The influence of location, source, and emission type

in estimates of the human health benefits of reducing

a ton of air pollution

Neal Fann & Charles M. Fulcher & Bryan J. Hubbell

Received: 17 October 2008 /Accepted: 11 May 2009

# The Author(s) 2009. This article is published with open access at Springerlink.com

Abstract The benefit per ton ($/ton) of reducing PM2.5

varies by the location of the emission reduction, the type of

source emitting the precursor, and the specific precursor

controlled. This paper examines how each of these factors

influences the magnitude of the $/ton estimate. We employ

a reduced-form air quality model to predict changes in

ambient PM2.5 resulting from an array of emission control

scenarios affecting 12 different combinations of sources

emitting carbonaceous particles, NOx, SOx, NH3, and

volatile organic compounds. We perform this modeling

for each of nine urban areas and one nationwide area. Upon

modeling the air quality change, we then divide the total

monetized health benefits by the PM2.5 precursor emission

reductions to generate $/ton metrics. The resulting $/ton

estimates exhibit the greatest variability across certain

precursors and sources such as area source SOx, point

source SOx, and mobile source NH3. Certain $/ton

estimates, including mobile source NOx, exhibit significant

variability across urban areas. Reductions in carbonaceous

particles generate the largest $/ton across all locations.

Keywords Benefit per ton . PM2.5 . Health benefits.

BenMAP. CMAQ . RSM . $/ton . Precursor. SOx, NOx,

NH3, VOC, carbonaceous particles . Industrial point

sources. Electrical generating units. Area sources.

Mobile sources. Air pollution . Population exposure .

Spatial heterogeneity. Source . Emission type

Introduction

The estimated human health benefits of reducing ambient

fine particulate matter are highly sensitive to the geographic

location and type of PM2.5 precursor emissions and the

characteristics of the emitting source. In this paper, we

apply an innovative reduced-form air quality model to

estimate changes in ambient PM2.5 resulting from a variety

of emission control strategies applied to different classes of

emission sources. We then estimate the monetized mortality

and morbidity benefits of the resulting changes in ambient

PM2.5 cross several U.S. urban areas and for the U.S. as a

whole. Finally, for each strategy and in each urban area and

for the U.S. as a whole, we divide the total monetized

human health benefits by the emission precursor reductions

to derive an array of benefit per ton ($/ton) metrics that are

specific to location, type of source, and emission precursor.

The resulting $/ton estimates illustrate the strong influence

that these three factors—location, source, and emission type—

exert on total PM2.5 benefits.

A common approach to estimating PM2.5 human health

benefit is to first estimate the air quality changes associated

with some discrete number of emission control strategies

using a photochemical grid model such as the Community

Multiscale Air Quality Model (CMAQ; EPA 2006a, 2008).

Having estimated the change in ambient PM2.5 air quality,

the health impacts would be quantified using a tool such as

the Environmental Benefits Mapping and Analysis Program

(BenMAP; Abt Associates 2009) that applies health effect

and economic valuation coefficients to estimate the benefits

of an incremental air quality change; these coefficients are

drawn (or transferred) from peer-reviewed epidemiological

and economic studies whose study populations are (ideally)

as similar as possible to the ones under consideration in the

benefits analysis. The strength of this approach is that it can

Air Qual Atmos Health

DOI 10.1007/s11869-009-0044-0

N. Fann (*) : C. M. Fulcher : B. J. Hubbell

U.S. Environmental Protection Agency,

Office of Air Quality Planning and Standards,

Research Triangle Park, NC 27711, USA

e-mail: Fann.neal@epa.gov
provide a refined estimate of the health benefits of a select

number of alternate scenarios. However, this approach

tends to be highly time- and resource-intensive and so is

generally applied only for a limited number of policy

scenarios.

Recognizing this limitation, the U.S. Environmental

Protection Agency (EPA) and other federal agencies have

sometimes used transfer coefficients to estimate the

monetized benefits resulting from emission reductions.

