Document ID: EPA-HQ-OAR-2007-0225-0012
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2007-08-02T04:00Z

Chapter 5.  Incremental Benefits of Attaining Alternative Ozone
Standards Relative to the Current 8-hour Standard (0.08 ppm) 

 5.1.  Introduction

similar to those used in several recent U.S. EPA regulatory impact
analyses, including those for the 2006 PM NAAQS (U.S. EPA, 2006) and the
Clean Air Interstate Rule (U.S. EPA, 2005).  This analysis largely
builds off of the analytical approach used in the 2006 PM NAAQS RIA and
in the analysis of ozone health impacts reported in Hubbell et al.
(2005).  For a more detailed discussion of the principles of benefits
analysis used here, we refer the reader to those documents, as well as
to the EPA Guidelines for Economic Analysis.  

.

For this assessment, we estimated benefits of changes in ozone and PM
resulting from application of illustrative control strategies on ozone
precursor emissions to attain alternative ozone NAAQS.  With the
exception of ozone-related mortality, we use methods consistent with
previous PM and ozone benefits assessments.  Specifically, the PM
benefits analysis uses an approach identical to that used in the 2006 PM
NAAQS RIA (U.S. EPA, 2006).  The ozone benefits analysis for
non-mortality endpoints uses an approach nearly identical to that for
the Clean Air Interstate Rule RIA (U.S. EPA, 2005).

New scientific studies published since 1996 have increased the body of
evidence supporting the association between ambient ozone and a number
of serious health effects, including premature death, hospital and
emergency room visits, and school absences (U.S. EPA, 2006, WHO 2004). 
For example, studies examining the association between ambient ozone and
premature mortality have increased the weight of evidence supporting a
link between ozone exposure and this important health impact (Bell et
al., 2004, 2005; Ito et al., 2005; Levy et al., 2005; Anderson et al.,
2004).  The ozone Criteria Document concluded that current scientific
evidence is highly suggestive of a relationship between ozone and
premature death.  In reviewing the Staff Paper, EPA’s Clean Air
Scientific Advisory Committee agreed that including ozone mortality in
risk analyses is appropriate.

There is considerable variability in the magnitude of the O3-related
mortality association reported in the scientific literature, which we
reflect by summarizing the primary estimates from four different studies
in the benefits analysis.  We also note that there are uncertainties
within each study that are not fully captured by this range of
estimates.  Recognizing that additional research is needed to more fully
establish underlying mechanisms by which such effects occur, we also
consider the possibility that the observed associations between ozone
and mortality may not be causal in nature.  The estimates of premature
deaths avoided from ozone provided in this benefits analysis reflect
EPA's interim approach to characterizing the benefits of reducing
premature mortality associated with ozone exposure.   EPA has requested
advice from the NAS on how best to quantify uncertainty in the
relationship between ozone exposure and premature mortality in the
context of quantifying benefits associated with alternative ozone
control strategies.

The remainder of this chapter describes the data and methods used in
this analysis, along with the results.  Additional details of the
analysis are provided in Appendix XX of this RIA.  Section 5.2 discusses
the probabilistic framework for the benefits analysis and how key
uncertainties are addressed in the analysis.  Section 5.3 discusses the
literature on ozone- and PM-related health effects and describes the
specific set of health impact functions we used in the benefits
analysis.  Section 5.4 describes the economic values selected to
estimate the dollar value of ozone- and PM- related health impacts. 
Finally, Section 5.5 presents the results and implications of the
analysis.  

5.2.  Probabilistic Framework for Benefits Assessment

The National Research Council (NRC) (2002) highlighted the need for EPA
to conduct rigorous quantitative analysis of uncertainty in its benefits
estimates and to present these estimates to decision makers in ways that
foster an appropriate appreciation of their inherent uncertainty.  In
response to these comments, EPA has initiated the development of a
comprehensive methodology for characterizing the aggregate impact of
uncertainty in key modeling elements on both health incidence and
benefits estimates.

uncertainty in the concentration-response (C-R) and economic valuation
functions.  This was done both for base estimates and in a series of
ancillary sensitivity analyses examining the impact of alternate
assumptions on the benefits estimates.  Specifically, we used Monte
Carlo methods to generate confidence intervals around the estimated
health impact and dollar benefits.  Monte Carlo simulation uses random
sampling from distributions of parameters to characterize the effects of
uncertainty on output variables, such as incidence of premature
mortality.  Distributions for individual effect estimates are based on
the reported standard errors in the epidemiological studies. 
Distributions for unit values are described in Table 4.

It should be noted that the Monte Carlo-generated distributions of
benefits reflect only some of the uncertainties in the input parameters.
 Uncertainties associated with emissions, air quality modeling,
populations, and baseline health effect incidence rates are not
represented in the distributions of benefits of attaining alternative
standards.  Issues such as correlation between input parameters and the
identification of reasonable upper and lower bounds for input
distributions characterizing uncertainty in additional model elements
will be addressed in future versions of the uncertainty framework.

In benefit analyses of air pollution regulations conducted to date, the
estimated impact of reductions in premature mortality has accounted for
85% to 95% of total benefits.  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.  The health impact functions used to
estimate avoided premature deaths associated with reductions in ozone
have associated standard errors that represent the statistical errors
around the effect estimates in the underlying epidemiological studies. 
In our results, we report credible intervals based on these standard
errors, reflecting the uncertainty in the estimated change in incidence
of avoided premature deaths.  We also provide multiple estimates, to
reflect model uncertainty between alternative study designs.  In
addition, we recognize additional uncertainty in the ability of existing
empirical models to confirm the existence of a causal relationship
between ozone and mortality.  

For premature mortality associated with exposure to PM, we follow the
same approach used in the RIA for 2006 PM NAAQS (U.S. EPA, 2006),
presenting several empirical estimates of premature deaths avoided, and
a set of twelve estimates based on the responses of an expert panel. 
Even these multiple characterizations, including confidence intervals,
omit the contribution to overall uncertainty of uncertainty in air
quality changes, baseline incidence rates, populations exposed and
transferability of the effect estimate to diverse locations.  As a
result, the reported confidence intervals and range of estimates give a
potentially misleading picture about the overall uncertainty in the
estimates.  This information should be interpreted within the context of
the larger uncertainty surrounding the entire analysis. 

5.3.  Health Impact Functions

; and 4) the estimated change in the relevant ozone or PM summary
measures.

A typical health impact function might look like:  

described the ozone air quality inputs to the health impact functions. 
The following subsections describe the sources for each of the other
elements:  size of potentially affected populations; effect estimates;
and baseline incidence rates.

	a.  Potentially Affected Populations

The starting point for estimating the size of potentially affected
populations is the 2000 U.S. Census block level dataset (Geolytics
2002).  BenMAP incorporates 250 age/gender/race categories to match
specific populations potentially affected by ozone and other air
pollutants.  The software constructs specific populations matching the
populations in each epidemiological study by accessing the appropriate
age-specific populations from the overall population database.  BenMAP
projects populations to 2001 and 2002 using growth factors based on
economic projections (Woods and Poole Inc. 2001). 

	b.  Effect Estimate Sources

The most significant benefits of reducing ambient concentrations of
ozone and PM are attributable to reductions in health risks.  EPA’s
Ozone and PM Criteria Documents and the World Health Organization’s
2003 and 2004 reports outline numerous health effects known or suspected
to be linked to exposure to ambient ozone and PM (US EPA, 2006; US EPA,
2005; WHO, 2003; Anderson et al., 2004).  EPA recently evaluated the PM
literature for use in the benefits analysis for the 2006 PM NAAQS RIA. 
Because we use the same literature for the PM benefits analysis in this
RIA, we do not provide a detailed discussion of individual effect
estimates for PM in this section.  Instead, we refer the reader to the
2006 PM NAAQS RIA for details. 

More than one thousand new ozone health and welfare studies have been
published since EPA issued the 8-hour ozone standard in 1997.  Many of
these studies investigated the impact of ozone exposure on health
effects such as: changes in lung structure and biochemistry; lung
inflammation; asthma exacerbation and causation; respiratory
illness-related school absence; hospital and emergency room visits for
asthma and other respiratory causes; and premature death.  

We excluded some PM and ozone health effects from this analysis for four
reasons: (1) the possibility of double counting (such as hospital
admissions for specific respiratory diseases); (2) uncertainties in
applying effect relationships that are based on clinical studies to the
potentially affected population; (3) the lack of an established
concentration-response relationship; or 4) the inability to
appropriately value the effect (for example, changes in forced
expiratory volume) in economic terms.  Table 1 lists the health
endpoints included in the primary and sensitivity analyses for this
analysis.

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

A number of endpoints that are not health-related also may significantly
contribute to monetized benefits.  These include: increased outdoor
worker productivity; increased yields for commercial and non-commercial
crops; increased commercial forest productivity; reduced damage to urban
ornamental plants; increased recreational demand for undamaged forest
aesthetics; and reduced damage to ecosystem functions (U.S. EPA 1999,
2006).  While we include estimates of the value of increased outdoor
worker productivity, estimation of other welfare impacts is beyond the
scope of this analysis.

Premature Mortality Effects Estimates

concludes:  “This overall body of evidence is highly suggestive that
ozone directly or indirectly contributes to non-accidental and
cardiopulmonary-related mortality, but additional research is needed to
more fully establish underlying mechanisms by which such effects
occur” (pg. E-18).	 

.  The 2006 Criteria Document states that “the results from these
meta-analyses, as well as several single- and multiple-city studies,
indicate that co-pollutants generally do not appear to substantially
confound the association between ozone and mortality” (p. 7-103). 
However, CASAC raised questions about the implications of these
time-series results in a policy context.  Specifically, CASAC emphasized
that “…while the time-series study design is a powerful tool to
detect very small effects that could not be detected using other
designs, it is also a blunt tool” (Henderson, 2006: 3).  They point to
findings (e.g., Stieb et al., 2002, 2003) that indicated associations
between premature mortality and all of the criteria pollutants,
indicating that “findings of time-series studies do not seem to allow
us to confidently attribute observed effects to individual pollutants”
(id.).  They note that “not only is the interpretation of these
associations complicated by the fact that the day-to-day variation in
concentrations of these pollutants is, to a varying degree, determined
by meteorology, the pollutants are often part of a large and highly
correlated mix of pollutants, only a very few of which are measured”
(id.).  Even with these uncertainties, the CASAC Ozone Panel, in its
review of EPA’s Staff Paper, found “…premature total
non-accidental and cardiorespiratory mortality for inclusion in the
quantitative risk assessment to be appropriate.”

