# Regulatory Impact Analysis of the Proposed Revisions to the National Ambient Air Quality Standards for
Lead(This page intentionally left blank)October 2008

Regulatory Impact Analysis of the Proposed Revisions to the National Ambient Air Quality Standards

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards

Health and Environmental Impact Division Air Benefit-Cost Group Research Triangle Park, North Carolina(This page intentionally left blank)ES-1 ES.1 OVERVIEW

This Regulatory Impact Analysis (RIA) estimates the incremental costs and monetized human health benefits of attaining a revised primary lead (Pb) National Ambient Air Quality Standard (NAAQS). There are important overall data limitations and uncertainties in these estimates. They are described in section E.S.4 below. Hypothetical co ntrol strategies were developed for final NAAQS of 0.15 μ g/m 3 plus several alternative lead standards. These
alternatives include at least one more stringent and one less stringent alternative than the selected standard, consistent with the OM B Circular A-4 Guidelines. This summary outlines the basis for and approach used for estimating the incremen tal costs and monetized benefits of these standards, presents the key results of the analysis, and highlights key uncertainties and
limitations.

This Regulatory Impact Analysis (RIA) provides illustrative estimates of the incremental
costs and monetized human health benefits of attaining a revised prim ary lead (Pb) National Ambient Air Quality Standard (NAAQS) within th e current monitoring ne twork of 189 monitors representing 86 counties. Many of the highest-emitting lead sources do not have nearby Pb-TSP monitors, and it is important to note that ther e may be many more potential nonattainment areas than have been analyzed in this RIA.

It is important to note at the outset that ove rall data limitations are very significant for this analysis, compared to other NAAQS reviews. One critical area of un certainty is the limited TSP-Pb monitoring network (discussed in chapter 2). Because monitors are present in only 86 counties nationwide, the univers e of monitors exceeding the final NAAQS level of 0.15 μg/m 3
represent only 16 counties. It is important to note that data limitations prevented us from identifying a full range of controls which woul d bring eight of these counties all the way to attainment of the final NAAQS. It is also impor tant to note that because many of the highestemitting 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 n onattainment areas than have been analyzed in
this RIA.

In addition, EPA would prefer to use a deta iled air quality model that simulates the dispersion and transport of lead to estimate local ambient lead concentrations with the hypothetical alternative emission control stra tegies expected under the NAAQS. Although models with such capabilities ar e available for pollutants for wh ich EPA frequently conducts air quality analyses (e.g., particulate matter and ozone), regional scale models are currently neither available nor appropriate for lead. 1 As discussed in Chapter 3, EPA developed an air quality assessment tool to estimate the air quality imp acts of each lead emissions control strategy.

In setting primary ambient air quality standard s, EPA’s responsibility under the law is to establish standards that protec t public health, regardless of the costs of implementing a new standard. The Clean Air Act requires EPA, for e ach criteria pollutant, to set a standard that

1 U.S. Environmental Protection Agency (2007c), Review of the National Ambient Air Quality Standards for Lead: Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper, section 2.4, EPA-452/R-07-013, Office of Air Quality Planning and Standards, RTP, NC.ES-2 protects public health with “an adequate margin of safety.” As interpreted by the Agency and the courts, the Act requires EPA to create standa rds based on health considerations only.

The prohibition against the consideration of co st 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 consid eration of costs and bene fits is essential to making efficient, cost effective decisions for im plementation of these standards. The impacts of cost and efficiency are considered by states duri ng 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 associated with a hypo thetical scenario that may result when a new lead standard is implemented, but is not relevant to establis hing the standards themselves.

The analysis year for this regulatory impact analysis is 2016, consistent with the attainment year for the final lead NAAQS. Fo r the purposes of this analysis, we assess attainment by 2016 for all areas. Some areas for which we assume 2016 attainment may in fact
need more time to meet one or more of the anal yzed standards, while others will need less time. This analysis does not prejudge the attainment date s that will ultimately be assigned to individual areas under the Clean Air Act, which provides fl exibility to postpone compliance dates, provided that the date is as expe ditious as practicable.

EPA presents this RIA pursuant to Ex ecutive Order 12866 and the guidelines of OMB
Circular A-4. 2 These documents present guidelines for EPA to assess the benefits and costs of the selected regulatory option, as well as one less stringent an d one more stringent option. OMB Circular A-4 also requires both a benefit-cost, and a cost-effectiveness analysis for rules where health is the primary effect. Within this RIA we provide a benefit-cost analysis.

ES.2 Summary of Analytic Approach

Our assessment of the selected lead NAAQS includes several key elements, including specification of baseline lead emissions and concentrations; development of illustrative control strategies to attain the standard in 2016; development of an air quality assessment tool to assess the air quality impacts of these control strategi es; and analyses of the incremental impacts 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 con centrations for this RIA are based on lead
emissions data from the 2002 National Emissions I nventory (NEI) and lead concentration values
for 21 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 f actors were applied to the 2002 NEI emissions estimates to generate the emissions or air quality projections for 2016. Where

2 U.S. Office of Management and Budget. Circular A-4, September 17, 2003. Found on the Internet at <http://www.whitehouse.gov/omb/c irculars/a004/a-4.pdf>.ES-3 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 2016. The analysis in cludes similar adjustments for compliance measures simulated 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. 3

Development of Illustrative Control Strategies

Our analysis of the emissions control measures required to meet the selected standard is limited to controls for point source emissions at active sources inventoried in the 2002 NEI. To identify point source lead emissions controls fo r 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 obt ained from EPA's AirControlNET database, but a
limited number of controls were identified from New Source Performance Standards and operating permits that apply to f acilities with similar Source Cl assification Codes as the point sources included in our analysis. 4 Controls identified throug h this process include major emissions controls, such as fabric filters, impingement-plate scrubbers, and electrostatic precipitators; and minor contro ls, such as increased monitoring frequency, upgrades to continuous emissions monitors, and diesel particul ate filters for stationa ry sources. In addition, we modeled replacement of the large primary l ead smelter in Jefferson County, Missouri with a more modern, lower-emitting smelter.

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 sour ce would implement if it were controlled as part of a local lead a ttainment strategy. Based on these controls, the optimization model then identifies sources to c ontrol such that each area would reach attainment at the least aggregate cost possible for the area.

It is important to remember that, co mpared 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
selected standard of 0.15 μ g/m 3 represents a substantial tightening of the existing NAAQS. As a result, although AirControlNET contains informati on on a large number of different point source controls, we would expect that State and lo cal air quality managers would have access to
additional information on the controls av ailable to the most significant source.

3 U.S. Environmental Pr otection 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 th e current lead standard by April 7, 2008. The SIP is available at http://www.dnr.mo.gov/env/apcp/docs/2007revision.pdf.

4 Source Classification Codes are the identifiers that EPA uses to classify different types of emissions activity.ES-4 In addition, as discussed in the final m onitoring provisions, th e existing monitoring network will need to be updated. It is possible that some areas shown to be out of attainment based on the current monitoring information will be shown to be in attainment with more recent monitoring. After the revised mon itoring network is in place, othe r areas not iden tified in this analysis may be found to be violating the new st andard. Since many of the existing sources of ambient Pb are relatively small, states are likel y to work closely with the sources to reduce
emissions in a cost-effective manner.

Note also that in this RIA we have not acc ounted for the effect of improvements that tend to occur, such as technology improvement, proce ss changes, efficiency improvements, materials substitution, etc. We believe these typical improvements will tend to result in more cost effective approaches than simply adding extrem ely expensive pollution c ontrols in many areas by the attainment date of 2016. Many industrial sources of lead emissions emit very small quantities of lead in absolute terms. Our co st modeling shows that some could face significant
costs to reduce these low levels of lead, costs which could be pr ohibitively expensive. Rather than applying additional controls, it may be possible for firms emitting small amounts of Pb to modify their production processes or other operational parameters , including pollution prevention techniques, which would be more co st effective than adding additional control technology. Such measures might include increasing th e enclosure of buildings, increasing air flow in hoods, modifying operation and maintenan ce procedures, changing feed materials to lower Pb content, measures to suppre ss dust from tailings piles, etc.

Finally, some monitor areas are not projecte d to reach attainment with the proposed NAAQS or alternative standard through the application of identified controls alone. (For the selected NAAQS, identified controls account for a bout 94% of the emission reductions needed to reach full attainment in all areas). For the se lected NAAQS and each al ternative standard, we applied unspecified emission reductions to all sources until attainment was reached in each county that failed to reach attainment with identified controls alone.ES-5 Figure ES-1. The Process Used to Create this RIA Analytic Sequence for Lead NAAQS RIA

Estimate

Initial Pb

Concentrations

Adjust for MACT and PM NAAQS

Compliance Pb

Monitor

Data

Anticipated

MACT and

PM Controls

Estimated Optional Compliance Path:

Identified Controls Base Case Pb

Concentrations

Apply Unidentified

Controls As Necessary

Estimate Costs

and PM Co-Control

Emissions

Direct

Cost
Estimates AirControl

NET
Database

Estimate Cost

of Unidentified

Controls

Compare
Benefits and

Costs

Net Benefits

For Each

Alternative PM

Data Estimate Pb Change by Standard

Estimate Blood

Pb Changes

Estimate Health

Effects (IQ)

Monetized

Benefit

Estimates Value Benefits Valuation

Estimates

DoseResponse

Data Air-Pb:

Blood-Pb

Ratios CONTROLS AND

COST ANALYSIS BENEFITS

ANALYSIS Analytic Sequence for Lead NAAQS RIA

Estimate

Initial Pb

Concentrations

Adjust for MACT and PM NAAQS

Compliance Estimate

Initial Pb

Concentrations

Adjust for MACT and PM NAAQS

Compliance Pb

Monitor

Data

Anticipated

MACT and

PM Controls

Estimated Optional Compliance Path:

Identified Controls Base Case Pb

Concentrations

Apply Unidentified

Controls As Necessary

Estimate Costs

and PM Co-Control

Emissions

Direct

Cost
Estimates AirControl

NET
Database

Estimate Cost

of Unidentified

Controls

Compare
Benefits and

Costs

Net Benefits

For Each

Alternative PM

Data Estimate Pb Change by Standard

Estimate Blood

Pb Changes

Estimate Health

Effects (IQ)

Monetized

Benefit

Estimates Value Benefits Valuation

Estimates

DoseResponse

Data Air-Pb:

Blood-Pb

Ratios CONTROLS AND

COST ANALYSIS BENEFITS

ANALYSISES-6 Air Quality Assessment Tool

To assess the air quality impact of the emissions controls implemented under the selected NAAQS, EPA would ideally use a detailed air qu ality model that simulates the dispersion and transport of lead to estimate local ambient l ead concentrations. A lthough models with such capabilities are available for pollutants for whic h EPA frequently conducts air quality analyses (e.g., particulate matter and ozone), regional scal e models are currently neither available nor appropriate for Pb. 5 Dispersion, or plume-based, models are recommended for compliance with the Pb NAAQS; however, dispersion models are da ta –intensive and more appropriate for local scale analyses of emissions from individual sources. It was not feasible to conduct such a largescale data-intensive analysis for this RIA.

Our air quality assessment tool, developed fo r 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 c oncentrations 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 6

After allocating a portion of the observed lead co ncentration for each monitor area to the first four categories listed above, the assessment t ool apportions the remaining concentration among all inventoried point sources within ten kilo meters of each monitor location by distanceweighting individual source contributi ons to ambient Pb concentrations. 7 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

5 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.

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

7 Note that although the air quality assessment tool distinguishes between the portion of the observed lead concentration attributable to point source emissions and th at attributable to indirect fugitive emissions from active point sources, this analysis assumes that the two contribu tions 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.ES-7 Our analysis of the benefits associated with the selected NAAQS in cludes benefits related to reducing ambient lead concentrat ions 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 h ealth benefits occurring as a result of implementing the selected NAAQS level. 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 year s of age. Cognitive ef fects are thought to strongly relate to a ch ild’s future productivity and earning potential. 8

The model was constructed in Microsoft Ex cel and provides an in tegrated tool to complete five benefits estimation steps: 1) estim ate lead in air concentrations for the “base case” and “control scenarios”; 2) estimate population e xposures to air lead concentrations for each scenario; 3) estimate blood lead levels in the population for each scenar io; 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.

Because most of the point source measures implemented to achieve the NAAQS standard are focused on controlling emissions of lead in particulate form, vi rtually all of these measures also have a significant impact on em issions 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 pr ovide the total monetized hum an health benefits (the sum of premature mortality and prematur e morbidity) of reducing one ton of PM 2.5 from a specified source. EPA has used a similar techni que in previous RIAs, including the recent ozone NAAQS RIA. 9 The complete methodology for creating the benefit per-ton estimates used in this analysis is available in the Technical Suppor t Document (TSD) accompanying the recent final ozone NAAQS RIA. 10

Analysis of Costs

Consistent with our development of the illustra tive control strategies described above, our analysis of the costs associated with the sel ected NAAQS focuses on point source PM controls. For the purposes of this analysis, these c ontrols largely include measures from the AirControlNET control technology da tabase, but also include addi tional measures associated with operating permits and/or New Source Performa nce Review standards applicable to sources similar to those included in our analysis. For controls identified in AirControlNET, we estimated

8 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.

9 U.S. Environmental Protection Agency. (2008). Final Ozone NAAQS Regulatory Impact Analysis . Office of Air and Radiation. Research Triangle Park, NC, March.

10 The Technical Support Document, entitled: Calculating Benefit Per-Ton Estimates , can be found in EPA Docket EPA-HQ-OAR-2007-0225-0284.ES-8 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 Ai rControlNET and other sources does not result in attainment with the selected NAAQS in several areas. In these areas, additional unspecified emission reductions will likely be necessary to reach attainment. In order to bring these monitor areas into attainment, we calculated control co sts using two different approaches. Under one approach, we extrapolated the cost of unspeci fied emission reductions by constructing a cost curve using data on identified cont rol costs. We then derived a total cost equation in quadratic
form which best fit the total cost curve. Under our second appro ach, we calculated the cost of unspecified emission reductions by deriving an average cost per microgram of air quality improvement obtained from identified controls. For each standard, we then selected all monitor areas that failed to reach attainment and app lied unspecified emission reductions to all sources
until attainment was reached.

ES.3 Results of Analysis

Air Quality

Table ES-1 summarizes the number of monito r sites that reach attainment with the selected NAAQS and alternative standards in 2016 following the implementation of identified and unspecified emission reductions. According to the data presented in Table ES-2, 13 of the 21 monitor areas are expected to reach a ttainment with the selected NAAQS following implementation of identified controls. Table ES -2 also shows results for alternative NAAQS of
0.10 and 0.40 μ g/m 3 . (In the body of the RIA, we also prov ide analysis of three other alternative NAAQS.) For the a lternative of 0.10 μ g/m 3 , only 9 of the 21 monitors are able to reach attainment from application of identified cont rols. By comparison, a ll but one monitor area reach attainment through the implementati on of identified controls under the 0.40 μ g/m 3

standard.ES-9 Table ES-1. Number of Monitor Sites Reaching Attainment with Each Alternative Standard using Identifi ed and Unidentified Controls

Standard Number
of Sites Analyzed Number of Sites
in Attainment
with No
Additional

Controls Number of
Sites in
Attainment
with Identified

Point Source

Controls Number of Sites in
Attainment with Unspecified

Emission
Reductions and
Identified Point
Source Controls

0.40 μ g/m3

Second

Maximum Monthly Mean 12 20 21

0.15 μ g/m3

Second

Maximum Monthly Mean 5 13 21

0.10 μ g/m3

Second

Maximum Monthly Mean 21

0 9 20

The failure of certain areas to reach attainment with iden tified controls may partially reflect the lack of control information for point sources in these areas. Sources for which the AirControlNET analysis identified no controls make up a small portion of the ambient lead concentration in many of the areas not projected to reach attainment with the selected standard. For such sources in nonattainment areas, we assume that unspecified emission reductions will be obtained. When unspecified emission reduction s are implemented in addition to identified controls, we project widespread attainment with the selected and alternative standards.

Benefit and Cost Estimates

Tables ES-2, ES-3 and ES-4 summarize the costs and benefits associated with the selected and alternative NAAQS standards in 2016, based on both 3 percent and 7 percent
discount rates.

The results in Table ES-2 show that the a ssumptions used in estimating the unspecified
emission reductions drive the cost estimates. Unde r the first approach, the majority of the costs for the selected standard (88%) come from our analysis of current known control technologies, with only 12% of the total costs coming from ex trapolated costs. Under the second approach,
5% of the total costs co me from our analysis of currently known control technologies, and the majority of the costs (95%) comes from our assu mptions about the cost of controlling the last
few ambient increments of Pb needed to reach full attainment. This reflects the limited
information available to EPA on the control measur es that lead sources may implement. It is important to remember that, compared to re cent NAAQS RIAs, our current knowledge of the
costs and nature of lead emissions controls is relatively poor. Lead in ambient air has not been aES-10 focus for all but a few areas of the country for th e 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 presents the benefits of the propos ed 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 an alysis 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 PM 2.5 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 analys is in EPA's final RIA for the PM NAAQS, published in September 2006. 12

Table ES-4 presents a comp arison of costs and benefits.

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.

112 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.ES-11 Table ES-2. Summary of Costs for Regu latory Alternatives (Millions of 2006$)*

Alternative NAAQS: 0.4

μ g/m 3 2 nd Maximum Monthly Mean Final NAAQS: 0.15 μ g/m 3

2 nd Maximum Monthly

Mean Alternative NAAQS: 0.1

μ g/m 3 2 nd Maximum Monthly Mean

3%
Discount

rate 7%
Discount

rate 3%
Discount

rate 7%
Discount

rate 3%
Discount

rate 7%
Discount

rate

Identified Control Costs $46 $57 $130 $150 $160 $180

Cost Curve Extrapolation $0.32 $0.32 $20 $20 $33 $33 Extrapolated
Costs Ambient Extrapolation $390 $460 $2,600 $3,100 $3,400 $3,900

Cost Curve $46 $57 $150 $170 $190 $210 Total RIA
Costs Ambient $430 $510 $2,800 $3,200 $3,500 $4,100
Monitoring Costs** $4.2 $4.2 $4.2 $4.2 $4.2 $4.2
* All estimates rounded to two significant figures . As such, totals will not sum down columns.

** Consistent with the scope of this rulemaking, which includes monitoring provi sions, monitoring costs are included here. See OMB 2060-0084, ICR #940.21 for a complete discussion.

Table ES-3. Summary of Benefits for Re gulatory Alternatives (Millions of 2006$)

Alternative Standard: 0.40

μ g/m 3 2 nd Maximum Monthly

Mean Final NAAQS: 0.15 μ g/m 3 2 nd
Maximum Monthly Mean Alternative Standard: 0.10

μ g/m 3 2 nd Maximum Monthly Mean

3% Discount

rate 7% Discount

rate 3% Discount

rate 7% Discount

rate 3% Discount

rate 7% Discount

rate Annualized Benefit - IQ
Gains (Range)** $2,000 - $2,800 $250 - $490 $3,500 - $5,000 $440 - $870 $4,500 - $6,400 $560 - $1,100

Annualized
Benefit - PM
Co-control (Range)*** $100 - $880 $100 - $800 $230 - $1,900 $210 - $1,700 $260 - $2,200 $240 - $2,000

Total
Benefits $2,100 - $3,700 $350 -$1,300 $3,700 - $6,900 $650 - $2,600 $4,800 - $8,600 $800 - $3,100ES-12 Table ES-4. Summary of Net Benefits for Regulatory Alternatives (Millions of 2006$) 13

Alternative Standard: 0.40 μ g/m 3

2 nd Maximum Monthly Mean Final NAAQS: 0.15 μ g/m 3 2 nd

Maximum Monthly Mean Alternative Standard: 0.10 μ g/m 3

2 nd Maximum Monthly Mean

3% Discount

rate 7% Discount

rate 3% Discount

rate 7% Discount

rate 3% Discount

rate 7% Discount

rate

Total RIA Costs +

Monitoring Costs $50—$430 $61—$510 $150—$2,800 $170—$3,200 $190—$3,500 $210—$4,100

Total Benefits $2,100—$3,700 $350—$1,300 $3,700—$6,900 $650—$2,600 $4,800—$8,600 $800—$3,100

Net Benefits $1,700 - $3,700 $(160) - $1,200 $900 - $6,800 $(2,600) - $2,400 $1,300 - $8,400 $(3,300) - $2,900

13 Note that bounds of the full range of net benefits is derived by subtracting the high costs from the low benefits at the lower end, and subtracting the low costs from the high be nefits at the upper end. This is the only way to fully represent the uncertainty.ES-13 To provide additional context for the results presented in Table ES-3, Table ES-5 presents the total number of IQ points expected to be gained in th e US in the year 2016 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 μ g/m 3 maximum quarterly mean) le ad NAAQS to the revised or alternative NAAQS would be expe cted to have a significant imp act on the IQ of young children. More specifically, the results in dicate that the number of IQ poi nts gained in 2016 ranges from 230,000 if a 0.4 μ g/m 3 second maximum monthly mean NAAQS is achieved up to 510,000 for a

0.10 μ g/m 3 second maximum monthly mean NAAQS.

Table ES-5. Number of IQ Points Gained in 2016

Standard IQ Points

Gained

0.40 μ g/m 3 Second Maximum Monthly

Mean 230,000

0.15 μ g/m 3 Second Maximum Monthly

Mean 400,000

0.10 μ g/m 3 Second Maximum Monthly

Mean 510,000

Our analysis suggests that the benefits presen ted in Table ES-5 will be concentrated in a
small number of counties. Table ES-6 below show s the distribution of tota l benefits due to IQ points gained for the 0.15 μ g/m 3 second maximum monthly mean NAA QS alternative. For this standard, approximately 60 percen t of the total benefits are due to changes in lead air concentrations in three counties: Hillsborough, Florid a; Delaware, Indiana; and Berks, PA. In these areas, sources of lead exposure and the monito rs that measure ambient lead appear to be in relatively close proximity to exposed populations.

Table ES-6. Percentage of Benefits by Monitor (0.15 μ g/m 3 Second Maximum Monthly Mean NAAQS)

County State Population of

Children in

Affected Area Affected Population (%) Percentage

of Benefits (%)
Hillsborough FL 67,359 17% 38%

Delaware IN 7,957 2% 13%

Berks PA 27,966 7% 13%

Collin TX 22,192 6% 12%

Denton TX 8,243 2% 5% Cuyahoga OH 60,605 16% 4%

Pike AL 2,621 1% 4%

Jefferson MO 6,472 2% 2% Orange NY 9,186 2% 2%

Dakota MN 23,216 6% 1%

Beaver PA 9,120 2% 1%ES-14 Fulton OH 1,644 0% 1%

Rutherford TN 707 0% 1%

Williamson TN 804 0% 1% Logan OH 2,993 1% 1%

Note: There were several other counties that constituted less than 1 percent of benefits that are not included in this table.ES-15 The costs of the selected NAAQS are also expected to be concentrated in a limited number of areas, as summarized in chapter 6. Many of the monitor sites listed in the exhibit represent areas with the largest sources of lead emissions, su ch as primary or secondary lead smelters, mining operations, or battery manufacturers.

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 exceedi ng the various target NAAQS levels is very small; only 21 counties exceeding the lowe st alternative NAAQS level of 0.10 μg/m 3

. Because we know that many of the highest-e mitting 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 ha ve been analyzed in this RIA.

• Simplified Air Quality Assessment Approach . Dispersion, or plume-based models are recommended for compliance with the Pb NAAQS; however, dispersion models are data–intensive and more appropr iate for local scale analyses of emissions from individual
sources. It was not feasible to conduct such a large-scale data intensive analysis for thisRIA. As a result, the simplified analysis developed for this RIA while distanceweighting individual source contributions to ambient Pb concentrations, could not account for such locally critical variable s as meteorology and s ource stack height.

• 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 ai r 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 sour ces may have contribu ted 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 by piston- engine powered aircraft. (EPA received a petition from Friends of the Earth requesting that the Agency find that aircraft lead emissions may reasonably be anticipated to en danger the public health or welfare, and to take action to control lead emissions from pi ston-engine aircraft. EPA, in coordination with FAA, is analyzing the petition.)

• 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 st andards using identified controls alone.ES-16 • Actual State Implementation Plan s May Differ from our Simulation . In order to reach attainment with each selected NAAQS, each state will develop its own implementation plan implementing a combination of emissi ons 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.

• Unspecified Emissions Reductions . In this RIA, we report emissions reductions from
both identified controls and unspecified emission reductions . We have taken care to report these separately, in rec ognition of the greater uncertain ty 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 2016 compliance deadline, a larg e number of existing measures will be adapted to be applicable to additional sour ces, and new measures may be developed that are specifically focused on cost-effectivel y reducing PM emissions with high lead
content. Because the current st andard is attained in all but a few areas of the country, and has been for many years since th e phase down of lead in gasoline, it is likely that very little effort has been devoted to developm ent 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.).

Costs

• Uncertainty associated with uns pecified emission reductions. As indicated above, some areas are expected to rely on unspecified emi ssion reductions to reach attainment with the standards. The cost of implementing these measures, though estimated here based on the costs for identified controls, is uncertai n. Many of these sources are already wellcontrolled for particulate matter, and additiona l control for the remaining increment of Pb might be difficult to achieve. Some sources have very low particulate matter (PM) emissions overall, and therefore controls are ge nerally not applied at that emissions level.

• Uncertainty associated with emissi ons estimates for smaller sources. Note that there is often greater inherent uncertainty in reporte d emissions from smaller sources than from larger sources, and that a large portion of the lead emissions inventory consists of sources emitting less than five tons per year of lead.

• Uncertainty associated with estimating the extrapolated co sts of unspecified emission
reductions. The ambient extrapolation methodology emphasizes control costs that are the most expensive within an area, and assumes that knowle dge of control costs from
monitor areas that attain ha ve no influence on the average control costs for areas that need unspecified emission reductions. It also assumes there will be no increased knowledge of sources or changed in technol ogy between now and 2016. Lastly, most of the costs are based upon areas that make le ss than 1% progress towards attainment, indicating what little knowledge we ha ve about controls in those areas.

The cost curve methodology for unspecified emission reductions also presents a poor conceptual relationship between the costs of id entified controls at a national level and theES-17 costs of control at a local level. The data underlying this curve cont ains data points which we believe to be invalid (pre sented as part of the distri butional analysis in Section 6.1.3.3). The estimated curve estimates ne gative costs over a portion of emission reductions. In addition this appr oach relies heavily on the co ntrol strategy for the tightest standard alternative analyzed in this RIA, and does not account for variability in control strategies across alternative st andards analyzed. Lastly, we do not believe this curve well represents the knowledge of how control costs behave over time.

Benefits

• Exposure

. The benefits of IQ point gains in ch ildren 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 w ithin a 10 km radius around an individual monitor, the results increase by 40 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 94 percent when a radius of 1 km is used.

• Dose-response relationship . The dose-response function se lected for quantifying the number of IQ points gained as a result of achieving the alternative NAAQS levels affected the results. Utilizing alternate ep idemiological studies decreased the primary estimate by as much as 72 percent or increased it by 140 percent.

• 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 lo ss of an IQ point. Welfare economics emphasizes WTP measures as the more complete estimate of economic value.

• Co-control benefits related to PM. Co-control benefits estimated here reflect the application of a national dolla r benefit per ton estimate of the benefits of reducing directly emitted fine particulates from pointsources. Because they are based on nationallevel analysis, the benefit-per-ton estimates used here do not reflect local meteorology, exposure, baseline health inciden ce rates, or other local factors that might lead to an overestimate or under-estimate of the actual benef its of controlling directly emitted fine particulates.

ES.5 Conclusions and Insights

Our analysis has estimated the health 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 benefits arising from reducing emissions from a variet y of sources in and around proj ected nonattaini ng counties inES-18 2016. While 2016 is the latest date by which stat es would generally need to demonstrate attainment with the revised standards, it is exp ected that benefits (and costs) may begin occurring earlier, as states begin implementing control me asures to show progre ss towards attainment.1-1 CHAPTER 1: INTRODUC TION AND BACKGROUND

Synopsis

This document estimates the incremental cost s and monetized human health benefits of attaining a revised primary lead (Pb) Na tional Ambient Air Qua lity Standard (NAAQS)
nationwide. This document contains illustrative an alyses that consider limited emission control scenarios that states, tribes and regional plan ning organizations might implement to achieve a revised lead NAAQS. EPA weighed the available empirical data to make judgments regarding the proposed attainment status of certain urban areas in the future. Acco rding to the Clean Air Act, EPA must use health-based criteria in se tting the NAAQS and cannot consider estimates of compliance cost. This Regulatory Impact Analys is (RIA) is intended to provide the public a sense of the benefits and costs of meeting new alternative lead NAAQS, and to meet the requirements of Executive Order 12866 and OMB Circular A-4 (describ ed below in Section 1.2.2).

This RIA provides illustrative estimates of the incremental costs and monetized human health benefits of attaining a revised primary lead (Pb) Nationa l Ambient Air Quality Standard (NAAQS) within the current monitoring network 1 . Many of the highest-emitting Pb sources do not have nearby Pb-TSP monitors, and it is impor tant to note that there may be many more potential nonattainment areas than ha ve been analyzed in this RIA.

1.1 Background

Two sections of the Clean Air Act (“Act” ) govern the establishment and revision of NAAQS. Section 108 (42 U.S.C. 74 08) directs the Administrator to identify pollutants which “may reasonably be anticipated to endanger public health or welfare,” an d to issue air quality criteria for them. These air quality criteria are intended to “accurately reflect the latest scientific knowledge useful in indicating the kind and extent of all identifiable eff ects on public health or welfare which may be expected from the presence of [a] pollutant in the ambient air.” Lead is one of six pollutants for which EPA ha s developed air quality criteria.

Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate “primary” and “secondary” NAAQS for polluta nts identified under section 108. Section 109(b)(1) defines a primary standard as “the attainment and maintenance of which in the judgment of the Administrator, based on [the] criter ia and allowing an adequate margin of safety, [are] requisite to protect the public health.” A secondary standard, as defined in section 109(b)(2), must “specify a level of air quality the attainment and mainte nance of which in the judgment of the Administrator, based on [the] cr iteria, [are] requisite to protect the public welfare from any known or anticipated adverse e ffects associated with the presence of [the] pollutant in the ambient air.” Welfare effects as defined in section 302(h) [42 U.S.C. 7602(h)]
include but are not limited to “effects on soils, water, crops, vegetation, manmade materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and

1 There are currently 189 monito rs representing 86 counties, bu t only 21 counties have monitors which exceed the final NAAQS of 0.15 ug/m 3

.1-2 hazards to transportation, as well as effects on economic values and on personal comfort and well-being.”

Section 109(d) of the Act directs the Admini strator to review existing criteria and standards at 5-year intervals. Wh en warranted by such review, the Administrator is to retain or revise the NAAQS. After promulgation or revision of the NAAQS, the standards are implemented by the States.

1.2 Role of the Regulatory Impact An alysis in the NAAQS Setting Process

1.2.1 Legislative Roles

In setting primary ambient air quality standard s, EPA’s responsibility under the law is to establish standards that protec t public health, regardless of the costs of implementing a new standard. 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 standa rds based on health considerations only.

The prohibition against the consideration of co st 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 consid eration of costs and bene fits are essential to making efficient, cost effective decisions for im plementation of these standards. The impact of cost and efficiency are considered by states duri ng this process, as they decide what timelines, strategies, and policies ma ke the most sense. 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 th e standards themselves.

1.2.2 Role of Statutory and Executive Orders

There are several statutory and executive orders that dictate the manner in which EPA considers rulemaking and public documents. Th is document is separate from the NAAQS decision making process, but there are several statut es and executive orders th at still apply to any public documentation. The analysis required by thes e statutes and executive orders is presented in Chapter 8.

EPA presents this RIA pursuant to Ex ecutive Order 12866 and the guidelines of OMB
Circular A-4. 2 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 benefit-cost, and a cost-effectiv eness analysis for rules where health is the primary effect. Within this RIA we provide a benefit-cost analysis. Methodological and data limitations prevent us from performing a cost-effectiven ess analysis and a meaningful more formal uncertainty analysis for the final RIA.

2 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>.1-3 1.2.3 Market Failure or Other Social Purpose

OMB Circular A-4 indicates that one of th e reasons a regulation such as the NAAQS may
be issued is to address market failure. The major types of ma rket failure include: externality, market power, and inadequate or asymmetric in formation. Correcting market failures is one reason for regulation, but it is not the only reason. Other possible justifications include improving the function of government, removing dist ributional unfairness, or promoting privacy and personal freedom.

An externality occurs when one party’s acti ons impose uncompensated benefits or costs on another party. Environmental problems are a cl assic case of external ity. For example, the smoke from a factory may adversel y affect the health of local residents while soiling the property in nearby neighborhoods. If bargaining was costle ss and all property righ ts were well defined, people would eliminate externalities through bargaining without the need for government regulation. From this perspective, externalities arise from high transact ion costs and/or poorly defined property rights that prevent people fr om reaching efficient outcomes through market
transactions.

Firms exercise market power when they redu ce output below what would be offered in a competitive industry in order to obtain highe r prices. They may exercise market power collectively or unilaterally. Govern ment action can be a source of market power, such as when regulatory actions exclude low-co st imports. Generally, regulations that increase market power
for selected entities should be avoided. Howe ver, there are some circumstances in which government may choose to validate a monopoly. If a market can be served at lowest cost only when production is limited to a single producer of local gas and electricity distribution services, a natural monopoly is said to ex ist. In such cases, the government may choose to approve the monopoly and to regulate its prices and/or production decisions. Ne vertheless, it should be noted that technological advances often affect economies of scale. This can, in turn, transform what was once considered a natural monopoly into a market where competition can flourish.

Market failures may also result from inad equate or asymmetric information. Because information, like other goods, is costly to produce and disseminate, an evaluation will need to do more than demonstrate the possible existence of incomplete or asymmetric information. Even though the market may supply less than the full amount of information, the amount it does supply may be reasonably adequate and theref ore not require governme nt regulation. Sellers have an incentive to provide information th rough advertising that can increase sales by highlighting distinctive characte ristics of their products. Buyers may also obtain reasonably adequate information about product characteris tics through other channels, such as a seller offering a warranty or a thir d party providing information.

There are justifications for regulations in addition to correcting market failures. A regulation may be appropriate wh en there are clearly identifi ed measures that can make government operate more efficiently. In a ddition, Congress establishes some regulatory programs to redistribute resource s to select groups. Such regulations should be examined to ensure that they are bo th effective and cost-eff ective. Congress also aut horizes some regulations to prohibit discrimination that conflicts with generally accepted norms within our society.1-4 Rulemaking may also be appropr iate to protect privacy, permit more personal freedom or promote other democratic aspirations.

From an economics perspective, setting an ai r quality standard is a straightforward case of addressing an externality, in this case where firms are emitting pollutants, which cause health and environmental problems without compensati on for those suffering the problems. Setting a standard with a reasonable margin of safety at tempts to place the cost of control on those who emit the pollutants and lessens the impact on th ose who suffer the health and environmental problems from higher levels of pollution.

1.2.4 Illustrative Nature of the Analysis

This Pb NAAQS RIA is an illustrative analysis that provides useful insights into a limited
number of emissions control scenarios that stat es might implement to achieve a revised lead NAAQS. Because states are ultimately responsible for implementing strategies to meet any revised standard, the control scenarios in this RIA are necessarily hypothetical in nature. They are not forecasts of expected future outcom es. Important uncertainties and limitations are
documented in the relevant portions of the analysis.

The illustrative goals of this RIA are somewhat different from other EPA analyses of national rules, or the im plementation plans states develop, an d the distinctions are worth brief mention. This RIA does not assess the regulator y impact of an EPA-prescribed national or regional rule such as the Clean Air Interstate Rule, nor does it attempt to model the specific actions that any state would take to implement a revised lead standa rd. This analysis attempts to
estimate the costs and human and welfare benefits of cost-effective implementation strategies which might be undertaken to achieve national at tainment of new standards. These hypothetical strategies represent a scenario where states use one set of cost -effective controls to attain a revised lead NAAQS. Because states—not EPA—will implement any revised NAAQS, they will ultimately determine appropriate emissions contro l scenarios. State implementation plans would likely vary from EPA’s estimates due to differences in the data and assumpti ons that states use to develop these plans.

The illustrative attainment scenarios presente d in this RIA were constructed with the understanding that there are inherent uncertain ties in projecting emissions and controls. Furthermore, certain emissions inventory, co ntrol, modeling and m onitoring limitations and uncertainties inhibit EPA’s ability to model full at tainment in all areas. Despite these limitations,
EPA has used the best available data and methods to produce this RIA.

1.3 Overview and Design of the RIA

This Regulatory Impact Analysis evaluates th e costs and benefits of hypothetical national strategies to attain several potential revised prim ary lead standards. The document is intended to be straightforward and written for the lay person with a minimal background in chemistry, economics, and/or epidemiology. Figure 1-1 provid es an illustration of th e framework of this

RIA.1-5 Figure 1-1: The Process Used to Create this RIA Analytic Sequence for Lead NAAQS RIA

Estimate

Initial Pb

Concentrations

Adjust for MACT and PM NAAQS

Compliance Pb

Monitor

Data

Anticipated

MACT and

PM Controls

Estimated Optional Compliance Path:

Identified Controls Base Case Pb

Concentrations

Apply Unidentified

Controls As Necessary

Estimate Costs

and PM Co-Control

Emissions

Direct

Cost

Estimates AirControl

NET

Database

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of Unidentified

Controls

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Benefits and

Costs

Net Benefits

For Each

Alternative PM

Data Estimate Pb Change by Standard

Estimate Blood

Pb Changes

Estimate Health

Effects (IQ)

Monetized

Benefit

Estimates Value Benefits Valuation

Estimates

DoseResponse

Data Air-Pb:

Blood-Pb

Ratios CONTROLS AND

COST ANALYSIS BENEFITS

ANALYSIS Analytic Sequence for Lead NAAQS RIA

Estimate

Initial Pb

Concentrations

Adjust for MACT and PM NAAQS

Compliance Estimate

Initial Pb

Concentrations

Adjust for MACT and PM NAAQS

Compliance Pb

Monitor

Data

Anticipated

MACT and

PM Controls

Estimated Optional Compliance Path:

Identified Controls Base Case Pb

Concentrations

Apply Unidentified

Controls As Necessary

Estimate Costs

and PM Co-Control

Emissions

Direct

Cost

Estimates AirControl

NET

Database

Estimate Cost

of Unidentified

Controls

Compare

Benefits and

Costs

Net Benefits

For Each

Alternative PM

Data Estimate Pb Change by Standard

Estimate Blood

Pb Changes

Estimate Health

Effects (IQ)

Monetized

Benefit

Estimates Value Benefits Valuation

Estimates

DoseResponse

Data Air-Pb:

Blood-Pb

Ratios CONTROLS AND

COST ANALYSIS BENEFITS

ANALYSIS1-6 1.3.1 Baseline and Years of Analysis

The analysis year for this regulatory impact analysis is 2016, which represents the required attainment year under the Clean Air Ac t. Many areas will reach attainment of any alternative Pb standard by 2016. For purposes of this analysis, we asse ss attainment by 2016 for
all areas. Some areas for which we assume 2016 atta inment 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 individua l areas under the Clean Air Act, which contains a variet y of potential dates and flexibili ty to move to later dates, provided that the date is as expeditious as practicable.

The methodology first estimates what baselin e lead levels might look like in 2016 with existing Clean Air Act programs, including appli cation of controls to meet the current Pb NAAQS, various maximum achievable control te chnology (MACT) standards, and the newly revised PM NAAQS standard, a nd then predicts the change in Pb levels following the application of additional controls to reach tighter alternative st andards. This allows for an analysis of the incremental change between the cu rrent standard and alternative standards. Since Pb is also a component of PM, it is impor tant that we account for the impact on Pb concentrations of PM controls used in the hypot hetical control scenario in the PM NAAQS RIA, so as to avoid double counting the bene fits and costs of these controls.

