Comparison of Program Costs to Program Emission Reductions and Air
Quality Benefits 

EPA traditionally evaluates the effectiveness of a proposal in terms of
net benefits and in terms of cost-effectiveness.  Section 8.1 below
presents the cost-benefit analysis of the proposal, while Section 8.2
presents the cost-effectiveness.

Cost-Benefit Analysis

The net benefits of the proposed 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.  

Program-Wide Costs 

The costs that would be incurred from our proposed 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-effectiveness we have summed them to
represent the estimated costs of the proposed program.  

All costs represent the fleet-weighted average of light-duty vehicles
and trucks.  All costs are represented in 2010 dollars.

Vehicle Costs

The vehicle costs include the technology costs projected to meet the
proposed exhaust and evaporative standards, as detailed in draft RIA
Chapter 2 and shown in   REF _Ref305507397 \h  Table 8-1 .  The fleet
mix of light-duty vehicles, light duty trucks, and medium-duty trucks
represents the 2016 MY fleet used in the 2012-2016MY light-duty GHG
final rulemaking.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  1 : Annual Vehicle
Technology Costs, 2010$

Year	Vehicle Exhaust Emission Control Costs ($Million)	Vehicle
Evaporative Emission Control Costs ($Million)	Facility Costs ($Million)
Total Proposed Vehicle Program Costs ($Million)

2016	$          -	$           -  	$    22.5 	$22.5

2017	$705	$79.4	$    3.75 	$788

2018	$1,300	$190	$    3.75 	$1,494

2019	$1,410	$180	$    3.75 	$1,594

2020	$1,530	$245	$    3.75 	$1,779

2021	$1,670	$237	$    3.75 	$1,911

2022	$1,810	$301	$    3.75 	$2,115

2023	$1,840	$288	$    3.75 	$2,132

2024	$1,960	$293	$    3.75 	$2,257

2025	$2,080	$281	$    3.75	$2,365

2026	$2,070	$281	$    3.75	$2,355

2027	$2,040	$281	$    3.75	$2,325

2028	$2,040	$281	$    3.75	$2,325

2029	$2,000	$281	$    3.75	$2,285

2030	$1,990	$281	$    3.75	$2,275

Fuel Costs

The fuel costs consist of the additional operating costs and capital
costs to the refiners to meet the proposed sulfur average of 10 ppm. 
The sulfur control costs, as described in detail in draft RIA Chapter 5,
assume a cost of 0.89 cents per gallon which includes the refinery
operating and capital costs.  The projected annual fuel consumption is
described in draft RIA Section 8.1.  The projected annual fuel
consumption and annual fuel costs of the proposed program are listed in 
 REF _Ref305507438 \h  Table 8-2 . 

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  2 : Annual Fuel
Costs, 2010$

Year	Annual Fuel Consumption (million gallons)	Fuel Sulfur Control Costs

($Million)

2016	          36,208 	 $          322 

2017	       144,878 	 $      1,289 

2018	       144,710 	 $      1,288 

2019	       144,435 	 $      1,285 

2020	       144,324 	 $      1,284 

2021	       144,562 	 $      1,287 

2022	       144,838 	 $      1,289 

2023	       144,774 	 $      1,288 

2024	       144,812 	 $      1,289 

2025	       145,057 	 $      1,291 

2026	       145,476 	 $      1,295 

2027	       145,988 	 $      1,299 

2028	       146,761 	 $      1,306 

2029	       147,280 	 $      1,311 

2030	       148,295 	 $      1,320 

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 proposed program, as
shown in   REF _Ref305507450 \h  Table 8-3 .

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  3 : Total Annual
Vehicle and Fuel Control Costs, 2010$

Year	Total Proposed Vehicle Program Costs ($Million)	Fuel Sulfur Control
Costs

($Million)	Total Proposed Program Costs ($Million)

2016	$22.5	 $          322 	$345

2017	$788	 $      1,289 	$2,078

2018	$1,494	 $      1,288 	$2,782

2019	$1,594	 $      1,285 	$2,879

2020	$1,779	 $      1,284 	$3,063

2021	$1,911	 $      1,287 	$3,197

2022	$2,115	 $      1,289 	$3,404

2023	$2,132	 $      1,288 	$3,420

2024	$2,257	 $      1,289 	$3,546

2025	$2,365	 $      1,291 	$3,656

2026	$2,355	 $      1,295 	$3,649

2027	$2,325	 $      1,299 	$3,624

2028	$2,325	 $      1,306 	$3,631

2029	$2,285	 $      1,311 	$3,596

2030	$2,275	 $      1,320 	$3,595

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
proposed Tier 3 standards.  The vehicles and fuels subject to the
proposed standards are significant sources of mobile source air
pollution such as direct PM, NOX, SOX, VOCs and air toxics.  The
standards would 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 directly-emitted 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
proposed standard by answering two key questions:

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?

 μg/m3 in 2030).  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 0.52 ppb
in 2030).

Using the most conservative premature mortality estimates (Pope et al.,
2002 for PM2.5 and Bell et al., 2004 for ozone),, we estimate that by
2030, implementation of the proposed standards would reduce
approximately 970 premature mortalities annually and yield approximately
$9.5 billion in total annual benefits.  The upper end of the range of
avoided premature mortality estimates associated with the proposed
standards (based on Laden et al., 2006 for PM2.5 and Levy et al., 2005
for ozone), results in approximately 2,800 premature mortalities avoided
in 2030 and yields approximately $27 billion in total benefits.  Thus,
even taking the most conservative premature mortality assumptions, the
health impacts of the proposed standards presented in this rule are
clearly 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 this proposal.  For this reason, the Tier 3 emission control
scenario used in the air quality and benefits modeling includes emission
reductions from Tier 3 across the nation, assuming no reductions
associated with California's LEV III program (as opposed to including 
California’s LEV III program and its associated emission reductions in
the baseline scenario).  This was because EPA had not granted California
a waiver of preemption under CAA section 209 for the LEV III program at
the time EPA conducted the air quality modeling.  EPA did include
California’s fuel program, which independent of LEV III was already
resulting in average gasoline sulfur levels of 10 ppm, in the baseline
scenario.  Since then, EPA granted a waiver for California's LEV III
program (78 FR 2112, January 9, 2013).  Based on this change in
circumstances, we will conduct new air quality modeling for the final
rule that will include emission reductions from California’s LEV III
program in the baseline scenario. 

Had we modeled the California LEV III emission impacts in the Tier 3 air
quality baseline, we estimate that benefits would decrease by
approximately 12-16 percent, depending on the particular health impact
functions used to characterize both PM- and ozone-related premature
mortality.  As a result, we estimate that in 2030, using the most
conservative premature mortality estimates (Pope et al., 2002 for PM2.5
and Bell et al., 2004 for ozone),, the proposed standards would reduce
approximately 820 premature mortalities annually and yield approximately
$8.0 billion in total annual benefits.  The upper end of the range of
avoided premature mortality estimates associated with the proposed
standards (based on Laden et al., 2006 for PM2.5 and Levy et al., 2005
for ozone), results in approximately 2,400 premature mortalities avoided
in 2030 and yields approximately $23 billion in total benefits.  These
are rough estimates since, without new photochemical air quality
modeling to reflect the revised baseline and control scenarios, we are
unable to account for cross-state transport of pollution.  However, we
believe this is a reasonable characterization of the small reduction in
benefits had we modeled California in the baseline; our overall
cost-benefit conclusions do not materially change with or without the
inclusion of California emissions in our analysis.  We will conduct new
air quality modeling for the final rule that will include emission
reductions from California’s LEV III program in the baseline scenario.
The rest of this Chapter presents benefits that include California LEV
III emission reductions.

Overview

This analysis reflects the impacts of the proposed Tier 3 rule in 2030
compared to a future-year reference scenario without the program in
place.  Overall, we estimate that the proposed rule would lead to a net
decrease in PM2.5-related health and environmental impacts (see Section
7.2.5 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 0.05 μg/m3 in 2030). 

The air quality modeling also projects decreases in ozone concentrations
(see Section 7.2.5).  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 0.52 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.,  Our benefits methods are also consistent with rulemaking
analyses such as the final 2012-2016 MY Light-Duty Vehicle Rule,  the
final Portland Cement National Emissions Standards for Hazardous Air
Pollutants (NESHAP) RIA,, and the final 2017-2025 MY Light-Duty Vehicle
Rule.  To model the ozone and PM air quality impacts of the proposal, we
used the Community Multiscale Air Quality (CMAQ) model (see Section
7.2.2).  The modeled ambient air quality data serves as an input to the
Environmental Benefits Mapping and Analysis Program version 4.0
(BenMAP).  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   REF _Ref306268111  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 ozone-related premature mortality (each with its own row in)
to estimates of PM-related premature mortality.   The analysis of the
proposed standards reflects EPA’s work to characterize benefits prior
to the most recent PM NAAQS.  EPA will update its benefits analysis, and
related uncertainty analysis, to be consistent with the final PM NAAQS
for the final Tier 3 regulatory impact analysis.  

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  4 :  Estimated
2030 Monetized PM-and Ozone-Related Health Benefitsa,d

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, 2010$, 3% Discount Rate)b,c	Total Benefits

(Billions, 2010$, 7% Discount Rate) b,c

Multi-city analyses	Bell et al., 2004	Total: $9.5 - $21

PM: $7.7 - $19

Ozone: $1.8	Total: $8.7 - $19

PM: $7.0 - $17

Ozone: $1.8

	Huang et al., 2005	Total: $10 - $21

PM: $7.7 - $19

Ozone: $2.6	Total: $9.5 - $20

PM: $7.0 - $17

Ozone: $2.6

	Schwartz, 2005	Total: $10 - $22

PM: $7.7 - $19

Ozone: $2.7	Total: $9.6 - $20

PM: $7.0 - $17

Ozone: $2.7

Meta-analyses	Bell et al., 2005	Total: $13 - $24

PM: $7.7 - $19

Ozone: $5.5	Total: $12 - $23

PM: $7.0 - $17

Ozone: $5.5

	Ito et al., 2005	Total: $15 - $26

PM: $7.7 - $19

Ozone: $7.5	Total: $15 - $25

PM: $7.0 - $17

Ozone: $7.5

	Levy et al., 2005	Total: $15 - $27

PM: $7.7 - $19

Ozone: $7.7	Total: $15 - $25

PM: $7.0 - $17

Ozone: $7.7

Notes:

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 (Pope et al.,
2002) or the Six-Cities study (Laden et al., 2006).

