45–64	0.0333	American Lung Association (2002a, Table 7)—based on
1999 HIS

65+	0.0221	American Lung Association (2002a, Table 7)—based on 1999
HIS

Male, 27+	0.021	2000 HIS public use data filesa

African American, 5 to 17	0.0726	American Lung Association (2002a, Table
9)—based on 1999 HIS

African American, <18	0.0735	American Lung Association (2002a, Table
9)—based on 1999 HIS

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

Selecting Unit Values for Monetizing Health Endpoints

The appropriate economic value for a change in a health effect depends
on whether the health effect is viewed ex ante (before the effect has
occurred) or ex post (after the effect has occurred).  Reductions in
ambient concentrations of air pollution generally lower the risk of
future adverse health affects by a small amount for a large population.
The appropriate economic measure is therefore ex ante WTP for changes in
risk.  However, epidemiological studies generally provide estimates of
the relative risks of a particular health effect avoided due to a
reduction in air pollution.  A convenient way to use this data in a
consistent framework is to convert probabilities to units of avoided
statistical incidences.  This measure is calculated by dividing
individual WTP for a risk reduction by the related observed change in
risk.  For example, suppose a measure is able to reduce the risk of
premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in
10,000).  If individual WTP for this risk reduction is $100, then the
WTP for an avoided statistical premature mortality amounts to $1 million
($100/0.0001 change in risk).  Using this approach, the size of the
affected population is automatically taken into account by the number of
incidences predicted by epidemiological studies applied to the relevant
population.  The same type of calculation can produce values for
statistical incidences of other health endpoints.

For some health effects, such as hospital admissions, WTP estimates are
generally not available.  In these cases, we use the cost of treating or
mitigating the effect as a primary estimate.  For example, for the
valuation of hospital admissions we use the avoided medical costs as an
estimate of the value of avoiding the health effects causing the
admission.  These COI estimates generally understate the true value of
reductions in risk of a health effect.  They tend to reflect the direct
expenditures related to treatment but not the value of avoided pain and
suffering from the health effect.  Table 5-11 summarizes the value
estimates per health effect that we used in this analysis.  Values are
presented both for a 1990 base income level and adjusted for income
growth in the two future analysis years, 2010 and 2015.  Note that the
unit values for hospital admissions are the weighted averages of the
ICD-9 code-specific values for the group of ICD-9 codes included in the
hospital admission categories.  A discussion of the valuation methods
for premature mortality and CB is provided here because of the relative
importance of these effects.  Discussions of the methods used to value
nonfatal myocardial infarctions (heart attacks) and school absence days
are provided because these endpoints have only recently been added to
the analysis and the valuation methods are still under development.  In
the following discussions, unit values are presented at 1990 levels of
income for consistency with previous analyses.  Equivalent future-year
values can be obtained from Table 5-11.  COI estimates are converted to
constant 1999 dollar equivalents using the medical CPI.

 estimate the monetary benefit of reducing premature mortality risk
using the VSL approach, which is a summary measure for the value of
small changes in mortality risk experienced by a large number of people.
 The mean value of avoiding one statistical death is assumed to be $5.5
million in 1999 dollars.  This represents a central value consistent
with the range of values suggested by recent meta-analyses of the
wage-risk VSL literature.  The distribution of VSL is characterized by a
confidence interval from $1 to $10 million, based on two meta-analyses
of the wage-risk VSL literature.  The $1 million lower confidence limit
represents the lower end of the interquartile range from the Mrozek and
Taylor (2002  XE "Mrozek and Taylor (2002"  ) meta-analysis.  The $10
million upper confidence limit represents

Table 5-11:  Unit Values Used for Economic Valuation of Health
Endpoints (1999$)

Health Endpoint	Central Estimate of Value Per Statistical Incidence

1990 Income Level	2020 Income Level	Derivation of Estimates [NEED TO ADD
DISTRIBUTIONAL INFORMATION]

Premature Mortality (Value of a Statistical Life)	$5,500,000	$6,600,000
Point estimate is the mean of a normal distribution with a 95%
confidence interval between $1 and $10 million.  Confidence interval is
based on two meta-analyses of the wage-risk VSL literature:  $1 million
represents the lower end of the interquartile range from the Mrozek and
Taylor (2002  XE "Mrozek and Taylor (2002"  ) meta-analysis and $10
million represents the upper end of the interquartile range from the
Viscusi and Aldy (2003  XE "Viscusi and Aldy (2003"  ) meta-analysis.
The VSL represents the value of a small change in mortality risk
aggregated over the affected population.

Chronic Bronchitis (CB)	$340,000	$420,000	Point estimate is the mean of
a generated distribution of WTP to avoid a case of pollution-related CB.
 WTP to avoid a case of pollution-related CB is derived by adjusting WTP
(as described in Viscusi, Magat, and Huber [1991  XE "Viscusi, Magat,
and Huber [1991"  ]) to avoid a severe case of CB for the difference in
severity and taking into account the elasticity of WTP with respect to
severity of CB.

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

$66,902

$74,676

$78,834

$140,649

$66,902

$65,293

$73,149

$76,871

$132,214

$65,293

$66,902

$74,676

$78,834

$140,649

$66,902

$65,293

$73,149

$76,871

$132,214

$65,293	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  XE "Cropper
and Krupnick (1990"  ).  Direct medical costs are based on simple
average of estimates from Russell et al. (1998  XE "Russell et al.
(1998"  ) and Wittels et al. (1990  XE "Wittels et al. (1990"  ).

Lost earnings:

Cropper and Krupnick (1990  XE "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  XE "Wittels et al. (1990"  ) ($102,658—no
discounting)

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

(continued)

Table 5-11:  Unit Values Used for Economic Valuation of Health Endpoints
(1999$) (continued)

Health Endpoint	Central Estimate of Value Per Statistical Incidence

1990 Income Level	2020 Income Level	Derivation of Estimates [NEED TO ADD
DISTRIBUTIONAL INFORMATION]

Hospital Admissions

Chronic Obstructive Pulmonary Disease (COPD)

(ICD codes 490-492, 494-496)	$12,378	$12,378	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  XE "Agency
for Healthcare Research and Quality (2000"  ) (www.ahrq.gov).

Asthma Admissions	$6,634	$6,634	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

(ICD codes 390-429)	$18,387	$18,387	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).

Emergency Room Visits for Asthma	$286	$286	Simple average of two unit
COI values:

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

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

Respiratory Ailments Not Requiring Hospitalization

Upper Respiratory Symptoms (URS)	$25	$27	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  XE "IEc,
1994"  ) to avoid each symptom in the cluster and assuming additivity of
WTPs.  The dollar value for URS is the average of the dollar values for
the seven different types of URS.

Lower Respiratory Symptoms (LRS)	$16	$18	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  XE "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.

(continued)

Table 5-11:  Unit Values Used for Economic Valuation of Health Endpoints
(1999$) (continued)

Health Endpoint	Central Estimate of Value Per Statistical Incidence

1990 Income Level	2020 Income Level	Derivation of Estimates [NEED TO ADD
DISTRIBUTIONAL INFORMATION]

Asthma Exacerbations	$42	$45	Asthma exacerbations are valued at $42 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  XE "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 attack is
assumed to be equivalent to a day in which asthma is moderate or worse
as reported in the Rowe and Chestnut (1986  XE "Rowe and Chestnut (1986"
 ) study.

Acute Bronchitis	$360	$380	Assumes a 6-day episode, with daily value
equal to the average of low and high values for related respiratory
symptoms recommended in Neumann et al. (1994  XE "Neumann et al. (1994"
).

Work Loss Days (WLDs)	Variable (U.S. median=$110)

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)	$51	$54	Median WTP estimate to
avoid one MRAD from Tolley et al. (1986  XE "Tolley et al. (1986"  ).

the upper end of the interquartile range from the Viscusi and Aldy
(2003  XE "Viscusi and Aldy (2003"  ) meta-analysis.  The mean estimate
of $5.5 million is consistent with the mean VSL of $5.4 million
estimated in the Kochi et al. (2006  XE "Kochi et al. (2006"  )
meta-analysis.  Because the majority of the studies in these
meta-analyses are based on datasets from the early 1990s or previous
decades, we continue to assume that the VSL estimates provided by those
meta-analyses are in 1990 income equivalents.  Future research might
provide income-adjusted VSL values for individual studies that can be
incorporated into the meta-analyses.  This would allow for a more
reliable base-year estimate for use in adjusting VSL for aggregate
changes in income over time.

As indicated in the previous section on quantification of premature
mortality benefits, we assumed for this analysis that some of the
incidences of premature mortality related to PM exposures occur in a
distributed fashion over the 20 years following exposure.  To take this
into account in the valuation of reductions in premature mortality, we
applied an annual 3% discount rate to the value of premature mortality
occurring in future years.

The economics literature concerning the appropriate method for valuing
reductions in premature mortality risk is still developing.  The
adoption of a value for the projected reduction in the risk of premature
mortality is the subject of continuing discussion within the economics
and public policy analysis community.  Regardless of the theoretical
economic considerations, EPA prefers not to draw distinctions in the
monetary value assigned to the lives saved even if they differ in age,
health status, socioeconomic status, gender, or other characteristic of
the adult population.

 Although there are several differences between the labor market studies
EPA uses to derive a VSL estimate and the PM air pollution context
addressed here, those differences in the affected populations and the
nature of the risks imply both upward and downward adjustments.  Table
5-12 lists some of these differences and the expected effect on the VSL
estimate for air pollution-related mortality. In the absence of a
comprehensive and balanced set of adjustment factors, EPA believes it is
reasonable to continue to use the $5.5 million value while acknowledging
the significant limitations and uncertainties in the available
literature.

The SAB-EEAC has reviewed many potential VSL adjustments and the state
of the economics literature.  The SAB-EEAC advised EPA to “continue to
use a wage-risk-based VSL as its primary estimate, including appropriate
sensitivity analyses to reflect the uncertainty of these estimates,”
and that “the only risk characteristic for which adjustments to the
VSL can be made is the timing of the risk” (EPA-SAB-EEAC-00-013, EPA,
2000a  XE "U.S. EPA-SAB-EEAC-00-013, EPA, 2000a"  ).  In developing our
primary estimate of the benefits of premature mortality reductions, we
have followed this advice and discounted over the lag period between
reduction in  exposure and reduction in premature mortality.

