#### CHAPTER 5 - Benefits Analysis Approach and Results

####### Synopsis

The results of this benefits analysis indicate that lowering the
National Ambient Air Quality Standard (NAAQS) for lead from its current
level of 1.5 μg/m^3^ maximum quarterly mean to one of the proposed
alternative second maximum monthly mean values would result in
significant reductions in adverse health effects due to reduced exposure
from lead and fine particles (PM~2.5~). We found that a large number of
intelligence quotient (IQ) points across the population could be gained
(between 200,000 and 700,000) if compliance with one of the proposed
alternative NAAQS is achieved in the year 2020.

As shown in Table 5-1 below, when applying a 3 percent discount rate,
these IQ point benefits translate into monetary benefits for the least
stringent standard alternative (0.3 μg/m^3^) ranging between \$1.7 and
\$2.5 billion (all values in 2006\$). For the most stringent standard
alternative (0.05 μg/m^3^), monetary benefits range from \$6.1 to \$8.7
billion. Additional co-control benefits of reduced PM emissions are
expected to range between \$0.4 and \$3.5 billion for the least
stringent standard alternative, up to a range of \$1.1 to \$8.9 billion
for the most stringent standard alternative. Therefore, the combined
monetized health benefits from reductions in both lead and PM exposures
as a result of lowering the current NAAQS range from \$2.1 to \$6
billion for the least stringent standard alternative, up to a range of
\$7.2 to \$18 billion for the most stringent standard alternative.

When applying a 7 percent discount rate, the monetary benefits for
changes in IQ the least stringent standard alternative (0.3 μg/m^3^)
range between \$0.2 and \$0.4 billion. For the most stringent standard
alternative (0.05 μg/m^3^), monetary benefits of IQ gains range from
\$0.8 to \$1.5 billion. Additional co-control benefits of reduced PM
emissions are expected to range between \$0.4 and \$3.1 billion for the
least stringent standard alternative, up to a range of \$1.0 to \$8.0
billion for the most stringent standard alternative. Therefore, the
combined monetized health benefits from reductions in both lead and PM
exposures as a result of lowering the current NAAQS range from \$0.6 to
\$3.5 billion for the least stringent standard alternative, up to a
range of \$1.8 and \$9.5 billion for the most stringent standard
alternative.

**Table 5-1.** **Monetary Benefits** **of Alternate Lead NAAQS** **(in
Millions of 2006\$) in 2020**

+---------+---------+---------+---------+---------+---------+---------+
| *S      | *Es     |         | *Mo     |         | *Total  |         |
| tandard | timated |         | netized |         | Benef   |         |
| A       | Net     |         | B       |         | its^4^* |         |
| lternat | Present |         | enefits |         |         |         |
| ive^1^* | Value   |         | of      |         |         |         |
|         | of IQ   |         | Co-Con  |         |         |         |
|         | Points  |         | trolled |         |         |         |
|         | Gai     |         | P       |         |         |         |
|         | ned^2^* |         | M~2.5~* |         |         |         |
|         |         |         | *Emissi |         |         |         |
|         |         |         | ons^3^* |         |         |         |
+---------+---------+---------+---------+---------+---------+---------+
|         | 3%      | 7%      | 3%      | 7%      | 3%      | 7%      |
|         | D       | D       | D       | D       | D       | D       |
|         | iscount | iscount | iscount | iscount | iscount | iscount |
|         | Rate    | Rate    | Rate    | Rate    | Rate    | Rate    |
+---------+---------+---------+---------+---------+---------+---------+
| 0.3     | \$      | \$220 - | \$410 - | \$380 - | \$      | \$600 - |
| μg/m^3^ | 1,700 - | \$430   | \$3,500 | \$3,100 | 2,100 - | \$3,500 |
|         | \$2,500 |         |         |         | \$6,000 |         |
+---------+---------+---------+---------+---------+---------+---------+
| 0.2     | \$      | \$310 - | \$560 - | \$520 - | \$      | \$830 - |
| μg/m^3^ | 2,500 - | \$610   | \$4,700 | \$4,300 | 3,100 - | \$4,900 |
|         | \$3,500 |         |         |         | \$8,200 |         |
+---------+---------+---------+---------+---------+---------+---------+
| 0.1     | \$      | \$480 - | \$690 - | \$640 - | \$      | \$      |
| μg/m^3^ | 3,900 - | \$950   | \$5,800 | \$5200  | 4,600 - | 1,100 - |
|         | \$5,500 |         |         |         | \       | \$6,200 |
|         |         |         |         |         | $11,000 |         |
+---------+---------+---------+---------+---------+---------+---------+
| 0.05    | \$      | \$760 - | \$      | \$      | \$      | \$      |
| μg/m^3^ | 6,100 - | \$1,500 | 1,100 - | 1,000 - | 7,200 - | 1,800 - |
|         | \$8,700 |         | \$8,900 | \$8,000 | \       | \$9,500 |
|         |         |         |         |         | $18,000 |         |
+---------+---------+---------+---------+---------+---------+---------+
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+---------+---------+---------+---------+---------+---------+---------+

Figures 5-1 and 5-2 below display some examples of the total health
benefits from both lead and PM~2.5~ exposure reductions for each of the
four alternative standards using a 3 percent and 7 percent discount
rate, respectively.

####### Introduction

This chapter documents our analysis of health benefits expected to
result from achieving alternative levels of the lead NAAQS, relative to
base case ambient air lead levels. We first describe our approach for
estimating and monetizing the health benefits associated with reductions
of lead in air. Next, we provide a summary of our results, including an
analysis of the sensitivity of the benefits model. We then review our
approach to and results from estimating benefits from co-control of
direct PM~2.5~ emissions associated with implementing measures necessary
to achieve alternative levels of the proposed lead NAAQS. Finally, we
discuss the key results of the benefits analysis and indicate areas of
uncertainty in our approach.

**Figure 5-1.** **Example Combined Lead and Total PM~2.5~** **Monetized
Benefits Estimates by Standard Alternative (3% Discount Rate)**

![](media/image1.wmf){width="5.584027777777778in"
height="2.8944444444444444in"}

**Figure 5-2.** **Example Combined Lead and Total** **PM~2.5~**
**Monetized** **Benefits** **Estimates** **by Standard** **Alternative**
**(7% Discount Rate)**

##### **Benefits Approach**

This section presents our approach for estimating avoided adverse health
effects in humans resulting from achieving alternative levels of the
lead NAAQS, relative to a base case ambient air lead level. We first
review the epidemiological evidence concerning potential health effects
of lead exposure and present the health endpoints we selected for our
primary benefits estimate. We then describe our screening-level
spreadsheet benefits model, including the data used and key assumptions.
Finally, we describe our approach for assigning an economic value to the
health benefits.

Benefits Scenario

We calculated the economic benefits from annual avoided health effects
expected to result from achieving alternative levels of the lead NAAQS
(the "control scenarios") in the year 2020. We measured benefits in the
control scenarios relative to the incidence of health effects consistent
with ambient lead levels in air expected under the current standard (1.5
μg/m^3^ maximum quarterly mean; the "base case") in 2020. Note that this
"base case" reflects emissions reductions and ambient air quality
improvements that we anticipate will result from implementation of other
air quality rules, including compliance with all relevant Maximum
Achievable Control Technology (MACT) rules and the recently revised
NAAQS for PM~2.5~.[^1] We compared benefits across four alternative
second maximum monthly mean NAAQS levels of 0.3, 0.2, 0.1, and 0.05
μg/m^3^.

Selection of Health Endpoints

Epidemiological researchers have associated lead exposure with adverse
health effects in numerous studies, as described in the *Air Quality
Criteria for Lead* (USEPA, 2006a; hereafter, *Lead Criteria Document*).
Young children are particularly sensitive to lead exposures;
neurobehavioral effects of lead exposure in infants and young children
(less than 7 years of age) have been observed consistently across
multiple studies that control for an array of confounding factors
(USEPA, 2006a). Adverse health effects in adults have also been
identified, such as cardiovascular effects (e.g., increased blood
pressure leading to increased incidence of hypertension as well as
coronary heart disease, stroke, and premature mortality), renal effects,
reproductive effects, immune system effects, and effects on other organ
systems (e.g., hematopoietic, endocrine, hepatic) (USEPA, 2006a).

Although a number of adverse health effects have been found to be
associated with lead exposure, this benefits analysis only includes a
subset, due to limitations in understanding and quantifying the
dose-response relationship for some of these health endpoints. We
analyzed only those endpoints with sufficient evidence to support a
quantified dose-response relationship. This determination was made using
the information presented in the *Lead Criteria Document*, which
contains an extensive literature review for several health endpoints
related to lead exposure. However, this document only included studies
published or accepted for publication through December 2005. Therefore,
we performed supplemental searches in the online search engine PubMed to
identify studies published between January 2006 and the present (see
Appendix A for more information). Finally, we reviewed previous EPA lead
benefits analyses to identify dose-response relationships that have been
used previously (USEPA, 1997, 2006b & 2007a).

Our analysis focuses primarily on children's health effects due to our
use of child-specific data to convert air quality data to a blood lead
level, which is the most common biomarker of exposure used in
dose-response functions.

This human health benefits analysis does not attempt to estimate the
changes in lead-related health effects among adults. Several key data
limitations prevented EPA from quantifying these important endpoints:

-   *The available peer reviewed air:blood ratios to estimate adult
    blood lead changes are dated.* Previous EPA analysis of the costs
    and benefits of the Clean Air Act (USEPA, 1997) utilized air:blood
    ratios for adults from based on Snee et al. (1981), a meta-analysis
    of several studies, including Johnson et al..(1976), Fugas et
    al.(1973), and Nordman (1975). While these studies do provide
    insight into the responsiveness of adult blood lead levels to
    changes in lead concentrations in air, the age of these studies
    suggests that these ratios may not be appropriate for application
    in 2020. The more-recent peer-reviewed estimates of air:blood ratios
    have been derived for children.Applying these ratios to adults would
    be inappropriate given the important differences between the two
    populations in their ambient exposure to Pb.

