The Recent and Future Health Burden of Air Pollution Apportioned

Across U.S. Sectors

Neal Fann,*,1 Charles M. Fulcher,1 and Kirk Baker1

1

Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive Research Triangle

Park, North Carolina 27711, United States

*S Supporting Information

ABSTRACT: Recent risk assessments have characterized the

overall burden of recent PM2.5 and ozone levels on public health,

but generally not the variability of these impacts over time or by

sector. Using photochemical source apportionment modeling and a

health impact function, we attribute PM2.5 and ozone air quality

levels, population exposure and health burden to 23 industrial point,

area, mobile and international emission sectors in the Continental

U.S. in 2005 and 2016. Our modeled policy scenarios account for a

suite of emission control requirements affecting many of these

sectors. Between these two years, the number of PM2.5 and ozonerelated deaths attributable to power plants and mobile sources falls

from about 68 000 (90% confidence interval from 48 000 to 87 000)

to about 36 000 (90% confidence intervals from 26 000 to 47 000).

Area source mortality risk grows slightly between 2005 and 2016,

due largely to population growth. Uncertainties relating to the

timing and magnitude of the emission reductions may affect the size

of these estimates. The detailed sector-level estimates of the size and

distribution of mortality and morbidity risk suggest that the air pollution mortality burden has fallen over time but that many

sectors continue to pose a substantial risk to human health.

■ INTRODUCTION

The epidemiological, toxicological, and clinical literature have

convincingly demonstrated the relationship between ambient

tropospheric ozone, fine particles, and the risk of adverse health

outcomes.1−3 The epidemiological literature in particular has

extensively characterized the relationship between populationlevel exposure to these two ubiquitous pollutants and the risk of

an array of acute and chronic adverse health impacts, including

premature death, hospital visits for respiratory and cardiovascular illnesses, and the exacerbation of asthma, among many

others.4−7 An increasing number of quantitative risk assessments have applied risk coefficients reported in this literature to

estimate the overall burden to public health attributable to

recent levels of PM2.5 (Particulate matter, 2.5 μm or less in

diameter) and ozone globally and in the Continental U.S.8−11

Such analyses give valuable insight to the size and spatial

distribution of air pollution impacts on human health due to

recent air quality levels, but they tell us less about the emission

sectors contributing most to this burden or how this

contribution could change over time. We build upon the

literature above by attempting to attribute the total PM2.5 and

ozone air quality and health burden to a host of stationary, area,

and mobile sectors located in the U.S. and globally.

We ran the source apportionment module in the

Comprehensive Air Quality Model with Extensions (CAMx)

photochemical model.12 This approach allowed us to attribute

ambient annual mean PM2.5 and summer season (May to

September) ozone levels at a 12 km grid resolution across the

Continental United States to each of 23 categories of emission

sectors in 2005 and 2016; these sectors account for all

inventoried anthropogenic and biogenic emissions affecting

PM2.5 and ozone levels in the Continental U.S. The 23 sectors

include nine industrial point sources (cement kilns, coke ovens,

electric arc furnaces, ferroalloy facilities, iron and steel facilities,

integrated iron and steel facilities, pulp and paper facilities,

refineries, and all other point source emissions); electricity

generating units (EGU); four area sources (residential wood

combustion, taconite mining, re-entrained fugitive emissions

from industrial sources and all other area sources); four mobile

sources (on-road, nonroad, aircraft/locomotives/marine vessels, and ocean-going vessels); two international sources

(Canada/Mexico and trans-boundary emissions); wildfires,

secondary organic aerosols, and biogenics. We stratified the

sectors in this way to ensure that we both represented the

spatial heterogeneity in air quality impacts among sectors, and

Received: November 27, 2012

Revised: March 13, 2013

Accepted: March 18, 2013

Published: March 18, 2013

Policy Analysis

pubs.acs.org/est

This article not subject to U.S. Copyright.

Published 2013 by the American Chemical

Society

3580 dx.doi.org/10.1021/es304831q | Environ. Sci. Technol. 2013, 47, 3580−3589
also to reflect the expected reduction in PM2.5 and ozone levels

due to regulatory requirements affecting certain sectors (EGUs

and mobile sources in particular).

Using the environmental benefits mapping and analysis

program (BenMAP), which contains a library of concentration−response relationships and demographic data necessary

to perform a health-impact assessment, we quantified the

number of premature deaths, chronic and acute illnesses due to

PM2.5 and ozone from these 23 sectors for each analytical

year.13 For these sectors we also assessed the geographic and

age distribution of health impacts, characterizing the change in

this distribution between 2005 and 2016, assuming the

implementation of several national air quality rules during

this time period.