Table 5-12:  Expected Impact on Estimated Benefits of Premature
Mortality Reductions of Differences Between Factors Used in Developing
Applied VSL and Theoretically Appropriate VSL

Attribute	Expected Direction of Bias

Age	Uncertain, perhaps overestimate

Life Expectancy/Health Status	Uncertain, perhaps overestimate

Attitudes Toward Risk	Underestimate

Income	Uncertain

Voluntary vs. Involuntary	Uncertain, perhaps underestimate

Catastrophic vs. Protracted Death	Uncertain, perhaps underestimate

 Because of the uncertainty in estimates of the value of premature
mortality avoidance, it is important to adequately characterize and
understand the various types of economic approaches available for
mortality valuation.  Such an assessment also requires an understanding
of how alternative valuation approaches reflect that some individuals
may be more susceptible to air pollution-induced mortality or reflect
differences in the nature of the risk presented by air pollution
relative to the risks studied in the relevant economics literature.

The health science literature on air pollution indicates that several
human characteristics affect the degree to which mortality risk affects
an individual.  For example, some age groups appear to be more
susceptible to air pollution than others (e.g., the elderly and
children).  Health status prior to exposure also affects susceptibility.
 An ideal benefits estimate of mortality risk reduction would reflect
these human characteristics, in addition to an individual’s WTP to
improve one’s own chances of survival plus WTP to improve other
individuals’ survival rates.  The ideal measure would also take into
account the specific nature of the risk reduction commodity that is
provided to individuals, as well as the context in which risk is
reduced.  To measure this value, it is important to assess how
reductions in air pollution reduce the risk of dying from the time that
reductions take effect onward and how individuals value these changes.
Each individual’s survival curve, or the probability of surviving
beyond a given age, should shift as a result of an environmental quality
improvement.  For example, changing the current probability of survival
for an individual also shifts future probabilities of that
individual’s survival.  This probability shift will differ across
individuals because survival curves depend on such characteristics as
age, health state, and the current age to which the individual is likely
to survive.

Although a survival curve approach provides a theoretically preferred
method for valuing the benefits of reduced risk of premature mortality
associated with reducing air pollution, the approach requires a great
deal of data to implement.  The economic valuation literature does not
yet include good estimates of the value of this risk reduction
commodity.  As a result, in this study we value avoided premature
mortality risk using the VSL approach.

Other uncertainties specific to premature mortality valuation include
the following:

Across-study variation:  There is considerable uncertainty as to whether
the available literature on VSL provides adequate estimates of the VSL
saved by air pollution reduction.  Although there is considerable
variation in the analytical designs and data used in the existing
literature, the majority of the studies involve the value of risks to a
middle-aged working population.  Most of the studies examine differences
in wages of risky occupations, using a wage-hedonic approach.  Certain
characteristics of both the population affected and the mortality risk
facing that population are believed to affect the average WTP to reduce
the risk.  The appropriateness of a distribution of WTP based on the
current VSL literature for valuing the mortality-related benefits of
reductions in air pollution concentrations therefore depends not only on
the quality of the studies (i.e., how well they measure what they are
trying to measure), but also on the extent to which the risks being
valued are similar and the extent to which the subjects in the studies
are similar to the population affected by changes in pollution
concentrations.

Level of risk reduction:  The transferability of estimates of the VSL
from the wage-risk studies to the context of the CAIR analysis rests on
the assumption that, within a reasonable range, WTP for reductions in
mortality risk is linear in risk reduction.  For example, suppose a
study estimates that the average WTP for a reduction in mortality risk
of 1/100,000 is $50, but that the actual mortality risk reduction
resulting from a given pollutant reduction is 1/10,000.  If WTP for
reductions in mortality risk is linear in risk reduction, then a WTP of
$50 for a reduction of 1/100,000 implies a WTP of $500 for a risk
reduction of 1/10,000 (which is 10 times the risk reduction valued in
the study).  Under the assumption of linearity, the estimate of the VSL
does not depend on the particular amount of risk reduction being valued.
 This assumption has been shown to be reasonable provided the change in
the risk being valued is within the range of risks evaluated in the
underlying studies (Rowlatt et al., 1998  XE "Rowlatt et al., 1998"  ).

Voluntariness of risks evaluated:  Although job-related mortality risks
may differ in several ways from air pollution-related mortality risks,
the most important difference may be that job-related risks are incurred
voluntarily, or generally assumed to be, whereas air pollution-related
risks are incurred involuntarily.  Some evidence suggests that people
will pay more to reduce involuntarily incurred risks than risks incurred
voluntarily.  If this is the case, WTP estimates based on wage-risk
studies may understate WTP to reduce involuntarily incurred air
pollution-related mortality risks.

Sudden versus protracted death:  A final important difference related to
the nature of the risk may be that some workplace mortality risks tend
to involve sudden, catastrophic events, whereas air pollution-related
risks tend to involve longer periods of disease and suffering prior to
death.  Some evidence suggests that WTP to avoid a risk of a protracted
death involving prolonged suffering and loss of dignity and personal
control is greater than the WTP to avoid a risk (of identical magnitude)
of sudden death.  To the extent that the mortality risks addressed in
this assessment are associated with longer periods of illness or greater
pain and suffering than are the risks addressed in the valuation
literature, the WTP measurements employed in the present analysis would
reflect a downward bias.

Self-selection and skill in avoiding risk:  Recent research (Shogren and
Stamland, 2002  XE "Shogren and Stamland, 2002"  ) suggests that VSL
estimates based on hedonic wage studies may overstate the average value
of a risk reduction.  This is based on the fact that the risk-wage
trade-off revealed in hedonic studies reflects the preferences of the
marginal worker (i.e., that worker who demands the highest compensation
for his risk reduction).  This worker must have either higher risk,
lower risk tolerance, or both.  However, the risk estimate used in
hedonic studies is generally based on average risk, so the VSL may be
upwardly biased because the wage differential and risk measures do not
match.

Baseline risk and age:  Recent research (Smith, Pattanayak, and Van
Houtven) finds that because individuals reevaluate their baseline risk
of death as they age, the marginal value of risk reductions does not
decline with age as predicted by some lifetime consumption models.  The
research questions the use of simple value of life year approaches to
adjusting VSL for age differences.

Valuing Reductions in the Risk of Chronic Bronchitis.  The best
available estimate of WTP to avoid a case of CB comes from Viscusi,
Magat, and Huber (1991  XE "Viscusi, Magat, and Huber (1991"  ).  The
Viscusi, Magat, and Huber study, however, describes a severe case of CB
to the survey respondents.  We therefore employ an estimate of WTP to
avoid a pollution-related case of CB, based on adjusting the Viscusi,
Magat, and Huber (1991  XE "Viscusi, Magat, and Huber (1991"  ) estimate
of the WTP to avoid a severe case.  This is done to account for the
likelihood that an average case of pollution-related CB is not as
severe.  The adjustment is made by applying the elasticity of WTP with
respect to severity reported in the Krupnick and Cropper (1992  XE
"Krupnick and Cropper (1992"  ) study.  Details of this adjustment
procedure are provided in the Benefits TSD for the Nonroad Diesel
rulemaking (Abt Associates, 2003  XE "Abt Associates, 2003"  ).

We use the mean of a distribution of WTP estimates as the central
tendency estimate of WTP to avoid a pollution-related case of CB in this
analysis.  The distribution incorporates uncertainty from three sources:
 the WTP to avoid a case of severe CB, as described by Viscusi, Magat,
and Huber; the severity level of an average pollution-related case of CB
(relative to that of the case described by Viscusi, Magat, and Huber);
and the elasticity of WTP with respect to severity of the illness.
Based on assumptions about the distributions of each of these three
uncertain components, we derive a distribution of WTP to avoid a
pollution-related case of CB by statistical uncertainty analysis
techniques.  The expected value (i.e., mean) of this distribution, which
is about $331,000 (2000$), is taken as the central tendency estimate of
WTP to avoid a PM-related case of CB.

Valuing Reductions in Nonfatal Myocardial Infarctions (Heart Attacks).
The Agency has recently incorporated into its analyses the impact of air
pollution on the expected number of nonfatal heart attacks, although it
has examined the impact of reductions in other related cardiovascular
endpoints.  We were not able to identify a suitable WTP value for
reductions in the risk of nonfatal heart attacks.  Instead, we use a COI
unit value with two components:  the direct medical costs and the
opportunity cost (lost earnings) associated with the illness event.
Because the costs associated with a myocardial infarction extend beyond
the initial event itself, we consider costs incurred over several years.
 Using age-specific annual lost earnings estimated by Cropper and
Krupnick (1990  XE "Cropper and Krupnick (1990"  ) and a 3% discount
rate, we estimated a present discounted value in lost earnings (in
2000$) over 5 years due to a myocardial infarction of $8,774 for someone
between the ages of 25 and 44, $12,932 for someone between the ages of
45 and 54, and $74,746 for someone between the ages of 55 and 65.  The
corresponding age-specific estimates of lost earnings (in 2000$) using a
7% discount rate are $7,855, $11,578, and $66,920, respectively.
Cropper and Krupnick (1990  XE "Cropper and Krupnick (1990"  ) do not
provide lost earnings estimates for populations under 25 or over 65.  As
such, we do not include lost earnings in the cost estimates for these
age groups.

We found three possible sources in the literature of estimates of the
direct medical costs of myocardial infarction:

Wittels et al. (1990  XE "Wittels et al. (1990"  ) estimated expected
total medical costs of myocardial infarction over 5 years to be $51,211
(in 1986$) for people who were admitted to the hospital and survived
hospitalization.  (There does not appear to be any discounting used.)
Wittels et al. was used to value coronary heart disease in the 812
Retrospective Analysis of the Clean Air Act.  Using the CPI-U for
medical care, the Wittels estimate is $109,474 in year 2000$.  This
estimated cost is based on a medical cost model, which incorporated
therapeutic options, projected outcomes, and prices (using
“knowledgeable cardiologists” as consultants).  The model used
medical data and medical decision algorithms to estimate the
probabilities of certain events and/or medical procedures being used.
The authors note that the average length of hospitalization for acute
myocardial infarction has decreased over time (from an average of 12.9
days in 1980 to an average of 11 days in 1983).  Wittels et al. used 10
days as the average in their study.  It is unclear how much further the
length of stay for myocardial infarction may have decreased from 1983 to
the present.  The average length of stay for ICD code 410 (myocardial
infarction) in the year-2000 Agency for Healthcare Research and Quality
(AHRQ) HCUP database is 5.5 days.  However, this may include patients
who died in the hospital (not included among our nonfatal myocardial
infarction cases), whose length of stay was therefore substantially
shorter than it would be if they had not died.

