Diesel Emissions Quantifier
Health Benefits Methodology
&EPA
United States
Environmental Protection
Agency
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Diesel Emissions Quantifier
Health Benefits Methodology
Transportation and Regional Programs Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
This technical report does not necessarily represent final EPA decisions or
positions. It is intended to present technical analysis of issues using data
that are currently available. The purpose in the release of such reports is to
facilitate the exchange of technical information and to inform the public of
technical developments.
ACKNOWLEDGEMENTS
EPA gratefully acknowledges the assistance of three peer reviewers in
reviewing this project and its documentation.
&EPA
United States
Environmental Protection
Agency
EPA-420-B-10-034
August 2010
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Table of Contents
I. Background
II. Estimating Changes in PM2.5 Air Quality Concentrations Resulting from Diesel Emissions
A. Data Inputs
i. National emissions inventory (NEI)
ii. National scale air toxics assessment (NATA)
B. Analysis and Calculations
III. Estimating the Human Health Benefits of Changes in PM2.5 Air Quality
A. Overview
B. Data Inputs and Health Endpoints
i. Annual vs. Annualized Monetized benefits
ii. Calculating the PM2.5 benefit-per-ton estimate
IV. Estimating Annualized Costs
V. Uncertainties, Limitations, and Quality Assurance
A. Input Data
B. Appropriate Use of This Application
C. Quality Assurance
VI. Example Results
VII. Literature Cited
VIII. Website Index/Internet Resources
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List of Tables and Figures
Table 1: Summary of health endpoints and health impact functions
Figure 1: Monetary benefit-per-ton of diesel emissions reduced ($/ton) for all counties in the
United States plotted versus source-specific diesel emissions (tons/year) in that county.
Results are presented for (a) total diesel sources, (b) on-road diesel sources, and (c)
non-road diesel sources
Figure 2: Monetary benefit-per-ton of diesel emissions reduced ($/ton) for all counties in the
United States plotted versus a ratio of change in concentration versus change in
emissions density for each county, i.e. c/( e/a,), where c,, e,, and a, are the
concentration, emissions, and area of county /'. Results are presented for (a) total diesel
sources, (b) on-road diesel sources, and (c) non-road diesel sources.
Table 2: Counties with highest predicted benefit-per-ton estimates ($/ton) for total diesel sources
Table 3: Counties with highest predicted benefit-per-ton estimates ($/ton) for on-road diesel
sources
Table 4: Counties with highest predicted benefit-per-ton estimates ($/ton) for non-road diesel
sources
Table 5: Benefit-per-ton of diesel emissions reduced ($/ton) for counties with the lowest
emissions of total diesel sources
Table 6: Benefit-per-ton of diesel emissions reduced ($/ton) for counties with the lowest
emissions of on-road diesel sources
Table 7: Benefit-per-ton of diesel emissions reduced ($/ton) for counties with the lowest
emissions of non-road diesel sources
Table 8: Counties with highest import/export factors for total diesel sources
Table 9: Counties with highest import/export factors for on-road diesel sources
Table 10: Counties with highest import/export factors for non-road diesel sources
Table 11: Counties with lowest import/export factors for total diesel sources
Table 12: Counties with lowest import/export factors for on-road diesel sources
Table 13: Counties with lowest import/export factors for non-road diesel sources
Figure 3: Distribution of county-level benefit-per-ton of diesel PM emission reductions: on-road
sources
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Figure 4: Distribution of county-level benefit-per-ton of diesel PM emission reductions: non-
road sources
Table 14: Example quantifier and benefits module results for Cook County, IL and Anderson
County, TX
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I. BACKGROUND
The Diesel Emissions Quantifier Benefits Module is a tool for estimating the health and
monetary benefits that could result from a decrease in diesel exhaust emissions. The Benefits
Module is a new component of EPA's existing web-based Diesel Emissions Quantifier (the
Quantifier). The Benefits Module uses the 2002 National Emissions Inventory (NEI) data and the
2002 National Air Toxics Assessment (NATA) model results to estimate the relationship of
changes in diesel emissions to changes in primary particulate matter air concentrations for each
county in the U.S. The Benefits Module then uses previously-generated outputs from the
Environmental Benefits Mapping and Analysis Program (BenMAP) model to estimate the value
of changes in the incidence of avoided premature mortality and several excess morbidity
endpoints.
The Quantifier, which was released on EPA's website in 2007, allows users to estimate the diesel
emission reductions that result from implementing a variety of control strategies for mobile or
stationary diesel engines that the user selects. It is designed for users who do not have technical
expertise in emissions modeling or air pollution in general, but it does include a substantial
amount of technical information for users who do have that expertise. The Quantifier's output
includes tabular estimates of particulate matter emission reductions as well as estimates of
emission reductions for NOX, CO, CO2, and hydrocarbons on both an annual and engine lifetime
basis. These tables can be exported in spreadsheet format. It also includes a User's Guide that
explains the data and calculations used to estimate the emission reductions. A more detailed
description of the Quantifier, and access to the tool itself, can be found at
www.epa.gov/cleandiesel/quantifier/.
The Benefits Module runs off of a county-scale "look-up table" within the larger Quantifier tool.
The look-up table includes estimates of the monetary benefits per unit of reduction in emissions
(tons/year) for each county in the United States. A user does not see this table directly but instead
answers a set of questions about the type of engine being controlled, the emission control(s)
used, and the location of the emission reductions. Once the Quantifier estimates the emission
changes, users can choose to have the Benefits Module estimate the health and monetary impacts
of reductions in fine particulate (PM2.5) emissions. Those results are calculated from the lookup
table and the combined monetary value of avoided mortality and morbidity is presented in
tabular format for the counties the user identified. Monetary values are based on avoided
incidences of the following health effects:
Premature mortality
Chronic bronchitis
Acute bronchitis
Upper and lower respiratory symptoms
Asthma exacerbation
Nonfatal heart attacks
Hospital admissions
Emergency room visits
Work loss days
Minor restricted-activity days
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EPA has developed look-up tables for total diesel PM sources, as well as for on-road diesel
sources and non-road diesel sources (diesel pleasure craft, diesel locomotives, diesel commercial
marine vessels, and all other non-road diesel sources). The look-up table for total diesel PM
sources was developed as part of the Quality Assurance for this tool; the tool uses the on-road
and non-road look-up tables and sums the results for the total benefits for projects that include
both types of engines. Due to the limitations of BenMAP (the benefits modeling component), the
Benefits Module results are only available for the contiguous 48 states. Therefore, it cannot be
used to provide benefits for diesel emission reductions strategies in Alaska, Hawaii or the U.S
territories.
The purpose of the Quantifier and the Benefits Module is to provide a screening-level estimate of
the emissions and health effects, respectively, of specific diesel engine emission reduction
options. These options include adding post-combustion control technologies (also known as
aftertreatment) to remove or reduce pollutants from the exhaust, replacing older engines with
newer, cleaner engines, and/or switching to lower-emitting fuels. Emission reductions for any
single project can be distributed in up to five counties. The Quantifier is not considered adequate
or appropriate for SIP planning or credit calculation purposes. Users wanting to estimate the air
quality or health benefits of a large number of diesel emission reduction programs spread out
over many counties should use more complex air quality modeling tools that account for longer
range transport of pollution and secondary pollutant formation.
The Quantifier allows the user to enter the size of the fleet affected by the strategy and the year
in which the changes will take effect, as well as the location (county) of the engines. For engines
used in multiple counties, such as long-haul trucks, the user should specify the county where the
majority of the emissions are located. (While the Benefits Module allows the user to allocate
emission reductions among multiple counties for the purpose of estimating monetary benefits,
currently the Quantifier requires users to pick a single county for the purposes of calculating the
effectiveness of each emission reduction strategy.) The Quantifier includes assumptions about
the effectiveness at reducing emissions of various emission control technologies; the Benefits
Module does not make any changes to those data. The Quantifier also includes scrappage
estimates to inform lifetime engine emission reduction estimates. In contrast, the Benefits
Module does not include estimates of the health impacts over the lifetime of an engine. Results
are instead presented for the single year in which the emission reduction strategy was
implemented. More information on the Quantifier can be found in the Users Guide at
www.epa.gov/cleandiesel/documents/420bl0033.pdf
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II. ESTIMATING CHANGES IN PM2.5 AIR QUALITY CONCENTRATIONS
RESULTING FROM DIESEL EMISSIONS
A. Data Inputs
/'. National Emissions Inventory
The NEI is a comprehensive inventory covering criteria pollutants and hazardous air pollutants
(HAPs) for the 50 states, Washington DC, Puerto Rico, and the U.S. Virgin Islands. The NEI is
assembled and reported every three years by EPA's Emission Inventory and Analysis Group.
Sources in the NEI are described as either stationary or mobile sources. Mobile sources are
categorized as either on-road or non-road sources. On-road sources include motorized vehicles
that are normally operated on public roadways. This includes passenger cars, motorcycles,
minivans, sport-utility vehicles, light-duty trucks, heavy-duty trucks, and buses. Non-road
sources include recreational marine and land-based vehicles, farm and construction machinery,
industrial, commercial, logging, and lawn and garden equipment, aircraft, airport ground support
equipment (GSE), locomotives, and rail maintenance equipment. These sources are powered by
diesel, gasoline, compressed natural gas (CNG), and liquefied petroleum gas (LPG) -fueled
engines, among others.
In developing the 2002 draft mobile source NEI, EPA provided state, local, and tribal agencies
the opportunity to review and provide comment on the preliminary NEI. EPA's National Mobile
Inventory Model (NMIM, www.epa.gov/oms/nmim.htm) was used to generate the preliminary
non-road estimates for the 2002 NEI. The preliminary on-road estimates were developed by E.H.
Pechan & Associates, Inc. using many of the same data and methods being used in NMIM (U.S.
EPA, 2004). The on-road emission estimates in the NEI are based on running EPA's MOBILE6
model (http://www.epa.gov/oms/m6.htm) to generate emission factors in grams per mile and
then determining total annual tons using annual vehicle miles traveled (VMT). The Highway
Performance Monitoring System (HPMS), on which VMT estimates are based, uses sampling
frames based on states, metropolitan areas, and non-metropolitan areas within states. EPA then
allocates VMT to the county level. The annual VMT used in the preliminary version of the NEI
was based on preliminary national 2002 VMT estimates made by the Federal Highway
Administration (FHWA). Thirteen states submitted revised VMT data to EPA for incorporation
in the final 2002 NEI. Once state, local, and tribal agencies submitted their review of preliminary
NEI information to EPA, these data were logged, reviewed, and quality-assured by EPA.
