**Overview**

This Regulatory Impact Analysis (RIA) estimates the incremental costs
and monetized human health and welfare benefits of attaining a revised
primary lead (Pb) National Ambient Air Quality Standard (NAAQS)
nationwide. Hypothetical control strategies were developed for four
alternative Pb standards encompassing the proposed range of 0.10 μg/m^3^
to 0.30 μg/m^3^, as well as an alternative standard of 0.05 μg/m^3^, in
order to illustrate how tighter standards might be met. This summary
outlines the basis for and approach used for estimating the incremental
costs and monetized benefits of these standards, presents the key
results of the analysis, highlights key uncertainties and limitations,
and outlines major conclusions and insights.

In setting primary ambient air quality standards, EPA's responsibility
under the law is to establish standards that protect public health. The
Clean Air Act requires EPA, for each criteria pollutant, to set a
standard that protects public health with "an adequate margin of
safety." As interpreted by the Agency and the courts, the Act requires
EPA to create standards based on health considerations only. Economic
factors cannot be considered.

The prohibition against the consideration of cost in the setting of the
primary air quality standard, however, does not mean that costs or other
economic considerations are unimportant or should be ignored. The Agency
believes that consideration of costs and benefits is essential to making
efficient, cost effective decisions for implementation of these
standards. The impact of cost and efficiency are considered by states
during this process, as they decide what timelines, strategies, and
policies are most appropriate. This RIA is intended to inform the public
about the potential costs and benefits that may result when a new lead
standard is implemented, but is not relevant to establishing the
standards themselves.

The analysis year for this regulatory impact analysis is 2020,
consistent with the previously completed PM~2.5~ NAAQS RIA analysis
which also used 2020 as its analysis year. For the purposes of this
analysis, we assess attainment by 2020 for all areas. Some areas for
which we assume 2020 attainment may in fact need more time to meet one
or more of the analyzed standards, while others will need less time.
This analysis does not prejudge the attainment dates that will
ultimately be assigned to individual areas under the Clean Air Act,
which provides flexibility to postpone compliance dates (up to 20
years), provided that the date is as expeditious as practicable.

EPA presents this RIA pursuant to Executive Order 12866 and the
guidelines of OMB Circular A-4.[^1] These documents present guidelines
for EPA to assess the benefits and costs of the selected regulatory
option, as well as one less stringent and one more stringent option. OMB
circular A-4 also requires both a cost-benefit, and a cost-effectiveness
analysis for rules where health is the primary effect. Within this RIA
we provide a cost benefit analysis.

**ES.2 Summary of Analytic Approach**

Our assessment of the proposed lead NAAQS includes several key elements,
including specification of baseline lead emissions and concentrations;
development of illustrative control strategies to attain the alternative
standards in 2020; development of an air quality assessment tool to
assess the air quality impacts of these control strategies; and analyses
of the incremental costs and benefits of attaining the alternative
standards. Figure ES.1 provides an illustration of the methodological
framework of this RIA. Additional information on the methods employed by
the Agency for this RIA is presented below.

*Overview of Baseline Emissions Forecast and Baseline Lead
Concentrations*

The baseline lead emissions and lead concentrations for this RIA are
based on lead emissions data from the 2002 National Emissions Inventory
(NEI) and lead concentration values for 36 lead monitors included in the
2003-2005 Pb-TSP NAAQS-review database. Consistent with the PM~2.5~
NAAQS RIA and ozone RIA, no growth factors were applied to the 2002 NEI
emissions estimates to generate the emissions or air quality projections
for 2020. Where possible, however, we adjusted these values to reflect
the estimated control efficiency of MACT standards with post-2002
compliance deadlines, because the 2002 NEI and observed lead
concentrations during the 2003-2005 period would not reflect the impact
of MACT controls reasonably anticipated to be in place by 2020. The
analysis includes similar adjustments for compliance measures required
by the September 2006 revision to the PM~2.5~ NAAQS (as included in the
illustrative PM~2.5~ control strategy described in the PM~2.5~ NAAQS
RIA) and measures listed in the 2007 Missouri Lead SIP revisions.[^2]

It should be noted again that overall data limitations are very
significant for this analysis. One critical area of uncertainty is the
limited TSP-Pb monitoring network. Because monitors are present in only
86 counties nationwide, the universe of monitors exceeding the various
target NAAQS levels is very small; only 36 counties above 0.05 ug/m3,
and only 24 counties exceeding the lowest proposed NAAQS level of 0.10
ug/m3. Because we know that many of the highest-emitting Pb sources in
the 2002 NEI do not have nearby Pb-TSP monitors, it is likely that there
may be many more potential nonattainment areas than have been analyzed
in this RIA.

