Health Benefits of Alternative PM2.5

Standards

July 2011

Prepared by:

Donald McCubbin, Ph.D.

Prepared for:

American Lung Association

Clean Air Task Force

Earthjustice
i

Executive Summary

The United States Environmental Protection Agency (EPA) is presently conducting a review of the

national ambient air quality standards (NAAQS) for particulate matter (PM). These standards are

established for pollutants that may reasonably be anticipated to endanger public health and welfare,

and whose presence in the ambient air results from numerous or diverse mobile or stationary sources.  

As part of its periodic review of the PM NAAQS, EPA released in June 2010 two reports: Quantitative

Health Risk Assessment for Particulate Matter and the Second External Review Draft of the Policy

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

reports are reviewed by the Clean Air Scientific Advisory Committee and help to inform the EPA

Administrator’s decision to revise the air quality standards as appropriate.

The present report seeks to augment the information developed by EPA and to highlight the potential

health benefits that might be realized nationwide from the standards under consideration in the current

NAAQS review.  EPA’s risk assessment focused on 15 major urban areas while looking at alternative

annual standards levels of 14, 13, and 12 μg/m3 combined with a daily standard of 35, and two

combinations of an annual and daily standard were analyzed – 13 μg/m3 annual coupled with a 30 μg/m3

daily standard (denoted 13/30) as well as a 12/25 standard.   This report evaluates more stringent

standard combinations, including an 11/35 standard put forth in the April 2011 Policy Assessment for 

the Review of the PM NAAQS (U.E. EPA 2011).   In addition, the risk assessment includes an

abbreviated national‐scale analysis using the Environmental Benefits Mapping and Analysis Program

(BenMAP), to examine the representativeness of its 15‐city risk assessment.

The goal of the present report is to conduct a national analysis of the mortality and morbidity benefits of

a greater range of annual and daily standards, and to conduct the analysis with the same types of tools

that EPA uses in its own analyses.   

To estimate PM2.5‐related human health impacts, this analysis uses the Benefits Mapping and Analysis

Program (BenMAP), the model which the EPA uses in regulatory benefit analyses (e.g., U.S. EPA2010b).  

Abt Associates developed this tool for EPA to analyze the health impacts of national‐scale air quality

regulations.   BenMAP calculates the difference in air quality between two scenarios, designated as

“baseline” and “control,” in each grid cell for which BenMAP has estimated population exposure to

PM2.5.  Given the difference in exposure between the two scenarios, BenMAP then calculates the

associated change in adverse health effects, such as premature mortality, using health impact functions.  

Table 1 presents key mortality results from the primary analysis by scenario.

Lastly, BenMAP assigns an economic value to these effects.  To estimate the economic benefit of the

estimated change in health incidence, BenMAP multiplies the number of adverse cases of a specific type

of effect (e.g., mortality) by its associated unit value and then adjusts for the estimated change in

income over time:

ݐ݉݁݊ݐݏݑ݆݀ܣ	݉݁݋݊ܿܫ ∗ ݁ݑܸ݈ܽ	ݐܷ݊݅ ∗ ݏݐ݂݂݁ܿܧ	hݐ݈݁ܽܪ	ݏ݁ݏܽܥ ൌ ݐ݂݁݊݁݅ܤ$
ii

A reasonable range of estimated results is presented as a 90 percent confidence interval (5th to 95th

percentiles along with the mean estimate ; however, not all sources of certainty can be quantified.  The

estimated population exposure uncertainty is unquantified, and similarly, the uncertainty associated

with the proportional rollback approach used in this analysis is unquantified.  

To help avoid over‐estimating the likely impacts of alternative standards, conservative assumptions are

used throughout the analysis.  When calculating mortality, no effects are assumed to occur below the

lowest measured level found in the study.  In addition, the rollback approach used in the present

analysis tends to result in relatively small changes in PM2.5 levels, in comparison to the rollback

approaches used by EPA (2010a).

Table 1.		Updated Air Quality Standards for Fine Particles Will Save Lives :		Estimated Premature

Deaths Avoided		in U.S. by Alternative PM2.5 Air Quality Standards and Epidemiological Study

(relative to current air quality), Mean and 90% Confidence Interval

Scenario Krewski et al. (2009) Laden et al. (2006)

A15D35 2,540 5,240

(1,850 – 3,220) (2,850 – 7,570)

A13D35 3,700 8,190

(2,700 – 4,700) (4,450 – 11,900)

A13D30 6,410 13,500

(4,680 – 8,130) (7,330 – 19,600)

A13D25 16,700 32,700

(12,200 – 21,200) (17,700 – 47,300)

A12D35 6,380 15,000

(4,650 – 8,100) (8,140 – 21,800)

A12D30 7,980 17,500

(5820 – 10,100) (9,490 – 25,400)

A12D25 16,800 33,000

(12,300 – 21,300) (17,900 – 47,800)

A11D35 11,200 27,300

(8,200 – 14,300) (14,800 – 39,600)

A11D30 12,100 27,900

(8,830 – 15,400) (15,100 – 40,400)

A11D25 17,900 35,700

(13,100 – 22,700) (19,400 – 51,800)

Bold type highlights options put forth by EPA in its Policy Assessment  
iii

Table of Contents

Executive Summary .............................................................................................................................. i

Table of Contents................................................................................................................................ iii

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

List of Acronyms .................................................................................................................................. v

1.  Introduction.................................................................................................................................... 1

2.  Estimating PM2.5 Exposure with Alternative Annual & Daily Standards............................................ 2

2.1  Monitoring Data.................................................................................................................................2

2.2  Rolling Back Monitoring Data to Meet Alternative Air Quality Standards......................................... 4

2. 3  Interpolating Monitoring Data to CMAQ 12 km Grid Cells............................................................... 5

3.  Estimating Human Health Benefits of Meeting Alternative PM2.5 Standards .................................... 7

3.1  Estimating Cases of Adverse Health Impacts..................................................................................... 7

3.2  Valuing Cases of Health Benefits...................................................................................................... 10

4.  Results .......................................................................................................................................... 16

Appendix A.  Human Health Impact Function Details ......................................................................... 26

A.1  Deriving Health Impact Functions.................................................................................................... 26

A.2  PM2.5 Human Health Impact Functions............................................................................................ 29

Appendix B.  Health Incidence Data ................................................................................................... 51

Appendix C.  Population Data ............................................................................................................ 53

Appendix D.  Resume of Dr. McCubbin............................................................................................... 55

References......................................................................................................................................... 57
iv

List of Tables

Table 1.  Updated Air Quality Standards for Fine Particles Will Save Lives :  Estimated Premature Deaths

Avoided  in U.S. by Alternative PM2.5 Air Quality Standards and Epidemiological Study (relative

to current air quality), Mean and 90% Confidence Interval.......................................................... ii

Table 2.  Rollback Scenarios.......................................................................................................................... 5

Table 3.  Population Coverage at Varying Fixed Radius Distances ............................................................... 6

Table 4.  Baseline and Control Scenarios Used to Calculate Health Impacts................................................ 7

Table 5.  Epidemiological Studies Used to Estimate Adverse Health Impacts of PM2.5 ................................ 9

Table 6.  Lowest Measured Levels for Mortality Epidemiological Studies ................................................. 10

Table 7.  Elasticity of WTP and Income Adjustment by Type of Health Effect ........................................... 13

Table 8.  Mean Value for Economic Valuation of Health Endpoints (based on 2008 income and 2008$).15

Table 9.  Adverse Health Impacts Avoided by Meeting Alternative PM2.5 Standards – Mean Number of

Cases and 90% Confidence Interval ............................................................................................ 18

Table 10.  Value of Adverse Health Impacts Avoided by Meeting Alternative PM2.5 Standards – Mean

Value and 90% Confidence Interval (million 2008 $).................................................................. 19

Table 11.  Alternative Mortality Estimates, Mean Number of Cases and 90% Confidence Interval ..........21

Table 12.  Alternative Nonfatal Heart Attack Estimate, Mean Number of Cases and 90% Confidence

Interval ........................................................................................................................................23

Table 13.  PM2.5 Levels by City, Comparison of Present Analysis & EPA (2010a)......................................24

Table 14.  Details of PM2.5 Human Health Impact Functions...................................................................... 30
v

List of Acronyms

Acronym Description

ALA American Lung Association

BenMAP Environmental Benefits Mapping and Analysis Program

CASAC Clean Air Scientific Advisory Committee

CATF Clean Air Task Force

CMAQ Community Multi‐Scale Air Quality Modeling System

COI cost of illness

DV design value

EPA U.S. Environmental Protection Agency

HCUP Healthcare Cost and Utilization Project

ICD International Classification of Disease

LML lowest measured level (in an epidemiological study)

NAAQS national ambient air quality standard

PM2.5 fine particulate matter less than 2.5 microns in aerodynamic diameter

PRB policy relevant background

μg/m3 micrograms per meter cubed

VNA voronoi neighbor averaging

VSL value of a statistical life

WTP willingness to pay
1

1.		Introduction

The United States Environmental Protection Agency (EPA) is presently conducting a review of the

national ambient air quality standards (NAAQS) for particulate matter (PM). These standards are

established for pollutants that may reasonably be anticipated to endanger public health and welfare,

and whose presence in the ambient air results from numerous or diverse mobile or stationary sources.  

As part of its periodic review of the PM NAAQS, EPA released in June 2010 two reports: Quantitative

Health Risk Assessment for Particulate Matter (EPA‐452/R‐10‐005) and the Second External Review Draft

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

Standards (EPA‐452/P‐10‐007).  These reports are reviewed by the Clean Air Scientific Advisory

Committee (CASAC) and help to inform the EPA Administrator’s decision to revise the air quality

standards as appropriate.

The present report seeks to augment the information developed by EPA and to highlight the potential

health benefits that might be realized nationwide from the standards under consideration in the current

NAAQS review.  EPA’s risk assessment focused on 15 major urban areas while looking at alternative

annual standards levels of 14, 13, and 12 μg/m3 combined with a daily standard of 35, and two

combinations of an annual and daily standard were analyzed – 13 μg/m3 annual coupled with a 30

μg/m3 daily standard (denoted 13/30) as well as a 12/25 standard. In addition, the risk assessment

conducted an abbreviated national‐scale analysis using the Environmental Benefits Mapping and

Analysis Program (BenMAP), using the limited national analysis, which focused on mortality impacts, to

examine the representativeness of its 15‐city risk assessment.   

In April 2011, EPA published a final Policy Assessment reflecting additional analysis and the comments of

the CASAC and the public on two prior draft documents.  The final policy assessment lays out EPA staff

scientists’ recommendations for revised PM2.5 standards. They proposed somewhat different levels than

used in the risk assessment.  Specifically, the policy assessment concluded that the current annual PM2.5

standard of 15 μg/m3 should be revised to a level within the range of 13 to 11 μg/m3

, with evidence

most strongly supporting a standard in the range of 12 to 11 μg/m3

.  In addition, staff recommended

retention of the current 24‐hour standard of 35 μg/m3 with possible consideration of a revised standard  

of 30 μg/m3 in conjunction with an annual  standard level of 13 μg/m3

.

The goal of the present report is to conduct a national analysis of the mortality and morbidity benefits of

a greater range of annual and daily standards, including those recommended in the final Policy

Assessment, and to conduct the analysis, using the same types of tools that EPA uses in its own analyses.  

The report is outlined as follows:  Section 2 describes the PM2.5 monitoring data used in this analysis, the

calculation of annual and daily design values, and the approach used to reduce, or rollback, daily PM2.5

values so as to meet alternative standards.  Section 3 describes the use of BenMAP in estimating the

avoided health impacts due to meeting more stringent PM2.5 standards, as well as the economic value of

these avoided health effects.  Section 4 presents the results of the analysis.  Finally, Appendix A details

the health impact functions used in this report; Appendix B provides a brief summary of some of the key

health incidence databases used; and Appendix C describes the population data.  
2

2.		Estimating PM2.5 Exposure with Alternative

Annual & Daily Standards

To estimate PM2.5 levels in a uniform grid across the United States, this analysis uses the domain from

the Community Multi‐Scale Air Quality Modeling System (CMAQ), which has grid cells that are

approximately 12 kilometers by 12 kilometers.  CMAQ  is one of EPA's preferred air quality modeling

tools.1

  The first step in estimating PM2.5 exposure is to work with the monitoring data to develop as

complete a set of data as possible.  The second step is to adjust, or roll back each monitor so that it just

meets the prescribed annual and daily standards.  The final step is to calculate for each grid cell an

inverse‐distance‐weighted concentration of the monitors within 60 kilometers of the center of each grid

cell, dropping from consideration any grid cell further than 60 kilometers from a monitor.   

The present analysis uses three years of monitoring data so as to follow as closely as possible EPA’s

approach for calculating whether a region is in attainment or not.  Note, however, that this analysis

differs in a number of ways from EPA’s (2010a) Quantitative Health Risk Assessment for Particulate

Matter, which evaluates the health impacts of alternative PM2.5 standards.  The present analysis is

national in scope, whereas the EPA analysis focuses on 15 urban areas representative of the continental

US.2

  The present analysis uses monitoring data from 2007–2009 (the most recent complete set of data

available), while the EPA analysis uses data from 2005–2007.  The present analysis combines the three

years of data to calculate design values and then calculates the associated health impacts for each

standard combination.  The EPA analysis used the maximum design value in an urban area to calculate

the required percent reduction in ambient PM2.5 to meet the standard combination, determined the

'controlling' standard, and then applied the reduction to a composite monitor in each urban area in each

year individually.3

  Finally, the present analysis uses a proportional rollback approach to estimate the

percent reduction needed to meet an alternative standard at each monitor, whereas the EPA used a

different proportional rollback approach as well as two other rollback approaches.

Below, there is a description of the monitoring data and how they are rolled back to meet different

combinations of annual and daily standards, and then how the BenMAP model averaged the monitoring

data to estimate exposure in each grid cell.   

2.1		Monitoring Data

This analysis relies on the three most recent available years of available quality‐controlled ambient air

quality PM2.5 data, 2007 to 2009.  For most monitoring sites, three years of data were available for the

                                                            

1 The CMAQ model is described at the EPA website: http://www.epa.gov/ttn/scram/photochemicalindex.htm 2 EPA (2010a, Appendix G) also conducted a national benefits analysis, but this was not designed to evaluate the

benefits of alternative scenarios. 3 Details on the rollback approaches used by EPA can be found in EPA (2010a) starting on page 3‐18.
3

calculation of annual and daily design values.4

  For sites with fewer than three years of data, annual and

98th percentile calculations were constructed based on available complete years.  In some cases, two

nearby sites with incomplete three‐year data sets were combined to cover the entire three‐year period.  

In a couple instances, the annual averages were constructed from multiple years such that all four

seasons were sampled (e.g., winter 2007 through fall 2008, reported as 2008).5

Included in the analysis are all monitors with at least one year of data, in which each quarter in that year

has at least nine daily values.  For these monitors, an annual design value was calculated, which is an

average of up to three years of data, using the following series of formulas:

௜,ହ.ଶܯܲ෍ ൌ௝ ܽ݃݁ݎ݁ݒܣ	ݕ݈ݎ݁ݐݎܽݑܳ

݊

௡

௜ୀଵ

where:  

n = number of daily observations in a given quarter “j”.

௝ܽ݃݁ݎ݁ݒܣ	ݕ݈ݎ݁ݐݎܽݑܳ෍ ൌ௞ ܽ݃݁ݎ݁ݒܣ	݈ܽݑ݊݊ܣ

4

ସ

௝ୀଵ

ೖ௘௥௔௚௘௩௔௟	஺௨ ∑ ஺௡௡ൌ௠ ݁ݑܸ݈ܽ	݅݃݊ݏ݁ܦ	݈ܽݑ݊݊ܣ

௟

௟

௞ୀଵ   

where:

l = number of annual averages available for a given monitor “m.”

For each monitor for which an annual design value was calculated, a daily design value was also

calculated.  The first step was to count the number of daily PM2.5measurements in a given year, and

then, based on this number, to calculate the 98th percentile value:

98th Percentilek = NumberDaysk – TruncationFunction(0.98 * NumberDaysk)

where:

NumberDaysk = number of days with daily PM2.5 values in year “k.”

TruncationFunction = function in Excel that keeps the integers to the left of the decimal point.

An annual peak is then calculated for each year:

Annual Peakk = Large(ArrayPM2.5, k, 98th Percentilek)

where:

Array PM2.5, k = array of PM2.5 values in a given year “k.”

Large = Excel function that identifies a peak value specified, in this case, by the 98th Percentilek value.

                                                            

4 Details on the calculation of design values can be found in 40 CFR, Part 50, Appendix N:

http://edocket.access.gpo.gov/cfr_2009/julqtr/pdf/40cfr50AppN.pdf.   5 The PM2.5 monitor data were downloaded from the U.S. Environmental Protection Agency’s website

(http://www.epa.gov/airexplorer/) between November 15 and November 23, 2010 by John Graham of the Clean

Air Task Force, who then added the data to an Excel spreadsheet.  As discussed in the text, he combined some

monitor data, so as to provide as complete a monitor record as possible, and then provided the Excel files to Don

McCubbin for use in this analysis.
4

The daily design value is then calculated as an average of the available annual peak values:

௞ܲ݁ܽ݇	݈ܽݑ݊݊ܣ෍ ൌ௠ ݁ݑܸ݈ܽ	݅݃݊ݏ݁ܦ	ݕ݈ܽ݅ܦ

݈

௟

௞ୀଵ

where:

l = number of annual peak values for a given monitor “m.”