■ MATERIALS AND METHODS

We quantified the number of PM2.5 and ozone-related deaths

and illnesses using a health impact function, which incorporates

information regarding air quality levels, population exposure,

baseline health data and a risk coefficient. The approach we

follow to calculating health impacts assessment has been

applied extensively in the literature and is methodologically

consistent with several recent quantitative risk assessments.11,14−16 As an example of this calculation, a log−linear

health impact function estimating changes in incidence as a

result of changes in air quality would look as follows:

Δ = −· β·Δy ye( 1) Pop x

0

Where β is the risk coefficient drawn from an epidemiological

study for the health end point of interest, y0 is the baseline

incidence rate for that end point, Δx is the change in air quality,

and Pop is the population of interest. We applied BenMAP

v4.0.52 to automate this process, as it contains the population,

concentration−response relationships and baseline health data

needed to calculate the health impact function. BenMAP has

been applied in several recent risk assessments.17,18 Briefly, in

this assessment we characterize PM2.5 and ozone-related

premature deaths, hospital admissions, emergency department

visits, and cases of acute respiratory symptoms, among other

impacts occurring throughout all populated areas in the

Continental U.S. for which we modeled PM2.5 and ozone

levels. In the interest of space, we place in Supporting

Information (SI) full details regarding the chronic and acute

health end points we assessed, the effect coefficients and

baseline incidence rates we applied and the steps we followed

to calculate health impacts and changes in life years; these

parameters are consistent with the values reported in a recent

U.S. EPA analysis.19

Because air pollution health impact assessments are wellestablished in the literature, we focus here on our novel use of

source apportionment modeling to generate the air quality

surfaces used in this analysis; the SI describes in detail how we

specified the health impact assessment. Figure 1 depicts the

sequence of analytical steps we followed to attribute air quality

levels and health impacts to each sector. We also acknowledge

that there is growing evidence that PM2.5 composition, which

varies across regions and by season, may affect its toxicity.

However, in our judgment the literature does not yet yield data

that could be used as the basis for a risk assessment that

quantitatively accounted for such differences.2 Thus, while in

this assessment we assume that particles from each source are

equally toxican approach that is consistent with recent advice

provided by the U.S. EPA Science Advisory Board 20we also

consider the potential influence of this assumption.

Emissions and Air Quality Modeling. Photochemical Air

Quality Modeling. Our first step was to quantify PM2.5 and

ozone levels attributable to each of 23 sectors. We applied a

state of the science 3D Eulerian photochemical transport model

to simulate the complex processes related to the formation,

transport, and fate of ozone and PM2.5. CAMx includes

treatment of emissions, wet and dry deposition of gases and

particles, gas-phase chemistry, aqueous-phase chemistry, and

particulate matter formation through inorganic and organic

pathways.12,21−24 The CAMx model has been used extensively

for regulatory modeling of both pollutants and has received

extensive peer review.12,25−27 We applied CAMx version 5.30

using a full year of 2005 meteorology and an emissions

inventory that accounted for both 2005 and 2016 emission

levels; below we describe how we projected emission levels to

2016. We selected the year of 2005 because it matched an

available national emissions inventory year and because the

meteorology for that year was conducive (e.g., summer stagnant

air masses with clear skies) to ozone and PM2.5 formation. We

performed photochemical modeling using domains that cover

the United States with 12 km square sized grid cells and that

stratify the vertical atmosphere with 14 layers, most of which

are nearest the surface, to resolve the diurnal variation in the

boundary layer; the height of the lowest model layer is

approximately 30 m. The 36 km domain covers a larger spatial

extent than both of the 12 km domains and is used to

downscale air quality predictions from an annual 2005

simulation of the Goddard Earth Observing System Chemistry

(GEOS-Chem) model.28 The use of a global model to provide

boundary inflow allows for more realistic representation of

PM2.5 and ozone transported from outside the model domains.

Additional information related to photochemical model

options, meteorological modeling used as input to the

Figure 1. Conceptual diagram of steps in health impact calculation for

2016.

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photochemical model, and performance evaluation is provided

in the SI.

Emissions Modeling. We used the 2005 National Emissions

Inventory to estimate anthropogenic emissions.29 We estimated

biogenic emissions using hour-specific meteorology as input to

the Biogenic Emissions Inventory System (BEIS) model.