1.3.2 Control Scenarios Considered in this RIA

Hypothetical control strategi es were developed for the final Pb NAAQS of 0.15 μg/m 3

. plus four alternative Pb standard s encompassing a range of 0.10 μg/m 3 to 0.50 μg/m 3 . First, EPA developed an air quality assessmen t tool to estimate air quality changes that would result from the application of emissions cont rol options that are known to be available to different types of sources in areas with monitoring levels currentl y exceeding the alternative standards. However, given the limitations of current technology and the amount of improvement in air quality needed
to reach some alternative standards in some areas, it was also expected that applying these
known controls would not reduce lead concentrations sufficiently to allow all areas to reach the more stringent standards. In or der to bring these monitor areas into attainment, we calculated controls costs using two different approaches. Under one scenari o, we calculated the cost of unspecified emission reductions by extrapolating from current av erage national identified control costs. Under our second scenario, we calculate d the cost of unspecified emission reductions by deriving an average cost per mi crogram of air quality improvement obtained from identified controls. For each standard, we then selected all m onitor areas that failed to reach attainment and applied unspecified emission reductions to all sources until atta inment was reached.

1.3.3 Evaluating Costs and Benefits

Applying a two step methodology for estimating emission reductions needed to reach full
attainment enabled EPA to evaluate nationwide costs and benefits of attaining a tighter Pb standard using hypothetical strategi es, albeit with substantial addi tional uncertainty regarding the second step estimates. First, the costs associated with applying known c ontrols were quantified.1-7 Second, EPA estimated costs of th e additional tons of extrapolat ed emission reductions estimated
which were needed to reach full attainment.

It is important to note that this analysis did not estimate any separate costs or benefits of attaining a secondary NAAQS standa rd due to resource and time c onstraints. Since the secondary is being set to be equivalent to the primary standard, no a dditional costs and benefits are expected.

To streamline this RIA, this document refers to several previously published documents, including three technical documents EPA produ ced to prepare for promulgation of the Pb NAAQS. The first was a Criteria Document created by EPA’s Office of Research and Development (published in 2006), which presented the latest available pe rtinent information on atmospheric science, air quality, exposure, dosimet ry, health effect, and environmental effects of lead. The second was a “Staff Paper” (published in 2007) that evaluated th e policy implications of the key studies and scientific information cont ained in the Criteria Document. The third was a
risk assessment for various standard levels. The Staff Paper also includes staff conclusions and recommendations to the Administrator regardin g potential revisions to the standards.

1.4 Pb Standard Alternatives Considered

EPA has performed an illustrative analysis of the potential costs and human health and visibility benefits of nationally attaining the fina l NAAQS of 0.15 μg/m 3 . Per Executive Order 12866 and the guidelines of OMB Ci rcular A-4, this Regulatory Impact Analysis (RIA) also presents analyses of a more stringent option of 0.10 μg/m 3 and a less stringent option of 0.40 μg/m 3 . EPA also analyzed alternative Pb standards of 0.20 μg/m 3 , 0.30 μg/m 3 , and 0.50 μg/m 3

. The benefit and cost estimates below are calculated incremen tal to a 2020 baseline that incorporates air quality improvements achieved through the projected implementation of existing regulations and full attainment of the existing Pb and particulate matter (PM) National Ambient Air Quality Standards (NAAQS). The baseline also includes the MACT program, which will help many areas move toward attainment of the current lead standard.

1.5 References

Henderson, R. 2006. October 24, 2006. Letter from CASAC Chairman Rogene Henderson to EPA Administrator Stephe n Johnson, EPA-CASAC-07-001.

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

U.S. EPA. 2006. Air Quality Criteria for Lead a nd Related Photochemical Oxidants (Final). U.S. Environmental Protection Agency, Washington, DC, EPA-452/R-07-013.

U.S. EPA. 2007. Review of the National Ambi ent Air Quality Standards for Lead: Policy
Assessment of Scientific and Technical Inform ation. OAQPS Staff Paper. North Carolina. EPA452/R-07-013, Office of Ai r Quality Planning and Standards, RTP, NC.1 CHAPTER 2. CHARACTERIZING PB AIR QUALITY AND EMISSIONS DATA

This chapter describes the available Pb air quality and emission data used to inform and develop the controls strategies outlined in this RI A. We first describe data sources for air quality measurement. We then provide an overview of data on Pb
emission sources contained in available EPA em ission inventories. For a more in-depth discussion of Pb air quality and emissions data, see the OAQPS Staff Paper for the Pb NAAQS. 1

2.1 Air Quality Monitoring Data

Ambient air Pb concentrations are measur ed by four monitoring networks in the United States, all funded in whole or in pa rt by EPA. These networks provide Pb
measurements for three different size classes of airborne particulat e matter (PM): total suspended PM (TSP), PM less than or equal to 2.5 μ m in diameter (PM 2.5 ), and PM less than or equal to 10 μ m in diameter (PM 10 ). The networks incl ude the Pb TSP network,
the PM 2.5 Chemical Speciation Network (CSN), th e Interagency Monitoring of Protected Visual Environments (IMPROVE ) network, and the National Ai r Toxics Trends Stations (NATTS) network. The subsections belo w describe each network and the Pb
measurements made at these sites.

In addition to these four networks, va rious organizations have operated other sampling sites yielding data on ambient air conc entrations of Pb, often for limited periods and/or for primary purposes other than quantificat ion of Pb itself. Most of these data are accessible via EPA’s Air Quality System (AQS): http://www.epa.gov/ttn/airs/airsaqs/. In an effort to gather as much air toxics data, including Pb, into one database, the EPA and National Association of Clean Air Agenci es (NACAA) created the Air Toxics Data
Archive. The Air Toxics Data Archive can be accessed at: http://vista.cira.colostate.edu/atda/

.

2.1.1 Ambient Pb Measurement Methods

A number of methods are used to collect Pb and measure Pb concentrations in the atmosphere. Most methods use similar samp le collection approaches. Ambient air is drawn through an inlet for a predetermined amount of time (typically 24 hours) and the
PM is collected on a suitable filter media. After the sample has been collected, the filter may be used to determine the mass of PM collected prior to then being used for determination of Pb. The filter is chemically extracted and analyzed to determine the Pb concentration in the particulate material . The concentration of Pb found in the atmosphere, in μg/m 3 , is calculated based on the con centration of Pb in the volume extracted, the size of the colle ction filter, and the volume of air drawn through the filter. The primary factors affecting the measur ements made are the sampling frequency, duration of sampling, type of inlet used, volume of air sampled, and the method of

1 U.S. Environmental Protection Agency (2007c), Review of the National Ambient Air Quality
Standards for Lead: Policy Assessment of Scientific and Technical Informati on, OAQPS Staff Paper,
Chapter 2, EPA-452/R-07-013, Office of Air Quality Planning and Standards, RTP, NC.2 analyzing the filter for Pb content. The following paragraphs describe how these factors
affect the Pb measurements.

2.1.2 Inlet Design

In ambient air monitors, a number of inlet designs have been de veloped that allow certain particle size ranges to be sampled. The inlets use either impaction or cyclone techniques to remove particles larger than a certain size (the size cutpoint) from the sample stream. Three particle size cutpoints are used in ambient Pb measurements including TSP, PM 2.5 , PM 10 . The TSP inlet is designed to allow as much suspended particulate into the sampling device as possi ble while protecting ag ainst precipitation and direct deposition on to the filter (nomina lly 25 to 45 micrometers) (USEPA, 2004c). Sampling systems employing inlets other than the TSP inlet will not collect Pb contained in the PM larger than the size cutpoint. Ther efore, they do not provide an estimate of the total Pb in the ambient air. This is part icularly important near sources which may emit Pb in the larger PM size fractions (e. g., fugitive dust from materials handling and storage).

2.1.3 Volume of Air Sampled

The amount of Pb collected is directly pr oportional to the volume of air sampled. Two different sampler types have evolved for PM and Pb sampling – a high-volume and a low-volume sampler. High-volume samplers draw between 70 and 100 m 3 /hr of air through an 8 inch by 10 inch filter (0.05 m 2 filter area). Low-volume samplers typically
draw 1 m 3 /hr through a 47 mm diam eter filter (0.002 m 2 filter area). Currently all Federal Reference Method (FRM) and Federal Equiva lence Method (FEM) for Pb-TSP are based on high-volume samplers.

2.1.4 Sampling Frequency

The frequency of Pb sampling used in th e U.S. varies between one sample every day (1 in 1 sampling) to the more common fre quency of one sample every 6 days (1 in 6 sampling). Semi-continuous methods for the measurement of ambient metals (including Pb) are currently being expl ored which would allow for more frequent sampling (as frequent as 1 sample per hour), but much mo re work is needed on these methods before they can be deployed in a network setting.

More frequent sampling reduces the uncer tainty in estimates of quarterly or annual averages associated with temporal variations in ambient concentrations. However, the costs of sampling and analysis are directly tied to sample frequency. As such, it is necessary to evaluate the reduction in measurement error ve rsus the increase in sampling and analysis costs when select ing the required sampling frequency. A discussion of the observed temporal variation of Pb measurements is given later in this
section.

2.1.5 Sample Analysis

After the samples have been collected on f ilters and the filters have been weighed, the filters are analyzed for Pb content. A nu mber of analytical methods can be used to analyze the filters for Pb c ontent including x-ray fluorescen ce analysis (XRF), proton-3 induced x-ray emission (PIXE), neutron activ ation analysis (NAA) , atomic absorption (AA), or inductively-coupled plasma ma ss spectrometry (ICP/MS) (CD, pp. 2-80 to 2- 81). A detailed discussion of these met hods was given in the 1986 CD (USEPA, 1986),
and the reader is referred to that documen t for more information on these analytical
methods. A search cond ucted on the AQS database 2 shows that the method detection limits for all of these analytical methods (coupled with the sampling methods) are very low, ranging from 0.01 μ g/m 3 to as low as 0.00001 μg/m 3 , and are more than adequate for determining compliance with the current NAAQS.

2.1.6 Pb-TSP

This network is comprised of state and locally managed Pb monitoring stations which measure Pb in TSP, i.e., particles up to 25 to 45 microns. These stations use samplers and laboratory analysis methods wh ich have either FRM or FEM status. The FRM and FEM method descriptions can be found in the U.S. Code of Federal Regulations, Section 40 part 50, Appendix G. Sampling is conducted for 24-hour periods, with a typical sampling schedule of 1 in 6 days. Some monitoring agencies “composite” samples by analyzing several cons ecutive samples together to save costs
and/or increase detection limits.

2.1.7 Monitor Locations

The locations of Pb-TSP sites in ope ration between 2003 and 2005 are shown in Figure 2-1. The state and local agencies wh ich operate these sites report the data to EPA’s AQS where they are accessible via seve ral web-based tools. EPA’s series of annual air quality trends reports have used da ta from this network to quantify trends in
ambient air Pb concentrations. The most r ecent Trends report for Pb-TSP can be found at http://www.epa.gov/airtrends/lead.html

.

A review of the Pb-TSP network's coverage of the highest Pb emitting sources (as
identified in the current version of the 2002 NEI) was conducted as part of preparing this
document. This review indicates that ma ny of the highest Pb emitting sources in the 2002 NEI do not have nearby Pb-TSP monitors. This review indicates that only 2 of 26 facilities (both Pb smelters 3 ) identified as emitting greater than 5 tpy have a Pb-TSP
monitor within 1 mile. The lack of monitors near large sources indicates we are likely currently underestimating the extent of occurrences of re latively higher Pb concentrations. Additionally, none of the 189 Pb-TSP are located within a mile of airports identified in the NEI as an airpor t where piston-engine ai rcraft operate (i.e.,
aircraft that still use leaded aviation fuel). However, there are historical data for 12 PbTSP monitoring sites operating w ithin 1 mile of such airports (going back to 1993). Nine of these sites reported maximum quarterly mean values (for 1993-2002) that ranged from 0.03 to 0.06 μg/m 3 and across all 12 sites, the maximum quarterly mean values ranged from 0.004 to 0.15 μg/m 3

.

2 EPA’s AQS can be accessed at h ttp://www.epa.gov/ttn /airs/airsaqs/

3 Primary and secondary smelters were the source types given particular priority at the time of the
last Pb NAAQS review (USEPA, 1990; USEPA, 1991).4 Figure 2-1. Pb-TSP monitoring sites: 2003-2005.

The number of sites in the Pb-TSP networ k has decreased significantly since the
1980s. The number of sites in the network r eached its highest point in 1981 (946 sites). About 250 sampling sites operated during 2005. This decline in the number of Pb-TSP
sites is attributable to the dramatic decr ease in Pb concentratio ns observed since the 1980s and the need to fund new monitoring objectives (e.g., PM 2.5 and ozone monitoring). Lead-TSP sites in lower con centration areas were shut down to free up resources needed for monitoring of other pollutants such as PM 2.5 and ozone.

2.2 Air Quality Modeling

As part of the Agency’s na tional air toxics assessment (NATA) activities, a national scale assessment of hazardous air pollutant s including Pb compounds has been performed twice over the past few years (USEPA 2006c, 2002c, 2001a). These two assessments included the use of the NEI for the year s 1996 and 1999, respectively, with atmospheric dispersion modeling to predict a ssociated annual average Pb ai r concentrations across the country. A national scale assessment is not yet available based on the 2002 NEI. A
number of limitations are associated w ith the 1996 and 1999 ambient concentration estimates and the underlying emissions estimates.

Historical studies show that Gaussian dispersion models, such as ASPEN, typically agree with monitoring data within a factor of 2 most of the time. In the case of Pb in the NATA assessment, model estimates at monitor locations were generally lower than the5 monitor averages for Pb, suggesting that th e modeling system (i.e., emissions estimates, spatial allocation estimates, dispersion modelin g) may be systematically underestimating ambient concentrations. This may be particul arly true for Pb as metals tend to deposit rapidly with distance from the source accordi ng to their particle size and weight. The model-to-monitor analysis is described in detail at http://www.epa.gov/ttn/atw/nata1999/99compare.html . The modeling system underestimation may also be due in part to a lack of accounting for emissions reentrainment (these "re-entraine d" particles may be observed by the monitors, but they are not accounted for in the emissions inventory, and thus would not contribute to the model estimate). For more details on the limitations of the 1999 NATA national scale
assessment, see http://www.epa.gov/ttn/atw/nata1999/limitations.html

.

For more information on Pb modeling, see section 2.4 of the OAQPS Staff Paper for the Pb NAAQS. 4 For reasons discussed in sectio n 3.1.1, we did not use an air quality model for this analysis.

2.3 Sources of Pb Emissions to Ambient Air

The primary data source for this discu ssion is the National Emissions Inventory (NEI) for 2002 (USEPA, 2007a). As a result of Clean Air Act requirements, emissions standards promulgated for many source categor ies that have taken effect since 2002 are projected to result in much lo wer emissions at the current time or in the near future. For a more comprehensive discussion of Pb s ources, see section 2.2 of the OAQPS Staff Paper for the Pb NAAQS. 5

2.3.1 Types of Pb Sources

Lead is emitted from a wide variety of source types, some of which are small individually but the cumulative emissions of which are large, and some for which the opposite is true. The categories of Pb sources estimated via the 2002 NEI to emit –as a category- more than five tons per year (tpy) of Pb are listed in Tabl e 2-2. Note that there is often greater inherent uncer tainty in reported emissions fr om small sources than from larger sources, and that a large portion of th e lead inventory consists of sources emitting less than than five tons per year of lead.

2.3.1.1 Stationary Sources

The main sources of emissions in th e 2002 NEI are comprised primarily of
combustion-related emissions and industrial process-related emissions. Point source
emissions account for about 66% of the natio nal Pb emissions in the 2002 NEI. The

4 U.S. Environmental Protection Agency (2007c), Review of the National Ambient Air Quality
Standards for Lead: Policy Assessment of Scientific and Technical Informati on, OAQPS Staff Paper,

section 2.4, EPA-452/R-07-013, Office of Air Quality Planning and Standards, RTP, NC.

5 Ibid ., section 2.2.6 point source emissions are roughly split betw een combustion and industrial processes, while mobile, non-road sources (e.g. piston-engi ne aircraft using leaded fuel) account for
29%.

Table 2-1 presents emissions estimate s for stationary sources grouped into descriptive categories. Presence and relativ e position of a source category on this list does not necessarily provide an indication of the significance of the emissions from
individual sources within th e source category. A source ca tegory, for example, may becomposed of many small (i.e., low-emitting) sources, or of just a few very large (highemitting) sources.7 Table 2-1. Source categories emitting greater than 5 tpy of Pb

ALL CATEGORIES - Total tons 1371 % of Total

Mobile sources 623 45.44
Iron and Steel Foundries 83 6.05 Primary Lead Smelting 59 4.30

Industrial/Commercial/ Institutional Boilers & Process Heaters 53 3.87

Hazardous Waste Incineration 47 3.43 Secondary Lead Smelting 44 3.21 Municipal Waste Combustors 33 2.41 Military Installations 27 1.97
Pressed and Blown Glass and Gl assware Manufacturing 26 1.90 Utility Boilers 23 1.68 Secondary Nonferrous Metals 22 1.60 Portland Cement Manufacturing 18 1.31 Integrated Iron & Steel Manufacturing 17 1.24 Lead Acid Battery Manufacturing 17 1.24 Stainless and Nonstainless Steel Manufacturing (EAF) 17 1.24 Mining 15 1.09 Primary Metal Products Manufacturing 13 0.95 Waste Disposal - Solid Waste Disposal 10 0.73 Primary Copper Smelting 10 0.73 Secondary Aluminum Production 9 0.66 Fabricated Metal Products Manufacturing 9 0.66 Pulp & Paper Production 9 0.66 Transportation Equipment Manufacturing 8 0.58 Electrical and Electronics Equipment Manufacturing 8 0.58 Sewage Sludge Incineration 7 0.51

Nonferrous Foundries 7 0.51 Ferroalloys Production 7 0.51 Industrial Inorganic Chemical Manufacturing 7 0.51 Industrial and Commercial Machinery Manufacturing 7 0.51 Residential Heating 6 0.44 Secondary Copper Smelting 6 0.44 Miscellaneous Metal Parts & Products (Surface Coating) 6 0.44

Commercial and Industrial Solid Waste Incineration 6 0.44 Autobody Refinishing Paint Shops 5 0.36

Coke Ovens 5 0.36 Stationary Reciprocating Internal Combustion Engines 5 0.36

Other 97 7.08

There are some 13,067 point sources (industria l, commercial or institutional) in the 2002 NEI, each with one or more processes that emit Pb to the atmosphere. Most of these sources emit less than 0.1 tpy Pb. Th ere are approximately 1300 point sources of Pb in the NEI with estimates of emissions gr eater than or equal to 0.1 tpy and these point sources, combined, emit 1058 tpy, or 94% of th e Pb point source emissions. In other words, 94% of Pb point source emissions are emitted by the largest 10% of these sources.8 Chapter 3 of this RIA discusses our me thodology for characterizing the relative contributions of stationary point sources (def ined in this analysis as sources emitting > 1 ton per year of Pb), area nonpoint sources (def ined in this analysis as sources emitting less than 1 ton per year), and mobile sources.

2.3.1.2 Mobile Sources

Thirty-five years ago, combustion of leaded gasoline was the main contributor of Pb to the air. In the early 1970s, EPA set na tional regulations to gr adually reduce the Pb content in gasoline. In 1975, unleaded gaso line was introduced for motor vehicles equipped with catalytic converters. EPA banne d the use of leaded gasoline in highway
vehicles after December 1995. Cu rrently, tetraethyl lead (TEL) is still added to aviation gasoline (avgas) which is used in most piston- engine powered aircraft. TEL is added to avgas to increase oc tane, prevent knock 6 , and prevent valve seat recession and subsequent loss of compression for engines without hard ened valves. The 2002 National Emissions Inventory (NEI) estimates that lead emissions from the use of leaded aviation gasoline (commonly referred to as avga s) are 491 tons; this accounts for 29% of the air emission inventory for lead. These estimates are ba sed on the volume of avgas supplied nationally,
the concentration of lead in a vgas and the retention of some l ead in the engine and engine oil of these aircraft. The Department of Energy estimates that about 281 million gallons of avgas were supplied in the U.S. in 2002. 7 In 2006 the volume was about 280 million gallons. The majority of avgas contains up to 0.56 grams of lead pe r liter (2.12 grams of lead/ gallon); this is referred to as 100 Lo w Lead (100LL). There is another grade of 100 octane avgas that contains 1.12 grams of lead per liter, but this product is not widely available. Based on newly available informati on, the retention of lead in the engine and oil of piston-engine aircraft was recently revised from a value of 25% which was more related to lead retention in light-duty vehicles operating on leaded fuel to 5% retention for piston-engine aircraft. 8 Using these recently available data on lead retention, EPA now
estimates that lead emissions from the use of avgas in 2002 were approximately 623 tons or 35% of the national inventory. This estimat e is based on all leaded avgas used in the
U.S. This estimate does not account for the fact that some lead is emitted in the local area of an airport facility and some lead is em itted at altitude. EPA’s method for estimating airport-specific lead inventories is discussed in detail elsewhere. 9

6 Knocking is the sound produced when some of the unburned fuel in the cylinder ignites spontaneously resulting in rapid burning and a precipitous rise in cylinder pressure that
creates the characteristic knocking or pinging sound (C hevron 2005 available at: http://www.chevronglobalaviation. com/docs/aviation_tech_review.pdf ).

7 data available at http://tonto.eia.doe.gov/dnav/pet/hist/mgaupus1A.htm

8 For more information see the memo to the docket titled “Revised Methodology for Esimating
Lead Emissions from Piston-Engine Aircraft Operating on Leaded Aviation Gasoline.”

9 See memo to the docket titled For more information see the memo to the docket titled “Revised
Methodology for Esimating Lead Emissions from Piston-Engine Aircraft Operating on Leaded Aviation

Gasoline.”9 Lead is also present as a trace contamin ant in gasoline and diesel fuel and is a component of lubricating oil (CD, pp. 2-45 to 2-48). Inventory estimates from these sources are not currently availa ble. Additional mobile sources of Pb include brake wear, tire wear, and loss of Pb wheel weights (CD, pp. 2-48 to 2-50). Emission rates for Pb from brake wear have been published but inventory estimates have not yet been developed from these data (Schauer et al., 2006 ). Robust estimates of Pb from tire wear and wheel weights are not available. Current ly, Pb from combustion of leaded avgas is the only mobile source of Pb included in the 2002 NEI.3-1 CHAPTER 3. AIR QUALITY ASSESSMENT METHODOLOGY

This chapter presents the methods used to estimate the air quality impacts of the emissions control strategies outlined in Chapter 4 of this document. To begin, we first describe the air quality assessment tool developed by EPA to relate lead emissions to ambient lead concentrations. We then explain how this tool was used to estimate the air quality impacts of each hypothetical emissions control strategy. The air quality impacts of these hypothetical control strategies are summarized in Chapter 4.

EPA used the air quality assessment methodology presented in this chapter to assess the final lead NAAQS of 0.15 μg/m 3 and the five alternative standard s included in this document. We note that the Agency is setting the final st andard as the maximum quarterly rolling average concentration, whereas the proposed rule include d two options for the averaging time and form of the standard: the maximum quarterly average c oncentration across a thre e-year period (i.e., the maximum quarterly mean) and the second highest monthly average concentration across a threeyear period (i.e., the second maximu m monthly mean). The decision to set the final standard as a maximum quarterly rolling average concentra tion, however, was made after much effort had been expended to assess the cost s and benefits of the final and alternative standards as second maximum monthly mean concentrations. Because th is decision was reached late in the analytic process, EPA used the air quality assessment met hodology presented in this chapter to assess the 0.15 μg/m 3 standard as a second maxi mum monthly mean concentr ation rather than as a maximum quarterly rolling av erage concentration.

To assess the implications of using second maximum monthly mean concentrations for this analysis, we compared second maximum monthly mean concentrations to maximum quarterly concentrations. Ideally, we would co mpare second maximum monthly concentrations for each monitor area to the corresponding maximu m quarterly rolling average concentration, but
historical data for the latter were not readily available. In the absence of these data, we compared the second maximum monthly mean to the maximum quarterly mean. For the full
universe of 86 counties where mon itor readings for lead were available, the second maximum monthly mean is, on average, 0.03 μg/m 3 higher than the maximum quarterly mean. In addition, when we statistically test the difference be tween the second maximum monthly mean and the maximum quarterly mean concentrations in thes e 86 counties, we confirm that the former is likely to be higher than the latter. 1 When we limit the analysis to the 21 counties included in this analysis (i.e., the 21 counties with second ma ximum monthly mean concentrations above 0.1 μg/m 3 , which is the most stringent standard analy zed in this document), we reach the same general conclusion—that the second maximum mont hly mean is, on average, higher than the maximum quarterly mean. 2 This suggests that we may overestimate the emissions reductions,

1 For all 86 counties where monitor data are available, the 95 percent confidence interval for the difference between the second maximum monthly mean and the maximum quarterly mean suggests that the former is 0.007 to 0.062 μg/m 3 higher than the latter.

2 For the 21 counties analyzed in this RIA, the 95 perc ent confidence interval for th e difference between the second maximum monthly mean and the maximum quarterly mean suggests that the second maximum monthly mean is 0.002 to 0.146 μg/m 3 higher than the maximum quarterly mean.3-2 costs, and benefits associated with the fina l and alternative standa rds and that we may
underestimate the number of areas able to attain each standard.

3.1 Air Quality Assessment Tool

To assess the air quality imp act of the hypothetical emissions controls implemented under the final NAAQS, EPA would ideally use a detail ed air quality model that simulates the dispersion and transport of lead to estimate loca l ambient lead concentrations. Although models with such capabilities are avai lable for pollutants for which EPA frequently conducts air quality analyses (e.g., particulate matter and ozone), regi onal scale models are currently neither available nor appropriate for Pb. 3 Dispersion, or plume-based mode ls, are recommended for compliance with the Pb NAAQS and were used for the Pb NAAQS risk assessment case studies. However, dispersion models are data-intensi ve and more appropriate for loca l scale analyses of emissions
from individual sources. It wa s not feasible to conduct such a la rge-scale data in tensive analysis for this RIA. As a result, the simplified anal ysis developed for this RIA, while distanceweighting individual source contri butions to ambient Pb concentr ations, could not account for such locally critical variables as meteorology a nd source stack height. In stead of using a dataintensive modeling approach, EPA developed a more simplified air quality assessment tool to estimate the air quality impacts of each lead emissions control strategy.

In general, air quality analyses conducted in support of the current Agency Pb NAAQS review focused on the Pb-TSP monitoring sites re presented in the Air Quality System (AQS) database with sufficient 1-, 2-, or 3-year data records for th e years 2003-2005; this database encompasses 189 monitoring sites located in 86 distin ct counties. For this particular analysis, we concentrated on county maxima monitors exceedi ng the lowest alternative target NAAQS level (0.1 μ g/m 3 ). The identification of the county maxima monitors and subsequent processing were based on the alternative NAAQS form of sec ond maximum monthly Pb -TSP average over a 3- year period (in this case, 2003-2005). 4 Specifically, we identified 21 monitors (located in 21 counties) which we analyzed with the hereto described air quality a ssessment tool. This assessment tool employs a source-apportionment a pproach to estimate the extent to which each of the following emissions sources contribute to observed lead c oncentrations in the proximate
areas of those 21 monitors:

3 U.S. Environmental Protection Agency (2007c), Review of the National Ambient Air Quality Standards for Lead: Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper, section 2.4, EPA-452/R-07-013, Office of Air Quality Planning and Standards, RTP, NC.

4 In the Proposed Rule Analysis, monitors / counties were initially selected based on an alternative NAAQS form of maximum monthly Pb-TSP average. The Agency focus sw itched to second maximum monthly after considerable effort had already been made in the Proposed Rule RIA assessment. Although the metric values were switched for all monitors included in the analysis and reprocessed acc ordingly, the initial monitor selection was not repeated using the different metric. Thus, in some isolated inst ances, a monitor utilized for the Proposed Rule RIA was not the monitor with the county highest second maximum monthly average (though it was the one with the county highest maximum monthly average). Fo r the Final Rule Analysis, the identification of monitors with maximum second monthly means was corrected; accordingly, some of th e monitors in this analysis differ from those used in the Proposed Rule Analysis.3-3 • Background lead

• Miscellaneous, re-entrained dust

• Emissions from area non-point sources

• Indirect fugitive emissions from active industrial sites

• Direct point source emissions 5

After allocating a portion of the observed lead concentration for each monitor area to the first three categories listed above, the assessmen t tool apportions the re maining concentration among all inventoried point sources within ten kilometers of each monitor location. 6 Once the tool has determined the contribution of each point source to the observed lead concentration, it is then possible to determine how the application of pollution controls to individual point sources translates into changes in the observed lead c oncentration for each monitor area. To apportion
the ambient lead concentration for each monitor area to the five categories presented above, the air quality assessment tool empl oys the following approach:

Step 1: Estimate baseline air quality value

. Drawing from the 2003-2005 Pb-TSP NAAQS-review database, the air quality assessment tool reco rds the second maximum monthly
mean ambient lead concentration for the 21 mon itor locations where this concentration exceeds

0.1 μ g/m 3 , the most stringent of the NAAQS alternativ es considered in this document. These concentrations, adjusted for the expected impl ementation of MACT cont rols implemented after 2002, PM 2.5 NAAQS controls included as part of the illustrative PM 2.5 control strategy described
in the PM 2.5 NAAQS RIA, 7 and the controls listed in the 2007 Missouri Lead SIP revisions, serve as the baseline air quali ty values for this analysis. 8

For the final rule, the specification of base line air quality values differs from the proposed rule in two ways:

1. First, in some areas, monitor geo-coordi nates and ambient lead concentration data were adjusted to reflect the air quality monitor with the limiting value for the alternative NAAQS form of second maximum monthly Pb-TSP average. For the Proposed Rule analysis, we incorrectly us ed the geo-coordinates and ambient lead

5 For the purposes of this analysis, airports servicing pi ston-engine aircraft that use leaded aviation gasoline are treated as point sources. The volume of avgas produced in the U.S. in 2002 was 6,682 thousand barrels or 280,644,000 gallons. This information is provided by the DOE Energy Information Administration. Fuel production volume data obtained from http://tonto.eia.doe.gov/dnav/pet/hist/mgaupus1A.htm accessed November

2006.

6 Note that although the air quality assessment tool distinguishes between the portion of the observed lead concentration attributable to point source emissions and th at attributable to indirect fugitive emissions from active point sources, this analysis assumes that the two contribu tions 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 below.

7 U.S. Environmental Protection Agency. Regulatory Impact Analysis: 2006 National Ambient Air Quality Standards for Particle Pollution . October 2006.

8 Note also that to estimate the value of the point source influence factor described above, the air quality assessment tool uses lead concentration data from 2003 through 2005 and lead emissions data for 2002. Ideally, this factor would be estimated based on concentration and emissions data for the same time period.3-4 concentration data from the monitor in each geographic area of analysis with the highest maximum monthly Pb-TSP average, ra ther than the monitor with the highest

second maximum monthly Pb-TSP average. In most areas, the limiting monitor was the same for both NAAQS forms, but in three monitor areas (Jefferson County, MO, Sullivan County, TN, and Dallas County, TX), the air quality monitor changed. Where the monitor location changed, the set of sources with emissions affecting each monitor area also changed to reflect the new range of influence surrounding the new
monitor locations.

2. Secondly, because the 0.05 μ g/m 3 standard examined in the proposed rule RIA is not considered in this analysis, monitors with lead concentrations exceeding only that standard (i.e., monitors with second ma ximum monthly Pb-TSP averages between

0.05 μ g/m 3 and 0.1 μ g/m 3 ) are excluded from the analys is. Thus, the number of monitor locations analyzed ha s therefore decreased from 36 in the proposed rule RIA to 21 in this analysis.

MACT controls : For most point sources, lead emissions as specified in the 2002 National Emissions Inventory (NEI) served as th e base case emissions for our 2016 analysis; as
with the PM 2.5 NAAQS RIA and ozone RIA, no growth f actors were applied to the 2002 NEI emissions estimates for industrial sources to ge nerate our emissions es timates for 2016. In general, lead emissions from these source ca tegories are trending downward over time due to various factors including lack of growth in pa rticular industrial sect ors, implementation of alternative lower-emitting production practices at facilities, and/or recent regulations coming into effect. However, where possible, we adjusted the 2002 NEI lead emissions values to reflect the estimated control efficiency of MACT standard s with post-2002 compliance deadlines, because the 2002 NEI would not reflect the imp act of those controls reasonabl y anticipated to be in place by 2016.

We identified 41 existing MACT rules with post-2002 compliance deadlines that affect sources included in this analys is. Of these, we focused on rules affecting the 20 industries responsible for the largest lead emissions accord ing to the 2002 NEI. Ideally, we would apply control efficiency data for each of these rules to the 2002 lead emissions estimates for the corresponding emissions sources. Consulting Federal Register documentation for these rules, as well as EPA’s internal MACT rule summary data, we were able to identify control efficiency information for just nine of these rules. The sources affected by these nine rules, however, represent 78 percent of the lead emissions fr om sources affected by MACT rules with post-2002 compliance deadlines. For three of these rules, EPA expects no incremental reduction in lead emissions. For two of these rules (integrated iron & steel and pressed & blown glass), the control efficiency information that we identifie d is specific to metal Hazardous Air Pollutants (HAPs, e.g., lead). For the remaining four ru les, we obtained information on their overall HAP control efficiency from the Federal Register and from EPA's internal MACT summary data.
Table 3-1 summarizes the control efficiencies found for each of the nine MACT rules with available control efficiency data. Due to the uncertainty that future MACT rules may cover sources of Pb emissions, this analysis does not assume the promulgation of future MACT rules.

PM 2.5 NAAQS controls : In addition to adjustments for MACT rules, we also adjusted
the 2002 NEI emissions estimates to account for compliance measures required by the3-5 September 2006 revision to the PM 2.5 NAAQS included as part of the illustrative PM 2.5 control strategy described in the PM 2.5 NAAQS RIA. Because EPA expects PM emissions controls to be implemented at certain of these sources in order to reach attainment with the PM 2.5 standard, we incorporated them into the base case em issions values used in our analysis.

Table 3-1.
CONTROL EFFICIENCIES FOR POST-2002 MACT RULES AFFECTING
SOURCES OF LEAD EMISSIONS

MACT Rule Data
Source Control Efficiency Observed

Pollutant Integrated Iron and Steel Manu facturing 1 65.4% Metal HAP Iron and Steel Foundries 2,3 36.5% HAP
Petroleum Refineries 4 86.6% HAP Secondary Aluminum Production 4 68.6% HAP

Industrial/Commercial/Institutional Boilers & Heaters –
Coal 4 33.3% HAP

Pressed and Blown Glass and Glassware Manufacturing 5 97.6% Metal HAP Primary Nonferrous Metals – Zinc, Cadmium, and Beryllium 6 0% N/A

Secondary Nonferrous Metals 5 0% N/A Primary Copper Smelting 6 0% N/A Key to Data sources:
1. Economic Impact Analysis of Final Integrated Iron and Steel NESHAP, Center for Regulatory Economics and Policy Research, September 2002

2. 67 FR 78273
3. Economic Impact Analysis of Final Iron and St eel Foundries NESHAP, RTI International, August

2003

4. EPA’s internal MACT summary data

5. 72 FR 73179

6. 72 FR 2929

Of the 21 lead monitor areas considered in this RIA, five are located in counties predicted
to be in nonattainment with the revised PM 2.5 standard in 2016, as specified in the PM 2.5 NAAQS RIA. For 20 point sources in these areas, EP A identified PM controls from the control technology database used in the controls and cost analysis for the PM NAAQS RIA. The controls anticipated to be applie d consisted of fabric filters (w ith a 99 percent expected control efficiency), upgrades to electros tatic precipitators ( 67 percent), and the in stallation of capture hoods vented to a baghouse (85 percent). For eac h source with controls identified in the PM NAAQS RIA, we applied the control efficiency for the appropriate control technology to its 2002 NEI emissions to produce the new, PM N AAQS-adjusted baseline emissions for that source. For this analysis, we assume that these expected control efficiencies will remain constant throughout the relevant time period.

Step 2: Estimate background lead concentration : EPA estimates that the average background lead concentration is so small (0.0005 μ g/m 3 ) as to be irrelevant for the purposes of this analysis. Given th e resolution of the lead monitoring devi ces supporting this analysis, the air quality assessment tool assumes that backgr ound lead concentrations have no measurable contribution to violations at the design value monitors. However, given the nature of the3-6 conducted analysis for estimating “miscellaneou s re-entrained dust” (see Step 3 below), background concentrations are, in fa ct, encompassed in that category.

Step 3: Estimate the contributio n of miscellaneous re-entrained dust

. Although the lead emissions constituting the miscellaneous re-e ntrained dust category ar e of uncertain origin, they are believed to encompass 1) re-entrained dust emitted from past stationary and past mobile sources (e.g., leaded gas), including the contribution from transport; and 2) dust emitted from demolition, construction, and/or sandblasting acti vities, and 3) uninventoried mobile-related emissions (e.g., from Pb wheel weights, brake we ar and trace Pb from gas/diesel and lube oil consumption) . Rather than estimating the site-b y-site contribution of mi scellaneous re-entrained dust, the air quality assessment tool applies a national estimate of the central tendency of the
contribution of miscellaneous re-entrained dust to ambient lead concentrations. EPA developed this national estimate by evaluating data from am bient TSP monitors with a negligible impact from NEI lead emission sources. For the purpose s of this analysis, EPA defines “negligible impact” to mean that NEI point and non-point lead-emitting sources, with associated fugitive emissions, have no contribution to the measured ambient lead concentration. Accordingly, EPA judged the ambient lead concentration measured at these TSP monitors to be entirely due to
miscellaneous re-entrained dust.

Of the 189 sites included in the 2003-2005 TSP NAAQS-revi ew database, EPA deemed 90 sites to have negligible impact from active sour ces based on two criteria: 1) each site was not identified as “source oriented” in previous EPA analysis; and 2) each site had cumulative point and area non-point emissions of 0.01 tons per y ear or less within a one-mile radius of the
monitor. 9 As a central tendency of the contributio n of miscellaneous, re -entrained dust, EPA

found the median ambient lead concen tration at these sites to be 0.0225 : g/m 3 . Although this represents the average concentration at the nationa l level, actual concentrations associated with miscellaneous re-entrained dust may vary by area. Nevertheless, in genera l this value typically represents a small portion of the baseline concentration at each monitor, as indicated by Figure 3- 1, which illustrates the compositi on of the baseline lead concentration at the Fulton County, Ohio
monitor.

Step 4: Estimate the contri bution of area nonpoint sources

. A portion of observed
lead concentrations results from emissions fr om area non-point sources (e.g., households). The air quality assessment tool estimates the contri bution of lead-emitting area non-point sources to
ambient lead concentrations based on data from the 2002 area non-point l ead emission inventory. This inventory is generally summarized at the county level, and EPA assumes that each county’s area non-point emissions were uniformly dist ributed within each county. Based on this assumption, the air quality assessment tool also as sumes that the extent to which area non-point
sources contribute to ambient lead concentrations is pr oportional to the ratio of county-level area non-point lead emissions to total county-level le ad emissions. Because this ratio differs by county, the area non-point source co ntribution to ambient lead con centrations also differs for each monitor site, but it generally composes a sm all portion of the overall concentration, as illustrated by the Fulton County, Ohio example in Figure 3-1.