Documentation for the 2002 NEI is provided at www.epa.gov/ttn/chief/net/2002inventory.html
Documentation for the 2002 Mobile NEI is located at
ftp://ftp.epa.gov/EmisInventorv/2002finalnei/documentation/mobile/2002 mobile nei version
3 report 092807.pdf
/'/'. National-Scale Air Toxics Assessment
The degree to which a reduction in diesel PM emissions results in a change in ambient diesel PM
concentrations has been determined based on the results of EPA's 2002 National-Scale Air
Toxics Assessment (NATA) (www.epa.gov/ttn/atw/natamain/). NATA, which is often referred
to as a "screening model" due to limitations in the underlying data and methodology, predicts
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ambient concentrations of diesel PM at the census tract level. NATA does this by performing
dispersion modeling of diesel PM emissions taken from the 2002 National Emissions Inventory
(NEI). The 2002 NATA includes 292 air pollutants, including all 187 hazardous air pollutants
and diesel PM. The assessment includes four steps:
1. Compiling a national emissions inventory of air toxics emissions from outdoor
sources.
2. Estimating ambient concentrations of air toxics across the United States.
3. Estimating outdoor population exposures across the United States.
4. Characterizing potential public health risk due to inhalation of air toxics including
both cancer and non-cancer effects.
The first step, developing emissions inventories for the
NEI, is described above. Since the NEI only provides
county-scale emissions for mobile sources and area-
wide stationary sources, the emissions must be
apportioned to the census tract level for NATA
modeling purposes. For diesel emission sources, the
emissions are apportioned based on source category,
for example:
On-road diesel emissions use roadway miles
(urban primary roads, rural primary roads,
urban secondary roads, rural secondary
roads) for all roads except local roadways.
This is because information on local
roadway miles is not generally available so
population was instead used as a surrogate
for roadway miles
Locomotive diesel emissions use railroad
miles
Commercial marine diesel emissions use
port locations and underway miles, i.e. miles
traveled under engine power
Construction diesel emissions use
population change (according to the census, i.e. 1990 - 2000)
Diesel pleasure craft emissions use miles of water coastline
A complete list of surrogates is available on Tables C-7 and C-8 of "User's Guide for the
Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) Version 3.0"
(www.epa.gov/scram001/userg/other/emshapv3ug.pdf).
For step 2, a computer simulation model called the Assessment System for Population Exposure
Nationwide (ASPEN; www.epa.gov/ttn/atw/natal999/aspen99.html) is used to estimate toxic air
pollutant concentrations. This model is based on EPA's Industrial Source Complex Long Term
model (ISCLT) which simulates the behavior of the pollutants after they are emitted into the
atmosphere. ASPEN uses estimates of toxic air pollutant emissions and meteorological data from
National Weather Service Stations to estimate air toxics concentrations nationwide by census
Types of Participate Matter
The Quantifier estimates changes in diesel
paniculate matter, or diesel PM. This is all
diesel particles, regardless of size.
Likewise, NATA models diesel PM.
Health effects and monetary benefits,
however, are related to exposures to fine
paniculate matter, or PM2 5. PM2.5 includes
diesel particles as well as other types of
small particles (the "2.5" means the
particles are smaller than 2.5 microns).
Most diesel paniculate matter is PM25;
some, however, is larger than 2.5 microns.
The Benefits Module uses the same
estimate as EPA's MOBILE 6.2 model
(the latest mobile source inventory
development tool) and assumes that 96%
of diesel paniculate matter is PM25.
Therefore, the Benefits Module benefits
are calculated on 96% of the total diesel
PM emission reductions estimated by the
Quantifier.
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tract. The ASPEN model takes into account important determinants of pollutant concentrations,
such as:
Rate of release
Location of release
The height at which the pollutants are released
Wind speeds and directions at the meteorological stations closest to the release
Breakdown of the pollutants in the atmosphere after release (i.e., reactive decay)
Settling of pollutants out of the atmosphere (i.e., deposition)
Transformation of one pollutant into another (i.e., secondary formation)
ASPEN estimates toxic air pollutant concentrations for every census tract in the continental
United States, Puerto Rico and the Virgin Islands. Census tracts are land areas defined by the
U.S. Bureau of the Census and typically contain about 4,000 residents each. Census tracts in
cities are usually smaller than 2 square miles in size but are much larger in rural areas (U.S.
Bureau of Census, 2000) The ASPEN user's guide is available at
www.epa.gov/scram001/userg/other/aspenug.pdf.
For emissions apportioned from the county-level to the census tract, such as on-road and non-
road diesel sources, the emission locations within each census tract are treated as pseudo-point
sources at locations in a radial grid around the census tract centroid. Pseudo-point sources are
assumed to be vented point sources with an effective stack height of 5 meters and for which no
plume rise calculations are made. ASPEN modeling was carried out to 40 km. Annual average
emissions rates were used - no diurnal patterns were assumed. Because of this approximation in
emissions source location, ASPEN was deemed sufficiently accurate for purposes of modeling
on-road and non-road diesel sources. The 2002 NATA uses a more sophisticated dispersion
model, AERMOD (see www.epa.gov/scram001/dispersion_prefrec.htm#aermod), to model large
stationary sources where more detailed emissions information is available, but the AERMOD
analysis does not apply to the module described here.
For some pollutants, the concentration estimates include a "background" concentration which is
based on monitored values. Background concentrations are the contributions to outdoor air toxics
concentrations resulting from natural sources, persistence in the environment of past years'
emissions, and long-range transport from sources that are more than 50 kilometers away. In
other words, background concentrations are levels of pollutants in the atmosphere that would be
present if there had been no anthropogenic emissions in the area being modeled.
(www.epa.gov/ttn/atw/nata 1999/background.html). For diesel PM, NATA does not use
monitored air quality concentrations to estimate background concentrations. Instead, it uses a
modeling-based approach that provides a rough approximation of air concentrations resulting
from transport from sources located between 50 km and 300 km from the receptors. These
estimates are included in the source category concentration estimates instead of being treated as a
separate source category in the 2002 NATA.
The results of NATA step 2, predicted ambient concentration for diesel PM at the census tract
level, are used in the Benefits Module. The results from steps 3 and 4 of the NATA analysis
(estimating population exposures and characterizing public health risk) have not been used to
support the Benefits Module and thus are not further described here. Instead, the Quantifier uses
the Environmental Benefits Mapping and Analysis Program (BenMAP) to estimate the health
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impacts. Further information on NATA's use of the Hazardous Air Pollutant Exposure Model 5
(HAPEM5) can be found at www.epa.gov/ttn/atw/nata 1999/ted/teddraft.html. For further
information summarizing the 2002 NATA and past results, see
www. epa. gov/ttn/atw/nata 1999/natafinalfact.html.
NATA results are publicly available on EPA's website at www.epa.gov/ttn/atw/natamain/.
Results can be found for the entire United States, at the county or census tract level, and by
source type or pollutant. These results are best used for comparing counties or census tracts to
one another, and do not define "hotspots" or areas of significantly higher concentrations within a
single census tract, or answer epidemiological questions such as whether proximity to sources
causes increased adverse health effects or higher risks.
The NATA methodology has undergone Science Advisory Board (SAB) peer review. Details of
the review, including slide presentations and user documentation for each step of the NATA
approach, are available at www.epa.gov/ttn/atw/sab/sabrev.html.
B. Analysis and Calculations
The census tract level NATA-predicted ambient concentrations of diesel PM were used to create
the lookup tables that are the basis for the Benefits Module. These predicted ambient
concentrations of diesel PM are used in conjunction with standard PM2 5 concentration-response
functions used in the BenMAP benefits modeling tool.
In order to create county-level ambient concentrations, the census tract level ambient
concentration NATA results (c) have been population-weighted to county values:
For a county /' and tract a:
X (c i,a * Population,^)
Ci =
Population
This analysis was performed for total ambient diesel PM, as well as for on-road diesel PM and
non-road diesel PM. County-level, versus census-tract level, concentrations have been used for
the Benefits Module because (a) county-level results are a better match for the standard PM2 5
concentration-response functions used in the health benefits analysis and (b) mobile source
emissions, taken from NEI, are estimated at the county-level and the use of census tract level
results would introduce additional uncertainty.
As an additional note, long range dispersion of diesel PM may contribute to an increase in diesel
PM concentrations in one county due to emissions from a neighboring county. In general, this
effect is likely to be insignificant because the large majority of diesel impacts occur in close
proximity to the source, but it is a potential concern for low-emitting counties in close proximity
to or downwind of one or more high-emitting counties. In areas where long-range transport is
more important, the uncertainty resulting from the approach used here may be more significant
and remains inadequately accounted for in this methodology. An inspection of the resulting
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county ratios, described in the Quality Assurance section below, reveals that there were only a
few counties that had very low emissions and large ambient PM diesel concentrations, none of
which were clearly an inaccurate result. Nonetheless, given the uncertainty in the results for
these counties, benefits have been calculated, but a flag has been added in the Benefits Module to
indicate where benefit per ton estimates for low-emitting counties may be underestimates, and
also where benefit per ton estimates for high-emitting counties may be overestimates (due to
transport of emissions into surrounding counties). The method used to identify and flag counties,
based on a ratio of predicted ambient concentration to emissions density in each county, is
described further in Section IV.C below.
III. ESTIMATING THE HUMAN HEALTH BENEFITS OF CHANGES IN PM2.5 AIR
QUALITY
A. Overview
Having first estimated change in PM2.5 ambient concentrations resulting from a change in diesel
PM2 5 emissions, the Benefits Module then estimates the per-ton benefit of reducing ambient
diesel PM2.5. To perform this benefits analysis, the Benefits Module uses the "damage function"
approach, which is a peer-reviewed technique for estimating the human health impacts
associated with exposure to ambient pollutants (Levy et al., 1999). As a result, the Benefits
Module calculates the benefit-per-ton of PM2 5 emission reduction in a manner generally
consistent with the methods found in the recently published Regulatory Impact Analysis (RIA)
for the Ozone NAAQS (U.S. EPA, 2008a).
Estimating PM2 5 benefit-per-ton entails three basic steps:
1. Estimating the change in PM2.5 air quality for the geographic area of interest
2. Loading the estimated air quality changes into the Environmental Benefits Mapping and
Analysis Program (BenMAP) and estimating the resulting change in the incidence of
health outcomes and monetizing the benefits of those outcomes (Abt Associates Inc.,
2005a)
3. Dividing the total monetized benefit by the total estimated emission reduction
The discussion in the preceding section described how the estimates of change in ambient diesel
PM air quality concentrations were derived for each county, which constitutes the first step
above. The following sections detail how we estimated health benefits of PM2.5 exposure and
performed the final benefit-per-ton calculations.
B. Data Inputs and Health Endpoints
The Benefits Module uses the BenMAP model to estimate the health endpoints (the health
effects that are caused, exacerbated, or otherwise affected by exposure to PM2 5 such as
premature mortality or asthma attacks) resulting from a unit change in diesel emissions in each
county. Table 1 below summarizes the health endpoints quantified and the health impact
functions applied for this analysis.
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Modeling was done for each of three air quality modeling scenarios—on-road, non-road and total
diesel PM. The model compared baseline air quality for each scenario (reflecting total county
level ambient PM2.5 from that particular source type alone) and a control air quality scenario
(reflecting a zero-out of ambient PM2 5) The modeling predicted relatively small incremental
changes in PM2.5 in each county. Because most of the health impact functions (equations that
explain the relationship between exposure and changes in health endpoints) used for our analysis
are log-linear (and thus produce different estimates of health impacts depending on the baseline
level of air quality change), the benefits are somewhat sensitive to the baseline levels of air
quality. For this reason, we modified the air quality inputs slightly by adding 10 |ig/m3 to the
baseline and control air quality files—ensuring that the benefits were calculated higher on the
curve. Because it is not possible to know ex-ante what the baseline air quality levels will be in
the counties in which users apply the benefit-per-ton estimates, this seemed like a reasonable
adjustment.