Previous EPA analyses (e.g., the 1997 Regulatory Impact

Analysis [RIA] for the integrated pulp and paper rule) have

developed national nonsource-specific $/ton estimates that

could be generalized from a study site to the estimated

reductions in emissions for a number of policy sites. These

national nonsource-specific $/ton estimates have also seen

use in the Office of Management and Budget's annual

reports to the Congress on the costs and benefits of federal

regulations (OMB 2007, 2008). This technique is essentially an extension of the approach described above, which

applies transfer coefficients in the quantification of both

health impacts and economic valuation.

However, this approach suffers from its own limitations,

as it relies on several strong assumptions, including that

populations, meteorology, and source attributes such as

plant stack heights, emission exit velocity, topography,

plant dimensions, and other factors at the policy site are

identical (or at least similar enough) to those of the study

site. If this assumption is violated, then the transfer values

per ton will overestimate or underestimate actual $/ton at

the policy site. In this article, we explore the sources of this

heterogeneity and propose an alternate model to estimating

PM2.5 $/ton metrics.

Understanding the sources of heterogeneity in benefits

per ton estimates

More recent EPA analyses have developed $/ton estimates

for specific source classes and emission types as a means of

addressing this variability. For example, in assessing the

benefits of utility PM and SO2 reductions for the Industrial

Boilers and Process Heaters NESHAP Rule, EPA developed $/ton estimates based on modeling of PM and SO2

reductions for a well-characterized emissions inventory and

applied these $/ton estimates to estimate the benefits of a

less-characterized set of sources (U.S. EPA 2004a). Another

example is EPA's analysis of the recent Mobile Source Air

Toxics Rule, which developed and applied $/ton estimates

based on air quality modeling conducted for the Clean Air

Nonroad Diesel Rule (EPA 2004b). These estimates

represented an improvement, but still failed to account for

the role of geographic heterogeneity, which may be

considerable.

Analyses by Levy et al. (1999, 2009) examined several

estimates of the environmental costs of power plant

emissions and concluded that there is the potential for

significant heterogeneity in the environmental and health

impacts per kilowatt hour of electricity generated. This

heterogeneity is a product of source location, meteorology,

mix of pollutants emitted, and atmospheric conditions,

including baseline atmospheric concentrations of pollutants.

While Levy et al. applied a case study methodology which

fails to account fully for the potential impact of interactions

among emission reductions from spatially dispersed sources, this research does suggest the importance of heterogeneity in benefits estimates—heterogeneity that should be

properly reflected in $/ton estimates.

Accounting for the key sources of heterogeneity is

especially important when estimating the formation of,

and the resulting benefits of reducing, fine particulate

matter, or PM2.5. Three inter-related sources of heterogeneity affect the magnitude of PM2.5 $/ton estimates. The

first relates to the chemical processes that govern the

formation of PM2.5 in the atmosphere. Ambient PM2.5 is a

complex mixture of primary and secondarily formed

particles, resulting from chemical interactions in the

atmosphere and physical transport of emissions of particulate matter precursors, including available SO2, NOx, and

NH3, meteorology (particularly temperature), and baseline

levels and composition of PM2.5. The complex nonlinear

chemistry governing PM formation suggests that the impact

of reducing precursor emissions could be very different

across areas due to base conditions at both the emitting

source and the receptor areas. As such, it is important to

account for this variability when estimating the benefits of

reducing a ton of PM2.5 precursor.

The second source of heterogeneity relates to the

characteristics of the emitting source. The potential for

transport of PM2.5 from particular sources to population

centers will depend on the local and regional meteorology

discussed above, but also on the characteristics of sources.

These attributes include stack heights, stack temperatures,

and velocity of emissions as they leave the stacks. Thus,

some sources may have more inherent potential to emit

precursors that will transport to population centers.

