Regulatory Impact Analysis of the 

Proposed Revisions to the 

National Ambient Air Quality Standards for 

Lead 
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 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 
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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 control strategies were 

developed for final NAAQS of 0.15 μg/m3 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 OMB Circular A-4 Guidelines. This summary outlines the basis for 

and approach used for estimating the incremental costs and monetized benefits of these 

standards, presents the key results of the analysis, 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 primary lead (Pb) National 

Ambient Air Quality Standard (NAAQS) within the current monitoring network 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 there may be many more potential nonattainment areas 

than have been analyzed in this RIA. 

It is important to note at the outset that overall data limitations are very significant for 

this analysis, compared to other NAAQS reviews. One critical area of uncertainty is the limited 

TSP-Pb monitoring network (discussed in chapter 2). Because monitors are present in only 86 

counties nationwide, the universe of monitors exceeding the final NAAQS level of 0.15 μg/m3

represent only 16 counties. It is important to note that data limitations prevented us from 

identifying a full range of controls which would bring eight of these counties all the way to 

attainment of the final NAAQS. It is also important 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 nonattainment areas than have been analyzed in 

this RIA. 

In addition, EPA would prefer to use a detailed air quality model that simulates the 

dispersion and transport of lead to estimate local ambient lead concentrations with the 

hypothetical alternative emission control strategies expected under the NAAQS. Although 

models with such capabilities are available for pollutants for which EPA frequently conducts air 

quality analyses (e.g., particulate matter and ozone), regional scale models are currently neither 

available nor appropriate for lead.1

 As discussed in Chapter 3, EPA developed an air quality 

assessment tool to estimate the air quality impacts of each lead emissions control strategy. 

In setting primary ambient air quality standards, EPA’s responsibility under the law is to 

establish standards that protect 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 

 

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 standards based on health considerations only. 

The prohibition against the consideration of cost in the setting of the primary air quality 

standard, however, does not mean that costs or other economic considerations are unimportant or 

should be ignored. The Agency believes that consideration of costs and benefits is essential to 

making efficient, cost effective decisions for implementation of these standards. The impacts of 

cost and efficiency are considered by states during this process, as they decide what timelines, 

strategies, and policies are most appropriate. This RIA is intended to inform the public about the 

potential costs and benefits associated with a hypothetical scenario that may result when a new 

lead standard is implemented, but is not relevant to establishing the standards themselves. 

The analysis year for this regulatory impact analysis is 2016, consistent with the 

attainment year for the final lead NAAQS. For 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 analyzed standards, while others will need less time. 

This analysis does not prejudge the attainment dates that will ultimately be assigned to individual 

areas under the Clean Air Act, which provides flexibility to postpone compliance dates, provided 

that the date is as expeditious as practicable. 

EPA presents this RIA pursuant to Executive Order 12866 and the guidelines of OMB 

Circular A-4.2

 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-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 strategies; 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 concentrations for this RIA are based on lead 

emissions data from the 2002 National Emissions Inventory (NEI) and lead concentration values 

for 21 lead monitors included in the 2003-2005 Pb-TSP NAAQS-review database. Consistent 

with the PM2.5 NAAQS RIA and ozone RIA, no growth factors were applied to the 2002 NEI 

emissions estimates to generate the emissions or air quality projections for 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/circulars/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 includes similar adjustments for 

compliance measures simulated by the September 2006 revision to the PM2.5 NAAQS (as 

included in the illustrative PM2.5 control strategy described in the PM2.5 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 for our analysis, we collected information on PM 

control technologies, assuming that the control efficiency for PM would also apply to lead 

emissions. Most of this information was obtained from EPA's AirControlNET database, but a 

limited number of controls were identified from New Source Performance Standards and 

operating permits that apply to facilities with similar Source Classification Codes as the point 

sources included in our analysis.4 Controls identified through this process include major 

emissions controls, such as fabric filters, impingement-plate scrubbers, and electrostatic 

precipitators; and minor controls, such as increased monitoring frequency, upgrades to 

continuous emissions monitors, and diesel particulate filters for stationary sources. In addition, 

we modeled replacement of the large primary lead 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 source would implement if it 

were controlled as part of a local lead attainment strategy. Based on these controls, the 

optimization model then identifies sources to control such that each area would reach attainment 

at the least aggregate cost possible for the area. 

It is important to remember that, compared to recent NAAQS RIAs, our current 

knowledge of the costs and nature of lead emissions controls is relatively poor. Lead in ambient 

air has not been a focus for all but a few areas of the country for the last decade or more; the 

selected standard of 0.15 μg/m3

 represents 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 source.

 

3

 U.S. Environmental Protection Agency. (2006). Final Regulatory Impact Analysis: PM2.5 NAAQS. Office of Air 

and Radiation, Research Triangle Park, NC. The Missouri lead SIP was finalized by EPA on April 14, 2006 with a 

requirement that this SIP will provide attainment with the current lead standard by April 7, 2008. The SIP is 

available at http://www.dnr.mo.gov/env/apcp/docs/2007revision.pdf. 

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 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 monitoring network is in place, other 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 in this RIA 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 typical 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. Many industrial sources of lead emissions emit very small 

quantities of lead in absolute terms. Our cost modeling shows that some could face significant 

costs to reduce these low levels of lead, costs which could be prohibitively 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 cost effective than adding additional control 

technology. Such measures might include increasing the enclosure of buildings, increasing air 

flow in hoods, modifying operation and maintenance procedures, changing feed materials to 

lower Pb content, measures to suppress dust from tailings piles, etc. 

Finally, some monitor areas are not projected 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 about 94% of the emission reductions needed to 

reach full attainment in all areas). For the selected NAAQS and each alternative 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

ANALYSIS
ES-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 quality model that simulates the dispersion and 

transport of lead to estimate local ambient lead concentrations. Although models with such 

capabilities are available for pollutants for which EPA frequently conducts air quality analyses 

(e.g., particulate matter and ozone), regional scale models are currently neither available nor 

appropriate for Pb.5

 Dispersion, or plume-based, models are recommended for compliance with 

the Pb NAAQS; however, dispersion models are data –intensive and more appropriate for local 

scale analyses of emissions from individual sources. It was not feasible to conduct such a largescale data-intensive analysis for this RIA. 

Our air quality assessment tool, developed for the purposes of this analysis, employs a 

source-apportionment approach to estimate the extent to which each of the following emissions 

sources contribute to observed lead concentrations in each monitor area: 

• Background lead 

• Miscellaneous, re-entrained dust 

• Emissions from area non-point sources 

• Indirect fugitive emissions from active industrial sites 

• Point source emissions6

After allocating a portion of the observed lead concentration for each monitor area to the first 

four categories listed above, the assessment tool apportions the remaining concentration among 

all inventoried point sources within ten kilometers of each monitor location by distanceweighting individual source contributions 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 that attributable to indirect fugitive emissions from active 

point sources, this analysis assumes that the two contributions are directly related, and any reduction in the air 

quality impact of point source emissions would produce a corresponding reduction in the air quality impact of 

indirect fugitive emissions from point sources in that monitor area. The process used to relate the contributions of 

these two categories is described in further detail in Chapter 3 of this RIA. 
ES-7

Our analysis of the benefits associated with the selected NAAQS includes benefits related 

to reducing ambient lead concentrations and the ancillary benefits of reducing direct emissions of 

particulate matter. To assess benefits specific to reduced lead concentrations, we created a 

spreadsheet model that provides a screening-level assessment of health benefits occurring as a 

result of implementing 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 years of age. Cognitive effects are thought to 

strongly relate to a child’s future productivity and earning potential.8

 

The model was constructed in Microsoft Excel and provides an integrated tool to 

complete five benefits estimation steps: 1) estimate lead in air concentrations for the “base case” 

and “control scenarios”; 2) estimate population exposures to air lead concentrations for each 

scenario; 3) estimate blood lead levels in the population for each scenario; 4) estimate avoided 

cases of health effects due to changes in blood lead levels; and 5) apply an economic unit value 

to each avoided case to calculate total monetized benefits. 

Because most of the point source measures implemented to achieve the NAAQS standard 

are focused on controlling emissions of lead in particulate form, virtually all of these measures 

also have a significant impact on emissions of directly emitted particulate matter. To estimate 

the value of these PM2.5 emissions reductions, EPA utilized PM2.5 benefit-per-ton estimates. 

These PM2.5 benefit-per-ton estimates provide the total monetized human health benefits (the 

sum of premature mortality and premature morbidity) of reducing one ton of PM2.5 from a 

specified source. EPA has used a similar technique 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 Support Document (TSD) accompanying the recent final 

ozone NAAQS RIA.10

Analysis of Costs 

Consistent with our development of the illustrative control strategies described above, our 

analysis of the costs associated with the selected NAAQS focuses on point source PM controls. 

For the purposes of this analysis, these controls largely include measures from the 

AirControlNET control technology database, but also include additional measures associated 

with operating permits and/or New Source Performance Review standards applicable to sources 

similar to those included in our analysis. For controls identified in AirControlNET, we estimated 

 

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 AirControlNET 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 costs using two different approaches. Under one 

approach, we extrapolated the cost of unspecified emission reductions by constructing a cost 

curve using data on identified control costs. We then derived a total cost equation in quadratic 

form which best fit the total cost curve. Under our second 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 applied unspecified emission reductions to all sources 

until attainment was reached. 

ES.3 Results of Analysis

Air Quality

Table ES-1 summarizes the number of monitor 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 attainment with the selected NAAQS following 

implementation of identified controls. Table ES-2 also shows results for alternative NAAQS of 

0.10 and 0.40 μg/m3

. (In the body of the RIA, we also provide analysis of three other alternative 

NAAQS.) For the alternative of 0.10 μg/m3

, only 9 of the 21 monitors are able to reach 

attainment from application of identified controls. By comparison, all but one monitor area 

reach attainment through the implementation of identified controls under the 0.40 μg/m3

standard. 
ES-9

Table ES-1. Number of Monitor Sites Reaching Attainment with Each 

Alternative Standard using Identified 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 μg/m3 

Second 

Maximum 

Monthly Mean 

12 20 21 

0.15 μg/m3 

Second 

Maximum 

Monthly Mean 

5 13 21 

0.10 μg/m3 

Second 

Maximum 

Monthly Mean 

21 

0 9 20 

The failure of certain areas to reach attainment with identified 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 reductions 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 assumptions used in estimating the unspecified 

emission reductions drive the cost estimates. Under 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 extrapolated costs. Under the second approach, 

5% of the total costs come from our analysis of currently known control technologies, and the 

majority of the costs (95%) comes from our assumptions 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 measures that lead sources may implement. It is 

important to remember that, compared to recent NAAQS RIAs, our current knowledge of the 

costs and nature of lead emissions controls is relatively poor. Lead in ambient air has not been a 
ES-10

focus for all but a few areas of the country for the last decade or more; the alternative standards 

represent a substantial tightening of the existing NAAQS. As a result, although AirControlNET 

contains information on a large number of different point source controls, we would expect that 

State and local air quality managers would have access to additional information on the controls 

available to the most significant sources. 

Table ES-3 presents the benefits of the proposed and alternative standards as a range to 

account for uncertainties associated with the benefits of the standards. The range in the benefits 

estimates related to IQ gains reflects two estimates of the earnings impacts associated with such 

gains. The low end of the range reflects an analysis by Schwartz, which estimated that a 1-point 

increase in IQ would increase earnings by 1.76 percent, while the high end of the range reflects 

the results of Salkever, which found that earnings increase by 2.38 percent for each 1-point 

increase in IQ.11 The range of estimates presented for PM-related benefits is based on the upper 

and lower ends of the range of PM2.5 premature mortality functions obtained by EPA through its 

expert elicitation study on the PM-mortality relationship, as first reported by Industrial 

Economics and interpreted for benefits analysis in EPA's final RIA for the PM NAAQS, 

published in September 2006.12

 Table ES-4 presents a comparison 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 PM2.5 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: PM2.5 NAAQS. Office of Air and Radiation, Research Triangle Park, NC. 
ES-11

Table ES-2. Summary of Costs for Regulatory Alternatives (Millions of 2006$)*

Alternative NAAQS: 0.4 

μg/m3

 2nd Maximum 

Monthly Mean 

Final NAAQS: 0.15 μg/m3

2nd Maximum Monthly 

Mean 

Alternative NAAQS: 0.1 

μg/m3

 2nd 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 

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

Costs Ambient 

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

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

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 provisions, monitoring costs are included here. 

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

 

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

Alternative Standard: 0.40 

μg/m3

 2nd Maximum Monthly 

Mean 

Final NAAQS: 0.15 μg/m3 2nd

Maximum Monthly Mean 

Alternative Standard: 0.10 

μg/m3

 2nd 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
ES-12

Table ES-4. Summary of Net Benefits for Regulatory Alternatives (Millions of 2006$)13

Alternative Standard: 0.40 μg/m3

2nd Maximum Monthly Mean 

Final NAAQS: 0.15 μg/m3 2nd

Maximum Monthly Mean 

Alternative Standard: 0.10 μg/m3

2nd 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 benefits 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 the 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 μg/m3

 maximum quarterly mean) lead NAAQS to the revised or 

alternative NAAQS would be expected to have a significant impact on the IQ of young children. 

More specifically, the results indicate that the number of IQ points gained in 2016 ranges from 

230,000 if a 0.4 μg/m3 second maximum monthly mean NAAQS is achieved up to 510,000 for a 

0.10 μg/m3

 second maximum monthly mean NAAQS. 

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

Standard 

IQ Points 

Gained 

0.40 μg/m3

 Second Maximum Monthly 

Mean 230,000 

0.15 μg/m3 Second Maximum Monthly 

Mean 400,000 

0.10 μg/m3 Second Maximum Monthly 

Mean 

510,000 

Our analysis suggests that the benefits presented in Table ES-5 will be concentrated in a 

small number of counties. Table ES-6 below shows the distribution of total benefits due to IQ 

points gained for the 0.15 μg/m3 second maximum monthly mean NAAQS alternative. For this 

standard, approximately 60 percent of the total benefits are due to changes in lead air 

concentrations in three counties: Hillsborough, Florida; Delaware, Indiana; and Berks, PA. In 

these areas, sources of lead exposure and the monitors that measure ambient lead appear to be in 

relatively close proximity to exposed populations. 

Table ES-6. Percentage of Benefits by Monitor (0.15 μg/m3 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, such 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 exceeding the various target NAAQS levels is very 

small; only 21 counties exceeding the lowest alternative NAAQS level of 0.10 μg/m3

. 

Because we know that many of the highest-emitting Pb sources in the 2002 NEI do not 

have nearby Pb-TSP monitors (see section 2.1.7), it is likely that there may be many more 

potential nonattainment areas than have been analyzed in this RIA. 

• Simplified Air Quality Assessment Approach. Dispersion, or plume-based models are 

recommended for compliance with the Pb NAAQS; however, dispersion models are 

data–intensive and more appropriate for local scale analyses of emissions from individual 

sources. It was not feasible to conduct such a large-scale data intensive analysis for this 

RIA. As a result, the simplified analysis developed for this RIA while distanceweighting individual source contributions to ambient Pb concentrations, could not 

account for such locally critical variables as meteorology and source 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 air quality impacts does not account for the 

potential implementation of such controls in areas where they might be effective. 

Although the analysis estimates the impact of point source controls on indirect fugitives, 

it does not consider the impact of controlling these emissions directly. This and the lack 

of control information for area nonpoint sources may have contributed to our projection 

of nonattainment in some areas. Additionally, for this analysis we have not modeled the 

effect of any potential changes in emissions at airports with lead emissions associated 

with use of leaded aviation gasoline 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 endanger the public health or welfare, and to 

take action to control lead emissions from piston-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 standards using identified controls alone. 
ES-16 

• Actual State Implementation Plans 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 emissions controls that may differ from those 

simulated in this analysis. This analysis therefore represents an approximation of the 

emissions reductions that would be required to reach attainment and should not be treated 

as a precise estimate. 

• 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 recognition of the greater uncertainty associated with achieving 

emissions reductions from measures that may not be currently in use or known to EPA. 

Nonetheless, EPA believes it is reasonable to project that, with at least 10 years of lead 

time before a 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 specifically focused on cost-effectively reducing PM emissions with high lead 

content. Because the current standard is attained in all but a few areas of the country, and 

has been for many years since the phase down of lead in gasoline, it is likely that very 

little effort has been devoted to development of lead emissions control technologies 

except for industries where regulations have been imposed to reduce lead (e.g., large 

MWC standard, primary and secondary lead smelter MACTs, etc.). 

Costs

• Uncertainty associated with unspecified emission reductions. As indicated above, some 

areas are expected to rely on unspecified emission reductions to reach attainment with the 

standards. The cost of implementing these measures, though estimated here based on the 

costs for identified controls, is uncertain. Many of these sources are already wellcontrolled for particulate matter, and additional 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 generally not applied at that emissions level.

• Uncertainty associated with emissions estimates for smaller sources. Note that there is 

often greater inherent uncertainty in reported 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 costs of unspecified emission 

reductions. The ambient extrapolation methodology emphasizes 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 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% progress 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 identified controls at a national level and the 
ES-17 

costs of control at a local level. The data underlying this curve contains data points which 

we believe to be invalid (presented as part of the distributional analysis in Section 

6.1.3.3). The estimated curve estimates negative costs over a portion of emission 

reductions. In addition this approach 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 standards 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 children were very sensitive to the method 

employed for estimating exposures to the population. When comparing the default 

method, which involved concentrations that were interpolated from multiple monitors, to 

the method assuming a uniform concentration within a 10 km radius around an individual 

monitor, the results increase by 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 selected for quantifying the 

number of IQ points gained as a result of achieving the alternative NAAQS levels 

affected the results. Utilizing alternate epidemiological studies decreased the primary 

estimate by as much as 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 loss 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 dollar benefit per ton estimate of the benefits of reducing 

directly emitted fine particulates from point sources. Because they are based on nationallevel analysis, the benefit-per-ton estimates used here do not reflect local meteorology, 

exposure, baseline health incidence rates, or other local factors that might lead to an overestimate or under-estimate of the actual benefits 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 variety of sources in and around projected nonattaining counties in 
ES-18 

2016. While 2016 is the latest date by which states would generally need to demonstrate 

attainment with the revised standards, it is expected that benefits (and costs) may begin occurring 

earlier, as states begin implementing control measures to show progress towards attainment. 
1-1 

CHAPTER 1: INTRODUCTION AND BACKGROUND

Synopsis 

This document estimates the incremental costs and monetized human health benefits of 

attaining a revised primary lead (Pb) National Ambient Air Quality Standard (NAAQS) 

nationwide. This document contains illustrative analyses that consider limited emission control 

scenarios that states, tribes and regional planning 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. According to the Clean Air 

Act, EPA must use health-based criteria in setting the NAAQS and cannot consider estimates of 

compliance cost. This Regulatory Impact Analysis (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 (described 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) National Ambient Air Quality Standard 

(NAAQS) within the current monitoring network1. Many of the highest-emitting Pb sources do 

not have nearby Pb-TSP monitors, and it is important to note that there may be many more 

potential nonattainment areas than have 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. 7408) directs the Administrator to identify pollutants which 

“may reasonably be anticipated to endanger public health or welfare,” and 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 effects 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 has developed air quality criteria. 

Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate 

“primary” and “secondary” NAAQS for pollutants 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] criteria 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 maintenance of which in the 

judgment of the Administrator, based on [the] criteria, [are] requisite to protect the public 

welfare from any known or anticipated adverse effects 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 monitors representing 86 counties, but only 21 counties have monitors 

which exceed the final NAAQS of 0.15 ug/m3

. 
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 Administrator to review existing criteria and 

standards at 5-year intervals. When 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 Analysis in the NAAQS Setting Process 

1.2.1 Legislative Roles 

In setting primary ambient air quality standards, EPA’s responsibility under the law is to 

establish standards that protect 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 standards based on health considerations only. 

The prohibition against the consideration of cost in the setting of the primary air quality 

standard, however, does not mean that costs or other economic considerations are unimportant or 

should be ignored. The Agency believes that consideration of costs and benefits are essential to 

making efficient, cost effective decisions for implementation of these standards. The impact of 

cost and efficiency are considered by states during this process, as they decide what timelines, 

strategies, and policies make 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 the 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. This document is separate from the NAAQS 

decision making process, but there are several statutes and executive orders that still apply to any 

public documentation. The analysis required by these statutes and executive orders is presented 

in Chapter 8. 

EPA presents this RIA pursuant to Executive 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-effectiveness 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-effectiveness 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 the reasons a regulation such as the NAAQS may 

be issued is to address market failure. The major types of market failure include: externality, 

market power, and inadequate or asymmetric information. 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 distributional unfairness, or promoting privacy 

and personal freedom. 

An externality occurs when one party’s actions impose uncompensated benefits or costs 

on another party. Environmental problems are a classic case of externality. For example, the 

smoke from a factory may adversely affect the health of local residents while soiling the property 

in nearby neighborhoods. If bargaining was costless and all property rights were well defined, 

people would eliminate externalities through bargaining without the need for government 

regulation. From this perspective, externalities arise from high transaction costs and/or poorly 

defined property rights that prevent people from reaching efficient outcomes through market 

transactions. 

Firms exercise market power when they reduce output below what would be offered in a 

competitive industry in order to obtain higher prices. They may exercise market power 

collectively or unilaterally. Government action can be a source of market power, such as when 

regulatory actions exclude low-cost imports. Generally, regulations that increase market power 

for selected entities should be avoided. However, 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 exist. In such cases, the government may choose to approve the 

monopoly and to regulate its prices and/or production decisions. Nevertheless, 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 inadequate 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 therefore not require government regulation. Sellers 

have an incentive to provide information through advertising that can increase sales by 

highlighting distinctive characteristics of their products. Buyers may also obtain reasonably 

adequate information about product characteristics through other channels, such as a seller 

offering a warranty or a third party providing information. 

There are justifications for regulations in addition to correcting market failures. A 

regulation may be appropriate when there are clearly identified measures that can make 

government operate more efficiently. In addition, Congress establishes some regulatory 

programs to redistribute resources to select groups. Such regulations should be examined to 

ensure that they are both effective and cost-effective. Congress also authorizes some regulations 

to prohibit discrimination that conflicts with generally accepted norms within our society. 
1-4 

Rulemaking may also be appropriate to protect privacy, permit more personal freedom or 

promote other democratic aspirations. 

From an economics perspective, setting an air 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 compensation for those suffering the problems. Setting a 

standard with a reasonable margin of safety attempts to place the cost of control on those who 

emit the pollutants and lessens the impact on those 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 states 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 outcomes. 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 implementation plans states develop, and the distinctions are worth brief 

mention. This RIA does not assess the regulatory 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 standard. This analysis attempts to 

estimate the costs and human and welfare benefits of cost-effective implementation strategies 

which might be undertaken to achieve national attainment 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 control scenarios. State implementation plans would 

likely vary from EPA’s estimates due to differences in the data and assumptions that states use to 

develop these plans. 

The illustrative attainment scenarios presented in this RIA were constructed with the 

understanding that there are inherent uncertainties in projecting emissions and controls. 

Furthermore, certain emissions inventory, control, modeling and monitoring limitations and 

uncertainties inhibit EPA’s ability to model full attainment 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 the costs and benefits of hypothetical national 

strategies to attain several potential revised primary 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 provides an illustration of the 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

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

ANALYSIS
1-6 

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 Act. Many areas will reach attainment of any 

alternative Pb standard by 2016. For 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 analyzed standards, while others will need less time. This analysis does not 

prejudge the attainment dates that will ultimately be assigned to individual areas under the Clean 

Air Act, which contains a variety of potential dates and flexibility to move to later dates, 

provided that the date is as expeditious as practicable. 

