)

 5.1.  Introduction

We applied a two-stage approach to estimate the benefits of fully
attaining each alternative standard.  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 (see Chapter XX for details on these control technologies and
the results of the air quality modeling).  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 (see Chapter XX for details on this methodology).   We
conducted analyses to examine the sensitivity of our results to a number
of different assumptions about the choice of health effects and effect
estimates from published epidemiological studies, as well as parameters
that affect the economic valuation of health effects.  A quantitative
assessment of non-health benefits, e.g. benefits from reduced
ozone-related crop damage, was outside of the scope of this analysis.

For this assessment, we estimated benefits of changes in ozone and PM
co-benefits 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 analysis of PM co-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).

add a copy of the discussion regarding the baseline

	That ozone benefits are incremental to …… (regs in full
implementation or whatever is the correct stmt)

that the PM co-benefits are incremental to assumptions of full
attainment of the 2006 PM2.5 NAAQS

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 (CASAC) 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.  Characterizing Uncertainty:  Moving Toward a 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’s Office of Air and Radiation (OAR) is
has initiated the developingment of a comprehensive strategymethodology
for characterizing the aggregate impact of uncertainty in key modeling
elements on both health incidence and benefits estimates.  Components of
that process include, emissions modeling, air quality modeling, health
effects incidence estimation, and valuation.

, are employed here.  First, we use Monte Carlo methods for estimating
characterizing random sampling error associated with the concentration
response functions from epidemiological studies and economic valuation
functions.  In the current analysis, EPA continues to move forward on
one of the key recommendations of the NRC – moving the assessment of
uncertainties from ancillary analyses into the main benefits
presentation through the conduct of probabilistic analyses.  In recent
this RIAs for its air rules,  EPA addressed key sources of uncertainty
in the benefits associated with reductions in PM and ozone
concentrations  by using a variety of techniques, including Monte Carlo
methods to characterize uncertainty in the concentration-response (C-R)
and economic valuation functions.

In this analysis we use the same techniques used in 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.

Second, we use a recently completed expert elicitation  of the
concentration response function describing the relationship between
premature mortality and ambient PM2.5 concentration.  We note that
incorporating only the uncertainty from random sampling error omits
important sources of uncertainty (e.g., in the functional form of the
model – e.g., whether or not a threshold may exist).  Use of the
expert elicitation and incorporation of the standard errors approaches
provide insights into the likelihood of different outcomes and about the
state of knowledge regarding the benefits estimates.  Both approaches
have different strengths and weaknesses, which are fully described in
Chapter 5 of the 2006 PM NAAQS RIA.

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 characterize therecognize additional uncertainty introduced
by in the inability of existing empirical studiesmodels to discern
whether the confirm the existence of a causal relationship between ozone
and pre-mortality is causal by providing an effect estimate
preconditions on an assumption that the effect estimate for pre-mature
mortality from ozone is zero..

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 results of the responses of an
expert elicitation studypanel.  Even these multiple characterizations,
including confidence intervals, omit the contribution to overall
uncertainty of uncertainty in air quality changes, baseline incidence
rates, populations exposed and transferability of the effect estimate to
diverse locations. Furthermore, the approach presented here does not yet
include methods for addressing correlation between input parameters and
the identification of reasonable upper and lower bounds for input
distributions characterizing uncertainty in additional model elements.
As a result, the reported confidence intervals and range of estimates
give an incomplete picture 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

Health impact functions measure the change in a health endpoint of
interest, such as hospital admissions, for a given change in ambient
ozone or PM concentration.  Health impact functions are derived from
primary epidemiology studies, meta-analyses of multiple epidemiology
studies, or expert elicitations (see IEc, 2006).  A standard health
impact function has four components: 1) an effect estimate from a
particular study; 2) a baseline incidence rate for the health effect
(obtained from either the epidemiology study or a source of public
health statistics such as the Centers for Disease Control); 3) the size
of the potentially affected population; and 4) the estimated change in
the relevant ozone or PM summary measures.

A typical health impact function might look like:

where y0 is the baseline incidence (the product of the , equal to the
baseline incidence rate and the times the potentially affected
population),  is the effect estimate, and x is the estimated
change in the summary ozone measure.  There are other functional forms,
but the basic elements remain the same.  Chapter __ described the ozone
and PM 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).  Benefits Modeling and Analysis Program (BenMAP) incorporates 250
age/gender/race categories to match specific populations potentially
affected by ozone and other air pollutants.  The software constructs
specific populations matching the populations in each epidemiological
study by accessing the appropriate age-specific populations from the
overall population database.  BenMAP projects populations to 2020 using
growth factors based on economic projections (Woods and Poole Inc.
2001).

	b.  Effect Estimate Sources

 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 were not able to separately quantify all of the excluded some PM and
ozone health effects that have been reported in the ozone and PM
criteria documents in 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 5-1 lists the health
endpoints included in the primary and sensitivity analyses for this
analysis.