Consistent with the methodology used in the Ozone Risk Assessment, we
included ozone mortality in the primary health effects analysis, with
the recognition that the exact magnitude of the effects estimate is
subject to continuing uncertainty.  We used effect estimates from the
Bell et al. (2004) NMMAPS analysis, as well as effect estimates from the
three meta-analyses.  In addition, we include the possibility that there
is not a causal association between ozone and mortality, i.e., that the
effect estimate for premature mortality could be zero.  	

credible interval resulting from application of the effect estimate from
each study and present them separately to reflect differences in the
study designs and assumptions about causality.   However, it is
important to note that this procedure only captures the uncertainty in
the underlying epidemiological work, and does not capture other sources
of uncertainty, such as uncertainty in the estimation of changes in air
pollution exposure (Levy et al., 2000).

Respiratory Hospital Admissions Effect Estimates

Detailed hospital admission and discharge records provide data for an
extensive body of literature examining the relationship between hospital
admissions and air pollution. This is especially true for the portion of
the population aged 65 and older, because of the availability of
detailed Medicare records.  In addition, there is one study (Burnett et
al., 2001) providing an effect estimate for respiratory hospital
admissions in children under two.

Because the number of hospital admission studies is so large, we used
results from a number of studies to pool some hospital admission
endpoints.  To estimate total respiratory hospital admissions for adults
over 65 associated with changes in ambient ozone concentrations, we
first estimated the change in hospital admissions for the separate
effects categories each study provided for each city, including
Minneapolis, Detroit, Tacoma and New Haven.  To estimate total
respiratory hospital admissions for Detroit, we added the pneumonia and
COPD estimates, based on the effect estimates in the Schwartz study
(1994b).  Similarly, we summed the estimated hospital admissions based
on the effect estimates the Moolgavkar study reported for Minneapolis
(Moolgavkar et al., 1997).  To estimate total respiratory hospital
admissions for Minneapolis using the Schwartz study (1994a), we simply
estimated pneumonia hospital admissions based on the effect estimate. 
Making this assumption that pneumonia admissions represent the total
impact of ozone on hospital admissions in this city will give some
weight to the possibility that there is no relationship between ozone
and COPD, reflecting the equivocal evidence represented by the different
studies.  We then used a fixed-effects pooling procedure to combine the
two total respiratory hospital admission estimates for Minneapolis. 
Finally, we used random effects pooling to combine the results for
Minneapolis and Detroit with results from studies in Tacoma and New
Haven.  As noted above, this pooling approach incorporates both the
precision of the individual effect estimates and between-study
variability characterizing differences across study locations.

Asthma-Related Emergency Room Visits Effect Estimates

We used three studies as the source of the concentration-response
functions we used to estimate the effects of ozone exposure on
asthma-related emergency room (ER) visits:  Peel et al. (2005); Wilson
et al. (2005); and Jaffe et al. (2003).  We estimated the change in ER
visits using the effect estimate(s) from each study and then pooled the
results using the random effects pooling technique (see Appendix XX for
details).    The study by Jaffe et al. (2003) examined the relationship
between ER visits and air pollution for populations aged five to 34 in
the Ohio cities of Cleveland, Columbus and Cincinnati from 1991 through
1996.  In single-pollutant Poisson regression models, ozone was linked
to asthma visits.  We use the pooled estimate across all three cities as
reported in the study.  The Peel et al. study (2005) estimated
asthma-related ER visits for all ages in Atlanta, using air quality data
from 1993 to 2000.  Using Poisson generalized estimating equations, the
authors found a marginal association between the maximum daily 8-hour
average ozone level and ER visits for asthma over a 3-day moving average
(lags of 0, 1, and 2 days) in a single pollutant model.  Wilson et al.
(2005) examined the relationship between ER visits for respiratory
illnesses and asthma and air pollution for all people residing in
Portland, Maine from 1998-2000 and Manchester, New Hampshire from
1996-2000.  For all models used in the analysis, the authors restricted
the ozone data incorporated into the model to the months ozone levels
are usually measured, the spring-summer months (April through
September).  Using the generalized additive model, Wilson et al. (2005)
found a significant association between the maximum daily 8-hour average
ozone level and ER visits for asthma in Portland, but found no
significant association for Manchester.   Similar to the approach used
to generate effect estimates for hospital admissions, we used random
effects pooling to combine the results across the individual study
estimates for ER visits for asthma.  The Peel et al. (2005) and Wilson
et al. (2005) Manchester estimates were not significant at the 95
percent level, and thus, the confidence interval for the pooled
incidence estimate based on these studies includes negative values. 
This is an artifact of the statistical power of the studies, and the
negative values in the tails of the estimated effect distributions do
not represent improvements in health as ozone concentrations are
increased  Instead these should be viewed as a measure of uncertainty
due to limitations in the statistical power of the study.  Note that we
included both hospital admissions and ER visits as separate endpoints
associated with ozone exposure, because our estimates of hospital
admission costs do not include the costs of ER visits, and because most
asthma ER visits do not result in a hospital admission. 

Minor Restricted Activity Days Effects Estimate

Minor restricted activity days (MRADs) occur when individuals reduce
most usual daily activities and replace them with less-strenuous
activities or rest, but do not miss work or school.  We estimated the
effect of ozone exposure on MRADs using a concentration-response
function derived from Ostro and Rothschild (1989).   These researchers
estimated the impact of ozone and PM2.5 on MRAD incidence in a national
sample of the adult working population (ages 18 to 65) living in
metropolitan areas.  We developed separate coefficients for each year of
the Ostro and Rothschild analysis (1976-1981), which we then combined
for use in EPA’s analysis.  The effect estimate used in the impact
function is a weighted average of the coefficients in Ostro and
Rothschild (1989, Table 4), using the inverse of the variance as the
weight.

School Absences Effect Estimate

Children may be absent from school due to respiratory or other acute
diseases caused, or aggravated by, exposure to air pollution.  Several
studies have found a significant association between ozone levels and
school absence rates.  We use two studies (Gilliland et al., 2001; Chen
et al., 2000) to estimate changes in school absences resulting from
changes in ozone levels.  The Gilliland et al. study estimated the
incidence of new periods of absence, while the Chen et al. study
examined absence on a given day.  We converted the Gilliland et al.
estimate to days of absence by multiplying the absence periods by the
average duration of an absence.  We estimated 1.6 days as the average
duration of a school absence, the result of dividing the average daily
school absence rate from Chen et al. (2000) and Ransom and Pope (1992)
by the episodic absence rate from Gilliland et al. (2001).  Thus, each
Gilliland et al. period of absence is converted into 1.6 absence days.

Following recent advice from the National Research Council (2002), we
calculated reductions in school absences for the full population of
school age children, ages five to 17.  This is consistent with recent
peer-reviewed literature on estimating the impact of ozone exposure on
school absences (Hall et al. 2003).  We estimated the change in school
absences using both Chen et al. (2000) and Gilliland et al. (2001) and
then, similar to hospital admissions and ER visits, pooled the results
using the random effects pooling procedure.

c.  Baseline Incidence Rates

Epidemiological studies of the association between pollution levels and
adverse health effects generally provide a direct estimate of the
relationship of air quality changes to the relative risk of a health
effect, rather than estimating the absolute number of avoided cases. 
For example, a typical result might be that a 100 ppb decrease in daily
ozone levels might, in turn, decrease hospital admissions by 3 percent. 
The baseline incidence of the health effect is necessary to convert this
relative change into a number of cases.  A baseline incidence rate is
the estimate of the number of cases of the health effect per year in the
assessment location, as it corresponds to baseline pollutant levels in
that location.  To derive the total baseline incidence per year, this
rate must be multiplied by the corresponding population number.  For
example, if the baseline incidence rate is the number of cases per year
per 100,000 people, that number must be multiplied by the number of
100,000s in the population.

Table 2 summarizes the sources of baseline incidence rates and provides
average incidence rates for the endpoints included in the analysis.  For
both baseline incidence and prevalence data, we used age-specific rates
where available.  We applied concentration-response functions to
individual age groups and then summed over the relevant age range to
provide an estimate of total population benefits.  In most cases, we
used a single national incidence rate, due to a lack of more spatially
disaggregated data.  Whenever possible, the national rates used are
national averages, because these data are most applicable to a national
assessment of benefits.  For some studies, however, the only available
incidence information comes from the studies themselves; in these cases,
incidence in the study population is assumed to represent typical
incidence at the national level.  Regional incidence rates are available
for hospital admissions, and county-level data are available for
premature mortality.  

d. The Role of Tropospheric Ozone in UVB-Related Human Health Outcomes

[Placehoder]

 V.  Economic Values for Health Outcomes 

Reductions in ambient concentrations of air pollution generally lower
the risk of future adverse health effects for a large population. 
Therefore, the appropriate economic measure is willingness-to-pay (WTP)
for changes in risk of a health effect rather than WTP for a health
effect that would occur with certainty (Freeman, 1993).  Epidemiological
studies generally provide estimates of the relative risks of a
particular health effect that is avoided because of a reduction in air
pollution. We converted those to units of avoided statistical incidence
for ease of presentation. We calculated the value of avoided statistical
incidences by dividing individual WTP for a risk reduction by the
related observed change in risk.  For example, suppose a
pollution-reduction regulation is able to reduce the risk of premature
mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000).
If individual WTP for this risk reduction is $100, then the WTP for an
avoided statistical premature death is $1 million ($100/0.0001 change in
risk).

WTP estimates generally are not available for some health effects, such
as hospital admissions.  In these cases, we used the cost of treating or
mitigating the effect as a primary estimate.  These cost-of-illness
(COI) estimates generally understate the true value of reducing the risk
of a health effect, because they reflect the direct expenditures related
to treatment, but not the value of avoided pain and suffering
(Harrington and Portney, 1987; Berger, 1987).  We provide unit values
for health endpoints (along with information on the distribution of the
unit value) in Table 3.  All values are in constant year 2000 dollars,
adjusted for growth in real income out to 2020.  Economic theory argues
that WTP for most goods (such as environmental protection) will increase
if real income increases.  Many of the valuation studies used in this
analysis were conducted in the late 1980s and early 1990s.  Because real
income has grown since the studies were conducted, people’s
willingness to pay for reductions in the risk of premature death and
disease likely has grown as well.  We did not adjust cost of
illness-based values because they are based on current costs. 
Similarly, we did not adjust the value of school absences, because that
value is based on current wage rates.  Table 3 presents the values for
individual endpoints adjusted to year 2020 income levels.  The
discussion below provides additional details on ozone related endpoints.
 For details on valuation estimates for PM related endpoints, see the
2006 PM NAAQS RIA. 	