Mexican emissions (1999) and Canadian emissions (2006)

are identical for the 2005 and 2016 simulations, allowing us to

isolate the impact of changes in U.S. anthropogenic and

biogenic emissions.30,31 Other international emissions are

included in the model simulations through boundary inflow

and are identical for both 2005 and 2016. We projected 2016

emissions from the 2005 emissions using economic and

population growth factors for some sectors and accounted for

reductions required by emissions control programs and

expected plant shut-downs. We projected electricity generating

unit emissions to 2016 using the Integrated Planning Model

(IPM), which the EPA has applied in a number of recent

regulatory analyses for the EGU sector and is documented

elsewhere.32 The point source inventory in 2016 reflects

emissions changes related to multiple Federal control

programs: the proposed Cross-State Air Pollution Rule

(CSAPR) affecting emissions from electricity generating units,

the maximum achievable technology (MACT) standards for

industrial boilers, the MACT for reciprocating internal

combustion engines, and the NOx state implementation

call.33−36 The 2016 air quality modeling simulations do not

reflect emission reductions anticipated from the EGU sector as

a result of the recently promulgated mercury and air toxics

standards, and so are likely to overstate the PM2.5 and ozone

contribution from this sector and do not account for the

vacatur of the CSAPR rule. 37

Attributing PM2.5 and Ozone Concentrations to Sources.

Source contribution techniques implemented in photochemical

grid models such as CAMx are used to estimate the

contribution of emissions sources to model estimates of

ambient concentration. Contribution information is similar to

observation based source apportionment approaches such as

positive matrix factorization or chemical mass balance receptor

models. These observation based receptor models are limited

to times/places where measurements are made and have

difficulty differentiating sources that have similar patterns of

emissions. Air quality models do not suffer from these

limitations and can be used to provide more specific distinction

between source and receptors. Particulate matter precursor

emissions are tracked in CAMx to secondarily formed sulfate

and nitrate. Sulfur dioxide, sulfuric acid, and primarily emitted

PM2.5 sulfate are tracked to PM2.5 sulfate. Nitrogen oxides,

nitrous oxides, and primarily emitted PM2.5 nitrate are tracked

to PM2.5 nitrate. Primarily emitted organic carbon, elemental

carbon, and soil/crustal species are tracked directly to the

associated ambient specie. CAMx has several options for ozone

source apportionment, and for this analysis we use the

anthropogenic precursor culpability assessment (APCA). This

algorithm directs ozone contribution to VOC or NOX sources

based on the estimated ozone formation regime, unless the

biogenic contribution is limiting in which case the model

redirects the contribution to the anthropogenic source

category.12 Photochemical model source apportionment has

been widely used by regulating agencies and peer reviewed in

the literature,38−41 and the strengths and limitations of this

approach are discussed elsewhere.42

We selected sectors for tracking to provide a broad

delineation of the total inventory into biogenic and

anthropogenic categories. These sectors represent a diverse

set of emissions groups in terms of emissions totals of ozone

and PM2.5 precursors and proximity to National Ambient Air

Quality Standard (NAAQS) nonattainment areas and population. The 2005 NEI and projected future year do not contain

emissions for outdoor wood boilers, suggesting that the

contribution estimates for residential wood combustion is

likely a lower bound estimate. The secondary organic aerosol

category is a total modeled estimate of PM2.5 organic carbon

formed through secondary reactions from anthropogenic and

biogenic emissions at each receptor and is not attributable to

any specific source group. As a result, the contributions from

sectors through SOA are understated. The contribution from

lateral boundary inflow was based on hourly CAMx output

from the 36 km domain that completely covers the continental

United States, ensuring that boundary contributions account

for all emissions from outside the United States and the

portions of Canada and Mexico that are outside the 12 km

model domains.

Post Processing Contributions. The model estimated ozone

and speciated PM2.5 originating from each sector noted below

in each 12 km sized grid cell for each hour. Two separate 12 km

domains were used and grid cells located in both were averaged

before subsequent analysis. We aggregated hourly ozone values

to average summer season 8 h maximum and aggregated hourly

PM2.5 species to annual averages to be consistent with the effect

coefficients used to quantify health impacts.

Modeled PM2.5 contribution estimates were expressed as a

percentage of bulk modeled speciated PM2.5 (i.e., predictions

from all sectors) to estimate annual average relative response

factors for each grid cell. Relative response factors for each

PM2.5 species were matched by grid cell with FRM monitor

locations using the modeled attainment test software (MATS)

program.43 The species specific RRFs (relative response factor)

representing sector contribution are multiplied to monitored

PM2.5 composition from colocated or nearby PM2.5 speciation

monitors to generate species specific contribution estimates.

