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Chapter 8 Comparison of Program Costs to Program Air Quality 

Benefits

EPA traditionally evaluates the effectiveness of a final rule in terms of net benefits. 

Section 8.1 below presents the cost-benefit analysis of the final rule.

8.1 Cost-Benefit Analysis

The net benefits of the final Tier 3 program are determined by the effects of the program 

on the costs to comply with the vehicle and fuel aspects of the program along with the benefits of 

improved air quality on health and the environment. 

8.1.1 Program-Wide Costs 

The costs that are incurred from our final program fall into three categories - for the Tier 

3 exhaust standards, Tier 3 evaporative standards, and reductions in sulfur content of gasoline. 

While we present these three categories of costs separately in this section, for purposes of the 

calculation of cost per ton of emissions reduced analysis we have summed them to represent the 

estimated costs of the final program. 

All costs represent the fleet-weighted average of light-duty vehicles and trucks. All costs 

are represented in 2011dollars.

8.1.1.1 Vehicle Program Costs

The vehicle costs include the technology costs projected to meet the final exhaust and 

evaporative standards including the facility and operating cost impacts, as detailed in RIA 

Chapter 2 and shown in Table 8-1. The fleet mix of light-duty vehicles, light duty trucks, and 

medium-duty trucks represents the MYs 2017-2025 fleet mix projected to result from the most 

recent GHG and fuel economy rules. The final vehicle costs are lower than the values projected 

in the proposal due to the items outlined in RIA Section 2.1 in addition to the exclusion of the 

vehicle sales in California and the states that have adopted the LEV III program.

Table 8-1: Annual Vehicle Program Costs, 2011$

Year

Vehicle Exhaust 

Emission Control 

Costs ($Million)

Vehicle Evaporative 

Emission Control 

Costs ($Million)

Operating

Costs 

($Million)

Facility Costs 

($Million)

Total Vehicle 

Program Costs 

($Million)a

2016 $0 $0 $0 $21 $21

2017 $268 $26 $0 $4 $297

2018 $539 $73 -$1 $4 $615

2019 $579 $72 -$2 $4 $653

2020 $599 $98 -$3 $4 $697

2021 $630 $97 -$5 $4 $725

2022 $640 $121 -$6 $4 $758

2023 $639 $116 -$8 $4 $751
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Year

Vehicle Exhaust 

Emission Control 

Costs ($Million)

Vehicle Evaporative 

Emission Control 

Costs ($Million)

Operating

Costs 

($Million)

Facility Costs 

($Million)

Total Vehicle 

Program Costs 

($Million)a

2024 $653 $114 -$9 $4 $761

2025 $668 $113 -$11 $4 $773

2030 $664 $113 -$19 $4 $761

a These estimates include costs associated with the Tier 3 vehicle standards in all states except California and states 

that have adopted the LEV III program.

8.1.1.2 Fuel Program Costs

The annual fuel costs of the Tier 3 program consist of the costs to the refiners to control 

sulfur on a per gallon basis and the total number of gallons consumed annually in the U.S. except 

in the state of California. 

The fuel costs associated with the additional operating and capital costs to refiners to 

meet the final sulfur average of 10 ppm, as described in detail in RIA Chapter 5, is 0.65 cents per 

gallon. The annual fuel costs of the final Tier 3 program are lower than the proposed fuel costs, 

0.89 cents, due to the reduction in cost per gallon to control sulfur.

The fuel consumption values used in the annual fuel cost projections are based on the 

U.S. Energy Information Administration’s Annual Energy Outlook (AEO) 2013. The latest 

AEO projection used in the final rule cost projections contain lower annual fuel consumption 

than the values used in the Tier 3 proposal because AEO 2013 now reflects the light-duty GHG 

and fuel economy standards for the 2017-2025 model years. The light-duty standards are 

projected to decrease the national gasoline fuel consumption due to more efficient vehicles. 

The annual fuel consumption in AEO 2013 represents the national fuel usage in terms of 

BTU. For the fuel program cost analysis, it was necessary to convert the national BTU fuel 

consumption to millions of gallons of fuel consumed in the U.S. in all states except California. 

This conversion process is explained here using a sample year of 2017. Based on AEO 2013, the 

annual fuel consumption of motor gasoline and E85 in the transportation sector is projected to be 

15,342 trillion BTU in 2017.1 AEO’s million BTU/barrel conversion factors for E85 and Motor 

Gasoline Average along with a conversion factor of 42 U.S. gallons of fuel per barrel were used 

to calculate the annual fuel consumption in terms of million gallons.2 Based on these 

conversions, the national fuel consumption in 2017 is projected to be 128,318 million gallons. 

Finally, the national fuel consumption values from AEO 2013 were reduced for this analysis to 

remove the fraction of gasoline sold in California. The AEO 2013 Prime Supplier Sales 

Volumes of Motor Gasoline provides the sales of motor gasoline by state.3 Based on the average 

between 1983 and 2012, the fraction of national gasoline sales in California was 3.6 percent. 

After removing the fraction of fuel consumed in California, the annual fuel costs in 2017 are 

calculated based on a fuel consumption level of 123,689 million gallons. 

The Tier 3 fuel program provides flexibilities, including the ability for refiners to phasein the sulfur standards and earn early credits. The fuel program costs presented here assume a 

start date of 2017 and do not reflect the sulfur control program phase in.
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A comparison of the annual fuel consumption values used in the proposal and this final 

rule is shown in Figure 8-1.

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

2016 2018 2020 2022 2024 2026 2028 2030 2032

Annual Fuel Consumption (millions of gallons)

Year

Tier 3 Impacted Annual Fuel Consumption 

Notice of Proposed Rulemaking vs. Final Rule

NPRM

Final Rule

Figure 8-1: Annual Fuel Consumption Comparison between NPRM and Final Rule

The projected annual fuel consumption and annual fuel costs of the final program are 

listed in Table 8-2. 

Table 8-2: Annual Fuel Costs, 2011$

Year Annual Fuel Consumption

Impacted by Tier 3 

Program (million gallons)

Fuel Sulfur Control 

Costs

($Million)a

2016 0 $0 

2017 123,698 $804 

2018 122,962 $799 

2019 122,121 $794 

2020 121,015 $787 

2021 119,625 $778 

2022 118,190 $768 

2023 116,645 $758 

2024 115,066 $748 

2025 113,335 $737 
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2030 107,065 $696 

a These estimates include costs associated with the Tier 3 fuel standards in all states except California.

8.1.1.3 Total Costs

The sum of the vehicle technology costs to control exhaust and evaporative emissions, in 

addition to the costs to control the sulfur level in the fuel, represent the total costs of the

program, as shown in Table 8-3.

Table 8-3: Total Annual Vehicle and Fuel Control Costs, 2011$

Year Total Vehicle 

Program Costs 

($Million)

Fuel Sulfur 

Control Costs

($Million)

Total Program Costs 

($Million)a

2016 $21.4 $0 $21

2017 $297 $804 $1,101

2018 $615 $799 $1,414

2019 $653 $794 $1,447

2020 $697 $787 $1,484

2021 $725 $778 $1,503

2022 $758 $768 $1,526

2023 $751 $758 $1,509

2024 $761 $748 $1,509

2025 $773 $737 $1,510

2030 $761 $696 $1,457

a These estimates include: (a) costs associated with the Tier 3 vehicle standards in all states except California and 

states that have adopted the LEV III program and (b) the Tier 3 fuel standards in all states except California.

8.1.2 Quantified and Monetized Health and Environmental Impacts

This section presents EPA’s analysis of the criteria pollutant-related health and 

environmental impacts that would occur as a result of the final Tier 3 standards. The vehicles 

and fuels subject to the final standards are significant sources of mobile source air pollution such 

as direct PM, NOX, SOX, VOCs and air toxics. The standards will affect exhaust and evaporative 

emissions of these pollutants from vehicles. Emissions of NOX (a precursor to ozone formation 

and secondarily-formed PM2.5), SOX (a precursor to secondarily-formed PM2.5), VOCs (a 

precursor to ozone formation and, to a lesser degree, secondarily-formed PM2.5) and directlyemitted PM2.5 contribute to ambient concentrations of PM2.5 and ozone. Exposure to ozone and 

PM2.5 is linked to adverse human health impacts such as premature deaths as well as other 

important public health and environmental effects.

The analysis in this section aims to characterize the benefits of the final standards by 

answering two key questions:
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1. What are the health and welfare effects of changes in ambient particulate matter 

(PM2.5) and ozone air quality resulting from reductions in precursors including NOX and SO2?

2. What is the economic value of these effects?

For the final rulemaking, we have quantified and monetized the health and environmental 

impacts in 2030, representing impacts associated with a year when the program is fully

implemented and most of the fleet is turned over. Overall, we estimate that the final standards 

will lead to a net decrease in PM2.5- and ozone-related health impacts in 2030. The decrease in 

population-weighted national average PM2.5 exposure results in a net decrease in adverse PMrelated human health impacts (the decrease in national population-weighted annual average 

PM2.5 is 0.04 μg/m3in 2030).A The decrease in population-weighted national average ozone 

exposure results in a net decrease in ozone-related health impacts (population-weighted 

maximum 8-hour average ozone decreases by 0.32 ppb in 2030).

Using the lower end of EPA’s range of preferred premature mortality estimates (Krewski

et al., 2009 for PM2.5 and Bell et al., 2004 for ozone),

4,5 we estimate that by 2030, 

implementation of the standards will reduce approximately 770 premature mortalities annually

and yield between $6.7 and $7.4 billion in total annual benefits, depending on the discount rate 

used.

B

 The upper end of the range of avoided premature mortality estimates associated with the 

final standards (based on Lepeule et al., 2012 for PM2.5 and Levy et al., 2005 for ozone)6,7results 

in approximately 2,000 premature mortalities avoided in 2030 and yields between $18 and $19

billion in total benefits. Thus, even using the lower end of the range of premature mortality

estimates, the health impacts of the final standards presented in this rule will clearly be 

substantial.

We note that of necessity, decisions on the emissions and other elements used in the air 

quality modeling were made early in the analytical process for the final rulemaking. For this 

reason, the modeled changes in emissions used to support the air quality and benefits analyses 

are slightly different than those used to represent the final emissions impacts of the Tier 3 

standards. The magnitude of the differences is small, however, and for that reason we do not 

expect these differences to materially impact our cost-benefit conclusions. See Chapter 7.2.1.1

for more details. 

 

A Note that the national, population-weighted PM2.5 and ozone air quality metrics presented in this Chapter represent 

an average for the entire, gridded U.S. CMAQ domain. These are different than the population-weighted PM2.5 and 

ozone design value metrics presented in Chapter 7, which represent the average for areas with a current air quality 

monitor.

B The monetized value of PM2.5-related mortality accounts for a twenty-year segmented cessation lag. To discount 

the value of premature mortality that occurs at different points in the future, we apply both a 3 and 7 percent 

discount rate. We also use both a 3 and 7 percent discount rate to value PM-related nonfatal heart attacks 

(myocardial infarctions). Nonfatal myocardial infarctions (MI) are valued using age-specific cost-of-illness values 

that reflect lost earnings and direct medical costs over a 5-year period following a nonfatal MI.
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8.1.2.1 Overview

This analysis reflects the impacts of the final Tier 3 rule in 2030 compared to a futureyear reference scenario without the program in place. Overall, we estimate that the final rule will

lead to a net decrease in PM2.5-related health and environmental impacts (see Section 7.2.4 for 

more information about the air quality modeling results). The decrease in population-weighted 

national average PM2.5 exposure results in a net decrease in adverse PM-related human health

and environmental impacts (the decrease in national population weighted annual average PM2.5 is 

0.04 μg/m3 in 2030). 

The air quality modeling also projects decreases in ozone concentrations (see Section 

7.2.4). The overall decrease in population-weighted national average ozone exposure results in 

decreases in ozone-related health and environmental impacts (population weighted maximum 8-

hour average ozone decreases by 0.32 ppb in 2030).

We base our analysis of the program’s impact on human health and the environment on 

peer-reviewed studies of air quality and human health effects.8,9,10 Our benefits methods are 

consistent with the RIA that accompanied the final revisions to the National Ambient Air Quality

Standards (NAAQS) for Particulate Matter, the 2008 final ozone NAAQS, and the 2010 ozone 

NAAQS reconsideration. To model the ozone and PM air quality impacts of the final standards, 

we used the Community Multiscale Air Quality (CMAQ) model (see Chapter 7.2.2). The 

modeled ambient air quality data serves as an input to the Environmental Benefits Mapping and 

Analysis Program version 4.065 (BenMAP).C BenMAP is a computer program developed by the 

U.S. EPA that integrates a number of the modeling elements used in previous analyses (e.g., 

interpolation functions, population projections, health impact functions, valuation functions, 

analysis and pooling methods) to translate modeled air concentration estimates into health effects 

incidence estimates and monetized benefits estimates.

The range of total monetized ozone- and PM-related health impacts in 2030 is presented 

in Table 8-4. We present total benefits based on the PM- and ozone-related premature mortality

function used. The benefits ranges therefore reflect the addition of each estimate of ozonerelated premature mortality (each with its own row in Table 8-4) to estimates of PM-related 

premature mortality. 

 

C

Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ 

benmodels.html.
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Table 8-4: Estimated 2030 Monetized PM-and Ozone-Related Health Benefits

2030 Total Ozone and PM Benefits – PM Mortality Derived from American Cancer Society Analysis and 

Six-Cities Analysisa

Premature Ozone 

Mortality Function

Reference Total Benefits

(Billions, 2011$, 3% 

Discount Rate)b,c

Total Benefits

(Billions, 2011$, 7% 

Discount Rate) b,c

Multi-city analyses Bell et al., 2004 Total: $7.4 - $15

PM: $6.0 - $14

Ozone: $1.1

Total: $6.7 - $14

PM: $5.4 - $12

Ozone: $1.1

Huang et al., 2005 Total: $7.9 - $16

PM: $6.0 - $14

Ozone: $1.7

Total: $7.3 - $14

PM: $5.4 - $12

Ozone: $1.7

Schwartz, 2005 Total: $8.0 - $16

PM: $6.0 - $14

Ozone: $1.7

Total: $7.3 - $14

PM: $5.4 - $12

Ozone: $1.7

Meta-analyses Bell et al., 2005 Total: $9.8 - $18

PM: $6.0 - $14

Ozone: $3.6

Total: $9.2 - $16

PM: $5.4 - $12

Ozone: $3.6

Ito et al., 2005 Total: $11 - $19

PM: $6.0 - $14

Ozone: $4.9

Total: $11 - $18

PM: $5.4 - $12

Ozone: $4.9

Levy et al., 2005 Total: $11 - $19

PM: $6.0 - $14

Ozone: $5.0

Total: $11 - $18

PM: $5.4 - $12

Ozone: $5.0

.a Total includes premature mortality-related and morbidity-related ozone and PM2.5 benefits. Range was 

developed by adding the estimate from the ozone premature mortality function to the estimate of PM2.5-related 

premature mortality derived from either the ACS study (Krewski et al., 2009) or the Six-Cities study (Lepeule et 

al., 2012). Range also reflects alternative estimates of non-fatal heart attacks avoided based on either Peters et al. 

