 9.00 12.62 9.91 18.70Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-18Table A-12. Air Quality Data for PittsburghQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q442003000889 90 92 89 360 13.80 15.29 20.72 13.40 15.80 42.2342003002128 27 30 27 112 12.91 14.99 22.00 11.49 15.35 35.0142003006488 90 92 86 356 16.28 22.26 25.94 21.10 21.40 69.4642003006726 28 29 27 110 12.32 13.95 20.35 10.26 14.22 33.8742003009313 11 12 13 49 10.66 13.83 23.66 9.63 14.44 41.6842003009514 13 14 15 56 12.79 14.49 21.55 9.83 14.67 36.0942003011623 29 28 26 106 13.82 16.42 21.68 12.66 16.15 38.7242003013314 13 13 9 49 13.54 12.62 20.51 9.51 14.04 27.3242003100830 29 30 29 118 12.79 15.60 21.90 13.52 15.95 40.1142003130129 29 29 26 113 14.39 16.86 23.90 13.37 17.13 38.2242003300715 13 14 15 57 14.13 14.25 24.36 12.71 16.36 30.6842003900213 13 14 15 55 12.95 14.01 21.32 11.25 14.88 37.93Composite Monitor for Pittsburgh 90 91 92 92 365 13.37 15.38 22.32 12.58 15.91 41.9242003000885 89 91 92 357 11.60 13.28 20.19 12.54 14.40 37.444200300210 0 0 0 0 --- --- --- --- ------42003006485 90 87 89 351 14.86 17.89 22.78 20.97 19.13 55.7042003006723 26 28 21 98 9.61 9.52 16.39 9.06 11.14 28.0442003009314 6 13 13 46 10.37 9.85 16.38 9.41 11.50 29.4642003009513 13 13 14 53 10.02 10.97 18.22 10.31 12.38 36.704200301160 0 0 0 0 --- --- --- --- ------4200301330 0 0 0 0 --- --- --- --- ------42003100827 23 28 25 103 11.87 14.30 18.32 11.63 14.03 37.5442003130126 28 29 29 112 12.56 14.55 19.89 13.11 15.03 37.7342003300715 15 14 15 59 12.93 13.51 19.16 12.36 14.49 34.734200390020 0 0 0 0 --- --- --- --- ------Composite Monitor for Pittsburgh 90 91 92 92 365 11.49 13.05 18.69 11.95 13.79 33.16AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-19Table A-12. Air Quality Data for PittsburghQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4420030008 85 86 86 89 346 11.80 14.72 20.30 12.74 14.89 39.35420030021 0 0 0 0 0 --- --- --- --- --- ---420030064 88 90 91 90 359 14.16 18.64 25.16 17.57 18.88 54.67420030067 19 25 28 26 98 10.28 13.40 19.46 10.73 13.47 40.80420030093 15 12 14 14 55 9.67 10.50 19.35 12.57 13.02 32.56420030095 14 13 15 14 56 10.96 9.89 20.79 12.90 13.64 32.40420030116 0 0 0 0 0 --- --- --- --- --- ---420030133 0 0 0 0 0 --- --- --- --- --- ---420031008 27 27 30 27 111 12.79 14.55 19.68 13.23 15.06 39.60420031301 28 27 31 26 112 14.02 15.18 21.90 15.16 16.56 43.57420033007 14 14 14 13 55 12.36 13.03 21.19 13.85 15.11 34.74420039002 0 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Pittsburgh 90 91 92 92 365 11.87 13.51 20.74 13.36 14.87 36.08Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-20Table A-13. Air Quality Data for Salt Lake CityQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4490350003 30 29 30 31 120 14.16 6.58 8.98 14.49 11.06 41.66490350012 82 89 85 85 341 16.73 9.59 12.68 17.24 14.06 43.36490351001 29 30 28 30 117 11.85 5.47 8.61 11.35 9.32 36.25490353006 88 90 90 85 353 13.95 6.27 9.56 14.17 10.99 43.23490353007 28 27 29 28 112 13.64 7.40 10.57 16.36 11.99 39.37490353008 30 31 24 31 116 9.90 6.03 7.76 7.45 7.79 26.61490353010 0 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Salt Lake City 90 91 92 92 365 13.37 6.89 9.69 13.51 10.87 36.45490350003 28 28 29 30 115 10.76 6.98 9.41 13.58 10.18 38.67490350012 76 87 82 90 335 11.80 11.22 14.19 14.91 13.03 37.93490351001 27 28 29 27 111 7.95 5.65 8.65 9.29 7.88 27.72490353006 88 90 90 88 356 10.59 7.21 8.54 12.37 9.68 37.54490353007 30 30 31 29 120 10.11 7.18 11.56 13.61 10.61 35.69490353008 29 26 30 30 115 6.14 6.85 9.26 7.09 7.33 21.97490353010 0 0 0 0 0 --- --- --- --- --- ---Composite Monitor for Salt Lake City 90 91 92 92 365 9.56 7.51 10.27 11.81 9.79 29.80490350003 30 30 29 28 117 18.12 6.97 10.99 13.89 12.49 55.65490350012 80 86 0 0 166 20.84 11.45 --- --- --- ---490351001 24 30 31 26 111 11.42 6.44 10.08 9.71 9.41 29.84490353006 89 85 78 89 341 18.17 6.11 9.42 12.05 11.44 54.28490353007 29 29 29 31 118 17.72 7.17 11.53 13.42 12.46 50.13490353008 23 28 28 30 109 10.03 6.06 9.66 7.09 8.21 23.02490353010 0 80 83 92 255 --- 7.68 11.62 13.00 --- ---Composite Monitor for Salt Lake City 90 91 92 92 365 16.05 7.41 10.55 11.53 11.39 49.06Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-21Table A-14. Air Quality Data for St. LouisQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4171190023* 28 28 29 29 114 18.01 19.10 21.49 16.95 18.89 41.17171190024* 00 0 0 0 --- --- --- --- --- ---171191007* 26 31 29 30 116 18.40 16.49 21.47 16.27 18.16 43.68171192009* 12 12 13 12 49 14.94 16.35 20.82 11.98 16.02 39.63171193007* 29 31 27 29 116 16.42 15.20 19.99 12.49 16.02 41.08171630010* 13 15 14 15 57 17.31 16.81 19.97 14.47 17.14 39.59171634001* 30 30 29 28 117 17.86 14.17 17.20 14.69 15.98 37.61290990012 90 87 90 91 358 15.22 14.69 19.26 12.42 15.40 39.86291890004* 29 29 28 31 117 16.01 12.64 17.80 11.87 14.58 37.57291892003* 57 30 29 31 147 16.73 14.15 18.44 12.65 15.49 40.00295100007* 88 88 83 81 340 16.99 14.67 18.92 12.87 15.86 38.44295100085* 90 86 78 88 342 16.78 14.46 19.67 13.33 16.06 39.81295100086* 84 26 30 29 169 15.11 14.34 18.43 13.14 15.26 39.57295100087* 90 87 82 81 340 17.02 14.80 18.74 12.94 15.88 40.80295100093* 00 0 0 0 --- --- --- --- --- ---Composite Monitor for St Louis - 1 90 91 92 92 365 16.68 15.22 19.40 13.54 16.21 37.87Composite Monitor for St Louis - 2 90 91 92 92 365 16.80 15.27 19.41 13.64 16.28 37.78171190023* 30 26 31 29 116 15.21 17.34 19.40 12.11 16.02 32.81171190024* 00 0 0 0 --- --- --- --- --- ---171191007* 27 24 24 27 102 14.95 16.12 20.18 14.05 16.32 36.24171192009* 15 15 14 16 60 12.59 13.35 13.49 12.92 13.08 27.28171193007* 28 30 31 31 120 13.08 12.00 16.47 10.87 13.11 27.54171630010* 12 14 15 14 55 14.18 13.75 15.72 14.48 14.53 29.18171634001* 28 28 31 29 116 13.43 12.87 15.20 12.00 13.38 27.92290990012 82 81 91 89 343 11.62 11.79 15.46 11.49 12.59 30.20291890004* 30 29 0 0 59 10.56 10.49 --- --- --- ---291892003* 29 29 28 26 112 11.36 10.69 13.87 11.00 11.73 27.61295100007* 78 88 91 90 347 12.27 11.82 15.89 12.51 13.12 29.39295100085* 86 77 84 92 339 13.04 12.46 15.26 12.68 13.36 28.52295100086* 30 30 31 29 120 11.94 11.55 15.48 10.90 12.47 30.46295100087* 85 90 86 91 352 12.92 12.32 16.17 13.18 13.65 29.60295100093* 00 0 0 0 --- --- --- --- --- ---Composite Monitor for St Louis - 1 90 91 92 92 365 12.86 12.81 16.05 12.35 13.52 25.08Composite Monitor for St Louis - 2 90 91 92 92 365 12.96 12.90 16.10 12.43 13.60 24.78AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterly Averages (ug/m3)A-22Table A-14 cont'd. Air Quality Data for St. LouisQ1 Q2 Q3 Q4Q1 Q2 Q3 Q4171190023*0 0 0 0 0 --- --- --- ---------171190024*0 0 6 29 35 --- --- 15.07 14.94------171191007*29 27 29 26 111 14.28 15.31 17.61 13.23 15.11 35.86171192009*15 12 14 13 54 14.31 16.02 15.66 13.51 14.88 34.98171193007*29 28 26 30 113 12.42 14.84 17.39 12.32 14.24 34.45171630010*13 13 14 14 54 14.94 17.65 15.94 13.79 15.58 33.08171634001*26 30 31 29 116 13.35 13.95 14.83 10.90 13.26 32.2729099001282 81 90 86 339 11.94 14.44 16.23 12.13 13.68 31.92291890004*0 0 0 0 0 --- --- --- ---------291892003*89 90 91 90 360 11.63 12.96 15.25 12.49 13.09 30.28295100007*88 91 91 92 362 12.56 14.50 16.13 12.97 14.04 31.61295100085*90 88 89 90 357 12.59 13.79 16.09 13.30 13.94 32.06295100086*27 30 0 0 57 11.79 14.50 --- ---------295100087*90 86 92 86 354 13.24 14.43 16.61 13.10 14.34 33.72295100093*0 0 24 29 53 --- --- 17.26 13.82------Composite Monitor for St Louis - 1 90 91 92 92 365 13.00 14.76 16.27 13.04 14.27 31.51Composite Monitor for St Louis - 2 90 91 92 92 365 13.11 14.79 16.28 13.13 14.33 31.52Note 1: The monitors marked with * are used for St Louis - 2. All monitors shown in the table are used for St Louis - 1.Note 2: The information on the composite monitors in this table is based on the composite monitors after missingvalues have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)A-23Table A-15. Air Quality Data for TacomaQ1 Q2 Q3 Q4 Q1 Q2 Q3 Q4530530029 29 30 30 31 120 16.46 5.34 7.13 17.07 11.50 40.42Composite Monitor for Tacoma 90 91 92 92 365 16.46 5.34 7.13 17.07 11.50 39.61530530029 30 30 31 26 117 8.92 5.89 7.45 15.93 9.55 39.82Composite Monitor for Tacoma 90 91 92 92 365 8.92 5.89 7.45 15.93 9.55 37.05530530029 29 28 31 29 117 13.76 5.94 5.23 13.76 9.67 45.11Composite Monitor for Tacoma 90 91 92 92 365 13.76 5.94 5.23 13.76 9.67 41.26Note: The information on the composite monitors in this table is based on the composite monitors after missing values have been filled in.2007AnnualAverage(ug/m3)98thPercentile(ug/m3)20052006MonitorQuarterly CountsAnnualTotalQuarterlyAverages(ug/m3)

# B-1 

# APPENDIX B: ADDITIONAL IN FORMATION 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 ch aracterization 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). Speci fically, section B1 considers an alternative approach for

interpolating missing data as part of constructing distribut ions of 24-hour PM 2.5 

# estimates at

composite monitors, section B2 provides additi onal 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 ar eas (Detroit).

B1. SENSITIVITY ANALYSIS ADDRESSING THE INTERPOLATION OF MISSING

DATA COMPLETED IN DEVELOPING COMPOSITE MONITOR 24-HOUR

PM 2.5 

# DISTRIBUTIONS

As noted in section 3.2.1, ther e are a variety of possible a pproaches for interpolating

missing 24-hour monitoring data as part of generating composite monitor 24-hour PM 2.5



# distributions for the study areas. For the risk as sessment, we have used an approach that relied

on other measurements at the specific monitor wh ere the interpolation wa s 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 availabl e, some of which make use of monitoring trend data across the

entire set of PM 2.5 

# monitors associated with a study area, rather than relying on data only from

the monitor undergoing the interpolation. In additi on, these alternative inte rpolation methods can

address another limitation of the method used in the risk assessment – the restriction of

interpolation to periods shorter than 8 days whic h excludes interpolation for 1 in 6 day monitors

missing data.

The availability of alternativ e interpolation methods highlig hts the potential uncertainty

associated with this component of the risk a ssessment. To further ex amine this source of

uncertainty, we have completed a sensitivity analys is based on the application of an alternative

interpolation approach based on centering. The cent ering approach uses variance-trend data from

all of the monitors in a study area (with empha sis on locations with measurements on the day

being interpolated) as the basis for interpolat ing missing daily measurements. This sensitivity

analysis has been implemented for Birmingha m (specifically the Birmingham 1 grouping of

counties and associated monitors). The alternat e 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 th e basis for the calculation of official design

values for the current annual NAAQS. All sa mples within Jan-March, April-June, JulySeptember, and October-December were average d. Then these four quarterly means were

averaged to get the annual mean for the calendar year.

2) The annual averages for the individual sites we re 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 th e 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 fr om 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 compos ite monitor was taken to be the sum of the

result from the second step and the result fr om the fourth step. No composite monitor

concentration was calculated for the si ngle day in 2005 mentioned in step 4.

It was observed that when the weighted annua l mean of the resulting estimates of daily

24-hour concentrations for the composite monitor fr om 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/m 3 

# in

2005 and 2006 and less than 0.2 μg/m 3 

# in 2007.

In comparing same-day estimates of 24-hour co ncentrations 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 e xpected when comparing 24-hour estimates versus

annual-average estimates) but occurred in both dir ections. Figure B-1 is a scatter plot of the two

sets of daily estimates.

# B-4 Figure B-1. Comparison of Composite Mo nitor 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 MethodEstimate from Alternative Method



# To investigate how the differences in same-day concentration estimates illustrated in

Figure B-1 would affect estimates of risk (s pecifically short-term exposure-related risk)

aggregated across individual years and acro ss all three years, we used the 24-hour PM 2.5



# concentration above PRB as a surrogate for risk , since the incidences of (short-term exposurerelated) health endpoints related to 24-hour concen trations are nearly pro portional to this metric.

We averaged this across all days in a year, and th en across the three years. Averaging rather than

summation was used because the completely mi ssing 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 Shor t-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 th at, while conceptually the two interpolation

methods considered in the sens itivity analysis diffe r 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 a lternative interpolation method as ap plied to one of the 15 urban study

areas), they do reduce concerns that this source of uncertainty significan tly impacts short-term

exposure-related risk.

B.2 ADDITIONAL DETAIL ON TH E HYBRID ROLLBACK APPROACH

This section provides additional detail on one of the three methods (the hybrid method)

used to simulate ambient PM 2.5 

# levels under both current and alte rnative 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 c ontext of assessing ri sk, see section 3.2.3.

The hybrid approach reflects the combined e ffects 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 al so includes a distance- weighted rollback was

conducted on a subset of the 15 study areas whic h contain source-oriente d monitors measuring

concentrations higher than t hose observed at other sites within a particular area. 1



# Unique sites with high design values exceedi ng the NAAQS were further investigated to

determine if they were in close proximity to a large source of PM 2.5 

# (Figure B-2). The presence

of possible source-oriented sites in each area wa s 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-orie nted adjustments were made include Detroit

MI, Pittsburgh PA, St. Louis MO-IL, Baltimor e MD, New York NY, Los Angeles CA and

Birmingham AL.

Figure B-2. Example of a mo nitor, 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 proporti onal 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 va lues were close to those of the source oriented site (Figure B3).

# B-7 Figure B-3. Plot of the 24-ho ur versus the annual average PM 2.5 

# 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 reduc tions in relation to the s ource-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 techniqu e for Detroit is presented in Table B-2. For

Detroit, adjustments were based on the differenc e between the two sites’ annual design values.

# B-8 Table B-2. Comparison of the original a nd 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 th e maximum non-source-orient ed 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 st andard after th e 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/m 3 

# 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 docke t (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 THR EE ROLLBACK METHODS AS APPLIED

TO THE DETROIT URBAN STUDY AREA

This section provides a sample calculati on of the three rollback methods (hybrid,

proportional, and locally focused) as applied to simulating attainment of the current suite of

standards for Detroit. This samp le calculation is intended to il lustrate how each rollback method

is applied, including equations (a nd associated intermediate cal culations) 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 fo r this study area and consequen tly for the sample calculations,

which means that simulated attainment of the 24-hour standard will determine the degree of

reduction in ambient PM 2.5 

# 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 PM 2.5 

# 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 ar e a better metric to cons ider in comparing and

contrasting the rollback methods than are 24-hour PM 2.5 

# distributions or percentiles summarizing

those distributions.

The calculations using the th ree rollback methods are presented in Table B-3, which

shows how values at individual monitors are adjusted in applying ro llback 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 me thod presented first followed by

the hybrid and the locally focused method. The fi rst 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 Me thods (proportional, hybrid and locally

focused) in Simulating Comp osite Monitor Annual-Average PM 2.5 

# 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 presen ted 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 discu ssion. In presenting the step-wis e explanations we provide only

sufficient explanation of the c onceptual 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 calculati on for the proportional method by presenting the

approach used to calculate the composite mon itor quarterly- and annual- averages (set of “A”

bullets below). We then present how design values are calculated (s et of “B” bullets below). We

then show how the design value is used to im plement 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 m onitors with at l east 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 averag es (last column in rows P3-P6).

3) Calculate the annual average at the com posite 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 rollb ack.

B. Calculating design values

1) At each monitor, calcula te the annual average PM 2.5 

# 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 μ g/m 3 

# , at monitor

261630033 (see Table A-5 in Appendix A).

3) At each monitor, calculate the 98th percentile 24-hour PM 2.5 

# 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 μ g/m 3 

# 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 annua l 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 dail y 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 qua lity - PRB)*(1 - 0.19) (see row P8)

Hybrid rollback

As discussed in section 3.2.3.2, the hybrid a pproach involves a two-phase adjustment

process with the first phase invol ving targeted reduction of sour ce-oriented monitors to bring

their levels down to that of near-by non-source-oriented mon itors and a second proportional

rollback phase to reach simulated attainment of th e standard for the study area. In presenting the

step-wise description of the hybr id rollback approach (and the sa mple calculation in Table B-3),

we illustrate the first phase of adjustment by presenting quarterly values at individual monitors

after the first phase of adjustme nt 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 presen ted in the set of “D” bullets below.

D. Calculating the composite monitor annual av erage when the 15/35 standard is just

met, using the hybrid rollback

1) Adjust daily concentrations at indivi dual monitors on a fi rst step (see rows

H3-H6);

2) Calculate an annual average at the co mposite 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 proporti onal 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 m eet both standards = 14.9%

# B-13 4) Rolled back annual average at compos ite 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 involve s targeted rollback of those monitors

exceeding the 24-hour standard under consider ation. As discussed in section 2.3.2.3, this

rollback approach is only applied for study area s 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) toge ther with the percent reductions

reflected in those adjusted monitor-specific levels (row LF3). The composite monitor annualaverage that results from this targeted monitor-le vel reduction is presented in the last column of

row LF8. It is interesting to point out that in the case of Detro it, all of the m onitors had some

degree of targeted reduction to achieve simula ted attainment of the 24-hour standard, although

the spread in magnitude of per cent reduction was substantial (see va lues in row LF3). The stepwise procedure used in implementing the hybrid a pproach is presented in the set of “E” bullets

below.

E. Calculating the composite monitor annual av erage 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 standa rd is controlling) (row LF2)

2) Calculate monitor-specific percent rollbacks (row LF3)

3) Roll back each quarterly av erage at each monitor, us ing the monitor-specific

percents for rollback (rows LF4-LF7):

Rolled back quarterly average at mon itor = PRB + (quarterly average at

monitor - PRB)*(1 - monitor- specific percent rollback)

4) Calculate the rolled back quarterly av erage 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 th e composite monitor as the average of these

4 rolled back quarterly averages (last column in row LF8).

# C-1 

# APPENDIX C: EPI STUDY SP ECIFIC INFORMATION ON PM 2.5

# C-2 Appendix C. Epidemiology Study -Specific Information for PM 2.5 

# Risk Assessment

This Appendix provides detailed summary in formation for the epidemiological studies

used to obtain the concentrati on-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-3Table C-1. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundMortality, all-causeAll0.00431 0.00276 0.00583Mortality,cardiopulmonary401-440, 460-5190.00898 0.00677 0.01115Mortality, ischemicheart disease410-4140.01689 0.01363 0.02005Mortality, lung cancer 1620.00880 0.00325 0.01432Mortality, all-causeAll0.00554 0.00354 0.00760Mortality,cardiopulmonary401-440, 460-5190.01293 0.01007 0.01587Mortality, ischemicheart disease410-4140.02167 0.01748 0.02585Mortality, lung cancer 1620.01293 0.00554 0.02029Mortality, all-causeAll0.00686 0.00315 0.01053Mortality, ischemicheart disease410-4140.02437 0.01450 0.03429Mortality, all-causeAll0.10966 0.06758 0.15306Mortality,cardiopulmonary401-440, 460-5190.17225 0.11261 0.23161Mortality, ischemicheart disease410-4140.35942 0.24629 0.47210Mortality, lung cancer 1620.19284 0.09861 0.28797Mortality, all-causeAllSix U.S. Cities 0.00414 0.00414 0.02071Mortality,cardiopulmonary400-440, 485-4950.00561 0.00561 0.02789Mortality, lung cancer 162-0.01133 -0.01133 0.04525Krewski et al.(2009) - exposureperiod 1979-1983Krewski et al.(2009) - exposureperiod 1999-2000Krewski et al.(2000) [reanalysisof Six Cities Study]nonelog-linearNationalNational30+none n/a annual mean30+ log-linear noneannual mean30+log-linear (randomeffects)none n/a annual meann/aannual mean25+ log-linear none n/a annual mean30+ log-logn/aHealth Effects Associatedwith Long-Term Exposure to PM2.5:C-4Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundNortheast 0.00107 0.00079 0.00136Northwest 0.00074 -0.00176 0.00324Southeast 0.00029 -0.00019 0.00077Southwest 0.00053 0.00000 0.00104Northeast 0.00028 -0.00017 0.00072Northwest 0.00019 -0.00255 0.00294Southeast 0.00035 -0.00044 0.00113Southwest 0.00094 0.00022 0.00166Ito et al. (2007) ER visits, asthma 493 all ages log-linear noneavg of 0-and 1-day24-hr avg. New York 0.00453 0.00286 0.00621log-linear, GAM(stringent), 30 df0.00099 0.00010 0.00188log-linear, GAM(stringent), 100 df0.00097 0.00014 0.00180log-linear, GLM,100 df0.00097 -0.00002 0.00196log-linear, GAM(stringent), 100 df0.00178 0.00075 0.00281log-linear, GLM,100 df0.00188 0.00067 0.00309log-linear, GAM(stringent), 30 df0.00103 0.00015 0.00191log-linear, GAM(stringent), 100 df0.00080 -0.00003 0.00163log-linear, GLM,100 df0.00069 -0.00032 0.00170log-linear, GAM(stringent), 100 df0.00091 -0.00013 0.00195log-linear, GLM,100 df0.00091 -0.00035 0.00217Los AngelesHealth Effects Associated with Short-Term Exposure to PM2.5:0-day2-day24-hr avg.24-hr avg.65+65+log-linearlog-linearMoolgavkar (2003)[reanalysis ofMoolgavkar(2000a)]426–427, 428,430–438; 410–414,429; 440–449490–492; 464–466,480–487nonenone24-hr avg.noneCOnoneCO0 day1 dayall ages390-429HA (unscheduled),respiratoryBell et al. (2008)Mortality,cardiovascularHA (unscheduled),cardiovascularC-5Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundlog-linear, GAM(stringent), 30 df0.00054 -0.00007 0.00115log-linear, GLM, 30df0.00040 -0.00034 0.00114log-linear, GAM(stringent), 100 df0.00032 -0.00023 0.00087log-linear, GLM,100 df0.00030 -0.00043 0.00103log-linear, GAM(stringent), 30 df0.00059 0.00000 0.00118log-linear, GLM, 30df0.00055 -0.00017 0.00127log-linear, GAM(stringent), 100 df0.00010 -0.00046 0.00066log-linear, GLM,100 df-0.00001 -0.00099 0.00097log-linear, GAM(stringent), 30 df-0.00053 -0.00131 0.00025log-linear, GAM(stringent), 100 df-0.00033 -0.00105 0.00039log-linear, GLM,100 df-0.00033 -0.00117 0.000510 day 0.00054 -0.00007 0.001151 day 0.00059 0.00000 0.001182 day 0.00038 -0.00019 0.000953 day -0.00015 -0.00073 0.000434 day -0.00009 -0.00064 0.000465 day -0.00056 -0.00115 0.00003Mortality, nonaccidentalall ages<800log-linear, GAM(stringent), 30 dfnonenoneMoolgavkar (2003)[reanalysis ofMoolgavkar(2000a)]Los Angeles24-hr avg.24-hr avg.CO 1 day 24-hr avg.1 day0 day24-hr avg.noneC-6Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundlog-linear, GAM(stringent), 30 df-0.00056 -0.00300 0.00188log-linear, GAM(stringent), 100 df-0.00142 -0.00380 0.00096log-linear, GLM,100 df-0.00121 -0.00407 0.00165log-linear, GAM(stringent), 30 df0.00038 -0.00210 0.00286log-linear, GAM(stringent), 100 df0.00086 -0.00158 0.00330log-linear, GLM,100 df0.00020 -0.00282 0.00322log-linear, GAM(stringent), 30 df0.00158 0.00091 0.00225log-linear, GAM(stringent), 100 df0.00116 0.00050 0.00182log-linear, GLM,100 df0.00126 0.00045 0.00207log-linear, GAM(stringent), 100 df0.00039 -0.00044 0.00122log-linear, GLM,100 df0.00058 -0.00041 0.00157log-linear, GAM(stringent), 30 df0.00139 0.00069 0.00209log-linear, GAM(stringent), 100 df0.00113 0.00046 0.00180log-linear, GLM,100 df0.00120 0.00038 0.00202log-linear, GAM(stringent), 100 df0.00024 -0.00065 0.00113log-linear, GLM,100 df0.00027 -0.00075 0.00129CO 1 day 24-hr avg.24-hr avg.24-hr avg.1 daynonenone 0 day 24-hr avg.CO 0 day 24-hr avg.none 0 day 24-hr avg.Los AngelesLos Angeles490-4961 daynone390-429 65+all agesMoolgavkar (2003)[reanalysis ofMoolgavkar(2000b)]Moolgavkar (2003)[reanalysis ofMoolgavkar(2000a)]HA, cardiovascularMortality, respiratory(COPD+)C-7Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundlog-linear, GAM(stringent), 30 df0.00167 0.00068 0.00266log-linear, GAM(stringent), 100 df0.00138 0.00052 0.00224log-linear, GLM,100 df0.00149 0.00041 0.00257log-linear, GAM(stringent), 30 df0.00119 0.00022 0.00216log-linear, GAM(stringent), 100 df0.00075 -0.00011 0.00161log-linear, GLM,100 df0.00077 -0.00027 0.00181log-linear, GAM(stringent), 30 df0.00185 0.00082 0.00288log-linear, GAM(stringent), 100 df0.00114 0.00021 0.00207log-linear, GLM,100 df0.00103 -0.00012 0.002180 day 0.00042 -0.00091 0.001751 day -0.00004 -0.00161 0.001532 day 0.00035 -0.00102 0.001723 day -0.00109 -0.00238 0.00020ER visits,cardiovascular410–414, 427, 428,433–437, 440,443–445, 451–453all ages log-linear noneavg of 0-,1-day, and 2-day24-hr avg. Atlanta 0.00046 -0.00064 0.00154ER visits, respiratory493, 786.07, 786.09;491, 492, and 496;460–465, 460.0, and477; 480–486; 466.1,466.11, and 466.19all ages log-linear noneavg of 0-,1-day, and 2-day24-hr avg. Atlanta 0.00046 -0.00046 0.00136HA, respiratory(COPD+)Moolgavkar (2003)[reanalysis ofMoolgavkar(2000c)]Tolbert et al.(2007)490-496 all ageslog-linear, GAM(stringent), 100 dfLos Angelesnone 1 day 24-hr avg.none 0 day 24-hr avg.NO2 24-hr avg.none 2 day 24-hr avg.C-8Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundAtlanta 0.00066 -0.00066 0.00198Baltimore 0.00128 -0.00009 0.00265Birmingham -0.00002 -0.00140 0.00135Dallas 0.00086 -0.00056 0.00228Detroit 0.00097 -0.00012 0.00205Fresno 0.00082 -0.00056 0.00219Houston 0.00084 -0.00056 0.00223Los Angeles -0.00018 -0.00080 0.00044New York 0.00196 0.00114 0.00278Philadelphia 0.00179 0.00046 0.00313Phoenix 0.00142 -0.00006 0.00291Pittsburgh 0.00102 -0.00020 0.00225Salt Lake City 0.00117 -0.00027 0.00260St. Louis 0.00158 0.00035 0.00282Tacoma 0.00104 -0.00055 0.0026224-hr avg.avg of 0-and 1-dayall agesI01-I59 nonelog-linearMortality,cardiovascularZanobetti andSchwartz (2009)C-9Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundAtlanta 0.00094 0.00018 0.00170Baltimore 0.00135 0.00054 0.00215Birmingham 0.00032 -0.00050 0.00115Dallas 0.00112 0.00027 0.00198Detroit 0.00068 -0.00012 0.00147Fresno 0.00096 0.00014 0.00178Houston 0.00104 0.00021 0.00188Los Angeles 0.00016 -0.00023 0.00055New York 0.00132 0.00077 0.00186Philadelphia 0.00126 0.00046 0.00206Phoenix 0.00110 0.00018 0.00202Pittsburgh 0.00104 0.00030 0.00177Salt Lake City 0.00105 0.00021 0.00188St. Louis 0.00105 0.00030 0.00180Tacoma 0.00117 0.00020 0.00214Zanobetti andSchwartz (2009)Mortality, nonaccidentalall agesA00-R99 24-hr avg.avg of 0-and 1-daynonelog-linearC-10Table C-1 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: All-Year FunctionsStudy Health Endpoint ICD-9 or 10 CodesAgesCoveredModelOtherPollutants inModelLag MetricRegionCoveredCoefficientLowerBoundUpperBoundAtlanta 0.00121 -0.00048 0.00290Baltimore 0.00211 0.00039 0.00384Birmingham 0.00096 -0.00076 0.00268Dallas 0.00093 -0.00084 0.00270Detroit 0.00169 0.00008 0.00330Fresno 0.00175 0.00006 0.00344Houston 0.00211 0.00033 0.00388Los Angeles 0.00112 0.00011 0.00213New York 0.00216 0.00075 0.00356Philadelphia 0.00157 -0.00015 0.00329Phoenix 0.00194 0.00015 0.00374Pittsburgh 0.00149 -0.00014 0.00313Salt Lake City 0.00194 0.00024 0.00364St. Louis 0.00132 -0.00034 0.00298Tacoma 0.00179 -0.00005 0.0036324-hr avg.avg of 0-and 1-daynonelog-linearall agesJ00-J99Zanobetti andSchwartz (2009)Mortality, respiratoryC-11Table C-2. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundWinter0.00199 0.00138 0.00260Spring0.00095 0.00032 0.00157Summer0.00055 0.00008 0.00101Fall0.00102 0.00048 0.00157Winter0.00085 -0.00420 0.00589Spring-0.00007 -0.01324 0.01309Summer-0.00156 -0.01651 0.01337Fall-0.00067 -0.00721 0.00587Winter0.00105 -0.00007 0.00219Spring0.00075 -0.00026 0.00176Summer-0.00067 -0.00161 0.00026Fall0.00017 -0.00072 0.00106Winter0.00076 -0.00025 0.00177Spring0.00176 -0.00087 0.00441Summer-0.00121 -0.00502 0.00262Fall0.00030 -0.00098 0.00158Winter0.00079 -0.00021 0.00178Spring0.00004 -0.00088 0.00097Summer0.00077 -0.00001 0.00155Fall0.00012 -0.00082 0.00106Winter-0.00006 -0.00674 0.00663Spring0.00226 -0.01539 0.01991Summer0.00074 -0.02074 0.02220Fall-0.00074 -0.01062 0.00915Winter0.00040 -0.00146 0.00224Spring0.00075 -0.00082 0.00231Summer-0.00052 -0.00209 0.00105Fall0.00014 -0.00130 0.00158Winter0.00119 -0.00010 0.00249Spring0.00104 -0.00220 0.00430Summer0.00238 -0.00264 0.00741Fall0.00097 -0.00137 0.00330noneNew York April-August0.00759 0.00486 0.01032O3New York April-August0.00602 0.00322 0.00883NO2New York April-August0.00334 0.00029 0.00640CONew York April-August0.00647 0.00356 0.00939SO2New York April-August0.00469 0.00163 0.00775Bell et al. (2008)Ito et al. (2007) ER visits, asthmaHA (unscheduled),respiratoryHA (unscheduled),cardiovascular493490–492;464–466,480–487426–427, 428,430–438;410–414, 429;440–449none65+65+0-day2-dayavg of 0-and 1-dayall agesNortheastNorthwestSoutheastSouthwestNortheastNorthwestSoutheastSouthwestC-12Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundAtlanta Winter0.00135 -0.00193 0.00462Atlanta Spring0.00076 -0.00273 0.00425Atlanta Summer0.00062 -0.00222 0.00347Atlanta Fall-0.00018 -0.00293 0.00257Baltimore Winter0.00104 -0.00196 0.00405Baltimore Spring0.00085 -0.00269 0.00438Baltimore Summer0.00067 -0.00251 0.00384Baltimore Fall0.00296 -0.00017 0.00609Birmingham Winter0.00080 -0.00283 0.00443Birmingham Spring0.00016 -0.00333 0.00365Birmingham Summer-0.00004 -0.00301 0.00293Birmingham Fall-0.00189 -0.00485 0.00106DallasWinter0.00120 -0.00214 0.00454DallasSpring0.00125 -0.00222 0.00472DallasSummer0.00115 -0.00223 0.00453DallasFall-0.00022 -0.00349 0.00306Detroit Winter-0.00006 -0.00203 0.00191Detroit Spring0.00166 -0.00045 0.00378Detroit Summer0.00136 -0.00099 0.00371Detroit Fall0.00226 -0.00001 0.00452Fresno Winter-0.00033 -0.00201 0.00135Fresno Spring0.00050 -0.00138 0.00238Fresno Summer0.00019 -0.00173 0.00211Fresno Fall0.00071 -0.00105 0.00248Houston Winter0.00070 -0.00285 0.00425Houston Spring0.00013 -0.00347 0.00373Houston Summer0.00183 -0.00142 0.00509Houston Fall0.00046 -0.00246 0.00337Los Angeles Winter-0.00014 -0.00109 0.00080Los Angeles Spring0.00007 -0.00113 0.00127Los Angeles Summer-0.00106 -0.00253 0.00042Los Angeles Fall0.00000 -0.00099 0.00099all agesavg of 0-and 1-dayZanobetti andSchwartz (2009)Mortality, short-termcardiovascularI01-I59noneC-13Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundNew York Winter0.00204 0.00048 0.00360New York Spring0.00231 0.00050 0.00412New York Summer0.00202 0.00038 0.00366New York Fall0.00205 0.00047 0.00363Philadelphia Winter0.00214 -0.00042 0.00470Philadelphia Spring0.00153 -0.00135 0.00441Philadelphia Summer0.00178 -0.00082 0.00438Philadelphia Fall0.00300 0.00044 0.00555Phoenix Winter---1------Phoenix Spring---------Phoenix Summer---------Phoenix Fall---------Pittsburgh Winter0.00150 -0.00102 0.00401Pittsburgh Spring0.00284 0.00026 0.00543Pittsburgh Summer0.00085 -0.00148 0.00318Pittsburgh Fall0.00047 -0.00185 0.00279Salt Lake City Winter---------Salt Lake City Spring---------Salt Lake City Summer---------Salt Lake City Fall---------St. Louis Winter-0.00013 -0.00297 0.00270St. Louis Spring0.00278 -0.00013 0.00568St. Louis Summer0.00188 -0.00084 0.00459St. Louis Fall0.00253 -0.00022 0.00527Tacoma Winter0.00006 -0.00182 0.00193Tacoma Spring0.00020 -0.00173 0.00212Tacoma Summer0.00025 -0.00168 0.00219Tacoma Fall0.00053 -0.00136 0.00242Mortality, short-termcardiovascularnoneZanobetti andSchwartz (2009)I01-I59 all agesavg of 0-and 1-dayC-14Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundAtlanta Winter0.00133 0.00020 0.00246Atlanta Spring0.00123 0.00007 0.00238Atlanta Summer0.00078 -0.00027 0.00184Atlanta Fall0.00069 -0.00035 0.00172Baltimore Winter0.00126 0.00016 0.00236Baltimore Spring0.00119 0.00002 0.00236Baltimore Summer0.00100 -0.00011 0.00212Baltimore Fall0.00129 0.00017 0.00240Birmingham Winter0.00097 -0.00022 0.00216Birmingham Spring0.00105 -0.00012 0.00222Birmingham Summer0.00049 -0.00061 0.00160Birmingham Fall0.00035 -0.00074 0.00144DallasWinter0.00099 -0.00017 0.00215DallasSpring0.00090 -0.00027 0.00208DallasSummer0.00106 -0.00008 0.00221DallasFall0.00132 0.00018 0.00247Detroit Winter-0.00009 -0.00125 0.00107Detroit Spring0.00174 0.00043 0.00304Detroit Summer0.00090 -0.00053 0.00233Detroit Fall0.00072 -0.00066 0.00210Fresno Winter0.00002 -0.00159 0.00163Fresno Spring0.00225 -0.00021 0.00471Fresno Summer0.00054 -0.00217 0.00325Fresno Fall0.00088 -0.00090 0.00266Houston Winter0.00106 -0.00011 0.00223Houston Spring0.00129 0.00010 0.00248Houston Summer0.00092 -0.00023 0.00207Houston Fall0.00092 -0.00015 0.00199Los Angeles Winter0.00012 -0.00059 0.00083Los Angeles Spring0.00059 -0.00031 0.00149Los Angeles Summer-0.00084 -0.00208 0.00039Los Angeles Fall-0.00002 -0.00067 0.00064avg of 0-and 1-dayZanobetti andSchwartz (2009)Mortality, short-term nonaccidentalA00-R99 all ages noneC-15Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundNew York Winter0.00168 0.00061 0.00275New York Spring0.00123 0.00001 0.00245New York Summer0.00074 -0.00029 0.00177New York Fall0.00181 0.00078 0.00285Philadelphia Winter0.00195 0.00041 0.00350Philadelphia Spring0.00078 -0.00090 0.00247Philadelphia Summer0.00064 -0.00089 0.00217Philadelphia Fall0.00200 0.00050 0.00350Phoenix Winter---------Phoenix Spring---------Phoenix Summer---------Phoenix Fall---------Pittsburgh Winter0.00135 -0.00013 0.00283Pittsburgh Spring0.00193 0.00034 0.00352Pittsburgh Summer0.00090 -0.00047 0.00227Pittsburgh Fall0.00062 -0.00073 0.00197Salt Lake City Winter0.00113 -0.00013 0.00240Salt Lake City Spring0.00152 -0.00047 0.00352Salt Lake City Summer0.00106 -0.00095 0.00308Salt Lake City Fall0.00131 -0.00051 0.00314St. Louis Winter0.00054 -0.00055 0.00164St. Louis Spring0.00136 0.00025 0.00247St. Louis Summer0.00097 -0.00009 0.00203St. Louis Fall0.00129 0.00022 0.00236Tacoma Winter0.00006 -0.00236 0.00249Tacoma Spring0.00154 -0.00123 0.00431Tacoma Summer0.00088 -0.00203 0.00378Tacoma Fall0.00145 -0.00099 0.00389A00-R99Zanobetti andSchwartz (2009)Mortality, short-term non-accidentalall ages noneavg of 0-and 1-dayC-16Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudy Health EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundAtlanta Winter0.00093 -0.00144 0.00329Atlanta Spring0.00035 -0.00205 0.00275Atlanta Summer0.00077 -0.00155 0.00310Atlanta Fall0.00096 -0.00134 0.00325Baltimore Winter0.00107 -0.00127 0.00340Baltimore Spring0.00144 -0.00097 0.00384Baltimore Summer0.00116 -0.00120 0.00353Baltimore Fall0.00103 -0.00134 0.00340Birmingham Winter0.00043 -0.00197 0.00282Birmingham Spring0.00079 -0.00160 0.00318Birmingham Summer-0.00018 -0.00252 0.00217Birmingham Fall0.00145 -0.00087 0.00377DallasWinter0.00040 -0.00198 0.00278DallasSpring0.00106 -0.00135 0.00347DallasSummer0.00060 -0.00180 0.00300DallasFall0.00038 -0.00202 0.00278Detroit Winter0.00104 -0.00128 0.00335Detroit Spring0.00226 -0.00015 0.00467Detroit Summer0.00253 0.00009 0.00498Detroit Fall0.00247 0.00001 0.00492Fresno Winter-0.00022 -0.00423 0.00380Fresno Spring0.00496 -0.00093 0.01085Fresno Summer0.00263 -0.00375 0.00900Fresno Fall0.00099 -0.00383 0.00580Houston Winter0.00138 -0.00102 0.00377Houston Spring0.00129 -0.00114 0.00372Houston Summer0.00100 -0.00140 0.00341Houston Fall0.00092 -0.00143 0.00327Los Angeles Winter0.00165 -0.00016 0.00345Los Angeles Spring0.00237 -0.00018 0.00493Los Angeles Summer-0.00134 -0.00500 0.00233Los Angeles Fall-0.00003 -0.00190 0.00183all ages noneavg of 0-and 1-dayZanobetti andSchwartz (2009)Mortality, short-termrespiratoryJ00-J99C-17Table C-2 cont'd. Information about the Concentration-Response Functions Used in the PM2.5Risk Assessment: Season-Specific FunctionsStudyHealth EndpointICD-9 or 10CodesAgesCoveredOtherPollutantsin ModelLagRegionCoveredSeasonCoveredCoefficient Lower Bound Upper BoundNew York Winter0.00334 0.00122 0.00547New York Spring0.00172 -0.00058 0.00403New York Summer0.00157 -0.00066 0.00381New York Fall0.00235 0.00013 0.00457Philadelphia Winter0.00217 -0.00030 0.00463Philadelphia Spring0.00219 -0.00033 0.00471Philadelphia Summer0.00182 -0.00068 0.00432Philadelphia Fall0.00186 -0.00062 0.00435Phoenix Winter0.00251 -0.00253 0.00755Phoenix Spring0.00538 -0.00140 0.01215Phoenix Summer0.00577 -0.00083 0.01238Phoenix Fall0.00887 0.00285 0.01489Pittsburgh Winter0.00134 -0.00110 0.00377Pittsburgh Spring0.00223 -0.00024 0.00470Pittsburgh Summer0.00188 -0.00052 0.00428Pittsburgh Fall0.00231 -0.00009 0.00472Salt Lake City Winter0.00301 -0.00088 0.00690Salt Lake City Spring0.00438 -0.00459 0.01336Salt Lake City Summer-0.00353 -0.01304 0.00598Salt Lake City Fall-0.00138 -0.00915 0.00639St. Louis Winter0.00019 -0.00212 0.00250St. Louis Spring0.00123 -0.00112 0.00357St. Louis Summer0.00060 -0.00171 0.00292St. Louis Fall0.00127 -0.00106 0.00360Tacoma Winter0.00011 -0.00563 0.00585Tacoma Spring0.00287 -0.00349 0.00924Tacoma Summer0.00190 -0.00467 0.00848Tacoma Fall0.00138 -0.00458 0.00733J00-J99none1 --- indicates that results were not available.Zanobetti andSchwartz (2009)Mortality, short-termrespiratoryavg of 0-and 1-dayall ages

# D-1 

# APPENDIX D: SUPPLEMENT TO THE REPRESENTATIVENESS

ANALYSIS OF THE 15 URBAN STUDY AREAS

# D-2 Appendix D. Supplement to the Representativeness Analysis of the 15 Urban Study Areas

(additional graphical comparisons of di stributions for key contributors to PM 2.5 

# risk )

Following the analysis discussed in Secti on 4.4.1, this appendix provides graphical

comparisons of the empirical distributions of components of the risk function, and additional

variables that have been identif ied as potentially influencing the risk associated with PM

exposures.

In each graph, the orange line represents th e 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 repr esents the empirical cumulative distribution

function for the variable based only on the da ta available for the se t 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 Po pulation with U.S. Distribution of

Population (all U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

100 1000 10000 100000 1000000 10000000

Population% of Counties



# All Counties CDF Case Study Counties CDF Case Study Counties Urban case study areas are

all above the 65th Percentile

of county populations 

# D.1 Elements of the Risk Equation

Figure D-1. Comparison of Distributions fo r Key Elements of the Risk Equation:

Total Population

# D-4 Comparison of Urban Case Study Area % Under 15 to U.S. Distribution of % Under

15

(3141 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

10 15 20 25 30 35

% of Population Under Age 15, 2005% of U.S. Counties



# All Counties CDF Case Study Counties Case Study Counties 

# Figure D-2. Comparison of Distributions fo r Key Elements of the Risk Equation:

Percent of Population Under 15 Years of Age

# D-5 Comparison of Urban Case Study Area % 65 and Older to U.S. Distribution of % 65

and Older

(3141 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 5 10 15 20 25 30 35

% of Population 65 and Older, 2005% of U.S. Counties



# All Counties CDF Case Study Counties CDF Case Study Counties Urban case study areas are

all below the 75th percentile

of county % of population 65

and older 

# Figure D-3. Comparison of Distributions fo r Key Elements of the Risk Equation:

Percent of Population 65 Years of Age and Older

# D-6 Comparison of Urban Case Study Area % 85 and Older to U.S. Distribution of % 85

and Older

(3141 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

12345678

% of Population 85 and Older, 2005% of U.S. Counties



# All Counties CDF Case Study Counties CDF Case Study Counties Urban case study areas are

all below the 85th percentile

of county % of population 85

and older 

# Figure D-4. Comparison of Distributions for Key Elements of the Risk Equation:

Percent of Population 85 Years of Age and Older

# D-7 Comparison of Urban Case Study Area Annual Mean PM2.5 with U.S.

Distribution of Annual Mean PM2.5 ( 617 U.S. Counties with PM2.5 Monitors)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 5 10 15 20 25

Annual Mean PM2.5% of Monitored Counties



# All Counties CDF Case Study Counties CDF Case Study Counties 

# Figure D-5. Comparison of Distributions for Key Elements of the Risk Equation: Annual

Mean PM 2.5

# D-8 Comparison of Urban Case St udy Area 98th %ile PM2.5 wi th U.S. Distribution of

98th %ile PM2.5

(617 U.S. Counties with PM2.5 Monitors)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 10 20 30 40 50 60 70 80 90

98th Percentile Daily PM2.5% of Monitored Counties



# All Counties CDF Case Study Counties CDF Case Study Counties 

# Figure D-6. Comparison of Distributions fo r Key Elements of the Risk Equation: 98 th



# %ile Daily Average PM 2.5

# D-9 Comparison of Urban Case Study Area % of Days with PM2.5>35 ug/m3 to U.S.

Distribution of % of Days with PM2.5>35 ug/m3

(204 U.S. Counties in MCAPS Study)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 5 10 15 20 25

% of Days Above 35 ug/m3% of MCAPS Counties



# All Counties CDF Case Study Counties CDF Case Study Counties 

# Figure D-7. Comparison of Distributions for Key Elements of the Risk Equation: % of

Days with PM 2.5 

# > 35 μg/m 3

# D-10 Comparison of Urban Case Study All Cause Mo rtality Rate to U.S. Distribution of All

Cause Mortality Rate

(3143 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

100 300 500 700 900 1100 1300 1500 1700 1900 2100

All Cause Mortality per 100,000 Population% of U.S. Counties



# All Counties CDF Case Study Counties CDF Case Study Counties Urban case study areas are

all below the 90th percentile

of county all cause mortality 

# Figure D-8. Comparison of Distributions for Key Elements of the Risk Equation: All

Cause Mortality Rate

# D-11 Comparison of Urban Case Study Non-accidental Mortality Rate to U.S. Distribution of

Non-accidental Mortality Rate

(3143 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

200 400 600 800 1000 1200 1400 1600 1800 2000

Non-accidental Mortality per 100,000 Population% of U.S. Counties



# All Counties CDF Case Study Counties CDF Case Study Counties Urban case study areas are

all below the 90th percentile

of county non-accidental

mortality 

# Figure D-9. Comparison of Distributions for Key Elements of the Risk Equation: Non-

Accidental Mortality Rate

# D-12 Comparison of Urban Case Study Cardiovascular Mortality Rate to U.S. Distribution of

Cardiovascular Mortality Rate

(3143 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

50 150 250 350 450 550 650 750 850 950

Cardiovascular Mortality per 100,000 Population% of U.S. Counties



# All Counties CDF Case Study Counties CDF Case Study Counties Urban case study areas are

all below the 90th percentile

of county cardiovascular

mortality 

# Figure D-10. Comparison of Distributions fo r Key Elements of the Risk Equation:

Cardiovascular Mortality Rate

# D-13 Comparison of Urban Case Study Respiratory Mortality Rate to U.S. Distribution of

Respiratory Mortality Rate

(3143 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

10 60 110 160 210 260

Respiratory Mortality per 100,000 Population% of U.S. Counties



# All Counties CDF Case Study Counties CDF Case Study Counties Urban case study areas are

all below the 90th percentile

of county respiratory

mortality 

# Figure D-11. Comparison of Distributions fo r Key Elements of the Risk Equation:

Respiratory Mortality Rate

# D-14 Comparison of Urban Case Study PM All-cause Mortality Risk ( β ) to

U.S. Distribution of PM All-cause Mortality Risk

(212 U.S. Urban Areas)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014 0.0016

Bayesian Shrunken PM All Cause Mortality Risk Coefficient ( β )% of Z&S Urban Areas



# All Z&S Urban Areas CDF Case Study Urban Area CDF Case Study Urban Areas 

# Figure D-12. Comparison of Distributions for Key Elements of the Risk Equation: All

Cause Mortality Risk Effect Estima te from Zanobetti and Schwartz (2008)

# D-15 Comparison of Urban Case Study PM Cardiovascular Mortality Risk ( β ) to U.S.

Distribution of PM Card iovascular Mortality Risk

(212 U.S. Urban Areas)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014 0.0016 0.0018 0.002

Bayesian Shrunken PM Cardiovascular Mortality Risk Coefficient ( β )% of Z&S Urban Areas



# All Z&S Urban Areas CDF Case Study Urban Area CDF Case Study Urban Areas 

# Figure D-13. Comparison of Distributions for Key Elements of the Risk Equation:

Cardiovascular Mortality Risk Effect Es timate from Zanobetti and Schwartz

(2008)

# D-16 Comparison of Urban Case Study PM Respiratory Mortality Risk ( β ) to U.S.

Distribution of PM Respiratory Mortality Risk

(212 U.S. Urban Areas)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 0.0005 0.001 0.0015 0.002

Bayesian Shrunken PM Respiratory Mortality Risk Coefficient ( β )% of Z&S Urban Areas



# All Z&S Urban Areas CDF Case Study Urban Area CDF Case Study Urban Areas 

# Figure D-14. Comparison of Distributions for Key Elements of the Risk Equation:

Respiratory Mortality Risk Effect Estimate from Zanobetti and Schwartz

(2008)

# D-17 Comparison of Urban Case Study Area Populat ion Density with U.S. Distribution of

Population Density (all U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1 10 100 1000 10000 100000

Population Density (persons per sq mile)% of Counties



# All Counties CDF Case Study Counties CDF Case Study Counties Urban case study areas are

all above the 65th Percentile

of county population density 

# D.2. Variables Expected to Influence the Relative Risk from PM 2.5



# D.2.1. Demographic Variables

Figure D-15. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : Population Density

# D-18 Comparison of Urban Case Study Area Unempl oyment Rate to U.S. Distribution of

Unemployment Rates

(3141 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 2 4 6 8 10 12 14 16 18 20

Unemployment Rate (%)% of U.S. Counties



# All Counties CDF Case Study Counties CDF Case Study Counties 

# Figure D-16. Comparison of Distributions for Selected Variables Expected to

Influence the Relative Risk from PM 2.5 

# : Unemployment Rate

# D-19 Comparison of Urban Case Study % Less than High School Education to U.S.

Distribution of % Less than High School Education

(3146 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

3 13 23 33 43 53 63

% of Population with Less than a High School Education, 2000% of U.S. Counties



# All Counties CDF Case Study Counties CDF Case Study Counties 

# Figure D-17. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : % with Less than a High School Education

# D-20 Comparison of Urban Case Study Area Per Capita Personal Income to U.S.

Distribution of Per Capita Personal Income

(3141 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

$10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000 $90,000

Per Capita Personal Income, 2005% of U.S. Counties



# All Counties CDF Case Study Counties CDF Case Study Counties Urban case study areas are

all above the 25th percentile

of county per capita income 

# Figure D-18. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : Per Capita Personal Income

# D-21 Comparison of Urban Case Study Air Conditi oning Prevalence to U.S. Distribution of

Air Conditioning Prevalence

(70 U.S. Urban Areas)



# 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 0.2 0.4 0.6 0.8 1

% Air Conditioning Prevalence% of Urban Areas



# All Urban Areas CDF Case Study Urban Areas CDF Case Study Urban Areas 

# Figure D-19. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : Air Conditioning Prevalence

# D-22 Comparison of Urban Case Study Air Conditi oning Prevalence to U.S. Distribution of

Air Conditioning Prevalence

(70 U.S. Urban Areas)



# 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 0.2 0.4 0.6 0.8 1

% Air Conditioning Prevalence% of Urban Areas



# All Urban Areas CDF Case Study Urban Areas CDF Case Study Urban Areas 

# Figure D-20. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : % Non-White Population

# D-23 Comparison of Urban Case Study Area Angi na/CHD Prevalence to U.S. Distribution

of Angina/CHD Prevalence

(183 U.S. MSA)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

123456789

% Prevalence of Angina or CHD, BRFSS, 2007% of BRFSS MSA



# All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA Urban case study areas are

all below the 85th percentile

of MSA angina or CHD

prevalence 

# D.2.2. Health Conditions

Figure D-21. Comparison of Distributions for Selected Variables Expected to

Influence the Relative Risk from PM 2.5 

# : Angina/Coronary Heart

Disease Prevalence

# D-24 Comparison of Urban Case Study Area Current Asthma Prevalence to U.S.

Distribution of Current Asthma Prevalence

(183 U.S. MSA)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

3 5 7 9 11 13

% Current Prevalence of Asthma, BRFSS 2007% of BRFSS MSA



# All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA 

# Figure D-22. Comparison of Distributions for Selected Variables Expected to

Influence the Relative Risk from PM 2.5 

# : Asthma Prevalence

# D-25 Comparison of Urban Case Study Area Diabet es Prevalence to U.S. Distribution of

Diabetes Prevalence

(183 U.S. MSA)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2 4 6 8 10 12 14 16

% Prevalence of Diabetes, BRFSS 2007% of BRFSS MSA



# All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA Urban case study areas are

all below the 85th percentile

of MSA diabetes prevalence 

# Figure D-23. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : Diabetes Prevalence

# D-26 Comparison of Urban Case Study Area Heart At tack Prevalence to U.S. Distribution of

Heart Attack Prevalence

(183 U.S. MSA)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2 3 4 5 6 7 8 9 10 11

% Prevalence of Heart Attacks, BRFSS 2007% of BRFSS MSA



# All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA Urban case study areas are

all below the 80th percentile

of MSA heart attack

prevalence 

# Figure D-24. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : Heart Attack Prevalence

# D-27 Comparison of Urban Case Study Area 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 PM 2.5 

# : Obesity Prevalence

# D-28 Comparison of Urban Case Study Area Stroke Prevalence to U.S. Distribution of

Stroke Prevalence

(183 U.S. MSA)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0123456

% Prevalence of Stroke, BRFSS 2007% of BRFSS MSA



# All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA 

# Figure D-26. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : Stroke Prevalence

# D-29 Comparison of Urban Case Study Area Smoki ng Prevalence to U.S. Distribution of

Smoking Prevalence

(183 U.S. MSA)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

6 1 1 1 6 2 1 2 6 3 1

% Prevalence of Smoking (ever), BRFSS 2007% of BRFSS MSA



# All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA Urban case study areas are

all below the 85th percentile

of MSA smoking prevalence 

# Figure D-27. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : Smoking Prevalence

# D-30 Comparison of Urban Case Study Area Exerci se Prevalence to U.S. Distribution of

Exercise Prevalence

(183 U.S. MSA)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

15 20 25 30 35 40 45

% Prevalence of Exercise (Adults with 20+ minutes of vigorous physical activity 3 or

more days per week), BRFSS 2007% of BRFSS MSA



# All BRFSS MSA CDF Case Study County MSA CDF Case Study County MSA 

# Figure D-28. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : Exercise Prevalence

# D-31 Comparison of Urban Case Study Area 4th Highest Daily 8-Hour Ozone with U.S.

Distribution of 4th Highest Daily 8-Hour Ozone

(725 U.S. Counties with Ozone Monitors)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0.02 0.04 0.06 0.08 0.1 0.12

4th Highest Daily 8-Hour Maximum% of Monitored Counties



# All Counties CDF Case Study Counties CDF Case Study Counties 

# D.2.3. Air Quality and Climate Variables

Figure D-29. Comparison of Distributions for Selected Variables Expected to

Influence the Relative Risk from PM 2.5 

# : 4 th 

# Highest Daily Max 8-

hour Average

# D-32 Comparison of Urban Case Study Mobile Sour ce Carbon to U.S. Distribution of Mobile

Source Carbon

(3142 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Mobile Source Proportion of Tota l Organic and Elemental Carbon Emissions% of U.S. Counties



# All Counties CDF Case Study Counties CDF Case Study Counties Urban case study areas are

all below the 80th percentile

of county mobile source

proportion of EC/OC

emissions 

# Figure D-30. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : % Mobile Source Direct PM 2.5 

# Emissions

# D-33 Comparison of Urban Case Study Area Long Term Average July Temperature to

U.S. Distribution of Long Term Average July Temperature

(3141 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

50 60 70 80 90 100

July 30 Year Average Temperature, 1941-1970% of U.S. Counties



# All Counties CDF Case Study Counties CDF Case Study Counties 

# Figure D-31. Comparison of Distributions for Selected Variables Expected to

Influence the Relative Risk from PM 2.5 

# : July Temperature Long

Term Average

# D-34 Comparison of Urban Case Study Area Long Term Average July Relative Humidity to

U.S. Distribution of Long Term Average July Relative Humidity

(3141 U.S. Counties)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 10 20 30 40 50 60 70 80 90

July 30 Year Average Relative Humidity, 1941-1970% of U.S. Counties



# All Counties CDF Case Study Counties CDF Case Study Counties 

# Figure D-32. Comparison of Distributions for Selected Variables Expected to Influence the

Relative Risk from PM 2.5 

# : July Relative Humidity Long Term Average

# E-1 

# APPENDIX E: RISK ESTI MATES (CORE ANALYSIS)

# E-1 Appendix E. Risk Estimates (core analysis)

This Appendix provides detailed risk estimates generated for the core analysis for the 15

urban study areas. The tables cove r all of the air quality scenar ios modeled, including recent

conditions, the current standard, and alternative st andard levels. For additional detail on the types

of risk metrics (and figures summarizing key me trics) presented in th is Appendix, see section