-   *There is a lack of current, peer reviewed non-air-related blood
    lead background estimates for adults*. Quantification of adult
    endpoints would require a non-air-related blood background for
    adults. CASAC recommends a range of values for children in their
    review of the Lead Risk Assessment. However, due to differences
    between adults and children in the routes of exposure to lead, it is
    possible that background levels would differ between these two
    receptor groups. Therefore, applying the child-specific
    non-air-related background blood lead levels to adults could
    mis-estimate the true adult background levels.

-   *The adult health impact functions relating changes in blood lead to
    health outcomes are dated*. Certain adult health impact functions,
    such as those quantifying the relationship between blood lead and
    diastolic blood pressure (Nawrot, 2002) are current. However, the
    functions relating changes in blood pressure to changes in premature
    mortality, chronic heart disease and stroke were each drawn from
    studies published in the 1970's; advances in the treatment of high
    blood pressure suggest that these functions may over-predict of
    changes in these health effects in the current population.

Taken together, these data limitations make a credible quantified
assessment of adult endpoints very challenging and subject to
considerable uncertainty. The Agency is committed to addressing these
data limitations so that it may be possible to provide a quantitative
estimate of the adult endpoints for the next Pb NAAQS review in
approximately 5 years. In the final RIA EPA will include a more detailed
discussion of the types of information and data that would improve its
ability to provide quantitative health benefit estimates for adults.

Table 5-2 below presents the health and welfare effects related to
exposure to lead in the air that are quantified in this benefits
analysis. In addition, the table includes a list of other endpoints that
potentially are linked to lead exposure, but which do not have
dose-response functions available for quantifying benefits.

As shown in Table 5-2, our primary estimate is based on the effect of IQ
loss on lifetime earnings. There are several recent epidemiological
analyses that have found potential adverse health impacts of blood lead
levels on cognitive function (most often measured as changes in IQ) in
young children under 7 years of age, as described in the *Lead Criteria
Document*. However, as also noted in that document, there has been
conflicting evidence as to whether there exists a discrete period of
neurological vulnerability to lead exposure during childhood.

For instance, the first three years of life represent the maximal period
of lead ingestion as well as a period of time when important development
of the central nervous system is occurring, which suggests that
biologically, this could be a vulnerable period (USEPA, 2006a). In
addition, there are two major meta-analyses that focused on the
association between school age IQ and blood lead concentrations at two
years of age or average blood lead concentrations up to three years of
age (Pocock et al, 1994; Schwartz, 1994a). However, several recent
prospective epidemiological studies have found concurrent blood lead
level (i.e., blood lead measured at the same time as school age IQ) or
lifetime average blood lead level (i.e., a mean of blood lead level from
infancy to measurement of school age IQ) to be more strongly associated
with school age IQ and other measures of neurodevelopment (Canfield et
al., 2003; Dietrich et al, 1993; Tong et al. 1996, Wasserman et al.,
2000). In addition, a large, international meta-analysis by Lanphear et
al. (2005) included four measures of blood lead level: concurrent, peak,
lifetime average, and early childhood. The authors found that the
concurrent and lifetime blood lead levels were the strongest predictors
of IQ deficits associated with lead exposure.

A study by Chen et al. (2005) specifically evaluated whether a window of
enhanced susceptibility to lead exists. This study examined whether
cross-sectional associations observed in school age children represent
residual effects from two years of age or "new" effects emerging among
these children (USEPA, 2006a). The authors found that the blood lead
metric with the strongest association with IQ was concurrent, and this
relationship grew stronger with age. The *Lead Criteria Document*
concluded that "\[t\]hese results support the idea that lead exposure
continues to be toxic to children as they reach school age, and do not
lend support to the interpretation that all damage is done by the time
the child reaches two to three years of age" (USEPA, 2006a, page 6-63).
Based on this evidence, it is reasonable to assume that all children
under seven years of age in the study area for this analysis will
experience some cognitive benefit (i.e., IQ loss avoided) from reduced
ambient air lead in 2020. Therefore, we have designed our benefits
analysis to measure benefits to all children under seven in our study
area.

**Table 5-2. Human Health and Welfare Effects of Lead**

+---------------------------------+------------------------------------+
| ***Quantified Health Effects*** | ***Unquantified Health             |
|                                 | Effects^a^***                      |
+---------------------------------+------------------------------------+
| -Intelligence Quotient (IQ)     | -Other neurobehavioral and         |
| loss effect on lifetime         | physiological effects              |
| earnings                        |                                    |
|                                 | -Delinquent and anti-social        |
|                                 | behavior                           |
|                                 |                                    |
|                                 | -IQ loss effects on compensatory   |
|                                 | education                          |
|                                 |                                    |
|                                 | -Hypertension                      |
|                                 |                                    |
|                                 | -Non-fatal coronary heart disease  |
|                                 |                                    |
|                                 | -Non-fatal strokes                 |
|                                 |                                    |
|                                 | -Premature mortality               |
|                                 |                                    |
|                                 | -Other cardiovascular diseases     |
|                                 |                                    |
|                                 | -Neurobehavioral function          |
|                                 |                                    |
|                                 | -Renal effects                     |
|                                 |                                    |
|                                 | -Reproductive effects              |
|                                 |                                    |
|                                 | -Fetal effects from maternal       |
|                                 | exposure (including diminished IQ) |
|                                 |                                    |
|                                 | -Neonatal mortality due to low     |
|                                 | birth weight caused by maternal    |
|                                 | exposure to lead                   |
+---------------------------------+------------------------------------+
| ^a^ The categorization of       |                                    |
| unquantified toxic health and   |                                    |
| welfare effects is not          |                                    |
| exhaustive. Health endpoints in |                                    |
| this column include both a)     |                                    |
| those for which there is not    |                                    |
| consensus on causality; and b)  |                                    |
| those for which causality has   |                                    |
| been determined but empirical   |                                    |
| data are not available to allow |                                    |
| calculation of benefits.        |                                    |
+---------------------------------+------------------------------------+
|                                 |                                    |
+---------------------------------+------------------------------------+
|                                 |                                    |
+---------------------------------+------------------------------------+
|                                 |                                    |
+---------------------------------+------------------------------------+

Benefits Estimation Model

###### 

###### **Overview**

For this benefits analysis, we created a spreadsheet model to provide a
screening-level assessment of health benefits occurring as a result of
implementing alternative NAAQS levels. The model uses various
simplifying assumptions and is intended only to provide an approximate,
preliminary estimate of the potential health benefits. EPA plans to
refine the model as it progresses towards a final NAAQS level for lead.

The model was constructed in Microsoft Excel™ and provides an integrated
tool to complete five benefits estimation steps: 1) estimate lead in air
concentrations for the "base case" and "control scenarios"; 2) estimate
population exposures to air lead concentrations for each scenario; 3)
estimate blood lead levels in the population for each scenario; 4)
estimate avoided cases of health effects due to changes in blood lead
levels; and 5) apply an economic unit value to each avoided case to
calculate total monetized benefits. These steps and the data inputs
required are shown in Figure 5-3 and are discussed in further detail
below.

###### 

###### **Estimating Lead in Air Concentrations**

We used estimates of the second maximum monthly mean lead total
suspended particles (TSP) for each monitor included in our study to
characterize ambient air lead concentrations for the "base case" in 2020
(USEPA, 2007b). These estimates were calculated by adjusting second
maximum monthly mean lead TSP monitoring values for the years 2003 to
2005 to account for emissions reductions due to compliance with MACT
requirements and the NAAQS for PM~2.5~ occurring by 2020 (see Chapter 4
for additional information). We assumed that under the "control
scenario," every monitor would meet the alternative NAAQS in 2020 and
therefore, assigned the proposed alternative NAAQS level as the second
maximum monthly mean to all monitors.

The benefits model used estimates of maximum quarterly mean lead
concentrations in order to calculate avoided cases of health endpoints.
This decision was based on a number of studies outlined in EPA's 2007
Staff Paper (USEPA, 2007c; Section 5.5.2), which indicate that changes
in blood lead levels resulting from changes in air lead concentrations
occur within a relatively short timeframe (i.e., within a few weeks to
months). This finding is also supported by a simulation of changes in
urban residential dust lead levels following a change in ambient air
lead using the hybrid mechanistic empirical model developed for the
*Lead Risk Assessment*. That analysis showed that changes in indoor dust
lead levels (the primary source of children's exposure) tracked closely
with changes in ambient lead air concentrations.

**Figure 5-3**

**OVERVIEW OF LEAD BENEFITS MODEL**

![](media/image3.wmf){width="5.990277777777778in"
height="6.735416666666667in"}

Therefore, for the "base case" estimates of lead air concentrations used
in the model, we estimated the expected maximum quarterly mean air lead
concentration in 2020 at each monitor based on the second maximum
monthly mean values for the "base case." This was achieved by
calculating monitor-specific ratios of the second maximum monthly mean
to the maximum quarterly mean for the period 2003-2005 and then dividing
the second maximum monthly mean for the "base case" by this ratio.

For the "control scenario" we estimated the maximum quarterly mean lead
in air concentration that would be expected in 2020, based on the second
maximum monthly mean NAAQS concentration. As in the "base case," we used
monitor-specific ratios of the second maximum monthly means to maximum
quarterly means for 2003-2005 and then divided the selected NAAQS by
this ratio.