In general, the benefits assessment used techniques, health impact functions and valuation
functions that are consistent with the PM2.5 health impacts assessments supporting the PM2.5 and
the Ozone National Ambient Air Quality Standards (U.S. EPA, 2006; U.S. EPA 2008a), with
two major exceptions. First, in contrast to those analyses, this assessment applies non-threshold
adjusted PM2.s health impact functions. Some researchers have hypothesized the presence of a
threshold relationship between PM2.5 exposure and the risk of adverse health effects, including
premature mortality. For this reason, EPA has traditionally applied an assumed 10 |ig/m3
cutpoint to the long-term mortality and short-term morbidity concentration-response functions.
We determined that such a threshold would be inappropriate for this analysis because we do not
know, ex ante, which areas would receive air quality improvements above or below this
hypothesized threshold. Further, we did not believe it appropriate to assign zero benefits to
counties where ambient PM levels were below a threshold level of 10 |ig/m3.
The second major divergence from the two RIAs noted above is that we estimated current year
population exposure (2008), rather than a projected exposure. We anticipated that most users
would wish to estimate the near-term benefits of diesel control strategies, which called for using
current year population to generate exposure estimates in BenMAP. Users interested in
additional details regarding the health impact assessment may refer to the most recent PM2.5 and
ozone RIAs (U.S. EPA 2006; U.S. EPA 2008a).
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Table 1: Summary of health endpoints and health impact functions
Endpoint
Pollutant
Study and Functional Form
Study
Population
Premature Mortality
Premature mortality —
cohort study, all-cause
Premature mortality —
all-cause
PM25 (annual)
PM25 (annual)
Laden et al. (2006), log-linear
Woodruff et al. (1 997), logistic
>25 years
Infant (26 years
Nonfatal heart attacks PM25 (daily)
Peters et al. (2001), logistic
Adults
Hospital Admissions
Respiratory
Cardiovascular
Asthma-related ER
visits
PM,5 (daily)
PM,5 (daily)
PM,5 (daily)
PM,5 (daily)
PM,5 (daily)
PM,5 (daily)
PM,5
Pooled estimate:
Moolgavkar (2003)— ICD 490-496 (COPD),
log-linear
Ito (2003)— ICD 490-496 (COPD), log-
linear
Moolgavkar (2000)— ICD 490-496 (COPD),
log-linear
Ito (2003)— ICD 480-486 (pneumonia), log-
linear
Sheppard (2003)— ICD 493 (asthma), log-
linear
Pooled estimate:
Moolgavkar (2003)— ICD 390-429 (all
cardiovascular), log-linear
Ito (2003)— ICD 4 1 0-4 1 4, 427-428
(ischemic heart disease, dysrhythmia, heart
failure), log-linear
Moolgavkar (2000)— ICD 390-429 (all
cardiovascular), log-linear
Morris et al. (1 999), log-linear
>64 years
20-64 years
>64 years
<65 years
>64 years
20-64 years
0- 1 8 years
Other Health Endpoints
Acute bronchitis
Upper respiratory
symptoms
Lower respiratory
symptoms
Asthma exacerbations
Work loss days
Minor restricted
activity days (MRADs)
PM,5
PMIO
PM,5
PM,5
PM,5
PM,5
Dockery et al. (1 996), logistic
Popeetal. (1 99 1),
Schwartz and Neas (2000)
Pooled estimate:
Ostro et al. (200 1) (cough, wheeze and
shortness of breath)
Vedaletal. (1 998) (cough)
Ostro (1 987), log-linear
Ostro and Rothschild (1 989), log-linear
8- 1 2 years
Asthmatics, 9- 1 I
years
7- 1 4 years
6-l8yearsa
1 8-65 years
1 8-65 years
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 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.
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The final stage of the benefits analysis is to estimate the monetary value of the health impacts for
each county and each of the three scenarios. As in the health incidence stage of the benefits
analysis, here we follow techniques that are generally consistent with previous EPA RIA benefits
analyses. As in those analyses, mortality benefits are estimated using the EPA standard Value of
Statistical Life of $5.5 million (1990 dollars income levels, 1999$). We also apply an EPA
Science Advisory Board-recommended 20-year distributed lag between exposure and premature
mortality.1 When calculating monetized benefits, it is necessary to discount over this time
period. Hence, we discount the mortality benefits at 3% and then sum the monetary value of each
independent endpoint. We estimated valuation for a cost year of 2006 and adjusted the
Willingness to Pay (WTP) valuation functions to reflect 2008 projected income levels. Users
interested in the complete technical details of the valuation stage may refer to the most recent
PM2.5 and ozone RIAs (U.S. EPA 2006; U.S. EPA 2008a).
/'. Annual versus AnnualizedMonetized Benefits
The steps above produce an annual estimate of the benefits of reducing an incremental ton of
PM2 5 from various emission sources for the year 2008. However, we expect that diesel retrofits
will provide a stream of benefits over a number of years. Moreover, the costs of these controls
are frequently expressed in annualized terms that take into account the expected "lifetime" of the
investment. Annualizing costs is the process of combining capital and operating-and-
maintenance costs and then distributing these costs on an annual basis over the life of the
equipment.
Thus, the benefits and costs are expressed in somewhat different temporal scales. Ideally, the
benefits should also be annualized as well. However, this process would require a year-to-year
estimate of the change in emissions and air quality over the life of each piece of equipment. For
this same time period we would calculate year-to-year benefits, and this stream of future benefits
would then be discounted back to the original year in which the emission control was installed.
Moreover, we would account for year-to-year changes in population growth and distribution. We
would also project changes in income growth to account for the increasing willingness to pay to
reduce mortality risk. These were not practical analyses for this project.
Instead, we have made the assumption that the annual benefits are a fair surrogate for the
annualized benefits. On one hand, we have neither modeled future population growth and
distribution, nor accounted for future income growth; these are factors that should increase
benefits over time. On the other hand, this stream of benefits would be discounted, which would
reduce the annualized benefits. In our judgment, these countervailing factors more or less
balance out such that the annual benefits are comparable to the annualized benefits. Each of the
tables and maps in this document treat annual benefits as annualized benefits.
/'/'. Calculating the PM2.s- Benefitper-ton Estimate
The final step is to simply divide the county level benefit estimate by the total change in
emissions—resulting in a benefit-per-ton estimate. The benefit-per-ton estimate can also be
represented as follows:
1 This lag reflects the hypothesis that some reductions in premature mortality from exposure to ambient PM2 5 will
occur over short periods of time in individuals with compromised health status, but other effects are likely to occur
among individuals who, at baseline, have reasonably good health that will deteriorate because of continued
exposure.
15
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BPT, 4^
where
ri = average health benefits (in 2002 dollars) in county /' per ton of reduced diesel PM
emissions in county /',
Aej = total reduction in diesel PM emissions (in tons) in county /',
/\wt = health benefits (in 2006 dollars) in county /' as a result of Ac,.
For this Benefits Module, no factor was used to convert the ambient diesel PM concentrations
(Ac,) in each county to ambient PM2.5 concentration, prior to calculating health benefits (Aw,).
Similarly, BPT,, were calculated by dividing by county diesel PM emissions (Ae,), and not just
the PM2.5 component. Diesel PM consists primarily of PM2.5, generally 96% by mass (U.S. EPA
MOBILE 6.2). Without additional information about how the percentage of PM2.5 to total diesel
PM may vary between sources and locations within a county, and because Aw, generally scales
linearly with Ac,, any factor that describes the proportion of diesel PM that is PM2.5 would be
multiplied in both the numerator (/\Wj*factor) and denominator (J\e*factor) of the BPT,,
calculation, and would cancel out. Thus, for purposes of deriving BPTa, the relative proportion
of diesel PM that is PM2.5 is unimportant.
When applying the BPTtt to determine the health benefits for specific diesel exhaust reductions, it
is important to remember that the health functions to derive Aw, are specific to PM2.5. Thus, the
derived BPTit most accurately describes health benefits per ton of PM2.5 reduced, and not total
diesel PM. The emissions changes predicted by the Quantifier are presented in the Quantifier as
changes in particulate matter (PM). The Benefits Module converts the Quantifier diesel PM into
changes in PM2 5 using the 96% conversion factor identified above before the health benefits can
be calculated.
IV. ESTIMATING ANNUALIZED COSTS
The Quantifier estimates the cost-effectiveness of each project over the average remaining
lifetime of the engine. These values are not easily comparable to the annual benefits presented in
the Benefits Module. Therefore, the Benefits Module also estimates the annualized cost of each
project.
The annualized cost is based on project cost data the user inputs into the Quantifier. Users can
enter two different costs into the Quantifier: the total project cost and the capital costs. The total
project costs refer to the entire cost of a retrofit project (for example, the amount of grant funding
received to do the project) whereas the capital costs refer to the portion of the total costs that go
towards purchasing and installing the retrofit equipment. Capital costs do not include any on-
going maintenance costs. To calculate the annualized cost, the Benefits Module uses the value
the user enters for the capital cost of the project.
16
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The formula for calculating annualized costs in essence "spreads out" the initial investment costs
of the project over the remaining lifetime of the engine being retrofitted. The remaining lifetime
is calculated from the existing scrappage tables in the Quantifier. These are the same data used to
calculate the cost-effectiveness estimates in the existing version of the Quantifier. This process is
used because although the costs are usually paid upfront, the benefits are spread out each year
over the remaining lifetime of the engine. By annualizing costs and benefits, the values can be
more easily compared.
The formula used for annualizing costs is:
AC = (P * r)/(l-(l+r)A-n)
Where:
AC = Annualized Cost
P = Principal (or upfront capital cost)
r = Discount rate
n = Years (remaining life of the engine)
In this case we use a discount rate of 3%. This rate is recommended by EPA draft guidance
ftp://ftp.epa.gov/EmisInventory/2002finalnei/documentation/mobile/2002_mobile_nei_version_3
report_092807.pdf regarding discounting of future costs and benefits in situations where all
costs and benefits occur as changes in consumption flows rather than changes in capital stocks,
i.e., capital displacement effects are negligible. As of the date of publication, current estimates of
the consumption rate of interest, based on recent returns to Government-backed securities, are
close to 3%.
Since the remaining lifetime of engines in a given retrofit project may vary, the annualized costs
must be calculated separately for each type and model year of engine in any given project. These
values are then summed to calculate the total annualized cost for each project.
