5-1

CHAPTER 5

HEALTH AND WELFARE CO-BENEFITS

Synopsis

Implementation of HAP emissions controls required by this rule is expected to have 

ancillary co-benefits, including lower overall ambient concentrations of SO2, NO2, PM2.5 and 

ozone across the U.S. Pollutants such as SO2, NOx, and direct PM2.5 contribute to ambient PM2.5

levelsin the atmosphere, and NOx contributes to ambient ozone concentrations. Furthermore, 

this rule is expected to reduce CO2 emissions affecting climate change. These health and 

welfare co-benefits comprise a significant share of the total monetized benefits from this rule.

This chapter provides estimates for this subset of the expected annual health and climate cobenefits of this rule in 2016.

Due to limits in available air quality modeling, the quantified co-benefits of this rule 

consist of only PM2.5-related health co-benefits from reductions in SO2 (a precursor to PM2.5 

formation) and direct PM2.5 and climate co-benefits from reductions in CO2. These co-benefits

are estimated by applying a benefit-per–ton (BPT) approach described below to estimated 

reductions in SO2 and direct PM2.5 emissions reported in Chapter 3. The monetized co-benefits 

assessment omits several important categories of benefits, including health and ecological cobenefits from reducing exposure to ozone, ecosystem co-benefits for reducing nitrogen and 

sulfate deposition, and the direct health co-benefits from reducing exposure to ozone, SO2 and 

NO2. We describe these co-benefits qualitatively in Section 5.5.

We estimate the monetized health and climate co-benefits of MATS to be $37 billion to 

$90 billion at a 3% discount rate and $33 billion to $81 billion at a 7% discount rate in 2016, 

depending on the epidemiological function used to estimate reductions in premature mortality.

All estimates are in 2007$. 

5.1 Overview

The analysis in this chapter aims to characterize the co-benefits of the Mercury and Air 

Toxics 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 SO2?

2. What is the economic value of these effects?
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Additionally, this chapter describes health effects that are not quantified for this rule, 

unquantified welfare effects, and visibility co-benefits.

In implementing these rules, emission controls may lead to reductions in ambient PM2.5

below the National Ambient Air Quality Standards (NAAQS) for PM in some areas and assist

other areas with attaining the PM NAAQS. Because the PM NAAQS RIAs also calculate PM

benefits, there are important differences worth noting in the design and analytical objectives of

each RIA. The NAAQS RIAs illustrate the potential costs and benefits of attaining a new air

quality standard nationwide based on an array of emission control strategies for different 

sources. In short, NAAQS RIAs hypothesize, but do not predict, the control strategies that States 

may choose to enact when implementing a NAAQS. The setting of a NAAQS does not directly 

result in costs or benefits, and as such, the 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. However, some costs and benefits estimated in this RIA 

account for the same air quality improvements as estimated in the illustrative PM2.5 NAAQS RIA.

By contrast, the emission reductions for this rule are from a specific class of wellcharacterized sources. In general, EPA is more confident in the magnitude and location of the

emission reductions for these rules. It is important to note that emission reductions anticipated

from these rules do not result in emission increases elsewhere (other than potential energy

disbenefits). Emission reductions achieved under these and other promulgated rules will

ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the

incremental costs and benefits associated with attaining the NAAQS. EPA remains forward

looking towards the next iteration of the 5-year review cycle for the NAAQS, and as a result 

does not issue updated RIAs for existing NAAQS that retroactively update the baseline for 

NAAQS implementation. For more information on the relationship between the NAAQS and 

rules such as analyzed here, please see Section 1.2.4 of the SO2 NAAQS RIA (U.S. EPA, 2010a).

To estimate a subset of the co-benefits from reducing PM2.5 exposure, EPA used an 

approach that is consistent with the approach utilized to estimate the co-benefits of the 

proposed MATS (U.S. EPA 2011a) and the Cross-State Air Pollution Rule (U.S. EPA 2011b). In this 

analysis we consider an array of health impacts attributable to changes in PM2.5 air quality. The 

2009 PM2.5 Integrated Science Assessment (U.S. EPA, 2009a) identified the human health 

effects associated with these ambient pollutants, which include premature mortality and a 

variety of morbidity effects associated with acute and chronic exposures. 
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Table 5-1 summarizes the total monetized co-benefits of the rule in 2016. This table 

reflects the economic value of the change in PM2.5-related human health impacts and the 

monetized value of CO2 reductions occurring as a result of the Mercury and Air Toxics

Standards.

Table 5-1. Estimated Monetized Co-benefits of the Mercury and Air Toxics Standards in 

2016 (billions of 2007$)a

Benefits Estimate Eastern U.S. b Western U.S. Total

Pope et al. (2002) PM2.5 mortality estimate

Using a 3% discount rate $35+B

($2.8 – $110)

$1.1+B

($0.03 – $3.4)

$37+B

($3.2 – $110)

Using a 7% discount rate $32+B

($2.5 – $98)

$1.0+B

($0.03 – $3.1)

$33+B

($2.9 – $100)

Laden et al. (2006) PM2.5 mortality estimate

Using a 3% discount rate $87+B

($7.5 – $250)

$2.7+B

($0.1 – $7.9)

$90+B

($8.0 – $260)

Using a 7% discount rate $78+B

($6.8 – $230)

$2.4+B

($0.1 – $7.2)

$81+B

($7.3 – $240)

a

For notational purposes, unquantified benefits are indicated with a “B” to represent the sum of additional 

monetary benefits and disbenefits. 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. A detailed 

listing of unquantified health and welfare effects is provided in Tables 5-2 and 5-3. Estimates here are subject to 

uncertainties discussed further in the body of the document. Estimates are rounded to two significant figures. 

Value of total co-benefits includes CO2-related co benefits discounted at 3%.

b

Includes Texas and those states to the north and east.

Tables 5-2 and 5-3 summarize the human health and environmental co-benefits

categories contained within the total monetized benefits estimate, and those categories that 

were unquantified due to limited data or time. It is important to emphasize that the list of 

unquantified benefit categories is not exhaustive, nor is quantification of each effect complete.

In order to identify the most meaningful human health and environmental co-benefits, 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 Integrated Science Assessment. This does not imply that additional 

relationships between these and other human health and environmental co-benefits and the 

affected pollutants do not exist. Due to this decision criterion, some effects that were identified 

in previous lists of unquantified benefits in other RIAs have been dropped (e.g., UVb exposure).

In addition, some quantified effects represent only a partial accounting of likely impacts due to 

limitations in the currently available data (e.g., climate effects from CO2, etc).
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Table 5-2. Human Health Effects of Pollutants Affected by the Mercury and Air Toxics 

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 5.4

Infant mortality (age <1)   Section 5.4

Reduced incidence of 

morbidity from 

exposure to PM2.5

Non-fatal heart attacks (age > 18)   Section 5.4

Hospital admissions—respiratory (all ages)   Section 5.4

Hospital admissions—cardiovascular (age 

>18)

  Section 5.4

Emergency room visits for asthma (<18)   Section 5.4

Acute bronchitis (age 8-12)   Section 5.4

Lower respiratory symptoms (age 7–14)   Section 5.4

Upper respiratory symptoms (asthmatics 

age 9–11)

  Section 5.4

Asthma exacerbation (asthmatics age 6–18)   Section 5.4

Lost work days (age 18–65)   Section 5.4

Minor restricted-activity days (age 18–65)   Section 5.4

Chronic Bronchitis (age >26)   Section 5.4

Other cardiovascular effects (e.g., other 

ages)

— — PM ISAb

Other respiratory effects (e.g., pulmonary 

function, non-asthma ER visits, nonbronchitis 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

Reduced incidence of 

mortality from 

exposure to ozone

Premature mortality based on short-term 

study estimates (all ages)

— — Ozone CD, Draft 

Ozone ISAa

Premature mortality based on long-term 

study estimates (age 30–99)

— — Ozone CD, Draft 

Ozone ISAa

Reduced incidence of 

morbidity from 

exposure to ozone

Hospital admissions—respiratory causes 

(age > 65)

— — Ozone CD, Draft 

Ozone ISAa

Hospital admissions—respiratory causes 

(age <2)

— — Ozone CD, Draft 

Ozone ISAa

Emergency room visits for asthma (all ages) — — Ozone CD, Draft 

Ozone ISAa

Minor restricted-activity days (age 18–65) — — Ozone CD, Draft 

Ozone ISAa

(continued)
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Table 5-2. Human Health Effects of Pollutants Affected by the Mercury and Air Toxics 

Standards (continued)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized More Information

School absence days (age 5–17) — — Ozone CD, Draft 

Ozone ISAa

Decreased outdoor worker productivity

(age 18–65)

— — Ozone CD, Draft 

Ozone ISAa

Other respiratory effects (e.g., premature 

aging of lungs)

— — Ozone CD, Draft 

Ozone ISAb

Cardiovascular and nervous system effects — — Ozone CD, Draft 

Ozone ISAc

Reproductive and developmental effects — — Ozone CD, Draft 

Ozone ISAc

Reduced incidence of 

morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — — NO2 ISAa

Chronic lung disease hospital admissions 

(age > 65)

— — NO2 ISAa

Respiratory emergency department visits 

(all ages)

— — NO2 ISAa

Asthma exacerbation (asthmatics age 4–18) — — NO2 ISAa

Acute respiratory symptoms (age 7–14) — — NO2 ISAa

Premature mortality — — NO2 ISAb,c

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, 

lung function, other ages and populations)

— — NO2 ISAb,c

Reduced incidence of 

morbidity from 

exposure to SO2

Respiratory hospital admissions (age > 65) — — SO2 ISAa

Asthma emergency room visits (all ages) — — SO2 ISAa

Asthma exacerbation (asthmatics age 4–12) — — SO2 ISAa

Acute respiratory symptoms (age 7–14) — — SO2 ISAa

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 reduced 

mercury deposition as 

well as the role of 

sulfate in methylation)

Neurologic effects - IQ loss   IRIS; NRC, 2000a

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 assess these co-benefits qualitatively due to time and resource limitations for this analysis.

b We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods.

c 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.
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Table 5-3. Environmental Effects of Pollutants Affected by the Mercury and Air Toxics 

Standards

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Improved Environment

Reduced visibility 

impairment

Visibility in Class I areas in SE, SW, and 

CA regions

— — PM ISAa

Visibility in Class I areas in other regions — — PM ISAa

Visibility in residential areas — — PM ISAa

Reduced climate 

effects

Global climate impacts from CO2 —  Section 5.6

Climate impacts from ozone and PM — — Section 5.6

Other climate impacts (e.g., other GHGs, 

other impacts) 

— — IPCCb

Reduced effects on 

materials

Household soiling — — PM ISAb

Materials damage (e.g., corrosion, 

increased wear)

— — PM ISAb

Reduced effects from 

PM deposition 

(metals and organics)

Effects on Individual organisms and 

ecosystems

— — PM ISAb

Reduced vegetation 

and ecosystem 

effects from 

exposure to ozone

Visible foliar injury on vegetation — — Ozone CD, Draft 

Ozone ISAb

Reduced vegetation growth and 

reproduction

— — Ozone CD, Draft 

Ozone ISAa

Yield and quality of commercial forest 

products and crops

— — Ozone CD, Draft 

Ozone ISAa,c

Damage to urban ornamental plants — — Ozone CD, Draft 

Ozone ISAb

Carbon sequestration in terrestrial 

ecosystems

— — Ozone CD, Draft 

Ozone ISAb

Recreational demand associated with 

forest aesthetics

— — Ozone CD, Draft 

Ozone ISAb

Other non-use effects Ozone CD, Draft 

Ozone ISAb

Ecosystem functions (e.g., water cycling, 

biogeochemical cycles, net primary 

productivity, leaf-gas exchange, 

community composition)

— — Ozone CD, Draft 

Ozone ISAb

(continued)
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Table 5-3. Environmental Effects of Pollutants Affected by the Mercury and Air Toxics 

Standards (continued)

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Reduced effects from 

acid deposition

Recreational fishing — — NOx SOx ISAa

Tree mortality and decline — — NOx SOx ISAb

Commercial fishing and forestry 

effects

— — NOx SOx ISAb

Recreational demand in terrestrial and 

aquatic ecosystems

— — NOx SOx ISAb

Other non-use effects NOx SOx ISAb

Ecosystem functions (e.g., 

biogeochemical cycles)

— — NOx SOx ISAb

Reduced effects from 

nutrient enrichment

Species composition and biodiversity 

in terrestrial and estuarine ecosystems

— — NOx SOx ISAb

Coastal eutrophication — — NOx SOx ISAb

Recreational demand in terrestrial and 

estuarine ecosystems

— — NOx SOx ISAb

Other non-use effects NOx SOx ISAb

Ecosystem functions (e.g., 

biogeochemical cycles, fire regulation)

— — NOx SOx ISAb

Reduced vegetation 

effects from ambient 

exposure to SO2 and 

NOx

Injury to vegetation from SO2 exposure — — NOx SOx ISAb

Injury to vegetation from NOx

exposure

— — NOx SOx ISAb

Reduced ecosystem 

effects from exposure 

to methylmercury 

(through reduced 

mercury deposition as 

well as the role of 

sulfate in methylation)

Effects on fish, birds, and mammals 

(e.g., reproductive effects)

— — Mercury Study 

RTCb,c

Commercial, subsistence and 

recreational fishing

— — Mercury Study 

RTCb

a We assess these co-benefits qualitatively due to time and resource limitations for this analysis.

b We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or 

methods.

c 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.

The co-benefits analysis in this chapter relies on an array of data inputs—including air 

quality modeling, health impact functions and valuation functions among others—which are 

themselves subject to uncertainty and may also contribute to the overall uncertainty in this 

analysis. As a means of characterizing this uncertainty we employ two primary techniques. First, 
5-8

we use Monte Carlo methods for characterizing random sampling error associated with the 

concentration response functions from epidemiological studies and economic valuation 

functions. Second, because this characterization of random statistical error may omit important 

sources of uncertainty we also employ the results of an expert elicitation on the relationship 

between premature mortality and ambient PM2.5 concentration (Roman et al., 2008). This 

provides additional insight into the likelihood of different outcomes and about the state of 

knowledge regarding the co-benefits estimates. Both approaches have different strengths and 

weaknesses, which are fully described in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006a).

While the contributions from additional data inputs to uncertainty in the results are not 

quantified here, this analysis employs best practices in every aspect of its development.

Given that co-benefits of reductions in premature mortality are a dominant share of the 

overall monetized co-benefits, more focus on uncertainty in mortality-related co-benefits gives 

us greater confidence in our uncertainty characterization surrounding total PM2.5-related cobenefits. Additional sensitivity analyses have been performed for the 2006 PM NAAQS RIA, and 

were not specifically included here as the results would be similar and would not change the 

conclusions of the analyses to support this rule. In particular, these analyses characterized the 

sensitivity of the monetized co-benefits to the specification of alternate cessation lags and 

income growth adjustment factors. As shown in these RIAs, the estimated co-benefits increased 

or decreased in proportion to the specification of alternate income growth adjustments and 

cessation lags. Therefore, readers can infer the sensitivity of the results in this RIA to these 

parameters by referring to the sensitivity analyses in the PM NAAQS RIA (2006d) and Ozone 

NAAQS RIA (2008a). For example, based on the results from previous analyses, the use of an 

alternate lag structure would change the PM2.5-related mortality co-benefits discounted at 3% 

discounted by between 10.4% and –27%; when discounted at 7%, these co-benefits change by 

between 31% and -49%. When applying higher and lower income growth adjustments, the 

monetary value of PM2.5 -related premature changes between 30% and -10%; the value of 

chronic endpoints change between 5% and -2% and the value of acute endpoints change 

between 6% and -7%.

Additionally, in this RIA we binned the estimated population exposed to projected 

future baseline PM2.5 air quality levels for comparison against the “Lowest Measured Level”

(LML) of PM2.5 air quality in the mortality studies. The purpose of this analysis is to show 

whether the estimated premature deaths associated with reduced PM2.5 exposure occur at or 

above the range of ambient PM2.5 observations studied in Pope et al. (2002) and Laden et al. 

(2006), which are the two epidemiological studies that EPA uses to estimate PM2.5-related 
5-9

prematuremortality co-benefits. We found that a significant proportion of the avoided PMrelated premature deaths we estimated in this analysis occurred among populations exposed at 

or above the LML of each study in the baseline, increasing our confidence in our estimate of the 

magnitude of the PM-related premature deaths avoided. Approximately 11% of the avoided 

premature deaths occur at or above an annual mean PM2.5 level of 10 μg/m3(the LML of the 

Laden et al. 2006 study), and about 73% occur at or above an annual mean PM2.5 level of 7.5 

μg/m3 (the LML of the Pope et al. 2002 study). As we model avoided premature deaths among 

populations exposed to levels of PM2.5 that are successively lower than the LML of each study 

our confidence in the results diminishes. 

5.2 Benefits Analysis Methods

We follow a “damage-function” approach in calculating health co-benefits of the 

modeled changes in environmental quality. This approach estimates changes in individual 

health and welfare endpoints (specific effects that can be associated with changes in air quality) 

and estimates values of those changes assuming independence between the values of

individual endpoints. Total benefits are calculated simply as the sum of the values for all nonoverlapping health and welfare 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; Hubbell et al., 2009; 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 health and 

welfare impact analysis must first be conducted to convert air quality changes into effects that 

can be assigned dollar values.

We note at the outset that 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 recent health impact analyses, our 

estimates are based on the best available methods of benefits transfer. Benefits transfer is a 

means 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.
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5.2.1 Health Impact Assessment

Health Impact Assessment (HIA) quantifies changes in the incidence of adverse health 

impacts resulting from changes in human exposure to specific pollutants, such as PM2.5. 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. Analysts 

have applied the HIA approach to estimate human health impacts resulting from hypothetical 

changes in pollutant levels (Hubbell et al. 2005; Davidson et al. 2007, Tagaris et al. 2009). 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 quality (e.g. U.S. EPA, 2008a). For 

this assessment, the HIAs are 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 impacts for coal miners.

The HIA approach used in this analysis involves three basic steps: (1) utilizing CAMxgenerated projections of PM2.5 and ozone air quality and estimating the change in the spatial 

distribution of the ambient air quality; (2) determining the subsequent change in populationlevel 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:

∆ =  ∙ 

∙∆ − 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. Tools such as BenMAP can systematize the HIA calculation process, 

allowing users to draw upon a library of existing air quality monitoring data, population data 

and health impact functions.

Figure 5-1 provides a simplified overview of this approach.
Figure 5-1. Illustration of BenMAP Approach

5.2.2 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 

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

for changes in risk. However, epidemiological studies generally provide estimates of the relative 

risks of a particular health effect avoided due to a reduction in air pollution. A convenient way 

to use this data in a consistent framework is to convert probabilities to units of avoided 

statistical incidences. 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 

risk of premature mortality from 2 in 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

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

5-11

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 

(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. However, epidemiological studies generally provide estimates of the relative 

risks of a particular health effect avoided due to a reduction in air pollution. A convenient way 

ata in a consistent framework is to convert probabilities to units of avoided 

statistical incidences. 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 

risk of premature mortality from 2 in 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.

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 

the effect has 

(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 

Willingness to Pay (WTP) 

for changes in risk. However, epidemiological studies generally provide estimates of the relative 

risks of a particular health effect avoided due to a reduction in air pollution. A convenient way 

ata in a consistent framework is to convert probabilities to units of avoided 

statistical incidences. 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 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 

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 

statistical incidences of other health endpoints.
5-12

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 as a primary 

estimate. 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 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 (Abt Associates, 2010) to estimate the health 

impacts and monetized health co-benefits for the Mercury and Air Toxics Standards. Figure 5-2 

shows the data inputs and outputs for the BenMAP model.

Figure 5-2. Data Inputs and Outputs for the BenMAP Model
5-13

5.2.3 Adjusting the Results of the PM2.5 co-benefits Analysis to Account for the Emission 

Reductions in the Final Mercury and Air Toxics Standards

As described in Chapter 3 of this RIA, EPA finalized the rule requirements after the 

completion of the air quality modeling for this rule. These changes to the rule affected both the 

overall level and distribution of PM2.5 precursor emissions across the U.S., which in turn affect 

the level of PM2.5 co-benefits. We determined that the geographic distribution of emissions 

reductions resulting from the final rule requirements were sufficiently similar to the modeled 

interim emissions reductions that we could adjust our co-benefits estimates to reflect these 

emission changes by applying benefit per-ton estimates generated using the modeled air 

quality changes.

Benefit per-ton (BPT) estimates quantify the health impacts and monetized human 

health co-benefits of an incremental change in air pollution precursor emissions. In 

circumstances where we are unable to perform air quality modeling because of resource or 

time constraints, this approach can provide a reasonable estimate of the co-benefits of 

emission reductions. EPA has used the BPT technique in previous RIAs, including the recent 

Ozone NAAQS RIA (U.S. EPA, 2008a), the NO2 NAAQS RIA (U.S. EPA, 2010b), the proposed 

Mercury and Air Toxics Standards RIA (U.S. EPA 2011a), and the Cross-State Air Pollution Rule

(U.S. EPA, 2011b).

For this co-benefits analysis we created per-ton estimates of PM2.5-related incidenceand monetized co-benefits based on the co-benefits of the air quality modeled scenario. Our 

approach here is methodologically consistent with the technique reported in Fann, Fulcher & 

Hubbell (2009), but adjusted for this analysis to better match the spatial distribution of air 

quality changes expected under the Mercury and Air Toxics Standards. To derive the BPT 

estimates for this analysis, we:

1. Quantified the PM2.5-related human and monetized health co-benefits of SO2 and 

direct PM2.5 changes for Eastern and Western states. We first estimated the health 

impacts and monetized co-benefits of reductions in directly emitted PM2.5 and

particulate sulfate.1 MATS is expected to reduce both SO2 and NOx emissions. In 

general SO2 is a precursor to particulate sulfate and NOx is a precursor to particulate 

nitrate. However, there are also several interactions between the PM2.5 precursors 

which cannot be easily quantified. For example, under conditions in which SO2 levels 

are reduced by a substantial margin, “nitrate replacement” may occur. This occurs 

 

1 Consistent with advice from the Health Effects Subcommittee of the Science Advisory Board (U.S. EPA-SAB, 

2010), we assume that each PM species is equally toxic. We quantify the change in incidence for each PM 

component by applying risk coefficients based on undifferentiated PM2.5 mass.
5-14

when particulate ammonium sulfate concentrations are reduced, thereby freeing up 

excess gaseous ammonia. The excess ammonia is then available to react with 

gaseous nitric acid to form particulate nitrate when meteorological conditions are 

conducive (cold temperatures and high humidity). The impact of nitrate replacement 

is also affected by concurrent NOx reductions. NOx reductions can lead to decreases 

in nitrate, which competes with the process of nitrate replacement. NOx reductions 

can also lead to reductions in photochemical by-products which can reduce both 

particulate sulfate and secondary organic carbon PM concentrations.

We found that reductions in NOx and SOx resulting from MATS led to significant 

decreases in particulate sulfate and small increases in particulate nitrate in some 

locations, indicating that nitrate replacement limited the nitrate decreases from NOx

reductions in some locations. Reductions in directly emitted crustal and 

carbonaceous PM2.5 (elemental carbon and organic carbon) were fairly modest.

Carbonaceous PM2.5 decreased slightly in the eastern US but did not significantly

change in the western US. We elected not to generate a NOx BPT for three reasons: 

(a) reductions in NOx emissions for this rule were relatively small; (b) previous EPA 

modeling indicates that PM2.5 formation is less sensitive to NOx emission reductions 

on a per-μg/m3basis (Fann, Fulcher and Hubbell, 2009); and (c) particulate nitrate 

formation is governed by complex non-linear chemistry that is difficult to 

characterize using BPT estimates that are derived from a single air quality modeling 

run which includes both NOx and SO2 reductions. Additional modeling runs with SO2

and NOx emissions changes modeled separately can provide information that can be 

used to estimate NOx benefits, and these runs have been conducted for other 

sectors, but have not been conducted for this rule. For the modeled scenario, 

sulfate reductions contributed 95% of the health co-benefits of all PM2.5 

components, with an additional 5% from direct PM2.5 reductions (see Appendix 5C).

Health co-benefits of sulfate reductions were two orders of magnitude larger than 

the health disbenefits of nitrate increases. Thus, the SO2 emission reductions are the 

main driver for the health co-benefits of this rule.

2. Divided the health impacts and monetized co-benefits by the emission reduction for 

the air quality modeling in the corresponding geographic area. For the reasons 

described above, we quantified BPT estimates for SO2 and directly emitted PM2.5 

(separately for carbonaceous and crustal). For SO2, we generated an array of eastern 

and western BPT estimates by dividing the particulate sulfate-related co-benefits in 

the eastern and western U.S. by the total SO2-related emission reductions in these

two areas. As the chemistry of nitrate formation is complex and non-linear, nitrate 

impacts were excluded from the BPT analysis. Nitrates can be reduced when NOx

emissions are reduced or increased when SO2 emissions are reduced. The increased 

nitrate health impacts in the modeled interim scenario were two orders of 

magnitude smaller than the sulfate health benefits. Thus, we estimate that including 

nitrate health impacts on the calculation for SO2 BPT would reduce the SO2 BPT by 1-

2%, with a similar magnitude impact on the total health benefits of the rule.
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Carbonaceous and crustal PM2.5 BPT estimates were similarly generated using the

co-benefits and emissions of those species.

The resulting BPT estimates (listed in Table 5C-3) were then multiplied by the projected 

SO2 (1.33 million tons), carbonaceous PM2.5 (6,100 tons), and crustal PM2.5 (39,000 tons) 

emission reductions for the final policy to produce an estimate of the PM2.5-related health 

impacts and monetized co-benefits. Due to time limitations, direct PM2.5 benefits are based on 

direct PM2.5 emission reductions from an earlier policy scenario. However, since direct PM2.5 

benefits contribute only approximately 5% to the total PM2.5 health co-benefits of this rule, and 

differences between direct PM2.5 emission reductions between the earlier and final policy 

scenarios are expected to be modest, use of earlier PM2.5 emission changes is unlikely to 

materially affect the results. Additional details on the BPT methodology and derivation are 

given in Appendix 5C.

An implicit assumption in our approach is that the size and distribution of SO2 emissions, 

and the relative levels of NOx and SO2 emissions, are fairly similar in the modeled and revised 

policy cases. In general, the modeled and revised policy cases achieve roughly similar levels of 

SO2 reductions (1.42 versus 1.33 million tons, respectively) with a similar distribution among 

states. However, for some states (notably Alabama, Colorado, Louisiana, Michigan, Missouri, 

North Dakota, Oklahoma, and Texas), SO2 emission reductions were lower for the final case 

versus the interim case. By far, the greatest difference in SO2 emission reductions was in 

Michigan where the final case emission reduction was 70% lower than for the interim case. In a 

few states (notably Arkansas, Ohio, and South Carolina), SO2 emission reductions were slightly 

larger for the final case versus the interim case. Since differences between the interim and final 

cases are not concentrated in any particular region of the country and the overall distribution of 

emission reductions is similar, we conclude that it is reasonable to apply BPT values derived 

from the interim case to the final case. While NOx emissions reductions decreased by 70% 

between the interim and final cases (141,000 vs. 46,000 tons), the impact of NOx on PM2.5 

concentrations and mortality is very minor relative to the impact of SO2 emission reductions.

Therefore, differences in the magnitude and distribution of NOx emission reductions are likely 

to have only a minor effect on results.

We did not develop ozone BPT estimates for this rule for two reasons. First, the overall 

level of ozone-related co-benefits in the modeled case is relatively small compared to those 

associated with PM2.5 reductions, due in part to the modest NOx emission reductions. Second, 

the complex non-linear chemistry of ozone formation introduces uncertainty to the 
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development and application of BPT estimates. Taken together, these factors argued against 

developing ozone BPT estimates for this RIA.

As there is no analogous approach for estimating visibility co-benefits using the BPT 

approach, visibility co-benefits are calculated for the modeled interim policy scenario only and 

are not included in estimate of co-benefits for the final rule. However, since the magnitude of 

SO2 emission reductions did not significantly change in the visibility study areas between the 

interim and final emissions scenarios, we expect the visibility benefit for the final policy 

scenario would be similar to that calculated for the interim policy scenario ($1.1 billion in total 

for the U.S., using 2007$; see Appendix 5C).

5.3 Uncertainty Characterization

As for any complex analysis using estimated parameters and inputs from numerous 

models, there are likely to be many sources of uncertainty affecting estimated results, including 

emission inventories, air quality models (with their associated parameters and inputs), 

epidemiological health effect estimates, estimates of values (both from WTP and COI studies), 

population estimates, income estimates, and estimates of the future state of the world (i.e., 

regulations, technology, and human behavior). Each of these inputs may be uncertain and, 

depending on its role in the co-benefits analysis, may have a disproportionately large impact on 

estimates of total monetized co-benefits. For example, emissions estimates are used in the first 

stage of the analysis. As such, any uncertainty in emissions estimates will be propagated 

through the entire analysis. When compounded with uncertainty in later stages, small 

uncertainties in emission levels can lead to large impacts on total monetized co-benefits.

The National Research Council (NRC) (2002, 2008) highlighted the need for EPA to 

conduct rigorous quantitative analysis of uncertainty in its benefits estimates and to present 

these estimates to decision makers in ways that foster an appropriate appreciation of their 

inherent uncertainty. In general, the NRC concluded that EPA’s methodology for calculating the 

benefits of reducing air pollution is reasonable and informative in spite of inherent 

uncertainties. Since the publication of these reports, EPA continues to improve the 

characterization of uncertainties for both health incidence and benefits estimates. We use both 

Monte Carlo analysis and expert-derived concentration-response functions to assess 

uncertainty quantitatively, as well as to provide a qualitative assessment for those aspects that 

we are unable to address quantitatively.

First, we used Monte Carlo methods to characterize both sampling error and variability 

across the economic valuation functions, including random sampling error associated with the 
5-17

concentration response functions from epidemiological studies and random effects modeling. 

Monte Carlo simulation uses random sampling from distributions of parameters to characterize 

the effects of uncertainty on output variables, such as incidence of premature mortality. 

Specifically, we used Monte Carlo methods to generate confidence intervals around the 

estimated health impact and dollar benefits. The reported standard errors in the 

epidemiological studies determined the distributions for individual effect estimates.

Second, because characterization of random statistical error omits important sources of 

uncertainty (e.g., in the functional form of the model—e.g., whether or not a threshold may 

exist), we also incorporate the results of an expert elicitation on the relationship between 

premature mortality and ambient PM2.5 concentration (Roman et al., 2008). Use of the expert 

elicitation and incorporation of the standard errors approaches provide insights into the 

likelihood of different outcomes and about the state of knowledge regarding the benefits 

estimates. However, there remain significant unquantified uncertainties present in upstream 

inputs including emission and air quality. Both uncertainty characterization approaches have 

different strengths and weaknesses, as detailed in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 

2006a).

In benefit analyses of air pollution regulations conducted to date, the estimated impact 

of reductions in premature mortality has accounted for 85% to 95% of total monetized benefits. 

Therefore, it is particularly important to attempt to characterize the uncertainties associated 

with reductions in premature mortality. The health impact functions used to estimate avoided 

premature deaths associated with reductions in ozone have associated standard errors that 

represent the statistical errors around the effect estimates in the underlying epidemiological 

studies. In our results, we report credible intervals based on these standard errors, reflecting 

the uncertainty in the estimated change in incidence of avoided premature deaths. We also 

provide multiple estimates, to reflect model uncertainty between alternative study designs.

For premature mortality associated with exposure to PM, we follow the same approach 

used in the RIA for 2006 PM NAAQS (U.S. EPA, 2006a), presenting two empirical estimates of 

premature deaths avoided, and a set of twelve estimates based on results of the expert 

elicitation study. Even these multiple characterizations, including confidence intervals, omit the 

contribution to overall uncertainty of uncertainty in air quality changes, baseline incidence 

rates, populations exposed and transferability of the effect estimate to diverse locations. 

Furthermore, the approach presented here does not 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 
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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.

EPA estimates PM-related mortality without assuming a health effect threshold at low 

concentrations, based on the current body of scientific literature (U.S. EPA-SAB, 2009a, U.S. 

EPA-SAB, 2009b). However, as we model mortality impacts among populations exposed to 

levels of PM2.5 that are successively lower than the lowest measured level (LML) in each

epidemiology study our confidence in the results diminishes. In addition to the uncertainty 

analyses described above, we therefore include an assessment of the mortality benefits 

accruing to populations exposed to baseline PM2.5 concentrations above the LML in the two 

main epidemiology studies used to quantify benefits (see Section 5.7). Based on the modeled 

interim baseline which is approximately equivalent to the final baseline (see Appendix 5A), 11% 

and 73% of the estimated avoided mortality impacts occur at or above an annual mean PM2.5

level of 10 μg/m3(the LML of the Laden et al. 2006 study) and 7.5 μg/m3 (the LML of the Pope 

et al. 2002 study), respectively. 

Key sources of uncertainty in the PM2.5 health impact assessment include:

 gaps in scientific data and inquiry;

 variability in estimated relationships, such as epidemiological effect estimates, 

introduced through differences in study design and statistical modeling;

 errors in measurement and projection for variables such as population growth rates;

 errors due to misspecification of model structures, including the use of surrogate 

variables, such as using PM10 when PM2.5 is not available, excluded variables, and 

simplification of complex functions; 

 biases due to omissions or other research limitations; and

 additional uncertainties from benefits transfer method using BPT estimates.

In Table 5-4 we summarize some of the key uncertainties in the benefits analysis.
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Table 5-4. Primary Sources of Uncertainty in the Benefits Analysis

1. Uncertainties Associated with Impact Functions

 The value of the ozone or PM effect estimate in each impact function.

 Application of a single impact function to pollutant changes and populations in all locations.

 Similarity of future-year impact functions to current impact functions.

 Correct functional form of each impact function.

 Extrapolation of effect estimates beyond the range of ozone or PM concentrations observed in the source 

epidemiological study.

 Application of impact functions only to those subpopulations matching the original study population.

2. Uncertainties Associated with CAMx-Modeled Ozone and PM Concentrations 

 Responsiveness of the models to changes in precursor emissions from the control policy.

 Projections of future levels of precursor emissions, especially ammonia and crustal materials.

 Lack of ozone and PM2.5 monitors in all rural areas requires extrapolation of observed ozone data from 

urban to rural areas.

3. Uncertainties Associated with PM Mortality Risk

 Limited scientific literature supporting a direct biological mechanism for observed epidemiological 

evidence.

 Direct causal agents within the complex mixture of PM have not been identified.

 The extent to which adverse health effects are associated with low-level exposures that occur many times 

in the year versus peak exposures.

 The extent to which effects reported in the long-term exposure studies are associated with historically 

higher levels of PM rather than the levels occurring during the period of study.

 Reliability of the PM2.5 monitoring data in reflecting actual PM2.5 exposures.

4. Uncertainties Associated with Possible Lagged Effects

 The portion of the PM-related long-term exposure mortality effects associated with changes in annual PM 

levels that would occur in a single year is uncertain as well as the portion that might occur in subsequent 

years.

5. Uncertainties Associated with Baseline Incidence Rates

 Some baseline incidence rates are not location specific (e.g., those taken from studies) and therefore may 

not accurately represent the actual location-specific rates.

 Current baseline incidence rates may not approximate well baseline incidence rates in 2016.

 Projected population and demographics may not represent well future-year population and demographics.

6. Uncertainties Associated with Economic Valuation

 Unit dollar values associated with health and welfare endpoints are only estimates of mean WTP and 

therefore have uncertainty surrounding them.

 Mean WTP (in constant dollars) for each type of risk reduction may differ from current estimates because 

of differences in income or other factors.