*Development of Illustrative Control Strategies*

Our analysis of the emissions control measures required to meet the
proposed and alternative standards is limited to controls for point
source emissions at active sources inventoried in the 2002 NEI. To
identify point source lead emissions controls for our analysis, we
collected information on PM control technologies, assuming that the
control efficiency for PM would also apply to lead emissions. Most of
this information was obtained from EPA\'s AirControlNET database, but a
limited number of controls were identified from New Source Performance
Standards and operating permits that apply to facilities with similar
Source Classification Codes as the point sources included in our
analysis.[^3]

![](media/image1.wmf){width="6.538194444444445in" height="8.175in"}

**Figure ES.1: The Process Used to Create this RIA**

Based on this information, we identified controls for approximately 28
percent of the total inventoried point sources in our analysis. However,
because of the skewed distribution of lead emissions in the 2002 NEI
(the top 10 percent of inventoried point sources account for over 98
percent of total lead emissions), these sources accounted for more than
75 percent of total lead emissions, as shown in Table ES.1. Controls
identified through this process include major emissions controls, such
as fabric filters, impingement-plate scrubbers, and electrostatic
precipitators; and minor controls, such as increased monitoring
frequency, upgrades to continuous emissions monitors, and diesel
particulate filters for stationary sources.

Table ES.1 Profile of Inventoried Point Sources, With and Without
Identified Controls

  ------------------------ ----------- ------------ ------------------- ---------------
                           **Count**   **Percent of **Emissions         **Percent of Pb
                                       Sources**    (tons**/**year)**   Emissions**

  Sources with Identified  642         28.2%        100.4               75.8%
  Controls                                                              

  Sources without          1,634       71.8%        32.1                24.2%
  Identified Controls                                                   

  **Total**                **2,276**   **100.0%**   **132.5**           **100.0%**
  ------------------------ ----------- ------------ ------------------- ---------------

To identify the least-cost approach for reaching attainment in each
area, EPA developed a linear programming optimization model that
systematically evaluates the changes in air quality and costs associated
with controlling each source to find the optimal control strategy for
each area. The optimization model first identifies the measures that
each source would implement if it were controlled as part of a local
lead attainment strategy. Based on these controls, the optimization
model then identifies sources to control such that each area would reach
attainment at the least aggregate cost possible for the area.

Some monitor areas are not projected to reach attainment with the
proposed NAAQS or alternative standard through the application of
identified controls alone. In order to bring these monitor areas into
attainment, we simulated the application of unidentified emissions
controls on "large" emissions sources, defined as those sources emitting
0.05 tons/year or more in the 2002 NEI. We limited our consideration of
unidentified controls to these sources in order to target facilities
that will likely be the focus of efforts of local air quality managers
to comply with the new NAAQS. Of the 2,230 point sources (excluding
airports) in our analysis, 7.8 percent (174 sources) satisfy the 0.05
annual tpy (100 pound) or greater criteria, but they account for more
than 97 percent of total adjusted baseline emissions. Based on the
estimated control efficiency of identified controls, our analysis
assumes that unidentified controls have a control efficiency of 90
percent. For each standard, we selected all monitor areas that failed to
reach attainment and applied unidentified controls to large sources
until attainment was reached.

*Air Quality Assessment Tool*

To assess the air quality impact of the emissions controls implemented
under the proposed NAAQS, EPA would ideally use a detailed air quality
model that simulates the dispersion and transport of lead to estimate
local ambient lead concentrations. Although models with such
capabilities are available for pollutants for which EPA frequently
conducts air quality analyses (e.g., particulate matter and ozone),
regional scale models are currently neither available nor appropriate
for Pb.[^4] Dispersion, or plume-based, models are recommended for
compliance with the Pb NAAQS; however, dispersion models are data
--intensive and more appropriate for local scale analyses of emissions
from individual sources. It was not feasible to conduct such a
large-scale data intensive analysis for this RIA.

Our air quality assessment tool, developed for the purposes of this
analysis, employs a source-apportionment approach to estimate the extent
to which each of the following emissions sources contribute to observed
lead concentrations in each monitor area:

-   Background lead

-   Miscellaneous, re-entrained dust

-   Emissions from area non-point sources

-   Indirect fugitive emissions from active industrial sites

-   Point source emissions[^5]

After allocating a portion of the observed lead concentration for each
monitor area to the first four categories listed above, the assessment
tool apportions the remaining concentration among all inventoried point
sources within ten kilometers of each monitor location by
distance-weighting individual source contributions to ambient Pb
concentrations,.[^6] Through this process, the tool establishes a point
source influence factor that can be used to translate changes in the
lead emissions of individual point sources to changes in the lead
concentration for each monitor area.

*Analysis of Benefits*

Our analysis of the benefits associated with the proposed Lead NAAQS
includes benefits related to reducing ambient lead concentrations and
the ancillary benefits of reducing direct emissions of particulate
matter. To assess benefits specific to reduced lead concentrations, we
created a spreadsheet model that provides 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. For the purposes of this analysis, the model
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.[^7]

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. EPA plans to refine the model as it
progresses towards a final NAAQS level for lead.

Because most of the point source measures implemented to achieve the
NAAQS standards are focused on controlling emissions of lead in
particulate form, virtually all of these measures also have a
significant impact on emissions of directly emitted particulate matter.
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.[^8] The complete methodology for creating the benefit per-ton
estimates used in this analysis is available in the Technical Support
Document (TSD) accompanying the recent final ozone NAAQS RIA.[^9]

*Analysis of Costs*

Consistent with our development of the illustrative control strategies
described above, our analysis of the costs associated with the proposed
lead NAAQS focuses on point source PM controls. For the purposes of this
analysis, these controls largely include measures from the AirControlNET
control technology database, but also include additional measures
associated with operating permits and/or New Source Performance Review
standards applicable to sources similar to those included in our
analysis. For controls identified in AirControlNET, we estimated costs
based on the cost equations included in AirControlNET. Our cost
estimates for controls associated with operating permits and/or New
Source Performance Review standards are based on cost data compiled by
EPA for previous analyses.