The constituent species are summed to generate an estimate of

contribution to total PM2.5 design values. The MATS program

also makes adjustments to the modeled concentrations to

match measurement and analytical artifacts known to exist

using federal reference methods (FRM) to measure total PM2.5

mass.44 These manipulations are largely related to changing

modeled PM2.5 ammonium nitrate to account for volatilization

off Teflon FRM filters during warmer months, assuming sulfate

and nitrate ions are nearly fully neutralized with ammonium,

and estimating particle bound water using an empirical

regression equation based on concentrations of sulfate, nitrate,

and ammonium ions.

Similarly, ozone source contribution estimates were expressed as a percentage of bulk modeled ozone to estimate

daily relative response factors for each grid cell, which were

averaged over all modeled elevated air quality days.45 Ozone

design values are multiplied by the relative response factor from

the grid cell containing the monitor to estimate source

contribution at that location. Rather than point estimates of

air quality and sector contribution, full model surfaces of annual

average PM2.5 and average summer season ozone are input to

BenMAP to estimate health impacts. The MATS program

creates a fused surface of annual average PM2.5 and summer

season ozone using observation data to minimize model

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3582 dx.doi.org/10.1021/es304831q | Environ. Sci. Technol. 2013, 47, 3580−3589
prediction outliers that may be related to instances where

meteorology or emissions are not well characterized.

■ RESULTS

CAMx Model Evaluation. Photochemical model output is

compared with speciated PM2.5 measurements taken from 402

monitors locations (SI) that are part of two different monitor

networks: IMPROVE and CSN. Recommended statistical

metrics for performance evaluation include mean bias, mean

error, fractional bias, and fractional error.45,46 Fractional bias

and error are bounded by 200%, which is the poorest model

performance. All of the statistical metrics presented approach 0

when agreement between observed and model predicted values

are best. Observations are matched with model predictions

using the grid cell where the monitor is located. Model

predictions and observations are paired matching the temporal

resolution of the observations before aggregate performance

metrics are estimated.46

The modeling systems slightly underestimates annual

aggregate total PM2.5 (mean bias = −0.7 ug/m3 and fractional

Figure 2. Distribution of sector contributions at all monitors to annual PM2.5 (left) and maximum 8 h ozone (right) for current year 2005 and

projected year 2016.

Figure 3. Number of premature PM2.5- and ozone-related deaths attributable to seven broad classes of sectors in 2016 (thousands of premature

deaths).

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Table 1. Ozone and PM2.5-Related Premature Deaths and Illnesses Attributable to Each of 7 Emission Sectors in 2016

emission sector

health outcomes industrial point area electricity generating units mobile wildfires international secondary organics & biogenics

ozone-related impactspremature deathmulti-citya 420

(190, 660)

460

(200, 720)

380

(170, 580)

2 300

(1 000, 3 600)

62

(27, 96)

3 300

(1 500, 5 200)

450

(200, 710)

meta-analysisb 1 900

(1 400, 2 400)

2 100

(1 600, 2 700)

1 7 00

(1 300, 2 200)

11 000

(7 800, 13 000)

290

(210, 360)

15 000

(11 000, 29 000)

2 100

(1 500, 2 600)

respiratory hospital visits 3 900

(1 100, 6 500)

4 200

(1 200, 7 000)

3 500

(950, 5 800)

21 000

(5 700, 34 000)

530

(150, 870)

29 000

(8 200, 48 000)

4 200

(1 100, 6 900)

respiratory ED visits 1 400

(0, 4 300)

1 600

(0, 4 900)

1,200

(0, 3 700)

7,800

(2, 23 000)

190

(0, 580)

11,000

(2, 32 000)

1,500

(0, 4 400)

acute respiratory symptoms 2 600 000

(1 300 000,

4 000 000)

3 100 000

(1 600 000,

4 600 000)

2 300 000

(1 200 000,

3 400 000)

15 000 000

(7 500 000,

22 000 000)

430 000

(220 000,

640 000)

21 000 000

(11 000 000,

31 000 000)

2 800 000

(1 400 000,

4 200 000)

lost school days 860 000

(390 000,

1 200 000)

990 000

(440 000,

1 400 000)

750 000

(330 000,

1 100 000)

4 800 000

(2 100 000,

6 800 000)

140 000

(63 000,

200 000)

7 000 000

(3 100 000,

9 800 000)

930 000

(420 000,

1 300 000)

PM2.5-related impacts

premature deathACSc 12 000

(8 800, 15 000)

31 000

(22 000, 39 000)