(2001) or a pooled estimate of four studies.

b Note that total benefits presented here do not include a number of unquantified benefits categories. A detailed 

listing of unquantified health effects is provided in Table 8-5.

c Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines for 

Preparing Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits for ease of 

presentation and computation. Totals may not sum due to rounding.

The benefits analysis presented in this chapter incorporates an array of policy and 

technical changes that the Agency has adopted since the Tier 3 proposal’s draft RIA. These 

changes reflect EPA’s work to update PM-related benefits reflected in the most recent PM 

NAAQS.11 Below we note the aspects of this analysis that differ from the Tier 3 proposal’s draft 

RIA:12
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 Incorporation of the newest American Cancer Society (ACS) mortality study and 

newest Harvard Six Cities mortality study. In 2012, Lepeule et al. published an 

extended analysis of the Six Cities cohort.13 Compared to the study it replaces 

(Laden et al., 2006),14 this new analysis follows the cohort for a longer time and 

includes more years of PM2.5 monitoring data. The all-cause PM2.5 mortality risk 

coefficient drawn from Lepeule et al. is roughly similar to the Laden et al. risk 

coefficient applied in the EPA’s recent analyses of long-term PM2.5 mortality and 

has narrower confidence intervals. 

In 2009, the Health Effects Institute published an extended analysis of the ACS 

cohort (Krewski et al., 2009).15 Compared to the study it replaces (Pope et al., 

2002),16 this new analysis incorporates a number of methodological 

improvements.DThe all-cause PM2.5 mortality risk estimate drawn from Krewski 

et al. (2009) is identical to the Pope et al. (2002) risk estimate applied in recent 

EPA analyses of long-term PM2.5 mortality but has narrower confidence intervals.

 Updated health endpoints. We have removed the quantification of chronic 

bronchitis from our main analysis. This change is consistent with the findings of 

the PM Integrated Science Assessment (ISA) that the evidence for an association 

between long-term exposure to PM2.5 and respiratory effects is more tenuous.17

 Updated demographic data. We updated the population demographic data in 

BenMAP to reflect the 2010 Census and future projections based on economic 

forecasting models developed by Woods and Poole, Inc.18 These data replace the 

earlier demographic projection data from Woods and Poole (2011).19

 Incorporation of new morbidity studies. Since the publication of the 2004 Criteria 

Document for Particulate Matter,20 the publication of the more recent PM ISA,21

and the Provisional Assessment of Recent Studies on Health Effects of Particulate 

Matter Exposure (“Provisional Assessment”),22 the epidemiological literature has 

produced several new studies examining the association between short-term PM2.5 

exposure and acute myocardial infarctions, respiratory and cardiovascular 

hospitalizations, respiratory and cardiovascular emergency department visits, 

acute respiratory symptoms and exacerbation of asthma, respiratory and 

cardiovascular hospitalizations. Upon careful evaluation of this new literature, we 

added several new studies to our health impact assessment; in many cases we 

have replaced older single-city time-series studies with newer multi-city timeseries analyses.

 Updated the survival rates for non-fatal acute myocardial infarctions. Based on 

recent data from Agency for Healthcare Research and Quality’s Healthcare 

Utilization Project National Inpatient Sample database,23 we identified death rates 

 

D Refer to the 2012 PM NAAQS RIA for more detail regarding the studies themselves.
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for adults hospitalized with acute myocardial infarction stratified by age. These 

rates replace the survival rates from Rosamond et al. (1999).24

 Updated hospital cost-of-illness (COI), including median wage data. In previous 

benefits analyses, estimates of hospital charges and lengths of hospital stays were 

based on discharge statistics provided by the Agency for Healthcare Research and 

Quality’s Healthcare Utilization Project National Inpatient Sample (NIS) database 

for 2000.25 The version of BenMAP (version 4.0.65) used in this analysis updated 

this information to use the 2007 database. The data source for the updated median 

annual income is the 2007 American Community Survey.

The benefits in Table 8-4 include all of the human health impacts we are able to quantify 

and monetize at this time. However, the full complement of human health effects associated

with PM and ozone remain unquantified because of current limitations in methods or available 

data. We have not quantified a number of known or suspected health effects linked with ozone 

and PM for which appropriate health impact functions are not available or which do not provide 

easily interpretable outcomes (e.g., changes in heart rate variability). These are listed in Table 

8-5. As a result, the health benefits quantified in this analysis are likely underestimates of the 

total benefits attributable to the final program.

Table 8-5: Quantified and Unquantified Health Effects

BENEFITS 

CATEGORY

SPECIFIC EFFECT EFFECT HAS 

BEEN 

QUANTIFIED

EFFECT HAS 

BEEN 

MONETIZED

MORE 

INFORMATION

Improved Human Health

Reduced 

incidence of 

premature 

mortality and 

morbidity from 

exposure to PM2.5

Adult premature mortality based on 

cohort study estimates and expert 

elicitation estimates (age >25 or age 

>30)

  PM NAAQS RIA, 

Section 5.6

Infant mortality (age <1)   PM NAAQS RIA, 

Section 5.6

Non-fatal heart attacks (age > 18)   PM NAAQS RIA, 

Section 5.6

Hospital admissions—respiratory (all 

ages)

  PM NAAQS RIA, 

Section 5.6

Hospital admissions—cardiovascular 

(age >20)

  PM NAAQS RIA, 

Section 5.6

Emergency department visits for 

asthma (all ages)

  PM NAAQS RIA, 

Section 5.6

Acute bronchitis (age 8–12)   PM NAAQS RIA, 

Section 5.6

Lower respiratory symptoms (age 7–   PM NAAQS RIA, 
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BENEFITS 

CATEGORY

SPECIFIC EFFECT EFFECT HAS 

BEEN 

QUANTIFIED

EFFECT HAS 

BEEN 

MONETIZED

MORE 

INFORMATION

14) Section 5.6

Upper respiratory symptoms 

(asthmatics age 9–11)

  PM NAAQS RIA, 

Section 5.6

Asthma exacerbation (asthmatics age 

6–18)

  PM NAAQS RIA, 

Section 5.6

Lost work days (age 18–65)   PM NAAQS RIA, 

Section 5.6

Minor restricted-activity days (age 

18–65)

  PM NAAQS RIA, 

Section 5.6

Chronic Bronchitis (age >26) — — PM NAAQS RIA, 

Section 5.6c

Emergency department visits for 

cardiovascular effects (all ages)

— — PM NAAQS RIA, 

Section 5.6c

Strokes and cerebrovascular disease 

(age 50–79)

— — PM NAAQS RIA, 

Section 5.6c

Other cardiovascular effects (e.g., 

other ages)

— — PM ISAa

Other respiratory effects (e.g., 

pulmonary function, non-asthma ER 

visits, non-bronchitis chronic 

diseases, other ages and populations)

— — PM ISAa

Reproductive and developmental 

effects (e.g., low birth weight, preterm births, etc.)

— — PM ISAa,b

Cancer, mutagenicity, and 

genotoxicity effects

— — PM ISAa,b

Reduced 

incidence of 

premature 

mortality and 

morbidity from 

exposure to 

ozone

Premature mortality based on shortterm study estimates (all ages)  Ozone ISA

Premature mortality based on longterm study estimates (age 30–99)— — Ozone ISAc

Hospital admissions—respiratory 

causes (age > 65)

  Ozone ISA

Hospital admissions—respiratory 

causes (age <2)

  Ozone ISA

Emergency department visits for 

asthma (all ages)

  Ozone ISA

Minor restricted-activity days (age   Ozone ISA
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BENEFITS 

CATEGORY

SPECIFIC EFFECT EFFECT HAS 

BEEN 

QUANTIFIED

EFFECT HAS 

BEEN 

MONETIZED

MORE 

INFORMATION

18–65)

School absence days (age 5–17)   Ozone ISA

Decreased outdoor worker 

productivity (age 18–65)

  Ozone ISA

Other respiratory effects (e.g., 

premature aging of lungs)

— — Ozone ISAa

Cardiovascular and nervous system 

effects

— — Ozone ISAb

Reproductive and developmental 

effects

— — Ozone ISAb

Reduced 

incidence of 

morbidity from 

exposure to air 

toxics

Cancer (benzene, 1,3-butadiene, 

formaldehyde, acetaldehyde)

Anemia (benzene)

Disruption of production of blood 

components (benzene)

Reduction in the number of blood 

platelets (benzene)

Excessive bone marrow formation 

(benzene)

Depression of lymphocyte counts 

(benzene)

Reproductive and developmental 

effects (1,3-butadiene)

Irritation of eyes and mucus 

membranes (formaldehyde)

Respiratory irritation (formaldehyde)

Asthma attacks in asthmatics 

(formaldehyde)

Asthma-like symptoms in nonasthmatics (formaldehyde)

Irritation of the eyes, skin, and 

respiratory tract (acetaldehyde)

Upper respiratory tract irritation and 

congestion (acrolein)

— — IRIS; NRC, 

2000a,b

a We assess these benefits qualitatively because we do not have sufficient confidence in available data or 

methods.

b We assess these benefits qualitatively because current evidence is only suggestive of causality or there are 

other significant concerns over the strength of the association.

c We assess these benefits qualitatively due to time and resource limitations for this analysis.

While there will be impacts associated with air toxic pollutant emission changes that 

result from the final program, we do not attempt to monetize those impacts. This is primarily

because currently available tools and methods to assess air toxics risk from mobile sources at the 

national scale are not adequate for extrapolation to incidence estimations or benefits assessment. 
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The best suite of tools and methods currently available for assessment at the national scale are 

those used in the National-Scale Air Toxics Assessment (NATA). The EPA Science Advisory

Board specifically commented in their review of the 1996 NATA that these tools were not yet 

ready for use in a national-scale benefits analysis, because they did not consider the full 

distribution of exposure and risk, or address sub-chronic health effects.26 While EPA has since 

improved these tools, there remain critical limitations for estimating incidence and assessing 

benefits of reducing mobile source air toxics. 

As part of the second prospective analysis of the benefits and costs of the Clean Air 

Act,

27 EPA conducted a case study analysis of the health effects associated with reducing 

exposure to benzene in Houston from implementation of the Clean Air Act. While reviewing the 

draft report, EPA’s Advisory Council on Clean Air Compliance Analysis concluded that “the 

challenges for assessing progress in health improvement as a result of reductions in emissions of 

hazardous air pollutants (HAPs) are daunting...due to a lack of exposure-response functions, 

uncertainties in emissions inventories and background levels, the difficulty of extrapolating risk 

estimates to low doses and the challenges of tracking health progress for diseases, such as 

cancer, that have long latency periods.”28 EPA continues to work to address these limitations; 

however, we did not have the methods and tools available for national-scale application in time 

for the analysis of the final program.

E

 

The reduction in air pollutants that will result from the final program will also have 

“welfare” co-benefits in addition to human health benefits, including changes in visibility, 

materials damage, ecological effects from PM deposition, ecological effects from nitrogen and 

sulfur emissions, vegetation effects from ozone exposure, and climate effects.

F

 Despite our goal 

to quantify and monetize as many of the benefits as possible for the final rulemaking, the welfare 

co-benefits of the Tier 3 standards remain unquantified and nonmonetized in this RIA due to 

data, methodology, and resource limitations. As a result, the benefits quantified in this analysis

are likely underestimates of the total benefits attributable to the final program. We refer the 

reader to Chapter 6 of the PM NAAQS RIA for a complete discussion of these welfare cobenefits. 29

8.1.2.2 Human Health Impacts

Table 8-6 and Table 8-7 present the core estimates of annual PM2.5 and ozone health 

impacts in the 48 contiguous U.S. states associated with the final Tier 3 program. For each 

 

E

In April, 2009, EPA hosted a workshop on estimating the benefits or reducing hazardous air pollutants. This 

workshop built upon the work accomplished in the June 2000 Science Advisory Board/EPA Workshop on the 

Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on 

approaches to estimating human health benefits from reductions in air toxics exposure, but no consensus was 

reached on methods that could be implemented in the near term for a broad selection of air toxics. Please visit 

http://epa.gov/air/toxicair/2009workshop.html for more information about the workshop and its associated materials.

F We project that the Tier 3 vehicle and fuel standards will reduce nitrous oxide (N2O) and methane (CH4) emissions 

from vehicles. The reductions in these potent greenhouse gases will be offset to some degree by the increase in CO2

emissions from refineries. The combined impact is a net decrease on a CO2-equivalent basis and would yield a net 

benefit if these reductions were monetized.
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endpoint presented in Table 8-6 and Table 8-7, we provide both the point estimate and the 90

percent confidence interval. 

Using EPA’s preferred estimates, based on the American Cancer Society (ACS) and SixCities studies and no threshold assumption in the model of mortality, we estimate that the final 

program would result in between 660 and 1,500 cases of avoided PM2.5-related premature deaths 

annually in 2030. A sensitivity analysis was conducted to understand the impact of alternative 

concentration response functions suggested by experts in the field. As shown in Table 8-8, when

the range of expert opinion is used, we estimate between 130 and 2,200 fewer premature 

mortalities in 2030.

The range of ozone impacts is based on changes in risk estimated using several sources of 

ozone-related mortality effect estimates. This analysis presents six alternative estimates for the 

association based upon different functions reported in the scientific literature, derived from both 

the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) 30,31,32 and from a series 

of meta-analyses. 33,34,35 This approach is not inconsistent with recommendations provided by

the NRC in their report (NRC, 2008) on the estimation of ozone-related mortality risk reductions, 

“The committee recommends that the greatest emphasis be placed on estimates from new 

systematic multicity analyses that use national databases of air pollution and mortality, such as in 

the NMMAPS, without excluding consideration of meta-analyses of previously published 

studies.”36 For ozone-related premature mortality in 2030, we estimate a range of between 110

to 500 fewer premature mortalities. 

Following these tables, we also provide a more comprehensive presentation of the 

distributions of incidence generated using the available information from empirical studies and 

expert elicitation. 

Table 8-8 presents the distributions of the reduction in PM2.5-related premature mortality 

based on the C-R distributions provided by each expert, as well as that from the data-derived 

health impact functions, based on the statistical error associated with the ACS study and the SixCities study. The 90 percent confidence interval for each separate estimate of PM-related 

mortality is also provided. 