As indicated in the above discussion on illustrative control strategies,
implementation of the PM control measures identified from AirControlNET
and other sources does not result in attainment with the proposed or
alternative lead standards in several areas. In these areas, additional
unidentified measures will likely be necessary to reach attainment. To
estimate the costs associated with unidentified measures, we assume an
annual fixed cost of \$32 million/ton, or \$16,000/pound. This value
represents the 98^th^ percentile of the cost/ton for identified controls
at large point sources (i.e., sources emitting at least 0.05 tons of
lead per year).[^10]

**ES.3 Results of Analysis**

*Air Quality*

Table ES.2 summarizes the number of monitor sites that reach attainment
with the proposed NAAQS and alternative standards in 2020 following the
implementation of identified and unidentified controls. According to the
data presented in Table ES.2, 20 of the 36 monitor areas are expected to
reach attainment with any target NAAQS in the proposed range of 0.10 to
0.30 μg/m^3^ following implementation of identified controls. For some
areas, however, identified controls are not sufficient to reach
attainment with one or more of the target alternatives in the proposed
range. For the alternative of 0.05 μg/m^3^, only 10 of the 36 monitors
are able to reach attainment from application of identified controls.

Table ES.2. Number of Monitor Sites Reaching Attainment with Each
Alternative Standard using Identified and Unidentified Controls

  ---------------- ------------ -------------- --------------- ----------------
  Standard         **Number of  **Number of    **Number of     **Number of
                   Sites        Sites in       Sites in        Sites in
                   Analyzed**   Attainment     Attainment with Attainment with
                                with No        Identified      Unidentified and
                                Additional     Point Source    Identified Point
                                Controls**     Controls**      Source
                                                               Controls**

  0.30 μg/m3       36           24             30              35
  Second Maximum                                               
  Monthly Mean                                                 

  0.20 μg/m3                    20             26              35
  Second Maximum                                               
  Monthly Mean                                                 

  0.10 μg/m3                    13             20              30
  Second Maximum                                               
  Monthly Mean                                                 

  0.05 μg/m3                    1              10              19
  Second Maximum                                               
  Monthly Mean                                                 
  ---------------- ------------ -------------- --------------- ----------------

The failure of certain areas to reach attainment with identified
controls partially reflects the lack of control information for point
sources in these areas. Sources for which the AirControlNET analysis
identified no controls make up a significant portion of the ambient lead
concentration in many of the areas not projected to reach attainment
with the proposed standard. For such sources in nonattainment areas, we
assume that unidentified controls will be applied.

In addition to the lack of point source control information, some areas
fail to reach attainment with the 0.05 μg/m^3^ target NAAQS following
the implementation of identified controls because the fraction of the
ambient lead concentration associated with area nonpoint sources and
miscellaneous re-entrained dust exceeds the standard itself. As
indicated above, our analysis relies only on point source controls,
which have no effect on the ambient lead fraction associated with
nonpoint sources or miscellaneous re-entrained dust. Therefore, even if
point source emissions were reduced to zero in these areas, they would
not reach attainment.

When unidentified point source controls are implemented in addition to
identified controls, we project more widespread attainment with the
alternative standards. As indicated above, we assume that these controls
have a control efficiency of 90 percent and that they may be installed
by any large point source (i.e., a point source emitting more than 0.05
tons of lead per year, as indicated in the 2002 NEI). Following the
application of unidentified controls, all monitor areas but one are
projected to reach attainment with the 0.3 g/m^3^ proposed standard and
the 0.2 g/m^3^ proposed standard. For the 0.1 g/m^3^ proposed
standard, six monitor areas are not projected to reach attainment with
the application of unidentified controls, either because control
efficiencies greater than 90 percent would be required at large sources
or because small sources would need to be controlled to sufficiently
reduce ambient lead concentrations. For the 0.05 g/m^3^ alternative
standard, seventeen monitor areas are not projected to reach attainment
with any application of unidentified controls, for the reasons given
above and because the fraction of the ambient concentration associated
with area nonpoint sources and miscellaneous re-entrained dust at some
areas exceeds the standard itself.