17 000

(12 000, 22 000)

17 000

(12 000, 21 000)

9 900

(7 200, 13 000)

14 000

(10 000, 18 000)

17 000

(12 000, 21 000)

H6Cd 31 000

(17 000, 45 000)

79 000

(43 000, 110 000)

44 000

(24 000, 63 000)

43 000

(23 000, 62 000)

25 000

(14 000, 37 000)

36 000

(20 000, 53 000)

43 000

(23 000, 62 000)

acute myocardial infarctions 13 000

(5 000, 22 000)

33 000

(12 000, 53 000)

19 000

(7 200, 31 000)

18 000

(6 700, 29 000)

11 000

(3 900, 17 000)

16 000

(5 800, 25 000)

19 000

(6 900, 30 000)

cardiovascular hospital admissions 4 200

(2 100, 7 100)

10 000

(5 300, 17 000)

6 000

(3 100, 10 000)

5 600

(2 900, 9 600)

3 300

(1 700, 5 600)

4 800

(2 500, 8 200)

5 700

(3 000, 9 800)

respiratory hospital admissions 4 600

(2 800, 6 500)

11 000

(6 700, 16 000)

6 800

(4 000, 9 500)

6 200

(3 700, 8 700)

3 500

(2 100, 5 000)

5 200

(3 100, 7 300)

6 400

(3 800, 9 000)

respiratory ED visitss 6 800

(210, 13 000)

18 000

(560, 35 000)

9 500

(300, 19 000)

9 800

(310, 19 000)

5 300

(160, 10 000)

7 800

(240, 15 000)

9 000

(280, 18 000)

acute respiratory symptomsd 9 500 000

(8 100 000,

11 000 000)

25 000 000

(21 000 000,

29 000 000)

13 000 000

(11 000 000,

15 000 000)

14 000 000

(12 00 0 000,

16 000 000)

7 900 000

(6 700 000,

9 100 000)

11 000 000

(9 400 000,

13 000 000)

13 000 000

(11 000 000,15 000 000)

upper & lower respiratory symptoms 420 000 (170 000,

660 000)

1 100 000

(440 000,

1 700 000)

560 000

(230 000,

880 000)

610 000

(250 000,

970 000)

350 000

(140 000,

550 000)

480 000

(190 000,

760 000)

560 000

(230 000,

890 000)

work loss days 1 600 000

(1 400 000,

1 800 000)

4 300 000

(3 700 000,

4 800 000)

2 200 000

(1 900 000,

2 400 000)

2 400 000

(2 100 000,

2 700 000)

1 300 000

(1 200 000,

1 500 000)

1 900 000

(1 600 000,

2 100 000)

2 200 000

(1 900 000,

2 400 000)

asthma exacerbation 710 000

(44 000,

3 600 000)

2 100 000

(120 000,

11 000 000)

1 100 000

(59 000,

5 800 000)

1 100 000

(65 000,

5 400 000)

690 000

(37 000,

3 700 000)

940 000

(51 000,

5 000 000)

1 100 000

(60 000,

5 800 000)

acute bronchitis 19 000

(−620, 37 000)

480 000

(−1 600, 95 000)

25 000

(−840, 49 000)

27 000

(−920, 55 000)

16 000

(−520, 31 000)

21 000

(−710, 43 000)

25 000

(−850, 50 000)

aNational Morbidity Mortality Air Pollution Study (Bell et al. 2004). bMeta-analysis of short-term ozone mortality studies (Levy et al. 2005). cAmerican Cancer Society cohort study (Krewski et al., 2009). dHarvard Six Cities cohort study (Laden et al., 2006).

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bias = −9%), although it is clear from SI Table 4 and Figure 5

that some of this good performance for total PM2.5 is the result

of over and under-predictions of the speciated components.

This is further evidence that only providing PM2.5 performance

metrics is not a good indicator of model performance;

validation must be done for the component species. The

modeling system does well at capturing the month to month

variability in observed components of PM2.5 (SI Figure 6).

Over all 1102 ozone monitors used in this analysis (SI Figure

7), average ozone season (May through September for this

analysis) 8 h maximum ozone is well characterized by the

modeling system. The mean observed value is 51.0 ppb and the

mean modeled value is 51.8 ppb. Over all sites, there is a mean

bias of 0.8 ppb and mean error of 9 ppb (fractional bias = 3%

and fractional error = 18%). Model estimates of ozone compare

well with observation data collected during 2005 (SI Figure 8),

giving us confidence that the modeling system is appropriately

characterizing the complex nature of precursor emissions and

secondary formation processes. Similarly to speciated PM2.5, the

modeling system does well at capturing the seasonal trends in

ozone (SI Figure 8).