  SEQ CHAPTER \h \r 1 Table 5-1.  Ozone and PM Related Health Endpoints
Included in Benefits Analysis, basis for the concentration-response
function associated with that endpoint, and  sub-populations for which
benefits were computed

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)

	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)

	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)

from the EPA Science Advisory Board Health Effects Subcommittee
(SAB-HES), we extended the applied population to 6 to 18, reflecting the
common biological basis for the effect in children in the broader age
group.

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 SubcommitteeSAB-HES, we have calculated reductions in school
absences for all school-aged children based on the biological similarity
between children aged 5 to 17.

Premature Mortality Effects Estimates

While particulate matter is the criteriaair pollutant most clearly
associated with premature mortality, recent research suggests that
repeated short-term ozone exposure likely contributes to premature
death.  The 2006 Ozone Criteria Document states:  “Consistent with
observed ozone-related increases in respiratory- and
cardiovascular-related morbidity, several newer multi-city studies,
single-city studies, and several meta-analyses of these studies have
provided relatively strong epidemiologic evidence for associations
between short-term ozone exposure and all-cause mortality, even after
adjustment for the influence of season and PM” (EPA, 2006: E-17).  The
epidemiologic data are also supported by newly available experimental
data from both animal and human studies which provide evidence
suggestive of plausible pathways by which risk of respiratory or
cardiovascular morbidity and mortality could be increased by ambient
ozone.  With respect to short-term exposure, the ozone Criteria Document
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).

 as well as recent meta-analyses by Bell et al. (2005), Ito et al.
(2005), and Levy et al. (2005), and a new analysis of the National
Morbidity, Mortality, and Air Pollution Study (NMMAPS) data set by Bell
et al. (2004), which specifically sought to disentangle the roles of
ozone, PM, weather-related variables, and seasonality.  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.”

, 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.

We estimate the change in mortality incidence and estimated 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.

.  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

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

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 5-2 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.

Table 5-2.  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

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.

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

[Placeholder]

 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).

.  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.

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

No studies are reported to have estimated WTP to avoid a minor
restricted activity day.  However one of EPA’s contractors,  IEc
(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.

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).

Table 5-3.  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 mean of the distribution is consistent with the mean estimate from a
third meta-analysis (Kochi et al 2006). 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

; 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).

(continued)

Table 5-3:  Unit Values Used for Economic Valuation of Health Endpoints
(1999$) (continued)

	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:

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).

Table 5-3:  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

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 5-3:  Unit Values Used for Economic Valuation of Health Endpoints
(1999$) (continued)

.  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

VII.  Results and Implications

 .  Any additional controls implemented to reduce ozone concentrations
at the highest monitor would likely result in some reductions in ozone
concentrations at attaining all monitors down-wind (i.e, the controls
would lead to concentrations below the standard in down-wind
locations)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.

Table 5-4: Illustrative Strategy to Attain 0.065 ppm: Estimated
Reductions in Premature Mortality associated with ozone exposure.

(Incremental to 0.084 ppm attainment)

Model or Assumptiona	Reference	Eastern U.S.	Western U.S. Excluding
California	California	National Total Full Attainment

Arithmetic Meanb

(95% Credible Intervals)c

NMMAPS	  Bell et al. 2004	500

(160—710)	43

(15-72)	120

(41—200)	640

(220—1,100)

Meta-Analysis	  Bell et al. 2005	2,000

(900—2,700)	180

(86—270)	500

(240—760)	2,600

(1,300—4,000)

	  Levy et al. 2005	2,100

(1,500—2,600)	190

(130—250)	540

(370—700)	2,900

(2,000—3,800)

	  Ito et al. 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

A Does not represent equal weighting among models or between assumption
of causality vs. no causality (see text on page__).

 B With the exception of the assumption of no causal relationship, the
arithmetic mean and 90% credible interval around the mean estimates of
the annual number    of lives saved are based on an assumption of a
normal distribution.

C 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 negative 5th percentile incidence estimates for this health
endpoint are a result of the weak statistical power of the study and
should not be inferred to indicate that decreased ozone exposure may
cause an increase in asthma-related emergency department visits.

Table 5-5: Illustrative Strategy to Attain 0.065 ppm: Estimated
Reductions in Premature Morbidity associated with ozone exposure

(Incremental to 0.084 ppm attainment, 95% Confidence Intervals in
Parentheses)

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

Hospital Admissions (ages 0-1)	2,700

(1,200—4,300)	330

(150—520)	1,100

(460—1,600)	4,100

(1,800—6,400)

Hospital Admissions (ages 65-99)	3,900

(180—9,800)	320

(16—790)	800

(40--2,000)	5,000

(240—13,000)

Emergency Department Visits, Asthma-Relateda	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 5-6: Illustrative Strategy to Attain 0.070 ppm: Estimated
Reductions in Premature Mortality associated with ozone exposure

(Incremental to 0.084 ppm attainment)

Model or Assumptiona	Reference	Eastern U.S.	Western U.S. Excluding
California	California	National Total Full Attainment

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

	Arithmetic Meanb

(95% Credible Intervals)c

NMMAPS	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)

Meta-Analysis	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

A Does not represent equal weighting among models or between assumption
of causality vs. no causality (see text on page__).