2.2		Rolling Back Monitoring Data to Meet Alternative Air

Quality Standards

For each monitor, the anthropogenic fraction of each daily PM2.5 value was rolled back by the

percentage reductions needed to attain various combinations of annual and daily standards (see Table

2).  Following EPA methods in their 2010 Risk Assessment (EPA 2010a), the anthropogenic fraction is

simply the daily value minus the “policy relevant background” or PRB; PRB has regional values as

determined by air quality modeling6

. The daily values were reduced until the annual standard was met.  

After this initial reduction, if a daily standard was not yet met, then a second round of reductions was

performed to meet the daily standard.   

The reduction needed to meet the annual standard was calculated as follows:

annualRollbackFraction = (baselineAnnualDV ‐ annualStandard) / (baselineAnthropogenicAnnualDV)

where:

baselineAnnualDV = annual design value calculated from the unadjusted, or baseline, PM2.5 monitor

values.

annualStandard = annual ambient air quality standard.

baselineAnthropogenicAnnualDV =  annual design value for anthropogenic PM2.5 values, calculated by

first subtracting the PRB from each day, and then calculating an annual design value the same

way as described in the previous section.

For each day above the PRB, the anthropogenic fraction of the daily value was reduced as follows:

adjustedAnthroDay = baselineAnthroDay * (1‐annualRollbackFraction)

where:

baselineAnthroDay = daily PM2.5 value minus the PRB.

The PRB was then added back to get an estimate of the daily value after meeting the annual standard.

rollbackDay = adjustedAnthroDay + PRB.

After this initial reduction, the daily design value (“estimatedDailyDV”) was calculated and compared to

the daily standard.  If the estimatedDailyDV exceeded the daily standard, then it was reduced as follows:

dailyRollbackFraction = (estimatedDailyDV ‐dailyStandard) / (estimatedDailyDV ‐ PRB)

                                                            

6 More details are available in Section 3.7 of the Integrated Science Assessment for Particulate Matter (U.S. EPA

2009).
5

where:

estimatedDailyDV = daily design value calculated after meeting the annual standard.

For each day above the PRB, the anthropogenic fraction of the previously reduced daily values was

reduced again, and then the PRB was added, as follows:

finalDailyValue = adjustedAnthroDay * (1‐dailyRollbackFraction) + PRB.

After adjusting the monitor values, a new annual average design value was calculated, which was then

used as input to BenMAP.  A variation of the above steps was performed for each of the scenarios listed

in Table 2.  These scenarios include most of the scenarios analyzed by EPA (2010a), as well as a  number

of additional scenarios so as to augment the available information for consideration in the current

NAAQS review.

Table 2.		Rollback Scenarios		

Name Annual Design Value

(μg/m3)

Daily Design Value

(μg/m3)

Included in EPA (2010a)

Risk Assessment*

  _A15 15 ‐‐ No

_A15D35 15 35 Yes

_A13 13 ‐‐ No

_A13D35 13 35 Yes

_A13D30 13 30 Yes

_A13D25 13 25 No

_A12 12 ‐‐ No

_A12D35 12 35 Yes

_A12D30 12 30 No

_A12D25 12 25 Yes

_A11 11 ‐‐ No

_A11D35 11 35 No

_A11D30 11 30 No

_A11D25 11 25 No

Note that as part of a sensitivity analysis some of the scenarios involve just meeting an annual standard (e.g., A15

meets an annual standard of 15 μg/m3

).  * The EPA (U.S. EPA, 2010a) Risk Assessment includes an analysis of a

14/35 scenario, which is not included here as the Policy Assessment (U.S. EPA, 2011) does not recommend this.

2. 3		Interpolating Monitoring Data to CMAQ 12 km Grid Cells

After loading into BenMAP the monitoring data for the baseline case and for each of the scenarios

described in Table 2, the fixed radius option in BenMAP was used to estimate the air quality in each
6

CMAQ grid cell that is within 60 kilometers of a monitor.7

  The fixed radius option works by calculating

an inverse distance‐weighted average of the monitoring data within a prescribed distance of the center

of each grid cell.  This is described in Appendix C of the BenMAP User Manual (Abt Associates Inc.,

2010).8

There is a tradeoff between choosing a relatively short maximum distance, which may better represent

the exposure estimate, and choosing a relatively large maximum distance, which will increase the

number of people included in the analysis.   As noted in Table 3, limiting the interpolation to 60

kilometers captures 91% of the population (ages 30 and up).  No estimate of adverse impacts is made

for the population that resides beyond the area with estimated air pollution levels.

Table 3.		Population Coverage at Varying Fixed Radius Distances

Fixed Radius

Distance

2008 Population Ages 30+

(million)

Percent of No Maximum

Distance Population

30 km 135.2 76%

40 km 147.8 83%

50 km 156.5 88%

60 km* 162.5 91%

100 km 173.0 97%

No Maximum 178.0 100%

* Distance used in this report.

   

                                                            

7 The California Air Resources Board (2009) used a similar approach when it interpolated monitor values within 50

kilometers of census tracts in their analysis of alternative PM2.5 standards.  The EPA (1999) also used a 50 kilometer

maximum distance when evaluating the benefits of the Clean Air Act. 8 An alternative to the fixed radius approach is Voronoi Neighbor Averaging (VNA), which has the option of using a

maximum interpolation distance.  The VNA approach uses the Voronoi algorithm to identify nearby or “neighbor”

monitors, and then BenMAP takes a distance‐weighted average of these "neighbor" monitors.  A downside is that

this approach excludes monitors that are within the specified distance, if there is another monitor between it and

the center of the CMAQ grid cell.  Nevertheless, both the fixed radius and VNA approaches give broadly similar

results, generally within 5‐10% based on test results using 2007 monitor data.
7

3.		Estimating Human Health Benefits of Meeting

Alternative PM2.5 Standards

To estimate PM2.5‐related human health impacts, this analysis used BenMAP, which the EPA used in its

analysis of alternative ambient air quality standards (e.g., U.S. EPA2010a) and air pollution regulations

(e.g., U.S. EPA2010b).  BenMAP calculates the difference in air quality between two scenarios,

designated as “baseline” and “control,” in each grid cell for which BenMAP has estimated population

exposure to PM2.5.  Given the difference in exposure between the two scenarios, BenMAP then

calculates the associated change in adverse health effects, such as premature mortality, using health

impact functions.  Lastly, BenMAP assigns an economic value to these effects.

Table 4 presents the baseline and control scenario pairs used in BenMAP to calculate health impacts.  

The next section details the approach used to estimate adverse health impacts.  The section after

describes the basic steps involved in calculating and placing an economic value on human health impacts

associated with PM2.5.

Table 4.		Baseline and Control Scenarios Used to Calculate Health Impacts

Results Name Baseline Scenario Control Scenario

A15 2007‐2009 PM2.5 _A15

A15D35 2007‐2009 PM2.5 _A15D35

A13 2007‐2009 PM2.5 _A13

A13D35 2007‐2009 PM2.5 _A13D35

A13D30 2007‐2009 PM2.5 _A13D30

A13D25 2007‐2009 PM2.5 _A13D25

A12 2007‐2009 PM2.5 _A12

A12D35 2007‐2009 PM2.5 _A12D35

A12D30 2007‐2009 PM2.5 _A12D30

A12D25 2007‐2009 PM2.5 _A12D25

A11 2007‐2009 PM2.5 _A11

A11D35 2007‐2009 PM2.5 _A11D35

A11D30 2007‐2009 PM2.5 _A11D30

A11D25 2007‐2009 PM2.5 _A11D25

Table 2 describes the baseline and control scenarios.

3.1		Estimating Cases of Adverse Health Impacts		

Derived from concentration‐response functions reported in the epidemiological literature, health impact

functions quantify the relationship between changes in air pollution and adverse health impacts.  A

typical health impact function has four components:  
8

 Effect estimate.  An effect estimate (“beta”) quantifies the change in health effects per unit of

change in a pollutant and is derived from an epidemiological study.

 PM2.5 change.  The estimated change in the concentration of ambient PM2.5.

 Incidence rate.  The baseline incidence rate for the health effect due to all causes.

 Population.  The affected population; the age range included depends on the ages included in

the epidemiological study.   This analysis used 2008 population estimates, the calculation of

which is described in Appendix C.

The typical log‐linear health impact function looks as follows:

∆ܪ݈݁ܽݐh	 ൌ ൬1 െ

1

ሻ2.5ܯܲ∆ ∗ ܽݐ݁ܤexpሺ

݊݋݅ݐ݈ܽݑ݌݋ܲ ∗ ݊ܿ݅݀݁݊ܿ݁ܫ ∗ ൰

Another common form for health impact functions is the logistic, which appears as follows:  

∆ܪ݈݁ܽݐh ൌ ൬1െሺ

1

݊݋݅ݐ݈ܽݑ݌݋ܲ ∗ ݊ܿ݅݀݁݊ܿ݁ܫ ∗ ሻ൰݊ܿ݅݀݁݊ܿ݁ܫ ൅ ሻ2.5ܯܲ∆ ∗ ܽݐ݁ܤexpሺ ∗ ሻ݊ܿ݅݀݁݊ܿ݁ܫ െ 1ሺ

All of the health impact functions used are in one of these two main forms.  Both types have the same

four elements.  Appendix A derives these two forms and presents details on the derivation of each effect

estimate, and Appendix B describes the health incidence databases used for the calculation of

premature mortality, hospital admissions, emergency room visits, and heart attacks.9

  Table 5 presents

the PM2.5‐related health endpoints included in the analysis, which follows the approach currently under

development by EPA for its benefits analyses.

                                                            

9 The incidence and prevalence rates for the other health endpoints, such as acute bronchitis, asthma

exacerbation, and other effects not requiring hospitalization are provided in the derivation of the health impact

functions in Appendix A.
9

Table 5.		Epidemiological Studies Used to Estimate Adverse Health Impacts of PM2.5

Endpoint Author Age

Mortality, All Cause Laden et al. (2006) 25 ‐ 99

Mortality, All Cause Pope et al. (2002) 30 ‐ 99

Mortality, Various* Krewski et al. (2009) 30 ‐ 99

Mortality, All Cause Woodruff et al. (1997) Infant

Heart Attack, Nonfatal Peters et al. (2001) 18 ‐ 99

Heart Attack, Nonfatal Pope et al. (2006) 18 ‐ 99

Heart Attack, Nonfatal Sullivan et al. (2005) 18 ‐ 99

Heart Attack, Nonfatal Zanobetti and Schwartz (2006) 18 ‐ 99

Heart Attack, Nonfatal Zanobetti et al. (2009) 18 ‐ 99

HA, All Cardiovascular (less Myocardial Infarctions) Bell et al. (2008) 65 ‐ 99

HA, All Cardiovascular (less Myocardial Infarctions) Peng et al. (2008) 65 ‐ 99

HA, All Cardiovascular (less Myocardial Infarctions) Peng et al. (2009) 65 ‐ 99

HA, All Cardiovascular (less Myocardial Infarctions) Zanobetti et al. (2009) 65 ‐ 99

HA, All Cardiovascular (less Myocardial Infarctions) Moolgavkar (2000b) 18 ‐ 64

HA, All Respiratory Zanobetti et al. (2009) 65 ‐ 99

HA, Chronic Lung Disease Moolgavkar (2000a) 18 ‐ 64

HA, Asthma Babin et al. (2007) 0 ‐ 17

HA, Asthma Sheppard (2003) 0 ‐ 17

Emergency Room Visits, Asthma Mar et al. (1999; 2010) 0 ‐ 99

Emergency Room Visits, Asthma Slaughter et al. (2005) 0 ‐ 99

Acute Bronchitis Dockery et al. (1996) 8 ‐ 12

Lower Respiratory Symptoms Schwartz and Neas (2000) 7 ‐ 14

Upper Respiratory Symptoms Pope et al. (1991) 9 ‐ 11

Asthma Exacerbation, Cough Ostro et al. (2001) 6 ‐ 18

Asthma Exacerbation, Cough Mar et al. (2004) 6 ‐ 18

Asthma Exacerbation, Shortness of Breath Ostro et al. (2001) 6 ‐ 18

Asthma Exacerbation, Shortness of Breath Mar et al. (2004) 6 ‐ 18

Asthma Exacerbation, Wheeze Ostro et al. (2001) 6 ‐ 18

Work Loss Days (WLD) Ostro (1987) 18 ‐ 64

Minor Restricted Activity Days (MRAD) Ostro and Rothschild (1989) 18 ‐ 64

Note: HA = hospital admissions. * The study by Krewski et al. (2009) was used to develop mortality functions for

four mortality endpoints: all‐cause, ischemic heart disease, cardiopulmonary, and lung cancer.

Sensitivity Analyses

As a sensitivity analysis, a variety of mortality functions were included in the present study.  The primary

estimate is based on studies by Laden et al. (2006) and Krewski et al. (2009) and assumes that no

impacts occurred when the baseline air quality level falls below the lowest measured level (LML) in the

underlying epidemiological studies.  This assumption may underestimate benefits as no known

threshold for health effects of PM2.5 has been identified.  Additional results show the impact of relaxing

this restriction.   Table 6 presents the LMLs for the mortality studies used in this analysis.

In addition, two estimates are presented for nonfatal heart attacks.  One is based on the study by Peters

et al. (2001), which EPA (2004; 2010b) has long used to estimate in regulatory impact assessments.  A

second estimate is based on four studies (Sullivan et al., 2005; Pope et al., 2006; Zanobetti and

Schwartz, 2006; Zanobetti et al., 2009), which give an estimate significantly lower than that based on
10

Peters et al.  This second estimate, based on a number of more recent studies over a broader

geographical area is included in the primary set of results, with the Peters et al. result presented as a

sensitivity analysis.

Table 6.		Lowest Measured Levels for Mortality Epidemiological Studies

Epidemiological Study LML (μg/m3)

Pope et al.   7.5

Laden et al. 10

Krewski et al. 5.8

Pooling Health Effect Estimates

When there are several effect estimates for a given health endpoint, these estimates are quantitatively

combined or “pooled” to derive a more robust estimate. This analysis generally used fixed or random

effects models to pool estimates from different studies of the same health endpoint.  Fixed effects

pooling simply weights each study’s estimate by the inverse variance, giving more weight to studies with

lower variance.  Random effects pooling accounts for both within‐study variance and between‐study

variability, due, for example, to differences in population susceptibility. The fixed effects model is used

as the null hypothesis, with a statistical test of the data determining whether the null should be

rejected, in which case the random effects model is used.10  Additional details on pooling are provided in

Appendix A.

3.2		Valuing Cases of Health Effects

To estimate the economic benefit of the estimated change in health incidence, BenMAP multiplies the

number of adverse cases of a specific type of effect (e.g., mortality) by its associated unit value and then

adjusts for the estimated change in income over time:

ݐ݉݁݊ݐݏݑ݆݀ܣ	݉݁݋݊ܿܫ ∗ ݁ݑܸ݈ܽ	ݐܷ݊݅ ∗ ݏݐ݂݂݁ܿܧ	hݐ݈݁ܽܪ	ݏ݁ݏܽܥ ൌ ݐ݂݁݊݁݅ܤ$

A unit value gives the estimated economic value of the avoidance of a single case of a particular

endpoint — a single death, for example, or a single hospital admission.  Unit values are derived from the

economics literature, and come in several varieties.   

 For some endpoints, such as hospital admissions, cost of illness (COI) unit values are used, which

estimate the cost of treating or mitigating the effect.  These estimates generally underestimate

the true value of reductions in risk of a health effect, since they include hospital costs and lost

wages, but do not include any estimate of the value of avoided pain and suffering.

 Other endpoints, such as asthma exacerbation, involve willingness to pay (WTP) unit values,

which estimate willingness to pay for reductions in the risk of a health effect.   

                                                            

10 This statistical test is described in the BenMAP User Manual (Abt Associates Inc., 2010, p. 404).
11

 Typically value of statistical life (VSL) unit values are used for reductions in risk of premature

mortality.   

Several issues arise when determining unit values, such as taking into account inflation and adjusting for

growth in income.  The following section discusses these issues.

Issues in Valuation

An air pollution benefit analysis tries to measure the full value to society of better health.  There are

different components to this value, including: (1) medical costs, (2) lost productivity when someone

cannot go to work, and (3) pain and suffering.  Medical costs and lost productivity are relatively easy to

value, since markets exist where these goods are bought and sold.  However, pain and suffering is more

difficult to measure, since there is no market.  

Economists have devised alternative approaches to valuing non‐market goods.  One is an indirect (or

“revealed preference”) approach, whereby one infers the values people place on non‐market “goods”

by examining their preferences revealed by their behaviors in associated markets.  For a long time

seatbelts were not standard equipment in automobiles, and car buyers needed to specifically order and

pay for them.  A market existed and seatbelts had a specific cost.  Seatbelts also reduced the risk of

premature mortality, and so economists inferred the value of avoiding premature mortality risk by

studying the associated seatbelt market.  There are many other similar examples.  Perhaps the most

common is calculating the value of avoiding risk by comparing jobs with different risk levels and

determining how much more people have to be paid to work the riskier job.