In 2030, the effect estimates of nine of the twelve experts included in the elicitation panel 

fall within the empirically-derived range provided by the ACS and Six-Cities studies. Only one 

expert falls below this range, while two of the experts are above this range. Although the overall 

range across experts is summarized in these tables, the full uncertainty in the estimates is 

reflected by the results for the full set of 12 experts. The twelve experts’ judgments as to the 

likely mean effect estimate are not evenly distributed across the range illustrated by arraying the 

highest and lowest expert means.
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Table 8-6: Estimated PM2.5-Related Health Impacts

Health Effect 2030 Annual 

Reduction in 

Incidence

(5th% - 95th%ile)

Premature Mortality – Derived from epidemiology literatureb

 Adult, age 30+, ACS Cohort Study (Krewski et al., 2009)

 Adult, age 25+, Six-Cities Study (Lepeule et al., 2012)

 Infant, age <1 year (Woodruff et al., 1997)

660

(480 – 840)

1,500

(860 – 2,100)

Non-fatal myocardial infarction (adult, age 18 and over)

 Peters et al. (2001)

 Pooled estimate of 4 studies

790

(290 – 1,300)

85

(42 – 190)

Hospital admissions - respiratory (all ages)c,e 210

(-38 – 380)

Hospital admissions - cardiovascular (adults, age >18)d250

(130 – 440)

Emergency room visits for asthma (age 18 years and younger)e340

(-58 – 660)

Acute bronchitis, (children, age 8-12)e980

(-35 – 2,000)

Lower respiratory symptoms (children, age 7-14) 13,000

(6,000 – 19,000)

Upper respiratory symptoms (asthmatic children, age 9-18) 18,000

(5,600 – 30,000)

Asthma exacerbation (asthmatic children, age 6-18) 19,000

(2,300 – 37,000)

Work loss days 81,000

(70,000 – 91,000)

Minor restricted activity days (adults age 18-65) 480,000

(400,000 – 550,000)

a

Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United 

States. 

b

PM-related adult mortality based upon the most recent American Cancer Society (ACS) Cohort Study 

(Krewski et al., 2009) and the most recent Six-Cities Study (Lepeule et al., 2012). Note that these are two 

alternative estimates of adult mortality and should not be summed. PM-related infant mortality based upon a 

study by Woodruff, Grillo, and Schoendorf, (1997).37

c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease 

(COPD), pneumonia and asthma.

d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart 

disease, dysrhythmias, and heart failure.

e The negative estimates at the 5th percentile confidence estimates for these morbidity endpoints reflect the 

statistical power of the study used to calculate these health impacts. These results do not suggest that reducing 

air pollution results in additional health impacts.
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Table 8-7: Estimated Ozone-Related Health Impacts

Health Effect 2030 Annual Reduction 

in Incidence

(5th% - 95th%ile)

Premature Mortality, All agesb

Multi-City Analyses 

 Bell et al. (2004) – Non-accidental

 Huang et al. (2005) – Cardiopulmonary

 Schwartz (2005) – Non-accidental

Meta-analyses:

 Bell et al. (2005) – All cause

 Ito et al. (2005) – Non-accidental

 Levy et al. (2005) – All cause

110

(46 – 170)

160

(74 – 250)

170

(68 – 270)

350

(190 – 510)

490

(320 – 660)

500

(360 – 630)

Hospital admissions- respiratory causes (adult, 65 and 

older)c

740

(87 – 1,400)

Hospital admissions -respiratory causes (children, under 2) 310

(160 – 450)

Emergency room visit for asthma (all ages)d330

(-8 – 990)

Minor restricted activity days (adults, age 18-65) 600,000

(290,000 – 910,000)

School absence days 210,000

(92,000 – 300,000)

a

Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous U.S. 

b Estimates of ozone-related premature mortality are based upon incidence estimates derived from several 

alternative studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. 

(2005); Levy et al. (2005). The estimates of ozone-related premature mortality should therefore not be 

summed.

c Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for 

COPD and pneumonia. 

d The negative estimate at the 5th percentile confidence estimate for this morbidity endpoint reflects the 

statistical power of the study used to calculate this health impact. This result does not suggest that reducing air 

pollution results in additional health impacts.

Table 8-8: Results of Application of Expert Elicitation: Annual Reductions in Premature 

Mortality in 2030 Associated with the Final Program

Source of Mortality 

Estimate

2030 Tier 3 Control

5th Percentile Mean 95th Percentile

Krewski et al. (2009) 480 660 840

Lepeule et al. (2012) 860 1,500 2,100

Expert A 340 1,700 3,300

Expert B 88 1,400 2,900

Expert C 480 1,300 2,200

Expert D 58 950 1,500

Expert E 1,100 2,200 3,300
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Source of Mortality 

Estimate

2030 Tier 3 Control

5th Percentile Mean 95th Percentile

Expert F 790 1,200 1,700

Expert G -1 790 1,400

Expert H -2 980 2,300

Expert I 110 1,300 2,300

Expert J 160 1,100 2,300

Expert K 0 130 590

Expert L 1 860 1,800

8.1.2.3 Monetized Estimates of Human Health and Environmental Impacts

Table 8-9 presents the estimated monetary value of changes in the incidence of ozone and 

PM2.5-related health and environmental effects. Total aggregate monetized benefits are 

presented in Table 8-10. All monetized estimates are presented in 2011$. Where appropriate,

estimates account for growth in real gross domestic product (GDP) per capita between 2000 and 

2030.G The monetized value of PM2.5-related mortality also accounts for a twenty-year 

segmented cessation lag.H To discount the value of premature mortality that occurs at different 

points in the future, we apply both a 3 and 7 percent discount rate. We also use both a 3 and 7 

percent discount rate to value PM-related nonfatal heart attacks (myocardial infarctions).I 

In addition to omitted benefits categories such as air toxics and various welfare effects, 

not all known PM2.5- and ozone-related health and welfare effects could be quantified or 

monetized. The estimate of total monetized health benefits of the final program is thus equal to 

the subset of monetized PM2.5- and ozone-related health impacts we are able to quantify plus the 

sum of the nonmonetized health and welfare benefits. Our estimate of total monetized benefits 

in 2030 for the final program, using the ACS and Six-Cities PM mortality studies and the range 

of ozone mortality assumptions, is between $7.4 and $19 billion billion, assuming a 3 percent 

 

G Our analysis accounts for expected growth in real income over time. Economic theory argues that WTP for most 

goods (such as environmental protection) will increase if real incomes increase. Benefits are therefore adjusted by 

multiplying the unadjusted benefits by the appropriate adjustment factor to account for income growth over time. 

For growth between 2000 and 2030, this factor is 1.23 for long-term mortality, 1.27 for chronic health impacts, and 

1.08 for minor health impacts. For a complete discussion of how these adjustment factors were derived, we refer the 

reader to the PM NAAQS regulatory impact analysis. Note that similar adjustments do not exist for cost-of-illnessbased unit values. For these, we apply the same unit value regardless of the future year of analysis.

H Based in part on prior SAB advice, EPA has typically assumed that there is a time lag between changes in 

pollution exposures and the total realization of changes in health effects. Within the context of benefits analyses, 

this term is often referred to as “cessation lag”. The existence of such a lag is important for the valuation of 

premature mortality incidence because economic theory suggests that benefits occurring in the future should be 

discounted. In this analysis, we apply a twenty-year distributed lag to PM mortality reductions. This method is 

consistent with the most recent recommendation by the EPA’s Science Advisory Board. Refer to: EPA – Science 

Advisory Board, 2004. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on 

Cessation Lag. Letter from the Health Effects Subcommittee to the U.S. Environmental Protection Agency 

Administrator, December.

I Nonfatal myocardial infarctions (MI) are valued using age-specific cost-of-illness values that reflect lost earnings 

and direct medical costs over a 5-year period following a nonfatal MI. 
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discount rate, or between $6.7 and $18 billion, assuming a 7 percent discount rate. As the results

indicate, total benefits are driven primarily by the reduction in PM2.5- and ozone-related 

premature fatalities each year and represent the benefits of the Tier 3 program anticipated to 

occur annually when the program is fully implemented and most of the fleet turned over.

The next largest benefit is for reductions in nonfatal heart attacks, although this value is 

more than an order of magnitude lower than for premature mortality. Hospital admissions for 

respiratory and cardiovascular causes, minor restricted activity days, and work loss days account 

for the majority of the remaining benefits. The remaining categories each account for a small 

percentage of total benefit; however, they represent a large number of avoided incidences 

affecting many individuals. A comparison of the incidence table to the monetary benefits table 

reveals that there is not always a close correspondence between the number of incidences 

avoided for a given endpoint and the monetary value associated with that endpoint. For

example, there are many more work loss days than PM-related premature mortalities, yet work 

loss days account for only a very small fraction of total monetized benefits. This reflects the fact 

that many of the less severe health effects, while more common, are valued at a lower level than 

the more severe health effects. Also, some effects, such as hospital admissions, are valued using 

a proxy measure of willingness-to-pay (e.g., cost-of-illness). As such, the true value of these 

effects may be higher than that reported here. 

Table 8-9: Estimated Monetary Value of Changes in Incidence of Health and Welfare 

Effects (millions of 2010$)

HEALTH ENDPOINTS 2030

(5TH AND 95TH

PERCENTILE)

PM2.5-Related Health Effects

Premature Mortality – Derived 

from Epidemiology Studiesb,c

Adult, age 30+ - ACS study 

(Krewski et al., 2009)

 3% discount rate

 7% discount rate

$6,100

($910 - $14,000)

$5,500

($820 - $13,000)

Adult, age 25+ - Six-Cities study 

(Lepeule et al., 2012)

 3% discount rate

 7% discount rate

$14,000

($2,000 - $33,000)

$12,000

($1,800 - $30,000)

Infant Mortality, <1 year –

(Woodruff et al. 1997)

$13

($1.8 - $32)

Non-fatal acute myocardial infarctions 

 Peters et al., 2001

 3% discount rate

 7% discount rate

 Pooled estimate of 4 studies

 3% discount rate

 7% discount rate

$96

($21 - $230)

$93

($19 - $220)

$10

($2.6 - $27)

$10

($2.4 - $27)
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Hospital admissions for respiratory causesd$5.9

(-$1.6 - $11)

Hospital admissions for cardiovascular causes $9.9

($5.0 - $17)

Emergency room visits for asthmad$0.15

(-$0.02 - $0.29)

Acute bronchitis (children, age 8–12)d$0.49

(-$0.02 - $1.2)

Lower respiratory symptoms (children, 7–14) $0.27

($0.11 - $0.51)

Upper respiratory symptoms (asthma, 9–11) $0.62

($0.18 - $1.4)

Asthma exacerbations $1.1

($0.14 - $2.7)

Work loss days $12

($11 – $14)

Minor restricted-activity days (MRADs) $34

($20 - $49)

Ozone-Related Health Effects

Premature Mortality, All ages –

Derived from Multi-city analyses

Bell et al., 2004 $1,100

($150 - $2,800)

Huang et al., 2005 $1,600

($220 - $4,100)

Schwartz, 2005 $1,700

($220 - $4,400)

Premature Mortality, All ages –

Derived from Meta-analyses

Bell et al., 2005 $3,600

($510 - $8,800)

Ito et al., 2005 $5,000

($740 - $12,000

Levy et al., 2005 $5,100

($760 - $12,000)

Hospital admissions- respiratory causes (adult, 65 and older) $21

($2.5 - $39)

Hospital admissions- respiratory causes (children, under 2) $3.7

($1.9 - $5.4)

Emergency room visit for asthma (all ages) $0.14

(-$0.003 - $0.41)

Minor restricted activity days (adults, age 18-65) $43

($19 - $73)

School absence days $21

($9.3 - $31)

a Monetary benefits are rounded to two significant digits for ease of presentation and computation. PM and 

ozone benefits are nationwide. 

b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis 

year (2030).

c Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results 

reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for 

preparing economic analyses.

d The negative estimate at the 5th percentile confidence estimate for this morbidity endpoint reflects the 

statistical power of the study used to calculate this health impact. This result does not suggest that reducing 

air pollution results in additional health impacts.
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Table 8-10: Total Monetized Ozone and PM-related Benefits Associated with the Final

Program in 2030

Total Ozone and PM Benefits (billions, 2011$) –

PM Mortality Derived from the ACS and Six-Cities Studies

3% Discount Rate 7% Discount Rate

Ozone 

Mortality 

Function

Reference Mean Total 

Benefits

Ozone 

Mortality 

Function

Reference Mean Total 

Benefits

Multi-city Bell et al., 

2004

$7.4 - $15 Multi-city Bell et al., 

2004

$6.7 - $14

Huang et al., 

2005

$7.9 - $16 Huang et al., 

2005

$7.3 - $14

Schwartz, 

2005

$8.0 - $16 Schwartz, 

2005

$7.3 - $14

Meta-analysis Bell et al., 

2005

$9.8 - $18 Meta-analysis Bell et al., 

2005

$9.2 - $16

Ito et al., 

2005

$11 - $19 Ito et al., 

2005

$11 - $18

Levy et al., 

2005

$11 - $19 Levy et al., 

2005

$11 - $18

Total Ozone and PM Benefits (billions, 2011$) –

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate)

3% Discount Rate 7% Discount Rate

Ozone 

Mortality 

Function

Reference Mean Total 

Benefits

Ozone 

Mortality 

Function

Reference Mean Total 

Benefits

Multi-city Bell et al., 

2004

$2.4 - $22 Multi-city Bell et al., 

2004

$2.4 - $20

Huang et al., 

2005

$2.9 - $22 Huang et al., 

2005

$2.9 - $20

Schwartz, 

2005

$3.0 - $22 Schwartz, 

2005

$3.0 - $20

Meta-analysis Bell et al., 

2005

$4.9 - $24 Meta-analysis Bell et al., 

2005

$4.9 - $22

Ito et al., 

2005

$6.3 - $26 Ito et al., 

2005

$6.3 - $24

Levy et al., 

2005

$6.3 - $26 Levy et al., 

2005

$6.3 - $26

8.1.2.4 Methodology

We follow a “damage-function” approach in calculating total benefits of the modeled 

changes in environmental quality. This approach estimates changes in individual health 

endpoints (specific effects that can be associated with changes in air quality) and assigns values 

to those changes assuming independence of the values for those individual endpoints. Total 

benefits are calculated simply as the sum of the values for all non-overlapping health endpoints. 

The “damage-function” approach is the standard method for assessing costs and benefits of 

environmental quality programs and has been used in several recent published analyses.38,39,40

To assess economic value in a damage-function framework, the changes in environmental 

quality must be translated into effects on people or on the things that people value. In some 

cases, the changes in environmental quality can be directly valued. In other cases, such as for 
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changes in ozone and PM, an impact analysis must first be conducted to convert air quality

changes into effects that can be assigned dollar values. For the purposes of this RIA, the health 

impacts analysis (HIA) includes those health effects that are directly linked to ambient levels of 

air pollution and specifically to those linked to ozone and PM2.5.

We note at the outset that the EPA rarely has the time or resources to perform extensive 

new research to measure directly either the health outcomes or their values for regulatory

analyses. Thus, similar to Kunzli et al. (2000)41 and other, more recent health impact analyses, 

our estimates are based on the best available methods of benefits transfer. Benefits transfer is the 

science and art of adapting primary research from similar contexts to obtain the most accurate 

measure of benefits for the environmental quality change under analysis. Adjustments are made 

for the level of environmental quality change, the socio-demographic and economic 

characteristics of the affected population, and other factors to improve the accuracy and 

robustness of benefits estimates.

