1-1

From: Lydia Wegman/RTP/USEPA/US

To: "Frey, Nathan J." <Nathan_J._Frey@omb.eop.gov>, "Lee, Amanda I." 

<Amanda_I._Lee@omb.eop.gov>

Cc: Joseph-J Dougherty/DC/USEPA/US@EPA, Darryl Weatherhead/RTP/USEPA/US@EPA, Lillian 

Bradley/RTP/USEPA/US@EPA, Bryan Hubbell/RTP/USEPA/US@EPA, Amy 

Lamson/RTP/USEPA/US@EPA

Date: 12/14/2012 10:48 AM

Subject: Response to interagency comments on the PM RIA

Nathan and Amanda, 

Attached below are the revisions to the RIA. As we discussed, we had made many edits to the Executive 

Summary prior to receiving your comments this morning. Those edits are:

1. Sections ES.2.1 - 3 was completely rewritten and we re-drafted the analytical flowchart from Chapter 1 

and added extensive language explaining in great detail what was performed at each step of the current 

analysis for the final rule. 

2. Section ES.5 was completely rewritten to explain and fully describe the updates and important 

analytical differences between proposal and final.

In addition, in response to your comments of today, we have added to this version of the ES language 

related to emission reductions and included the table the comments identified. We have also updated the 

benefits language. The new language added to section ES.5 addresses the analytical differences 

between proposal and final. We also added a statement to the beginning paragraph of ES 2.1 pointing to 

section ES. 5.

We plan to create a final version of the document based on this version so please let us know if you have 

any questions. 

Also, the revised benefits Chapters 5 and 6 are attached below as well.
1-2

EXECUTIVE SUMMARY

ES.1 Overview

Based on its review of the air quality criteria and the national ambient air quality 

standards (NAAQS) for particulate matter (PM), the U.S. Environmental Protection Agency (EPA)

is making revisions to the primary standards for PM to provide requisite protection of public 

health and welfare. The EPA is revising the primary annual (health-based) standard, retaining 

the primary 24-hour standard, and retaining the secondary (welfare-based) NAAQS for fine 

particles (generally referring to particles less than or equal to 2.5 micrometers [μm] in 

diameter—PM2.5). The EPA is retaining the current primary and secondary 24-hour PM10

standards.

As has traditionally been done in NAAQS rulemakings, the EPA has conducted a 

Regulatory Impact Analysis (RIA) to provide the public with illustrative estimates of the 

potential costs and health and welfare benefits of attaining the revised annual standard along 

with two alternative standards. In NAAQS rulemakings, the RIA is prepared for informational 

purposes only, and the decisions related to the setting of the PM NAAQS standards are not in 

any way based on consideration of the information or analyses in the RIA. The RIA fulfills the 

requirements of Executive Orders 12866 and 13563 and guidelines of the Office of 

Management and Budget’s (OMB) Circular A-4.

1

The control strategies presented in this RIA are illustrative and represent one set of 

control strategies States might choose to implement in order to meet the final standards. As a 

result, benefit and cost estimates provided in this RIA cannot be added to benefits and costs 

from other regulations because each regulation is based on a different set of analytical 

assumptions and policy decisions. The costs and benefits identified in this RIA will not be 

realized until specific controls are mandated by future State Implementation Plans (SIPs) or 

other Federal regulations.

ES.2 Existing and Revised PM Air Quality Standards

Two primary PM2.5 standards provide public health protection from effects associated 

with fine particle exposures: the annual standard and the 24-hour standard. The current annual 

standard is set at a level of 15.0 μg/m3, based on the 3-year average of annual arithmetic mean 

PM2.5 concentrations. The current 24-hour standard is set at a level of 35 μg/m3, based on the 

 

1

U.S. Office of Management and Budget. Circular A-4, September 17, 2003. Available at 

http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf.
1-3

3-year average of the 98th percentile of 24-hour PM2.5 concentration. In the RIA, the current 

primary PM2.5 standards, including both annual and 24-hour standards, are denoted as 15/35

μg/m3. Attainment of the 24-hour standard is analyzed only in developing the scenario of 

attainment with the existing standards of 15/35 μg/m3. All other scenarios evaluate additional 

emissions reductions needed to attain alternative annual standards only.

In this PM NAAQS review, the EPA has revised and lowered the level of the primary 

annual PM2.5 standard to 12 μg/m3in conjunction with retaining the level of the 24-hour 

standard at 35 μg/m3and this standard is denoted as 12 μg/m3. In addition to the revised 

annual standard of 12 μg/m3, the RIA also analyzes the benefits and costs of incremental 

control strategies for two alternative annual standards of 13 μg/m3and 11 μg/m3.

Currently, the existing secondary (welfare-based) PM2.5 standards are identical in all 

respects to the primary standards. In this PM NAAQS review, the EPA is retaining the current 

suite of secondary standards for 24-hour and annual PM2.5. Thus, while the new primary annual 

standard will be revised to 12 μg/m3, the secondary annual standard will remain at 15 μg/m3.

Non-visibility welfare effects are addressed by this suite of secondary standards, and PMrelated visibility impairment is addressed by the secondary 24-hour PM2.5 standard, which EPA 

is leaving unchanged at 35 μg/m3. The secondary standards will thus remain at 15/35 μg/m3.

With regard to the primary and secondary standards for particles less than or equal to 

10 μm in diameter (PM10), the EPA is retaining the current primary and secondary 24-hour PM10

standards, which are both set at a level of 150 μg/m3, not to be exceeded more than once per 

year on average over 3 years (U.S. EPA, 1997).

2

Because the benefit-cost analysis of the 

alternative PM10 standards was conducted when the standard was promulgated in 1997, this 

RIA does not repeat that analysis here.

ES.2.1 Overview of the Analytical Steps in this RIA

The goal of this RIA is to provide the best estimates of the costs and benefits of an 

illustrative attainment strategy to the meet the revised annual standard. The flowchart below

(Figure ES-1) outlines the analytical steps taken to illustrate attainment with the revised annual 

standard of 12 μg/m3, and the following discussion, by primary flowchart section, describes 

each of the steps taken. For important updates and analytical differences between proposal 

and final, see section ES.5 of this Executive Summary.

 

2

U.S. Environmental Protection Agency. 1997. Regulatory Impact Analyses for the Particulate Matter and Ozone 

National Ambient Air Quality Standards and Proposed Regional Haze Rule. Available at: 

http://www.epa.gov/ttn/oarpg/naaqsfin/ria.html.
1-4

Figure ES-1. PM RIA Analytical Flowchart

Establishing the Analytical Baseline (Flowchart Section 1)

This section of the flowchart reflects the analytical steps taken to account for Federal 

and State rules and programs currently underway, as well as to reflect attainment of the 

current annual and daily standards of 15/35 μg/m3for the purpose of estimating the 

incremental costs and benefits of attaining the revised annual standard. Detailed discussions of 

the elements of this section of the flowchart are in Chapters 1 and 3 of the final RIA.

 Adjusted 2020 Base Case with “On the Books” Federal and State Rules and 

Programs—The adjusted 2020 base case includes reductions expected to occur 

between 2007 and 2020 from existing (i.e., “on-the-books”) Federal and State

control programs. This projection reflects air quality modeling of 2020 that accounts 

for major Federal and State programs along with adjustments to these national-level 

modeling results to account for PM2.5 reductions expected, largely in the western 

US, from the implementation of episodic burn ban programs in certain counties and 
1-5

to remove the effects of atypical events such as wildfires and fireworks displays. 

(See Figure ES-2 below for illustration.) Below is a list of some of the major national 

rules reflected in the base case. Refer to Chapter 3, Section 3.2.1.4 for a more 

detailed discussion of the rules reflected in the 2020 base case emissions inventory.

– Light-Duty Vehicle Tier 2 Rule (U.S. EPA, 1999)

– Heavy Duty Diesel Rule (U.S. EPA, 2000)

– Clean Air Nonroad Diesel Rule (U.S. EPA, 2004)

– Regional Haze Regulations and Guidelines for Best Available Retrofit 

Technology Determinations (U.S. EPA, 2005b)

– NOx Emission Standard for New Commercial Aircraft Engines (U.S. EPA, 

2005a)

– Emissions Standards for Locomotives and Marine Compression-Ignition 

Engines (U.S. EPA, 2008a)

– Control of Emissions for Nonroad Spark Ignition Engines and Equipment (U.S. 

EPA, 2008b)

– C3 Oceangoing Vessels (U.S. EPA, 2010a)

– Boiler MACT (U.S. EPA, 2011d)

– Hospital/Medical/Infectious Waste Incinerators: New Source Performance 

Standards and Emission Guidelines: Final Rule Amendments (U.S. EPA, 2009)

– Reciprocating Internal Combustion Engines (RICE) NESHAPs (U.S. EPA, 2010b)

– Mercury and Air Toxics Standards (U.S. EPA, 2011b)

– Cross-State Air Pollution Rule (U.S. EPA, 2011a)

3

 

3

See Chapter 3, Section 3.2.1.4 for a discussion of the role CSAPR plays in the PM2.5 RIA and the reasons we believe 

CSAPR remains an appropriate proxy for this analysis.
1-6

Figure ES-2. Annual NAAQS Exceedances*in “Adjusted 2020 Base Case” Scenario

*

24-hr PM2.5 NAAQS Exceedances as Follows:

• San Joaquin Valley (6 counties); South Coast (2 counties); Imperial, CA; Allegheny, PA; Salt Lake, UT; Lake, 

OR; and Sacramento, CA

 Analytical Baseline with Attainment of 15/35—The analytical baseline includes 

reductions from additional controls that the EPA estimates are needed to attain the 

current standards (15/35 μg/m3) for the purpose of estimating the incremental costs 

and benefits of attaining the revised annual standard of 12 μg/m3. Determining the 

level of emissions reductions needed to meet both the current annual and daily 

standards is done through adjusting a county’s projected design value (DV) 

concentration using the geographic area and pollutant specific air quality ratios (as 

described in Chapter 3 of the final RIA). In addition, for each area, it is necessary to 

determine which of the standards will be “controlling,” i.e., which standard will 

require the most reductions to reach attainment of current standard. This is 

important for establishing the analytical baseline because when the daily standard is 

controlling (as is the case in much of California), the emissions reductions required 

to meet the daily standard will result in reductions in the annual design value to 

below the annual standard of 15 μg/m3. As a result, the annual PM2.5 increment 

needed to attain alternative standards for each county will not be relative to an 

annual design value of 15 μg/m3, rather, the increment will vary by county based on 

the annual design value in that county that resulted from applying emissions 
1-7

controls to meet the 35 μg/m3daily standard. As shown in the map below, for 

counties labeled as exceeding the 12 μg/m3annual standard, the baseline design 

values can be any value between 12 and 15 μg/m3. Similarly, exceedances of the 11 

μg/m3and 13 μg/m3standards can be anywhere between 11 or 13 and 15 μg/m3. 

The analytical baseline reflects attainment of 15/35 μg/m3by2020. We also 

modified the analytical baseline for counties in the South Coast Air Quality 

Management District and the San Joaquin Valley Air Pollution Control District to 

reflect reductions in mobile NOx emissions that these areas are expected to achieve

between 2020 and 2025 due to fleet turnover. These reductions in NOx emissions 

are not attributable to attainment of the current or revised PM standards, but 

reflect the impacts of other ”on-the-books” mobile programs so that these 

reductions are not included as either an incremental cost nor benefit to these area’s 

attaining the revised annual standard. 

Figure ES-3. Annual NAAQS Exceedances in “Analytical Baseline” Scenario

• 13 Level: 2 Counties (Tulare, CA and Riverside, CA)

• 12 Level: 7 Counties (All in CA)

• 11 Level: 23 Counties

Control Strategies (Flowchart Section 2)

This section of the flowchart reflects analytical steps taken to analyze controls and 

emissions reductions needed beyond the current standard (15/35 μg/m3) and other existing 

major rules to achieve the revised standard (12 μg/m3). We apply control options that might be 

available to States for application by 2020. Detailed discussion of the elements of this section of 

the flowchart is in Chapter 4 of the final RIA.
1-8

 Select Controls by Emissions Sector and Pollutant—Non-EGU point and nonpoint 

control measures were applied for the revised and alternative standards’ control 

strategies. These controls were identified using the U.S. EPA’s Control Strategy Tool. 

Additional control measures were not applied to EGUs because of the extensive 

nature of controls resulting from the inclusion of MATS.

 Adjust 2020 Control Scenario to Meet Alternate Standards—After identifying the 

known controls in the control scenario that were needed to meet the analytical 

baseline, additional known controls needed to meet the revised and alternative 

standards were identified. The EPA used air quality modeling results to determine 

whether the control scenario was sufficient to meet the revised and alternative 

standards for each geographic area. Where the control scenario modeling resulted 

in design value reductions below the level needed for the revised or alternative 

standards for specific geographic areas, county-specific ratios of air quality response 

to emission reductions were used to determine the subset of controls that were 

needed to attain the standard. Where it was determined that the control scenario 

was not sufficient in attaining the standard, these same response factors were used 

to calculate the amount of additional emission reductions beyond known controls 

needed to meet the standard. For the revised and alternative control strategy 

analysis, known controls for two sectors were used: non-EGU point and area 

sources. Onroad mobile source controls were not used in the revised and alternative 

standards analysis because they were applied previously in the analytical baseline 

analysis where they were deemed to be most cost effective. Emission reductions 

were calculated for the known control strategy analysis and the cost analysis for 

emission reductions needed beyond known controls (“extrapolated” costs) for each 

alternative standard being analyzed. The EPA estimates the national-scale emission 

reductions for revised annual standard of 12 μg/m3and two alternative annual 

standards (13 μg/m3and 11 μg/m3) as shown in Table ES-1.

 Due to Because the rules listed above and other emissions reductions should have 

substantially reduced ambient PM2.5 concentrations by 2020 in the East, no 

additional controls are anticipated to be needed outside of California (assuming no

the absence of new sources). Specifically, our analysis estimates that in 2020 only 7

counties, all in California, will be out of attainment with the revised annual standard 

of 12ug/m3. Emissions reductions are needed in more locations for the alternative 

standard of 11ug/m3.
1-9

Table ES-1. Emission Reductions in Illustrative Emission Reduction Strategies for the Revised 

and Alternative Annual Primary PM2.5 Standards, by Pollutant and Region in 2020 

(tons)a

13 μg/m312 μg/m311 μg/m3

Directly emitted PM2.5

East 0 0 8,200

West 0 0 160

CA 730 4,000 10,600

SO2

East 0 0 21,000

West 0 0 43

CA 0 0 0

NOx

East 0 0 9

West 0 0 0

CA 0 0 0

a

See Chapter 4 for more information on the illustrative emission reduction strategies. The emissions in this 

table reflect both known and unknown controls. Estimates are rounded to two significant figure.

Emission Reduction Estimates by Standard in 2020 (annual tons/year)a

Alternative Annual 

Standards (μg/m3) PM2.5 SO2 NOx

13 730 - -

12 4,000 - -

11 19,000 21,000 9

a

Estimates are rounded to two significant figures.

Analysis of Costs (Flowchart Section 3) 

This section of the flowchart reflects analytical steps taken to estimate the costs of both 

known and unknown controls. Detailed discussion of the elements of this section of the 

flowchart is in Chapter 7 of the final RIA.

 Estimate Known Controls Engineering Costs, Estimate Extrapolated Costs—We 

provide engineering cost estimates for the control strategies identified in Chapter 4 
1-10

that include control technologies on non-EGU point sources and area sources. 

Engineering costs generally refer to the capital equipment expense, the site 

preparation costs for the application, and annual operating and maintenance costs. 

For this analysis, we included known controls for all of the geographic areas likely to 

exceed the revised and/or alternative standards. We also provide estimates for the 

engineering costs of the additional emissions reductions that are needed beyond the 

application of known controls to reach full attainment of the alternative standards 

analyzed; the cost estimates derived from this approach are referred to as 

“extrapolated” costs. By definition, no cost data currently exists for the additional 

emissions reductions needed beyond known controls. We employ two 

methodologies for estimating the costs of unidentified future controls, and both 

approaches assume that innovative strategies and new control options make 

possible the emissions reductions needed for attainment by 2020.

Full Attainment (Flowchart Section 4) 

This section of the flowchart reflects analytical steps taken to estimate the costs of 

attainment, the benefits of attainment and the net benefits for the revised standard of 12 

μg/m3. Detailed discussions of the elements of this section of the flowchart are in Chapter 5, 

Chapter 7, and Chapter 8 of the final RIA.

 Calculate Full Attainment Costs—In Chapter 7 we present a summary of the total 

national costs of attaining the revised annual standard of 12 μg/m3 and the 

alternative annual standards of 13 μg/m3and 11 μg/m3 in 2020. This summary 

includes the known and extrapolated costs. The total cost estimates are $53 million 

(2010$) and $350 million (2010$) for the revised annual standard of 12 μg/m3; $11 

million and $100 million for the alternative annual standard of 13 μg/m3; and $320 

million and $1,700 million for the alternative annual standard of 11 μg/m3.

 Calculate Full Attainment Benefits—Chapter 5 presents the estimated human health 

benefits for the revised NAAQS. We quantify the health-related benefits of the fine 

particulate matter (PM2.5)-related air quality improvements resulting from the 

illustrative emissions control scenarios that reduce emissions of directly emitted 

particles and precursor pollutants including SO2 and NOx to reach alternative PM2.5

NAAQS levels in 2020. These benefits are relative to an analytical baseline reflecting 

nationwide attainment of the current primary PM2.5 standards (i.e., annual standard 

of 15 μg/m3and 24-hour standard of 35 μg/m3) that includes promulgated national 

regulations and illustrative emissions controls to simulate attainment with 15/35 as 

well as a NOx emission adjustment to reflect expected reductions in mobile NOx

emissions between 2020 and 2025. 

The estimated benefits for the revised and alternative standards are in addition to 

the substantial benefits estimated for several recent implementation rules. Rules 

such as the Mercury and Air Toxics Standard (MATS) and other emission reductions 

will have substantially reduced ambient PM2.5 concentrations by 2020 in the East, 
1-11

such that no additional controls would be needed in the East for the revised annual 

standard of 12 μg/m3. Thus, all of the estimated benefits occur in California because 

this is the only State that needs additional air quality improvement beyond the 

analytical baseline after accounting for air quality improvements from recent rules.

 Net Benefits—Chapter 8 compares estimates of the benefits with costs and 

summarizes the net benefits of revised annual standard of 12 μg/m3and the 

alternative annual standards of 13 μg/m3and 11 μg/m3relative to the analytical 

baseline that includes recently promulgated national regulations and additional 

emissions reductions needed to attain the existing 15/35 μg/m3standards, as well as 

adjustments to NOx emissions in the San Joaquin and South Coast areas.

ES.2.4 Health and Welfare Co-Benefits

The EPA estimated impacts on human health (e.g., mortality and morbidity effects) 

under full attainment of the three alternative annual PM2.5 standards. We considered an array 

of health impacts attributable to changes in PM2.5 exposure and estimated these benefits using 

the BenMAP model (Abt Associates, 2012), which has been used in many recent RIAs (e.g., U.S. 

EPA, 2006, 2011a, 2011b), and The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. 

EPA, 2011c). The monetized benefits estimated in the core analysis include avoided premature 

deaths (derived from effect coefficients in tow cohort studies [Krewski et al. (2009) and Lepeule 

et al. (2012)] for adults and one for infants [Woodruff et al. (1997)] for infants) as well as 

avoided morbidity effects for 10 non-fatal endpoints ranging in severity from lower respiratory 

symptoms to heart attacks. As noted above, because California is the only state that needs 

additional air quality improvement beyond the analytical baseline after accounting for expected 

air quality improvements expected from recent rules, all of the benefits associated with the

revised standard of 12ug/m3 occur in California.

Since the proposed rule, tThe EPA has incorporated an array of policy and technical 

updates to the benefits analysis approach applied in this RIA, including incorporation of the 

most recent follow-up to the Harvard Six Cities cohort study (Lepeule et al., 2012), more recent 

demographic data projections, additional hospitalization and emergency department visit

studies, inflation adjustment to 2010 dollars, and an expanded uncertainty assessment. Each of 

these updates is fully described in the health benefits chapter (Chapter 5) and summarized 

below in section ES.5. Compared with the proposal benefits, the estimated benefits for the 

revised standard are about double due to a combination of updates to the analytic baseline The 

monetized benefits estimated in the core analysis include avoided premature deaths (derived 

from effect coefficients in Krewski et al. (2009) and Lepeule et al. (2012) for adults and 
1-12

Woodruff et al. (1997) for infants) as well as avoided morbidity effects for 10 non-fatal 

endpoints ranging in severity from lower respiratory symptoms to heart attacks.

Even though the primary standards are designed to protect against adverse effects to 

human health, the emission reductions will have welfare co-benefits in addition to the direct 

human health benefits. The term welfare co-benefits covers both environmental and societal 

benefits of reducing pollution, such as reductions in visibility impairment, materials damage, 

and ecosystem damage. Despite our attempts to quantify and monetize as many of the benefits 

as possible, the welfare co-benefits associated with meeting the alternative standards are not 

quantified or monetized in this analysis. Unquantified health benefits are discussed in Chapter 

5, and unquantified welfare co-benefits are discussed in Chapter 6.

It is important to note that estimates of the health benefits from reduced PM2.5 

exposure reported here contain uncertainties, which are described in detail in Chapter 5 and 

Appendix 5b. Below are two key assumptions in the benefits analysis:

1. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 varies considerably in composition across sources, but the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle 

type. The Integrated Science Assessment for Particulate Matter (PM ISA), which was 

twice reviewed by CASAC, concluded that “many constituents of PM2.5 can be linked 

with multiple health effects, and the evidence is not yet sufficient to allow 

differentiation of those constituents or sources that are more closely related to 

specific outcomes” (U.S. EPA, 2009). These uncertainties are likely to be magnified in 

the current analysis to the extent that the emissions controls are less diverse when 

focusing on one small region of the country rather than a broader geography with 

more diverse emissions sources and the application of a more diverse set of 

controls.

2. We assume that health impact functions based on national studies are 

representative for exposures and populations in California. In addition to the 

national risk coefficients we use as our core estimates, the EPA considered the 

cohort studies conducted in California specifically. Although we have not calculated 

the benefits results using the cohort studies conducted in California, we provided 

these risk coefficients to show how much the monetized benefits could have 

changed. Most of the California cohort studies report central effect estimates similar 

to the (nation-wide) all-cause mortality risk estimate we applied from Krewski et al. 

(2009) and Lepeule et al. (2012) albeit with wider confidence intervals. Three cohort 

studies conducted in California indicate statistically significant higher risks than the 
1-13

risk estimates we applied from Lepeule et al. (2012), and four studies showed 

insignificant results.

3. We assume that the health impact function for fine particles is log-linear without a 

threshold in this analysis. Thus, the estimates include health benefits from reducing 

fine particles in areas with varied concentrations of PM2.5, including both areas that 

do not meet the fine particle standard and those areas that are in attainment, down 

to the lowest modeled concentrations.

In general, we are more confident in the magnitude of the risks we estimate from 

simulated PM2.5 concentrations that coincide with the bulk of the observed PM concentrations 

in the epidemiological studies that are used to estimate the benefits. Likewise, we are less 

confident in the risk we estimate from simulated PM2.5 concentrations that fall below the bulk 

of the observed data in these studies. As noted in the preamble to the rule, the range from the 

25th to 10th percentiles of the air quality data in the epidemiology studies is a reasonable 

range below which we start to have appreciably less confidence in the magnitude of the 

associations observed in the epidemiological studies. Concentration benchmark analyses (e.g., 

25th percentile, 10th percentile, one standard deviation below the mean,

4

and lowest 

measured level [LML]) provide some insight into the level of uncertainty in the estimated PM2.5 

mortality benefits. Most of the estimated avoided premature deaths for this rulemaking occur 

at or above the lowest measured PM2.5 concentration in the two studies that are used to 

estimate mortality benefits. There are uncertainties inherent in identifying any particular point 

at which our confidence in reported associations becomes appreciably less, and the scientific 

evidence provides no clear dividing line. However, the EPA does not view these concentration 

benchmarks as a concentration threshold below which we would not quantify health benefits 

of air quality improvements. Rather, the core benefits estimates reported in this RIA (i.e., those 

based on Krewski et al. [2009] and Lepeule et al. [2012]) are the best measures because they 

reflect the full range of modeled air quality concentrations associated with the emission 

reduction strategies and because the current body of scientific literature indicates that a nothreshold model provides the best estimate of PM-related long-term mortality. It is important 

to emphasize that “less confidence” does not mean “no confidence.” 

The estimated benefits reflect illustrative control measures and emission reductions to 

lower PM2.5 concentrations at monitors projected to exceed the revised and alternative annual 

standards. The result is that air quality is expected to improve in counties that exceed these 

standards as well as surrounding areas that do not exceed the alternative standards. In order to 

 

4

A range of one standard deviation around the mean represents approximately 68% of normally distributed data 

and below the mean falls between the 25th and 10th percentiles.
1-14

make a direct comparison between the benefits and costs of the emission reduction strategies, 

it is appropriate to include all the benefits occurring as a result of the emission reduction 

strategies applied regardless of where they occur. Therefore, it is not appropriate to estimate 

the fraction of benefits that occur only in the counties that exceed the standards because it 

would omit benefits attributable to emission reduction in exceeding counties. In addition, we 

estimate benefits using modeled air quality data with 12 km grid cells, which is important 

because the grid cells are often substantially smaller than counties, and PM2.5 concentrations 

can vary spatially within a county. Some grid cells in a county can be below the level of the 

alternative standard even though the highest monitor exceeds the alternative standard. Thus, 

emission reductions can lead to benefits in grid cells that are below the alternative standards 

within an exceeding county. 

ES.2.5 Cost Analysis Approach

The EPA estimated total costs under partial and full attainment of the alternative PM2.5

standards. The engineering costs generally include the costs of purchasing, installing, and 

operating the referenced control technologies. The technologies and control strategies selected 

for analysis are illustrative of one way in which nonattainment areas could meet a revised 

standard. There are numerous ways to construct and evaluate potential control programs that 

would bring areas into attainment with alternative standards, and the EPA anticipates that 

State and local governments will consider programs that are best suited for local conditions.

The partial attainment cost analysis reflects the costs associated with applying known 

controls. Costs for full attainment include estimates for the engineering costs of the additional 

tons of emissions reductions that are needed beyond identified controls, referred to as 

extrapolated costs. By definition, no cost data currently exist for the additional emissions 

reductions needed beyond known controls. We employ two methodologies for estimating the 

costs of unidentified future controls: a fixed-cost methodology and a hybrid methodology; both 

approaches assume either that existing technologies can be applied in particular combinations 

or to specific sources that we currently can’t predict or that innovative strategies and new 

control options make possible the emissions reductions needed for attainment by 2020. The 

two approaches, however, implicitly reflect different assumptions about technological progress 

and innovation in emissions reductions strategies. The fixed-cost methodology uses a 

$15,000/ton estimate for each ton of PM2.5 reduced, and the hybrid methodology generates a 

total annual cost curve for PM2.5 for unknown future controls that might be applied in order to 

move toward 2020 attainment. The hybrid methodology has the advantage of incorporating 

information about how significant the needed reductions from unspecified control technologies 
1-15

are relative to the known control measures and matching that information with expected 

increasing per-ton cost for applying unknown controls. Employing the fixed-cost methodology, 

approximately 90% of total costs for attaining the revised annual standard of 12 μg/m3are from 

unspecified control technologies. Employing the hybrid methodology, approximately 97% of 

total costs for attaining the revised annual standard of 12 μg/m3are from unspecified control 

technologies. The EPA recognizes that the extrapolated portion of the engineering cost 

estimates reflects substantial uncertainty about which sectors and which technologies might 

become available for cost-effective application in the future.

The engineering cost estimates are limited in their scope. Our analysis focuses on the 

emissions reductions needed for attainment of the revised and alternative standards. Also, the

amendments to the ambient air monitoring regulations will revise the network design 

requirements for PM2.5 monitoring sites, resulting in moving 21 monitors to established nearroad monitoring stations by January 1, 2015. The incremental cost associated with moving 

these 21 monitors is a one-time cost of $28,570. Lastly, the EPA understands that some States 

will incur costs designing SIPs and implementing new control strategies to meet the revised 

standard. However, the EPA does not know what specific actions States will take to design their 

SIPs to meet the revised standards; therefore, we do not include estimated costs that 

government agencies may incur for managing the requirement, implementing these (or other) 

control strategies, or for offering incentives that may be necessary to encourage the 

implementation of specific technologies, especially for technologies that are not necessarily 

market driven.

ES.2.6 Comparison of Benefits and Costs

The EPA’s illustrative analysis has estimated the health and welfare benefits and costs 

associated with the revised annual PM NAAQS. The results for 2020 suggest there will be 

significant health and welfare benefits and these benefits will outweigh the costs associated 

with the illustrative control strategies in 2020. In the analysis, we estimate the net benefits of 

the revised annual PM2.5 standard of 12 μg/m3and alternative annual standards of 13 μg/m3

and 11 μg/m3, incremental to the 2020 analytical baseline. For the revised annual standard of 

12 μg/m3, net benefits are estimated to be $3.7 billion to $9 billion at a 3% discount rate and 

$3.3 billion to $8.1 billion at a 7% discount rate in 2020 (2010 dollars). For an alternative annual 

standard of 13 μg/m3, net benefits are estimated to be $1.2 billion to $2.9 billion at the 3% 

discount rate and $1.1 billion to $2.6 billion at the 7% discount rate. Net benefits of an

alternative annual PM2.5 standard of 11 μg/m3are estimated to be $11 billion to $29 billion at a 

3% discount rate and $10 billion to $26 billion at a 7% discount rate in 2020. See Table ES-2.
1-16

For the revised annual standard of 12 μg/m3, the EPA estimates that the benefits of full 

attainment exceed the costs of full attainment by 12 to 171 times at a 3% discount rate. For the 

alternative annual standard of 13 μg/m3, the EPA estimates that the benefits of full attainment 

exceed the costs of full attainment by 13 to 272 times at a 3% discount rate. For the alternative 

annual standards of 11 μg/m3, the EPA estimates that the benefits of full attainment exceed the 

costs of full attainment by 8 to 90 times at a 3% discount rate.

Table ES-2. Total Monetized Benefits, Total Costs, and Net Benefits in 2020 (millions of 

2010$)—Full Attainmenta

Alternative 

Annual 

Standard

(μg/m3)

Total Costsb Monetized Benefitsd Net Benefits

3% Discount 

Ratec

7% Discount 

Rate

3% Discount 

Rate

7% Discount 

Rate

3% Discount 

Rateb

7% Discount 

Rate

13 $11 to $100 $11 to $100 $1,300 to 

$2,900

$1,200 to 

$2,600

$1,200 to 

$2,900

$1,100 to 

$2,600

12 $53 to $350 $53 to 350 $4,000 to 

$9,100

$3,600 to 

$8,200

$3,700 to 

$9,000

$3,300 to 

$8,100

11 $320 to 

$1,700

$320 to 

$1,700

$13,000 to 

$29,000

$12,000 to

$26,000

$11,000 to 

$29,000

$10,000 to 

$26,000

a

These estimates reflect incremental emissions reductions from an analytical baseline that gives an “adjustment” 

to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 

2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full 

benefits of the revised standards in those two areas will not be realized until 2025.

b

The two cost estimates do not represent lower- and upper-bound estimates but represent estimates generated 

by two different methodologies. The lower estimate is generated using the fixed-cost methodology, which 

assumes that technological change and innovation will result in the availability of additional controls by 2020 that 

are similar in cost to the higher end of the cost range for current, known controls. The higher estimate is generated 

using the hybrid methodology, which assumes that while additional controls may become available by 2020, they 

become available at an increasing cost and the increasing cost varies by geographic area and by degree of difficulty 

associated with obtaining the needed emissions reductions.

c

Due to data limitations, we were unable to discount compliance costs for all sectors at 3%. See Chapter 7, Section 

7.2.2 for additional details on the data limitations. As a result, the net benefit calculations at 3% were computed by 

subtracting the costs at 7% from the monetized benefits at 3%.

d

The reduction in premature deaths each year accounts for over 90% of total monetized benefits. Mortality risk 

valuation assumes discounting over the SAB-recommended 20-year segmented lag structure. Not all possible 

benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all unquantified benefits. Data 

limitations prevented us from quantifying these endpoints, and as such, these benefits are inherently more 

uncertain than those benefits that we were able to quantify. The range of benefits reflects the range of the central 

estimates from two mortality cohort studies (i.e., Krewski et al., 2009 and Lepeule et al., 2012).
1-17

Table ES-3. Benefit-to-Cost Ratios for Alternative Standards at 3% and 7% Based on 

Projected Benefits and Costs in 2020

13 μg/m312 μg/m311 μg/m3

Benefit-Cost Ratio 3%a13 to 272 12 to 171 8 to 90

Benefit-Cost Ratio 7% 11 to 246 11 to 154 7 to 81

a Due to data limitations, we were unable to discount compliance costs for all sectors at 3%. See Chapter 7, Section 

7.2.2 for additional details on the data limitations. As a result, the net benefit calculations at 3% were computed by 

subtracting the costs at 7% from the monetized benefits at 3%.

Figure ES-4 reflects the range of costs based on the calculation of costs using the fixedcost approach and the hybrid approach. Additionally, we see the difference in the calculation of 

benefits based on using various studies. 
1-18

Figure ES-4. Monetized Benefit to Cost Comparison for 12 μg/m3in 2020 (7% Discount Rate)
1-19

Table ES-4. Estimated Number of Avoided PM2.5 Health Impacts for Standard Alternatives—

Full Attainmentin 2020a

Health Effect

Alternative Annual Standards

13 μg/m312 μg/m311 μg/m3

Adult Mortality 

Krewski et al. (2009) (adult) 140 460 1,500

Lepeule et al. (2012) (adult) 330 1,000 3,300

Woodruff et al. (1997) (infant) 0 1 4

Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 160 480 1,600

Pooled estimate of 4 studies 17 52 170

Hospital admissions—respiratory (all ages) 31 110 380

Hospital admissions—cardiovascular (age > 18) 43 140 480

Emergency department visits for asthma (all ages) 67 230 810

Acute bronchitis (age 8–12) 280 870 2,700

Lower respiratory symptoms (age 7–14) 3,500 11,000 34,000

Upper respiratory symptoms (asthmatics age 9–11) 5,100 16,000 49,000

Asthma exacerbation (age 6–18) 13,000 40,000 120,000

Lost work days (age 18–65) 22,000 71,000 230,000

Minor restricted-activity days (age 18–65) 130,000 420,000 1,300,000

a

Incidence estimates are rounded to whole numbers with no more than two significant figures.

ES.3 Discussion and Conclusions

An extensive body of scientific evidence documented in PM ISA indicates that PM2.5 can 

penetrate deep into the lungs and cause serious health effects, including premature death and 

other non-fatal illnesses (U.S. EPA, 2009). As described in the preamble to the rule, the 

revisions to the standards are based on an integrative assessment of an extensive body of new 

scientific evidence (U.S. EPA, 2009). Health studies published since the PM ISA (e.g., Pope et al. 

[2009]) confirm that recent levels of PM2.5 have had a significant impact on public health. Based 

on the air quality analysis in this RIA, the EPA projects that nearly all counties with PM2.5 

monitors in the United States would meet an annual standard of 12 μg/m3without additional 

Federal, State, or local PM control programs. This demonstrates the substantial progress that 

the United States has made in reducing air pollution emissions over the last several decades. 
1-20

Regulations such as the EPA’s recent Mercury and Air Toxics Standards (MATS) and other 

Federal programs such as diesel standards will provide substantial improvements in regional 

concentrations of PM2.5. Our analysis shows a few areas would still need additional emissions 

reductions to address local sources of air pollution, including ports and uncontrolled industrial 

emissions. For this reason, we have designed the RIA analysis to focus on local controls in these 

few areas. We estimate that these additional local controls would yield benefits well in excess 

of costs.

The setting of a NAAQS does not compel specific pollution reductions and as such does 

not directly result in costs or benefits. For this reason, NAAQS RIAs are merely illustrative. The 

NAAQS RIAs illustrate the potential costs and benefits of additional steps States could take to 

attain a revised air quality standard nationwide beyond rules already on the books. We base 

our illustrative estimates on an array of emission control strategies for different sources. The 

costs and benefits identified in this RIA will not be realized until specific controls are mandated 

by SIPs or other Federal regulations. In short, NAAQS RIAs hypothesize, but do not prescribe, 

the control strategies that States may choose to enact when implementing a revised NAAQS.

It is important to emphasize that the EPA does not “double count” the costs or the 

benefits of our rules. Emission reductions achieved under rules that require specific actions 

from sources—such as MATS—are in the baseline of this NAAQS analysis, as are emission 

reductions needed to meet the current NAAQS. For this reason, the cost and benefits estimates 

provided in this RIA and all other NAAQS RIAs should not be added to the estimates for 

implementation rules.

In calculating the costs, the EPA assumed the application of a significant number of 

unidentified future controls that would make possible the additional emissions reductions 

needed for attainment in 2020. EPA used two methodologies—the fixed-cost and hybrid 

methodologies—for estimating the costs of unidentified future controls, and both approaches 

assume either that existing technologies can be applied in particular combinations or to specific 

sources that we currently can’t predict or that innovative strategies and new control options 

make possible the emissions reductions needed for attainment by 2020. Estimates generated 

by the two approaches do not represent lower- and upper-bound estimates but simply 

represent estimates generated by two different methodologies. The fixed-cost methodology 

implicitly assumes that technological change and innovation will result in the availability of 

additional controls by 2020 that are similar in cost to the higher end of the cost range for 

current controls. The hybrid methodology implicitly assumes that while additional controls 

become available by 2020, they become available at an increasing cost and the increasing cost 
1-21

varies by geographic area and by degree of difficulty associated with obtaining the needed 

emissions reductions.

For the revised annual standard of 12 μg/m3, the total cost estimates comprise between 

90 and 97% extrapolated cost estimates, and the estimated total cost using the hybrid 

methodology is roughly 6.5 times more than the estimated total cost using the fixed-cost 

methodology.Because the hybrid methodology reflects increasing marginal costs in areas 

needing a higher ratio of emissions reductions from unknown to known controls, it could be 

more representative of total costs. In an effort to consider the potential fitness of the

extrapolated cost estimates, we reviewed the South Coast Air Quality Management District’s 

(SCAQMD) 2012 Air Quality Management Plan (AQMP), and we located data on recent emission 

reduction credit (ERC) transactions in both the SCAQMD and San Joaquin Valley Air Pollution 

Control District (SJV APCD). While this information provides context for the extrapolated cost 

estimates, the current relationship between available controls and costs to reduce emissions 

may or may not be applicable in 2020 because of changes in innovation and advances in 

technology.

The SCAQMD’s 2012 AQMP includes information on control measures to meet the 

current 24-hour standard of 35 μg/m3, including further PM2.5 controls for under-fired 

charbroilers at a cost per ton reduced of $15,000. This control cost matches the parameter used 

in the fixed-cost methodology, as well as the initial value used for the hybrid methodology and 

is supportive of our selection of that value. In addition, the California Air Resources Board’s 

2009 and 2010 Emission Reduction Offset Transaction Costs, Summary Report included PM10

ERC prices in both the SCAQMD and the SJV APCD. To some degree, ERC transaction prices 

reflect a choice between installing a more stringent control and purchasing ERCs. Between 2009 

and 2010 PM10 ERC prices in SJV APCD ranged from $40,000 per ton per year (tpy) to 

$70,000/tpy, and PM10 ERC prices in the SCAQMD ranged from $575,000/tpy to more than $1.9 

million/tpy. These prices reflect both marginal costs that are higher than the fixed-cost 

estimates and marginal costs that are not inconsistent with the higher cost estimates generated 

using the hybrid methodology. For further discussion of the total cost estimates, refer to 

Section 7.2.4 in Chapter 7 of this RIA.

Furthermore, the monetized benefits estimates presented in this RIA are not intended 

to capture the full burden of PM to public health but rather represent the incremental benefits 

expected upon attaining the revised annual primary standard of 12 μg/m3. In comparison, 

modeling by Fann et al. (2012) estimated that 2005 levels of air pollution were responsible for 

between 130,000 and 320,000 PM2.5-related deaths, or between 6.1% and 15% of total deaths 
1-22

from all causes in the continental United States. The monetized benefits associated with 

attaining the proposed range of standards appear modest when viewed within the context of 

the potential overall public health burden of PM2.5 and ozone air pollution estimated by Fann et 

al. (2012), but this is primarily because regulations already on the books will make great strides 

toward reducing future levels of PM. One important distinction between the total public health 

burden estimated for 2005 air pollution levels and the estimated benefits in this RIA is that 

ambient levels of PM2.5 will have improved substantially by 2020, due to major emissions 

reductions resulting from implementation of Federal regulations. For example, we estimate 

that SO2 emissions (an important PM2.5 precursor) in the United States would fall from 14 

million tons in 2005 to less than 5 million tons by 2020 (a reduction of 66%). For this reason, 

States will only need to achieve small air quality improvements to reach the proposed PM 

standards. As shown in the recent RIA for MATS (U.S. EPA, 2011b), implementing other Federal 

and State air quality actions will address a substantial fraction of the total public health burden 

of PM2.5 and ozone air pollution.

The NAAQS are not set at levels that eliminate the risk of air pollution. Instead, the 

Administrator sets the NAAQS at a level requisite to protect public health with an adequate 

margin of safety, taking into consideration effects on susceptible populations based on the 

scientific literature. The risk analysis prepared in support of this PM NAAQS reported risks 

below these levels, while acknowledging that the confidence in those effect estimates is higher 

at levels closer to the standard (U.S. EPA, 2010c). While benefits occurring below the standard 

are assumed to be more uncertain than those occurring above the standard, the EPA considers 

these to be legitimate components of the total benefits estimate. Although there are greater 

uncertainties at lower PM2.5 concentrations, there is no evidence of a population-level 

threshold in PM2.5-related health effects in the epidemiology literature.

Lastly, the EPA recognizes that there are uncertainties in both the cost and benefit 

estimates provided in this RIA. The EPA was unable to monetize fully all of the benefits 

associated with reaching these standards in this RIA, including other health effects of PM, 

visibility effects, ecosystem effects, and climate effects. If the EPA were able to monetize all of 

the benefits, the benefits would exceed the estimated costs by an even greater margin. 
1-23

ES.4 Caveats and Limitations

EPA acknowledges several important limitations of this analysis. These include the 

following:

ES.4.1 Benefits Caveats

 PM2.5 mortality benefits represent a substantial proportion of total monetized 

benefits (over 98%). To characterize the uncertainty in the relationship between 

PM2.5 and premature mortality, we include a set of 12 estimates of the 

concentration-response function based on results of the PM2.5 mortality expert 

elicitation study in addition to our core estimates. Even these multiple 

characterizations omit the uncertainty in air quality estimates, baseline incidence 

rates, populations exposed, chemical composition, transferability of the effect 

estimate to diverse locations, and additional uncertainty around the mean estimates 

expressed by the experts. As a result, the reported confidence intervals and range of 

estimates give an incomplete picture about the overall uncertainty in the PM2.5 

estimates. This information should be interpreted within the context of the larger 

uncertainty surrounding the entire analysis.

 Most of the estimated avoided premature deaths occur at or above the lowest 

measured PM2.5 concentration in the two studies used to estimate mortality 

benefits. In general, we have greater confidence in risk estimates based on PM2.5 

concentrations where the bulk of the health and air quality data reside and 

somewhat less confidence where data density is lower.

 We analyzed full attainment in 2020, and projecting key variables introduces 

uncertainty. Inherent in any analysis of future regulatory programs are uncertainties 

in projecting atmospheric conditions and source-level emissions, as well as 

population, health baselines, incomes, technology, and other factors.

 There are uncertainties related to the health impact functions used in the analysis. 

These include within-study variability; pooling across studies; the application of C-R 

functions nationwide and for all particle species; extrapolation of impact functions 

across populations; and various uncertainties in the C-R function, including causality 

and shape of the function at low concentrations. Therefore, benefits may be underor over-estimates.

 This analysis omits certain unquantified effects due to lack of data, time, and 

resources. These unquantified endpoints include other health and ecosystem 

effects. The EPA will continue to evaluate new methods and models and select those 

most appropriate for estimating the benefits of reductions in air pollution.

 Full benefits of the revised standards in San Joaquin and South Coast will not be 

realized until 2025 when those areas are expected to demonstrate attainment with 

the revised standards. If we were to estimate the monetized benefits for 2025, those 
1-24

benefits would be higher due to population growth, aging of the population, and 

income growth over time.

ES.5.2 Control Strategy and Cost Analysis Caveats and Limitations

 The control technologies applied as part of the illustrative control strategies 

represent technologies that are available currently and may not reflect emerging 

devices that may be available in future years to aid in attainment of the revised 

standard. In addition, the emission reductions calculated from the known controls 

(control efficiencies) assume that the control devices are properly installed and 

maintained. There is also variability in scale of application that is difficult to reflect 

for small area sources of emissions.

 The illustrative control strategy analysis estimates only one potential pathway to 

attainment. The control strategies are not recommendations for how the revised 

PM2.5 standard should be implemented, and States will make all final decisions 

regarding implementation strategies for the revised NAAQS.

 The application of known controls is based on source information obtained from the 

emissions inventory. To the extent the inventory is lacking data on baseline controls 

from SIPs, we may analyze control options that are currently in place. 

 The future-year emissions used as a basis for developing the control strategies in 

this RIA have implicit assumptions regarding emissions growth and control, which 

differ by sector. For some emission sectors, these future-year emissions may not 

reflect new sources locating in these areas.

 For two areas in California (South Coast Air Quality Management District and San 

Joaquin Valley Air Pollution Control District) the degree of projected nonattainment 

with the revised annual standard of 12 μg/m3is such that these areas are not 

expected to be able to demonstrate attainment with the new standard by 2020.

These areas may qualify for up to a 5-year extension of their attainment date and 

are likely to have until 2025 to demonstrate attainment with the revised annual 

standard.

 The control technologies applied do not reflect potential effects of technological 

change that may be available in future years and the effects of “learning by doing” 

are not accounted for in the emissions reduction estimates. In our analysis, we do 

not have the necessary data for cumulative output, fuel sales, or emission 

reductions for all sectors included in order to properly generate control costs that 

reflect learning-curve impacts. We believe the effect of including these impacts 

would be to lower our estimates of costs for our control strategies in 2020.

 In addition to the application of known controls, the EPA assumes the application of 

unidentified future controls that make possible the additional emission reductions 

needed for attainment in 2020. By definition, no cost data currently exist for 
1-25

unidentified future technologies or innovative strategies and the cost estimates for

unidentified future controls reflect some uncertainty.

 Because we obtain control cost data from many sources, we are not always able to 

obtain consistent data across original data sources. If disaggregated control cost

data are unavailable (i.e., where capital, equipment life value, and operation and 

maintenance [O&M] costs are not separated out), the EPA typically assumes that the 

estimated control costs are annualized using a 7% discount rate. When 

disaggregated control cost data are available (i.e., where capital, equipment life 

value, and O&M costs are explicit), we can recalculate costs using a 3% discount 

rate. In general, we have some disaggregated data available for non-EGU point

source controls, and we do not have any disaggregated control cost data for area 

source controls. In this analysis, for the revised annual standard of 12 μg/m3and the 

alternative standard of 13 μg/m3 we did not have any disaggregated known control 

cost data; therefore, we were not able to recalculate known control costs using a 3% 

discount rate. 

 The EPA understands that some States will incur costs designing SIPs and 

implementing new control strategies to meet the revised annual standard. However,

the EPA does not know what specific actions States will take to design their SIPs to 

meet the revised standards; therefore we do not present estimated costs that 

government agencies may incur for managing the requirement, implementing these 

(or other) control strategies, or for offering incentives that may be necessary to 

encourage the implementation of specific technologies, especially for technologies 

that are not necessarily market driven.

ES.5 Important Updates and Analytical Differences Between the PM NAAQS Proposal RIA 

and the Final RIA

There have been several major improvements in the analytical components the EPA 

used to estimate benefits and costs between the proposal RIA (June 2012) and this RIA 

accompanying the final PM NAAQS. Important updates to our emissions, air quality modeling 

and ambient data, air quality ratios, population projections, as well as currency year valuation 

resulted in an improved analytical base for our analysis for the final rule. Based on the 

complexity and magnitude of the updates and improvements made between the PM RIA 

proposal and final RIA, it would not be appropriate to perform a simple direct comparison of 

results. Therefore, each analysis stands alone and must be evaluated independently as such.

Below is a list summarizing some of the analytical changes between the proposal RIA 

and the final RIA. Between the proposal RIA and the final RIA, we developed the control 

strategies based on an improved modeling platform and updated the approach in designing the 

control strategies. The improved modeling platform updated the current and projected air 
1-26

quality levels for each area across the nation and the updated approach allowed for more 

effective emissions reductions for each area in both attaining the current annual and daily 

standards for our analytical baseline and in attaining the revised annual standard. For example, 

in the proposal RIA we controlled precursor emissions of NOx and SO2 over a broader region 

and these emissions reductions were not as effective in reducing design value for each area as 

the direct PM2.5 emissions reductions targeted in the final RIA. These analytical improvements 

resulted in different estimates of costs and benefits between the proposal RIA and the final RIA

in which we have more confidence in reflecting the approaches that States will pursue to attain 

the current and revised standards.

 New modeling platform—In the modeling platform for the final rule we included 

key updates to the current ambient data that generally show improved air quality 

when compared to modeling for the proposed rule, although daily design values 

(DVs) for some areas in California increased due largely to wildfires in 2008. To 

address the increases in these areas we adjusted the ambient data for these atypical 

events.

 Future air quality with “on-the-books” controls—We also project PM2.5 air quality 

levels between 2007 and the 2020 base case which were generally lower in the final 

RIA compared to the proposal RIA largely because the starting values for the 

ambient data were lower (i.e., final RIA air quality projections showed more 

improvements than proposal RIA). 

 Analytical baseline with attainment of 15/35—For the final RIA, EPA’s approach to 

attaining the existing standards of 15/35 μg/m3 was improved with “air quality 

adjustment ratios” that were based on more focused sensitivity model runs for 

(i) specific areas like California counties; (ii) influential sources like residential wood 

combustion; and (iii) specific PM emission species like directly emitted PM2.5. In the

proposal RIA, we conducted the analysis using more general air quality ratios that 

reflected multiple sources such as point and area and precursor PM emissions like 

NOx and SO2. As a result, in the final RIA, the daily standard of 35 μg/m3 was attained 

more effectively and had less impact on annual DVs because of episodic, direct PM2.5

reductions, while in the proposal RIA, the daily standard was attained less effectively 

because we pursued year-around NOx and SO2 reductions that necessitated more 

emissions reductions and had more impact on the annual DVs. 

 Incremental air quality changes—In the final RIA, the use of improved air quality 

adjustment ratios resulted in more incremental air quality improvement needed to 

attain the annual standard of 12 μg/m3in California. Thus, these larger incremental 

air quality improvements needed to attain the revised standard resulted in higher 

estimated health benefits in the final rule compared to the proposal RIA. As 

described above, this is because the daily standard of 35 μg/m3 was attained more 
1-27

effectively and had less impact on annual DVs across the counties in California. In 

addition, the focus on direct PM2.5 emissions reductions allowed for more effective 

controls to attain the revised standard with fewer PM2.5 emissions reductions 

thereby resulting in similar fixed costs estimates.5

 Benefits analysis—EPA incorporated several updates that affected the core benefits 

estimates in this RIA. Specifically, the EPA incorporated the most recent follow-up to 

the Harvard Six Cities cohort study (Lepeule et al., 2012), which decreases the high 

end of the monetized benefits range by 13%. The EPA also updated the demographic 

data projections to reflect the 2010 Census, which increased the monetized benefits 

by 4% percent for the revised standard. Additional epidemiology studies for 

hospitalizations and emergency department visits and updated survival rates for 

non-fatal heart attacks did not affect the rounded benefits estimates.

 Cost estimates—In the final RIA, the EPA presents a range of costs using both the 

fixed and hybrid methodologies to estimate the costs associated with unknown 

controls.

 Inflation—The EPA updated the currency year in this RIA to use 2010 dollars, which 

increased both the costs and the monetized benefits by approximately 8% since the 

proposal.

ES.6 References

Abt Associates, Inc. 2012. BenMAP User’s Manual Appendices. Prepared for U.S. Environmental 

Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, 

NC. September. 

Fann, N., A. Lamson, K. Wesson, D. Risley, S.C. Anenberg, and B.J. Hubbell. 2012. “Estimating 

the National Public Health Burden Associated with Exposure to Ambient PM2.5 and 

Ozone. Risk Analysis.” Risk Analysis 32(1): 81-95.

Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M. Abrahamowicz, 

and W.H. White. 2009. “Reanalysis of the Harvard Six Cities Study and the American 

Cancer Society Study of Particulate Air Pollution and Mortality.” Special Report to the 

Health Effects Institute. Cambridge, MA. July.

Lepeule, J., F. Laden, D. Dockery, and J. Schwartz. 2012. “Chronic Exposure to Fine Particles and 

Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.”

Environ Health Perspect. In press. Available at: http://dx.doi.org/10.1289/ehp.1104660.

Peters, A., D. W. Dockery, J. E. Muller and M. A. Mittleman. 2001. “Increased particulate air 

pollution and the triggering of myocardial infarction.” Circulation 103(23): 2810-5.

 

5

The hybrid methodology cost estimates increased between the proposal RIA and the final RIA largely because a 

large amount of emissions reductions were needed from one county with a low amount of known controls. 
1-28

Pope, C.A. III, E. Majid, and D. Dockery. 2009. “Fine Particle Air Pollution and Life Expectancy in 

the United States.” New England Journal of Medicine 360: 376-386.

U.S. Environmental Protection Agency (U.S. EPA). 1997. Regulatory Impact Analyses for the 

Particulate Matter and Ozone National Ambient Air Quality Standards and Proposed 

Regional Haze Rule. Available at: http://www.epa.gov/ttn/oarpg/naaqsfin/ria.html.

U.S. Environmental Protection Agency (U.S. EPA). 1999. Regulatory Impact Analysis—Control of 

Air Pollution from New Motor Vehicles: Tier 2 Motor Vehicle Emissions Standards and 

Gasoline Sulfur Control Requirements. EPA 420-R-99-023. Available at:

http://www.epa.gov/otaq/regs/ld-hwy/tier-2/documents/r99023.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2000. Regulatory Impact Analysis: Control of 

Emissions of Air Pollution from Highway Heavy-Duty Engines. EPA 420-R-00-010. 

Available at: http://www.epa.gov/otaq/regs/hd-hwy/2000frm/r00010.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2004. Final Regulatory Analysis: Control of 

Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Available at:

http://www.epa.gov/otaq/documents/nonroad-diesel/420r04007.pdf.

U.S. Environmental Protection Agency (U.S. EPA). November 17, 2005a. Federal Register. Vol. 

70, No. 221. “Control of Air Pollution From Aircraft and Aircraft Engines; Emission 

Standards and Test Procedures.” 40 CFR Part 87. Available at: 

http://www.epa.gov/fedrgstr/EPA-AIR/2005/November/Day-17/a22704.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2005b. Regulatory Impact Analysis for the

Final Clean Air Visibility Rule or the Guidelines for Best Available Retrofit Technology 

(BART) Determinations Under the Regional Haze Regulations. EPA-452/R-05-004. 

Available at: http://www.epa.gov/oar/visibility/pdfs/bart_ria_2005_6_15.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 

National Ambient Air Quality Standards for Particulate Matter, Chapter 5. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. October. Available at:

http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205—Benefits.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis: Control of 

Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition 

Engines Less than 30 Liters Per Cylinder. Available at:

http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2003-0190-0938.

U.S. Environmental Protection Agency (U.S. EPA). 2008b. Control of Emissions from Marine SI 

and Small SI Engines, Vessels, and Equipment. Final Regulatory Impact Analysis. 

Available at: http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2004-

0008-0929.
1-29

U.S. Environmental Protection Agency (U.S. EPA). 2009a. Economic Impacts of Revised MACT 

Standards for Hospital/Medical/Infectious Waste Incinerators. Available at: 

http://www.epa.gov/ttn/atw/129/hmiwi/hmiwi_eia_090803.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2009b. Integrated Science Assessment for 

Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for 

Environmental Assessment—RTP Division. December. Available at:

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546.

U.S. Environmental Protection Agency (U.S. EPA). 2010a. Regulatory Impact Analysis: Control of 

Emissions of Air Pollution from Category 3 Marine Diesel Engines. Available at: 

http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09019.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Regulatory Impact Analysis: 

Regulatory Impact Analysis (RIA) for Existing Stationary Spark Ignition (SI) RICE NESHAP. 

Available at: http://www.epa.gov/ttn/atw/rice/fnl_si_rice_ria.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2010c. Quantitative Health Risk Assessment 

for Particulate Matter—Final Report. EPA-452/R-10-005. Office of Air Quality Planning 

and Standards, Research Triangle Park, NC. September. Available on the Internet at 

<http://www.epa.gov/ttnnaaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011a. Regulatory Impact Analysis for the 

Federal Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter 

and Ozone in 27 States; Correction of SIP Approvals for 22 States. June. Available at: 

http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Analysis for the 

Final Mercury and Air Toxics Standards. EPA-452/R-11-011. December. Available at:

http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2011c. The Benefits and Costs of the Clean Air 

Act 1990 to 2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, 

Washington, DC. March. Available at:

http://www.epa.gov/oar/sect812/feb11/fullreport.pdf.

U.S. Environmental Protection Agency (U.S. EPA). 2011d. Regulatory Impact Analysis: National 

Emission Standards for Hazardous Air Pollutants for Industrial, Commercial, and

Institutional Boilers and Process Heaters. February. Available at:

http://www.epa.gov/ttnecas1/regdata/RIAs/boilersriafinal110221_psg.pdf.

U.S. Office of Management and Budget. Circular A-4, September 17, 2003. Available at:

http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf.
1-30

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected of 

postneonatal infant mortality and particulate air pollution in the United States.”

Environmental Health Perspectives 105(6): 608-612.

CHAPTER 1

HUMAN HEALTH BENEFITS ANALYSIS APPROACH AND RESULTS

Synopsis

This chapter presents the estimated human health benefits for the revised National 

Ambient Air Quality Standards (NAAQS) for particulate matter (PM). In this chapter, we quantify 

the health-related benefits of the fine particulate matter (PM2.5)-related air quality 

improvements resulting from the illustrative emission control scenarios that reduce emissions 

of directly emitted particles and precursor pollutants including SO2 and NOx to reach alternative

PM2.5 NAAQS levels in 2020.

6

These benefits are relative to an analytical baseline reflecting nationwide attainment of 

the current primary PM2.5 standards (i.e., annual standard of 15 μg/m3and 24-hour standard of 

35 μg/m3) that includes promulgated national regulations and illustrative emission controls to 

simulate attainment with 15/35 as well as a NOx emission adjustment to reflect expected 

reductions in mobile NOx emissions between 2020 and 2025. We project PM2.5 levels in certain 

areas that would exceed the revised annual standard of 12 μg/m3as well as alternative annual 

standards of 13 and 11 μg/m3after simulated attainment with 15/35 in the analytical baseline.

Table 5-1 summarizes the total monetized benefits of the revised and alternative PM2.5

standards in 2020. These estimates reflect the sum of the economic value of estimated PM2.5

mortality impacts identified and the value of all morbidity impacts.

The estimated benefits for the revised and alternative standards are in addition to the 

substantial benefits estimated for several recent implementation rules (U.S. EPA, 2009a, 2011c, 

2011d, 2011e). Rules such as the Mercury and Air Toxics Standard (MATS) and other emission 

reductions will have substantially reduced ambient PM2.5 concentrations by 2020 in the East, 

such that no additional controls would be needed in the East for the revised annual standard of 

12 μg/m3. Thus, all of the estimated benefits occur in California because this is the only state 

 

6

The estimates in this chapter reflect incremental emissions reductions from an analytical baseline that gives an 

adjustment to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to 

occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised 

standards. Full benefits of the revised standards in those two areas will not be realized until 2025.
1-31

that needs additional air quality improvement beyond the analytical baseline after accounting 

for air quality improvements from recent rules. Because of the national focus of many of the 

inputs to the benefits model, benefits estimated for any particular location have greater 

uncertainty than benefits occurring nationally. Compared with the proposal benefits, the 

estimated benefits for the revised standard are about double due to a combination of updates 

to the analytic baseline (See Chapter 3). We believe that all of these updates improve our 

estimate of benefits for the revised annual primary standard.