9 Sites classified as source oriented in previous EPA analysis were identified via a reference list used in EPA Trends Report analyses. This list encompasses 119 sources and was last updated in 2003.3-7 Figure 3-1.
APPORTIONMENT OF THE BASELINE SE COND MAXIMUM MONTHLY MEAN LEAD CONCENTRATION AMONG SOURCE CATEGORIES IN FULTON COUNTY, OHIO

Step 5: Estimate the residual concentrat ion after removing the contributions of miscellaneous re-entrained dust and area non-point source emissions

. Based on the results of the four previous steps, the air quality assessment tool estimates the intermediate remaining second maximum monthly mean lead concentra tion (hereafter, “resi dual concentration”) by subtracting the contributions of miscellaneou s re-entrained dust a nd area non-point source emissions from the baseline air quality value. The residual concentration represents the total
concentration fraction associated with emissions from inventoried point sources and indirect fugitive emissions from industrial sites. In the case of Fulton County, Ohio, the residual

concentration is 0.5053 : g/m 3 , or the baseline concentration of 0.5300 : g/m 3 less the 0.0225

: g/m 3 and 0.0022 : g/m 3 concentration fractions associated with miscellaneous dust and area non-point sources, respectively. 0.00 0.10 0.20 0.30 0.40 0.50 0.60

Monitor 390510001: Fulton County, OHAmbient Lead Concentration (μg/m3)

Contribution from
Inventoried Point Source Emissions, Including Indirect Fugitive Emissions (i.e., Residual Concentration): 0.5053 μg/m 3

Contribution from Area
Non-Point Sources: 0.0022 μg/m 3 Contribution from
Miscellaneous Dust: 0.0225 μg/m 3 Total Second Maximum Monthly Mean Ambient
Lead Concentration: 0.5300 μg/m 3

0.00 0.10 0.20 0.30 0.40 0.50 0.60

Monitor 390510001: Fulton County, OHAmbient Lead Concentration (μg/m3)

Contribution from
Inventoried Point Source Emissions, Including Indirect Fugitive Emissions (i.e., Residual Concentration): 0.5053 μg/m 3

Contribution from Area
Non-Point Sources: 0.0022 μg/m 3 Contribution from
Miscellaneous Dust: 0.0225 μg/m 3 Total Second Maximum Monthly Mean Ambient
Lead Concentration: 0.5300 μg/m 33-8 Step 6: Determine the contribution of ea ch inventoried point source to the ambient lead concentration at each monitor, accountin g for indirect fugitive emissions from nearby
active industrial sites

. For each monitor area, the air qual ity assessment tool attributes the residual concentration derived in Step 5 to each point source according to its lead emissions as
well as its distance from the monitor. After wei ghting each source’s emi ssions according to its distance from the monitor and applying an adju stment to account for the impact of indirect fugitive emissions, the air quality assessment tool then estimates the total contribution of each
source to the ambient lead concentration at the ne arest monitor. We describe this approach in
more detail below.

Step 6.1: Weight the emissions from each source by its distance from the monitor

.

To account for the fact that, al l else equal, lead emissions closer to the monitor have a greater impact on ambient lead c oncentrations, the tool assumes that each source’s contribution to the concentration is proportional to its shar e of the total distance -weighted point source emissions for the monitor area. [Note that the to ol does not contain data sufficient to assess the influence of other factors, such as stackheight and local meteorologica l conditions, that could affect the relative contribution of each point source to monitored Pb concentrations.]

For each source, the tool calculates distan ce-weighted emissions using the following equation:

(Equation 3-1)

2 3

S S
S D E DWE =

where:

• DWE S = Distance-weighted 2002 NEI emissions for source S

,

• E S = 2002 NEI emissions for source S , and

• D S = Distance between source S and the monitor location.

Step 6.2: Adjust the distance-weighted emissi ons to incorporate the impacts of indirect fugitive emissions.

After calculating the distance-weighted em issions for each source, the air quality assessment tool applies an additional adjustment to account for indirect fugitive emissions from
active industrial sites near each monitor. 10 These indirect fugitive emissions are thought to result from materials handling and on- site activities that re-entrain previously deposited leadcontaining dust. Unlike area non- point source emissions, indirect fugitive emissions are linked to point sources and are not captured in the 2002 NEI. Indirect fugitive emissions, however, do

not include fugitives associated with industrial processes at point sources, as these direct, process-based fugitive emissions are reflected in the 2002 NEI point source inventory. Relative to point source emissions, fugitive emissions tend to consist of coarser particles that are emitted closer to the ground and are ther efore assumed to have a more localized effect on ambient air

10 Airport emissions are also reflected in the residual concentration. For the purposes of this analysis, airports are treated as point sources, a lthough as discussed further in chapter 4, no controls are applied to airports.3-9 quality. Reflecting this consider ation, the air quality assessment tool assumes that only indirect fugitive emissions from sources within one mile of the monitor would have an impact on the monitor reading, and even then only in situations where the cumulative emissions of such nearby sources are “significant” (i.e., typically where the aggregate 1-mile radius point source emissions are greater than one ton).

To estimate the extent to which indirect fugi tive emissions contribu te to ambient lead
concentrations near active industr ial sites, EPA conducted a study of nine sites where previously active lead-emitting sources had ceased or paused production. Assuming that activities
conducive to re-entrainment continue for a short period after production had ceased, EPA compared ambient lead concentrations before and after these produc tion stoppages. After subtracting the contribution from un-inventoried miscellaneous dust (as in Step 3 above) and from area non-point sources (as in Step 4 above), EPA found that the aver age post-stoppage lead concentration represented approxi mately 15 percent of the averag e pre-stoppage concentration. For this analysis, therefore, EPA assumes that the contribution of indirect fugitive emissions
from active industrial sites within one mile of the lead monitors represents approximately 15 percent of the total contribution a ttributable to these sources.

The air quality assessment tool estimates co ntribution of indirect fugitive emissions to
observed lead concentrations as follows:

• First EPA identified large sources within one mile of the monitor location that were expected to have indi rect fugitive emissions that would affect the monitor’s air quality reading.

• For each identified source, EPA adjusted its distance-weighted emissions to account for the additional fugitive emissi ons emanating from that source. Reflecting the results of the analysis de scribed above, EPA adjusted sources with indirect fugitive emissions by multiplying their distance-weighted emissions by 20/17, or 117.65 percent. 11

For all sources, the air quality assessment tool applies the adjustment for indirect fugitive emissions using the following equation:

(Equation 3-2) 

# ( )

17 3 1

⋅

+

⋅ = S S S f DWE fDWE

where:

• fDWE S = Distance-weighted 2002 NEI emissions for source S , adjusted to account for indirect fugitive emissions,

• DWE S = Distance-weighted 2002 NEI emissions for source S

,

11 As stated above, EPA estimates that 15 percent of the total contribution from active industrial sites within one mile of lead monitors is attributable to indirect fugitive emissions. Accordingly, 85 percent of the total contribution
from these sites is attributable to direct emissions. The ratio of the total contribution from these sources to the contribution from direct emissions is therefore 100/85, or 20/17.3-10 • f S = Indicator for whether a source has indirect fugitive emissions affecting the monitor (assigned a va lue of 1 if a source is within one mile of the monitor and has indirect fugitive emissions, and 0 otherwise).

Step 6.3: Estimate contribution of each sour ce to ambient lead concentrations based on fugitive-adjusted distance-weighted emissions.

After calculating the distance-weighted emi ssions for each source using Equation 3-1 and incorporating the impacts of indirect fugitiv e emissions using Equation 3-2, the air quality
assessment tool estimates each source’s contri bution to the ambient lead concentration as
follows:

(Equation 3-3)

P S

P S fDWE fDWE C C

⋅ =

where:

• C S = The portion of that monitor area’s ambi ent lead concentration attributable to

source S

,

• C P = Total contribution of point sour ce emissions (and associated industrial fugitives) to the ambient lead concentration (i.e., the remaining concentration after subtracting background and area s ource contributions from the baseline air quality value),

• fDWE S = Fugitive-adjusted, distance-weighted 2002 NEI emissions for source S

, and

• fDWE P = Sum of fugitive-adjusted, di stance-weighted 2002 NEI emissions for all point sources in the monitor area.

Continuing with the Fulton County, Ohio ex ample presented in Figure 3-1, Table 3-2 illustrates the process by which the contribu tion of each point source in Fulton County is apportioned based on its emissions a nd distance from the monitor loca tion. Note that the source
contribution from Source B is several orders of magnitude larger than the source contribution from Source A, even though lead emissions from Source B are only twice those from Source A.3-11 Table 3-2.
APPORTIONMENT OF THE TOTA L POINT SOURCE CONTRIBUTION TO THE AMBIENT LEAD CONCENTRATION AMONG INVENTORIED POINT SOURCES IN FULTON COUNTY, OHIO

Monitor Location Fulton County, OH

Total Point Source Contribution to Ambient Lead Concentration ( : g/m 3 ) [C P ] 0.5053

Source Source A Source B

2002 NEI Emissions (tpy) [E S ] 0.1500 0.338

Distance from Monitor to Source (km) [D S ] 3.4707 0.0554

2002 NEI Distance-Weighted Emissions (tpy/km 3/2 ) [DWE S ] 0.0232 25.8982

Fugitive Emissions Affect Monitor? [f S ] 1 N o Yes

Distance-Weighted Emissions with Fugitive Adjustment (tpy/km 3/2 ) [fDWE S ] 0.0232 30.4685

Total Distance-Weighted Emissions (tpy/km 3/2 ) [fDWE P ] 30.4917

Share of Total Distance-Weighted Emissions [fDWE S /
fDWE P ] 0.0761% 99.9239%

Source Contribution to ambient lead concentration ( : g/m 3 ) [C S = C P * fDWE S /fDWE P ] 0.0004 0.5049

1 “No” and “Yes” in this row corres pond to values of 0 and 1 for f S , respectively, as defined in Equation 3-2.

In this analysis, airports we re treated as point sources. Currently, there are 3,410 aviation facilities in the NEI of which, 24 ar e included in this analysis due to their proximity to one of the
24 monitors that were identif ied using the criteria describe d in Section 3.1. Among the 21 monitors in this analysis, there are 18 monitors with at least one airport located within ten kilometers of the monitor (six monitors have two airport facilities within ten kilometers). This analysis estimates that the cont ribution of leaded aviation gaso line to lead measured at the monitors ranges from 0.00002 to 0.047 : g/m 3 . There are currently no TSP lead monitors located within one mile of an airport servicing aircraft that operate on leaded aviation gasoline. In addition to the 24 airport facilities within ten kilometers of the monitors in this analysis, there are heliport and airport facilities wher e piston-engine aircraft might ope rate that are not currently in the NEI and for which we do not currently have lead emissions estimates. 12

12 Memo to the RIA Docket -HQ-OAR-2008-0253 Titled ‘Small airport facilities within ten kilometers of monitors in the Lead Regulatory Impact Analysis th at are currently missing from EPA’s National Emissions Inventory’ submitted by Marion Hoyer, Meredith Pedde and Bryan Manning.3-12 3.2. Using the Air Quality Assessment Tool to Estimate Impacts of Point Source
Emissions Controls

Through the process described in Chapter 4, we used a least-cost optimization model to estimate the extent to which point source lead emissions could dec line under the control strategies developed for the final NAAQS and the alternative standards summarized in Chapter

1. 13 To estimate the air quality impact of these reductions, we developed a three-step process for estimating ambient lead concentrat ions based on the air quality assessment tool described above. This process is as follows:

1. For each alternative standard, we applied identified controls to individual point sources, according to the cost optimiza tion model. Because the air quality
assessment tool translates lead emissions to air quality impacts by applying a constant distance-weighting for each source, the percent reduction in a source’s
contribution to ambient lead concentrations was the same as the combined control efficiency of all emissions controls applie d to that source. Based on these sourcespecific reductions, we estimated each point source’s contri bution (including the contribution from indirect fugitive emissions) to the ambient lead concentration following the implementation of emissions controls.

2. For each monitor area, we summed the individual point source contributions estimated in Step 1 to obtain the total am bient lead concentration attributable to inventoried point sources (including ambient lead associated with indirect fugitive emissions).

3. Holding the contributions from area non- point sources and miscellaneous reentrained dust constant between the base line and policy case, we added these to the total contribution from point sources (e stimated in step 2) to yield the new
estimate for the total ambient lead concentration.

13 As described in Chapter 4, our analysis did not consider controls on lead emissions from airports. Therefore, we kept lead emissions from airports constant in both the baseline and policy scenarios.4-1 CHAPTER 4. EMISSIONS CONTROL ANALYSIS: DESIGN AND ANALYTIC
RESULTS

This chapter documents the illustrative emi ssion control strategy we applied to simulate
attainment with the selected standard and al ternative standards. Section 4.1 describes the approach we followed to select cost-effective emissions controls to simulate attainment in each geographic area of analysis. Section 4.2 summarizes the emission reductions we simulated in each area based on current knowledge of emissions controls applicable to existing sources of lead emissions, while Section 4.3 presents the ai r quality impacts of thes e emissions reductions. Section 4.4 discusses the applicat ion of additional controls, beyond those already known to be available, that we estimate will be necessary to reach attainment in certain monitor areas. Section 4.5 discusses key limitations in the approach we used to estimate the optimal control strategies for each a lternative standard.

4.1

. Estimation of Optimal Emissions Control Strategies

Our analysis of the emissions control measures required to meet the selected standard and
alternative standards is limited to controls for point source emissions at active sources inventoried in the 2002 NEI. [Not e that while airports are include d as point sources in the NEI, our analysis considers the impact of emissions from use of lead ed aviation gasoline (avgas) at airports, but does not consider controls on thos e emissions as a strategy for compliance. EPA received a petition from Friends of the Earth requesting that the Agency find that aircraft lead emissions may reasonably be anticipated to endang er the public health or welfare, and to take action to control lead emissions from pist on-engine aircraft. We published a Federal Register notice discussing the petition and requested comme nt on specific aspects of the use of leaded avgas and potential control of lead em issions from the consumption of avgas. 1 ] As discussed in Chapter 3, a portion of ambient lead concentrations can be attribut ed not to point sources but to miscellaneous re-entrained dust and area nonpoint emissions. Nevertheless, this RIA deals only with the application of controls on emissions at active non-av iation point sources, including stack emissions and fugitive emissions from industrial processes.

1 The petition requested that EPA find that such emissions cause or contribute to air pollution which may reasonably be anticipated to endanger public health or welfare. An d, if EPA makes such a finding, the petitioner requested that EPA take steps to reduce lead emissions under the authority of the Clean Air Act Section 231 Approximately 70 different parties commented on the petition and the questions presented in the notice (72 FR 64570, November 16, 2007). These comments can be found in EPA public docket OAR-2007-0294 (at www.regulations.gov). A clear theme in many of the comments was the dependence of much of the current piston-powered aircraft fleet on leaded avgas either because of engine design, performance demand s, or lack of mogas availability at airports. However, several comments identified potential ne ar and longer term measures to reduc e these lead emissions. These potential measures fall into five general categories: (1) Continued work on identifying fuel blends or additives which would provide the octane and other performan ce characteristics needed fo r a transparent fuel replacement, (2) Measures to ensure greater availability of ethanol-free unleaded avgas at airports for those aircraft which otherwise could use it, (3) Laboratory and field work to assess the potential to reduce the amount of lead now added to current leaded avgas, (4) Add-on engine technology or fuel management technology to allow for equivalent engine performance at lower avgas octane ratings and (5) Long-term measures or standards for new engines which provide the needed and desired performance characteristics using modified engine designs and calibrations on fuels or fuel blends not containing lead. For more information about th e petition, see http://www.epa.gov/otaq/aviation.htm.4-2 To simulate attainment with the selected standard and the five regulatory alternatives considered in all 21 monitor areas, we first modeled the most cost-effective application of identified emissions controls in each area, usin g the following four-step process:

1. Specification of baseline emissions for inventor ied point sources in each geographic area of analysis.

2. Identification of potential controls for inventoried point sources.

3. Identification of an alternative lead abatement strategy for the primary lead smelter in Jefferson County, Missouri.

4. Identification of the least cost stra tegy for using point source controls.

In areas where identified emissions controls were not sufficient to reach attainment with one or more of the alternative standards considered, we also simulated emission reductions needed beyond identified controls at invent oried point sources. Further di scussion of theseunspecified emission reductions is presented in Section 4.4.

The analysis used for the Final Rule differs from that presented in the Proposed Rule RIA in the following ways:

1. We no longer remove from consideration all iden tified controls with a cost/ton higher than
the 98 th percentile of control costs at point source s emitting more than 0.05 tons/year of lead. This was described as the “Stage 3 Filter in the Proposed Rule RIA.”

2. We updated control efficiency and cost info rmation for many of the identified emission controls used in the Proposed Rule RIA, after determining that the prior values were unlikely to reflect the performance and cost of these co ntrols in 2016, the analysis year for this RIA.

3. Rather than applying PM emission controls to the primary lead smelter in Jefferson County, Missouri, we simulated a complete rebuild of the smelter to utilize a less-polluting smelting

process.

4. We modified the analysis of em ission reductions needed beyond id entified controls such that
these reductions are distributed across all sources in areas projected to violate any alternate standard. In the Proposed Rule analysis, thes e reductions were achieve d by controls applied
to a limited number of sources.

These changes to the analysis relative to the Proposed Rule RIA are described in greater detail throughout this chapter.

4.1.1. Specification of Baseline Lead Emissions for Inventoried Point Sources in Each Geographic Area of Analysis

.

For most sources, lead emissions as specified in the 2002 National Emi ssions Inventory (NEI) served as the baseline for our analysis. Howeve r, for some sources (e.g., natural gas-fired utility boilers), we corrected the 2002 NE I lead emissions data with updated information. As discussed4-3 in Chapter 2, we did not apply growth factor s to the 2002 NEI emissions estimates to predict emissions in 2016 (the analysis year for this RI A) because we believe that the number of Pb emitting sources will not increase with populat ion growth. We did, however, adjust the 2002
NEI lead emissions values to re flect anticipated emissions contro ls necessary to comply with other regulations that have compliance dead lines after 2002, wherever possible. These adjustments included applicati on of MACT for air toxics ru les with post 2002 compliance deadlines, 2 PM controls at sources in designated nonattainment area s in the 2006 revisions to the

PM 2.5 NAAQS as modeled in the illust rative control strategy in the PM 2.5 NAAQS RIA, 3 and controls planned for the primar y lead smelter in Jefferson County, Missouri, as part of the 2007 Missouri lead SIP (at the one current nonattainment area for ambient lead under the Federal CAA). 4 After applying these adjustments to all affected point sources, the remaining lead
emissions served as our baseline for the application of identified controls. Table 4-1 illustrates the process used to specify the baseline lead emissions for in ventoried point sources in the analysis.

Table 4-1.
TOTAL BASELINE LEAD EMISSIONS FO R ALL INVENTORIED POINT SOURCES
IN 23 DESIGNATED MONITOR AREAS Original Baseline: 2002 NEI Emissions (point sources, excluding airports) 109.2 tons/year (tpy)
2002 NEI Emissions adjusted for PM NAAQS controls 109.0 tpy
2002 NEI Emissions adjusted for PM NAAQS and Missouri SIP controls 101.2 tpy
Final Baseline: 2002 NEI Emissions adjusted for PM NAAQS, Missouri SIP, and MACT
controls 99.7 tpy

Following the same process as describe d above, we also sp ecified baseline PM 10 and PM 2.5 emissions for all inventoried point sources. A lthough the non-lead fraction of PM emissions did not play a role in simulating attainment with the selected standard and alternative standards, we
did use these baseline values to estimate the anc illary benefits of co-controlling PM emissions in the process of implementing lead control stra tegies, as discussed in Chapter 5. Recent promulgation of mobile source rules that redu ce PM is not relevant for this analysis.

4.1.2. Identification of Potential Cont rols for Inventoried Point Sources

.

To identify point source lead em issions controls for our analys is, we collected information on PM control technologies, assuming that the cont rol efficiency for PM emissions would also apply to lead emissions. We collected th is information in the following way:

2 The MACT standards included covered the following industries: Integrated Iron and Steel Manufacturing; Iron and Steel Foundries; Petroleum Refineries; Secondary Aluminum Production; Industrial/Commercial/Institutional Boilers & Heaters – Coal; Pressed and Blown Glass and Glassware Manufacturing; Primary Nonferrous Metals – Zinc, Cadmium, and Beryllium; Secondary Nonf errous Metals; and Primary Copper Smelting.

3 Available at http://www.epa.gov /ttn/ecas/ria.html

4 This 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.pdf4-4 1. We queried EPA's AirControlNET database fo r information on potential PM controls available for each source, accounting for any cont rol measures already in place, according to
the 2002 NEI. 5

2. For sources with Standard Indus trial Classifications (SICs) but without identified NEI Source Classification Codes (SCCs), we used th e SIC/SCC crosswalk in Appendix C of AirControlNET’s Documentation Report to identify SCCs for those sources. 6 We then found
controls in AirControlNET’s database associated with these SCCs.

3. EPA identified additional controls from techni cal documents prepared in support of New Source Performance Standards, EPA memos pr epared to support analyses for the PM 2.5 RIA, and operating permits that apply to facilities with similar SCCs as the point sources in our analysis. These controls include the following:

• Capture hoods vented to a baghou se at iron and steel mills. Virtually all iron and steel mills have some type of PM control measure, but there is additional equipment that could
be installed to reduce emissions further. Ca pture hoods that route PM emissions from a
blast furnace casthouse to a fabric filter ca n provide 80 to 90 per cent additional emission
reductions from an iron or steel mill.

• Diesel particulate filter (for stationary sources such as diesel generators). This control incorporates directly-emitted PM 2.5 reductions from stationary internal combustion engines that will be affected by the compre ssion-ignition internal combustion engine new source performance standard (NSPS) promul gated on June 28, 2006. Diesel particulate filters (DPF) are likely to be the control tec hnology required for these engines to meet the NSPS requirements. The control is applied here as a retrofit to existing stationary internal combustion engines in our inventor y. Based on the techni cal support documents prepared for the final compression-ignition NSPS, the PM 2.5 control efficiency for DPF is 90 percent. 7

• Upgrade of CEMs and increased monitoring frequency of PM controls (for sources where not already identified as a control by ACN). This control is an upgrade to existing control measures or an improvement in control efficiency due to how existing control measures operate from increases in monitoring. Such controls can lead to small reductions in PM emissions (5 to 7 percent). 8

5 Documentation available at http://www.epa.gov/ttn ecas1/models/DocumentationReport.pdf . AirControlNET’s database of PM controls normally excludes sources emitting fewer than 10 tons/year of PM 10 . Because many of the point sources included in our analysis fall below this th reshold and because this analysis focuses entirely on obtaining emission reductions from point sources, we effectively reduced the threshold from 10 tons/year to zero in order to identify controls for a larger number of inventoried point sources.

6 Available at http://www.epa.gov/ttnecas1/mode ls/DocumentationReport.pdf

. 7 U.S. Environmental Protection Agency. “Emission Reduction Associated with NSPS for Stationary CI ICE.” Prepared by Alpha-Gamma, Inc. June 3, 2005, and U.S. Environmental Protection Agency. “Cost per Ton for NSPS for Stationary CI ICE.” Prepared by Alpha-Gamma, Inc. June 9, 2005.

8 U.S. Environmental Protection Agency. Regulatory Impact Analysis for the Particulate Matter NAAQS. October, 2006. Appendix E, pp. E-16 to E-24. This document is available at http://www.epa.gov/ttn /ecas/regdata/RIAs/Appendix %20E--Controls%20List.pdf

.4-5 4. In response to the degree of residual nonattainment found in a nu mber of monito r areas in the Proposed Rule analysis, we reviewed the PM co ntrol measures in our databases in order to
determine if the data for these measures were fu lly up to date and appropriate for an analysis year of 2016. In the course of our review, we found that the control effi ciencies for a variety of PM control measures as applied in our pr oposal RIA were quite conservative (i.e., more likely to be underestimates than overestimates) for control strategy analyses to be conducted for 2016. A number of recent EPA references provided findings that showed that increases in PM control efficiencies from those appl ied in our proposal RIA were reasonable for a future year analysis. Based on these findings, we increased the control efficiencies and costs
for a number of the PM control measures in our database, as summarized below: 9

• Dry and Wet ESPs: Control efficiency modified fr om 95 percent to 99 percent.

• Fabric Filters (pulse jet type and mechanical shaker type): Control efficiency modified from 99.5 percent to 99.9 percent.

• Venturi Scrubbers: For those source classifi cation codes (SCCs) to which AirControlNET applies a control efficiency of 50 percent, we modified this value to 90 percent for the 2016 target year. For S CCs, where the control efficiency in AirControlNET is 25 percent, we adju sted this value to 70 percent.

• Paper/Nonwoven Filters – Cartridge Collectors: The AirControlNET control efficiency value of 99 percent was m odified to 99.5 percent.

Completion of the procedure outlined above yielde d identified controls for about 43 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 pe rcent of inventoried point sources account for more than 97 percent of total lead emissions ), these sources accounted for approximately 92 percent of total lead emissi ons, as shown in Table 4-2.

Table 4-2. PROFILE OF INVENTORIED POINT SOURCES, WITH AND WITHOUT IDENTIFIED CONTROLS

Count Percent of Total Emissions (tons / year) Percent of Total

Sources with Identified Controls 1 266 42.6% 91.8 92.1%

Sources without Identified Controls 359 57.4% 7.9 7.9%

Total 625 100.0% 99.7 100.0%

1 Identified controls, as represented in this table, include the potential rebuild of the primary lead smelter in Jefferson County, MO, as desc ribed in greater detail below. Therefore, all emissions sources at this facility are
included in this table as sources with identified controls.

9 PM control efficiencies were increas ed for the following control measures: dry and wet ESPs, all types of fabric filters, venturi scrubbers, impingement-pla te/tray-tower scrubbers, and paper/nonw oven filters - cartridge collectors. We also revised the capital and annualized costs for these control devices to refl ect the increased control efficiencies
associated with these control meas ures, as discussed in Chapter 6.4-6 Controls identified through this process include ma jor emissions controls, such as fabric filters, impingement-plate scrubbers, and electrostati c precipitators; and minor controls, such as increased monitoring frequency, upgrades to continuous emissions monitors, and diesel particulate filters. For each identi fied control, we identified both the expected control efficiency for the technology and the annualized cost of installing and op erating the control. 10 For those point sources where the 2002 NEI indicated that c ontrol measures were already in place, we
estimated the effective emissions control efficien cy for each identified control by estimating the
emissions reductions that would re sult if the pre-existing control were replaced by the identified control technology. For example, while a fabric f ilter might have an expected control efficiency of 90 percent when installed in th e absence of pre-existing controls, if it were applie d at a source that already had an electrosta tic precipitator with an 80 pe rcent control efficiency, the effective control efficiency of the Fabr ic Filter would be 50 percent. 11 We also assumed that each identified control technology woul d be installed in addition to any controls required under the

2006 PM 2.5 NAAQS and any MACT rules with enfo rcement dates after 2002, but before 2016. We therefore applied each control’s effective co ntrol efficiency to the adjusted baseline lead emissions at each inventoried point source. 12

4.1.3. Identification of an Alternative Lead Abatement Strategy for the Primary Lead Smelter in Jefferson County, Missouri.

In the Proposed Rule analysis, a significant por tion of the estimated costs of the rule—ranging from 55 percent for the 0.05 μ g/m 3 standard alternative to 95 percent for the 0.3 μ g/m 3 standard alternative - represented reducti ons beyond identified controls at the primary lead smelter in Jefferson County, Missouri. To reduce the extent to which the costs and emissions reductions associated with the lead NAAQS depend on reduc tions beyond identified controls for a single source, we have modeled the replacement of the primary lead smelter in Jefferson County with a more modern, lower-emitting smelter, utilizing the Kivcet smelting process, as a means of reducing the facility’s lead emissions. The Kiv cet process is currently employed at the primary lead smelter operated by Teck Cominco in Trail, British Columbia, as well as in plants in Kazakhstan, Bolivia, and Italy. 13 While it may be more cost-effective for the facility to implement more targeted emissions controls und er the selected standard, information on such
controls is not available.

To estimate the emissions reductions associated w ith transitioning to Kivcet technology at the smelter in Jefferson County, we relied on emissions data for Teck Cominco’s Trail, British Columbia, facility, which began using the Kivcet process in 1997. We derived lead emissions per ton of lead produced at th is facility by obtaining lead em ission values from Canada’s

10 See Chapter 6 for a detailed discussion of how annualized control costs were estimated.

11 With the electrostatic precipitator, 20 percent of th e source’s original, uncontrolled emissions would remain uncontrolled, but with the fabric filter, only 10 percen t of the source’s original emissions would remain uncontrolled. Thus, replacing the electrostatic precipitator with the fabric filter would represent a 50 percent (10/20 = 0.5) decrease in uncontrolled emissions.

12 The one exception to this assumption is the installation of capture hoods vented to baghouses, a control included at some sites as part of the control strategies applied for the 2006 PM 2.5 revised NAAQS RIA. Because baghouses are major controls which would be replaced by the installation of any other major control, we applied the effective control efficiency of major controls to the unadjusted baseline emissions at any site with a capture hood installed.

13 The Eastern Mining and Metallurgical Research In stitute for Non-Ferrous Metals, Pyrometallurgy. http://vcm.ukg.kz/eng/v3_6.htm . Accessed September 23, 2008.4-7 National Pollutant Release Inventory (NPRI) 14 and annual lead production values from Teck Cominco’s annual reports. Taking the average value for the past five years for which NPRI data are available, we estimated that the Trail, BC, facility emits 0.07 pounds of lead for every ton of lead produced using its Kivcet smel ter. Applying this emissions ra te to the facility in Jefferson County, which produced 150,000 thousand tons of lead in 2002, we estimate lead emissions of 5.50 tons per year for this facility. 15 This represents an 89 percen t reduction in lead emissions relative to the facility’s baseline emissions of 51 tons per year. Wh en modeling this lead emissions control strategy, we divided these re ductions among the emi ssions sources at the Jefferson County primary lead smelter in proport ion to each source’s 2002 NEI emissions.

4.1.4. Identification of the Optimal Strategy for Using Point Source Controls to Reach
Attainment in Each Area.

To identify the least-cost approach for reaching attainment in each area pr ojected to violate the NAAQS, EPA developed a linear programming optimi zation model that systematically evaluates the air quality and cost information discussed be low and in Chapter 6 to find the optimal control strategy for each area. The optimization model firs t 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 po ssible for the area. Minimizing total costs across all sources is not always equivalent to minimi zing marginal costs at each source. Therefore, although the model selects major controls for each source by minimizing the marginal cost/ton of lead controlled at the source, the objective for each area is to minimize total costs associated with reaching attainment. It should be noted that unlik e major controls, all minor controls identified can be implemented in conjunction with other c ontrols, so the optimization model selects all
minor controls as well.

Rather than considering all emissions controls at every inventoried point source, the optimization model utilizes a two-stage filteri ng process to select only the most cost-effective controls at sources making a significant impact on ambient air quality. The stages are as follows:

1. Stage 1 filter: First, the model selects all controls at sources deemed “relevant” by virtue of the fact that they account for at least 0.001 percent of all point source contributions to the
ambient lead concentration in their monitor ar ea. This stage mostly affects monitor areas with large numbers of invent oried point sources, such as Dakota County, Minnesota, where
105 out of 126 inventoried sources do not meet the 0.001 percent threshold.

2. Stage 2 filter: Because we identified multiple major emissions controls for many sources, the second stage of the model assumes that th e most cost-effective major control for each relevant source would be installed, as determin ed by the cost/ton of lead emissions reduced. For example, consider a source that could install either an electrostatic precipitator (ESP) that would reduce lead emissions by 0.1 tons/year w ith an annualized cost of $1 million or a

14 Available at http://www.ec.gc.ca/pdb/npri/npri_home_e.cfm . Communication with David Niemi, Head Emissions Inventory Reporting and Outreach at En vironment Canada, confirmed that the methods used to collect the NPRI were comparable to the method s used to collect the NEI.

15 The estimate of 2002 lead produc tion at this smelter comes from The Doe Run Company, Primary Mining and Smelting Division , 2002 Annual Report to our Community.4-8 fabric filter that would reduce lead emissions by 0.11 tons/year at a cost of $2 million/year. Because the cost/ton is lower for the ESP, th e optimization model assumes that the source would (potentially) install the ESP rather than the fabric filter. 16

In the Proposed Rule RIA, we implemented a third filter, in which we removed from consideration all point source controls with a cost/ton higher than the 98 th percentile of control costs at point sources emitting more than 0.05 tons /year of lead. For this analysis, we have eliminated that filter, in order to maximize the emission reductions achieved with identified
controls.

After selecting the most cost-effective emissions c ontrols at all relevant point sources for each monitor area, the model then proceeds to eval uate every possible combination of control technologies until the monitor area re aches attainment with the sele cted standard or alternative standard at the lowest possible cost . If the monitor area is already in attainment with the selected standard, the model applies no cont rols. On the other hand, if the monitor area is unable to reach
attainment with the selected standard when all cost-effective controls at relevant sources are applied, then the model assumes that all sources in the area are controlled, including those that account for less than 0.001 percent of point source contributions in the area (i.e., the model eliminates the stage 1 filter described above and thus applies controls to smaller sources).

As indicated above, this approach is not the equivalent of movi ng up the marginal abatement cost curve for lead. If the control strategy were selected based on the marginal cost per μ g/m 3
reduced, we would not necessarily identify the least-cost strategy fo r attainment in each area.

4.2. Lead Emissions Reductions Achieved with each Control Strategy

Utilizing the optimization model described above, we determined the most cost-effective control strategies required to meet attainment at the largest number of monitor areas. 17 Table 4-3 presents the lead emissions reductions realized at each monitor area under the control strategies
followed for each alternative standard.

4.3. Impacts Using Identified Controls

Following the steps described in Section 3.2, we estimated the overall change in ambient air quality achieved as a result of each of the cont rol strategies identifie d in the AirControlNETbased emissions analysis. Table 4-4 presents a detailed breakdown of the estimated ambient lead

16 If there are two available control options , the least-cost approach chooses the option with a lower cost/ton. It does this even if a slightly more expensive control option can ac hieve greater emission reduction. Although in theory this filter could cause some emission reduction to be missed, in practice, the impact is negligible. For example, in the
simulation of attainment with the 0.1 μ g/m 3 standard, removal of this filter increases the emission reduction by less than 0.0001 tons per year.

17 As will be discussed below, the appli cation of identified controls was insufficient to bring all monitor areas into compliance with the selected standard and the alternative standards.4-9 concentrations in 2016 at each of the 21 monitor s ites under the selected standard and the five alternative standards described in Chapter 1.

According to the data presented in Table 4-4, 13 of the 21 monitor areas are expected to reach
attainment with the selected standard of 0.15 μ g/m 3 following implementation of the controls identified in the AirControlNET analysis (i.e., identified controls). In addition, 20 areas are expected to reach attainme nt with identified controls under a NAAQS of 0.5 or 0.4 μ g/m 3 . For the most stringent alte rnative considered, 0.1 μ g/m 3 , 9 of the 21 monitors are expected to reach attainment following the application of identified controls. For some areas, identified controls
are not sufficient to re ach attainment with the selected standard.

The failure of certain areas to r each attainment with identified cont rols partially reflects the lack of control information for point sources in thes e areas. As indicated in Table 4-5, emissions from sources for which the AirControlNET analys is identified no contro ls contribute to a significant portion of the ambient lead concentrat ion in many of the areas not projected to reach
attainment with the selected standard and four al ternative standards. Fo r such sources in areas projected to violate the NAAQS w ith the application of identified controls, we assume that emission reductions beyond identified controls wi ll be applied, as discussed further below.

Table 4-6 presents the additional air quality change needed for monitor areas that did not attain at
least one of the alternative sta ndards analyzed in this RIA. In addition, Figure 1 presents the additional air quality improvement needed in each monitor area that did not attain the 0.15 ug/m3 selected standard with the application of iden tified controls. This figure illustrates that the progress made through the application of identif ied controls varies greatly by monitor area.4-10 FIGURE 1. AIR QUALITY CHANGE ACHIEVED THROUGH APPLICATION OF IDENTIFIED CONTROLS AND
ADDITIONAL INCREMENT NEEDED TO REACH ATTAINMENT OF SELECTED STANDARD 0.15
UG/M34-11TABLE 4-3.REDUCTION IN LEAD EMISSIONS UNDER ALTERNATIVE NAAQS AT EACHMONITOR AREA, IDENTIFIED CONTROLS ONLYReduction in Lead Emissions (tpy)MonitorStateMonitorCountyBaselineLeadEmissionsin 2016StandardAlternative: 0.5μg/m32ndMaximumMonthly MeanStandardAlternative: 0.4μg/m32ndMaximumMonthly MeanStandardAlternative: 0.3μg/m32ndMaximumMonthly MeanStandardAlternative: 0.2μg/m32ndMaximumMonthly MeanSelectedStandard: 0.15μg/m32ndMaximumMonthly MeanStandardAlternative: 0.1μg/m32ndMaximumMonthly MeanAL Pike4.45 4.02 4.02 4.02 4.134.31 4.31CO El Paso0.95 0.00 0.00 0.00 0.000.00 0.00*FL Hillsborough1.48 1.00 1.00 1.09 1.191.19 1.26IL Madison0.53 0.00 0.00 0.00 0.000.00 0.09*IN Delaware1.53 1.37 1.37 1.40 1.421.44 1.46MN Dakota3.55 0.00 0.00 0.00 0.001.29 3.07MO Iron16.12 12.26 12.26 12.26 12.2612.26 12.26MO Jefferson51.02 45.52 45.52 45.52 45.52*45.52* 45.52*NY Orange1.80 0.00 0.00 0.00 1.391.39 1.39OH Cuyahoga0.94 0.00 0.00 0.13* 0.13*0.13* 0.13*OH Fulton0.49 0.14* 0.14* 0.14* 0.14*0.14* 0.14*OH Logan0.12 0.00 0.00 0.00* 0.00*0.00* 0.00*OK Ottawa0.00 0.00 0.00 0.00 0.000.00 0.00*PA Beaver4.28 0.00 0.00 0.00 0.640.73* 0.73*PA Berks2.16 1.00 1.02 1.61* 1.61*1.61* 1.61*PA Carbon0.45 0.00 0.00 0.00 0.00*0.00* 0.00*TN Sullivan0.38 0.00 0.00 0.00 0.00*0.00* 0.00*TN Williamson2.55 1.25 1.35 1.95 2.002.19 2.32TX Collin3.18 2.19 2.19 2.20 2.692.75 2.95TX Dallas0.06 0.00 0.00 0.00 0.000.00 0.00*UT Salt Lake3.66 0.00 0.00 0.00 0.000.00 0.62Total99.7 68.74 68.86 70.31 73.1174.96 77.87* Indicates monitor area does not reach attainment using identified controls.4-12Table 4-4.AMBIENT LEAD CONCENTRATIONS ACHIEVED WITH IDENTIFIED CONTROLSUNDER THE ALTERNATIVE STANDARDS IN 2016Ambient Lead Concentration (μg/m3)MonitorStateMonitorCountyBaseline LeadConcentrationin 2016StandardAlternative: 0.5μg/m32ndMaximumMonthly MeanStandardAlternative: 0.4μg/m32ndMaximumMonthly MeanStandardAlternative: 0.3μg/m32ndMaximumMonthly MeanStandardAlternative: 0.2μg/m32ndMaximumMonthly MeanSelectedStandard: 0.15μg/m32ndMaximumMonthly MeanStandardAlternative: 0.1μg/m32ndMaximumMonthly MeanAL Pike 2.420 0.256 0.256 0.256 0.1970.098 0.098CO El Paso 0.131 0.131 0.131 0.131 0.1310.131 0.131*FL Hillsborough 1.380 0.327 0.327 0.222 0.1230.123 0.049IL Madison 0.128 0.128 0.128 0.128 0.1280.128 0.104*IN Delaware 5.022 0.397 0.397 0.285 0.1990.148 0.078MN Dakota 0.192 0.192 0.192 0.192 0.1920.127 0.039MO Iron 1.454 0.091 0.091 0.091 0.0910.091 0.091MO Jefferson 1.998 0.236 0.236 0.236 0.236*0.236* 0.236*NY Orange 0.240 0.240 0.240 0.240 0.0850.085 0.085OH Cuyahoga 0.357 0.357 0.357 0.316* 0.316*0.316* 0.316*OH Fulton 0.530 0.530* 0.530* 0.530* 0.530*0.530* 0.530*OH Logan 0.360 0.360 0.360 0.360* 0.360*0.360* 0.360*OK Ottawa 0.114 0.114 0.114 0.114 0.1140.114 0.114*PA Beaver 0.228 0.228 0.228 0.228 0.2000.196* 0.196*PA Berks 0.518 0.404 0.400 0.331* 0.331*0.331* 0.331*PA Carbon 0.294 0.294 0.294 0.294 0.294*0.294* 0.294*TN Sullivan 0.236 0.236 0.236 0.236 0.236*0.236* 0.236*TN Williamson 0.820 0.429 0.398 0.212 0.1980.137 0.097TX Collin 0.891 0.302 0.302 0.300 0.1680.150 0.098TX Dallas 0.101 0.101 0.101 0.101 0.1010.101 0.101*UT Salt Lake 0.107 0.107 0.107 0.107 0.1070.107 0.093* Indicates that this monitorarea did not reach attainmentwith the alternative standard.4-13TABLE 4-5.BASELINE LEAD CONCENTRATIONS IN μg/m3IN AREAS WITH MONITORED CONCENTRATIONS GREATER THAN ANY OF THEALTERNATIVE STANDARDS USING ONLY IDENTIFIED CONTROLSBaseline Pb Concentration related to indirectfugitive and point source emissionsMonitorStateMonitorCountyBaseline PbConcentrationin 2016Pb Concentration relatedto area non-point emissionsand misc. re-entrained dustPoint sources with noIdentified ControlsPoint sources withIdentified ControlsTotal concentrationassociated with sourcesfor which no controlinformation availableCO El Paso0.131 0.024 0.101 0.006 0.125IL Madison0.128 0.024 0.000 0.104 0.024MO Jefferson1.998 0.023 0.000 1.975 0.023OH Cuyahoga0.357 0.027 0.288 0.042 0.315OH Fulton0.530 0.025 0.505 0.000 0.530OH Logan0.360 0.027 0.333 0.000 0.360OK Ottawa0.114 0.023 0.091 0.000 0.114PA Beaver0.228 0.027 0.000 0.201 0.027PA Berks0.518 0.037 0.275 0.205 0.312PA Carbon0.294 0.036 0.259 0.000 0.294TN Sullivan0.236 0.024 0.212 0.000 0.236TX Dallas0.101 0.046 0.055 0.000 0.1014-14TABLE 4-6.ADDITIONAL AIR QUALITY INCREMENT (μg/m3) POST APPLICATION OF IDENTIFIED CONTROLS IN AREAS WITH MONITOREDCONCENTRATIONS GREATER THAN ANY OF THE ALTERNATIVE STANDARDSMonitor State Monitor CountyStandardAlternative: 0.5μg/m32ndMaximumMonthly MeanStandardAlternative: 0.4μg/m32ndMaximumMonthly MeanStandardAlternative: 0.3μg/m32ndMaximumMonthly MeanStandardAlternative: 0.2μg/m32ndMaximumMonthly MeanSelectedStandard: 0.15μg/m32ndMaximumMonthly MeanStandardAlternative: 0.1μg/m32ndMaximumMonthly MeanCO El Paso0.031IL Madison0.004MO Jefferson 0.0360.086 0.136OH Cuyahoga 0.016 0.1160.166 0.216OH Fulton 0.030 0.130 0.230 0.3300.380 0.430OH Logan 0.060 0.1600.210 0.260OK Ottawa0.014PA Beaver0.046 0.096PA Berks 0.031 0.1310.181 0.231PA Carbon 0.0940.144 0.194TN Sullivan 0.0360.086 0.136TX Dallas0.0014-15 4.4. Emission Reductions Needed Beyond Identified Controls

As discussed above, some monitor areas did not reach attainment with the selected standard or alternative standards through the application of identifie d controls alone in
these illustrative control scenarios. In order to bring these monitor areas into attainment, we simulated the effects of unspecified em ission reductions beyond identified controls. The manner in which these reductions would be achieved is yet to be determined.