Mortality Valuation

To estimate the monetary benefit of reducing the risk of premature
death, we used the “value of statistical lives” saved (VSL)
approach, which is a summary measure for the value of small changes in
mortality risk for a large number of people.  The VSL approach applies
information from several published value-of-life studies to determine a
reasonable monetary value of preventing premature mortality.  The mean
value of avoiding one statistical death is estimated to be roughly $5.5
million at 1990 income levels (2000 $), and $6.6 million at 2020 income
levels.  This represents an intermediate value from a variety of
estimates in the economics literature (see the 2006 PM NAAQS RIA for
more details on the calculation of VSL). 

Hospital Admissions Valuation

In the absence of estimates of societal WTP to avoid hospital
visits/admissions for specific illnesses, estimates of total cost of
illness (total medical costs plus the value of lost productivity)
typically are used as conservative, or lower bound, estimates. These
estimates are biased downward, because they do not include the
willingness-to-pay value of avoiding pain and suffering.  

 “all items.”  The resulting estimate is $109.35.  The total
cost-of-illness estimate for an ICD code-specific hospital stay lasting
n days, then, was the mean hospital charge plus $109 n.  

Asthma-Related Emergency Room Visits Valuation

To value asthma emergency room visits, we used a simple average of two
estimates from the health economics literature.  The first estimate
comes from Smith et al. (1997), who reported approximately 1.2 million
asthma-related emergency room visits in 1987, at a total cost of $186.5
million (1987$).  The average cost per visit that year was $155; in
2000$, that cost was $311.55 (using the CPI-U for medical care to adjust
to 2000$).  The second estimate comes from Stanford et al. (1999), who
reported the cost of an average asthma-related emergency room visit at
$260.67, based on 1996-1997 data.  A simple average of the two estimates
yields a (rounded) unit value of $286.

Minor Restricted Activity Days Valuation 

 (1993) has derived an estimate of willingness to pay to avoid a minor
respiratory restricted activity day, using estimates from Tolley et al.
(1986) of WTP for avoiding a combination of coughing, throat congestion
and sinusitis.  The IEc estimate of WTP to avoid a minor respiratory
restricted activity day is $38.37 (1990$), or about $52 ($2000).

Although Ostro and Rothschild (1989) statistically linked ozone and
minor restricted activity days, it is likely that most MRADs associated
with ozone exposure are, in fact, minor respiratory restricted activity
days. For the purpose of valuing this health endpoint, we used the
estimate of mean WTP to avoid a minor respiratory restricted activity
day.

School Absences

To value a school absence, we:  (1) estimated the probability that if a
school child stays home from school, a parent will have to stay home
from work to care for the child; and (2) valued the lost productivity at
the parent’s wage.  To do this, we estimated the number of families
with school-age children in which both parents work, and we valued a
school-loss day as the probability that such a day also would result in
a work-loss day. We calculated this value by multiplying the proportion
of households with school-age children by a measure of lost wages.

We used this method in the absence of a preferable WTP method. However,
this approach is likely to understate the value of school-loss days in
three ways.  First, it omits willingness to pay to avoid the
symptoms/illness that resulted in the school absence; second, it
effectively gives zero value to school absences that do not result in
work-loss days; and third, it uses conservative assumptions about the
wages of the parent staying home with the child. 

For this valuation approach, we assumed that in a household with two
working parents, the female parent will stay home with a sick child. 
From the Statistical Abstract of the United States (U.S. Census Bureau,
2001), we obtained:  (1) the numbers of single, married and “other”
(widowed, divorced or separated) working women with children; and (2)
the rates of participation in the workforce of single, married and
“other” women with children.  From these two sets of statistics, we
calculated a weighted average participation rate of 72.85 percent.

Our estimate of daily lost wage (wages lost if a mother must stay at
home with a sick child) is based on the year 2000 median weekly wage
among women ages 25 and older (U.S. Census Bureau, 2001). This median
weekly wage is $551. Dividing by five gives an estimated median daily
wage of $103. To estimate the expected lost wages on a day when a mother
has to stay home with a school-age child, we first estimated the
probability that the mother is in the workforce then multiplied that
estimate by the daily wage she would lose by missing a work day: 72.85
percent times $103, for a total loss of $75.   This valuation approach
is similar to that used by Hall et al. (2003).

VII.  Results and Implications

Tables 5-1 through 5-5 summarize the reduction in incidence for ozone-
related health endpoints for each of the alternative ozone standards
evaluated and the PM-related health endpoints for the 0.070 ppm
standard.  Tables 5-6 through 5-11 summarize the economic benefits for
each of the alternative standards.     In addition to the mean incidence
estimates, we have included 5th and 95th percentile estimates, based on
the Monte Carlo simulations described above.  In each table, the total
change in incidence from fully attaining the alternative standards is
broken out into the change in incidence associated with the modeled
partial attainment scenario and the sum of the change in incidence
associated with achieving the partial attainment increment plus the
residual attainment increment.  As described in Chapter XX, to calculate
the change in ozone concentrations to reach full attainment, we rolled
back the ozone monitor data so that the 4th highest daily maximum 8-hour
average just met the level required to attain the alternative standard. 
This approach will likely understate the benefits that would occur due
to implementation of actual controls to reduce ozone precursor
emissions.  Any additional controls implemented to reduce ozone
concentrations at the highest monitor would likely result in some
reductions in ozone concentrations at all monitors within a
non-attainment area.  Therefore, air quality improvements and resulting
health benefits from full attainment would be more widespread than we
have estimated in our rollback analyses. 

In addition to disaggregating benefits between modeled and rollback for
the 0.070 ppm standard alternative, we also provide disaggregation by
region, with separate benefits estimates for the Eastern U.S.,
California, and the Western U.S. outside of California.  As discussed
further in section __, the estimates of incidence and valuation in
tables 5-3 and 5-8 through 5-10 reflect the PM2.5 benefits associated
with partial attainment of the 0.070 ppm alternative. To derive a
valuation estimate of the PM2.5 related benefits of full attainment of
each ozone standard alternative, we used a benefits scaling technique;
for this reason, we do not provide a comprehensive estimate of PM2.5
incidence and valuation.

Table 5-1: Illustrative Strategy to Attain 0.065 ppm: Estimated
Reductions in Mortality and Morbidity 

(Incremental to 0.084 ppm attainment) 

	Eastern U.S.	Western U.S. Excluding California	California	National
Total Full Attainment

Ozone-Related Health Endpoints 

Premature Mortalitya	Arithmetic Mean, 95% Credible Intervals in
Parenthesesb

  Bell (2004)	500

(160—710)	43

(15-72)	120

(41—200)	640

(220—1,100)

  Bell (2005)	2,000

(900—2,700)	180

(86—270)	500

(240—760)	2,600

(1,300—4,000)

  Levy (2005)	2,100

(1,500—2,600)	190

(130—250)	540

(370—700)	2,900

(2,000—3,800)

  Ito (2005)	2,100

(1,300—2,700)	190

(120—270)	540

(330—750)	2,800

(1,700—3,900)

  Assumption that association is not causal	0	0	0	0

Morbidity Endpoints	Arithmetic Mean, 95% Confidence Intervals in
Parentheses

Hospital Admissions (0-1)	2,700

(1,200—4,300)	330

(150—520)	1,100

(460—1,600)	4,100

(1,800—6,400)

Hospital Admissions 

(65-99)	3,900

(180—9,800)	320

(16—790)	800

(40--2,000)	5,000

(240—13,000)

Emergency Department Visits, Asthma-Related	2,500

(-680—7,700)	130

(-35—400)	370

(-100—1,100)	3,000

(-810—9,200)

School Absences	1,200,000

(290,000—3,000,000)	120,000

(30,000—310,000)	410,000

(98,000—1,000,00)	1,700,000

(420,000—4,300,000)

Minor Restricted Activity Days	3,200,000

(1,300,000—5,000,000)	310,000

(130,000—490,000)	990,000

(410,000—1,600,000)	4,500,000

(1,900,000—7,000,000)

Table 5-2: Illustrative Scenario to Attain 0.070 ppm: Estimated
Reductions in Ozone Mortality and Morbidity 

	Eastern U.S.	Western U.S. Excluding California	California	National
Total Full Attainment

 	Modeled Partial Attainment	Full Attainment	Modeled Partial Attainment
Full Attainment	Glidepath Attainment	Full Attainment

	Ozone-Related Health Endpoints 	 	 	 	 	 

Premature Mortalitya	Arithmetic Mean, 95% Credible Intervals in
Parenthesesb

Bell et. al (2004)	130 

(45--220)	260 

(88--440)	0.23 

(0.075--0.37)	11 

(3.8--19)	5.5 

(1.8--9.1)	79 

(27--130)	350 

(120--590)

Bell et. al (2005)	540 

(260--820)	1,100 

(510--1,600)	0.86 

(0.42--1.3)	47 

(23--71)	22 

(11--34)	320 

(160--490)	1,400 

(690--2,200)

Levy et. al (2005)	780 

(540--1,000)	1,300 

(900--1,700)	31 

(22--41)	73 

(50--95)	20 

(14--27)	350 

(240--460)	1,700 

(1,200--2,300)

Ito et al. (2005)	590 

(360--820)	1,200 

(700--1,600)	0.98 

(0.6--1.4)	50 

(30--70)	24 

(15--34)	350 

(210--480)	1,600 

(940--2,200)

  Assumption that association is not causal	0	0	0	0	0	0	0

Morbidity Endpoints

         Arithmetic Mean, 95% Confidence Intervals in Parentheses

Hospital Admissions  (ages 0-1)	960 

(410--1,500)	1,700 

(720--2,600)	53 

(23--83)	130 

(55--200)	33 

(14--51)	710 

(310--1,100)	2,500 

(1,100--3,900)

Hospital Admissions 

(ages 65-99)	1,100 

(52—2,800)	2,100 

(100--5,400)	3.8 

(0.17--9.4)	86 

(4.2--210)	37 

(1.8--92)	520 

(26--1,300)	2,700 

(130--6,900)

Emergency Department Visits for Asthma	830 

(-230--2,500)	1,500 

(-400--4,300)	21 

(-5.8--66)	50 

(-13--150)	13 

(-3.5--37)	250 

(-66--750)	1,800 

(-480--5,200)

School Absences	410,000 

(100,000--1,000,000)	720,000 

(170,000--1,800,000)	20,000 

(4,900--53,000)	47,000 

(11,000--120,000)	13,000 

(3,100--33,000)	270,000 

(66,000--680,000)	1,000,000 

(250,000--2,600,000)

Minor Restricted Activity Days	1,100,000 

(460,000--1,800,000)	1,900,000 

(790,000--3,000,000)	49,000 

(20,000--78,000)	120,000 

(49,000--190,000)	34,000 

(14,000--53,000)	660,000 

(280,000--1,100,000)	2,700,000 

(1,100,000--4,300,000)