Eisenstein et al. (2001  XE "Eisenstein et al. (2001"  ) estimated
10-year costs of $44,663 in 1997$, or $49,651 in 2000$ for myocardial
infarction patients, using statistical prediction (regression) models to
estimate inpatient costs.  Only inpatient costs (physician fees and
hospital costs) were included.

Russell et al. (1998  XE "Russell et al. (1998"  ) estimated first-year
direct medical costs of treating nonfatal myocardial infarction of
$15,540 (in 1995$) and $1,051 annually thereafter.  Converting to year
2000$, that would be $23,353 for a 5-year period (without discounting)
or $29,568 for a 10-year period.

In summary, the three different studies provided significantly different
values (see Table 5-13).

Table 5-13:  Alternative Direct Medical Cost of Illness Estimates for
Nonfatal Heart Attacks

Study	Direct Medical Costs (2000$)	Over an x-Year Period, for x =

Wittels et al. (1990  XE "Wittels et al. (1990"  )	$109,474a	5

Russell et al. (1998  XE "Russell et al. (1998"  )	$22,331b	5

Eisenstein et al. (2001  XE "Eisenstein et al. (2001"  )	$49,651b	10

Russell et al. (1998  XE "Russell et al. (1998"  )	$27,242b	10

a	Wittels et al. (1990  XE "Wittels et al. (1990"  ) did not appear to
discount costs incurred in future years.

b	Using a 3% discount rate.

As noted above, the estimates from these three studies are substantially
different, and we have not adequately resolved the sources of
differences in the estimates.  Because the wage-related opportunity cost
estimates from Cropper and Krupnick (1990  XE "Cropper and Krupnick
(1990"  ) cover a 5-year period, we used estimates for medical costs
that similarly cover a 5-year period (i.e., estimates from Wittels et
al. (1990  XE "Wittels et al. (1990"  ) and Russell et al. (1998  XE
"Russell et al. (1998"  ).  We used a simple average of the two 5-year
estimates, or $65,902, and added it to the 5-year opportunity cost
estimate.  The resulting estimates are given in Table 5-14.

Table 5-14:  Estimated Costs Over a 5-Year Period (in 2000$) of a
Nonfatal Myocardial Infarction

Age Group	Opportunity Cost	Medical Costa	Total Cost

0–24	$0	$65,902	$65,902

25–44	$8,774b	$65,902	$74,676

45–54	$12,253b	$65,902	$78,834

55–65	$70,619b	$65,902	$140,649

> 65	$0	$65,902	$65,902

a	An average of the 5-year costs estimated by Wittels et al. (1990  XE
"Wittels et al. (1990"  ) and Russell et al. (1998  XE "Russell et al.
(1998"  ).

b	From Cropper and Krupnick (1990  XE "Cropper and Krupnick (1990"  ),
using a 3% discount rate.

5.1.6	Human Welfare Impact Assessment

have numerous documented effects on environmental quality that affect
human welfare.  These welfare effects include direct damages to
property, either through impacts on material structures or by soiling of
surfaces, direct economic damages in the form of lost productivity of
crops and trees, indirect damages through alteration of ecosystem
functions, and indirect economic damages through the loss in value of
recreational experiences or the existence value of important resources.
EPA’s Criteria Documents for PM and ozone list numerous physical and
ecological effects known to be linked to ambient concentrations of these
pollutants (EPA, 1996a; 1996b  XE "U.S. EPA, 1996a\; 1996b"  ).  This
section describes individual effects and how we quantify and monetize
them.  These effects include changes in commercial crop and forest
yields, visibility, and nitrogen deposition to estuaries.

Visibility Benefits

Changes in the level of ambient PM caused by the reduction in emissions
from CAIR will change the level of visibility in much of the Eastern
United States.  Visibility directly affects people’s enjoyment of a
variety of daily activities.  Individuals value visibility both in the
places they live and work, in the places they travel to for recreational
purposes, and at sites of unique public value, such as the Great Smokey
Mountains National Park.  This section discusses the measurement of the
economic benefits of improved visibility.

It is difficult to quantitatively define a visibility endpoint that can
be used for valuation.  Increases in PM concentrations cause increases
in light extinction, a measure of how much the components of the
atmosphere absorb light.  More light absorption means that the clarity
of visual images and visual range is reduced, ceteris paribus.  Light
absorption is a variable that can be accurately measured.  Sisler (1996
XE "Sisler (1996"  ) created a unitless measure of visibility, the
deciview, based directly on the degree of measured light absorption.
Deciviews are standardized for a reference distance in such a way that
one deciview corresponds to a change of about 10% in available light.
Sisler characterized a change in light extinction of one deciview as
“a small but perceptible scenic change under many circumstances.”
Air quality models were used to predict the change in visibility,
measured in deciviews, of the areas affected by the control options.

EPA considers benefits from two categories of visibility changes:
residential visibility and recreational visibility.  In both cases
economic benefits are believed to consist of use values and nonuse
values.  Use values include the aesthetic benefits of better visibility,
improved road and air safety, and enhanced recreation in activities like
hunting and birdwatching.  Nonuse values are based on people’s beliefs
that the environment ought to exist free of human-induced haze.  Nonuse
values may be more important for recreational areas, particularly
national parks and monuments.

Residential visibility benefits are those that occur from visibility
changes in urban, suburban, and rural areas and also in recreational
areas not listed as federal Class I areas.  For the purposes of this
analysis, recreational visibility improvements are defined as those that
occur specifically in federal Class I areas.  A key distinction between
recreational and residential benefits is that only those people living
in residential areas are assumed to receive benefits from residential
visibility, while all households in the United States are assumed to
derive some benefit from improvements in Class I areas.  Values are
assumed to be higher if the Class I area is located close to their home.

Only two existing studies provide defensible monetary estimates of the
value of visibility changes.  One is a study on residential visibility
conducted in 1990 (McClelland et al., 1993  XE "McClelland et al., 1993"
 ) and the other is a 1988 survey on recreational visibility value
(Chestnut and Rowe, 1990a; 1990b  XE "Chestnut and Rowe, 1990a\; 1990b"
).  Although there are a number of other studies in the literature, they
were conducted in the early 1980s and did not use methods that are
considered defensible by current standards.  Both the Chestnut and Rowe
and McClelland et al. studies use the CV method.  There has been a great
deal of controversy and significant development of both theoretical and
empirical knowledge about how to conduct CV surveys in the past decade.
In EPA’s judgment, the Chestnut and Rowe study contains many of the
elements of a valid CV study and is sufficiently reliable to serve as
the basis for monetary estimates of the benefits of visibility changes
in recreational areas.  This study serves as an essential input to our
estimates of the benefits of recreational visibility improvements in the
primary benefits estimates.  Consistent with SAB advice, EPA has
designated the McClelland et al. study as significantly less reliable
for regulatory benefit-cost analysis, although it does provide useful
estimates on the order of magnitude of residential visibility benefits
(EPA-SAB-COUNCIL-ADV-00-002, 1999  XE "U.S. EPA-SAB-COUNCIL-ADV-00-002,
1999"  ).  Residential visibility benefits are not calculated for this
analysis.

The Chestnut and Rowe study measured the demand for visibility in Class
I areas managed by the National Park Service (NPS) in three broad
regions of the country:  California, the Southwest, and the Southeast.
Respondents in five states were asked about their WTP to protect
national parks or NPS-managed wilderness areas within a particular
region.  The survey used photographs reflecting different visibility
levels in the specified recreational areas.  The visibility levels in
these photographs were later converted to deciviews for the current
analysis.  The survey data collected were used to estimate a WTP
equation for improved visibility.  In addition to the visibility change
variable, the estimating equation also included household income as an
explanatory variable.

The Chestnut and Rowe study did not measure values for visibility
improvement in Class I areas outside the three regions.  Their study
covered 86 of the 156 Class I areas in the United States.  We can infer
the value of visibility changes in the other Class I areas by
transferring values of visibility changes at Class I areas in the study
regions.  A complete description of the benefits transfer method used to
infer values for visibility changes in Class I areas outside the study
regions is provided in the Benefits TSD for the Nonroad Diesel
rulemaking (Abt Associates, 2003  XE "Abt Associates, 2003"  ).

The Chestnut and Rowe study (Chestnut and Rowe, 1990a; 1990b  XE
"Chestnut and Rowe, 1990a\; 1990b"  ), although representing the best
available estimates, has a number of limitations.  These include the
following:

The age of the study (late 1980s) will increase the uncertainty about
the correspondence of the estimated values to those that might be
provided by current or future populations.

The survey focused only on populations in five states, so the
application of the estimated values to populations outside those states
requires that preferences of populations in the five surveyed states be
similar to those of nonsurveyed states.

There is an inherent difficulty in separating values expressed for
visibility improvements from an overall value for improved air quality.
The Chestnut and Rowe study attempted to control for this by informing
respondents that “other households are being asked about visibility,
human health, and vegetation protections in urban areas and at national
parks in other regions.”  However, most of the respondents did not
feel that they were able to segregate visibility at national parks
entirely from residential visibility and health effects.

It is not clear exactly what visibility improvements the respondents to
the Chestnut and Rowe survey were valuing.  For the purpose of the
benefits analysis for this rule, EPA assumed that respondents provided
values for changes in annual average visibility.  Because most policies
will result in a shift in the distribution of visibility (usually
affecting the worst days more than the best days), the annual average
may not be the most relevant metric for policy analysis.

The WTP question asked about changes in average visibility.  However,
the survey respondents were shown photographs of only summertime
conditions, when visibility is generally at its worst.  It is possible
that the respondents believed those visibility conditions held
year-round, in which case they would have been valuing much larger
overall improvements in visibility than what otherwise would be the
case.

The survey did not include reminders of possible substitutes (e.g.,
visibility at other parks) or budget constraints.  These reminders are
considered to be best practice for stated preference surveys.