The methodology first estimates what baseline lead levels might look like in 2016 with 

existing Clean Air Act programs, including application of controls to meet the current Pb 

NAAQS, various maximum achievable control technology (MACT) standards, and the newly 

revised PM NAAQS standard, and then predicts the change in Pb levels following the 

application of additional controls to reach tighter alternative standards. This allows for an 

analysis of the incremental change between the current standard and alternative standards. Since 

Pb is also a component of PM, it is important that we account for the impact on Pb 

concentrations of PM controls used in the hypothetical control scenario in the PM NAAQS RIA, 

so as to avoid double counting the benefits and costs of these controls. 

1.3.2 Control Scenarios Considered in this RIA 

Hypothetical control strategies were developed for the final Pb NAAQS of 0.15 μg/m3

. 

plus four alternative Pb standards encompassing a range of 0.10 μg/m3

 to 0.50 μg/m3. First, EPA 

developed an air quality assessment tool to estimate air quality changes that would result from 

the application of emissions control options that are known to be available to different types of 

sources in areas with monitoring levels currently 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 order to bring these monitor areas into attainment, we calculated 

controls costs using two different approaches. Under one scenario, we calculated the cost of 

unspecified emission reductions by extrapolating from current average national identified control 

costs. Under our second scenario, 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 

applied unspecified emission reductions to all sources until attainment 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 strategies, albeit with substantial additional uncertainty regarding the 

second step estimates. First, the costs associated with applying known controls were quantified. 
1-7 

Second, EPA estimated costs of the additional tons of extrapolated 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 standard due to resource and time constraints. Since the secondary 

is being set to be equivalent to the primary standard, no additional costs and benefits are 

expected. 

To streamline this RIA, this document refers to several previously published documents, 

including three technical documents EPA produced 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 pertinent information on 

atmospheric science, air quality, exposure, dosimetry, health effect, and environmental effects of 

lead. The second was a “Staff Paper” (published in 2007) that evaluated the policy implications 

of the key studies and scientific information contained 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 regarding 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 final NAAQS of 0.15 μg/m3

. Per Executive Order 

12866 and the guidelines of OMB Circular A-4, this Regulatory Impact Analysis (RIA) also 

presents analyses of a more stringent option of 0.10 μg/m3

 and a less stringent option of 0.40 

μg/m3

. EPA also analyzed alternative Pb standards of 0.20 μg/m3, 0.30 μg/m3, and 0.50 μg/m3. 

The benefit and cost estimates below are calculated incremental 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 Stephen Johnson, EPA-CASAC-07-001. 

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

U.S. EPA. 2006. Air Quality Criteria for Lead and Related Photochemical Oxidants (Final). U.S. 

Environmental Protection Agency, Washington, DC, EPA-452/R-07-013. 

U.S. EPA. 2007. Review of the National Ambient Air Quality Standards for Lead: Policy 

Assessment of Scientific and Technical Information. OAQPS Staff Paper. North Carolina. EPA452/R-07-013, Office of Air 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 RIA. We first describe data 

sources for air quality measurement. We then provide an overview of data on Pb 

emission sources contained in available EPA emission 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 measured by four monitoring networks in the 

United States, all funded in whole or in part by EPA. These networks provide Pb 

measurements for three different size classes of airborne particulate matter (PM): total 

suspended PM (TSP), PM less than or equal to 2.5 μm in diameter (PM2.5), and PM less 

than or equal to 10 μm in diameter (PM10). The networks include the Pb TSP network, 

the PM2.5 Chemical Speciation Network (CSN), the Interagency Monitoring of Protected 

Visual Environments (IMPROVE) network, and the National Air Toxics Trends Stations 

(NATTS) network. The subsections below describe each network and the Pb 

measurements made at these sites. 

In addition to these four networks, various organizations have operated other 

sampling sites yielding data on ambient air concentrations of Pb, often for limited periods 

and/or for primary purposes other than quantification 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 Agencies (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 sample 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/m3, is calculated based on the concentration of Pb in the volume 

extracted, the size of the collection filter, and the volume of air drawn through the filter. 

The primary factors affecting the measurements 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 Information, 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 developed 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, PM2.5, PM10. The TSP inlet is designed to allow as much suspended 

particulate into the sampling device as possible while protecting against precipitation and 

direct deposition on to the filter (nominally 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. Therefore, they do not provide an estimate of the 

total Pb in the ambient air. This is particularly 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 proportional 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 m3/hr of air 

through an 8 inch by 10 inch filter (0.05 m2 filter area). Low-volume samplers typically 

draw 1 m3

/hr through a 47 mm diameter filter (0.002 m2 filter area). Currently all Federal 

Reference Method (FRM) and Federal Equivalence Method (FEM) for Pb-TSP are based 

on high-volume samplers. 

2.1.4 Sampling Frequency 

The frequency of Pb sampling used in the U.S. varies between one sample every 

day (1 in 1 sampling) to the more common frequency of one sample every 6 days (1 in 6 

sampling). Semi-continuous methods for the measurement of ambient metals (including 

Pb) are currently being explored which would allow for more frequent sampling (as 

frequent as 1 sample per hour), but much more work is needed on these methods before 

they can be deployed in a network setting. 

More frequent sampling reduces the uncertainty 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 versus the increase in 

sampling and analysis costs when selecting 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 filters and the filters have been weighed, 

the filters are analyzed for Pb content. A number of analytical methods can be used to 

analyze the filters for Pb content including x-ray fluorescence analysis (XRF), proton-
3

induced x-ray emission (PIXE), neutron activation analysis (NAA), atomic absorption 

(AA), or inductively-coupled plasma mass spectrometry (ICP/MS) (CD, pp. 2-80 to 2-

81). A detailed discussion of these methods was given in the 1986 CD (USEPA, 1986), 

and the reader is referred to that document for more information on these analytical 

methods. A search conducted on the AQS database2

 shows that the method detection 

limits for all of these analytical methods (coupled with the sampling methods) are very 

low, ranging from 0.01 μg/m3

 to as low as 0.00001 μg/m3, 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 which 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 consecutive samples together to save costs 

and/or increase detection limits. 

2.1.7 Monitor Locations 

The locations of Pb-TSP sites in operation between 2003 and 2005 are shown in 

Figure 2-1. The state and local agencies which operate these sites report the data to 

EPA’s AQS where they are accessible via several web-based tools. EPA’s series of 

annual air quality trends reports have used data from this network to quantify trends in 

ambient air Pb concentrations. The most recent 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 many 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 smelters3

) 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 relatively higher Pb 

concentrations. Additionally, none of the 189 Pb-TSP are located within a mile of 

airports identified in the NEI as an airport where piston-engine aircraft operate (i.e., 

aircraft that still use leaded aviation fuel). However, there are historical data for 12 PbTSP monitoring sites operating within 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/m3

 and across all 12 sites, the maximum quarterly mean values ranged 

from 0.004 to 0.15 μg/m3

. 

 2

 EPA’s AQS can be accessed at http://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 network has decreased significantly since the 

1980s. The number of sites in the network reached 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 decrease in Pb concentrations observed since the 

1980s and the need to fund new monitoring objectives (e.g., PM2.5 and ozone 

monitoring). Lead-TSP sites in lower concentration areas were shut down to free up 

resources needed for monitoring of other pollutants such as PM2.5 and ozone. 

2.2 Air Quality Modeling 

As part of the Agency’s national air toxics assessment (NATA) activities, a national 

scale assessment of hazardous air pollutants 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 years 1996 and 1999, respectively, with atmospheric 

dispersion modeling to predict associated annual average Pb air concentrations across the 

country. A national scale assessment is not yet available based on the 2002 NEI. A 

number of limitations are associated with 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 the 
5

monitor averages for Pb, suggesting that the modeling system (i.e., emissions estimates, 

spatial allocation estimates, dispersion modeling) may be systematically underestimating 

ambient concentrations. This may be particularly true for Pb as metals tend to deposit 

rapidly with distance from the source according 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-entrained" 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 section 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 discussion is the National Emissions Inventory 

(NEI) for 2002 (USEPA, 2007a). As a result of Clean Air Act requirements, emissions 

standards promulgated for many source categories that have taken effect since 2002 are 

projected to result in much lower emissions at the current time or in the near future. For 

a more comprehensive discussion of Pb sources, 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 Table 2-2. Note that there 

is often greater inherent uncertainty in reported emissions from small sources than from 

larger sources, and that a large portion of the 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 the 2002 NEI are comprised primarily of 

combustion-related emissions and industrial process-related emissions. Point source 

emissions account for about 66% of the national 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 Information, 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 between combustion and industrial processes, 

while mobile, non-road sources (e.g. piston-engine aircraft using leaded fuel) account for 

29%. 

Table 2-1 presents emissions estimates for stationary sources grouped into 

descriptive categories. Presence and relative position of a source category on this list 

does not necessarily provide an indication of the significance of the emissions from 

individual sources within the source category. A source category, for example, may be 

composed 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 Glassware 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 (industrial, 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. There are approximately 1300 point sources of 

Pb in the NEI with estimates of emissions greater than or equal to 0.1 tpy and these point 

sources, combined, emit 1058 tpy, or 94% of the 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 methodology for characterizing the relative 

contributions of stationary point sources (defined in this analysis as sources emitting > 1 

ton per year of Pb), area nonpoint sources (defined 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 national regulations to gradually reduce the Pb 

content in gasoline. In 1975, unleaded gasoline was introduced for motor vehicles 

equipped with catalytic converters. EPA banned the use of leaded gasoline in highway 

vehicles after December 1995. Currently, 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 octane, prevent knock6

, and prevent valve seat recession and subsequent 

loss of compression for engines without hardened valves. The 2002 National Emissions 

Inventory (NEI) estimates that lead emissions from the use of leaded aviation gasoline 

(commonly referred to as avgas) are 491 tons; this accounts for 29% of the air emission 

inventory for lead. These estimates are based on the volume of avgas supplied nationally, 

the concentration of lead in avgas and the retention of some lead 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 per liter (2.12 grams of 

lead/ gallon); this is referred to as 100 Low 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 information, 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 estimate 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 emitted 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 (Chevron 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 contaminant 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 available. 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. Currently, 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/m3 and the five alternative standards included in this document. 

We note that the Agency is setting the final standard as the maximum quarterly rolling average 

concentration, whereas the proposed rule included two options for the averaging time and form 

of the standard: the maximum quarterly average concentration across a three-year period (i.e., the 

maximum quarterly mean) and the second highest monthly average concentration across a threeyear period (i.e., the second maximum monthly mean). The decision to set the final standard as 

a maximum quarterly rolling average concentration, however, was made after much effort had 

been expended to assess the costs and benefits of the final and alternative standards as second 

maximum monthly mean concentrations. Because this decision was reached late in the analytic 

process, EPA used the air quality assessment methodology presented in this chapter to assess the 

0.15 μg/m3

 standard as a second maximum monthly mean concentration rather than as a 

maximum quarterly rolling average 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 compare second maximum monthly concentrations 

for each monitor area to the corresponding maximum 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 monitor readings for lead were available, the second maximum 

monthly mean is, on average, 0.03 μg/m3

 higher than the maximum quarterly mean. In addition, 

when we statistically test the difference between the second maximum monthly mean and the 

maximum quarterly mean concentrations in these 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 maximum monthly mean concentrations above 0.1 

μg/m3

, which is the most stringent standard analyzed in this document), we reach the same 

general conclusion—that the second maximum monthly 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/m3

 higher than the latter. 

2

 For the 21 counties analyzed in this RIA, the 95 percent confidence interval for the 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/m3

 higher than the maximum quarterly mean. 
3-2 

costs, and benefits associated with the final and alternative standards 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 impact of the hypothetical emissions controls implemented under 

the final NAAQS, EPA would ideally use a detailed air quality model that simulates the 

dispersion and transport of lead to estimate local ambient lead concentrations. Although models 

with such capabilities are available for pollutants for which EPA frequently conducts air quality 

analyses (e.g., particulate matter and ozone), regional scale models are currently neither available 

nor appropriate for Pb.3 Dispersion, or plume-based models, are recommended for compliance 

with the Pb NAAQS and were used for the Pb NAAQS risk assessment case studies. However, 

dispersion models are data-intensive and more appropriate for local scale analyses of emissions 

from individual sources. It was not feasible to conduct such a large-scale data intensive analysis 

for this RIA. As a result, the simplified analysis developed for this RIA, while distanceweighting individual source contributions to ambient Pb concentrations, could not account for 

such locally critical variables as meteorology and source stack height. Instead 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 represented in the Air Quality System (AQS) 

database with sufficient 1-, 2-, or 3-year data records for the years 2003-2005; this database 

encompasses 189 monitoring sites located in 86 distinct counties. For this particular analysis, we 

concentrated on county maxima monitors exceeding the lowest alternative target NAAQS level 

(0.1 μg/m3

). The identification of the county maxima monitors and subsequent processing were 

based on the alternative NAAQS form of second 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 assessment tool. This 

assessment tool employs a source-apportionment approach to estimate the extent to which each 

of the following emissions sources contribute to observed lead concentrations 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 switched 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 accordingly, the initial monitor selection was not repeated 

using the different metric. Thus, in some isolated instances, 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). For the Final Rule Analysis, the identification of monitors with maximum 

second monthly means was corrected; accordingly, some of the 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 emissions5

After allocating a portion of the observed lead concentration for each monitor area to the 

first three categories listed above, the assessment tool apportions the remaining 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 concentration 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 employs 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 records the second maximum monthly 

mean ambient lead concentration for the 21 monitor locations where this concentration exceeds 

0.1 μg/m3, the most stringent of the NAAQS alternatives considered in this document. These 

concentrations, adjusted for the expected implementation of MACT controls implemented after 

2002, PM2.5 NAAQS controls included as part of the illustrative PM2.5 control strategy described 

in the PM2.5 NAAQS RIA,7 and the controls listed in the 2007 Missouri Lead SIP revisions, 

serve as the baseline air quality values for this analysis.8

 

For the final rule, the specification of baseline air quality values differs from the 

proposed rule in two ways: 

1. First, in some areas, monitor geo-coordinates 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 used the geo-coordinates and ambient lead 

 

5

 For the purposes of this analysis, airports servicing piston-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 that attributable to indirect fugitive emissions from active 

point sources, this analysis assumes that the two contributions are directly related, and any reduction in the air 

quality impact of point source emissions would produce a corresponding reduction in the air quality impact of 

indirect fugitive emissions from point sources in that monitor area. The process used to relate the contributions of 

these two categories is described in further detail 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, rather 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 μg/m3

 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 maximum monthly Pb-TSP averages between 

0.05 μg/m3

 and 0.1 μg/m3) are excluded from the analysis. Thus, the number of 

monitor locations analyzed has 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 the base case emissions for our 2016 analysis; as 

with the PM2.5 NAAQS RIA and ozone RIA, no growth factors were applied to the 2002 NEI 

emissions estimates for industrial sources to generate our emissions estimates for 2016. In 

general, lead emissions from these source categories are trending downward over time due to 

various factors including lack of growth in particular industrial sectors, 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 standards with post-2002 compliance deadlines, because 

the 2002 NEI would not reflect the impact of those controls reasonably anticipated to be in place 

by 2016. 

We identified 41 existing MACT rules with post-2002 compliance deadlines that affect 

sources included in this analysis. Of these, we focused on rules affecting the 20 industries 

responsible for the largest lead emissions according 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 from 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 identified is specific to metal Hazardous Air Pollutants 

(HAPs, e.g., lead). For the remaining four rules, 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. 

PM2.5 NAAQS controls: In addition to adjustments for MACT rules, we also adjusted 

the 2002 NEI emissions estimates to account for compliance measures required by the 
3-5 

September 2006 revision to the PM2.5 NAAQS included as part of the illustrative PM2.5 control 

strategy described in the PM2.5 NAAQS RIA. Because EPA expects PM emissions controls to be 

implemented at certain of these sources in order to reach attainment with the PM2.5 standard, we 

incorporated them into the base case emissions 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 Manufacturing 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 Steel 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 PM2.5 standard in 2016, as specified in the PM2.5 NAAQS 

RIA. For 20 point sources in these areas, EPA 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 applied consisted of fabric filters (with a 99 percent expected control 

efficiency), upgrades to electrostatic precipitators (67 percent), and the installation of capture 

hoods vented to a baghouse (85 percent). For each 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 NAAQS-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 μg/m3) as to be irrelevant for the purposes of 

this analysis. Given the resolution of the lead monitoring devices supporting this analysis, the air 

quality assessment tool assumes that background lead concentrations have no measurable 

contribution to violations at the design value monitors. However, given the nature of the 
3-6 

conducted analysis for estimating “miscellaneous re-entrained dust” (see Step 3 below), 

background concentrations are, in fact, encompassed in that category. 

Step 3: Estimate the contribution of miscellaneous re-entrained dust. Although the 

lead emissions constituting the miscellaneous re-entrained dust category are 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 activities, and 3) uninventoried mobile-related 

emissions (e.g., from Pb wheel weights, brake wear and trace Pb from gas/diesel and lube oil 

consumption) . Rather than estimating the site-by-site contribution of miscellaneous 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 ambient TSP monitors with a negligible impact 

from NEI lead emission sources. For the purposes 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-review database, EPA deemed 

90 sites to have negligible impact from active sources 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 year or less within a one-mile radius of the 

monitor.9

 As a central tendency of the contribution of miscellaneous, re-entrained dust, EPA 

found the median ambient lead concentration at these sites to be 0.0225 :g/m3. Although this 

represents the average concentration at the national level, actual concentrations associated with 

miscellaneous re-entrained dust may vary by area. Nevertheless, in general this value typically 

represents a small portion of the baseline concentration at each monitor, as indicated by Figure 3-

1, which illustrates the composition of the baseline lead concentration at the Fulton County, Ohio 

monitor. 

Step 4: Estimate the contribution of area nonpoint sources. A portion of observed 

lead concentrations results from emissions from area non-point sources (e.g., households). The 

air quality assessment tool estimates the contribution of lead-emitting area non-point sources to 

ambient lead concentrations based on data from the 2002 area non-point lead emission inventory. 

This inventory is generally summarized at the county level, and EPA assumes that each county’s 

area non-point emissions were uniformly distributed within each county. Based on this 

assumption, the air quality assessment tool also assumes that the extent to which area non-point 

sources contribute to ambient lead concentrations is proportional to the ratio of county-level area 

non-point lead emissions to total county-level lead emissions. Because this ratio differs by 

county, the area non-point source contribution to ambient lead concentrations also differs for 

each monitor site, but it generally composes a small 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 SECOND MAXIMUM MONTHLY MEAN LEAD 

CONCENTRATION AMONG SOURCE CATEGORIES IN FULTON COUNTY, OHIO 

Step 5: Estimate the residual concentration 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 concentration (hereafter, “residual concentration”) by 

subtracting the contributions of miscellaneous re-entrained dust and 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/m3

, or the baseline concentration of 0.5300 :g/m3 less the 0.0225 

:g/m3

 and 0.0022 :g/m3 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, OH

Ambient Lead Concentration (μg/m3)

Contribution from 

Inventoried Point Source 

Emissions, Including 

Indirect Fugitive 

Emissions (i.e., Residual 

Concentration): 0.5053 

μg/m3

Contribution from Area 

Non-Point Sources: 

0.0022 μg/m3

Contribution from 

Miscellaneous Dust: 

0.0225 μg/m3

Total Second Maximum 

Monthly Mean Ambient 

Lead Concentration: 

0.5300 μg/m3

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Monitor 390510001: Fulton County, OH

Ambient Lead Concentration (μg/m3)

Contribution from 

Inventoried Point Source 

Emissions, Including 

Indirect Fugitive 

Emissions (i.e., Residual 

Concentration): 0.5053 

μg/m3

Contribution from Area 

Non-Point Sources: 

0.0022 μg/m3

Contribution from 

Miscellaneous Dust: 

0.0225 μg/m3

Total Second Maximum 

Monthly Mean Ambient 

Lead Concentration: 

0.5300 μg/m3
3-8 

Step 6: Determine the contribution of each inventoried point source to the ambient 

lead concentration at each monitor, accounting for indirect fugitive emissions from nearby 

active industrial sites. For each monitor area, the air quality 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 weighting each source’s emissions according to its 

distance from the monitor and applying an adjustment 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 nearest 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, all else equal, lead emissions closer to the monitor have a 

greater impact on ambient lead concentrations, the tool assumes that each source’s contribution 

to the concentration is proportional to its share of the total distance-weighted point source 

emissions for the monitor area. [Note that the tool does not contain data sufficient to assess the 

influence of other factors, such as stackheight and local meteorological conditions, that could 

affect the relative contribution of each point source to monitored Pb concentrations.] 

For each source, the tool calculates distance-weighted emissions using the following 

equation: 

(Equation 3-1) 

2

3

S

S

S

D

E

DWE =

where: 

• DWES = Distance-weighted 2002 NEI emissions for source S, 

• ES = 2002 NEI emissions for source S, and 

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

Step 6.2: Adjust the distance-weighted emissions to incorporate the impacts of indirect 

fugitive emissions.

After calculating the distance-weighted emissions 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 therefore 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, although as discussed further in chapter 4, no controls are applied to airports. 
3-9 

quality. Reflecting this consideration, 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 fugitive emissions contribute to ambient lead 

concentrations near active industrial 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 production 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 average post-stoppage lead 

concentration represented approximately 15 percent of the average 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 attributable to these sources. 

The air quality assessment tool estimates contribution 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 indirect 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 emissions emanating from that source. 

Reflecting the results of the analysis described 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 fDWE DWE f

where: 

• fDWES = Distance-weighted 2002 NEI emissions for source S, adjusted to account 

for indirect fugitive emissions, 

• DWES = 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 

• fS = Indicator for whether a source has indirect fugitive emissions affecting the 

monitor (assigned a value 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 source to ambient lead concentrations based on 

fugitive-adjusted distance-weighted emissions. 

After calculating the distance-weighted emissions for each source using Equation 3-1 and 

incorporating the impacts of indirect fugitive emissions using Equation 3-2, the air quality 

assessment tool estimates each source’s contribution to the ambient lead concentration as 

follows: 

(Equation 3-3) 

P

S

S P fDWE

fDWE C = C ⋅

where: 

• CS = The portion of that monitor area’s ambient lead concentration attributable to 

source S,

• CP = Total contribution of point source emissions (and associated industrial 

fugitives) to the ambient lead concentration (i.e., the remaining concentration 

after subtracting background and area source contributions from the baseline air 

quality value), 

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

and 

• fDWEP = Sum of fugitive-adjusted, distance-weighted 2002 NEI emissions for 

all point sources in the monitor area. 

Continuing with the Fulton County, Ohio example presented in Figure 3-1, Table 3-2 

illustrates the process by which the contribution of each point source in Fulton County is 

apportioned based on its emissions and distance from the monitor location. 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 TOTAL 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/m3

) [CP] 0.5053 

Source Source A Source B 

2002 NEI Emissions (tpy) [ES] 0.1500 0.338 

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

2002 NEI Distance-Weighted Emissions (tpy/km3/2) 

[DWES] 0.0232 25.8982 

Fugitive Emissions Affect Monitor? [fS]

1 No Yes 

Distance-Weighted Emissions with Fugitive Adjustment 

(tpy/km3/2) [fDWES] 0.0232 30.4685 

Total Distance-Weighted Emissions (tpy/km3/2) [fDWEP] 30.4917 

Share of Total Distance-Weighted Emissions [fDWES / 

fDWEP] 0.0761% 99.9239% 

Source Contribution to ambient lead concentration (:g/m3) 

[CS = CP * fDWES/fDWEP] 0.0004 0.5049 

1

 “No” and “Yes” in this row correspond to values of 0 and 1 for fS, respectively, as defined in Equation 3-2. 