Table 5-3: Illustrative Scenario to Attain 0.070 ppm: Estimated
Reductions in PM Mortality (95th percentile confidence intervals
provided in parentheses)

	Eastern U.S.	Western U.S. Excluding California	California	National PM
Benefits

PM-Related Health Endpoints	 	 	 

Mortality Impact Functions Derived from Epidemiology Literature

	ACS Studya	510

(200--810)	0.17

(0.07--0.3)	47

(18--75)	550

(220--890)

Harvard Six-City Studyb 	1,100

(620--1,700)	0.4

(0.2--0.6)	110

(57--150)	1,300

(680--1,800)

Woodruff et al 1997 (infant mortality)	1.1

(0.5--1.7)	0.04

(0.02--0.06)	0.14

(0.07--0.2)	1.3

(0.6--1.2)

Mortality Impact Functions Derived from Expert Elicitation

Expert A	1,600

(290--2,800)	77

(14--140)	140

(26--260)	1,800

(330--3,200)

Expert B	1,200

(170--2,600)	56

(4.6--120)	110

(16--230)	1,400

(190--2,900)

Expert C	1,200

(210--2,600)	58

(10--130)	110

(20--240)	1,400

(240--2,900)

Expert D	820

(170--1,400)	40

(9--66)	76

(16--130)	940

(200--1,600)

Expert E	2,000

(980--3,000)	95

(48--150)	180

(90--270)	2,200

(1,100--3,400)

Expert F	1,100

(760--1,600)	51

(34--73)	100

(70--140)	1, 200

(860--1,800)

Expert G	690

(0--1,300)	34

(0--63)	63

(0--120)	790

(0--1,500)

Expert H	880

(3--2,000)	43

(0.2--99)	80

(0.3--190)	1,000

(3.7--2,300)

Expert I	1,200

(190--2,100)	57

(9--100)	110

(17--190)	1,300

(210--2,300)

Expert J	950

(280--2,100)	46

(14--103)	87

(26--190)	1,100

(320--2,400)

Expert K	190

(0--880)	9

(0--43)	19

(0--87)	220

(0--1,000)

Expert L	860

(150--1,600)	33

(0.05--79)	79

(14--150)	970

(170--1,800)

Table 5-4: Illustrative Scenario to Attain 0.070 ppm: Estimated
Reductions in PM Morbidity (95th percentile confidence intervals
provided in parentheses)

	Eastern U.S.	Western U.S. Excluding California	California	National PM
Benefits

PM-Related Health Endpoints	 	 	 

Morbidity Impact Functions Derived from Epidemiology Literature

	Chronic Bronchitis (age >25 and over)	380

(70-690)	12

(2.1—21)	43

(8—77)	440

(80—790)

Nonfatal myocardial infarction (age >17)	1,100

(610—1,600)	0.4

(0.2—0.5)	94

(50—140)	1,200

(660—1,800)

Hospital admissions--respiratory (all ages)	130

(65—200)	--	10

(5-15)	140

(70—210)

Hospital admissions-- cardiovascular 

(age >17)	270

(170—360)	--	20

(12—27)	290

(180—390)

Emergency room visits for asthma 

(age <19)	560

(330—800)	--	22

(13—31)	590

(340—830)

Acute bronchitis (age 8-12)	990

(-34—2,000)	32

(-1.1—64)	130

(-4.4—260)	1,200

(-39—2,300)

Lower respiratory symptoms (age 7-14)	8,400

(4,000—13,000)	3.6

(1.7—5.5)	1,200

(580—1,800)	9,600

(4,600—15,000)

Upper respiratory symptoms (asthmatic children age 9-18)	6,100

(1,900—10,000)	2.7

(0.83—4.4)	870

(270—1,500)	7,000

(2,200—12,000)

Asthma exacerbation (asthmatic children age 6--18)	7,700

(840—22,000)	3.4

(0.4—9.6)	1,100

(120—3,100)	8,700

(950—25,000)

Work loss days (age 18-65)	53,000

(47,000—60,000)	20

(18—23)	7,200

(6,200—8,100)	61,000

(53,000—68,000)

Minor restricted activity days (age 18-65)	320,000

(270,000—370,000)	120

(100—140)	42,000

(35,000—48,000)	360,000

(300,000—420,000)

 	 	 	 	 

Table 5-5: Illustrative Strategy to Attain 0.075 ppm: Estimated
Reductions in Mortality and Morbidity (Incremental to 0.084 ppm
attainment)

	Eastern U.S.	Western U.S. Excluding California	California	National
Total Full Attainment

Ozone-Related Health Endpoints 

Premature Mortality

	  Bell (2004)	190	9	54	250

  Bell (2005)	840	40	220	1,100

  Levy (2005)	1,100	70	240	1,400

  Ito (2005)	920	43	240	1,200

  Assumption that association is not causal	0	0	0	0

Morbidity Endpoints

	Hospital Admissions (0-1)	1,300	110	490	1,900

Hospital Admissions 

(65-99)	1,700	76	350	2,100

Emergency Department Visits, Asthma-Related	1,200	44	170	1,400

School Absences	570,000	42,000	190,000	800,000

Minor Restricted Activity Days	1,500,000	110,000	450,000	2,100,000

Table 5-6: Illustrative Strategy to Attain 0.065 ppm: Estimated
Valuation of Mortality and Morbidity  (Millions 1999$)

	Eastern U.S.	Western U.S. Excluding California	California	National
Total Full Attainment

Ozone-Related Health Endpoints 

Premature Mortalitya	Arithmetic Mean, 95% Credible Intervals in
Parenthesesb

  Bell (2004)	$3,100

($800--$6,200)	$280

($70--$560)	$790

($200--$1,600)	$4,100

($1,000--$8,400)

  Bell (2005)	$12,000

($3,600--$24,000)	$1,100

($330--$2,200)	$3,200

($930--$6,200)	$17,000

($4,900--$32,000)

  Levy (2005)	$14,000

(4,400--$25,000)	$1,200

($390--$2,200)	$3,400

($1,100--$6,200)	$18,000

($5,900--$33,000)

  Ito (2005)	$13,000

($4,200--$25,000)	$1,200

($380--$2,300)	$3,500

($1,100--$6,400)	$18,000

($5,600--$33,000)

  Assumption that association is not causal	0	0	0	0

Morbidity Endpoints	         Arithmetic Mean, 95% Confidence Intervals
in Parentheses

Hospital Admissions (0-1)	$20

($12--$32)	$2.5

($1.5--$3.9)	$7.9

($4.6--$12)	$31

($18--$48)

Hospital Admissions 

(65-99)	$68

($22--$170)	$5.6

($1.9--$14)	$14

($4.9--$35)	$88

($29--$220)

Emergency Department Visits, Asthma-Related	$0.7

($0.5--$1.9)	$0. 04

($0.003--$0.1)	$0.1

($0.007--$0.3)	$0.8

($0.06--$2.3)

School Absences	$87

($36--$210)	$8.9

($3.7--$22)	$29

($12--$73)	$130

($52--$310)

Worker Productivity	$38	$3.9	$35	$76

Minor Restricted Activity Days	$79

($7.8--$180)	$7.6

($0.8--$17)	$25

($2.4--$55)	$110

($10--$240)

Table 5-7: Illustrative Strategy to Attain 0.070 ppm: Estimated
Monetary Value of Reductions in Risk of Ozone-Related Premature
Mortality (millions of 1999$)

	Eastern U.S.	Western U.S. Excluding California	California	National
Total Full Attainment

 	Modeled Partial Attainment	Full Attainment	Modeled Partial Attainment
Full Attainment	Glidepath Attainment	Full Attainment

	Ozone-Related Health Endpoints 	 	 	 	 	 

	Premature Mortalitya	Arithmetic Mean, 95% Credible Intervals in
Parenthesesb

  Bell (2004)	$850

($220—$1,700)	$1,700

($420--$3,400)	$1.4

($0.36--$2.9)	$73

($18--$150)	$35

($8.8--$71)	$510

($130--$1,000)	$2,300

($570--$4,600)

  Bell (2005)	$3,500

($1,000--$6,700)	$6,800

($2,000--$13,000)	$5.5

($1.6--$11)	$300

($88--$580)	$140

($41--$270)	$2,100

($600--$4,000)	$9,100

($2,700--$18,000)

  Levy (2005)	$5,000

($1,300--$9,300)	$8,300

($2,200--$16,000)	$200

($53--$380)	$470

($120--$870)	$130

($35--$250)	$2,300

($600--$4,200)	$11,000

($2,900--$21,000)

  Ito (2005)	$3,800

($1,200--$7,000)	$7,400

($2,400--$14,000)	$6.3

($1.9--$12)	$320

($99--$590)	$150

($48--$280)	$2,200

($690--$4,100)	$9,900

($3,100--$18,000)

Assumption     that association is not causal	0	0	0	0	0	0	0

Morbidity Endpoints	         Arithmetic Mean, 95% Confidence Intervals
in Parentheses

Hospital Admissions 

(ages 0-1)	$7.1

($4.1--$11)	$12

($7.2--$19)	$0.39

($0.2--$0.6)	$1

($0.56--$1.5)	$0.24

($0.14--$0.38)	$5.3

($3.1--$8.3)	$19

($11-$29)

Hospital Admissions 

(ages 65-99)	$19

($6.3--$49)	$38

($12--$95)	$0.7

($0.0002--$0.002)	$1.5

($0.5--$3.8)	$0.65

($0.23--$1.6)	$9.2

($3.2--$23)	$48

($16--$120)

Emergency Department Visits for Asthma	$0.23

($0.017--$0.62)	$0.4

($0.03--$1.1)	--	--	--	$0.067

($0.005--$0.19)	$0.48

($0.035--$1.3)

School Absences	$30

($13-$72)	$52

($22--$130)	$1.5

($0.6--$3.8)	$3.4

($1.4--$8.4)	$0.93

($0.39--$2.4)	$20

($8.3--$49)	$75

($32--$180)

Worker Productivity	$15	$22	$0.38	$1.4	$1.9	$22	$46

Minor Restricted Activity Days	$27

($2.7--$62)	$47

($4.7--$110)	$1.2

($0.12--$2.8)	$2.9

($0.3--$6.6)	$0.83

($0.08--$1.9)	$16

($1.6--$37)	$63

($6.2--$140)

Table 5-8: Illustrative Strategy to Attain 0.070 ppm: Estimated
Monetary Value of Reductions in Risk of PM2.5-Related Premature
Mortality (3 percent discout rate, in millions of 1999$) 90th Percentile
Confidence Intervals Provided in Parentheses