Valuing “Statistical Cases” Avoided

Reductions in ambient concentrations of air pollution generally lower the risk of adverse health effects

by a small amount for a large population.  The health benefits conferred on individuals by a reduction in

pollution concentrations are, then, actually reductions in the risk of having to endure certain health

problems.  This reduction in risk for individuals results in a decrease in the expected number of cases of

the adverse health effect in the population.   

Suppose, for example, that a given reduction in particulate matter (PM) concentrations results in a

decrease in mortality risk of 1/10,000.  Then for every 10,000 individuals, we expect one fewer death

than would have been expected if PM concentrations had not been reduced.  Whose lives will be saved

cannot be known ex ante.  We therefore refer to “statistical lives” and, in benefits analysis, the “value of

a statistical life.”  Similarly with other adverse health effects, such as hospitalization for a respiratory

illness, it is reductions in risk that are conferred on individuals and “statistical cases” that are avoided by

a reduction in pollution concentrations.     
12

In theory, it is the risk reductions resulting from a reduction in pollutant concentrations that should be

valued, and some studies have been designed to estimate individuals’ willingness to pay (WTP)11 for

small reductions in risk.  Wage‐risk studies, for example, use data on wages in occupations with varying

degrees of mortal risk (e.g., from accidents) to tease out estimates of the average willingness to pay for

mortal risk reductions.  From this, the “value of a statistical life” can be inferred.   

Taking the example above, suppose that a given reduction in PM concentrations results in a decrease in

mortality risk of 1/10,000.  If the average WTP for this 1/10,000 decrease in mortality risk is $500, then

the value of a “statistical life saved” is 10,000 x $500, or $5 million.  Note that no one individual is willing

to pay $5 million dollars to avoid certain death; instead, 10,000 individuals are each willing to pay $500

to reduce the risk of dying by 1/10,000.   

Change over Time in WTP in Real Dollars

The WTP for health‐related environmental improvements (in real dollars) could change over time if, for

example, real income changes.  Generally, the more income a person has, the more he is willing to pay

for a good or service (although the percent increase in WTP does not necessarily equal the percent

increase in income).  If average income (in real dollars) has increased since the time that a wage‐risk

study was conducted, for example, it is reasonable to expect that WTP, in real dollars, would have

increased as well.  If a WTP estimate is not adjusted for increases (over time) in real income, it will be

biased downward as a result.  As a result, this analysis includes an adjustment for income change over

time, following an approach used in EPA analyses.12

Economists estimate “elasticities” to describe by what percent WTP goes up for a given percentage

increase in income.  As it turns out, these estimated elasticities are much less than one, however, there

is considerable uncertainty over the precise value.  This report follows the approach used by EPA in

recent regulatory analyses (U.S. EPA, 2008), which used elasticity estimates that vary by type of health

effect, with relatively minor effects having a smaller elasticity than more severe effects. Multiplying

these elasticities by historical and forecasted income data, EPA developed income adjustment factors

which are used in this report.   Table 7 presents the elasticities and associated income adjustment

factors used in this report.

                                                            

11 WTP is a measure of the value an individual places on gaining an outcome viewed as desirable, whether it be

purchased in a market or not.  The WTP measure, therefore, is the amount of money such that the individual

would be indifferent between having the good (e.g., avoiding premature death) and having the money.    12 See for example the regulatory impact analysis of the Proposed Federal Transport Rule (U.S. EPA, 2010b).  
13

Table 7.		Elasticity of WTP and Income Adjustment by Type of Health Effect

Health Effect Central Elasticity Estimate Income Adjustment for 2008

Minor Health Effect 0.14 1.030

Severe & Chronic Health Effects 0.45 1.098

Premature Mortality 0.4 1.087

Source: EPA (2005, p. 4‐18).

Note that because of a lack of data on the dependence of the cost of illness (COI) on income and a lack

of data on projected growth in average wages, no adjustments are made to benefits estimates based on

the COI approach or to work loss days and worker productivity benefits estimates. This lack of

adjustment would tend to result in an under‐prediction of benefits in future years, because it is likely

that increases in real U.S. income would also result in increased COI (due, for example, to increases in

wages paid to medical workers) and increased cost of work loss days and lost worker productivity

(reflecting that if worker incomes are higher, the losses resulting from reduced worker production

would also be higher).

Adjusting for Inflation

An adjustment was made for inflation, which increases the nominal value – i.e., the price of goods –

from one year to the next without a corresponding increase in the actual value of those goods.  So, for

example, a good that cost $200 in 1996 might cost $250 in 2008.  This increase in the nominal price of

the good does not imply an increase in the value of the good, if both prices and incomes have increased

by the same percentage over that time period.  

To get all dollar values in year 2008 dollars, this analysis used standard inflation adjusters.  In particular,

to adjust WTP estimates, the consumer price index for “all items” (CPI‐U “all items”) was used.  To adjust

estimated hospital costs, the consumer price index for medical care and services was used (see Bureau

of Labor Statistics website, at: http://data.bls.gov/cgi‐bin/surveymost). Similarly, a wage index was used

to adjust for wage inflation.13

Mortality Adjustments

The delay, or lag, between changes in PM exposures and changes in mortality rates is not precisely

known.  The current scientific literature on adverse health effects, such as those associated with PM2.5

(e.g., smoking‐related disease) and the difference in the estimated effect of chronic exposure studies

versus daily mortality studies, suggests that it is likely that not all cases of avoided premature mortality

associated with a given incremental reduction in PM exposure would occur in the same year as the

exposure reduction.

EPA analyses (U.S. EPA, 2006, p. 5‐21) have used a 20‐year lag structure, with 30 percent of premature

deaths occurring in the first year, 50 percent occurring evenly over years 2 to 5 after the reduction in

                                                            

13 Additional details on the inflation adjustment can be found in the BenMAP User Manual (Abt Associates Inc.,

2010)
14

PM2.5, and 20 percent occurring evenly over years 6 to 20 after the reduction in PM2.5.  It should be

noted, however, that the selection of a 20‐year lag structure is not directly supported by any PM‐specific

literature.  Rather, it is intended to be a reasonable estimate of the appropriate time distribution of

avoided cases of PM‐related mortality.  The distribution of deaths over the latency period is intended to

reflect the contribution of short‐term exposures in the first year, cardiopulmonary deaths in the 2‐ to 5‐

year period, and long‐term lung disease and lung cancer in the 6‐ to 20‐year period.  This is a

conservative approach, as recent work by Schwartz et al. (2008) suggests that most deaths occur within

the first two years of exposure.

Accounting for the lag is important because people are generally willing to pay more for something now

than for the same thing later.  They would, for example, be willing to pay more for a reduction in the risk

of premature death in the same year as exposure is reduced than for that same risk reduction to be

received the following year.  This time preference for receiving benefits now, rather than later, is

expressed by discounting benefits received later.  The exact discount rate that is appropriate (i.e., that

represents people’s time preference) is a topic of much debate.  EPA has typically presented results

based on a discount rate of three percent and seven percent; a similar procedure is followed here.

Quantifying the Value of Avoiding Adverse Health Impacts

Table 8 presents the mean estimate of the unit values used in the analysis.  The approach used is the

same as that used in recent EPA analyses, and details on the values can be found in the BenMAP User

Manual (Abt Associates Inc., 2010) and in recent EPA regulatory impact assessments (e.g., EPA2010b).
15

Table 8.		Mean Value for Economic Valuation of Health Endpoints (based on 2008 income and 2008$)

Health Endpoint Age

Range

No Discount

Used ($)

3% Discount

Rate ($)

7% Discount

Rate ($)

Mortality* 0 – 99 ‐‐ $7,790,000 $7,010,000

Acute Myocardial Infarction, Nonfatal** 0 – 24 ‐‐ $92,000 $91,100

Acute Myocardial Infarction, Nonfatal** 25 – 44 ‐‐ $103,000 $101,000

Acute Myocardial Infarction, Nonfatal** 45 – 54 ‐‐ $109,000 $106,000

Acute Myocardial Infarction, Nonfatal** 55 – 64 ‐‐ $190,000 $179,000

Acute Myocardial Infarction, Nonfatal** 65 – 99 ‐‐ $92,000 $91,100

HA, All Cardiovascular (less AMI) 65 – 99 $29,500 ‐‐  ‐‐

HA, All Cardiovascular (less AMI) 18 – 64 $31,700 ‐‐  ‐‐

HA, All Respiratory 65 – 99 $25,600 ‐‐  ‐‐

HA, Chronic Lung Disease 18 – 64 $14,600 ‐‐  ‐‐

HA, Asthma*** 0 – 64 $10,800 ‐‐  ‐‐

Asthma ER Visits  0 – 99 $399 ‐‐  ‐‐

Acute Bronchitis 8 – 12 $458 ‐‐  ‐‐

Lower Resp. Symptoms 7 – 14 $20 ‐‐  ‐‐

Upper Resp. Symptoms 9 – 11 $32 ‐‐  ‐‐

Asthma Exacerbation 6 – 18 $55 ‐‐  ‐‐

Work Loss Days** *** 18 – 64 $146 ‐‐  ‐‐

Minor Restricted Activity Days 18 – 64 $69 ‐‐  ‐‐

Note: Numbers rounded to three significant digits.  HA = hospital admissions.  * Mortality value after adjustment

for 20‐year lag. **The age‐specific acute myocardial infarction unit values are based on an average of two

estimates: one based on Russell (1998) and one based on Wittels (1990).  *** Asthma hospital admissions

valuation based on data for ages 0‐64 and applied to the age group 0‐17.    Asthma ER visits based on the

average of two studies by Smith et al. (1997) and Stanford et al. (1999).  ** *** BenMAP uses county‐specific

median daily wage; a national average of the county‐level results is presented here.   

   
16

4.		Results	

BenMAP calculates the health benefits of alternative standards for grid cell in the CMAQ 12 kilometer

modeling domain.  These results were then aggregated to the county and national level.  National‐level

results are presented here.   

Table 9 presents the estimated adverse health impacts avoided nationwide each year by moving from

typical PM2.5 air quality levels in the period 2007‐2009 and attaining a variety of standards.  The results

for an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3 are in the column denoted A15D35,

and the results for other combinations of annual and daily standards are presented in a similar way in

Table 9.  Table 10 presents the dollar value of the avoided health impacts for each of the scenarios

presented in Table 9.  

The mortality estimates in Table 9 and Table 10 assume that no deaths occur below the lowest

measured level (LML).  This is a relatively conservative assumption.  For the Laden et al. (2006) estimate,

with an LML of 10 μg/m3

, assuming no effect below the LML reduces the estimated mortality by almost

30 percent for some standards (see Table 11).  For Krewski et al. (2009), with an LML of 5.8 μg/m3

, the

LML has no effect, while for Pope et al. (2002), with a LML of 7.5 μg/m3

, the effect is very small.

To quantify some of the uncertainty in the estimated results, a 90 percent confidence interval (5th to 95th

percentiles) is presented along with the mean estimate.  The quantified uncertainty is based on the

estimated uncertainty in the underlying epidemiological studies used in the effect estimates and in the

economic literature used to value the estimated health impacts.  It should be noted that not all sources

of uncertainty are quantified.  The estimated population exposure uncertainty is unquantified; similarly,

the uncertainty associated with the proportional rollback approach used in this analysis is unquantified.  

In its quantitative assessment of alternative standards, EPA (2010a, p. 4‐37) noted that the rollback

choice can have a “notable impact” on the results.  To provide greater coverage for the variability

associated with rollbacks, EPA used three rollback approaches, “proportional,” “locally focused,” and

“hybrid,” with the “proportional” approach tending to produce the biggest reduction in city‐wide PM2.5

levels, and the “locally focused” approach the smallest.14  The rollback approach in the present analysis,

though also termed a “proportional” rollback, is generally much more conservative than the

“proportional” approach used by EPA, as well as the “hybrid” and “locally focused” approaches, and

tends to generate relatively small reductions in PM2.5, as seen in Table 13.  The approach here closely

resembles EPA's locally focused approach, except for the spatial averaging.  EPA takes a straight average

of the PM changes and applies that result to the entire urban area, while the approach in this report

uses inverse distance weighting of the PM reductions.  As a result, the estimated health benefits in the

present analysis will tend to be more conservatively estimated.

                                                            

14 This is noted by EPA (U.S. EPA, 2010a, p. 3‐17): “In considering the three rollback methods collectively, the

proportional and locally focused methods represent approaches more likely to capture “bounding” behavior

related to the spatial pattern of future reductions in ambient PM2.5 levels. By contrast, the hybrid approach can be

interpreted as reflecting a more plausible or representative rollback strategy in principle...”
17

To help characterize the uncertainty of some of the assumptions made in this analysis, alternative sets

of results are presented.  Table 10 presents mortality and nonfatal heart attack valuations with three

percent and seven percent discount rates, the effect of which is relatively minor.  Table 11 presents a

range of mortality health impact functions based on Krewski et al. (2009), Laden et al. (2006) and Pope

et al. (2002), and it presents the effect of the LML on the estimated mortality estimates.  As noted

above, the LML has a strong effect on the Laden et al. result.  Finally, Table 12 presents the pooled

estimate of nonfatal heart attacks used in the primary set of health impact results (Table 9) as well as an

estimate based on the study by Peters et al. (2001), which has been used by EPA in recent regulatory

impact analyses (e.g., EPA2010b) to estimate nonfatal heart attacks.  The Peters et al. estimate is much

larger than the pooled estimate; however, the pooled estimate is based on more recent studies from a

greater number of areas, so it is used as the primary estimate.  
18

Table 9. Adverse Health Impacts Avoided by Meeting Alternative PM2.5 Standards – Mean Number of Cases and 90% Co

Effect A15D35 A13D35 A13D30 A13D25 A12D35 A12D30 A12D25 A11D35 Mortality,Krewski, LML

2,540 3,700 6,410 16,700 6,380 7,980 16,800 11,200 

1,850 –

3,220

2,700 –

4,700 4,680 – 8,130 12200 ‐ 21200

4,650 –

8,100 5,820 – 10,100 12,300 – 21,300 8,200 – 14,300 

Mortality,Laden, LML

5,240 8,190 13,500 32,700 15,000 17,500 33,000 27,300 

2,850 –

7,570

4,450 –

11,900

7,330 –

19,600

17700 ‐ 

47300

8,140 –

21,800

9,490 –

25,400

17,900 –

47,800

14,800 –

39,600

Mortality,Infants

7 10 16 36 18 21 37 32 

3 ‐ 10 5 ‐ 15 8 ‐ 24 18 ‐ 54 9 ‐ 27 10 ‐ 31 18 ‐ 55 16 ‐ 48 

Heart Attack,Nonfatal

300 460 803 2,180 828 1,020 2,200 1,490 

60 ‐ 663 92 – 1,020 163 – 1,780 448 ‐ 4820 166 – 1,830 206 – 2,270 451 – 4,850 299 – 3,300 

HospitalAdmis.,Cardiovascular

708 1,120 1,960 5,420 2,050 2,510 5,460 3,730 

369 – 1,200 582 – 1,900 1,020 – 3,330 2820 ‐ 9210

1,070 –

3,470 1,310 – 4,260 2,840 – 9,280 1,940 – 6,320 

HospitalAdmis.,Respiratory

761 1,220 2,120 5,890 2,250 2,750 5,930 4,110 

455 – 1,070 726 – 1,720 1,260 – 2,990 3500 ‐ 8280

1,340 –

3,170 1,640 – 3,870 3,530 – 8,350 2,450 – 5,800 

EmergencyRoom Visits,Asthma

1,530 2,310 3,960 10,400 3,980 4,930 10,500 7,040 

48 ‐ 2980 72 – 4,500 124 – 7,730 324 ‐ 20200 124 – 7,780 154 – 9,630 327 – 20,400 219 – 13,700 

UpperRespiratorySymptoms

51,800 70,000 118,000 279,000 112,000 141,000 281,000 187,000 

16,300 –

87,200

22,000 –

118,000

37,100 –

198,000

87900 ‐ 

470000

35,100 –

188,000

44,400 –

237,000

88,600 –

473,000

58,900 –

315,000

LowerRespiratorySymptoms

66,500 90,300 152,000 358,000 145,000 182,000 361,000 242,000 

32,500 –

99,500

44,000 –

135,000

74,000 –

227,000

175000 ‐ 

536000

70,300 –

217,000

88,600 –

272,000

176,000 –

540,000

118,000 –

363,000

AsthmaExacerbation

244,000 329,000 554,000 1,310,000 525,000 663,000 1,320,000 880,000 

13,200 –

1,280,000

17,800 –

1,740,000

30,000 –

2,920,000

71100 ‐ 

6900000

28,400 –

2,780,000

35,900 –

3,490,000

71,600 –

6,950,000

47,600 –

4,640,000

Work Loss Days

431,000 596,000 1,020,000 2,490,000 975,000 1,230,000 2,510,000 1,660,000 

376,000 –

486,000

519,000 –

672,000

888,000 –

1,150,000

2170000 ‐ 

2810000

850,000 –

1,100,000

1,070,000 –

1,390,000

2,190,000 –

2,830,000

1,450,000 –

1,870,000

MinorRestrictedActivity Days

2,520,000 3,490,000 5,970,000 14,700,000 5,730,000 7,240,000 14,800,000 9,770,000 

2,130,000 –

2,900,000

2,950,000 –

4,030000

5,050,000 –

6,890,000

12400000 ‐ 

16900000

4,840,000 –

6,610,000

6,120,000 –

8,350,000

12,500,000 –

17,000,000

8,260,000 –

11,300,000
19

Table 10. Value of Adverse Health Impacts Avoided by Meeting Alternative PM2.5 Standards – Mean Value and 90% Confi