In this analysis, airports were treated as point sources. Currently, there are 3,410 aviation 

facilities in the NEI of which, 24 are included in this analysis due to their proximity to one of the 

24 monitors that were identified using the criteria described 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 contribution of leaded aviation gasoline to lead measured at the 

monitors ranges from 0.00002 to 0.047 :g/m3

. 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 where piston-engine aircraft might operate 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 that 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 decline 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 concentrations 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 optimization 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 applied to that source. Based on these sourcespecific reductions, we estimated each point source’s contribution (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 ambient 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 baseline and policy case, we added these to 

the total contribution from point sources (estimated 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 emission control strategy we applied to simulate 

attainment with the selected standard and alternative 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 air quality impacts of these emissions reductions. 

Section 4.4 discusses the application 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 alternative 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. [Note that while airports are included as point sources in the NEI, 

our analysis considers the impact of emissions from use of leaded aviation gasoline (avgas) at 

airports, but does not consider controls on those 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 endanger the public health or welfare, and to take 

action to control lead emissions from piston-engine aircraft. We published a Federal Register

notice discussing the petition and requested comment on specific aspects of the use of leaded 

avgas and potential control of lead emissions from the consumption of avgas.1

] As discussed in 

Chapter 3, a portion of ambient lead concentrations can be attributed 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-aviation 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. And, 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 demands, or lack of mogas availability at airports. However, 

several comments identified potential near and longer term measures to reduce 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 performance characteristics needed for 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 the 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, using the following four-step process: 

1. Specification of baseline emissions for inventoried 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 strategy 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 inventoried point sources. Further discussion 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 identified controls with a cost/ton higher than 

the 98th percentile of control costs at point sources 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 information 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 controls 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 emission reductions needed beyond identified controls such that 

these reductions are distributed across all sources in areas projected to violate any alternate 

standard. In the Proposed Rule analysis, these reductions were achieved 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 Emissions Inventory (NEI) 

served as the baseline for our analysis. However, for some sources (e.g., natural gas-fired utility 

boilers), we corrected the 2002 NEI lead emissions data with updated information. As discussed 
4-3 

in Chapter 2, we did not apply growth factors to the 2002 NEI emissions estimates to predict 

emissions in 2016 (the analysis year for this RIA) because we believe that the number of Pb 

emitting sources will not increase with population growth. We did, however, adjust the 2002 

NEI lead emissions values to reflect anticipated emissions controls necessary to comply with 

other regulations that have compliance deadlines after 2002, wherever possible. These 

adjustments included application of MACT for air toxics rules with post 2002 compliance 

deadlines,2

 PM controls at sources in designated nonattainment areas in the 2006 revisions to the 

PM2.5 NAAQS as modeled in the illustrative control strategy in the PM2.5 NAAQS RIA,3 and 

controls planned for the primary 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 inventoried point sources in the 

analysis. 

Table 4-1. 

TOTAL BASELINE LEAD EMISSIONS FOR 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 described above, we also specified baseline PM10 and PM2.5

emissions for all inventoried point sources. Although 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 ancillary benefits of co-controlling PM emissions in 

the process of implementing lead control strategies, as discussed in Chapter 5. Recent 

promulgation of mobile source rules that reduce PM is not relevant for this analysis. 

4.1.2. Identification of Potential Controls for Inventoried Point Sources. 

To identify point source lead emissions controls for our analysis, we collected information on 

PM control technologies, assuming that the control efficiency for PM emissions would also 

apply to lead emissions. We collected this 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 Nonferrous 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.pdf
4-4 

1. We queried EPA's AirControlNET database for information on potential PM controls 

available for each source, accounting for any control measures already in place, according to 

the 2002 NEI.5

 

2. For sources with Standard Industrial Classifications (SICs) but without identified NEI Source 

Classification Codes (SCCs), we used the 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 technical documents prepared in support of New 

Source Performance Standards, EPA memos prepared to support analyses for the PM2.5 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 baghouse 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. Capture hoods that route PM emissions from a 

blast furnace casthouse to a fabric filter can provide 80 to 90 percent additional emission 

reductions from an iron or steel mill. 

• Diesel particulate filter (for stationary sources such as diesel generators). This control 

incorporates directly-emitted PM2.5 reductions from stationary internal combustion 

engines that will be affected by the compression-ignition internal combustion engine new 

source performance standard (NSPS) promulgated on June 28, 2006. Diesel particulate 

filters (DPF) are likely to be the control technology 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 inventory. Based on the technical support documents 

prepared for the final compression-ignition NSPS, the PM2.5 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/ttnecas1/models/DocumentationReport.pdf. AirControlNET’s 

database of PM controls normally excludes sources emitting fewer than 10 tons/year of PM10. Because many of the 

point sources included in our analysis fall below this threshold 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/models/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 number of monitor areas in the 

Proposed Rule analysis, we reviewed the PM control measures in our databases in order to 

determine if the data for these measures were fully up to date and appropriate for an analysis 

year of 2016. In the course of our review, we found that the control efficiencies for a variety 

of PM control measures as applied in our proposal 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 applied 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 from 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 classification 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 SCCs, where the control efficiency in 

AirControlNET is 25 percent, we adjusted this value to 70 percent. 

• Paper/Nonwoven Filters – Cartridge Collectors: The AirControlNET control efficiency 

value of 99 percent was modified to 99.5 percent. 

Completion of the procedure outlined above yielded 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 percent of inventoried point sources account for 

more than 97 percent of total lead emissions), these sources accounted for approximately 92 

percent of total lead emissions, 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 Controls1 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 described 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 increased for the following control measures: dry and wet ESPs, all types of fabric 

filters, venturi scrubbers, impingement-plate/tray-tower scrubbers, and paper/nonwoven filters - cartridge collectors. 

We also revised the capital and annualized costs for these control devices to reflect the increased control efficiencies 

associated with these control measures, as discussed in Chapter 6. 
4-6 

Controls identified through this process include major emissions controls, such as fabric filters, 

impingement-plate scrubbers, and electrostatic precipitators; and minor controls, such as 

increased monitoring frequency, upgrades to continuous emissions monitors, and diesel 

particulate filters. For each identified control, we identified both the expected control efficiency 

for the technology and the annualized cost of installing and operating the control.10 For those 

point sources where the 2002 NEI indicated that control measures were already in place, we 

estimated the effective emissions control efficiency for each identified control by estimating the 

emissions reductions that would result if the pre-existing control were replaced by the identified 

control technology. For example, while a fabric filter might have an expected control efficiency 

of 90 percent when installed in the absence of pre-existing controls, if it were applied at a source 

that already had an electrostatic precipitator with an 80 percent control efficiency, the effective

control efficiency of the Fabric Filter would be 50 percent.11 We also assumed that each 

identified control technology would be installed in addition to any controls required under the 

2006 PM2.5 NAAQS and any MACT rules with enforcement dates after 2002, but before 2016. 

We therefore applied each control’s effective control 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 portion of the estimated costs of the rule—ranging 

from 55 percent for the 0.05 μg/m3 standard alternative to 95 percent for the 0.3 μg/m3 standard 

alternative - represented reductions 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 reductions 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 Kivcet 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 under the selected standard, information on such 

controls is not available. 

To estimate the emissions reductions associated with 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 this facility by obtaining lead emission 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 the source’s original, uncontrolled emissions would remain 

uncontrolled, but with the fabric filter, only 10 percent 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 PM2.5 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 Institute 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 smelter. Applying this emissions rate 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 percent reduction in lead emissions 

relative to the facility’s baseline emissions of 51 tons per year. When modeling this lead 

emissions control strategy, we divided these reductions among the emissions sources at the 

Jefferson County primary lead smelter in proportion 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 projected to violate the 

NAAQS, EPA developed a linear programming optimization model that systematically evaluates 

the air quality and cost information discussed below and in Chapter 6 to find the optimal control 

strategy for each area. The optimization model first identifies the measures that each source 

would implement if it were controlled as part of a local lead attainment strategy. Based on these 

controls, the optimization model then identifies sources to control such that each area would 

reach attainment at the least aggregate cost possible for the area. Minimizing total costs across 

all sources is not always equivalent to minimizing 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 unlike major controls, all minor controls identified 

can be implemented in conjunction with other controls, 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 filtering 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 area. This stage mostly affects monitor areas 

with large numbers of inventoried 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 the most cost-effective major control for each 

relevant source would be installed, as determined 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 with 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 Environment Canada, confirmed that the methods used to collect the NPRI 

were comparable to the methods used to collect the NEI. 15 The estimate of 2002 lead production 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, the 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 98th 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 controls at all relevant point sources for each 

monitor area, the model then proceeds to evaluate every possible combination of control 

technologies until the monitor area reaches attainment with the selected 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 controls. 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 moving up the marginal abatement cost 

curve for lead. If the control strategy were selected based on the marginal cost per μg/m3

reduced, we would not necessarily identify the least-cost strategy for 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 control strategies identified 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 achieve 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 μg/m3

 standard, removal of this filter increases the emission reduction by less 

than 0.0001 tons per year. 

17 As will be discussed below, the application 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 sites 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 μg/m3

 following implementation of the controls 

identified in the AirControlNET analysis (i.e., identified controls). In addition, 20 areas are 

expected to reach attainment with identified controls under a NAAQS of 0.5 or 0.4 μg/m3

. For 

the most stringent alternative considered, 0.1 μg/m3

, 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 reach attainment with the selected standard. 

The failure of certain areas to reach attainment with identified controls partially reflects the lack 

of control information for point sources in these areas. As indicated in Table 4-5, emissions 

from sources for which the AirControlNET analysis identified no controls contribute to a 

significant portion of the ambient lead concentration in many of the areas not projected to reach 

attainment with the selected standard and four alternative standards. For such sources in areas 

projected to violate the NAAQS with the application of identified controls, we assume that 

emission reductions beyond identified controls will 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 standards 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 identified controls. This figure illustrates that the 

progress made through the application of identified 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/M3 
4-11 

TABLE 4-3. 

REDUCTION IN LEAD EMISSIONS UNDER ALTERNATIVE NAAQS AT EACH 

MONITOR AREA, IDENTIFIED CONTROLS ONLY 

Reduction in Lead Emissions (tpy) 

Monitor 

State 

Monitor 

County 

Baseline 

Lead 

Emissions 

in 2016 

Standard 

Alternative: 0.5 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.4 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.3 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.2 

μg/m3 2nd

Maximum 

Monthly Mean 

Selected 

Standard: 0.15 

μg/m3 2nd

Maximum 

Monthly Mean 

AL Pike 4.45 4.02 4.02 4.02 4.13 4.31 CO El Paso 0.95 0.00 0.00 0.00 0.00 0.00 FL Hillsborough 1.48 1.00 1.00 1.09 1.19 1.19 IL Madison 0.53 0.00 0.00 0.00 0.00 0.00 IN Delaware 1.53 1.37 1.37 1.40 1.42 1.44 MN Dakota 3.55 0.00 0.00 0.00 0.00 1.29 MO Iron 16.12 12.26 12.26 12.26 12.26 12.26 MO Jefferson 51.02 45.52 45.52 45.52 45.52* 45.52* NY Orange 1.80 0.00 0.00 0.00 1.39 1.39 OH Cuyahoga 0.94 0.00 0.00 0.13* 0.13* 0.13* OH Fulton 0.49 0.14* 0.14* 0.14* 0.14* 0.14* OH Logan 0.12 0.00 0.00 0.00* 0.00* 0.00* OK Ottawa 0.00 0.00 0.00 0.00 0.00 0.00 PA Beaver 4.28 0.00 0.00 0.00 0.64 0.73* PA Berks 2.16 1.00 1.02 1.61* 1.61* 1.61* PA Carbon 0.45 0.00 0.00 0.00 0.00* 0.00* TN Sullivan 0.38 0.00 0.00 0.00 0.00* 0.00* TN Williamson 2.55 1.25 1.35 1.95 2.00 2.19 TX Collin 3.18 2.19 2.19 2.20 2.69 2.75 TX Dallas 0.06 0.00 0.00 0.00 0.00 0.00 UT Salt Lake 3.66 0.00 0.00 0.00 0.00 0.00 Total 99.7 68.74 68.86 70.31 73.11 74.96 

* Indicates monitor area does not reach attainment using identified controls. 
4-12 

Table 4-4. 

AMBIENT LEAD CONCENTRATIONS ACHIEVED WITH IDENTIFIED CONTROLS 

UNDER THE ALTERNATIVE STANDARDS IN 2016 

Ambient Lead Concentration (μg/m3) 

Monitor 

State 

Monitor 

County 

Baseline Lead 

Concentration 

in 2016 

Standard 

Alternative: 0.5 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.4 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.3 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.2 

μg/m3 2nd

Maximum 

Monthly Mean 

Selected 

Standard: 0.15 

μg/m3 2nd

Maximum 

Monthly Mean 

AL Pike 2.420 0.256 0.256 0.256 0.197 0.098 CO El Paso 0.131 0.131 0.131 0.131 0.131 0.131 FL Hillsborough 1.380 0.327 0.327 0.222 0.123 0.123 IL Madison 0.128 0.128 0.128 0.128 0.128 0.128 IN Delaware 5.022 0.397 0.397 0.285 0.199 0.148 MN Dakota 0.192 0.192 0.192 0.192 0.192 0.127 MO Iron 1.454 0.091 0.091 0.091 0.091 0.091 MO Jefferson 1.998 0.236 0.236 0.236 0.236* 0.236* NY Orange 0.240 0.240 0.240 0.240 0.085 0.085 OH Cuyahoga 0.357 0.357 0.357 0.316* 0.316* 0.316* OH Fulton 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* OK Ottawa 0.114 0.114 0.114 0.114 0.114 0.114 PA Beaver 0.228 0.228 0.228 0.228 0.200 0.196* PA Berks 0.518 0.404 0.400 0.331* 0.331* 0.331* PA Carbon 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* TN Williamson 0.820 0.429 0.398 0.212 0.198 0.137 TX Collin 0.891 0.302 0.302 0.300 0.168 0.150 TX Dallas 0.101 0.101 0.101 0.101 0.101 0.101 UT Salt Lake 0.107 0.107 0.107 0.107 0.107 0.107 

* Indicates that this monitor area did not reach attainment with the alternative standard.
4-13 

TABLE 4-5. 

BASELINE LEAD CONCENTRATIONS IN μg/m3 IN AREAS WITH MONITORED CONCENTRATIONS GREATER T

ALTERNATIVE STANDARDS USING ONLY IDENTIFIED CONTROLS 

Baseline Pb Concentration related to indirect 

Monitor fugitive and point source emissions 

State 

Monitor 

County 

Baseline Pb 

Concentration 

in 2016 

Pb Concentration related 

to area non-point emissions 

and misc. re-entrained dust Point sources with no 

Identified Controls 

Point sources with 

Identified Controls 

Total co

associated 

for whic

informat

CO El Paso 0.131 0.024 0.101 0.006 0.125 IL Madison 0.128 0.024 0.000 0.104 0.024 MO Jefferson 1.998 0.023 0.000 1.975 0.023 OH Cuyahoga 0.357 0.027 0.288 0.042 0.315 OH Fulton 0.530 0.025 0.505 0.000 0.530 OH Logan 0.360 0.027 0.333 0.000 0.360 OK Ottawa 0.114 0.023 0.091 0.000 0.114 PA Beaver 0.228 0.027 0.000 0.201 0.027 PA Berks 0.518 0.037 0.275 0.205 0.312 PA Carbon 0.294 0.036 0.259 0.000 0.294 TN Sullivan 0.236 0.024 0.212 0.000 0.236 TX Dallas 0.101 0.046 0.055 0.000 0.101 
4-14 

TABLE 4-6. 

ADDITIONAL AIR QUALITY INCREMENT (μg/m3) POST APPLICATION OF IDENTIFIED CONTROLS IN AREAS W

CONCENTRATIONS GREATER THAN ANY OF THE ALTERNATIVE STANDARDS 

Monitor State Monitor County 

Standard 

Alternative: 0.5 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.4 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.3 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.2 

μg/m3 2nd

Maximum 

Monthly Mean 

Selected 

Standard: 0.15 

μg/m3 2nd

Maximum 

Monthly Mean 

A

M

CO El Paso 0.IL Madison 0.MO Jefferson 0.036 0.086 0.OH Cuyahoga 0.016 0.116 0.166 0.2OH Fulton 0.030 0.130 0.230 0.330 0.380 0.4OH Logan 0.060 0.160 0.210 0.2OK Ottawa 0.PA Beaver 0.046 0.PA Berks 0.031 0.131 0.181 0.2PA Carbon 0.094 0.144 0.TN Sullivan 0.036 0.086 0.TX Dallas 0.
4-15 

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 identified controls alone in 

these illustrative control scenarios. In order to bring these monitor areas into attainment, 

we simulated the effects of unspecified emission 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 Alternatives with the Application of Identified 

Controls

To model emission reductions beyond identified controls, we assumed that all point 

sources in an area projected to violate a standard alternative (excluding airports) would 

be controlled with measures employing the same control efficiency. To simulate 

attainment with each alternative standard, 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 considered. As a result, the effective control 

efficiency applied to point sources differs by area and by standard alternative. For 

example, for the 0.2 μg/m3

 standard alternative, we apply 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, Ohio. 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 used 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 attainment. In this analysis, we instead apply 

the same control efficiency to all point sources within each area projected to violate any 

alternate standard. 

4.4.2. Lead Emission Reductions Needed Beyond Identified Controls 

After applying unspecified emission reductions beyond identified controls using the 

process described above, all monitor areas reached attainment with the 0.5 μg/m3

, 0.4 

μg/m3, 0.3 μg/m3, 0.2 μg/m3, and 0.15 μg/m3 alternative standards. Under the 0.1 μg/m3 

standard alternative, however, Ottawa County, 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 bring 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 based on both identified controls alone and 

emission reductions beyond identified controls. Tables 4-9 and 4-10 present the air 

quality impacts of these emissions reductions and summarize the number of areas 

reaching attainment with the application of identified controls and emission reductions 

beyond identified controls. Lastly, Figure 2 presents the quantity of emissions reductions 
4-16 

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 REACH 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 μg/m3 2nd Maximum 

Monthly Mean 30.96 0.02 30.94* 

0.4 μg/m3 2nd Maximum 

Monthly Mean 30.84 0.08 30.76* 

0.3 μg/m3 2nd Maximum 

Monthly Mean 29.39 0.29 29.10* 

0.2 μg/m3 2nd Maximum 

Monthly Mean 26.59 2.06 24.53* 

0.15 μg/m3 2nd Maximum 

Monthly Mean 24.74 4.79 19.95* 

0.1 μg/m3 2nd Maximum 

Monthly Mean 21.83 7.91 13.92** 

* 21 out of 21 monitor areas reached attainment with this standard alternative using identified point source 

emissions controls and unspecified emission reductions. 

** 20 out of 21 monitor areas reached attainment with this standard alternative using identified point source 

emissions controls and unspecified emission reductions. 
4-18 

Table 4-8. 

REDUCTION IN LEAD EMISSIONS UNDER ALTERNATIVE STANDARDS AT EACH MONITOR AREA WITH IDENTI

UNSPECIFIED EMISSION REDUCTIONS BEYOND IDENTIFIED CONTROLS 

Reduction in Lead Emissions (tpy) 

Monitor 

State 

Monitor 

County 

Baseline 

Lead 

Emissions 

in 2016 

Standard 

Alternative: 0.5 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.4 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.3 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.2 

μg/m3 2nd

Maximum 

Monthly Mean 

Selected 

Standard: 0.15 

μg/m3 2nd

Maximum 

Monthly Mean 

AL Pike 4.45 4.02 4.02 4.02 4.13 4.31 CO El Paso 0.95 0.00 0.00 0.00 0.00 0.00 FL Hillsborough 1.48 1.00 1.00 1.09 1.19 1.19 IL Madison 0.53 0.00 0.00 0.00 0.00 0.00 IN Delaware 1.53 1.37 1.37 1.40 1.42 1.44 MN Dakota 3.55 0.00 0.00 0.00 0.00 1.29 MO Iron 16.12 12.26 12.26 12.26 12.26 12.26 MO Jefferson 51.02 45.52 45.52 45.52 46.46 47.75 NY Orange 1.80 0.00 0.00 0.00 1.39 1.39 OH Cuyahoga 0.94 0.00 0.00 0.18 0.49 0.65 OH Fulton 0.49 0.16 0.23 0.30 0.37 0.40 OH Logan 0.12 0.00 0.00 0.02 0.06 0.08 OK Ottawa 0.00 0.00 0.00 0.00 0.00 0.00 PA Beaver 4.28 0.00 0.00 0.00 0.64 1.69 PA Berks 2.16 1.00 1.02 1.66 1.86 1.95 PA Carbon 0.45 0.00 0.00 0.00 0.16 0.25 TN Sullivan 0.38 0.00 0.00 0.00 0.06 0.15 TN Williamson 2.55 1.25 1.35 1.95 2.00 2.19 TX Collin 3.18 2.19 2.19 2.20 2.69 2.75 TX Dallas 0.06 0.00 0.00 0.00 0.00 0.00 UT Salt Lake 3.66 0.00 0.00 0.00 0.00 0.00 Total 99.7 68.76 68.94 70.6 75.17 79.75 

* Indicates monitor area does not reach attainment with identified controls and unspecified emission reductions beyond identified controls. Ottawa

sources and a large Superfund site. 
4-19 

Table 4-9. 

AMBIENT LEAD CONCENTRATIONS ACHIEVED WITH IDENTIFIED CONTROLS AND UNSPECIFIED EMISSION R

IDENTIFIED CONTROLS UNDER ALTERNATIVE STANDARDS IN 2016 

Ambient Lead Concentration (μg/m3) 

Monitor 

State 

Monitor 

County 

Baseline Lead 

Concentration 

in 2016 

Standard 

Alternative: 0.5 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.4 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.3 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.2 

μg/m3 2nd

Maximum 

Monthly Mean 

Selected 

Standard: 0.15 

μg/m3 2nd

Maximum 

Monthly Mean 

AL Pike 2.420 0.256 0.256 0.256 0.197 0.098 CO El Paso 0.131 0.131 0.131 0.131 0.131 0.131 FL Hillsborough 1.380 0.327 0.327 0.222 0.123 0.123 IL Madison 0.128 0.128 0.128 0.128 0.128 0.128 IN Delaware 5.022 0.397 0.397 0.285 0.199 0.148 MN Dakota 0.192 0.192 0.192 0.192 0.192 0.127 MO Iron 1.454 0.091 0.091 0.091 0.091 0.091 MO Jefferson 1.998 0.236 0.236 0.236 0.200 0.150 NY Orange 0.240 0.240 0.240 0.240 0.085 0.085 OH Cuyahoga 0.357 0.357 0.357 0.300 0.200 0.150 OH Fulton 0.530 0.500 0.400 0.300 0.200 0.150 OH Logan 0.360 0.360 0.360 0.300 0.200 0.150 OK Ottawa 0.114 0.114 0.114 0.114 0.114 0.114 PA Beaver 0.228 0.228 0.228 0.228 0.200 0.150 PA Berks 0.518 0.404 0.400 0.300 0.200 0.150 PA Carbon 0.294 0.294 0.294 0.294 0.200 0.150 TN Sullivan 0.236 0.236 0.236 0.236 0.200 0.150 TN Williamson 0.820 0.429 0.398 0.212 0.198 0.137 TX Collin 0.891 0.302 0.302 0.300 0.168 0.150 TX Dallas 0.101 0.101 0.101 0.101 0.101 0.101 UT Salt Lake 0.107 0.107 0.107 0.107 0.107 0.107 

* Indicates monitor area does not reach attainment with identified controls and unspecified emission reductions beyond identified controls. Ottawa

sources 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 μg/m3 

Second 

Maximum 

Monthly Mean 

12 20 21 

0.40 μg/m3 

Second 

Maximum 

Monthly Mean 

12 20 21 

0.30 μg/m3 

Second 

Maximum 

Monthly Mean 

10 17 21 

0.20 μg/m3 

Second 

Maximum 

Monthly Mean 

6 14 21 

0.15 μg/m3 

Second 

Maximum 

Monthly Mean 

5 13 21 

0.10 μg/m3 

Second 

Maximum 

Monthly Mean 

21 

0 9 20 
4-21 

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/m3

Emission Reductions (tons/year)

Emission Reductions from Identified Controls Unspecified Emission Reductions

4.5 Key Limitations

The estimates of emission reductions associated with 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 impact of indirect fugitive or area nonpoint source 

controls, the analysis of air quality impacts does not account for the potential implementation 

of such controls in areas where they might be effective. Although the analysis estimates the 

impact of point source controls on indirect fugitives, it does not consider the impact of 

controlling these emissions directly. This and the lack of control information for area 

nonpoint sources may have contributed to our projection that some areas would violate the 

NAAQS. 