Table 5-1. Estimated Monetized Benefits of the of Revised and Alternative Annual PM2.5

Standards in 2020 Incremental to the Analytical Baseline (billions of 2010$)

a

Benefits Estimate 13 μg/m312 μg/m311 μg/m3

Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from 

Krewski et al. (2009)

3% discount rate $1.3 + B $4.0 +B $13 + B

7% discount rate $1.2 + B $3.6 +B $12 + B

Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from 

Lepeule et al. (2012)

3% discount rate $2.9 + B $9.1 +B $29 + B

7% discount rate $2.6 + B $8.2 +B $26 + B

a

Rounded to two significant figures. Avoided premature deaths account for over 98% of monetized 

benefits here, which are discounted over the SAB-recommended 20-year segmented lag. It was not all possible to 

quantify all benefits due to data limitations in this analysis. “B” is the sum of all unquantified health benefits and 

welfare co-benefits.

As we describe in detail below, we estimate PM-related health impacts using 

concentration-response relationships drawn from the epidemiological literature. In general, we 

are more confident in the magnitude of the risks we estimate from simulated PM2.5 

concentrations that coincide with the bulk of the observed PM concentrations in the 

epidemiological studies that are used in the benefits estimates. Likewise, we are less confident

in the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the 

observed data in these studies. As noted in the preamble to the rule, the range from the 25th to 

10th percentiles of the air quality data in the epidemiology studies is a reasonable range below 

which we start to have appreciably less confidence in the magnitude of the associations 

observed in the epidemiological studies. Most of the estimated avoided premature deaths 

occur at or above the lowest measured PM2.5 concentration in the two studies that are used to 

estimate mortality benefits.
1-32

In addition to PM2.5 benefits, implementation of emissions controls to reach some of the 

alternative PM2.5 standards would reduce other ambient pollutants, such as SO2, NO2, and 

ozone. However, because the method used in this analysis to simulate attainment does not 

account for changes in ambient concentrations of other pollutants, we were not able to 

quantify the co-benefits of reduced exposure to other pollutants. In addition, due to data and 

methodology limitations, we were unable to estimate additional health benefits associated 

with exposure to PM2.5 or the additional co-benefits from improvements in welfare effects (i.e., 

non-health effects) associated with emission reductions to attain the primary standard, such as 

visibility. We describe the unquantified health benefits in this chapter and the unquantified 

welfare co-benefits in Chapter 6.

Overview

This chapter contains a subset of the estimated health benefits of the revised and 

alternative PM2.5 standards in 2020 that the EPA was able to quantify, given the available

resources and methods. The analysis in this chapter aims to characterize the benefits of the air 

quality changes resulting from the implementation of new PM standards by answering two key 

questions:

1. What are the health effects of changes in ambient particulate matter (PM2.5) 

resulting from reductions in directly emitted PM2.5 and precursors due to the 

attainment of a new PM2.5 standard?

2. What is the economic value of these effects?

In this analysis, we consider an array of health impacts attributable to changes in PM2.5.

The Integrated Science Assessment for Particulate Matter (“PM ISA”)(U.S. EPA, 2009b) identifies

the human health effects associated with ambient particles, which include premature mortality 

and a variety of morbidity effects associated with acute and chronic exposures. Table 5-2

summarizes human health categories contained within the core benefits estimate as well as 

those categories that were unquantified due to limited data or resources. It is important to 

emphasize that the list of unquantified benefit categories is not exhaustive, nor is quantification 

of each effect complete. To identify human health benefits that we could quantify with 

confidence we selected endpoints that were classified as causal or likely causal in the PM ISA, 

and we excluded effects not identified as having at least a causal, likely causal, or suggestive 

relationship with the affected pollutants in the most recent comprehensive scientific 

assessment, such as an ISA. This decision does not imply that additional relationships between 

these and other human health and environmental co-benefits and the affected pollutants do 
1-33

not exist. Due to this decision criterion, we excluded some effects that were identified in 

previous lists of unquantified benefits in other RIAs (e.g., UVb exposure).

The benefits analysis in this chapter relies on an array of data inputs—including air 

quality modeling, health impact functions and valuation estimates among others—which are 

themselves subject to uncertainty and may also in turn contribute to the overall uncertainty in 

this analysis. We employ several techniques to characterize this uncertainty, which are 

described in detail in section 5.4.
1-34

Table 5-2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain

the Primary PM2.5 Standards

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized More Information

Improved Human Health

Reduced incidence 

of premature 

mortality from 

exposure to PM2.5

Adult premature mortality based on cohort 

study estimates and expert elicitation 

estimates (age >25 or age >30)

  Section 5.6

Infant mortality (age <1)   Section 5.6

Non-fatal heart attacks (age > 18)   Section 5.6

Hospital admissions—respiratory (all ages)   Section 5.6

Hospital admissions—cardiovascular (age >20)   Section 5.6

Emergency department visits for asthma (all 

ages)

  Section 5.6

Acute bronchitis (age 8–12)   Section 5.6

Lower respiratory symptoms (age 7–14)   Section 5.6

Upper respiratory symptoms (asthmatics age 

9–11)

  Section 5.6

Asthma exacerbation (asthmatics age 6–18)   Section 5.6

Lost work days (age 18–65)   Section 5.6

Minor restricted-activity days (age 18–65)   Section 5.6

Chronic Bronchitis (age >26) —

a —a

Section 5.6

Emergency department visits for 

cardiovascular effects (all ages)

—

a —a

Section 5.6

Strokes and cerebrovascular disease (age 50–

79)

—

a —a

Section 5.6

Other cardiovascular effects (e.g., other ages) — — PM ISAb

Other respiratory effects (e.g., pulmonary 

function, non-asthma ER visits, non-bronchitis 

chronic diseases, other ages and populations)

— — PM ISAb

Reproductive and developmental effects (e.g., 

low birth weight, pre-term births, etc.)

— — PM ISAb,c

Cancer, mutagenicity, and genotoxicity effects — — PM ISAb,c

(continued)
1-35

Table 5-2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain 

the Primary PM2.5 Standards (continued)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized More Information

Reduced incidence 

of mortality from 

exposure to ozone

Premature mortality based on short-term 

study estimates (all ages)

— — Ozone ISAd

Premature mortality based on long-term study 

estimates (age 30–99)

— — Ozone ISAd

Hospital admissions—respiratory causes (age 

> 65)

— — Ozone ISAd

Hospital admissions—respiratory causes (age 

<2)

— — Ozone ISAd

Emergency department visits for asthma (all 

ages)

— — Ozone ISAd

Minor restricted-activity days (age 18–65) — — Ozone ISAd

School absence days (age 5–17) — — Ozone ISAd

Decreased outdoor worker productivity (age 

18–65)

— — Ozone ISAd

Other respiratory effects (e.g., premature 

aging of lungs)

— — Ozone ISAb

Cardiovascular and nervous system effects — — Ozone ISAc

Reproductive and developmental effects — — Ozone ISAc

Reduced incidence 

of morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — — NO2 ISAd

Chronic lung disease hospital admissions (age 

> 65)

— — NO2 ISAd

Respiratory emergency department visits (all 

ages)

— — NO2 ISAd

Asthma exacerbation (asthmatics age 4–18) — — NO2 ISAd

Acute respiratory symptoms (age 7–14) — — NO2 ISAd

Premature mortality — — NO2 ISAb,c

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — NO2 ISAb,c

(continued)
1-36

Table 5-2. Human Health Effects of Pollutants Potentially Affected by Strategies to Attain 

the Primary PM2.5 Standards (continued)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized More Information

Reduced incidence 

of morbidity from 

exposure to SO2

Respiratory hospital admissions (age > 65) — — SO2 ISAd

Asthma emergency department visits (all 

ages)

— — SO2 ISAd

Asthma exacerbation (asthmatics age 4–12) — — SO2 ISAd

Acute respiratory symptoms (age 7–14) — — SO2 ISAd

Premature mortality — — SO2 ISAb,c

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — SO2 ISAb,c

Reduced incidence 

of morbidity from 

exposure to 

methylmercury 

(through role of 

sulfate in 

methylation)

Neurologic effects—IQ loss — — IRIS; NRC, 2000d

Other neurologic effects (e.g., developmental 

delays, memory, behavior)

— — IRIS; NRC, 2000b

Cardiovascular effects — — IRIS; NRC, 2000b,c

Genotoxic, immunologic, and other toxic 

effects

— — IRIS; NRC, 2000b,c

a We quantify these benefits in a sensitivity analysis, but not in the core analysis.

b We assess these benefits qualitatively because we do not have sufficient confidence in available data or 

methods.

c We assess these benefits qualitatively because current evidence is only suggestive of causality or there 

are other significant concerns over the strength of the association.

d We assess these benefits qualitatively due to time and resource limitations for this analysis.

As described in Chapter 1 of this RIA, there are important differences worth noting in 

the design and analytical objectives of NAAQS RIAs compared to RIAs for implementation rules, 

such as the recent MATS rule (U.S. EPA, 2011d). The NAAQS RIAs illustrate the potential costs 

and benefits of attaining a revised air quality standard nationwide based on an array of 

emission reduction strategies for different sources including known and unknown controls, 

incremental to implementation of existing regulations and controls needed to attain the 

current standards. In short, NAAQS RIAs hypothesize, but do not predict, the emission 

reduction strategies that States may choose to enact when implementing a revised NAAQS. The 

setting of a NAAQS does not directly result in costs or benefits, and as such, NAAQS RIAs are 

merely illustrative and the estimated costs and benefits are not intended to be added to the 
1-37

costs and benefits of other regulations that result in specific costs of control and emission 

reductions. By contrast, the emission reductions from implementation rules are generally for 

specific, well-characterized sources, such as the recent MATS rule addressing emissions from 

coal and oil-fired electricity generating units (U.S. EPA, 2011d). In general, the EPA is more 

confident in the magnitude and location of the emission reductions for implementation rules. 

As such, emission reductions achieved under promulgated implementation rules such as MATS 

have been reflected in the baseline of this NAAQS analysis. Subsequent implementation rules 

will be reflected in the baseline for the next PM NAAQS review. For this reason, the benefits 

estimated provided in this RIA and all other NAAQS RIAs should not be added to the benefits 

estimated for implementation rules.

Updated Methodology Presented in this RIA

The benefits analysis presented in this chapter incorporates an array of policy and 

technical changes that the Agency has adopted since the previous review of the PM2.5 standards 

in 2006, and since publication of the proposal RIA for this rulemaking. Below we note the 

aspects of this analysis that differ from the proposal RIA (U.S. EPA, 2012a):

1. Incorporation of the newest Harvard Six Cities mortality study. In 2012, Lepeule et al.

published an extended analysis of the Six Cities cohort. Compared to the study it 

replaces (Laden et al., 2006), this new analysis follows the cohort for a longer time 

and includes more years of PM2.5 monitoring data. The all-cause PM2.5 mortality risk 

coefficient drawn from Lepeule et al. (2012) is roughly similar to the Laden et al. 

(2006) risk coefficient applied in the EPA’s recent analyses of long-term PM2.5

mortality and has narrower confidence intervals.

2. Updated demographic data. We updated the population demographic data in 

BenMAP to reflect the 2010 Census and future projections based on economic 

forecasting models developed by Woods and Poole, Inc. (Woods and Poole, 2012). 

These data replace the earlier demographic projection data from Woods and Poole 

(2007).

3. Incorporation of new morbidity studies. Since the publication of the PM ISA (U.S. 

EPA, 2009) the epidemiological literature has produced several new studies 

examining the association between short-term PM2.5 exposure and respiratory and 

cardiovascular hospitalizations, respiratory and cardiovascular emergency 

department visits, and stroke. Upon careful evaluation of this new literature 

identified in the Provisional Assessment of Recent Studies on Health Effects of

Particulate Matter Exposure (“Provisional Assessment”)(U.S. EPA, 2012b) we added 

several new studies to our health impact assessment.
1-38

4. Updated the survival rates for non-fatal acute myocardial infarctions. Based on 

recent data from Agency for Healthcare Research and Quality’s Healthcare 

Utilization Project National Inpatient Sample database (AHRQ, 2009), we identified 

death rates for adults hospitalized with acute myocardial infarction stratified by age.

These rates replace the survival rates from Rosamond et al. (1999).

5. Expanded uncertainty assessment. We clarified the comprehensive assessment of 

the various uncertain parameters and assumptions within the benefits analysis and 

expanded the evaluation of air quality benchmarks (previously the LML analysis).

Although the list above identifies the major changes implemented since the proposal 

RIA, the EPA has also updated several additional components of the benefits analysis since the 

2006 PM NAAQS RIA (U.S. EPA, 2006a). In the Portland Cement NESHAP proposal RIA (U.S. EPA, 

2009a), the Agency no longer assumed a concentration threshold in the concentrationresponse function for PM2.5-related health effects and began using the benefits derived from 

the two major cohort studies of PM2.5 and mortality as the core benefits estimates, while still 

including a range of sensitivity estimates based on the EPA’s PM2.5mortality expert elicitation.

In the NO2 NAAQS proposal RIA (U.S. EPA, 2009a), we revised the estimate used for the valueof-a-statistical life to be consistent with Agency guidance. In the proposed CSAPR (previously 

the “Transport Rule”) (U.S. EPA, 2010g), we incorporated the “lowest measured level”

assessment to help characterize uncertainty in estimates of benefits of reductions in PM2.5 at 

lower baseline concentrations of PM2.5. In the final CSAPR (U.S. EPA, 2011c), we updated the 

baseline incidence rates for hospital admissions and emergency department visits and asthma 

prevalence rates. We direct the reader to each of these RIAs for more information on these 

changes. In the proposal RIA for this NAAQS review (U.S. EPA, 2012a), we updated the 

American Cancer Society cohort study to Krewski et al. (2009), updated health endpoints in the 

core and sensitivity analyses, incorporated new morbidity studies, updated the median wage 

data in the cost-of-illness studies, and expanded the uncertainty assessment.

Human Health Benefits Analysis Methods

We follow a “damage-function” approach in calculating total benefits of the modeled 

changes in environmental quality.7This approach estimates changes in individual health 

endpoints (specific effects that can be associated with changes in air quality) and assigns values 

to those changes assuming independence of the values for those individual endpoints. Total 

benefits are calculated simply as the sum of the values for all non-overlapping health 

 

7

The damage function approach is a more comprehensive method of estimating total benefits than the hedonic 

price approach applied to housing prices, which requires homebuyers to be knowledgeable of the full magnitude 

of health risks associated with their home purchase. See discussion of hedonic studies in Chapter 6.
1-39

endpoints. The “damage-function” approach is the standard method for assessing costs and 

benefits of environmental quality programs and has been used in several recent published 

analyses (Levy et al., 2009; Fann et al., 2012a; Tagaris et al., 2009).

To assess economic value in a damage-function framework, the changes in 

environmental quality must be translated into effects on people or on the things that people 

value. In some cases, the changes in environmental quality can be directly valued, as is the case 

for changes in visibility. In other cases, such as for changes in ozone and PM, a impact analysis 

must first be conducted to convert air quality changes into effects that can be assigned dollar 

values. For the purposes of this RIA, the health impacts analysis (HIA) is limited to those health 

effects that are directly linked to ambient levels of air pollution and specifically to those linked 

to PM2.5.

We note at the outset that the EPA rarely has the time or resources to perform 

extensive new research to measure directly either the health outcomes or their values for 

regulatory analyses. Thus, similar to Kunzli et al. (2000) and other, more recent health impact 

analyses, our estimates are based on the best available methods of benefits transfer. Benefits 

transfer is the science and art of adapting primary research from similar contexts to obtain the 

most accurate measure of benefits for the environmental quality change under analysis.

Adjustments are made for the level of environmental quality change, the socio-demographic 

and economic characteristics of the affected population, and other factors to improve the 

accuracy and robustness of benefits estimates.

Health Impact Assessment

The health impact assessment (HIA) quantifies the changes in the incidence of adverse 

health impacts resulting from changes in human exposure to PM2.5 and ozone air quality. HIAs 

are a well-established approach for estimating the retrospective or prospective change in 

adverse health impacts expected to result from population-level changes in exposure to 

pollutants (Levy et al., 2009). PC-based tools such as the environmental Benefits Mapping and 

Analysis Program (BenMAP) can systematize health impact analyses by applying a database of 

key input parameters, including health impact functions and population projections—provided 

that key input data are available, including air quality estimates and risk coefficients (Abt 

Associates, 2010). Analysts have applied the HIA approach to estimate human health impacts 

resulting from hypothetical changes in pollutant levels (Hubbell et al., 2005; Tagaris et al., 2009; 

Fann et al., 2012a). The EPA and others have relied upon this method to predict future changes 

in health impacts expected to result from the implementation of regulations affecting air 
1-40

quality (e.g., U.S. EPA, 2011d). For this assessment, the HIA is limited to those health effects 

that are directly linked to ambient PM2.5 concentrations. There may be other indirect health 

impacts associated with implementing emissions controls, such as occupational health 

exposures.

The HIA approach used in this analysis involves three basic steps: (1) utilizing projections 

of PM2.5 air quality8and estimating the change in the spatial distribution of the ambient air 

quality; (2) determining the subsequent change in population-level exposure; (3) calculating 

health impacts by applying concentration-response relationships drawn from the 

epidemiological literature (Hubbell et al., 2009) to this change in population exposure.

A typical health impact function might look as follows:

(5.1)

where y0 is the baseline incidence rate for the health endpoint being quantified (for example, a 

health impact function quantifying changes in mortality would use the baseline, or background, 

mortality rate for the given population of interest); Pop is the population affected by the 

change in air quality; x is the change in air quality; and β is the effect coefficient drawn from 

the epidemiological study. Figure 5-1 provides a simplified overview of this approach.

 

8

Projections of ambient PM2.5 concentrations for this analysis were generated using the Community Multiscale Air 

Quality model (CMAQ). See Chapter 3 of this RIA for more information on the air quality modeling.
1-41

Figure 5-1. Illustration of BenMAP Approach

Economic Valuation of Health Impacts

After quantifying the change in adverse health impacts, the final step is to estimate the 

economic value of these avoided impacts. The appropriate economic value for a change in a 

health effect depends on whether the health effect is viewed ex ante (before the effect has 

occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air 

pollution generally lower the risk of future adverse health effects by a small amount for a large 

population. The appropriate economic measure is therefore ex ante willingness to pay (WTP) 

for changes in risk. Epidemiological studies generally provide estimates of the relative risks of a 

particular health effect for a given increment of air pollution (often per 10 μg/m3for PM2.5). 

These relative risks can be used to develop risk coefficients that relate a unit reduction in PM2.5 

to changes in the incidence of a health effect. In order to value these changes in incidence, WTP 

for changes in risk need to be converted into WTP per statistical incidence. This measure is 

calculated by dividing individual WTP for a risk reduction by the related observed change in risk. 

For example, suppose a measure is able to reduce the risk of premature mortality from 2 in 
1-42

10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is 

$100, then the WTP for an avoided statistical premature mortality amounts to $1 million 

($100/0.0001 change in risk). Using this approach, the size of the affected population is 

automatically taken into account by the number of incidences predicted by epidemiological 

studies applied to the relevant population. The same type of calculation can produce values for 

statistical incidences of other health endpoints.

For some health effects, such as hospital admissions, WTP estimates are generally not 

available. In these cases, we use the cost of treating or mitigating the effect. For example, for 

the valuation of hospital admissions we use the avoided medical costs as an estimate of the 

value of avoiding the health effects causing the admission. These cost-of-illness (COI) estimates 

generally (although not necessarily in every case) understate the true value of reductions in risk 

of a health effect. They tend to reflect the direct expenditures related to treatment but not the 

value of avoided pain and suffering from the health effect.

We use the BenMAP model version 4.0.52 (Abt Associates, 2010) to estimate the health 

impacts and monetized health benefits for the proposed standard. Figure 5-2 shows the data 

inputs and outputs for the BenMAP model.

Modeled Baseline 

and Post-Control 

Ambient PM2.5

PM2.5 Health 

Functions

Economic 

Valuation 

Functions

2020 Population 

Projections

PM2.5 Incremental Air 

Quality Change

PM2.5-Related Health 

Impacts

Woods & Poole 

Population 

Projections

Background 

Incidence and 

Prevalence Rates

Monetized PM2.5-

related Benefits

Blue identifies a user-selected input within the BenMAP model

Green identifies a data input generated outside of the BenMAP 

model

Census 

Population Data
1-43

Figure 5-2. Data Inputs and Outputs for the BenMAP Model

Uncertainty Characterization

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty. This analysis is no exception. As outlined 

both in this and preceding chapters, this analysis includes many data sources as inputs, 

including emission inventories, air quality data from models (with their associated parameters 

and inputs), population data, population estimates, health effect estimates from epidemiology 

studies, economic data for monetizing benefits, and assumptions regarding the future state of 

the world (i.e., regulations, technology, and human behavior). Each of these inputs may be 

uncertain and would affect the benefits estimate. When the uncertainties from each stage of 

the analysis are compounded, even small uncertainties can have large effects on the total 

quantified benefits.

After reviewing the EPA’s approach, the National Research Council (NRC) (2002, 2008),

which is part of the National Academies of Science, concluded that the EPA’s general 

methodology for calculating the benefits of reducing air pollution is reasonable and informative 

in spite of inherent uncertainties. The NRC also highlighted the need to conduct rigorous 

quantitative analyses of uncertainty and to present benefits estimates to decision makers in 

ways that foster an appropriate appreciation of their inherent uncertainty. Since the 

publication of these reports, the EPA has continued work to improve the characterization of 

uncertainty in both health incidence and benefits estimates. In response to these 

recommendations, we have expanded our previous analyses to incorporate additional 

quantitative and qualitative characterizations of uncertainty. Although we have not yet been 

able to make as much progress towards a full, probabilistic uncertainty assessment as 

envisioned by the NAS as we had hoped, we have added a number of additional quantitative 

and qualitative analyses to highlight the impact that uncertain assumptions may have on the 

benefits estimates. In addition, for some inputs into the benefits analysis, such as the air quality 

data, it is difficult to address uncertainty probabilistically due to the complexity of the 

underlying air quality models and emission inputs. Therefore, we decline to make up alternative 

assumptions simply for the purpose of probabilistic uncertainty characterization when there is 

no scientific literature to support alternate assumptions.

To characterize uncertainty and variability, we follow an approach that combines 

elements from two recent analyses by the EPA (U.S. EPA, 2010b; 2011a), and uses a tiered 

approach developed by the World Health Organization (WHO) for characterizing uncertainty 
1-44

(WHO, 2008). We present this tiered assessment as well as an assessment of the potential 

impact and magnitude of each aspect of uncertainty In Appendix 5c. Data limitations prevent us 

from treating each source of uncertainty quantitatively and from reaching a full-probabilistic 

simulation of our results, but we were able to consider the influence of uncertainty in the risk 

coefficients and economic valuation functions by incorporating six quantitative analyses

described in more detail below:

1. A Monte Carlo assessment that accounts for random sampling error and between 

study variability in the epidemiological and economic valuation studies;

2. A concentration benchmark assessment that characterizes the distribution of 

avoided PM2.5-related deaths relative to specific concentrations in the long-term 

epidemiological studies used to estimate PM2.5-related mortality;

3. The quantification of PM-related mortality using alternative PM2.5 mortality effect 

estimates drawn from two long-term cohort studies and an expert elicitation;

4. Sensitivity analyses of several aspects of PM-related benefits;

5. Distributional analyses of PM2.5-related benefits by location, race, income, and 

education; and

6. An analysis of the influence of various parameters on total monetized benefits.

Monte Carlo Assessment

Similar to other recent RIAs, we used Monte Carlo methods for characterizing random 

sampling error associated with the concentration response functions from epidemiological 

studies and random effects modeling to characterize both sampling error and variability across 

the economic valuation functions. The Monte Carlo simulation in the BenMAP software 

randomly samples from a distribution of incidence and valuation estimates to characterize the 

effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to 

generate confidence intervals around the estimated health impact and monetized benefits. The 

reported standard errors in the epidemiological studies determined the distributions for 

individual effect estimates for endpoints estimated using a single study. For endpoints 

estimated using a pooled estimate of multiple studies, the confidence intervals reflect both the 

standard errors and the variance across studies. The confidence intervals around the monetized 

benefits incorporate the epidemiology standard errors as well as the distribution of the 

valuation function. These confidence intervals do not reflect other sources of uncertainty 

inherent within the estimates, such as baseline incidence rates, populations exposed and 

transferability of the effect estimate to diverse locations. As a result, the reported confidence 
1-45

intervals and range of estimates give an incomplete picture about the overall uncertainty in the 

benefits estimates.

Concentration Benchmark Analysis for PM2.5

We include a concentration benchmark assessment, which identifies the baseline (i.e.,

pre-rule) annual mean PM2.5 levels at which populations are exposed and specific 

concentrations in the two long-term cohort studies we use to quantify mortality impacts. This 

analysis characterizes avoided PM2.5-related deaths relative to the 10th and 25th percentiles of 

the air quality data used the Krewski et al. (2009) study as well as the lowest measured level 

(LML) of the Krewski et al. (2009) and Lepeule et al. (20) studies.

Alternative Concentration-Response Functions for PM2.5–Related Mortality

We assign the greatest economic value to the reduction in PM2.5 relatedmortality risk. 

Therefore, it is particularly important to attempt to characterize the uncertainties associated 

with reductions in premature mortality. To better understand the concentration-response 

relationship between PM2.5 exposure and premature mortality, the EPA conducted an expert 

elicitation in 2006 (Roman et al., 2008; IEc, 2006).9In general, the results of the expert 

elicitation support the conclusion that the benefits of PM2.5 control are very likely to be 

substantial.

Alternative concentration-response functions are useful for assessing uncertainty 

beyond random statistical error, including uncertainty in the functional form of the model or 

alternative study design. Thus, we include the expert elicitation results as well as standard 

errors approaches to provide insights into the likelihood of different outcomes and about the 

state of knowledge regarding the benefits estimates. In this analysis, we present the results 

derived from the expert elicitation as indicative of the uncertainty associated with a major 

component of the health impact functions, and we provide the independent estimates derived 

from each of the twelve experts to better characterize the degree of variability in the expert 

responses.

In previous RIAs, the EPA presented benefits estimates using concentration response 

functions derived from the PM2.5 Expert Elicitation (Roman et al., 2008) as a range from the 

lowest expert value (Expert K) to the highest expert value (Expert E). However, this approach 

did not indicate the agency’s judgment on what the best estimate of PM2.5 benefits may be, and 

 

9

Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually 

of multiple experts, are obtained (Ayyub, 2002).
1-46

the EPA’s independent Science Advisory Board (SAB) recommended refinements to the way 

EPA presented the results of the elicitation (U.S. EPA-SAB, 2008). Therefore, we began to 

present the cohort-based studies (Krewski et al., 2009; Laden et al., 2006)10 as our core 

estimates in the proposal RIA for the Portland Cement NESHAP (U.S. EPA, 2009a). Using 

alternate relationships between PM2.5 and premature mortality supplied by experts, higher and 

lower benefits estimates are plausible, but most of the expert-based estimates of the mean 

PM2.5 effect on mortality fall between the two epidemiology-based estimates (Roman et al., 

2008). In addition to these studies, we have included a discussion or other recent multi-state 

cohort studies conducted in North America, but we have not estimated benefits using the effect 

coefficients from these studies. Please note that the benefits estimates results presented are 

not the direct results from the studies or expert elicitation; rather, the estimates are based in 

part on the effect coefficients provided in those studies or by experts. In addition, the experts 

provided distributions around their mean PM2.5 effect estimates, which provides more 

information regarding the overall range of uncertainty, and this overall range is larger than the 

range of the mean effect estimates from each of the experts.

Even these multiple characterizations with confidence intervals omit the contribution to 

overall uncertainty from uncertainty in air quality changes, baseline incidence rates, and 

populations exposed. Furthermore, the approach presented here does not yet include methods 

for addressing correlation between input parameters and the identification of reasonable upper 

and lower bounds for input distributions characterizing uncertainty in additional model 

elements. As a result, the reported confidence intervals and range of estimates give an 

incomplete picture about the overall uncertainty in the estimates. This information should be 

interpreted within the context of the larger uncertainty surrounding the entire analysis.

Sensitivity Analyses

For some aspects of uncertainty, we have sufficient data to conduct sensitivity analyses.

In this analysis, we performed five such analyses for the revised standard level. In particular, 

we:

1. Assessed the sensitivity of the economic value of reductions in the risk of PM2.5-

related death according to differing assumptions regarding the lag between PM2.5

exposure and premature death. The timing of such premature deaths affects the 

magnitude of the discounted PM2.5-related mortality benefits. In this sensitivity 

assessment, we consider 6 alternative cessation lags.

 

10 We have since updated the the Harvard Six Cities cohort study from Laden et al. (2006) to use the most recent 

follow-up publication of this cohort (Lepeule et al, 2012).
1-47

2. Characterized the sensitivity of the economic value of the health endpoints valued 

using willingness-to-pay estimates to a higher and a lower assumption regarding 

income elasticity. As we discuss below, economic theory argues that individual 

willingness to pay increases as personal income grows. The relationship between 

growth in personal income and willingness-to-pay to reduce mortality and morbidity 

risk is characterized by the income growth factor.

3. Summarized the avoided cases of certain health endpoints for which we either 

lacked an appropriate economic value (cardiovascular hospital admissions and 

stroke) or in which we no longer had sufficient confidence to retain in our primary 

benefits estimate (chronic bronchitis).

4. Assessed the sensitivity of the benefits results to the new population data from the 

2010 Census.

5. Assessed the sensitivity of the benefits results to an analysis year of 2025.

Distributional Assessment

In an Appendix to the proposal RIA, we characterized the distribution of PM2.5-related 

benefits based on the geographic distribution of race and education in areas where the 

illustrative emission reduction strategies would reduce PM2.5 concentrations. In that 

assessment, we aimed to answer two key questions:

1. What was the estimated distribution of PM2.5-related mortality risk based on the

race and education characteristics of the population living within areas projected to 

exceed alternative combinations of the proposed primary PM2.5 standards?

2. How would air quality improvements within these counties change the distribution 

of risk among populations of different races and educational attainment?

11

That assessment was generally consistent with the distributional assessments 

performed in support of MATS (U.S. EPA, 2011c), with one key difference. The environmental 

justice analyses accompanying the MATS RIA applied CMAQ-modeled PM2.5 predictions that 

represent the change in air quality after the implementation of each rule. By contrast, the 

proposal RIA aimed to illustrate the potential benefits and costs of attaining alternative primary 

PM2.5 standards; the states will ultimately implement attainment strategies, which may differ 

greatly from the least-cost strategy the EPA modeled here. Alternative emission reduction

strategies—particularly those that maximize benefits to human health and provide a more

 

11 In this analysis we assess the change in risk among populations of different race and educational attainment. As 

we discuss further in the methodology, we consider this last variable because of the availability of educationmodified PM2.5 mortality risk estimates.
1-48

equitable distribution of risk—are also available to the states, though not modeled here (Fann 

et al., 2012b). Due in part to time constraints, the EPA did not perform such an analysis for the 

final RIA, and instead refers readers to the Appendix noted above.

Influence Analysis—Quantitative Assessment of Uncertainty

In the past few years, the EPA has initiated several projects to improve the 

characterization of uncertainty for benefits analysis. In particular, the EPA recently completed 

the first phase of a quantitative uncertainty analysis of benefits, hereafter referred to as the 

“Influence Analysis” (Mansfield et al., 2009). The Influence Analysis diagramed the uncertain 

components of each step within the benefits analysis process, identified plausible ranges for a 

sensitivity analysis, and assessed the sensitivity to total benefits to changes in each component.

Although this analysis does not quite fulfill the goal of a full probabilistic assessment, it 

accomplished the necessary first steps and identified the challenges to accomplishing that goal.

Below are some of the preliminary observations from the first phase of the project.

 The components that contribute the most to uncertainty of the monetized benefits 

and mortality incidence (in order of importance) are the value-of-a-statistical-life 

(VSL), the concentration-response (C-R) function for mortality, and change in PM2.5 

concentration.

 The components that contribute the least to uncertainty of the monetized benefits 

and mortality incidence are population, morbidity valuation, and income elasticity.

 The choice of a C-R function for mortality affects the mortality incidence and 

monetized benefits more than other sources of uncertainty within each C-R 

function.

 Alternative cessation lag structures for mortality have a moderate effect on the 

monetized benefits.

 Because the health impact function is essentially linear, the key components show 

the same sensitivity across all mortality C-R functions even if the midpoints differ 

significantly from one expert to another.

Qualitative Assessment of Uncertainty and Other Analysis Limitations

Although we strive to incorporate as many quantitative assessments of uncertainty as 

possible, there are several aspects we are only able to address qualitatively. These aspects are 

important factors to consider when evaluating the relative benefits of the emission reduction 

strategies for the revised and alternative annual standards:
1-49

The total monetized benefits presented in this chapter are based on our interpretation 

of the best available scientific literature and methods and supported by the EPA’s independent 

SAB (Health Effects Subcommittee) (SAB-HES) (U.S. EPA- SAB, 2010a) and the National 

Academies of Science (NAS) (NRC, 2002). The benefits estimates are subject to a number of 

assumptions and uncertainties. For example, the key assumptions underlying the estimates for 

premature mortality, which account for over 98% of the total monetized benefits in this 

analysis, include the following:

1. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 varies considerably in composition across sources, but the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle 

type. The PM ISA, which was twice reviewed by Clean Air Scientific Advisory 

Committee (SAB-CASAC), concluded that “many constituents of PM2.5 can be linked 

with multiple health effects, and the evidence is not yet sufficient to allow 

differentiation of those constituents or sources that are more closely related to 

specific outcomes” (U.S. EPA, 2009b). These uncertainties are likely to be magnified 

in the current analysis to the extent that the emissions controls are less diverse 

when focusing on one small region of the country rather than a broader geography 

with more diverse emissions sources and the application of a more diverse set of 

controls.

2. We assume that health impact functions based on national studies are 

representative for exposures and populations in California. In addition to the 

national risk coefficients we use as our core estimates, the EPA considered the 

cohort studies conducted in California specifically. Although we have not calculated 

the benefits results using the cohort studies conducted in California, we provided 

these risk coefficients to show how much the monetized benefits could have 

changed. Most of the California cohort studies report central effect estimates similar 

to the (nation-wide) all-cause mortality risk estimate we applied from Krewski et al. 

(2009) and Lepeule et al. (2012) albeit with wider confidence intervals. Three cohort 

studies conducted in California indicate statistically significant higher risks than the 

risk estimates we applied from Lepeule et al. (2012), and four studies showed 

insignificant results.

3. We assume that the health impact function for fine particles is log-linear without a 

threshold in this analysis. Thus, the estimates include health benefits from reducing 

fine particles in areas with varied concentrations of PM2.5, including both areas that 

do not meet the fine particle standard and those areas that are in attainment, down 

to the lowest modeled concentrations.

4. We assume that there is a “cessation” lag between the change in PM exposures and 

the total realization of changes in mortality effects. Specifically, we assume that 
1-50

some of the incidences of premature mortality related to PM2.5 exposures occur in a 

distributed fashion over the 20 years following exposure based on the advice of the 

SAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality benefits at 

different discount rates.

5. To characterize the uncertainty in the relationship between PM2.5 and premature 

mortality (which account for over 98% of total monetized benefits in this analysis), 

we include a set of twelve estimates based on results of the expert elicitation study 

in addition to our core estimates. Even these multiple characterizations omit the 

uncertainty in air quality estimates, baseline incidence rates, populations exposed 

and transferability of the effect estimate to diverse locations. As a result, the 

reported confidence intervals and range of estimates give an incomplete picture 

about the overall uncertainty in the PM2.5 estimates. This information should be 

interpreted within the context of the larger uncertainty surrounding the entire 

analysis.

As previously described, we strive to monetize as many of the benefits anticipated from 

the revised and alternative standards as possible given data and resource limitations, but the 

monetized benefits estimated in this RIA inevitably only reflect a portion of the benefits. 

Specifically, only certain benefits attributable to the health impacts associated with exposure to 

ambient fine particles have been monetized in this analysis. Data and methodological 

limitations prevented the EPA from quantifying or monetizing the benefits from several 

important health benefit categories from emission reduction strategies to reach the revised 

annual standard in this RIA, including potential co-benefits from reducing ozone exposure, NO2

exposure, SO2 exposure, and methylmercury exposure (see section 5.6.5 for more information). 

If we could fully monetize all of the benefit categories, the total monetized benefits would 

exceed the costs by an even greater margin than we currently estimate.

To more fully address all these uncertainties including those we cannot quantify, we 

apply a four-tiered approach using the WHO uncertainty framework (WHO, 2008), which 

provides a means for systematically linking the characterization of uncertainty to the 

sophistication of the underlying risk assessment. The EPA has applied similar approaches in 

analyses (U.S. EPA, 2010b, 2011a). Using this framework, we summarize the key uncertainties in 

the health benefits analysis, including our assessment of the direction of potential bias, 

magnitude of impact on the monetized benefits, degree of confidence in our analytical 

approach, and our ability to assess the source of uncertainty. More information on this 

approach and the uncertainty characterization are available in Appendix 5B.
1-51

Benefits Analysis Data Inputs

In Figure 5-2, we summarized the key data inputs to the health impact and economic 

valuation estimate. Below we summarize the data sources for each of these inputs, including 

demographic projections, incidence and prevalence rates, effect coefficients, and economic 

valuation. We indicate where we have updated key data inputs since the benefits analysis

conducted for the MATS RIA (U.S. EPA, 2011d) and the proposal RIA (U.S. EPA, 2012a).

Demographic Data

Quantified and monetized human health impacts depend on the demographic 

characteristics of the population, including age, location, and income. We use population 

projections based on economic forecasting models developed by Woods and Poole, Inc.

(Woods and Poole, 2012). The Woods and Poole (WP) database contains county-level 

projections of population by age, sex, and race out to 2040, relative to a baseline using the 

2010 Census data; the proposal RIA incorporated WP projections relative to a baseline using 

2000 Census data. An analysis exploring the sensitivity of population and health impact 

estimates to this update can be found in Appendix 5.A. Projections in each county are 

determined simultaneously with every other county in the United States to take into account 

patterns of economic growth and migration. The sum of growth in county-level populations is 

constrained to equal a previously determined national population growth, based on Bureau of 

Census estimates (Hollman et al., 2000). According to WP, linking county-level growth 

projections together and constraining to a national-level total growth avoids potential errors 

introduced by forecasting each county independently. County projections are developed in a 

four-stage process:

 First, national-level variables such as income, employment, and populations are

forecasted.

 Second, employment projections are made for 179 economic areas defined by the 

Bureau of Economic Analysis (U.S. BEA, 2004), using an “export-base” approach, 

which relies on linking industrial-sector production of non-locally consumed 

production items, such as outputs from mining, agriculture, and manufacturing with 

the national economy. The export-based approach requires estimation of demand 

equations or calculation of historical growth rates for output and employment by 

sector.

 Third, population is projected for each economic area based on net migration rates 

derived from employment opportunities and following a cohort-component method 

based on fertility and mortality in each area.
1-52

 Fourth, employment and population projections are repeated for counties, using the 

economic region totals as bounds. The age, sex, and race distributions for each 

region or county are determined by aging the population by single year of age by sex 

and race for each year through 2040 based on historical rates of mortality, fertility, 

and migration.

Baseline Incidence and Prevalence Estimates

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide a direct estimate of the relationship of air quality changes to the 

relative risk of a health effect, rather than estimating the absolute number of avoided cases. For 

example, a typical result might be that a 10 μg/m3decrease in daily PM2.5 levels might be 

associated with a decrease in hospital admissions of 3%. The baseline incidence of the health 

effect is necessary to convert this relative change into a number of cases. A baseline incidence 

rate is the estimate of the number of cases of the health effect per year in the assessment 

location, as it corresponds to baseline pollutant levels in that location. To derive the total 

baseline incidence per year, this rate must be multiplied by the corresponding population 

number. For example, if the baseline incidence rate is the number of cases per year per million 

people, that number must be multiplied by the millions of people in the total population.

Table 5-3 summarizes the sources of baseline incidence rates and provides average 

incidence rates for the endpoints included in the analysis. For both baseline incidence and 

prevalence data, we used age-specific rates where available. We applied concentrationresponse functions to individual age groups and then summed over the relevant age range to 

provide an estimate of total population benefits. In most cases, we used a single national 

incidence rate, due to a lack of more spatially disaggregated data. Whenever possible, the 

national rates used are national averages, because these data are most applicable to a national 

assessment of benefits. For some studies, however, the only available incidence information 

comes from the studies themselves; in these cases, incidence in the study population is 

assumed to represent typical incidence at the national level. County, state and regional 

incidence rates are available for hospital admissions, and county-level data are available for 

premature mortality. 

We projected mortality rates such that future mortality rates are consistent with our 

projections of population growth (Abt Associates, 2012). To perform this calculation, we began 

first with an average of 2004–2006 cause-specific mortality rates. Using Census Bureau 

projected national-level annual mortality rates stratified by age range, we projected these 
1-53

mortality rates to 2050 in 5-year increments (Abt Associates, 2012; U.S. Bureau of the Census 

2002).

The baseline incidence rates for hospital admissions and emergency department visits reflect 

the revised rates first applied in the CSAPR RIA (U.S. EPA, 2011c). In addition, we have revised 

the baseline incidence rates for acute myocardial infarction. These revised rates are more 

recent (AHRQ, 2007), which provides a better representation of the rates at which populations 

of different ages, and in different locations, visit the hospital and emergency department for air 

pollution-related illnesses. Also, the new baseline incidence rates are more spatially refined. For 

many locations within the U.S., these data are resolved at the county- or state-level, providing a 

better characterization of the geographic distribution of hospital and emergency department 

visits than the previous national rates. Lastly, these rates reflect unscheduled hospital 

admissions only, which represents a conservative assumption that most air pollution-related 

visits are likely to be unscheduled. If air pollution-related hospital admissions are scheduled, 

this assumption would underestimate these benefits.

For the set of endpoints affecting the asthmatic population, in addition to baseline 

incidence rates, prevalence rates of asthma in the population are needed to define the 

applicable population. Table 5-4 lists the prevalence rates used to determine the applicable 

population for asthma symptoms. Note that these reflect current asthma prevalence and 

assume no change in prevalence rates in future years. We updated these rates in the CSAPR RIA 

(U.S. EPA, 2011c).
1-54

Table 5-3. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population

Endpoint Parameter

Rates

Value Source

Mortality Daily or annual mortality rate 

projected to 2020

a

Age-, cause-, and countyspecific rateCDC WONDER (2004–

2006)

U.S. Census bureau, 2000

Hospitalizations Daily hospitalization rate Age-, region-, state-, 

county- and cause-specific 

rate

2007 HCUP data filesb

ER Visits Daily ER visit rate for asthma and 

cardiovascular events

Age-, region-, state-, 

county- and cause-specific 

rate

2007 HCUP data filesb

Cerebrovascular 

events

Incidence of new cerebrovascular 

events among populations 50–79

0.001575I Table 3 of Miller et al. 

(2007)

Chronic BronchitiscAnnual prevalence rate per 

person

Aged 18–44

Aged 45–64

Aged 65 and older

0.0315

0.0549

0.0563

American Lung Association 

(2010a, Table 4). 

Annual incidence rate per person 0.00378 Abbey et al. (1993, 

Table 3)

Nonfatal Myocardial 

Infarction (heart 

attacks)

Daily nonfatal myocardial 

infarction incidence rate per 

person, 18+

Age-, region-, state-, and 

county-specific rate

2007 HCUP data files;

b

adjusted by 0.93 for 

probability of surviving 

after 28 days (Rosamond 

et al., 1999)

Asthma 

Exacerbations

Incidence among asthmatic 

African-American children

daily wheeze

daily cough

daily shortness of breath

0.173

0.145

0.074

Ostro et al. (2001) 

Acute Bronchitis Annual bronchitis incidence rate, 

children

0.043 American Lung Association 

(2002c, Table 11)

Lower Respiratory 

Symptoms

Daily lower respiratory symptom 

incidence among childrend

0.0012 Schwartz et al. (1994, 

Table 2)

Upper Respiratory 

Symptoms

Daily upper respiratory symptom 

incidence among asthmatic 

children

0.3419 Pope et al. (1991, Table 2)

(continued)
1-55

Table 5-3. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population (continued)

Endpoint Parameter

Rates

Value Source

Work Loss Days Daily WLD incidence rate per 

person (18–65)

Aged 18–24

Aged 25–44

Aged 45–64

0.00540

0.00678

0.00492

1996 HIS (Adams, 

Hendershot, and Marano, 

1999, Table 41); U.S. 

Census Bureau (2000)

School Loss Days Rate per person per year, 

assuming 180 school days per 

year

9.9 National Center for 

Education Statistics (1996) 

and 1996 HIS (Adams 

et al., 1999, Table 47); 

Minor RestrictedActivity DaysDaily MRAD incidence rate per 

person

0.02137 Ostro and Rothschild 

(1989, p. 243)

a Mortality rates are only available at 5-year increments.

b

Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regionallevel hospital and emergency department discharges for a variety of ICD codes (AHRQ, 2007).

c

Assessed in sensitivity analysis only. The rate numbers may be slightly different from those in Table 4 

because we received more current estimates from ALA.

d

Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and 

wheeze.

Table 5-4. Asthma Prevalence Rates 

Population Group

Asthma Prevalence Rates

Value Source

All Ages 0.0780 American Lung Association (2010b, Table 7)

< 18 0.0941

5–17 0.1070

18–44 0.0719

45–64 0.0745

65+ 0.0716

African American, 5–17 0.1776 American Lung Association (2010b, Table 9)

African American, <18 0.1553 American Lung Associationa

a

Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008).

Effect Coefficients

The first step in selecting effect coefficients is to identify the health endpoints to be 

quantified. We base our selection of health endpoints on consistency with the EPA’s Integrated 
1-56

Science Assessments (which replace previous Criteria Documents), with input and advice from 

the SAB-HES, a scientific review panel specifically established to provide advice on the use of 

the scientific literature in developing benefits analyses for the EPA’s Report to Congress on The 

Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a). In addition, we have 

included more recent epidemiology studies from the PM ISA (U.S. EPA, 2009b) and the 

Provisional Assessment (U.S. EPA, 2012b).

12 In general, we follow a weight of evidence 

approach, based on the biological plausibility of effects, availability of concentration-response 

functions from well conducted peer-reviewed epidemiological studies, cohesiveness of results 

across studies, and a focus on endpoints reflecting public health impacts (like hospital 

admissions) rather than physiological responses (such as changes in clinical measures like 

Forced Expiratory Volume [FEV1]).

There are several types of data that can support the determination of types and 

magnitude of health effects associated with air pollution exposures. These sources of data 

include toxicological studies (including animal and cellular studies), human clinical trials, and 

observational epidemiology studies. All of these data sources provide important contributions 

to the weight of evidence surrounding a particular health impact. However, only epidemiology 

studies provide direct concentration-response relationships that can be used to evaluate 

population-level impacts of reductions in ambient pollution levels in a health impact 

assessment.

For the data-derived estimates, we relied on the published scientific literature to 

ascertain the relationship between PM2.5 and adverse human health effects. We evaluated 

epidemiological studies using the selection criteria summarized in Table 5-5. These criteria 

include consideration of whether the study was peer-reviewed, the match between the 

pollutant studied and the pollutant of interest, the study design and location, and 

characteristics of the study population, among other considerations. In general, the use of 

concentration-response functions from more than a single study can provide a more 

representative distribution of the effect estimate. However, there are often differences 

between studies examining the same endpoint, making it difficult to pool the results in a 

consistent manner. For example, studies may examine different pollutants or different age 

 

12 The peer-reviewed studies in the Provisional Assessment have not yet undergone external review by the SAB. 

The new studies from the PM ISA and Provisional Assessment for health endpoints not previously quantified in 

EPA’s RIAs are presented in a sensitivity analysis in Appendix 5B, but these new endpoints have not been 

incorporated into the core benefits analysis.
1-57

Table 5-5. Criteria Used When Selecting C-R Functions

Consideration Comments

Peer-Reviewed 

Research

Peer-reviewed research is preferred to research that has not undergone the peer-review 

process.

Study Type Among studies that consider chronic exposure (e.g., over a year or longer), prospective 

cohort studies are preferred over ecological studies because they control for important 

individual-level confounding variables that cannot be controlled for in ecological studies. 

Study Period Studies examining a relatively longer period of time (and therefore having more data) are 

preferred, because they have greater statistical power to detect effects. Studies that are 

more recent are also preferred because of possible changes in pollution mixes, medical 

care, and lifestyle over time. However, when there are only a few studies available, 

studies from all years will be included.

Population Attributes The most technically appropriate measures of benefits would be based on impact 

functions that cover the entire sensitive population but allow for heterogeneity across 

age or other relevant demographic factors. In the absence of effect estimates specific to 

age, sex, preexisting condition status, or other relevant factors, it may be appropriate to 

select effect estimates that cover the broadest population to match with the desired 

outcome of the analysis, which is total national-level health impacts. When available, 

multi-city studies are preferred to single city studies because they provide a more 

generalizable representation of the concentration-response function.

Study Size Studies examining a relatively large sample are preferred because they generally have 

more power to detect small magnitude effects. A large sample can be obtained in several 

ways, including through a large population or through repeated observations on a smaller 

population (e.g., through a symptom diary recorded for a panel of asthmatic children).

Study Location U.S. studies are more desirable than non-U.S. studies because of potential differences in 

pollution characteristics, exposure patterns, medical care system, population behavior, 

and lifestyle. National estimates are most appropriate when benefits are nationally 

distributed; the impact of regional differences may be important when benefits only 

accrue to a single area.

Pollutants Included in 

Model

When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it 

is important to use properly specified impact functions that include both pollutants. 

Using single-pollutant models in cases where both pollutants are expected to affect a 

health outcome can lead to double-counting when pollutants are correlated.

Measure of PM For this analysis, impact functions based on PM2.5 are preferred to PM10 because of the 

focus on reducing emissions of PM2.5 precursors, and because air quality modeling was 

conducted for this size fraction of PM. Where PM2.5 functions are not available, PM10

functions are used as surrogates, recognizing that there will be potential downward 

(upward) biases if the fine fraction of PM10 is more (less) toxic than the coarse fraction. 

Economically Valuable 

Health Effects

Some health effects, such as forced expiratory volume and other technical measurements 

of lung function, are difficult to value in monetary terms. These health effects are not 

quantified in this analysis.

Non-overlapping 

Endpoints

Although the benefits associated with each individual health endpoint may be analyzed 

separately, care must be exercised in selecting health endpoints to include in the overall 

benefits analysis because of the possibility of double-counting of benefits. 

groups. For this reason, we consider very carefully the set of studies available examining each 

endpoint and select a consistent subset that provides a good balance of population coverage 
1-58

and match with the pollutant of interest. In many cases, either because of a lack of multiple 

studies, consistency problems, or clear superiority in the quality or comprehensiveness of one 

study over others, a single published study is selected as the basis of the effect estimate.

When several effect estimates for a pollutant and a given health endpoint have been 

selected, they are quantitatively combined or pooled to derive a more robust estimate of the 

relationship. The BenMAP Manual Technical Appendices provides details of the procedures 

used to combine multiple impact functions (Abt Associates, 2012). In general, we used fixed or 

random effects models to pool estimates from different single city studies of the same 

endpoint. Fixed effects pooling simply weights each study’s estimate by the inverse variance, 

giving more weight to studies with greater statistical power (lower variance). Random effects 

pooling accounts for both within-study variance and between-study variability, due, for 

example, to differences in population susceptibility. We used the fixed effects model as our null 

hypothesis and then determined whether the data suggest that we should reject this null 

hypothesis, in which case we would use the random effects model. 13 Pooled impact functions 

are used to estimate hospital admissions and asthma exacerbations. When combining evidence 

across multi-city studies (e.g., cardiovascular hospital admission studies), we use equal weights 

pooling. The effect estimates drawn from each multi-city study are themselves pooled across a 

large number of urban areas. For this reason, we elected to give each study an equal weight 

rather than weighting by the inverse of the variance reported in each study. For more details on 

methods used to pool incidence estimates, see the BenMAP Manual Appendices (Abt 

Associates, 2012).

Effect estimates selected for a given health endpoint were applied consistently across all 

locations nationwide. This applies to both impact functions defined by a single effect estimate 

and those defined by a pooling of multiple effect estimates. Although the effect estimate may, 

in fact, vary from one location to another (e.g., because of differences in population 

susceptibilities or differences in the composition of PM), location-specific effect estimates are 

generally not available.

The specific studies from which effect estimates for the core analysis are drawn are 

included in Table 5-6. We highlight in blue those studies that have been added since the 

benefits analysis conducted for the MATS RIA (U.S. EPA, 2011d), and we highlight those studies 

 

13 EPA recently changed the algorithm BenMAP uses to calculate study variance, which is used in the pooling 

process. Prior versions of the model calculated population variance, while the version used here calculated 

sample variance. This change did not affect the selection of random or fixed effects for the pooled incidence 

estimates between the proposal and final RIA. 
1-59

in red that have been added since the proposal RIA (U.S. EPA, 2012a). In all cases where effect 

estimates are drawn directly from epidemiological studies, standard errors are used as a partial 

representation of the uncertainty in the size of the effect estimate. Table 5-7 summarizes those 

health endpoints and studies we have included as in sensitivity analyses.

Table 5-6. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts 

in the Core Analysis a

Endpoint Study Study Population

Risk Estimate

(95th Percentile 

Confidence Interval)a

Premature Mortality

Premature 

mortality—cohort 

study, all-cause

Krewski et al. (2009) >29 years

>24 years

RR = 1.06 (1.04–1.06) per 

10 μg/m3

Lepeule et al. (2012) RR = 1.14 (1.07–1.22) per 

10 μg/m3

Premature mortality, 

total exposures

PM2.5 Expert Elicitation (Roman et al., 

2008)

>24 years Varies by expert

Premature 

mortality—all-cause

Woodruff et al. (1997) Infant (<1 year) OR = 1.04 (1.02–1.07) per 

10 μg/m3

Chronic Illness

Nonfatal heart 

attacks

Peters et al. (2001) Adults (>18 

years)

OR = 1.62 (1.13–2.34) per 

20 μg/m3

Pooled estimate:

Pope et al. (2006) β = 0.00481 (0.00199)

Sullivan et al. (2005) β = 0.00198 (0.00224)

Nonfatal heart 

attacks (cont’d)

Zanobetti et al. (2009) β = 0.00225 (0.000591)

Zanobetti and Schwartz (2006) β = 0.0053 (0.00221)

Hospital Admissions 

Respiratory Zanobetti et al. (2009)—ICD 460-519 (All 

respiratory)

>64 years β=0.00207 (0.00446)

Kloog et al. (2012)—ICD 460-519 (All 

Respiratory

β=0.0007 (0.000961)

Moolgavkar (2000)—ICD 490–496 

(Chronic lung disease)

18–64 years 1.02 (1.01–1.03) per 

36 μg/m3

Babin et al. (2007)—ICD 493 (asthma) <19 β=0.002 (0.004337)

Sheppard (2003)—ICD 493 (asthma) < 18 RR = 1.04 (1.01–1.06) per 

11.8 μg/m3

(continued)

Table 5-6. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts 

in the Core Analysis a(continued)

Endpoint Study

Study 

Population

Risk Estimate

(95th Percentile 

Confidence Interval)a

Cardiovascular Pooled estimate:

Zanobetti et al. (2009)—ICD 390-459 (all 

cardiovascular)

>64 years

β=0.00189 (0.000283)

Peng et al. (2009)—ICD 426-427; 428; 430-438; 

410-414; 429; 440-449 (Cardio-, cerebro- and 

β=0.00068

(0.000214)
1-60

peripheral vascular disease)

Peng et al. (2008)—ICD 426-427; 428; 430-438; 

410-414; 429; 440-449 (Cardio-, cerebro- and 

peripheral vascular disease)

β=0.00071

(0.00013)

Bell et al. (2008)—ICD 426-427; 428; 430-438; 

410-414; 429; 440-449 (Cardio-, cerebro- and 

peripheral vascular disease)

β=0.0008

(0.000107)

Moolgavkar (2000)—ICD 390–429 (all 

cardiovascular)

20–64 years RR=1.04 (t statistic: 

4.1) per 10 μg/m3

Asthma-related ER 

visits

Pooled estimate:

Mar et al. (2010)

All ages RR = 1.04 (1.01–1.07) 

per 7 μg/m3

Slaughter et al. (2005) RR = 1.03 (0.98–1.09) 

per 10 μg/m3

Glad et al. (2012) β=0.00392 (0.002843)

Other Health Endpoints

Acute bronchitis Dockery et al. (1996) 8–12 years OR = 1.50 (0.91–2.47) 

per 14.9 μg/m3

Asthma 

exacerbations

Pooled estimate:

Ostro et al. (2001) (cough, wheeze and 

shortness of breath) b

6–18 years b

OR = 1.03 (0.98–1.07) 

OR = 1.06 (1.01–1.11) 

OR = 1.08 (1.00–1.17) 

per 30 μg/m3

Mar et al. (2004) (cough, shortness of breath) RR = 1.21 (1–1.47) per 

RR = 1.13 (0.86–1.48) 

per 10 μg/m3

Work loss days Ostro (1987) 18–65 years β=0.0046 (0.00036)

Acute respiratory 

symptoms

Ostro and Rothschild (1989) (Minor restricted 

activity days) 18–65 years β=0.00220 (0.000658)

Upper respiratory 

symptoms

Pope et al. (1991) Asthmatics, 9–

11 years

1.003 (1–1.006) per 

10 μg/m3

Lower respiratory 

symptoms

Schwartz and Neas (2000) 7–14 years OR = 1.11 (1.58–1.58) 

per 15 μg/m3

a

Studies highlighted in blue represent updates incorporated since the RIA for MATS (U.S. EPA, 2011d).

Studies highlighted in red represent updates incorporated since the proposal RIA (U.S. EPA, 2012a).

b

The original study populations were 8 to 13 for the Ostro et al. (2001) study and 7 to 12 for the Mar et al. 

(2004) study. Based on advice from the SAB-HES, we extended the applied population to 6-18, reflecting the 

common biological basis for the effect in children in the broader age group. See: U.S. EPA-SAB (2004) and NRC 

(2002).
1-61

Table 5-7. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts 

in the Sensitivity Analysisa

Endpoint Study Study Population

Chronic Illness

Chronic bronchitis Abbey et al. (1995) >26 years

Stroke Miller et al. (2007) 50–79 years

Hospital Admissions

Cardiovascular ED Visits Metzger et al. (2004) 0–99

Tolbert et al. (2007) 0–99

Mathes et al. (2011) 40–99

a

Studies highlighted in blue represent updates incorporated since the RIA for MATS (U.S. EPA, 2011d).

Studies highlighted in red represent updated incorporated since the proposal RIA (U.S. EPA, 2012a).

5.6.3.1 PM2.5 Premature Mortality Effect Coefficients

Core Mortality Effect Coefficients for Adults. A substantial body of published scientific 

literature documents the association between elevated PM2.5 concentrations and increased 

premature mortality (U.S. EPA, 2009b). This body of literature reflects thousands of 

epidemiology, toxicology, and clinical studies. The PM ISA completed as part of the this review 

of the PM standards, which was twice reviewed by the SAB-CASAC (U.S. EPA-SAB, 2009b, 

2009c), concluded that there is a causal relationship between mortality and both long-term and 

short-term exposure to PM2.5 based on the entire body of scientific evidence (U.S. EPA, 2009b). 

The size of the mortality effect estimates from epidemiological studies, the serious nature of 

the effect itself, and the high monetary value ascribed to prolonging life make mortality risk 

reduction the most significant health endpoint quantified in this analysis. 