***Estimating Population Exposure***

The first input to any benefits assessment is the estimated changes in
ambient air quality expected to result from simulated attainment of a
NAAQS. EPA typically relies upon air quality modeling to generate these
data. For this analysis, time and technical limitations prevented us
from performing formal air quality modeling. Instead, EPA employed two
alternate approaches to approximate the air quality change resulting
from attainment of alternate lead NAAQS. Each approach relies upon the
lead monitoring network as the basis for subsequent air quality
estimates. The first approach, which we employed to generate our primary
benefits estimate, uses an interpolation method utilized in previous
RIA's to estimate changes in lead concentrations in projected
non-attainment areas. The second approach, which we utilized as a
sensitivity analysis, applies a radius of a fixed size around each
non-attaining lead monitor and estimates a fixed concentration of lead
within that radius. We describe the process for using each approach
below.

[Interpolation Method]{.underline}

This approach applies an interpolation method to generate an air quality
surface from available lead monitoring data to better represent the
spatial heterogeneity of lead concentrations in a projected
non-attainment area. It utilizes both the lead monitoring network as
well as the lead-speciating TSP monitoring network; we added the
lead-speciating monitors to increase the number of data points available
for the interpolation. We interpolated lead concentrations to the census
tract, rather than census block group, to increase the computational
efficiency of the model.

To create an air quality surface of ambient lead values we applied the
Voronoi Neighborhood Averaging (VNA) method. The VNA is an
inverse-distance-weighting technique that interpolates point monitor
data to a user-defined grid cell for the purpose of creating an air
quality surface. The VNA approach is well suited for this type of
analysis because the inverse distance weighting approach can approximate
the gradient of ambient lead surrounding each monitor. VNA is a
well-established technique that EPA has used in combination with modeled
air quality changes to estimate the air quality change associated with
full attainment of PM~2.5~ and Ozone NAAQS (USEPA, 2006c & 2008).[^2]

Figure 5-4 below summarizes how we applied the VNA method in this
analysis.

The VNA approach is expected to provide a better representation of the
gradient of ambient lead around each monitor as compared to the radius
approach. For this reason, we utilized this approach to generate our
primary benefits estimate. However, this validity of this method is to
some extent contingent upon the availability of a sufficient number of
monitors to support an interpolation. In certain locations, such as
Hillsborough County, FL, there are a sufficient number of lead and TSP
monitors to generate an interpolation with a pronounced gradient around
each monitor (see Figure 5-5). The lead and TSP monitoring network in
other non-attainment areas can in some cases be sparse, and the
resulting interpolation does not appear to generate a meaningful
gradient, such as in Delaware County, IN (see Figure 5-6). To the extent
that there were a more dense lead monitoring network in such locations,
the interpolation approach would produce a gradient that better
represents actual ambient lead concentrations. While both the VNA and
radius approaches exhibit limitations, we hold more confidence in the
results of the interpolation approach and so rely upon it as our primary
method of simulating air quality changes. We also acknowledge the
limitations to the interpolation method and provide sensitivity
estimates using the radius method.

**Figure 5-4**

![](media/image4.wmf){width="5.995833333333334in"
height="6.736111111111111in"}**STEPS IN THE VNA INTERPOLATION**
**METHOD**

![](media/image5.wmf){width="7.104861111111111in"
height="5.684027777777778in"}**Figure 5-5. Air Lead Concentration
Gradient in Hillsborough County, Florida**

**Figure 5-6. Air Lead Concentration Gradient in Delaware County,
Indiana**

![](media/image6.wmf){width="7.322222222222222in"
height="5.658333333333333in"}

[Radius Method]{.underline}

In this approach we focused on the 36 monitors in counties that
potentially could be designated as non-attainment areas under at least
one of these alternative lead NAAQS levels. These monitor concentration
values likely only apply to the population of people living within the
vicinity of these monitors, especially if the monitor is oriented near a
source of lead contamination (e.g., a primary or secondary lead
smelter). As a default, we defined the affected population as those
individuals living within a 10-kilometer radius around the monitor. The
10-kilometer radius is consistent with source-specific modeling in the
EPA *Lead Risk Assessment* case studies for primary and secondary
sources (USEPA, 2007a). In the absence of detailed air quality modeling
for the lead sources in the vicinity of each monitor, we assumed in this
screening-level analysis that the lead concentrations in air measured at
each monitor are uniform throughout the specified radius. To develop an
upper-bound estimate of lead benefits, we assumed the entire population
of the county was exposed to the concentration measured at the monitor
(the geographic extent of a county generally exceeds 10 km). Also, for
the 19 source-oriented monitors in our dataset we performed sensitivity
analysis using alternate, smaller radii of one, two, and five
kilometers, since lead air concentrations can in some cases display

significant gradients with distance from a source-oriented monitor. For
example, second maximum monthly mean values measured at monitors in
close proximity to the Herculaneum, MO lead smelter drop off 40 percent
within roughly 1 km of the source and decrease by an additional 95
percent within 2 km.[^3]

We used ArcGIS to establish the radii around each monitor. Our spatial
dataset contained US Census population data at the block group level for
the year 2000. We calculated the total population within each radius in
2000 by adding the population of each Census block group that resided
completely within the radius and the relative fraction of the population
of block groups only partially falling within the radius, assuming that
the population was uniformly distributed throughout the block group.[^4]
For instance, if 50 percent of the block group was located inside the
radius, we added 50 percent of its population to the total population of
that radius.

We next took the estimate of the total population for each radius in
2000 and distributed it into gender- and age-specific groups (in
five-year increments) according to the county-level Census data for the
county in which the monitor resides.[^5] In a few instances where a
radius extended into a neighboring county, we assumed the age and
gender-specific proportions would be the same as the county in which the
monitor resides.

[Population Projections]{.underline}

For both the interpolation and radius methods, we extrapolated the 2000
age- and gender-specific population data to 2020, using Woods and Poole
county-level projection data (Woods and Poole, 2001). We calculated a
growth rate for each gender and age group combination by taking the
ratio of the 2020 estimate from Woods and Poole to the corresponding
2000 county-level estimates from the Census. We applied the calculated
growth rates to each gender and age group to estimate the total
population in 2020 residing within each census tract or radius.

In order to determine the number of children aged six and under, we
added the population of children in the 0-4 age group for both genders
and then added two-fifths of the population in the 5-9 age group,
assuming the population was uniformly distributed across all five ages
in that group.

***Estimating Blood Lead Levels***

The concentration-response functions we employ in this benefits analysis
require estimates of blood lead levels in the exposed population to
calculate avoided incidence of adverse health effects. We chose to
develop a first approximation of the blood lead levels associated with
reductions in air lead concentrations for each of the alternative NAAQS
by using the air lead to blood lead ratio ("air:blood ratio") approach
applied by EPA in deriving the current NAAQS in 1978 (43 FR 46246).
These ratios predict geometric blood lead levels due to direct lead
exposure via inhalation as well as indirect exposures via ingestion of
dust and soils contaminated by lead deposition, based on comparisons of
historical data on lead in ambient air and measured or modeled geometric
mean blood lead levels in an exposed population. Table 5-3 lists the
ratios considered for the current NAAQS analysis; for its primary
estimate, EPA chose a ratio of 1:5 μg/m^3^ to μg/dl. That is, for every
one microgram per cubic meter reduction in air lead, EPA assumed that
geometric mean blood lead levels would be reduced by five micrograms per
deciliter. We selected this value based on advice from the Clean Air
Scientific Advisory Committee (CASAC) and analysis conducted as part of
EPA's *Lead Risk Assessment* (USEPA, 2007a & 2007d).

CASAC in its March 2007 review of EPA\'s *Lead Risk Assessment*
recommended that EPA apply these ratios as part of a population level
lead risk analysis to inform alternative proposals for a new lead NAAQS
(USEPA, 2007d; see Appendix D). In its previous NAAQS analysis, EPA used
a ratio of 1:2 μg/m^3^ to μg/dl; however, CASAC suggested that ratios
higher than 1:2 may be appropriate based on more recent literature.
CASAC cites the use of a ratio of 1:5 by the World Health Organization
(WHO) in 2000 to better account for lead deposition from air to dust and
soil, and they cite a ratio of 1:9-1:10 based on the data in Schwartz
and Pitcher (1989) on blood lead changes resulting from the phase-out of
lead in gasoline.

As part of its *Lead Risk Assessment*, EPA calculated air:blood ratios
based on the extensive modeling conducted for its case studies and
compared these ratios to values reported in the literature (USEPA,
2007a). For the benefits analysis, we focused on the ratios in Table 5-7
of the *Lead Risk Assessment* that compare the incremental reduction in
air concentrations required to meet lower alternative NAAQS levels to
the corresponding incremental change in blood lead. The ratios for the
general urban and primary lead smelter case studies range from 1:2 to
1:6 for scenarios ranging from the current NAAQS to an alternative NAAQS
of 0.05 g/m^3^ maximum monthly mean. EPA found these values to be
similar to ratios available in the literature, specifically to ratios
reported in a 1984 meta-analysis by Brunekreef (1:3 to 1:6) and to
values calculated from a more recent 2003 study by Hilts (1:7). We
selected as our default estimate a 1:5 air:blood ratio, which
represented the ratio for the change in the urban case study from
current (mean) conditions to an alternative NAAQS of 0.2 g/m^3^ maximum
monthly mean. For sensitivity analysis, we selected a lower bound of the
1:2 ratio from the previous NAAQS and an upper bound of 1:8 as the
midpoint between the Hilts and the Schwartz and Pitcher study-based
estimates.