4.0.E-2Table E-1. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a RecentYear (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25649575513451389451379(421 - 873) (373 - 774) (333 - 692) (292 - 608) (252 - 525) (292 - 608) (245 - 512)597548502442382426303(387 - 803) (355 - 738) (325 - 676) (286 - 596) (247 - 516) (276 - 574) (196 - 409)424297262227192227160(275 - 571) (192 - 400) (170 - 354) (147 - 307) (124 - 260) (147 - 307) (103 - 216)379379379379336379336(245 - 511) (245 - 511) (245 - 511) (245 - 511) (218 - 454) (245 - 511) (218 - 454)798580573502431442303(518 - 1073) (376 - 782) (371 - 772) (325 - 677) (279 - 581) (286 - 597) (196 - 410)254898989895928(165 - 342) (57 - 120) (57 - 120)(57 - 120) (57 - 120)(38 - 79)(18 - 38)609557491426360426360(394 - 820) (360 - 751) (318 - 663) (275 - 575) (233 - 486) (275 - 575) (233 - 486)2333104510451045919719390(1514 - 3141) (676 - 1413) (676 - 1413) (676 - 1413) (593 - 1242) (464 - 972) (252 - 528)200014771477141012051100721(1297 - 2693) (956 - 1992) (956 - 1992) (912 - 1902) (779 - 1627) (711 - 1486) (465 - 975)521455455406348345233(338 - 703) (295 - 614) (295 - 614) (263 - 548) (225 - 470) (223 - 465) (151 - 315)483483483483433420263(312 - 652) (312 - 652) (312 - 652) (312 - 652) (280 - 586) (271 - 568) (170 - 356)593387387363318289190(385 - 798) (251 - 523) (251 - 523) (235 - 490) (206 - 430) (187 - 390) (122 - 257)10229292929100(66 - 138)(19 - 39)(19 - 39)(19 - 39)(19 - 39)(7 - 14)(0 - 0)826700634557480543383(536 - 1111) (454 - 943) (411 - 855) (361 - 752) (310 - 648) (351 - 732) (248 - 518)123808080805326(80 - 166)(52 - 109) (52 - 109)(52 - 109) (52 - 109)(34 - 72)(17 - 36)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-3Table E-2. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a RecentYear (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25668592528464400464390(434 - 899) (384 - 797) (342 - 712) (301 - 626) (259 - 540) (301 - 626) (253 - 527)483441400347295333225(313 - 651) (285 - 594) (259 - 539) (225 - 469) (191 - 398) (216 - 450) (145 - 304)399276243209176209144(259 - 536) (179 - 373) (157 - 328) (135 - 283) (114 - 238) (135 - 283) (93 - 195)284284284284247284247(183 - 383) (183 - 383) (183 - 383) (183 - 383) (160 - 334) (183 - 383) (160 - 334)576398392334276285172(373 - 776) (257 - 537) (253 - 529) (216 - 451) (178 - 373) (184 - 386) (111 - 233)265 94 94 94 94 63 32(172 - 356) (61 - 127) (61 - 127) (61 - 127) (61 - 127)(41 - 85)(20 - 43)589537472407342407342(381 - 794) (348 - 725) (306 - 638) (263 - 550) (221 - 462) (263 - 550) (221 - 462)2054863863863745561257(1332 - 2767) (557 - 1166) (557 - 1166) (557 - 1166) (481 - 1008) (362 - 759) (166 - 348)1548109610961038861771444(1002 - 2087) (708 - 1481) (708 - 1481) (671 - 1403) (556 - 1164) (498 - 1043) (286 - 601)471409409363308305200(305 - 636) (265 - 552) (265 - 552) (235 - 490) (199 - 417) (197 - 412) (129 - 271)512512512512460446281(331 - 691) (331 - 691) (331 - 691) (331 - 691) (297 - 622) (288 - 603) (181 - 380)468290290270231205120(303 - 631) (187 - 392) (187 - 392) (174 - 364) (149 - 312) (133 - 278) (78 - 163)841616161600(54 - 113)(10 - 21)(10 - 21)(10 - 21)(10 - 21)(0 - 0)(0 - 0)618514458394329382249(401 - 834) (332 - 693) (296 - 619) (255 - 532) (213 - 445) (247 - 515) (160 - 336)8347474747252(54 - 112)(30 - 64)(30 - 64)(30 - 64)(30 - 64)(16 - 33)(1 - 3)Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-4Table E-3. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a RecentYear (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski etal. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25642567505442379442370(416 - 864) (368 - 764) (327 - 680) (286 - 596) (245 - 512) (286 - 596) (239 - 499)482440399347294333225(313 - 650) (285 - 593) (258 - 538) (224 - 468) (190 - 398) (215 - 449) (145 - 304)418291257222187222155(271 - 563) (189 - 393) (166 - 347) (144 - 300) (121 - 253) (144 - 300) (100 - 209)317317317317278317278(205 - 428) (205 - 428) (205 - 428) (205 - 428) (180 - 375) (205 - 428) (180 - 375)607424418358299308192(393 - 818) (274 - 572) (270 - 564) (232 - 484) (193 - 404) (199 - 417) (124 - 260)2791011011011016936(181 - 375) (65 - 137) (65 - 137) (65 - 137) (65 - 137)(44 - 93)(23 - 49)615561494427359427359(398 - 829) (363 - 757) (320 - 667) (276 - 577) (232 - 486) (276 - 577) (232 - 486)2134911911911791601289(1384 - 2874) (588 - 1232) (588 - 1232) (588 - 1232) (511 - 1070) (388 - 813) (187 - 392)18121316131612531058959600(1174 - 2443) (852 - 1777) (852 - 1777) (810 - 1692) (684 - 1430) (620 - 1296) (387 - 811)466405405359304301197(302 - 629) (262 - 546) (262 - 546) (232 - 484) (197 - 411) (195 - 407) (127 - 266)433433433433385371216(280 - 586) (280 - 586) (280 - 586) (280 - 586) (248 - 520) (240 - 502) (139 - 292)527339339316274247156(342 - 710) (219 - 457) (219 - 457) (205 - 427) (177 - 371) (160 - 334) (100 - 211)12038383838170(78 - 162)(24 - 51)(24 - 51)(24 - 51)(24 - 51)(11 - 23)(0 - 0)679568509441373428287(440 - 915) (368 - 766) (330 - 687) (285 - 596) (241 - 504) (277 - 578) (186 - 389)8750505050274(56 - 118)(32 - 68)(32 - 68)(32 - 68)(32 - 68)(17 - 36)(2 - 5)Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest whole number.Numbers in parentheses are 95% confidenceor credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-5Table E-4. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/254.3%3.8%3.4%3%2.6%3%2.5%(2.8% - 5.8%) (2.5% - 5.1%) (2.2% - 4.6%) (1.9% - 4%) (1.7% - 3.5%) (1.9% - 4%) (1.6% - 3.4%)4.2%3.9%3.6%3.1%2.7%3%2.1%(2.7% - 5.7%) (2.5% - 5.2%) (2.3% - 4.8%) (2% - 4.2%) (1.8% - 3.7%) (2% - 4.1%) (1.4% - 2.9%)4.3%3%2.7%2.3%2%2.3%1.6%(2.8% - 5.8%) (2% - 4.1%) (1.7% - 3.6%) (1.5% - 3.1%) (1.3% - 2.6%) (1.5% - 3.1%) (1.1% - 2.2%)3%3%3%3%2.6%3%2.6%(1.9% - 4%) (1.9% - 4%) (1.9% - 4%) (1.9% - 4%) (1.7% - 3.5%) (1.9% - 4%) (1.7% - 3.5%)4.5%3.3%3.2%2.8%2.4%2.5%1.7%(2.9% - 6%) (2.1% - 4.4%) (2.1% - 4.3%) (1.8% - 3.8%) (1.6% - 3.3%) (1.6% - 3.3%) (1.1% - 2.3%)4.6%1.6%1.6%1.6%1.6%1.1%0.5%(3% - 6.1%) (1% - 2.2%) (1% - 2.2%) (1% - 2.2%) (1% - 2.2%) (0.7% - 1.4%) (0.3% - 0.7%)3.3%3%2.6%2.3%1.9%2.3%1.9%(2.1% - 4.4%) (1.9% - 4%) (1.7% - 3.6%) (1.5% - 3.1%) (1.2% - 2.6%) (1.5% - 3.1%) (1.2% - 2.6%)4.1%1.8%1.8%1.8%1.6%1.3%0.7%(2.7% - 5.5%) (1.2% - 2.5%) (1.2% - 2.5%) (1.2% - 2.5%) (1% - 2.2%) (0.8% - 1.7%) (0.4% - 0.9%)3.8%2.8%2.8%2.7%2.3%2.1%1.4%(2.5% - 5.1%) (1.8% - 3.8%) (1.8% - 3.8%) (1.7% - 3.6%) (1.5% - 3.1%) (1.3% - 2.8%) (0.9% - 1.8%)3.6%3.1%3.1%2.8%2.4%2.4%1.6%(2.3% - 4.8%) (2% - 4.2%) (2% - 4.2%) (1.8% - 3.8%) (1.5% - 3.2%) (1.5% - 3.2%) (1% - 2.2%)2.1%2.1%2.1%2.1%1.9%1.8%1.1%(1.4% - 2.8%) (1.4% - 2.8%) (1.4% - 2.8%) (1.4% - 2.8%) (1.2% - 2.5%) (1.2% - 2.5%) (0.7% - 1.5%)4.3%2.8%2.8%2.6%2.3%2.1%1.4%(2.8% - 5.7%) (1.8% - 3.8%) (1.8% - 3.8%) (1.7% - 3.5%) (1.5% - 3.1%) (1.3% - 2.8%) (0.9% - 1.8%)2.2%0.6%0.6%0.6%0.6%0.2%0%(1.4% - 2.9%) (0.4% - 0.8%) (0.4% - 0.8%) (0.4% - 0.8%) (0.4% - 0.8%) (0.1% - 0.3%) (0% - 0%)4.4%3.7%3.4%3%2.5%2.9%2%(2.8% - 5.9%) (2.4% - 5%) (2.2% - 4.5%) (1.9% - 4%) (1.6% - 3.4%) (1.9% - 3.9%) (1.3% - 2.7%)2.4%1.6%1.6%1.6%1.6%1.1%0.5%(1.6% - 3.3%) (1% - 2.1%) (1% - 2.1%) (1% - 2.1%) (1% - 2.1%) (0.7% - 1.4%) (0.3% - 0.7%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-6Table E-5. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/254.3%3.8%3.4%3%2.6%3%2.5%(2.8% - 5.8%) (2.5% - 5.1%) (2.2% - 4.6%) (1.9% - 4%) (1.7% - 3.5%) (1.9% - 4%) (1.6% - 3.4%)3.4%3.1%2.8%2.5%2.1%2.4%1.6%(2.2% - 4.6%) (2% - 4.2%) (1.8% - 3.8%) (1.6% - 3.3%) (1.3% - 2.8%) (1.5% - 3.2%) (1% - 2.2%)4%2.8%2.4%2.1%1.8%2.1%1.5%(2.6% - 5.4%) (1.8% - 3.8%) (1.6% - 3.3%) (1.4% - 2.8%) (1.1% - 2.4%) (1.4% - 2.8%) (0.9% - 2%)2.2%2.2%2.2%2.2%1.9%2.2%1.9%(1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.2% - 2.5%) (1.4% - 2.9%) (1.2% - 2.5%)3.2%2.2%2.2%1.9%1.5%1.6%1%(2.1% - 4.4%) (1.4% - 3%) (1.4% - 3%) (1.2% - 2.5%) (1% - 2.1%) (1% - 2.2%) (0.6% - 1.3%)4.7%1.7%1.7%1.7%1.7%1.1%0.6%(3% - 6.3%) (1.1% - 2.2%) (1.1% - 2.2%) (1.1% - 2.2%) (1.1% - 2.2%) (0.7% - 1.5%) (0.4% - 0.8%)3.1%2.8%2.5%2.1%1.8%2.1%1.8%(2% - 4.1%) (1.8% - 3.8%) (1.6% - 3.3%) (1.4% - 2.9%) (1.1% - 2.4%) (1.4% - 2.9%) (1.1% - 2.4%)3.6%1.5%1.5%1.5%1.3%1%0.5%(2.3% - 4.8%) (1% - 2%) (1% - 2%) (1% - 2%) (0.8% - 1.8%) (0.6% - 1.3%) (0.3% - 0.6%)2.9%2.1%2.1%2%1.6%1.4%0.8%(1.9% - 3.9%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.6%) (1% - 2.2%) (0.9% - 2%) (0.5% - 1.1%)3.2%2.8%2.8%2.5%2.1%2.1%1.4%(2.1% - 4.4%) (1.8% - 3.8%) (1.8% - 3.8%) (1.6% - 3.4%) (1.4% - 2.9%) (1.4% - 2.8%) (0.9% - 1.9%)2.1%2.1%2.1%2.1%1.9%1.9%1.2%(1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.2% - 2.6%) (1.2% - 2.5%) (0.8% - 1.6%)3.4%2.1%2.1%1.9%1.7%1.5%0.9%(2.2% - 4.6%) (1.4% - 2.8%) (1.4% - 2.8%) (1.3% - 2.6%) (1.1% - 2.3%) (1% - 2%) (0.6% - 1.2%)1.7%0.3%0.3%0.3%0.3%0%0%(1.1% - 2.3%) (0.2% - 0.4%) (0.2% - 0.4%) (0.2% - 0.4%) (0.2% - 0.4%) (0% - 0%) (0% - 0%)#DIV/0!2.7%2.4%2.1%1.7%2%1.3%#DIV/0! (1.8% - 3.7%) (1.6% - 3.3%) (1.3% - 2.8%) (1.1% - 2.4%) (1.3% - 2.7%) (0.8% - 1.8%)3.3%0.9%0.9%0.9%0.9%0.5%0%(2.1% - 4.4%) (0.6% - 1.2%) (0.6% - 1.2%) (0.6% - 1.2%) (0.6% - 1.2%) (0.3% - 0.6%) (0% - 0.1%)Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-7Table E-6. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/254%3.6%3.2%2.8%2.4%2.8%2.3%(2.6% - 5.4%) (2.3% - 4.8%) (2% - 4.3%) (1.8% - 3.7%) (1.5% - 3.2%) (1.8% - 3.7%) (1.5% - 3.1%)3.4%3.1%2.8%2.5%2.1%2.4%1.6%(2.2% - 4.6%) (2% - 4.2%) (1.8% - 3.8%) (1.6% - 3.3%) (1.3% - 2.8%) (1.5% - 3.2%) (1% - 2.2%)4.2%2.9%2.6%2.2%1.9%2.2%1.5%(2.7% - 5.6%) (1.9% - 3.9%) (1.7% - 3.5%) (1.4% - 3%) (1.2% - 2.5%) (1.4% - 3%) (1% - 2.1%)2.4%2.4%2.4%2.4%2.1%2.4%2.1%(1.5% - 3.2%) (1.5% - 3.2%) (1.5% - 3.2%) (1.5% - 3.2%) (1.3% - 2.8%) (1.5% - 3.2%) (1.3% - 2.8%)3.4%2.4%2.4%2%1.7%1.7%1.1%(2.2% - 4.6%) (1.5% - 3.2%) (1.5% - 3.2%) (1.3% - 2.7%) (1.1% - 2.3%) (1.1% - 2.4%) (0.7% - 1.5%)4.9%1.8%1.8%1.8%1.8%1.2%0.6%(3.2% - 6.5%) (1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (0.8% - 1.6%) (0.4% - 0.9%)3.1%2.9%2.5%2.2%1.8%2.2%1.8%(2% - 4.2%) (1.8% - 3.9%) (1.6% - 3.4%) (1.4% - 2.9%) (1.2% - 2.5%) (1.4% - 2.9%) (1.2% - 2.5%)3.7%1.6%1.6%1.6%1.4%1%0.5%(2.4% - 5%) (1% - 2.1%) (1% - 2.1%) (1% - 2.1%) (0.9% - 1.9%) (0.7% - 1.4%) (0.3% - 0.7%)3.4%2.5%2.5%2.3%2%1.8%1.1%(2.2% - 4.6%) (1.6% - 3.3%) (1.6% - 3.3%) (1.5% - 3.2%) (1.3% - 2.7%) (1.2% - 2.4%) (0.7% - 1.5%)3.2%2.8%2.8%2.5%2.1%2.1%1.4%(2.1% - 4.3%) (1.8% - 3.8%) (1.8% - 3.8%) (1.6% - 3.3%) (1.4% - 2.8%) (1.3% - 2.8%) (0.9% - 1.8%)1.8%1.8%1.8%1.8%1.6%1.5%0.9%(1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (1.1% - 2.4%) (1% - 2.1%) (1% - 2%) (0.6% - 1.2%)3.8%2.5%2.5%2.3%2%1.8%1.1%(2.5% - 5.2%) (1.6% - 3.3%) (1.6% - 3.3%) (1.5% - 3.1%) (1.3% - 2.7%) (1.2% - 2.4%) (0.7% - 1.5%)2.4%0.7%0.7%0.7%0.7%0.3%0%(1.5% - 3.2%) (0.5% - 1%) (0.5% - 1%) (0.5% - 1%) (0.5% - 1%) (0.2% - 0.5%) (0% - 0%)3.6%3%2.7%2.3%2%2.3%1.5%(2.3% - 4.8%) (1.9% - 4%) (1.7% - 3.6%) (1.5% - 3.1%) (1.3% - 2.7%) (1.5% - 3.1%) (1% - 2.1%)1.7%1%1%1%1%0.5%0.1%(1.1% - 2.2%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.3% - 0.7%) (0% - 0.1%)Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-8Table E-7. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated with Long-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-13%0%11%22%32%22%34%(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 22%) (34% - 34%)-9%0%8%19%30%22%45%(-9% - -9%) (0% - 0%) (8% - 9%) (19% - 19%) (30% - 30%) (22% - 22%) (45% - 45%)-43%0%12%23%35%23%46%(-43% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 35%) (23% - 24%) (46% - 46%)0%0%0%0%11%0%11%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-38%0%1%13%26%24%48%(-37% - -38%) (0% - 0%) (1% - 1%) (13% - 14%) (26% - 26%) (24% - 24%) (48% - 48%)-187%0%0%0%0%34%68%(-185% - -188%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (34% - 34%) (68% - 68%)-9%0%12%24%35%24%35%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 35%) (23% - 24%) (35% - 35%)-123%0%0%0%12%31%63%(-122% - -124%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 31%) (63% - 63%)-35%0%0%5%18%26%51%(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (18% - 18%) (25% - 26%) (51% - 51%)-14%0%0%11%24%24%49%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 24%) (49% - 49%)0%0%0%0%10%13%46%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 46%)-53%0%0%6%18%25%51%(-53% - -54%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (25% - 26%) (51% - 51%)-255%0%0%0%0%64%100%(-254% - -256%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 64%) (100% - 100%)-18%0%9%20%31%23%45%(-18% - -18%) (0% - 0%) (9% - 9%) (20% - 21%) (31% - 32%) (22% - 23%) (45% - 45%)-53%0%0%0%0%34%67%(-53% - -54%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (34% - 34%) (67% - 67%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with Long-Term Exposure toPM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily(m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-9Table E-8. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated with Long-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-13%0%11%22%32%22%34%(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 22%) (34% - 34%)-10%0%9%21%33%24%49%(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 24%) (49% - 49%)-44%0%12%24%36%24%48%(-44% - -45%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 24%) (48% - 48%)0%0%0%0%13%0%13%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 0%) (13% - 13%)-45%0%1%16%31%28%57%(-45% - -45%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 31%) (28% - 28%) (57% - 57%)-182%0%0%0%0%33%66%(-180% - -184%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (66% - 66%)-10%0%12%24%36%24%36%(-10% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 24%) (36% - 36%)-138%0%0%0%14%35%70%(-137% - -139%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 14%) (35% - 35%) (70% - 70%)-41%0%0%5%21%30%59%(-41% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (30% - 30%) (59% - 60%)-15%0%0%11%25%25%51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (25% - 25%) (25% - 26%) (51% - 51%)0%0%0%0%10%13%45%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 45%)-61%0%0%7%20%29%58%(-61% - -62%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 29%) (58% - 59%)-438%0%0%0%0%100%100%(-437% - -440%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%)-20%0%11%23%36%26%52%(-20% - -21%) (0% - 0%) (11% - 11%) (23% - 23%) (36% - 36%) (26% - 26%) (51% - 52%)-76%0%0%0%0%48%96%(-76% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (48% - 48%) (96% - 96%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with Long-Term Exposure toPM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily(m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-10Table E-9. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated with Long-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-13%0%11%22%33%22%35%(-13% - -13%) (0% - 0%) (11% - 11%) (22%- 22%) (33% - 33%) (22% - 22%) (35% - 35%)-10%0%9%21%33%24%49%(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 24%) (49% - 49%)-44%0%12%24%36%24%47%(-43% - -44%) (0% - 0%) (12% - 12%) (24%- 24%) (36% - 36%) (24% - 24%) (47% - 47%)0%0%0%0%12%0%12%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-43%0%1%15%30%27%55%(-43% - -44%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 30%) (27% - 27%) (55% - 55%)-176%0%0%0%0%32%64%(-175% - -178%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (32% - 32%) (64% - 64%)-10%0%12%24%36%24%36%(-9% - -10%) (0% - 0%) (12%- 12%) (24% - 24%) (36% - 36%) (24% - 24%) (36% - 36%)-134%0%0%0%13%34%68%(-133% - -135%) (0% - 0%) (0% - 0%) (0%- 0%) (13% - 13%) (34% - 34%) (68% - 68%)-38%0%0%5%20%27%54%(-37% - -38%) (0% - 0%) (0% - 0%) (5% - 5%) (20% - 20%) (27% - 27%) (54% - 55%)-15%0%0%11%25%26%51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (25% - 25%) (26% - 26%) (51% - 52%)0%0%0%0%11%14%50%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (50% - 50%)-56%0%0%7%19%27%54%(-55% - -56%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 19%) (27% - 27%) (54% - 54%)-218%0%0%0%0%55%100%(-217% - -219%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (55% - 55%) (100% - 100%)-20%0%10%22%34%25%49%(-19% - -20%) (0% - 0%) (10% - 10%) (22%- 22%) (34% - 34%) (25% - 25%) (49% - 50%)-74%0%0%0%0%46%93%(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with Long-Term Exposure toPM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily(m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-11Table E-10. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a RecentYear (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25830736657578499578487(531 - 1123) (470 - 997) (420 - 891) (369 - 785) (318 - 677) (369 - 785) (310 - 661)763702643566490546388(488 - 1033) (448 - 950) (410 - 871) (361 - 768) (312 - 665) (348 - 741) (247 - 528)543380336292247292205(347 - 734) (243 - 516) (214 - 457) (186 - 397) (157 - 336) (186 - 397) (130 - 280)486486486486431486431(310 - 659) (310 - 659) (310 - 659) (310 - 659) (275 - 586) (310 - 659) (275 - 586)1021743734643552567389(653 - 1380) (474 - 1008) (468 - 996) (410 - 874) (352 - 751) (361 - 770) (247 - 530)3251141141141147536(208 - 439) (72 - 155) (72 - 155) (72 - 155) (72 - 155) (48 - 103)(23 - 50)780713630546462546462(498 - 1058) (455 - 968) (401 - 856) (348 - 743) (294 - 629) (348 - 743) (294 - 629)29861342134213421180924502(1910 - 4042) (854 - 1827) (854 - 1827) (854 - 1827) (750 - 1607) (587 - 1258) (318 - 684)256018931893180815461412926(1636 - 3468) (1207 - 2571) (1207 - 2571) (1152 - 2455) (984 - 2101) (898 - 1920) (588 - 1261)668584584521447442299(427 - 905) (372 - 792) (372 - 792) (332 - 707) (285 - 607) (282 - 601) (190 - 408)620620620620557539338(394 - 843) (394 - 843) (394 - 843) (394 - 843) (354 - 757) (343 - 734) (214 - 460)759497497466409371244(485 - 1026) (317 - 674) (317 - 674) (297 - 633) (260 - 555) (236 - 504) (155 - 332)13237373737130(84 - 179)(24 - 51)(24 - 51)(24 - 51)(24 - 51)(8 - 18)(0 - 0)1056897813714616696492(676 - 1429) (573 - 1215) (519 - 1102) (456 - 970) (392 - 836) (443 - 944) (313 - 669)1581031031031036934(101 - 215) (66 - 141) (66 - 141) (66 - 141) (66 - 141)(44 - 94)(21 - 46)Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-12Table E-11. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a RecentYear (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25854758677595513595501(547 - 1156) (484 - 1026) (432 - 918) (380 - 808) (327 - 697) (380 - 808) (319 - 681)619565513446378428289(395 - 839) (360 - 766) (327 - 696) (284 - 606) (241 - 515) (272 - 581) (184 - 394)510354312269226269186(326 - 691) (226 - 481) (198 - 423) (171 - 365) (144 - 307) (171 - 365) (118 - 253)364364364364317364317(232 - 495) (232 - 495) (232 - 495) (232 - 495) (202 - 432) (232 - 495) (202 - 432)737510503429355366222(471 - 1000) (325 - 694) (320 - 684) (273 - 584) (225 - 483) (233 - 499) (141 - 302)3381211211211218141(217 - 457) (77 - 164) (77 - 164) (77 - 164) (77 - 164) (51 - 110)(26 - 55)755689606523439523439(481 - 1024) (439 - 935) (386 - 823) (333 - 711) (279 - 598) (333 - 711) (279 - 598)2631110811081108958721331(1680 - 3565) (704 - 1509) (704 - 1509) (704 - 1509) (608 - 1305) (457 - 983) (210 - 451)19841407140713331106990571(1265 - 2693) (895 - 1913) (895 - 1913) (848 - 1813) (703 - 1506) (629 - 1349) (362 - 779)604525525466396392257(386 - 819) (335 - 713) (335 - 713) (297 - 633) (252 - 538) (249 - 533) (163 - 350)657657657657591572361(418 - 893) (418 - 893) (418 - 893) (418 - 893) (376 - 803) (364 - 779) (229 - 492)599372372346297264155(383 - 813) (237 - 506) (237 - 506) (220 - 471) (189 - 404) (168 - 359) (98 - 211)107 20 20 20 20 0 0(68 - 146)(13 - 27)(13 - 27)(13 - 27)(13 - 27)(0 - 0)(0 - 0)792659588506423490319(506 - 1075) (420 - 894) (374 - 799) (322 - 688) (269 - 575) (312 - 666) (203 - 435)107 61 61 61 61 32 3(68 - 145)(38 - 83)(38 - 83)(38 - 83)(38 - 83)(20 - 43)(2 - 3)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-13Table E-12. Estimated Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a RecentYear (2007) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25821726647567486567474(525 - 1112) (464 - 984) (413 - 877) (361 - 769) (310 - 661) (361 - 769) (302 - 644)618564512445378427289(394 - 838) (360 - 765) (326 - 695) (283 - 605) (240 - 514) (272 - 580) (184 - 393)535374330285241285199(342 - 724) (238 - 507) (210 - 448) (182 - 388) (153 - 327) (182 - 388) (126 - 271)407407407407356407356(259 - 553) (259 - 553) (259 - 553) (259 - 553) (227 - 485) (259 - 553) (227 - 485)778544536460384396247(496 - 1054) (346 - 739) (341 - 729) (293 - 626) (244 - 522) (252 - 539) (157 - 336)3571301301301308846(228 - 482) (82 - 177) (82 - 177) (82 - 177) (82 - 177) (56 - 120)(29 - 63)788719634548461548461(502 - 1069) (459 - 977) (404 - 861) (349 - 745) (293 - 628) (349 - 745) (293 - 628)27321170117011701016773372(1746 - 3702) (744 - 1593) (744 - 1593) (744 - 1593) (645 - 1384) (490 - 1053) (236 - 508)232216891689160713591232771(1482 - 3148) (1076 - 2295) (1076 - 2295) (1023 - 2185) (864 - 1848) (783 - 1676) (489 - 1051)598519519460391386253(381 - 811) (331 - 704) (331 - 704) (293 - 625) (249 - 531) (246 - 525) (161 - 344)556556556556494477278(354 - 757) (354 - 757) (354 - 757) (354 - 757) (314 - 673) (303 - 650) (176 - 379)675434434406352318200(431 - 914) (277 - 590) (277 - 590) (258 - 552) (224 - 479) (202 - 432) (127 - 273)154 48 48 48 48 22 0(98 - 209)(31 - 66)(31 - 66)(31 - 66)(31 - 66)(14 - 30)(0 - 0)869728653566478549369(555 - 1178) (464 - 988) (416 - 887) (360 - 769) (304 - 651) (350 - 747) (235 - 503)112 64 64 64 64 35 5(71 - 152)(41 - 88)(41 - 88)(41 - 88)(41 - 88)(22 - 47)(3 - 6)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-14Table E-13. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/255.5%4.9%4.4%3.8%3.3%3.8%3.2%(3.5% - 7.4%) (3.1% - 6.6%) (2.8% - 5.9%) (2.4% - 5.2%) (2.1% - 4.5%) (2.4% - 5.2%) (2.1% - 4.4%)5.4%5%4.5%4%3.5%3.9%2.8%(3.5% - 7.3%) (3.2% - 6.7%) (2.9% - 6.2%) (2.6% - 5.4%) (2.2% - 4.7%) (2.5% - 5.2%) (1.8% - 3.7%)5.5%3.9%3.4%3%2.5%3%2.1%(3.5% - 7.5%) (2.5% - 5.3%) (2.2% - 4.7%) (1.9% - 4%) (1.6% - 3.4%) (1.9% - 4%) (1.3% - 2.8%)3.8%3.8%3.8%3.8%3.4%3.8%3.4%(2.4% - 5.1%) (2.4% - 5.1%) (2.4% - 5.1%) (2.4% - 5.1%) (2.1% - 4.6%) (2.4% - 5.1%) (2.1% - 4.6%)5.7%4.2%4.1%3.6%3.1%3.2%2.2%(3.7% - 7.7%) (2.7% - 5.6%) (2.6% - 5.6%) (2.3% - 4.9%) (2% - 4.2%) (2% - 4.3%) (1.4% - 3%)5.8%2%2%2%2%1.4%0.7%(3.7% - 7.9%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (0.9% - 1.8%) (0.4% - 0.9%)4.2%3.8%3.4%2.9%2.5%2.9%2.5%(2.7% - 5.7%) (2.4% - 5.2%) (2.2% - 4.6%) (1.9% - 4%) (1.6% - 3.4%) (1.9% - 4%) (1.6% - 3.4%)5.3%2.4%2.4%2.4%2.1%1.6%0.9%(3.4% - 7.1%) (1.5% - 3.2%) (1.5% - 3.2%) (1.5% - 3.2%) (1.3% - 2.8%) (1% - 2.2%) (0.6% - 1.2%)4.9%3.6%3.6%3.4%2.9%2.7%1.8%(3.1% - 6.6%) (2.3% - 4.9%) (2.3% - 4.9%) (2.2% - 4.7%) (1.9% - 4%) (1.7% - 3.6%) (1.1% - 2.4%)4.6%4%4%3.6%3.1%3%2.1%(2.9% - 6.2%) (2.6% - 5.4%) (2.6% - 5.4%) (2.3% - 4.9%) (2% - 4.2%) (1.9% - 4.1%) (1.3% - 2.8%)2.7%2.7%2.7%2.7%2.4%2.3%1.5%(1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.5% - 3.3%) (1.5% - 3.2%) (0.9% - 2%)5.5%3.6%3.6%3.3%2.9%2.7%1.8%(3.5% - 7.4%) (2.3% - 4.8%) (2.3% - 4.8%) (2.1% - 4.5%) (1.9% - 4%) (1.7% - 3.6%) (1.1% - 2.4%)2.8%0.8%0.8%0.8%0.8%0.3%0%(1.8% - 3.8%) (0.5% - 1.1%) (0.5% - 1.1%) (0.5% - 1.1%) (0.5% - 1.1%) (0.2% - 0.4%) (0% - 0%)5.6%4.8%4.3%3.8%3.3%3.7%2.6%(3.6% - 7.6%) (3% - 6.5%) (2.8% - 5.9%) (2.4% - 5.1%) (2.1% - 4.4%) (2.4% - 5%) (1.7% - 3.6%)3.1%2%2%2%2%1.3%0.7%(2% - 4.2%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (0.9% - 1.8%) (0.4% - 0.9%)Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-15Table E-14. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/255.5%4.9%4.4%3.8%3.3%3.8%3.2%(3.5% - 7.4%) (3.1% - 6.6%) (2.8% - 5.9%) (2.4% - 5.2%) (2.1% - 4.5%) (2.4% - 5.2%) (2.1% - 4.4%)4.4%4%3.6%3.1%2.7%3%2%(2.8% - 5.9%) (2.5% - 5.4%) (2.3% - 4.9%) (2% - 4.3%) (1.7% - 3.6%) (1.9% - 4.1%) (1.3% - 2.8%)5.1%3.6%3.1%2.7%2.3%2.7%1.9%(3.3% - 7%) (2.3% - 4.8%) (2% - 4.3%) (1.7% - 3.7%) (1.4% - 3.1%) (1.7% - 3.7%) (1.2% - 2.5%)2.8%2.8%2.8%2.8%2.4%2.8%2.4%(1.8% - 3.8%) (1.8% - 3.8%) (1.8% - 3.8%) (1.8% - 3.8%) (1.5% - 3.3%) (1.8% - 3.8%) (1.5% - 3.3%)4.1%2.9%2.8%2.4%2%2.1%1.2%(2.6% - 5.6%) (1.8% - 3.9%) (1.8% - 3.8%) (1.5% - 3.3%) (1.3% - 2.7%) (1.3% - 2.8%) (0.8% - 1.7%)6%2.1%2.1%2.1%2.1%1.4%0.7%(3.8% - 8.1%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (1.4% - 2.9%) (0.9% - 1.9%) (0.5% - 1%)3.9%3.6%3.1%2.7%2.3%2.7%2.3%(2.5% - 5.3%) (2.3% - 4.9%) (2% - 4.3%) (1.7% - 3.7%) (1.4% - 3.1%) (1.7% - 3.7%) (1.4% - 3.1%)4.6%1.9%1.9%1.9%1.7%1.3%0.6%(2.9% - 6.2%) (1.2% - 2.6%) (1.2% - 2.6%) (1.2% - 2.6%) (1.1% - 2.3%) (0.8% - 1.7%) (0.4% - 0.8%)3.7%2.6%2.6%2.5%2.1%1.9%1.1%(2.4% - 5.1%) (1.7% - 3.6%) (1.7% - 3.6%) (1.6% - 3.4%) (1.3% - 2.8%) (1.2% - 2.5%) (0.7% - 1.5%)4.2%3.6%3.6%3.2%2.7%2.7%1.8%(2.7% - 5.6%) (2.3% - 4.9%) (2.3% - 4.9%) (2% - 4.4%) (1.7% - 3.7%) (1.7% - 3.7%) (1.1% - 2.4%)2.7%2.7%2.7%2.7%2.5%2.4%1.5%(1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.7% - 3.7%) (1.6% - 3.4%) (1.5% - 3.3%) (1% - 2.1%)4.3%2.7%2.7%2.5%2.1%1.9%1.1%(2.8% - 5.9%) (1.7% - 3.7%) (1.7% - 3.7%) (1.6% - 3.4%) (1.4% - 2.9%) (1.2% - 2.6%) (0.7% - 1.5%)2.2%0.4%0.4%0.4%0.4%0%0%(1.4% - 3%) (0.3% - 0.6%) (0.3% - 0.6%) (0.3% - 0.6%) (0.3% - 0.6%) (0% - 0%) (0% - 0%)4.2%3.5%3.1%2.7%2.2%2.6%1.7%(2.7% - 5.7%) (2.2% - 4.7%) (2% - 4.2%) (1.7% - 3.6%) (1.4% - 3%) (1.6% - 3.5%) (1.1% - 2.3%)2.1%1.2%1.2%1.2%1.2%0.6%0%(1.3% - 2.8%) (0.7% - 1.6%) (0.7% - 1.6%) (0.7% - 1.6%) (0.7% - 1.6%) (0.4% - 0.8%) (0% - 0.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-16Table E-15. Estimated Percent of Total Annual Incidence of All-Cause Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/255.1%4.6%4.1%3.6%3%3.6%3%(3.3% - 7%) (2.9% - 6.2%) (2.6% - 5.5%) (2.3% - 4.8%) (1.9% - 4.1%) (2.3% - 4.8%) (1.9% - 4%)4.4%4%3.6%3.2%2.7%3%2%(2.8% - 5.9%) (2.5% - 5.4%) (2.3% - 4.9%) (2% - 4.3%) (1.7% - 3.6%) (1.9% - 4.1%) (1.3% - 2.8%)5.3%3.7%3.3%2.8%2.4%2.8%2%(3.4% - 7.2%) (2.4% - 5.1%) (2.1% - 4.5%) (1.8% - 3.9%) (1.5% - 3.3%) (1.8% - 3.9%) (1.3% - 2.7%)3%3%3%3%2.7%3%2.7%(1.9% - 4.1%) (1.9% - 4.1%) (1.9% - 4.1%) (1.9% - 4.1%) (1.7% - 3.6%) (1.9% - 4.1%) (1.7% - 3.6%)4.4%3.1%3%2.6%2.2%2.2%1.4%(2.8% - 6%) (2% - 4.2%) (1.9% - 4.1%) (1.7% - 3.5%) (1.4% - 3%) (1.4% - 3%) (0.9% - 1.9%)6.2%2.3%2.3%2.3%2.3%1.5%0.8%(4% - 8.4%) (1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1% - 2.1%) (0.5% - 1.1%)4%3.7%3.2%2.8%2.3%2.8%2.3%(2.6% - 5.4%) (2.3% - 5%) (2.1% - 4.4%) (1.8% - 3.8%) (1.5% - 3.2%) (1.8% - 3.8%) (1.5% - 3.2%)4.8%2%2%2%1.8%1.3%0.6%(3% - 6.4%) (1.3% - 2.8%) (1.3% - 2.8%) (1.3% - 2.8%) (1.1% - 2.4%) (0.9% - 1.8%) (0.4% - 0.9%)4.3%3.2%3.2%3%2.5%2.3%1.4%(2.8% - 5.9%) (2% - 4.3%) (2% - 4.3%) (1.9% - 4.1%) (1.6% - 3.4%) (1.5% - 3.1%) (0.9% - 2%)4.1%3.6%3.6%3.2%2.7%2.7%1.7%(2.6% - 5.6%) (2.3% - 4.8%) (2.3% - 4.8%) (2% - 4.3%) (1.7% - 3.7%) (1.7% - 3.6%) (1.1% - 2.4%)2.3%2.3%2.3%2.3%2%1.9%1.1%(1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1.4% - 3.1%) (1.3% - 2.7%) (1.2% - 2.6%) (0.7% - 1.5%)4.9%3.2%3.2%2.9%2.6%2.3%1.5%(3.1% - 6.6%) (2% - 4.3%) (2% - 4.3%) (1.9% - 4%) (1.6% - 3.5%) (1.5% - 3.1%) (0.9% - 2%)3%1%1%1%1%0.4%0%(1.9% - 4.1%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.6% - 1.3%) (0.3% - 0.6%) (0% - 0%)4.6%3.8%3.4%3%2.5%2.9%1.9%(2.9% - 6.2%) (2.4% - 5.2%) (2.2% - 4.7%) (1.9% - 4.1%) (1.6% - 3.4%) (1.8% - 3.9%) (1.2% - 2.7%)2.1%1.2%1.2%1.2%1.2%0.7%0.1%(1.3% - 2.9%) (0.8% - 1.7%) (0.8% - 1.7%) (0.8% - 1.7%) (0.8% - 1.7%) (0.4% - 0.9%) (0.1% - 0.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of All-Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence orcredible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-17Table E-16. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated with Long-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-13%0%11%21%32%21%34%(-13% - -13%) (0% - 0%) (11% - 11%) (21% -22%) (32% - 32%) (21% - 22%) (34% - 34%)-9%0%8%19%30%22%45%(-9% - -9%) (0% - 0%) (8% - 8%) (19% - 19%) (30% - 30%) (22% - 22%) (44% - 45%)-43%0%12%23%35%23%46%(-42% - -43%) (0% - 0%) (12% - 12%) (23% -23%) (35% - 35%) (23% - 23%) (46% - 46%)0%0%0%0%11%0%11%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-37%0%1%13%26%24%48%(-37% - -38%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (24% - 24%) (47% - 48%)-185% 0% 0% 0% 0% 34% 68%(-183% - -187%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (34% - 34%) (68% - 68%)-9%0%12%23%35%23%35%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 35%) (23% - 24%) (35% - 35%)-122%0%0%0%12%31%63%(-121% - -124%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 31%) (63% - 63%)-35%0%0%5%18%25%51%(-35% - -36%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 26%) (51% - 51%)-14%0%0%11%23%24%49%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 24%) (49% - 49%)0%0%0%0%10%13%45%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 46%)-53%0%0%6%18%25%51%(-52% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (25% - 25%) (51% - 51%)-254% 0% 0% 0% 0% 64% 100%(-253% - -256%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (64% - 64%) (100% - 100%)-18%0%9%20%31%22%45%(-18% - -18%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (22% - 23%) (45% - 45%)-53%0%0%0%0%34%67%(-53% - -53%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (67% - 67%)Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with Long-Term Exposure toPM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily(m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-18Table E-17. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated with Long-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-13%0%11%21%32%21%34%(-13% - -13%) (0% - 0%) (11% - 11%) (21% -22%) (32% - 32%) (21% - 22%) (34% - 34%)-10%0%9%21%33%24%49%(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 24%) (49% - 49%)-44%0%12%24%36%24%48%(-44% - -44%) (0% - 0%) (12% - 12%) (24% -24%) (36% - 36%) (24% - 24%) (47% - 48%)0%0%0%0%13%0%13%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 0%) (13% - 13%)-45%0%1%16%30%28%57%(-44% - -45%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 31%) (28% - 28%) (56% - 57%)-181% 0% 0% 0% 0% 33% 66%(-179% - -183%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (33% - 33%) (66% - 66%)-10%0%12%24%36%24%36%(-10% - -10%) (0% - 0%) (12% - 12%) (24% -24%) (36% - 36%) (24% - 24%) (36% - 36%)-137%0%0%0%14%35%70%(-136% - -139%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 14%) (35% - 35%) (70% - 70%)-41%0%0%5%21%30%59%(-41% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 30%) (59% - 60%)-15%0%0%11%25%25%51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 26%) (51% - 51%)0%0%0%0%10%13%45%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 45%)-61%0%0%7%20%29%58%(-61% - -62%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 29%) (58% - 58%)-437% 0% 0% 0% 0% 100% 100%(-435% - -439%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (100% - 100%) (100% - 100%)-20%0%11%23%36%26%51%(-20% - -20%) (0% - 0%) (11% - 11%) (23% -23%) (36% - 36%) (26% - 26%) (51% - 52%)-76%0%0%0%0%48%96%(-76% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (48% - 48%) (96% - 96%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with Long-Term Exposure toPM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily(m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-19Table E-18. Percent Reduction from the Current Standards: Estimated Annual Incidence of All Cause Mortality Associated with Long-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-13%0%11%22%33%22%35%(-13% - -13%) (0% - 0%) (11% - 11%) (22% -22%) (33% - 33%) (22% - 22%) (35% - 35%)-10%0%9%21%33%24%49%(-10% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 24%) (49% - 49%)-43%0%12%24%36%24%47%(-43% - -44%) (0% - 0%) (12% - 12%) (24% -24%) (35% - 36%) (24% - 24%) (47% - 47%)0%0%0%0%12%0%12%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-43%0%1%15%29%27%55%(-43% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 30%) (27% - 27%) (54% - 55%)-175% 0% 0% 0% 0% 32% 64%(-173% - -177%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (32% - 32%) (64% - 64%)-9%0%12%24%36%24%36%(-9% - -10%) (0% - 0%) (12% - 12%) (24% -24%) (36% - 36%) (24% - 24%) (36% - 36%)-134%0%0%0%13%34%68%(-132% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 34%) (68% - 68%)-37%0%0%5%20%27%54%(-37% - -38%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 20%) (27% - 27%) (54% - 55%)-15%0%0%11%25%26%51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (25% - 25%) (25% - 26%) (51% - 51%)0%0%0%0%11%14%50%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (50% - 50%)-55%0%0%7%19%27%54%(-55% - -56%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 19%) (27% - 27%) (54% - 54%)-217% 0% 0% 0% 0% 55% 100%(-216% - -218%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (55% - 55%) (100% - 100%)-19%0%10%22%34%25%49%(-19% - -20%) (0% - 0%) (10% - 10%) (22% -22%) (34% - 34%) (24% - 25%) (49% - 49%)-74%0%0%0%0%46%93%(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)Percent Reduction from the Current Standards: Annual Incidence of All Cause Mortality Associated with Long-Term Exposure toPM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily(m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-20Table E-19. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25249222199176153176149(205 - 291) (182 - 260) (163 - 234) (144 - 207) (125 - 180) (144 - 207) (122 - 176)396366337298259288207(326 - 464) (301 - 429) (276 - 395) (244 - 351) (212 - 306) (236 - 339) (169 - 245)1861331181038710373(153 - 218) (109 - 156) (96 - 139) (84 - 121) (71 - 103) (84 - 121)(59 - 86)231231231231206231206(189 - 272) (189 - 272) (189 - 272) (189 - 272) (169 - 243) (189 - 272) (169 - 243)689509504444383393272(567 - 806) (418 - 599) (413 - 592) (363 - 523) (313 - 452) (321 - 463) (222 - 322)187686868684522(154 - 219)(56 - 81)(56 - 81)(56 - 81)(56 - 81)(37 - 54)(18 - 26)370340302263223263223(304 - 435) (278 - 400) (247 - 356) (215 - 310) (182 - 264) (215 - 310) (182 - 264)2124984984984867682373(1746 - 2489) (802 - 1163) (802 - 1163) (802 - 1163) (707 - 1026) (555 - 808) (303 - 443)261419591959187416101475976(2147 - 3068) (1603 - 2307) (1603 - 2307) (1533 - 2208) (1315 - 1900) (1204 - 1742) (795 - 1156)333293293263226224153(273 - 391) (240 - 345) (240 - 345) (215 - 309) (185 - 267) (183 - 264) (125 - 181)351351351351316307194(286 - 414) (286 - 414) (286 - 414) (286 - 414) (258 - 374) (250 - 362) (158 - 230)436291291274241219146(359 - 511) (238 - 343) (238 - 343) (224 - 323) (197 - 285) (179 - 259) (119 - 172)401212121240(33 - 47)(9 - 14)(9 - 14)(9 - 14)(9 - 14)(3 - 5)(0 - 0)636544496438379427305(523 - 744) (447 - 639) (406 - 583) (359 - 516) (310 - 447) (350 - 503) (249 - 361)93616161614120(76 - 109)(50 - 72)(50 - 72)(50 - 72)(50 - 72)(33 - 48)(16 - 24)Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-21Table E-20. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2006) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25256229205181157181154(211 - 300) (188 - 268) (168 - 241) (149 - 214) (129 - 185) (149 - 214) (126 - 181)325297271237202228155(266 - 382) (244 - 350) (222 - 319) (194 - 279) (165 - 239) (186 - 268) (127 - 184)17612411095809566(144 - 206) (101 - 146) (90 - 129) (77 - 112)(65 - 95)(77 - 112)(54 - 78)175175175175153175153(143 - 207) (143 - 207) (143 - 207) (143 - 207) (125 - 181) (143 - 207) (125 - 181)506355350300249257157(415 - 595) (290 - 418) (286 - 413) (244 - 354) (203 - 294) (209 - 304) (127 - 186)194 72 72 72 72 49 25(160 - 227)(59 - 85)(59 - 85)(59 - 85)(59 - 85)(40 - 58)(20 - 29)359329291252213252213(294 - 423) (269 - 388) (238 - 343) (206 - 298) (173 - 252) (206 - 298) (173 - 252)1884815815815707534247(1546 - 2212) (664 - 965) (664 - 965) (664 - 965) (575 - 837) (434 - 633) (200 - 293)205014701470139411631043606(1678 - 2413) (1200 - 1736) (1200 - 1736) (1138 - 1648) (947 - 1375) (850 - 1235) (492 - 719)303264264236201199132(248 - 356) (216 - 311) (216 - 311) (193 - 278) (164 - 238) (163 - 235) (107 - 156)372372372372335325207(303 - 439) (303 - 439) (303 - 439) (303 - 439) (273 - 396) (265 - 384) (168 - 245)34922022020517715793(286 - 410) (180 - 260) (180 - 260) (167 - 242) (144 - 209) (128 - 186) (76 - 110)33666600(27 - 39)(5 - 7)(5 - 7)(5 - 7)(5 - 7)(0 - 0)(0 - 0)484405363314263304200(397 - 569) (331 - 477) (297 - 428) (256 - 370) (215 - 311) (248 - 359) (163 - 237)6336363636192(51 - 75)(29 - 43)(29 - 43)(29 - 43)(29 - 43)(15 - 23)(1 - 2)Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-22Table E-21. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25247220197173149173146(203 - 290) (180 - 258) (161 - 231) (142 - 204) (122 - 176) (142 - 204) (119 - 172)324297271236202227155(266 - 381) (243 - 349) (221 - 319) (193 - 279) (165 - 238) (186 - 268) (126 - 184)1841311161018510171(151 - 216) (107 - 154) (95 - 136) (82 - 119) (70 - 101) (82 - 119)(58 - 84)195195195195172195172(159 - 230) (159 - 230) (159 - 230) (159 - 230) (140 - 203) (159 - 230) (140 - 203)532377372321269277174(436 - 625) (308 - 445) (304 - 439) (262 - 379) (219 - 318) (226 - 327) (142 - 206)204777777775328(169 - 239)(63 - 92)(63 - 92)(63 - 92)(63 - 92)(43 - 63)(23 - 33)375344304264223264223(307 - 441) (281 - 405) (249 - 358) (215 - 312) (182 - 264) (215 - 312) (182 - 264)1953860860860749572278(1604 - 2293) (701 - 1018) (701 - 1018) (701 - 1018) (610 - 887) (465 - 678) (225 - 330)238417551755167314211292815(1955 - 2802) (1435 - 2070) (1435 - 2070) (1367 - 1974) (1160 - 1679) (1053 - 1527) (663 - 966)300261261233199197130(245 - 352) (214 - 308) (214 - 308) (190 - 275) (162 - 235) (160 - 232) (106 - 154)317317317317282272160(258 - 374) (258 - 374) (258 - 374) (258 - 374) (230 - 333) (222 - 322) (130 - 189)390256256239209189120(321 - 458) (209 - 302) (209 - 302) (196 - 283) (170 - 246) (154 - 223) (98 - 142)471515151570(38 - 55)(12 - 18)(12 - 18)(12 - 18)(12 - 18)(6 - 8)(0 - 0)529446402350297340231(434 - 621) (365 - 525) (329 - 474) (286 - 413) (243 - 351) (278 - 401) (188 - 273)6638383838213(54 - 78)(31 - 46)(31 - 46)(31 - 46)(31 - 46)(17 - 25)(2 - 3)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidenceor credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-23Table E-22. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2515.8%14.1%12.7%11.2%9.7%11.2%9.5%(13% - 18.6%) (11.6% - 16.6%) (10.4% - 14.9%) (9.2%- 13.2%) (8% - 11.5%) (9.2% - 13.2%) (7.8% - 11.2%)15.6%14.4%13.2%11.7%10.2%11.3%8.1%(12.8% - 18.2%) (11.8% - 16.9%) (10.9% - 15.5%) (9.6% - 13.8%) (8.3% - 12%) (9.3% - 13.3%) (6.7% - 9.6%)15.9%11.3%10.1%8.8%7.5%8.8%6.2%(13.1% - 18.6%) (9.3% - 13.4%) (8.2% - 11.9%) (7.2%- 10.4%) (6.1% - 8.8%) (7.2% - 10.4%) (5.1% - 7.4%)11.1%11.1%11.1%11.1%9.9%11.1%9.9%(9.1% - 13.1%) (9.1% - 13.1%) (9.1% - 13.1%) (9.1% - 13.1%) (8.1% - 11.7%) (9.1% - 13.1%) (8.1% - 11.7%)16.4%12.2%12%10.6%9.1%9.4%6.5%(13.5% - 19.2%) (10% - 14.3%) (9.8% - 14.1%) (8.7% - 12.5%) (7.5% - 10.8%) (7.7% - 11.1%) (5.3% - 7.7%)16.8%6.1%6.1%6.1%6.1%4.1%2%(13.8% - 19.6%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (3.3% - 4.8%) (1.6% - 2.4%)12.2%11.2%9.9%8.7%7.4%8.7%7.4%(10% - 14.4%) (9.2% - 13.2%) (8.1% - 11.7%) (7.1% - 10.2%) (6% - 8.7%) (7.1% - 10.2%) (6% - 8.7%)15.2%7%7%7%6.2%4.9%2.7%(12.5% - 17.8%) (5.7% - 8.3%) (5.7% - 8.3%) (5.7% - 8.3%) (5.1% - 7.3%) (4% - 5.8%) (2.2% - 3.2%)14.1%10.6%10.6%10.1%8.7%7.9%5.3%(11.6% - 16.5%) (8.6% - 12.4%) (8.6% - 12.4%) (8.3% - 11.9%) (7.1% - 10.2%) (6.5% - 9.4%) (4.3% - 6.2%)13.3%11.7%11.7%10.5%9.1%9%6.1%(10.9% - 15.6%) (9.6% - 13.8%) (9.6% - 13.8%) (8.6% - 12.4%) (7.4% - 10.7%) (7.3% - 10.6%) (5% - 7.3%)8%8%8%8%7.2%7%4.4%(6.5% - 9.4%) (6.5% - 9.4%) (6.5% - 9.4%) (6.5% -9.4%) (5.9% - 8.5%) (5.7% - 8.2%) (3.6% - 5.2%)15.7%10.5%10.5%9.9%8.7%7.9%5.2%(12.9% - 18.4%) (8.6% - 12.3%) (8.6% - 12.3%) (8.1% - 11.6%) (7.1% - 10.2%) (6.4% - 9.3%) (4.3% - 6.2%)8.2%2.4%2.4%2.4%2.4%0.8%0%(6.7% - 9.7%) (1.9% - 2.8%) (1.9% - 2.8%) (1.9%- 2.8%) (1.9% - 2.8%) (0.7% - 1%) (0% - 0%)16.1%13.8%12.6%11.1%9.6%10.8%7.7%(13.3% - 18.9%) (11.3% - 16.2%) (10.3% - 14.8%) (9.1% - 13.1%) (7.9% - 11.4%) (8.9% - 12.8%) (6.3% - 9.2%)9.2%6.1%6.1%6.1%6.1%4.1%2%(7.5% - 10.8%)(4.9% - 7.2%)(4.9% - 7.2%)(4.9% - 7.2%)(4.9% - 7.2%)(3.3% - 4.8%)(1.6% - 2.4%)Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-24Table E-23. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2515.8%14.1%12.7%11.2%9.7%11.2%9.5%(13% - 18.5%) (11.6% - 16.6%) (10.4% - 14.9%) (9.2%- 13.2%) (8% - 11.5%) (9.2% - 13.2%) (7.8% - 11.2%)12.7%11.7%10.6%9.3%7.9%8.9%6.1%(10.5% - 15%) (9.6% - 13.7%) (8.7% - 12.5%) (7.6%- 11%) (6.5% - 9.4%) (7.3% - 10.5%) (5% - 7.2%)14.8%10.5%9.3%8%6.8%8%5.6%(12.2% - 17.4%) (8.6% - 12.3%) (7.6% - 10.9%) (6.5% - 9.5%) (5.5% - 8%) (6.5% - 9.5%) (4.5% - 6.6%)8.2%8.2%8.2%8.2%7.2%8.2%7.2%(6.7% - 9.7%) (6.7% - 9.7%) (6.7% - 9.7%) (6.7% -9.7%) (5.9% - 8.5%) (6.7% - 9.7%) (5.9% - 8.5%)12.1%8.5%8.4%7.2%5.9%6.1%3.7%(9.9% - 14.2%) (6.9% - 10%) (6.8% - 9.9%) (5.8% - 8.5%) (4.8% - 7%) (5% - 7.3%) (3% - 4.4%)17.2%6.4%6.4%6.4%6.4%4.3%2.2%(14.1% - 20.1%) (5.2% - 7.5%) (5.2% - 7.5%) (5.2%- 7.5%) (5.2% - 7.5%) (3.5% - 5.1%) (1.8% - 2.6%)11.5%10.5%9.3%8%6.8%8%6.8%(9.4% - 13.5%) (8.6% - 12.4%) (7.6% - 10.9%) (6.6% - 9.5%) (5.5% - 8%) (6.6% - 9.5%) (5.5% - 8%)13.4%5.8%5.8%5.8%5%3.8%1.8%(11% - 15.7%) (4.7% - 6.9%) (4.7% - 6.9%) (4.7% -6.9%) (4.1% - 5.9%) (3.1% - 4.5%) (1.4% - 2.1%)10.9%7.8%7.8%7.4%6.2%5.6%3.2%(9% - 12.9%) (6.4% - 9.3%) (6.4% - 9.3%) (6.1% -8.8%) (5.1% - 7.3%) (4.5% - 6.6%) (2.6% - 3.8%)12.1%10.6%10.6%9.4%8.1%8%5.3%(9.9% - 14.3%) (8.7% - 12.5%) (8.7% - 12.5%) (7.7% -11.1%) (6.6% - 9.5%) (6.5% - 9.4%) (4.3% - 6.3%)8.1%8.1%8.1%8.1%7.3%7.1%4.5%(6.6% - 9.6%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6%- 9.6%) (6% - 8.7%) (5.8% - 8.4%) (3.7% - 5.4%)12.6%8%8%7.4%6.4%5.7%3.4%(10.4% - 14.8%) (6.5% - 9.4%) (6.5% - 9.4%) (6.1% - 8.8%) (5.2% - 7.6%) (4.6% - 6.7%) (2.7% - 4%)6.5%1.2%1.2%1.2%1.2%0%0%(5.3% - 7.7%) (1% - 1.5%) (1% - 1.5%) (1% - 1.5%) (1% - 1.5%) (0% - 0%) (0% - 0%)12.2%10.2%9.2%7.9%6.7%7.7%5.1%(10% - 14.4%) (8.4% - 12.1%) (7.5% - 10.8%) (6.5% - 9.4%) (5.4% - 7.9%) (6.3% - 9.1%) (4.1% - 6%)6.1%3.5%3.5%3.5%3.5%1.8%0.1%(5% - 7.3%)(2.9% - 4.2%)(2.9% - 4.2%)(2.9% - 4.2%)(2.9% - 4.2%)(1.5% - 2.2%)(0.1% - 0.2%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-25Table E-24. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2514.9%13.2%11.8%10.4%9%10.4%8.8%(12.2% - 17.4%) (10.9% - 15.5%) (9.7% - 13.9%) (8.5%- 12.3%) (7.4% - 10.6%) (8.5% - 12.3%) (7.2% - 10.4%)12.7%11.7%10.6%9.3%7.9%8.9%6.1%(10.5% - 15%) (9.6% - 13.7%) (8.7% - 12.5%) (7.6%- 11%) (6.5% - 9.4%) (7.3% - 10.5%) (5% - 7.2%)15.4%10.9%9.7%8.4%7.1%8.4%5.9%(12.7% - 18%) (8.9% - 12.9%) (7.9% - 11.4%) (6.9%- 9.9%) (5.8% - 8.4%) (6.9% - 9.9%) (4.8% - 7%)9%9%9%9%7.9%9%7.9%(7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (6.5% - 9.3%) (7.3% - 10.6%) (6.5% - 9.3%)12.8%9.1%9%7.7%6.5%6.7%4.2%(10.5% - 15%) (7.4% - 10.7%) (7.3% - 10.6%) (6.3%- 9.1%) (5.3% - 7.6%) (5.4% - 7.9%) (3.4% - 5%)17.7%6.7%6.7%6.7%6.7%4.6%2.4%(14.6% - 20.7%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (3.7% - 5.5%) (2% - 2.9%)11.7%10.7%9.5%8.2%7%8.2%7%(9.6% - 13.8%) (8.8% - 12.6%) (7.8% - 11.2%) (6.7% -9.7%) (5.7% - 8.3%) (6.7% - 9.7%) (5.7% - 8.3%)13.8%6.1%6.1%6.1%5.3%4%2%(11.3% - 16.2%) (4.9% - 7.2%) (4.9% - 7.2%) (4.9%- 7.2%) (4.3% - 6.3%) (3.3% - 4.8%) (1.6% - 2.3%)12.6%9.3%9.3%8.9%7.5%6.8%4.3%(10.4% - 14.8%) (7.6% - 11%) (7.6% - 11%) (7.2% - 10.5%) (6.1% - 8.9%) (5.6% - 8.1%) (3.5% - 5.1%)12%10.5%10.5%9.3%8%7.9%5.2%(9.8% - 14.1%) (8.6% - 12.3%) (8.6% - 12.3%) (7.6% - 11%) (6.5% - 9.4%) (6.4% - 9.3%) (4.2% - 6.2%)6.7%6.7%6.7%6.7%6%5.8%3.4%(5.5% - 7.9%) (5.5% - 7.9%) (5.5% - 7.9%) (5.5%- 7.9%) (4.9% - 7.1%) (4.7% - 6.8%) (2.8% - 4%)14.2%9.3%9.3%8.7%7.6%6.9%4.4%(11.7% - 16.7%) (7.6% - 11%) (7.6% - 11%) (7.1% -10.3%) (6.2% - 9%) (5.6% - 8.1%) (3.5% - 5.2%)9%2.9%2.9%2.9%2.9%1.3%0%(7.4% - 10.6%) (2.4% - 3.4%) (2.4% - 3.4%) (2.4% - 3.4%) (2.4% - 3.4%) (1.1% - 1.6%) (0% - 0%)13.3%11.2%10.1%8.8%7.5%8.6%5.8%(10.9% - 15.7%) (9.2% - 13.2%) (8.3% - 11.9%) (7.2% - 10.4%) (6.1% - 8.9%) (7% - 10.1%) (4.7% - 6.9%)6.3%3.7%3.7%3.7%3.7%2%0.3%(5.2% - 7.5%)(3% - 4.4%)(3% - 4.4%)(3% - 4.4%)(3% - 4.4%)(1.6% - 2.4%)(0.2% - 0.3%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-26Table E-25. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-12%0%10%21%31%21%33%(-12% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (31% - 31%) (20% - 21%) (32% - 33%)-8%0%8%18%29%21%43%(-8% - -8%) (0% - 0%) (8% - 8%) (18% - 19%) (29% - 29%) (21% - 22%) (43% - 44%)-40%0%11%23%34%23%45%(-39% - -41%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 34%) (22% - 23%) (45% - 45%)0%0%0%0%11%0%11%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-35%0%1%13%25%23%47%(-35% - -36%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 25%) (23% - 23%) (46% - 47%)-174% 0% 0% 0% 0% 33% 68%(-171% - -177%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (67% - 68%)-9%0%11%23%34%23%34%(-9% - -9%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (23% - 23%) (34% - 35%)-116%0%0%0%12%31%62%(-114% - -118%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 31%) (62% - 62%)-33%0%0%4%18%25%50%(-33% - -34%) (0% - 0%) (0% - 0%) (4% - 4%) (18% - 18%) (25% - 25%) (50% - 50%)-14%0%0%10%23%23%48%(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 10%) (22% - 23%) (23% - 24%) (47% - 48%)0%0%0%0%10%13%45%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 45%)-50%0%0%6%17%25%50%(-49% - -51%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 17%) (24% - 25%) (50% - 50%)-247% 0% 0% 0% 0% 64% 100%(-245% - -249%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 64%) (100% - 100%)-17%0%9%20%30%22%44%(-16% - -17%) (0% - 0%) (9% - 9%) (19% - 20%) (30% - 31%) (21% - 22%) (44% - 44%)-51%0%0%0%0%33%67%(-51% - -52%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (67% - 67%)Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-27Table E-26. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-12%0%10%21%31%21%33%(-12% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (31% - 31%) (20% - 21%) (32% - 33%)-9%0%9%20%32%24%48%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 32%) (23% - 24%) (47% - 48%)-42%0%12%23%35%23%47%(-41% - -42%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (46% - 47%)0%0%0%0%13%0%13%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (0% - 0%) (12% - 13%)-43%0%1%16%30%28%56%(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 16%) (30% - 30%) (27% - 28%) (56% - 56%)-170% 0% 0% 0% 0% 33% 66%(-167% - -173%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 33%) (66% - 66%)-9%0%12%23%35%23%35%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-131%0%0%0%13%35%70%(-129% - -133%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 35%) (70% - 70%)-39%0%0%5%21%29%59%(-39% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 29%) (59% - 59%)-14%0%0%11%24%25%50%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (24% - 25%) (50% - 50%)0%0%0%0%10%12%44%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 13%) (44% - 45%)-58%0%0%7%20%28%58%(-58% - -59%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (28% - 29%) (58% - 58%)-427% 0% 0% 0% 0% 100% 100%(-425% - -430%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%)-19%0%10%23%35%25%51%(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 23%) (35% - 35%) (25% - 25%) (50% - 51%)-74%0%0%0%0%47%96%(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 48%) (96% - 96%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-28Table E-27. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-12%0%10%21%32%21%34%(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 21%) (32% - 32%) (21% - 21%) (33% - 34%)-9%0%9%20%32%24%48%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 32%) (23% - 24%) (47% - 48%)-41%0%11%23%35%23%46%(-40% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 23%) (46% - 46%)0%0%0%0%12%0%12%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-41%0%1%15%29%27%54%(-41% - -42%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (26% - 27%) (54% - 54%)-164% 0% 0% 0% 0% 31% 64%(-161% - -167%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 32%) (64% - 64%)-9%0%12%23%35%23%35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 23%) (35% - 35%)-127%0%0%0%13%34%68%(-125% - -129%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (33% - 34%) (68% - 68%)-36%0%0%5%19%26%54%(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (26% - 27%) (53% - 54%)-15%0%0%11%24%25%50%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% - 25%) (50% - 51%)0%0%0%0%11%14%50%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (49% - 50%)-53%0%0%6%18%26%53%(-52% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 19%) (26% - 26%) (53% - 53%)-210% 0% 0% 0% 0% 55% 100%(-209% - -212%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 55%) (100% - 100%)-19%0%10%22%33%24%48%(-18% - -19%) (0% - 0%) (10% - 10%) (21% - 22%) (33% - 34%) (24% - 24%) (48% - 49%)-72%0%0%0%0%46%93%(-71% - -72%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-29Table E-28. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski etal. (2009), Using Ambient PM2.5 from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25312279251222193222189(257 - 364) (229 - 327) (206 - 295) (182 - 262) (158 - 228) (182 - 262) (154 - 223)497460423376328363263(409 - 581) (378 - 538) (348 - 497) (308 - 442) (268 - 386) (298 - 427) (214 - 310)23316814913011113093(192 - 273) (137 - 197) (122 - 176) (106 - 154) (90 - 131) (106 - 154) (75 - 110)292292292292261292261(239 - 344) (239 - 344) (239 - 344) (239 - 344) (213 - 307) (239 - 344) (213 - 307)862642635561485497346(711 - 1007) (526 - 754) (520 - 746) (459 - 660) (396 - 572) (406 - 586) (282 - 410)234878787875828(193 - 273) (71 - 103) (71 - 103) (71 - 103) (71 - 103)(47 - 69)(23 - 34)467429382333284333284(383 - 548) (351 - 505) (312 - 449) (272 - 393) (231 - 335) (272 - 393) (231 - 335)26641249124912491103869477(2192 - 3117) (1017 - 1477) (1017 - 1477) (1017 - 1477) (897 - 1306) (705 - 1030) (386 - 567)3285247524752369204018711243(2700 - 3849) (2024 - 2914) (2024 - 2914) (1936 - 2790) (1665 - 2408) (1525 - 2210) (1010 - 1474)419369369332287284195(344 - 492) (303 - 434) (303 - 434) (271 - 391) (234 - 338) (232 - 335) (159 - 231)445445445445401389247(363 - 526) (363 - 526) (363 - 526) (363 - 526) (327 - 475) (317 - 461) (200 - 293)547368368346305278185(450 - 639) (301 - 433) (301 - 433) (283 - 408) (249 - 361) (227 - 329) (151 - 220)511515151550(42 - 60)(12 - 18)(12 - 18)(12 - 18)(12 - 18)(4 - 6)(0 - 0)796684624553480539388(656 - 930) (562 - 802) (512 - 733) (453 - 650) (392 - 566) (441 - 635) (316 - 458)117787878785226(96 - 138)(63 - 92)(63 - 92)(63 - 92)(63 - 92)(42 - 62)(21 - 31)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest whole number.Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-30Table E-29. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25321287258229199229194(264 - 375) (236 - 336) (212 - 303) (187 - 269) (163 - 235) (187 - 269) (159 - 229)409375342300256288198(335 - 480) (307 - 441) (280 - 403) (245 - 353) (209 - 303) (235 - 340) (161 - 234)22115713912010212084(181 - 258) (128 - 184) (113 - 164) (98 - 142) (83 - 120) (98 - 142) (68 - 100)222222222222195222195(181 - 262) (181 - 262) (181 - 262) (181 - 262) (158 - 230) (181 - 262) (158 - 230)638449443380316327200(523 - 749) (367 - 530) (361 - 523) (310 - 450) (257 - 375) (266 - 387) (162 - 237)243929292926232(201 - 284) (75 - 108) (75 - 108) (75 - 108) (75 - 108)(50 - 74)(26 - 38)453416368320270320270(371 - 533) (340 - 490) (301 - 434) (261 - 378) (220 - 320) (261 - 378) (220 - 320)2370103810381038901682316(1945 - 2779) (843 - 1229) (843 - 1229) (843 - 1229) (731 - 1068) (553 - 809) (255 - 376)258818651865177014781328774(2118 - 3046) (1520 - 2203) (1520 - 2203) (1442 - 2092) (1202 - 1750) (1079 - 1573) (627 - 920)381334334298255253168(313 - 448) (273 - 393) (273 - 393) (244 - 352) (208 - 302) (206 - 298) (137 - 199)471471471471426413264(384 - 557) (384 - 557) (384 - 557) (384 - 557) (347 - 503) (336 - 488) (214 - 313)439279279260225200119(360 - 516) (228 - 330) (228 - 330) (212 - 308) (183 - 266) (163 - 237) (96 - 141)42888800(34 - 50)(6 - 10)(6 - 10)(6 - 10)(6 - 10)(0 - 0)(0 - 0)610512460398335386255(500 - 716) (419 - 603) (375 - 542) (324 - 470) (272 - 396) (314 - 456) (207 - 302)8046464646242(65 - 95)(37 - 55)(37 - 55)(37 - 55)(37 - 55)(20 - 29)(2 - 2)Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-31Table E-30. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25310277248219189219185(255 - 363) (227 - 324) (203 - 291) (179 - 258) (154 - 223) (179 - 258) (151 - 218)408374342299256288197(335 - 479) (307 - 440) (280 - 402) (244 - 353) (209 - 302) (235 - 339) (160 - 234)23116514612810812890(190 - 270) (135 - 194) (120 - 173) (104 - 151) (88 - 128) (104 - 151) (73 - 107)247247247247218247218(202 - 291) (202 - 291) (202 - 291) (202 - 291) (178 - 257) (202 - 291) (178 - 257)670478471407341352222(549 - 786) (390 - 563) (385 - 556) (332 - 481) (278 - 404) (286 - 416) (180 - 264)255989898986836(211 - 298) (80 - 116) (80 - 116) (80 - 116) (80 - 116)(55 - 80)(29 - 43)473434385335284335284(387 - 556) (355 - 511) (314 - 453) (273 - 395) (231 - 335) (273 - 395) (231 - 335)2456109410941094954730355(2017 - 2879) (890 - 1296) (890 - 1296) (890 - 1296) (775 - 1131) (592 - 866) (287 - 423)3003222222222120180416411040(2462 - 3525) (1814 - 2620) (1814 - 2620) (1730 - 2501) (1469 - 2132) (1336 - 1941) (843 - 1234)378330330295252249165(309 - 444) (270 - 389) (270 - 389) (241 - 347) (205 - 298) (203 - 295) (134 - 196)402402402402359347204(327 - 476) (327 - 476) (327 - 476) (327 - 476) (291 - 425) (282 - 410) (165 - 242)490324324303265240153(403 - 574) (264 - 382) (264 - 382) (248 - 358) (216 - 313) (195 - 284) (124 - 181)591919191990(48 - 70)(16 - 23)(16 - 23)(16 - 23)(16 - 23)(7 - 10)(0 - 0)665563508443377431294(546 - 780) (461 - 662) (415 - 599) (362 - 523) (307 - 446) (351 - 509) (239 - 348)8449494949274(68 - 99)(40 - 58)(40 - 58)(40 - 58)(40 - 58)(21 - 32)(3 - 4)Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Yearand PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-32Table E-31. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 2001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2519.9%17.8%16%14.2%12.3%14.2%12%(16.4% - 23.2%) (14.6% - 20.8%) (13.1% - 18.8%) (11.6%- 16.7%) (10.1% - 14.5%) (11.6% - 16.7%) (9.8% - 14.2%)19.5%18.1%16.6%14.8%12.9%14.3%10.3%(16.1% - 22.8%) (14.8% - 21.2%) (13.7% - 19.5%) (12.1%- 17.4%) (10.5% - 15.2%) (11.7% - 16.8%) (8.4% - 12.2%)19.9%14.3%12.7%11.1%9.5%11.1%7.9%(16.4% - 23.3%) (11.7% - 16.8%) (10.4% - 15%) (9.1% - 13.1%) (7.7% - 11.2%) (9.1% - 13.1%) (6.4% - 9.4%)14%14%14%14%12.5%14%12.5%(11.5% - 16.5%) (11.5% - 16.5%) (11.5% - 16.5%) (11.5%- 16.5%) (10.2% - 14.7%) (11.5% - 16.5%) (10.2% - 14.7%)20.6%15.3%15.1%13.4%11.6%11.9%8.3%(17% - 24%) (12.6% - 18%) (12.4% - 17.8%) (10.9%- 15.7%) (9.4% - 13.6%) (9.7% - 14%) (6.7% - 9.8%)21% 7.8% 7.8% 7.8% 7.8% 5.2% 2.5%(17.3% - 24.5%) (6.3% - 9.2%) (6.3% - 9.2%) (6.3%- 9.2%) (6.3% - 9.2%) (4.2% - 6.2%) (2.1% - 3%)15.4%14.2%12.6%11%9.4%11%9.4%(12.6% - 18.1%) (11.6% - 16.6%) (10.3% - 14.8%) (9% - 13%) (7.6% - 11.1%) (9% - 13%) (7.6% - 11.1%)19% 8.9% 8.9% 8.9% 7.9% 6.2% 3.4%(15.7% - 22.3%) (7.3% - 10.6%) (7.3% - 10.6%) (7.3% - 10.6%) (6.4% - 9.3%) (5% - 7.4%) (2.8% - 4.1%)17.7%13.3%13.3%12.8%11%10.1%6.7%(14.5% - 20.7%) (10.9% - 15.7%) (10.9% - 15.7%) (10.4% - 15%) (9% - 13%) (8.2% - 11.9%) (5.4% - 7.9%)16.8%14.8%14.8%13.3%11.5%11.4%7.8%(13.8% - 19.7%) (12.1% - 17.4%) (12.1% - 17.4%) (10.8% - 15.6%) (9.4% - 13.5%) (9.3% - 13.4%) (6.3% - 9.2%)10.1%10.1%10.1%10.1%9.1%8.8%5.6%(8.2% - 11.9%) (8.2% - 11.9%) (8.2% - 11.9%) (8.2% - 11.9%) (7.4% - 10.8%) (7.2% - 10.5%) (4.5% - 6.6%)19.7%13.2%13.2%12.5%11%10%6.7%(16.2% - 23%) (10.8% - 15.6%) (10.8% - 15.6%) (10.2% - 14.7%) (9% - 13%) (8.2% - 11.8%) (5.4% - 7.9%)10.4% 3% 3% 3% 3% 1.1% 0%(8.5% - 12.3%) (2.4% - 3.6%) (2.4% - 3.6%) (2.4% - 3.6%) (2.4% - 3.6%) (0.9% - 1.3%) (0% - 0%)20.2%17.4%15.8%14%12.2%13.7%9.8%(16.6% - 23.6%) (14.3% - 20.4%) (13% - 18.6%) (11.5% - 16.5%) (10% - 14.4%) (11.2% - 16.1%) (8% - 11.6%)11.6% 7.7% 7.7% 7.7% 7.7% 5.2% 2.6%(9.5% - 13.7%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% -9.1%) (6.3% - 9.1%) (4.2% - 6.1%) (2.1% - 3.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-33Table E-32. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 2001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2519.8%17.7%16%14.2%12.3%14.2%12%(16.3% - 23.2%) (14.6% - 20.8%) (13.1% - 18.7%) (11.6% - 16.6%) (10% - 14.5%) (11.6% - 16.6%) (9.8% - 14.2%)16%14.7%13.4%11.8%10.1%11.3%7.8%(13.2% - 18.8%) (12.1% - 17.3%) (11% - 15.8%) (9.6% - 13.9%) (8.2% - 11.9%) (9.2% - 13.3%) (6.3% - 9.2%)18.6%13.2%11.7%10.2%8.6%10.2%7.1%(15.3% - 21.8%) (10.8% - 15.6%) (9.6% - 13.8%) (8.3% - 12%) (7% - 10.2%) (8.3% - 12%) (5.8% - 8.4%)10.4%10.4%10.4%10.4%9.1%10.4%9.1%(8.5% - 12.3%) (8.5% - 12.3%) (8.5% - 12.3%) (8.5% - 12.3%) (7.4% - 10.8%) (8.5% - 12.3%) (7.4% - 10.8%)15.2%10.7%10.6%9.1%7.6%7.8%4.8%(12.5% - 17.9%) (8.8% - 12.7%) (8.6% - 12.5%) (7.4% - 10.7%) (6.1% - 8.9%) (6.3% - 9.2%) (3.9% - 5.7%)21.5% 8.1% 8.1% 8.1% 8.1% 5.5% 2.8%(17.7% - 25.1%) (6.6% - 9.6%) (6.6% - 9.6%) (6.6%- 9.6%) (6.6% - 9.6%) (4.4%- 6.5%) (2.3% - 3.3%)14.5%13.3%11.7%10.2%8.6%10.2%8.6%(11.8% - 17%) (10.8% - 15.6%) (9.6% - 13.8%) (8.3% - 12%) (7% - 10.2%) (8.3% - 12%) (7% - 10.2%)16.8% 7.4% 7.4% 7.4% 6.4% 4.8% 2.2%(13.8% - 19.7%) (6% - 8.7%) (6% - 8.7%) (6% - 8.7%) (5.2% - 7.6%) (3.9% - 5.8%) (1.8% - 2.7%)13.8% 9.9% 9.9% 9.4% 7.9% 7.1% 4.1%(11.3% - 16.2%) (8.1% - 11.7%) (8.1% - 11.7%) (7.7% - 11.2%) (6.4% - 9.3%) (5.8% - 8.4%) (3.3% - 4.9%)15.3%13.4%13.4%11.9%10.2%10.1%6.7%(12.5% - 18%) (10.9% - 15.8%) (10.9% - 15.8%) (9.8% - 14.1%) (8.3%- 12.1%) (8.3% - 12%) (5.5% - 8%)10.3%10.3%10.3%10.3%9.3%9%5.8%(8.4% - 12.2%) (8.4% - 12.2%) (8.4% - 12.2%) (8.4% - 12.2%) (7.6% - 11%) (7.3% - 10.7%) (4.7% - 6.8%)15.9%10.1%10.1%9.4%8.1%7.3%4.3%(13% - 18.7%) (8.2% - 11.9%) (8.2% - 11.9%) (7.7% - 11.1%) (6.6% - 9.6%) (5.9% - 8.6%) (3.5% - 5.1%)8.3%1.6%1.6%1.6%1.6%0%0%(6.7% - 9.8%) (1.3% - 1.9%) (1.3% - 1.9%) (1.3%- 1.9%) (1.3% - 1.9%) (0% - 0%) (0% - 0%)15.4%12.9%11.6%10%8.5%9.7%6.4%(12.6% - 18.1%) (10.6% - 15.2%) (9.5% - 13.7%) (8.2% - 11.9%) (6.9% - 10%) (7.9% - 11.5%) (5.2% - 7.6%)7.8%4.5%4.5%4.5%4.5%2.4%0.2%(6.3% - 9.2%) (3.6% - 5.3%) (3.6% - 5.3%) (3.6% -5.3%) (3.6% - 5.3%) (1.9% - 2.8%) (0.2% - 0.2%)Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-34Table E-33. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2518.7%16.7%14.9%13.2%11.4%13.2%11.1%(15.4% - 21.8%) (13.7% - 19.5%) (12.2% - 17.6%) (10.8% - 15.5%) (9.3% - 13.4%) (10.8% - 15.5%) (9.1% - 13.1%)16.1%14.7%13.4%11.8%10.1%11.3%7.8%(13.2% - 18.8%) (12.1% - 17.3%) (11% - 15.8%) (9.6% - 13.9%) (8.2% - 11.9%) (9.2% - 13.3%) (6.3% - 9.2%)19.3%13.8%12.2%10.7%9.1%10.7%7.5%(15.9% - 22.6%) (11.3% - 16.2%) (10% - 14.4%) (8.7% - 12.6%) (7.4% - 10.7%) (8.7% - 12.6%) (6.1% - 8.9%)11.4%11.4%11.4%11.4%10%11.4%10%(9.3% - 13.4%) (9.3% - 13.4%) (9.3% - 13.4%) (9.3% - 13.4%) (8.2% - 11.9%) (9.3% - 13.4%) (8.2% - 11.9%)16.1%11.5%11.3%9.8%8.2%8.5%5.3%(13.2% - 18.9%) (9.4% - 13.5%) (9.3% - 13.4%) (8% - 11.6%) (6.7% - 9.7%) (6.9% - 10%) (4.3% - 6.3%)22.2% 8.5% 8.5% 8.5% 8.5% 5.9% 3.1%(18.3% - 25.9%) (7% - 10.1%) (7% - 10.1%) (7% - 10.1%) (7% - 10.1%) (4.8% - 7%) (2.5% - 3.7%)14.8%13.6%12%10.5%8.9%10.5%8.9%(12.1% - 17.4%) (11.1% - 16%) (9.8% - 14.2%) (8.5% - 12.3%) (7.2% - 10.5%) (8.5% - 12.3%) (7.2% - 10.5%)17.3% 7.7% 7.7% 7.7% 6.7% 5.2% 2.5%(14.2% - 20.3%) (6.3% - 9.1%) (6.3% - 9.1%) (6.3% - 9.1%) (5.5% - 8%) (4.2% - 6.1%) (2% - 3%)15.9%11.8%11.8%11.2%9.6%8.7%5.5%(13% - 18.7%) (9.6% - 13.9%) (9.6% - 13.9%) (9.2% - 13.2%) (7.8% - 11.3%) (7.1% - 10.3%) (4.5% - 6.5%)15.1%13.2%13.2%11.8%10.1%10%6.6%(12.4% - 17.8%) (10.8% - 15.6%) (10.8% - 15.6%) (9.6% - 13.9%) (8.2% - 11.9%) (8.1% - 11.8%) (5.4% - 7.8%)8.5%8.5%8.5%8.5%7.6%7.3%4.3%(6.9% - 10.1%) (6.9% - 10.1%) (6.9% - 10.1%) (6.9%- 10.1%) (6.2% - 9%) (6% - 8.7%) (3.5% - 5.1%)17.8%11.8%11.8%11%9.6%8.7%5.6%(14.7% - 20.9%) (9.6% - 13.9%) (9.6% - 13.9%) (9% - 13%) (7.8% - 11.4%) (7.1% - 10.3%) (4.5% - 6.6%)11.4%3.7%3.7%3.7%3.7%1.7%0%(9.3% - 13.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (1.4% - 2%) (0% - 0%)16.8%14.2%12.8%11.2%9.5%10.9%7.4%(13.8% - 19.7%) (11.6% - 16.7%) (10.5% - 15.1%) (9.1%- 13.2%) (7.7% - 11.2%) (8.9% - 12.8%) (6% - 8.8%)8%4.7%4.7%4.7%4.7%2.5%0.3%(6.5% - 9.5%) (3.8% - 5.6%) (3.8% - 5.6%) (3.8%- 5.6%) (3.8% - 5.6%) (2.1% - 3%) (0.3% - 0.4%)Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-35Table E-34. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-12%0%10%20%31%20%32%(-11% - -12%) (0% - 0%) (10% - 10%) (20% -21%) (30% - 31%) (20% - 21%) (32% - 33%)-8%0%8%18%29%21%43%(-8% - -8%) (0% - 0%) (8% - 8%) (18% - 18%) (28% - 29%) (21% - 21%) (42% - 43%)-39%0%11%22%34%22%45%(-38% - -40%) (0% - 0%) (11% - 11%) (22% -23%) (33% - 34%) (22% - 23%) (44% - 45%)0%0%0%0%11%0%11%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-34%0%1%13%24%23%46%(-34% - -35%) (0% - 0%) (1% - 1%) (12% - 13%) (24% - 25%) (22% - 23%) (46% - 46%)-170% 0% 0% 0% 0% 33% 67%(-166% - -174%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (33% - 33%) (67% - 67%)-9%0%11%22%34%22%34%(-9% - -9%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 34%) (22% - 23%) (34% - 34%)-113%0%0%0%12%30%62%(-111% - -116%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (30% - 31%) (62% - 62%)-33%0%0%4%18%24%50%(-32% - -33%) (0% - 0%) (0% - 0%) (4% - 4%) (17% - 18%) (24% - 25%) (49% - 50%)-13%0%0%10%22%23%47%(-13% - -14%) (0% - 0%) (0% - 0%) (10% - 10%) (22% - 23%) (23% - 23%) (47% - 48%)0%0%0%0%10%12%45%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 13%) (44% - 45%)-49%0%0%6%17%24%50%(-48% - -50%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 17%) (24% - 25%) (49% - 50%)-244% 0% 0% 0% 0% 64% 100%(-242% - -247%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (64% - 64%) (100% - 100%)-16%0%9%19%30%21%43%(-16% - -17%) (0% - 0%) (9% - 9%) (19% - 19%) (29% - 30%) (21% - 22%) (43% - 44%)-51%0%0%0%0%33%67%(-50% - -51%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (66% - 67%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-36Table E-35. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-12%0%10%20%31%20%32%(-11% - -12%) (0% - 0%) (10% - 10%) (20% -21%) (30% - 31%) (20% - 21%) (32% - 33%)-9%0%9%20%32%23%47%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (23% - 23%) (47% - 48%)-41%0%11%23%35%23%46%(-40% - -42%) (0% - 0%) (11% - 12%) (23% -23%) (35% - 35%) (23% - 23%) (46% - 47%)0%0%0%0%12%0%12%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (0% - 0%) (12% - 13%)-42%0%1%15%30%27%56%(-41% - -43%) (0% - 0%) (1% - 1%) (15% - 16%) (29% - 30%) (27% - 28%) (55% - 56%)-165% 0% 0% 0% 0% 32% 66%(-162% - -169%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (32% - 33%) (65% - 66%)-9%0%11%23%35%23%35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 23%) (35% - 35%)-128%0%0%0%13%34%70%(-126% - -131%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 34%) (69% - 70%)-39%0%0%5%21%29%58%(-38% - -39%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 29%) (58% - 59%)-14%0%0%11%24%24%50%(-14% - -14%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 25%) (49% - 50%)0%0%0%0%10%12%44%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 12%) (44% - 44%)-57%0%0%7%20%28%57%(-56% - -58%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 20%) (28% - 28%) (57% - 58%)-423% 0% 0% 0% 0% 100% 100%(-420% - -427%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (100% - 100%) (100% - 100%)-19%0%10%22%35%25%50%(-19% - -19%) (0% - 0%) (10% - 10%) (22% -23%) (34% - 35%) (24% - 25%) (50% - 51%)-74%0%0%0%0%47%96%(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 47%) (96% - 96%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-37Table E-36. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35214/3513/3512/3513/3012/25-12%0%10%21%32%21%33%(-12% - -12%) (0% - 0%) (10% - 10%) (21% -21%) (31% - 32%) (21% - 21%) (33% - 34%)-9%0%9%20%32%23%47%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (23% - 23%) (47% - 48%)-40%0%11%23%34%23%45%(-39% - -41%) (0% - 0%) (11% - 11%) (22% -23%) (34% - 35%) (22% - 23%) (45% - 46%)0%0%0%0%12%0%12%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-40%0%1%15%28%26%53%(-40% - -41%) (0% - 0%) (1% - 1%) (15% - 15%) (28% - 29%) (26% - 27%) (53% - 54%)-159% 0% 0% 0% 0% 31% 63%(-156% - -163%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (31% - 31%) (63% - 64%)-9%0%11%23%35%23%35%(-9% - -9%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (23% - 23%) (34% - 35%)-124%0%0%0%13%33%68%(-122% - -127%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (33% - 33%) (67% - 68%)-35%0%0%5%19%26%53%(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (26% - 26%) (53% - 54%)-14%0%0%11%24%25%50%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 24%) (24% - 25%) (50% - 50%)0%0%0%0%11%14%49%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (49% - 50%)-51%0%0%6%18%26%53%(-51% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (26% - 26%) (52% - 53%)-208% 0% 0% 0% 0% 55% 100%(-205% - -210%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (55% - 55%) (100% - 100%)-18%0%10%21%33%23%48%(-18% - -18%) (0% - 0%) (10% - 10%) (21% -22%) (33% - 33%) (23% - 24%) (47% - 48%)-71%0%0%0%0%46%93%(-71% - -72%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-38Table E-37. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2005) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25512455407359310359302(391 - 630) (347 - 561) (310 - 502) (273 - 443) (236 - 383) (273 - 443) (230 - 374)507467428378327364260(387 - 624) (356 - 575) (326 - 528) (288 - 466) (249 - 405) (278 - 450) (198 - 322)365258228198168198140(279 - 450) (196 - 318) (174 - 282) (151 - 245) (128 - 208) (151 - 245) (106 - 173)321321321321285321285(244 - 396) (244 - 396) (244 - 396) (244 - 396) (217 - 352) (244 - 396) (217 - 352)748547540474408419288(572 - 920) (417 - 675) (412 - 667) (361 - 586) (310 - 505) (318 - 518) (219 - 357)240 85 85 85 85 56 27(184 - 295) (65 - 105) (65 - 105) (65 - 105) (65 - 105)(43 - 70)(21 - 34)499457404351297351297(380 - 616) (348 - 564) (307 - 499) (267 - 434) (226 - 368) (267 - 434) (226 - 368)2357106910691069941737401(1800 - 2902) (812 - 1324) (812 - 1324) (812 - 1324) (714 - 1166) (559 - 915) (304 - 499)220516371637156413391224805(1683 - 2717) (1246 - 2022) (1246 - 2022) (1190 - 1933) (1018 - 1657) (930 - 1515) (611 - 998)439384384343295292198(335 - 541) (293 - 474) (293 - 474) (261 - 424) (224 - 365) (222 - 361) (151 - 246)406406406406365354222(309 - 503) (309 - 503) (309 - 503) (309 - 503) (278 - 453) (269 - 439) (169 - 276)529349349328287261172(405 - 652) (265 - 431) (265 - 431) (249 - 405) (219 - 356) (198 - 323) (131 - 213)762222222280(58 - 94)(16 - 27)(16 - 27)(16 - 27)(16 - 27)(6 - 10)(0 - 0)758646586516445503357(580 - 933) (493 - 796) (447 - 723) (393 - 637) (339 - 550) (383 - 621) (271 - 442)110 72 72 72 72 48 24(84 - 136)(55 - 89)(55 - 89)(55 - 89)(55 - 89)(36 - 60)(18 - 29)Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-39Table E-38. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2006PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25527469419369319369311(403 - 649) (358 - 577) (320 - 517) (281 - 456) (243 - 394) (281 - 456) (237 - 385)412377342298253286194(314 - 509) (287 - 465) (261 - 423) (227 - 369) (193 - 314) (218 - 354) (147 - 241)344240211183154183127(263 - 424) (183 - 297) (161 - 261) (139 - 226) (117 - 190) (139 - 226) (96 - 157)241241241241210241210(183 - 298) (183 - 298) (183 - 298) (183 - 298) (160 - 260) (183 - 298) (160 - 260)543377372317263272165(414 - 670) (287 - 466) (283 - 460) (241 - 393) (199 - 326) (206 - 336) (125 - 204)250909090906030(191 - 307) (68 - 112) (68 - 112) (68 - 112) (68 - 112)(46 - 75)(23 - 38)483441389336283336283(368 - 597) (336 - 545) (296 - 481) (255 - 416) (215 - 350) (255 - 416) (215 - 350)2081884884884765576265(1587 - 2566) (671 - 1095) (671 - 1095) (671 - 1095) (580 - 948) (436 - 715) (200 - 329)1715122012201156961861497(1306 - 2118) (927 - 1510) (927 - 1510) (878 - 1431) (729 - 1191) (653 - 1067) (377 - 618)398346346307262259171(303 - 491) (263 - 427) (263 - 427) (234 - 380) (199 - 324) (197 - 320) (129 - 211)431431431431388376238(327 - 533) (327 - 533) (327 - 533) (327 - 533) (294 - 480) (286 - 466) (180 - 295)420262262244209186110(320 - 518) (199 - 324) (199 - 324) (185 - 302) (159 - 259) (141 - 231) (83 - 136)621212121200(47 - 77)(9 - 15)(9 - 15)(9 - 15)(9 - 15)(0 - 0)(0 - 0)572476426366307355232(436 - 705) (362 - 588) (324 - 526) (278 - 453) (233 - 380) (270 - 440) (176 - 288)7442424242222(56 - 92)(32 - 53)(32 - 53)(32 - 53)(32 - 53)(17 - 28)(1 - 2)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-40Table E-39. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2007) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25507449401352302352295(387 - 625) (343 - 554) (305 - 495) (268 - 435) (230 - 374) (268 - 435) (224 - 365)412376342298253286194(314 - 508) (286 - 464) (260 - 422) (226 - 368) (192 - 313) (217 - 353) (147 - 240)361253224194164194136(276 - 444) (193 - 313) (170 - 276) (147 - 240) (124 - 203) (147 - 240) (103 - 168)269269269269236269236(205 - 333) (205 - 333) (205 - 333) (205 - 333) (179 - 292) (205 - 333) (179 - 292)572402396340284293183(436 - 705) (305 - 497) (301 - 490) (259 - 421) (216 - 352) (223 - 363) (139 - 227)263979797976635(201 - 323) (74 - 120) (74 - 120) (74 - 120) (74 - 120)(50 - 82)(26 - 43)504461407352297352297(384 - 622) (351 - 569) (309 - 503) (268 - 436) (225 - 368) (268 - 436) (225 - 368)2160933933933811617298(1648 - 2663) (708 - 1156) (708 - 1156) (708 - 1156) (615 - 1006) (468 - 766) (226 - 370)200314621462139211791069671(1527 - 2470) (1112 - 1808) (1112 - 1808) (1059 - 1722) (895 - 1459) (812 - 1324) (508 - 832)393342342304258255168(300 - 485) (260 - 423) (260 - 423) (231 - 375) (196 - 320) (194 - 316) (127 - 208)366366366366325314183(278 - 453) (278 - 453) (278 - 453) (278 - 453) (247 - 403) (238 - 389) (139 - 227)472305305286248224141(360 - 581) (232 - 378) (232 - 378) (217 - 353) (188 - 307) (170 - 277) (107 - 175)8928282828130(68 - 110)(21 - 35)(21 - 35)(21 - 35)(21 - 35)(10 - 16)(0 - 0)626526472410347398268(478 - 772) (400 - 649) (359 - 584) (312 - 507) (264 - 430) (302 - 492) (203 - 332)7845454545243(59 - 97)(34 - 56)(34 - 56)(34 - 56)(34 - 56)(18 - 30)(2 - 4)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidenceor credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-41Table E-40. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/258.8%7.8%7%6.1%5.3%6.1%5.2%(6.7% - 10.8%) (5.9% - 9.6%) (5.3% - 8.6%) (4.7% - 7.6%) (4% - 6.6%) (4.7% - 7.6%) (3.9% - 6.4%)8.6%7.9%7.3%6.4%5.6%6.2%4.4%(6.6% - 10.6%) (6% - 9.8%) (5.5% - 9%) (4.9% - 7.9%) (4.2% - 6.9%) (4.7% - 7.6%) (3.4% - 5.5%)8.8%6.2%5.5%4.8%4%4.8%3.4%(6.7% - 10.8%) (4.7% - 7.7%) (4.2% - 6.8%) (3.6% - 5.9%) (3.1% - 5%) (3.6% - 5.9%) (2.6% - 4.2%)6.1%6.1%6.1%6.1%5.4%6.1%5.4%(4.6% - 7.5%) (4.6% - 7.5%) (4.6% - 7.5%) (4.6% - 7.5%) (4.1% - 6.7%) (4.6% - 7.5%) (4.1% - 6.7%)9.1%6.7%6.6%5.8%5%5.1%3.5%(7% - 11.2%) (5.1% - 8.2%) (5% - 8.1%) (4.4% - 7.1%) (3.8% - 6.1%) (3.9% - 6.3%) (2.7% - 4.3%)9.3%3.3%3.3%3.3%3.3%2.2%1.1%(7.1% - 11.4%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (1.7% - 2.7%) (0.8% - 1.3%)6.7%6.1%5.4%4.7%4%4.7%4%(5.1% - 8.3%) (4.7% - 7.6%) (4.1% - 6.7%) (3.6% - 5.8%) (3% - 4.9%) (3.6% - 5.8%) (3% - 4.9%)8.4%3.8%3.8%3.8%3.3%2.6%1.4%(6.4% - 10.3%) (2.9% - 4.7%) (2.9% - 4.7%) (2.9% - 4.7%) (2.5% - 4.1%) (2% - 3.3%) (1.1% - 1.8%)7.8%5.8%5.8%5.5%4.7%4.3%2.8%(5.9% - 9.6%) (4.4% - 7.1%) (4.4% - 7.1%) (4.2% - 6.8%) (3.6% - 5.8%) (3.3% - 5.3%) (2.1% - 3.5%)7.3%6.4%6.4%5.7%4.9%4.9%3.3%(5.6% - 9%) (4.9% - 7.9%) (4.9% - 7.9%) (4.4% - 7.1%) (3.7% - 6.1%) (3.7% - 6%) (2.5% - 4.1%)4.3%4.3%4.3%4.3%3.9%3.8%2.4%(3.3% - 5.3%) (3.3% - 5.3%) (3.3% - 5.3%) (3.3% - 5.3%) (2.9% - 4.8%) (2.9% - 4.7%) (1.8% - 2.9%)8.7%5.7%5.7%5.4%4.7%4.3%2.8%(6.6% - 10.7%) (4.4% - 7.1%) (4.4% - 7.1%) (4.1% - 6.6%) (3.6% - 5.8%) (3.3% - 5.3%) (2.1% - 3.5%)4.5%1.3%1.3%1.3%1.3%0.5%0%(3.4% - 5.5%) (1% - 1.6%) (1% - 1.6%) (1%- 1.6%) (1% - 1.6%) (0.3% - 0.6%) (0% - 0%)8.9%7.6%6.9%6.1%5.2%5.9%4.2%(6.8% - 11%) (5.8% - 9.4%) (5.3% - 8.5%) (4.6% - 7.5%) (4% - 6.5%) (4.5% - 7.3%) (3.2% - 5.2%)5%3.3%3.3%3.3%3.3%2.2%1.1%(3.8% - 6.2%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (1.7% - 2.7%) (0.8% - 1.3%)Percent of Total Incidence of CardiopulmonaryMortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-42Table E-41. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/258.8%7.8%7%6.1%5.3%6.1%5.2%(6.7% - 10.8%) (5.9% - 9.6%) (5.3% - 8.6%) (4.7% - 7.6%) (4% - 6.5%) (4.7% - 7.6%) (3.9% - 6.4%)7%6.4%5.8%5.1%4.3%4.9%3.3%(5.3% - 8.6%) (4.9% - 7.9%) (4.4% - 7.2%) (3.8% - 6.2%) (3.3% - 5.3%) (3.7% - 6%) (2.5% - 4.1%)8.2%5.7%5%4.3%3.7%4.3%3%(6.3% - 10.1%) (4.3% - 7.1%) (3.8% - 6.2%) (3.3% - 5.4%) (2.8% - 4.5%) (3.3% - 5.4%) (2.3% - 3.7%)4.5%4.5%4.5%4.5%3.9%4.5%3.9%(3.4% - 5.5%) (3.4% - 5.5%) (3.4% - 5.5%) (3.4% - 5.5%) (3% - 4.8%) (3.4% - 5.5%) (3% - 4.8%)6.6%4.6%4.5%3.9%3.2%3.3%2%(5% - 8.2%) (3.5% - 5.7%) (3.4% - 5.6%) (2.9% - 4.8%) (2.4% - 4%) (2.5% - 4.1%) (1.5% - 2.5%)9.5%3.4%3.4%3.4%3.4%2.3%1.2%(7.3% - 11.7%) (2.6% - 4.3%) (2.6% - 4.3%) (2.6% - 4.3%) (2.6% - 4.3%) (1.7% - 2.9%) (0.9% - 1.4%)6.3%5.7%5%4.4%3.7%4.4%3.7%(4.8% - 7.7%) (4.4% - 7.1%) (3.8% - 6.2%) (3.3% - 5.4%) (2.8% - 4.5%) (3.3% - 5.4%) (2.8% - 4.5%)7.4%3.1%3.1%3.1%2.7%2%0.9%(5.6% - 9.1%) (2.4% - 3.9%) (2.4% - 3.9%) (2.4% - 3.9%) (2.1% - 3.4%) (1.5% - 2.5%) (0.7% - 1.2%)6%4.2%4.2%4%3.3%3%1.7%(4.5% - 7.4%) (3.2% - 5.3%) (3.2% - 5.3%) (3.1% - 5%) (2.5% - 4.1%) (2.3% - 3.7%) (1.3% - 2.2%)6.6%5.8%5.8%5.1%4.4%4.3%2.9%(5.1% - 8.2%) (4.4% - 7.1%) (4.4% - 7.1%) (3.9% - 6.4%) (3.3% - 5.4%) (3.3% - 5.4%) (2.2% - 3.5%)4.4%4.4%4.4%4.4%4%3.8%2.4%(3.3% - 5.4%) (3.3% - 5.4%) (3.3% - 5.4%) (3.3% - 5.4%) (3% - 4.9%) (2.9% - 4.8%) (1.8% - 3%)6.9%4.3%4.3%4%3.5%3.1%1.8%(5.3% - 8.5%) (3.3% - 5.4%) (3.3% - 5.4%) (3.1% - 5%) (2.6% - 4.3%) (2.3% - 3.8%) (1.4% - 2.2%)3.5%0.7%0.7%0.7%0.7%0%0%(2.7% - 4.4%) (0.5% - 0.8%) (0.5% - 0.8%) (0.5% - 0.8%) (0.5% - 0.8%) (0% - 0%) (0% - 0%)6.7%5.6%5%4.3%3.6%4.2%2.7%(5.1% - 8.3%) (4.2% - 6.9%) (3.8% - 6.2%) (3.3% - 5.3%) (2.7% - 4.5%) (3.2% - 5.1%) (2.1% - 3.4%)3.3%1.9%1.9%1.9%1.9%1%0.1%(2.5% - 4.1%) (1.4% - 2.3%) (1.4% - 2.3%) (1.4% - 2.3%) (1.4% - 2.3%) (0.7% - 1.2%) (0.1% - 0.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of CardiopulmonaryMortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-43Table E-42. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/258.2%7.3%6.5%5.7%4.9%5.7%4.8%(6.3% - 10.1%) (5.5% - 9%) (4.9% - 8%) (4.3% - 7%) (3.7% - 6.1%) (4.3% - 7%) (3.6% - 5.9%)7%6.4%5.8%5.1%4.3%4.9%3.3%(5.3% - 8.6%) (4.9% - 7.9%) (4.4% - 7.2%) (3.8% - 6.3%) (3.3% - 5.3%) (3.7% - 6%) (2.5% - 4.1%)8.5%6%5.3%4.6%3.9%4.6%3.2%(6.5% - 10.5%) (4.5% - 7.4%) (4% - 6.5%) (3.5% - 5.7%) (2.9% - 4.8%) (3.5% - 5.7%) (2.4% - 4%)4.9%4.9%4.9%4.9%4.3%4.9%4.3%(3.7% - 6%) (3.7% - 6%) (3.7% - 6%) (3.7% - 6%) (3.3% - 5.3%) (3.7% - 6%) (3.3% - 5.3%)7%4.9%4.9%4.2%3.5%3.6%2.3%(5.4% - 8.7%) (3.8% - 6.1%) (3.7% - 6%) (3.2% - 5.2%) (2.6% - 4.3%) (2.7% - 4.5%) (1.7% - 2.8%)9.9%3.6%3.6%3.6%3.6%2.5%1.3%(7.6% - 12.1%) (2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (1.9% - 3.1%) (1% - 1.6%)6.4%5.9%5.2%4.5%3.8%4.5%3.8%(4.9% - 7.9%) (4.5% - 7.2%) (3.9% - 6.4%) (3.4% - 5.5%) (2.9% - 4.7%) (3.4% - 5.5%) (2.9% - 4.7%)7.6%3.3%3.3%3.3%2.8%2.2%1%(5.8% - 9.4%) (2.5% - 4.1%) (2.5% - 4.1%) (2.5% - 4.1%) (2.2% - 3.5%) (1.6% - 2.7%) (0.8% - 1.3%)6.9%5.1%5.1%4.8%4.1%3.7%2.3%(5.3% - 8.5%) (3.8% - 6.3%) (3.8% - 6.3%) (3.7% - 6%) (3.1% - 5%) (2.8% - 4.6%) (1.8% - 2.9%)6.6%5.7%5.7%5.1%4.3%4.3%2.8%(5% - 8.1%) (4.3% - 7.1%) (4.3% - 7.1%) (3.9% - 6.3%) (3.3% - 5.3%) (3.2% - 5.3%) (2.1% - 3.5%)3.6%3.6%3.6%3.6%3.2%3.1%1.8%(2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (2.8% - 4.5%) (2.4% - 4%) (2.4% - 3.9%) (1.4% - 2.3%)7.8%5.1%5.1%4.7%4.1%3.7%2.3%(6% - 9.6%) (3.8% - 6.3%) (3.8% - 6.3%) (3.6% - 5.9%) (3.1% - 5.1%) (2.8% - 4.6%) (1.8% - 2.9%)4.9%1.6%1.6%1.6%1.6%0.7%0%(3.7% - 6.1%) (1.2% - 1.9%) (1.2% - 1.9%) (1.2% - 1.9%) (1.2% - 1.9%) (0.5% - 0.9%) (0% - 0%)7.3%6.1%5.5%4.8%4.1%4.7%3.1%(5.6% - 9%) (4.7% - 7.6%) (4.2% - 6.8%) (3.6% - 5.9%) (3.1% - 5%) (3.5% - 5.8%) (2.4% - 3.9%)3.4%2%2%2%2%1.1%0.1%(2.6% - 4.2%) (1.5% - 2.5%) (1.5% - 2.5%) (1.5% - 2.5%) (1.5% - 2.5%) (0.8% - 1.3%) (0.1% - 0.2%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of CardiopulmonaryMortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-44Table E-43. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-13%0%11%21%32%21%34%(-12% - -13%) (0% - 0%) (10% - 11%) (21% -21%) (32% - 32%) (21% - 21%) (33% - 34%)-9%0%8%19%30%22%44%(-9% - -9%) (0% - 0%) (8% - 8%) (19% - 19%) (30% - 30%) (22% - 22%) (44% - 44%)-42%0%12%23%35%23%46%(-41% - -42%) (0% - 0%) (11% - 12%) (23% -23%) (35% - 35%) (23% - 23%) (46% - 46%)0%0%0%0%11%0%11%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-37%0%1%13%25%23%47%(-36% - -37%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (23% - 24%) (47% - 47%)-182% 0% 0% 0% 0% 34% 68%(-180% - -184%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (34% - 34%) (68% - 68%)-9%0%12%23%35%23%35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 23%) (35% - 35%)-120%0%0%0%12%31%62%(-119% - -122%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 31%) (62% - 63%)-35%0%0%4%18%25%51%(-34% - -35%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 25%) (51% - 51%)-14%0%0%11%23%24%48%(-14% - -14%) (0% - 0%) (0% - 0%) (11% - 11%) (23% - 23%) (24% - 24%) (48% - 49%)0%0%0%0%10%13%45%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 45%)-52%0%0%6%18%25%51%(-51% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (25% - 25%) (50% - 51%)-252% 0% 0% 0% 0% 64% 100%(-251% - -254%) (0% - 0%) (0% - 0%) (0% -0%) (0% - 0%) (64% - 64%) (100% - 100%)-17%0%9%20%31%22%45%(-17% - -18%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 31%) (22% - 22%) (45% - 45%)-53%0%0%0%0%33%67%(-52% - -53%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (67% - 67%)Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-45Table E-44. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-12%0%10%21%32%21%33%(-12% - -12%) (0% - 0%) (10% - 11%) (21% - 21%) (31% - 32%) (21% - 21%) (33% - 33%)-9%0%9%21%32%24%48%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (24% - 24%) (48% - 48%)-43%0%12%24%36%24%47%(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (47% - 47%)0%0%0%0%13%0%13%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 0%) (13% - 13%)-43%0%1%16%30%28%56%(-43% - -44%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 30%) (28% - 28%) (56% - 56%)-173%0%0%0%0%33%66%(-171% - -176%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (66% - 66%)-9%0%12%24%36%24%36%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-133%0%0%0%13%35%70%(-132% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (35% - 35%) (70% - 70%)-40%0%0%5%21%29%59%(-40% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 29%) (59% - 59%)-15%0%0%11%24%25%50%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% - 25%) (50% - 51%)0%0%0%0%10%13%45%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (44% - 45%)-59%0%0%7%20%29%58%(-59% - -60%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 29%) (58% - 58%)-431%0%0%0%0%100%100%(-428% - -433%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%)-20%0%10%23%35%25%51%(-20% - -20%) (0% - 0%) (10% - 11%) (23% - 23%) (35% - 35%) (25% - 25%) (51% - 51%)-75%0%0%0%0%48%96%(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 48%) (96% - 96%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundingthe PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-46Table E-45. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-13%0%11%22%33%22%34%(-13% - -13%) (0% - 0%) (11% - 11%) (22% - 22%) (33% - 33%) (22% - 22%) (34% - 35%)-10%0%9%21%33%24%48%(-9% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (33% - 33%) (24% - 24%) (48% - 49%)-43%0%12%23%35%23%46%(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (46% - 47%)0%0%0%0%12%0%12%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-42%0%1%15%29%27%54%(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (27% - 27%) (54% - 55%)-171%0%0%0%0%32%64%(-169% - -174%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (32% - 32%) (64% - 64%)-9%0%12%24%36%24%36%(-9% - -9%) (0% - 0%) (12% - 12%) (24% - 24%) (35% - 36%) (24% - 24%) (35% - 36%)-132%0%0%0%13%34%68%(-130% - -133%) (0% - 0%) (0% - 0%) (0%- 0%) (13% - 13%) (34% - 34%) (68% - 68%)-37%0%0%5%19%27%54%(-37% - -37%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (27% - 27%) (54% - 54%)-15%0%0%11%24%25%51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 25%) (51% - 51%)0%0%0%0%11%14%50%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (50% - 50%)-55%0%0%6%19%27%54%(-54% - -55%) (0% - 0%) (0% - 0%) (6% - 7%) (19% - 19%) (27% - 27%) (54% - 54%)-215%0%0%0%0%55%100%(-214% - -216%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (55% - 55%) (100% - 100%)-19%0%10%22%34%24%49%(-19% - -19%) (0% - 0%) (10% - 10%) (22% - 22%) (34% - 34%) (24% - 24%) (49% - 49%)-73%0%0%0%0%46%93%(-73% - -73%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-47Table E-46. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations ina Recent Year (2005) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations: Estimates Based on Krewski etal. (2009), Using Ambient PM2.5 from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25722643577509441509430(569 - 872) (506 - 778) (453 - 698) (399 - 617) (345 - 535) (399 - 617) (337 - 522)715660606536465517371(563 - 863) (518 - 797) (476 - 733) (420 - 649) (364 - 564) (405 - 627) (290 - 451)516366324282240282200(406 - 622) (287 - 443) (254 - 393) (221 - 343) (187 - 291) (221 - 343) (156 - 243)455455455455405455405(357 - 552) (357 - 552) (357 - 552) (357 - 552) (317 - 492) (357 - 552) (317 - 492)1054775766674581596412(830 - 1271) (608 - 939) (601 - 928) (528 - 817) (454 - 705) (466 - 723) (321 - 501)3381221221221228139(266 - 408) (95 - 148) (95 - 148) (95 - 148) (95 - 148)(63 - 99)(31 - 48)707649574500424500424(555 - 856) (508 - 786) (450 - 697) (391 - 607) (331 - 516) (391 - 607) (331 - 516)332815261526152613441055576(2618 - 4019) (1191 - 1856) (1191 - 1856) (1191 - 1856) (1048 - 1636) (822 - 1286) (448 - 703)3117232623262223190717451151(2450 - 3768) (1821 - 2820) (1821 - 2820) (1740 - 2697) (1491 - 2317) (1363 - 2121) (897 - 1403)621545545488420416283(488 - 752) (427 - 660) (427 - 660) (382 - 592) (328 - 510) (325 - 505) (221 - 345)579579579579521506318(453 - 704) (453 - 704) (453 - 704) (453 - 704) (407 - 634) (395 - 615) (248 - 388)747495495466409372246(588 - 902) (388 - 601) (388 - 601) (364 - 565) (320 - 497) (291 - 452) (192 - 300)109 31 31 31 31 11 0(85 - 132)(24 - 38)(24 - 38)(24 - 38)(24 - 38)(9 - 14)(0 - 0)1069913830732633714509(842 - 1290) (718 - 1104) (651 - 1005) (574 - 888) (496 - 769) (559 - 865) (397 - 618)1561031031031036934(122 - 190) (80 - 125) (80 - 125) (80 - 125) (80 - 125)(54 - 84)(26 - 42)Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-48Table E-47. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations ina Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25744662594524454524443(586 - 898) (521 - 801) (466 - 719) (411 - 635) (355 - 550) (411 - 635) (346 - 538)584534486424361407277(459 - 707) (419 - 647) (381 - 590) (332 - 515) (282 - 439) (319 - 495) (216 - 338)486341301260219260181(382 - 587) (267 - 414) (235 - 365) (203 - 316) (171 - 267) (203 - 316) (141 - 220)344344344344300344300(268 - 417) (268 - 417) (268 - 417) (268 - 417) (234 - 365) (268 - 417) (234 - 365)770537530453375388236(604 - 932) (420 - 652) (414 - 643) (354 - 551) (293 - 457) (303 - 472) (184 - 288)3521291291291298744(277 - 424) (100 - 157) (100 - 157) (100 - 157) (100 - 157) (67 - 106)(34 - 53)686627553479404479404(537 - 831) (491 - 761) (433 - 672) (374 - 582) (315 - 491) (374 - 582) (315 - 491)29451263126312631094825380(2313 - 3562) (985 - 1538) (985 - 1538) (985 - 1538) (852 - 1333) (642 - 1007) (296 - 465)243517391739164913731231713(1907 - 2951) (1358 - 2114) (1358 - 2114) (1288 - 2005) (1071 - 1671) (960 - 1499) (555 - 870)564491491437373369244(442 - 683) (385 - 596) (385 - 596) (342 - 531) (291 - 453) (288 - 449) (190 - 297)614614614614553536340(480 - 746) (480 - 746) (480 - 746) (480 - 746) (432 - 673) (419 - 653) (265 - 415)595373373348299266157(467 - 720) (292 - 454) (292 - 454) (271 - 423) (233 - 364) (208 - 324) (122 - 192)891717171700(69 - 108)(13 - 21)(13 - 21)(13 - 21)(13 - 21)(0 - 0)(0 - 0)810677606522438506332(636 - 981) (530 - 821) (474 - 735) (408 - 635) (342 - 533) (395 - 616) (259 - 405)10661616161323(83 - 129)(47 - 74)(47 - 74)(47 - 74)(47 - 74)(25 - 39)(2 - 3)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-49Table E-48. Estimated Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations ina Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25717636568500430500420(563 - 865) (500 - 770) (446 - 689) (391 - 606) (337 - 523) (391 - 606) (328 - 510)583533485423361407277(458 - 706) (418 - 646) (380 - 589) (331 - 514) (282 - 438) (318 - 494) (216 - 337)509359318276234276194(401 - 615) (282 - 436) (249 - 386) (216 - 335) (182 - 284) (216 - 335) (151 - 236)383383383383337383337(299 - 465) (299 - 465) (299 - 465) (299 - 465) (263 - 409) (299 - 465) (263 - 409)810572564485406419262(636 - 980) (447 - 694) (441 - 685) (379 - 590) (317 - 494) (327 - 509) (204 - 320)3701381381381389550(292 - 446) (108 - 168) (108 - 168) (108 - 168) (108 - 168) (74 - 115)(39 - 61)715655579501424501424(561 - 866) (513 - 794) (453 - 702) (392 - 609) (331 - 515) (392 - 609) (331 - 515)30561333133313331160884428(2401 - 3695) (1040 - 1623) (1040 - 1623) (1040 - 1623) (904 - 1413) (688 - 1079) (333 - 523)283720802080198216811526960(2227 - 3434) (1627 - 2526) (1627 - 2526) (1550 - 2408) (1313 - 2044) (1191 - 1857) (748 - 1171)558486486432368364240(437 - 675) (381 - 589) (381 - 589) (338 - 525) (288 - 447) (284 - 443) (187 - 292)522522522522464448262(407 - 635) (407 - 635) (407 - 635) (407 - 635) (362 - 565) (350 - 546) (204 - 320)667434434407354320202(524 - 806) (340 - 527) (340 - 527) (318 - 494) (276 - 430) (249 - 389) (158 - 247)127 41 41 41 41 18 0(99 - 154)(32 - 50)(32 - 50)(32 - 50)(32 - 50)(14 - 22)(0 - 0)887746671584495567383(696 - 1072) (585 - 904) (526 - 814) (456 - 709) (386 - 602) (443 - 688) (299 - 467)111 65 65 65 65 35 5(87 - 135)(50 - 79)(50 - 79)(50 - 79)(50 - 79)(27 - 43)(4 - 6)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m)2:1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-50Table E-49. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2512.4%11%9.9%8.7%7.5%8.7%7.4%(9.7% - 14.9%) (8.7% - 13.3%) (7.7% - 11.9%) (6.8% - 10.6%) (5.9% - 9.2%) (6.8% - 10.6%) (5.8% - 8.9%)12.2%11.2%10.3%9.1%7.9%8.8%6.3%(9.6% - 14.7%) (8.8% - 13.5%) (8.1% - 12.4%) (7.1% - 11%) (6.2% - 9.6%) (6.9% - 10.6%) (4.9% - 7.7%)12.4%8.8%7.8%6.8%5.8%6.8%4.8%(9.8% - 15%) (6.9% - 10.7%) (6.1% - 9.5%) (5.3% - 8.3%) (4.5% - 7%) (5.3% - 8.3%) (3.8% - 5.9%)8.6%8.6%8.6%8.6%7.7%8.6%7.7%(6.7% - 10.4%) (6.7% - 10.4%) (6.7% - 10.4%) (6.7% - 10.4%) (6% - 9.3%) (6.7% - 10.4%) (6% - 9.3%)12.8%9.4%9.3%8.2%7.1%7.3%5%(10.1% - 15.5%) (7.4% - 11.4%) (7.3% - 11.3%) (6.4% - 10%) (5.5% - 8.6%) (5.7% - 8.8%) (3.9% - 6.1%)13.1%4.7%4.7%4.7%4.7%3.1%1.5%(10.3% - 15.8%) (3.7% - 5.7%) (3.7% - 5.7%) (3.7% - 5.7%) (3.7% - 5.7%) (2.4% - 3.8%) (1.2% - 1.9%)9.5%8.7%7.7%6.7%5.7%6.7%5.7%(7.4% - 11.5%) (6.8% - 10.5%) (6% - 9.4%) (5.2% - 8.1%) (4.4% - 6.9%) (5.2% - 8.1%) (4.4% - 6.9%)11.8%5.4%5.4%5.4%4.8%3.8%2%(9.3% - 14.3%) (4.2% - 6.6%) (4.2% - 6.6%) (4.2% - 6.6%) (3.7% - 5.8%) (2.9% - 4.6%) (1.6% - 2.5%)11%8.2%8.2%7.8%6.7%6.1%4%(8.6% - 13.3%) (6.4% - 9.9%) (6.4% - 9.9%) (6.1% - 9.5%) (5.2% - 8.1%) (4.8% - 7.5%) (3.2% - 4.9%)10.4%9.1%9.1%8.1%7%6.9%4.7%(8.1% - 12.5%) (7.1% - 11%) (7.1% - 11%) (6.4% - 9.9%) (5.5% - 8.5%) (5.4% - 8.4%) (3.7% - 5.8%)6.2%6.2%6.2%6.2%5.5%5.4%3.4%(4.8% - 7.5%) (4.8% - 7.5%) (4.8% - 7.5%) (4.8% - 7.5%) (4.3% - 6.7%) (4.2% - 6.5%) (2.6% - 4.1%)12.3%8.1%8.1%7.6%6.7%6.1%4%(9.6% - 14.8%) (6.4% - 9.9%) (6.4% - 9.9%) (6% - 9.3%) (5.2% - 8.2%) (4.8% - 7.4%) (3.1% - 4.9%)6.3%1.8%1.8%1.8%1.8%0.6%0%(5% - 7.7%) (1.4% - 2.2%) (1.4% - 2.2%) (1.4% - 2.2%) (1.4% - 2.2%) (0.5% - 0.8%) (0% - 0%)12.6%10.8%9.8%8.6%7.5%8.4%6%(9.9% - 15.2%) (8.4% - 13%) (7.7% - 11.8%) (6.8% - 10.5%) (5.8% - 9%) (6.6% - 10.2%) (4.7% - 7.3%)7.1%4.7%4.7%4.7%4.7%3.1%1.5%(5.6% - 8.6%) (3.6% - 5.7%) (3.6% - 5.7%) (3.6% - 5.7%) (3.6% - 5.7%) (2.4% - 3.8%) (1.2% - 1.9%)Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-51Table E-50. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2512.4%11%9.9%8.7%7.5%8.7%7.4%(9.7% - 14.9%) (8.6% - 13.3%) (7.7% - 11.9%) (6.8% - 10.5%) (5.9% - 9.1%) (6.8% - 10.5%) (5.8% - 8.9%)9.9%9.1%8.2%7.2%6.1%6.9%4.7%(7.8% - 12%) (7.1% - 11%) (6.5% - 10%) (5.6% - 8.7%) (4.8% - 7.4%) (5.4% - 8.4%) (3.7% - 5.7%)11.6%8.1%7.2%6.2%5.2%6.2%4.3%(9.1% - 14%) (6.4% - 9.8%) (5.6% - 8.7%) (4.8% - 7.5%) (4.1% - 6.4%) (4.8% - 7.5%) (3.4% - 5.2%)6.4%6.4%6.4%6.4%5.5%6.4%5.5%(5% - 7.7%) (5% - 7.7%) (5% - 7.7%) (5% - 7.7%) (4.3% - 6.7%) (5% - 7.7%) (4.3% - 6.7%)9.4%6.5%6.5%5.5%4.6%4.7%2.9%(7.4% - 11.4%) (5.1% - 8%) (5% - 7.8%) (4.3% - 6.7%) (3.6% - 5.6%) (3.7% - 5.8%) (2.2% - 3.5%)13.4%4.9%4.9%4.9%4.9%3.3%1.7%(10.6% - 16.2%) (3.8% - 6%) (3.8% - 6%) (3.8% - 6%) (3.8% - 6%) (2.6% - 4%) (1.3% - 2%)8.9%8.1%7.2%6.2%5.2%6.2%5.2%(7% - 10.8%) (6.4% - 9.9%) (5.6% - 8.7%) (4.9% - 7.6%) (4.1% - 6.4%) (4.9% - 7.6%) (4.1% - 6.4%)10.4%4.5%4.5%4.5%3.9%2.9%1.3%(8.2% - 12.6%) (3.5% - 5.4%) (3.5% - 5.4%) (3.5% - 5.4%) (3% - 4.7%) (2.3% - 3.6%) (1% - 1.6%)8.5%6.1%6.1%5.7%4.8%4.3%2.5%(6.6% - 10.3%) (4.7% - 7.4%) (4.7% - 7.4%) (4.5% - 7%) (3.7% - 5.8%) (3.3% - 5.2%) (1.9% - 3%)9.4%8.2%8.2%7.3%6.2%6.2%4.1%(7.4% - 11.4%) (6.4% - 10%) (6.4% - 10%) (5.7% - 8.9%) (4.9% - 7.6%) (4.8% - 7.5%) (3.2% - 5%)6.3%6.3%6.3%6.3%5.7%5.5%3.5%(4.9% - 7.6%) (4.9% - 7.6%) (4.9% - 7.6%) (4.9% - 7.6%) (4.4% - 6.9%) (4.3% - 6.7%) (2.7% - 4.2%)9.8%6.2%6.2%5.7%4.9%4.4%2.6%(7.7% - 11.9%) (4.8% - 7.5%) (4.8% - 7.5%) (4.5% - 7%) (3.8% - 6%) (3.4% - 5.4%) (2% - 3.2%)5%0.9%0.9%0.9%0.9%0%0%(3.9% - 6.1%) (0.7% - 1.2%) (0.7% - 1.2%) (0.7% - 1.2%) (0.7% - 1.2%) (0% - 0%) (0% - 0%)9.5%7.9%7.1%6.1%5.1%5.9%3.9%(7.4% - 11.5%) (6.2% - 9.6%) (5.5% - 8.6%) (4.8% - 7.4%) (4% - 6.2%) (4.6% - 7.2%) (3% - 4.7%)4.7%2.7%2.7%2.7%2.7%1.4%0.1%(3.7% - 5.8%) (2.1% - 3.3%) (2.1% - 3.3%) (2.1% - 3.3%) (2.1% - 3.3%) (1.1% - 1.7%) (0.1% - 0.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence orcredible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-52Table E-51. Estimated Percent of Total Annual Incidence of Cardiopulmonary Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 2001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2511.6%10.3%9.2%8.1%7%8.1%6.8%(9.1% - 14%) (8.1% - 12.5%) (7.2% - 11.1%) (6.3% - 9.8%) (5.4% - 8.5%) (6.3% - 9.8%) (5.3% - 8.3%)9.9%9.1%8.2%7.2%6.1%6.9%4.7%(7.8% - 12%) (7.1% - 11%) (6.5% - 10%) (5.6% - 8.7%) (4.8% - 7.5%) (5.4% - 8.4%) (3.7% - 5.7%)12%8.5%7.5%6.5%5.5%6.5%4.6%(9.5% - 14.5%) (6.6% - 10.3%) (5.9% - 9.1%) (5.1% - 7.9%) (4.3% - 6.7%) (5.1% - 7.9%) (3.6% - 5.6%)7%7%7%7%6.1%7%6.1%(5.4% - 8.4%) (5.4% - 8.4%) (5.4% - 8.4%) (5.4% - 8.4%) (4.8% - 7.4%) (5.4% - 8.4%) (4.8% - 7.4%)9.9%7%6.9%6%5%5.1%3.2%(7.8% - 12%) (5.5% - 8.5%) (5.4% - 8.4%) (4.7% - 7.2%) (3.9% - 6.1%) (4% - 6.3%) (2.5% - 3.9%)13.9% 5.2% 5.2% 5.2% 5.2% 3.5% 1.9%(11% - 16.7%) (4.1% - 6.3%) (4.1% - 6.3%) (4.1% - 6.3%) (4.1% - 6.3%) (2.8% - 4.3%) (1.5% - 2.3%)9.1%8.3%7.4%6.4%5.4%6.4%5.4%(7.1% - 11%) (6.5% - 10.1%) (5.8% - 8.9%) (5% - 7.8%) (4.2% - 6.6%) (5% - 7.8%) (4.2% - 6.6%)10.7% 4.7% 4.7% 4.7% 4.1% 3.1% 1.5%(8.4% - 13%) (3.7% - 5.7%) (3.7% - 5.7%) (3.7% - 5.7%) (3.2% - 5%) (2.4% - 3.8%) (1.2% - 1.8%)9.8%7.2%7.2%6.9%5.8%5.3%3.3%(7.7% - 11.9%) (5.6% - 8.7%) (5.6% - 8.7%) (5.4% - 8.3%) (4.5% - 7.1%) (4.1% - 6.4%) (2.6% - 4.1%)9.3%8.1%8.1%7.2%6.2%6.1%4%(7.3% - 11.3%) (6.4% - 9.8%) (6.4% - 9.8%) (5.6% - 8.8%) (4.8% - 7.5%) (4.8% - 7.4%) (3.1% - 4.9%)5.2%5.2%5.2%5.2%4.6%4.4%2.6%(4% - 6.3%) (4% - 6.3%) (4% - 6.3%) (4% - 6.3%) (3.6% - 5.6%) (3.5% - 5.4%) (2% - 3.2%)11.1% 7.2% 7.2% 6.7% 5.9% 5.3% 3.4%(8.7% - 13.4%) (5.6% - 8.7%) (5.6% - 8.7%) (5.3% - 8.2%) (4.6% - 7.1%) (4.1% - 6.4%) (2.6% - 4.1%)7%2.2%2.2%2.2%2.2%1%0%(5.4% - 8.5%) (1.7% - 2.7%) (1.7% - 2.7%) (1.7% - 2.7%) (1.7% - 2.7%) (0.8% - 1.2%) (0% - 0%)10.4% 8.7% 7.9% 6.8% 5.8% 6.6% 4.5%(8.1% - 12.5%) (6.8% - 10.6%) (6.1% - 9.5%) (5.3% - 8.3%) (4.5% - 7%) (5.2% - 8.1%) (3.5% - 5.5%)4.9%2.8%2.8%2.8%2.8%1.5%0.2%(3.8% - 5.9%) (2.2% - 3.5%) (2.2% - 3.5%) (2.2% - 3.5%) (2.2% - 3.5%) (1.2% - 1.9%) (0.2% - 0.2%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of CardiopulmonaryMortality Associated with Long-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-53Table E-52. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-12%0%10%21%32%21%33%(-12% - -12%) (0% - 0%) (10% - 10%) (21% - 21%) (31% - 32%) (21% - 21%) (33% - 33%)-8%0%8%19%29%22%44%(-8% - -9%)(0% - 0%)(8% - 8%) (19% - 19%) (29% - 30%) (21% - 22%) (43% - 44%)-41%0%11%23%34%23%45%(-40% - -42%) (0% - 0%) (11% - 11%) (23% - 23%) (34% - 35%) (23% - 23%) (45% - 46%)0%0%0%0%11%0%11%(0% - 0%)(0% - 0%)(0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-36%0%1%13%25%23%47%(-35% - -37%) (0% - 0%)(1% - 1%) (13% - 13%) (25% - 25%) (23% - 23%) (47% - 47%)-178%0%0%0%0%34%68%(-175% - -181%) (0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (68% - 68%)-9%0%11%23%35%23%35%(-9% - -9%)(0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 23%) (34% - 35%)-118%0%0%0%12%31%62%(-116% - -120%) (0% - 0%)(0% - 0%) (0% - 0%) (12% - 12%) (31% - 31%) (62% - 62%)-34%0%0%4%18%25%50%(-34% - -34%) (0% - 0%)(0% - 0%) (4% - 4%) (18% - 18%) (25% - 25%) (50% - 51%)-14%0%0%10%23%24%48%(-14% - -14%) (0% - 0%)(0% - 0%) (10% - 11%) (23% - 23%) (24% - 24%) (48% - 48%)0%0%0%0%10%13%45%(0% - 0%)(0% - 0%)(0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 45%)-51%0%0%6%17%25%50%(-50% - -52%) (0% - 0%)(0% - 0%) (6% - 6%) (17% - 18%) (25% - 25%) (50% - 51%)-250%0%0%0%0%64%100%(-248% - -251%) (0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (64% - 64%) (100% - 100%)-17%0%9%20%31%22%44%(-17% - -17%) (0% - 0%)(9% - 9%) (20% - 20%) (30% - 31%) (22% - 22%) (44% - 45%)-52%0%0%0%0%33%67%(-52% - -52%) (0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (67% - 67%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-54Table E-53. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-12%0%10%21%32%21%33%(-12% - -12%) (0% - 0%) (10% - 11%) (21% - 21%) (31% - 32%) (21% - 21%) (33% - 33%)-9%0%9%21%32%24%48%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (24% - 24%) (48% - 48%)-43%0%12%24%36%24%47%(-42% - -43%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (47% - 47%)0%0%0%0%13%0%13%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 0%) (13% - 13%)-43%0%1%16%30%28%56%(-43% - -44%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 30%) (28% - 28%) (56% - 56%)-173%0%0%0%0%33%66%(-171% - -176%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (66% - 66%)-9%0%12%24%36%24%36%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-133%0%0%0%13%35%70%(-132% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (35% - 35%) (70% - 70%)-40%0%0%5%21%29%59%(-40% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 29%) (59% - 59%)-15%0%0%11%24%25%50%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% - 25%) (50% - 51%)0%0%0%0%10%13%45%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (44% - 45%)-59%0%0%7%20%29%58%(-59% - -60%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 29%) (58% - 58%)-431%0%0%0%0%100%100%(-428% - -433%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%)-20%0%10%23%35%25%51%(-20% - -20%) (0% - 0%) (10% - 11%) (23% - 23%) (35% - 35%) (25% - 25%) (51% - 51%)-75%0%0%0%0%48%96%(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 48%) (96% - 96%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-55Table E-54. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiopulmonary Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al.(2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-13%0%11%21%32%21%34%(-12% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 22%) (34% - 34%)-9%0%9%21%32%24%48%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (24% - 24%) (48% - 48%)-42%0%12%23%35%23%46%(-41% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (35% - 35%) (23% - 23%) (46% - 46%)0%0%0%0%12%0%12%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-42%0%1%15%29%27%54%(-41% - -42%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (27% - 27%) (54% - 54%)-168%0%0%0%0%32%64%(-165% - -170%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 32%) (64% - 64%)-9%0%12%23%35%23%35%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-129%0%0%0%13%34%68%(-128% - -131%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 34%) (68% - 68%)-36%0%0%5%19%27%54%(-36% - -37%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (26% - 27%) (54% - 54%)-15%0%0%11%24%25%51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 24%) (25% - 25%) (50% - 51%)0%0%0%0%11%14%50%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (50% - 50%)-54%0%0%6%19%26%53%(-53% - -54%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 19%) (26% - 27%) (53% - 54%)-213%0%0%0%0%55%100%(-211% - -215%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 55%) (100% - 100%)-19%0%10%22%34%24%49%(-19% - -19%) (0% - 0%) (10% - 10%) (22% - 22%) (33% - 34%) (24% - 24%) (48% - 49%)-72%0%0%0%0%46%93%(-72% - -73%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Cardiopulmonary Disease Mortality Associated with LongTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-56Table E-55. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2577686154465445(29 - 122)(26 - 108)(23 - 97)(20 - 86)(17 - 74)(20 - 86)(17 - 73)81746860525842(31 - 128)(28 - 118) (26 - 109)(23 - 96)(20 - 84)(22 - 93)(16 - 67)55393430253021(21 - 87)(15 - 62)(13 - 55)(11 - 48)(10 - 41)(11 - 48)(8 - 34)50505050445044(19 - 79)(19 - 79)(19 - 79)(19 - 79)(17 - 71)(19 - 79)(17 - 71)112828171616343(43 - 178)(31 - 131) (31 - 129) (27 - 114)(23 - 98)(24 - 101)(16 - 70)26999963(10 - 41)(3 - 15)(3 - 15)(3 - 15)(3 - 15)(2 - 10)(1 - 5)76706254465446(29 - 122)(26 - 112)(23 - 99)(20 - 86)(17 - 73)(20 - 86)(17 - 73)248112112112997842(94 - 393)(42 - 181) (42 - 181) (42 - 181) (37 - 160) (29 - 125)(16 - 68)20815515514812611676(79 - 331)(58 - 247) (58 - 247) (56 - 236) (48 - 203) (43 - 186) (28 - 123)70616155474632(26 - 111)(23 - 98)(23 - 98)(21 - 87)(18 - 75)(17 - 75)(12 - 51)58585858535132(22 - 94)(22 - 94)(22 - 94)(22 - 94)(20 - 85)(19 - 82)(12 - 52)80535350444026(31 - 127)(20 - 84)(20 - 84)(19 - 79)(16 - 70)(15 - 64)(10 - 42)8 222210(3 - 13)(1 - 4)(1 - 4)(1 - 4)(1 - 4)(0 - 1)(0 - 0)116999079687754(44 - 184)(37 - 157) (34 - 143) (30 - 126) (26 - 109) (29 - 123)(20 - 88)191212121284(7 - 30)(5 - 20)(5 - 20)(5 - 20)(5 - 20)(3 - 13)(1 - 6)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-57Table E-56. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2579706355485547(30 - 125)(27 - 112) (24 - 100)(21 - 88)(18 - 77)(21 - 88)(18 - 75)66605548404631(25 - 105)(23 - 96)(21 - 87)(18 - 76)(15 - 65)(17 - 73)(12 - 50)52363228232819(20 - 82)(14 - 58)(12 - 51)(10 - 44)(9 - 37)(10 - 44)(7 - 31)37373737333733(14 - 60)(14 - 60)(14 - 60)(14 - 60)(12 - 52)(14 - 60)(12 - 52)82575648394125(31 - 130)(21 - 91)(21 - 90)(18 - 77)(15 - 64)(15 - 66)(9 - 40)271010101073(10 - 43)(4 - 16)(4 - 16)(4 - 16)(4 - 16)(2 - 11)(1 - 5)74686052435243(28 - 118)(26 - 108)(22 - 96)(19 - 83)(16 - 70)(19 - 83)(16 - 70)219 93 93 93 80 61 28(83 - 348)(35 - 150) (35 - 150) (35 - 150) (30 - 130)(23 - 98)(10 - 45)162115115109918147(61 - 259)(43 - 185) (43 - 185) (41 - 176) (34 - 146) (30 - 131)(17 - 76)63555549424127(24 - 101)(21 - 88)(21 - 88)(18 - 78)(16 - 67)(15 - 66)(10 - 44)62626262565434(23 - 100)(23 - 100) (23 - 100) (23 - 100)(21 - 90)(20 - 87)(13 - 55)64404037322817(24 - 101)(15 - 64)(15 - 64)(14 - 59)(12 - 51)(11 - 45)(6 - 27)6 111100(2 - 10)(0 - 2)(0 - 2)(0 - 2)(0 - 2)(0 - 0)(0 - 0)87736556475435(33 - 139)(27 - 116) (24 - 104)(21 - 90)(18 - 75)(20 - 87)(13 - 57)13777740(5 - 20)(3 - 12)(3 - 12)(3 - 12)(3 - 12)(1 - 6)(0 - 0)Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-58Table E-57. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2576676053455344(29 - 121)(26 - 107)(23 - 96)(20 - 84)(17 - 73)(20 - 84)(17 - 71)66605547404631(25 - 105)(23 - 96)(21 - 87)(18 - 76)(15 - 65)(17 - 73)(12 - 50)54383429252920(21 - 86)(14 - 61)(13 - 54)(11 - 47)(9 - 40)(11 - 47)(8 - 33)42424242374237(16 - 67)(16 - 67)(16 - 67)(16 - 67)(14 - 59)(16 - 67)(14 - 59)86605951434428(33 - 137)(23 - 97)(22 - 95)(19 - 82)(16 - 69)(16 - 71)(10 - 44)291111111174(11 - 45)(4 - 17)(4 - 17)(4 - 17)(4 - 17)(3 - 12)(1 - 6)77716254465446(29 - 123)(27 - 113) (23 - 100)(20 - 87)(17 - 73)(20 - 87)(17 - 73)227 98 98 98 85 65 31(86 - 361)(37 - 158) (37 - 158) (37 - 158) (32 - 138) (24 - 105)(12 - 51)18913813813111110163(72 - 301)(52 - 221) (52 - 221) (49 - 211) (42 - 179) (38 - 163) (24 - 102)63545448414127(24 - 100)(21 - 87)(21 - 87)(18 - 77)(15 - 66)(15 - 65)(10 - 43)53535353474526(20 - 85)(20 - 85)(20 - 85)(20 - 85)(17 - 75)(17 - 73)(10 - 43)71464643383421(27 - 114)(17 - 74)(17 - 74)(16 - 69)(14 - 60)(13 - 55)(8 - 35)9 333310(3 - 15)(1 - 5)(1 - 5)(1 - 5)(1 - 5)(0 - 2)(0 - 0)96807263536141(36 - 152)(30 - 128) (27 - 116) (24 - 100)(20 - 85)(23 - 98)(15 - 66)13888841(5 - 21)(3 - 12)(3 - 12)(3 - 12)(3 - 12)(2 - 7)(0 - 1)Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:2Numbers rounded to the nearest whole number. Numbers in parenthesesare 95% confidence or credibleintervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annualstandard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-59Table E-58. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to AmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/258.6%7.6%6.8%6%5.2%6%5.1%(3.3% - 13.6%) (2.9% - 12.1%) (2.6% - 10.9%) (2.3% - 9.6%) (2% - 8.3%) (2.3% - 9.6%) (1.9% - 8.1%)8.4%7.8%7.1%6.3%5.5%6.1%4.3%(3.2% - 13.4%) (3% - 12.3%) (2.7% - 11.3%) (2.4% - 10%) (2.1% - 8.7%) (2.3% - 9.7%) (1.6% - 7%)8.6%6.1%5.4%4.7%4%4.7%3.3%(3.3% - 13.7%) (2.3% - 9.7%) (2% - 8.6%) (1.8% - 7.5%) (1.5% - 6.4%) (1.8% - 7.5%) (1.2% - 5.3%)5.9%5.9%5.9%5.9%5.3%5.9%5.3%(2.2% - 9.5%) (2.2% - 9.5%) (2.2% - 9.5%) (2.2%- 9.5%) (2% - 8.5%) (2.2% - 9.5%) (2% - 8.5%)8.9%6.5%6.5%5.7%4.9%5%3.4%(3.4% - 14.1%) (2.5% - 10.4%) (2.4% - 10.3%) (2.1% - 9.1%) (1.8% - 7.8%) (1.9% - 8%) (1.3% - 5.5%)9.1%3.2%3.2%3.2%3.2%2.1%1%(3.5% - 14.4%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (0.8% - 3.5%) (0.4% - 1.7%)6.6%6%5.3%4.6%3.9%4.6%3.9%(2.5% - 10.5%) (2.3% - 9.6%) (2% - 8.5%) (1.7% - 7.4%) (1.5% - 6.3%) (1.7% - 7.4%) (1.5% - 6.3%)8.2%3.7%3.7%3.7%3.3%2.6%1.4%(3.1% - 13%) (1.4% - 6%) (1.4% - 6%) (1.4% - 6%) (1.2% - 5.3%) (1% - 4.2%) (0.5% - 2.3%)7.6%5.6%5.6%5.4%4.6%4.2%2.8%(2.9% - 12.1%) (2.1% - 9%) (2.1% - 9%) (2% - 8.6%) (1.7% - 7.4%) (1.6% - 6.8%) (1% - 4.5%)7.2%6.3%6.3%5.6%4.8%4.8%3.2%(2.7% - 11.4%) (2.4% - 10%) (2.4% - 10%) (2.1% - 9%) (1.8% - 7.7%) (1.8% - 7.7%) (1.2% - 5.2%)4.2%4.2%4.2%4.2%3.8%3.7%2.3%(1.6% - 6.8%) (1.6% - 6.8%) (1.6% - 6.8%) (1.6% -6.8%) (1.4% - 6.1%) (1.4% - 5.9%) (0.9% - 3.7%)8.5%5.6%5.6%5.3%4.6%4.2%2.8%(3.2% - 13.5%) (2.1% - 9%) (2.1% - 9%) (2% - 8.4%) (1.7% - 7.4%) (1.6% - 6.7%) (1% - 4.5%)4.4%1.2%1.2%1.2%1.2%0.4%0%(1.6% - 7%) (0.5% - 2%) (0.5% - 2%) (0.5% - 2%) (0.5% - 2%) (0.2% - 0.7%) (0% - 0%)8.8%7.5%6.8%6%5.1%5.8%4.1%(3.3% - 13.9%) (2.8% - 11.9%) (2.6% - 10.8%) (2.3% - 9.5%) (1.9% - 8.2%) (2.2% - 9.3%) (1.5% - 6.6%)4.9%3.2%3.2%3.2%3.2%2.1%1.1%(1.8% - 7.8%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% -5.2%) (1.2% - 5.2%) (0.8% - 3.5%) (0.4% - 1.7%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-60Table E-59. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposureto Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Currentand Alternative Standards, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009),Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/258.6%7.6%6.8%6%5.2%6%5.1%(3.3% - 13.6%) (2.9% - 12.1%) (2.6% - 10.9%) (2.3% - 9.6%) (2% - 8.3%) (2.3% - 9.6%) (1.9% - 8.1%)6.9%6.3%5.7%5%4.2%4.8%3.2%(2.6% - 10.9%) (2.4% - 10%) (2.1% - 9.1%) (1.9% - 7.9%) (1.6% - 6.8%) (1.8% - 7.6%) (1.2% - 5.2%)8%5.6%4.9%4.3%3.6%4.3%3%(3.1% - 12.7%) (2.1% - 9%) (1.9% - 7.9%) (1.6% - 6.8%) (1.3% - 5.8%) (1.6% - 6.8%) (1.1% - 4.8%)4.4%4.4%4.4%4.4%3.8%4.4%3.8%(1.6% - 7%) (1.6% - 7%) (1.6% - 7%) (1.6% - 7%) (1.4% - 6.1%) (1.6% - 7%) (1.4% - 6.1%)6.5%4.5%4.4%3.8%3.1%3.2%2%(2.5% - 10.4%) (1.7% - 7.2%) (1.7% - 7.1%) (1.4% - 6.1%) (1.2% - 5.1%) (1.2% - 5.2%) (0.7% - 3.2%)9.3%3.4%3.4%3.4%3.4%2.3%1.1%(3.6% - 14.8%) (1.3% - 5.4%) (1.3% - 5.4%) (1.3% - 5.4%) (1.3% - 5.4%) (0.8% - 3.7%) (0.4% - 1.9%)6.1%5.6%4.9%4.3%3.6%4.3%3.6%(2.3% - 9.8%) (2.1% - 9%) (1.9% - 7.9%) (1.6% -6.9%) (1.3% - 5.8%) (1.6% - 6.9%) (1.3% - 5.8%)7.2%3.1%3.1%3.1%2.6%2%0.9%(2.7% - 11.5%) (1.1% - 4.9%) (1.1% - 4.9%) (1.1% - 4.9%) (1% - 4.3%) (0.7% - 3.2%) (0.3% - 1.5%)5.9%4.2%4.2%3.9%3.3%2.9%1.7%(2.2% - 9.4%) (1.6% - 6.7%) (1.6% - 6.7%) (1.5% -6.4%) (1.2% - 5.3%) (1.1% - 4.7%) (0.6% - 2.8%)6.5%5.7%5.7%5%4.3%4.2%2.8%(2.5% - 10.4%) (2.1% - 9.1%) (2.1% - 9.1%) (1.9% - 8.1%) (1.6% - 6.9%) (1.6% - 6.8%) (1% - 4.5%)4.3%4.3%4.3%4.3%3.9%3.8%2.4%(1.6% - 6.9%) (1.6% - 6.9%) (1.6% - 6.9%) (1.6% -6.9%) (1.5% - 6.3%) (1.4% - 6.1%) (0.9% - 3.9%)6.8%4.2%4.2%3.9%3.4%3%1.8%(2.6% - 10.8%) (1.6% - 6.8%) (1.6% - 6.8%) (1.5% - 6.3%) (1.3% - 5.5%) (1.1% - 4.9%) (0.7% - 2.9%)3.4%0.6%0.6%0.6%0.6%0%0%(1.3% - 5.6%) (0.2% - 1%) (0.2% - 1%) (0.2%- 1%) (0.2% - 1%) (0% - 0%) (0% - 0%)6.6%5.5%4.9%4.2%3.5%4.1%2.7%(2.5% - 10.5%) (2.1% - 8.8%) (1.8% - 7.8%) (1.6% - 6.8%) (1.3% - 5.7%) (1.5% - 6.6%) (1% - 4.3%)3.2%1.9%1.9%1.9%1.9%1%0.1%(1.2% - 5.2%) (0.7% - 3%) (0.7% - 3%) (0.7%- 3%) (0.7% - 3%) (0.4% - 1.6%) (0% - 0.1%)Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-61Table E-60. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to AmbientPM2.5 Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/258%7.1%6.4%5.6%4.8%5.6%4.7%(3.1% - 12.8%) (2.7% - 11.4%) (2.4% - 10.1%) (2.1% - 8.9%) (1.8% - 7.7%) (2.1% - 8.9%) (1.8% - 7.5%)6.9%6.3%5.7%5%4.2%4.8%3.2%(2.6% - 10.9%) (2.4% - 10%) (2.1% - 9.1%) (1.9% - 7.9%) (1.6% - 6.8%) (1.8% - 7.6%) (1.2% - 5.2%)8.3%5.9%5.2%4.5%3.8%4.5%3.1%(3.2% - 13.2%) (2.2% - 9.4%) (1.9% - 8.3%) (1.7% - 7.2%) (1.4% - 6.1%) (1.7% - 7.2%) (1.2% - 5.1%)4.8%4.8%4.8%4.8%4.2%4.8%4.2%(1.8% - 7.7%) (1.8% - 7.7%) (1.8% - 7.7%) (1.8% -7.7%) (1.6% - 6.8%) (1.8% - 7.7%) (1.6% - 6.8%)6.9%4.8%4.8%4.1%3.4%3.5%2.2%(2.6% - 11%) (1.8% - 7.8%) (1.8% - 7.7%) (1.5% -6.6%) (1.3% - 5.5%) (1.3% - 5.7%) (0.8% - 3.6%)9.7%3.6%3.6%3.6%3.6%2.4%1.3%(3.7% - 15.3%) (1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (0.9% - 3.9%) (0.5% - 2.1%)6.3%5.7%5.1%4.4%3.7%4.4%3.7%(2.4% - 10%) (2.2% - 9.2%) (1.9% - 8.1%) (1.6%- 7%) (1.4% - 6%) (1.6% - 7%) (1.4% - 6%)7.4%3.2%3.2%3.2%2.8%2.1%1%(2.8% - 11.8%) (1.2% - 5.2%) (1.2% - 5.2%) (1.2% - 5.2%) (1% - 4.5%) (0.8% - 3.4%) (0.4% - 1.7%)6.8%5%5%4.7%4%3.6%2.3%(2.6% - 10.8%) (1.9% - 7.9%) (1.9% - 7.9%) (1.8% - 7.6%) (1.5% - 6.4%) (1.4% - 5.8%) (0.8% - 3.7%)6.4%5.6%5.6%5%4.2%4.2%2.7%(2.4% - 10.3%) (2.1% - 9%) (2.1% - 9%) (1.9% - 8%) (1.6% - 6.8%) (1.6% - 6.7%) (1% - 4.4%)3.6%3.6%3.6%3.6%3.2%3.1%1.8%(1.3% - 5.7%) (1.3% - 5.7%) (1.3% - 5.7%) (1.3% -5.7%) (1.2% - 5.1%) (1.1% - 4.9%) (0.7% - 2.9%)7.7%5%5%4.6%4%3.6%2.3%(2.9% - 12.2%) (1.9% - 8%) (1.9% - 8%) (1.7% - 7.4%) (1.5% - 6.5%) (1.4% - 5.9%) (0.9% - 3.7%)4.8%1.5%1.5%1.5%1.5%0.7%0%(1.8% - 7.7%) (0.6% - 2.5%) (0.6% - 2.5%) (0.6%- 2.5%) (0.6% - 2.5%) (0.3% - 1.1%) (0% - 0%)7.2%6%5.4%4.7%4%4.6%3.1%(2.7% - 11.4%) (2.3% - 9.6%) (2% - 8.7%) (1.8% - 7.5%) (1.5% - 6.4%) (1.7% - 7.3%) (1.2% - 5%)3.3%1.9%1.9%1.9%1.9%1%0.1%(1.3% - 5.4%) (0.7% - 3.1%) (0.7% - 3.1%) (0.7% -3.1%) (0.7% - 3.1%) (0.4% - 1.7%) (0.1% - 0.2%)Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-62Table E-61. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: EstimatesBased onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/353143513/3512/3513/3012/25-13%0%11%21%32%21%34%(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 22%) (31% - 32%) (21% - 22%) (33% - 34%)-9%0%8%19%30%22%44%(-8% - -9%) (0% - 0%) (8% - 9%) (19% - 19%) (29% - 30%) (21% - 22%) (44% - 45%)-42%0%12%23%35%23%46%(-41% - -43%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 23%) (45% - 46%)0%0%0%0%11%0%11%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-37%0%1%13%25%23%47%(-36% - -38%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (23% - 24%) (47% - 48%)-182% 0% 0% 0% 0% 34% 68%(-177% - -188%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (68% - 68%)-9%0%12%23%35%23%35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 24%) (35% - 35%) (23% - 24%) (35% - 35%)-121%0%0%0%12%31%62%(-117% - -124%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 31%) (62% - 63%)-35%0%0%4%18%25%51%(-34% - -36%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 26%) (50% - 51%)-14%0%0%11%23%24%48%(-14% - -15%) (0% - 0%) (0% - 0%) (10% - 11%) (23% - 24%) (24% - 24%) (48% - 49%)0%0%0%0%10%13%45%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 46%)-52%0%0%6%18%25%51%(-50% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (25% - 25%) (50% - 51%)-252% 0% 0% 0% 0% 64% 100%(-249% - -256%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 64%) (100% - 100%)-17%0%9%20%31%22%45%(-17% - -18%) (0% - 0%) (9% - 9%) (20% - 21%) (31% - 32%) (22% - 23%) (44% - 45%)-53%0%0%0%0%33%67%(-52% - -54%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (67% - 67%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-63Table E-62. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/353143513/3512/3513/3012/25-13%0%11%21%32%21%34%(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 22%) (31% - 32%) (21% - 22%) (33% - 34%)-10%0%9%21%33%24%48%(-9% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (32% - 33%) (24% - 24%) (48% - 49%)-43%0%12%24%36%24%47%(-42% - -45%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 24%) (47% - 48%)0%0%0%0%13%0%13%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (0% - 0%) (13% - 13%)-44%0%1%16%30%28%56%(-43% - -45%) (0% - 0%) (1% - 1%) (16% - 16%) (30% - 31%) (28% - 28%) (56% - 57%)-177% 0% 0% 0% 0% 33% 66%(-172% - -183%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (66% - 66%)-9%0%12%24%36%24%36%(-9% - -10%) (0% - 0%) (12% - 12%) (24% - 24%) (36% - 36%) (24% - 24%) (36% - 36%)-136%0%0%0%13%35%70%(-132% - -139%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 14%) (35% - 35%) (70% - 70%)-41%0%0%5%21%29%59%(-40% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 30%) (59% - 60%)-15%0%0%11%24%25%51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 26%) (50% - 51%)0%0%0%0%10%13%45%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (44% - 45%)-60%0%0%7%20%29%58%(-59% - -62%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (29% - 29%) (58% - 59%)-434% 0% 0% 0% 0% 100% 100%(-430% - -439%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%)-20%0%11%23%36%25%51%(-20% - -21%) (0% - 0%) (10% - 11%) (23% - 23%) (35% - 36%) (25% - 26%) (51% - 52%)-75%0%0%0%0%48%96%(-75% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (48% - 48%) (96% - 96%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-64Table E-63. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/353143513/3512/3513/3012/25-13%0%11%22%33%22%34%(-13% - -13%) (0% - 0%) (11% - 11%) (21% - 22%) (32% - 33%) (21% - 22%) (34% - 35%)-10%0%9%21%33%24%48%(-9% - -10%) (0% - 0%) (9% - 9%) (21% - 21%) (32% - 33%) (24% - 24%) (48% - 49%)-43%0%12%23%35%23%46%(-41% - -44%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (46% - 47%)0%0%0%0%12%0%12%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-42%0%1%15%29%27%54%(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 30%) (27% - 27%) (54% - 55%)-172% 0% 0% 0% 0% 32% 64%(-166% - -177%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 32%) (64% - 64%)-9%0%12%24%36%24%36%(-9% - -10%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-132%0%0%0%13%34%68%(-128% - -135%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (34% - 34%) (68% - 68%)-37%0%0%5%19%27%54%(-36% - -38%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 20%) (27% - 27%) (54% - 55%)-15%0%0%11%24%25%51%(-15% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 26%) (51% - 51%)0%0%0%0%11%14%50%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (50% - 50%)-55%0%0%6%19%27%54%(-53% - -56%) (0% - 0%) (0% - 0%) (6% - 7%) (19% - 19%) (26% - 27%) (53% - 54%)-215% 0% 0% 0% 0% 55% 100%(-212% - -219%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 55%) (100% - 100%)-19%0%10%22%34%24%49%(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 22%) (34% - 34%) (24% - 25%) (49% - 49%)-73%0%0%0%0%46%93%(-72% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-65Table E-64. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in aRecent Year (2005) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2005PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25110 98 88 78 67 78 66(49 - 167)(44 - 149) (39 - 134) (34 - 119) (30 - 103) (34 - 119) (29 - 101)1161079987768460(52 - 176)(48 - 163) (44 - 150) (38 - 133) (33 - 116) (37 - 129)(26 - 93)79565043374331(35 - 120)(25 - 86)(22 - 77)(19 - 67)(16 - 57)(19 - 67)(13 - 48)72727272647264(32 - 110)(32 - 110) (32 - 110) (32 - 110)(28 - 98)(32 - 110)(28 - 98)161119117103899163(72 - 244)(52 - 181) (52 - 179) (45 - 158) (39 - 137) (40 - 140)(27 - 98)381414141494(17 - 57)(6 - 21)(6 - 21)(6 - 21)(6 - 21)(4 - 14)(2 - 7)1111019078667866(49 - 169)(45 - 155) (39 - 138) (34 - 120) (29 - 102) (34 - 120) (29 - 102)35716416416414411362(159 - 541) (71 - 253) (71 - 253) (71 - 253) (63 - 223) (49 - 176)(27 - 96)300224224214184168111(133 - 457) (99 - 343) (99 - 343) (94 - 329) (80 - 283) (73 - 259) (48 - 172)101 88 88 79 68 67 46(45 - 154)(39 - 135) (39 - 135) (35 - 121) (30 - 105) (30 - 104)(20 - 71)85858585767447(37 - 131)(37 - 131) (37 - 131) (37 - 131) (33 - 118) (32 - 115)(20 - 73)115 76 76 72 63 57 38(51 - 175)(34 - 117) (34 - 117) (32 - 110)(28 - 97)(25 - 89)(17 - 59)11333310(5 - 18)(1 - 5)(1 - 5)(1 - 5)(1 - 5)(1 - 2)(0 - 0)1671421291149911179(74 - 252)(63 - 217) (57 - 197) (50 - 175) (43 - 152) (49 - 170) (35 - 122)2718181818126(12 - 41)(8 - 27)(8 - 27)(8 - 27)(8 - 27)(5 - 18)(3 - 9)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-66Table E-65. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in aRecent Year (2006) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations: Estimates Based on Krewskiet al. (2009), Using Ambient PM2.5 from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25114 101 91 80 69 80 68(51 - 172)(45 - 154) (40 - 138) (35 - 123) (30 - 106) (35 - 123) (30 - 104)95877969596645(42 - 145)(38 - 133) (35 - 121) (30 - 106)(26 - 91)(29 - 102)(20 - 70)75524640344028(33 - 113)(23 - 80)(20 - 71)(18 - 62)(15 - 52)(18 - 62)(12 - 43)54545454475447(24 - 84)(24 - 84)(24 - 84)(24 - 84)(21 - 73)(24 - 84)(21 - 73)118828169585936(52 - 180)(36 - 127) (35 - 125) (30 - 107)(25 - 89)(26 - 92)(16 - 56)3914141414105(18 - 59)(6 - 22)(6 - 22)(6 - 22)(6 - 22)(4 - 15)(2 - 8)107988775637563(47 - 164)(43 - 150) (38 - 133) (33 - 116)(28 - 98)(33 - 116)(28 - 98)3161351351351178941(140 - 481) (59 - 210) (59 - 210) (59 - 210) (51 - 182) (38 - 138)(18 - 64)23416716715913211969(103 - 359) (73 - 258) (73 - 258) (69 - 245) (58 - 205) (52 - 184) (30 - 107)91808071616040(40 - 140)(35 - 122) (35 - 122) (31 - 109)(26 - 93)(26 - 92)(17 - 61)90909090817950(39 - 139)(39 - 139) (39 - 139) (39 - 139) (35 - 125) (34 - 122)(22 - 78)92585854464124(41 - 140)(25 - 89)(25 - 89)(23 - 83)(20 - 71)(18 - 64)(10 - 38)9 222200(4 - 14)(1 - 3)(1 - 3)(1 - 3)(1 - 3)(0 - 0)(0 - 0)126 105 94 81 68 79 52(56 - 193)(46 - 162) (41 - 145) (36 - 126) (30 - 106) (34 - 122)(22 - 80)181010101050(8 - 28)(5 - 16)(5 - 16)(5 - 16)(5 - 16)(2 - 9)(0 - 1)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Baltimore, MDDetroit, MIHouston, TXSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UT3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Based on follow-up through 2000, using models with 44 individualand 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAIncidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:E-67Table E-66. Estimated Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in aRecent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5 from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25109978776667664(49 - 166)(43 - 148) (38 - 133) (34 - 117) (29 - 101) (34 - 117)(28 - 99)95877969596645(42 - 145)(38 - 133) (35 - 121) (30 - 106)(26 - 91)(29 - 102)(20 - 70)78554942364230(35 - 119)(24 - 85)(21 - 75)(19 - 65)(16 - 56)(19 - 65)(13 - 46)60606060536053(27 - 93)(27 - 93)(27 - 93)(27 - 93)(23 - 82)(27 - 93)(23 - 82)124888674626440(55 - 189)(38 - 135) (38 - 133) (32 - 115)(27 - 96)(28 - 99)(17 - 63)4115151515116(18 - 62)(7 - 24)(7 - 24)(7 - 24)(7 - 24)(5 - 16)(2 - 9)112 102 90 78 66 78 66(49 - 171)(45 - 157) (40 - 139) (34 - 121) (29 - 102) (34 - 121) (29 - 102)3281431431431249546(145 - 499) (62 - 222) (62 - 222) (62 - 222) (54 - 193) (41 - 148)(20 - 72)27320020019116214792(121 - 417) (88 - 308) (88 - 308) (84 - 294) (71 - 250) (64 - 227) (40 - 144)91797970605939(40 - 138)(35 - 121) (35 - 121) (31 - 108)(26 - 92)(26 - 91)(17 - 60)77777777686638(33 - 118)(33 - 118) (33 - 118) (33 - 118) (30 - 106) (29 - 102)(17 - 60)103676763554931(46 - 157)(29 - 103) (29 - 103)(28 - 97)(24 - 84)(22 - 76)(14 - 49)13444420(6 - 21)(2 - 7)(2 - 7)(2 - 7)(2 - 7)(1 - 3)(0 - 0)13811610591778860(61 - 210)(51 - 178) (46 - 161) (40 - 140) (34 - 119) (39 - 136)(26 - 93)191111111161(8 - 30)(5 - 17)(5 - 17)(5 - 17)(5 - 17)(3 - 9)(0 - 1)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Baltimore, MDDetroit, MIHouston, TXSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UT3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAIncidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:E-68Table E-67. Estimated Percent of Total Annual Incidence of LungCancer Mortality Associated withLong-Term Exposure to AmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2512.4%11%9.9%8.7%7.5%8.7%7.4%(5.5% - 18.7%) (4.9% - 16.7%) (4.4% - 15.1%) (3.8% -13.3%) (3.3% - 11.6%) (3.8%- 13.3%) (3.2% - 11.3%)12.2%11.2%10.3%9.1%7.9%8.8%6.3%(5.4% - 18.4%) (5% - 17%) (4.6% - 15.7%) (4% - 13.9%) (3.5% - 12.1%) (3.9% - 13.4%) (2.8% - 9.7%)12.4%8.8%7.8%6.8%5.8%6.8%4.8%(5.5% - 18.8%) (3.9% - 13.5%) (3.4% - 12%) (3% - 10.5%) (2.5% - 8.9%) (3% - 10.5%) (2.1% - 7.4%)8.6%8.6%8.6%8.6%7.7%8.6%7.7%(3.8% - 13.2%) (3.8% - 13.2%) (3.8% - 13.2%) (3.8% -13.2%) (3.4% - 11.8%) (3.8%- 13.2%) (3.4% - 11.8%)12.8%9.4%9.3%8.2%7.1%7.3%5%(5.7% - 19.4%) (4.2% - 14.4%) (4.1% - 14.3%) (3.6% -12.6%) (3.1% - 10.9%) (3.2% - 11.2%) (2.2% - 7.8%)13.1%4.7%4.7%4.7%4.7%3.1%1.5%(5.8% - 19.8%) (2.1% - 7.3%) (2.1% - 7.3%) (2.1% -7.3%) (2.1% - 7.3%) (1.4% - 4.9%) (0.7% - 2.4%)9.5%8.7%7.7%6.7%5.7%6.7%5.7%(4.2% - 14.5%) (3.8% - 13.3%) (3.4% - 11.8%) (2.9% -10.3%) (2.5% - 8.8%) (2.9% - 10.3%) (2.5% - 8.8%)11.8%5.4%5.4%5.4%4.8%3.8%2%(5.3% - 18%) (2.4% - 8.4%) (2.4% - 8.4%) (2.4% -8.4%) (2.1% - 7.4%) (1.6% - 5.8%) (0.9% - 3.2%)11%8.2%8.2%7.8%6.7%6.1%4%(4.9% - 16.7%) (3.6% - 12.5%) (3.6% - 12.5%) (3.4% - 12%) (2.9% - 10.3%) (2.7% - 9.5%) (1.8% - 6.3%)10.4%9.1%9.1%8.1%7%6.9%4.7%(4.6% - 15.8%) (4% - 13.9%) (4% - 13.9%) (3.6% - 12.5%) (3.1% - 10.8%) (3% - 10.7%) (2.1% - 7.3%)6.2%6.2%6.2%6.2%5.5%5.4%3.4%(2.7% - 9.5%) (2.7% - 9.5%) (2.7% - 9.5%) (2.7% - 9.5%) (2.4% - 8.6%) (2.3% - 8.3%) (1.5% - 5.3%)12.3%8.1%8.1%7.6%6.7%6.1%4%(5.5% - 18.6%) (3.6% - 12.5%) (3.6% - 12.5%) (3.4% -11.7%) (2.9% - 10.3%) (2.7% - 9.4%) (1.8% - 6.3%)6.3%1.8%1.8%1.8%1.8%0.6%0%(2.8% - 9.8%) (0.8% - 2.8%) (0.8% - 2.8%) (0.8% - 2.8%) (0.8% - 2.8%) (0.3% - 1%) (0% - 0%)12.6%10.8%9.8%8.6%7.5%8.4%6%(5.6% - 19.1%) (4.8% - 16.4%) (4.3% - 14.9%) (3.8% -13.2%) (3.3% - 11.5%) (3.7% - 12.9%) (2.6% - 9.2%)7.1%4.7%4.7%4.7%4.7%3.1%1.5%(3.1% - 10.9%) (2% - 7.2%) (2% - 7.2%) (2% - 7.2%) (2% - 7.2%) (1.4% - 4.9%) (0.7% - 2.4%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-69Table E-68. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to AmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2512.4%11%9.9%8.7%7.5%8.7%7.4%(5.5% - 18.7%) (4.9% - 16.7%) (4.4% - 15%) (3.8% - 13.3%) (3.3% - 11.6%) (3.8% - 13.3%) (3.2% - 11.3%)9.9%9.1%8.2%7.2%6.1%6.9%4.7%(4.4% - 15.1%) (4% - 13.9%) (3.6% - 12.6%) (3.2% - 11.1%) (2.7% - 9.5%) (3% - 10.6%) (2% - 7.3%)11.6%8.1%7.2%6.2%5.2%6.2%4.3%(5.1% - 17.6%) (3.6% - 12.4%) (3.1% - 11%) (2.7% - 9.6%) (2.3% - 8.1%) (2.7% - 9.6%) (1.9% - 6.7%)6.4%6.4%6.4%6.4%5.5%6.4%5.5%(2.8% - 9.8%) (2.8% - 9.8%) (2.8% - 9.8%) (2.8% -9.8%) (2.4% - 8.6%) (2.8% - 9.8%) (2.4% - 8.6%)9.4%6.5%6.5%5.5%4.6%4.7%2.9%(4.1% - 14.3%) (2.9% - 10.1%) (2.8% - 10%) (2.4% - 8.5%) (2% - 7.1%) (2.1% - 7.3%) (1.2% - 4.5%)13.4%4.9%4.9%4.9%4.9%3.3%1.7%(6% - 20.3%) (2.1% - 7.6%) (2.1% - 7.6%) (2.1% -7.6%) (2.1% - 7.6%) (1.4% - 5.1%) (0.7% - 2.6%)8.9%8.1%7.2%6.2%5.2%6.2%5.2%(3.9% - 13.6%) (3.6% - 12.5%) (3.1% - 11%) (2.7% - 9.6%) (2.3% - 8.1%) (2.7% - 9.6%) (2.3% - 8.1%)10.4%4.5%4.5%4.5%3.9%2.9%1.3%(4.6% - 15.9%) (1.9% - 6.9%) (1.9% - 6.9%) (1.9%- 6.9%) (1.7% - 6%) (1.3% - 4.5%) (0.6% - 2.1%)8.5%6.1%6.1%5.7%4.8%4.3%2.5%(3.7% - 13%) (2.6% - 9.3%) (2.6% - 9.3%) (2.5% -8.9%) (2.1% - 7.4%) (1.9% - 6.7%) (1.1% - 3.9%)9.4%8.2%8.2%7.3%6.2%6.2%4.1%(4.2% - 14.4%) (3.6% - 12.6%) (3.6% - 12.6%) (3.2% - 11.2%) (2.7% - 9.6%) (2.7% - 9.5%) (1.8% - 6.3%)6.3%6.3%6.3%6.3%5.7%5.5%3.5%(2.7% - 9.7%) (2.7% - 9.7%) (2.7% - 9.7%) (2.7% -9.7%) (2.5% - 8.7%) (2.4% - 8.5%) (1.5% - 5.4%)9.8%6.2%6.2%5.7%4.9%4.4%2.6%(4.3% - 15%) (2.7% - 9.5%) (2.7% - 9.5%) (2.5% - 8.9%) (2.1% - 7.6%) (1.9% - 6.8%) (1.1% - 4%)5%0.9%0.9%0.9%0.9%0%0%(2.2% - 7.8%) (0.4% - 1.5%) (0.4% - 1.5%) (0.4% - 1.5%) (0.4% - 1.5%) (0% - 0%) (0% - 0%)9.5%7.9%7.1%6.1%5.1%5.9%3.9%(4.2% - 14.5%) (3.5% - 12.2%) (3.1% - 10.9%) (2.7% - 9.4%) (2.2% - 7.9%) (2.6% - 9.2%) (1.7% - 6%)4.7%2.7%2.7%2.7%2.7%1.4%0.1%(2.1% - 7.3%)(1.2% - 4.2%)(1.2% - 4.2%)(1.2% - 4.2%)(1.2% - 4.2%)(0.6% - 2.2%)(0% - 0.2%)Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-70Table E-69. Estimated Percent of Total Annual Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to AmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2511.6%10.3%9.2%8.1%7%8.1%6.8%(5.2% - 17.6%) (4.6% - 15.7%) (4.1% - 14.1%) (3.6% - 12.4%) (3.1% - 10.7%) (3.6% - 12.4%) (3% - 10.5%)9.9%9.1%8.2%7.2%6.1%6.9%4.7%(4.4% - 15.1%) (4% - 13.9%) (3.6% - 12.6%) (3.2% - 11.1%) (2.7% - 9.5%) (3% - 10.6%) (2% - 7.3%)12%8.5%7.5%6.5%5.5%6.5%4.6%(5.3% - 18.2%) (3.7% - 13%) (3.3% - 11.5%) (2.8% - 10%) (2.4% - 8.5%) (2.8% - 10%) (2% - 7.1%)7%7%7%7%6.1%7%6.1%(3% - 10.7%) (3% - 10.7%) (3% - 10.7%) (3% - 10.7%) (2.7% - 9.4%) (3% - 10.7%) (2.7% - 9.4%)9.9%7%6.9%6%5%5.1%3.2%(4.4% - 15.2%) (3.1% - 10.8%) (3% - 10.7%) (2.6% - 9.2%) (2.2% - 7.7%) (2.2% - 8%) (1.4% - 5%)13.9%5.2%5.2%5.2%5.2%3.5%1.9%(6.2% - 20.9%) (2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (1.5% - 5.5%) (0.8% - 2.9%)9.1%8.3%7.4%6.4%5.4%6.4%5.4%(4% - 13.9%) (3.7% - 12.8%) (3.2% - 11.3%) (2.8% - 9.8%) (2.4% - 8.3%) (2.8% - 9.8%) (2.4% - 8.3%)10.7%4.7%4.7%4.7%4.1%3.1%1.5%(4.8% - 16.3%) (2% - 7.3%) (2% - 7.3%) (2% - 7.3%) (1.8% - 6.3%) (1.3% - 4.8%) (0.6% - 2.4%)9.8%7.2%7.2%6.9%5.8%5.3%3.3%(4.3% - 15%) (3.2% - 11.1%) (3.2% - 11.1%) (3% - 10.6%) (2.5% - 9%) (2.3% - 8.2%) (1.4% - 5.2%)9.3%8.1%8.1%7.2%6.2%6.1%4%(4.1% - 14.2%) (3.6% - 12.5%) (3.6% - 12.5%) (3.2% - 11.1%) (2.7% - 9.5%) (2.7% - 9.4%) (1.7% - 6.2%)5.2%5.2%5.2%5.2%4.6%4.4%2.6%(2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (2.3% - 8%) (2% - 7.1%) (1.9% - 6.9%) (1.1% - 4.1%)11.1%7.2%7.2%6.7%5.9%5.3%3.4%(4.9% - 16.8%) (3.2% - 11.1%) (3.2% - 11.1%) (3% - 10.4%) (2.6% - 9.1%) (2.3% - 8.2%) (1.5% - 5.2%)7%2.2%2.2%2.2%2.2%1%0%(3.1% - 10.7%) (1% - 3.5%) (1% - 3.5%) (1% - 3.5%) (1% - 3.5%) (0.4% - 1.6%) (0% - 0%)10.4%8.7%7.9%6.8%5.8%6.6%4.5%(4.6% - 15.8%) (3.8% - 13.4%) (3.5% - 12.1%) (3% - 10.5%) (2.5% - 8.9%) (2.9% - 10.2%) (2% - 6.9%)4.9%2.8%2.8%2.8%2.8%1.5%0.2%(2.1% - 7.6%) (1.2% - 4.4%) (1.2% - 4.4%) (1.2% - 4.4%) (1.2% - 4.4%) (0.7% - 2.4%) (0.1% - 0.3%)Percent of Total Incidence of Lung Cancer Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-71Table E-70. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations -- Estimates Basedon Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-12%0%10%21%32%21%33%(-12% - -13%) (0% - 0%) (10% - 11%) (20% - 21%) (31% - 32%) (20% - 21%) (32% - 34%)-8%0%8%19%29%22%44%(-8% - -9%) (0% - 0%) (8% - 8%) (18% - 19%) (29% - 30%) (21% - 22%) (43% - 45%)-41%0%11%23%34%23%45%(-39% - -43%) (0% - 0%) (11% - 12%) (22% - 23%) (34% - 35%) (22% - 23%) (45% - 46%)0%0%0%0%11%0%11%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (0% - 0%) (11% - 11%)-36%0%1%13%25%23%47%(-35% - -37%) (0% - 0%) (1% - 1%) (13% - 13%) (25% - 26%) (23% - 24%) (46% - 48%)-178% 0% 0% 0% 0% 34% 68%(-171% - -185%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (67% - 68%)-9%0%11%23%35%23%35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (34% - 35%) (23% - 23%) (34% - 35%)-118%0%0%0%12%31%62%(-114% - -122%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (31% - 31%) (62% - 63%)-34%0%0%4%18%25%50%(-33% - -35%) (0% - 0%) (0% - 0%) (4% - 5%) (18% - 18%) (25% - 25%) (50% - 51%)-14%0%0%10%23%24%48%(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 11%) (22% - 23%) (23% - 24%) (47% - 49%)0%0%0%0%10%13%45%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (13% - 13%) (45% - 45%)-51%0%0%6%17%25%50%(-49% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 18%) (24% - 25%) (50% - 51%)-250% 0% 0% 0% 0% 64% 100%(-245% - -254%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 64%) (100% - 100%)-17%0%9%20%31%22%44%(-16% - -18%) (0% - 0%) (9% - 9%) (19% - 20%) (30% - 31%) (21% - 22%) (44% - 45%)-52%0%0%0%0%33%67%(-51% - -53%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (67% - 67%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidenceof Lung Cancer Mortality Associated with Long-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-72Table E-71. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations -- Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-12%0%10%21%32%21%33%(-12% - -13%) (0% - 0%) (10% - 11%) (20% - 21%) (31% - 32%) (20% - 21%) (32% - 34%)-9%0%9%21%32%24%48%(-9% - -10%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (23% - 24%) (47% - 49%)-43%0%12%24%36%24%47%(-41% - -44%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (46% - 48%)0%0%0%0%13%0%13%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (0% - 0%) (12% - 13%)-43%0%1%16%30%28%56%(-42% - -45%) (0% - 0%) (1% - 1%) (15% - 16%) (30% - 30%) (27% - 28%) (56% - 57%)-173% 0% 0% 0% 0% 33% 66%(-167% - -181%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 33%) (66% - 66%)-9%0%12%24%36%24%36%(-9% - -10%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-133%0%0%0%13%35%70%(-129% - -137%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 14%) (34% - 35%) (70% - 70%)-40%0%0%5%21%29%59%(-39% - -41%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (29% - 30%) (59% - 59%)-15%0%0%11%24%25%50%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (24% - 25%) (50% - 51%)0%0%0%0%10%13%45%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%) (12% - 13%) (44% - 45%)-59%0%0%7%20%29%58%(-58% - -61%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (28% - 29%) (57% - 58%)-431% 0% 0% 0% 0% 100% 100%(-425% - -437%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%)-20%0%10%23%35%25%51%(-19% - -20%) (0% - 0%) (10% - 11%) (22% - 23%) (35% - 36%) (25% - 26%) (50% - 51%)-75%0%0%0%0%48%96%(-74% - -76%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 48%) (96% - 96%)Percent Reduction from the Current Standards: Annual Incidenceof Lung Cancer Mortality Associated with Long-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-73Table E-72. Percent Reduction from the Current Standards: Estimated Annual Incidence of Lung Cancer Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations -- Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-13%0%11%21%32%21%34%(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 22%) (32% - 33%) (21% - 22%) (33% - 35%)-9%0%9%21%32%24%48%(-9% - -10%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 33%) (23% - 24%) (47% - 49%)-42%0%12%23%35%23%46%(-40% - -43%) (0% - 0%) (11% - 12%) (23% - 24%) (34% - 36%) (23% - 24%) (45% - 47%)0%0%0%0%12%0%12%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (0% - 0%) (12% - 12%)-42%0%1%15%29%27%54%(-41% - -43%) (0% - 0%) (1% - 1%) (15% - 15%) (29% - 29%) (26% - 27%) (54% - 55%)-168% 0% 0% 0% 0% 32% 64%(-161% - -175%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 32%) (64% - 64%)-9%0%12%23%35%23%35%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 24%) (35% - 36%) (23% - 24%) (35% - 36%)-129%0%0%0%13%34%68%(-125% - -134%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (33% - 34%) (68% - 68%)-36%0%0%5%19%27%54%(-35% - -37%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 20%) (26% - 27%) (53% - 54%)-15%0%0%11%24%25%51%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% - 25%) (25% - 26%) (50% - 51%)0%0%0%0%11%14%50%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (14% - 14%) (49% - 50%)-54%0%0%6%19%26%53%(-52% - -55%) (0% - 0%) (0% - 0%) (6% - 7%) (18% - 19%) (26% - 27%) (53% - 54%)-213% 0% 0% 0% 0% 55% 100%(-209% - -217%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 55%) (100% - 100%)-19%0%10%22%34%24%49%(-18% - -19%) (0% - 0%) (10% - 10%) (21% - 22%) (33% - 34%) (24% - 25%) (48% - 49%)-72%0%0%0%0%46%93%(-71% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%)Percent Reduction from the Current Standards: Annual Incidenceof Lung Cancer Mortality Associated with Long-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAE-74Table E-73. Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25193177164151137151135(37 - 347)(34 - 319) (31 - 295) (29 - 272) (26 - 248) (29 - 272) (26 - 244)271256242224206219182(110 - 430) (104 - 406) (98 - 384) (91 - 356) (83 - 327) (89 - 348) (74 - 289)44343229272924(-68 - 154) (-53 - 121) (-49 - 112) (-45 - 103) (-41 - 94) (-45 - 103) (-38 - 85)156156156156145156145(37 - 273)(37 - 273) (37 - 273) (37 - 273) (35 - 253) (37 - 273) (35 - 253)181147146135124125104(-32 - 390) (-26 - 317) (-26 - 315) (-24 - 291) (-22 - 267) (-22 - 271) (-18 - 225)79444444443731(11 - 145)(6 - 81)(6 - 81)(6 - 81)(6 - 81)(5 - 69)(4 - 57)227214198182166182166(46 - 405)(44 - 383) (40 - 354) (37 - 326) (34 - 297) (37 - 326) (34 - 297)129 81 81 81 77 69 58(-185 - 441) (-117 - 278) (-117 - 278) (-117 - 278) (-110 - 263) (-100 - 238) (-82 - 197)939781781761700668555(552 - 1323) (459 - 1102) (459 - 1102) (447 - 1073) (411 - 987) (392 - 943) (325 - 783)234216216202185184153(86 - 380)(79 - 350) (79 - 350) (74 - 328) (68 - 301) (68 - 300) (56 - 249)242242242242230227188(40 - 442)(40 - 442) (40 - 442) (40 - 442) (38 - 420) (38 - 414) (31 - 344)224159159155147136112(66 - 380)(47 - 270) (47 - 270) (45 - 263) (43 - 249) (40 - 231) (33 - 191)48303030302621(10 - 85)(6 - 54)(6 - 54)(6 - 54)(6 - 54)(5 - 46)(4 - 38)290260244226207222184(84 - 494)(75 - 443) (71 - 416) (65 - 385) (60 - 354) (64 - 379) (53 - 315)59484848484134(10 - 107)(8 - 87)(8 - 87)(8 - 87)(8 - 87)(7 - 74)(6 - 62)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standarderrors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-75Table E-74. Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25196180166153139153137(37 - 353)(34 - 324) (32 - 300) (29 - 276) (26 - 251) (29 - 276) (26 - 248)237224212196180192159(96 - 376)(91 - 356) (86 - 336) (79 - 311) (73 - 286) (78 - 305) (64 - 253)42333028262823(-66 - 148) (-51 - 116) (-47 - 108) (-44 - 99)(-40 - 90)(-44 - 99)(-36 - 82)130130130130121130121(31 - 228)(31 - 228) (31 - 228) (31 - 228) (29 - 212) (31 - 228) (29 - 212)1451181171089910183(-25 - 314) (-21 - 255) (-20 - 253) (-19 - 234) (-17 - 215) (-18 - 218) (-15 - 181)84474747474033(12 - 155)(7 - 86)(7 - 86)(7 - 86)(7 - 86)(6 - 74)(5 - 61)221208193177162177162(45 - 395)(42 - 373) (39 - 345) (36 - 317) (33 - 289) (36 - 317) (33 - 289)119 75 75 75 71 64 53(-171 - 407) (-108 - 257) (-108 - 257) (-108 - 257) (-101 - 242) (-92 - 219) (-76 - 182)807671671654601574476(474 - 1137) (394 - 946) (394 - 946) (383 - 922) (352 - 847) (336 - 809) (279 - 672)222204204191175174145(82 - 359)(75 - 331) (75 - 331) (70 - 310) (65 - 285) (64 - 283) (53 - 235)254254254254241238198(42 - 463)(42 - 463) (42 - 463) (42 - 463) (40 - 440) (39 - 434) (33 - 361)19413613613312611696(57 - 329)(40 - 232) (40 - 232) (39 - 226) (37 - 215) (34 - 198) (28 - 164)44272727272319(9 - 78)(6 - 49)(6 - 49)(6 - 49)(6 - 49)(5 - 42)(4 - 35)240215202187171184152(69 - 409)(62 - 367) (58 - 345) (54 - 319) (49 - 293) (53 - 314) (44 - 260)50404040403428(9 - 90)(7 - 73)(7 - 73)(7 - 73)(7 - 73)(6 - 62)(5 - 52)Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UT3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.E-76Table E-75. Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25193177164151137151135(37 - 348)(34 - 319) (31 - 296) (29 - 272) (26 - 248) (29 - 272) (26 - 244)240227214198182194161(97 - 380)(92 - 360) (87 - 340) (80 - 315) (74 - 289) (79 - 308) (65 - 256)44343229262924(-68 - 154) (-53 - 120) (-49 - 111) (-45 - 102) (-41 - 93) (-45 - 102) (-37 - 85)139139139139129139129(33 - 243)(33 - 243) (33 - 243) (33 - 243) (31 - 225) (33 - 243) (31 - 225)15012112011110210486(-26 - 323) (-21 - 262) (-21 - 261) (-19 - 241) (-18 - 221) (-18 - 224) (-15 - 186)87484848484134(12 - 160)(7 - 89)(7 - 89)(7 - 89)(7 - 89)(6 - 76)(5 - 63)224212196180164180164(46 - 401)(43 - 378) (40 - 350) (37 - 322) (33 - 294) (37 - 322) (33 - 294)121 77 77 77 72 65 54(-174 - 415) (-110 - 262) (-110 - 262) (-110 - 262) (-104 - 247) (-94 - 224) (-78 - 186)882734734715657627521(518 - 1243) (431 - 1035) (431 - 1035) (419 - 1008) (385 - 927) (368 - 885) (305 - 735)226208208195179178148(83 - 367)(77 - 338) (77 - 338) (72 - 316) (66 - 291) (66 - 289) (54 - 240)242242242242230227188(40 - 442)(40 - 442) (40 - 442) (40 - 442) (38 - 420) (38 - 414) (31 - 344)204143143140133122102(60 - 346)(42 - 244) (42 - 244) (41 - 237) (39 - 226) (36 - 208) (30 - 173)54343434342924(11 - 96)(7 - 61)(7 - 61)(7 - 61)(7 - 61)(6 - 52)(5 - 43)251225211195179192160(73 - 428)(65 - 384) (61 - 360) (56 - 333) (52 - 306) (55 - 328) (46 - 272)52424242423630(9 - 94)(7 - 76)(7 - 76)(7 - 76)(7 - 76)(6 - 65)(5 - 54)Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UT3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.E-77Table E-76. Estimated Percent of Total Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/251.3%1.2%1.1%1%1%1%0.9%(0.3% - 2.4%) (0.2% - 2.2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% - 1.9%) (0.2% - 1.7%)2%1.9%1.8%1.7%1.5%1.6%1.3%(0.8% - 3.2%) (0.8% - 3%) (0.7% - 2.8%) (0.7% - 2.6%) (0.6% - 2.4%) (0.7% - 2.6%) (0.5% - 2.1%)0.5%0.4%0.3%0.3%0.3%0.3%0.3%(-0.7% - 1.6%) (-0.6% - 1.3%) (-0.5% - 1.2%) (-0.5% - 1.1%) (-0.4% - 1%) (-0.5% - 1.1%) (-0.4% - 0.9%)1.2%1.2%1.2%1.2%1.2%1.2%1.2%(0.3% - 2.2%) (0.3% - 2.2%) (0.3% - 2.2%) (0.3% - 2.2%) (0.3% - 2%) (0.3% - 2.2%) (0.3% - 2%)1%0.8%0.8%0.8%0.7%0.7%0.6%(-0.2% - 2.3%) (-0.1% - 1.8%) (-0.1% - 1.8%) (-0.1% - 1.7%) (-0.1% - 1.5%) (-0.1% - 1.6%) (-0.1% - 1.3%)1.5%0.8%0.8%0.8%0.8%0.7%0.6%(0.2% - 2.7%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.3%) (0.1% - 1.1%)1.3%1.2%1.1%1%0.9%1%0.9%(0.3% - 2.3%) (0.2% - 2.1%) (0.2% - 2%) (0.2% - 1.8%) (0.2% - 1.7%) (0.2% - 1.8%) (0.2% - 1.7%)0.2%0.1%0.1%0.1%0.1%0.1%0.1%(-0.3% - 0.8%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.4%) (-0.1% - 0.4%)1.8%1.5%1.5%1.5%1.4%1.3%1.1%(1.1% - 2.6%) (0.9% - 2.1%) (0.9% - 2.1%) (0.9% - 2.1%) (0.8% - 1.9%) (0.8% - 1.8%) (0.6% - 1.5%)1.7%1.5%1.5%1.4%1.3%1.3%1.1%(0.6% - 2.7%) (0.6% - 2.5%) (0.6% - 2.5%) (0.5% - 2.3%) (0.5% - 2.1%) (0.5% - 2.1%) (0.4% - 1.8%)1.1%1.1%1.1%1.1%1.1%1%0.9%(0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.9%) (0.1% - 1.6%)1.7%1.2%1.2%1.1%1.1%1%0.8%(0.5% - 2.8%) (0.3% - 2%) (0.3% - 2%) (0.3% - 1.9%) (0.3% - 1.8%) (0.3% - 1.7%) (0.2% - 1.4%)1%0.6%0.6%0.6%0.6%0.6%0.5%(0.2% - 1.8%) (0.1% - 1.2%) (0.1% - 1.2%) (0.1% - 1.2%) (0.1% - 1.2%) (0.1% - 1%) (0.1% - 0.8%)1.6%1.4%1.3%1.2%1.1%1.2%1%(0.5% - 2.7%) (0.4% - 2.4%) (0.4% - 2.3%) (0.4% - 2.1%) (0.3% - 1.9%) (0.4% - 2.1%) (0.3% - 1.7%)1.2%1%1%1%1%0.8%0.7%(0.2% - 2.2%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.1% - 1.5%) (0.1% - 1.3%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional means."Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-78Table E-77. Estimated Percent of Total Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/251.3%1.2%1.1%1%0.9%1%0.9%(0.3% - 2.4%) (0.2% - 2.2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% - 1.9%) (0.2% - 1.7%)1.7%1.6%1.6%1.4%1.3%1.4%1.2%(0.7% - 2.8%) (0.7% - 2.6%) (0.6% - 2.5%) (0.6% - 2.3%) (0.5% - 2.1%) (0.6% - 2.2%) (0.5% - 1.9%)0.4%0.3%0.3%0.3%0.3%0.3%0.2%(-0.7% - 1.6%) (-0.5% - 1.2%) (-0.5% - 1.1%) (-0.5% - 1%) (-0.4% - 0.9%) (-0.5% - 1%) (-0.4% - 0.9%)1%1%1%1%0.9%1%0.9%(0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.8%) (0.2% - 1.7%) (0.2% - 1.8%) (0.2% - 1.7%)0.8%0.7%0.7%0.6%0.6%0.6%0.5%(-0.1% - 1.8%) (-0.1% - 1.5%) (-0.1% - 1.5%) (-0.1% - 1.4%) (-0.1% - 1.3%) (-0.1% - 1.3%) (-0.1% - 1.1%)1.5%0.8%0.8%0.8%0.8%0.7%0.6%(0.2% - 2.8%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.3%) (0.1% - 1.1%)1.2%1.1%1%1%0.9%1%0.9%(0.2% - 2.1%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% - 1.6%) (0.2% - 1.7%) (0.2% - 1.6%)0.2%0.1%0.1%0.1%0.1%0.1%0.1%(-0.3% - 0.7%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.4%) (-0.2% - 0.4%) (-0.1% - 0.3%)1.6%1.3%1.3%1.3%1.2%1.1%0.9%(0.9% - 2.2%) (0.8% - 1.8%) (0.8% - 1.8%) (0.7% - 1.8%) (0.7% - 1.6%) (0.6% - 1.6%) (0.5% - 1.3%)1.6%1.5%1.5%1.4%1.2%1.2%1%(0.6% - 2.6%) (0.5% - 2.4%) (0.5% - 2.4%) (0.5% - 2.2%) (0.5% - 2%) (0.5% - 2%) (0.4% - 1.7%)1.1%1.1%1.1%1.1%1.1%1.1%0.9%(0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.9%) (0.1% - 1.6%)1.4%1%1%1%0.9%0.9%0.7%(0.4% - 2.5%) (0.3% - 1.7%) (0.3% - 1.7%) (0.3% - 1.7%) (0.3% - 1.6%) (0.3% - 1.5%) (0.2% - 1.2%)0.9%0.6%0.6%0.6%0.6%0.5%0.4%(0.2% - 1.6%) (0.1% - 1%) (0.1% - 1%) (0.1% - 1%) (0.1% - 1%) (0.1% - 0.9%) (0.1% - 0.7%)1.3%1.2%1.1%1%0.9%1%0.8%(0.4% - 2.2%) (0.3% - 2%) (0.3% - 1.9%) (0.3% - 1.7%) (0.3% - 1.6%) (0.3% - 1.7%) (0.2% - 1.4%)1%0.8%0.8%0.8%0.8%0.7%0.6%(0.2% - 1.8%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.2%) (0.1% - 1%)Percent of Total Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional means."Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-79Table E-78. Estimated Percent of Total Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/251.3%1.2%1.1%1%0.9%1%0.9%(0.2% - 2.3%) (0.2% - 2.1%) (0.2% - 1.9%) (0.2% - 1.8%) (0.2% - 1.6%) (0.2% - 1.8%) (0.2% - 1.6%)1.8%1.7%1.6%1.5%1.3%1.4%1.2%(0.7% - 2.8%) (0.7% - 2.6%) (0.6% - 2.5%) (0.6% - 2.3%) (0.5% - 2.1%) (0.6% - 2.3%) (0.5% - 1.9%)0.5%0.4%0.3%0.3%0.3%0.3%0.2%(-0.7% - 1.6%) (-0.6% - 1.2%) (-0.5% - 1.2%) (-0.5% - 1.1%) (-0.4% - 1%) (-0.5% - 1.1%) (-0.4% - 0.9%)1.1%1.1%1.1%1.1%1%1.1%1%(0.3% - 1.9%) (0.3% - 1.9%) (0.3% - 1.9%) (0.3% - 1.9%) (0.2% - 1.8%) (0.3% - 1.9%) (0.2% - 1.8%)0.9%0.7%0.7%0.7%0.6%0.6%0.5%(-0.2% - 1.9%) (-0.1% - 1.6%) (-0.1% - 1.5%) (-0.1% - 1.4%) (-0.1% - 1.3%) (-0.1% - 1.3%) (-0.1% - 1.1%)1.6%0.9%0.9%0.9%0.9%0.7%0.6%(0.2% - 2.9%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.6%) (0.1% - 1.4%) (0.1% - 1.1%)1.2%1.1%1%1%0.9%1%0.9%(0.2% - 2.1%) (0.2% - 2%) (0.2% - 1.9%) (0.2% - 1.7%) (0.2% - 1.6%) (0.2% - 1.7%) (0.2% - 1.6%)0.2%0.1%0.1%0.1%0.1%0.1%0.1%(-0.3% - 0.7%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.5%) (-0.2% - 0.4%) (-0.2% - 0.4%) (-0.1% - 0.3%)1.7%1.4%1.4%1.4%1.3%1.2%1%(1% - 2.4%) (0.8% - 2%) (0.8% - 2%) (0.8% - 1.9%) (0.7% - 1.8%) (0.7% - 1.7%) (0.6% - 1.4%)1.6%1.5%1.5%1.4%1.3%1.3%1.1%(0.6% - 2.6%) (0.5% - 2.4%) (0.5% - 2.4%) (0.5% - 2.3%) (0.5% - 2.1%) (0.5% - 2.1%) (0.4% - 1.7%)1%1%1%1%1%1%0.8%(0.2% - 1.9%) (0.2% - 1.9%) (0.2% - 1.9%) (0.2% - 1.9%) (0.2% - 1.8%) (0.2% - 1.8%) (0.1% - 1.5%)1.5%1.1%1.1%1%1%0.9%0.8%(0.4% - 2.6%) (0.3% - 1.8%) (0.3% - 1.8%) (0.3% - 1.8%) (0.3% - 1.7%) (0.3% - 1.6%) (0.2% - 1.3%)1.1%0.7%0.7%0.7%0.7%0.6%0.5%(0.2% - 2%) (0.1% - 1.3%) (0.1% - 1.3%) (0.1% - 1.3%) (0.1% - 1.3%) (0.1% - 1.1%) (0.1% - 0.9%)1.4%1.2%1.2%1.1%1%1.1%0.9%(0.4% - 2.4%) (0.4% - 2.1%) (0.3% - 2%) (0.3% - 1.8%) (0.3% - 1.7%) (0.3% - 1.8%) (0.3% - 1.5%)1%0.8%0.8%0.8%0.8%0.7%0.6%(0.2% - 1.9%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.5%) (0.1% - 1.3%) (0.1% - 1.1%)Percent of Total Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence orcredible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-responsefunction estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional means."Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-80Table E-79. Percent Reduction from the Current Standards: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25Atlanta, GA-9%0%7%15%22%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (23% - 24%)Baltimore, MD-6%0%5%13%20%14%29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)Birmingham, AL-28%0%8%15%23%15%30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)Dallas, TX0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)Detroit, MI-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15%) (29% - 29%)Fresno, CA-81%0%0%0%0%15%29%(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)Houston, TX-6%0%7%15%22%15%22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)Los Angeles, CA-58%0%0%0%6%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)New York, NY-20%0%0%3%10%14%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15%) (29% - 29%)Philadelphia, PA-9%0%0%6%14%14%29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)Phoenix, AZ0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)Pittsburgh, PA-41%0%0%3%8%15%29%(-41% - -41%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (14% - 15%) (29% - 29%)Salt Lake City, UT-58%0%0%0%0%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)St. Louis, MO-12%0%6%13%20%15%29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)Tacoma, WA-23%0%0%0%0%15%29%(-23% - -23%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional means."Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Risk AssessmentLocationEstimated Percent Reduction From the Current Standards to Several Alternative Standards in Non-Accidental MortalityAssociated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet theCurrent and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:E-81Table E-80. Percent Reduction fromthe Current Standards: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25Atlanta, GA-9%0%7%15%22%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (23% - 24%)Baltimore, MD-6%0%6%13%20%14%29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)Birmingham, AL-28%0%8%15%23%15%30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)Dallas, TX0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)Detroit, MI-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)Fresno, CA-81%0%0%0%0%15%29%(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)Houston, TX-6%0%7%15%22%15%22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)Los Angeles, CA-58%0%0%0%6%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)New York, NY-20%0%0%3%10%14%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%) (29% - 29%)Philadelphia, PA-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)Phoenix, AZ0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)Pittsburgh, PA-42%0%0%3%7%15%29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%) (29% - 29%)Salt Lake City, UT-58%0%0%0%0%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)St. Louis, MO-12%0%6%13%20%15%29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)Tacoma, WA-23%0%0%0%0%15%29%(-23% - -23%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Risk AssessmentLocationEstimated Percent Reduction From the Current Standards to Several Alternative Standards in Non-Accidental MortalityAssociated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet theCurrent and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:E-82Table E-81. Percent Reduction from the Current Standards: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25Atlanta, GA-9%0%7%15%22%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (23% - 24%)Baltimore, MD-6%0%6%13%20%14%29%(-6% - -6%) (0% - 0%) (5% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)Birmingham, AL-28%0%8%15%23%15%30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)Dallas, TX0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)Detroit, MI-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)Fresno, CA-81%0%0%0%0%15%29%(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)Houston, TX-6%0%7%15%22%15%22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)Los Angeles, CA-58%0%0%0%6%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)New York, NY-20%0%0%3%10%14%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%) (29% - 29%)Philadelphia, PA-9%0%0%6%14%14%29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)Phoenix, AZ0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)Pittsburgh, PA-42%0%0%3%7%15%29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%) (29% - 29%)Salt Lake City, UT-58%0%0%0%0%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)St. Louis, MO-12%0%6%13%20%15%29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)Tacoma, WA-23%0%0%0%0%15%29%(-23% - -23%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Risk AssessmentLocationEstimated Percent Reduction From the Current Standards to Several Alternative Standards in Non-Accidental MortalityAssociated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet theCurrent and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:E-83Table E-82. Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2535323027252724(-36 - 104)(-33 - 95)(-30 - 88)(-28 - 81)(-25 - 74)(-28 - 81)(-25 - 73)74706661566050(-5 - 151)(-5 - 143)(-4 - 135)(-4 - 125)(-4 - 115)(-4 - 122)(-3 - 102)-1-1-1-1-1-10(-55 - 52)(-43 - 40)(-39 - 37)(-36 - 34)(-33 - 31)(-36 - 34)(-30 - 29)32323232303230(-21 - 85)(-21 - 85)(-21 - 85)(-21 - 85)(-20 - 79)(-21 - 85)(-20 - 79)89737267616251(-11 - 188)(-9 - 153)(-9 - 152)(-8 - 140)(-8 - 129)(-8 - 131)(-6 - 109)2011111111108(-14 - 54)(-8 - 30)(-8 - 30)(-8 - 30)(-8 - 30)(-7 - 26)(-6 - 21)50474340364036(-34 - 131) (-32 - 124) (-29 - 114) (-27 - 105) (-25 - 96) (-27 - 105) (-25 - 96)-50-31-31-31-30-27-22(-223 - 121) (-140 - 76) (-140 - 76) (-140 - 76) (-132 - 72) (-119 - 65) (-99 - 54)605504504491451431358(353 - 853) (294 - 711) (294 - 711) (286 - 693) (263 - 637) (251 - 609) (208 - 506)94878781757462(25 - 163)(23 - 150) (23 - 150) (21 - 140) (19 - 129) (19 - 129) (16 - 107)84848484807965(-4 - 170)(-4 - 170)(-4 - 170)(-4 - 170)(-3 - 161)(-3 - 159)(-3 - 132)67474746444134(-13 - 145)(-9 - 103)(-9 - 103)(-9 - 101)(-9 - 96)(-8 - 88)(-7 - 73)13888876(-3 - 28)(-2 - 18)(-2 - 18)(-2 - 18)(-2 - 18)(-2 - 15)(-1 - 12)1361221151069810587(30 - 240)(27 - 215) (26 - 203) (24 - 187) (22 - 172) (23 - 185) (19 - 153)1512121212119(-8 - 38)(-7 - 31)(-7 - 31)(-7 - 31)(-7 - 31)(-6 - 27)(-5 - 22)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIRisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-84Table E-83. Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2535323028252825(-36 - 105)(-33 - 97)(-31 - 90)(-28 - 82)(-26 - 75)(-28 - 82)(-25 - 74)65615853495243(-4 - 132)(-4 - 125)(-4 - 118)(-4 - 109)(-3 - 101)(-4 - 107)(-3 - 89)-1-1-1-10-10(-52 - 50)(-41 - 39)(-38 - 36)(-35 - 33)(-32 - 30)(-35 - 33)(-29 - 27)27272727252725(-18 - 71)(-18 - 71)(-18 - 71)(-18 - 71)(-16 - 66)(-18 - 71)(-16 - 66)72585854495041(-9 - 152)(-7 - 123)(-7 - 122)(-7 - 113)(-6 - 104)(-6 - 105)(-5 - 87)2212121212109(-15 - 58)(-8 - 32)(-8 - 32)(-8 - 32)(-8 - 32)(-7 - 27)(-6 - 23)48454239353935(-33 - 128) (-31 - 120) (-29 - 112) (-26 - 103) (-24 - 94) (-26 - 103) (-24 - 94)-46-29-29-29-27-25-20(-205 - 112) (-129 - 70) (-129 - 70) (-129 - 70) (-122 - 66) (-110 - 60) (-91 - 50)519432432421387370307(303 - 733) (252 - 611) (252 - 611) (246 - 595) (226 - 548) (216 - 523) (179 - 435)89828277717058(23 - 154)(21 - 142) (21 - 142) (20 - 133) (18 - 122) (18 - 122) (15 - 101)88888888848269(-4 - 178)(-4 - 178)(-4 - 178)(-4 - 178)(-4 - 169)(-4 - 167)(-3 - 139)58414140383529(-12 - 126)(-8 - 89)(-8 - 89)(-8 - 86)(-8 - 82)(-7 - 76)(-6 - 63)11777765(-3 - 25)(-2 - 16)(-2 - 16)(-2 - 16)(-2 - 16)(-1 - 14)(-1 - 11)1131019588818772(25 - 199)(23 - 179) (21 - 168) (20 - 155) (18 - 143) (19 - 153) (16 - 127)131010101097(-7 - 32)(-6 - 26)(-6 - 26)(-6 - 26)(-6 - 26)(-5 - 22)(-4 - 19)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXRisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-85Table E-84. Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2535323027252724(-36 - 104)(-33 - 95)(-30 - 88)(-28 - 81)(-25 - 74)(-28 - 81)(-25 - 73)65625854505344(-4 - 133)(-4 - 126)(-4 - 119)(-4 - 111)(-3 - 102)(-4 - 108)(-3 - 90)-1-1-1-1-1-10(-54 - 51)(-42 - 40)(-39 - 37)(-36 - 34)(-33 - 31)(-36 - 34)(-30 - 28)29292929272927(-19 - 76)(-19 - 76)(-19 - 76)(-19 - 76)(-17 - 70)(-19 - 76)(-17 - 70)74606055515143(-9 - 156)(-8 - 127)(-7 - 126)(-7 - 116)(-6 - 107)(-6 - 108)(-5 - 90)2312121212119(-16 - 59)(-9 - 33)(-9 - 33)(-9 - 33)(-9 - 33)(-7 - 28)(-6 - 24)49464339363936(-33 - 130) (-31 - 122) (-29 - 113) (-27 - 104) (-24 - 95) (-27 - 104) (-24 - 95)-47-30-30-30-28-25-21(-209 - 114) (-132 - 72) (-132 - 72) (-132 - 72) (-124 - 68) (-112 - 61) (-93 - 51)568473473461424405336(332 - 802) (276 - 668) (276 - 668) (269 - 651) (247 - 599) (236 - 572) (196 - 476)91848479727260(24 - 157)(22 - 145) (22 - 145) (20 - 136) (19 - 125) (19 - 124) (15 - 103)84848484807965(-4 - 170)(-4 - 170)(-4 - 170)(-4 - 170)(-3 - 162)(-3 - 159)(-3 - 133)61434342403730(-12 - 132)(-9 - 93)(-9 - 93)(-8 - 91)(-8 - 87)(-7 - 80)(-6 - 66)14999986(-3 - 31)(-2 - 20)(-2 - 20)(-2 - 20)(-2 - 20)(-2 - 17)(-1 - 14)1181069992849175(26 - 208)(24 - 187) (22 - 176) (20 - 162) (19 - 149) (20 - 160) (17 - 133)141111111198(-7 - 34)(-6 - 27)(-6 - 27)(-6 - 27)(-6 - 27)(-5 - 23)(-4 - 19)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXRisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-86Table E-85. Estimated Percent of Total Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/250.9%0.9%0.8%0.7%0.7%0.7%0.7%(-1% - 2.8%) (-0.9% - 2.6%) (-0.8% - 2.4%) (-0.7% - 2.2%) (-0.7% - 2%) (-0.7% - 2.2%) (-0.7% - 2%)1.9%1.8%1.7%1.6%1.4%1.5%1.3%(-0.1% - 3.9%) (-0.1% - 3.7%) (-0.1% - 3.5%) (-0.1% - 3.2%) (-0.1% - 3%) (-0.1% - 3.1%) (-0.1% - 2.6%)0%0%0%0%0%0%0%(-2% - 1.9%) (-1.6% - 1.5%) (-1.5% - 1.4%) (-1.3% - 1.3%) (-1.2% - 1.2%) (-1.3% - 1.3%) (-1.1% - 1.1%)1%1%1%1%0.9%1%0.9%(-0.6% - 2.5%) (-0.6% - 2.5%) (-0.6% - 2.5%) (-0.6% - 2.5%) (-0.6% - 2.3%) (-0.6% - 2.5%) (-0.6% - 2.3%)1.5%1.2%1.2%1.1%1%1%0.9%(-0.2% - 3.1%) (-0.2% - 2.5%) (-0.2% - 2.5%) (-0.1% - 2.3%) (-0.1% - 2.1%) (-0.1% - 2.2%) (-0.1% - 1.8%)1.2%0.7%0.7%0.7%0.7%0.6%0.5%(-0.9% - 3.3%) (-0.5% - 1.8%) (-0.5% - 1.8%) (-0.5% - 1.8%) (-0.5% - 1.8%) (-0.4% - 1.6%) (-0.3% - 1.3%)1%1%0.9%0.8%0.7%0.8%0.7%(-0.7% - 2.7%) (-0.7% - 2.5%) (-0.6% - 2.3%) (-0.6% - 2.2%) (-0.5% - 2%) (-0.6% - 2.2%) (-0.5% - 2%)-0.3%-0.2%-0.2%-0.2%-0.2%-0.1%-0.1%(-1.2% - 0.6%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.6% - 0.3%) (-0.5% - 0.3%)2.7%2.2%2.2%2.2%2%1.9%1.6%(1.6% - 3.8%) (1.3% - 3.2%) (1.3% - 3.2%) (1.3% - 3.1%) (1.2% - 2.8%) (1.1% - 2.7%) (0.9% - 2.3%)2.3%2.2%2.2%2%1.9%1.8%1.5%(0.6% - 4%) (0.6% - 3.7%) (0.6% - 3.7%) (0.5% - 3.5%) (0.5% - 3.2%) (0.5% - 3.2%) (0.4% - 2.7%)1.4%1.4%1.4%1.4%1.4%1.3%1.1%(-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.7%) (-0.1% - 2.7%) (0% - 2.3%)1.6%1.2%1.2%1.1%1.1%1%0.8%(-0.3% - 3.6%) (-0.2% - 2.5%) (-0.2% - 2.5%) (-0.2% - 2.5%) (-0.2% - 2.3%) (-0.2% - 2.2%) (-0.2% - 1.8%)1.1%0.7%0.7%0.7%0.7%0.6%0.5%(-0.3% - 2.5%) (-0.2% - 1.6%) (-0.2% - 1.6%) (-0.2% - 1.6%) (-0.2% - 1.6%) (-0.1% - 1.4%) (-0.1% - 1.1%)2.4%2.2%2%1.9%1.7%1.8%1.5%(0.5% - 4.2%) (0.5% - 3.8%) (0.5% - 3.6%) (0.4% - 3.3%) (0.4% - 3%) (0.4% - 3.2%) (0.3% - 2.7%)1.1%0.9%0.9%0.9%0.9%0.7%0.6%(-0.6% - 2.7%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.4% - 1.8%) (-0.3% - 1.5%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional means."Shrunken" coefficient estimates and their standard errorswere sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-87Table E-86. Estimated Percent of Total Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/250.9%0.8%0.8%0.7%0.7%0.7%0.6%(-0.9% - 2.8%) (-0.9% - 2.5%) (-0.8% - 2.3%) (-0.7% - 2.2%) (-0.7% - 2%) (-0.7% - 2.2%) (-0.7% - 1.9%)1.7%1.6%1.5%1.4%1.3%1.3%1.1%(-0.1% - 3.4%) (-0.1% - 3.2%) (-0.1% - 3%) (-0.1% - 2.8%) (-0.1% - 2.6%) (-0.1% - 2.7%) (-0.1% - 2.3%)0%0%0%0%0%0%0%(-1.9% - 1.8%) (-1.5% - 1.4%) (-1.4% - 1.3%) (-1.3% - 1.2%) (-1.2% - 1.1%) (-1.3% - 1.2%) (-1.1% - 1%)0.8%0.8%0.8%0.8%0.7%0.8%0.7%(-0.5% - 2.1%) (-0.5% - 2.1%) (-0.5% - 2.1%) (-0.5% - 2.1%) (-0.5% - 1.9%) (-0.5% - 2.1%) (-0.5% - 1.9%)1.2%1%1%0.9%0.8%0.8%0.7%(-0.2% - 2.5%) (-0.1% - 2.1%) (-0.1% - 2.1%) (-0.1% - 1.9%) (-0.1% - 1.7%) (-0.1% - 1.8%) (-0.1% - 1.5%)1.3%0.7%0.7%0.7%0.7%0.6%0.5%(-0.9% - 3.5%) (-0.5% - 1.9%) (-0.5% - 1.9%) (-0.5% - 1.9%) (-0.5% - 1.9%) (-0.4% - 1.6%) (-0.4% - 1.4%)1%0.9%0.8%0.8%0.7%0.8%0.7%(-0.6% - 2.5%) (-0.6% - 2.4%) (-0.6% - 2.2%) (-0.5% - 2%) (-0.5% - 1.9%) (-0.5% - 2%) (-0.5% - 1.9%)-0.2%-0.2%-0.2%-0.2%-0.1%-0.1%-0.1%(-1.1% - 0.6%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.6% - 0.4%) (-0.6% - 0.3%) (-0.5% - 0.3%)2.3%1.9%1.9%1.9%1.7%1.6%1.4%(1.3% - 3.3%) (1.1% - 2.7%) (1.1% - 2.7%) (1.1% - 2.6%) (1% - 2.4%) (1% - 2.3%) (0.8% - 1.9%)2.2%2.1%2.1%1.9%1.8%1.8%1.5%(0.6% - 3.8%) (0.5% - 3.5%) (0.5% - 3.5%) (0.5% - 3.3%) (0.5% - 3.1%) (0.5% - 3%) (0.4% - 2.5%)1.4%1.4%1.4%1.4%1.4%1.4%1.1%(-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.9%) (-0.1% - 2.8%) (-0.1% - 2.7%) (0% - 2.3%)1.4%1%1%1%0.9%0.9%0.7%(-0.3% - 3.1%) (-0.2% - 2.2%) (-0.2% - 2.2%) (-0.2% - 2.1%) (-0.2% - 2%) (-0.2% - 1.9%) (-0.1% - 1.6%)1%0.6%0.6%0.6%0.6%0.5%0.5%(-0.2% - 2.2%) (-0.1% - 1.4%) (-0.1% - 1.4%) (-0.1% - 1.4%) (-0.1% - 1.4%) (-0.1% - 1.2%) (-0.1% - 1%)2%1.8%1.7%1.5%1.4%1.5%1.3%(0.4% - 3.5%) (0.4% - 3.1%) (0.4% - 3%) (0.3% - 2.7%) (0.3% - 2.5%) (0.3% - 2.7%) (0.3% - 2.2%)0.9%0.7%0.7%0.7%0.7%0.6%0.5%(-0.5% - 2.2%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.3% - 1.5%) (-0.3% - 1.3%)Percent of Total Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence orcredible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutantconcentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards theappropriate regional means."Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-88Table E-87. Estimated Percent of Total Annual Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/250.9%0.8%0.8%0.7%0.6%0.7%0.6%(-0.9% - 2.7%) (-0.8% - 2.4%) (-0.8% - 2.3%) (-0.7% - 2.1%) (-0.6% - 1.9%) (-0.7% - 2.1%) (-0.6% - 1.9%)1.7%1.6%1.5%1.4%1.3%1.4%1.1%(-0.1% - 3.4%) (-0.1% - 3.2%) (-0.1% - 3.1%) (-0.1% - 2.8%) (-0.1% - 2.6%) (-0.1% - 2.8%) (-0.1% - 2.3%)0%0%0%0%0%0%0%(-2% - 1.9%) (-1.5% - 1.5%) (-1.4% - 1.4%) (-1.3% - 1.2%) (-1.2% - 1.1%) (-1.3% - 1.2%) (-1.1% - 1%)0.8%0.8%0.8%0.8%0.8%0.8%0.8%(-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2.2%) (-0.5% - 2%) (-0.5% - 2.2%) (-0.5% - 2%)1.3%1%1%0.9%0.9%0.9%0.7%(-0.2% - 2.7%) (-0.1% - 2.2%) (-0.1% - 2.1%) (-0.1% - 2%) (-0.1% - 1.8%) (-0.1% - 1.8%) (-0.1% - 1.5%)1.3%0.7%0.7%0.7%0.7%0.6%0.5%(-0.9% - 3.5%) (-0.5% - 2%) (-0.5% - 2%) (-0.5% - 2%) (-0.5% - 2%) (-0.4% - 1.7%) (-0.4% - 1.4%)1%0.9%0.8%0.8%0.7%0.8%0.7%(-0.6% - 2.5%) (-0.6% - 2.4%) (-0.6% - 2.2%) (-0.5% - 2%) (-0.5% - 1.9%) (-0.5% - 2%) (-0.5% - 1.9%)-0.2%-0.2%-0.2%-0.2%-0.1%-0.1%-0.1%(-1.1% - 0.6%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.7% - 0.4%) (-0.6% - 0.3%) (-0.5% - 0.3%)2.5%2.1%2.1%2%1.9%1.8%1.5%(1.5% - 3.5%) (1.2% - 3%) (1.2% - 3%) (1.2% - 2.9%) (1.1% - 2.6%) (1% - 2.5%) (0.9% - 2.1%)2.3%2.1%2.1%2%1.8%1.8%1.5%(0.6% - 3.9%) (0.5% - 3.6%) (0.5%- 3.6%) (0.5% - 3.4%) (0.5% - 3.1%) (0.5% - 3.1%) (0.4% - 2.6%)1.3%1.3%1.3%1.3%1.3%1.3%1%(-0.1% - 2.7%) (-0.1% - 2.7%) (-0.1% - 2.7%) (-0.1% - 2.7%) (-0.1% - 2.6%) (-0.1% - 2.5%) (0% - 2.1%)1.5%1.1%1.1%1%1%0.9%0.8%(-0.3% - 3.3%) (-0.2% - 2.3%) (-0.2% - 2.3%) (-0.2% - 2.3%) (-0.2% - 2.1%) (-0.2% - 2%) (-0.1% - 1.6%)1.2%0.8%0.8%0.8%0.8%0.7%0.6%(-0.3% - 2.7%) (-0.2% - 1.7%) (-0.2% - 1.7%) (-0.2% - 1.7%) (-0.2% - 1.7%) (-0.2% - 1.5%) (-0.1% - 1.2%)2.1%1.9%1.7%1.6%1.5%1.6%1.3%(0.5% - 3.7%) (0.4% - 3.3%) (0.4%- 3.1%) (0.4% - 2.9%) (0.3% - 2.6%) (0.4% - 2.8%) (0.3% - 2.3%)0.9%0.7%0.7%0.7%0.7%0.6%0.5%(-0.5% - 2.3%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.4% - 1.8%) (-0.3% - 1.6%) (-0.3% - 1.3%)Percent of Total Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional means."Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-89Table E-88. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-9%0%7%15%23%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (23% - 24%)-6%0%6%13%20%14%29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 15%) (29% - 29%)-28%0%8%15%23%15%30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15%) (29% - 29%)-81%0%0%0%0%15%29%(-80% - -83%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6%0%7%15%22%15%22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)-59%0%0%0%6%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20%0%0%3%10%14%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 14%) (29% - 29%)-9%0%0%6%14%14%29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-41%0%0%3%8%15%29%(-41% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (14% - 15%) (29% - 29%)-58%0%0%0%0%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12%0%6%13%20%14%29%(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-23%0%0%0%0%15%29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 30%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutantconcentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional means."Shrunken" coefficient estimates andtheir standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Cardiovascular Mortality Associated with Short-TermExposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual(n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-90Table E-89. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular MortalityAssociated with Short-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-9%0%7%15%23%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (23% - 24%)-6%0%6%13%20%14%29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% -13%) (19% - 20%) (14% - 15%) (29% - 29%)-28%0%8%15%23%15%30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15%) (29% - 29%)-81%0%0%0%0%15%29%(-80% - -83%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6%0%7%15%23%15%23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (22% - 23%)-59%0%0%0%6%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20%0%0%3%10%14%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% -6%) (14% - 14%) (14% - 15%) (29% - 29%)0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-42%0%0%3%7%15%29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 15%) (29% - 29%)-58%0%0%0%0%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12%0%6%13%20%15%29%(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-23%0%0%0%0%15%29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Cardiovascular Mortality Associated with Short-TermExposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meetthe Current and Alternative Annual(n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-91Table E-90. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Mortality Associated with Short-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-9%0%7%15%23%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (23% - 24%)-6%0%6%13%20%14%29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% - 13%) (19% - 20%) (14% - 15%) (29% - 29%)-28%0%8%15%23%15%30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% -0%) (7% - 7%) (0% - 0%) (7% - 7%)-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15%) (29% - 29%)-81%0%0%0%0%15%29%(-79% - -83%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6%0%7%15%23%15%23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)-59%0%0%0%6%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0%- 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20%0%0%3%10%14%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15%) (29% - 29%)-9%0%0%6%14%14%29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-42%0%0%3%7%15%29%(-42% - -43%) (0% - 0%) (0% - 0%) (3%- 3%) (7% - 7%) (14% - 15%) (29% - 29%)-58%0%0%0%0%15%29%(-57% - -59%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (14% - 15%) (29% - 29%)-12%0%6%13%20%15%29%(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-23%0%0%0%0%15%29%(-23% - -24%) (0% - 0%) (0% - 0%) (0%- 0%) (0% - 0%) (14% - 15%) (29% - 30%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parenthesesare 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional means."Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidenceof Cardiovascular Mortality Associated with Short-TermExposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual(n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-92Table E-91. Estimated Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in aRecent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2521201817151715(-9 - 51)(-8 - 47)(-7 - 43)(-7 - 40)(-6 - 36)(-7 - 40)(-6 - 36)38363431293025(7 - 67)(7 - 64)(6 - 60)(6 - 56)(5 - 51)(6 - 55)(5 - 45)12998787(-10 - 33)(-7 - 26)(-7 - 24)(-6 - 22)(-6 - 20)(-6 - 22)(-5 - 18)11111111101110(-10 - 32)(-10 - 32)(-10 - 32)(-10 - 32)(-10 - 30)(-10 - 32)(-10 - 30)35282826242420(2 - 67)(1 - 55)(1 - 54)(1 - 50)(1 - 46)(1 - 47)(1 - 39)15999976(0 - 30)(0 - 17)(0 - 17)(0 - 17)(0 - 17)(0 - 14)(0 - 12)36343129262926(6 - 65)(5 - 61)(5 - 57)(5 - 52)(4 - 48)(5 - 52)(4 - 48)90575757544941(9 - 171)(6 - 108)(6 - 108)(6 - 108)(5 - 102)(5 - 93)(4 - 77)128106106104959176(45 - 208)(37 - 174) (37 - 174) (37 - 169) (34 - 156) (32 - 149) (27 - 124)25232322202016(-2 - 52)(-2 - 48)(-2 - 48)(-2 - 45)(-2 - 41)(-2 - 41)(-2 - 34)47474747454437(4 - 90)(4 - 90)(4 - 90)(4 - 90)(4 - 85)(4 - 84)(3 - 70)28202020191714(-3 - 58)(-2 - 42)(-2 - 42)(-2 - 40)(-2 - 38)(-2 - 36)(-1 - 30)8 555544(1 - 15)(1 - 10)(1 - 10)(1 - 10)(1 - 10)(1 - 8)(0 - 7)35312927252722(-9 - 78)(-8 - 70)(-8 - 65)(-7 - 61)(-7 - 56)(-7 - 60)(-6 - 50)9 777765(0 - 18)(0 - 15)(0 - 15)(0 - 15)(0 - 15)(0 - 13)(0 - 10)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-93Table E-92. Estimated Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in aRecent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2522201817161715(-9 - 51)(-8 - 47)(-7 - 44)(-7 - 40)(-6 - 37)(-7 - 40)(-6 - 36)33312927252722(6 - 59)(6 - 56)(5 - 53)(5 - 49)(5 - 45)(5 - 48)(4 - 40)11988786(-9 - 31)(-7 - 25)(-7 - 23)(-6 - 21)(-6 - 19)(-6 - 21)(-5 - 17)9 999999(-9 - 27)(-9 - 27)(-9 - 27)(-9 - 27)(-8 - 25)(-9 - 27)(-8 - 25)28232321192016(1 - 54)(1 - 44)(1 - 44)(1 - 41)(1 - 37)(1 - 38)(1 - 31)16999986(1 - 32)(0 - 18)(0 - 18)(0 - 18)(0 - 18)(0 - 15)(0 - 13)35333028252825(6 - 63)(5 - 60)(5 - 55)(4 - 51)(4 - 46)(4 - 51)(4 - 46)84535353504537(8 - 158)(5 - 100)(5 - 100)(5 - 100)(5 - 95)(4 - 86)(4 - 71)110 91 91 89 82 78 65(39 - 179)(32 - 149) (32 - 149) (31 - 146) (29 - 134) (27 - 128) (23 - 107)24222220191915(-2 - 49)(-2 - 45)(-2 - 45)(-2 - 42)(-2 - 39)(-2 - 39)(-1 - 32)50505050474639(4 - 94)(4 - 94)(4 - 94)(4 - 94)(4 - 90)(4 - 88)(3 - 74)24171717161512(-2 - 51)(-2 - 36)(-2 - 36)(-2 - 35)(-2 - 33)(-1 - 31)(-1 - 25)8 555543(1 - 14)(1 - 9)(1 - 9)(1 - 9)(1 - 9)(1 - 8)(0 - 6)29262422212218(-8 - 64)(-7 - 58)(-6 - 54)(-6 - 50)(-5 - 46)(-6 - 49)(-5 - 41)8 666654(0 - 15)(0 - 12)(0 - 12)(0 - 12)(0 - 12)(0 - 11)(0 - 9)Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UT3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.E-94Table E-93. Estimated Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in aRecent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2521201817151715(-9 - 51)(-8 - 47)(-7 - 43)(-7 - 40)(-6 - 36)(-7 - 40)(-6 - 36)33313028252722(6 - 60)(6 - 56)(6 - 53)(5 - 49)(5 - 45)(5 - 48)(4 - 40)12998787(-10 - 32)(-7 - 25)(-7 - 24)(-6 - 22)(-6 - 20)(-6 - 22)(-5 - 18)101010109109(-9 - 29)(-9 - 29)(-9 - 29)(-9 - 29)(-8 - 27)(-9 - 29)(-8 - 27)29242322202017(1 - 56)(1 - 45)(1 - 45)(1 - 42)(1 - 38)(1 - 39)(1 - 32)17999987(1 - 33)(0 - 18)(0 - 18)(0 - 18)(0 - 18)(0 - 16)(0 - 13)35333128262826(6 - 64)(5 - 61)(5 - 56)(5 - 52)(4 - 47)(5 - 52)(4 - 47)85545454514638(8 - 161)(5 - 102)(5 - 102)(5 - 102)(5 - 96)(4 - 87)(4 - 73)120 100 100 97 89 85 71(42 - 196)(35 - 163) (35 - 163) (34 - 159) (31 - 147) (30 - 140) (25 - 117)24222221191916(-2 - 50)(-2 - 46)(-2 - 46)(-2 - 43)(-2 - 40)(-2 - 39)(-1 - 33)47474747454437(4 - 90)(4 - 90)(4 - 90)(4 - 90)(4 - 85)(4 - 84)(3 - 70)26181818171513(-3 - 53)(-2 - 38)(-2 - 38)(-2 - 37)(-2 - 35)(-2 - 32)(-1 - 27)9 666654(1 - 17)(1 - 11)(1 - 11)(1 - 11)(1 - 11)(1 - 9)(1 - 8)30272523222319(-8 - 67)(-7 - 60)(-7 - 57)(-6 - 53)(-6 - 48)(-6 - 52)(-5 - 43)8 666655(0 - 16)(0 - 13)(0 - 13)(0 - 13)(0 - 13)(0 - 11)(0 - 9)Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denotedn/m)2:1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UT3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.E-95Table E-94. Estimated Percent of Total Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/251.7%1.6%1.5%1.3%1.2%1.3%1.2%(-0.7% - 4.1%) (-0.6% - 3.7%) (-0.6% - 3.5%) (-0.5% - 3.2%) (-0.5% - 2.9%) (-0.5% - 3.2%) (-0.5% - 2.9%)3.1%2.9%2.8%2.6%2.4%2.5%2.1%(0.6% - 5.6%) (0.5% - 5.3%) (0.5% - 5%) (0.5% - 4.6%) (0.4% - 4.2%) (0.5% - 4.5%) (0.4% - 3.8%)1.4%1.1%1%0.9%0.8%0.9%0.8%(-1.1% - 3.7%) (-0.9% - 2.9%) (-0.8% - 2.7%) (-0.7% - 2.5%) (-0.7% - 2.3%) (-0.7% - 2.5%) (-0.6% - 2.1%)1%1%1%1%1%1%1%(-0.9% - 3%) (-0.9% - 3%) (-0.9% - 3%) (-0.9% - 3%) (-0.9% - 2.7%) (-0.9% - 3%) (-0.9% - 2.7%)2.6%2.1%2.1%1.9%1.8%1.8%1.5%(0.1% - 5%) (0.1% - 4.1%) (0.1% - 4%) (0.1% - 3.7%) (0.1% - 3.4%) (0.1% - 3.5%) (0.1% - 2.9%)2.6%1.5%1.5%1.5%1.5%1.2%1%(0.1% - 5.1%) (0% - 2.8%) (0% - 2.8%) (0% - 2.8%) (0% - 2.8%) (0% - 2.4%) (0% - 2%)2.5%2.4%2.2%2%1.9%2%1.9%(0.4% - 4.6%) (0.4% - 4.4%) (0.4% - 4%) (0.3% - 3.7%) (0.3% - 3.4%) (0.3% - 3.7%) (0.3% - 3.4%)1.6%1%1%1%1%0.9%0.7%(0.2% - 3.1%) (0.1% - 1.9%) (0.1% - 1.9%) (0.1% - 1.9%) (0.1% - 1.8%) (0.1% - 1.7%) (0.1% - 1.4%)3%2.5%2.5%2.4%2.2%2.1%1.8%(1% - 4.8%) (0.9% - 4%) (0.9% - 4%) (0.8% - 3.9%) (0.8% - 3.6%) (0.7% - 3.5%) (0.6% - 2.9%)2.1%1.9%1.9%1.8%1.6%1.6%1.3%(-0.2% - 4.3%) (-0.2% - 3.9%) (-0.2% - 3.9%) (-0.2% - 3.7%) (-0.2% - 3.4%) (-0.2% - 3.4%) (-0.1% - 2.8%)1.9%1.9%1.9%1.9%1.8%1.8%1.5%(0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 2.9%)2.4%1.7%1.7%1.6%1.6%1.4%1.2%(-0.2% - 4.9%) (-0.2% - 3.5%) (-0.2% - 3.5%) (-0.2% - 3.4%) (-0.2% - 3.2%) (-0.1% - 3%) (-0.1% - 2.5%)1.9%1.2%1.2%1.2%1.2%1%0.8%(0.2% - 3.5%) (0.1% - 2.2%) (0.1% - 2.2%) (0.1% - 2.2%) (0.1% - 2.2%) (0.1% - 1.9%) (0.1% - 1.6%)2%1.8%1.7%1.6%1.4%1.5%1.3%(-0.5% - 4.5%) (-0.5% - 4%) (-0.4% - 3.8%) (-0.4% - 3.5%) (-0.4% - 3.2%) (-0.4% - 3.4%) (-0.3% - 2.9%)1.8%1.5%1.5%1.5%1.5%1.3%1.1%(0% - 3.6%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 2.5%) (0% - 2.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-96Table E-95. Estimated Percent of Total Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/251.7%1.6%1.4%1.3%1.2%1.3%1.2%(-0.7% - 4%) (-0.6% - 3.7%) (-0.6% - 3.4%) (-0.5% - 3.1%) (-0.5% - 2.9%) (-0.5% - 3.1%) (-0.5% - 2.8%)2.7%2.6%2.4%2.2%2.1%2.2%1.8%(0.5% - 4.9%) (0.5% - 4.6%) (0.4% - 4.3%) (0.4% - 4%) (0.4% - 3.7%) (0.4% - 3.9%) (0.3% - 3.3%)1.3%1%0.9%0.9%0.8%0.9%0.7%(-1% - 3.6%) (-0.8% - 2.8%) (-0.8% - 2.6%) (-0.7% - 2.4%) (-0.6% - 2.2%) (-0.7% - 2.4%) (-0.6% - 2%)0.8%0.8%0.8%0.8%0.8%0.8%0.8%(-0.8% - 2.4%) (-0.8% - 2.4%) (-0.8% - 2.4%) (-0.8% - 2.4%) (-0.7% - 2.3%) (-0.8% - 2.4%) (-0.7% - 2.3%)2.1%1.7%1.7%1.6%1.4%1.5%1.2%(0.1% - 4.1%) (0.1% - 3.3%) (0.1% - 3.3%) (0.1% - 3%) (0.1% - 2.8%) (0.1% - 2.8%) (0.1% - 2.3%)2.8%1.5%1.5%1.5%1.5%1.3%1.1%(0.1% - 5.3%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 3%) (0% - 2.6%) (0% - 2.1%)2.4%2.3%2.1%1.9%1.8%1.9%1.8%(0.4% - 4.4%) (0.4% - 4.1%) (0.3% - 3.8%) (0.3% - 3.5%) (0.3% - 3.2%) (0.3% - 3.5%) (0.3% - 3.2%)1.5%0.9%0.9%0.9%0.9%0.8%0.7%(0.1% - 2.8%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.7%) (0.1% - 1.5%) (0.1% - 1.3%)2.5%2.1%2.1%2.1%1.9%1.8%1.5%(0.9% - 4.1%) (0.7% - 3.5%) (0.7% - 3.5%) (0.7% - 3.4%) (0.7% - 3.1%) (0.6% - 3%) (0.5% - 2.5%)2%1.8%1.8%1.7%1.6%1.5%1.3%(-0.2% - 4%) (-0.2% - 3.7%) (-0.2% - 3.7%) (-0.2% - 3.5%) (-0.1% - 3.2%) (-0.1% - 3.2%) (-0.1% - 2.7%)2%2%2%2%1.9%1.8%1.5%(0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.2% - 3.7%) (0.1% - 3.6%) (0.1% - 3.5%) (0.1% - 2.9%)2.1%1.5%1.5%1.4%1.4%1.2%1%(-0.2% - 4.3%) (-0.1% - 3%) (-0.1% - 3%) (-0.1% - 3%) (-0.1% - 2.8%) (-0.1% - 2.6%) (-0.1% - 2.2%)1.7%1.1%1.1%1.1%1.1%0.9%0.8%(0.2% - 3.1%) (0.1% - 2%) (0.1% - 2%) (0.1% - 2%) (0.1% - 2%) (0.1% - 1.7%) (0.1% - 1.4%)1.7%1.5%1.4%1.3%1.2%1.3%1.1%(-0.4% - 3.7%) (-0.4% - 3.3%) (-0.4% - 3.1%) (-0.3% - 2.9%) (-0.3% - 2.7%) (-0.3% - 2.8%) (-0.3% - 2.4%)1.5%1.2%1.2%1.2%1.2%1%0.9%(0% - 3%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.1%) (0% - 1.7%)Percent of Total Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-97Table E-96. Estimated Percent of Total Annual Incidence of Respiratory Mortality Associated with Short-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based onAdjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/251.6%1.5%1.4%1.3%1.2%1.3%1.2%(-0.7% - 3.9%) (-0.6% - 3.6%) (-0.6% - 3.3%) (-0.5% - 3%) (-0.5% - 2.8%) (-0.5% - 3%) (-0.5% - 2.7%)2.7%2.6%2.5%2.3%2.1%2.2%1.8%(0.5% - 4.9%) (0.5% - 4.7%) (0.5% - 4.4%) (0.4% - 4.1%) (0.4% - 3.8%) (0.4% - 4%) (0.3% - 3.3%)1.3%1%1%0.9%0.8%0.9%0.7%(-1.1% - 3.7%) (-0.8% - 2.9%) (-0.8% - 2.7%) (-0.7% - 2.5%) (-0.7% - 2.2%) (-0.7% - 2.5%) (-0.6% - 2%)0.9%0.9%0.9%0.9%0.8%0.9%0.8%(-0.8% - 2.6%) (-0.8% - 2.6%) (-0.8% - 2.6%) (-0.8% - 2.6%) (-0.8% - 2.4%) (-0.8% - 2.6%) (-0.8% - 2.4%)2.2%1.8%1.8%1.6%1.5%1.5%1.3%(0.1% - 4.2%) (0.1% - 3.4%) (0.1% - 3.4%) (0.1% - 3.2%) (0.1% - 2.9%) (0.1% - 2.9%) (0.1% - 2.4%)2.8%1.6%1.6%1.6%1.6%1.3%1.1%(0.1% - 5.4%) (0.1% - 3%) (0.1% - 3%) (0.1% - 3%) (0.1% - 3%) (0% - 2.6%) (0% - 2.2%)2.4%2.3%2.1%1.9%1.8%1.9%1.8%(0.4% - 4.4%) (0.4% - 4.1%) (0.3% - 3.8%) (0.3% - 3.5%) (0.3% - 3.2%) (0.3% - 3.5%) (0.3% - 3.2%)1.5%1%1%1%0.9%0.8%0.7%(0.1% - 2.9%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.8%) (0.1% - 1.7%) (0.1% - 1.6%) (0.1% - 1.3%)2.8%2.3%2.3%2.2%2.1%2%1.6%(1% - 4.5%) (0.8% - 3.8%) (0.8% - 3.8%) (0.8% - 3.7%) (0.7% - 3.4%) (0.7% - 3.2%) (0.6% - 2.7%)2%1.8%1.8%1.7%1.6%1.6%1.3%(-0.2% - 4.1%) (-0.2% - 3.8%) (-0.2% - 3.8%) (-0.2% - 3.6%) (-0.1% - 3.3%) (-0.1% - 3.3%) (-0.1% - 2.7%)1.8%1.8%1.8%1.8%1.7%1.7%1.4%(0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 3.5%) (0.1% - 3.3%) (0.1% - 3.3%) (0.1% - 2.7%)2.2%1.5%1.5%1.5%1.4%1.3%1.1%(-0.2% - 4.5%) (-0.2% - 3.2%) (-0.2% - 3.2%) (-0.1% - 3.1%) (-0.1% - 3%) (-0.1% - 2.7%) (-0.1% - 2.3%)2%1.3%1.3%1.3%1.3%1.1%0.9%(0.3% - 3.8%) (0.2% - 2.4%) (0.2% - 2.4%) (0.2% - 2.4%) (0.2% - 2.4%) (0.1% - 2%) (0.1% - 1.7%)1.7%1.6%1.5%1.3%1.2%1.3%1.1%(-0.5% - 3.9%) (-0.4% - 3.5%) (-0.4% - 3.3%) (-0.4% - 3%) (-0.3% - 2.8%) (-0.3% - 3%) (-0.3% - 2.5%)1.6%1.3%1.3%1.3%1.3%1.1%0.9%(0% - 3.1%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.5%) (0% - 2.2%) (0% - 1.8%)Percent of Total Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrations in a RecentYear and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-98Table E-97. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Mortality Associated withShort-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-9%0%7%15%22%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (23% - 24%)-6%0%5%12%20%14%29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% -13%) (19% - 20%) (14% - 15%) (29% - 29%)-28%0%7%15%22%15%29%(-27% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23%0%1%8%16%14%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (15% - 16%) (14% - 15%) (29% - 29%)-80%0%0%0%0%14%29%(-78% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6%0%7%15%22%15%22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (22% - 23%)-58%0%0%0%6%15%29%(-57% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 15%) (29% - 29%)-20%0%0%3%10%14%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% -7%) (14% - 14%) (14% - 15%) (29% - 29%)0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-41%0%0%3%8%15%29%(-40% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (14% - 15%) (29% - 29%)-58%0%0%0%0%15%29%(-57% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-12%0%6%13%20%15%29%(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-23%0%0%0%0%15%29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidenceof Respiratory Mortality Associated with Short-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-99Table E-98. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Mortality Associated withShort-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-9%0%7%15%22%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (23% - 24%)-6%0%5%12%20%14%29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% -13%) (19% - 20%) (14% - 15%) (29% - 29%)-28%0%7%15%22%15%29%(-27% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15%) (29% - 29%)-80%0%0%0%0%14%29%(-79% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6%0%7%15%22%15%22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (22% - 23%)-58%0%0%0%6%15%29%(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20%0%0%3%10%14%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% -7%) (14% - 14%) (14% - 15%) (29% - 29%)0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-42%0%0%3%7%15%29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 15%) (29% - 29%)-58%0%0%0%0%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12%0%6%13%20%15%29%(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-23%0%0%0%0%15%29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)Percent Reduction from the Current Standards: Annual Incidenceof Respiratory Mortality Associated with Short-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-100Table E-99. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Mortality Associated withShort-TermExposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-9%0%7%15%22%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (23% - 24%)-6%0%5%13%20%14%29%(-6% - -6%) (0% - 0%) (5% - 6%) (12% -13%) (19% - 20%) (14% - 15%) (29% - 29%)-28%0%7%15%22%15%29%(-27% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (14% - 15%) (29% - 29%)-80%0%0%0%0%14%29%(-78% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6%0%7%15%22%15%22%(-6% - -6%) (0% - 0%) (7% - 8%) (15% -15%) (22% - 23%) (15% - 15%) (22% - 23%)-58%0%0%0%6%15%29%(-57% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 15%) (29% - 29%)-20%0%0%3%10%14%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 14% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% -7%) (14% - 14%) (14% - 15%) (29% - 29%)0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-42%0%0%3%7%15%29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (14% - 15%) (29% - 29%)-58%0%0%0%0%15%29%(-57% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-12%0%6%13%20%15%29%(-11% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-23%0%0%0%0%15%29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)Percent Reduction from the Current Standards: Annual Incidenceof Respiratory Mortality Associated with Short-Term Exposureto PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) andDaily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. "Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email.Dallas, TXFresno, CALos Angeles, CAE-101Table E-100. Estimated Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2543403734313430(-28 - 115) (-26 - 105) (-24 - 98)(-22 - 90)(-20 - 82)(-22 - 90)(-20 - 81)262247234216199212176(192 - 331) (182 - 313) (172 - 295) (159 - 273) (146 - 251) (155 - 267) (129 - 222)21171514131412(-14 - 56)(-11 - 44)(-10 - 41)(-9 - 37)(-8 - 34)(-9 - 37)(-8 - 31)31313131283128(-20 - 81)(-20 - 81)(-20 - 81)(-20 - 81)(-19 - 75)(-20 - 81)(-19 - 75)345280278257236239198(253 - 435) (206 - 354) (204 - 351) (189 - 325) (173 - 298) (176 - 302) (146 - 251)38212121211815(0 - 75)(0 - 41)(0 - 41)(0 - 41)(0 - 41)(0 - 35)(0 - 29)60565248444844(-39 - 158) (-37 - 149) (-34 - 138) (-31 - 127) (-29 - 115) (-31 - 127) (-29 - 115)418264264264249225187(5 - 827)(3 - 523)(3 - 523)(3 - 523)(3 - 494)(3 - 447)(2 - 371)952792792772709677562(700 - 1204) (582 - 1002) (582 - 1002) (567 - 976) (521 - 897) (497 - 857) (413 - 711)233214214200184183152(171 - 294) (157 - 271) (157 - 271) (147 - 253) (135 - 233) (134 - 232) (112 - 192)10810810810810210184(1 - 213)(1 - 213)(1 - 213)(1 - 213)(1 - 203)(1 - 200)(1 - 166)222157157153145134111(163 - 280) (115 - 199) (115 - 199) (112 - 193) (106 - 183) (98 - 170) (82 - 141)13888876(0 - 25)(0 - 16)(0 - 16)(0 - 16)(0 - 16)(0 - 13)(0 - 11)231207195180165177147(170 - 293) (152 - 262) (143 - 246) (132 - 228) (121 - 209) (130 - 224) (108 - 186)26212121211815(-65 - 113)(-52 - 92)(-52 - 92)(-52 - 92)(-52 - 92)(-44 - 79)(-37 - 65)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table2 of Bell et al. (2008). Location-specific C-R functionestimates were not available from this study.Dallas, TXFresno, CALos Angeles, CATotal Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrationsin a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards(Standard Combination Denoted n/m)2:Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXSt. Louis, MOE-102Table E-101. Estimated Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2544413835313531(-29 - 117) (-27 - 108) (-25 - 99)(-23 - 91)(-21 - 83)(-23 - 91)(-20 - 82)227214203187172183152(167 - 287) (157 - 271) (149 - 256) (138 - 237) (126 - 218) (135 - 232) (112 - 192)20161513121311(-13 - 53)(-10 - 42)(-10 - 38)(-9 - 35)(-8 - 32)(-9 - 35)(-7 - 29)26262626242624(-17 - 68)(-17 - 68)(-17 - 68)(-17 - 68)(-16 - 63)(-17 - 68)(-16 - 63)278225224207190192160(204 - 351) (165 - 285) (164 - 283) (152 - 261) (139 - 240) (141 - 243) (117 - 202)40222222221916(0 - 80)(0 - 44)(0 - 44)(0 - 44)(0 - 44)(0 - 38)(0 - 31)58555147434743(-38 - 154) (-36 - 145) (-33 - 134) (-31 - 123) (-28 - 113) (-31 - 123) (-28 - 113)392248248248234211175(5 - 776)(3 - 491)(3 - 491)(3 - 491)(3 - 463)(3 - 419)(2 - 348)822684684666612585485(604 - 1040) (502 - 865) (502 - 865) (489 - 843) (449 - 774) (429 - 740) (356 - 614)218201201188173172142(160 - 276) (147 - 254) (147 - 254) (138 - 237) (127 - 218) (126 - 217) (105 - 180)11311311311310710688(1 - 224)(1 - 224)(1 - 224)(1 - 224)(1 - 212)(1 - 209)(1 - 174)19013413413012411495(140 - 240) (98 - 169) (98 - 169) (96 - 165) (91 - 157) (84 - 144) (69 - 120)12777765(0 - 23)(0 - 15)(0 - 15)(0 - 15)(0 - 15)(0 - 12)(0 - 10)191171160148136146121(140 - 241) (126 - 216) (118 - 203) (109 - 188) (100 - 172) (107 - 185) (89 - 153)22181818181513(-54 - 95)(-44 - 78)(-44 - 78)(-44 - 78)(-44 - 78)(-37 - 66)(-31 - 55)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table2 of Bell et al. (2008). Location-specific C-R functionestimates were not available from this study.Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrationsin a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards(Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-103Table E-102. Estimated Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations1Recent AmbientPM2.5Concentrations15/35314/3513/3512/3513/3012/2545413835323531(-29 - 119) (-27 - 109) (-25 - 101) (-23 - 92)(-21 - 84)(-23 - 92)(-21 - 83)229216204189174185153(168 - 289) (159 - 273) (150 - 258) (139 - 239) (127 - 220) (136 - 234) (113 - 194)21161514121411(-14 - 54)(-11 - 43)(-10 - 39)(-9 - 36)(-8 - 33)(-9 - 36)(-7 - 30)28282828262826(-18 - 73)(-18 - 73)(-18 - 73)(-18 - 73)(-17 - 68)(-18 - 73)(-17 - 68)288233232214197199165(211 - 364) (171 - 295) (170 - 293) (157 - 271) (144 - 249) (146 - 252) (121 - 209)42232323232016(1 - 83)(0 - 46)(0 - 46)(0 - 46)(0 - 46)(0 - 39)(0 - 32)60565248444844(-39 - 158) (-37 - 149) (-34 - 138) (-31 - 127) (-29 - 116) (-31 - 127) (-29 - 116)408258258258243220182(5 - 807)(3 - 511)(3 - 511)(3 - 511)(3 - 482)(3 - 436)(2 - 362)905752752733673643534(665 - 1144) (552 - 951) (552 - 951) (538 - 927) (494 - 852) (472 - 814) (392 - 676)221203203190175174144(162 - 279) (149 - 257) (149 - 257) (140 - 240) (128 - 221) (128 - 220) (106 - 183)10810810810810310284(1 - 215)(1 - 215)(1 - 215)(1 - 215)(1 - 204)(1 - 201)(1 - 167)19914014013612911999(146 - 251) (103 - 177) (103 - 177) (100 - 172) (95 - 164) (88 - 151) (73 - 125)15999987(0 - 29)(0 - 18)(0 - 18)(0 - 18)(0 - 18)(0 - 16)(0 - 13)199178167155142152126(146 - 251) (131 - 225) (123 - 212) (114 - 196) (104 - 180) (112 - 193) (93 - 160)23191919191613(-57 - 101)(-46 - 82)(-46 - 82)(-46 - 82)(-46 - 82)(-39 - 70)(-33 - 58)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table2 of Bell et al. (2008). Location-specific C-R functionestimates were not available from this study.Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrationsin a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards(Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-104Table E-103. Estimated Percent of Total Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-TermExposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/250.41%0.4%0.35%0.32%0.29%0.32%0.29%(-0.27% - 1.09%) (-0.2% - 1%) (-0.23% - 0.93%) (-0.21% - 0.85%) (-0.19% - 0.78%) (-0.21% - 0.85%) (-0.19% - 0.77%)1.59%1.5%1.42%1.32%1.21%1.29%1.07%(1.17% - 2.01%) (1.1% - 1.9%) (1.05% - 1.8%) (0.97% - 1.67%) (0.89% - 1.53%) (0.95% - 1.63%) (0.79% - 1.35%)0.42%0.3%0.31%0.28%0.26%0.28%0.23%(-0.28% - 1.12%) (-0.2% - 0.9%) (-0.2% - 0.81%) (-0.18% - 0.75%) (-0.17% - 0.68%) (-0.18% - 0.75%) (-0.15% - 0.62%)0.32%0.3%0.32%0.32%0.3%0.32%0.3%(-0.21% - 0.85%) (-0.2% - 0.9%) (-0.21% - 0.85%) (-0.21% - 0.85%) (-0.2% - 0.79%) (-0.21% - 0.85%) (-0.2% - 0.79%)1.65%1.3%1.33%1.23%1.13%1.15%0.95%(1.22% - 2.09%) (1% - 1.7%) (0.98% - 1.68%) (0.91% - 1.56%) (0.83% - 1.43%) (0.84% - 1.45%) (0.7% - 1.2%)0.81%0.4%0.44%0.44%0.44%0.38%0.31%(0.01% - 1.59%) (0% - 0.9%) (0.01% - 0.88%) (0.01% - 0.88%) (0.01% - 0.88%) (0% - 0.75%) (0% - 0.62%)0.35%0.3%0.31%0.28%0.26%0.28%0.26%(-0.23% - 0.93%) (-0.2% - 0.9%) (-0.2% - 0.82%) (-0.19% - 0.75%) (-0.17% - 0.68%) (-0.19% - 0.75%) (-0.17% - 0.68%)0.77%0.5%0.49%0.49%0.46%0.41%0.34%(0.01% - 1.52%) (0% - 1%) (0.01% - 0.96%) (0.01% - 0.96%) (0.01% - 0.91%) (0% - 0.82%) (0% - 0.68%)1.49%1.2%1.24%1.21%1.11%1.06%0.88%(1.09% - 1.88%) (0.9% - 1.6%) (0.91% - 1.57%) (0.89% - 1.53%) (0.81% - 1.4%) (0.78% - 1.34%) (0.65% - 1.11%)1.41%1.3%1.3%1.22%1.12%1.11%0.92%(1.04% - 1.79%) (1% - 1.6%) (0.96% - 1.64%) (0.89% - 1.54%) (0.82% - 1.41%) (0.82% - 1.41%) (0.68% - 1.17%)0.53%0.5%0.53%0.53%0.51%0.5%0.41%(0.01% - 1.05%) (0% - 1.1%) (0.01% - 1.05%) (0.01% -1.05%) (0.01% - 1%) (0.01% - 0.99%) (0% - 0.82%)1.72%1.2%1.22%1.19%1.13%1.04%0.86%(1.26% - 2.17%) (0.9% - 1.5%) (0.89% - 1.54%) (0.87% - 1.5%) (0.83% - 1.42%) (0.76% - 1.32%) (0.63% - 1.09%)0.52%0.3%0.33%0.33%0.33%0.28%0.23%(0.01% - 1.03%) (0% - 0.7%) (0% - 0.65%) (0% -0.65%) (0% - 0.65%) (0% - 0.56%) (0% - 0.46%)1.64%1.5%1.38%1.28%1.17%1.26%1.04%(1.21% - 2.08%) (1.1% - 1.9%) (1.02% - 1.75%) (0.94% - 1.62%) (0.86% - 1.48%) (0.92% - 1.59%) (0.77% - 1.32%)0.76%0.6%0.62%0.62%0.62%0.53%0.44%(-1.86% - 3.26%) (-1.5% - 2.7%) (-1.5% - 2.65%) (-1.5% - 2.65%) (-1.5% - 2.65%) (-1.28% - 2.27%) (-1.05% - 1.89%)2Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table2 of Bell et al. (2008). Location-specific C-R functionestimates were not available from this study.Dallas, TXFresno, CALos Angeles, CAPercent of Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)StandardSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-105Table E-104. Estimated Percent of Total Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-TermExposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/250.41%0.4%0.35%0.32%0.29%0.32%0.29%(-0.27% - 1.08%) (-0.2% - 1%) (-0.23% - 0.92%) (-0.21% - 0.84%) (-0.19% - 0.77%) (-0.21% - 0.84%) (-0.19% - 0.76%)1.39%1.3%1.24%1.15%1.05%1.12%0.93%(1.02% - 1.75%) (1% - 1.7%) (0.91% - 1.57%) (0.84% - 1.45%) (0.77% - 1.33%) (0.82% - 1.42%) (0.68% - 1.18%)0.4%0.3%0.29%0.27%0.24%0.27%0.22%(-0.26% - 1.06%) (-0.2% - 0.8%) (-0.19% - 0.77%) (-0.17% - 0.71%) (-0.16% - 0.64%) (-0.17% - 0.71%) (-0.14% - 0.59%)0.27%0.3%0.27%0.27%0.25%0.27%0.25%(-0.17% - 0.7%) (-0.2% - 0.7%) (-0.17% - 0.7%) (-0.17% - 0.7%) (-0.16% - 0.65%) (-0.17% - 0.7%) (-0.16% - 0.65%)1.34%1.1%1.08%1%0.92%0.93%0.77%(0.98% - 1.69%) (0.8% - 1.4%) (0.79% - 1.37%) (0.73% - 1.26%) (0.67% - 1.16%) (0.68% - 1.18%) (0.57% - 0.97%)0.85%0.5%0.47%0.47%0.47%0.4%0.33%(0.01% - 1.68%) (0% - 0.9%) (0.01% - 0.93%) (0.01% - 0.93%) (0.01% - 0.93%) (0% - 0.79%) (0% - 0.66%)0.33%0.3%0.29%0.27%0.24%0.27%0.24%(-0.22% - 0.88%) (-0.2% - 0.8%) (-0.19% - 0.77%) (-0.17% - 0.71%) (-0.16% - 0.64%) (-0.17% - 0.71%) (-0.16% - 0.64%)0.71%0.4%0.45%0.45%0.42%0.38%0.32%(0.01% - 1.41%) (0% - 0.9%) (0.01% - 0.89%) (0.01% - 0.89%) (0.01% - 0.84%) (0% - 0.76%) (0% - 0.63%)1.27%1.1%1.06%1.03%0.95%0.9%0.75%(0.93% - 1.61%) (0.8% - 1.3%) (0.78% - 1.34%) (0.76% - 1.3%) (0.7% - 1.2%) (0.66% - 1.14%) (0.55% - 0.95%)1.34%1.2%1.24%1.16%1.06%1.06%0.88%(0.99% - 1.7%) (0.9% - 1.6%) (0.91% - 1.56%) (0.85% - 1.46%) (0.78% - 1.34%) (0.78% - 1.34%) (0.64% - 1.11%)0.54%0.5%0.54%0.54%0.51%0.5%0.42%(0.01% - 1.07%) (0% - 1.1%) (0.01% - 1.07%) (0.01% - 1.07%) (0.01% - 1.02%) (0.01% - 1%) (0.01% - 0.83%)1.5%1.1%1.05%1.03%0.98%0.9%0.75%(1.1% - 1.89%) (0.8% - 1.3%) (0.77% - 1.33%) (0.75% - 1.3%) (0.72% - 1.23%) (0.66% - 1.14%) (0.55% - 0.94%)0.46%0.3%0.29%0.29%0.29%0.25%0.21%(0.01% - 0.92%) (0% - 0.6%) (0% - 0.58%) (0% -0.58%) (0% - 0.58%) (0% - 0.49%) (0% - 0.41%)1.36%1.2%1.14%1.06%0.97%1.04%0.86%(1% - 1.72%) (0.9% - 1.5%) (0.84% - 1.45%) (0.78% - 1.34%) (0.71% - 1.23%) (0.76% - 1.32%) (0.63% - 1.09%)0.63%0.5%0.51%0.51%0.51%0.43%0.36%(-1.53% - 2.69%) (-1.2% - 2.2%) (-1.23% - 2.19%) (-1.23% - 2.19%) (-1.23% - 2.19%) (-1.05% - 1.87%) (-0.87% - 1.56%)2Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table2 of Bell et al. (2008). Location-specific C-R functionestimates were not available from this study.Dallas, TXFresno, CALos Angeles, CAPercent of Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)StandardSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-106Table E-105. Estimated Percent of Total Annual Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-TermExposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/250.4%0.4%0.34%0.31%0.28%0.31%0.28%(-0.26% - 1.06%) (-0.2% - 1%) (-0.22% - 0.9%) (-0.2% - 0.83%) (-0.19% - 0.75%) (-0.2% - 0.83%) (-0.18% - 0.74%)1.41%1.3%1.26%1.16%1.07%1.14%0.94%(1.03% - 1.78%) (1% - 1.7%) (0.92% - 1.59%) (0.85% - 1.47%) (0.78% - 1.35%) (0.83% - 1.44%) (0.69% - 1.19%)0.41%0.3%0.3%0.27%0.25%0.27%0.23%(-0.27% - 1.09%) (-0.2% - 0.9%) (-0.19% - 0.79%) (-0.18% - 0.72%) (-0.16% - 0.66%) (-0.18% - 0.72%) (-0.15% - 0.6%)0.28%0.3%0.28%0.28%0.26%0.28%0.26%(-0.18% - 0.74%) (-0.2% - 0.7%) (-0.18% - 0.74%) (-0.18% - 0.74%) (-0.17% - 0.68%) (-0.18% - 0.74%) (-0.17% - 0.68%)1.4%1.1%1.13%1.04%0.96%0.97%0.8%(1.03% - 1.77%) (0.8% - 1.4%) (0.83% - 1.42%) (0.76% - 1.32%) (0.7% - 1.21%) (0.71% - 1.23%) (0.59% - 1.02%)0.86%0.5%0.48%0.48%0.48%0.41%0.34%(0.01% - 1.7%) (0% - 0.9%) (0.01% - 0.94%) (0.01% - 0.94%) (0.01% - 0.94%) (0% - 0.81%) (0% - 0.67%)0.33%0.3%0.29%0.27%0.24%0.27%0.24%(-0.22% - 0.88%) (-0.2% - 0.8%) (-0.19% - 0.77%) (-0.17% - 0.71%) (-0.16% - 0.64%) (-0.17% - 0.71%) (-0.16% - 0.64%)0.72%0.5%0.46%0.46%0.43%0.39%0.32%(0.01% - 1.43%) (0% - 0.9%) (0.01% - 0.91%) (0.01% - 0.91%) (0.01% - 0.86%) (0% - 0.78%) (0% - 0.64%)1.39%1.2%1.15%1.12%1.03%0.99%0.82%(1.02% - 1.75%) (0.8% - 1.5%) (0.85% - 1.46%) (0.83% - 1.42%) (0.76% - 1.31%) (0.72% - 1.25%) (0.6% - 1.04%)1.38%1.3%1.27%1.18%1.09%1.08%0.9%(1.01% - 1.74%) (0.9% - 1.6%) (0.93% - 1.6%) (0.87% - 1.5%) (0.8% - 1.38%) (0.79% - 1.37%) (0.66% - 1.14%)0.5%0.5%0.5%0.5%0.47%0.47%0.39%(0.01% - 0.99%) (0% - 1%) (0.01% - 0.99%) (0.01% - 0.99%) (0.01% - 0.94%) (0.01% - 0.93%) (0% - 0.77%)1.58%1.1%1.11%1.08%1.03%0.95%0.79%(1.16% - 2%) (0.8% - 1.4%) (0.82% - 1.41%) (0.8% - 1.37%) (0.76% - 1.3%) (0.7% - 1.2%) (0.58% - 1%)0.56%0.4%0.36%0.36%0.36%0.3%0.25%(0.01% - 1.11%) (0% - 0.7%) (0% - 0.7%) (0% - 0.7%) (0% - 0.7%) (0% - 0.6%) (0% - 0.5%)1.42%1.3%1.19%1.1%1.01%1.09%0.9%(1.04% - 1.79%) (0.9% - 1.6%) (0.88% - 1.51%) (0.81% - 1.4%) (0.74% - 1.28%) (0.8% - 1.37%) (0.66% - 1.14%)0.65%0.5%0.52%0.52%0.52%0.45%0.37%(-1.58% - 2.77%) (-1.3% - 2.3%) (-1.28% - 2.26%) (-1.28% - 2.26%) (-1.28% - 2.26%) (-1.09% - 1.93%) (-0.9% - 1.6%)2Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY1Incidence estimates were calculated using the appropriate regional concentration-response function estimates reported in Table2 of Bell et al. (2008). Location-specific C-R functionestimates were not available from this study.Dallas, TXFresno, CALos Angeles, CAPercent of Total Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)StandardSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-107Table E-106. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Hospital Admissions Associated withShort-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-9%0%8%15%23%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (24% - 24%)-6%0%6%13%20%14%29%(-6% - -6%) (0% - 0%) (5% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-28%0%8%15%23%15%30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)-81%0%0%0%0%15%29%(-81% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-6%0%8%15%23%15%23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% - 15%) (23% - 23%)-58%0%0%0%6%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20%0%0%3%10%14%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 10%) (14% - 15%) (29% - 29%)-9%0%0%6%14%14%29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-41%0%0%3%8%15%29%(-41% - -41%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (15% - 15%) (29% - 29%)-59%0%0%0%0%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12%0%6%13%20%15%29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-23%0%0%0%0%15%29%(-23% - -24%)(0% - 0%)(0% - 0%)(0% - 0%)(0% - 0%)(14% - 15%)(29% - 30%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidenceof Cardiovascular Hospital Admissions Associated with ShortTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundingthe PM coefficient.1Incidence estimates were calculated usingthe appropriate regional concentration-responsefunction estimates reported in Table2 of Bell et al. (2008). Location-specific C-R function estimateswere not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXE-108Table E-107. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Hospital Admissions Associated withShort-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-9%0%8%15%23%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (24% - 24%)-6%0%6%13%20%14%29%(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-28%0%8%15%23%15%30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% - 15%) (29% - 30%)0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)-81%0%0%0%0%15%29%(-81% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-6%0%8%15%23%15%23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% - 15%) (23% - 23%)-58%0%0%0%6%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20%0%0%3%10%15%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%) (29% - 29%)-9%0%0%6%14%14%29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-42%0%0%3%7%15%29%(-42% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%) (29% - 29%)-59%0%0%0%0%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12%0%6%13%20%15%29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-23%0%0%0%0%15%29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 30%)Percent Reduction from the Current Standards: Annual Incidenceof Cardiovascular Hospital Admissions Associated with ShortTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundingthe PM coefficient.1Incidence estimates were calculated usingthe appropriate regional concentration-responsefunction estimates reported in Table2 of Bell et al. (2008). Location-specific C-R function estimateswere not available from this study.Dallas, TXFresno, CALos Angeles, CAE-109Table E-108. Percent Reduction from the Current Standards: Estimated Annual Incidence of Cardiovascular Hospital Admissions Associated withShort-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-9%0%8%15%23%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (24% - 24%)-6%0%6%13%20%14%29%(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (14% - 15%) (29% - 29%)-28%0%8%15%23%15%30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)-81%0%0%0%0%15%29%(-81% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-6%0%8%15%23%15%23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (23% - 23%) (15% - 15%) (23% - 23%)-58%0%0%0%6%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20%0%0%3%10%15%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (10% - 11%) (14% - 15%) (29% - 29%)-9%0%0%6%14%14%29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 6%) (14% - 14%) (14% - 15%) (29% - 29%)0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-42%0%0%3%7%15%29%(-42% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%) (29% - 29%)-59%0%0%0%0%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12%0%6%13%20%15%29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-23%0%0%0%0%15%29%(-23% - -24%)(0% - 0%)(0% - 0%)(0% - 0%)(0% - 0%)(14% - 15%)(29% - 30%)Percent Reduction from the Current Standards: Annual Incidenceof Cardiovascular Hospital Admissions Associated with ShortTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surroundingthe PM coefficient.1Incidence estimates were calculated usingthe appropriate regional concentration-responsefunction estimates reported in Table2 of Bell et al. (2008). Location-specific C-R function estimateswere not available from this study.Dallas, TXFresno, CALos Angeles, CAE-110Table E-109. Estimated Annual Incidence ofHospital Admissions for Respiratory Illness Associated with Short-Term Exposure toAmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2519171615131513(-23 - 60)(-22 - 55)(-20 - 51)(-18 - 47)(-17 - 43)(-18 - 47)(-16 - 42)21201917161714(-12 - 54)(-12 - 51)(-11 - 48)(-10 - 45)(-9 - 41)(-10 - 44)(-8 - 36)9 776665(-11 - 29)(-9 - 23)(-8 - 21)(-8 - 20)(-7 - 18)(-8 - 20)(-6 - 16)15151515141514(-18 - 47)(-18 - 47)(-18 - 47)(-18 - 47)(-17 - 44)(-18 - 47)(-17 - 44)31252523212118(-18 - 79)(-15 - 64)(-15 - 64)(-13 - 59)(-12 - 54)(-13 - 55)(-10 - 46)25141414141210(6 - 44)(3 - 25)(3 - 25)(3 - 25)(3 - 25)(3 - 21)(2 - 17)27252321192119(-34 - 86)(-32 - 81)(-29 - 75)(-27 - 69)(-24 - 63)(-27 - 69)(-24 - 63)269170170170161145121(63 - 473)(40 - 300) (40 - 300) (40 - 300) (37 - 283) (34 - 256) (28 - 213)79656564585646(-46 - 203) (-38 - 169) (-38 - 169) (-37 - 164) (-34 - 151) (-33 - 144) (-27 - 120)19171716151512(-11 - 48)(-10 - 44)(-10 - 44)(-9 - 41)(-9 - 38)(-9 - 38)(-7 - 31)61616161585747(14 - 107)(14 - 107) (14 - 107) (14 - 107) (14 - 102) (13 - 101)(11 - 84)1813131212119(-11 - 47)(-8 - 33)(-8 - 33)(-7 - 32)(-7 - 30)(-6 - 28)(-5 - 23)9 666654(2 - 16)(1 - 10)(1 - 10)(1 - 10)(1 - 10)(1 - 9)(1 - 7)28252322202118(-16 - 72)(-15 - 64)(-14 - 60)(-13 - 56)(-12 - 51)(-13 - 55)(-10 - 46)2 222221(-34 - 37)(-27 - 30)(-27 - 30)(-27 - 30)(-27 - 30)(-23 - 26)(-19 - 21)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTDetroit, MIHouston, TXNew York, NYSt. Louis, MOIncidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Incidence estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital admissions reported inTable 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDE-111Table E-110. Estimated Annual Incidence ofHospital Admissions for Respiratory Illness Associated with Short-Term Exposure toAmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2519171615131513(-24 - 61)(-22 - 56)(-20 - 52)(-19 - 48)(-17 - 44)(-19 - 48)(-17 - 43)18171615141512(-11 - 47)(-10 - 44)(-10 - 42)(-9 - 39)(-8 - 36)(-9 - 38)(-7 - 31)9 766565(-11 - 28)(-8 - 22)(-8 - 20)(-7 - 19)(-7 - 17)(-7 - 19)(-6 - 15)12121212111211(-15 - 40)(-15 - 40)(-15 - 40)(-15 - 40)(-14 - 37)(-15 - 40)(-14 - 37)25202018171714(-15 - 64)(-12 - 52)(-12 - 51)(-11 - 47)(-10 - 44)(-10 - 44)(-8 - 37)27151515151311(6 - 47)(3 - 26)(3 - 26)(3 - 26)(3 - 26)(3 - 22)(2 - 19)26252321192119(-33 - 84)(-31 - 79)(-29 - 73)(-26 - 68)(-24 - 62)(-26 - 68)(-24 - 62)253160160160151136113(59 - 444)(37 - 281) (37 - 281) (37 - 281) (35 - 265) (32 - 240) (26 - 199)68565655504840(-40 - 175) (-33 - 145) (-33 - 145) (-32 - 142) (-30 - 130) (-28 - 124) (-24 - 103)17161615141411(-10 - 45)(-9 - 41)(-9 - 41)(-9 - 39)(-8 - 36)(-8 - 35)(-7 - 29)64646464616050(15 - 112)(15 - 112) (15 - 112) (15 - 112) (14 - 107) (14 - 105)(12 - 87)161111111098(-9 - 40)(-6 - 28)(-6 - 28)(-6 - 27)(-6 - 26)(-5 - 24)(-5 - 20)8 555554(2 - 15)(1 - 9)(1 - 9)(1 - 9)(1 - 9)(1 - 8)(1 - 7)23211918161815(-14 - 59)(-12 - 53)(-11 - 50)(-10 - 46)(-10 - 42)(-10 - 45)(-9 - 38)2 222211(-28 - 31)(-23 - 25)(-23 - 25)(-23 - 25)(-23 - 25)(-19 - 22)(-16 - 18)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:1Incidence estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital admissions reported inTable 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDE-112Table E-111. Estimated Annual Incidence ofHospital Admissions for Respiratory Illness Associated with Short-Term Exposure toAmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/2519181615141513(-24 - 62)(-22 - 57)(-21 - 53)(-19 - 48)(-17 - 44)(-19 - 48)(-17 - 44)18171615141512(-11 - 47)(-10 - 45)(-10 - 42)(-9 - 39)(-8 - 36)(-9 - 38)(-7 - 32)9 766565(-11 - 29)(-9 - 22)(-8 - 21)(-7 - 19)(-7 - 17)(-7 - 19)(-6 - 16)13131313121312(-17 - 43)(-17 - 43)(-17 - 43)(-17 - 43)(-15 - 40)(-17 - 43)(-15 - 40)26212119181815(-15 - 66)(-12 - 54)(-12 - 53)(-11 - 49)(-10 - 45)(-10 - 46)(-9 - 38)28151515151311(7 - 49)(4 - 27)(4 - 27)(4 - 27)(4 - 27)(3 - 23)(3 - 19)27252321202120(-34 - 87)(-32 - 82)(-29 - 76)(-27 - 69)(-25 - 63)(-27 - 69)(-25 - 63)263166166166157142118(61 - 461)(39 - 293) (39 - 293) (39 - 293) (37 - 276) (33 - 250) (27 - 207)75626260565344(-44 - 193) (-37 - 160) (-37 - 160) (-36 - 156) (-33 - 143) (-31 - 137) (-26 - 113)18161615141412(-10 - 46)(-10 - 42)(-10 - 42)(-9 - 39)(-8 - 36)(-8 - 36)(-7 - 30)61616161585748(14 - 108)(14 - 108) (14 - 108) (14 - 108) (14 - 103) (13 - 101)(11 - 84)1611111111108(-10 - 42)(-7 - 29)(-7 - 29)(-7 - 29)(-6 - 27)(-6 - 25)(-5 - 21)11777765(2 - 19)(2 - 12)(2 - 12)(2 - 12)(2 - 12)(1 - 10)(1 - 8)24212019171815(-14 - 62)(-13 - 55)(-12 - 52)(-11 - 48)(-10 - 44)(-11 - 47)(-9 - 39)2 222221(-30 - 33)(-24 - 27)(-24 - 27)(-24 - 27)(-24 - 27)(-21 - 23)(-17 - 19)2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:1Incidence estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lag for respiratory hospital admissions reported inTable 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDE-113Table E-112. Estimated Percent of Total Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure toAmbient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and AlternativeStandards, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/250.5%0.46%0.42%0.39%0.35%0.39%0.35%(-0.63% - 1.61%) (-0.58% - 1.48%) (-0.53% - 1.37%) (-0.49%- 1.26%) (-0.45% - 1.15%) (-0.49% - 1.26%) (-0.44% - 1.13%)0.42%0.39%0.37%0.34%0.32%0.34%0.28%(-0.25% - 1.08%) (-0.23% - 1.02%) (-0.22% - 0.96%) (-0.2% - 0.89%)(-0.19% - 0.82%) (-0.2%- 0.87%) (-0.16% - 0.72%)0.51%0.4%0.37%0.34%0.31%0.34%0.28%(-0.65% - 1.65%) (-0.51% - 1.3%) (-0.47% - 1.2%) (-0.43%- 1.1%) (-0.39% - 1.01%) (-0.43% - 1.1%) (-0.36% - 0.92%)0.39%0.39%0.39%0.39%0.36%0.39%0.36%(-0.49% - 1.26%) (-0.49% - 1.26%) (-0.49% - 1.26%) (-0.49%- 1.26%) (-0.45% - 1.17%) (-0.49% - 1.26%) (-0.45% - 1.17%)0.43%0.35%0.35%0.32%0.3%0.3%0.25%(-0.26% - 1.12%) (-0.21% - 0.91%) (-0.21% - 0.9%) (-0.19% - 0.83%) (-0.17% - 0.76%) (-0.18% - 0.77%) (-0.15% - 0.64%)1.42%0.78%0.78%0.78%0.78%0.67%0.56%(0.33% - 2.49%) (0.18% - 1.38%) (0.18% - 1.38%) (0.18% - 1.38%) (0.18% - 1.38%) (0.16% - 1.18%) (0.13% - 0.98%)0.43%0.4%0.37%0.34%0.31%0.34%0.31%(-0.54% - 1.38%) (-0.51% - 1.3%) (-0.47% - 1.2%) (-0.43%- 1.11%) (-0.39% - 1.01%) (-0.43% - 1.11%) (-0.39% - 1.01%)1.36%0.86%0.86%0.86%0.81%0.73%0.61%(0.32% - 2.38%) (0.2% - 1.51%) (0.2% - 1.51%) (0.2% -1.51%) (0.19% - 1.42%) (0.17% - 1.29%) (0.14% - 1.07%)0.39%0.32%0.32%0.32%0.29%0.28%0.23%(-0.23% - 1.01%) (-0.19% - 0.84%) (-0.19% - 0.84%) (-0.19%- 0.81%) (-0.17% - 0.75%) (-0.16% - 0.71%) (-0.14% - 0.59%)0.37%0.34%0.34%0.32%0.29%0.29%0.24%(-0.22% - 0.95%) (-0.2% - 0.88%) (-0.2% - 0.88%) (-0.19% -0.82%) (-0.17% - 0.75%) (-0.17% - 0.75%) (-0.14% - 0.62%)0.94%0.94%0.94%0.94%0.89%0.88%0.73%(0.22% - 1.65%) (0.22% - 1.65%) (0.22% - 1.65%) (0.22% - 1.65%) (0.21% - 1.57%) (0.21% - 1.55%) (0.17% - 1.29%)0.45%0.32%0.32%0.31%0.29%0.27%0.23%(-0.27% - 1.16%) (-0.19% - 0.82%) (-0.19% - 0.82%) (-0.18% - 0.8%) (-0.17% - 0.76%) (-0.16%- 0.7%) (-0.13% - 0.58%)0.92%0.58%0.58%0.58%0.58%0.49%0.41%(0.21% - 1.61%) (0.14% - 1.02%) (0.14% - 1.02%) (0.14% - 1.02%) (0.14% - 1.02%) (0.12% - 0.87%) (0.1% - 0.72%)0.43%0.39%0.36%0.33%0.31%0.33%0.27%(-0.25% - 1.11%) (-0.23% - 0.99%) (-0.21% - 0.93%) (-0.2% - 0.86%)(-0.18% - 0.79%) (-0.19%- 0.85%) (-0.16% - 0.7%)0.2%0.16%0.16%0.16%0.16%0.14%0.11%(-2.72% - 2.96%) (-2.19% - 2.41%) (-2.19% - 2.41%) (-2.19%- 2.41%) (-2.19% - 2.41%) (-1.87% - 2.06%) (-1.54% - 1.71%)2Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)Standards (Standard Combination Denoted n/m)2:1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lagfor respiratory hospital admissions reported in Table 2 ofBell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-114Table E-113. Estimated Percent of Total Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure toAmbient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and AlternativeStandards, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/250.49%0.45%0.42%0.38%0.35%0.38%0.34%(-0.62% - 1.59%) (-0.57% - 1.46%) (-0.53% - 1.35%) (-0.48% - 1.24%) (-0.44% - 1.13%) (-0.48% - 1.24%) (-0.43% - 1.12%)0.36%0.34%0.32%0.3%0.28%0.29%0.24%(-0.21% - 0.94%) (-0.2% - 0.89%) (-0.19% - 0.84%) (-0.18% - 0.77%) (-0.16% - 0.71%) (-0.17% - 0.76%) (-0.14% - 0.63%)0.48%0.38%0.35%0.32%0.29%0.32%0.27%(-0.61% - 1.57%) (-0.48% - 1.23%) (-0.44% - 1.14%) (-0.41% - 1.04%) (-0.37% - 0.95%) (-0.41% - 1.04%) (-0.34% - 0.87%)0.32%0.32%0.32%0.32%0.3%0.32%0.3%(-0.4% - 1.04%) (-0.4% - 1.04%) (-0.4% - 1.04%) (-0.4% - 1.04%) (-0.37% - 0.96%) (-0.4% - 1.04%) (-0.37% - 0.96%)0.35%0.28%0.28%0.26%0.24%0.24%0.2%(-0.21% - 0.9%) (-0.17% - 0.73%) (-0.17% - 0.73%) (-0.15% - 0.67%) (-0.14% - 0.62%) (-0.14% - 0.63%) (-0.12% - 0.52%)1.49%0.83%0.83%0.83%0.83%0.71%0.58%(0.35% - 2.62%) (0.19% - 1.45%) (0.19% - 1.45%) (0.19% - 1.45%) (0.19% - 1.45%) (0.16% - 1.24%) (0.14% - 1.03%)0.4%0.38%0.35%0.32%0.29%0.32%0.29%(-0.51% - 1.3%) (-0.48% - 1.23%) (-0.44% - 1.13%) (-0.4% - 1.04%) (-0.37% - 0.95%) (-0.4% - 1.04%) (-0.37% - 0.95%)1.25%0.79%0.79%0.79%0.75%0.68%0.56%(0.29% - 2.2%) (0.18% - 1.4%) (0.18% - 1.4%) (0.18% - 1.4%) (0.17% - 1.32%) (0.16% - 1.19%) (0.13% - 0.99%)0.33%0.28%0.28%0.27%0.25%0.24%0.2%(-0.2% - 0.86%) (-0.16% - 0.71%) (-0.16% - 0.71%) (-0.16% - 0.7%) (-0.15% - 0.64%) (-0.14% - 0.61%) (-0.12% - 0.51%)0.35%0.32%0.32%0.3%0.28%0.28%0.23%(-0.21% - 0.91%) (-0.19% - 0.83%) (-0.19% - 0.83%) (-0.18% - 0.78%) (-0.16% - 0.72%) (-0.16% - 0.71%) (-0.14% - 0.59%)0.95%0.95%0.95%0.95%0.9%0.89%0.74%(0.22% - 1.67%) (0.22% - 1.67%) (0.22% - 1.67%) (0.22% - 1.67%) (0.21% - 1.59%) (0.21% - 1.57%) (0.17% - 1.3%)0.39%0.28%0.28%0.27%0.26%0.24%0.19%(-0.23% - 1.01%) (-0.16% - 0.71%) (-0.16% - 0.71%) (-0.16% - 0.69%) (-0.15% - 0.66%) (-0.14% - 0.61%) (-0.11% - 0.5%)0.82%0.51%0.51%0.51%0.51%0.44%0.36%(0.19% - 1.43%) (0.12% - 0.91%) (0.12% - 0.91%) (0.12% - 0.91%) (0.12% - 0.91%) (0.1% - 0.77%) (0.08% - 0.64%)0.36%0.32%0.3%0.28%0.25%0.27%0.23%(-0.21% - 0.92%) (-0.19% - 0.82%) (-0.18% - 0.77%) (-0.16% - 0.71%) (-0.15% - 0.65%) (-0.16% - 0.7%) (-0.13% - 0.58%)0.16%0.13%0.13%0.13%0.13%0.11%0.09%(-2.23% - 2.45%) (-1.8% - 1.99%) (-1.8% - 1.99%) (-1.8% - 1.99%) (-1.8% - 1.99%) (-1.53% - 1.7%) (-1.26% - 1.41%)2Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)Standards (Standard Combination Denoted n/m)2:1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lagfor respiratory hospital admissions reported in Table 2 ofBell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-115Table E-114. Estimated Percent of Total Annual Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure toAmbient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and AlternativeStandards, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/250.48%0.44%0.41%0.38%0.34%0.38%0.34%(-0.61% - 1.56%) (-0.56% - 1.43%) (-0.52% - 1.32%) (-0.47% - 1.22%) (-0.43% - 1.11%) (-0.47% - 1.22%) (-0.43% - 1.09%)0.37%0.35%0.33%0.3%0.28%0.3%0.25%(-0.22% - 0.95%) (-0.2% - 0.9%) (-0.19% - 0.85%) (-0.18% - 0.78%) (-0.16% - 0.72%) (-0.18% - 0.77%) (-0.15% - 0.64%)0.5%0.39%0.36%0.33%0.3%0.33%0.27%(-0.63% - 1.6%) (-0.49% - 1.26%) (-0.45% - 1.16%) (-0.42% - 1.07%) (-0.38% - 0.97%) (-0.42% - 1.07%) (-0.34% - 0.89%)0.34%0.34%0.34%0.34%0.31%0.34%0.31%(-0.42% - 1.09%) (-0.42% - 1.09%) (-0.42% - 1.09%) (-0.42% - 1.09%) (-0.39% - 1.01%) (-0.42% - 1.09%) (-0.39% - 1.01%)0.37%0.3%0.29%0.27%0.25%0.25%0.21%(-0.22% - 0.94%) (-0.17% - 0.77%) (-0.17% - 0.76%) (-0.16% - 0.7%) (-0.15% - 0.64%) (-0.15% - 0.65%) (-0.12% - 0.54%)1.52%0.84%0.84%0.84%0.84%0.72%0.6%(0.36% - 2.66%) (0.2% - 1.48%) (0.2% - 1.48%) (0.2% - 1.48%) (0.2% - 1.48%) (0.17% - 1.26%) (0.14% - 1.05%)0.4%0.38%0.35%0.32%0.29%0.32%0.29%(-0.51% - 1.3%) (-0.48% - 1.23%) (-0.44% - 1.13%) (-0.41% - 1.04%) (-0.37% - 0.95%) (-0.41% - 1.04%) (-0.37% - 0.95%)1.28%0.81%0.81%0.81%0.76%0.69%0.57%(0.3% - 2.25%) (0.19% - 1.42%) (0.19% - 1.42%) (0.19% - 1.42%) (0.18% - 1.34%) (0.16% - 1.22%) (0.13% - 1.01%)0.36%0.3%0.3%0.29%0.27%0.26%0.21%(-0.21% - 0.94%) (-0.18% - 0.78%) (-0.18% - 0.78%) (-0.17% - 0.76%) (-0.16% - 0.7%) (-0.15% - 0.67%) (-0.13% - 0.55%)0.36%0.33%0.33%0.31%0.28%0.28%0.23%(-0.21% - 0.93%) (-0.2% - 0.85%) (-0.2% - 0.85%) (-0.18% - 0.8%) (-0.17% - 0.73%) (-0.17% - 0.73%) (-0.14% - 0.61%)0.88%0.88%0.88%0.88%0.84%0.83%0.69%(0.21% - 1.55%) (0.21% - 1.55%) (0.21% - 1.55%) (0.21%- 1.55%) (0.2% - 1.48%) (0.19% - 1.46%) (0.16% - 1.21%)0.42%0.29%0.29%0.28%0.27%0.25%0.21%(-0.24% - 1.07%) (-0.17% - 0.75%) (-0.17% - 0.75%) (-0.17% - 0.73%) (-0.16% - 0.7%) (-0.15% - 0.64%) (-0.12% - 0.53%)0.99%0.63%0.63%0.63%0.63%0.54%0.44%(0.23% - 1.74%) (0.15% - 1.1%) (0.15% - 1.1%) (0.15% - 1.1%) (0.15% - 1.1%) (0.12% - 0.94%) (0.1% - 0.78%)0.37%0.33%0.31%0.29%0.26%0.28%0.24%(-0.22% - 0.96%) (-0.2% - 0.86%) (-0.18% - 0.81%) (-0.17% - 0.74%) (-0.16% - 0.68%) (-0.17% - 0.73%) (-0.14% - 0.61%)0.17%0.14%0.14%0.14%0.14%0.12%0.1%(-2.31% - 2.52%) (-1.86% - 2.05%) (-1.86% - 2.05%) (-1.86% - 2.05%) (-1.86% - 2.05%) (-1.59% - 1.76%) (-1.31% - 1.46%)2Percents rounded to the nearest hundredth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)Standards (Standard Combination Denoted n/m)2:1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lagfor respiratory hospital admissions reported in Table 2 ofBell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTE-116Table E-115. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Hospital Admissions Associated withShort-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-9%0%8%15%23%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (24% - 24%)-6%0%6%13%20%15%29%(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-28%0%8%15%23%15%30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)-81%0%0%0%0%15%29%(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6%0%8%15%23%15%23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)-58%0%0%0%6%15%29%(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20%0%0%3%11%15%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (11% - 11%) (15% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 15% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (15% - 15%) (29% - 29%)0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-41%0%0%3%8%15%29%(-41% - -42%) (0% - 0%) (0% - 0%) (3% - 3%) (8% - 8%) (15% - 15%) (29% - 29%)-58%0%0%0%0%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12%0%6%13%20%15%29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-23%0%0%0%0%15%29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 30%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lagfor respiratory hospital admissions reported in Table 2 ofBell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidenceof Respiratory Hospital Admissions Associated with ShortTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXE-117Table E-116. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Hospital Admissions Associated withShort-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-9%0%8%15%23%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (24% - 24%)-6%0%6%13%20%15%29%(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-28%0%8%15%23%15%30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)-81%0%0%0%0%15%29%(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-6%0%8%15%23%15%23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)-58%0%0%0%6%15%29%(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20%0%0%3%11%15%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (11% - 11%) (15% - 15%) (29% - 29%)-9%0%0%6%14%15%29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (15% - 15%) (29% - 29%)0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-43%0%0%3%7%15%29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%) (29% - 29%)-58%0%0%0%0%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12%0%6%13%20%15%29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-23%0%0%0%0%15%29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 30%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lagfor respiratory hospital admissions reported in Table 2 ofBell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Respiratory Hospital Admissions Associated with ShortTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-118Table E-117. Percent Reduction from the Current Standards: Estimated Annual Incidence of Respiratory Hospital Admissions Associated withShort-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35314/3513/3512/3513/3012/25-9%0%8%15%23%15%24%(-9% - -9%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (24% - 24%)-6%0%6%13%20%15%29%(-6% - -6%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-28%0%8%15%23%15%30%(-28% - -28%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (29% - 30%)0%0%0%0%7%0%7%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (7% - 7%) (0% - 0%) (7% - 7%)-23%0%1%8%16%15%29%(-23% - -23%) (0% - 0%) (1% - 1%) (8% - 8%) (16% - 16%) (15% - 15%) (29% - 29%)-81%0%0%0%0%15%29%(-80% - -82%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 29%)-6%0%8%15%23%15%23%(-6% - -6%) (0% - 0%) (7% - 8%) (15% - 15%) (22% - 23%) (15% - 15%) (22% - 23%)-58%0%0%0%6%15%29%(-58% - -58%) (0% - 0%) (0% - 0%) (0% - 0%) (6% - 6%) (15% - 15%) (29% - 29%)-20%0%0%3%11%15%29%(-20% - -20%) (0% - 0%) (0% - 0%) (3% - 3%) (11% - 11%) (15% - 15%) (29% - 29%)-9% 0% 0% 6% 14% 15% 29%(-9% - -9%) (0% - 0%) (0% - 0%) (6% - 7%) (14% - 14%) (15% - 15%) (29% - 29%)0%0%0%0%5%6%22%(0% - 0%)(0% - 0%) (0% - 0%) (0% - 0%) (5% - 5%) (6% - 6%) (22% - 22%)-43%0%0%3%7%15%29%(-42% - -43%) (0% - 0%) (0% - 0%) (3% - 3%) (7% - 7%) (15% - 15%) (29% - 29%)-58%0%0%0%0%15%29%(-58% - -59%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (15% - 15%) (29% - 29%)-12%0%6%13%20%15%29%(-12% - -12%) (0% - 0%) (6% - 6%) (13% - 13%) (20% - 20%) (15% - 15%) (29% - 29%)-23%0%0%0%0%15%29%(-23% - -24%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (14% - 15%) (29% - 30%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Estimates were calculated using the appropriate regional concentration-response function estimates from models with a 2-day lagfor respiratory hospital admissions reported in Table 2 ofBell et al. (2008). Location-specific C-R function estimates were not available from this study.Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidenceof Respiratory Hospital Admissions Associated with ShortTerm Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and AlternativeAnnual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIE-119Table E-118. Estimated AnnualIncidence of Emergency Room (ER) Visits Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2005) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/25216198183169154169151(-304 - 727) (-279 - 668) (-258 - 618) (-237 - 568) (-216 - 518) (-237 - 568) (-212 - 511)814746690634578634570(-816 - 2419) (-748 - 2220) (-691 - 2055) (-635 - 1889) (-578 - 1723) (-635 - 1889) (-570 - 1698)5235437543754265392737543127(3346 - 7071) (2790 - 5923) (2790 - 5923) (2719 - 5776) (2501 - 5323) (2390 - 5091) (1987 - 4248)Tolbert et al. (2007)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.StudyTolbert et al. (2007)Ito et al. (2007)Location ER Visit for:Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards(Standard Combination Denoted n/m):New York, NY AsthmaAtlanta, GACardiovascularillnessAtlanta, GARespiratoryillnessE-120Table E-119. Estimated Annual Incidenceof Emergency Room (ER) Visits Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current andAlternative Standards, Based on Adjusting 2006PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/25220202187172157172154(-310 - 741) (-284 - 681) (-263 - 630) (-241 - 579) (-220 - 528) (-241 - 579) (-216 - 521)829761704647589647581(-831 - 2465) (-762 - 2263) (-705 - 2094) (-647 - 1925) (-590 - 1756) (-647 - 1925) (-581 - 1730)4506376437643669337732282688(2876 - 6095) (2397 - 5102) (2397 - 5102) (2336 - 4974) (2149 - 4582) (2053 - 4382) (1707 - 3654)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tolbert et al. (2007)1Numbers rounded to the nearest whole number.Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.StudyTolbert et al. (2007)Ito et al. (2007)Location ER Visit for:Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m):New York, NY AsthmaAtlanta, GACardiovascularillnessAtlanta, GARespiratoryillnessE-121Table E-120. Estimated Annual Incidence ofEmergency Room (ER) Visits Associated with Short-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2007) and PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/25219201186171156171154(-308 - 738) (-283 - 677) (-261 - 627) (-240 - 576) (-219 - 526) (-240 - 576) (-215 - 518)825757700643586643578(-827 - 2453) (-758 - 2251) (-701 - 2084) (-644 - 1915) (-587 - 1747) (-644 - 1915) (-578 - 1721)4926411541154011369235292939(3145 - 6660) (2622 - 5575) (2622 - 5575) (2555 - 5436) (2350 - 5008) (2245 - 4790) (1867 - 3995)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tolbert et al. (2007)1Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.StudyTolbert et al. (2007)Ito et al. (2007)Location ER Visit for:Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards(Standard Combination Denoted n/m):New York, NY AsthmaAtlanta, GACardiovascularillnessAtlanta, GARespiratoryillnessE-122Table E-121. Estimated Percent of Total Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exposure toAmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/250.6%0.6%0.5%0.5%0.5%0.5%0.5%(-0.9% - 2.2%) (-0.8% - 2%) (-0.8% - 1.9%) (-0.7% - 1.7%) (-0.6% - 1.6%) (-0.7% - 1.7%) (-0.6% - 1.5%)0.6%0.6%0.5%0.5%0.5%0.5%0.5%(-0.6% - 1.9%) (-0.6% - 1.8%) (-0.6% - 1.6%) (-0.5% - 1.5%) (-0.5% - 1.4%) (-0.5% - 1.5%) (-0.5% - 1.4%)6.1%5.1%5.1%5%4.6%4.4%3.6%(3.9% - 8.2%) (3.3% - 6.9%) (3.3% - 6.9%) (3.2% - 6.7%) (2.9% - 6.2%) (2.8% - 5.9%) (2.3% - 5%)AsthmaAtlanta, GACardiovascularillnessAtlanta, GARespiratoryillnessPercent of Total Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)Standards (Standard Combination Denoted n/m):Tolbert et al. (2007)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.StudyTolbert et al. (2007)Ito et al. (2007)Location ER Visit for:New York, NYE-123Table E-122. Estimated Percent of Total Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exposure toAmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/250.6%0.6%0.5%0.5%0.5%0.5%0.4%(-0.9% - 2.1%) (-0.8% - 2%) (-0.8% - 1.8%) (-0.7% - 1.7%) (-0.6% - 1.5%) (-0.7% - 1.7%) (-0.6% - 1.5%)0.6%0.6%0.5%0.5%0.5%0.5%0.4%(-0.6% - 1.9%) (-0.6% - 1.7%) (-0.5% - 1.6%) (-0.5% - 1.5%) (-0.5% - 1.4%) (-0.5% - 1.5%) (-0.4% - 1.3%)5.2%4.4%4.4%4.3%3.9%3.7%3.1%(3.3% - 7.1%) (2.8% - 5.9%) (2.8% - 5.9%) (2.7% - 5.8%) (2.5% - 5.3%) (2.4% - 5.1%) (2% - 4.2%)AsthmaAtlanta, GACardiovascularillnessAtlanta, GARespiratoryillnessPercent of Total Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)Standards (Standard Combination Denoted n/m):Tolbert et al. (2007)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.StudyTolbert et al. (2007)Ito et al. (2007)Location ER Visit for:New York, NYE-124Table E-123. Estimated Percent of Total Annual Incidence of Emergency Room (ER) Visits Associated with Short-Term Exposure toAmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35214/35 13/35 12/35 13/30 12/250.6%0.6%0.5%0.5%0.4%0.5%0.4%(-0.9% - 2.1%) (-0.8% - 1.9%) (-0.7% - 1.8%) (-0.7% - 1.6%) (-0.6% - 1.5%) (-0.7% - 1.6%) (-0.6% - 1.5%)0.6%0.6%0.5%0.5%0.4%0.5%0.4%(-0.6% - 1.8%) (-0.6% - 1.7%) (-0.5% - 1.6%) (-0.5% - 1.4%) (-0.4% - 1.3%) (-0.5% - 1.4%) (-0.4% - 1.3%)5.7%4.8%4.8%4.6%4.3%4.1%3.4%(3.6% - 7.7%) (3% - 6.5%) (3% - 6.5%) (3% - 6.3%) (2.7% - 5.8%) (2.6% - 5.5%) (2.2% - 4.6%)AsthmaAtlanta, GACardiovascularillnessAtlanta, GARespiratoryillnessPercent of Total Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in aRecent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m)Standards (Standard Combination Denoted n/m):Tolbert et al. (2007)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Percents rounded to the nearest tenth. Numbers in parenthesesare 95% confidence or credibleintervals based on statistical uncertainty surrounding the PM coefficient.StudyTolbert et al. (2007)Ito et al. (2007)Location ER Visit for:New York, NY