4.4.1. Application of Unspecified Emission Reductions to Point Sources in Areas Projected to Violate the Standard Alternat ives with the Application of Identified
Controls

To model emission reductions beyond identifi ed controls, we assumed that all point sources in an area projected to violate a standard alternat ive (excluding airports) would be controlled with measures employing the same control efficiency. To simulate
attainment with each alternative standar d, we find the minimum control efficiency required to bring each area’s second maximum monthly mean lead concentration exactly
to the level of the standard alternative cons idered. As a result, the effective control efficiency applied to point sources differs by area and by standard alternative. For example, for the 0.2 μ g/m 3 standard alternative, we a pply a control efficiency of 16.9 percent to all sources in Sullivan County, Tennessee, but a control efficiency of 65.3 percent to all sources in Fulton County, Oh io. We multiply the appropriate control efficiency by the remaining emissions for each point source in each county. We then sum the point source emission reductions to get a total for each county.

This process differs from the method we us ed in the Proposed Rule RIA for modeling emission reductions beyond identified controls. In that analysis, we applied controls to a limited number of point sources, beginning with those sources closest to the monitor and proceeding outward until each area reached attain ment. In this analysis, we instead apply the same control efficiency to all point sour ces within each area projected to violate any
alternate standard.

4.4.2. Lead Emission Reductions Needed Beyond Identified Controls

After applying unspecified emission reducti ons beyond identified controls using the process described above, all monitor ar eas reached attainment with the 0.5 μ g/m 3 , 0.4

μ g/m 3 , 0.3 μ g/m 3 , 0.2 μ g/m 3 , and 0.15 μ g/m 3 alternative standards. Under the 0.1 μ g/m 3
standard alternative, however, Ottawa C ounty, Oklahoma fails to reach attainment because there are no point sources of lead to control in this county. Table 4-7 presents the lead emissions reductions required to br ing the maximum number of monitor areas into attainment with each standard alternative. Table 4-8 presents the lead emissions
reductions realized for each monitor area ba sed on both identified controls alone and emission reductions beyond identified contro ls. Tables 4-9 and 4-10 present the air quality impacts of these emissions reduc tions and summarize the number of areas reaching attainment with the application of identified controls and emission reductions beyond identified controls. Lastly, Figure 2 pres ents the quantity of emissions reductions4-16 needed through the identified controls analysis, and the emissions reductions needed beyond identified controls.4-17 Table 4-7. TOTAL LEAD EMISSIONS REMAINING AND LEAD EMISSIONS REDUCTIONS REQUIRED
BEYOND IDENTIFIED CONTROLS TO REAC H ATTAINMENT WITH THE ALTERNATIVE
STANDARDS

Standard Alternative Lead emissions Remaining after applying identified controls (Tons/Year) Additional emission
reductions needed beyond identified controls (Tons/Year) Emissions remaining after applying
identified controls

and unspecified
emission reductions

beyond identified controls (Tons/Year)

0.5 μ g/m 3 2 nd Maximum Monthly Mean 30.96 0.02 30.94*

0.4 μ g/m 3 2 nd Maximum Monthly Mean 30.84 0.08 30.76*

0.3 μ g/m 3 2 nd Maximum Monthly Mean 29.39 0.29 29.10*

0.2 μ g/m 3 2 nd Maximum Monthly Mean 26.59 2.06 24.53*

0.15 μ g/m 3 2 nd Maximum Monthly Mean 24.74 4.79 19.95*

0.1 μ g/m 3 2 nd Maximum Monthly Mean 21.83 7.91 13.92**

* 21 out of 21 monitor areas reached attainment with th is standard alternative using identified point source emissions controls and unspe cified emission reductions.
** 20 out of 21 monitor areas reached attainment with this standard alternative using identified point source emissions controls and unspe cified emission reductions.4-18Table 4-8.REDUCTION IN LEAD EMISSIONS UNDER ALTERNATIVE STANDARDS AT EACH MONITOR AREA WITH IDENTIFIED CONTROLS ANDUNSPECIFIED EMISSION REDUCTIONS BEYOND IDENTIFIED CONTROLSReduction in Lead Emissions (tpy)MonitorStateMonitorCountyBaselineLeadEmissionsin 2016StandardAlternative: 0.5μg/m32ndMaximumMonthly MeanStandardAlternative: 0.4μg/m32ndMaximumMonthly MeanStandardAlternative: 0.3μg/m32ndMaximumMonthly MeanStandardAlternative: 0.2μg/m32ndMaximumMonthly MeanSelectedStandard: 0.15μg/m32ndMaximumMonthly MeanStandardAlternative: 0.1μg/m32ndMaximumMonthly MeanAL Pike4.45 4.02 4.02 4.02 4.134.31 4.31CO El Paso0.95 0.00 0.00 0.00 0.000.00 0.27FL Hillsborough1.48 1.00 1.00 1.09 1.191.19 1.26IL Madison0.53 0.00 0.00 0.00 0.000.00 0.11IN Delaware1.53 1.37 1.37 1.40 1.421.44 1.46MN Dakota3.55 0.00 0.00 0.00 0.001.29 3.07MO Iron16.12 12.26 12.26 12.26 12.2612.26 12.26MO Jefferson51.02 45.52 45.52 45.52 46.4647.75 49.04NY Orange1.80 0.00 0.00 0.00 1.391.39 1.39OH Cuyahoga0.94 0.00 0.00 0.18 0.490.65 0.81OH Fulton0.49 0.16 0.23 0.30 0.370.40 0.44OH Logan0.12 0.00 0.00 0.02 0.060.08 0.09OK Ottawa0.00 0.00 0.00 0.00 0.000.00 0.00*PA Beaver4.28 0.00 0.00 0.00 0.641.69 2.74PA Berks2.16 1.00 1.02 1.66 1.861.95 2.05PA Carbon0.45 0.00 0.00 0.00 0.160.25 0.34TN Sullivan0.38 0.00 0.00 0.00 0.060.15 0.24TN Williamson2.55 1.25 1.35 1.95 2.002.19 2.32TX Collin3.18 2.19 2.19 2.20 2.692.75 2.95TX Dallas0.06 0.00 0.00 0.00 0.000.00 0.01UT Salt Lake3.66 0.00 0.00 0.00 0.000.00 0.62Total99.7 68.76 68.94 70.6 75.1779.75 85.78*Indicates monitor area does not reach attainment with identified controls and unspecified emission reductions beyond identifiedcontrols. Ottawa, OK contains no pointsources and a large Superfund site.4-19Table 4-9.AMBIENT LEAD CONCENTRATIONS ACHIEVEDWITH IDENTIFIED CONTROLS AND UNSPECIFIED EMISSION REDUCTIONS BEYONDIDENTIFIED CONTROLS UNDER ALTERNATIVE STANDARDS IN 2016Ambient Lead Concentration (μg/m3)MonitorStateMonitorCountyBaseline LeadConcentrationin 2016StandardAlternative: 0.5μg/m32ndMaximumMonthly MeanStandardAlternative: 0.4μg/m32ndMaximumMonthly MeanStandardAlternative: 0.3μg/m32ndMaximumMonthly MeanStandardAlternative: 0.2μg/m32ndMaximumMonthly MeanSelectedStandard: 0.15μg/m32ndMaximumMonthly MeanStandardAlternative: 0.1μg/m32ndMaximumMonthly MeanAL Pike2.420 0.256 0.256 0.256 0.1970.098 0.098CO El Paso0.131 0.131 0.131 0.131 0.1310.131 0.100FL Hillsborough1.380 0.327 0.327 0.222 0.1230.123 0.049IL Madison0.128 0.128 0.128 0.128 0.1280.128 0.100IN Delaware5.022 0.397 0.397 0.285 0.1990.148 0.078MN Dakota0.192 0.192 0.192 0.192 0.1920.127 0.039MO Iron1.454 0.091 0.091 0.091 0.0910.091 0.091MO Jefferson1.998 0.236 0.236 0.236 0.2000.150 0.100NY Orange0.240 0.240 0.240 0.240 0.0850.085 0.085OH Cuyahoga0.357 0.357 0.357 0.300 0.2000.150 0.100OH Fulton0.530 0.500 0.400 0.300 0.2000.150 0.100OH Logan0.360 0.360 0.360 0.300 0.2000.150 0.100OK Ottawa0.114 0.114 0.114 0.114 0.1140.114 0.114*PA Beaver0.228 0.228 0.228 0.228 0.2000.150 0.100PA Berks0.518 0.404 0.400 0.300 0.2000.150 0.100PA Carbon0.294 0.294 0.294 0.294 0.2000.150 0.100TN Sullivan0.236 0.236 0.236 0.236 0.2000.150 0.100TN Williamson0.820 0.429 0.398 0.212 0.1980.137 0.097TX Collin0.891 0.302 0.302 0.300 0.1680.150 0.098TX Dallas0.101 0.101 0.101 0.101 0.1010.101 0.100UT Salt Lake0.107 0.107 0.107 0.107 0.1070.107 0.093* Indicates monitor area does not reach attainment with identified controls and unspecified emission reductions beyond identified controls. Ottawa, OK contains no pointsources and a large Superfund site.4-20 Table 4-10.
NUMBER OF MONITOR SITES REACHING ATTAINMENT WITH EACH ALTERNATIVE
STANDARD WITH IDENTIFIED CONTROLS AND EMISSION REDUCTIONS BEYOND IDENTIFIED
CONTROLS

Standard

Alternative Number of
Sites Analyzed Number of Sites in
Attainment with No
Additional Controls Number of Sites in
Attainment with
Identified Point Source
Controls Number of Sites in
Attainment with
Identified Point Source Controls and Unspecified

Emission Reductions

0.50 μ g/m 3

Second

Maximum Monthly Mean 12 20 21

0.40 μ g/m 3

Second

Maximum Monthly Mean 12 20 21

0.30 μ g/m 3

Second

Maximum Monthly Mean 10 17 21

0.20 μ g/m 3

Second

Maximum Monthly Mean 6 14 21

0.15 μ g/m 3

Second

Maximum Monthly Mean 5 13 21

0.10 μ g/m 3

Second

Maximum Monthly Mean 21

0 9 204-21 FIGURE 2.
EMISSIONS REDUCTIONS FROM IDENTIFIED CONTROLS AND REDUCTIONS NEEDED BEYOND
IDENTIFIED CONTROLS

50 55 60 65 70 75 80 85 90

0.5 μg/m3 0.4 μg/m3 0.3 μg/m3 0.2 μg/m3 0.15 μg/m3 0.1 μg/m3EmissionReductions(tons/year)

Emission Reductions from Identified Controls Unspecified Emission Reductions

4.5 Key Limitations

The estimates of emission reductions associated w ith the control strategies described above are subject to important limitations and uncertainties. We summarize these limitations as follows:

• Analysis Only Considers Controls on Point Source Emissions. Because the available data are not sufficiently detailed to assess the imp act 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 a nd the lack of control information for area nonpoint sources may have contributed to our pr ojection that some areas would violate the NAAQS.

• Actual State Implementation Plan s May Differ from our Simulation : In order to reach attainment with the selected standard, each state will develop its own implementation plan implementing a combination of emissions contro ls that may differ from those simulated in this analysis. This analysis therefore re presents an approximation of the emissions4-22 reductions that would be required to reach atta inment and should not be treated as a precise
estimate.

• Limited Sources Considered : 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. Furthermore, as discussed ab ove, we were not able to obtain emissions control information for a large number of poi nt sources in our an alysis. Although these sources collectively accounted fo r less than one tenth of all le ad emissions considered, many
of those sources were located in areas that we re not able to reach attainment with one or
more of the standard alternatives using identified controls alon e. If more emissions control information were available, it may not be nece ssary to rely on estimated emissions reductions beyond identified controls in orde r to simulate attainment with the alternative standards.

• Emissions Reductions from the Rebuild of the Primary Lead Smelter in Jefferson County,
Missouri: To estimate the emissions reductions asso ciated with the selected standard for Jefferson County, this analysis models the repla cement of the primary lead smelter in this area with a more modern, lower emitting Kiv cet smelter. We estimate the emissions reductions that such a project would achieve based on the emissions performance of Teck Cominco’s Kivcet smelter in Trail, British Columbia, scaling for differences in lead production volumes between the two facilities. While this is a reasonable approach for estimating the extent to which the Jefferson County smelter’s emissions may decline if it rebuilds its smelter, facility-specific charac teristics not included in our analysis may influence lead smelter emissions. Therefore, we may overestimate or underestimate the lead reductions that would be achieved by a rebuild of this smelter.

• Emissions Reduction Beyond Identified Controls : In this chapter we report both emissions
reductions from identified emissions controls and unspecified emissi on reductions beyond
identified controls. We have taken care to report these separatel y, in recognition of the greater uncertainty associated with achieving emissions reductions from measures that may not be currently in use or known to EPA. N onetheless, EPA believes it is reasonable to project that, with at least seve n years of lead time before a 2016 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 specifi cally focused on cost-e ffectively 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 year s since the phase down of lead in gasoline, it is likely that very little effo rt has been devoted to development of lead emissions control technologies except in industries where regulati ons have been imposed to reduce lead (e.g., large MWC standard, primary and secondary lead smelter MACTs, etc.). As a result, EPA believes that the projection of emission reducti ons beyond identified controls is particularly appropriate for compliance with a more stringent lead standard.1 Chapter 5 - Benefits Analysis Approach and Results

Synopsis

This chapter describes our analysis of the bene fits associated with attaining the selected National Ambient Air Quality Standard (NAAQS) fo r lead and the alternat ive standards outlined in Chapter 1. 1 The estimates outlined in this benef its analysis indicate that achieving a lower National Ambient Air Quality Standard (NAAQS ) for lead from its current level of 1.5 μ g/m 3
maximum quarterly mean to a second maximum monthly value of 0.15 μ g/m 3 could result in significant reductions in adverse health effects due to reduced exposure from lead and fine particles (PM 2.5 ). We estimate a potential increase in in telligence quotient (IQ ) points across the population (approximately 400,000) with the se lected NAAQS under various assumptions, including baseline non-air background blood lead levels at 2002 levels.

This Regulatory Impact Analysis (RIA) seeks to estimate benefits for the year 2016 using a 2002 baseline blood lead level; th is may result in an under- or ove r-estimate of benefits in the year 2016. 2 State and federal regulat ory interventions, including the recently promulgated Renovation and Repair Rule (RRP), are likely to reduce non-air background blood lead levels significantly. In the draft RIA, EPA committed to explore the possibility of updating the baseline to reflect expected effects on bl ood lead levels from other lead rules and potentially from an anticipated decline in p opulation blood lead levels. EPA has dete rmined that such a projection of baseline non-air background blood le ad levels is not technically f easible in the time available. Specifically, EPA lacks data regarding the dist ribution of the housing stock and populations to which rules such as the RRP apply. As an altern ative, we provide a sensitivity analysis, found in Table 5-8, which indicates that the total benef its estimate shows little sensitivity to alternate background non-air blood lead levels.

This RIA provides illustrative estimates of the incremental monetized human health benefits of attaining a revised primary lead (Pb) National Ambient Air Quality Standard (NAAQS) within the current monitoring network. 3 Some of the highest-emitting Pb sources do not have nearby Pb-TSP monitors, and it is importa nt to note that there may be more potential nonattainment areas than have been analyzed in this RIA. Because monitors are present in only 86 counties nationwide, the univers e of monitors exceeding the va rious target NAAQS levels is

1 The costs presented in this chapte r represent the direct pollution contro l expenditures associ ated with NAAQS
compliance. As such, they do not reflect the ge neral equilibrium impacts of the proposed rule.

2 The level of non-air background blood lead levels affects the portion of the health impact function curve on which
IQ changes are estimated. A change in the background level may cause IQ changes to be estimated on a shallower or
steeper portion of the curve.

3 There are currently 189 monitors representing 86 counties, but only 21 counties have monitors which exceed 0.10
ug/m 3

.2 very small; only 21 counties exceed the lo west alternate NAAQS level of 0.10 ug/m 3 . Because
we know that Pb-TSP monitors are not located ne ar some of the highest-emitting Pb sources in the 2002 NEI (see Chapter 2), it is likely that there may be more poten tial nonattainment areas than have been analyzed in this RIA.

As shown in Table 5-1 below, when applyi ng a 3 percent discount rate, the monetary value of avoided IQ point loss for the l east stringent standard alternative (0.5 μ g/m 3 ) ranges between $2.0 and $2.8 billi on (all values in 2006$). 4 If future non-air background blood levels change, benefits may be higher or lower. For the selected standard of 0.15 μ g/m 3

, benefits range from $3.5 to $5.0 billion. For the most stringent standard alternative (0.1 μ g/m 3 ), monetary benefits range from $4.5 to $6.4 billion. Additional co-control benefits of reduced PM emissions are expected to range between $0.1 and $0.9 billion for the least stringent standard alternative, between $0.2 and $1.9 billion for the selected sta ndard and up to a range of $0.3 to $2.2 billion for the most stringent standard alternative. Th erefore, the combined mo netized health benefits from reductions in both lead and PM exposures as a result of lowering the current NAAQS range from $2.1 to $3.7 billion for the least stringen t standard alternative, between $3.7 and $6.9

billion for the selected standard and between $4.8 to $8.6 billion fo r the most stringent standard
alternative.

When applying a 7 percent discount rate, the monetary benefits for changes in IQ the least stringent standa rd alternative (0.5 μ g/m 3 ) range between $0.3 and $0.5 billion. For the selected standard, benefits range from $0.4 a nd $0.9 billion. For the most stringent standard alternative (0.1 μ g/m 3 ), monetary benefits of IQ gains range from $0.6 to $1.1 billion.

Additional co-control benefits of reduced PM emissions are exp ected to range between $0.1 and $0.8 billion for the least string ent standard alternative, be tween $0.2 and $1.7 billion for the selected standard and a range of $0.2 to $2 billion for the most st ringent standard alternative. Therefore, the combined monetized health bene fits from reductions in both lead and PM exposures as a result of lowering the current NAAQS range from $0.4 to $1.3 billion for the least stringent standard alternative, between $0.7 a nd $2.6 billion for the selected standard and between $0.8 and $3.1 billion for the most stringent standard alternative.

The benefits summarized in the table below are the product of the air quality change
associated with both identified and unidentified emission controls. The proportion of benefits
attributable to identified controls varies by standard alternative. At the less stringent alternatives
of 0.5 μ g/m 3 , 0.4 μ g/m 3 , 0.3 μ g/m 3 and 0.2 μ g/m 3 the identified emission controls account for all, or nearly all, of the estimated bene fits. For the selected standard of 0.15 μ g/m 3 , the identified controls represent about 85% of total benefits. Finally, the more stringent alternative standard of

0.1 μ g/m 3 , the identified controls repres ent about 80% of total benefits.

4 When monetizing benefits, we applied two alternate valuation functions. These functions are discussed further in
this chapter.3 Table 5-1. Monetary Benefits of Alternat e Lead NAAQS (in Millions of 2006$) in 2016

Estimated Net Present

Value of IQ Points

Gained 23 Monetized Benefits of

Co-Controlled PM 2.5 Emissions 4 Total Benefits 5

Standard

Alternativ

e 1 3%

Discount

Rate 7%

Discount

Rate 3%

Discount

Rate 7%

Discount

Rate 3%

Discount

Rate 7%

Discount

Rate

0.5 μ g/m 3 $2,000—
$2,800 $250—$490 $110—$880 $100—$790 $2,100—
$3,700 $350—
$1,300

0.4 μ g/m 3 $2,000—
$2,800 $250—$490 $100—$880 $100—$800 $2,100—
$3,700 $350—
$1,300

0.3 μ g/m 3 $2,400—
$3,400 $300—$580 $190—
$1,600 $170—
$1,400 $2,600—
$5,000 $470—
$2,000

0.2 μ g/m 3 $3,200—
$4,500 $390—$780 $220—
$1,800 $200—
$1,600 $3,400—
$6,300 $590—
$2,400

0.15 μ g/m 3 $3,500—
$5,000 $440—$870 $230—
$1,900 $210—
$1,700 $3,700—
$6,900 $650—
$2,600

0.1 μ g/m 3 $4,500—
$6,400 $560—
$1,100 $260—
$2,200 $240—
$2,000 $4,800—
$8,600 $800—
$3,100

1 All standard alternatives are for a s econd maximum monthly mean concentration.

2 Results reflect the use a 2002 der ived non-air background blood lead applied to analysis year of 2016. To the

extent that state and federal interventions such as the Renovation and Repair Rule (EPA, 2008c) reduce future

non-air blood lead levels, the estima te of IQ change above may be different.

3 The lower end of the range of presented values was calculated using the S chwartz (1994b) valuation

estimate; the upper end was calculated using th e Salkever (1995) valuation estimate.

4 The range of presented values repres ent 14 different estimates from the PM epidemiological literature and

an expert judgment study.

5 Numbers are rounded to two significant figures. Ther efore, the sums in these columns may not total.

Figures 5-1 and 5-2 below display the h ealth benefits from both lead and PM 2.5 exposure reductions for each of the six alternative standard s using a 3 percent and 7 percent discount rate, respectively. 5 Figures 5-3 and 5-4 below display some ex amples of the total health benefits from

5 Note that these figures present the lead benefits results that incorporate valuation estimates from Schwartz (1994b)
and PM co-control benefits using the Pope et al. (2002) epidemiological study and therefore do not represent the full
range of uncertainty in the expected benefits.4 both lead and PM2.5 exposure reductions using di fferent input assumptions for each of the six alternative standards using a 3 percent and 7 percent discoun t rate, respectively.5 Figure 5-1. Lead and PM 2.5 Benefits by Standard Alternative (3% Discount Rate)6 Figure 5-2. Lead and PM 2.5 Benefits by Standard Alternative (7% Discount Rate)7 Figure 5-3. Example Combined Lead and Total PM 2.5 Monetized Benefits Estimates by

Standard Alternative (3% Discount Rate)8 Figure 5-4. Example Combined Lead and Total PM 2.5 Monetized Benefits Estimates by Standard

Alternative (7% Discount Rate)9 Introduction

This chapter documents our analysis of health benefits expected to result from achieving

alternative levels of the lead NAAQS, relative to baseline ambien t air lead levels. We first describe our approach for estimating and mone tizing the health benefits associated with reductions of lead in air. Next, we provide a summa ry of our results, including an analysis of the sensitivity of the benefits model. We then re view 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 of the selected lead NAAQS. Fi nally, we discuss the key results of the benefits analysis and indicate ar eas of uncertainty in our approach.

Benefits Approach

This section presents our approach for es timating 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 fi rst review the epidemiological ev idence concerning potential health effects of lead exposure and pres ent 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 a nnual avoided health effects expected to result from achieving alternative levels of the lead NAAQS (the “control scenarios”) in the year

2016. We measured benefits in th e control scenarios re lative to the inciden ce of health effects

consistent with ambient lead levels in air expected under the current standard (1.5 μ g/m 3
maximum quarterly mean; the “base case”) in 2016. Note that this “base case” reflects emissions reductions and ambient air quality improvement s that we anticipate will result from implementation of other air quality rules, in cluding compliance with all relevant Maximum Achievable Control Technology (MACT) rule s and the recently revised NAAQS for PM 2.5

. 6 We compared benefits across six alternative s econd maximum monthly mean NAAQS levels of 0.5, 0.4, 0.3, 0.2, 0.15, and 0.1 μ g/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 child ren (less than 7 years of age)

6 Development of this base case is described further in Chapter 3.10 have been observed consistently across multiple studies that control for an array of confounding factors (USEPA, 2006a).

The Criteria Document provide s a comprehensive review of the current evidence of

health and environmental effects of Pb. With regard to health effects, the Criteria document summarizes the evidence as follows (CD, Section 8.4.1):

“...Pb has been shown to exert a broad array of deleterious effects on multiple organ systems via widely diverse mechanisms of action. Trul y remarkable progress has been made during the past several decades with regard to (a) more fully delin eating over time the wide variety of pathophysiologic effects associated with Pb exposure of human population groups and laboratory animals and (b) the characterizati on of applicable expos ure durations and doseresponse relationships for the induction of the mu ltifaceted Pb effects. This progress has been well documented by the previous Pb NAAQS crite ria reviews carried out by EPA in the late 1970s and during the 1980s, as well as being well reflected by previous chapters of this
document. The 1977 Lead AQCD (U.S. Environmental Protection Agency, 1977) that provided key scientific bases for the setting in 1978 of the current Pb NAAQS included discussion of both: (a) historical literature accumulated du ring several preceding decades that established Pb encephalopathy and other signs and sympto ms of persisting se vere central and/or peripheral nervous system damage, as well as renal and hepatic damage, and anemia as typifying the classic syndrome of acute and/ or chronic high-level Pb poisoning among human pediatric and /or adu lt population groups, and (b) eval uation of then newly-emerging
evidence for more subtle and difficult-to-de tect “subclinical” Pb effects on IQ, other neurological endpoints, and m oderate blood hemoglobin defici ts or other erythropoietic indicators of heme synthesis impairment, whic h collectively were judge d to constitute an array of adverse Pb health effects associat ed with Pb exposures indexed by blood Pb concentrations ranging down to ~30 μg/dL. The next Pb NAAQS criteria review during the 1980's, as contained in the 1986 Lead AQC D/Addendum and its 1990 Supplement (U.S. Environmental Protection Agency, 1986a, b, 1990) documented further rapid advances in Pb health effects research that provided (a) increas ingly stronger evidence th at substantiated still lower fetal and/or postnatal Pb-exposure levels (indexed by blood-Pb levels extending to as low as 10 to 15 μg/dL or, possibly, below) as being associated with slowed physical and neurobehavioral development, lower IQ, impa ired learning, and/or other indicators of adverse neurological impacts and (b) othe r pathophysiological effects of Pb on cardiovascular function, immune system com ponents, calcium and vitamin D metabolism, and other selected health endpoints. Newly available scientific inform ation published since the 1986 Lead AQCD/Addendum and the 1990 Supplement, as a ssessed in previous chapters of this document, further expands our understanding of a wide array of Pb-induced health effects, underlying mechanisms, and factors that enhance or lessen susceptibility to Pb effects. Very importantly, the newly available toxicologic a nd epidemiologic information, as integrated below, includes assessment of new evidence substantiating risks of deleterious effects on certain health endpoints being i nduced by distinctly lower than previously demonstrated Pb exposures indexed by blood-Pb levels extend ing well below 10 μg/dL in children and/or
adults.11 The ensuing subsections [of the CD] provide concise summarization and integrative synthesis of the most salient health-related fi ndings and conclusions derived from the current
criteria assessment. This includes discu ssion of new toxicologic and/or epidemiologic evidence concerning Pb induced (a) effects on neurobehavioral development and other indicators of nervous system effects; (b) cardi ovascular effects; (c) he me synthesis effects; (d) renal effects; (e) immune system func tions; (f) effects on calcium and vitamin D metabolism; (g) inter-relationships to bone a nd teeth formation and demineralization; (h) effects on reproduction and other neuroendocri ne effects; and (i) genotoxicity and carcinogenic effects.”

The differing evidence and associated strength of the evidence for these different effects

is described in detail in the Cr iteria Document. The evidence w ith regard to adverse effects on plants and animals is also desc ribed in the Criteria Document.

Although a number of adverse health effects ha ve 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 and the fact that for some of these endpoints the science is less ce rtain. We analyzed only those endpoints with sufficient evidence to support a quantified dose-response relations hip. This determination was made using the information presented in the Lead Criteria Document , which contains an

extensive literature review for several health end points related to lead exposure. However, this document only included studies published or accepted for publication through December 2005. Therefore, we performed supplemen tal searches in the online search engine PubMed to identify studies published between January 2006 and the pr esent (see Appendix A for more information). Finally, we reviewed previous EP A lead benefits analyses to id entify 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 childspecific 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 ra tios 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 metaanalysis of several studie s, 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 concen trations in air, the age of these studies suggests that these ratios may not be appr opriate for application in 2016. The morerecent peer-reviewed estimates of air:bl ood ratios have been derived for children.12 Applying these ratios to adults would be inappropriate give n the important differences between the two populations in th eir ambient exposure to Pb.

• There is a lack of current, peer revi ewed non-air-related blood lead background estimates for adults . Quantification of adu lt endpoints would requi re a non-air-related blood background for adults. CASAC recommends a range of values for children in their
review of the Lead Risk Assessment. Howe ver, due to differences between adults and
children in the routes of e xposure to lead, it is possibl e that background levels would differ between these two receptor groups. Th erefore, applying the child-specific non-airrelated background blood lead levels to a dults could misestimate the true adult background levels.

• The adult health impact functions relating c hanges in blood lead to health outcomes are
dated . Certain adult health impact functions, such as those quantifying the relationship
between blood lead and diasto lic 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 1970s;
advances in the treatment of high blood pressure suggest that these functions may overpredict of changes in these health eff ects in the current population. One newer study, Schober et al. (2006), quantifies the relations hip between blood lead and cardiovascular mortality. However, according to the Lead Criteria Document, “...until the Schober et al. findings are replicated and more fully understood, the Schober et al. (2006) estimates for Pb-induced cardiovascular mortality should probably not be used for quantitative risk assessment” USEPA, 2006a, page 8-89.

Taken together, these data limitations make a credible quantified assessment of adult endpoints very challenging and subject to consider able uncertainty. The Agency is working 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.

Table 5-3 below presents the hea lth effects related to exposure to lead in the air that are quantified in this benefits analysis. In addition, the table incl udes a list of other endpoints that potentially are linked to lead exposure, but whic h do not have dose-respon se functions available for quantifying benefits.

As shown in Table 5-3, 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 leve ls on cognitive function (most of ten 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 vulnerab ility to lead exposure during childhood.

For instance, the first three y ears of life represent the maxima l period of lead ingestion as well as a period of time when important developmen t of the central nervous system is occurring,13 which suggests that biologically this could be a vulnerable period (USE PA, 2006a). In addition, there are two major meta-analyses that focuse d 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). Howeve r, 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 m ean 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 (Canfi eld et al., 2003; Dietrich et al., 1993; Tong et al., 1996, Wasserman et al., 2000). In addition, a la rge, international me ta-analysis by Lanphear et al. (2005) included four measur es of blood lead level: concurre nt, peak, lifetime average, and early childhood. The authors found that the concurrent and lifetim e blood lead levels were the strongest predictors of IQ deficits as sociated with lead exposure.

A study by Chen et al. (2005) specifically evaluated whether a window of enhanced susceptibility to lead exists. This study examin ed whether cross-sectional associations observed in school age children repr esent residual effects from two year s of age or “new” effects emerging among these children (USEPA, 2006a). Chen et al . found that the blood lead metric with the strongest association with IQ wa s concurrent, and this relationshi p grew stronger with age. The authors did not find any associa tion between peak blood lead leve l and IQ measured at seven years of age. In addition, a stronger relationship was found between IQ at seven years of age and blood lead level at seven years of age compared with blood lead at tw o years of age. The Lead

Criteria Document concluded that “[t]hese results suppor t 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 child ren under seven years of age in the study area for this analysis will e xperience some cognitive benefit (i.e., IQ loss avoided) from reduced ambient air lead in 2016. Therefore, we have designed our benefits analysis to measure benefits to a ll children under seven in our study area.14 Table 5-3. Human Health Effects of Lead

Quantified Health Effects Unquantified Health Effects a

-Intelligence Quotient (IQ) loss effect
on lifetime earnings -Other neurobehavioral and physiological effects
-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)

a The categorization of unquantified toxic health effects is not exhaustive. Health endpoints in this column include

both a) those for which there is not consensus; and b) t hose for which associations, to various degrees, has been

determined but empirical data are not av ailable to allow calculation of benefits.

Benefits Estimation Model

Overview

For this benefits analysis, we created a sp readsheet model to provide a screening-level
assessment of health benefits occurring as a result of implem enting alternative NAAQS levels. The model uses various simplifying assumptions a nd is intended only to provide an approximate, preliminary estimate of the potential health benefits.

The model was constructed in Microsoft Ex cel and provides an in tegrated tool to complete five benefits estimation steps: 1) estim ate lead in air concentrations for the “base case” and “control scenarios”; 2) estimate population e xposures to air lead concentrations for each scenario; 3) estimate blood lead levels in the population for each scenar io; 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 5-5 and are discussed in further detail below.

Estimating Lead in Air Concentrations

We used estimates of the second maximum m onthly mean lead total suspended particles (TSP) for each monitor included in our study to ch aracterize ambient air lead concentrations for the “base case” in 2016 (USEPA, 2007b). These es timates were calculated by adjusting second maximum monthly mean lead TSP monitoring va lues for the years 2003 to 2005 to account for15 emissions reductions due to compliance w ith MACT requirements and the NAAQS for PM 2.5 occurring by 2016 (see Chapter 4 for additional information). We assumed that under the
“control scenario” for each standard alternativ e, each monitor would meet the second maximum monthly mean level achieved under th e cost analysis for that alte rnative described in Chapter 4.

While the alternative standards were speci fied in terms of second maximum monthly mean lead concentrations, the benefits model us ed estimates of maximum quarterly mean lead

concentrations in order to calcu late avoided cases of health en dpoints. 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., with in a few weeks to months). This finding is also supported by a simulation of changes in ur ban residential dust lead levels following a change in ambient air lead us ing 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. The hybrid model developed for th e general urban case study suggest ed that 90% of steady-state impacts will be recognized within the three mont hs and take up to one year for a full change to

be realized.16 Figure 5-5

OVERVIEW OF LEAD BENEFITS MODEL17 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 2016 in each census block group based on the second maximum monthly m ean values for the “base case.” This was achieved by calculating census block group-spec ific ratios of the second maximum monthly mean to the maximum quarterly mean for th e period 2003-2005 and then dividing the second maximum monthly mean for the “base case” by this ratio. 7

For the “control scenario” we estimated the maximum quarterly mean lead in air concentration that would be expected in 2016, based on the second maximum monthly mean NAAQS concentration. As in th e “base case,” we used census bl ock group-specific ratios of the second maximum monthly means to maximum qu arterly 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 ch ange resulting from attainment of alternate lead NAAQS. Each approach relies upon the lead monitoring networ k as the basis for s ubsequent air quality estimates. The first approach, which we employe d to generate our primary benefits estimate, uses an interpolation method utili zed in previous RIAs to estimate changes in lead concentrations in projected non-attainment areas. The second a pproach, 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

This approach applies an interpolation met hod 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 ne twork as well as the lead-speciating TSP monitoring ne twork; we added the lead-speci ating monitors to increase the number of data points available for the interpolat ion. We interpolated lead concentrations to the census tract, rather than census block group, to increase the computational efficiency of the

model.

7 This ratio technique is detailed further in Chapter 3.18 To create an air quality su rface of ambient lead values we applied the Voronoi Neighborhood Averaging (VNA) method. 8 The VNA is an inverse-di stance-weighting technique that interpolates point monitor da ta 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 th e gradient of ambient lead surrounding each

monitor. VNA is a well-establishe d 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 & 2008a).

Figure 5-6 below summarizes how we appl ied the VNA method in this analysis. The VNA approach is expected to provi de 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 5-7). The lead and T SP monitoring network in other non-attainment areas can in some cases be sparse, and the result ing interpolation does not appear to generate a meaningful gradient, such as in Delaware Count y, IN (see Figure 5-8). To the extent that there was a denser lead monitoring network in such locations, the interpolation approach would produce a gradient that better represents actual ambient lead concentrat ions. 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. As a means of acknowledging the limita tions to the interpolation method we also provide sensitivity estimates using the radius method.