*\
Benefit and Cost Estimates*

Table ES.3 summarizes the net benefits associated with the proposed and
alternative NAAQS standards in 2020, based on both 3 percent and 7
percent discount rates. The table presents the net benefits of the
proposed and alternative standards as a range to account for
uncertainties associated with the benefits of the standards. The range
in the benefits estimates related to IQ gains reflects two estimates of
the earnings impacts associated with such gains. The low end of the
range reflects an analysis by Schwartz, which estimated that a 1-point
increase in IQ would increase earnings by 1.76 percent, while the high
end of the range reflects the results of Salkever, which found that
earnings increase by 2.38 percent for each 1-point increase in IQ.[^11]
The range of estimates presented for PM-related benefits is based on the
upper and lower ends of the range of PM2.5 premature mortality functions
obtained by EPA through its expert elicitation study on the PM-mortality
relationship, as first reported by Industrial Economics and interpreted
for benefits analysis in EPA\'s final RIA for the PM NAAQS, published in
September 2006.[^12]

The results in Table ES.3 show that unidentified controls represent the
majority of costs incurred by affected sources. This reflects the
limited information available to EPA on the control measures that lead
sources may implement. It is important to remember that, compared to
recent NAAQS RIAs, our current knowledge of the costs and nature of lead
emissions controls is relatively poor. Lead in ambient air has not been
a focus for all but a few areas of the country for the last decade or
more; the alternative standards represent a substantial tightening of
the existing NAAQS. As a result, although AirControlNET contains
information on a large number of different point source controls, we
would expect that State and local air quality managers would have access
to additional information on the controls available to the most
significant sources.

**Table ES.3. Summary of Costs, Benefits, and Net Benefits for
Regulatory Alternatives (Millions of 2006\$)**

+----------+------+------+------+------+------+------+------+------+
|          | Sum  |      |      |      |      |      |      |      |
|          | mary |      |      |      |      |      |      |      |
|          | of   |      |      |      |      |      |      |      |
|          | An   |      |      |      |      |      |      |      |
|          | nual |      |      |      |      |      |      |      |
|          | Co   |      |      |      |      |      |      |      |
|          | sts, |      |      |      |      |      |      |      |
|          | B    |      |      |      |      |      |      |      |
|          | enef |      |      |      |      |      |      |      |
|          | its, |      |      |      |      |      |      |      |
|          | and  |      |      |      |      |      |      |      |
|          | Net  |      |      |      |      |      |      |      |
|          | Bene |      |      |      |      |      |      |      |
|          | fits |      |      |      |      |      |      |      |
|          | in   |      |      |      |      |      |      |      |
|          | 2020 |      |      |      |      |      |      |      |
|          | (    |      |      |      |      |      |      |      |
|          | Mill |      |      |      |      |      |      |      |
|          | ions |      |      |      |      |      |      |      |
|          | of   |      |      |      |      |      |      |      |
|          | 200  |      |      |      |      |      |      |      |
|          | 6\$) |      |      |      |      |      |      |      |
+----------+------+------+------+------+------+------+------+------+
|          | Prop |      | Prop |      | Prop |      | Alt  |      |
|          | osed |      | osed |      | osed |      | erna |      |
|          | NA   |      | NA   |      | NA   |      | tive |      |
|          | AQS: |      | AQS: |      | AQS: |      | S    |      |
|          | 0.3  |      | 0.2  |      | 0.1  |      | tand |      |
|          | μg/  |      | μg/  |      | μg/  |      | ard: |      |
|          | m^3^ |      | m^3^ |      | m^3^ |      | 0.05 |      |
|          | 2    |      | 2    |      | 2    |      | μg/  |      |
|          | ^nd^ |      | ^nd^ |      | ^nd^ |      | m^3^ |      |
|          | Max  |      | Max  |      | Max  |      | 2    |      |
|          | imum |      | imum |      | imum |      | ^nd^ |      |
|          | Mon  |      | Mon  |      | Mon  |      | Max  |      |
|          | thly |      | thly |      | thly |      | imum |      |
|          | Mean |      | Mean |      | Mean |      | Mon  |      |
|          |      |      |      |      |      |      | thly |      |
|          |      |      |      |      |      |      | Mean |      |
+----------+------+------+------+------+------+------+------+------+
|          | **3% | **7% | **3% | **7% | **3% | **7% | **3% | **7% |
|          | Disc | Disc | Disc | Disc | Disc | Disc | Disc | Disc |
|          | ount | ount | ount | ount | ount | ount | ount | ount |
|          | ra   | ra   | ra   | ra   | ra   | ra   | ra   | ra   |
|          | te** | te** | te** | te** | te** | te** | te** | te** |
+----------+------+------+------+------+------+------+------+------+
| Id       | \$47 | \$49 | \$55 | \$57 | \$63 | \$66 | \$84 | \$88 |
| entified |      |      |      |      |      |      |      |      |
| Controls |      |      |      |      |      |      |      |      |
+----------+------+------+------+------+------+------+------+------+
| Unid     | \    |      | \    |      | \$1  |      | \$2  |      |
| entified | $400 |      | $790 |      | ,600 |      | ,000 |      |
| Co       |      |      |      |      |      |      |      |      |
| ntrols\* |      |      |      |      |      |      |      |      |
+----------+------+------+------+------+------+------+------+------+
| **Total  | *    | *    | *    | *    | **\  | **\  | **\  | **\  |
| Eng      | *\$4 | *\$4 | *\$8 | *\$8 | $1,6 | $1,6 | $2,1 | $2,1 |
| ineering | 50** | 50** | 40** | 40** | 00** | 00** | 00** | 00** |
| Costs**  |      |      |      |      |      |      |      |      |
+----------+------+------+------+------+------+------+------+------+
| An       | \$1  | \    | \$2  | \    | \$3  | \    | \$6  | \    |
| nualized | ,700 | $220 | ,500 | $310 | ,900 | $480 | ,100 | $760 |
| B        | to   | to   | to   | to   | to   | to   | to   | to   |
| enefit - | \$2  | \    | \$3  | \    | \$5  | \    | \$8  | \$1  |
| IQ Gains | ,500 | $430 | ,500 | $610 | ,500 | $950 | ,700 | ,500 |
| (Ra      |      |      |      |      |      |      |      |      |
| nge)\*\* |      |      |      |      |      |      |      |      |
+----------+------+------+------+------+------+------+------+------+
| An       | \    | \    | \    | \    | \    | \    | \$1  | \$1  |
| nualized | $410 | $380 | $560 | $520 | $690 | $640 | ,100 | ,000 |
| B        | to   | to   | to   | to   | to   | to   | to   | to   |
| enefit - | \$3  | \$3  | \$4  | \$4  | \$5  | \$5  | \$8  | \$8  |
| PM       | ,500 | ,100 | ,700 | ,300 | ,800 | ,200 | ,900 | ,000 |
| Co       |      |      |      |      |      |      |      |      |
| -control |      |      |      |      |      |      |      |      |
| (Rang    |      |      |      |      |      |      |      |      |
| e)\*\*\* |      |      |      |      |      |      |      |      |
+----------+------+------+------+------+------+------+------+------+
| **Total  | *    | **\  | *    | **\  | *    | *    | *    | *    |
| Be       | *\$2 | $600 | *\$3 | $830 | *\$4 | *\$1 | *\$7 | *\$1 |
| nefits** | ,100 | to   | ,100 | to   | ,600 | ,100 | ,200 | ,800 |
|          | to   | \    | to   | \    | to   | to   | to   | to   |
|          | \    | $3,5 | \    | $4,9 | \$   | \    | \$   | \    |
|          | $6,0 | 00** | $8,2 | 00** | 11,0 | $6,2 | 18,0 | $9,5 |
|          | 00** |      | 00** |      | 00** | 00** | 00** | 00** |
+----------+------+------+------+------+------+------+------+------+
| **Total  | *    | **\  | *    | **-  | *    | **-\ | *    | **-\ |
| Net      | *\$1 | $150 | *\$2 | \$10 | *\$3 | $480 | *\$5 | $340 |
| Be       | ,700 | to   | ,200 | to   | ,000 | to   | ,100 | to   |
| nefits** | to   | \    | to   | \    | to   | \    | to   | \    |
|          | \    | $3,1 | \    | $4,1 | \    | $4,6 | \$   | $7,4 |
|          | $5,6 | 00** | $7,4 | 00** | $9,7 | 00** | 16,0 | 00** |
|          | 00** |      | 00** |      | 00** |      | 00** |      |
+----------+------+------+------+------+------+------+------+------+
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+----------+------+------+------+------+------+------+------+------+