V. UNCERTAINTIES, LIMITATIONS, AND QUALITY ASSURANCE
The Benefits Module represents a new way to bring together existing tools and databases to
provide information to state and local agencies, the public and other parties as they seek to
implement diesel reduction strategies. These existing data and tools have at various times been
subjected to comment and peer review and reflect the recommendations of many experts in
multiple disciplines. Nonetheless, the approach and data used by the Benefits Module contain
multiple uncertainties and limitations that can limit the application of this tool. These
uncertainties and limitations are discussed in more detail below.
A. Input Data
The emissions inventory for diesel PM from the 2002 NEI includes uncertainties associated with
the emissions factors, particularly those built into NMIM and the activity information either
included by default by EPA or provided by state and local agencies. It also includes the
17
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methodology used to apportion diesel PM emissions to the census tract level using surrogates in
NATA
The NATA modeling approach has a series of limitations as well. First, the results are considered
most reliable at comparing geographic areas, not analyzing specific locations. The assessment
focused on variation between geographic areas such as census tracts, counties and states. It
cannot be used to identify "hot spots" where the air concentration, exposure and/or risk might be
significantly higher within a census tract or county. In addition, this kind of modeling assessment
cannot address the kinds of questions an epidemiology study might, such as the relationship
between asthma and proximity of residences to point sources, roadways and other sources of air
toxics emissions.
Second, the results do not include impacts from sources in neighboring countries (i.e., Canada or
Mexico). Since the assessment did not include the emissions of sources in Canada and Mexico,
the results for states that border either of these countries would not reflect these potentially
significant sources of transported emissions.
Third, the assessment does not fully reflect variations in background ambient air concentrations.
This includes both emissions from natural sources unrelated to anthropogenic emissions as well
as transport of emissions from other counties. The assessment uses background ambient air
concentrations that are average values over broad geographic regions. Much more research is
needed before an accurate estimate of background concentrations at the level of census tracts, or
even at the higher geographic scales (i.e. counties or states), can be made. Since background
levels are significant contributors to the overall exposure in this assessment, the lack of detailed
information on variations in background exposures probably causes the amount of variation in
total exposure and risk between census tracts to be smaller than would otherwise be the case.
It is also important to keep in mind that NATA might systematically underestimate ambient air
concentration for some compounds. A comparison of the 1996 and 1999 NATA results with
ambient monitoring found good agreement for benzene (which primarily comes from gasoline
engines), but underestimates for several other species, especially metals. Diesel PM monitoring
is not generally available, so no comparison between NATA-predicted diesel PM concentrations
and ambient monitoring has been made. There are several possible reasons for the
underestimation of pollutant concentrations by NATA:
The National Emissions Inventory (NEI) may be missing specific emissions sources (for
many of the sources in the NEI some of the emissions parameters are defaulted or
missing). Where data were missing or of poor quality, NATA uses default, or simplified
assumptions.
If the emission rates are underestimated in many locations. EPA believes the ASPEN
model itself is contributing in only a minor way to the underestimation. This is mainly
due to output from the predecessor of the ASPEN model comparing favorably to
monitoring data in cases where the emissions and meteorology were accurately
characterized and the monitors took more frequent readings.
18
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If there are problems in monitor siting. Sites are normally situated to find peak pollutant
concentrations, which imply that errors in the characterization of sources would tend to
make the model underestimate the monitor values.
Uncertainty in the accuracy of the monitor averages, which, in turn, have their own
sources of uncertainty. The results suggest that the model estimates are uncertain on a
local scale (i.e., at the census tract level). EPA believes that the model estimates are
more reliably interpreted as being a value likely to be found within 30 km of the census
tract location.
With respect to diesel PM specifically, the ASPEN modeling used in NATA does not take into
account secondary formation of PM2.5 (i.e. atmospheric transformation into PM2.5 of other
pollutants present in diesel exhaust such as oxides of sulfur and nitrogen along with volatile
organic carbons). Many of the emission controls included in the Quantifier will reduce mobile
source NOx, which is an important precursor to the formation of ambient PM2 5. By not
modeling the influence of NOx reductions on PM2.5 formation, our benefit-per-ton estimates may
be biased downward. While we are aware of no published estimates quantifying this bias, it is
possible to generate a bounding estimate by using previously published PM2.5 benefit-per-ton
estimates.
EPA published a series of PM2 5 benefit-per-ton estimates in 2008 that relate changes in PM
precursors to monetary benefits (U.S. EPA 2008b). These estimates vary by precursor reduced
and source type affected. These estimates indicate that the value in 2015 of reducing one ton of
directly emitted carbonaceous particles from a mobile source is about $380,000 (Laden et al.
mortality estimate, 3% discount rate). Conversely, the value of reducing one ton of NOx
emissions from mobile sources is about $10,000 (Laden et al. mortality estimate, 3% discount
rate). The significant difference in valuation estimates reflects the differing potential for these
precursors to form PM2.5 in the atmosphere. This difference suggests, in turn, that not modeling
NOx emissions may bias our estimates of PM2 5 formation by only a small degree.2
In summary, the uncertainties and limitations associated with several key components of the
analysis propagate through the analysis. The estimated health effects are calculated based on an
array of "upstream" data and assumptions, the most significant of which relate to the change in
ambient PM concentrations resulting from changes in emissions. We note that diesel PM is
predominately but not exclusively PM2.5, and PM2.5 includes but is not limited to diesel particles.
Based on these predicted air quality changes, we draw upon the vast body of PM2.5 health effects
literature to apply well-established benefit estimation techniques.
There are several key limitations and uncertainties associated with the benefit-per-ton estimates
as well:
Estimating benefits at the local scale carries special uncertainties. This benefits analysis
combines county-level air quality data with a substantial amount of national- and
regional-level baseline incidence data to estimate the change in PM2.5-related health
outcomes. With the exception of baseline incidence rates for mortality, the health inputs
2 In addition, as noted further in the document, we do not estimate ozone-related benefits or other benefits categories
such as visibility. As such, the benefit-per-ton estimates likely understate total benefits.
19
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to the analysis are defined at a much broader geographic scale than the air quality data.
Moreover, the study we use to estimate PM2 5 mortality benefits (Laden et al., 2006) is
based upon population exposure data in six cities across the U.S. To the extent that
populations in that study and the populations exposed to diesel PM are different, we may
under- or over-state total benefits. For these reasons, this analysis is unlikely to have
completely characterized the spatial variability in benefits.
The benefit-per-ton metrics contain each of the uncertainties inherent in a PM2.5 benefits
analysis. As discussed in the PM2.5 NAAQS RIA (Table 5.5; U.S. EPA 2006), there are a
variety of uncertainties associated with calculating PM benefits; these uncertainties are
passed through to the benefit-per-ton estimates included in the Benefits Module. To
some extent these uncertainties are exacerbated when applied at smaller scales.
These estimates omit certain benefits categories. Reductions in PM2.5 precursors may
provide visibility benefits, which are not expressed in the benefit-per-ton metrics.
Certain unquantified benefit categories, described fully in the PM2.5 NAAQS RIA (U.S.
EPA 2006), are also omitted. These categories include ecological benefits, changes in
pulmonary function, low birth weight, and non-asthma respiratory ER visits.
The full description of the limitations and uncertainties of the BenMAP modeling tool are
available in the BenMAP User's Guide Technical Appendices, Appendix I: Uncertainty and
Pooling (pg 254-263) (Abt, 2005a) and online at
www.epa.gov/air/benmap/models/BenMAPappendicesSept08.pdf
B. Appropriate Use of This Application
For all of these factors, the uncertainty may lead to either a positive or negative bias in the
results. The potential magnitude of the uncertainty in results is difficult to quantify. Past
experience with emissions inventories would suggest that the magnitude of emissions, a product
of emissions factors and activity, would be one of the largest uncertainties associated with the
use of these data. However, basing our estimate on the ratio of the ambient concentration to total
emissions, as is done for the Benefits Module, tends to minimize the importance of uncertainties
in the emissions. For example, doubling emissions in a specific area would tend to double
ambient concentrations, but keep the ratio relatively static, and thus the absolute uncertainty in
emissions is not as significant a concern as other uncertainties in this analysis. Conversely, to the
extent that these emissions transport to other areas, the uncertainty may be larger.
One of the main factors determining magnitude of health benefits associated with a given
emissions reduction is the proximity of the emissions to people. Thus, uncertainty in the
apportionment of emissions could be an important factor in this analysis. There are two things to
consider for this uncertainty. First, if emissions are assigned to a larger census tract, then the
same level of emissions will result in a lower ambient concentration, on average (the pollution,
in effect, being spread out over a larger area means that there is less of it at any given point in
that area). The opposite is true as well (i.e., assigning emissions to a smaller census tract will
result in higher average concentrations). Second, if emissions are assigned to a less populated
census tract, fewer people will be exposed to the resulting concentration of air pollution and the
population-weighting at the county scale will predict a lower concentration and thus a lower
20
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ratio. Again, the opposite is true (i.e., emissions assigned to higher-populated tracts leads to an
overestimate of concentration and ratio).
We do not anticipate a high degree of uncertainty associated with treating mobile sources as a
series of radial points within census tracts, although this may be more of a concern for counties
and census tracts that cover a large geographic area. The Benefits Module uses average
concentrations at a much larger geographic scale (i.e., county-level), which would tend to
underestimate the importance of local hotspot impacts that are not detected by the NATA
approach. Some bias may result, however, if the population within a census tract is located closer
to and therefore more exposed to pollution from major roads or other low-level releases than our
analysis assumes.
The health benefits in the Benefits Module are for PM2.5 generically and are not dependent on the
precise chemical composition of the PM2 5 emissions in a particular area. Therefore the only
likely significance associated with not considering atmospheric chemistry is if chemical
reactions could lead to either loss or formation of PM25. The loss of directly-emitted diesel PM
through chemical reactions is unlikely, since the impacts from diesel PM tend to be highly local
for these source types (e.g., no high stacks, minimal exit velocity) and there is insufficient time
for reactions to occur before concentrations have been diluted by dispersion alone. Dilution of
diesel PM occurs in less than 1 mile, or less than 20 minutes at even slow wind speeds, which is
much faster than the typical atmospheric half-life of PM2.5, which is considered to be on the
order of days to weeks (e.g. Wilson and Suh, 1997).
In addition, the exposure and benefit-per-ton values to not include highly localized exposures,
such as those that occur when diesel exhaust "self-pollutes" the cabin on the vehicle from which
it has been emitted. This phenomenon has been studied extensively in diesel school buses, and
the data indicate it can be a significant source of exposure from older diesel engines (e.g.
Marshall and Behrentz, 2005). This Benefits Module does not capture this type of micro-scale
exposure and thus the benefits estimate does not include the benefits of reducing these types of
exposures.
Uncertainties in the use of NEI emissions and NATA-predicted ambient concentration may be
reduced by considering the following when calculating health benefits using the Benefits
Module:
The highest uncertainties in the Benefits Module's emissions, dispersion, health, and
monetary benefits calculations are likely all associated with considering only a single
location or project. Uncertainties that may have either a positive or negative bias when
considered together are more likely to be substantially smaller when considering multiple
emissions reductions over larger geographic areas, to the extent that such bias is not
highly correlated with population.