 B With the exception of the assumption of no causal relationship, the
arithmetic mean and 90% credible interval around the mean estimates of
the annual number    of lives saved are based on an assumption of a
normal distribution.

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

D Certain California projected non-attainment counties are required to
meet an ozone target above the actual standard  (that is, a
“glidepath”) by 2020 due to the severity of non-attainment. The
estimates in this column reflect the benefits of meeting this target.
See chapter __ for a discussion of how glidepath targets were
calculated.

A The negative 5th percentile incidence estimates for this health
endpoint are a result of the weak statistical power of the study and
should not be inferred to indicate that decreased ozone exposure may
cause an increase in asthma-related emergency department visits..

B Certain California projected non-attainment counties are required to
meet an ozone target above the actual standard (that is, a
“glidepath”) by 2020 due to the severity of non-attainment. The
estimates in this column reflect the benefits of meeting this target.
See chapter __ for a discussion of how glidepath targets were
calculated.

Table 5-7: Illustrative Strategy to Attain 0.070 ppm: Estimated
Reductions in Morbidity associated with ozone exposure

(Incremental to 0.084 ppm attainment, 95% Confidence Intervals in
Parentheses)

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

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

	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, Asthma-Relateda	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 5-8: Illustrative Scenario to Attain 0.070 ppm: Estimated
Reductions in Premature  PM Mortality associated with PM co-benefit

(95th percentile confidence intervals provided in parentheses)

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

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

Table 5-9: Illustrative Scenario to Attain 0.070 ppm: Estimated
Reductions in PM Morbidity Associated with PM Co-benefit

(95th percentile confidence intervals provided in parentheses)

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

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 5-10: Illustrative Strategy to Attain 0.075 ppm: Estimated
Reductions in Premature Mortality Ozone exposure

(Incremental to 0.084 ppm attainment)

Model or Assumptiona	Reference	Eastern U.S.	Western U.S. Excluding
California	California	National Total Full Attainment

Arithmetic Meanb

(95% Credible Intervals)c

NMMAPS	  Bell et al. 2004	190	9	54	250

Meta-Analysis	  Bell et al. 2005	840	40	220	1,100

	  Levy et al. 2005	1,100	70	240	1,400

	  Ito et al. 2005	920	43	240	1,200

Assumption that association

is not causal	0	0	0	0

A Does not represent equal weighting among models or between assumption
of causality vs. no causality (see text on page__).

 B With the exception of the assumption of no causal relationship, the
arithmetic mean and 90% credible interval around the mean estimates of
the annual number    of lives saved are based on an assumption of a
normal distribution.

C A credible interval is a posterior probability interval used in
Bayesian statistics, which is similar to a confidence interval used in
frequentist statistics. Credible intervals not provided due to the fact
that the incidence estimates were derived through an interpolation
technique (see chapter__) that precluded us from generating such
estimates.

A Confidence intervals not provided due to the fact that the incidence
estimates were derived through an interpolation technique (see
chapter__) that precluded us from generating such estimates.

Table 5-11: Illustrative Strategy to Attain 0.075 ppm: Estimated
Reductions in Morbidity

(Incremental to 0.084 ppm attainment)a

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

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

Hospital Admissions (ages 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 5-12: Illustrative Strategy to Attain 0.065 ppm: Estimated
Valuation of Reductions in Mortality

(Incremental to 0.084 ppm attainment, Millions of 1999$)

Model or Assumptiona	Reference	Eastern U.S.	Western U.S. Excluding
California	California	National Total Full Attainment

Arithmetic Meanb

(95% Credible Intervals)c

NMMAPS	  Bell et al. 2004	$3,100

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

($70--$560)	$790

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

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

Meta-Analysis	  Bell et al. 2005	$12,000

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

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

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

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

	  Levy et al. 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 et al. 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

A Does not represent equal weighting among models or between assumption
of causality vs. no causality (see text on page__).

 B With the exception of the assumption of no causal relationship, the
arithmetic mean and 90% credible interval around the mean estimates of
the annual number    of lives saved are based on an assumption of a
normal distribution.

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

Table 5-13: Illustrative Strategy to Attain 0.065 ppm: Estimated
Valuation of Reductions in Morbidity

(95% Confidence Intervals in Parentheses, Millions of 1999$)

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

Hospital Admissions (ages 0-1)	$20

($12--$32)	$2.5

($1.5--$3.9)	$7.9

($4.6--$12)	$31

($18--$48)

Hospital Admissions (ages 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

	$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)

Worker Productivity should be in a separate ‘welfare’ table.