# F-1 

# APPENDIX F: SENSITIVITY ANALYSIS RESULTS

# F-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 ge nerated, see section 4.3.F-3Table F-1. Sensitivity Analysis: Impact of Using Different Model Choices to Estimate the Incidence of Mortality Associated with Long-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1Standard FixedEffects Log-Linear(Cox ProportionalHazard)Model3Random Effects LogLinear Model4Random Effects Log-Log Model5Fixed Effects vs.Random Effects Log-Linear ModelsFixed Effects vs.Random Effects Log-Log Models134216563360(854 - 1827)(772 - 2527)(2075 - 4615)15262569(1191 - 1856)(1709 - 3400)124913972535(1017 - 1477)(847 - 1924)(1793 - 3232)164307(71 - 253)(160 - 446)5847191254(372 - 792)(337 - 1090)(779 - 1713)545790(427 - 660)(530 - 1038)369411639(303 - 434)(253 - 558)(458 - 803)88142(39 - 135)(75 - 204)6Calculated as (core analysis model estimate - alternative model estimate)/(core analysis model estimate.)Health EndpointIncidence of Mortality Associated with Long-Term Exposure toPM2.5Concentrations Using:2Percent Difference6Los Angeles, CAAll Cause Mortality23%150%Cardiopulmonary Mortality---7---68%Ischemic Heart Disease Mortality12%103%Lung Cancer Mortality------87%Philadelphia, PAAll Cause Mortality23%115%Cardiopulmonary Mortality------45%Ischemic Heart Disease Mortality11%73%Lung Cancer Mortality------61%5Estimates based on Table 11, "MSA and DIFF" rows, in Krewski et al. (2009) -- exposure period from 1999 - 2000.7Estimates for cardiopulmonary mortality and lung cancer mortality were not available for the random effects log-linear model.1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.2Mortality incidence was estimated for PM2.5concentrations down to the lowest measured level in the study (5.8 ug/m3). Numbers rounded to the nearest whole number. Numbers inparentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).4Estimates based on Table 9. Autocorrelation at MSA and ZCA levels; MSA & DIFF, in Krewski et al. (2009) -- exposure period from 1999 - 2000.F-4Table F-2. Sensitivity Analysis: Impact of Using Different Model Choices to Estimate the Incidence of Mortality Associated with Long-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1Standard FixedEffects Log-Linear(Cox ProportionalHazard)Model3Random Effects LogLinear Model4Random Effects Log-Log Model5Fixed Effects vs.Random Effects Log-Linear ModelsFixed Effects vs.Random Effects Log-Log Models110813682904(704 - 1509)(637 - 2090)(1790 - 3995)12632225(985 - 1538)(1477 - 2953)103811622212(843 - 1229)(702 - 1605)(1558 - 2833)135266(59 - 210)(138 - 388)5256471166(335 - 713)(303 - 982)(723 - 1595)491736(385 - 596)(493 - 969)334372598(273 - 393)(228 - 507)(428 - 755)80133(35 - 122)(70 - 191)6Calculated as (core analysis model estimate - alternative model estimate)/(core analysis model estimate.)5Estimates based on Table 11, "MSA and DIFF" rows, in Krewski et al. (2009) -- exposure period from 1999 - 2000.7Estimates for cardiopulmonary mortality and lung cancer mortality were not available for the random effects log-linear model.1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.2Mortality incidence was estimated for PM2.5concentrations down to the lowest measured level in the study (5.8 ug/m3). Numbers rounded to the nearest whole number. Numbers inparentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.3Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).4Estimates based on Table 9. Autocorrelation at MSA and ZCA levels; MSA & DIFF, in Krewski et al. (2009) -- exposure period from 1999 - 2000.Ischemic Heart Disease Mortality11%79%Lung Cancer Mortality------66%Cardiopulmonary Mortality------50%Philadelphia, PAAll Cause Mortality23%122%Ischemic Heart Disease Mortality12%113%Lung Cancer Mortality------97%All Cause Mortality23%162%Cardiopulmonary Mortality---7---76%Health EndpointIncidence of Mortality Associated with Long-Term Exposure toPM2.5Concentrations Using:2Percent Difference6Los Angeles, CAF-5Table F-3. Sensitivity Analysis: Impact of Using Different Model Choices to Estimate the Incidence of Mortality Associated with Long-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5Concentrations1Standard FixedEffects Log-Linear(Cox ProportionalHazard)Model3Random Effects LogLinear Model4Random Effects Log-Log Model5Fixed Effects vs.Random Effects Log-Linear ModelsFixed Effects vs.Random Effects Log-Log Models117014443034(744 - 1593)(672 - 2206)(1871 - 4173)13332324(1040 - 1623)(1544 - 3082)109412252306(890 - 1296)(741 - 1691)(1626 - 2950)143278(62 - 222)(145 - 405)5196391157(331 - 704)(299 - 971)(718 - 1583)486731(381 - 589)(489 - 962)330368594(270 - 389)(226 - 502)(424 - 750)79132(35 - 121)(69 - 190)6Calculated as (core analysis model estimate - alternative model estimate)/(core analysis model estimate.)All Cause MortalityCardiopulmonary MortalityIschemic Heart Disease MortalityLung Cancer Mortality5Estimates based on Table 11, "MSA and DIFF" rows, in Krewski et al. (2009) -- exposure period from 1999 - 2000.3Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.All Cause MortalityIschemic Heart Disease Mortality2Mortality incidence was estimated for PM2.5concentrations down to the lowest measured level in the study (5.8 ug/m3). Numbers rounded to the nearest whole number. Numbers inparentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Lung Cancer Mortality80%67%23%Incidence of Mortality Associated with Long-Term Exposure toPM2.5Concentrations Using:2123%50%159%Los Angeles, CACardiopulmonary Mortality---Percent Difference623%12%Philadelphia, PA74%111%94%---Health Endpoint---4Estimates based on Table 9. Autocorrelation at MSA and ZCA levels; MSA & DIFF, in Krewski et al. (2009) -- exposure period from 1999 - 2000.7Estimates for cardiopulmonary mortality and lung cancer mortality were not available for the random effects log-linear model.---7---------12%---

# F-6 Table F-4. Sensitivity Analysis: Impact of Limiting Estimated Annual Incidence of All-Cause Mortality

Associated with Long-Term Exposure to PM 2.5 

# Concentrations that Just Meet the Current

Standards to the Lowest Measured Level in the Study vs. to PRB, Based on Adjusting 2005

PM 2.5 

# Concentrations 1, 2



# Lowest Measured Level in

Study (5.8 ug/m3) Estimated PRB

736 1057

(470 - 997) (678 - 1426)

702 1073

(448 - 950) (689 - 1446)

380 592

(243 - 516) (379 - 800)

486 762

(310 - 659) (488 - 1030)

743 1205

(474 - 1008) (773 - 1626)

114 262

(72 - 155) (167 - 355)

713 1114

(455 - 968) (713 - 1506)

1342 2845

(854 - 1827) (1819 - 3853)

1893 3299

(1207 - 2571) (2113 - 4456)

584 971

(372 - 792) (622 - 1310)

620 1255

(394 - 843) (803 - 1698)

497 859

(317 - 674) (550 - 1161)

37 161

(24 - 51) (102 - 218)

897 1381

(573 - 1215) (887 - 1862)

103 234

(66 - 141) (149 - 317)

3 

# Calculated as (mortality estimated down to PRB - mortality estimated down to LML)/(mortality estimated down to LML). 

# 62%

130%

56%

112% 44%

53%

56%

57% 

# Dallas, TX

Fresno, CA

Los Angeles, CA

Risk Assessment Location

Atlanta, GA

Birmingham, AL

Houston, TX

New York, NY

2 

# The current primary PM 2.5 

# standards include an annual standard set at 15 ug/m 3 

# and a daily standard set at 35 ug/m 3



# . 1 

# Estimates based on Table 33 in Krewski et al. (2009) -- exposure period from 1999 - 2000, follow-up through 2000, models with 4 4 individual

and 7 ecological covariates. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credibl e intervals

based on statistical uncertainty surrounding the PM coefficient. 

# 74%

66%

102%

73%

335%

54%

127%

# Incidence of All Cause Mortality Associated with LongTerm Exposure to PM 2.5 

# Concentrations Measured

Down to: Percent Difference 3



# St. Louis, MO

Tacoma, WA Philadelphia, PA

Phoenix, AZ

Pittsburgh, PA

Salt Lake City, UT Baltimore, MD

Detroit, MI

# F-7 Table F-5. Sensitivity Analysis: Impact of Limiting Estimated Annual Incidence of Ischemic Heart Disease

Mortality As sociated with Long-Term Exposure to PM 2.5 

# Concentrations that Just Meet the Current

Standards to the Lowest Measu red Level in the Study vs. to PRB, Based on Adjusting 2006 PM 2.5



# Concentrations 1, 2



# Lowest Measured Level in

Study (5.8 ug/m 3 

# ) Estimated PRB

287 400

(236 - 336) (331 - 465)

375 601

(307 - 441) (497 - 699)

157 244

(128 - 184) (201 - 285)

222 384

(181 - 262) (315 - 450)

449 829

(367 - 530) (683 - 969)

92 198

(75 - 108) (162 - 232)

416 646

(340 - 490) (533 - 755)

1038 2366

(843 - 1229) (1943 - 2775)

1865 3618

(1520 - 2203) (2979 - 4232)

334 559

(273 - 393) (461 - 651)

471 907

(384 - 557) (747 - 1061)

279 531

(228 - 330) (437 - 621)

8 5 7

(6 - 10) (47 - 67)

512 862

(419 - 603) (712 - 1006)

46 143

(37 - 55) (117 - 168)

3 

# Calculated as (IHD mortality estimated down to PRB - IHD mortalit y estimated down to LML)/(IHD mortality estimated down to LML ). 

# 613%

68%

211% 94%

67%

93%

90% 85%

115%

55%

128%



# Tacoma, WA Philadelphia, PA

Phoenix, AZ

Pittsburgh, PA

Salt Lake City, UT

2 

# The current primary PM 2.5 

# standards include an annual standard set at 15 ug/m 3 

# and a daily standard set at 35 ug/m 3



# . 1 

# Estimates based on Table 33 in Krewski et al. (2009) -- exposure period from 1999 - 2000, follow-up through 2000, models with 4 4 individual

and 7 ecological covariates. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals

based on statistical uncertainty surrounding the PM coefficient. 

# Dallas, TX

Fresno, CA

Los Angeles, CA 73%



# Detroit, MI

Houston, TX

New York, NY

St. Louis, MO

Risk Assessment Location

Atlanta, GA

Birmingham, AL 55% 

# Baltimore, MD Incidence of Ischemic Heart Disease Mortality

Associated with Long-Term Exposure to PM 2.5



# Concentrations Measured Down to: Percent Difference 3



# 39%

60%

# F-8 Table F-6. Sensitivity Analysis: Impact of Limiting Estimated Annual Incidence of All-Cause Mortality

Associated with Long-Term Exposure to PM 2.5 

# Concentrations that Just Meet the Current

Standards to the Lowest Measured Level in the Study vs. to PRB, Based on Adjusting 2007

PM 2.5 

# Concentrations 1, 2



# Lowest Measured Level in

Study (5.8 ug/m3) Estimated PRB

726 1067

(464 - 984) (684 - 1440)

564 938

(360 - 765) (602 - 1267)

374 590

(238 - 507) (377 - 797)

407 696

(259 - 553) (445 - 942)

544 1007

(346 - 739) (644 - 1362)

130 282

(82 - 177) (180 - 382)

719 1143

(459 - 977) (732 - 1545)

1170 2697

(744 - 1593) (1723 - 3654)

1689 3124

(1076 - 2295) (2000 - 4224)

519 907

(331 - 704) (581 - 1225)

556 1240

(354 - 757) (792 - 1678)

434 795

(277 - 590) (509 - 1074)

48 179

(31 - 66) (114 - 244)

728 1220

(464 - 988) (782 - 1648)

64 201

(41 - 88) (128 - 272)

3 

# Calculated as (mortality estimated down to PRB - mortality estimated down to LML)/(mortality estimated down to LML). 

# 85%

117%

59%

131% 47%

66%

58%

71% 

# Dallas, TX

Fresno, CA

Los Angeles, CA

Risk Assessment Location

Atlanta, GA

Birmingham, AL

Houston, TX

New York, NY

2 

# The current primary PM 2.5 

# standards include an annual standard set at 15 ug/m 3 

# and a daily standard set at 35 ug/m 3



# . 1 

# Estimates based on Table 33 in Krewski et al. (2009) -- exposure period from 1999 - 2000, follow-up through 2000, models with 4 4 individual

and 7 ecological covariates. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credibl e intervals

based on statistical uncertainty surrounding the PM coefficient. 

# 85%

75%

123%

83%

273%

68%

214%

# Incidence of All Cause Mortality Associated with LongTerm Exposure to PM 2.5 

# Concentrations Measured

Down to: Percent Difference 3



# St. Louis, MO

Tacoma, WA Philadelphia, PA

Phoenix, AZ

Pittsburgh, PA

Salt Lake City, UT Baltimore, MD

Detroit, MI

# F-9 Table F-7. Sensitivity Analysis: Impact of Using a Different Study to Estimate the Incidence of Mortality

Associated with Long-Term Exposure to PM 2.5 

# Concentrations that Just Meet the Current

Standards, Based on Adjusting 2005 PM 2.5 

# Concentrations 1



# Krewski et al. (2009) 3 

# Krewski et al. (2000) 4



# 1342 2965

(854 - 1827) (1005 - 4855)

1526 1981

(1191 - 1856) (693 - 3207)

164 212

(71 - 253) (-152 - 535)

584 1276

(372 - 792) (438 - 2064)

545 704

(427 - 660) (250 - 1121)

88 114

(39 - 135) (-85 - 276) 30%

29%

2 

# Mortality incidence was estimated for PM 2.5 

# concentrations down to the lowest measured level in Krewski et al., 2009 (5.8 ug/m 3 

# ). Numbers

rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncer tainty

5 

# Calculated as (Krewski et al. (2000) estimate - Krewski et al. (2009) estimate)/(Krewski et al. (2009) estimate). 4 

# Estimates based on Table 21b in Krewski et al. (2000) [reanalysis of Six Cities Study]. 3 

# Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological co variates

(see Table 33 in Krewski et al., 2009). 1 

# The current primary PM 2.5 

# standards include an annual standard set at 15 ug/m 3 

# and a daily standard set at 35 ug/m 3



# . 

# 30% 

# Incidence of Mortality Associated with Long-Term

Exposure to PM 2.5 

# Concentrations Using: 2



# Percent Difference 5



# 118%

29% 121% 

# Los Angeles, CA

Cardiopulmonary Mortality Philadelphia, PA Health Endpoint

All Cause Mortality All Cause Mortality

Cardiopulmonary Mortality

Lung Cancer Mortality

Lung Cancer Mortality

# F-10 Table F-8. Sensitivity Analysis: Impact of Using a Different Study to Estimate the Incidence of Mortality

Associated with Long-Term Exposure to PM 2.5 

# Concentrations that Just Meet the Current

Standards, Based on Adjusting 2006 PM 2.5 

# Concentrations 1



# Krewski et al. (2009) 3 

# Krewski et al. (2000) 4



# 1108 2454

(704 - 1509) (829 - 4031)

1263 1642

(985 - 1538) (572 - 2671)

135 176

(59 - 210) (-124 - 448)

525 1150

(335 - 713) (394 - 1866)

491 635

(385 - 596) (225 - 1016)

80 103

(35 - 122) (-76 - 251) 30%

30%

2 

# Mortality incidence was estimated for PM 2.5 

# concentrations down to the lowest measured level in Krewski et al., 2009 (5.8 ug/m 3 

# ). Numbers

rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncer tainty

5 

# Calculated as (Krewski et al. (2000) estimate - Krewski et al. (2009) estimate)/(Krewski et al. (2009) estimate). 4 

# Estimates based on Table 21b in Krewski et al. (2000) [reanalysis of Six Cities Study]. 3 

# Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological co variates

(see Table 33 in Krewski et al., 2009). 1 

# The current primary PM 2.5 

# standards include an annual standard set at 15 ug/m 3 

# and a daily standard set at 35 ug/m 3



# . 

# 29% 

# Incidence of Mortality Associated with Long-Term

Exposure to PM 2.5 

# Concentrations Using: 2



# Percent Difference 5



# 119%

29% 121% 

# Los Angeles, CA

Cardiopulmonary Mortality Philadelphia, PA Health Endpoint

All Cause Mortality All Cause Mortality

Cardiopulmonary Mortality

Lung Cancer Mortality

Lung Cancer Mortality

# F-11 Table F-9. Sensitivity Analysis: Impact of Using a Different Study to Estimate the Incidence of Mortality

Associated with Long-Term Exposure to PM 2.5 

# Concentrations that Just Meet the Current

Standards, Based on Adjusting 2007 PM 2.5 

# Concentrations 1



# Krewski et al. (2009) 3 

# Krewski et al. (2000) 4



# 1170 2590

(744 - 1593) (876 - 4252)

1333 1732

(1040 - 1623) (604 - 2815)

143 186

(62 - 222) (-131 - 472)

519 1137

(331 - 704) (389 - 1846)

486 628

(381 - 589) (223 - 1005)

79 102

(35 - 121) (-75 - 249) 30%

30%

2 

# Mortality incidence was estimated for PM 2.5 

# concentrations down to the lowest measured level in Krewski et al., 2009 (5.8 ug/m 3 

# ). Numbers

rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncer tainty

surrounding the PM coefficient.

5 

# Calculated as (Krewski et al. (2000) estimate - Krewski et al. (2009) estimate)/(Krewski et al. (2009) estimate). 4 

# Estimates based on Table 21b in Krewski et al. (2000) [reanalysis of Six Cities Study]. 3 

# Estimates Based on Krewski et al. (2009), exposure period from 1999 - 2000, using models with 44 individual and 7 ecological co variates