b Note that total benefits presented here do not include a number of
unquantified benefits categories.  A detailed listing of unquantified
health and welfare effects is provided in   REF _Ref306268166  \*
MERGEFORMAT  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 in   REF _Ref306268111  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 and welfare 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).  Additionally, we are unable to quantify a number of known
welfare effects, including reduced acid and particulate deposition
damage to cultural monuments and other materials, and environmental
benefits due to reductions of impacts of eutrophication in coastal
areas.  These are listed in   REF _Ref306268166  \* MERGEFORMAT  Table
8-5 .  As a result, the health benefits quantified in this section are
likely underestimates of the total benefits attributable to the proposed
program.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  5 :  Human Health
and Welfare Effects of Pollutants Affected by the Proposed

Tier 3 Program

Pollutant/ Effect	Quantified and monetized in primary estimate
Unquantified

PM: healtha	Premature mortality based on cohort study estimatesb  and
expert elicitation estimates

Hospital admissions: respiratory and cardiovascular

Emergency room visits for asthma

Nonfatal heart attacks (myocardial infarctions)

Lower and upper respiratory illness

Minor restricted activity days

Work loss days

Asthma exacerbations (among asthmatic populations

Respiratory symptoms (among asthmatic populations)

Infant mortality	Low birth weight, pre-term birth and other reproductive
outcomes

Pulmonary function

Chronic respiratory diseases other than chronic bronchitis

Non-asthma respiratory emergency room visits

UVb exposure (+/-)c

PM: welfare

Visibility in Class I areas in SE, SW, and CA regions

Household soiling

Visibility in residential areas

Visibility in non-class I areas and class 1 areas in NW, NE, and Central
regions

UVb exposure (+/-)c

Global climate impactsc

Ozone: health	Premature mortality based on short-term study estimates

Hospital admissions: respiratory

Emergency room visits for asthma

Minor restricted activity days

School loss days	Chronic respiratory damage

Premature aging of the lungs

Non-asthma respiratory emergency room visits

UVb exposure (+/-)c



Ozone: welfare	

Decreased outdoor worker productivity	Yields for:

--Commercial forests

--Fruits and vegetables, and

--Other commercial and noncommercial crops

Damage to urban ornamental plants

Recreational demand from damaged forest aesthetics

Ecosystem functions

UVb exposure (+/-)c

Climate impacts

CO: health

Behavioral effects

Nitrate Deposition: welfare

Commercial fishing and forestry from acidic deposition effects

Commercial fishing, agriculture and forestry from nutrient deposition
effects

Recreation in terrestrial and estuarine ecosystems from nutrient
deposition effects 

Other ecosystem services and existence values for currently healthy
ecosystems

Coastal eutrophication from nitrogen deposition effects

Sulfate Deposition: welfare

Commercial fishing and forestry from acidic deposition effects

Recreation in terrestrial and aquatic ecosystems from acid deposition
effects

Increased mercury methylation

HC/Toxics: healthd

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 non-asthmatics (formaldehyde)

Irritation of the eyes, skin, and respiratory tract (acetaldehyde)

Upper respiratory tract irritation and congestion (acrolein)

HC/Toxics: welfare

Direct toxic effects to animals

Bioaccumulation in the food chain

Damage to ecosystem function

Odor

Notes:

a In addition to primary economic endpoints, there are a number of
biological responses that have been associated with PM health effects
including morphological changes and altered host defense mechanisms. 
The public health impact of these biological responses may be partly
represented by our quantified endpoints.

b Cohort estimates are designed to examine the effects of long term
exposures to ambient pollution, but relative risk estimates may also
incorporate some effects due to shorter term exposures (see Kunzli et
al., 2001 xe "Kunzli, 2001"  for a discussion of this issue). While some
of the effects of short term exposure are likely to be captured by the
cohort estimates, there may be additional premature mortality from short
term PM exposure not captured in the cohort estimates included in the
primary analysis.

c May result in benefits or disbenefits.

d Many of the key hydrocarbons related to this action are also hazardous
air pollutants listed in the CAA. 

While there would be impacts associated with air toxic pollutant
emission changes that result from the proposed 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.  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.  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, 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.”  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 proposed
program.  

Human Health Impacts

  REF _Ref294119395  \* MERGEFORMAT  Table 8-6  and   REF _Ref306268441 
\* MERGEFORMAT  Table 8-7  present EPA’s preferred estimates of the
annual PM2.5 and ozone health impacts in the 48 contiguous U.S. states
associated with the proposed Tier 3 program.  For each endpoint
presented in   REF _Ref294119395  \* MERGEFORMAT  Table 8-6  and   REF
_Ref306268441  \* MERGEFORMAT  Table 8-7 , we provide both the point
estimate and the 90 percent confidence interval. Table 8-8 presents the
sensitivity analysis.

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 proposed program would result in between
800 and 2,100 cases of avoided PM2.5-related premature deaths annually
in 2030.  As a sensitivity analysis was conducted to understand the
impact of alternative concentration response functions suggested by
experts in the field.  W, when the range of expert opinion is used, we
estimate between 270 and 2,700 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) (Bell et al., 2004; Huang et al., 2005; Schwartz, 2005) and
from a series of meta-analyses (Bell et al., 2005, Ito et al., 2005, and
Levy et al., 2005).  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.”  For
ozone-related premature mortality in 2030, we estimate a range of
between 170 to 770 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. 

  REF _Ref306268768  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 (Pope et al., 2002) and the Six-Cities
study (Laden et al., 2006).  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.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  6 :  Estimated
PM2.5-Related Health Impactsa

Health Effect	2030 Annual Reduction in Incidence  (5th - 95th
percentile)

Premature Mortality – Derived from epidemiology literatureb

  Adult, age 30+, ACS Cohort Study (Pope et al., 2002)

  Adult, age 25+, Six-Cities Study (Laden et al., 2006)

  Infant, age <1 year (Woodruff et al., 1997)	800

(310 – 1,300)

2,100

(1,100 – 3,000)

3

(0 – 8)

Chronic bronchitis (adult, age 26 and over)	560

(100 – 1,000)

Non-fatal myocardial infarction (adult, age 18 and over)	980

(360 – 1,600)

Hospital admissions - respiratory (all ages)c	160

(77 – 230)

Hospital admissions - cardiovascular (adults, age >18)d 	380

(270 – 440)

Emergency room visits for asthma (age 18 years and younger) 	600

(350 – 850)

Acute bronchitis, (children, age 8-12)	1,300

(0 – 2,500)

Lower respiratory symptoms (children, age 7-14)	16,000

(7,700 – 24,000)

Upper respiratory symptoms (asthmatic children, age 9-18)	12,000

(3,800 – 20,000)

Asthma exacerbation (asthmatic children, age 6-18)	27,000

(3,000 – 74,000)

Work loss days	100,000

(88,000 – 110,000)

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

(500,000 – 690,000)

Notes:

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 American Cancer Society
(ACS) Cohort Study (Pope et al., 2002) and the Six-Cities Study (Laden
et al., 2006).  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).

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.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  7 :  Estimated
Ozone-Related Health Impactsa

Health Effect	2030 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

	

170

(73 – 260)

250

(120 – 380)

260

(110 – 410)

540

(300 – 780)

750

(500 – 1,000)

770

(560 – 970)

Hospital admissions- respiratory causes (adult, 65 and older)c	1,200

(160 – 2,200)

Hospital admissions -respiratory causes (children, under 2)	550

(290 – 810)

Emergency room visit for asthma (all ages)	580

(0 – 1,500)

Minor restricted activity days (adults, age 18-65)	970,000

(490,000 – 1,500,000)

School absence days	350,000

(150,000 – 490,000)

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   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  8 :  Results of
Application of Expert Elicitation: Annual Reductions in Premature
Mortality in 2030 Associated with the Proposed Program

Source of Mortality Estimate	2030 Tier 3 Control

	5th Percentile	Mean	95th Percentile

Pope et al. (2002)	310	800	1,300

Laden et al. (2006)	1,100	2,100	3,000

Expert A	400	2,200	4,000

Expert B	170	1,600	3,600

Expert C	300	1,700	3,600

Expert D	240	1,200	1,900

Expert E	1,400	2,700	4,100

Expert F	1,000	1,500	2,100

Expert G	0	960	1,800

Expert H	4	1,200	2,800

Expert I	260	1,600	2,900

Expert J	390	1,300	2,900

Expert K	0	270	1,300

Expert L	65	1,000	2,300



Monetized Estimates of Human Health and Environmental Impacts

  REF _Ref306268940  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   REF _Ref306268961  Table 8-10 .  All monetized estimates are
presented in 2010$.  Where appropriate, estimates account for growth in
real gross domestic product (GDP) per capita between 2000 and 2030.  The
monetized value of PM2.5-related mortality also 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).  

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
proposed program, using the ACS and Six-Cities PM mortality studies and
the range of ozone mortality assumptions, is between $9.5 and $27
billion, assuming a 3 percent discount rate, or between $8.7 and $25
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 chronic illness (chronic
bronchitis and 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   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  9 :  Estimated
Monetary Value of Changes in Incidence of Health and Welfare Effects
(millions of 2010$) a,b

	2030

PM2.5-Related Health Effect	(5th and 95th Percentile)

Premature Mortality – Derived from Epidemiology Studiesc,d

	Adult, age 30+ - ACS study 

(Pope et al., 2002)

          3% discount rate

          7% discount rate

	

$7,200

($920 – $18,000)

$6,500

($830 - $17,000)

	Adult, age 25+ - Six-Cities study (Laden et al., 2006)

          3% discount rate

          7% discount rate

	

$18,000

($2,600 - $45,000)

$17,000

($2,300 - $41,000)

	Infant Mortality, <1 year – (Woodruff et al. 1997)	$27

($0 - $100)

Chronic bronchitis (adults, 26 and over)	$310

($25 - $1,000)

Non-fatal acute myocardial infarctions 

          3% discount rate

          7% discount rate

	

$110

($24 - $260)

$90

($19 - $210)

Hospital admissions for respiratory causes	$2.5

($1.2 – $3.6)

Hospital admissions for cardiovascular causes	$5.5

($1.2 - $10)

Emergency room visits for asthma	$0.24

($0.13 - $0.36)

Acute bronchitis (children, age 8–12)	$0.61

($0.00 - $1.5)

Lower respiratory symptoms (children, 7–14)	$0.34

($0.13 - $0.63)

Upper respiratory symptoms (asthma, 9–11)	$0.40

($0.12 - $0.89)

Asthma exacerbations	$1.6

($0.17 - $4.4)

Work loss days	$16

($14 - $19)

Minor restricted-activity days (MRADs)	$41

($24 - $59)

Ozone-related Health Effect

Premature Mortality, All ages – Derived from Multi-city analyses	Bell
et al., 2004	$1,700

($220 - $4,200)

	Huang et al., 2005	$2,500

($340 - $6,200)

	Schwartz, 2005	$2,600

($330 - $6,500)

Premature Mortality, All ages – Derived from Meta-analyses	Bell et
al., 2005	$5,400

($760 - $13,000)

	Ito et al., 2005	$7,400

($1,100 - $18,000)