To provide additional context for the results presented in Table ES.3,
Table ES.4 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, relative to the "base case" (i.e., the lead NAAQS remains
at its current level). The results presented in the table 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. More specifically, the
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.

**Table ES.4. Number of IQ Points Gained in 2020**

  ---------------------------------------------------- ------------------
  **Standard**                                         **IQ Points
                                                       Gained**

  **0.3 μg/m^3^ Second Maximum Monthly Mean**          200,000

  **0.2 μg/m^3^ Second Maximum Monthly Mean**          280,000

  **0.1 μg/m^3^ Second Maximum Monthly Mean**          440,000

  **0.05 μg/m^3^Second Maximum Monthly Mean**          700,000
  ---------------------------------------------------- ------------------

Our analysis suggests that the benefits presented in Table ES.4 will be
concentrated in a small number of counties. Table ES.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 57 percent of the total benefits are due to
changes in lead air concentrations in three counties: Hillsborough,
Florida; Delaware, Indiana; and Berks, PA. In these areas, sources of
lead exposure and the monitors that measure ambient lead appear to be in
relatively close proximity to exposed populations.

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

  ------------------------------------------------------------------------------
  **County**     **State**   **Population of  **Affected          **Percentage
                             Children in      Population\         of Benefits\
                             Affected Area**  (%)**               (%)**
  -------------- ----------- ---------------- ------------------- --------------
  Hillsborough   FL          46923            15                  29

  Delaware       IN          9236             3                   18

  Berks          PA          23977            8                   10

  Collin         TX          16593            5                   7

  Adams          CO          25746            8                   6

  Denver         CO          40395            13                  5

  Denton         TX          6301             2                   4

  Cuyahoga       OH          35680            12                  4

  Pike           AL          2342             1                   4

  St. Louis      MO          26365            9                   2

  Jefferson      CO          8689             3                   2

  Jefferson      MO          7358             2                   1

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

The costs of the proposed and alternative lead NAAQS are also expected
to be concentrated in a limited number of areas, as summarized in Table
ES.6. Many of the monitor sites listed in the exhibit represent areas
with the largest sources of lead emissions, such as primary or secondary
lead smelters, mining operations, or battery manufacturers.