• Actual State Implementation Plans 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 controls that may differ from those simulated in 

this analysis. This analysis therefore represents an approximation of the emissions 
4-22 

reductions that would be required to reach attainment 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 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 tenth 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 standard alternatives using identified controls alone. If more emissions control 

information were available, it may not be necessary to rely on estimated emissions reductions 

beyond identified controls in order 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 associated with the selected standard for 

Jefferson County, this analysis models the replacement of the primary lead smelter in this 

area with a more modern, lower emitting Kivcet 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 characteristics 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 emission reductions beyond 

identified controls. We have taken care to report these separately, in recognition of the 

greater uncertainty associated with achieving emissions reductions from measures that may 

not be currently in use or known to EPA. Nonetheless, EPA believes it is reasonable to 

project that, with at least seven 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 specifically focused on cost-effectively reducing PM 

emissions with high lead content. Because the current standard is attained in all but a few 

areas of the country, and has been for many years since the phase down of lead in gasoline, it 

is likely that very little effort has been devoted to development of lead emissions control 

technologies except in industries where regulations have been imposed to reduce lead (e.g., 

large MWC standard, primary and secondary lead smelter MACTs, etc.). As a result, EPA 

believes that the projection of emission reductions 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 benefits associated with attaining the selected 

National Ambient Air Quality Standard (NAAQS) for lead and the alternative standards outlined 

in Chapter 1.1 The estimates outlined in this benefits analysis indicate that achieving a lower 

National Ambient Air Quality Standard (NAAQS) for lead from its current level of 1.5 μg/m3 

maximum quarterly mean to a second maximum monthly value of 0.15 μg/m3 could result in 

significant reductions in adverse health effects due to reduced exposure from lead and fine 

particles (PM2.5). We estimate a potential increase in intelligence quotient (IQ) points across the 

population (approximately 400,000) with the selected 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; this may result in an under- or over-estimate of benefits in the 

year 2016.2

 State and federal regulatory 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 blood lead levels from other lead rules and potentially from an 

anticipated decline in population blood lead levels. EPA has determined that such a projection of 

baseline non-air background blood lead levels is not technically feasible in the time available. 

Specifically, EPA lacks data regarding the distribution of the housing stock and populations to 

which rules such as the RRP apply. As an alternative, we provide a sensitivity analysis, found in 

Table 5-8, which indicates that the total benefits 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 important 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 universe of monitors exceeding the various target NAAQS levels is 

 

1

 The costs presented in this chapter represent the direct pollution control expenditures associated with NAAQS 

compliance. As such, they do not reflect the general 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/m3

. 
2

very small; only 21 counties exceed the lowest alternate NAAQS level of 0.10 ug/m3. Because 

we know that Pb-TSP monitors are not located near some of the highest-emitting Pb sources in 

the 2002 NEI (see Chapter 2), it is likely that there may be more potential nonattainment areas 

than have been analyzed in this RIA. 

As shown in Table 5-1 below, when applying a 3 percent discount rate, the monetary 

value of avoided IQ point loss for the least stringent standard alternative (0.5 μg/m3) ranges 

between $2.0 and $2.8 billion (all values in 2006$).4

 If future non-air background blood levels 

change, benefits may be higher or lower. For the selected standard of 0.15 μg/m3, benefits range 

from $3.5 to $5.0 billion. For the most stringent standard alternative (0.1 μg/m3), 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 standard and up to a range of $0.3 to $2.2 billion 

for the most stringent standard alternative. Therefore, the combined monetized health benefits 

from reductions in both lead and PM exposures as a result of lowering the current NAAQS range 

from $2.1 to $3.7 billion for the least stringent standard alternative, between $3.7 and $6.9 

billion for the selected standard and between $4.8 to $8.6 billion for the most stringent standard 

alternative. 

When applying a 7 percent discount rate, the monetary benefits for changes in IQ the 

least stringent standard alternative (0.5 μg/m3) range between $0.3 and $0.5 billion. For the 

selected standard, benefits range from $0.4 and $0.9 billion. For the most stringent standard 

alternative (0.1 μg/m3

), monetary benefits of IQ gains range from $0.6 to $1.1 billion. 

Additional co-control benefits of reduced PM emissions are expected to range between $0.1 and 

$0.8 billion for the least stringent standard alternative, between $0.2 and $1.7 billion for the 

selected standard and a range of $0.2 to $2 billion for the most stringent standard alternative. 

Therefore, the combined monetized health benefits from reductions in both lead and PM 

exposures as a result of lowering the current NAAQS range from $0.4 to $1.3 billion for the least 

stringent standard alternative, between $0.7 and $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 μg/m3

, 0.4 μg/m3, 0.3 μg/m3 and 0.2 μg/m3 the identified emission controls account for all, 

or nearly all, of the estimated benefits. For the selected standard of 0.15 μg/m3

, the identified 

controls represent about 85% of total benefits. Finally, the more stringent alternative standard of 

0.1 μg/m3

, the identified controls represent 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 Alternate Lead NAAQS (in Millions of 2006$) in 2016

Estimated Net Present 

Value of IQ Points 

Gained23 

Monetized Benefits of 

Co-Controlled PM2.5 

Emissions4 Total Benefits5 

Standard 

Alternativ

e1 

3% 

Discount 

Rate 

7% 

Discount 

Rate 

3% 

Discount 

Rate 

7% 

Discount 

Rate 

3% 

Discount 

Rate 

7% 

Discount 

Rate 

0.5 μg/m3 $2,000—

$2,800 

$250—$490 $110—$880 $100—$790 $2,100—

$3,700 

$350—

$1,300 

0.4 μg/m3 $2,000—

$2,800 

$250—$490 $100—$880 $100—$800 $2,100—

$3,700 

$350—

$1,300 

0.3 μg/m3 $2,400—

$3,400 

$300—$580 $190—

$1,600 

$170—

$1,400 

$2,600—

$5,000 

$470—

$2,000 

0.2 μg/m3 $3,200—

$4,500 

$390—$780 $220—

$1,800 

$200—

$1,600 

$3,400—

$6,300 

$590—

$2,400 

0.15 μg/m3 $3,500—

$5,000 

$440—$870 $230—

$1,900 

$210—

$1,700 

$3,700—

$6,900 

$650—

$2,600 

0.1 μg/m3 $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 second maximum monthly mean concentration. 

2 Results reflect the use a 2002 derived 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 estimate of IQ change above may be different. 

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

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

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

an expert judgment study. 

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

Figures 5-1 and 5-2 below display the health benefits from both lead and PM2.5 exposure 

reductions for each of the six alternative standards using a 3 percent and 7 percent discount rate, 

respectively.5

 Figures 5-3 and 5-4 below display some examples 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 different input assumptions for each of the six 

alternative standards using a 3 percent and 7 percent discount 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 PM2.5 Monetized Benefits Estimates by 

Standard Alternative (3% Discount Rate) 
8

Figure 5-4. Example Combined Lead and Total PM2.5 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 ambient air lead levels. We first 

describe our approach for estimating and monetizing the health benefits associated with 

reductions of lead in air. Next, we provide a summary of our results, including an analysis of the 

sensitivity of the benefits model. We then review our approach to and results from estimating 

benefits from co-control of direct PM2.5 emissions associated with implementing measures 

necessary to achieve alternative of the selected lead NAAQS. Finally, we discuss the key results 

of the benefits analysis and indicate areas of uncertainty in our approach. 

Benefits Approach

This section presents our approach for estimating avoided adverse health effects in 

humans resulting from achieving alternative levels of the lead NAAQS, relative to a base case 

ambient air lead level. We first review the epidemiological evidence concerning potential health 

effects of lead exposure and present the health endpoints we selected for our primary benefits 

estimate. We then describe our screening-level spreadsheet benefits model, including the data 

used and key assumptions. Finally, we describe our approach for assigning an economic value to 

the health benefits. 

Benefits Scenario 

We calculated the economic benefits from annual avoided health effects expected to 

result from achieving alternative levels of the lead NAAQS (the “control scenarios”) in the year 

2016. We measured benefits in the control scenarios relative to the incidence of health effects 

consistent with ambient lead levels in air expected under the current standard (1.5 μg/m3 

maximum quarterly mean; the “base case”) in 2016. Note that this “base case” reflects emissions 

reductions and ambient air quality improvements that we anticipate will result from 

implementation of other air quality rules, including compliance with all relevant Maximum 

Achievable Control Technology (MACT) rules and the recently revised NAAQS for PM2.5.

6

 We 

compared benefits across six alternative second maximum monthly mean NAAQS levels of 0.5, 

0.4, 0.3, 0.2, 0.15, and 0.1 μg/m3. 

Selection of Health Endpoints 

Epidemiological researchers have associated lead exposure with adverse health effects in 

numerous studies, as described in the Air Quality Criteria for Lead (USEPA, 2006a; hereafter, 

Lead Criteria Document). Young children are particularly sensitive to lead exposures; 

neurobehavioral effects of lead exposure in infants and young children (less than 7 years of age) 

 

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 provides 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. Truly remarkable progress has been made during 

the past several decades with regard to (a) more fully delineating over time the wide variety 

of pathophysiologic effects associated with Pb exposure of human population groups and 

laboratory animals and (b) the characterization of applicable exposure durations and doseresponse relationships for the induction of the multifaceted Pb effects. This progress has been 

well documented by the previous Pb NAAQS criteria 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 during several preceding decades that established 

Pb encephalopathy and other signs and symptoms of persisting severe 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 adult population groups, and (b) evaluation of then newly-emerging 

evidence for more subtle and difficult-to-detect “subclinical” Pb effects on IQ, other 

neurological endpoints, and moderate blood hemoglobin deficits or other erythropoietic 

indicators of heme synthesis impairment, which collectively were judged to constitute an 

array of adverse Pb health effects associated 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 AQCD/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) increasingly stronger evidence that 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, impaired learning, and/or other indicators of 

adverse neurological impacts and (b) other pathophysiological effects of Pb on 

cardiovascular function, immune system components, calcium and vitamin D metabolism, 

and other selected health endpoints. 

 Newly available scientific information published since the 1986 Lead 

AQCD/Addendum and the 1990 Supplement, as assessed 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 and epidemiologic information, as integrated 

below, includes assessment of new evidence substantiating risks of deleterious effects on 

certain health endpoints being induced by distinctly lower than previously demonstrated Pb 

exposures indexed by blood-Pb levels extending 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 findings and conclusions derived from the current 

criteria assessment. This includes discussion of new toxicologic and/or epidemiologic 

evidence concerning Pb induced (a) effects on neurobehavioral development and other 

indicators of nervous system effects; (b) cardiovascular effects; (c) heme synthesis effects; 

(d) renal effects; (e) immune system functions; (f) effects on calcium and vitamin D 

metabolism; (g) inter-relationships to bone and teeth formation and demineralization; (h) 

effects on reproduction and other neuroendocrine 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 Criteria Document. The evidence with regard to adverse effects on 

plants and animals is also described in the Criteria Document. 

Although a number of adverse health effects have been found to be associated with lead 

exposure, this benefits analysis only includes a subset, due to limitations in understanding and 

quantifying the dose-response relationship for some of these health endpoints and the fact that 

for some of these endpoints the science is less certain. We analyzed only those endpoints with 

sufficient evidence to support a quantified dose-response relationship. This determination was 

made using the information presented in the Lead Criteria Document, which contains an 

extensive literature review for several health endpoints related to lead exposure. However, this 

document only included studies published or accepted for publication through December 2005. 

Therefore, we performed supplemental searches in the online search engine PubMed to identify 

studies published between January 2006 and the present (see Appendix A for more information). 

Finally, we reviewed previous EPA lead benefits analyses to identify dose-response relationships 

that have been used previously (USEPA, 1997, 2006b & 2007a). 

Our analysis focuses primarily on children’s health effects due to our use of 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 ratios to estimate adult blood lead changes are 

dated. Previous EPA analysis of the costs and benefits of the Clean Air Act (USEPA, 

1997) utilized air:blood ratios for adults from based on Snee et al. (1981), a metaanalysis of several studies, including Johnson et al..(1976), Fugas et al.(1973), and 

Nordman (1975). While these studies do provide insight into the responsiveness of adult 

blood lead levels to changes in lead concentrations in air, the age of these studies 

suggests that these ratios may not be appropriate for application in 2016. The morerecent peer-reviewed estimates of air:blood ratios have been derived for children. 
12

Applying these ratios to adults would be inappropriate given the important differences 

between the two populations in their ambient exposure to Pb. 

• There is a lack of current, peer reviewed non-air-related blood lead background 

estimates for adults. Quantification of adult endpoints would require a non-air-related 

blood background for adults. CASAC recommends a range of values for children in their 

review of the Lead Risk Assessment. However, due to differences between adults and 

children in the routes of exposure to lead, it is possible that background levels would 

differ between these two receptor groups. Therefore, applying the child-specific non-airrelated background blood lead levels to adults could misestimate the true adult 

background levels. 

• The adult health impact functions relating changes in blood lead to health outcomes are 

dated. Certain adult health impact functions, such as those quantifying the relationship 

between blood lead and diastolic blood pressure (Nawrot, 2002) are current. However, 

the functions relating changes in blood pressure to changes in premature mortality, 

chronic heart disease and stroke were each drawn from studies published in the 1970s; 

advances in the treatment of high blood pressure suggest that these functions may overpredict of changes in these health effects in the current population. One newer study, 

Schober et al. (2006), quantifies the relationship 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 considerable 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 health effects related to exposure to lead in the air that are 

quantified in this benefits analysis. In addition, the table includes a list of other endpoints that 

potentially are linked to lead exposure, but which do not have dose-response functions available 

for quantifying benefits. 

As shown in Table 5-3, 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 levels on cognitive function (most often measured as changes in IQ) 

in young children under 7 years of age, as described in the Lead Criteria Document. However, 

as also noted in that document, there has been conflicting evidence as to whether there exists a 

discrete period of neurological vulnerability to lead exposure during childhood. 

For instance, the first three years of life represent the maximal period of lead ingestion as 

well as a period of time when important development of the central nervous system is occurring, 
13

which suggests that biologically this could be a vulnerable period (USEPA, 2006a). In addition, 

there are two major meta-analyses that focused on the association between school age IQ and 

blood lead concentrations at two years of age or average blood lead concentrations up to three 

years of age (Pocock et al., 1994; Schwartz, 1994a). However, several recent prospective 

epidemiological studies have found concurrent blood lead level (i.e., blood lead measured at the 

same time as school age IQ) or lifetime average blood lead level (i.e., a mean of blood lead level 

from infancy to measurement of school age IQ) to be more strongly associated with school age 

IQ and other measures of neurodevelopment (Canfield et al., 2003; Dietrich et al., 1993; Tong et 

al., 1996, Wasserman et al., 2000). In addition, a large, international meta-analysis by Lanphear 

et al. (2005) included four measures of blood lead level: concurrent, peak, lifetime average, and 

early childhood. The authors found that the concurrent and lifetime blood lead levels were the 

strongest predictors of IQ deficits associated with lead exposure. 

A study by Chen et al. (2005) specifically evaluated whether a window of enhanced 

susceptibility to lead exists. This study examined whether cross-sectional associations observed 

in school age children represent residual effects from two years of age or “new” effects emerging 

among these children (USEPA, 2006a). Chen et al. found that the blood lead metric with the 

strongest association with IQ was concurrent, and this relationship grew stronger with age. The 

authors did not find any association between peak blood lead level 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 two years of age. The Lead 

Criteria Document concluded that “[t]hese results support the idea that lead exposure continues 

to be toxic to children as they reach school age, and do not lend support to the interpretation that 

all damage is done by the time the child reaches two to three years of age” (USEPA, 2006a, page 

6-63). Based on this evidence, it is reasonable to assume that all children under seven years of 

age in the study area for this analysis will experience some cognitive benefit (i.e., IQ loss 

avoided) from reduced ambient air lead in 2016. Therefore, we have designed our benefits 

analysis to measure benefits to all children under seven in our study area. 
14

Table 5-3. Human Health Effects of Lead 

Quantified Health Effects Unquantified Health Effectsa

-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) those for which associations, to various degrees, has been 

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

Benefits Estimation Model 

Overview 

For this benefits analysis, we created a spreadsheet model to provide a screening-level 

assessment of health benefits occurring as a result of implementing alternative NAAQS levels. 

The model uses various simplifying assumptions and is intended only to provide an approximate, 

preliminary estimate of the potential health benefits. 

The model was constructed in Microsoft Excel and provides an integrated tool to 

complete five benefits estimation steps: 1) estimate lead in air concentrations for the “base case” 

and “control scenarios”; 2) estimate population exposures to air lead concentrations for each 

scenario; 3) estimate blood lead levels in the population for each scenario; 4) estimate avoided 

cases of health effects due to changes in blood lead levels; and 5) apply an economic unit value 

to each avoided case to calculate total monetized benefits. These steps and the data inputs 

required are shown in Figure 5-5 and are discussed in further detail below. 

Estimating Lead in Air Concentrations 

We used estimates of the second maximum monthly mean lead total suspended particles 

(TSP) for each monitor included in our study to characterize ambient air lead concentrations for 

the “base case” in 2016 (USEPA, 2007b). These estimates were calculated by adjusting second 

maximum monthly mean lead TSP monitoring values for the years 2003 to 2005 to account for 
15

emissions reductions due to compliance with MACT requirements and the NAAQS for PM2.5

occurring by 2016 (see Chapter 4 for additional information). We assumed that under the 

“control scenario” for each standard alternative, each monitor would meet the second maximum 

monthly mean level achieved under the cost analysis for that alternative described in Chapter 4. 

While the alternative standards were specified in terms of second maximum monthly 

mean lead concentrations, the benefits model used estimates of maximum quarterly mean lead 

concentrations in order to calculate avoided cases of health endpoints. This decision was based 

on a number of studies outlined in EPA’s 2007 Staff Paper (USEPA, 2007c; Section 5.5.2), 

which indicate that changes in blood lead levels resulting from changes in air lead concentrations 

occur within a relatively short timeframe (i.e., within a few weeks to months). This finding is 

also supported by a simulation of changes in urban residential dust lead levels following a 

change in ambient air lead using the hybrid mechanistic empirical model developed for the Lead 

Risk Assessment. That analysis showed that changes in indoor dust lead levels (the primary 

source of children’s exposure) tracked closely with changes in ambient lead air concentrations. 

The hybrid model developed for the general urban case study suggested that 90% of steady-state 

impacts will be recognized within the three months and take up to one year for a full change to 

be realized. 
16

Figure 5-5 

OVERVIEW OF LEAD BENEFITS MODEL
17

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 mean values for the “base case.” This was 

achieved by calculating census block group-specific ratios of the second maximum monthly 

mean to the maximum quarterly mean for the period 2003-2005 and then dividing the second 

maximum monthly mean for the “base case” by this ratio.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 the “base case,” we used census block group-specific ratios of the 

second maximum monthly means to maximum quarterly means for 2003-2005 and then divided 

the selected NAAQS by this ratio. 

Estimating Population Exposure 

The first input to any benefits assessment is the estimated changes in ambient air quality 

expected to result from simulated attainment of a NAAQS. EPA typically relies upon air quality 

modeling to generate these data. For this analysis, time and technical limitations prevented us 

from performing formal air quality modeling. Instead, EPA employed two alternate approaches 

to approximate the air quality change resulting from attainment of alternate lead NAAQS. Each 

approach relies upon the lead monitoring network as the basis for subsequent air quality 

estimates. The first approach, which we employed to generate our primary benefits estimate, 

uses an interpolation method utilized in previous RIAs to estimate changes in lead concentrations 

in projected non-attainment areas. The second approach, which we utilized as a sensitivity 

analysis, applies a radius of a fixed size around each non-attaining lead monitor and estimates a 

fixed concentration of lead within that radius. We describe the process for using each approach 

below. 

Interpolation Method

This approach applies an interpolation method to generate an air quality surface from 

available lead monitoring data to better represent the spatial heterogeneity of lead concentrations 

in a projected non-attainment area. It utilizes both the lead monitoring network as well as the 

lead-speciating TSP monitoring network; we added the lead-speciating monitors to increase the 

number of data points available for the interpolation. We interpolated lead concentrations to the 

census tract, rather than census block group, to increase the computational efficiency of the 

model. 

 

7

 This ratio technique is detailed further in Chapter 3. 
18

To create an air quality surface of ambient lead values we applied the Voronoi 

Neighborhood Averaging (VNA) method.8 The VNA is an inverse-distance-weighting technique 

that interpolates point monitor data to a user-defined grid cell for the purpose of creating an air 

quality surface. The VNA approach is well suited for this type of analysis because the inverse 

distance weighting approach can approximate the gradient of ambient lead surrounding each 

monitor. VNA is a well-established technique that EPA has used in combination with modeled 

air quality changes to estimate the air quality change associated with full attainment of PM2.5 and 

Ozone NAAQS (USEPA, 2006c & 2008a). 

Figure 5-6 below summarizes how we applied the VNA method in this analysis. The 

VNA approach is expected to provide a better representation of the gradient of ambient lead 

around each monitor as compared to the radius approach. For this reason, we utilized this 

approach to generate our primary benefits estimate. However, this validity of this method is to 

some extent contingent upon the availability of a sufficient number of monitors to support an 

interpolation. In certain locations, such as Hillsborough County, FL, there are a sufficient 

number of lead and TSP monitors to generate an interpolation with a pronounced gradient around 

each monitor (see Figure 5-7). The lead and TSP monitoring network in other non-attainment 

areas can in some cases be sparse, and the resulting interpolation does not appear to generate a 

meaningful gradient, such as in Delaware County, IN (see Figure 5-8). 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 concentrations. While both the VNA 

and radius approaches exhibit limitations, we hold more confidence in the results of the 

interpolation approach and so rely upon it as our primary method of simulating air quality 

changes. As a means of acknowledging the limitations to the interpolation method we also 

provide sensitivity estimates using the radius method. 

 

8

 For technical details of the VNA approach, see the technical appendices to the BenMAP User manual , found at: 

http://www.epa.gov/air/benmap/models/BenMAPTechnicalAppendicesDraftMay2005.pdf 
19

Figure 5-6 

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

attain each standard alternative

0.5 μg/m3

0.4 μg/m3

0.3 μg/m3

0.2 μg/m3 

0.15 μg/m3 

0.1 μg/m3

Step 2. Apply VNA to 

create baseline air 

quality grid 

Convert baseline 2nd

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 RIA and MACT emission controls. The 

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-related air quality improvement from the Pb monitors to the Pbspeciating TSP monitors. 