Table 5-14: Illustrative Strategy to Attain 0.070 ppm: Estimated
Valuation of Reductions in Mortality

(Incremental to 0.084 ppm attainment, Millions of 1999$)

Model or Assumptiona	Reference	Eastern U.S.	Western U.S. Excluding
California	California	National Total Full Attainment

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

	Arithmetic Meanb

(95% Credible Intervals)c

NMMAPS	Bell et al. 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)

Meta-Analysis	Bell et al. 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 et al. 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 et al. 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

A Does not represent equal weighting among models or between assumption
of causality vs. no causality (see text on page__).

 B With the exception of the assumption of no causal relationship, the
arithmetic mean and 90% credible interval around the mean estimates of
the annual number    of lives saved are based on an assumption of a
normal distribution.

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

D Certain California projected non-attainment counties are required to
meet an ozone target above the actual standard (that is, a
“glidepath”) by 2020 due to the severity of non-attainment. The
estimates in this column reflect the benefits of meeting this target.
See chapter __ for a discussion of how glidepath targets were
calculated.

Table 5-15: Illustrative Strategy to Attain 0.070 ppm: Estimated
Valuation of Reductions in Morbidity

(95% Confidence Intervals in Parentheses, Millions of 1999$)

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

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

	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, Asthma-Related	$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)

A Certain California projected non-attainment counties are required to
meet an ozone target above the actual standard  (that is, a
“glidepath”) by 2020 due to the severity of non-attainment. The
estimates in this column reflect the benefits of meeting this target.
See chapter __ for a discussion of how glidepath targets were
calculated.

Table 5-16: Illustrative Strategy to Attain 0.070 ppm: Estimated Value
of Reductions in PM2.5-Related Premature Mortality

(3 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

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

Table 5-17: Illustrative Strategy to Attain 0.070 ppm: Estimated
Monetary Value of Reductions in 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

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

Expert L	$4,200

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

($0.21--$480)	$380

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

($660--$12,000)

Table 5-18: 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

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)

A Does not represent equal weighting among models or between assumption
of causality vs. no causality (see text on page__).

 B With the exception of the assumption of no causal relationship, the
arithmetic mean and 90% credible interval around the mean estimates of
the annual number    of lives saved are based on an assumption of a
normal distribution.

C A credible interval is a posterior probability interval used in
Bayesian statistics, which is similar to a confidence interval used in
frequentist statistics. Credible intervals not provided due to the fact
that the incidence estimates were derived through an interpolation
technique (see chapter__) that precluded us from generating such
estimates.

Table 5-19: Illustrative Strategy to Attain 0.075 ppm: Estimated
Monetary Value of Reductions in Mortality

(Incremental to 0.084 ppm attainment)

Model or Assumptiona	Reference	Eastern U.S.	Western U.S. Excluding
California	California	National Total Full Attainment

Arithmetic Meanb

(95% Credible Intervals)c

NMMAPS	  Bell et al. 2004	$1,400	$66	$350	$1,800

Meta-Analysis	  Bell et al. 2005	$5,400	$270	$1,400	$7,100

	  Levy et al. 2005	$6,700	$430	$1,500	$8,700

	  Ito et al. 2005	$5,900	$290	$1,500	$7,800

Assumption that association

is not causal	0	0	0	0

A Confidence intervals not provided due to the fact that the incidence
estimates were derived through an interpolation technique (see
chapter__) that precluded us from generating such estimates.

Table 5-20: Illustrative Strategy to Attain 0.075 ppm: Estimated
Monetary Value of Reductions in Morbidity

(Incremental to 0.084 ppm attainment)a

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

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

Hospital Admissions (ages 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
5-4, 5-5 and 5-8 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 5-4, 5-5 and 5-8 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 5-4, 5-5 and 5-8, 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 a the proposed
standard of 0.070 ppm, 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 proposedless stringent standard of 0.075ppm75 ppb, using the
NMMAPS ozone mortality study, we estimate resulted in 190 premature
deaths avoided and total monetized benefits of $1,400 billion/yr.  Using
the three synthesis studies, we 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.  Because
EPA is taking comment on alternatives down as low as 0.065ppm. we show
that For a more stringent standard of 0.065ppm65 ppb, using the NMMAPS
ozone mortality study is estimated to 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.

 0.070 ppm incremental to full attainment of the 0.084 ppm standard.
That is, the estimates in the previous tables overstate the benefits of
0.070 ppm partial attainment scenario relative to the actual incremental
benefits of this scenario; this is due to the fact that the benefits
estimates in these tables include the benefits of NOx reductions that
would be required to attain a baseline of 0.084 ppm.

Thus, 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.

Estimating the PM2.5 benefits that represented the full attainment of
both 0.070 ppm incremental to full attainment of 0.084 ppm 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
extrapolated NOx emissions reductions necessary to attain a baseline of
0.084 full attainment.