	Levy et al., 2005	$7,600

($1,100 - $18,000)

Hospital admissions- respiratory causes (adult, 65 and older)	$32

($4.2 - $56)

Hospital admissions- respiratory causes (children, under 2)	$6.0

($3.1 - $8.9)

Emergency room visit for asthma (all ages)	$0.23

($0.0 - $0.57)

Minor restricted activity days (adults, age 18-65)	$67

($31 - $110)

School absence days	$34

($15 - $48)

Notes:

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.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  10 :  Total
Monetized Ozone and PM-related Benefits Associated with the Proposed
Program in 2030

Total Ozone and PM Benefits (billions, 2010$) – 

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	$9.5 - $21	Multi-city	Bell et al., 2004
$8.7 - $19

	Huang et al., 2005	$10 - $21

Huang et al., 2005	$9.5 - $20

	Schwartz, 2005	$10 - $22

Schwartz, 2005	$9.6 - $20

Meta-analysis	Bell et al., 2005	$13 - $24	Meta-analysis	Bell et al.,
2005	$12 - $23

	Ito et al., 

2005	$15 - $26

Ito et al., 

2005	$15 - $25

	Levy et al., 2005	$15 - $27

Levy et al., 2005	$15 - $25

Total Ozone and PM Benefits (billions, 2010$) – 

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	$4.6 - $27	Multi-city	Bell et al., 2004
$4.4 - $24

	Huang et al., 2005	$5.4 - $27

Huang et al., 2005	$5.2 - $25

	Schwartz, 2005	$5.5 - $28

Schwartz, 2005	$5.3 - $25

Meta-analysis	Bell et al., 2005	$8.3 - $30	Meta-analysis	Bell et al.,
2005	$8.1 - $28

	Ito et al., 

2005	$10 - $32

Ito et al., 

2005	$10 - $30

	Levy et al., 2005	$11 - $33

Levy et al., 2005	$10 - $30



Methodology

Human Health Impact Functions

Health impact functions measure the change in a health endpoint of
interest, such as hospital admissions, for a given change in ambient
ozone or PM concentration.  Health impact functions are derived from
primary epidemiology studies, meta-analyses of multiple epidemiology
studies, or expert elicitations.  A standard health impact function has
four components: (1) an effect estimate from a particular study; (2) a
baseline incidence rate for the health effect (obtained from either the
epidemiology study or a source of public health statistics such as the
Centers for Disease Control); (3) the size of the potentially affected
population; and (4) the estimated change in the relevant ozone or PM
summary measures.

A typical health impact function might look like:  

 ,

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
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. Section 8.2 describes the ozone
and PM air quality inputs to the health impact functions.  

Potentially Affected Populations

The starting point for estimating the size of potentially affected
populations is the 2000 U.S. Census block level dataset.  Benefits
Modeling and Analysis Program (BenMAP) incorporates 250 age/gender/race
categories to match specific populations potentially affected by ozone
and other air pollutants.  The software constructs specific populations
matching the populations in each epidemiological study by accessing the
appropriate age-specific populations from the overall population
database.  BenMAP projects populations to 2030 using growth factors
based on economic projections.

Effect Estimate Sources

The most significant quantifiable benefits of reducing ambient
concentrations of ozone and PM are attributable to reductions in human
health risks.  EPA’s Ozone and PM Criteria Documents, and the World
Health Organization’s 2003 and 2004, reports outline numerous human
health effects known or suspected to be linked to exposure to ambient
ozone and PM.  EPA evaluated the ozone and PM literature for use in the
benefits analysis for the final 2008 Ozone NAAQS and final 2006 PM NAAQS
analyses.  We use the same literature in this analysis; for more
information on the studies that underlie the health impacts quantified
in this RIA, please refer to those documents.

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.    REF
_Ref294119495 \h  \* MERGEFORMAT  Table 8-11  lists the health endpoints
included in this analysis.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  11 :  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) – 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	All ages

Premature mortality —cohort study, all-cause	PM2.5 	Pope et al. (2002)

Laden et al. (2006)	>29 years

>25 years

Premature mortality, total exposures	PM2.5 	Expert Elicitation (IEc,
2006)	>24 years

Premature mortality — all-cause	PM2.5 	Woodruff et al. (1997)	Infant
(<1 year)

Chronic Illness

Chronic bronchitis	PM2.5	Abbey et al. (1995)	>26 years

Nonfatal heart attacks	PM2.5 	Peters et al. (2001)	Adults (>18 years)

Hospital Admissions 

Respiratory	

O3 	Pooled estimate:

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

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

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

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

Moolgavkar et al. (1997) – ICD 490-496 (COPD)	>64 years



Burnett et al. (2001)	<2 years

	PM2.5 	Pooled estimate:

Moolgavkar (2003)—ICD 490-496 (COPD)

Ito (2003)—ICD 490-496 (COPD)	>64 years

	PM2.5	Moolgavkar (2000)—ICD 490-496 (COPD)	20–64 years

	PM2.5	Ito (2003)—ICD 480-486 (pneumonia)	>64 years

	PM2.5 	Sheppard (2003)—ICD 493 (asthma)	<65 years

Cardiovascular	PM2.5 	Pooled estimate:

Moolgavkar (2003)—ICD 390-429 (all cardiovascular)

Ito (2003)—ICD 410-414, 427-428 (ischemic heart disease, dysrhythmia,
heart failure)	>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)

Wilson et al (2005)	

All ages

All ages

Asthma-related ER visits (cont’d)	PM2.5 	Norris et al. (1999)	0–18
years

Other Health Endpoints

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

Upper respiratory symptoms	PM2.5	Pope et al. (1991)	Asthmatics, 9–11
years

Lower respiratory symptoms	PM2.5 	Schwartz and Neas (2000)	7–14 years

Asthma exacerbations	PM2.5 	Pooled estimate:

Ostro et al. (2001) (cough, wheeze and shortness of breath)

Vedal et al. (1998) (cough)	6–18 yearsa

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

School absence days	

O3 	Pooled estimate:

Gilliland et al. (2001)

Chen et al. (2000)	

5–17 yearsb

Minor Restricted Activity Days (MRADs)	O3	Ostro and Rothschild (1989)
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 6 to 13 for the Vedal et al. (1998) study.  Based on
advice from the Science Advisory Board Health Effects Subcommittee
(SAB-HES), we extended the applied population to 6 to 18, reflecting the
common biological basis for the effect in children in the broader age
group. See: U.S. Science Advisory Board. 2004.  Advisory Plans for
Health Effects Analysis in the Analytical Plan for EPA’s Second
Prospective Analysis –Benefits and Costs of the Clean Air Act,
1990—2020. EPA-SAB-COUNCIL-ADV-04-004. See also National Research
Council (NRC).  2002.  Estimating the Public Health Benefits of Proposed
Air Pollution Regulations.  Washington, DC:  The National Academies
Press.

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.

In selecting epidemiological studies as sources of effect estimates, we
applied several criteria to develop a set of studies that is likely to
provide the best estimates of impacts in the U.S.  To account for the
potential impacts of different health care systems or underlying health
status of populations, we give preference to U.S. studies over non-U.S.
studies.  In addition, due to the potential for confounding by
co-pollutants, we give preference to effect estimates from models
including both ozone and PM over effect estimates from single-pollutant
models., 

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 100 ppb decrease in daily
ozone levels might, in turn, decrease hospital admissions by 3 percent. 
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 100,000 people, that number must be multiplied by the number of
100,000s in the population.

  REF _Ref294119509 \h  \* MERGEFORMAT  Table 8-12  summarizes the
sources of baseline incidence rates and provides average incidence rates
for the endpoints included in the analysis.    REF _Ref306271004  Table
8-13  presents the asthma prevalence rates used in this 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 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.  Regional incidence rates are available
for hospital admissions, and county-level data are available for
premature mortality.  We have projected mortality rates such that future
mortality rates are consistent with our projections of population
growth.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  12 :  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)

U.S. Census bureau

Hospitalizations	Daily hospitalization rate	Age-, region-, state-,
county- and cause- specific rate	2007 HCUP data filesa,

Asthma ER Visits	Daily asthma ER visit rate	Age-, region-, state-,
county- and cause- specific rate	2007 HCUP data filesa

Chronic Bronchitis	Annual prevalence rate per person

Aged 18–44

Aged 45–64

Aged 65 and older	

0.0367

0.0505

0.0587	1999 NHIS (American Lung Association, 2002, Table 4) 

	Annual incidence rate per person	0.00378	Abbey et al. (1993, Table 3)

Nonfatal Myocardial Infarction (heart attacks)	Daily nonfatal myocardial
infarction incidence rate per person, 18+	Age-, region-, state-, and
county- specific rate	2007 HCUP data filesa; adjusted by 0.93 for
probability of surviving after 28 days (Rosamond et al., 1999)

Asthma Exacerbations	Incidence among asthmatic African-American children

daily wheeze

daily cough

daily dyspnea	0.076

0.067

0.037 	Ostro et al. (2001)

Acute Bronchitis	Annual bronchitis incidence rate, children	0.043
American Lung Association (2002, Table 11)

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); U.S.
Bureau of the Census (2000)

School Loss Days	Rate per person per year, assuming 180 school days per
year	9.9	National Center for Education Statistics (1996) and 1996 HIS
(Adams et al., 1999, Table 47); 

Minor Restricted-Activity Days	Daily 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   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  13 :  Asthma
Prevalence Rates Used for this Analysis

Population Group	Asthma Prevalence Rates

	Value	Source

All Ages	0.0780	American Lung Association (2010, Table 7)

< 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 Associationb

Notes:

a See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/.

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

PM2.5-Related Premature Mortality “Lowest Measured Level” (LML)
Assessment

Based on our review of the current body of scientific literature, EPA
estimated PM-related mortality without applying an assumed concentration
threshold. EPA’s Integrated Science Assessment for Particulate Matter
(U.S. EPA, 2009), which was reviewed by EPA’s Clean Air Scientific
Advisory Committee (U.S. EPA-SAB, 2009; U.S. EPA-SAB, 2009),, concluded
that the scientific literature consistently finds that a no-threshold
log-linear model most adequately portrays the PM-mortality
concentration-response relationship while recognizing potential
uncertainty about the exact shape of the concentration-response
function. Consistent with this finding, we have conformed the threshold
sensitivity analysis to the current state of the PM science and improved
upon our previous approach for estimating the sensitivity of the
benefits estimates to the presence of an assumed threshold by
incorporating a new “Lowest Measured Level” (LML) assessment.