2008 $)

Effect A15D35 A13D35 A13D30 A13D25 A12D35 A12D30 A12D25 A11D35 Mortality,Krewski, LML, 3%DR

$19,800 $28,800 $49,900 $130,000 $49,700 $62,100 $131,000 $87,500 

($2,970 ‐ 

$46,400)

($4,330 ‐ 

$67,700)

($7,490 ‐ 

$117,000)

($19,500 ‐ 

$306,000)

($7,460 ‐ 

$117,000)

($9,330 ‐ 

$146,000)

($19,700 ‐ 

$308,000)

($13,100 ‐ 

$206,000)

Mortality,Krewski, LML, 7%DR

$17,800 $26,000 $44,900 $117,000 $44,800 $56,000 $118,000 $78,800 

($2,670 ‐ 

$41,800)

($3,900 ‐ 

$61,000)

($6,750 ‐ 

$106,000)

($17,600 ‐ 

$275,000)

($6,720 ‐ 

$105,000)

($8,400 ‐ 

$131,000)

($17,700 ‐ 

$277,000)

($11,800 ‐ 

$185,000)

Mortality, Laden,LML, 3% DR

$40,800 $63,800 $105,000 $254,000 $117,000 $136,000 $257,000 $212,000 

($5,740 ‐ 

$100,000)

($8,960 ‐ 

$157,000)

($14,800 ‐ 

$258,000)

($35,700 ‐ 

$625,000)

($16,400 ‐ 

$287,000)

($19,100 ‐ 

$335,000)

($36,100 ‐ 

$630,000)

($29,800 ‐ 

$521,000)

Mortality, Laden,LML, 7% DR

$36,700 $57,500 $94,700 $229,000 $105,000 $123,000 $231,000 $191,000 

($5,170 ‐ 

$90,100)

($8,070 ‐ 

$141,000)

($13,300 ‐ 

$233,000)

($32,200 ‐ 

$563,000)

($14,800 ‐ 

$258,000)

($17,200 ‐ 

$301,000)

($32,500 ‐ 

$568,000)

($26,800 ‐ 

$469,000)

Mortality, Infants $58 $87 $135 $309 $153 $176 $314 $272 

($8 ‐ $144) ($12 ‐ $217) ($19 ‐ $336) ($42 ‐ $769) ($21 ‐ $380) ($24 ‐ $438) ($43 ‐ $780) ($37 ‐ $677) 

Heart Attack,Nonfatal, 3% DR

$34 $53 $93 $257 $97 $120 $259 $176 

($1 ‐ $98) ($1 ‐ $152) ($2 ‐ $265) ($5 ‐ $725) ($2 ‐ $275) ($2 ‐ $340) ($5 ‐ $730) ($3 ‐ $498) 

Heart Attack,Nonfatal, 7% DR

$33 $52 $91 $249 $94 $116 $251 $171 

($1 ‐ $97) ($1 ‐ $149) ($2 ‐ $261) ($4 ‐ $713) ($2 ‐ $271) ($2 ‐ $334) ($4 ‐ $718) ($3 ‐ $490) 

Hospital Admis.,Cardiovascular

$22 $34 $60 $166 $63 $77 $167 $114 

($11 ‐ $37) ($18 ‐ $58) ($31 ‐ $101) ($87 ‐ $280) ($33 ‐ $105) ($40 ‐ $130) ($87 ‐ $282) ($60 ‐ $192) 

Hospital Admis.,Respiratory

$17 $27 $47 $131 $50 $61 $132 $92 

($11 ‐ $24) ($17 ‐ $38) ($29 ‐ $66) ($81 ‐ $182) ($31 ‐ $70) ($38 ‐ $85) ($81 ‐ $183) ($56 ‐ $127) 

Emergency RoomVisits, Respiratory

$0.6 $0.9 $1.6 $4.1 $1.6 $2.0 $4.2 $2.8 

($0 ‐ $1.2) ($0 ‐ $1.8) ($0 ‐ $3) ($0.1 ‐ $7.9) ($0 ‐ $3.1) ($0.1 ‐ $3.8) ($0.1 ‐ $8) ($0.1 ‐ $5.4) 

UpperRespiratorySymptoms

$1.6 $2.2 $3.7 $8.8 $3.5 $4.5 $8.9 $5.9 

($0.5 ‐ $3.6) ($0.6 ‐ $4.9) ($1.1 ‐ $8.2)

($2.5 ‐ 

$19.5) ($1 ‐ $7.8) ($1.3 ‐ $9.8)

($2.5 ‐ 

$19.6) ($1.7 ‐ $13)
20

Effect A15D35 A13D35 A13D30 A13D25 A12D35 A12D30 A12D25 A11D35 LowerRespiratorySymptoms

$1.3 $1.8 $3.0 $7.2 $2.9 $3.6 $7.2 $4.8 

($0.5 ‐ $2.5) ($0.7 ‐ $3.3) ($1.2 ‐ $5.6)

($2.8 ‐ 

$13.2) ($1.1 ‐ $5.3) ($1.4 ‐ $6.7)

($2.9 ‐ 

$13.3) ($1.9 ‐ $8.9) 

AsthmaExacerbation

$13 $18 $31 $72 $29 $37 $73 $49 

($1 ‐ $71) ($1 ‐ $97) ($2 ‐ $162) ($4 ‐ $384) ($2 ‐ $154) ($2 ‐ $194) ($4 ‐ $387) ($3 ‐ $258) 

Work Loss Days

$69 $96 $163 $400 $156 $198 $403 $266 

($60 ‐ $78) ($83 ‐ $108)

($143 ‐ 

$184)

($349 ‐ 

$451)

($136 ‐ 

$176)

($172 ‐ 

$223)

($351 ‐ 

$454)

($232 ‐ 

$301)

Minor RestrictedActivity Days

$164 $227 $389 $954 $373 $471 $961 $636 

($96 ‐ $236)

($133 ‐ 

$327)

($227 ‐ 

$560)

($558 ‐ 

$1,370)

($218 ‐ 

$537)

($275 ‐ 

$678)

($562 ‐ 

$1,380)

($372 ‐ 

$916)

Total, 3% DR,Krewski

$20,200 $29,400 $50,800 $132,000 $50,600 $63,300 $133,000 $89,100 

($3,160 ‐ 

$47,100)

($4,590 ‐ 

$68,700)

($7,950 ‐ 

$119,000)

($20,700 ‐ 

$310,000)

($7,910 ‐ 

$118,000)

($9880 ‐ 

$148,000)

($20,800 ‐ 

$312,000)

($13,900 ‐ 

$209,000)

Total, 7% DR,Krewski

$18,200 $26,500 $45,900 $119,000 $45,700 $57,100 $120,000 $80,400 

($2,860 ‐ 

$42,500)

($4,160 ‐ 

$62,000)

($7,200 ‐ 

$107,000)

($18,700 ‐ 

$279,000)

($7,160 ‐ 

$107,000)

($8,960 ‐ 

$134,000)

($18,900 ‐ 

$281,000)

($12,600 ‐ 

$188,000)

Total, 3% DR,Laden

$41,200 $64,400 $106,000 $257,000 $118,000 $137,000 $259,000 $214,000 

($5,920 ‐ 

$101,000)

($9,220 ‐ 

$158,000)

($15,200 ‐ 

$260,000)

($36,900 ‐ 

$629,000)

($16,800 ‐ 

$289,000)

($19,700 ‐ 

$337,000)

($37,200 ‐ 

$635,000)

($30,600 ‐ 

$524,000)

Total, 7% DR,Laden

$37,100 $58,000 $95,700 $232,000 $106,000 $124,000 $234,000 $193,000 

($5,350 ‐ 

$90,800)

($8,330 ‐ 

$142,000)

($13,800 ‐ 

$234,000)

($33,300 ‐ 

$567,000)

($15,200 ‐ 

$260,000)

($17,800 ‐ 

$303,000)

($33,600 ‐ 

$572,000)

($27,600 ‐ 

$472,000)

Results rounded to three digits. Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines for Preparing E

Circular A‐4.
21

Table 11. Alternative Mortality Estimates, Mean Number of Cases and 90% Confidence IntervalDescription A15 A15D35 A13 A13D35 A13D30 A13D2Pope, All‐Cause,LML 7.5

582 2,530 2,600 3,690 6,330 16,20

(228 ‐ 932) (995 – 4,050) (1,020 – 4,180) (1,450 – 5,910) (2,490 – 10,100) (6,380 – 26,000

Pope, All‐Cause

582 2,540 2,610 3,700 6,410 16,70

(228 ‐ 932) (999 – 4,060) (1,020 – 4,180) (1,450 – 5,930) (2,520 – 10,300) (6,570 – 26,700

Laden, All‐Cause,LML 10

1,450 5,240 6,590 8,190 13,500 32,70

(787 – 2,110) (2,850 – 7,570) (3,570 – 9,580) (4,450 – 11,900) (7,330 – 19,600) (17,700 – 47,300

Laden, All‐Cause 1,490 6,450 6,650 9,430 16,300 42,40

(806 – 2,160) (3,510 – 9,330) (3,600 – 9,670) (5,110 – 13,700) (8,840 – 23,600) (23,000 – 61,400

Krewski, All‐Cause,LML 5.8

582 2,540 2,610 3,700 6,410 16,70

(424 ‐ 739) (1,850 – 3,220) (1,900 – 3,310) (2,700 – 4,700) (4,680 – 8,130) (12,200 – 21,200

Krewski, All‐Cause

582 2,540 2,610 3,700 6,410 16,70

(424 ‐ 739) (1,850 – 3,220) (1,900 – 3,310) (2,700 – 4,700) (4,680 – 8,130) (12,200 – 21,200

Krewski, Ischemic,LML 5.8

480 1,940 2,030 2,780 4,800 12,20

(406 ‐ 553) (1,640 – 2,230) (1,710 – 2,340) (2,350 – 3,200) (4,060 – 5,530) (10,300 – 14,000

Krewski, Ischemic

480 1,940 2,030 2,780 4,800 12,20

(406 ‐ 553) (1,640 – 2,230) (1,710 – 2,340) (2,350 – 3,200) (4,060 – 5,530) (10,300 – 14,000

Krewski, Cardio.,LML 5.8

589 2,510 2,560 3,610 6,230 16,00

(483 ‐ 694) (2,060 – 2,960) (2,100 – 3,030) (2,960 – 4,260) (5,110 – 7,340) (13,100 – 18,800

Krewski, Cardio.

589 2,510 2,560 3,610 6,230 16,00

(483 ‐ 694) (2,060 – 2,960) (2,100 – 3,030) (2,960 – 4,260) (5,110 – 7,340) (13,100 – 18,900

Krewski, LungCancer, LML 5.8

74 330 361 507 878 2,39

(40 ‐ 109) (175 ‐ 483) (190 ‐ 530) (267 ‐ 743) (464 – 1,290) (1,260 – 3,490

Krewski, LungCancer

74 330 361 507 878 2,39

(40 ‐ 109) (175 ‐ 483) (190 ‐ 530) (267 ‐ 743) (464 – 1,290) (1,260 – 3,490
22

Description A12 A12D35 A12D30 A12D25 A11 A11D35 APope, All‐Cause,LML 7.5

5,590 6,370 7,900 16,400 10,600 11,200 1

(2,190 – 8,970) (2,500 – 10,200) (3,100 – 12,700) (6,430 – 26,200) (4,180 – 17,100) (4,410 – 18,000) (4,720 – 

Pope, All‐Cause 5,590 6,380 7,980 16,800 10,600 11,200 1

(2,190 – 8,970) (2,510 – 10,200) (3,130 – 12,800) (6,620 – 26,900) (4,180 – 17,100) (4,410 – 18,000) (4,750 – 

Laden, All‐Cause,LML 10

14,200 15,000 17,500 33,000 26,900 27,300 2

(7,680 – 20,600) (8,140 – 21,800) (9,490 – 25,400)

(17,900 –

47,800)

(14,600 –

39,000)

(14,800 –

39,600)

(1

Laden, All‐Cause

14,300 16,300 20,300 42,700 27,100 28,600 3

(7,720 – 20,700) (8,810 – 23,600)

(11,000 –

29,400)

(23,200 –

61,800)

(14,700 –

39,300)

(15,500 –

41,500)

(1

Krewski, All‐Cause, LML 5.8

5,590 6,380 7,980 16,800 10,600 11,200 1

(4,080 – 7,100) (4,650 – 8,100) (5,820 – 10,100)

(12,300 –

21,300) (7,770 – 13,500) (8,200 – 14,300) (8,830 – 

Krewski, All‐Cause

5,590 6,380 7,980 16,800 10,600 11,200 1

(4,080 – 7,100) (4,650 – 8,100) (5,820 – 10,100)

(12,300 –

21,400) (7,770 – 13,500) (8,200 – 14,300) (8,830 – 

Krewski,Ischemic, LML5.8

4,170 4,690 5,840 12,200 7,750 8,120 

(3,530 – 4,810) (3,960 – 5,400) (4,940 – 6,730)

(10,400 –

14,100) (6,550 – 8,930) (6,870 – 9,360) (7,340 – 

Krewski, Ischemic

4,170 4,690 5,840 12,300 7,750 8,120 

(3,530 – 4,810) (3,960 – 5,400) (4,940 – 6,730)

(10,400 –

14,100) (6,550 ‐ 8,930) (6,870 – 9,360) (7,340 – 

Krewski, Cardio.,LML 5.8

5,410 6,160 7,690 16,100 10,200 10,800 1

(4,430 – 6,380) (5,050 – 7,260) (6,300 – 9,060)

(13,200 –

19,000) (8,360 – 12,000) (8,820 – 12,700) (9,480 – 

Krewski, Cardio.

5,410 6,160 7,690 16,100 10,200 10,800 1

(4,430 – 6,380) (5,050 – 7,260) (6,300 – 9,060)

(13,200 –

19,000) (8,360 – 12,000) (8,820 – 12,700) (9,480 – 

Krewski, LungCancer, LML 5.8

804 911 1,130 2,410 1,560 1,640 

(424 – 1,180) (480 – 1,340) (595 – 1,650) (1,270 – 3,520) (825 – 2,290) (867 – 2,410) (929 – 

Krewski, LungCancer

804 911 1,130 2,410 1,560 1,640 

(424 – 1,180) (480 – 1,340) (595 – 1,650) (1,270 – 3,520) (825 – 2,290) (867 – 2,410) (929 – 
23

Table 12. Alternative Nonfatal Heart Attack Estimate, Mean Number of Cases and 90% Confidence IntervalAuthor A15 A15D35 A13 A13D35 A13D30 A13D25 A12 A12D35 A12D30 A12D25 A11 APooledEstimate

69 300 333 460 803 2,180 738 828 1,020 2,200 1,430 1

(14 ‐ 

153)

(60 ‐ 

663)

(67 ‐ 

738)

(92 ‐ 

1,020)

(163 ‐ 

1,780)

(448 –

4,820)

(148 –

1,630)

(166 –

1,830)

(206 –

2,270)

(451 –

4,850)

(286 –

3,160)

Peters etal. (2001)

543 2,330 2,630 3,600 6,280 17,000 5,820 6,510 8,020 17,200 11,200 11

(202 ‐ 

874)

(876 ‐ 

3,720)

(977 ‐ 

4,240)

(1,350 ‐ 

5,790)

(2,350 –

10,100)

(6,390 –

27,200)

(2,160 –

9,370)

(2,420 –

10,500)

(3,000 –

12,900)

(64,30 –

27,400)

(4,170 –

18,000)

(4

1

Results rounded to three digits.
24

Table 13.		PM2.5 Levels by City, Comparison of Present Analysis & EPA (2010a)

Source of Rollback

Results

Baseline

(ug/m3)

Baseline Minus PM2.5 Level in Scenario (ug/m3)