The third factor that may influence the heterogeneity in

PM2.5-related $/ton estimates is the size of the population

exposed to PM2.5 and the susceptibility of that population

to adverse health outcomes. For example, we would expect

a larger $/ton associated with a class of sources that emit

large volumes of PM2.5 and that are located in close

proximity to major population centers. Likewise, we might

expect that the $/ton would be larger for classes of sources

located in proximity to populations whose susceptibility

was greater for certain adverse health impacts such as

respiratory outcomes.

Air Qual Atmos Health
Producing PM2.5 benefit per ton estimates

that better account for key sources of heterogeneity

The purpose of this analysis is to develop a matrix of $/ton

benefit transfer estimates that account for a variety of

emission types, industry classifications, and locations and

to examine the heterogeneity within that set of estimates. In

addition to examining those factors highlighted by Levy et

al. (1999, 2009), we also explore the influence of other

factors that may lead to heterogeneity in the $/ton of

pollutant reduced, as described above. We focus on those

factors influencing the heterogeneity in the incremental

change in ambient PM2.5 concentration associated with a

given amount of emissions among different urban areas,

rather than factors related to health concentration–response

functions or economic valuation estimates. We make use of

EPA's response surface model (RSM), generated from the

CMAQ, to estimate PM air quality PM2.5 ambient air

concentration changes associated with emission reductions

(EPA 2006b). The CMAQ model simulates the multiple

physical and chemical processes involved in the formation,

transport, and destruction of fine particulate matter and

ozone (Byun and Schere 2006) and has been utilized for

regulatory modeling applications (EPA 2006a, 2008). For

ease of discussion, we translate these air quality changes

into dollars using EPA's regulatory benefits analysis model,

BenMAP (Abt Associates 2009). In this paper, we

demonstrate that there is a substantial level of heterogeneity

in the fine particulate matter (PM2.5) air quality benefits

associated with a ton of emission reductions across several

dimensions, including type of precursor emission, source

category, location, and levels of pollutants reduced.

For this analysis, we use the RSM to calculate the

benefits of reducing ambient PM2.5 resulting from reductions in emissions of NOx, SO2, NH3, organic and

elemental particles, and volatile organic compounds

(VOC). The RSM allows users to model the air quality

impacts of changes in emission levels within nine urban

areas and a broad region encompassing the rest of the

continental U.S outside of these nine urban areas. The RSM

used in this study is based on air quality modeling using

CMAQ version 4.4 with a 36-km horizontal domain (148×

112 grid cells) and 14 vertical layers. The modeling domain

encompasses the contiguous U.S. and extends from 126° to

66° west longitude and from 24° to 52° north latitude. A

rigorous area-of-influence analysis was conducted for the

selection of RSM urban locations to discern the degree of

overlap between different urban areas in terms of air quality

impacts and to tease out local versus regional impacts. The

area-of-influence analysis identified nine urban areas where

ambient PM2.5 is largely independent of the precursor

emissions in all other included urban areas. Thus, selection

of these areas allows the RSM to analyze air quality

changes in these nine urban areas and associated counties

independent of one another. These nine urban areas include

New York/Philadelphia (combined), Chicago, Atlanta,

Dallas, San Joaquin, Salt Lake City, Phoenix, Seattle, and

Denver. The emissions baseline for all air quality modeled

was the 2001 National Emissions Inventory projected to the

year 2015. This inventory accounts for emission reductions

from such national rules as the Clean Air Interstate Rule,

the Nonroad Diesel Rule, and the Tier-2 Rule, among

others. A complete description of CMAQ, meteorological,

emission, and initial and boundary condition inputs used for

this analysis are discussed in the technical support

document for the U.S. EPA Clean Air Interstate Rule

(EPA 2005).

We analyze local and regional strategies that reduce

emissions by a fixed percentage. For example, one

experimental run might reduce NOx emissions by 50% in

each of the nine urban areas, including Atlanta, Chicago,

Dallas, Denver, New York/Philadelphia, Phoenix, Salt Lake

City, San Joaquin Valley, and Seattle; we also estimate

benefits from emission reductions nationwide, both within

and outside of these nine urban areas. Figure 1 illustrates

the location of the RSM urban areas across the U.S.