This approach summarizes the distribution of avoided PM mortality
impacts according to the baseline (i.e. pre-Tier 3 Program) PM2.5 levels
experienced by the population receiving the PM2.5 mortality benefit (see
  REF _Ref311723249 \h  Figure 8-1 ). We identify on this figure the
lowest air quality levels measured in each of the two primary
epidemiological studies EPA uses to quantify PM-related mortality. This
information allows readers to determine the portion of PM-related
mortality benefits occurring above or below the LML of each study; in
general, our confidence in the estimated PM mortality decreases as we
consider air quality levels further below the LML in the two
epidemiological studies. While the LML analysis provides some insight
into the level of uncertainty in the estimated PM mortality benefits,
EPA does not view the LML as a threshold and continues to quantify
PM-related mortality impacts using a full range of modeled air quality
concentrations.

The large proportion of the avoided PM-related impacts we estimate in
this analysis occur among populations exposed at or above the LML of
each study (  REF _Ref311723249 \h  Figure 8-1 ), increasing our
confidence in the PM mortality analysis. Approximately 25 percent of the
avoided impacts occur at or above an annual mean PM2.5 level of 10
µg/m3 (the LML of the Laden et al. 2006 study); about 81 percent occur
at or above an annual mean PM2.5 level of 7.5 µg/m3 (the LML of the
Pope et al. 2002 study). As we model mortality impacts among populations
exposed to levels of PM2.5 that are successively lower than the LML of
each study our confidence in the results diminishes. However, the
analysis above confirms that the great majority of the impacts occur at
or above the Pope et al. LML.

As an example, when considering mortality impacts among populations
living in areas with an annual mean PM level of 8 µg/m3, we would place
greater confidence in estimates drawn from the Pope et al. 2002 study,
as this air quality level is above the LML of this study. Conversely, we
would place equal confidence when estimating mortality impacts among
populations living in locations where the annual mean PM levels are
above 10 µg/m3 because this value is at or above the LML of each study.


While the LML of each study is important to consider when characterizing
and interpreting the overall level PM-related benefits, EPA believes
that both cohort-based mortality estimates are suitable for use in air
pollution health impact analyses. When estimating PM mortality impacts
using risk coefficients drawn from the Laden et al. analysis of the
Harvard Six Cities and the Pope et al. analysis of the American Cancer
Society cohorts there are innumerable other attributes that may affect
the size of the reported risk 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.

Figure   STYLEREF 1 \s  8 -  SEQ Figure \* ARABIC \s 1  1  Percentage of
Total PM-related Mortalities Avoided by Baseline Air Quality Level

Economic Values for Health Outcomes

Reductions in ambient concentrations of air pollution generally lower
the risk of future adverse health effects for a large population. 
Therefore, the appropriate economic measure is willingness-to-pay (WTP)
for changes in risk of a health effect rather than WTP for a health
effect that would occur with certainty (Freeman, 1993).  Epidemiological
studies generally provide estimates of the relative risks of a
particular health effect that is avoided because of a reduction in air
pollution. We converted those to units of avoided statistical incidence
for ease of presentation. We calculated the value of avoided statistical
incidences by dividing individual WTP for a risk reduction by the
related observed change in risk.  For example, suppose a
pollution-reduction regulation 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 death is $1 million ($100/0.0001 change in
risk).

WTP estimates generally are not available for some health effects, such
as hospital admissions.  In these cases, we used the cost of treating or
mitigating the effect as a primary estimate.  These cost-of-illness
(COI) estimates generally understate the true value of reducing the risk
of a health effect, because they reflect the direct expenditures related
to treatment, but not the value of avoided pain and suffering
(Harrington and Portney, 1987; Berger, 1987).,  We provide unit values
for health endpoints (along with information on the distribution of the
unit value) in   REF _Ref294119565 \h  \* MERGEFORMAT  Table 8-14 .  All
values are in constant year 2010 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 2006 PM NAAQS RIA.  For
details on valuation estimates for ozone-related endpoints, see the 2008
Ozone NAAQS RIA.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  14 : Unit Values
for Economic Valuation of Health Endpoints (2010$)

Health Endpoint	Central Estimate of Value Per Statistical Incidence



2000 Income Level	2030 Income Level	Derivation of Distributions of
Estimates

Premature Mortality (Value of a Statistical Life)	$8,000,000	$9,900,000
EPA currently recommends a central VSL of $6.3m (2000$) 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, 2000).  



Chronic Bronchitis (CB)	$450,000	$550,000	The WTP to avoid a case of
pollution-related CB is calculated as where x is the severity of an
average CB case, WTP13 is the WTP for a severe case of CB, and $ is the
parameter relating WTP to severity, based on the regression results
reported in Krupnick and Cropper (1992). The distribution of WTP for an
average severity-level case of CB was generated by Monte Carlo methods,
drawing from each of three distributions: (1) WTP to avoid a severe case
of CB is assigned a 1/9 probability of being each of the first nine
deciles of the distribution of WTP responses in Viscusi et al. (1991);
(2) the severity of a pollution-related case of CB (relative to the case
described in the Viscusi study) is assumed to have a triangular
distribution, with the most likely value at severity level 6.5 and
endpoints at 1.0 and 12.0; and (3) the constant in the elasticity of WTP
with respect to severity is normally distributed with mean = 0.18 and
standard deviation = 0.0669 (from Krupnick and Cropper [1992]). This
process and the rationale for choosing it is described in detail in the
Costs and Benefits of the Clean Air Act, 1990 to 2010 (U.S. EPA, 1999). 

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	

$89,373

$100,690$106,053

$185,785

$89,373

$88,547

$98,680

$103,481

$174,866

$88,548	

$89,373

$100,690

$106,053

$185,785

$89,373

$88,547

$98,680

$103,481

$174,866

$88,548	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). Direct medical costs are based
on simple average of estimates from Russell et al. (1998) and Wittels et
al. (1990).

Lost earnings:

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

age of onset:   at 3%   	    at 7%

25–44             $8,774	     $7,855

45–54            $12,932	  $11,578

55–65            $74,746	  $66,920

Direct medical expenses: An average of:

1. Wittels et al. (1990) ($102,658—no discounting)

2.  Russell et al. (1998), 5-year period ($22,331 at 3% discount rate;
$21,113 at 7% discount rate)

Hospital Admissions



	Chronic Obstructive Pulmonary Disease (COPD)	$17,996	$17,996	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 (2000) (www.ahrq.gov). 

Asthma Admissions	$11,957	$11,957	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 (2000) (www.ahrq.gov). 

All Cardiovascular	$30,256	$30,256	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 (2000) (www.ahrq.gov). 

All respiratory (ages 65+)	$25,413	$25,413	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, 2000 (www.ahrq.gov).

All respiratory (ages 0–2)	$10,943	$10,943	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, 2000 (www.ahrq.gov).

Emergency Room Visits for Asthma	$405	$405	No distributional information
available. Simple average of two unit COI values:

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

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

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 (IEc, 1994) 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 $43.1.

Lower Respiratory Symptoms (LRS)	$20	$21	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 (IEc, 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 $24.46.

Asthma Exacerbations	$55	$57	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). 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 $15.6 and $70.8.

Acute Bronchitis	$452	$494	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. 

Work Loss Days (WLDs)	Variable (U.S. median = $137)	Variable (U.S.
median = $137)	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.

Minor Restricted Activity Days (MRADs)	$64	$69	Median WTP estimate to
avoid one MRAD from Tolley et al. (1986). Distribution is assumed to be
triangular with a minimum of $22 and a maximum of $83, with a most
likely value of $52. 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	$95	$95	No distribution available

Manipulating Air Quality Modeling Data for Health Impacts Analysis

In Section 7.2, 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., 
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
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., 

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).  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.  

  REF _Ref294119583 \h  \* MERGEFORMAT  Table 8-15  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   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  15 : Summary of
CMAQ-Derived Population-Weighted Ozone and PM2.5 Air Quality Metrics for
Health Benefits Endpoints Associated with the Proposed Tier 3 Program

	2030

Statistica	Baseline	Changeb

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

Daily Maximum 8-Hour Average Concentration 	42.8652	0.5235

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

Annual Average Concentration	8.3941	0.0479

Notes:

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 proposed program. 
Please refer to Section 7.2.1 for more information about the inventories
used in the air quality modeling that supports the health impacts
analysis.  

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 and
this analysis is no exception.  As outlined both in this and preceding
chapters, many inputs were used to derive the estimate of benefits for
the proposal, including emission inventories, air quality models (with
their associated parameters and inputs), epidemiological health effect
estimates, estimates of values (both from WTP and COI studies),
population estimates, income estimates, and estimates of the future
state of the world (i.e., regulations, technology, and human behavior). 
Each of these inputs may be uncertain and, depending on its role in the
benefits analysis, may have a disproportionately large impact on
estimates of total benefits.  For example, emissions estimates are used
in the first stage of the analysis.  As such, any uncertainty in
emissions estimates will be propagated through the entire analysis. 
When compounded with uncertainty in later stages, small uncertainties in
emission levels can lead to large impacts on total benefits.

The National Research Council (NRC) (2002, 2008), highlighted the need
for EPA to conduct rigorous quantitative analysis of uncertainty in its
benefits estimates and to present these estimates to decision makers in
ways that foster an appropriate appreciation of their inherent
uncertainty. In general, the NRC concluded that EPA’s general
methodology for calculating the benefits of reducing air pollution is
reasonable and informative in spite of inherent uncertainties.  Since
the publication of these reports, EPA’s Office of Air and Radiation
(OAR) continues to make progress toward the goal of characterizing the
aggregate impact of uncertainty in key modeling elements on both health
incidence and benefits estimates in two key ways: Monte Carlo analysis
and expert-derived concentration-response functions.  In this analysis,
we use both of these two methods to assess uncertainty quantitatively,
as well as provide a qualitative assessment for those aspects that we
are unable to address quantitatively.  

First, 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.
Monte Carlo simulation uses random sampling from distributions of
parameters to characterize the effects of uncertainty on output
variables, such as incidence of premature mortality. Specifically, we
used Monte Carlo methods to generate confidence intervals around the
estimated health impact and dollar benefits. The reported standard
errors in the epidemiological studies determined the distributions for
individual effect estimates.

Second, because characterization of random statistical error omits
important sources of uncertainty (e.g., in the functional form of the
model—e.g., whether or not a threshold may exist), we also incorporate
the results of an expert elicitation on the relationship between
premature mortality and ambient PM2.5 concentration (Roman et al.,
2008).  Use of the expert elicitation and incorporation of the standard
errors approaches provide insights into the likelihood of different
outcomes and about the state of knowledge regarding the benefits
estimates. However, there are significant unquantified uncertainties
present in upstream inputs including emission and air quality. Both
approaches have different strengths and weaknesses, which are fully
described in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006). 

In benefit analyses of air pollution regulations conducted to date, the
estimated impact of reductions in premature mortality has accounted for
85 to 95 percent of total monetized benefits. Therefore, it is
particularly important to attempt to characterize the uncertainties
associated with reductions in premature mortality. The health impact
functions used to estimate avoided premature deaths associated with
reductions in ozone have associated standard errors that represent the
statistical errors around the effect estimates in the underlying
epidemiological studies. In our results, we report credible intervals
based on these standard errors, reflecting the uncertainty in the
estimated change in incidence of avoided premature deaths. We also
provide multiple estimates, to reflect model uncertainty between
alternative study designs. 