We divided the maximum quarterly mean lead in air concentrations for
each scenario by the air:blood ratio to estimate the blood lead level in
the population due solely to exposure to ambient air. We then added an
estimate of non-air-related background blood lead level (e.g., from
ingestion of indoor dust or outdoor soil contaminated by lead paint) to
calculate the total geometric mean blood lead level expected in the
population.[^6] For our estimate of non-air-related background, we
selected the midpoint from a range of values reported by CASAC (USEPA,
2007d).[^7] ^,^[^8]

The air:blood ratio provided us with an estimate of the geometric mean
blood lead level across the population of exposed children, which we
then used to estimate the magnitude of health effects benefits. We
assumed that the blood lead level changes in 2020 estimated in this way
are a reasonable representation of lifetime average blood lead level for
children under seven years of age in our study and were used with the
selected dose-response functions without further adjustment.

**Table** **5-3 Air Lead to Blood Lead Ratios**

+--------+------------+------------------------------------------------+
| >      | **         | ***Description***                              |
|  ***Ra | *Source*** |                                                |
| tio*** |            |                                                |
+========+============+================================================+
| *      | USEPA,     | Air:blood ratio applied in EPA's previous      |
| *1:2** | 1978       | NAAQS RIA. More recent evidence suggests blood |
|        |            | lead more sensitive to air concentrations than |
|        |            | previously thought, particularly at lower      |
|        |            | exposure levels; thus, a higher ratio may be   |
|        |            | appropriate for changes from current           |
|        |            | conditions.                                    |
+--------+------------+------------------------------------------------+
| **1:2  | USEPA,     | Ratios in Table 5-7 of EPA's current *Lead     |
| to     | 2007a      | Risk Assessment* (USEPA, 2007a) estimated from |
| 1:6**  |            | modeling of exposures in urban areas and areas |
|        |            | near lead smelters. These ratios compare the   |
|        |            | incremental reduction in air concentrations    |
|        |            | required to meet lower alternative NAAQS       |
|        |            | levels to the corresponding incremental change |
|        |            | in blood lead. This ratio is likely to provide |
|        |            | the best estimate of blood lead associated     |
|        |            | with recent changes in air lead                |
|        |            | concentrations. These ratios for the general   |
|        |            | urban and primary lead smelter case studies    |
|        |            | range from 1:2 to 1:6 for scenarios ranging    |
|        |            | from the current NAAQS to an alternative NAAQS |
|        |            | of 0.05 g/m^3^ maximum monthly mean,           |
|        |            | respectively.                                  |
+--------+------------+------------------------------------------------+
| *      | USEPA,     | Ratio applied by WHO to establish current lead |
| *1:5** | 2007a      | Air Quality Guideline for Europe. Also         |
|        |            | reported in Table 5-7 of EPA's *Lead Risk      |
|        | WHO, 2005  | Assessment* (USEPA, 2007a; see above) for the  |
|        |            | ratio for the change in the urban case study   |
|        |            | from current (mean) conditions to an           |
|        |            | alternative NAAQS of 0.2 g/m^3^ maximum        |
|        |            | monthly mean. Selected as default air:blood    |
|        |            | ratio because it represents reasonable central |
|        |            | estimate of the change from current conditions |
|        |            | to a proposed alternative NAAQS level.         |
+--------+------------+------------------------------------------------+
| **1:3  | B          | Ratios reported in a meta-analysis of surveys  |
| to     | runekreef, | of smelters and urban areas. Based on older    |
| 1:6**  | 1984       | studies that typically reflect ratios for      |
|        |            | children with blood lead levels \> 10 g/dl.    |
+--------+------------+------------------------------------------------+
| *      | Hilts,     | Ratio calculated from more recent study of air |
| *1:7** | 2003       | concentrations and blood lead levels for       |
|        |            | children living near a British Columbia        |
|        |            | smelter during a period of decreasing lead     |
|        |            | emissions. Blood lead levels in this study (4  |
|        |            | -- 10 g/dl) are lower than in the Brunekreef   |
|        |            | studies, but still higher than those modeled   |
|        |            | in EPA's 2007 *Lead Risk Assessment*.          |
+--------+------------+------------------------------------------------+
| **1:9- | USEPA,     | Ratio cited by CASAC in its March 2007         |
| 1:10** | 2007d;     | advisory that was derived from calculations in |
|        | Schwartz   | Schwartz and Pitcher analysis of the impacts   |
|        | and        | of phasing out lead in gasoline. Reflects      |
|        | Pitcher    | ratios for changes from higher baseline lead   |
|        | (1989)     | concentrations than expected under current     |
|        |            | conditions.                                    |
+--------+------------+------------------------------------------------+
|        |            |                                                |
+--------+------------+------------------------------------------------+

***Estimating Avoided Health Effects***

The following section presents the approach we used to quantify the
health benefits of lead due to reductions in the blood lead levels in
the population resulting from lowering the NAAQS.

This analysis estimates the adverse health impact of blood lead levels
on cognitive function (which is most often measured as changes in IQ) in
young children below seven years of age. Cognitive effects are thought
to strongly relate to a child's future productivity and earning
potential (USEPA, 2006b).

The literature does not support the presence of a threshold in the
relationship between blood lead level in children and cognitive effects.
Therefore, we did not incorporate a threshold in our analysis. Many
epidemiological studies examining the link between blood lead level and
children's IQ have found an inverse relationship (i.e., increases in
blood lead levels are associated with decreases in children's IQ), with
more potent effects occurring at lower blood lead levels (e.g., Lanphear
et al., 2005; Canfield et al., 2003). The Workgroup of the Advisory
Committee on Childhood Lead Poisoning Prevention to the Centers for
Disease Control and Prevention (CDC) concluded that overall, the weight
of available evidence supports an inverse association between blood lead
levels and cognitive function in children in the low range of blood lead
levels (i.e., below 10 μg/dl) (CDC, 2005). The CDC also indicates that,
"\[a\] steeper slope in the dose-response curve was observed at lower
rather than higher \[blood lead levels\] BLLs" (page iv). In addition,
EPA's Integrated Risk Information System (IRIS) concluded the following:
"by comparison to most other environmental toxicants, the degree of
uncertainty about the health effects of lead is quite low. It appears
that some of these effects, particularly changes in the levels of
certain blood enzymes and in aspects of children's neurobehavioral
development, may occur at blood lead levels so low as to be essentially
without a threshold" (USEPA, 2004).

In order to quantify the expected changes in IQ points in the population
of children due to the implementation of alternative NAAQS, we utilized
available dose-response functions in the literature. For our primary
estimate, we selected a dose-response relationship from a pooled
analysis of seven prospective studies in North America and Europe
examining the effect of lead on full-scale IQ in children (Lanphear et
al., 2005).[^9]^,^[^10] Blood lead levels were measured in each study
five times over early childhood (at 6, 12 (or 15), 36, 48, and 60
months). Full-scale IQ was measured when the children were between 4 and
10 years of age. Four measures of blood lead were examined by the
authors: concurrent blood lead (defined as the blood lead measured
closest to the IQ test), maximum blood lead (defined as the peak blood
lead measured at any time before the IQ test), average lifetime blood
lead (defined as the mean blood lead from six months to concurrent blood
lead tests), and early childhood blood lead (defined as the mean blood
lead from 6 to 24 months). The authors found that the concurrent and
lifetime blood lead levels were the strongest predictors of IQ deficits
associated with lead exposure.

We used an estimate from this study based on a log-linear relationship
between lifetime blood lead level and IQ score.[^11] The log-linear
relationship was found to be the best fit for the data and the lifetime
blood lead levels exhibited a strong relationship with IQ. In addition,
we found this measure to be the most consistent with the benefits
scenario (see the section in this chapter entitled "Selection of Health
Endpoints for further information). Lanphear reports an IQ decrement of
6.2 points for an increase in lifetime blood lead level from 6.1 to 47.0
μg/dl for the selected model. However, the lowest measured lifetime
blood lead level represented in the Lanphear pooled analysis was 1.47
μg/dl. To estimate IQ effects at blood lead levels below this
"cutpoint," we used a linearized slope, obtained by taking the tangent
to the log-linear function at the point of departure (USEPA, 2007a).

To estimate IQ benefits from blood lead reductions, we first calculated
the expected IQ point loss per child under each of the two scenarios
(the "base case" and the "control scenarios") for each monitor (Equation
1). We then subtracted the "base case" IQ loss from the "control
scenario" IQ loss and multiplied by the population of children six years
of age and younger living within the radius of influence of each monitor
to estimate the total number of IQ points that would be gained by
reducing the NAAQS (Equation 2).

**Equation 1**

[For blood lead levels ≥ cutpoint:]{.underline}

IQ loss = β~1~ ln(PbB/cutpoint) + β~2~ cutpoint

[For blood lead levels \< cutpoint:]{.underline}

IQ loss = β~2~ PbB

Where:

Cutpoint = 1.47 μg/dl (i.e., the lowest observed lifetime blood lead
level);

β~1~ = -3.04 (log-linear regression coefficient from Lanphear (2005),
Table 4);

β~2~ = -2.1 (linear slope); and

PbB = blood lead level (μg/dl).

**Equation 2**

> Δ IQ = (IQ loss ~Control~ -- IQ loss ~Base~) P
>
> Where:
>
> Δ IQ = total number of IQ points gained under the "control scenario"
> in comparison with the "base case" in 2020;
>
> IQ loss ~Control~ = IQ point loss under the "control scenario" per
> child;
>
> IQ loss ~Base~ = IQ point loss under the "base case" per child; and

P = the population of children aged 0 -- 6 within the monitor's radius
of influence.