(see Table 33 in Krewski et al., 2009). 1 

# The current primary PM 2.5 

# standards include an annual standard set at 15 ug/m 3 

# and a daily standard set at 35 ug/m 3



# . 

# 29% 

# Incidence of Mortality Associated with Long-Term

Exposure to PM 2.5 

# Concentrations Using: 2



# Percent Difference 5



# 119%

29% 121% 

# Los Angeles, CA

Cardiopulmonary Mortality Philadelphia, PA Health Endpoint

All Cause Mortality All Cause Mortality

Cardiopulmonary Mortality

Lung Cancer Mortality

Lung Cancer MortalityF-12Table F-10. Sensitivity Analysis: Estimated Annual Incidence ofAll Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005 PM2.5Concentrations:Comparison of Proportional and Hybrid Rollback Methods115/35214/3513/3512/3513/3012/25Proportional702643566490546388(448 - 950) (410 - 871) (361 - 768) (312 - 665) (348 - 741) (247 - 528)Hybrid691667589511537381(442 - 936) (426 - 904) (376 - 799) (326 - 694) (342 - 729) (242 - 518)Percent Difference3-2% 4% 4% 4% -2% -2%Proportional380336292247292205(243 - 516) (214 - 457) (186 - 397) (157 - 336) (186 - 397) (130 - 280)Hybrid461411360310360274(294 - 624) (262 - 557) (230 - 489) (197 - 421) (230 - 489) (174 - 372)Percent Difference21%22%23%26%23%34%Proportional743734643552567389(474 - 1008) (468 - 996) (410 - 874) (352 - 751) (361 - 770) (247 - 530)Hybrid773773750651593411(493 - 1048) (493 - 1048) (479 - 1018) (415 - 884) (378 - 805) (261 - 559)Percent Difference4%5%17%18%5%6%Proportional1342134213421180924502(854 - 1827) (854 - 1827) (854 - 1827) (750 - 1607) (587 - 1258) (318 - 684)Hybrid16751675159913441209740(1066 - 2276) (1066 - 2276) (1018 - 2175) (855 - 1830) (769 - 1647) (470 - 1010)Percent Difference25%25%19%14%31%47%Proportional18931893180815461412926(1207 - 2571) (1207 - 2571) (1152 - 2455) (984 - 2101) (898 - 1920) (588 - 1261)Hybrid19501950180615441461967(1244 - 2648) (1244 - 2648) (1151 - 2452) (983 - 2099) (930 - 1987) (614 - 1317)Percent Difference3%3%0%0%3%4%Proportional897813714616696492(573 - 1215) (519 - 1102) (456 - 970) (392 - 836) (443 - 944) (313 - 669)Hybrid956855754652754548(611 - 1294) (546 - 1159) (481 - 1022) (415 - 885) (481 - 1022) (349 - 745)Percent Difference7%5%6%6%8%11%3Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet theCurrent and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m):Type of Rollback1Estimates Based on Krewski et al. (2009), exposure period from1999 - 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009). Numbers rounded tothe nearest whole number. Numbers in parentheses are 95% confidenceor credible intervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationSt. Louis, MOBaltimore, MDBirmingham, ALDetroit, MILos Angeles, CANew York, NYF-13Table F-11. Sensitivity Analysis: Estimated Annual Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006 PM2.5Concentrations:Comparisonof Proportional and Hybrid Rollback Methods115/35214/3513/3512/3513/3012/25Proportional565513446378428289(360 - 766) (327 - 696) (284 - 606) (241 - 515) (272 - 581) (184 - 394)Hybrid554533465396419282(354 - 752) (340 - 724) (296 - 632) (252 - 539) (267 - 569) (179 - 384)Percent Difference3-2% 4% 4% 5% -2% -2%Proportional354312269226269186(226 - 481) (198 - 423) (171 - 365) (144 - 307) (171 - 365) (118 - 253)Hybrid430382334286334251(275 - 584) (244 - 519) (213 - 454) (182 - 388) (213 - 454) (159 - 341)Percent Difference21%22%24%27%24%35%Proportional510503429355366222(325 - 694) (320 - 684) (273 - 584) (225 - 483) (233 - 499) (141 - 302)Hybrid534534515434387238(340 - 725) (340 - 725) (328 - 700) (276 - 591) (246 - 526) (151 - 325)Percent Difference5%6%20%22%6%7%Proportional110811081108958721331(704 - 1509) (704 - 1509) (704 - 1509) (608 - 1305) (457 - 983) (210 - 451)Hybrid1414141413441108984550(899 - 1923) (899 - 1923) (855 - 1829) (704 - 1510) (625 - 1340) (349 - 750)Percent Difference28%28%21%16%36%66%Proportional1407140713331106990571(895 - 1913) (895 - 1913) (848 - 1813) (703 - 1506) (629 - 1349) (362 - 779)Hybrid14531453132711011030604(924 - 1975) (924 - 1975) (844 - 1806) (700 - 1499) (654 - 1403) (383 - 823)Percent Difference3%3%0%0%4%6%Proportional659588506423490319(420 - 894) (374 - 799) (322 - 688) (269 - 575) (312 - 666) (203 - 435)Hybrid704620535450535363(449 - 956) (395 - 842) (341 - 727) (286 - 612) (341 - 727) (231 - 495)Percent Difference7%5%6%6%9%14%3Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).New York, NY2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet theCurrent and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m):Type of Rollback1Estimates Based on Krewski et al. (2009), exposure period from1999 - 2000, using models with 44individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009). Numbers rounded tothe nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationBaltimore, MDBirmingham, ALSt. Louis, MODetroit, MILos Angeles, CAF-14Table F-12. Sensitivity Analysis: Estimated Annual Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007 PM2.5Concentrations:Comparisonof Proportional and Hybrid Rollback Methods115/35214/3513/3512/3513/3012/25Proportional564512445378427289(360 - 765) (326 - 695) (283 - 605) (240 - 514) (272 - 580) (184 - 393)Hybrid553532464395418281(353 - 751) (339 - 722) (296 - 630) (252 - 537) (266 - 568) (179 - 383)Percent Difference3-2% 4% 4% 4% -2% -3%Proportional374330285241285199(238 - 507) (210 - 448) (182 - 388) (153 - 327) (182 - 388) (126 - 271)Hybrid454404354304354268(290 - 615) (258 - 548) (226 - 481) (193 - 413) (226 - 481) (170 - 364)Percent Difference21%22%24%26%24%35%Proportional544536460384396247(346 - 739) (341 - 729) (293 - 626) (244 - 522) (252 - 539) (157 - 336)Hybrid568568549466417265(362 - 772) (362 - 772) (350 - 747) (297 - 634) (265 - 568) (168 - 361)Percent Difference4%6%19%21%5%7%Proportional1170117011701016773372(744 - 1593) (744 - 1593) (744 - 1593) (645 - 1384) (490 - 1053) (236 - 508)Hybrid14841484141311711043598(944 - 2019) (944 - 2019) (899 - 1922) (744 - 1594) (662 - 1420) (379 - 815)Percent Difference27%27%21%15%35%61%Proportional16891689160713591232771(1076 - 2295) (1076 - 2295) (1023 - 2185) (864 - 1848) (783 - 1676) (489 - 1051)Hybrid17411741160413551277809(1109 - 2366) (1109 - 2366) (1021 - 2180) (862 - 1844) (812 - 1738) (513 - 1102)Percent Difference3%3%0%0%4%5%Proportional728653566478549369(464 - 988) (416 - 887) (360 - 769) (304 - 651) (350 - 747) (235 - 503)Hybrid787698607516607424(503 - 1068) (445 - 947) (387 - 825) (329 - 702) (387 - 825) (269 - 577)Percent Difference8%7%7%8%11%15%3Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).Incidence of All Cause Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet theCurrent and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m):Type of Rollback1Estimates Based on Krewski et al. (2009), exposure period from1999 - 2000, using models with 44individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009). Numbers rounded tothe nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationSt. Louis, MONew York, NYLos Angeles, CADetroit, MIBirmingham, ALBaltimore, MD2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.F-15Table F-13. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence ofNon-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year55534333184177(8 - 102)(3 - 101)(-15 - 99)(-17 - 83)---4(34 - 319)66 46 60 50 222256(9 - 122) (1 - 91) (-7 - 126) (7 - 92) ---(104 - 406)18 25 17 10 7034(-4 - 41) (-3 - 51) (-21 - 55) (-21 - 40) ---(-53 - 121)30 30 43 46 149156(-5 - 64) (-9 - 68) (-3 - 88) (6 - 84) ---(37 - 273)-6 77 54 34 159147(-83 - 69) (19 - 134) (-32 - 137) (-31 - 98) ---(-26 - 317)0 16 3 11 3044(-33 - 33) (-1 - 32) (-14 - 20) (-12 - 34) ---(6 - 81)45 61 51 55 212214(-5 - 94) (5 - 116) (-13 - 113) (-9 - 117) ---(44 - 383)17 66 -104 -2 -2381(-84 - 117) (-35 - 166) (-257 - 48) (-90 - 85) ---(-117 - 278)279 159 136 206 780781(102 - 453) (1 - 315) (-55 - 323) (89 - 321) ---(459 - 1102)93 28 34 65 220216(20 - 165) (-33 - 89) (-48 - 114) (16 - 112) ---(79 - 350)--- --- --- --- ---242--- --- --- --- ---(40 - 442)43 65 44 23 175159(-4 - 90) (12 - 117) (-23 - 109) (-28 - 73) ---(47 - 270)16 6 6 8 3630(-2 - 32) (-2 - 14) (-5 - 17) (-3 - 19) ---(6 - 54)37 75 66 73 251260(-37 - 109) (14 - 136) (-6 - 136) (13 - 133) ---(75 - 443)1 9 4 14 2848(-53 - 53) (-7 - 25) (-9 - 17) (-10 - 37) ---(8 - 87)3Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-specificcoefficient estimators was not available.10%20%-3%-42%1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriate regional means.Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.-128%0%2%----4%8%-32%-1%PercentDifference34%-13%106%Dallas, TXFresno, CAHouston, TXDetroit, MI5Season-specific coefficientestimates were not available from Zanobetti and Schwartz (2009) for this location.St. Louis, MOEstimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALPhiladelphia, PAPhoenix, AZ5Pittsburgh, PATacoma, WASalt Lake City, UTF-16Table F-14. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year48575129185180(7 - 89)(3 - 109)(-18 - 119) (-15 - 72)---4(34 - 324)54 41 52 46 193224(7 - 101) (1 - 81) (-6 - 109) (6 - 86) ---(91 - 356)16 27 18 8 6933(-4 - 36) (-3 - 56) (-23 - 58) (-16 - 32) ---(-51 - 116)24 28 36 34 122130(-4 - 52) (-8 - 64) (-3 - 75) (5 - 63) ---(31 - 228)-5 77 39 26 137118(-64 - 54) (19 - 134) (-23 - 100) (-24 - 75) ---(-21 - 255)1 14 4 12 3147(-36 - 36) (-1 - 30) (-16 - 24) (-12 - 35) ---(7 - 86)40 68 51 48 207208(-4 - 84) (5 - 130) (-13 - 115) (-8 - 102) ---(42 - 373)17 57 -97 -2 -2575(-86 - 120) (-30 - 143) (-239 - 45) (-78 - 74) ---(-108 - 257)242 141 111 183 677671(89 - 394) (1 - 279) (-44 - 263) (79 - 286) ---(394 - 946)79 26 33 70 208204(17 - 140) (-31 - 83) (-46 - 109) (18 - 121) ---(75 - 331)--- --- --- --- ---254--- --- --- --- ---(42 - 463)39 58 40 17 154136(-4 - 81) (10 - 104) (-21 - 100) (-20 - 53) ---(40 - 232)12 7 7 7 3327(-1 - 25) (-2 - 15) (-6 - 19) (-3 - 17) ---(6 - 49)26 67 58 60 211215(-27 - 79) (12 - 120) (-5 - 120) (10 - 110) ---(62 - 367)1 10 4 12 2740(-38 - 38)(-8 - 26)(-10 - 19)(-8 - 30)---(7 - 73)3Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).13%22%-2%-33%-133%1%2%----6%16%-34%0%PercentDifference33%-14%109%Dallas, TXFresno, CAHouston, TXDetroit, MI5Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.St. Louis, MOEstimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALPhiladelphia, PAPhoenix, AZ5Pittsburgh, PA4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriateregional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WASalt Lake City, UTF-17Table F-15. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting 2007 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year48705330201177(7 - 88)(4 - 134)(-18 - 122) (-16 - 76)---4(34 - 319)56 46 56 49 207227(7 - 104) (1 - 90) (-6 - 118) (7 - 91) ---(92 - 360)20 44 21 10 9534(-5 - 45) (-5 - 92) (-27 - 67) (-21 - 39) ---(-53 - 120)28 25 39 39 131139(-5 - 60) (-8 - 58) (-3 - 80) (5 - 73) ---(33 - 243)-6 84 46 42 166121(-79 - 66) (21 - 145) (-28 - 119) (-39 - 121) ---(-21 - 262)1 25 6 22 5448(-78 - 76) (-2 - 53) (-23 - 33) (-23 - 64) ---(7 - 89)49 63 57 52 221212(-5 - 102) (5 - 120) (-14 - 127) (-9 - 112) ---(43 - 378)23 112 -144 -3 -1277(-115 - 160) (-59 - 280) (-359 - 66) (-140 - 131) ---(-110 - 262)319 177 150 241 887734(117 - 517) (1 - 350) (-60 - 355) (105 - 376) ---(431 - 1035)88 32 34 78 232208(19 - 156) (-37 - 99) (-48 - 114) (20 - 134) ---(77 - 338)--- --- --- --- ---242--- --- --- --- ---(40 - 442)54 84 57 30 225143(-5 - 113) (15 - 152) (-30 - 140) (-35 - 93) ---(42 - 244)28 11 11 13 6334(-3 - 57) (-4 - 26) (-10 - 32) (-5 - 32) ---(7 - 61)32 83 63 70 248225(-32 - 95) (15 - 150) (-6 - 130) (12 - 127) ---(65 - 384)1 12 4 20 3742(-47 - 47) (-9 - 32) (-9 - 16) (-14 - 52) ---(7 - 76)3Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).85%10%-12%21%12%---57%37%13%4%-116%14%-9%179%-6%5Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.Philadelphia, PAPhoenix, AZ5Pittsburgh, PA4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriateregional means. Numbers are rounded to the nearest whole number.Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WASt. Louis, MOEstimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXPercentDifference3Fresno, CAHouston, TXSalt Lake City, UTDetroit, MIF-18Table F-16. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction toEstimate the Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year1499-23032(-21 - 49)(-32 - 48)(-31 - 46)(-37 - 31)---4(-33 - 95)16 10 11 32 6970(-30 - 59) (-31 - 48) (-45 - 64) (-2 - 65) ---(-5 - 143)4 1 0 -15 -10-1(-16 - 24) (-23 - 25) (-29 - 27) (-40 - 8) ---(-43 - 40)10 11 13 -2 3232(-18 - 36) (-21 - 42) (-25 - 49) (-33 - 27) ---(-21 - 85)-1 25 28 36 8873(-48 - 43) (-7 - 57) (-21 - 74) (0 - 72) ---(-9 - 153)-2 1 0 3 211(-13 - 8) (-3 - 5) (-3 - 4) (-4 - 9) ---(-8 - 30)8 2 27 7 4447(-34 - 49) (-46 - 47) (-21 - 73) (-41 - 54) ---(-32 - 124)-7 3 -43 0 -47-31(-54 - 39) (-45 - 49) (-105 - 17) (-43 - 43) ---(-140 - 76)149 130 160 100 539504(35 - 261) (29 - 228) (30 - 286) (23 - 174) ---(294 - 711)28 16 27 27 9887(-6 - 60) (-14 - 46) (-13 - 65) (4 - 50) ---(23 - 150)--- --- --- --- ---84--- --- --- --- ---(-4 - 170)14 30 13 5 6247(-10 - 38) (3 - 56) (-23 - 47) (-20 - 29) ---(-9 - 103)--- --- --- --- ---8--- --- --- --- ---(-2 - 18)-3 48 38 43 126122(-68 - 59) (-2 - 95) (-17 - 90) (-4 - 88) ---(27 - 215)0002212(-12 - 13) (-3 - 4) (-2 - 3) (-4 - 7) ---(-7 - 31)3Calculated as (sum-of-4-seasons mortality -all-year mortality)/(all-year mortality).2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.4It was not possible to calculate the 2.5th and 97.5th percentileestimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-specificcoefficient estimators was not available.---3%-83%1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriate regional means.Numbers rounded to the nearest whole number.Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.7%13%---32%21%-82%-6%52%Birmingham, AL900%0%Dallas, TXPittsburgh, PAEstimated Incidence of Cardiovascular MortalityAssociated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CARisk AssessmentLocationAtlanta, GABaltimore, MDPercentDifference3-6%-1%Detroit, MI5Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.Fresno, CAHouston, TXSalt Lake City, UT5St. Louis, MOPhiladelphia, PAPhoenix, AZ5Tacoma, WANew York, NYF-19Table F-17. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year13 9 10 -2 3032(-18 - 42)(-35 - 52)(-38 - 56)(-32 - 27)---4(-33 - 97)13 8 10 30 6161(-25 - 49) (-28 - 43) (-39 - 56) (-2 - 61) ---(-4 - 125)4 1 0-12 -7-1(-14 - 21)(-25 - 27)(-31 - 29)(-31 - 7)---(-41 - 39)8 11 11 -1 2927(-14 - 29)(-19 - 40)(-21 - 41)(-24 - 21)---(-18 - 71)-12520287258(-37 - 34)(-7 - 57)(-15 - 55)(0 - 55)---(-7 - 123)-21 0 3 212(-14 - 9)(-3 - 5)(-4 - 5)(-4 - 9)---(-8 - 32)7 22764245(-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)13011513088463432(31 - 227) (25 - 202) (25 - 233) (21 - 155)---(252 - 611)241526309582(-5 - 51)(-13 - 42)(-12 - 62)(4 - 54)---(21 - 142)---------------88---------------(-4 - 178)13271245641(-9 - 35)(2 - 50)(-21 - 43)(-14 - 21)---(-8 - 89)---------------7---------------(-2 - 16)-2423336109101(-49 - 43)(-2 - 85)(-15 - 80)(-3 - 73)---(23 - 179)0001110(-9 - 9)(-3 - 4)(-2 - 3)(-3 - 6)---(-6 - 26)3Calculated as (sum-of-4-seasons mortality -all-year mortality)/(all-year mortality).59%7%16%-90%---37%---8%7%24%-83%-7%Estimated Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CARisk AssessmentLocationAtlanta, GABaltimore, MDPercentDifference3-6%0%600%5Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.Fresno, CAHouston, TXSalt Lake City, UT5St. Louis, MOPhiladelphia, PAPhoenix, AZ51Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriateregional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WANew York, NYPittsburgh, PA4It was not possible to calculate the 2.5th and 97.5th percentileestimates of the sum of the season-specific incidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Birmingham, ALDallas, TXDetroit, MIF-20Table F-18. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards,Based on Adjusting 2007 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year111110-23032(-17 - 39)(-40 - 58)(-35 - 52)(-31 - 26)---4(-33 - 95)13 9 10 30 6262(-24 - 48)(-29 - 45)(-39 - 57)(-2 - 61)---(-4 - 126)4 2 0-12 -6-1(-14 - 21)(-33 - 35)(-28 - 26)(-31 - 6)---(-42 - 40)9 10 11 -2 2829(-16 - 34)(-18 - 36)(-23 - 44)(-28 - 24)---(-19 - 76)-12219367660(-37 - 34)(-6 - 50)(-14 - 52)(0 - 72)---(-8 - 127)-31 0 3 112(-16 - 11)(-3 - 5)(-3 - 4)(-4 - 9)---(-9 - 33)8 22974646(-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)14212014797506473(34 - 248) (26 - 212) (28 - 262) (23 - 170)---(276 - 668)241625309584(-5 - 52)(-15 - 47)(-12 - 60)(5 - 55)---(22 - 145)---------------84---------------(-4 - 170)13271245643(-9 - 34)(2 - 51)(-21 - 43)(-18 - 26)---(-9 - 93)---------------9---------------(-2 - 20)-2473237114106(-53 - 46)(-2 - 94)(-15 - 78)(-3 - 76)---(24 - 187)0002211(-9 - 9)(-3 - 4)(-2 - 2)(-5 - 8)---(-6 - 27)3Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).30%---8%-82%37%7%13%----3%27%-92%0%Phoenix, AZ5Pittsburgh, PA2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Birmingham, ALDallas, TXDetroit, MI5Season-specific coefficient estimates were not available from Zanobetti and Schwartz (2009) for this location.4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.Fresno, CAHouston, TXSalt Lake City, UT5St. Louis, MOPhiladelphia, PA1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriateregional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WANew York, NYEstimated Incidence of Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CARisk AssessmentLocationAtlanta, GABaltimore, MDPercentDifference3-6%0%500%F-21Table F-19. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidenceof Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year4 1 3 31120(-6 - 14)(-8 - 11)(-7 - 13)(-5 - 11)---4(-8 - 47)5 6 6 3 2036(-6 - 15) (-4 - 15) (-6 - 17) (-4 - 11) ---(7 - 64)1 2 -1 3 59(-4 - 5) (-4 - 7) (-8 - 7) (-2 - 9) ---(-7 - 26)1321711(-6 - 9) (-4 - 10) (-6 - 9) (-6 - 7) ---(-10 - 32)5 9 10 9 3328(-7 - 16) (-1 - 18) (0 - 19) (0 - 18) ---(1 - 55)-1 4 1 1 59(-11 - 9) (-1 - 9) (-2 - 4) (-6 - 8) ---(0 - 17)5 5 4 4 1834(-4 - 14)(-4 - 13)(-5 - 13)(-7 - 15)---(5 - 61)2727-1503957(-3 - 56)(-2 - 56)(-58 - 26)(-23 - 21)---(6 - 108)51182222113106(19 - 82)(-6 - 41)(-10 - 53)(1 - 42)---(37 - 174)107752923(-1 - 21)(-1 - 15)(-3 - 16)(-2 - 11)---(-2 - 48)2730214111947(-29 - 79)(-8 - 66)(-3 - 45)(14 - 67)---(4 - 90)4 7 8 72620(-3 - 11)(-1 - 15)(-2 - 17)(0 - 14)---(-2 - 42)42-2-135(-1 - 9)(-2 - 6)(-6 - 3)(-5 - 3)---(1 - 10)1 7 4 71931(-15 - 17)(-6 - 20)(-10 - 17)(-6 - 18)---(-8 - 70)021147(-15 - 13)(-2 - 6)(-2 - 3)(-4 - 6)---(0 - 15)3Calculated as (sum-of-4-seasons mortality -all-year mortality)/(all-year mortality).St. Louis, MO26%153%30%-40%Salt Lake City, UTEstimated Incidence of Respiratory MortalityAssociated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current StandardsLos Angeles, CANew York, NY-44%-47%-32%7%Risk AssessmentLocationAtlanta, GABaltimore, MDPhiladelphia, PAPhoenix, AZPittsburgh, PATacoma, WA4It was not possible to calculate the 2.5th and 97.5th percentileestimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-specificcoefficient estimators was not available.-39%-43%1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriate regional means.Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Birmingham, ALDallas, TXDetroit, MIHouston, TXFresno, CA-44%-36%18%PercentDifference3-45%-44%F-22Table F-20. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting2006 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year3 2 4 31220(-5 - 12)(-9 - 12)(-8 - 16)(-4 - 10)---4(-8 - 47)4 5 5 31731(-5 - 13)(-3 - 13)(-5 - 15)(-4 - 10)---(6 - 56)1 2-13 59(-3 - 5)(-4 - 8)(-9 - 7)(-2 - 7)---(-7 - 25)132179(-5 - 7)(-4 - 10)(-5 - 8)(-4 - 6)---(-9 - 27)4 9 7 72723(-5 - 13)(-1 - 18)(0 - 14)(0 - 14)---(1 - 44)-14 1 1 59(-11 - 10)(-1 - 9)(-2 - 5)(-6 - 8)---(0 - 18)5 5 4 41833(-4 - 13)(-5 - 15)(-5 - 13)(-6 - 13)---(5 - 60)2824-1403853(-3 - 57)(-2 - 48)(-54 - 24)(-19 - 18)---(5 - 100)441618209891(16 - 72)(-5 - 37)(-8 - 43)(1 - 37)---(32 - 149)9 7 7 52822(-1 - 18)(-1 - 14)(-3 - 16)(-2 - 12)---(-2 - 45)3130224112450(-33 - 90)(-8 - 65)(-3 - 46)(14 - 66)---(4 - 94)4 6 7 52217(-3 - 10)(-1 - 13)(-2 - 15)(0 - 10)---(-2 - 36)3 2-2 -1 25(-1 - 7)(-2 - 6)(-7 - 3)(-5 - 3)---(1 - 9)1 6 3 51526(-10 - 12)(-6 - 18)(-9 - 15)(-5 - 15)---(-7 - 58)021146(-11 - 10)(-2 - 6)(-2 - 4)(-3 - 5)---(0 - 12)3Calculated as (sum-of-4-seasons mortality - all-year mortality)/(all-year mortality).-42%-33%27%148%29%-60%-44%-45%-28%8%Philadelphia, PAPhoenix, AZPittsburgh, PA4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of theseason-specificincidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Based on season-specific and all-year location-specificcoefficient estimates from Zanobetti and Schwartz (2009) that have been"shrunken" towards the appropriateregional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WAFresno, CAEstimated Incidence of Respiratory MortalityAssociated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIHouston, TXSalt Lake City, UTSt. Louis, MOPercentDifference3-40%-45%-44%-22%17%F-23Table F-21. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Respiratory Mortality Associated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current Standards, Based on Adjusting2007 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year3 2 4 31220(-5 - 11)(-10 - 13)(-8 - 15)(-4 - 9)---4(-8 - 47)4 5 5 31731(-5 - 12)(-4 - 14)(-5 - 15)(-4 - 10)---(6 - 56)1 2-13 59(-3 - 4)(-5 - 10)(-8 - 7)(-2 - 7)---(-7 - 25)1321710(-6 - 8)(-3 - 9)(-5 - 8)(-5 - 6)---(-9 - 29)4 8 7 92824(-5 - 13)(-1 - 16)(0 - 14)(0 - 18)---(1 - 45)-14 1 1 59(-14 - 11)(-1 - 8)(-2 - 4)(-6 - 8)---(0 - 18)5 4 4 41733(-4 - 15)(-4 - 13)(-6 - 14)(-6 - 13)---(5 - 61)2329-1303954(-2 - 48)(-2 - 60)(-51 - 23)(-22 - 21)---(5 - 102)49172021107100(18 - 79)(-6 - 38)(-9 - 48)(1 - 41)---(35 - 163)9 7 7 52822(-1 - 19)(-1 - 15)(-3 - 15)(-2 - 12)---(-2 - 46)2429254512347(-24 - 68)(-8 - 63)(-4 - 51)(15 - 73)---(4 - 90)4 6 7 62318(-3 - 10)(-1 - 13)(-2 - 15)(0 - 13)---(-2 - 38)52-2-146(-1 - 10)(-2 - 6)(-7 - 3)(-5 - 4)---(1 - 11)1 7 3 61727(-11 - 13)(-6 - 20)(-9 - 14)(-5 - 16)---(-7 - 60)021146(-11 - 10)(-2 - 6)(-1 - 3)(-5 - 7)---(0 - 13)3Calculated as (sum-of-4-seasons mortality -all-year mortality)/(all-year mortality).-28%7%27%-33%162%28%-33%-37%St. Louis, MOPercentDifference3-40%-45%-44%-30%17%-44%-48%Estimated Incidence of Respiratory MortalityAssociated with Short-Term Exposure to PM2.5Concentrationsthat Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIHouston, TXFresno, CAPhiladelphia, PAPhoenix, AZPittsburgh, PA2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Based on season-specific and all-year location-specific coefficient estimates fromZanobetti and Schwartz (2009) that have been"shrunken" towards the appropriateregional means. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WA4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of theseason-specific incidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.Salt Lake City, UTF-24Table F-22. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence ofHospital Admissions for Cardiovascular Illness Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year2924-2863140(-2 - 60)(-9 - 57)(-68 - 11)(-27 - 39)---4(-26 - 105)129 45 40 48 262247(90 - 168) (16 - 75) (6 - 74) (23 - 73) ---(182 - 313)10 10 -13 3 1017(-1 - 21) (-3 - 23) (-32 - 5) (-12 - 17) ---(-11 - 44)24 19 -21 5 2731(-2 - 50) (-7 - 45) (-50 - 8) (-19 - 28) ---(-20 - 81)153 54 40 57 304280(107 - 198) (18 - 89) (6 - 74) (27 - 87) ---(206 - 354)14 11 -7 3 2121(-5 - 31) (-6 - 27) (-28 - 14) (-11 - 17) ---(0 - 41)44 34 -35 10 5356(-3 - 91)(-12 - 80)(-84 - 14)(-41 - 59)---(-37 - 149)104194-14442196264(-35 - 241) (-98 - 479) (-613 - 307) (-138 - 218)---(3 - 523)391161131145828792(273 - 509) (55 - 266) (19 - 241) (68 - 221)---(582 - 1002)118393539231214(82 - 153)(13 - 64)(5 - 65)(18 - 59)---(157 - 271)5881-4714106108(-20 - 135) (-41 - 200) (-198 - 99) (-47 - 75)---(1 - 213)59312836154157(41 - 77)(11 - 51)(4 - 51)(17 - 55)---(115 - 199)5 4-31 78(-2 - 13)(-2 - 10)(-15 - 7)(-3 - 5)---(0 - 16)103443044221207(72 - 134)(15 - 73)(4 - 55)(20 - 66)---(152 - 262)120-5 -5 221(-65 - 82)(-61 - 55)(-56 - 42)(-53 - 40)---(-52 - 92)3Calculated as (sum-of-4-seasons hospital admissions - all-year hospitaladmissions)/(all-yearhospital admissions).-90%1Incidence estimates were calculated usingthe appropriate season-specific or all-year regional concentration-response functionestimates reported in Table 2 of Bell et al. (2008). Locationspecific C-R function estimates were not available from thisstudy. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervalsbased on statistical uncertainty surrounding the PM coefficient.5%8%-2%-2%9%0%-5%-26%-23%6%-41%-13%Fresno, CAHouston, TXSalt Lake City, UTDetroit, MIEstimated Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposureto PM2.5Concentrations that JustMeet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXPercentDifference3Philadelphia, PAPhoenix, AZPittsburgh, PATacoma, WASt. Louis, MO2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.4It was not possible to calculate the 2.5th and 97.5th percentileestimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-specificcoefficient estimators was not available.-13%7%F-25Table F-23. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting2006 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year2726-3462541(-2 - 55)(-9 - 60)(-81 - 13)(-24 - 35)---4(-27 - 108)106 40 35 44 225214(74 - 137) (14 - 66) (5 - 64) (21 - 68) ---(157 - 271)9 10 -14 2 716(-1 - 19) (-4 - 24) (-33 - 5) (-9 - 13) ---(-10 - 42)19 18 -18 3 2226(-1 - 40) (-6 - 43) (-43 - 7) (-15 - 21) ---(-17 - 68)119 54 29 44 246225(83 - 155) (19 - 90) (4 - 54) (20 - 66) ---(165 - 285)15 10 -8 3 2022(-5 - 34) (-5 - 25) (-34 - 17) (-11 - 18) ---(0 - 44)39 38 -36 8 4955(-3 - 81) (-14 - 90) (-86 - 14) (-36 - 52) ---(-36 - 145)108 170 -137 37 178248(-37 - 252) (-86 - 419) (-580 - 291) (-121 - 192) ---(3 - 491)342 143 107 129 721684(239 - 445) (49 - 237) (16 - 197) (61 - 198) ---(502 - 865)99 35 33 41 208201(69 - 129) (12 - 59) (5 - 62) (19 - 63) ---(147 - 254)66 80 -48 14 112113(-23 - 154) (-40 - 198) (-202 - 101) (-47 - 74) ---(1 - 224)53 27 25 26 131134(37 - 69) (9 - 45) (4 - 46) (12 - 40) ---(98 - 169)4 4 -4 1 57(-1 - 10) (-2 - 11) (-17 - 9) (-3 - 4) ---(0 - 15)74 39 26 36 175171(52 - 96) (13 - 64) (4 - 48) (17 - 54) ---(126 - 216)9 0 -6 -4 -118(-48 - 60) (-64 - 59) (-61 - 46) (-43 - 33) ---(-44 - 78)3Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).-29%2%-106%5%3%-1%-2%9%-9%-11%-28%-39%5%-56%-15%Fresno, CAHouston, TXSalt Lake City, UTDetroit, MIEstimated Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposureto PM2.5Concentrations that JustMeet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXPercentDifference3Philadelphia, PAPhoenix, AZPittsburgh, PA4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of theseason-specificincidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Incidence estimates were calculated usingthe appropriate season-specific or all-yearregional concentration-response functionestimates reported in Table 2 of Bell et al.(2008). Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest wholenumber. Numbers in parentheses are 95%confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WASt. Louis, MOF-26Table F-24. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence ofHospital Admissions for Cardiovascular Illness Associated with Short-TermExposure to PM2.5Concentrations that JustMeet the Current Standards,Based on Adjusting 2007 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year2430-3352641(-2 - 50)(-11 - 70)(-80 - 13)(-23 - 34)---4(-27 - 109)103 42 35 44 224216(72 - 134) (14 - 70) (5 - 65) (21 - 67) ---(159 - 273)9 13 -12 2 1216(-1 - 18) (-5 - 31) (-30 - 5) (-9 - 13) ---(-11 - 43)23 17 -19 4 2528(-2 - 47) (-6 - 39) (-46 - 7) (-17 - 25) ---(-18 - 73)121 48 28 58 255233(84 - 157) (16 - 79) (4 - 52) (27 - 88) ---(171 - 295)17 10 -6 3 2423(-6 - 40) (-5 - 25) (-26 - 13) (-11 - 18) ---(0 - 46)46 34 -38 9 5156(-3 - 95) (-12 - 79) (-91 - 15) (-37 - 54) ---(-37 - 149)93 215 -131 42 219258(-31 - 216) (-109 - 531) (-556 - 279) (-139 - 220) ---(3 - 511)377 151 121 143 792752(263 - 490) (51 - 249) (18 - 223) (67 - 218) ---(552 - 951)101 39 32 42 214203(71 - 131) (13 - 64) (5 - 59) (20 - 64) ---(149 - 257)50 78 -54 16 90108(-17 - 117) (-39 - 193) (-230 - 115) (-52 - 83) ---(1 - 215)51 28 25 32 136140(36 - 67) (9 - 46) (4 - 46) (15 - 49) ---(103 - 177)6 5 -4 1 89(-2 - 15) (-2 - 12) (-18 - 9) (-3 - 5) ---(0 - 18)80 43 25 37 185178(56 - 104) (15 - 71) (4 - 47) (17 - 56) ---(131 - 225)9 0 -4 -6 -119(-48 - 60) (-64 - 59) (-43 - 33) (-63 - 47) ---(-46 - 82)3Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).-11%4%-105%5%5%-17%-3%9%4%-9%-15%-37%4%-25%-11%Philadelphia, PAPhoenix, AZPittsburgh, PA2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Incidence estimates were calculated usingthe appropriate season-specific or all-year regional concentration-response functionestimates reported in Table 2 of Bell et al.(2008). Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest wholenumber. Numbers in parentheses are 95%confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.Tacoma, WA4It was not possible to calculate the 2.5th and 97.5th percentileestimates of the sum of the season-specific incidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.St. Louis, MOEstimated Incidence of Hospital Admissions for Cardiovascular Illness Associated with Short-Term Exposureto PM2.5Concentrations that JustMeet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXPercentDifference3Fresno, CAHouston, TXSalt Lake City, UTDetroit, MIF-27Table F-25. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year6 9-611017(-21 - 31)(-10 - 28)(-25 - 12)(-13 - 15)---4(-22 - 55)18 1 14 2 3520(-5 - 40) (-14 - 15) (0 - 28) (-12 - 15) ---(-12 - 51)2 4 -3 1 47(-7 - 11) (-4 - 11) (-12 - 6) (-5 - 6) ---(-9 - 23)4 7 -5 1 715(-17 - 25) (-8 - 23) (-21 - 10) (-13 - 16) ---(-18 - 47)24 1 17 2 4425(-6 - 53) (-18 - 20) (0 - 35) (-14 - 18) ---(-15 - 64)10 3 4 3 2014(-1 - 20) (-6 - 11) (-4 - 11) (-5 - 11) ---(3 - 25)7 12 -8 3 1425(-27 - 42)(-14 - 38)(-33 - 16)(-27 - 32)---(-32 - 81)71458642244170(-6 - 148) (-97 - 183) (-98 - 261) (-60 - 140)---(40 - 300)57249 511365(-15 - 129) (-52 - 56)(-1 - 99)(-33 - 42)---(-38 - 169)1611213017(-4 - 37)(-12 - 12)(0 - 25)(-9 - 12)---(-10 - 44)351723138861(-3 - 72)(-36 - 68)(-26 - 70)(-18 - 43)---(14 - 107)8 0 9 11813(-2 - 18)(-10 - 11)(0 - 19)(-9 - 12)---(-8 - 33)412186(0 - 8)(-2 - 5)(-3 - 7)(-2 - 4)---(1 - 10)2311524125(-6 - 53)(-20 - 21)(0 - 29)(-15 - 19)---(-15 - 64)0 4 1-2 32(-50 - 43)(-27 - 32)(-19 - 19)(-24 - 18)---(-27 - 30)3Calculated as (sum-of-4-seasons hospital admissions - all-year hospitaladmissions)/(all-yearhospital admissions).St. Louis, MO76%44%38%33%Salt Lake City, UTEstimated Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure toPM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CANew York, NY43%-44%44%74%Risk AssessmentLocationAtlanta, GABaltimore, MDPhiladelphia, PAPhoenix, AZPittsburgh, PATacoma, WA4It was not possible to calculate the 2.5th and 97.5th percentileestimates of the sum of the season-specific incidences because the variance-covariance matrix for the season-specificcoefficient estimators was not available.64%50%1Incidence estimates were calculated usingthe appropriate season-specific or all-year regional concentration-response functionestimates from models with a 2-day lag for respiratoryhospital admissions reported in Table 2 of Bell et al. (2008).Location-specific C-R function estimates were not available from this study. Numbers are rounded to the nearest whole number.Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Birmingham, ALDallas, TXDetroit, MIHouston, TXFresno, CA-43%-53%76%PercentDifference3-41%75%F-28Table F-26. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations that JustMeet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year5 10 -71 917(-19 - 28)(-11 - 30)(-30 - 14)(-11 - 13)---4(-22 - 56)1511223017(-4 - 33)(-12 - 13)(0 - 24)(-11 - 14)---(-10 - 44)2 4-30 37(-6 - 10)(-4 - 12)(-12 - 6)(-4 - 5)---(-8 - 22)4 7-41 812(-13 - 20)(-8 - 22)(-18 - 9)(-10 - 12)---(-15 - 40)1811323420(-5 - 41)(-19 - 20)(0 - 25)(-11 - 13)---(-12 - 52)113532215(-1 - 22)(-5 - 10)(-5 - 14)(-5 - 12)---(3 - 26)7 14 -831625(-24 - 37)(-15 - 42)(-34 - 17)(-24 - 28)---(-31 - 79)75408237234160(-6 - 155) (-85 - 160) (-93 - 248) (-53 - 124)---(37 - 281)5024049656(-13 - 113) (-46 - 50)(-1 - 81)(-30 - 38)---(-33 - 145)1401212716(-4 - 31)(-11 - 12)(0 - 23)(-10 - 13)---(-9 - 41)401724139464(-3 - 82)(-35 - 67)(-27 - 72)(-18 - 43)---(15 - 112)7 0 8 11611(-2 - 16)(-9 - 10)(0 - 17)(-7 - 8)---(-6 - 28)313185(0 - 6)(-3 - 5)(-3 - 8)(-1 - 3)---(1 - 9)1711323321(-5 - 38)(-17 - 18)(0 - 26)(-13 - 16)---(-12 - 53)0 4 1-1 42(-37 - 31)(-29 - 34)(-21 - 20)(-20 - 15)---(-23 - 25)3Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).57%100%69%47%45%60%47%-36%46%71%Philadelphia, PAPhoenix, AZPittsburgh, PA4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response functionestimates from models with a 2-day lag forrespiratory hospital admissions reported in Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study. Numbers are roundedto the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WAFresno, CAEstimated Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure toPM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIHouston, TXSalt Lake City, UTSt. Louis, MOPercentDifference3-47%76%-57%-33%70%F-29Table F-27. Sensitivity Analysis: Impact of Using Season-Specific Concentration-Response Functions vs. an Annual Concentration-ResponseFunction to Estimate the Incidence ofHospital Admissions for Respiratory Illness Associated with Short-Term Exposure to PM2.5Concentrations that JustMeet the Current Standards,Based on Adjusting 2007 PM2.5Concentrations1,2WinterSpringSummerFallSum of FourSeasonsAll Year5 11 -711018(-17 - 26)(-13 - 35)(-29 - 14)(-11 - 13)---4(-22 - 57)14 1 12 2 2917(-4 - 32) (-13 - 14) (0 - 25) (-11 - 14) ---(-10 - 45)2 5 -3 0 47(-6 - 9) (-6 - 16) (-11 - 5) (-4 - 5) ---(-9 - 22)4 6 -5 1 613(-16 - 24) (-7 - 20) (-19 - 10) (-12 - 14) ---(-17 - 43)19 1 12 2 3421(-5 - 42) (-16 - 18) (0 - 25) (-14 - 18) ---(-12 - 54)13 2 3 4 2215(-1 - 26) (-5 - 10) (-4 - 11) (-5 - 12) ---(4 - 27)8 12 -9 3 1425(-28 - 43) (-14 - 38) (-36 - 18) (-25 - 29) ---(-32 - 82)64 50 78 42 234166(-5 - 133) (-108 - 203) (-89 - 239) (-61 - 142) ---(39 - 293)55 2 46 5 10862(-15 - 124) (-48 - 52) (-1 - 91) (-33 - 42) ---(-37 - 160)14 1 11 1 2716(-4 - 32) (-12 - 13) (0 - 22) (-10 - 13) ---(-10 - 42)30 16 27 14 8761(-3 - 63) (-34 - 65) (-31 - 82) (-20 - 48) ---(14 - 108)7 0 8 1 1611(-2 - 16) (-9 - 10) (0 - 17) (-8 - 10) ---(-7 - 29)5 1 3 1 107(0 - 9) (-3 - 5) (-3 - 9) (-2 - 4) ---(2 - 12)18 1 12 2 3321(-5 - 41) (-19 - 21) (0 - 25) (-13 - 17) ---(-13 - 55)0 4 1 -2 32(-37 - 31) (-29 - 34) (-15 - 15) (-29 - 22) ---(-24 - 27)3Calculated as (sum-of-4-seasons hospital admissions - all-year hospital admissions)/(all-year hospital admissions).41%74%69%50%43%45%43%57%St. Louis, MOPercentDifference3-44%71%-43%-54%62%47%-44%Estimated Incidence of Hospital Admissions for Respiratory Illness Associated with Short-Term Exposure toPM2.5Concentrations that Just Meet the Current StandardsLos Angeles, CANew York, NYRisk AssessmentLocationAtlanta, GABaltimore, MDBirmingham, ALDallas, TXDetroit, MIHouston, TXFresno, CAPhiladelphia, PAPhoenix, AZPittsburgh, PA2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Incidence estimates were calculated using the appropriate season-specific or all-year regional concentration-response functionestimates from models with a 2-day lag forrespiratory hospital admissions reported in Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this study. Numbers are roundedto the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertaintysurrounding the PM coefficient.Tacoma, WA4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for theseason-specific coefficient estimators was not available.Salt Lake City, UTF-30Table F-28. Sensitivity Analysis: Impact of Using an Annual Concentration-Response Function vs. a Seasonal Function (for April - August)Applied Only to that Period to Estimatethe Incidence of Emergency Room Visits for Asthma Associated with Short-TermExposure to Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and AlternativeStandards in New York City, Based on Adjusting 2005 PM2.5Concentrations1Recent PM2.5Concentrations15/35214/3513/35 12/35 13/30 12/255235437543754265392737543127(3346 - 7071) (2790 - 5923) (2790 - 5923) (2719 -5776) (2501 - 5323) (2390 - 5091) (1987 - 4248)3136263426342569237022681896(2058 - 4162) (1722 - 3509) (1722 - 3509) (1678 -3425) (1546 - 3164) (1478 - 3031) (1232 - 2541)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Based on Ito et al. (2007). Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credibleintervals based on statistical uncertainty surrounding the PMcoefficient.Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m):Annual C-R Function Applied to the WholeYearSeasonal C-R Function for April - AugustApplied Only to that Period:Concentration-Response (C-R) Functionand Period to Which Applied:F-31Table F-29. Sensitivity Analysis: Impactof Using an Annual Concentration-ResponseFunction vs. a Seasonal Function (for April - August)Applied Only to that Period to Estimatethe Incidence of Emergency Room Visits for Asthma Associated with Short-Term Exposureto Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards inNew York City, Based on Adjusting 2006 PM2.5Concentrations1Recent PM2.5Concentrations15/35214/3513/35 12/35 13/30 12/254506376437643669337732282688(2876 - 6095) (2397 - 5102) (2397 - 5102) (2336 - 4974) (2149 - 4582) (2053 - 4382) (1707 - 3654)2732229322932237206319741649(1791 - 3631) (1497 - 3059) (1497 - 3059) (1460 - 2985) (1344 - 2757) (1285 - 2640) (1071 - 2212)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Based on Ito et al. (2007). Numbers rounded to the nearest wholenumber. Numbers in parentheses are 95% confidence or credibleintervals based on statistical uncertainty surrounding the PMcoefficient.Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m):Annual C-R Function Applied to the WholeYearSeasonal C-R Function for April - AugustApplied Only to that Period:Concentration-Response (C-R) Functionand Period to Which Applied:F-32Table F-30. Sensitivity Analysis: Impact of Using an Annual Concentration-Response Function vs. a Seasonal Function (for April - August)Applied Only to that Period to Estimatethe Incidence of Emergency Room Visits for Asthma Associated with Short-TermExposure to Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and AlternativeStandards in New York City, Based on Adjusting 2007 PM2.5Concentrations1Recent PM2.5Concentrations15/35214/3513/35 12/35 13/30 12/254926411541154011369235292939(3145 - 6660) (2622 - 5575) (2622 - 5575) (2555- 5436) (2350 - 5008) (2245 - 4790) (1867 - 3995)2908244124412380219521011755(1906 - 3864) (1593 - 3256) (1593 - 3256) (1553- 3177) (1431 - 2934) (1368 - 2810) (1140 - 2354)2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.1Based on Ito et al. (2007). Numbers rounded to the nearest wholenumber. Numbers in parentheses are 95% confidence or credibleintervals based on statistical uncertainty surrounding the PMcoefficient.Incidence of ER Visits Associated with Short-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m):Annual C-R Function Applied to the WholeYearSeasonal C-R Function for April - AugustApplied Only to that Period:Concentration-Response (C-R) Functionand Period to Which Applied:F-33Table F-31. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none275Max. positive est. =81(-35 - 584)301(-117 - 278)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none301Min. positive est. =(0 - 600)194Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 2 day none194(-97 - 483)Percent diff. =Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 3 day none-7755%(-373 - 218)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 4 day none-46(-329 - 235)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 5 day none-287(-592 - 15)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none275Max. positive est. =81(-35 - 584)275(-117 - 278)Mortality, short-term non-accidentallog-linear, GLM, 30 df 0 day none204Min. positive est. =(-174 - 579)153Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 0 day none163(-115 - 441)Percent diff. =Mortality, short-term non-accidental log-linear, GLM, 100 df 0 day none15380%(-218 - 522)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none301Max. positive est. =81(0 - 600)301(-117 - 278)Mortality, short-term non-accidentallog-linear, GLM, 30 df 1 day none281Min. positive est. =(-86 - 644)51Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day none51(-236 - 336)Percent diff. =Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day none-5490%(-509 - 494)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day CO-27281(-676 - 128)(-117 - 278)Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day CO-169(-540 - 198)Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day CO-169(-603 - 260)Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day LagHealth Effects ModelIncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Lag Structure:240%LagOtherPollutantsin ModelNon-Accidental MortalityAssociated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model-436%-309%-309%IncidenceAssociated withPM2.5Above PolicyRelevantBackgroundRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2272%140%-195%-157%-454%Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag240%152%101%89%272%247%-37%-106%F-34Table F-31 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 0 day none171Max. positive est. = -31(17 - 324)171(-140 - 76)Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day none168Min. positive est. =(24 - 310)168Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day none168Percent diff. =(-4 - 337)2%Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 1 day none178Max. positive est. = -31(26 - 328)178(-140 - 76)Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day none139Min. positive est. =(-6 - 282)120Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day none120Percent diff. =(-56 - 293)48%Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day CO307Max. positive est. = -31(130 - 481)324(-140 - 76)Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day CO324Min. positive est. =(116 - 529)158Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day CO158Percent diff. =(-22 - 335)105%Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day CO158(-60 - 372)log-linear, GAM (stringent), 30 df 0 day none-15---5(-80 - 49)log-linear, GAM (stringent), 100 df 0 day none-37(-102 - 25)log-linear, GLM, 100 df 0 day none-32(-109 - 43)log-linear, GAM (stringent), 30 df 1 day none10Max. positive est. =---(-56 - 74)22log-linear, GAM (stringent), 100 df 1 day none22Min. positive est. =(-42 - 85)5log-linear, GLM, 100 df 1 day none5Percent diff. =(-75 - 83)340%Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag120%Health EffectsRespiratory Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day LagRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4ModelMortality, short-term respiratory(COPD+)300%95%95%Respiratory Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag---Mortality, short-term respiratory(COPD+)---Mortality, short-term respiratory(COPD+)---Mortality, short-term respiratory(COPD+)---LagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundCardiovascular Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag111%107%107%72%48%Cardiovascular MortalityAssociated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model279%---Mortality, short-term respiratory(COPD+)---Mortality, short-term respiratory(COPD+)F-35Table F-31 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1HA, cardiovascularlog-linear, GAM (stringent), 30 df 0 day none794Max. positive est. =35(457 - 1128)794(-60 - 130)HA, cardiovascularlog-linear, GAM (stringent), 100 df 0 day none584Min. positive est. =(254 - 912)584HA, cardiovascularlog-linear, GLM, 100 df 0 day none634Percent diff. =(226 - 1038)36%HA, cardiovascularlog-linear, GAM (stringent), 30 df 1 day none699Max. positive est. =35(347 - 1048)699(-60 - 130)HA, cardiovascularlog-linear, GAM (stringent), 100 df 1 day none569Min. positive est. =(234 - 902)569HA, cardiovascularlog-linear, GLM, 100 df 1 day none604Percent diff. =(194 - 1011)23%HA, cardiovascularlog-linear, GAM (stringent), 100 df 0 day CO197Max. positive est. =35(-224 - 615)293(-60 - 130)HA, cardiovascularlog-linear, GLM, 100 df 0 day CO293Min. positive est. =(-208 - 788)122HA, cardiovascularlog-linear, GAM (stringent), 100 df 1 day CO122Percent diff. =(-330 - 568)140%HA, cardiovascularlog-linear, GLM, 100 df 1 day CO137(-381 - 648)HA, respiratory (COPD+)log-linear, GAM (stringent), 30 df 0 day none336Max. positive est. =---(138 - 531)336HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 0 day none278Min. positive est. =(104 - 450)278HA, respiratory (COPD+)log-linear, GLM, 100 df 0 day none300Percent diff. =(83 - 514)21%HA, respiratory (COPD+)log-linear, GAM (stringent), 30 df 1 day none240Max. positive est. =---(45 - 432)240HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 1 day none152Min. positive est. =(-22 - 324)152HA, respiratory (COPD+)log-linear, GLM, 100 df 1 day none156Percent diff. =(-55 - 364)58%Range of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4ModelLagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundHealth EffectsCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag880%621%683%Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag763%602%646%Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model143%262%51%69%Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag---------Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag---------F-36Table F-31 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2005 PM2.5Concentrations1HA, respiratory (COPD+)log-linear, GAM (stringent), 30 df 2 day none371Max. positive est. =---(166 - 574)371HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 2 day none230Min. positive est. =(43 - 414)208HA, respiratory (COPD+)log-linear, GLM, 100 df 2 day none208Percent diff. =(-24 - 436)78%HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 0 day NO285Max. positive est. =---(-185 - 351)85HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 1 day NO2-8Min. positive est. =(-329 - 307)71HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 2 day NO271Percent diff. =(-209 - 346)20%HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 3 day NO2-223(-491 - 41)Range of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4ModelLagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundHealth EffectsRespiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 2-Day Lag---------Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Lag Structure, with a Copollutant Model------------5Because "respiratory illness" was much more broadly defined in both Zanobetti and Schwartz (2009) and Bell et al. (2008) than in Moolgavkar (2003), a comparison between the Moolgavkar (2003) estimatesand the corresponding core analysis estimates is not shown.1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3. Results are based on Moolgavkar (2003) [reanalysis of Moolgavkar (2000a, 2000b, and2000c)]. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.2The core analysis estimates for non-accidental mortality and cardiovascular mortality associated with short-term exposure to PM2.5 are from Zanobetti and Schwartz (2009). The core analysis estimates forcardiovascular hospital admissions associated with short-term exposure to PM2.5 are from Bell et al. (2008).3Calculated as (maximum positive estimate - minimum positive estimate)/(minimum positive estimate).4Calculated as (Moolgavkar (2003) estimate - core analysis estimate)/(core analysis estimate).F-37Table F-32. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none254Max. positive est. =75(-32 - 539)278(-108 - 257)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none278Min. positive est. =(0 - 554)179Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 2 day none179(-89 - 445)Percent diff. =Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 3 day none-7155%(-344 - 201)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 4 day none-42(-304 - 217)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 5 day none-265(-546 - 14)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none254Max. positive est. =75(-32 - 539)254(-108 - 257)Mortality, short-term non-accidentallog-linear, GLM, 30 df 0 day none188Min. positive est. =(-161 - 535)141Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 0 day none151(-106 - 407)Percent diff. =Mortality, short-term non-accidental log-linear, GLM, 100 df 0 day none14180%(-201 - 482)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none278Max. positive est. =75(0 - 554)278(-108 - 257)Mortality, short-term non-accidentallog-linear, GLM, 30 df 1 day none259Min. positive est. =(-80 - 595)47Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day none47(-218 - 310)Percent diff. =Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day none-5491%(-469 - 455)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day CO-25175(-623 - 118)(-108 - 257)Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day CO-156(-497 - 183)Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day CO-156(-555 - 240)271%245%-37%-107%239%151%101%88%-195%-156%-453%Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day LagIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2271%139%Non-Accidental MortalityAssociated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model-435%-308%-308%Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day LagHealth Effects ModelIncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Lag Structure:239%LagOtherPollutantsin ModelF-38Table F-32 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 0 day none158Max. positive est. = -29(15 - 299)158(-129 - 70)Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day none155Min. positive est. =(22 - 286)155Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day none155Percent diff. =(-3 - 311)2%Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 1 day none164Max. positive est. = -29(24 - 303)164(-129 - 70)Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day none128Min. positive est. =(-5 - 260)110Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day none110Percent diff. =(-51 - 270)49%Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day CO283Max. positive est. = -29(120 - 444)299(-129 - 70)Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day CO299Min. positive est. =(107 - 489)145Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day CO145Percent diff. =(-20 - 309)106%Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day CO145(-56 - 344)log-linear, GAM (stringent), 30 df 0 day none-14---5(-74 - 45)log-linear, GAM (stringent), 100 df 0 day none-35(-94 - 23)log-linear, GLM, 100 df 0 day none-29(-100 - 39)log-linear, GAM (stringent), 30 df 1 day none9Max. positive est. =---(-51 - 68)21log-linear, GAM (stringent), 100 df 1 day none21Min. positive est. =(-39 - 78)5log-linear, GLM, 100 df 1 day none5Percent diff. =(-69 - 76)320%---Mortality, short-term respiratory(COPD+)---Mortality, short-term respiratory(COPD+)47%Cardiovascular MortalityAssociated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model277%107%107%71%Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag119%IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag111%Health EffectsMortality, short-term respiratory(COPD+)---Mortality, short-term respiratory(COPD+)---Mortality, short-term respiratory(COPD+)---93%93%Respiratory Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag---Respiratory Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day LagModel LagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2Mortality, short-term respiratory(COPD+)299%F-39Table F-32 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1HA, cardiovascularlog-linear, GAM (stringent), 30 df 0 day none745Max. positive est. = 248(428 - 1060)745(3 - 491)HA, cardiovascularlog-linear, GAM (stringent), 100 df 0 day none548Min. positive est. =(238 - 856)548HA, cardiovascularlog-linear, GLM, 100 df 0 day none595Percent diff. =(212 - 975)36%HA, cardiovascularlog-linear, GAM (stringent), 30 df 1 day none656Max. positive est. = 248(326 - 984)656(3 - 491)HA, cardiovascularlog-linear, GAM (stringent), 100 df 1 day none534Min. positive est. =(220 - 847)534HA, cardiovascularlog-linear, GLM, 100 df 1 day none567Percent diff. =(182 - 949)23%HA, cardiovascularlog-linear, GAM (stringent), 100 df 0 day CO185Max. positive est. = 248(-210 - 577)275(3 - 491)HA, cardiovascularlog-linear, GLM, 100 df 0 day CO275Min. positive est. =(-195 - 740)114HA, cardiovascularlog-linear, GAM (stringent), 100 df 1 day CO114Percent diff. =(-309 - 533)141%HA, cardiovascularlog-linear, GLM, 100 df 1 day CO128(-357 - 608)HA, respiratory (COPD+)log-linear, GAM (stringent), 30 df 0 day none310Max. positive est. =---(127 - 491)310HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 0 day none256Min. positive est. =(96 - 415)256HA, respiratory (COPD+)log-linear, GLM, 100 df 0 day none277Percent diff. =(76 - 475)21%HA, respiratory (COPD+)log-linear, GAM (stringent), 30 df 1 day none221Max. positive est. =---(42 - 399)221HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 1 day none140Min. positive est. =(-21 - 299)140HA, respiratory (COPD+)log-linear, GLM, 100 df 1 day none144Percent diff. =(-51 - 336)58%Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag---Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag693%---------------71%Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag147%267%52%775%612%656%Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model893%631%Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day LagRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4ModelLagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundHealth EffectsF-40Table F-32 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2006 PM2.5Concentrations1HA, respiratory (COPD+)log-linear, GAM (stringent), 30 df 2 day none343Max. positive est. =---(153 - 531)343HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 2 day none212Min. positive est. =(40 - 383)192HA, respiratory (COPD+)log-linear, GLM, 100 df 2 day none192Percent diff. =(-22 - 403)79%HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 0 day NO278Max. positive est. =---(-171 - 324)78HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 1 day NO2-7Min. positive est. =(-303 - 284)65HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 2 day NO265Percent diff. =(-192 - 319)20%HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 3 day NO2-205(-452 - 38)5Because "respiratory illness" was much more broadly defined in both Zanobetti and Schwartz (2009) and Bell et al. (2008) than in Moolgavkar (2003), a comparison between the Moolgavkar (2003) estimatesand the corresponding core analysis estimates is not shown.1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3. Results are based on Moolgavkar (2003) [reanalysis of Moolgavkar (2000a, 2000b, and2000c)].2The core analysis estimates for non-accidental mortality and cardiovascular mortality associated with short-term exposure to PM2.5 are from Zanobetti and Schwartz (2009). The core analysis estimates forcardiovascular hospital admissions associated with short-term exposure to PM2.5 are from Bell et al. (2008). Numbers rounded tothe nearest whole number. Numbers in parentheses are 95% confidence orcredible intervals based on statistical uncertainty surrounding the PM coefficient.3Calculated as (maximum positive estimate - minimum positive estimate)/(minimum positive estimate).4Calculated as (Moolgavkar (2003) estimate - core analysis estimate)/(core analysis estimate).---------Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Lag Structure, with a Copollutant Model------------Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 2-Day LagRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4ModelLagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundHealth EffectsF-41Table F-33. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5Concentrations1Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none259Max. positive est. =77(-33 - 550)283(-110 - 262)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none283Min. positive est. =(0 - 565)183Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 2 day none183(-91 - 455)Percent diff. =Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 3 day none-7255%(-351 - 205)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 4 day none-43(-310 - 222)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 5 day none-271(-558 - 14)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 0 day none259Max. positive est. =77(-33 - 550)259(-110 - 262)Mortality, short-term non-accidentallog-linear, GLM, 30 df 0 day none192Min. positive est. =(-164 - 546)144Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 0 day none154(-109 - 415)Percent diff. =Mortality, short-term non-accidental log-linear, GLM, 100 df 0 day none14480%(-206 - 492)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day none283Max. positive est. =77(0 - 565)283(-110 - 262)Mortality, short-term non-accidentallog-linear, GLM, 30 df 1 day none264Min. positive est. =(-81 - 607)48Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day none48(-222 - 317)Percent diff. =Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day none-5490%(-480 - 465)Mortality, short-term non-accidental log-linear, GAM (stringent), 30 df 1 day CO-25677(-636 - 121)(-110 - 262)Mortality, short-term non-accidental log-linear, GAM (stringent), 100 df 1 day CO-159(-508 - 187)Mortality, short-term non-accidental log-linear, GLM, 100 df 1 day CO-159(-567 - 245)-306%Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag236%149%100%87%Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Lag Structure:236%268%138%-194%-156%-452%IncidenceAssociated withPM2.5Above PolicyRelevantBackgroundRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4Health EffectsModelLagOtherPollutantsin ModelNon-Accidental Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag268%243%-38%-106%Non-Accidental MortalityAssociated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model-432%-306%F-42Table F-33 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5Concentrations1Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 0 day none161Max. positive est. = -30(16 - 306)161(-132 - 72)Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day none158Min. positive est. =(23 - 292)158Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day none158Percent diff. =(-3 - 318)2%Mortality, short-term cardiovascular log-linear, GAM (stringent), 30 df 1 day none168Max. positive est. = -30(25 - 309)168(-132 - 72)Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day none130Min. positive est. =(-6 - 265)113Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day none113Percent diff. =(-52 - 276)49%Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 0 day CO289Max. positive est. = -30(123 - 453)305(-132 - 72)Mortality, short-term cardiovascular log-linear, GLM, 100 df 0 day CO305Min. positive est. =(109 - 498)148Mortality, short-term cardiovascular log-linear, GAM (stringent), 100 df 1 day CO148Percent diff. =(-21 - 316)106%Mortality, short-term cardiovascular log-linear, GLM, 100 df 1 day CO148(-57 - 351)log-linear, GAM (stringent), 30 df 0 day none-14---5(-75 - 46)log-linear, GAM (stringent), 100 df 0 day none -35(-96 - 24)log-linear, GLM, 100 df 0 day none -30(-103 - 40)log-linear, GAM (stringent), 30 df 1 day none 9 Max. positive est. = ---(-52 - 70) 21log-linear, GAM (stringent), 100 df 1 day none 21 Min. positive est. =(-39 - 80) 5log-linear, GLM, 100 df 1 day none 5Percent diff. =(-71 - 78)320%Respiratory Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag---Mortality, short-term respiratory(COPD+)---Mortality, short-term respiratory(COPD+)---92%---Mortality, short-term respiratory(COPD+)---Respiratory Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day LagMortality, short-term respiratory(COPD+)---296%92%Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag109%105%105%PercentDifference(Compared toCore AnalysisModel)4Mortality, short-term respiratory(COPD+)Mortality, short-term respiratory(COPD+)47%Cardiovascular MortalityAssociated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model275%Cardiovascular Mortality Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag118%69%Health EffectsModelLagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3F-43Table F-33 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5Concentrations1HA, cardiovascularlog-linear, GAM (stringent), 30 df 0 day none775Max. positive est. = 258(446 - 1102)775(3 - 511)HA, cardiovascularlog-linear, GAM (stringent), 100 df 0 day none570Min. positive est. =(248 - 890)570HA, cardiovascularlog-linear, GLM, 100 df 0 day none619Percent diff. =(221 - 1014)36%HA, cardiovascularlog-linear, GAM (stringent), 30 df 1 day none682Max. positive est. = 258(339 - 1023)682(3 - 511)HA, cardiovascularlog-linear, GAM (stringent), 100 df 1 day none556Min. positive est. =(228 - 880)556HA, cardiovascularlog-linear, GLM, 100 df 1 day none590Percent diff. =(189 - 987)23%HA, cardiovascularlog-linear, GAM (stringent), 100 df 0 day CO193Max. positive est. = 258(-219 - 600)286(3 - 511)HA, cardiovascularlog-linear, GLM, 100 df 0 day CO286Min. positive est. =(-203 - 769)119HA, cardiovascularlog-linear, GAM (stringent), 100 df 1 day CO119Percent diff. =(-321 - 554)140%HA, cardiovascularlog-linear, GLM, 100 df 1 day CO133(-371 - 633)HA, respiratory (COPD+)log-linear, GAM (stringent), 30 df 0 day none316Max. positive est. =---(130 - 501)316HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 0 day none262Min. positive est. =(98 - 424)262HA, respiratory (COPD+)log-linear, GLM, 100 df 0 day none282Percent diff. =(78 - 485)21%HA, respiratory (COPD+)log-linear, GAM (stringent), 30 df 1 day none226Max. positive est. =---(42 - 407)226HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 1 day none143Min. positive est. =(-21 - 305)143HA, respiratory (COPD+)log-linear, GLM, 100 df 1 day none146Percent diff. =(-52 - 343)58%Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag---------Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag---------Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of a Copollutant Model151%271%55%73%Cardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 1-Day Lag786%622%666%Range of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3PercentDifference(Compared toCore AnalysisModel)4ModelLagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundHealth EffectsCardiovascular Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 0-Day Lag906%640%704%F-44Table F-33 cont'd. Sensitivity Analysis: Estimated Annual Incidence and Percent of Total Incidence of Mortality in Los Angeles, CA Associated with Short-TermExposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting 2007 PM2.5Concentrations1HA, respiratory (COPD+)log-linear, GAM (stringent), 30 df 2 day none350Max. positive est. =---(156 - 541)350HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 2 day none216Min. positive est. =(41 - 391)196HA, respiratory (COPD+)log-linear, GLM, 100 df 2 day none196Percent diff. =(-22 - 411)79%HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 0 day NO280Max. positive est. =---(-174 - 331)80HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 1 day NO2-8Min. positive est. =(-310 - 290)67HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 2 day NO267Percent diff. =(-196 - 326)19%HA, respiratory (COPD+)log-linear, GAM (stringent), 100 df 3 day NO2-209(-462 - 39)5Because "respiratory illness" was much more broadly defined in both Zanobetti and Schwartz (2009) and Bell et al. (2008) than in Moolgavkar (2003), a comparison between the Moolgavkar (2003) estimatesand the corresponding core analysis estimates is not shown.1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3. Results are based on Moolgavkar (2003) [reanalysis of Moolgavkar (2000a, 2000b, and2000c)]. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.2The core analysis estimates for non-accidental mortality and cardiovascular mortality associated with short-term exposure to PM2.5 are from Zanobetti and Schwartz (2009). The core analysis estimates forcardiovascular hospital admissions associated with short-term exposure to PM2.5 are from Bell et al. (2008).3Calculated as (maximum positive estimate - minimum positive estimate)/(minimum positive estimate).4Calculated as (Moolgavkar (2003) estimate - core analysis estimate)/(core analysis estimate).Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Lag Structure, with a Copollutant Model------------Respiratory Hospital Admissions Associated with Short-Term Exposure to PM2.5-- Impact of Changing the Type of Model, with a 2-Day Lag---------PercentDifference(Compared toCore AnalysisModel)4Health EffectsModelLagOtherPollutantsin ModelIncidenceAssociated withPM2.5Above PolicyRelevantBackgroundRange of PositiveEstimates andPercent DifferenceBetween Maximumand Minimum2IncidenceEstimate UsingCore AnalysisModel3F-45Table F-34. Sensitivity Analysis: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term ExposuretoPM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005 PM2.5Concentrations:Comparisonof Proportional and Hybrid Rollback Methods115/35214/3513/3512/3513/3012/25Proportional256242224206219182(104 - 406)(98 - 384)(91 - 356)(83 - 327)(89 - 348)(74 - 289)Hybrid254248229211217180(103 - 402) (101 - 393)(93 - 364)(86 - 335)(88 - 344)(73 - 286)Percent Difference3-1%2%2%2%-1%-1%Proportional343229272924(-53 - 121)(-49 - 112)(-45 - 103)(-41 - 94)(-45 - 103)(-38 - 85)Hybrid393633303328(-61 - 137)(-56 - 127)(-52 - 117)(-47 - 107)(-52 - 117)(-44 - 99)Percent Difference15%13%14%11%14%17%Proportional147146135124125104(-26 - 317)(-26 - 315)(-24 - 291)(-22 - 267)(-22 - 271)(-18 - 225)Hybrid151151148136129107(-26 - 325)(-26 - 325)(-26 - 319)(-24 - 293)(-23 - 278)(-19 - 231)Percent Difference3%3%10%10%3%3%Proportional818181776958(-117 - 278) (-117 - 278) (-117 - 278) (-110 - 263) (-100 - 238) (-82 - 197)Hybrid919189817864(-130 - 311) (-130 - 311) (-127 - 304) (-117 - 279) (-111 - 266) (-92 - 220)Percent Difference12%12%10%5%13%10%Proportional781781761700668555(459 - 1102) (459 - 1102) (447 - 1073) (411 - 987) (392 - 943) (325 - 783)Hybrid795795761699680564(467 - 1121) (467 - 1121) (446 - 1073) (410 - 986) (399 - 959) (331 - 797)Percent Difference2%2%0%0%2%2%Proportional260244226207222184(75 - 443)(71 - 416)(65 - 385)(60 - 354)(64 - 379)(53 - 315)Hybrid271252233214233195(78 - 462)(73 - 429)(67 - 397)(62 - 365)(67 - 397)(56 - 332)Percent Difference4%3%3%3%5%6%3Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).St. Louis, MO2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that JustMeet the Current and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m):1Based on location-specific single pollutant concentration-responsefunction estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional means. "Shrunken"coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credibleintervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationBaltimore, MDBirmingham, ALType of RollbackDetroit, MILos Angeles, CANew York, NYF-46Table F-35. Sensitivity Analysis: Estimated Annual Incidence of Non-Accidental Mortality Associated with Short-Term Exposureto PM2.5Concentrations that Just Meetthe Current and Alternative Standards, Based on Adjusting 2006 PM2.5Concentrations:Comparisonof Proportional and Hybrid Rollback Methods115/35214/3513/3512/3513/3012/25Proportional224212196180192159(91 - 356)(86 - 336)(79 - 311)(73 - 286)(78 - 305)(64 - 253)Hybrid222217200184190157(90 - 352)(88 - 344)(81 - 318)(75 - 293)(77 - 301)(64 - 250)Percent Difference3-1% 2% 2% 2% -1% -1%Proportional333028262823(-51 - 116)(-47 - 108)(-44 - 99)(-40 - 90)(-44 - 99)(-36 - 82)Hybrid373532293227(-58 - 132)(-54 - 122)(-49 - 112)(-45 - 102)(-49 - 112)(-42 - 95)Percent Difference12%17%14%12%14%17%Proportional1181171089910183(-21 - 255)(-20 - 253)(-19 - 234)(-17 - 215)(-18 - 218)(-15 - 181)Hybrid12112111810910385(-21 - 261)(-21 - 261)(-21 - 256)(-19 - 235)(-18 - 223)(-15 - 185)Percent Difference3%3%9%10%2%2%Proportional757575716453(-108 - 257) (-108 - 257) (-108 - 257) (-101 - 242) (-92 - 219)(-76 - 182)Hybrid848482757259(-120 - 287) (-120 - 287) (-117 - 280) (-108 - 257) (-102 - 245) (-85 - 203)Percent Difference12%12%9%6%13%11%Proportional671671654601574476(394 - 946) (394 - 946) (383 - 922) (352 - 847) (336 - 809) (279 - 672)Hybrid682682652599583484(400 - 961) (400 - 961) (383 - 920) (352 - 846) (342 - 822) (284 - 683)Percent Difference2%2%0%0%2%2%Proportional215202187171184152(62 - 367)(58 - 345)(54 - 319)(49 - 293)(53 - 314)(44 - 260)Hybrid224208192176192160(64 - 381)(60 - 354)(55 - 328)(51 - 301)(55 - 328)(46 - 274)Percent Difference4%3%3%3%4%5%3Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).New York, NYBaltimore, MDBirmingham, AL2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that JustMeet the Current and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m):1Based on location-specific single pollutant concentration-responsefunction estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional means."Shrunken" coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence orcredible intervals based on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationType of RollbackSt. Louis, MODetroit, MILos Angeles, CAF-47Table F-36. Sensitivity Analysis: Estimated Annual Incidence ofNon-Accidental Mortality Associated with Short-Term Exposureto PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007 PM2.5Concentrations:Comparison of Proportional and Hybrid Rollback Methods115/35214/3513/3512/3513/3012/25Proportional227214198182194161(92 - 360)(87 - 340)(80 - 315)(74 - 289)(79 - 308)(65 - 256)Hybrid224219203186192159(91 - 356)(89 - 348)(82 - 322)(75 - 296)(78 - 304)(64 - 253)Percent Difference3-1% 2% 3% 2% -1% -1%Proportional343229262924(-53 - 120)(-49 - 111)(-45 - 102)(-41 - 93)(-45 - 102)(-37 - 85)Hybrid393633303328(-60 - 137)(-56 - 127)(-51 - 116)(-47 - 106)(-51 - 116)(-43 - 99)Percent Difference15%13%14%15%14%17%Proportional12112011110210486(-21 - 262)(-21 - 261)(-19 - 241)(-18 - 221)(-18 - 224)(-15 - 186)Hybrid12412412211210688(-22 - 269)(-22 - 269)(-21 - 264)(-20 - 242)(-19 - 230)(-15 - 191)Percent Difference2%3%10%10%2%2%Proportional777777726554(-110 - 262) (-110 - 262) (-110 - 262) (-104 - 247) (-94 - 224)(-78 - 186)Hybrid868683777361(-123 - 293) (-123 - 293) (-120 - 286) (-110 - 262) (-105 - 250) (-87 - 207)Percent Difference12%12%8%7%12%13%Proportional734734715657627521(431 - 1035) (431 - 1035) (419 - 1008) (385 - 927) (368 - 885) (305 - 735)Hybrid746746714656638530(438 - 1052) (438 - 1052) (419 - 1007) (385 - 926) (374 - 900) (310 - 748)Percent Difference2%2%0%0%2%2%Proportional225211195179192160(65 - 384)(61 - 360)(56 - 333)(52 - 306)(55 - 328)(46 - 272)Hybrid236219203186203169(68 - 402)(63 - 374)(59 - 346)(54 - 318)(59 - 346)(49 - 289)Percent Difference5%4%4%4%6%6%3Calculated as (mortality based on hybrid rollbacks - mortality based on proportional rollbacks)/(mortality based on proportional rollbacks).Los Angeles, CA2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.St. Louis, MONew York, NYIncidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that JustMeet the Current and Alternative Combinations of Annual (n) and Daily (m) Standards (Standard CombinationDenoted n/m):1Based on location-specific single pollutantconcentration-response function estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regional means. "Shrunken"coefficient estimates and their standard errors were sent to EPA by A. Zanobetti via email. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervalsbased on statistical uncertainty surrounding the PM coefficient.Risk AssessmentLocationType of RollbackDetroit, MIBirmingham, ALBaltimore, MDF-48Table F-37. Multi-Factor Sensitivity Analysis: Impact of Usinga Log-Linear vs. a Log-Log Model, Estimating Incidence Down tothe LowestMeasured Level (LML) in the Study vs. PRB,and Using a Proportional vs. a Hybrid Rollback to Estimate the Incidence of All Causeand Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet the CurrentStandards, Based on Adjusting 2005 PM2.5Concentrations1Modeling Choices:Fixed Effects (FE) Log-linear vs.Random Effects (RE) log-log modelFE Log-Linear FE Log-Linear FE Log-Linear FE Log-Linear RE Log-Log RE Log-Log RE Log-Log RE Log-LogDown to LML (5.8 ug/m3) vs. PRBLML LML PRB PRB LML LML PRB PRBProportional vs. hybrid rollback Proportional Hybrid Proportional Hybrid Proportional Hybrid Proportional Hybrid1342167528453169336039531355714037(854 - 1827) (1066 - 2276)(1819 - 3853)(2027 - 4286) (2075 - 4615) (2446 - 5418) (8709 - 17917) (9035 - 18516)Percent Difference:3---25%112%136%150%195%910%946%12491545254828132535294782698475(1017 - 1477) (1261 - 1824)(2095 - 2983)(2318 - 3288) (1793 - 3232) (2095 - 3738) (6414 - 9670) (6602 - 9873)Percent Difference:---24%104%125%103%136%562%579%58485912543946(372 - 792)(550 - 1161)(779 - 1713)(2554 - 5176)Percent Difference:------47%---115%---576%---3695916391612(303 - 434)(489 - 688)(458 - 803)(1271 - 1859)Percent Difference------60%---73%---337%---Incidence of Mortality Associated with Long-Term Exposure to PM2.5Concentrations Using:2Los Angeles, CAAll Cause MortalityIschemic Heart Disease Mortality2Estimates Based on Krewski et al. (2009),exposure period from 1999 - 2000. The fixedeffects log-linear estimates are from Table 33, using models with 44 individual and 7 ecological covariates; the randomeffects log-log estimates are from Table 11, "MSA and DIFF" rows. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statisticaluncertainty surrounding the PM coefficient.3Percent differences are calculated relative to the model selections used in the core analysis (fixed effects log-linear model; LML, and proportional rollbacks). So, for example, the percent difference in estimatedall cause mortality in Los Angeles resulting from changing from the core analysis inputselections to instead using (1) a fixedeffects log-linear model, (2) PRB, and (3) hybrid rollbacks is (3169 - 1342)/1342 =136%.4Philadelphia was not among the risk assessment urban areas for which hybrid rollbacks were calculated.Philadelphia, PAAll Cause MortalityIschemic Heart Disease Mortality1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.---------------------4---F-49Table F-38. Multi-Factor Sensitivity Analysis: Impact of Usinga Log-Linear vs. a Log-Log Model, Estimating Incidence Down tothe LowestMeasured Level (LML) in the Study vs. PRB,and Using a Proportional vs. a Hybrid Rollback to Estimate the Incidence of All Causeand Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet the CurrentStandards, Based on Adjusting 2006 PM2.5Concentrations1Modeling Choices:Fixed Effects (FE) Log-linear vs.Random Effects (RE) log-log modelFE Log-Linear FE Log-Linear FE Log-Linear FE Log-Linear RE Log-Log RE Log-Log RE Log-Log RE Log-LogDown to LML (5.8 ug/m3) vs. PRBLML LML PRB PRB LML LML PRB PRBProportional vs. hybrid rollback Proportional Hybrid Proportional Hybrid Proportional Hybrid Proportional Hybrid1108141426272924290434981325513736(704 - 1509) (899 - 1923)(1678 - 3560)(1869 - 3959) (1790 - 3995) (2161 - 4803) (8501 - 17544) (8827 - 18146)Percent Difference:3---28%137%164%162%216%1096%1140%10381314236626142212263381518361(843 - 1229) (1070 - 1553)(1943 - 2775)(2150 - 3060) (1558 - 2833) (1864 - 3354) (6301 - 9561) (6491 - 9770)Percent Difference:---27%128%152%113%154%685%705%52591211663869(335 - 713)(585 - 1233)(723 - 1595)(2502 - 5082)Percent Difference:------74%---122%---637%---3345595981590(273 - 393)(461 - 651)(428 - 755)(1251 - 1837)Percent Difference------67%---79%---376%---Incidence of Mortality Associated with Long-Term Exposure to PM2.5Concentrations Using:2Los Angeles, CAAll Cause MortalityIschemic Heart Disease MortalityPhiladelphia, PAAll Cause MortalityIschemic Heart Disease Mortality1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.---42Estimates Based on Krewski et al. (2009),exposure period from 1999 - 2000. The fixedeffects log-linear estimates are from Table 33, using models with 44 individual and 7 ecological covariates; the randomeffects log-log estimates are from Table 11, "MSA and DIFF" rows. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statisticaluncertainty surrounding the PM coefficient.3Percent differences are calculated relative to the model selections used in the core analysis (fixed effects log-linear model; LML, and proportional rollbacks). So, for example, the percent difference in estimatedall cause mortality in Los Angeles resulting from changing from the core analysis inputselections to instead using (1) a fixedeffects log-linear model, (2) PRB, and (3) hybrid rollbacks is (2924 - 1108)/1108 =164%.4Philadelphia was not among the risk assessment urban areas for which hybrid rollbacks were calculated.---------------------F-50Table F-39. Multi-Factor Sensitivity Analysis: Impact of Usinga Log-Linear vs. a Log-Log Model, Estimating Incidence Down tothe LowestMeasured Level (LML) in the Study vs. PRB,and Using a Proportional vs. a Hybrid Rollback to Estimate the Incidence of All Causeand Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations that Just Meet the CurrentStandards, Based on Adjusting 2007 PM2.5Concentrations1Modeling Choices:Fixed Effects (FE) Log-linear vs.Random Effects (RE) log-log modelFE Log-Linear FE Log-Linear FE Log-Linear FE Log-Linear RE Log-Log RE Log-Log RE Log-Log RE Log-LogDown to LML (5.8 ug/m3) vs. PRBLML LML PRB PRB LML LML PRB PRBProportional vs. hybrid rollback Proportional Hybrid Proportional Hybrid Proportional Hybrid Proportional Hybrid1170148426973003303436331343013914(744 - 1593) (944 - 2019) (1723 - 3654) (1920 - 4064) (1871 - 4173) (2245 - 4986) (8616 - 17770) (8945 - 18375)Percent Difference:3---27%131%157%159%211%1048%1089%10941377242626802306272882438454(890 - 1296) (1122 - 1627)(1993 - 2845)(2205 - 3136) (1626 - 2950) (1933 - 3472) (6377 - 9662) (6568 - 9871)Percent Difference:---26%122%145%111%149%653%673%519---4907---1157---3864---(331 - 704)--- (581 - 1225) ---(718 - 1583)---(2498 - 5075)---Percent Difference:------75%---123%---645%---330---555---594---1589---(270 - 389)---(459 - 647)---(424 - 750)---(1249 - 1836)---Percent Difference------68%---80%---382%---Los Angeles, CAPhiladelphia, PA3Percent differences are calculated relative to the model selections used in the core analysis (fixed effects log-linear model; LML, and proportional rollbacks). So, for example, the percent difference in estimatedall cause mortality in Los Angeles resulting from changing from the core analysis inputselections to instead using (1) a fixedeffects log-linear model, (2) PRB, and (3) hybrid rollbacks is (3003 - 1170)/1170 =157%.Incidence of Mortality Associated with Long-Term Exposure to PM2.5Concentrations Using:2All Cause MortalityIschemic Heart Disease Mortality4Philadelphia was not among the risk assessment urban areas for which hybrid rollbacks were calculated.2Estimates Based on Krewski et al. (2009),exposure period from 1999 - 2000. The fixedeffects log-linear estimates are from Table 33, using models with 44 individual and 7 ecological covariates; the randomeffects log-log estimates are from Table 11, "MSA and DIFF" rows. Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statisticaluncertainty surrounding the PM coefficient.1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.All Cause MortalityIschemic Heart Disease MortalityF-51Table F-40. Sensitivity Analysis: Impact of Using Season-Specific vs. AnnualConcentration-Response Functions andProportional vs. Hybrid Rollbacks to Estimatethe Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting2005 PM2.5Concentrations1,2Modeling Choices:Seasonal C-R Functions vs. an All-YearFunctionAll Year All Year Sum of Four Seasons Sum of Four SeasonsProportional vs. Hybrid Rollback Proportional Hybrid Proportional Hybrid256254222220(104 - 406)(103 - 402)---4---Percent Difference3----1%-13%-14%34397079(-53 - 121)(-61 - 137)------Percent Difference---15%106%132%147151159163(-26 - 317)(-26 - 325)------Percent Difference---3%8%11%8191-23-25(-117 - 278)(-130 - 311)------Percent Difference---12%-128%-131%781795780792(459 - 1102)(467 - 1121)------Percent Difference---2%0%1%159163175182(47 - 270)(48 - 277)------Percent Difference---3%10%14%260271251261(75 - 443)(78 - 462)------Percent Difference---4%-3%0%1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PMcoefficient.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.3Percent differences are calculated relative to the model selections used in the core analysis (all-year C-R function and proportional rollback). So, for example, the percentdifference in estimated non-accidental mortality in Baltimore resulting from changing from the core analysis input selections to instead using the sum of four season-specific mortalityestimates and hybrid rollbacks is (192 - 225)/225 = -15%.4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the seasonspecific coefficient estimators was not available.Pittsburgh, PASt. Louis, MOLos Angeles, CANew York, NYBirmingham, ALDetroit, MIEstimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsBaltimore, MDF-52Table F-41. Sensitivity Analysis: Impact of Using Season-Specific vs. AnnualConcentration-Response Functions andProportional vs. Hybrid Rollbacks to Estimatethe Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting2006 PM2.5Concentrations1,2Modeling Choices:Seasonal C-R Functions vs. an All-YearFunctionAll Year All Year Sum of Four Seasons Sum of Four SeasonsProportional vs. Hybrid Rollback Proportional Hybrid Proportional Hybrid224222193193(91 - 356)(90 - 352)---4---Percent Difference3----1%-14%-14%33376978(-51 - 116)(-58 - 132)------Percent Difference---12%109%136%118121137140(-21 - 255)(-21 - 261)------Percent Difference---3%16%19%7584-25-28(-108 - 257)(-120 - 287)------Percent Difference---12%-133%-137%671682677688(394 - 946)(400 - 961)------Percent Difference---2%1%3%136147154164(40 - 232)(43 - 249)------Percent Difference---8%13%21%215224211219(62 - 367)(64 - 381)------Percent Difference---4%-2%2%Estimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current StandardsBaltimore, MDBirmingham, ALDetroit, MILos Angeles, CANew York, NYPittsburgh, PASt. Louis, MO1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PMcoefficient.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.3Percent differences are calculated relative to the model selections used in the core analysis (all-year C-R function and proportional rollback). So, for example, the percentdifference in estimated non-accidental mortality in Baltimore resulting from changing from the core analysis input selections to instead using the sum of four season-specific mortalityestimates and hybrid rollbacks is (192 - 225)/225 = -15%.4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the seasonspecific coefficient estimators was not available.F-53Table F-42. Sensitivity Analysis: Impact of Using Season-Specific vs. AnnualConcentration-Response Functions andProportional vs. Hybrid Rollbacks to Estimatethe Incidence of Non-Accidental Mortality Associated with Short-Term Exposure to PM2.5Concentrations that Just Meet the Current Standards, Based on Adjusting2007 PM2.5Concentrations1,2Modeling Choices:Seasonal C-R Functions vs. an All-YearFunctionAll Year All Year Sum of Four Seasons Sum of Four SeasonsProportional vs. Hybrid Rollback Proportional Hybrid Proportional Hybrid227224207194(92 - 360)(91 - 356)---4---Percent Difference3----1%-9%-15%34399586(-53 - 120)(-60 - 137)------Percent Difference---15%179%153%121124166137(-21 - 262)(-22 - 269)------Percent Difference---2%37%13%7786-12-8(-110 - 262)(-123 - 293)------Percent Difference---12%-116%-110%734746887750(431 - 1035)(438 - 1052)------Percent Difference---2%21%2%143147225162(42 - 244)(43 - 250)------Percent Difference---3%57%13%225236248232(65 - 384)(68 - 402)------Percent Difference---5%10%3%St. Louis, MONew York, NYPittsburgh, PADetroit, MILos Angeles, CABirmingham, ALEstimated Incidence of Non-Accidental Mortality Associated with Short-Term Exposure toPM2.5Concentrations that Just Meet the Current StandardsBaltimore, MD1Based on season-specific and all-year location-specific coefficient estimates from Zanobetti and Schwartz (2009) that have been "shrunken" towards the appropriate regionalmeans. Numbers are rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PMcoefficient.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.3Percent differences are calculated relative to the model selections used in the core analysis (all-year C-R function and proportional rollback). So, for example, the percentdifference in estimated non-accidental mortality in Baltimore resulting from changing from the core analysis input selections to instead using the sum of four season-specific mortalityestimates and hybrid rollbacks is (192 - 225)/225 = -15%.4It was not possible to calculate the 2.5th and 97.5th percentile estimates of the sum of the season-specific incidences because the variance-covariance matrix for the seasonspecific coefficient estimators was not available.

# F-54 Table F-43. Sensitivity Analysis: Impact of Copollutant Models in Estimating the Incidence of All Cause

Mortality Associated with Long-Term Exposure to PM

2.5 

# Concentrations that Just Meet the

Current Standards, Based on Adjusting 2005 PM

2.5 

# Concentrations 1

, 2



# Copollutant in Model Incidence Percent Difference 3



# 1122

(580 - 1713)

1632

(945 - 2341)

1954

(1034 - 2782)

1632

(945 - 2341)

295

(-515 - 1209)

489

(253 - 743)

708

(412 - 1012)

847

(451 - 1199)

708

(412 - 1012)

129

(-227 - 526) 

# Los Angeles, CA

CO Philadelphia, PA

None 45% 

# None

CO

-74% 0%

3 

# Calculated as (estimate with copollutant - estimate without copollutant)/(estimate without copollutant). 1 

# The current primary PM 2.5 

# standards include an annual standard set at 15 ug/m 3 

# and a daily standard set at 35 ug/m 3



# . 

# -74% 

# SO

2 

# 45%

74%

45%

2 

# Estimates based on Krewski et al. (2000) [reanalysis of the ACS study]. Mortality incidence was estimated for PM 2.5 

# concentrations down to

5.8 ug/m 3 

# (the lowest measured level used for the analyses of long-term exposure). Numbers rounded to the nearest whole number.

Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient

. 

# 73%

45% 

# NO 2



# O

3



# SO 2



# 0%



# NO 2



# O

3

# F-55 Table F-44. Sensitivity Analysis: Impact of Copollutant Models in Estimating the Incidence of All Cause

Mortality Associated with Long-Term Exposure to PM

2.5 

# Concentrations that Just Meet the

Current Standards, Based on Adjusting 2006 PM

2.5 

# Concentrations 1

, 2



# Copollutant in Model Incidence Percent Difference 3



# 926

(478 - 1415)

1347

(780 - 1936)

1615

(853 - 2302)

1347

(780 - 1936)

243

(-424 - 998)

439

(228 - 669)

637

(370 - 911)

762

(405 - 1080)

637

(370 - 911)

116

(-203 - 473) 

# Los Angeles, CA

None 0%



# CO 45%



# NO 2 

# 74%



# O

3 

# 45%



# SO 2 

# -74%



# Philadelphia, PA

None 0%



# CO 45%



# NO 2 

# 74%

1 

# The current primary PM 2.5 

# standards include an annual standard set at 15 ug/m 3 

# and a daily standard set at 35 ug/m 3



# .

2 

# Estimates based on Krewski et al. (2000) [reanalysis of the ACS study]. Mortality incidence was estimated for PM 2.5 

# concentrations down to

5.8 ug/m 3 

# (the lowest measured level used for the analyses of long-term exposure). Numbers rounded to the nearest whole number.

Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient

.

3 

# Calculated as (estimate with copollutant - estimate without copollutant)/(estimate without copollutant). 

# O

3 

# 45%



# SO

2 

# -74%

# F-56 Table F-45. Sensitivity Analysis: Impact of Copollutant Models in Estimating the Incidence of All Cause

Mortality Associated with Long-Term Exposure to PM

2.5 

# Concentrations that Just Meet the

Current Standards, Based on Adjusting 2007 PM

2.5 

# Concentrations 1

, 2



# Copollutant in Model Incidence Percent Difference 3



# 978

(505 - 1494)

1423

(824 - 2043)

1705

(901 - 2429)

1423

(824 - 2043)

257

(-448 - 1054)

434

(225 - 661)

630

(366 - 901)

753

(400 - 1068)

630

(366 - 901)

115

(-201 - 468)

1 

# The current primary PM 2.5 

# standards include an annual standard set at 15 ug/m 3 

# and a daily standard set at 35 ug/m 3



# .

2 

# Estimates based on Krewski et al. (2000) [reanalysis of the ACS study]. Mortality incidence was estimated for PM 2.5 

# concentrations down to

5.8 ug/m 3 

# (the lowest measured level used for the analyses of long-term exposure). Numbers rounded to the nearest whole number.

Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient

.

3 

# Calculated as (estimate with copollutant - estimate without copollutant)/(estimate without copollutant). 

# O

3 

# 45%



# SO

2 

# -74% 

# CO 45%



# NO 2 

# 74% 

# SO 2 

# -74%



# Philadelphia, PA

None 0% 

# NO 2 

# 74%



# O

3 

# 46% 

# Los Angeles, CA

None 0%



# CO 46%F-57Table F-46. Sensitivity Analysis: Impact of Different Lag Models on Estimated Annual Incidence of HospitalAdmissions Associated with Short-Term Exposure to Ambient PM2.5Concentrations that Just Meet theCurrent Standards, Based on Adjusting 2005 PM2.5Concentrations1,20-Day Lag 1-Day Lag 2-Day Lag 0-Day Lag 1-Day Lag 2-Day Lag397 35 30 40975(294 - 501) (-60 - 130) (-58 - 118) (-22 - 102) (-53 - 71) (16 - 133)159 14 12 13325(118 - 200) (-24 - 52) (-23 - 47) (-7 - 34) (-18 - 24) (5 - 45)Risk AssessmentLocationCardiovascular Hospital AdmissionsRespiratory Hospital Admissions2Incidence estimates were calculated using the national concentration-response function estimates reported in Table 1 of Bell etal. (2008). Locationspecific C-R function estimates were not available from this study.1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Los Angeles, CAPhiladelphia, PAF-58Table F-47. Sensitivity Analysis: Impact of Different Lag Models on Estimated Annual Incidence of HospitalAdmissions Associated with Short-Term Exposure to Ambient PM2.5Concentrations that Just Meet theCurrent Standards, Based on Adjusting 2006 PM2.5Concentrations1,20-Day Lag 1-Day Lag 2-Day Lag 0-Day Lag 1-Day Lag 2-Day Lag373 33 28 38 9 70(276 - 470) (-56 - 122) (-54 - 110) (-21 - 96) (-50 - 67) (15 - 125)149 13 11 13 3 24(110 - 188) (-23 - 49) (-22 - 44) (-7 - 32) (-17 - 22) (5 - 42)Los Angeles, CAPhiladelphia, PA2Incidence estimates were calculated using the national concentration-response function estimates reported in Table 1 of Bell etal. (2008). Locationspecific C-R function estimates were not available from this study.Risk AssessmentLocationCardiovascular Hospital Admissions Respiratory Hospital Admissions1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.F-59Table F-48. Sensitivity Analysis: Impact of Different Lag Models on Estimated Annual Incidence of HospitalAdmissions Associated with Short-Term Exposure to Ambient PM2.5Concentrations that Just Meet theCurrent Standards, Based on Adjusting 2007 PM2.5Concentrations1,20-Day Lag 1-Day Lag 2-Day Lag 0-Day Lag 1-Day Lag 2-Day Lag388 34 29 39 9 73(287 - 489) (-59 - 127) (-56 - 115) (-21 - 99) (-52 - 69) (15 - 130)151 13 11 13 3 24(112 - 190) (-23 - 49) (-22 - 45) (-7 - 32) (-17 - 23) (5 - 43)2Incidence estimates were calculated using the national concentration-response function estimates reported in Table 1 of Bell etal. (2008). Locationspecific C-R function estimates were not available from this study.Los Angeles, CAPhiladelphia, PARisk AssessmentLocationCardiovascular Hospital Admissions Respiratory Hospital Admissions1The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.F-60Annual 24-Hr 2007 CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CM14/35 13/35 12/35 13/30 12/25Proportional15.0 14.2 14.0 13.3 13.0 12.3 12.0 11.4 13.0 12.3 11.8 11.2 11% 22% 34% 22% 35%Hybrid3--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused4--- --- --- --- --- --- --- --- --- --- 14 11.76--- --- --- --- ---Proportional14.8 13.1 14.0 12.5 13.0 11.6 12.010.7 12.7 11.3 10.7 9.5 9% 21% 33% 25% 49%Hybrid14.3 13.0 14.0 12.7 13.0 11.8 12.010.9 12.3 11.2 10.3 9.4 4% 16% 29% 25% 50%Locally Focused15.2 13.6 --- --- --- --- --- --- 13.1 12.0 11.0 10.0--- --- --- 21% 46%Proportional15.0 12.7 14.0 11.8 13.0 11.0 12.010.2 13.0 11.0 11.1 9.4 12% 24% 36% 24% 47%Hybrid15.0 14.2 14.0 13.2 13.0 12.3 12.0 11.4 13.0 12.3 11.3 10.7 11% 22% 34% 22% 42%Locally Focused--- --- --- --- --- --- --- --- --- --- 12.3 11.4--- --- --- --- ---Proportional12.8 11.4 12.8 11.4 12.8 11.4 12.0 10.7 12.8 11.4 12.0 10.7 0% 0% 13% 0% 13%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- ---Proportional14.1 11.4 14.0 11.4 13.0 10.6 12.0 9.8 12.2 9.9 10.2 8.3 1% 16% 30% 27% 55%Hybrid13.2 11.7 13.2 11.7 13.0 11.5 12.010.6 11.4 10.1 9.6 8.5 0% 3% 18% 27% 54%Locally Focused14.1 12.6 --- --- --- --- --- --- 12.2 11.0 10.2 9.2--- --- --- 25% 50%Proportional9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 8.6 8.6 7.3 7.3 0% 0% 0% 32% 64%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused10.1 10.3 10.1 10.3 10.1 10.3 10.1 10.3 8.8 8.9 7.4 7.50% 0% 0% 31% 62%Proportional15.0 12.5 14.0 11.7 13.0 10.9 12.0 10.1 13.0 10.9 12.0 10.1 12% 24% 36% 24% 36%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- ---Proportional12.7 9.5 12.7 9.5 12.7 9.5 12.0 9.0 10.9 8.2 9.2 7.0 0% 0% 13% 34% 68%Hybrid13.3 10.5 13.3 10.5 13.0 10.3 12.0 9.5 11.5 9.1 9.6 7.7 0% 5% 21% 30% 60%Locally Focused13.9 12.1 13.9 12.1 13.9 12.1 --- --- 12.0 10.6 10.1 9.10% 0% --- 24% 48%Proportional13.3 11.6 13.3 11.6 13.0 11.3 12.010.4 11.5 10.0 9.7 8.4 0% 5% 20% 27% 55%Hybrid13.6 11.8 13.6 11.8 13.0 11.3 12.010.4 11.7 10.2 9.8 8.5 0% 8% 22% 27% 54%Locally Focused14.3 13.3 14.3 13.3 --- --- --- --- 12.3 11.6 10.3 9.80% --- --- 22% 47%Proportional13.9 12.3 13.9 12.3 13.0 11.6 12.010.7 11.9 10.7 10.0 9.0 0% 12% 25% 26% 52%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused15.5 13.0 15.5 13.0 --- --- --- --- 14.1 11.3 11.8 9.50% --- --- 23% 48%Proportional12.6 9.9 12.6 9.9 12.6 9.9 12.0 9.4 11.8 9.3 9.9 7.8 0% 0% 11% 14% 50%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---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 ofpercent reduction in surrogate for long-term mortality risk across rollback methods)Percent reduction in a surrogate forlong-term exposure-related mortality(alternative standard compared withcurrent standard)6Risk AssessmentLocation1Rollback MethodDesign ValueRecentAirQuality(2007)Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007Annual Average at Composite Monitor (2007CM) (in ug/m3)15/35214/35 13/3513/30 12/2512/35Atlanta, GA15.3Baltimore, MD13.915.6 37.016.2 35.0Birmingham, AL15.7Dallas, TX11.412.8 26.018.7 44.0Detroit, MI13.9Fresno, CA17.417.4 63.017.2 43.0Houston, TX13.2Los Angeles, CA14.619.6 55.015.8 31.0New York, NY13.8Philadelphia, PA13.415.0 38.015.9 42.0Phoenix, AZ9.912.6 32.0F-61Annual 24-Hr2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CM14/35 13/35 12/35 13/30 12/25Proportional13.3 11.6 13.3 11.6 12.8 11.2 11.8 10.5 11.5 10.0 9.7 8.4 0% 7% 19% 27% 54%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused15.6 13.2 15.6 13.2 15.3 11.8 15.3 11.2 15.6 11.4 13.9 9.60% 18% 27% 24% 48%Proportional7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 6.7 6.6 5.7 5.6 0% 0% 0% 55% 110%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused10.8 9.7 10.8 9.7 10.8 9.7 10.8 9.7 10.8 8.8 9.1 7.70% 0% 0% 21% 51%Proportional14.9 12.9 14.0 12.1 13.0 11.3 12.0 10.4 12.8 11.1 10.8 9.3 10% 23% 35% 25% 50%Hybrid15.0 13.5 14.0 12.6 13.0 11.7 12.0 10.8 13.0 11.7 11.0 9.9 12% 23% 35% 23% 47%Locally Focused16.5 14.1 --- --- --- --- --- --- 14.2 12.4 11.9 10.4--- --- --- 21% 44%Proportional8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 7.4 7.0 6.3 6.0 0% 0% 0% 46% 93%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused8.5 8.0 8.5 8.0 8.5 8.0 8.5 8.0 7.4 7.0 6.3 6.00% 0% 0% 46% 93%Table F-49 (cont'd). Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods(with comparisonof percent reduction in surrogate for long-term mortality risk across rollback methods)6Percent reduction in composite monitor value with consideration for LML of 5.8 ug/m3 (note: composite monitor value denoted asCMV): %reduction = (CMVcurrent standard- CMValternative standard)/(CMVcurrerntstandard-LML). Note, greyed cells identify instances 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 tableonly the version that was used for mortality associated with short-term exposureto PM2.5(Zanobetti and Schwartz, 2009) is included.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.3The hybrid rollback method was applied to only a subset of the risk assessment locations. The "---" for a given location indicates that the hybrid rollback method was not applied to that location.Tacoma, WA9.710.2 43.04The 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 thatset of annual and daily standards in that location, the annual standard was controlling and so the locally focused method was not applied.5The proportional rollback and locally focused methods were applied to Pittsburgh differently from the way they were applied inthe other locations. See text for details.Salt Lake City, UT11.4St. Louis, MO14.316.5 39.011.6 55.012/2512/35Pittsburgh, PA514.919.8 60.0Percent reduction in a surrogate forlong-term exposure-related mortality(alternative standard compared withcurrent standard)6Risk AssessmentLocation1RollbackMethodDesign ValueRecentAirQuality(2007)Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007Annual Average at Composite Monitor (2007CM) (in ug/m3)15/35214/35 13/3513/30F-62Annual 24-Hr 2007 CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CM15/35 14/35 13/35 12/35 13/30 12/25Proportional15.0 14.2 14.0 13.3 13.0 12.312.0 11.4 13.0 12.3 11.8 11.2Hybrid3--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Locally Focused4--- --- --- --- --- --- --- --- --- --- 14 11.76--- --- --- --- --- 9%Proportional14.8 13.1 14.0 12.5 13.0 11.612.0 10.7 12.7 11.3 10.7 9.5Hybrid14.3 13.0 14.0 12.7 13.0 11.812.0 10.9 12.3 11.2 10.3 9.4-2% 4% 4% 4% -2% -3%Locally Focused15.2 13.6 --- --- --- --- --- --- 13.1 12.0 11.0 10.06% --- --- --- 10% 12%Proportional15.0 12.7 14.0 11.8 13.0 11.012.0 10.2 13.0 11.0 11.1 9.4Hybrid15.0 14.2 14.0 13.2 13.0 12.312.0 11.4 13.0 12.3 11.3 10.718% 19% 20% 21% 20% 26%Locally Focused--- --- --- --- --- --- --- --- --- --- 12.3 11.4--- --- --- --- --- 36%Proportional12.8 11.4 12.8 11.4 12.8 11.412.0 10.7 12.8 11.4 12.0 10.7Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Locally Focused--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Proportional14.1 11.4 14.0 11.4 13.0 10.6 12.0 9.8 12.2 9.9 10.2 8.3Hybrid13.2 11.7 13.2 11.7 13.0 11.512.0 10.6 11.4 10.1 9.6 8.54% 6% 16% 18% 5% 7%Locally Focused14.1 12.6 --- --- --- --- --- --- 12.2 11.0 10.2 9.218% --- --- --- 21% 26%Proportional9.9 9.9 9.9 9.9 9.9 9.9 9.9 9.9 8.6 8.6 7.3 7.3Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Locally Focused10.1 10.3 10.1 10.3 10.1 10.3 10.1 10.3 8.8 8.9 7.4 7.58% 8% 8% 8% 10% 14%Proportional15.0 12.5 14.0 11.7 13.0 10.912.0 10.1 13.0 10.9 12.0 10.1Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Locally Focused--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Proportional12.7 9.5 12.7 9.5 12.7 9.5 12.0 9.0 10.9 8.2 9.2 7.0Hybrid13.3 10.5 13.3 10.5 13.0 10.3 12.0 9.5 11.5 9.1 9.6 7.721% 21% 17% 13% 26% 38%Locally Focused13.9 12.1 13.9 12.1 13.9 12.1 --- --- 12.0 10.6 10.1 9.141% 41% 41% --- 49% 64%Proportional13.3 11.6 13.3 11.6 13.0 11.312.0 10.4 11.5 10.0 9.7 8.4Hybrid13.6 11.8 13.6 11.8 13.0 11.312.0 10.4 11.7 10.2 9.8 8.53% 3% 0% 0% 4% 5%Locally Focused14.3 13.3 14.3 13.3 --- --- --- --- 12.3 11.6 10.3 9.823% 23% --- --- 28% 34%Proportional13.9 12.3 13.9 12.3 13.0 11.612.0 10.7 11.9 10.7 10.0 9.0Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Locally Focused15.5 13.0 15.5 13.0 --- --- --- --- 14.1 11.3 11.8 9.59% 9% --- --- 12% 15%Proportional12.6 9.9 12.6 9.9 12.6 9.9 12.0 9.4 11.8 9.3 9.9 7.8Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Locally Focused--- --- --- --- --- --- --- --- 12.2 9.7 10.2 9.0--- --- --- --- 10% 36%Proportional13.3 11.6 13.3 11.6 12.8 11.211.8 10.5 11.5 10.0 9.7 8.4Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Locally Focused15.6 13.2 15.6 13.2 15.3 11.815.3 11.2 15.6 11.4 13.9 9.622% 22% 11% 13% 25% 31%Table F-50. Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (with percent difference insurrogate for long-term exposure-related mortality across rollback methods)cells used as basis for calculation13/30 12/25Atlanta, GA15.3RiskAssessmentLocation1RollbackMethodDesign ValueRecent AirQuality(2007)16.2 35.0Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs.(Max. M-S) and 2007Annual Average at Composite Monitor (2007CM) (in ug/m3)15/35214/35 13/35 12/35Baltimore, MD13.915.6 37.0Birmingham,AL15.718.7 44.0Dallas, TX11.4Detroit, MI13.917.2 43.012.8 26.0Fresno, CA17.4Houston, TX13.215.8 31.017.4 63.0Los Angeles,CA14.6New York, NY13.815.9 42.019.6 55.0Philadelphia,PA13.4Phoenix, AZ9.9Pittsburgh, PA514.919.8cells used as basis for calculationcells used as basis for calculationcells used as basis for calculationcells used as basis for calculationcells used as basis for calculationcells used as basis for calculationcells used as basis for calculationcells used as basis for calculationPercent difference between compositemonitor value with hybrid or peak shavingcompared with proportional(surrogate fordifference in long-term exposure-relatedmortality)6cells used as basis for calculationcells used as basis for calculationcells used as basis for calculation60.012.6 32.015.0 38.0F-63Annual 24-Hr 2007 CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CM15/35 14/35 13/35 12/35 13/30 12/25Proportional7.7 7.5 7.7 7.5 7.7 7.5 7.7 7.5 6.7 6.6 5.7 5.6Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Locally Focused10.8 9.7 10.8 9.7 10.8 9.7 10.8 9.7 10.8 8.8 9.1 7.755% 55% 55% 55% 74% 109%Proportional14.9 12.9 14.0 12.1 13.0 11.3 12.0 10.4 12.8 11.1 10.8 9.3Hybrid15.0 13.5 14.0 12.6 13.0 11.7 12.0 10.8 13.0 11.7 11.0 9.98% 6% 7% 7% 10% 13%Locally Focused16.5 14.1 --- --- --- --- --- --- 14.2 12.4 11.9 10.415% --- --- --- 19% 23%Proportional8.4 8.0 8.4 8.0 8.4 8.0 8.4 8.0 7.4 7.0 6.3 6.0Hybrid--- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- ---Locally Focused8.5 8.0 8.5 8.0 8.5 8.0 8.5 8.0 7.4 7.0 6.3 6.00% 0% 0% 0% 0% 0%Table F-50 (cont'd). Maximum 3yr Monitor-Specific Average and Annual Composite Monitor Value Given Different Rollback Methods (withpercent difference insurrogate for long-term exposure-related mortality across rollback methods)6Percent reduction in composite monitor value (CMV) with consideration for LML of 5.8 ug/m3. Percent reduction = (CMVlocally focused or hybrid- CMVproportional)/(CMVlocally focusedor hybrid-LML). Note that greyed cellsidentify 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 tableonly the version that was used for mortality associated with short-term exposure to PM2.5(Zanobetti and Schwartz, 2009) is included.3The hybrid rollback method was applied to only a subset of the risk assessment locations. The "---" for a given location indicates that the hybrid rollback method was not applied to that location.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.13/30 12/25RiskAssessmentLocation1RollbackMethodDesign ValueRecent AirQuality(2007)Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007Annual Average at Composite Monitor (2007CM) (in ug/m3)15/35214/35 13/35 12/35Tacoma, WA9.7Salt Lake City,UT11.4St. Louis, MO14.355.011.64The 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 ofannual and daily standards in that location, the annual standard was controlling and so the locally focused method was not applied.5The proportional rollback and locally focused methods were applied to Pittsburgh differently from the way they were applied inthe other locations. See Sections 3.2.3.2 and 3.2.3.3 for details.Percent difference between compositemonitor value with hybrid or peak shavingcompared with proportional(surrogate fordifference in long-term exposure-relatedmortality)6cells used as basis for calculationcells used as basis for calculationcells used as basis for calculation10.2 43.016.5 39.0

# G-1 

# APPENDIX G: SUPPLEMEMN T TO THE NATIONAL-SCALE

ASSESSMENT OF LONG-TERM MORTALITY RELATED TO PM 2.5



# EXPOSURE

# G-2 Appendix G. National-Scale Assessment of Long-Term Mortality Related to PM 2.5 

# 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 repres entativeness of the 15 urban

study areas in the national-conte xt, we completed a national-s cale 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 likel y to experience relative elevated PM 2.5 

# -related long-term

exposure-related mortality risk. This appendix provides additiona l information related to the

national-scale assessment of long-te rm mortality that was completed as part of the risk analysis.

It begins with an overview of the national-scal e analysis (section G.1), including presentation of

the national-scale mortality estimates that were generated as part of th e analysis and a brief

description of the technical appr oach used in conducting the anal ysis. Then, additi onal technical

detail on air quality (CMAQ) modeling is pres ented 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 PM 2.5. 