The results of the Benefits Module may be used to characterize the relative benefits of
diesel emission reduction projects between areas, but comparisons are likely to be more
uncertain when comparing areas in different states, where differences in underlying
methodology (e.g., local submission of emissions information to NEI) are likely to be
more significant.
The benefits module is most appropriate when used to estimate scale and relative
distribution of results (as opposed to precise predictions) and thus should be used for
21
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purposes where this type of estimate is appropriate only. These results are not an
adequate substitute for a more refined emissions, dispersion, and health impacts analysis
in support of broader decision-making.
Both the calculation of air concentrations from emissions estimates and the subsequent
estimation of the health benefits of those improvements in air quality are subject to significant
uncertainty. As stated earlier, these estimates should be considered just that: estimates, and not
precise calculations or predictions.
C. Quality Assurance
Figure 1 and Tables 2 through 4 are designed to examine whether the highest predicted benefit-
per-ton results are reasonable. One of the primary concerns with our methodology is with
counties that may experience substantial diesel impacts due to atmospheric transport from
surrounding counties, but may not themselves have substantial emissions. This would likely
skew the results towards unusually high benefit-per-ton numbers in those counties (i.e., skewed
higher ratios of NATA-predicted diesel PM concentrations versus county emissions would be
used as inputs for benefits calculations in BenMAP).
Figure 1 is a plot of monetary benefit-per-ton of diesel emissions reduced (expressed in $/ton)
for each county in the United States versus total emissions (tons/year), by source, in that county.
This figure illustrates two main points. First, there are few, if any, outliers with high benefit-per-
ton but low local emissions. Although this figure cannot illustrate sufficiently whether the low-
emitting counties are nonetheless skewed higher by atmospheric transport than would otherwise
be expected, no low-emitting counties have benefit-per-ton results beyond what is observed for
higher emitting, and thus more certain, counties. Second, the distributions show a relative
positive trend; that is, benefit-per-ton estimates increase with county emissions. This result is
reasonable because higher emitting counties also tend to be more populated counties and the
combination of a higher density of sources and population in proximity to each other would lead
to higher anticipated health benefits for diesel exhaust reductions.
Another way to consider the impacts of atmospheric transport either into or out of a county is to
estimate the import/export factor. This factor describes the relationship between the change in
NATA-predicted ambient concentration to the change in emissions density for that county.
Figure 2 shows a plot of monetary benefit-per-ton of diesel emissions reduced (expressed in
$/ton) for each county in the United States versus the ratio of change in concentration versus
change in emissions density. This can be indicated by. c,/( e/a,), where c,, e;, and a, are the
concentration, emissions, and area of county /'. Counties that are highest in c/( e/a,) would be
indicative of those that are most likely to import a relatively large portion of diesel PM, while
counties that are lowest in c,/( e/a,) would be indicative of those that are most likely to export
a relatively large portion of diesel PM. A high import/export value indicates the air
concentrations in the county are likely affected by imports of diesel PM from other counties. A
low value indicates the county is likely to export a large portion of the diesel PM emitted there to
other counties.
22
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Figure 1: Monetary benefit-per-ton of diesel emissions reduced ($/ton) for all counties in the
United States plotted versus source-specific diesel emissions (tons/year) in that county. Results
are presented for (a) total diesel sources, (b) on-road diesel sources, and (c) non-road diesel
sources.
1.E+07
1.E+02
OQl
1 10 100
Emissions (ton/year)
1000
10000
23
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Figure 2: Monetary benefit-per-ton of diesel emissions reduced ($/ton) for all counties in the
United State versus the import/export factor. This factor is a ratio of change in concentration
versus change in emissions density for each county, i.e. c//( e//a/), where c/, e/, and a, are the
concentration, emissions, and area of county /. Results are presented for (a) total diesel sources,
(b) on-road diesel sources, and (c) non-road diesel sources.
1.E+07
l.E+02
0.01
0.1 1 10 100
Concentration / [Emissions / Area) (ug'riectare / m^tpy)
24
1003
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Tables 2 through 4 show the counties with highest predicted benefit-per-ton due to reductions
from total diesel sources, on-road diesel sources, and non-road diesel sources, respectively.
Tables 5 through 7 show the benefit-per-ton for counties with the lowest emissions for total
diesel sources, on-road diesel sources, and non-road diesel sources, respectively. Tables 8
through 10 show the counties with the highest import/export factor (i.e. counties likely to import)
and Tables 11 through 13 show the counties with the lowest import/export factor (i.e. counties
likely to export) nationally.
A closer examination of the counties with the highest-predicted benefit-per-ton estimates (Tables
2 through 4) shows that counties with a high density of sources and/or high population density
(such as Bronx, Kings, New York, Manhattan, and Queens Counties, which are part of the City
of New York) have some of the highest benefit-per-ton estimates, which is expected. The
independent cities of Virginia, i.e. Fairfax, Poquoson, Portsmouth, Winchester, Franklin,
Lexington, and Falls Church, also show very high benefit-per-ton results, especially relative to
their local emissions. These results do not appear unreasonable since these cities tend to be fairly
dense with both sources and receptors. Many of these same counties have the lowest
import/export factors in Tables 11 through 13, supporting the assertion that, if anything, the
counties are mostly exporters of diesel emissions and the benefit-per-ton estimates may be
underestimates.
Most of the instances of unusually high or low benefit-per-ton results are for non-road emissions.
For example, the Loving County, TX, benefits of $42,000 per ton (Table 7), while small, is most
likely due entirely to transport of outside pollutants, because there are essentially no local
sources. Similarly, the $520,000 per ton for Alpine County is quite large, given the minimal local
sources (0.3 tons/year) and sparsely populated, low density county. The import/export factor
analysis supports both of these assertions, since both counties have a very high ratio (Table 13),
and could thus be interpreted as diesel importers. Two other counties with low emissions (<2
tons/year) but high predicted benefit-per-ton (> $500,000 per ton) are Owsley County, KY, and
Clay County, WV.
In order to acknowledge this uncertainty, the diesel benefits calculator takes the following
approach. First, in addition to reporting results for the county selected, the results are also
calculated and reported using statewide benefit-per-ton values in order to provide context.
Second, for all counties with import/export factors in the lowest 5th percentile - for either on-
road or non-road sources, depending on the query - the results are flagged with the following
message:
Benefits estimates are "flagged" for this county, indicating that we have less confidence
in these results due to a large amount of inter-county transport of emissions. The impacts
estimation tool may be underestimating benefits for emissions reduction projects in this
county because it has a relatively high density of emissions compared to surrounding
areas. As a result, this county is likely to be a net exporter of diesel emissions, and many
of the benefits of reducing these emissions are likely to take place in downwind counties.
Please take this increased uncertainty into account when interpreting your results.
25
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Also, for all counties with import/export factors in the highest 5th percentile - for either on-road
or non-road sources, depending on the query - the results are flagged with the following
message:
Benefits estimates are "flagged" for this county, indicating that we have less confidence
in these results due to a large amount of inter-county transport of emissions. The impacts
estimation tool may be overestimating the benefits for emissions reduction projects in this
county because it has relatively few emissions compared to surrounding areas. As a
result, this county is likely to be a net importer of diesel emissions, and air quality is
significantly affected by emissions in upwind counties. Please take this increased
uncertainty into account when interpreting your results.
EPA also calculated a population-weighted average of the county benefit-per-ton values within
each state and within the entire United States. The procedure was identical to the population-
weighting performed for averaging census tract ambient concentrations to the county level.
For total diesel sources, we calculated a range from $3.2 million per ton for New York State to
$68,000 per ton for Wyoming. The national population-weighted average was $1.2 million per
ton. The national benefit-per-ton value is somewhat higher than the national mobile source
benefit-per-ton from carbonaceous particles from all mobile sources of $730,000 that was
calculated as part of the ozone NAAQS RIA (U. S. EPA, 2008a). For on-road diesel sources we
calculated a range from $3.8 million per ton for New York State to $63,000 per ton for
Wyoming. For non-road diesel sources we calculated a range from $3.2 million per ton for New
York State to $73,000 per ton for Wyoming. The national population-weighted average for on-
road sources and non-road sources are $1.2 million per ton of diesel reduced. This is also
somewhat higher than the on-road and non-road estimates calculated as part of the ozone
NAAQS RIA, which are $740,000 per ton and $720,000, respectively.
The benefit-per-ton estimates from this project are clearly very different from those in the most
recent ozone RIA. However, the divergence may be due to the fact that the diesel PM benefit-
per-ton estimates reflect air quality changes from diesel sources alone. Conversely, the benefit-
per-ton estimates developed for the ozone RIA reflect air quality changes from reductions in
carbonaceous particles across all on-road and non-road mobile sources. Finally, these two
benefit-per-ton estimates may diverge due to inherent differences in the model used to estimate
air quality impacts. As described above, EPA used a dispersion model to estimate diesel PM air
quality changes; conversely, EPA used a photochemical grid model to generate air quality
estimates for the benefit-per-ton estimates that supported the ozone RIA.
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Table 2: Counties with Highest Predicted Benefit-per-ton Estimates ($/ton) for Total Diesel
Sources.
County
Bronx
County
Kings County
Baltimore city
New York
County
Queens
County
Fairfax city
Philadelphia
County
Poquoson
city
Portsmouth
city
Winchester
city
Ocean
County
Hudson
County
Passaic
County
Falls Church
city
Richmond
County
Bergen
County
Camden
County
Essex County
Franklin city
Hopewell city
State
NEW YORK
NEW YORK
MARYLAND
NEW YORK
NEW YORK
VIRGINIA
PENNSYLVANIA
VIRGINIA
VIRGINIA
VIRGINIA
NEW JERSEY
NEW JERSEY
NEW JERSEY
VIRGINIA
NEW YORK
NEW JERSEY
NEW JERSEY
NEW JERSEY
VIRGINIA
VIRGINIA
2000
Population
1,332,650
2,465,326
651,154
1,537,195
2,229,379
21,498
1 ,5 1 7,550
11,566
100,565
23,585
510,916
608,975
489,049
10,377
443,728
884,118
508,932
793,633
8346
22,354
Emissions
input
(tons/year)
290
630
200
820
610
5.3
700
1.8
16
6.9
210
400
ISO
2.6
260
400
240
340
2.5
5.4
County
area
(hectares)
40
60
85
23
110
10
150
20
35
11
620
57
200
3.5
48
250
230
130
3.2
8.8
Import/
export
factor
0.31
0.20
0.64
0.11
0.33
0.99
0.44
5.1
1.4
1.7
3.1
0.44
1.8
1.3
0.39
1.0
1.6
0.61
0.60
1.0
Benefits
output
($/ton)
7,800,000
6,200,000
5,300,000
5,100,000
5,000,000
4,500,000
4,500,000
3,900,000
3,800,000
3,800,000
3,800,000
3,500,000
3,400,000
3,200,000
3,200,000
3,100,000
3,100,000
3,100,000
2,900,000
2,800,000
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Table 3: Counties with Highest Predicted Benefit-per-ton Estimates ($/ton) for On-road Diesel
Sources.