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. To estimate the benefits of fully
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 the benefits of attaining 0.070 ppm incremental to full
attainment of 0.084 ppm. Subtract the benefits of attaining 0.084 ppm
fully incremental to the partial attainment of 0.084 to create a new
estimate of incremental 0.070 ppm partial attainment.

Calculate the PM2.5 benefits of fully attaining 0.070 ppm. Multiplying
the estimate of the extrapolated NOx tons necessary to attain 0.070 ppm
fully (found in chapter __) produces an estimate of the incremental
benefits of attaining 0.070 ppm incremental to partial attainment of
0.070 ppm. By adding this benefit estimate with the benefits generated
in step 3, we derived a total benefit estimate of attaining 0.070 ppm
incremental to 0.084 ppm.

To estimate the PM2.5 benefits of fully attaining 0.065 ppm and 0.075
ppm we followed steps 1 through 3 above. In step four we substituted the
number of extrapolated tons necessary to attain 0.065 ppm and 0.075 ppm,
respectively.  Table 5-12 below provides the inputs to the calculation
steps described above. To remain consistent with the mortality estimate
used to derive the original benefit per-ton estimates, we derived the
valuation estimates using the Pope (2002) mortality estimate at a 3
percent discount rate in 1999$. Alternate mortality valuation functions
would affect total benefits in a similar way as the partial benefits
presented in tables 5-16 and 5-17 above. Note that while our benefit per
ton estimates are associated with broad source categories (in this case,
NOx Electrical Generating Units, Other NOx point sources and Mobile NOx
sources) the extrapolated tons were not. For this reason we simply
assumed that the total number of extrapolated NOx tons were evenly
distributed between these three source types.

Table 5-21: Estimated PM2.5 Benefits Associated with Full Attainment of
0.070 ppm incremental to 0.084 ppma

Calculation	Extrapolated NOx Tons	Benefit per ton estimate	Valuation of
PM2.5 Benefits

(Billions 1999$)

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

	Benefits of attaining 0.084 from a baseline of 0.084 partial
attainment:	NOx EGU: 140,000	$3,400	$1.6B

	NOx Point: 140,000	$3,100

NOx Mobile: 140,000	$5,000

Benefits of attaining 0.070 partially, incremental to attainment of
0.084	---	---	            = $3.2B - $1.6B

                                      =$1.7 B

	Benefits of attaining 0.070 incremental to partial attainment of 0.070
NOx EGU: 410,000	$3,400	$4.7B

	NOx Point: 410,000	$3,100

NOx Mobile: 410,000	$5,000

Benefits of attaining 0.070 incremetnal to attainment of 0.084

	           =$1.7.6B + 4.7B

                                      =$6.4B

	a Numbers have been rounded to two significant figures and therefore
summation may not match table estimates. PM2.5 benefit estimates do not
include confidence intervals because they are derived using benefit
per-ton estimates.

b From table 5-16 above

The procedure for calculating the PM2.5 benefits resulting from full
attainment of 0.075 ppm and 0.065 ppm is identical to this example, with
the exception of step 4; the PM2.5 benefits of attaining 0.065 ppm and
0.075 ppm incremental to partial attainment of 0.070 ppm are $9.7B and
$1.4B respectively. Thus, the total PM2.5 benefits of attaining 0.065
ppm and 0.075 ppm are $11B and $3B, respectively. 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 5-22 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 co-benefits. Also note that the combined ozone
benefits and PM2.5 co-benefits for the attainment of the 0.070 standard
do not include confidence intervals. As discussed in the preceding
section, it was not feasible to derive confidence intervals for the
adjusted PM2.5 full attainment benefits.

Table 5-22: Estimate of Total Ozone and PM2.5 Benefits (95% Confidence
Intervals, Millions of $1999) for Each Standard Alternative and Ozone
Mortality Function

Standard Alternative and

Model or Assumptiona	Ozone Benefits,

Arithmetic Meanb

(95% Credible Intervals)c	PM2.5 Benefits	Total Benefits

0.065

	NMMAPS	Bell (2004)	$4,500

($1,200--$9,300)	$11,000	$16,000

Meta-Analysis	Bell (2005)	$17,000

($5,100--$33,000)	$11,000	$28,000

	Ito (2005)	$19,000

($5,800--$34,000)	$11,000	$30,000

	Levy (2005)	$19,000

($6,000--$34,000)	$11,000	$30,000

No Causality	$430

($190--$900)	$11,000	$12,000

0.070

	NMMAPS	Bell (2004)	$2,500

($680--$5,100)	$6,400	$8,900

Meta-Analysis	Bell (2005)	$9,400

($2,800--$18,000)	$6,400	$16,000

	Ito (2005)	$10,000

($3,200--$19,000)	$6,400	$17,000

	Levy (2005)	$11,000

($3,000--$21,000)	$6,400	$18,000

No Causality	$260

($110--$530)	$6,400	$6,600

0.075

	NMMAPS	Bell (2004)	$2,000	$3,000	$5,000

Meta-Analysis	Bell (2005)	$7,300	$3,000	$10,000

	Ito (2005)	$7,900	$3,000	$11,000

	Levy (2005)	$8,900	$3,000	$12,000

No Causality	$200	$3,000	$3,200

 A Does not represent equal weighting among models or between assumption
of causality vs. no causality (see text on page__).

