Diesel Emissions Quantifier 


Health Benefits Methodology 

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-420-B-10-034 

August 2010 
 

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 

3
 
 

 

 

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. ci/( ei/ai), where ci, ei, and ai are the 

concentration, emissions, and area of county i. 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: nonroad 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/420b10033.pdf 

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II. ESTIMATING CHANGES IN PM2.5 AIR QUALITY CONCENTRATIONS 

RESULTING FROM DIESEL EMISSIONS 

A. Data Inputs 

i. 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/EmisInventory/2002finalnei/documentation/mobile/2002_mobile_nei_version_ 

3_report_092807.pdf 

ii. 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 

8
 
 

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 areawide 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 

Types of Particulate Matter 

The Quantifier estimates changes in diesel 

particulate 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 

particulate 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 particulate matter is PM2.5; 

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 particulate matter is PM2.5. 

Therefore, the Benefits Module benefits 

are calculated on 96% of the total diesel 

PM emission reductions estimated by the 

Quantifier. 

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/nata1999/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 

9
 
 

 

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 nonroad 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/nata1999/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 

10
 
 

 

 

 

 

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/nata1999/ted/teddraft.html. For further 

information summarizing the 2002 NATA and past results, see 

www.epa.gov/ttn/atw/nata1999/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 i and tract a: 

∑ (c i,a * Populationi,a ) 

ci = 

Populationi 

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 

11
 
 

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 μg/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 PM2.5 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 μg/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 μg/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). 

13
 
 

 

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 

PM2.5 (annual) Laden et al. (2006), log-linear >25 years 

Premature mortality — 

all-cause 

PM2.5 (annual) Woodruff et al. (1997), logistic Infant (<1 year) 

Chronic Illness 

Chronic bronchitis PM2.5 (annual) Abbey et al. (1995), logistic >26 years 

Nonfatal heart attacks PM2.5 (daily) Peters et al. (2001), logistic Adults 

Hospital Admissions 

Respiratory PM2.5 (daily) Pooled estimate: 

Moolgavkar (2003)—ICD 490-496 (COPD), 

log-linear 

Ito (2003)—ICD 490-496 (COPD), loglinear 

>64 years 

PM2.5 (daily) Moolgavkar (2000)—ICD 490-496 (COPD), 

log-linear 

20–64 years 

PM2.5 (daily) Ito (2003)—ICD 480-486 (pneumonia), loglinear 

>64 years 

PM2.5 (daily) Sheppard (2003)—ICD 493 (asthma), loglinear 

<65 years 

Cardiovascular PM2.5 (daily) Pooled estimate: 

Moolgavkar (2003)—ICD 390-429 (all 

cardiovascular), log-linear 

Ito (2003)—ICD 410-414, 427-428 

(ischemic heart disease, dysrhythmia, heart 

failure), log-linear 

>64 years 

PM2.5 (daily) Moolgavkar (2000)—ICD 390-429 (all 

cardiovascular), log-linear 

20–64 years 

Asthma-related ER 

visits 

PM2.5 Norris et al. (1999), log-linear 0–18 years 

Other Health Endpoints 

Acute bronchitis PM2.5 Dockery et al. (1996), logistic 8–12 years 

Upper respiratory 

symptoms 

PM10 Pope et al. (1991), Asthmatics, 9–11 

years 

Lower respiratory 

symptoms 

PM2.5 Schwartz and Neas (2000) 7–14 years 

Asthma exacerbations PM2.5 Pooled estimate: 

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

shortness of breath) 

Vedal et al. (1998) (cough) 

6–18 yearsa 

Work loss days PM2.5 Ostro (1987), log-linear 18–65 years 

Minor restricted 

activity days (MRADs) 

PM2.5 Ostro and Rothschild (1989), log-linear 18–65 years 

a	 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. 