The Chestnut and Rowe survey focused on visibility improvements in and
around national parks and wilderness areas.  The survey also focused on
visibility improvements of national parks in the southwest United
States.  Given that national parks and wilderness areas exhibit unique
characteristics, it is not clear whether the WTP estimate obtained from
Chestnut and Rowe can be transferred to other national parks and
wilderness areas, without introducing additional uncertainty.

In general, the survey design and implementation reflect the period in
which the survey was conducted.  Since that time, many improvements to
the stated preference methodology have been developed.  As future survey
efforts are completed, EPA will incorporate values for visibility
improvements reflecting the improved survey designs.

The estimated relationship from the Chestnut and Rowe study is only
directly applicable to the populations represented by survey
respondents.  EPA used benefits transfer methodology to extrapolate
these results to the population affected by the reductions in precursor
emissions associated with attainment strategies for the PM NAAQS.  A
general WTP equation for improved visibility (measured in deciviews) was
developed as a function of the baseline level of visibility, the
magnitude of the visibility improvement, and household income.  The
behavioral parameters of this equation were taken from analysis of the
Chestnut and Rowe data.  These parameters were used to calibrate WTP for
the visibility changes resulting from CAIR.  The method for developing
calibrated WTP functions is based on the approach developed by Smith et
al. (2002  XE "Smith et al. (2002"  ).  Available evidence indicates
that households are willing to pay more for a given visibility
improvement as their income increases (Chestnut, 1997  XE "Chestnut,
1997"  ).  The benefits estimates here incorporate Chestnut’s estimate
that a 1% increase in income is associated with a 0.9% increase in WTP
for a given change in visibility.

Using the methodology outlined above, EPA estimates that the total WTP
for the visibility improvements in Southeastern Class I areas brought
about by attainment strategies for the PM NAAQS is $XX billion in 2020
for attainment of the 15/35 option and $ billion for attainment of the
14/35 option.  This value includes the value to households living in the
same state as the Class I area as well as values for all households in
the United States living outside the state containing the Class I area,
and the value accounts for growth in real income.

The benefits resulting from visibility improvements in Southeastern
Class I areas for the 15/35 and 14/35 options are presented in Figures
5-2 and 5-3.  These figures present these benefits both in terms of the
total benefits modeled for each of the Class I areas (i.e., the “Park
Benefits” map) and the benefits realized by the populations in each of
the 48 contiguous states (i.e., the “State Benefits” map).  The
latter results reflect the WTP of state residents for visibility
improvements occurring in Class I areas in the Southeastern United
States.

Figure 5-3.  CAIR Final Rule Visibility Improvements in Class I Areas in
the Southeast

One major source of uncertainty for the visibility benefits estimate is
the benefits transfer process used.  Judgments used to choose the
functional form and key parameters of the estimating equation for WTP
for the affected population could have significant effects on the size
of the estimates.  Assumptions about how individuals respond to changes
in visibility that are either very small or outside the range covered in
the Chestnut and Rowe study could also affect the results.

Agricultural, Forestry, and Other Vegetation-Related Benefits

Certain illustrative attainment strategies which reduce NOx emissions
will also reduce nitrogen deposition on agricultural land and forests.
There is some evidence that nitrogen deposition may have positive
effects on agricultural output through passive fertilization.  Holding
all other factors constant, farmers’ use of purchased fertilizers or
manure may increase as deposited nitrogen is reduced.  Estimates of the
potential value of this possible increase in the use of purchased
fertilizers are not available, but it is likely that the overall value
is very small relative to other health and welfare effects.  The share
of nitrogen requirements provided by this deposition is small, and the
marginal cost of providing this nitrogen from alternative sources is
quite low.  In some areas, agricultural lands suffer from nitrogen
oversaturation due to an abundance of on-farm nitrogen production,
primarily from animal manure.  In these areas, reductions in atmospheric
deposition of nitrogen represent additional agricultural benefits.

Information on the effects of changes in passive nitrogen deposition on
forests and other terrestrial ecosystems is very limited.  The
multiplicity of factors affecting forests, including other potential
stressors such as ozone, and limiting factors such as moisture and other
nutrients, confound assessments of marginal changes in any one stressor
or nutrient in forest ecosystems.  However, reductions in the deposition
of nitrogen could have negative effects on forest and vegetation growth
in ecosystems where nitrogen is a limiting factor (EPA, 1993  XE "U.S.
EPA, 1993"  ).

On the other hand, there is evidence that forest ecosystems in some
areas of the United States are nitrogen saturated (EPA, 1993  XE "U.S.
EPA, 1993"  ).  Once saturation is reached, adverse effects of
additional nitrogen begin to occur such as soil acidification, which can
lead to leaching of nutrients needed for plant growth and mobilization
of harmful elements such as aluminum.  Increased soil acidification is
also linked to higher amounts of acidic runoff to streams and lakes and
leaching of harmful elements into aquatic ecosystems.

Climate Related Impacts

INSERT FROM DOUG/NEAL

Benefits from Reductions in Materials Damage

also have corrosive effects on commercial/industrial buildings and
structures of cultural and historical significance.  The effects on
historic buildings and outdoor works of art are of particular concern
because of the uniqueness and irreplaceability of many of these objects.

Previous EPA benefits analyses have been able to provide quantitative
estimates of household soiling damage.  Consistent with SAB advice, we
determined that the existing data (based on consumer expenditures from
the early 1970s) are too out of date to provide a reliable estimate of
current household soiling damages (EPA, 1998  XE "U.S. EPA, 1998"  ).

EPA is unable to estimate any benefits to commercial and industrial
entities from reduced materials damage.  Nor is EPA able to estimate the
benefits of reductions in PM-related damage to historic buildings and
outdoor works of art.  Existing studies of damage to this latter
category in Sweden (Grosclaude and Soguel, 1994  XE "Grosclaude and
Soguel, 1994"  ) indicate that these benefits could be an order of
magnitude larger than household soiling benefits.

Benefits from Reduced Ecosystem Damage

The effects of air pollution on the health and stability of ecosystems
are potentially very important but are at present poorly understood and
difficult to measure.  Excess nutrient loads, especially of nitrogen,
cause a variety of adverse consequences to the health of estuarine and
coastal waters.  These effects include toxic and/or noxious algal blooms
such as brown and red tides, low (hypoxic) or zero (anoxic)
concentrations of dissolved oxygen in bottom waters, the loss of
submerged aquatic vegetation due to the light-filtering effect of thick
algal mats, and fundamental shifts in phytoplankton community structure
(Bricker et al., 1999  XE "Bricker et al., 1999"  ).

Direct functions relating changes in nitrogen loadings to changes in
estuarine benefits are not available.  The preferred WTP-based measure
of benefits depends on the availability of these functions and on
estimates of the value of environmental responses.  Because neither
appropriate functions nor sufficient information to estimate the
marginal value of changes in water quality exist at present, calculation
of a WTP measure is not possible.

If better models of ecological effects can be defined, EPA believes that
progress can be made in estimating WTP measures for ecosystem functions.
 These estimates would be superior to avoided cost estimates in placing
economic values on the welfare changes associated with air pollution
damage to ecosystem health.  For example, if nitrogen or sulfate
loadings can be linked to measurable and definable changes in fish
populations or definable indexes of biodiversity, then CV studies can be
designed to elicit individuals’ WTP for changes in these effects.
This is an important area for further research and analysis and will
require close collaboration among air quality modelers, natural
scientists, and economists.

5.2	Benefits: Results of Probabilistic Analysis—Results and
Probabilistic Uncertainty Analysis

Applying the impact and valuation functions described previously in this
chapter to the estimated changes in PM yields estimates of the changes
in physical damages (e.g., premature mortalities, cases, admissions,
change in light extinction) and the associated monetary values for those
changes.  As noted earlier, benefits are provided for three regions of
the U.S. (Eastern, Western excluding CA, and CA).  Benefits are also
separately provided for the modeled scenarios (which result in partial
attainment) and for residual attainment based on “rolling back”
PM2.5 design values to the level of the standards (see Chapter XX).
Because of the differences in the sources of effect estimates for
mortality versus morbidity (mortality includes both epidemiology and
expert elicitation based impact functions), mortality estimates are
presented separately from morbidity.  Estimates of mortality and
morbidity impacts are presented in Tables 5-16 through 5-19.  Following
the recommendations of the NRC report (NRC, 2002  XE "NRC, 2002"  ), we
identify those estimates which are based on empirical data, and those
which are based on expert judgments.  Monetized values for both health
and welfare endpoints are presented in Tables 5-XX through 5-XX, along
with total aggregate monetized benefits in Table 5-XX.  All of the
monetary benefits are in constant-year 1999 dollars.  For each endpoint
and total benefits, we provide both the mean estimate and the 95%
confidence interval.  Note that in the case of the premature mortality
estimates derived from the expert elicitation, we report the 95%
credible interval, which encompasses a broader representation of
uncertainty relative to the statistical confidence intervals provided
for the effect estimates derived from the epidemiology literature.

Following these tables, we also provide a more comprehensive graphical
presentation of the distributions of benefits generated using the
available information from empirical studies and expert elicitation.
Not all known PM- and ozone-related health and welfare effects could be
quantified or monetized.  The monetized value of these unquantified
effects is represented by adding an unknown “B” to the aggregate
total.  The estimate of total monetized health benefits is thus equal to
the subset of monetized PM- and ozone-related health and welfare
benefits plus B, the sum of the nonmonetized health and welfare
benefits.

Total monetized benefits are dominated by benefits of mortality risk
reductions.  The range of mean estimates across the full set of
mortality effect estimates projects that attainment of the final
standards of 15/35 will result in XXXX avoided premature deaths annually
in 2020, and that an attainment strategy for the more stringent annual
standard would result in XXXX avoided premature deaths incremental to
the 15/65 attainment strategy with XXXX avoided premature deaths
incremental to attainment of the final standards.

Our estimate of total monetized benefits in 2020 for the final rule is
$XXX billion using a 3% discount rate and $XXX billion using a 7%
discount rate.  Health benefits account for 98% of total benefits, in
part because we are unable to quantify most of the nonhealth benefits.
These unquantified benefits may be substantial and could exceed the
costs of the rule, although the magnitude of these benefits is highly
uncertain.  The monetized benefit associated with reductions in the risk
of premature mortality, which accounts for $XXX billion in 2020 is over
XX% of total monetized health benefits.  The next largest benefit is for
reductions in chronic illness (CB 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, visibility, MRADs, 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 almost 100 times more work loss days
than 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 WTP.
As such, the true value of these effects may be higher than that
reported in Table 5-16.