8 For technical details of the VNA approach, see the tec hnical appendices to the BenMAP User manual , found at:
http://www.epa.gov/air/benmap/models/Ben MAPTechnicalAppendicesDraftMay2005.pdf19 Figure 5-6

Step 3 . Roll-back Pb and Pbspeciating TSP monitor values to just

attain each standard alternative 0.5 μ g/m 3

0.4 μ g/m 3

0.3 μ g/m 3

0.2 μ g/m 3

0.15 μ g/m 3

0.1 μ g/m 3 Step 2. Apply VNA to

create baseline air quality grid Convert baseline 2 nd

max monthly mean to
max quarterly value Adjust baseline monitor values

to reflect PM RIA and MACT

controls * Step 1. Identify all Pb and Pb-speciating TSP monitors

Step 4. Apply VNA to

create control air quality grid Interpolate Pb values to
census block groups

within 10km radius of

each monitor

Interpolate Pb values to
census block groups

within 10km radius of

each monitor

* This step required us to adjust the Pb-speciating TSP monitors to reflect the presence of PM RI A and MACT emission controls. Th e
emissions controls team performed this adjustment for the Pb monitors. To make a conforming adjustment to the Pb-speciating TSP

monitors, we used VNA to interpolate the PM RIA and MACT-rel ated air quality improvement from the Pb monitors to the Pbspeciating TSP monitors. Convert control 2 nd

max monthly mean to
max quarterly value Steps in the VNA Interpolation Technique20 Figure 5-7. Air Lead Concentration Grad ient in Hillsborough County, Florida21 Figure 5-8. Air Lead Concentration Gr adient in Delaware County, Indiana22 Radius Method

In this approach we focused on the 21 mon itors in counties that potentially could be designated as non-attainment areas under at least one of these a lternative lead NAAQS levels.
These monitor concentration values likely only apply to the popul ation of people living within the vicinity of these monitors, especially if the monitor is or iented near a source of lead contamination (e.g., a primary or se condary lead smelter). As a de fault, 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 seconda ry sources (USEPA, 2007a). In the absence of detailed air quality modeling for the lead sources in the vi cinity of each monitor, we assumed in this screening-level analysis that the lead concentrations in air measur ed at each monitor are uniform throughout the specified radius. To develop a conservative 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, we performed sensitivity analysis us ing alternate, smaller radii of one, two, and five kilometers,
since lead air concentrations can in some cases display significant gradie nts with distance from a source-oriented monitor. For example, s econd 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. 9

We used ArcGIS to establish the radii around each monitor. Our spatial dataset contained US Census population data at the bl ock 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 at least partially reside d within the radius. We then dist ributed the estimate of the total population for each radius in 2000 into gender- and age-specific groups (in five-year increments, consistent with the age ranges reported by the Census). 10

Population Projections

For both the interpolation and radius methods, we extrapolated the 2000 age- and genderspecific population data to 2016, using Woods a nd 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 2016 estimate from Woods and Poole to the corresponding 2000 countylevel estimates from the Census. We applied the calculated growth rates to each gender and age group to estimate the total population in 2016 residi ng within each census tract or radius. This approach to population projection is consistent with previous EPA RIA’s that estimate futureyear human health benefits (USEPA 2006c, 2007c , 2008a). However EPA does not assume that

9 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).

10 The five-year age groups were 0-4, 5-9, 10-14, ... up to 85 and above.23 the number of Pb emitting sources will grow co rrespondingly with the population growth as discussed in Chapter 4.

In order to determine the number of ch ildren under the age of seven, we added the population of children in the 0-4 age group for both genders and th en added two-fifths of the population in the 5-9 age group, assuming the popul ation was uniformly distributed across all five ages in that group.

Estimating Blood Lead Levels

The concentration-response functions we em ploy 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 approxi mation of the blood lead levels
associated with reductions in air lead concentr ations 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 inhalati on 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 5-4 lists the ratios considered for the current NAAQS analysis; for its primary estimate, EPA chose a
ratio of 1:7 μ g/m 3 to μ g/dl. That is, for every one microgr am per cubic mete r reduction in air lead, EPA assumed that geometric mean bl ood lead levels would be reduced by seven micrograms per deciliter. We se lected this value based on advice from the Clean Air Scientific Advisory Committee (CASAC) and analys is 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 le ad 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 μ g/m 3 to 1:6 μ g/dl; however, CASAC su ggested that ratios higher than 1:2 may be appropria te based on more recent literatur e. CASAC cites the use of a ratio of 1:5 by the World Health Organizati on (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 bl ood 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 a nd compared these ratios to values reported in the literature (USEPA , 2007a). For the benefits analysis, we reviewed the ratios in Table 5-7 of
the Lead Risk Assessment that compare the incremental reduc tion in air concentrations required to meet lower alternative NAAQS levels to the co rresponding incremental cha nge 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 N AAQS to an alternative NAAQS of 0.05 μ g/m 3 maximum monthly mean. EPA found these values to be si milar to ratios availabl e in the literature, specifically to ratios reported in a 1984 meta-analy sis by Brunekreef (1:3 to 1:6) and to values24 calculated from a more recent 2003 study by Hilts (1 :7). More recently, a study of changes in
children’s blood Pb levels associated with reduced Pb emissions and associated air concentrations near a Pb smelter in Canada (for children through age six in age) reports a ratio of 1:6 and additional analysis of the data by EPA for the initial time period of the study resulted in a ratio of 1:7 (CD, pp. 3-23 to 3-24; Hilts, 2003). 11 Ambient air and blood Pb levels associated with the Hilts (2003) study range from 1.1 to 0.03 μg/m3, and associat ed population mean blood Pb levels range from 11.5 to 4.7 μg/dL, which ar e lower than levels asso ciated with the older
studies cited in the 1986 Crite ria Document (USEPA, 1986).

We also reviewed the ratio of 1:9-1:10 c ited by CASAC that was based on the data in Schwartz and Pitcher (1989) on bl ood lead changes resulting from the phase-out of lead in gasoline. Schwartz and Pitche r developed a regression equation to estimate blood lead levels from gasoline-based lead usage ( hundreds of metric tons per da y) in the period 1976-1980. Their main analysis used a nationally representative sample of the U.S. population from the second
National Health and Nutrition Examination Surv ey (NHANES II) and controlled for a range of factors such as age, income, smoking, educational attainment, nutrition, occupational exposure, alcohol intake, and urban/rural status. 12 (Another potential confounder, dietary lead intake, was found to exhibit no temporal trend over the study period and thus wa s not included in the model.) Schwartz and Pitcher’s analysis found that for every 100 metric t ons (MT) of gasoline lead used per day, the general pop ulation blood lead level would increase 2.14 μ g/dL. This finding was corroborated by a supplemental analysis by the au thors of a dataset of blood lead screening results for inner-city children in Chicago over the same time pe riod. CASAC developed its ratio of 1:9 – 1:10 by first multiplying the 2.14 μ g/dL per hundred MT of gasoline lead to an estimate of gasoline usage in 1976 (426 MT per day) to produce a blood lead attr ibutable to lead in gasoline of 9.12 μ g/dL. CASAC then divided this va lue by EPA’s estimated reduction in
ambient air levels in urban cities of 1 μ g/m 3 between 1976 and the elimination of lead in gasoline (USEPA, 1986) to produce the ratio.

We also considered a 1:5 air: blood ratio, which represented the ratio for the change in the urban case study from current (mean) condi tions to an alternative NAAQS of 0.2 μ g/m 3
maximum monthly mean. According to the Notice of Proposed Rulemaking

, “There are a number of sources of uncertainty associated w ith these model-derived ratios. The hybrid indoor dust Pb model, which is used in estimating indoor dust Pb levels for the urban case studies, uses a HUD dataset reflecting housing co nstructed before 1980 in esta blishing the relationship

11 This study considered changes in ambient air Pb levels and associated blood Pb levels over a five-year period which included closure of an older Pb smelter and subsequent opening of a newer facility in 1997 and a temporary (3 month) shutdown of all smelting activity in the summer of 2001. The author observed that the air-to-blood ratio for children in the area over the full period was approxima tely 1:6. The author noted limitations in the dataset associated with exposures in the second time period, after the temporary shutdown of the facility in 2001, including sampling of a different age group at th at time and a shorter time period (3 months) at these lower ambient air Pb levels prior to collection of blood Pb levels. Consequently, EPA calculated an alternate air-to blood Pb ratio based

on consideration for ambient air Pb and blood Pb reductions in the first time period (after opening of the new facility in 1997).

12 Race was addressed using stratified regression analysis and was not found to modify results by more than 10
percent (Schwartz and Pitcher, 1989).25 between dust loading and concentr ation, which is a key component in the hybrid dust model (see Section Attachment G-1 of the Risk Assessment, Volume II). Given this application of the HUD dataset, there is the potential th at the non-linear relationship betw een indoor dust Pb loading and concentration (which is reflected in the structure of the hybrid dust model) could be driven more by the presence of indoor Pb paint than contribu tions from outdoor ambient air Pb. We also note that only recent air pathways were adjusted in modeling the impact of ambient air Pb reductions on blood Pb levels in the urban case studies, whic h could have implications for the air-to-blood ratios.” (US EPA, 2008b).

As a sensitivity analysis, we selected a lo wer bound of the 1:2 ratio from the U.S. EPA

Lead Risk Assessment and an upper bound of 1:10 as upper bound of the CASAC Schwartz and Pitcher study-based estimates. We believe the inclusion of th e 1:10 ratio as an upper bound is justified by the strengths of the design of th e Schwartz and Pitcher study, which like the Hilts study, captured the effect s of a natural experiment, by its extensive control for confounders, including dietary exposures, and by the robustness of its findings using national-level and urban inner-city samples.

We divided the maximum quarterly mean lead in air concentrations for each scenario by
the air:blood ratio to estimate the blood lead le vel 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 cont aminated by lead paint) to calculate the total geometric mean blood lead leve l expected in the population. 13 For our estimate of non-airrelated background, we selected the midpoint from a range of values reported by CASAC as being most appropriate for children under 7 years of age (USEPA, 2007d). 14 We apply this estimate of current-year non-air background blood lead for an an alysis year of 2016. State and
federal interventions such as the Renovation and Repair Rule (EPA, 2008c) may reduce future
non-air blood lead to a level below this estimate . EPA alternate approaches to projecting non-air
blood lead levels to reflect such regulatory interventions. Howe ver, data limitations prevented EPA from generating credible estimates.

The air:blood ratio provided us with an esti mate 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 a ssumed that the blood lead level changes in 2016 estimated in this way are a reasonable representation of lifetime av erage blood lead level for children under seven years of age in our study and were used with the selected dose-re sponse functions without further adjustment.

13 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 chan ges in lead exposure, which are based on total blood lead

level.

14 CASAC provided a range of non-air-related background geometric mean concentrations of 1.0 – 1.4 μ g/dl in their

comments on EPA’s Lead Risk Assessment (USEPA, 2007a). We selected the midpoint of this range, 1.2 μ g/dl, for
this analysis.26 Table 5-4. Air Lead to Blood Lead Ratios

Ratio Source Description

1:2 to 1:6 USEPA, 2007a Ratios in Table 5-7 of EPA’s current Lead Risk Assessment (USEPA,
2007a) estimated from modeling of exposures in urban areas and areas

near lead smelters. These ratios co mpare the incremental reduction in air
concentrations required to meet lo wer 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 ratio s 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 μ g/m 3 maximum
monthly mean, respectively.

1:5 USEPA, 2007a
WHO, 2005 Ratio applied by WHO to establish current lead Air Quality Guideline for
Europe. Also reported in Table 5-7 of EPA’s Lead Risk 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

μ g/m 3 maximum monthly mean.

1:3 to 1:6 Brunekreef, 1984 Ratios reported in a meta-analysis of surveys of smelters and urban areas.
Based on older studies that typically reflect ratios for children with blood

lead levels > 10 μ g/dl.

1:6 to 1:7 Hilts, 2003 15 Ratio calculated from more recent study of air 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 μ g/dl) are lower than in the Brunekreef studies, but still higher than

those modeled in EPA’s 2007 Lead Risk Assessment

.

1:9-1:10 USEPA, 2007d;

Schwartz and
Pitcher (1989) Ratio cited by CASAC in its March 2007 advisory that was derived from

calculations in Schwartz and Pitcher analysis of the impacts of phasing
out lead in gasoline. Reflects ratios for changes from higher baseline lead
concentrations than expected under current conditions.

15 This study considered changes in ambient air Pb levels and associated blood Pb levels over a five-year period
which included closure of an older Pb smelter and subsequent opening of a newer facility in 1997 and a temporary
(3 month) shutdown of all smelting activity in the summer of 2001. The author observed that the air-to-blood ratio
for children in the area over the full period was approxima tely 1:6. The author noted limitations in the dataset
associated with exposures in the second time period, after the temporary shutdown of the facility in 2001, including
sampling of a different age group at th at time and a shorter time period (3 months) at these lower ambient air Pb
levels prior to collection of blood Pb levels. Consequently, EPA calculated an alternate air-to blood Pb ratio based

on consideration for ambient air Pb and blood Pb reductions in the first time period (after opening of the new facility
in 1997).27 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 im pact of blood lead levels on changes in IQ in young children below seven years of age. C ognitive effects are thought to strongly relate to a child’s future productivity and earning potential (USEPA, 2006b).

Multiple epidemiologic studies of Pb and child development have demonstrated inverse

associations between blood Pb concentrations and children’s IQ and ot her cognitive-related outcomes at successively lower Pb exposure levels over the past 30 years (as discussed in the CD, section 6.2.13). Numerous epidemiological studies have reported neurocognitive, neurobehavioral, sensory, and motor function effect s in children with blood Pb levels below 10 μg/dL (CD, sections 6.2 and 8.4). As disc ussed in the Criteria Document, “extensive experimental laboratory animal evidence has b een generated that (a) substantiates well the plausibility of the epidemiologi c findings observed in human child ren and adults and (b) expands our understanding of likely mechanisms underlying th e neurotoxic effects” (CD, p. 8-25; section 5.3).

Threshold levels for neurological effects, in terms of blood Pb levels in individual children, cannot be discerned from the currently available studies (CD, pp. 8-60 to 8-63). The Criteria Document states “[t]here is no level of Pb exposure that can ye t be identified, with confidence, as clearly not being a ssociated with some risk of dele terious health effects” (CD, p. 8-63). The Criteria Document also notes that “a threshold for Pb neurotoxic effects may exist at levels distinctly lower than the lowest exposures examined in these epidemiologic studies” (CD, p. 8-67). The current evidence indicates the occurr ence of a variety of health effects, including neurological effects in children, associated w ith blood Pb levels extending well below 10 μg/dL (CD, Sections 6.2, 8.4 and 8.5). For example, as not ed in the Criteria Document with regard to the neurocognitive effects in children, the “wei ght of overall evidence strongly substantiates likely occurrence of [this] type of effect in association with blood- Pb concentrations in range of 5-10 μg/dL, or possibly lower ... Although no eviden t threshold has yet been clearly established for those effects, the existence of such effects at still lower bl ood-Pb levels cannot be ruled out based on available data.” (CD, p. 8-61). The Cr iteria Document further notes that any such threshold may exist “at levels distinctly lower than the lowest exposures examined in these epidemiological studies” (CD, p. 8-67).

In comparing across the individual epidemio logical studies and th e international pooled analysis, the Criteria Document observed that at higher blood Pb levels (e.g., above 10 μg/dL), the slopes (for change in IQ with blood Pb) derived for log-lin ear and linear models are almost identical, and for studies with lo wer blood Pb levels, the slopes a ppear to be steeper than those

observed in studies involvi ng higher blood Pb levels (C D, p. 8-78, Figure 8-7).

The current evidence reviewed in the Criteria Document with regard to the quantitative relationship between neurocognitive decrement, such as IQ, and blood Pb levels indicates that28 the slope for Pb effects on IQ is nonlinear and is steeper at lower blood Pb levels, such that each μg/dL increase in blood Pb may have a greater e ffect on IQ at lower blood Pb levels (e.g., below 10 μg/dL) than at higher levels (CD, section 6.2.1 3; pp. 8-63 to 8-64; Figure 8-7). As stated in the CD, “the most compelling ev idence for effects at blood Pb levels <10 μg/dL, as well as a nonlinear relationship between blood Pb levels and IQ, comes from the international pooled analysis of seven prospective cohort studies (n=1,333) by Lanphear et al. (2005)” (CD, pp. 6-67 and 8-37 and section 6.2.3.1.11). Using the full pooled dataset with concurrent blood Pb level as the exposure metric and IQ as the response fr om the pooled dataset of seven international studies, Lanphear and others (2005) employed math ematical models of various forms, including linear, cubic spline, log-linear , and piece-wise linear, in their investigation of the blood Pb concentration-response relationship (CD, p. 6-29; Lanphear et al., 2005). They observed for this pooled dataset that the shape of the concentration-re sponse relationship is nonlinear and the loglinear model provides a better fit over the full range of blood Pb measurements than a linear one (CD, p. 6-29 and pp. 6-67 to 6-70; Lanphear et al., 2005). In addition, they found that no individual study among the seven was responsib le for the estimated nonlinear relationship between Pb and deficits in IQ (CD p. 6-30). Ot hers have also analyzed the same dataset and similarly concluded that, acro ss the range of the dataset’s blood Pb levels, a log-linear relationship was a significantly better fit than the linear relationship (p=0.009) with little evidence of residual confounding from incl uded model variables (CD, section 6.2.13; Rothenberg and Rothenberg, 2005).

In order to quantify the expect ed changes in IQ points in the population of children due to the implementation of alternative NAAQS, we uti lized available dose-resp onse functions in the literature. For our primary estimate, we select ed a dose-response rela tionship from a pooled analysis of seven prospective st udies in North America and Europe examining the effect of lead on full-scale IQ in children (Lanphear et al., 2005). 16,17 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 l ead (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 (def ined as the mean blood lead from 6 to 24 months). The authors found that th e concurrent and lifetime blood lead levels were the strongest predictors of IQ defic its associated with lead exposure.

16 Full-scale IQ is a composite score of verbal and performance tests. Childre n were administered a version of the
Wechsler Intelligence Scales for Children under uniform co nditions within each study (Lanphear et al., 2005).

17 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 Ernha rt et al., 1989); Mexico City, Mexico (Schnaas et al.,
2000); Rochester, New York (Canfield et al., 2003); and Yugoslavia (Wasserman et al., 1997).29 We used an estimate from this study base d on a log-linear relati onship between lifetime blood lead level and IQ score. 18 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 (s ee the section in this chapter entitled “Selection of Health Endpoints fo r 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 μ g/dl for the selected model. However, the lowest measur ed lifetime blood lead level represented in the Lanphear pooled analysis was 1.47 μ g/dl. To estimate IQ effects at blood lead levels below this “cutpoint,” we used a linearized slope, obtained by taking the tangent to th e log-linear function at the point of departure (USEPA, 2007a).

To estimate IQ benefits from blood lead reduc tions, we first calculated the expected IQ point loss per child under each of the two scenar ios (the “base case” and the “control scenarios”) for each monitor (Equation 1). We then subtra cted the “base case” IQ loss from the “control scenario” IQ loss and multiplied by the populatio n of children six years of age and younger living within the radius of influence of each mo nitor to estimate the tota l number of IQ points that would be gained by re ducing the NAAQS (Equation 2).

Equation 1

For blood lead levels ≥ cutpoint:

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

For blood lead levels < cutpoint:

IQ loss = β 2 × PbB

Where:

Cutpoint = 1.47 μ g/dl (i.e., the lowest observe d lifetime blood lead level); β 1 = -3.04 (log-linear regre ssion coefficient from La nphear (2005), Table 4); β 2 = -2.1 (linear slope); and
PbB = blood lead level ( μ g/dl).

Equation 2

Δ IQ = (IQ loss Control – IQ loss Base ) × P

Where:

Δ IQ = total number of IQ points gained unde r the “control scenario ” in comparison with the “base case” in 2016;

18 The natural log of the blood lead levels were used for this analysis.30 IQ loss Control = IQ point loss under the “c ontrol scenario ” per child;

IQ loss Base = IQ point loss under the “base case” per child; and

P = the population of children aged 0 – 6 w ithin the monitor’s radius of influence.

To assess the sensitivity of selecting the log- linear model with low-exposure linearization from Lanphear et al. (2005), we estimated the IQ points gained using another function from this study. This function represents an analysis performed by stratifying the study population based on the child’s peak blood lead level measurem ent (i.e., maximal blood lead level above and
below 7.5 μ g/dl). A linear slope was then was then fit to each group relating the concurrent blood lead level index to IQ score. We used these slopes to calculate the IQ loss for each scenario on a per child basis, using Equation 3 be low. We then calculated the total IQ points gained as in the primary estimate, by using Equation 2 described above.

Equation 3

For blood lead levels ≥ cutpoint:

IQ loss = β 1 × (PbB - cutpoint) + β 2 × cutpoint

For blood lead levels < cutpoint:

IQ loss = β 2 × PbB

Where:

Cutpoint = 5.17 μ g/dl; 19

β 1 = -0.16 (linear coefficients from Lanphear et al. (2005) for blood lead levels greater than or equal to the cutpoint);

β 2 = -2.94 (linear coefficients from Lanphear et al. (2005) for blood lead levels below the cutpoint); and

PbB = blood lead level ( μ g/dl).

We also assessed the sensitivity of the IQ be nefits to the epidemiological study selected, using alternative estimates from a study of 172 child ren in Rochester, New York (Canfield et al, 2003), a linear function developed for the Lead Renovation and Repair Rule (U.S. EPA, 2008c)

19 This cutpoint refers to the lifetime av erage blood lead level. Using the data presented in Table 1 of Lanphear et al.
(2005), we multiplied the peak blood lead level cutpoint value of 7.5 μ g/dl by the ratio of median lifetime average
(12.4 μ g/dl) to the median peak (18.0 μ g/dl) blood lead levels in the study population.31 and the linear function found in table 3 of the Lead NAAQS rule. Using a linear model between lifetime blood lead level and IQ score, Canfield et al. (2003) found a decr ement of 0.46 IQ points per 1 μ g/dl increase in blood lead level. 20 The RRP rule specified a linear function, finding a decrement of 0.88 IQ points per 1 μ g/dl increase in blood lead. 21 Finally, the Lead NAAQS cites four key studies that have “a mean blood Pb le vel closest to today’s mean for U.S. children yields four slopes ranging from -1.56 to -2.94, with a median of -1.75 IQ points per μ g/dL.” 22

We summarize these studies below in table 5-5. We used the following equation (Equation 4) for
these three linear dos e-response functions:

Equation 4

Δ IQ = [ β × (PbB 1 – PbB 2 )] × P

Where:

Δ IQ = total number of IQ points gained unde r the “control scenario ” in comparison with the “base case” in 2016;

β = linear regression coefficien t (-0.46 for Canfield, -0.88 for RRP and -1.75 for the lead NAAQS);

PbB 1 = blood lead level under the “control scenario” ( μ g/dl);

PbB 2 = blood lead level under the “base case” ( μ g/dl); and

P = the population of children aged 0 – 6 w ithin the monitor’s radius of influence.

20 See table 3 of Canfield, R.L, Henderson, C.R., Cory-S lechta, D.A., et al. (2003). Intellectual Impairment in

Children with Blood Lead Concentrations below 10 μ g per Deciliter. The New England Journal of Medicine
348(16): 1517-26.

21 Note that we include this function for comparative purposes. This function was designed to characterize IQ
changes among children exposed to lead dur ing renovation activities, a policy cont ext that differs from the analysis

here.

22 U.S. Environmental Protection Agency (2008b) National Ambient Air Quality Standards for Lead . Office of Air
and Radiation. EPA-HQ-OAR-2006-0735. Pp156.32 Table 5-5. Summary of Quantitative Relationsh ips of IQ and Blood Pb for Analyses with Blood Pb Levels Closest to those of Children in the U.S. Today.

Blood Pb Levels ( μ g/dL)

Geometric

Mean Range (min-max) Study/Analysis Average Linear Slope A
(IQ points per

μ g/dL)
2.9 0.8 – 4.9 Tellez-Rojo et al 2006, <5 subgroup -1.71

3.24 0.9 – 7.4 Lanphear et al 2005 B , <7.5 peak
subgroup -2.94

3.32 0.5 – 8.4 Canfield et al 2003 B , <10 peak
subgroup -1.79

3.8 1 - 9.3 Bellinger and Needleman 2003 B , <10
peak subgroup -1.56

Median value -1.75

A Average linear slope estimates here are for relati onship between IQ and concurrent blood Pb levels (BLL),

except for Bellinger & Needleman for which study reports re lationship fir 10 year old IQ with 24 month blood

Pb levels.

B The Lanphear et al 2005 pooled Internati onal study includes blood Pb dat a from the Rochester and Boston

cohorts, although for different ages (6 and 5 years, re spectively) than the ages analyzed in Canfield et al

2003 and Bellinger and Needleman 2003.

Benefit Valuation

Value of Avoided IQ decrements

The valuation approach we apply for assess ing monetary losses associated with IQ decrements is based on an approach applie d 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 ea rnings 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 birt h for a person born in the U.S., based on earnings and labor force participation rate data fr om the 2006 Current Population Survey (CPS). 23 When calculating the lifetime earnings estimate, we assumed an individual born today would begin working at age 16 and retire at ag e 67. We assumed a real growthrate for wages of one percent per year, as assumed in EPA’s Section 812 retros pective analysis (US EPA, 1997); adjusted for survival probabilities based on current US vital statistics from the CDC’s National Center for Health Statistics; 24 and adjusted for labor force participa tion by age. We then discounted the

23 See http://www.bls.gov/cps/home.htm - data.

24 See http://www.cdc.gov/nchs/da ta/nvsr/nvsr54/nvsr54_14.pdf.33 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, 2008c), we assumed children will be affected by lead at ag e three, the midpoint of the range during which children are thought to be most susceptible to le ad. 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). 25
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 di rect impact of IQ on hourly wages and indirect effects on annual earn ings as the result of additional schooling and increased labor force participati on. A recent review of literature from the labor economics and environmental health fields by CDC economist Sco tt Grosse suggests that both of these studies may have overestimated the association of IQ with earnings (2007). Sp ecifically, he found the Salkever estimate of direct impacts of IQ on wa ges 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 bot h 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 repr esents a gross impact on earnings; it does
not account for the costs of a dditional schooling. EPA’s Clean Air Mercury Rule (CAMR) RIA (USEPA, 2005) reported an estimate of $16,425 per additional year of sch ooling 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 co sts 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 pres ent value at age 3, to be consistent with the present value of lifetime earnings. We then adju st this value to 2006 do llars, 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

25 The 812 Retrospective analysis also included an estimate based on older work by Needleman et al. (1990).34 ($606,930 × 0.0176 = $10,682) minus the change in aver age 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 Sa lkever (1995), we followed the same set of steps as above, substituting the Salkever estima te 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 result s and the associated monetary benefits. We first present the expected IQ point gains in 2016, comparing each of the “control scenarios” to the “base case.” We then provide the expected monetized value of those gains in IQ in 2016. We also describe an analysis we performed to assess the sensitivity of the model to the various inputs used and assumptions made. Finall y, we explain the met hodology we applied for estimating monetized health benefits from co-control of PM 2.5 and the results of that analysis.

Changes in IQ

Table 5-6 below presents the tota l number of IQ points expected to be gained in the US in the year 2016 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 2016 ranges from 230,000 if a 0.5 maximum quarterly mean NAAQS is achieved up to 510,000 fo r a 0.1 maximum quarterly mean NAAQS. These IQ point gains are valued at between $2.0 and $6.4 billion at a 3% discount rate and between $0.3 and
$1.1 billion at a 7% disc ount rate (2006$).

Table 5-6. Number of IQ Points Gained and Monetary Benefits (in Millions of 2006$) in 2016

Estimated Net Present Value of IQ

Points Gained*

Standard

Alternative IQ Points

Gained 3% Discount

Rate 7% Discount

Rate
0.5 Maximum Quarterly

Mean 230,000 $2,000—$2,800 $250—$490

0.4 Maximum Quarterly

Mean 230,000 $2,000—$2,800 $250—$490

0.3 Maximum Quarterly

Mean 270,000 $2,400—$3,400 $300—$580

0.2 Maximum Quarterly

Mean 360,000 $3,200—$4,500 $390—$780

0.15 Maximum Quarterly Mean 400,000 $3,500—$5,000 $440—$87035 0.1 Maximum Quarterly

Mean 510,000 $4,500—$6,400 $560—$1,100

*Lower end of range calculated using Schwartz (1994b) estimate; upper end calculated using
Salkever (1995) estimate.
** Results reflect the use a 2002 derived non-air background blood lead applied to analysis
year of 2016. To the extent t hat state and federal interventi ons such as the Renovation and
Repair Rule (EPA, 2008c) reduce future non-air blood lead levels, the estimate of IQ change

above may be overstated.

We also assessed the geographic distribution of these benefits. We found that the
benefits were concentrated in a small number of counties. Table 5-7 below is an example of the
distribution of total benefits due to IQ points gained for the selected 0.15 μ g/m 3 maximum quarterly mean NAAQS standard. For this stan dard, approximately 60 percent of the total benefits are due to changes in lead air c oncentrations in four counties: Hillsborough, FL; Delaware, IN; and Berks, PA.36 Table 5-7. Percentage of Benefits by County (0.15 μ g/m 3 Second Maximum Monthly Mean NAAQS)

County State Population of

Children in

Affected

Area Affected
Population (%) Percentage

of Benefits (%)

Hillsborough FL 67,359 17% 38%
Delaware IN 7,957 2% 13%
Berks PA 27,966 7% 13%
Collin TX 22,192 6% 12%
Denton TX 8,243 2% 5%
Cuyahoga OH 60,605 16% 4%
Pike AL 2,621 1% 4%
Jefferson MO 6,472 2% 2%
Orange NY 9,186 2% 2%
Dakota MN 23,216 6% 1%
Beaver PA 9,120 2% 1%
Fulton OH 1,644 0% 1%

Rutherford TN 707 0% 1%

Williamson TN 804 0% 1%
Logan OH 2,993 1% 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 be nefits model in order to assess the total range of potential benefits and to determine th e 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 0.15 maximum quarter ly monthly mean NAAQS option using our default model input assumptions. 26 We then changed each default input one at a time and recalculated the total benefits to assess the pe rcent change from the default. Table 5-8 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 th e 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 th e population of exposed children. Our primary estimate relied on an interpolation method, where several monitor concentrations were used in determining the ex posure concentration. When the radius method

26 Note that for the sensitivity analysis, we relied on the re sults that incorporated the valuation estimate for IQ from
Schwartz (1994b).37 was employed as part of the sensitivity analysis , the results varied. We assumed that monitor concentration applied to the populat ion residing within a 10 km radi us as a best estimate of the exposed population, which as we noted above, produces a conservative upper-bound estimate of exposure. When compared with the interpolation method, this in creased results by 40 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 48, 87, and 94 percent, respectively). In addition, if the monitor concentration is assumed to apply to the population of the entire county in which that mon itor resides, the benefits increase significantly (320 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 be nefits fell by 83 percent.

Table 5-8. Sensitivity Analysis for the Prim ary Estimate of Health Benefits (for the 0.15

μ g/m 3 Maximum Quarterly Mean Results)

Model Input Total

Benefits (Millions

2006$) Percent Change

from Default

Interpolation $5,000 N/A County $21,000 320%

10 km Radius $7,000 40%

5 km Radius $2,600 -48%

2 km Radius $670 -87% Exposure Estimation Method

1 km Radius $300 -94%

3 Percent $5,000 N/A Discount Rate 7 Percent $870 -83%
Lanphear et. al (2005) $5,000 N/A Canfield et. al (2003) Linear slope of - 0.46 $2,600 -48%

Lanphear et al. (2005) Dual Linear $12,000 140%

Median of 4 Functions in Rule $10,000 100%
LRRP Function Linear slope of -0.88 $5,000 0% Epidemiological Study for IQ

1:7 $5,000 N/A

1:10 $5,900 18%

1:2 $2,500 -50% Air:Blood ( μ g/m3 in air: μ g/dl in blood) 1.2 $5,000 N/A

1.0 $5,400 8%

1.4 $4,700 -6% Non-Air-Related Background
Geometric Mean Blood Lead Level ( μ g/dl) Salkever (1995) $5,000 N/A
Schwartz (1994) $3,500 -30% Valuation Study38 Finally, the results are also sensitivity to the selection of the concentration-response function. Compared to the default function, the use of the Canfield et al. (2003) estimate generates IQ change estimates that aer 48% lo wer. Conversely, the Lanph ear dual-linear function generates an IQ change estimate that is 140% that of the defau lt, while the function based on the median of the four functions in Table 5-5 genera tes an estimate twice that of the default.

Inputs that had a moderate impact on the be nefits results include the air:blood ratio
selected to convert lead air co ncentrations into blood lead leve ls in the popul ation, the non-airrelated geometric mean blood lead level us ed, as well as the IQ valuation function.

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 5-9 lists the PM-related health eff ects that are included in our monetized benefits estimate incorporating PM co-benefits. 27

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 show n in Table 5-10 below. These emission reduction estimates are
incremental to a baseline that reflects emissi on reductions from MACT controls and the PM 2.5 NAAQS RIA.

27 Because the PM co-benefits are estimated on a $-per-ton basis, we do not report quantitative estimates for

individual PM health effects.39 Table 5-9. Health Effects of PM 2.5 .

Effect Quantified Health Effects Unquantified Health Effects e

Health a,b -Premature mortality based on
both cohort study estimates and on
expert elicitation c,d

-Bronchitis: chronic and acute
-Hospital admissions:
respiratory and cardiovascular
-Emergency room 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 -Subchronic bronchitis cases

-Low birth weight

-Pulmonary function

-Chronic respiratory diseases other than

chronic bronchitis
-Non-asthma respiratory emergency

room visits

a Because the PM co-benefits are estimated on a $-per-ton basis, we do not report quantitative es timates 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 an d altered host defense mechanisms. The pub lic 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-te rm exposures to ambient pollution, but relative risk estimates may also incorporate some effects due to s horter-term exposures (s ee 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 estimat es included in the primary analysis.

e The categorization of unquantified toxic hea lth effects is not exhaustive. Health endpoints in this column include both a) tho se for which there is not consensus on causality; and b) those for wh ich causality has been determin ed but empirical data are not

available to allow calculation of benefits.40 Table 5-10. Summary of Estimated Co-Controlled PM 2.5 Emissions Reductions (in Tons)

Alternate NAAQS (Second

Maximum Monthly Mean) Identified

Controls Unidentified

Controls All Controls

0.5 μ g/m 3 1,532 1 1,534

0.4 μ g/m 3 1,538 5 1,544

0.3 μ g/m 3 2,743 30 2,772

0.2 μ g/m 3 3,037 114 3,150

0.15 μ g/m 3 3,122 175 3,297

0.1 μ g/m 3 3,492 304 3,795

To estimate the value of these PM 2.5 emissions reductions, EPA utilized PM 2.5 benefitper-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 sim ilar technique in previous RIAs, including the recent ozone NAAQS RIA (USEPA, 2008a). The f ourteen 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) c ohort reported in Pope et al. (2002), which has previously been reported as the prim ary 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 epid emiological study that was used as an alternative in the PM NAAQS RIA.

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

Readers interested in revi ewing the complete methodology for creating the benefit perton estimates used in this analysis can consult the Technical Support Document (TSD) accompanying the recent final ozone NAAQS RIA (USEPA 2008a). 28 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

28 The Technical Support Document, entitled: Calculating Benefit Per-Ton Estimates , can be found in EPA Docket
EPA-HQ-OAR-2007-0225-0284.41 therefore apply only to tons re duced from those specific polluta nt/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 industr ial facilities involved in ha ndling lead as a manufacturing product, byproduct, or input. From among the lis t of pollutant/source combinations outlined in the TSD referenced above, the combinati on 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 pe r-ton value for a 3 percent discount rate vary from a low of $68,000 per ton to a high of $570,000 pe r ton (based on a change in emissions of 50 percent or less from a 2015 PM emissions base, in 2006$). Our estimate of PM 2.5 co-control
benefits is therefor e based on the total PM 2.5 emissions controlled multiplied by this per-ton
value. The results of this cal culation are provided in Table 5- 11 below. Figures 5-9 and 5-10 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.42Table 5-11. Monetized Benefits of Co-Controlled PM2.5Emissions (in Millions of 2006$)AlternativePopeet al.(2002)Ladenet al.(2006)Expert AExpertBExpertCExpertDExpertEExpert FExpertGExpertHExpert IExpert JExpert KExpertL3 Percent Discount Rate0.5μg/m3$240 $510 $710 $550 $540 $380 $880 $490 $320 $410 $540 $440 $110 $4000.4μg/m3$240 $510 $710 $550 $540 $390 $880 $500 $330 $410 $540 $440 $110 $4000.3μg/m3$430 $920$1,280$990 $980 $690 $1,600 $890 $590 $740 $970 $790 $190 $7200.2μg/m3$480 $1,000$1,500$1,100 $1,100 $790 $1,800$1,000$670 $840$1,100$900 $220 $8200.15μg/m3$510$1,100$1,500$1,200$1,200$820$1,900$1,100$700$880$1,200$940$230$8600.1μg/m3$580 $1,300$1,800$1,400 $1,300 $950 $2,200$1,200$800 $1,000$1,300$1,100$260 $9907 Percent Discount Rate0.5μg/m3$210 $460 $640 $490 $490 $350 $790 $450 $290 $370 $480 $400 $96 $3600.4μg/m3$220 $460 $640 $500 $490 $350 $800 $450 $300 $370 $490 $400 $97 $3600.3μg/m3$390 $830$1,200$890 $880 $630 $1,400 $810 $530 $670 $870 $710 $170 $6500.2μg/m3$440 $940$1,300$1,000 $1,000 $710 $1,600 $920 $600 $760 $990 $810 $200 $7400.15μg/m3$460$980$1,400$1,100$1,100$750$1,700$960$630$790$1,000$850$210$7800.1μg/m3$530 $1,100$1,600$1,200 $1,200 $860 $2,000$1,100$730 $910$1,200$980 $240 $890Note: All estimates have been rounded to two significant figures. All estimates are incremental to the 2006 PM NAAQSRIA. These estimates do not include confidence intervals because they werederived through a scaling technique43described in the text.44 Figure 5-9. Distribution of Total PM 2.5 Monetized Co-Benefits by Lead Standard Alternative (3% Discount Rate)45 Figure 5-10. Distribution of Total PM 2.5 Monetized Co-Benefits by Lead Standard Alternative (7% Discount Rate)46 Discussion

The results of this benefits analysis demonstrate that lowering the current (1.5 μ g/m 3
maximum quarterly mean) lead NAAQS to the selected standard of 0.15 μ g/m 3 would be expected to have a significan t impact on the IQ of young childre n. Lowering the standard could cause an increase in total IQ points to about 400,000 points in 2016, which would be valued at between $3.5 and $5.0 billion (2006$, 3% discount rate). In a ddition, controls installed to achieve the lead NAAQS standards w ill 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 significan t benefit in terms of improved particulate air quality, reduced health effects, and increased economic welf are of currently exposed individuals.