Table ES.6. Geographic Distribution of the Costs Associated with
Identified and Unidentified Controls\*

  -------------- ----------- ------------- ------------- ------------- ---------------
  **Monitor      **Monitor   **Proposed    **Proposed    **Proposed    **Alternative
  State**        County**    NAAQS: 0.3    NAAQS: 0.2    NAAQS: 0.1    Standard: 0.05
                             μg/m^3^ 2^nd^ μg/m^3^ 2^nd^ μg/m^3^ 2^nd^ μg/m^3^ 2^nd^
                             Maximum       Maximum       Maximum       Maximum Monthly
                             Monthly       Monthly       Monthly       Mean**
                             Mean**        Mean**        Mean**        

  MO             Jefferson   91%           85%           63%           55%

  MO             Iron        1%            6%            19%           21%

  PA             Beaver      0%            0%            4%            5%

  CO             Denver      0%            1%            3%            4%

  CO             Adams       2%            3%            3%            3%

  NJ             Middlesex   0%            0%            1%            2%

  CO             El Paso     0%            0%            1%            1%

  OH             Cuyahoga    1%            1%            1%            1%

  PA             Berks       2%            2%            1%            1%

  Other Monitors             3%            3%            3%            6%

  **Total**                  **100%**      **100%**      **100%**      **100%**

  \*The                                                                
  percentages                                                          
  presented in                                                         
  this table                                                           
  reflect the                                                          
  approximate                                                          
  distribution                                                         
  of costs                                                             
  estimated with                                                       
  both 3 percent                                                       
  and 7 percent                                                        
  discount                                                             
  rates.                                                               
  -------------- ----------- ------------- ------------- ------------- ---------------

The following set of graphs is included to provide the reader with a
richer presentation of the range of costs and benefits of the
alternative standards. The graphs supplement the tables by displaying
the various combinations of net benefits, utilizing the two different
lead benefits estimates, the fourteen different PM~2.5~ co-benefits
estimates, and the cost estimate. Each of the 28 bars in each graph
represents an independent and equally probably point estimate of net
benefits under a certain combination of cost and benefit estimation
methods. Thus it is not possible to infer the likelihood of any single
net benefit estimate. The blue bars indicate combinations where the net
benefits are negative, whereas the green bars indicate combinations
where net benefits are positive. Figures ES.1 and ES.2 show all of these
combinations for all alternative standards analyzed at discount rates of
3% and 7%, respectively.

**Figure ES.1: Range of Net Benefits Estimates across all standard
Alternatives at 3% discount rate**

![](media/image2.jpeg){width="4.497916666666667in"
height="3.0756944444444443in"}
![](media/image3.jpeg){width="4.497916666666667in"
height="3.0756944444444443in"}

![](media/image4.jpeg){width="4.497916666666667in"
height="3.0756944444444443in"}
![](media/image5.jpeg){width="4.497916666666667in"
height="3.0756944444444443in"}

**Figure ES.2: Range of Net Benefits Estimates across all standard
Alternatives at 7% discount rate**

![](media/image6.jpeg){width="4.497916666666667in"
height="3.0756944444444443in"}![](media/image7.jpeg){width="4.497916666666667in"
height="3.0756944444444443in"}![](media/image8.jpeg){width="4.497916666666667in"
height="3.0756944444444443in"}![](media/image9.jpeg){width="4.497916666666667in"
height="3.0756944444444443in"}

**ES.4 Caveats and Limitations**

*Air Quality Data, Modeling and Emissions*

-   ***Limited TSP-Pb monitoring network.*** Because monitors are
    present in only 86 counties nationwide, the universe of monitors
    exceeding the various target NAAQS levels is very small; only 36
    counties above 0.05 ug/m3, and only 24 counties exceeding the lowest
    proposed NAAQS level of 0.10 ug/m3. Because we know that many of the
    highest-emitting Pb sources in the 2002 NEI do not have nearby
    Pb-TSP monitors (see section 2.1.7), it is likely that there may be
    many more potential nonattainment areas than have been analyzed in
    this RIA.

-   ***Simplified Air Quality Assessment Approach***. It is not
    appropriate to conduct regional scale modeling for Pb similar to the
    regional scale modeling conducted for PM and ozone. Dispersion, or
    plume-based models are recommended for compliance with the Pb NAAQS;
    however, dispersion models are data --intensive and more appropriate
    for local scale analyses of emissions from individual sources. It
    was not feasible to conduct such a large-scale data intensive
    analysis for this RIA. As a result, the simplified analysis
    developed for this RIA while distance-weighting individual source
    contributions to ambient Pb concentrations, could not account for
    such locally critical variables as meteorology and source stack
    height.

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-   ***Analysis Only Considers Controls on Point Source Emission
    Reductions.*** Because the available data are not sufficiently
    detailed to assess the impact of indirect fugitive or area nonpoint
    source controls, the analysis of air quality impacts does not
    account for the potential implementation of such controls in areas
    where they might be effective. Although the analysis estimates the
    impact of point source controls on indirect fugitives, it does not
    consider the impact of controlling these emissions directly. This
    and the lack of control information for area nonpoint sources may
    have contributed to our projection of nonattainment in some areas.
    Additionally, for this analysis we have not modeled the effect of
    any potential changes in emissions at airports with lead emissions
    associated with use of leaded aviation gasoline. It is currently
    unclear if measures to reduce lead in aviation gasoline might be
    pursued, or if they were, what the impact might be on lead monitor
    areas.