14
 
 

 

 

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 o f 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). 

i. Annual versus Annualized Monetized 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-andmaintenance 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. 

ii. Calculating the PM2.5- Benefit per-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
 
 

 

⎛ Δwi ⎞ BPTii = ⎜

⎜ ⎟⎟ 

Δe ⎝ i ⎠ 

where 

BPTii = average health benefits (in 2002 dollars) in county i per ton of reduced diesel PM 

emissions in county i, 

∆ei = total reduction in diesel PM emissions (in tons) in county i, 

∆wi = health benefits (in 2006 dollars) in county i as a result of ∆ci. 

For this Benefits Module, no factor was used to convert the ambient diesel PM concentrations 

(∆ci) in each county to ambient PM2.5 concentration, prior to calculating health benefits (∆wi). 

Similarly, BPTii were calculated by dividing by county diesel PM emissions (∆ei), 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 ∆wi generally scales 

linearly with ∆ci, any factor that describes the proportion of diesel PM that is PM2.5 would be 

multiplied in both the numerator (∆wi*factor) and denominator (∆ei*factor) of the BPTii 

calculation, and would cancel out. Thus, for purposes of deriving BPTii, the relative proportion 

of diesel PM that is PM2.5 is unimportant. 

When applying the BPTii to determine the health benefits for specific diesel exhaust reductions, it 

is important to remember that the health functions to derive ∆wi are specific to PM2.5. Thus, the 

derived BPTii 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 ongoing maintenance costs. To calculate the annualized cost, the Benefits Module uses the value 

the user enters for the capital cost of the project. 

16
 
 

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)/(1-(1+r)^-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
 
 

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
 
 

 

 

 

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
 
 

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
 
 

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 PM2.5. 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
 
 

 

 

 

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 benefitper-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-perton but low local emissions. Although this figure cannot illustrate sufficiently whether the lowemitting 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. ci/( ei/ai), where ci, ei, and ai are the 

concentration, emissions, and area of county i. Counties that are highest in ci/( ei/ai) would be 

indicative of those that are most likely to import a relatively large portion of diesel PM, while 

counties that are lowest in ci/( ei/ai) 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
 
 

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. 

23
 
 

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. ci/( ei/ai), where ci, ei, and ai are the 

concentration, emissions, and area of county i. Results are presented for (a) total diesel sources, 

(b) on-road diesel sources, and (c) non-road diesel sources. 

24
 
 

 

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 onroad 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
 
 

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 populationweighting 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 onroad 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 benefitper-ton estimates reflect air quality changes from diesel sources alone. Conversely, the benefitper-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. 

26
 
 

 

 

 

Table 2: Counties with Highest Predicted Benefit-per-ton Estimates ($/ton) for Total Diesel 

Sources. 

County State 2000 

Population 

Emissions 

input 

(tons/year) 

County 

area 

(hectares) 

Import/ 

export 

factor 

Benefits 

output 

($/ton) 