Contribution to PM2.5 and O3 Air Quality. We estimated

contributions from each sector tracked in this assessment at all

PM2.5 federal reference method and ozone monitors (SI).

Figure 2 summarizes: the annual average PM2.5 contribution

from the sectors analyzed in this study as a distribution over all

FRM monitors for both the 2005 and 2016 simulations and to

daily 8 h maximum ozone concentration at all ozone monitors..

For annual average PM2.5, the biggest contributors (on

average) are EGU point sources, nonpoint area, mobile (onroad and nonroad), and residential wood combustion. The

largest contributing anthropogenic sectors to ozone concentrations in 2005 are mobile sectors: onroad, nonroad, and air/

rail/marine. Between 2005 and 2016, large decreases in

contribution from the onroad sector to ozone and to a lesser

degree to annual PM2.5 are evident, which reflects a significant

Figure 4. Percentage of annual all-cause deaths due to PM2.5 and ozone precursors from the electricity generating and mobile source sectors in 2005

and 2016 in U.S. counties.

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decline in emissions from this sector in the modeled scenario

due to the greater market penetration of lower emitting vehicles

in the future and implementation of Federal control programs.

There is a similarly large drop in the contribution from the

EGU sector to annual (SI) PM2.5, which is consistent with

emissions reductions expected as part of planned Federal

emissions reductions for this sector.

Lateral boundary inflow is a notable source group in terms of

contribution for ozone and PM2.5. While not as well

characterized as U.S. anthropogenic sources, the portions of

Canada and Mexico in the model domain are tracked for

contribution and have notable contributions to areas inside the

U.S. with close proximity to the border.

Health Impacts Attributable to Each Sector from

Anthropogenic Emissions in 2005 and 2016. For 2005,

we estimate that EGU emissions contribute most to annual

PM2.5-related health impacts (38 000 premature deaths, 90%

confidence intervals from 27 000 to 48 000, and 620 000 life

years lost), followed by area sources (19 000 premature deaths,

90% confidence intervals from 14 000 to 24 000, and 310 000

life years lost) (Figure 3 and SI). We estimate that 2005

emissions from the mobile sector emissions contribute the

second largest number of PM2.5 and ozone-related health

impacts (Figure 3 and SI). For each emission sector modeled,

the number of PM2.5-related deaths is greater than the number

of ozone-related premature deaths, with the exception of

biogenic emissions, where the ozone-related deaths are slightly

greater in both 2005 and 2016 (SI). As noted above, these

estimates are sensitive to our assumptions in our model

emissions projections regarding the timing and magnitude of

emission reductions from each sector. As discussed in detail

below, we lack projected emissions inventories for certain

anthropogenic emission sectors, including residential wood

combustion, and so the projected health impacts for these

sources account for changes in population growth but not

emissions.

We also estimated the number of cases of PM2.5-related acute

myocardial infarctions, cardiovascular and respiratory hospital

admissions, respiratory emergency department visits, acute

respiratory symptoms, upper and lower respiratory symptoms,

lost work days, cases of asthma exacerbation and acute

bronchitis. For ozone, we quantified the number of respiratory

hospital admissions, lost school days, and acute respiratory

symptoms. For ease of presentation, we aggregate these

morbidity impacts among seven groups of sectors in 2016:

(1) industrial point sources; (2) area sources; (3) EGUs; (4)

mobile sources; (5) wildfires; (6) international emissions; and,

(7) secondary organic, and biogenic emissions (Table 1).

For 2016, we estimate the greatest number of PM2.5 and

ozone morbidity impacts among the area sector group, where

we quantify 33 000 acute myocardial infarctions, 45 000 total

cardiovascular and respiratory hospital admissions and

emergency departments, 1.1 million upper and lower

respiratory symptoms, 480 000 cases of acute bronchitis, 2.1

million cases of asthma exacerbation, 4.3 million lost work days,

and 28 million cases of acute respiratory symptoms.

To provide insight to the PM species that are associated with

each of these broad source groupings we report the populationweighted exposure to directly emitted PM2.5, particulate nitrate

and particulate sulfate in 2005 and 2016 for each of seven broad

sector definitions (SI). To the extent that certain of these

species are more toxic than others, then our estimates of health

impacts for each sector may be under- or overstated. Between

2005 and 2016 the population-weighted exposure to directly

emitted PM2.5 and particulate sulfate each decline, while

exposure to particulate nitrate increases modestly. The

significant decline in overall particulate sulfate exposure is

driven largely by reductions in the EGU and mobile sectors.