# To perform this assessment we use 2005 PM 2.5 

# fused air

quality estimates from the Community Model for Air Quality (CMAQ) (Byun and Schere, 2006)

in conjunction with the envir onmental Benefits Mapping and Analysis Program (BenMAP, Abt

Associates Inc, 2008) to estimate long-term PM 2.5 

# -related premature mort ality nationwide. We

estimate excess PM 2.5 

# -related long-term mort ality by applying two estimates of all-cause

mortality risk found in th e Krewski et al. (2009) PM 2.5 

# mortality extended analysis of the

American Cancer Society (ACS) cohort, and an es timate of all-cause mortality risk found in the

Laden et al. (2006) PM 2.5 

# mortality extended analysis of the Six-Cities cohort. We estimate that

total PM 2.5 

# -related premature mortality ranges from 63,000 (39,000—87,000) (95 th 

# percentile

confidence interval) and 88,000 (49,000—130,000), resp ectively; in each case we estimated

deaths per year down to the lowest measured levels (LMLs) in each epidemiological study.

We had considered expanding th e national-scale mortality to include additional health

endpoints (related to short-term PM 2.5 

# exposure) or additional air qua lity 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 th is 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 provid e perspective on the

magnitude of PM 2.5 

# health impacts on a national-scale and to help to place the risk estimates

generated for the urban study areas in a national c ontext. We note, however that the second goal

(assessing the representativeness of the 15 urba n 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 achieve s the first goal by providing estimates of longterm exposure-related all-cause mortality under r ecent conditions. While simulation of risk for

the current and alternative standard levels woul d provide additional perspective on the magnitude

of national-scale risk, that asse ssment would be resource-intensiv e 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., ap plication of a combination of rollback methods that reflects

both local and regional patterns in ambient PM 2.5 

# reductions implemented at the monitor-level).

A particular area of uncertainty (and technical complexity) rela ted to air quality simulation

would be addressing the inte rplay between regional-scale reductions in ambient PM 2.5 

# in adjacent

urbanized areas. In the urban study area analysis, each location is treated independently with

regard to simulating ambient PM 2.5 

# under alternative suites of standards. However, if we were to

expand the national analysis to include alternativ e standards, then simulation of rollbacks in

ambient PM 2.5 

# levels would necessarily have to addre ss this contiguity issue between adjacent

urban areas and even between suburban areas an d adjacent urbanized area s in the context of

simulating monitor rollback.

In addition, because long-term expos ure-related mortality dominates PM 2.5 

# in terms of

total incidence, providing coverage for this e ndpoint category ensures th at the majority of PM 2.5 

# -

related mortality incidence is reflected in the analysis, without includ ing short-term exposurerelated mortality.

The national-scale mortality analysis, as curr ently 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 PM 2.5 

# -related health impacts, the analysis allows us to assess how the urban study

areas "fall" across a nati onal distribution of risk for this ke y health endpoint category (see section

4.4.2 of the RA). This then allows us to charact erize the degree to which the set of urban study

areas provides coverage for areas of the country likely to experience relatively elevated levels of

PM 2.5 

# -related health impacts.

This assessment combines information regarding estimated PM 2.5 

# air quality levels,

population projections, baseline mort ality rates, and mortality risk coefficients to estimate PM 2.5 

# -

# G-4 related premature mortality. Fi gure G-1 below provides a conceptu al diagram, detailing each of

the key steps involved in performing this BenMAP-based health impact assessment.

Figure G-1 Conceptual diagram of data i nputs and outputs for national long-term

mortality risk assessment

Population Estimates

The starting point for estimating the size a nd 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 quali ty 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 geographi c distribution of th e potentially exposed

population, BenMAP then matches these population projections with estimates of the ambient

levels of PM 2.5 

# . In contrast to the urban study areas analysis, the national-scale analysis

employed a data fusion approach, which joined 2005 monitored PM 2.5 

# concentrations with 2005

CMAQ-modeled air quality levels using the Voronoi Neighbor Averag ing (VNA) technique

(Abt, 2003). CMAQ was run at a ho rizontal grid resolution of 12k m for the east and 36km in the

# G-5 west using 2005 estimated emission levels a nd meteorology. Figure G- 2 shows the geographic

distribution of baseline annual mean PM 2.5 

# concentrations across the continental U.S. The

maximum predicted value within the U.S. is 31 μg/m 3 

# , the mean PM 2.5 

# value is 8.7 μg/m 3



# , median is 8.8 μg/m 3 

# and the 95 th 

# percentile value is about 14 μg/m 3



# .

Figure G-2 2005 fused surface baseline PM 2.5 

# concentrations

This assessment applies PM 2.5 

# mortality risk coefficients drawn from long-term cohort

studies which estimate changes in risk based on annual mean changes in PM 2.5 

# concentration.

For this reason, EPA used the CMAQ model to estimat e annual mean concentrations at each grid

cell. These grid-level annual average con centrations were then input to BenMAP.

Premature Mortality Estimates

In this assessment of PM 2.5 

# -related premature mortality we considered risk estimates

drawn from studies based on two prospective cohort s. 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 a pplied 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 cohor t 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; fo r the Krewski et al. ( 2009) study this is 5.8 μg/m 3



# and for the Laden et al. (2006) study this is 10 μg/m 3 

# . In general, we place a higher degree of

confidence in health impacts estimated at air quality levels at or a bove the LML because the

portion of the concentration-res ponse curve below this point is extrapolated beyond the observed

data. We also estimated health impacts dow n 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/m 3 

# in the southwest to 1.72 μg/m 3 

# in the

southeast.

BenMAP contains baseline age-, cause- and county-specific mortal ity rates drawn from

the CDC-WONDER. Current baseline mortality es timates 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 PM 2.5 

# -related premature mortality associated with

modeled 2005 PM 2.5 

# levels.

Table G-1 Estimated PM 2.5 

# -related premature mortalit y associated with

incremental air quality differen ces between 2005 ambient mean

PM 2.5 

# levels and lowest measured leve l from the epidemiology studies

or policy relevant background (90 th 

# 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/m 3 

# (LML

for Laden et al.,

2006) 26,000

(16,000—36,000) 33,000

(22,000—44,000) 88,000

(49,000—130,000)

# G-7 Estimates Based on Krewski et al. (2009)

5.8 μg/m 3 

# (LML

for Krewski et

al., 2009) 63,000

(39,000—87,000) 80,000

(54,000—110,000) 210,000

(120,000—300,000)

Policy-Relevant

Background 110,000

(68,000—150,000) 140,000

(94,000—180,000) 360,000

(200,000—500,000)

Bold indicates that the minimum air quality level used to calculate this estimate corresponds to the

lowest measured level identified in the epidemiological study



# In this table, the bold figures indicate the estimate that corre sponds with the LML

identified in the epidemiological study. The bold estimates in the column Krewski et al. (2009)

were calculated using the same risk coefficien ts as the urban case study analysis. We place a

greater emphasis on those results calculated us ing the LML reported in the epidemiological

studies. 3 

# Figure G-3 illustrates the percentage of baseline mortality attributable to PM 2.5



# exposure in each of the grid cells according to the 2005 PM 2.5 

# air quality levels, using the

Krewski et al. (2009) estimate ba sed 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 (w hich conceptually supports estimation of risk below LML), we do not have

information characterizing the nature of the C-R function for long-term mortality below the LML and consequently

estimates of mortality based on incremental exposure below LML (and down to PRB) is subject to greater

uncertainty.

# G-8 Figure G-3 Percentage of prematu re mortality attributable to PM 2.5 

# exposure at various

2005 annual average PM 2.5 

# levels*

Figure G-3 illustrates the number of deat hs 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 th e 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 th e 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 numbe r of grid cells summarized by that plot.

We note that the results of the nationa l-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 ar e 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

PM 2.5 

# concentrations for the year 2005 for the contin ental US. These data were then combined

with ambient monitored PM 2.5 

# measurements to create “fused ” spatial surfaces supplied to

BenMAP.

CMAQ Model Application and Evaluation

# G-9 CMAQ is a non-proprietary computer model th at simulates the formation and fate of

photochemical oxidants, including PM 2.5 

# and ozone, for given input sets of meteorological

conditions and emissions. This analysis empl oyed a version of CMAQ based on the latest

publicly released version (i.e. CMAQ version 4.7 4 

# ).

Model Domain and Grid Resolution

The CMAQ modeling analyses we re performed for two domains covering the continental

United States, as shown in Figure G-4. These dom ains 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. Tabl e 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 ye ar of 2005. All 365 model days were used in

the annual average levels of PM 2.5



# .

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 D ecember 1, 2008. It is availabl e from the Community Modeling and

Analysis System (CMAS) a t: 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 inne r 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 (P oint and Nonpoint); c) Onroad and Nonroad

Mobile Sources; d) Biogenic Emissions; and e) Fi res. The fires portion of the inventory included

emissions from wildfires and prescribed bur ning computed as hour-specific point sources.

Electric Generating Units (EGUs)

# G-11 Annual emissions estimates for EGUs for a ll National Emissions Inventory (NEI) air

pollutants for 2005 were developed using data re ported 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 amount s. These three values are reported to the

database by the largest electri c generating facilities, usually based upon Continuous Emissions

Monitors (CEMs). For all pollutants except the di rectly 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 Vers ion 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 ve hicle type level of de tail was developed from

Federal Highway Administration (FHWA) informa tion. However, state and local agencies had

the opportunity to provide model inputs (vehicle popul ations, fuel characteristics, VMT, etc) for

2002 and 2005. If the state or lo cal 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 re placed the baseline FHWA-based VMT data.

Otherwise, the FHWA-based VMT data were used.

Emission estimates for NONROAD model engi nes were developed using EPA’s National

Mobile Inventory Model (NMIM), which in corporates NONROAD2005. Wh ere 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 Sate llite Mapping Automatic Reanalysis Tool for

Fire Incident Reconciliation (SMARTFIRE) as th e 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 meteorol ogy data using the

BEIS3.13 model (Schwede, et. al, 2005) from the Sparse Matrix Operator Kernel Emissions

(SMOKE). The BEIS3.13 model creates gridde d, 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 Em issions Landuse Database, version 3

(BELD3), which provides data on the 230 vegeta tion 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 refe rred 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 mode l input fields were

# G-13 prepared separately for both of the domains shown in Figure G-4 usi ng MM5 version 3.7.4. The

MM5 simulations were run on the same map projection as CMAQ.

Both meteorological model runs were configur ed 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 temper ature 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 10 4 

# for wind fields and

temperatures and 1.0 x 10 5 

# for moisture fields. The 12 km domain nudging weighting factors

were 1.0 x 10 4 

# for wind fields and te mperatures and 1.0 x 10 5 

# for moisture fields.

All model runs were conducted in 5.5 day se gments with 12 hours of overlap for spin-up

purposes. Both domains contained 34 vertical la yers with an approximately 38 m deep surface

layer and a 100 millibar top. The MM5 and CMAQ ve rtical 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 (heigh ts are layer top).

CMAQ

La y ers MM5

La y ers Sigma P Approximate

Hei g ht ( m ) Approximate

Pressure ( mb )

0 0 1 0 1000

1 1 0.995 38 995

2 2 0.99 77 991

3 0.985 115 987 3

4 0.98 154 982

4 5 0.97 232 973

5 6 0.96 310 964

7 0.95 389 955 6

8 0.94 469 946

9 0.93 550 937 7

10 0.92 631 928

11 0.91 712 919 8

12 0.9 794 910

# G-14 CMAQ

La y ers MM5

La y ers Sigma P Approximate

Hei g ht ( 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 Pro cessor (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 m odeling inputs. The 2005 MM5 model performance

evaluations used an approach which include d a combination of qualitative and quantitative

analyses to assess the adequacy of the MM5 si mulated fields. The qua litative aspects involved

comparisons of the model-estimated synoptic patterns against obser ved patterns from historical

weather chart archives. Additionally, the eval uations compared spatial patterns of monthly

average rainfall and monthly maximum planetar y boundary layer (PBL) heights. Qualitatively,

the model fields closely matched the observed sy noptic patterns, which is not unexpected given

the use of nudging. The operational evaluati on 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 init ial species concentrations are provided by a threedimensional global atmospheric chemistry mode l, the GEOS-CHEM model (Yantosca, 2004).

The global GEOS-CHEM model simulates atmosphe ric 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 da ta described above. The predictions were

used to provide one-way dynamic boundary conditions and an initial concen tration field for the

CMAQ simulations. More information is avai lable 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 PM 2.5 

# 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 repl icate the concentrations for the 12-km Eastern

domain and 36-km domain in the west. The prin cipal evaluation statisti cs used to evaluate

CMAQ performance included two bias metrics, norm alized mean bias and fractional bias; and

two error metrics, normalized mean error and fr actional error. For the 12-km Eastern domain,

performance evaluation statistics were computed fo r the entire domain as well as its subregions.

For the 36-km domain, evaluation focuses on th e parts of the US not covered by the 12-km

Eastern domain by computing performance evaluati on statistics for the states included in the

Western Regional Air Partnership (WRAP).

The PM 2.5 

# evaluation focuses on PM 2.5 

# total mass and its com ponents, 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: Speci ation 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. Ta ble G-4 provides annual model

performance statistics for PM 2.5 

# and its component species. Based on the bias and error values

associated with the 2005 CMAQ-modeled PM 2.5 

# concentration data, it was determined that the

annual average PM 2.5 

# data were generally within the range of past modeling results used for air

quality applications and are app licable to be used for this national-scale current conditions

analysis.

Table G-4. CMAQ modeled performance ev aluation 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

(NO 3 

# +HNO 3 

# ) CASTNet

36-km

West

WRAP 4065 37.7 51.9 24.2 45.1

12-km EUS 13317 1.8 41.9 8.3 45.6

Northeast 3247 7.1 42.9 18.9 45.7

Midwest 2495 7.1 40.5 16.4 41.4

Southeast 3499 -2.1 40.5 2.9 43.3 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 20 05 data were created by fusing CMAQ-modeled annual average

PM2.5 concentrations with total PM 2.5 

# 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 employ ees the Voronoi Nei ghbor Averaging (VNA)

interpolation technique (Abt, 2008). This technique identifies the set of m onitors 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 fiel ds 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 divi ded by the modeled concentration at the nearest

neighbor monitor locations (w eighted by distance).

To create the spatial surfaces for use in BenMAP, the 2005 CMAQ-modeled annual

average PM 2.5 

# concentrations were “fused” with 2005 total PM 2.5 

# ambient monitoring data from

STN, IMPROVE, and CASTNET sites. This wa s done for both the 36km national domain and

the 12km eastern US domain. The spatial surface of annual aver age PM2.5 air quality

concentrations produced by this technique is show n in Figure G-5 for the continental U.S. Where

available, the 12km spatial surface was used to supply BenMAP with annual average PM 2.5



# concentrations. In the western part of the U.S., annual average PM 2.5 

# concentrations were

supplied from the 36km domain.

# G-20 Figure G-5: 2005 Predicted Annual Mean PM 2.5 

# Levels

Advantages and Limitations

As compared to using monitored data al one, 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, “f using” 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 PM 2.5 

# , the better the

performance of the “fused” spatial fields. Theref ore, it is important to use model outputs that

have adequate PM 2.5 

# performance. As discussed a bove, we believe that the 2005 CMAQmodeled PM 2.5 

# concentration data showed adequate m odel performance to be used for this

national-scale current conditi ons 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 uncertai nties, in particular

# G-21 for unusual emission or meteorological events. Th ere are also uncertaintie s 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 il lustrate the spatial distribu tion of air quality impacts.

Figure G-6 illustrates the modeled 2005 PM 2.5 

# air quality levels across the U.S. Figures G-7 and

G-8 display the PM2.5 air quality le vels 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 PM 2.5 

# Levels

# G-23 Figure G-7: 2005 Predicted Annual Mean PM 2.5 

# Levels Adjusted for LML of the

Krewski et al. (2009) study

# G-24 Figure G-8: 2005 Predicted Annual Mean PM 2.5 

# 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 di stribution of grid cells at different baseline PM 2.5 

# air quality

levels. Figures G-11 and G-13 displays the distribu tion of grid cells according to the incremental

change in PM 2.5 

# 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 PM 2.5 

# 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/m 3 

# ) (Current Conditions – 10

μg/m 3 

# )

# G-28 Figure G-12: The Number of CMAQ Grid Cells Experiencing an Incremental

Change in Annual Mean PM 2.5 

# (μg/m 3 

# ) (Current Conditions – 5.8

μg/m 3 

# )

# G-29 Figure G-13: The Number of CMAQ Grid Cells Experiencing an Incremental

Change in Annual Mean PM 2.5 

# (μg/m 3 

# ) (Current Conditions – Policy

Relevant Background)

Figure G-14 displays the cumulative distri bution 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 PM 2.5 

# Concentrations (μg/m 3 

# )

# G-31 Figure G-15: Cumulative Distribution of PM 2.5 

# (μg/m 3 

# ) Changes (Baseline – 10

μg/m 3 

# )

# G-32 Figure G-16: Cumulative Distribution of PM 2.5 

# (μg/m 3 

# ) Changes (Baseline – 5.8

μg/m 3 

# )

# G-33 Figure G-14: Cumulative Distribution of PM 2.5 

# (μg/m 3 

# ) (Baseline – Policy Relevant

Background)

Exposure Estimates

Below we provide additional details regardi ng the estimated exposure changes occurring

as a result of each of the air quality changes assumed in each of the three health impact

assessments: current conditions incremental to 10 μg/m3, 5.8 μg/m3 and PRB. Table G-5

summarizes the population-weighted air quality change occurring among populations 30-99 (the

age range considered in the AC S cohort) for each scenario.

Population-weighted air quality change is the average per-person change in PM 2.5 

# . It is

estimated by calculating the summation of the popul ation in each grid cell multiplied against the

change in annual mean PM 2.5 

# concentration in that grid ce ll and then dividing by the total

population.

# G-34 Table G-5. Estimated Change in Annual Mean Population-Weighted PM 2.5 

# by

Model Scenario

Model scenario Population-weighted air quality change or

baseline

Current conditions to 10 μg/m 3 

# 2.6 μg/m 3



# Current conditions to 5.8 μg/m 3 

# 6.3 μg/m 3



# Current conditions to PRB 11 μg/m 3



# Current conditions 12 μg/m 3



# Health Impact Estimates

Figure G-14 through G-16 illustrate the distribut ion of total mortality attributable to

PM 2.5 

# exposure for each of three scenar ios: current conditions to 10 μg/m 3 

# , 5.8 μg/m 3 

# and PRB.

# G-35 Figure G-14: The Percentage of To tal Mortality Attributable to PM 2.5 

# Exposure:

Baseline – 10 μg/m 3

# G-36 Figure G-15: The Percentage of To tal Mortality Attributable to PM 2.5 

# Exposure:

Baseline – 5.8 μg/m 3

# G-37 Figure G-16: The Percentage of To tal Mortality Attributable to PM 2.5 

# Exposure:

Baseline – Policy Relevant Background

Figures G-17 through G-19 illustrate the cu mulative distribution of total mortality

attributable to PM 2.5 

# exposure for each of three scen arios: current conditions to 10 μg/m 3 

# , 5.8

μg/m 3 

# and PRB.

# G-38 Figure G-17: The Cumulative Distribution of the Percentage of Total Mortality

Attributable to PM 2.5 

# Exposure: Baseline – 10 μg/m 3

# G-39 Figure G-18: The Cumulative Distribution of the Percentage of Total Mortality

Attributable to PM2.5 Exposure: Baseline – 5.8 μg/m 3

# G-40 Figure G-19: The Cumulative Distribution of the Percentage of Total Mortality

Attributable to PM 2.5 

# Exposure: Baseline – Policy Relevant Background

# H-1 

# APPENDIX H: CONSIDERATION OF RISK ASSOCIATED WITH

EXPOSURE TO THORACI C COARESE PM (PM 10-2.5 

# )

# H-2 H.1 OVERVIEW

This appendix discusses the issu e of assessing public health risk associated with exposure

to thoracic coarse PM (PM 10-2.5 

# ). As mentioned in Section 2.3, due to limitations in available

monitoring data characterizing ambient levels of PM 10-2.5 

# in prospective urban study areas,

together with limitations in the epidemiological study data available fo r deriving C-R functions

for this PM size fraction, EPA staff has concluded that uncertainties in characterizing risk for

PM 10-2.5 

# are potentially significant enough at this time to limit the u tility of those estimates in

informing the review of the PM coarse standard level. Therefore, we have not conducted a PM 10-

2.5 

# risk assessment for this review; instead, we have included a summary of risk estimates for

PM 10-2.5 

# generated as part of the last PM NAAQS review completed in 2005. 5



# As part of our summarizing PM 10-2.5 

# risk estimates from the last review below in section

H.2, we have included a discussion of the limitations and uncertainties associated with those risk

estimates which resulted in the decision by EPA not to use those risk es timates in recommending

specific standard levels (USE PA, 2006 – Final Rule FR Notice, p. 61178). This discussion

provides the basis for a more de tailed discussion (in Section H.3) of our rationale for not

conducting a PM 10-2.5 

# risk assessment as part of the curr ent review. Specifically, in Section H-3,

we consider each of the limitations in the PM 10-2.5 

# 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 lim itation. Our conclusion is that additional

information on PM 10-2.5 

# that has become available since th e last review does not substantially

reduce overall uncertainty associated with m odeling risk for this PM size fraction, and

consequently, we conclude that conducting a PM 10-2.5 

# risk assessment is not supported at this

time.

H.2 SUMMARY OF PM 10-2.5 

# RISK ESTIMATES GENERATED FOR THE

PREVIOUS REVIEW

This section provides a brief overview of the approach used in completing the PM 10-2.5



# risk assessment for the previous review and pr ovides 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 PM 10-2.5 

# risk assessment completed for the previ ous review is similar in design to the

PM 2.5 

# risk assessment, although the scope is sign ificantly more limited, reflecting the more

5 

# We note that inclusion in this appendix of a summary of the PM 10-2.5 

# risk assessment completed for the

previous review should not be construed as implying that overall conclusions regarding limitations and uncertainties

in that risk assessment have changed. Conc lusions reached in the last review, that PM 10-2.5 

# risk estimates should not

be used in recommending specific standard levels, still holds. Rather, we have included a summary of the PM 10-2.5 

# risk assessment completed for the last re view in the interest of completeness.

# H-3 limited body of epidemiological evidence and air quality information available for PM 10-2.5 

# . The

PM 10-2.5 

# risk assessment assessed risk for populations in three urban study ar eas (Detroit, Seattle

and St. Louis), with a set of short-term e xposure-related morbidity health endpoints being

modeled, including: respiratory hospital admissions (for Detro it 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 func tions, as well as availabi lity of co-located PM 10 

# and PM 2.5 

# monitoring

data used in deriving estimates of ambient PM 10-2.5 

# levels for urban study areas. EPA staff noted

in the last review that the locations used in the PM 10-2.5 

# risk assessment were not representative

of urban locations in the U.S. that expe rience the most significant elevated 24-hour PM 10-2.5



# ambient concentrations. Thus, observations regard ing risk reductions associ ated 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 PM 10-2.5 

# concentrations. This is a key

limitation impacting the PM 10-2.5 

# risk assessment and remains a primary concern in conducting a

PM 10-2.5 

# risk assessment (see below).

In summarizing PM 10-2.5 

# risk estimates from the last review, we focus here on risk

estimates generated for the recent conditions air quality scenario. 6 

# In the risk assessment, risk

estimates are provided for Detroit for several cate gories of cardiovascular and respiratory-related

hospital admissions and show point estimates ra nging from about 2 to 7% of cause-specific

admissions being associated with “a s is” short-term exposures to PM 10-2.5 

# . The point estimate for

asthma hospital admissions associated with short-term PM 10-2.5 

# exposures for Seattle, an area

with lower PM 10-2.5 

# ambient concentrations than either Detr oit 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 resu lted in EPA staff concluding that the PM 10-2.5



# risk estimates should not be used in recommending specific standard levels include, but are not

limited to, the following (see USEPA, 2005, PM SP, Section 5.4.4.2):

 Concerns that the current PM 10-2.5 

# 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 epidem iologic studies based on monitoring sites in

different location, thus po ssibly over- or underestimating population risk levels;

 Greater uncertainty about the reasonablene ss of the use of proportional rollback to

simulate attainment of alternative PM 10-2.5 

# daily standards in any urban area due to the

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 PM 10-2.5 

# air quality data over time (this uncertainty only being

relevant to risk estimates generated fo r the alternative standard levels);

 Concerns that the locations us ed in the risk assessment are not representative of urban

areas in the U.S. that experience the most significant 24-hour peak PM 10-2.5 

# concentrations, and thus, observations about re lative risk reductions associated with

alternative standards may not be relevant to the areas expected to have the greatest health

risks associated with elevated ambient PM 10-2.5 

# levels; and

 Concerns about the much smaller health effects database that supplies the C-R

relationships used in the risk assessment, compared to that available for PM 2.5 

# , which

limits our ability to evaluate the robustness of the risk es timates for the same health

endpoints across different locations.

H.3 RATIONALE FOR THE DECISI ON NOT TO CONDUCT A PM 10-2.5 

# RISK

ASSESSMENT AS PART OF THE CURRENT REVIEW

The decision not to conduct a PM 10-2.5 

# risk assessment for the current review is

based on consideration of key uncer tainties identified in the last re view and an assessment as to

whether newly available information has significan tly reduced those uncerta inties. Each of the

sources of uncertainty is addressed below:

 Concerns that monitoring data that would be used in a PM 10-2.5 

# risk assessment (i.e., for

the period 2005-2007) would not match ambien t 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 PM 10-2.5 

# ambient levels (see final PM ISA, Sections 2.1.1.2 and 2.2.1,

USEPA 2009b), the potential for discrepancies between the m onitoring networks used in

epidemiological studies providi ng C-R functions and the monitoring network used in the

risk assessment introducing uncertain ty is increased relative to PM 2.5 

# . That is, the

potential for greater spatial variation in PM 10-2.5 

# levels means that the particular mix of

collocated monitors used in ge nerating an exposure surrogate in epidemiological studies

needs to be more closely matched to the m onitoring network used in conducting the risk

assessment if significant uncerta inty is to be avoided.

 Uncertainty in the predicti on of ambient levels under cu rrent and alternative standard

levels: This remains a significant fact or introducing uncertainty into PM 10-2.5 

# risk

estimates generated for alternative standard le vels, and continues to weigh against the use

of these risk estimates in identifying alternat ive standard levels for consideration in this

review. Not only is the monitori ng network (i.e., co-located PM 10 

# and PM 2.5 

# monitors)

available for characterizing PM 10-2.5 

# levels in candidate ur ban study areas limited (see

above), given the potential for gr eater spatial heterogeneity in PM 10-2.5 

# levels (relative to

PM 2.5 

# levels), generating representative es timates of ambient air profiles for PM 10-2.5 

# under alternative standard levels is substantially more challenging than for PM 2.5 

# . In

particular, the use of proportional rollback as a means for conducting rollbacks would be

subject to significant uncertainty given the gr eater potential for local-scale gradients in

PM 10-2.5 

# levels and the linkage of PM 10-2.5 

# to local-scale sources.

# H-5  Concerns that locations used in the risk assessment may not be representative of areas

experiencing the most sign ificant 24-hour peak PM 10-2.5 

# concentrations (and

consequently, may not capture lo cations with the highest risk) : This concern still holds

since the monitoring network available for characterizing PM 10-2.5 

# levels in urban areas

has not been significantly expa nded (final PM ISA, Secti on 3.5.1.2, ). Specifically, the

final PM ISA states that: “Given the lim ited number of co-located low-volume FRM

PM 10 

# and FRM PM 2.5 

# monitors, only a very limited i nvestigation into the intra-urban

spatial variability of PM 10-2.5 

# was possible using AQS data. Of the 15 cities under

investigation, only six (Atlanta, Boston, Chicago, Denver, New York and Phoenix)

contained data sufficient for calculating PM 10-2.5 

# according to the data completeness and

monitor specification requireme nts discussed earlier.” As noted in the previous risk

assessment, these urban study areas may not capture locations with the highest peak

levels of PM 10-2.5 

# based on consideration of general patterns in PM 10 

# and PM 2.5 

# levels.

 Concerns about the much smaller health effects database that supplies the C-R

relationships (relative to PM2.5): While a number of epidemiological studies have been

published since completion of the previous PM NAAQS review, incl uding several large

multi-city studies that inform consideration of the effects of short-term exposure to PM 10-

2.5 

# , limitations in the available studies still result in uncertainty in specifying C-R

functions for PM 10-2.5 

# . For example, while Peng et al . (2008) and Zanobetti and Schwartz

(2009) both provide affect estimates for s hort-term exposure-related mortality (with

consideration of copollutant confounding by PM 2.5 

# ), both have specific limitations that

impact their use in risk assessment. For example, Zanobetti and Schwartz (2009) derives

estimates of PM 10-2.5 

# by subtracting county-level PM 10 

# and PM 2.5 

# levels, rather than using

collocated monitors. Given the significant spatial gradients associated with PM 10-2.5



# relative to PM 2.5 

# , the use of this approach for as sessing exposure intr oduces significant

uncertainty (i.e., exposure measurement er ror). In the case of Peng et al. (2008),

significant uncertainty results from the st udy not providing regiona l and/or seasonallydifferentiated effects estimates that control for PM 2.5 

# . Given the potential for regional

differences in the composition of PM 10-2.5 

# which could impact risk estimates, combined

with the potential for PM 2.5 

# to vary regionally as a c onfounder for the effect of PM 10-2.5 

# , EPA staff believes that C-R functions with control for PM 2.5 

# would ideally be available at

the regional level. .

When considered together, the limitati ons outlined above resulted in EPA staff

concluding that a quantitative PM 10-2.5 

# risk assessment would not significantly enhance the

review of the NAAQS for coarse-fraction PM. Sp ecifically, these limitati ons would likely result

in sufficient uncertainty in the resulting risk es timates to significantly limit their utility in

informing policy-related questions, including the a ssessment of whether the current standard is

protective of public health and characterizati on of the degree of a dditional public health

protection potentially afforded by alternative standards. Because of the decision not to conduct a

quantitative PM 10-2.5 

# risk assessment, these questions will draw more heavily on the 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 PM 2.5 

# LEVELS FOR

DETROIT AND NEW YORK

# 6/30/2010 I-2 I.1 OVERVIEW

As discussed in section 3.1.2.2, we have upda ted an earlier analys is from the 1996 PM

NAAQS Risk Assessment which showed that shor t-term exposure-related mortality incidence

was driven more by days with PM 2.5 

# levels near the typical annual level, than by the days with

higher PM 2.5 

# levels that comprise the ta il of the annual daily PM2.5 di stribution. This analysis

was completed using short-term exposure-rela ted incidence estimates for cardiovascular

mortality for the simulation year 2007 (recent co nditions) for Detroit (Fig ure I-1) and New York

(Figure I-2). The daily PM 2.5 

# distributions are the composite m onitor 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 tw o urban study areas is driven more by daily PM 2.5



# levels nearer to the annual average levels, than by higher- end concentrations.

# I-3 Figure I-1: Comparison of Short-Term Ex posure-Related Cardiovascular Mortality

Against 24-hour PM 2.5 

# 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/m 3 

# PM 2.5 

# "Bins"Frequency of Daily Average PM2.5 Concentrations



# -10 0 10 20 30 40 50 60Associated Cardiovascular Mortality (95% CI) 

# Frequency of Daily Average PM2.5

Concentrations

Associated Cardiovascular Mortality

# I-4 Figure I-2: Comparison of Short-Term Ex posure-Related Cardiovascular Mortality

Against 24-hour PM 2.5 

# Distribution (New York – recent conditions for

2007 - total PM-attributab le 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/m 3 

# PM 2.5 

# "Bins"Frequency of Daily Average PM2.5 Concentrations



# 0 50 100 150 200 250Associated 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 IN VOLVING THE ALTERNATIVE

ANNUAL STANDARD OF 10 μg/m 3

# J-2 This appendix provides risk estimates generated for two sets of alternative standard levels

involving an alternative a nnual standard of 10 μg/m 3 

# , 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/m 3



# (10/25). As noted in section 2.4, these risk estima tes are potentially subject to greater uncertainty

than risk estimates generated for the other altern ative annual standard le vels considered in the

RA due to the need to simulate ambient PM 2.5 

# 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 a ssessment, 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 appropria te). This does mean that the risk estimates

presented in these tables repeat many of the same estimates pres ented in tables in Appendix E.

The organization of the tables in this appendix mirror those pr esented 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 e xposure-related health endpoints modeled in the

formal risk assessment. The final tables presente d 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-3Table J-1. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2005PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/35 13/3512/35 10/3513/30 12/2510/25249222199176153105176149105(205 - 291) (182 - 260) (163 - 234) (144 - 207) (125 - 180) (85 - 124) (144 - 207) (122 - 176) (85 - 124)396366337298259180288207180(326 - 464) (301 - 429) (276 - 395) (244 - 351) (212 - 306) (147 - 212) (236 - 339) (169 - 245) (147 - 212)18613311810387561037356(153 - 218) (109 - 156) (96 - 139) (84 - 121) (71 - 103) (46 - 66) (84 - 121) (59 - 86) (46 - 66)231231231231206142231206142(189 - 272) (189 - 272) (189 - 272) (189 - 272) (169 - 243) (116 - 168) (189 - 272) (169 - 243) (116 - 168)689509504444383258393272258(567 - 806) (418 - 599) (413 - 592) (363 - 523) (313 - 452) (211 - 306) (321 - 463) (222 - 322) (211 - 306)1876868686868452222(154 - 219) (56 - 81) (56 - 81) (56 - 81) (56 - 81) (56 - 81) (37 - 54) (18 - 26) (18 - 26)370340302263223143263223143(304 - 435) (278 - 400) (247 - 356) (215 - 310) (182 - 264) (116 - 169) (215 - 310) (182 - 264) (116 - 169)2124984984984867515682373373(1746 - 2489) (802 - 1163) (802 - 1163) (802 - 1163) (707 - 1026) (419 - 611) (555 - 808) (303 - 443) (303 - 443)2614195919591874161010711475976976(2147 - 3068) (1603 - 2307) (1603 - 2307) (1533 - 2208) (1315 - 1900) (873 - 1268) (1204 - 1742) (795 - 1156) (795 - 1156)333293293263226152224153152(273 - 391) (240 - 345) (240 - 345) (215 - 309) (185 - 267) (124 - 180) (183 - 264) (125 - 181) (124 - 180)351351351351316198307194194(286 - 414) (286 - 414) (286 - 414) (286 - 414) (258 - 374) (161 - 235) (250 - 362) (158 - 230) (158 - 230)436291291274241162219146146(359 - 511) (238 - 343) (238 - 343) (224 - 323) (197 - 285) (132 - 191) (179 - 259) (119 - 172) (119 - 172)401212121212400(33 - 47)(9 - 14) (9 - 14) (9 - 14)(9 - 14) (9 - 14)(3 - 5)(0 - 0)(0 - 0)636544496438379259427305259(523 - 744) (447 - 639) (406 - 583) (359 - 516) (310 - 447) (211 - 307) (350 - 503) (249 - 361) (211 - 307)936161616161412020(76 - 109) (50 - 72) (50 - 72) (50 - 72) (50 - 72) (50 - 72) (33 - 48) (16 - 24) (16 - 24)Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination 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 the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTJ-4Table J-2. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2006PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/25256229205181157108181154108(211 - 300) (188 - 268) (168 - 241) (149 - 214) (129 - 185) (88 - 128) (149 - 214) (126 - 181) (88 - 128)325297271237202131228155131(266 - 382) (244 - 350) (222 - 319) (194 - 279) (165 - 239) (107 - 155) (186 - 268) (127 - 184) (107 - 155)176124110958050956650(144 - 206) (101 - 146) (90 - 129) (77 - 112) (65 - 95) (41 - 59) (77 - 112) (54 - 78) (41 - 59)1751751751751539717515397(143 - 207) (143 - 207) (143 - 207) (143 - 207) (125 - 181) (79 - 115) (143 - 207) (125 - 181) (79 - 115)506355350300249145257157145(415 - 595) (290 - 418) (286 - 413) (244 - 354) (203 - 294) (118 - 172) (209 - 304) (127 - 186) (118 - 172)1947272727272492525(160 - 227) (59 - 85) (59 - 85) (59 - 85) (59 - 85) (59 - 85) (40 - 58) (20 - 29) (20 - 29)359329291252213133252213133(294 - 423) (269 - 388) (238 - 343) (206 - 298) (173 - 252) (108 - 157) (206 - 298) (173 - 252) (108 - 157)1884815815815707379534247247(1546 - 2212) (664 - 965) (664 - 965) (664 - 965) (575 - 837) (308 - 450) (434 - 633) (200 - 293) (200 - 293)205014701470139411636891043606606(1678 - 2413) (1200 - 1736) (1200 - 1736) (1138 - 1648) (947 - 1375) (560 - 817) (850 - 1235) (492 - 719) (492 - 719)303264264236201131199132131(248 - 356) (216 - 311) (216 - 311) (193 - 278) (164 - 238) (107 - 155) (163 - 235) (107 - 156) (107 - 155)372372372372335212325207207(303 - 439) (303 - 439) (303 - 439) (303 - 439) (273 - 396) (172 - 251) (265 - 384) (168 - 245) (168 - 245)3492202202051771071579393(286 - 410) (180 - 260) (180 - 260) (167 - 242) (144 - 209) (87 - 127) (128 - 186) (76 - 110) (76 - 110)33 66666000(27 - 39)(5 - 7)(5 - 7)(5 - 7)(5 - 7)(5 - 7)(0 - 0)(0 - 0)(0 - 0)484405363314263161304200161(397 - 569) (331 - 477) (297 - 428) (256 - 370) (215 - 311) (131 - 191) (248 - 359) (163 - 237) (131 - 191)6336363636361922(51 - 75)(29 - 43) (29 - 43) (29 - 43) (29 - 43) (29 - 43) (15 - 23) (1 - 2)(1 - 2)Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTJ-5Table J-3. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrationsin a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Based on Adjusting 2007PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/25247220197173149101173146101(203 - 290) (180 - 258) (161 - 231) (142 - 204) (122 - 176) (82 - 119) (142 - 204) (119 - 172) (82 - 119)324297271236202131227155131(266 - 381) (243 - 349) (221 - 319) (193 - 279) (165 - 238) (106 - 155) (186 - 268) (126 - 184) (106 - 155)18413111610185541017154(151 - 216) (107 - 154) (95 - 136) (82 - 119) (70 - 101) (44 - 64) (82 - 119) (58 - 84) (44 - 64)195195195195172112195172112(159 - 230) (159 - 230) (159 - 230) (159 - 230) (140 - 203) (91 - 133) (159 - 230) (140 - 203) (91 - 133)532377372321269162277174162(436 - 625) (308 - 445) (304 - 439) (262 - 379) (219 - 318) (132 - 192) (226 - 327) (142 - 206) (132 - 192)2047777777777532828(169 - 239) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (43 - 63) (23 - 33) (23 - 33)375344304264223140264223140(307 - 441) (281 - 405) (249 - 358) (215 - 312) (182 - 264) (114 - 166) (215 - 312) (182 - 264) (114 - 166)1953860860860749413572278278(1604 - 2293) (701 - 1018) (701 - 1018) (701 - 1018) (610 - 887) (335 - 490) (465 - 678) (225 - 330) (225 - 330)238417551755167314219061292815815(1955 - 2802) (1435 - 2070) (1435 - 2070) (1367 - 1974) (1160 - 1679) (737 - 1073) (1053 - 1527) (663 - 966) (663 - 966)300261261233199129197130129(245 - 352) (214 - 308) (214 - 308) (190 - 275) (162 - 235) (105 - 153) (160 - 232) (106 - 154) (105 - 153)317317317317282164272160160(258 - 374) (258 - 374) (258 - 374) (258 - 374) (230 - 333) (133 - 195) (222 - 322) (130 - 189) (130 - 189)390256256239209135189120120(321 - 458) (209 - 302) (209 - 302) (196 - 283) (170 - 246) (109 - 159) (154 - 223) (98 - 142) (98 - 142)471515151515700(38 - 55)(12 - 18) (12 - 18) (12 - 18) (12 - 18) (12 - 18) (6 - 8)(0 - 0)(0 - 0)529446402350297189340231189(434 - 621) (365 - 525) (329 - 474) (286 - 413) (243 - 351) (154 - 224) (278 - 401) (188 - 273) (154 - 224)6638383838382133(54 - 78)(31 - 46) (31 - 46) (31 - 46) (31 - 46) (31 - 46) (17 - 25) (2 - 3)(2 - 3)St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXNew York, NY3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination 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 the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALJ-6Table J-4. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/2515.8%14.1%12.7%11.2%9.7%6.7%11.2%9.5%6.7%(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%) (7.8% - 11.2%) (5.4% - 7.9%)15.6%14.4%13.2%11.7%10.2%7.1%11.3%8.1%7.1%(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%) (6.7% - 9.6%) (5.8% - 8.4%)15.9%11.3%10.1%8.8%7.5%4.8%8.8%6.2%4.8%(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%) (5.1% - 7.4%) (3.9% - 5.7%)11.1%11.1%11.1%11.1%9.9%6.8%11.1%9.9%6.8%(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%) (8.1% - 11.7%) (5.5% - 8%)16.4%12.2%12%10.6%9.1%6.2%9.4%6.5%6.2%(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%) (5.3% - 7.7%) (5% - 7.3%)16.8%6.1%6.1%6.1%6.1%6.1%4.1%2%2%(13.8% - 19.6%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (5% - 7.2%) (3.3% - 4.8%) (1.6% - 2.4%) (1.6% - 2.4%)12.2%11.2%9.9%8.7%7.4%4.7%8.7%7.4%4.7%(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%) (6% - 8.7%) (3.8% - 5.6%)15.2%7%7%7%6.2%3.7%4.9%2.7%2.7%(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%) (2.2% - 3.2%) (2.2% - 3.2%)14.1%10.6%10.6%10.1%8.7%5.8%7.9%5.3%5.3%(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%) (4.3% - 6.2%) (4.3% - 6.2%)13.3%11.7%11.7%10.5%9.1%6.1%9%6.1%6.1%(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%) (5% - 7.3%) (5% - 7.2%)8%8%8%8%7.2%4.5%7%4.4%4.4%(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%) (3.6% - 5.2%) (3.6% - 5.2%)15.7%10.5%10.5%9.9%8.7%5.8%7.9%5.2%5.2%(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%) (4.3% - 6.2%) (4.3% - 6.2%)8.2%2.4%2.4%2.4%2.4%2.4%0.8%0%0%(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%) (0% - 0%) (0% - 0%)16.1%13.8%12.6%11.1%9.6%6.6%10.8%7.7%6.6%(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%) (6.3% - 9.2%) (5.4% - 7.8%)9.2%6.1%6.1%6.1%6.1%6.1%4.1%2%2%(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%) (1.6% - 2.4%) (1.6% - 2.4%)Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAJ-7Table J-5. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/2515.8%14.1%12.7%11.2%9.7%6.7%11.2%9.5%6.7%(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%) (7.8% - 11.2%) (5.4% - 7.9%)12.7% 11.7% 10.6% 9.3% 7.9% 5.1% 8.9% 6.1% 5.1%(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% - 7.2%) (4.2% - 6.1%)14.8%10.5%9.3%8%6.8%4.2%8%5.6%4.2%(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%) (4.5% - 6.6%) (3.4% - 5%)8.2%8.2%8.2%8.2%7.2%4.6%8.2%7.2%4.6%(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%) (5.9% - 8.5%) (3.7% - 5.4%)12.1% 8.5% 8.4% 7.2% 5.9% 3.5% 6.1% 3.7% 3.5%(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%) (3% - 4.4%) (2.8% - 4.1%)17.2% 6.4% 6.4% 6.4% 6.4% 6.4% 4.3% 2.2% 2.2%(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%) (1.8% - 2.6%) (1.8% - 2.6%)11.5%10.5%9.3%8%6.8%4.2%8%6.8%4.2%(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%) (5.5% - 8%) (3.4% - 5%)13.4% 5.8% 5.8% 5.8% 5% 2.7% 3.8% 1.8% 1.8%(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%) (1.4% - 2.1%) (1.4% - 2.1%)10.9% 7.8% 7.8% 7.4% 6.2% 3.7% 5.6% 3.2% 3.2%(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%) (2.6% - 3.8%) (2.6% - 3.8%)12.1%10.6%10.6%9.4%8.1%5.3%8%5.3%5.3%(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%) (4.3% - 6.3%) (4.3% - 6.2%)8.1%8.1%8.1%8.1%7.3%4.6%7.1%4.5%4.5%(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%) (3.7% - 5.4%) (3.7% - 5.4%)12.6% 8% 8% 7.4% 6.4% 3.9% 5.7% 3.4% 3.4%(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%) (2.7% - 4%) (2.7% - 4%)6.5%1.2%1.2%1.2%1.2%1.2%0%0%0%(5.3% - 7.7%) (1% - 1.5%) (1% - 1.5%) (1% - 1.5%) (1%- 1.5%) (1% - 1.5%) (0% - 0%) (0% - 0%) (0% - 0%)12.2%10.2%9.2%7.9%6.7%4.1%7.7%5.1%4.1%(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%) (4.1% - 6%) (3.3% - 4.8%)6.1%3.5%3.5%3.5%3.5%3.5%1.8%0.1%0.1%(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%) (0.1% - 0.2%) (0.1% - 0.2%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXJ-8Table J-6. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/25 10/2514.9%13.2%11.8%10.4%9%6.1%10.4%8.8%6.1%(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%) (7.2% - 10.4%) (4.9% - 7.2%)12.7% 11.7% 10.6% 9.3% 7.9% 5.1% 8.9% 6.1% 5.1%(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% - 7.2%) (4.2% - 6.1%)15.4%10.9%9.7%8.4%7.1%4.5%8.4%5.9%4.5%(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%) (4.8% - 7%) (3.7% - 5.4%)9%9%9%9%7.9%5.2%9%7.9%5.2%(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%) (6.5% - 9.3%) (4.2% - 6.1%)12.8% 9.1% 9% 7.7% 6.5% 3.9% 6.7% 4.2% 3.9%(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%) (3.4% - 5%) (3.2% - 4.6%)17.7% 6.7% 6.7% 6.7% 6.7% 6.7% 4.6% 2.4% 2.4%(14.6% - 20.7%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (5.5% - 8%) (3.7% - 5.5%) (2% - 2.9%) (2% - 2.9%)11.7%10.7%9.5%8.2%7%4.4%8.2%7%4.4%(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%) (5.7% - 8.3%) (3.6% - 5.2%)13.8%6.1%6.1%6.1%5.3%2.9%4%2%2%(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%) (1.6% - 2.3%) (1.6% - 2.3%)12.6% 9.3% 9.3% 8.9% 7.5% 4.8% 6.8% 4.3% 4.3%(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%) (3.5% - 5.1%) (3.5% - 5.1%)12%10.5%10.5%9.3%8%5.2%7.9%5.2%5.2%(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%) (4.2% - 6.2%) (4.2% - 6.1%)6.7%6.7%6.7%6.7%6%3.5%5.8%3.4%3.4%(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%) (2.8% - 4%) (2.8% - 4%)14.2% 9.3% 9.3% 8.7% 7.6% 4.9% 6.9% 4.4% 4.4%(11.7% - 16.7%) (7.6% - 11%) (7.6% - 11%) (7.1% - 10.3%) (6.2%- 9%) (4% - 5.8%) (5.6% - 8.1%) (3.5% - 5.2%) (3.5% - 5.2%)9%2.9%2.9%2.9%2.9%2.9%1.3%0%0%(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%) (0% - 0%) (0% - 0%)13.3% 11.2% 10.1% 8.8% 7.5% 4.8% 8.6% 5.8% 4.8%(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%) (4.7% - 6.9%) (3.9% - 5.7%)6.3%3.7%3.7%3.7%3.7%3.7%2%0.3%0.3%(5.2% - 7.5%)(3% - 4.4%)(3% - 4.4%)(3% - 4.4%)(3% - 4.4%)(3% - 4.4%)(1.6% - 2.4%)(0.2% - 0.3%)(0.2% - 0.3%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIJ-9Table J-7. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/25-12%0%10%21%31%53%21%33%53%(-12% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (31% - 31%) (52% - 53%) (20% - 21%) (32% - 33%) (52% - 53%)-8%0%8%18%29%51%21%43%51%(-8% - -8%) (0% - 0%) (8% - 8%) (18% - 19%) (29% - 29%) (50% - 51%) (21% - 22%) (43% - 44%) (50% - 51%)-40%0%11%23%34%58%23%45%58%(-39% - -41%) (0% - 0%) (11% - 11%) (22% - 23%) (34% - 34%) (57% - 58%) (22% - 23%) (45% - 45%) (57% - 58%)0%0%0%0%11%39%0%11%39%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (38% - 39%) (0% - 0%) (11% - 11%) (38% - 39%)-35%0%1%13%25%49%23%47%49%(-35% - -36%) (0% - 0%) (1% - 1%) (13% - 13%) (25% -25%) (49% - 50%) (23% - 23%) (46% - 47%) (49% - 50%)-174%0%0%0%0%0%33%68%68%(-171% - -177%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 34%) (67% - 68%) (67% - 68%)-9%0%11%23%34%58%23%34%58%(-9% - -9%) (0% - 0%) (11% - 11%) (23% - 23%) (34% -35%) (58% - 58%) (23% - 23%) (34% - 35%) (58% - 58%)-116%0%0%0%12%48%31%62%62%(-114% - -118%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (47% - 48%) (31% - 31%) (62% - 62%) (62% - 62%)-33%0%0%4%18%45%25%50%50%(-33% - -34%) (0% - 0%) (0% - 0%) (4% - 4%) (18% - 18%) (45% - 46%) (25% - 25%) (50% - 50%) (50% - 50%)-14%0%0%10%23%48%23%48%48%(-14% - -14%) (0% - 0%) (0% - 0%) (10% - 10%) (22% -23%) (48% - 48%) (23% - 24%) (47% - 48%) (48% - 48%)0%0%0%0%10%44%13%45%45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%)(43% - 44%) (13% - 13%) (45% - 45%) (45% - 45%)-50%0%0%6%17%44%25%50%50%(-49% - -51%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 17%) (44% - 45%) (24% - 25%) (50% - 50%) (50% - 50%)-247%0%0%0%0%0%64%100%100%(-245% - -249%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (64% - 64%) (100% - 100%) (100% - 100%)-17%0%9%20%30%52%22%44%52%(-16% - -17%) (0% - 0%) (9% - 9%) (19% - 20%) (30% -31%) (52% - 53%) (21% - 22%) (44% - 44%) (52% - 53%)-51%0%0%0%0%0%33%67%67%(-51% - -52%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (33% - 33%) (67% - 67%) (67% - 67%)Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAJ-10Table J-8. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/25-12%0%10%21%31%53%21%33%53%(-12% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (31% - 31%) (52% - 53%) (20% - 21%) (32% - 33%) (52% - 53%)-9%0%9%20%32%56%24%48%56%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 32%) (56% - 56%) (23% - 24%) (47% - 48%) (56% - 56%)-42%0%12%23%35%60%23%47%60%(-41% - -42%) (0% - 0%) (12% - 12%) (23% - 24%) (35% - 36%) (60% - 60%) (23% - 24%) (46% - 47%) (60% - 60%)0%0%0%0%13%45%0%13%45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (44% - 45%) (0% - 0%) (12% - 13%) (44% - 45%)-43%0%1%16%30%59%28%56%59%(-42% - -43%) (0% - 0%) (1% - 1%) (15% - 16%) (30% -30%) (59% - 59%) (27% - 28%) (56% - 56%) (59% - 59%)-170%0%0%0%0%0%33%66%66%(-167% - -173%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 33%) (66% - 66%) (66% - 66%)-9%0%12%23%35%60%23%35%60%(-9% - -9%) (0% - 0%) (12% - 12%) (23% - 24%) (35% -36%) (59% - 60%) (23% - 24%) (35% - 36%) (59% - 60%)-131%0%0%0%13%54%35%70%70%(-129% - -133%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (53% - 54%) (34% - 35%) (70% - 70%) (70% - 70%)-39%0%0%5%21%53%29%59%59%(-39% - -40%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (53% - 53%) (29% - 29%) (59% - 59%) (59% - 59%)-14%0%0%11%24%50%25%50%50%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% -24%) (50% - 51%) (24% - 25%) (50% - 50%) (50% - 51%)0%0%0%0%10%43%12%44%44%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%)(43% - 43%) (12% - 13%) (44% - 45%) (44% - 45%)-58%0%0%7%20%51%28%58%58%(-58% - -59%) (0% - 0%) (0% - 0%) (7% - 7%) (20% - 20%) (51% - 51%) (28% - 29%) (58% - 58%) (58% - 58%)-427%0%0%0%0%0%100%100%100%(-425% - -430%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%) (100% - 100%)-19%0%10%23%35%60%25%51%60%(-19% - -20%) (0% - 0%) (10% - 10%) (22% - 23%) (35% - 35%) (60% - 61%) (25% - 25%) (50% - 51%) (60% - 61%)-74%0%0%0%0%0%47%96%96%(-74% - -75%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 48%) (96% - 96%) (96% - 96%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MI2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TXJ-11Table J-9. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated withLong-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1979 - 19831Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/25-12%0%10%21%32%54%21%34%54%(-12% - -13%) (0% - 0%) (10% - 11%) (21% - 21%) (32% -32%) (54% - 55%) (21% - 21%) (33% - 34%) (54% - 55%)-9%0%9%20%32%56%24%48%56%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 21%) (32% - 32%) (56% - 56%) (23% - 24%) (47% - 48%) (56% - 56%)-41%0%11%23%35%59%23%46%59%(-40% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (34% -35%) (58% - 59%) (23% - 23%) (46% - 46%) (58% - 59%)0%0%0%0%12%43%0%12%43%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (42% - 43%) (0% - 0%) (12% - 12%) (42% - 43%)-41%0%1%15%29%57%27%54%57%(-41% - -42%) (0% - 0%) (1% - 1%) (15% - 15%) (29% -29%) (57% - 57%) (26% - 27%) (54% - 54%) (57% - 57%)-164%0%0%0%0%0%31%64%64%(-161% - -167%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 32%) (64% - 64%) (64% - 64%)-9%0%12%23%35%59%23%35%59%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% -35%) (59% - 59%) (23% - 23%) (35% - 35%) (59% - 59%)-127%0%0%0%13%52%34%68%68%(-125% - -129%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (52% - 52%) (33% - 34%) (68% - 68%) (68% - 68%)-36%0%0%5%19%48%26%54%54%(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (48% - 49%) (26% - 27%) (53% - 54%) (53% - 54%)-15%0%0%11%24%51%25%50%51%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (24% -24%) (50% - 51%) (25% - 25%) (50% - 51%) (50% - 51%)0%0%0%0%11%48%14%50%50%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%)(48% - 48%) (14% - 14%) (49% - 50%) (49% - 50%)-53%0%0%6%18%47%26%53%53%(-52% - -53%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 19%) (47% - 48%) (26% - 26%) (53% - 53%) (53% - 53%)-210%0%0%0%0%0%55%100%100%(-209% - -212%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 55%) (100% - 100%) (100% - 100%)-19%0%10%22%33%58%24%48%58%(-18% - -19%) (0% - 0%) (10% - 10%) (21% - 22%) (33% -34%) (57% - 58%) (24% - 24%) (48% - 49%) (57% - 58%)-72%0%0%0%0%0%46%93%93%(-71% - -72%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%) (93% - 93%)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIJ-12Table J-10. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/25312279251222193133222189133(257 - 364) (229 - 327) (206 - 295) (182 - 262) (158 - 228) (108 - 158) (182 - 262) (154 - 223) (108 - 158)497460423376328228363263228(409 - 581) (378 - 538) (348 - 497) (308 - 442) (268 - 386) (186 - 270) (298 - 427) (214 - 310) (186 - 270)233168149130111721309372(192 - 273) (137 - 197) (122 - 176) (106 - 154) (90 - 131) (58 - 85) (106 - 154) (75 - 110) (58 - 85)292292292292261180292261180(239 - 344) (239 - 344) (239 - 344) (239 - 344) (213 - 307) (147 - 213) (239 - 344) (213 - 307) (147 - 213)862642635561485329497346329(711 - 1007) (526 - 754) (520 - 746) (459 - 660) (396 - 572) (267 - 389) (406 - 586) (282 - 410) (267 - 389)2348787878787582828(193 - 273) (71 - 103) (71 - 103) (71 - 103) (71 - 103) (71 - 103) (47 - 69) (23 - 34) (23 - 34)467429382333284182333284182(383 - 548) (351 - 505) (312 - 449) (272 - 393) (231 - 335) (148 - 216) (272 - 393) (231 - 335) (148 - 216)26641249124912491103658869477477(2192 - 3117) (1017 - 1477) (1017 - 1477) (1017 - 1477) (897 - 1306) (533 - 781) (705 - 1030) (386 - 567) (386 - 567)328524752475236920401363187112431243(2700 - 3849) (2024 - 2914) (2024 - 2914) (1936 - 2790) (1665 - 2408) (1108 - 1615) (1525 - 2210) (1010 - 1474) (1010 - 1474)419369369332287194284195194(344 - 492) (303 - 434) (303 - 434) (271 - 391) (234 - 338) (158 - 229) (232 - 335) (159 - 231) (158 - 229)445445445445401252389247247(363 - 526) (363 - 526) (363 - 526) (363 - 526) (327 - 475) (205 - 300) (317 - 461) (200 - 293) (200 - 293)547368368346305206278185185(450 - 639) (301 - 433) (301 - 433) (283 - 408) (249 - 361) (167 - 244) (227 - 329) (151 - 220) (151 - 220)511515151515500(42 - 60)(12 - 18) (12 - 18) (12 - 18) (12 - 18) (12 - 18) (4 - 6)(0 - 0)(0 - 0)796684624553480330539388330(656 - 930) (562 - 802) (512 - 733) (453 - 650) (392 - 566) (268 - 390) (441 - 635) (316 - 458) (268 - 390)1177878787878522626(96 - 138) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (63 - 92) (42 - 62) (21 - 31) (21 - 31)Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NYHouston, TX2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CA3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIJ-13Table J-11. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/25321287258229199137229194137(264 - 375) (236 - 336) (212 - 303) (187 - 269) (163 - 235) (112 - 162) (187 - 269) (159 - 229) (112 - 162)409375342300256167288198167(335 - 480) (307 - 441) (280 - 403) (245 - 353) (209 - 303) (135 - 198) (235 - 340) (161 - 234) (135 - 198)221157139120102641208464(181 - 258) (128 - 184) (113 - 164) (98 - 142) (83 - 120) (52 - 75) (98 - 142) (68 - 100) (52 - 75)222222222222195124222195124(181 - 262) (181 - 262) (181 - 262) (181 - 262) (158 - 230) (101 - 147) (181 - 262) (158 - 230) (101 - 147)638449443380316185327200185(523 - 749) (367 - 530) (361 - 523) (310 - 450) (257 - 375) (150 - 220) (266 - 387) (162 - 237) (150 - 220)2439292929292623232(201 - 284) (75 - 108) (75 - 108) (75 - 108) (75 - 108) (75 - 108) (50 - 74) (26 - 38) (26 - 38)453416368320270169320270169(371 - 533) (340 - 490) (301 - 434) (261 - 378) (220 - 320) (137 - 201) (261 - 378) (220 - 320) (137 - 201)2370103810381038901484682316316(1945 - 2779) (843 - 1229) (843 - 1229) (843 - 1229) (731 - 1068) (392 - 576) (553 - 809) (255 - 376) (255 - 376)258818651865177014788801328774774(2118 - 3046) (1520 - 2203) (1520 - 2203) (1442 - 2092) (1202 - 1750) (713 - 1045) (1079 - 1573) (627 - 920) (627 - 920)381334334298255167253168167(313 - 448) (273 - 393) (273 - 393) (244 - 352) (208 - 302) (136 - 198) (206 - 298) (137 - 199) (136 - 198)471471471471426270413264264(384 - 557) (384 - 557) (384 - 557) (384 - 557) (347 - 503) (219 - 320) (336 - 488) (214 - 313) (214 - 313)439279279260225137200119119(360 - 516) (228 - 330) (228 - 330) (212 - 308) (183 - 266) (111 - 163) (163 - 237) (96 - 141) (96 - 141)42 88888000(34 - 50)(6 - 10) (6 - 10) (6 - 10)(6 - 10) (6 - 10)(0 - 0)(0 - 0)(0 - 0)610512460398335205386255205(500 - 716) (419 - 603) (375 - 542) (324 - 470) (272 - 396) (167 - 244) (314 - 456) (207 - 302) (167 - 244)8046464646462422(65 - 95)(37 - 55) (37 - 55) (37 - 55) (37 - 55) (37 - 55) (20 - 29) (2 - 2)(2 - 2)Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAJ-14Table J-12. Estimated Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to Ambient PM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards, Basedon Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/25310277248219189128219185128(255 - 363) (227 - 324) (203 - 291) (179 - 258) (154 - 223) (104 - 152) (179 - 258) (151 - 218) (104 - 152)408374342299256167288197167(335 - 479) (307 - 440) (280 - 402) (244 - 353) (209 - 302) (135 - 197) (235 - 339) (160 - 234) (135 - 197)231165146128108691289069(190 - 270) (135 - 194) (120 - 173) (104 - 151) (88 - 128) (56 - 82) (104 - 151) (73 - 107) (56 - 82)247247247247218143247218143(202 - 291) (202 - 291) (202 - 291) (202 - 291) (178 - 257) (116 - 169) (202 - 291) (178 - 257) (116 - 169)670478471407341207352222207(549 - 786) (390 - 563) (385 - 556) (332 - 481) (278 - 404) (168 - 246) (286 - 416) (180 - 264) (168 - 246)2559898989898683636(211 - 298) (80 - 116) (80 - 116) (80 - 116) (80 - 116) (80 - 116) (55 - 80) (29 - 43) (29 - 43)473434385335284179335284179(387 - 556) (355 - 511) (314 - 453) (273 - 395) (231 - 335) (145 - 212) (273 - 395) (231 - 335) (145 - 212)2456109410941094954528730355355(2017 - 2879) (890 - 1296) (890 - 1296) (890 - 1296) (775 - 1131) (427 - 627) (592 - 866) (287 - 423) (287 - 423)300322222222212018041155164110401040(2462 - 3525) (1814 - 2620) (1814 - 2620) (1730 - 2501) (1469 - 2132) (937 - 1369) (1336 - 1941) (843 - 1234) (843 - 1234)378330330295252164249165164(309 - 444) (270 - 389) (270 - 389) (241 - 347) (205 - 298) (133 - 195) (203 - 295) (134 - 196) (133 - 195)402402402402359209347204204(327 - 476) (327 - 476) (327 - 476) (327 - 476) (291 - 425) (170 - 249) (282 - 410) (165 - 242) (165 - 242)490324324303265171240153153(403 - 574) (264 - 382) (264 - 382) (248 - 358) (216 - 313) (139 - 203) (195 - 284) (124 - 181) (124 - 181)591919191919900(48 - 70)(16 - 23) (16 - 23) (16 - 23) (16 - 23) (16 - 23) (7 - 10)(0 - 0)(0 - 0)665563508443377241431294241(546 - 780) (461 - 662) (415 - 599) (362 - 523) (307 - 446) (196 - 286) (351 - 509) (239 - 348) (196 - 286)8449494949492744(68 - 99)(40 - 58) (40 - 58) (40 - 58) (40 - 58) (40 - 58) (21 - 32) (3 - 4)(3 - 4)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAIncidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard Combination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXJ-15Table J-13. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2005) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2005 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 2001Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/2519.9%17.8%16%14.2%12.3%8.5%14.2%12%8.5%(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%) (9.8% - 14.2%) (6.9% - 10%)19.5%18.1%16.6%14.8%12.9%9%14.3%10.3%9%(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%) (8.4% - 12.2%) (7.3% - 10.6%)19.9%14.3%12.7%11.1%9.5%6.1%11.1%7.9%6.1%(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%) (6.4% - 9.4%) (5% - 7.2%)14%14%14%14%12.5%8.6%14%12.5%8.6%(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%) (10.2% - 14.7%) (7% - 10.2%)20.6%15.3%15.1%13.4%11.6%7.8%11.9%8.3%7.8%(17% - 24%) (12.6% - 18%) (12.4% - 17.8%) (10.9% - 15.7%) (9.4%- 13.6%) (6.4% - 9.3%) (9.7% - 14%) (6.7% - 9.8%) (6.4% - 9.3%)21%7.8%7.8%7.8%7.8%7.8%5.2%2.5%2.5%(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%) (2.1% - 3%) (2.1% - 3%)15.4%14.2%12.6%11%9.4%6%11%9.4%6%(12.6% - 18.1%) (11.6% - 16.6%) (10.3% - 14.8%) (9% - 13%) (7.6% - 11.1%) (4.9% - 7.1%) (9% - 13%) (7.6% - 11.1%) (4.9% - 7.1%)19%8.9%8.9%8.9%7.9%4.7%6.2%3.4%3.4%(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%) (2.8% - 4.1%) (2.8% - 4.1%)17.7%13.3%13.3%12.8%11%7.3%10.1%6.7%6.7%(14.5% - 20.7%) (10.9% - 15.7%) (10.9% - 15.7%) (10.4% - 15%) (9% - 13%) (6% - 8.7%) (8.2% - 11.9%) (5.4% - 7.9%) (5.4% - 7.9%)16.8%14.8%14.8%13.3%11.5%7.7%11.4%7.8%7.7%(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%) (6.3% - 9.2%) (6.3% - 9.2%)10.1%10.1%10.1%10.1%9.1%5.7%8.8%5.6%5.6%(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%) (4.5% - 6.6%) (4.5% - 6.6%)19.7%13.2%13.2%12.5%11%7.4%10%6.7%6.7%(16.2% - 23%) (10.8% - 15.6%) (10.8% - 15.6%) (10.2% - 14.7%) (9% - 13%) (6% - 8.8%) (8.2% - 11.8%) (5.4% - 7.9%) (5.4% - 7.9%)10.4% 3% 3% 3% 3% 3% 1.1% 0% 0%(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%) (0% - 0%) (0% - 0%)20.2%17.4%15.8%14%12.2%8.4%13.7%9.8%8.4%(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%) (6.8% - 9.9%)11.6% 7.7% 7.7% 7.7% 7.7% 7.7% 5.2% 2.6% 2.6%(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%)(2.1% - 3.1%)(2.1% - 3.1%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTJ-16Table J-14. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2006) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2006 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 2001Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/2519.8%17.7%16%14.2%12.3%8.5%14.2%12%8.5%(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%) (9.8% - 14.2%) (6.9% - 10%)16%14.7%13.4%11.8%10.1%6.5%11.3%7.8%6.5%(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%) (6.3% - 9.2%) (5.3% - 7.8%)18.6%13.2%11.7%10.2%8.6%5.4%10.2%7.1%5.4%(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%) (5.8% - 8.4%) (4.4% - 6.4%)10.4%10.4%10.4%10.4%9.1%5.8%10.4%9.1%5.8%(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%) (7.4% - 10.8%) (4.7% - 6.9%)15.2% 10.7% 10.6% 9.1% 7.6% 4.4% 7.8% 4.8% 4.4%(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%) (3.9% - 5.7%) (3.6% - 5.3%)21.5% 8.1% 8.1% 8.1% 8.1% 8.1% 5.5% 2.8% 2.8%(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%) (2.3% - 3.3%) (2.3% - 3.3%)14.5%13.3%11.7%10.2%8.6%5.4%10.2%8.6%5.4%(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% - 10.2%) (4.4% - 6.4%)16.8% 7.4% 7.4% 7.4% 6.4% 3.4% 4.8% 2.2% 2.2%(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%) (1.8% - 2.7%) (1.8% - 2.7%)13.8% 9.9% 9.9% 9.4% 7.9% 4.7% 7.1% 4.1% 4.1%(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%) (3.3% - 4.9%) (3.3% - 4.9%)15.3%13.4%13.4%11.9%10.2%6.7%10.1%6.7%6.7%(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%) (5.5% - 8%) (5.4% - 7.9%)10.3%10.3%10.3%10.3%9.3%5.9%9%5.8%5.8%(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%) (4.7% - 6.8%) (4.7% - 6.8%)15.9% 10.1% 10.1% 9.4% 8.1% 5% 7.3% 4.3% 4.3%(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%) (3.5% - 5.1%) (3.5% - 5.1%)8.3%1.6%1.6%1.6%1.6%1.6%0%0%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%) (0% - 0%) (0% - 0%)15.4%12.9%11.6%10%8.5%5.2%9.7%6.4%5.2%(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%) (5.2% - 7.6%) (4.2% - 6.2%)7.8%4.5%4.5%4.5%4.5%4.5%2.4%0.2%0.2%(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%)(0.2% - 0.2%)(0.2% - 0.2%)Percent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDDetroit, MIHouston, TXNew York, NYSt. Louis, MO3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTJ-17Table J-15. Estimated Percent of Total Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposureto AmbientPM2.5Concentrations in a Recent Year (2007) and PM2.5Concentrations that Just Meet the Current and Alternative Standards,Based on Adjusting 2007 PM2.5Concentrations: Estimates Based on Krewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/2518.7%16.7%14.9%13.2%11.4%7.7%13.2%11.1%7.7%(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%) (9.1% - 13.1%) (6.3% - 9.1%)16.1%14.7%13.4%11.8%10.1%6.5%11.3%7.8%6.5%(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%) (6.3% - 9.2%) (5.3% - 7.8%)19.3%13.8%12.2%10.7%9.1%5.8%10.7%7.5%5.8%(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%) (6.1% - 8.9%) (4.7% - 6.8%)11.4%11.4%11.4%11.4%10%6.6%11.4%10%6.6%(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%) (8.2% - 11.9%) (5.3% - 7.8%)16.1%11.5%11.3%9.8%8.2%5%8.5%5.3%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%) (4.3% - 6.3%) (4% - 5.9%)22.2% 8.5% 8.5% 8.5% 8.5% 8.5% 5.9% 3.1% 3.1%(18.3% - 25.9%) (7% - 10.1%) (7% - 10.1%) (7% - 10.1%) (7% -10.1%) (7% - 10.1%) (4.8% - 7%) (2.5% - 3.7%) (2.5% - 3.7%)14.8%13.6%12%10.5%8.9%5.6%10.5%8.9%5.6%(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%) (7.2% - 10.5%) (4.5% - 6.6%)17.3% 7.7% 7.7% 7.7% 6.7% 3.7% 5.2% 2.5% 2.5%(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%) (2% - 3%) (2% - 3%)15.9%11.8%11.8%11.2%9.6%6.1%8.7%5.5%5.5%(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%) (4.5% - 6.5%) (4.5% - 6.5%)15.1%13.2%13.2%11.8%10.1%6.6%10%6.6%6.6%(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%) (5.4% - 7.8%) (5.3% - 7.8%)8.5%8.5%8.5%8.5%7.6%4.4%7.3%4.3%4.3%(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%) (3.5% - 5.1%) (3.5% - 5.1%)17.8%11.8%11.8%11%9.6%6.2%8.7%5.6%5.6%(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%) (4.5% - 6.6%) (4.5% - 6.6%)11.4% 3.7% 3.7% 3.7% 3.7% 3.7% 1.7% 0% 0%(9.3% - 13.4%) (3% - 4.4%) (3% - 4.4%) (3% - 4.4%) (3%- 4.4%) (3% - 4.4%) (1.4%- 2%) (0% - 0%) (0% - 0%)16.8%14.2%12.8%11.2%9.5%6.1%10.9%7.4%6.1%(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% - 8.8%) (4.9% - 7.2%)8%4.7%4.7%4.7%4.7%4.7%2.5%0.3%0.3%(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%)(0.3% - 0.4%)(0.3% - 0.4%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Tacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTDetroit, MIHouston, TXNew York, NYSt. Louis, MOPercent of Total Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year andPM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (Standard 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 coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CARisk AssessmentLocationAtlanta, GABirmingham, ALBaltimore, MDJ-18Table J-16. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2005 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/25-12%0%10%20%31%52%20%32%52%(-11% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (30% -31%) (59% - 42%) (20% - 21%) (32% - 33%) (52% - 53%)-8%0%8%18%29%50%21%43%50%(-8% - -8%) (0% - 0%) (8% - 8%) (18% - 18%) (28% - 29%) (58% - 40%) (21% - 21%) (42% - 43%) (50% - 51%)-39%0%11%22%34%57%22%45%57%(-38% - -40%) (0% - 0%) (11% - 11%) (22% - 23%) (33% -34%) (64% - 48%) (22% - 23%) (44% - 45%) (57% - 58%)0%0%0%0%11%38%0%11%38%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%) (48% - 25%) (0% - 0%) (11% - 11%) (38% - 39%)-34%0%1%13%24%49%23%46%49%(-34% - -35%) (0% - 0%) (1% - 1%) (12% - 13%) (24% -25%) (56% - 38%) (22% - 23%) (46% - 46%) (48% - 49%)-170% 0% 0% 0% 0% 0% 33% 67% 67%(-166% - -174%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (16% - -23%) (33% - 33%) (67% - 67%) (67% - 67%)-9%0%11%22%34%58%22%34%58%(-9% - -9%) (0% - 0%) (11% - 11%) (22% - 23%) (34% -34%) (64% - 48%) (22% - 23%) (34% - 34%) (57% - 58%)-113%0%0%0%12%47%30%62%62%(-111% - -116%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (55% - 35%) (30% - 31%) (62% - 62%) (62% - 62%)-33%0%0%4%18%45%24%50%50%(-32% - -33%) (0% - 0%) (0% - 0%) (4% - 4%) (17% - 18%) (53% - 33%) (24% - 25%) (49% - 50%) (49% - 50%)-13%0%0%10%22%48%23%47%48%(-13% - -14%) (0% - 0%) (0% - 0%) (10% - 10%) (22% -23%) (55% - 36%) (23% - 23%) (47% - 48%) (47% - 48%)0%0%0%0%10%43%12%45%45%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%)(52% - 30%) (12% - 13%) (44% - 45%) (44% - 45%)-49%0%0%6%17%44%24%50%50%(-48% - -50%) (0% - 0%) (0% - 0%) (6% - 6%) (17% - 17%) (52% - 32%) (24% - 25%) (49% - 50%) (49% - 50%)-244%0%0%0%0%0%64%100%100%(-242% - -247%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (16% - -24%) (64% - 64%) (100% - 100%) (100% - 100%)-16%0%9%19%30%52%21%43%52%(-16% - -17%) (0% - 0%) (9% - 9%) (19% - 19%) (29% -30%) (59% - 41%) (21% - 22%) (43% - 44%) (51% - 52%)-51%0%0%0%0%0%33%67%67%(-50% - -51%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (16% - -23%) (33% - 33%) (66% - 67%) (66% - 67%)3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAPercent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TXJ-19Table J-17. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2006 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35314/3513/3512/3510/3513/30 12/2510/25-12%0%10%20%31%52%20%32%52%(-11% - -12%) (0% - 0%) (10% - 10%) (20% - 21%) (30% -31%) (52% - 53%) (20% - 21%) (32% - 33%) (52% - 53%)-9%0%9%20%32%56%23%47%56%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (55% - 56%) (23% - 23%) (47% - 48%) (55% - 56%)-41%0%11%23%35%59%23%46%59%(-40% - -42%) (0% - 0%) (11% - 12%) (23% - 23%) (35% -35%) (59% - 60%) (23% - 23%) (46% - 47%) (59% - 60%)0%0%0%0%12%44%0%12%44%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 13%) (44% - 44%) (0% - 0%) (12% - 13%) (44% - 44%)-42%0%1%15%30%59%27%56%59%(-41% - -43%) (0% - 0%) (1% - 1%) (15% - 16%) (29% -30%) (59% - 59%) (27% - 28%) (55% - 56%) (59% - 59%)-165% 0% 0% 0% 0% 0% 32% 66% 66%(-162% - -169%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (32% - 33%) (65% - 66%) (65% - 66%)-9%0%11%23%35%59%23%35%59%(-9% - -9%) (0% - 0%) (11% - 12%) (23% - 23%) (35% -35%) (59% - 60%) (23% - 23%) (35% - 35%) (59% - 60%)-128%0%0%0%13%53%34%70%70%(-126% - -131%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (53% - 54%) (34% - 34%) (69% - 70%) (69% - 70%)-39%0%0%5%21%53%29%58%58%(-38% - -39%) (0% - 0%) (0% - 0%) (5% - 5%) (21% - 21%) (53% - 53%) (29% - 29%) (58% - 59%) (58% - 59%)-14%0%0%11%24%50%24%50%50%(-14% - -14%) (0% - 0%) (0% - 0%) (11% - 11%) (23% -24%) (50% - 50%) (24% - 25%) (49% - 50%) (50% - 50%)0%0%0%0%10%43%12%44%44%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (10% - 10%)(43% - 43%) (12% - 12%) (44% - 44%) (44% - 44%)-57%0%0%7%20%51%28%57%57%(-56% - -58%) (0% - 0%) (0% - 0%) (7% - 7%) (19% - 20%) (51% - 51%) (28% - 28%) (57% - 58%) (57% - 58%)-423%0%0%0%0%0%100%100%100%(-420% - -427%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (100% - 100%) (100% - 100%) (100% - 100%)-19%0%10%22%35%60%25%50%60%(-19% - -19%) (0% - 0%) (10% - 10%) (22% - 23%) (34% -35%) (60% - 60%) (24% - 25%) (50% - 51%) (60% - 60%)-74%0%0%0%0%0%47%96%96%(-73% - -74%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (47% - 47%) (96% - 96%) (96% - 96%)Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAJ-20Table J-18. Percent Reduction from the Current Standards: Estimated Annual Incidence of Ischemic Heart Disease Mortality Associatedwith Long-Term Exposure to Ambient PM2.5Concentrations, Based on Adjusting 2007 PM2.5Concentrations: Estimates Based onKrewski et al. (2009), Using Ambient PM2.5from 1999 - 20001Recent PM2.5Concentrations15/35214/3513/3512/3510/3513/30 12/2510/25-12%0%10%21%32%54%21%33%54%(-12% - -12%) (0% - 0%) (10% - 10%) (21% - 21%) (31% -32%) (53% - 54%) (21% - 21%) (33% - 34%) (53% - 54%)-9%0%9%20%32%56%23%47%56%(-9% - -9%) (0% - 0%) (9% - 9%) (20% - 20%) (31% - 32%) (55% - 56%) (23% - 23%) (47% - 48%) (55% - 56%)-40%0%11%23%34%58%23%45%58%(-39% - -41%) (0% - 0%) (11% - 11%) (22% - 23%) (34% -35%) (58% - 59%) (22% - 23%) (45% - 46%) (58% - 59%)0%0%0%0%12%42%0%12%42%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (12% - 12%) (42% - 43%) (0% - 0%) (12% - 12%) (42% - 43%)-40%0%1%15%28%57%26%53%57%(-40% - -41%) (0% - 0%) (1% - 1%) (15% - 15%) (28% -29%) (56% - 57%) (26% - 27%) (53% - 54%) (56% - 57%)-159% 0% 0% 0% 0% 0% 31% 63% 63%(-156% - -163%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (31% - 31%) (63% - 64%) (63% - 64%)-9%0%11%23%35%59%23%35%59%(-9% - -9%) (0% - 0%) (11% - 11%) (23% - 23%) (34% -35%) (58% - 59%) (23% - 23%) (34% - 35%) (58% - 59%)-124%0%0%0%13%52%33%68%68%(-122% - -127%) (0% - 0%) (0% - 0%) (0% - 0%) (13% - 13%) (52% - 52%) (33% - 33%) (67% - 68%) (67% - 68%)-35%0%0%5%19%48%26%53%53%(-35% - -36%) (0% - 0%) (0% - 0%) (5% - 5%) (19% - 19%) (48% - 48%) (26% - 26%) (53% - 54%) (53% - 54%)-14%0%0%11%24%50%25%50%50%(-14% - -15%) (0% - 0%) (0% - 0%) (11% - 11%) (23% -24%) (50% - 51%) (24% - 25%) (50% - 50%) (50% - 51%)0%0%0%0%11%48%14%49%49%(0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (11% - 11%)(48% - 48%) (14% - 14%) (49% - 50%) (49% - 50%)-51%0%0%6%18%47%26%53%53%(-51% - -52%) (0% - 0%) (0% - 0%) (6% - 6%) (18% - 18%) (47% - 47%) (26% - 26%) (52% - 53%) (52% - 53%)-208%0%0%0%0%0%55%100%100%(-205% - -210%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (55% - 55%) (100% - 100%) (100% - 100%)-18%0%10%21%33%57%23%48%57%(-18% - -18%) (0% - 0%) (10% - 10%) (21% - 22%) (33% -33%) (57% - 57%) (23% - 24%) (47% - 48%) (57% - 57%)-71%0%0%0%0%0%46%93%93%(-71% - -72%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (0% - 0%) (46% - 46%) (93% - 93%) (93% - 93%)Percent Reduction from the Current Standards: Annual Incidence of Ischemic Heart Disease Mortality Associated with Long-Term Exposure to PM2.5Concentrations in a Recent Year and PM2.5Concentrations that Just Meet the Current and Alternative Annual (n) and Daily (m) Standards (StandardCombination Denoted n/m)2:St. Louis, MOTacoma, WAPhiladelphia, PAPhoenix, AZPittsburgh, PASalt Lake City, UTBaltimore, MDDetroit, MIHouston, TX3The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.Risk AssessmentLocationAtlanta, GABirmingham, ALNew York, NY2Numbers rounded to the nearest percent. Numbers in parentheses are 95% confidence or credible intervals based on statistical uncertainty surrounding the PM coefficient.1Based on follow-up through 2000, using models with 44 individual and 7 ecological covariates (see Table 33 in Krewski et al., 2009).Dallas, TXFresno, CALos Angeles, CAJ-21Annual 24-Hr2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CM14/35 13/35 12/35 10/35 13/30 12/25 10/25Proportional15.0 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% 34% 56% 22% 35% 56%Hybrid3--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused4--- --- --- --- --- --- --- --- --- --- --- --- 14 11.76 --- ------ --- --- --- --- --- ---Proportional14.8 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% 33% 57% 25% 49% 57%Hybrid14.3 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% 29% 54% 25% 50% 54%Locally Focused15.2 13.6 --- --- --- --- --- --- --- --- 13.1 12.0 11.0 10.0 --- ------ --- --- --- 21% 46% ---Proportional15.0 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% 36% 60% 24% 47% 60%Hybrid15.0 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% 34% 56% 22% 42% 56%Locally Focused--- --- --- --- --- --- --- --- --- --- --- --- 12.3 11.4 --- ------ --- --- --- --- --- ---Proportional12.8 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% 13% 44% 0% 13% 44%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- ---Proportional14.1 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% 30% 58% 27% 55% 58%Hybrid13.2 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% 18% 48% 27% 54% 54%Locally Focused14.1 12.6 --- --- --- --- --- --- --- --- 12.2 11.0 10.2 9.2 --- ------ --- --- --- 25% 50% ---Proportional9.9 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% 0% 0% 32% 64% 64%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused10.1 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 7.50% 0% 0% 0% 31% 62% 62%Proportional15.0 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% 36% 60% 24% 36% 60%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- ---Proportional12.7 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% 13% 53% 34% 68% 68%Hybrid13.3 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% 21% 54% 30% 60% 60%Locally Focused13.9 12.1 13.9 12.1 13.9 12.1 --- --- --- --- 12.0 10.6 10.1 9.1 10.1 9.10% 0% --- --- 24% 48% 48%Proportional13.3 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% 20% 50% 27% 55% 55%Hybrid13.6 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% 22% 51% 27% 54% 54%Locally Focused14.3 13.3 14.3 13.3 --- --- --- --- --- --- 12.3 11.6 10.3 9.8 10.3 9.80% --- --- --- 22% 47% 47%Proportional13.9 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% 25% 52% 26% 52% 52%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused15.5 13.0 15.5 13.0 --- --- --- --- --- --- 14.1 11.3 11.8 9.5 --- ---0% --- --- --- 23% 48% ---Proportional12.6 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% 11% 49% 14% 50% 50%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 ofpercent reduction in surrogate for long-term mortalityriskacross rollback methods)Percent reduction in a surrogate for long-termexposure-related mortality(alternative standardcompared with current standard)6Risk AssessmentLocation1Rollback MethodDesign ValueRecentAirQuality(2007)Maximum Monitor-Specific Avg. of 2005, 2006, 2007 AnnualAvgs. (Max. M-S)and 2007 Annual Average atCompositeMonitor (2007CM) (in ug/m3)15/35214/35 13/3510/35 13/3010/2512/2512/35Atlanta, GA15.3Baltimore, MD13.915.6 37.016.2 35.0Birmingham, AL15.7Dallas, TX11.412.8 26.018.7 44.0Detroit, MI13.9Fresno, CA17.417.4 63.017.2 43.0Houston, TX13.2Los Angeles, CA14.619.6 55.015.8 31.0New York, NY13.8Philadelphia, PA13.415.0 38.015.9 42.0Phoenix, AZ9.912.6 32.0J-22Annual 24-Hr2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CM14/35 13/35 12/35 10/35 13/30 12/25 10/25Proportional13.3 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% 19% 49% 27% 54% 54%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused15.6 13.2 15.6 13.2 15.3 11.8 15.3 11.2 --- --- 15.6 11.4 13.9 9.6 --- ---0% 18% 27% --- 24% 48% ---Proportional7.7 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% 0% 0% 55% 110% 110%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused10.8 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 7.70% 0% 0% 0% 21% 51% 51%Proportional14.9 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% 35% 59% 25% 50% 59%Hybrid15.0 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% 35% 58% 23% 47% 58%Locally Focused16.5 14.1 --- --- --- --- --- --- --- --- 14.2 12.4 11.9 10.4 --- ------ --- --- --- 21% 44% ---Proportional8.4 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% 0% 0% 46% 93% 93%Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---Locally Focused8.5 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 6.00% 0% 0% 0% 46% 93% 93%Table J-19 (cont'd). Maximum 3yr Monitor-Specific Average andAnnual Composite Monitor Value Given Different Rollback Methods(with comparison of percent reduction in surrogate for long-term mortality riskacross rollback methods)6Percent 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). Note, greyed cells identify instanceswhere 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 tableonly the version that was used for mortality associated with short-term exposure to PM2.5(Zanobetti and Schwartz, 2009) isincluded.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.3The hybrid rollback method was applied to only a subset of the risk assessment locations. The "---" for a given location indicates that the hybrid rollback method was not applied to that location.Tacoma, WA9.710.2 43.04The 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 daily standards in thatlocation, the annual standard was controlling and so the locally focused method was not applied.5The proportional rollback and locally focused methods were applied to Pittsburgh differently from the way they were applied inthe other locations. See text for details.Salt Lake City, UT11.4St. Louis, MO14.316.5 39.011.6 55.0Pittsburgh, PA514.919.8 60.013/3010/2512/2512/35Percent reduction in a surrogate for long-termexposure-related mortality(alternative standardcompared with current standard)6Risk AssessmentLocation1RollbackMethodDesign ValueRecentAirQuality(2007)Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 Annual Average at CompositeMonitor (2007CM) (in ug/m3)15/35214/35 13/3510/35J-23Annual 24-Hr 2007 CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CM15/35 14/35 13/35 12/35 10/35 13/30 12/25 10/25Proportional15.0 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 9.5Hybrid3--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- --- ---Locally Focused4--- --- --- --- --- --- --- --- --- --- --- --- 14 11.76 --- ------ --- --- --- --- --- 9% ---Proportional14.8 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 8.9Hybrid14.3 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% 4% 6% -2% -3% 6%Locally Focused15.2 13.6 --- --- --- --- --- --- --- --- 13.1 12.0 11.0 10.0 --- ---6% --- --- --- --- 10% 12% ---Proportional15.0 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 8.5Hybrid15.0 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 9.518% 19% 20% 21% 26% 20% 26% 26%Locally Focused--- --- --- --- --- --- --- --- --- --- --- --- 12.3 11.4 --- ------ --- --- --- --- --- 36% ---Proportional12.8 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 8.9Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- --- ---Locally Focused--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- --- ---Proportional14.1 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 8.2Hybrid13.2 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 8.54% 6% 16% 18% 23% 5% 7% 13%Locally Focused14.1 12.6 --- --- --- --- --- --- --- --- 12.2 11.0 10.2 9.2 --- ---18% --- --- --- --- 21% 26% ---Proportional9.9 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 7.3Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- --- ---Locally Focused10.1 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 7.58% 8% 8% 8% 8% 10% 14% 14%Proportional15.0 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 8.5Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- --- ---Locally Focused--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- --- ---Proportional12.7 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 7.0Hybrid13.3 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 7.721% 21% 17% 13% 19% 26% 38% 38%Locally Focused13.9 12.1 13.9 12.1 13.9 12.1 --- --- --- --- 12.0 10.6 10.1 9.1 10.1 9.141% 41% 41% --- --- 49% 64% 64%Proportional13.3 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 8.4Hybrid13.6 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 8.53% 3% 0% 0% 0% 4% 5% 5%Locally Focused14.3 13.3 14.3 13.3 --- --- --- --- --- --- 12.3 11.6 10.3 9.8 10.3 9.823% 23% --- --- --- 28% 34% 34%Proportional13.9 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 8.9Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- --- ---Locally Focused15.5 13.0 15.5 13.0 --- --- --- --- --- --- 14.1 11.3 11.8 9.5 --- ---9% 9% --- --- --- 12% 15% ---Proportional12.6 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 7.8Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- --- ---Locally Focused--- --- --- --- --- --- --- --- --- --- 12.2 9.7 10.2 9.0 10.2 9.0--- --- --- --- --- 10% 36% 36%Proportional13.3 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 8.4Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- --- ---Locally Focused15.6 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% 13% --- 25% 31% ---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-related mortality across rollbackmethods)10/25cells used as basis for calculation13/30 12/25Atlanta, GA15.3RiskAssessmentLocation1Rollback MethodDesign ValueRecent AirQuality(2007)16.2 35.0Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 Annual Average at CompositeMonitor (2007CM) (in ug/m3)15/35214/35 13/35 12/35 10/35Baltimore, MD13.915.6 37.0Birmingham,AL15.718.7 44.0Dallas, TX11.4Detroit, MI13.917.2 43.012.8 26.0Fresno, CA17.4Houston, TX13.215.8 31.017.4 63.0Los Angeles,CA14.6New York, NY13.815.9 42.019.6 55.0Philadelphia,PA13.4Phoenix, AZ9.9Pittsburgh, PA514.919.8cells used as basis for calculationcells used as basis for calculationcells used as basis for calculationcells used as basis for calculationcells used as basis for calculationcells used as basis for calculationcells used as basis for calculationcells used as basis for calculationPercent difference between composite monitor value withhybrid or peak shaving compared with proportional(surrogatefor difference in long-term exposure-related mortality)6cells used as basis for calculationcells used as basis for calculationcells used as basis for calculation60.012.6 32.015.0 38.0J-24Annual 24-Hr 2007 CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CMMax. M-S2007CM15/35 14/35 13/35 12/35 10/35 13/30 12/25 10/25Proportional7.7 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 5.6Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- --- ---Locally Focused10.8 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 7.755% 55% 55% 55% 55% 74% 109% 109%Proportional14.9 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 8.7Hybrid15.0 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 9.08% 6% 7% 7% 9% 10% 13% 9%Locally Focused16.5 14.1 --- --- --- --- --- --- --- --- 14.2 12.4 11.9 10.4 --- ---15% --- --- --- --- 19% 23% ---Proportional8.4 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 6.0Hybrid--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ------ --- --- --- --- --- --- ---Locally Focused8.5 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 6.00% 0% 0% 0% 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 exposure-related mortality acrossrollback methods)6Percent reduction in composite monitor value (CMV) with consideration for LML of 5.8 ug/m3. Percent reduction = (CMVlocally focusedor hybrid- CMVproportional)/(CMVlocally focusedor hybrid-LML). Note that greyed cells identify instances where two values differ by >25%across alternative rollback methods (for a given alternative standard level/study area combination).10/251For some locations (e.g., Atlanta) more than one "version" (group of counties) was used in the risk assessment. In this tableonly the version that was used for mortality associated with short-term exposure to PM2.5(Zanobetti and Schwartz, 2009) is included.3The hybrid rollback method was applied to only a subset of the risk assessment locations. The "---" for a given location indicates that the hybrid rollback method was not applied to that location.2The current primary PM2.5standards include an annual standard set at 15 ug/m3and a daily standard set at 35 ug/m3.13/30 12/25RiskAssessmentLocation1Rollback MethodDesign ValueRecent AirQuality(2007)Maximum Monitor-Specific Avg. of 2005, 2006, 2007 Annual Avgs. (Max. M-S) and 2007 Annual Average at CompositeMonitor (2007CM) (in ug/m3)15/35214/35 13/35 12/35 10/35Tacoma, WA9.7Salt Lake City,UT11.4St. Louis, MO14.355.011.64The 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 daily standards in thatlocation, the annualstandard was controlling and so the locally focused method was not applied.5The 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 between composite monitor value withhybrid or peak shaving compared with proportional(surrogatefor difference in long-term exposure-related mortality)6cells used as basis for calculationcells used as basis for calculationcells used as basis for calculation10.2 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 spatia l templates used in defining 13 of the 15 study areas were