While there are many source categories, we aggregated

source emissions to broader groups to reduce the number of

model runs required. Certain precursor emissions from

source categories were omitted where they were considered

de minimus. For example, there are very low levels of

ammonia emitted by electric utilities (<1% of the total

ammonia inventory), so no response surface factors were

developed for ammonia from electric utilities. Table 1

provides a listing of the 12 source/pollutant combinations

that were examined in the response surface modeling.

Fig. 1 The geographic definition of the nine RSM urban areas

Air Qual Atmos Health
Generating the matrix of benefits per ton estimates

Generation of a $/ton value for PM precursors is a multistep

process, illustrated in Fig. 2:

1. generate a set of air quality changes associated with a

predetermined reduction in emissions for a specific

emission type, location, and source using the CMAQ

RSM,

2. model the human health benefits associated with the

changes in air quality using BenMAP, and

3. divide the total benefits by the tons of emissions

reduced.

We executed the first step by developing a set of

emission reduction scenarios covering the complete set of

12 emission/source combinations in the CMAQ RSM,

evaluated at three reduction levels, 25%, 50%, and 75%,

to allow for exploration of nonlinearities in the $/ton

estimates (for brevity of presentation and because nonlinearities were generally found to be small, we include

only the results based on the 50% emission reductions).

We then modeled the grid level changes in PM2.5

associated with these emission reduction scenarios using

the RSM, implemented through the PROC MIXED

procedure in SAS 9.1. These gridded changes in ambient

PM2.5 are then used as inputs to the BenMAP model to

complete step 2 (Fig. 3).

To provide estimates of human health benefits for each

emission reduction scenario, we load the changes in

ambient PM2.5 estimated using the CMAQ RSM into the

BenMAP. BenMAP is a Windows-based computer program

that estimates the health benefits from improvements in air

quality. BenMAP is primarily intended as a tool for

Response Surface Model estimate 

of air quality change across U.S. 

and 9 urban areas 

BenMAP estimate 

of monetized 

human health

Scenario selection

Divide total 

monetized benefits by 

scenario emission 

reductions abated 

Iterate for each

scenario

Fig. 2 The sequence of steps for calculating the $/ton of reducing

PM2.5 precursor emissions

Census 

Population 

Data 

Woods & 

Poole 

Population 

Projections 

RSM Modeled 

Baseline 2015 

Ambient 

PM2.5

RSM Modeled 

2015 Scenario 

Ambient PM2.5

PM2.5 Health 

Impact Functions 

Incidence and 

Prevalence Rates

Valuation 

Functions 

2015 

Population 

Projections 

∆ PM2.5

Population 

Exposure

PM2.5-Related 

Health Impacts 

Monetized 

Benefits 

Fig. 3 The data inputs and model outputs for estimating the $/ton of

reducing PM2.5 precursor emissions

Table 1 Sources and emission types modeled in the RSM

Emissions Source types

Industrial

point sources

Electrical

generating units

Industrial point sources and

electrical generating units combined

Area

sources

Mobile

sources

Primary organic and elemental carbon X X X

SOx XX X

NOx XX X

NH3 X X

VOC X

Air Qual Atmos Health
estimating the health impacts and associated economic

values of changes in ambient air pollution. It accomplishes

this by running health impact functions, which relate a

change in the concentration of a pollutant with a change in

the incidence of a health endpoint. A typical health impact

function might look like:

$y 1⁄4 y0  eb$x 1  

where y0 is the baseline incidence (the product of the

baseline incidence rate times the potentially affected

population), β is the effect estimate, and ∆x is the estimated

change in the summary pollutant measure. For example, in

the case of a premature mortality health impact function, we

would utilize the following inputs:

& Air pollution change. The air quality change is

calculated as the difference between the starting air

pollution level, also called the baseline, and the air

pollution level after some change, such as that caused

by a regulation. In this exercise, we used the RSM to

estimate the air pollution change.