 B With the exception of the assumption of no causal relationship, the
arithmetic mean and 90% credible interval around the mean estimates of
the annual number    of lives saved are based on an assumption of a
normal distribution.

C A credible interval is a posterior probability interval used in
Bayesian statistics, which is similar to a confidence interval used in
frequentist statistics. Credible intervals not provided due to the fact
that the incidence estimates were derived through an interpolation
technique (see chapter__) that precluded us from generating such
estimates.

[Placeholder discussion of benefits results]

Above   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 relative? 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.

[Placeholder discussing that if one were to use a more complete set of
PM mortality functions, the total estimated PM co-benefits would cover a
broader range than presented in previous table]

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

Agency for Healthcare Research and Quality.  2000.  HCUPnet, Healthcare
Cost and Utilization Project. Rockville, Maryland. Available:
HYPERLINK "http://www.ahrq.gov/HCUPnet/"  http://www.ahrq.gov/HCUPnet/
[accessed 29 September 2004].

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

Berger MC, Blomquist GC, Kenkel D, Tolley GS.  1987. Valuing Changes in
Health Risks:  A Comparison of Alternative Measures. The Southern
Economic Journal 53:977-984.

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

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

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

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

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

Hall JV, Brajer V, Lurmann FW.  2003.  Economic Valuation of
Ozone-related School Absences in the South Coast Air Basin of
California.  Contemporary Economic Policy 21(4):407-417.

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

Industrial Economics Incorporated (IEc).  1993.  Memorandum to Jim
DeMocker, U.S. Environmental Protection Agency, Office of Air and
Radiation, Office of Policy Analysis and Review.  September 30.
Washington, D.C.

International Classification of Diseases, Ninth Revision. 1979.
Hyattsville, MD: National Center for Health Statistics. Available:
HYPERLINK "http://www.cdc.gov/nchs/about/major/dvs/icd9des.htm
[accessed"  http://www.cdc.gov/nchs/about/major/dvs/icd9des.htm
[accessed  29 September 2004].

Jaffe DH, Singer ME, Rimm AA.  2003.  Air pollution and emergency
department visits for asthma among Ohio Medicaid recipients, 1991-1996.
Environ Res 91(1):21-28.

Levy JI, Carrothers TJ, Tuomisto JT, Hammitt JK, Evans JS.  2001.
Assessing the Public Health Benefits of Reduced Ozone Concentrations.
Environ Health Perspect 109(12):1215-1226

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

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

Ostro BD, Rothschild S.  1989.  Air Pollution and Acute Respiratory
Morbidity - an Observational Study of Multiple Pollutants.  Environ Res
50(2):238-247.

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

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

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

Smith DH, Malone DC, Lawson KA, Okamoto LJ, Battista C, Saunders WB.
1997.  A national estimate of the economic costs of asthma.  Am J Respir
Crit Care Med. 156(3 Pt 1):787-793.

Stanford R, McLaughlin T, Okamoto LJ.  1999.  The cost of asthma in the
emergency department and hospital.  Am J Respir Crit Care Med
160(1):211-215.

Thurston GD, Ito K.   2001.  Epidemiological studies of acute ozone
exposures and mortality.  J Expo Anal Environ Epidemiol 11(4):286-294.

Tolley GS, Babcock L, Berger M, Bilotti A, Blomquist G, Brien M, et al.
1986.  Valuation of Reductions in Human Health Symptoms and Risks.
Prepared for U.S. Environmental Protection Agency.  January.
Washington, D.C.

U.S. Bureau of the Census. 2001.  Statistical Abstract of the United
States, 2001, Section 12: Labor Force, Employment, and Earnings, Table
No. 521. Washington, DC.

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

U.S. EPA. 1970. Clean Air Act. 40CFR50.

U.S. Environmental Protection Agency.  1999.  The Benefits and Costs of
the Clean Air Act, 1990-2010.  Prepared for U.S. Congress by U.S. EPA,
Office of Air and Radiation/Office of Policy Analysis and Review,
Washington, DC, November; EPA report no. EPA-410-R-99-001.

U.S. Environmental Protection Agency, 2004.  Final Regulatory Analysis:
Control of Emissions from Nonroad Diesel Engines.  EPA420-R-04-007.
Prepared by Office of Air and Radiation.  Available:   HYPERLINK
"http://www.epa.gov/nonroad-diesel/2004fr/420r04007.pdf"
http://www.epa.gov/nonroad-diesel/2004fr/420r04007.pdf  [accessed 29
September 2004].

Woods & Poole Economics Inc.  2001.  Population by Single Year of Age
CD.  CD-ROM.  Woods & Poole Economics, Inc. Washington, D.C.