City A15D35 A13D35 A12D35 A13D30 A12D25

Atlanta Present Analysis 13.3 0.0 0.4 1.3 0.4 1.7

EPA ‐ Proportional 15.3 1.1 3.0 3.9 3.0 4.1

Baltimore Present Analysis 12.3 0.0 0.1 0.5 0.4 2.0

   EPA ‐ Proportional

13.9

0.8 2.3 3.2 2.6 4.4

   EPA ‐ Hybrid 0.9 2.1 3.0 2.7 4.5

   EPA ‐ Locally Focused 0.3          3.9

Birmingham Present Analysis 13.1 0.0 0.5 1.1 0.6 2.1

EPA ‐ Proportional

15.7

3.0 4.7 5.5 4.7 6.3

EPA ‐ Hybrid 1.5 3.4 4.3 3.4 5.0

EPA ‐ Locally Focused 4.3

Dallas Present Analysis 10.7 0.0 0.0 0.0 0.0 0.2

   EPA ‐ Proportional 11.4 0.0 0.0 0.7 0.0 0.7

Detroit Present Analysis 12.1 0.0 0.1 0.3 0.6 2.3

EPA ‐ Proportional

13.9

2.5 3.3 4.1 4.0 5.6

EPA ‐ Hybrid 2.2 2.4 3.3 3.8 5.4

EPA ‐ Locally Focused 1.4 2.9 4.7

Fresno Present Analysis 16.4 5.8 5.8 5.8 7.3 8.7

   EPA ‐ Proportional 17.47.5 7.5 7.5 8.8 10.1

   EPA ‐ Locally Focused 7.1 7.1 7.1 8.5 9.9

Houston Present Analysis 12.6 0.0 0.4 0.9 0.4 1.0

EPA ‐ Proportional 13.2 0.7 2.3 3.1 2.3 3.1

Los Angeles Present Analysis 14.0 1.3 1.5 2.2 2.8 4.4

   EPA ‐ Proportional

14.6

5.1 5.1 5.6 6.4 7.6

   EPA ‐ Hybrid 4.1 4.3 5.1 5.5 6.9

   EPA ‐ Locally Focused 2.5 2.5    4.0 5.5

New York Present Analysis 12.1 0.1 0.3 0.6 0.6 2.3

EPA ‐ Proportional

13.8

2.2 2.5 3.4 3.8 5.4

EPA ‐ Hybrid 2.0 2.5 3.4 3.6 5.3

EPA ‐ Locally Focused 0.5 2.2 4.0

Philadelphia Present Analysis 12.4 0.0 0.1 0.6 0.4 2.1

   EPA ‐ Proportional 13.41.1 1.8 2.7 2.7 4.4

   EPA ‐ Locally Focused 0.4       2.1 3.9

Phoenix Present Analysis 9.5 0.1 0.2 0.2 0.2 0.6

EPA ‐ Proportional 9.90.0 0.0 0.5 0.6 2.1

EPA ‐ Locally Focused 0.2 0.9

Pittsburgh Present Analysis 13.4 0.6 0.9 1.6 1.5 3.3

   EPA ‐ Proportional 14.93.3 3.7 4.4 4.9 6.5

   EPA ‐ Locally Focused 1.7 3.1 3.7 3.5 5.3
25

Source of Rollback

Results

Baseline

(ug/m3)

Baseline Minus PM2.5 Level in Scenario (ug/m3)

City A15D35 A13D35 A12D35 A13D30 A12D25

Salt Lake City Present Analysis 10.4 1.9 1.9 1.9 2.8 3.8

EPA ‐ Proportional 11.43.9 3.9 3.9 4.8 5.8

EPA ‐ Locally Focused 1.7 1.7 1.7 2.6 3.7

St. Louis Present Analysis 12.8 0.0 0.1 0.8 0.2 1.5

   EPA ‐ Proportional

14.3

1.4 3.0 3.9 3.2 5.0

   EPA ‐ Hybrid 0.8 2.6 3.5 2.6 4.4

   EPA ‐ Locally Focused 0.2       1.9 3.9

Tacoma Present Analysis 9.2 1.3 1.3 1.3 2.0 2.7

EPA ‐ Proportional 9.71.7 1.7 1.7 2.7 3.7

EPA ‐ Locally Focused 1.7 1.7 1.7 2.7 3.7

Note: The EPA estimates are from Table F‐50 of EPA (2010a), which presents results based on 2007 monitoring data.  The

results for the present analysis are a population‐weighted (ages 30+) estimate of PM2.5 based on data for 2007‐2009.
26

Appendix A.		Human Health Impact Function

Details

This appendix presents the derivation of the two main health impact functions used in this analysis (log‐

linear and logistic), as well as details on each function used.

A.1		Deriving Health Impact Functions

Presented below is a derivation of the mean coefficient estimates for log‐linear and logistic health

impact functions.

Log‐Linear Derivation

PM

c

c

c

c

y Be

y PM

PM PM PM

PM PM levels under control conditions

PM PM levels under baseline conditions

y y y

y Incidence under control conditions

y Incidence under baseline conditions



 



 

  





  





ln( )

0

0

0

0





















   

  





0

0

0

0

1

0

PM

PM

PM

PM PM

PM

c

PM

Be

Be

y Be

y Be Be

y Be

y Be

c

c

c







 




27

 

 

 





 



    

   

   





 



    



 

 

 

PM

PM

PM PM

PM PM PM

e

y y

y y e

y Be e

y Be e c





 

 

1 1

1

1

1

0

0

0

0 0

Logistic Derivation

coefficient of the PM variable

vector of coefficients

X vector of explanatory variables

PM PM PM

PM PM levels under control conditions

PM PM levels under baseline conditions

y y y

y Incidence under control conditions

y Incidence under baseline conditions

c

c

c

c



 



  





  







0

0

0

0



 

 

PM

PM

PM

X

X

X

X

X

X

X

X

X

X X

X

e

e e

e e

e

e odds ratio

e

e

e

e

e odds

e

e

y

y odds

e e

e

y

c c









 

 

 

 

 

 

  



 

  

 























 





 





 





 





 





   

 















 

0 0

1

1

1

1

1

1 1

1

1

1

1

1

1
28

c

PM c

PM

c

c

y

y

e

y

y

e

y

y

y

y

   



























































1 1

1

1

0

0

0

0





 

 

 



PM PM

c

PM PM

c c

PM

c c

e

y

y

e

y

y

y

e

y

y

e

y

y

y y

e

y

y

y y

 

 



 















  



 



  















  

 















   















  

 















   

0

0

0

0

0

0

0

0

0

0

1 1

1

1 1

1

1









PM

PM

c

PM

PM

c

y y e

y e

y

e

y

y

e

y

y

y









  

 

 















 

 















 

0 0

0

0

0

0

0

1

1

1

1

 

 

  















  

   

     

   







0 0

0

0 0

0

0 0

0 0

0

1

1 1

1

1

y e y

y y

y e y

y

y y y

y e y

y

y

PM

c PM

c PM






29

A.2		PM2.5 Human Health Impact Functions		

This analysis uses a range of health impact functions, including those used to estimate premature

mortality, chronic bronchitis, and hospital admissions. These health impact functions are the same ones

used in recent EPA analyses (e.g., U.S. EPA2010b).  Presented below is a table with the health impact

functions used to estimate PM2.5‐related adverse health effects.  Following the table is a brief summary

of each of the studies along with details not in the summary table.

Some additional details should be noted regarding the calculation of heart attacks and cardiovascular

hospital admissions.  Rosamond et al. (1999) report that approximately six percent of male and eight

percent of female hospitalized heart attack patients die within 28 days (either in or outside of the

hospital).  As a result, this analysis assumes that 93 percent of heart attacks are not fatal and that

nonfatal heart attacks result in a hospitalization. In addition, the studies used to estimate hospital

admissions for all cardiovascular causes reported results that include ICD code 410 (heart attack).  Since

this analysis estimates avoided nonfatal heart attacks separately, ICD code 410 was excluded from the

baseline incidence rate used in the calculation of hospital admissions for all cardiovascular causes, so as

to avoid double counting heart attack hospitalizations.   
30

Table 14. Details of PM2.5 Human Health Impact FunctionsEndpoint Name Study Location Age Beta Std Error FunctAdult mortality, All‐Cause Laden et al. (2006) 6 cities 25‐99 0.01484 0.00417 Log‐liAdult mortality, All‐Cause Pope et al. (2002) 51 cities 30‐99 0.00583 0.00216 Log‐liAdult mortality, All‐Cause Krewski et al. (2009) 116 cities 30‐99 0.00583 0.00096 Log‐liAdult mortality, Cardiopulmonary Krewski et al. (2009) 116 cities 30‐99 0.01222 0.00135 Log‐liAdult mortality, Ischemic Heart Disease Krewski et al. (2009) 116 cities 30‐99 0.02151 0.00206 Log‐liAdult mortality, Lung Cancer Krewski et al. (2009) 116 cities 30‐99 0.01310 0.00379 Log‐liInfant mortality, All‐Cause Woodruff et al. (1997) 86 cities 0‐0 0.00392 0.00122 LogistHeart Attack, Nonfatal Peters et al. (2001) Boston, MA 18‐99 0.02412 0.00929 LogistHeart Attack, Nonfatal Pope et al. (2006) Wasatch Front, UT 18‐99 0.00481 0.00199 LogistHeart Attack, Nonfatal Sullivan et al. (2005) King County, WA 18‐99 0.00198 0.00224 LogistHeart Attack, Nonfatal Zanobetti et al. (2009) 26 cities 18‐99 0.00225 0.00059 Log‐liHeart Attack, Nonfatal Zanobetti and Schwartz (2006) Boston, MA 18‐99 0.00530 0.00221 LogistHA, All Cardiovascular (less AMI) Bell et al. (2008) 202 counties 65‐99 0.00080 0.00011 Log‐liHA, All Cardiovascular (less AMI) Peng et al. (2008) 108 counties 65‐99 0.00071 0.00013 Log‐liHA, All Cardiovascular (less AMI) Peng et al. (2009) 119 counties 65‐99 0.00068 0.00021 Log‐liHA, All Cardiovascular (less AMI) Zanobetti et al. (2009) 26 cities 65‐99 0.00189 0.00028 Log‐liHA, All Cardiovascular (less AMI) Moolgavkar (2000b) Los Angeles, CA 18‐64 0.00140 0.00034 Log‐liHA, All Respiratory Zanobetti et al. (2009) 26 cities 65‐99 0.00207 0.00045 Log‐liHA, Chronic Lung Disease Moolgavkar (2000a) Los Angeles, CA 18‐64 0.002200 0.00073 Log‐liHA, Asthma Babin et al. (2007) Washington, DC 0‐17 0.00200 0.00430 Log‐liHA, Asthma Sheppard (2003) Seattle, WA 0‐17 0.003324 0.00105 Log‐liEmergency Room Visits, Asthma Mar et al. (1999; 2010) Tacoma, WA 0‐99 0.005603 0.00210 Log‐liEmergency Room Visits, Asthma Slaughter et al. (2005) Spokane, WA 0‐99 0.002956 0.00271 Log‐liAcute Bronchitis Dockery et al. (1996) 24 communities 8‐12 0.027212 0.017096 LogistLower Respiratory Symptoms Schwartz and Neas

(2000)

6 U.S. cities 7‐14 0.019012 0.006005 Logist

Upper Respiratory Symptoms Pope et al. (1991) Utah Valley 9‐11 0.0036 0.0015 LogistAsthma Exacerbation, Cough Ostro et al. (2001) Los Angeles, CA 6‐18 0.000985 0.000747 LogistAsthma Exacerbation, Cough Mar et al. (2004) Spokane, WA 6‐18 0.01906 0.00983 Logist
31

Endpoint Name Study Location Age Beta Std Error FunctAsthma Exacerbation, Shortness ofBreath

Ostro et al. Los Angeles, CA 6‐18 0.002565 0.001335 Logist

Asthma Exacerbation, Shortness ofBreath

Mar et al. (2004) Spokane, WA 6‐18 0.01222 0.01385 Logist

Asthma Exacerbation, Wheeze Ostro et al. Los Angeles, CA 6‐18 0.00194 0.00080 LogistWork Loss Days (WLD) Ostro (1987) Nationwide 18‐64 0.00460 0.00036 Log‐liMinor Restricted Activity Days (MRAD) Ostro & Rothschild

(1989)

Nationwide 18‐64 0.00741 0.00070 Log‐li
32

Mortality, All Cause (Laden et al., 2006)

Laden, et al. performed an extended mortality follow‐up for eight years in a period of reduced air

pollution concentrations using data from the Harvard Six Cities adult cohort study.  They used annual

city‐specific PM2.5 concentrations measured from1979‐1988, and estimated the air quality data for the

subsequent eight years using publicly available data.  The authors used a Cox proportional hazards

regression controlling for individual risk factors to examine the relationship between long‐term

exposure to PM2.5 and mortality.  Laden, et al. found a significant increase in the overall mean mortality

associated with a 10‐μg/m3 increase in PM2.5 (Laden et al., 2006).    

The coefficient and standard error are estimated from the relative risk (1.16) and 95% confidence

interval (1.07‐1.26) associated with a 10‐μg/m3 increase in PM2.5.      

Functional Form: Log‐linear

Coefficient: 0.01484

Standard Error: 0.00417

Incidence Rate: county‐specific annual all‐cause mortality rate per person ages 30 and older

Population: population of ages 25 and older.

Mortality, All Cause (Pope et al., 2002)		

The Pope et al. (2002) analysis is a longitudinal cohort tracking study that uses the same American

Cancer Society cohort as the original Pope et al. (1995) study, and the Krewski et al. (2000) reanalysis.  

Pope et al. (2002) analyzed survival data for the cohort from 1982 through 1998, 9 years longer than the

original Pope study.  Pope et al. (2002) followed Krewski et al. (2000) and Pope et al. (1995, Table 2) and

reported results for all‐cause deaths, lung cancer (ICD‐9 code: 162), cardiopulmonary deaths (ICD‐9

codes: 401‐440 and 460‐519), and “all other” deaths.15 Like the earlier studies, Pope et al. (2002) found

that mean PM2.5 is significantly related to all‐cause and cardiopulmonary mortality.  In addition, Pope et

al. (2002) found a significant relationship with lung cancer mortality, which was not found in the earlier

studies.  None of the three studies found a significant relationship with “all other” deaths.

The coefficient and standard error for PM2.5 using the average of ’79‐’83 and ’99‐’00 PM data are

estimated from the relative risk (1.06) and 95% confidence interval (1.02‐1.11) associated with a change

in annual mean exposure of 10 μg/m3 Pope et al. (2002, Table 2).

Functional Form: Log‐linear

Coefficient: 0.005827

Standard Error: 0.002157

Incidence Rate: county‐specific annual all‐cause mortality rate per person ages 30 and older

Population: population of ages 30 and older.

                                                            

15  All‐cause mortality includes accidents, suicides, homicides and legal interventions.  The category “all other”

deaths is all‐cause mortality less lung cancer and cardiopulmonary deaths.
33

Mortality	‐‐	All Cause, Cardiopulmonary, Ischemic Heart Disease, and Lung Cancer

(Krewski et al., 2009)

Krewski et al. (2009) analysis is a longitudinal cohort tracking study that uses an updated version of the

same American Cancer Society cohort as the original Pope et al. (1995) study, and the Krewski et al.

(2000) reanalysis.  Krewski et al. (2009) analyzed survival data for the cohort from 1982 through 2000,

11 years longer than the original Pope study.  Krewski et al. (2002) reported significant impacts for all‐

cause deaths, lung cancer (ICD‐9 code: 162), cardiopulmonary deaths (ICD‐9 codes: 401‐440 and 460‐

519), and ischemic heart disease (ICD‐9 codes: 410‐414).  

Krewski et al. (2009) present results for two exposure estimates.  One based on PM2.5 levels in 1979‐

1983 and the other for PM2.5 levels in 1999‐2000.  The effect estimates below for are based on the

1999‐2000 exposure estimate, as the later exposure period has better monitor coverage with

participants from 116 cities versus 58 cities for the earlier exposure period.  In addition, to the extent

that the number of deaths is increasing as the cohort ages, then the later exposure period will be more

representative of the majority of the deaths that are occurring.

All‐Cause Mortality

The coefficient and standard error for PM2.5 (using the’99‐’00 PM data and the Random Effects Cox

Model) are estimated from the relative risk (1.06) and 95% confidence interval (1.04‐1.08) associated

with a change in annual mean exposure of 10 μg/m3 Krewski et al. (2009, Commentary Table 4).

Functional Form: Log‐linear

Coefficient: 0.00583

Standard Error: 0.00096

Incidence Rate: county‐specific annual all‐cause mortality rate per person ages 30 and older

Population: population of ages 30 and older.

Cardiopulmonary Mortality

The coefficient and standard error for PM2.5 (using the’99‐’00 PM data and the Random Effects Cox

Model) are estimated from the relative risk (1.13) and 95% confidence interval (1.10‐1.16) associated

with a change in annual mean exposure of 10 μg/m3 Krewski et al. (2009, Commentary Table 4).

Functional Form: Log‐linear

Coefficient: 0.01222

Standard Error: 0.00135

Incidence Rate: county‐specific annual cardiopulmonary mortality rate per person ages 30 and older

Population: population of ages 30 and older.

Ischemic Heart Disease Mortality

The coefficient and standard error for PM2.5 (using the’99‐’00 PM data and the Random Effects Cox

Model) are estimated from the relative risk (1.24) and 95% confidence interval (1.19‐1.29) associated

with a change in annual mean exposure of 10 μg/m3 Krewski et al. (2009, Commentary Table 4).

Functional Form: Log‐linear
34

Coefficient: 0.02151

Standard Error: 0.00206

Incidence Rate: county‐specific annual ischemic heart disease mortality rate per person ages 30 and

older

Population: population of ages 30 and older.

Lung Cancer Mortality

The coefficient and standard error for PM2.5 (using the’99‐’00 PM data and the Random Effects Cox

Model) are estimated from the relative risk (1.14) and 95% confidence interval (1.06‐1.23) associated

with a change in annual mean exposure of 10 μg/m3 Krewski et al. (2009, Commentary Table 4).

Functional Form: Log‐linear

Coefficient: 0.01310

Standard Error: 0.00379

Incidence Rate: county‐specific annual lung cancer mortality rate per person ages 30 and older

Population: population of ages 30 and older.

Infant Mortality (Woodruff et al., 1997)

Woodruff et al. (1997) examined the relationship between post‐neonatal all‐cause mortality and

exposure to PM2.5 in infants born In a study of four million infants in 86 U.S. metropolitan areas

conducted from1989 to 1991.  Woodruff et al. (1997) found a significant link between PM10 exposure in

the first two months of an infant’s life with the probability of dying between the ages of 28 days and 364

days.  PM10 exposure was significant for all‐cause mortality. PM10 was also significant for respiratory

mortality in average birth‐weight infants, but not low birth‐weight infants.