Convert control 2nd

max monthly mean to 

max quarterly value 

Steps in the VNA Interpolation Technique 
20

Figure 5-7. Air Lead Concentration Gradient in Hillsborough County, Florida 
21

Figure 5-8. Air Lead Concentration Gradient in Delaware County, Indiana 
22

Radius Method

In this approach we focused on the 21 monitors in counties that potentially could be 

designated as non-attainment areas under at least one of these alternative lead NAAQS levels. 

These monitor concentration values likely only apply to the population of people living within 

the vicinity of these monitors, especially if the monitor is oriented near a source of lead 

contamination (e.g., a primary or secondary lead smelter). As a default, we defined the affected 

population as those individuals living within a 10-kilometer radius around the monitor. The 10-

kilometer radius is consistent with source-specific modeling in the EPA Lead Risk Assessment

case studies for primary and secondary sources (USEPA, 2007a). In the absence of detailed air 

quality modeling for the lead sources in the vicinity of each monitor, we assumed in this 

screening-level analysis that the lead concentrations in air measured at each monitor are uniform 

throughout the specified radius. To develop 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 using alternate, smaller radii of one, two, and five kilometers, 

since lead air concentrations can in some cases display significant gradients with distance from a 

source-oriented monitor. For example, second maximum monthly mean values measured at 

monitors in close proximity to the Herculaneum, MO lead smelter drop off 40 percent within 

roughly 1 km of the source and decrease by an additional 95 percent within 2 km.9

 

We used ArcGIS to establish the radii around each monitor. Our spatial dataset 

contained US Census population data at the block group level for the year 2000. We calculated 

the total population within each radius in 2000 by adding the population of each Census block 

group that at least partially resided within the radius. We then distributed 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 and Poole county-level projection data (Woods 

and Poole, 2001). We calculated a growth rate for each gender and age group combination by 

taking the ratio of the 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 residing 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 correspondingly with the population growth as 

discussed in Chapter 4. 

In order to determine the number of children under the age of seven, we added the 

population of children in the 0-4 age group for both genders and then added two-fifths of the 

population in the 5-9 age group, assuming the population was uniformly distributed across all 

five ages in that group. 

Estimating Blood Lead Levels 

The concentration-response functions we employ in this benefits analysis require 

estimates of blood lead levels in the exposed population to calculate avoided incidence of 

adverse health effects. We chose to develop a first approximation of the blood lead levels 

associated with reductions in air lead concentrations for each of the alternative NAAQS by using 

the air lead to blood lead ratio (“air:blood ratio”) approach applied by EPA in deriving the 

current NAAQS in 1978 (43 FR 46246). These ratios predict geometric blood lead levels due to 

direct lead exposure via inhalation as well as indirect exposures via ingestion of dust and soils 

contaminated by lead deposition, based on comparisons of historical data on lead in ambient air 

and measured or modeled geometric mean blood lead levels in an exposed population. Table 5-4 

lists the ratios considered for the current NAAQS analysis; for its primary estimate, EPA chose a 

ratio of 1:7 μg/m3

 to μg/dl. That is, for every one microgram per cubic meter reduction in air 

lead, EPA assumed that geometric mean blood lead levels would be reduced by seven 

micrograms per deciliter. We selected this value based on advice from the Clean Air Scientific 

Advisory Committee (CASAC) and analysis conducted as part of EPA’s Lead Risk Assessment

(USEPA, 2007a & 2007d). 

CASAC in its March 2007 review of EPA's Lead Risk Assessment recommended that 

EPA apply these ratios as part of a population level lead risk analysis to inform alternative 

proposals for a new lead NAAQS (USEPA, 2007d; see Appendix D). In its previous NAAQS 

analysis, EPA used a ratio of 1:2 μg/m3 to 1:6 μg/dl; however, CASAC suggested that ratios 

higher than 1:2 may be appropriate based on more recent literature. CASAC cites the use of a 

ratio of 1:5 by the World Health Organization (WHO) in 2000 to better account for lead 

deposition from air to dust and soil, and they cite a ratio of 1:9-1:10 based on the data in 

Schwartz and Pitcher (1989) on blood lead changes resulting from the phase-out of lead in 

gasoline. 

As part of its Lead Risk Assessment, EPA calculated air:blood ratios based on the 

extensive modeling conducted for its case studies and compared these ratios to values reported in 

the literature (USEPA, 2007a). For the benefits analysis, we reviewed the ratios in Table 5-7 of 

the Lead Risk Assessment that compare the incremental reduction in air concentrations required 

to meet lower alternative NAAQS levels to the corresponding incremental change in blood lead. 

The ratios for the general urban and primary lead smelter case studies range from 1:2 to 1:6 for 

scenarios ranging from the current NAAQS to an alternative NAAQS of 0.05 μg/m3 maximum 

monthly mean. EPA found these values to be similar to ratios available in the literature, 

specifically to ratios reported in a 1984 meta-analysis by Brunekreef (1:3 to 1:6) and to values 
24

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 associated population mean blood 

Pb levels range from 11.5 to 4.7 μg/dL, which are lower than levels associated with the older 

studies cited in the 1986 Criteria Document (USEPA, 1986). 

We also reviewed the ratio of 1:9-1:10 cited by CASAC that was based on the data in 

Schwartz and Pitcher (1989) on blood lead changes resulting from the phase-out of lead in 

gasoline. Schwartz and Pitcher developed a regression equation to estimate blood lead levels 

from gasoline-based lead usage (hundreds of metric tons per day) 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 Survey (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 was not included in the model.) 

Schwartz and Pitcher’s analysis found that for every 100 metric tons (MT) of gasoline lead used 

per day, the general population blood lead level would increase 2.14 μg/dL. This finding was 

corroborated by a supplemental analysis by the authors of a dataset of blood lead screening 

results for inner-city children in Chicago over the same time period. CASAC developed its ratio 

of 1:9 – 1:10 by first multiplying the 2.14 μg/dL per hundred MT of gasoline lead to an estimate 

of gasoline usage in 1976 (426 MT per day) to produce a blood lead attributable to lead in 

gasoline of 9.12 μg/dL. CASAC then divided this value by EPA’s estimated reduction in 

ambient air levels in urban cities of 1 μg/m3 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) conditions to an alternative NAAQS of 0.2 μg/m3

maximum monthly mean. According to the Notice of Proposed Rulemaking, “There are a 

number of sources of uncertainty associated with 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 constructed before 1980 in establishing 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 approximately 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 that 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 concentration, 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 that the non-linear relationship between 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 contributions 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, which could have implications for the air-to-blood 

ratios.” (US EPA, 2008b). 

As a sensitivity analysis, we selected a lower 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 the 1:10 ratio as an upper bound is 

justified by the strengths of the design of the Schwartz and Pitcher study, which like the Hilts 

study, captured the effects 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 level in the population due solely to exposure to 

ambient air. We then added an estimate of non-air-related background blood lead level (e.g., 

from ingestion of indoor dust or outdoor soil contaminated by lead paint) to calculate the total 

geometric mean blood lead level expected in the population.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 analysis 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. However, data limitations prevented 

EPA from generating credible estimates.

The air:blood ratio provided us with an estimate of the geometric mean blood lead level 

across the population of exposed children, which we then used to estimate the magnitude of 

health effects benefits. We assumed that the blood lead level changes in 2016 estimated in this 

way are a reasonable representation of lifetime average blood lead level for children under seven 

years of age in our study and were used with the selected dose-response functions without further 

adjustment.

 

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 changes 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 μg/dl in their 

comments on EPA’s Lead Risk Assessment (USEPA, 2007a). We selected the midpoint of this range, 1.2 μg/dl, for 

this analysis. 
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 compare the incremental reduction in air 

concentrations required to meet lower alternative NAAQS levels to the 

corresponding incremental change in blood lead. This ratio is likely to 

provide the best estimate of blood lead associated with recent changes in 

air lead concentrations. These ratios for the general urban and primary 

lead smelter case studies range from 1:2 to 1:6 for scenarios ranging from 

the current NAAQS to an alternative NAAQS of 0.05 μg/m3

 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 

μg/m3

 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 μg/dl. 

1:6 to 1:7 Hilts, 200315 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 μg/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 approximately 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 that 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 impact of blood lead levels on changes in 

IQ in young children below seven years of age. Cognitive effects are thought to strongly relate 

to a child’s future productivity and earning potential (USEPA, 2006b). 

Multiple epidemiologic studies of Pb and child development have demonstrated inverse 

associations between blood Pb concentrations and children’s IQ and other 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 effects in children with blood Pb levels below 10 

μg/dL (CD, sections 6.2 and 8.4). As discussed in the Criteria Document, “extensive 

experimental laboratory animal evidence has been generated that (a) substantiates well the 

plausibility of the epidemiologic findings observed in human children and adults and (b) expands 

our understanding of likely mechanisms underlying the 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 yet be identified, with 

confidence, as clearly not being associated with some risk of deleterious 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 occurrence of a variety of health effects, including 

neurological effects in children, associated with blood Pb levels extending well below 10 μg/dL 

(CD, Sections 6.2, 8.4 and 8.5). For example, as noted in the Criteria Document with regard to 

the neurocognitive effects in children, the “weight 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 evident threshold has yet been clearly established 

for those effects, the existence of such effects at still lower blood-Pb levels cannot be ruled out 

based on available data.” (CD, p. 8-61). The Criteria 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 epidemiological studies and the 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-linear and linear models are almost 

identical, and for studies with lower blood Pb levels, the slopes appear to be steeper than those 

observed in studies involving higher blood Pb levels (CD, 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 that 
28

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 effect on IQ at lower blood Pb levels (e.g., below 

10 μg/dL) than at higher levels (CD, section 6.2.13; pp. 8-63 to 8-64; Figure 8-7). As stated in 

the CD, “the most compelling evidence 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 from the pooled dataset of seven international 

studies, Lanphear and others (2005) employed mathematical 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-response 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 responsible for the estimated nonlinear relationship 

between Pb and deficits in IQ (CD p. 6-30). Others have also analyzed the same dataset and 

similarly concluded that, across 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 included model variables (CD, section 6.2.13; 

Rothenberg and Rothenberg, 2005). 

In order to quantify the expected changes in IQ points in the population of children due to 

the implementation of alternative NAAQS, we utilized available dose-response functions in the 

literature. For our primary estimate, we selected a dose-response relationship from a pooled 

analysis of seven prospective studies in North America and Europe examining the effect of lead 

on full-scale IQ in children (Lanphear et al., 2005).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 lead (defined as the blood lead measured closest 

to the IQ test), maximum blood lead (defined as the peak blood lead measured at any time before 

the IQ test), average lifetime blood lead (defined as the mean blood lead from six months to 

concurrent blood lead tests), and early childhood blood lead (defined as the mean blood lead 

from 6 to 24 months). The authors found that the concurrent and lifetime blood lead levels were 

the strongest predictors of IQ deficits associated with lead exposure. 

 

16 Full-scale IQ is a composite score of verbal and performance tests. Children were administered a version of the 

Wechsler Intelligence Scales for Children under uniform conditions within each study (Lanphear et al., 2005). 

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 Ernhart 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 based on a log-linear relationship 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 (see the section in this 

chapter entitled “Selection of Health Endpoints for further information). Lanphear reports an IQ 

decrement of 6.2 points for an increase in lifetime blood lead level from 6.1 to 47.0 μg/dl for the 

selected model. However, the lowest measured lifetime blood lead level represented in the 

Lanphear pooled analysis was 1.47 μg/dl. To estimate IQ effects at blood lead levels below this 

“cutpoint,” we used a linearized slope, obtained by taking the tangent to the log-linear function at 

the point of departure (USEPA, 2007a). 

To estimate IQ benefits from blood lead reductions, we first calculated the expected IQ 

point loss per child under each of the two scenarios (the “base case” and the “control scenarios”) 

for each monitor (Equation 1). We then subtracted the “base case” IQ loss from the “control 

scenario” IQ loss and multiplied by the population of children six years of age and younger 

living within the radius of influence of each monitor to estimate the total number of IQ points 

that would be gained by reducing the NAAQS (Equation 2). 

Equation 1 

For blood lead levels ≥ cutpoint:

 

 IQ loss = β1 × ln(PbB/cutpoint) + β2 × cutpoint 

 For blood lead levels < cutpoint:

 IQ loss = β2 × PbB 

Where: 

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

β1 = -3.04 (log-linear regression coefficient from Lanphear (2005), Table 4); 

β2 = -2.1 (linear slope); and 

PbB= blood lead level (μg/dl). 

Equation 2 

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

Where: 

Δ IQ = total number of IQ points gained under the “control scenario” in comparison with 

the “base case” in 2016; 

 

18 The natural log of the blood lead levels were used for this analysis. 
30

IQ loss Control = IQ point loss under the “control scenario” per child; 

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

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

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 measurement (i.e., maximal blood lead level above and 

below 7.5 μg/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 below. 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 = β1 × (PbB - cutpoint) + β2 × cutpoint 

 For blood lead levels < cutpoint:

 IQ loss = β2 × PbB 

Where: 

Cutpoint = 5.17 μg/dl;19

β1 = -0.16 (linear coefficients from Lanphear et al. (2005) for blood lead levels greater 

than or equal to the cutpoint); 

β2 = -2.94 (linear coefficients from Lanphear et al. (2005) for blood lead levels below the 

cutpoint); and 

PbB= blood lead level (μg/dl). 

We also assessed the sensitivity of the IQ benefits to the epidemiological study selected, 

using alternative estimates from a study of 172 children 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 average 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 μg/dl by the ratio of median lifetime average 

(12.4 μg/dl) to the median peak (18.0 μg/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 decrement of 0.46 IQ points 

per 1 μg/dl increase in blood lead level.20 The RRP rule specified a linear function, finding a 

decrement of 0.88 IQ points per 1 μg/dl increase in blood lead.21 Finally, the Lead NAAQS cites 

four key studies that have “a mean blood Pb level 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 μg/dL.”22

We summarize these studies below in table 5-5. We used the following equation (Equation 4) for 

these three linear dose-response functions: 

Equation 4 

Δ IQ = [β × (PbB1 – PbB2)] × P 

Where: 

Δ IQ = total number of IQ points gained under the “control scenario” in comparison with 

the “base case” in 2016; 

β = linear regression coefficient (-0.46 for Canfield, -0.88 for RRP and -1.75 for the lead 

NAAQS); 

PbB1 = blood lead level under the “control scenario” (μg/dl); 

PbB2 = blood lead level under the “base case” (μg/dl); and 

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

 

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

Children with Blood Lead Concentrations below 10 μg 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 during renovation activities, a policy context 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 Relationships of IQ and Blood Pb for Analyses with 

Blood Pb Levels Closest to those of Children in the U.S. Today. 

Blood Pb Levels 

(μg/dL) 

Geometric 

Mean 

Range 

(min-max) Study/Analysis 

Average Linear 

SlopeA 

(IQ points per 

μg/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 2005B, <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 relationship between IQ and concurrent blood Pb levels (BLL), 

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

Pb levels. 

B The Lanphear et al 2005 pooled International study includes blood Pb data from the Rochester and Boston 

cohorts, although for different ages (6 and 5 years, respectively) 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 assessing monetary losses associated with IQ 

decrements is based on an approach applied in previous EPA analyses (USEPA, 1997, 2005 & 

2006b). The approach expresses the loss to an affected individual resulting from IQ decrements 

in terms of foregone future earnings for that individual. 

To estimate the expected monetary value of these effects, we first estimated the median 

present value of future earnings at time of birth for a person born in the U.S., based on earnings 

and labor force participation rate data from the 2006 Current Population Survey (CPS).23 When 

calculating the lifetime earnings estimate, we assumed an individual born today would begin 

working at age 16 and retire at age 67. We assumed a real growth rate for wages of one percent 

per year, as assumed in EPA’s Section 812 retrospective analysis (US EPA, 1997); adjusted for 

survival probabilities based on current US vital statistics from the CDC’s National Center for 

Health Statistics;24 and adjusted for labor force participation by age. We then discounted the 

 

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

24 See http://www.cdc.gov/nchs/data/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 age three, the midpoint of the range during which 

children are thought to be most susceptible to lead. Therefore, we discounted lifetime earnings 

back to age three. We estimated present value median lifetime earnings to be $606,930 in 2006 

dollars. 

In the previous EPA analyses cited above, the Agency has applied an average estimate of 

the effect of IQ on earnings of 2.379 percent per IQ point from an analysis by Salkever (1995).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 direct impact of IQ on 

hourly wages and indirect effects on annual earnings as the result of additional schooling and 

increased labor force participation. A recent review of literature from the labor economics and 

environmental health fields by CDC economist Scott Grosse suggests that both of these studies 

may have overestimated the association of IQ with earnings (2007). Specifically, he found the 

Salkever estimate of direct impacts of IQ on wages to be higher than estimates reported in the 

labor economics literature. Grosse also found that the Schwartz study overestimates the 

cognitive impact of lead exposure on earnings, but he argues that the Schwartz estimate may still 

be appropriate for estimating the total effect of lead on earnings, because it includes the effects 

of lead on education and earnings that result from both cognitive and non-cognitive changes. 

Thus, it may be a more comprehensive estimate than one based on cognitive changes alone. 

In recognition of the fact that the economics literature continues to evolve, and because 

EPA has traditionally relied upon the Salkever (1995) estimate to value changes in IQ, for this 

analysis we provide a range of valuation estimates based on both the Salkever (1995) and the 

Schwartz (1994b) functions. Below we describe how we estimate the cost per IQ decrement 

using each function. 

 

The 1.76 percent estimate from Schwartz represents a gross impact on earnings; it does 

not account for the costs of additional schooling. EPA’s Clean Air Mercury Rule (CAMR) RIA 

(USEPA, 2005) reported an estimate of $16,425 per additional year of schooling in 1992 dollars, 

based on U.S. Department of Education data reflecting both direct annual expenditures per 

student and annual average opportunity cost (i.e., lost income from being in school). Consistent 

with the CAMR analysis, we assume that these costs are incurred when an individual born today 

turns 19, based on an average 12.9 years of education among people aged 25 and over in the U.S. 

We discount the educational costs back to a present value at age 3, to be consistent with the 

present value of lifetime earnings. We then adjust this value to 2006 dollars, resulting in an 

estimated $14,700 per additional year of schooling. Schwartz reports an increase of 0.131 years 

of schooling per IQ point (1994b); thus the change in average education costs per IQ point is 

$14,700 × 0.131 = $1,930. 

Using the Schwartz function, we calculated the present value of the median net earnings 

loss associated with one IQ point as the present value of median lost earnings per IQ point lost 

 

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 average education costs per IQ point 

($1,930). These calculations yield a value of $8,760 of net earnings lost per a one-point decrease 

in IQ using a 3% discount rate and a value of $1,094 at a 7% discount rate. 

To estimate the cost per IQ point using Salkever (1995), we followed the same set of 

steps as above, substituting the Salkever estimate of the change in lifetime earnings. These 

calculations yield a value of $12,512 of net earnings lost per a one point decrease in IQ using a 

3% discount rate and a value of $2,156 at a 7% discount rate. 

Results 

This section presents the health effects results and the associated monetary benefits. We 

first present the expected IQ point gains in 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. Finally, we explain the methodology we applied for 

estimating monetized health benefits from co-control of PM2.5 and the results of that analysis. 

Changes in IQ 

Table 5-6 below presents the total 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 for 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 

$1.1 billion at a 7% discount rate (2006$). 

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—$870 
35

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 that state and federal interventions 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 μg/m3 maximum 

quarterly mean NAAQS standard. For this standard, approximately 60 percent of the total 

benefits are due to changes in lead air concentrations in four counties: Hillsborough, FL; 

Delaware, IN; and Berks, PA. 
36

Table 5-7. Percentage of Benefits by County (0.15 μg/m3 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 benefits model in order to assess the total 

range of potential benefits and to determine the sensitivity of the primary model results to 

various data inputs and assumptions. We used the model to calculate the total monetary benefits 

due to gains in children’s IQ for the 0.15 maximum quarterly 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 percent 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 the default value. 

Our sensitivity analysis results indicate that the benefits model is most sensitive to the 

method used for assigning air lead exposure concentrations to the population of exposed 

children. Our primary estimate relied on an interpolation method, where several monitor 

concentrations were used in determining the exposure concentration. When the radius method 

 

26 Note that for the sensitivity analysis, we relied on the results 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 population residing within a 10 km radius 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 increased 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 monitor 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 benefits fell by 83 percent. 

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

μg/m3 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 (μg/m3 in air:μg/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 (μg/dl) Salkever (1995) $5,000 N/A

Schwartz (1994) $3,500 -30%

Valuation Study 
38

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% lower. Conversely, the Lanphear dual-linear function 

generates an IQ change estimate that is 140% that of the default, while the function based on the 

median of the four functions in Table 5-5 generates an estimate twice that of the default. 

 

Inputs that had a moderate impact on the benefits results include the air:blood ratio 

selected to convert lead air concentrations into blood lead levels in the population, the non-airrelated geometric mean blood lead level used, 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 effects 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 PM2.5. However, in some areas, more emission reductions are needed than can be 

achieved through identified control options (i.e., unidentified controls). The identified and 

unidentified controls are shown in Table 5-10 below. These emission reduction estimates are 

incremental to a baseline that reflects emission reductions from MACT controls and the PM2.5

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 PM2.5. 

Effect Quantified Health Effects Unquantified Health Effectse

Healtha,b -Premature mortality based on 

both cohort study estimates and on 

expert elicitationc,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 estimates for individual PM health

effects. 

b

 In addition to primary economic endpoints, there are a number of biological responses that have been associated with PM health

effects, including morphological changes and altered host defense mechanisms. The public health impact of these biological 

responses may be partly represented by our quantified endpoints. 

c

 Cohort estimates are designed to examine the effects of long-term exposures to ambient pollution, but relative risk estimates 

may also incorporate some effects due to shorter-term exposures (see Kunzli et al., 20 01). d While some of the effects of short-term exposure are likely to be captured by the cohort estimates, there may be additional 

premature mortality from short-term PM exposure not captured in the estimates included in the primary analysis. e The categorization of unquantified toxic health effects is not exhaustive. Health endpoints in this column include both a) those 

for which there is not consensus on causality; and b) those for which causality has been determined but empirical data are not 

available to allow calculation of benefits. 
40

Table 5-10. Summary of Estimated Co-Controlled PM2.5 Emissions Reductions (in Tons) 

Alternate NAAQS 

(Second 

Maximum 

Monthly Mean) 

Identified 

Controls 

Unidentified 

Controls All Controls 

0.5 μg/m3 1,532 1 1,534 

0.4 μg/m3 1,538 5 1,544 

0.3 μg/m3 2,743 30 2,772 

0.2 μg/m3 3,037 114 3,150 

0.15 μg/m3 3,122 175 3,297 

0.1 μg/m3 3,492 304 3,795 

To estimate the value of these PM2.5 emissions reductions, EPA utilized PM2.5 benefitper-ton estimates. These PM2.5 benefit-per-ton estimates provide the total monetized human 

health benefits (the sum of premature mortality and premature morbidity) of reducing one ton of 

PM2.5 from a specified source. EPA has used a similar technique in previous RIAs, including the 

recent ozone NAAQS RIA (USEPA, 2008a). The fourteen estimates presented below derive 

from the application of three alternative methods: 

• One estimate is based on the concentration-response (C-R) function developed from a 

study of the American Cancer Society (ACS) cohort reported in Pope et al. (2002), which 

has previously been reported as the primary estimate in recent RIAs (USEPA, 2006c). 