7. Uncertainties Associated with Aggregation of Monetized Benefits

 Health and welfare benefits estimates are limited to the available impact functions. Thus, unquantified or 

unmonetized benefits are not included.

PM2.5 mortality benefits represent a substantial proportion of total monetized cobenefits (over 90%), and these estimates have following key assumptions and uncertainties. 

1. The PM2.5-related co-benefits were derived through a benefit per-ton approach, 

which does not fully reflect local variability in population density, meteorology,

exposure, baseline health incidence rates, or other local factors that might lead to 
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an over-estimate or under-estimate of the actual co-benefits of controlling PM 

precursors. In addition, differences in the distribution of emissions reductions 

across states between the modeled scenario and the final rule scenario add

uncertainty to the final benefits estimates.

2. 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 via transported precursors emitted from EGUs may differ 

significantly from direct PM2.5 released from diesel engines and other industrial 

sources, but the scientific evidence is not yet sufficient to allow differential effects 

estimates by particle type. 

3. We assume that the health impact function for fine particles is linear within the 

range of ambient concentrations under consideration. Thus, the estimates include 

health co-benefits from reducing fine particles in areas with varied concentrations of 

PM2.5, including both regions that are in attainment with fine particle standard and 

those that do not meet the standard down to the lowest modeled concentrations. 

5.4 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, effect coefficients, incidence rates and economic valuation. Our 

approach here is generally consistent with the Regulatory Impact Analysis for the Cross-State 

Air Pollution Rule (U.S. EPA, 2011b).

5.4.1 Demographic Data

Quantified and monetized human health impacts depend on the demographic 

characteristics of the population, including age, location, and income. We use projections based 

on economic forecasting models developed by Woods and Poole, Inc. (Woods and Poole, 2008).

The Woods and Poole (WP) database contains county-level projections of population by age, 

sex, and race out to 2030. 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:

1. First, national-level variables such as income, employment, and populations are 

forecasted.
5-21

2. Second, employment projections are made for 172 economic areas defined by the 

Bureau of Economic Analysis, 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.

3. 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.

4. 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 2016 based on historical rates of mortality, fertility, 

and migration.

5.4.2 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 EPA’s Integrated 

Science Assessments (which replace the Criteria Document), with input and advice from the 

EPA Science Advisory Board - Health Effects Subcommittee (SAB-HES), a scientific review panel 

specifically established to provide advice on the use of the scientific literature in developing 

benefits analyses for air pollution regulations (http://www.epa.gov/sab/). 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 which can be used to evaluate 

population-level impacts of reductions in ambient pollution levels in a health impact 

assessment.
5-22

For the data-derived estimates, we relied on the published scientific literature to 

ascertain the relationship between PM 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. The selection of C-R 

functions for the benefits analysis is guided by the goal of achieving a balance between 

comprehensiveness and scientific defensibility. In general, the use of results from more than a 

single study can provide a more robust estimate of the relationship between a pollutant and a 

given health effect. 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 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 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.

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. More recent 

studies 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.

(continued)
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Table 5-5. Criteria Used when Selecting C-R Functions (continued)

Consideration Comments

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 C-R 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, either 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.

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. 

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 Technical Appendices provides details of the procedures used to 

combine multiple impact functions (Abt Associates, 2010). In general, we used fixed or random 

effects models to pool estimates from different 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 
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determined whether the data suggest that we should reject this null hypothesis, in which case 

we would use the random effects model. Pooled impact functions are used to estimate hospital 

admissions and asthma exacerbations. For more details on methods used to pool incidence 

estimates, see the BenMAP Manual Appendices (Abt Associates, 2010), which are available with 

the BenMAP software at http://www.epa.gov/benmap.html.

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 primary analysis are drawn are 

included in Table 5-6. 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. Below we provide the basis for selecting these studies.

5.4.2.1 PM2.5 Premature Mortality Effect Coefficients

Both long- and short-term exposures to ambient levels of PM2.5 air pollution have been 

associated with increased risk of premature mortality. The size of the mortality risk 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.

Table 5-6. Health Endpoints and Epidemiological Studies Used to Quantify Health Impactsa

Endpoint Pollutant Study

Study 

Population

Premature Mortality

Premature mortality—

cohort study, all-cause

PM2.5

(annual avg)

Pope et al. (2002)

Laden et al. (2006)

>29 years

>25 years

Premature mortality, total 

exposures

PM2.5

(annual avg)

Expert Elicitation (Roman et al., 2008) >24 years

Premature mortality—allcausePM2.5

(annual avg)

Woodruff et al. (2006) Infant (<1 year)

(continued)
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Table 5-6. Health Endpoints and Epidemiological Studies Used to Quantify Health Impactsa

(continued)

Endpoint Pollutant Study

Study 

Population

Chronic Illness

Chronic bronchitis PM2.5

(annual avg)

Abbey et al. (1995) >26 years

Non-fatal heart attacks PM2.5

(24-hour avg)

Peters et al. (2001) Adults (>18 

years)

Hospital Admissions 

Respiratory PM2.5

(24-hour avg)

Pooled estimate:

Moolgavkar (2003)—ICD 490–496 (COPD)

Ito (2003)—ICD 490–496 (COPD)

>64 years

PM2.5

(24-hour avg)

Moolgavkar (2000)—ICD 490–496 (COPD) 20–64 years

PM2.5

(24-hour avg)

Ito (2003)—ICD 480–486 (pneumonia) >64 years

PM2.5

(24-hour avg)

Sheppard (2003)—ICD 493 (asthma) <65 years

Cardiovascular PM2.5

(24-hour avg)

Pooled estimate:

Moolgavkar (2003)—ICD 390–429 (all 

cardiovascular)

Ito (2003)—ICD 410–414, 427–428 (ischemic 

heart disease, dysrhythmia, heart failure)

>64 years

PM2.5

(24-hour avg)

Moolgavkar (2000)—ICD 390–429 (all 

cardiovascular)

20–64 years

Asthma-related ER visits PM2.5

(24-hour avg)

Norris et al. (1999) 0–18 years

Other Health Endpoints

Acute bronchitis PM2.5

(annual avg)

Dockery et al. (1996) 8–12 years

Upper respiratory symptoms PM10

(24-hour avg)

Pope et al. (1991) Asthmatics, 9–11 

years

Lower respiratory symptoms PM2.5

(24-hour avg)

Schwartz and Neas (2000) 7–14 years

Asthma exacerbations PM2.5

(24-hour avg)

Pooled estimate:

Ostro et al. (2001) (cough, wheeze and shortness 

of breath)

Vedal et al. (1998) (cough)

6–18 yearsb

(continued)
5-26

Table 5-6. Health Endpoints and Epidemiological Studies Used to Quantify Health Impactsa

(continued)

Endpoint Pollutant Study

Study 

Population

Work loss days PM2.5

(24-hour avg)

Ostro (1987) 18–65 years

Minor Restricted Activity 

Days (MRADs)

PM2.5

(24-hour avg)

Ostro and Rothschild (1989) 18–65 years

a

Studies or air quality metrics highlighted in blue represent updates incorporated since the 2005 CAIR RIA

b

The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. 

(1998) study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), we 

extended the applied population to 6 to 18, reflecting the common biological basis for the effect in children in 

the broader age group. See: U.S. Science Advisory Board. 2004. Advisory 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. EPASAB-COUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health 

Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press.

Although a number of uncertainties remain to be addressed by continued research 

(NRC, 2002), a substantial body of published scientific literature documents the correlation 

between elevated PM2.5 concentrations and increased mortality rates (U.S. EPA, 2009a). Timeseries 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 PM exposures over multiple years (i.e., long-term exposures) and 

community-level annual mortality rates. Researchers have found statistically significant 

associations between PM2.5 and premature mortality using both types of studies. In general, the 

risk estimates based on the cohort studies are larger than those derived from time-series 

studies. Cohort analyses are thought to better capture the full public health impact of exposure 

to air pollution over time, because they account for the effects of long-term exposures and 

possibly some component of short-term exposures (Kunzli et al., 2001; NRC, 2002). This section 

discusses some of the issues surrounding the estimation of PM2.5-related premature mortality. 

To demonstrate the sensitivity of the benefits estimates to the specific sources of information 

regarding the impact of PM2.5 exposures on the risk of premature death, we are providing 

estimates in our results tables based on studies derived from the epidemiological literature and 

from the EPA sponsored expert elicitation. The epidemiological studies from which these 

estimates are drawn are described below. The expert elicitation project and the derivation of 

effect estimates from the expert elicitation results are described in the 2006 PM2.5 NAAQS RIA 

and Roman et al. (2008). In the interest of brevity we do not repeat those details here. 
5-27

However, Figure 5-13 summarizes the estimated PM2.5-related premature mortalities avoided 

using risk estimates drawn from the expert elicitation.

Over a dozen epidemiological studies have found significant associations between 

various measures of long-term exposure to PM and elevated rates of annual mortality, 

beginning with Lave and Seskin (1977). Most of the published studies found positive (but not 

always statistically significant) associations with available PM indices such as total suspended 

particles (TSP). However, exploration of alternative model specifications sometimes raised 

questions about causal relationships (e.g., Lipfert et al., 1989). These early “ecological crosssectional” studies (Lave and Seskin, 1977; Ozkaynak and Thurston, 1987) were criticized for a 

number of methodological limitations, particularly for inadequate control at the individual level 

for variables that are potentially important in causing mortality, such as wealth, smoking, and 

diet.

Over the last 17 years, several studies using “prospective cohort” designs have been 

published that appear to be consistent with the earlier body of literature. These new 

“prospective cohort” studies reflect a significant improvement over the earlier work because 

they include individual level information with respect to health status and residence. The most 

extensive analyses have been based on data from two prospective cohort groups, often 

referred to as the Harvard “Six-Cities Study” (Dockery et al., 1993; Laden et al., 2006) and the 

“American Cancer Society or ACS study” (Pope et al., 1995; Pope et al., 2002; Pope et al., 2004, 

Krewski et al. 2009); these studies have found consistent relationships between fine particle 

indicators and premature mortality across multiple locations in the United States. A third major 

data set comes from the California-based 7th Day Adventist Study (e.g., Abbey et al., 1999), 

which reported associations between long-term PM exposure and mortality in men. Results 

from this cohort, however, have been inconsistent, and the air quality results are not 

geographically representative of most of the United States, and the lifestyle of the population is 

not reflective of much of the U.S. population. Analysis is also available for a cohort of adult 

male veterans diagnosed with hypertension has been examined (Lipfert et al., 2000; Lipfert 

et al., 2003, 2006). The characteristics of this group differ from the cohorts in the Six-Cities, 

ACS, and 7th Day Adventist studies with respect to income, race, health status, and smoking

status. Unlike previous long-term analyses, this study found some associations between 

mortality and ozone but found inconsistent results for PM indicators. Because of the selective 

nature of the population in the veteran’s cohort, we have chosen not to include any effect 

estimates from the Lipfert et al. (2000) study in our co-benefits assessment.
5-28

Given their consistent results and broad geographic coverage, and importance in 

informing the NAAQS development process, the Six-Cities and ACS data have been particularly 

important in benefits analyses. The credibility of these two studies is further enhanced by the 

fact that the initial published studies (Pope et al., 1995 and 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) (Krewski et al., 2000). The final results of the 

reanalysis were then independently peer reviewed by a Special Panel of the HEI Health Review 

Committee. The results of these reanalyses confirmed and expanded the conclusions of the 

original investigators. While the HEI reexamination lends credibility to the original studies, it 

also highlights sensitivities concerning the relative impact of various pollutants, such as SO2, the 

potential role of education in mediating the association between pollution and mortality, and 

the influence of spatial correlation modeling. Further confirmation and extension of the 

findings of the 1993 Six City Study and the 1995 ACS study were recently completed using more 

recent air quality and a longer follow-up period for the ACS cohort was published over the past 

several years (Pope et al., 2002, 2004; Laden et al., 2006, Krewski et al. 2009). The follow up to 

the Harvard Six City Study both confirmed the effect size from the first analysis and provided 

additional confirmation that reductions in PM2.5 are likely to result in reductions in the risk of 

premature death. This additional evidence stems from the observed reductions in PM2.5 in each 

city during the extended follow-up period. Laden et al. (2006) found that mortality rates 

consistently went down at a rate proportionate to the observed reductions in PM2.5.

A number of additional analyses have been conducted on the ACS cohort data (Jerrett et 

al., 2009; Pope et al., 2009). These studies have continued to find a strong significant 

relationship between PM2.5 and mortality outcomes and life expectancy. Specifically, much of 

the recent research has suggested a stronger relationship between cardiovascular mortality and 

lung cancer mortality with PM2.5, and a less significant relationship between respiratory-related 

mortality and PM2.5. The extended analyses of the ACS cohort data (Krewski et al. 2009) 

provides additional refinements to the analysis of PM-related mortality by (a) extend the 

follow-up period by 2 years to the year 2000, for a total of 18 years; (b) incorporate ecological., 

or neighborhood-level co-variates so as to better estimate personal exposure; (c) perform an 

extensive spatial analysis using land use regression modeling. These additional refinements may 

make this analysis well-suited for the assessment of PM-related mortality for EPA benefits 

analyses.

In developing and improving the methods for estimating and valuing the potential 

reductions in mortality risk over the years, EPA consulted with the SAB-HES. That panel 
5-29

recommended using long-term prospective cohort studies in estimating mortality risk reduction 

(U.S. EPA-SAB, 1999). This recommendation has been confirmed by a report from the National 

Research Council, which stated that “it is essential to use the cohort studies in benefits analysis 

to capture all important effects from air pollution exposure” (NRC, 2002, p. 108). More 

specifically, the SAB recommended emphasis on the ACS study because it includes a much 

larger sample size and longer exposure interval and covers more locations (e.g., 50 cities 

compared to the Six Cities Study) than other studies of its kind. Because of the refinements in 

the extended follow-up analysis, the SAB-HES recommended using the Pope et al. (2002) study 

as the basis for the primary mortality estimate for adults and suggests that alternate estimates 

of mortality generated using other cohort and time-series studies could be included as part of 

the sensitivity analysis (U.S. EPA-SAB, 2004a). The PM NAAQS Risk and Exposure Assessment 

(U.S. EPA, 2010c) utilized risk coefficients drawn from the Krewski et al. (2009) study. In a 

December of 2009 consultation with the SAB-HES, the Agency proposed utilizing the Krewski 

et al. (2009) extended analysis of the ACS cohort data. The panel is scheduled to issue an 

advisory in early 2010.

As noted above, since 2004 SAB review, an extended follow-up of the Harvard Six cities 

study has been published (Laden et al., 2006) and in recent RIAs (see for example the CrossState Air Pollution Rule RIA, U.S. EPA 2011b), we have included this estimate of mortality 

impacts based on application of the C-R function derived from this study. We use this specific 

estimate to represent the Six Cities study because it both reflects among the most up-to-date 

science and was cited by many of the experts in their elicitation responses. It is clear from the 

expert elicitation that the results published in Laden et al. (2006) are potentially influential, and 

in fact the expert elicitation results encompass within their range the estimates from both the 

Pope et al. (2002) and Laden et al. (2006) studies (see Figure 5-3). These are logical choices for 

anchor points in our presentation because, while both studies are well designed and peer 

reviewed, there are strengths and weaknesses inherent in each, which we believe argues for 

using both studies to generate benefits estimates.

5.4.2.2 Chronic Bronchitis (CB)

CB 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. CB affects an estimated 5 percent of the U.S. 

population (American Lung Association, 1999). A limited number of studies have estimated the 

impact of air pollution on new incidences of CB. Schwartz (1993) and Abbey et al. (1995)

provide evidence that long-term PM exposure gives rise to the development of CB in the United 

States. Because PM2.5 reductions are expected from MATS, this analysis uses only the Abbey et 
5-30

al. (1995) study, because it is the only study focusing on the relationship between PM2.5 and 

new incidences of CB.

5.4.2.3 Non-fatal Myocardial Infarctions (Heart Attacks)

Non-fatal heart attacks have been linked with short-term exposures to PM2.5 in the 

United States (Peters et al., 2001) and other countries (Poloniecki et al., 1997). We used a 

recent study by Peters et al. (2001) as the basis for the impact function estimating the 

relationship between PM2.5 and non-fatal heart attacks. Peters et al. is the only available U.S. 

study to provide a specific estimate for heart attacks. Other studies, such as Samet et al. (2000)

and Moolgavkar (2000), show a consistent relationship between all cardiovascular hospital 

admissions, including those for non-fatal heart attacks, and PM. Given the lasting impact of a 

heart attack on long-term health costs and earnings, we provide a separate estimate for nonfatal heart attacks. The estimate used in the MATS analysis is based on the single available U.S. 

effect estimate. The finding of a specific impact on heart attacks is consistent with hospital 

admission and other studies showing relationships between fine particles and cardiovascular 

effects both within and outside the United States. Several epidemiologic studies (Liao et al., 

1999; Gold et al., 2000; Magari et al., 2001) have shown that heart rate variability (an indicator 

of how much the heart is able to speed up or slow down in response to momentary stresses) is 

negatively related to PM levels. Heart rate variability is a risk factor for heart attacks and other 

coronary heart diseases (Carthenon et al., 2002; Dekker et al., 2000; Liao et al., 1997; Tsuji et 

al., 1996). As such, significant impacts of PM on heart rate variability are consistent with an 

increased risk of heart attacks.

5.4.2.4 Hospital and Emergency Room Admissions

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. Because of this, many of the hospital admission endpoints use pooled impact 

functions based on the results of a number of studies. In addition, some studies have examined 

the relationship between air pollution and emergency room visits. Since most emergency room 

visits do not result in an admission to the hospital (the majority of people going to the 

emergency room are treated and return home), we treat hospital admissions and emergency 

room visits separately, taking account of the fraction of emergency room visits that are 

admitted to the hospital.

The two main groups of hospital admissions estimated in this analysis are respiratory 

admissions and cardiovascular admissions. There is not much evidence linking ozone or PM 
5-31

with other types of hospital admissions. The only type of emergency room visits that have been 

consistently linked to ozone and PM in the United States are asthma-related visits.

To estimate avoided incidences of cardiovascular hospital admissions associated with 

PM2.5, we used studies by Moolgavkar (2003) and Ito (2003). Additional published studies show 

a statistically significant relationship between PM10 and cardiovascular hospital admissions.

However, given that the control options we are analyzing are expected to reduce primarily 

PM2.5, we focus on the two studies that examine PM2.5. Both of these studies provide an effect 

estimate for populations over 65, allowing us to pool the impact functions for this age group.

Only Moolgavkar (2000) provided a separate effect estimate for populations 20 to 64.2Total 

cardiovascular hospital admissions are thus the sum of the pooled estimate for populations 

over 65 and the single study estimate for populations 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.

To estimate total avoided incidences of respiratory hospital admissions, we used impact 

functions for several respiratory causes, including chronic obstructive pulmonary disease 

(COPD), pneumonia, and asthma. As with cardiovascular admissions, additional published 

studies show a statistically significant relationship between PM10 and respiratory hospital 

admissions. We used only those focusing on PM2.5. Both Moolgavkar (2000) and Ito (2003) 

provide effect estimates for COPD in populations over 65, allowing us to pool the impact 

functions for this group. Only Moolgavkar (2000) provides a separate effect estimate for 

populations 20 to 64. Total COPD hospital admissions are thus the sum of the pooled estimate 

for populations over 65 and the single study estimate for populations 20 to 64. Only Ito (2003) 

estimated pneumonia and only for the population 65 and older. In addition, Sheppard (2003)

provided an effect estimate for asthma hospital admissions for populations under age 65. Total 

avoided incidences of PM-related respiratory-related hospital admissions are the sum of COPD, 

pneumonia, and asthma admissions.

 

2

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 percent) 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.
5-32

To estimate the effects of PM air pollution reductions on asthma-related ER visits, we 

use the effect estimate from a study of children 18 and under by Norris et al. (1999). As noted 

earlier, there is another study by Schwartz examining a broader age group (less than 65), but 

the Schwartz study focused on PM10 rather than PM2.5. We selected the Norris et al. (1999) 

effect estimate because it better matched the pollutant of interest. Because children tend to 

have higher rates of hospitalization for asthma relative to adults under 65, we will likely capture 

the majority of the impact of PM2.5 on asthma emergency room visits in populations under 65, 

although there may still be significant impacts in the adult population under 65.

To estimate avoided incidences of respiratory hospital admissions associated with 

ozone, we used a number of studies examining hospital admissions for a range of respiratory 

illnesses, including pneumonia and COPD. Two age groups, adults over 65 and children under 2, 

were examined. For adults over 65, Schwartz (1995) provides effect estimates for two different 

cities relating ozone and hospital admissions for all respiratory causes (defined as ICD codes 

460–519). Impact functions based on these studies were pooled first before being pooled with 

other studies. Two studies (Moolgavkar et al., 1997; Schwartz, 1994a) examine ozone and 

pneumonia hospital admissions in Minneapolis. One additional study (Schwartz, 1994b) 

examines ozone and pneumonia hospital admissions in Detroit. The impact functions for 

Minneapolis were pooled together first, and the resulting impact function was then pooled with 

the impact function for Detroit. This avoids assigning too much weight to the information 

coming from one city. For COPD hospital admissions, two studies are available: Moolgavkar 

et al. (1997), conducted in Minneapolis, and Schwartz (1994b), conducted in Detroit. These two 

studies were pooled together. To estimate total respiratory hospital admissions for adults over 

65, COPD admissions were added to pneumonia admissions, and the result was pooled with the 

Schwartz (1995) estimate of total respiratory admissions. Burnett et al. (2001) is the only study 

providing an effect estimate for respiratory hospital admissions in children under 2.

We used two studies as the source of the concentration-response functions we used to 

estimate the effects of ozone exposure on asthma-related emergency room (ER) visits: Peel et 

al. (2005) and Wilson et al. (2005). We estimated the change in ER visits using the effect 

estimate(s) from each study and then pooled the results using the random effects pooling 

technique (see Abt, 2005). The Peel et al. (2005) study estimated asthma-related ER visits for all 

ages in Atlanta, using air quality data from 1993 to 2000. Using Poisson generalized estimating 

equations, the authors found a marginal association between the maximum daily 8-hour 

average ozone level and ER visits for asthma over a 3-day moving average (lags of 0, 1, and 2 

days) in a single pollutant model. Wilson et al. (2005) examined the relationship between ER 
5-33

visits for respiratory illnesses and asthma and air pollution for all people residing in Portland, 

Maine from 1998–2000 and Manchester, New Hampshire from 1996–2000. For all models used 

in the analysis, the authors restricted the ozone data incorporated into the model to the 

months ozone levels are usually measured, the spring-summer months (April through 

September). Using the generalized additive model, Wilson et al. (2005) found a significant 

association between the maximum daily 8-hour average ozone level and ER visits for asthma in 

Portland, but found no significant association for Manchester. Similar to the approach used to 

generate effect estimates for hospital admissions, we used random effects pooling to combine 

the results across the individual study estimates for ER visits for asthma. The Peel et al. (2005)

and Wilson et al. (2005) Manchester estimates were not significant at the 95 percent level, and 

thus, the confidence interval for the pooled incidence estimate based on these studies includes 

negative values. This is an artifact of the statistical power of the studies, and the negative 

values in the tails of the estimated effect distributions do not represent improvements in health 

as ozone concentrations are increased. Instead, these should be viewed as a measure of 

uncertainty due to limitations in the statistical power of the study. We included both hospital 

admissions and ER visits as separate endpoints associated with ozone exposure because our 

estimates of hospital admission costs do not include the costs of ER visits and most asthma ER 

visits do not result in a hospital admission.

5.4.2.5 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 ambient levels of 

ozone and PM. The sources for the effect estimates used to quantify these effects are described 

below.

Around 4 percent of U.S. children between the ages of 5 and 17 experience episodes of 

acute bronchitis annually (American Lung Association, 2002c). 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,3 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).

 

3

See http://www.nlm.nih.gov/medlineplus/ency/article/000124.htm, accessed January 2002. 
5-34

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).

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. Incidences 

of upper respiratory symptoms in asthmatic children aged 9 to 11 are estimated using an effect 

estimate developed from Pope et al. (1991).

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. Most studies examining school absence rates have found little or no association with 

PM2.5, but several studies have found a significant association between ozone levels and school 

absence rates. We used two recent studies, Gilliland et al. (2001) and Chen et al. (2000), to 

estimate changes in absences (school loss days) due to changes in ozone levels. The Gilliland et 

al. study estimated the incidence of new periods of absence, while the Chen et al. study 

examined absence on a given day. We converted the Gilliland estimate to days of absence by 

multiplying the absence periods by the average duration of an absence. We estimated an 

average duration of school absence of 1.6 days by dividing the average daily school absence 

rate from Chen et al. (2000) and Ransom and Pope (1992) by the episodic absence rate from 

Gilliland et al. (2001). This provides estimates from Chen et al. (2000) and Gilliland et al. (2001), 

which can be pooled to provide an overall estimate.

Minor Restricted Activity Days (MRAD) occur 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 and ozone on MRAD was estimated using an effect 

estimate derived from Ostro and Rothschild (1989).

For this analysis, we have followed the SAB-HES recommendations regarding asthma 

exacerbations in developing the primary estimate. To prevent double-counting, we focused the 

estimation on asthma exacerbations occurring in children and excluded adults from the 
5-35

calculation.4Asthma exacerbations occurring in adults are assumed to be captured in the 

general population endpoints such as work loss days and MRADs. Consequently, if we had 

included an adult-specific asthma exacerbation estimate, we would likely double-count 

incidence for this endpoint. However, because the general population endpoints do not cover 

children (with regard to asthmatic effects), an analysis focused specifically on asthma 

exacerbations for children (6 to 18 years of age) could be conducted without concern for 

double-counting.

To characterize asthma exacerbations in children, we selected two studies (Ostro et al., 

2001; Vedal et al., 1998) that followed panels of asthmatic children. Ostro et al. (2001) followed 

a 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). Vedal et al. (1998) followed a group of elementary school children, including 74 

asthmatics, located on the west coast of Vancouver Island for 18 months including 

measurements of daily peak expiratory flow (PEF) and the tracking of respiratory symptoms 

(e.g., cough, phlegm, wheeze, chest tightness) through the use of daily diaries. Association 

between PM10 and respiratory symptoms for the asthmatic population was only reported for 

two endpoints: cough and PEF. Because it is difficult to translate PEF measures into clearly 

 

4

Estimating asthma exacerbations associated with air pollution exposures is difficult, due to concerns about 

double counting of benefits. Concerns over double counting stem from the fact that studies of the general 

population also include asthmatics, so estimates based solely on the asthmatic population cannot be directly 

added to the general population numbers without double counting. In one specific case (upper respiratory 

symptoms in children), the only study available is limited to asthmatic children, so this endpoint can be readily 

included in the calculation of total benefits. However, other endpoints, such as lower respiratory symptoms and 

MRADs, are estimated for the total population that includes asthmatics. Therefore, to simply add predictions of 

asthma-related symptoms generated for the population of asthmatics to these total population-based estimates 

could result in double counting, especially if they evaluate similar endpoints. The SAB-HES, in commenting on 

the analytical blueprint for 812, acknowledged these challenges in evaluating asthmatic symptoms and 

appropriately adding them into the primary analysis (SAB-HES, 2004). However, despite these challenges, the 

SAB-HES recommends the addition of asthma-related symptoms (i.e., asthma exacerbations) to the primary 

analysis, provided that the studies use the panel study approach and that they have comparable design and 

baseline frequencies in both asthma prevalence and exacerbation rates. Note also, that the SAB-HES, while 

supporting the incorporation of asthma exacerbation estimates, does not believe that the association between 

ambient air pollution, including ozone and PM, and the new onset of asthma is sufficiently strong to support 

inclusion of this asthma-related endpoint in the primary estimate. 
5-36

defined health endpoints that can be monetized, we only included the cough-related effect 

estimate from this study in quantifying asthma exacerbations. 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 pooled the separate 

incidence estimates for shortness of breath, wheeze, and cough generated using effect 

estimates from the Ostro et al. study, because each of these endpoints is aimed at capturing 

the same overall endpoint (asthma exacerbations) and there could be overlap in their 

predictions. The pooled estimate from the Ostro et al. study is then pooled with the coughrelated estimate generated using the Vedal study. The rationale for this second pooling step is 

similar to the first; both studies are attempting to quantify the same overall endpoint (asthma 

exacerbations).

5.4.3 Baseline Incidence 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 ppb decrease in daily ozone levels might, in turn, 

decrease hospital admissions by 3 percent. 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 5-7 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. Rates for mortality, hospitalizations, asthma 

ER visits, and non-fatal myocardial infarction (heart attacks) have been updated since the MATS 

Proposal RIA, consistent with the Cross-State Air Pollution Rule RIA (U.S. EPA 2011b).
5-37

Table 5-7. 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 2015

Age-, cause-, and countyspecific rateCDC Wonder (2004–2006)

U.S. Census bureau

Hospitalizations Daily hospitalization rate Age-, region-, state-, 

county- and causespecific rate

2007 HCUP data filesa

Asthma ER Visits Daily asthma ER visit rate Age-, region-, state-, 

county- and causespecific rate

2007 HCUP data filesa

Chronic Bronchitis Annual prevalence rate per 

person

 Aged 18–44

 Aged 45–64

 Aged 65 and older

0.0367

0.0505

0.0587

1999 NHIS (American Lung 

Association, 2002b, Table 4) 

Annual incidence rate per 

person

0.00378 Abbey et al. (1995, Table 3)

(continued)
5-38

Table 5-7. Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population (continued)

Endpoint Parameter

Rates

Value Source

Non-fatal Myocardial 

Infarction (heart 

attacks)

Daily non-fatal myocardial 

infarction incidence rate per 

person, 18+

Age-, region-, state-, and 

county- specific rate

2007 HCUP data filesa; 

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 dyspnea

0.076

0.067

0.037

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 

childrenb

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)

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. Bureau 

of the Census (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

Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level 

hospital and emergency department discharges for a variety of ICD codes.

b

Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and 

wheeze.

The baseline incidence rates for hospital and emergency department visits that we 

applied in this analysis are an improvement over the rates we used in the proposal analysis in 

two ways. First, these data are newer, and so are a more recent representation of the rates at 

which populations of different ages, and in different locations, visit the hospital and emergency 
5-39

department for illnesses that may be air pollution related. Second, these newer data are also 

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. Newer and more spatially resolved incidence rates 

are likely to yield a more reliable estimate of air pollution-related hospitalizations and 

emergency department visits. Consistent with the proposal RIA, we continue to use countylevel mortality rates. We have projected mortality rates such that future mortality rates are 

consistent with our projections of population growth (Abt Associates, 2010).

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 5-8 lists the prevalence rates used to determine the applicable 

population for asthma symptom endpoints. Note that these reflect current asthma prevalence 

and assume no change in prevalence rates in future years. These rates have all been updated 

since the MATS proposal RIA, consistent with the Cross-State Air Pollution Rule (U.S. EPA 

2011b).

Table 5-8. Asthma Prevalence Rates Used for this Analysisa

Population Group

Asthma Prevalence Rates

Value Source

All Ages 0.0780 American Lung Association (2010, Table 7)

< 18 0.0941

5–17 0.1070

18–44 0.0719

45–64 0.0745

65+ 0.0716

African American, 5 to 17 0.1776 American Lung Association (2010, Table 9)

African American, <18 0.1553 American Lung Associationb

a

See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/.

b

Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2009)

5.4.4 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 
5-40

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 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.5

WTP estimates generally are not available for some health effects, such as hospital 

admissions. In these cases, we used the cost of treating or mitigating the effect as a primary 

estimate. These cost-of-illness (COI) estimates generally 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 Tables 5-10 through 5-12. All values are in constant year 2006 dollars, adjusted for 

growth in real income out to 2016 using projections provided by Standard and Poor’s. Economic 

theory argues that WTP for most goods (such as environmental protection) will increase if real 

income increases. Many of the valuation studies used in this analysis were conducted in the late 

1980s and early 1990s. Because real income has grown since the studies were conducted, 

people’s willingness to pay for reductions in the risk of premature death and disease likely has 

grown as well. We did not adjust cost of illness-based values because they are based on current 

costs. Similarly, we did not adjust the value of school absences, because that value is based on 

current wage rates. For these two reasons, these cost of illness estimates may underestimate 

the economic value of avoided health impacts in 2016. The discussion below provides 

additional details on ozone and PM2.5-related related endpoints.

5.4.4.1 Mortality Valuation

Following the advice of the EEAC of the SAB, EPA currently uses the VSL approach in 

calculating the primary estimate of mortality co-benefits, because we believe this calculation 

 

5

To comply with Circular A-4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003). 

These benefits are estimated for a specific analysis year (i.e., 2016), and most of the PM benefits occur within 

that year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we 

assume 5 years of follow-up medical costs and lost wages. For premature mortality, we assume that there is a 

“cessation” lag between PM exposures and the total realization of changes in health effects. Although the 

structure of the lag is uncertain, 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). Changes in the lag assumptions do not change the total 

number of estimated deaths but rather the timing of those deaths. Therefore, discounting only affects the AMI 

costs after the analysis year and the valuation of premature mortalities that occur after the analysis year. As 

such, the monetized benefits using a 7% discount rate are only approximately 10% less than the monetized 

benefits using a 3% discount rate.
5-41

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 value of statistical life (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) meta-analysis 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) metaanalysis. However, the Agency neither changed its official guidance on the use of VSL in rulemakings nor subjected the interim estimate to a scientific peer-review process through the 

Science Advisory Board (SAB) or other peer-review group.

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 EPA and the SAB on combining estimates from the various 

data sources. In addition, the Agency consulted several times with the Science Advisory Board 

Environmental Economics Advisory Committee (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)

6 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 

 

6

In EPA’s recently revised Economic Guidelines (U.S. EPA, 2010d), 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. Therefore, this report does not represent final agency policy.
5-42

1991. The mean VSL across these studies is $6.3 million (2000$).7The 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.

The Agency anticipates presenting results from this effort to the SAB-EEAC in Spring 2010 and 

that draft guidance will be available shortly thereafter.

As indicated in the previous section on quantification of premature mortality benefits, 

we assumed for this analysis that some of the incidences of premature mortality related to PM 

exposures occur in a distributed fashion over the 20 years following exposure. To take this into 

account in the valuation of reductions in premature mortality, we applied an annual 3% 

discount rate to the value of premature mortality occurring in future years.8

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. 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, we use a single VSL for all reductions in mortality 

risk.

Although there are several differences between the labor market studies EPA uses to 

derive a VSL estimate and the PM air pollution context addressed here, those differences in the 

affected populations and the nature of the risks imply both upward and downward 

adjustments. Table 5-9 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, EPA believes it is reasonable to continue to use the $6.3 million value 

while acknowledging the significant limitations and uncertainties in the available literature.

 

7

In this analysis, we adjust the VSL to account for a different currency year (2007$) and to account for income 

growth to 2016. After applying these adjustments to the $6.3 million value, the VSL is $8.9M.

8

The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the 

federal government. EPA adopted a 3% discount rate for its base estimate in this case to reflect reliance on a 

“social rate of time preference” discounting concept. We have also calculated benefits and costs using a 7% rate 

consistent with an “opportunity cost of capital” concept to reflect the time value of resources directed to meet 

regulatory requirements. In this case, the benefit and cost estimates were not significantly affected by the 

choice of discount rate. Further discussion of this topic appears in EPA’s Guidelines for Preparing Economic 

Analyses (EPA, 2010).
5-43

Table 5-9. Expected Impact on Estimated Benefits of Premature Mortality Reductions of 

Differences Between Factors Used in Developing Applied VSL and Theoretically 

Appropriate VSL

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 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, 2000). In developing our primary estimate of the cobenefits of premature mortality reductions, we have followed this advice and discounted over 

the lag period between exposure and premature mortality.