& Mortality or morbidity effect estimate. The mortality or

morbidity effect estimate is an estimate of the percentage change in mortality or morbidity due to a one unit

change in ambient air pollution. For this analysis, we

apply PM2.5 mortality effect coefficients drawn from the

reanalysis of the American Cancer Society cohort (Pope

et al. 2002) and the reanalysis of the Harvard Six Cities

cohort (Laden et al. 2006). For ease of presentation, we

present the derived results of Laden et al. (2006) only;

estimates calculated using the effect coefficient of Pope

et al. (2002) would be smaller. We do not apply

threshold adjustments to any of the PM2.5 mortality or

morbidity effect estimates. We also consider an array of

morbidity health endpoints including cases of chronic

bronchitis, nonfatal acute myocardial infarctions, and

hospitalizations for respiratory symptoms. For the sake

of brevity, we do not enumerate each of the epidemiological studies applied; readers interested in a comprehensive list of PM2.5 morbidity endpoints may consult

EPA (2008).

& Mortality and morbidity incidence. The incidence rate is

an estimate of the average number of people that suffer

a given adverse health outcome in a given population

over a given period of time. For example, the mortality

incidence rate might be the probability that a person

will die in a given year. For this analysis, we used

county mortality rates from the CDC-WONDER database, projected to 2015; in this respect, we account for

the variability in population vulnerability across the

urban areas. BenMAP also contains baseline incidence

rates for each morbidity endpoint. These rates are

generally at the regional or national level; complete

information may be found in the BenMAP user manual

(Abt Associates 2008). When calculating health

impacts, BenMAP allocates mortality and morbidity

rates to the CMAQ grid cell.

& Exposed population. The exposed population is the

number of people affected by the air pollution reduction. This analysis uses Woods and Poole's (2007)

projections of census population to 2015.

Finally, BenMAP calculates the economic value of

health impacts. For example, after the calculation of the

mortality change, the program values these premature

deaths by multiplying the change in mortality reduction

by an estimate of the value of a statistical life:

Value mortality 1⁄4 Mortality change

 Value of statistical life

In this analysis, we applied a value of $6.2M, which

represents the default EPA value used in RIA’s. BenMAP

generated estimates of total monetized benefits for each of

the model scenarios. Because of the very large number of

RSM model scenarios, BenMAP was run in batch mode for

each of the 81 scenarios. Each of these total monetized

benefit estimates were then divided by the relevant total

emission reductions to derive a $/ton estimate.

Results and discussion

As Fig. 4 illustrates, the value of the $/ton estimates varies

by geographic location and PM2.5 precursor. The national $/

ton estimates, found on the top row of the figure, are

national-level averages that represent the benefits of PM2.5

precursor emission reductions from sources located

throughout the United States, including the nine separately

defined urban areas. As such, they essentially average out

much of the geographic variability observed in specific

locations. This is to say that spatial heterogeneity is masked

in the national estimate because of the very large

geographic area covered.

However, two sources of variability are apparent in the

national estimates. First, there is a high degree of

heterogeneity among the $/ton by PM2.5 precursors.

Clearly, reductions in directly emitted carbonaceous

particles offer the largest $/ton. This relatively large

estimate is likely due to the fact that these particles are

emitted as total PM2.5 and thus do not undergo any

additional transformation in the atmosphere before affecting population centers. Moreover, carbonaceous particles

tend to be emitted in close proximity to population

centers. In fact, area source and mobile source carbonaAir Qual Atmos Health
ceous particle emissions, in particular, show the highest

$/ton—suggesting that the emissions and population

centers exposed are colocated.

The next highest national $/ton estimates are associated

with emissions of SO2. These estimates are not as high as

directly emitted carbonaceous particles likely due to the fact

that, as a gas, SO2 must be transformed into particles, and

this occurs over moderate to long transport distances from

the source during which much of the SO2 is removed by

wet or dry deposition before it is transformed. Both

phenomena reduce the unit (per ton) impact on population

centers near the source. Among the lowest $/ton estimates

is for NOx reductions. An even smaller amount of NOx is

transformed into PM2.5 before it is removed than SO2.