8.1.2.4.1 Human Health Impact Assessment

The health impact assessment (HIA) quantifies the changes in the incidence of adverse 

health impacts resulting from changes in human exposure to PM2.5 and ozone air quality. HIAs 

are a well-established approach for estimating the retrospective or prospective change in adverse 

health impacts expected to result from population-level changes in exposure to pollutants. 42 PCbased tools such as the environmental Benefits Mapping and Analysis Program (BenMAP) can 

systematize health impact analyses by applying a database of key input parameters, including 

health impact functions and population projections—provided that key input data are available, 

including air quality estimates and risk coefficients.43 Analysts have applied the HIA approach to 

estimate human health impacts resulting from hypothetical changes in pollutant levels.44, 45,

46

The EPA and others have relied upon this method to predict future changes in health impacts 

expected to result from the implementation of regulations affecting air quality.

47 For this 

assessment, the HIA is limited to those health effects that are directly linked to ambient ozone 

and PM2.5 concentrations. 

The HIA approach used in this analysis involves three basic steps: (1) utilizing 

projections of PM2.5 air qualityJand estimating the change in the spatial distribution of the 

ambient air quality; (2) determining the subsequent change in population-level exposure; (3) 

calculating health impacts by applying concentration-response relationships drawn from the 

epidemiological literature to this change in population exposure.

A typical health impact function might look like: 

 1   0

 

x

y y e



,

where y0 is the baseline incidence (the product of the baseline incidence rate times the potentially

affected population), β is the effect estimate, and Δx is the estimated change in the summary

 

J

Projections of ambient PM2.5 concentrations for this analysis were generated using the Community Multiscale Air 

Quality model (CMAQ). See Chapter 7 of this RIA for more information on the air quality modeling.
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pollutant measure. There are other functional forms, but the basic elements remain the same. 

The following subsections describe the sources for each of the first three elements: size of the 

potentially affected populations; PM2.5 and ozone effect estimates; and baseline incidence rates. 

We also describe the treatment of potential thresholds in PM-related health impact functions in 

Section 8.1.2.5.3. Section 8.1.2.4.6 describes the ozone and PM air quality inputs to the health 

impact functions. 

8.1.2.4.2 Potentially Affected Populations

Quantified and monetized human health impacts depend on the demographic 

characteristics of the population, including age, location, and income. We use population 

projections based on economic forecasting models developed by Woods and Poole, Inc.48 The 

Woods and Poole (WP) database contains county-level projections of population by age, sex, and 

race out to 2040, relative to a baseline using the 2010 Census data. Projections in each county are 

determined simultaneously with every other county in the United States 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.49 According to WP, linking county-level growth projections together and 

constraining to a national-level total growth avoids potential errors introduced by forecasting 

each county independently. County projections are developed in a four-stage process:

 First, national-level variables such as income, employment, and populations are 

forecasted.

 Second, employment projections are made for 179 economic areas defined by the 

Bureau of Economic Analysis,50 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-based 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 2040 based on historical rates of mortality, fertility, and 

migration.

8.1.2.4.3 Effect Estimate Sources

The first step in selecting effect coefficients is to identify the health endpoints to be 

quantified. We base our selection of health endpoints on consistency with the EPA’s Integrated 

Science Assessments (which replace previous Criteria Documents), with input and advice from 

the SAB-HES, a scientific review panel specifically established to provide advice on the use of 

the scientific literature in developing benefits analyses for the EPA’s Report to Congress on The 

Benefits and Costs of the Clean Air Act 1990 to 2020.

51 In addition, we have included more 
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recent epidemiology studies from the PM ISA and the Provisional Assessment.

52,53 In general, 

we follow a weight of evidence approach, based on the biological plausibility of effects, 

availability of concentration-response functions from well conducted peer-reviewed 

epidemiological studies, cohesiveness of results across studies, and a focus on endpoints 

reflecting public health impacts (like hospital admissions) rather than physiological responses 

(such as changes in clinical measures like Forced Expiratory Volume [FEV1]).

There are several types of data that can support the determination of types and magnitude 

of health effects associated with air pollution exposures. These sources of data include 

toxicological studies (including animal and cellular studies), human clinical trials, and 

observational epidemiology studies. All of these data sources provide important contributions to 

the weight of evidence surrounding a particular health impact. However, only epidemiology

studies provide direct concentration-response relationships that can be used to evaluate 

population-level impacts of reductions in ambient pollution levels in a health impact assessment.

For the data-derived estimates, we relied on the published scientific literature to ascertain 

the relationship between PM2.5,, ozone, and adverse human health effects. We evaluated 

epidemiological studies using the selection criteria summarized in Table 8-11. These criteria 

include consideration of whether the study was peer-reviewed, the match between the pollutant 

studied and the pollutant of interest, the study design and location, and characteristics of the 

study population, among other considerations. In general, the use of concentration-response 

functions from more than a single study can provide a more representative distribution of the 

effect estimate. However, there are often differences between studies examining the same 

endpoint, making it difficult to pool the results in a consistent manner. For example, studies may

examine different pollutants or different age groups. For this reason, we consider very carefully

the set of studies available examining each endpoint and select a consistent subset that provides a 

good balance of population coverage and match with the pollutant of interest. In many cases, 

either because of a lack of multiple studies, consistency problems, or clear superiority in the 

quality or comprehensiveness of one study over others, a single published study is selected as the 

basis of the effect estimate.

When several effect estimates for a pollutant and a given health endpoint have been 

selected, they are quantitatively combined or pooled to derive a more robust estimate of the 

relationship. The BenMAP Manual Technical Appendices provides details of the procedures 

used to combine multiple impact functions.54 In general, we used fixed or random effects models 

to pool estimates from different single city studies of the same endpoint. Fixed effects pooling 

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

greater statistical power (lower variance). Random effects pooling accounts for both within-study

variance and between-study variability, due, for example, to differences in population 

susceptibility. We used the fixed effects model as our null hypothesis and then determined 

whether the data suggest that we should reject this null hypothesis, in which case we would use 

the random effects model. KPooled impact functions are used to estimate hospital admissions 

 

K EPA recently changed the algorithm BenMAP uses to calculate study variance, which is used in the pooling 

process. Prior versions of the model calculated population variance, while the version used here calculated sample 
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and asthma exacerbations. When combining evidence across multi-city studies (e.g., 

cardiovascular hospital admission studies), we use equal weights pooling. The effect estimates 

drawn from each multi-city study are themselves pooled across a large number of urban areas. 

For this reason, we elected to give each study an equal weight rather than weighting by the 

inverse of the variance reported in each study. For more details on methods used to pool 

incidence estimates, see the BenMAP Manual Appendices.

Effect estimates selected for a given health endpoint were applied consistently across all 

locations nationwide. This applies to both impact functions defined by a single effect estimate 

and those defined by a pooling of multiple effect estimates. Although the effect estimate may, in 

fact, vary from one location to another (e.g., because of differences in population susceptibilities 

or differences in the composition of PM), location-specific effect estimates are generally not 

available.

Table 8-11: Criteria Used When Selecting C-R Functions

Consideration Comments

Peer-Reviewed 

Research

Peer-reviewed research is preferred to research that has not undergone the peer-review 

process.

Study Type Among studies that consider chronic exposure (e.g., over a year or longer), prospective 

cohort studies are preferred over ecological studies because they control for important 

individual-level confounding variables that cannot be controlled for in ecological studies. 

Study Period Studies examining a relatively longer period of time (and therefore having more data) are 

preferred, because they have greater statistical power to detect effects. Studies that are 

more recent are also preferred because of possible changes in pollution mixes, medical 

care, and lifestyle over time. However, when there are only a few studies available, studies 

from all years will be included.

Population Attributes The most technically appropriate measures of benefits would be based on impact functions 

that cover the entire sensitive population but allow for heterogeneity across age or other 

relevant demographic factors. In the absence of effect estimates specific to age, sex, 

preexisting condition status, or other relevant factors, it may be appropriate to select effect 

estimates that cover the broadest population to match with the desired outcome of the 

analysis, which is total national-level health impacts. When available, multi-city studies 

are preferred to single city studies because they provide a more generalizable 

representation of the concentration-response function.

Study Size Studies examining a relatively large sample are preferred because they generally have 

more power to detect small magnitude effects. A large sample can be obtained in several 

ways, including through a large population or through repeated observations on a smaller 

population (e.g., through a symptom diary recorded for a panel of asthmatic children).

Study Location U.S. studies are more desirable than non-U.S. studies because of potential differences in 

pollution characteristics, exposure patterns, medical care system, population behavior, and 

lifestyle. National estimates are most appropriate when benefits are nationally distributed; 

the impact of regional differences may be important when benefits only accrue to a single 

area.

Pollutants Included in 

Model

When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it is 

important to use properly specified impact functions that include both pollutants. Using 

single-pollutant models in cases where both pollutants are expected to affect a health 

 

variance. This change did not affect the selection of random or fixed effects for the pooled incidence estimates 

between the proposal and final RIA. 
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outcome can lead to double-counting when pollutants are correlated.

Measure of PM For this analysis, impact functions based on PM2.5 are preferred to PM10 because of the 

focus on reducing emissions of PM2.5 precursors, and because air quality modeling was 

conducted for this size fraction of PM. Where PM2.5 functions are not available, PM10

functions are used as surrogates, recognizing that there will be potential downward 

(upward) biases if the fine fraction of PM10 is more (less) toxic than the coarse fraction. 

Economically Valuable 

Health Effects

Some health effects, such as forced expiratory volume and other technical measurements 

of lung function, are difficult to value in monetary terms. These health effects are not 

quantified in this analysis.

Non-overlapping 

Endpoints

Although the benefits associated with each individual health endpoint may be analyzed 

separately, care must be exercised in selecting health endpoints to include in the overall 

benefits analysis because of the possibility of double-counting of benefits. 

It is important to note that we are unable to separately quantify all of the possible PM and 

ozone health effects that have been reported in the literature for three reasons: (1) the possibility

of double counting (such as hospital admissions for specific respiratory diseases versus hospital 

admissions for all or a sub-set of respiratory diseases); (2) uncertainties in applying effect 

relationships that are based on clinical studies to the potentially affected population; or (3) the 

lack of an established concentration-response (CR) relationship. Table 8-12 lists the health 

endpoints included in this analysis.

Table 8-12: Health Impact Functions Used in BenMAP to Estimate Impacts of PM2.5 and 

Ozone Reductions

ENDPOINT POLLUTANT STUDY STUDY POPULATION

Premature Mortality

Premature mortality –

daily time series

O3 Multi-city

Bell et al (2004) (NMMAPS study)55– Nonaccidental

Huang et al (2005)56- Cardiopulmonary

Schwartz (2005)57– Non-accidental

Meta-analyses:

Bell et al (2005)58– All cause

Ito et al (2005)59– Non-accidental

Levy et al (2005)60– All cause

All ages

Premature mortality 

—cohort study, allcause

PM2.5 Krewski et al. (2009)

61

Lepeule et al. (2012)

62

>29 years

>25 years

Premature mortality, 

total exposures

PM2.5 Expert Elicitation (IEc, 2006)63 >24 years

Premature mortality 

— all-cause

PM2.5 Woodruff et al. (1997)64 Infant (<1 year)

Chronic Illness
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ENDPOINT POLLUTANT STUDY STUDY POPULATION

Nonfatal heart attacks PM2.5 Peters et al. (2001)65

Pooled estimate:

 Pope et al. (2006)66

 Sullivan et al. (2005)67

 Zanobetti et al. (2009)68

 Zanobetti and Schwartz (2006)69

Adults (>18 years)

Hospital Admissions 

Respiratory

O3

Pooled estimate:

Schwartz (1995) - ICD 460-519 (all resp)70

Schwartz (1994a; 1994b) - ICD 480-486 

(pneumonia)71,72

Moolgavkar et al. (1997) - ICD 480-487 

(pneumonia)73

Schwartz (1994b) - ICD 491-492, 494-496 

(COPD)

Moolgavkar et al. (1997) – ICD 490-496 

(COPD)

>64 years

Burnett et al. (2001)74 <2 years

PM2.5 Pooled estimate:

Zanobetti et al. (2009)—ICD 460-519 (All 

respiratory)

Kloog et al. (2012)—ICD 460-519 (All 

Respiratory)

75

>64 years

PM2.5 Moolgavkar (2000)—ICD 490–496 (Chronic 

lung disease)76

18–64 years

PM2.5 Pooled estimate:

Babin et al. (2007)—ICD 493 (asthma)77

Sheppard (2003)—ICD 493 (asthma)78

<18 years

Cardiovascular PM2.5 Pooled estimate:Zanobetti et al. (2009)—ICD 

390-459 (all cardiovascular)

Peng et al. (2009)—ICD 426-427; 428; 430-

438; 410-414; 429; 440-449 (Cardio-, cerebroand peripheral vascular disease)79

Peng et al. (2008)—ICD 426-427; 428; 430-

438; 410-414; 429; 440-449 (Cardio-, cerebroand peripheral vascular disease)80

Bell et al. (2008)—ICD 426-427; 428; 430-

438; 410-414; 429; 440-449 (Cardio-, cerebroand peripheral vascular disease)81

>64 years

PM2.5 Moolgavkar (2000)—ICD 390-429 (all 

cardiovascular)

20–64 years

Asthma-related ER 

visits

O3 Pooled estimate:

Peel et al (2005)82

Wilson et al (2005)83

All ages

All ages

Asthma-related ER 

visits (cont’d)

PM2.5 Pooled estimate:

Mar et al. (2010)84

Slaughter et al. (2005)85

Glad et al. (2012)86

All ages
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ENDPOINT POLLUTANT STUDY STUDY POPULATION

Other Health Endpoints

Acute bronchitis PM2.5 Dockery et al. (1996)87 8–12 years

Upper respiratory 

symptoms

PM2.5 Pope et al. (1991)88 Asthmatics, 9–11 

years

Lower respiratory 

symptoms

PM2.5 Schwartz and Neas (2000)89 7–14 years

Asthma exacerbations PM2.5 Pooled estimate:

Ostro et al. (2001)90 (cough, wheeze and 

shortness of breath)

Mar et al. (2004) (cough, shortness of breath)

6–18 yearsa

Work loss days PM2.5 Ostro (1987)91 18–65 years

School absence days

O3 

Pooled estimate:

Gilliland et al. (2001)

92

Chen et al. (2000)93

5–17 yearsb

Minor Restricted 

Activity Days 

(MRADs)

O3 Ostro and Rothschild (1989)94 18–65 years

PM2.5 Ostro and Rothschild (1989) 18–65 years

Notes:

a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 7 to 12 for the Mar et al. (2004) 

study. Based on advice from the SAB-HES, we extended the applied population to 6-18, reflecting the common 

biological basis for the effect in children in the broader age group. See: U.S. EPA-SAB (2004) and NRC (2002).

b Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on advice 

from the National Research Council and the EPA SAB-HES, we have calculated reductions in school absences for 

all school-aged children based on the biological similarity between children aged 5 to 17.