We also assessed the sensitivity of the IQ benefits to the
epidemiological study selected, using alternative estimates from a
meta-analysis of seven studies (Schwartz, 1993) and a study of 172
children in Rochester, New York (Canfield et al, 2003). The Schwartz
study calculated an overall estimate by linearizing coefficients from
included studies that used natural logarithms of lead as the exposure
index. Regression coefficients for studies with untransformed blood lead
levels were used directly. The Schwartz analysis found a decrease of
0.25 IQ points per 1 μg/dl increase in blood lead level. Using a linear
model between lifetime blood lead level and IQ score, Canfield et al.
(2003) found a decrement of 0.46 IQ points per 1 μg/dl increase in blood
lead level. We used the following equation (Equation 3) for these two
linear dose-response functions:

**Equation 3**

> Δ IQ = \[β (PbB~1~ -- PbB~2~)\] P

Where:

> Δ IQ = total number of IQ points gained under the "control scenario"
> in comparison with the "base case" in 2020;
>
> β = linear regression coefficient (-0.25 for Schwartz and -0.46 for
> Canfield);
>
> PbB~1~ = blood lead level under the "control scenario" (μg/dl);
>
> PbB~2~ = blood lead level under the "base case" (μg/dl); and

P = the population of children aged 0 -- 6 within the monitor's radius
of influence.

#### Benefit Valuation

######### Value of Avoided IQ decrements

The valuation approach we apply for assessing monetary losses associated
with IQ decrements is based on an approach applied in previous EPA
analyses (USEPA, 1997, 2005 & 2006b). The approach expresses the loss to
an affected individual resulting from IQ decrements in terms of foregone
future earnings for that individual.

To estimate the expected monetary value of these effects, we first
estimated the median present value of future earnings at time of birth
for a person born in the U.S., based on earnings and labor force
participation rate data from the 2006 Current Population Survey
(CPS).[^12] When calculating the lifetime earnings estimate, we assumed
an individual born today would begin working at age 16 and retire at age
67. We assumed a real growth rate for wages of one percent per year, as
assumed in EPA's Section 812 retrospective analysis (US EPA, 1997);
adjusted for survival probabilities based on current US vital statistics
from the CDC's National Center for Health Statistics;[^13] and adjusted
for labor force participation by age. We then discounted the expected
lifetime stream of wages using a three percent annual rate. As in EPA's
*Economic Analysis for the Renovation, Repair, and Painting Program
Proposed Rule* (EPA, 2006b), we assumed children will be affected by
lead at age three, the midpoint of the range during which children are
thought to be most susceptible to lead. Therefore, we discounted
lifetime earnings back to age three. We estimated present value median
lifetime earnings to be \$606,930 in 2006 dollars.

In the previous EPA analyses cited above, the Agency has applied an
average estimate of the effect of IQ on earnings of 2.379 percent per IQ
point from an analysis by Salkever (1995).[^14] An analysis by Schwartz
(1994b) estimated that a 1-point increase in IQ would increase earnings
by 1.76 percent. The percentage increases in both studies reflect both
direct impact of IQ on hourly wages and indirect effects on annual
earnings as the result of additional schooling and increased labor force
participation. A recent review of literature from the labor economics
and environmental health fields by CDC economist Scott Grosse suggests
that both of these studies may have overestimated the association of IQ
with earnings (2007). Specifically, he found the Salkever estimate of
direct impacts of IQ on wages to be higher than estimates reported in
the labor economics literature. Grosse also found that the Schwartz
study overestimates the cognitive impact of lead exposure on earnings,
but he argues that the Schwartz estimate may still be appropriate for
estimating the total effect of lead on earnings, because it includes the
effects of lead on education and earnings that result from both
cognitive and non-cognitive changes. Thus, it may be a more
comprehensive estimate than one based on cognitive changes alone.

In recognition of the fact that the economics literature continues to
evolve, and because EPA has traditionally relied upon the Salkever
(1995) estimate to value changes in IQ, for this analysis we provide a
range of valuation estimates based on both the Salkever (1995) and the
Schwartz (1994b) functions. Below we describe how we estimate the cost
per IQ decrement using each function.

The 1.76 percent estimate from Schwartz represents a gross impact on
earnings; it does not account for the costs of additional schooling.
EPA's Clean Air Mercury Rule (CAMR) RIA (USEPA, 2005) reported an
estimate of \$16,425 per additional year of schooling in 1992 dollars,
based on U.S. Department of Education data reflecting both direct annual
expenditures per student and annual average opportunity cost (i.e., lost
income from being in school). Consistent with the CAMR analysis, we
assume that these costs are incurred when an individual born today turns
19, based on an average 12.9 years of education among people aged 25 and
over in the U.S. We discount the educational costs back to a present
value at age 3, to be consistent with the present value of lifetime
earnings. We then adjust this value to 2006 dollars, resulting in an
estimated \$14,700 per additional year of schooling. Schwartz reports an
increase of 0.131 years of schooling per IQ point (1994b); thus the
change in average education costs per IQ point is \$14,700 0.131 =
\$1,930.

Using the Schwartz function, we calculated the present value of the
median net earnings loss associated with one IQ point as the present
value of median lost earnings per IQ point lost (\$606,930 0.0176 =
\$10,682) minus the change in average education costs per IQ point
(\$1,930). These calculations yield a value of \$8,760 of net earnings
lost per a one-point decrease in IQ using a 3% discount rate and a value
of \$1,094 at a 7% discount rate.

#### 

To estimate the cost per IQ point using Salkever (1995), we followed the
same set of steps as above, substituting the Salkever estimate of the
change in lifetime earnings. These calculations yield a value of
\$12,512 of net earnings lost per a one point decrease in IQ using a 3%
discount rate and a value of \$2,156 at a 7% discount rate.

#### Results

This section presents the health effects results and the associated
monetary benefits. We first present the expected IQ point gains in 2020,
comparing each of the "control scenarios" to the "base case." We then
provide the expected monetized value of those gains in IQ in 2020. We
also describe an analysis we performed to assess the sensitivity of the
model to the various inputs used and assumptions made. Finally, we
explain the methodology we applied for estimating monetized health
benefits from co-control of PM~2.5~ and the results of that analysis.

###### 

###### **Changes in IQ**

Table 5-4 below presents the total number of IQ points expected to be
gained in the US in the year 2020 by achieving each of the alternate
NAAQS level options, when compared to the "base case" (i.e., the lead
NAAQS remains at its current level). Our results indicate that the
number of IQ points gained in 2020 ranges from 200,000 if a 0.3 second
maximum monthly mean NAAQS is achieved up to 700,000 for a 0.05 second
maximum monthly mean NAAQS. These IQ point gains are valued at between
\$1.7 and \$6.1 billion at a 3% discount rate and between \$220 and
\$760 million at a 7% discount rate (2006\$).

**Table** **5-4. Number of IQ Points Gained and Monetary Benefits (in
Millions of 2006\$) in 2020**

+-----------------------------+----------+-------------+--------------+
| > *Standard Alternative*    | *IQ      | *Estimated  |              |
|                             | Points   | Net Present |              |
|                             | Gained*  | Value of IQ |              |
|                             |          | Points      |              |
|                             |          | Gained\**   |              |
+-----------------------------+----------+-------------+--------------+
|                             |          | 3% Discount | 7% Discount  |
|                             |          | Rate        | Rate         |
+-----------------------------+----------+-------------+--------------+
| 0.3 Second Maximum Monthly  | 200,000  | \$1,70      | \            |
| Mean                        |          | 0\--\$2,500 | $220\--\$430 |
+-----------------------------+----------+-------------+--------------+
| 0.2 Second Maximum Monthly  | 280,000  | \$2,50      | \            |
| Mean                        |          | 0\--\$3,500 | $310\--\$610 |
+-----------------------------+----------+-------------+--------------+
| 0.1 Second Maximum Monthly  | 440,000  | \$3,90      | \            |
| Mean                        |          | 0\--\$5,500 | $480\--\$950 |
+-----------------------------+----------+-------------+--------------+
| 0.05 Second Maximum Monthly | 700,000  | \$6,10      | \$7          |
| Mean                        |          | 0\--\$8,700 | 60\--\$1,500 |
+-----------------------------+----------+-------------+--------------+
| \*Lower end of range        |          |             |              |
| calculated using Schwartz   |          |             |              |
| (1994b) estimate; upper end |          |             |              |
| calculated using Salkever   |          |             |              |
| (1995) estimate.            |          |             |              |
+-----------------------------+----------+-------------+--------------+

We also assessed the geographic distribution of these benefits. We found
that the benefits were concentrated in a small number of counties. Table
5-5 below is an example of the distribution of total benefits due to IQ
points gained for the 0.2 μg/m^3^ second maximum monthly mean NAAQS
alternative. For this standard, approximately 60 percent of the total
benefits are due to changes in lead air concentrations in three
counties: Hillsborough, Florida; Delaware, Indiana; and Berks, PA.
Please see Appendix B for tables providing the percentage of total
health benefits by county for all of the four alternative NAAQS levels.