For premature mortality associated with exposure to PM, we follow the
same approach used in the RIA for 2006 PM NAAQS (U.S. EPA, 2006),
presenting two empirical estimates of premature deaths avoided, and a
set of twelve estimates based on results of the expert elicitation
study. Even these multiple characterizations, including confidence
intervals, omit the contribution to overall uncertainty of uncertainty
in air quality changes, baseline incidence rates, populations exposed
and transferability of the effect estimate to diverse locations.
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.

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 mortality (NRC, 2008) 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.

Report additional risk metrics, including the percentage of baseline
mortality attributable to short-term exposure.

Remove reference to a no-causal relationship between ozone exposure and
premature mortality.

	The quantification and presentation of ozone-related premature
mortality in this chapter is responsive to these NRC recommendations. 

	Some key sources of uncertainty in each stage of both the PM and ozone
health impact assessment are the following:

gaps in scientific data and inquiry;

variability in estimated relationships, such as epidemiological effect
estimates, introduced through differences in study design and
statistical modeling;

errors in measurement and projection for variables such as population
growth rates;

errors due to misspecification of model structures, including the use of
surrogate variables, such as using PM10 when PM2.5 is not available,
excluded variables, and simplification of complex functions; and

biases due to omissions or other research limitations.

In   REF _Ref294119597 \h  Table 8-16  we summarize some of the key
uncertainties in the benefits analysis. 

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  16 :  Primary
Sources of Uncertainty in the Benefits Analysis

1.  Uncertainties Associated with Impact Functions

The value of the ozone or PM effect estimate in each impact function.

Application of a single impact function to pollutant changes and
populations in all locations.

Similarity of future-year impact functions to current impact functions. 

Correct functional form of each impact function. 

Extrapolation of effect estimates beyond the range of ozone or PM
concentrations observed in the source epidemiological study. 

Application of impact functions only to those subpopulations matching
the original study population.

2.  Uncertainties Associated with CMAQ-Modeled Ozone and PM
Concentrations 

Responsiveness of the models to changes in precursor emissions from the
control policy.

Projections of future levels of precursor emissions, especially ammonia
and crustal materials.

Lack of ozone and PM2.5 monitors in all rural areas requires
extrapolation of observed ozone data from urban to rural areas.

3.  Uncertainties Associated with PM Mortality Risk

Limited scientific literature supporting a direct biological mechanism
for observed epidemiological evidence.

Direct causal agents within the complex mixture of PM have not been
identified.

The extent to which adverse health effects are associated with low-level
exposures that occur many times in the year versus peak exposures.

The extent to which effects reported in the long-term exposure studies
are associated with historically higher levels of PM rather than the
levels occurring during the period of study.

Reliability of the PM2.5 monitoring data in reflecting actual PM2.5
exposures.

4.  Uncertainties Associated with Possible Lagged Effects

The portion of the PM-related long-term exposure mortality effects
associated with changes in annual PM levels that would occur in a single
year is uncertain as well as the portion that might occur in subsequent
years.

5.  Uncertainties Associated with Baseline Incidence Rates

Some baseline incidence rates are not location specific (e.g., those
taken from studies) and therefore may not accurately represent the
actual location-specific rates.

Current baseline incidence rates may not approximate well baseline
incidence rates in 2030.

Projected population and demographics may not represent well future-year
population and demographics.

6.  Uncertainties Associated with Economic Valuation

Unit dollar values associated with health and welfare endpoints are only
estimates of mean WTP and therefore have uncertainty surrounding them.

Mean WTP (in constant dollars) for each type of risk reduction may
differ from current estimates because of differences in income or other
factors.

7.  Uncertainties Associated with Aggregation of Monetized Benefits

Health and welfare benefits estimates are limited to the available
impact functions.  Thus, unquantified or unmonetized benefits are not
included.



Comparison of Costs and Benefits

This section presents the cost-benefit comparison related to the
expected impacts of the proposed 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.   
REF _Ref308697823 \h  Table 8-17  contains the estimated social costs
and the estimated monetized benefits of the program.

The results in   REF _Ref308697823 \h  Table 8-17  suggest that the 2030
monetized benefits of the proposed standards are greater than the
expected costs.  Specifically, the annual benefits of the total program
will range between $9.5 to $27 billion annually in 2030 using a three
percent discount rate, or between $8.7 to $25 billion assuming a 7
percent discount rate, compared to estimated social costs of
approximately $3.6 billion in that same year.  Though there are a number
of health and environmental effects associated with the proposed
standards that we are unable to quantify or monetize (see   REF
_Ref306268166 \h  \* MERGEFORMAT  Table 8-5 ), the benefits of the
proposed standards outweigh the projected costs. 

	Using a conservative benefits estimate, the 2030 benefits outweigh the
costs by a factor of 2.4.  Using the upper end of the benefits range,
the benefits could outweigh the costs by a factor of 7.5.  Thus, even
taking the most conservative benefits assumptions, benefits of the
proposed standards clearly outweigh the costs.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  17 : Summary of
Annual Benefts and Costs Associated with the Proposed Tier 3 Program
(Billions, 2010$)a

Description	2030

Vehicle Program Costs

Fuels Program Costs

Total Estimated Costsb 	$2.3

$1.3

$3.6

Total Estimated Health Benefitsc,d,e,f

     3 percent discount rate

     7 percent discount rate	

$9.5 - $27

$8.7 - $25

Annual Net Benefits (Total Benefits – Total Costs)

     3 percent discount rate

     7 percent discount rate	

$5.9 - $23

$5.1 - $21

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 draft
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 (Pope et al., 2002) 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 (Laden et
al., 2006) 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 2006 PM
National Ambient Air Quality Standards (September, 2006). 

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.

Illustrative Analysis of Quantified and Monetized Impacts Associated
with the Proposal in 2017

For illustrative purposes, this section presents the quantified and
monetized impacts associated with the proposed standards in 2017.  As
presented in Section 7.1.5, the emissions impacts of the proposed
standards in 2017 are primarily due to the effects of sulfur on the
existing (pre-Tier 3) fleet.  For reasons explained in Section
7.1.3.2.2, our analysis of the air quality impacts in 2017 reflects an
increase in direct PM emissions from vehicles (along with reductions in
NOX, VOCs and other pollutants).  This emissions increase results from a
series of conservative assumptions and uncertainties related to fuel
parameters in 2017, and is not expected to occur in reality.  Because
our air quality modeling assumes this increase, as well as increased
direct PM emissions due to an emissions processing error (see Section
7.2.1.1.2), our illustrative benefits analysis in 2017 overestimates
ambient concentrations of PM and underestimates the benefits of the
proposed Tier 3 standards.  

μg/m3 in 2017).  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 (population-weighted maximum 8-hour
average ozone decreases by 0.17 ppb in 2017).

  REF _Ref311720637 \h  Table 8-18  and   REF _Ref311720660 \h  Table
8-19  present the annual PM2.5 and ozone health impacts in the 48
contiguous U.S. states associated with the proposed Tier 3 program.  For
each endpoint presented in   REF _Ref311720637 \h  Table 8-18  and   REF
_Ref311720660 \h  Table 8-19 , 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
proposed standards would result in between 57 and 150 cases of avoided
PM2.5-related premature mortalities annually in 2017.  For ozone-related
premature mortality in 2017, we estimate a range of between 49 to 230
fewer premature mortalities.  

  REF _Ref311720728 \h  Table 8-20  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   REF _Ref311549396 \h  Table 8-21 .  All monetized estimates are
presented in 2010$.  Where appropriate, estimates account for growth in
real gross domestic product (GDP) per capita between 2000 and 2017.  The
monetized value of PM2.5-related mortality also 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 PM2.5-related nonfatal heart attacks (myocardial
infarctions).  

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 proposed 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 associated with the
proposed standards in 2017, using the ACS and Six-Cities PM mortality
studies and the range of ozone mortality assumptions, is between $1.0
and $3.4 billion, assuming a 3 percent discount rate, or between $1.0
and $3.3 billion, assuming a 7 percent discount rate.  Had our ambient
air quality modeling of PM2.5 not included the increase in direct PM
emissions, we estimate that benefits would increase by a range of $400
to $970 million (assuming a 3 percent discount rate) or increase by a
range of $360 to $880 million (assuming a 7 percent discount rate),
using current EPA benefit-per-ton estimates for direct PM.

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.  Accounting for
the removal of the increase in direct PM emissions in the 2017 air
quality modeling, total monetized benefits increase even more, to
between $1.4 and $4.3 billion, assuming a 3 percent discount rate, or
between $1.3 and $4.2 billion, assuming a 7 percent discount rate.   The
benefits increase substantially after 2017, as the vehicle standards
phase in after 2017 and as the fleet turns over.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  18 :  Estimated
PM2.5-Related Health Impactsa

Health Effect	2017 Annual Reduction in Incidence (5th - 95th percentile)

Premature Mortality – Derived from epidemiology literatureb

  Adult, age 30+, ACS Cohort Study (Pope et al., 2002)

  Adult, age 25+, Six-Cities Study (Laden et al., 2006)

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

57

(9 – 110)

150

(54 – 240)

0

(0 – 1)

Chronic bronchitis (adult, age 26 and over)	38

(-7 – 84)

Non-fatal myocardial infarction (adult, age 18 and over)	64

(7 – 120)

Hospital admissions - respiratory (all ages)c	10

(3 – 17)

Hospital admissions - cardiovascular (adults, age >18)d 	23

14 – 28)

Emergency room visits for asthma (age 18 years and younger) 	40

(15 – 64)

Acute bronchitis, (children, age 8-12)	87

(-45 – 220)

Lower respiratory symptoms (children, age 7-14)	1,100

(270 – 1,900)

Upper respiratory symptoms (asthmatic children, age 9-18)	830

(0 – 1,700)

Asthma exacerbation (asthmatic children, age 6-18)	1,800

(-360 – 5,100)

Work loss days	7,300

(5,900 – 8,700)

Minor restricted activity days (adults age 18-65)	43,000

(33,000 – 53,000)

Notes:

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 American Cancer Society
(ACS) Cohort Study (Pope et al., 2002) and the Six-Cities Study (Laden
et al., 2006).  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).