Uncertainties Specific to Premature Mortality Valuation. The economic co-benefits

associated with reductions in the risk of premature mortality are the largest category of 

monetized co-benefits of the MATS. In addition, in prior analyses, EPA has identified valuation 

of mortality-related benefits as the largest contributor to the range of uncertainty in monetized 

benefits (U.S. EPA, 1999).9Because 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.

 

9

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 

PM mortality expert elicitation may result in different conclusions about the relative contribution of sources of 

uncertainty.
5-44

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 

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 
5-45

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).

 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. 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. 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.
5-46

 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). This 

worker must have either a higher workplace risk than the average worker, a lower 

risk tolerance than the average worker, or both. However, the risk estimate used in 

hedonic studies is generally based on average risk, so the VSL may be upwardly 

biased 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.

5.4.4.2 Chronic Bronchitis Valuation

The best available estimate of WTP to avoid a case of CB comes from Viscusi, Magat, 

and Huber (1991). The Viscusi, Magat, and Huber study, however, describes a severe case of CB 

to the survey respondents. We therefore employ an estimate of WTP to avoid a pollutionrelated case of CB, based on adjusting the Viscusi, Magat, and Huber (1991) estimate of the 

WTP to avoid a severe case. This is done to account for the likelihood that an average case of 

pollution-related CB is not as severe. The adjustment is made by applying the elasticity of WTP 

with respect to severity reported in the Krupnick and Cropper (1992) study. Details of this 

adjustment procedure are provided in the Benefits Technical Support Document (TSD) for the 

Nonroad Diesel rulemaking (Abt Associates, 2003).

We use the mean of a distribution of WTP estimates as the central tendency estimate of 

WTP to avoid a pollution-related case of CB in this analysis. The distribution incorporates 

uncertainty from three sources: the WTP to avoid a case of severe CB, as described by Viscusi, 

Magat, and Huber; the severity level of an average pollution-related case of CB (relative to that 

of the case described by Viscusi, Magat, and Huber); and the elasticity of WTP with respect to 

severity of the illness. Based on assumptions about the distributions of each of these three 

uncertain components, we derive a distribution of WTP to avoid a pollution-related case of CB 

by statistical uncertainty analysis techniques. The expected value (i.e., mean) of this 
5-47

distribution, which is about $340,000 (2006$), is taken as the central tendency estimate of WTP 

to avoid a PM-related case of CB.

5.4.4.3 Non-fatal Myocardial Infarctions Valuation

We were not able to identify a suitable WTP value for reductions in the risk of non-fatal

heart attacks. Instead, we use a COI unit value with two components: the direct medical costs 

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 present discounted value in lost 

earnings (in 2006$) over 5 years due to a myocardial infarction of $8,774 for someone between 

the ages of 25 and 44, $12,932 for someone between the ages of 45 and 54, and $74,746 for 

someone between the ages of 55 and 65. The corresponding age-specific estimates of lost 

earnings (in 2006$) using a 7% discount rate are $7,855, $11,578, and $66,920, 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:

 Wittels et al. (1990) estimated expected total medical costs of myocardial infarction 

over 5 years to be $51,211 (in 1986$) for people who were admitted to the hospital 

and survived hospitalization. (There does not appear to be any discounting used.)

Wittels et al. was used to value coronary heart disease in the 812 Retrospective 

Analysis of the Clean Air Act. Using the CPI-U for medical care, the Wittels estimate 

is $144,111 in year 2006$. 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.
5-48

However, this may include patients who died in the hospital (not included among 

our non-fatal myocardial infarction cases), 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 $44,663 in 1997$, or $64,003 in 

2006$ for myocardial infarction patients, using statistical prediction (regression) 

models to estimate inpatient costs. Only inpatient costs (physician fees and hospital 

costs) were included.

 Russell et al. (1998) estimated first-year direct medical costs of treating non-fatal

myocardial infarction of $15,540 (in 1995$) and $1,051 annually thereafter.

Converting to year 2006$, that would be $30,102 for a 5-year period (without 

discounting) or $38,113 for a 10-year period.

In summary, the three different studies provided significantly different values (see 

Table 5-10).

Table 5-10. Alternative Direct Medical Cost of Illness Estimates for Non-fatal Heart Attacks

Study Direct Medical Costs (2006$) Over an x-Year Period, for x =

Wittels et al. (1990) 144,111a5

Russell et al. (1998) 30,102b5

Eisenstein et al. (2001) 64,003b10

Russell et al. (1998) $38,113b10

a Wittels et al. (1990) did not appear to discount costs incurred in future years.

b

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 $65,902, and added it to the 5-year opportunity cost estimate. The resulting 

estimates are given in Table 5-11.
5-49

Table 5-11. Estimated Costs Over a 5-Year Period (in 2006$) of a Non-fatal Myocardial 

Infarction

Age Group

Cost, $

Opportunity MedicalaTotal

0–24 0 84,955 84,955

25–44 10,757b84,955 95,713

45–54 15,855b84,955 100,811

55–65 91,647b84,955 176,602

> 65 0 84,955 84,955

a

An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998).

b

From Cropper and Krupnick (1990), using a 3% discount rate.

5.4.5 Hospital Admissions Valuation

In the absence of estimates of societal WTP to avoid hospital visits/admissions for 

specific illnesses, estimates of total cost of illness (total medical costs plus the value of lost 

productivity) typically are used as conservative, or lower bound, estimates. These estimates are 

biased downward, because they do not include the willingness-to-pay value of avoiding pain 

and suffering.

The International Classification of Diseases (ICD-9, 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 (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 (AHRQ 2000). 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 1990 median weekly wage by five and inflated the result to year 2006$ using the 

CPI-U “all items.” The resulting estimate is $127.93. The total cost-of-illness estimate for an ICD 

code-specific hospital stay lasting n days, then, was the mean hospital charge plus $127.93 

multiplied by n.

5.4.5.1 Asthma-Related Emergency Room Visits Valuation

To value asthma emergency room 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 room visits in 1987, at a total 

cost of $186.5 million (1987$). The average cost per visit that year was $155; in 2006$, that cost 
5-50

was $400.88 (using the CPI-U for medical care to adjust to 2006$). The second estimate comes 

from Stanford et al. (1999), who reported the cost of an average asthma-related emergency 

room visit at $335.14, based on 1996–1997 data. A simple average of the two estimates yields a 

(rounded) unit value of $368.

5.4.5.2 Minor Restricted Activity Days Valuation

No studies are reported to have estimated WTP to avoid a minor restricted activity day. 

However, one of EPA’s contractors, IEc (1994) has 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. The IEc estimate 

of WTP to avoid a minor respiratory restricted activity day is $38.37 (1990$), or about $62.04 

(2006$).
5-51

Table 5-12. Unit Values for Economic Valuation of Health Endpoints (2006$)a

Health Endpoint

Central Estimate of Value Per 

Statistical Incidence, 

Income Level

2000 2016 Derivation of Distributions of Estimates

Premature Mortality (Value 

of a Statistical Life)

$6,300,000 $8,600,000 EPA currently recommends a central VSL of $6.3m 

(2000$) 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 5B of EPA’s current Guidelines for 

Preparing Economic Analyses (U.S. EPA, 2000). 

Chronic Bronchitis (CB) $340,000 $470,000 The WTP to avoid a case of pollution-related CB is 

calculated as where x is the severity of an average 

CB case, WTP13 is the WTP for a severe case of CB, 

and $ is the parameter relating WTP to severity, 

based on the regression results reported in 

Krupnick and Cropper (1992). The distribution of 

WTP for an average severity-level case of CB was 

generated by Monte Carlo methods, drawing 

from each of three distributions: (1) WTP to avoid 

a severe case of CB is assigned a 1/9 probability of 

being each of the first nine deciles of the 

distribution of WTP responses in Viscusi et al. 

(1991); (2) the severity of a pollution-related case 

of CB (relative to the case described in the Viscusi 

study) is assumed to have a triangular 

distribution, with the most likely value at severity 

level 6.5 and endpoints at 1.0 and 12.0; and (3) 

the constant in the elasticity of WTP with respect 

to severity is normally distributed with mean = 

0.18 and standard deviation = 0.0669 (from 

Krupnick and Cropper [1992]). This process and 

the rationale for choosing it is described in detail 

in the Costs and Benefits of the Clean Air Act, 

1990 to 2010 (U.S. EPA, 1999). 

(continued)
5-52

Table 5-12. Unit Values for Economic Valuation of Health Endpoints (2006$) (continued)

Health Endpoint

Central Estimate of Value Per 

Statistical Incidence, 

Income Level

2000 2016 Derivation of Distributions of Estimates

Non-fatal Myocardial 

Infarction (heart attack)

3% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–65

Age 66 and over

7% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–65

Age 66 and over

$79,685

$88,975

$93,897

$167,532

$79,685

$77,769

$87,126

$91,559

$157,477

$77,769

$79,685

$88,975

$93,897

$167,532

$79,685

$77,769

$87,126

$91,559

$157,477

$77,769

No distributional information available. Agespecific cost-of-illness values reflect lost earnings 

and direct medical costs over a 5-year period 

following a non-fatal 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:

age of onset: at 3% at 7%

25–44 $8,774 $7,855

45–54 $12,932 11,578

55–65 $74,746 66,920

Direct medical expenses: An average of:

1. Wittels et al. (1990) ($102,658—no 

discounting)

2. Russell et al. (1998), 5-year period ($22,331 

at 3% discount rate; $21,113 at 7% discount 

rate)

Hospital Admissions

Chronic Obstructive 

Pulmonary Disease (COPD)

$16,606 $16,606 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 COPD 

category illnesses) reported in Agency for 

Healthcare Research and Quality (2000)

(www.ahrq.gov). 

Asthma Admissions $8,900 $8,900 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 (2000) 

(www.ahrq.gov). 

(continued)
5-53

Table 5-12. Unit Values for Economic Valuation of Health Endpoints (2006$) (continued)

Health Endpoint

Central Estimate of Value Per 

Statistical Incidence, 

Income Level

2000 2016 Derivation of Distributions of Estimates

All Cardiovascular $24,668 $24,668 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 

(2000) (www.ahrq.gov). 

All respiratory (ages 65+) $24,622 $24,622 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 COPD 

category illnesses) reported in Agency for 

Healthcare Research and Quality, 2000 

(www.ahrq.gov).

All respiratory (ages 0–2) $10,385 $10,385 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 COPD 

category illnesses) reported in Agency for 

Healthcare Research and Quality, 2000 

(www.ahrq.gov).

Emergency Room Visits for 

Asthma

$384 $384 No distributional information available. Simple 

average of two unit COI values:

(1) $311.55, from Smith et al. (1997) and

(2) $260.67, from Stanford et al. (1999).

(continued)
5-54

Table 5-12. Unit Values for Economic Valuation of Health Endpoints (2006$) (continued)

Health Endpoint

Central Estimate of Value Per 

Statistical Incidence, 

Income Level

2000 2016 Derivation of Distributions of Estimates

Respiratory Ailments Not Requiring Hospitalization

Upper Respiratory 

Symptoms (URS)

$30 $30 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 $43.1.

Lower Respiratory 

Symptoms (LRS)

$16 $19 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 $24.46.

Asthma Exacerbations $43 $53 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 $15.6 and $70.8.

(continued)
5-55

Table 5-12. Unit Values for Economic Valuation of Health Endpoints (2006$) (continued)

Health Endpoint

Central Estimate of Value Per 

Statistical Incidence, 

Income Level

2000 2016 Derivation of Distributions of Estimates

Acute Bronchitis $360 $440 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. 

Work Loss Days (WLDs) Variable (U.S. 

median = 

$130)

Variable (U.S. 

median = 

$130)

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.

Minor Restricted Activity 

Days (MRADs)

$51 $62 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. 

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

Values reported in this table are in 2006$, but we used 2007$ for this analysis. Inflating to 2007$ would increase 

the values approximately 2.8% for WTP estimates up to 4.4% for COI estimates.

Although Ostro and Rothschild (1989) statistically linked ozone and minor restricted 

activity days, it is likely that most MRADs associated with ozone 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.

5.4.5.3 Growth in WTP Reflecting National Income Growth Over Time

Our analysis accounts for expected growth in real income over time. Economic theory 

argues that WTP for most goods (such as environmental protection) will increase if real incomes 
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increase. There is substantial empirical evidence that the income elasticity10 of 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 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. Empirical 

evidence of the effect of real income on WTP gathered to date is based on studies examining 

the former. The Environmental Economics Advisory Committee (EEAC) of the Science Advisory 

Board (SAB) advised 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). A recent advisory by another committee associated with the SAB, the Advisory Council 

on Clean Air Compliance Analysis, 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 (U.S. EPA-SAB, 2004b, p. 52)” and that “The same increase should be 

assumed for the WTP for serious non-fatal health effects (U.S. EPA-SAB, 2004b, p. 52),” 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, p. 53). Until these conflicting advisories 

have been reconciled, 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 

 

10 Income elasticity is a common economic measure equal to the percentage change in WTP for a 1% change in 

income.
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found in Kleckner and Neumann (1999). An abbreviated description of the procedure we used 

to account for WTP for real income growth between 1990 and 2016 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. 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. We also 

expect that the WTP for improved visibility in Class I areas would increase with growth in real 

income. 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, WTP 

is more elastic with respect to income. The elasticity values used to adjust estimates of benefits 

in 2016 are presented in Table 5-13.

Table 5-13. 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

Visibility 0.90

a

Derivation of estimates can be found in Kleckner and Neumann (1999) and Chestnut (1997). COI estimates are 

assigned an adjustment factor of 1.0.

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 
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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 2016, 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 2010.11 We used projections of 

real GDP (in chained 1996 dollars) provided by Standard and Poor’s (2000) for the years 2010 to 

2016.12

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 5-14. 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. Note that, for premature mortality, 

we applied the income adjustment factor to the present discounted value of the stream of 

avoided mortalities occurring over the lag period. Also note that because of a lack of data on 

the dependence of COI and income, and a lack of data on projected growth in average wages, 

no adjustments are made to benefits based on the COI approach or to work loss days and 

worker productivity. This assumption leads us to 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).

 

11 U.S. Bureau of Economic Analysis, Table 2A (1992$) (available at http://www.bea.doc.gov/bea/dn/0897nip2/ 

tab2a.htm.) and U.S. Bureau of Economic Analysis, Economics and Budget Outlook. Note that projections for 

2007 to 2010 are based on average GDP growth rates between 1999 and 2007.

12 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.
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Table 5-14. Adjustment Factors Used to Account for Projected Real Income Growtha

Benefit Category 2016

Minor Health Effect 1.06

Severe and Chronic Health Effects 1.19

Premature Mortality 1.16

Visibility 1.41

a

Based on elasticity values reported in Table 5-13, U.S. Census population projections, and projections of real 

GDP per capita.

5.5 Unquantified Health and Welfare Benefits

This analysis is limited by the available data and resources. As such, we are not able to 

quantify several welfare benefit categories, as shown in Tables 5-3 and 5-4. This section 

provides an overview of what is meant by ecosystem services as well as a description of 

visibility benefits, which are typically assessed and monetized in relevant RIAs but that were not 

quantified in this benefits analysis. The RIA for the final Cross-State Air Pollution Rule (U.S. EPA, 

2011b) provides more information on additional major health and welfare benefit categories 

associated with reducing NO2 and SO2 emissions including: health and ecosystem benefits of 

reducing nitrogen and sulfur emissions and deposition; vegetation benefits from reducing 

ozone; mercury benefits associated with reducing mercury emissions; and the role of sulfate 

deposition in mercury methylation. While we are unable to quantify these benefits, previous 

relevant EPA assessments show that these benefits could be substantial (U.S. EPA, 2008a; U.S. 

EPA, 2009a; U.S. EPA, 2007; U.S. EPA, 1999, U.S. EPA, 2011b). The omission of these endpoints 

from the monetized results should not imply that the impacts are small or unimportant. 

5.5.1 Visibility Valuation

Reductions in NO2 and SO2 emissions along with the secondary formation of PM2.5 would 

improve the level of visibility throughout the United States because these suspended particles 

and gases degrade visibility by scattering and absorbing light (U.S. EPA, 2009a). Visibility has 

direct significance to people’s enjoyment of daily activities and their overall sense of wellbeing 

(U.S. EPA, 2009a). Individuals value visibility both in the places they live and work, in the places 

they travel to for recreational purposes, and at sites of unique public value, such as the Great 

Smokey Mountains National Park. This section discusses the measurement of the economic 

benefits of improved visibility. As there is no analogous approach for estimating visibility

benefits using the BPT approach, visibility benefits are calculated for the modeled interim policy 

scenario only and are not included in the co-benefits estimate of the final policy. However, 
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since the magnitude of SO2 emission reductions did not significantly change in the visibility 

study areas between the interim and final emissions scenarios, we expect the visibility benefit 

for the final policy scenario would be similar to that calculated for the interim policy scenario 

($1.1 billion in total for the U.S., using 2007$; see Appendix 5C).

Visual air quality (VAQ) is commonly measured as either light extinction, which is 

defined as the loss of light per unit of distance in terms of inverse megameters (Mm-1) or the 

deciview (dv) metric (Pitchford and Malm, 1993), which is a logarithmic function of extinction.

Extinction and deciviews are 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. Pitchford and Malm characterize a change of one deciview as “a small but 

perceptible scenic change under many circumstances.” 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 (i.e. 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 5-3 for an illustration of the important factors affecting visibility.

In conjunction with the U.S. National Park Service, the U.S. Forest Service, other Federal 

land managers, and State organizations in the U.S., the U.S. EPA has supported visibility 

monitoring in national parks and wilderness areas since 1988. The monitoring network known 

as IMPROVE (Interagency Monitoring of Protected Visual Environments) now includes 150 sites 

that represent almost all of the Class I areas across the country (see Figure 5-4) (U.S. EPA, 

2009a).

Annual average visibility conditions (reflecting light extinction due to both 

anthropogenic and non-anthropogenic sources) vary regionally across the U.S. (U.S. EPA, 

2009a). The rural East generally has higher levels of impairment than remote sites in the West, 

with the exception of urban-influenced sites such as San Gorgonio Wilderness (CA) and Point 

Reyes National Seashore (CA), which have annual average levels comparable to certain sites in 

the Northeast (U.S. EPA, 2004). Higher visibility impairment levels in the East are due to 

generally higher concentrations of fine particles, particularly sulfates, and higher average 

relative humidity levels. While visibility trends have improved in most Class I areas, the recent 

data show that these areas continue to suffer from visibility impairment. In eastern parks, 
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average visual range has decreased from 90 miles to 15-25 miles, and in the West, visual range 

has decreased from 140 miles to 35-90 miles (U.S. EPA, 2004; U.S. EPA, 1999).

Figure 5-3. Important Factors Involved in Seeing a Scenic Vista (Malm, 1999)
5-62

Figure 5-4. Mandatory Class I Areas in the U.S.

EPA distinguishes benefits from two categories of visibility changes: residential visibility 

and recreational visibility. In both cases economic benefits are believed to 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 people’s beliefs that the environment ought to exist free of human-induced 

haze. Nonuse values may be more important for recreational areas, particularly national parks 

and monuments.

Residential visibility benefits are those that occur from visibility changes in urban, 

suburban, and rural areas. In previous assessments, EPA used a study on residential visibility 

valuation conducted in 1990 (McClelland et al., 1993). Subsequently, EPA designated the 

McClelland et al. study as significantly less reliable for regulatory benefit-cost analysis 

consistent with SAB advice (U.S. EPA-SAB, 1999). Although a wide range of published, peerreview literature supports a non-zero value for residential visibility (Brookshire et al., 1982; Rae, 

1983; Tolley et al., 1986; Chestnut and Rowe, 1990c; McClelland et al., 1993; Loehman et al., 

1994), the residential visibility benefits have not been calculated in this analysis.
5-63

For recreational visibility, only one existing study provides defensible monetary 

estimates of the value of visibility changes in a 1988 survey on recreational visibility value 

(Chestnut and Rowe, 1990a; 1990b). Although there are a number of other studies in the 

literature, they were conducted in the early 1980s and did not use methods that are considered 

defensible by current standards. The Chestnut and Rowe study uses the CV method. There has 

been a great deal of controversy and significant development of both theoretical and empirical 

knowledge about how to conduct CV surveys in the past decade. In EPA’s judgment, 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 benefits of visibility changes in 

recreational areas.13 This study serves as an essential input to our estimates of the benefits of 

recreational visibility improvements in the primary benefits estimates.

For the purposes of the analysis of the visibility benefits of the modeled interim policy 

(Appendix 5C), recreational visibility improvements are defined as those that occur specifically 

in federal Class I areas.14 A key distinction between recreational and residential benefits is that 

only those people living in residential areas are assumed to receive benefits from residential 

visibility, while all households in the United States are assumed to derive some benefit from 

improvements in Class I areas. Values are assumed to be higher if the Class I area is located 

close to their home.15 The Chestnut and Rowe study measured the demand for visibility in Class 

I areas managed by the National Park Service (NPS) in three broad regions of the country:

California, the Southwest, and the Southeast. 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 visibility levels in these photographs were later converted to deciviews for the current 

analysis. The survey data collected were used to estimate a WTP equation for improved 

visibility. In addition to the visibility change variable, the estimating equation also included 

household income as an explanatory variable.

 

13 In SAB advisory letter indicates that “many members of the Council believe that the Chestnut and Rowe study is 

the best available” (EPA-SAB-COUNCIL-ADV-00-002, 1999, p. 13). However, the committee did not formally 

approve use of these estimates because of concerns about the peer-reviewed status of the study. EPA believes 

the study has received adequate review and has been cited in numerous peer-reviewed publications (Chestnut 

and Dennis, 1997).

14 The Clean Air Act designates 156 national parks and wilderness areas as Class I areas for visibility protection.

15 For details of the visibility estimates discussed in this chapter, please refer to the Benefits TSD for the Nonroad 

Diesel rulemaking (Abt Associates, 2003).
5-64

The Chestnut and Rowe study did not measure values for visibility improvement in Class 

I areas outside the three regions. Their study covered 86 of the 156 Class I areas in the United 

States. We can infer the value of visibility changes in the other Class I areas by transferring 

values of visibility changes at Class I areas in the study regions. A complete description of the 

benefits transfer method used to infer values for visibility changes in Class I areas outside the 

study regions is provided in the Benefits TSD for the Nonroad Diesel rulemaking (Abt 

Associates, 2003).

The Chestnut and Rowe study (Chestnut and Rowe, 1990a; 1990b), although 

representing the best available estimates, has a number of limitations. These include the 

following:

 The age of the study (late 1980s) will increase the uncertainty about the 

correspondence of the estimated values to those that might be provided by current 

or future populations.

 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.

 There is an inherent difficulty in separating values expressed for visibility 

improvements from an overall value for improved air quality. The Chestnut and 

Rowe study 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 visibility at national 

parks entirely from residential visibility and health effects.

 It is not clear exactly what visibility improvements the respondents to the Chestnut 

and Rowe survey were valuing. The WTP question asked about changes in average 

visibility, but the survey respondents were shown photographs of only summertime 

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. For the purpose of the benefits analysis for this rule, EPA 

assumed that respondents provided values for changes in annual average visibility.

Because most policies will result in a shift in the distribution of visibility (usually 
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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.

 The Chestnut and Rowe survey focused on visibility improvements in and around 

national parks and wilderness areas. The survey also focused on visibility 

improvements of national parks in the southwest United States. Given that national 

parks and wilderness areas exhibit unique characteristics, it is not clear whether the 

WTP estimate obtained from Chestnut and Rowe can be transferred to other 

national parks and wilderness areas, without introducing additional uncertainty.

In general, the survey design and implementation reflect the period in which the survey 

was conducted. Since that time, many improvements to the stated preference methodology 

have been developed. As future survey efforts are completed, EPA will incorporate values for 

visibility improvements reflecting the improved survey designs.

The estimated relationship from the Chestnut and Rowe study is only directly applicable 

to the populations represented by survey respondents. EPA used benefits transfer methodology 

to extrapolate these results to the population affected by the reductions in precursor emissions 

associated with this rule. A general WTP equation for improved visibility (measured in 

deciviews) was developed as a function of the baseline level of visibility, the magnitude of the 

visibility improvement, and household income. The behavioral parameters of this equation 

were taken from analysis of the Chestnut and Rowe data. These parameters were used to 

calibrate WTP for the visibility changes resulting from this rule. The method for developing 

calibrated WTP functions is based on the approach developed by Smith et al. (2002). Available 

evidence indicates that households are willing to pay more for a given visibility improvement as 

their income increases (Chestnut, 1997). The benefits estimates here incorporate Chestnut’s 

estimate that a 1% increase in income is associated with a 0.9% increase in WTP for a given 

change in visibility. A more detailed explanation of the visibility benefits methodology is 

provided in Appendix I of the PM NAAQS RIA (U.S. EPA, 2006a).

One major source of uncertainty for the visibility benefits estimate is the benefits 

transfer process used. Judgments used to choose the functional form and key parameters of 

the estimating equation for WTP for the affected population could have significant effects on 
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the size 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 results.

In addition, our estimate of visibility benefits of the modeled interim policy in Appendix 

5C is incomplete. For example, we anticipate improvement in visibility in residential areas for 

which we are currently unable to monetize benefits, such as the Northeastern and Central 

regions of the U.S. The value of visibility benefits in areas where we were unable to monetize 

benefits could also be substantial. EPA requests public comment on the approach taken here to 

quantify the monetary value of changes in visibility in Class I areas.

5.5.2 Ecosystem Services

Ecosystem services can be generally defined as the benefits that individuals and 

organizations obtain from ecosystems. 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, 2006b). Figure 5-5 provides the Millennium Ecosystem 

Assessment’s schematic demonstrating the connections between the categories of ecosystem 

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).

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
5-67

Figure 5-5. Linkages Between Categories of Ecosystem Services and Components of Human 

Well-Being from Millennium Ecosystem Assessment (MEA, 2005)

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 

reductions in them (U.S. EPA, 2006b). 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, 2006b). 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 5-6.
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Figure 5-6. Schematic of the Benefits Assessment Process (U.S. EPA, 2006b)

5.5.3 Ecosystem Benefits of Reduced Nitrogen and Sulfur Deposition

5.5.3.1 Science of Deposition

Nitrogen and sulfur emissions 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, 2008b). Sulfur is primarily emitted as SO2, and nitrogen can be emitted as NO, NO2, or NH3.

Secondary particles are formed from NOx and SOX 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 Figure 5-7.

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.
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Figure 5-7. Schematics of Ecological Effects of Nitrogen and Sulfur Deposition

The 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 gas-phase species that lead to the 

dry deposition of acidity are also precursors of particles. Therefore, reductions in NO2 and SO2
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emissions will decrease both acid deposition and PM concentrations, but not necessarily in a 

linear fashion. (U.S. EPA, 2008b). Sulfuric acid is also deposited on surfaces by dry deposition 

and can contribute to environmental effects (U.S. EPA, 2008b).

5.5.3.2 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, 2008b). Major 

terrestrial effects include a decline in sensitive tree species, such as red spruce (Picea rubens) 

and sugar maple (Acer saccharum) (U.S. EPA, 2008b). 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, 2008b). 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, 2008b).

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, 2008b). 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, 2008b).

5.5.3.3 Aquatic Ecosystems

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. 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 factor16, changes in species composition and declines in aquatic species richness 

 

16 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, 2008b).
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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 5-15 provides a summary of the biological effects experienced at various ANC 

levels.

Table 5-15. 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.

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, 2008b). 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 5-8 illustrates those areas of the U.S. where 

aquatic ecosystems are at risk from acidification.
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Figure 5-8. Areas Potentially Sensitive to Aquatic Acidification (U.S. EPA, 2008b)

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.

Inland surface waters support several cultural services, including aesthetic and 

educational services and recreational fishing. Recreational fishing in lakes and streams is among 
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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 2006.17 For recreation days, consumer surplus value is most commonly 

measured using recreation demand, travel cost models.

Another estimate of the overarching ecological 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 

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, 2009b).18

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.

5.5.3.4 Terrestrial Ecosystems

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 

 

17 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.

18 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.
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restricts the ability of the plant to take up water and nutrients, especially calcium (U. S. EPA, 

2008b). 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, 

2008b).

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 

(2008b). Figure 5-9 depicts the areas across the U.S. that are potentially sensitive to terrestrial 

acidification.

Figure 5-9. Areas Potentially Sensitive to Terrestrial Acidification (U.S. EPA, 2008b)

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 
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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 5-10 shows the distribution of 

red spruce (brown) and sugar maple (green) in the eastern U.S.

Figure 5-10. Distribution of Red Spruce (Pink) and Sugar Maple (Green) in the Eastern U.S. 

(U.S. EPA, 2008b)

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).

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). Red spruce is also used in a variety of products 

including lumber, pulpwood, poles, plywood, and musical instruments. The total removal of red 
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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), 

recreational, and aesthetic services. Red spruce forests are home to two federally listed species 

and one delisted species:

 Spruce-fir moss spider (Microhexura montivaga)—endangered

 Rock gnome lichen (Gymnoderma lineare)—endangered

 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.

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, 
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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 

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.19

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.

5.5.4 Ecological 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.

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 

 

19 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.
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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 nitrogen than 

vascular plants (U.S. EPA, 2008b). 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, 

2008b). 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).

In addition to the role of sulfate deposition on methylation, the technologies installed to 

reduce emissions of NOx and SO2 associated with this rule would also reduce mercury 

emissions. EPA recently commissioned an information collection request that will soon provide 

greatly improved power industry mercury emissions estimates that will enable the Agency to 

better estimate mercury emissions changes from its air emissions control actions. For this 

reason, the Agency did not estimate Hg changes in this rule and will instead wait for these new 

data which will be available in the near future. Due to time and resource limitations, we were 

unable in any event to model mercury dispersion, deposition, methylation, bioaccumulation in 

fish tissue, and human consumption of mercury-contaminated fish that would be needed in 

order to estimate the human health benefits from reducing these mercury emissions.

5.5.5 Nitrogen Enrichment

5.5.5.1 Aquatic Enrichment

One of the main adverse ecological effects resulting from N 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 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).
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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 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 

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 
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$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). 20 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. 21

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.

5.5.5.2 Terrestrial Enrichment

Terrestrial enrichment occurs when terrestrial ecosystems receive N loadings in excess 

of natural background levels, either through atmospheric deposition or direct application.

Evidence presented in the Integrated Science Assessment (U.S. EPA, 2008b) 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.

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 

 

20 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.

21 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.
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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. 22 However, the contribution of N deposition to these losses is still uncertain.

5.5.6 Benefits of Reducing Ozone Effects on Vegetation and Ecosystems

Ozone causes discernible injury to a wide array of vegetation (U.S. EPA, 2006c; Fox and 

Mickler, 1996). 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, 2006c). Studies 

have demonstrated repeatedly that 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, 

2006c; 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, 2006c).

 

22 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.
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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). Because ozone damage can consist of visible 

injury to leaves, it can also reduce 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 

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) 

(U.S. EPA, 2006c; Winner, 1994). After injuries have occurred, plants may be capable of 

repairing the damage to a limited extent (U.S. EPA, 2006c). 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, 2006c). 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, 2006c, McBride et al., 1985; Miller et al., 1982). It is not yet possible to 

predict ecosystem responses to ozone with much certainty; however, considerable knowledge 

of potential ecosystem responses has been acquired through long-term observations in highly 

damaged forests in the United States (U.S. EPA, 2006c).
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5.5.6.1 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, 2006c). 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, 2007).

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 examines ozone injury to ozone-sensitive plant species 

at ground monitoring sites in forestland across the country (excluding woodlots and urban 

trees). FIA looks for damage on the foliage of ozone-sensitive forest plant species at each site 

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 injury is typically 

highest.

Monitoring of ozone injury to plants by the USDA Forest Service has expanded over the 

last 10 years from monitoring sites in 10 states in 1994 to nearly 1,000 monitoring sites in 41 

states in 2002. The data underlying the indictor in Figure 5-11 are based on averages of all 

observations collected in 2002, the latest year for which data are publicly available at the time 

the study was conducted, and are broken down by U.S. EPA Regions. 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, 2006c; 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.
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Figure 5-11. Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002

Assessing the impact of ground-level ozone on forests in the eastern United States 

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 ozoneexposure/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 magnitude of the effect may be higher or lower depending on 

the tree species (Chappelka and Samuelson, 1998). In areas where certain ozone-sensitive 
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species dominate the forest community, the biomass loss from ozone can be significant.

Significant biomass loss can be defined as a more than 2% annual biomass loss, which would 

cause long term ecological harm as the short-term negative effects on seedlings compound to 

affect long-term forest health (Heck, 1997).

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, 2007). Other common tree species, such as oak (Quercus spp.) and 

hickory (Carya spp.), are not as sensitive to ozone. Consequently, with knowledge of the 

distribution of sensitive species and the level of ozone at particular locations, it is possible to 

estimate a “biomass loss” for each species across their range. As shown in Figure 5-12, current 

ambient levels of ozone are associated with significant biomass loss across large geographic 

areas (U.S. EPA, 2009b). However, this information is unavailable for this rule.

Figure 5-12. Estimated Black Cherry, Yellow Poplar, Sugar Maple, Eastern White Pine, Virginia 

Pine, Red Maple, and Quaking Aspen Biomass Loss due to Current Ozone Exposure, 2006-

2008 (U.S. EPA, 2009b)
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To estimate the biomass loss for forest ecosystems across the eastern United States, the 

biomass loss for each of the seven tree species was calculated using the three-month, 12-hour 

W126 exposure metric at each location, along with each tree’s individual C-R functions. The 

W126 exposure metric was calculated using monitored ozone data from CASTNET and AQS 

sites, and a three-year average was used to mitigate the effect of variations in meteorological 

and soil moisture conditions. The biomass loss estimate for each species was then multiplied by 

its prevalence in the forest community using the U.S. Department of Agriculture (USDA) Forest 

Service IV index of tree abundance calculated from Forest Inventory and Analysis (FIA) 

measurements (Prasad, 2003). Sources of uncertainty include the ozone-exposure/plantresponse functions, the tree abundance index, and other factors (e.g., soil moisture). Although 

these factors were not considered, they can affect ozone damage (Chappelka, 1998).

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 

and educational services that may be impacted 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 (Felzer et al., 2005).

5.5.6.2 Ozone Effects on Crops and Urban Ornamentals

Laboratory and field experiments have also 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, 

2007). 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, 2006c). In addition, economic studies have shown 

reduced economic benefits as a result of predicted reductions in crop yields, directly affecting 
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the amount and quality of the provisioning service provided by the crops in question, 

associated with observed ozone levels (Kopp et al., 1985; Adams et al., 1986; Adams et al., 

1989). According to the Ozone Staff Paper, there has been no evidence that crops are becoming 

more tolerant of ozone (U.S. EPA, 2007). Using the Agriculture Simulation Model (AGSIM) 

(Taylor, 1994) to calculate the agricultural benefits of reductions in ozone exposure, U.S. EPA 

estimated that meeting a W126 standard of 21 ppm-hr would produce monetized benefits of 

approximately $160 million to $300 million (inflated to 2006 dollars) (U.S. EPA, 2007). 23

Urban ornamentals are an additional vegetation category likely to experience some 

degree of negative effects associated with exposure to ambient ozone levels. Because ozone 

causes visible foliar injury, the aesthetic value of ornamentals (such as petunia, geranium, and 

poinsettia) in urban landscapes would be reduced (U.S. EPA, 2007). 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, 2007). In addition, many businesses rely on healthy-looking vegetation for 

their livelihoods (e.g., horticulturalists, landscapers, Christmas tree growers, farmers of leafy 

crops, etc.) and a variety of ornamental species have been listed as sensitive to ozone (Abt 

Associates, 2010). The ornamental landscaping industry is valued at more than $30 billion 

(inflated to 2006 dollars) annually, by both private property owners/tenants and by 

governmental units responsible for public areas (Abt Associates, 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 the absence of adequate exposure-response functions 

and economic damage functions for the potential range of effects relevant to these types of 

vegetation, we cannot conduct a quantitative analysis to estimate these effects.