Bronx 

County NEW YORK 1,332,650 290 40 0.31 7,800,000 

Kings County NEW YORK 2,465,326 630 60 0.20 6,200,000 

Baltimore city MARYLAND 651,154 200 85 0.64 5,300,000 

New York 

County NEW YORK 1,537,195 820 23 0.11 5,100,000 

Queens 

County NEW YORK 2,229,379 610 110 0.33 5,000,000 

Fairfax city VIRGINIA 21,498 5.3 10 0.99 4,500,000 

Philadelphia 

County PENNSYLVANIA 1,517,550 700 150 0.44 4,500,000 

Poquoson 

city VIRGINIA 11,566 1.8 20 5.1 3,900,000 

Portsmouth 

city VIRGINIA 100,565 16 35 1.4 3,800,000 

Winchester 

city VIRGINIA 23,585 6.9 11 1.7 3,800,000 

Ocean 

County NEW JERSEY 510,916 210 620 3.1 3,800,000 

Hudson 

County NEW JERSEY 608,975 400 57 0.44 3,500,000 

Passaic 

County NEW JERSEY 489,049 150 200 1.8 3,400,000 

Falls Church 

city VIRGINIA 10,377 2.6 3.5 1.3 3,200,000 

Richmond 

County NEW YORK 443,728 260 48 0.39 3,200,000 

Bergen 

County NEW JERSEY 884,118 400 250 1.0 3,100,000 

Camden 

County NEW JERSEY 508,932 240 230 1.6 3,100,000 

Essex County NEW JERSEY 793,633 340 130 0.61 3,100,000 

Franklin city VIRGINIA 8346 2.5 3.2 0.60 2,900,000 

Hopewell city VIRGINIA 22,354 5.4 8.8 1.0 2,800,000 

27
 
 

 

 

Table 3: Counties with Highest Predicted Benefit-per-ton Estimates ($/ton) for On-road Diesel 

Sources. 

County State 2000 

Population 

Emissions 

input 

(tons/year) 

County 

area 

(hectares) 

Import/ 

export 

factor 

Benefits 

output 

($/ton) 

New York 

County NEW YORK 1,537,195 91 23 0.20 9,900,000 

Kings County NEW YORK 2,465,326 100 60 0.27 8,700,000 

Bronx 

County NEW YORK 1,332,650 94 40 0.28 7,000,000 

Philadelphia 

County PENNSYLVANIA 1,517,550 140 150 0.56 5,800,000 

Queens 

County NEW YORK 2,229,379 150 110 0.37 5,700,000 

Hudson 

County NEW JERSEY 608,975 50 57 0.71 5,700,000 

Baltimore city MARYLAND 651,154 79 85 0.60 5,000,000 

Ocean 

County NEW JERSEY 510,916 49 620 3.7 4,600,000 

Richmond 

County NEW YORK 443,728 41 48 0.55 4,500,000 

Essex County NEW JERSEY 793,633 68 130 0.87 4,400,000 

Bristol 

County 

RHODE 

ISLAND 50,648 2.6 23 1.8 3,600,000 

Winchester 

city VIRGINIA 23,585 2.3 11 1.5 3,500,000 

Bergen 

County NEW JERSEY 884,118 100 250 1.2 3,500,000 

Passaic 

County NEW JERSEY 489,049 47 200 1.8 3,400,000 

Fairfax city VIRGINIA 21,498 2.6 10 1.4 3,300,000 

Providence 

County 

RHODE 

ISLAND 621,602 48 430 2.3 3,000,000 

Orange 

County CALIFORNIA 2,846,289 400 800 1.3 2,900,000 

Union 

County NEW JERSEY 522,541 69 110 0.73 2,800,000 

District of 

Columbia 

DISTRICT OF 

COLUMBIA 572,059 90 66 0.37 2,800,000 

Delaware 

County 

PENNSYLVA 

NIA 550,864 83 190 1.0 2,800,000 

28
 
 

 

 

 

Table 4: Counties with Highest Predicted Benefit-per-ton Estimates ($/ton) for Non-road Diesel 

Sources. 

County State 2000 

Population 

Emissions 

input 

(tons/year) 

County 

area 

(hectares) 

Import/ 

export 

factor 

Benefits 

output 

($/ton) 