County
New York
County
Kings County
Bronx
County
Philadelphia
County
Queens
County
Hudson
County
Baltimore city
Ocean
County
Richmond
County
Essex County
Bristol
County
Winchester
city
Bergen
County
Passaic
County
Fairfax city
Providence
County
Orange
County
Union
County
District of
Columbia
Delaware
County
State
NEW YORK
NEW YORK
NEW YORK
PENNSYLVANIA
NEW YORK
NEW JERSEY
MARYLAND
NEW JERSEY
NEW YORK
NEW JERSEY
RHODE
ISLAND
VIRGINIA
NEW JERSEY
NEW JERSEY
VIRGINIA
RHODE
ISLAND
CALIFORNIA
NEW JERSEY
DISTRICT OF
COLUMBIA
PENNSYLVA
NIA
2000
Population
1,537,195
2,465,326
1,332,650
1,517,550
2,229,379
608,975
651,154
510,916
443,728
793,633
50,648
23,585
884,118
489,049
21,498
621,602
2,846,289
522,541
572,059
550,864
Emissions
input
(tons/year)
91
100
94
140
ISO
50
79
49
41
68
2.6
2.3
100
47
2.6
48
400
69
90
83
County
area
(hectares)
23
60
40
150
110
57
85
620
48
130
23
11
250
200
10
430
800
110
66
190
Import/
export
factor
0.20
0.27
0.28
0.56
0.37
0.71
0.60
3.7
0.55
0.87
1.8
1.5
1.2
1.8
1.4
2.3
1.3
0.73
0.37
1.0
Benefits
output
($/ton)
9,900,000
8,700,000
7,000,000
5,800,000
5,700,000
5,700,000
5,000,000
4,600,000
4,500,000
4,400,000
3,600,000
3,500,000
3,500,000
3,400,000
3,300,000
3,000,000
2,900,000
2,800,000
2,800,000
2,800,000
28
-------
Table 4: Counties with Highest Predicted Benefit-per-ton Estimates ($/ton) for Non-road Diesel
Sources.
County
Bronx
County
Portsmouth
city
Kings County
Fairfax city
Baltimore city
Franklin city
Hampton city
Queens
County
New York
County
Poquoson city
Lexington city
Camden
County
Philadelphia
County
Winchester
city
Falls Church
city
Staunton city
Colonial
Heights city
Hopewell city
Ocean
County
Passaic
County
State
NEW YORK
VIRGINIA
NEW YORK
VIRGINIA
MARYLAND
VIRGINIA
VIRGINIA
NEW YORK
NEW YORK
VIRGINIA
VIRGINIA
NEW JERSEY
PENNSYLVANIA
VIRGINIA
VIRGINIA
VIRGINIA
VIRGINIA
VIRGINIA
NEW JERSEY
NEW JERSEY
2000
Population
1,332,650
100,565
2,465,326
21,498
651,154
8346
146,437
2,229,379
1,537,195
11,566
6867
508,932
1,517,550
23,585
10,377
23,853
1 6,897
22,354
510,916
489,049
Emissions
input
(tons/year)
190
6.2
530
2.7
121
0.84
8.2
460
730
I.I
0.39
130
560
4.6
1.4
2.3
2.4
2.6
160
110
County
area
(hectares)
40
35
60
10
85
3.2
51
105
23
20
5.1
230
150
11
3.5
13
7.0
8.8
620
200
Import/
export
factor
0.32
2.7
0.18
2.5
0.66
1.2
2.4
0.31
0.093
6.0
3.1
2.2
0.41
1.7
1.6
1.6
1.3
1.3
2.8
1.8
Benefits
output
($/ton)
8,100,000
7,800,000
5,800,000
5,800,000
5,500,000
5,500,000
5,400,000
4,800,000
4,600,000
4,500,000
4,300,000
4,200,000
4,200,000
4,000,000
3,900,000
3,900,000
3,900,000
3,600,000
3,500,000
3,400,000
29
-------
Table 5: Benefit-per-ton of Diesel Emissions Reduced ($/ton) for Counties with the Lowest
Emissions of Total Diesel Sources.
County
Loving
County
Alpine
County
Lexington city
Hinsdale
County
Poquoson city
Franklin city
Buena Vista
city
Daggett
County
Falls Church
city
Edwards
County
Owsley
County
Robertson
County
Real County
Wirt County
McMullen
County
Norton city
Irion County
Mineral
County
Esmeralda
County
Glascock
County
State
TEXAS
CALIFORNIA
VIRGINIA
COLORADO
VIRGINIA
VIRGINIA
VIRGINIA
UTAH
VIRGINIA
TEXAS
KENTUCKY
KENTUCKY
TEXAS
WEST
VIRGINIA
TEXAS
VIRGINIA
TEXAS
NEVADA
NEVADA
GEORGIA
2000
Population
67
1208
6867
790
11,566
8346
6349
921
10,377
2162
4858
2266
3047
5873
851
3904
1771
5071
971
2556
Emissions
input
(tons/year)
0.53
0.87
1.2
1.7
1.8
2.5
2.5
2.5
2.6
2.7
3.0
3.1
3.2
3.3
3.3
3.3
3.4
3.4
3.5
3.6
County
area
(hectares)
660
730
5.1
1100
20
3.2
4.9
710
3.5
2100
200
110
690
230
1100
5.0
1100
3800
3600
150
Import/
export
factor
60
160
1.8
30
5.1
0.60
0.87
14
1.3
53
23
21
19
26
53
0.79
20
80
36
14
Benefits
output
($/ton)
4900
280,000
2,400,000
4IOO
3,900,000
2,800,000
1 ,600,000
1 7,000
3,200,000
1 9,000
670,000
510,000
120,000
740,000
56,000
820,000
32,000
120,000
10,000
230,000
30
-------
Table 6: Benefit-per-ton of Diesel Emissions Reduced ($/ton) Results for Counties with the
Lowest Emissions of On-road Diesel Sources.
County
Arthur
County
McPherson
County
Petroleum
County
Loup County
Esmeralda
County
Thomas
County
Hooker
County
Keya Paha
County
Blaine County
Banner
County
Harding
County
Slope County
Storey
County
Loving
County
Greeley
County
Grant County
Alpine
County
Buffalo
County
Stanley
County
Logan County
State
NEBRASKA
NEBRASKA
MONTANA
NEBRASKA
NEVADA
NEBRASKA
NEBRASKA
NEBRASKA
NEBRASKA
NEBRASKA
NEW
MEXICO
NORTH
DAKOTA
NEVADA
TEXAS
KANSAS
NEBRASKA
CALIFORNIA
SOUTH
DAKOTA
SOUTH
DAKOTA
NEBRASKA
2000
Population
444
533
493
712
971
729
783
983
583
819
810
767
3399
67
1534
747
1208
2032
2772
774
Emissions
Input
(tons/year)
0.18
0.24
0.25
0.32
0.36
0.39
0.40
0.41
0.41
0.42
0.42
0.47
0.48
0.49
0.53
0.55
0.57
0.58
0.58
0.58
County
Area
(hectares)
720
870
1700
570
3600
700
720
780
710
750
2100
1200
260
660
790
770
730
500
1500
560
Import/
export
factor
66
62
29
29
100
22
21
21
23
54
95
19
24
29
19
17
88
12
24
16
Benefits
Output
($/ton)
34,000
1 9,000
7600
28,000
29,000
28,000
43,000
34,000
31,000
55,000
45,000
6700
320,000
2300
39,000
12,000
1 50,000
61,000
34,000
22,000
-------
Table 7: Benefit-per-ton of Diesel Emissions Reduced ($/ton) Results for Counties with the Lowest
Emissions of Non-road Diesel Sources.
County
Loving
County
Alpine
County
Lexington city
Edwards
County
Hinsdale
County
San Juan
County
Franklin city
Poquoson city
Daggett
County
Irion County
Falls Church
city
Catron
County
Norton city
Sterling
County
Real County
Crockett
County
Owsley
County
Kimble
County
King County
Clay County
State
TEXAS
CALIFORNIA
VIRGINIA
TEXAS
COLORADO
COLORADO
VIRGINIA
VIRGINIA
UTAH
TEXAS
VIRGINIA
NEW
MEXICO
VIRGINIA
TEXAS
TEXAS
TEXAS
KENTUCKY
TEXAS
TEXAS
WEST
VIRGINIA
2000
Population
67
1208
6867
2162
790
558
8346
11,566
921
1771
10,377
3543
3904
1393
3047
4099
4858
4468
356
10,330
Emissions
Input
(tons/year)
0.034
0.30
0.39
0.70
0.71
0.75
0.84
I.I
1.3
1.4
1.4
1.4
1.4
1.4
1.4
1.4
1.5
1.5
1.6
1.6
County
area
(hectares)
660
730
5.1
2100
1100
400
3.2
20
710
1100
3.5
7000
5.0
920
690
2800
196
1300
940
350
Import/
export
factor
520
300
3.1
120
46
13
1.2
6.0
19
28
1.6
120
1.1
32
27
47
27
52
45
38
Benefits
Output
($/ton)
42,000
520,000
4,300,000
41,000
6200
1 7,000
5,500,000
4,500,000
22,000
45,000
3,900,000
56,000
1,100,000
35,000
1 70,000
57,000
790,000
200,000
8300
1 ,300,000
32
-------
Table 8: Counties with Highest Import/Export Factors for Total Diesel Sources
County
Alpine
County
Nye County
Mineral
County
Inyo County
Catron
County
Loving
County
Edwards
County
McMullen
County
Moffat
County
Hamilton
County
Sierra County
Esmeralda
County
Graham
County
Hinsdale
County
Malheur
County
Highland
County
Coconino
County
Mono County
Pendleton
County
Greenlee
County
State
CALIFORNIA
NEVADA
NEVADA
CALIFORNIA
NEW
MEXICO
TEXAS
TEXAS
TEXAS
COLORADO
NEW YORK
CALIFORNIA
NEVADA
NORTH
CAROLINA
COLORADO
OREGON
VIRGINIA
ARIZONA
CALIFORNIA
WEST
VIRGINIA
ARIZONA
2000
Population
1208
32485
5071
17945
3543
67
2162
851
13184
5379
3555
971
7993
790
31615
2536
116320
12853
8196
8547
Emissions
Input
(tons/year)
0.87
24
3.4
20
4.5
0.53
2.7
3.3
27
7.8
4.9
3.5
4.0
1.7
75
4.1
260
15
7.8
4.1
County
Area
(hectares)
730
18,000
3800
10,000
7000
660
2000
MOO
4800
1800
960
3600
300
MOO
9900
420
1 9,000
3100
690
1800
Import/
export
factor
160
100
80
69
63
60
53
53
38
38
36
36
31
30
30
29
29
29
29
28
Benefits
Output
($/ton)
280,000
240,000
120,000
140,000
29,000
4900
1 9,000
56,000
62,000
120,000
140,000
10,000
930,000
4100
85,000
250,000
93,000
59,000
380,000
86,000
33
-------
Table 9: Counties with Highest Import/Export Factors for On-road Diesel Sources
County
Mineral
County
Esmeralda
County
Harding
County
Alpine
County
Nye County
Arthur
County
McPherson
County
Banner
County
Hancock
County
Brewster
County
McMullen
County
Inyo County
Skamania
County
Sierra County
Meagher
County
Catron
County
Lincoln
County
Highland
County
Mariposa
County
Webster
County
State
NEVADA
NEVADA
NEW MEXICO
CALIFORNIA
NEVADA
NEBRASKA
NEBRASKA
NEBRASKA
TENNESSEE
TEXAS
TEXAS
CALIFORNIA
WASHINGTON
CALIFORNIA
MONTANA
NEW MEXICO
NEVADA
VIRGINIA
CALIFORNIA
WEST
VIRGINIA
2000
Population
5071
971
810
1208
32485
444
533
819
6786
8866
851
17945
9872
3555
1932
3543
4165
2536
17130
9719
Emissions
Input
(tons/year)
0.61
0.36
0.42
0.57
3.9
0.18
0.24
0.42
0.75
2.3
1.4
10
4.2
1.3
0.60
3.1
1.3
1.2
4.3
1.5
County
Area
(hectares)
3800
3600
2100
730
18,000
720
870
750
220
6100
MOO
10,000
1700
960
2400
7000
11,000
420
1500
560
Import/
export
factor
140
100
95
88
77
66
62
54
46
45
45
43
42
42
42
39
38
38
36
36
Benefits
Output
($/ton)
220,000
29,000
45,000
153,000
180,000
34,000
1 9,000
55,000
1 ,600,000
52,000
48,000
89,000
210,000
1 60,000
36,000
18,000
14,000
320,000
460,000
680,000
34
-------
Table 10: Counties with Highest Import/Export Factors for Non-road Diesel Sources
County
Loving
County
Alpine
County
Catron
County
Edwards
County
Nye County
Inyo County
Mineral
County
McMullen
County
Kimble
County
Brooks
County
Crockett
County
Hinsdale
County
King County
Hamilton
County
Moffat
County
St. Helena
Parish
Coconino
County
Clay County
Blanco
County
Park County
State
TEXAS
CALIFORNIA
NEW
MEXICO
TEXAS
NEVADA
CALIFORNIA
NEVADA
TEXAS
TEXAS
TEXAS
TEXAS
COLORADO
TEXAS
NEW YORK
COLORADO
LOUISIANA
ARIZONA
WEST
VIRGINIA
TEXAS
COLORADO
2000
Population
67
1 208
3543
2I62
32485
1 7945
507 1
85!