The coefficient and standard error are based on the odds ratio (1.04) and the 95% confidence interval

(1.02‐1.07) associated with a 10 μg/m3 change in PM10 (Woodruff et al., 1997, Table 3).

Functional Form: Logistic

Coefficient: 0.003922

Standard Error: 0.001221

Incidence Rate: county‐specific annual post‐neonatal16 infant deaths per infant under the age of one  

Population: population of infants under one year old.

Heart Attacks (Acute Myocardial Infarction), Nonfatal (Peters et al., 2001)

Peters et al. (2001) studied the relationship between increased particulate air pollution and onset of

heart attacks in the Boston area from 1995 to 1996.  The authors used air quality data for PM10, PM10‐2.5,

PM2.5, “black carbon”, O3, CO, NO2, and SO2 in a case‐crossover analysis.  For each subject, the case

period was matched to three control periods, each 24 hours apart.  In univariate analyses, the authors

observed a positive association between heart attack occurrence and PM2.5 levels hours before and days

before onset.  The authors estimated multivariate conditional logistic models including two‐hour and

                                                            

16 Post‐neonatal refers to infants that are 28 days to 364 days old.
35

twenty‐four hour pollutant concentrations for each pollutant.  They found significant and independent

associations between heart attack occurrence and both two‐hour and twenty‐four hour PM2.5

concentrations before onset.  Significant associations were observed for PM10 as well.  None of the other

particle measures or gaseous pollutants was significantly associated with acute myocardial infarction for

the two hour or twenty‐four hour period before onset.

The mean age of participants was 62 years old, with 21% of the study population under the age of 50.  In

order to capture the full magnitude of heart attack occurrence potentially associated with air pollution

and because age was not listed as an inclusion criteria for sample selection, this analysis assumes an age

range of 18 and over in the C‐R function.  According to the National Hospital Discharge Survey, there

were no hospitalizations for heart attacks among children <15 years of age in 1999 and only 5.5% of all

hospitalizations occurred in 15‐44 year olds (Popovic, 2001, Table 10).

The coefficient and standard error are calculated from an odds ratio of 1.62 (95% CI 1.13‐2.34) for a 20

μg/m3 increase in twenty‐four hour average PM2.5 (Peters et al., 2001, Table 4, p. 2813).

Functional Form: Logistic

Coefficient: 0.024121

Standard Error: 0.009285

Incidence Rate: region‐specific daily nonfatal heart attack rate per person 18+ = 93% of region‐specific

daily heart attack hospitalization rate (ICD code 410)

Population: population of ages 18 and older.

Heart Attacks (Acute Myocardial Infarction), Nonfatal (Pope et al., 2006)

Pope et al. (2006) evaluated the association between short‐term exposure to PM2.5 and acute ischemic

heart disease events, including acute nonfatal myocardial infarction, all acute coronary events, and

subsequent myocardial infarctions in individuals living in greater Salt Lake City, Utah.  In a case‐

crossover study, these ischemic events were assessed in relation to a 10 ug/m3 increase in PM2.5.  

The coefficient and standard error are calculated from a 4.81% increase in index myocardial infarction

and unstable angina (95% CI 0.98%‐8.79%) for a 10 μg/m3 increase in twenty‐four hour average PM2.5

(Pope et al., 2006, Table 3).

Functional Form: Logistic

Coefficient: 0.00481

Standard Error: 0.00199

Incidence Rate: region‐specific daily nonfatal heart attack rate per person 18+ = 93% of region‐specific

daily heart attack hospitalization rate (ICD code 410)

Population: population of ages 18 and older.17

                                                            

17 The study included people of all ages. To be comparable with the age groups used with Peters et al (2001) and

because the rate of heart attack in the age group 0‐17 is very low, effects were calculated for ages 18 and older.
36

Heart Attacks (Acute Myocardial Infarction), Nonfatal (Sullivan et al., 2005)

Sullivan et al. (2005) studied the relationship between onset time of acute myocardial infarction and the

preceding hourly PM2.5 concentrations in 5,793 confirmed cased of myocardial infarction through King

County, Washington.  In this case‐crossover study from 1988‐1994, air pollution exposure levels

averaged 1 hour, 2 hours, 4 hours, and 24 hours before onset of myocardial infarction were compared

to a set of time‐stratified referent exposures from the same day of the week in the month of the case

event.  The authors reported a relatively weak association between myocardial infarction onset and

exposure to PM2.5.  

The coefficient and standard error are calculated from an odds ratio of 1.02 (95% CI 0.98‐1.07) for a 10

μg/m3 increase in twenty‐four hour average PM2.5 (Sullivan et al., 2005, Table 3).

Functional Form: Logistic

Coefficient: 0.00198

Standard Error: 0.00224

Incidence Rate: region‐specific daily nonfatal heart attack rate per person 18+ = 93% of region‐specific

daily heart attack hospitalization rate (ICD code 410)

Population: population of ages 18 and older. 18

Heart Attacks (Acute Myocardial Infarction), Nonfatal (Zanobetti et al., 2009)

Zanobetti et al. (2009) examined the relationship between daily PM2.5 levels and emergency hospital

admissions for cardiovascular causes, myocardial infarction, congestive heart failure, respiratory disease

and diabetes among people ages 65 and older in 26 U.S. communities from 2000‐2003.  The authors

used meta‐regression to examine how this association was modified by season‐ and community‐specific

PM2.5 composition while controlling for seasonal temperature as a substitute for ventilation.  The

authors found that PM2.5 mass higher in a variety of elements, including arsenic and organic carbon,

significantly increased its effects on hospital admissions.   

The coefficient and standard error are calculated from a 2.25% increase in hospital admissions for

myocardial infarction (95% CI 1.10%‐3.42%) for a 10 μg/m3 increase in two‐day averaged PM2.5

(Zanobetti et al., 2009, Table 3).

Functional Form: Log‐linear

Coefficient: 0.00225

Standard Error: 0.00059

Incidence Rate: region‐specific daily nonfatal heart attack rate per person 65+ = 93% of region‐specific

daily heart attack hospitalization rate (ICD code 410)

Population: population of ages 65 and older.

                                                            

18 The study included people of all ages. To be comparable with the age groups used with Peters et al (2001) and

because the rate of heart attack in the age group 0‐17 is very low, effects were calculated for ages 18 and older.
37

Heart Attacks (Acute Myocardial Infarction), Nonfatal (Zanobetti and Schwartz,

2006)

Zanobetti and Schwartz (2006) analyzed hospital admissions through emergency department for

myocardial infarction (ICD‐9 code 410) and pneumonia (ICD‐9 codes 480‐487) for associations with fine

particulate air pollution, ozone, black carbon, nitrogen dioxide (NO2), PM not from traffic, and CO in the

greater Boston area from 1995‐1999.  The authors used a case‐crossover analysis with control days

matched on temperature.  Significant associations were detected for NO2, PM2.5, and black carbon in

emergency myocardial infarction hospitalizations.  Significant associations were also identified for PM2.5

in pneumonia hospitalizations.   

The coefficient and standard error are calculated from a 8.65% increase in hospital admissions for

myocardial infarction (95% CI 1.22%‐15.38%) for a 16.32 μg/m3 increase in daily PM2.5 (Zanobetti and

Schwartz, 2006, Table 4).

Functional Form: Log‐linear

Coefficient: 0.00530

Standard Error: 0.00221

Incidence Rate: region‐specific daily nonfatal heart attack rate per person 65+ = 93% of region‐specific

daily heart attack hospitalization rate (ICD code 410)

Population: population of ages 65 and older.

Pooling Estimates of Heart Attacks (Acute Myocardial Infarction), Nonfatal

As the valuation of a nonfatal heart varies by age, he heart attack results were calculated for five age

groups: 18‐24, 25‐4, 45‐54, 55‐64, and 65+.  For the first four age groups, the studies by Pope et al.

(2006) and Sullivan et al. (2005) were combined with a random/fixed effects approach for each age

group.  For the 65 and older age group, the Zanobetti et al. (2009) and Zanobetti and Schwartz (2006)

studies, which are based on just the elderly population, were included in the random/fixed effects

pooling.   Once each of the age groups were pooled separately, they were added together to get an

overall estimate of nonfatal heart attacks.
38

Hospital Admissions for All Cardiovascular Causes (less AMI) (Bell et al., 2008)

Bell et al. (2008) evaluated the association between short‐term exposure to PM2.5 and the risk of

cardiovascular (ICD‐9 codes 410‐414, 26‐427, 428, 429, 430‐438, and 440‐449) and respiratory (ICD‐9

codes 464‐466, 480‐487, and 490‐492) hospital admissions.  The target population was Medicare

enrollees ≥65 years old in 202 U.S. counties with populations greater than 200,000 from 1999‐2005.  

Three time‐series models were used to provide three key variables: consistent PM effects across the

year, different PM effects by season, and smoothly varying PM effects throughout the year.  A two‐stage

Bayesian hierarchical model was used to estimate the association between PM2.5 and hospitalization

rates, with the first stage estimating the association within a single county and the second stage

combining county‐specific estimates.   

The coefficient and standard error are calculated from a 0.80% increase in hospital admissions for

cardiovascular‐related problems (95% CI 0.59%‐1.01%) for a 10 μg/m3 increase in daily PM2.5 (Bell et al.,

2008, Table 1).

Functional Form: Log‐linear

Coefficient: 0.00080

Standard Error: 0.00011

Incidence Rate: region‐specific daily hospital admission rate for all cardiovascular admissions (less acute

myocardial infarction) per person ages 65 and older (ICD codes 390‐409, 411‐429)

Population: population of ages 65 and older.

Hospital Admissions for All Cardiovascular Causes (less AMI) (Peng et al., 2008)

Peng et al. (2008) examined the risk of hospital admissions for cardiovascular and respiratory diseases in

relation to particulate matter (PM10‐2.5 and PM2.5).  To accomplish this, the authors utilized a database of

108 U.S. counties with daily emergency hospital admission rates for cardiovascular and respiratory

diseases among Medicare enrollees living 9 miles from air monitors, temperature, and dew‐point

temperature.  PM10‐2.5 and PM2.5 concentrations were calculated by using monitoring data from January

1, 1999 through December 31, 2005.  Overall, there were 3.7 million cardiovascular disease and 1.4

million respiratory disease‐related hospital admissions for the time period assessed.   

The coefficient and standard error are calculated from a 0.71% increase in hospital admissions for

cardiovascular‐related problems (95% CI 0.45%‐0.96%) for a 10 μg/m3 increase in daily PM2.5 (Peng et al.,

2008, p. 2175).

Functional Form: Log‐linear

Coefficient: 0.00071

Standard Error: 0.00013

Incidence Rate: region‐specific daily hospital admission rate for all cardiovascular admissions (less acute

myocardial infarction) per person ages 65 and older (ICD codes 390‐409, 411‐429)

Population: population of ages 65 and older.
39

Hospital Admissions for All Cardiovascular Causes (less AMI) (Peng et al., 2009)

Peng et al. (2009) investigated the relationship between hospital admissions for cardiovascular and

respiratory disease and the chemical components of PM2.5 across 119 U.S. urban communities for 12

million Medicare enrollees using log‐linear Poisson regression models.  This was achieved using a

national database with daily data from 2000‐2006 on emergency hospital admissions of cardiovascular

and respiratory outcomes, ambient levels of PM2.5 components and weather variables.  Bayesian

hierarchical statistical models were used to estimate the associations.  

The coefficient and standard error are calculated from a 0.68% increase in hospital admissions for

cardiovascular‐related problems (95% CI 0.26%‐1.10%) for a 10 μg/m3 increase in daily PM2.5 (Peng et al.,

2009, p. 960).

Functional Form: Log‐linear

Coefficient: 0.00068

Standard Error: 0.00021

Incidence Rate: region‐specific daily hospital admission rate for all cardiovascular admissions (less acute

myocardial infarction) per person ages 65 and older (ICD codes 390‐409, 411‐429)

Population: population of ages 65 and older.

Hospital Admissions for All Cardiovascular Causes (less AMI) (Zanobetti et al., 2009)

Zanobetti et al. (2009) examined the relationship between daily PM2.5 levels and emergency hospital

admissions for cardiovascular causes, myocardial infarction, congestive heart failure, respiratory disease

and diabetes among people ages 65 and older in 26 U.S. communities from 2000‐2003.  The authors

used meta‐regression to examine how this association was modified by season‐ and community‐specific

PM2.5 composition while controlling for seasonal temperature as a substitute for ventilation.  The

authors found that PM2.5 mass higher in a variety of elements, including arsenic and organic carbon,

significantly increased its effects on hospital admissions.   

The coefficient and standard error are calculated from a 1.89% increase in hospital admissions for

myocardial infarction (95% CI 1.34%‐2.45%) for a 10 μg/m3 increase in two‐day averaged PM2.5

(Zanobetti et al., 2009, Table 3).

Functional Form: Log‐linear

Coefficient: 0.00189

Standard Error: 0.00028

Incidence Rate: region‐specific daily hospital admission rate for all cardiovascular admissions (less acute

myocardial infarction) per person ages 65 and older (ICD codes 390‐409, 411‐429)

Population: population of ages 65 and older.
40

Hospital Admissions for All Cardiovascular Causes (less AMI) (Moolgavkar, 2000b)

Moolgavkar (2000b) examined the association between air pollution and cardiovascular hospital

admissions (ICD 390‐448) in the Chicago, Los Angeles, and Phoenix metropolitan areas.  He collected

daily air pollution data for ozone, SO2, NO2, CO, and PM10 in all three areas.  PM2.5 data was available

only in Los Angeles.  The data were analyzed using a Poisson regression model with generalized additive

models to adjust for temporal trends.  Separate models were run for 0 to 5 day lags in each location.  

Among the 65+ age group, the gaseous pollutants generally exhibited stronger effects than PM10 or

PM2.5.  The strongest overall effects were observed for SO2 and CO.  In a single pollutant model, PM2.5

was statistically significant for lag 0 and lag 1.  In co‐pollutant models with CO, the PM2.5 effect dropped

out and CO remained significant.  For ages 20‐64, SO2 and CO exhibited the strongest effect and any

PM2.5 effect dropped out in co‐pollutant models with CO.  

In response to concerns with the Splus issue, Moolgavkar (2003) reanalyzed his earlier study.  In the

reanalysis, he reported that more generalized additive models with stringent convergence criteria and

generalized linear models resulted in smaller relative risk estimates.  Not all of the original results were

replicated, however, so this analysis uses a result from the original study.

The single pollutant coefficient and standard error are calculated from an estimated percent change of

1.4 and t‐statistic of 4.1 for a 10 μg/m3 increase in PM2.5 in the zero lag model (Moolgavkar, 2000b, Table

4).

Functional Form: Log‐linear

Coefficient: 0.00140

Standard Error: 0.00034

Incidence Rate: region‐specific daily hospital admission rate for all cardiovascular admissions per person

ages 18 to 64 (ICD codes 390‐409, 411‐429)

Population: population of ages 18 to 64.19

Pooling Estimates of Hospital Admissions for All Cardiovascular Causes (less AMI)

The all‐cardiovascular hospital admission studies for ages 65 and older are themselves large, multi‐city

cities. Because the authors themselves report a pooled estimate, it would not be appropriate to then

used random/fixed effects to pool their individual estimates.  The results from the two studies by Peng

et al. (2008; 2009), which are based on similar databases, were averaged, and then this average was

given an equal weight with the studies by Bell et al. (2008) and Zanobetti et al (2009).  The pooled result

from the 65 and older studies was then added to the result for ages 18‐64 based on Moolgavkar

(2000b).

                                                            

19 Although Moolgavkar (2000a) reports results for the 20‐64 year old age range, for comparability to other

studies, we apply the results to the population of ages 18 to 64.
41

Hospital Admissions for All Respiratory Causes (Zanobetti et al., 2009)

Zanobetti et al (2009) examined the relationship between daily PM2.5 levels and emergency hospital

admissions for cardiovascular causes, myocardial infarction, congestive heart failure, respiratory disease

and diabetes among people ages 65 and older in 26 U.S. communities from 2000‐2003.  The authors

used meta‐regression to examine how this association was modified by season‐ and community‐specific

PM2.5 composition while controlling for seasonal temperature as a substitute for ventilation.  The

authors found that PM2.5 mass higher in a variety of elements, including arsenic and organic carbon,

significantly increased its effects on hospital admissions.   

The coefficient and standard error are calculated from a 2.07% increase in hospital admissions for

myocardial infarction (95% CI 1.20%‐2.95%) for a 10 μg/m3 increase in two‐day averaged PM2.5

(Zanobetti et al., 2009, Table 3).

Functional Form: Log‐linear

Coefficient: 0.00207

Standard Error: 0.00045

Incidence Rate: region‐specific daily hospital admission rate for all respiratory admissions per person 65

and older (ICD codes 460‐519)

Population: population of ages 65 and older.

Hospital Admissions for Chronic Lung Disease (Moolgavkar, 2000a)

Moolgavkar (2000a) examined the association between air pollution and COPD hospital admissions (ICD

490‐496) in the Chicago, Los Angeles, and Phoenix metropolitan areas.  He collected daily air pollution

data for ozone, SO2, NO2, CO, and PM10 in all three areas.  PM2.5 data was available only in Los Angeles.  