Bronx 

County NEW YORK 1,332,650 190 40 0.32 8,100,000 

Portsmouth 

city VIRGINIA 100,565 6.2 35 2.7 7,800,000 

Kings County NEW YORK 2,465,326 530 60 0.18 5,800,000 

Fairfax city VIRGINIA 21,498 2.7 10 2.5 5,800,000 

Baltimore city MARYLAND 651,154 121 85 0.66 5,500,000 

Franklin city VIRGINIA 8346 0.84 3.2 1.2 5,500,000 

Hampton city VIRGINIA 146,437 8.2 51 2.4 5,400,000 

Queens 

County NEW YORK 2,229,379 460 105 0.31 4,800,000 

New York 

County NEW YORK 1,537,195 730 23 0.093 4,600,000 

Poquoson city VIRGINIA 11,566 1.1 20 6.0 4,500,000 

Lexington city VIRGINIA 6867 0.39 5.1 3.1 4,300,000 

Camden 

County NEW JERSEY 508,932 130 230 2.2 4,200,000 

Philadelphia 

County PENNSYLVANIA 1,517,550 560 150 0.41 4,200,000 

Winchester 

city VIRGINIA 23,585 4.6 11 1.7 4,000,000 

Falls Church 

city VIRGINIA 10,377 1.4 3.5 1.6 3,900,000 

Staunton city VIRGINIA 23,853 2.3 13 1.6 3,900,000 

Colonial 

Heights city VIRGINIA 16,897 2.4 7.0 1.3 3,900,000 

Hopewell city VIRGINIA 22,354 2.6 8.8 1.3 3,600,000 

Ocean 

County NEW JERSEY 510,916 160 620 2.8 3,500,000 

Passaic 

County NEW JERSEY 489,049 110 200 1.8 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 State 2000 

Population 

Emissions 

input 

(tons/year) 

County 

area 

(hectares) 

Import/ 

export 

factor 

Benefits 

output 

($/ton) 

Loving 

County TEXAS 67 0.53 660 60 4900 

Alpine 

County CALIFORNIA 1208 0.87 730 160 280,000 

Lexington city VIRGINIA 6867 1.2 5.1 1.8 2,400,000 

Hinsdale 

County COLORADO 790 1.7 1100 30 4100 

Poquoson city VIRGINIA 11,566 1.8 20 5.1 3,900,000 

Franklin city VIRGINIA 8346 2.5 3.2 0.60 2,800,000 

Buena Vista 

city VIRGINIA 6349 2.5 4.9 0.87 1,600,000 

Daggett 

County UTAH 921 2.5 710 14 17,000 

Falls Church 

city VIRGINIA 10,377 2.6 3.5 1.3 3,200,000 

Edwards 

County TEXAS 2162 2.7 2100 53 19,000 

Owsley 

County KENTUCKY 4858 3.0 200 23 670,000 

Robertson 

County KENTUCKY 2266 3.1 110 21 510,000 

Real County TEXAS 3047 3.2 690 19 120,000 

Wirt County WEST 

VIRGINIA 5873 3.3 230 26 740,000 

McMullen 

County TEXAS 851 3.3 1100 53 56,000 

Norton city VIRGINIA 3904 3.3 5.0 0.79 820,000 

Irion County TEXAS 1771 3.4 1100 20 32,000 

Mineral 

County NEVADA 5071 3.4 3800 80 120,000 

Esmeralda 

County NEVADA 971 3.5 3600 36 10,000 

Glascock 

County GEORGIA 2556 3.6 150 14 230,000 

30
 
 

 

 

 

Table 6: Benefit-per-ton of Diesel Emissions Reduced ($/ton) Results for Counties with the 

Lowest Emissions of On-road Diesel Sources. 

County State 2000 

Population 

Emissions 

Input 

(tons/year) 

County 

Area 

(hectares) 

Import/ 

export 

factor 

Benefits 

Output 

($/ton) 