**Table** **5-5. Percentage of Benefits by** **County** **(0.2 μg/m^3^**
Second Maximum Monthly Mean NAAQS)

  -------------- --------- --------------- ---------------- -------------------
  *County*       *State*   *Population of  *Affected        *Percentage of
                           Children in     Population*      Benefits\
                           Affected Area*  *(%)*            (%)*

  Hillsborough   FL        46,923          18               31

  Delaware       IN        9,236           3                19

  Berks          PA        23,977          9                10

  Collin         TX        16,593          6                7

  Adams          CO        25,746          10               6

  Denver         CO        40,395          15               5

  Pike           AL        2,342           1                4

  Denton         TX        6,301           2                4

  Cuyahoga       OH        35,680          13               3

                                                            

                                                            

  Jefferson      CO        8,689           3                2

  Jefferson      MO        7,358           3                1

  Note: There                                               
  were several                                              
  other counties                                            
  that                                                      
  constituted                                               
  less than 1                                               
  percent of                                                
  benefits that                                             
  are not                                                   
  included in                                               
  this table.                                               
  -------------- --------- --------------- ---------------- -------------------

###### 

###### **IQ Sensitivity Analysis**

####### We performed a sensitivity analysis on the benefits model in order to assess the total range of potential benefits and to determine the sensitivity of the primary model results to various data inputs and assumptions. We used the model to calculate the total monetary benefits due to gains in children's IQ for the 0.2 second maximum monthly mean NAAQS option using our default model input assumptions.[^15] We then changed each default input one at a time and recalculated the total benefits to assess the percent change from the default. Table 5-6 below presents the results of this sensitivity analysis. The table indicates for each input parameter the value used as the default (in bold) and the values for the sensitivity analyses. It then provides the total monetary benefits for each input and the percent change from the default value. 

####### 

Our sensitivity analysis results indicate that the benefits model is
most sensitive to the method used for assigning air lead exposure
concentrations to the population of exposed children. Our primary
estimate relied on an interpolation method, where several monitor
concentrations were used in determining the exposure concentration. When
the radius method was employed as part of the sensitivity analysis, the
results varied. We assumed that monitor concentration applied to the
population residing within a 10 km radius as a best estimate of the
exposed population. When compared with the interpolation method, this
increased results by 31 percent. The size of the radius assumed when
using the radius method also had a large impact on the results. When the
radius size was reduced to 5, 2, and 1 km for monitors associated with a
lead source, the benefits are significantly reduced (i.e., total
monetary benefits are reduced by 66, 94, and 98 percent, respectively).
In addition, if the monitor concentration is assumed to apply to the
population of the entire county in which that monitor resides, the
benefits increase significantly (323 percent).

The discount rate also had a significant impact on results, because the
benefits of lead on earnings occur over a lifetime, and the net present
value of those earnings is highly sensitive to the discount rate
applied. When the discount rate was changed from the default (3 percent)
to a rate of 7 percent, the benefits fell by 88 percent.

**Table** **5-6. Sensitivity Analysis for the Primary Estimate of Health
Benefits (for the 0.2 μg/m^3^ Second Maximum Monthly Mean Results^\*^**

![](media/image7.wmf){width="5.648611111111111in"
height="4.215277777777778in"}

  ------------------- ---------------------- --------------- --------------
                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             

                                                             
  ------------------- ---------------------- --------------- --------------

The results were also found to be sensitive to the epidemiological study
selected for calculating IQ point gains in 2020, with results decreasing
by between 54 and 74 percent when dose-response functions derived from
the Canfield et al. (2003) and Schwartz (1993) studies are used, as
compared to the default function from Lanphear et al. (2005).

Inputs that had a moderate impact on the benefits results include the
air:blood ratio selected to convert lead air concentrations into blood
lead levels in the population and the non-air-related geometric mean
blood lead level used.

####### 

####### *PM Co-Control Benefits -- Methodology and Results*

As outlined in Chapter 4, most of the point source measures implemented
to achieve the NAAQS standards are focused on controlling emissions of
lead in particulate form. As a result, virtually all of these measures
also have a significant impact on emissions of directly emitted
particulate matter. Table 5-7 lists the PM-related health effects that
are included in our monetized benefits estimate incorporating PM
co-benefits.[^16]

In Chapter 4 we identified control technologies to reduce emissions of
lead that also reduce PM~2.5~. However, in some areas, more emission
reductions are needed than can be achieved through identified control
options (i.e., unidentified controls). The identified and unidentified
controls are shown in Table 5-8 below. These emission reduction
estimates are incremental to a baseline that reflects emission
reductions from MACT controls and the PM~2.5~ NAAQS RIA.

#### Table 5-7. Health and Welfare Effects of PM~2.5~.

#### 

+---------------------+-----------------------+------------------------+
| ***Effect***        | ***Quantified Health  | ***Unquantified Health |
|                     | Effects***            | Effects^e^***          |
+---------------------+-----------------------+------------------------+
| Health^a,b^         | -Premature mortality  | -Subchronic bronchitis |
|                     | based on both cohort  | cases                  |
|                     | study estimates and   |                        |
|                     | on expert             | -Low birth weight      |
|                     | elicitation^c,d^      |                        |
|                     |                       | -Pulmonary function    |
|                     | -Bronchitis: chronic  |                        |
|                     | and acute             | -Chronic respiratory   |
|                     |                       | diseases other than    |
|                     | -Hospital admissions: | chronic bronchitis     |
|                     | respiratory and       |                        |
|                     | cardiovascular        | -Non-asthma            |
|                     |                       | respiratory emergency  |
|                     | -Emergency room       | room visits            |
|                     | visits for asthma     |                        |
|                     |                       |                        |
|                     | -Nonfatal heart       |                        |
|                     | attacks (myocardial   |                        |
|                     | infarction)           |                        |
|                     |                       |                        |
|                     | -Lower and upper      |                        |
|                     | respiratory illness   |                        |
|                     |                       |                        |
|                     | -Minor                |                        |
|                     | restricted-activity   |                        |
|                     | days                  |                        |
|                     |                       |                        |
|                     | -Work loss days       |                        |
|                     |                       |                        |
|                     | -Asthma exacerbations |                        |
|                     | (asthmatic            |                        |
|                     | population)           |                        |
|                     |                       |                        |
|                     | -Respiratory symptoms |                        |
|                     | (asthmatic            |                        |
|                     | population)           |                        |
|                     |                       |                        |
|                     | -Infant mortality     |                        |
+---------------------+-----------------------+------------------------+
| Welfare             |                       | -Visibility in Class I |
|                     |                       | areas                  |
|                     |                       |                        |
|                     |                       | -Household soiling     |
|                     |                       |                        |
|                     |                       | -Visibility in         |
|                     |                       | residential and        |
|                     |                       | non-Class I areas      |
+---------------------+-----------------------+------------------------+
| ^a^ Because the PM  |                       |                        |
| co-benefits are     |                       |                        |
| estimated on a      |                       |                        |
| \$-per-ton basis,   |                       |                        |
| we do not report    |                       |                        |
| quantitative        |                       |                        |
| estimates for       |                       |                        |
| individual PM       |                       |                        |
| health effects.     |                       |                        |
|                     |                       |                        |
| ^b^ In addition to  |                       |                        |
| primary economic    |                       |                        |
| endpoints, there    |                       |                        |
| are a number of     |                       |                        |
| biological          |                       |                        |
| responses that have |                       |                        |
| been associated     |                       |                        |
| with PM health      |                       |                        |
| effects, including  |                       |                        |
| morphological       |                       |                        |
| changes and altered |                       |                        |
| host defense        |                       |                        |
| mechanisms. The     |                       |                        |
| public health       |                       |                        |
| impact of these     |                       |                        |
| biological          |                       |                        |
| responses may be    |                       |                        |
| partly represented  |                       |                        |
| by our quantified   |                       |                        |
| endpoints.          |                       |                        |
|                     |                       |                        |
| ^c^ Cohort          |                       |                        |
| estimates are       |                       |                        |
| designed to examine |                       |                        |
| the effects of      |                       |                        |
| long-term exposures |                       |                        |
| to ambient          |                       |                        |
| pollution, but      |                       |                        |
| relative risk       |                       |                        |
| estimates may also  |                       |                        |
| incorporate some    |                       |                        |
| effects due to      |                       |                        |
| shorter-term        |                       |                        |
| exposures (see      |                       |                        |
| Kunzli et al., 20   |                       |                        |
| 01).                |                       |                        |
|                     |                       |                        |
| ^d^ While some of   |                       |                        |
| the effects of      |                       |                        |
| short-term exposure |                       |                        |
| are likely to be    |                       |                        |
| captured by the     |                       |                        |
| cohort estimates,   |                       |                        |
| there may be        |                       |                        |
| additional          |                       |                        |
| premature mortality |                       |                        |
| from short-term PM  |                       |                        |
| exposure not        |                       |                        |
| captured in the     |                       |                        |
| estimates included  |                       |                        |
| in the primary      |                       |                        |
| analysis.           |                       |                        |
|                     |                       |                        |
| ^e^ The             |                       |                        |
| categorization of   |                       |                        |
| unquantified toxic  |                       |                        |
| health and welfare  |                       |                        |
| effects is not      |                       |                        |
| exhaustive. Health  |                       |                        |
| endpoints in this   |                       |                        |
| column include both |                       |                        |
| a) those for which  |                       |                        |
| there is not        |                       |                        |
| consensus on        |                       |                        |
| causality; and b)   |                       |                        |
| those for which     |                       |                        |
| causality has been  |                       |                        |
| determined but      |                       |                        |
| empirical data are  |                       |                        |
| not available to    |                       |                        |
| allow calculation   |                       |                        |
| of benefits.        |                       |                        |
+---------------------+-----------------------+------------------------+

#### Table 5-8. Summary of Estimated Co-Controlled PM~2.5~ Emissions Reductions (in Tons)

  ----------------- ----------------- ----------------- -----------------
  *Alternate NAAQS  *Identified       *Unidentified     *All Controls*
  (Second Maximum   Controls*         Controls*         
  Monthly Mean)*                                        

  0.3 μg/m^3^       6,073             46                **6,120**

  0.2 μg/m^3^       8,134             248               **8,382**

  0.1 μg/m^3^       9,065             1,237             **10,302**

  0.05 μg/m^3^      9,648             6,044             **15,692**

                                                        
  ----------------- ----------------- ----------------- -----------------

To estimate the value of these PM~2.5~ emissions reductions, EPA
utilized PM~2.5~ benefit-per-ton estimates. These PM~2.5~
benefit-per-ton estimates provide the total monetized human health
benefits (the sum of premature mortality and premature morbidity) of
reducing one ton of PM~2.5~ from a specified source. EPA has used a
similar technique in previous RIAs, including the recent ozone NAAQS RIA
(USEPA, 2008). The fourteen estimates presented below derive from the
application of three alternative methods:

-   One estimate is based on the concentration-response (C-R) function
    developed from a study of the American Cancer Society (ACS) cohort
    reported in Pope et al. (2002), which has previously been reported
    as the primary estimate in recent RIAs (USEPA, 2006c).