Researchers have found statistically significant associations between PM2.5 and 

premature mortality using different types of study designs. Time-series methods have been 

used to relate short-term (often day-to-day) changes in PM2.5 concentrations and changes in 

daily mortality rates up to several days after a period of elevated PM2.5 concentrations. Cohort 

methods have been used to examine the potential relationship between community-level PM2.5 

exposures over multiple years (i.e., long-term exposures) and community-level annual mortality 

rates that have been adjusted for individual level risk factors. When choosing between using 

short-term studies or cohort studies for estimating mortality benefits, cohort analyses are 

thought to capture more of the public health impact of exposure to air pollution over time 

because they account for the effects of long-term exposures as well as some fraction of shortterm exposures (Kunzli et al., 2001; NRC, 2002). The NRC stated that “it is essential to use the 

cohort studies in benefits analysis to capture all important effects from air pollution exposure”
1-62

(NRC, 2002, p. 108). The NRC further notes that “the overall effect estimates may be a 

combination of effects from long-term exposure plus some fraction from short-term exposure. 

The amount of overlap is unknown” (NRC, 2002, p. 108-9). To avoid double counting, we focus 

on applying the risk coefficients from the long-term cohort studies in estimating the mortality 

impacts of reductions in PM2.5.

Over the last two decades, several studies using “prospective cohort” designs have been 

published that are consistent with the earlier body of literature. Two prospective cohort 

studies, often referred to as the Harvard “Six Cities Study” (Dockery et al., 1993; Laden et al., 

2006; Lepeule et al., 2012) and the “American Cancer Society” or “ACS study” (Pope et al., 

1995; Pope et al., 2002; Pope et al., 2004; Krewski et al., 2009), provide the most extensive 

analyses of ambient PM2.5 concentrations and mortality. These studies have found consistent 

relationships between fine particle indicators and premature mortality across multiple locations 

in the United States. The credibility of these two studies is further enhanced by the fact that the 

initial published studies (Pope et al., 1995; Dockery et al., 1993) were subject to extensive 

reexamination and reanalysis by an independent team of scientific experts commissioned by 

the Health Effect Institute (HEI) and by a Special Panel of the HEI Health Review Committee 

(Krewski et al., 2000). Publication of studies confirming and extending the findings of the 1993 

Six Cities Study and the 1995 ACS study using more recent air quality and a longer follow-up 

period for the ACS cohort provides additional validation of the findings of these original studies 

(Pope et al., 2002, 2004; Laden et al., 2006; Krewski et al., 2009; Lepeule et al., 2012). The SABHES also supported using these two cohorts for analyses of the benefits of PM reductions, and 

concluded, “the selection of these cohort studies as the underlying basis for PM mortality 

benefit estimates to be a good choice. These are widely cited, well studied and extensively 

reviewed data sets” (U.S. EPA-SAB, 2010a). As both the ACS and Six Cities studies have inherent 

strengths and weaknesses, we present benefits estimates using relative risk estimates from the 

most recent extended reanalysis of these cohorts (Krewski et al., 2009; Lepeule et al., 2012). 

Presenting results using both ACS and Six Cities is consistent with other recent RIAs (e.g., U.S. 

EPA, 2006a, 2010c, 2011c, 2011d). The PM ISA concludes that the ACS and Six Cities cohorts 

provide the strongest evidence of the association between long-term PM2.5 exposure and 

premature mortality with support from a number of additional cohort studies (described 

below).

The extended analyses of the ACS cohort data (Krewski et al., 2009) provides additional 

refinements to the analysis of PM-related mortality by (a) extending the follow-up period by 

2 years to the year 2000, for a total of 18 years; (b) incorporating almost double the number of 
1-63

urban areas (c) addressing confounding by spatial autocorrelation by incorporating ecological, 

or community-level, co-variates; (d) performing an extensive spatial analysis using land use 

regression modeling in two large urban areas. These enhancements make this analysis wellsuited for the assessment of mortality risk from long-term PM2.5 exposures for the EPA’s

benefits analyses. 

In 2009, the SAB-HES again reviewed the choice of mortality risk coefficients for benefits 

analysis, concluding that “[t]he Krewski et al. (2009) findings, while informative, have not yet 

undergone the same degree of peer review as have the aforementioned studies. Thus, the SABHES recommends that EPA not use the Krewski et al. (2009) findings for generating the Primary 

Estimate” (U.S. EPA-SAB, 2010a). Since this time, the Krewski et al. (2009) has undergone 

additional peer review, which we believe strengthens the support for including this study in this 

RIA. For example, the PM ISA (U.S. EPA, 2009b) included this study among the key mortality 

studies. In addition, the risk assessment supporting the PM NAAQS (U.S. EPA, 2010b) utilized 

risk coefficients drawn from the Krewski et al. (2009) study, the most recent reanalysis of the 

ACS cohort data. The risk assessment cited a number of advantages that informed the selection 

of the Krewski et al. (2009) study as the source of the core effect estimates, including the 

extended period of observation, the rigorous examination of model forms and effect estimates, 

the coverage for ecological variables, and the large dataset with over 1.2 million individuals and 

156 MSAs (U.S. EPA, 2010b). The SAB-CASAC also provided extensive peer review of the risk 

assessment and supported the use of effect estimates from this study (U.S. EPA-SAB, 2009a, 

2010b, c). 

Consistent with the Quantitative Health Risk Assessment for Particulate Matter (U.S. 

EPA, 2010b) which was reviewed by the SAB-CASAC (U.S. EPA-SAB, 2009), we use the all-cause 

mortality risk estimate based on the random-effects Cox proportional hazard model that 

incorporates 44 individual and 7 ecological covariates (RR=1.06, 95% confidence intervals 1.04–

1.08 per 10μg/m3increase in PM2.5). The relative risk estimate (1.06 per 10μg/m3increase in 

PM2.5) is identical to the risk estimate drawn from the earlier Pope et al. (2002) study, though 

the confidence interval around the Krewski et al. (2009) risk estimate is tighter.

In the most recent Six Cities study, which was published after the last SAB-HES review, 

Lepeule et al. (2012) evaluated the sensitivity of previous Six Cities results to model 

specifications, lower exposures, and averaging time using eleven additional years of cohort 

follow-up that incorporated recent lower exposures. The authors found significant associations 

between PM2.5 exposure and increased risk of all-cause, cardiovascular and lung cancer 

mortality. The authors also concluded that the concentration-response relationship was linear 
1-64

down to PM2.5 concentrations of 8 μg/m3, and that mortality rate ratios for PM2.5 fluctuated 

over time, but without clear trends, despite a substantial drop in the sulfate fraction. We use 

the all-cause mortality risk estimate based on a Cox proportional hazard model that 

incorporates 3 individual covariates. (RR=1.14, 95% confidence intervals 1.07–1.22 per 10 

μg/m3increase in PM2.5). The relative risk estimate is slightly smaller than the risk estimate 

drawn from Laden et al. (2006), with relatively smaller confidence intervals.

Implicit in the calculation of PM2.5-related premature mortality impacts are several key 

assumptions, which are described in further detail later in this chapter. First, we assume that 

there is a “cessation” lag in time between the reduction in PM exposure and the full reduction 

in mortality risk that affects the timing (and thus discounted monetary valuation) of the 

resulting premature deaths (see section 5.6.6.1). Second, following conclusions of the PM ISA, 

we assume that all fine particles are equally potent in causing premature mortality (see section 

5.7.2). Third, following conclusions of the PM ISA, we assume that the health impact function 

for fine particles is linear within the range of ambient concentrations affected by these 

standards (see section 5.7.4).

Alternate Mortality Effect Coefficients for Adults. In addition to the ACS and Six Cities 

cohorts, several recent cohort studies conducted in North America provide evidence for the 

relationship between long-term exposure to PM2.5 and the risk of premature death. Many of 

these additional cohort studies are described in the PM ISA (U.S. EPA, 2009) and the Provisional 

Assessment (U.S. EPA, 2012b) (and thus not summarized here).14,15 Table 5-8 provides the 

effect estimates from each of these cohort studies for all-cause, cardiovascular, 

cardiopulmonary, and ischemic heart disease (IHD) mortality as well as the lowest measured air 

quality level (LML) and mean concentration in the study. 

We also draw upon the results of the 2006 expert elicitation sponsored by the EPA

(Roman et al., 2008; IEc, 2006) to demonstrate the sensitivity of the benefits estimates to 12 

expert-defined concentration-response functions. The PM2.5 expert elicitation and the 

derivation of effect estimates from the expert elicitation results are described in detail in the 

 

14 It is important to note that the newer studies in the Provisional Assessment are published in peer-reviewed 

journals and meet our study selection criteria, but they have not been assessed in the context of an Integrated 

Science Assessment nor gone through review by the SAB. In addition, only the ACS and H6C cohort studies have 

been recommended by the SAB as appropriate for benefits analysis of national rulemakings.

15 In this chapter, we only describe multi-state cohort studies. There are additional cohort studies that we have not 

included in this list, including cohort studies that focus on single cities (e.g., Gan et al., 2012) and cohort studies 

focusing on methods development. In Appendix 5A, we provide additional information regarding cohort studies in 

California, which is the only state for which we identified single state cohorts. 
1-65

2006 PM2.5 NAAQS RIA (U.S. EPA, 2006a), the elicitation summary report (IEc, 2006) and Roman 

et al. (2008), and so we summarize here the key attributes of this study relative to the 

interpretation of the estimates of PM-related mortality reported here. We describe also how 

the epidemiological literature has evolved since the expert elicitation was conducted in 2005 

and 2006.

Table 5-8. Summary of Effect estimates from Associated With Change in Long-Term 

Exposure to PM2.5 in Recent Cohort Studies in North America

Study Cohort (age)

LML 

(μg/m3)

Mean

(μg/m3)

Hazard Ratios per 10 μg/m3Change in PM2.5

(95th percentile confidence intervals)

All Causes Cardiovascular Cardiopulmonary IHD

Pope et al. 

(2002)

ACS 

(age >30)

7.5 18.2 1.06

(1.02–1.11)

1.12

(1.08–1.15)

1.09

(1.03–1.16)

N/A

Laden et al. 

(2006)

Six Cities

(age > 25)

10 16.4 1.16

(1.07–1.26)

1.28

(1.13–1.44)

N/A N/A

Lipfert et al. 

(2006)a

Veterans

(age 39–63)

<14.1 14.3 1.15

(1.05–1.25)

N/A N/A N/A

Miller et al. 

(2007)b

WHI

(age 50–79)

3.4 13.5 N/A 1.76

(1.25–2.47)

N/A 2.21

(1.17–4.16)

Eftim et al. 

(2008)

Medicare (age 

> 65)

6 13.6 1.21

(1.15–1.27)

N/A N/A N/A

Zeger et al. 

(2008)c

Medicare (age 

> 65)

<9.8 13.2 1.068

(1.049–1.087)

N/A N/A N/A

Krewski et 

al. (2009)d

ACS

(age >30)

5.8 14 1.06

(1.04–1.08)

N/A 1.13

(1.10–1.16)

1.24

(1.19–1.29)

Puett et al. 

(2009)b

NHS 

(age 30–55)

5.8 13.9 1.26

(1.02–1.54)

N/A N/A 2.02

(1.07–3.78)

Crouse et 

al. (2011)d,e

Canadian 

census

1.9 8.7 1.06

(1.01–1.10)

N/A N/A N/A

Puett et al. 

(2011)f

Health 

Professionals 

(age 40–75)

<14.4 17.8 0.86

(0.70–1.00)

1.02

(0.84–1.23)

N/A N/A

Lepeule et 

al. (2012)d

Six Cities

(age > 25)

8 15.9 1.14

(1.07–1.22)

1.26

(1.14–1.40)

N/A N/A

a

Low socio-economic status (SES) men only. Used traffic proximity as a surrogate of exposure. 

b Women only.

c

Reflects risks in the Eastern U.S. Risks in the Central U.S. were higher, but the authors found no 

association in the Western U.S.

d

Random effects Cox model with individual and ecologic covariates.

e

Canadian population.

f Men with high socioeconomic status only.

The primary goal of the 2006 study was to elicit from a sample of health experts 

probabilistic distributions describing uncertainty in estimates of the reduction in mortality 

Formatted: Spanish (International

Sort)
1-66

among the adult U.S. population resulting from reductions in ambient annual average PM2.5 

levels. These distributions were obtained through a formal interview protocol using methods 

designed to elicit subjective expert judgments. These experts were selected through a peernomination process and included experts in epidemiology, toxicology, and medicine. The

elicitation interview consisted of a protocol of carefully structured questions, both qualitative 

and quantitative, about the nature of the PM2.5-mortality relationship designed to build twelve 

individual distributions for the coefficient (or slope) of the C-R function relating changes in 

annual average PM2.5 exposures to annual, adult all-cause mortality. The elicitation also 

provided useful information regarding uncertainty characterization in the PM2.5-mortality 

relationship. Specifically, during their interviews, the experts highlighted several uncertainties 

inherent within the epidemiology literature, such as causality, concentration thresholds, effect 

modification, the role of short- and long-term exposures, potential confounding, and exposure 

misclassification. In Appendix 5c, we evaluate each of these uncertainties in the context of this 

health impact assessment. For several of these uncertainties, such as causality, we are able to 

use the expert-derived functions to quantify the impacts of applying different assumptions. The 

elicitation received favorable peer review in 2006 (Mansfield and Patil, 2006).

Prior to providing a quantitative estimate of the risk of premature death associated with 

long-term PM2.5 exposure, the experts answered a series of “conditioning questions.” One such 

question asked the experts to identify which epidemiological studies they found most 

informative. The “ideal study attributes”

16 according to the experts included:

 Geographic representation of the entire U.S. (e.g., monitoring sites across the 

country)

 Collection of information on individual risk factors and residential information both 

at the beginning and throughout the follow-up period

 Large sample size that is representative of the general U.S. population

 Collection of genetic information from cohort members to identify and assess 

potential effect modifiers

 Monitoring of individual exposures (e.g., with a personal monitor)

 Collection of data on levels of several co-pollutants (not only those that are 

monitored for compliance purposes

 Accurate characterization of outcome (i.e., cause of death)

 

16 These criteria are substantively similar to EPA’s study selection criteria identified in Table 5-5 of this chapter.
1-67

 Follow-up for a long period of time, up to a lifetime

 Prospective study design

Although no single epidemiological study completely satisfies each of these criteria, the 

experts determined that the ACS and Six Cities cohort studies best satisfy a majority of these 

ideal attributes. To varying degrees the studies examining these two cohorts: are geographically 

representative; have collected information on individual risk factors; include a large sample 

size; have collected data on co-pollutants in the case of the ACS study; have accurately 

characterized the health outcome; include a long (and growing) follow-up period; and, are 

prospective in nature. The experts also noted a series of limitations in these two cohort studies. 

In the case of the Six Cities study (Laden et al., 2006), the experts identified the “small sample 

size, limited number of cities, and concerns about representativeness of the six cities for the 

U.S. as a whole” as weaknesses. When considering the ACS study (Pope et al., 2002), the 

experts indicated that the “method of recruitment for the study, which resulted in a group with 

higher income, more education, and a greater proportion of whites than is representative of 

the general U.S. population” represented a shortcoming. Several experts also argued that 

because the ACS study relied upon “...whatever monitors were available to the study...a single 

monitor represent[ed] exposure for an entire metropolitan area...whereas [the Six Cities study] 

often had exposures assigned at the county level.” Despite these limitations, the experts 

considered the Pope et al. (2002) extended analysis of the ACS cohort and the Laden et al. 

(2006) extended analysis of the Six Cities cohort to be particularly influential in their opinions 

(see Exhibit 3-3 of the elicitation summary report [IEc, 2006]).

Since the completion of the EPA’s expert elicitation in 2006, additional epidemiology 

literature has become available, including 9 new multi-state cohort studies shown in Table 5-8. 

This newer literature addresses some of the weaknesses identified in the prior literature. For 

example, in an attempt to improve its characterization of population exposure the most recent 

extended analysis of the ACS cohort Krewski et al. (2009) incorporates two case studies that 

employ more spatially resolved estimates of population exposure. 

In light of the availability of this newer literature, we have updated the presentation of 

results in the RIA. Specifically, we focus the core analysis on results derived from the two most 

recent studies of the ACS and Six Cities cohorts (Krewski et al., 2009; Lepeule et al., 2012). 

Because the other multi-state cohorts generally have limited geography and age/gender 

representativeness, these limitations preclude us from using these studies in our core benefits 

results, and we instead present the risk coefficients from these other multi-state cohorts in 
1-68

Table 5-8. In addition, we now present the full distributions of the expert-derived results in a 

probabilistic graphic (rather than cascading the expert-derived results throughout the results 

tables as done in prior RIAs). We do not combine the expert results in order to preserve the 

breadth and diversity of opinion on the expert panel (Roman et. al., 2006). This presentation of 

the expert-derived results is generally consistent with SAB advice (U.S. EPA-SAB, 2008), which 

recommended that the EPA emphasize that “scientific differences existed only with respect to 

the magnitude of the effect of PM2.5 on mortality, not whether such an effect existed” and that 

the expert elicitation “supports the conclusion that the benefits of PM2.5 control are very likely 

to be substantial”. Although it is possible that the newer literature could revise the experts’ 

quantitative responses if elicited again, we believe that these general conclusions are unlikely 

to change.

Mortality Effect Coefficients for Infants. In addition to the adult mortality studies 

described above, several studies show an association between PM exposure and premature 

mortality in children under 5 years of age.17 The PM ISA states that less evidence is available 

regarding the potential impact of PM2.5 exposure on infant mortality than on adult mortality 

and the results of studies in several countries include a range of findings with some finding 

significant associations. Specifically, the PM ISA concluded that evidence exists for a stronger 

effect at the post-neonatal period and for respiratory-related mortality, although this trend is 

not consistent across all studies. In addition, compared to avoided premature deaths estimated

for adult mortality, avoided premature deaths for infants are significantly smaller because the 

number of infants in the population is much smaller than the number of adults and the 

epidemiology studies on infant mortality provide smaller risk coefficients associated with 

exposure to PM2.5.

In 2004, the SAB-HES noted the release of the WHO Global Burden of Disease Study 

focusing on ambient air, which cites several recently published time-series studies relating daily 

PM exposure to mortality in children (U.S. EPA-SAB, 2004a). The SAB-HES also cites the study by 

Belanger et al. (2003) as corroborating findings linking PM exposure to increased respiratory 

inflammation and infections in children. A study by Chay and Greenstone (2003) found that 

reductions in TSP caused by the recession of 1981–1982 were statistically associated with

reductions in infant mortality at the county level. With regard to the cohort study conducted by 

Woodruff et al. (1997), the SAB-HES notes several strengths of the study, including the use of a 

larger cohort drawn from a large number of metropolitan areas and efforts to control for a 

 

17 For the purposes of this analysis, we only calculate benefits for infants age 0–1, not all children under 5 years 

old.
1-69

variety of individual risk factors in infants (e.g., maternal educational level, maternal ethnicity, 

parental marital status, and maternal smoking status). Based on these findings, the SAB-HES 

recommended that the EPA incorporate infant mortality into the primary benefits estimate and 

that infant mortality be evaluated using an impact function developed from the Woodruff et al. 

(1997) study (U.S. EPA-SAB, 2004a).

In 2010, the SAB-HES again noted the increasing body of literature relating infant 

mortality and PM exposure and supported the inclusion of infant mortality in the monetized 

benefits (U.S. EPA-SAB, 2010a). The SAB-HES generally supported the approach of estimating 

infant mortality based on Woodruff et al. (1997) and noted that a more recent study by 

Woodruff et al. (2006) continued to find associations between PM2.5 and infant mortality in 

California. The SAB-HES also noted, “when PM10 results are scaled to estimate PM2.5 impacts, 

the results yield similar risk estimates.” Consistent with the Costs and Benefits of the Clean Air 

Act (U.S. EPA, 2011a), we continue to rely on the earlier 1997 study in part due to the national–

scale of the earlier study.

5.6.3.2 Nonfatal Acute Myocardial Infarctions (AMI) (Heart Attacks)

Nonfatal heart attacks have been linked with short-term exposures to PM2.5 in the 

United States (Mustafić et al., 2012; Peters et al., 2001; Sullivan et al., 2005; Pope et al., 2006; 

Zanobetti and Schwartz, 2006; Zanobetti et al., 2009) and other countries (Poloniecki et al., 

1997; Barnett et al., 2006; Peters et al., 2005). In previous health impact assessments, we have 

relied upon a study by Peters et al. (2001) as the basis for the impact function estimating the 

relationship between PM2.5 and nonfatal heart attacks. The Peters et al. (2001) study exhibits a 

number of strengths. In particular, it includes a robust characterization of populations 

experiencing acute myocardial infarctions (AMIs). The researchers interviewed patients within 4 

days of their AMI events and, for inclusion in the study, patients were required to meet a series 

of criteria including minimum kinase levels, an identifiable onset of pain or other symptoms and 

the ability to indicate the time, place and other characteristics of their AMI pain in an interview.

Since the publication of Peters et al. (2001), a number of other single and multi-city 

studies have appeared in the literature. These studies include Sullivan et al. (2005), which 

considered the risk of PM2.5-related hospitalization for AMIs in King County, WA; Pope et al. 

(2006), based in Wasatch Range, UT; Zanobetti and Schwartz (2006), based in Boston, MA; and, 

Zanobetti et al. (2009), a multi-city study of 26 U.S. communities. Each of these single and 

multi-city studies, with the exception of Pope et al. (2006), measure AMIs using hospital 

discharge rates. Conversely, the Pope et al. (2006) study is based on a large registry with 
1-70

angiographically characterized patients—arguably a more precise indicator of AMI. Because the 

Pope et al. (2006) study reflected both myocardial infarctions and unstable angina, this 

produces a more comprehensive estimate of acute ischemic heart disease events than the 

other studies. However, unlike the Peters study (Peters et al., 2006), Pope and colleagues did 

not measure the time of symptom onset, and PM2.5 data were not measured on an hourly basis.

As a means of recognizing the strengths of the Peters study while also incorporating the 

newer evidence found in the four single and multi-city studies, we present a range of AMI 

estimates. The upper end of the range is calculated using the Peters study while the lower end 

of the range is the result of an equal-weights pooling of these four newer studies. It is 

important to note that when calculating the incidence of nonfatal AMI, the fraction of fatal 

heart attacks is subtracted to ensure that there is no double-counting with premature mortality 

estimates. Specifically, we apply an adjustment factor in the concentration-response function 

to reflect the probability of surviving a heart attack. Based on recent data from the Agency for 

Healthcare Research and Quality’s Healthcare Utilization Project National Inpatient Sample 

database (AHRQ, 2009), we identified death rates for adults hospitalized with acute myocardial 

infarction stratified by age (e.g., 1.852% for ages 18–44, 2.8188% for ages 45–64, and 7.4339% 

for ages 65+). These rates show a clear downward trend over time between 1994 and 2009 for 

the average adult and thus replace the 7% survival rate previously applied across all age groups 

from Rosamond et al. (1999).

5.6.3.3 Hospital Admissions and Emergency Department Visits

Because of the availability of detailed hospital admission and discharge records, there is 

an extensive body of literature examining the relationship between hospital admissions and air 

pollution. For this reason, we pool together the incidence estimates using several different 

studies for many of the hospital admission endpoints. In addition, some studies have examined 

the relationship between air pollution and emergency department visits. Since most emergency 

department visits do not result in an admission to the hospital (i.e., most people going to the 

emergency department are treated and return home), we treat hospital admissions and 

emergency department visits separately, taking account of the fraction of emergency 

department visits that are admitted to the hospital. Specifically, within the baseline incidence 

rates, we parse out the scheduled hospital visits from unscheduled ones as well as the hospital 

visits that originated in the emergency department.

The two main groups of hospital admissions estimated in this analysis are respiratory 

admissions and cardiovascular admissions. There is not much evidence linking PM2.5 with other 
1-71

types of hospital admissions. Both asthma- and cardiovascular-related visits have been linked to 

PM2.5 in the United States, though as we note below, we are able to assign an economic value 

to asthma-related events only. To estimate the effects of PM2.5 air pollution reductions on 

asthma-related ER visits, we use the effect estimate from a study of children 18 and under by 

Mar et al. (2010), Slaughter et al. (2005), and Glad et al. (2012). The first two studies examined

populations 0 to 99 in Washington State, while Glad et al. examined populations 0-99 in 

Pittsburgh, PA. Mar and colleagues perform their study in Tacoma, while Slaughter and 

colleagues base their study in Spokane. We apply random/fixed effects pooling to combine 

evidence across these two studies.

To estimate avoided incidences of cardiovascular hospital admissions associated with 

PM2.5, we used studies by Moolgavkar (2000), Zanobetti et al. (2009), Peng et al. (2008, 2009) 

and Bell et al., (2008). Only Moolgavkar (2000) provided a separate effect estimate for adults 20 

to 64, while the remainder estimate risk among adults over 64.18 Total cardiovascular hospital 

admissions are thus the sum of the pooled estimate for adults over 65 and the single study 

estimate for adults 20 to 64. Cardiovascular hospital admissions include admissions for 

myocardial infarctions. To avoid double-counting benefits from reductions in myocardial 

infarctions when applying the impact function for cardiovascular hospital admissions, we first 

adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial 

infarctions. We applied equal weights pooling to the multi-city studies assessing risk among 

adults over 64 because these studies already incorporated pooling across the city-level 

estimates. One potential limitation of our approach is that while the Zanobetti et al. (2009) 

study assesses all cardiovascular risk, Bell et al. (2008), and Peng et al., (2008, 2009) studies 

estimate a subset of cardiovascular hospitalizations as well as certain cerebro- and peripheralvascular diseases. To address the potential for the pooling of these four studies to produce a 

biased estimate, we match the pooled risk estimate with a baseline incidence rate that excludes 

cerebro- and peripheral-vascular disease. An alternative approach would be to use the 

Zanobetti et al. (2009) study alone, though this would prevent us from drawing upon the 

strengths of the three multi-city studies.

 

18 Note that the Moolgavkar (2000) study has not been updated to reflect the more stringent GAM convergence 

criteria. However, given that no other estimates are available for this age group, we chose to use the existing 

study. Given the very small (<5%) difference in the effect estimates for people 65 and older with cardiovascular 

hospital admissions between the original and reanalyzed results, we do not expect this choice to introduce much 

bias. For a discussion of the GAM convergence criteria, and how it affected the size of effect coefficients reported 

by time series epidemiological studies using NMMAPS data, see: http://www.healtheffects.org/Pubs/sttimeseries.htm.
1-72

To estimate avoided incidences of respiratory hospital admissions associated with PM2.5, 

we used a number of studies examining total respiratory hospital admissions as well as asthma 

and chronic lung disease. We estimated impacts among three age groups: adults over 65, adults 

18 to 64 and children 0 to 17. For adults over 65, the multi-city studies by Zanobetti et al. 

(2009) and Kloog et al. (2012) provide effect coefficients for total respiratory hospital 

admissions (defined as ICD codes 460–519). We pool these two studies using equal weights. 

Moolgavkar et al. (2003) examines PM2.5 and chronic lung disease hospital admissions (less 

asthma) in Los Angeles, CA among adults 18 to 64. For children 0 to 18, we pool two studies 

using random/fixed effects. The first is Babin et al. (2007) which assessed PM2.5 and asthma 

hospital admissions in Washington, DC among children 1 to 18; we adjusted the age range for 

this study to apply to children 0 to 18. The second is Sheppard et al. (2003) which assessed 

PM2.5 and asthma hospitalizations in Seattle, Washington, among children 0 to 18.

5.6.3.4 Acute Health Events and School/Work Loss Days

In addition to mortality, chronic illness, and hospital admissions, a number of acute 

health effects not requiring hospitalization are associated with exposure to PM2.5. The sources 

for the effect estimates used to quantify these effects are described below.

Approximately 4% of U.S. children between the ages of 5 and 17 experience episodes of 

acute bronchitis annually (ALA, 2002). Acute bronchitis is characterized by coughing, chest 

discomfort, slight fever, and extreme tiredness, lasting for a number of days. According to the 

MedlinePlus medical encyclopedia,19 with the exception of cough, most acute bronchitis 

symptoms abate within 7 to 10 days. Incidence of episodes of acute bronchitis in children 

between the ages of 5 and 17 were estimated using an effect estimate developed from Dockery 

et al. (1996). Health effects from air pollution can also result in missed days of work (either 

from personal symptoms or from caring for a sick family member). Days of work lost due to 

PM2.5 were estimated using an effect estimate developed from Ostro (1987). Children may also

be absent from school because of respiratory or other diseases caused by exposure to air 

pollution, but we have not quantified these effects for this rule.

We estimate three types of acute respiratory symptoms: lower respiratory symptoms, 

upper respiratory symptoms, and minor restricted activity days (MRAD). Incidences of lower 

respiratory symptoms (e.g., wheezing, deep cough) in children aged 7 to 14 were estimated 

using an effect estimate from Schwartz and Neas (2000). Incidences of upper respiratory 

symptoms in asthmatic children aged 9 to 11 are estimated using an effect estimate developed 

 

19 See http://www.nlm.nih.gov/medlineplus/ency/article/001087.htm, accessed April 2012. 
1-73

from Pope et al. (1991). Because asthmatics have greater sensitivity to stimuli (including air 

pollution), children with asthma can be more susceptible to a variety of upper respiratory 

symptoms (e.g., runny or stuffy nose; wet cough; and burning, aching, or red eyes). Research on 

the effects of air pollution on upper respiratory symptoms has thus focused on effects in 

asthmatics. 

Minor restricted activity days (MRAD) result when individuals reduce most usual daily 

activities and replace them with less strenuous activities or rest, yet not to the point of missing 

work or school. For example, a mechanic who would usually be doing physical work most of the 

day will instead spend the day at a desk doing paper and phone work because of difficulty 

breathing or chest pain. The effect of PM2.5 on MRAD was estimated using an effect estimate 

derived from Ostro and Rothschild (1989).

More recently published literature examining the relationship between short-term PM2.5 

exposure and acute respiratory symptoms was available in the PM ISA (U.S. EPA, 2009), but 

proved to be unsuitable for use in this benefits analysis. In particular, the best available study 

(Patel et al., 2010) specified a population aged 13–20, which overlaps with the population in 

which we asses asthma exacerbation. As we describe in detail below, to avoid the chance of 

double-counting impacts, we do not estimate changes in acute respiratory symptoms and 

asthma exacerbation among populations of the same age.

For this RIA, we have followed the SAB-HES recommendations regarding asthma 

exacerbations in developing the core estimate (U.S. EPA-SAB, 2004a). Although certain studies 

of acute respiratory events characterize these impacts among only asthmatic populations, 

others consider the full population, including both asthmatics and non-asthmatics. For this 

reason, incidence estimates derived from studies focused only on asthmatics cannot be added 

to estimates from studies that consider the full population—to do so would double-count 

impacts. To prevent such double-counting, we estimated the exacerbation of asthma among

children and excluded adults from the calculation. Asthma exacerbations occurring in adults are 

assumed to be captured in the general population endpoints such as work loss days and 

MRADs. Finally, note also the important distinction between the exacerbation of asthma among 

asthmatic populations, and the onset of asthma among populations not previously suffering 

from asthma; in this RIA, we quantify the exacerbation of asthma among asthmatic populations 

and not the onset of new cases of asthma.

To characterize asthma exacerbations in children, we selected two studies (Ostro et al., 

2001; Mar et al., 2004) that followed panels of asthmatic children. Ostro et al. (2001) followed a 
1-74

group of 138 African-American children in Los Angeles for 13 weeks, recording daily 

occurrences of respiratory symptoms associated with asthma exacerbations (e.g., shortness of 

breath, wheeze, and cough). This study found a statistically significant association between 

PM2.5, measured as a 12-hour average, and the daily prevalence of shortness of breath and 

wheeze endpoints. Although the association was not statistically significant for cough, the 

results were still positive and close to significance; consequently, we decided to include this 

endpoint, along with shortness of breath and wheeze, in generating incidence estimates (see 

below).

Mar et al. (2004) studied the effects of various size fractions of particulate matter on 

respiratory symptoms of adults and children with asthma, monitored over many months. The 

study was conducted in Spokane, Washington, a semiarid city with diverse sources of 

particulate matter. Data on respiratory symptoms and medication use were recorded daily by 

the study’s subjects, while air pollution data was collected by the local air agency and 

Washington State University. Subjects in the study consisted of 16 adults—the majority of 

whom participated for over a year—and nine children, all of whom were studied for over eight 

months. Among the children, the authors found a strong association between cough symptoms 

and several metrics of particulate matter, including PM2.5. However, the authors found no 

association between respiratory symptoms and PM of any metric in adults. Mar et al. therefore 

concluded that the discrepancy in results between children and adults was due either to the 

way in which air quality was monitored, or a greater sensitivity of children than adults to 

increased levels of PM air pollution.

We employed the following pooling approach in combining estimates generated using 

effect estimates from the two studies to produce a single asthma exacerbation incidence 

estimate. First, we used random/fixed effects pooling to combine the Ostro and Mar estimates 

for shortness of breath and cough. Next, we pooled the Ostro estimate of wheeze with the 

pooled cough and shortness of breath estimates to derive an overall estimate of asthma 

exacerbation.

5.6.3.5 Effect Coefficients Selected for the Sensitivity Analyses

Chronic Bronchitis. Chronic bronchitis is characterized by mucus in the lungs and a 

persistent wet cough for at least 3 months a year for several years in a row. Chronic bronchitis

affects an estimated 5% of the U.S. population (ALA, 1999). A limited number of studies have 

estimated the impact of air pollution on new incidences of chronic bronchitis. Schwartz (1993) 

and Abbey et al. (1995) provide evidence that long-term PM2.5 exposure gives rise to the 
1-75

development of chronic bronchitis in adults in the United States; these remain the two most 

recent studies observing a relationship between long-term exposure to PM2.5 and the onset of 

chronic bronchitis in the U.S. The absence of newer studies finding a relationship between longterm PM2.5 exposure and chronic bronchitis argues for moving this endpoint from the core

benefits analysis to a sensitivity analysis. In their review of the scientific literature on chronic 

obstructive pulmonary disease (COPD), which includes chronic bronchitis and emphysema, the 

American Thoracic Society concluded that air pollution is “associated with COPD, but sufficient 

criteria for causation were not met” (Eisner et al., 2010).

Stroke. The PM ISA (U.S. EPA, 2009) includes several new studies that have examined 

the relationship between PM2.5 exposure and cerebrovascular events (U.S. EPA, 2009). Timeseries studies have generally been inconsistent with several studies showing positive 

associations (Dominici et al., 2006; Metzger et al., 2004; Lippman et al., 2000; Lisabeth et al.,

2008). Several other studies have demonstrated null or negative associations (Anderson et al.,

2001; Barnett et al., 2006; Peel et al., 2007). In general, these studies examined cerebrovascular 

disease as a group, though a few studies partition ischemic and hemorrhagic strokes separately 

(Lisabeth et al., 2008). A key limitation of these time-series studies is that they use hospital 

discharge rates as the diagnosis and relatively short lags (0–2 days)—this is problematic, as 

discharge rates are an imperfect diagnosis and strokes may occur several days before admission 

to the hospital.

Longer-term prospective cohort studies of PM2.5 and stroke include Miller et al. (2007).

Miller et al. (2007) estimated the change in risk among post-menopausal women enrolled in the 

Women’s Health Initiative (U.S. EPA, 2009b). After adjusting for age, race, smoking status, 

educational level, household income, body-mass index, diabetes, hypertension, and 

hypercholesterolemia, hazard ratios were estimated for the first cardiovascular event. Because 

this study considers first-time cardiovascular events, a key challenge to incorporating this study 

into the core health impact assessment is matching the baseline incidence rate correctly, and 

we have approximated this information using the data in the study.

In addition, Wellenius et al. (2012) examined the association of PM2.5 with neurologist 

confirmed ischemic stroke in Boston adults in a time-stratified case-crossover study. A key 

feature of this study is that it included the time of stroke symptom onset for most patients. 

Similar to the challenge with Miller et al. (2007), we do not have baseline incidence rates, and 

we do not have sufficient data from the study to approximate it. Three factors argue for 

treating this endpoint in the sensitivity analysis: (1) the epidemiological literature examining 

PM-related cerebrovascular events is still evolving; (2) there are special uncertainties associated 
1-76

with quantifying this endpoint; (3) we have not yet identified an appropriate method for 

estimating the economic value of this endpoint.

Cardiovascular Emergency Department Visits. A large number of recent U.S.-based 

studies provide support for an association between short-term increases in PM2.5 and increased 

risk of ED visits for ischemic heart diseases (U.S. EPA, 2009b). Both Metzger et al. (2004) and 

Tolbert et al. (2007) published interim results from the Study of Particles and Health in Atlanta

(SOPHIA), finding a relationship between PM2.5 exposure and cardiovascular emergency 

department visits. These cardiovascular emergency department visits are distinct from 

cardiovascular hospital admissions and non-fatal heart attacks. To ensure no double-counting, 

we excluded ICD-9-411 (ischemic heart disease) from the baseline incidence rates for 

cardiovascular emergency department visits. Mathes et al. (2011) find relationships between 

PM2.5 levels and cardiovascular emergency department visits in New York City. The principal 

challenge to incorporating these studies is the absence of readily-available economic valuation 

estimates for cardiovascular emergency department visits. Until we develop an approach for 

estimating the economic value of this endpoint, we will quantify these ED visits in a sensitivity

analysis only.

Unquantified Human Health Benefits

The illustrative emission reduction strategies to reach the revised and alternative annual 

standards described in Chapter 4 would reduce emissions of directly emitted particles, as well 

as SO2, and NOx for an alternative standard for 11 μg/m3. The extent to which down wind 

exposure to secondary pollutants ozone, and mercury would actually be reduced would depend 

on the specific control strategy that States would use to reduce PM2.5 in a given area as well as 

local geographic and meteorological conditions. Although we have quantified many of the 

health benefits associated with reducing exposure to PM2.5, as shown in Table 5-2, we are 

unable to quantify the health benefits associated with reducing the potential for ozone 

exposure, SO2 exposure, NO2 exposure or contamination of local water bodies with mercury

due to the absence of air quality modeling data for these pollutants in this analysis. Although 

the method we applied simulated the impact of attaining the revised and alternative annual 

standards on ambient levels of PM2.5, this method does not simulate how the illustrative 

emission reductions would affect ambient levels of ozone, SO2, or NO2. Furthermore, the air 

quality modeling conducted for this analysis did not assess mercury, so we are unable to 

estimate mercury deposition associated with the illustrative controls or subsequent 

bioaccumulation and exposure. Below we provide a qualitative description of these health 
1-77

benefits. In general, previous analyses have shown that the monetized value of these additional 

health benefits is much smaller than PM2.5-related benefits (U.S. EPA, 2010a, 2010c, 2010d).

Reducing NOx emissions also reduces ozone concentrations in most areas. Reducing 

ambient ozone concentrations is associated with significant human health benefits, including 

mortality and respiratory morbidity (U.S. EPA, 2008a, 2010d). Epidemiological researchers have 

associated ozone exposure with adverse health effects in numerous toxicological, clinical and 

epidemiological studies (U.S. EPA, 2006b; U.S. EPA, 2012c). When adequate data and resources 

are available, the EPA generally quantifies several health effects associated with exposure to 

ozone (e.g., U.S. EPA, 2008a, 2010d, 2011a, 2011c). These health effects include respiratory 

morbidity such as asthma attacks, hospital and emergency department visits, school loss days, 

as well as premature mortality. The scientific literature suggests that exposure to ozone is also 

associated with chronic respiratory damage and premature aging of the lungs, but the EPA has 

not quantified these effects in benefits analyses previously.

Following an extensive evaluation of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment for Sulfur Dioxide—Health Criteria (SO2 ISA) 

concluded that there is a causal relationship between respiratory health effects and short-term 

exposure to SO2 (U.S. EPA, 2008c). The immediate effect of SO2 on the respiratory system in 

humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely 

resulting from preexisting inflammation associated with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO2

at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and 

percentage of asthmatics adversely affected. Based on our review of this information, we 

identified four short-term morbidity endpoints that the SO2 ISA identified as a “causal 

relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

“suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects 

to SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 

relationship between SO2 exposure and mortality, there was a lack of robustness of the 

observed associations to adjustment for pollutants. We did not quantify these benefits due to 

data constraints.

Epidemiological researchers have associated NO2 exposure with adverse health effects 

in numerous toxicological, clinical and epidemiological studies, as described in the Integrated
1-78

Science Assessment for Oxides of Nitrogen—Health Criteria (NO2 ISA) (U.S. EPA, 2008b). The 

NO2 ISA provides a comprehensive review of the current evidence of health and environmental 

effects of NO2. The NO2 ISA concluded that the evidence “is sufficient to infer a likely causal 

relationship between short-term NO2 exposure and adverse effects on the respiratory system.”

These epidemiologic and experimental studies encompass a number of endpoints including 

emergency department visits and hospitalizations, respiratory symptoms, airway 

hyperresponsiveness, airway inflammation, and lung function. Effect estimates from 

epidemiologic studies conducted in the United States and Canada generally indicate a 2–20% 

increase in risks for ED visits and hospital admissions and higher risks for respiratory symptoms.

The NO2 ISA concluded that the relationship between short-term NO2 exposure and premature 

mortality was “suggestive but not sufficient to infer a causal relationship” because it is difficult 

to attribute the mortality risk effects to NO2 alone. Although the NO2 ISA stated that studies 

consistently reported a relationship between NO2 exposure and mortality, the effect was 

generally smaller than that for other pollutants such as PM. We did not quantify these benefits 

due to data constraints.

Economic Valuation Estimates

Reductions in ambient concentrations of air pollution generally lower the risk of future 

adverse health effects for a large population. Therefore, the appropriate economic measure is 

WTP for changes in risk of a health effect rather than WTP for a health effect that would occur 

with certainty (Freeman, 1993). Epidemiological studies generally provide estimates of the 

relative risks of a particular health effect that is avoided because of a reduction in air pollution. 

We converted those changes in risk to units of avoided statistical incidence for ease of 

presentation. We calculated the value of avoided statistical incidences by dividing individual 

WTP for a risk reduction by the related observed change in risk.

WTP estimates generally are not available for some health effects, such as hospital 

admissions. In these cases, we instead used the cost of treating or mitigating the effect to 

estimate the economic value. COI estimates generally (although not necessarily in all cases) 

understate the true value of reducing the risk of a health effect, because they reflect the direct 

expenditures related to treatment, but not the value of avoided pain and suffering (Harrington 

and Portney, 1987; Berger, 1987). 

We provide unit values for health endpoints (along with information on the distribution 

of the unit value) in Table 5-9. All values are in constant year 2006 dollars, adjusted for growth 

in real income for WTP estimates out to 2020 using projections provided by Standard and 
1-79

Poor’s, which is discussed in further detail in Section 5.6.8. Economic theory argues that WTP 

for most goods (such as environmental protection) will increase if real income increases. 

Several of the valuation studies used in this analysis were conducted in the late 1980s and early 

1990s, and we are in the process of reviewing the literature to update these unit values. The 

discussion below provides additional details on valuing specific PM2.5-related related endpoints.

5.6.6.1 Mortality Valuation

Following the advice of the SAB’s Environmental Economics Advisory Committee (SABEEAC), the EPA currently uses the value of statistical life (VSL) approach in calculating the core

estimate of mortality benefits, because we believe this calculation provides the most 

reasonable single estimate of an individual’s willingness to trade off money for reductions in 

mortality risk (U.S. EPA-SAB, 2000). The VSL approach is a summary measure for the value of 

small changes in mortality risk experienced by a large number of people. For a period of time 

(2004–2008), the Office of Air and Radiation (OAR) valued mortality risk reductions using a VSL 

estimate derived from a limited analysis of some of the available studies. OAR arrived at a VSL 

using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of the 

wage-risk literature. The $1 million value represented the lower end of the interquartile range 

from the Mrozek and Taylor (2002) meta-analysis of 33 studies. The $10 million value 

represented the upper end of the interquartile range from the Viscusi and Aldy (2003) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$) was also consistent with the 

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the 

Agency neither changed its official guidance on the use of VSL in rule-makings nor subjected the 

interim estimate to a scientific peer-review process through SAB or other peer-review group.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

1-80

Table 5-9. Unit Values for Economic Valuation of Health Endpoints (2010$)

a

Health Endpoint

Central Estimate of Value Per Statistical Incidence

1990 Income Level 2020 Income Level Derivation of Distributions of Estimates

Premature Mortality (Value of a 

Statistical Life)

$8,000,000 $9,600,000 The EPA currently recommends a central VSL of $4.8m (1990$, 1990 

income) based on a Weibull distribution fitted to 26 published VSL 

estimates (5 contingent valuation and 21 labor market studies). The 

underlying studies, the distribution parameters, and other useful 

information are available in Appendix B of the EPA’s Guidelines for 

Preparing Economic Analyses (U.S. EPA, 2010e).

Nonfatal Myocardial Infarction 

(heart attack)

3% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–64

Age 65 and over

7% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–64

Age 65 and over

$98,000

$110,000

$120,000

$200,000

$98,000

$97,000

$110,000

$110,000

$190,000

$97,000

$98,000

$110,000

$120,000

$200,000

$98,000

$97,000

$110,000

$110,000

$190,000

$97,000

No distributional information available. Age-specific cost-of-illness

values reflect lost earnings and direct medical costs over a 5-year 

period following a nonfatal MI. Lost earnings estimates are based on 

Cropper and Krupnick (1990). Direct medical costs are based on 

simple average of estimates from Russell et al. (1998) and Wittels 

et al. (1990).

Lost earnings:

Cropper and Krupnick (1990). Present discounted value of 5 years of 

lost earnings in 2000$:

age of onset: at 3% at 7%

25–44 $9,000 $8,000

45–54 $13,000 $12,000

55–65 $77,000 $69,000

Direct medical expenses (2000$): An average of:

1. Wittels et al. (1990) ($100,000—no discounting)

2. Russell et al. (1998), 5-year period ($22,000 at 3% discount rate; 

$21,000at 7% discount rate)

(continued)

Table 5-9. Unit Values for Economic Valuation of Health Endpoints (2010$)

a

(continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence

2000 Income Level 2020 Income Level Derivation of Distributions of Estimates

Hospital Admissions 

Chronic Lung Disease (18–64) $21,000 $21,000 No distributional information available. The COI estimates (lost 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

1-81

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of 

hospital stay, and weighted share of total chronic lung illnesses) 

reported in Agency for Healthcare Research and Quality (2007) 

(www.ahrq.gov). 

Asthma Admissions (0–64) $16,000 $16,000 No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of 

hospital stay, and weighted share of total asthma category illnesses) 

reported in Agency for Healthcare Research and Quality (2007) 

(www.ahrq.gov). 

All Cardiovascular

Age 18–64

Age 65–99

$42,000

$41,000

$42,000

$41,000

No distributional information available. The COI estimates (lost 

earnings plus direct medical costs) are based on ICD-9 code-level 

information (e.g., average hospital care costs, average length of 

hospital stay, and weighted share of total cardiovascular category 

illnesses) reported in Agency for Healthcare Research and Quality 

(2007) (www.ahrq.gov). 

All respiratory (ages 65+) $36,000 $36,000 No distributions available. The COI point estimates (lost earnings plus 

direct medical costs) are based on ICD-9 code level information (e.g., 

average hospital care costs, average length of hospital stay, and 

weighted share of total respiratory category illnesses) reported in 

Agency for Healthcare Research and Quality, 2007 (www.ahrq.gov).

Emergency Department Visits 

for Asthma

$430 $430 No distributional information available. Simple average of two unit 

COI values (2000$):

(1) $310, from Smith et al. (1997) and

(2) $260, from Stanford et al. (1999).

(continued)
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

1-82

Table 5-9. Unit Values for Economic Valuation of Health Endpoints (2010$)

a

(continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence

2000 Income Level 2020 Income Level Derivation of Distributions of Estimates

Respiratory Ailments Not Requiring Hospitalization

Upper Respiratory Symptoms 

(URS)

$31 $33 Combinations of the three symptoms for which WTP estimates are 

available that closely match those listed by Pope et al. result in seven 

different “symptom clusters,” each describing a “type” of URS. A 

dollar value was derived for each type of URS, using mid-range 

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster 

and assuming additivity of WTPs. In the absence of information 

surrounding the frequency with which each of the seven types of URS 

occurs within the URS symptom complex, we assumed a uniform 

distribution between $9.2 and $43 (2000$).

Lower Respiratory Symptoms 

(LRS)

$20 $21 Combinations of the four symptoms for which WTP estimates are 

available that closely match those listed by Schwartz et al. result in 11 

different “symptom clusters,” each describing a “type” of LRS. A 

dollar value was derived for each type of LRS, using mid-range 

estimates of WTP (IEc, 1994) to avoid each symptom in the cluster 

and assuming additivity of WTPs. The dollar value for LRS is the 

average of the dollar values for the 11 different types of LRS. In the 

absence of information surrounding the frequency with which each of 

the 11 types of LRS occurs within the LRS symptom complex, we 

assumed a uniform distribution between $6.9 and $25 (2000$).

Asthma Exacerbations $54 $58 Asthma exacerbations are valued at $45 per incidence, based on the 

mean of average WTP estimates for the four severity definitions of a 

“bad asthma day,” described in Rowe and Chestnut (1986). This study 

surveyed asthmatics to estimate WTP for avoidance of a “bad asthma 

day,” as defined by the subjects. For purposes of valuation, an asthma 

exacerbation is assumed to be equivalent to a day in which asthma is 

moderate or worse as reported in the Rowe and Chestnut (1986) 

study. The value is assumed have a uniform distribution between $16 

and $71 (2000$).

(continued)
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

1-83

Table 5-9. Unit Values for Economic Valuation of Health Endpoints (2010$)

a

(continued)

Health Endpoint

Central Estimate of Value Per Statistical Incidence

2000 Income Level 2020 Income Level Derivation of Distributions of Estimates

Respiratory Ailments Not Requiring Hospitalization (continued)

Acute Bronchitis $450 $480 Assumes a 6-day episode, with the distribution of the daily value 

specified as uniform with the low and high values based on those 

recommended for related respiratory symptoms in Neumann et al. 

(1994). The low daily estimate of $10 is the sum of the mid-range 

values recommended by IEc (1994) for two symptoms believed to be 

associated with acute bronchitis: coughing and chest tightness. The 

high daily estimate was taken to be twice the value of a minor 

respiratory restricted-activity day, or $110 (2000$). 

Work Loss Days (WLDs) Variable 

(U.S. median = $150)

Variable 

(U.S. median = $150)

No distribution available. Point estimate is based on county-specific 

median annual wages divided by 52 and then by 5—to get median 

daily wage. U.S. Year 2000 Census, compiled by Geolytics, Inc.

(Geolytics, 2002)

Minor Restricted Activity Days 

(MRADs)

$64 $68 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). 

Distribution is assumed to be triangular with a minimum of $22 and a 

maximum of $83, with a most likely value of $52 (2000$). Range is 

based on assumption that value should exceed WTP for a single mild 

symptom (the highest estimate for a single symptom—for eye 

irritation—is $16.00) and be less than that for a WLD. The triangular

distribution acknowledges that the actual value is likely to be closer 

to the point estimate than either extreme.

a

All estimates are rounded to two significant digits. Unrounded estimates in 2000$ are available in the Appendix J of the BenMAP user manual (Abt 

Associates, 2012).
ES-1

During this time, the Agency continued work to update its guidance on valuing mortality 

risk reductions, including commissioning a report from meta-analytic experts to evaluate 

methodological questions raised by the EPA and the SAB on combining estimates from the 

various data sources. In addition, the Agency consulted several times with the SAB-EEAC on the 

issue. With input from the meta-analytic experts, the SAB-EEAC advised the Agency to update 

its guidance using specific, appropriate meta-analytic techniques to combine estimates from 

unique data sources and different studies, including those using different methodologies (i.e., 

wage-risk and stated preference) (U.S. EPA-SAB, 2007).

Until updated guidance is available, the Agency determined that a single, peer-reviewed 

estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, the 

Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines 

for Preparing Economic Analyses (U.S. EPA, 2000)1 while the Agency continues its efforts to 

update its guidance on this issue. This approach calculates a mean value across VSL estimates 

derived from 26 labor market and contingent valuation studies published between 1974 and 

1991. The mean VSL across these studies is $4.8 million (1990$) or $6.3 million (2000$).2The 

Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing 

mortality risk reductions and has made significant progress in responding to the SAB-EEAC’s 

specific recommendations. In the process, the Agency has identified a number of important 

issues to be considered in updating its mortality risk valuation estimates. These are detailed in a 

white paper on “Valuing Mortality Risk Reductions in Environmental Policy,” which recently 

underwent review by the SAB-EEAC. A meeting with the SAB on this paper was held on March 

14, 2011 and formal recommendations were transmitted on July 29, 2011 (U.S. EPA-SAB, 2011). 

Draft guidance responding to SAB recommendations will be developed shortly.

The economics literature concerning the appropriate method for valuing reductions in 

premature mortality risk is still developing. The adoption of a value for the projected reduction 

in the risk of premature mortality is the subject of continuing discussion within the economics 

and public policy analysis community. The EPA strives to use the best economic science in its 

analyses. Given the mixed theoretical finding and empirical evidence regarding adjustments to 

VSL for risk and population characteristics (e.g., Smith et al., 2004; Alberini et al., 2004; Aldy 

and Viscusi, 2008), we use a single VSL for all reductions in mortality risk.

 

1

In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA retained the VSL endorsed by 

the SAB with the understanding that further updates to the mortality risk valuation guidance would be 

forthcoming in the near future.

2

In this analysis, we adjust the VSL to account for a different currency year (2010$) and to account for income 

growth to 2020. After applying these adjustments to the $6.3 million value, the VSL is $8.9M. 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-2

Although there are several differences between the labor market studies the EPA uses 

to derive a VSL estimate and the PM2.5 air pollution context addressed here, those differences in 

the affected populations and the nature of the risks imply both upward and downward 

adjustments. Table 5-10 lists some of these differences and the expected effect on the VSL 

estimate for air pollution-related mortality. In the absence of a comprehensive and balanced 

set of adjustment factors, the EPA believes it is reasonable to continue to use the $4.8 million 

(1990$) value adjusted for inflation and income growth over time while acknowledging the 

significant limitations and uncertainties in the available literature.

Table 5-10. Influence of Applied VSL Attributes on the Size of the Economic Benefits of 

Reductions in the Risk of Premature Death (U.S. EPA, 2006a)

Attribute Expected Direction of Bias

Age Uncertain, perhaps overestimate

Life Expectancy/Health Status Uncertain, perhaps overestimate

Attitudes Toward Risk Underestimate

Income Uncertain

Voluntary vs. Involuntary Uncertain, perhaps underestimate

Catastrophic vs. Protracted Death Uncertain, perhaps underestimate

The SAB-EEAC has reviewed many potential VSL adjustments and the state of the 

economics literature. The SAB-EEAC advised the EPA to “continue to use a wage-risk-based VSL 

as its primary estimate, including appropriate sensitivity analyses to reflect the uncertainty of 

these estimates,” and that “the only risk characteristic for which adjustments to the VSL can be 

made is the timing of the risk” (U.S. EPA-SAB, 2000). In developing our core estimate of the 

benefits of premature mortality reductions, we have followed this advice. 

For premature mortality, we assume that there is a “cessation” lag between PM 

exposures and the total realization of changes in health effects. We assumed for this analysis 

that some of the incidences of premature mortality related to PM2.5 exposures occur in a 

distributed fashion over the 20 years following exposure and discounted over the period 

between exposure and premature mortality. Although the structure of the lag is uncertain, the 

EPA follows the advice of the SAB-HES to assume a segmented lag structure characterized by 

30% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 

20 after the reduction in PM2.5 (U.S. EPA-SAB, 2004c). Additional cessation lag structures are 

described and assessed in Appendix 5.A of this RIA. To take this into account in the valuation of 

reductions in premature mortality, we discount the value of premature mortality occurring in 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-3

future years using rates of 3% and 7%.3Changes in the cessation lag assumptions do not change 

the total number of estimated deaths but rather the timing of those deaths. As such, the 

monetized benefits using a 7% discount rate are only approximately 10% less than the 

monetized benefits using a 3% discount rate. Further discussion of this topic appears in the 

EPA’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e).

Uncertainties Specific to Premature Mortality Valuation. The economic benefits 

associated with reductions in the risk of premature mortality are the largest category of 

monetized benefits in this RIA. In addition, in prior analyses, the EPA has identified valuation of 

mortality-related benefits as the largest contributor to the range of uncertainty in monetized 

benefits (Mansfield et al., 2009).4Because of the uncertainty in estimates of the value of 

reducing premature mortality risk, it is important to adequately characterize and understand 

the various types of economic approaches available for valuing reductions in mortality risk. 

Such an assessment also requires an understanding of how alternative valuation approaches 

reflect that some individuals may be more susceptible to air pollution-induced mortality or 

reflect differences in the nature of the risk presented by air pollution relative to the risks 

studied in the relevant economics literature.

The health science literature on air pollution indicates that several human 

characteristics affect the degree to which mortality risk affects an individual. For example, some 

age groups appear to be more susceptible to air pollution than others (e.g., the elderly and 

children). Health status prior to exposure also affects susceptibility. An ideal benefits estimate 

of mortality risk reduction would reflect these human characteristics, in addition to an 

individual’s WTP to improve one’s own chances of survival plus WTP to improve other 

individuals’ survival rates. The ideal measure would also take into account the specific nature of 

the risk reduction commodity that is provided to individuals, as well as the context in which risk 

is reduced. To measure this value, it is important to assess how reductions in air pollution 

reduce the risk of dying from the time that reductions take effect onward and how individuals 

value these changes. Each individual’s survival curve, or the probability of surviving beyond a 

 

3

The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the 

federal government. To comply with Circular A-4, EPA provides monetized benefits using discount rates of 3% and 

7% (OMB, 2003). A 3% discount reflects reliance on a “social rate of time preference” discounting concept. A 7% 

rate is consistent with an “opportunity cost of capital” concept to reflect the time value of resources directed to 

meet regulatory requirements. 

4

This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological effect 

estimates and economic valuation estimates. Additional sources of model error such as those examined in the 

PM2.5 mortality expert elicitation (Roman et al., 2008) may result in different conclusions about the relative 

contribution of sources of uncertainty.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-4

given age, should shift as a result of an environmental quality improvement. For example, 

changing the current probability of survival for an individual also shifts future probabilities of 

that individual’s survival. This probability shift will differ across individuals because survival 

curves depend on such characteristics as age, health state, and the current age to which the 

individual is likely to survive.

Although a survival curve approach provides a theoretically preferred method for 

valuing the benefits of reduced risk of premature mortality associated with reducing air 

pollution, the approach requires a great deal of data to implement. The economic valuation 

literature does not yet include good estimates of the value of this risk reduction commodity. As 

a result, in this study we value reductions in premature mortality risk using the VSL approach.

Other uncertainties specific to premature mortality valuation include the following:

 Across-study variation: There is considerable uncertainty as to whether the available 

literature on VSL provides adequate estimates of the VSL for risk reductions from air 

pollution reduction. Although there is considerable variation in the analytical designs 

and data used in the existing literature, the majority of the studies involve the value 

of risks to a middle-aged working population. Most of the studies examine 

differences in wages of risky occupations, using a hedonic wage approach. Certain 

characteristics of both the population affected and the mortality risk facing that 

population are believed to affect the average WTP to reduce the risk. The 

appropriateness of a distribution of WTP based on the current VSL literature for 

valuing the mortality-related benefits of reductions in air pollution concentrations 

therefore depends not only on the quality of the studies (i.e., how well they 

measure what they are trying to measure), but also on the extent to which the risks 

being valued are similar and the extent to which the subjects in the studies are 

similar to the population affected by changes in pollution concentrations.

 Level of risk reduction: The transferability of estimates of the VSL from the wage-risk 

studies to the context of the PM NAAQS analysis rests on the assumption that, 

within a reasonable range, WTP for reductions in mortality risk is linear in risk 

reduction. For example, suppose a study provides a result that the average WTP for 

a reduction in mortality risk of 1/100,000 is $50, but that the actual mortality risk 

reduction resulting from a given pollutant reduction is 1/10,000. If WTP for 

reductions in mortality risk is linear in risk reduction, then a WTP of $50 for a 

reduction of 1/100,000 implies a WTP of $500 for a risk reduction of 1/10,000 

(which is 10 times the risk reduction valued in the study). Under the assumption of 

linearity, the estimate of the VSL does not depend on the particular amount of risk 

reduction being valued. This assumption has been shown to be reasonable provided 

the change in the risk being valued is within the range of risks evaluated in the 

underlying studies (Rowlatt et al., 1998).
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-5

 Voluntariness of risks evaluated: Although job-related mortality risks may differ in 

several ways from air pollution-related mortality risks, the most important 

difference may be that job-related risks are incurred voluntarily, or generally 

assumed to be, whereas air pollution-related risks are incurred involuntarily. Some 

evidence suggests that people will pay more to reduce involuntarily incurred risks 

than risks incurred voluntarily (e.g., Lichtenstein and Slovic, 2006). If this is the case, 

WTP estimates based on wage-risk studies may understate WTP to reduce 

involuntarily incurred air pollution-related mortality risks.