	Eastern US	Western US Excluding CA	California	Total Nationwide
Attainment

	Modeled Partial Attainment	Residual Attainment	Modeled Partial
Attainment	Residual Attainment	Modeled Partial Attainment	Residual
Attainment

	Mortality Impact Functions Derived from Epidemiology Literatureb

Adult Premature Mortality:

		Extended Follow-up Analysis of ACS Cohort (Pope et al., 2002  XE "Pope
et al., 2002"  )	700

(190 – 1,200)	17

(5 - 30)	570

(160 - 1,000)	150

(42 - 260)	650

(180 - 1100)	2,100

(580 - 3,600)	4,200

(1,160-7,200)

	Extended Follow-up Analysis of Harvard 6-City Study (Laden et al., 2006
 XE "Laden et al., 2006"  )	1,600

(730 - 2,500)	39

(18 - 60)	1,300

(590 - 2,000)	340

(160 - 530)	1,500

(670 - 2,200)	4,800

(2,200-7,300)	9,500

(4,300-15,000)

Infant Mortality:

		Woodruff et al. (1997  XE "Woodruff et al. (1997"  )	1

(0 - 2)	0

(0 - 0)	1

(0 - 1)	0

(0 - 1)	1

(1 - 2)	5

(2 - 8)	8

(3 – 13)

	Woodruff et al. (2005  XE "Woodruff et al. (2005"  )

	Mortality Impact Functions Derived from Expert Elicitationc

Adult Premature Mortality:

		Expert A	1,700	41	1,400	370	1,600	5,100	10,000

	Expert B	1,300	33	1,000	280	1,200	4,000	7,800

	Expert C	1,300	31	1,000	280	1,200	3,800	7,600

	Expert D	900	22	730	190	820	2,700	5,300

	Expert E	2,100	52	1,700	460	2,000	6,400	13,000

	Expert F	1,200	29	950	250	1,100	3,500	7,000

	Expert G	750	18	610	160	690	2,300	4,500

	Expert H	960	23	780	210	880	2,900	5,700

	Expert I	1,300	31	1,000	270	1,200	3,800	7,600

	Expert J	1,200	29	950	250	1,100	3,500	7,000

	Expert K	200	05	160	43	210	610	1,200

	Expert L	340	0	520	110	70	480	1,500

a	Incidences are rounded to two significant digits.

b	95% confidence intervals are provided in parentheses.

c	95% credible intervals are provided in parentheses.

Table 5-17:  Illustrative Strategy to Attain 15/35:  Estimated
Reductions in Morbidity (Incremental to 15/65 Attainment Strategy)

	Eastern US	Western US Excluding CA	California	Total Nationwide
Attainment

	Modeled Partial Attainment	Residual Attainment	Modeled Partial
Attainment	Residual Attainment	Modeled Partial Attainment	Residual
Attainment

	Chronic bronchitis (age >25 and over)	370	8	370	100	450	1,500	2,800

Nonfatal myocardial infarction (age >17)	0,800	38	140	30	1,000	3,000
5,000

Hospital admissions—respiratory (all ages)b	90	4	13	3	100	320	530

Hospital admissions—cardiovascular (age >17)c	190	9	30	6	220	650	1,100

Emergency room visits for asthma (age <19)	290	7	25	6	210	690	1,200

Acute bronchitis (age 8–12)	0,900	17	650	280	1,200	4,200	7,300

Lower respiratory symptoms (age 7–14) 	5,000	180	1,400	300	12,000
38,000	56,000

Upper respiratory symptoms (asthmatic children, age 9–18)	3,600	130
1,000	220	8,500	28,000	41,000

Asthma exacerbation (asthmatic children, age 6–18)	5,000	160	1,200	280
11,000	35,000	52,000

Work loss days (age 18–65)	33,000	1,300	7,900	1,800	73,000	230,000
350,000

Minor restricted-activity days (age 18–65)	200,000	8,000	46,000	10,000
430,000	1,300,000	2,000,000

a	Incidences are rounded to two significant digits.

b	Respiratory hospital admissions for PM include admissions for COPD,
pneumonia, and asthma.

c	Cardiovascular hospital admissions for PM include total cardiovascular
and subcategories for ischemic heart disease, dysrhythmias, and heart
failure.

Table 5-18:  Illustrative Strategy to Attain 14/35:  Estimated Reduction
in Premature Mortality (Incremental to 15/65 Attainment Strategy)

	Eastern US	Western US Excluding CA	California	Total Nationwide
Attainment

	Modeled Partial Attainment	Residual Attainment	Modeled Partial
Attainment	Residual Attainment	Modeled Partial Attainment	Residual
Attainment

	Mortality Impact Functions Derived from Epidemiology Literatureb

Adult Premature Mortality:

		Extended Follow-up Analysis of ACS Cohort (Pope et al., 2002)	4,200

560

630

	Extended Follow-up Analysis of Harvard 6-City Study (Laden et al.,
2006)	9,500

1,300

1,400

Infant Mortality:

		Woodruff et al. (1997)	0,008

1

1

	Woodruff et al. (2005)

	Mortality Impact Functions Derived from Expert Elicitationc

Adult Premature Mortality:

		Expert A	10,000

1,400

1,500

	Expert B	7,800

1,000

1,200

	Expert C	7,700

1,000

1,300

	Expert D	5,300

710

800

	Expert E	13,000

1,700

1,900

	Expert F	7,000

930

1,100

	Expert G	4,500

600

670

	Expert H	5,700

770

860

	Expert I	7,600

1,000

1,100

	Expert J	7,000

940

1,000

	Expert K	1,200

160

200

	Expert L	2,000

510

70

a	Incidences are rounded to two significant digits.

b	95% confidence intervals are provided in parentheses

c	95% credible intervals are provided in parentheses

Table 5-19:  Illustrative Strategy to Attain 14/35:  Estimated
Reductions in Morbidity (Incremental to 15/65 Attainment Strategy)

	Eastern US	Western US Excluding CA	California	Total Nationwide
Attainment

	Modeled Partial Attainment	Residual Attainment	Modeled Partial
Attainment	Residual Attainment	Modeled Partial Attainment	Residual
Attainment

	Chronic bronchitis (age >25 and over)	2,400

360

440

Nonfatal myocardial infarction (age >17)	4,200

140

1,000

Hospital admissions—respiratory (all ages)b	0,500

13

100

Hospital admissions—cardiovascular (age >17)c	1,100

31

210

Emergency room visits for asthma (age <19)	2,200

25

210

Acute bronchitis (age 8–12)	5,900

640

1,200

Lower respiratory symptoms (age 7–14) 	34,000

1,400

11,000

Upper respiratory symptoms (asthmatic children, age 9–18)	25,000

1,000

8,300

Asthma exacerbation (asthmatic children, age 6–18)	31,000

1,200

10,000

Work loss days (age 18–65)	220,000

8,000

71,000

Minor restricted-activity days (age 18–65)	1,300,000

47,000

420,000

a	Incidences are rounded to two significant digits.

b	Respiratory hospital admissions for PM include admissions for COPD,
pneumonia, and asthma.

c	Cardiovascular hospital admissions for PM include total cardiovascular
and subcategories for ischemic heart disease, dysrhythmias, and heart
failure.

Table 5-20:  Illustrative Strategy to Attain 15/35:  Estimated Monetary
Value of Reductions in Risk of Premature Mortality (3 Percent Discount
Rate, in millions of 1999$)a,b

	Eastern US	Western US Excluding CA	California	Total Nationwide
Attainment

	Modeled Partial Attainment	Residual Attainment	Modeled Partial
Attainment	Residual Attainment	Modeled Partial Attainment	Residual
Attainment

	Mortality Impact Functions Derived from Epidemiology Literature

Adult Premature Mortality:

		Extended Follow-up Analysis of ACS Cohort (Pope et al., 2002)	$4,100
 	$3,300	$880	$3,700	$12,000

		Extended Follow-up Analysis of Harvard 6-City Study (Laden et al.,
2006)	$9,200	 	$7,400	$2,000	$8,400	$27,000

	Infant Mortality:

		Woodruff et al. (1997)	$6	 	$4	$2	$8	$28

		Woodruff et al. (2005)

	Mortality Impact Functions Derived from Expert Elicitation

Adult Premature Mortality:

		Expert A	$9,800	 	$8,000	$2,100	$9,000	$29,000

		Expert B	$7,600	 	$6,000	$1,600	$7,100	$23,000

		Expert C	$7,400	 	$6,000	$1,600	$6,800	$22,000

		Expert D	$5,200	 	$4,200	$1,100	$4,700	$16,000

		Expert E	$12,000	 	$10,000	$3,000	$11,000	$37,000

		Expert F	$6,800	 	$5,500	$1,500	$6,200	$20,000

		Expert G	$4,300	 	$3,500	$0,940	$4,000	$13,000

		Expert H	$5,600	 	$4,500	$1,200	$5,100	$17,000

		Expert I	$7,400	 	$6,000	$1,600	$6,700	$22,000

		Expert J	$6,800	 	$5,500	$1,500	$6,200	$20,000

		Expert K	$1200	 	$900	$250	$1,200	$3500

		Expert L	$2,000	 	$3,000	$0,660	$380	$2,800

	a	Incidences are rounded to two significant digits.