The data were analyzed using a Poisson regression model with generalized additive models to adjust for

temporal trends.  Separate models were run for 0 to 5 day lags in each location.  Among the 65+ age

group in Chicago and Phoenix, weak associations were observed between the gaseous pollutants and

admissions.  No consistent associations were observed for PM10.  In Los Angeles, marginally significant

associations were observed for PM2.5, which were generally lower than for the gases.  In co‐pollutant

models with CO, the PM2.5 effect was reduced.  Similar results were observed in the 0‐19 and 20‐64 year

old age groups.   

In response to concerns with the Splus issue, Moolgavkar (2003) reanalyzed his earlier study.  In the

reanalysis, he reported that more generalized additive models with stringent convergence criteria and

generalized linear models resulted in smaller relative risk estimates.  Not all of the original results were

replicated, however, so this analysis uses a result from the original study.  The PM2.5 C‐R functions for

the 20‐64 age group are based on the original study’s single‐pollutant model.  Since the true PM effect is

most likely best represented by a distributed lag model, then any single lag model should underestimate

the total PM effect.  As a result, the lag models with the greatest effect estimates were selected for use

in the C‐R functions.
42

The single pollutant coefficient and standard error are calculated from an estimated percent change of

2.2 and t‐statistic of 3.0 for a 10 μg/m3 increase in PM2.5 in the two‐day lag model (Moolgavkar, 2000a,

Table 4).

Functional Form: Log‐linear

Coefficient: 0.0022

Standard Error: 0.000733

Incidence Rate: region‐specific daily hospital admission rate for chronic lung disease admissions per

person 18‐64 (ICD codes 490‐492, 494‐496)20

Population: population of ages 18 to 64.21

Hospital Admissions for Asthma (Sheppard et al., 1999; 2003; Babin et al., 2007)

Babin et al. (2007) examined pediatric asthma‐related emergency room (ER) visits and hospital

admissions (ICD‐9 code 493) in Washington, D.C. from 2001‐2004 and their short‐term associations with

ozone, particulate matter, socioeconomic status, and age group.  Applying Poisson regression analyses,

the authors found significant associations between asthma ER visits and outdoor ozone concentrations

for the 5‐12 year old age group. A weak association was found between PM2.5 and asthma

hospitalization.

The coefficient and standard error are calculated from a 0.2% increase in hospital admissions for

myocardial infarction (95% CI ‐0.6%‐1.1%) for a 1 μg/m3 increase in daily PM2.5 (Babin et al., 2007, Table

2).

Functional Form: Log‐linear

Coefficient: 0.00200

Standard Error: 0.00434

Incidence Rate: region‐specific daily hospital admission rate for asthma admissions per person ages 0 to

17 (ICD code 493)

Population: population of ages 0 to 17.

Hospital Admissions for Asthma (Sheppard et al., 1999; 2003)

Sheppard et al. (1999) studied the relation between air pollution in Seattle and nonelderly (ages <65)

hospital admissions for asthma from 1987 to 1994.  They used air quality data for PM10, PM2.5, coarse

PM1010‐2.5, SO2, ozone, and CO in a Poisson regression model with control for time trends, seasonal

variations, and temperature‐related weather effects. They found asthma hospital admissions associated

with PM10, PM2.5, PM10‐2.5, CO, and ozone.  They did not observe an association for SO2.  They found PM

and CO to be jointly associated with asthma admissions.  The best fitting co‐pollutant models were

                                                            

20 Moolgavkar (2000a)  reports results for ICD codes 490‐496.  In order to avoid double counting non‐elderly

asthma hospitalizations (ICD code 493), this analysis excludes ICD code 493 from the baseline incidence rate used

in this function. 21 Although Moolgavkar (2000a) reports results for the 20‐64 year old age range, for comparability to other

studies, we apply the results to the population of ages 18 to 64.
43

found using ozone.  However, ozone data was only available April through October, so they did not

consider ozone further.  For the remaining pollutants, the best fitting models included PM2.5 and CO.  

Results for other co‐pollutant models were not reported.   

In response to concerns that the work by Sheppard et al. (1999) may be biased because of the Splus

issue, Sheppard (2003) reanalyzed some of this work, in particular Sheppard reanalyzed the original

study’s PM2.5 single pollutant model.  

The coefficient and standard error are based on the relative risk (1.04) and 95% confidence interval

(1.01‐1.06) for a 11.8 μg/m3 increase in PM2.5 in the 1‐day lag GAM stringent model (Sheppard, 2003, pp.

228‐299).

Functional Form: Log‐linear

Coefficient: 0.003324

Standard Error: 0.001045

Incidence Rate: region‐specific daily hospital admission rate for asthma admissions per person ages 0 to

17 (ICD code 493)

Population: population of ages 0 to 17.

Pooling Estimates of Hospital Admissions for Asthma

The estimates generated from the Babin et al. (2007) and Sheppard et al. (2003) studies were pooled

using a random/fixed effects approach to get a single estimate of hospital admissions due to asthma.

Emergency Room Visits for Asthma (Mar et al., 2010)

Mar et al. (2010) assessed the effect of particulate matter air pollution, including emissions from diesel

generators, on emergency room visits for asthma in the greater Tacoma, Washington area from January

3, 1998 to May 30, 2002 using Poisson regression models. Health data were collected for individuals of

all ages from six Tacoma hospitals.  Overall, the researchers found an association between daily PM2.5

levels and emergency room visits for asthma at lag days 2 and 3, with a relative risk for lag day 2 of 1.04

(95% CI: 1.01‐1.07) and a relative risk for lag day 3 of 1.03 (95% CI: 1.0‐1.06).  No significant association

between emergency room visits for asthma and increased use of the diesel generators was observed.  

The coefficient and standard error are calculated from a relative risk of 1.04 (95% CI 1.01‐1.07) for a 7

μg/m3 increase in PM2.5 (Mar et al., 2010, Table 5).

Functional Form: Log‐linear

Coefficient: 0.00560

Standard Error: 0.00210

Incidence Rate: region‐specific daily emergency room rate for asthma admissions per person ages 0 to

17 (ICD code 493)

Population: population of ages 0 to 17.
44

Emergency Room Visits for Asthma (Slaughter et al., 2005)

Slaughter et al. (2005) examined the short‐term association of particulate matter (PM1, PM2.5, PM10, and

PM10‐2.5) and carbon monoxide with hospital admissions and emergency room visits for respiratory and

cardiac outcomes and mortality in Spokane, Washington from January 1995 to June 2001 using a log‐

linear generalized linear model.  

The coefficient and standard error are calculated from a relative risk of 1.03 (95% CI 0.98‐1.09) for a 10

μg/m3 increase in PM2.5 (Slaughter et al., 2005, Table 4).

Functional Form: Log‐linear

Coefficient: 0.00296

Standard Error: 0.00271

Incidence Rate: region‐specific daily emergency room rate for asthma admissions per person ages 0 to

17 (ICD code 493)  

Population: population of ages 0 to 17.

Pooling Estimates of Emergency Room Visits for Asthma

The estimates generated from the Mar et al. (2010) and Slaughter et al. (2005) studies were pooled

using a random/fixed effects approach to get a single estimate of emergency room visits due to asthma.

Acute Bronchitis (Dockery et al., 1996)

Dockery et al. (1996) examined the relationship between PM and other pollutants on the reported rates

of asthma, persistent wheeze, chronic cough, and bronchitis, in a study of 13,369 children ages 8‐12

living in 24 communities in U.S. and Canada.  Health data were collected in 1988‐1991, and single‐

pollutant models were used in the analysis to test a number of measures of particulate air pollution.  

Dockery et al. found that annual level of sulfates and particle acidity were significantly related to

bronchitis, and PM2.1 and PM10 were marginally significantly related to bronchitis.22 They also found

nitrates were linked to asthma, and sulfates linked to chronic phlegm.  It is important to note that the

study examined annual pollution exposures, and the authors did not rule out that acute (daily)

exposures could be related to asthma attacks and other acute episodes.

Bronchitis was counted in the study only if there were “reports of symptoms in the past 12

months”(Dockery et al., 1996, p. 501).  It is unclear, however, if the cases of bronchitis are acute and

temporary, or if the bronchitis is a chronic condition.  Dockery et al. found no relationship between PM

and chronic cough and chronic phlegm, which are important indicators of chronic bronchitis.  This

analysis assumes that the C‐R function based on Dockery et al. is measuring acute bronchitis.

The estimated logistic coefficient and standard error are based on the odds ratio (1.50) and 95%

confidence interval (0.91‐2.47) associated with being in the most polluted city (PM2.1 = 20.7 μg/m3

)

                                                            

22 The original study measured PM2.1, however when using the study's results we use PM2.5.  This makes only a

negligible difference, assuming that the adverse effects of PM2.1 and PM2.5 are comparable.
45

versus the least polluted city (PM2.1 = 5.8 μg/m3

) (Dockery et al., 1996, Tables 1 and 4). The original

study used PM2.1, however, this analysis uses the PM2.1 coefficient and applies it to PM2.5 data.

Functional Form: Logistic

Coefficient: 0.027212

Standard Error: 0.017096

Incidence Rate: annual bronchitis incidence rate per person = 0.043 (American Lung Association, 2002b,

Table 11)

Population: population of ages 8‐12.

Lower Respiratory Symptoms (Schwartz and Neas, 2000)

Schwartz and Neas (2000) used logistic regression to link lower respiratory symptoms and cough in

children with coarse PM10, PM2.5, sulfate and H+ (hydrogen ion).  Children were selected for the study if

they were exposed to indoor sources of air pollution: gas stoves and parental smoking.  The study

enrolled 1,844 children into a year‐long study that was conducted in different years (1984 to 1988) in six

cities.  The students were in grades two through five at the time of enrollment in 1984.  By the

completion of the final study, the cohort would then be in the eighth grade (ages 13‐14); this suggests

an age range of 7 to 14.

The coefficient and standard error are calculated from the reported odds ratio (1.33) and 95%

confidence interval (1.11‐1.58) associated with a 15 μg/m3 change in PM2.5 (Schwartz and Neas, 2000,

Table 2).

Functional Form: Logistic

Coefficient: 0.01901

Standard Error: 0.006005

Incidence Rate: daily lower respiratory symptom incidence rate per person = 0.0012 (Schwartz et al.,

1994, Table 2).

Population: population of ages 7 to 14.

Upper Respiratory Symptoms (Pope, 1991)

Using logistic regression, Pope et al. (1991)estimated the impact of PM10 on the incidence of a variety of

minor symptoms in 55 subjects (34 “school‐based” and 21 “patient‐based”) living in the Utah Valley

from December 1989 through March 1990.  The children in the Pope et al. study were asked to record

respiratory symptoms in a daily diary.  With this information, the daily occurrences of upper respiratory

symptoms (URS) and lower respiratory symptoms (LRS) were related to daily PM10 concentrations.  Pope

et al. describe URS as consisting of one or more of the following symptoms:  runny or stuffy nose; wet

cough; and burning, aching, or red eyes.  Levels of ozone, NO2, and SO2 were reported low during this

period, and were not included in the analysis.  The sample in this study is relatively small and is most

representative of the asthmatic population, rather than the general population.  The school‐based

subjects (ranging in age from 9 to 11) were chosen based on “a positive response to one or more of
46

three questions: ever wheezed without a cold, wheezed for 3 days or more out of the week for a month

or longer, and/or had a doctor say the ‘child has asthma’(Pope, 1991, p. 669).”  The patient‐based

subjects (ranging in age from 8 to 72) were receiving treatment for asthma and were referred by local

physicians.  Regression results for the school‐based sample (Pope, 1991, Table 5) show PM10 significantly

associated with both upper and lower respiratory symptoms.  The patient‐based sample did not find a

significant PM10 effect.  The results from the school‐based sample are used here.

The coefficient and standard error for a one μg/m3 change in PM10 is reported in Table 5.

Functional Form: Logistic

Coefficient: 0.0036

Standard Error: 0.0015

Incidence Rate: daily upper respiratory symptom incidence rate per person = 0.3419 (Pope, 1991, Table

2)

Population: asthmatic population ages 9 to 11 = 5.67%23 of population ages 9 to 11.

Asthma Exacerbation: Cough, Wheeze, and Shortness of Breath (Ostro et al., 2001)

Ostro et al. (2001)studied the relation between air pollution in Los Angeles and asthma exacerbation in

African‐American children (8 to 13 years old) from August to November 1993.  They used air quality data

for PM10, PM2.5, NO2, and O3 in a logistic regression model with control for age, income, time trends, and

temperature‐related weather effects.24  Asthma symptom endpoints were defined in two ways:

“probability of a day with symptoms” and “onset of symptom episodes”.  New onset of a symptom

episode was defined as a day with symptoms followed by a symptom‐free day.  The authors found

cough prevalence associated with PM10 and PM2.5 and cough incidence associated with PM2.5, PM10, and

NO2.  Ozone was not significantly associated with cough among asthmatics.

Note that the study focused on African‐American children ages 8 to 13 years old.  However, it is

assumed that the results from this study are applicable to the general population ages 6 to 18 years old.

Asthma Exacerbation, Cough

The coefficient and standard error are based on an odds ratio of 1.03 (95% CI 0.98‐1.07) for a 30 μg/m3

increase in 12‐hour average PM2.5 concentration (Ostro et al., 2001, Table 4, p. 204).  

Functional Form: Logistic

Coefficient: 0.000985

Standard Error: 0.000747

Incidence Rate: daily cough rate per person (Ostro et al., 2001, p. 202)  = 0.145

                                                            

23 The American Lung Association (2002a, Table 7) estimates asthma prevalence for children ages 5 to 17 at 5.67%

(based on data from the 1999 National Health Interview Survey). 24 The authors note that there were 26 days in which PM2.5 concentrations were reported higher than PM10

concentrations.  The majority of results the authors reported were based on the full dataset.  These results were

used for the basis for the C‐R functions.
47

Population: asthmatic population ages 6 to 18	ൌ 10.7%25

Asthma Exacerbation, Shortness of Breath

The coefficient and standard error are based on an odds ratio of 1.08 (95% CI 1.00‐1.17) for a 30 μg/m3

increase in 12‐hour average PM2.5 concentration (Ostro et al., 2001, Table 4, p. 204).  

Functional Form: Logistic

Coefficient: 0.002565

Standard Error: 0.001335

Incidence Rate: daily shortness of breath rate per person (Ostro et al., 2001, p. 202) = 0.074

Population: asthmatic population ages 6 to 18	ൌ 10.7%

Asthma Exacerbation, Wheeze

The coefficient and standard error are based on an odds ratio of 1.06 (95% CI 1.01‐1.11) for a 30 μg/m3

increase in 12‐hour average PM2.5 concentration (Ostro et al., 2001, Table 4, p. 204).  

Functional Form: Logistic

Coefficient: 0.001942

Standard Error: 0.000803

Incidence Rate: daily wheeze rate per person (Ostro et al., 2001, p. 202)   = 0.173

Population: asthmatic population ages 6 to 18	ൌ 10.7%

Asthma Exacerbation: Cough and Shortness of Breath (Vedal et al., 1998; Mar et al.,

2004)

Mar et al. (2004) studied the effects of various size fractions of particulate matter on respiratory

symptoms of adults and children with asthma, monitored over many months. The study was conducted

in Spokane, Washington, a semiarid city with diverse sources of particulate matter. Data on respiratory

symptoms and medication use were recorded daily by the study’s subjects, while air pollution data was

collected by the local air agency and Washington State University. Subjects in the study consisted of 16

adults—the majority of whom participated for over a year—and nine children, all of whom were studied

for over eight months. Among the children, the authors found a strong association between cough

symptoms and several metrics of particulate matter, including PM2.5. However, the authors found no

association between respiratory symptoms and PM of any metric in adults.   

Asthma Exacerbation, Cough

The coefficient and standard error are based on an odds ratio of 1.21 (95% CI 1.99‐1.47) for a 10 μg/m3

increase in daily average PM2.5 concentration (Mar et al., 2004, Table 7).

Functional Form: Logistic

Coefficient: 0.01906

                                                            

25 The American Lung Association (2010, Table 7) estimates asthma prevalence for children 5‐17 at 10.7% in 2008.
48

Standard Error: 0.00983

Incidence Rate: daily cough rate per person (Ostro et al., 2001, p. 202)  = 0.145

Population: asthmatic population ages 6 to 18	ൌ 10.7%26

Asthma Exacerbation, Shortness of Breath

The coefficient and standard error are based on an odds ratio of 1.13 (95% CI 0.86‐1.48) for a 10 μg/m3

increase in daily average PM2.5 concentration (Mar et al., 2004, Table 7).

Functional Form: Logistic

Coefficient: 0.01222

Standard Error: 0.01385

Incidence Rate: daily shortness of breath rate per person (Ostro et al., 2001, p. 202) = 0.074

Population: asthmatic population ages 6 to 18 = 10.7%

Pooling Estimates of Asthma Exacerbation

The following approach was used to combine the estimates generated using effect estimates from the

Ostro et al. (2001) and Mar et al. (2004) studies to produce a single asthma exacerbation incidence

estimate.  First, the separate incidence estimates from the Ostro et al. (2001) study for cough, shortness

of breath, and wheeze and from the Mar et al. (2004) study for cough and shortness of breath were

generated.  The two estimates for cough were pooled using a random/fixed effects approach, and the

two estimates for shortness of breath were pooled.  The pooled estimates for cough and shortness of

were then averaged with wheeze estimate, because each of these endpoints is aimed at capturing the

same overall endpoint (asthma exacerbations) and there could be overlap in their predictions.  