• One estimate is based on Laden et al.’s (2006) reporting of the extended Six Cities cohort 

study; this study is a more recent PM epidemiological study that was used as an 

alternative in the PM NAAQS RIA. 

• The other twelve estimates are based on the results of EPA's expert elicitation study on 

the PM-mortality relationship, as first reported in Industrial Economics (2006) and 

interpreted for benefits analysis in EPA's final RIA for the PM NAAQS, published in 

September 2006 (USEPA, 2006c). For that study, twelve experts (labeled A through L) 

provided independent estimates of the PM-mortality C-R function. EPA practice has 

been to develop independent estimates of PM-mortality estimates corresponding to the CR function provided by each of the twelve experts. 

Readers interested in reviewing 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 reduced from those specific pollutant/source combinations (e.g., SO2

emitted from electric generating units; NOx emitted from mobile sources). Emissions controls 

modeled in this RIA are all applied to point sources; a few are at electric generating units 

(EGUs), but most are at industrial facilities involved in handling lead as a manufacturing 

product, byproduct, or input. From among the list of pollutant/source combinations outlined in 

the TSD referenced above, the combination most appropriate for valuation of PM2.5 emissions 

reductions from the sources controlled for lead emissions is the combination for PM2.5 from EGU 

and non-EGU point sources. Estimates of this per-ton value for a 3 percent discount rate vary 

from a low of $68,000 per ton to a high of $570,000 per ton (based on a change in emissions of 

50 percent or less from a 2015 PM emissions base, in 2006$). Our estimate of PM2.5 co-control 

benefits is therefore based on the total PM2.5 emissions controlled multiplied by this per-ton 

value. The results of this calculation 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 PM2.5, using 

both a 3 percent and 7 percent discount rate. 
42

Table 5-11. Monetized Benefits of Co-Controlled PM2.5 Emissions (in Millions of 2006$) 

Alternati

ve 

Pope 

et al. 

(2002

) 

Laden 

et al. 

(2006) 

Exper

t A 

Expert 

B 

Expert 

C 

Expert 

D 

Expert 

E 

Exper

t F 

Expert 

G 

Expert 

H 

Exper

t I 

Ex

3 Percent Discount Rate

0.5 μg/m3 $240 $510 $710 $550 $540 $380 $880 $490 $320 $410 $540 $

0.4 μg/m3 $240 $510 $710 $550 $540 $390 $880 $500 $330 $410 $540 $0.3 μg/m3 $430 $920 $1,28 0 $990 $980 $690 $1,600 $890 $590 $740 $970 $

0.2 μg/m3 $480 $1,000 $1,50 0 $1,100 $1,100 $790 $1,800 $1,00 0 $670 $840 $1,10 0 $

0.15 μg/m3 $510 $1,100 $1,50 0 $1,200 $1,200 $820 $1,900 $1,10 0 $700 $880 $1,20 0 $

0.1 μg/m3 $580 $1,300 $1,80 0 $1,400 $1,300 $950 $2,200 $1,20 0 $800 $1,000 $1,30 0 $1

7 Percent Discount Rate

0.5 μg/m3 $210 $460 $640 $490 $490 $350 $790 $450 $290 $370 $480 $

0.4 μg/m3 $220 $460 $640 $500 $490 $350 $800 $450 $300 $370 $490 $0.3 μg/m3 $390 $830 $1,20 0 $890 $880 $630 $1,400 $810 $530 $670 $870 $

0.2 μg/m3 $440 $940 $1,30 0 $1,000 $1,000 $710 $1,600 $920 $600 $760 $990 $

0.15 μg/m3 $460 $980 $1,40 0 $1,100 $1,100 $750 $1,700 $960 $630 $790 $1,00 0 $

0.1 μg/m3 $530 $1,100 $1,60 0 $1,200 $1,200 $860 $2,000 $1,10 0 $730 $910 $1,20 0 $

Note: All estimates have been rounded to two significant figures. All estimates are incremental to thRIA. These estimates do not include confidence intervals because they were derived through a scalin
43

described in the text. 
44

Figure 5-9. Distribution of Total PM2.5 Monetized Co-Benefits by Lead Standard 

Alternative (3% Discount Rate) 
45

Figure 5-10. Distribution of Total PM2.5 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 μg/m3

maximum quarterly mean) lead NAAQS to the selected standard of 0.15 μg/m3 would be 

expected to have a significant impact on the IQ of young children. Lowering the standard could 

cause an increase in total IQ points to about 400,000 points in 2016, which would be valued at 

between $3.5 and $5.0 billion (2006$, 3% discount rate). In addition, controls installed to 

achieve the lead NAAQS standards will also reduce emissions of fine particulates. As a result, 

this analysis includes a screening level calculation that indicates each of the alternatives 

considered could have a significant benefit in terms of improved particulate air quality, reduced 

health effects, and increased economic welfare of currently exposed individuals. 

This benefits analysis is intended to be a screening investigation to provide an estimate of 

the potential magnitude of the benefits of reducing the lead NAAQS. Therefore, the results of 

this analysis are associated with a number of uncertainties. The benefits of IQ point gains in 

children were very sensitive to the method employed for estimating exposures to the population. 

When comparing the default method, which involved concentrations interpolated from multiple 

monitors, to the method assuming a uniform concentration within 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. The results were also fairly sensitive to the discount rate selected. 

When a 7 percent discount rate was used in place of the default rate of 3 percent, results 

decreased by 83 percent. This is in part because the benefits of lead on earnings occur over a 

lifetime, and the net present value of those earnings is highly sensitive to the discount rate 

applied. The dose-response function selected for quantifying the number of IQ points gained 

as a result of achieving the alternative NAAQS levels affected the results. Utilizing alternate 

epidemiological studies decreased the primary estimate by as much as 72 percent or increased 

the primary estimate by as much as 140 percent. However, we believe the Lanphear et al. (2005) 

study was the best choice for our primary estimate. This study was a meta-analysis that 

synthesized a range of existing information and is based on more recent data than the studies 

included in the Schwartz (1993) study. In addition, the log-linear model was the most robust 

estimate from this study, in that it was the best fit for the data. 

Additional uncertainties related to the benefits estimates include the following: 

• For our primary estimate of the benefits due to gains in children’s IQ, we used a loglinear estimate from a recently published pooled analysis of seven studies (Lanphear et 

al., 2005). Using alternate estimates from other epidemiological studies examining the 

link between blood lead level and children’s IQ has significant impact on benefits results. 

We found the benefits to decrease by as much as 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 found to be the best fit for the data (i.e., the natural logtransformed blood lead level is used to predict changes in IQ score). This model implies 
47

that the magnitude of changes in IQ increases with lower blood lead levels. However, in 

the Schwartz (1993) and Canfield et al. (2003) studies, a single linear model is assumed 

(i.e., untransformed blood lead levels are used to predict changes in IQ score). The single 

linear model implies that the magnitude of change in IQ is constant over the entire range 

of blood lead levels. Therefore, at lower blood lead levels, the log-linear model predicts 

larger changes in IQ than the linear model. Note that CASAC, in their review of EPA’s 

Lead Risk Assessment indicated that “studies show that the decrements in intellectual 

(cognitive) functions in children are proportionately greater at PbB concentrations <10 

μg/dl” (USEPA, 2007d, page 3). However, if the true dose-response relationship is 

linear, than our primary estimate of benefits is an overestimation. 

• Some uncertainty is involved in the estimates of maximum quarterly mean lead air 

concentrations used for the benefits model. We used ratios of second maximum monthly 

mean values to maximum quarterly mean values from lead monitoring data from 2003-

2005 to convert the second maximum monthly mean values in 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 because the pattern and distribution of daily 

values differs, then our results could be either over- or underestimated. 

 

• The interpolation method of estimating exposure concentrations that we used for our 

primary estimate is associated with some uncertainty. The validity of this method is to 

some extent contingent upon the availability of a sufficient number of monitors to 

support an interpolation. In certain locations, such as Hillsborough County, FL, there are 

a sufficient number of lead and TSP monitors to generate an interpolation with a 

pronounced gradient around each monitor. The lead and TSP monitoring network in 

other non-attainment areas can in some cases be sparse, and the resulting interpolation 

does not appear to generate a meaningful gradient, such as in Delaware County, IN. 

• We assumed that the IQ point effects of a change in lifetime 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 critical window of exposure for IQ effects (e.g., 

between the ages of one and two), then we could potentially be overestimating benefits in 

2016 because we would have overestimated the population affected by reduced lead 

exposure in that year. However, if partial or full achievement of the alternative NAAQS 

levels might occur earlier than 2016, the children in our 0-6 age cohort who are past any 

critical window in 2016 would have realized the partial or full benefits of reduced lead 

exposures in those earlier years. Thus, the issue of a potential critical developmental 

window reflects uncertainty in both the timing and size of benefits. 

• The use of air:blood ratios represents an approximation to the impact of changes in 

ambient air concentrations of lead on concurrent blood lead levels, applied in the absence 

of modeling data on lead transport and deposition and the on direct and indirect human 

exposures. While the values we apply match fairly well with available literature, there 
48

are relatively few studies that report such values or provide sufficient data to calculate 

such ratios. Further, the lead concentrations in those studies tend to be higher than those 

modeled here (USEPA, 2007a); thus uncertainty remains as to whether the same ratios 

would be expected at lower levels, or whether air exposures are more or less efficient at 

changing concurrent blood lead levels at these lower concentrations. 

• If the air:blood ratio we apply for children or a similar value is also valid for estimating 

adult exposures, then our primary benefits understate the true health benefits accruing to 

the lead-exposed populations because they exclude impacts on morbidity and mortality 

impacts on adults as well as impacts on prenatal mortality. Additional research is needed 

to improve our understanding of the impacts of adult air exposure on adult blood lead 

levels.

• The earnings-based value-per-IQ-point lost that we apply in this analysis most likely 

represents a lower bound on the true value of a lost IQ point, because it is essentially a 

cost-of-illness measure, not a measure of an individual’s willingness-to-pay (WTP) to 

avoid the loss of an IQ point. Welfare economics emphasizes WTP measures as the more 

complete estimate of economic value. 

• The earnings-based estimate of the value-per-IQ-point lost is based on current data on 

labor-force participation rates, survival probabilities, and assumptions about educational 

costs and real wage growth in the future. To the extent these factors diverge from these 

values in the future, our lifetime earnings estimate may be under- or overestimated. 

• Co-control benefits estimated here reflect the application of a national dollar benefit per 

ton estimate of the benefits of reducing directly emitted fine particulates from point 

sources. Because they are based on national-level analysis, the benefit-per-ton estimates 

used here do not reflect local meteorology, exposure, baseline health incidence rates, or 

other local factors that might lead to an over-estimate or under-estimate of the actual 

benefits of controlling directly emitted fine particulates. 
49

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50

Industrial Economics, Inc. (2006). Expanded Expert Judgment Assessment of the ConcentrationResponse Relationship between PM2.5 Exposure and Mortality. Prepared for: Office of 

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51

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6-1

 CHAPTER 6. COST ANALYSIS APPROACH AND RESULTS 

This chapter describes our analysis of the engineering 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 these costs in four separate sections. 

Section 6.1 presents the cost estimates. Sections 6.2 and 6.3 summarize the economic and energy 

impacts of the Final Rule, respectively, while Section 6.4 outlines the main 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 employed 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 the costs of identified 

controls associated with the regulatory alternatives examined in this RIA. Following this 

discussion, the third subsection describes the two approaches 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 brief discussion of the monitoring costs associated 

with the NAAQS. The fifth subsection provides the estimated total costs of the regulatory 

alternatives examined. This section concludes with a discussion of technological change in 

regulatory cost estimates. 

It should be noted again that overall data limitations are very significant for this analysis. One 

critical area of uncertainty is the limited TSP-Pb monitoring network (discussed in chapter 2). 

Because monitors are present in only 86 counties nationwide, the universe of monitors exceeding 

the various target NAAQS levels is very small—only 21 counties above 0.1 μg/m3

, the lowest 

alternative NAAQS level examined in this RIA. Because we know that 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 nonattainment areas than have been analyzed in 

this RIA. We should also emphasize that these cost estimates represent hypothetical emission 

control strategies that, in some cases, reflect assumptions about technological advances that will 

improve the effectiveness of specific control technologies. 

It is important also to note that this chapter presents cost estimates associated with both 

identified point source measures and unspecified emission reductions needed to reach attainment. 

For the selected standard of 0.15 μg/m3

, 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 point source controls include known measures for 

known sources that may be implemented to attain the selected standard, whereas the achievement 

of unspecified emission reductions requires implementation of hypothetical additional measures 

in areas that would not attain the selected standard following the implementation of identified 

controls to known sources. 

 

1

 The costs presented in this chapter represent the direct 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 sources achieving unspecified emission reductions beyond identified 

controls is not completely understood; therefore we are not able to identify known control 

devices or work practices to achieve these reductions. We calculated extrapolated costs for 

unspecified emission reductions using two different methods. Both acknowledge the lack of data 

for this analysis. One estimates the extrapolated 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 costs of identified controls and the extrapolated 

costs of unspecified emission reductions needed beyond identified controls. 

As is discussed throughout this RIA, the technologies 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 programs may be designed and implemented in a 

number of ways, and EPA anticipates that State 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 annualized costs of purchasing, installing, 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 Implementation 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 NAAQS differ from those used in the analysis for 

the proposed NAAQS in the following ways: 

1. As discussed in Chapter 4, we updated the control 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 were 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 fixed cost/ton of lead emissions reduced beyond 

identified controls, based on the 98th percentile of the cost/ton for identified controls at large 

point sources. We recognize that a single fixed cost of control does not account for the 

different sets of conditions that might describe situations where controls are needed beyond 

identified controls. In this analysis, we instead estimate extrapolated costs by two 

approaches, the cost curve approach and 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 smelter 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 relative 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 presented in Chapter 3, our analysis of the costs 

associated with the final lead NAAQS focuses on point source particulate matter (PM) controls. 

For the purposes of this analysis, these controls largely include measures from the 

AirControlNET control technology database. The analysis also includes additional measures 

associated with operating permits, upgrades to existing 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 costs, served as the primary source of cost information for our analysis of 

the final lead NAAQS. For the analysis of the final lead NAAQS, AirControlNET used one of 

two methods to estimate the costs of a given control: 

1. Dollar per ton of PM10 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 cost equation that estimates costs as a function of several 

key engineering parameters, such as the source’s capacity or stack flow rate. 

AirControlNET uses these equations only if a source's stack flow rate is at least 5 cubic 

feet/minute. If the stack flow rate is below this threshold, AirControlNET applies a fixed 

cost per ton value to the tonnage of PM10 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 values described under items 1 and 2 above, it was 

necessary to first estimate the reduction in PM2.5 or PM10 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 PM2.5, while in other cases it is specific to all PM. 3

 This stands in contrast to the cost/ton values for the controls summarized under method 1, which are estimated as a 

cost/ton of PM2.5 controlled or cost/ton of total PM controlled. 4

 By major controls, we mean all controls except for 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 sources, we employed one of two approaches to 

estimate baseline PM emissions: 

1. SPECIATE approach. EPA's SPECIATE database includes sample PM speciation profiles 

for a variety of sources and maps these profiles to individual source classification codes 

(SCCs). For lead sources with SCCs represented by these profiles, we estimated baseline 

PM2.5 and PM10 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 SPECIATE database suggests that lead, on average, 

represents two percent of the PM10 emissions for other sources that share its SCC, we assume 

that the source emits one ton of PM10/year in the baseline. We used this approach to estimate 

the baseline PM2.5 and PM10 emissions of all sources for which the 2002 NEI includes no PM 

emissions data but that are represented by the speciation profiles in SPECIATE.6

 

2. Average speciation approach. Although SPECIATE includes speciation profiles for a wide 

range of sources, the database does not include profiles applicable to all of the sources 

included in our analysis. Therefore, we were unable to use SPECIATE to estimate the 

baseline PM2.5 and PM10 emissions for several of the lead sources for which the 2002 NEI 

includes no PM data. For these sources, we estimated baseline PM2.5 and PM10 emissions 

based on the PM speciation profile of lead sources included in our analysis for which the 

2002 NEI includes PM emissions data. Our analysis of these sources suggests that lead 

represents approximately 1.4 percent of their PM10 emissions and 1.9 percent of their PM2.5

emissions. We assume that these values apply 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 PM10 per year. Because many of 

the point sources included in our analysis fall below this emissions threshold, we have changed 

this standard format so that AirControlNET can apply direct PM controls to sources emitting any 

amount of PM10.

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 controls for such small point sources, we used 

AirControlNET’s technology-specific cost estimates for these sources in order to estimate cost 

per ton values for small sources. Such costs may be quite high on a per ton basis; for example, 

applying a pulse-jet fabric filter on a PM10 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/software/speciate/index.html>. 7

 In this analysis, we apply controls to small point sources with emissions below the 10 tons per year PM10

AirControlNET application threshold. We apply controls to small point sources due to the extent of lead 

nonattainment associated with the alternative standards examined 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 AirControlNET control measures documentation.8

 

As noted above, we adjusted AirControlNET’s control 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 published 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 measures than is currently in AirControlNET. All 

of these fact sheets can be found at http://www.epa.gov/ttn/catc/products.html#aptecfacts. We 

revised the control efficiencies, and also the capital and annualized costs for these PM control 

devices as listed above to reflect the increased control efficiencies associated 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 filters reflects an upgrade 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 percent. O&M costs increased 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 increased 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/models/DocumentationReport.pdf. 9

 PM control efficiencies were increased for the following control measures: dry and wet ESPs, all types of fabric 

filters, venturi scrubbers, impingement-plate/tray-tower scrubbers, and paper/nonwoven 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-Jet Cleaning. EPA-452/F-03-004. 2003. 

U.S. EPA-CICA Air Pollution Technology Fact Sheet: Impingement-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: Fabric Filter - Reverse-Air Cleaned 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 scrubbers 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 implementation 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/reconstructed sources of 

PM emissions are expected to implement for compliance with New Source Performance 

Standards. The specific measures identified for these sources include the following: 

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

o We estimate the annualized costs for this control measure at $5,000/ton of PM2.5 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 analyses undertaken for the final compressionignition NSPS.12 The annualized cost for PM2.5 reductions from applying DPF is 

$9,000/ton. 

• Upgrade of CEMs and increased monitoring frequency 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 PM2.5. This 

control is applicable to ESPs and baghouses 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 μg/m3 standard to 95 percent for the 0.3 μg/m3 standard—reflected the 

implementation of emission reductions 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 identified controls at a single facility, EPA 

estimated the cost of replacing the Jefferson County smelter’s current lead smelting unit with a 

more modern, lower-emitting Kivcet furnace, similar to the unit that Teck Cominco built in the 

mid-1990s at the primary lead smelter it operates in Trail, British Columbia15. In addition to the 

facility in Trail, BC, the Kivcet process is currently employed at plants in Kazakhstan, Bolivia, 

and Italy.14 While more targeted measures may allow 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 furnace at the Jefferson County facility 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 the 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 incurred additional costs of approximately $30.4 

million (year 1996 Canadian dollars) to address unanticipated operational problems with the 

Kivcet furnace after it was initially brought online in 1997, bringing the total cost of the 

project to approximately $182.4 million (year 1996 Canadian dollars).16 Converting to U.S. 

dollars and adjusting for inflation, this translates to approximately $184.5 million in year 

2006 U.S. dollars.17 

2. Scale Teck Cominco’s costs to reflect the capacity of the primary smelter in Jefferson 

County: The capacity of the primary lead smelter in Jefferson County is significantly higher 

than that of Teck Cominco’s Trail facility—250,000 tons per year for the Jefferson County 

smelter versus 132,000 tons per year for the Trail facility. Scaling the 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 Institute 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 construction 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 percent discount rate, and $42.0 million, 

applying a 7 percent discount rate. 

Based on Teck Cominco’s experience, transitioning to a Kivcet furnace could potentially reduce 

O&M costs for the primary lead smelter in Jefferson 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 potential 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 strategies presented in Chapter 4. Table 6-1 summarizes these costs by 

monitor area. As indicated in the 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 value becomes $130 million per year. The annual 

costs of identified controls under the alternative standards included in Table 6-1 range from $57 

million under the 0.5 μg/m3 standard to $180 million under the 0.1 μg/m3 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 μg/m3 standards, respectively. Consistent with 

Chapter 4's summary of the air quality impacts associated 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 μg/m3, 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 identified measures are expected to be highest in Jefferson County, 

Missouri. As indicated in Chapter 4, Jefferson County has the highest baseline lead emissions of 

any county in the U.S. This largely reflects emissions from the primary lead smelter located in 

the county. Table 6-2 presents the 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 significantly for a given monitor as the standard 

becomes more stringent. For example, the cost/pound for Delaware County, Indiana steadily 

increases from $700/pound under the 0.5 μg/m3 standard to $3,100/pound under the 0.1 μg/m3 

standard, assuming a 7 percent discount 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 manager of Teck Cominco’s Trail operations, August 25, 

2008. 
6-9

Table 6-1 

ANNUAL COSTS RELATED TO IDENTIFIED CONTROLS

*

Annual Cost of Identified Controls in 2016 (Millions of 2006$) 

Standard 

Alternative: 0.5 

μg/m3 2nd Maximum 

Monthly Mean** 

Standard 

Alternative: 0.4 

μg/m3 2nd Maximum 

Monthly Mean** 

Standard 

Alternative: 0.3 

μg/m3 2nd Maximum 

Monthly Mean 

Standard 

Alternative: 0.2 

μg/m3 2nd Maximum 

Monthly Mean 

Selected Standa

0.15 μg/m3 2n

Maximum Mon

Mean 

Monitor 

State 

Monitor 

County 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Disc

ra

AL Pike $3.1 $3.3 $3.1 $3.3 $3.1 $3.3 $4.0 $4.2 $4.3 $4.6 CO El Paso - - - - - - - - - - FL Hillsborough <$0.1 <$0.1 <$0.1 <$0.1 $0.4 $0.4 $0.8 $0.9 $0.8 $0.9 IL Madison - - - - - - - - - - IN Delaware $1.8 $1.9 $1.8 $1.9 $3.6 $3.8 $5.4 $5.7 $6.1 $6.5 MN Dakota - - - - - - - - <$0.1 <$0.MO Iron $6.3 $6.7 $6.3 $6.7 $6.3 $6.7 $6.3 $6.7 $6.3 $6.7 MO Jefferson $32 $42 $32 $42 $32 $42 $32 $42 $32 $42 NY Orange - - - - - - <$0.1 <$0.1 <$0.1 <$0.OH Cuyahoga - - - - $30 $32 $30 $32 $30 $32 OH Fulton $1.5 $1.5 $1.5 $1.5 $1.5 $1.5 $1.5 $1.5 $1.5 $1.5 OH Logan - - - - - - - - - - OK Ottawa - - - - - - - - - - PA Beaver - - - - - - $11 $12 $35 $37 PA Berks <$0.1 <$0.1 <$0.1 <$0.1 $11 $12 $11 $12 $11 $12 PA Carbon - - - - - - <$0.1 <$0.1 <$0.1 <$0.TN Sullivan - - - - - - $0.4 $0.4 $0.4 $0.4 TN Williamson $1.0 $1.1 $1.0 $1.1 $1.3 $1.4 $1.3 $1.4 $2.8 $3.0 TX Collin <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 $2.0 $2.1 $2.3 $2.5 TX Dallas - - - - - - - - - - UT Salt Lake - - - - - - - - - - Total $46 $57 $46 $57 $89 $100 $110 $120 $130 $150 

*All estimates rounded to two significant figures. As such, totals will not sum down columns. ** Identified Control Costs are the same for alternative standard 0.5 μg/m3 and for alternative standard 0.4 μg/m3. This is because the same monitor a

and the same controls were chosen in the least cost optimization for both standard alternatives. 
6-10

Table 6-2. 