5.5.7 Unquantified SO2 and NO2-Related Human Health Benefits

Following an extensive evaluation of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment for Sulfur Dioxide 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 concentration-response 

 

23 These 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.
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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 

time constraints.

Epidemiological researchers have associated NO2 exposure with adverse health effects 

in numerous toxicological, clinical and epidemiological studies, as described in the Integrated 

Science Assessment for Oxides of Nitrogen - Health Criteria (Final Report) (U.S. EPA, 2008c). 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”

(ISA, section 5.3.2.1). These epidemiologic and experimental studies encompass a number of 

endpoints including [Emergency Department (ED)] 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 (ISA, section 5.4). 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 co-benefits due to time constraints.

5.6 Social Cost of Carbon and Greenhouse Gas Co-Benefits

EPA has assigned a dollar value to reductions in carbon dioxide (CO2) emissions using 

recent estimates of the “social cost of carbon” (SCC). The SCC is an estimate of the monetized 

damages associated with an incremental increase in carbon emissions in a given year. It is 
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intended to include (but is not limited to) changes in net agricultural productivity, human 

health, property damages from increased flood risk, and the value of ecosystem services due to 

climate change. The SCC estimates used in this analysis were developed through an interagency 

process that included EPA and other executive branch entities, and concluded in February 2010.

EPA first used these SCC estimates in the benefits analysis for the final joint EPA/DOT 

Rulemaking to establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate 

Average Fuel Economy Standards; see the rule’s preamble for discussion about application of 

SCC (75 FR 25324; 5/7/10). The SCC Technical Support Document (SCC TSD) provides a 

complete discussion of the methods used to develop these SCC estimates.24

The interagency group selected four SCC values for use in regulatory analyses, which we 

have applied in this analysis: $5.9, $24.3, $39, and $74.4 per metric ton of CO2 emissions25 in 

2016, in 2007 dollars. The first three values are based on the average SCC from three integrated 

assessment models, at discount rates of 2.5%, 3%, and 5%, respectively. SCCs at several 

discount rates are included because the literature shows that the SCC is quite sensitive to 

assumptions about the discount rate, and because no consensus exists on the appropriate rate 

to use in an intergenerational context. The fourth value is the 95th percentile of the SCC from 

all three models at a 3% discount rate. It is included to represent higher-than-expected impacts 

from temperature change further out in the tails of the SCC distribution. Low probability, high 

impact events are incorporated into all of the SCC values through explicit consideration of their 

effects in two of the three models as well as the use of a probability density function for 

equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high 

temperature outcomes, which in turn lead to higher projections of damages.

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that the interagency group estimated the growth rate of the SCC 

directly using the three integrated assessment models rather than assuming a constant annual 

 

24 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for Regulatory 

Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with 

participation by Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, 

Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection 

Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, 

Office of Science and Technology Policy, and Department of Treasury (February 2010). Also available at 

http://www.epa.gov/otaq/climate/regulations.htm

25 Note that upstream and downstream emission changes were not considered for this rule. For example, there 

may be changes in greenhouse gas emissions (in particular, methane) due to changes in fossil fuel extraction and 

transport in response to this proposal, but those emission changes were not quantified. 
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growth rate. This helps to ensure that the estimates are internally consistent with other 

modeling assumptions. The SCC estimates for the analysis year of 2016 in 2007$ are provided in 

Table 5-16.

Table 5-16. Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2016 (in 2007$)a

Discount Rate and Statistic SCC Estimate, $

5% Average 5.9

3% Average 24.3

2.5% Average 39.0

3% 95th percentile 74.4

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts.

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A recent report from the National 

Academies of Science (NRC 2009) points out that any assessment will suffer from uncertainty, 

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the 

effects of past and future emissions on the climate system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages. As a result, any effort to quantify and monetize the harms 

associated with climate change will raise serious questions of science, economics, and ethics 

and should be viewed as provisional.

The interagency group noted a number of limitations to the SCC analysis, including the 

incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding 

risk aversion. Current integrated assessment models do not assign value to all of the important 

physical, ecological, and economic impacts of climate change because models understandably 

lag behind the most recent research. The limited amount of research linking climate impacts to 

economic damages makes the interagency modeling exercise even more difficult. The 

interagency group hopes that over time researchers and modelers will work to fill these gaps 

and that the SCC estimates used for regulatory analysis by the federal government will continue 

to evolve with improvements in modeling. Additional details on these limitations are discussed 

in the SCC TSD.
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In light of these limitations, the interagency group has committed to updating the 

current estimates as the science and economic understanding of climate change and its impacts 

on society improves over time. Specifically, the interagency group has set a preliminary goal of 

revisiting the SCC values in the next few years or at such time as substantially updated models 

become available, and to continue to support research in this area.

Applying the global SCC estimates shown in Table 5-16 to the estimated reductions in 

annual CO2 emissions of 15 million metric tons for the policy scenario, we estimate the dollar 

value of the climate related co-benefits captured by the models for 2016 using three discount 

rates 5%, 3%, and 2.5% rather than 3% and 7%.26 These climate co-benefit estimates are 

provided in Table 5-17. The CO2 emission reductions associated with the policy scenario were 

developed using IPM and result largely from projected increases in electricity generation from 

natural gas sources and reductions in coal-fired generation by 2016. Even within the coal 

generation fleet, there are likely some modest generation shifts away from the least efficient 

units towards units that are more efficient to operate. These CO2 emission reductions are net of 

any CO2 emission increases associated with the energy usage for control technologies required 

by the rule.

Table 5-17. Monetized Co-Benefits of CO2 Emissions Reductions in 2016 (in millions of 

2007$)a,b,c,d

Discount Rate and Statistic SCC Estimate, $

5% Average 89

3% Average 360

2.5% Average 590

3% 95th percentile 1,100

a

All estimates have been rounded.

b

The SCC values are dollar-year and emissions-year specific. 

c

SCC values represent only a partial accounting of climate impacts.

d

Three discount rates are used to estimate the dollar value of the climate related co-benefits.

As noted above, there are a number of limitations associated with the SCC and its use to 

assess the climate benefits of regulations. Beyond the SCC’s incomplete treatment of impacts 

associated with CO2 emissions, it is important to note that SCC is limited to assessing the 

 

26 See SCC TSD for more information about discount rate selection. Also, it is possible that other benefits or costs 

of proposed regulations unrelated to CO2 emissions will be discounted at rates that differ from those used to 

develop the SCC estimates.
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climate benefits associated with changes in CO2 emissions only. However this rule will have an 

impact on the emissions of other pollutants that will affect the climate. These other pollutants 

include other greenhouse gases, aerosols and aerosols precursors such as black carbon, organic 

carbon, sulfur dioxide and nitrogen oxides, and ozone precursors such as nitrogen oxides and 

volatile organic carbon compounds. Changes in these pollutants (both increases and decreases) 

can be a direct result of changes in electricity generation, including but not limited to the 

changes in SO2, NOx, and filterable particulate matter identified in Chapter 3 of the RIA, but can 

also result from upstream changes in emissions due to changes in fossil fuel extraction and 

transport or downstream emission changes for secondary market impacts (not calculated for 

this rule). Reductions in black carbon or ozone precursors would lead to further cooling, but 

reductions in the other aerosol species and precursors would lead to warming. Therefore, 

changes in non-CO2 pollutants could potentially augment or offset the climate benefits 

calculated here. These pollutants can act in different ways and on different timescales than 

carbon dioxide. For example, aerosols reflect (and in the case of black carbon, absorb) incoming 

radiation, whereas greenhouse gases absorb outgoing infrared radiation. These aerosols can 

also affect climate indirectly by altering properties of clouds. Black carbon can also deposit on 

snow and ice, darkening these surfaces and accelerating melting. In terms of lifetime, while 

carbon dioxide emissions can increase concentrations in the atmosphere for hundreds to 

thousands of years, many of these other pollutants are short lived and remain in the 

atmosphere for short periods of time ranging from days to weeks and can therefore exhibit 

large spatial and temporal variability. The climate impacts of these other pollutants can be 

complex and have not been calculated for this rule.

5.7 Co-Benefits Results

Applying the impact and valuation functions described previously in this chapter to the 

estimated changes in ambient PM yields estimates of the changes in physical damages (e.g.,

premature deaths, hospital admissions). Since the air quality modeling performed for this RIA 

does not reflect the changes in emissions of PM2.5 precursors associated with the final 

emissions control requirements of the rule, we extrapolate the co-benefits of the final rule from 

the co-benefits of the air quality modeled emissions (see Appendices 5A and 5B). From these 

modeled co-benefits, we calculate BPT values for SO2 and direct PM (carbonaceous and crustal), 

separately for Eastern and Western states, following the general methodology described by 

Fann et al. (2009). We then apply the BPT values to the final emission changes associated with 

the revised policy scenario. Since the geographic distribution of emission changes did not 

change dramatically from the modeled emission scenarios to the final policy scenario, 

extrapolating co-benefits using the BPT approach reasonably approximates the co-benefits of 
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the final policy scenario. However, there is additional uncertainty in the extrapolated benefits 

estimates relative to the benefits estimated for the air quality modeled emissions.

This section summarizes the health co-benefits estimated for the final policy scenario in 

2016. Co-benefits associated with the modeled air quality changes are described in Appendix 

5C. Although extrapolating recreational visibility impacts to the final revised policy scenario is 

not possible, we estimate that visibility co-benefits add $1.1 billion to the total monetized 

benefits of the modeled interim policy scenario (see Appendix 5C). Visibility benefits are not 

included in the co-benefits estimate for the final policy. Table 5-18 presents health impacts 

among eastern and western states. Monetized values for both health and welfare endpoints are 

presented in Table 5-19. All monetary benefits are in constant-year 2007$. 

Not all known health and welfare co-benefits for non-HAP pollutants could be quantified 

or monetized in this analysis. The monetized value of these unquantified effects is represented 

by adding an unknown “B” to the aggregate total. The estimate of total monetized co-benefits

is thus equal to the subset of monetized PM- and CO2-related health and welfare co-benefits

plus B, the sum of the non-monetized health and welfare; this B represents both uncertainty 

and a bias in this analysis, as it reflects those co-benefits categories that we are unable quantify 

in this analysis.

This assessment estimates that in 2016 MATS will result in between 4,200 and 11,000

PM2.5-related avoided premature deaths annually. The total monetized health and climate cobenefits of MATS in 2016 are between $37 billion and $90 billion using a 3% discount rate and 

between $33 billion and $81 using a 7% discount rate. As shown in Appendix 5C, 95% of the 

health co-benefits result from reduced exposure to sulfate particles. Mortality co-benefits

account for approximately 93% to 97% of total monetized co-benefits depending on the PM2.5

estimates used, in part because we are unable to quantify most of the non-health co-benefits. 

The next largest benefit is for reductions in chronic illness (CB and non-fatal heart attacks), 

although this value is more than an order of magnitude lower than for premature mortality.

Hospital admissions for respiratory and cardiovascular causes, visibility, MRADs and work loss 

days account for the majority of the remaining co-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.

Figure 5-13 summarizes an array of PM2.5-related monetized co-benefits estimates 

based on alternative epidemiology and expert-derived PM-mortality estimate. A comparison of 

the incidence table to the monetary co-benefits table reveals that there is not always a close 
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correspondence between the number of incidences avoided for a given endpoint and the 

monetary value associated with that endpoint. For example, there are over 100 times more 

work loss days than premature mortalities, yet work loss days account for only a very small 

fraction of total monetized co-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 Table 5-19. 
5-95

Table 5-18. Estimated Reduction in Incidence of Adverse Health Effects of the Mercury and 

Air Toxics Standards in 2016 (95% confidence intervals)a,b

Impact Eastern U.S.c Western U.S. Total

Premature death

Pope et al. (2002) (age >30) 4,100

(1,100 – 7,000)

130

(30 – 220)

4,200

(1,200 – 7,200)

Laden et al. (2006) (age 

>25)

10,000

(4,800 – 16,000)

320

(140 – 510)

11,000

(5,000 – 17,000)

Infant (< 1 year) 19

(-21 – 59)

1

(-1 – 2)

20

(-22 – 61)

Chronic bronchitis 2,700

(89 – 5,400)

100

(-1 – 210)

2,800

(88 – 5,600)

Non-fatal heart attacks (age > 

18)

4,600

(1,200 – 8,100)

120

(25 – 210)

4,700

(1,200 – 8,300)

Hospital admissions—

respiratory (all ages)

820

(320 – 1,300)

17

(6 – 27)

830

(330 – 1,300)

Hospital admissions—

cardiovascular (age > 18)

1,800

(1,200 – 2,100)

42

(27 – 50)

1,800

(1,200 – 2,200)

Emergency room visits for 

asthma (age < 18)

3,000

(1,500 – 4,500)

110

(52 – 160)

3,100

(1,600 – 4,700)

Acute bronchitis (age 8-12) 6,000

(-1,400 – 13,000)

250

(-69 – 560)

6,300

(-1,400 – 14,000)

Lower respiratory symptoms 

(age 7-14)

77,000

(30,000 – 120,000)

3,100

(1,100 – 5,200)

80,000

(31,000 – 130,000)

Upper respiratory symptoms

(asthmatics age 9-18)

58,000

(11,000 – 110,000)

2,400

(360 – 4,400)

60,000

(11,000 – 110,000)

Asthma exacerbation

(asthmatics 6-18)

130,000

(4,500 – 430,000)

5,200

(-6 – 18,000)

130,000

(4,500 – 450,000)

Lost work days (ages 18-65) 520,000

(440,000 – 600,000)

21,000

(18,000 – 24,000)

540,000

(460,000 – 620,000)

Minor restricted-activity days

(ages 18-65)

3,100,000

(2,500,000 – 3,700,000)

120,000

(99,000 – 150,000)

3,200,000

(2,600,000 – 3,800,000)

a

Estimates rounded to two significant figures; column values will not sum to total value.

b

The negative estimates for certain endpoints are the result of the weak statistical power of the study used to 

calculate these health impacts and do not suggest that increases in air pollution exposure result in decreased 

health impacts.

c

Includes Texas and those states to the north and east.
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Table 5-19. Estimated Economic Value of Health and Welfare co-benefits of the Mercury and 

Air Toxics Standards in 2016 (95% confidence intervals, billions of 2007$)a

Impact Pollutant Eastern U.S.b Western U.S. Total

Adult premature death (Pope et al. 2002 PM mortality estimate)

3% discount rate PM2.5 $33

($2.6 - $99)

$1.0

(<$0.01 - $3.1)

$34

($2.6 - $100)

7% discount rate PM2.5 $30

($2.3 - $90)

$0.9

(<$0.01 - $2.8)

$30($2.4 - $92)

Adult premature death (Laden et al. 2006 PM mortality estimate)

3% discount rate PM2.5 $84

($7.4 - $240)

$2.6

($0.1 - $7.6)

$87

($7.5 - $250)

7% discount rate PM2.5 $76

($6.7 - $220)

$2.3

($0.1 - $6.9)

$78

($6.8 - $230)

Infant premature death PM2.5 $0.2

($-0.2 – $0.8)

<$0.01 $0.2

($-0.2 - $0.8)

Chronic Bronchitis PM2.5 $1.3

($0.1 - $6.1)

$0.1

(<$0.01 - $0.2)

$1.4

($0.1 - $6.4)

Non-fatal heart attacks 

3% discount rate PM2.5 $0.5

($0.1 - $1.3)

<$0.01 $0.5

($0.1 - $1.3)

7% discount rate PM2.5 $0.4

($0.1 - $1.0)

<$0.01 $0.4

($0.1 - $1.0)

Hospital admissions—

respiratory 

PM2.5 $0.01

(<$0.01 - $0.02)

<$0.01 $0.01

($0.01 - $0.02)

Hospital admissions—

cardiovascular 

PM2.5 $0.03

(<$0.01 - $0.05)

<$0.01 $0.03

(<$0.01 - $0.05)

Emergency room visits for 

asthma 

PM2.5 <$0.01 <$0.01 <$0.01

Acute bronchitis PM2.5 <$0.01 <$0.01 <$0.01

Lower respiratory symptoms PM2.5 <$0.01 <$0.01 <$0.01

Upper respiratory symptoms PM2.5 <$0.01 <$0.01 <$0.01

Asthma exacerbation PM2.5 <$0.01 <$0.01 <$0.01

Lost work days PM2.5 $0.1

($0.1 - $0.1)

<$0.01 $0.1

($0.1 - $0.1)

Minor restricted-activity days PM2.5 $0.2

($0.1 - $0.3)

<$0.01 $0.2

($0.1 - $0.3)

CO2-related co-benefits (3% 

discount rate)

CO2 $0.36

(continued)
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Table 5-19. Estimated Economic Value of Health and Welfare co-benefits of the Mercury and 

Air Toxics Standards in 2016 (95% confidence intervals, billions of 2007$)a

(continued)

Total Monetized co-benefits (Pope et al. 2002 PM2.5 mortality estimate)

3% discount rate $35+B

($2.8 - $110)

$1.1+B

($0.03 - $3.4)

$37+B

($3.2 - $110)

7% discount rate $32+B

($2.5 - $98)

$1.0+B

($0.03 - $3.1)

$33+B

($2.9 - $100)

Total Monetized Benefits (Laden et al. 2006 PM2.5 mortality estimate)

3% discount rate $87+B

($7.5 - $250)

$2.7+B

($0.1 - $7.9)

$90+B

($8.0 - $260)

7% discount rate $78+B

($6.8 - $230)

$2.4+B

($0.1 - $7.2)

$81+B

($7.3 - $240)

a

Estimates rounded to two significant figures. The negative estimates for certain endpoints are the result of the 

weak statistical power of the study used to calculate these health impacts and do not suggest that increases in 

air pollution exposure result in decreased health impacts. Confidence intervals reflect random sampling error 

and not the additional uncertainty associated with co-benefits scaling described above. The net present value of 

reduced CO2 emissions are calculated differently than other co-benefits. The same discount rate used to 

discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present 

value of SCC for internal consistency. This table shows monetized CO2 co-benefits at discount rates at 3 and 7 

percent that were calculated using the global average SCC estimate at a 3% discount rate because the 

interagency workgroup on this topic deemed this marginal value to be the central value. In Section 5.6 we also 

report CO2 co-benefits using discount rates of 5 percent (average), 2.5 percent (average), and 3 percent (95th

percentile).

b

Includes Texas and those states to the north and east.

PM2.5 mortality benefits represent a substantial proportion of total monetized cobenefits (over 90%), and these estimates have the following key assumptions and uncertainties. 

1. The PM2.5-related co-benefits were derived through a benefit per-ton approach, 

which does not fully reflect local variability in population density, meteorology, 

exposure, baseline health incidence rates, or other local factors that might lead to 

an over-estimate or under-estimate of the actual co-benefits of controlling PM 

precursors. In addition, differences in the distribution of emissions reductions 

across states between the modeled scenario and the final rule scenario add 

uncertainty to the final benefits estimates.

2. This rule is expected to reduce emissions of SO2, NOx, and directly emitted PM2.5. 

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 via transported precursors emitted from EGUs may differ 

significantly from direct PM2.5 released from diesel engines and other industrial 
5-98

sources, but the scientific evidence is not yet sufficient to allow differential effects 

estimates by particle type. 

3. We assume that the health impact function for fine particles is linear within the 

range of ambient concentrations under consideration. Thus, the estimates include 

health co-benefits from reducing fine particles in areas with varied concentrations of 

PM2.5, including both regions that are in attainment with fine particle standard and 

those that do not meet the standard down to the lowest modeled concentrations. 

Based on our review of the current body of scientific literature, EPA estimated PMrelated mortality without applying an assumed concentration threshold. EPA’s Integrated 

Science Assessment for Particulate Matter (U.S. EPA, 2009a), which was reviewed by EPA’s 

Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 2009b), concluded 

that the scientific literature consistently finds that a no-threshold log-linear model most 

adequately portrays the PM-mortality concentration-response relationship while also 

recognizing potential uncertainty about the exact shape of the concentration-response 

function. Consistent with this finding, we incorporated a “Lowest Measured Level” (LML) 

assessment, which is a method EPA has employed in several recent RIA’s including the CrossState Air Pollution Rule (U.S. EPA, 2011b).

This approach summarizes the distribution of avoided PM mortality impacts according 

to the baseline (i.e. pre-MATS) PM2.5 levels experienced by the population receiving the PM2.5

mortality benefit (Figures 5-14 and 5-15). We identify on this figure the lowest air quality levels 

measured in each of the two primary epidemiological studies EPA used to quantify PM-related 

mortality. This information allows readers to determine the portion of PM-related premature 

deaths avoided occurring at or above the LML of each study; in general, our confidence in the 

estimated PM-related premature deaths avoided decreases as we consider air quality levels 

further below the LML in the two epidemiological studies. While the LML analysis provides 

some insight into the level of uncertainty in the estimated PM mortality co-benefits, EPA does 

not view the LML as a threshold and continues to quantify PM-related mortality impacts using a 

full range of modeled air quality concentrations. 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, 2010e), 

which is provided in Appendix 5E of this RIA. While this figure describes the relationship 

between baseline PM2.5 exposure and avoided premature deaths for the modeled air quality 

scenario, we expect the distribution of mortality impacts to be fairly similar between the two 

cases.
Figure 5-13. Economic Value of Estimated PM

and Air Toxics Standards in 2016 

Risk Estimatea,b

a

Based on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appen

5A).

b

Column total equals sum of PM2.5-related mortality and morbidity

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Economic Value of Estimated PM2.5-Related Health co-benefits of the Mercury 

in 2016 According to Epidemiology or Expert-Derived PM Mortality 

sed on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appen

related mortality and morbidity co-benefits.

of the Mercury 

Derived PM Mortality 

sed on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appendix 
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Figure 5-14. Percentage of Total PM-Related Mortalities of the Mercury and Air Toxics 

Standards in 2016 Avoided by Baseline Air Quality Levela

a

Based on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appendix 

5A)

Some proportion of the avoided PM-related impacts we estimate in this analysis occur 

among populations exposed at or above the LML of the Laden et al. (2006) study, while a 

majority of the impacts occur at or above the LML of the Pope et al. (2002) study (Figure 5-14), 

increasing our confidence in the PM-related premature mortality analysis. Based on the 

modeled interim baseline which is approximately equivalent to the final baseline (see Appendix 

5A), 11% and 73% of the estimated avoided premature deaths occur at or above an annual 

mean PM2.5 level of 10 μg/m3(the LML of the Laden et al. 2006 study) and 7.5 μg/m3 (the LML 

of the Pope et al. 2002 study), respectively. Using these percentages derived from the modeled 

interim baseline, Table 5-20 shows the allocation of reduced incidence above and below the 

LMLs of Laden et al. (2006) and Pope et al. (2002). As we model avoided premature deaths 

among populations exposed to levels of PM2.5, we have lower confidence in levels below the 

LML for each study. 
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Table 5-20. Estimated Reduction in Incidence of Premature Adult Mortality due to the 

Mercury and Air Toxics Standards in 2016 Occurring Above and Below the Lowest Measured 

Levels in the Underlying Epidemiology Studiesa

a

These estimates are rounded to two significant digits. 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. See Appendix 5E for more 

information.

A large fraction of the PM2.5-related benefits associated with this rule occur below the 

level of the National Ambient Air Quality Standard (NAAQS) for annual PM2.5 at 15 μg/m3, which 

wasset in 2006. It is important to emphasize that NAAQS are not set at a level of zero risk. 

Instead, the NAAQS reflect the level determined by the Administrator to be protective of public 

health within an adequate margin of safety, taking into consideration effects on susceptible 

populations. While benefits occurring below the standard may be less certain than those 

occurring above the standard, EPA considers them to be legitimate components of the total 

benefits estimate.

Study and 

Lowest Measured Level (LML)

Total Reduced 

Mortality Incidence

Allocation of Reduced Mortality Incidence

Below LML At or Above LML

Pope et al. (2002), 7.5 μg/m34,200 1,100 3,100

Laden et al. (2006), 10 μg/m311,000 9,600 1,200
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Figure 5-15. Cumulative Percentage of Total PM-Related Mortalities of the Mercury and Air 

Toxics Standards in 2016 Avoided by Baseline Air Quality Levela

a

Based on the modeled interim baseline, which is approximately equivalent to the final baseline (see Appendix 

5A)

While the LML of each study is important to consider when characterizing and 

interpreting the overall level PM2.5-related co-benefits, as discussed earlier in this chapter, EPA 

believes that both 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 Laden et al. (2006) analysis of the Harvard Six Cities and the Pope et al. (2002)

analysis of the American Cancer Society cohorts there are innumerable other attributes that 

may affect the size of the reported risk 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.
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5.8 Discussion

This analysis demonstrates the significant health and welfare co-benefits of MATS. We 

estimate that in 2016 the rule will have reduced the number of PM2.5-related premature deaths 

by between 4,200 and 11,000 and produce substantial non-mortality co-benefits. We estimate 

the monetized health and climate co-benefits of MATS to be $37 billion to $90 billion at a 3% 

discount rate and $33 billion to $81 billion at a 7% discount rate in 2016, depending on the 

epidemiological function used to estimate reductions in premature mortality. All estimates are 

in 2007$. Health co-benefits comprise approximately 99% of these total monetized co-benefits. 

This co-benefits assessment omits several important categories of co-benefits that we were 

unable to quantify, including health and ecological co-benefits from reducing exposure to 

ozone, ecosystem co-benefits for reducing nitrogen and sulfate deposition, the direct health cobenefits from reducing exposure to SO2 and NO2, and reduced visibility impairment in 

recreational areas. 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 response relationships and our use of the expert elicitation-derived PM2.5

mortality functions. Others are unquantified, including the projection of atmospheric conditions 

and source-level emissions, the projection of baseline morbidity rates, incomes and 

technological development. 

The emissions scenarios for the RIA reflects the Cross-State Air Pollution Rule (CSAPR) as 

finalized in July 2011 and the emissions reductions of SOx, NOx, directly emitted PM, and CO2

are consistent with application of federal rules, state rules and statutes, and other binding, 

enforceable commitments in place by December 2010 for the analysis timeframe27. EPA has 

proposed minor modifications to the state level SO2 budgets in the Cross State Air Pollution 

Rule (CSAPR; see http://www.epa.gov/airtransport/actions.html). These modifications are 

expected to result in small changes in the levels of SO2 emission reductions expected in a 

number of states, with the largest impact expected in Texas. EPA expects that these changes 

will slightly reduce the benefits of CSAPR, and will have a small impact on the baseline 

emissions for MATS. Because of the change in the baseline SO2 emissions for MATS, the MACT 

controls may result in slightly larger reductions in SO2 and other emissions, and consequently 

slightly higher benefits. It is important to note that the total monetized benefits of both rules is 

not expected to change significantly, rather, the allocation of the SO2 emissions reductions and 

 

27 Consistent with the mercury risk deposition modeling for MATS, EPA did not model non-federally enforceable 

mercury-specific emissions reduction rules in the base case or MATS policy case (see preamble Section III. A.).

Note that this approach does not significantly affect SO2 and NOx projections underlying the cost and benefit 

results presented in this RIA.
5-104

benefits between the rules is changed, so that MATS accounts for slightly more of the total SO2

emissions reductions and benefits, and CSAPR slightly less.

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5A-1

APPENDIX 5A

IMPACT OF THE INTERIM POLICY SCENARIO ON EMISSIONS

5A.1 Introduction

This section summarizes the emissions inventories that are used to create emissions 

inputs to the air quality modeling performed for this rule. A summary of the emissions 

reductions that were modeled for this rule is provided. Note that the emissions processing and 

corresponding air quality modeling were used to develop BPT scaling factors for the benefits 

calculation as described in this RIA. More information on this approach can be found in 

Appendix 5C. The emissions inventories were processed into the form required by the 

Community Multi-scale Air Quality (CMAQ) model. CMAQ simulates the numerous physical and 

chemical processes involved in the formation, transport, and destruction of ozone, particulate 

matter and air toxics. 

As part of the analysis for this rulemaking, the modeling system was used to calculate 

daily and annual PM2.5 concentrations, 8-hr maximum ozone and visibility impairment. Model 

predictions of PM2.5 and ozone are used in a relative sense to estimate scenario-specific, futureyear design values of PM2.5 and ozone. These are combined with monitoring data to estimate 

population-level exposures to changes in ambient concentrations for use in estimating health 

and welfare effects. In the remainder of this section we provide an overview of (1) the 

emissions components of the modeling platform, (2) the development of the 2005 base year 

emissions, (3) the development of the future year baseline emissions, and (4) the development 

of the future year control case emissions. 

5A.2 Overview of Modeling Platform and Emissions Processing Performed

A modeling platform is the collection of the inputs to an air quality model, including the 

settings and data used for the model, including emissions data, meteorology, initial conditions, 

and boundary conditions. The 2005-based air quality modeling platform used for this RIA 

includes 2005 base year emissions and 2005 meteorology for modeling ozone and PM2.5 with 

CMAQ. In support of this rule, EPA modeled the air quality in the Eastern and the Western 

United States using two separate model runs, each with a horizontal grid resolution of 12 km x 

12 km. These 12 km modeling domains were “nested” within a modeling domain covering the 

remainder of the lower 48 states and surrounding areas using a grid resolution of 36 x 36 km. 

The results from the 36-km modeling were used to provide incoming “boundary” for the 12km 

grids. Additional details on the non-emissions portion of the 2005v4.3 modeling platform used 

for this RIA are described in the air quality modeling section (Appendix 5B).
5A-2

The 2005-based air quality modeling platform used in support of this RIA is version 4.3 

and is referred to as the 2005v4.3 platform. It is an update to the 2005-based platform, version 

4.1 (i.e., 2005v4.1) used for the proposal modeling and for the appropriate and necessary 

finding. The Technical Support Document “Preparation of Emissions Inventories for the Version 

4.1, 2005-based Platform” (see http://www.epa.gov/ttn/chief/emch/index.html#toxics) 

provides information on the platform used for the proposed version of this rule and for the 

appropriate and necessary finding. The 2005v4.3 platform builds upon the 2005-based 

platform, version 4.2 which was the version of the platform used for the final Cross-State Air 

Pollution Rule and incorporated changes made in response to public comments on the 

proposed version of that rule. Detailed documentation about the 2005v4.3 platform emissions 

inventories used for this rule can be found in the “Emissions Modeling for the Final Mercury 

and Air Toxics Standards Technical Support Document”.

5A.3 Development of 2005 Base Year Emissions 

Emissions inventory inputs representing the year 2005 were developed to provide a 

base year for forecasting future air quality. The emission source sectors and the basis for 

current and future-year inventories include Electric Generating Utility point sources, non-EGU 

point sources, and the following types of sources with inventories primarily at the county level: 

onroad mobile, nonroad mobile, nonpoint, and fires. The specific sectors used for modeling are 

listed and defined in detail in the “Emissions Modeling for the Final Mercury and Air Toxics 

Standards Technical Support Document”. The inventories used include emissions of criteria 

pollutants, and for some sectors the pollutants benzene, formaldehyde, acetaldehyde and 

methanol are used to speciate VOC into the chemical species needed by CMAQ.

The 2005v4 platform was the initial starting point for the 2005v4.3 platform used for 

this modeling. There were two intermediate versions: the version used for the MATS proposal 

modeling (2005v4.1), and the version used for the final Cross-State Air Pollution Rule modeling 

(2005v4.2). The basis of the 2005v4 platform and subsequent versions is the U.S. inventory is 

the 2005 National Emission Inventory (NEI), version 2 from October 6, 2008 

(http://www.epa.gov/ttn/chief/net/2005inventory.html). The 2005 NEI v2 includes 2005-

specific data for point and mobile sources, while most nonpoint data were carried forward from 

version 3 of the 2002 NEI. 

Emissions for point sources were primarily from the 2005 NEI v2 inventory, consisting 

mostly of 2005 values with some 2002 emissions values used where 2005 data were not 

available. The point sources are split into “EGU” (aka “ptipm”) and “Non-EGU” (aka 
5A-3

“ptnonipm”) sectors for modeling purposes based on the matching of the unit level data in the 

NEI units in the National Electric Energy Database System (NEEDS) version 4.10 database. All 

units that matched NEEDS were included in the EGU sector so that the future year emissions 

could easily be taken from the Integrated Planning Model (IPM) as its outputs are also based on 

the NEEDS units. Efforts made to ensure the quality of the 2005 EGU inventory included 

ensuring that there were not duplicate emissions (e.g., data pulled forward from earlier 

inventories), accounting for plants or units that shutdown prior to 2005, adding estimates for 

ethanol plants, and accounting for installed emissions control devices.

The 2005 annual NOX and SO2 emissions for sources in the EGU sector are based 

primarily on data from EPA’s Clean Air Markets Division’s Continuous Emissions Monitoring 

(CEM) program, with other pollutants estimated using emission factors and the CEM annual 

heat input. For EGUs without CEMs, emissions were obtained from the state-submitted data in 

the NEI. For the 2005 base year, the annual EGU NEI emissions were allocated to hourly 

emissions values needed for modeling based on the 2004, 2005, and 2006 CEM data. The NOX

CEM data were used to create NOX-specific profiles, the SO2 data were used to create SO2-

specific profiles, and the heat input data were used to allocate all other pollutants. The three 

years of data were used to create monthly profiles by state, while the 2005 data were used to 

create state-averaged profiles for allocating monthly emissions to daily. These daily values were 

input into SMOKE, which utilized state-averaged 2005-based hourly profiles to allocate to 

hourly values. This approach to temporal allocation was used for all base and control cases 

modeled to provide a temporal consistency between the years modeled without tying the 

temporalization to the events of a single year.

For nonpoint sources, the 2002 NEI v2 inventory was augmented with updated oil and 

gas exploration emissions for Texas and Oklahoma (for CO, NOX, PM, SO2, VOC). These oil and 

gas exploration emissions were in addition to oil and gas data previously available in the 2005 

v4 platform that includes emissions within the following states: Arizona, Colorado, Montana, 

Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, and Wyoming.

The commercial marine category 3 (C3) vessel emissions portion of the nonroad sector

used point-based gridded 2005 emissions that reflect the final projections developed for the 

category 3 commercial marine Emissions Control Area (ECA) proposal to the International 

Maritime Organization (EPA-420-F-10-041, August 2010). These emissions include Canada as 

part of the ECA, and were updated using region-specific growth rates and thus contain

Canadian province codes. The state/federal water boundaries were based on a file available 
5A-4

from the Mineral Management Service (MMS) that specifies boundaries ranging from three to 

ten nautical miles from the coast.

The onroad emissions were primarily based on the version of the Motor Vehicle 

Emissions Simulator (MOVES) (http://www.epa.gov/otaq/models/moves/) used for the Tier 3 

proposed rule. The first step was to run MOVES to output emission factors for a set of counties 

with characteristics representative of the counties within the continental United States. Data 

for each representative county included county-specific fuels, vehicle age distribution, 

inspection and maintenance programs, temperatures and relative humidity. The emission 

factors produced by MOVES were then combined by SMOKE with county-based activity data 

(vehicle miles traveled, speed data, and vehicle population) and gridded temperature data to 

create hourly, gridded emissions. Additional information on this approach is available in the 

“Emissions Modeling for the Final Mercury and Air Toxics Standards Technical Support 

Document”.

The nonroad emissions utilized the National Mobile Inventory Model (NMIM) to run the 

NONROAD model for all states to create county/month emissions, updated from the annual 

emissions in the 2005 NEI v2 with some improvements. For this case, NMIM was run using

representing county mode, with improved fuels, an improved toxics emission factor (1,3-

butadiene for 2-stroke snowmobiles), and a small coding change that enabled NONROAD to 

process 15% ethanol (E15) fuels.