To prevent double‐counting, this analysis focused the estimation on asthma exacerbations occurring in

children and excluded adults from the calculation.  Asthma exacerbations occurring in adults are

assumed to be captured in the general population endpoints such as work loss days and MRADs.

Consequently, if adult‐specific asthma exacerbation estimate had been included, this would likely have

double‐counted incidence for this endpoint. However, because the general population endpoints do not

cover children (with regard to asthmatic effects), an analysis focused specifically on asthma

exacerbations for children (6 to 18 years of age) could be conducted without concern for double‐

counting.

Work Loss Days (Ostro, 1987)

Ostro (1987) estimated the impact of PM2.5 on the incidence of work‐loss days (WLDs), restricted activity

days (RADs), and respiratory‐related RADs (RRADs) in a national sample of the adult working population,

ages 18 to 65, living in metropolitan areas.  The annual national survey results used in this analysis were

conducted in 1976‐1981.  Ostro reported that two‐week average PM2.5 levels were significantly linked to

work‐loss days, restricted activity days (RADs), and respiratory‐related restricted activity days (RRADs),

                                                            

26 The American Lung Association (2010, Table 7) estimates asthma prevalence for children 5‐17 at 10.7% in 2008.
49

however there was some year‐to‐year variability in the results.27  Separate coefficients were developed

for each year in the analysis (1976‐1981); these coefficients were pooled.  The coefficient used in the

concentration‐response function presented here is a weighted average of the coefficients in Ostro

(1987, Table 3) using the inverse of the variance as the weight.

The coefficient used in the C‐R function is a weighted average of the coefficients in Ostro (1987, Table 3)

using the inverse of the variance as the weight.  The standard error of the coefficient is calculated as

follows, assuming that the estimated year‐specific coefficients are independent.

Functional Form: Log‐linear

Coefficient: 0.0046

Standard Error: 0.00036

Incidence Rate: daily work‐loss‐day incidence rate per person ages 18 to 64 = 0.00595 (U.S. Bureau of

the Census, 1997, No. 22; Adams et al., 1999, Table 41)

Population: adult population ages 18 to 64.28

Minor Restricted Activity Days (Ostro, 1989)

Ostro and Rothschild (1989) estimated the impact of PM2.5 and ozone on the incidence of minor

restricted activity days (MRADs) and respiratory‐related restricted activity days (RRADs) in a national

sample of the adult working population, ages 18 to 65, living in metropolitan areas.  The annual national

survey results used in this analysis were conducted in 1976‐1981.  Controlling for PM2.5, two‐week

average ozone has highly variable association with RRADs and MRADs.  Controlling for ozone, two‐week

average PM2.5 was significantly linked to both health endpoints in most years.29  The C‐R function for PM

is based on this co‐pollutant model.

Using the results of the two‐pollutant model, separate coefficients were developed for each year in the

analysis, which were then combined for use in this analysis.  The coefficient is a weighted average of the

coefficients in Ostro and Rothschild (1989, Table 4) using the inverse of the variance as the weight.   The

standard error of the coefficient is calculated as follows, assuming that the estimated year‐specific

coefficients are independent.

Functional Form: Log‐linear

Coefficient: 0.00741

Standard Error: 0.00070

Incidence Rate: daily incidence rate for minor restricted activity days (MRAD) = 0.02137 (Ostro and

Rothschild, 1989, p. 243)

                                                            

27 The study used a two‐week average pollution concentration; the C‐R function uses a daily average, which is

assumed to be a reasonable approximation.    28 The study is based on a “convenience” sample of non‐elderly individuals.  Applying the C‐R function to this age

group is likely a slight underestimate, as it seems likely that elderly are at least as susceptible to PM as individuals

under 65. 29 The study used a two‐week average pollution concentration; the C‐R function uses a daily average, which is

assumed to be a reasonable approximation.
50

Population: adult population ages 18 to 64.30

                                                            

30 The study is based on a “convenience” sample of non‐elderly individuals.  Applying the C‐R function to this age

group is likely a slight underestimate, as it seems likely that elderly are at least as susceptible to PM as individuals

under 65.
51

Appendix B.		Health Incidence Data

Health impact functions developed from log‐linear or logistic models estimate the percent change in an

adverse health effect associated with a given pollutant change.  In order to estimate the absolute

change in incidence using these functions, this analysis needed the baseline incidence rate of the

adverse health effect, or the number of cases experienced by a given population per unit of time.  And in

the case of asthma, a prevalence rate, or percentage of the population affected (in this case asthmatic),

was needed as well.  This appendix describes the data used to estimate baseline incidence and

prevalence rates for the health effects considered in this analysis.

The incidence data used in this analysis are available in version 4.0 of BenMAP and are described in

Appendix E of the BenMAP User Manual.31  The description below for mortality, hospitalization,

emergency room visits, and heart attacks is drawn from the BenMAP User Manual.  Details on incidence

and prevalence rates for other health impacts are described in the Appendix detailing each health

impact function.

Mortality Incidence Rate

The mortality data used in this analysis is a forecasted estimate for 2010 that comes with the BenMAP

software.  It would have been preferred to have a 2008 mortality estimate, as this is the midpoint of our

ambient PM2.5 data range of 2007‐2009, but this was not available.  Since mortality rates have gradually

declined over time, using a 2010 rate rather than 2008 is a conservative approach, tending to slightly

under‐estimate the mortality rate.

The 2010 mortality rate estimates are ultimately based on individual‐level mortality data from2004‐

2006 for the whole United States were obtained from the Centers for Disease Control (CDC), National

Center for Health Statistics (NCHS).  Since the detailed mortality data obtained from the CDC do not

include population, these data were combined with U.S. Census Bureau post‐censal population

estimates exported from BenMAP.  Age‐, cause‐, and county‐specific mortality rates were then

generated.  

To estimate age‐ and county‐specific mortality rates in years 2010, the developers of BenMAP calculated

adjustment factors, based on a series of Census Bureau projected national mortality rates (for all‐cause

mortality), to adjust the age‐ and county‐specific mortality rates calculated using 2004‐2006 data.  

Details of these calculations are described in Appendix E of the BenMAP User Manual (Abt Associates

Inc., 2010).

                                                            

31 The BenMAP software and its user manual are available from the BenMAP website:

http://www.epa.gov/oaqps001/benmap/download.html.  
52

Hospital Admission Incidence Rate

Hospitalization rates were calculated using data from the Healthcare Cost and Utilization Project (HCUP).  

As noted in Appendix E of the BenMAP User Manual, HCUP is a family of health care databases

developed through a Federal‐State‐Industry partnership and sponsored by the Agency for Healthcare

Research and Quality. HCUP products include the State Inpatient Databases, the State Emergency

Department Databases, the Nationwide Inpatient Sample, and the Nationwide Emergency Department

Sample.  HCUP databases can be obtained from the following data services:

HCUP Central Distributor: Many of the HCUP databases are available for purchase through the HCUP

Central Distributor. The databases include detailed information for individual discharges, such as

primary diagnosis (in ICD‐9 codes), patient’s age and residence county.

HCUP State Partners: Some HCUP participating states do not release their data to the Central

Distributor; however, the data may be obtained through contacting the State Partners. Some State

Partners (e.g., CA, TX, and NY) provided discharge‐level data; others (e.g., OH) provided summarized

data.

HCUPnet: This is a free, on‐line query system based on data from HCUP.

The developers of BenMAP combined the data from these different sources and developed a nominally

county‐level database, however, in some cases more aggregated data were used.  For some states

neither discharge‐level nor state‐level data were available. In such cases regional statistics were used

from HCUPnet to estimate hospitalization rates for those states.  The data year for most states is 2007;

the exception is MA, for which the data year is 2006.

Non‐Fatal Heart Attack Incidence Rate

As in the case of hospital admissions, the emergency room visit incidence rate is based on HCUP data,

and thus comes from a variety of sources.  The data year for most states is 2007; the exception is MA,

for which the data year is 2006.

Emergency Room Visit Incidence Rate

As in the case of hospital admissions, the emergency room visit incidence rate is based on HCUP data,

and thus comes from a variety of sources.  The data year varies across the states from 2005 to 2007; we

assumed that ER visit rates are reasonably constant across these three years and consider them as 2006

rates.
53

Appendix C.		Population Data

The population estimate used in this analysis is for 2008, which is the midpoint of the 2007‐2009 air

quality monitoring used in the rollback analyses.  To develop the 2008 population estimates, the analysis

started with block data from the 2000 Census.32  To account for the change in population between 2000

and 2008, the analysis adjusted the U.S. Census Bureau data with population projections based on

economic forecasting models developed by Woods and Poole,33 which has the most detailed (county‐

level) forecasts available covering the entire nation.

The Woods and Poole database contains county level projections of population by age, sex, race, and

ethnicity.  Projections in each county are determined simultaneously with every other county in the U.S.

to take into account patterns of economic growth and migration.  The sum of growth in county level

populations is constrained to equal a previously determined national population growth, based on

Bureau of Census estimates.34  According to Woods and Poole, linking county level growth projections

together and constraining to a national level total growth avoids potential errors introduced by

forecasting each county independently.

Woods and Poole developed county projections in a four stage process.  First, national level variables

such as income, employment, populations, etc. are forecasted.  Second, employment projections are

made for 172 economic areas defined by the Bureau of Economic Analysis, using an "export‐base"

approach, which relies on linking industrial sector production of non‐locally consumed production items,

such as outputs from mining, agriculture, and manufacturing with the national economy.  The export‐

base approach requires estimation of demand equations or calculation of historical growth rates for

output and employment by sector.  Third, population is projected for each economic area based on net

migration rates derived from employment opportunities, and following a cohort‐component method

based on fertility and mortality in each area.  Fourth, employment and population projections are

repeated for counties, using the economic region totals as bounds.  The age, sex, and race distributions

for each region or county are determined by aging the population by single year of age by sex and race

for each year through 2025 based on historical rates of mortality, fertility, and migration.

The present analysis estimated 2008 grid‐cell population by using 2000 Census block‐level population

estimates and the percentage change in population estimates based on the county‐level Woods & Poole

data.  This occurred in a three‐step process.  

Step 1.  Using the PopGrid model, the analysis summed the 2000 block data to the level of the air quality

modeling data (CAMx grid‐cell), keeping track of the (one or more) counties that overlap with the grid‐

cell.  The 2000 block data are the most disaggregated data provided by the Census Bureau, and typically

cover a much smaller geographic area than the air quality models.  This summing generated CAMx

                                                            

32 (GeoLytics Inc., 2002) 33 (Woods & Poole Economics Inc., 2001) 34 (Hollman et al., 2000)
54

population grid‐cells with year 2000 population data, as well as the percentage of the total population in

each grid‐cell falling within a particular county.

Step 2.  The analysis used the Woods and Poole data to estimate the percentage change (from 2000 to

2008) in the population by age, sex, and race.  The analysis then calculated a growth adjustment factor

equal to one plus the percentage change (e.g., if the percentage change equals two percent, then the

adjustment factor is 1.02).

Step 3.  Finally, the analysis multiplied the appropriate age, sex, and race growth adjustment factor for

each county with the appropriate population in each grid‐cell.

For example, when forecasting a single population variable, say, children ages 4 to 9 in the year 2008,

BenMAP calculated:

ܽ݃݁ସିଽ,	௚,	ଶ଴଴଼ ൌ ܽ݃݁ସିଽ,	௚,	ଶ଴଴଴ ∗

ܽ݃݁ସିଽ,	௖௢௨௡௧௬,	ଶ଴଴଼

ܽ݃݁ସିଽ,	௖௢௨௡௧௬,	ଶ଴଴଴

where the gth population grid‐cell is wholly located within a given county.

In the case, where the gth grid‐cell includes more than one county in its boundary, the situation is

somewhat more complicated.  BenMAP first estimates the fraction of individuals in a given age group

(e.g., ages 4 to 9) that reside in each county within the gth grid‐cell.  BenMAP calculates this fraction by

simply dividing the all‐age population of a given county within the gth grid‐cell by the total population in

the gth grid‐cell:

fraction of age

age

age g in county

all g in county

all g c

c

4 9  ,

,

,

BenMAP multiplied this fraction with the number of individuals ages 4 to 9 in the year 2000, to give an

estimate of the number of individuals ages 4 to 9 that reside in the fraction of the county within the gth

grid‐cell in the year 2000:

ܽ݃݁ସିଽ,	௚	௜௡	௖௢௨௡௧௬೎,	ଶ଴଴଴ ൌ ܽ݃݁ସିଽ,	௚,	ଶ଴଴଴ ∗ ݂ݎܽܿݐ݅݋݊	ܽ݃݁ସିଽ,	௚	௜௡	௖௢௨௡௧௬೎

To then forecast the population in 2008, BenMAP scaled the 2000 estimate with the ratio of the county

projection for 2008 to the county projection for 2000:  

ܽ݃݁ସିଽ,	௚	௜௡	௖௢௨௡௧௬೎,	ଶ଴଴଼ ൌ ܽ݃݁ସିଽ,	௚,	ଶ଴଴଴ ∗

ܽ݃݁ସିଽ,	௖௢௨௡௧௬೎,	ଶ଴଴଼

ܽ݃݁ସିଽ,	௖௢௨௡௧௬೎,	ଶ଴଴଴

Combining the contributions from each county within each given grid‐cell gave population of persons

ages 4 to 9 in the year 2008.
55

Appendix D.		Resume of Dr. McCubbin

Dr. Donald McCubbin has over 15 years of experience conducting analyses of environmental issues.  He

managed the development of BenMAP, led training programs for state, federal and international clients,

wrote the BenMAP user manual, and generated and maintained extensive databases of population,

health statistics, epidemiological results, and health costs.  Dr. McCubbin analyzed the health benefit of

the Clean Air Act, major regulations, such as the Heavy Duty Diesel and Clean Air Interstate rules, and

the impacts of power plants, motor vehicles, and other pollution sources.  He has written a number of

peer‐reviewed articles and book chapters, as well as many reports on air quality issues and health

impacts.  He has given expert witness testimony for the Department of Justice, and he received EPA’s

Level 1 Science and Technological Achievement Award for a peer‐reviewed research article on the

health benefits of alternative ozone standards.

Education

Ph.D. Economics, University of California, San Diego

M.S. Agricultural Economics, University of California, Davis

M.S. Ecology, University of California, Davis

B.A. Biological Basis of Behavior, University of Pennsylvania

Relevant Professional Experience

Institute of Transportation Studies, University of CA, Davis. Research Analyst. Developing a model of

the climate change impacts associated with transportation fuels. (2009‐Present)

Abt Associates Inc. Principal Associate. Designed and managed the development of innovative software

tools to estimate air pollution exposure and the associated health and economic costs. Prepared

numerous reports, including examining the co‐control benefits of greenhouse gas emission reductions,

dioxin fate and transport, and the environmental and health impacts of ammonia from livestock

production. Worked on a variety of international projects in Mexico, Dominican Republic, Guatemala,

Belize, Nicaragua, Chile, Bolivia, India, Tanzania, and Ghana. (1998‐2009)

Awards																																																																																																																								

EPA Level I Scientific And Technological Achievement Award.  2005.  Given by EPA for an exceptionally

high‐quality research of national significance, recognizable as a major scientific/technological

achievement within its discipline or field of study

Daniel Bell Award. 2005.  Given by Abt Associates each year for outstanding social science research.

Selected Publications

Delucchi, M.A. & D.R. McCubbin (2010). The external costs of transport in the U.S. A. de Palma, R.

Lindsey, E. Quinet, and R. Vickerman, editors.  Handbook of Transport Economics.  Northamptom, MA:

Edward Elgar Publishing.
56

Davidson, K, A. Hallberg, B. Hubbell, D.R. McCubbin (2007). Analysis of PM2.5 Using the Environmental

Benefits Mapping and Analysis Program (BenMAP).  Journal of Toxicology and Environmental Health,

Part A, 70: 332‐346.

Hubbell, B., Hallberg, D.R. McCubbin, E. Post (2005). Health related benefits of attaining the eight‐hour

ozone standard.  Environmental Health Perspectives, 113(1): 73‐82.

McCubbin, D. R., & M.A. Delucchi (2003).  The health effects of motor vehicle‐related air pollution.  

David Hensher & Ken Button, editors.  Handbooks in Transport.  Volume 4: Transport and Environment.  

Sydney, Australia: Institute of Transport Studies.

Murphy, J., M.A. Delucchi, & D.R. McCubbin (2002). The health and visibility cost of air pollution: A

comparison of estimation methods. Journal of Environmental Management, 64: 139‐152.

McCubbin, D. R., B.J. Apelberg, S. Roe, & F. Divita Jr.  (2002). Livestock ammonia management and

particulate‐related health benefits.  Environmental Science & Technology, 36(6): 1141‐1146.

Murphy, J., M.A. Delucchi, D.R. McCubbin, & J. Kim (1999). The cost of crop losses caused by ozone air

pollution from motor vehicles.  Journal of Environmental Management, 55: 273‐289.

McCubbin, D. R., & M.A. Delucchi (1999). The cost of the health effects of air pollution from motor

vehicles. Journal of Transport Economics and Policy, 33(part 3): 253‐286.
57

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