	Eastern U.S.	Western U.S. Excluding California	California	National PM
Benefits

PM-Related Health Endpoints	 	 	 

Mortality Impact Functions Derived from Epidemiology Literature

	ACS Studya	$2,900

($730--$6,100)	$1

($0.24--$2)	$270

($67--$560)	$3,200

(800--$6,600)

Harvard Six-City Studyb	$6,600

($1,900--$13,000)	$2.1

($0.6--$4.2)	$610

($180--$1,200)	$7,200

($2,000--$15,000)

Woodruff et al 1997 (infant mortality)	$6

($1.7--$13)	$0.2

($0.06--$0.4)	$0.8

($0.2--$1.6)	$7.3

($2--$15)

Mortality Impact Functions Derived from Expert Elicitation

Expert A	$9,100

($1,400--$21,000)	$440

($66--$1,000)	$830

($120--$1,900)	$10,000

($1.5--$24)

Expert B	$7,000

($780--$19,000)	$320

($21--$940)	$630

($71--$1,800)	$7,900

($880--$22,000)

Expert C	$6,800

($1,000--$18,000)	$340

($50--$890)	$630

($94--$1,700)	$7,800

($1,200--$21,000)

Expert D	$4,800

($820--$10,000)	$230

($40--$500)	$440

($75--$950)	$5,400

($930--$12,000)

Expert E	$11,000

($3,200--$23,000)	$600

($160--$1,100)	$1,000

($290--$2,100)	$13,000

($3,600--$26,000)

Expert F	$6,200

($2,000--$12,000)	$290

($92--$580)	$570

(200--$1,300)	$7,100

($2,300--$14,000)

Expert G	$4,000

(0--$10,000)	$200

(0--$480)	$370

(0--$910)	$4,600

(0--$11,000)

Expert H	$5,100

($17--$14,000)	$250

(0.9--$690)	$470

($1.6--$1,300)	$5,800

($20--$16,000)

Expert I	$6,800

($930--$15,000)	$330

($46--$760)	$620

($86--$1,400)	$7,700

($1,100--$18,000)

Expert J	$5,500

($1,100--$15,000)	$270

($57--$720)	$500

($110--$1,400)	$6,200

($1,300--$17,000)

Expert K	$1,100

(0--$5,500)	$51

(0--$270)	$110

(0--$560)	$1,300

(0--$6,300)

Expert L	$5,000

($710--$12,000)	$190

($0.3--$580)	$460

($64--$1,100)	$5,700

($780--$14,000)

Table 5-9: Illustrative Strategy to Attain 0.070 ppm: Estimated Monetary
Value of Reductions in Risk of PM2.5-Related Premature Mortality (7
percent discount rate, in millions of 1999$) 90th Percentile Confidence
Intervals Provided in Parentheses

	Eastern U.S.	Western U.S. Excluding California	California	National PM
Benefits

PM-Related Health Endpoints	 	 	 

Mortality Impact Functions Derived from Epidemiology Literature

	ACS Studya	$2,500

($620--$5,100)	$0.8

(0.2--$1.7)	$230

($57--$470)	$2,700

($670--$5,600)

Harvard Six-City Studyb	$5,600

($1,600--$11,000)	$1.8

($0.5--$3.6)	$510

($150--$990)	$6,100

($1,800--$12,000)

Woodruff et al 1997 (infant mortality)	$5.3

($1.5--$11)	$0.2

($0.05--$0.35)	$0.7

($0.2--$1.3)	$6.2

($1,7--$12,000)

Mortality Impact Functions Derived from Expert Elicitation

Expert A	$7,700

($1.1--$17,000)	$370

($56--$850)	$700

($110--$1,600)	$8,700

($1,300--$20,000)

Expert B	$5,800

($660--$16,000)	$270

($17--$790)	$530

($60--$1,500)	$6,600

($740--$19,000)

Expert C	$5,800

($860--$15,000)	$280

($42--$750)	$530

($79--$1,400)	$6,600

($980--$17,400)

Expert D	$4,000

($690--$8,700)	$200 

($34--$420)	$370

($63--$800)	$4,600

($780--$10,000)

Expert E	$9,500

($2,700--$19,000)	$470

($130--$930)	$870

($250--$1,700)	$11,000

($3,100--$22,000)

Expert F	$5,300

($1,700--$10,000)	$250

($77--$480)	$480

($160--$950)	$6,000

($2,000--$12,000)

Expert G	$3,400

(0--$8,300)	$160

(0--$410)	$310

(0--$760)	$3,800

(0--$9,500)

Expert H	$4,300

($15--$12,000)	$210

($0.7--$580)	$390

($1.4--$1,100)	$4,900

($17--$14,000)

Expert I	$5,900

($780--$13,000)	$290

($38--$640)	$540

($72--$1,200)	$6,700

($890--$15,000)

Expert J	$4,600

($970--$12,000)	$230

($48--$610)	$420

($90--$1,100)	$5,300

($1,100--$14,000)

Expert K	$920

(0--$4,700)	$43

(0--$220)	$93

(0--$460)	$1,100

(0--$5,300)

Expert L	$4,200

($600--$10,000)	$160

($0.21--$480)	$380

($54--$940)	$4,700

($660--$12,000)

Table 5-10: Illustrative Strategy to Attain 0.070 ppm: Estimated
Monetary Value of Reductions in Risk of PM2.5-Related Morbidity
Reductions (in millions of 1999$) 90th Percentile Confidence Intervals
Provided in Parentheses

	Eastern U.S.	Western U.S. Excluding California	California	National PM
Benefits

PM-Related Health Endpoints	 	 	 

Morbidity Impact Functions Derived from Epidemiology Literature

	Chronic Bronchitis (age >25 and over)	$160

($13--$540)	$4.7

($0.4--$17)	

$17

($1.5--$60)	$180

($15--$620)

Nonfatal myocardial infarction (age >17)

3% discount rate	$92

($32--$200)	$0.03

($0.01--$0.7)	$8

($2.9--$17)	$100

($35--$220)

Nonfatal myocardial infarction (age >17)

7% discount rate	$90

($30--$200)	$0.3

($0.1--$0.7)	$7.7

($2.6--$17)	$100

($33--$220)

Hospital admissions--respiratory (all ages)	$2.1

($1--$3.1)	---	$0.2

($0.08--$0.2)	$2.3

($1.1--$3.4)

Hospital admissions-- cardiovascular (age >17)	$5.5

($3.4--$7.5)	---	$0.4

($0.3--$0.57)	$5.9

($3.7--$8.1)

Emergency room visits for asthma (age <19)	$0.2

($0.09--$0.2)	---	$0.006

($0.003--$0.01)	$0.2

($0.09--$0.2)

Acute bronchitis (age 8-12)	$0.4

($-0.01--$0.9)	---	$0.05

($-0.002--$0.1)	$0.4

($-0.01--$1.1)

Lower respiratory symptoms (age 7-14)	$0.1

($0.05--$0.25)	---	$0.02

($0.01--$0.04)	$0.15

($0.06--$0.3)

Upper respiratory symptoms (asthmatic children age 9-18)	$0.2

($0.05—0.4)	---	$0.022

($0.007--$0.05)	$0.2

($0.05--$0.4)

Asthma exacerbation (asthmatic children age 6--18)	$0.34

($0.04--$1)	---	$0.05

($0.005--$0.2)	$0.4

($0.04--$1.2)

Work loss days (age 18-65)	$5.3

($4.6--$6)	$0.002

($0.002--$0.002)	$0.9

($0.7--$1)	$7.4

($6.4--$8.3)

Minor restricted activity days (age 18-65)	$7.9

($0.9--$16)	---	$1

($0.1--$2)	$8.9

($1--$18)

 	 	 	 	 

Table 5-11: Illustrative Strategy to Attain 0.075 ppm: Estimated
Valuation of Mortality and Morbidity (Incremental to 0.084 ppm
attainment) 

	Eastern U.S.	Western U.S. Excluding California	California	National
Total Full Attainment

Ozone-Related Health Endpoints 

Premature Mortality

	  Bell (2004)	$1,400	$66	$350	$1,800

  Bell (2005)	$5,400	$270	$1,400	$7,100

  Levy (2005)	$6,700	$430	$1,500	$8,700

  Ito (2005)	$5,900	$290	$1,500	$7,800

  Assumption that association is not causal	0	0	0	0

Morbidity Endpoints

	Hospital Admissions (0-1)	$9.9	$0.9	$3.6	$14

Hospital Admissions 

(65-99)	$31	$1.4	$6.2	$38

Emergency Department Visits, Asthma-Related	$0.3	$0.013	$0.05	$0.4

School Absences	$41	$3	$13	$58

Worker Productivity	$20	$1.3	$14	$35

Minor Restricted Activity Days	$38	$2.6	$11	$49

Estimated reductions in ozone mortality incidence provided in Tables
5-1, 5-2 and 5-5 represent the number of premature deaths due to ozone
exposure avoided in 2020 based on the warm season results of a recent
ozone-mortality NMMAPS analysis of 95 U.S. communities (Bell et al.,
2004) and three meta-analyses of the available published literature on
ozone-mortality effects (Bell et al., 2005; Ito et al., 2005; Levy et
al., 2005).  Tables 5-6, 5-7 and 5-11 also include the possibility that
there is not a causal association between ozone and mortality, i.e.,
that the estimate for premature mortality avoided could be zero.  Model
uncertainty, including whether or not the relationship is assumed to be
causal, is a key source of uncertainty.  Although multiple estimates are
presented in Tables 5-6, 5-7 and 5-11, no attempt was made to quantify
the likelihood of a causal relationship between short-term ozone
exposure and increased mortality or to weigh the results of the various
models.  

The estimate of central tendency for premature mortality is expressed as
the arithmetic mean, with the assumption of a normal distribution, and
represents the central estimate of the number of premature deaths
avoided in association with the proposed standard based on each study. 
Statistical uncertainty associated with the model estimate for each
study is characterized by the 90% credible interval around the mean
estimate (i.e., 5th and 95th percent interval).  Of the four available
studies, the NMMAPS study by Bell et al. (2004) is considered to be the
most representative for evaluating potential mortality-related benefits
associated with the proposed standard due to its extensive coverage
(examination of 95 large communities across the United States over an
extended period of time, from 1987 to 2000) and its specific focus on
the ozone-mortality relationship.  Annual estimates of lives saved from
this study are lower than those from the three meta-analyses, possibly
due to more stringent adjustment for meteorological factors (Ito et al.,
2005; Ostro et al., 2006), publication bias in the meta-analyses (Bell
et al., 2005; Ito et al., 2005) or other factors.  Clearly, the
ozone-mortality reduction estimates are conditional on a causal
relationship.  