ANNUAL COST/POUND OF REDUCED LEAD EMISSIONS: IDENTIFIED CONTROLS

*

Annual Cost/Pound for Identified Controls in 2016 (2006$) 

Standard 

Alternative: 0.5 

μg/m3 2nd Maximum 

Monthly Mean** 

Standard 

Alternative: 0.4 

μg/m3 2nd Maximum 

Monthly Mean** 

Standard 

Alternative: 0.3 

μg/m3 2nd Maximum 

Monthly Mean 

Standard 

Alternative: 0.2 

μg/m3 2nd Maximum 

Monthly Mean 

Selected Standa

0.15 μg/m3 2n

Maximum Mon

Mean 

Monitor 

State 

Monitor 

County 

3% Discount rate 

7% Discount rate 

3% Discount rate 

7% Discount rate 

3% Discount rate 

7% Discount rate 

3% Discount rate 

7% Discount rate 

3% Discount rate 

7% Discrate 

AL Pike $400 $400 $400 $400 $400 $400 $500 $500 $500 $500 CO El Paso - - - - - - - - - - FL Hillsborough <$100 <$100 <$100 <$100 $200 $200 $300 $400 $300 $400 IL Madison - - - - - - - - - - IN Delaware $700 $700 $700 $700 $1,300 $1,400 $1,900 $2,000 $2,100 $2,20MN Dakota - - - - - - - - <$100 <$10MO Iron $300 $300 $300 $300 $300 $300 $300 $300 $300 $300 MO Jefferson $300 $500 $300 $500 $300 $500 $400 $500 $400 $500 NY Orange - - - - - - <$100 <$100 <$100 <$10OH Cuyahoga - - - - $120,000 $130,000 $120,000 $130,000 $120,000 $130OH Fulton $5,100 $5,100 $5,100 $5,100 $5,100 $5,100 $5,100 $5,100 $5,100 $5,10OH Logan - - - - - - - - - - OK Ottawa - - - - - - - - - - PA Beaver - - - - - - $8,300 $9,000 $24,000 $25,0PA Berks <$100 <$100 <$100 <$100 $3,500 $3,700 $3,500 $3,700 $3,500 $3,70PA Carbon - - - - - - $1,400 $1,400 $1,400 $1,40TN Sullivan - - - - - - $120,000 $130,000 $120,000 $130TN Williamson $400 $400 $400 $400 $300 $400 $300 $400 $600 $700 TX Collin <$100 <$100 <$100 <$100 <$100 <$100 $400 $400 $400 $500 TX Dallas - - - - - - - - - - UT Salt Lake - - - - - - - - - - Total $300 $400 $300 $400 $600 $700 $700 $800 $900 $1,00

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

** Identified Control Costs are the same for alternative standard 0.5 μg/m3 and for alternative standard 0.4 μg/m3. This is because the same monitor aand 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 unable 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 assigning costs to these 

 unidentified measures. It suggests taking great care in reporting 

 cost estimates in cases where unidentified measures account for a 

 significant share of emission reductions. At a minimum, the 

 components of the total cost associated with identified and 

 unidentified measures should 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 reductions attributed to these measures. 

 “When assigning costs to unidentified measures, the Council 

 suggests that a simple, transparent method that is sensitive to 

 the degree of uncertainty about these costs is best. Of the three 

 approaches outlined, assuming a fixed cost/ton appears to be the 

 simplest and most straightforward. Uncertainty might be 

 represented using alternative fixed costs per ton of emissions 

 avoided.” 

EPA has considered this advice and the requirements 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 identified control costs and extrapolating 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 Council on Clean Air Compliance 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 represents a fixed cost approach given the data limitations of this 

analysis. Please note, the fixed cost methodology 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 approaches to try to estimate 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 intend to seek additional SAB advice on extrapolated 

cost approaches after further investigation 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 standards in several areas. In these areas, unspecified 

emission reductions needed beyond identified controls will likely be necessary to reach 

attainment. As noted in chapter 4, the overwhelming majority of emissions reductions needed to 

attain the various target NAAQS levels would be obtained from identified controls (see table 4-

6). Also, the unspecified emission reductions needed beyond identified controls are small 

enough to fall within the expected error associated 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/m3

Second Maximum Monthly Mean

Emission Reductions from Identified Controls

Emission Reductions Needed Beyond Identified Controls

The estimation of engineering costs for unspecified 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 Clean Air Act implementation shows that technological advances 
6-13

and development of innovative strategies can make possible emissions reductions that are 

unforeseen today, and to reduce 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 controlled. 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 implemented and would still not provide 

the needed additional control for full attainment in the analysis year 2016. 

The emission inventories, air quality 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 beyond identified controls. This fixed cost was 

consistent with the 98th percentile of total possible emission reductions for identified controls. 

We recognize that a single fixed cost of control does not account for the different sets of 

conditions that might describe situations where controls are needed beyond identified controls. 

Recall that we did not cap identified control costs 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 the techniques described 

below. Therefore in this RIA for the final Pb NAAQS, we take a broader approach to estimating 

the costs associated with the remaining 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 work 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 control of data in the emission inventory may not 

have been appropriate for this use of the emission inventory. For example, some sources for 

which we know of no control may be incorrectly characterized; leading us to believe, 

incorrectly, that no appropriate control is available. 

2. Our knowledge of add-on controls is greater than our knowledge of process modifications. 

For example in the months since proposal of the revised NAAQS, we conducted additional 

research and discovered the process modification 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 quality 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 emissions from several very large sources. This 

is true in only a few areas (e.g., Jefferson County, 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 the 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 identified controls. 

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

emissions. 

Finally, there is uncertainty inherent in estimating the progress in development of control 

measures that will occur between today and 2016. For example: 

1. Technological change unrelated to this regulation may lead to different baseline emissions or 

changes in industrial processes (e.g. will battery technology changes lead to fewer lead 

battery recycling facilities?). 

2. Changes in pollution control technology may make more control options available or reduce 

the costs of current ones. 

3. Some emissions sources may close rather than impose the identified controls (in this case the 

cost of identified controls has been overestimated and the emission reduction has been 

underestimated). 

There are many limitations to each approach taken to estimate the extrapolated costs of 

unspecified emissions, for example: 

• The ambient extrapolation methodology emphasizes 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 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% progress towards 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 

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 invalid 
6-15

(presented as part of the distributional 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 

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 associated total cost of 

meeting the selected and alternative standards 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 costs of unidentified control measures (U.S. 

Environmental Protection Agency. June 2007. Advisory Council on Clean Air Compliance 

Analysis (COUNCIL), Council Advisory on OAR’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 SAB 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 emission reductions achieved through identified controls up to the 

emission reductions required to meet the standard is the extrapolated 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 Reduced2

. The resulting regression equation is Cumulative Total Cost = -

1,175,430 ⋅ Cumulative Emissions Reduced + 32,690 ⋅ Cumulative Emissions Reduced2

, a graph 

of which can be seen in Figure 6-2. The adjusted R-squared value for this equation is 0.88, 

indicating a relatively good fit to the data. All variables are highly significant. 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 required. These estimated costs range from $79,000 for 

the 0.5 standard to $33 million for the 0.1 standard. 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 μg/m3 2nd Maximum 

Monthly Mean 0.02 $0.08 

0.4 μg/m3 2nd Maximum 

Monthly Mean 0.08 $0.32 

0.3 μg/m3 2nd Maximum 

Monthly Mean 0.29 $1.1 

0.2 μg/m3 2nd Maximum 

Monthly Mean 2.06 $8.3 

0.15 μg/m3 2nd Maximum 

Monthly Mean 4.79 $20 

0.1 μg/m3 2nd Maximum 

Monthly Mean 7.91 $33 

6.1.3.2.1 Regression Diagnostics 

While the deletion of outliers is a controversial practice in statistics, especially when there is 

uncertainty as to the underlying distribution of the 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, and leverage statistics were used to identify 

potentially influential observations. 

Before discussion of these results, it may be informative to analyze 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 extrapolated cost curve from the regression analysis are 

based. These statistics are presented in Table 6-4 below. 

Table 6-4. 

DESCRIPTIVE STATISTICS FOR VARIABLES 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, studentized residuals, and leverage were calculated 

for the regression equation Cumulative Total Cost = Cumulative Emissions Reduced + 

Cumulative Emissions Reduced2

. 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 2 ⋅ k / n 11 

Studentized Residuals ±2 19 

Leverage (2k+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 these points are outliers. 

Nonetheless, there is significant evidence that the distributions of the underlying variables used 

to construct the variables used in the regression are non-normal, and three measures of influence 

identify the upper 10-20 observations as suspect. This 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 extrapolation methodology, we first examined the seven 

monitor areas that make some progress towards attainment with identified controls, but cannot 

reach the target NAAQS. We calculated an average cost per microgram of air quality 

improvement for each area based upon the identified 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 extrapolated cost of attainment. We performed these calculations for 

each target NAAQS in our analysis. 

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

There were four areas that made little to no progress towards attainment with identified controls 

for one or more of the target NAAQS. For these monitor areas, we did not have a cost/ug for 

identified controls to plug into the above equation. As a surrogate cost/ug, we used the overall 

average cost/ug of the 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 monitor areas. 

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

Additional cost to attain 
6-19

Table 6-6 presents the cost per air quality improvement 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 approach are approximately $3.1 billion per year, 

assuming a discount rate of seven percent. Applying a three percent discount rate this value 

becomes $2.6 billion per year. The annual extrapolated costs for the ambient approach under the 

each alternative standard analyzed included in Table 6-7 range from $100 million under the 0.5 

μg/m3 standard alternative to $3.9 billion under the 0.1 μg/m3 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 μg/m3 standard alternatives, respectively. These 

costs come out to be much larger than costs under 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 

Monitor Annual Cost/Microgram (Millions of 2006$) 

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.
6-20

Table 6-7. 

AMBIENT APPROACH EXTRAPOLATED COSTS BY MONITOR AREA* 

Annual Extrapolated Costs of Emission Reductions Needed Beyond Identified Controls in 2016 (M

Standard 

Alternative: 0.5 

μg/m3 2nd Maximum 

Monthly Mean 

Standard 

Alternative: 0.4 

μg/m3 2nd Maximum 

Monthly Mean 

Standard 

Alternative: 0.3 

μg/m3 2nd Maximum 

Monthly Mean 

Standard 

Alternative: 0.2 

μg/m3 2nd Maximum 

Monthly Mean 

Selected Standa

0.15 μg/m3 2n

Maximum Mon

Mean 

Monitor 

State 

Monitor 

County 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Disc

ra

CO El Paso - - - - - - - - - - IL Madison - - - - - - - - - - MO Jefferson - - - - - - $0.7 $0.9 $1.6 $2.1 OH Cuyahoga - - - - $12 $12 $85 $90 $120 $130 OH Fulton $89 $100 $390 $460 $690 $810 $990 $1,200 $1,100 $1,30OH Logan - - - - $180 $210 $480 $560 $630 $740 PA Beaver - - - - - - - - $49 $53 PA Berks - - - - $1.8 $1.9 $7.8 $8.2 $11 $11 PA Carbon - - - - - - $280 $330 $430 $510 TN Sullivan - - - - - - $110 $130 $260 $300 TX Dallas - - - - - - - - - - Total $89 $100 $390 $460 $880 $1,000 $1,900 $2,300 $2,600 $3,10

*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 regulatory package accompanying the revised standard, 

revised lead monitoring requirements are being issued. The rule includes revisions to the 

network design requirements, QA requirements, and the minimum sampling frequency for lead 

monitoring. These changes will ensure that adequate lead monitoring will be performed to 

determine compliance with the proposed lead NAAQS. In addition, the level of the standard and 

the averaging time for the standard directly affect the associated monitoring burden. For the 

final Pb NAAQS, total monitoring costs are estimated 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/m3

; rolling quarterly average). EPA estimates that there are 

approximately 260 facilities that would require monitoring at a level of 0.15 μg/m3

 (see: 

http://www.epa.gov/oar/lead/pdfs/20080502_maps4.pdf). Monitors will also be required in 52 

urban areas (those metropolitan statistical 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 total costs to achieve the selected standard and each alternative 

standard. As suggested by Tables20 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 presented 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 identified controls alone and the cost of achieving 

attainment (i.e. including both identified controls and emission reductions beyond identified 

controls) in this and other areas reflects the limited 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 but a few areas of the country for 

the last decade or more; the alternative standards represent a substantial tightening of the existing 

NAAQS. As a result, although AirControlNET contains information on a large number of 

different point source controls, we would expect that State and local air quality managers would 

have access to additional information on the controls available to the most significant sources. 

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 the cost curve approach. This approach was generated at 

an aggregate level and therefore does not break down costs to local areas. 
6-22

monitoring network is in place, other 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 chooses 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 other facilities to be lower. We did not attempt to quantify 

facility closures in this RIA. However, in interpreting the costs, the possibility of savings from 

closures should be recalled. 

Finally, many of the control techniques identified in this analysis 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, including pollution prevention techniques, which would be more 

cost effective than adding additional control technology. Such measures might include increasing 

the enclosure of buildings, increasing air flow in hoods, modifying operation and maintenance 

procedures, changing feed materials to lower Pb content, 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 typical 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 deposited Pb, when disturbed, may be re-entrained into the 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. 

To further inform our understanding of the potential difficulties in reaching attainment, EPA 

took a closer look at the eight counties for which identified controls are insufficient to attain the 

final NAAQS of 0.15 ug/m3

 in our analysis. All eight counties contained some type of metals 

processing facilities, such as battery manufacturers, zinc smelters, and foundries. Some have 

baghouses for controlling particulate matter, but for others we found no information on 

applicable controls (possibly because of their relatively low overall particulate emissions). Three 

of the eight counties host contaminated 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 including Pb battery manufacturing, Pb battery 

recycling and disposal, zinc processing, metal alloy production, and iron and steel 

manufacturing. 
6-23

Table 6-8. ANNUAL TOTAL COSTS BY MONITOR AREA* 

(INCLUDES IDENTIFIED CONTROL COSTS AND EXTRAPOLATED COSTS USING AMBIENT EXTRAPOLATI

Annual Total Costs in 2016 (Millions of 2006$) 

Standard 

Alternative: 0.5 

μg/m3 2nd Maximum 

Monthly Mean 

Standard 

Alternative: 0.4 

μg/m3 2nd Maximum 

Monthly Mean 

Standard 

Alternative: 0.3 

μg/m3 2nd Maximum 

Monthly Mean 

Standard 

Alternative: 0.2 

μg/m3 2nd Maximum 

Monthly Mean 

Selected Stan

0.15 μg/m3 

Maximum Mo

Mean 

Monitor 

State Monitor County 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

Dis

r

AL Pike $3.1 $3.3 $3.1 $3.3 $3.1 $3.3 $4.0 $4.2 $4.3 $4.CO Adams - - - - - - - - - - CO Denver $0.01 $0.01 $0.01 $0.01 $0.38 $0.40 $0.81 $0.87 $0.81 $0.CO El Paso - - - - - - - - - - FL Hillsborough $1.8 $1.9 $1.8 $1.9 $3.6 $3.8 $5.4 $5.7 $6.1 $6.IL Madison - - - - - - - - $0.02 $0.IN Delaware $6.3 $6.7 $6.3 $6.7 $6.3 $6.7 $6.3 $6.7 $6.3 $6.MN Dakota $32 $42 $32 $42 $32 $42 $33 $43 $34 $44 MO Iron - - - - - - $0.02 $0.02 $0.02 $0.MO Jefferson - - - - $42 $44 $110 $120 $150 $16NY Orange $90 $110 $390 $460 $690 $810 $990 $1,200 $1,100 $1,OH Cuyahoga - - - - $180 $210 $480 $560 $630 $74OH Fulton - - - - - - - - - - OH Logan - - - - - - $11 $12 $84 $90 OK Ottawa $0.01 $0.01 $0.01 $0.01 $13 $14 $19 $20 $22 $23 PA Beaver - - - - - - $280 $330 $430 $5PA Berks - - - - - - $110 $130 $260 $30PA Carbon $1.0 $1.1 $1.0 $1.1 $1.3 $1.4 $1.3 $1.4 $2.8 $3.TN Sullivan $0.03 $0.03 $0.03 $0.03 $0.03 $0.03 $2.0 $2.1 $2.3 $2.TN Williamson - - - - - - - - - - TX Collin - - - - - - - - - - TX Dallas $3.1 $3.3 $3.1 $3.3 $3.1 $3.3 $4.0 $4.2 $4.3 $4.UT Salt Lake - - - - - - - - - - Total $130 $160 $430 $510 $970 $1,100 $2,100 $2,400 $2,800 $3,

*All estimates rounded to two significant figures. As such, totals will not sum down columns. 
6-24

Table 6-9. 

TOTAL COSTS BY STANDARD*

Annual Total Costs in 2016 (Millions of 2006$) 

Standard 

Alternative: 0.5 

μg/m3 2nd Maximum 

Monthly Mean 

Standard 

Alternative: 0.4 

μg/m3 2nd Maximum 

Monthly Mean 

Standard 

Alternative: 0.3 

μg/m3 2nd Maximum 

Monthly Mean 

Standard 

Alternative: 0.2 

μg/m3 2nd Maximum 

Monthly Mean 

Selected Sta

0.15 μg/m

Maximum M

Mean 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

7% 

Discount 

rate 

3% 

Discount 

rate 

D

Monitoring Costs** $4.2 $Identified Control Costs*** $46 $57 $46 $57 $89 $100 $110 $120 $130 $

Cost Curve 

Extrapolated Extrapolation $0.08 $0.08 $0.32 $0.32 $1.1 $1.1 $8.3 $8.3 $20 $Costs Ambient 

Extrapolation $89 $100 $390 $460 $880 $1,000 $1,900 $2,300 $2,600 $Cost Curve Extrapolation**** $46 $57 $46 $57 $90 $100 $110 $130 $150 $

Total Costs Ambient 

Extrapolation $130 $160 $430 $510 $970 $1,100 $2,100 $2,400 $2,800 $

* 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 provisions, monitoring costs are included here. See OMB 2060-0084, ICR 

discussion. *** Identified Control Costs are the same for alternative standard 0.5 μg/m3 and for alternative standard 0.4 μg/m3. This is because the same monitor 

and the same controls were chosen in the least cost optimization for both standard alternatives. **** Estimates for the 0.5 μg/m3 and the 0.4 μg/m3 appear similar; this is due 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  ‐ Ambient
6-26

Figure 6-4. 

TOTAL COSTS IN 2016 BY EXTRAPOLATED COST APPROACH 

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

$4,500

Total Costs (M 2006$)

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 technological innovation and “learning by doing” have 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 estimates. Studies21 have suggested that costs 

of some EPA programs have been less than originally estimated due in part to inadequate 

inability to predict and account for future technological innovation in regulatory impact analyses. 

Constantly increasing marginal costs 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 shift 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 assumption 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 abatement cost curve. It is possible that the shape of the marginal abatement 

cost curve is non-linear.
6-27

production and would likely induce production efficiencies, in particular those related to energy 

inputs, which would lower emissions from the production side. 

Figure 6-5. 

TECHNOLOGICAL INNOVATION REFLECTED BY MARGINAL COST SHIFT 

MC0 MC1

Cumulative NOx Reductions

Cost/Ton

Induced Technology Shift

Slope = β0

Slope = β1

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 detection 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 believed possible in the late 1980s due to improvements in 

evaporative controls, catalyst design and fuel control systems for light-duty vehicles; and 

treatment devices and retrofit technologies for heavy-duty engines 

• Idle-reduction technologies for engines, including truck stop electrification efforts 

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

These technologies were not commercially available 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 doing” 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 estimates. Learning curve impacts can be defined 

generally as the extent to which variable costs (of production and/or pollution 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 production processes. Impacts such as these would 

manifest themselves as a lowering of expected costs for operation of technologies in the future 

below what they may have been. 

The magnitude of learning curve impacts on pollution control costs has been estimated for a 

variety of sectors as part of the 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 adjustments were included for those sectors and technologies for which 

learning curve data was available. A typical learning curve adjustment example is to reduce 

either capital or O&M costs by a certain percentage given a doubling 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 the given sector or technology. 

T.P. Wright, in 1936, was the first to characterize the relationship between increased productivity 

and cumulative production. He analyzed man-hours required to assemble successive airplane 

bodies. He suggested the relationship is a log linear function, since he observed a constant linear 

reduction in man-hours every time the total number of airplanes assembled was doubled. The 

relationship he devised between number assembled 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: CN = Co * Nb, 

Where: 

N = cumulative production 

CN = cost to produce Nth unit of capacity 

Co = 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 Economics, 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/mar07/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, depending on the sector and 

technology. Learning curve adjustments were prepared in a memo by IEc supplied to US EPA 

and applied for the mobile source sector (both onroad and nonroad) and for application of 

various EGU control technologies within the Draft Direct Cost Report.25 Advice received from 

the SAB Advisory Council on Clean Air Compliance Analysis in June 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 available. 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. The recommended learning curve impact adjustment 

for virtually every sector considered in the Draft 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 predict technological advances. Over longer time 

horizons the opportunity for technical advances is greater. 

• Multi-rule study: Harrington et al. of Resources for the Future26 conducted an analysis of the 

predicted and actual costs of 28 federal and state 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 fall within 25 percent (greater or less than) the 

predicted amount. They found that predicted total costs were overestimated for 14 of the 28 

rules, while total costs were underestimated for only three rules. Differences can result 

because of quantity differences (e.g., overestimate of pollution reductions) or differences in 

per-unit costs (e.g., cost per unit of pollution reduction). Per-unit costs of regulations were 

overestimated in 14 cases, while they were underestimated in six cases. In the case of EPA 

rules, the agency overestimated per-unit costs for five regulations, underestimated them for 

four regulations (three of these were relatively small pesticide rules), and accurately 

estimated them for four. Based on examination of eight economic incentive rules, “for those 

rules that employed economic incentive 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. 2000. “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 literature, the authors identified technological 

innovation as one of five explanations of why predicted and actual regulatory 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 lower than predicted because of unanticipated use of new 

technology.” 

 It should be noted that many (though not all) of the EPA rules examined by Harrington had 

compliance dates of several years, which allowed 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 results 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 spurred 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 unanticipated opportunities arose to blend low and high 

sulfur coal in older boilers up to a 40/60 mixture, compared with the 5/95 mixture originally 

estimated. 

Phase 2 Cost Estimates 

Ex ante estimates $2.7 to $6.2 billiona

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 Quality28 examined EPA vehicle and fuels 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 notes 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 and 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 simple statement reflects the fact that a lot happens between 

a producer’s determination of manufacturing 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 manufacturers), 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 phaseout for Class I substances was implemented 

4-6 years faster, included 13 more chemicals, and 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 HFC-134a could be substituted 

for CFC-12 in refrigeration. However, as Hammit31 notes, ‘since 1991 most new U.S. 

automobile air conditioners have contained 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, which 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 decreasing 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 capital cost dropped from 

$25-$38/kw to $15/kw32. 

We can not estimate the interplay between EPA regulation and technology improvement, but it is 

clear that a priori cost estimation often results in overestimation of costs because changes in 

technology (whatever the cause) make less costly control possible. 