Table 5-21:  Illustrative Strategy to Attain 15/35:  Estimated Monetary
Value of Reductions in Risk of Premature Mortality (7 Percent Discount
Rate, in millions of 1999$)a,b

	Eastern US	Western US Excluding CA	California	Total Nationwide
Attainment

	Modeled Partial Attainment	Residual Attainment	Modeled Partial
Attainment	Residual Attainment	Modeled Partial Attainment	Residual
Attainment

	Mortality Impact Functions Derived from Epidemiology Literature

Adult Premature Mortality:

		Extended Follow-up Analysis of ACS Cohort (Pope et al., 2002)	$3,400
 	$2,800	$0,740	$3,100	$10,300

		Extended Follow-up Analysis of Harvard 6-City Study (Laden et al.,
2006)	$7,700	 	$6,300	$1,700	$7,100	$23,100

	Infant Mortality:

		Woodruff et al. (1997)	$5	 	$4	$2	$6	$23

		Woodruff et al. (2005)

	Mortality Impact Functions Derived from Expert Elicitation

Adult Premature Mortality:

		Expert A	$8,300	 	$6,700	$1,800	$7,600	$25,000

		Expert B	$6,400	 	$5,100	$1,400	$5,900	$19,000

		Expert C	$6,300	 	$5,100	$1,300	$5,700	$19,000

		Expert D	$4,400	 	$3,500	$0,940	$4,000	$13,000

		Expert E	$10,000	 	$8,000	$2,200	$10,000	$31,000

		Expert F	$5,700	 	$4,600	$1,200	$5,300	$17,200

		Expert G	$3,700	 	$3,000	$0,790	$3,300	$11,000

		Expert H	$4,700	 	$3,800	$1,000	$4,300	$14,000

		Expert I	$6,200	 	$5,000	$1,300	$5,700	$19,000

		Expert J	$5,700	 	$4,600	$1,200	$5,200	$17,000

		Expert K	$1000	 	$800	$210	$1,000	$3000

		Expert L	$1,700	 	$2,500	$0,550	$320	$2,400

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

b	Monetary benefits adjusted to account for growth in real GDP per
capita between 1990 and 2020

c	Valuation assumes discounting over the SAB recommended 20 year
segmented lag structure described earlier.  Results reflect the use of
3% and 7% discount rates consistent with EPA and OMB guidelines for
preparing economic analyses (EPA, 2000b  XE "U.S. EPA, 2000b"  ; OMB,
2003  XE "OMB, 2003"  ).

Table 5-22:  Illustrative Strategy to Attain 15/35:  Estimated Monetary
Value of Morbidity Reductions (in millions of 1999$)a,b

	Eastern US	Western US Excluding CA	California	Total Nationwide
Attainment

	Modeled Partial Attainment	Residual Attainment	Modeled Partial
Attainment	Residual Attainment	Modeled Partial Attainment	Residual
Attainment

	Chronic bronchitis (age >25 and over)	$150	 	$150	$040	$180	$600

	Nonfatal myocardial infarction (age >17)

	3% Discount Rate	$63	 	$11	$02	$87	$253

	7% Discount Rate	$61	 	$11	$02	$84	$244

	Hospital admissions—respiratory (all ages)d	$1.1	 	$0.2	$0.0	$1	$3.7

	Hospital admissions—cardiovascular (age >17)e	$4.0	 	$0.6	$0.1	$5
$14.0

	Emergency room visits for asthma (age <19)	$0.08	 	$0.01	$0.00	$0.1
$0.19

	Acute bronchitis (age 8–12)	$0.3	 	$0.2	$0.1	$0.5	$1.6

	Lower respiratory symptoms (age 7–14) 	$1.14	 	$0.02	$0.00	$0.2
$0.61

	Upper respiratory symptoms (asthmatic children, age 9–18)	$0.10
$0.03	$0.01	$0.2	$0.75

	Asthma exacerbation (asthmatic children, age 6–18)	$0.19	 	$0.05
$0.01	$0.4	$1.50

	Work loss days (age 18–65)	$4.3	 	$1.0	$0.2	$11	$33.0

	Minor restricted-activity days (age 18–65)	$10.3	 	$2.4	$0.5	$23
$70.0

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

b	Monetary benefits adjusted to account for growth in real GDP per
capita between 1990 and 2020

c	Results reflect the use of 3% and 7% discount rates consistent with
EPA and OMB guidelines for preparing economic analyses (EPA, 2000b  XE
"U.S. EPA, 2000b"  ; OMB, 2003  XE "OMB, 2003"  ).

Table 5-23:  Illustrative Strategy to Attain 14/35:  Estimated Monetary
Value of Reductions in Risk of Premature Mortality (3 Percent Discount
Rate, in millions of 1999$)a,b

	Eastern US	Western US Excluding CA	California	Total Nationwide
Attainment

	Modeled Partial Attainment	Residual Attainment	Modeled Partial
Attainment	Residual Attainment	Modeled Partial Attainment	Residual
Attainment

	Mortality Impact Functions Derived from Epidemiology Literature

Adult Premature Mortality:

		Extended Follow-up Analysis of ACS Cohort (Pope et al., 2002)	$24,000
 	$3,300	 	$3,600

	Extended Follow-up Analysis of Harvard 6-City Study (Laden et al.,
2006)	$55,000	 	$7,300	 	$8,200

Infant Mortality:

		Woodruff et al. (1997)	$43	 	$4	 	$7

	Woodruff et al. (2005)

	Mortality Impact Functions Derived from Expert Elicitation

Adult Premature Mortality:

		Expert A	$59,000	 	$7,800	 	$8,800

	Expert B	$45,000	 	$5,900	 	$6,900

	Expert C	$44,000	 	$5,900	 	$6,600

	Expert D	$31,000	 	$4,100	 	$4,600

	Expert E	$74,000	 	$10,000	 	$11,000

	Expert F	$40,000	 	$5,400	 	$6,100

	Expert G	$26,000	 	$3,500	 	$3,900

	Expert H	$33,000	 	$4,400	 	$5,000

	Expert I	$44,000	 	$5,900	 	$6,600

	Expert J	$40,000	 	$5,400	 	$6,100

	Expert K	$7,000	 	$900	 	$1,200

	Expert L	$11,000	 	$2,900	 	$380

a	Incidences are rounded to two significant digits.

Table 5-24:  Illustrative Strategy to Attain 14/35:  Estimated Monetary
Value of Reductions in Risk of Premature Mortality (7 Percent Discount
Rate, in millions of 1999$)a,b

	Eastern US	Western US Excluding CA	California	Total Nationwide
Attainment

	Modeled Partial Attainment	Residual Attainment	Modeled Partial
Attainment	Residual Attainment	Modeled Partial Attainment	Residual
Attainment

	Mortality Impact Functions Derived from Epidemiology Literature

Adult Premature Mortality:

		Extended Follow-up Analysis of ACS Cohort (Pope et al., 2002)	$20,000
 	$2,700	 	$3,100

	Extended Follow-up Analysis of Harvard 6-City Study (Laden et al.,
2006)	$46,000	 	$6,200	 	$6,900

Infant Mortality:

		Woodruff et al. (1997)	$36	 	$3	 	$6

	Woodruff et al. (2005)

	Mortality Impact Functions Derived from Expert Elicitation

Adult Premature Mortality:

		Expert A	$49,000	 	$6,600	 	$7,400

		Expert B	$38,000	 	$5,000	 	$5,800

	Expert C	$37,000	 	$5,000	 	$5,600

	Expert D	$26,000	 	$3,500	 	$3,900

	Expert E	$62,000	 	$8,000	 	$9,000

	Expert F	$34,000	 	$4,500	 	$5,100

	Expert G	$22,000	 	$2,900	 	$3,300

	Expert H	$28,000	 	$3,700	 	$4,200

	Expert I	$37,000	 	$4,900	 	$5,500

	Expert J	$34,000	 	$4,600	 	$5,100

	Expert K	$5,800	 	$800	 	$1,000

	Expert L	$9700	 	$2,500	 	$320

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

b	Monetary benefits adjusted to account for growth in real GDP per
capita between 1990 and 2020

c	Valuation assumes discounting over the SAB recommended 20 year
segmented lag structure described earlier.  Results reflect the use of
3% and 7% discount rates consistent with EPA and OMB guidelines for
preparing economic analyses (EPA, 2000b; OMB, 2003).

Table 5-25:  Illustrative Strategy to Attain 14/35:  Estimated Monetary
Value of Morbidity Reductions (in millions of 1999$)a,b

	Eastern US	Western US Excluding CA	California	Total Nationwide
Attainment

	Modeled Partial Attainment	Residual Attainment	Modeled Partial
Attainment	Residual Attainment	Modeled Partial Attainment	Residual
Attainment

	Chronic bronchitis (age >25 and over)	$950	 	$150	 	$180

	Nonfatal myocardial infarction (age >17)

	3% Discount Rate	$350	 	$11	 	$84

7% Discount Rate	$330	 	$11	 	$82

Hospital admissions—respiratory (all ages)d	$6	 	$0.2	 	$1

Hospital admissions—cardiovascular (age >17)e	$22	 	$0.6	 	$4

Emergency room visits for asthma (age <19)	$0.6	 	$0.01	 	$0.1

Acute bronchitis (age 8–12)	$2.1	 	$0.2	 	$0.4

Lower respiratory symptoms (age 7–14) 	$0.5	 	$0.02	 	$0.2

Upper respiratory symptoms (asthmatic children, age 9–18)	$0.7
$0.03	 	$0.2

Asthma exacerbation (asthmatic children, age 6–18)	$1.3	 	$0.05
$0.4

Work loss days (age 18–65)	$28	 	$1.0	 	$10

Minor restricted-activity days (age 18–65)	$68	 	$2.4	 	$22

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

b	Monetary benefits adjusted to account for growth in real GDP per
capita between 1990 and 2020

c	Results reflect the use of 3% and 7% discount rates consistent with
EPA and OMB guidelines for preparing economic analyses (EPA, 2000b; OMB,
2003).

Table 5-26:  Monetary Benefits Associated with Improvements in
Visibility in Selected Federal Class I Areas in 2020 Incremental to
15/65 Attainment Strategy (in millions of 1999$)

Suite of Standards	California	Southwest	Southeast	Total

15/35

14/35

Table 5-27:  Total Monetized Benefits (Health and Visibility) Associated
with Attainment of 15/35 and 14/35 in 2020 (in millions of 1999$)a

Source of Mortality Effect Estimate	3% Discount Rate	7% Discount Rate

	15/35	14/35	15/35	14/35

Data-derived

Pope et al. (2002)	+B

	Laden et al. (2006)	+B

	Expert Elicitation Derived

Expert A	+B

	Expert B	+B

	Expert C	+B

	Expert D	+B

	Expert E	+B

	Expert F	+B

	Expert G	+B

	Expert H	+B

	Expert I	+B

	Expert J	+B

	Expert K	+B

	Expert L	+B

	a	B represents the monetary value of health and welfare benefits and
disbenefits not monetized.  A detailed listing is provided in Table 5-2.