For detailed descriptions of each of the individual studies referenced in Table 8-12, 

please refer to the RIAs that accompanied the final revisions to the National Ambient Air Quality

Standards (NAAQS) for Particulate Matter, the 2008 final ozone NAAQS, and the 2010 ozone 

NAAQS reconsideration.95,96,97 

8.1.2.4.4 Baseline Incidence Rates

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide a direct estimate of the relationship of air quality changes to the relative 

risk of a health effect, rather than estimating the absolute number of avoided cases. For example, 

a typical result might be that a 10 μg/m3 decrease in daily PM2.5 levels might be associated with 

a decrease in hospital admissions of 3%. The baseline incidence of the health effect is necessary

to convert this relative change into a number of cases. A baseline incidence rate is the estimate of 

the number of cases of the health effect per year in the assessment location, as it corresponds to 

baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate 

must be multiplied by the corresponding population number. For example, if the baseline 

incidence rate is the number of cases per year per million people, that number must be multiplied 

by the millions of people in the total population.

Table 8-13 summarizes the sources of baseline incidence rates and provides average 

incidence rates for the endpoints included in the analysis. For both baseline incidence and 

prevalence data, we used age-specific rates where available. We applied concentration-response 

functions to individual age groups and then summed over the relevant age range to provide an 
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estimate of total population benefits. In most cases, we used a single national incidence rate, due 

to a lack of more spatially disaggregated data. Whenever possible, the national rates used are 

national averages, because these data are most applicable to a national assessment of benefits. 

For some studies, however, the only available incidence information comes from the studies 

themselves; in these cases, incidence in the study population is assumed to represent typical 

incidence at the national level. County, state and regional incidence rates are available for 

hospital admissions, and county-level data are available for premature mortality. 

We projected mortality rates such that future mortality rates are consistent with our 

projections of population growth.98 To perform this calculation, we began first with an average 

of 2004–2006 cause-specific mortality rates. Using Census Bureau projected national-level 

annual mortality rates stratified by age range, we projected these mortality rates to 2050 in 5-year 

increments. 99,100

The baseline incidence rates for hospital admissions and emergency department visits 

reflect the revised rates first applied in the CSAPR RIA.101 In addition, we have revised the 

baseline incidence rates for acute myocardial infarction. These revised rates are more recent, 

which provides a better representation of the rates at which populations of different ages, and in 

different locations, visit the hospital and emergency department for air pollution-related 

illnesses. Also, the new baseline incidence rates are more spatially refined. For many locations 

within the U.S., these data are resolved at the county- or state-level, providing a better 

characterization of the geographic distribution of hospital and emergency department visits than 

the previous national rates. Lastly, these rates reflect unscheduled hospital admissions only, 

which represent a conservative assumption that most air pollution-related visits are likely to be 

unscheduled. If air pollution-related hospital admissions are scheduled, this assumption would 

underestimate these benefits.

For the set of endpoints affecting the asthmatic population, in addition to baseline 

incidence rates, prevalence rates of asthma in the population are needed to define the applicable 

population. Table 8-14 lists the prevalence rates used to determine the applicable population for 

asthma symptoms. Note that these reflect current asthma prevalence and assume no change in 

prevalence rates in future years. We updated these rates in the CSAPR RIA.

Table 8-13: Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population

Endpoint Parameter Rates

Value Source

Mortality Daily or annual mortality 

rate projected to 2020

Age-, cause-, and 

county-specific rate

CDC Wonder (2006–2008)102

U.S. Census bureau

Hospitalizations Daily hospitalization rate Age-, region-, state-, 

county- and causespecific rate

2007 HCUP data filesa,103

Asthma ER Visits Daily asthma ER visit rate Age-, region-, state-, 

county- and causespecific rate

2007 HCUP data filesa

Nonfatal Myocardial Daily nonfatal myocardial Age-, region-, state-, 2007 HCUP data files;

a

adjusted by 
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Endpoint Parameter Rates

Value Source

Infarction (heart 

attacks)

infarction incidence rate 

per person, 18+

and county- specific 

rate

0.93 for probability of surviving after 

28 days (Rosamond et al., 1999)104

Asthma Exacerbations Incidence among asthmatic 

African-American children

daily wheeze

daily cough

daily shortness of breath

0.173

0.145

0.074

Ostro et al. (2001)

Acute Bronchitis Annual bronchitis 

incidence rate, children

0.043 American Lung Association (2002, 

Table 11)105

Lower Respiratory 

Symptoms

Daily lower respiratory 

symptom incidence among 

childrenb

0.0012 Schwartz et al. (1994, Table 2)

Upper Respiratory 

Symptoms

Daily upper respiratory 

symptom incidence among 

asthmatic children

0.3419 Pope et al. (1991, Table 2)

Work Loss Days Daily WLD incidence rate 

per person (18–65)

Aged 18–24

Aged 25–44

Aged 45–64

0.00540

0.00678

0.00492

1996 HIS (Adams, Hendershot, and 

Marano, 1999, Table 41);106 U.S. 

Bureau of the Census (2000)107

School Loss Days Rate per person per year, 

assuming 180 school days 

per year

9.9 National Center for Education 

Statistics (1996)108 and 1996 HIS 

(Adams et al., 1999, Table 47); 

Minor RestrictedActivity DaysDaily MRAD incidence 

rate per person

0.02137 Ostro and Rothschild (1989, p. 243)

Notes:

a Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level 

hospital and emergency department discharges for a variety of ICD codes.

b Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and wheeze.

Table 8-14: Asthma Prevalence Rates Used for this Analysis

Population Group Asthma Prevalence Rates

Value Source

All Ages 0.0780

American Lung Association (2010, Table 7)109

< 18 0.0941

5–17 0.1070

18–44 0.0719

45–64 0.0745

65+ 0.0716

African American, 5 to 17 0.1776 American Lung Association (2010, Table 9)

African American, <18 0.1553 American Lung Associationa

Notes:

ab Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008).110

8.1.2.4.5 Economic Values for Health Outcomes

After quantifying the change in adverse health impacts, we estimate the economic value 

of these avoided impacts. The appropriate economic value for a change in a health effect depends 

on whether the health effect is viewed ex ante (before the effect has occurred) or ex post (after 

the effect has occurred). Reductions in ambient concentrations of air pollution generally lower 
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the risk of future adverse health effects by a small amount for a large population. The appropriate 

economic measure is therefore ex ante willingness to pay (WTP) for changes in risk. 111

Epidemiological studies generally provide estimates of the relative risks of a particular health 

effect for a given increment of air pollution (often per 10 μg/m3 for PM2.5). These relative risks 

can be used to develop risk coefficients that relate a unit reduction in PM2.5 to changes in the 

incidence of a health effect. In order to value these changes in incidence, WTP for changes in 

risk need to be converted into WTP per statistical incidence. This measure is calculated by 

dividing individual WTP for a risk reduction by the related observed change in risk. For 

example, suppose a measure is able to reduce the risk of premature mortality from 2 in 10,000 to 

1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then 

the WTP for an avoided statistical premature mortality amounts to $1 million ($100/0.0001 

change in risk). Using this approach, the size of the affected population is automatically taken 

into account by the number of incidences predicted by epidemiological studies applied to the 

relevant population. The same type of calculation can produce values for statistical incidences of 

other health endpoints.

For some health effects, such as hospital admissions, WTP estimates are generally not 

available. In these cases, we use the cost of treating or mitigating the effect. For example, for the 

valuation of hospital admissions we use the avoided medical costs as an estimate of the value of 

avoiding the health effects causing the admission. These cost-of-illness (COI) estimates 

generally (although not necessarily in every case) understate the true value of reductions in risk 

of a health effect. They tend to reflect the direct expenditures related to treatment but not the 

value of avoided pain and suffering from the health effect.

112,113

We provide unit values for health endpoints (along with information on the distribution 

of the unit value) in Table 8-15. All values are in constant year 2011 dollars, adjusted for growth 

in real income out to 2030 using projections provided by Standard and Poor’s. Economic theory

argues that WTP for most goods (such as environmental protection) will increase if real income 

increases. Many of the valuation studies used in this analysis were conducted in the late 1980s 

and early 1990s. Because real income has grown since the studies were conducted, people’s 

willingness to pay for reductions in the risk of premature death and disease likely has grown as 

well. We did not adjust cost of illness-based values because they are based on current costs. 

Similarly, we did not adjust the value of school absences, because that value is based on current 

wage rates. For details on valuation estimates for PM-related endpoints, see the the RIA that 

accompanied the final revisions to the National Ambient Air Quality Standards (NAAQS) for 

Particulate Matter. 114 For details on valuation estimates for ozone-related endpoints, see the 

RIAs for the 2008 Ozone NAAQS RIA and the 2010 ozone NAAQS reconsideration.

115,116
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Table 8-15: Unit Values for Economic Valuation of Health Endpoints (2011$)

Health Endpoint Central Estimate of 

Value Per Statistical 

Incidence

1990

Income 

Level

2030 

Income 

Levela

Derivation of Distributions of Estimates

Premature Mortality 

(Value of a Statistical 

Life)

$8,300,000 $10,200,000 EPA currently recommends a central VSL of $6.3m (2000$, 1990 

income) based on a Weibull distribution fitted to 26 published VSL 

estimates (5 contingent valuation and 21 labor market studies). The 

underlying studies, the distribution parameters, and other useful 

information are available in Appendix B of EPA's current Guidelines 

for Preparing Economic Analyses (U.S. EPA, 2010).117 

Nonfatal Myocardial 

Infarction (heart 

attack)

3% discount rate

Age 0–24

Age 25–44

 Age 45–54

Age 55–65

Age 66 and over

7% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–65

Age 66 and over

$100,000

$110,000

$120,000

$210,000

$100,000

$100,000

$110,000

$110,000

$200,000

$100,000

$100,000

$110,000

$120,000

$210,000

$100,000

$100,000

$110,000

$110,000

$200,000

$100,000

No distributional information available. Age-specific cost-of-illness 

values reflect lost earnings and direct medical costs over a 5-year 

period following a nonfatal MI. Lost earnings estimates are based on 

Cropper and Krupnick (1990).118 Direct medical costs are based on 

simple average of estimates from Russell et al. (1998)119 and Wittels et 

al. (1990).120

Lost earnings:

Cropper and Krupnick (1990). Present discounted value of 5 years of 

lost earnings:

age of onset: at 3% at 7%

25–44 $9,000 $8,000

45–54 $13,000 $12,000

55–65 $77,000 $69,000

Direct medical expenses: An average of:

1. Wittels et al. (1990) ($100,000—no discounting)

2. Russell et al. (1998), 5-year period ($22,000 at 3% discount rate; 

$21,000 at 7% discount rate)

Hospital Admissions

Chronic Obstructive 

Pulmonary Disease 

(COPD)

$22,000 $22,000 No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of 

hospital stay, and weighted share of total COPD category illnesses) 

reported in Agency for Healthcare Research and Quality (2007) 

(www.ahrq.gov).121

Asthma Admissions $17,000 $17,000 No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of 

hospital stay, and weighted share of total asthma category illnesses) 

reported in Agency for Healthcare Research and Quality (2007) 

(www.ahrq.gov). 

All Cardiovascular

 Age 18-64

 Age 65-99

$43,000

$42,000

$43,000

$42,000

No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of 

hospital stay, and weighted share of total cardiovascular category 

illnesses) reported in Agency for Healthcare Research and Quality 

(2007) (www.ahrq.gov). 

All respiratory (ages 

65+)

$37,000 $37,000 No distributions available. The COI point estimates (lost earnings plus 

direct medical costs) are based on ICD-9 code level information (e.g., 

average hospital care costs, average length of hospital stay, and 

weighted share of total COPD category illnesses) reported in Agency 
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for Healthcare Research and Quality, 2007 (www.ahrq.gov).

All respiratory (ages 

0–2)

$13,000 $13,000 No distributions available. The COI point estimates (lost earnings plus 

direct medical costs) are based on ICD-9 code level information (e.g., 

average hospital care costs, average length of hospital stay, and 

weighted share of total COPD category illnesses) reported in Agency 

for Healthcare Research and Quality, 2007 (www.ahrq.gov).

Emergency Room 

Visits for Asthma

$440 $440 No distributional information available. Simple average of two unit 

COI values:

(1) $311.55, from Smith et al. (1997)122 and

(2) $260.67, from Stanford et al. (1999).123

Respiratory Ailments Not Requiring Hospitalization

Upper Respiratory 

Symptoms (URS)

$32 $34 Combinations of the three symptoms for which WTP estimates are 

available that closely match those listed by Pope et al. result in seven 

different “symptom clusters,” each describing a “type” of URS. A 

dollar value was derived for each type of URS, using mid-range 

estimates of WTP (Neumann et al., 1994)124 to avoid each symptom in 

the cluster and assuming additivity of WTPs. In the absence of 

information surrounding the frequency with which each of the seven 

types of URS occurs within the URS symptom complex, we assumed a 

uniform distribution between $9.2 and $43 (2000$).

Lower Respiratory 

Symptoms (LRS)

$21 $22 Combinations of the four symptoms for which WTP estimates are 

available that closely match those listed by Schwartz et al. result in 11 

different “symptom clusters,” each describing a “type” of LRS. A 

dollar value was derived for each type of LRS, using mid-range 

estimates of WTP (Neumann et al., 1994) to avoid each symptom in 

the cluster and assuming additivity of WTPs. The dollar value for LRS 

is the average of the dollar values for the 11 different types of LRS. In 

the absence of information surrounding the frequency with which each 

of the 11 types of LRS occurs within the LRS symptom complex, we 

assumed a uniform distribution between $6.9 and $25 (2000$).

Asthma 

Exacerbations

$57 $59 Asthma exacerbations are valued at $45 per incidence, based on the 

mean of average WTP estimates for the four severity definitions of a 

“bad asthma day,” described in Rowe and Chestnut (1986).125 This 

study surveyed asthmatics to estimate WTP for avoidance of a “bad 

asthma day,” as defined by the subjects. For purposes of valuation, an 

asthma exacerbation is assumed to be equivalent to a day in which 

asthma is moderate or worse as reported in the Rowe and Chestnut 

(1986) study. The value is assumed have a uniform distribution 

between $16 and $71 (2000$).

Acute Bronchitis $470 $510 Assumes a 6-day episode, with the distribution of the daily value 

specified as uniform with the low and high values based on those 

recommended for related respiratory symptoms in Neumann et al. 

(1994). The low daily estimate of $10 is the sum of the mid-range 

values recommended by IEc 1994 for two symptoms believed to be 

associated with acute bronchitis: coughing and chest tightness. The 

high daily estimate was taken to be twice the value of a minor 

respiratory restricted-activity day, or $110 (2000$). 