-   One estimate is based on Laden et al.'s (2006) reporting of the
    extended Six Cities cohort study; this study is a more recent PM
    epidemiological study that was used as an alternative in the PM
    NAAQS RIA.

-   The other twelve estimates are based on the results of EPA\'s expert
    elicitation study on the PM-mortality relationship, as first
    reported in Industrial Economics (2006) and interpreted for benefits
    analysis in EPA\'s final RIA for the PM NAAQS, published in
    September 2006 (USEPA, 2006c). For that study, twelve experts
    (labeled A through L) provided independent estimates of the
    PM-mortality C-R function. EPA practice has been to develop
    independent estimates of PM-mortality estimates corresponding to the
    C-R function provided by each of the twelve experts.

Readers interested in reviewing the complete methodology for creating
the benefit per-ton estimates used in this analysis can consult the
Technical Support Document (TSD) accompanying the recent final ozone
NAAQS RIA (USEPA 2008).[^17]

As described in the documentation for the benefit per-ton estimates
cited above, national per-ton estimates are developed for selected
pollutant/source category combinations. The per ton values calculated
therefore apply only to tons reduced from those specific
pollutant/source combinations (e.g., SO~2~ emitted from electric
generating units; NO~x~ emitted from mobile sources). Emissions controls
modeled in this RIA are all applied to point sources; a few are at
electric generating units (EGUs), but most are at industrial facilities
involved in handling lead as a manufacturing product, byproduct, or
input. From among the list of pollutant/source combinations outlined in
the TSD referenced above, the combination most appropriate for valuation
of PM~2.5~ emissions reductions from the sources controlled for lead
emissions is the combination for PM~2.5~ from EGU and non-EGU point
sources. Estimates of this per-ton value for a 3 percent discount rate
vary from a low of \$67,000 per ton to a high of \$560,000 per ton
(based on a change in emissions of 25 percent or less from a 2015 PM
emissions base, in 2006\$). Our estimate of PM~2.5~ co-control benefits
is therefore based on the total PM~2.5~ emissions controlled multiplied
by this per-ton value. The results of this calculation are provided in
Table 5-9 below. Figures 5-7 and 5-8 provide a graphical representation
of the 14 estimates of PM co-control benefits for PM~2.5~, using both a
3 percent and 7 percent discount rate.

**Table** **5-9. Monetized Benefits of Co-Controlled PM~2.5~ Emissions
(in Millions of 2006\$)**

  ------------------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- ---------
  *Alternative*       *Pope et  *Laden et *Expert   *Expert   *Expert   *Expert   *Expert   *Expert   *Expert   *Expert   *Expert   *Expert   *Expert   *Expert
                      al.       al.       A*        B*        C*        D*        E*        F*        G*        H*        I*        J*        K*        L*
                      (2002)*   (2006)*                                                                                                                 

  [3 Percent Discount                                                                                                                                   
  Rate]{.underline}                                                                                                                                     

  0.3 μg/m^3^         940       2,000     2,800     2,200     2,100     1,500     3,500     1,900     1,300     1,600     2,100     1,700     410       1,600

  0.2 μg/m^3^         1,300     2,800     3,800     3,000     2,900     2,100     4,700     2,700     1,700     2,200     2,900     2,400     560       2,100

  0.1 μg/m^3^         1,600     3,400     4,700     3,600     3,600     2,500     5,800     3,300     2,100     2,700     3,500     2,900     690       2,600

  0.05 μg/m^3^        2,400     5,200     7,200     5,500     5,500     3,900     8,900     5,000     3,300     4,100     5,400     4,400     1,100     4,000

  [7 Percent Discount                                                                                                                                   
  Rate]{.underline}                                                                                                                                     

  0.3 μg/m^3^         850       1,800     2,500     1,900     1,900     1,400     3,100     1,800     1,200     1,500     1,900     1,600     380       1,400

  0.2 μg/m^3^         1,200     2,500     3,500     2,700     2,600     1,900     4,300     2,400     1,600     2,000     2,600     2,100     520       1,900

  0.1 μg/m^3^         1,400     3,100     4,200     3,300     3,200     2,300     5,200     3,000     1,900     2,400     3,200     2,600     640       2,400

  0.05 μg/m^3^        2,200     4,700     6,500     5,000     4,900     3,500     8,000     4,500     3,000     3,700     4,900     4,000     1,000     3,600

  Note: All estimates                                                                                                                                   
  have been rounded                                                                                                                                     
  to two significant                                                                                                                                    
  figures. All                                                                                                                                          
  estimates are                                                                                                                                         
  incremental to the                                                                                                                                    
  2006 PM NAAQS RIA.                                                                                                                                    
  These estimates do                                                                                                                                    
  not include                                                                                                                                           
  confidence                                                                                                                                            
  intervals because                                                                                                                                     
  they were derived                                                                                                                                     
  through a scaling                                                                                                                                     
  technique described                                                                                                                                   
  in the text.                                                                                                                                          
  ------------------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- --------- ---------

**Figure 5-7. Distribution of Total PM~2.5~ Monetized Co-Benefits by
Lead Standard Alternative (3% Discount Rate)**

![](media/image8.png){width="6.990277777777778in"
height="4.695833333333334in"}

**Figure 5-8. Distribution of Total PM~2.5~ Monetized Co-Benefits by
Lead Standard Alternative (7% Discount Rate)**

![](media/image9.png){width="7.0in" height="4.702083333333333in"}

**Discussion**

#### 

#### The results of this benefits analysis demonstrate that lowering the current (1.5 μg/m^3^ maximum quarterly mean) lead NAAQS to one of the proposed alternative NAAQS would be expected to have a significant impact on the IQ of young children. Lowering the standard could cause an increase in total IQ points by between 200,000 and 700,000 points in 2020, which would be valued at between 1.7 and 6.1 billion 2006\$. In addition, controls installed to achieve the lead NAAQS standards will also reduce emissions of fine particulates. As a result, this analysis includes a screening level calculation that indicates each of the alternatives considered could have a significant benefit in terms of improved particulate air quality, reduced health effects, and increased economic welfare of currently exposed individuals. 

#### 

This benefits analysis is intended to be an initial screening
investigation to provide a first estimate of the potential magnitude of
the benefits of reducing the lead NAAQS. Therefore, the results of this
analysis are associated with a number of uncertainties. The benefits of
IQ point gains in children were very sensitive to the method employed
for estimating exposures to the population. When comparing the default
method, which involved concentrations that were interpolated from
multiple monitors, to the method assuming a uniform concentration within
a 10 km radius around an individual monitor, the results increase by 31
percent. Increasing the radius to include the entire county in which the
monitor resides results in roughly 3-fold increase in benefits.
Decreasing the radius size also has a large impact on benefits,
decreasing the value by as much as 98 percent when a radius of 1 km is
used. The results were also fairly sensitive to the discount rate
selected. When a 7 percent discount rate was used in place of the
default rate of 3 percent, results decreased by 88 percent. This is in
part because the benefits of lead on earnings occur over a lifetime, and
the net present value of those earnings is highly sensitive to the
discount rate applied. The dose-response function selected for
quantifying the number of IQ points gained as a result of achieving the
alternative NAAQS levels affected the results. Utilizing alternate
epidemiological studies decreased the primary estimate by as much as 74
percent. However, we believe the Lanphear et al. (2005) study was the
best choice for our primary estimate. This study was a meta-analysis
that synthesized a range of existing information and is based on more
recent data than the studies included in the Schwartz (1993) study. In
addition, the log-linear model was the most robust estimate from this
study, in that it was the best fit for the data.

Additional uncertainties related to the benefits estimates include the
following:

-   For our primary estimate of the benefits due to gains in children's
    IQ, we used a log-linear estimate from a recently published pooled
    analysis of seven studies (Lanphear et al., 2005). Using alternate
    estimates from other epidemiological studies examining the link
    between blood lead level and children's IQ has significant impact on
    benefits results. We found the benefits to decrease by as much as 74
    percent when an alternate estimate from a paper by Schwartz (1993)
    is used. This is due in part to the underlying shape of the
    dose-response relationship assumed by each of the functions. In the
    Lanphear study, a log-linear relationship was found to be the best
    fit for the data (i.e., the natural log-transformed blood lead level
    is used to predict changes in IQ score). This model implies that the
    magnitude of changes in IQ increases with lower blood lead levels.
    However, in the Schwartz (1993) and Canfield et al. (2003) studies,
    a single linear model is assumed (i.e., untransformed blood lead
    levels are used to predict changes in IQ score). The single linear
    model implies that the magnitude of change in IQ is constant over
    the entire range of blood lead levels. Therefore, at lower blood
    lead levels, the log-linear model predicts larger changes in IQ than
    the linear model. Note that CASAC, in their review of EPA's *Lead
    Risk Assessment* indicated that "studies show that the decrements in
    intellectual (cognitive) functions in children are proportionately
    greater at PbB concentrations \<10 μg/dl" (USEPA, 2007d, page 3).
    However, if the true dose-response relationship is linear, than our
    primary estimate of benefits is an overestimation.