This benefits analysis is inte nded to be a screening investiga tion to provide an estimate of the potential magnitude of the bene fits of reducing the lead NAAQS. Therefore, the results of this analysis are associated with a number of un certainties. The benefits of IQ point gains in children were very sensitive to the method empl oyed for estimating exposures to the population. When comparing the default method, which involve d concentrations interpolated from multiple monitors, to the method assuming a uniform con centration within a 10 km radius around an individual monitor, the results in crease by 40 percent. Increasing th e radius to include the entire county in which the monitor resides results in roughl y 3-fold increase in be nefits. Decreasing the radius size also has a large impact on benefits, decreasing the value by as much as 94 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 pl ace of the default rate of 3 percent, results decreased by 83 percent. This is in part becau se the benefits of lead on earnings occur over a lifetime, and the net present value of those earn ings is highly sensitiv e 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 leve ls affected the result s. Utilizing alternate epidemiological studies decreased the primary es timate by as much as 72 percent or increased the primary estimate by as much as 140 percent. However, we be lieve the Lanphear et al. (2005) study was the best choice for our primary estim ate. 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 addi tion, 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 be nefits estimates include the following:

• For our primary estimate of the benefits due to gains in children’s IQ, we used a loglinear estimate from a recently published pooled analysis of seven studies (Lanphear et al., 2005). Using alternate estimates from ot her epidemiological studies examining the
link between blood lead level a nd children’s IQ has significant impact on benefits results.
We found the benefits to decrea se by as much as 72 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 f ound to be the best fit for th e data (i.e., the natural logtransformed blood lead level is used to predic t changes in IQ score) . This model implies47 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 sing le 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 bl ood 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 th at the decrements in intellectual (cognitive) functions in children are proportiona tely greater at PbB concentrations <10

μ g/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 estim ates 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 va lues from lead monitoring data from 2003-
2005 to convert the second maximum monthl y mean values in 2016 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 2016 than in 2003-2005 be cause the pattern and distribution of daily values differs, then our results could be either over- or underestimated.

• The interpolation method of estimating exposur e concentrations that we used for our primary estimate is associated with some un certainty. 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 locatio ns, such as Hillsborough County, FL, there are
a sufficient number of lead and TSP monito rs 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.

• We assumed that the IQ point effects of a change in lifetim e blood lead (i.e., the effects of a change in 2016) apply to all children in our study population that were under seven years of age in 2016. If ther e is a critical window of exposure for IQ effects (e.g., between the ages of one and two), then we c ould potentially be overestimating benefits in
2016 because we would have overestimated the population affected by reduced lead exposure in that year. However, if partia l or full achievement of the alternative NAAQS levels might occur earlier than 2016, the childr en in our 0-6 age cohort who are past any
critical window in 2016 would ha ve realized the partial or fu ll benefits of reduced lead exposures in those earl ier 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 an approximation to the impact of changes in
ambient air concentrations of lead on concurre nt blood lead levels, ap plied in the absence of modeling data on lead transport and depos ition and the on direct and indirect human exposures. While the values we apply match fairly well with availa ble literature, there48 are relatively few studies that report such valu es or provide sufficien t 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 whethe r air exposures are more or less efficient at changing concurrent blood lead leve ls 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 unde rstate 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 prenat al mortality. Additiona l 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 appl y 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 ec onomics emphasizes WTP measures as the more complete estimate of economic value.

• The earnings-based estimate of the value-per -IQ-point lost is ba sed on current data on labor-force participation rates, survival proba bilities, and assumpti ons 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.

• Co-control benefits estimated he re 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, e xposure, baseline health incidence rates, or other local factors that might lead to an over-estimate or under-es timate of the actual benefits of controlling directly emitted fine particulates.49 REFERENCES

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Lead . Office of Air and Radiation. EPA-HQ-OAR-2006-0735.

U.S. Environmental Protection Agency (2008c) Economic Analysis for the TSCA Lead Renovation, Repair, and Painting Program Final Rule for Target Housing and Child-Occupied
Facilities

. Economic and Policy Analysis Branch Economics, Exposure and Technology Division Office of Pollution Prevention and Toxics, 2008

Wasserman, G.A., Liu, X., Lolacono, N.J., et al . (1997). Lead Exposure and Intelligence in 7-
Year-Old Children: the Y ugoslavia Prospective Study. Environmental Health Perspectives 105: 956-962.

Wasserman, G.A., Liu, X., Popovac, D., et al. (2 000). The Yugoslavia Prospective Lead Industry Study: Contributions of Prenatal and Postna tal Lead Exposure to Early Intelligence. Neurotoxicology and Teratology 22: 811-818.

Woods and Poole Economics, Inc. (2001). Popul ation by Single Year of Age CD. CD-ROM. Woods & Poole Economics, Inc. Washington, D.C.

World Health Organization. (2000). Air Quality Guidelines for Europe, Second Edition

. Regional Office for Europe, Copenhagen.6-1 CHAPTER 6. COST ANALY SIS APPROACH AND RESULTS

This chapter describes our analysis of the engine ering costs and monitoring costs associated with attaining the final National Ambient Air Quality Standard (NAAQS) for lead and the alternative standards outlined in Chapter 1. 1 We present our analysis of thes e costs in four separate sections. Section 6.1 presents the cost estimates. Sections 6.2 and 6.3 su mmarize the economic and energy impacts of the Final Rule, respectively, while Se ction 6.4 outlines the ma in limitations of the analysis.

Section 6.1 breaks out discussion of cost estimates into five subsections . The first subsection summarizes the data and methods that we employ ed to estimate the costs associated with the control strategies outlined in Chapter 4. As indicated in Chapter 4, these strategies rely exclusively on the application of point source controls and, in the case of Jefferson County, Missouri, the rebuild of a primary lead smelter with modern, low-emitting lead smelting technology. The second subsection presents county level estimates of th e costs of identified controls associated with the regulatory altern atives examined in this RIA. Following this discussion, the third subsection describes the two a pproaches used to estimate the extrapolated costs of unspecified emission reductions that may be needed to comply with the selected standard. The fourth subsection provides a brie f discussion of the monitoring costs associated with the NAAQS. The fifth subsection provides the estimated total co sts of the regulatory
alternatives examined. This section concludes with a discussi on of technological change in regulatory cost estimates.

It should be noted again that ove rall data limitations are very significant for this analysis. One critical area of uncertainty is the limited TSP-Pb monitoring network (dis cussed in chapter 2). Because monitors are present in only 86 counties nationwide, the universe of monitors exceeding the various target NAAQS levels is ve ry small—only 21 counties above 0.1 μg/m 3 , the lowest alternative NAAQS level examined in this RIA. Because we know thatmany of the highestemitting 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 n onattainment areas than have been analyzed in this RIA. We should also emphasize that thes e cost estimates repres ent hypothetical emission control strategies that, in some cases, reflect a ssumptions about technologi cal advances that will improve the effectiveness of specific control technologies.

It is important also to note that this chapte r presents cost estimates associated with both identified point source measures and unspecified em ission reductions needed to reach attainment. For the selected standard of 0.15 μg/m 3

, over 94% of the estimated emission reductions needed for attainment are achieved through application of identified controls, and less than 6% through unspecified emission reductions. Identified po int source controls incl ude known measures for known sources that may be implemented to attain the selected standard, whereas the achievement of unspecified emission reductions requires impl ementation of hypothetical additional measures
in areas that would not attain the selected st andard following the implementation of identified
controls to known sources.

1 The costs presented in this chapter represent the dir ect pollution control expenditures associated with NAAQS compliance. As such, they do not reflect the general equilibrium impacts of the final rule.6-2 Note that the universe of sour ces achieving unspecified emi ssion reductions beyond identified controls is not completely understood; therefor e we are not able to identify known control devices or work practices to achieve these re ductions. We calculated extrapolated costs for unspecified emission reductions us ing two different methods. Both acknowledge the lack of data for this analysis. One estimates the extrapolat ed cost based upon recognition that the marginal cost of reducing progressively smaller amounts of emissions will increase over time. This approach estimates a total cost curve given the identified control data and estimates the additional incremental extrapolated cost. The other approach estimates extrapolated costs by costing the remaining air quality increment to attainment in each monitor area using an areaspecific cost per microgram of air quality improvement. Section 6.1 below describes in more detail our approaches for estimating both the cost s of identified controls and the extrapolated costs of unspecified emission reducti ons needed beyond identified controls.

As is discussed throughout this RIA, the technol ogies and control strategies selected for this analysis are illustrative of one approach that nonattainment areas may employ to comply with the revised lead standard. Potential control pr ograms may be designed and implemented in a number of ways, and EPA anticipates that St ate and Local governments will consider those programs that are best suited for local conditions. As such, the costs described in this chapter generally cover the annu alized costs of purchasing, instal ling, and operating the referenced technologies. We also present monitoring costs. Because we are uncertain of the specific actions that State Agencies will take to design State Im plementation Plans to meet the revised standard, we do not estimate the costs that government agencies may incur to implement these control strategies.

The methods used in the analysis for the final N AAQS differ from those used in the analysis for the proposed NAAQS in the following ways:

1. As discussed in Chapter 4, we updated the cont rol efficiency and cost information for many
of the identified emission controls used in the Proposed Rule RIA. We determined that the values originally provided by AirControlNET we re unlikely to reflect the performance and cost of these controls in 2016, the analysis year for this RIA.

2. We changed the way we estimated extrapolated costs of unspecified emission reductions. In the Proposed Rule analysis, we applied a fi xed cost/ton of lead emissions reduced beyond identified controls, based on the 98 th percentile of the cost/ton for identified controls at large point sources. We recognize that a single fi xed cost of control does not account for the different sets of conditions that might descri be situations where controls are needed beyond identified controls. In this analysis, we instead estimate extrapolated costs by two approaches, the cost curve approach a nd the ambient extrapolation approach.

3. Rather than applying particulate matter (PM) emission controls to the primary lead smelter in Jefferson County, Missouri, we simulated a complete rebuild of the smelte r to utilize a lesspolluting smelting process. We estimated the cost of this rebuild based on the experience of a lead smelter in Trail, British Columbia that underwent a similar process in the mid 1990s.6-3 We describe these changes to the analysis rela tive to the RIA for the proposed NAAQS in greater detail throughout this chapter.

6.1 Engineering Cost Estimates

6.1.1 Data and Methods: Identified Control Costs

Consistent with the emissions analysis presen ted in Chapter 3, our analysis of the costs associated with the final lead NAAQS focuses on poi nt source particulate matter (PM) controls. For the purposes of this analysis, these c ontrols largely include measures from the AirControlNET control technology database. The an alysis also includes additional measures associated with operating permits, upgrades to ex isting PM control devices, and/or New Source Performance Review standards applicable to sources similar to those included in our analysis.

6.1.1.1 AirControlNET Control Costs

AirControlNET, a PC based database tool that EPA has used extensively for previous analyses of air pollution control cost s, served as the primary source of cost information for our analysis of the final lead NAAQS. For the analysis of the final lead NAAQS, AirC ontrolNET used one of two methods to estimate the costs of a given control:

1. Dollar per ton of PM 10 controlled. For minor PM controls (i.e., increased monitoring frequency for PM controls and improvements to continuous emissions monitoring systems), AirControlNET estimates costs by applying a fixed cost per ton value to the tonnage of PM
controlled. 2

2. Hybrid - cost per ton/detailed cost function. For several PM emissions controls,
AirControlNET includes a detailed co st equation that estimates costs as a function of several key engineering parameters, such as the s ource’s capacity or stack flow rate. AirControlNET uses these equations only if a so urce's stack flow rate is at least 5 cubic
feet/minute. If the stack flow rate is belo w this threshold, AirControlNET applies a fixed cost per ton value to the tonnage of PM 10 controlled. 3 AirControlNET employs this hybrid approach for most of the major PM controls included in our analysis. 4

To estimate costs based on the cost per ton valu es described under items 1 and 2 above, it was necessary to first estimate the reduction in PM 2.5 or PM 10 emissions for each relevant source. Where possible, we developed these estimates based on the baseline PM emissions of each source, as adapted from the 2002 NEI, and the estimated control efficiency of each measure. 5
The 2002 NEI, however, does not include baseline PM emissions data for many of the sources

2 In some cases, this cost/ton value is specific to PM 2.5 , while in other cases it is specific to all PM.

3 This stands in contrast to the cost/ton values for the co ntrols summarized under method 1, which are estimated as a
cost/ton of PM 2.5 controlled or cost/ton of total PM controlled.

4 By major controls, we mean all controls except fo r increased monitoring frequency and improvements to continuous monitoring systems.

5 Chapter 4 describes the adjustments that we made to the 2002 NEI to account for controls expected to be implemented prior to the 2016 target year for this analysis.6-4 included in our analysis. For each of these so urces, we employed one of two approaches to
estimate baseline PM emissions:

1. SPECIATE approach. EPA's SPECIATE database include s sample PM speciation profiles for a variety of sources and maps these prof iles to individual sour ce classification codes (SCCs). For lead sources with SCCs represen ted by these profiles, we estimated baseline

PM 2.5 and PM 10 emissions based on the baseline lead emissions of these sources and the corresponding PM speciation profile in SPECIATE. For example, if a source has baseline lead emissions of 0.02 tons/year and the SPECIA TE database suggests that lead, on average, represents two percent of the PM 10 emissions for other sources th at share its SCC, we assume
that the source emits one ton of PM 10 /year in the baseline. We us ed this approach to estimate
the baseline PM 2.5 and PM 10 emissions of all sources for which the 2002 NEI includes no PM emissions data but that are represente d by the speciation profiles in SPECIATE. 6

2. Average speciation approach . Although SPECIATE includes sp eciation profiles for a wide range of sources, the database does not include profiles applicable to all of the sources included in our analysis. Th erefore, we were unable to use SPECIATE to estimate the
baseline PM 2.5 and PM 10 emissions for several of the lead sources for which the 2002 NEI includes no PM data. For these sources, we estimated baseline PM 2.5 and PM 10 emissions based on the PM speciation profile of lead sour ces included in our an alysis for which the 2002 NEI includes PM emissions data. Our analys is of these sources suggests that lead represents approximately 1.4 percent of their PM 10 emissions and 1.9 percent of their PM 2.5 emissions. We assume that these values a pply to all lead sources for which the 2002 NEI
includes no PM emissions estimates and for which SPECIATE includes no relevant PM speciation profiles.

Within AirControlNET’s standard format, the tool provides estimates of control costs for direct PM controls only for those sources emitting 10 or more tons of PM 10 per year. Because many of the point sources included in our analysis fall below this emissi ons threshold, we have changed this standard format so that AirControlNET can apply direct PM controls to sources emitting any
amount of PM 10

. 7 The cost equations and cost per ton values for major controls, however, do not necessarily apply with great accuracy to such controls for sources below this PM emissions
threshold.

To better estimate the costs of direct PM c ontrols for such small point sources, we used AirControlNET’s technology-specific cost estimates for these sour ces in order to estimate cost per ton values for small sources. Such costs ma y be quite high on a per ton basis; for example, applying a pulse-jet fabric filter on a PM 10 source with less than 10 tons per year yields a cost per ton of $558,000. We expect that application of these point source controls to such small PM

6 U.S. EPA, SPECIATE Version 4.0, updated January 18, 2007, http://www.epa.gov/ttn/chief/sof tware/speciate/index.html>.

7 In this analysis, we apply controls to small point sources with emissions below the 10 tons per year PM 10 AirControlNET application threshold. We apply controls to small point sources due to the extent of lead
nonattainment associated with the alternative standards exam ined in this RIA and the lack of identified point source
and other controls available to examine attainment with these standards as noted in Chapter 4.6-5 sources will be highly limited in actual practice for lead SIPs. More information on these point
source controls can be found in the AirC ontrolNET control measures documentation. 8

As noted above, we adjusted AirControlNET’s c ontrol efficiency values for a number of the emissions controls used in the Proposed Rule analysis to reflect improvements in control efficiency likely to be realized for these technologies by the 2016 target year of this analysis. 9 A number of recent EPA references provided findings that showed that increases in PM control efficiencies from those applied in our proposal RIA were reasonable for a future year analysis. These references include EPA fact sheets publis hed in 2003 that summarize the capabilities of
PM control measures and associated data. This information provided more recent data on
control efficiencies and costs for PM control meas ures than is currently in AirControlNET. All of these fact sheets can be found at http://www.ep a.gov/ttn/catc/products.html#aptecfacts. We revised the control efficiencies, and also the cap ital and annualized costs for these PM control
devices as listed above to reflect the increased co ntrol efficiencies associ ated with these control

measures. 10

To account for the costs of these improvements, we also modified AirControlNET’s cost estimates for these technologies as follows:

• Fabric filters: The estimated cost increase for fabric fi lters reflects an upgrad e of fabric filter bags and an increase in the number of bags used by each fabric filter.

o Pulse-Jet: Capital costs increased by 75 percen t. O&M costs increa sed by 20 percent.

o Mechanical Shaker: Capital costs increased by 30 percent. O&M increased by 7 percent.

o Reverse-Air or Reverse-Jet: Capital costs in creased by 40 percent. O&M increased by 5
percent.

• Paper/Nonwoven Filters - Cartridge Collector Type: The estimated cost increase for these
units reflects the incremental cost of a more advanced filter material.

8 Available at http://www.epa.gov/ttnecas1/mode ls/DocumentationReport.pdf

. 9 PM control efficiencies were increas ed for the following control measures: dry and wet ESPs, all types of fabric filters, venturi scrubbers, impingement-p late/tray-tower scrubbers, and paper/n onwoven filters - cartridge collectors.

10 These fact sheets are the following: U.S. EPA-CICA Air Pollution Technology Fact Sheet: Paper/Nonwoven Filters - Cartridge Collector Type with Pulse-Je t Cleaning. EPA-452/F-03-004. 2003. U.S. EPA-CICA Air Pollution Technology Fact Sheet: Im pingement-Plate/Tray-Tower Scrubber. EPA-452/F-03-

012. 2003. U.S. EPA-CICA Air Pollution Technology Fact Sheet: Venturi Scrubber. EPA-452/F-03-017. 2003. U.S. EPA-CICA Air Pollution Technology Fact Sheet: Fabric Filter - Mechanical Shaker Cleaned Type. EPA452/F-03-024. 2003. U.S. EPA-CICA Air Pollution Technology Fact Sheet: Fabric Filter - Pulse-Jet Cleaned Type. EPA-452/F-03-025.

2003. U.S. EPA-CICA Air Pollution Technology Fact Sheet: Fa bric Filter - Reverse-Air Cl eaned Type. EPA-452/F-03-

026. 2003. U.S. EPA-CICA Air Pollution Technology Fact Sheet: Dry Electrostatic Precipitator (ESP) - Wire-Plate Type.

EPA-452/F-03-028. 2003. U.S. EPA-CICA Air Pollution Technology Fact Sheet: Wet Electrostatic Precipitator (ESP) - Wire-Plate Type.

EPA-452/F-03-030. 2003.6-6 o Capital cost increase: 75 percent.

o O&M increase: 10 percent.

• Dry or Wet ESP: For ESPs, the estimated increase in costs reflects additional charging inside
the ESP.

o Capital cost increase: 25 percent.

o O&M increase: 10 percent.

• Venturi Scrubber: The cost increase for venturi scrubbe rs reflects an upgrade in scrubber material and higher operating pressure.

o Capital cost increase: 35 percent.

o O&M increase: 40 percent.

6.1.1.2 Control Measure Upgrades and Controls Identified from New Source Performance Standards and Recent Operating Permits

In addition to controls included in AirControlNET, our analysis of the costs associated with the final lead NAAQS reflects the potential implemen tation of controls identified from other
sources. These include measures enumerated in recent operating permits for sources similar to
those included in this RIA as well as measures that new and modified/r econstructed sources of PM emissions are expected to implement for compliance with New Source Performance Standards. The specific measures identifie d for these sources include the following:

• Capture hoods vented to a baghouse at iron and steel mills:

o We estimate the annualized costs for th is control measure at $5,000/ton of PM 2.5 emission
reduction. 11

• Diesel particulate filter (DPF ) (for stationary sources such as diesel generators);

o We have taken the control efficiency and cost data from technical support documents prepared for the U.S. EPA as part of an alyses undertaken for the final compressionignition NSPS. 12 The annualized cost for PM 2.5 reductions from applying DPF is $9,000/ton.

• Upgrade of CEMs and increased monitoring freq uency of PM controls (for sources where not already identified as a control by ACN).

o The annualized costs for this control range from $600 to $5,200/ton of PM 2.5. This control is applicable to ESPs and ba ghouses at both EGU and non-EGU sources. 13 This

11 U.S. Environmental Protection Agency. Regulatory Impact Analysis for the Particulate Matter NAAQS. October, 2006. Chapter 3, p. 3-13. This document is available at http://www.epa.gov/ttn/ ecas/regdata/RIAs/Chapter%203--Controls.pdf

. 12 U.S. Environmental Protection Agency. “Emission Reduction Associated with NSPS for Stationary CI ICE.” Prepared by Alpha-Gamma, Inc. June 3, 2005, and U.S. Environmental Protection Agency. “Cost per Ton for NSPS for Stationary CI ICE.” Prepared by Alpha-Gamma, Inc. June 9, 2005.

13 U.S. Environmental Protection Agency. Regulatory Impact Analysis for the Particulate Matter NAAQS. October, 2006. Appendix E, pp. E-16 to E-24. This document is available at http://www.epa.gov/ttn /ecas/regdata/RIAs/Appendix %20E--Controls%20List.pdf

.6-7 control was applied to existing sources identified as having ESPs and baghouses according to their operating permits.

6.1.1.3 Costs of Rebuilding the Primary Lead Smelter in Jefferson County, Missouri

In the proposed rule RIA, a significant portion of the estimated costs of the rule— ranging from 55 percent for the 0.05 μ g/m 3 standard to 95 percent for the 0.3 μ g/m 3 standard—reflected the implementation of emission reductio ns beyond identified controls at the primary lead smelter in Jefferson County, Missouri. To reduce the extent to which the estimated costs of the lead NAAQS depend on emission reductions beyond iden tified controls at a single facility, EPA estimated the cost of replacing the Jefferson Coun ty smelter’s current lead smelting unit with a more modern, lower-emitting Kivcet furnace, simila r to the unit that Teck Cominco built in the mid-1990s at the primary lead smelter it operates in Trail, British Columbia 15 . In addition to the facility in Trail, BC, the Kivcet process is cu rrently employed at plants in Kazakhstan, Bolivia, and Italy. 14 While more targeted measures may allo w the smelter in Jefferson County to reduce its lead emissions more cost effectively than the replacement of its smelting unit, information on
such measures is not available. In the absence of this information, we use the estimated costs of building and opening a Kivcet furn ace at the Jefferson County facili ty as a reasonable indicator of the facility’s potential identified control lead abatement costs. Our approach for estimating
these costs is as follows:

1. Obtain cost information on th e Teck Cominco Kivcet furnace: According to Cominco’s 1996 annual report, the total cost of building the Kivcet furnace was approximately $152 million (year 1996 Canadian dollars). 15 In addition, based on communications with the facility’s management, we estimate that the facility in curred additional cost s of approximately $30.4 million (year 1996 Canadian dollars) to address unanticipated operational problems with the
Kivcet furnace after it was in itially brought online in 1997, bri nging the total cost of the project to approximately $182.4 milli on (year 1996 Canadian dollars). 16 Converting to U.S. dollars and adjusting for inflation, this tran slates to approximately $184.5 million in year
2006 U.S. dollars. 17

2. Scale Teck Cominco’s costs to reflect the c apacity of the primary smelter in Jefferson County: The capacity of the primary lead smelte r in Jefferson County is significantly higher
than that of Teck Cominco’s Trail facil ity—250,000 tons per year for the Jefferson County smelter versus 132,000 tons per year for the Tra il facility. Scaling th e cost of the Teck Cominco Kivcet furnace to account for this difference in capacity, we estimate that the total upfront cost of building a Kivcet furnace at the Jefferson County smelter would be approximately $349 million.

14 The Eastern Mining and Metallurgical Research In stitute for Non-Ferrous Metals, Pyrometallurgy. http://vcm.ukg.kz/eng/v3_6.htm . Accessed September 23, 2008.

15 Cominco Ltd. 1996 Annual Report, p. 12. Cominco and Teck Corporation merged in 2001 to form Teck
Cominco Limited.

16 According to the general manager of Teck Cominco’s Trail operations, these additional costs were equal to approximately 20 percent of the cons truction costs for the Kivcet furnace. Personal communication with Mike Martin, general manager of Teck Cominco’s Trail operations, August 25, 2008.

17 To adjust for inflation, we use the Engineering News Record construction cost index.6-8 3. Annualize Kivcet furnace construction costs: To annualize the estimated $349 million in costs for the construction of a Kivcet furnace at the primary lead smelter in Jefferson County,
we assume that the furnace would have a 30- year lifespan. Based on this assumption, we
estimate annualized costs of $31.7 million, using a 3 percen t discount rate, and $42.0 million, applying a 7 percent discount rate.

Based on Teck Cominco’s experience, transitioni ng to a Kivcet furnace could potentially reduce O&M costs for the primary lead smelter in Je fferson County. After the Kivcet furnace went online at Teck Cominco’s Trail facility in 1997, its O&M costs declined significantly, largely
due to reduced labor costs. 18 We do not incorporate potenti al O&M savings into our cost assessment for the Jefferson County smelter; therefore, we may overestimate the net costs associated with installing a Kivcet furnace at the facility.

6.1.2 Engineering Cost Estimates for Identified Controls

Based on the data and methods outlined above, we estimated the costs associated with implementing the control strategi es presented in Chapter 4. Ta ble 6-1 summarizes these costs by
monitor area. As indicated in th e table, the estimated costs of these controls under the selected standard are approximately $150 million per year, assuming a discount rate of seven percent. Applying a three percent discount rate this va lue becomes $130 million per year. The annual
costs of identified controls unde r the alternative standards incl uded in Table 6-1 range from $57
million under the 0.5 μ g/m 3 standard to $180 million under the 0.1 μ g/m 3 standard, assuming a discount rate of seven percent. If we apply a three percent discount rate, these values adjust to $46 million and $160 million for the 0.5 and 0.1 μ g/m 3 standards, respectively. Consistent with Chapter 4's summary of the air quality impacts a ssociated with identified controls, the cost estimates in Table 6-1 reflect partial attainment with several of the standards examined in this
RIA. For the selected standard of 0.15 μ g/m 3 , the costs in Table 6-1 reflect attainment in 13 of the 21 monitor areas included in this analysis.

The results in Table 6-1 illustrate that the costs of the selected standard are likely to vary by
monitor area. The costs of iden tified measures are expected to be highest in Jefferson County, Missouri. As indicated in Chapter 4, Jefferson County has the highest base line lead emissions of any county in the U.S. This largely reflects emi ssions from the primary lead smelter located in the county. Table 6-2 presents th e costs of identified controls /pound of lead emissions avoided in each monitor area. The estimates presented in this table suggest that, in general, the cost/pound of avoided lead emissions increases si gnificantly for a given m onitor as the standard becomes more stringent. For example, the cost/pound for Delaware County, Indiana steadily increases from $700/pound under the 0.5 μ g/m 3 standard to $3,100/pound under the 0.1 μ g/m 3
standard, assuming a 7 percent disco unt rate. This is consistent with local areas first targeting sources where relatively low cost reductions may be achieved and subsequently achieving reductions from sources that are more costly to control (i.e., moving up the area's marginal abatement cost curve).

18 Personal communication with Mike Martin, general ma nager of Teck Cominco’s Trail operations, August 25,

2008.6-9Table 6-1ANNUAL COSTS RELATED TO IDENTIFIED CONTROLS*Annual Cost of Identified Controls in 2016 (Millions of 2006$)StandardAlternative: 0.5μg/m32ndMaximumMonthly Mean**StandardAlternative: 0.4μg/m32ndMaximumMonthly Mean**StandardAlternative: 0.3μg/m32ndMaximumMonthly MeanStandardAlternative: 0.2μg/m32ndMaximumMonthly MeanSelected Standard:0.15μg/m32ndMaximum MonthlyMeanStandardAlternative: 0.1μg/m32ndMaximumMonthly MeanMonitorStateMonitorCounty3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%DiscountrateAL Pike$3.1 $3.3 $3.1 $3.3$3.1 $3.3 $4.0 $4.2$4.3$4.6 $4.3 $4.6CO El Paso- - - - - - - --- $0.2 $0.2FL Hillsborough<$0.1 <$0.1 <$0.1 <$0.1 $0.4 $0.4 $0.8 $0.9$0.8$0.9 $1.4 $1.5IL Madison- - - - - - - --- $13 $14IN Delaware$1.8 $1.9 $1.8 $1.9$3.6 $3.8 $5.4 $5.7$6.1$6.5 $8.5 $9.0MN Dakota- - - - - - - -<$0.1<$0.1 $2.4 $2.5MO Iron$6.3 $6.7 $6.3 $6.7$6.3 $6.7 $6.3 $6.7$6.3$6.7 $6.3 $6.7MO Jefferson$32 $42 $32 $42 $32 $42 $32 $42$32$42 $32 $42NY Orange- - - - - - <$0.1 <$0.1<$0.1<$0.1 <$0.1 <$0.1OH Cuyahoga- - - - $30 $32 $30 $32$30$32 $30 $32OH Fulton$1.5 $1.5 $1.5 $1.5$1.5 $1.5 $1.5 $1.5$1.5$1.5 $1.5 $1.5OH Logan- - - - - - - --- - -OK Ottawa- - - - - - - --- - -PA Beaver- - - - - - $11 $12$35$37 $35 $37PA Berks<$0.1 <$0.1 <$0.1 <$0.1 $11 $12 $11 $12$11$12 $11 $12PA Carbon- - - - - - <$0.1 <$0.1<$0.1<$0.1 <$0.1 <$0.1TN Sullivan- - - - - - $0.4 $0.4$0.4$0.4 $0.4 $0.4TN Williamson$1.0 $1.1 $1.0 $1.1$1.3 $1.4 $1.3 $1.4$2.8$3.0 $3.4 $3.6TX Collin<$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 $2.0 $2.1$2.3$2.5 $3.3 $3.5TX Dallas- - - - - - - --- $7.0 $8.4UT Salt Lake- - - - - - - --- <$0.1 <$0.1Total$46 $57 $46 $57 $89 $100 $110 $120$130$150 $160 $180*All estimates rounded to two significant figures. As such, totals will not sum down columns.** Identified Control Costs are the same for alternative standard 0.5μg/m3and for alternative standard 0.4μg/m3. This is because the same monitor areas violate both standardsand the same controls were chosen in the least cost optimization for both standard alternatives.6-10Table 6-2.ANNUAL COST/POUND OF REDUCED LEAD EMISSIONS: IDENTIFIED CONTROLS*Annual Cost/Pound for Identified Controls in 2016 (2006$)StandardAlternative: 0.5μg/m32ndMaximumMonthly Mean**StandardAlternative: 0.4μg/m32ndMaximumMonthly Mean**StandardAlternative: 0.3μg/m32ndMaximumMonthly MeanStandardAlternative: 0.2μg/m32ndMaximumMonthly MeanSelected Standard:0.15μg/m32ndMaximum MonthlyMeanStandard Alternative:0.1μg/m32ndMaximum MonthlyMeanMonitorStateMonitorCounty3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%DiscountrateAL Pike$400 $400 $400 $400 $400 $400 $500 $500$500$500 $500 $500CO El Paso- - - - - - - --- $170,000 $170,000FL Hillsborough<$100 <$100 <$100 <$100 $200 $200 $300 $400$300$400 $600 $600IL Madison- - - - - - - --- $75,000 $79,000IN Delaware$700 $700 $700 $700 $1,300 $1,400 $1,900 $2,000$2,100$2,200 $2,900 $3,100MN Dakota- - - - - - - -<$100<$100 $400 $400MO Iron$300 $300 $300 $300 $300 $300 $300 $300$300$300 $300 $300MO Jefferson$300 $500 $300 $500 $300 $500 $400 $500$400$500 $400 $500NY Orange- - - - - - <$100 <$100<$100<$100 <$100 <$100OH Cuyahoga- - - - $120,000 $130,000 $120,000 $130,000$120,000$130,000 $120,000 $130,000OH Fulton$5,100 $5,100 $5,100 $5,100$5,100 $5,100 $5,100 $5,100$5,100$5,100 $5,100 $5,100OH Logan- - - - - - - --- - -OK Ottawa- - - - - - - --- - -PA Beaver- - - - - - $8,300 $9,000$24,000$25,000 $24,000 $25,000PA Berks<$100 <$100 <$100 <$100 $3,500 $3,700 $3,500 $3,700$3,500$3,700 $3,500 $3,700PA Carbon- - - - - - $1,400 $1,400$1,400$1,400 $1,400 $1,400TN Sullivan- - - - - - $120,000 $130,000$120,000$130,000 $120,000 $130,000TN Williamson$400 $400 $400 $400 $300 $400 $300 $400$600$700 $700 $800TX Collin<$100 <$100 <$100 <$100 <$100 <$100 $400 $400$400$500 $600 $600TX Dallas- - - - - - - --- $90,000$1,100,000UT Salt Lake- - - - - - - --- <$100 <$100Total $300 $400 $300 $400 $600 $700 $700 $800$900$1,000 $1,000 $1,200*All estimates rounded to two significant figures. As such, totals will not sum down columns** Identified Control Costs are the same for alternative standard 0.5μg/m3and for alternative standard 0.4μg/m3. This is because the same monitor areas violate both standardsand the same controls were chosen in the least cost optimization for both standard alternatives.6-11 6.1.3 Extrapolated Costs

Prior to presenting the methodology for estimating costs for unspecified emission reductions, it is important to provide information from EPA’s Science Advisory Board Council Advisory on the issue of estimating costs of unidentified control measures. 19

812 Council Advisory, Direct Cost Report, Unidentified Measures (charge question 2.a):

“The Project Team has been un able to identify measures that yield sufficient emission reductions to comply with the National Ambient Air Quality Standards (NAAQS ) and relies on unidentified pollution
control measures to make up the difference. Emission reductions attributed to unidentified measures appear to account for a large share of emission reductions required for a few large metropolitan areas but a relatively small share of emission reductions in other
locations and nationwide.

“The Council agrees with the Project Team that there is little credibility and hence limited value to assignin g costs to these unidentified measures. It suggests taking great care in reporting
cost estimates in cases wh ere unidentified measures account for a significant share of emi ssion reductions. At a minimum, the components of the total co st associated with identified and unidentified measures shoul d be clearly distinguished. In some cases, it may be preferable to not quantify the costs of unidentified measures and to simply report the quantity and share of emissions reduction s attributed to these measures.

“When assigning costs to unidentified measures, the Council suggests that a simple, tr ansparent method that is sensitive to the degree of uncertainty abou t these costs is best. Of the three approaches outlined, assumi ng a fixed cost/ton appears to be the simplest and most stra ightforward. Uncertainty might be represented using alternat ive fixed costs per ton of emissions
avoided.”

EPA has considered this advice and the requi rements of E.O. 12866 and OMB circular A-4, which provides guidance on the estimation of benefits and costs of regulations.

EPA estimated the costs of unspecified future controls using two approaches. The first is calculated by estimating a cost curve for the identif ied control costs and extr apolating the cost of the remaining unspecified emission reductions . The second approach is the ambient extrapolation approach, this approach uses an average cost per microgram for each area. We

19 U.S. Environmental Protection Agency, Advisory C ouncil on Clean Air Complia nce Analysis (COUNCIL), Council Advisory on OAR’s Direct Cost Report and Uncertainty Analysis Plan , Washington, DC. June 8, 2007.6-12 believe this approach best repr esents a fixed cost approach gi ven the data limitations of this analysis. Please note, the fixed cost met hodology was preferred by EPA’s Science Advisory Board over two other options, including a marginal cost-based approach. Extrapolated costs were calculated for the recent Ozone NAAQS RIA, and they are calculated again here for the final Lead NAAQS. EPA has developed a few appr oaches to try to esti mate the extrapolated costs of future unspecified emissions reductions, which take into account the analysis context and data availability. Now that EPA has gained some additional experience in estimating the extrapolated costs for these two RIAs, we inte nd to seek additional SA B advice on extrapolated cost approaches after further investig ation of these and other approaches.

As indicated above the identified control costs reflected in Tables 6-1 and 6-2 do not result in
attainment of the selected or alternative standard s in several areas. In these areas, unspecified emission reductions needed beyond identified co ntrols will likely be necessary to reach attainment. As noted in chapte r 4, the overwhelming majority of emissions reductions needed to attain the various target NAAQS levels would be obtained from identifie d controls (see table 4- 6). Also, the unspecified emission reductions needed beyond identified controls are small enough to fall within the expected error associat ed with the precision of the modeling of the emissions. Figure 6-1 below illustrates this point: the percentage of remaining emissions after applying identified controls ranges from a low of 0.03% to a high of only 9.23%.

Figure 6-1.
PERCENTAGE OF REMAINING EMISSION REDUCTIONS NEEDED POST APPLICATION OF
IDENTIFIED CONTROLS

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0.5 μg/m3

0.4 μg/m3

0.3 μg/m3

0.2 μg/m3

0.15 μg/m3

0.1 μg/m3SecondMaximumMonthlyMean

Emission Reductions from Identified Controls

Emission Reductions Needed Beyond Identified Controls

The estimation of engineering costs for unsp ecified emission reductions needed to reach attainment many years in the future is inherently a difficult issue. As described later in this chapter, our experience with Cl ean Air Act implementation show s that technological advances6-13 and development of innovative strategies can make possible emissions reductions that are unforeseen today, and to redu ce costs of emerging technologies over time. But we cannot quantitatively predict the amount of technology advance in the future. For areas needing significant additional emission reductions, much of the control must be for sources that historically have not been contro lled. The relationship of the cost of such control to the cost of control options available today is not at all clear. Available, current known control measures increase in cost beyond the range of what has ever been implemen ted and would still not provide
the needed additional control for full attainment in the analysis year 2016.

The emission inventories, air quali ty modeling, and control options that would address these very small remaining increments of lead in ambient air are highly uncertain. In the RIA for the proposed NAAQS, a fixed cost of $32 million/ton, or $16,000/lb. in 2006 dollars was applied to the unspecified emission reductions needed beyo nd identified controls. This fixed cost was
consistent with the 98 th percentile of total possible emission re ductions for identified controls.

We recognize that a single fixe d cost of control does not acco unt for the different sets of conditions that might describe situ ations where controls are needed beyond identified controls. Recall that we did not cap identified control cost s in the final RIA analysis because we lack good information regarding lead emission controls on small emission sources. This same lack of information also makes it difficult to extrapolated costs using either of th e techniques described
below. Therefore in this RIA for the final Pb NAAQS, we take a broader approach to estimating
the costs associated with the rema ining increment to attainment.

Finally, it is also important to emphasize here that the universe of sources where unspecified emission reductions beyond identified controls are achieved is not completely understood; therefore, we are not able to identify known control devices or wo rk practices to achieve these
reductions.

We expect that additional control measures that we were not able to identify may be available today, or may be developed by 2016. We believe such an expectation is reasonable for the following reasons:

1. Because the revised Pb NAAQS is ten times lower than the previous standard, the Pb emission inventory has not been used in the past for estimating controls for such small sources of lead emissions. Thus the quality cont rol of data in the emission inventory may not have been appropriate for this use of the emi ssion inventory. For example, some sources for which we know of no control may be incorrectly characterized; lead ing us to believe, incorrectly, that no appropriate control is available.

2. Our knowledge of add-on controls is greater th an our knowledge of process modifications. For example in the months since proposal of the revised NAAQS, we conducted additional research and discovered the process modificatio n used in the analysis for Herculaneum. There may be other similar modifications available today that we do not know about.

3. Controls for emission sources other than point sources may be available but are not captured under the data constraints built into the air qu ality monitor to emissions source relationship used to estimate air quality improvement.6-14 On the other hand, there are several reasons why identified controls may not be sufficient to reach attainment in a given monitor area:

1. There may be no other available controls that are similar to the identified controls. For example, the area might be characterized by emi ssions from several very large sources. This is true in only a few areas (e.g., Jefferson Count y, MO). In these areas, there may be large
reductions achieved with identified or even pre-existing controls, but there are no further known controls available to reduce the remaining emissions after those identified controls are applied.

2. It is possible that fugitive dust emissions from area sources containing deposited Pb will also
contribute to violations of th e NAAQS, or that historically deposited Pb, when disturbed, may be re-entrained into the ambient air. These fugitive or area source controls may be more expensive than the id entified controls.

3. More expensive process modifications may be the only way to reduce the remaining
emissions.

Finally, there is uncertainty i nherent in estimating the progre ss in development of control measures that will occur between today and 2016. For example:

1. Technological change unrelated to this regulati on may lead to different baseline emissions or changes in industrial processes (e.g. will batte ry technology changes lead to fewer lead battery recycling facilities?).