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.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  19 :  Estimated
Ozone-Related Health Impactsa

Health Effect	2017 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

	

49

(21 – 77)

71

(33 – 110)

75

(31 – 120)

160

(88 – 230)

220

(140 – 290)

230

(160 – 290)

Hospital admissions- respiratory causes (adult, 65 and older)c	290

(37 – 520)

Hospital admissions -respiratory causes (children, under 2)	170

(85 – 250)

Emergency room visit for asthma (all ages)	170

(-3 – 441)

Minor restricted activity days (adults, age 18-65)	300,000

(150,000 – 440,000)

School absence days	98,000

(43,000 – 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   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  20 :  Estimated
Monetary Value of Changes in Incidence of Health and Welfare Effects
(millions of 2010$) a,b

	2017

PM2.5-Related Health Effect	(5th and 95th Percentile)

Premature Mortality – Derived from Epidemiology Studiesc,d

	Adult, age 30+ - ACS study 

(Pope et al., 2002)

          3% discount rate

          7% discount rate

	

$490

($41 - $1,300)

$440

($37 - $1,200)

	Adult, age 25+ - Six-Cities study (Laden et al., 2006)

          3% discount rate

          7% discount rate

	

$1,300

($160 - $3,200)

$1,100

($140 - $2,900)

	Infant Mortality, <1 year – (Woodruff et al. 1997)	$2.5

(-$3.3 - $11)

Chronic bronchitis (adults, 26 and over)	$20

(-$2.6 - $70)

Non-fatal acute myocardial infarctions 

          3% discount rate

          7% discount rate

	

$7.4

($0.80 - $20)

$5.9

($0.60 - $15)

Hospital admissions for respiratory causes	$0.16

($0.05 - $0.23)

Hospital admissions for cardiovascular causes	$0.38

($0.01 - $0.74)

Emergency room visits for asthma	$0.016

($0.006 - $0.026)

Acute bronchitis (children, age 8–12)	$0.041

(-$0.021 - $0.12)

Lower respiratory symptoms (children, 7–14)	$0.023

($0.005 - $0.047)

Upper respiratory symptoms (asthma, 9–11)	$0.028

($0 - $0.070)

Asthma exacerbations	$0.10

(-$0.02 - $0.30)

Work loss days	$1.2

($0.96 - $1.4)

Minor restricted-activity days (MRADs)	$2.9

($1.7 - $4.3)

Ozone-related Health Effect

Premature Mortality, All ages – Derived from Multi-city analyses	Bell
et al., 2004	$460

($60 - $1,200)

	Huang et al., 2005	$670

($90 - $1,700)

	Schwartz, 2005	$700

($91 - $1,800

Premature Mortality, All ages – Derived from Meta-analyses	Bell et
al., 2005	$1,500

($210 - $3,700)

	Ito et al., 2005	$2,100

($300 - $4,900)

	Levy et al., 2005	$2,100

($320 - $5,000)

Hospital admissions- respiratory causes (adult, 65 and older)	$7.6

($0.97 - $14)

Hospital admissions- respiratory causes (children, under 2)	$1.8

($0.93 - $2.7)

Emergency room visit for asthma (all ages)	$0.07

($0 - $0.17)

Minor restricted activity days (adults, age 18-65)	$20

($9.0 - $34)

School absence days	$9.4

($4.1 - $13)

Notes:

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 (2017).

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.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  21 :  Total
Monetized Ozone and PM-related Benefits Associated with the Proposed
Program in 2017

Total Ozone and PM Benefits (billions, 2010$) – 

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	$1.0 - $1.8	Multi-city	Bell et al., 2004
$0.96 - $1.7

	Huang et al., 2005	$1.2 - $1.9

Huang et al., 2005	$1.2 - $1.9

	Schwartz, 2005	$1.3 - $2.0

Schwartz, 2005	$1.2 - $1.9

Meta-analysis	Bell et al., 2005	$2.1 - $2.8	Meta-analysis	Bell et al.,
2005	$2.0 - $2.7

	Ito et al., 

2005	$2.6 - $3.4

Ito et al., 

2005	$2.6 - $3.3

	Levy et al., 2005	$2.7 - $3.4

Levy et al., 2005	$2.6 - $3.3



Cost-Effectiveness

This section will present the cost-effectiveness analysis we completed
for the proposed combined Tier 3 vehicle and fuel standards.  This
analysis relies in part on cost information from draft RIA Chapters 2
and 5 and emissions information from draft RIA Chapter 8 to estimate the
dollars per ton ($/ton) of total NOX + NMOG emission reductions after
the proposed Tier 3 standards would have been fully implemented.  We
have calculated the cost-effectiveness on an aggregate basis to provide
a comprehensive means for capturing the effectiveness of the proposed
program on vehicles and fuels.  This chapter also compares the
cost-effectiveness of the proposed provisions with the
cost-effectiveness of other NOX and NMOG control strategies from
previous and potential future EPA emission control programs.   

Overview

We have calculated the aggregate cost-effectiveness which uses the costs
and emission reductions for calendar years 2017 and 2030, consistent
with the years that we evaluated for air quality and the cost-benefit
analysis.  All of our results are presented and discussed in Section
8.2.5 below.

Baselines

An average approach to cost-effectiveness requires that we compare the
costs and emission reductions associated with the proposed standards to
those for the previous set of standards that are being met by
manufacturers.  In this case, the $/ton values represent the full range
of control from the last applicable standard to our proposed standards. 


Since today's program includes both proposed fuel standards and proposed
vehicle standards, it was necessary for us to define a baseline for both
fuels and vehicles from which to calculate reductions in emissions and
increases in costs.  For sulfur content of fuel and vehicle emissions,
the previous standards were set under the Tier 2 program.  The baseline
sulfur level in the fuel is therefore 30 ppm and the baseline vehicle
exhaust standard is 0.07 g/mi NOX.  The baseline vehicle evaporative
standards are listed below in   REF _Ref303346937 \h  Table 8-22 .

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  22 :  Light-Duty
Vehicle, Light-Duty Truck, and Medium-Duty Passenger Vehicle Evaporative
Standardsa  

Vehicle Type	3 Day Diurnal + Hot Soak (g/test)	2 Day Diurnal + Hot Soak
(g/test)

LDV	0.50	0.65

LLDT 	0.65	0.85

MDPV	1.00	1.25

a 72 Federal Register at 8471 (February 26, 2007)

Costs

Costs that would be incurred from our proposed program would be due to
the proposed Tier 3 exhaust standards, Tier 3 evaporative standards, and
reductions in sulfur content of gasoline, as discussed above in Section
8.1.1.  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, are as shown in   REF _Ref303349746 \h  Table 8-23 . 
All costs represent the fleet-weighted average of light-duty vehicles
and trucks.  All costs are represented in 2010 dollars.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  23 : Total Annual
Vehicle and Fuel Control Costs, 2010$

	Total Vehicle and Fuel Control Costs

($Million)

2017	$2,078



2030	$3,595

Emission Reductions

In order to determine the overall cost-effectiveness of the standards we
are proposing, it was necessary to calculate the tons of each pollutant
reduced on an aggregate basis.  Our proposed standards are intended
primarily to reduce emissions of NOX and NMOG.  As a result, we have
determined that the cost-effectiveness of our standards should be
determined for both NOX and NMOG.  It is true that our program does
include new proposed standards for PM.  However, as discussed in Chapter
2, we believe that the efforts manufacturers make to meet the NOX+NMOG
standards will also result in sufficient PM reductions to meet our
proposed PM standards.  Thus we estimate that manufacturers would incur
no additional costs to comply with the Tier 3 PM standard and a
cost-effectiveness analysis of the PM standards is therefore
unnecessary.

NOX and NMOG

Several past rulemakings which produced reductions in both NOX and NMOG
have taken an approach to cost-effectiveness that sums the NOX and NMOG
emission reductions.  This approach leads to $/ton NOX+NMOG.  In
addition, many standards for mobile sources have been established in
terms of NOX+NMOG caps, including the previous Tier 2 vehicle standards.
 Thus we believe that this approach to cost-effectiveness is appropriate
for our Tier 3 standards as well.  

The projected annual reductions in NOX and NMOG in 2017 and 2030 are
included in   REF _Ref304544975 \h  Table 8-24 .

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  24 : Annual NOX
and VOC Reductions (tons) in 2017 and 2030

	NOX Reductions (tons)	VOC Reductions (tons)	Total NOX+VOC Reductions
(tons

2017	284,381	44,782	329,162

2030	524,790	226,028	750,818

Results

The results of our cost-effectiveness analysis are provided in   REF
_Ref304545141 \h  Table 8-25 .  Costs are provided above in   REF
_Ref303349746 \h  Table 8-23 .  The tons reduced are from the values in 
 REF _Ref304544975 \h  Table 8-24  as the difference between our Tier 2
baseline at our baseline fuel sulfur level of 30 ppm, and our Tier 3
standards at our fuel sulfur standard of 10 ppm.  

The costs of the proposed program would be higher immediately after it
is implemented than they would be after several years, since both
vehicle manufacturers and refiners can take advantage of decreasing
capital and operating costs over time.  In addition, the reductions in
NOX and VOC emissions will become greater as a greater percentage of the
fleet contains the technologies required to meet the proposed standards.

Table   STYLEREF 1 \s  8 -  SEQ Table \* ARABIC \s 1  25  
Cost-Effectiveness of the Proposed Vehicle and Fuel Standards

	Total Proposed Program Cost ($million, 2010$)	Total NOX + VOC
Reductions (tons)	Cost Effectiveness ($/ton)

2017	$2,078	329,162	$6,312

2030	$3,595	750,818	$4,788



References

 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.

 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K.
Ito, and G.D. Thurston.  (2002).  Lung Cancer, Cardiopulmonary
Mortality, and Long-term Exposure to Fine Particulate Air Pollution. 
Journal of the American Medical Association, 287, 1132-1141.

 Bell, M.L., et al. (2004).  Ozone and short-term mortality in 95 US
urban communities, 1987-2000.Journal of the American Medical
Association, 292(19), 2372-2378.

 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery.  (2006). 
Reduction in Fine Particulate Air Pollution and Mortality.  American
Journal of Respiratory and Critical Care Medicine.  173, 667-672.

 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005).  Ozone exposure
and mortality: an empiric bayes metaregression analysis. Epidemiology.
16(4), 458-68.

 To conduct this sensitivity analysis, we simply assumed no air quality
change in the California portion of the CMAQ domain.  We then exported
the reference and control air quality surfaces to be used as inputs to
BenMAP.  Note that this simple approach is unable to account for
legitimate emissions impacts related to cross-state transport of
pollution.

 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K.
Ito, and G.D. Thurston.  (2002).  Lung Cancer, Cardiopulmonary
Mortality, and Long-term Exposure to Fine Particulate Air Pollution. 
Journal of the American Medical Association, 287, 1132-1141.

 Bell, M.L., et al. (2004).  Ozone and short-term mortality in 95 US
urban communities, 1987-2000.Journal of the American Medical
Association, 292(19), 2372-2378.

 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery.  (2006). 
Reduction in Fine Particulate Air Pollution and Mortality.  American
Journal of Respiratory and Critical Care Medicine.  173, 667-672.