Other emissions inventories used included average-year county-based inventories for 

emissions from wildfires and prescribed burning. These emissions are intended to be 

representative of both base and future years and are held constant for each. As a result, postprocessing techniques minimize their impact on the modeling results. The 2005v4.3 platform 

utilizes the same 2006 Canadian inventory and a 1999 Mexican inventory as were used since 

the v4 platform, as these were the latest available data from these countries.

Once developed, the emissions inventories were processed to provide the hourly, 

gridded emissions for the model-species needed by CMAQ. Details on this processing are 

further described in the “Emissions Modeling for the Final Mercury and Air Toxics Standards 

Technical Support Document”. Table 5A-1 provides summaries of the 2005 U.S. emissions 

inventories modeled for this rule by sector. Tables 5A-2 through 5A-3 provide state-level 

summaries of SO2, and PM2.5 by sector. Note that the nonroad columns include emissions from 

traditional nonroad sources that are found “on-land,” along with commercial marine sources. 

The nonpoint columns include area fugitive dust, agriculture, and other nonpoint emissions.
5A-5

Table 5A-1. 2005 US Emissions by Sector

Emissions Sector

2005 NOX

[tons/yr]

2005 SO2

[tons/yr]

2005 PM2.5

[tons/yr]

2005 PM10

[tons/yr]

2005 NH3

[tons/yr]

2005 CO 

[tons/yr]

2005 VOC 

[tons/yr]

Agriculture 3,251,990

Area fugitive dust 1,030,391 8,858,992

Average fires 189,428 49,094 684,035 796,229 36,777 8,554,551 1,958,992

Commercial marine 

Category 3 (US)

130,164 97,485 10,673 11,628 11,862 4,570

EGU 3,729,161 10,380,883 496,877 602,236 21,995 603,788 41,089

Locomotive/marine 1,922,723 153,068 56,666 59,342 773 270,007 67,690

Non-EGU point 2,213,471 2,030,759 433,346 647,873 158,342 3,201,418 1,279,308

Nonpoint 1,696,902 1,216,362 1,079,906 1,349,639 133,962 7,410,946 7,560,061

Nonroad 2,031,527 196,277 201,406 210,767 1,971 20,742,873 2,806,422

Onroad 8,235,002 168,480 301,073 369,911 144,409 41,117,658 3,267,931

US Total 20,148,378 14,292,410 4,294,373 12,906,616 3,750,218 81,913,104 16,986,064

Table 5A-2. 2005 Base Year SO2 Emissions (tons/year) for States by Sector

State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total

Alabama 460,123 66,373 52,325 5,622 3,554 983 588,980

Arizona 52,733 23,966 2,571 6,151 3,622 2,888 91,931

Arkansas 66,384 13,039 27,260 5,678 1,918 728 115,008

California 601 33,097 77,672 40,222 4,526 6,735 162,852

Colorado 64,174 1,550 6,810 4,897 2,948 1,719 82,098

(continued)
5A-6

Table 5A-2. 2005 Base Year SO2 Emissions (tons/year) for States by Sector (continued)

State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total

Connecticut 10,356 1,831 18,455 2,557 1,337 4 34,540

Delaware 32,378 34,859 1,030 2,657 486 6 71,416

District of Columbia 1,082 686 1,559 414 205 0 3,947

Florida 417,321 57,429 70,490 31,190 12,388 7,018 595,836

Georgia 616,063 52,827 56,829 9,224 6,939 2,010 743,893

Idaho 0 17,151 2,915 2,304 902 3,845 27,117

Illinois 330,382 131,357 5,395 19,305 6,881 20 493,339

Indiana 878,979 86,337 59,775 9,437 4,641 24 1,039,194

Iowa 130,264 41,010 19,832 8,838 2,036 25 202,004

Kansas 136,520 12,926 36,381 8,035 1,978 103 195,943

Kentucky 502,731 25,808 34,229 6,943 3,240 364 573,315

Louisiana 109,875 165,705 2,378 25,451 2,902 892 307,202

Maine 3,887 18,512 9,969 1,625 963 150 35,106

Maryland 283,205 34,988 40,864 9,353 3,016 32 371,458

Massachusetts 84,234 19,620 25,261 6,524 2,669 93 138,402

Michigan 349,877 76,510 42,066 14,626 8,253 91 491,423

Minnesota 101,678 24,603 14,747 10,409 2,934 631 155,002

Mississippi 75,047 29,892 6,796 5,930 2,590 1,051 121,306

Missouri 284,384 78,308 44,573 10,464 4,901 186 422,816

Montana 19,715 11,056 2,600 3,813 874 1,422 39,480

Nebraska 74,955 7,910 7,659 9,199 1,510 105 101,337

Nevada 53,363 2,253 12,477 2,880 656 1,346 72,975

New Hampshire 51,445 3,155 7,408 789 746 38 63,580

New Jersey 57,044 7,639 10,726 13,321 3,038 61 91,830

New Mexico 30,628 7,831 3,193 3,541 1,801 3,450 50,445

New York 180,847 58,426 125,158 15,666 6,258 113 386,468

North Carolina 512,231 59,433 22,020 8,766 6,287 696 609,433

North Dakota 137,371 9,582 6,455 5,986 533 66 159,994

Ohio 1,116,095 115,155 19,810 15,425 7,336 22 1,273,843

(continued)
5A-7

Table 5A-2. 2005 Base Year SO2 Emissions (tons/year) for States by Sector (continued)

State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total

Oklahoma 110,081 40,482 8,556 5,015 3,039 469 167,642

Oregon 12,304 9,825 9,845 5,697 1,790 4,896 44,357

Pennsylvania 1,002,203 83,375 68,349 11,999 6,266 32 1,172,224

Rhode Island 176 2,743 3,365 816 254 1 7,354

South Carolina 218,781 31,495 13,489 7,719 3,589 646 275,719

South Dakota 12,215 1,702 10,347 3,412 623 498 28,797

Tennessee 266,148 65,693 32,714 6,288 5,538 277 376,659

Texas 534,949 223,625 115,192 34,944 16,592 1,178 926,480

Tribal 3 1,511 0 0 0 0 1,515

Utah 34,813 9,132 3,577 2,439 1,890 1,934 53,784

Vermont 9 902 5,385 385 342 49 7,073

Virginia 220,287 69,401 32,923 10,095 4,600 399 337,705

Washington 3,409 24,211 7,254 18,810 3,343 407 57,433

West Virginia 469,456 46,710 14,589 2,133 1,378 215 534,481

Wisconsin 180,200 66,807 6,369 7,163 3,647 70 264,256

Wyoming 89,874 22,321 6,721 2,674 721 1,106 123,417

Total 10,380,883 2,030,759 1,216,362 446,831 168,480 49,094 14,292,410

Table 5A-3. 2005 Base Year PM2.5 Emissions (tons/year) for States by Sector

State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total

Alabama 23,366 19,498 35,555 4,142 5,775 13,938 102,273

Arizona 7,418 3,940 21,402 4,486 6,920 37,151 81,316

Arkansas 1,688 10,820 34,744 3,803 3,102 10,315 64,472

California 347 21,517 94,200 22,815 26,501 97,302 262,682

Colorado 4,342 7,116 25,340 3,960 4,377 24,054 69,189

Connecticut 562 224 11,460 1,740 2,544 56 16,586

Delaware 2,169 1,810 1,590 818 922 87 7,397

District of Columbia 17 172 589 277 367 0 1,421

(continued)
5A-8

Table 5A-3. 2005 Base Year PM2.5 Emissions (tons/year) for States by Sector (continued)

State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total

Florida 24,217 25,193 52,955 15,035 16,241 99,484 233,125

Georgia 28,057 12,666 63,133 6,504 12,449 24,082 146,892

Idaho 0 2,072 41,492 2,140 1,402 52,808 99,914

Illinois 16,585 15,155 74,045 12,880 12,574 277 131,516

Indiana 34,439 14,124 74,443 6,515 7,585 344 137,450

Iowa 8,898 6,439 54,312 6,969 3,468 349 80,436

Kansas 5,549 7,387 138,437 5,719 3,109 1,468 161,669

Kentucky 19,830 10,453 31,245 4,762 5,566 5,155 77,010

Louisiana 5,599 32,201 28,164 9,440 4,288 12,647 92,339

Maine 52 3,783 15,037 1,363 1,759 2,127 24,120

Maryland 15,417 6,768 23,323 3,410 5,504 531 54,952

Massachusetts 3,110 2,245 31,116 3,293 5,913 1,324 47,001

Michigan 11,022 12,926 47,722 8,561 13,006 1,283 94,520

Minnesota 3,262 10,538 73,990 8,541 6,842 8,943 112,116

Mississippi 2,029 10,602 34,217 4,133 4,195 14,897 70,074

Missouri 6,471 6,966 76,419 7,230 7,665 2,636 107,388

Montana 2,398 2,729 30,096 2,654 1,347 17,311 56,536

Nebraska 1,246 2,340 45,661 5,848 2,620 1,483 59,198

Nevada 3,341 4,095 9,920 2,212 1,290 19,018 39,876

New Hampshire 2,586 568 13,316 907 1,512 534 19,423

New Jersey 4,625 2,588 13,623 5,042 5,963 865 32,707

New Mexico 5,583 1,460 50,698 1,959 2,861 48,662 111,224

New York 9,648 4,994 48,540 8,607 11,139 1,601 84,529

North Carolina 16,967 12,665 49,551 6,272 8,939 9,870 104,264

North Dakota 6,397 598 41,504 4,552 976 934 54,962

Ohio 53,572 12,847 52,348 9,847 11,785 316 140,715

Oklahoma 1,411 6,246 90,047 3,765 4,559 6,644 112,672

Oregon 412 8,852 58,145 3,741 3,375 65,350 139,874

Pennsylvania 55,547 16,263 44,607 7,565 11,058 454 135,494

(continued)
5A-9

Table 5A-3. 2005 Base Year PM2.5 Emissions (tons/year) for States by Sector (continued)

State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total

Rhode Island 10 256 1,289 394 577 14 2,540

South Carolina 14,455 4,779 26,598 3,491 5,061 9,163 63,548

South Dakota 390 2,982 33,678 2,910 1,056 7,062 48,079

Tennessee 12,856 21,912 32,563 5,072 8,514 3,934 84,851

Texas 21,464 37,563 194,036 21,361 29,859 21,578 325,861

Tribal 0 1,569 0 0 0 0 1,569

Utah 5,055 3,595 14,761 1,627 2,703 27,412 55,153

Vermont 37 337 6,943 479 605 696 9,098

Virginia 12,357 11,455 38,140 5,968 6,661 5,659 80,241

Washington 2,396 4,618 45,599 6,697 6,721 4,487 70,519

West Virginia 26,377 5,154 14,778 1,702 1,930 3,050 52,991

Wisconsin 5,233 7,967 37,277 6,083 6,783 994 64,337

Wyoming 8,068 10,298 31,645 1,455 1,103 15,686 68,254

Total 496,877 433,346 2,110,298 268,745 301,073 684,035 4,294,373

5A.4 Development of Future year baseline Emissions 

The future year baseline scenario, also known as the “reference case”, represents

predicted emissions including adjustments for known promulgated federal measures for all 

sectors as of the year 2017, which is assumed to be representative of 2016. The EGU and 

mobile sectors reflect projected economic and fuel usage changes. Emissions from non-EGU 

stationary sectors have previously been shown to not be well correlated with economic 

forecasts, and therefore economic impacts were not included for non-EGU stationary sources.

Like the 2005 base case, these emissions cases include criteria pollutants and for some sectors, 

benzene, formaldehyde, acetaldehyde and methanol from the inventory is used in VOC 

speciation. The future year baseline scenario represents predicted emissions in the absence of 

any further controls beyond those Federal measures already promulgated. For EGUs, all state 

and other programs available at the time of modeling have been included. For mobile sources, 

all national measures promulgated at the time of modeling have been included. Additional 

details on the future year baseline (i.e., reference case) emissions modeling can be found in the 
5A-10

“Emissions Modeling for the Final Mercury and Air Toxics Standards Technical Support 

Document”.

The future year baseline EGU emissions were obtained using version 4.10 Final of the 

Integrated Planning Model (IPM) (http://www.epa.gov/airmarkt/progsregs/epaipm/index.html). The IPM is a multiregional, dynamic, deterministic linear programming model 

of the U.S. electric power sector. Version 4.10 Final reflects state rules and consent decrees 

through December 1, 2010, information obtained from the 2010 Information Collection 

Request (ICR), and information from comments received on the IPM-related Notice of Data 

Availability (NODA) published on September 1, 2010. Notably, IPM 4.1 Final included the 

addition of over 20 GW of existing Activated Carbon Injection (ACI) for coal-fired EGUs reported 

to EPA via the ICR. Additional unit-level updates that identified existing pollution controls (such 

as scrubbers) were also made based on the ICR and on comments from the IPM NODA. Units 

with SO2 or NOX advanced controls (e.g., scrubber, SCR) that were not required to run for 

compliance with Title IV, New Source Review (NSR), state settlements, or state-specific rules 

were modeled by IPM to either operate those controls or not based on economic efficiency 

parameters. The IPM run for future year baseline case modeled with CMAQ assumed that 100% 

of the HCl found in the coal was emitted into the atmosphere. However, in the final IPM results 

for the rule, neutralization of 75% of the available HCl was included based on recent findings.

Further details on the future year baseline EGU emissions inventory used for this rule 

can be found in the IPM v.4.10 Documentation, available at

http://www.epa.gov/airmarkets/progsregs/epa-ipm/transport.html. The future year baseline 

modeled in IPM for this rule includes estimates of emissions reductions that will result from the 

Cross-State Air Pollution Rule. However, reductions from the Boiler MACT rule were not 

represented this modeling because the rule was stayed at the time the modeling was 

performed. A complete list of state regulations, NSR settlements, and state settlements 

included in the IPM modeling is given in Appendices 3-2, 3-3, and 3-4 beginning on p. 68 of 

http://www.epa.gov/airmarkets/progsregs/epaipm/CSAPR/docs/DocSuppv410_FTransport.pdf. For the future year baseline EGU emissions, 

the IPM outputs for 2020, which are also representative of the year 2017, were used as part of 

the 2017 reference case modeling. These emissions were very similar to the year 2015 

emissions output from the same IPM modeling case.

Inventories of onroad mobile emissions for the future year baseline and control cases 

were created using the MOVES model with an approach consistent with the 2005 base year. As 

with the 2005 emissions, the future year onroad emissions were based on emission factors 
5A-11

developed using the Tier 3 version of MOVES processed through the SMOKE-MOVES interface.

Future-year vehicle miles travelled (VMT) were projected from the 2005 NEI v2 VMT using 

growth rates from the 2009 Annual Energy Outlook (AEO) data. The VMT for heavy duty diesel 

vehicles class 8a and 8b was updated based on data from Oak Ridge National Laboratory. The 

future year onroad emissions reflect control program implementation through 2017 and 

include the Light-Duty Vehicle Tier 2 Rule, the Onroad Heavy-Duty Rule, the Mobile Source Air 

Toxics (MSAT) final rule, and the Renewable Fuel Standard version 2 (RFS2).

Future year nonroad mobile emissions were created using NMIM to run NONROAD in a 

consistent manner as was done for 2005, but with future-year equipment population estimates, 

fuels, and control programs. The fuels in 2017 are assumed to be E10. Emissions for 

locomotives and category 1 and 2 (C1 and C2) commercial marine vessels were derived based 

on emissions published in the Final Locomotive Marine Rule, Regulatory Impact Assessment, 

Chapter 3 (see http://www.epa.gov/otaq/locomotives.htm#2008final). The future year baseline 

nonroad mobile emissions reductions include emissions reductions to locomotives, various 

nonroad engines including diesel engines and various marine engine types, fuel sulfur content, 

and evaporative emissions standards, including the category 3 marine residual and diesel 

fuelled engines and International Maritime Organization standards which include the 

establishment of emission control areas for these ships. A summary of the onroad and nonroad 

mobile source control programs included in the projected future year baseline is shown in Table 

5A-4.

Table 5A-4. Summary of Mobile Source Control Programs Included in the Future Year

Baseline

National Onroad Rules:

Tier 2 rule (Signature date: February 28, 2000)

Onroad heavy-duty rule (February 24, 2009)

Final mobile source air toxics rule (MSAT2) (February 9, 2007)

Renewable fuel standard Version 2 (March 26, 2010)

Light duty greenhouse gas standards (May, 2010)

Corporate Average Fuel Economy (CAFE) standards for 2008–2011

Local Onroad Programs:

National low emission vehicle program (NLEV) (March 2, 1998)

Ozone transport commission (OTC) LEV Program (January, 1995)

(continued)
5A-12

Table 5A-4. Summary of Mobile Source Control Programs Included in the Future Year

Baseline (continued)

National Nonroad Controls:

Tier 1 nonroad diesel rule (June 17, 2004)

Phase 1 nonroad SI rule (July 3, 1995)

Marine SI rule (October 4, 1996)

Nonroad large SI and recreational engine rule (November 8, 2002)

Clean Air Nonroad Diesel Rule—Tier 4 (June 29, 2004)

Locomotive and marine rule (May 6, 2008)

Nonroad SI rule (October 8, 2008)

Aircraft:

Itinerant (ITN) operations at airports adjusted to year 2017

Locomotives:

Locomotive Emissions Final Rulemaking (December 17, 1997)

Clean Air nonroad diesel final rule—Tier 4 (June 29, 2004)

Locomotive rule (April 16, 2008)

Locomotive and marine rule (May 6, 2008)

Commercial Marine:

Locomotive and marine rule (May 6, 2008)

EIA fuel consumption projections for diesel-fueled vessels

Clean Air Nonroad Diesel Final Rule – Tier 4

Emissions Standards for Commercial Marine Diesel Engines (December 29,1999)

Tier 1 Marine Diesel Engines (February 28, 2003)

Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards (April, 30, 

2010) 

For non-EGU point sources, emissions were projected by including emissions reductions 

and increases from a variety of source data. Other than for certain large municipal waste 

combustors and airports, non-EGU point source emissions were not grown using economic 

growth projections, but rather were held constant at the emissions levels in 2005. Emissions 

reductions were applied to non-EGU point source to reflect final federal measures, known plant 

closures, and consent decrees. The starting inventories for this rule were the projected 
5A-13

emission inventories developed for the 2005v4.2 platform for the final Cross-State Air Pollution 

Rule (see http://www.epa.gov/ttn/chief/emch/index.html#final). The most significant updates 

to the emission projections for this rule are the addition of future year ethanol, biodiesel and 

cellulosic plants that account for increased ethanol production from the Renewable Fuel 

Standard Rule that is incorporated into the base case for 2017.

Since aircraft at airports were treated as point emissions sources in the 2005 NEI v2, we 

developed future year baseline emissions for airports by applying projection factors based on 

activity growth projected by the Federal Aviation Administration Terminal Area Forecast (TAF) 

system, published in January 2010 for these sources.

Emissions from stationary nonpoint sources were projected using procedures specific to 

individual source categories. Refueling emissions were projected using refueling emissions from 

MOVES inventory mode runs. Portable fuel container emissions were projected using estimates 

from previous rulemaking inventories compiled by the Office of Transportation and Air Quality 

(OTAQ). Emissions of ammonia and dust from animal operations were projected based on 

animal population data from the Department of Agriculture and EPA. Residential wood 

combustion emissions were projected by replacement of obsolete woodstoves with new 

woodstoves and a 1 percent annual increase in fireplaces. Landfill emissions were projected 

using MACT controls. Other nonpoint sources were held constant between the 2005 and future 

year scenarios.

A summary of all rules and growth assumptions impacting non-EGU stationary sources is 

provided in Table 5A-5, along with the affected pollutants. Note that reductions associated with 

the Boiler MACT are not included in the future year baseline.

Table 5A-5. Control Strategies and/or Growth Assumptions Included in the Future Year 

Baseline for Non-EGU Stationary Sources

MACT rules, national, VOC: national applied by SCC, MACT VOC

Consent decrees and settlements, including refinery consent decrees, and settlements 

for: Alcoa, TX and Premcor (formerly MOTIVA), DE 

All

Municipal waste combustor reductions—plant level PM

Hazardous waste combustion PM

Hospital/medical/infectious waste incinerator regulations NOx, PM, SO2

Large municipal waste combustors—growth applied to specific plants All

(continued)
5A-14

Table 5A-5. Control Strategies and/or Growth Assumptions Included in the Future Year 

Baseline for Non-EGU Stationary Sources (continued)

MACT rules, plant-level, VOC: auto plants VOC

MACT rules, plant-level, PM & SO2: lime manufacturing PM, SO2

MACT rules, plant-level, PM: taconite ore PM

Municipal waste landfills: projection factor of 0.25 applied All

Livestock emissions growth from year 2002 to year 2017 NH3, PM

Residential wood combustion growth and change-outs from years 2005 to year 2017 All

Gasoline Stage II growth and control via MOVES from year 2005 to year 2017 VOC

Portable fuel container mobile source air toxics rule 2: inventory growth and control 

from year 2005 to year 2017

VOC

NESHAP: Portland Cement (09/09/10)—plant level based on industrial sector 

integrated solutions (ISIS) policy emissions in 2013. The ISIS results are from the ISIScement model runs for the NESHAP and NSPS analysis of July 28, 2010 and include 

closures. 

Hg, NOX, SO2, PM, HCl

New York ozone SIP standards VOC, HAP VOC, NOX

Additional plant and unit closures provided by state, regional, and EPA agencies All

Emission reductions resulting from controls put on specific boiler units (not due to 

MACT) after 2005, identified through analysis of the control data gathered from the 

ICR from the ICI boiler NESHAP.

NOX, SO2, HCL

NESHAP: Reciprocating Internal Combustion Engines (RICE). NOX, CO, PM, SO2

RICE controls applied to Phase II WRAP 2018 oil and gas emissions VOC, SO2, NOX, CO

RICE controls applied to 2008 Oklahoma and Texas Oil and gas emissions VOC, SO2, NOX, CO, PM

Ethanol plants that account for increased ethanol due to RFS2 All

State fuel sulfur content rules for fuel oil—effective in 2017, only in Maine, New Jersey, 

and New York

SO2

In all future year cases, the same emissions were used for Canada and Mexico as were 

used in the 2005 base case because appropriate future year emissions for sources in these 

countries were not available. Future year emissions need to reflect expected percent 

reductions or increases between the base year and the future year to be considered 

appropriate for this type of modeling and such emissions were not available.

Table 5A-6 shows a summary of the 2005 and modeled future year baseline emissions 

for the lower 48 states. Tables 5A-7 and 5A-8 below provide summaries of SO2 and PM2.5 in the 
5A-15

2017 baseline for each sector by state. Table 5A-9 shows the future year baseline EGU 

emissions by state for all criteria air pollutants.

Table 5A-6. Summary of Modeled Base Case Annual Emissions (tons/year) for 48 States by 

Sector: SO2 and PM2.5

Source Sector SO2 Emissions 2005 2017

EGU point 10,380,883 3,281,364

Non-EGU point 2,030,759 1,534,991

Nonpoint 1,216,362 1,125,985

Nonroad 446,831 15,759

On-road 168,480 29,288

Average fire 49,094 49,094

Total SO2, all sources 14,292,410 6,036,480

Source Sector PM2.5 Emissions 2005 2017

EGU point 496,877 276,430

Non-EGU point 433,346 411,437

Nonpoint 2,110,298 1,912,757

Nonroad 268,745 150,221

On-road 301,073 129,416

Average fire 684,035 684,035

Total PM2.5, all sources 4,294,373 3,564,296

Table 5A-7. Future Year Baseline SO2 Emissions (tons/year) for States by Sector

State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total

Alabama 186,084 63,053 52,341 146 569 983 303,177

Arizona 36,996 24,191 2,467 59 724 2,888 67,324

Arkansas 92,804 12,160 26,801 123 314 728 132,929

California 5,346 21,046 67,846 3,311 2,087 6,735 106,370

Colorado 74,255 1,415 6,210 50 532 1,719 84,181

Connecticut 3,581 1,833 18,149 100 311 4 23,978

(continued)
5A-16

Table 5A-7. Future Year Baseline SO2 Emissions (tons/year) for States by Sector (continued)

State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total

Delaware 2,835 4,770 1,018 500 91 6 9,220

District of Columbia 5 686 1,505 3 38 0 2,237

Florida 117,702 49,082 70,073 1,255 2,111 7,018 247,241

Georgia 96,712 44,248 55,946 192 1,158 2,010 200,266

Idaho 182 17,133 2,894 23 162 3,845 24,240

Illinois 118,217 81,683 5,650 295 1,107 20 206,971

Indiana 200,969 73,930 59,771 150 760 24 335,604

Iowa 85,178 22,865 19,929 86 324 25 128,407

Kansas 45,740 10,288 36,140 57 294 103 92,622

Kentucky 116,927 23,530 33,852 215 463 364 175,350

Louisiana 142,447 129,730 2,669 1,449 447 892 277,634

Maine 2,564 14,285 2,007 72 149 150 19,226

Maryland 29,786 33,562 40,642 494 593 32 105,110

Massachusetts 15,133 17,077 24,907 266 565 93 58,041

Michigan 163,168 48,697 42,185 448 995 91 255,584

Minnesota 52,380 24,742 14,635 220 558 631 93,164

Mississippi 34,865 24,284 6,635 208 396 1,051 67,440

Missouri 178,143 33,757 44,680 191 722 186 257,679

Montana 24,018 7,212 1,875 25 106 1,422 34,657

Nebraska 70,910 6,885 7,899 58 202 105 86,058

Nevada 14,140 2,132 12,028 27 200 1,346 29,873

New Hampshire 6,719 2,471 7,284 21 137 38 16,671

New Jersey 9,042 6,700 9,528 686 757 61 26,774

New Mexico 10,211 7,813 2,719 26 262 3,450 24,480

New York 14,653 45,222 71,060 659 1,466 113 133,173

North Carolina 71,113 58,517 21,713 197 890 696 153,125

North Dakota 105,344 9,915 5,559 36 71 66 120,991

Ohio 180,935 93,600 19,777 373 1,093 22 295,799

(continued)
5A-17

Table 5A-7. Future Year Baseline SO2 Emissions (tons/year) for States by Sector (continued)

State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total

Oklahoma 141,433 27,873 7,731 49 501 469 178,056

Oregon 13,211 9,790 9,508 218 361 4,896 37,985

Pennsylvania 126,316 64,697 67,650 427 1,060 32 260,183

Rhode Island 0 2,745 3,338 33 85 1 6,202

South Carolina 103,694 28,536 13,310 294 500 646 146,980

South Dakota 29,711 1,655 10,301 23 86 498 42,273

Tennessee 33,080 59,145 32,624 154 757 277 126,037

Texas 249,748 129,667 108,633 1,146 2,483 1,178 492,855

Tribal 0 676 0 0 0 0 676

Utah 34,912 6,599 3,365 27 291 1,934 47,128

Vermont 264 902 5,283 8 129 49 6,634

Virginia 51,004 50,387 32,439 275 849 399 135,353

Washington 5,569 19,780 6,885 881 633 407 34,156

West Virginia 84,344 32,458 14,322 64 178 215 131,582

Wisconsin 50,777 61,080 6,260 122 633 70 118,941

Wyoming 48,198 20,491 5,944 18 87 1,106 75,844

Total 3,281,364 1,534,991 1,125,985 15,759 29,288 49,094 6,036,480

Table 5A-8. Future Year Baseline PM2.5 Emissions (tons/year) for States by Sector

State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total

Alabama 13,154 17,052 33,235 2,403 2,217 13,938 81,999

Arizona 3,889 3,809 20,214 2,674 2,762 37,151 70,498

Arkansas 2,838 10,527 33,486 2,042 1,242 10,315 60,450

California 475 20,693 73,607 14,875 13,492 97,302 220,443

Colorado 3,845 7,037 19,868 2,350 2,387 24,054 59,540

Connecticut 400 222 6,838 1,038 1,414 56 9,968

Delaware 434 772 1,207 383 375 87 3,259

(continued)
5A-18

Table 5A-8. Future Year Baseline PM2.5 Emissions (tons/year) for States by Sector (continued)

State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total

District of Columbia 1 172 536 139 154 0 1,002

Florida 12,723 24,620 50,472 8,100 7,652 99,484 203,050

Georgia 13,445 12,105 59,412 3,803 4,863 24,082 117,711

Idaho 36 2,076 40,288 1,186 714 52,808 97,108

Illinois 8,587 13,471 70,775 6,885 4,926 277 104,922

Indiana 22,354 13,570 72,501 3,491 3,380 344 115,640

Iowa 4,298 7,000 51,684 3,348 1,519 349 68,198

Kansas 3,199 6,895 136,633 2,872 1,268 1,468 152,335

Kentucky 12,078 10,353 26,811 2,717 2,059 5,155 59,173

Louisiana 3,093 30,865 27,082 5,107 1,673 12,647 80,467

Maine 355 3,543 8,213 881 750 2,127 15,869

Maryland 3,969 6,382 18,960 1,975 2,492 531 34,310

Massachusetts 1,465 2,123 23,729 1,914 2,590 1,324 33,145

Michigan 8,102 11,688 43,055 4,696 4,949 1,283 73,773

Minnesota 2,598 9,867 68,121 4,483 2,882 8,943 96,893

Mississippi 2,201 10,492 31,474 2,337 1,525 14,897 62,926

Missouri 7,061 6,384 69,722 3,954 3,059 2,636 92,816

Montana 3,870 2,562 28,479 1,332 492 17,311 54,048

Nebraska 2,358 2,834 44,904 2,967 919 1,483 55,465

Nevada 2,505 4,032 9,351 1,319 857 19,018 37,083

New Hampshire 1,130 464 8,981 576 663 534 12,348

New Jersey 2,452 2,520 8,559 2,929 3,244 865 20,569

New Mexico 3,153 1,442 49,789 1,148 1,103 48,662 105,298

New York 2,331 4,859 44,334 5,032 6,723 1,601 64,879

North Carolina 9,983 12,656 43,398 3,583 3,521 9,870 83,011

North Dakota 5,870 795 40,802 2,126 383 934 50,910

Ohio 18,920 12,353 47,811 5,302 5,013 316 89,715

Oklahoma 3,530 5,695 88,862 2,029 2,006 6,644 108,767

(continued)
5A-19

Table 5A-8. Future Year Baseline PM2.5 Emissions (tons/year) for States by Sector (continued)

State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total

Oregon 381 8,869 39,503 2,148 1,627 65,350 117,877

Pennsylvania 16,727 14,874 38,523 4,582 4,854 454 80,014

Rhode Island 4 256 1,070 222 383 14 1,949

South Carolina 9,997 4,527 23,430 1,932 1,929 9,163 50,978

South Dakota 737 2,399 32,697 1,339 416 7,062 44,650

Tennessee 5,053 21,553 28,449 2,939 3,057 3,934 64,985

Texas 21,677 34,648 187,604 11,901 9,289 21,578 286,698

Tribal 1 1,568 0 0 0 0 1,569

Utah 4,524 3,530 13,978 963 1,318 27,412 51,724

Vermont 67 336 4,930 307 653 696 6,989

Virginia 4,529 10,165 32,254 3,507 3,446 5,659 59,561

Washington 1,444 4,421 35,706 3,328 2,874 4,487 52,259

West Virginia 13,602 4,281 12,951 1,048 762 3,050 35,695

Wisconsin 5,323 7,853 27,656 3,161 3,148 994 48,135

Wyoming 5,662 10,225 30,812 850 392 15,686 63,626

Total 276,430 411,437 1,912,757 150,221 129,416 684,035 3,564,296

Table 5A-9. Future Year Baseline EGU CAP Emissions (tons/year) by State

State CO NOX VOC SO2 NH3 PM10 PM2.5

Alabama 27,024 64,064 1,524 186,084 1,472 16,686 13,154

Arizona 16,797 36,971 825 36,996 1,163 5,038 3,889

Arkansas 9,925 36,297 658 92,804 560 3,507 2,838

California 45,388 20,910 1,031 5,346 2,519 580 475

Colorado 9,006 50,879 636 74,255 398 4,605 3,845

Connecticut 9,180 2,738 139 3,581 313 431 400

Delaware 4,256 2,452 132 2,835 119 580 434

District of Columbia 67 11 2 5 3 1 1

(continued)
5A-20

Table 5A-9. Future Year Baseline EGU CAP Emissions (tons/year) by State (continued)

State CO NOX VOC SO2 NH3 PM10 PM2.5

Florida 72,915 83,174 2,253 117,702 3,997 19,098 12,723

Georgia 16,537 43,778 1,293 96,712 903 18,668 13,445

Idaho 1,532 613 41 182 57 38 36

Illinois 51,862 56,128 3,091 118,217 1,437 9,926 8,587

Indiana 30,587 106,881 2,295 200,969 1,317 33,816 22,354

Iowa 8,316 42,698 791 85,178 452 5,735 4,298

Kansas 5,066 25,163 683 45,740 305 3,996 3,199

Kentucky 37,287 71,259 1,604 116,927 928 16,279 12,078

Louisiana 32,626 33,509 852 142,447 1,427 3,677 3,093

Maine 12,789 6,121 306 2,564 269 366 355

Maryland 13,446 17,933 533 29,786 301 5,322 3,969

Massachusetts 7,128 7,991 279 15,133 395 1,915 1,465

Michigan 25,856 66,846 1,497 163,168 874 11,056 8,102

Minnesota 9,365 36,867 746 52,380 460 3,034 2,598

Mississippi 9,704 27,319 440 34,865 469 3,113 2,201

Missouri 16,499 52,464 1,714 178,143 740 9,093 7,061

Montana 5,266 20,946 338 24,018 198 6,117 3,870

Nebraska 4,691 28,898 542 70,910 292 2,948 2,358

Nevada 9,677 15,627 438 14,140 953 3,095 2,505

New Hampshire 5,667 4,908 206 6,719 207 1,234 1,130

New Jersey 25,831 11,178 823 9,042 747 2,948 2,452

New Mexico 9,079 65,189 574 10,211 570 3,833 3,153

New York 19,731 21,172 731 14,653 1,076 3,248 2,331

North Carolina 17,367 44,141 1,076 71,113 654 13,368 9,983

North Dakota 7,437 53,778 867 105,344 383 6,757 5,870

Ohio 33,481 93,150 2,005 180,935 1,317 25,688 18,920

Oklahoma 26,165 47,454 957 141,433 1,073 4,457 3,530

Oregon 5,905 10,828 203 13,211 381 446 381

(continued)
5A-21

Table 5A-9. Future Year Baseline EGU CAP Emissions (tons/year) by State (continued)

State CO NOX VOC SO2 NH3 PM10 PM2.5

Pennsylvania 38,767 123,501 2,023 126,316 1,522 22,117 16,727

Rhode Island 1,748 456 44 0 136 7 4

South Carolina 10,305 37,516 726 103,694 515 14,469 9,997

South Dakota 742 14,293 129 29,711 48 764 737

Tennessee 10,693 16,982 862 33,080 406 6,313 5,053

Texas 78,317 145,182 4,975 249,748 5,304 31,404 21,677

Tribal 601 73 15 0 47 2 1

Utah 5,632 67,476 526 34,912 279 5,843 4,524

Vermont 1,868 458 52 264 25 69 67

Virginia 30,205 39,408 821 51,004 1,115 5,404 4,529

Washington 7,183 14,284 326 5,569 346 1,706 1,444

West Virginia 15,496 54,247 1,320 84,344 658 18,415 13,602

Wisconsin 19,247 35,179 1,137 50,777 649 6,503 5,323

Wyoming 9,087 71,380 970 48,198 481 7,385 5,662

Total 873,344 1,930,769 46,050 3,281,364 40,259 371,101 276,430

Note: Emission estimates apply to all fossil Electric Generating Units, including those with capacity < 25MW.