The Ozone Criteria Document (U.S. EPA, 2006) and Staff Paper (U.S. EPA,
2007) concluded that the overall body of evidence is highly suggestive
that (short-term exposure to) ozone directly or indirectly contributes
to non-accidental cardiopulmonary-related mortality.  However, various
sources of uncertainty remain, including the possibility that there is
no causal relationship between ozone and mortality (i.e., zero effect). 
For instance, because results of time-series studies implicate all of
the criteria air pollutants, and those who would be expected to be
potentially more susceptible to ozone exposure are likely to have lower
exposure to ozone due to the amount of time that they spend indoors,
CASAC stated that it seems unlikely that the observed associations
between short-term ozone concentrations and daily mortality are due
solely to ozone itself (i.e., ozone may be serving as a marker for other
agents that are contributing to the short-term exposure effects on
mortality).  Even so, CASAC concluded that the evidence was strong
enough to support a quantitative risk assessment of the relationship
between short-term exposure to ozone and premature mortality as part of
the Staff Paper.  EPA has asked the National Academy of Sciences for
their advice on how best to quantify the uncertainty about the
relationship between ambient ozone exposure and premature mortality
within the context of quantifying projected benefits of alternative
control strategies.

Using the NMMAPS study that was used as the basis for the risk analysis
presented in our Staff Paper, we estimate 350 avoided premature deaths
annually in 2020 from reducing ozone levels to meet the proposed
standard, which, when added to the other projected benefits, leads to an
estimated total benefit of $2.5 billion/yr. Using three studies that
synthesize data across a large number of individual studies, we estimate
between 1,400 and 1,700 avoided premature deaths annually in 2020,
leading to total monetized benefits of between $9.4 and $11 billion/yr.
Alternatively, if there is no causal relationship between ozone and
mortality, avoided premature deaths would be zero.  For a less stringent
standard of 75 ppb, using the NMMAPS ozone mortality study resulted in
190 premature deaths avoided and total monetized benefits of $1,400
billion/yr.  Using the three synthesis studies, estimated premature
deaths avoided for the less stringent standard are between 840 and
1,100, with total monetized ozone benefits between $5,400 and $6,700
billion/yr.  For a more stringent standard of 65 ppb, using the NMMAPS
ozone mortality study resulted in 500 premature deaths avoided and total
monetized benefits of $3.1 billion/yr.  Using the three synthesis
studies, estimated premature deaths avoided for the more stringent
standard are between 2,000 and 2,100, with total monetized ozone
benefits between $12 and $14 billion/yr.  Including premature mortality
in our estimates had the largest impact on the overall magnitude of
benefits:  Premature mortality benefits account for more than 95 percent
of the total benefits we can monetize.  We note that these estimates
reflect EPA's interim approach to characterizing the benefits of
reducing premature mortality associated with ozone exposure.   EPA has
requested advice from the NAS on how best to quantify uncertainty in the
relationship between ozone exposure and premature mortality in the
context of quantifying benefits associated with alternative ozone
control strategies.

The results of our analysis suggest that moving from current monitored
ozone levels to full attainment of the 8-hour standard may yield
substantial health benefits.  We estimate total benefits (including
premature mortality) of meeting the 0.084 ppm standard as reaching up to
$5.7 billion (averaged over the three years, 2000-2002).  These dollar
benefits are associated with average reductions in health effects
including more than 800 avoided premature deaths, more than 4,000
avoided hospital admissions, about 500 avoided asthma emergency room
visits per year, over one million avoided restricted activity days, and
more than 900,000 avoided school absences. 

  SEQ CHAPTER \h \r 1 We provide sensitivity analyses to examine key
modeling assumptions.  In addition, there are other uncertainties that
we could not quantify, such as the importance of unquantified effects
and uncertainties in the interpolation of ambient air quality.  Inherent
in any analysis of health impacts are uncertainties in the size of
potentially affected populations, health baselines, incomes, effect
estimates and other factors.  The assumptions used to capture these
elements are reasonable based on the available evidence.  However, these
data limitations prevent a full scale quantitative estimate of the
uncertainty associated with estimates of total economic benefits.  If
one is mindful of these limitations, the magnitude of the benefit
estimates presented here can be useful information in expanding the
understanding of the potential public health impacts of attaining
alternative 8-hour ozone standards.

PM2.5 Benefits Resulting from Full Attainment of 0.070 ppm incremental
to 0.084 ppm

The summary of PM2.5 related benefits in the preceding tables were
estimated to result from the partial attainment of 0.070 ppm incremental
to a baseline of 0.084 ppm partial attainment. Of greater analytical
value would be an estimate of the PM2.5 benefits associated with
attaining 0.070 ppm incremental to 0.084 ppm. Estimating these PM2.5
benefits necessitated the creation of a new PM2.5 baseline that
established the PM2.5 air quality associated with full attainment of
0.084 ppm. To create such a baseline, EPA used previously-created PM2.5
benefit per-ton estimates that provide the total monetized human health
benefits (mortality and morbidity) of reducing one ton of PM2.5 from a
specified source. EPA has used such benefit per-ton estimates in
previous Regulatory Impact Analyses. These estimates are based on the
sum of the valuation of the Pope (2002) estimates of mortality (3%
discount rate, 1999$) and valuation of the morbidity incidence. 

Creating a new PM2.5 baseline that incorporated the full attainment of
0.084 entailed the following steps:

Estimate the number of tons of NOx necessary to attain a baseline of
0.084 ppm. Chapter___ described the method used to estimating the
emissions reductions necessary to attain a baseline of 0.084. 

Multiply this emission reduction estimate by the relevant benefit
per-ton estimate. EPA estimated the total valuation benefits per ton of
NOx abated on a national scale. In chapter __, EPA also estimated the
number of NOx tons necessary to attain 0.084 ppm fully. To estimate the
benefits of attaining 0.084 ppm incremental to partial attainment of
0.084, the benefit per ton is simply multiplied by the total number of
extrapolated NOx tons abated.

Calculate an adjusted baseline. Subtract the benefits of attaining 0.084
ppm fully incremental to the partial attainment of 0.084 to create a new
baseline.

Calculate the PM2.5 benefits of attaining 0.070 ppm. Multiplying the the
estimate of the extrapolated tons necessary to attain 0.070 ppm found in
chapter __ produces an estimate of the incremental benefits of attaining
0.070 ppm incremental to partial attainment of 0.070 ppm. 

 below summarizes the calculation steps necessary to estimate the PM2.5
accruing from full attainment of 0.070 ppm incremental to 0.084 ppm.
Consistent with the benefit per-ton estimates, the valuation estimate
below reflects the use of the Pope (2002) mortality estimate at a 3
percent discount rate in 1999$. 

Table 5-12: Estimated PM2.5 Benefits Associated with Full Attainment of
0.070 ppm incremental to 0.084 ppm

Calculation Step	Valuation of PM2.5 Benefits 

(Billions 1999$)

	Benefits of attaining 0.084 from a baseline of 0.084 partial
attainment:	$1.5B

Benefits of attaining 0.084 partially and 0.070 partially:	$3.2B

	Benefits of attaining 0.070 partially, incremental to attainment of
0.084	                                      = $3.2B - $1.5B

                                      =$1.2 B

	Benefits of attaining 0.070 incremental to partial attainment of 0.070
$4.5B

	Benefits of attaining 0.070 incremetnal to attainment of 0.084	        
                             =$1.2B + 4.5B

                                      =$6.2B

	

The full attainment PM2.5 benefits do not include confidence intervals.
Because this full attainment estimate was derived by summing the modeled
PM2.5 benefits and the benefits derived using the benefit per-ton
metrics—and these benefit per ton metrics do not include confidence
intervals—the resulting sum of total PM2.5 benefits do not include
confidence intervals.

Estimate of Full Attainment Benefits 

Table 5-13 below summarizes the full attainment benefit estimate for
each of the standard alternatives. Note that only the 0.070 ppm benefit
estimate includes PM2.5 benefits. Also note that the combined ozone and
PM2.5 benefits for the attainment of the 0.070 standard do not include
confidence intervals. As discussed in the preceding section, it was not
possible to derive confidence intervals for the adjusted PM2.5 full
attainment benefits.

Table 5-13: Estimate of Total Benefits (95% Confidence Intervals,
Millions of $1999) for Each Standard Alternative and Ozone Mortality
Function

Standard Alternative and 

Ozone Mortality Estimate 	Ozone Benefits	PM2.5 Benefits	Total Benefits

0.065

Bell (2004)	$4,500

($1,200--$9,300)	TBA	TBA

	Bell (2005)	$17,000

($5,100--$33,000)	TBA	TBA

	Ito (2005)	$19,000

($5,800--$34,000)	TBA	TBA

	Levy (2005)	$19,000

($6,000--$34,000)	TBA	TBA

	No Causality	$430

($190--$900)	TBA	TBA

0.070

Bell (2004)	$2,500

($680--$5,100)	$6,200	$8,800

	Bell (2005)	$9,400

($2,800--$18,000)	$6,200	$16,000

	Ito (2005)	$10,000

($3,200--$19,000)	$6,200	$16,000

	Levy (2005)	$11,000

($3,000--$21,000)	$6,200	$18,000

	No Causality	$260

($110--$530)	$6,200	$6,500

0.075

Bell (2004)	$2,000	TBA	TBA

	Bell (2005)	$7,300	TBA	TBA

	Ito (2005)	$7,900	TBA	TBA

	Levy (2005)	$8,900	TBA	TBA

	No Causality	$200	TBA	TBA

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EUR/03/5042688.  SEQ CHAPTER \h \r 1 Table 2.  Ozone and PM Related
Health Endpoints Included in Benefits Analysis

Endpoint	Pollutant	Study	Study Population

Premature Mortality

Premature mortality – daily time series, non-accidental	O3 (24-hour
avg)

O3 (24-hour avg)

O3 (1-hour max)

O3 (1-hour max)	Bell et al (2004) (NMMAPS study)

Meta-analyses:

Bell et al (2005)

Ito et al (2005)

Levy et al (2005)	All ages

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

Laden et al. (2006)	>29 years

>25 years

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

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

Chronic Illness

Chronic bronchitis	PM2.5 (annual avg)	Abbey et al. (1995  XE "Abbey et
al. (1995"  )	>26 years

Nonfatal heart attacks	PM2.5 (24-hour avg)	Peters et al. (2001  XE
"Peters et al. (2001"  )	Adults (>18 years)

Hospital Admissions 

Respiratory	

O3 (24-hour avg)	Pooled estimate:

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

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

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

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

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

Burnett et al. (2001)	<2 years

	PM2.5 (24-hour avg)	Pooled estimate:

Moolgavkar (2003  XE "Moolgavkar (2003"  )—ICD 490-496 (COPD)

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

	PM2.5 (24-hour avg)	Moolgavkar (2000  XE "Moolgavkar (2000"  )—ICD
490-496 (COPD)	20–64 years

	PM2.5 (24-hour avg)	Ito (2003  XE "Ito (2003"  )—ICD 480-486
(pneumonia)	>64 years

	PM2.5 (24-hour avg)	Sheppard (2003  XE "Sheppard (2003"  )—ICD 493
(asthma)	<65 years

Cardiovascular	PM2.5 (24-hour avg)	Pooled estimate:

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

Ito (2003  XE "Ito (2003"  )—ICD 410-414, 427-428 (ischemic heart
disease, dysrhythmia, heart failure)	>64 years

	PM2.5 (24-hour avg)	Moolgavkar (2000  XE "Moolgavkar (2000"  )—ICD
390-429 (all cardiovascular)	20–64 years

Asthma-related ER visits	O3 (8-hour max)	Pooled estimate:

Jaffe et al (2003)

Peel et al (2005)

Wilson et al (2005)	

5–34 years

All ages

All ages

	PM2.5 (24-hour avg)	Norris et al. (1999  XE "Norris et al. (1999"  )
0–18 years

Other Health Endpoints

Acute bronchitis	PM2.5 (annual avg)	Dockery et al. (1996  XE "Dockery et
al. (1996"  )	8–12 years

Upper respiratory symptoms	PM10 (24-hour avg)	Pope et al. (1991  XE
"Pope et al. (1991"  )	Asthmatics, 9–11 years

Lower respiratory symptoms	PM2.5 (24-hour avg)	Schwartz and Neas (2000 
XE "Schwartz and Neas (2000"  )	7–14 years

Asthma exacerbations	PM2.5 (24-hour avg)	Pooled estimate:

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

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

Work loss days	PM2.5 (24-hour avg)	Ostro (1987  XE "Ostro (1987"  )
18–65 years

School absence days	

O3 (8-hour avg)

O3 (1-hour max)	Pooled estimate:

Gilliland et al. (2001)

Chen et al. (2000)	

5–17 yearsb

MRADs	O3 (24-hour avg)	Ostro and Rothschild (1989  XE "Ostro and
Rothschild (1989"  )	18–65 years

	PM2.5 (24-hour avg)	Ostro and Rothschild (1989  XE "Ostro and
Rothschild (1989"  )	18–65 years

a	The original study populations were 8 to 13 for the Ostro et al. (2001
 XE "Ostro et al. (2001"  ) study and 6 to 13 for the Vedal et al. (1998
 XE "Vedal et al. (1998"  ) study.  Based on advice from the SAB-HES, we
extended the applied population to 6 to 18, reflecting the common
biological basis for the effect in children in the broader age group.

b 	Gilliland et al. (2001) studied children aged 9 and 10.  Chen et al.
(2000) studied children 6 to 11.  Based on recent advice from the
National Research Council and the EPA Science Advisory Board Health
Effects Subcommittee, we have calculated reductions in school absences
for all school-aged children based on the biological similarity between
children aged 5 to 17.

Table 3.  National Average Baseline Incidence Rates

Endpoint	Source	Notes	Rate per 100 people per year 4 by Age Group

	<18	18-24	25-34	35-44	45-54	55-64	65+

Mortality	CDC Compressed Mortality File, accessed through CDC Wonder
(1996-1998)	non-accidental	0.025	0.022	0.057	0.150	0.383	1.006	4.937

Respiratory Hospital Admissions. 	1999 NHDS public use data files2
incidence	0.043	0.084	0.206	0.678	1.926	4.389	11.629

Asthma ER visits	2000 NHAMCS public use data files3; 1999 NHDS public
use data files2	incidence	1.011	1.087	0.751	0.438	0.352	0.425	0.232

Minor Restricted Activity Days (MRADs)	Ostro and Rothschild  
MACROBUTTON endnote+.cit (1989, p. 243) 	incidence	–	780	780	780	780
780	–

School Loss Days	National Center for Education Statistics (1996) and

1996 HIS (Adams et al., 1999, Table 47); estimate of 180 school days per
year	all-cause	990.0	–	–	–	–	–	–

a The following abbreviations are used to describe the national surveys
conducted by the National Center for Health Statistics: HIS refers to
the National Health Interview Survey; NHDS - National Hospital Discharge
Survey; NHAMCS - National Hospital Ambulatory Medical Care Survey.

b See   HYPERLINK
ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/
ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/ 

c See   HYPERLINK
ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/
ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/  

d All of the rates reported here are population-weighted incidence rates
per 100 people per year.  Additional details on the incidence and
prevalence rates, as well as the sources for these rates are available
upon request.Table 4.  Unit Values for Economic Valuation of Health
Endpoints (2000$)

Health Endpoint	Central Estimate of Value Per Statistical Incidence

1990 Income Level	2020 Income Level	Derivation of Distributions of
Estimates

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	The WTP to avoid a case of
pollution-related CB is calculated as 

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

(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 Distributions of
Estimates

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	No distributional information available.  Age-specific
cost-of-illness values reflect lost earnings and direct medical costs
over a 5-year period following a nonfatal MI.  Lost earnings estimates
are based on Cropper and Krupnick (1990  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)

Hospital Admissions

Chronic Obstructive Pulmonary Disease (COPD)

	$12,378	$12,378	No distributional information available. The COI
estimates (lost earnings plus direct medical costs) are based on ICD-9
code-level information (e.g., average hospital care costs, average
length of hospital stay, and weighted share of total COPD category
illnesses) reported in Agency for Healthcare Research and Quality (2000 
XE "Agency for Healthcare Research and Quality (2000"  ) (www.ahrq.gov).

Asthma Admissions	$6,634	$6,634	No distributional information available.
The COI estimates (lost earnings plus direct medical costs) are based on
ICD-9 code-level information (e.g., average hospital care costs, average
length of hospital stay, and weighted share of total asthma category
illnesses) reported in Agency for Healthcare Research and Quality (2000)
(www.ahrq.gov). 

All Cardiovascular

	$18,387	$18,387	No distributional information available. The COI
estimates (lost earnings plus direct medical costs) are based on ICD-9
code-level information (e.g., average hospital care costs, average
length of hospital stay, and weighted share of total cardiovascular
category illnesses) reported in Agency for Healthcare Research and
Quality (2000) (www.ahrq.gov). 

All respiratory (ages 65+)	$18,353	$18,353	No distributions available.
The COI point estimates (lost earnings plus direct medical costs) are
based on ICD-9 code level information (e.g., average hospital care
costs, average length of hospital stay, and weighted share of total COPD
category illnesses) reported in Agency for Healthcare Research and
Quality, 2000 (www.ahrq.gov).

(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 Distributions of
Estimates

All respiratory (ages 0-2)	$7,741   	$7,741   	No distributions
available. The COI point estimates (lost earnings plus direct medical
costs) are based on ICD-9 code level information (e.g., average hospital
care costs, average length of hospital stay, and weighted share of total
COPD category illnesses) reported in Agency for Healthcare Research and
Quality, 2000 (www.ahrq.gov).

Emergency Room Visits for Asthma	$286	$286	No distributional information
available.  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) to avoid
each symptom in the cluster and assuming additivity of WTPs.  In the
absence of information surrounding the frequency with which each of the
seven types of URS occurs within the URS symptom complex, we assumed a
uniform distribution between $10 and $45.

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) to avoid
each symptom in the cluster and assuming additivity of WTPs.  The dollar
value for LRS is the average of the dollar values for the 11 different
types of LRS.  In the absence of information surrounding the frequency
with which each of the 11 types of LRS occurs within the LRS symptom
complex, we assumed a uniform distribution between $8 and $25.

Asthma Exacerbations	$42	$45	Asthma exacerbations are valued at $45 per
incidence, based on the mean of average WTP estimates for the four
severity definitions of a “bad asthma day,” described in   XE "Rowe
and Chestnut (1986"  Rowe and Chestnut (1986).  This study surveyed
asthmatics to estimate WTP for avoidance of a “bad asthma day,” as
defined by the subjects.  For purposes of valuation, an asthma
exacerbation is assumed to be equivalent to a day in which asthma is
moderate or worse as reported in the   XE "Rowe and Chestnut (1986" 
Rowe and Chestnut (1986) study.  The value is assumed have a uniform
distribution between $17 and $73.

(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 Distributions of
Estimates

Acute Bronchitis	$360	$380	Assumes a 6-day episode, with the
distribution of the daily value specified as uniform with the low and
high values based on those recommended for related respiratory symptoms
in   XE "Neumann et al. (1994"  Neumann et al. (1994).  The low daily
estimate of $10 is the sum of the mid-range values recommended by   XE
"IEc, 1994"  IEc   MACROBUTTON endnote+.cit (1994)  for two symptoms
believed to be associated with acute bronchitis:  coughing and chest
tightness.  The high daily estimate was taken to be twice the value of a
minor respiratory restricted-activity day, or $110.  

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

No distribution available.  Point estimate is based on county-specific
median annual wages divided by 50 (assuming 2 weeks of vacation) and
then by 5—to get median daily wage. U.S. Year 2000 Census, compiled by
Geolytics, Inc.

Minor Restricted Activity Days (MRADs)	$51	$54	Median WTP estimate to
avoid one MRAD from Tolley et al. (1986).  Distribution is assumed to be
triangular with a minimum of $22 and a maximum of $83, with a most
likely value of $55.  Range is based on assumption that value should
exceed WTP for a single mild symptom (the highest estimate for a single
symptom—for eye irritation—is $16.00) and be less than that for a
WLD.  The triangular distribution acknowledges that the actual value is
likely to be closer to the point estimate than either extreme.