 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005).  Ozone exposure
and mortality: an empiric bayes metaregression analysis. Epidemiology.
16(4), 458-68.

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

 US EPA (2012).  National Ambient Air Quality Standards for Particulate
Matter.  http://www.epa.gov/PM/2012/finalrule.pdf

 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.

 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.

 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.9  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.

 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.

 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.  

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

 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).

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

 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.

 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.

 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.  

 The PM2.5-related benefit-per-ton estimates provide the total monetized
human health benefits (the sum of premature mortality and premature
morbidity) of reducing one ton of directly emitted PM2.5.  The benefit
per-ton technique has been used in previous analyses, including the
2012-2016 Light-Duty Greenhouse Gas Rule (U.S. EPA, 2010. Regulatory
Impact Analysis, Final Rulemaking to Establish Light-Duty Vehicle
Greenhouse Gas Emission Standards and Corporate Average Fuel Economy
Standards.  Office of Transportation and Air Quality.  April.  Available
at http://www.epa.gov/otaq/climate/regulations/420r10009.pdf.
EPA-420-R-10-009). The benefits-per-ton values are available at  
HYPERLINK "http://www.epa.gov/oaqps001/benmap/bpt.html" 
http://www.epa.gov/oaqps001/benmap/bpt.html .  Note that the values on
the website are presented in year 2006$; the values underlying the
estimates here have been adjusted to 2010$ using the CPI-U “all”
index.

 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.

Chapter 1 

*** Working Comments on Draft Language under E.O. 13563 and 12866
Interagency Review.  Subject to Further Policy Review.E.O. 12866 Review
– Revised Version – Do Not Cite, Quote, or Release During Review ***

Page   PAGE  2 

  PAGE   \* MERGEFORMAT  8-41 

 U.S. Environmental Protection Agency.  (2006).  Final Regulatory Impact
Analysis (RIA) for the Proposed National Ambient Air Quality Standards
for Particulate Matter.  Prepared by: Office of Air and Radiation. 
Retrieved March, 26, 2009 at  HYPERLINK
"http://www.epa.gov/ttn/ecas/ria.html"
http://www.epa.gov/ttn/ecas/ria.html . EPA-HQ-OAR-2009-0472-0240 

 U.S. Environmental Protection Agency.  (2008).  Final Ozone NAAQS
Regulatory Impact Analysis.  Prepared by: Office of Air and Radiation,
Office of Air Quality Planning and Standards.  Retrieved March, 26, 2009
at  HYPERLINK "http://www.epa.gov/ttn/ecas/ria.html"
http://www.epa.gov/ttn/ecas/ria.html . EPA-HQ-OAR-2009-0472-0238

 U.S. Environmental Protection Agency.  (2010).  Final Rulemaking to
Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and
Corporate Average Fuel Economy Standards: Regulatory Impact Analysis,
Assessment and Standards Division, Office of Transportation and Air
Quality, EPA-420-R-10-009, April 2010.  Available on the internet:
http://www.epa.gov/otaq/climate/regulations/420r10009.pdf

 U.S. Environmental Protection Agency (U.S. EPA).  2010.  Regulatory
Impact Analysis: National Emission Standards for Hazardous Air
Pollutants from the Portland Cement Manufacturing Industry.  Office of
Air Quality Planning and Standards, Research Triangle Park, NC.  Augues.
 Available on the Internet at <
http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementfinalria.pdf >.
EPA-HQ-OAR-2009-0472-0241

 U.S. Environmental Protection Agency.  (2012).  Regulatory Impact
Analysis: Final Rulemaking for 2017-2025 Light-Duty Vehicle Greenhouse
Gas Emission Standards and Corporate Average Fuel Economy Standards,
Assessment and Standards Division, Office of Transportation and Air
Quality, EPA-420-R-12-016, August 2012.  Available on the internet:
http://www.epa.gov/otaq/climate/documents/420r12016.pdf

 Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M.
Studnicka. 2001. Assessment of Deaths Attributable to Air Pollution:
Should We Use Risk Estimates Based on Time Series or on Cohort Studies?
American Journal of Epidemiology 153(11):1050-55.

 Science Advisory Board.  2001.  NATA – Evaluating the National-Scale
Air Toxics Assessment for 1996 – an SAB Advisory. 
http://www.epa.gov/ttn/atw/sab/sabrev.html.

 U.S. Environmental Protection Agency (U.S. EPA). 2011. The Benefits and
Costs of the Clean Air Act from 1990 to 2020. Office of Air and
Radiation, Washington, DC.  March.  Available on the Internet at
<http://www.epa.gov/air/sect812/feb11/fullreport.pdf>.

 U.S. Environmental Protection Agency—Science Advisory Board (U.S.
EPA-SAB). 2008. Benefits of Reducing Benzene Emissions in Houston,
1990–2020. EPA-COUNCIL-08-001. July. Available at
<http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9DAEDA8A54852574860072
8A83/$File/EPA-COUNCIL-08-001-unsigned.pdf>.

 National Research Council (NRC).  2008.  Estimating Mortality Risk
Reduction and Economic Benefits from Controlling Ozone Air Pollution. 
National Academies Press.  Washington, DC.  

 GeoLytics Inc.  (2002).  Geolytics CensusCD® 2000 Short Form Blocks. 
CD-ROM Release 1.0.  GeoLytics, Inc. East Brunswick, NJ. Available:
http://www.geolytics.com/ [accessed 29 September 2004]

 Woods & Poole Economics Inc.  2008.  Population by Single Year of Age
CD.  CD-ROM.  Woods & Poole Economics, Inc. Washington, D.C.
EPA-HQ-OAR-2009-0472-0011

 U.S. Environmental Protection Agency. (2006). Air quality criteria for
ozone and related photochemical oxidants (second external review draft).
Research Triangle Park, NC:  National Center for Environmental
Assessment; report no. EPA/600R-05/004aB-cB, 3v. Available: 
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=137307[March 2006]
EPA-HQ-OAR-2009-0472-0099, EPA-HQ-OAR-2009-0472-0100,
EPA-HQ-OAR-2009-0472-0101

 U.S. Environmental Protection Agency, 2004.  Air Quality Criteria for
Particulate Matter Volume II of II. National Center for Environmental
Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC EPA/600/P-99/002bF.
EPA-HQ-OAR-2009-0472-0097

 World Health Organization (WHO).  (2003).  Health Aspects of Air
Pollution with Particulate Matter, Ozone and Nitrogen Dioxide: Report on
a WHO Working Group.  World Health Organization.  Bonn, Germany. 
EUR/03/5042688.

 Anderson HR, Atkinson RW, Peacock JL, Marston L, Konstantinou K.
(2004).  Meta-analysis of time-series studies and panel studies of
Particulate Matter (PM) and Ozone (O3): Report of a WHO task group.
Copenhagen, Denmark: World Health Organization.  

 Bell, M.L., et al. (2004).  Ozone and short-term mortality in 95 U.S.
urban communities, 1987-2000. JAMA, 2004. 292(19): p. 2372-8.
EPA-HQ-OAR-2009-0472-1662

 Huang, Y.; Dominici, F.; Bell, M. L. (2005) Bayesian hierarchical
distributed lag models for summer ozone exposure and cardio-respiratory
mortality. Environmetrics. 16: 547-562. EPA-HQ-OAR-2009-0472-0233

 Schwartz, J. (2005) How sensitive is the association between ozone and
daily deaths to control for temperature? Am. J. Respir. Crit. Care Med.
171: 627-631. EPA-HQ-OAR-2009-0472-1678

 Bell, M.L., F. Dominici, and J.M. Samet. (2005). A meta-analysis of
time-series studies of ozone and mortality with comparison to the
national morbidity, mortality, and air pollution study. Epidemiology.
16(4): p. 436-45. EPA-HQ-OAR-2009-0472-0222

 Ito, K., S.F. De Leon, and M. Lippmann (2005). Associations between
ozone and daily mortality: analysis and meta-analysis. Epidemiology.
16(4): p. 446-57. EPA-HQ-OAR-2009-0472-0231

 Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (2005).  Ozone exposure
and mortality: an empiric bayes metaregression analysis. Epidemiology.
16(4): p. 458-68. EPA-HQ-OAR-2009-0472-0236

 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K.
Ito, and G.D. Thurston.  (2002).  “Lung Cancer, Cardiopulmonary
Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” 
Journal of the American Medical Association 287:1132-1141.
EPA-HQ-OAR-2009-0472-0263

 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery.  (2006). 
Reduction in Fine Particulate Air Pollution and Mortality.  American
Journal of Respiratory and Critical Care Medicine.  173: 667-672.
EPA-HQ-OAR-2009-0472-1661

 Industrial Economics, Incorporated (IEc).  (2006).  Expanded Expert
Judgment Assessment of the Concentration-Response Relationship Between
PM2.5 Exposure and Mortality.  Peer Review Draft.  Prepared for: Office
of Air Quality Planning and Standards, U.S. Environmental Protection
Agency, Research Triangle Park, NC. August. EPA-HQ-OAR-2009-0472-0242

 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. EPA-HQ-OAR-2009-0472-0382

 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills. 
(1995).  Estimated Long-Term Ambient Concentrations of PM(10) and
Development of Respiratory Symptoms in a Nonsmoking Population. 
Archives of Environmental Health. 50(2): 139-152.
EPA-HQ-OAR-2009-0472-0432

 Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman.  (2001). 
Increased Particulate Air Pollution and the Triggering of Myocardial
Infarction.  Circulation. 103:2810-2815. EPA-HQ-OAR-2009-0472-0239

 Schwartz J.  (1995).  Short term fluctuations in air pollution and
hospital admissions of the elderly for respiratory disease.  Thorax.
50(5):531-538.

 Schwartz J.  (1994a).  PM(10) Ozone, and Hospital Admissions For the
Elderly in Minneapolis St Paul, Minnesota.  Arch Environ Health.
49(5):366-374. EPA-HQ-OAR-2009-0472-1673

 Schwartz J.  (1994b).  Air Pollution and Hospital Admissions For the
Elderly in Detroit, Michigan.  Am J Respir Crit Care Med.
150(3):648-655. EPA-HQ-OAR-2009-0472-1674

 Moolgavkar SH, Luebeck EG, Anderson EL. (1997).  Air pollution and
hospital admissions for respiratory causes in Minneapolis St. Paul and
Birmingham.  Epidemiology. 8(4):364-370. EPA-HQ-OAR-2009-0472-1673 

 Burnett RT, Smith-Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE,
et al. (2001).  Association between ozone and hospitalization for acute
respiratory diseases in children less than 2 years of age.  Am J
Epidemiol. 153(5):444-452. EPA-HQ-OAR-2009-0472-0223

 Moolgavkar, S.H.  (2003).  “Air Pollution and Daily Deaths and
Hospital Admissions in Los Angeles and Cook Counties.”  In Revised
Analyses of Time-Series Studies of Air Pollution and Health.  Special
Report.  Boston, MA:  Health Effects Institute.