5A.5 Development of Future Year Control Case Emissions for Air Quality Modeling

For the future year control case (i.e., policy case) air quality modeling, the emissions for 

all sectors were unchanged from the base case modeling except for those from EGUs. The IPM 

model was used to prepare the future year policy case for EGU emissions. The air quality 

modeling for MATS relied on EGU emission projections from an interim IPM platform based on 

the Cross-state Air Pollution Rule version 4.10_FTransport, and was subsequently updated 

during the rulemaking process. The updates made include: updated assumptions regarding the 

removal of HCl by alkaline fly ash in subbituminous and lignite coals; an update to the fuelbased mercury emission factor for petroleum coke, which was corrected based on reexamination of the 1999 ICR data; updated capital cost for new nuclear capacity and nuclear life 

extension costs; corrected variable operating and maintenance cost (VOM) for ACI retrofits; 

adjusted coal rank availability for some units, consistent with EIA From 923 (2008); updated 

state rules in Washington and Colorado; and numerous unit-level revisions based on comments 

received through the notice and comment process. In particular, the policy case modeled with 
5A-22

CMAQ did not include the neutralization of 75% of HCl as did the final policy case. Additional 

details on the version of IPM used to develop the control case are available in Chapter 3.

The changes in EGU SO2, and PM2.5 emissions as a result of the policy case for the lower 

48 states are summarized in Table 5A-10. Table 5A-11 shows the CAP emissions for the 

modeled MATS control case by State. State-specific difference summaries of EGU SO2 and PM2.5

for the sum of the lower 48 states are shown in Tables 5A-12 and 5A-13, respectively. 

Table 5A-10.Summary of Emissions Changes for the MATS AQ Modeling in the Lower 48 

States

Future Year EGU Emissions SO2 PM2.5

Base case EGU emissions (tons) 3,281,364 276,430

Control case EGU emissions (tons) 1,866,247 223,320

Reductions to base case in control case (tons) 1,415,117 53,110

Percentage reduction of base EGU emissions 43% 19%

Total Man-Made Emissionsa

Total base case emissions (tons) 6,036,480 3,564,296

Total control case emissions (tons) 4,621,363 3,511,186

Percentage reduction of all man-made emissions 23% 1%

a

In this table, man-made emissions includes average fires.

Table 5A-11.EGU Emissions Totals for the Modeled MATS Control Case in the Lower 48 States

State CO NOX VOC SO2 NH3 PM10 PM2.5

Alabama 20,873 61,863 1,313 68,517 1,235 9,734 7,844

Arizona 13,238 34,804 749 23,459 921 4,264 3,494

Arkansas 9,036 35,788 642 35,112 490 1,696 1,593

California 56,360 27,159 1,307 5,041 2,548 1,057 942

Colorado 8,219 44,409 582 19,564 358 3,492 2,859

Connecticut 8,017 2,800 136 1,400 313 439 412

Delaware 1,312 2,527 67 4,160 93 3,056 1,455

District of Columbia

Florida 66,378 61,676 2,055 64,791 3,482 16,434 11,377

(continued)
5A-23

Table 5A-11.EGU Emissions Totals for the Modeled MATS Control Case in the Lower 48 States

(continued)

State CO NOX VOC SO2 NH3 PM10 PM2.5

Georgia 14,217 41,006 1,197 78,197 790 11,165 9,742

Idaho 1,523 609 41 182 56 38 36

Illinois 24,365 50,655 2,353 103,867 1,050 7,309 6,588

Indiana 17,061 102,045 1,872 156,781 1,110 29,683 20,388

Iowa 7,340 41,247 747 48,030 410 3,318 2,947

Kansas 4,683 22,136 623 22,767 282 2,504 2,263

Kentucky 25,911 70,126 1,476 125,430 882 12,544 10,635

Louisiana 28,171 31,655 767 30,509 1,261 2,003 1,899

Maine 10,992 5,683 302 1,372 267 342 331

Maryland 4,283 16,554 400 18,091 211 3,851 3,143

Massachusetts 5,408 7,211 226 5,033 344 1,702 1,267

Michigan 18,792 60,982 1,215 82,834 718 8,261 6,893

Minnesota 8,699 34,942 709 33,214 430 3,332 2,936

Mississippi 8,782 20,749 410 15,975 397 1,949 1,720

Missouri 12,249 52,755 1,605 95,965 686 5,216 4,809

Montana 2,223 19,758 264 6,399 133 2,637 1,727

Nebraska 4,493 28,180 533 34,631 277 2,152 1,828

Nevada 7,178 14,382 336 6,372 725 2,626 2,073

New Hampshire 6,781 4,862 232 2,102 232 1,336 1,264

New Jersey 8,350 7,699 315 6,404 546 2,020 1,583

New Mexico 7,987 64,922 545 9,984 554 2,961 2,750

New York 18,725 20,863 699 28,174 1,086 3,123 2,350

North Carolina 15,195 35,309 1,033 59,551 602 8,885 7,988

North Dakota 7,266 53,267 858 23,889 371 5,940 5,051

Ohio 29,956 85,565 1,852 139,208 1,229 19,599 15,823

Oklahoma 26,687 44,725 892 44,602 970 2,293 2,056

Oregon 6,002 9,671 198 3,565 379 241 233

Pennsylvania 24,865 104,906 1,645 93,606 1,349 17,330 14,080

(continued)
5A-24

Table 5A-11.EGU Emissions Totals for the Modeled MATS Control Case in the Lower 48 States

(continued)

State CO NOX VOC SO2 NH3 PM10 PM2.5

Rhode Island 1,721 443 43 0 134 7 4

South Carolina 9,826 37,849 725 40,901 459 9,627 6,963

South Dakota 641 14,290 117 2,483 41 260 245

Tennessee 5,551 16,931 723 42,666 334 6,721 5,272

Texas 71,475 138,086 4,444 105,958 4,774 25,359 17,601

Tribal 266 32 7 0 21 1 1

Utah 4,003 65,286 474 17,007 241 4,755 3,896

Vermont 1,868 458 52 264 25 69 67

Virginia 26,778 37,255 707 33,704 748 5,306 4,506

Washington 6,334 3,834 179 854 254 183 176

West Virginia 13,923 47,836 1,263 66,857 632 14,321 11,572

Wisconsin 16,124 32,865 1,012 28,322 578 4,725 3,969

Wyoming 7,516 71,135 932 28,456 467 5,946 4,671

Total 707,640 1,789,790 40,875 1,866,247 35,493 281,811 223,320

Table 5A-12. State Specific Changes in Annual EGU SO2 for the Lower 48 States

State

Future Year 

Baseline SO2

(tons)

Future Year Policy 

Case SO2

(tons)

EGU SO2 Reduction

(tons)

EGU SO2 Reduction

(%)

Alabama 186,084 68,517 117,568 63%

Arizona 36,996 23,459 13,537 37%

Arkansas 92,804 35,112 57,692 62%

California 5,346 5,041 305 6%

Colorado 74,255 19,564 54,690 74%

Connecticut 3,581 1,400 2,181 61%

Delaware 2,835 4,160 -1,324 -47%

District of Columbia 5 0 5 100%

(continued)
5A-25

Table 5A-12. State Specific Changes in Annual EGU SO2 for the Lower 48 States (continued)

State

Future Year 

Baseline SO2

(tons)

Future Year Policy 

Case SO2

(tons)

EGU SO2 Reduction

(tons)

EGU SO2 Reduction

(%)

Florida 117,702 64,791 52,911 45%

Georgia 96,712 78,197 18,515 19%

Idaho 182 182 0 0%

Illinois 118,217 103,867 14,350 12%

Indiana 200,969 156,781 44,189 22%

Iowa 85,178 48,030 37,148 44%

Kansas 45,740 22,767 22,973 50%

Kentucky 116,927 125,430 -8,503 -7%

Louisiana 142,447 30,509 111,938 79%

Maine 2,564 1,372 1,191 46%

Maryland 29,786 18,091 11,695 39%

Massachusetts 15,133 5,033 10,100 67%

Michigan 163,168 82,834 80,334 49%

Minnesota 52,380 33,214 19,165 37%

Mississippi 34,865 15,975 18,890 54%

Missouri 178,143 95,965 82,177 46%

Montana 24,018 6,399 17,618 73%

Nebraska 70,910 34,631 36,279 51%

Nevada 14,140 6,372 7,768 55%

New Hampshire 6,719 2,102 4,618 69%

New Jersey 9,042 6,404 2,638 29%

New Mexico 10,211 9,984 228 2%

New York 14,653 28,174 -13,521 -92%

North Carolina 71,113 59,551 11,562 16%

North Dakota 105,344 23,889 81,455 77%

Ohio 180,935 139,208 41,727 23%

Oklahoma 141,433 44,602 96,831 68%

(continued)
5A-26

Table 5A-12. State Specific Changes in Annual EGU SO2 for the Lower 48 States (continued)

State

Future Year 

Baseline SO2

(tons)

Future Year Policy 

Case SO2

(tons)

EGU SO2 Reduction

(tons)

EGU SO2 Reduction

(%)

Oregon 13,211 3,565 9,646 73%

Pennsylvania 126,316 93,606 32,710 26%

Rhode Island 0 0 0 N/A

South Carolina 103,694 40,901 62,793 61%

South Dakota 29,711 2,483 27,228 92%

Tennessee 33,080 42,666 -9,586 -29%

Texas 249,748 105,958 143,790 58%

Tribal 0 0 0 N/A

Utah 34,912 17,007 17,905 51%

Vermont 264 264 0 0%

Virginia 51,004 33,704 17,300 34%

Washington 5,569 854 4,716 85%

West Virginia 84,344 66,857 17,488 21%

Wisconsin 50,777 28,322 22,454 44%

Wyoming 48,198 28,456 19,742 41%

Total 3,281,364 1,866,247 1,415,117

Table 5A-13.State Specific Changes in Annual EGU PM2.5 for the Lower 48 States

State

Future Year 

Baseline PM2.5

(tons)

Future Year Policy 

Case PM2.5

(tons)

EGU PM2.5

Reduction

(tons)

EGU PM2.5

Reduction

(%)

Alabama 13,154 7,844 5,310 40%

Arizona 3,889 3,494 395 10%

Arkansas 2,838 1,593 1,246 44%

California 475 942 -467 -98%

Colorado 3,845 2,859 985 26%

Connecticut 400 412 -12 -3%

(continued)
5A-27

Table 5A-13. State Specific Changes in Annual EGU PM2.5 for the Lower 48 States (continued)

State

Future Year 

Baseline PM2.5

(tons)

Future Year Policy 

Case PM2.5

(tons)

EGU PM2.5

Reduction

(tons)

EGU PM2.5

Reduction

(%)

Delaware 434 1,455 -1,021 -235%

District of Columbia 1 0 1 100%

Florida 12,723 11,377 1,346 11%

Georgia 13,445 9,742 3,703 28%

Idaho 36 36 0 0%

Illinois 8,587 6,588 2,000 23%

Indiana 22,354 20,388 1,966 9%

Iowa 4,298 2,947 1,351 31%

Kansas 3,199 2,263 936 29%

Kentucky 12,078 10,635 1,443 12%

Louisiana 3,093 1,899 1,193 39%

Maine 355 331 24 7%

Maryland 3,969 3,143 826 21%

Massachusetts 1,465 1,267 198 14%

Michigan 8,102 6,893 1,210 15%

Minnesota 2,598 2,936 -339 -13%

Mississippi 2,201 1,720 481 22%

Missouri 7,061 4,809 2,252 32%

Montana 3,870 1,727 2,143 55%

Nebraska 2,358 1,828 530 22%

Nevada 2,505 2,073 432 17%

New Hampshire 1,130 1,264 -134 -12%

New Jersey 2,452 1,583 868 35%

New Mexico 3,153 2,750 403 13%

New York 2,331 2,350 -19 -1%

North Carolina 9,983 7,988 1,995 20%

North Dakota 5,870 5,051 819 14%

(continued)
5A-28

Table 5A-13. State Specific Changes in Annual EGU PM2.5 for the Lower 48 States (continued)

State

Future Year 

Baseline PM2.5

(tons)

Future Year Policy 

Case PM2.5

(tons)

EGU PM2.5

Reduction

(tons)

EGU PM2.5

Reduction

(%)

Ohio 18,920 15,823 3,097 16%

Oklahoma 3,530 2,056 1,474 42%

Oregon 381 233 148 39%

Pennsylvania 16,727 14,080 2,646 16%

Rhode Island 4 4 0 2%

South Carolina 9,997 6,963 3,033 30%

South Dakota 737 245 492 67%

Tennessee 5,053 5,272 -219 -4%

Texas 21,677 17,601 4,077 19%

Tribal 1 1 1 56%

Utah 4,524 3,896 627 14%

Vermont 67 67 0 0%

Virginia 4,529 4,506 24 1%

Washington 1,444 176 1,268 88%

West Virginia 13,602 11,572 2,031 15%

Wisconsin 5,323 3,969 1,354 25%

Wyoming 5,662 4,671 991 17%

Total 276,430 223,320 53,110
5B-1

APPENDIX 5B

IMPACT OF THE INTERIM POLICY SCENARIO ON AIR QUALITY

5B.1 Air Quality Modeling Platform

This document describes the air quality modeling performed by EPA in support of the 

final National Emissions Standard for Hazardous Air Pollutants (NESHAP) related to electrical 

generating utilities. A national scale air quality modeling analysis was performed to estimate 

the impact of the sector emissions changes on future year annual and 24-hour PM2.5 

concentrations, 8-hr maximum ozone, as well as visibility impairment. Air quality benefits are 

estimated with the Community Multi-scale Air Quality (CMAQ) model. CMAQ simulates the 

numerous physical and chemical processes involved in the formation, transport, and 

destruction of ozone, particulate matter and other air pollutants. In addition to the CMAQ 

model, the modeling platform includes the emissions, meteorology, and initial and boundary 

condition data which are inputs to this model.

Emissions and air quality modeling decisions are made early in the analytical process. 

For this reason, it is important to note that the inventories used in the air quality modeling may 

be slightly different than the final utility sector inventories presented in the RIA. However, the 

air quality inventories and the final rule inventories are generally consistent, so the air quality 

modeling adequately reflects the effects of the rule. Similarly, the projected future year 

inventory used for this analysis is generally representative of several years around 2017 such as 

2016.

Photochemical grid models use state of the science numerical algorithms to estimate 

pollutant formation, transport, and deposition over a variety of spatial scales that range from 

urban to continental. Emissions of precursor species are injected into the model where they 

react to form secondary species such as ozone and then transport around the modeling domain 

before ultimately being removed by deposition or chemical reaction. The 2005-based CMAQ

modeling platform was used as the basis for the air quality modeling for this rule. This platform 

represents a structured system of connected modeling-related tools and data that provide a 

consistent and transparent basis for assessing the air quality response to projected changes in 

emissions. The base year of data used to construct this platform includes emissions and 

meteorology for 2005. This modeling platform is described in more detail in the modeling 

technical support document for this rule (USEPA, 2011).
5.B.1.1 Photochemical Model Background

The Community Multi-scale Air Quality (CMAQ) model v4.7.1 (www.cmaq

a state of the science three-dimensional Eularian 

model used to estimate air quality (Appel et al., 2008; Appel et al., 2007; Byun and Schere, 

2006). CMAQ simulates the formation and fate of photochemical oxidants, ozone, primary and 

secondary PM concentrations, and air toxics over regional and urban sp

input sets of meteorological conditions and emissions. CMAQ is applied with the AERO5 aerosol 

module, which includes the ISORROPIA inorganic chemistry (Nenes et al., 1998) and a 

secondary organic aerosol module (Carlton et al., 2010)

and organic oxidation aqueous phase chemistry (Carlton et al., 2008) and the carbon

(CB05) gas-phase chemistry module (Gery et al., 1989).

5.B.1.2 Model Setup, Application, and Post

The modeling analyses were performed for a domain covering the continental United 

States, as shown in Figure 5B-1.

finer-scale 12 km grids over portions of the eastern and western U.S.

vertically from the surface to 100 millibars (approximately 15 km) using a sigma

coordinate system. Air quality conditions at the outer boundary of the 36 km domain were 

taken from a global model and vary in time and space. The 36 km grid was only used t

establish the incoming air quality concentrations along the boundaries of the 12 km grids.

the finer grid data were used in determining

provides geographic information about the photochemical model do

Figure 5B-1. Map of the Photochemical Modeling Domains.

36 km national modeling domain; the red inner box is the 12 km western U.S. grid; and the 

blue inner box is the 12 km eastern U.S. grid.

5B-2

Photochemical Model Background

scale Air Quality (CMAQ) model v4.7.1 (www.cmaq

dimensional Eularian “one-atmosphere” photochemical transpor

used to estimate air quality (Appel et al., 2008; Appel et al., 2007; Byun and Schere, 

2006). CMAQ simulates the formation and fate of photochemical oxidants, ozone, primary and 

secondary PM concentrations, and air toxics over regional and urban spatial scales for given 

input sets of meteorological conditions and emissions. CMAQ is applied with the AERO5 aerosol 

module, which includes the ISORROPIA inorganic chemistry (Nenes et al., 1998) and a 

secondary organic aerosol module (Carlton et al., 2010). The CMAQ model is applied with sulfur 

and organic oxidation aqueous phase chemistry (Carlton et al., 2008) and the carbon

phase chemistry module (Gery et al., 1989).

Model Setup, Application, and Post-Processing

analyses were performed for a domain covering the continental United 

This domain has a parent horizontal grid of 36 km with two 

scale 12 km grids over portions of the eastern and western U.S. The model extends 

lly from the surface to 100 millibars (approximately 15 km) using a sigma

Air quality conditions at the outer boundary of the 36 km domain were 

taken from a global model and vary in time and space. The 36 km grid was only used t

establish the incoming air quality concentrations along the boundaries of the 12 km grids.

the finer grid data were used in determining the impacts of the emissions changes. Table 

provides geographic information about the photochemical model domains.

Map of the Photochemical Modeling Domains. The black outer box denotes the 

36 km national modeling domain; the red inner box is the 12 km western U.S. grid; and the 

s the 12 km eastern U.S. grid.

scale Air Quality (CMAQ) model v4.7.1 (www.cmaq-model.org) is 

photochemical transport 

used to estimate air quality (Appel et al., 2008; Appel et al., 2007; Byun and Schere, 

2006). CMAQ simulates the formation and fate of photochemical oxidants, ozone, primary and 

atial scales for given 

input sets of meteorological conditions and emissions. CMAQ is applied with the AERO5 aerosol 

module, which includes the ISORROPIA inorganic chemistry (Nenes et al., 1998) and a 

. The CMAQ model is applied with sulfur 

and organic oxidation aqueous phase chemistry (Carlton et al., 2008) and the carbon-bond 2005 

analyses were performed for a domain covering the continental United 

This domain has a parent horizontal grid of 36 km with two 

The model extends 

lly from the surface to 100 millibars (approximately 15 km) using a sigma-pressure 

Air quality conditions at the outer boundary of the 36 km domain were 

taken from a global model and vary in time and space. The 36 km grid was only used to 

establish the incoming air quality concentrations along the boundaries of the 12 km grids. Only 

changes. Table 5B-1 

The black outer box denotes the 

36 km national modeling domain; the red inner box is the 12 km western U.S. grid; and the 
5B-3

Table 5B-1. Geographic Elements of Domains Used in Photochemical Modeling

Photochemical Modeling Configuration

National Grid Western U.S. Fine Grid Eastern U.S. Fine Grid

Map Projection Lambert Conformal Projection

Grid Resolution 36 km 12 km 12 km

Coordinate Center 97 deg W, 40 deg N

True Latitudes 33 deg N and 45 deg N

Dimensions 148 x 112 x 14 213 x 192 x 14 279 x 240 x 14

Vertical extent 14 Layers: Surface to 100 millibar level

The 36 km and both 12 km modeling domains were modeled for the entire year of 2005. 

Data from the entire year were utilized when looking at the estimation of PM2.5 and visibility 

impacts from the regulation. Data from April through October is used to estimate ozone 

impacts. 

As part of the analysis for this rulemaking, the modeling system was used to calculate 

daily and annual PM2.5 concentrations, 8-hr maximum ozone, and visibility impairment. Model 

predictions are used to estimate future-year design values of PM2.5 and ozone. Specifically, we 

compare a 2017 reference scenario to a 2017 control scenario. This is done by calculating the 

simulated air quality ratios between any particular future year simulation and the 2005 base. 

These predicted ratios are then applied to ambient base year design values. The design value 

projection methodology used here followed EPA guidance for such analyses (USEPA, 2007). 

5.B.1.3 Emissions Input Data

The emissions data used in the base year and future reference and future emissions 

adjustment case are based on the 2005 v4.1 platform. Emissions are processed to 

photochemical model inputs with the SMOKE emissions modeling system (Houyoux et al., 

2000). The 2017 reference case is intended to represent the emissions associated with growth 

and controls in that year projected from the 2005 simulation year. The United States EGU point 

source emissions estimates for the future year reference and control case are based on an 

Integrated Planning Model (IPM) run for criteria pollutants. Both control and growth factors 

were applied to a subset of the 2005 non-EGU point and non-point emissions to create the 

2017 reference case. The 2005 v4 platform projection factors were the starting point for most 
5B-4

of the 2017 SMOKE-based projections. The estimated total anthropogenic emissions and 

emissions for the utility sector used in this modeling assessment are shown in Appendix 5A. 

Other North American emissions are based on a 2006 Canadian inventory and 1999 Mexican 

inventory. Both inventories are not grown or controlled when used as part of future year 

inventories. Global emissions of criteria pollutants are included in the modeling system through 

boundary condition inflow. More details on these emissions are available in Appendix 5A. 

5B.2 Impacts of Sector on Future Annual PM2.5 Levels

This section summarizes the results of our modeling of annual average PM2.5 air quality 

impacts in the future due to reductions in emissions from this sector. Specifically, we compare a 

2017 baseline scenario to a 2017 control scenario. The modeling assessment indicates a 

decrease up to 1.03 μg/m3in annual PM2.5 design values is possible given an area’s proximity to 

controlled sources. The median reduction in annual PM2.5 design value over all monitor 

locations is 0.36 μg/m3. The change in future year projected design value is shown in Figure 

5B-2. Negative changes indicate an improvement in air quality. 

An annual PM2.5 design value is the concentration that determines whether a 

monitoring site meets the annual NAAQS for PM2.5. The full details involved in calculating an 

annual PM2.5 design value are given in appendix N of 40 CFR part 50. Projected air quality 

benefits are estimated using procedures outlined by United States Environmental Protection 

Agency modeling guidance (USEPA, 2007).
5B-5

Figure 5B-2. Change in Design Values Between the 2017 Baseline and 2017 Control 

Simulations. Negative numbers indicate lower (improved) design values in the control case 

compared to the baseline

5B.3 Impacts of Sector on Future 24-hour PM2.5 Levels

This section summarizes the results of our modeling of 24-hr average PM2.5 air quality 

impacts in the future due to reductions in emissions from this sector. Specifically, we compare a 

2017 baseline scenario to a 2017 control scenario. A decrease up to 1.9 μg/m3in 24-hr average 

PM2.5 design value at monitor locations in the United States is possible given an area’s 

proximity to controlled sources and the amount of reduced emissions from those sources. A 

median decrease of 0.6 μg/m3in 24-hr average PM2.5 design value at monitor locations in the 

United States is possible given an area’s proximity to controlled sources and the amount of 

reduced emissions from those sources. The change in future year projected design value is 

shown in Figure 5B-3. Negative changes indicate an improvement in air quality. 

A 24-hour PM2.5 design value is the concentration that determines whether a 

monitoring site meets the 24-hour NAAQS for PM2.5. The full details involved in calculating a 24-

hour PM2.5 design value are given in appendix N of 40 CFR part 50. Projected air quality benefits 

are estimated using procedures outlined by United States Environmental Protection Agency

modeling guidance (USEPA, 2007). 
5B-6

Figure 5B-3. Change in Design Values Between the 2017 Base Case and 2017 Control 

Simulations. Negative numbers indicate lower (improved) design values in the control case 

compared to the baseline.

5B.4 Impacts of Sector on Future Visibility Levels

Air quality modeling conducted for this rule was used to project visibility conditions in 

138 mandatory Class I federal areas across the U.S. in 2017 (USEPA, 2007). The level of visibility 

impairment in an area is based on the light-extinction coefficient and a unitless visibility index, 

called a “deciview,” which is used in the valuation of visibility. The deciview metric provides a 

scale for perceived visual changes over the entire range of conditions, from clear to hazy. Under 

many scenic conditions, the average person can generally perceive a change of one deciview. 

Higher deciview values are indicative of worse visibility. Thus, an improvement in visibility is a 

decrease in deciview value. 

The modeling assessment indicates a median visibility improvement of 0.09 deciviews in 

annual 20% worst visibility days over all Class I area monitors. An improvement in visibility up to 

0.97 deciviews on the 20% worst visibility days at Class I monitor locations in the United States 

is possible given an area’s proximity to controlled sources and the amount of reduced emissions 
5B-7

from these sources. The change in future year projected visibility is shown in Figure 5B-4. 

Negative changes indicate an improvement in air quality. 

Figure 5B-4. Change in 20% Worst Days Between the 2017 Baseline and 2017 Control 

Simulations. Negative numbers indicate lower (improved) visibility expressed in deciviews in 

the control case compared to the baseline

5B.5 Impacts of Sector on Future Ozone Levels

This section summarizes the results of our modeling of 8-hr maximum ozone air quality 

impacts in the future due to reductions in emissions from this sector. Specifically, we compare a 

2017 baseline scenario to a 2017 control scenario. The modeling assessment indicates a 

decrease of up to 3.5 ppb in 8-hr averaged ozone design value is possible given an area’s 

proximity to controlled sources and the amount of reduced emissions from these sources. A 

median decrease across all monitors of 0.20 ppb in 8-hr averaged ozone design value is possible 

given an area’s proximity to controlled sources and the amount of reduced emissions from 

these sources. The change in future year projected design value is shown in Figure 5B-5. 

Negative changes indicate an improvement in air quality. The full details involved in calculating 
5B-8

design value are given in appendix P of 40 CFR part 50. Projected air quality benefits are 

estimated using procedures outlined by United States Environmental Protection Agency

modeling guidance (USEPA, 2007).

Figure 5B-5. Change in Design Values Between the 2017 Baseline and 2017 Control 

Simulations. Negative numbers indicate lower (improved) design values in the control case 

compared to the baseline

5B.6 References

Appel, K.W., Bhave, P.V., Gilliland, A.B., Sarwar, G., Roselle, S.J., 2008. Evaluation of the 

community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting 

model performance; Part II—particulate matter. Atmospheric Environment 42, 6057-

6066.

Appel, K.W., Gilliland, A.B., Sarwar, G., Gilliam, R.C., 2007. Evaluation of the Community 

Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model 

performance Part I—Ozone. Atmospheric Environment 41, 9603-9615.
5B-9

Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and 

other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling 

system. Applied Mechanics Reviews 59, 51-77.

Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.D., Sarwar, G., Pinder, R.W., Pouliot, G.A., 

Houyoux, M., 2010. Model Representation of Secondary Organic Aerosol in CMAQv4.7. 

Environmental Science & Technology 44, 8553-8560.

Carlton, A.G., Turpin, B.J., Altieri, K.E., Seitzinger, S.P., Mathur, R., Roselle, S.J., Weber, R.J., 

2008. CMAQ Model Performance Enhanced When In-Cloud Secondary Organic Aerosol 

is Included: Comparisons of Organic Carbon Predictions with Measurements. 

Environmental Science & Technology 42, 8798-8802.

Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A Photochemical Kinetics Mechanism 

for Urban and Regional Scale Computer Modeling. Journal of Geophysical ResearchAtmospheres 94, 12925-12956.

Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S., 2000. Emission 

inventory development and processing for the Seasonal Model for Regional Air Quality 

(SMRAQ) project. Journal of Geophysical Research-Atmospheres 105, 9079-9090.

Nenes, A., Pandis, S.N., Pilinis, C., 1998. ISORROPIA: A new thermodynamic equilibrium model 

for multiphase multicomponent inorganic aerosols. Aquatic Geochemistry 4, 123-152.

USEPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze, RTP.

USEPA, 2011. Air Quality Modeling Technical Support Document: Final EGU NESHAP (EPA454/R-11-009), Research Triangle Park, North Carolina.
5C-1

APPENDIX 5C

HEALTH AND WELFARE CO-BENEFITS OF THE MODELED INTERIM POLICY SCENARIO

In this appendix to the co-benefits chapter we report the estimates of the benefits of 

reductions in emissions of SO2 and directly emitted PM2.5 based on air quality modeling of an 

interim policy scenario. 

As noted in Chapter 5 of the RIA, the air quality modeling performed for the RIA does 

not reflect the emission changes associated with the final rule requirements. To estimate the 

benefits of those emissions changes for the final rule, we developed BPT estimates for SO2 and 

directly emitted PM2.5 based on air quality modeling of an interim policy scenario. These BPT

values were used to adjust benefits estimates for changes in the emission reductions resulting 

from the final policy scenario. This appendix reports the results of the benefits analysis 

associated with the modeled interim policy scenario described in Appendix 5A and 5B, along 

with the derivation of BPT values used to estimate the health benefits of the final policy 

scenario.

As described in the benefits chapter, the chief difference between the modeled and 

revised scenarios relates to the magnitude and distribution of SO2 emission reductions (Figure 

5C-1). In general, the modeled and revised policy cases achieve roughly similar levels of SO2

reductions (1.42 versus 1.33 million tons, respectively) with a similar distribution among states.

However, for some states (notably Alabama, Colorado, Louisiana, Michigan, Missouri, North 

Dakota, Oklahoma, and Texas), SO2 emission reductions were lower for the final case versus the 

interim case. By far, the greatest difference in SO2 emission reductions was in Michigan where 

the final case emission reduction was 70% lower than for the interim case. In a few states 

(notably Arkansas, Ohio, and South Carolina), SO2 emission reductions were slightly larger for 

the final case versus the interim case. Since differences between the interim and final cases are 

not concentrated in any particular region of the country and the overall distribution of emission 

reductions is similar, we conclude that it is reasonable to apply BPT values derived from the 

interim case to the final case. While NOx emissions reductions decreased by 70% between the 

interim and final cases (141,000 vs. 46,000 tons), the impact of NOx on PM2.5 concentrations 

and mortality is very minor relative to the impact of SO2 emission reductions. Therefore, 

differences in the magnitude and distribution of NOx emission reductions are likely to have only 

a minor effect on results.
5C-2

5C.1 PM2.5-Related Health Impacts and Monetized Benefits of Reductions in Emissions of 

SO2 and Directly Emitted PM2.5 for the Air Quality Modeled Interim Policy Scenario

Health benefits of the interim policy scenario are calculated using the modeled changes 

in PM2.5 concentration described in Appendix 5B, which result from the emission changes 

described in Appendix 5A. Concentration changes are input into BenMAP to calculate the 

changes in incidence of an array of health endpoints, along with their associated monetary 

value. BenMAP is described in more detail in Chapter 5. In addition, more information can be 

found at http://www.epa.gov/air/benmap/.

Tables 5C-1 and 5C-2 summarize the PM2.5-related health impacts and monetized 

benefits of the air quality modeled interim policy scenario. Not all known PM- and ozonerelated health and welfare 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 equal to the subset of monetized PM- and 

ozone-related health and welfare benefits plus B, the sum of the nonmonetized health and 

welfare benefits; this B represents both uncertainty and a bias in this analysis, as it reflects 

those benefits categories that we are unable quantify in this analysis. Figure 5C-2 illustrates the 

distribution of avoided PM-related deaths by county across the U.S.

Methods for quantifying recreational visibility are described in Section 5.5.1. Visibility 

benefits are calculated for the modeled interim policy scenario only since there is no analogous 

approach for estimating visibility benefits using the BPT approach. However, the magnitude of 

SO2 emission reductions did not significantly change in the visibility study areas between the 

interim and final emissions scenarios. Therefore, we expect the visibility benefit for the final 

policy scenario would be similar to that calculated for the interim policy scenario ($1.1 billion in 

total for the U.S., using 2007$). These benefits are not included in the co-benefits estimate of 

the final policy.
Figure 5C-1. Comparison of state

scenario and the final policy.

5C-3

1. Comparison of state-level SO2 emission changes between the interim modeled 
5C-4

Table 5C-1. Estimated Reduction in Incidence of Adverse Health Effects of the Interim 

Modeled Mercury and Air Toxics Standards in 2016 (95% confidence intervals)a,b

Impact Eastern U.S.c Western U.S. Total

Premature Mortality

Pope et al. (2002) (age >30) 4,200

(1,200—7,300)

120

(19—220)

4,400

(1,200—7,500)

Laden et al. (2006) (age 

>25)

11,000

(5,000—17,000)

310

(110—510)

11,000

(5,100—17,000)

Infant (< 1 year) 20

(-22—61)

1

(-1—2)

20

(-23—63)

Chronic Bronchitis 2,800

(94—5,500)

100

(-12—210)

2,900

(82—5,800)

Non-fatal heart attacks (age > 

18)

4,800

(1,200—8,400)

110

(13—220)

4,900

(1,200—8,600)

Hospital admissions—

respiratory (all ages)

840

(340—1,300)

16

(5—27)

860

(340—1,400)

Hospital admissions—

cardiovascular (age > 18)

1,800

(1,200—2,200)

41

(26—51)

1,900

(1,300—2,200)

Emergency room visits for 

asthma (age < 18)

3,100

(1,600—4,700)

100

(44—160)

3,200

(1,700—4,800)

Acute bronchitis (age 8-12) 6,200

(-1,400—14,000)

240

(-100—570)

6,500

(-1,500—14,000)

Lower respiratory symptoms 

(age 7-14)

80,000

(31,000—130,000)

3,000

(860—5,200)

83,000

(32,000—130,000)

Upper respiratory symptoms

(asthmatics age 9-18)

60,000

(11,000—110,000)

2,300

(130—4,400)

62,000

(11,000—110,000)

Asthma exacerbation

(asthmatics 6-18)

130,000

(4,700—450,000)

5,000

(-520—17,000)

140,000

(4,200—460,000)

Lost work days (ages 18-65) 540,000

(460,000—620,000)

20,000

(16,000—24,000)

560,000

(470,000—640,000)

Minor restricted-activity days

(ages 18-65)

3,200,000

(2,600,000—3,800,000)

120,000

(93,000—140,000)

3,300,000

(2,700,000—3,900,000)

a

Estimates rounded to two significant figures; column values will not sum to total value.

b

The negative estimates for certain endpoints are the result of the weak statistical power of the study used to 

calculate these health impacts and do not suggest that increases in air pollution exposure result in decreased 

health impacts.

c

Includes Texas and those states to the north and east.
5C-5

Table 5C-2. Estimated Economic Value of Health and Welfare Benefits of the Interim

Modeled Mercury and Air Toxics Standards in 2016 (95% confidence intervals, 

billions of 2007$)

Impact Pollutant Eastern U.S.a Western U.S. Total

Premature mortality (Pope et al. 2002 PM mortality estimate)

3% discount rate PM2.5$34

($2.7—$100)

$1.0

($0.1—$3.1)

$35

($2.8—$100)

7% discount rate PM2.5$31

($2.4—$93)

$0.9

($0.1—$2.8)

$32

($2.5—$96)

Premature mortality (Laden et al. 2006 PM mortality estimate)

3% discount rate PM2.5$87

($7.7—$250)

$2.5

($0.2—$7.5)

$90

($7.9—$260)

7% discount rate PM2.5$79

($6.9—$230)

$2.3

($0.2—$6.7)

$81

($7.1—$240)

Infant mortality PM2.5$0.2

($-0.2—$0.8)

<$0.01 $0.2

($-0.2—$0.8)

Chronic bronchitis PM2.5$1.4

($0.1—$6.4)

$0.05

(<$0.01—$0.23)

$1.4

($0.1—$6.6)

Non-fatal heart attacks 

3% discount rate PM2.5$0.5

($0.1—$1.3)

$0.01

(<$0.01—$0.03)

$0.6

($0.1—$1.3)

7% discount rate PM2.5$0.4

($0.1—$1.0)

$0.01

(<$0.01—$0.03)

$0.4

($0.1—$1.0)

Hospital admissions—

respiratory PM2.5

$0.01

($0.01—$0.02)

<$0.01 $0.01

($0.01—$0.02)

Hospital admissions—

cardiovascular PM2.5

$0.03

(<$0.01—$0.05)

<$0.01 $0.03

(<$0.01—$0.06)

Emergency room visits for 

asthma PM2.5

<$0.01 <$0.01 <$0.01

Acute bronchitis PM2.5 <$0.01 <$0.01 <$0.01

Lower respiratory 

symptoms 

PM2.5

<$0.01 <$0.01 <$0.01

Upper respiratory 

symptoms 

PM2.5

<$0.01 <$0.01 <$0.01

Asthma exacerbation PM2.5 <$0.01 <$0.01 <$0.01

Lost work days PM2.5$0.1

($0.1—$0.1)

<$0.01 $0.1

($0.1—$0.1)

Minor restricted-activity 

days PM2.5

$0.2

($0.1—$0.3)

<$0.01 $0.2

($0.1—$0.3)

Recreational visibility, Class 

I areas PM2.5

$0.9 $0.2 $1.1

Social cost of carbon (3% 

discount rate, 2016 value) CO2

(continued)
Table 5C-2. Estimated Economic Value of Health and Welfare Benefits 

Modeled Mercury and Air Toxics Standards 

billions of 2007$) (continued)

Impact Pollutant

Total Monetized benefits (Pope et al. 2002 PM

3% discount rate

7% discount rate

Total Monetized benefits (Laden et al. 2006 PM

3% discount rate

7% discount rate

a

Includes Texas and those states to the north and east.