Work Loss Days 

(WLDs)

Variable 

(U.S. 

median = 

$150)

Variable 

(U.S. 

median = 

$150)

No distribution available. Point estimate is based on county-specific 

median annual wages divided by 50 (assuming 2 weeks of vacation) 

and then by 5—to get median daily wage. U.S. Year 2000 Census, 

compiled by Geolytics, Inc. (Geolytics, 2002)126
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Minor Restricted 

Activity Days 

(MRADs)

$66 $71 Median WTP estimate to avoid one MRAD from Tolley et al. 

(1986).127 Distribution is assumed to be triangular with a minimum of 

$22 and a maximum of $83, with a most likely value of $52 (2000$). 

Range is based on assumption that value should exceed WTP for a 

single mild symptom (the highest estimate for a single symptom—for 

eye irritation—is $16.00) and be less than that for a WLD. The 

triangular distribution acknowledges that the actual value is likely to be 

closer to the point estimate than either extreme.

School Absence Days $98 $98 No distribution available

Note:

a Willingness-to-pay-based benefits are adjusted by multiplying the unadjusted benefits by the appropriate 

adjustment factor to account for income growth over time using projections provided by Standard and Poor’s. Due 

to a lack of reliable projections of income growth past 2024, we assume constant WTP from 2024 through 2030. 

This results in an underestimate of benefits occurring between 2024 and 2030. For growth between 2000 and 2024, 

this factor is 1.23 for long-term mortality, 1.27 for chronic health impacts, and 1.08 for minor health impacts. Note 

that similar adjustments do not exist for cost-of-illness-based unit values. For these, we apply the same unit value 

regardless of the future year of analysis.

8.1.2.4.6 Manipulating Air Quality Modeling Data for Health 

Impacts Analysis

In Chapter 7, we summarized the methods for and results of estimating air quality for the 

program. These air quality results are in turn associated with human populations to estimate 

changes in health effects. For the purposes of this analysis, we focus on the health effects that 

have been linked to ambient changes in ozone and PM2.5 related to emission reductions estimated 

to occur due to the implementation of the program. We estimate ambient PM2.5 and ozone 

concentrations using the Community Multiscale Air Quality model (CMAQ). This section 

describes how we converted the CMAQ modeling output into full-season profiles suitable for the 

health impacts analysis. 

General Methodology

First, we extracted hourly, surface-layer PM and ozone concentrations for each grid cell 

from the standard CMAQ output files. For ozone, these model predictions are used in 

conjunction with the observed concentrations obtained from the Aerometric Information 

Retrieval System (AIRS) to generate ozone concentrations for the entire ozone season.L,M The 

predicted changes in ozone concentrations from the future-year base case to future-year control 

scenario serve as inputs to the health and welfare impact functions of the benefits analysis (i.e., 

BenMAP). 

To estimate ozone-related health effects for the contiguous United States, full-season 

ozone data are required for every BenMAP grid-cell. Given available ozone monitoring data, we 

generated full-season ozone profiles for each location in two steps: (1) we combined monitored 

 

L The ozone season for this analysis is defined as the 5-month period from May to September.

M Based on AIRS, there were 961 ozone monitors with sufficient data (i.e., 50 percent or more days reporting at 

least nine hourly observations per day [8 am to 8 pm] during the ozone season).
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observations and modeled ozone predictions to interpolate hourly ozone concentrations to a grid 

of 12-km by 12-km population grid cells for the contiguous 48 states, and (2) we converted these 

full-season hourly ozone profiles to an ozone measure of interest, such as the daily 8-hour 

maximum.N,O

For PM2.5, we also use the model predictions in conjunction with observed monitor data. 

CMAQ generates predictions of hourly PM species concentrations for every grid. The species 

include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron size range), a 

primary fine fraction (corresponding to PM less than 2.5 microns in diameter), and several 

secondary particles (e.g., sulfates, nitrates, and organics). PM2.5 is calculated as the sum of the 

primary fine fraction and all of the secondarily formed particles. Future-year estimates of PM2.5

were calculated using relative reduction factors (RRFs) applied to 2005 ambient PM2.5 and PM2.5

species concentrations. A gridded field of PM2.5 concentrations was created by interpolating

Federal Reference Monitor ambient data and IMPROVE ambient data. Gridded fields of PM2.5 

species concentrations were created by interpolating EPA speciation network (ESPN) ambient 

data and IMPROVE data. The ambient data were interpolated to the CMAQ 12 km grid. 

The procedures for determining the RRFs are similar to those in EPA’s draft guidance for 

modeling the PM2.5 standard (EPA, 2001).128 The guidance recommends that model predictions 

be used in a relative sense to estimate changes expected to occur in each major PM2.5 species. 

The procedure for calculating future-year PM2.5 design values is called the “Speciated Modeled 

Attainment Test (SMAT).” EPA used this procedure to estimate the ambient impacts of the final 

program. 

Table 8-16 provides those ozone and PM2.5 metrics for grid cells in the modeled domain 

that enter the health impact functions for health benefits endpoints. The population-weighted 

average reflects the baseline levels and predicted changes for more populated areas of the nation. 

This measure better reflects the potential benefits through exposure changes to these populations.

Table 8-16: Summary of CMAQ-Derived Population-Weighted Ozone and PM2.5 Air 

Quality Metrics for Health Benefits Endpoints Associated with the Final Tier 3 Program

2030

Statistica Baseline Changeb

Ozone Metric: National Population-Weighted Average (ppb)c

Daily Maximum 8-Hour Average 

Concentration 

43.65 0.32

PM2.5 Metric: National Population-Weighted Average (μg/m3)

Annual Average Concentration 7.94 0.04

Notes:

 

N The 12-km grid squares contain the population data used in the health benefits analysis model, BenMAP. 

O This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi 

Neighbor Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for 

download at http://www.epa.gov/air/benmap.
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a Ozone and PM2.5 metrics are calculated at the CMAQ grid-cell level for use in health effects 

estimates. Ozone metrics are calculated over relevant time periods during the daylight hours of 

the “ozone season” (i.e., May through September). Note that the national, population-weighted 

PM2.5 and ozone air quality metrics presented in this chapter represent an average for the entire, 

gridded U.S. CMAQ domain. These are different than the population-weighted PM2.5 and ozone 

design value metrics presented in Chapter 7, which represent the average for areas with a current 

air quality monitor.

b The change is defined as the base-case value minus the control-case value. 

c Calculated by summing the product of the projected CMAQ grid-cell population and the 

estimated CMAQ grid cell seasonal ozone concentration and then dividing by the total population.

Emissions and air quality modeling decisions are made early in the analytical process. 

For this reason, the emission control scenarios used in the air quality and benefits modeling are 

slightly different than the final emission inventories estimated for the Tier 3 standards. Please 

refer to Section 7.2.1.1 for more information about the inventories used in the air quality

modeling that supports the health impacts analysis. 

8.1.2.5 Methods for Describing Uncertainty

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty. This analysis is no exception. As outlined both 

in this and preceding chapters, this analysis includes many data sources as inputs, including 

emission inventories, air quality data from models (with their associated parameters and inputs), 

population data, population estimates, health effect estimates from epidemiology studies, 

economic data for monetizing benefits, and assumptions regarding the future state of the world 

(i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain and 

would affect the benefits estimate. When the uncertainties from each stage of the analysis are 

compounded, even small uncertainties can have large effects on the total quantified benefits.

After reviewing the EPA’s approach, the National Research Council (NRC) (2002, 2008),

129,130 which is part of the National Academies of Science, concluded that the EPA’s general 

methodology for calculating the benefits of reducing air pollution is reasonable and informative 

in spite of inherent uncertainties. The NRC also highlighted the need to conduct rigorous 

quantitative analyses of uncertainty and to present benefits estimates to decision makers in ways 

that foster an appropriate appreciation of their inherent uncertainty. Since the publication of these 

reports, the EPA has continued work to improve the characterization of uncertainty in both 

health incidence and benefits estimates. In response to these recommendations, we have 

expanded our previous analyses to incorporate additional quantitative and qualitative 

characterizations of uncertainty. Although we have not yet been able to make as much progress 

towards a full, probabilistic uncertainty assessment as envisioned by the NAS as we had hoped, 

we have added a number of additional quantitative and qualitative analyses to highlight the 

impact that uncertain assumptions may have on the benefits estimates. In addition, for some 

inputs into the benefits analysis, such as the air quality data, it is difficult to address uncertainty 

probabilistically due to the complexity of the underlying air quality models and emission inputs. 

Therefore, we decline to make up alternative assumptions simply for the purpose of probabilistic 

uncertainty characterization when there is no scientific literature to support alternate 

assumptions.
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To characterize uncertainty and variability, the 2012 PM NAAQS RIA utilized an 

approach that combined elements from two recent analyses by the EPA,131,132 and used a tiered 

approach developed by the World Health Organization (WHO) for characterizing uncertainty.

133

We refer the reader to this tiered assessment as an assessment of the potential impact and 

magnitude of each aspect of uncertainty that is also present in the final Tier 3 RIA (see Appendix 

5c of the 2012 PM NAAQS RIA). 

Data limitations prevent us from treating each source of uncertainty quantitatively and 

from reaching a full-probabilistic simulation of our results, but we were able to consider the 

influence of uncertainty in the risk coefficients and economic valuation functions by

incorporating four quantitative analyses described in more detail below:

1. A Monte Carlo assessment that accounts for random sampling error and between 

study variability in the epidemiological and economic valuation studies;

2. The quantification of PM-related mortality using alternative PM2.5 mortality effect 

estimates drawn from two long-term cohort studies and an expert elicitation;

3. A concentration benchmark assessment that characterizes the distribution of 

avoided PM2.5-related deaths relative to specific concentrations in the long-term epidemiological 

studies used to estimate PM2.5-related mortality;

4. The quantification of ozone-related mortality using alternative ozone mortality

effect estimates drawn from six short-term studies: three multi-city studies and three metaanalyses of the existing literature;

5. An analysis of the influence of various parameters on total monetized benefits.

8.1.2.5.1 Monte Carlo Assessment

Similar to other recent RIAs, we used Monte Carlo methods for characterizing random 

sampling error associated with the concentration response functions from epidemiological 

studies and random effects modeling to characterize both sampling error and variability across 

the economic valuation functions. The Monte Carlo simulation in the BenMAP software 

randomly samples from a distribution of incidence and valuation estimates to characterize the 

effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to 

generate confidence intervals around the estimated health impact and monetized benefits. The 

reported standard errors in the epidemiological studies determined the distributions for individual 

effect estimates for endpoints estimated using a single study. For endpoints estimated using a 

pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and 

the variance across studies. The confidence intervals around the monetized benefits incorporate 

the epidemiology standard errors as well as the distribution of the valuation function. These 

confidence intervals do not reflect other sources of uncertainty inherent within the estimates, 

such as baseline incidence rates, populations exposed and transferability of the effect estimate to 

diverse locations. As a result, the reported confidence intervals and range of estimates give an 

incomplete picture about the overall uncertainty in the benefits estimates.
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8.1.2.5.2 Alternative Concentration-Response Functions for PM2.5-

Related Mortality

We assign the greatest economic value to the reduction in PM2.5 related mortality risk. 

Therefore, it is particularly important to attempt to characterize the uncertainties associated with 

reductions in premature mortality. To better understand the concentration-response relationship 

between PM2.5 exposure and premature mortality, the EPA conducted an expert elicitation in 

2006.134,135 In general, the results of the expert elicitation support the conclusion that the 

benefits of PM2.5 control are very likely to be substantial.

Alternative concentration-response functions are useful for assessing uncertainty beyond 

random statistical error, including uncertainty in the functional form of the model or alternative 

study design. Thus, we include the expert elicitation results as well as standard errors approaches 

to provide insights into the likelihood of different outcomes and about the state of knowledge 

regarding the benefits estimates. In this analysis, we present the results derived from the expert 

elicitation as indicative of the uncertainty associated with a major component of the health 

impact functions, and we provide the independent estimates derived from each of the twelve 

experts to better characterize the degree of variability in the expert responses.

In previous RIAs, the EPA presented benefits estimates using concentration response 

functions derived from the PM2.5 Expert Elicitation as a range from the lowest expert value 

(Expert K) to the highest expert value (Expert E). However, this approach did not indicate the 

agency’s judgment on what the best estimate of PM2.5 benefits may be, and the EPA’s 

independent Science Advisory Board (SAB) recommended refinements to the way EPA 

presented the results of the elicitation.136 As a result of this recommendation, we have presented 

the ACS and Six-Cities cohort-based studies as our core premature mortality estimates, such as 

in the RIA for the final PM NAAQS. Using alternate relationships between PM2.5 and premature 

mortality supplied by experts, higher and lower benefits estimates are plausible, but most of the 

expert-based estimates of the mean PM2.5 effect on mortality fall between the two epidemiologybased estimates. Please note that the benefits estimates results presented are not the direct results 

from the studies or expert elicitation; rather, the estimates are based in part on the effect 

coefficients provided in those studies or by experts. In addition, the experts provided 

distributions around their mean PM2.5 effect estimates, which provide more information 

regarding the overall range of uncertainty, and this overall range is larger than the range of the 

mean effect estimates from each of the experts.

Even these multiple characterizations with confidence intervals omit the contribution to 

overall uncertainty from uncertainty in air quality changes, baseline incidence rates, and 

populations exposed. Furthermore, the approach presented here does not yet include methods for 

addressing correlation between input parameters and the identification of reasonable upper and 

lower bounds for input distributions characterizing uncertainty in additional model elements. As 

a result, the reported confidence intervals and range of estimates give an incomplete picture 

about the overall uncertainty in the estimates. This information should be interpreted within the 

context of the larger uncertainty surrounding the entire analysis.
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8.1.2.5.3 Concentration Benchmark Analysis for PM2.5

In this analysis, we estimate the number of avoided PM2.5-related deaths occurring due to 

PM2.5 reductions down to various PM2.5 concentration benchmarks, including the Lowest 

Measured Level (LML) of each long-term PM2.5 mortality study. This analysis is one of several 

sensitivities that the EPA has historically performed that characterize the uncertainty associated 

with the PM-mortality relationship and the economic value of reducing the risk of premature 

death. 137,138,139

Our review of the current body of scientific literature indicates that a log-linear nothreshold model provides the best estimate of PM-related long-term mortality. The PM ISA,

140

which was twice reviewed by the EPA’s Clean Air Scientific Advisory Committee,

141,142

concluded that the evidence supports the use of a no-threshold log-linear model while also 

recognizing potential uncertainty about the exact shape of the concentration-response function.P

Consistent with this finding, we estimate benefits associated with the full range of PM2.5 

exposure in conjunction with sensitivity analyses to recognize the potential uncertainty at lower 

concentrations. Specifically, we incorporated a LML assessment, a method the EPA has 

employed in several recent RIAs.143,144,145 In addition, we have incorporated an assessment using 

specific concentration benchmarks identified in the EPA’s Policy Assessment for Particulate 

Matter.