However, it should be noted that the $/ton of SOx and NOx

reflect the PM2.5-related benefits alone and does not

consider any independent health impact associated with

either NO2 or SO2 exposure.

The urban area-specific $/ton estimates (Fig. 4) suggest a

significant amount of inter-regional and intraregional

variability. While reductions in directly emitted carbonaceous particles produce the highest $/ton estimates in each

of the nine areas, the magnitude of the benefit varies across

the areas. Areas in the west, such as Phoenix, Salt Lake

City, and San Joaquin, enjoy the largest $/ton ratios for

reductions of carbonaceous particles, while other urban

areas see smaller ratios. These differences may be due to

the baseline health status of populations in these areas.

Alternately, urban areas with very large populations, such

as the New York/Philadelphia area located in coastal areas,

National $720,000 $550,000 $460,000 $40,000 $82,000 $59,000 $2,400 $38,000 $95,000 $15,000 $9,700 $10,000

Atlanta $670,000 $590,000 $620,000 $48,000 $15,000 $42,000 $1,200 -$4,100 $56,000 $7,900 -$4,500 -$4,100

Chicago $510,000 $580,000 $600,000 $29,000 $18,000 $15,000 $3,100 $36,000 $100,000 $1,100 $2,000 -$8,700

Dallas $1,100,000 $790,000 $1,100,000 $140,000 $29,000 $600 $16,000 $36,000 $34,000 $920 $370

Denver $280,000 $450,000 $220,000 $75,000 $6,400 $19,000 $1,400 $10,000 $58,000 $3,200 $3,800 $2,700

NY/Phi $570,000 $710,000 $780,000 $14,000 $74,000 $50,000 $4,300 $53,000 $140,000 $1,500 -$2,600 -$8,200

Phoenix $2,500,000 $1,700,000 $980,000 $73,000 $550,000 $2,000 $15,000 $43,000 $11,000 -$2,100 -$680

Salt Lake $140,000 $150,000 $65,000 $15,000 $9,100 $2,600 $29,000 $43,000 $4,200 $1,500

San Joaquin $910,000 $560,000 $720,000 $140,000 $350,000 $46,000 $5,700 $36,000 $140,000 $28,000 $28,000 $43,000

Seattle $500,000 $570,000 $720,000 $54,000 $6,300 $52,000 $560 $18,000 $49,000 $120,000 -$2,300 -$8,100 Area source carbon Mobile source carbon EGU & Non-EGU carbon Area source SOx EGU SOx Non-EGU SOx VOC Area source NH3 Mobile source NH3 EGU NOx Non-EGU NOx Mobile source NOx

Fig. 4 The monetized $/ton in 2015 of reductions in PM2.5 precursor emissions by area of the country (Laden et al. (2006) mortality estimate,

2006$, 3% discount rate)

Air Qual Atmos Health
may see smaller $/ton estimates as PM2.5 transports away

from the city and toward unpopulated areas.

Other precursor- and source category-specific $/ton

ratios are far smaller by comparison and show some degree

of variability. The smaller size may be due to the fact that

the transformation of these precursors into total PM2.5

occurs downwind from the urban area. Certain precursors,

such as mobile source NH3 and all-source VOC, exhibit a

fairly consistent $/ton estimate across the urban areas.

Others, such as mobile source NOx, show a disbenefit in

eastern cities such as Chicago, New York/Philadelphia, and

Atlanta; this is due to the well-documented NO titration of

O3 effect where additional NOx reductions in NOx-rich

areas lessen the titration of O3. Under this NOx-rich

situation, the formation of particulate nitrate is not limited

to NOx, but to ozone and other oxidants (e.g., OH and

H2O2). Less NOx titration of O3 results in more O3 and

oxidants available to oxidize NOx to form HNO3, which

subsequently react with NH3 to form more particulate

nitrate (and thus disbenefit). It should be noted that, while

NOx reductions may occasionally generate PM2.5 disbenefits in certain urban areas, because NOx is also an O3

precursor, additional NOx reductions—even in areas where

PM2.5 disbenefits are possible—may produce a downwind

O3 benefit. Moreover, NOx emission controls may also

affect other copollutants, such as directly emitting PM2.5,

that may result in a health benefit.