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-   ***Limited Point Source Controls Considered***. As discussed above,
    we were not able to obtain emissions control information for a large
    number of point sources in our analysis. Although these sources
    collectively accounted for less than one fourth of all lead
    emissions considered, many of those sources were located in areas
    that were not able to reach attainment with one or more of the
    standards using identified controls alone. If more emissions control
    information were available, it may not be necessary to rely on
    estimated emissions reductions from unidentified point sources in
    order to simulate attainment with the alternative NAAQS.

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-   ***Actual State Implementation Plans May Differ from our
    Simulation***. In order to reach attainment with the proposed NAAQS,
    each state will develop its own implementation plan implementing a
    combination of emissions controls that may differ from those
    simulated in this analysis. This analysis therefore represents an
    approximation of the emissions reductions that would be required to
    reach attainment and should not be treated as a precise estimate.

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```
-   ***Emissions Reduction from Unidentified Controls***. In this RIA,
    we report emissions reductions from both identified and unidentified
    emissions controls. We have taken care to report these separately,
    in recognition of the greater uncertainty associated with achieving
    emissions reductions from measures that may not be currently in use
    or known to EPA. Nonetheless, EPA believes it is reasonable to
    project that, with at least 10 years of lead time before a 2020
    compliance deadline, a large number of existing measures will be
    adapted to be applicable to additional sources, and new measures may
    be developed that are specifically focused on cost-effectively
    reducing PM emissions with high lead content. Because the current
    standard is attained in all but a few areas of the country, and has
    been for many years since the phase down of lead in gasoline, it is
    likely that very little effort has been devoted to development of
    lead emissions control technologies except for industries where
    regulations have been imposed to reduce lead (e.g., large MWC
    standard, primary and secondary lead smelter MACTs, etc.). As a
    result, EPA believes that application of unidentified controls is
    particularly appropriate for compliance with a more stringent lead
    NAAQS.

*Costs*

-   ***Uncertainty associated with unidentified measures.*** As
    indicated above, many areas are expected to rely heavily on
    unidentified controls to reach attainment with the standards. The
    cost of implementing these measures, though estimated here based on
    the costs for identified controls, is uncertain. Many of these
    sources are already well-controlled for particulate matter, and
    additional control for the remaining increment of Pb might be
    difficult to achieve. Many other sources are boilers fired by
    natural gas, whose emissions we are currently investigating and
    which are likely to be overstated. Some sources have very low
    particulate matter (PM) emissions overall, and therefore controls
    are generally not found at that emissions level.

*Benefits*

-   ***Lead Concentrations.*** 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.

-   ***Exposure.*** 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.

-   ***Dose-response relationship***. 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.

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-   ***Earnings-based metric of IQ.*** 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.

-   ***Co-control benefits related to PM.*** 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.

**ES.5 Conclusions and Insights**

Our analysis has estimated the health and welfare benefits of reductions
in ambient concentrations of lead resulting from a set of illustrative
control strategies to reduce emissions of lead at point sources. The
results suggest there will be significant additional health and welfare
benefits arising from reducing emissions from a variety of sources in
and around projected nonattaining counties in 2020. While 2020 is the
latest date by which states would generally need to demonstrate
attainment with the revised standards, it is expected that benefits (and
costs) will begin occurring earlier, as states begin implementing
control measures to show progress towards attainment.

There are several important factors to consider when evaluating the
relative benefits and costs of the attainment strategies for the four
alternative standards assessed in this RIA:

-   Overall net benefits for most of the alternative standards appear to
    be positive, with the exception of net benefit estimates that rely
    on the low-end co-benefits estimation assumption and a 7 percent
    discount rate, for the three most stringent alternatives. In
    general, the discount rate has a substantial effect on the primary
    lead benefits, because the majority of future lifetime earnings
    effects that are the basis for estimating the value of avoided IQ
    decrements are expected to occur several decades into the future.
    For that reason, primary lead benefits alone are sufficient to yield
    positive net benefits for all alternatives using a 3 percent
    discount rate. At 7 percent, the estimate of net benefits depends to
    a greater degree on the estimated co-benefits of PM control.

-   Our estimates of costs of attainment in 2020 assume a particular
    trajectory of what might be aggressive technological change. This
    trajectory leads to a particular level of emissions reductions and
    costs which we have estimated based on costs of identified controls.
    An alternative storyline might hypothesize a much less optimistic
    technological change path, such that emissions reductions
    technologies for industrial sources would be more expensive or would
    be unavailable, so that emissions reductions from many smaller
    sources might be required for 2020 attainment, at a potentially
    greater cost per ton. Under this alternative storyline, two outcomes
    are hypothetically possible: Under one scenario, total costs
    associated with full attainment might be substantially higher. Under
    the second scenario, states may choose to take advantage of
    flexibility in the Clean Air Act to adopt plans with later
    attainment dates to allow for additional technologies to be
    developed and for other programs be fully implemented. If states
    were to submit plans with attainment dates beyond our 2020 analysis
    year, benefits would clearly be lower than we have estimated under
    our analytical storyline. However, in this case, state decision
    makers, seeking to maximize economic efficiency, would not impose
    costs, including potential opportunity costs of not meeting their
    attainment date, when they exceed the expected health benefits that
    states would realize from meeting their modeled 2020 attainment
    date. In this case, upper bound costs are difficult to estimate
    because we do not have an estimate of the point where marginal costs
    are equal to marginal benefits plus the costs of nonattainment.