4468
7976
4099
790
356
5379
I3I84
1 0525
I 1 6320
1 0330
84I8
1 4523
Emissions
Input
(tons/year)
0.034
0.30
1. 4
0.70
20
10
2.8
1. 9
1. 5
2. 1
1. 4
0.7 1
1. 6
4.0
18
4.2
1 30
1. 6
3.6
8.2
County
Area
(hectares)
660
730
7000
2IOO
18,000
10,000
3800
MOO
1300
970
2800
MOO
940
1800
4800
410
1 9,000
350
720
2200
Import/
export
factor
520
300
120
120
110
96
67
59
52
47
47
46
45
43
42
41
39
38
37
37
Benefits
Output
($/ton)
42,000
520,000
56,000
41,000
250,000
200,000
100,000
62,000
1 95,000
360,000
57,000
6200
8300
140,000
67,000
960,000
120,000
1 ,300,000
310,000
1 30,000
35
-------
Table 11: Counties with Lowest Import/Export Factors for Total Diesel Sources
County
New York
County
Norfolk city
San Francisco
County
Kings County
Suffolk
County
Denver
County
Bristol city
San Juan
County
Newport
News city
Arlington
County
Bronx
County
Emporia city
Fredericks-
burg city
Williamsburg
city
District of
Columbia
Dukes
County
Queens
County
Manassas
Park city
Lynchburg
city
Richmond
County
State
NEW YORK
VIRGINIA
CALIFORNIA
NEW YORK
MASSACHUSETTS
COLORADO
VIRGINIA
WASHINGTON
VIRGINIA
VIRGINIA
NEW YORK
VIRGINIA
VIRGINIA
VIRGINIA
DISTRICT OF
COLUMBIA
MASSACHUSETTS
NEW YORK
VIRGINIA
VIRGINIA
NEW YORK
2000
Populatio
n
I537I95
234403
776733
2465326
689807
554636
1 7367
1 4077
I80I50
1 89453
1 332650
5665
1 9279
I 1 998
572059
1 4987
2229379
1 0290
65269
443728
Emissions
Input
(tons/year)
820
460
870
630
370
400
17
52
ISO
ISO
290
10
26
9.7
370
ISO
6IO
6.6
48
260
County
Area
(hectares)
23
48
47
60
69
1 00
5. 1
56
75
26
40
3.5
7.3
4.8
6
95
MO
1. 7
23
48
Import/
export
factor
O.I I
O.I6
O.I9
0.20
0.24
0.24
0.27
0.28
0.28
0.29
0.3 1
0.3 1
0.3 1
0.32
0.32
0.33
0.33
0.34
0.38
0.39
Benefits
Output
($/ton)
5,200,000
590,000
2,500,000
6,200,000
1 ,700,000
940,000
1 ,200,000
70,000
510,000
1 ,300,000
7,700,000
1 ,000,000
880,000
890,000
2,400,000
5 1 ,000
5,000,000
990,000
1 ,200,000
3,200,000
36
-------
Table 12: Counties with Lowest Import/Export Factors for On-road Diesel Sources
County
San Francisco
County
New York
County
Norfolk city
Bristol city
Denver
County
Kings County
Bronx
County
Fredericks-
burg city
Emporia city
Danville city
Lynchburg
city
Franklin city
Hampton city
Harrisonburg
city
Queens
County
Suffolk
County
District of
Columbia
St. Louis city
Arlington
County
Pinellas
County
State
CALIFORNIA
NEW YORK
VIRGINIA
VIRGINIA
COLORADO
NEW YORK
NEW YORK
VIRGINIA
VIRGINIA
VIRGINIA
VIRGINIA
VIRGINIA
VIRGINIA
VIRGINIA
NEW YORK
MASSACHUSETTS
DISTRICT OF
COLUMBIA
MISSOURI
VIRGINIA
FLORIDA
2000
Population
776733
I537I95
234403
1 7367
554636
2465326
1 332650
1 9279
5665
484 1 I
65269
8346
1 46437
40468
2229379
689807
572059
348 1 89
1 89453
92 1 482
Emissions
Input
(tons/year)
260
91
33
8.5
1 40
1 00
94
9.7
2.9
16
22
1. 7
25
12
ISO
84
90
1 40
35
2IO
County
area
(hectares)
47
23
48
5. 1
1 00
60
40
7.3
3.5
17
23
3.2
51
II
MO
69
66
72
26
3IO
Import/
export
factor
O.I9
0.20
0.23
0.24
0.24
0.27
0.28
0.29
0.3 1
0.32
0.33
0.33
0.34
0.34
0.37
0.37
0.37
0.37
0.38
0.4 1
Benefits
Output
($/ton)
2,500,000
9,900,000
840,000
1,100,000
940,000
8,600,000
7,100,000
830,000
1 ,000,000
1 ,200,000
1 ,000,000
1 ,600,000
750,000
870,000
5,700,000
2,600,000
2,800,000
1 ,700,000
1 ,700,000
1 ,400,000
37
-------
Table 13: Counties With Lowest Import/Export Factors for Non-road Diesel Sources
County
New York
County
Norfolk city
Kings County
San Francisco
County
Suffolk
County
San Juan
County
Denver
County
Newport
News city
Arlington
County
Dukes
County
Williamsburg
city
Bristol city
District of
Columbia
Emporia city
Queens
County
Manassas
Park city
Bronx
County
Fredericks-
burg city
Salem city
Richmond
County
State
NEW YORK
VIRGINIA
NEW YORK
CALIFORNIA
MASSACHUSETTS
WASHINGTON
COLORADO
VIRGINIA
VIRGINIA
MASSACHUSETTS
VIRGINIA
VIRGINIA
DISTRICT OF
COLUMBIA
VIRGINIA
NEW YORK
VIRGINIA
NEW YORK
VIRGINIA
VIRGINIA
NEW YORK
2000
Population
I537I95
234403
2465326
776733
689807
1 4077
554636
I80I50
1 8945 3
1 4987
I 1 998
1 7367
572059
5665
2229379
1 0290
1 332650
1 9279
24747
443728
Emissions
Input
(tons/year)
730
430
530
6IO
280
51
260
ISO
1 40
1 70
8.6
8. 1
280
7.2
460
5.3
1 90
17
17
220
County
area
(hectares)
23
48
60
47
69
56
1 00
75
26
95
4.8
5. 1
66
3.5
MO
1. 7
40
7.3
10
48
Import/
export
factor
0.094
O.I6
O.I8
0.20
0.20
0.22
0.24
0.25
0.27
0.28
0.29
0.29
0.3 1
0.3 1
0.3 1
0.32
0.32
0.33
0.35
0.36
Benefits
Output
($/ton)
4,600,000
580,000
5,800,000
2,500,000
1 ,400,000
55,000
940,000
450,000
1 ,200,000
43,000
800,000
1 ,300,000
2,300,000
1,000,000
4,800,000
920,000
8,100,000
920,000
940,000
2,900,000
38
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Figures 3 and 4 below illustrate the geographic distribution of county-level PM2.5 benefit-per-ton
estimates by source type. Two key summary conclusions may be drawn:
There is a high degree of spatial heterogeneity. For example, the states of California,
New Jersey and Florida contain among the highest benefit-per-ton estimates, while
interior states such as North and South Dakota contain very low estimates. Human health
benefit estimates are strongly influenced by population exposure. Other things being
equal, counties with higher population density will exhibit larger benefit-per-ton
estimates.
Estimates are not equally accurate for all counties. The Benefits Module "flags" results
for counties where the non-road and on-road benefit-per-ton values are likely to be more
uncertain due to transport of fine particle concentrations into or out of a county. These
counties, identified by the import/export factors, are hashed on these maps. They are
often but not always counties with very high and very low emissions.
39
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;* • v'isi
• iv-r'fl
r:-• •*
f< J-X
i.aaa|
Benefit per Ton of On-Road Diesel Emission Reductions
(results for hatched counties are flagged in the Benefits Module
to indicate results are less certain)
$0 - $250.000
$250,001-3750,000
^B $750,001 -$1,500,000
H $1500,001 -$3,000,000
^H $3.000,000 -$9,900.000
(51
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(Q
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Benefit per Ton of Non-Road Diesel Emission Reductions
(results for hatched counties are flagged in the Benefits Module
to indicate results are less certain)
$0 - $250,000
$250,001 - $750,000
(Hi $750,001 -$1500,000
m| $1,500.001 -$3,000.000
j^H $3,000.001 -$8,100.000
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-------
VI. EXAMPLE RESULTS
An example set of results for the Benefits Module are presented to assist users in understanding
how the tools works. To provide example results, the Quantifier was run twice with two different
scenarios. Results are presented for the "current" year - the year the emission reductions take
place — and dollar values are presented in 2006 dollars. These benefits would be expected to be
similar in subsequent years, assuming that the performance of the emission reduction technology
stays constant (for example, installed diesel catalysts continue to perform at the same efficiency).