The fall in direct PM2.5 exposure, and the small rise in

particulate nitrate exposure, are each attributable to the mobile

sector.

Temporal and Spatial Distribution of Premature

Deaths from Anthropogenic Emissions. When characterizing the change in health impacts over time and space, we

focus on changes in the incidence of premature death.

Comparing mortality results for 2005 with 2016, we find that

the number of premature deaths attributable to EGUs and

mobile sources decreases significantly over time in the modeled

scenario (by about 53% and 36%, respectively; Figure 3). The

number of premature deaths associated with emissions from

industrial sources show a modest decline (about 6%).

Conversely, area sources, and international sources, show

small increases. Increases in premature deaths from these latter

sectors are attributable to increases in the number of individuals

exposed rather than increases in emissions from these sectors.

For the mobile source sector, the overall magnitude of the

PM and ozone mortality risk, and the geographic scope of that

risk, decline between 2005 and 2016 (Figure 4). In 2005, we

estimate that the percentage of all deaths attributable to PM2.5

and ozone from mobile source emissions is at or above 1% in

large portions of the Midwest, Northeast, and California, and in

some areas of Texas and the Pacific Northwest. By 2016, the

number of counties at or above this level declines significantly;

very few counties in either Texas or the Pacific Northwest

exceed this level. The percentage of total deaths attributable to

PM2.5 and ozone due to EGUs also declines significantly. In

2005, the percentage of deaths due to PM2.5 and ozone

precursors originating from this sector is at or above 1.7%

among most counties in the Eastern U.S. In the 2016 scenario,

we estimate that no Eastern counties are at or above this level,

and most counties are at or below 1.1%.

■ DISCUSSION

Using photochemical source apportionment modeling and

health impact functions, we estimated the public health burden

of PM2.5 and ozone that is attributable to each of emission

sectors in 2005 and 2016. Applying 2005 air quality estimates,

we quantify tens of thousands of premature deaths, hospital

admissions, non-fatal heart attacks and other acute impacts due

to PM2.5 and/or ozone precursor emissions from industrial

point, EGU, mobile, international and secondary organic

aerosols, and thousands of deaths from other sources including

industrial point sources, wildfires, and residential wood

combustion among others (SI). We estimate thousands of

PM2.5 and ozone-related life years lost due to emissions from

these same sources.

The level of PM2.5 and ozone mortality risk drops markedly

for EGUs and mobile sources between 2005 and 2016, such

that the mortality impacts these sectors pose declines from the

first and second largest among all sectors to third and fourth

(Figure 3). The reduction in mortality risk from EGUs and

mobile sources reflects the implementation of various rules

affecting the emissions of directly emitted PM2.5 and PM2.5 and

ozone precursors from these sectors between 2005 and 2016.

The Mercury and Air Toxics Standards, not accounted for in

this analysis, promise to further reduce the mortality burden

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3586 dx.doi.org/10.1021/es304831q | Environ. Sci. Technol. 2013, 47, 3580−3589
from this sector. The continuing shift to newer, lower emitting

cars (i.e., vehicle fleet turnover) and rules including Tier-2

vehicle standards, the on-road and nonroad rules, among

others, will reduce emissions of PM2.5 and ozone precursors

between 2005 and 2016; indeed, continued turnover in the

vehicle fleet promises to further reduce emissions well beyond

2016.

The changes in mortality impacts for other sectors are more

muted. The reduction in premature mortality from industrial

point sources is a result of National Emission Standards for

Hazardous Air Pollutants, whose emission control requirements

for air toxics frequently reduce emissions of PM2.5 and ozone

precursors as a cobenefit. As we note above, the increases in

premature deaths attributable to certain sectors including area

sources, international emissions, secondary organic aerosols,

biogenic emissions, and wildfires are due largely to increases in

population growth, rather than growth in emissions from those

sectors. The heterogeneity in air quality and health impacts

estimated in this paper argues for the importance of tracking

high resolution sectors with source contribution techniques to

provide more specificity of culpability beyond very broadly

defined sectors such as point, area, and mobile.

This analysis is subject to important limitations and

uncertainties, some of which are common to quantitative

health risk assessments, but certain of which are unique to this

assessment. For example, uncertainties associated with

predicting recent and future total air quality levels, transferring

risk coefficients from the epidemiological literature, projecting

the growth and distribution of the population, are each

described in detail elsewhere.47 However, we believe that some

of these uncertainties and limitations have the potential to

influence our analysis greatly and are worth noting here.