Figure 5C-2. Estimated Reduction in Excess PM

Occur in Each County in 2016 as a Result of the 

Standards

5C-6

Estimated Economic Value of Health and Welfare Benefits of the 

Mercury and Air Toxics Standards in 2016 (95% confidence intervals, 

(continued)

Pollutant Eastern U.S. Western U.S.

(Pope et al. 2002 PM2.5 mortality estimates)

$37+B

($3.7—$110)

$1.2+B

($0.2—$3.6) ($4.0

$34+B

($3.5—$100)

$1.1+B

($0.2—$3.3) ($3.7

(Laden et al. 2006 PM2.5 mortality estimates)

$91+B

($8.7—$260)

$2.8+B

($0.4—$8.0) ($9.1

$82+B

($7.9—$240)

$2.5+B

($0.4—$7.2) ($8.3

Includes Texas and those states to the north and east.

. Estimated Reduction in Excess PM2.5-Related Premature Deaths Estimated to 

Occur in Each County in 2016 as a Result of the Interim Modeled Mercury and Air Toxics 

of the Interim 

(95% confidence intervals, 

Total

$39+B

($4.0—$120)

$35+B

($3.7—$110)

$93+B

($9.1—$270)

$84+B

($8.3—$240)

Related Premature Deaths Estimated to 

Mercury and Air Toxics 
5C-7

5C.2 Derivation of the BPT Values Used to Calculate the Health Benefits of the Final Policy 

Scenario

The health benefits summarized in Tables C-1 and C-2 include impacts of changes in 

sulfate, nitrate, and direct PM2.5. To quantify the health benefits of the final policy scenario 

reported in Chapter 5, we calculate the health benefits per ton of emission reduced, separately 

for eastern and western states, and separately for SO2, directly emitted carbonaceous PM2.5, 

and directly emitted crustal PM2.5. This calculation is shown by Equation 1:

,, =

,,,

∆,,

where BPT is the BPT for a particular pollutant i (SO2, directly emitted carbonaceous PM2.5, or 

directly emitted crustal PM2.5), region j (Eastern U.S. or Western U.S.), health endpoint k (e.g. 

adult mortality, infant mortality, etc.), and the interim baseline and policy scenario (denoted as 

“1”). As described in Chapter 5, we do not generate BPT values for NOx. Nitrate increases in the 

modeled policy scenario were two orders of magnitude smaller than the sulfate decreases and 

were not included in the BPT estimates. Including nitrate in the SO2 BPT estimate would reduce 

the SO2 BPT by 1-2%, with a corresponding impact on the total health benefits of the rule.

Furthermore, as described in Appendix 5A, NOx emission changes resulting from this rule were 

75% smaller for the final policy scenario relative to the interim modeled policy scenario.

Therefore, excluding the impacts of NOx emission changes is unlikely to materially impact the 

final benefit results.

Table 5C-3 reports the economic value of the adult mortality benefits resulting from 

reductions in SO2, directly emitted carbonaceous PM2.5, and directly emitted crustal PM2.5 for 

the modeled interim policy scenario, along with the BPT values derived from these benefits.

Only adult mortality benefits are shown here as they contribute 93-97% of the total health 

benefits, however, BPT values were calculated and applied separately for each health endpoint.

Since premature mortality is discounted after BPT values are applied to the final emissions, the 

values reported in Table 5C-3 are not discounted. Sulfate reductions resulting from SO2

emission reductions contribute approximately 95% of the benefits of SO2 and directly emitted 

PM2.5 combined. In some locations, directly emitted carbonaceous PM2.5 increased slightly in 

the Western U.S. for the interim policy scenario relative to the interim baseline, which overall 

resulted in negative BPT values for the West. However, since the magnitudes of the emission 

and concentration changes are small relative to the changes in SO2 emissions and sulfate 

concentrations, the resulting increase in premature mortality is only 0.04% of the total health 

impact of the rule. 
5C-8

Table 5C-3. Estimated Economic Value of Adult Mortality Benefits by Pollutant, in Total and 

Per Ton of Emissions Reduced Interim Modeled Mercury and Air Toxics Standard 

in 2016 (95% confidence intervals, 2007$)

Pollutant and Source of Adult 

Mortality Estimate

Total Monetized Benefits 

(billions) BPT (thousands)

Eastern U.S.a Western U.S. Eastern U.S. a Western U.S.

SO2 emissions (tons)b1,268,961 146,155

Pope et al. (2002) estimate $36

($2.9—$110)

$1.2

($0.1—$3.7)

$29

($2.3-$87)

$8.3

($0.1-$25)

Laden et al. (2006) estimate $93

($8.2—$270)

$3.1

($0.3—$9.0)

$73

($6.4-$210)

$21

($1.9-$62)

Carbonaceous PM2.5 emissions (tons) b5,860 231

Pope et al. (2002) estimatec$1.3

($0.1—$3.9)

<-$0.01 $220

($17-$670)

-$66

(-$450-$210)

Laden et al. (2006) estimatec$3.3

($0.3—$9.6)

<-$0.01 $560

($49-$1,600)

-$170

(-$960-$350)

Crustal PM2.5 emissions (tons) b34,742 29,148

Pope et al. (2002) estimate $0.6

(<$0.01—$1.9)

$0.1

(<$0.01—$0.2)

$18

($1.4-$55)

$9.6

($0.1-$31)

Laden et al. (2006) estimate $1.6

($0.1—$4.7)

$0.1

(<$0.01—$0.4)

$47

($4.1-$140)

$25

($2.1-$74)

a

Includes Texas and those states to the north and east.

b

Emission reductions are reported for the modeled interim policy case, from which the BPT values were 

generated.

C

Directly emitted carbonaceous PM2.5 increased slightly in some locations in the Western U.S. for the interim 

policy scenario relative to the interim baseline, which overall resulted in negative BPT values for the West. 

However, since the magnitudes of the emission and concentration changes are small relative to the changes in 

SO2 emissions and sulfate concentrations, the resulting increase in premature mortality is only 0.04% of the total 

health impact of the rule. 

The BPT values reported in Table 5C-3, along with those calculated for the other health 

endpoints listed in Table 5C-2, are applied to the final emission changes described in Chapter 3, 

resulting in the final benefit values summarized in Chapter 5. This calculation is shown by 

Equation 2:

,,, = ∆,,  ,,

where 2 refers to the final baseline and policy scenarios.
5D-1

APPENDIX 5D

PM2.5 CO-BENEFITS OF THE FINAL RULE BY STATE

5D.1 Introduction

This appendix describes the distribution of the health-related PM2.5 co-benefits 

associated with this rule by state. We describe our approach for allocating the national-level 

PM2.5-related mortality and monetized benefits to the state-level. We also summarize the 

results of this analysis and describe the limitations and uncertainties associated with our 

approach. This rule is expected to achieve PM2.5-related health benefits in all states, resulting 

from both emission reductions in that state and reduced transport of PM2.5 between states. A 

key limitation of our approach is that it does not account for differences in the distribution of 

SO2 and direct PM2.5 emission reductions between the modeled interim scenario and the final 

policy (see Appendix 5C). PM2.5-related co-benefits may therefore be under- or over-estimated 

for certain states. 

5D.2 Methods

As described in Appendix 5C, the PM2.5 health co-benefits of the final rule are calculated 

using a BPT approach. The BPT values are derived from air quality modeling of an interim 

emissions scenario. Since the distribution of the SO2 emission reductions in the interim 

modeled scenario and the final policy were generally consistent, applying BPT values from the 

interim modeled scenario to the final policy reasonably approximates the total monetized 

benefits of the final policy. However, this approach requires aggregation of benefits in the 

interim scenario to larger spatial scales to account for transport of pollution across state 

boundaries. Therefore, the final rule benefits described in Appendix 5C are estimated for the 

eastern and western US, the same resolution at which BPT values were generated from the 

interim scenario. 

Since spatially resolved estimates of the co-benefits are useful for understanding how 

the expected benefits of this rule are distributed across the U.S., we developed an 

approximating approach for allocating national-level PM2.5 co-benefits estimated for the final 

policy to the state level. This approach follows three steps. First, we quantified the state-level 

mortality and monetized health co-benefits of the air quality modeled scenario using the 

BenMAP software. From these results, we calculated the percentage of national health 

benefits occurring in each state. Finally, these percentages were used to scale the national 

health benefits of the final policy down to the state level. 
5D-2

As another approach, EPA considered scaling the health co-benefits of the final policy 

scenario by the percentage of the national total SO2 emission change occurring in each state, 

since the distribution of emission changes across the U.S. changed between the modeled 

interim scenario and the final policy. However, such an approach would not account for the 

population in each state which is a main driver for air pollution health impacts, nor would it 

account for transport of pollution across state lines. Therefore, EPA judged that scaling 

national health co-benefits of the final rule by the state distribution of the co-benefits of the 

interim modeled scenario is a more appropriate approach. 

5D.3 Limitations and uncertainties

The method described above adds unique uncertainties and limitations beyond those 

already described in detail in Chapter 5. A key limitation of this approach is that the 

distribution of SO2 and direct PM2.5 emissions changed between the modeled interim scenario 

and the final policy (see Appendix 5C). Differences in the emission changes would have an 

effect on the percentage of health co-benefits occurring in each state for the final policy. 

However, our approach necessarily assumes that the state distribution of health co-benefits for 

the final policy is equivalent to that of the modeled interim scenario. PM2.5-related health cobenefits for this rule could therefore be under- or over-estimated for certain states.

5D.4 Results

The reduction in incidence of adult premature PM2.5-related mortality for the final rule 

by state is shown in Table 5D-1. Additional non-mortality heath benefits are also expected in 

each state but are not included here. The greatest percentage of interim mortality benefits in 

any one state is in Texas (10.8%), followed by Florida (6.7%). For the final policy, 460 to 1,200 

avoided premature deaths are estimated to be avoided in Texas, and 280 to 750 in Florida, 

depending on the concentration-response factor. Although SO2 emissions in some states (e.g. 

Kentucky, New York, Tennessee) increase between the interim baseline and interim policy 

scenario (see Appendix 5A), mortality decreases in these states due to reduced transport of 

pollution from other states. All states, therefore, experience health benefits from the interim 

scenario and the final rule. Table 5D-2 shows the estimated economic value of health and 

welfare benefits by state for the final rule. Approximately $4.0 to $9.7 billion (2007$, 3%

discount rate) in benefits are expected to occur in Texas, and $2.4 to $6.0 billion in Florida, 

depending on the concentration-response function used for adult mortality.
5D-3

Table 5D-1. Estimated Reduction in Incidence of Premature Adult Mortality for the Mercury 

and Air Toxics Standards in 2016 by Statea,b

State Percent of total 

interim benefits

Final policy benefits – adult mortality

Pope et al. (2002) estimate Laden et al. (2006) estimate

Alabama 3.34 140 360

Arizona 0.32 14 35

Arkansas 2.28 96 250

California 0.13 6 14

Colorado 1.26 53 140

Connecticut 0.84 35 90

Delaware 0.30 13 32

DC 0.14 6 15

Florida 6.73 280 730

Georgia 4.53 190 490

Idaho 0.06 3 6

Illinois 5.31 220 570

Indiana 2.64 110 290

Iowa 1.45 61 160

Kansas 1.43 60 160

Kentucky 1.97 83 210

Louisiana 2.68 110 290

Maine 0.19 8 20

Maryland 1.99 84 220

Massachusetts 1.23 52 130

Michigan 3.78 160 410

Minnesota 1.34 57 150

Mississippi 2.21 93 240

Missouri 3.79 160 410

Montana 0.07 3 8

Nebraska 0.67 28 72

Nevada 0.09 4 10

New Hampshire 0.23 10 25

New Jersey 2.96 130 320

New Mexico 0.22 9 24

New York 4.11 170 440

North Carolina 4.42 190 480

North Dakota 0.17 7 19

Ohio 5.19 220 560

Oklahoma 2.82 120 300

Oregon 0.11 5 12

Pennsylvania 4.91 210 530

Rhode Island 0.27 11 29

South Carolina 3.01 130 330

South Dakota 0.25 11 27

Tennessee 3.38 140 370

Texas 10.82 460 1200

Utah 0.20 8 22

Vermont 0.09 4 10

Virginia 2.76 120 300

Washington 0.28 12 31
5D-4

West Virginia 0.89 38 96

Wisconsin 2.06 87 220

Wyoming 0.05 2 6

National Total 4,200 11,000

a

State level benefits of the final rule are scaled by the distribution of mortality benefits simulated from the 

interim scenario described in Appendices 5A, 5B, and 5C.

b

Estimates rounded to two significant figures; column values will not sum to total value. These estimates do not 

include confidence intervals.
5D-5

Table 5D-2. Estimated Economic Value of Health Benefits of the Mercury and Air Toxics 

Standard in 2016 by State (billions of 2007$, 3% discount rate)

a,b

State Percent of total 

interim benefits

Health benefits (billions of 2007$, 3% discount rate)

Pope et al. (2002) estimate Laden et al. (2006) estimate

Alabama 3.32 $1.20 $3.00

Arizona 0.32 $0.12 $0.29

Arkansas 2.27 $0.82 $2.00

California 0.13 $0.05 $0.12

Colorado 1.28 $0.46 $1.10

Connecticut 0.84 $0.30 $0.75

Delaware 0.30 $0.11 $0.27

DC 0.14 $0.05 $0.12

Florida 6.68 $2.40 $6.00

Georgia 4.56 $1.70 $4.10

Idaho 0.06 $0.02 $0.05

Illinois 5.34 $1.90 $4.70

Indiana 2.64 $0.96 $2.40

Iowa 1.45 $0.52 $1.30

Kansas 1.43 $0.52 $1.30

Kentucky 1.97 $0.71 $1.80

Louisiana 2.67 $0.97 $2.40

Maine 0.19 $0.07 $0.17

Maryland 1.99 $0.72 $1.80

Massachusetts 1.23 $0.45 $1.10

Michigan 3.78 $1.40 $3.40

Minnesota 1.35 $0.49 $1.20

Mississippi 2.20 $0.80 $2.00

Missouri 3.78 $1.40 $3.40

Montana 0.07 $0.03 $0.06

Nebraska 0.67 $0.24 $0.60

Nevada 0.09 $0.03 $0.08

New Hampshire 0.23 $0.08 $0.21

New Jersey 2.96 $1.10 $2.60

New Mexico 0.22 $0.08 $0.20

New York 4.12 $1.50 $3.70

North Carolina 4.41 $1.60 $3.90

North Dakota 0.17 $0.06 $0.15

Ohio 5.17 $1.90 $4.60

Oklahoma 2.81 $1.00 $2.50

Oregon 0.11 $0.04 $0.10

Pennsylvania 4.87 $1.80 $4.40

Rhode Island 0.27 $0.10 $0.24

South Carolina 2.99 $1.10 $2.70

South Dakota 0.25 $0.09 $0.23

Tennessee 3.38 $1.20 $3.00

Texas 10.95 $4.00 $9.70

Utah 0.20 $0.07 $0.18

Vermont 0.09 $0.03 $0.08

Virginia 2.77 $1.00 $2.50

Washington 0.28 $0.10 $0.25
5D-6

West Virginia 0.89 $0.32 $0.79

Wisconsin 2.06 $0.75 $1.80

Wyoming 0.05 $0.02 $0.05

National Totalc$36.00 $89.00

a

State level benefits of the final rule are scaled by the distribution of mortality benefits simulated from the 

interim scenario described in Appendices 5A, 5B, and 5C.

b

Estimates rounded to two significant figures; column values will not sum to total value. These estimates do not 

include confidence intervals.

c While climate benefits are included in the total co-benefits of this rule as described in Chapter 5, only health 

benefits (sum of mortality and morbidity endpoints) are included in the national total here. 
5E-1

APPENDIX 5E

TECHNICAL SUPPORT DOCUMENT:

SUMMARY OF EXPERT OPINIONS ON THE EXISTENCE OF A THRESHOLD IN THE 

CONCENTRATION-RESPONSE FUNCTION FOR PM2.5-RELATED MORTALITY28

 

28 U.S. Environmental Protection Agency. 2010. 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>.
5E-2

Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-related Mortality

Technical Support Document (TSD)

June 2010

Compiled by:

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Health and Environmental Impact Division

Air Benefit-Cost Group

Research Triangle Park, North Carolina

Contents:

A. HES comments on 812 Analysis (2010)

B. American Heart Association Scientific Statement (2010)

C. Integrated Science Assessment for Particulate Matter (2009)

D. CASAC comments on PM ISA and REA (2009)

E. Krewski et al. (2009)

F. Schwartz et al. (2008)

G. Expert Elicitation on PM Mortality (2006, 2008)

H. CASAC comments on PM Staff Paper (2005)

I. HES comments on 812 Analysis (2004)

J. NRC (2002)
5E-3

A. HES Comments on 812 Analysis (2010)

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2010. 

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/0/72D4EFA39E48CDB28525774500738776/$

File/EPA-COUNCIL-10-001-unsigned.pdf>.

Pg 2: “The HES generally agrees with other decisions made by the EPA project team with 

respect to PM, in particular, the PM mortality effect threshold model, the cessation lag model, 

the inclusion of infant mortality estimation, and differential toxicity of PM.”

Pg 2: “Further, the HES fully supports EPA’s use of a no-threshold model to estimate the 

mortality reductions associated with reduced PM exposure.”

Pg 6: “The HES also supports the Agency’s choice of a no-threshold model for PM-related 

effects.”

Pg 13: “The HES fully supports EPA’s decision to use a no-threshold model to estimate mortality 

reductions. This 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. Therefore, there is 

no evidence to support a truncation of the CRF.”

HES Panel Members

Dr. John Bailar, Chair of the Health Effects Subcommittee, Scholar in Residence, The National 

Academies, Washington, DC

Dr. Michelle Bell, Associate Professor, School of Forestry and Environmental Studies, Yale 

University, New Haven, CT

Dr. James K. Hammitt, Professor, Department of Health Policy and Management, Harvard 

School of Public Health, Boston, MA

Dr. Jonathan Levy, Associate Professor, Department of Environmental Health, Harvard School 

of Public Health, Boston, MA

Dr. C. Arden Pope, III Professor, Department of Economics, Brigham Young University, 

Provo, UT

Mr. John Fintan Hurley, Research Director, Institute of Occupational Medicine (IOM), 

Edinburgh, United Kingdom, UK

Dr. Patrick Kinney, Professor, Department of Environmental Health Sciences, Mailman School 

of Public Health, Columbia University, New York, NY

Dr. Michael T. Kleinman, Professor, Department of Medicine, Division of Occupational and 

Environmental Medicine, University of California, Irvine, Irvine, CA

Dr. Bart Ostro, Chief, Air Pollution Epidemiology Unit, Office of Environmental Health 

Hazard Assessment, California Environmental Protection Agency, Oakland, CA
5E-4

Dr. Rebecca Parkin, Professor and Associate Dean, Environmental and Occupational Health, 

School of Public Health and Health Services, The George Washington University Medical 

Center, Washington, DC
5E-5

B. Scientific Statement from American Heart Association (2010)

Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux AV, Holguin 

F, Hong Y, Luepker RV, Mittleman MA, Peters A, Siscovick D, Smith SC Jr, Whitsel L, 

Kaufman JD; on behalf of the American Heart Association Council on Epidemiology and 

Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition, 

Physical Activity and Metabolism. (2010). “Particulate matter air pollution and 

cardiovascular disease: an update to the scientific statement from the American Heart 

Association.” Circulation. 121: 2331-2378.

Pg 2338: “Finally, there appeared to be no lower-limit threshold below which PM10 was not 

associated with excess mortality across all regions.”

Pg 2350: “There also appears to be a monotonic (eg, linear or log-linear) concentration-response 

relationship between PM2.5 and mortality risk observed in cohort studies that extends below 

present-day regulations of 15 μg/m3for mean annual levels, without a discernable “safe” 

threshold.” (cites Pope 2004, Krewski 2009, and Schwartz 2008)

Pg 2364: “The PM2.5 concentration– cardiovascular risk relationships for both short- and longterm exposures appear to be monotonic, extending below 15 μg/m3 (the 2006 annual NAAQS 

level) without a discernable “safe” threshold.”

Pg 2365: “This updated review by the AHA writing group corroborates and strengthens the 

conclusions of the initial scientific statement. In this context, we agree with the concept and 

continue to support measures based on scientific evidence, such as the US EPA NAAQS, that 

seek to control PM levels to protect the public health. Because the evidence reviewed supports 

that there is no safe threshold, it appears that public health benefits would accrue from lowering 

PM2.5 concentrations even below present-day annual (15 μg/m3) and 24-hour (35 μg/m3) 

NAAQS, if feasible, to optimally protect the most susceptible populations.”

Pg 2366: “Although numerous insights have greatly enhanced our understanding of the PMcardiovascular relationship since the first AHA statement was published, the following list 

represents broad strategic avenues for future investigation: ... Determine whether any “safe” PM 

threshold concentration exists that eliminates both acute and chronic cardiovascular effects in 

healthy and susceptible individuals and at a population level.”

Scientific Statement Authors

Dr. Robert D. Brook, MD

Dr. Sanjay Rajagopalan, MD

Dr. C. Arden Pope, PhD

Dr. Jeffrey R. Brook, PhD

Dr. Aruni Bhatnagar, PhD, FAHA

Dr. Ana V. Diez-Roux, MD, PhD, MPH

Dr. Fernando Holguin, MD
5E-6

Dr. Yuling Hong, MD, PhD, FAHA

Dr. Russell V. Luepker, MD, MS, FAHA

Dr. Murray A. Mittleman, MD, DrPH, FAHA

Dr. Annette Peters, PhD 

Dr. David Siscovick, MD, MPH, FAHA

Dr. Sidney C. Smith, Jr, MD, FAHA

Dr. Laurie Whitsel, PhD

Dr. Joel D. Kaufman, MD, MPH
5E-7

C. Integrated Science Assessment for Particulate Matter (2009)

U.S. Environmental Protection Agency (U.S. EPA). 2009. 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>.

Pg 1-22: “An important consideration in characterizing the public health impacts associated with 

exposure to a pollutant is whether the concentration-response relationship is linear across the full 

concentration range encountered, or if nonlinear relationships exist along any part of this range. 

Of particular interest is the shape of the concentration-response curve at and below the level of 

the current standards. The shape of the concentration-response curve varies, depending on the 

type of health outcome, underlying biological mechanisms and dose. At the human population 

level, however, various sources of variability and uncertainty tend to smooth and “linearize” the 

concentration-response function (such as the low data density in the lower concentration range, 

possible influence of measurement error, and individual differences in susceptibility to air 

pollution health effects). In addition, many chemicals and agents may act by perturbing naturally 

occurring background processes that lead to disease, which also linearizes population 

concentration-response relationships (Clewell and Crump, 2005, 156359; Crump et al., 1976, 

003192; Hoel, 1980, 156555). These attributes of population dose-response may explain why the 

available human data at ambient concentrations for some environmental pollutants (e.g., PM, O3, 

lead [Pb], ETS, radiation) do not exhibit evident thresholds for health effects, even though likely 

mechanisms include nonlinear processes for some key events. These attributes of human 

population dose-response relationships have been extensively discussed in the broader 

epidemiologic literature (Rothman and Greenland, 1998, 086599).”

Pg 2-16: “In addition, cardiovascular hospital admission and mortality studies that examined the 

PM10 concentration-response relationship found evidence of a log-linear no-threshold 

relationship between PM exposure and cardiovascular-related morbidity (Section 6.2) and 

mortality (Section 6.5).”

Pg 2-25: “2.4.3. PM Concentration-Response Relationship

An important consideration in characterizing the PM-morbidity and mortality association is 

whether the concentration-response relationship is linear across the full concentration range that 

is encountered or if there are concentration ranges where there are departures from linearity (i.e., 

nonlinearity). In this ISA studies have been identified that attempt to characterize the shape of 

the concentration-response curve along with possible PM “thresholds” (i.e., levels which PM 

concentrations must exceed in order to elicit a health response). The epidemiologic studies 

evaluated that examined the shape of the concentration-response curve and the potential presence 

of a threshold have focused on cardiovascular hospital admissions and ED visits and mortality

associated with short-term exposure to PM10 and mortality associated with long-term exposure to 

PM2.5. 

“A limited number of studies have been identified that examined the shape of the PM 

cardiovascular hospital admission and ED visit concentration-response relationship. Of these 

studies, some conducted an exploratory analysis during model selection to determine if a linear 
5E-8

curve most adequately represented the concentration-response relationship; whereas, only one 

study conducted an extensive analysis to examine the shape of the concentration-response curve 

at different concentrations (Section 6.2.10.10). Overall, the limited evidence from the studies 

evaluated supports the use of a no-threshold, log-linear model, which is consistent with the 

observations made in studies that examined the PM-mortality relationship.

“Although multiple studies have previously examined the PM-mortality concentration-response 

relationship and whether a threshold exists, more complex statistical analyses continue to be 

developed to analyze this association. Using a variety of methods and models, most of the 

studies evaluated support the use of a no-threshold, log-linear model; however, one study did 

observe heterogeneity in the shape of the concentration-response curve across cities (Section 

6.5). Overall, the studies evaluated further support the use of a no-threshold log-linear model, but 

additional issues such as the influence of heterogeneity in estimates between cities, and the effect 

of seasonal and regional differences in PM on the concentration-response relationship still 

require further investigation.

“In addition to examining the concentration-response relationship between short-term exposure 

to PM and mortality, Schwartz et al. (2008, 156963) conducted an analysis of the shape of the 

concentration-response relationship associated with long-term exposure to PM. Using a variety 

of statistical methods, the concentration-response curve was found to be indistinguishable from 

linear, and, therefore, little evidence was observed to suggest that a threshold exists in the 

association between long-term exposure to PM2.5 and the risk of death (Section 7.6).”

Pg 6-75: “6.2.10.10. Concentration Response

The concentration-response relationship has been extensively analyzed primarily through studies 

that examined the relationship between PM and mortality. These studies, which have focused on 

short- and long-term exposures to PM have consistently found no evidence for deviations from 

linearity or a safe threshold (Daniels et al., 2004, 087343; Samoli et al., 2005, 087436; Schwartz, 

2004, 078998; Schwartz et al., 2008, 156963) (Sections 6.5.2.7 and 7.1.4). Although on a more 

limited basis, studies that have examined PM effects on cardiovascular hospital admissions and 

ED visits have also analyzed the PM concentration-response relationship, and contributed to the 

overall body of evidence which suggests a log-linear, no-threshold PM concentration-response 

relationship.

“The results from the three multicity studies discussed above support no-threshold log-linear 

models, but issues such as the possible influence of exposure error and heterogeneity of shapes 

across cities remain to be resolved. Also, given the pattern of seasonal and regional differences 

in PM risk estimates depicted in recent multicity study results (e.g., Peng et al., 2005, 087463), 

the very concept of a concentration-response relationship estimated across cities and for all-year 

data may not be very informative.”

Pg 6-197: “6.5.2.7. Investigation of Concentration-Response Relationship

The results from large multicity studies reviewed in the 2004 PM AQCD (U.S. EPA, 2004, 

056905) suggested that strong evidence did not exist for a clear threshold for PM mortality

effects. However, as discussed in the 2004 PM AQCD (U.S. EPA, 2004, 056905), there are 

several challenges in determining and interpreting the shape of PM-mortality concentration-
5E-9

response functions and the presence of a threshold, including: (1) limited range of available 

concentration levels (i.e., sparse data at the low and high end); (2) heterogeneity of susceptible 

populations; and (3) investigate the PM-mortality concentration-response relationship.

“Daniels et al. (2004, 087343) evaluated three concentration-response models: (1) log-linear 

models (i.e., the most commonly used approach, from which the majority of risk estimates are 

derived); (2) spline models that allow data to fit possibly non-linear relationship; and (3) 

threshold models, using PM10 data in 20 cities from the 1987-1994 NMMAPS data. They

reported that the spline model, combined across the cities, showed a linear relation without 

indicating a threshold for the relative risks of death for all-causes and for cardiovascularrespiratory causes in relation to PM10, but “the other cause” deaths (i.e., all cause minus 

cardiovascular-respiratory) showed an apparent threshold at around 50 μg/m3PM10, as shown in 

Figure 6-35. For all-cause and cardio-respiratory deaths, based on the Akaike’s Information 

Criterion (AIC), a log-linear model without threshold was preferred to the threshold model and 

to the spline model.

“The HEI review committee commented that interpretation of these results required caution, 

because (1) the measurement error could obscure any threshold; (2) the city-specific 

concentration-response curves exhibited a variety of shapes; and (3) the use of AIC to choose 

among the models might not be appropriate due to the fact it was not designed to assess scientific 

theories of etiology. Note, however, that there has been no etiologically credible reason 

suggested thus far to choose one model over others for aggregate outcomes. Thus, at least 

statistically, the result of Daniels et al. (2004, 087343) suggests that the log-linear model is 

appropriate in describing the relationship between PM10 and mortality.

“The Schwartz (2004, 078998) analysis of PM10 and mortality in 14 U.S. cities, described in 

Section 6.5.2.1, also examined the shape of the concentration-response relationship by including 

indicator variables for days when concentrations were between 15 and 25 μg/m3, between 25 and 

34 μg/m3, between 35 and 44 μg/m3, and 45 μg/m3and above. In the model, days with 

concentrations below 15 μg/m3served as the reference level. This model was fit using the single 

stage method, combining strata across all cities in the case-crossover design. Figure 6-36 shows 

the resulting relationship, which does not provide sufficient evidence to suggest that a threshold 

exists. The authors did not examine city-to-city variation in the concentration-response 

relationship in this study.

“PM10 and mortality in 22 European cities (and BS in 15 of the cities) participating in the 

APHEA project. In nine of the 22 cities, PM10 levels were estimated using a regression model 

relating co-located PM10 to BS or TSP. They used regression spline models with two knots (30 

and 50 μg/m3) and then combined the individual city estimates of the splines across cities. The 

investigators concluded that the association between PM and mortality in these cities could be 

adequately estimated using the log-linear model. However, in an ancillary analysis of the 

concentration-response curves for the largest cities in each of the three distinct geographic areas 

(western, southern, and eastern European cities): London, England; Athens, Greece; and Cracow, 

Poland, Samoli et al. (2005, 087436) observed a difference in the shape of the concentrationresponse curve across cities. Thus, while the combined curves (Figure 6-37) appear to support 
5E-10

no-threshold relationships between PM10 and mortality, the heterogeneity of the shapes across 

cities makes it difficult to interpret the biological relevance of the shape of the combined curves.

“The results from the three multicity studies discussed above support no-threshold log-linear 

models, but issues such as the possible influence of exposure error and heterogeneity of shapes 

across cities remain to be resolved. Also, given the pattern of seasonal and regional differences 

in PM risk estimates depicted in recent multicity study results (e.g., Peng et al., 2005, 087463), 

the very concept of a concentration-response relationship estimated across cities and for all-year 

data may not be very informative.”

Authors of ISA

Dr. Lindsay Wichers Stanek (PM Team Leader)—National Center for Environmental 

Assessment (NCEA), U.S. Environmental Protection Agency (U.S. EPA), Research Triangle 

Park, NC

Dr. Jeffrey Arnold—NCEA, U.S. EPA, Research Triangle Park, NC (now at Institute for Water 

Resources, U.S. Army Corps of Engineers, Washington, D.C)

Dr. Christal Bowman—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. James S. Brown—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Barbara Buckley—NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Allen Davis—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Jean-Jacques Dubois—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Steven J. Dutton—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Tara Greaver—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Erin Hines—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Douglas Johns—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Ellen Kirrane—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Dennis Kotchmar—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Thomas Long—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Thomas Luben—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Qingyu Meng—Oak Ridge Institute for Science and Education, Postdoctoral Research 

Fellow to NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Kristopher Novak—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Joseph Pinto—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Jennifer Richmond-Bryant—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Mary Ross—NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Jason Sacks—NCEA, U.S. EPA, Research Triangle Park, NC
5E-11

Dr. Timothy J. Sullivan—E&S Environmental Chemistry, Inc., Corvallis, OR

Dr. David Svendsgaard—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Lisa Vinikoor—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. William Wilson—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Lori White— NCEA, U.S. EPA, Research Triangle Park, NC (now at National Institute for 

Environmental Health Sciences, Research Triangle Park, NC)

Dr. Christy Avery—University of North Carolina, Chapel Hill, NC

Dr. Kathleen Belanger —Center for Perinatal, Pediatric and Environmental Epidemiology, 

Yale University, New Haven, CT

Dr. Michelle Bell—School of Forestry & Environmental Studies, Yale University, New Haven, 

CT

Dr. William D. Bennett—Center for Environmental Medicine, Asthma and Lung Biology, 

University of North Carolina, Chapel Hill, NC

Dr. Matthew J. Campen—Lovelace Respiratory Research Institute, Albuquerque, NM

Dr. Leland B. Deck— Stratus Consulting, Inc., Washington, DC

Dr. Janneane F. Gent—Center for Perinatal, Pediatric and Environmental Epidemiology, Yale 

University, New Haven, CT

Dr. Yuh-Chin Tony Huang—Department of Medicine, Division of Pulmonary Medicine, Duke 

University Medical Center, Durham, NC

Dr. Kazuhiko Ito—Nelson Institute of Environmental Medicine, NYU School of Medicine, 

Tuxedo, NY

Mr. Marc Jackson—Integrated Laboratory Systems, Inc., Research Triangle Park, NC

Dr. Michael Kleinman—Department of Community and Environmental Medicine, University 

of California, Irvine

Dr. Sergey Napelenok—National Exposure Research Laboratory, U.S. EPA, Research Triangle 

Park, NC

Dr. Marc Pitchford—National Oceanic and Atmospheric Administration, Las Vegas, NV

Dr. Les Recio—Genetic Toxicology Division, Integrated Laboratory Systems, Inc., Research 

Triangle Park, NC

Dr. David Quincy Rich—Department of Epidemiology, University of Medicine and Dentistry 

of New Jersey, Piscataway, NJ

Dr. Timothy Sullivan— E&S Environmental Chemistry, Inc., Corvallis, OR

Dr. George Thurston—Department of Environmental Medicine, NYU, Tuxedo, NY

Dr. Gregory Wellenius—Cardiovascular Epidemiology Research Unit, Beth Israel Deaconess 

Medical Center, Boston, MA
5E-12

Dr. Eric Whitsel—Departments of Epidemiology and Medicine, University of North Carolina, 

Chapel Hill, NC

Peer Reviewers

Dr. Sara Dubowsky Adar, Department of Epidemiology, University of Washington, Seattle, 

WA

Mr. Chad Bailey, Office of Transportation and Air Quality, Ann Arbor, MI

Mr. Richard Baldauf, Office of Transportation and Air Quality, Ann Arbor, MI

Dr. Prakash Bhave, National Exposure Research Laboratory, U.S. EPA, Research Triangle 

Park, NC

Mr. George Bowker, Office of Atmospheric Programs, U.S. EPA, Washington, D.C.