 Sudden versus protracted death: A final important difference related to the nature of 

the risk may be that some workplace mortality risks tend to involve sudden, 

catastrophic events, whereas air pollution-related risks tend to involve longer 

periods of disease and suffering prior to death. Some evidence suggests that WTP to 

avoid a risk of a protracted death involving prolonged suffering and loss of dignity 

and personal control is greater than the WTP to avoid a risk (of identical magnitude) 

of sudden death (e.g., Tsuge et al., 2005; Alberini and Scasny, 2011). To the extent 

that the mortality risks addressed in this assessment are associated with longer 

periods of illness or greater pain and suffering than are the risks addressed in the 

valuation literature, the WTP measurements employed in the present analysis would 

reflect a downward bias.

 Self-selection and skill in avoiding risk: Recent research (Shogren and Stamland, 

2002) suggests that VSL estimates based on hedonic wage studies may overstate the 

average value of a risk reduction. This is based on the fact that the risk-wage tradeoff revealed in hedonic studies reflects the preferences of the marginal worker (i.e., 

that worker who demands the highest compensation for his risk reduction for a 

given job). This worker must have either a higher workplace risk than the average 

worker in a given occupation, a lower risk tolerance than the average worker in that 

occupation, or both. Conversely, the marginal worker should have a higher risk 

tolerance than workers employed in less-risky sectors. However, the risk estimate 

used in hedonic studies is generally based on average risk, so the VSL may be biased, 

in an ambiguous direction, because the wage differential and risk measures do not 

match.

 Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) 

finds that because individuals reevaluate their baseline risk of death as they age, the 

marginal value of risk reductions does not decline with age as predicted by some 

lifetime consumption models. This research supports findings in recent stated 

preference studies that suggest only small reductions in the value of mortality risk 

reductions with increasing age (e.g., Alberini et al., 2004).

5.6.6.2 Nonfatal Myocardial Infarctions Valuation

We were not able to identify a suitable WTP value for reductions in the risk of nonfatal 

heart attacks. Instead, we use a COI unit value with two components: the direct medical costs 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-6

and the opportunity cost (lost earnings) associated with the illness event. Because the costs 

associated with a myocardial infarction extend beyond the initial event itself, we consider costs 

incurred over several years. Using age-specific annual lost earnings estimated by Cropper and 

Krupnick (1990) and a 3% discount rate, we estimated a rounded present discounted value in 

lost earnings (in 2000$) over 5 years due to a myocardial infarction of $8,800 for someone 

between the ages of 25 and 44, $13,000 for someone between the ages of 45 and 54, and 

$75,000 for someone between the ages of 55 and 65. The rounded corresponding age-specific 

estimates of lost earnings (in 2000$) using a 7% discount rate are $7,900, $12,000, and $67,000, 

respectively. Cropper and Krupnick (1990) do not provide lost earnings estimates for 

populations under 25 or over 65. As such, we do not include lost earnings in the cost estimates 

for these age groups.

We found three possible sources in the literature of estimates of the direct medical 

costs of myocardial infarction, which provide significantly different values (see Table 5-11):

 Wittels et al. (1990) estimated expected total medical costs of myocardial infarction 

over 5 years to be $51,000 (rounded in 1986$) for people who were admitted to the 

hospital and survived hospitalization. (There does not appear to be any discounting 

used.) This estimated cost is based on a medical cost model, which incorporated 

therapeutic options, projected outcomes, and prices (using “knowledgeable 

cardiologists” as consultants). The model used medical data and medical decision 

algorithms to estimate the probabilities of certain events and/or medical procedures 

being used. The authors note that the average length of hospitalization for acute 

myocardial infarction has decreased over time (from an average of 12.9 days in 1980 

to an average of 11 days in 1983). Wittels et al. used 10 days as the average in their 

study. It is unclear how much further the length of stay for myocardial infarction 

may have decreased from 1983 to the present. The average length of stay for ICD 

code 410 (myocardial infarction) in the year-2000 Agency for Healthcare Research 

and Quality (AHRQ) HCUP database is 5.5 days (AHRQ, 2000). However, this may 

include patients who died in the hospital (not included among our nonfatal 

myocardial infarction cases), and whose length of stay was therefore substantially 

shorter than it would be if they had not died.

 Eisenstein et al. (2001) estimated 10-year costs of $45,000 in rounded 1997$ (using 

a 3% discount rate) for myocardial infarction patients, using statistical prediction 

(regression) models to estimate inpatient costs. Only inpatient costs (physician fees 

and hospital costs) were included.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-7

Table 5-11. Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacksa

Study Direct Medical Costs (2010$) Over an x-Year Period, for x =

Wittels et al. (1990) $160,000b5

Russell et al. (1998) $33,000c5

Average (5-year) costs $98,000 5

Eisenstein et al. (2001) $74,000c10

a

All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in appendix J 

of the BenMAP user manual (Abt Associates, 2012).

b Wittels et al. (1990) did not appear to discount costs incurred in future years.

c

Using a 3% discount rate. Discounted values as reported in the study.

As noted above, the estimates from these three studies are substantially different, and 

we have not adequately resolved the sources of differences in the estimates. Because the 

wage-related opportunity cost estimates from Cropper and Krupnick (1990) cover a 5-year 

period, we used estimates for medical costs that similarly cover a 5-year period (i.e., estimates 

from Wittels et al. (1990) and Russell et al. (1998). We used a simple average of the two 5-year 

estimates, or rounded to $85,000, and added it to the 5-year opportunity cost estimate. The 

resulting estimates are given in Table 5-12.

Table 5-12. Estimated Costs Over a 5-Year Period of a Nonfatal Myocardial Infarction (in 

2010$)

a

Age Group Opportunity Cost Medical CostbTotal Cost

0–24 $0 $98,000 $98,000

25–44 $12,000c$98,000 $110,000

45–54 $17,000c$98,000 $120,000

55–65 $100,000c$98,000 $200,000

> 65 $0 $98,000 $98,000

a

All estimates rounded to two significant digits, so estimates may not sum across columns. Unrounded 

estimates in 2000$ are available in appendix J of the BenMAP user manual (Abt Associates, 2012).

b

An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998).

c

From Cropper and Krupnick (1990), using a 3% discount rate for illustration.

Hospital Admissions and Emergency Department Valuation

In the absence of estimates of societal WTP to avoid hospital visits/admissions for 

specific illnesses, we derive COI estimates for use in the benefits analysis. The International 

Classification of Diseases (WHO, 1977) code-specific COI estimates used in this analysis consist 

of estimated hospital charges and the estimated opportunity cost of time spent in the hospital 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-8

(based on the average length of a hospital stay for the illness). We based all estimates of 

hospital charges and length of stays on statistics provided by the Agency for Healthcare 

Research and Quality’s Healthcare Utilization Project National Inpatient Sample (NIS) database 

(AHRQ, 2007). We estimated the opportunity cost of a day spent in the hospital as the value of 

the lost daily wage, regardless of whether the hospitalized individual is in the workforce. To 

estimate the lost daily wage, we divided the median weekly wage reported by the 2007 

American Community Survey (ACS) by five and deflated the result to year 2010$ using the CPI-U 

“all items” (Abt Associates, 2012). The resulting national average lost daily wage is $148. The 

total cost-of-illness estimate for an ICD code-specific hospital stay lasting n days, then, was the 

mean hospital charge plus $148 multiplied by n. In general, the mean length of stay has 

decreased since the 2000 database used in previous version of BenMAP while the mean 

hospital charge has increased. We provide the rounded unit values in 2010$ for the COI 

functions used in this analysis in Table 5-13.

Table 5-13. Unit Values for Hospital Admissions

End Point ICD Codes

Age Range

Mean Hospital 

Charge (2010$)

Mean Length 

of Stay (days)

Total Cost of Illness 

(unit value in 

min. max. 2010$)

HA, Chronic Lung Disease 490–496 18 64 $19,000 3.9 $21,000

HA, Asthma 493 0 64 $14,000 3.0 $16,000

HA, All Cardiovascular 390–429 18 64 $40,000 4.1 $42,000

HA, All Cardiovascular 390–429 65 99 $37,000 4.9 $41,000

HA, All Respiratory 460–519 65 99 $31,000 6.1 $36,000

* All estimates rounded to two significant digits. Unrounded estimates in 2000$ are available in Appendix J 

of the BenMAP user manual (Abt Associates, 2012).

To value asthma emergency department visits, we used a simple average of two 

estimates from the health economics literature. The first estimate comes from Smith et al. 

(1997), who reported approximately 1.2 million asthma-related emergency department visits in 

1987, at a total cost of $186 million (1987$). The average cost per visit that year was $155; in 

2010$, that cost was $464 (using the CPI-U for medical care to adjust to 2010$). The second 

estimate comes from Stanford et al. (1999), who reported the cost of an average asthmarelated emergency department visit at $335, based on 1996–1997 data. A simple average of the 

two estimates yields a unit value of $388.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-9

Minor Restricted Activity Days Valuation

No studies are reported to have estimated WTP to avoid a minor restricted activity day. 

However, Neumann et al. (1994) derived an estimate of willingness to pay to avoid a minor 

respiratory restricted activity day, using estimates from Tolley et al. (1986) of WTP for avoiding 

a combination of coughing, throat congestion and sinusitis. This estimate of WTP to avoid a 

minor respiratory restricted activity day is $38 (1990$), or about $68 (2010$). Although Ostro 

and Rothschild (1989) statistically linked ozone and minor restricted activity days, it is likely that 

most MRADs associated with ozone and PM2.5 exposure are, in fact, minor respiratory restricted 

activity days. For the purpose of valuing this health endpoint, we used the estimate of mean 

WTP to avoid a minor respiratory restricted activity day.

Growth in WTP Reflecting National Income Growth Over Time

Our analysis accounts for expected growth in real income over time. This is a distinct 

concept from inflation and currency year. Economic theory argues that WTP for most goods 

(such as environmental protection) will increase if real incomes increase. There is substantial 

empirical evidence that the income elasticity5of WTP for health risk reductions is positive, 

although there is uncertainty about its exact value. Thus, as real income increases, the WTP for

environmental improvements also increases. Although many analyses assume that the income 

elasticity of WTP is unit elastic (i.e., a 10% higher real income level implies a 10% higher WTP to 

reduce risk changes), empirical evidence suggests that income elasticity is substantially less 

than one and thus relatively inelastic. As real income rises, the WTP value also rises but at a 

slower rate than real income.

The effects of real income changes on WTP estimates can influence benefits estimates 

in two different ways: through real (national average) income growth between the year a WTP 

study was conducted and the year for which benefits are estimated, and through differences in 

income between study populations and the affected populations at a particular time. The SABEEAC advised the EPA to adjust WTP for increases in real income over time but not to adjust 

WTP to account for cross-sectional income differences “because of the sensitivity of making 

such distinctions, and because of insufficient evidence available at present” (U.S. EPA-SAB, 

2000). An advisory by another committee associated with the SAB, the Advisory Council on 

Clean Air Compliance Analysis (SAB-Council), has provided conflicting advice. While agreeing 

with “the general principle that the willingness to pay to reduce mortality risks is likely to 

increase with growth in real income” and that “[t]he same increase should be assumed for the 

 

5

Income elasticity is a common economic measure equal to the percentage change in WTP for a 1% change in 

income.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-10

WTP for serious nonfatal health effects,” they note that “given the limitations and uncertainties 

in the available empirical evidence, the Council does not support the use of the proposed 

adjustments for aggregate income growth as part of the primary analysis” (U.S. EPA-SAB, 

2004b). Until these conflicting advisories have been reconciled, the EPA will continue to adjust 

valuation estimates to reflect income growth using the methods described below, while 

providing sensitivity analyses for alternative income growth adjustment factors.

Based on a review of the available income elasticity literature, we adjusted the valuation 

of human health benefits upward to account for projected growth in real U.S. income. Faced 

with a dearth of estimates of income elasticities derived from time-series studies, we applied 

estimates derived from cross-sectional studies in our analysis. Details of the procedure can be 

found in Kleckner and Neumann (1999). We note that the literature has evolved since the 

publication of this memo and that an array of newer studies identifying potentially suitable 

income elasticity estimates are available (IEc, 2012). The EPA anticipates seeking an SAB review 

of these studies, and its approach to adjusting WTP estimates to account for changes in 

personal income, in 2013. As such, these newer studies have not yet been incorporated into the 

benefits analysis. An abbreviated description of the procedure we used to account for WTP for 

real income growth between 1990 and 2020 is presented below.

Reported income elasticities suggest that the severity of a health effect is a primary 

determinant of the strength of the relationship between changes in real income and WTP. As 

such, we use different elasticity estimates to adjust the WTP for minor health effects, severe 

and chronic health effects, and premature mortality. Note that because of the variety of 

empirical sources used in deriving the income elasticities, there may appear to be 

inconsistencies in the magnitudes of the income elasticities relative to the severity of the 

effects (a priori one might expect that more severe outcomes would show less income elasticity 

of WTP). We have not imposed any additional restrictions on the empirical estimates of income 

elasticity. One explanation for the seeming inconsistency is the difference in timing of 

conditions. WTP for minor illnesses is often expressed as a short term payment to avoid a single 

episode. WTP for major illnesses and mortality risk reductions are based on longer term 

measures of payment (such as wages or annual income). Economic theory suggests that 

relationships become more elastic as the length of time grows, reflecting the ability to adjust 

spending over a longer time period (U.S. EPA, 2010e, p.A-9). Based on this theory, it would be 

expected that WTP for reducing long term risks would be more elastic than WTP for reducing 

short term risks. The relative magnitude of the income elasticity of WTP for visibility compared 

with those for health effects suggests that visibility is not as much of a necessity as health, thus, 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-11

WTP is more elastic with respect to income. The elasticity values used to adjust estimates of 

benefits in 2020 are presented in Table 5-14.

6

Table 5-14. Elasticity Values Used to Account for Projected Real Income Growtha

Benefit Category Central Elasticity Estimate

Minor Health Effect 0.14

Severe and Chronic Health Effects 0.45

Premature Mortality 0.40

a

Derivation of estimates can be found in Kleckner and Neumann (1999). COI estimates are not adjusted for 

income growth.

In addition to elasticity estimates, projections of real gross domestic product (GDP) and 

populations from 1990 to 2020 are needed to adjust benefits to reflect real per capita income 

growth. For consistency with the emissions and benefits modeling, we used national population 

estimates for the years 1990 to 1999 based on U.S. Census Bureau estimates (Hollman, Mulder, 

and Kallan, 2000). These population estimates are based on application of a cohort-component 

model applied to 1990 U.S. Census data projections (U.S. Bureau of Census, 2000). For the years 

between 2000 and 2020, we applied growth rates based on the U.S. Census Bureau projections 

to the U.S. Census estimate of national population in 2000. We used projections of real GDP 

provided in Kleckner and Neumann (1999) for the years 1990 to 2010.7 We used projections of 

real GDP (in chained 1996 dollars) provided by Standard and Poor’s (2000) for the years 2010 to 

2020.

8

Using the method outlined in Kleckner and Neumann (1999) and the population and 

income data described above, we calculated WTP adjustment factors for each of the elasticity 

estimates listed in Table 5-15. Benefits for each of the categories (minor health effects, severe 

and chronic health effects, premature mortality, and visibility) are adjusted by multiplying the 

unadjusted benefits by the appropriate adjustment factor. For premature mortality, we applied 

 

6 We expect that the WTP for improved visibility in Class 1 areas would also increase with growth in real income

(see Chapter 6).

7

U.S. Bureau of Economic Analysis, Table 2A—Real Gross Domestic Product (1997) and U.S. Bureau of Economic 

Analysis, The Economic and Budget Outlook: An Update, Table 4—Economic Projections for Calendar Years 1997 

Through 2007 (1997). Note that projections for 2007 to 2010 are based on average GDP growth rates between 

1999 and 2007.

8

In previous analyses, we used the Standard and Poor’s projections of GDP directly. This led to an apparent 

discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the relative 

growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP based on the 

Bureau of Economic Analysis projections.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-12

the income adjustment factor specific to the analysis year, but we do not adjust for income 

growth over the 20-year cessation lag. Our approach could underestimate the benefits for the 

later years of the lag.

There is some uncertainty regarding the total costs of illness in the future. Specifically, 

the nature of medical treatment is changing, including a shift towards more outpatient

treatment. Although we adjust the COI estimates for inflation, we do not have data to project 

COI estimates for the cost of treatment in the future or income growth over time, which leads 

to an inherent though unavoidable inconsistency between COI- and WTP-based estimates This 

approach may underpredict benefits in future years because it is likely that increases in real 

U.S. income would also result in increased COI (due, for example, to increases in wages paid to 

medical workers) and increased cost of work loss days and lost worker productivity (reflecting 

that if worker incomes are higher, the losses resulting from reduced worker production would 

also be higher). In addition, cost-of-illness estimates do not include sequelae costs or pain and 

suffering, the value of which would likely increase in the future. To the extent that costs would 

be expected to increase over time, this increase may be partially offset by advancement in 

medical technology that improves the effectiveness of treatment at lower costs. For these 

reasons, we believe that the cost-of-illness estimates in this RIA may underestimate (on net)

the total economic value of avoided health impacts.

Table 5-15. Adjustment Factors Used to Account for Projected Real Income Growtha

Benefit Category 2020

Minor Health Effect 1.07

Severe and Chronic Health Effects 1.22

Premature Mortality 1.20

a

Based on elasticity values reported in Table 5-3, U.S. Census population projections, and projections of 

real GDP per capita.

Benefits Results

Benefits of the Revised and Alternative Annual Primary PM2.5 Standards

Applying the impact and valuation functions described previously in this chapter to the 

estimated changes in PM2.5 yields estimates of the changes in physical damages (e.g., 

premature mortalities, cases of acute bronchitis and hospital admissions) and the associated 

monetary values for those changes. Not all known PM health effects could be quantified or 

monetized. The monetized value of these unquantified effects is represented by adding an 

unknown “B” to the aggregate total. The estimate of total monetized health benefits is thus 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-13

equal to the subset of monetized PM-related health benefits plus B, the sum of the nonmonetized health benefits and welfare co-benefits; this B represents both uncertainty and a 

bias in this analysis, as it reflects those benefits categories that we are unable to monetize in 

this analysis.

Table 5-16 shows the population-weighted air quality change for the alternative

standards averaged across the continental U.S. Tables 5-17 through 5-22 present the benefits 

results for the annual PM2.5 standards. These benefits are relative to a 2020 analytical baseline 

reflecting attainment nationwide of the current primary PM2.5 standards (i.e., 15/35) that 

includes promulgated national regulations and illustrative emission controls to simulate 

attainment with 15/35 as well as an adjustment to NOx emissions to reflect expected reductions 

in mobile NOx emissions between 2020 and 2025.9Figure 5-3 graphically displays the total 

monetized benefits of the revised annual primary standard of 12 μg/m3, using alternative

concentration-response functions at discount rates of 3% and 7%. Figure 5-4 graphically 

displays the cumulative distribution of total monetized benefits using the 2 epidemiologyderived and the 12 expert-derived relationships between PM2.5 and mortality for the revised 

standard, which provides the full range of uncertainty within and across the expert-derived 

relationships.

Table 5-16. Population-Weighted Air Quality Change for the Revised and Alternative Annual 

Primary PM2.5 Standards Relative to Analytical Baseline

Standard Population-Weighted Air Quality Change

13 μg/m30.014 μg/m3

12 μg/m30.043 μg/m3

11 μg/m30.207 μg/m3

 

9

The estimates in this chapter reflect incremental emissions reductions from an analytical baseline that gives “an 

adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to 

occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised 

standards. Full benefits of the revised standards in those two areas will not be realized until 2025.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-14

Table 5-17. Emission Reductions in Illustrative Emission Reduction Strategies for the Revised 

and Alternative Annual Primary PM2.5 Standards, by Pollutant and Region in 2020 

(tons)a

13 μg/m312 μg/m311 μg/m3

Directly emitted PM2.5

East 0 0 8,200

West 0 0 160

CA 730 4,000 10,600

SO2

East 0 0 21,000

West 0 0 43

CA 0 0 0

NOx

East 0 0 9

West 0 0 0

CA 0 0 0

a

See Chapter 4 for more information on the illustrative emission reduction strategies. The emissions in this 

table reflect both known and unknown controls.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-15

Table 5-18. Estimated number of Avoided PM2.5 Health Impacts for the Revised and 

Alternative Annual Primary PM2.5 Standards (Incremental to the Analytical 

Baseline)

a

Health Effect

Revised and Alterative Annual Standards

(95th percentile confidence interval)

13 μg/m312 μg/m311 μg/m3

Avoided Mortality

Krewski et al. (2009)

(adult mortality age 30+)

140 460 1,500

(100--190) (320--590) (1,000--1,900)

Lepeule et al. (2012)

(adult mortality age 25+)

330 1,000 3,300

(180--480) (560--1,500) (1,800--4,800)

Woodruff et al. (1997)

(infant mortality)

0 1 4

(0--1) (1--2) (2--6)

Avoided Morbidity

Non-fatal heart attacks

Peters et al. (2001)

(age >18)

160 480 1,600

(49--260) (150--800) (480--2,600)

Pooled estimate of 4 studies

(age >18)

17 52 170

(8--41) (24--130) (78--410)

Hospital admissions—respiratory

(all ages)b

31 110 380

(-9--58) (-30--200) (-100--720)

Hospital admissions—cardiovascular

(age > 18)

43 140 480

(20--76) (66--240) (230--0,840)

Emergency department visits for 

asthma (all ages)

b

67 230 810

(-22--140) (-74--470) (-260--1,600)

Acute bronchitis 

(ages 8–12)b

280 870 2,700

(-36--580) (-110--1,800) (-350--5,500)

Lower respiratory symptoms

(ages 7–14)

3,500 11,000 34,000

(1500--5500) (4,900--17,000) (15,000--53,000)

Upper respiratory symptoms

(asthmatics ages 9–11)

5,100 16,000 49,000

(1300--8900) (4,100--28,000) (12,000--86,000)

Asthma exacerbation

(asthmatics ages 6–18)

13,000 40,000 120,000

(270--81000) (850--250,000) (2,600--770,000)

Lost work days 

(ages 18–65)

22,000 71,000 230,000

(19000--25000) (61,000--81,000) (190,000--260,000)

Minor restricted-activity days

(ages 18–65)

130,000 420,000 1,300,000

(110,000--150,000) (350,000--490,000) (1,100,000--1,600,000)

a

All incidence estimates are rounded to whole numbers with a maximum of two significant digits. These 

estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” to the 

San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 

2025, when those areas are expected to demonstrate attainment with the revised standards. Full benefits of the 

revised standards in those two areas will not be realized until 2025. Additional health endpoints, such as 

cardiovascular emergency department visits, are only quantified in a sensitivity analysis in Table 5.A-6 because we 

do not yet have a valuation estimate for this endpoint. 

b

The negative estimates at the 5th percentile confidence estimates for these morbidity endpoints reflect 

the statistical power of the study used to calculate these health impacts. These results do not suggest that 

reducing air pollution results in additional health impacts.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-16

Table 5-19. Monetized PM2.5 Health Benefits for the Revised and Alternative Annual Primary 

PM2.5 Standards (Incremental to Analytical Baseline) (Millions of 2006$, 3% 

discount rate)

a

Health Effect

Revised and Annual Standards

(95th percentile confidence interval)

13 μg/m312 μg/m311 μg/m3

Avoided Mortalityb

Krewski et al. (2009)

(adult mortality age 30+)

$1,300 $4,000 $13,000

($120--$3,500) ($370--$11,000) ($1,200--$35,000)

Lepeule et al. (2012)

(adult mortality age 25+)

$2,900 $9,000 $29,000

($250--$8,100) ($800--$26,000) ($2,600--$82,000)

Woodruff et al. (1997)

(infant mortality)

$3.4 $11 $35

($0.29--$10) ($0.91--$32) ($3.0--$100)

Avoided Morbidity

Non-fatal heart attacks 

Peters et al. (2001)

(age >18)

$18 $55 $180

($3.0--$46) ($9.1--$140) ($31--$460)

Pooled estimate of 4 studies

(age >18)

$2.0 $6.0 $20.0

($0.43--$6.8) ($1.3--$21) ($4.4--$68)

Hospital admissions—respiratory

(all ages)c

$0.86 $3.0 $11

(-$0.22--$1.6) (-$0.8--$5.5) (-$2.7--$20)

Hospital admissions—cardiovascular 

(age > 18)

$1.70 $5.3 $18

($0.85--$2.8) ($2.7--$9.2) ($10--$32)

Emergency department visits for asthma

(all ages)

$0.03 $0.10 $0.34

(-$0.0052--$0.061) (-$0.018--$0.21) (-$0.063--$0.73)

Acute bronchitis

(ages 8–12)c

$0.13 $0.42 $1.30

(-$0.0060--$0.37) (-$0.019--$1.2) (-$0.059--$3.5)

Lower respiratory symptoms

(ages 7–14)

$0.08 $0.24 $0.71

($0.025--$0.10) ($0.078--$0.47) ($0.24--$1.4)

Upper respiratory symptoms

(asthmatics ages 9–11)

$0.17 $0.54 $1.6

($0.038--$0.42) ($0.12--$1.30) ($0.36--$4.0)

Asthma exacerbation

(asthmatics ages 6–18)

$0.7 $2.30 $7.0

($0.027--$5.2) ($0.085--$16) ($0.26--$49)

Lost work days

(ages 18–65)

$3.3 $11 $35

($2.90--$3.70) ($9.4--$12) ($30--$39)

Minor restricted-activity days

(ages 18–65)

$8.8 $29 $91

($4.70--$13) ($15--$43) ($48--$140)

a

All estimates are rounded to two significant digits. Estimates do not include unquantified health benefits noted in 

Table 5-2 or Section 5.6.5 or welfare co-benefits noted in Chapter 6. These estimates reflect incremental emissions reductions 

from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions 

reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the 

revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.

b

The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.

c

The negative estimates at the 5th percentile confidence estimates for this morbidity endpoint reflects the statistical 

power of the study used to calculate these health impacts. These results do not suggest that reducing air pollution results in 

additional health impacts.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-17

Table 5-20. Monetized PM2.5 Health Benefits for the Revised and Alternative Annual Primary 

PM2.5 Standards (Incremental to Analytical Baseline) (Millions of 2006$, 7% 

discount rate)

a

Health Effect

Revised and Alterative Annual Standards

(95th percentile confidence interval)

13 μg/m312 μg/m311 μg/m3

Avoided Mortalityb

Krewski et al. (2009)

(adult mortality age 30+)

$1,100 $3,600 $11,000

($110--$3,100) ($330--$9,800) ($1,100--$31,000)

Lepeule et al. (2012)

(adult mortality age 25+)

$2,600 $8,100 $26,000

($230--$7,300) ($720--$23,000) ($2,300--$74,000)

Woodruff et al. (1997)

(infant mortality)

$3.4 $11 $35

($0.29--$100) ($0.91--$32) ($3.0--$100)

Avoided Morbidity

Non-fatal heart attacks 

Peters et al. (2001)

(age >18)

$18 $54 $180

($2.8--$46) ($8.4--$140) ($28--$450)

Pooled estimate of 4 studies

(age >18)

$1.9 $5.9 $20

($0.40--$6.7) ($1.2--$20) ($4.1--$67)

Hospital admissions—respiratory

(all ages)c

$0.86 $3.0 $11

(-$0.22--$1.6) (-$0.8--$5.5) (-$2.7--$20)

Hospital admissions—cardiovascular 

(age > 18)

$1.70 $5.3 $18

($0.85--$2.8) ($2.7--$9.2) ($10--$32)

Emergency department visits for asthma

(all ages)

$0.03 $0.10 $0.34

(-$0.0052--$0.061) (-$0.018--$0.21) (-$0.063--$0.73)

Acute bronchitis

(ages 8–12)c

$0.13 $0.42 $1.3

(-$0.0060--$0.37) (-$0.019--$1.2) (-$0.059--$3.5)

Lower respiratory symptoms

(ages 7–14)

$0.08 $0.24 $0.71

($0.025--$0.10) ($0.078--$0.47) ($0.24--$1.4)

Upper respiratory symptoms

(asthmatics ages 9–11)

$0.17 $0.54 $1.6

($0.038--$0.42) ($0.12--$1.30) ($0.36--$4.0)

Asthma exacerbation

(asthmatics ages 6–18)

$0.7 $2.30 $7.0

($0.027--$5.2) ($0.085--$16) ($0.26--$49)

Lost work days

(ages 18–65)

$3.3 $11 $35

($2.90--$3.70) ($9.4--$12) ($30--$39)

Minor restricted-activity days

(ages 18–65)

$8.8 $29 $91

($4.70--$13) ($15--$43) ($48--$140)

a

All estimates are rounded to two significant digits. Estimates do not include unquantified health benefits noted in 

Table 5-2 or Section 5.6.5 or welfare co-benefits noted in Chapter 6. These estimates reflect incremental emissions reductions 

from an analytical baseline that gives “an adjustment” to the San Joaquin and South Coast areas in California for NOx emissions 

reductions expected to occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the 

revised standards. Full benefits of the revised standards in those two areas will not be realized until 2025.

b

The reduction in premature fatalities each year accounts for over 98% of total monetized benefits in this analysis. 

Mortality risk valuation assumes discounting over the SAB-recommended 20-year segmented lag structure.

c

The negative estimates at the 5th percentile confidence estimates for this morbidity endpoint reflects the statistical 

power of the study used to calculate these health impacts. These results do not suggest that reducing air pollution results in 

additional health impacts.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-18

Table 5-21. Total Estimated Monetized Benefits of the for Revised and Alternative Annual 

Primary PM2.5 Standards (Incremental to the Analytical Baseline) (billions of 

2006$) a,b

Benefits Estimate 13 μg/m312 μg/m311 μg/m

Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from 

Krewski et al. (2009)

3% discount rate $1.3 + B $4.0 +B $13 + B

7% discount rate $1.2 + B $3.6 +B $12 + B

Economic value of avoided PM2.5-related morbidities and premature deaths using PM2.5 mortality estimate from 

Lepeule et al. (2012)

3% discount rate $2.9 + B $9.1 +B $29 + B

7% discount rate $2.6 + B $8.2 +B $26 + B

a

Rounded to two significant figures. The reduction in premature fatalities each year accounts for over 98% 

of total monetized benefits in this analysis. Mortality risk valuation assumes discounting over the SABrecommended 20-year segmented lag structure. Not all possible benefits are quantified and monetized in this 

analysis. B is the sum of all unquantified health and welfare co-benefits. Data limitations prevented us from 

quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we 

were able to quantify.

b

These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” 

to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 

2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full 

benefits of the revised standards in those two areas will not be realized until 2025.

Table 5-22. Regional Breakdown of Monetized Benefits Results

Region

Revised and Alterative Annual Standards

13 μg/m312 μg/m311 μg/m3

Easta0% 0% 23%

Californiab100% 100% 77%

Rest of West 0% 0% <1%

a

Includes Texas and those states to the north and east. Several recent rules such as MATS and CSAPR will 

have substantially reduced PM2.5 levels by 2020 in the East, thus few additional controls would be needed to reach 

12 μg/m3.

b

For 12 and 13 μg/m3, all of the benefits occur in California because this highly populated area is where the 

most air quality improvement beyond the analytical baseline is needed to reach these levels. These estimates 

reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” to the San Joaquin 

and South Coast areas in California for NOx emissions reductions expected to occur between 2020 and 2025, when 

those areas are expected to demonstrate attainment with the revised standards. Full benefits of the revised 

standards in those two areas will not be realized until 2025.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-19

Figure 5-3. Estimated PM2.5-Related Premature Mortalities Avoided According to 

Epidemiology or Expert-Derived PM2.5 Mortality Risk Estimate for 12 μg/m3in 2020 a

a

These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” 

to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 

2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full 

benefits of the revised standards in those two areas will not be realized until 2025.

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

Krewski et al.

Lepeuele et al.

Millions (2010$)

PM2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions

Total Monetized PM2.5 Benefits of Standard at 12/35 μg/m3in 

2020*

3% Discount Rate

7% Discount Rate
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-20

Figure 5-4. Total Monetized Benefits Using 2 Epidemiology-Derived and 12-Expert Derived 

Relationships Between PM2.5 and Premature Mortality for 12 μg/m3in 2020 a

a

These estimates reflect incremental emissions reductions from an analytical baseline that gives “an adjustment” 

to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to occur between 

2020 and 2025, when those areas are expected to demonstrate attainment with the revised standards. Full 

benefits of the revised standards in those two areas will not be realized until 2025.

Uncertainty in Benefits Results

Mortality benefits account for 98% of total monetized benefits, in part because we are 

unable to quantify most of the non-health benefits. The next largest benefit is for reductions in 

chronic illness (nonfatal heart attacks), although this value is more than an order of magnitude 

lower than for premature mortality. Hospital admissions for respiratory and cardiovascular 

causes, MRADs and work loss days account for the majority of the remaining benefits. The 

remaining categories each account for a small percentage of total benefit; however, they 

represent a large number of avoided incidences affecting many individuals. A comparison of the 

incidence table to the monetary benefits table reveals that there is not always a close 

correspondence between the number of incidences avoided for a given endpoint and the 

monetary value associated with that endpoint. For example, we estimate almost 1,000 times 

more work loss days would be avoided than premature mortalities, yet work loss days account 

0 %

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

$ 0 $ 5 $10 $15 $20 $25 $30 Cumulative Percentage of Monetized Benefits

Monetized Benefits (Billions of 2010$)

Total Monetized Benefits Using 2 Epidemiology-Derived and 12 Expert-Derived 

Relationships Between PM2.5 and Mortality for 12/35 μg/m3(3% discount rate)

Expert A

Expert B

Expert C

Expert D

Expert E

Expert F

Expert G

Expert H

Expert I

Expert J

Expert K

Expert L

Krewski et al.

Lepeule et al.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-21

for only a very small fraction of total monetized benefits. This reflects the fact that many of the 

less severe health effects, while more common, are valued at a lower level than the more 

severe health effects. Also, some effects, such as hospital admissions, are valued using a proxy 

measure of WTP. As such, the true value of these effects may be higher than that reported in 

the tables above.

PM2.5 mortality benefits represent a substantial proportion of total monetized benefits 

(over 98% in this analysis), and these estimates have the following key assumptions and 

uncertainties.

1. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 produced varies considerably in composition across sources, but 

the scientific evidence is not yet sufficient to allow differential effects estimates 

by particle type. The PM ISA, which was twice reviewed by SAB-CASAC, 

concluded that “many constituents of PM2.5 can be linked with multiple health 

effects, and the evidence is not yet sufficient to allow differentiation of those 

constituents or sources that are more closely related to specific outcomes” (U.S. 

EPA, 2009b).

2. We assume that the health impact function for fine particles is log-linear without 

a threshold in this analysis. Thus, the estimates include health benefits from 

reducing fine particles in areas with varied concentrations of PM2.5, including 

both areas that do not meet the fine particle standard and those areas that are 

in attainment, down to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between the change in PM exposures 

and the total realization of changes in mortality effects. Specifically, we assume 

that some of the incidences of premature mortality related to PM2.5 exposures 

occur in a distributed fashion over the 20 years following exposure based on the 

advice of the SAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of 

mortality benefits at different discount rates.

Given that reductions in premature mortality dominate the size of the overall monetized 

benefits, more focus on uncertainty in mortality-related benefits gives us greater confidence in 

our uncertainty characterization surrounding total benefits.

Estimated Life Years Gained and Reduction in the Percentage of Deaths Attributable to PM2.5

In their 2008 review of the EPA’s approach to estimating ozone-related mortality 

benefits, NRC indicated, “EPA should consider placing greater emphasis on reporting decreases 

in age-specific death rates in the relevant population and develop models for consistent 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-22

calculation of changes in life expectancy and changes in number of deaths at all ages” (NRC, 

2008). In addition, NRC noted in an earlier report that “[f]rom a public-health perspective, lifeyears lost might be more relevant than annual number of mortality cases” (NRC, 2002). This 

advice is consistent with that of the SAB-HES, which agreed that “...the interpretation of 

mortality risk results is enhanced if estimates of lost life-years can be made” (U.S. EPA-SAB,

2004a). To address these recommendations, we use simplifying assumptions to estimate the 

number of life years that might be gained. We also estimate the reduction in the percentage of 

deaths attributed to PM2.5 resulting from the illustrative emission reduction strategies to reach

the revised annual primary standard. The EPA included similar estimates of life years gained in a 

previous assessment of PM2.5 benefits (U.S. EPA, 2006a, 2011a), the latter of which was peer 

reviewed by the SAB-HES (U.S. EPA-SAB, 2010a).

Changes in life years and changes in life expectancy at birth are frequently conflated, 

thus it is important to distinguish these two very different metrics at the outset. Life expectancy 

varies by age. CDC defines life expectancy as the “average number of years of life remaining for 

persons who have attained a given age” (CDC, 2011). In other words, changes in life expectancy 

refer to an average change for the entire population, and refer to the future. Over the past 50 

years, average life expectancy at birth in the U.S. has increased by 8.4 years (CDC, 2001). For 

example, life expectancy at birth was estimated in 2007 to be 77.9 years for an average person 

born in the U.S., but for people surviving to age 60, estimated life expectancy is 82.5 years (i.e., 

4.6 years more than life expectancy at birth) (CDC, 2011). Life years, on the other hand, 

measure the amount of time that an individual loses if they die before the age of their life 

expectancy. Life years refer to individuals, and refer to the past, e.g., when the individual has 

already died. If a 60-year old individual dies, we estimate that this individual would lose about 

22.5 years of life (i.e., the average population life expectancy for an individual of this age minus 

this person’s age at death).

5.7.3.1 Estimated Life Years Gained

For estimating the potential life years gained by reducing exposure to PM2.5 in the U.S. 

adult population, we use the same general approach as Hubbell (2006) and Fann et al. (2012a). 

We have not estimated the change in average life expectancy at birth in this RIA. Hubbell (2006) 

estimated that reducing exposure to PM2.5 from air pollution regulations may result in an 

average gain of 15 years of life for those adults prematurely dying from PM2.5 exposure. In 

contrast, Pope et al. (2009) estimated changes in average life expectancy at birth over a twenty 

year period, suggesting that reducing exposure to air pollution may increase average life 

expectancy at birth by approximately 7 months, which was 15% of the overall increase in life 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-23

expectancy at birth from 1980 through 2000. These results are not necessarily inconsistent 

because they are reporting different metrics. Because life expectancy is an average of the entire 

population (including both those whose deaths would likely be attributed to PM exposure as 

well as those whose deaths would not), average life expectancy changes associated with PM 

exposure would be expected to always be significantly smaller than the average number of life 

years lost by an individual who is projected to die prematurely from PM exposure.

To estimate the potential distribution of life years gained for population subgroups 

defined by the age range at which their reduction in PM2.5 exposure is modeled to occur we use 

standard life tables available from the CDC (2003) and the following formula:

(5.2)

where LEiis the average remaining life expectancy for age interval i, Miis the estimated change 

in number of deaths in age interval i, and n is the number of age intervals. 

To get Mi (the estimate the number of avoided premature deaths attributed to changes 

in PM2.5 exposure in 2020), we use a health impact function that incorporates risk coefficients 

estimated for the adult population in the U.S. and age-specific mortality rates. That is, we use 

risk coefficients that do not vary by age, but use baseline mortality rates that do. Because 

mortality rates for younger populations are much lower than mortality rates for older 

populations, most but not all, of the avoided deaths tend to be in older populations. Table 5-23

summarizes the modeled number of life years gained by reducing PM2.5 exposure to 12 μg/m3

in 2020. We then calculated the average number of life years gained per avoided premature

mortality. Figure 5-5 shows the potential life years gained as a result of meeting a primary 

standard of 12 μg/m3in 2020, partitioned by the age when exposure reduction occurred, not

necessarily age at death. 

By comparing the projected age distribution of the avoided premature deaths with the 

age distribution of life years gained, we observed that about half of the deaths occur in 

populations age 75–99, but half of the life years would occur in populations younger than 65. 

This is because the younger populations have the potential to lose more life years per death 

than older populations based on changes in PM2.5 exposure in 2020. We estimate that the 

average individual who would otherwise have died prematurely from PM exposure would gain 

16 additional years of life. However, this approach does not account for whether or not people 

who are older are more likely to be susceptible to the health effects of PM or whether that 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-24

susceptibility was in and of itself caused by PM exposure (for a more complete discussion of 

this issue, see Kunzli et al., 2001). 

Table 5-23. Sum of Life Years Gained by Age Range Attributed to the Revised Annual Primary 

PM2.5 Standard of 12 μg/m3 in 2020a,b

Age Rangeb

Krewski et al. (2009) Risk 

Coefficient c

Lepeule et al. (2012)

Risk Coefficient

25–29 — 610

30–34 210 470

35–44 550 1,200

45–54 1,000 2,300

55–64 1,600 3,500

65–74 1,700 3,900

75–84 1,300 2,900

85–99 560 1,300

Total life years gained 7,000 16,000

Average life years gained per individual 15.0 16.0

a

Estimates rounded to two significant figures.

b

Because we assume that there is a “cessation” lag between PM exposures and the reduction in the risk of 

premature death, there is uncertainty regarding the specific ages that people die relative to the change in 

exposure.

c

The youngest age in the population cohort of this study is 30.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-25

Figure 5-5. Estimated Life Years Gained as a Result of the Revised Annual Primary PM2.5 

Standard of 12 μg/m3in 2020, Partitioned by the Age When Exposure Reduction Occurred, 

Not Necessarily Age at Deatha

a

As shown in these charts, slightly more than half of the avoided premature deaths occur in populations 

age 75–99, but slightly more than half of the avoided life years occur in populations age <65 due to the fact that 

the younger populations would lose more life years per death than older populations. Results would be similar for 

other standard levels on a percentage basis. Because we assume that there is a “cessation” lag between PM 

exposures and the reduction in the risk of premature death, there is uncertainty regarding the specific ages that 

people die relative to the change in exposure.

b

The youngest age in the population cohort of this study used to estimate PM2.5 mortality incidence is 30.

5.7.3.2 Percent of PM-related Mortality Reduced

To estimate the percentage of all-cause mortality attributed to reduced PM2.5 exposure 

in 2020 as a result of the illustrative emission reduction strategies, we use Mi from the equation 

above, dividing the number of excess deaths estimated for each alternative standard by the 

total number of deaths in each county. Table 5-24 shows the reduction in all-cause mortality 

attributed to reducing PM2.5 exposure to the revised annual standard of 12 μg/m3in 2020. 

Figure 5-6 shows the percentage of avoided premature deaths attributed to meeting the 

revised primary annual standard of 12 μg/m3in 2020, partitioned by the age when exposure 

reduction occurred, not necessarily age at death.

Age 

30-34

Age

35-44

Age

45-54

Age

55-64

Age

65-74

Age

75-84

Age

85-99

Age

25-29

Age 

30-34

Age

35-44

Age

45-54

Age

55-64

Age

65-74

Age

75-84

Age

85-99

Calculated using Lepeule et al. (2012) risk 

coefficient Calculated using Krewski et al. (2009) risk 

coefficient b
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-26

Table 5-24. Estimated Reduction in the Percentage of All-Cause Mortality Attributed to the 

Revised Annual Primary PM2.5 Standard of 12 μg/m3in 2020a,b

Age Range

b

Krewski et al. (2009) Risk CoefficientcLepeule et al. (2012) Risk Coefficient

25–29 — 0.80%

30–34 0.35% 0.78%

35–44 0.32% 0.73%

45–54 0.32% 0.73%

55–64 0.33% 0.73%

65–74 0.33% 0.73%

75–84 0.32% 0.73%

85–99 0.29% 0.65%

a

Rounded to two significant figures. Results would be similar for other standard levels on a percentage 

basis. These estimates reflect incremental emissions reductions from an analytical baseline that gives “an 

adjustment” to the San Joaquin and South Coast areas in California for NOx emissions reductions expected to 

occur between 2020 and 2025, when those areas are expected to demonstrate attainment with the revised 

standards. Full benefits of the revised standards in those two areas will not be realized until 2025.

b

Because we assume that there is a “cessation” lag between PM exposures and the reduction in the risk of 

premature death, there is uncertainty regarding the specific ages that people die relative to the change in 

exposure.

c

The youngest age in the population cohort of this study is 30.

The relative distributions of the potential number of life years gained (Figure 5-5) and 

the estimated avoided mortalities (Figure 5-6) would be similar across alternative annual 

standards. Because reduction in PM exposure is not associated with an immediate 

improvement in chronic health conditions, we assume that there is a “cessation” lag between 

PM exposures and the reduction in the risk of premature death. There is uncertainty regarding 

the specific ages at which people “avoid” death relative to the change in exposure. While the 

structure of the lag is uncertain, some studies suggest that most of the premature deaths are 

avoided within the first 3 years after the change in exposure, while others are unable to identify 

conclusively a critical window of exposure (U.S. EPA, 2004c; Schwartz, 2008; Krewski et al. 

2009). These studies did not examine whether the cessation lag was modified by either age at 

the time when exposure is reduced or the extent of cumulative lifetime exposure.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-27

Figure 5-6. Estimate of Avoided Premature Deaths Attributed to the Revised Annual

Primary PM2.5 Standard of 12 μg/m3in 2020, Partitioned by the Age When Exposure 

Reduction Occurred, Not Necessarily Age at Deatha

a

As shown in these charts, slightly more than half of the avoided premature deaths occur in populations age 75-

99, but slightly more than half of the avoided life years occur in populations age <65 due to the fact that the 

younger populations would lose more life years per death than older populations. Results would be similar for 

other standard levels on a percentage basis. Because we assume that there is a “cessation” lag between PM 

exposures and the reduction in the risk of premature death, there is uncertainty regarding the specific ages that 

people die relative to the change in exposure.

b

The youngest age in the population cohort of this study is 30.

Evaluation of Mortality Impacts Relative to Various Concentration Benchmarks

In this analysis, we estimate the number of avoided PM2.5-related deaths occurring due 

to PM2.5 reductions down to various PM2.5 concentration benchmarks, including the Lowest 

Measured Level (LML) of each long-term PM2.5 mortality study. This analysis is one of several 

sensitivities that the EPA has historically performed that characterize the uncertainty associated 

with the PM-mortality relationship and the economic value of reducing the risk of premature 

death (Roman et al., 2008; U.S. EPA, 2006a, 2011a; Mansfield, 2009).

Our review of the current body of scientific literature indicates that a log-linear nothreshold model provides the best estimate of PM-related long-term mortality. The PM ISA 

(U.S. EPA, 2009b), which was twice reviewed by the EPA’s Clean Air Scientific Advisory 

Committee (U.S. EPA-SAB, 2009a, 2009b), concluded that the evidence supports the use of a 

no-threshold log-linear model while also recognizing potential uncertainty about the exact 

Age

30-34

Age

35-44 Age

45-54

Age

55-64

Age 

65-74 Age 

75-84

Age 

85-99

Age

25-29

Age

30-34 Age

35-44

Age

45-54

Age

55-64

Age

65-74

Age

75-84

Age

85-99

Calculated using Krewski et al. (2009) risk 

coefficient b

Calculated using Lepeule et al. (2012) risk 

coefficient.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-28

shape of the concentration-response function.10 Consistent with this finding, we estimate 

benefits associated with the full range of PM2.5 exposure in conjunction with sensitivity analyses 

to recognize the potential uncertainty at lower concentrations. Specifically, we incorporated a 

LML assessment, a method the EPA has employed in several recent RIAs (U.S. EPA, 2010g, 

2011c, 2011d). In addition, we have incorporated an assessment using specific concentration 

benchmarks identified in the EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011b).

These two approaches summarize the distribution of avoided PM2.5-related mortality 

impacts relative to baseline (i.e., pre-rule) annual mean PM2.5 levels. The LML approach 

compares the percentage of avoided premature deaths estimated to occur above and below 

the minimum observed air quality level of each long-term cohort study we use to quantify PM. 

In the air quality benchmark approach, we summarize the impacts occurring at different points 

in the distribution of the air quality data used in these same epidemiology studies.

Our confidence in the estimated number of premature deaths avoided (but not in the 

existence of a causal relationship between PM and premature mortality) diminishes as we 

estimate these impacts in locations where PM2.5 levels are below the LML. This interpretation is 

consistent with the Policy Assessment (U.S. EPA, 2011b) and advice from SAB-CASAC during 

their peer review (U.S. EPA-SAB, 2010d). As noted in the preamble to the final rule, the Policy 

Assessment (U.S. EPA, 2011b) concludes that the range from the 25th to the 10th percentile is a 

reasonable range of the air quality distribution below which we start to have appreciably less 

confidence in the magnitude of the associations observed in the epidemiological studies. In 

general, we are more confident in the magnitude of the risks we estimate from simulated PM2.5 

concentrations that coincide with the bulk of the observed PM concentrations in the 

epidemiological studies at are used to estimate the benefits. Likewise, we are less confident in 

the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the 

observed data in these studies. However, there are uncertainties inherent in identifying any 

particular point at which our confidence in reported associations becomes appreciably less, and 

the scientific evidence provides no clear dividing line. 

For these reasons, we consider the LML as well as one standard deviation below the 

mean11 air quality levels when characterizing the distribution of mortality impacts. It is 

 

10 For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-mortality 

relationship, see the Technical Support Document (TSD) entitled Summary of Expert Opinions on the Existence of 

a Threshold in the Concentration-Response Function for PM2.5-related Mortality (U.S. EPA, 2010f).

11 A range of one standard deviation around the mean represents approximately 68 percent of normally 

distributed data, and, below the mean falls between the 25th and 10th percentiles. 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-29

important to emphasize that “less confidence” does not mean “no confidence.” In addition, 

while we may have less confidence in the magnitude of the risk, we still have high confidence 

that PM2.5 is causally associated with risk at those lower air quality concentrations. To clarify 

this concept, Figure 5-7 graphically displays the spectrum of confidence using illustrative 

concentration benchmarks from the major epidemiology studies cited in this chapter.

Figure 5-7. Relationship between the Size of the PM Mortality Estimates and the PM2.5

Concentration Observed in the Epidemiology Study

Although these types of concentration benchmark analyses (e.g., LML, one standard 

deviation below the mean, etc.) provide some insight into the level of uncertainty in the 

estimated PM2.5 mortality benefits, the EPA does not view these concentration benchmarks as a 

concentration threshold below which we would not quantify health benefits of air quality 

improvements Rather, the core benefits estimates reported in this RIA (i.e., those based on 

Krewski et al. (2009) and Lepeule et al. (2012)) are the best measures because they reflect the 

full range of modeled air quality concentrations associated with the emission reduction 

strategies. In reviewing the Policy Assessment, SAB-CASAC confirmed that “[a]lthough there is 

increasing uncertainty at lower levels, there is no evidence of a threshold (i.e., a level below 

which there is no risk for adverse health effects)” (U.S. EPA-SAB, 2010d). In addition, in 

reviewing the Costs and Benefits of the Clean Air Act (U.S. EPA, 2011a), the SAB-HES noted that 

“[t]his [no-threshold] decision is supported by the data, which are quite consistent in showing 

effects down to the lowest measured levels. Analyses of cohorts using data from more recent 

years, during which time PM concentrations have fallen, continue to report strong associations 

with mortality” (U.S. EPA-SAB, 2010a). Therefore, the best estimate of benefits includes 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-30

estimates below and above these concentration benchmarks but uncertainty is higher in the 

magnitude of health benefits estimated at lower concentrations, with the lowest confidence 

below the LML. Estimated health impacts reflecting air quality improvements below and above 

these concentration benchmarks are appropriately included in the total benefits estimate. In 

other words, our higher confidence in the estimated benefits above these concentration 

benchmarks should not imply an absence of confidence in the benefits estimated below these 

concentration benchmarks.

We estimate that most of the avoided PM-related impacts we estimate in this analysis 

occur among populations exposed at or above the LML of the Lepeule et al. (2012) study, while 

a majority of the impacts occur at or above the LML of the Krewski et al. (2009) study. We show 

the estimated reduction in incidence of premature mortality above and below the LML or air 

quality benchmarks of these studies in Tables 5-25, and we graphically display the distribution 

of PM2.5-related mortality impacts for the selected standard in Figures 5-8 and 5-9. 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-31

Table 5-25. Estimated Reduction in Incidence of Adult Premature Mortality Occurring Above 

and Below Various Concentration Benchmarks in the Underlying Epidemiology 

Studiesa

Revised and 

Alternative 

Standard Level

Epidemiology 

Study

Total Reduced 

Mortality 

Incidence

Allocation of Reduced Mortality Incidence

Below 1 Std.

Dev.

Below AQ 

Mean

At or Above 

1 Std. Dev.

Below AQ 

Mean

Below 

LML

At or 

Above 

LML

13 μg/m3Krewski et al. 

(2009) 140

79 66 6 140

(54%) (46%) (4%) (96%)

Lepeule et al. 

(2012) 330 N/A N/A

130 200

(38%) (62%)

12 μg/m3Krewski et al. 

(2009) 460

200 260 14 440

(43%) (57%) (3%) (97%)

Lepeule et al. 

(2012) 1,000 N/A N/A

310 720

(30%) (70%)

11 μg/m3Krewski et al. 

(2009) 1,500

690 770 27 1,400

(47%) (53%) (2%) (98%)

Lepeule et al. 

(2012) 3,300 N/A N/A

1,000 2,300

(31%) (69%)

a Mortality incidence estimates are rounded to whole numbers and two significant digits, so estimates may 

not sum across columns. One standard deviation below the mean is equivalent to the middle of the range between 

the 10th and 25th percentile. For Krewski, the LML is 5.8 μg/m3and one standard deviation below the mean is 11.0 

μg/m3. For Lepeule et al., the LML is 8 μg/m3and we do not have the data for one standard deviation below the 

mean. It is important to emphasize that although we have lower levels of confidence in levels below the LML for 

each study, the scientific evidence does not support the existence of a level below which health effects from 

exposure to PM2.5 do not occur.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-32

Of total PM2.5-Related deaths avoided for 12 μg/m3:

97% occur among populations exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study.

70% occur among populations exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study.

Figure 5-8. Number of Premature PM2.5-related Deaths Avoided for the Revised Annual 

Primary PM2.5 Standard of 12 μg/m3in 2020 According to the Baseline Level of PM2.5 and the 

Lowest Measured Air Quality Levels of Each Mortality Study

0%

5%

10%

15%

20%

25%

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 Percentage of Avoided PM2.5Mortalities

Baseline Annual Mean PM2.5 Level (μg/m3)

Percentage of Total PM-related Mortalities Avoided by Baseline Air 

Quality Level

LML of Lepeule et 

al. (2012) study

LML of Krewski et 

al. (2009) study
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-33

Of total PM2.5-Related deaths avoided for 12 μg/m3:

97% occur among populations exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study.

70% occur among populations exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study.

Figure 5-9. Number of Premature PM2.5-related Deaths Avoided for the Revised Annual 

Primary PM2.5 Standard of 12 μg/m3in 2020 According to the Baseline Level of PM2.5 and the 

Lowest Measured Air Quality Levels of Each Mortality Study

When interpreting these LML and air quality benchmarks results, it is important to 

understand that the avoided PM2.5 deaths are estimated to occur from PM2.5 reductions in the 

baseline air quality simulation, which assumes that 15/35 is already met. When simulating 

attainment with revised and alternative standards, we adjust the design value at each monitor 

exceeding the standard alternative to equal that standard and use an air quality interpolation 

technique to simulate the change in PM levels surrounding that monitor. This technique tends 

to simulate the greatest air quality changes nearest the monitor. We estimate benefits using 

modeled air quality data with 12 km grid cells, which is important because the grid cells are 

often substantially smaller than counties and PM2.5 concentrations vary spatially within a 

county. Therefore, there may be a small number of grid cells with concentrations slightly 

greater than 15 μg/m3 in the gridded baseline even though all monitors could meet an annual 

standard of 15 μg/m3. In addition, some grid cells in a county can be below the level of a 

standard even though the highest monitor value is above that standard. Thus, emission 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Cumulative percentage of avoided PM Mortalities

Baseline Annual Mean PM2.5 Level (μg/m3)

Cumulative Percentage of Total PM-related Mortalities Avoided by 

Baseline Air Quality Level

LML of Krewski et 

al. (2009) study

LML of Lepeuele et 

al. (2012) study
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-34

reductions can lead to benefits in grid cells that are below a standard even within a county with 

a monitor that exceeds that standard. Furthermore, our approach to simulating attainment can 

lead to benefits in counties that are below alternative standard. Emission reduction strategies 

designed to reduce PM2.5 concentrations at a given monitor will frequently improve air quality 

in neighboring counties. In order to make a direct comparison between the benefits and costs 

of these emission reduction strategies, it is appropriate to include all the benefits occurring as a 

result of the emission reduction strategies applied regardless of where they occur. Therefore, it 

is not appropriate to estimate the fraction of benefits that occur only in counties that exceed 

the alternative standards because it would omit benefits attributable to emission reductions in 

exceeding counties. Figure 5-10 provides an illustration of this concept.

As this illustration shows, because 12km grid cells are much smaller than counties and because PM2.5 

concentrations vary within a county, there can be modeled grid cells with PM2.5 concentrations below an 

standard level even when the county exceeds that level. Because we model benefits using grid cells, this is a 

key reason why the LML graphs show benefits at levels below the alternative standards. In addition, emission 

reductions in an exceeding county can have benefits in a neighboring county that does not exceed. 

Figure 5-10. Illustration of Relative Size of County with Exceeding Monitor and Modeled 

Grid Cells
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-35

While the LML of each study is important to consider when characterizing and 

interpreting the overall level of PM2.5-related benefits, as discussed earlier in this chapter, the 

EPA believes that both of the cohort-based mortality estimates are suitable for use in air 

pollution health impact analyses. When estimating PM-related premature deaths avoided using 

risk coefficients drawn from the Lepeule et al. (2012) analysis of the Harvard Six Cities and the 

Krewski et al. (2009) analysis of the ACS cohorts there are innumerable other attributes that 

may affect the size of the reported effect estimates—including differences in population 

demographics, the size of the cohort, activity patterns and particle composition among others. 

The LML assessment presented here provides a limited representation of one key difference 

between the two studies.

Additional Sensitivity Analyses

The details of these sensitivity analyses are provided in Appendix 5.A, and summarized 

here. The use of an alternate lag structure would change the PM2.5-related mortality benefits 

discounted at 3% discounted by between 10% and –27%; when discounted at 7%, these 

benefits change by between 22% and −52%. When applying higher and lower income growth 

adjustments, the monetary value of PM2.5-related premature mortality changes between 33% 

and −14%; the value of acute endpoints changes between 8% and −4%. Using the updated 

population projection data, the rounded estimates of total monetized benefits increases by 

4.4% for 12 μg/m3. These sensitivity analysis results would be similar on a percentage basis for 

the alternative annual standards.

Discussion

The analysis in this Chapter demonstrates the potential for significant health benefits of 

the illustrative emission controls applied to simulate attainment with the revised annual 

primary PM2.5 standard. We estimate that by 2020 the emissions reductions to reach the

revised annual primary standard would have reduced the number of PM2.5-related premature 

mortalities and produce substantial non-mortality benefits. This rule promises to yield 

significant welfare impacts as well (see Chapter 6), though the quantification of those 

endpoints is absent in this RIA. Even considering the quantified and unquantified uncertainties 

identified in this chapter, we believe that the implementing the revised annual PM2.5 standard 

of 12 μg/m3 would have substantial public health benefits that outweigh the costs (see Chapter 

7).

Inherent in any complex RIA such as this one are multiple sources of uncertainty. Some 

of these we characterized through our quantification of statistical error in the concentration 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-36

response relationships and our use of the expert elicitation-derived PM2.5 mortality functions. 