Table 5-28:  Mortality Threshold Sensitivity Analysis for 15/35 and
14/35 Attainment Scenarios (Using Pope et al., 2002 Effect Estimate)

7%

 	12 µg/m3 c	3%

7%

 	10 µg/m3 d	3%

7%

 	7.5 µg/m3 e	3%

7%

	3 µg/m3 f	3%

Less Certain

7%

a	Note that the threshold is the cutpoint below which no benefits acrue

b	Lowest annual NAAQS considered

c	Intermediate value

d	CASAC (2005  XE "CASAC (2005"  )

e	SAB-HES (2004  XE "SAB-HES (2004"  )

f	NAS (2002  XE "NAS (2002"  )

We provide likelihood distributions both for the total dollar benefits
estimate and for the incidence of premature mortality to show the
uncertainty described by each expert’s judgment relative to the range
of uncertainty associated with the standard error in the Pope et al.
(2002  XE "Pope et al. (2002"  ) study.  The uncertainty about the total
dollar benefit associated with any single endpoint combines the
uncertainties from two sources—the C-R relationship and the
valuation—and is estimated with a Monte Carlo method.  Our estimates
of the likelihood distributions for total benefits should be viewed
within the context of the wide range of sources of uncertainty that we
have not incorporated, including uncertainty in emissions, air quality,
and baseline health effect incidence rates.

Therefore, in characterizing the uncertainty related to the estimates of
total benefits it is particularly important to attempt to characterize
the uncertainties associated with this endpoint.  We conducted two
different Monte Carlo analyses, one based on the distribution of
reductions in premature mortality characterized by the mean effect
estimate and standard error from the Pope et al. (2002  XE "Pope et al.
(2002"  ) study (our primary estimate), and one based on the results
from a pilot expert elicitation project (IEc, 2004  XE "IEc, 2004"  ).
In each case, uncertainty in other aspects of the analysis (morbidity
effect estimates and valuation for all endpoints) is characterized using
data-derived distributions.

We are unable at this time to characterize the uncertainty in the
estimate of benefits of improvements in visibility at Class I areas.  As
such, we treat the visibility benefits as fixed and add them to all
percentiles of the health benefits distribution.

Given this unequal treatment of endpoints, it is likely that these
distributions do not capture the full range of benefits, and in fact are
likely to understate the uncertainty, especially on the high end of the
range due to omission of potentially significant benefit categories.  We
include them here primarily as an illustration of the impacts of using
probabilistic (expert elicitation and statistical error-based)
distributions for premature mortality associated with PM2.5.

Figures 5-4 and 5-5 presents box plots of 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 Pope et al. (2002  XE "Pope et al. (2002"  ) and Laden
et al. (2006  XE "Laden et al. (2006"  ).

Figure 5-4.  Results of Illustrative Application of Pilot Expert
Elicitation:  Annual Reductions in Premature Mortality in 2020
Associated with Illustrative Strategies to Attain 15/35

Figure 5-5.  Results of Illustrative Application of Pilot Expert
Elicitation:  Annual Reductions in Premature Mortality in 2020
Associated with Illustrative Strategies to Attain 14/35

The distributions are depicted as box plots with the diamond symbol
() showing the mean, the dash (–) showing the median (50th
percentile), the box defining the interquartile range (bounded by the
25th and 75th percentiles), and the whiskers defining the 90% confidence
interval (bounded by the 5th and 95th percentiles of the distribution).
The data-derived estimates based on Pope et al. (2002  XE "Pope et al.
(2002"  ) and Laden et al. (2006  XE "Laden et al. (2006"  ) show that
the mean predicted number of premature deaths avoided in 2020 ranges
from XXXX to XXXX.  This is higher than XX of the experts and lower than
XX experts and falls within the uncertainty bounds of all but one
expert.  The figure shows that the average annual number of premature
deaths avoided in 2020 ranges from approximately XXXX (based on the
judgments of Expert X) to XXXXX (based on the judgments of Expert X).
The medians span zero to XXXX, with the zero value due to the low
probability of a causal relationship associated with one of the
expert’s distributions.  The statistical uncertainty bounds of all of
the estimates, including the data-derived distributions, overlap.
Although the uncertainty bounds for all but one expert include zero, and
some distributions have significant percentiles at zero, all of the
distributions have a positive mean estimate.

Figure 5-4 presents box plots of the distributions of monetized benefits
of reductions in premature mortality associated with use of the Pope et
al. (2002  XE "Pope et al. (2002"  ), Laden et al. (2006  XE "Laden et
al. (2006"  ), and expert-based mortality incidence distributions.  The
data-derived estimates based on Pope et al. (2002  XE "Pope et al.
(2002"  ) and Laden et al. (2006  XE "Laden et al. (2006"  ) show that
the mean annual benefit ranges from $XX billion to $XX billion.  Mean
annual benefits for each expert elicited during the pilot expert
elicitation range from approximately $XXX billion (based on judgments of
Expert X) to $XXX billion (based on the judgments of Expert X).

The uncertainty estimates based on statistical error have the strength
of presenting a statistical measure of the uncertainty in the underlying
studies serving as the basis for the estimates used in the analysis.
However, this approach captures only a limited portion of the
uncertainty about the parameters.  The 5th and 95th percentile points of
the distributions are based on statistical error and cross-study
variability and provide some insight into how uncertain our estimate is
with regard to those sources of uncertainty.  However, it does not
capture other sources of uncertainty regarding the model specification
and other inputs to the model, including emissions, air quality, and
aspects of the health science not captured in the studies, such as the
likelihood that PM is causally related to premature mortality and other
serious health effects.

Figure 5-6.  Results of Probabalistic Uncertainty Analysis:  Dollar
Value of Health and Welfare Impacts Associated with Illustrative
Strategies to Attain 15/35

Figure 5-7.  Results of Probabalistic Uncertainty Analysis:  Dollar
Value of Health and Welfare Impacts Associated with Illustrative
Strategies to Attain 14/35

These distributions can also be displayed in terms of cumulative
distribution functions.  The cumulative distributions of monetized
benefits are provided in Figures 5-8 and 5-9 for the 15/35 and 14/35
attainment scenarios, respectively.

Figure 5-8.  Results of Probabalistic Uncertainty Analysis:  Cumulative
Distributions of Dollar Value of Health and Welfare Impacts Associated
with Illustrative Strategies to Attain 15/35

Figure 5-9.  Results of Probabalistic Uncertainty Analysis:  Cumulative
Distributions of Dollar Value of Health and Welfare Impacts Associated
with Illustrative Strategies to Attain 14/35

5.3	Discussion

This analysis has estimated the health and welfare benefits of
reductions in ambient concentrations of particulate matter resulting
from a set of illustrative control strategies to reduce emissions of
PM2.5 precursors.  The result suggests there will be significant health
and welfare benefits arising from reducing emissions from a variety of
sources in and around projected nonattaining counties in 2020.  While
2020 is the expected date that states would need to demonstrate
attainment with the standards, it is expected that benefits (and costs)
will begin occurring much earlier, as states begin implementing control
measures to show reasonable progress towards attainment. Our estimate
that between XXXX and XXXX premature mortalities would be avoided when
the emissions reductions from implementing the standards are fully
realized provides additional evidence of the important role that
implementation of the standards plays in reducing the health risks
associated with exceeding the standards.

Other uncertainties that we could not quantify include the importance of
unquantified effects and uncertainties in the modeling of ambient air
quality.  Inherent in any analysis of future regulatory programs are
uncertainties in projecting atmospheric conditions and source-level
emissions, as well as population, health baselines, incomes, technology,
and other factors.  The assumptions used to capture these elements are
reasonable based on the available evidence.  However, data limitations
prevent an overall quantitative estimate of the uncertainty associated
with estimates of total economic benefits.  If one is mindful of these
limitations, the magnitude of the benefits estimates presented here can
be useful information in expanding the understanding of the public
health impacts of reducing PM2.5 precursor emissions.

EPA will continue to evaluate new methods and models and select those
most appropriate for estimating the health benefits of reductions in air
pollution.  It is important to continue improving benefits transfer
methods in terms of transferring economic values and transferring
estimated impact functions.  The development of both better models of
current health outcomes and new models for additional health effects
such as asthma, high blood pressure, and adverse birth outcomes (such as
low birth weight) will be essential to future improvements in the
accuracy and reliability of benefits analyses (Guo et al., 1999  XE "Guo
et al., 1999"  ; Ibald-Mulli et al., 2001  XE "Ibald-Mulli et al., 2001"
 ).  Enhanced collaboration between air quality modelers,
epidemiologists, toxicologists, and economists should result in a more
tightly integrated analytical framework for measuring health benefits of
air pollution policies.

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<www.epa.gov/ttn/ecase/benefits.html>.

McConnell, R., K. Berhane, F. Gilliland, S.J. London, T. Islam, W.J.
Gauderman, E. Avol, H.G. Margolis, and J.M. Peters.  2002.  “Asthma in
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Moolgavkar, S.H., E.G. Luebeck, and E.L. Anderson.  1997.  “Air
Pollution and Hospital Admissions for Respiratory Causes in
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National Center for Education Statistics.  1996.  “The Condition of
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National Hospital Ambulatory Medical Care Survey (NHAMCS).  2000.
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at:  <http://www.cdc.gov/nchs/about/major/ ahcd/ahcd1.htm>.

NHDS Public Use Data Files.  1999.  Centers for Disease Control and
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NPS/NERCHAL/NRTR-03/090.  Philadelphia, PA:  National Park Service,
Northeast Region.  Available at http://www.nps.gov/shen/air_quality.htm.