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

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

 Health impact functions measure the change in a health endpoint of
interest, such as hospital admissions, for a given change in ambient
ozone or PM concentration.

 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.

 Final Regulatory Impact Analysis: Industrial Boilers and Process
Heaters. Prepared by Office of Air and Radiation. Available:
http://www.epa.gov/ttn/ecas/regdata/EIAs/chapter10.pdf [accessed 18 May
2007].

 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

 Final Regulatory Impact Analysis: Industrial Boilers and Process
Heaters. Prepared by Office of Air and Radiation. Available:
http://www.epa.gov/ttn/ecas/regdata/EIAs/chapter10.pdf
[ac散獳摥ㄠ‸慍⁹〲㜰⹝ȍ䘠湩污删来汵瑡牯⁹浉慰瑣
䄠慮祬楳㩳䤠摮獵牴慩潂汩牥⁳湡牐捯獥⁳效瑡牥
⹳倠敲慰敲祢传晦捩景䄠物愠摮删摡慩楴湯癁楡
慬汢㩥栠瑴㩰⼯睷⹷灥⹡潧⽶瑴⽮捥獡爯来慤慴䔯䅉⽳
档灡整ㅲ⸰摰慛捣獥敳㠱䴠祡㈠〰崷മ̍഍ഄ̍഍
ഄ

഍

഍഍഍

䄍 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.

We’ll want to talk about this ‘summary’ once the chapter is
complete.  Also, please remove the text box format so that editing is
easier on the next round.  Many  thanks.

Shouldn’t the 203 be 126 and the 360 be 280 from 16May cost slides?

Second paragraph:  the combined hospital and ER admission number is 5910
in 03May slides, combined hospital/ER totals 7000 on 16May slides.  Do
we know reason for >2X increase in ER visits which is the source of the
discrepancy?

Separate footnotes for each document, pls.

Footnote w/ refs

 Cite.

Should this be included in O3, 24 hr. avg row?

Do no O3 studies cover work loss days, asthma exacerbations, URS, LRS,
acute bronchitis?

Add the citation to the ‘advice’ to which you are referring.  Same
comment for footnote b.

Say something about SAB HES advice in both endpoints and recommendations
for studies prefernece.

Move this in to a separate section for Welfare benefits – it adds
unnecessary confusion by coming in the middle of the health section
(5..3).  Footnote 6 below needs to be moved (and taken out of a
footnote)

 Need text on how worker productivity being estimated.  The description
of method and underlying study should be parallel to that used for
school absences – but should not be “in” the health section.

Provide references here for these.  Begin next sentence with references
on meta-analyses.

Provide more formal definition here.

Confusing paragraph.

Add a couple of sentences about the method and the page numbers in the
CAIR or PM RIA where the method is described in more detail.

Reference for  Tacoma and New Haven?  Was data manipulation performed on
these studies prior to combination?

Will this Appendix detail the random/fixed effects pooling technique?
If so, perhaps a blanket statement upfront would be wise to refer
readers to the Appendix, rather than sometimes referring and sometimes
not.

This paragraph is not clear in explaining the data manipulation.
Gilliland is converted to total absence days, but then an episodic
absence rate for Gilliland is discussed.  It would be helpful here to
include units in parentheses to aid in clarity.

Adding asthma prevalence rates as well

Foot notes should be letters OR numbers.

Where are income growth assumptions coming from?  I saw in a previous
document where GDP growth estimates came from S&P.  Is that the case
here as well?  Please note.

This also assumes parent unable to work from home; therefore, no lost
wages.  To not account for this is to overestimate benefits.  This
paragraph should instead discuss sources of uncertainty in the benefits
estimate, not focus only on its underestimation.

13 most severe?

Why are the values in these columns identical for different time
periods?

Is this a 5 year period estimate, and does this “no discounting”
disclaimer imply that the author did not discount and you have
discounted the estimated sum, or have you not discounted this number at
all?

How were the numbers here calculated?  Uniform distributions apply.
Should one column be considered the midpoint and the other column
adjusted to account for time value of money?

Aren’t most people paid for vacation?  Annual wages should be divided
by 260 instead.

Text indicates $52 as WTP to avoid MRAD.

See edits – is this what you mean?

Will also reference a separate TSD that describes the process for
generating the benefit per ton estimates.

This

This section is not at all clear, although the example clarifies what
you did.  Pls take another stab at this explanation. Thanks.

full or partial?

References for Peel, Wilson, Crocker and Horst, Bell, Ito, Levy?

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 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 between $6.5 and
$18B using a combination of the Pope (2002) PM2.5 mortality function and
the range of ozone mortality functions as well as the possibility that
there is no causal relationship between ozone and mortality.

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.