based either on the combined statistical ar ea (CSA) or the core-b ased statistical area

(CBSA), if the CSA was not available. 7 

# The three remaining urban study areas

(Baltimore, Philadelphia and Tacoma) are specia l cases and were handled as described in

section 3.3.2. The maps presented in this ap pendix provide several types of information

including: (a) annual and daily (i.e., 24-hour) design values (DV) for each PM 2.5 

# monitor

in each study area with DV values based on monitoring data from 2005-2007, (b) sources

of PM 2.5 

# greater than 50 tons/year (c) major high ways 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 ar ea associated with each study area that would be

used in identifying which counties and PM 2.5 

# monitors were associated with a particular study area.K-3Figure K-1: Atlanta Urban Study AreaK-4Figure K-2: Baltimore Urban Study AreaAQSite 240032002AQSite 240032002AQSite 240032002AQSite 240032002AQSite 240032002AQSite 240032002AQSite 240032002AQSite 240032002AQSite 240032002Annual DV 15.7 μg/m3Annual DV 15.7 μg/m3Annual DV 15.7 μg/m3Annual DV 15.7 μg/m3Annual DV 15.7 μg/m3Annual DV 15.7 μg/m3Annual DV 15.7 μg/m3Annual DV 15.7 μg/m3Annual DV 15.7 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3AQSite 240031003AQSite 240031003AQSite 240031003AQSite 240031003AQSite 240031003AQSite 240031003AQSite 240031003AQSite 240031003AQSite 240031003Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3AQSite 245100007AQSite 245100007AQSite 245100007AQSite 245100007AQSite 245100007AQSite 245100007AQSite 245100007AQSite 245100007AQSite 245100007Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3AQSite 245100040AQSite 245100040AQSite 245100040AQSite 245100040AQSite 245100040AQSite 245100040AQSite 245100040AQSite 245100040AQSite 245100040Annual DV 15.2 μg/m3Annual DV 15.2 μg/m3Annual DV 15.2 μg/m3Annual DV 15.2 μg/m3Annual DV 15.2 μg/m3Annual DV 15.2 μg/m3Annual DV 15.2 μg/m3Annual DV 15.2 μg/m3Annual DV 15.2 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3AQSite 245100035AQSite 245100035AQSite 245100035AQSite 245100035AQSite 245100035AQSite 245100035AQSite 245100035AQSite 245100035AQSite 245100035Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3AQSite 245100049AQSite 245100049AQSite 245100049AQSite 245100049AQSite 245100049AQSite 245100049AQSite 245100049AQSite 245100049AQSite 245100049Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3AQSite 240053001AQSite 240053001AQSite 240053001AQSite 240053001AQSite 240053001AQSite 240053001AQSite 240053001AQSite 240053001AQSite 240053001Annual DV 14.5 μg/m3Annual DV 14.5 μg/m3Annual DV 14.5 μg/m3Annual DV 14.5 μg/m3Annual DV 14.5 μg/m3Annual DV 14.5 μg/m3Annual DV 14.5 μg/m3Annual DV 14.5 μg/m3Annual DV 14.5 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3AQSite 240251001AQSite 240251001AQSite 240251001AQSite 240251001AQSite 240251001AQSite 240251001AQSite 240251001AQSite 240251001AQSite 240251001Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3AQSite 245100008AQSite 245100008AQSite 245100008AQSite 245100008AQSite 245100008AQSite 245100008AQSite 245100008AQSite 245100008AQSite 245100008Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3AQSite 245100006AQSite 245100006AQSite 245100006AQSite 245100006AQSite 245100006AQSite 245100006AQSite 245100006AQSite 245100006AQSite 245100006Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3AQSite 240030014AQSite 240030014AQSite 240030014AQSite 240030014AQSite 240030014AQSite 240030014AQSite 240030014AQSite 240030014AQSite 240030014Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3AQSite 240051007AQSite 240051007AQSite 240051007AQSite 240051007AQSite 240051007AQSite 240051007AQSite 240051007AQSite 240051007AQSite 240051007Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3CONSTELLATION POWER SOURCE GENERATION - WAGNERPrimary PM25 Annual Emission799.3 tpyCONSTELLATION POWER SOURCE GENERATION - BRANDON SHORESPrimary PM25 Annual Emission1660 tpyCONSTELLATION POWER SOURCE GENERATION - BRANDON SHORPrimary PM25 Annual Emission1968 tpyTATE & LYLE NORTH AMERICAN SUGARSPrimary PM25 Annual Emission88.4 tpyRED STAR YEASTPrimary PM25 Annual Emission56.6 tpyUNITED STATES GYPSUM COMPANYPrimary PM25 Annual Emission132 tpyBETHLEHEM STEELPrimary PM25 Annual Emission1326.2 tpyGRACE - DAVISON CHEMICALPrimary PM25 Annual Emission87.2 tpyBaltimoreBaltimoreBaltimoreBaltimoreBaltimoreBaltimoreBaltimoreBaltimoreBaltimoreBaltimore CityBaltimore CityBaltimore CityBaltimore CityBaltimore CityBaltimore CityBaltimore CityBaltimore CityBaltimore CityAnne ArundelAnne ArundelAnne ArundelAnne ArundelAnne ArundelAnne ArundelAnne ArundelAnne ArundelAnne ArundelCarrollCarrollCarrollCarrollCarrollCarrollHarfordHarfordHarfordHarfordHarfordHarfordHowardHowardHowardHowardHowardHowardK-5Figure K-3: Birmingham Urban Study AreaAQSite 010735002AQSite 010735002AQSite 010735002AQSite 010735002AQSite 010735002AQSite 010735002AQSite 010735002AQSite 010735002AQSite 010735002Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3AQSite 010731010AQSite 010731010AQSite 010731010AQSite 010731010AQSite 010731010AQSite 010731010AQSite 010731010AQSite 010731010AQSite 010731010Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3AQSite 010730023AQSite 010730023AQSite 010730023AQSite 010730023AQSite 010730023AQSite 010730023AQSite 010730023AQSite 010730023AQSite 010730023Annual DV 18.7 μg/m3Annual DV 18.7 μg/m3Annual DV 18.7 μg/m3Annual DV 18.7 μg/m3Annual DV 18.7 μg/m3Annual DV 18.7 μg/m3Annual DV 18.7 μg/m3Annual DV 18.7 μg/m3Annual DV 18.7 μg/m3Daily DV 44 μg/m3Daily DV 44 μg/m3Daily DV 44 μg/m3Daily DV 44 μg/m3Daily DV 44 μg/m3Daily DV 44 μg/m3Daily DV 44 μg/m3Daily DV 44 μg/m3Daily DV 44 μg/m3AQSite 010732006AQSite 010732006AQSite 010732006AQSite 010732006AQSite 010732006AQSite 010732006AQSite 010732006AQSite 010732006AQSite 010732006Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Annual DV 15.1 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3AQSite 010732003AQSite 010732003AQSite 010732003AQSite 010732003AQSite 010732003AQSite 010732003AQSite 010732003AQSite 010732003AQSite 010732003Annual DV 17.5 μg/m3Annual DV 17.5 μg/m3Annual DV 17.5 μg/m3Annual DV 17.5 μg/m3Annual DV 17.5 μg/m3Annual DV 17.5 μg/m3Annual DV 17.5 μg/m3Annual DV 17.5 μg/m3Annual DV 17.5 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3AQSite 010731005AQSite 010731005AQSite 010731005AQSite 010731005AQSite 010731005AQSite 010731005AQSite 010731005AQSite 010731005AQSite 010731005Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Annual DV 15.6 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3AQSite 010731009AQSite 010731009AQSite 010731009AQSite 010731009AQSite 010731009AQSite 010731009AQSite 010731009AQSite 010731009AQSite 010731009Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3AQSite 010735003AQSite 010735003AQSite 010735003AQSite 010735003AQSite 010735003AQSite 010735003AQSite 010735003AQSite 010735003AQSite 010735003Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3AQSite 011270002AQSite 011270002AQSite 011270002AQSite 011270002AQSite 011270002AQSite 011270002AQSite 011270002AQSite 011270002AQSite 011270002Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3National Cement Co of AlabamaPrimary PM25 Annual Emission129.6 tpyDRUMMOND COMPANY, INC.Primary PM25 Annual Emission1222.2 tpyLEHIGH CEMENT COMPANYPrimary PM25 Annual Emission215.2 tpySMI STEEL, INC.Primary PM25 Annual Emission134 tpyNUCOR STEEL BIRMINGHAM,INC.Primary PM25 Annual Emission85.6 tpyU. S. PIPE & FOUNDRY COMPANY INC.(NO. B'HAM PLANT)Primary PM25 Annual Emission356.8 tpySLOSS INDUSTRIES CORPORATION - COKE/UTILITIES/BTFPrimary PM25 Annual Emission478.2 tpySLOSS INDUSTRIES CORPORATION - MINERAL WOOL PLANTPrimary PM25 Annual Emission161.9 tpyMCWANE CAST IRON PIPE CO.Primary PM25 Annual Emission308.3 tpyAMERICAN CAST IRON PIPE COMPANYPrimary PM25 Annual Emission436.9 tpyUNITED STATES STEEL CORPORATION-FAIRFIELD PIPE MILPrimary PM25 Annual Emission187.2 tpyUNITED STATES STEEL CORPORATION - FAIRFIELD WORKSPrimary PM25 Annual Emission635.1 tpyAlabama Power CompanyPrimary PM25 Annual Emission6804.7 tpyLaFarge Building MaterialsPrimary PM25 Annual Emission344 tpySouthern LimePrimary PM25 Annual Emission51.8 tpyU. S. PIPE & FOUNDRY COMPANY,INC.(BESSEMER PLANT)Primary PM25 Annual Emission295.4 tpyAlabama Power CompanyPrimary PM25 Annual Emission2738.8 tpyALABAMA POWER COMPANY (MILLER POWER PLANT)Primary PM25 Annual Emission1666.5 tpyOAK GROVE RESOURCES, LLCPrimary PM25 Annual Emission169.1 tpyBlountBlountBlountBlountBlountBlountJeffersonJeffersonJeffersonJeffersonJeffersonJeffersonSt. ClairSt. ClairSt. ClairSt. ClairSt. ClairSt. ClairSt. ClairSt. ClairSt. ClairShelbyShelbyShelbyShelbyShelbyShelbyWalkerWalkerWalkerWalkerWalkerBibbBibbBibbBibbChiltonChiltonChiltonChiltonChiltonChiltonChiltonCullmanCullmanCullmanCullmanCullmanCullmanCullmanCullmanCullmanK-6Figure K-4: Dallas Urban Study AreaAQSite 484393006AQSite 484393006AQSite 484393006AQSite 484393006AQSite 484393006AQSite 484393006AQSite 484393006AQSite 484393006AQSite 484393006Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3AQSite 484391006AQSite 484391006AQSite 484391006AQSite 484391006AQSite 484391006AQSite 484391006AQSite 484391006AQSite 484391006AQSite 484391006Annual DV 11.9 μg/m3Annual DV 11.9 μg/m3Annual DV 11.9 μg/m3Annual DV 11.9 μg/m3Annual DV 11.9 μg/m3Annual DV 11.9 μg/m3Annual DV 11.9 μg/m3Annual DV 11.9 μg/m3Annual DV 11.9 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3AQSite 484391002AQSite 484391002AQSite 484391002AQSite 484391002AQSite 484391002AQSite 484391002AQSite 484391002AQSite 484391002AQSite 484391002Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3AQSite 481390016AQSite 481390016AQSite 481390016AQSite 481390016AQSite 481390016AQSite 481390016AQSite 481390016AQSite 481390016AQSite 481390016Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3AQSite 481390015AQSite 481390015AQSite 481390015AQSite 481390015AQSite 481390015AQSite 481390015AQSite 481390015AQSite 481390015AQSite 481390015Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3AQSite 481130087AQSite 481130087AQSite 481130087AQSite 481130087AQSite 481130087AQSite 481130087AQSite 481130087AQSite 481130087AQSite 481130087Annual DV 10.8 μg/m3Annual DV 10.8 μg/m3Annual DV 10.8 μg/m3Annual DV 10.8 μg/m3Annual DV 10.8 μg/m3Annual DV 10.8 μg/m3Annual DV 10.8 μg/m3Annual DV 10.8 μg/m3Annual DV 10.8 μg/m3Daily DV 22 μg/m3Daily DV 22 μg/m3Daily DV 22 μg/m3Daily DV 22 μg/m3Daily DV 22 μg/m3Daily DV 22 μg/m3Daily DV 22 μg/m3Daily DV 22 μg/m3Daily DV 22 μg/m3AQSite 482570005AQSite 482570005AQSite 482570005AQSite 482570005AQSite 482570005AQSite 482570005AQSite 482570005AQSite 482570005AQSite 482570005Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3AQSite 481133004AQSite 481133004AQSite 481133004AQSite 481133004AQSite 481133004AQSite 481133004AQSite 481133004AQSite 481133004AQSite 481133004Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3AQSite 481130050AQSite 481130050AQSite 481130050AQSite 481130050AQSite 481130050AQSite 481130050AQSite 481130050AQSite 481130050AQSite 481130050Annual DV 12.8 μg/m3Annual DV 12.8 μg/m3Annual DV 12.8 μg/m3Annual DV 12.8 μg/m3Annual DV 12.8 μg/m3Annual DV 12.8 μg/m3Annual DV 12.8 μg/m3Annual DV 12.8 μg/m3Annual DV 12.8 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3Daily DV 26 μg/m3AQSite 481130035AQSite 481130035AQSite 481130035AQSite 481130035AQSite 481130035AQSite 481130035AQSite 481130035AQSite 481130035AQSite 481130035Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3Daily DV 27 μg/m3AQSite 481130069AQSite 481130069AQSite 481130069AQSite 481130069AQSite 481130069AQSite 481130069AQSite 481130069AQSite 481130069AQSite 481130069Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3AQSite 481130057AQSite 481130057AQSite 481130057AQSite 481130057AQSite 481130057AQSite 481130057AQSite 481130057AQSite 481130057AQSite 481130057Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3NORTH TEXAS CEMENT CO.Primary PM25 Annual Emission349.4 tpyHOLCIM (TEXAS) LPPrimary PM25 Annual Emission378.1 tpyNORTH LAKE STEAM ELECTRIC STATIONPrimary PM25 Annual Emission58.2 tpyHANDLEY STEAM ELEC. STA.Primary PM25 Annual Emission105.1 tpyJOHNS MANVILLEPrimary PM25 Annual Emission308 tpyPONDEROSA PINE ENERGYPrimary PM25 Annual Emission51.2 tpyCHAPARRAL STEEL MIDLOTHIANPrimary PM25 Annual Emission129.9 tpyWAXAHACHIE PLANTPrimary PM25 Annual Emission419.8 tpySAINT GOBAIN CONTAINERSPrimary PM25 Annual Emission61.6 tpyG.S. ROOFING PRODUCTSPrimary PM25 Annual Emission61 tpyDAL-TILE CORPORATIONPrimary PM25 Annual Emission52.9 tpyC.O. MFG. GROUP OF AMERICAPrimary PM25 Annual Emission61.2 tpyLAKE RAY HUBBARDPrimary PM25 Annual Emission77.7 tpyCERAMIC TILE GLAZED MFG.Primary PM25 Annual Emission58.2 tpyMIDLOTHIAN PLANTPrimary PM25 Annual Emission115.5 tpyTEXAS LIME COMPANYPrimary PM25 Annual Emission65.3 tpyDallaso PintoParkerHoodJohnsonEllisHendersonKaufmanRockwallCookeGraysonFanninDeltaHuntCollinDentonWiseTarrantK-7Figure K-5: Detroit Urban Study AreaAQSite 260990009AQSite 260990009AQSite 260990009AQSite 260990009AQSite 260990009AQSite 260990009AQSite 260990009AQSite 260990009AQSite 260990009Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3AQSite 260490021AQSite 260490021AQSite 260490021AQSite 260490021AQSite 260490021AQSite 260490021AQSite 260490021AQSite 260490021AQSite 260490021Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3AQSite 261150005AQSite 261150005AQSite 261150005AQSite 261150005AQSite 261150005AQSite 261150005AQSite 261150005AQSite 261150005AQSite 261150005Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Daily DV 38 μg/m3DailyDV 38μg/m3Day DV 38 μg/m3DailDV38mDaily DV 38 μg/m3DailyDV 38μg/3AQSite 261630001AQSite 261630001AQSite 261630001AQSite 261630001AQSite 261630001AQSite 261630001AQSite 261630001AQSite 261630001AQSite 261630001Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3AQSite 261630036AQSite 261630036AQSite 261630036AQSite 261630036AQSite 261630036AQSite 261630036AQSite 261630036AQSite 261630036AQSite 261630036Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3AQSite 261630015AQSite 261630015AQSite 261630015AQSite 261630015AQSite 261630015AQSite 261630015AQSite 261630015AQSite 261630015AQSite 261630015Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3AQSite 261630033AQSite 261630033AQSite 261630033AQSite 261630033AQSite 261630033AQSite 261630033AQSite 261630033AQSite 261630033AQSite 261630033Annual DV 17.2 μg/m3Annual DV 17.2 μg/m3Annual DV 17.2 μg/m3Annual DV 17.2 μg/m3Annual DV 17.2 μg/m3Annual DV 17.2 μg/m3Annual DV 17.2 μg/m3Annual DV 17.2 μg/m3Annual DV 17.2 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3AQSite 261630038AQSite 261630038AQSite 261630038AQSite 261630038AQSite 261630038AQSite 261630038AQSite 261630038AQSite 261630038AQSite 261630038Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3AQSite 261630016AQSite 261630016AQSite 261630016AQSite 261630016AQSite 261630016AQSite 261630016AQSite 261630016AQSite 261630016AQSite 261630016Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3AQSite 261630039AQSite 261630039AQSite 261630039AQSite 261630039AQSite 261630039AQSite 261630039AQSite 261630039AQSite 261630039AQSite 261630039Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3AQSite 261630019AQSite 261630019AQSite 261630019AQSite 261630019AQSite 261630019AQSite 261630019AQSite 261630019AQSite 261630019AQSite 261630019Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Annual DV 14.1 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3AQSite 261250001AQSite 261250001AQSite 261250001AQSite 261250001AQSite 261250001AQSite 261250001AQSite 261250001AQSite 261250001AQSite 261250001Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3AQSite 261610008AQSite 261610008AQSite 261610008AQSite 261610008AQSite 261610008AQSite 261610008AQSite 261610008AQSite 261610008AQSite 261610008Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Annual DV 13.7 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3AQSite 261610005AQSite 261610005AQSite 261610005AQSite 261610005AQSite 261610005AQSite 261610005AQSite 261610005AQSite 261610005AQSite 261610005Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3AQSite 261630025AQSite 261630025AQSite 261630025AQSite 261630025AQSite 261630025AQSite 261630025AQSite 261630025AQSite 261630025AQSite 261630025Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3AQSite 261470005AQSite 261470005AQSite 261470005AQSite 261470005AQSite 261470005AQSite 261470005AQSite 261470005AQSite 261470005AQSite 261470005Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3J.R. WHITING COPrimary PM25 Annual Emission1029.2 tpyHOLCIM (US) INC.Primary PM25 Annual Emission656.5 tpyDETROIT EDISON/MONROE POWERPrimary PM25 Annual Emission2814.8 tpyROCKWOOD QUARRYPrimary PM25 Annual Emission52.5 tpyGUARDIAN INDUSTRIESPrimary PM25 Annual Emission124.9 tpyDETROIT EDISON TRENTON CHANNPrimary PM25 Annual Emission1127.9 tpyDETROIT EDISON RIVER ROUGEPrimary PM25 Annual Emission378 tpyNATIONAL STEEL CORPPrimary PM25 Annual Emission1983 tpyMARATHON ASHLAND PETROLEUMPrimary PM25 Annual Emission144.6 tpyROUGE STEEL COPrimary PM25 Annual Emission1335.9 tpySIX S INC PT 5Primary PM25 Annual Emission74.3 tpySIX S INC. PT 4Primary PM25 Annual Emission64.9 tpyST. CLAIR / BELLE RIVER POWER PLANTPrimary PM25 Annual Emission1593.1 tpyCARGILL SALTPrimary PM25 Annual Emission80.5 tpyDETROIT EDISON GREENWOOD ENERGY CENTERPrimary PM25 Annual Emission87.6 tpyMonroeLivingstonWayneWashtenawMacombOaklandGeneseeSt. ClairLapeerCLEVELAND ELECCLEVELAPrimaryP2108.7 tpyK-8Figure K-6: Fresno Urban Study AreaAQSite 060195025AQSite 060195025AQSite 060195025AQSite 060195025AQSite 060195025AQSite 060195025AQSite 060195025AQSite 060195025AQSite 060195025Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Daily DV 61 μg/m3Daily DV 61 μg/m3Daily DV 61 μg/m3AQSite 060190008AQSite 060190008AQSite 060190008AQSite 060190008AQSite 060190008AQSite 060190008AQSite 060190008AQSite 060190008AQSite 060190008Annual DV 17.4 μg/m3Annual DV 17.4 μg/m3Annual DV 17.4 μg/m3Daily DV 63 μg/m3Daily DV 63 μg/m3Daily DV 63 μg/m3AQSite 060195001AQSite 060195001AQSite 060195001AQSite 060195001AQSite 060195001AQSite 060195001AQSite 060195001AQSite 060195001AQSite 060195001Annual DV 16.4 μg/m3Annual DV 16.4 μg/m3Annual DV 16.4 μg/m3Annual DV 16.4 μg/m3Annual DV 16.4 μg/m3Annual DV 16.4 μg/m3Daily DV 58 μg/m3Daily DV 58 μg/m3Daily DV 58 μg/m3SAINT-GOBAIN CONTAINERS, INCPrimary PM25 Annual Emission51.4 tpyAES MENDOTA, L.P.Primary PM25 Annual Emission50.3 tpyMADERA POWER, LLCPrimary PM25 Annual Emission77.8 tpyMaderaFresnoK-9Figure K-7: Houston Urban Study AreaAQSite 482010055AQSite 482010055AQSite 482010055AQSite 482010055AQSite 482010055AQSite 482010055AQSite 482010055AQSite 482010055AQSite 482010055Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3AQSite 482010058AQSite 482010058AQSite 482010058AQSite 482010058AQSite 482010058AQSite 482010058Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3AQSite 482010026AQSite 482010026AQSite 482010026AQSite 482010026Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3AQSite 483390078AQSite 483390078AQSite 483390078AQSite 483390078AQSite 483390078AQSite 483390078AQSite 483390078AQSite 483390078AQSite 483390078Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3AQSite 482010024AQSite 482010024AQSite 482010024AQSite 482010024AQSite 482010024AQSite 482010024AQSite 482010024AQSite 482010024AQSite 482010024Annual DV 13.1 μg/m3Annual DV 13.1 μg/m3Annual DV 13.1 μg/m3Annual DV 13.1 μg/m3Annual DV 13.1 μg/m3Annual DV 13.1 μg/m3Annual DV 13.1 μg/m3Annual DV 13.1 μg/m3Annual DV 13.1 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3AQSite 482011034AQSite 482011034AQSite 482011034AQSite 482011034AQSite 482011034AQSite 482011034AQSite 482011034AQSite 482011034AQSite 482011034Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Annual DV 13.9 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3AQSite 482011035AQSite 482011035AQSite 482011035AQSite 482011035AQSite 482011035AQSite 482011035Annual DV 15.8 μg/m3Annual DV 15.8 μg/m3Annual DV 15.8 μg/m3Annual DV 15.8 μg/m3Annual DV 15.8 μg/m3Annual DV 15.8 μg/m3Annual DV 15.8 μg/m3Annual DV 15.8 μg/m3Annual DV 15.8 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3AQSite 481670014AQSite 481670014AQSite 481670014AQSite 481670014AQSite 481670014AQSite 481670014AQSite 481670014AQSite 481670014AQSite 481670014Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Daily DV 21 μg/m3Daily DV 21 μg/m3Daily DV 21 μg/m3Daily DV 21 μg/m3Daily DV 21 μg/m3Daily DV 21 μg/m3CLEAR LAKE COGENERATIONPrimary PM25 Annual Emission116.7 tpyKANEKA TEXAS CORPORATIONPrimary PM25 Annual Emission50.9 tpyAKZO NOBEL CHEMICALS, INC.Primary PM25 Annual Emission66 tpyHOUSTON REFINERYPrimary PM25 Annual Emission133.8 tpyLYONDELL-CITGO REFININGPrimary PM25 Annual Emission177.9 tpyCHANNEL ENERGY ENERGYPrimary PM25 Annual Emission80.2 tpyCLEAR LAKE PLANTPrimary PM25 Annual Emission515.5 tpyBP AMOCO CHEMICAL TEXAS CPrimary PM25 Annual Emission71.6 tpyVINYL ACETATE FACILITY 5Primary PM25 Annual Emission97.7 tpyCHOCOLATE BAYOU PLNTPrimary PM25 Annual Emission90.2 tpyTEXAS CITY REFINERYPrimary PM25 Annual Emission264.4 tpyTEXAS CITY REFINERYPrimary PM25 Annual Emission178.7 tpyTEXAS CITY REFINERYPrimary PM25 Annual Emission912.1 tpyCEDAR BAYOU STATIONPrimary PM25 Annual Emission164.2 tpyBAYTOWN OLEFINS PLANTPrimary PM25 Annual Emission62.9 tpySAN JACINTO STEAM ELE STAPrimary PM25 Annual Emission73.1 tpyDEER PARK PLANTPrimary PM25 Annual Emission194.3 tpyAGRIFOS L L CPrimary PM25 Annual Emission80.7 tpyEXXONMOBIL REF & SUPPLYPrimary PM25 Annual Emissio915.2 tpyQUALITY ELECTRIC STEEL CASTING, INC.Primary PM25 Annual Emission56.6 tpyCHANNELVIEW COMPLEXPrimary PM25 Annual Emission269.5 tpyDEER PARK PLANTPrimary PM25 Annual Emission233.1 tpyTX PETROCHEMICALS LPPrimary PM25 Annual Emission119.1 tpyCHANNELVIEW PLANTPrimary PM25 Annual Emission410.5 tpyCHANNELVIEW COGENERGATION FACILITYPrimary PM25 Annual Emission212 tpyLONGHORN GLASSPrimary PM25 Annual Emission72.2 tpyKATY GAS PLANTPrimary PM25 Annual Emission174.9 tpyPASADENA PLANTPrimary PM25 Annual Emission449.1 tpyMAXWELL HOUSE DIVISIONPrimary PM25 Annual Emission53.6 tpyPLANT BPrimary PM25 Annual Emission171.7 tpyFREEPORT SITEPrimary PM25 Annual Emission154 9 tpyCHEVRON CHEMICAL COPrimary PM25 Annual Emission129.9 tpyMONT BELVIEU FRACTIONATORPrimary PM25 Annual Emission67.9 tpyP H ROBINSON STATIONPrimary PM25 Annual Emission177.5 tpyW A PARISH STATIONPrimary PM25 Annual Emission2186.7 tpySWEENY REFINERY PETROCHEMPrimary PM25 Annual Emission54.6 tpySWEENY COMPLEXPrimary PM25 Annual Emission218.2 tpyLEWIS CREEK PLANTPrimary PM25 Annual Emission95.4 tpyFort BendGalvestonBrazoriaChambersLibertyMontgomeryWallerAustinHarrisK-10Figure K-8: Los Angeles Urban Study AreaAQSite 060710025AQSite 060710025AQSite 060710025AQSite 060710025AQSite 060710025AQSite 060710025Annual DV 18.5 μg/m3Annual DV 18.5 μg/m3Annual DV 18.5 μg/m3Annual DV 18.5 μg/m3Annual DV 18.5 μg/m3Annual DV 18.5 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3AQSite 060370002AQSite 060370002AQSite 060370002AQSite 060370002AQSite 060370002AQSite 060370002AQSite 060370002AQSite 060370002AQSite 060370002Annual DV 16 μg/m3Annual DV 16 μg/m3Annual DV 16 μg/m3Annual DV 16 μg/m3Annual DV 16 μg/m3Annual DV 16 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3AQSite 060590007AQSite 060590007AQSite 060590007AQSite 060590007AQSite 060590007AQSite 060590007Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3DailyDV 42μg/m3DailyDV 42μg/m3DailDV 42μg/m3DV 42μg/m3DailyV3DDailyDV 42/m3DV 42/m3AQSite 060658005AQSite 060658005AQSite 060658005AQSite 060658005AQSite 060658005AQSite 060658005AQSite 060658005AQSite 060658005AQSite 060658005Annual DV 20.9 μg/m3Annual DV 20.9 μg/m3Annual DV 20.9 μg/m3Annual DV 20.9 μg/m3Annual DV 20.9 μg/m3Annual DV 20.9 μg/m3Daily DV 56 μg/m3Daily DV 56 μg/m3Daily DV 56 μg/m3Daily DV 56 μg/m3Daily DV 56 μg/m3Daily DV 56 μg/m3AQSite 060651003AQSite 060651003AQSite 060651003AQSite 060651003AQSite 060651003AQSite 060651003Annual DV 17.7 μg/m3Annual DV 17.7 μg/m3Annual DV 17.7 μg/m3Annual DV 17.7 μg/m3Annual DV 17.7 μg/m3Annual DV 17.7 μg/m3Daily DV 49 μg/m3Daily DV 49 μg/m3Daily DV 49 μg/m3AQSite 060658001AQSite 060658001AQSite 060658001AQSite 060658001AQSite 060658001AQSite 060658001Annual DV 19.6 μg/m3Annual DV 19.6 μg/m3Annual DV 19.6 μg/m3Annual DV 19.6 μg/m3Annual DV 19.6 μg/m3Annual DV 19.6 μg/m3Daily DV 55 μg/m3Daily DV 55 μg/m3Daily DV 55 μg/m3AQSite 060374004AQSite 060374004AQSite 060374004AQSite 060374004AQSite 060374004AQSite 060374004Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3DailyDV 36μg/m3y DV 36 μg/m3Dail36μgDaily DV 36 μg/mDailyDV/m3Daily DV 36 μg/m3AQSite 060374002AQSite 060374002AQSite 060374002AQSite 060374002AQSite 060374002AQSite 060374002Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3AQSite 060371201AQSite 060371201AQSite 060371201AQSite 060371201AQSite 060371201AQSite 060371201AQSite 060371201AQSite 060371201AQSite 060371201Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3AQSite 060371103AQSite 060371103AQSite 060371103AQSite 060371103AQSite 060371103AQSite 060371103Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Daily DV 48 μg/m3Daily DV 48 μg/m3Daily DV 48 μg/m3Daily DV 48 μg/m3Daily DV 48 μg/m3Daily DV 48 μg/m3AQSite 060371301AQSite 060371301AQSite 060371301AQSite 060371301AQSite 060371301AQSite 060371301Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Daily DV 46 μg/m3Daily DV 46 μg/m3Daily DV 46 μg/m3Daily DV 46 μg/m3Daily DV 46 μg/m3Daily DV 46 μg/m3AQSite 060712002AQSite 060712002AQSite 060712002AQSite 060712002AQSite 060712002AQSite 060712002Annual DV 18.4 μg/m3Annual DV 18.4 μg/m3Annual DV 18.4 μg/m3Annual DV 18.4 μg/m3Annual DV 18.4 μg/m3Annual DV 18.4 μg/m3Daily DV 52 μg/m3Daily DV 52 μg/m3Daily DV 52 μg/m3AQSite 060372005AQSite 060372005AQSite 060372005AQSite 060372005AQSite 060372005AQSite 060372005Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Annual DV 14.3 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3AQSite 060371002AQSite 060371002AQSite 060371002AQSite 060371002AQSite 060371002AQSite 060371002Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Annual DV 17.1 μg/m3Daily DV 48 μg/m3Daily DV 48 μg/m3Daily DV 48 μg/m3Daily DV 48 μg/m3Daily DV 48 μg/m3Daily DV 48 μg/m3AQSite 060719004AQSite 060719004AQSite 060719004AQSite 060719004AQSite 060719004AQSite 060719004AQSite 060719004AQSite 060719004AQSite 060719004Annual DV 17.6 μg/m3Annual DV 17.6 μg/m3Annual DV 17.6 μg/m3Annual DV 17.6 μg/m3Annual DV 17.6 μg/m3Annual DV 17.6 μg/m3Daily DV 54 μg/m3Daily DV 54 μg/m3Daily DV 54 μg/m3AQSite 061112002AQSite 061112002AQSite 061112002AQSite 061112002AQSite 061112002AQSite 061112002Annual DV 11.1 μg/m3Annual DV 11.1 μg/m3Annual DV 11.1 μg/m3Annual DV 11.1 μg/m3Annual DV 11.1 μg/m3Annual DV 11.1 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3AQSite 060592022AQSite 060592022AQSite 060592022AQSite 060592022AQSite 060592022AQSite 060592022Annual DV 10.9 μg/m3Annual DV 10.9 μg/m3Annual DV 10.9 μg/m3Annual DV 10.9 μg/m3Annual DV 10.9 μg/m3Annual DV 10.9 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3AQSite 060710306AQSite 060710306AQSite 060710306AQSite 060710306AQSite 060710306AQSite 060710306AQSite 060710306AQSite 060710306AQSite 060710306Annual DV 9.9 μg/m3Annual DV 9.9 μg/m3Annual DV 9.9 μg/m3Annual DV 9.9 μg/m3Annual DV 9.9 μg/m3Annual DV 9.9 μg/m3Annual DV 9.9 μg/m3Annual DV 9.9 μg/m3Annual DV 9.9 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3AQSite 060718001AQSite 060718001AQSite 060718001AQSite 060718001AQSite 060718001AQSite 060718001Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3AQSite 061110007AQSite 061110007AQSite 061110007AQSite 061110007AQSite 061110007AQSite 061110007Annual DV 10.3 μg/m3Annual DV 10.3 μg/m3Annual DV 10.3 μg/m3Annual DV 10.3 μg/m3Annual DV 10.3 μg/m3Annual DV 10.3 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3GRANITE CONS. INC/ LITTLEROCKPrimary PM25 Annual Emission88.6 tpySCE-ORMOND BEACH GEN STATIONPrimary PM25 Annual Emission77.7 tpyLA CO., SANITATION DISTRICTPrimary PM25 Annual Emission70.3 tpyTAMCOPrimary PM25 Annual Emission67.2 tpyJOHNS MANVILLE CORPPrimary PM25 Annual Emission122.8 tpyAES ALAMITOS, LLCPrimary PM25 Annual Emission243.4 tpyCHEVRON U.S.A. INC.Primary PM25 Annual Emission223 tpyEL SEGUNDO POWER, LLCPrimary PM25 Annual Emission108.7 tpyLA CITY, DWP SCATTERGOOD GENERATING STAPrimary PM25 Annual Emission50.8 tpyWEST COAST PRODUCTS LLCPrimary PM25 Annual Emission172.4 tpyEQUILON ENTERPRISES, LLCPrimary PM25 Annual Emission89 tpyULTRAMAR INC (NSR USE ONLY)Primary PM25 Annual Emission140.5 tpyTOSCO REFINING COMPANYPrimary PM25 Annual Emission140.5 tpyAES REDONDO BEACH, LLCPrimary PM25 Annual Emission109 tpyMOBIL OIL CORPPrimary PM25 Annual Emission119 tpySAN CLEMENTE ISLAND RANGEPrimary PM25 Annual Emission57.9 tpyMARCH AIR FORCE BASEPrimary PM25 Annual Emission221.1 tpyAFG INDUSTRIES INCPrimary PM25 Annual Emission97.2 tpyCALIFORNIA PORTLAND CEMENT COPrimary PM25 Annual Emission71.1 tpyCEMEX - BLACK MOUNTAIN QUARRYPrimary PM25 Annual Emission183.1 tpyTXI RIVERSIDE CEMENT COMPANYPrimary PM25 Annual Emission312.2 tpyMITSUBISHI CEMENT 2000Primary PM25 Annual Emission260.2 tpyANTELOPE VALLEY AGGREGATE INCPrimary PM25 Annual Emission257.2 tpyVenturaSan BernardinoRiversideOrangeLos AngelesAQSite 060371602AQSite 060371602AQSite 060371602AQSite 060371602AQSite 060371602AQSite 060371602Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Annual DV 16.7 μg/m3Daily DV 49 μg/m3Daily DV 49 μg/m3Daily DV 49 μg/m3AQSite 060379033AQSite 060379033AQSite 060379033AQSite 060379033AQSite 060379033AQSite 060379033Annual DV 8.1 μg/m3Annual DV 8.1 μg/m3Annual DV 8.1 μg/m3Annual DV 8.1 μg/m3Annual DV 8.1 μg/m3Annual DV 8.1 μg/m3Daily DV 17 μg/m3Daily DV 17 μg/m3Daily DV 17 μg/m3Daily DV 17 μg/m3Daily DV 17 μg/m3Daily DV 17 μg/m3AQSiteQSiteQSitAQSitAQeQSiteQSitAnnuannuannuannuAnnuAnnuannuannuannuDailyDDailyDailyDailyAQSite 060655001AQSite 060655001AQSite 060655001AQSite 060655001AQSite 060655001AQSite 060655001Annual DV 8.3 μg/m3Annual DV 8.3 μg/m3Annual DV 8.3 μg/m3Annual DV 8.3 μg/m3Annual DV 8.3 μg/m3Annual DV 8.3 μg/m3Daily DV 20 μg/m3Daily DV 20 μg/m3Daily DV 20 μg/m3AQSite 061110009AQSite 061110009AQSite 061110009AQSite 061110009AQSite 061110009AQSite 061110009Annual DV 9.6 μg/m3Annual DV 9.6 μg/m3Annual DV 9.6 μg/m3Annual DV 9.6 μg/m3Annual DV 9.6 μg/m3Annual DV 9.6 μg/m3Daily DV 21 μg/m3Daily DV 21 μg/m3Daily DV 21 μg/m3AQSite 061113001AQSite 061113001AQSite 061113001AQSite 061113001AQSite 061113001AQSite 061113001Annual DV 10.3 μg/m3Annual DV 10.3 μg/m3Annual DV 10.3 μg/m3Annual DV 10.3 μg/m3Annual DV 10.3 μg/m3Annual DV 10.3 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3HANSON AGGREGPrimary PM25 Annu75.2 tpyK-11Figure K-9: New York Urban Study AreaAQSite 360610079AQSite 360610079AQSite 360610079AQSite 360610079AQSite 360610079AQSite 360610079AQSite 360610079AQSite 360610079AQSite 360610079Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3AQSite 360050110AQSite 360050110AQSite 360050110AQSite 360050110AQSite 360050110AQSite 360050110AQSite 360050110AQSite 360050110AQSite 360050110Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3AQSite 360050083AQSite 360050083AQSite 360050083AQSite 360050083AQSite 360050083AQSite 360050083AQSite 360050083AQSite 360050083AQSite 360050083Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3AQSite 360050080AQSite 360050080AQSite 360050080AQSite 360050080AQSite 360050080AQSite 360050080AQSite 360050080AQSite 360050080AQSite 360050080Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3AQSite 340171003AQSite 340171003AQSite 340171003AQSite 340171003AQSite 340171003AQSite 340171003AQSite 340171003AQSite 340171003AQSite 340171003Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3AQSite 360850067AQSite 360850067AQSite 360850067AQSite 360850067AQSite 360850067AQSite 360850067Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Annual DV 11.5 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3AQSite 360850055AQSite 360850055AQSite 360850055AQSite 360850055AQSite 360850055AQSite 360850055AQSite 360850055AQSite 360850055AQSite 360850055Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3DailyDV 34μg/m3Daily DV 34 μg/m3DailyDV 34μg/mDaily DV 34 μg/m3Daily DV 34 μg/m3DailyDV34μg/m3Daily DV 34 μg/m3AQSite 360470122AQSite 360470122AQSite 360470122AQSite 360470122AQSite 360470122AQSite 360470122Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3AQSite 360610128AQSite 360610128AQSite 360610128AQSite 360610128AQSite 360610128AQSite 360610128AQSite 360610128AQSite 360610128AQSite 360610128Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3AQSite 360610134AQSite 360610134AQSite 360610134AQSite 360610134AQSite 360610134AQSite 360610134AQSite 360610134AQSite 360610134AQSite 360610134Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3DailyDV 37μg/m3Daily DV 37 μg/m3DailyDV 37μgm3Daily DV 37 μg/m3Daily DV 37 μg/m3DailyD 3μg/m3Daily DV 37 μg/m3AQSite 360610062AQSite 360610062AQSite 360610062AQSite 360610062AQSite 360610062AQSite 360610062AQSite 360610062AQSite 360610062AQSite 360610062Annual DV 14.8 μg/m3Annual DV 14.8 μg/m3Annual DV 14.8 μg/m3Annual DV 14.8 μg/m3Annual DV 14.8 μg/m3Annual DV 14.8 μg/m3Annual DV 14.8 μg/m3Annual DV 14.8 μg/m3Annual DV 14.8 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3AQSite 360610056AQSite 360610056AQSite 360610056AQSite 360610056AQSite 360610056AQSite 360610056AQSite 360610056AQSite 360610056AQSite 360610056Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Annual DV 15.9 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3AQSite 340172002AQSite 340172002AQSite 340172002AQSite 340172002AQSite 340172002AQSite 340172002AQSite 340172002AQSite 340172002AQSite 340172002Annual DV 15.4 μg/m3Annual DV 15.4 μg/m3Annual DV 15.4 μg/m3Annual DV 15.4 μg/m3Annual DV 15.4 μg/m3Annual DV 15.4 μg/m3Annual DV 15.4 μg/m3Annual DV 15.4 μg/m3Annual DV 15.4 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3Daily DV 41 μg/m3AQSite 360810124AQSite 360810124AQSite 360810124AQSite 360810124AQSite 360810124AQSite 360810124Annual DV 11.8 μg/m3Annual DV 11.8 μg/m3Annual DV 11.8 μg/m3Annual DV 11.8 μg/m3Annual DV 11.8 μg/m3Annual DV 11.8 μg/m3Annual DV 11.8 μg/m3Annual DV 11.8 μg/m3Annual DV 11.8 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3AQSite 361191002AQSite 361191002AQSite 361191002AQSite 361191002AQSite 361191002AQSite 361191002AQSite 361191002AQSite 361191002AQSite 361191002Annual DV 11.7 μg/m3Annual DV 11.7 μg/m3Annual DV 11.7 μg/m3Annual DV 11.7 μg/m3Annual DV 11.7 μg/m3Annual DV 11.7 μg/m3Annual DV 11.7 μg/m3Annual DV 11.7 μg/m3Annual DV 11.7 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3AQSite 361030002AQSite 361030002AQSite 361030002AQSite 361030002AQSite 361030002AQSite 361030002Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3AQSite 361030001AQSite 361030001AQSite 361030001AQSite 361030001AQSite 361030001AQSite 361030001Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3AQSite 340030003AQSite 340030003AQSite 340030003AQSite 340030003AQSite 340030003AQSite 340030003Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3AQSite 340390006AQSite 340390006AQSite 340390006AQSite 340390006AQSite 340390006AQSite 340390006AQSite 340390006AQSite 340390006AQSite 340390006Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3AQSite 340130015AQSite 340130015AQSite 340130015AQSite 340130015AQSite 340130015AQSite 340130015Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3AQSite 340310005AQSite 340310005AQSite 340310005AQSite 340310005Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3AQSite 360590008AQSite 360590008AQSite 360590008AQSite 360590008AQSite 360590008AQSite 360590008Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Annual DV 11.4 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3AQSite 340392003AQSite 340392003AQSite 340392003AQSite 340392003AQSite 340392003AQSite 340392003Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3AQSite 340390004AQSite 340390004AQSite 340390004AQSite 340390004AQSite 340390004AQSite 340390004AQSite 340390004AQSite 340390004AQSite 340390004Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Daily DV 42 μg/m3Daily DV 42 μg/m3Daily DV 42 μg/m3Daily DV 42 μg/m3Daily DV 42 μg/m3Daily DV 42 μg/m3Daily DV 42 μg/m3Daily DV 42 μg/m3Daily DV 42 μg/m3ASTORIA GENERATING STATIONPrimary PM25 Annual Emission254.9 tpyPOLETTI POWER PROJECTPrimary PM25 Annual Emission158.5 tpySTATEN ISLAND LANDFILLPrimary PM25 Annual Emission70.8 tpyCON ED-74TH STREET STAPrimary PM25 Annual Emission83.7 tpyRAVENSWOOD GENERATING STATIONPrimary PM25 Annual Emission269.7 tpyCON ED-WATERSIDE STATIONPrimary PM25 Annual Emission50.2 tpyCON ED-EAST RIVER GENERATING STATPrimary PM25 Annual Emission96.8 tpyCON EDISON - HUDSON AVE STATIONPrimary PM25 Annual Emission80.1 tpyHudson Generating StationPrimary PM25 Annual Emission1816.1 tpyEF BARRETT POWER STATIONPrimary PM25 Annual Emission97 tpyBayway RefineryPrimary PM25 Annual Emission147.9 tpyNew York CityNew York CityNew York CityNew York CityNew York CityNew York City(Manhattan)(Manhattan)(Manhattan)BronxRichmondQueensKingsAQSite 340270004AQSite 340270004AQSite 340270004AQSite 340270004AQSite 340270004AQSite 340270004Annual DV 11.3 μg/m3Annual DV 11.3 μg/m3Annual DV 11.3 μg/m3Annual DV 11.3 μg/m3Annual DV 11.3 μg/m3Annual DV 11.3 μg/m3Annual DV 11.3 μg/m3Annual DV 11.3 μg/m3Annual DV 11.3 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3NORTHPORT POWER STATIONPrimary PM25 Annual Emission543.2 tpyK-12Figure K-10: Philadelphia Urban Study AreaAQSite 421010136AQSite 421010136AQSite 421010136AQSite 421010136AQSite 421010136AQSite 421010136AQSite 421010136AQSite 421010136AQSite 421010136Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3AQSite 420450002AQSite 420450002AQSite 420450002AQSite 420450002AQSite 420450002AQSite 420450002AQSite 420450002AQSite 420450002AQSite 420450002Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3AQSite 340155001AQSite 340155001AQSite 340155001AQSite 340155001AQSite 340155001AQSite 340155001AQSite 340155001AQSite 340155001AQSite 340155001Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Annual DV 12.2 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3AQSite 421010047AQSite 421010047AQSite 421010047AQSite 421010047AQSite 421010047AQSite 421010047AQSite 421010047AQSite 421010047AQSite 421010047Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3AQSite 421010057AQSite 421010057AQSite 421010057AQSite 421010057AQSite 421010057AQSite 421010057AQSite 421010057AQSite 421010057AQSite 421010057Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Annual DV 12 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3AQSite 421010003AQSite 421010003AQSite 421010003AQSite 421010003AQSite 421010003AQSite 421010003AQSite 421010003AQSite 421010003AQSite 421010003Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Annual DV 12.4 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3AQSite 340070003AQSite 340070003AQSite 340070003AQSite 340070003AQSite 340070003AQSite 340070003AQSite 340070003AQSite 340070003AQSite 340070003Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3AQSite 420910013AQSite 420910013AQSite 420910013AQSite 420910013AQSite 420910013AQSite 420910013AQSite 420910013AQSite 420910013AQSite 420910013Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3AQSite 340071007AQSite 340071007AQSite 340071007AQSite 340071007AQSite 340071007AQSite 340071007AQSite 340071007AQSite 340071007AQSite 340071007Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Annual DV 13.5 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3AQSite 421010004AQSite 421010004AQSite 421010004AQSite 421010004AQSite 421010004AQSite 421010004AQSite 421010004AQSite 421010004AQSite 421010004Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Annual DV 13.8 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3AQSite 421010020AQSite 421010020AQSite 421010020AQSite 421010020AQSite 421010020AQSite 421010020AQSite 421010020AQSite 421010020AQSite 421010020Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3AQSite 421010024AQSite 421010024AQSite 421010024AQSite 421010024AQSite 421010024AQSite 421010024AQSite 421010024AQSite 421010024AQSite 421010024Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Annual DV 12.7 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3AQSite 100031003AQSite 100031003AQSite 100031003AQSite 100031003AQSite 100031003AQSite 100031003AQSite 100031003AQSite 100031003AQSite 100031003Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Annual DV 13.4 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3Daily DV 33 μg/m3AQSite 100032004AQSite 100032004AQSite 100032004AQSite 100032004AQSite 100032004AQSite 100032004AQSite 100032004AQSite 100032004AQSite 100032004Annual DV 14.7 μg/m3Annual DV 14.7 μg/m3Annual DV 14.7 μg/m3Annual DV 14.7 μg/m3Annual DV 14.7 μg/m3Annual DV 14.7 μg/m3Annual DV 14.7 μg/m3Annual DV 14.7 μg/m3Annual DV 14.7 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3AQSite 420170012AQSite 420170012AQSite 420170012AQSite 420170012AQSite 420170012AQSite 420170012AQSite 420170012AQSite 420170012AQSite 420170012Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Annual DV 13.2 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3PQ CORP/CHESTERPrimary PM25 Annual Emission52.3 tpyEXELON GENERATION CO/EDDYSTONEPrimary PM25 Annual Emission400.4 tpyValero Refining Co.- N.J.Primary PM25 Annual Emission128.7 tpySUNOCO INC. (R&M)Primary PM25 Annual Emission212.1 tpyCOASTAL EAGLE POINT OIL COMPANYPrimary PM25 Annual Emission165.5 tpyEXELON GENERATION CO/CROMBY GENERATION STATIONPrimary PM25 Annual Emission160.6 tpySUNOCO CHEMICALS (FORMER ALLIED SIGNAL)Primary PM25 Annual Emission113.9 tpyOCCIDENTAL CHEM CORP/POTTSTOWNPrimary PM25 Annual Emission64.2 tpyDeepwater Generating StationPrimary PM25 Annual Emission107.6 tpyCARNEYS POINT GENERATING PLANTPrimary PM25 Annual Emission257.3 tpyCONECTIV DELMARVA GENERATION-EDGE MOORPrimary PM25 Annual Emission519 tpyLogan Generating Plant, L.P.Primary PM25 Annual Emission145.5 tpySUNOCO INC (R&M)/MARCUS HOOK REFINERYPrimary PM25 Annual Emission240.9 tpyCONOCOPHILLIPS CO/TRAINER REFPrimary PM25 Annual Emission89 tpyKIMBERLY CLARK PA LLC/CHESTER OPERATIONSPrimary PM25 Annual Emission97.8 tpyMercer Generating StationPrimary PM25 Annual Emission746.3 tpyDelawareBurlingtonBucksMontgomeryPhiladelphiaAQSite 420290100AQSite 420290100AQSite 420290100AQSite 420290100AQSite 420290100AQSite 420290100AQSite 420290100AQSite 420290100AQSite 420290100Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Annual DV 14.2 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3AQSite 340210008AQSite 340210008AQSite 340210008AQSite 340210008AQSite 340210008AQSite 340210008AQSite 340210008AQSite 340210008AQSite 340210008Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Annual DV 12.5 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3K-13Figure K-11: Phoenix Urban Study AreaAQSite 040131003AQSite 040131003AQSite 040131003AQSite 040131003AQSite 040131003AQSite 040131003AQSite 040131003AQSite 040131003AQSite 040131003Annual DV 9.4 μg/m3Annual DV 9.4 μg/m3Annual DV 9.4 μg/m3Annual DV 9.4 μg/m3Annual DV 9.4 μg/m3Annual DV 9.4 μg/m3Annual DV 9.4 μg/m3Annual DV 9.4 μg/m3Annual DV 9.4 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3Daily DV 19 μg/m3AQSite 040137020AQSite 040137020AQSite 040137020AQSite 040137020AQSite 040137020AQSite 040137020AQSite 040137020AQSite 040137020AQSite 040137020Annual DV 7.9 μg/m3Annual DV 7.9 μg/m3Annual DV 7.9 μg/m3Annual DV 7.9 μg/m3Annual DV 7.9 μg/m3Annual DV 7.9 μg/m3Annual DV 7.9 μg/m3Annual DV 7.9 μg/m3Annual DV 7.9 μg/m3Daily DV 15 μg/m3Daily DV 15 μg/m3Daily DV 15 μg/m3Daily DV 15 μg/m3Daily DV 15 μg/m3Daily DV 15 μg/m3Daily DV 15 μg/m3Daily DV 15 μg/m3Daily DV 15 μg/m3AQSite 040210001AQSite 040210001AQSite 040210001AQSite 040210001AQSite 040210001AQSite 040210001Annual DV 8.4 μg/m3Annual DV 8.4 μg/m3Annual DV 8.4 μg/m3Annual DV 8.4 μg/m3Annual DV 8.4 μg/m3Annual DV 8.4 μg/m3Annual DV 8.4 μg/m3Annual DV 8.4 μg/m3Annual DV 8.4 μg/m3Daily DV 18 μg/m3Daily DV 18 μg/m3Daily DV 18 μg/m3Daily DV 18 μg/m3Daily DV 18 μg/m3Daily DV 18 μg/m3Daily DV 18 μg/m3Daily DV 18 μg/m3Daily DV 18 μg/m3AQSite 040213013AQSite 040213013AQSite 040213013AQSite 040213013AQSite 040213013AQSite 040213013Annual DV 22.6 μg/m3Annual DV 22.6 μg/m3Annual DV 22.6 μg/m3Annual DV 22.6 μg/m3Annual DV 22.6 μg/m3Annual DV 22.6 μg/m3Annual DV 22.6 μg/m3Annual DV 22.6 μg/m3Annual DV 22.6 μg/m3Daily DV 51 μg/m3Daily DV 51 μg/m3Daily DV 51 μg/m3Daily DV 51 μg/m3Daily DV 51 μg/m3Daily DV 51 μg/m3Daily DV 51 μg/m3Daily DV 51 μg/m3Daily DV 51 μg/m3AQSite 040213002AQSite 040213002AQSite 040213002AQSite 040213002AQSite 040213002AQSite 040213002Annual DV 5.9 μg/m3Annual DV 5.9 μg/m3Annual DV 5.9 μg/m3Annual DV 5.9 μg/m3Annual DV 5.9 μg/m3Annual DV 5.9 μg/m3Annual DV 5.9 μg/m3Annual DV 5.9 μg/m3Annual DV 5.9 μg/m3Daily DV 12 μg/m3Daily DV 12 μg/m3Daily DV 12 μg/m3Daily DV 12 μg/m3Daily DV 12 μg/m3Daily DV 12 μg/m3Daily DV 12 μg/m3Daily DV 12 μg/m3Daily DV 12 μg/m3AQSite 040134003AQSite 040134003AQSite 040134003AQSite 040134003AQSite 040134003AQSite 040134003Annual DV 12.6 μg/m3Annual DV 12.6 μg/m3Annual DV 12.6 μg/m3Annual DV 12.6 μg/m3Annual DV 12.6 μg/m3Annual DV 12.6 μg/m3Annual DV 12.6 μg/m3Annual DV 12.6 μg/m3Annual DV 12.6 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3AQSite 040130019AQSite 040130019AQSite 040130019AQSite 040130019AQSite 040130019AQSite 040130019Annual DV 12.3 μg/m3Annual DV 12.3 μg/m3Annual DV 12.3 μg/m3Annual DV 12.3 μg/m3Annual DV 12.3 μg/m3Annual DV 12.3 μg/m3Annual DV 12.3 μg/m3Annual DV 12.3 μg/m3Annual DV 12.3 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3AQSite 040139997AQSite 040139997AQSite 040139997AQSite 040139997AQSite 040139997AQSite 040139997AQSite 040139997AQSite 040139997AQSite 040139997Annual DV 9.8 μg/m3Annual DV 9.8 μg/m3Annual DV 9.8 μg/m3Annual DV 9.8 μg/m3Annual DV 9.8 μg/m3Annual DV 9.8 μg/m3Annual DV 9.8 μg/m3Annual DV 9.8 μg/m3Annual DV 9.8 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Daily DV 25 μg/m3Asarco Ray MinePrimary PM25 Annual Emission992.8 tpyCactus Waste Systems - Cactus LandfillPrimary PM25 Annual Emission64.5 tpyMaricopaPinalASARCO - RAY COMPLEX HAYDPrimary PM25 Annual Emission126.6 tpyK-14Figure K-12: Pittsburgh Urban Study AreaAQSite 421255001AQSite 421255001AQSite 421255001AQSite 421255001AQSite 421255001AQSite 421255001AQSite 421255001AQSite 421255001AQSite 421255001Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Annual DV 13.3 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3AQSite 420030021AQSite 420030021AQSite 420030021AQSite 420030021AQSite 420030021AQSite 420030021AQSite 420030021AQSite 420030021AQSite 420030021Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3AQSite 420030008AQSite 420030008AQSite 420030008AQSite 420030008AQSite 420030008AQSite 420030008AQSite 420030008AQSite 420030008AQSite 420030008Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3AQSite 420030093AQSite 420030093AQSite 420030093AQSite 420030093AQSite 420030093AQSite 420030093AQSite 420030093AQSite 420030093AQSite 420030093Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Annual DV 13 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3AQSite 420030095AQSite 420030095AQSite 420030095AQSite 420030095AQSite 420030095AQSite 420030095AQSite 420030095AQSite 420030095AQSite 420030095Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Annual DV 13.6 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3AQSite 420030116AQSite 420030116AQSite 420030116AQSite 420030116AQSite 420030116AQSite 420030116AQSite 420030116AQSite 420030116AQSite 420030116Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Annual DV 16.1 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3AQSite 420030067AQSite 420030067AQSite 420030067AQSite 420030067AQSite 420030067AQSite 420030067AQSite 420030067AQSite 420030067AQSite 420030067Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Annual DV 12.9 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3AQSite 420031301AQSite 420031301AQSite 420031301AQSite 420031301AQSite 420031301AQSite 420031301AQSite 420031301AQSite 420031301AQSite 420031301Annual DV 16.2 μg/m3Annual DV 16.2 μg/m3Annual DV 16.2 μg/m3Annual DV 16.2 μg/m3Annual DV 16.2 μg/m3Annual DV 16.2 μg/m3Annual DV 16.2 μg/m3Annual DV 16.2 μg/m3Annual DV 16.2 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3AQSite 420039002AQSite 420039002AQSite 420039002AQSite 420039002AQSite 420039002AQSite 420039002AQSite 420039002AQSite 420039002AQSite 420039002Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Annual DV 14.9 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3Daily DV 39 μg/m3AQSite 420031008AQSite 420031008AQSite 420031008AQSite 420031008AQSite 420031008AQSite 420031008AQSite 420031008AQSite 420031008AQSite 420031008Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Annual DV 15 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3Daily DV 40 μg/m3AQSite 420033007AQSite 420033007AQSite 420033007AQSite 420033007AQSite 420033007AQSite 420033007AQSite 420033007AQSite 420033007AQSite 420033007Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Annual DV 15.3 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3Daily DV 34 μg/m3AQSite 420030064AQSite 420030064AQSite 420030064AQSite 420030064AQSite 420030064AQSite 420030064AQSite 420030064AQSite 420030064AQSite 420030064Annual DV 19.8 μg/m3Annual DV 19.8 μg/m3Annual DV 19.8 μg/m3Annual DV 19.8 μg/m3Annual DV 19.8 μg/m3Annual DV 19.8 μg/m3Annual DV 19.8 μg/m3Annual DV 19.8 μg/m3Annual DV 19.8 μg/m3Daily DV 60 μg/m3Daily DV 60 μg/m3Daily DV 60 μg/m3Daily DV 60 μg/m3Daily DV 60 μg/m3Daily DV 60 μg/m3Daily DV 60 μg/m3Daily DV 60 μg/m3Daily DV 60 μg/m3AQSite 420030133AQSite 420030133AQSite 420030133AQSite 420030133AQSite 420030133AQSite 420030133AQSite 420030133AQSite 420030133AQSite 420030133Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Annual DV 14 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3Daily DV 28 μg/m3AQSite 421290008AQSite 421290008AQSite 421290008AQSite 421290008AQSite 421290008AQSite 421290008AQSite 421290008AQSite 421290008AQSite 421290008Annual DV 15.5 μg/mnnual DV 15.5 μg/nnual DV 15.5 μg/nnual DV 15.5 μg/mAnnual DV 15.5 μg/mAnnual DV 15.5 μg/nnual DV 15.5 μg/nnual DV 15.5 μg/nnual DV 15.5 μg/Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3Daily DV 37 μg/m3AQSite 421250005AQSite 421250005AQSite 421250005AQSite 421250005AQSite 421250005AQSite 421250005AQSite 421250005AQSite 421250005AQSite 421250005Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Annual DV 15.5 μg/m3Daily DV 36 μg/m33Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/mDaily DV 36 μg/mAQSite 421250200AQSite 421250200AQSite 421250200AQSite 421250200AQSite 421250200AQSite 421250200AQSite 421250200AQSite 421250200AQSite 421250200Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Annual DV 14.6 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3AQSite 420070014AQSite 420070014AQSite 420070014AQSite 420070014AQSite 420070014AQSite 420070014AQSite 420070014AQSite 420070014AQSite 420070014Annual DV 16.5 μg/m3Annual DV 16.5 μg/m3Annual DV 16.5 μg/m3Annual DV 16.5 μg/m3Annual DV 16.5 μg/m3Annual DV 16.5 μg/m3Annual DV 16.5 μg/m3Annual DV 16.5 μg/m3Annual DV 16.5 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3WHEELING-PITTSBURGH STEEL CORPORATION - STEUBENVILPrimary PM25 Annual Emission251.5 tpyUSS - CLAIRTON WORKSPrimary PM25 Annual Emission394.3 tpyALLEGHENY ENERGY SUPPLY CO/MITCHELL POWER STAPrimary PM25 Annual Emission181.2 tpyGLENSHAW GLASS COMPANY, INC.Primary PM25 Annual Emission58.8 tpyORION POWER MIDWEST, CHESWICK STATIOPrimary PM25 Annual Emission591.3 tpySHENANGO INC.Primary PM25 Annual Emission70.2 tpyAES BEAVER VALLEY LLC/BEAVER VALLY COGENPrimary PM25 Annual Emission417.8 tpyPA POWER CO/BRUCE MANSFIELD PLTPrimary PM25 Annual Emission1602.8 tpyALLEGHENY LUDLUM CORP - BRACKENRIDGEPrimary PM25 Annual Emission271.4 tpyUS STEEL CORPORATION - IRVIN PLANTPrimary PM25 Annual Emission63.5 tpyORION POWER MIDWEST/ELRAMAPrimary PM25 Annual Emission555.8 tpyUSS CORPORATION - EDGAR THOMSON WORKSPrimary PM25 Annual Emission472.1 tpyBrookeHancockWashingtonAlleghenyFayetteWestmorelandButlerBeaverCARDINAL POWER PLANT (CARDINAL OPERATING COMPANY)Primary PM25 Annual Emission3581.4 tpyW. H. SAMMIS PLANTPrimary PM25 Annual Emission6485.3 tpyRELIANT ENERGYPrimary PM25 Ann8553.5 tpyWHEELING-PITTSBURGH STEEL CORPORATIONPrimary PM25 Annual Emission223.7 tpyWEIRTON STEEL CORPORATIONPrimary PM25 Annual Emission3610 tpyK-15Figure K-13 Salt Lake City Urban Study AreaAQSite 490450003AQSite 490450003AQSite 490450003AQSite 490450003Annual DV 7.6 μg/m3Annual DV 7.6 μg/m3Annual DV 7.6 μg/m3Annual DV 7.6 μg/m3Annual DV 7.6 μg/m3Annual DV 7.6 μg/m3Annual DV 7.6 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3AQSite 490353010AQSite 490353010AQSite 490353010Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Annual DV 10.5 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3Daily DV 30 μg/m3AQSite 490353006AQSite 490353006AQSite 490353006AQSite 490353006AQSite 490353006AQSite 490353006Annual DV 10.7 μg/m3Annual DV 10.7 μg/m3Annual DV 10.7 μg/m3Annual DV 10.7 μg/m3Annual DV 10.7 μg/m3Annual DV 10.7 μg/m3Annual DV 10.7 μg/m3Annual DV 10.7 μg/m3Annual DV 10.7 μg/m3Daily DV 48 μg/m3Daily DV 48 μg/m3Daily DV 48 μg/m3AQSite 490353007AQSite 490353007AQSite 490353007AQSite 490353007AQSite 490353007AQSite 490353007AQSite 490353007Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Annual DV 11.6 μg/m3Daily DV 42 μg/m3Daily DV 42 μg/m3Daily DV 42 μg/m3AQSite 490350012AQSite 490350012Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Annual DV 14.4 μg/m3Daily DV 55 μg/m3Daily DV 55 μg/m3Daily DV 55 μg/m3Daily DV 55 μg/m3Daily DV 55 μg/m3Daily DV 55 μg/m3AQSite 490350003AQSite 490350003Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Annual DV 11.2 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3Daily DV 47 μg/m3AQSite 490351001AQSite 490351001AQSite 490351001Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3Daily DV 32 μg/m3AQSite 490570007AQSite 490570007AQSite 490570007Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Annual DV 8.9 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3Daily DV 31 μg/m3AQSite 490110004AQSite 490110004AQSite 490110004Annual DV 10 μg/m3Annual DV 10 μg/m3Annual DV 10 μg/m3Annual DV 10 μg/m3Annual DV 10 μg/m3Annual DV 10 μg/m3Annual DV 10 μg/m3Annual DV 10 μg/m3Annual DV 10 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3Daily DV 38 μg/m3AQSite 490353008AQSite 490353008AQSite 490353008AQSite 490353008AQSite 490353008AQSite 490353008AQSite 490353008AQSite 490353008AQSite 490353008Annual DV 7.8 μg/m3Annual DV 7.8 μg/m3Annual DV 7.8 μg/m3Annual DV 7.8 μg/m3Annual DV 7.8 μg/m3Annual DV 7.8 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3Daily DV 24 μg/m3AQSite 490571003AQSite 490571003AQSite 490571003AQSite 490571003Annual DV 9.1 μg/m3Annual DV 9.1 μg/m3Annual DV 9.1 μg/m3Annual DV 9.1 μg/m3Annual DV 9.1 μg/m3Annual DV 9.1 μg/m3Annual DV 9.1 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3Daily DV 35 μg/m3AQSite 490030003AQSite 490030003Annual DV 8.2 μg/m3Annual DV 8.2 μg/m3Annual DV 8.2 μg/m3Annual DV 8.2 μg/m3Annual DV 8.2 μg/m3Annual DV 8.2 μg/m3Annual DV 8.2 μg/m3Annual DV 8.2 μg/m3Annual DV 8.2 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3Daily DV 29 μg/m3AQSite 490570002AQSite 490570002AQSite 490570002AQSite 490570002AQSite 490570002AQSite 490570002AQSite 490570002Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Annual DV 10.6 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Daily DV 36 μg/m3Production PlantPrimary PM25 Annual Emission65.2 tpySmelter & RefineryPrimary PM25 Annual Emission152.2 tpyPower Plt/ Lab/ Tailings ImpoundmentPrimary PM25 Annual Emission64.3 tpySalt Lake City RefineryPrimary PM25 Annual Emission70.7 tpySalt Lake Valley Landfill & Transfer StationPrimary PM25 Annual Emission61.1 tpyWestern Fiberglass - Salt Lake City PlantPrimary PM25 Annual Emission58.7 tpyRowley PlantPrimary PM25 Annual Emission219.5 tpyMine & Copperton ConcentratorPrimary PM25 Annual Emission312 tpyU.S. Army-Dugway Proving GroundPrimary PM25 Annual Emission174.7 tpyBox ElderWeberTooeleSalt LakeSalt LakeSalt LakeSalt LakeSalt LakeSalt LakeSalt LakeSalt LakeSalt LakeSummitMorganWasatchUtahASTARPrimar139.3tK-16Figure K-14 St. Louis Urban Study AreaK-17Figure K-15 Tacoma Urban Study AreaAQSite 530530029AQSite 530530029AQSite 530530029AQSite 530530029AQSite 530530029AQSite 530530029AQSite 530530029AQSite 530530029AQSite 530530029Annual DV 10.2 μg/m3Annual DV 10.2 μg/m3Annual DV 10.2 μg/m3Annual DV 10.2 μg/m3Annual DV 10.2 μg/m3Annual DV 10.2 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3Daily DV 43 μg/m3SIMPSON TIMBER COPrimary PM25 Annual Emission137 tpySIMPSON DOOR COPrimary PM25 Annual Emission57.1 tpyWEYERHAEUSER CO.Primary PM25 Annual Emission113.8 tpyGRAYS HARBOR PAPER LPPrimary PM25 Annual Emission174.6 tpySaint-Gobain Containers IncPrimary PM25 Annual Emission70.3 tpySIMPSON TACOMA KRAFTPrimary PM25 Annual Emission79.3 tpyGraymont Western US IncPrimary PM25 Annual Emission53.5 tpyWeyerhaeuser CompanyPrimary PM25 Annual Emission60.9 tpyTransAlta Centralia GenerationPrimary PM25 Annual Emission2052.8 tpyTransAlta Centralia Mining, LLCPrimary PM25 Annual Emission112.2 tpySnohomishSnohomishSnohomishSnohomishSnohomishSnohomishSnohomishKingKingPiercePiercePiercePierceJeffersonGrays HarborPacificLewisMasonThurston

# 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