146

These two approaches summarize the distribution of avoided PM2.5-related mortality

impacts relative to baseline (i.e., pre-rule) annual mean PM2.5 levels. The LML approach 

compares the percentage of avoided premature deaths estimated to occur above and below the 

minimum observed air quality level of each long-term cohort study we use to quantify PM. In the 

air quality benchmark approach, we summarize the impacts occurring at different points in the 

distribution of the air quality data used in these same epidemiology studies.

Our confidence in the estimated number of premature deaths avoided (but not in the 

existence of a causal relationship between PM and premature mortality) diminishes as we 

estimate these impacts in locations where PM2.5 levels are below the LML. This interpretation is 

consistent with the Policy Assessment and advice from SAB-CASAC during their peer review.147

The Policy Assessment concludes that the range from the 25th to the 10th percentile is a 

reasonable range of the air quality distribution below which we start to have appreciably less 

confidence in the magnitude of the associations observed in the epidemiological studies. In

general, we are more confident in the magnitude of the risks we estimate from simulated PM2.5 

concentrations that coincide with the bulk of the observed PM concentrations in the 

epidemiological studies at are used to estimate the benefits. Likewise, we are less confident in 

the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the observed 

data in these studies. However, there are uncertainties inherent in identifying any particular point 

at which our confidence in reported associations becomes appreciably less, and the scientific 

evidence provides no clear dividing line. 

 

P

For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-mortality 

relationship, see the Technical Support Document (TSD) entitled Summary of Expert Opinions on the Existence of 

a Threshold in the Concentration-Response Function for PM2.5-related Mortality (U.S. EPA, 2010f).
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For these reasons, we consider the LML as well as one standard deviation below the 

meanQair quality levels when characterizing the distribution of mortality impacts. It is important 

to emphasize that “less confidence” does not mean “no confidence.” In addition, while we may 

have less confidence in the magnitude of the risk, we still have high confidence that PM2.5 is 

causally associated with risk at those lower air quality concentrations. To clarify this concept, 

Figure 8-2graphically displays the spectrum of confidence using illustrative concentration 

benchmarks from the major epidemiology studies cited in this chapter.

Figure 8-2: Relationship between the Size of the PM Mortality Estimates and the PM2.5

Concentration Observed in the Epidemiology Study

Although these types of concentration benchmark analyses (e.g., LML, one standard 

deviation below the mean, etc.) provide some insight into the level of uncertainty in the 

estimated PM2.5 mortality benefits, the EPA does not view these concentration benchmarks as a 

concentration threshold below which we would not quantify health benefits of air quality 

improvements. Rather, the core benefits estimates reported in this RIA (i.e., those based on 

Krewski et al. (2009) and Lepeule et al. (2012)) are the best measures because they reflect the 

full range of modeled air quality concentrations associated with the emission reduction 

strategies. In reviewing the Policy Assessment, SAB-CASAC confirmed that “[a]lthough there is 

increasing uncertainty at lower levels, there is no evidence of a threshold (i.e., a level below 

which there is no risk for adverse health effects)”. 148 In addition, in reviewing the Costs and 

Benefits of the Clean Air Act,

149 the SAB-HES noted that “[t]his [no-threshold] decision is 

supported by the data, which are quite consistent in showing effects down to the lowest measured 

levels. Analyses of cohorts using data from more recent years, during which time PM 

concentrations have fallen, continue to report strong associations with mortality”.150 Therefore, 

the best estimate of benefits includes estimates below and above these concentration benchmarks 

 

Q A range of one standard deviation around the mean represents approximately 68 percent of normally distributed 

data, and, below the mean falls between the 25th and 10th percentiles. 
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but uncertainty is higher in the magnitude of health benefits estimated at lower concentrations, 

with the lowest confidence below the LML. Estimated health impacts reflecting air quality 

improvements below and above these concentration benchmarks are appropriately included in 

the total benefits estimate. In other words, our higher confidence in the estimated benefits above 

these concentration benchmarks should not imply an absence of confidence in the benefits 

estimated below these concentration benchmarks.

We estimate that most of the avoided PM-related impacts quantified in this analysis occur 

among populations exposed at or above the LML of the Lepeule et al. (2012) study, while a 

majority of the impacts occur at or above the LML of the Krewski et al. (2009) study. We show 

the estimated reduction in incidence of premature mortality above and below the LML or air 

quality benchmarks of these studies in Table 8-17, and we graphically display the distribution of 

PM2.5-related mortality impacts for the final standard in Figure 8-3 and Figure 8-4. 

Table 8-17: Estimated Reduction in Incidence of Adult Premature Mortality Occurring 

Above and Below Various Concentration Benchmarks in the Underlying Epidemiology 

Studiesa

Epidemiology 

Study

Total 

Reduced 

Mortality 

Incidence

Allocation of Reduced Mortality Incidence

Below 1 Std. 

Dev.

Below AQ 

Mean

At or 

Above 1 

Std. Dev. 

Below AQ 

Mean

Below 

LML

At or Above 

LML

Krewski et al. 

(2009) 660

620 38 37 620

(94%) (6%) (6%) (94%)

Lepeule et al. 

(2012) 1,500 N/A N/A

630 860

(42%) (58%)

a Mortality incidence estimates are rounded to whole numbers and two significant digits, so estimates may not 

sum across columns. One standard deviation below the mean is equivalent to the middle of the range between 

the 10th and 25th percentile. For Krewski, the LML is 5.8 μg/m3and one standard deviation below the mean is 

11.0 μg/m3. For Lepeule et al., the LML is 8 μg/m3and we do not have the data for one standard deviation 

below the mean. It is important to emphasize that although we have lower levels of confidence in levels below

the LML for each study, the scientific evidence does not support the existence of a level below which health 

effects from exposure to PM2.5 do not occur.
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0%

5%

10%

15%

20%

25%

30%

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20

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Baseline Annual Mean PM2.5 Level (μg/m3)

LML of Lepeule et al. (2012) study

LML of Krewski et al. (2009) study

Of total PM2.5-Related deaths avoided for 12 μg/m3:

94% occur among populations exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study.

58% occur among populations exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study.

Figure 8-3. Number of Premature PM2.5-related Deaths Avoided for the Final Tier 3 

Standards in 2030 According to the Baseline Level of PM2.5 and the Lowest Measured Air 

Quality Levels of Each Mortality Study
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0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

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Baseline Annual Mean PM2.5 Level (μg/m3)

Cumulative Percentage of Total PM-related Mortalities Avoided by 

Baseline Air Quality Level

LML of Krewski et al. (2009) study

LML of Lepeule et al. (2012) study

Of total PM2.5-Related deaths avoided for 12 μg/m3:

94% occur among populations exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study.

58% occur among populations exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study.

Figure 8-4. Number of Premature PM2.5-related Deaths Avoided for the Final Tier 3 

Standards in 2030 According to the Baseline Level of PM2.5 and the Lowest Measured Air 

Quality Levels of Each Mortality Study

While the LML of each study is important to consider when characterizing and 

interpreting the overall level of PM2.5-related benefits, as discussed earlier in this chapter, the 

EPA believes that both of the cohort-based mortality estimates are suitable for use in air 

pollution health impact analyses. When estimating PM-related premature deaths avoided using 

risk coefficients drawn from the Lepeule et al. (2012) analysis of the Harvard Six Cities and the 

Krewski et al. (2009) analysis of the ACS cohorts there are innumerable other attributes that may

affect the size of the reported effect estimates—including differences in population 

demographics, the size of the cohort, activity patterns and particle composition among others. 

The LML assessment presented here provides a limited representation of one key difference 

between the two studies.
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8.1.2.5.4 Alternative Concentration-Response Functions for OzoneRelated Mortality

In 2006 the EPA requested an NAS study to evaluate the extent to which the 

epidemiological literature to that point improved the understanding of ozone-related mortality. 

The NAS found that short-term ozone exposure was likely to contribute to ozone-related 

mortality151 and issued a series of recommendations to EPA, including that the Agency should

present multiple short-term ozone mortality estimates, including those based on multi-city

analyses such as the National Morbidity, Mortality and Air Pollution Study (NMMAPS) as well 

as meta-analytic studies. The NAS also recommended that EPA remove reference to a no-causal 

relationship between ozone exposure and premature mortality. The quantification and 

presentation of ozone-related premature mortality in this analysis is responsive to these 

recommendations.

8.1.2.5.5 Influence Analysis – Quantitative Assessment of 

Uncertainty

In the past few years, the EPA has initiated several projects to improve the 

characterization of uncertainty for benefits analysis. In particular, the EPA recently completed 

the first phase of a quantitative uncertainty analysis of benefits, hereafter referred to as the 

“Influence Analysis”. 152 The Influence Analysis diagramed the uncertain components of each 

step within the benefits analysis process, identified plausible ranges for a sensitivity analysis, and 

assessed the sensitivity to total benefits to changes in each component. Although this analysis 

does not quite fulfill the goal of a full probabilistic assessment, it accomplished the necessary

first steps and identified the challenges to accomplishing that goal. Below are some of the 

preliminary observations from the first phase of the project.

 The components that contribute the most to uncertainty of the monetized benefits 

and mortality incidence (in order of importance) are the value-of-a-statistical-life 

(VSL), the concentration-response (C-R) function for mortality, and change in 

PM2.5 concentration.

 The components that contribute the least to uncertainty of the monetized benefits 

and mortality incidence are population, morbidity valuation, and income 

elasticity.

 The choice of a C-R function for mortality affects the mortality incidence and 

monetized benefits more than other sources of uncertainty within each C-R 

function.

 Alternative cessation lag structures for mortality have a moderate effect on the 

monetized benefits.

 Because the health impact function is essentially linear, the key components show 

the same sensitivity across all mortality C-R functions even if the midpoints differ 

significantly from one expert to another.

8.1.2.5.6 Qualitative Assessment of Uncertainty and Other Analysis 

Limitations
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Although we strive to incorporate as many quantitative assessments of uncertainty as 

possible, there are several aspects we are only able to address qualitatively. These aspects are 

important factors to consider when evaluating the benefits of the final Tier 3 standards.

The total monetized benefits presented in this chapter are based on our interpretation of 

the best available scientific literature and methods and supported by the EPA’s independent SAB 

(Health Effects Subcommittee) (SAB-HES) and the National Academies of Science (NAS).153,154

The benefits estimates are subject to a number of assumptions and uncertainties. For example, 

the key assumptions underlying the estimates for premature mortality, which account for over 

98% of the total monetized benefits in this analysis, include the following:

1. We assume that all fine particles, regardless of their chemical 

composition, are equally potent in causing premature mortality. This is an 

important assumption, because PM2.5 varies considerably in composition across 

sources, but the scientific evidence is not yet sufficient to allow differentiation of 

effect estimates by particle type. The PM ISA, which was twice reviewed by

Clean Air Scientific Advisory Committee (SAB-CASAC), concluded that “many

constituents of PM2.5 can be linked with multiple health effects, and the evidence 

is not yet sufficient to allow differentiation of those constituents or sources that 

are more closely related to specific outcomes”.155

2. We assume that the health impact function for fine particles is log-linear 

without a threshold in this analysis. Thus, the estimates include health benefits 

from reducing fine particles in areas with varied concentrations of PM2.5, 

including both areas that do not meet the fine particle standard and those areas 

that are in attainment, down to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between the change in PM 

exposures and the total realization of changes in mortality effects. Specifically, 

we assume that some of the incidences of premature mortality related to PM2.5

exposures occur in a distributed fashion over the 20 years following exposure 

based on the advice of the SAB-HES,156 which affects the valuation of mortality

benefits at different discount rates.

4. To characterize the uncertainty in the relationship between PM2.5 and 

premature mortality, we include a set of twelve estimates based on results of the 

expert elicitation study in addition to our core estimates. Even these multiple 

characterizations omit the uncertainty in air quality estimates, baseline incidence 

rates, populations exposed and transferability of the effect estimate to diverse 

locations. As a result, the reported confidence intervals and range of estimates 

give an incomplete picture about the overall uncertainty in the PM2.5 estimates. 

This information should be interpreted within the context of the larger uncertainty

surrounding the entire analysis.

5. There is uncertainty in the magnitude of the association between ozone 

and premature mortality. The range of ozone benefits associated with the final 
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standards is estimated based on the risk of several sources of ozone-related 

mortality effect estimates. In a report on the estimation of ozone-related 

premature mortality published by the National Research Council, a panel of 

experts and reviewers concluded that short-term exposure to ambient ozone is 

likely to contribute to premature deaths and that ozone-related mortality should be 

included in estimates of the health benefits of reducing ozone exposure.R

As previously described, we strive to monetize as many of the benefits anticipated from 

the revised and alternative standards as possible given data and resource limitations, but the 

monetized benefits estimated in this RIA inevitably only reflect a portion of the benefits. 

Specifically, only certain benefits attributable to the health impacts associated with exposure to 

ambient fine particles have been monetized in this analysis. Data and methodological limitations 

prevented the EPA from quantifying or monetizing the benefits from several important health 

benefit categories (see Table 8-5 for more information). If we could fully monetize all of the 

benefit categories, the total monetized benefits would exceed the costs by an even greater margin 

than we currently estimate.

To more fully address these uncertainties, including those we cannot quantify, the 2012 

PM NAAQS RIA utilized a four-tiered approach using the WHO uncertainty framework,157

which provides a means for systematically linking the characterization of uncertainty to the 

sophistication of the underlying risk assessment. The EPA has applied similar approaches in

other analyses.158,159 Using this framework, the PM NAAQS summarized the key uncertainties in 

the health benefits analysis, including our assessment of the direction of potential bias, 

magnitude of impact on the monetized benefits, degree of confidence in our analytical approach, 

and our ability to assess the source of uncertainty. We refer the reader to this tiered assessment as 

an assessment of the potential impact and magnitude of each aspect of uncertainty that is also 

present in the final Tier 3 RIA (see Appendix 5b of the 2012 PM NAAQS RIA).160

8.1.3 Comparison of Costs and Benefits

This section presents the cost-benefit comparison related to the expected impacts of the 

final Tier 3 program. In estimating the net benefits of the program, the appropriate cost measure 

is ‘social costs.’ Social costs represent the welfare costs of a rule to society and do not consider

transfer payments (such as taxes) that are simply redistributions of wealth. For this analysis, we 

estimate that the social costs of the program are equivalent to the estimated vehicle and fuel 

compliance costs of the program. While vehicle manufacturers and fuel producers would see 

their costs increase by the amount of those compliance costs, they are expected to pass them on 

in their entirety to vehicle and fuel consumers in the form of increased prices. Ultimately, these 

costs will be borne by the final consumers of these goods. The social benefits of the program are 

represented by the monetized value of health and welfare improvements experienced by the U.S. 

population. Table 8-18 contains the estimated social costs and the estimated monetized benefits 

of the program.