-   Benefits and costs are distributed differently across potential
    non-attainment counties. As presented in Table ES.5, most of the
    primary lead benefits of the standards are expected to be realized
    in a small number of areas. These are areas where the sources of
    lead exposure and the monitors that measure ambient lead appear to
    be in relatively close proximity to exposed populations. The
    identified control costs, on the other hand, are greatest in those
    areas with the largest sources of lead emissions - usually around
    primary or secondary lead smelters, mining operations, or battery
    manufacturers. PM co-control benefits tend to be distributed in
    better correlation to control costs. In general, PM co-control
    benefits tend to be highest in those areas where our attainment
    strategy suggests controls on combustion sources, rather than metals
    processing, are necessary.

-   Because of the limitations and uncertainties in the emissions and
    air quality components of our assessment, the specific control
    strategies that might be the most effective in helping areas to
    reach attainment are still very uncertain. For example, we employ a
    fairly simple distance-weighted dispersion approach to approximate
    the effect of controls on specific point sources in reducing
    concentrations at current monitor locations.

[^1]: U.S. Office of Management and Budget. Circular A-4, September 17,
    2003. Found on the Internet at
    \<<http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf>\>.

[^2]: U.S. Environmental Protection Agency. (2006). *Final Regulatory
    Impact Analysis: PM~2.5~ NAAQS*. Office of Air and Radiation,
    Research Triangle Park, NC. The Missouri lead SIP was finalized by
    EPA on April 14, 2006 with a requirement that this SIP will provide
    attainment with the current lead standard by April 7, 2008. The SIP
    is available at
    http://www.dnr.mo.gov/env/apcp/docs/2007revision.pdf.

[^3]: Source Classification Codes are the identifiers that EPA uses to
    classify different types of emissions activity.

[^4]:
    > See Chapter 2 of U.S. Environmental Protection Agency. (2007).
    > Review of the National Ambient Air Quality Standards for Lead:
    > Policy Assessment of Scientific and Technical Information -- OAQPS
    > Staff Paper. Office of Air Quality Planning and Standards,
    > Research Triangle Park, NC. EPA-452/R-07-013.

[^5]: For the purposes of this analysis, airports servicing
    piston-engine aircraft that use leaded aviation gasoline are treated
    as point sources.

[^6]: Note that although the air quality assessment tool distinguishes
    between the portion of the observed lead concentration attributable
    to point source emissions and that attributable to indirect fugitive
    emissions from active point sources, this analysis assumes that the
    two contributions are directly related, and any reduction in the air
    quality impact of point source emissions would produce a
    corresponding reduction in the air quality impact of indirect
    fugitive emissions from point sources in that monitor area. The
    process used to relate the contributions of these two categories is
    described in further detail in Chapter 3 of this RIA.

[^7]: U.S. Environmental Protection Agency. (2006b). *Economic Analysis
    for the Renovation, Repair, and Painting Program Proposed Rule*.
    Office of Pollution Prevention and Toxics. Washington, DC.

[^8]: U.S. Environmental Protection Agency. (2008). *Final Ozone NAAQS
    Regulatory Impact Analysis*. Office of Air and Radiation. Research
    Triangle Park, NC, March.

[^9]: The Technical Support Document, entitled: *Calculating Benefit
    Per-Ton Estimates*, can be found in EPA Docket
    EPA-HQ-OAR-2007-0225-0284.

[^10]: The fixed cost estimate for unidentified controls was developed
    based on the cost of controls with a 3 percent discount rate.
    Because it is a fixed cost, however, when applied to estimate the
    costs of unidentified controls it is assumed not to be affected by
    the discount rate assumption. That is, costs for *unidentified*
    controls are assumed to be the same for the 3 and 7 percent discount
    rate.

[^11]: Schwartz, J. (1994). Societal Benefits of Reducing Lead Exposure.
    *Environmental Research* 66: 105-124 and Salkever, D.S. (1995).
    Updated Estimates of Earnings Benefits from Reduced Exposure of
    Children to Environmental Lead. *Environmental Research* 70:1-6.

[^12]: Industrial Economics, Inc. (2006). *Expanded Expert Judgment
    Assessment of the Concentration-Response Relationship between
    PM~2.5~ Exposure and Mortality*. Prepared for: Office of Air Quality
    Planning and Standards, U.S. Environmental Protection Agency,
    Research Triangle Park, NC; U.S. Environmental Protection Agency.
    (2006). *Final Regulatory Impact Analysis: PM~2.5~ NAAQS*. Office of
    Air and Radiation, Research Triangle Park, NC.