 Ito, K.  (2003).  “Associations of Particulate Matter Components with
Daily Mortality and Morbidity in Detroit, Michigan.”  In Revised
Analyses of Time-Series Studies of Air Pollution and Health. Special
Report. Health Effects Institute, Boston, MA. EPA-HQ-OAR-2009-0472-1674

 Moolgavkar, S.H.  (2000).  Air Pollution and Hospital Admissions for
Diseases of the Circulatory System in Three U.S. Metropolitan Areas. 
Journal of the Air and Waste Management Association 50:1199-1206.
EPA-HQ-OAR-2009-0472-1664

 Sheppard, L.  (2003).  Ambient Air Pollution and Nonelderly Asthma
Hospital Admissions in Seattle, Washington, 1987-1994.  In Revised
Analyses of Time-Series Studies of Air Pollution and Health.  Special
Report.  Boston, MA:  Health Effects Institute.
EPA-HQ-OAR-2009-0472-0318

 Peel, J. L., P. E. Tolbert, M. Klein, et al. (2005). Ambient air
pollution and respiratory emergency department visits. Epidemiology.
Vol. 16 (2): 164-74. EPA-HQ-OAR-2009-0472-1663

 Wilson, A. M., C. P. Wake, T. Kelly, et al. (2005). Air pollution,
weather, and respiratory emergency room visits in two northern New
England cities: an ecological time-series study. Environ Res. Vol. 97
(3): 312-21. EPA-HQ-OAR-2009-0472-0246

 Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. Larson, L. Sheppard, and
J.W. Stout.  (1999).  An Association between Fine Particles and Asthma
Emergency Department Visits for Children in Seattle.  Environmental
Health Perspectives 107(6):489-493. EPA-HQ-OAR-2009-0472-0318

 Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler,
P. Koutrakis, J.H. Ware, M. Raizenne, and F.E. Speizer.  (1996).  Health
Effects of Acid Aerosols On North American Children-Respiratory
Symptoms.  Environmental Health Perspectives 104(5):500-505.
EPA-HQ-OAR-2009-0472-0225

 Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. 
(1991).  Respiratory Health and PM10 Pollution:  A Daily Time Series
Analysis.  American Review of Respiratory Diseases 144:668-674.
EPA-HQ-OAR-2009-0472-1672

 Schwartz, J., and L.M. Neas.  (2000).  Fine Particles are More Strongly
Associated than Coarse Particles with Acute Respiratory Health Effects
in Schoolchildren.  Epidemiology 11:6-10. 

 Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. 
(2001).  Air Pollution and Exacerbation of Asthma in African-American
Children in Los Angeles.  Epidemiology 12(2):200-208. 

 Vedal, S., J. Petkau, R. White, and J. Blair.  (1998).  Acute Effects
of Ambient Inhalable Particles in Asthmatic and Nonasthmatic Children. 
American Journal of Respiratory and Critical Care Medicine
157(4):1034-1043. EPA-HQ-OAR-2009-0472-1671

 Ostro, B.D.  (1987).  Air Pollution and Morbidity Revisited: A
Specification Test.  Journal of  Environmental Economics Management
14:87-98. EPA-HQ-OAR-2009-0472-1670

 Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ,
et al. (2001).  The effects of ambient air pollution on school
absenteeism due to respiratory illnesses.  Epidemiology 12(1):43-54.
EPA-HQ-OAR-2009-0472-1675

 Chen L, Jennison BL, Yang W, Omaye ST.  (2000).  Elementary school
absenteeism and air pollution.  Inhal Toxicol 12(11):997-1016.
EPA-HQ-OAR-2009-0472-0224

 Ostro, B.D. and S. Rothschild.  (1989).  Air Pollution and Acute
Respiratory Morbidity:  An Observational Study of Multiple Pollutants. 
Environmental Research 50:238-247. EPA-HQ-OAR-2009-0472-0364

 U.S. Science Advisory Board. (2004).  Advisory Plans for Health Effects
Analysis in the Analytical Plan for EPA’s Second Prospective Analysis
–Benefits and Costs of the Clean Air Act, 1990—2020.
EPA-SAB-COUNCIL-ADV-04-004. EPA-HQ-OAR-2009-0472-4664

 National Research Council (NRC).  (2002).  Estimating the Public Health
Benefits of Proposed Air Pollution Regulations.  Washington, DC:  The
National Academies Press. 

 Abt Associates, Inc. October 2005.  Methodology for County-level
Mortality Rate Projections.  Memorandum to Bryan Hubbell and Zachary
Pekar, U.S. EPA. 

 Centers for Disease Control: Wide-ranging OnLine Data for Epidemiologic
Research (CDC Wonder) (data from years 1996-1998), Centers for Disease
Control and Prevention (CDC), U.S. Department of Health and Human
Services, Available on the Internet at <http://wonder.cdc.gov>.

 Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet,
Healthcare Cost and Utilization Project.

 American Lung Association. 1999. Chronic Bronchitis. Available on the
Internet at <http://www.lungusa.org/diseases/lungchronic.html>.

 American Lung Association.  2002. Trends in Asthma Morbidity and
Mortality.  American Lung Association, Best Practices and Program
Services, Epidemiology and Statistics Unit.  Available on the Internet
at <http://www.lungusa.org/data/asthma/ASTHMAdt.pdf>.

 Adams PF, Hendershot GE, Marano MA. 1999. Current Estimates from the
National Health Interview Survey, 1996. Vital Health Stat 10(200):1-212.

 U.S. Bureau of Census. 2000. Population Projections of the United
States by Age, Sex, Race, Hispanic Origin and Nativity: 1999 to 2100.
Population Projections Program, Population Division, U.S. Census Bureau,
Washington, DC. Available on the Internet at
<http://www.census.gov/population/projections/nation/summary/np-t.txt>.

 National Center for Education Statistics (NCHS). 1996. The Condition of
Education 1996, Indicator 42: Student Absenteeism and Tardiness. U.S.
Department of Education. Washington, DC.

 Centers for Disease Control: Wide-ranging OnLine Data for Epidemiologic
Research (CDC Wonder) (data from years 1996-1998), Centers for Disease
Control and Prevention (CDC), U.S. Department of Health and Human
Services, Available on the Internet at <http://wonder.cdc.gov>.

 U.S. Environmental Protection Agency (U.S. EPA).  2009. Integrated
Science Assessment for Particulate Matter (Final Report). 
EPA-600-R-08-139F. National Center for Environmental Assessment – RTP
Division.  December.  Available on the Internet at
<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

 U.S. Environmental Protection Agency - Science Advisory Board (U.S.
EPA-SAB).  2009.   Consultation on EPA’s Particulate Matter National
Ambient Air Quality Standards: Scope and Methods Plan for Health Risk
and Exposure Assessment.  EPA-COUNCIL-09-009.  May.  Available on the
Internet at
<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006b
e86e/723FE644C5D758DF852575BD00763A32/$File/EPA-CASAC-09-009-unsigned.pd
f>.

 U.S. Environmental Protection Agency - Science Advisory Board (U.S.
EPA-SAB).  2009.   Review of EPA’s Integrated Science Assessment for
Particulate Matter (First External Review Draft, December 2008). 
EPA-COUNCIL-09-008.  May.  Available on the Internet at
<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006b
e86e/73ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pd
f>.

 Freeman(III), AM. 1993. The Measurement of Environmental and Resource
Values: Theory and Methods. Washington, DC: Resources for the Future.

 Harrington, W., and P.R. Portney. 1987. Valuing the Benefits of Health
and Safety Regulation. Journal of Urban Economics 22:101-112.

 Berger, M.C., G.C. Blomquist, D. Kenkel, and G.S. Tolley. 1987. Valuing
Changes in Health Risks: A Comparison of Alternative Measures. The
Southern Economic Journal 53:977-984.

*

+

.

/

0

F

G

H

N

O

P

Q

R

û

kd

h"

h"

·¬¤¬¤¬¤·¬¤¬ h°

 h°

摧深ú

摧深ú

 h°

摧深ú

摧深ú

	”§

 h7

 h7

 h7



























Ȇ

Ȇ

Ȇ

Ȇ

Ȇ

Ȇ

Ȇ

 h°

䀆

@

@

 h°

;

;

;

;

”ÿÎ	$

 hÇ

hÇ

̀Ĥ萏

̀Ĥ萏

̃萏

¸ÿ2	³

g

i

j

}

̀Ĥ萏

̀萏

Ͽ萏

̃萏

̀Ĥ萏

Ͽ萏

h

i

j

¸ÿ2	³

怀愀Ĥ摧ῖ`Ѐj

~

‚

†

 

̀萏

Ѐ}

~

€

‚

„

…

†

Ÿ

 

¡



ä

ç

ï

ò

4

7

m

t

‹

’

«

®

j

1

 ԀĤ옍)

옍)

　␆ਁ&䘋

@

萯਀&䘋

@

萯਀&䘋

@

萯਀&䘋

@

萯਀&䘋

@

萯਀&䘋

@

萯਀&䘋

옍)

å

























 h°

䀆

䀆

䀆

 h°

 h°

;

hv

hv

hv

h"

h"

h"

h"

h"

h"

B

B

B

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

옍)

옍)

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

h"

옍)

h"

h"

h"

h"

ЀN摧㹢¦ЀN摧嶅³ЀN摧ῖ`ЀN摧⊶öఀarticulate Matter, Chapter
5.  Office of Air Quality Planning and Standards, Research Triangle
Park, NC.  October.  Available on the Internet at
<http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf>.

 U.S. Environmental Protection Agency (U.S. EPA).  2008.  Regulatory
Impact Analysis, 2008 National Ambient Air Quality Standards for
Ground-level Ozone, Chapter 6.  Office of Air Quality Planning and
Standards, Research Triangle Park, NC.  March.  Available at
<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>.

 U.S. EPA, (2001), “Guidance for Demonstrating Attainment of Air
Quality Goals for PM2.5 and Regional Haze”,
http://www.epa.gov/ttn/scram/guidance_sip.htm, Modeling Guidance,
DRAFT-PM

 National Research Council (NRC).  2002. Estimating the Public Health
Benefits of Proposed Air Pollution Regulations.  Washington, DC: The
National Academies Press.

 National Research Council (NRC).  2008.  Estimating Mortality Risk
Reduction and Economic Benefits from Controlling Ozone Air Pollution. 
National Academies Press.  Washington, DC.

 Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner,
Harvey M. Richmond, Bryan J. Hubbell, and Patrick L. Kinney.  2008.
Expert Judgment Assessment of the Mortality Impact of Changes in Ambient
Fine Particulate Matter in the U.S. Environ.  Sci. Technol.,
42(7):2268-2274.