This is based on existing assumptions inherent in the Quantifier and some field research
(Chandler et al., 2003). Given the scales and uncertainty in this analysis, we assume that
population growth would slightly increase the benefits at roughly the same rate that discounting
future benefits would reduce them. Therefore, this annual benefits number can be used as a
rough estimate of annual benefits for each year of the lifetime of the engine retrofit.
To calculate these example results, the Quantifier was run for two counties: Cook County, IL and
Anderson County, Texas. Cook County is a highly urban county, including the city of Chicago
(land area 1,635 square miles and population in 2000 of 5.3 million), while Anderson County is a
highly rural county southeast of Dallas (land area 1,078 square miles and population in 2000 of
55,109).
In the example scenario, 100 school buses were retrofitted in 2008 with diesel particulate filters
and began using ultra low-sulfur diesel fuel (15 ppm sulfur). The buses were model year 2002
and traveled 13,000 miles per year. Before the retrofit, these 100 buses emitted a total of 0.32
tons/year of diesel PM. The retrofit reduced emissions 85%, or 0.27 tons per year.
In addition, 10 pieces of construction equipment (e.g. tractors, loaders, backhoes) were
retrofitted in 2008 with diesel particulate filters and began using low-sulfur diesel fuel (500 ppm
sulfur). The equipment was all model year 2000. Before the retrofit, the construction equipment
emitted 0.20 tons/year. The retrofit reduced emissions 85%, or 0.17 tons per year.
Table 14 presents the estimates of the economic value of the emission reductions from both
scenarios.
Table 14. Example Quantifier and Benefits Module results for Cook County, IL and Anderson
County, TX
Benefits Module Results
county
Cook County, IL
Anderson County, TX
annual tons
diesel PM
reduction
0.44
0.45
annualized costs
$15,203
$15,203
annual benefits
$1,000,000
$224,000
42
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When reporting benefits estimates, we believe that there are two key uncertainties:
The assumptions used by EPA to derive the benefits-per-ton may differ significantly from
the policy scenario in which users apply the benefit-per-ton. Specifically, the types of
emission sources controlled, the temporal distribution of emission controls, the types of
emissions, the source locations and background PM2.5 levels may differ between the
modeling scenario used to generate the benefit-per-ton estimates and the user-defined
scenario.
The benefits-per-ton do not reflect certain non-linear relationships. Because the benefit-
per-ton estimates are averages, they may not reflect non-linear relationships between air
quality changes and background PM2.5 levels. For example, because the concentration-
response functions are non-linear, the estimated change in health impacts is sensitive to
the background levels of PM2.5 in the atmosphere. Overall we expect this to contribute a
small amount to total uncertainty because the functional form of the mortality estimate
(which represents the great majority of total benefits) is a nearly flat log-linear form.
43
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VII. LITERATURE CITED
Abbey, D.E., B.L. Hwang, RJ. Burchette, T. Vancuren, and P.K. Mills. 1995. "Estimated Long-
Term Ambient Concentrations of PM(10) and Development of Respiratory Symptoms in
a Nonsmoking Population." Archives of Environmental Health 50(2): 139-152.
Abt Associates, Inc. 2005a. Environmental Benefits Mapping and Analysis Program (Version
2.4). Prepared for Environmental Protection Agency, Office of Air Quality Planning and
Standards, Air Benefits and Cost group. Research Triangle Park, NC.
Chandler, K., Vertin, K., and Alleman, C. N. 2003. Ralph's Grocery Company EC-Diesel truck
fleet: final results. Truck evaluation project. National Renewable Energy Laboratory
report to U.S. Department of Energy.
Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis, J.H.
Ware, M. Raizenne, and F.E. Speizer. 1996. "Health Effects of Acid Aerosols On North
American Children-Respiratory Symptoms." Environmental Health Perspectives
104(5):500-505.
Ito, K. 2003. "Associations of Parti culate Matter Components with Daily Mortality and
Morbidity in Detroit, Michigan." In Revised Analyses of Time-Series Studies of Air
Pollution and Health. Special Report. Health Effects Institute, Boston, MA.
Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. Reduction in Fine Particulate Air
Pollution and Mortality. American Journal of Respiratory and Critical Care Medicine.
173:667-672.
Levy, J.I., Hammitt, J.K., Yanagisawa, Y.,d and Spengler, J.D. 1999. Development of a New
Damage Function Model for Power Plants: Methodology and Applications.
Environmental Science and Technology. 33 (24): 4364-4372.
Marshall, J., Behrentz, E., 2005. Vehicle self-pollution intake fraction: children's exposure to
school bus emissions. Environmental Science and Technology 39, 2559-2563.
Moolgavkar, S.H. 2000. "Air Pollution and Hospital Admissions for Diseases of the Circulatory
System in Three U.S. Metropolitan Areas." Journal of the Air and Waste Management
Association 50:1199-1206.
Moolgavkar, S.H. 2003. "Air Pollution and Daily Deaths and Hospital Admissions in Los
Angeles and Cook Counties." In Revised Analyses of Time-Series Studies of Air
Pollution and Health. Special Report. Boston, MA: Health Effects Institute.
Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. Larson, L. Sheppard, and J.W. Stout. 1999. "An
Association between Fine Particles and Asthma Emergency Department Visits for
Children in Seattle." Environmental Health Perspectives 107(6):489-493.
44
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Ostro, B.D. 1987. "Air Pollution and Morbidity Revisited: A Specification Test." Journal of
Environmental Economics Management 14:87-98.
Ostro, B.D. and S. Rothschild. 1989. "Air Pollution and Acute Respiratory Morbidity: An
Observational Study of Multiple Pollutants." Environmental Research 50:238-247.
Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. 2001. "Air Pollution and
Exacerbation of Asthma in African-American Children in Los Angeles." Epidemiology
12(2):200-208.
Peters, A., D.W. Dockery, I.E. Muller, and M.A. Mittleman. 2001. "Increased Particulate Air
Pollution and the Triggering of Myocardial Infarction." Circulation 103:2810-2815.
Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. 1991. "Respiratory Health
and PMio Pollution: A Daily Time Series Analysis." American Review of Respiratory
Diseases 144:668-674.
Schwartz, J., and L.M. Neas. 2000. "Fine Particles are More Strongly Associated than Coarse
Particles with Acute Respiratory Health Effects in Schoolchildren." Epidemiology 11:6-
10.
Sheppard, L. 2003. "Ambient Air Pollution and Nonelderly Asthma Hospital Admissions in
Seattle, Washington, 1987-1994." In Revised Analyses of Time-Series Studies of Air
Pollution and Health. Special Report. Boston, MA: Health Effects Institute.
U.S. Bureau of Census. 2000. Population Projections of the United States by Age, Sex, Race,
Hispanic Origin and Nativity: 1999 to 2100. Population Projections Program, Population
Division, U.S. Census Bureau, Washington, DC. Available at:
www. census, gov/population/proj ections/nation/summary/np-t.txt
U.S. Environmental Protection Agency. 2004. Documentation for the Onroad National
Emissions Inventory (NEI) For Base Years 1970-2002. Prepared by E.H. Pechan &
Associates, Inc., 5528-B Hempstead Way, Springfield, VA 22151
U.S. Environmental Protection Agency (EPA). 2006. "Regulatory Impact Analysis for the PM2.5
NAAQS." EPA-HQ-OAR-2006-0834
U.S. Environmental Protection Agency (EPA). 2008a. "Regulatory Impact Analysis for the
Ozone NAAQS." Available at: www.epa.gov/ttn/ecas/ria.htmltfria2007
U.S. Environmental Protection Agency (EPA). 2008b. "Technical Support Document:
Calculating Benefit Per-Ton Estimates" prepared for the Regulatory Impact Analysis for
the Ozone NAAQS. Available at www.regulations.gov/fdmspublic/
component/main?main=DocumentDetail&o=09000064803f33e4
45
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Vedal, S., J. Petkau, R. White, and J. Blair. 1998. "Acute Effects of Ambient Inhalable Particles
in Asthmatic and Nonasthmatic Children." American Journal of Respiratory and Critical
Care Medicine 157(4): 1034-1043.
Wilson, W.E. and Suh H.H., J. 1997. "Fine Particles and Coarse Particles: Concentration
Relationships Relevant to Epidemiologic Studies." Air and Waste Management
Association, 47:123 8-1249
Woodruff, T.J., J. Grille, andK.C. Schoendorf 1997. "The Relationship Between Selected
Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United
States." Environmental Health Perspectives 105(6):608-612.
46
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VIII. WEBSITE INDEX/INTERNET RESOURCES
AERMOD model: www.epa.gov/scratnOO 1 /dispersion_prefrec.htm#aermod
Assessment System for Population Exposure Nationwide (ASPEN) user's guide is available at
www.epa.gov/scratn001/userg/other/aspenug.pdf
BenMAP User's Guide Technical Appendices, Appendix I: Uncertainty and Pooling:
www.epa.gov/air/benmap/models/BenMAPappendicesSept08.pdf
Diesel Emissions Quantifier
A more detailed description of the Quantifier, and access to the tool itself, can be found at
www.epa.gov/cleandiesel/quantifier/
More information on the Quantifier can be found in the Users Guide, which is available
on the website at www.epa.gov/cleandiesel/documents/420bl0033.pdf
Documentation for the 2002 NEI is provided at www.epa.gov/ttn/chief/net/2002inventory.html
Documentation for the 2002 Mobile NEI is located at
ftp://ftp.epa.gov/EmisInventory/2002finalnei/documentation/mobile/2002 mobile nei version 3
report 092807.pdf
EPA's MOBILE6 model is used to generate emission factors in grams per mile and then
determining total annual tons using annual vehicle miles traveled (VMT):
www. epa. gov/oms/m6 .htm
EPA's National Mobile Inventory Model: NMIM, www.epa.gov/oms/nmim.htm
EPA's 2002 National-Scale Air Toxics Assessment (NAT A): www. epa. gov/ttn/atw/natamain/
EPA's Draft Guidance for Discounting Future Costs and Benefits
http://vosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0516-06.pdf/$File/EE-0516-
Q6.pdf?OpenElement
NATA's use of the Hazardous Air Pollutant Exposure Model 5 (HAPEM5) can be found at
www. epa. gov/ttn/atw/nata 1999/ted/teddraft. html
2002 NATA and past results summarized:
www.epa.gov/ttn/atw/natal999/natafinalfact.html
NATA results: www.epa.gov/ttn/atw/natamain/
Science Advisory Board (SAB) peer review of the NATA approach:
www.epa.gov/ttn/atw/sab/sabrev.html
47
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Technology Transfer Network 1999 National-Scale Air Toxic Assessment:
www.epa.gov/ttn/atw/natal999/background.httnl
User's Guide for the Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP)
Version 3.0: www.epa.gov/scram001/userg/other/emshapv3ug.pdf
48
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