Others, including the projection of atmospheric conditions and source-level emissions, the 

projection of baseline morbidity rates, incomes and technological development are 

unquantified. When evaluated within the context of these uncertainties, the health impact and 

monetized benefits estimates in this RIA can provide useful information regarding the public 

health benefits associated with the revised PM NAAQS.

There are important differences worth noting in the design and analytical objectives of 

NAAQS RIAs compared to RIAs for implementation rules, such as the recent MATS rule (U.S. 

EPA, 2011d). The NAAQS RIAs illustrate the potential costs and benefits of a revised air quality 

standard nationwide based on an array of emission reduction strategies for different sources, 

incremental to implementation of existing regulations and controls needed to attain the 

current standards. In short, NAAQS RIAs hypothesize, but do not predict, the emission 

reduction strategies that States may choose to enact when implementing a revised NAAQS. The 

setting of a NAAQS does not directly result in costs or benefits, and as such, NAAQS RIAs are 

merely illustrative and are not intended to be added to the costs and benefits of other 

regulations that result in specific costs of control and emission reductions. By contrast, the 

emission reductions from implementation rules are generally for specific, well-characterized 

sources, such as the recent MATS rule (U.S. EPA, 2011d). In general, the EPA is more confident 

in the magnitude and location of the emission reductions for implementation rules. As such, 

emission reductions achieved under promulgated implementation rules such as MATS have 

been reflected in the baseline of this NAAQS analysis. Subsequent implementation rules will be 

reflected in the baseline for the next PM NAAQS review. For this reason, the benefits estimated 

provided in this RIA and all other NAAQS RIAs should not be added to the benefits estimated for 

implementation rules.

In setting the NAAQS, the EPA considers that PM2.5 concentrations vary over space and 

time. While the standard is designed to limit concentrations at the highest monitor in an area, it 

is understood that emission controls put in place to meet the standard of the highest monitor 

will simultaneously result in lower PM2.5 concentrations throughout the entire area. In fact, the 

Quantitative Risk and Exposure Assessment for Particulate Matter (U.S. EPA, 2010b) shows how 

different standard levels would affect the entire distribution of PM2.5 concentrations, and thus 

people’s exposures and risk, across urban areas. For this reason, it is inappropriate to use the 

NAAQS level as a bright line for health effects.

The NAAQS are not set at levels that eliminate the risk of air pollution completely.

Instead, the Administrator sets the NAAQS at a level requisite to protect public health with an 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-37

adequate margin of safety, taking into consideration effects on susceptible populations based 

on the scientific literature. The risk analysis prepared in support of this PM NAAQS reported 

risks below these levels, while acknowledging that the confidence in those effect estimates is 

higher at levels closer to the standard (U.S. EPA, 2010b). While benefits occurring below the 

standard may be somewhat more uncertain than those occurring above the standard, the EPA

considers these to be legitimate components of the total benefits estimate. Though there are 

greater uncertainties at lower PM2.5 concentrations, there is no evidence of a threshold in 

PM2.5-related health effects in the epidemiology literature. Given that the epidemiological 

literature in most cases has not provided estimates based on threshold models, there would be 

additional uncertainties imposed by assuming thresholds or other non-linear concentrationresponse functions for the purposes of benefits analysis.

The estimated benefits for the revised and alternative annual standards are in addition 

to the substantial benefits estimated for several recent implementation rules (U.S. EPA, 2009a, 

2011c, 2011d, 2011e). Rules such as MATS and other emission reductions will have substantially 

reduced ambient PM2.5 concentrations by 2020 in the East, such that no additional controls 

would be needed to reach 12 μg/m3 in the East beyond the analytical baseline. These rules that 

have already been promulgated have tremendous combined benefits that explain why the 

number of avoided premature deaths associated with this NAAQS revision are smaller than 

were estimated in the previous PM NAAQS RIA (U.S. EPA, 2006a) for the year 2020 and even 

smaller than the mortality risks estimated for the current year in the PM REA (U.S. EPA, 2010b). 

In addition, because our analytical baseline excludes benefits associated with attaining the 

current annual and daily standards as well as the mobile NOx emissions anticipated by 2025, 

including the benefits associated with those air quality improvements would result in higher 

benefits than we have estimated here.

For the revised annual standard of 12 μg/m3, all of the estimated benefits occur in 

California because this is the only state that needs additional air quality improvement beyond 

the analytical baseline after accounting for the air quality improvements from recent rules.

Because all of the monetized human health benefits are projected to occur in California, we 

have considered the cohort studies conducted in California specifically in addition to the 

national risk coefficients we use as our core estimates. Although we have not calculated the 

benefits results using these cohort studies, we provided the risk coefficients from these 

California cohorts to show how much the monetized benefits could have changed if we used 

these cohort studies. Most of the California cohort studies report central effect estimates 

similar to the (nation-wide) all-cause mortality risk estimate we applied from Krewski et al. 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-38

(2009) and Lepeule et al. (2012) albeit with wider confidence intervals. Three cohort studies 

conducted in California indicate statistically significant higher risks than the risk estimates we 

applied from Lepeule et al. (2012), and four studies showed insignificant results.

References

Abbey, David E et al. 1995. “Chronic Respiratory Symptoms Associated with Estimated LongTerm Ambient Concentrations of Fine Particulates less than 2.5 Microns in aerodynamic 

diameter (PM2.5) and Other Air Pollutants.” Journal of Exposure Analysis and 

Environmental Epidemiology 5(2): 137-158.

Abbey, D.E., N. Nishino, W.F. McDonnell, R.J. Burchette, S.F. Knutsen, W. Lawrence Beeson, and 

J.X. Yang. 1999. “Long-term inhalable particles and other air pollutants related to 

mortality in nonsmokers.” American Journal of Respiratory and Critical Care Medicine

159(2):373-382.

Abt Associates, Inc. 2012. BenMAP User’s Manual Appendices. Prepared for U.S. Environmental 

Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, 

NC. September. Available on the Internet at 

<http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>.

Abt Associates, Inc. 2010. Environmental Benefits and Mapping Program (Version 4.0). 

Bethesda, MD. Prepared for U.S. Environmental Protection Agency Office of Air Quality 

Planning and Standards. Research Triangle Park, NC. Available on the Internet at 

<http://www.epa.gov/air/benmap>.

Adams PF, Hendershot GE, Marano MA. 1999. “Current Estimates from the National Health 

Interview Survey, 1996.” Vital Health Stat 10(200):1-212.

Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet, Healthcare Cost and 

Utilization Project. Rockville, MD. Available on the Internet at 

<http://hcupnet.ahrq.gov>.

Agency for Healthcare Research and Quality (AHRQ). 2007. HCUPnet, Healthcare Cost and 

Utilization Project. Rockville, MD. Available on the Internet at 

<http://hcupnet.ahrq.gov>.

Agency for Healthcare Research and Quality (AHRQ). 2009. HCUPnet, Healthcare Cost and 

Utilization Project. Rockville, MD. Available on the Internet at 

<http://hcupnet.ahrq.gov>.American Lung Association (ALA). 1999. Chronic Bronchitis. 

Available on the Internet at <http://www.lungusa.org/diseases/lungchronic.html>.

Alberini A, Cropper M, Krupnick A, Simon NB. 2004. “Does the value of a statistical life vary with 

age and health status? Evidence from the US and Canada.” Journal of Environmental 

Economics and Management 48(1): 769-792.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-39

Alberini, Anna & Milan Ščasný. 2011. "Context and the VSL: Evidence from a Stated Preference 

Study in Italy and the Czech Republic," Environmental & Resource Economics, European 

Association of Environmental and Resource Economists 49(4), pages 511-538.

Aldy JE, Viscusi WK. 2008. “Adjusting the value of a statistical life for age and cohort effects.” 

The Review of Economics and Statistics 90(3):573-581.

American Lung Association (ALA). 2002. Trends in Asthma Morbidity and Mortality. American 

Lung Association, Best Practices and Program Services, Epidemiology and Statistics Unit.

Available on the Internet at <http://www.lungusa.org/data/asthma/ASTHMAdt.pdf>.

American Lung Association (ALA). 2010a. Trends in COPD (Chronic Bronchitis and Emphysema): 

Morbidity and Mortality. American Lung Association Epidemiology and Statistics Unit 

Research and Program Services Division. February. Table 4. Based on NHIS data (CDC, 

2008). Available at http://www.lungusa.org/finding-cures/our-research/trendreports/copdtrend-report.pdf.

American Lung Association (ALA). 2010b. Trends in Asthma Morbidity and Mortality. American 

Lung Association Epidemiology and Statistics Unit Research and Program Services 

Division. February. Table 7. Based on NHIS data (CDC, 2008). Available at 

http://www.lungusa.org/finding-cures/our-research/trend-reports/asthma-trendreport.pdf.

Anderson HR, Bremner SA, Atkinson RW, Harrison RM, Walters S. 2001. “Particulate matter and 

daily mortality and hospital admissions in the west midlands conurbation of the UK: 

associations with fine and coarse particles, black smoke and sulphate.” Occup Environ 

Med 58:504–510.

Ayyub, B.M. 2002. Elicitation of Expert Opinions for Uncertainty and Risk. CRC Press, Florida.

Babin, S. M., H. S. Burkom, et al. 2007. “Pediatric patient asthma-related emergency 

department visits and admissions in Washington, DC, from 2001–2004, and associations 

with air quality, socio-economic status and age group.” Environ Health 6: 9.

Barnett, Adrian G. et al. 2006. “The Effects of Air Pollution on Hospitalizations for 

Cardiovascular Disease in Elderly People in Australian and New Zealand Cities.”

Environmental Health Perspectives 114(7): 1018-1023.

Belanger, K., W. Beckett, E. Triche, M.B. Bracken, T. Holford, P. Ren, J.E. McSharry, D.R. Gold, 

T.A. Platts-Mills, and B.P. Leaderer. 2003. “Symptoms of Wheeze and Persistent Cough 

in the First Year of Life: Associations with Indoor Allergens, Air Contaminants, and 

Maternal History of Asthma.” American Journal of Epidemiology 158:195-202.

Bell, M. L., K. Ebisu, et al. 2008. “Seasonal and Regional Short-term Effects of Fine Particles on 

Hospital Admissions in 202 US Counties, 1999–2005.” American Journal of Epidemiology

168(11): 1301-1310.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-40

Berger, M.C., G.C. Blomquist, D. Kenkel, and G.S. Tolley. 1987. “Valuing Changes in Health Risks: 

A Comparison of Alternative Measures.” The Southern Economic Journal 53:977-984.

Centers for Disease Control and Prevention (CDC). 2003. Health, United States. Table 30. Years 

of Potential Life Lost Before Age 75 for Selected Causes of Death, According to Sex, Race 

and Hispanic Origin: United States, Selected Years 1980– 2000. Available on the Internet 

at <http://www.cdc.gov/nchs/data/hus/hus03.pdf>.

Centers for Disease Control and Prevention (CDC). 2008. National Center for Health Statistics. 

National Health Interview Survey, 1999-2008.

Centers for Disease Control and Prevention (CDC). 2011. United States Life Tables, 2007 

National Vital Statistics Reports. Volume 59, Number 9. September. Available on the 

Internet at <http://www.cdc.gov/nchs/data/nvsr/nvsr59/nvsr59_09.pdf>.

Chay, K.Y., and M. Greenstone. 2003. “The Impact of Air Pollution on Infant Mortality: Evidence 

from Geographic Variation in Pollution Shocks Induced by a Recession.” Quarterly 

Journal of Economics 118(3).

Chen L, Jennison BL, Yang W, and Omaye ST. 2000. “Elementary school absenteeism and air 

pollution.” Inhal Toxicol 12(11):997-1016.

Cropper, M. L. and A. J. Krupnick. 1990. The Social Costs of Chronic Heart and Lung Disease.

Resources for the Future. Washington, DC. Discussion Paper QE 89-16-REV.

Crouse DL, Peters PA, van Donkelaar A, Goldberg MS, Villeneuve PJ, Brion O, et al. 2012. “Risk 

of Non-accidental and Cardiovascular Mortality in Relation to Long-term Exposure to 

Low Concentrations of Fine Particulate Matter: A Canadian National-level Cohort Study.”

Environ Health Perspect. In press. http://dx.doi.org/10.1289/ehp.1104049.

Dockery, D.W., C.A. Pope, X.P. Xu, J.D. Spengler, J.H. Ware, M.E. Fay, B.G. Ferris, and F.E. 

Speizer. 1993. “An Association between Air Pollution and Mortality in Six U.S. Cities.”

New England Journal of Medicine 329(24):1753-1759.

Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis, J.H. Ware, 

M. Raizenne, and F.E. Speizer. 1996. “Health Effects of Acid Aerosols On North American 

Children-Respiratory Symptoms.” Environmental Health Perspectives 104(5):500-505.

Dominici, F., R.D. Peng, M.L. Bell, L. Pham, A. McDermott, S.L. Zeger, J.M. Samet. 2006. “Fine 

Particulate Air Pollution and Hospital Admission for Cardiovascular and Respiratory 

Diseases.” Journal of the American Medical Association 295: 1127-1134.

Eftim SE, Samet JM, Janes H, McDermott A, Dominici F. 2008. “Fine particulate matter and mortality: a comparison of the Six Cities and American Cancer Society cohorts with a 

Medicare cohort.” Epidemiology 19:209–216.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-41

Eisenstein, E.L., L.K. Shaw, K.J. Anstrom, C.L. Nelson, Z. Hakim, V. Hasselblad and D.B. Mark. 

2001. “Assessing the Clinical and Economic Burden of Coronary Artery Disease: 1986-

1998. “Medical Care 39(8):824-35.

Eisner M, N Anthonisen, D Coultas et al. 2010. “An official American Thoracic Society public 

policy statement: novel risk factors and the global burden of chronic obstructive 

pulmonary disease,” Am J Respir Crit Care Med 182: 693–718.

Fann N, Lamson A, Wesson K, Risley D, Anenberg SC, Hubbell BJ. 2012a. “Estimating the 

National Public Health Burden Associated with Exposure to Ambient PM2.5 and Ozone. 

Risk Analysis,” Risk Analysis. 32(1): 81-95.

Fann N, Roman HR, Fulcher CF, Gentile M, Wesson K, Hubbell BJ, Levy JI. 2012b. “Maximizing 

Health Benefits and Minimizing Inequality: Incorporating Local Scale Data in the Design 

and Evaluation of Air Quality Policies.” Risk Analysis. 32(6): 908—922.

Freeman (III), AM. 1993. The Measurement of Environmental and Resource Values: Theory and 

Methods. Washington, DC: Resources for the Future.

Gan, W. Q., H. W. Davies, et al. 2012. “Association of Long-term Exposure to Community Noise 

and Traffic-related Air Pollution With Coronary Heart Disease Mortality.” American 

Journal of Epidemiology. In press. doi: 10.1093/aje/kwr424.

GeoLytics Inc. 2002. GeoLytics CensusCD® 2000 Short Form Blocks. CD-ROM Release 1.0. 

GeoLytics, Inc. East Brunswick, NJ. Available: http://www.geolytics.com/ [accessed 29 

September 2004].

Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. 2001. “The 

effects of ambient air pollution on school absenteeism due to respiratory illnesses.”

Epidemiology 12(1):43-54.

Glad, J.A., L.L. Brink, E.O. Talbott, P.C. Lee, X. Xu, M. Saul, and J. Rager. 2012. The Relationship 

of Ambient Ozone and PM2.5 Levels and Asthma Emergency Department Visits: Possible 

Influence of Gender and Ethnicity. Archives of Environmental & Occupational Health. 

Vol 62 (2): 103-108.

Hall JV, Brajer V, Lurmann FW. 2003. Economic Valuation of Ozone-related School Absences in 

the South Coast Air Basin of California. Contemporary Economic Policy 21(4):407-417.

Harrington, W., and P.R. Portney. 1987. “Valuing the Benefits of Health and Safety Regulation.”

Journal of Urban Economics 22:101-112.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-42

Hollman, F.W., T.J. Mulder, and J.E. Kallan. 2000. Methodology and Assumptions for the 

Population Projections of the United States: 1999 to 2100. Population Division Working 

Paper No. 38, Population Projections Branch, Population Division, U.S. Census Bureau, 

Department of Commerce. January. Available on the Internet at 

<http://www.census.gov/population/www/documentation/twps0038/twps0038.html>.

Hubbell, B., A. Hallberg, D.R. McCubbin, and E. Post. 2005. “Health-Related Benefits of Attaining 

the 8-Hr Ozone Standard.” Environmental Health Perspectives 113:73–82. ICD, 1979

Hubbell BL. 2006. “Implementing QALYs in the analysis of air pollution regulations.”

Environmental and Resource Economics 34:365–384.

Industrial Economics, Incorporated (IEc). 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. September. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_tsd_expert_interview_sum

maries.pdf>.

Industrial Economics, Incorporated (IEc). 2012. Updating BenMAP Income Elasticity Estimates—

Literature Review . Prepared for: Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. September. Available on 

the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/IncomeElasticityUpdate_Recommenda

tionswithappendices.pdf>.

Kleckner, N., and J. Neumann. 1999. Recommended Approach to Adjusting WTP Estimates to 

Reflect Changes in Real Income. Memorandum to Jim Democker, U.S. EPA/OPAR. June 3.

Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/klecknerandneumann199

9.pdf>.

Kochi, I., B. Hubbell, and R. Kramer. 2006. “An Empirical Bayes Approach to Combining 

Estimates of the Value of Statistical Life for Environmental Policy Analysis.”

Environmental and Resource Economics. 34: 385-406.

Kloog, I., B.A. Coull, A. Zanobetti, P. Koutrakis, J.D. Schwartz. 2012. Acute and Chronic Effects of 

Particles on Hospital Admissions in New-England. PLoS ONE. Vol 7 (4): 1-8.

Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. “Extended follow-up and 

spatial analysis of the American Cancer Society study linking particulate air pollution and 

mortality.” HEI Research Report, 140, Health Effects Institute, Boston, MA.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-43

Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M. Abrahamowicz, 

and W.H. White. 2000. “Reanalysis of the Harvard Six Cities Study and the American 

Cancer Society Study of Particulate Air Pollution and Mortality.” Special Report to the 

Health Effects Institute. Cambridge MA. July.

Krupnick, A.J., and M.L. Cropper. 1992. “The Effect of Information on Health Risk Valuations.”

Journal of Risk and Uncertainty 5(2):29-48.

Kunzli, N., R. Kaiser, S. Medina, M. Studnicka, O. Chanel, P. Filliger, M. Herry, F. Horak Jr., V. 

Puybonnieux-Texier, P. Quenel, J. Schneider, R. Seethaler, J-C Vergnaud, and H. Sommer. 

2000. “Public-Health Impact of Outdoor and Traffic-Related Air Pollution: A European 

Assessment.” The Lancet 356:795-801.

Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M. Studnicka. 2001. “Assessment of 

Deaths Attributable to Air Pollution: Should We Use Risk Estimates Based on Time Series 

or on Cohort Studies?” American Journal of Epidemiology 153(11):1050-55.

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air 

Pollution and Mortality.” American Journal of Respiratory and Critical Care Medicine

173:667-672.

Lave, L.B., and E.P. Seskin. 1977. Air Pollution and Human Health. Baltimore: Johns Hopkins 

University Press for Resources for the Future.

Lepeule J, Laden F, Dockery D, Schwartz J 2012. “Chronic Exposure to Fine Particles and 

Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.”

Environ Health Perspect. Jul;120(7):965-70.

Levy JI, Baxter LK, Schwartz J. 2009. “Uncertainty and variability in health-related damages from 

coal-fired power plants in the United States.” Risk Anal. 29(7) 1000-1014.

Lichtenstein, S. and P. Slovic, eds. 2006. The construction of preference. New York: Cambridge University 

Press.

Lipfert, F.W., H. Mitchell Perry Jr., J. Philip Miller, Jack D. Baty, Ronald E. Wyzg, and Sharon E. 

Carmody. 2000. “The Washington University-EPRI Veterans’ Cohort Mortality Study: 

Preliminary Results.” Inhalation Toxicology 12:41-74.

Lipfert, F.W., S.C. Morris, and R.E. Wyzga. 1989. “Acid Aerosols—the Next Criteria Air 

Pollutant.” Environmental Science & Technology 23(11):1316-1322.

Lipfert, F.W.; Perry, H.M., Jr.; Miller, J.P.; Baty, J.D.; Wyzga, R.E.; Carmody, S.E. 2003. “Air 

Pollution, Blood Pressure, and Their Long-Term Associations with Mortality.” Inhalation 

Toxicology. 15, 493-512.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-44

Lipfert, F.W.; Wyzga, R.E.; Baty, J.D.; Miller, J.P. 2006. “Traffic Density as a Surrogate Measure of

Environmental Exposures in Studies of Air Pollution Health Effects: Long-Term Mortality 

in a Cohort of US Veterans.” Atmospheric Environment 40: 154-169.

Lippmann M, Ito K, Nádas A, Burnett RT. 2000. “Association of particulate matter components 

with daily mortality and morbidity in urban populations.” Res Rep Health Eff Inst. 95:5-

72, discussion 73-82.

Lisabeth, L.D. et al. 2008. “Ambient air pollution and risk for ischemic stroke and transient 

ischemic attack.” Annals of Neurology. July (64): 53—59.

Mansfield, Carol; Paramita Sinha; Max Henrion. 2009. Influence Analysis in Support of 

Characterizing Uncertainty in Human Health Benefits Analysis: Final Report. Prepared 

for U.S. EPA, Office of Air Quality Planning and Standards. November. Available on the 

internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/influence_analysis_final_report_psg.p

df>.

Mar, T. F., J. Q. Koenig and J. Primomo. 2010. “Associations between asthma emergency visits 

and particulate matter sources, including diesel emissions from stationary generators in 

Tacoma, Washington.” Inhal Toxicol. Vol. 22 (6): 445-8.Mar, T. F., T. V. Larson, et al. 

2004. “An analysis of the association between respiratory symptoms in subjects with 

asthma and daily air pollution in Spokane, Washington.” Inhal Toxicol 16(13): 809-15.

Mathes, R.W., K. Ito, and T. Matte. 2011. Assessing Syndromic Surveillance of Cardiovascular 

Outcomes from Emergency Department Chief Complaint Data in New York City. PloS 

ONE. Vol 6(2): 1-10.

Metzger, K. B., P. E. Tolbert, et al. 2004. “Ambient air pollution and cardiovascular emergency 

department visits.” Epidemiology 15(1): 46-56.

Miller, Kristin A., David S. Siscovick, Lianne Sheppard, Kristen Shepherd, Jeffrey H. Sullivan, 

Garnet L. Anderson, and Joel D. Kaufman. 2007. “Long-Term Exposure to Air Pollution 

and Incidence of Cardiovascular Events in Women.” New England Journal of Medicine. 

356 (5) :447-458.

Moolgavkar, S.H. 2000. “Air Pollution and Hospital Admissions for Diseases of the Circulatory 

System in Three U.S. Metropolitan Areas.” Journal of the Air and Waste Management 

Association 50:1199-1206.

Moolgavkar, S.H. 2003. “Air Pollution and Daily Deaths and Hospital Admissions in Los Angeles 

and Cook Counties.” In Revised Analyses of Time-Series Studies of Air Pollution and 

Health. Special Report. Boston, MA: Health Effects Institute.

Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A Meta-Analysis.”

Journal of Policy Analysis and Management 21(2):253-270.

Formatted: Spanish (International

Sort)
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-45

Mustafić H, Jabre P, Caussin C et al. 2012. “Main Air Pollutants and Myocardial Infarction: A 

Systematic Review and Meta-analysis” JAMA. 2012;307(7):713-721.

National Center for Education Statistics (NCHS). 1996. The Condition of Education 1996, 

Indicator 42: Student Absenteeism and Tardiness. U.S. Department of Education. 

Washington, DC.

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air 

Pollution Regulations. Washington, DC: The National Academies Press. Washington, DC.

National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic 

Benefits from Controlling Ozone Air Pollution. National Academies Press. Washington, 

DC.

Neumann, J.E., M.T. Dickie, and R.E. Unsworth. 1994. “Linkage Between Health Effects 

Estimation and Morbidity Valuation in the Section 812 Analysis—Draft Valuation 

Document.” Industrial Economics Incorporated (IEc) Memorandum to Jim DeMocker, 

U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Policy 

Analysis and Review. March 31. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/neumannn1994.pdf>.

Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis. Washington, 

DC. Available on the Internet at <http://www.whitehouse.gov/omb/circulars/a004/a4.html.>

Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. 2001. “Air Pollution and 

Exacerbation of Asthma in African-American Children in Los Angeles.” Epidemiology

12(2):200–208.

Ostro, B.D. 1987. “Air Pollution and Morbidity Revisited: A Specification Test.” Journal of

Environmental Economics Management 14:87-98.

Ostro, B.D. and S. Rothschild. 1989. “Air Pollution and Acute Respiratory Morbidity: An 

Observational Study of Multiple Pollutants.” Environmental Research 50:238-247.

Ozkaynak, H., and G.D. Thurston. 1987. “Associations between 1980 U.S. Mortality Rates and 

Alternative Measures of Airborne Particle Concentration.” Risk Analysis 7(4):449-461.

Patel, M. M., S. N. Chillrud, J. C. Correa, Y. Hazi, M. Feinberg, D. Kc, S. Prakash, J. M. Ross, D. 

Levy and P. L. Kinney. 2010. “Traffic-Related Particulate Matter and Acute Respiratory 

Symptoms among New York City Area Adolescents.” Environ Health Perspect. 

Sep;118(9):1338-43.

Peel JL; Metzger KB; Klein M; Flanders WD; Mulholland JA; Tolbert PE. 2007. “Ambient air 

pollution and cardiovascular emergency department visits in potentially sensitive 

groups.” Am J Epidemiol 165: 625-633.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-46

Peng, R. D., M. L. Bell, et al. 2009. “Emergency admissions for cardiovascular and respiratory 

diseases and the chemical composition of fine particle air pollution.” Environ Health 

Perspect 117(6): 957-63.

Peng, R. D., H. H. Chang, et al. 2008. “Coarse particulate matter air pollution and hospital 

admissions for cardiovascular and respiratory diseases among Medicare patients.” JAMA

299(18): 2172-9.

Peters A. 2005. “Particulate matter and heart disease: evidence from epidemiological studies.”

Toxicol Appl Pharmacol. Sep 1;207(2 Suppl):477-82.

Peters A. 2006. “When a myocardial infarction comes out of the not-so-blue air.” Circulation. 

Dec 5;114(23):2430-1

Peters, A., D. W. Dockery, J. E. Muller and M. A. Mittleman. 2001. “Increased particulate air 

pollution and the triggering of myocardial infarction.” Circulation. Vol. 103 (23): 2810-5.

Poloniecki, J.D., R.W. Atkinson., A.P. de Leon., and H.R. Anderson. 1997. “Daily Time Series for 

Cardiovascular Hospital Admissions and Previous Day’s Air Pollution in London, UK.”

Occupational and Environmental Medicine 54(8):535-540

Pope, C.A., III, R.T. Burnett, G.D. Thurston, M.J. Thun, E.E. Calle, D. Krewski, and J.J. Godleski. 

2004. “Cardiovascular Mortality and Long-term Exposure to Particulate Air Pollution.”

Circulation 109: 71-77.

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. 

“Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate 

Air Pollution.” Journal of the American Medical Association 287:1132-1141.

Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. 1991. “Respiratory Health and 

PM10 Pollution: A Daily Time Series Analysis.” American Review of Respiratory Diseases

144:668-674.

Pope, CA III, E Majid, D Dockery. 2009. “Fine Particle Air Pollution and Life Expectancy in the 

United States.” New England Journal of Medicine 360: 376–386.

Pope, C. A., III, J. B. Muhlestein, et al. 2006. “Ischemic heart disease events triggered by shortterm exposure to fine particulate air pollution.” Circulation 114(23): 2443-8.

Pope, C.A., III, M.J. Thun, M.M. Namboodiri, D.W. Dockery, J.S. Evans, F.E. Speizer, and C.W. 

Heath, Jr. 1995. “Particulate Air Pollution as a Predictor of Mortality in a Prospective 

Study of U.S. Adults.” American Journal of Respiratory Critical Care Medicine 151:669-

674.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-47

Puett RC, Hart JE, Suh H, Mittleman M, Laden F. 2011. “Particulate Matter Exposures, Mortality 

and Cardiovascular Disease in the Health Professionals Follow-up Study.” Environ Health 

Perspect. August; 119(8): 1130–1135.

Puett RC, Hart JE, Yanosky JD, Paciorek C, Schwartz J, Suh H, et al. 2009. “Chronic fine and 

coarse particulate exposure, mortality and coronary heart disease in the Nurses’ Health 

Study.” Environ Health Perspect 117:1697–1701.

Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan 

J. Hubbell, and Patrick L. Kinney. 2008. “Expert Judgment Assessment of the Mortality 

Impact of Changes in Ambient Fine Particulate Matter in the U.S.” Environ. Sci. Technol., 

42(7):2268-2274.

Rosamond, W., G. Broda, E. Kawalec, S. Rywik, A. Pajak, L. Cooper, and L. Chambless. 1999. 

“Comparison of Medical Care and Survival of Hospitalized Patients with Acute 

Myocardial Infarction in Poland and the United States.” American Journal of Cardiology

83:1180-1185

Rowe, R.D., and L.G. Chestnut. 1986. “Oxidants and Asthmatics in Los Angeles: A Benefits 

Analysis—Executive Summary.” Prepared by Energy and Resource Consultants, Inc. 

Report to the U.S. Environmental Protection Agency, Office of Policy Analysis. EPA- 230-

09-86-018. Washington, DC.

Rowlatt, P., Spackman, M., Jones, S., Jones-Lee, M., Loomes, G. 1998. Valuation of deaths from 

air pollution. A Report for the Department of Environment, Transport and the Regions 

and the Department of Trade and Industry. National Economic Research Associates 

(NERA), London.

Russell, M.W., D.M. Huse, S. Drowns, E.C. Hamel, and S.C. Hartz. 1998. “Direct Medical Costs of 

Coronary Artery Disease in the United States.” American Journal of Cardiology

81(9):1110-1115.

Schwartz, J. 1993. “Particulate Air Pollution and Chronic Respiratory Disease.” Environment 

Research 62:7-13.

Schwartz, J., and L.M. Neas. 2000. “Fine Particles are More Strongly Associated than Coarse 

Particles with Acute Respiratory Health Effects in Schoolchildren.” Epidemiology 11:6-

10.

Schwartz J, Dockery DW, Neas LM, Wypij D, Ware JH, Spengler JD, Koutrakis P, Speizer FE, Ferris 

BG Jr. 1994. “Acute effects of summer air pollution on respiratory symptom reporting in 

children.” American Journal of Respiratory and Critical Care Medicine 150(5 Pt 1):1234-

42.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-48

Schwartz J, Coull B, Laden F. 2008. “The Effect of Dose and Timing of Dose on the Association 

between Airborne Particles and Survival.” Environmental Health Perspectives 116: 64-

69.

Sheppard, L. 2003. “Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in 

Seattle, Washington, 1987-1994.” In Revised Analyses of Time-Series Studies of Air 

Pollution and Health. Special Report. Boston, MA: Health Effects Institute.

Shogren, J., and T. Stamland. 2002. “Skill and the Value of Life.” Journal of Political Economy

110:1168-1197.

Slaughter, James C et al. 2005. “Association between particulate matter and emergency room 

visits, hospital admissions and mortality in Spokane, Washington.” Journal of Exposure 

Analysis and Environmental Epidemiology (2005) 15, 153–159. 

doi:10.1038/sj.jea.7500382

Smith et al. 2004. “Do the Near Elderly Value Mortality Risks Differently?” The Review of 

Economics and Statistics 86(1): 423-429.

Smith, D.H., D.C. Malone, K.A. Lawson, L.J. Okamoto, C. Battista, and W.B. Saunders. 1997. “A 

National Estimate of the Economic Costs of Asthma.” American Journal of Respiratory 

and Critical Care Medicine 156(3 Pt 1):787-793.

Smith, K., Pattanayak SK, Van Houtven G. 2006. “Structural benefit transfer: An example using 

VSL estimates.” Ecological Economics 60: 361-371.

Standard and Poor’s. 2000. “The U.S. Economy: The 25 Year Focus.” Winter. Available on the 

Internet at http://:www.standardandpoors.com

Stanford, R., T. McLaughlin and L. J. Okamoto. 1999. “The cost of asthma in the emergency 

department and hospital.” Am J Respir Crit Care Med.160 (1): 211-5.

Sullivan, J., L. Sheppard, et al. 2005. “Relation between short-term fine-particulate matter 

exposure and onset of myocardial infarction.” Epidemiology 16(1): 41-8.

Tagaris E, Liao KJ, Delucia AJ, et al. 2009. “Potential impact of climate change on air-pollution 

related human health effects.” Environ. Sci. Technol. 43: 4979–4988.

Tolbert, P. E., M. Klein, et al. 2007. “Multipollutant modeling issues in a study of ambient air 

quality and emergency department visits in Atlanta.” J Expo Sci Environ Epidemiol 17 

Suppl 2: S29-35.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-49

Tolley, G.S. et al. 1986. Valuation of Reductions in Human Health Symptoms and Risks.

University of Chicago. Final Report for the U.S. Environmental Protection Agency, Office 

of Policy Analysis. January. Available at 

<http://yosemite.epa.gov/ee/epa/eerm.nsf/a7a2ee5c6158cedd852563970080ee30/eaa

2842b3d2bc2798525644d0053bf48!OpenDocument#_Section6>.

Tsuge T, Kishimot A, Takeuchi K. 2005. “A choice experiment approach to the valuation of 

mortality.” Journal of Risk and Uncertainty 31(1):73-95.

U.S. Bureau of Economic Analysis (U.S. BEA). 2004. BEA Economic Areas (EAs). U.S. Department 

of Commerce. Available on the Internet at 

<http://www.bea.gov/regional/docs/econlist.cfm>.

U.S. Bureau of Economic Analysis (U.S. BEA). 1997. Real Gross Domestic Product—Table 2A. U.S. 

Department of Commerce.

U.S. Bureau of Economic Analysis (U.S. BEA). 1997. The Economic and Budget Outlook: An 

Update, Table 4—Economic Projections for Calendar Years 1997 Through 2007. 

September. U.S. Department of Commerce.

U.S. Bureau of the Census. 2001. Statistical Abstract of the United States, 2001, Section 12: 

Labor Force, Employment, and Earnings, Table No. 521. Washington, DC.

U.S. Bureau of the Census. 2002a. Modified Race Data Summary File 2000 Technical 

Documentation. Census of Population and Housing: Washington DC. Available on the 

Internet at <http://www.census.gov/popest/archives/files/MRSF-01-US1.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 1999a. The Benefits and Costs of the Clean Air 

Act, 1990–2010. Prepared for U.S. Congress by U.S. EPA, Office of Air and 

Radiation/Office of Policy Analysis and Review, Washington, DC, November; EPA report 

no. EPA-410-R-99-001. Available on the Internet at 

<http://www.epa.gov/air/sect812/1990-2010/fullrept.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic 

Analyses. EPA 240-R-00-003. National Center for Environmental Economics, Office of 

Policy Economics and Innovation. Washington, DC. September. Available on the Internet 

at 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2004. Air Quality Criteria for Particulate 

Matter. National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

EPA/600/P-99/002bF. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=87903>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-50

U.S. Environmental Protection Agency (U.S. EPA). 2006a. Regulatory Impact Analysis, 2006 

National Ambient Air Quality Standards for Particulate Matter, Chapter 5. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. October. Available on the 

Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205—Benefits.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2006b. Air Quality Criteria for Ozone and 

Related Photochemical Oxidants (Final). EPA/600/R-05/004aF-cF. Washington, DC: U.S. 

EPA. February. Available on the Internet at 

http://cfpub.epa.gov/ncea/CFM/recordisplay.cfm?deid=149923.U.S. Environmental 

Protection Agency (U.S. EPA). 2008. Regulatory Impact Analysis, 2008 National Ambient 

Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air Quality Planning 

and Standards, Research Triangle Park, NC. March. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis, 2008 

National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. March. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008b. Integrated Science Assessment for 

Oxides of Nitrogen—Health Criteria (Final Report). National Center for Environmental 

Assessment, Research Triangle Park, NC. July. Available on the Internet at 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645

U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment for 

Sulfur Oxides—Health Criteria (Final Report). National Center for Environmental 

Assessment, Research Triangle Park, NC. September. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>.

U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: National 

Emission Standards for Hazardous Air Pollutants from the Portland Cement 

Manufacturing Industry. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. April. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2009b. Integrated Science Assessment for 

Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for 

Environmental Assessment—RTP Division. December. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

U.S. Environmental Protection Agency (U.S. EPA). 2010a. Final Regulatory Impact Analysis (RIA) 

for the NO2 National Ambient Air Quality Standards (NAAQS). Office of Air Quality 

Planning and Standards, Research Triangle Park, NC. January. Available on the Internet 

at <http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-51

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Quantitative Health Risk Assessment 

for Particulate Matter—Final Report. EPA-452/R-10-005. Office of Air Quality Planning 

and Standards, Research Triangle Park, NC. September. Available on the Internet at 

<http://www.epa.gov/ttnnaaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010c. Final Regulatory Impact Analysis (RIA) 

for the SO2 National Ambient Air Quality Standards (NAAQS). Office of Air Quality 

Planning and Standards, Research Triangle Park, NC. June. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/fso2ria100602full.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010d. Regulatory Impact Analysis (RIA) for 

the Proposed Reconsideration of the Ozone National Ambient Air Quality Standards 

(NAAQS). Office of Air Quality Planning and Standards, Research Triangle Park, NC.

January. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/s1-

supplemental_analysis_full.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010e. Guidelines for Preparing Economic 

Analyses. EPA 240-R-10-001. National Center for Environmental Economics, Office of 

Policy Economics and Innovation. Washington, DC. December. Available on the Internet 

at <http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0568-50.pdf/$file/EE-0568-

50.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010f. Technical Support Document: Summary 

of Expert Opinions on the Existence of a Threshold in the Concentration-Response 

Function for PM2.5-related Mortality. Research Triangle Park, NC. June. Available on the 

Internet at <www.epa.gov/ttn/ecas/regdata/Benefits/thresholdstsd.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010g. Proposed Regulatory Impact Analysis 

(RIA) for the Transport Rule. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. January. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/proposaltrria_final.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011a. The Benefits and Costs of the Clean Air 

Act 1990 to 2020: EPA Report to Congress. Office of Air and Radiation, Office of Policy, 

Washington, DC. March. Available on the Internet at 

<http://www.epa.gov/oar/sect812/feb11/fullreport.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011b. Policy Assessment for the Review of 

the Particulate Matter National Ambient Air Quality Standards. EPA-452/D-11-003. April. 

Available on the Internet at 

<http://www.epa.gov/ttnnaaqs/standards/pm/s_pm_2007_pa.html>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-52

U.S. Environmental Protection Agency (U.S. EPA). 2011c. Regulatory Impact Analysis for the 

Federal Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter 

and Ozone in 27 States; Correction of SIP Approvals for 22 States. June. Available on the 

Internet at <http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011d. Regulatory Impact Analysis for the 

Final Mercury and Air Toxics Standards. EPA-452/R-11-011. December. Available on the 

Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011e. Integrated Science Assessment for 

Ozone and Related Photochemical Oxidants (Second External Review Draft). EPA/600/R10/076B. U.S. Environmental Protection Agency, National Center for Environmental 

Assessment—RTP Division. September. Available at 

<http://oaspub.epa.gov/eims/eimscomm.getfile?p_download_id=503567>.

U.S. Environmental Protection Agency (U.S. EPA). 2011e. Regulatory Impact Results for the 

Reconsideration Proposal for National Emission Standards for Hazardous Air Pollutants 

for Industrial, Commercial, and Institutional Boilers and Process Heaters at Major 

Sources. December. Available on the Internet at 

<http://www.epa.gov/ttnecas1/regdata/RIAs/boilermactreconria.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the 

Proposed Revisions to the National Ambient Air Quality Standards for Particulate Matter.

EPA-452/R-12-003. Office of Air Quality Planning and Standards, Health and 

Environmental Impacts Division. June. Available at 

<http://www.epa.gov/ttnecas1/regdata/RIAs/PMRIACombinedFile_Bookmarked.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2012b. Provisional Assessment of Recent 

Studies on Health Effects of Particulate Matter Exposure. EPA/600/R-12/056A. National

Center for Environmental Assessment—RTP Division. December. 

U.S. Environmental Protection Agency (U.S. EPA). 2012c. Integrated Science Assessment for 

Particulate Ozone and Related Photochemical Oxidants (Third External Review Draft). 

EPA/600/R-10/076C. National Center for Environmental Assessment—RTP Division. 

June. Available on the Internet at 

<http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=242490>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 1999. An SAB 

Advisory on the Clean Air Act Section 812 Prospective Study Health and Ecological Initial 

Studies. Prepared by the Health and Ecological Effects Subcommittee (HEES) of the 

Advisory Council on the Clean Air Compliance Analysis, Science Advisory Board, U.S. 

Environmental Protection Agency. Washington DC. EPA-SAB-COUNCIL-ADV-99-005.

Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/1AFDEBB45C57F6E585257193005

8C1A5/$File/coua9905.pdf>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-53

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2000. An SAB 

Report on EPA’s White Paper Valuing the Benefits of Fatal Cancer Risk Reduction. EPASAB-EEAC-00-013. July. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/41334524148BCCD6852571A7005

16498/$File/eeacf013.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004a. Advisory 

on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective 

Analysis—Benefits and Costs of the Clean Air Act, 1990–2020. EPA-SAB-COUNCIL-ADV04-002. March. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E54004

33D5D/$File/council_adv_04002.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004b. Review 

of the Draft Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of 

the Clean Air Act, 1990-2020: An Advisory by the Advisory Council for Clean Air 

Compliance Analysis. EPA-SAB-COUNCIL-ADV-04-004. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/7CCBBFE15CD4C8B185256F17005E3079/

$File/council_adv_04004.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004c. Advisory 

Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag.

EPA-COUNCIL-LTR-05-001. December. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E

0/$File/council_ltr_05_001.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2007. SAB 

Advisory on EPA’s Issues in Valuing Mortality Risk Reduction. EPA-SAB-08-001. October. 

Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/

$File/sab-08-001.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2008. 

Characterizing Uncertainty in Particulate Matter Benefits Using Expert Elicitation. EPACOUNCIL-08-002. July. Available on the Internet at

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/43B91173651AED9E85257487004EA6C

B/$File/EPA-COUNCIL-08-002-unsigned.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2009a.

Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: Scope 

and Methods Plan for Health Risk and Exposure Assessment. EPA-COUNCIL-09-009. May. 

Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86

e/723FE644C5D758DF852575BD00763A32/$File/EPA-CASAC-09-009-unsigned.pdf>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-54

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009b. Review 

of EPA’s Integrated Science Assessment for Particulate Matter (First External Review 

Draft, December 2008). EPA-COUNCIL-09-008. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86

e/73ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009c. Review 

of Integrated Science Assessment for Particulate Matter (Second External Review Draft, 

July 2009). EPA-CASAC-10-001. November. Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86

e/151B1F83B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010a. Review of 

EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air

Act. EPA-COUNCIL-10-001. June. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/

59e06b6c5ca66597852575e7006c5d09!OpenDocument&TableRow=2.3#2.>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010b. Review of 

Risk Assessment to Support the Review of the Particulate Matter (PM) Primary National 

Ambient Air Quality Standards—External Review Draft (September 2009). EPA-CASAC10-003. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/

BC1ECC5D539EF72385257678006D5754/$File/EPA-CASAC-10-003-unsigned.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010c. CASAC 

Review of Quantitative Health Risk Assessment for Particulate Matter—Second External 

Review Draft (February 2010). EPA-CASAC-10-008. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/

BC4F6E77B6385155852577070002F09F/$File/EPA-CASAC-10-008-unsigned.pdf>.

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2010d. CASAC 

Review of Policy Assessment for the Review of the PM NAAQS—Second External Review 

Draft (June 2010). EPA-CASAC-10-015. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/

CCF9F4C0500C500F8525779D0073C593/$File/EPA-CASAC-10-015-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011. Review of 

Valuing Mortality Risk Reductions for Environmental Policy: A White Paper (December 

10, 2010). EPA-SAB-11-011 July. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/

$File/EPA-SAB-11-011-unsigned.pdf>.

Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market 

Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5-76.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-55

Viscusi, W.K., W.A. Magat, and J. Huber. 1991. “Pricing Environmental Health Risks: Survey 

Assessments of Risk-Risk and Risk-Dollar Trade-Offs for Chronic Bronchitis.” Journal of 

Environmental Economics and Management 21:32-51.

Wellenius, G.A., M.R. Burger, B.A. Coull, J. Schwartz, H.H. Suh, P. Koutrakis, G. Schlaug, D.R. 

Gold, M.A. Mittlemen. 2012.”Ambient air pollution and the risk of acute ischemic 

stroke.” Arch. Intern. Med., 172 (3), pp. 229–234.

Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr. 1990. “Medical Costs of Coronary Artery Disease in 

the United States.” American Journal of Cardiology 65(7):432-440.

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected of 

postneonatal infant mortality and particulate air pollution in the United States.”

Environmental Health Perspectives. Vol. 105 (6): 608-612.

Woodruff TJ, Parker JD, Schoendorf KC. 2006. “Fine particulate matter (PM2.5) air pollution and 

selected causes of postneonatal infant mortality in California.” Environmental Health 

Perspectives 114(5):786-90.

Woods & Poole Economics Inc. 2007. Population by Single Year of Age. CD-ROM. Woods & 

Poole Economics, Inc. Washington, D.C.

World Health Organization (WHO). 1977. International Classification of Diseases, 9th Revision 

(ICD-9). Geneva: WHO.

World Health Organization (WHO). 2008. Part 1: Guidance Document on Characterizing and 

Communicating Uncertainty in Exposure Assessment, Harmonization Project Document 

No. 6. Published under joint sponsorship of the World Health Organization, the 

International Labour Organization and the United Nations Environment Programme. 

WHO Press: Geneva, Switzerland. Available on the Internet at 

<www.who.int/ipcs/methods/harmonization/areas/uncertainty%20.pdf>.

Zanobetti, A., M. Franklin, et al. 2009. “Fine particulate air pollution and its components in 

association with cause-specific emergency admissions.” Environmental Health 8: 58-

60.Zeger S; Dominici F; McDermott A; Samet J. 2008. “Mortality in the Medicare 

population and chronic exposure to fine particulate air pollution in urban centers (2000-

2005).” Environ Health Perspect 116: 1614-1619.

Zanobetti A. and Schwartz, J. 2006. “Air pollution and emergency admissions in Boston, MA.” J 

Epidemiol Community Health 60(10): 890-5.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-56

CHAPTER 2

WELFARE CO-BENEFITS OF THE PRIMARY STANDARD

6.1 Synopsis

The emission reductions to meet the revised primary standard will have “welfare” cobenefits in addition to human health benefits, including changes in visibility, materials damage, 

ecological effects from PM deposition, ecological effects from nitrogen and sulfur emissions, 

vegetation effects from ozone exposure, ecological effects from mercury deposition, and 

climate effects.12 Despite our goal to quantify and monetize as many of the benefits as possible 

for the revised primary standard, the welfare co-benefits of the revised primary standard 

remain unquantified and nonmonetized in this RIA due to data, methodology, and resource 

limitations. Specifically, we do not have air quality model runs for the regulatory baseline and 

the alternative standard levels that would allow us to calculate the visibility co-benefits of 

attaining the revised primary standard even though we have a complete methodology for 

estimating these co-benefits. However, using the approach described in this chapter, we 

provide the results of an illustrative analysis in Appendix 6.B using the 2020 base case and 2020 

control case simulations that were used to develop the air quality ratios. 

6.2 Introduction to Welfare Benefits 

Illustrative emission reduction strategies to attain the revised and alternative annual 

primary standard have numerous documented effects on environmental quality that affect 

human welfare. The Clean Air Act defines welfare effects to include any non-health effects, 

including direct damages to property, either through impacts on material structures or by 

soiling of surfaces, direct economic damages in the form of lost productivity of crops and trees, 

indirect damages through alteration of ecosystem functions, and indirect economic damages 

through the loss in value of recreational experiences or the existence value of important 

resources. The EPA’s Integrated Science Assessments for Particulate Matter (hereafter, “PM 

ISA”) (U.S. EPA, 2009b), NOX/SOX—Ecological Criteria (U.S. EPA, 2008), and Ozone (U.S. EPA, 

2012a) identify numerous physical and ecological effects known to be causally linked to these 

pollutants. This chapter describes these individual effects and how we would quantify and 

monetize them if there is enough data to do so. These welfare effects include changes in 

visibility, materials damage, ecological effects from PM deposition, ecological effects from 

 

12 While we understand that “welfare” can include health and non-health effects in the economic sense, we use 

the term “welfare” in this RIA in the same context as the definitions in the Clean Air Act for NAAQS. In practice, 

welfare benefits represent non-health effects.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-57

nitrogen and sulfur emissions, vegetation effects from ozone exposure, ecological effects from 

mercury deposition, and climate effects.

These welfare co-benefits are associated with reductions in emissions of specific 

pollutants resulting from illustrative emission reduction strategies to attain the revised and 

alternative annual primary standard, not the form or intent of any specific standard. Even 

though the primary standards are designed to protect against adverse effects to human health, 

the emission reductions have welfare co-benefits in addition to the direct human health 

benefits.

The impacts of the illustrative emission reduction strategies can be grouped into four 

categories: directly emitted PM (e.g., metals, organic compounds, dust), reductions of PM2.5 

precursors (e.g., NOx, SOx, VOCs), other ancillary reductions from the illustrative emission 

reduction strategies (e.g., mercury and CO2), and secondary co-pollutant formation from PM 

precursors (e.g., ozone from NOx and VOCs). Regardless of the category, these emission 

changes are anticipated to affect ambient concentrations and deposition, and consequently 

affect public welfare. It is therefore appropriate and reasonable to include all the benefits and 

co-benefits associated with these emission reductions to provide a comprehensive 

understanding of the likely public impacts of attaining the revised or alternative annual 

standards. Table 6-1 shows the welfare effects associated with the various pollutants (either 

directly or as a precursor to secondary formation of PM or ozone) that would be reduced by the 

illustrative emission reduction strategies to attain the revised and alternative annual standard.

Based on the EPA’s previous analyses, we believe the welfare co-benefits associated 

with these non-health benefit categories could be significant (U.S. EPA, 2011b). Despite our 

goal to quantify and monetize as many of the benefits and co-benefits as possible, welfare cobenefits of the revised primary standard remain unquantified and nonmonetized in this RIA due 

to data, methodology, and resource limitations. Therefore, the total benefits would be larger 

than we have estimated. The monetized value of these unquantified effects is represented by 

adding an unknown “B,” which includes both unmonetized health benefits and welfare cobenefits, to the aggregate total for the cost-benefit comparison. These unquantified benefits 

and co-benefits may be substantial, although the magnitude is highly uncertain. We include a 

qualitative description of the anticipated welfare effects in this chapter to characterize the type 

and potential extent of those co-benefits, as identified in Table 6-2.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-58

Table 6-1. Welfare Effects by Pollutants Potentially Affected by Attainment of the PM 

NAAQS

Pollutant

Atmospheric Effects Atmospheric and 

Deposition Effects Deposition Effects

Visibility 

Impairment

Vegetation 

Injury 

(SO2)

Vegetation 

Injury 

(Ozone)

Materials 

Damage Climate

Ecosystem 

Effects—

(Organics 

& Metals)

Acidification 

(freshwater)

Nitrogen 

Enrichment

Mercury 

Methylation

Direct

PM2.5

   

NOx      

SO2      

VOCs    

PM10   

Hg  

CO2 

 = Welfare category affected by this pollutant.

The remainder of this chapter is organized as follows: Section 6.3 provides a qualitative 

discussion of the visibility co-benefits and describes our approach to estimate those visibility 

co-benefits if we had the data to do so. Sections 6.4 through 6.6 provide qualitative co-benefits 

for the unquantified benefits categories of materials damage, climate, and ecosystem effects. 

References are provided in Section 6.7. Additional information regarding technical details of the 

visibility co-benefits approach is provided in Appendix 6A. The illustrative visibility co-benefits 

results for the specific modeled scenario (not the revised standard scenario) are provided in 

Appendix 6.B. 

Table 6-2. Quantified and Unquantified Welfare Co-Benefits

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-59

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Improved Environment

Reduced visibility 

impairment

Visibility in Class 1 areas in 

SE, SW, and CA regions

a a Section 6.3, 

Appendix 6.B

Visibility in Class 1 areas in 

other regions

— a Section 6.3, 

Appendix 6.B

Visibility in 8 cities — a Section 6.3, 

Appendix 6.B

Visibility in other residential 

areas

— a Section 6.3, 

Appendix 6.B

Reduced climate effects Global climate impacts from 

CO2 

— — Section 6.5, 

SCC TSDb

Climate impacts from ozone 

and PM

— — Section 6.5, 

Ozone ISA, 

PM ISAc

Other climate impacts (e.g., 

other GHGs, other impacts) 

— — Section 6.5, 

IPCCc

Reduced effects on 

materials

Household soiling — — Section 6.4, 

PM ISAc

Materials damage (e.g., 

corrosion, increased wear)

— — Section 6.4, 

PM ISAc

Reduced effects from 

PM deposition (metals 

and organics)

Effects on Individual 

organisms and ecosystems

— — Section 6.6.1, 

PM ISAc

Reduced vegetation and 

ecosystem effects from 

exposure to ozone

Visible foliar injury on 

vegetation

— — Section 6.6.4, 

Ozone ISAc

Reduced vegetation growth 

and reproduction

— — Section 6.6.4, 

Ozone ISAb

Yield and quality of 

commercial forest products 

and crops

— — Section 6.6.4, 

Ozone ISAb,d

Damage to urban ornamental 

plants

— — Section 6.6.4, 

Ozone ISAc

Carbon sequestration in 

terrestrial ecosystems

— — Ozone ISAc

Recreational demand 

associated with forest 

aesthetics

— — Ozone ISAc
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-60

(continued)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Other non-use effects Ozone ISAc

Ecosystem functions (e.g., 

water cycling, 

biogeochemical cycles, net 

primary productivity, leafgas exchange, community 

composition)

— — Ozone ISA2

Table 6-2. Quantified and Unquantified Welfare Co-Benefits (Cont.)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Improved Environment (Cont.)

Reduced effects from 

acid deposition

Recreational fishing — — Section 6.6.2, 

NOx SOx ISAb

Tree mortality and decline — — Section 6.6.2, 

NOx SOx ISAc

Commercial fishing and 

forestry effects

— — Section 6.6.2, 

NOx SOx ISAc

Recreational demand in 

terrestrial and aquatic 

ecosystems

— — Section 6.6.2, 

NOx SOx ISAc

Other non-use effects Section 6.6.2, 

NOx SOx ISAc

Ecosystem functions (e.g., 

biogeochemical cycles)

— — Section 6.6.2, 

NOx SOx ISAc

Reduced effects from 

nutrient enrichment

Species composition and 

biodiversity in terrestrial and 

estuarine ecosystems

— — Section 6.6.2, 

NOx SOx ISAc

Coastal eutrophication — — Section 6.6.2, 

NOx SOx ISAc

Recreational demand in 

terrestrial and estuarine 

ecosystems

— — Section 6.6.2, 

NOx SOx ISAc

Other non-use effects Section 6.6.2, 

NOx SOx ISAc

Ecosystem functions (e.g., 

biogeochemical cycles, fire 

regulation)

— — Section 6.6.2, 

NOx SOx ISAc
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-61

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Reduced vegetation 

effects from ambient 

exposure to SO2 and 

NOx

Injury to vegetation from 

SO2 exposure

— — Section 6.6.2, 

NOx SOx ISAc

Injury to vegetation from 

NOx exposure

— — Section 6.6.2, 

NOx SOx ISAc

Reduced ecosystem 

effects from exposure to 

methylmercury (through 

the role of sulfate in 

methylation)

Effects on fish, birds, and 

mammals (e.g., reproductive 

effects)

— — Section 6.2 

and 6.6.3, 

Mercury Study 

RTCc,d

Commercial, subsistence and 

recreational fishing

— — Section 6.2 

and 6.6.3, 

Mercury Study 

RTCc

aWe quantify these co-benefits in an illustrative analysis using the methods discussed in this chapter for the 

specific modeled scenario. These results are provided in Appendix 6.B, but these results of that illustrative scenario 

are not an estimate of the co-benefits for the revised primary standard.

b We assess these co-benefits qualitatively due to time and resource limitations for this RIA.

cWe assess these co-benefits qualitatively because we do not have sufficient confidence in available data or 

methods.

d We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are 

other significant concerns over the strength of the association.

6.3 Visibility Co-Benefits Approach

6.3.1 Visibility and Light Extinction Background

The illustrative emission reduction strategies designed to attain the revised and 

alternative annual standards would reduce emissions of directly emitted PM2.5 as well as 

precursor emissions such as NOx and SO2 for an alternative annualstandard at 11 μg/m3. These 

emission reductions would improve the level of visibility because these suspended particles and 

gases impair visibility by scattering and absorbing light (U.S. EPA, 2009b). Visibility is also 

referred to as visual air quality (VAQ),

13 and it directly affects people’s enjoyment of a variety of 

daily activities (U.S. EPA, 2009b). Good visibility increases quality of life where individuals live 

and work, and where they travel for recreational activities, including sites of unique public 

value, such as the Great Smoky Mountains National Park (U. S. EPA, 2009b). This section 

discusses the economic co-benefits associated with improved visibility as a result of emission 

reductions associated with the revised and alternative annual standards.

 

13 We use the term VAQ to refer to the visibility effects caused solely by air quality conditions, excluding fog.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-62

Air pollution affects light extinction, a measure of how much the components of the 

atmosphere scatter and absorb light. More light extinction means that the clarity of visual 

images and visual range is reduced, all else held constant. Light extinction is the optical 

characteristic of the atmosphere that occurs when light is either scattered or absorbed, which 

converts the light to heat. Particulate matter and gases can both scatter and absorb light. Fine 

particles with significant light-extinction efficiencies include sulfates, nitrates, organic carbon, 

elemental carbon, and soil (Sisler, 1996). The extent to which any amount of light extinction 

affects a person’s ability to view a scene depends on both scene and light characteristics. For 

example, the appearance of a nearby object (e.g., a building) is generally less sensitive to a 

change in light extinction than the appearance of a similar object at a greater distance. See 

Figure 6-1 for an illustration of the important factors affecting visibility.

According to the PM ISA, there is strong and consistent evidence that PM is the 

overwhelming source of visibility impairment in both urban and remote areas (U.S. EPA, 

2009b). After reviewing all of the evidence, the PM ISA concluded that the evidence was 

sufficient to conclude that a causal relationship exists between PM and visibility impairment.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-63

Figure 6-1. Important Factors Involved in Seeing a Scenic Vista (Malm, 1999)

Visibility is commonly measured as either light extinction (βext), which is defined as the 

loss of light per unit of distance in terms of inverse megameters (Mm-1), or using the deciview 

(dv) metric, which is a logarithmic function of extinction (Pitchford and Malm, 1994). Deciviews, 

a unitless measure of visibility, are standardized for a reference distance in such a way that one 

deciview corresponds to a change of about 10% in available light.14 Pitchford and Malm (1994) 

characterize a change of one deciview as “a small but perceptible scenic change under many 

circumstances.”

15 Extinction and deciviews are both physical measures of the amount of 

visibility impairment (e.g., the amount of “haze”), with both extinction and deciview increasing 

as the amount of haze increases. Using the relationships derived by Pitchford and Malm (1994),

 

14 Note that deciviews are inversely related to visual range, such that a decrease in deciviews implies an increase in 

visual range (i.e., improved visibility). Conversely, an increase in deciviews implies a decrease in visual range (i.e., 

decreased visibility). Deciview, in effect, is a measure of the lack of visibility.

15 An instantaneous change of less than 1 deciview (i.e., less than 10% in the light extinction budget) represents a 

measurable improvement in visibility but may not be perceptible to the eye. The visibility co-benefits approach

described in this chapter reflects annual average changes in visibility, which are likely made up of periods with 

changes less than one deciview and periods with changes exceeding one deciview. Annual averages appear to 

more closely correspond to the economic literature relied upon for valuation of visibility changes in this analysis.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-64

(6.1)

where VR denotes visual range (in kilometers) and βext denotes light extinction (in Mm-1).16

Annual average visibility conditions (reflecting light extinction due to both 

anthropogenic and non-anthropogenic sources) vary regionally across the U.S. and by season 

(U.S. EPA, 2009b). Particulate sulfate is the dominant source of regional haze in the eastern U.S.