The results in Fig. 4 are sensitive to several key input

parameters including the estimate of PM2.5-related mortality and the VSL, among others; as the science continues to

evolve, these parameters are likely to change, affecting the

magnitude of the benefit per ton estimates. For this reason,

we encourage readers to consult the latest array of PM2.5

benefit per ton estimates available on EPA’s website (www.

epa.gov/air/benmap/bpt.html) that reflect the best available

science.

Limitations and uncertainties

There are several key limitations and uncertainties associated with these $/ton estimates:

& Care should be used when applying these estimates to

urban areas other than those analyzed. As discussed

above, the unique attributes of each urban area cause

the $/ton of emission reduction to vary by a significant

amount. For this reason, there are special uncertainties

incurred when transferring these metrics from one urban

area to another.

& The $/ton metrics contain each of the uncertainties

inherent in a PM2.5 benefits analysis. As discussed in

the RIA for the PM2.5 National Ambient Air Quality

Standards (NAAQS; EPA 2006a, b), there are a variety

of uncertainties associated with calculating PM benefits;

these uncertainties are propagated through to the $/ton

estimates discussed in this article.

& These estimates omit certain benefits categories.

Reductions in PM2.5 precursors may provide visibility

benefits, which are not expressed in the $/ton metrics.

Certain unquantified benefit categories, described fully

in the PM2.5 NAAQS RIA (EPA 2006a, b), are also

omitted. Moreover, these metrics reflect the monetized

health benefits of reductions in exposure to ambient

PM2.5 alone and do not incorporate the benefits of

reductions in other pollutants such as O3, NO2, or SO2.

A comprehensive benefits analysis frequently reports

confidence intervals around the mean incidence and

valuation estimates, which provide a limited degree of

quantitative characterization of uncertainty around these

mean values; these are based on the standard errors reported

in the underlying epidemiology and economic valuation

studies. When calculating $/ton estimates, we use the mean

monetized benefits estimate alone.

Conclusions

The PM2.5 $/ton estimates in this paper reflect three

principal sources of heterogeneity:

& Variability across precursors. The $/ton for certain

pollutants, such as directly emitted PM2.5, is much

higher than others. This is due to the differences among

the precursors in their potential to form PM2.5, local

atmospheric conditions, and proximity to populations.

& Variability across sources. Certain sources may emit a

common precursor, but may produce very different $/

ton estimates. These differences are likely due to

variations in source parameters, such as stack height

or exit velocity, which in turn influence subsequent

dispersion of the pollutant and human exposure.

& Variability across location. The $/ton for a given

pollutant showed some degree of variation based on

the urban area in which the pollutant was emitted. This

is likely due to differences in atmospheric conditions

among each of the nine urban areas as well as

differences in population densities and baseline health

status.

These $/ton estimates account for many, but not all, of

the important sources of heterogeneity associated with

PM2.5 formation, transport, and human exposure across

the nation and in a small number of urban areas. By

accounting for these key sources of variability, these

estimates can inform the development of air quality

Air Qual Atmos Health
management plans. For example, the estimates demonstrate

that emission control strategies affecting directly emitted

particles promise to reduce ambient levels of PM2.5—and

produce greater health benefits—than those reducing VOC

emissions. However, maximizing net benefits will require

users to consider both the benefit, and the cost, per ton of

emission control. For example, while the benefits of

reducing emissions of SO2 are significantly smaller than

the per-ton benefits of reducing emissions such as directly

emitted carbonaceous particles, controlling SO2 may be

significantly less expensive. Thus, when applying these

$/ton metrics, analysts should consider both the expected

benefits and costs.

Open Access This article is distributed under the terms of the

Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any

medium, provided the original author(s) and source are credited.

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