 

R National Research Council (NRC), (2008). Estimating Mortality Risk Reduction and Economic Benefits from 

Controlling Ozone Air Pollution. The National Academies Press: Washington, D.C.
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The results in Table 8-18 suggest that the 2030 monetized benefits of the final standards

are greater than the expected costs. Specifically, the annual benefits of the total program will 

range between $7.4 to $19 billion annually in 2030 using a three percent discount rate, or 

between $6.7 to $18 billion assuming a 7 percent discount rate, compared to estimated social 

costs of approximately $1.5 billion in that same year. Though there are a number of health and 

environmental effects associated with the final standards that we are unable to quantify or 

monetize (see Table 8-5), the benefits of the final standards outweigh the projected costs.

Using a conservative benefits estimate, the 2030 benefits outweigh the costs by a factor 

of 4.5. Using the upper end of the benefits range, the benefits could outweigh the costs by a 

factor of 13. Thus, even taking the most conservative benefits assumptions, benefits of the final 

standards clearly outweigh the costs.

Table 8-18: Summary of Annual Benefts and Costs Associated with the Final Tier 3 

Program (Billions, 2011$)a

Description 2030

Vehicle Program Costs

Fuels Program Costs

Total Estimated Costsb

$0.76

$0.70

$1.5

Total Estimated Health Benefitsc,d,e,f

 3 percent discount rate

 7 percent discount rate

$7.4 - $19

$6.7 - $18

Annual Net Benefits (Total Benefits – Total Costs)

 3 percent discount rate

 7 percent discount rate

$5.9 - $18

$5.2 - $17

Notes:

a All estimates represent annual benefits and costs anticipated for the year 2030. Totals are rounded to two 

significant digits and may not sum due to rounding.

b

 The calculation of annual costs does not require amortization of costs over time. Therefore, the estimates of annual 

cost do not include a discount rate or rate of return assumption (see Chapter 2 of the RIA for more information on 

vehicle costs, Chapter 5 for fuel costs, and Section 8.1.1 for a summary of total program costs). 

c Total includes ozone and PM2.5 benefits. Range was developed by adding the estimate from the Bell et al., 2004

ozone premature mortality function to PM2.5-related premature mortality derived from the American Cancer Society 

cohort study (Krewski et al., 2009) for the low estimate and ozone premature mortality derived from the Levy et al., 

2005 study to PM2.5-related premature mortality derived from the Six-Cities (Lepeule et al., 2012) study for the high 

estimate.

d Annual benefits analysis results reflect the use of a 3 percent and 7 percent discount rate in the valuation of 

premature mortality and nonfatal myocardial infarctions, consistent with EPA and OMB guidelines for preparing 

economic analyses. 

e Valuation of premature mortality based on long-term PM exposure assumes discounting over the SAB 

recommended 20-year segmented lag structure described in the Regulatory Impact Analysis for the 2012 PM 

National Ambient Air Quality Standards (December, 2012). 

f Not all possible benefits or disbenefits are quantified and monetized in this analysis. Potential benefit categories 

that have not been quantified and monetized are listed in Table 8-5.
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8.1.4 Illustrative Analysis of Quantified and Monetized Impacts Associated with the Rule in 

2018

For illustrative purposes, this section presents the quantified and monetized impacts 

associated with the final standards in 2018. As presented in Section 7.1.5, the emissions impacts 

of the final standards in 2018 are primarily due to the effects of sulfur on the existing (pre-Tier 3) 

fleet. 

This analysis reflects the impacts of the final Tier 3 rule in 2018 compared to a futureyear reference scenario without the program in place. Overall, we estimate that the final rule will

lead to a net decrease in PM2.5-related health and environmental impacts in 2018 (see Section 

7.2.4 for more information about the air quality modeling results). The decrease in populationweighted national average PM2.5 exposure results in a net decrease in adverse PM2.5-related 

human health and environmental impacts (the decrease in national population-weighted annual 

average PM2.5 is 0.012 μg/m3in 2018). The air quality modeling also projects decreases in 

ozone concentrations. The overall decrease in population-weighted national average ozone 

exposure results in decreases in ozone-related health and environmental impacts (populationweighted maximum 8-hour average ozone decreases by 0.15 ppb in 2018).

Table 8-19 and 

Table 8-20 present the annual PM2.5 and ozone health impacts in the 48 contiguous U.S. 

states associated with the final Tier 3 program. For each endpoint presented in Table 8-19

and

Table 8-20, we provide both the point estimate and the 90 percent confidence interval. 

Using EPA’s preferred estimates, based on the American Cancer Society (ACS) and Six-Cities 

studies and no threshold assumption in the model of mortality, we estimate that the final

standards would result in between 180 and 400 cases of avoided PM2.5-related premature 

mortalities annually in 2018. For ozone-related premature mortality in 2018, we estimate a range 

of between 45 to 210 fewer premature mortalities. 

Table 8-21 presents the estimated monetary value of changes in the incidence of ozone 

and PM2.5-related health and environmental effects. Total aggregate monetized benefits are 

presented in Table 8-22. All monetized estimates are presented in 2011$. Where appropriate,

estimates account for growth in real gross domestic product (GDP) per capita between 2000 and 

2018. The monetized value of PM2.5-related mortality also accounts for a twenty-year segmented 

cessation lag.S To discount the value of premature mortality that occurs at different points in the 

 

S Based in part on prior SAB advice, EPA has typically assumed that there is a time lag between changes in 

pollution exposures and the total realization of changes in health effects. Within the context of benefits analyses, 

this term is often referred to as “cessation lag”. The existence of such a lag is important for the valuation of 

premature mortality incidence because economic theory suggests that benefits occurring in the future should be

discounted. In this analysis, we apply a twenty-year distributed lag to PM mortality reductions. This method is 

consistent with the most recent recommendation by the EPA’s Science Advisory Board. Refer to: EPA – Science 

Advisory Board, 2004. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on 
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future, we apply both a 3 and 7 percent discount rate. We also use both a 3 and 7 percent 

discount rate to value PM2.5-related nonfatal heart attacks (myocardial infarctions).T 

In addition to omitted benefits categories such as air toxics and various welfare effects, 

not all known PM2.5- and ozone-related health and welfare effects could be quantified or 

monetized. The estimate of total monetized health benefits of the final standards is thus equal to 

the subset of monetized PM2.5- and ozone-related health impacts we are able to quantify plus the 

sum of the nonmonetized health and welfare benefits. Our estimate of total monetized benefits

associated with the final standards in 2018, using the ACS and Six-Cities PM mortality studies 

and the range of ozone mortality assumptions, is between $2.1 and $5.6 billion, assuming a 3 

percent discount rate, or between $1.9 and $5.3 billion, assuming a 7 percent discount rate.

The results in Table 8-21 demonstrate that the gasoline sulfur standards provide large 

immediate benefits in the program’s first year, related to emission reductions from existing 

gasoline vehicles. The benefits increase substantially after 2018, as the vehicle standards phase 

in after 2018 and as the fleet turns over.

Table 8-19: Estimated PM2.5-Related Health Impactsa

Health Effect 2018 Annual 

Reduction in 

Incidence

(5th% - 95th%ile)

Premature Mortality – Derived from epidemiology literatureb

 Adult, age 30+, ACS Cohort Study (Krewski et al., 2009)

 Adult, age 25+, Six-Cities Study (Lepeule et al., 2012)

 Infant, age <1 year (Woodruff et al., 1997)

180

(130 – 220)

400

(230 – 570)

0

(0 – 1)

Non-fatal myocardial infarction (adult, age 18 and over)

 Peters et al. (2001)

 Pooled estimate of 4 studies

200

(74 – 330)

22

(11 – 48)

Hospital admissions - respiratory (all ages)c,e 51

(-6 – 93)

Hospital admissions - cardiovascular (adults, age >18)d64

(33 – 110)

Emergency room visits for asthma (age 18 years and younger)e100

(-18 – 200)

Acute bronchitis, (children, age 8-12)e280

(-10 – 560)

Lower respiratory symptoms (children, age 7-14) 3,500

(1,700 – 5,300)

 

Cessation Lag. Letter from the Health Effects Subcommittee to the U.S. Environmental Protection Agency 

Administrator, December.

T Nonfatal myocardial infarctions (MI) are valued using age-specific cost-of-illness values that reflect lost earnings 

and direct medical costs over a 5-year period following a nonfatal MI. 
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Upper respiratory symptoms (asthmatic children, age 9-18) 5,000

(1,600 – 8,500)

Asthma exacerbation (asthmatic children, age 6-18) 5,200

(650 – 10,000)

Work loss days 23,000

(20,000 – 27,000)

Minor restricted activity days (adults age 18-65) 140,000

(120,000 – 160,000)

a

Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United 

States. 

b

PM-related adult mortality based upon the most recent American Cancer Society (ACS) Cohort Study 

(Krewski et al., 2009) and the most recent Six-Cities Study (Lepeule et al., 2012). Note that these are two 

alternative estimates of adult mortality and should not be summed. PM-related infant mortality based upon a 

study by Woodruff, Grillo, and Schoendorf, (1997).U

c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease 

(COPD), pneumonia and asthma.

d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart 

disease, dysrhythmias, and heart failure.

e The negative estimates at the 5th percentile confidence estimates for these morbidity endpoints reflect the 

statistical power of the study used to calculate these health impacts. These results do not suggest that reducing 

air pollution results in additional health impacts.

Table 8-20: Estimated Ozone-Related Health Impactsa

Health Effect 2018 Annual Reduction in 

Incidence (5th- 95th

percentile)

Premature Mortality, All agesb

Multi-City Analyses 

 Bell et al. (2004) – Non-accidental

 Huang et al. (2005) – Cardiopulmonary

 Schwartz (2005) – Non-accidental

Meta-analyses:

 Bell et al. (2005) – All cause

 Ito et al. (2005) – Non-accidental

 Levy et al. (2005) – All cause

45

(17 – 73)

65

(27 – 100)

69

(25 – 110)

150

(75 – 220)

200

(130 – 280)

210

(150 – 270)

Hospital admissions- respiratory causes (adult, 65 and older)c 260

(130 – 280)

Hospital admissions -respiratory causes (children, under 2) 130

(60 – 210)

Emergency room visit for asthma (all ages) 140

 

U Woodruff, T.J., J. Grillo, and K.C. Schoendorf. (1997). The Relationship Between Selected Causes of 

Postneonatal Infant Mortality and Particulate Air Pollution in the United States. Environmental Health Perspectives

105(6):608-612.
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Health Effect 2018 Annual Reduction in 

Incidence (5th- 95th

percentile)

(-19 – 430)

Minor restricted activity days (adults, age 18-65) 270,000

(120,000 – 420,000)

School absence days 92,000

(36,000 – 140,000)

Notes:

a

Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous U.S. 

b Estimates of ozone-related premature mortality are based upon incidence estimates derived from several alternative 

studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. (2005); Levy et al. (2005). 

The estimates of ozone-related premature mortality should therefore not be summed.

c Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for 

COPD and pneumonia. 

Table 8-21: Estimated Monetary Value of Changes in Incidence of Health and Welfare 

Effects (millions of 2011$) a,b

HEALTH ENDPOINTS 2018

(5TH AND 95TH

PERCENTILE)

PM2.5-Related Health Effects

Premature Mortality – Derived 

from Epidemiology Studiesb,c

Adult, age 30+ - ACS study 

(Krewski et al., 2009)

 3% discount rate

 7% discount rate

$1,600

($230 - $3,700)

$1,400

($210 - $3,300)

Adult, age 25+ - Six-Cities study 

(Lepeule et al., 2012)

 3% discount rate

 7% discount rate

$3,500

($510 - $8,600)

$3,200

($460 - $7,700)

Infant Mortality, <1 year –

(Woodruff et al. 1997)

$4.0

($0.55 - $10)

Non-fatal acute myocardial infarctions 

 Peters et al., 2001

 3% discount rate

 7% discount rate

 Pooled estimate of 4 studies

 3% discount rate

 7% discount rate

$26

($5.9 - $60)

$25

($5.5 - $59)

$2.8

($0.74 - $7.3)

$2.7

($0.68 - $7.2)

Hospital admissions for respiratory causesd$1.4

(-$0.32 - $2.7)

Hospital admissions for cardiovascular causes $2.6

($1.3 - $4.4)

Emergency room visits for asthmad$0.045

(-$0.007 - $0.087)
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Acute bronchitis (children, age 8–12)d$0.14

(-$0.005 - $0.34)

Lower respiratory symptoms (children, 7–14) $0.076

($0.030 - $0.14)

Upper respiratory symptoms (asthma, 9–11) $0.17

($0.049 - $0.38)

Asthma exacerbations $0.31

($0.039 – $0.74)

Work loss days $3.5

($3.1 - $4.0)

Minor restricted-activity days (MRADs) $9.8

($5.7 - $14)

Ozone-Related Health Effects

Premature Mortality, All ages –

Derived from Multi-city analyses

Bell et al., 2004 $440

($56 – $1,100)

Huang et al., 2005 $670

($95 - $1,700)

Schwartz, 2005 $710

($96 - $1,800)

Premature Mortality, All ages –

Derived from Meta-analyses

Bell et al., 2005 $1,400

($200 - $3,600)

Ito et al., 2005 $2,000

($290 - $4,800)

Levy et al., 2005 $2,000

($300 - $4,800)

Hospital admissions- respiratory causes (adult, 65 and older) $7.5

($0.62 - $14)

Hospital admissions- respiratory causes (children, under 2) $1.6

($0.74 - $2.5)

Emergency room visit for asthma (all ages) $0.061

(-$0.008 - $0.18)

Minor restricted activity days (adults, age 18-65) $19

($7.7 - $33)

School absence days $9.4

($3.6 - $14)

a Monetary benefits are rounded to two significant digits for ease of presentation and computation. PM and 

ozone benefits are nationwide. 

b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis 

year (2018).

c Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results 

reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for 

preparing economic analyses.

d The negative estimate at the 5th percentile confidence estimate for this morbidity endpoint reflects the 

statistical power of the study used to calculate this health impact. This result does not suggest that reducing 

air pollution results in additional health impacts.
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Table 8-22: Total Monetized Ozone and PM-related Benefits Associated with the Final

Program in 2018

Total Ozone and PM Benefits (billions, 2011$) –

PM Mortality Derived from the ACS and Six-Cities Studies

3% Discount Rate 7% Discount Rate

Ozone 

Mortality 

Function

Reference Mean Total 

Benefits

Ozone 

Mortality 

Function

Reference Mean Total 

Benefits

Multi-city Bell et al., 

2004

$2.1 - $4.1 Multi-city Bell et al., 

2004

$1.9 - $3.7

Huang et al., 

2005

$2.3 - $4.2 Huang et al., 

2005

$2.1 - $3.9

Schwartz, 

2005

$2.3 - $4.3 Schwartz, 

2005

$2.1 - $3.9

Meta-analysis Bell et al., 

2005

$3.1 - $5.0 Meta-analysis Bell et al., 

2005

$2.9 - $4.7

Ito et al., 

2005

$3.6 - $5.6 Ito et al., 

2005

$3.4 - $5.2

Levy et al., 

2005

$3.6 - $5.6 Levy et al., 

2005

$3.5 - $5.3
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