(>50% of the particulate light extinction) and an important contributor to haze elsewhere in the 

country (>20% of particulate light extinction) (U.S. EPA, 2009b). Particulate nitrate is an 

important contributor to light extinction in California and the upper Midwestern U.S., 

particularly during winter (U.S. EPA, 2009b). Smoke plumes from large wildfires dominate many 

of the worst haze periods in the western U.S., while Asian dust only caused a few of the worst 

haze episodes, primarily in the more northerly regions of the west (U.S. EPA, 2009b). Higher 

visibility impairment levels in the East are due to generally higher concentrations of fine 

particles, particularly sulfates, and higher average relative humidity levels (U.S. EPA, 2009b).

Humidity increases visibility impairment because some particles such as ammonium sulfate and 

ammonium nitrate absorb water and form droplets that become larger when relative humidity 

increases, thus resulting in increased light scattering (U.S. EPA, 2009b).

Reductions in air pollution from implementation of various programs associated with 

the Clean Air Act Amendments of 1990 (CAAA) provisions have resulted in substantial 

improvements in visibility, and will continue to do so in the future. Because trends in haze are 

closely associated with trends in particulate sulfate and nitrate due to the simple relationship 

between their concentration and light extinction, visibility trends have improved as emissions 

of SO2 and NOx have decreased over time due to air pollution regulations such as the Acid Rain 

Program (U.S. EPA, 2009b). For example, Figure 6-2 shows that visual range increased nearly 

50% in the eastern U.S. since 1992.17 The EPA’s recent regulations such as the Cross-State Air 

Pollution Rule (U.S. EPA, 2011c) and the Mercury and Air Toxics Standard (U.S. EPA, 2011d) are 

anticipated to reduce SO2 emissions down to 2 million tons nationally, which would lead to 

substantial further improvement in visibility levels in the Eastern U.S. Calculated from light 

extinction efficiencies from Trijonis et al. (1987, 1988), annual average visual range under 

natural conditions in the East is estimated to be 150 km ± 45 km (i.e., 65 to 120 miles) and 230 

km ± 35 km (i.e., 120 to 165 miles) in the West (Irving, 1991). Figure 6-2 reflects the average 

 

16 It has been noted that, for a given deciview value, there can be many different visual ranges, depending on the 

other factors that affect visual range—such as light angle and altitude. See Appendix 6a for more detail.

17 In Figure 6-2, the “best days” are defined as the best 20% of days, the “mid-range days” are defined as the 

middle 20%, and the “worst days” are defined as the worst 20% of days (IMPROVE, 2010).
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-65

trends in visual ranges at select monitors in the eastern and western areas of the U.S. since 

1992 using data from the IMPROVE monitoring network (U.S. EPA (2008) updated; IMPROVE 

(2010)). As an illustration of the improvements in visibility attributable to the CAAA, Figure 6-3 

depicts the modeled improvements in visibility associated with all the CAAA provisions in 2020 

compared to a counterfactual scenario without the CAAA (U.S. EPA, 2011b). While visibility 

trends have improved in most National Parks, the recent data show that these areas continue 

to suffer from visibility impairment beyond natural background levels (U.S. EPA, 2009b).

Figure 6-2. Visibility in Selected National Parks and Wilderness Areas in the U.S., 1992–

2008a,b

(Source: U.S. EPA (2008) updated, IMPROVE (2010))
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-66

Figure 6-3. Estimated Improvement in Annual Average Visibility Levels Associated with the 

CAAA Provisions in 2020

Source: U.S. EPA, 2011b18

 

18 It is important to note that visibility levels shown in these maps were modeled differently than the modeling 

conducted for this analysis using an earlier method that we would currently use, including coarser grid resolution 

(i.e., 36 km instead of 12 km). In addition, please note that these maps present annual average visibility levels, 

which are different than the short-term averages being considered for the secondary standard.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-67

6.3.3 Quantifying Light Extinction for Assessing Visibility Co-benefits

For this RIA, we do not have air quality model runs for the regulatory baseline and the 

alternative standard levels that would allow us to calculate the visibility co-benefits of attaining 

the revised primary standard. However, we provide an illustrative analysis in Appendix 6.B 

using the 2020 base case and 2020 control case simulations that were used to develop the air 

quality ratios.19 In our approach, we generate light extinction estimates using the CMAQ model 

in conjunction with the IMPROVE (Interagency Monitoring of Protected Visual Environments) 

algorithm that estimates light extinction as a function of PM concentrations and relative 

humidity levels (U.S. EPA, 2009b).20 The procedure for calculating light extinction associated 

with the revised and alternative annual standards is described in detail in Chapter 3 of this RIA. 

In addition, Appendix 6.A describes how the spatial resolution of the light extinction estimates 

would be adjusted in our approach.

It is important to note that the light extinction estimates used in our approach represent 

annual averages, which is different from the averaging times currently being considered for the 

secondary PM NAAQS. While the annual averages are influenced by days with extremely 

impaired visibility, the light extinction data is not sufficient to provide higher temporal 

resolution than quarterly averages. While we suspect that the most impaired days would have 

disproportionately improved visibility from the emission reductions associated with attaining 

the revised or alternative primary standards, we are not able to quantify those impacts. These 

data gaps result in an underestimate of visibility co-benefits associated with extreme days. We 

recognize that recent advice from the Science Advisory Board’s Advisory Council on Clean Air 

Compliance Analysis (SAB-Council) recommends estimating visibility co-benefits considering 

daytime visibility on days with severe impairment (U.S. EPA-SAB, 2010a), but the available data 

and valuation studies do not allow such fine temporal resolution.

 

19 These simulations are described in Chapter 3.

20 According to the PM ISA, the IMPROVE algorithm performs reasonably well despite its simplicity (U.S. EPA, 

2009b).
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-68

While our approach is a substantial improvement in the methods to estimate light 

extinction nationally, we are still developing a method to estimate coarse particle 

concentrations for the entire continental U.S. for estimating light extinction. As an interim 

solution, our approach includes sensitivity analyses to show the potential impact of omitting 

coarse particles from the light extinction estimates for recreational and residential visibility. For 

these sensitivity analyses, we selected the levels of coarse particles to represent the full range 

of possible annual concentrations from a recent report on the IMPROVE monitoring network 

(Debell et al., 2006). Specifically, for these sensitivity analyses, we assume four levels of coarse 

particles: no coarse particles, 5 μg/m3nationwide, 15 μg/m3in the Southwest with 5 μg/m3in 

the rest of the country, and 15 μg/m3in the Southwest with 8 μg/m3in the rest of the 

country.21 In Table 6-3, we provide a qualitative assessment of how key assumptions in the 

estimation of light extinction would affect the visibility co-benefits.

Table 6-3. Key Assumptions in the Light Extinction Estimates Affecting the Visibility CoBenefits Approacha

Key Assumption Direction of 

Bias

Magnitude of 

Effect

The light extinction estimates are annual averages to 

correspond with the valuation studies. People may 

value large changes to the haziest days differently 

than small changes to many days. We assume that 

annual average light extinction is the most appropriate 

temporal scale for estimating visibility benefits.

Potential 

Underestimate Medium

Coarse particles are a component of light extinction, 

but we were unable to include coarse particles in the 

light extinction estimates. We provide sensitivity 

analyses with up to 15 μg/m3in the Southwest and 8 

μg/m3in the rest of the country.

Potential 

Overestimate Very Low

a

A description of the classifications for magnitude of effects can be found in Appendix 5.B of this RIA.

 

21 We define “Southwest” for this sensitivity analysis to be the states of California, Nevada, Utah, Arizona, New 

Mexico, Colorado, and Texas.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-69

6.3.3 Visibility Valuation Overview

In the Clean Air Act Amendments of 1977, the U.S. Government recognized visibility’s 

value to society by establishing a national goal to protect national parks and wilderness areas 

from visibility impairment caused by manmade pollution.22 Air pollution impairs visibility in both 

residential and recreational settings, and an individual’s willingness to pay (WTP) to improve 

visibility differs in these two settings. Benefits of residential visibility relate to the impact of 

visibility changes on an individual’s daily life (e.g., at home, at work, and while engaged in 

routine recreational activities). Benefits of recreational visibility relate to the impact of visibility 

changes manifested at parks and wilderness areas that are expected to be experienced by 

recreational visitors.

Both recreational and residential visibility benefits consist of use values and nonuse 

values. Use values include the aesthetic benefits of better visibility, improved road and air 

safety, and enhanced recreation in activities like hunting and birdwatching. Nonuse values are 

based on a belief that the environment ought to exist free of human-induced haze. This 

includes the value of better visibility for use by others now and in the future (bequest value).

Nonuse values may be more important for recreational areas, particularly national parks and 

monuments.

The relationship between a household’s WTP and changes in visibility can be derived 

from a number of contingent valuation (CV) studies published in the peer-reviewed economics 

literature. The studies used in the approach to estimate the residential and recreational 

visibility co-benefits associated with the revised and alternative annual standards are described 

in the following sections. In addition to CV studies, hedonic valuation studies (Beron et al., 

2001, 2004) also demonstrate that visibility has value, but we are unable to apply these 

valuation estimates in the context of estimating the visibility co-benefits associated with 

national regulations that reduce air pollution (Leggett and Neumann, 2004).

6.3.3.1 Visibility Valuation Approach

In our approach, we assume that individuals value visibility for aesthetic reasons rather 

than viewing visibility as a proxy for other impacts associated with air pollution, such as health 

or ecological improvements. Some studies in the literature indicate that individuals may have 

difficulty distinguishing visibility from other aspects of air pollution (e.g., McClelland et al., 

1993; Chestnut and Rowe, 1990c; Carson, Mitchell, and Rudd, 1990). Because visual air quality 

is inherently multi-attribute, it is a challenge for all visibility valuation studies to isolate the 

 

22 See Section 169(a) of the Clean Air Act.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-70

value of visibility from the collection of intertwined benefits. Each study used in our approach

attempts to isolate visibility from other effect categories, but the different studies take 

different approaches (U.S. EPA, 2009b).23 However, the degree to which the studies were 

successful in convincing respondents to focus solely on visibility is unclear

Similarly, it is important to try to distinguish residential visibility from recreational 

visibility co-benefits, specifically whether these can these can be treated as distinct and additive 

benefit categories based on the available literature. In our approach, we assume that 

residential and recreational visibility co-benefits are distinct and separable. It is conceivable 

that respondents to the recreational visibility survey may have partially included values for their 

own residential visibility when evaluating changes at national parks and wilderness areas 

located in their region of the country. In our approach, we take care to minimize the number of 

overlapping areas and their contributions. Specifically, we believe that the potential for doublecounting recreational and residential visibility is minimal for several reasons. First, in our 

approach, we only include a subset of areas in the primary estimates of recreational and 

residential visibility co-benefits, which overlap in only a few places.24 Second, a number of the 

overlapping counties are wilderness areas, which would contribute little to the overall

monetized visibility co-benefits due to low visitation rates, rather than highly visited national 

parks. For example, Los Angeles County is home to the San Gabriel Wilderness Area, which has 

10 thousand annual visitors (NPS, 2008). If we were to exclude the residential visibility cobenefits that accrue to 10 million residents in Los Angeles County and only include the very 

small recreational visibility co-benefits for the wilderness area, we would be substantially 

biasing the overall estimates downward. For these reasons, we believe that the potential for 

double-counting is minimal.

In the next sections, we describe the methodology and limitations of the recreational 

and residential visibility approach. Consistent with the health benefits analysis, the monetized 

visibility co-benefits would be adjusted for inflation and income growth. These co-benefits

would be specific to the analysis year, and as population and income increase over time, these 

co-benefits would be expected to increase each year for the same incremental change in light 

extinction.

 

23 See Leggett and Neumann (2004) for a more detailed discussion of this issue. 

24 As described in detail in Sections 6.3.3 and 6.3.4, our approach includes only a subset of visibility co-benefits in 

the main benefits estimates, while providing the rest of the visibility co-benefits in sensitivity analyses.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-71

6.3.4 Recreational Visibility

6.3.4.1 Methodology

Our approach for estimating recreational visibility co-benefits is well-established and 

has been used in numerous analyses by the EPA (U.S. EPA, 1999; 2005; 2006; 2010; 2011b). In 

our approach, recreational visibility co-benefits apply to Class 1 areas, such as National Parks 

and Wilderness Areas.25 Although other recreational settings such as National Forests, state 

parks, or even hiking trails or roadside areas have important scenic vistas, a lack of suitable 

economic valuation literature to identify these other areas and/or a lack of visitation data 

prevents us from generating estimates for those recreational vista areas.

Under the 1999 Regional Haze Rule (64 FR 35714), states are required to set goals 

develop long-term strategies to improve visibility in Class 1 areas, with the goal of achieving 

natural background visibility levels by 2064. In conjunction with the U.S. National Park Service 

(NPS), the U.S. Forest Service (USFS), other Federal land managers, and State organizations in 

the U.S., the EPA has supported visibility monitoring in national parks and wilderness areas 

since 1988. The monitoring network known as IMPROVE includes 156 sites that represent the 

Class 1 areas across the country (U.S. EPA, 2009b).26 The IMPROVE monitoring network 

measures fine particles, coarse particles, and key PM2.5 constituents that affect visibility, such 

as sulfate, nitrate, organic and elemental carbon, soil dust, and several other elements.

Figure 6-4 identifies where each of these parks are located in the U.S.

 

25 Hereafter referred to as Class 1 areas, which are defined as areas of the country such as national parks, national 

wilderness areas, and national monuments that have been set aside under Section 169(a) of the Clean Air Act to 

receive the most stringent degree of air quality protection. Mandatory Class 1 federal lands fall under the 

jurisdiction of three federal agencies, the National Park Service, the Fish and Wildlife Service, and the Forest 

Service. EPA has designated 156 areas as mandatory Class 1 federal areas for visibility protection, including 

national parks that exceed 6,000 acres and wilderness areas that exceed 5,000 acres (40 CFR §81.400).

26 The formula used to estimate light extinction from concentrations of PM constituents and relative humidity is 

referred to as the IMPROVE algorithm.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-72

Figure 6-4. Mandatory Class 1 Areas in the U.S.

For recreational visibility, the EPA relies upon a contingent valuation (CV) survey 

conducted in 1988 (Chestnut and Rowe, 1990a; 1990b) to estimate the recreational visibility cobenefits. Although there are several other studies in the literature on recreational visibility 

valuation, they are even older and use less robust methods. In the EPA’s judgment, despite the 

inherent limitations in the survey, the Chestnut and Rowe study contains many of the elements 

of a valid CV study and is sufficiently reliable to serve as the basis for monetary estimates of the 

co-benefits of visibility changes in recreational areas. Even though this specific report has not 

been published in a peer-reviewed journal, these data were subjected to peer review as part of 

the National Acid Precipitation Assessment Program (NAPAP) and have been applied to 

estimate visibility benefits in several peer-reviewed journal articles (Chestnut and Dennis, 1997; 

Burtraw et al., 1998; Delucchi et al, 2002; Chestnut and Mills, 2005), as well as several peer 

reviewed in reports (NAPAP, 1991; SAMI, 2002; U.S. EPA, 1999b; 2011b). 

27 This study serves as 

 

27 In 1999 as part of The Benefits and Costs of the Clean Air Act, 1990–2010 (U.S. EPA, 1999b), the EPA asked the 

SAB-Council whether they concurred with the EPA’s proposed use of the WTP values from Chestnut and Rowe 

(1990a,b) to support the main (“primary”) benefit estimate. The SAB-Council responded, “[t]he Council believes 

that EPA should rely on the peer-reviewed literature as the basis for its regulatory analyses. Although many 

members of the Council believe that the Chestnut and Rowe study is the best available, we believe that the 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-73

an essential input to our approach for estimating the co-benefits from improving recreational 

visibility.

In our approach, we assume that the household WTP is higher if the Class 1 recreational 

area is located close to the person’s home (i.e., in the same region of the country). People 

appear to be willing to pay more for visibility improvements at parks and wilderness areas that 

are in the same region as their household than at those that are not in the same region as their 

household (Chestnut and Rowe, 1990a, 1990b). This is plausible, because people are more likely 

to visit, be familiar with, and care about parks and wilderness areas in their own part of the 

country. However, studies have also found many people who had never visited and never 

planned to visit the parks still had positive values for visibility improvements in those locations 

(Chestnut and Rowe, 1990b).

The Chestnut and Rowe survey measured the demand for visibility in Class 1 areas 

managed by the NPS in three broad regions of the country: California, the Colorado Plateau 

(Southwest), and the Southeast.28 Respondents in five states were asked about their WTP to 

protect national parks or NPS-managed wilderness areas within a particular region. The survey 

used photographs reflecting different visibility levels in the specified recreational areas. The 

authors used the survey data to estimate household WTP values for improved visibility in each 

region.

The separate regions were developed to capture differences in household WTP values 

based on proximity to recreational areas. Chestnut (1997) also concluded that, for a given 

region, a substantial proportion of the WTP is attributable to one specific park within the 

region. This so called “indicator park” is the most well-known and frequently visited park within 

a particular region. The indicator parks for the three studied park regions are Yosemite National 

 

general principle should be adhered to.” ” (U.S. EPA-SAB, 1999). In response to this comment, the EPA clarified to 

the SAB-Council that the visibility valuation functions and the sources of estimates for the coefficients for the 

functions were originally developed as part of NAPAP and were subjected to peer review as part of that program 

(Chestnut and Rowe, 1990c). The Integrated Assessment Report, including the data contained therein, received 

several rounds of public and peer review (NAPAP, 1991). After this clarification, the SAB-Council identified no 

concerns in their final review letter with the EPA using the Chestnut and Rowe data to quantify recreational 

visibility benefits in the main benefits estimate (U.S. EPA-SAB, 1999b). In July 2010 as part of The Benefits and 

Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011b), the SAB-Council stated that the studies were dated, but 

the EPA “used what the Council understands to be the only relevant studies” (U.S. EPA-SAB, 2010a). In addition, 

the SAB-Council identified no concerns with quantifying recreational visibility benefits using these data in the 

main benefits estimate (U.S. EPA-SAB, 2010a). 

28 The Colorado Plateau (Southwest) region is defined as the states of Colorado, New Mexico, Arizona, and Utah. 

The Southeast region is defined as the states of West Virginia, Virginia, North Carolina, South Carolina, Georgia, 

Florida, Alabama, Mississippi, Louisiana, Tennessee, and Kentucky. The California region includes the state of 

California and one wilderness area in Nevada.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-74

Park for the California region, the Grand Canyon National Park for the Southwest region, and 

Shenandoah National Park for the Southeast region. In accordance with the methodology in 

Chestnut (1997), our approach calculates the benefits from households for a particular region 

for a given change in visibility at a particular Class 1 area. In theory, summing benefits from 

households in all regions would yield the total monetary benefits associated with a given 

visibility improvement at a particular park, which could then be summed with other parks and 

regions to estimate national benefits. Because recreational visibility benefits may reflect the 

value an individual places on visibility improvements regardless of whether the person plans to 

visit the park, all households in the U.S. are assumed to derive some benefit from 

improvements to Class 1 areas.

To value recreational visibility improvements associated with its rulemakings, the EPA 

developed a valuation WTP equation function based on the baseline of visibility, the magnitude 

of the visibility improvement, and household income. This function requires light extinction 

estimates measured as visual range. The behavioral parameters of this equation were taken 

from an analysis of the survey described in Chestnut and Rowe (1990a, 1990b). These 

parameters were used to calibrate WTP for the visibility changes resulting from this rule.29 As an 

example, household WTP for a visibility improvement at a park in its region takes the following 

form:

(6.2)

where:

i indexes region,

k indexes park,

m = household income,

ρ = shape parameter (0.1),

γ = parameter corresponding to the visibility at in-region parks,

Q0 = starting visibility, and

Q1 = visibility after change.

 

29 The parameters for each region are available in Appendix 6a of this RIA.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-75

As discussed in more detail in Appendix 6.A of this RIA, our approach to valuing 

recreational visibility changes is an application of the Constant Elasticity of Substitution (CES) 

utility function approach and is based on the preference calibration method developed by 

Smith, Van Houtven, and Pattanayak (2002).30 Available evidence indicates that households are 

willing to pay more for a given visibility improvement as their income increases (Chestnut, 

1997). Using the income elasticity calculated by Chestnut (1997), the recreational visibility 

benefits assume a 1% increase in income is associated with a 0.9% increase in WTP for a given 

change in visibility. WTP responses reported in Chestnut and Rowe (1990a, 1990b) were also 

region-specific, rather than park-specific. As visibility improvements are not constant across all 

parks in a region, we must infer park-specific visibility parameters in order to calculate WTP for 

projected visibility changes. As the quantity and quality of parks differs between regions, we 

apportion the regional WTP parameters based on relative visitation rates at the different parks, 

because this statistic likely captures both park quality (more people visit parks with more 

desirable attributes, so collective WTP is likely higher) and quantity (more people visit parks in a 

region if the parks are more numerous, so collective WTP is likely higher).31 We also adjust the 

co-benefits for inflation and growth in real income.

Recreational visibility co-benefits can be calculated as the sum of the household WTPs 

for changes in light extinction. We assume that each household is valuing the first or only 

visibility change that occurs in a particular area. The co-benefits at particular areas can be 

calculated by assuming that the subset of visibility changes of interest is the first or the only set 

of changes being valued by households. Estimating benefit components in this way will yield 

slightly upwardly biased estimates of co-benefits, because disposable income is not reduced by 

the WTPs for any prior visibility improvements. The upward bias should be extremely small, 

however, because all of the WTPs for visibility changes are very small relative to income.

In our approach, the primary estimate for recreational visibility only includes co-benefits

for 86 Class 1 areas in the original study regions (i.e., California, the Southwest, and the 

 

30 The Constant Elasticity of Substitution utility function has been chosen for use in this analysis due to its flexibility 

when illustrating the degree of substitutability present in various economic relationships (in this case, the 

tradeoff between income and improvements in visibility).

31 We use 2008 park visitation data from the National Park Service Statistical Abstracts (NPS, 2008), as this is the 

most current data available. Where the data for a particular park was not representative of normal visitation 

rates at that park (for example due to fire damage that occurred during that year), we substitute data from the 

prior year. We use 1997 visitation data for those wilderness areas not included in the National Park Service 

Statistical Abstracts, as more current data is not readily available. As visitation rates for Wilderness Areas are 

small compared to visitation rates in National Parks, the inaccuracies generated by using 1997 data are likely to 

also be small. 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-76

Southeast).32 These co-benefits reflect the value to households living in the same region as the 

Class 1 area as well as values for all households in the United States living outside the state 

containing the Class 1 area.

The Chestnut and Rowe study did not measure values for visibility improvement in Class 

1 areas in the Northwest, Northern Rockies, and Rest of U.S. regions.33 In order to obtain 

estimates of WTP for visibility changes for the 70 additional Class 1 areas in these non-studied

regions, we have to transfer the WTP values from the studied regions. 34 This co-benefits

transfer approach introduces additional uncertainty into the estimates. However, we have 

taken steps to adjust the WTP values to account for the possibility that a visibility improvement 

in parks within one region may not necessarily represent the same visibility improvement at 

parks within a different region in terms of environmental improvement. This may be due to 

differences in the scenic vistas at different parks, uniqueness of the parks, or other factors, such 

as public familiarity with the park resource. To account for this potential difference, we 

adjusted the transferred WTP being transferred by the ratio of visitor days in the two regions. 35

A complete description of the co-benefits transfer method used to infer values for visibility 

changes in Class 1 areas outside the study regions is provided in Appendix 6a of this RIA.

Table 6-4 indicates which studied park regions we used to estimate the value in the nonstudied park regions in our approach. Figure 6-5 shows how the visitation rates vary across 

Class 1 areas and regions and indicates whether each Class 1 area is located within one of the 

studied regions.

Table 6-4. WTP for Visibility Improvements in Class 1 Areas in Non-Studied Park Regions

Park Region Source of WTP Estimate

1. Northwest Benefits transfer from California 

2. Northern Rockies Benefits transfer from Colorado Plateau

3. Rest of U.S. Benefits transfer from Southeast

 

32 The 86 Class 1 areas in the three studied park regions represented 68% of the total visitor days to Class 1 areas in 

2008 (NPS, 2008).

33 The Northwest region is defined as the states of Washington and Oregon. The Northern Rockies region includes

the states of Idaho, Montana, Wyoming, North Dakota, and South Dakota. The Rest of the U.S. region includes all 

other states not included in the other 5 regions.

34 The 70 additional Class 1 areas represented 32% of the total visitor days to Class 1 areas in 2008 (NPS, 2008).

35 For example, if total park visitation in a transfer region was less than visitation in a study region, transferred WTP 

would be adjusted downward by the ratio of the two.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-77

Figure 6-5. Visitation Rates and Park Regions for Class 1 Areasa

a

The colors in this map correspond to the park regions used in the valuation study and the extrapolation to parks 

in other regions. Red = California, light red = Northwest (extrapolated from California), blue = Colorado Plateau, 

light blue = Northern Rockies (extrapolated from Colorado Plateau), green = Southeast, light green = Rest of U.S. 

(extrapolated from Southeast).

In a more recent study, Smith et al. (2005) conducted a contingent valuation survey that 

updated and expanded a portion of the 1988 survey by Chestnut and Rowe (1990). Specifically, 

the Smith et al. (2005) survey relied on a panel maintained by Knowledge Networks with 2,020 

participants completing the survey. Similar to the Chestnut and Rowe survey, the Smith et al. 

survey assessed WTP for changes in summertime visibility using the base photograph of 

Shenandoah National Park. Unlike the Chestnut and Rowe survey, the Smith et al. survey only 

assessed the Shenandoah National Park, did not estimate in-region estimates of WTP, and 

evaluated several options for incorporating budgetary constraints into the survey. The authors 

concluded that WTP for recreational visibility is skewed and sensitive to information about 

budgetary constraints. We are still evaluating the potential error identified by Smith et al. 

(2005) regarding the visibility levels in the photographs for Shenandoah National Park in the 

Chestnut and Rowe survey (1990a,b).

Even though this survey represents several advantages over the older survey (e.g., more 

recent, national, demographically representative, larger sample, etc.), we are unable 

ys
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-78

incorporate the results generated by this survey into our existing method for calculating 

recreational visibility co-benefits because the survey did not account for the differences 

between WTP for in-region parks and out-of-region parks. This omission precludes us from 

combining this new survey for only 1 region of the country with the WTP for the other regions 

of the country from Chestnut and Rowe (1990). Furthermore, Smith et al. (2005) provide a 

variety of WTP estimates reflecting different versions of the survey and different methods of 

summarizing the typical response, which makes it difficult to select estimates to incorporate 

into the recreational visibility benefits calculation.

6.3.4.2 Recreational Visibility Limitations, Caveats, and Uncertainties

Our approach relies upon several data sources as inputs, including emission inventories, 

air quality data from models (with their associated parameters and inputs), relative humidity 

measurements, park information, economic data and assumptions for monetizing co-benefits.

Each of these inputs may contain uncertainty that would affect the recreational visibility cobenefits estimates. Though we are unable to quantify the cumulative effect of all of these 

uncertainties in our approach, we do provide information on uncertainty based on the available 

data, including model evaluation36 and sensitivity analyses to characterize major omissions (i.e., 

benefits from parks in non-studied park regions and inclusion of coarse particles). Although we 

strive to incorporate as many quantitative assessments of uncertainty as possible, we are 

severely limited by the available data, and there are several aspects that we are only able to 

address qualitatively. A summary of the key assumptions including direction and magnitude of 

bias is provided in Table 6-5.

One major source of uncertainty for the estimation of recreational visibility co-benefits

is the benefits transfer process. Choices regarding the functional form and key parameters of 

the estimating equation for WTP for the affected population could have significant effects on 

the magnitude of the estimates. Assumptions about how individuals respond to changes in 

visibility that are either very small or outside the range covered in the Chestnut and Rowe study 

could also affect the estimates.

Table 6-5. Summary of Key Assumptions in Estimating Recreational Visibility Co-benefitsa

Key Assumption

Direction of 

Bias

Potential 

Magnitude of 

Effect

 

36See Chapter 4 for more information on model evaluation.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-79

Chestnut and Rowe study covers parks in three regions: 

California, Southwest, and Southeast. Benefits to other 

regions in the U.S. are not included in the primary 

benefits estimate.

Underestimate Medium

Benefits to other recreational settings, such as National 

Forests and state parks, are not included in our 

approach.

Underestimate Medium-Low

Chestnut and Rowe study conducted on populations in 

five states. These results are applied to the entire U.S. 

population.

Unclear Unclear

Individuals have a greater WTP for visibility changes in 

parks within their region.

Unclear Unclear

WTP values reflect only visibility improvements and 

not overall air quality improvements.

Potential 

Overestimate

Unclear

We assume that there are 2.68 people per household.

Because this estimate has been decreasing over time, 

this may underestimate the number of households.

Potential 

Underestimate

Medium-Low

a

A description of the classifications for magnitude of effects can be found in Appendix 5.B of this RIA.

Since the valuation of recreational visibility co-benefits relies upon one study (Chestnut 

and Rowe, 1990a; 1990b), all of the uncertainties within that study also pertain to any analysis

that uses it. In general, the survey design and implementation reflect the period in which the 

Chestnut and Rowe study was conducted. Since that time, many improvements to the design of 

stated preference surveys have been developed (e.g., Arrow, 1993), but we are currently 

unaware of newer studies that we could incorporate into our visibility co-benefits

methodology. Although Chestnut and Rowe still offers the best available WTP estimates, the 

study has a number of limitations, including:

 The vintage of the survey (late 1980s) invites questions whether the values would 

still be valid for current populations, or more importantly for our approach, future 

populations in 2020.

 The survey focused on visibility improvements in and around specific national parks 

and wilderness areas. Given that national parks and wilderness areas exhibit unique 

characteristics, it is not clear whether the WTP estimate obtained from this survey 

can be transferred to other national parks and wilderness areas, even other parks 

within the studied park regions, without introducing additional uncertainty.

 The survey focused only on populations in five states, so the application of the 

estimated values to populations outside those states requires that preferences of 

populations in the five surveyed states be similar to those of non-surveyed states.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-80

 There is an inherent difficulty in separating values expressed for visibility 

improvements from an overall value for improved air quality. The survey attempted 

to control for this by informing respondents that “other households are being asked 

about visibility, human health, and vegetation protections in urban areas and at 

national parks in other regions.” However, most of the respondents did not feel that 

they were able to segregate recreational visibility at national parks entirely from 

residential visibility and health effects.

 It is not clear exactly what visibility improvements the respondents to the survey 

were valuing. The WTP question asked about changes in average visibility, but the 

survey respondents were shown photographs of only daytime, summer conditions, 

when visibility is generally at its worst. It is possible that the respondents believed 

those visibility conditions held year-round, in which case they would have been 

valuing much larger overall improvements in visibility than what otherwise would be 

the case. In our approach, the EPA assumed that respondents provided values for 

changes in annual average visibility. Because most policies would result in a shift in 

the distribution of visibility (usually affecting the worst days more than the best 

days), the annual average may not be the most relevant metric for policy analysis.

 The survey did not include reminders of possible substitutes (e.g., visibility at other 

parks) or budget constraints. These reminders are considered to be best practice for 

stated preference surveys.

6.3.5 Residential Visibility

6.3.5.1 Methodology

Residential visibility co-benefits are those that occur from visibility changes in urban, 

suburban, and rural areas where people live. These co-benefits are important because some 

people living in certain urban areas may place a high value on unique scenic resources in or 

near these areas that are outside of Class 1 areas. For example, the State of Colorado 

established a local visibility standard for the Denver metropolitan area in 1990 (Ely et al., 1991). 

In our approach, residential visibility improvements are defined as those that occur specifically 

in Metropolitan Statistical Areas (MSAs).

In the Urban-focused Visibility Assessment (U.S. EPA, 2010b) and the Policy Assessment 

for the Review of the PM NAAQS (U.S. EPA, 2011a), several preference studies provide the 

foundation for the secondary PM NAAQS.37 The three completed survey studies (all in the west) 

included Denver, Colorado (Ely et al., 1991), one in the lower Fraser River valley near 

Vancouver, British Columbia (BC), Canada (Pryor, 1996), and one in Phoenix, Arizona (BBC 

 

37 For more detail about these preference studies, including information about study designs and sampling 

protocols, please see Section 2 of the Particulate Matter Urban-Focused Visibility Assessment (U.S. EPA, 2010b).
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-81

Research & Consulting, 2003). A pilot focus group study was conducted in Washington, DC on 

behalf of the EPA to inform the 2006 PM NAAQS review (Abt Associates Inc., 2001). Although 

these studies indicate that some individuals considered the visual air quality associated with 

ambient levels of air pollution in urban areas to be unacceptable, these studies do not provide 

sufficient information on which to develop monetized co-benefits estimates. Specifically, the 

public perception studies do not provide preferences expressed in dollar values, even though 

they do suggest that the co-benefits associated with improving residential visibility are positive.

Studies in the peer-reviewed literature support a non-zero value for residential visibility 

(e.g., Brookshire et al., 1982; Loehman et al., 1994). Furthermore, Chestnut and Rowe (1990c) 

conclude that residential visibility co-benefits are likely to be at least as high as recreational 

visibility co-benefits because of the quantity of time most people spend in and near their 

homes and the substantial number of people affected. In previous assessments, the EPA used a 

study on residential visibility valuation conducted in 1990 (McClelland et al., 1993). Consistent 

with advice from SAB-Council, the EPA designated the McClelland et al. study as significantly 

less reliable for regulatory benefit-cost analysis, although it does provide useful estimates on 

the order of magnitude of residential visibility co-benefits (U.S. EPA-SAB, 1999).38 In our 

approach for estimating residential visibility co-benefits, we replaced the previous methodology 

with a new benefits transfer approach and incorporated additional valuation studies. This new 

approach was developed for The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA 

Report to Congress (U.S. EPA, 2011)39 and reviewed by the SAB-Council (U. S. EPA-SAB, 2004, 

2010a, 2010b).

To value residential visibility improvements, the new approach draws upon information 

reported in the Brookshire et al. (1979), Loehman et al. (1985) and Tolley et al. (1986) studies.40

 

38 EPA’s Advisory Council on Clean Air Compliance Analysis noted that the McClelland et al. (1993) study may not 

incorporate two potentially important adjustments. First, their study does not account for the “warm glow”

effect, in which respondents may provide higher willingness to pay estimates simply because they favor “good 

causes” such as environmental improvement. Second, while the study accounts for non-response bias, it may not 

employ the best available methods. As a result of these concerns, the Council recommended that residential 

visibility be omitted from the overall primary benefits estimate. (U.S. EPA-SAB, 1999)

39 This report is also known as the Second Prospective 812 analysis.

40 Loehman et al. (1994) and Brookshire et al. (1982) published results in peer-reviewed journals based on the 

same underlying data we obtained from Loehman et al. (1985) and Brookshire et al. (1979). While the specific 

details need to compute visibility benefits using Tolley et al. (1986) were not published in a peer-reviewed

journal, the overall work including study and survey design was subject to peer review during study development

(see Leggett and Neumann, 2004 and Patterson et al., 2005). In addition, Tolley subsequently published a book

(Tolley and Fabian, 1988) based on this research, which notes in the preface that the methods were critiqued 

throughout by various external economists. The EPA does not claim that this external critique necessarily 

constituted a formal peer review process, but we provide this information for transparency regarding the review 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-82

Each of the studies provides estimates of household WTP to improve visibility conditions. While 

uncertainty exists regarding the precision of these older, stated-preference residential 

valuation studies, we believe their results support the argument that individuals have a nonzero value for residential visibility improvements. These studies provide primary visibility values 

for Atlanta, Boston, Chicago, Denver, Los Angeles, Mobile, San Francisco, and Washington D.C.41

In accordance with Chestnut and Rowe (1990c), we utilize the WTP estimates and the 

associated change in visual range from each study to estimate the β parameter for the eight 

study areas. The β parameter represents the WTP for a specific improvement in visibility in a 

specific location. Where studies provide multiple estimates for visual range improvements, we 

estimate β by regressing the natural log of the ratio of visual range following and prior to 

improvement against WTP. To express these value estimates in comparable terms across study 

locations, we express household WTP for a change in visual range in a specific MSA using the 

following function:

(6.3)

where:

VR0 = mean annual visual range in miles before the improvement,

VR1 = mean annual visual range in miles after the improvement, and

β = parameter.

Total residential visibility co-benefits within a particular MSA are driven by visibility 

improvements, population density, and the WTP value applied. Only those people living within 

in an MSA are assumed to receive co-benefits from improved residential visibility. In other 

words, unlike recreational visibility, we do not assume a non-use value by people who live 

outside the MSA for residential visibility. Table 6-6 provides a summary of these valuation 

estimates for each study location, as well as an illustrative implied WTP value for a 10% 

improvement in visual range. As shown, the implied annual per-household WTP estimates for a 

hypothetical 10% improvement ranges from $21 to $220, depending on the study area. It is not 

 

of this work. The use of these studies as the only available information to estimate residential visibility co-benefits 

in the main estimate was supported by the SAB-Council (U.S. EPA-SAB, 1999a, 2010a). 

41 Recognizing potential fundamental issues associated with data collected in Cincinnati and Miami (e.g., see 

Chestnut et al. (1986) and Chestnut and Rowe (1990c), we do not include values for these cities in our analysis. 

The 8 MSAs where the valuation studies were conducted represent 15% of the total US population in 2020 (U.S. 

Census).
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-83

surprising that such a range of values exists, as these study areas all feature different 

landscapes and vistas, populations and prevailing visibility conditions.

Table 6-6. Summary of Residential Visibility Valuation Estimates

City Study

β 

Estimate

Implied WTP for 

10% 

Improvement in 

Visual Range 

(1990$, 1990 

income)

Implied WTP for 

10% 

Improvement in 

Visual Range 

(2006$, 2020 

income)

Atlanta Tolley et al. (1986) 321 $31 $72

Boston Tolley et al. (1986) 398 $38 $89

Chicago Tolley et al. (1986) 310 $30 $69

Denver Tolley et al. (1986) 696 $66 $155

Los Angeles Brookshire et al. 

(1979)

94 $9 $21

Mobile Tolley et al. (1986) 313 $30 $70

San Francisco Loehman et al. 

(1985)

989 $94 $220

Washington, 

DC

Tolley et al. (1986) 614 $59 $137

Similar to recreational visibility co-benefits, we then incorporate preference calibration 

using the method developed by Smith, Van Houtven, and Pattanayak (2002), which is discussed

in more detail in Appendix 6a of this RIA. This preference calibration is a change since The 

Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011) 

intended to address the SAB-Council’s concern (U.S. EPA-SAB, 2010a) regarding the 

inconsistency regarding household income in the estimation of recreational and residential 

visibility. To express these “preference-calibrated” value estimates across study locations, we 

express household WTP for a change in visual range in a specific MSA using the following 

function:

(6-4)

where:

m = household income,

ρ = shape parameter (0.1),
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-84

θ = WTP parameter corresponding to the visibility at MSA,

VR0 = starting visibility, and

VR1 = visibility after change.

While the primary estimate for residential visibility includes co-benefits in only the eight 

MSAs included in the valuation studies, people living in other urban areas also have non-zero 

values for residential visibility. For this reason, our approach includes a sensitivity analysis for 

the extrapolated residential visibility in the 351 additional MSAs.42 Because there is 

considerable uncertainty about the validity of this benefit transfer approach, these 

extrapolated co-benefits are included in a sensitivity analysis only. This is an important 

distinction between the approach used in The Benefits and Costs of the Clean Air Act 1990 to 

2020: EPA Report to Congress (U.S. EPA, 2011), where all cities were included in the total 

benefits approach. We believe that it is appropriate to deviate from the previous approach in 

order to be consistent with the approach used to estimate recreational visibility co-benefits and 

to recognize the uncertainty associated with extrapolating beyond the studied cities. Figure 6-6 

indicates the study cities as well as the assignment of the other MSAs to the study cities.

The degree to which the three studies were successful in convincing respondents to 

focus solely on visibility is unclear, as none of the three studies includes follow-up questions 

necessary to investigate the issue. Furthermore, no other residential visibility CV studies 

provide evidence regarding the degree to which health effects are embedded in visibility 

values. Although the McClelland et al. (1991) study has a follow-up question designed to 

allocate WTP across several categories, the CV question in the McClelland et al. study was 

focused on air pollution generally rather than visibility. As a result, we do not adjust the results 

from these studies to account for potentially embedded health effects.

There are many factors that could influence WTP for residential visibility, and these 

factors vary across urban areas. In our approach, we utilize the benefit transfer approach 

developed for The Benefits and Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress

(U.S. EPA, 2011) report, but we recognize that there are alternative methods that we could 

have used. We assigned a valuation study area to each MSA based on two factors: geographic 

proximity to one of the eight study cities and elevation. Any MSA with a county elevation above 

1,500 meters was assigned the Denver valuation instead of the nearest study area.43 Because 

 

42 The 351 additional MSAs plus the 8 study area MSAs represent 84% of the total US population in 2020 (U.S. 

Census).

43 Elevation data represent the county-level maximum, which were calculated using the ArcGIS Spatial Analyst tool 

“Zonal Statistics” using the geographic database HYDRO1K for North America (U.S. Geological Survey, 1997). This 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-85

residents of Denver have a dramatic view of the Rocky Mountains that is rarely obstructed by 

trees, it is plausible that they might have a greater interest in protecting visibility than a city 

without nearby mountains. The geographic proximity factor is constrained in two areas. The 

San Francisco valuation study is only assigned to the six counties in the San Francisco Bay area 

MSAs because the study is unique among the three regarding the temporal description of 

visibility conditions, landscape/vistas, and prevailing weather conditions. In addition, the Los 

Angeles valuation was assigned to the Riverside MSA despite exceeding the elevation 

threshold.44

 

dataset and associated documentation are available on the Internet at 

DEMhttp://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/gtopo30/hydro/namerica.

44 Riverside MSA is assigned to the Los Angeles study area because a significant portion of Riverside County itself is 

located in the South Coast Air Quality Management District, which can be considered by to be part of the same 

regulated airshed as Los Angeles. The geographic assignment is preserved despite exceeding the elevation 

threshold because Riverside is adjacent to one of the study cities and this region has a particular set of locationspecific characteristics that set it apart from Denver. 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-86

Figure 6-6. Residential Visibility Study City Assignment

6.3.5.2 Residential Visibility Limitations, Caveats, and Uncertainties

Similar to recreational visibility co-benefits, there are many data inputs into the 

residential visibility co-benefits that contribute to overall uncertainty. Our approach includes 

sensitivity analyses to characterize major omissions (i.e., co-benefits in other MSAs and coarse 

particles). A summary of the key assumptions including direction and magnitude of bias in our 

approach is provided in Table 6-7.

The valuation studies relied upon for the residential visibility co-benefits, although 

representing the best available estimates, have a number of limitations. These include the 

following:

 The survey design and implementation reflects the period in which the surveys were 

conducted. Since that time, many improvements to the stated preference methods 

have been developed.

 The vintage of the surveys (1970s and 1980s) invites questions whether the values 

are still valid for current populations, or more importantly for our approach, future 

populations in 2020.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-87

 The survey focused only on populations in eight cities, so the transfer of the WTP 

estimates values to populations outside those cities requires that their preferences 

be similar to those in non-surveyed cities, as well as the visibility attributes be 

similar across study and transfer MSAs.

 There is an inherent difficulty in separating values expressed for visibility 

improvements from an overall value for improved air quality. The studies attempted 

to control for this, but most of the respondents did not feel that they were able to 

segregate residential visibility entirely from recreational visibility and health effects.

Table 6-7. Summary of Key Assumptions in the Residential Visibility Co-benefitsa

Key Assumption

Direction of 

Bias

Magnitude of 

Effect

Residential and recreational visibility benefits are 

distinct and separable.

Potential 

Overestimate

Medium-Low

Estimates residential visibility benefits are limited to 

populations within the boundaries of MSAs. Areas 

outside of an MSA are not included in our approach.

Underestimate Low

WTP values reflect only visibility improvements and 

not overall air quality improvements.

Potential 

Overestimate

Medium-Low

WTP values from studies in Atlanta, Boston, Chicago, 

Denver, Los Angeles, Mobile, San Francisco, and 

Washington D.C. can be accurately transferred to 

MSAs across the U.S. based on proximity and elevation

Unclear Unclear

We assume that there are 2.68 people per household.

Because this estimate has been decreasing over time, 

this may underestimate the number of households.

Potential 

Underestimate

Medium-Low

a

A description of the classifications for magnitude of effects can be found in Appendix 5.B of this RIA.

6.3.5.3 Using Hedonic Economic Literature to Estimate Visibility Co-benefits

The hedonic model assumes that consumers do not value the consumption of a good 

directly, but rather value the characteristics contained within a good. In the context of property 

values, the consumer values both the physical attributes of the property (i.e., number of rooms, 

square footage, etc.) as well as geographic and environmental attributes (e.g., proximity to 

parks, visibility, etc.). Following the technique developed by Rosen (1974), property 

characteristics are regressed on the observed price of the properties within a given housing 

market to estimate the contribution of each characteristic to the overall price. 

Numerous studies have applied hedonic methods to estimate the willingness to pay 

(WTP) for air quality changes (see Smith and Huang (1995) and Boyle and Kiel (2001) for 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-88

literature reviews), but fewer researchers have focused specifically on visibility. Studies that 

have estimated the WTP to improve visibility have focused on specific housing markets like Los 

Angeles (Murdoch and Thayer, 1998; Beron et al., 2001) or San Francisco (Graves et al., 1998), 

and it is unclear if these results would be more broadly applicable to the rest of the county. 

While the literature demonstrates a link between pollutant concentrations and home-buying 

behavior, it is difficult to partition the WTP for changes in pollution between health and 

aesthetic concerns. Murdoch and Thayer (1998) use visibility as a surrogate for environmental 

quality and Beron et al. (2001) acknowledge that their parameter estimates likely reflect a 

combination of visual aesthetics and an absence of health effects. Delucchi et al. (2002) deals 

with this issue by partitioning WTP estimates from a hedonic model into health and visibility 

components using results from previous contingent valuation studies, and find that the 

estimate of visibility co-benefits is similar to estimates based on contingent valuation alone. 

In 2004, the Advisory Council on Clean Air Compliance Analysis (SAB-Council)

recommended that the EPA evaluate the available studies addressing residential visibility and

consider the possibility of using hedonic property models to estimate residential visibility cobenefits (U.S. EPA-SAB, 2004). In response to this recommendation, the EPA evaluated the 

existing economic literature, and determined that there were substantial limitations that 

precluded the Agency from using these studies to make inferences regarding individuals’ WTP 

for improved visibility (Leggett and Neumann, 2004). Specifically, the literature did not provide 

support for the assumption that market participants are aware of the spatial variation in 

visibility, and consider this variation when purchasing a home, and can successfully separate 

visibility effects from health effects (Leggett and Neumann, 2004). This conclusion is also 

supported by Delucchi et al. (2004), which found that hedonic price analysis does not capture 

all of the health effects of air pollution because homebuyers may not be fully informed about 

these effects .

Research since 2004 has attempted to address limitations of the hedonic method 

through the use of U.S. Census microdata (Bayer et al., 2009), spatial statistical methods 

(Anselin and Le Gallo, 2006; Anselin and Lozana-Gracia, 2009; Beron et al., 2004; Kim et al., 

2010) and more complete air quality data and information about nonattainment status (Chay 

and Greenstone, 2005). However, none of these studies specifically address visibility, and they 

are therefore of limited use at this time. While the current state of the literature does not 

provide a basis for using hedonics-based approaches, continued innovations in methodology 

and the further development of national, micro-level housing and demographic datasets may 

open possibilities for national-scale hedonics-based benefit analysis in the future.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-89

Regardless of whether we use hedonic models or stated preference surveys to estimate 

co-benefits arising from improved visibility, it is important to emphasize that estimates of WTP 

for residential or recreational visibility improvements are not substitutes for health benefits. As 

previously mentioned, people often have difficulty separating their health concerns from their 

aesthetic concerns when evaluating preferences for visibility, which could overestimate 

visibility co-benefits if not properly controlled. However, because we use a damage-function 

approach to estimate health benefits (see Chapter 5 of this RIA), the health benefits estimates 

are unaffected by any potential confounding with visibility preferences. 

6.3.6 Discussion of Visibility Co-benefits

As described in the previous sections of this chapter, the estimation of visibility cobenefits is complex and suffers from unavoidable limitations. While we are confident that the 

underlying scientific literature supports a non-zero estimate for visibility co-benefits

attributable to emission reductions, we are less confident in the magnitude of those co-benefits

outside of previously studied locations. While acknowledging these limitations, it is important 

to note that this general approach was included in The Benefits and Costs of the Clean Air Act 

1990 to 2020: EPA Report to Congress (U.S. EPA, 2011), which was reviewed by the SAB-Council 

(U.S. EPA-SAB, 2010a, 2010b). Although the SAB-Council highlighted concerns with the visibility 

approach used in the study, it did not recommend that visibility benefits be excluded. We have 

addressed the SAB-Council’s concern regarding inconsistency between estimation of residential 

and recreational visibility in our approach. However, we do not have the data to address the 

SAB-Council’s concern regarding inclusion of night-time benefits of visibility improvements in 

our annual average, which may lead to an underestimation of visibility benefits. To minimize 

uncertainties related to extrapolation and geographical double counting, our approach only 

includes a subset of monetized visibility co-benefits in the core monetized visibility co-benefits 

estimate to correspond with our higher level of confidence in recreational co-benefits within 

the study regions and residential co-benefits within the study cities. Although we are confident

that visibility co-benefits extend beyond these studied areas, we are less confident about the 

magnitude of those co-benefits. However, it is unclear the degree to which the visibility 

valuation surveys were successful in controlling for potential double counting embedded health 

benefits.

Consistent with the approach described in the proposal RIA, we have described a 

revised approach for estimating visibility co-benefits, including light extinction estimation 

methods, visitation data for Class 1 areas (used in extrapolating co-benefits), valuation studies 

for residential visibility co-benefits, and the benefit transfer technique for residential co-
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-90

benefits. Including residential visibility co-benefits in the core visibility co-benefits estimates 

reflects an evolution in our understanding of the nature and importance of the effect on public 

welfare from visibility impairment to a more multifaceted approach that includes non-Class 1

areas, such as urban areas. This evolution has occurred in conjunction with the expansion of 

available PM data and information from associated studies of public perception, valuation and 

personal comfort and well-being. While visibility preference studies (Abt Associates Inc., 2001, 

Ely et al., 1991, Pryor, 1996, BBC Research & Consulting, 2003) also provide support for a nonzero benefits estimate, these surveys did not include questions that would enable monetization 

of those preferences.

Despite these improvements, we are limited by the available peer-reviewed studies on 

visibility co-benefits, which have not undergone a similar expansion as the health literature.

Each of these valuation studies has limitations, which are identified in the sections 6.3.4.2 and 

6.3.5.2. When the SAB-Council reviewed the visibility benefits analysis for The Benefits and 

Costs of the Clean Air Act 1990 to 2020: EPA Report to Congress (U.S. EPA, 2011), they also 

lamented on the need for additional research to improve methods and estimates (U.S. EPASAB, 2010a, 2010b). Because of time and resource constraints, performing original research for 

regulatory analyses of specific policy actions is infeasible. Most importantly, we are interested 

in recently published national-scale visibility valuation studies that incorporate current CV best 

practices, as the existing studies are limited to specific subset of geographic areas. Other 

important research questions that remain unresolved include identifying factors that affect 

valuation preferences in order to facilitate benefits transfer from the original studies to transfer 

sites across localities, disentangling health and ecosystem valuation from visibility valuation, 

usefulness of preference calibration, and potential role of hedonic valuation approaches. Many 

of these same research needs were identified by Cropper (2000), but they have yet to be 

addressed by the research community.

In Appendix 6.B, we provide the results of an illustrative analysis of the visibility cobenefits associated with the 2020 base case 2020 control case simulation described in Chapter 

3 that were used to develop the air quality ratios; however, we do not have air quality model 

runs for the regulatory baseline and the alternative standard levels that would allow us to 

calculate the visibility co-benefits of attaining the revised primary standard.

6.4 Materials Damage Co-benefits

Building materials including metals, stones, cements, and paints undergo natural 

weathering processes from exposure to environmental elements (e.g., wind, moisture, 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-91

temperature fluctuations, sunlight, etc.). Pollution can worsen and accelerate these effects.

Deposition of PM is associated with both physical damage (materials damage effects) and 

impaired aesthetic qualities (soiling effects). Wet and dry deposition of PM can physically affect 

materials, adding to the effects of natural weathering processes, by potentially promoting or 

accelerating the corrosion of metals, by degrading paints and by deteriorating building 

materials such as stone, concrete and marble (U.S. EPA, 2009b). The effects of PM are 

exacerbated by the presence of acidic gases and can be additive or synergistic due to the 

complex mixture of pollutants in the air and surface characteristics of the material. Acidic 

deposition has been shown to have an effect on materials including zinc/galvanized steel and 

other metal, carbonate stone (as monuments and building facings), and surface coatings 

(paints) (Irving, 1991). The effects on historic buildings and outdoor works of art are of 

particular concern because of the uniqueness and irreplaceability of many of these objects.

The PM ISA concludes that evidence is sufficient to support a causal relationship 

between PM and effects on materials (U.S. EPA, 2009b). Considerable research has been 

conducted on the effects of air pollutants on metal surfaces due to the economic importance of 

these materials, especially steel, zinc, aluminum, and copper. Moisture is the single greatest 

factor promoting metal corrosion; however, deposited PM can have additive, antagonistic or 

synergistic effects. In general, SO2 is more corrosive than NOx although mixtures of NOx, SO2

and other particulate matter corrode some metals at a faster rate than either pollutant alone 

(U.S. EPA, 2008). Metal structures are usually coated by alkaline corrosion product layers and 

thus are subject to increased corrosion by acidic deposition. In addition, research has 

demonstrated that iron, copper, and aluminum-based products are subject to increased 

corrosion due to pollution (Irving, 1991). Information from both the PM ISA (U.S. EPA, 2009b) 

and NOx/SOx ISA (U.S. EPA, 2008) suggest that the extent of damage to metals due to ambient 

PM is variable and dependent upon the type of metal, prevailing environmental conditions, rate 

of natural weathering and presence or absence of other pollutants

In addition, the deposition of PM can cause soiling, which is the accumulation of dirt, 

dust, and ash on exposed surfaces such as metal, glass, stone and paint. Particles consisting 

primarily of carbonaceous compounds can cause soiling of commonly used building materials 

and culturally important items such as statues and works of art. Soiling occurs when PM 

accumulates on an object and alters the optical characteristics (appearance). The reflectivity of 

a surface may be changed or presence of particulates may alter light transmission. These 

effects can reduce the aesthetic value of a structure or result in reversible or irreversible 

damage to statues, artwork and architecturally or culturally significant buildings. Due to soiling 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-92

of building surfaces by PM, the frequency and duration of cleaning or repainting may be 

increased. In addition to natural factors, exposure to PM may give painted surfaces a dirty 

appearance. Pigments in works of art can be degraded or discolored by atmospheric pollutants, 

especially sulfates (U.S. EPA, 2008). Previous assessments estimated household soiling cobenefits based on the Manuel et al. (1982) study of consumer expenditures on cleaning and 

household maintenance. However, the data used to estimate household soiling damages in the 

Manuel et al. study is from a 1972 consumer expenditure survey and as such may not 

accurately represent consumer preferences in the future. In light of this significant limitation, 

we believe that this study cannot provide reliable estimates of the likely magnitude of the cobenefits of reduced PM household soiling.

In order to estimate the monetized co-benefits associated with reducing materials 

damage and household soiling, quantitative relationships are needed between particle size, 

concentration, chemical concentrations and frequency of maintenance and repair. Such an 

analysis would require three steps:

1. Develop a national inventory of sensitive materials;

2. Derive concentration-response functions that relate material damage to change in 

pollution concentration or deposition; and,

3. Estimate the value of lost materials and/or repair of damage.

Due to data limitations and uncertainties inherent in each of these steps, we have 

chosen not to include a monetized estimate of materials damage and household soiling in this 

analysis. The PM ISA concluded that there is considerable uncertainty with regard to interaction 

of co-pollutants in regards to materials damage and soiling processes (U.S. EPA, 2009b).

Previous benefits analyses by the EPA have provided quantitative estimates of materials 

damage (U.S. EPA, 2011b) and household soiling damage (U.S. EPA, 1999). Consistent with SAB 

advice (U.S. EPA, 1998), we determined that the existing data are not sufficient to calculate a 

reliable estimate of future year household soiling damages (U.S. EPA, 1998). These previous 

analyses have shown that materials damage co-benefits are significantly smaller than the 

health benefits associated with reduced exposure to PM2.5 and ozone, or even visibility cobenefits. However, studies of materials damage to historic buildings and outdoor artwork in 

Sweden (Grosclaude and Soguel, 1994) indicate that these co-benefits could be an order of 

magnitude larger than household soiling co-benefits.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-93

In the absence of quantified co-benefits, we provide a qualitative description of the 

avoided damage associated with reducing PM and PM precursor pollutants. Table 6-8 shows 

the effect of various PM2.5 precursor pollutants and other co-pollutants on various materials.

Table 6-8. Materials Damaged by Pollutants Affected by this Rule (U.S. EPA, 2011b)

Pollutant Unquantified Effects / Damage to:

Sulfur oxides Infrastructural materials—galvanized and painted carbon steel

Commercial buildings—carbonate stone, metal, and painted wood 

surfaces

Residential buildings—carbonate stone, metal, and painted wood 

surfaces

Monuments—carbonate stone and metal

Structural aesthetics

Automotive finishes—painted metal

Hydrogen ion and 

nitrogen oxides

Infrastructural materials—galvanized and painted carbon steel

Zinc-based metal products, such as galvanized steel

Commercial and residential buildings—carbonate stone, metal, and 

wood surfaces

Monuments—carbonate stone and metal

Structural aesthetics

Automotive finishes—painted metal

Carbon dioxide Zinc-based metal products, such as galvanized steel

Formaldehyde Zinc-based metal products, such as galvanized steel

Particulate matter Household cleanliness (i.e., household soiling)

Ozone Rubber products (e.g., tires)

6.5 Climate Co-benefits

Actions taken by state and local governments to implement the revised annual primary

standard are likely to have implications for climate change because emission reductions

ultimately implemented to meet the standard may have impacts on emissions of long-lived 

greenhouse gas (GHG) such as carbon dioxide (CO2), short-lived climate forcers such as black 

carbon (BC), and cooling aerosols like organic carbon (OC). Our ability to quantify the climate 

effects of these revised standard is limited due to lack of available information on the cocontrolled GHG emission reductions, the energy and associated climate gas implications of 

control technologies assumed in the illustrative regulatory alternatives, and remaining 

uncertainties regarding the impact of long-lived and short-lived climate forcer impacts on 

climate change. For this RIA, we discuss qualitatively the implications of potential emission 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-94

reductions in warming and cooling aerosols and changes in long-lived GHG emissions such as 

CO2 for the regulatory alternatives. Implementation strategies undertaken by state and local 

governments to comply with the standards may differ from the illustrative emission reduction

strategies in this RIA. It is important to note that the net climate forcing depends on the specific 

combinations of emission reductions chosen to meet the revised standard because of the 

differences in warming and cooling potential of the difference pollutants.

6.5.1 Climate Effects of Short Lived Climate Forcers

Pollutants that affect the energy balance of the earth are referred to as climate forcers. 

A pollutant that increases the amount of energy in the Earth’s climate system is said to exert 

“positive radiative forcing,” which leads to warming and climate change. In contrast, a pollutant 

that exerts negative radiative forcing reduces the amount of energy in the Earth’s system and 

leads to cooling.