Dr. Judith Chow, Division of Atmospheric Sciences, Desert Research Institute, Reno, NV 

Dr. Dan Costa, U.S. EPA, Research Triangle Park, NC

Dr. Ila Cote, NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Robert Devlin, National Health and Environmental Effects Research Laboratory, U.S. EPA, 

Research Triangle Park, NC

Dr. David DeMarini, National Health and Environmental Effects Research Laboratory, U.S. 

EPA, Research Triangle Park, NC

Dr. Neil Donahue, Department of Chemical Engineering, Carnegie Mellon University, 

Pittsburgh, PA

Dr. Aimen Farraj, National Health and Environmental Effects Research Laboratory, U.S. EPA, 

Research Triangle Park, NC

Dr. Mark Frampton, Department of Environmental Medicine, University of Rochester Medical 

Center, Rochester, NY

Mr. Neil Frank, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC

Mr. Tyler Fox, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC

Dr. Jim Gauderman, Department of Environmental Medicine, Department of Preventive 

Medicine, University of Southern California, Los Angeles, CA

Dr. Barbara Glenn, National Center for Environmental Research, U.S. EPA, Washington, D.C.

Dr. Terry Gordon, School of Medicine, New York University, Tuxedo, NY

Mr. Tim Hanley, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC

Dr. Jack Harkema, Department of Pathobiology and Diagnostic Investigation, Michigan State 

University, East Lansing, MI

Ms. Beth Hassett-Sipple, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC
5E-13

Dr. Amy Herring, Department of Biostatistics, University of North Carolina, Chapel Hill, NC

Dr. Israel Jirak, Department of Meteorology, Embry-Riddle Aeronautical University, Prescott, 

AZ

Dr. Mike Kleeman, Department of Civil and Environmental Engineering, University of 

California, Davis, CA

Dr. Petros Koutrakis, Exposure, Epidemiology and Risk Program, Harvard School of Public 

Health, Boston, MA

Dr. Sagar Krupa, Department of Plant Pathology, University of Minnesota, St. Paul, MN

Mr. John Langstaff, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC

Dr. Meredith Lassiter, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC

Mr. Phil Lorang, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC

Dr. Karen Martin, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC

Ms. Connie Meacham, NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Tom Pace, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC

Dr. Jennifer Peel, Department of Environmental and Radiological Health Sciences, College of 

Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO

Dr. Zackary Pekar, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC

Mr. Rob Pinder, National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, 

NC

Mr. Norm Possiel, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC

Dr. Sanjay Rajagopalan, Division of Cardiovascular Medicine, Ohio State University, 

Columbus, OH

Dr. Pradeep Rajan, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC

Mr. Venkatesh Rao, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC

Ms. Joann Rice, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC

Mr. Harvey Richmond, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC
5E-14

Ms. Victoria Sandiford, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC

Dr. Stefanie Sarnat, Department of Environmental and Occupational Health, Emory University, 

Atlanta, GA

Dr. Frances Silverman, Gage Occupational and Environmental Health, University of Toronto, 

Toronto, ON

Mr. Steven Silverman, Office of General Council, U.S. EPA, Washington, D.C.

Dr. Barbara Turpin, Department of Environmental Sciences, Rutgers University, New 

Brunswick, NJ

Dr. Robert Vanderpool, National Exposure Research Laboratory, U.S. EPA, Research Triangle 

Park, NC

Dr. John Vandenberg (Director)—NCEA-RTP Division, U.S. EPA, Research Triangle Park, 

NC

Dr. Alan Vette, National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, 

NC

Ms. Debra Walsh (Deputy Director)—NCEA-RTP Division, U.S. EPA, Research Triangle 

Park, NC

Mr. Tim Watkins, National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, 

NC

Dr. Christopher Weaver, NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Lewis Weinstock, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC

Ms. Karen Wesson, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC

Dr. Jason West, Department of Environmental Sciences and Engineering, University of North 

Carolina, Chapel Hill, NC

Mr. Ronald Williams, National Exposure Research Laboratory, U.S. EPA, Research Triangle 

Park, NC

Dr. George Woodall, NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Antonella Zanobetti, Department of Environmental Health, Harvard School of Public 

Health, Boston, MA
5E-15

D. CASAC comments on PM ISA and REA (2009)

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009. 

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/81e39f4c09954fcb85256ead006be86e/7

3ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>.

Pg 9: “There is an appropriate discussion of the time-series studies, but this section needs to 

have an explicit finding that the evidence supports a relationship between PM and mortality that 

is seen in these studies. This conclusion should be followed by the discussion of statistical 

methodology and the identification of any threshold that may exist.”

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2009. 

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/81e39f4c09954fcb85256ead006be86e/7

23FE644C5D758DF852575BD00763A32/$File/EPA-CASAC-09-009-unsigned.pdf>.

Pg 6: “On the issue of cut-points raised on 3-18, the authors should be prepared to offer a 

scientifically cogent reason for selection of a specific cut-point, and not simply try different cutpoints to see what effect this has on the analysis. The draft ISA was clear that there is little 

evidence for a population threshold in the C-R function.”

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009. 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/81e39f4c09954fcb85256ead006be86e/151B1F8

3B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>.

Pg 2: “The paragraph on lines 22-30 of page 2-37 is not clearly written. Twice in succession it 

states that the use of a no-threshold log-linear model is supported, but then cites other studies 

that suggest otherwise. It would be good to revise this paragraph to more clearly state – well, I’m 

not sure what. Probably that more research is needed.”

CASAC Panel Members

Dr. Jonathan M. Samet, Professor and Chair, Department of Preventive Medicine, University of 

Southern California, Los Angeles, CA 

Dr. Joseph Brain, Philip Drinker Professor of Environmental Physiology, Department of Environmental 

Health, Harvard School of Public Health, Harvard University, Boston, MA 

Dr. Ellis B. Cowling, University Distinguished Professor At-Large Emeritus, Colleges of Natural 

Resources and Agriculture and Life Sciences, North Carolina State University, Raleigh, NC 

Dr. James Crapo, Professor of Medicine, Department of Medicine, National Jewish Medical and 

Research Center, Denver, CO 
5E-16

Dr. H. Christopher Frey, Professor, Department of Civil, Construction and Environmental Engineering, 

College of Engineering, North Carolina State University, Raleigh, NC 

Dr. Armistead (Ted) Russell, Professor, Department of Civil and Environmental Engineering, Georgia 

Institute of Technology, Atlanta, GA 

Dr. Lowell Ashbaugh, Associate Research Ecologist, Crocker Nuclear Lab, University of California, 

Davis, Davis, CA 

Prof. Ed Avol, Professor, Preventive Medicine, Keck School of Medicine, University of Southern 

California, Los Angeles, CA 

Dr. Wayne Cascio, Professor, Medicine, Cardiology, Brody School of Medicine at East Carolina 

University, Greenville, NC 

Dr. David Grantz, Director, Botany and Plant Sciences and Air Pollution Research Center, Riverside 

Campus and Kearney Agricultural Center, University of California, Parlier, CA 

Dr. Joseph Helble, Dean and Professor, Thayer School of Engineering, Dartmouth College, Hanover, 

NH 

Dr. Rogene Henderson, Senior Scientist Emeritus, Lovelace Respiratory Research Institute, 

Albuquerque, NM

Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department of Chemical Engineering, 

Clarkson University, Potsdam, NY 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York University 

School of Medicine, Tuxedo, NY 

Dr. Helen Suh MacIntosh, Associate Professor, Environmental Health, School of Public Health, 

Harvard University, Boston, MA 

Dr. William Malm, Research Physicist, National Park Service Air Resources Division, Cooperative 

Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 

Mr. Charles Thomas (Tom) Moore, Jr., Air Quality Program Manager, Western Governors' 

Association, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort 

Collins, CO 

Dr. Robert F. Phalen, Professor, Department of Community & Environmental Medicine; Director, Air 

Pollution Health Effects Laboratory; Professor of Occupational & Environmental Health, Center for 

Occupation & Environment Health, College of Medicine, University of California Irvine, Irvine, CA 

Dr. Kent Pinkerton, Professor, Regents of the University of California, Center for Health and the 

Environment, University of California, Davis, CA 

Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of 

Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT 

Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing Laboratory, Harvard Medical School, 

Boston, MA 

Dr. Sverre Vedal, Professor, Department of Environmental and Occupational Health Sciences, School of 

Public Health and Community Medicine, University of Washington, Seattle, WA 

Dr. Donna Kenski, Data Analysis Director, Lake Michigan Air Directors Consortium, Rosemont, IL

Dr. Kathy Weathers, Senior Scientist, Cary Institute of Ecosystem Studies, Millbrook, NY
5E-17

E. Krewski et al. (2009)

Krewski, Daniel, Michael Jerrett, Richard T. Burnett, Renjun Ma, Edward Hughes, Yuanli 

Shi, Michelle C. Turner, C. Arden Pope III, George Thurston, Eugenia E. Calle, and 

Michael J. Thun with Bernie Beckerman, Pat DeLuca, Norm Finkelstein, Kaz Ito, D.K. 

Moore, K. Bruce Newbold, Tim Ramsay, Zev Ross, Hwashin Shin, and Barbara 

Tempalski. (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.

Pg 119: [About Pope et al. (2002)] “Each 10-μg/m3increase in longterm average ambient PM2.5

concentrations was associated with approximately a 4%, 6%, or 8% increase in risk of death 

from all causes, cardiopulmonary disease, and lung cancer, respectively. There was no evidence 

of a threshold exposure level within the range of observed PM2.5 concentrations. “

Krewski (2009). Letter from Dr. Daniel Krewski to HEI’s Dr. Kate Adams (dated July July 

7, 2009) regarding “EPA queries regarding HEI Report 140”. Dr. Adams then forwarded 

the letter on July 10, 2009 to EPA’s Beth Hassett-Sipple. (letter placed in docket #EPAHQ-OAR-2007-0492).

Pg 4: “6. The Health Review Committee commented that the Updated Analysis completed by

Pope et al. 2002 reported “no evidence of a threshold exposure level within the range of 

observed PM2.5 concentrations” (p. 119). In the Extended Follow-Up study, did the analyses 

provide continued support for a no-threshold response or was there evidence of a threshold?

“Response: As noted above, the HEI Health Review Committee commented on the lack of 

evidence for a threshold exposure level in Pope et al. (2002) with follow-up through the year 

1998. The present report, which included follow-up through the year 2000, also does not appear 

to demonstrate the existence of a threshold in the exposure-response function within the range of 

observed PM2.5 concentrations.”

HEI Health Review Committee Members

Dr. Homer A. Boushey, MD, Chair, Professor of Medicine, Department of Medicine, 

University of California–San Francisco 

Dr. Ben Armstrong, Reader, in Epidemiological Statistics, Department of Public Health and 

Policy, London School of Hygiene and Tropical Medicine, United Kingdom 

Dr. Michael Brauer, ScD, Professor, School of Environmental Health, University of British 

Columbia, Canada 

Dr. Bert Brunekreef, PhD, Professor of Environmental Epidemiology, Institute of Risk 

Assessment Sciences, University of Utrecht, The Netherlands 

Dr. Mark W. Frampton, MD, Professor of Medicine & Environmental Medicine, University of 

Rochester Medical Center, Rochester, NY 

Dr. Stephanie London, MD, PhD, Senior Investigator, Epidemiology Branch, National Institute 

of Environmental Health Sciences 
5E-18

Dr. William N. Rom, MD, MPH, Sol and Judith Bergstein Professor of Medicine and 

Environmental Medicine and Director of Pulmonary and Critical Care Medicine, New York 

University Medical Center 

Dr. Armistead Russell, Georgia Power Distinguished Professor of Environmental Engineering, 

School of Civil and Environmental Engineering, Georgia Institute of Technology

Dr. Lianne Sheppard, PhD, Professor, Department of Biostatistics, University of Washington
5E-19

F. Schwartz et al. (2008)

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.

Pg 67: “A key finding of this study is that there is little evidence for a threshold in the 

association between exposure to fine particles and the risk of death on follow-up, which 

continues well below the U.S. EPA standard of 15 μg/m3.” 

Pg 68: “In conclusion, penalized spline smoothing and model averaging represent reasonable, 

feasible approaches to addressing questions of the shape of the exposure–response curve, and can 

provide valuable information to decisionmakers. In this example, both approaches are consistent, 

and suggest that the association of particles with mortality has no threshold down to close to 

background levels.”
5E-20

G. Expert Elicitation on PM-Mortality (2006, 2008)

Industrial Economics, Inc., 2006. Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared 

for the U.S.EPA, Office of Air Quality Planning and Standards, September. Available on 

the Internet at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf>.

Pg v: “Each expert was given the option to integrate their judgments about the likelihood of a 

causal relationship and/or threshold in the C-R function into his distribution or to provide a 

distribution "conditional on" one or both of these factors.”

Pg vii: “Only one of 12 experts explicitly incorporated a threshold into his C-R function.3 The 

rest believed there was a lack of empirical and/or theoretical support for a population threshold. 

However, three other experts gave differing effect estimate distributions above and below some 

cut-off concentration. The adjustments these experts made to median estimates and/or 

uncertainty at lower PM2.5 concentrations were modest.”

“

3

Expert K indicated that he was 50 percent sure that a threshold existed. If there 

were a threshold, he thought that there was an 80 percent chance that it would be 

less than or equal to 5 μg/m3, and a 20 percent chance that it would fall between 5 

and 10 μg/m3.”

Pg ix: “Compared to the pilot study, experts in this study were in general more confident in a 

causal relationship, less likely to incorporate thresholds, and reported higher mortality effect 

estimates. The differences in results compared with the pilot appear to reflect the influence of 

new research on the interpretation of the key epidemiological studies that were the focus of both 

elicitation studies, more than the influence of changes to the structure of the protocol.”

Pg 3-25: “3.1.8 THRESHOLDS 

The protocol asked experts for their judgments regarding whether a threshold exists in the PM2.5 

mortality C-R function. The protocol focused on assessing expert judgments regarding theory

and evidential support for a population threshold (i.e., the concentration below which no member 

of the study population would experience an increased risk of death).32 If an expert wished to 

incorporate a threshold in his characterization of the concentration-response relationship, the 

team then asked the expert to specify the threshold PM2.5 concentration probabilistically, 

incorporating his uncertainty about the true threshold level.

“From a theoretical and conceptual standpoint, all experts generally believed that individuals 

exhibit thresholds for PM-related mortality. However, 11 of them discounted the idea of a 

population threshold in the C-R function on a theoretical and/or empirical basis. Seven of these 

experts noted that theoretically one would be unlikely to observe a population threshold due to 

the variation in susceptibility at any given time in the study population resulting from 

combinations of genetic, environmental, and socioeconomic factors.33 All 11 thought that there 

was insufficient empirical support for a population threshold in the C-R function. In addition, 

two experts (E and L) cited analyses of the ACS cohort data in Pope et al. (2002) and another (J) 

cited Krewski et al. (2000a & b) as supportive of a linear relationship in the study range. 
5E-21

“Seven of the experts favored epidemiological studies as ideally the best means of addressing the 

population threshold issue, because they are best able to evaluate the full range of susceptible 

individuals at environmentally relevant exposure levels. However, those who favored 

epidemiologic studies generally acknowledged that definitive studies addressing thresholds 

would be difficult or impossible to conduct, because they would need to include a very large and 

diverse population with wide variation in exposure and a long follow-up period. Furthermore, 

two experts (B and I) cited studies documenting difficulties in detecting a threshold using 

epidemiological studies (Cakmak et al. 1999, and Brauer et al., 2002, respectively). The experts 

generally thought that clinical and toxicological studies are best suited for researching 

mechanisms and for addressing thresholds in very narrowly defined groups. One expert, B, 

thought that a better understanding of the detailed biological mechanism is critical to addressing 

the question of a threshold.

“One expert, K, believed it was possible to make a conceptual argument for a population 

threshold. He drew an analogy with smoking, indicating that among heavy smokers, only a 

proportion of them gets lung cancer or demonstrates an accelerated decline in lung function. He 

thought that the idea that there is no level that is biologically safe is fundamentally at odds with 

toxicological theory. He did not think that a population threshold was detectable in the currently 

available epidemiologic studies. He indicated that some of the cohort studies showed greater 

uncertainty in the shape of the C-R function at lower levels, which could be indicative of a 

threshold. 

“Expert K chose to incorporate a threshold into his C-R function. He indicated that he was 50 

percent sure that a threshold existed. If there were a threshold, he thought that there was an 80 

percent chance that it would be less than or equal to 5 μg/m3, and a 20 percent chance that it 

would fall between 5 and 10 μg/m3.”

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.

Pg 2271: “Eight experts thought the true C-R function relating mortality to changes in annual 

average PM2.5 was log-linear across the entire study range (ln(mortality) ) β × PM). Four experts 

(B, F, K, and L) specified a “piecewise” log-linear function, with different β coefficients for PM 

concentrations above and below an expert-specified break point. This approach allowed them to 

express increased uncertainty in mortality effects seen at lower concentrations in major 

epidemiological studies. Expert K thought the relationship would be log-linear above a 

threshold.”

Pg 2271: “Expert K also applied a threshold, T, to his function, which he described 

probabilistically. He specified P(T > 0) = 0.5. Given T > 0, he indicated P(T ≤ 5 μg/m3) = 0.8 

and P(5 μg/m3< T ≤ 10 μg/m3) = 0.2. Figure 3 does not include the impact of applying expert 

K’s threshold, as the size of the reduction in benefits will depend on the distribution of baseline 

PM levels in a benefits analysis.”
5E-22

Experts:

Dr. Doug W. Dockery, Harvard School of Public Health

Dr. Kazuhiko Ito, Nelson Institute of Environmental Medicine, NYU School of Medicine, 

Tuxedo, NY

Dr. Dan Krewski, University of Ottawa

Dr. Nino Künzli, University of Southern California Keck School of Medicine 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York University 

School of Medicine, Tuxedo, NY 

Dr. Joe Mauderly, Lovelace Respiratory Research Institute

Dr. Bart Ostro, Chief, Air Pollution Epidemiology Unit, Office of Environmental Health 

Hazard Assessment, California Environmental Protection Agency, Oakland, CA

Dr. Arden Pope, Professor, Department of Economics, Brigham Young University, Provo, UT

Dr. Richard Schlesinger, Pace University

Dr. Joel Schwartz, Harvard School of Public Health

Dr. George Thurston—Department of Environmental Medicine, NYU, Tuxedo, NY

Dr. Mark Utell, University of Rochester School of Medicine and Dentistry
5E-23

H. CASAC comments on PM Staff Paper (2005)

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2005. 

EPA’s Review of the National Ambient Air Quality Standards for Particulate Matter 

(Second Draft PM Staff Paper, January 2005). EPA-SAB-CASAC-05-007. June. Available 

on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/E523DD36175EB5AD8525701B007332AE/$Fil

e/SAB-CASAC-05-007_unsigned.pdf>.

Pg 6: “A second concern is with methodological issues. The issue of the selection of 

concentration-response (C-R) relationships based on locally-derived coefficients needs more 

discussion. The Panel did not agree with EPA staff in calculating the burden of associated 

incidence in their risk assessment using either the predicted background or the lowest measured 

level (LML) in the utilized epidemiological analysis. The available epidemiological database on 

daily mortality and morbidity does not establish either the presence or absence of threshold 

concentrations for adverse health effects. Thus, in order to avoid emphasizing an approach that 

assumes effects that extend to either predicted background concentrations or LML, and to 

standardize the approach across cities, for the purpose of estimating public health impacts, the 

Panel favored the primary use of an assumed threshold of 10 μg/m3. The original approach of 

using background or LML, as well as the other postulated thresholds, could still be used in a 

sensitivity analysis of threshold assumptions.

“The analyses in this chapter highlight the impact of assumptions regarding thresholds, or lack of 

threshold, on the estimates of risk. The uncertainty associated with threshold or nonlinear models 

needs more thorough discussion. A major research need is for more work to determine the 

existence and level of any thresholds that may exist or the shape of nonlinear concentrationresponse curves at low levels of exposure that may exist, and to reduce uncertainty in estimated 

risks at the lowest PM concentrations.”

CASAC Panel Members

Dr. Rogene Henderson, Scientist Emeritus, Lovelace Respiratory Research Institute, Albuquerque, NM 

Dr. Ellis Cowling, University Distinguished Professor-at-Large, North Carolina State University, 

Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina State University, 

Raleigh, NC 

Dr. James D. Crapo, Professor, Department of Medicine, Biomedical Research and PatientCare, 

National Jewish Medical and Research Center, Denver, CO 

Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department of Chemical Engineering, 

Clarkson University, Potsdam, NY 

Dr. Jane Q. Koenig, Professor, Department of Environmental Health, School of Public Health and 

Community Medicine, University of Washington, Seattle, WA

Dr. Petros Koutrakis, Professor of Environmental Science, Environmental Health , School of Public 

Health, Harvard University (HSPH), Boston, MA

Dr. Allan Legge, President, Biosphere Solutions, Calgary, Alberta 
5E-24

Dr. Paul J. Lioy, Associate Director and Professor, Environmental and Occupational Health Sciences 

Institute, UMDNJ - Robert Wood Johnson Medical School, NJ 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York 

University School of Medicine, Tuxedo, NY 

Dr. Joe Mauderly, Vice President, Senior Scientist, and Director, National Environmental 

Respiratory Center, Lovelace Respiratory Research Institute, Albuquerque, NM 

Dr. Roger O. McClellan, Consultant, Albuquerque, NM 

Dr. Frederick J. Miller, Consultant, Cary, NC

Dr. Gunter Oberdorster, Professor of Toxicology, Department of Environmental Medicine, School 

of Medicine and Dentistry, University of Rochester, Rochester, NY 

Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of 

Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT 

Dr. Robert D. Rowe, President, Stratus Consulting, Inc., Boulder, CO 

Dr. Jonathan M. Samet, Professor and Chair, Department of Epidemiology, Bloomberg School of 

Public Health, Johns Hopkins University, Baltimore, MD 

Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing Laboratory, Harvard Medical 

School, Boston, MA 

Dr. Sverre Vedal, Professor of Medicine, School of Public Health and Community Medicine 

University of Washington, Seattle, WA 

Mr. Ronald White, Research Scientist, Epidemiology, Bloomberg School of Public Health, Johns 

Hopkins University, Baltimore, MD 

Dr. Warren H. White, Visiting Professor, Crocker Nuclear Laboratory, University of California -Davis, 

Davis, CA 

Dr. George T. Wolff, Principal Scientist, General Motors Corporation, Detroit, MI 

Dr. Barbara Zielinska, Research Professor, Division of Atmospheric Science, Desert Research 

Institute, Reno, NV
5E-25

I. HES Comments on 812 Analysis (2004)

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2004. 

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. Advisory by 

the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance 

Analysis. EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400

433D5D/$File/council_adv_04002.pdf>.

Pg 20: “The Subcommittee agrees that the whole range of uncertainties, such as the questions of 

causality, shape of C-R functions and thresholds, relative toxicity, years of life lost, cessation lag 

structure, cause of death, biologic pathways, or susceptibilities may be viewed differently for 

acute effects versus long-term effects. 

“For the studies of long-term exposure, the HES notes that Krewski et al. (2000) have conducted 

the most careful work on this issue. They report that the associations between PM2.5 and both allcause and cardiopulmonary mortality were near linear within the relevant ranges, with no 

apparent threshold. Graphical analyses of these studies (Dockery et al., 1993, Figure 3 and 

Krewski et al., 2000, page 162) also suggest a continuum of effects down to lower levels. 

Therefore, it is reasonable for EPA to assume a no threshold model down to, at least, the low end 

of the concentrations reported in the studies.”

HES Panel Members

Dr. Bart Ostro, California Office of Environmental Health Hazard Assessment (OEHHA), 

Oakland, CA 

Mr. John Fintan Hurley, Institute of Occupational Medicine (IOM), Edinburgh, Scotland 

Dr. Patrick Kinney, Columbia University, New York, NY 

Dr. Michael Kleinman, University of California, Irvine, CA 

Dr. Nino Künzli, University of Southern California, Los Angeles, CA 

Dr. Morton Lippmann, New York University School of Medicine, Tuxedo, NY Dr. Rebecca 

Parkin, The George Washington University, Washington, DC

Dr. Trudy Cameron, University of Oregon, Eugene, OR 

Dr. David T. Allen, University of Texas, Austin, TX 

Ms. Lauraine Chestnut, Stratus Consulting Inc., Boulder, CO 

Dr. Lawrence Goulder, Stanford University, Stanford, CA 

Dr. James Hammitt, Harvard University, Boston, MA 

Dr. F. Reed Johnson, Research Triangle Institute, Research Triangle Park, NC 

Dr. Charles Kolstad, University of California, Santa Barbara, CA 

Dr. Lester B. Lave, Carnegie Mellon University, Pittsburgh, PA 
5E-26

Dr. Virginia McConnell, Resources for the Future, Washington, DC 

Dr. V. Kerry Smith, North Carolina State University, Raleigh, NC 

Other Panel Members

Dr. John Evans, Harvard University, Portsmouth, NH Dr. Dale Hattis, Clark University, 

Worcester, MA Dr. D. Warner North, NorthWorks Inc., Belmont, CA Dr. Thomas S. Wallsten, 

University of Maryland, College Park, MD
5E-27

J. NRC – Committee on Estimating the Health Risk Reduction Benefits of Proposed Air 

Pollution Regulations (2002)

National Research Council (NRC). 2002. Estimating the Public Health Benefits of 

Proposed Air Pollution Regulations. Washington, DC: The National Academies Press.

Pg 109: “Linearity and Thresholds

“The shape of the concentration-response functions may influence the overall estimate of 

benefits. The shape is particularly important for lower ambient air pollution concentrations to 

which a large portion of the population is exposed. For this reason, the impact of the existence of 

a threshold may be considerable.

“In epidemiological studies, air pollution concentrations are usually measured and modeled as 

continuous variables. Thus, it may be feasible to test linearity and the existence of thresholds, 

depending on the study design. In time-series studies with the large number of repeated 

measurements, linearity and thresholds have been formally addressed with reasonable statistical 

power. For pollutants such as PM10 and PM2.5, there is no evidence for any departure of linearity

in the observed range of exposure, nor any indication of a threshold. For example, examination 

of the mortality effects of short-term exposure to PM10 in 88 cities indicates that the 

concentration-response functions are not due to the high concentrations and that the slopes of 

these functions do not appear to increase at higher concentrations (Samet et al. 2000). Many

other mortality studies have examined the shape of the concentration-response function and 

indicated that a linear (nonthreshold) model fit the data well (Pope 2000). Furthermore, studies 

conducted in cities with very low ambient pollution concentrations have similar effects per unit 

change in concentration as those studies conducted in cities with higher concentrations. Again, 

this finding suggests a fairly linear concentration-response function over the observed range of 

exposures.

“Regarding the studies of long-term exposure, Krewski et al. (2000) found that the assumption of a linear 

concentration-response function for mortality outcomes was not unreasonable. However, the statistical 

power to assess the shape of these functions is weakest at the upper and lower end of the observed 

exposure ranges. Most of the studies examining the effects of long-term exposure on morbidity compare 

subjects living in a small number of communities (Dockery et al. 1996; Ackermmann-Liebrich 1997; 

Braun-Fahrländer et al. 1997). Because the number of long-term effects studies are few and the number of 

communities studied is relatively small (8 to 24), the ability to test formally the absence or existence of a 

no-effect threshold is not feasible. However, even if thresholds exist, they may not be at the same 

concentration for all health outcomes.

“A review of the time-series and cohort studies may lead to the conclusion that although a threshold is not 

apparent at commonly observed concentrations, one may exist at lower levels. An important point to 

acknowledge regarding thresholds is that for health benefits analysis a key threshold is the population 

threshold (the lowest of the individual thresholds). However, the population threshold would be very 

difficult to observe empirically through epidemiology, because epidemiology integrates information from 

very large groups of people (thousands). Air pollution regulations affect even larger groups of people 

(millions). It is reasonable to assume that among such large groups susceptibility to air pollution health 

effects varies considerably across individuals and depends on a large set of underlying factors, including 
5E-28

genetic makeup, age, exposure measurement error, preexisting disease, and simultaneous exposures from 

smoking and occupational hazards. This variation in individual susceptibilities and the resulting 

distribution of individual thresholds underlies the concentration-response function observed in 

epidemiology. Thus, until biologically based models of the distribution of individual thresholds are 

developed, it may be productive to assume that the population concentration-response function is 

continuous and to focus on finding evidence of changes in its slope as one approaches lower 

concentrations.

EPA’s Use of Thresholds

“In EPA’s benefits analyses, threshold issues were discussed and interpreted. For the PM and ozone 

National Ambient Air Quality Standards (NAAQS), EPA investigated the effects of a potential threshold 

or reference value below which health consequences were assumed to be zero (EPA 1997). Specifically, 

the high-end benefits estimate assumed a 12-microgram per cubic meter (μg/m3) mean threshold for 

mortality associated with long-term exposure to PM2.5. The low-end benefits estimate assumed a 15-

μg/m3threshold for all PM-related health effects. The studies, however, included concentrations as low as 

7.5 μg/m3. For the Tier 2 rule and the HD engine and diesel-fuel rule, no threshold was assumed (EPA 

1999, 2000). EPA in these analyses acknowledged that there was no evidence for a threshold for PM.

“Several points should be noted regarding the threshold assumptions. If a threshold is assumed where one 

was not apparent in the original study, then the data should be refit and a new curve generated with the 

assumption of a zero slope over a segment of the concentration-response function that was originally 

found to be positively sloped. The assumption of a zero slope over a portion of the curve will force the 

slope in the remaining segment of the positively sloped concentration-response function to be greater than 

was indicated in the original study. A new concentration-response function was not generated for EPA’s 

benefits analysis for the PM and ozone NAAQS for which threshold assumptions were made. The 

generation of the steeper slope in the remaining portion of the concentration-response function may fully 

offset the effect of assuming a threshold. These aspects of assuming a threshold in a benefits analysis 

where one was not indicated in the original study should be conveyed to the reader. The committee notes 

that the treatment of thresholds should be evaluated in a consistent and transparent framework by using 

different explicit assumptions in the formal uncertainty analyses (see Chapter 5).”

Pg 117: “Although the assumption of no thresholds in the most recent EPA benefits analyses was 

appropriate, EPA should evaluate threshold assumptions in a consistent and transparent framework using 

several alternative assumptions in the formal uncertainty analysis.”

Pg 136: “Two additional illustrative examples are thresholds for adverse effects and lag structures.2 EPA 

considers implausible any threshold for mortality in the particulate matter (PM) exposure ranges under 

consideration (EPA 1999a, p. 3-8). Although the agency conducts sensitivity analyses incorporating 

thresholds, it provides no judgment as to their relative plausibility. In a probabilistic uncertainty analysis, 

EPA could assign appropriate weights to various threshold models. For PM-related mortality in the Tier 2 

analysis, the committee expects that this approach would have resulted in only a slight widening of the

probability distribution for avoided mortality and a slight reduction in the mean of that distribution, thus 

reflecting EPA’s views about the implausibility of thresholds. The committee finds that such formal 

incorporation of EPA’s expert judgments about the plausibility of thresholds into its primary analysis 

would have been an improvement.

“Uncertainty about thresholds is a special aspect of uncertainty about the shape of concentration-response 

functions. Typically, EPA and authors of epidemiological studies assume that these functions are linear 

on some scale. Often, the scale is a logarithmic transformation of the risk or rate of the health outcome, 
5E-29

but when a rate or risk is low, a linear function on the logarithmic scale is approximately linear on the

scale of the rate or risk itself. Increasingly, epidemiological investigators are employing analytic methods 

that permit the estimation of nonlinear shapes for concentration-response functions (Greenland et al. 

1999). As a consequence, EPA will need to be prepared to incorporate nonlinear concentration-response 

functions from epidemiological studies into the agency’s health benefits analyses. Any source of error or 

bias that can distort an epidemiological association can also distort the shape of an estimated 

concentration -response function, as can variation in individual susceptibility (Hattis and Burmaster 1994; 

Hattis et al. 2001).”

Pg 137: “In principle, many components of the health benefits model need realistic probabilistic models 

(see Table 5-1 for a listing of such components), in addition to concentration-response thresholds and 

time lags between exposure and response. For example, additional features of the concentration-response 

function—such as projection of the results from the study population to the target populations (which may 

have etiologically relevant characteristics outside the range seen in the study population) and the 

projection of baseline frequencies of morbidity and mortality into the future—must be characterized 

probabilistically. Other uncertainties that might affect the probability distributions are the estimations of 

population exposure (or even concentration) from emissions, estimates of emissions themselves, and the 

relative toxicity of various classes of particles. Similarly, many aspects of the analysis of the impact of 

regulation on ambient concentrations and on population exposure involve considerable uncertainty and, 

therefore, may be beneficially modeled in this way. Depending on the analytic approach used, joint 

probability distributions will have to be specified to incorporate correlations between model components 

that are structurally dependent upon each other, or the analysis will have to be conducted in a sequential 

fashion that follows the model for the data-generating process.

“EPA should explore alternative options for incorporating expert judgment into its probabilistic 

uncertainty analyses. The agency possesses considerable internal expertise, which should be employed as 

fully as possible. Outside experts should also be consulted as needed, individually or in panels. In all 

cases, when expert judgment is used in the construction of a model component, the experts should be 

identified and the rationales and empirical bases for their judgments should be made available.”

NRC members

Dr. JOHN C. BAILAR, III (Chair), (emeritus) University of Chicago, Chicago, Illinois

Dr. HUGH ROSS ANDERSON, University of London, London, England

Dr. MAUREEN L. CROPPER, University of Maryland, College Park

Dr. JOHN S. EVANS, Harvard University, Boston, Massachusetts

Dr. DALE B. HATTIS, Clark University, Worcester, Massachusetts

Dr. ROGENE F. HENDERSON, Lovelace Respiratory Research Institute, Albuquerque, New Mexico

Dr. PATRICK L. KINNEY, Columbia University, New York, New York

Dr. NINO KÜNZLI, University of Basel, Basel, Switzerland; as of September 2002, University of 

Southern California, Los Angeles

Dr. BART D. OSTRO, California Environmental Protection Agency, Oakland

Dr. CHARLES POOLE, University of North Carolina, Chapel Hill

Dr. KIRK R. SMITH, University of California, Berkeley

Dr. PETER A. VALBERG, Gradient Corporation, Cambridge, Massachusetts
5E-30

Dr. SCOTT L. ZEGER, Johns Hopkins University, Baltimore, Maryland