Pope, C.A.  2000.  “Invited Commentary:  Particulate Matter-Mortality
Exposure-Response Relations and Thresholds.”  American Journal of
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Ransom, M.R., and C.A. Pope.  1992.  “Elementary School Absences and
PM(10) Pollution in Utah Valley.”  Environmental Research
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Rossi, G., M.A. Vigotti, A. Zanobetti, F. Repetto, V. Gianelle, and J.
Schwartz.  1999.  “Air Pollution and Cause-Specific Mortality in
Milan, Italy, 1980-1989.”  Archives of Environmental Health
54(3):158-164

Samet, J.M., S.L. Zeger, J.E. Kelsall, J. Xu, and L.S. Kalkstein.  March
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Schwartz, J.  1994a.  “PM(10) Ozone, and Hospital Admissions for the
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Schwartz, J.  1994b.  “Air Pollution and Hospital Admissions for the
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Schwartz, J., and A. Zanobetti.  2000.  “Using Meta-Smoothing to
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Schwartz, J., D.W. Dockery, L.M. Neas, D. Wypij, J.H. Ware, J.D.
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 The term “impact function” as used here refers to the combination
of a) an effect estimate obtained from the epidemiological literature,
b) the baseline incidence estimate for the health effect of interest in
the modeled population, c) the size of that modeled population, and d)
the change in the ambient air pollution metric of interest.  These
elements are combined in the impact function to generate estimates of
changes in incidence of the health effect.  The impact function is
distinct from the C-R function, which strictly refers to the estimated
equation from the epidemiological study relating incidence of the health
effect and ambient pollution.  We refer to the specific value of the
relative risk or estimated coefficients in the epidemiological study as
the “effect estimate.”  In referencing the functions used to
generate changes in incidence of health effects for this RIA, we use the
term “impact function” rather than C-R function because “impact
function” includes all key input parameters used in the incidence
calculation.

 Expert elicitation is a formal, highly structured and well documented
process whereby expert judgments, usually of multiple experts, are
obtained (Ayyub, 2002  XE "Ayyub, 2002"  ).

 For many goods, WTP can be observed by examining actual market
transactions.  For example, if a gallon of bottled drinking water sells
for $1, it can be observed that at least some people are willing to pay
$1 for such water.  For goods not exchanged in the market, such as most
environmental “goods,” valuation is not as straightforward.
Nevertheless, a value may be inferred from observed behavior, such as
sales and prices of products that result in similar effects or risk
reductions (e.g., nontoxic cleaners or bike helmets).  Alternatively,
surveys can be used in an attempt to directly elicit WTP for an
environmental improvement.

 In general, economists tend to view an individual’s WTP for an
improvement in environmental quality as the appropriate measure of the
value of a risk reduction.  An individual’s willingness to accept
(WTA) compensation for not receiving the improvement is also a valid
measure.  However, WTP is generally considered to be a more readily
available and conservative measure of benefits.

 Concerns about the reliability of value estimates from CV studies arose
because research has shown that bias can be introduced easily into these
studies if they are not carefully conducted.  Accurately measuring WTP
for avoided health and welfare losses depends on the reliability and
validity of the data collected.  There are several issues to consider
when evaluating study quality, including but not limited to 1) whether
the sample estimates of WTP are representative of the population WTP; 2)
whether the good to be valued is comprehended and accepted by the
respondent; 3) whether the WTP elicitation format is designed to
minimize strategic responses; 4) whether WTP is sensitive to respondent
familiarity with the good, to the size of the change in the good, and to
income; 5) whether the estimates of WTP are broadly consistent with
other estimates of WTP for similar goods; and 6) the extent to which WTP
responses are consistent with established economic principles.

 Income elasticity is a common economic measure equal to the percentage
change in WTP for a 1% change in income.

 U.S. Bureau of Economic Analysis, Table 2A (1992$) (available at
http://www.bea.doc.gov/bea/dn/0897nip2/ tab2a.htm.) and U.S. Bureau of
Economic Analysis, Economics and Budget Outlook.  Note that projections
for 2007 to 2010 are based on average GDP growth rates between 1999 and
2007.

 In previous analyses, we used the Standard and Poor’s projections of
GDP directly.  This led to an apparent discontinuity in the adjustment
factors between 2010 and 2011.  We refined the method by applying the
relative growth rates for GDP derived from the Standard and Poor’s
projections to the 2010 projected GDP based on the Bureau of Economic
Analysis projections.

 In this analysis, the fixed effects model assumes that there is only
one pollutant coefficient for the entire modeled area.  The random
effects model assumes that studies conducted in different locations are
estimating different parameters; therefore, there may be a number of
different underlying pollutant coefficients.

 EPA recognizes that the ACS cohort also is not representative of the
demographic mix in the general population.  The ACS cohort is almost
entirely white and has higher income and education levels relative to
the general population.  EPA’s approach to this problem is to match
populations based on the potential for demographic characteristics to
modify the effect of air pollution on mortality risk.  Thus, for the
various ACS-based models, we are careful to apply the effect estimate
only to ages matching those in the original studies, because age has a
potentially large modifying impact on the effect estimate, especially
when younger individuals are excluded from the study population.  For
the Lipfert analysis, the applied population should be limited to that
matching the sample used in the analysis.  This sample was all male,
veterans, and diagnosed hypertensive.  There are also a number of
differences between the composition of the sample and the general
population, including a higher percentage of African Americans (35%) and
a much higher percentage of smokers (81% former smokers, 57% current
smokers) than the general population (12% African American, 24% current
smokers).

 Note that in the expert elicitation protocol, we specified the relevant
range of exposure as between 4 and 30 µg/m3.  As such, when applying
the expert elicitation based functions, benefits are only estimated for
starting concentrations greater than 4 µg/m3.

 Note that the Moolgavkar (2000  XE "Moolgavkar (2000"  ) study has not
been updated to reflect the more stringent GAM convergence criteria.
However, given that no other estimates are available for this age group,
we chose to use the existing study.  Given the very small (<5%)
difference in the effect estimates for people 65 and older with
cardiovascular hospital admissions between the original and reanalyzed
results, we do not expect this choice to introduce much bias.

 See http://www.nlm.nih.gov/medlineplus/ency/article/000124.htm,
accessed January 2002.

 Estimating asthma exacerbations associated with air pollution exposures
is difficult, due to concerns about double-counting of benefits.
Concerns over double-counting stem from the fact that studies of the
general population also include asthmatics, so estimates based solely on
the asthmatic population cannot be directly added to the general
population numbers without double-counting.  In one specific case (upper
respiratory symptoms in children), the only study available is limited
to asthmatic children, so this endpoint can be readily included in the
calculation of total benefits.  However, other endpoints, such as lower
respiratory symptoms and MRADs, are estimated for the total population
that includes asthmatics.  Therefore, to simply add predictions of
asthma-related symptoms generated for the population of asthmatics to
these total population-based estimates could result in double-counting,
especially if they evaluate similar endpoints.  The SAB-HES, in
commenting on the analytical blueprint for 812, acknowledged these
challenges in evaluating asthmatic symptoms and appropriately adding
them into the primary analysis (SAB-HES, 2004  XE "SAB-HES, 2004"  ).
However, despite these challenges, the SAB-HES recommends the addition
of asthma-related symptoms (i.e., asthma exacerbations) to the primary
analysis, provided that the studies use the panel study approach and
that they have comparable design and baseline frequencies in both asthma
prevalence and exacerbation rates.  Note also, that the SAB-HES, while
supporting the incorporation of asthma exacerbation estimates, does not
believe that the association between ambient air pollution, including
ozone and PM, and the new onset of asthma is sufficiently strong to
support inclusion of this asthma-related endpoint in the primary
estimate.

 Note, that the adjustment to the mortality slopes was only done for the
10 µg/m3 and 15 µg/m3 cutpoints since the 7.5 µg/m3 and background
cutpoints are at or below the lowest measured exposure levels reported
in the Pope 2002, for the combined exposure dataset.

 Although we are not able to use region-specific effect estimates, we
use region-specific baseline incidence rates where available.  This
allows us to take into account regional differences in health status,
which can have a significant impact on estimated health benefits.

 The advice from the 2004 SAB-HES (EPA-SAB-COUNCIL-ADV-04-002) is
characterized by the following: “For the studies of long-term
exposure, the HES notes that Krewski et al. (2000  XE "Krewski et al.
(2000"  ) have conducted the most careful work on this issue.  They
report that the associations between PM2.5 and both all-cause and
cardiopulmonary mortality were near linear within the relevant ranges,
with no apparent threshold.  Graphical analyses of these studies
(Dockery et al., 1993  XE "Dockery et al., 1993"  , Figure 3, and
Krewski et al., 2000  XE "Krewski et al., 2000"  , page 162) also
suggest a continuum of effects down to lower levels.  Therefore, it is
reasonable for EPA to assume a no threshold model down to, at least, the
low end of the concentrations reported in the studies.”

 The choice of a discount rate, and its associated conceptual basis, is
a topic of ongoing discussion within the federal government.  EPA
adopted a 3% discount rate for its base estimate in this case to reflect
reliance on a “social rate of time preference” discounting concept.
We have also calculated benefits and costs using a 7% rate consistent
with an “opportunity cost of capital” concept to reflect the time
value of resources directed to meet regulatory requirements.  In this
case, the benefit and cost estimates were not significantly affected by
the choice of discount rate.  Further discussion of this topic appears
in EPA’s Guidelines for Preparing Economic Analyses (EPA, 2000b  XE
"U.S. EPA, 2000b"  ).

 This conclusion was based on a assessment of uncertainty based on
statistical error in epidemiological effect estimates and economic
valuation estimates.  Additional sources of model error such as those
examined in the pilot PM mortality expert elicitation may result in
different conclusions about the relative contribution of sources of
uncertainty.

 A change of less than 10% in the light extinction budget represents a
measurable improvement in visibility but may not be perceptible to the
eye in many cases.  Some of the average regional changes in visibility
are less than one deciview (i.e., less than 10% of the light extinction
budget) and thus less than perceptible.  However, this does not mean
that these changes are not real or significant.  Our assumption is then
that individuals can place values on changes in visibility that may not
be perceptible.  This is quite plausible if individuals are aware that
many regulations lead to small improvements in visibility that, when
considered together, amount to perceptible changes in visibility.

 The Clean Air Act designates 156 national parks and wilderness areas as
Class I areas for visibility protection.

 For details of the visibility estimates discussed in this chapter,
please refer to the Benefits TSD for the Nonroad Diesel rulemaking (Abt
Associates, 2003  XE "Abt Associates, 2003"  ).

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  EPA believes the study has received adequate review and has been cited
in numerous peer-reviewed publications (Chestnut and Dennis, 1997  XE
"Chestnut and Dennis, 1997"  ).

 In each iteration of the Monte Carlo procedure, a value is randomly
drawn from the incidence distribution, and a value is randomly drawn
from the unit dollar value distribution.  The total dollar benefit for
that iteration is the product of the two.  If this is repeated for many
(e.g., thousands of) iterations, the distribution of total dollar
benefits associated with the endpoint is generated.  For details on the
specific Monte Carlo approach we used, see Appendix B.

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