Long-lived gases such as CO2 differ from short-lived pollutants such as BC in the length 

of time they remain in the atmosphere affecting the earth’s energy balance. Long-lived gases 

remain in the atmosphere for hundreds to thousands of years. Short-lived climate forcers 

(SLCFs), in contrast, remain in the atmosphere for short periods of time ranging from days to 

weeks. The potential to affect near-term climate change and the rate of climate change with 

policies to address these emissions is gaining attention nationally and internationally (e.g., 

Black Carbon Report to Congress (U.S. EPA, 2012b), Arctic Council Task Force, Global Methane 

Initiative, and Convention on Long-Range Trans-boundary Air Pollution of the United Nations 

Economic Commission for Europe). A recent United Nations Environmental Programme (UNEP) 

study provides the most comprehensive analysis to date of the co-benefits of measures to 

reduce SLCFs including methane, ozone, and black carbon assessing the health, climate, and 

agricultural co-benefits of a suite of mitigation technologies. The report concludes that the 

climate is changing now, and these changes have the potential to “trigger abrupt transitions 

such as the release of carbon from thawing permafrost and biodiversity loss.” While reducing 

long-lived GHGs such as CO2 is necessary to protect against long-term climate change, reducing 

SLCF gases including BC and ozone is beneficial and will slow the rate of climate change within 

the first half of this century (UNEP, 2011).

6.5.1.1 Climate Effects of Black Carbon

Black carbon is the most strongly light‐absorbing component of PM2.5, and is formed by 

incomplete combustion of fossil fuels, biofuels, and biomass. The short atmospheric lifetime of 

BC lasting from days to weeks and the mechanisms by which BC affects climate distinguish it 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-95

from long‐lived GHGs like CO2. This means that actions taken to reduce the BC constituents in 

direct PM2.5 will have almost immediate effects on climate change. Emissions sources and 

ambient concentrations of BC vary geographically and temporally resulting in climate effects 

that are more regionally and seasonally dependent than the effects of long‐lived, well‐mixed 

GHGs. Likewise, mitigation actions for BC will produce different climate impacts depending on 

the region, season, and emission source category affected.

BC influences climate in multiple ways: directly absorbing light, reducing the reflectivity 

(“albedo”) of snow and ice through deposition, and interacting with clouds. BC affects climate 

directly by absorbing both incoming and outgoing radiation of all wavelengths. In contrast, 

GHGs mainly trap outgoing infrared radiation from the earth’s surface. Per unit of mass in the 

atmosphere, BC can absorb a million times more energy than CO2 (Bond and Sun 2005). This 

strong absorptive capacity is the property most relevant to its potential to affect the Earth’s 

climate. When BC is deposited on snow and ice, it darkens the surface and decreases albedo, 

thereby increasing absorption and accelerating melting. Finally, BC also affects climate 

indirectly by altering the properties of clouds, affecting cloud reflectivity, precipitation, and 

surface dimming. These indirect impacts of BC are associated with all ambient particles and 

may lead to cooling, but are not associated with long-lived well mixed GHGs.

Regional climate impacts of BC are highly variable, and sensitive regions such as the 

Arctic and the Himalayas are particularly vulnerable to the warming and melting effects of BC. 

Snow and ice cover in the Western U.S. has also been affected by BC. Specifically, deposition of 

BC on mountain glaciers and snow packs produces a positive snow and ice albedo effect, 

contributing to the melting of snowpack earlier in the spring and reducing the amount of 

snowmelt that normally would occur later in the spring and summer (Hadley et al. 2010). This 

has implications for freshwater resources in regions of the U.S. dependent on snow‐fed or 

glacier‐fed water systems. In the Sierra Nevada mountain range, Hadley et al. (2010) found BC 

at different depths in the snowpack, deposited over the winter months by snowfall. In the 

spring, the continuous uncovering of the BC contributed to the early melt. A model capturing 

the effects of soot on snow in the western U.S. shows significant decreases in snowpack 

between December and May (Qian et al., 2009). Snow water equivalent (the amount of water 

that would be produced by melting all the snow) is reduced 2‐50 millimeters (mm) in 

mountainous areas, particularly over the Central Rockies, Sierra Nevadas, and western Canada. 

A study found that biomass burning emissions in Alaska and the Rocky Mountain region during 

the summer can enhance snowmelt. Dust deposition on snow, at high concentrations, can have 

similar effects to BC (Koch et al., 2007). Similarly, a study done by Painter et al. (2007) in the 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-96

San Juan Mountains in Colorado indicated a decrease in snow cover duration of 18‐35 days as a 

result of dust transported from non‐local desert sources.

The illustrative emission reduction strategies evaluated for this rule include reductions 

in BC emissions that will tend to have a beneficial cooling effect on the atmosphere. BC and 

elemental carbon (EC) (or particulate elemental carbon (PEC)) are used interchangeably in this 

report because the EPA traditionally estimates EC emissions rather than BC and for the purpose 

of this analysis these measures are essentially equivalent.

6.5.1.2 Climate Effects of Nitrates, Sulfate, and Organic Carbon (excluding BC)

The composition of the total emissions mixture is also relevant as to whether emissions 

are warming or cooling to the atmosphere. Pollutants such as SO2, NOX, and most OC particles 

tend to produce a cooling influence on climate. Exceptions include OC deposition on snow and 

ice, which leads to increased melting.

In addition, it is important to account for the indirect effects of all PM constituents on 

climate: all aerosols (including BC) affect climate indirectly by changing the reflectivity and 

lifetime of clouds. The net indirect effect of all aerosols is very uncertain but is thought to be a 

net cooling influence.

6.5.1.3 Climate Effects of Ozone

Ozone changes due to this revised annual standard are not estimated for this analysis 

but may occur due to the NOx reductions estimated. Ozone is a well-known SLCF (U.S. EPA, 

2006). Stratospheric ozone (the upper ozone layer) is beneficial because it protects life on Earth 

from the sun’s harmful ultraviolet (UV) radiation. In contrast, tropospheric ozone (ozone in the 

lower atmosphere) is a harmful air pollutant that adversely affects human health and the 

environment and contributes significantly to regional and global climate change. Due to its 

short atmospheric lifetime, tropospheric ozone concentrations exhibit large spatial and 

temporal variability (U.S. EPA, 2009). The discernible influence of ground level ozone on climate 

leads to increases in global surface temperature and changes in hydrological cycles. While 

reducing long-lived GHGs such as CO2 is necessary to protect against long-term climate change, 

reducing SLCF gases including ozone is beneficial and will slow the rate of climate change within 

the first half of this century (UNEP, 2011).

6.5.1.4 SLCFs Summary and Conclusions

Assessing the net climate impact of SLCFs for the illustrative emission reduction

strategies is outside the scope of this regulatory analysis and requires climate atmospheric 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-97

modeling not undertaken due to time and resource constraints. Information about the amount 

of BC relative to non-BC constituents emitted from a source is important. In general, these nonBC constituents are emitted in greater volume than BC, counteracting the warming influence of 

BC. Qualitatively, it seems likely that BC emission reductions associated with directly emitted 

PM2.5 reductions will be beneficial for the climate in terms of reduced radiative forcing and 

deposition on snow and ice. Reductions in OC, sulfates and nitrates are likely to produce 

warming in the atmosphere. The indirect impacts of aerosols on clouds and precipitation 

remain the subject of great uncertainty making it more difficult to estimate the quantitative 

impact of aerosol reductions on climate.

6.5.2 Climate Effects of Long-Lived Greenhouse Gases

The EPA Administrator found in 2009 that elevated concentrations of the six major 

GHGs, including CO2, endanger the public health and public welfare of current and future 

generations (FR 77 66496). While addressing short-lived climate forcers can result in near-term 

(and sometimes regionally specific) co-benefits as well as reductions in the rate of warming, 

reductions of long-term warming would require mitigation of long-lived GHGs. We are unable 

to quantify the impact of the illustrative emission reduction strategies for this rulemaking on 

long-lived climate gases due lack of available data. However, State and Local governments may 

want to consider human health, welfare, and climate implications of regulatory strategies 

undertaken to implement the promulgated PM standards.

6.6 Ecosystem Co-benefits and Services

The effects of air pollution on the health and stability of ecosystems are potentially very 

important. At present, it is difficult to measure the impact of reducing air pollution in a national 

scale analysis across different types of ecosystems and different pollutant effects. Previous 

science assessments by the EPA (U.S. EPA, 2006a; 2008c; 2009b) have determined that air 

pollution can be directly linked to aquatic and terrestrial acidification, nutrient enrichment, 

vegetation injury, and metal bioaccumulation in animals. Ecosystem services are a useful 

conceptual framework for analyzing the impact of ecosystem changes on public welfare.

Ecosystem services can be generally defined as the benefits that individuals and 

organizations obtain from ecosystems. The EPA has defined ecological goods and services as 

the “outputs of ecological functions or processes that directly or indirectly contribute to social 

welfare or have the potential to do so in the future. Some outputs may be bought and sold, but 

most are not marketed” (U.S. EPA, 2006c). Figure 6-7 provides the Millennium Ecosystem 

Assessment’s schematic demonstrating the connections between the categories of ecosystem 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-98

services and human well-being. The interrelatedness of these categories means that any one 

ecosystem may provide multiple services. Changes in these services can affect human wellbeing by affecting security, health, social relationships, and access to basic material goods 

(MEA, 2005).

Figure 6-7. Linkages between Categories of Ecosystem Services and Components of Human 

Well-Being from Millennium Ecosystem Assessment (MEA, 2005)

In the Millennium Ecosystem Assessment (MEA, 2005), ecosystem services are classified 

into four main categories:

1. Provisioning: Products obtained from ecosystems, such as the production of food 

and water

2. Regulating: Benefits obtained from the regulation of ecosystem processes, such as 

the control of climate and disease

3. Cultural: Nonmaterial benefits that people obtain from ecosystems through spiritual 

enrichment, cognitive development, reflection, recreation, and aesthetic 

experiences

4. Supporting: Services necessary for the production of all other ecosystem services, 

such as nutrient cycles and crop pollination

The monetization of ecosystem services generally involves estimating the value of 

ecological goods and services based on what people are willing to pay (WTP) to increase 

ecological services or by what people are willing to accept (WTA) in compensation for 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-99

reductions in them (U.S. EPA, 2006c). There are three primary approaches for estimating the 

monetary value of ecosystem services: market-based approaches, revealed preference 

methods, and stated preference methods (U.S. EPA, 2006c). Because economic valuation of 

ecosystem services can be difficult, nonmonetary valuation using biophysical measurements 

and concepts also can be used. An example of a nonmonetary valuation method is the use of 

relative-value indicators (e.g., a flow chart indicating uses of a water body, such as boatable, 

fishable, swimmable, etc.). It is necessary to recognize that in the analysis of the environmental 

responses associated with any particular policy or environmental management action, only a 

subset of the ecosystem services likely to be affected are readily identified. Of those ecosystem 

services that are identified, only a subset of the changes can be quantified. Within those 

services whose changes can be quantified, only a few will likely be monetized, and many will 

remain nonmonetized. The stepwise concept leading up to the valuation of ecosystems services 

is graphically depicted in Figure 6-8.

Figure 6-8. Schematic of the Benefits Assessment Process (U.S. EPA, 2006c)

6.6.1 Ecosystem Co-benefits for Metallic and Organic Constituents of PM

Several significant ecological effects are associated with deposition of chemical 

constituents of ambient PM such as metals and organics (U.S. EPA, 2009b). The trace metal 

constituents of PM include cadmium, copper, chromium, mercury, nickel, zinc, and lead. The 

organics include persistent organic pollutants (POPs), polyaromatic hydrocarbons (PAHs) and 

polybromiated diphenyl ethers (PBDEs). Exposure to PM for direct effects occur via deposition 

(e.g., wet, dry or occult) to vegetation surfaces, while indirect effects occur via deposition to 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-100

ecosystem soils or surface waters where the deposited constituents of PM then interacts with 

biological organisms. While both fine and coarse-mode particles may affect plants and other 

organisms, more often the chemical constituents drive the ecosystem response to PM (Grantz 

et al., 2003). Ecological effects of PM include direct effects to metabolic processes of plant 

foliage; contribution to total metal loading resulting in alteration of soil biogeochemistry and 

microbiology, plant and animal growth and reproduction; and contribution to total organics 

loading resulting in bioaccumulation and biomagnification across trophic levels.

The PM ISA concludes that a causal relationship is likely to exist between deposition of 

PM and a variety of effects on individual organisms and ecosystems (U.S. EPA 2009b). Most 

direct ecosystem effects associated with particulate pollution occur in severely polluted areas 

near industrial point sources (quarries, cement kilns, metal smelting) (U.S. EPA, 2009b).

However the PM ISA also finds, in many cases, it is difficult to characterize the nature and 

magnitude of effects and to quantify relationships between ambient concentrations of PM and 

ecosystem response due to significant data gaps and uncertainties as well as considerable 

variability that exists in the components of PM and their various ecological effects (U.S. EPA, 

2009b).

Particulate matter can adversely impact plants and ecosystem services provided by 

plants by deposition to vegetative surfaces (U.S. EPA, 2009b). Particulates deposited on the 

surfaces of leaves and needles can block light, altering the radiation received by the plant. PM 

deposition near sources of heavy deposition can obstruct stomata limiting gas exchange, 

damage leaf cuticles and increase plant temperatures (U.S. EPA, 2009b). Plants growing on 

roadsides exhibit impact damage from near-road PM deposition, having higher levels of 

organics and heavy metals, and accumulate salt from road de-icing during winter months (U.S. 

EPA, 2009b). In addition, atmospheric PM can convert direct solar radiation to diffuse radiation, 

which is more uniformly distributed in a tree canopy, allowing radiation to reach lower leaves 

(U.S. EPA, 2009b). Decreases in crop yields (a provisioning service) due to reductions in solar 

radiation have been attributed to regional scale air pollution in other counties with especially 

severe regional haze (Chameides et al., 1999).

In addition to damage to plant surfaces, deposited PM can be taken up by plants from 

soil or foliage. Copper, zinc, and nickel have been shown to be directly toxic to vegetation under 

field conditions (U.S. EPA, 2009b). The ability of vegetation to take up heavy metals is 

dependent upon the amount, solubility and chemical composition of the deposited PM. Uptake 

of PM by plants from soils and vegetative surfaces can disrupt photosynthesis, alter pigments 

and mineral content, reduce plant vigor, decrease frost hardiness and impair root development.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-101

Particulate matter can also contain organic air toxic pollutants, including PAHs, which 

are a class of polycyclic organic matter (POM). PAHs can accumulate in sediments and 

bioaccumulate in freshwater, flora and fauna. The uptake of organics depends on the plant 

species, site of deposition, physical and chemical properties of the organic compound and 

prevailing environmental conditions (U.S. EPA, 2009b). Different species can have different 

uptake rates of PAHs. For example, zucchini (Cucurbita pepo) accumulated significantly more 

PAHs than related plant species (Parrish et al., 2006). PAHs can accumulate to high enough 

concentrations in some coastal environments to pose an environmental health threat that 

includes cancer in fish populations, toxicity to organisms living in the sediment and risks to 

those (e.g., migratory birds) that consume these organisms (Simcik et al., 1996; Simcik et al., 

1999).Atmospheric deposition of particles is thought to be the major source of PAHs to the 

sediments of Lake Michigan, Chesapeake Bay, Tampa Bay and other coastal areas of the U.S. 

(Arzavus, Dickhut, and Canuel, 2001).

Contamination of plant leaves by heavy metals can lead to elevated concentrations in 

the soil. Trace metals absorbed into the plant, frequently bind to the leaf tissue, and then are 

lost when the leaf drops. As the fallen leaves decompose, the heavy metals are transferred into 

the soil (Cotrufo et al., 1995; Niklinska et al., 1998). Many of the major indirect plant responses 

to PM deposition are chiefly soil-mediated and depend on the chemical composition of 

individual components of deposited PM. Upon entering the soil environment, PM pollutants 

can alter ecological processes of energy flow and nutrient cycling, inhibit nutrient uptake to 

plants, change microbial community structure and, affect biodiversity. Accumulation of heavy 

metals in soils depends on factors such as local soil characteristics, geologic origin of parent 

soils, and metal bioavailability. Heavy metals, such as zinc, copper, and cadmium, and some 

pesticides can interfere with microorganisms that are responsible for decomposition of soil 

litter, an important regulating ecosystem service that serves as a source of soil nutrients (U.S. 

EPA, 2009b). Surface litter decomposition is reduced in soils having high metal concentrations.

Soil communities have associated bacteria, fungi, and invertebrates that are essential to soil 

nutrient cycling processes. Changes to the relative species abundance and community 

composition are associated with deposited PM to soil biota (U.S. EPA, 2009b).

Atmospheric deposition can be the primary source of some organics and metals to 

watersheds. Deposition of PM to surfaces in urban settings increases the metal and organic 

component of storm water runoff (U.S. EPA, 2009b). This atmospherically-associated pollutant 

burden can then be toxic to aquatic biota. The contribution of atmospherically deposited PAHs 

to aquatic food webs was demonstrated in high elevation mountain lakes with no other 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-102

anthropogenic contaminant sources (U.S. EPA, 2009b). Metals associated with PM deposition 

limit phytoplankton growth, affecting aquatic trophic structure. Long-range atmospheric 

transport of 47 pesticides and degradation products to the snowpack in seven national parks in 

the Western U.S. was recently quantified indicating PM-associated contaminant inputs to 

receiving waters during spring snowmelt (Hageman et al., 2006).

The recently completed Western Airborne Contaminants Assessment Project (WACAP) is 

the most comprehensive database on contaminant transport and PM depositional effects on 

sensitive ecosystems in the Western U.S. (Landers et al., 2008). In this project, the transport, 

fate, and ecological impacts of anthropogenic contaminants from atmospheric sources were 

assessed from 2002 to 2007 in seven ecosystem components (air, snow, water, sediment, 

lichen, conifer needles and fish) in eight core national parks. The study concluded that 

bioaccumulation of semi-volatile organic compounds occurred throughout park ecosystems, an 

elevational gradient in PM deposition exists with greater accumulation in higher altitude areas, 

and contaminants accumulate in proximity to individual agriculture and industry sources, which 

is counter to the original working hypothesis that most of the contaminants would originate 

from Eastern Europe and Asia.

Although there is considerable data on impacts of PM on ecological receptors, few 

studies link ambient PM levels to observed effect. This is due, in part, to the nature, deposition, 

transport and fate of PM in ecosystems. Some of the difficulties in quantifying the ecosystem 

co-benefits associated with reduced PM deposition include the following:

 PM is not a single pollutant, but a heterogeneous mixture of particles differing in 

size, origin and chemical composition. Since vegetation and other ecosystem 

components are affected more by particulate chemistry than size fraction, exposure 

to a given mass concentration of airborne PM may lead to widely differing plant or 

ecosystem responses, depending on the particular mix of deposited particles.

 Composition of ambient PM varies in time and space and the particulate mixture 

may have synergistic, antagonistic or additive effects on ecological receptors 

depending upon the chemical species present.

 Presence of co-pollutants makes it difficult to attribute observed effects to 

ecological receptors to PM alone or one component of deposited PM.

 Ecosystem effects linked to PM are difficult to determine because the changes may 

not be observed until pollutant deposition has occurred for many decades.

Furthermore, many PM components bioaccumulate over time in organisms or 

plants, making correlations to ambient levels of PM difficult.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-103

 Multiple ecological stressors can confound attempts to link specific ecosystem 

responses to PM deposition. These stressors can be anthropogenic (e.g., habitat 

destruction, eutrophication, other pollutants) or natural (e.g., drought, fire, disease).

Deposited PM interacts with other stressors to affect ecosystem patterns and 

processes.

 Each ecosystem has a unique topography, underlying bedrock, soils, climate, 

meteorology, hydrologic regime, natural and land use history, and species 

composition. Sensitivity of ecosystem response can be highly variable in space and 

time. Because of this variety and lack of data for most ecosystems, extrapolating 

these effects from one ecosystem to another is highly uncertain.

6.6.2 Ecosystem Co-benefits from Reductions in Nitrogen and Sulfur Emissions

Emissions of the PM precursors, such as nitrogen and sulfur oxides occur over large 

regions of North America. Once these pollutants are lofted to the middle and upper 

troposphere, they typically have a much longer lifetime and, with the generally stronger winds 

at these altitudes, can be transported long distances from their source regions. The length scale 

of this transport is highly variable owing to differing chemical and meteorological conditions 

encountered along the transport path (U.S. EPA, 2008c). Secondary particles are formed from 

NOX and SO2 gaseous emissions and associated chemical reactions in the atmosphere.

Deposition can occur in either a wet (i.e., rain, snow, sleet, hail, clouds, or fog) or dry form (i.e., 

gases or particles). Together these emissions are deposited onto terrestrial and aquatic 

ecosystems across the U.S., contributing to the problems of acidification, nutrient enrichment, 

and methylmercury production as represented in Figures 6-9 and 6-10. Although there is some 

evidence that nitrogen deposition may have positive effects on agricultural and forest output 

through passive fertilization, it is likely that the overall value is very small relative to other 

health and welfare effects. In addition to deposition effects, SO2 can affect vegetation at 

ambient levels near pollution sources.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-104

NOX 

Atmospheric 

Fate and 

Transport

Deposition 

Process

Acidification

Aquatic 

Ecosystems

Terrestrial 

Ecosystems

Nutrient 

Enrichment

Aquatic 

Ecosystems

Terrestrial 

Ecosystems
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-105

Figure 6-9. Schematics of Ecological Effects of Nitrogen and Sulfur Deposition

Figure 6-10. Nitrogen and Sulfur Cycling, and Interactions in the Environment

Source: U.S. EPA, 2008c

SO2 

Atmospheric 

Fate and 

Transport

Deposition 

Process

Acidification

Aquatic 

Ecosystems

Terrestrial 

Ecosystems

Methylmercury 

Production
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-106

The atmospheric lifetimes of particles vary with particle size. Accumulation-mode 

particles such as sulfates are kept in suspension by normal air motions and have a lower 

deposition velocity than coarse-mode particles; they can be transported thousands of 

kilometers and remain in the atmosphere for a number of days. They are removed from the 

atmosphere primarily by cloud processes. Particulates affect acid deposition by serving as cloud 

condensation nuclei and contribute directly to the acidification of rain. In addition, the gasphase species that lead to the dry deposition of acidity are also precursors of particles. 

Therefore, reductions in NOX and SO2 emissions will decrease both acid deposition and PM 

concentrations, but not necessarily in a linear fashion (U.S. EPA, 2008c). Sulfuric acid is also 

deposited on surfaces by dry deposition and can contribute to environmental effects (U.S. EPA, 

2008c).

6.6.2.1 Ecological Effects of Acidification

Deposition of nitrogen and sulfur can cause acidification, which alters biogeochemistry 

and affects animal and plant life in terrestrial and aquatic ecosystems across the U.S. Soil 

acidification is a natural process, but is often accelerated by acidifying deposition, which can 

decrease concentrations of exchangeable base cations in soils (U.S. EPA, 2008c). Major 

terrestrial effects include a decline in sensitive tree species, such as red spruce (Picea rubens) 

and sugar maple (Acer saccharum) (U.S. EPA, 2008c). Biological effects of acidification in 

terrestrial ecosystems are generally linked to aluminum toxicity and decreased ability of plant 

roots to take up base cations (U.S. EPA, 2008c). Decreases in the acid neutralizing capacity and 

increases in inorganic aluminum concentration contribute to declines in zooplankton, macro 

invertebrates, and fish species richness in aquatic ecosystems (U.S. EPA, 2008c).

Geology (particularly surficial geology) is the principal factor governing the sensitivity of 

terrestrial and aquatic ecosystems to acidification from nitrogen and sulfur deposition (U.S. 

EPA, 2008c). Geologic formations having low base cation supply generally underlie the 

watersheds of acid-sensitive lakes and streams. Other factors contribute to the sensitivity of 

soils and surface waters to acidifying deposition, including topography, soil chemistry, land use, 

and hydrologic flow path (U.S. EPA, 2008c).

Aquatic Acidification. Aquatic effects of acidification have been well studied in the U.S. 

and elsewhere at various trophic levels. These studies indicate that aquatic biota have been 

affected by acidification at virtually all levels of the food web in acid sensitive aquatic 

ecosystems. The ISA for NOx/SOx—Ecological Criteria concluded that the evidence is sufficient 

to infer a causal relationship between acidifying deposition and effects on biogeochemistry 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-107

related to aquatic ecosystems and biota in aquatic ecosystems (U.S. EPA, 2008c). Effects have 

been most clearly documented for fish, aquatic insects, other invertebrates, and algae.

Biological effects are primarily attributable to a combination of low pH and high inorganic 

aluminum concentrations. Such conditions occur more frequently during rainfall and snowmelt 

that cause high flows of water and less commonly during low-flow conditions, except where 

chronic acidity conditions are severe. Biological effects of episodes include reduced fish 

condition factor45, changes in species composition and declines in aquatic species richness 

across multiple taxa, ecosystems and regions. These conditions may also result in direct fish 

mortality (Van Sickle et al., 1996). Biological effects in aquatic ecosystems can be divided into 

two major categories: effects on health, vigor, and reproductive success; and effects on 

biodiversity. Surface water with ANC values greater than 50 μeq/L generally provides moderate 

protection for most fish (i.e., brook trout, others) and other aquatic organisms (U.S. EPA, 

2009c). Table 6-9 provides a summary of the biological effects experienced at various ANC 

levels.

Table 6-9. Aquatic Status Categories

Category Label ANC Levels Expected Ecological Effects

Acute 

Concern

<0 micro 

equivalent 

per Liter 

(μeq/L)

Near complete loss of fish populations is expected. Planktonic 

communities have extremely low diversity and are dominated by 

acidophilic forms. The number of individuals in plankton species 

that are present is greatly reduced.

Severe

Concern

0–20 μeq/L Highly sensitive to episodic acidification. During episodes of high 

acidifying deposition, brook trout populations may experience lethal 

effects. Diversity and distribution of zooplankton communities 

decline sharply.

Elevated 

Concern

20–50 μeq/L Fish species richness is greatly reduced (i.e., more than half of 

expected species can be missing). On average, brook trout 

populations experience sublethal effects, including loss of health, 

reproduction capacity, and fitness. Diversity and distribution of 

zooplankton communities decline.

Moderate 

Concern

50–100 

μeq/L

Fish species richness begins to decline (i.e., sensitive species are lost 

from lakes). Brook trout populations are sensitive and variable, with 

possible sublethal effects. Diversity and distribution of zooplankton 

communities also begin to decline as species that are sensitive to 

acidifying deposition are affected.

 

45 Condition factor is an index that describes the relationship between fish weight and length, and is one measure 

of sublethal acidification stress that has been used to quantify effects of acidification on an individual fish (U.S.

EPA, 2008f).
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-108

Category Label ANC Levels Expected Ecological Effects

Low 

Concern

>100 μeq/L Fish species richness may be unaffected. Reproducing brook trout 

populations are expected where habitat is suitable. Zooplankton 

communities are unaffected and exhibit expected diversity and 

distribution.

A number of national and regional assessments have been conducted to estimate the 

distribution and extent of surface water acidity in the U.S. (U.S. EPA, 2008c). As a result, several 

regions of the U.S. have been identified as containing a large number of lakes and streams that 

are seriously impacted by acidification. Figure 6-11 illustrates those areas of the U.S. where 

aquatic ecosystems are at risk from acidification.

Because acidification primarily affects the diversity and abundance of aquatic biota, it 

also affects the ecosystem services that are derived from the fish and other aquatic life found in 

these surface waters.

While acidification is unlikely to have serious negative effects on, for example, water 

supplies, it can limit the productivity of surface waters as a source of food (i.e., fish). In the 

northeastern United States, the surface waters affected by acidification are not a major source 

of commercially raised or caught fish; however, they are a source of food for some recreational 

and subsistence fishermen and for other consumers. For example, there is evidence that certain 

population subgroups in the northeastern United States, such as the Hmong and Chippewa 

ethnic groups, have particularly high rates of self-caught fish consumption (Hutchison and Kraft, 

1994; Peterson et al., 1994). However, it is not known if and how their consumption patterns 

are affected by the reductions in available fish populations caused by surface water 

acidification.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-109

Figure 6-11. Areas Potentially Sensitive to Aquatic Acidification

Source: U.S. EPA, 2008c

Inland surface waters support several cultural services, including aesthetic and 

educational services and recreational fishing. Recreational fishing in lakes and streams is among 

the most popular outdoor recreational activities in the northeastern United States. Based on 

studies conducted in the northeastern United States, Kaval and Loomis (2003) estimated 

average consumer surplus values per day of $36 for recreational fishing (in 2007 dollars); 

therefore, the implied total annual value of freshwater fishing in the northeastern United States 

was $5.1 billion in 2006.46 For recreation days, consumer surplus value is most commonly 

measured using recreation demand, travel cost models.

Another estimate of the overarching ecological co-benefits associated with reducing 

lake acidification levels in Adirondacks National Park can be derived from the contingent 

valuation (CV) survey (Banzhaf et al., 2006), which elicited values for specific improvements in 

acidification-related water quality and ecological conditions in Adirondack lakes. The survey 

described a base version with minor improvements said to result from the program, and a 

 

46 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-110

scope version with large improvements due to the program and a gradually worsening status 

quo. After adapting and transferring the results of this study and converting the 10-year annual 

payments to permanent annual payments using discount rates of 3% and 5%, the WTP 

estimates ranged from $48 to $107 per year per household (in 2004 dollars) for the base 

version and $54 to $154 for the scope version. Using these estimates, the aggregate annual 

benefits of eliminating all anthropogenic sources of NOx and SOx emissions were estimated to 

range from $291 million to $829 million (U.S. EPA, 2009c).47

In addition, inland surface waters provide a number of regulating services associated 

with hydrological and climate regulation by providing environments that sustain aquatic food 

webs. These services are disrupted by the toxic effects of acidification on fish and other aquatic 

life. Although it is difficult to quantify these services and how they are affected by acidification, 

some of these services may be captured through measures of provisioning and cultural services.

Terrestrial Acidification. Acidifying deposition has altered major biogeochemical 

processes in the U.S. by increasing the nitrogen and sulfur content of soils, accelerating nitrate 

and sulfate leaching from soil to drainage waters, depleting base cations (especially calcium and 

magnesium) from soils, and increasing the mobility of aluminum. Inorganic aluminum is toxic to 

some tree roots. Plants affected by high levels of aluminum from the soil often have reduced 

root growth, which restricts the ability of the plant to take up water and nutrients, especially 

calcium (U. S. EPA, 2008c). These direct effects can, in turn, influence the response of these 

plants to climatic stresses such as droughts and cold temperatures. They can also influence the 

sensitivity of plants to other stresses, including insect pests and disease (Joslin et al., 1992) 

leading to increased mortality of canopy trees. In the U.S., terrestrial effects of acidification are 

best described for forested ecosystems (especially red spruce and sugar maple ecosystems) 

with additional information on other plant communities, including shrubs and lichen (U.S. EPA, 

2008c). The ISA for NOx/SOx—Ecological Criteria concluded that the evidence is sufficient to 

infer a causal relationship between acidifying deposition and effects on biogeochemistry 

related to terrestrial ecosystems and biota in terrestrial ecosystems (U.S. EPA, 2008c).

Certain ecosystems in the continental U.S. are potentially sensitive to terrestrial 

acidification, which is the greatest concern regarding nitrogen and sulfur deposition U.S. EPA 

(2008c). Figure 6-12 depicts the areas across the U.S. that are potentially sensitive to terrestrial 

acidification.

 

47 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-111

Figure 6-12. Areas Potentially Sensitive to Terrestrial Acidification

Source: U.S. EPA, 2008c

Both coniferous and deciduous forests throughout the eastern U.S. are experiencing 

gradual losses of base cation nutrients from the soil due to accelerated leaching from acidifying 

deposition. This change in nutrient availability may reduce the quality of forest nutrition over 

the long term. Evidence suggests that red spruce and sugar maple in some areas in the eastern 

U.S. have experienced declining health because of this deposition. For red spruce, (Picea 

rubens) dieback or decline has been observed across high elevation landscapes of the 

northeastern U.S., and to a lesser extent, the southeastern U.S., and acidifying deposition has 

been implicated as a causal factor (DeHayes et al., 1999). Figure 6-13 shows the distribution of 

red spruce (brown) and sugar maple (green) in the eastern U.S.

Terrestrial acidification affects several important ecological endpoints, including 

declines in habitat for threatened and endangered species (cultural), declines in forest 

aesthetics (cultural), declines in forest productivity (provisioning), and increases in forest soil 

erosion and reductions in water retention (cultural and regulating).
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-112

Figure 6-13. Distribution of Red Spruce (pink) and Sugar Maple (green) in the Eastern U.S.

Source: U.S. EPA, 2008c

Forests in the northeastern United States provide several important and valuable 

provisioning services in the form of tree products. Sugar maples are a particularly important 

commercial hardwood tree species, providing timber and maple syrup. In the United States, 

sugar maple saw timber was nearly 900 million board feet in 2006 (USFS, 2006), and annual 

production of maple syrup was nearly 1.4 million gallons, accounting for approximately 19% of 

worldwide production. The total annual value of U.S. production in these years was 

approximately $160 million (NASS, 2008).48 Red spruce is also used in a variety of products 

including lumber, pulpwood, poles, plywood, and musical instruments. The total removal of red 

spruce saw timber from timberland in the United States was over 300 million board feet in 

2006 (USFS, 2006).

Forests in the northeastern United States are also an important source of cultural 

ecosystem services—nonuse (i.e., existence value for threatened and endangered species), 

 

48 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-113

recreational, and aesthetic services. Red spruce forests are home to two federally listed species 

and one delisted species:

1. Spruce-fir moss spider (Microhexura montivaga)—endangered

2. Rock gnome lichen (Gymnoderma lineare)—endangered

3. Virginia northern flying squirrel (Glaucomys sabrinus fuscus)—delisted, but 

important

Forestlands support a wide variety of outdoor recreational activities, including fishing, 

hiking, camping, off-road driving, hunting, and wildlife viewing. Regional statistics on 

recreational activities that are specifically forest based are not available; however, more 

general data on outdoor recreation provide some insights into the overall level of recreational 

services provided by forests. More than 30% of the U.S. adult population visited a wilderness or 

primitive area during the previous year and engaged in day hiking (Cordell et al., 2008). From 

1999 to 2004, 16% of adults in the northeastern United States participated in off-road vehicle 

recreation, for an average of 27 days per year (Cordell et al., 2005). The average consumer 

surplus value per day of off-road driving in the United States was $25 (in 2007 dollars), and the 

implied total annual value of off-road driving recreation in the northeastern United States was 

more than $9 billion (Kaval and Loomis, 2003). More than 5% of adults in the northeastern 

United States participated in nearly 84 million hunting days (U.S. FWS and U.S. Census Bureau, 

2007). Ten percent of adults in northeastern states participated in wildlife viewing away from 

home on 122 million days in 2006. For these recreational activities in the northeastern United 

States, Kaval and Loomis (2003) estimated average consumer surplus values per day of $52 for 

hunting and $34 for wildlife viewing (in 2007 dollars). The implied total annual value of hunting 

and wildlife viewing in the northeastern United States was, therefore, $4.4 billion and $4.2 

billion, respectively, in 2006 (U.S. EPA, 2009c).49

As previously mentioned, it is difficult to estimate the portion of these recreational 

services that are specifically attributable to forests and to the health of specific tree species.

However, one recreational activity that is directly dependent on forest conditions is fall color 

viewing. Sugar maple trees, in particular, are known for their bright colors and are, therefore, 

an essential aesthetic component of most fall color landscapes. A survey of residents in the 

Great Lakes area found that roughly 30% of residents reported at least one trip in the previous 

year involving fall color viewing (Spencer and Holecek, 2007). In a separate study conducted in 

 

49 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-114

Vermont, Brown (2002) reported that more than 22% of households visiting Vermont in 2001 

made the trip primarily for viewing fall colors.

Two studies estimated values for protecting high-elevation spruce forests in the 

southern Appalachian Mountains. Kramer et al. (2003) conducted a contingent valuation study 

estimating households’ WTP for programs to protect remaining high-elevation spruce forests 

from damages associated with air pollution and insect infestation. Median household WTP was 

estimated to be roughly $29 (in 2007 dollars) for a smaller program, and $44 for the more 

extensive program. Jenkins et al. (2002) conducted a very similar study in seven Southern

Appalachian states on a potential program to maintain forest conditions at status quo levels.

The overall mean annual WTP for the forest protection programs was $208 (in 2007 dollars).

Multiplying the average WTP estimate from these studies by the total number of households in 

the seven-state Appalachian region results in an aggregate annual range of $470 million to $3.4 

billion for avoiding a significant decline in the health of high-elevation spruce forests in the 

Southern Appalachian region (U.S. EPA, 2009c).50

Forests in the northeastern United States also support and provide a wide variety of 

valuable regulating services, including soil stabilization and erosion control, water regulation, 

and climate regulation. The total value of these ecosystem services is very difficult to quantify 

in a meaningful way, as is the reduction in the value of these services associated with total 

nitrogen and sulfur deposition. As terrestrial acidification contributes to root damages, reduced 

biomass growth, and tree mortality, all of these services are likely to be affected; however, the 

magnitude of these impacts is currently very uncertain.

6.6.2.2 Ecological Effects from Nitrogen Enrichment

Aquatic Enrichment. The ISA for NOx/SOx—Ecological Criteria concluded that the 

evidence is sufficient to infer a causal relationship between nitrogen deposition and the 

alteration of species richness, species composition, and biodiversity in wetland, freshwater 

aquatic and coastal marine ecosystems (U.S. EPA, 2008c).

One of the main adverse ecological effects resulting from nitrogen deposition, 

particularly in the Mid-Atlantic region of the United States, is the effect associated with nutrient 

enrichment in estuarine waters. A recent assessment of 141 estuaries nationwide by the 

National Oceanic and Atmospheric Administration (NOAA) concluded that 19 estuaries (13%) 

suffered from moderately high or high levels of eutrophication due to excessive inputs of both 

 

50 These estimates reflect the marginal value of the service for the hypothetical program described in the survey, 

not the marginal change in the value of the service as a result of the emission reductions achieved by this rule.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-115

N and phosphorus, and a majority of these estuaries are located in the coastal area from North 

Carolina to Massachusetts (NOAA, 2007). For estuaries in the Mid-Atlantic region, the 

contribution of atmospheric distribution to total N loads is estimated to range between 10% 

and 58% (Valigura et al., 2001).

Eutrophication in estuaries is associated with a range of adverse ecological effects. The 

conceptual framework developed by NOAA emphasizes four main types of eutrophication 

effects—low dissolved oxygen (DO), harmful algal blooms (HABs), loss of submerged aquatic 

vegetation (SAV), and low water clarity. Low DO disrupts aquatic habitats, causing stress to fish 

and shellfish, which, in the short-term, can lead to episodic fish kills and, in the long-term, can 

damage overall growth in fish and shellfish populations. Low DO also degrades the aesthetic 

qualities of surface water. In addition to often being toxic to fish and shellfish, and leading to 

fish kills and aesthetic impairments of estuaries, HABs can, in some instances, also be harmful 

to human health. SAV provides critical habitat for many aquatic species in estuaries and, in 

some instances, can also protect shorelines by reducing wave strength; therefore, declines in 

SAV due to nutrient enrichment are an important source of concern. Low water clarity is in part 

the result of accumulations of both algae and sediments in estuarine waters. In addition to 

contributing to declines in SAV, high levels of turbidity also degrade the aesthetic qualities of 

the estuarine environment.

Estuaries in the eastern United States are an important source of food production, in 

particular fish and shellfish production. The estuaries are capable of supporting large stocks of 

resident commercial species, and they serve as the breeding grounds and interim habitat for 

several migratory species. To provide an indication of the magnitude of provisioning services 

associated with coastal fisheries, from 2005 to 2007, the average value of total catch was $1.5 

billion per year. It is not known, however, what percentage of this value is directly attributable 

to or dependent upon the estuaries in these states.

In addition to affecting provisioning services through commercial fish harvests, 

eutrophication in estuaries may also affect the demand for seafood. For example, a wellpublicized toxic pfiesteria bloom in the Maryland Eastern Shore in 1997, which involved 

thousands of dead and lesioned fish, led to an estimated $56 million (in 2007 dollars) in lost 

seafood sales for 360 seafood firms in Maryland in the months following the outbreak (Lipton, 

1999).

Estuaries in the United States also provide an important and substantial variety of 

cultural ecosystem services, including water-based recreational and aesthetic services. The 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-116

water quality in the estuary directly affects the quality of these experiences. For example, there 

were 26 million days of saltwater fishing coastal states from North Carolina to Massachusetts in 

2006 (FWA and Census, 2007). Assuming an average consumer surplus value for a fishing day at 

$36 (in 2007 dollars) in the Northeast and $87 in the Southeast (Kaval and Loomis, 2003), the 

aggregate value was approximately $1.3 billion (in 2007 dollars) (U.S. EPA, 2009c). 51 In addition, 

almost 6 million adults participated in motorboating in coastal states from North Carolina to 

Massachusetts, for a total of nearly 63 million days annually during 1999–2000 (Leeworthy and 

Wiley, 2001). Using a national daily value estimate of $32 (in 2007 dollars) for motorboating 

(Kaval and Loomis (2003), the aggregate value of these coastal motorboating outings was $2 

billion per year (U.S. EPA, 2009c). 52 Almost 7 million participated in birdwatching for 175 million 

days per year, and more than 3 million participated in visits to non-beach coastal waterside 

areas.

Estuaries and marshes have the potential to support a wide range of regulating services, 

including climate, biological, and water regulation; pollution detoxification; erosion prevention; 

and protection against natural hazards from declines in SAV (MEA, 2005). SAV can help reduce 

wave energy levels and thus protect shorelines against excessive erosion, which increases the 

risks of episodic flooding and associated damages to near-shore properties or public 

infrastructure or even contribute to shoreline retreat.

We are unable to provide an estimate of the aquatic enrichment co-benefits associated 

with the revised or alternative annual standards due to data, time, and resource limitations.

Terrestrial Enrichment. Terrestrial enrichment occurs when terrestrial ecosystems 

receive N loadings in excess of natural background levels, through either atmospheric 

deposition or direct application. Evidence presented in the ISA for NOx/SOx (U.S. EPA, 2008c) 

supports a causal relationship between atmospheric N deposition and biogeochemical cycling 

and fluxes of N and carbon in terrestrial systems. Furthermore, evidence summarized in the 

report supports a causal link between atmospheric N deposition and changes in the types and 

number of species and biodiversity in terrestrial systems. Nitrogen enrichment occurs over a 

long time period; as a result, it may take as much as 50 years or more to see changes in 

ecosystem conditions and indicators. This long time scale also affects the timing of the 

ecosystem service changes. The ISA for NOx/SOx—Ecological Criteria concluded that the 

 

51 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule.

52 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-117

evidence is sufficient to infer a causal relationship between nitrogen deposition and the 

alteration of species richness, species composition, and biodiversity in terrestrial ecosystems 

(U.S. EPA, 2008c).

One of the main provisioning services potentially affected by N deposition is grazing 

opportunities offered by grasslands for livestock production in the Central U.S. Although N 

deposition on these grasslands can offer supplementary nutritive value and promote overall 

grass production, there are concerns that fertilization may favor invasive grasses and shift the 

species composition away from native grasses. This process may ultimately reduce the 

productivity of grasslands for livestock production. Losses due to invasive grasses can be 

significant; for example, based on a bioeconomic model of cattle grazing in the upper Great 

Plains, Leitch, Leistritz, and Bangsund (1996) and Leistritz, Bangsund, and Hodur (2004) 

estimated $130 million in losses due to a leafy spurge infestation in the Dakotas, Montana, and 

Wyoming.53 However, the contribution of N deposition to these losses is still uncertain.

Terrestrial nutrient enrichment also affects cultural and regulating services. For 

example, in California, Coastal Sage Scrub (CSS) habitat concerns focus on a decline in CSS and 

an increase in nonnative grasses and other species, impacts on the viability of threatened and 

endangered species associated with CSS, and an increase in fire frequency. Changes in Mixed 

Conifer Forest (MCF) include changes in habitat suitability and increased tree mortality, 

increased fire intensity, and a change in the forest’s nutrient cycling that may affect surface 

water quality through nitrate leaching (U.S. EPA, 2008c). CSS and MCF are an integral part of 

the California landscape, and together the ranges of these habitats include the densely 

populated and valuable coastline and the mountain areas. Numerous threatened and 

endangered species at both the state and federal levels reside in CSS and MCF. The value that 

California residents and the U.S. population as a whole place on CSS and MCF habitats is 

reflected in the various federal, state, and local government measures that have been put in 

place to protect these habitats, including the Endangered Species Act, conservation planning 

programs, and private and local land trusts. CSS and MCF habitat are showcased in many 

popular recreation areas in California, including several national parks and monuments. In 

addition, millions of individuals are involved in fishing, hunting, and wildlife viewing in California 

every year (DOI, 2007). The quality of these trips depends in part on the health of the 

ecosystems and their ability to support the diversity of plants and animals found in important 

habitats found in CSS or MCF ecosystems and the parks associated with those ecosystems. 

 

53 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-118

Based on analyses in the NOx SOx REA average values of the total benefits in 2006 from fishing, 

hunting, and wildlife viewing away from home in California were approximately $950 million, 

$170 million, and $3.6 billion, respectively (U.S. EPA, 2009c).54 In addition, data from California 

State Parks (2003) indicate that in 2002, 69% of adult residents participated in trail hiking for an 

average of 24 days per year. The aggregate annual benefit for California residents from trail 

hiking in 2007 was $11 billion (U.S. EPA, 2009c).55 It is not currently possible to quantify the loss 

in value of services due to nitrogen deposition as those losses are already reflected in the 

estimates of the contemporaneous total value of these recreational activities. Restoration of 

services through decreases in nitrogen deposition would likely increase the total value of 

recreational services.

Fire regulation is also an important regulating service that could be affected by nutrient 

enrichment of the CSS and MCF ecosystems by encouraging growth of more flammable grasses, 

increasing fuel loads, and altering the fire cycle. Over the 5-year period from 2004 to 2008, 

Southern California experienced, on average, over 4,000 fires per year burning, on average, 

over 400,000 acres per year (National Association of State Foresters [NASF], 2009). It is not 

possible at this time to quantify the contribution of nitrogen deposition, among many other 

factors, to increased fire risk.

We are unable to provide an estimate of the terrestrial nutrient enrichment co-benefits

associated with the revised or alternative annual standards due to data, time, and resource 

limitations. Methods are not yet available to allow estimation of changes in ecosystem services 

due to nitrogen deposition.

6.7.2.3 Vegetation Effects Associated with Gaseous Sulfur Dioxide

Uptake of gaseous sulfur dioxide in a plant canopy is a complex process involving 

adsorption to surfaces (leaves, stems, and soil) and absorption into leaves. SO2 penetrates into 

leaves through to the stomata, although there is evidence for limited pathways via the cuticle 

(U.S. EPA, 2008c). Pollutants must be transported from the bulk air to the leaf boundary layer in 

order to get to the stomata. When the stomata are closed, as occurs under dark or drought 

conditions, resistance to gas uptake is very high and the plant has a very low degree of 

susceptibility to injury. In contrast, mosses and lichens do not have a protective cuticle barrier 

to gaseous pollutants or stomates and are generally more sensitive to gaseous sulfur and 

 

54 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule.

55 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-119

nitrogen than vascular plants (U.S. EPA, 2008c). Acute foliar injury usually happens within hours 

of exposure, involves a rapid absorption of a toxic dose, and involves collapse or necrosis of 

plant tissues. Another type of visible injury is termed chronic injury and is usually a result of 

variable SO2 exposures over the growing season. Besides foliar injury, chronic exposure to low 

SO2 concentrations can result in reduced photosynthesis, growth, and yield of plants (U.S. EPA, 

2008c). These effects are cumulative over the season and are often not associated with visible 

foliar injury. As with foliar injury, these effects vary among species and growing environment.

SO2 is also considered the primary factor causing the death of lichens in many urban and 

industrial areas (Hutchinson et al., 1996). The ISA for NOx/SOx—Ecological Criteria concluded 

that the evidence is sufficient to infer a causal relationship between SO2 injury to vegetation 

(U.S. EPA, 2008c).

6.6.2.4 Mercury-Related Co-benefits Associated with the Role of Sulfate in Mercury Methylation

Mercury is a persistent, bioaccumulative toxic metal that is emitted from in three forms:

gaseous elemental Hg (Hg0), oxidized Hg compounds (Hg+2), and particle-bound Hg (HgP).

Methylmercury (MeHg) is formed by microbial action in the top layers of sediment and soils, 

after Hg has precipitated from the air and deposited into waterbodies or land. Once formed, 

MeHg is taken up by aquatic organisms and bioaccumulates up the aquatic food web. Larger 

predatory fish may have MeHg concentrations many times, typically on the order of one million 

times, that of the concentrations in the freshwater body in which they live.

The NOx SOx ISA—Ecological Criteria concluded that evidence is sufficient to infer a 

causal relationship between sulfur deposition and increased mercury methylation in wetlands 

and aquatic environments (U.S. EPA, 2008c). Specifically, there appears to be a relationship 

between SO4

2-

deposition and mercury methylation; however, the rate of mercury methylation 

varies according to several spatial and biogeochemical factors whose influence has not been 

fully quantified (see Figure 6-14). Therefore, the correlation between SO4

2-

deposition and 

MeHg could not be quantified for the purpose of interpolating the association across 

waterbodies or regions. Nevertheless, because changes in MeHg in ecosystems represent 

changes in significant human and ecological health risks, the association between sulfur and 

mercury cannot be neglected (U.S. EPA, 2008c).
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-120

Figure 6-14. Spatial and Biogeochemical Factors Influencing MeHg Production

As research evolves and the computational capacity of models expands to meet the 

complexity of mercury methylation processes in ecosystems, the role of interacting factors may 

be better parsed out to identify ecosystems or regions that are more likely to generate higher 

concentrations of MeHg. Figure 6-15 illustrates the type of current and forward-looking 

research being developed by the U.S. Geological Survey (USGS) to synthesize the contributing 

factors of mercury and to develop a map of sensitive watersheds. The mercury score 

referenced in Figure 6-15 is based on SO4

2-

concentrations, acid neutralizing capacity (ANC), 

levels of dissolved organic carbon and pH, mercury species concentrations, and soil types to 

gauge the methylation sensitivity (Myers et al., 2007).
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-121

Figure 6-15. Preliminary USGS Map of Mercury Methylation–Sensitive Watersheds

Source: Myers et al., 2007

Interdependent biogeochemical factors preclude the existence of simple sulfate-related 

mercury methylation models. It is clear that decreasing sulfate deposition is likely to result in 

decreased MeHg concentrations. Future research may allow for the characterization of a usable 

sulfate-MeHg response curve; however, no regional or classification calculation scale can be 

created at this time because of the number of confounding factors.

Decreases in SO4

2-

deposition have already shown promising reductions in MeHg. 

Observed decreases in MeHg fish tissue concentrations have been linked to decreased 

acidification and declining SO4

2-

and mercury deposition in Little Rock Lake, WI (Hrabik and 

Watras, 2002), and to decreased SO4

2-

deposition in Isle Royale in Lake Superior, MI (Drevnick 

et al., 2007). Although the possibility exists that reductions in SO4

2-

emissions could generate a 

pulse in MeHg production because of decreased sulfide inhibition in sulfate-saturated waters, 

this effect would likely involve a limited number of U.S. waters (Harmon et al., 2007). Also, 

because of the diffusion and outward flow of both mercury-sulfide complexes and SO4

2-

, 

increased mercury methylation downstream may still occur in sulfate-enriched ecosystems with 

increased organic matter and/or downstream transport capabilities.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-122

Remediation of sediments heavily contaminated with mercury has yielded significant 

reductions of MeHg in biotic tissues. Establishing quantitative relations in biotic responses to 

MeHg levels as a result of changes in atmospheric mercury deposition, however, presents 

difficulties because direct associations can be confounded by all of the factors discussed in this 

section. Current research does suggest that the levels of MeHg and total mercury in ecosystems 

are positively correlated, so that reductions in mercury deposited into ecosystems would also 

eventually lead to reductions in MeHg in biotic tissues. Ultimately, an integrated approach that 

involves the reduction of both sulfur and mercury emissions may be most efficient because of 

the variability in ecosystem responses. Reducing SOX emissions could have a beneficial effect on 

levels of MeHg in many waters of the United States.

MeHg is the only form of mercury that biomagnifies in the food web. Concentrations of

MeHg in fish are generally on the order of a million times the MeHg concentration in water. In 

addition to mercury deposition, key factors affecting MeHg production and accumulation in fish 

include the amount and forms of sulfur and carbon species present in a given waterbody. Thus, 

two adjoining water bodies receiving the same deposition can have significantly different fish 

mercury concentrations.

Methylmercury builds up more in some types of fish and shellfish than in others. The 

levels of methylmercury in high and shellfish vary widely depending on what they eat, how long 

they live, and how high they are in the food chain. Most fish, including ocean species and local 

freshwater fish, contain some methylmercury. In general, higher mercury concentrations are 

expected in top predators, which are often large fish relative to other species in a waterbody.

The ecosystem service most directly affected by sulfate-mediated mercury methylation 

is the provision of fish for consumption as a food source. This service is of particular importance 

to groups engaged in subsistence fishing, pregnant women and young children.

6.6.3 Ecosystem Co-benefits from Reductions in Mercury Emissions

Deposition of mercury to waterbodies can also have an impact on ecosystems and 

wildlife. Mercury contamination is present in all environmental media with aquatic systems 

experiencing the greatest exposures due to bioaccumulation. Bioaccumulation refers to the net 

uptake of a contaminant from all possible pathways and includes the accumulation that may 

occur by direct exposure to contaminated media as well as uptake from food.

Atmospheric mercury enters freshwater ecosystems by direct deposition and through 

runoff from terrestrial watersheds. Once mercury deposits, it may be converted to organic 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-123

methylmercury mediated primarily by sulfate-reducing bacteria. Methylation is enhanced in 

anaerobic and acidic environments, greatly increasing mercury toxicity and potential to 

bioaccumulate in aquatic foodwebs. A number of key biogeochemical controls influence the 

production of methylmercury in aquatic ecosystems. These include sulfur, pH, organic matter, 

iron, mercury “aging,” and bacteria type and activity (Munthe et al., 2007).

Wet and dry deposition of oxidized mercury is a dominant pathway for bringing mercury 

to terrestrial surfaces. In forest ecosystems, elemental mercury may also be absorbed by plants 

stomatally, incorporated by foliar tissues and released in litterfall (Ericksen et al., 2003). 

Mercury in throughfall, direct deposition in precipitation, and uptake of dissolved mercury by 

roots (Rea et al., 2002) are also important in mercury accumulation in terrestrial ecosystems.

Soils have significant capacity to store large quantities of atmospherically deposited 

mercury where it can leach into groundwater and surface waters. The risk of mercury exposure 

extends to insectivorous terrestrial species such as songbirds, bats, spiders, and amphibians 

that receive mercury deposition or from aquatic systems near the forest areas they inhabit 

(Bergeron et al., 2010a, b; Cristol et al., 2008; Rimmer et al., 2005; Wada et al., 2009 & 2010).

Numerous studies have generated field data on the levels of mercury in a variety of wild 

species. Many of the data from these environmental studies are anecdotal in nature rather than 

representative or statistically designed studies. The body of work examining the effects of these 

exposures is growing but still incomplete given the complexities of the natural world. A large 

portion of the adverse effect research conducted to date has been carried out in the laboratory 

setting rather than in the wild; thus, conclusions about overarching ecosystem health and 

population effects are difficult to make at this time. In the sections that follow numerous 

effects have been identified at differing exposure levels.

6.6.3.1 Mercury Effects on Fish

A review of the literature on effects of mercury on fish (Crump and Trudeau, 2009) 

reports results for numerous species including trout, bass (large and smallmouth), northern 

pike, carp, walleye, salmon and others from laboratory and field studies. The effects studied are 

reproductive and include deficits in sperm and egg formation, histopathological changes in 

testes and ovaries, and disruption of reproductive hormone synthesis. These studies were 

conducted in areas from New York to Washington and while many were conducted by adding 

MeHg to water or diet many were conducted at current environmental levels. While we cannot 

determine at this time whether these reproductive deficits are affecting fish populations across 

the United States it should be noted that it is possible that over time reproductive deficits could 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-124

have an effect on populations. Lower fish populations would conceivably impact the ecosystem 

services like recreational fishing derived from having healthy aquatic ecosystems quite apart 

from the effects of consumption advisories due to the human health effects of mercury.

6.6.3.2 Mercury Effects on Birds

In addition to effects on fish, mercury also affects avian species. In previous reports (U.S. 

EPA, 1997; 2005), much of the focus has been on large piscivorous species in particular the 

common loon. The loon is most visible to the public during the summer breeding season on 

northern lakes and they have become an important symbol of wilderness in these areas 

(McIntyre and Barr, 1997). A multitude of loon watch, preservation, and protection groups have 

formed over the past few decades and have been instrumental in promoting conservation, 

education, monitoring, and research of breeding loons (McIntyre and Evers, 2000; Evers, 2006).

Significant adverse effects on breeding loons from mercury have been found to occur including 

behavioral (reduced nest-sitting), physiological (flight feather asymmetry) and reproductive 

(chicks fledged/territorial pair) effects (Evers, 2008). Additionally Evers, et al (2008) report that 

they believe that the weight of evidence indicates that population-level effects occur in parts of 

Maine and New Hampshire, and potentially in broad areas of the loon’s range.

Recently attention has turned to other piscivorous species such as the white ibis, and 

great snowy egret. While considered to be fish-eating generally these wading birds have a very 

wide diet including crayfish, crabs, snails, insects and frogs. These species are experiencing a 

range of adverse effects due to exposure to mercury. The white ibis has been observed to have 

decreased foraging efficiency (Adams and Frederick, 2008). Additionally ibises have been shown 

to exhibit decreased reproductive success and altered pair behavior (Frederick and Jayasena, 

2010). These effects include significantly more unproductive nests, male/male pairing, reduced 

courtship behavior (head bobbing and pair bowing) and lower nestling production by exposed 

males. In this study, a worst-case scenario suggested by the results could involve up to a 50% 

reduction in fledglings due to MeHg in diet. These estimates may be conservative if male/male 

pairing in the wild it could result in a shortage of partners for females and the effect of 

homosexual breeding would be magnified. In egrets, mercury has been implicated in the 

decline of the species in south Florida (Sepulveda, et al., 1999) and Hoffman (2010) has shown 

that egrets show liver and possibly kidney effects. While ibises and egrets are most abundant in 

coastal areas and these studies were conducted in south Florida and Nevada the ranges of 

ibises and egrets extend to a large portion of the United States. Ibis territory can range inland 

to Oklahoma, Arkansas and Tennessee. Egret range covers virtually the entire United States 

except the mountain west.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-125

Insectivorous birds have also been shown to suffer adverse effects due to mercury 

exposure. These songbirds such as Bicknell’s thrush, tree swallows and the great tit have shown 

reduced reproduction, survival, and changes in singing behavior. Exposed tree swallows 

produced fewer fledglings (Brasso, 2008), lower survival (Hallinger, 2010) and had 

compromised immune competence (Hawley, 2009). The great tit has exhibited reduced singing 

behavior and smaller song repertoire in an area of high contamination in the vicinity of a 

metallurgic smelter in Flanders (Gorissen, 2005).

6.6.3.3 Mercury Effects on Mammals

In mammals, adverse effects have been observed in mink and river otter, both fish 

eating species. For otter from Maine and Vermont maximum concentrations on Hg in fur nearly 

equal or exceed a concentration associated with mortality and concentration in liver for mink in 

Massachusetts/Connecticut and the levels in fur from mink in Maine exceed concentrations 

associated with acute mortality (Yates, 2005). Adverse sublethal effects may be associated with

lower Hg concentrations and consequently be more widespread than potential acute effects.

These effects may include increased activity, poorer maze performance, abnormal startle reflex, 

and impaired escape and avoidance behavior (Scheuhammer et al., 2007).

6.6.3.4 Mercury Ecological Conclusions

The studies cited here provide a glimpse of the scope of mercury effects on wildlife 

particularly reproductive and survival effects. These effects range across species from fish to 

mammals and spatially across a wide area of the United States. The literature is far from 

complete however. Much more research is required to establish a link between the ecological 

effects on wildlife and the effect on ecosystem services (services that the environment provides 

to people) for example recreational fishing, bird watching and wildlife viewing. The EPA is not, 

however, currently able to quantify or monetize the co-benefits of reducing mercury exposures 

affecting provision of ecosystem services.