-   Some uncertainty is involved in the estimates of maximum quarterly
    mean lead air concentrations used for the benefits model. We used
    ratios of second maximum monthly mean values to maximum quarterly
    mean values from lead monitoring data from 2003-2005 to convert the
    second maximum monthly mean values in 2020 into a maximum quarterly
    mean for the "base case" as well as to convert the alternative
    second maximum monthly mean NAAQS into a maximum quarterly mean for
    the "control scenarios." If the true ratio between the second
    maximum monthly means to the maximum quarterly mean is different in
    2020 than in 2003-2005 because the pattern and distribution of daily
    values differs, then our results could be either over- or
    underestimated.

-   The interpolation method of estimating exposure concentrations that
    we used for our primary estimate is associated with some
    uncertainty. The validity of this method is to some extent
    contingent upon the availability of a sufficient number of monitors
    to support an interpolation. In certain locations, such as
    Hillsborough County, FL, there are a sufficient number of lead and
    TSP monitors to generate an interpolation with a pronounced gradient
    around each monitor. The lead and TSP monitoring network in other
    non-attainment areas can in some cases be sparse, and the resulting
    interpolation does not appear to generate a meaningful gradient,
    such as in Delaware County, IN.

-   The application of the monitor rollback technique to estimate full
    attainment air quality changes benefits introduces some uncertainty
    to the analysis. This technique simulates the air quality change
    associated with an emissions control strategy that is capable of
    just attaining each standard alternative at each monitor., This
    approach to estimating air quality changes is different from the
    reduced-form air quality model employed to develop the emissions
    control strategy. When utilizing this reduced-form model to identify
    control strategies for each standard alternative, in certain cases
    emission controls achieved reductions in ambient lead below the
    standard alternative under analysis,. In other cases, the modeled
    control strategies were insufficient to model full attainment with
    all monitors. The monitor rollback approach used to estimate full
    attainment benefits does not reflect this variability in attainment
    status, because it adjusts the violating monitor value down to, but
    not below, the standard alternative. Thus, where the control
    strategy attains air quality improvements below the standard at
    violating monitors, the monitor rollback approach will not reflect
    the additional benefits associated with this air quality
    improvement. Conversely, where the control strategy does not fully
    attain the standard alternative at a given monitor, the rollback
    technique would overstate benefits because it adjusts the monitor
    value all the way down to the standard, below a level actually
    achieved by the control strategy.

-   The estimation of the population to which the benefits apply when
    using the radius method of exposure estimation is uncertain. We made
    a number of assumptions in the process of estimating the population
    living within the 10 km radius around each monitor. First, we
    assumed that the population within each census block group is
    uniformly distributed, and therefore, that the fraction of the block
    group geographically that overlapped with the radius corresponded to
    the fraction of the population living within the radius. In
    addition, we used 2000 Census data to calculate the population
    living within each radius and distributed it into five-year age
    groups by gender using the 2000 Census data for the county in which
    the monitor resides. We assumed that block groups falling inside the
    radius that reside in neighboring counties had the same age and
    gender distributions as the county in which the monitor resides. If
    these assumptions are inaccurate, the benefits results could
    potentially be under- or overestimated.

-   We assumed that the IQ point effects of a change in concurrent blood
    lead (i.e., the effects of a change in 2020) apply to all children
    in our study population that were under seven years of age in 2020.
    If there is a critical window of exposure for IQ effects (e.g.,
    between the ages of one and two), then we could potentially be
    overestimating benefits in 2020 because we would have overestimated
    the population affected by reduced lead exposure in that year.
    However, if partial or full achievement of the alternative NAAQS
    levels might occur earlier than 2020, the children in our 0-6 age
    cohort who are past any critical window in 2020 would have realized
    the partial or full benefits of reduced lead exposures in those
    earlier years. Thus, the issue of a potential critical developmental
    window reflects uncertainty in both the timing and size of benefits.

-   The use of air:blood ratios represents a first approximation to the
    impact of changes in ambient air concentrations of lead on
    concurrent blood lead levels, applied in the absence of modeling
    data on lead transport and deposition and the on direct and indirect
    human exposures. While the values we apply match fairly well with
    available literature, there are relatively few studies that report
    such values or provide sufficient data to calculate such ratios.
    Further, the lead concentrations in those studies tend to be higher
    than those modeled here (USEPA, 2007a); thus uncertainty remains as
    to whether the same ratios would be expected at lower levels, or
    whether air exposures are more or less efficient at changing
    concurrent blood lead levels at these lower concentrations.

-   If the air:blood ratio we apply for children or a similar value is
    also valid for estimating adult exposures, then our primary benefits
    understate the true health benefits accruing to the lead-exposed
    populations because they exclude impacts on morbidity and mortality
    impacts on adults as well as impacts on prenatal mortality.
    Additional research is needed to improve our understanding of the
    impacts of adult air exposure on adult blood lead levels.

-   The earnings-based value-per-IQ-point lost that we apply in this
    analysis most likely represents a lower bound on the true value of a
    lost IQ point, because it is essentially a cost-of-illness measure,
    not a measure of an individual's willingness-to-pay (WTP) to avoid
    the loss of an IQ point. Welfare economics emphasizes WTP measures
    as the more complete estimate of economic value; for example, the
    earnings-based value does not include losses in utility due to pain
    and suffering, nor does it assess the costs of averting behaviors
    that may be undertaken by households to avoid or mitigate IQ loss
    from lead exposure.

-   The earnings-based estimate of the value-per-IQ-point lost is based
    on current data on labor-force participation rates, survival
    probabilities, and assumptions about educational costs and real wage
    growth in the future. To the extent these factors diverge from these
    values in the future, our lifetime earnings estimate may be under-
    or overestimated. Another factor suggesting that our lifetime
    earnings estimate may be an underestimate is that it does not
    account for the value of productive services occurring outside the
    labor force (e.g., child rearing and housework).

-   Co-control benefits estimated here reflect the application of a
    national dollar benefit per ton estimate of the benefits of reducing
    directly emitted fine particulates from point sources. Because they
    are based on national-level analysis, the benefit-per-ton estimates
    used here do not reflect local meteorology, exposure, baseline
    health incidence rates, or other local factors that might lead to an
    over-estimate or under-estimate of the actual benefits of
    controlling directly emitted fine particulates.

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[^1]: Development of this base case is described further in Chapter 4.

[^2]: Readers interested in reviewing the technical details of the VNA
    approach may consult the technical appendices to the BenMAP User
    manual, found at:
    <http://www.epa.gov/air/benmap/models/BenMAPTechnicalAppendicesDraftMay2005.pdf>

[^3]: This was assessed using second maximum monthly mean monitoring
    data between 2003-2005 for eight monitors located near the
    Herculaneum Lead Smelter (operated by the Doe Run Company) (USEPA,
    2007b).

[^4]: In two instances, the radius drawn around one monitor overlapped
    with the radius drawn around another monitor. The first case
    affected monitors located in Adams and Denver counties in Colorado
    and the second affected monitors located in Madison and St. Claire
    counties in Illinois. We assigned the highest measured concentration
    at the two monitors to the population residing in the overlapping
    area.

[^5]: The five-year age groups were 0-4, 5-9, 10-14, ... up to 85 and
    above.

[^6]: We estimated total blood lead level to be consistent with the
    epidemiological studies underlying the dose-response functions we
    used for estimating changes in IQ due to changes in lead exposure,
    which are based on total blood lead level.

[^7]: CASAC provided a range of non-air-related background geometric
    mean concentrations of 1.0 -- 1.4 μg/dl in their comments on EPA's
    *Lead Risk Assessment* (USEPA, 2007a). We selected the midpoint of
    this range, 1.2 μg/dl, for this analysis.

[^8]: We apply this estimate of current-year non-air background blood
    lead for an analysis year of 2020. State and federal interventions
    such as the Renovation and Repair Rule (EPA, 2008) may reduce future
    non-air blood lead to a level below this estimate.

[^9]: Full-scale IQ is a composite score of verbal and performance
    tests. Children were administered a version of the Wechsler
    Intelligence Scales for Children under uniform conditions within
    each study (Lanphear et al., 2005).

[^10]: The seven cohort studies included in this analysis include sites
    in Boston, Massachusetts (Bellinger et al., 1992); Cincinnati and
    Cleveland, Ohio (Dietrich et al., 1993 and Ernhart et al., 1989);
    Mexico City, Mexico (Schnaas et al., 2000); Rochester, New York
    (Canfield et al., 2003); and Yugoslavia (Wasserman et al., 1997).

[^11]: The natural log of the blood lead levels were used for this
    analysis.

[^12]: See [http://www.bls.gov/cps/home.htm -
    data](http://www.bls.gov/cps/home.htm#data).

[^13]: See <http://www.cdc.gov/nchs/data/nvsr/nvsr54/nvsr54_14.pdf>.

[^14]: The 812 Retrospective analysis also included an estimate based on
    older work by Needleman et al. (1990).

[^15]: Note that for the sensitivity analysis, we relied on the results
    that incorporated the valuation estimate for IQ from Schwartz
    (1994b).

[^16]: Because the PM co-benefits are estimated on a \$-per-ton basis,
    we do not report quantitative estimates for individual PM health
    effects.

[^17]: The Technical Support Document, entitled: *Calculating Benefit
    Per-Ton Estimates*, can be found in EPA Docket
    EPA-HQ-OAR-2007-0225-0284.