First, our analysis does not reflect the substantial improvements in PM2.5, and to a lesser extent ozone, air quality levels

expected to result from the implementation of the Mercury and

Air Toxics Standards. The Regulatory Impact Analysis

accompanying that rule estimated about 4200 fewer PM2.5-

related premature deaths as a cobenefit of emission controls

reducing releases of mercury from EGUs in 2016. These results

suggest that the overall contribution of PM2.5-related mortality

attributable to EGUs in 2016 is somewhat lower than estimated

in this paper and so the decline in premature deaths would be

steeper for this sector between 2005 and 2016. Moreover, this

assessment does not reflect reductions in PM2.5 and ozone

expected from the full implementation of emission controls for

mobile sources due to fleet turnover expected beyond 2016.

Finally, our analysis does not account for human health risks

from air toxics, and so it underestimates the overall magnitude

of health risks attributable to recent and projected levels of air

pollution.

Second, when projecting the emission reductions expected to

result from the implementation of these programs, we used the

best available information. However, the timing and magnitude

of emission reductions from these programs may differ from

our modeling in ways that could affect the size and distribution

of health impacts attributable to each sector. Moreover, we have

greater confidence in the emissions projections for some

sectors than others. For example, the emissions for electrical

generating units are projected using IPM, which characterizes

well the unit-level emissions of PM2.5 and ozone precursors.

Conversely, we estimate emissions from the residential wood

combustion sector indirectly, using population surrogates, and

resolve these emissions at the county level.

Third, this modeling does not perturb the atmosphere by

changing the relative levels of PM2.5 and ozone precursors, and

so the health burden we estimate is associated with the fraction

of total PM2.5 and ozone levels attributable to each sector at a

given point in time (2005 or 2016), holding all else constant.

Thus, the health benefits resulting from emission controls

placed on a given sector are likely to differ from those reported

here and will depend on the relative levels of other precursors

and the level of emission reductions. Further, uncertainties

associated with the emissions or meteorological inputs may lead

to deficiencies in the characterization of the source contribution

mix at a receptor location. Our approach used a complete year

of meteorology to capture the variety of ozone formation

regimes. However, it is possible that the meteorology used for

these model applications may not represent all ozone formation

regimes at every individual receptor location in the continental

United States.

Finally, this analysis assumes that all fine particles are equally

toxic. We acknowledge above that a growing body of literature

suggests that variability in the size of risk estimates may be

explained in part by both particle composition, season, and

source type. However, we argue that this literature does not yet

provide the data necessary to estimate PM-related impacts that

are differentiated by specie. The population-weighted estimates

of exposure to each PM species above confirm that reductions

in total PM2.5 levels have a nonuniform affect on particle

composition. And, to the extent that certain species are more

toxic than others, then this assessment may have under- or

overestimated the PM health impacts attributable to each

sector. And, to the extent that certain sectors affect PM levels

differently across seasons, and risks also differ across seasons,

then the impacts reported here may be under- or overestimated.

Our analysis suggests that emissions from EGUs and area

sources contribute more than other emission source sectors to

air pollution-related health impacts in the U.S. in 2005. We

estimate that the overall burden of outdoor air pollution on

mortality will decline from 2005 to 2016, particularly for EGUs

and mobile sources due to implementation of regulatory

programs affecting emissions from these sources.. Notwithstanding the limitations and uncertainties noted above, our

results indicate that while that overall levels of PM2.5 and ozone

are improving, many sectors continue to pose a considerable

burden to public health. Moreover, even if the emissions and air

quality impacts attributable to these sectors were to remain

constant, population growth would likely increase the total

number of premature PM2.5 and ozone-related deaths in the

future.

■ ASSOCIATED CONTENT

*S Supporting Information

The Supporting Information accompanying this article contains

additional results and information regarding our approach for

estimating population exposure; the health impact functions we

applied; and the model performance evaluation. This material is

available free of charge via the Internet at http://pubs.acs.org.

■ AUTHOR INFORMATION

Corresponding Author

*Phone: (919) 541-0209; fax: (919) 541-0839; e-mail: Fann.

Neal@epa.gov.

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3587 dx.doi.org/10.1021/es304831q | Environ. Sci. Technol. 2013, 47, 3580−3589
Notes

The authors declare no competing financial interest.

■ ACKNOWLEDGMENTS

We gratefully acknowledge the assistance we received from

both Susan C. Anenberg and Bryan J. Hubbell, whose feedback

greatly improved early versions of this manuscript.

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