 

32 ICF Consulting. October 2005. The Clean Air Act Amendment: Spurring Innovation and Growth While Cleaning 

the Air. Washington, DC. Available at http://www.icfi.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 impacts presented here are an extension of the 

engineering costs, where engineering costs are allocated to specific source categories by North 

American Industry Classification 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 primary smelters & refiners of nonferrous metals 

(NAICS 331419) are expected to incur most of the identified control costs associated with the 

alternative standards included in this RIA. For 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 alloying 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 industry for identified measures under the Final 

NAAQS.33 Costs associated with emission reductions 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 approximately 54 percent of costs 

under the selected standard of 0.15μg/m3

. This implies that although the upfront capital costs 

associated with the selected standard may be significant for affected industries, these costs are 

expected to represent a fraction of the 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. 
6-34

Table 6-11. ANNUAL COSTS OF IDENTIFIED CONTROLS BY INDUSTRY

Total Cost with Identified Controls (Millions of 2006$) 

Standard 

Alternative: 

0.5 μg/m3 2nd

Maximum 

Monthly 

Mean 

Standard 

Alternative: 0.4 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.3 

μg/m3 2nd

Maximum 

Monthly Mean 

Standard 

Alternative: 0.2 

μg/m3 2nd

Maximum 

Monthly Mean 

Selected 

Standard: 0.15 

μg/m3 2nd

Maximum 

Monthly Mean 

Standar

Alternative

μg/m3 2

Maximu

Monthly M

NAICS 

Code Industry Description 

3% 

Disc 

rate 

7% 

Disc 

rate 

3% 

Disc 

rate 

7% 

Disc 

rate 

3% 

Disc 

rate 

7% 

Disc 

rate 

3% 

Disc 

rate 

7% 

Disc 

rate 

3% 

Disc 

rate 

7% 

Disc 

rate 

3% 

Disc 

rate 

D

r

2211 Electric Power Generation, 

Transmission and Distribution - - - - $2 $2.3 $4.4 $5.1 $4.6 $5.2 $11.2 $

331419 Primary Smelting and 

Refining of Nonferrous Metal (except Copper and Aluminum) $31.7 $42 $31.7 $42 $31.7 $42 $40 $50.8 $63.9 $76.3 $63.9 $7

331492 Secondary Smelting, 

Refining, and Alloying of Nonferrous Metal (except Copper and Aluminum) $12.3 $13 $12.3 $13.1 $23.3 $24.7 $28.4 $30.2 $31.2 $33.3 $35.8 $3

331111 Iron 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 $2221330 

Steam and AirConditioning Supply - - - - $7.3 $7.6 $7.3 $7.6 $7.3 $7.6 $7.3 $7

331491 

Nonferrous Metal (except Copper and Aluminum) Rolling, Drawing, and Extruding - - - - - - - - - - $4.6 $4

331513 

Steel Foundries (except Investment) - - - - - - - - - - $3.9 $4

331314 

Secondary Smelting and Alloying of Aluminum - - - - - - - - <$0.1 <$0.1 $2.4 $2

32511 

Petrochemical Manufacturing - - - - - - - - - - $1.1 $

622110 

General Medical and Surgical Hospitals - - - - - - - - - - - - 

335911 

Storage Battery Manufacturing - - <$0.1 <$0.1 $0.8 $0.8 $0.8 $0.8 $0.8 $0.8 $0.8 $0

331511 Iron Foundries - - - - $0.4 $0.4 $0.4 $0.4 $0.4 $0.4 $0.7 $0Other Industries - - - - $1.5 $1.8 $2.2 $2.5 $2.2 $2.5 $3.8 $4Total $45.5 $56.5 $45.5 $56.6 $89.1 $103 $105.5 $120.7 $132.5 $149.4 $159.9 $11. Source: U.S. Census Bureau 2002 Economic Census 
6-35

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 μg/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 Nonferrous 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 Alloying 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 represents 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 strategies will be developed by States on a case-bycase basis, and EPA cannot 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. 

For this RIA, implementation of the control measures needed for attainment with the alternative 

standards will likely lead to increased energy consumption among lead emitting facilities. To 

control emissions effectively, these measures require a significant amount of electricity that 

affected facilities are not expected to consume under baseline 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 energy impacts focuses exclusively on electricity 

consumption. In addition, because the energy consumption associated with emission reductions 

beyond identified controls is uncertain, we only consider the energy impacts associated with 
6-36

identified controls. Similarly, the electricity consumption 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 facility from our analysis of energy impacts. 

To assess the electricity consumption impacts associated with identified controls, we relied on 

the AirControlNET outputs generated for this analysis. For most identified controls, 

AirControlNET estimates electricity costs separately from other operating and maintenance 

(O&M) costs. Therefore, for sources expected to implement these controls, AirControlNET 

provides direct estimates of the additional electricity costs expected under the standard 

alternatives. We calculate the electricity consumption associated with these costs based on the 

unit cost of electricity assumed by AirControlNET (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 cost data for several controls identified from sources other than 

AirControlNET do not distinguish between electricity 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. For 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 controls where O&M and capital are calculated 

separately. 

Table 6-13 summarizes the estimated energy impacts associated with the selected and alternative 

standards. As indicated in the table, we estimate that sources installing identified controls under 

the alternative standards will increase their electricity consumption in 2016 by approximately 

121,800 megawatt-hours (MWh) under the selected standard. By comparison, the iron and steel 

industry alone is projected to purchase 66 million MWh of electricity in 2016.34

Table 6-13. 

SUMMARY OF ENERGY IMPACTS 

 

Standard 

Alternative: 

0.5 μg/m3

 2nd

Maximum 

Monthly 

Mean 

Standard 

Alternative: 

0.4 μg/m3 2nd

Maximum 

Monthly 

Mean 

Standard 

Alternative: 

0.3 μg/m3 2nd

Maximum 

Monthly 

Mean 

Standard 

Alternative: 

0.2 μg/m3 2nd

Maximum 

Monthly 

Mean 

Selected 

Standard: 

0.15 μg/m3 2nd

Maximum 

Monthly 

Mean 

Standard 

Alternative: 

0.1 μg/m3

 2nd

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 provides a reasonable approximation of the 

costs associated with the final lead NAAQS using hypothetical control scenarios given the 

available information, we note the following 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 associated with the final 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 the 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 relies 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 emissions included in our 

analysis (i.e., AirControlNET contains control measure data for those lead sources that 

represent 91 percent of the lead emissions included in the analysis). 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 tighter control levels in the future. It is not 

known whether industrial sources will in the future make improvements to existing 

particulate matter controls to control Pb emissions, 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 estimates for the primary lead smelter in Jefferson County, 

Missouri: To estimate the costs of the selected standard for Jefferson County, Missouri, this 

analysis models the replacement of the primary 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, British Columbia, scaling for differences in lead 

production capacities between the Teck Cominco facility and the smelter in Jefferson 

County. While this is a reasonable approach for estimating costs for the smelter in Jefferson 

County, our analysis may not capture facility-specific characteristics that 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 more 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 extrapolated 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 with 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 additional control for the remaining increment of 

Pb might be difficult to achieve. In addition 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 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 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% progress towards 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 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 invalid (presented as part of the distributional 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 control 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 benefits 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 primary lead (Pb) National 

Ambient Air Quality Standard (NAAQS) within the current monitoring network 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 there may be many more potential nonattainment areas 

than have been analyzed in this RIA. 

It is important to note at the outset that overall data limitations are very significant for 

this analysis, compared to other NAAQS reviews. One critical area of uncertainty is the limited 

TSP-Pb monitoring network (discussed in chapter 2). Because monitors are present in only 86 

counties nationwide, the universe of monitors exceeding the final NAAQS level of 0.15 μg/m3

represent only 16 counties. It is important to note that data limitations prevented us from 

identifying a full range of controls which would bring eight of these counties all the way to 

attainment of the final NAAQS. It is also important 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 nonattainment 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 for compliance with the Pb NAAQS; however, 

dispersion models are data –intensive and more appropriate for local scale analyses of emissions 

from individual sources. It was not feasible to conduct such a large-scale data intensive analysis 

for this RIA. As a result, the simplified analysis developed for this RIA, while distanceweighting individual source contributions to ambient Pb concentrations, 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 inputs 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 NAAQS revision) are consistently applied in all 
 7-2 

estimates presented here. In addition, the two alternative 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 illustrative control strategies described above, our 

analysis of the costs associated with the selected NAAQS focuses on point source PM controls. 

For the purposes of this analysis, these controls largely include measures from the 

AirControlNET control technology database, but also include additional measures associated 

with operating permits and/or New Source Performance Review standards applicable to sources 

similar to those included in our analysis. For controls identified in AirControlNET, we estimated 

costs based on the cost equations included in AirControlNET. Our cost estimates for controls 

associated with operating permits and/or New Source Performance Review standards are based 

on cost data compiled by EPA for previous analyses. 

As indicated in the above discussion on illustrative control strategies, implementation of 

the PM control measures identified from AirControlNET and other sources does not result in 

attainment with the 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 costs using two different approaches. Under one 

approach, we extrapolated the cost of unspecified emission reductions by constructing a total 

cost curve using data on identified control costs. We then derived a total cost equation in 

quadratic form which best fit the total cost curve. Under our second 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 applied 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 standard (88%) come from our analysis of current known control 

technologies, with only 12% of the total costs coming from extrapolated costs. Under the second 

scenario, 5% of the total costs come from our analysis of currently known control technologies, 

and the majority of the costs (95%) comes from our assumptions about 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 primary 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 

μg/m3

 2nd Maximum 

Monthly Mean 

Final NAAQS: 0.15 μg/m3

2nd Maximum Monthly 

Mean 

Alternative NAAQS: 0.1 

μg/m3

 2nd 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 

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

Costs Ambient 

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

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

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 provisions, monitoring costs are included here. See

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

The ranges of benefits presented reflect uncertainty 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 benefits to the low and high ends of the range of 

PM co-benefits, and then subtracting the total cost 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 

μg/m3 2nd Maximum Monthly 

Mean 

Final NAAQS: 0.15 μg/m3 2nd

Maximum Monthly Mean 

Alternative Standard: 0.10 

μg/m3

 2nd 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 selected NAAQS and alternative standards. 

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

Alternative Standard: 0.40 μg/m3

2nd Maximum Monthly Mean 

Final NAAQS: 0.15 μg/m3 2nd

Maximum Monthly Mean 

Alternative Standard: 0.10 μg/m3

2nd 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 recognized 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 reach attainment of the lead NAAQS, with the 

timing of attainment being determined by future decisions by states and EPA. Our estimates are 

intended to provide information on the general magnitude of the costs and benefits 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 predictions 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 health benefits of reductions in ambient lead 

levels, as well as coincident reductions in ambient fine particulates. 

Compared to recent NAAQS RIAs, however, our current knowledge of the costs and 

nature of lead emissions controls and the effects 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 proposed standards, while supported by and consistent 

with EPA's review of recent scientific research, represent a substantial tightening of the existing 

NAAQS. As a result, many of the analyses conducted for this RIA could be significantly 

improved through enhanced research and data in these areas. Perhaps the greatest need is for 

research on available pollution control technology, work practice changes, and pollution 

prevention options to most cost-effectively control lead emissions directly, rather than as a 

component of lead-bearing PM emissions. 

It is important to note again that overall data limitations are very significant for this 

analysis, compared to other NAAQS reviews. One critical area of uncertainty is the limited TSPPb monitoring network (discussed in chapter 2). Because monitors are present in only 86 

counties nationwide, the universe of monitors exceeding the final NAAQS level of 0.15 μg/m3

represent only 16 counties. It is important to note that data limitations prevented us from 

identifying a full range of controls which would bring eight of these counties all the way to 

attainment of the final NAAQS. It is also important 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 nonattainment 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 for compliance with the Pb NAAQS; however, 

dispersion models are data –intensive and more appropriate for local scale analyses of emissions 

from individual sources. It was not feasible to conduct such a large-scale data intensive analysis 

for this RIA. As a result, the simplified analysis developed for this RIA, while distanceweighting individual source contributions to ambient Pb concentrations, 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 the 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 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. 

• To assess the cost of reducing point source lead emissions, this analysis relies upon PM 

control cost information from AirControlNET. EPA's database of lead-focused controls 

is very limited, however, and there are no lead-specific 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 control 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 sources may be less than or greater than costs 

to control emissions from other sources. 

• The ambient extrapolation methodology emphasizes 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 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% progress 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 identified controls at a national level and the 

costs of control at a local level. The data underlying this curve contains data points which 

we believe to be invalid (presented as part of the distributional analysis in Section 

6.1.3.3). The estimated curve estimates negative costs over a portion of emission 

reductions. In addition this approach 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 standards 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 other epidemiological studies examining the 

link between blood lead level and children’s IQ has significant impact on benefits results. 

We found the benefits to decrease by as much as 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 found to be the best fit for the data (i.e., the natural logtransformed blood lead level is used to predict changes in IQ score). This model implies 

that the magnitude of changes in IQ increases with lower blood lead levels. However, in 
 7-7 

the Schwartz (1993) and Canfield et al. (2003) studies, a single linear model is assumed 

(i.e., untransformed blood lead levels are used to predict changes in IQ score). The single 

linear model implies that the magnitude of change in IQ is constant over the entire range 

of blood lead levels. Therefore, at lower blood lead levels, the log-linear model predicts 

larger changes in IQ than the linear model. Note that CASAC, in their review of EPA’s 

Lead Risk Assessment indicated that “studies show that the decrements in intellectual 

(cognitive) functions in children are proportionately greater at Pb concentrations <10 

μg/dl” (USEPA, 2007d, page 3). However, if the true dose-response relationship is 

linear, than our primary estimate of benefits is an overestimation. 

• Some uncertainty is involved in the estimates of maximum quarterly mean lead air 

concentrations used for the benefits model. We used ratios of second maximum monthly 

mean values to maximum quarterly mean values from lead monitoring data from 2003-

2005 to convert the baseline second maximum 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 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 because the pattern and 

distribution of daily values differs, then our results could be either over- or 

underestimated. 

• The interpolation method of estimating exposure concentrations that we used for our 

primary estimate is associated with some uncertainty. The validity of this method is to 

some extent contingent upon the availability of a sufficient number of monitors to 

support an interpolation. In certain locations, such as Hillsborough County, FL, there are 

a sufficient number of lead and TSP monitors to generate an interpolation with a 

pronounced gradient around each monitor. The lead and TSP monitoring network in 

other non-attainment areas can in some cases be sparse, and the resulting interpolation 

does not appear to generate a meaningful gradient, such as in Delaware County, IN. 

• We assumed that the IQ point effects of a change in concurrent 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 critical window of exposure for IQ effects (e.g., 

between the ages of one and two), then we could potentially be overestimating benefits in 

2016 because we would have overestimated the population affected by reduced lead 

exposure in that year. However, if partial or full achievement of the alternative NAAQS 

levels might occur earlier than 2016, the children in our 0-6 age cohort who are past any 

critical window in 2016 would have realized the partial or full benefits of reduced lead 

exposures in those earlier years. Thus, the issue of a potential critical developmental 

window reflects uncertainty in both the timing and size of benefits.

• The use of air:blood ratios represents a first approximation to the impact of changes in 

ambient air concentrations of lead on concurrent blood lead levels, applied in the absence 

of modeling data on lead transport and deposition and the on direct and indirect human 

exposures. While the values we apply match fairly well with available literature, there 
 7-8 

are relatively few studies that report such values or provide sufficient data to calculate 

such ratios. Further, the lead concentrations in those studies tend to be higher than those 

modeled here (EPA, 2007a); thus uncertainty remains as to whether the same ratios 

would be expected at lower levels, or whether air exposures are more or less efficient at 

changing concurrent blood lead levels at these lower concentrations. 

• If the air:blood ratio we apply for children or a similar value is also valid for estimating 

adult exposures, then our primary benefits understate the true health benefits accruing to 

the lead-exposed populations because they exclude impacts on morbidity and mortality 

impacts on adults as well as impacts on prenatal mortality. Additional research is needed 

to improve our understanding of the impacts of adult air exposure on adult blood lead 

levels.

• The earnings-based value-per-IQ-point lost that we apply in this analysis most likely 

represents a lower bound on the true value of a lost IQ point, because it is essentially a 

cost-of-illness measure, not a measure of an individual’s willingness-to-pay (WTP) to 

avoid the loss of an IQ point. Welfare economics emphasizes WTP measures as the more 

complete estimate of economic value; for example, the earnings-based value does not 

include losses in utility due to pain and suffering, nor does it assess the costs of averting 

behaviors that may be undertaken by households to avoid or mitigate IQ loss from lead 

exposure. 

• The earnings-based estimate of the value-per-IQ-point lost is based on current data on 

labor-force participation rates, survival probabilities, and assumptions about educational 

costs and real wage growth in the future. To the extent these factors diverge from these 

values in the future, our lifetime earnings estimate may be under- or overestimated. 

Another factor suggesting that our lifetime earnings estimate may be an underestimate is 

that it does not account for the value of productive services occurring outside the labor 

force (e.g., child rearing and housework). 

• Because of the relatively strong relationship between PM2.5 concentrations and premature 

mortality, PM co-benefits resulting from reductions 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 lesser extend on the relative magnitude of direct 

lead benefits. The lower end of the range assumes PM2.5 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 here reflect the application of a national dollar benefit per 

ton estimate of the benefits of reducing directly emitted fine particulates from point 

sources. Because they are based on national-level analysis, the benefit-per-ton estimates 

used here do not reflect local meteorology, exposure, baseline health incidence rates, or 

other local factors that might lead to an over-estimate or under-estimate of the actual 

benefits of controlling directly emitted fine particulates. 
 7-9 

Conclusions and Insights

EPA’s analysis has estimated the health and welfare benefits of reductions in ambient 

concentrations of lead resulting from a set of illustrative control strategies to reduce emissions of 

lead at point sources. The results suggest there will be significant additional health and welfare 

benefits arising from reducing emissions from a variety of sources in and around projected 

nonattaining counties in 2016. While 2016 is the latest date by which states would generally 

need to demonstrate attainment with the revised standards, it is expected that benefits (and costs) 

will begin occurring earlier, as states begin implementing control measures to show progress 

towards attainment. 

There are several important factors to consider when evaluating the relative benefits and 

costs of the attainment strategies for the six alternative standards assessed in this RIA: 

• Benefits and costs are distributed differently across potential non-attainment 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 identified control costs, on the other hand, are 

greatest in those areas with the largest sources of lead emissions - usually around primary 

or secondary lead smelters, mining operations, or battery manufacturers. PM co-control 

benefits tend to be distributed in better correlation to control costs. In general, PM cocontrol benefits tend to be highest in those areas where our attainment strategy suggests 

controls on combustion sources, rather than metals processing, are necessary. 

• Our analysis considers controls on point source emissions only. Local areas might find 

that controls of area nonpoint 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 Federal action might reduce the contribution 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 Order (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 million 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 potential costs and benefits associated with this 

action. However, the CAA and judicial decisions make clear that the economic and 

technical feasibility of attaining ambient standards are not to be considered in setting or 

revising NAAQS, although such factors may be considered in the development of State 

plans to implement the standards. Accordingly, although an RIA has been prepared, the 

results of the RIA have not been considered 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 collection 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 associated information) is needed 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 provide additional flexibility in meeting the 

FRM/FEM determination requirements. Furthermore, 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/FEM 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 made 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 amendments 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 ambient Pb monitoring data to 

EPA’s Air Quality System (AQS). We have estimated the burden based on the final 
monitoring requirements of this rule. Based on these requirements, the annual average 

reporting burden for the collection under 40 CFR part 58 (averaged over the first 3 years 

of this ICR) for 150 respondents is estimated to increase by a total 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) generally requires an agency to prepare a 

regulatory flexibility analysis of any rule subject to notice and comment rulemaking 

requirements under the Administrative Procedure Act or any other statute unless the 

agency certifies that the rule will not have a significant economic 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 rule 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 that is any not-forprofit enterprise which is independently 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 establishes 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, the 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 tribal governments and the private sector. Unless otherwise 

prohibited by law, under section 202 of the UMRA, EPA generally must prepare a 

written statement, including a cost-benefit analysis, for proposed and final rules with 

“Federal mandates” that may result in expenditures to State, local, and tribal 

governments, in the aggregate, or to the private 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 and 
consider a reasonable number of regulatory alternatives 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, section 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 why that alternative was not adopted. Before 

EPA establishes any regulatory requirements that may significantly or uniquely affect 

small governments, including tribal governments, it must have developed under section 

203 of the UMRA a small government agency plan. The plan must provide for notifying 

potentially affected small governments, enabling 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 with the regulatory requirements. 

This action is not subject to the requirements 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 for State, local, and tribal governments, in 

the aggregate, or the private sector in any one year. The 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 those costs are not expected to exceed $100 

million in the aggregate for any year. Furthermore, as indicated previously, in setting a 

NAAQS EPA cannot consider the economic or technological feasibility of attaining 

ambient air quality standards. Because the Clean Air Act prohibits EPA from 

considering the types of estimates 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 contains no regulatory requirements that might 

significantly or uniquely affect small governments 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 ensure “meaningful and timely input by State 

and local officials in the development of regulatory policies that have federalism 

implications.” “Policies that have federalism implications” is defined in the Executive 

Order to include regulations that have “substantial direct effects on the States, on the 

relationship between the national government and the States, or on the distribution of 

power and responsibilities among the various levels of government.” 

This final rule does not have federalism implications. It will not have substantial direct 

effects on the States, on the relationship between the national government and the States, 
or on the distribution of power and responsibilities among the various levels of 

government, as specified in Executive Order 13132. The rule does not alter the 

relationship between the Federal 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 States to establish more stringent requirements if deemed 

necessary by a State. Furthermore, under 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 significant 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 implications, 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 Children from Environmental Health & 

Safety Risks 

This action is subject to EO 13045 (62 FR 19885, April 23, 1997) because it is an 

economically significant regulatory action as defined by EO 12866, and we believe that 

the environmental health risk addressed by this action has a disproportionate effect on 

children. The final rule establishes 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, the protection offered by these standards may 

be especially important for children because neurological 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 evaluated the environmental health effects of 

exposure to Pb pollution among children. These 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 sections 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 Significantly Affect Energy Supply, Distribution, 
or Use” (66 FR 28355 (May 22, 2001)) because it is not likely to have a significant 

adverse effect on the supply, distribution, or use of energy. The purpose of this rule is to 

establish revised NAAQS for Pb. The rule does not prescribe specific 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 cannot 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 National 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 regulatory activities unless to do so would be 

inconsistent with applicable law or otherwise impractical. Voluntary consensus standards 

are technical standards (e.g., materials specifications, test methods, sampling procedures, 

and business practices) that are developed or adopted by voluntary consensus standards 

bodies. The NTTAA directs EPA to provide Congress, through OMB, explanations 

when the Agency decides not to use available and applicable voluntary consensus 

standards. 

This final rule involves technical standards. EPA has established low-volume PM10 

samplers coupled with XRF analysis as the FRM for Pb-PM10 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 analysis method does not provide for the method 

detection limits necessary to adequately characterize ambient Pb concentrations 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 agencies, to the greatest 

extent practicable and permitted by law, to make environmental justice part of their 

mission by identifying and addressing, as appropriate, disproportionately high and 

adverse human health or environmental effects 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 minority or low-income populations because it 

increases the level of environmental 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 rule 
establishes uniform national standards 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 ambient air Pb in those areas where ambient air 

concentrations are currently the highest. Thus, to the extent that any population groups, 

including minorities or low-income populations, are currently experiencing 

disproportionate exposure to ambient air-related Pb, those groups can be expected to 

experience relatively greater air quality improvements under the revised standards. 

Nationwide, these revised, more stringent standards will not have adverse health impacts 

on any population, including any minority or low-income population.