2. Changes in pollution control t echnology may make more contro l options available or reduce
the costs of current ones.

3. Some emissions sources may close rather than im pose the identified controls (in this case the
cost of identified controls has been over estimated and the emission reduction has been underestimated).

There are many limitations to each approach ta ken to estimate the extrapolated costs of unspecified emissions, for example:

• The ambient extrapolation methodology empha sizes control costs that are the most expensive within an area, and assumes that knowledge of control costs from monitor
areas that attain have no influence on the average control costs for areas that need unspecified emission reductions . It also assumes there wi ll be no increased knowledge of sources or changed in technology between now and 2016. Lastly, most of the costs are based upon areas that make less than 1% pr ogress towards attainment, indicating what little knowledge we have about controls in those areas.

• The cost curve methodology presents a poor conc eptual relationship between the costs of
identified controls at a national level and the costs of control at a local level. The data this curve is developed upon contains data points which we believe to be invalid6-15 (presented as part of the distributional anal ysis in Section 6.1.3.2). The estimated curve estimates negative costs over a portion of emission reductions. In addition this approach relies heavily on the control strategy for tight est standard alternative analyzed in this RIA, and does not account for variability in co ntrol strategies across alternative standards analyzed. Lastly, we do not believe this curve well represents the knowledge of how
control costs behave over time.

6.1.3.2 Estimated Cost Curve Approach

The cost curve method was used to estimate extrapolated costs and the asso ciated total cost of meeting the selected and alternative standard s analyzed. However, before describing this approach it is again important to note the EPA Science Advisory Board Council’s position on the use of similar methods when estimating cost s of unidentified control measures (U.S. Environmental Protection Agency. June 2007. Advisory Council on Clean Air Compliance Analysis (COUNCIL), Council Advisory on OA R’s Direct Cost Report and Uncertainty Analysis Plan. Washington, DC). The Council advises against any approach that deviates from using a fixed cost/ton estimate such as the approach described below.

This noted, in addition to the SA B advised fixed cost per ton in cases of extreme uncertainty presented earlier, an alternative methods was used to estimate extrapolated costs, involving the use of regression analysis to estimate a total cost curve for the identified controls. The portion of the curve extending beyond emissi on reductions achieved through iden tified controls up to the emission reductions required to m eet the standard is the extrapolat ed cost curve. This curve is then appended to the total cost curve whose data was used in the regression to calculate a total cost estimate. These curves can be seen in Figure 6-2 below.6-16 Figure 6-2. COST CURVE APPROACH

The regression approach to estimating extrapolated costs uses the data used to construct the cost curve and estimates the equation Cumulative Total Cost = Cumulative Emissions Reduced +
Cumulative Emissions Reduced 2 . The resulting regression equation is Cumulative Total Cost = -
1,175,430

⋅ Cumulative Emissions Reduced + 32,690

⋅ Cumulative Emissions Reduced 2 , a graph of which can be seen in Figure 6-2. The adju sted R-squared value for this equation is 0.88, indicating a relatively good fit to the data. All variables are highly signi ficant. Table 6-3 shows the extrapolated costs for each of the analyzed standard levels when using this regression to estimate the cost for the remaining tons requi red. These estimated costs range from $79,000 for the 0.5 standard to $33 million for the 0.1 standar d. For the selected 0.15 standard, the costs are $20 million.

Table 6-3.
EXTRAPOLATED COSTS USING TOTAL COST REGRESSION (7% Discount Rate)6-17 Standard Alternative Additional emission reductions needed) Extrapolated cost (M 2006$)

0.5 μ g/m 3 2 nd Maximum Monthly Mean 0.02 $0.08

0.4 μ g/m 3 2 nd Maximum Monthly Mean 0.08 $0.32

0.3 μ g/m 3 2 nd Maximum Monthly Mean 0.29 $1.1

0.2 μ g/m 3 2 nd Maximum Monthly Mean 2.06 $8.3

0.15 μ g/m 3 2 nd Maximum Monthly Mean 4.79 $20

0.1 μ g/m 3 2 nd Maximum Monthly Mean 7.91 $33

6.1.3.2.1 Regression Diagnostics

While the deletion of outliers is a controversial pr actice in statistics, especially when there is uncertainty as to the underlying distribution of th e data, it is an accepted practice in regression to exclude data points that exhibit a large degree of influence on the resulting parameter estimates, while dutifully noting that such exclusions have taken place. Several statistics for measuring the influence of specific data points on estimation results are readily calculated, and for this analysis Cook’s Distance, DFFITS, studentized residuals, a nd leverage statistics were used to identify potentially influential observations.

Before discussion of these results, it may be informative to analy ze the descriptive statistics of
the variables used to construct the cost curve upon which the extrapolated cost curve using the maximum marginal costs as well as the extrapolat ed cost curve from the regression analysis are based. These statistics are pres ented in Table 6-4 below.

Table 6-4.
DESCRIPTIVE STATISTICS FOR VA RIABLES USED IN COST CURVE Emissions Reduced Cost per Ton

Number of Observations 211 211
Minimum 0.000000039 2,656
Median 0.0042 12,277,347
Mean 0.37 34,754,959,220
Max 40.05 6,179,483,400,000 Interquartile Range 0.038 224,882,280
Standard Error 2.83 426,342,369,723

Skewness 13.31 14.40

Kurtosis 185.86 208.39

As can be seen by these statistics, these data exhibit a wide range with the median values considerably different from the mean values. The statistics together provide evidence that the
distribution of these variables is non-normal. As a result, we may expect the presence of outliers that may bias the regression results.6-18 Estimates of were Cook’s Distance, DFFITS, stude ntized residuals, and le verage were calculated for the regression equation Cumulative Total Cost = Cumulative Emissions Reduced +
Cumulative Emissions Reduced 2 . The number of points identified by each of these techniques as influential varied slightly, as seen in Table 6-5.

Table 6-5.
REGRESSION DIAGNOSTICS

Statistic Cutoff Identified observations

Cook's Distance 4/ n 9

DFFITS n k / 2

⋅ 11

Studentized Residuals ±2 19

Leverage (2 k+2)/n 0

All of the observations that were deemed influential under the various measures are at the upper portion of the cumulative cost curve (i.e., the last 9, 11, and 19 points on the curve for Cook’s Distance, DFFITS, and studentized residuals, respectively). That no points are identified by the leverage statistic is puzzling, and provides some counter-evidence that th ese points are outliers. Nonetheless, there is significan t evidence that the distributions of the underlying variables used to construct the variables used in the regressi on are non-normal, and three measures of influence identify the upper 10-20 observations as suspect. Th is calls into question the use of the estimated regression to calculate additional extrapolated costs.

6.1.3.3 Ambient Extrapolation Methodology

To derive costs using this ambient extrapol ation methodology, we first examined the seven monitor areas that make some progress towards attainment with identif ied controls, but cannot reach the target NAAQS. We calculated an av erage cost per microgram of air quality improvement for each area based upon the identifi ed control costs analysis. For each monitor area, we then applied its area-specific cost per microgram to the remaining air quality increment, in order to calculate the extrap olated cost of attainment. We performed these calculations for each target NAAQS in our analysis.

(Additional ug needed to attain) x ($/ug for iden tified controls) = Additional cost to attain

There were four areas that made little to no progr ess towards attainment with identified controls for one or more of the target NAAQS. For thes e monitor areas, we did not have a cost/ug for identified controls to plug into the above equation. As a surr ogate cost/ug, we used the overall average cost/ug of th e monitor areas that had made some progress towards attainment. To derive the extrapolated costs for each monitor area, we then multiplied that overall average cost/ug by the remaining air quality increment in each of the m onitor areas.

(Additional ug needed to attain) x (overall averag e $/ug for identified controls) =

Additional cost to attain6-19 Table 6-6 presents the cost per air quality im provement for all 11 monitor areas. Table 6-7 presents the total extrapolated costs for each monitor area. As indicated in Table 6-7, the estimated extrapolated costs using this appr oach are approximately $3.1 billion per year, assuming a discount rate of seven percent. A pplying a three percent di scount rate this value becomes $2.6 billion per year. The annual extrap olated costs for the ambient approach under the each alternative standard analyzed included in Table 6-7 range from $100 million under the 0.5

μ g/m 3 standard alternative to $3.9 billion under the 0.1 μ g/m 3 standard alternative, assuming a discount rate of seven percent. If we apply a three percent discount rate, these values adjust to $89 million and $3.4 billion for the 0.5 and 0.1 μ g/m 3 standard alternatives, respectively. These costs come out to be much larger than costs unde r the cost curve approach; this is because they are driven by extremely expensive controls that provide only a very small increment of air quality improvement.

Table 6-6. COST PER AIR QUALITY IMPROVEMENT BY MONITOR AREA

Annual Cost/Microgram (Millions of 2006$) Monitor

State Monitor County 3% Discount rate 7% Discount rate
CO El Paso $330 $340

IL Madison $550 $580

MO Jefferson $18 $24 OH Cuyahoga $730 $780

OH Fulton * $3,000 $3,500 OH Logan * $3,000 $3,500

PA Beaver $1,100 $1,200

PA Berks $60 $63

PA Carbon* $3,000 $3,500

TN Sullivan* $3,000 $3,500

TX Dallas $18,000 $22,000 *Represents monitor areas that made less than 1% progress towards attainment with identified controls.6-20Table 6-7.AMBIENT APPROACH EXTRAPOLATED COSTS BY MONITOR AREA*Annual Extrapolated Costs of Emission Reductions Needed Beyond Identified Controls in 2016 (Millions of 2006$)StandardAlternative: 0.5μg/m32ndMaximumMonthly MeanStandardAlternative: 0.4μg/m32ndMaximumMonthly MeanStandardAlternative: 0.3μg/m32ndMaximumMonthly MeanStandardAlternative: 0.2μg/m32ndMaximumMonthly MeanSelected Standard:0.15μg/m32ndMaximum MonthlyMeanStandard Alternative:0.1μg/m32ndMaximum MonthlyMeanMonitorStateMonitorCounty3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%DiscountrateCO El Paso - - - - - - - --- $10 $10IL Madison - - - - - - - --- $2.3 $2.4MO Jefferson - - - - - - $0.7 $0.9$1.6$2.1 $2.5 $3.3OH Cuyahoga - - - - $12 $12 $85 $90$120$130 $160 $170OH Fulton $89 $100 $390 $460 $690 $810 $990 $1,200$1,100$1,300 $1,300 $1,500OH Logan - - - - $180 $210 $480 $560$630$740 $780 $920PA Beaver - - - - - - - -$49$53 $100 $110PA Berks - - - - $1.8 $1.9 $7.8 $8.2$11$11 $14 $15PA Carbon - - - - - - $280 $330$430$510 $580 $690TN Sullivan - - - - - - $110 $130$260$300 $410 $480TX Dallas - - - - - - - --- $19 $23Total $89 $100 $390 $460 $880 $1,000 $1,900 $2,300$2,600$3,100 $3,400 $3,900*All estimates rounded to two significant figures. As such, totals will not sum down columns.6-21 6.1.4 Monitoring Costs

Consistent with the scope of this rulemaking, which includes monitoring provisions, monitoring costs are included here. As part of the regul atory package accompanyi ng the revised standard, revised lead monitoring requireme nts are being issued. The ru le includes revisions to the network design requirements, QA requirements, and the minimum sampling frequency for lead monitoring. These changes will ensure that ad equate lead monitoring will be performed to determine compliance with the proposed lead NAAQS . In addition, the leve l of the standard and the averaging time for the standard directly aff ect the associated monitoring burden. For the final Pb NAAQS, total monitoring costs are es timated to be $4.2 million, which include $3.2 million for expansion of the monitoring network.

In the final information collection request (ICR), EPA has estimated the potential burden for the final Pb NAAQS (i.e. 0.15 ug/m 3 ; rolling quarterly average). EPA estimates that there are approximately 260 facilities that would re quire monitoring at a level of 0.15 μg/m 3 (see: http://www.epa.gov/oar/lead/pdfs/20080502_maps4.pdf ). Monitors will also be required in 52 urban areas (those metropolitan st atistical areas with a population greater than one million). For more detail, see OMB 2060-0084, ICR #940.21.

6.1.5 Summary of Cost Estimates

Table 6-9 provides a summary of to tal costs to achieve the selected standard and each alternative standard. As suggested by Tables 20 6-1, 6-8, the extent to which these costs exceed those
associated with identified controls alone increases with the stringency of the standard. This is consistent with the emissions analysis presente d in Chapter 4, which suggests that local areas’ reliance on emission reductions beyond identified controls would increase as the standard becomes more stringent. Figures 6- 3 and 6-4 present the portion of total costs that is represented by identified controls and the portion of costs that is represented by unspecified emission
reductions.

The significant difference between the costs of id entified controls alone and the cost of achieving attainment (i.e. including bot h identified controls and emi ssion reductions beyond identified controls) in this and other areas reflects the lim ited information available to EPA on the control measures that 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 bu t a few areas of the country for the last decade or more; the alternative standards represent a substantial ti ghtening of the existing NAAQS. As a result, although AirControlNET c ontains information on a large number of different point source controls, we would expect that State and lo cal air quality managers would
have access to additional information on the contro ls available to the most significant sources.

It is important to remember also that these cost estimates are highly uncertain. As discussed in the final monitoring provisions, the existing monitoring network will need to be updated. It is possible that some areas shown to be out of attainment based on the current monitoring information will be shown to be in attainment with more recent monitoring. After the revised

20 Note there is no breakdown of costs by monitor area for th e cost curve approach. This approach was generated at an aggregate level and therefore does no t break down costs to local areas.6-22 monitoring network is in place, ot her areas not identified in this analysis may be found to be violating the new standard. Since many of the existing sources of ambient Pb are relatively small, states are likely to work closely with the sources to reduce emissions in a cost-effective

manner.

Note also that if any facility c hooses to close rather than incur either the costs associated with identified controls or extrapolated costs then for that facility costs are overestimated and
emission reductions are underestimated. We would then also expect the required emission
reductions and control costs for ot her facilities to be lower. We did not attempt to quantify facility closures in this RIA. However, in inte rpreting the costs, the possibility of savings from
closures should be recalled.

Finally, many of the control techni ques identified in this analys is do not appear to be cost effective because a high dollar value control has been added to reduce a small amount of Pb emissions – sometimes only a few pounds per year. Rather than applying additional controls, it may be possible for firms emitting small amounts of Pb to modify their production processes or other operational parameters, in cluding pollution prevention tec hniques, which would be more cost effective than adding additional control technology. Such measures might include increasing the enclosure of buildings, increasing air fl ow in hoods, modifying operation and maintenance procedures, changing feed materials to lower Pb c ontent, etc. While we have estimated the cost of attainment of this revised NAAQS, we have not accounted for the effect of improvements that tend to occur, such as technology improvement, process changes, efficiency improvements, materials substitution, etc. We believe these t ypical improvements will tend to result in more cost effective approaches than simply adding extremely expensive pollution controls in many areas by the attainment date of 2016. It is also worth noting that it is possible that fugitive dust emissions from area sources containing deposited Pb will also contribute to violations of the NAAQS, or that historically depo sited Pb, when disturbed, may be re-entrained into the ambient air. For areas where point source controls ar e sufficient to reach attainment, we may have
overestimated costs if more cost effective fugitive or area sour ce controls are likely to be
available in these areas.

To further inform our understanding of the poten tial difficulties in reaching attainment, EPA took a closer look at the eight coun ties for which identified controls are insufficient to attain the final NAAQS of 0.15 ug/m 3 in our analysis. All eight counti es contained some type of metals processing facilities, such as ba ttery manufacturers, zinc smelte rs, and foundries. Some have baghouses for controlling part iculate matter, but for others we found no information on applicable controls (pos sibly because of their relatively low ove rall particulate emissions). Three of the eight counties host contam inated industrial soil sites that are the focus of significant ongoing remediation efforts (Fulton, OH; Berks, PA ; and Carbon, PA). Many of those sites are
associated with industrial metals sources in cluding Pb battery manu facturing, Pb battery recycling and disposal, zinc processing, metal alloy pr oduction, and iron and steel manufacturing.6-23Table 6-8. ANNUAL TOTALCOSTS BY MONITOR AREA*(INCLUDES IDENTIFIED CONTROL COSTS AND EXTRAPOLATED COSTS USINGAMBIENT EXTRAPOLATION APPROACH)Annual Total Costs in 2016 (Millions of 2006$)StandardAlternative: 0.5μg/m32ndMaximumMonthly MeanStandardAlternative: 0.4μg/m32ndMaximumMonthly MeanStandardAlternative: 0.3μg/m32ndMaximumMonthly MeanStandardAlternative: 0.2μg/m32ndMaximumMonthly MeanSelected Standard:0.15μg/m32ndMaximum MonthlyMeanStandardAlternative: 0.1μg/m32ndMaximumMonthly MeanMonitorStateMonitor County3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%DiscountrateAL Pike$3.1 $3.3 $3.1 $3.3$3.1 $3.3 $4.0 $4.2$4.3$4.6 $4.3 $4.6CO Adams- - - - - - - --- $10 $11CO Denver$0.01 $0.01 $0.01 $0.01$0.38 $0.40 $0.81 $0.87$0.81$0.87 $1.4 $1.5CO El Paso- - - - - - - --- $16 $16FL Hillsborough$1.8 $1.9 $1.8 $1.9$3.6 $3.8 $5.4 $5.7$6.1$6.5 $8.5 $9.0IL Madison- - - - - - - -$0.02$0.02 $2.4 $2.5IN Delaware$6.3 $6.7 $6.3 $6.7$6.3 $6.7 $6.3 $6.7$6.3$6.7 $6.3 $6.7MN Dakota$32 $42 $32 $42 $32 $42 $33 $43$34$44 $35 $46MO Iron- - - - - - $0.02 $0.02$0.02$0.02 $0.02 $0.02MO Jefferson- - - - $42 $44 $110 $120$150$160 $190 $200NY Orange$90 $110 $390 $460 $690 $810 $990 $1,200$1,100$1,300 $1,300 $1,500OH Cuyahoga- - - - $180 $210 $480 $560$630$740 $780 $920OH Fulton- - - - - - - --- - -OH Logan- - - - - - $11 $12$84$90 $140 $150OK Ottawa$0.01 $0.01 $0.01 $0.01 $13 $14 $19 $20$22$23 $25 $26PA Beaver- - - - - - $280 $330$430$510 $580 $690PA Berks- - - - - - $110 $130$260$300 $410 $480PA Carbon$1.0 $1.1 $1.0 $1.1$1.3 $1.4 $1.3 $1.4$2.8$3.0 $3.4 $3.6TN Sullivan$0.03 $0.03 $0.03 $0.03$0.03 $0.03 $2.0 $2.1$2.3$2.5 $3.3 $3.5TN Williamson- - - - - - - --- $26 $31TX Collin- - - - - - - --- $0.01 $0.01TX Dallas$3.1 $3.3 $3.1 $3.3$3.1 $3.3 $4.0 $4.2$4.3$4.6 $4.3 $4.6UT Salt Lake- - - - - - - --- $10 $11Total$130 $160 $430 $510 $970 $1,100 $2,100 $2,400$2,800$3,200 $3,500 $4,100*All estimates rounded to two significant figures. As such, totals will not sum down columns.6-24Table 6-9.TOTAL COSTS BY STANDARD*Annual Total Costs in 2016 (Millions of 2006$)StandardAlternative: 0.5μg/m32ndMaximumMonthly MeanStandardAlternative: 0.4μg/m32ndMaximumMonthly MeanStandardAlternative: 0.3μg/m32ndMaximumMonthly MeanStandardAlternative: 0.2μg/m32ndMaximumMonthly MeanSelected Standard:0.15μg/m32ndMaximum MonthlyMeanStandardAlternative: 0.1μg/m32ndMaximumMonthly Mean3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%Discountrate3%Discountrate7%DiscountrateMonitoring Costs**$4.2$4.2Identified Control Costs***$46 $57 $46 $57 $89 $100 $110 $120$130$150 $160 $180Cost CurveExtrapolation$0.08 $0.08 $0.32 $0.32$1.1 $1.1 $8.3 $8.3$20$20 $33 $33ExtrapolatedCostsAmbientExtrapolation$89 $100 $390 $460 $880 $1,000 $1,900 $2,300$2,600$3,100 $3,400 $3,900Cost CurveExtrapolation**** $46 $57 $46 $57 $90 $100 $110 $130$150$170 $190 $210Total CostsAmbientExtrapolation$130 $160 $430 $510 $970 $1,100 $2,100 $2,400$2,800$3,200 $3,500 $4,100* All estimates rounded to two significant figures. As such, totals will not sum down columns.** Consistent with the scope ofthis rulemaking, which includes monitoring provisions, monitoring costs are included here. SeeOMB 2060-0084, ICR #940.21 for a completediscussion.*** Identified Control Costs are the same for alternative standard 0.5μg/m3and for alternative standard 0.4μg/m3. This is because the same monitor areas violate both standardsand the same controls were chosen in the least cost optimization for both standard alternatives.**** Estimates for the 0.5μg/m3and the 0.4μg/m3appear similar; this isdue to rounding conventions.6-25 Figure 6-3.
PERCENTAGE OF TOTAL COSTS BY EXTRAPOLATED COST APPROACH

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0.5 μg/m3

0.4 μg/m3

0.3 μg/m3

0.2 μg/m3

0.15 μg/m3

0.1 μg/m3

0.5 μg/m3

0.4 μg/m3

0.3 μg/m3

0.2 μg/m3

0.15 μg/m3

0.1 μg/m3 Percentage of Total Costs by Type

Identified Control Costs Extrapolated Costs ‐ Cost Curve Extrapolated Costs ‐ Ambient6-26 Figure 6-4.
TOTAL COSTS IN 2016 BY EXTR APOLATED COST APPROACH

$0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500 $4,000 $4,500TotalCosts(M2006$)

Identified Control Costs Extrapolated Costs ‐ Cost Curve Extrapolated Costs ‐ Ambient

6.1.6 Technology Innovation and Regulatory Cost Estimates

There are many examples in which technologica l innovation and “learning by doing” have made it possible to achieve greater emi ssions reductions than had been f easible earlier, or have reduced
the costs of emission control in re lation to original estimates. Studies 21 have suggested that costs of some EPA programs have been less than orig inally estimated due in part to inadequate inability to predict and account for future technological innovation in regulatory impact analyses.

Constantly increasing marginal co sts are likely to induce the type of innovation that would result in lower costs than estimated early in this chapter. Breakthrough technologies in control equipment could by 2016 result in a rightward sh ift in the marginal cost curve for such equipment (Figure 6-5) 22 as well as perhaps a decrease in its slope, reducing marginal costs per unit of abatement, and thus deviate from the a ssumption of one constantly increasing marginal cost curve. In addition, elevated abatement costs may result in significant increases in the cost of

21 Harrington et al. (2000) and previous studies cited by Harrington. Harrington, W., R.D. Morgenstern, and P. Nelson. 2000 . “On the Accuracy of Regulatory Cost Estimates.” Journal of Policy Analysis and Management 19(2):297-322.

22 Figure 5.2 shows a linear marginal abat ement cost curve. It is possible that the shape of the marginal abatement
cost curve is non-linear.6-27 production and would likely induce pr oduction efficiencies, in partic ular those related to energy inputs, which would lower emissions from the production side.

Figure 6-5.
TECHNOLOGICAL INNOVATION REFLECTED BY MARGINAL COST SHIFT

MC 0 MC 1

Cumulative NOx ReductionsCost/Ton

Induced Technology Shift Sl

o

p

e

=

β

0

S l o

p

e

=

β

1

6.1.6.1 Examples of Technological Advances in Pollution Control

There are numerous examples of low-emission technologies developed and/or commercialized over the past 15 or 20 years, such as:

• Selective catalytic reduction (SCR) and ultra-low NOx burners for NOx emissions

• Scrubbers which achieve 95% and even greater SO2 control on boilers

• Sophisticated new valve seals and leak detecti on equipment for refineries and chemical plans

• Low or zero VOC paints, consumer products and cleaning processes

• Chlorofluorocarbon ( CFC) free air conditioners, refrigerators, and solvents

• Water and powder-based coatings to replace petroleum-based formulations

• Vehicles far cleaner than be lieved possible in the late 1980s due to improvements in evaporative controls, catalyst design and fuel control system s for light-duty vehicles; and
treatment devices and retrofit te chnologies for heavy-duty engines

• Idle-reduction technologies for engines, in cluding truck stop elec trification efforts

• Market penetration of gas-electric hybrid vehicles, and clean fuels

These technologies were not commercially ava ilable two decades ago, and some were not even in existence. Yet today, all of these technologies are on the market, and many are widely employed. Several are key components of major pollution control programs.6-28 What is known as “learning by doi ng” or “learning curve impacts” have also made it possible to achieve greater emissions reductions than had been feasible earlier, or have reduced the costs of emission control in relation to original estim ates. Learning curve impacts can be defined generally as the extent to which variable costs (of production and/or polluti on control) decline as firms gain experience with a specific technology. Such impacts have been identified to occur in a number of studies conducted for various produc tion processes. Impacts such as these would manifest themselves as a lowering of expected co sts for operation of technologies in the future below what they may have been.

The magnitude of learning curve impacts on po llution control costs has been estimated for a variety of sectors as part of th e cost analyses done for the Draft Direct Cost Report for the second EPA Section 812 Prospective Analysis of the Clean Air Act Amendments of 1990. 23 In that report, learning curve adjustment s were included for those sect ors and technologies for which learning curve data was available. A typical le arning curve adjustment example is to reduce either capital or O&M costs by a certain percentage given a doubli ng of output from that sector or for that technology. In other words, capital or O&M costs will be reduced by some percentage for every doubling of output for th e given sector or technology.

T.P. Wright, in 1936, was the first to characteri ze the relationship between increased productivity and cumulative production. He analyzed man-hours re quired to assemble successive airplane bodies. He suggested the relations hip is a log linear function, sin ce he observed a constant linear reduction in man-hours every time the total num ber of airplanes assembled was doubled. The relationship he devised between number assemb led and assembly time is called Wright’s Equation (Gumerman and Marnay, 2004) 24 . This equation, shown below, has been shown to be widely applicable in manufacturing:

Wright’s Equation: C N = C
o * N b

,

Where:

N = cumulative production

C N = cost to produce N th unit of capacity

C
o = cost to produce the first unit

B = learning parameter = ln (1-LR)/ln(2), where

LR = learning by doing rate, or cost reduction per doubling of capacity or output.

23 E.H. Pechan and Associates and Industrial Economic s, Direct Cost Estimates for the Clean Air Act Second Section 812 Prospective Analysis: Draft Report, prepared for U.S. EPA, Office of Air and Radiation, February 2007.
Available at http://www.epa.gov/oar/sect812/m ar07/direct_cost_draft.pdf

. 24 Gumerman, Etan and Marnay, Chris. Learning and Cost Reductions for Generating Technologies in the National Energy Modeling System (NEMS), Ernest Orlando Lawrence Berkeley National Laboratory, University of California at Berkeley, Berkeley, CA. January 2004, LBNL-52559.6-29 The percentage adjustments can range from 5 to 20 percent, dependi ng on the sector and technology. Learning curve adjustments were prep ared in a memo by IEc supplied to US EPA and applied for the mobile source sector (b oth onroad and nonroad) and for application of various EGU control technologies with in the Draft Direct Cost Report. 25 Advice received from the SAB Advisory Council on Clea n Air Compliance Analysis in J une 2007 indicated an interest in expanding the treatment of learning curves to those portions of the cost analysis for which no learning curve impact data are currently availabl e. Examples of these sectors are non-EGU point sources and area sources. The memo by IEc outlined various approaches by which learning curve impacts can be addressed for those sectors. Th e recommended learning curve impact adjustment for virtually every sector considered in the Dr aft Direct Cost Report is a 10% reduction in O&M costs for two doubling of cumulative output, with proxies such as cumulative fuel sales or cumulative emission reductions being used when output data was unavailable.

For this RIA, we do not have the necessary data for cumulative output, fuel sales, or emission reductions for sectors included in our analysis in order to properly generate control costs that reflect learning curve impacts. Clearly, the effect of including these impacts would be to lower our estimates of costs for our control strategies in 2016, but we are not able to include such an analysis in this RIA.

6.1.6.2 Influence on Regulatory Cost Estimates

Studies indicate that it is not uncommon for pre-regulatory cost estimates to be higher than later estimates, in part because of inability to predic t technological advances. Over longer time horizons the opportunity for tec hnical advances is greater.

• Multi-rule study: Harrington et al. of Resources for the Future 26 conducted an analysis of the predicted and actual costs of 28 federal and st ate rules, including 21 issued by EPA and the Occupational Safety and Health Administration (OSHA), and found a tendency for predicted costs to overstate actual implementation costs. Costs were considered accurate if they fell within the analysis error bounds or if they fa ll within 25 percent (greater or less than) the predicted amount. They found that predicted tota l costs were overestimated for 14 of the 28 rules, while total costs were underestimated for only three ru les. Differences can result because of quantity differences (e.g., overestim ate of pollution reductions) or differences in per-unit costs (e.g., cost per unit of pollution re duction). Per-unit costs of regulations were overestimated in 14 cases, while they were unde restimated in six cases. In the case of EPA rules, the agency overestimated per-unit costs for five regulat ions, underestimated them for four regulations (three of these were relati vely small pesticide rules), and accurately
estimated them for four. Based on examination of eight economic incentiv e rules, “for those rules that employed economic in centive mechanisms, overestimation of per-unit costs seems to be the norm,” the study said.

25 Industrial Economics, Inc. Proposed Approach for Expanding the Treatment of Learning Curve Impacts for the Second Section 812 Prospective Analysis: Memorandum, prepared for U.S. EPA, Office of Air and Radiation, August 13, 2007.

26 Harrington, W., R.D. Morgenstern, and P. Nelson. 2 000. “On the Accuracy of Regulatory Cost Estimates.” Journal of Policy Analysis and Management 19(2):297-322.6-30 Based on the case study results and existing lite rature, the authors id entified technological innovation as one of five explanations of why predicted and actual re gulatory cost estimates differ: “Most regulatory cost estimates ignore the possibility of technological innovation ... Technical change is, after all, notoriously difficult to forecast ... In numerous case studies actual compliance costs are lowe r than predicted because of unanticipated use of new technology.”

It should be noted that ma ny (though not all) of the EPA ru les examined by Harrington had compliance dates of several years, which allo wed a limited period for technical innovation.

• Acid Rain SO2 Trading Program : Recent cost estimates of the Acid Rain SO2 trading program by Resources for the Future (RFF) and MIT have been as much as 83 percent lower than originally projected by EPA. 27 Note that the original EPA cost analysis also relied on an optimization model like IPM to approximate the re sults of emissions trading. As noted in the RIA for the Clean Air Interstate Rule, the ex ante numbers in 1989 were an overestimate in part because of the limitation of economic modeling to predict technological improvement of pollution controls and other compliance options such as fuel switching. The fuel switching from high-sulfur to low-sulfur coal was spu rred by a reduction in rail transportation costs due to deregulation of rail rates during the 1990’sHarrington et al. report that scrubbing turned out to be more efficient (95% removal vs. 80- 85% removal) and more reliable (95% vs. 85% reliability) than expected, and that unanticip ated opportunities arose to blend low and high sulfur coal in older boilers up to a 40/60 mixture, compared w ith the 5/95 mixture originally
estimated.

Phase 2 Cost Estimates Ex ante estimates $2.7 to $6.2 billion a Ex post estimates $1.0 to $1.4 billion

a 2010 Phase II cost estimate in $1995.

• EPA Fuel Control Rules: A 2002 study by two economists with EPA’s Office of Transportation and Air Quality 28 examined EPA vehicle and fu els rules and found a general pattern that “all ex ante estimates tended to exceed actual price impacts, with the EPA estimates exceeding actual prices by the smallest amount.” The paper note s that cost is not the same as price, but suggests that a comparison nonetheless can be instructive. 29 An example focusing on fuel rules is provided:

27 Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000. “Sulfur Dioxide Control by
Electric Utilities: What Are the Gains from Trade?” Journal of Political Economy 108(#6):1292-1326. Ellerman, Denny. January 2003. Ex Post Evaluation of Tradable Permits: The U.S. SO2 Cap-and-Trade Program. Massachusetts Institute of Technology Center for Energy and Environmental Policy Research.

28 Anderson, J.F., and Sherwood, T., 2002. “Comparison of EPA and Other Estimates of Mobile Source Rule Costs to Actual Price Changes,” Office of Transportation an d Air Quality, U.S. Environmental Protection Agency. Technical Paper published by the Society of Automotive Engineers. SAE 2002-01-1980.

29 The paper notes: “Cost is not the same as price. This si mple statement reflects the fact that a lot happens between a producer’s determination of manufacturin g cost and its decisions about what the market will bear in terms of price change.”6-31 Table 6-10.
COMPARISON OF INFLATION-ADJUSTED ESTIMATED COSTS AND ACTUAL PRICE
CHANGES FOR EPA FUEL CONTROL RULES A

Inflation-adjusted Cost Estimates (c/gal)

EPA DOE API Other Actual
Price Changes

(c/gal)

Gasoline Phase 2 RVP Control (7.8

RVP—Summer) (1995$) 1.1 1.8 0.5

Reformulated Gasoline Phase 1

(1997$) 3.1-5.1 3.4-4.1 8.2-
14.0 7.4 (CRA) 2.2

Reformulated Gasoline Phase 2

(Summer) (2000$) 4.6-6.8 7.6-
10.2 10.8-
19.4 12 7.2 (5.1,
when
corrected to 5yr MTBE

price) 30 ppm sulfur gasoline (Tier 2) 1.7-1.9 2.9-3.4 2.6 5.7 (NPRA),

3.1 (AIAM) N/A

Diesel 500 ppm sulfur highway diesel

fuel (1997$) 1.9-2.4 3.3 (NPR

A) 2.2

15 ppm sulfur highway diesel

fuel 4.5 4.2-6.0 6.2 4.2-6.1 (NPRA) N/A

a Anderson, J.F., and Sherwood, T., 2002. “Comparison of EPA and Other Estimates of Mobile Source Rule Costs to Actual Price Changes,” Office of Transportation and Air Quality, U.S. Environmental Protection Agency. Technical Paper published by the Society of Automotive Engineers. SAE 2002-01-1980.

• Chlorofluorocarbon ( CFC) Phase-Out: EPA used a combination of regulatory, market based (i.e., a cap-and-trade system among manuf acturers), and voluntary approaches to phase out the most harmful ozone depleting substances. This was done more efficiently than either EPA or industry originally anticipated. The pha seout for Class I substances was implemented 4-6 years faster, included 13 more chemicals, a nd cost 30 percent less than was predicted at
the time the 1990 Clean Air Act Amendments were enacted. 30

The Harrington study states, “When the original cost analysis was performed for the CFC phase-out it was not anticipated that the hydrofluorocarbon HF C-134a could be substituted for CFC-12 in refrigeration. However, as Hammit 31 notes, ‘since 1991 most new U.S.
automobile air conditioners have co ntained HFC-134a (a compound for which no commercial production technology was available in 1986) instead of CFC-12” (p.13). He cites a similar story for HCFRC-141b and 142b, wh ich are currently substituting for CFC-11 in important foam-blowing applications.”

30 Holmstead, Jeffrey, 2002. “Testimony of Jeffrey Holmstead, Assistant Administrator, Office of Air and Radiation, U.S. Environmental Protection Agency , Before the Subcommittee on Energy and air Quality of the committee on Energy and Commerce, U.S. House of Representatives, May 1, 2002, p. 10.

31 Hammit, J.K. (1997). “Are the costs of proposed environmental regulations overestimated? Evidence from the CFC phaseout.” Unpublished paper, Center for Risk Analysis, Harvard School of Public Health, Cambridge, MA.6-32 • Additional examples of decrea sing costs of emissions controls include: SCR catalyst costs decreasing from $11k-$14k in 1998 to $3.5k-$5k in 2004, and improved low NOx burners reduced emissions by 50% from 1993-2003 while the associated capita l cost dropped from $25-$38/kw to $15/kw 32

.

We can not estimate the interplay between EPA regulation and technology improvement, but it is
clear that a priori cost estimation often results in overe stimation of costs because changes in technology (whatever the cause) make less costly control possible.

32 ICF Consulting. October 2005. The Clean Air Act Amen dment: Spurring Innovation and Growth While Cleaning the Air. Washington, DC. Available at http://www.icf i.com/Markets/Environment/ doc_files/caaa-success.pdf.6-33 6.2 Economic Impacts

The assessment of economic impacts was conducted simply based on those source categories which are controlled in this analysis. The im pacts presented here are an extension of the engineering costs, where engineering costs are allocated to specific source categories by North American Industry Classificat ion System (NAICS) code. Although the costs outlined in the previous section may affect a range of industries, we expect that most of the costs associated
with the selected standard will be concentrated in a limited number of industry sectors. As indicated in Table 6-11, we estimate that primar y smelters & refiners of nonferrous metals (NAICS 331419) are expected to in cur most of the identified cont rol costs associated with the
alternative standards included in this RIA. Fo r primary smelters, the estimated costs of the alternative standards range from $32 million to $76 million per year, depending on the standard and the discount rate. This represents 1.2 to 2.9 percent of the industry’s value of shipments in 2002. Table 6-11 also shows that other industry sectors expected to incur significant costs include secondary smelting, refining, and alloyi ng of nonferrous metals excluding copper and aluminum (NAICS 331492); iron and steel mills (NAICS 331111); and steam and airconditioning supply (NAICS 221330). The projected compliance costs for these industries range from 0.0 to 1.3 percent of their total shipments (or receipts, in the case of steam and airconditioning supply) in 2002.

Table 6-12 presents capital and O&M costs by in dustry for identified measures under the Final NAAQS. 33 Costs associated with emission reduc tions beyond identified controls are not
reflected in the table because the distribution of costs between capital and O&M is uncertain for these measures. As indicated in the table, O& M represents approximate ly 54 percent of costs
under the selected standard of 0.15 μ g/m 3 . This implies that alt hough the upfront capital costs
associated with the selected st andard may be significant for aff ected industries, these costs are expected to represent a fraction of th e selected standard’s total costs.