"

#

*

W

@

©欀ᱤ

©欀

©欀Ꝥ

ȏ脈栕⡓

h

h

h

愀Ĥ摧䢼CЀ

CJ

aJ

@

	@

	@

	@

ÿÿÿÿ

Ȁ沖̀Ĵ鐇ʜ혈F鐃擿㐋䀗8 'Ð

ÿÿÿÿ 	Ð

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿÿÿÿÿ

Ȁ沖̀Ĵ鐇Ǵ혈鐆擿㐋먞퀧䀯ꀸ퀦

ÿÿÿÿÿÿÿÿ

Ȁ沖̀Ĵ鐇Ȉ혈鐆擿㐋먞퀧䀯ꀸ퀦

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

	y

ÿÿÿÿ

G

ˆ

‰

Ä

Æ

Ç

Ð

＀胿䀆

＀胿䀆

愀Ĥ摧罏܀V

[

h

m

y

}

ˆ

‰

@

@

＀胿䀆

＀胿䀆

@

@

@

@

愀Ĥ摧罏܀V

Y

b

f

p

w

„

…

@

@

愀Ĥ摧罏܀…

Ž

’

¨

«

´

¸

Ã

@

@

@

@

	@

@

@

@

@

@

@

@

@

@

@

@

'@

”ÿd

@8 'Ð

ÿÿÿÿ 	Ð

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿÿÿÿÿ

Ȁ沖̀Ĵ鐇Ǵ혈鐆擿㐋먞퀧䀯ꀸ퀦

ÿÿÿÿÿÿÿÿ

Ȁ沖̀Ĵ鐇Ȉ혈鐆擿㐋먞퀧䀯ꀸ퀦

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

”ÿd

ð-º'Ð/@8`'Ð

ÿÿÿÿ`	Ð

ÿÿÿÿ

”ÿd

@8 'Ð

ÿÿÿÿ 	Ð

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿÿÿÿÿ

Ȁ沖̀Ĵ鐇Ǵ혈鐆擿㐋먞퀧䀯ꀸ퀦

ÿÿÿÿÿÿÿÿ

Ȁ沖̀Ĵ鐇Ȉ혈鐆擿㐋먞퀧䀯ꀸ퀦

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

	y

ÿÿÿÿ

„

ÿÿÿÿ

”

„

ÿÿÿÿ

”

„

„

ÿÿÿÿÿÿÿÿ

”

ÿÿÿÿÿÿÿÿ

”

„

ÿÿÿÿÿÿÿÿ

”

„

ÿÿÿÿÿÿÿÿ

”

ÿÿÿÿÿÿÿÿ

”

 hí

ÿÿÿÿÿÿÿÿ

”

„

ÿÿÿÿÿÿÿÿ

”

„

ÿÿÿÿÿÿÿÿ

”

	@

	@

ÿÿÿÿÿÿÿÿ

”

„

„

„

ÿÿÿÿÿÿÿÿ

”

„

ÿÿÿÿÿÿÿÿ

”

„

ÿÿÿÿÿÿÿÿ

”

 hí

„

ÿÿÿÿÿÿÿÿ

”

„

ÿÿÿÿÿÿÿÿ

”

„

ÿÿÿÿÿÿÿÿ

”

„

ÿÿÿÿ

”

„

ÿÿÿÿ

”

ÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

hês

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿÿÿÿÿ

ÿÿÿÿ

ÿÿÿÿ

hês

hês

hês

hês

ÿÿÿÿ

愀Ĥ摧珪

摧珪

Ȁ沖̀Ĵ鐇ʜ혈F鐃擿㐋䀗8 'Ð

ÿÿÿÿ 	Ð

ÿÿÿÿ

”ÿd

ð-º'Ð/@8€&Ð

Ð

ÿÿÿÿ

愀Ĥ摧珪

愀Ĥ摧珪

摧珪

摧珪

ÿÿÿÿÿÿÿÿ

愀Ĥ摧珪

摧珪

Ȁ沖̀Ĵ鐇Ǵ혈鐆擿㐋먞퀧䀯ꀸ퀦

ÿÿÿÿÿÿÿÿ

愀Ĥ摧珪

hês

hês

hês

hês

hês

hês

hês

hês

摧珪

Ȁ沖̀Ĵ鐇Ȉ혈鐆擿㐋먞퀧䀯ꀸ퀦

ÿÿÿÿ

愀Ĥ摧珪

ÿÿÿÿ

hês

hês

hês

hês

hês

hês

hês

愀Ĥ摧珪

摧珪

ÿÿÿÿ

摧珪

愀Ĥ摧珪

愀Ĥ摧珪

愀Ĥ摧珪

摧珪

愀Ĥ摧珪

摧珪

ÿÿÿÿÿÿÿÿ

摧珪

愀Ĥ摧珪

愀Ĥ摧珪

愀Ĥ摧珪

摧珪

ÿÿÿÿ

 h

 h

 h

hZ

hZ

 h

 h

 h

€&

＀ÿܧ

 h

@

@

@

@

h

h

sponse (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 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.