School Absence Days	$75	$75	No distribution available

Abbreviations:

AHRQ: Agency for Healthcare Research and Quality 

AQS:  Air Quality System

BenMAP:  Environmental Benefits Mapping and Analysis Program

CAPMS: Criteria Air Pollutant Modeling System

CDC: Centers for Disease Control

CDC WONDER: Centers for Disease Control Wide-Ranging Online Data for
Epidemiological Research

CI: Confidence interval

COI: Cost of illness

COPD: Chronic obstructive pulmonary disease

CPI-U: Consumer price index – urban

EPA: Environmental Protection Agency

ER: Emergency room  

HIS: National Health Interview Survey

ICD: International Classification of Disease

MRAD: Minor restricted activity days

NCHS: National Center for Health Statistics 

NHDS: National Hospital Discharge Survey 

NHAMCS: National Hospital Ambulatory Medical Care Survey

NMMAPS:  National Morbidity, Mortality and Air Pollution Study

NOx:  Nitrogen oxides

PM10: Particulate matter less than or equal to 10 microns

PM2.5: Particulate matter less than or equal to 2.5 microns

ppb: parts per billion

POC: Parameter occurrence code

RIA: Regulatory impact analysis

SO2: Sulfur dioxide

U.S.:  United States

VOC:  Volatile organic compounds

VNA: Voronoi neighbor averaging

VSL: Value of statistical life

WHO: World Health Organization

WTP: Willingness to pay

 U.S. EPA.  2006.  Regulatory Impact Analysis, 2006 National Ambient Air
Quality Standards for Particle Pollution.  Available at    HYPERLINK
"http://www.epa.gov/ttn/ecas/ria.html" 
http://www.epa.gov/ttn/ecas/ria.html  .

Hubbell, B., A. Hallberg, D.R. McCubbin, and E. Post. 2005.
Health-Related Benefits of Attaining the 8-Hr Ozone Standard. 
Environmental Health Perspectives 113:73–82.

U.S. EPA. 2000.  Guidelines for Preparing Economic Analyses.   
HYPERLINK
"http://yosemite1.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/
Guidelines.pdf" 
http://yosemite1.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/G
uidelines.pdf  

 The one exception relates to the use of updated health impact functions
for emergency department visits. These new functions are detailed
further in this chapter.

 Industrial Economics, Inc.  2006.  Expanded Expert Judgment Assessment
of the Concentration-Response Relationship Between PM2.5 Exposure and
Mortality.  Prepared for EPA Office of Air Quality Planning and
Standards, September.  Available at:    HYPERLINK
"http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf" 
http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf  

 Add note here about the potential magnitude of the agricultural
benefits based on the staff paper analysis.

 A credible interval is a posterior probability interval used in
Bayesian statistics, which is similar to a confidence interval used in
frequentist statistics.

 Citations: refinery RIA, industrial boilers RIA

 A credible interval is a posterior probability interval used in
Bayesian statistics, which is similar to a confidence interval used in
frequentist statistics.

 Clean Air Scientific Advisory Committee's Peer Review of the Agency's
2nd Draft Ozone Staff Paper, October 24, 2006. EPA-CASAC-07-001.
Available at   HYPERLINK "http://www.epa.gov/sab/pdf/casac-07-001.pdf" 
http://www.epa.gov/sab/pdf/casac-07-001.pdf  

 National Academy of Sciences (2007) Project Scope.  Estimating
Mortality Risk Reduction Benefits from Decreasing Tropospheric Ozone
Exposure.  Division on Earth and Life Studies, Board on Environmental
Studies and Toxicology.  Available at   HYPERLINK
"http://www8.nationalacademies.org/cp/projectview.aspx?key=48768" 
http://www8.nationalacademies.org/cp/projectview.aspx?key=48768  

 See: Industrial Boilers RIA, Refinery RIA [provide citations]

A Does not represent equal weighting among models or between assumption
of causality vs. no causality. See note on page __  

B With the exception of the assumption of no causal relationship, the
arithmetic mean and 95% credible interval around the mean estimates of
the number of lives saved are based on an assumption of a normal
distribution.  A credible interval is a posterior probability interval
used in Bayesian statistics, which is similar to a confidence interval
used in frequentist statistics.

A Does not represent equal weighting among models or between assumption
of causality vs. no causality. See note on page __  

B With the exception of the assumption of no causal relationship, the
arithmetic mean and 95% credible interval around the mean estimates of
the number of lives saved are based on an assumption of a normal
distribution.  A credible interval is a posterior probability interval
used in Bayesian statistics, which is similar to a confidence interval
used in frequentist statistics.

A The estimate is based on the concentration-response (C-R) function
developed from the study of the American Cancer Society cohort reported
in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs

B Based on Laden et al (2006) reporting of the extended Six-cities
study; to be reviewed by the EPA-SAB for advice on the appropriate
method for incorporating what has previously been a sensitivity
estimate.

A Does not represent equal weighting among models or between assumption
of causality vs. no causality. See note on page __  

B With the exception of the assumption of no causal relationship, the
arithmetic mean and 95% credible interval around the mean estimates of
the number of lives saved are based on an assumption of a normal
distribution.  A credible interval is a posterior probability interval
used in Bayesian statistics, which is similar to a confidence interval
used in frequentist statistics.

A Does not represent equal weighting among models or between assumption
of causality vs. no causality. See note on page __  

B With the exception of the assumption of no causal relationship, the
arithmetic mean and 95% credible interval around the mean estimates of
the number of lives saved are based on an assumption of a normal
distribution.  A credible interval is a posterior probability interval
used in Bayesian statistics, which is similar to a confidence interval
used in frequentist statistics.

A Does not represent equal weighting among models or between assumption
of causality vs. no causality. See note on page __  

B With the exception of the assumption of no causal relationship, the
arithmetic mean and 95% credible interval around the mean estimates of
the number of lives saved are based on an assumption of a normal
distribution.  A credible interval is a posterior probability interval
used in Bayesian statistics, which is similar to a confidence interval
used in frequentist statistics.

b  The estimate is based on the concentration-response (C-R) function
developed from the study of the American Cancer Society cohort reported
in Pope et al (2002), which has previously been reported as the primary
estimate in recent RIAs

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distribution.  A credible interval is a posterior probability interval
used in Bayesian
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Does this clause apply only to Bell et al. 2004 or to all of these
studies?

Are we estimating the change in the estimated credible interval, or are
we estimating a change in mortality incidence and the credible interval
associated with that change?  If the latter, omit “estimated” here
and substitute “the”

???

Write out?

Will include additional tables summarizing PM2.5 co-benefits estimation
for 0.075 and 0.065.

Based on projected emissions and air quality modeling, in 2020, 203
counties in the U.S. with ozone monitors are estimated to fail to meet
an ozone standard of 0.070 ppm for the 4th highest maximum 8-hour ozone
concentration.   This number falls to 82 for an alternative standard of
0.075 ppm, and increases to 360 for an alternative standard of 0.065
ppm.  We estimated the health benefits of attaining these alternative
ozone standards across the U.S. using the EPA Environmental Benefits
Modeling and Analysis Program (BenMAP).  We performed a two-stage
analysis.  In the first stage we estimated the benefits associated with
changes in modeled air quality following application of control
technologies known to be currently available.  These control strategies
were sufficient to bring some, but not all, areas into attainment with
the various standard levels.  Thus, the benefits computed during this
first stage were for partial attainment in some areas.  In the second
stage, we estimated the benefits of fully attaining the standards in all
areas by using a “rollback” methodology to reduce ozone
concentrations at residually nonattaining monitors to a level that would
just meet the standards. To calculate the monetary value of the adverse
health outcomes avoided due to these improvements in air quality, we
used health impact functions based on published epidemiological studies,
and valuation functions derived from the economics literature.  Key
health endpoints included premature mortality, hospital and emergency
room visits, school absences, and minor restricted activity days.

There is considerable variability in the magnitude of the O3-related
mortality association reported in the scientific literature, which we
reflect by summarizing the primary estimates from four different studies
below.  We also note that there are uncertainties within each study that
are not fully captured by this range of estimates.  Recognizing that
additional research is needed to more fully establish underlying
mechanisms by which such effects occur, we also consider the possibility
that the observed associations between ozone and mortality may not be
causal in nature.  Using the NMMAPS study that was used as the primary
basis for the risk analysis presented in our Staff Paper and reviewed by
CASAC, we estimated 350 avoided premature deaths annually in 2020 from
reducing ozone levels to meet a standard of 0.070 ppm, which, when added
to the other projected benefits from reduced ozone, including  7,000
hospital and emergency room admissions, 1,000,000 school absences, and
over 2,700,000 minor restricted activity days, leads to an estimated
total ozone-related benefit of $2.5 billion/yr (1999$).  Using three
studies that synthesize data across a large number of individual
studies, we estimate between 1,400 and 1,700 avoided premature deaths
annually in 2020 from reducing ozone to 0.070 ppm, leading to total
monetized ozone-related benefits of between $9.4 and $11 billion/yr. 
Alternatively, if there is no causal relationship between ozone and
mortality, avoided premature deaths associated with reduced ozone
exposure would be zero and total monetized ozone-related morbidity
benefits would be $250 million/yr.  For a less stringent standard of
0.075 ppm, using the NMMAPS ozone mortality study resulted in 250
premature deaths avoided and total monetized benefits of $2 billion/yr. 
Using the three synthesis studies, estimated premature deaths avoided
for the less stringent standard are between 1,100 and 1,400, with total
monetized ozone benefits between $7.3 and $8.9 billion/yr.
Alternatively, if there is no causal relationship between ozone and
mortality, avoided premature deaths associated with reduced ozone
exposure would be zero and total monetized ozone-related morbidity
benefits would be $200 million/yr.  For a more stringent standard of
0.065 ppm, using the NMMAPS ozone mortality study resulted in 640
premature deaths avoided and total monetized benefits of $4.5
billion/yr.  Using the three synthesis studies, estimated premature
deaths avoided for the more stringent standard are between 2,600 and
2,900, with total monetized ozone benefits between $17 and $19
billion/yr.  Alternatively, if there is no causal relationship between
ozone and mortality, avoided premature deaths associated with reduced
ozone exposure would be zero and total monetized ozone-related morbidity
benefits would be $430 million/yr. These estimates reflect EPA's interim
approach to characterizing the benefits of reducing premature mortality
associated with ozone exposure.   EPA has requested advice from the NAS
on how best to quantify uncertainty in the relationship between ozone
exposure and premature mortality in the context of quantifying benefits
associated with alternative ozone control strategies. 

In addition to the direct benefits from reduced ozone concentrations,
attainment of the standards would likely result in health and welfare
benefits from the reduction of PM2.5 that would occur as ozone precursor
emissions (NOx and VOC) are reduced.  Using both modeled and
extrapolated reductions in these precursor emissions, we estimated
PM-related co-benefits for the three alternative standards.  For each
alternative standard, we provide a range of estimated benefits based on
several different PM mortality effect estimates.  These effect estimates
were derived from two different sources:  the published epidemiology
literature and an expert elicitation study conducted by EPA in 2006. 
For the partial attainment of the 0.070 ppm standard, we estimated PM
co-benefits including between 220 and 2,200 premature deaths avoided,
with total monetized PM co-benefits of between $1.3 and 13,000
billion/yr (3% discount rate).  For the full attainment of the 0.070 ppm
alternative, incremental to attainment of the 0.084 ppm standard, we
estimate total ozone and PM2.5-related benefits to be ___.

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