33 The costs presented in the table reflect a discount rate of seven percent. Because the purpose of the table is to show the approximate distribution of costs between capital and O&M, the table presents costs based on just one discount rate for ease of presentation.6-34Table 6-11. ANNUAL COSTS OF IDENTIFIED CONTROLS BY INDUSTRYTotal Cost with IdentifiedControls (Millions of 2006$)StandardAlternative:0.5μg/m32ndMaximumMonthlyMeanStandardAlternative: 0.4μg/m32ndMaximumMonthly MeanStandardAlternative: 0.3μg/m32ndMaximumMonthly MeanStandardAlternative: 0.2μg/m32ndMaximumMonthly MeanSelectedStandard: 0.15μg/m32ndMaximumMonthly MeanStandardAlternative: 0.1μg/m32ndMaximumMonthly MeanNAICSCodeIndustry Description3%Discrate7%Discrate3%Discrate7%Discrate3%Discrate7%Discrate3%Discrate7%Discrate3%Discrate7%Discrate3%Discrate7%DiscrateIndustryRevenue in2005(millionsof 2006$)12211 Electric Power Generation,Transmission andDistribution - - - - $2 $2.3 $4.4 $5.1$4.6$5.2 $11.2 $13.2 $363,990331419 Primary Smelting andRefining of NonferrousMetal (except Copper andAluminum) $31.7 $42 $31.7 $42 $31.7 $42 $40 $50.8$63.9$76.3 $63.9 $76.3 $2,649331492 Secondary Smelting,Refining, and Alloying ofNonferrous Metal (exceptCopper and Aluminum)$12.3 $13 $12.3 $13.1 $23.3 $24.7 $28.4 $30.2$31.2$33.3 $35.8 $38.1 $3,009331111Iron and Steel Mills $1.5 $1.5 $1.5 $1.5 $22.1 $23.3 $22.1 $23.3$22.1$23.3 $24.4 $25.8 $51,762221330Steam and AirConditioning Supply- - - - $7.3 $7.6 $7.3 $7.6$7.3$7.6 $7.3 $7.6 $723331491Nonferrous Metal (exceptCopper and Aluminum)Rolling, Drawing, andExtruding - - - - - - - --- $4.6 $4.9 $5,041331513Steel Foundries (exceptInvestment) - - - - - - - --- $3.9 $4.0 $2,941331314Secondary Smelting andAlloying of Aluminum- - - - - - - -<$0.1<$0.1 $2.4 $2.5 $4,15032511PetrochemicalManufacturing - - - - - - - --- $1.1 $1.1 $23,611622110General Medical andSurgical Hospitals - - - - - - - --- - - $526,034335911Storage BatteryManufacturing- - <$0.1 <$0.1 $0.8 $0.8 $0.8 $0.8$0.8$0.8 $0.8 $0.8 $3,961331511Iron Foundries- - - - $0.4 $0.4 $0.4 $0.4$0.4$0.4 $0.7 $0.8 $11,541Other Industries - - - - $1.5 $1.8 $2.2 $2.5$2.2$2.5 $3.8 $4.2Total$45.5 $56.5 $45.5 $56.6 $89.1 $103 $105.5 $120.7$132.5$149.4 $159.9 $179.41. Source: U.S. Census Bureau 2002 Economic Census6-35 Table 6-12.
ANNUAL CAPITAL AND O&M COSTS BY INDUSTRY FOR IDENTIFIED CONTROLS (7 percent discount rate)

Annual Cost of Identified Controls in 2016 (Millions of 2006$) Final NAAQS: 0.15 μ g/m3 2nd

Maximum Monthly Mean

SIC
Code Description Capital O&M Total
Annual

Cost 2211 Electric Power Generation, Transmission and Distribution $3.1 $2.2 $5.2 331419 Primary Smelting and Refining of N onferrous Metal (except Copper and Aluminum) $52.4 $24 $76.3 331492 Secondary Smelting, Refining, and Alloying of Nonferrous Metal (except Copper and Aluminum) $10 $23.2 $33.3

331111 Iron and Steel Mills $0.6 $22.7 $23.3

221330 Steam and Air-Conditioning Supply $1.5 $6.2 $7.6

331491 Nonferrous Metal (except Copper and Aluminum) Rolling, Drawing, and Extruding

- - -

331513 Steel Foundries (except Investment)

- - - 331314 Secondary Smelting and A lloying of Aluminum <$0.1 <$0.1 <$0.1

32511 Petrochemical Manufacturing

- - - 622110 General Medical and Surgical Hospitals

- - -

335911 Storage Battery Manufacturing $0.2 $0.6 $0.8

331511 Iron Foundries $0.1 $0.3 $0.4

Other $0.6 $1.9 $2.5

Total $68.5 $80.9 $149.4

6.3 Energy Impacts

This section summarizes the energy consumption impacts of the final and alternative lead NAAQS. The Pb NAAQS revisions do not constitute a “significant energy action” as defined in Executive Order 13211; this information merely re presents impacts of the illustrative control strategies applied in the RIA. The rule does not prescribe specific control strategies by which
these ambient standards will be met. Such strate gies will be developed by States on a case-bycase basis, and EPA cannot predic t whether the control options selected by States will include regulations on energy suppliers, distri butors, or users. Thus, EPA c oncludes that this rule is not likely to have any adverse energy effects.

For this RIA, implementation of the control measur es needed for attainment with the alternative standards will likely lead to increased energy consumption among lead emitting facilities. To control emissions effectively, these measures re quire a significant amount of electricity that
affected facilities are not e xpected to consume under base line conditions. The available information on these controls suggests that they are not typically powered by natural gas or other fossil fuels; therefore, our analysis of en ergy impacts focuses exclusively on electricity consumption. In addition, because the energy co nsumption associated with emission reductions beyond identified controls is uncer tain, we only consider the energy impacts associated with6-36 identified controls. Similarly, the electricity c onsumption associated with the construction of a Kivcet furnace at the primary lead smelter in Jefferson County, MO is also uncertain. We therefore exclude energy impacts for this faci lity from our analysis of energy impacts.

To assess the electricity consumption impacts asso ciated with identified controls, we relied on the AirControlNET outputs generated for this an alysis. For most identified controls, AirControlNET estimates electricity costs sepa rately from other operating and maintenance (O&M) costs. Therefore, for sources expect ed to implement these controls, AirControlNET provides direct estimates of the additional el ectricity costs expected under the standard
alternatives. We calculate the electricity consumption associated with these costs based on the unit cost of electricity assumed by AirControlN ET (7.8 cents/kilowatt hour in 2006 dollars).

For a number of identified controls , AirControlNET does not separate the cost of electricity from other O&M costs. Similarly, the co st data for several controls iden tified from sources other than AirControlNET do not distinguish between electric ity and other O&M costs. We estimate the electricity costs associated with these measures based on electricity's assumed share of total O&M, which we estimate based on AirControlNET's results for those controls where it separates electricity costs from other O&M costs. Fo r some controls, O&M costs are not estimated separately from capital costs. In these cases, we assume that O&M represents a fixed share of
annual costs based on the cost data for those co ntrols where O&M and capital are calculated separately.

Table 6-13 summarizes the estimated energy impacts associated with the se lected and alternative
standards. As indicated in the table, we estimate that sources installing identified controls under
the alternative standards will increase their el ectricity consumption in 2016 by approximately 121,800 megawatt-hours (MWh) under the selected st andard. By comparison, the iron and steel industry alone is projected to purchas e 66 million MWh of electricity in 2016. 34

Table 6-13.
SUMMARY OF ENERGY IMPACTS

Standard
Alternative:

0.5 μ g/m 3 2 nd

Maximum Monthly

Mean Standard
Alternative:

0.4 μ g/m 3 2 nd

Maximum Monthly

Mean Standard
Alternative:

0.3 μ g/m 3 2 nd

Maximum Monthly

Mean Standard
Alternative:

0.2 μ g/m 3 2 nd

Maximum Monthly

Mean Selected
Standard:

0.15 μ g/m 3 2 nd

Maximum Monthly

Mean Standard
Alternative:

0.1 μ g/m 3 2 nd

Maximum Monthly

Mean Electricity Cost (millions of year 2006$) $1.4 $1.4 $6.2 $8.7 $9.5 $17.3 Electricity Consumption(Megawatthours
consumed in 2016) 17,800 18,200 79,900 111,700 121,800 222,000

34 U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook 2007 , February 2007.6-37 6.4 Limitations

Although the cost analysis presented in this chapter provide s a reasonable a pproximation of the
costs associated with the fi nal lead NAAQS using hypothetical control scenarios given the available information, we note the fo llowing limitations of the analysis:

• Analysis limited to point source controls. Because limited data are available on fugitive and area source emissions, our analysis of the costs a ssociated with the fina l and alternative lead NAAQS does not consider area source and direct fugitive controls that may be implemented to comply with these standards. 35 Therefore, in the few areas where point source controls are insufficient to attain the standard, and where we were unable to model full compliance with the revised NAAQS, it is possible that fugitive dust emissions from area sources containing deposited Pb will also contribute to violations of the NAAQS, or that historically deposited Pb, when disturbed, may be re-entrained into th e ambient air. For areas where point source
controls are sufficient to reach attainment, we may have overestimated costs if more cost effective fugitive or area source controls are likely to be available in these areas.

• Incomplete information for point source controls. To assess the cost of reducing point source lead emissions, this analysis reli es upon PM control cost information from AirControlNET. For several sources, however , AirControlNET contains no information on the PM controls available, and we are unable to estimate costs for these sources. Such sources represent approximately 9 percent of the point source lead emi ssions included in our analysis (i.e., AirControlNET contains contro l measure data for those lead sources that represent 91 percent of the lead emissions included in the anal ysis). Costs to control lead emissions from these sources may be less than or greater than the costs to control emissions
from other sources.

• Uncertainty about methods for reaching tig hter control levels in the future. It is not
known whether industrial sources will in th e future make improvements to existing particulate matter controls to control Pb emi ssions, whether there will be further application of existing control technology in series with controls that might already be employed at a source, or whether we might expect new control technology to be developed.

• Uncertainty in the cost esti mates for the primary lead smelter in Jefferson County,
Missouri: To estimate the costs of the selected st andard for Jefferson County, Missouri, this analysis models the replacement of the primar y lead smelter in that area with a modern, lower emitting Kivcet smelter. We estimate the cost of this project based on the costs of Teck Cominco’s Kivcet smelter in Trail, Brit ish Columbia, scaling for differences in lead production capacities between the Teck Cominc o facility and the smelter in Jefferson County. While this is a reasona ble approach for estimating costs for the smelter in Jefferson County, our analysis may not capture facility-sp ecific characteristics th at would affect the

35 Although our analysis considers the impact of point source controls on certain fugitive emissions, as described in Chapter 4, it does not consider direct fugitive controls.6-38 costs of building and operating a Kivcet smelter at this facility. Furthermore, more targeted measures for reducing lead emissions may be mo re cost-effective than the construction of a
new Kivcet unit. Given these uncertainties, we may overestimate or underestimate costs for Jefferson County.

• Uncertainty associated with ex trapolated costs of emission reductions beyond identified
controls. As indicated above, many areas are expected to rely heavily on emission reductions beyond identified controls to reach attainment wi th the selected and alternative standards. The cost of implementing these measures is uncertain. Many of these sources are already well-controlled for particulate matter, and addi tional control for the remaining increment of Pb might be difficult to achieve. In add ition to these uncertainties there are also many uncertainties associated with the two approaches used in this RIA to estimated extrapolated costs. The ambient extrapolation methodology emphasizes control cost s that are the most expensive within an area, and assumes that knowledge of control costs from monitor areas
that attain have no influence on the average control costs for areas that need unspecified
emission reductions. It also assumes there will be no increased knowledge of sources or changed in technology between now and 2016. La stly, most of the costs are based upon areas that make less than 1% progress toward s attainment, indicating what little knowledge
we have about controls in those areas. The cost curve methodology presents a poor conceptual relationship between the costs of id entified controls at a national level and the
costs of control at a local level. The data th is curve is developed upon contains data points which we believe to be invalid (presented as part of the distributiona l analysis in Section 6.1.3.2). The estimated curve estimates negative costs over a portion of emission reductions. In addition this approach relies heavily on the control strategy for tightest standard alternative analyzed in this RIA, and does not account for variability in control strategies across alternative standards analyzed. Lastly, we do not believe this curve well represents the knowledge of how contro l costs behave over time.

•7-1 CHAPTER 7. ESTIMATES OF BENEFITS AND COSTS

EPA has performed an illustrative analysis to estimate the costs and human health benefits of nationally attaining the selected lead National Ambient Air Quality Standards (NAAQS). This chapter first presents benef its and costs for scenarios using consistent assumptions. We then discuss key uncertainties and limitations. Finally, we provide a summary of key conclusions, considering both the primary results and key uncertainties.

This Regulatory Impact Analysis (RIA) provides illustrative estimates of the incremental

costs and monetized human health benefits of attaining a revised prim ary lead (Pb) National Ambient Air Quality Standard (NAAQS) within th e current monitoring ne twork of 189 monitors representing 86 counties. Many of the highest-emitting lead sources do not have nearby Pb-TSP monitors, and it is important to note that ther e may be many more potential nonattainment areas than have been analyzed in this RIA.

It is important to note at the outset that ove rall data limitations are very significant for this analysis, compared to other NAAQS reviews. One critical area of un certainty is the limited TSP-Pb monitoring network (discussed in chapter 2). Because monitors are present in only 86 counties nationwide, the univers e of monitors exceeding the final NAAQS level of 0.15 μg/m 3
represent only 16 counties. It is important to note that data limitations prevented us from identifying a full range of controls which woul d bring eight of these counties all the way to attainment of the final NAAQS. It is also impor tant to note that because many of the highestemitting 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 n onattainment areas than have been analyzed in
this RIA.

In addition, as discussed in chapter 3 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 fo r compliance with the Pb NAAQS; however, dispersion models are data –inten sive and more appropriate for lo cal scale analyses of emissions

from individual sources. It wa s not feasible to conduct such a la rge-scale data in tensive analysis for this RIA. As a result, the simplified anal ysis developed for this RIA, while distanceweighting individual source contri butions to ambient Pb concentr ations, could not account for such locally critical variables as meteorology and source stack height.

Benefits and Costs

The estimates of benefits and costs presented here reflect illustrative scenarios of future lead NAAQS compliance that are consistent in most respects. In all cases, estimates are based on a 2016 compliance date; as a result, such inpu ts as population and baseline emissions and air quality compliance with existing Clean Air Act requirements (including MACT rules affecting lead emissions and the recently promulgated PM NAA QS revision) are consistently applied in all7-2 estimates presented here. In addition, the two a lternative discount rates - 3% and 7% - are used in all relevant components of both benefit and cost calculations, for all estimates presented here.

Consistent with our development of the illustra tive control strategies described above, our analysis of the costs associated with the sel ected NAAQS focuses on point source PM controls. For the purposes of this analysis, these c ontrols largely include measures from the AirControlNET control technology da tabase, but also include addi tional measures associated with operating permits and/or New Source Performa nce 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 Ai rControlNET and other sources does not result in attainment with the selected NAAQS in several areas. In these areas, additional unspecified emission reductions will likely be necessary to reach attainment. In order to bring these monitor areas into attainment, we calculated control co sts using two different approaches. Under one approach, we extrapolated the cost of unspeci fied emission reductions by constructing a total cost curve using data on identified control cost s. We then derived a total cost equation in quadratic form which best fit the total cost curve. Under our s econd approach, we calculated the cost of unspecified emission reductions by deriving an average cost per microgram of air quality improvement obtained from identified controls. For each standard, we then selected all monitor areas that failed to reach attainment and app lied unspecified emission reductions to all sources
until attainment was reached.

For the selected standard and each alternative, we then selected all monitor areas that failed to reach attainment and applied unspecified emission reductions to all sources until attainment was reached. It is important to remember that under the first approach, the majority of
the costs for the selected sta ndard (88%) come from our an alysis of current known control technologies, with only 12% of th e total costs coming fr om extrapolated costs. Under the second scenario, 5% of the total costs come from our analysis of currently kno wn control technologies, and the majority of the costs (9 5%) comes from our assumptions a bout the cost of controlling the
last few ambient increments of Pb needed to reach full attainment.

Tables 7.1 and 7.2 presents total national primar y estimates of costs and benefits for a 3%
discount rate and a 7% discount rate.7-3 Table 7.1. Summary of Costs (Millions of 2006$)

Alternative NAAQS: 0.4

μ g/m 3 2 nd Maximum

Monthly Mean Final NAAQS: 0.15 μ g/m 3

2 nd Maximum Monthly

Mean Alternative NAAQS: 0.1

μ g/m 3 2 nd Maximum

Monthly Mean

3%

Discount

rate 7%

Discount

rate 3%

Discount

rate 7%

Discount

rate 3%

Discount

rate 7%

Discount

rate

Identified Control Costs $46 $57 $130 $150 $160 $180

Cost Curve

Extrapolation $0.32 $0.32 $20 $20 $33 $33 Extrapolated

Costs Ambient

Extrapolation $390 $460 $2,600 $3,100 $3,400 $3,900

Cost Curve $46 $57 $150 $170 $190 $210 Total RIA

Costs Ambient $430 $510 $2,800 $3,200 $3,500 $4,100

Monitoring Costs** $4.2 $4.2 $4.2 $4.2 $4.2 $4.2

* All estimates rounded to two significant figures . As such, totals will not sum down columns.

** Consistent with the scope of this rulemaking, which includes monitoring provisi ons, monitoring costs are included here. See

OMB 2060-0084, ICR #940.21 for a complete discussion.

The ranges of benefits presented reflect uncer tainty about the earnings impact associated with IQ gains and variability in the estimates of the PM mortality co-benefits across the available effects estimates. The total benefits range of estimates was developed by first adding the low and high ends of the range of monetized lead IQ be nefits to the low and high ends of the range of PM co-benefits, and then subtracting the total co st estimate from the low and high end of the resulting range of total benefits.7-4 Table 7.2. Summary of Benefits (Millions of 2006$)

Alternative Standard: 0.40

μ g/m 3 2 nd Maximum Monthly

Mean Final NAAQS: 0.15 μ g/m 3 2 nd

Maximum Monthly Mean Alternative Standard: 0.10

μ g/m 3 2 nd Maximum Monthly

Mean

3% Discount

rate 7% Discount

rate 3% Discount

rate 7% Discount

rate 3% Discount

rate 7% Discount

rate

Annualized Benefit - IQ

Gains (Range)** $2,000—$2,800 $250—$490 $3,500—$5,000 $440—$870 $4,500—$6,400 $560—$1,100

Annualized Benefit - PM

Co-control (Range)*** $100—$880 $100—$800 $230—$1,900 $210—$1,700 $260—$2,200 $240—$2,000

Total Benefits $2,100—$3,700 $350—$1,300 $3,700—$6,900 $650—$2,600 $4,800—$8,600 $800—$3,100

Table 7.3 shows net benefits of the sele cted NAAQS and alternative standards.

Table 7.3. Summary of Net Benefits (Millions of 2006$) 1

Alternative Standard: 0.40 μ g/m 3

2 nd Maximum Monthly Mean Final NAAQS: 0.15 μ g/m 3 2 nd

Maximum Monthly Mean Alternative Standard: 0.10 μ g/m 3

2 nd Maximum Monthly Mean

3% Discount

rate 7% Discount

rate 3% Discount

rate 7% Discount

rate 3% Discount

rate 7% Discount

rate

Total RIA Costs +

Monitoring Costs $50—$430 $61—$510 $150—$2,800 $170—$3,200 $190—$3,500 $210—$4,100

Total Benefits $2,100—$3,700 $350—$1,300 $3,700—$6,900 $650—$2,600 $4,800—$8,600 $800—$3,100

Net Benefits $1,700 - $3,700 $(160) - $1,200 $900 - $6,800 $(2,600) - $2,400 $1,300 - $8,400 $(3,300) - $2,900

Discussion of Uncertainties and Limitations

As with other NAAQS RIAs, it should be recogni zed that all estimates of future costs and

benefits are not intended to be forecasts of the actual costs and benefits of implementing revised standards. Ultimately, states and urban areas will be responsible for developing and implementing emissions control programs to r each attainment of the lead NAAQS, with the timing of attainment being determined by future d ecisions by states and EPA. Our estimates are intended to provide information on the general magn itude of the costs and be nefits of alternative

1 Note that bounds of the full range of net benefits is derived by subtracting the high costs from the low benefits at
the lower end, and subtracting the low costs from the high benefits at the upper end. This is the only way to fully
represent the uncertainty.7-5 standards, rather than precise pr edictions of control measures, costs, or benefits. With these caveats, we expect that this analysis can provide a reasonable picture of the types of emissions controls that are currently available, the direct costs of those controls, the levels of emissions reductions that may be achieved with these controls , the air quality impact that can be expected to result from reducing emissions, and the public h ealth benefits of reduc tions in ambient lead levels, as well as coincident reductio ns in ambient fine particulates.

Compared to recent NAAQS RIAs, however, our current knowledge of the costs and
nature of lead emissions controls and the eff ects of changes in emissions on air quality and exposure is less robust. 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 propos ed standards, while suppo rted by and consistent
with EPA's review of recent scie ntific research, repres ent a substantial tight ening of the existing NAAQS. As a result, many of the analyses co nducted for this RIA could be significantly improved through enhanced research and data in these areas. Perh aps the greatest need is for research on available pollution control techno logy, work practice ch anges, and pollution prevention options to most cost-effectively contro l lead emissions directly, rather than as a component of lead-bearing PM emissions.

It is important to note again that overall da ta limitations are very significant for this analysis, compared to other NAAQS reviews. One cr itical area of uncertainty is the limited TSPPb monitoring network (discusse d in chapter 2). Because mo nitors are present in only 86 counties nationwide, the univers e of monitors exceeding the final NAAQS level of 0.15 μg/m 3
represent only 16 counties. It is important to note that data limitations prevented us from identifying a full range of controls which woul d bring eight of these counties all the way to attainment of the final NAAQS. It is also impor tant to note that because many of the highestemitting 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 n onattainment areas than have been analyzed in
this RIA.

In addition, as discussed in chapter 3 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 fo r compliance with the Pb NAAQS; however, dispersion models are data –inten sive and more appropriate for lo cal scale analyses of emissions

from individual sources. It wa s not feasible to conduct such a la rge-scale data in tensive analysis for this RIA. As a result, the simplified anal ysis developed for this RIA, while distanceweighting individual source contri butions to ambient Pb concentr ations, could not account for such locally critical variables as meteorology and source stack height. Note also that the emissions inventory has limitations for Pb sources with very low emissions.

In the remainder of this section we re-state the most important limitations and

uncertainties in the cost and benefit estimates.

Uncertainties specifically related to th e cost estimates include the following:7-6 • Because limited data are available on fugitive and area source emissions, our analysis of the costs associated with the proposed and alternative lead NAAQS does not consider fugitive and area source controls that may be implemented to comply with these standards. For areas where point source cont rols are sufficient to reach attainment, we may have overestimated costs if more cost-effective fugitive or area source controls are likely to be available in these areas.

• To assess the cost of reducing point source l ead emissions, this an alysis relies upon PM
control cost information from AirControlNET. EPA's database of lead-focused controls is very limited, however, and there are no lead-s pecific controls in AirControlNET. In addition, for several sources AirControlNET contains no information on PM controls either. Such sources represent approximately 8 percent of the point source lead emissions included in our analysis (i.e ., AirControlNET contains cont rol measure data for those lead sources that represent 92 percent of the lead emissions included in the analysis).
Costs to control lead emissions from these so urces may be less than or greater than costs
to control emissions from other sources.

• The ambient extrapolation methodology empha sizes control costs that are the most expensive within an area, and assumes that knowledge of control costs from monitor
areas that attain have no influence on the average control costs for areas that need unspecified emission reductions. It also assu mes there will be no increased knowledge of sources or changed in technology between now and 2016. Lastly, most of the costs are based upon areas that make less than 1% pr ogress towards attainment, indicating what little knowledge we have about controls in those areas.

The cost curve methodology for unspecified emission reductions also presents a poor conceptual relationship between the costs of id entified controls at a national level and the
costs of control at a local level. The data underlying this curve cont ains data points which we believe to be invalid (pre sented as part of the distri butional analysis in Section 6.1.3.3). The estimated curve estimates ne gative costs over a portion of emission reductions. In addition this appr oach relies heavily on the control strategy for the tightest standard alternative analyzed in this RIA, and does not account for variability in control strategies across alternative st andards analyzed. Lastly, we do not believe this curve well represents the knowledge of how control costs behave over time.

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 loglinear estimate from a recently published pooled analysis of seven studies (Lanphear et al., 2005). Using alternate estimates from ot her epidemiological studies examining the
link between blood lead level a nd children’s IQ has significant impact on benefits results.
We found the benefits to decrea se by as much as 72 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 f ound to be the best fit for th e data (i.e., the natural logtransformed blood lead level is used to predic t changes in IQ score) . This model implies that the magnitude of changes in IQ increases with lower blood lead levels. However, in7-7 the Schwartz (1993) and Canfield et al. (2003) studies, a sing le 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 bl ood 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 th at the decrements in intellectual (cognitive) functions in children are proportiona tely greater at Pb concentrations <10

μ g/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 estim ates 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 va lues from lead monitoring data from 2003-
2005 to convert the baseline second maximu m monthly mean values in 2016 into maximum quarterly mean for the “base case” as well as to convert the alternative second maximum monthly mean NAAQS into a ma ximum quarterly mean for the “control
scenarios.” If the true ratio between the second maxi mum monthly means to the maximum quarterly mean is different in 2016 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 exposur e concentrations that we used for our primary estimate is associated with some un certainty. 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 locatio ns, such as Hillsborough County, FL, there are
a sufficient number of lead and TSP monito rs 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.

• We assumed that the IQ point effects of a change in c oncurrent blood lead (i.e., the effects of a change in 2016) apply to all children in our study population that were under seven years of age in 2016. If there is a cr itical window of exposur e for IQ effects (e.g., between the ages of one and two), then we c ould potentially be overestimating benefits in
2016 because we would have overestimated the population affected by reduced lead exposure in that year. However, if partia l or full achievement of the alternative NAAQS levels might occur earlier than 2016, the childr en in our 0-6 age cohort who are past any
critical window in 2016 would ha ve realized the partial or fu ll benefits of reduced lead exposures in those earl ier 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 firs t approximation to the impact of changes in
ambient air concentrations of lead on concurre nt blood lead levels, ap plied in the absence of modeling data on lead transport and depos ition and the on direct and indirect human exposures. While the values we apply match fairly well with availa ble literature, there7-8 are relatively few studies that report such valu es or provide sufficien t data to calculate such ratios. Further, the lead concentrations in those studies tend to be higher than those modeled here (EPA, 2007a); thus uncertainty remains as to whether the same ratios would be expected at lower levels, or whethe r air exposures are more or less efficient at changing concurrent blood lead leve ls 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 unde rstate 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 prenat al mortality. Additiona l 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 appl y 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 ec onomics 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 ba sed on current data on labor-force participation rates, survival proba bilities, and assumpti ons 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 produc tive services occurr ing outside the labor force (e.g., child rearing and housework).

• Because of the relatively strong relationship between PM 2.5 concentrations and premature mortality, PM co-benefits resulting from re ductions in fine particulate emissions can make up a large fraction of total monetized benefits, depending on the specific PM mortality impact function used, and to a lesse r extend on the relative magnitude of direct

lead benefits. The lower end of the range assumes PM 2.5 benefits are based on the PMmortality concentration-response relationship provided by Expert K; the upper end of the range assumes the relationship provided by Expert E. The relative share of co-control to primary lead benefits varies only modestly across the four alternative standards.

• Co-control benefits estimated he re 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, e xposure, baseline health incidence rates, or other local factors that might lead to an over-estimate or under-es timate of the actual benefits of controlling directly emitted fine particulates.7-9 Conclusions and Insights

EPA’s analysis has estimated the health and welfare benefits of reductions in ambient concentrations of lead resulting fr om a set of illustrative control st rategies 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 2016. While 2016 is the late st date by which states would generally
need to demonstrate attainment with the revised st andards, it is expected th at benefits (and costs) will begin occurring earlier, as states begin implementing control measures to show progress
towards attainment.

There are several important fact ors to consider when evalua ting the relative benefits and costs of the attainment strategies for the si x alternative standards assessed in this RIA:

• Benefits and costs are distributed differently across potential non-attain ment counties. As presented in Chapter 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 iden tified control costs, on the other hand, are greatest in those areas with th e 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 corr elation to control costs. In general, PM cocontrol benefits tend to be hi ghest in those areas where our attainment strategy suggests controls on combustion sources, rather th an metals processing, are necessary.

• Our analysis considers controls on point s ource emissions only. Local areas might find that controls of area nonpoi nt sources would be more cost-effective or better demonstrated than the point source controls we model. In addition, at this time we have
not considered whether Federa l action might reduce the cont ribution of leaded aviation gasoline to local lead concentrations, particularly in areas where we find it difficult or impossible to reach attainment based on point sources controls alone.CHAPTER 8. STATUTORY AND EXECUTIVE ORDER REVIEWS

A. Executive Order 12866: Regulatory Planning and Review

Under section 3(f)(1) of Executive Orde r (EO) 12866 (58 FR 51735, October 4, 1993), this action is an “economically significant regulatory action” because it is likely to have an annual effect on the economy of $100 m illion or more. Accordingly, EPA submitted this action to the Office of Management and Budget (OMB) for review under EO 12866 and any changes made in response to OMB recommendations have been documented in the docket for this action (EPA-HQ-OAR-2006- 0735). In addition, EPA prepared this Regulatory Impact Analysis (RIA) of the potentia l costs and benefits associated with this action. However, the CAA and judicial deci sions make clear that the economic and technical feasibility of attaining ambient standa rds are not to be cons idered in setting or revising NAAQS, although such factors may be c onsidered in the development of State plans to implement the standards. Accord ingly, although an RIA has been prepared, the
results of the RIA have not been co nsidered in issuing the final rule.

B. Paperwork Reduction Act

The information collection requirements in this final rule will be submitted for approval to the Office of Management and Budget (OMB) under the Paperwork Reduction Act, 44 U.S.C. 3501 et seq. The information collec tion requirements are not enforceable until OMB approves them.

The information collected under 40 CFR part 53 (e.g., test results, monitoring records, instruction manual, and other as sociated information) is n eeded to determine whether a
candidate method intended for use in determining attainment of the National Ambient Air Quality Standards (NAAQS) in 40 CFR part 50 will meet the design, performance, and/or comparability requirements for designation as a Federal reference method (FRM) or Federal equivalent method (FEM). While this final rule amends the requirements for Pb FRM and FEM determinations, they merely pr ovide additional flexibility in meeting the FRM/FEM determination requirements. Furt hermore, we do not expect the number of
FRM or FEM determinations to increase over the number that is currently used to
estimate burden associated with Pb FRM/FE M determinations provided in the current ICR for 40 CFR part 53 (EPA ICR numbers 0559.12). As such, no change in the burden estimate for 40 CFR part 53 has been ma de as part of this rulemaking.

The information collected and reported under 40 CFR part 58 is needed to determine compliance with the NAAQS, to characterize air quality and associated health and ecosystem impacts, to develop emissions control strategies, and to measure progress for the air pollution program. The proposed am endments would revise the technical requirements for Pb monitoring sites, require the siting and operation of additional Pb ambient air monitors, and the reporting of the collected am bient Pb monitoring data to EPA’s Air Quality System (AQS). We have estimated the burden based on the finalmonitoring requirements of this rule. Base d on these requirements, the annual average reporting burden for the collec tion under 40 CFR part 58 (ave raged over the first 3 years of this ICR) for 150 respondents is estimated to increase by a tota l of 42,165 labor hours per year with an increase of $3,207,407 per year . Burden is defined at 5 CFR 1320.3(b).

C. Regulatory Flexibility Act

The Regulatory Flexibility Act (RFA) gene rally requires an agency to prepare a regulatory flexibility analysis of any rule subject to notice and comment rulemaking requirements under the Administrative Proce dure Act or any other statute unless the agency certifies that the rule will not have a significant ec onomic impact on a substantial
number of small entities. Small entities include small businesses, small organizations, and small governmental jurisdictions.

For purposes of assessing the impacts of this ru le on small entities, small entity is defined as: (1) a small business that is a small industrial entity as defined by the Small Business Administration’s (SBA) regulations at 13 CFR 121.201; (2) a small governmental jurisdiction that is a government of a city, county, town, school district or special district with a population of less than 50,000; and (3) a small organization th at is any not-forprofit enterprise which is i ndependently owned and operated and is not dominant in its
field.

After considering the economic impacts of this final rule on small entities, the
Administrator certified this action will not have a significant economic impact on a
substantial number of small entities. This final rule will not impose any requirements on small entities. Rather, this rule esta blishes national standards for allowable
concentrations of Pb in ambient air as required by section 109 of the CAA. American Trucking Ass’ns v. EPA, 175 F. 3d 1027, 1044-45 (D.C. cir. 1999) (NAAQS do not have significant impacts upon small entities because NAAQS themselves impose no regulations upon small entities). Similarly, th e amendments to 40 CFR part 58 address the requirements for States to collect information and report compliance with the NAAQS and will not impose any requirements on small entities.

D. Unfunded Mandates Reform Act

Title II of the Unfunded Mandates Reform Act of 1995 (UMRA), Public Law 104-4, establishes requirements for Federal agencies to assess the effects of their regulatory actions on State, local, and tr ibal governments and the priv ate sector. Unless otherwise prohibited by law, under section 202 of th e UMRA, EPA generally must prepare a written statement, including a cost-benefit an alysis, for proposed and final rules with “Federal mandates” that may result in e xpenditures to State, local, and tribal governments, in the aggregate, or to the pr ivate sector, of $100 million or more in any one year. Before promulgating an EPA rule for which a written statement is required under section 202, section 205 of the UMRA generally requires EPA to identify andconsider a reasonable number of regulatory al ternatives and to adopt the least costly, most cost-effective or least burdensome alternative that achieves the objectives of the rule. The provisions of section 205 do not apply when they are inconsistent with applicable law. Moreover, s ection 205 allows EPA to adopt an alternative other than the least costly, most cost-effective or least burdensome alternative if the Administrator publishes with the final rule an explanation w hy that alternative was not adopted. Before EPA establishes any regulatory requirements that may significantly or uniquely affect small governments, including tribal government s, it must have developed under section 203 of the UMRA a small government agency pl an. The plan must provide for notifying potentially affected small governments, enab ling officials of affected small governments to have meaningful and timely input in the development of EPA regulatory proposals with significant Federal intergovernmental mandates, and informing, educating, and advising small governments on compliance w ith the regulatory requirements.

This action is not subject to the requireme nts of sections 202 and 205 of the UMRA.
EPA has determined that this final rule does not contain a Federal mandate that may result in expenditures of $100 million or more fo r State, local, and tribal governments, in the aggregate, or the private sector in any one year. Th e revisions to the Pb NAAQS impose no enforceable duty on any State, local or Tribal governments or the private sector. The expected costs associated with the increased monitoring requirements are described in EPA’s ICR document, but thos e costs are not expected to exceed $100 million in the aggregate for any year. Furtherm ore, as indicated previously, in setting a NAAQS EPA cannot consider th e economic or technological feasibility of attaining ambient air quality standard s. Because the Clean Air Act prohibits EPA from considering the types of estimat es and assessments described in section 202 when setting the NAAQS, the UMRA does not require EPA to prepare a written statement under section 202 for the revisions to the Pb NAAQS.

EPA has determined that this final rule c ontains no regulatory re quirements that might significantly or uniquely affect small gove rnments because it imposes no enforceable duty on any small governments. Therefore, this rule is not subject to the requirements of
section 203 of the UMRA.

E. Executive Order 13132: Federalism

Executive Order 13132, entitled “Federalism” (64 FR 43255, August 10, 1999), requires EPA to develop an accountable process to en sure “meaningful and timely input by State and local officials in the development of regulatory policies that have federalism implications.” “Policies that have federali sm implications” is defined in the Executive Order to include regulations that have “subs tantial direct effects on the States, on the relationship between the nati onal government and the States, or on the distribution of power and responsibilities among the va rious levels of government.”

This final rule does not have federalism impli cations. It will not ha ve substantial direct effects on the States, on the relationship betw een the national government and the States,or on the distribution of power and res ponsibilities among the various levels of government, as specified in Executive Order 13132. The rule does not alter the relationship between the Federa l government and the States regarding the establishment and implementation of air quality improvement programs as codified in the CAA. Under section 109 of the CAA, EPA is mandated to establish NAAQS; however, CAA section 116 preserves the rights of Stat es to establish more stringent requirements if deemed necessary by a State. Furthermore, unde r CAA section 107, the States have primary responsibility for implementation of the NAAQS. Finally, as noted in section E (above) on UMRA, this rule does not impose signifi cant costs on State, local, or tribal governments or the private sector. Thus, Executive Order 13132 does not apply to this
rule.

F. Executive Order 13175: Consultation and Coordination with Indian Tribal

Governments

This action does not have tribal implicati ons, as specified in Executive Order 13175 (65 FR 67249, November 9, 2000). It does not have a substantial direct effect on one or more Indian Tribes, since Tribes are not obligated to adopt or implement any NAAQS or monitoring requirements for NAAQS. Thus , Executive Order 13175 does not apply to
this action.

G. Executive Order 13045: Protection of Child ren from Environmental Health & Safety Risks

This action is subject to EO 13045 (62 FR 19885, April 23, 1997) because it is an economically significant regulat ory action as defined by EO 12866, and we believe that the environmental health risk addressed by th is action has a disproportionate effect on
children. The final rule establ ishes uniform national ambient air quality standards for Pb; these standards are designed to protect public health with an adequate margin of safety, as required by CAA section 109. However, th e protection offered by these standards may be especially important for children because ne urological effects in children are among if not the most sensitive health endpoints for Pb exposure. Because children are considered a sensitive population, we have carefully eval uated the environmental health effects of exposure to Pb pollution among children. Th ese effects and the size of the population affected are summarized in chapters 6 and 8 of the Criteria Document and sections 3.3 and 3.4 of the Staff Paper, and the results of our evaluation of the effects of Pb pollution
on children are discussed in se ctions II.B and II.C of the notice of proposed rulemaking, and section II.A of this preamble.

H. Executive Order 13211: Actions that Significantly Affect Energy Supply,
Distribution or Use

This rule is not a “significant energy action” as defined in Executive Order 13211, “Actions Concerning Regulations That Signi ficantly Affect Ener gy Supply, Distribution,or Use” (66 FR 28355 (May 22, 2001)) because it is not likely to have a significant adverse effect on the supply, di stribution, or use of energy. The purpose of this rule is to establish revised NAAQS for Pb. The rule does not prescribe specifi c control strategies by which these ambient standards will be met. Such strategies will be developed by States on a case-by-case basis, and EPA ca nnot predict whether the control options selected by States will include regulations on energy suppliers , distributors, or users. Thus, EPA concludes that this rule is not likely to have any adverse energy effects.

H. National Technology Transfer and Advancement Act

Section 12(d) of the Nati onal Technology Transfer and Advancement Act of 1995 (NTTAA), Public Law No. 104-113, §12(d) (15 U.S.C. 272 note) directs EPA to use voluntary consensus standards in its regulat ory activities unless to do so would be inconsistent with applicable law or otherwis e impractical. Voluntary consensus standards are technical standards (e.g., materials specifi cations, test methods, sampling procedures, and business practices) that are developed or adopted by voluntary consensus standards
bodies. The NTTAA directs EP A to provide Congress, th rough OMB, explanations when the Agency decides not to use avai lable and applicable voluntary consensus
standards.

This final rule involves t echnical standards. EPA has established low-volume PM 10 samplers coupled with XRF analysis as the FRM for Pb-PM 10 measurement. While EPA
identified the ISO standard “Determination of the particulate lead content of aerosols collected on filters” (ISO 9855: 1993) as being potentially applicable, the final rule does not permit its use. EPA determined that the use of this voluntary consensus standard would be impractical because the analys is method does not provide for the method detection limits necessary to adequately char acterize ambient Pb c oncentrations for the purpose of determining compliance with the revisions to the Pb NAAQS.

J. Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populations and Low-Income Populations

Executive Order 12898 (59 FR 7629; Feb. 16, 1994) establishes federal executive policy on environmental justice. Its main provision directs federal agenci es, to the greatest extent practicable and permitted by law, to make environmental justice part of their mission by identifying and addressing, as a ppropriate, dispropor tionately high and
adverse human health or environmental effect s of their programs, policies, and activities on minority populations and low-income populations in the United States.

EPA has determined that this final rule will not have disproportionately high and adverse
human health or environmental effects on mi nority or low-income populations because it
increases the level of envi ronmental protection for all affected populations without having any disproportionately high and adverse human health or environmental effects on any population, including any minority or low-income population. The final ruleestablishes uniform national sta ndards for Pb in ambient air. In the Administrator’s judgment, the revised Pb NAAQS protect public health, including the health of sensitive groups, with an adequate margin of safety.

However, EPA believes that the newly strengthened Pb standards and the new requirements for ambient air monitoring for Pb will have the greatest benefit in reducing health risks associated with exposure to ambi ent air Pb in those areas where ambient air concentrations are currently the highest. T hus, to the extent that any population groups, including minorities or low-income p opulations, are currently experiencing disproportionate exposure to am bient air-related Pb, those groups can be expected to experience relatively greater air quality improvements under the revised standards. Nationwide, these revised, more stringent sta ndards will not have adverse health impacts on any population, including any minor ity or low-income population.