6.6.4 Vegetation Co-benefits from Reductions in Ambient Ozone

Illustrative emission reduction strategies that include NOx emission reductions would 

affect ambient ozone concentrations. Ozone causes discernible injury to a wide array of 

vegetation (U.S. EPA, 2006a; Fox and Mickler, 1996). Air pollution can affect the environment 

and affect ecological systems, leading to changes in the ecological community and influencing 

the diversity, health, and vigor of individual species (U.S. EPA, 2006a). In terms of forest 

productivity and ecosystem diversity, ozone may be the pollutant with the greatest potential 

for regional-scale forest impacts (U.S. EPA, 2006a). Studies have demonstrated repeatedly that 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-126

ozone concentrations commonly observed in polluted areas can have substantial impacts on 

plant function (De Steiguer et al., 1990; Pye, 1988).

When ozone is present in the air, it can enter the leaves of plants, where it can cause 

significant cellular damage. Like carbon dioxide (CO2) and other gaseous substances, ozone 

enters plant tissues primarily through the stomata in leaves in a process called “uptake”

(Winner and Atkinson, 1986). Once sufficient levels of ozone (a highly reactive substance), or its 

reaction products, reaches the interior of plant cells, it can inhibit or damage essential cellular 

components and functions, including enzyme activities, lipids, and cellular membranes, 

disrupting the plant’s osmotic (i.e., water) balance and energy utilization patterns (U.S. EPA, 

2006a; Tingey and Taylor, 1982). With fewer resources available, the plant reallocates existing 

resources away from root growth and storage, above ground growth or yield, and reproductive 

processes, toward leaf repair and maintenance, leading to reduced growth and/or 

reproduction. Studies have shown that plants stressed in these ways may exhibit a general loss 

of vigor, which can lead to secondary impacts that modify plants’ responses to other 

environmental factors. Specifically, plants may become more sensitive to other air pollutants, 

or more susceptible to disease, pest infestation, harsh weather (e.g., drought, frost) and other 

environmental stresses, which can all produce a loss in plant vigor in ozone-sensitive species 

that over time may lead to premature plant death. Furthermore, there is evidence that ozone 

can interfere with the formation of mycorrhiza, essential symbiotic fungi associated with the 

roots of most terrestrial plants, by reducing the amount of carbon available for transfer from 

the host to the symbiont (U.S. EPA, 2006a).

This ozone damage may or may not be accompanied by visible injury on leaves, and 

likewise, visible foliar injury may or may not be a symptom of the other types of plant damage 

described above. Foliar injury is usually the first visible sign of injury to plants from ozone 

exposure and indicates impaired physiological processes in the leaves (Grulke, 2003). When 

visible injury is present, it is commonly manifested as chlorotic or necrotic spots, and/or 

increased leaf senescence (accelerated leaf aging). Visible foliar injury reduces the aesthetic 

value of ornamental vegetation and trees in urban landscapes and negatively affects scenic 

vistas in protected natural areas.

Ozone can produce both acute and chronic injury in sensitive species depending on the 

concentration level and the duration of the exposure. Ozone effects also tend to accumulate 

over the growing season of the plant, so that even lower concentrations experienced for a 

longer duration have the potential to create chronic stress on sensitive vegetation. Not all 

plants, however, are equally sensitive to ozone. Much of the variation in sensitivity between 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-127

individual plants or whole species is related to the plant’s ability to regulate the extent of gas 

exchange via leaf stomata (e.g., avoidance of ozone uptake through closure of stomata) and the 

relative ability of species to detoxify ozone-generated reactive oxygen free radicals (U.S. EPA, 

2006a; Winner, 1994). After injuries have occurred, plants may be capable of repairing the 

damage to a limited extent (U.S. EPA, 2006a). Because of the differing sensitivities among 

plants to ozone, ozone pollution can also exert a selective pressure that leads to changes in 

plant community composition. Given the range of plant sensitivities and the fact that numerous 

other environmental factors modify plant uptake and response to ozone, it is not possible to 

identify threshold values above which ozone is consistently toxic for all plants.

Because plants are at the base of the food web in many ecosystems, changes to the 

plant community can affect associated organisms and ecosystems (including the suitability of 

habitats that support threatened or endangered species and below ground organisms living in 

the root zone). Ozone impacts at the community and ecosystem level vary widely depending 

upon numerous factors, including concentration and temporal variation of tropospheric ozone, 

species composition, soil properties and climatic factors (U.S. EPA, 2006a). In most instances, 

responses to chronic or recurrent exposure in forested ecosystems are subtle and not 

observable for many years. These injuries can cause stand-level forest decline in sensitive 

ecosystems (U.S. EPA, 2006a, McBride et al., 1985; Miller et al., 1982). It is not yet possible to 

predict ecosystem responses to ozone with certainty; however, considerable knowledge of 

potential ecosystem responses is available through long-term observations in highly damaged 

forests in the U.S. (U.S. EPA, 2006a).

6.6.4.1 Ozone Effects on Forests

Air pollution can affect the environment and affect ecological systems, leading to 

changes in the ecological community and influencing the diversity, health, and vigor of 

individual species (U.S. EPA, 2006a). Ozone has been shown in numerous studies to have a 

strong effect on the health of many plants, including a variety of commercial and ecologically 

important forest tree species throughout the United States (U.S. EPA, 2007b).

In the U.S., this data comes from the U.S. Department of Agriculture (USDA) Forest 

Service Forest Inventory and Analysis (FIA) program. As part of its Phase 3 program (formerly 

known as Forest Health Monitoring), FIA looks for visible foliar injury of ozone-sensitive forest 

plant species at each ground monitoring site across the country (excluding woodlots and urban 

trees) that meets certain minimum criteria. Because ozone injury is cumulative over the course 

of the growing season, examinations are conducted in July and August, when ozone 

concentrations and associated injury are typically highest.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-128

Monitoring of ozone injury to plants by the U.S. Forest Service has expanded over the 

last 15 years from monitoring sites in 10 states in 1994 to nearly 1,000 monitoring sites in 41 

states in 2002. Since 2002, the monitoring program has further expanded to 1,130 monitoring 

sites in 45 states. Figure 6-16 shows the results of this monitoring program for the year 2002 

broken down by U.S. EPA Regions.56 Figure 6-17 identifies the counties that were included in 

Figure 6-16, and provides the county-level data regarding the presence or absence of ozonerelated injury. As shown in Figure 6-16, large geographic areas of EPA Regions 6, 8, and 10 were 

not included in the assessment. Ozone damage to forest plants is classified using a subjective 

five-category biosite index based on expert opinion, but designed to be equivalent from site to 

site. Ranges of biosite values translate to no injury, low or moderate foliar injury (visible foliar 

injury to highly sensitive or moderately sensitive plants, respectively), and high or severe foliar 

injury, which would be expected to result in tree-level or ecosystem-level responses, 

respectively (U.S. EPA, 2006a; Coulston, 2004). The highest percentages of observed high and 

severe foliar injury, which are most likely to be associated with tree or ecosystem-level 

responses, are primarily found in the Mid-Atlantic and Southeast regions. While the assessment 

showed considerable regional variation in ozone injury, this assessment targeted different 

ozone-sensitive species in different parts of the country with varying ozone sensitivity, which 

contributes to the apparent regional differences. It is important to note that ozone can have 

other, more significant impacts on forest plants (e.g., reduced biomass growth in trees) prior to 

showing signs of visible foliar injury (U.S. EPA, 2006a).

Assessing the impact of ground-level ozone on forests in the U.S. involves understanding 

the risks to sensitive tree species from ambient ozone concentrations and accounting for the 

prevalence of those species within the forest. As a way to quantify the risks to particular plants 

from ground-level ozone, scientists have developed ozone-exposure/tree-response functions by 

exposing tree seedlings to different ozone levels and measuring reductions in growth as 

“biomass loss.” Typically, seedlings are used because they are easy to manipulate and measure 

their growth loss from ozone pollution. The mechanisms of susceptibility to ozone within the 

leaves of seedlings and mature trees are identical, and the decreases predicted using the 

seedlings should be related to the decrease in overall plant fitness for mature trees, but the

 

56 The data are based on averages of all observations collected in 2002, which is the last year for which data are 

publicly available. For more information, please consult EPA’s 2008 Report on the Environment (U.S. EPA, 2008b).
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-129

c

Degree of Injury: These categories reflect a subjective index based on expert opinion. Ozone can have other, 

more significant impacts on forest plants (e.g., reduced biomass growth in trees) prior to showing signs of visible 

foliar injury.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-130

Figure 6-16. Visible Foliar Injury to Forest Plants from Ozone in U.S. by EPA Regionsa,b,c

Figure 6-17. Presence and Absence of Visible Foliar Injury, as Measured by U.S. Forest 

Service, 2002

Source: U.S. EPA, 2007b

magnitude of the effect may be higher or lower depending on the tree species (Chappelka and 

Samuelson, 1998). In areas where certain ozone-sensitive species dominate the forest 

community, the biomass loss from ozone can be significant. Experts have identified 2% annual 

biomass loss as a level of concern, which would cause long term ecological harm as the shortterm negative effects on seedlings compound to affect long-term forest health (Heck and 

Cowling, 1997).

Ozone damage to the plants including the trees and understory in a forest can affect the 

ability of the forest to sustain suitable habitat for associated species particularly threatened and 

endangered species that have existence value—a nonuse ecosystem service—for the public.

Similarly, damage to trees and the loss of biomass can affect the forest’s provisioning services 

in the form of timber for various commercial uses. In addition, ozone can cause discoloration of 

leaves and more rapid senescence (early shedding of leaves), which could negatively affect fallcolor tourism because the fall foliage would be less available or less attractive. Beyond the 

aesthetic damage to fall color vistas, forests provide the public with many other recreational 
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-131

and educational services that may be affected by reduced forest health including hiking, wildlife 

viewing (including bird watching), camping, picnicking, and hunting. Another potential effect of 

biomass loss in forests is the subsequent loss of climate regulation service in the form of 

reduced ability to sequester carbon and alteration of hydrologic cycles.

Some of the common tree species in the United States that are sensitive to ozone are 

black cherry (Prunus serotina), tulip-poplar (Liriodendron tulipifera), and eastern white pine 

(Pinus strobus). Ozone-exposure/tree-response functions have been developed for each of 

these tree species, as well as for aspen (Populus tremuliodes), and ponderosa pine (Pinus 

ponderosa) (U.S. EPA, 2007b).

6.6.4.2 Ozone Effects on Crops

Laboratory and field experiments have shown reductions in yields for agronomic crops 

exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and wheat).

Damage to crops from ozone exposures includes yield losses (i.e., in terms of weight, number, 

or size of the plant part that is harvested), as well as changes in crop quality (i.e., physical 

appearance, chemical composition, or the ability to withstand storage) (U.S. EPA, 2007b). The 

most extensive field experiments, conducted under the National Crop Loss Assessment 

Network (NCLAN) examined 15 species and numerous cultivars. The NCLAN results show that 

“several economically important crop species are sensitive to ozone levels typical of those 

found in the United States” (U.S. EPA, 2006a). In addition, economic studies have shown 

reduced economic co-benefits as a result of predicted reductions in crop yields, directly 

affecting the amount and quality of the provisioning service provided by these crops, associated 

with observed ozone levels (Kopp et al., 1985; Adams et al., 1986; Adams et al., 1989). In 

addition, visible foliar injury by itself can reduce the market value of certain leafy crops (such as 

spinach, lettuce). According to the Ozone Staff Paper, there has been no evidence that crops 

are becoming more tolerant of ozone (U.S. EPA, 2007b). Using the Agriculture Simulation Model 

(AGSIM) (Taylor, 1994) to calculate the agricultural benefits of reductions in ozone exposure, 

the EPA estimated that attaining a W126 standard of 13 ppm-hr would produce monetized 

benefits of approximately $400 million to $620 million in 2006 (inflated to 2006 dollars) (U.S. 

EPA, 2007b).57

 

57These estimates illustrate the value of vegetation effects from a substantial reduction of ozone concentrations, 

not the marginal change in ozone concentrations anticipated a result of the emission reductions achieved by this 

rule.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-132

6.6.4.3 Ozone Effects on Ornamental Plants

Urban ornamental plants are an additional vegetation category likely to experience 

some degree of negative effects associated with exposure to ambient ozone levels. Several 

ornamental species have been listed as sensitive to ozone (Abt Associates, 1995). Because 

ozone causes visible foliar injury, the aesthetic value of ornamental plants (such as petunia, 

geranium, and poinsettia) in urban landscapes would be reduced (U.S. EPA, 2007b). Sensitive 

ornamental species would require more frequent replacement and/or increased maintenance 

(fertilizer or pesticide application) to maintain the desired appearance because of exposure to 

ambient ozone (U.S. EPA, 2007b). In addition, many businesses rely on healthy-looking 

vegetation for their livelihoods (e.g., horticulturalists, landscapers, Christmas tree growers, 

farmers of leafy crops, etc.). The ornamental landscaping industry is a multi-billion dollar 

industry that affects both private property owners/tenants and governmental units responsible 

for public areas (Abt Associates, 1995). Preliminary data from the 2007 Economic Census 

indicate that the landscaping services industry, which is primarily engaged in providing 

landscape care and maintenance services and installing trees, shrubs, plants, lawns, or gardens, 

was valued at $53 billion (U.S. Census Bureau, 2010). Therefore, urban ornamentals represent a 

potentially large unquantified benefit category. This aesthetic damage may affect the 

enjoyment of urban parks by the public and homeowners’ enjoyment of their landscaping and 

gardening activities. In addition, homeowners may experience a reduction in home value or a 

home may linger on the market longer due to decreased aesthetic appeal. In the absence of 

adequate exposure-response functions and economic damage functions for the potential range 

of effects relevant to ornamental plants, we cannot conduct a quantitative analysis to estimate 

these effects.

We are unable to provide an estimate of the ozone crop co-benefits associated with the 

revised or alternative annual standards due to data, time, and resource limitations.

6.7 References

Abt Associates, Inc. 1995. Urban ornamental plants: sensitivity to ozone and potential economic 

losses. U.S. EPA, Office of Air Quality Planning and Standards, Research Triangle Park.

Under contract to RADIAN Corporation, contract no. 68-D3-0033, WA no. 6. pp. 9-10. 

Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/abt1995.pdf>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-133

Abt Associates, Inc. 2001. Assessing Public Opinions on Visibility Impairment due to Air 

Pollution: Summary Report. Prepared for EPA Office of Air Quality Planning and 

Standards; funded under EPA Contract No. 68-D-98-001. Abt Associates Inc., Bethesda, 

MD. January. Available on the Internet at 

<http://www.epa.gov/ttncaaa1/t1/reports/vis_rpt_final.pdf>.

Adams, Evan M., and Frederick, Peter C. 2008. “Effects of methylmercury and spatial complexity 

on foraging behavior and foraging efficiency in juvenile white ibises (Eudocimus albus).”

Environmental Toxicology and Chemistry. Vol 27, No. 8.

Adams, R. M., Glyer, J. D., Johnson, S. L., McCarl, B. A. 1989. “A reassessment of the economic 

effects of ozone on U.S. agriculture.” Journal of the Air Pollution Control Association, 39, 

960-968.

Adams, R. M., Hamilton, S. A., McCarl, B. A. 1986. “The benefits of pollution control: the case of 

ozone and U.S. agriculture.” American Journal of Agricultural Economics, 34, 3-19.

Anselin, L., Le Gallo, J. 2006. “Interpolation of air quality measures in hedonic house price 

models: Spatial aspects.” Spatial Economic Analysis 1: 31–52

Anselin, L., Lozano-Gracia, N. 2008. “Errors in variables and spatial effects in hedonic house 

price models of ambient air quality.” Empirical Economics 34: 5–34.

Arrow, K.J., et al. 1993. Report of the NOAA Panel on Contingent Valuation. Federal Register.

Volume 58, Number 10. Pages 4601 to 4614. Available on the Internet at 

<http://www.darp.noaa.gov/library/pdf/cvblue.pdf>.

Arzavus K.M., Dickhut R.M., and Canuel E.A. 2001. “Fate of Atmospherically Deposited 

Polycyclic Aromatic Hydrocarbons (PAHs) in Chesapeake Bay.” Environmental Science & 

Technology, 35, 2178-2183.

Banzhaf, S., D. Burtraw, D. Evans, and A. Krupnick. 2006. “Valuation of Natural Resource 

Improvements in the Adirondacks.” Land Economics 82:445-464.

Bayer, P., Keohane, N.O. and Timmins, C. 2009. “Migration and hedonic valuation: the case of 

air quality,” Journal of Environmental Economics and Management, 58 (2009) 1–14.

BBC Research & Consulting. 2003. Phoenix Area Visibility Survey. Draft Report. Available on the 

Internet at <http://www.azdeq.gov/environ/air/download/vis_021903f.pdf>.

Bergeron, CM., Bodinof, CM., Unrine, JM., Hopkins, WA. 2010a. “Mercury accumulation along a 

contamination gradient and nondestructive indices of bioaccumulation in amphibians.”

Environmental Toxicology and Chemistry 29(4), 980-988.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-134

Bergeron, CM., Bodinof, CM., Unrine, JM., Hopkins, WA. 2010b. “Bioaccumulation and maternal 

transfer of mercury and selenium in amphibians.” Environmental Toxicology and 

Chemistry 29(4), 989-997.

Beron KJ, Murdoch J, Thayer M. 2001. “The benefits of visibility improvement: New evidence 

from Los Angeles metropolitan area.” J Real Estate Finance Econ 22(2–3):319–337.

Beron KJ, Hanson Y, Murdoch JC, Thayer MA. 2004. “Hedonic price functions and spatial 

dependence: implications for the demand for urban air quality.” In: Anselin L, Florax RJ, 

Rey SJ (eds) Advances in spatial econometrics: methodology, tools and applications. 

Springer-Verlag, Berlin, pp 267–281.

Bond, T.C., and Sun H.L. (2005) Can reducing BC emissions counteract global warming? 

Environmental Science & Technology 39 (16).

Brasso, Rebecka L., and Cristol, Daniel A. 2008. “Effects of mercury exposure in the reproductive 

success of tree swallows (Tachycineta bicolor).” Ecotoxicology. 17:133-141.

Brookshire, D.S., R.C. d’Arge, W.D. Schulze and M.A. Thayer. 1979. Methods Development for 

Assessing Tradeoffs in Environmental Management, Vol. II: Experiments in Valuing NonMarket Goods: A Case Study of Alternative Benefit Measures of Air Pollution Control in 

the South Coast Air Basin of Southern California. Prepared for the U.S. Environmental 

Protection Agency, Office of Research and Development. Available on the Internet at 

<http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0271B-1.pdf/$file/EE-0271B1.pdf>.

Brookshire, D.S., Thayer, M.A., Schulze, W.D. & D’Arge, R.C. 1982. “Valuing Public Goods: A 

Comparison of Survey and Hedonic Approaches.” The American Economic Review. 72(1): 

165-177.

Burgess, Neil M., and Meyer, Michael W. 2008. “Methylmercury exposure associated with 

reduced productivity in common loons.” Ecotoxicology. 17:83-91.

Burtraw, Dallas, Alan J. Krupnick, Erin Mansur, David Austin and Deirdre Farrell, 1998. "The 

Costs and Benefits of Reducing Air Pollutants Related to Acid Rain." Contemporary 

Economic Policy 16: 379-400.

California State Parks. 2003. Public Opinions and Attitudes on Outdoor Recreation in California 

2002. California State Parks, Sacramento, California. December 2003. Available on the 

Internet at <http://parks.ca.gov/pages/795/files/poa2002final.pdf>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-135

Carson, R.T., R.C. Mitchell, and P.A. Rudd. 1990. “Valuing Air Quality Improvements: Simulating 

a Hedonic Pricing Equation in the Context of a Contingent Valuation Scenario.” In 

Visibility and Fine Particles, Transactions of an AWMA/EPA International Specialty 

Conference, C.V. Mathai, ed. Air and Waste Management Association, Pittsburgh. 

Available on the Internet at 

<http://www.econ.ucsd.edu/~rcarson/papers/AirQuality.pdf>.

Chameides. W.L., Yu, H., Liu, S.C., Bergin, M., Zhou, X., Mearns, L., Wang, G., Kiang, C.S., Saylor,

R.D., Luo, C. Huang, Y., Steiner, A., and Giorgi, F. 1999. “Case study of the effects of 

atmospheric aerosols and regional haze on agriculture: an opportunity to enhance crop 

yields in China through emission controls?” PNAS, 96: 13626-13633.

Chappelka, A.H., Samuelson, L.J. 1998. “Ambient ozone effects on forest trees of the eastern 

United States: a review.” New Phytologist, 139, 91-108.

Chay KY, Greenstone M. 2005. “Does air quality matter? evidence form the housing market.” J 

Polit Econ 113(2):376–424.

Chestnut, L.G. 1997. Methodology for Estimating Values for Changes in Visibility in National 

Parks. Prepared for Office of Policy, Planning and Evaluation, May. Available on the 

Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/chestnut1997.pdf>.

Chestnut, L.G. and D.M. Mills. 2005. “A fresh look at the benefits and cost of the US acid rain 

program.” J Environ Manage 77:252–66. 

Chestnut, L.G. and R. Dennis. 1997. “Economic Benefits of Improvements in Visibility: Acid Rain 

Provisions of the 1990 Clean Air Act Amendments.” Journal of Air and Waste 

Management Association 47: 395-402.

Chestnut, L.G., and R.D. Rowe. 1990a. “New National Park Visibility Value Estimates.” In 

Visibility and Fine Particles, Transactions of an AWMA/EPA International Specialty 

Conference, C.V. Mathai, ed. Air and Waste Management Association, Pittsburgh.

Chestnut, L.G. and R.D. Rowe. 1990b. Preservation Values for Visibility Protection at the 

National Parks. Prepared for U.S. Environmental Protection Agency, Office of Air Quality 

Planning and Standards, and National Park Service, Air Quality Management Division. 

Available on the Internet at 

<http://yosemite.epa.gov/ee/epa/eerm.nsf/vwGA/EE97D10D71E3FAD08525662400766

534 >.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-136

Chestnut, L.G. and R.D. Rowe. 1990c. “Economic Valuation of Changes in Visibility: A State of 

the Science Assessment for NAPAP.” Section B5 in Irving, Patricia M., e.d., 1991. Acid 

Deposition: State of Science and Technology, Volume IV, Control Technologies, Future 

Emissions, and Effects Valuation, The U.S. National Acid Precipitation Assessment 

Program, Report 27, page 27–153.

Chestnut, L.G., R.D. Rowe and J. Murdoch. 1986. Review of ‘Establishing and Valuing the Effects 

of Improved Visibility in Eastern United States.’ Prepared for the U.S. Environmental 

Protection Agency. October. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/chestnut1986.pdf>.

Cordell, H.K., C.J. Betz, G. Green, and M. Owens. 2005. Off-Highway Vehicle Recreation in the 

United States, Regions and States: A National Report from the National Survey on 

Recreation and the Environment (NSRE). Prepared for the U.S. Department of 

Agriculture Forest Service, Southern Research Station, National OHV Policy and 

Implementation Teams, Athens, GA. Available on the Internet at 

<http://www.fs.fed.us/recreation/programs/ohv/OHV_final_report.pdf>

Cordell, K., B. Leeworthy, G.T. Green, C. Betz, B. Stephens. 2008. The National Survey on 

Recreation & the Environment. Research Work Unit 4953. Pioneering Research on 

Changing Forest Values in the South and Nation, U.S. Department of Agriculture Forest 

Service, Southern Research Station, Athens, GA. Available on the Internet at 

<http://www.srs.fs.fed.us/trends>.

Cotrufo, M.F., De Santo A.V., Alfani A., Bartoli G., De Cristofaro A. 1995. “Effects of urban heavy 

metal pollution on organic matter decomposition in Quercus ilex L. Woods.”

Environmental Pollution, 89(1), 81-87.

Coulston, J.W., Riitters, K.H., Smith, G.C. 2004. “A preliminary assessment of the Montreal 

process indicators of air pollution for the United States.” Environmental Monitoring and 

Assessment, 95, 57-74.

Cristol D. A., Brasso R. L., Condon A. M., Fovargue R. E., Friedman S. L., Hallinger K. K., Monroe 

A. P., White A. E. 2008. “The movement of aquatic mercury through terrestrial food 

webs.” Science 320, 335–335.

Cropper, Maureen. 2000. “Has Economic Research Answered the Needs of Environmental 

Policy?” Journal of Environmental Economics and Management 39, 328-350.

Crump, Kate L., and Trudeau, Vance L. 2009. “Mercury-induced reproductive impairment in 

fish.” Environmental Toxicology and Chemistry. Vol. 28, No. 5.

De Steiguer, J., Pye, J., Love, C. 1990. “Air Pollution Damage to U.S. Forests.” Journal of Forestry, 

88(8), 17-22.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-137

DeBell, L. J.; Gebhart, K. A.; Hand, J. L.; Malm, W. C.; Pitchford, M. L.; Schichtel, B. A.; White, W. 

H. 2006. Spatial and Seasonal Patterns and Temporal Variability of Haze and its 

Constituents in the United States, Report IV; Cooperative Institute for Research in the 

Atmosphere, Nov; ISSN 0737-5352-74. Available on the Internet at 

<http://vista.cira.colostate.edu/improve/Publications/Reports/2006/2006.htm>.

DeHayes, D.H., P.G. Schaberg, G.J. Hawley, and G.R. Strimbeck. 1999. “Acid rain impacts on 

calcium nutrition and forest health.” Bioscience 49(10):789–800.

DeLucchi, M.A., J.J. Murphy, D.R. McCubbin. 2002. “The health and visibility cost of air 

pollution: A comparison of estimation methods,” Journal of Environmental 

Management, 64 (2), pp. 139–152.

Ely, D.W., Leary, J.T., Stewart, T.R., Ross, D.M. 1991. “The Establishment of the Denver Visibility 

Standard.” Presented at Air and Waste Management Association 84th Annual Meeting, 

June 16-21, Paper 91-48.4, 17pp. Available on the Internet at 

<http://www.albany.edu/cpr/stewart/Papers/T0210-ElyDenverVisStandard1991cap.pdf>.

Ericksen, J. A., Gustin, M. S., Schorran, D. E., Johnson, D. W., Lindberg, S. E., & Coleman, J. S. 

(2003). “Accumulation of atmospheric mercury in forest foliage.” Atmospheric 

Environment 37(12), 1613-1622.

Evers, D.C., 2006. Status assessment and conservation plan for the common loon (Gavia immer) 

in North America. U.S. Fish and Wildlife Service, Hadley, MA, USA.

Evers, David C., Savoy, Lucas J., DeSorbo, Christopher R., Yates, David E., Hanson, William, 

Taylor, Kate M., Siegel, Lori S., Cooley, John H. Jr., Bank, Michael S., Major, Andrew, 

Munney, Kenneth, Mower, Barry F., Vogel, Harry S., Schoch, Nina, Pokras, Mark, 

Goodale, Morgan W., Fair, Jeff. 2008. “Adverse effects from environmental mercury 

loads on breeding common loons.” Ecotoxicology. 17:69-81.

Fox, S., Mickler, R. A. (Eds.). 1996. Impact of Air Pollutants on Southern Pine Forests, Ecological 

Studies. (Vol. 118, 513 pp.) New York: Springer-Verlag.

Frederick, Peter, and Jayasena, Nilmini. 2010. “Altered pairing behavior and reproductive

success in white ibises exposed to environmentally relevant concentrations of 

methylmercury.” Proceedings of the Royal Society B. doi: 10-1098.

Gorissen, Leen, Snoeijs, Tinne, Van Duyse, Els, and Eens, Marcel. 2005. “Heavy metal pollution 

affects dawn singing behavior in a small passerine bird.” Oecologia. 145: 540-509.

Grantz DA; Garner JHB; Johnson DW. 2003. “Ecological effects of particulate matter.” Environ 

Int, 29: 213-239.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-138

Graves, Phil, James C. Murdoch, Mark A. Thayer, and Don Waldman. 1988. “The Robustness of 

Hedonic Price Estimation: Urban Air Quality.” Land Economics, 64: 220-33. 

Grosclaude P. and N. Soguel. 1994. “Valuing damage to historic buildings using a contingent 

market: a case study of road traffic externalities,” Journal of Environmental Planning and 

Management 37 (1994) (3), pp. 279–287.

Grulke, N.E. 2003. “The physiological basis of ozone injury assessment attributes in Sierran 

conifers.” In A. Bytnerowicz, M.J. Arbaugh, & R. Alonso (Eds.), Ozone air pollution in the 

Sierra Nevada: Distribution and effects on forests. (pp. 55-81). New York, NY: Elsevier 

Science, Ltd.

Hadley O.L., Corrigan C.E., Kirchstetter T.W., Cliff S.S., and Ramanathan V. (2010) Measured 

black carbon deposition on the Sierra Nevada snow pack and implication for snow pack 

retreat. Atmos. Chem. Phys. Discuss. 10, 10463‐10485 

(doi:10.5194/acpd‐10‐10463‐2010).

Hageman KJ; Simonich SL; Campbell DH; Wilson GR; Landers DH. 2006. “Atmospheric deposition 

of current-use and historic-use pesticides in snow at national parks in the western 

United States.” Environ Sci Technol, 40: 3174-3180

Hallinger, Kelly K., Cornell, Kerri L., Brasso, Rebecka L., and Cristol, Daniel A. 2010. “Mercury 

exposure and survival in free-living tree swallows (Tachycineta bicolor).” Ecotoxicology. 

Doi: 10.1007/s10646-010-0554-4.

Hawley, Dana M., Hallinger, Kelly K., Cristol, Daniel A. 2009. “Compromised immune 

competence in free-living tree swallows exposed to mercury.” Ecotoxicology. 18:499-

503.

Heck, W.W. &Cowling E.B. 1997. “The need for a long term cumulative secondary ozone 

standard—an ecological perspective.” Environmental Management, January, 23-33.

Hoffman, David J., Henny, Charles J., Hill, Elwood F., Grover, Robert A., Kaiser, James L., 

Stebbins, Katherine R. 2009. “Mercury and drought along the lower Carson River, 

Nevada: III. Effects on blood and organ biochemistry and histopathology of snowy egrets 

and black-crowned night-herons on Lahontan Reservoir, 2002–2006.” Journal of 

Toxicology and Environmental Health, Part A. 72: 20, 1223-1241.

Hutchison, R., and C.E. Kraft. 1994. “Hmong Fishing Activity and Fish Consumption.” Journal of 

Great Lakes Research 20(2):471–487.

Intergovernmental Panel on Climate Change 4th Assessment Report: Climate Change 2007 

Synthesis Report Summary for Policy Makers Pachauri, R.K. and Reisinger, A. (Eds.). 

Available on the Internet at 

<http://ipcc.ch/publications_and_data/ar4/syr/en/spm.html>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-139

IMPROVE (Interagency Monitoring of Protected Visual Environments). 2010. 1992–2008 data 

from the IMPROVE network based on the “New IMPROVE algorithm” (updated 

November, 2008). Available on the Internet at 

<http://vista.cira.colostate.edu/improve/Data/IMPROVE/summary_data.htm.>

Irving, P.M., e.d. 1991. Acid Deposition: State of Science and Technology, Volume III, Terrestrial, 

Materials, Health, and Visibility Effects, The U.S. National Acid Precipitation Assessment 

Program, Chapter 24, page 24–76.

Joslin, J.D., Kelly, J.M., and van Miegroet, H. 1992. “Soil chemistry and nutrition of North 

American spruce-fir stands: evidence for recent change. “ Journal of Environmental 

Quality, 21, 12-30.

Kaval, P., and J. Loomis. 2003. Updated Outdoor Recreation Use Values With Emphasis On 

National Park Recreation. Final Report October 2003, under Cooperative Agreement CA 

1200-99-009, Project number IMDE-02-0070. Available on the Internet at 

<http://www.mngt.waikato.ac.nz/departments/staff/pkaval/Kaval%26LoomisNPSRepor

t10-03.pdf>.

Kim, CW, Phipps, T, and Anselin L. 2003. “Measuring the benefits of air quality improvement: a 

spatial hedonic approach.” J Environ Econ Manage 45:24–39.

Koch, D., Bond, T.C., Streets D., and Unger N. (2007) Linking future aerosol radiative forcing to 

shifts in source activities. Geophys. Res. Lett. 34 (L05821), AGU 

(doi:10.1029/2006GL028360, 2007). Available on the Internet at 

<http://dx.doi.org/10.1029/2006GL028360>.

Koch D., Bond T.C., Streets D., Unger N., and van der Werf G.R. (2007) Global impacts of 

aerosols from particular source regions and sectors. Journal of Geophysical 

Research‐Atmospheres 112 (D02205).

Kopp, R. J., Vaughn, W. J., Hazilla, M., Carson, R. 1985. “Implications of environmental policy for 

U.S. agriculture: the case of ambient ozone standards.” Journal of Environmental 

Management, 20, 321-331.

Kramer, A., T. Holmes, and M. Haefel. 2003. Contingent valuation of forest ecosystem 

protection. Pp. 303–320 in Forests in a Market Economy. Edited by E.O. Sills and K.L. Abt. 

Dordrecht, The Netherlands: Kluwer Academic Publishers.

Landers DH; Simonich SL; Jaffe DA; Geiser LH; Campbell DH; Schwindt AR; Schreck CB; Kent ML; 

Hafner WD; Taylor HE; Hageman KJ; Usenko S; Ackerman LK; Schrlau JE; Rose NL; Blett 

TF; Erway MM. (2008). The Fate, Transport and Ecological Impacts of Airborne 

Contaminants in Western National Parks (USA). EPA/600/R-07/138. U.S. Environmental 

Protection Agency, Office of Research and Development, NHEERL, Western Ecology 

Division. Corvallis, Oregon.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-140

Leeworthy, V.R., and P.C. Wiley. 2001. Current Participation Patterns in Marine Recreation. 

Silver Spring, MD: U.S. Department of Commerce, National Oceanic and Atmospheric 

Administration, National Ocean Service, Special Projects. November. Available on the 

Internet at < http://www.naturetourismplanning.com/pdfs/NSRE_2.pdf>.

Leggett, C.G. and J.E. Neumann. 2004. Responding to SAB Council Comments on the May 2003 

Draft Analytical Plan for the Section 812 Second Prospective—Visibility Benefits.

Prepared for U.S. Environmental Protection Agency OPAR. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/leggettandneumann2004.

pdf>.

Leistritz, F. L., D. A. Bangsund, and N. M. Hodur. 2004. “Assessing the Economic Impact of 

Invasive Weeds: The Case of Leafy Spurge (Euphorbia esula).” Weed Technology

18:1392-1395.

Leitch, J. A, F. L Leistritz, and D. A Bangsund. 1996. “Economic effect of leafy spurge in the 

upper Great Plains: methods, models, and results.” Impact Assessment 14: 419–434.

Lipton, D. W. 1999. “Pfiesteria’s economic impact on seafood industry sales and recreational 

fishing, p. 35–38.” In B. L. Gardner and L. Koch (ed.), Proceedings of the Conference, 

Economics of Policy Options for Nutrient Management and Pfiesteria. Center for 

Agricultural and Natural Resource Policy, University of Maryland, College Park.

Loehman, E.T., D. Boldt, K. Chaikin. 1985. Measuring the Benefits of Air Quality Improvements

in the San Francisco Bay Area. From Methods Development for Environmental Control 

Benefits Assessment, Volume IV. Prepared for the U.S. Environmental Protection 

Agency, Office of Policy, Planning and Evaluation, September. Grant #R805059-01-0 

Available on the Internet at 

<http://yosemite.epa.gov/ee/epa/eerm.nsf/vwGA/e85705387833398f8525644d0053bd

d0!OpenDocument#_Section6>.

Loehman, E.T., S. Park, and D. Boldt. 1994. “Willingness to Pay for Gains and Losses in Visibility 

and Health.” Land Economics 70(4): 478-498.

Malm, WC. 1999. Introduction to visibility. Colorado State University, Fort Collins, CO, USA, 

1983, Revised edition, 1999. Available on the Internet at 

<vista.cira.colostate.edu/improve/Education/IntroToVisinstr.htm>.

McBride, J.R., Miller, P.R., Laven, R.D. 1985. “Effects of oxidant air pollutants on forest 

succession in the mixed conifer forest type of southern California.” In: Air Pollutants 

Effects on Forest Ecosystems, Symposium Proceedings, St. P, 1985, p. 157-167.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-141

McClelland, G., W. Schulze, D. Waldman, J. Irwin, D. Schenk, T. Stewart, L. Deck and M. Thayer. 

1993. Valuing Eastern Visibility: A Field Test of the Contingent Valuation Method. 

Prepared for U.S. Environmental Protection Agency, Office of Policy, Planning and 

Evaluation. September. Available on the Internet at 

<http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0008-1.pdf/$file/EE-0008-1.pdf >.

McIntyre, J.W., Barr, J.F. 1997. “Common Loon (Gavia immer)” in: Pool A, Gill F (eds.) The Birds 

of North America. Academy of Natural Sciences, Philadelphia, PA, 313

McIntrye, J.W., and Evers, D.C.,(eds). 2000. “Loons: old history and new finding.” Proceedings of 

a Symposium from the 1997 meeting, American Ornithologists’ Union. North American 

Loon Fund, 15 August 1997, Holderness, NH, USA.

Millennium Ecosystem Assessment Board (MEA). 2005. Ecosystems and Human Well-being: 

Synthesis. Washington, DC: World Resources Institute. Available on the Internet at 

<http://www.who.int/globalchange/ecosystems/ecosystems05/en/index.html>.

Miller, P.R., O.C. Taylor, R.G. Wilhour. 1982. Oxidant air pollution effects on a western 

coniferous forest ecosystem. Corvallis, OR: U.S. Environmental Protection Agency, 

Environmental Research Laboratory (EPA600-D-82-276).

Mitro, Matthew G., Evers, David C., Meyer, Michael W., and Piper, Walter H. 2008. “Common 

loon survival rates and mercury in New England and Wisconsin.” Journal of Wildlife 

Management 72(3): 665-673.

Munthe, J., Bodaly, R. A., Branfireun, B. A., Driscoll, C. T., Gilmour, C. C., Harris, R., et al. (2007). 

“Recovery of Mercury-Contaminated Fisheries.” Environmental Science & Technology

36(1), 33-44.

Murdoch, J. C. and M. A. Thayer. 1988. “Hedonic price estimation of variable urban air quality,”

J. Environ. Econ. Management 15, 143-146. 

National Acid Precipitation Assessment Program (NAPAP). 1991. 1990 Integrated Assessment 

Report. NAPAP Office of the Director. Washington, DC. November.

National Agricultural Statistics Service (NASS). 2008. Maple Syrup—June 12, 2008: Maple Syrup 

Production Up 30 Percent Nationwide. U.S. Department of Agriculture, National 

Agricultural Statistics Service, New England Agricultural Statistics, Concord, NH.

National Association of State Foresters (NASF). 2009. Quadrennial Fire Review 

2009.Washington, DC: NASF. Quadrennial Fire and Fuel Review Final Report 2009. 

National Wildfire Coordinating Group Executive Board. January.

National Oceanic and Atmospheric Administration (NOAA). 2007. Annual Commercial Landing 

Statistics. August. Available on the Internet at <http://www.st.nmfs.noaa.gov/ 

st1/commercial/landings/annual_landings.html>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-142

National Park Service (NPS). 1997. Statistical Abstract: 1997. Department of Interior, National 

Park Service Social Science Program, Public Use Statistics Office, Denver, Colorado. 

Available on the Internet at 

<http://www.nature.nps.gov/stats/abstracts/abst1997.pdf>.

National Park Service (NPS). 2008. Statistical Abstract: 2008. Department of Interior, National 

Park Service Social Science Program, Public Use Statistics Office, Denver, Colorado. 

Available on the Internet at 

<http://www.nature.nps.gov/stats/abstracts/abst2008.pdf>.

Neill, Helen R., David M. Hassenzahl and Djeto D. Assane. 2007. “Estimating the Effect of Air 

Quality: Spatial versus Traditional Hedonic Price Models,” Southern Economic Journal

73, No. 4, pp. 1088-1111.

Neumann, J.E. and R.W. Patterson. 2009. Alternative Approach to Estimating Monetized 

Benefits of Residential Visibility for the Section 812 Second Prospective. Prepared for U.S. 

Environmental Protection Agency OAR/OPAR. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/neumannpaterson2009.p

df>.

Niklinska M., Laskowski R., Maryanski M. (1998). “Effect of heavy metals and storage time on 

two types of forest litter: basal respiration rate and exchangeable metals”

Ecotoxicological Environmental Safety, 41, 8-18.

Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis. Washington, 

DC. Available on the internet at <http://www.whitehouse.gov/omb/circulars/a004/a4.html>.

Painter, et al. 2007. “Impact of disturbed desert soils on duration of mountain snow cover.”

Geophys. Res. Lett. 34, L12502.

Parrish ZD; White JC; Isleyen M; Gent MPN; Iannucci-Berger W; Eitzer BD; Kelsey JW; Mattina 

MI. 2006. “Accumulation of weathered polycyclic aromatic hydrocarbons (PAHs) by 

plant and earthworm species.” Chemosphere, 64: 609-618.

Patterson, R.W., J.E. Neumann, and C.G. Leggett. 2005. Recommended Residential Visibility 

Values for the Section 812 Second Prospective Analysis. Prepared for U.S. Environmental 

Protection Agency OPAR. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/Benefits/background/patersonneumanleggett2

005.pdf>.

Peterson, D.E., M.S. Kanarek, M.A. Kuykendall, J.M. Diedrich, H.A. Anderson, P.L. Remington, 

and T.B. Sheffy. 1994. “Fish Consumption Patterns and Blood Mercury Levels in 

Wisconsin Chippewa Indians.” Archives of Environmental Health 49(1):53–58.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-143

Pitchford, M and W. Malm. 1994. “Development and Application of a Standard Visual Index.”

Atmospheric Environment 28(5): 1049-1054.

Prasad, A.M. and Iverson, L.R. 2003. “Little’s range and FIA importance value database for 135 

eastern U.S. tree species.” Northeastern Research Station, USDA Forest Service.

Available on the Internet at 

<http://www.fs.fed.us/ne/delaware/4153/global/littlefia/index.html>.

Pryor, S.C. 1996. “Assessing Public Perception of Visibility for Standard Setting Exercises.”

Atmospheric Environment 30, no. 15, pp. 2705-2716.

Pye, J.M. 1988. “Impact of ozone on the growth and yield of trees: A review.” Journal of 

Environmental Quality, 17, 347-360.

Qian Y., Gustafson W.I., Leung L.R., and Ghan S.J. 2009. “Effects of soot-induced snow albedo 

change on snowpack and hydrological cycle in western United States based on Weather 

Research Forecasting Chemistry and Regional Climate Simulations.” Journal of 

Geophysical Research 114 (DO2108).

Rae, D.A. 1983. Benefits of Visual Air Quality in Cincinnati Results of a Contingent Ranking 

Survey. Prepared for Electric Power Research Institute. May.

Rea, A. W., Lindberg, S. E., Scherbatskoy, T., & Keeler, G. J. 2002. “Mercury Accumulation in 

Foliage over Time in Two Northern Mixed-Hardwood Forests.” Water, Air, & Soil 

Pollution 133(1), 49-67.

Rimmer, C. C., McFarland, K. P., Evers, D. C., Miller, E. K., Aubry, Y., Busby, D., et al. 2005. 

“Mercury Concentrations in Bicknell’s Thrush and Other Insectivorous Passerines in 

Montane Forests of Northeastern North America.” Ecotoxicology 14(1), 223-240.

Rosen, S. 1974. “Hedonic prices and implicit markets: product differentiation in pure 

competition.” Journal of Political Economy 82(1): 34-55.

Scheuhammer, Anton M., Meyer Michael W., Sandheinrich, Mark B., and Murray, Michael W. 

2007. “Effects of environmental methylmercury on the health of wild birds, mammals, 

and fish,” Ambio Vol.36, No.1.

Sepulveda, Maria S., Frederick, Peter C., Spalding, Marilyn G., and Williams, Gary E. Jr. 1999. 

“Mercury contamination in free-ranging great egret nestlings (Ardea albus) from 

southern Florida, USA,” Environmental Toxicology and Chemistry Vol. 18, No.5.

Simcik M.F., Eisenreich S.J., Lioy P.J. 1999. “Source apportionment and source/sink relationship 

of PAHs in the coastal atmosphere of Chicago and Lake Michigan.” Atmospheric 

Environment, 33, 5071-5079.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-144

Simcik M.F., Eisenreich, S.J., Golden K.A., et al. 1996. “Atmospheric Loading of Polycyclic 

Aromatic Hydrocarbons to Lake Michigan as Recorded in the Sediments.” Environmental 

Science and Technology, 30, 3039-3046.

Sisler, J.F. 1996. Spatial and seasonal patterns and long-term variability of the composition of 

the haze in the United States: an analysis of data from the IMPROVE network. CIRA 

Report, ISSN 0737-5352-32, Colorado State University.

Smith, G., Coulston, J., Jepsen, E., Prichard, T. 2003. “A national ozone biomonitoring program—

results from field surveys of ozone sensitive plants in Northeastern forests (1994–

2000).” Environmental Monitoring and Assessment, 87, 271-291.

Smith, A.E.; Kemp, M.A.; Savage, T.H.; Taylor, C.L. 2005. “Methods and Results from a New 

Survey of Values for Eastern Regional Haze Improvements,” J. Air & Waste Manage 

Assoc 55, 1767-1779.

Smith, V.K., G. Van Houtven, and S.K. Pattanayak. 2002. “Benefit Transfer via Preference 

Calibration.” Land Economics 78:132-152.

Smith, V.K., and J.C. Huang. 1995. “Can markets value air quality? A meta-analysis of hedonic 

property value models,” J. Polit. Econ, 103, pp. 209–227.

Southern Appalachian Mountains Initiative (SAMI). 2002. Southern Appalachian Mountains 

Initiative: Final Report. Asheville, NC. 172pp. Available on the Internet at 

<http://www.nature.nps.gov/air/pubs/pdf/SAMI_Final_Report_0802.pdf>.

Spencer, D.M., and D.F. Holecek. 2007. “Basic characteristics of the fall tourism market.”

Tourism Management 28:491–504.

Taylor R. 1994. “Deterministic versus stochastic evaluation of the aggregate economic effects of 

price support programs.” Agricultural Systems 44: 461-473.

Tingey, D.T., and Taylor, G.E. 1982. “Variation in plant response to ozone: a conceptual model 

of physiological events.” In M.H. Unsworth & D.P. Omrod (Eds.), Effects of Gaseous Air 

Pollution in Agriculture and Horticulture. (pp.113-138). London, UK: Butterworth 

Scientific.

Tolley, G., A. Randall, G. Blomquist, M. Brien, R. Fabian, G. Fishelson, A. Frankel, M. Grenchik, J. 

Hoehn, A. Kelly, R. Krumm, E. Mensah, and T. Smith. 1986. Establishing and Valuing the 

Effects of Improved Visibility in Eastern United States. Prepared for U.S. Environmental 

Protection Agency, Office of Policy, Planning and Evaluation. March. U.S. Environmental 

Protection Agency Grant #807768-01-0. Available on the Internet at 

<http://yosemite.epa.gov/ee/epa/eerm.nsf/vwGA/e673ccafbda23639852565f00007412

f!OpenDocument&ExpandSection=3>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-145

Tolley, George and Robert Fabian (eds). 1988. The Economic Value of Visibility. Series : Studies 

in urban and resource economics. Mount Pleasant, MO: Blackstone.

Trijonis, J.C. et al. 1987. “Preliminary extinction budget results from the RESOLVE program,” pp. 

872-883. In: P.J. Bhardwaja, et al. Visibility Protection Research and Policy Aspects. Air 

Pollution Control Assoc., Pittsburgh, PA.

Trijonis, J.C. et al. 1988. RESOLVE Project Final Report: Visibility conditions and Causes of 

Visibility Degradation in the Mojave Desert of California. NWC TP #6869. Naval Weapons 

Center, China Lake, CA. Available on the Internet at 

<www.dtic.mil/dtic/tr/fulltext/u2/a206322.pdf>.

United Nations Environmental Programme, World Meteorological Organization and Stockholm 

Environment Institute (UNEP). 2011. Integrated Assessment of Black Carbon and 

Tropospheric Ozone Summary for Decision Makers. Available on the Internet at 

<http://www.unep.org/dewa/Portals/67/pdf/Black_Carbon.pdf>.

U.S. Census Bureau. 2010. “Sector 56: EC075612: Administrative Support and Waste 

Management and Remediation Services: Industry Series: Preliminary Comparative 

Statistics for the United States (2002 NAICS Basis): 2007 and 2002.” American FactFinder 

Available on the Internet at http://factfinder.census.gov (accessed August 25, 2010)

U.S. Department of the Interior (DOI). 2007. 2006 National Survey of Fishing, Hunting, and 

Wildlife-Associated Recreation. U.S. Department of the Interior, Fish and Wildlife 

Service, and U.S. Department of Commerce, U.S. Census Bureau. Washington, DC. 

Available on the Internet at <http://www.census.gov/prod/2008pubs/fhw06-nat.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 1999a. The 

Clean Air Act Amendments (CAAA) Section 812 Prospective Study of Costs and Benefits 

(1999): Advisory by the Advisory Council on Clean Air Compliance Analysis: Costs and 

Benefits of the CAAA. EPA-SAB-COUNCIL-ADV-00-002. October. Available on the Internet 

at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/E992F918DF32131A852571B9004571F7/

$File/coua0002.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 1999b. Final 

Advisory by the Advisory Council on Clean Air Compliance Analysis on the 1999 

Prospective Study of Costs and Benefits (1999) of Implementation of the Clean Air Act 

Amendments (CAAA). EPA-SAB-COUNCIL-ADV-00-003. November. Available on the 

Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/93BE842F5CBA7BEF852571B900461197/

$File/coua0003.pdf>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-146

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004. Review of 

the Draft Analytical Plan for EPA’s Second Prospective Analysis—Benefits and Costs of 

the Clean Air Act, 1990-2020: An Advisory by the Advisory Council for Clean Air 

Compliance Analysis. EPA-SAB-COUNCIL-ADV-04-004. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/7CCBBFE15.BD4C8B185256F17005E3079

/$File/council_adv_04004.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2007. Clean Air 

Scientific Advisory Committee’s (CASAC) Review of the Agency’s Final Ozone Staff Paper.

EPA-CASAC-07-002. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/FE915E916333D776852572AC007397B5/

$File/casac-07-002.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2010a. Advisory 

on a Preliminary Draft of the Second Section 812 Prospective Study of the Benefits and 

Costs of the Clean Air Act (April 2010). EPA-COUNCIL-10-004. July. Available on the 

Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/553D59A280CDCF388525776D005EE83A

/$File/EPA-COUNCIL-10-004-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2010b. Review 

of the Final Integrated Report for the Second Section 812 Prospective Study of the 

Benefits and Costs of the Clean Air Act (August 2010). EPA-COUNCIL-11-001. December. 

Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/1E6218DE3BFF682E852577FB005

D46F1/$File/EPA-COUNCIL-11-001-unsigned.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 1997. Mercury Study Report to Congress. 

Volume V: Health Effects of Mercury and Mercury Compounds. EPA-452/R-97-007. U.S. 

EPA Office of Air Quality Planning and Standards, and Office of Research and 

Development. Available on the Internet at 

<http://www.epa.gov/ttncaaa1/t3/reports/volume5.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 1998. Advisory Council on Clean Air 

Compliance Analysis Advisory on the Clean Air Act Amendments (CAAA) of 1990 Section 

812 Prospective Study: Overview of Air Quality and Emissions Estimates: Modeling, 

Health and Ecological Valuation Issues Initial Studies. EPA-SAB-COUNCIL-ADV-98-003.

Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/D20F5AE06F98883F852571AF00474927/

$File/coad9803.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 1999a. Regional Haze Regulations, July 1, 

1999. (64 FR 35714, July 1, 1999).
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-147

U.S. Environmental Protection Agency (U.S. EPA). 1999b. The Benefits and Costs of the Clean Air 

Act, 1990–2010. Prepared for U.S. Congress by U.S. EPA, Office of Air and Radiation, 

Office of Policy Analysis and Review, Washington, DC, November; EPA report no.

EPA410-R-99-001. Available on the Internet at <http://www.epa.gov/oar/sect812/1990-

2010/chap1130.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2004. Air Quality Criteria for Particulate 

Matter Volume II of II. National Center for Environmental Assessment, Office of 

Research and Development, U.S. Environmental Protection Agency, Research Triangle 

Park, NC EPA/600/P-99/002bF. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=87903>.

U.S. Environmental Protection Agency (U.S. EPA). 2005. Regulatory Impact Analysis of the Final 

Clean Air Mercury Rule. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC., March; EPA report no. EPA-452/R-05-003. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/mercury_ria_final.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2006b. Regulatory Impact Analysis, 2006 

National Ambient Air Quality Standards for Particulate Matter. Office of Air Quality 

Planning and Standards, Research Triangle Park, NC. October. Available on the Internet 

at < http://www.epa.gov/ttn/ecas/regdata/RIAs/Appendix%20I--

Visibility%20Benefits%20Methodology.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2006c. Ecological Benefits Assessment 

Strategic Plan. EPA-240-R-06-001. Office of the Administrator. Washington, DC. October.

Available on the Internet at 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/EcologBenefitsPlan.html>.

U.S. Environmental Protection Agency (U.S. EPA). 2007a. Guidance on the Use of Models and 

Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and 

Regional Haze. Office of Air Quality Planning and Standards. EPA-454/B-07-002. April.

<Available on the Internet at http://www.epa.gov/scram001/guidance/guide/final-03-

pm-rh-guidance.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2007b. Review of the National Ambient Air 

Quality Standards for Ozone: Policy assessment of scientific and technical information.

Staff Paper. Office of Air Quality Planning and Standards. EPA-452/R-07-007a. July.

Available on the Internet at 

<http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_07_ozone_staff_paper.p

df>.

U.S. Environmental Protection Agency (U.S. EPA). 2007c. Technical Report on Ozone Exposure, 

Risk, and Impact Assessments for Vegetation. Office of Air Quality Planning and 

Standards. EPA-452/R-07-002. January. Available on the Internet at 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_pr.html
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-148

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis, 2008 

National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. March. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008b. U.S. EPA’s 2008 Report on the 

Environment (Final Report). Washington, D.C., EPA/600/R-07/045F (NTIS PB2008-

112484). May. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=190806>. Updated in 

December 2010, available on the Internet at 

<http://cfpub.epa.gov/eroe/index.cfm?fuseaction=detail.viewInd&showQues=Air&ch=4

7&lShowInd=0,287&subtop=341&lv=list.listByQues&r=188207>.

U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment for 

Oxides of Nitrogen and Sulfur—Ecological Criteria National (Final Report). National 

Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139.

December. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>.

U.S. Environmental Protection Agency (U.S. EPA). 2009a. The NOx Budget Trading Program: 

2008 Environmental Results. Clean Air Markets Division. September. Available on the 

Internet at <http://www.epa.gov/airmarkt/progress/NBP_3/

NBP_2008_Environmental_Results.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2009b. Integrated Science Assessment for 

Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for 

Environmental Assessment—RTP Division. December. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

U.S. Environmental Protection Agency (U.S. EPA). 2009c. Risk and Exposure Assessment for 

Review of the Secondary National Ambient Air Quality Standards for Oxides of Nitrogen 

and Oxides of Sulfur (Final Report). EPA-452/R-09-008a. Office of Air Quality Planning 

and Standards, Research Triangle Park, NC. September. Available on the Internet at 

<http://www.epa.gov/ttn/naaqs/standards/no2so2sec/data/NOxSOxREASep2009MainC

ontent.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2009d. Endangerment and Cause or 

Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air Act. 

December 15, 2009. (74 Fed. Reg. 66,496, December 15, 2009).

U.S. Environmental Protection Agency (U.S. EPA). 2010a. Updated Regulatory Impact Analysis 

(RIA) for the Reconsideration of the 2008 Ozone National Ambient Air Quality Standard 

(NAAQS). Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

January. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/s1-

supplemental_analysis_full.pdf>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-149

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Particulate Matter Urban-Focused 

Visibility Assessment—Final Document (Corrected). Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. July. Available on the Internet at <Particulate 

Matter Urban-Focused Visibility Assessment—Final Document>.

U.S. Environmental Protection Agency (U.S. EPA). 2011a. Policy Assessment for the Review of 

the PM NAAQS—Final Document. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. April. Available on the Internet at 

<http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html>.

U.S. Environmental Protection Agency (U.S. EPA). 2011b. The Benefits and Costs of the Clean Air 

Act 1990 to 2020: EPA Report to Congress. Office of Air and Radiation, Washington, DC.

March. Available on the Internet at 

<http://www.epa.gov/air/sect812/feb11/fullreport.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011c. Regulatory Impact Analysis for the 

Federal Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter 

and Ozone in 27 States; Correction of SIP Approvals for 22 States. June. Available on the 

Internet at: <http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf>

U.S. Environmental Protection Agency (U.S. EPA). 2011d. Regulatory Impact Analysis for the 

Final Mercury and Air Toxics Standards. EPA-452/R-11-011. December. Available on the 

Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2012a. Integrated Science Assessment for 

Particulate Ozone and Related Photochemical Oxidants (Third External Review Draft).

EPA/600/R-10/076C. National Center for Environmental Assessment—RTP Division. 

June. Available on the Internet at 

<http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=242490>.

U.S. Environmental Protection Agency (U.S. EPA). 2012b. Black Carbon Report to Congress. EPA450/R-12-001. March. Available on the Internet at 

<http://www.epa.gov/blackcarbon/2012report/fullreport.pdf>.

U.S. Fish and Wildlife Service and U.S. Census Bureau (FWS and Census). 2007. 2006 National 

Survey of Fishing, Hunting, and Wildlife-Associated Recreation (FHWAR). FHW/06-NAT. 

U.S. Department of the Interior, U.S. Fish and Wildlife Service, Washington, DC, and U.S. 

Department of Commerce, U.S. Census Bureau, Washington, DC.

U.S. Forest Service (USFS). 2006. Forest Inventory and Analysis National Program: Forest 

Inventory Data Online. Online database. U.S. Department of Agriculture Forest Service, 

Forest Inventory and Analysis, Arlington, VA. Available on the Internet at 

<http://fia.fs.fed.us/tools-data>.
*** E.O. 12866 Review - Draft - Do Not Cite, Quote, or Release During Review ***

ES-150

Valigura, R.A., R.B. Alexander, M.S. Castro, T.P. Meyers, H.W. Paerl, P.E. Stacy, and R.E. Turner. 

2001. Nitrogen Loading in Coastal Water Bodies: An Atmospheric Perspective. 

Washington, DC: American Geophysical Union.

Van Sickle, J., Baker, J.P., Simonin, H.A., Baldigo, B.P., Kretser, W.A., Sharpe, W.E. 1996.

“Episodic acidification of small streams in the northeastern United States: Fish mortality 

in field bioassays.” Ecological Applications, 6, 408-421.

Wada, H. and Cristol, D.A. and McNabb, F.M.A. and Hopkins, W.A. 2009. “Suppressed 

adrenocortical responses and thyroid hormone levels in birds near a mercurycontaminated river.” Environmental Science & Technology 43(15), 6031-6038.

Wada, H., Yates, DE., Evers, DC., Taylor, RJ., Hopkins, WA. 2010. “Tissue mercury concentrations 

and adrenocortical responses of female big brown bats (Eptesicus fuscus) near a 

contaminated river.” Ecotoxicology 19(7), 1277-1284.

Winner, W.E. 1994. “Mechanistic analysis of plant responses to air pollution.” Ecological 

Applications, 4(4), 651-661.

Winner, W.E., and C.J. Atkinson. 1986. “Absorption of air pollution by plants, and consequences 

for growth.” Trends in Ecology and Evolution 1:15-18.

Wiscombe W. and Warren S. 1980. “A model for the spectral albedo of snow. Snow containing 

atmospheric aerosols.” J. Atmos. Sci. 37, 2734-2274.

Yates, David E., Mayack, David T., Munney, Kenneth, Evers David C., Major, Andrew, Kaur, 

Taranjit, and Taylor, Robert J. 2005. “Mercury levels in mink (Mustela vison) and river 

otter (Lonra canadensis) from northeastern North America,” Ecotoxicology 14, 263-274.