Arthur 

County NEBRASKA 444 0.18 720 66 34,000 

McPherson 

County NEBRASKA 533 0.24 870 62 19,000 

Petroleum 

County MONTANA 493 0.25 1700 29 7600 

Loup County NEBRASKA 712 0.32 570 29 28,000 

Esmeralda 

County NEVADA 971 0.36 3600 100 29,000 

Thomas 

County NEBRASKA 729 0.39 700 22 28,000 

Hooker 

County NEBRASKA 783 0.40 720 21 43,000 

Keya Paha 

County NEBRASKA 983 0.41 780 21 34,000 

Blaine County NEBRASKA 583 0.41 710 23 31,000 

Banner 

County NEBRASKA 819 0.42 750 54 55,000 

Harding 

County 

NEW 

MEXICO 810 0.42 2100 95 45,000 

Slope County NORTH 

DAKOTA 767 0.47 1200 19 6700 

Storey 

County NEVADA 3399 0.48 260 24 320,000 

Loving 

County TEXAS 67 0.49 660 29 2300 

Greeley 

County KANSAS 1534 0.53 790 19 39,000 

Grant County NEBRASKA 747 0.55 770 17 12,000 

Alpine 

County CALIFORNIA 1208 0.57 730 88 150,000 

Buffalo 

County 

SOUTH 

DAKOTA 2032 0.58 500 12 61,000 

Stanley 

County 

SOUTH 

DAKOTA 2772 0.58 1500 24 34,000 

Logan County NEBRASKA 774 0.58 560 16 22,000 

31
 
 

 

 

 

 

 

 

Table 7: Benefit-per-ton of Diesel Emissions Reduced ($/ton) Results for Counties with the Lowest 

Emissions of Non-road Diesel Sources. 

County State 2000 

Population 

Emissions 

Input 

(tons/year) 

County 

area 

(hectares) 

Import/ 

export 

factor 

Benefits 

Output 

($/ton) 

Loving 

County TEXAS 67 0.034 660 520 42,000 

Alpine 

County CALIFORNIA 1208 0.30 730 300 520,000 

Lexington city VIRGINIA 6867 0.39 5.1 3.1 4,300,000 

Edwards 

County TEXAS 2162 0.70 2100 120 41,000 

Hinsdale 

County COLORADO 790 0.71 1100 46 6200 

San Juan 

County COLORADO 558 0.75 400 13 17,000 

Franklin city VIRGINIA 8346 0.84 3.2 1.2 5,500,000 

Poquoson city VIRGINIA 11,566 1.1 20 6.0 4,500,000 

Daggett 

County UTAH 921 1.3 710 19 22,000 

Irion County TEXAS 1771 1.4 1100 28 45,000 

Falls Church 

city VIRGINIA 10,377 1.4 3.5 1.6 3,900,000 

Catron 

County 

NEW 

MEXICO 3543 1.4 7000 120 56,000 

Norton city VIRGINIA 3904 1.4 5.0 1.1 1,100,000 

Sterling 

County TEXAS 1393 1.4 920 32 35,000 

Real County TEXAS 3047 1.4 690 27 170,000 

Crockett 

County TEXAS 4099 1.4 2800 47 57,000 

Owsley 

County KENTUCKY 4858 1.5 196 27 790,000 

Kimble 

County TEXAS 4468 1.5 1300 52 200,000 

King County TEXAS 356 1.6 940 45 8300 

Clay County WEST 

VIRGINIA 10,330 1.6 350 38 1,300,000 

32
 
 

 

 

 

 

 

Table 8: Counties with Highest Import/Export Factors for Total Diesel Sources 

County State 2000 

Population 

Emissions 

Input 

(tons/year) 

County 

Area 

(hectares) 

Import/ 

export 

factor 

Benefits 

Output 

($/ton) 

Alpine 

County CALIFORNIA 1208 0.87 730 160 280,000 

Nye County NEVADA 32485 24 18,000 100 240,000 

Mineral 

County NEVADA 5071 3.4 3800 80 120,000 

Inyo County CALIFORNIA 17945 20 10,000 69 140,000 

Catron 

County 

NEW 

MEXICO 3543 4.5 7000 63 29,000 

Loving 

County TEXAS 67 0.53 660 60 4900 

Edwards 

County TEXAS 2162 2.7 2000 53 19,000 

McMullen 

County TEXAS 851 3.3 1100 53 56,000 

Moffat 

County COLORADO 13184 27 4800 38 62,000 

Hamilton 

County NEW YORK 5379 7.8 1800 38 120,000 

Sierra County CALIFORNIA 3555 4.9 960 36 140,000 

Esmeralda 

County NEVADA 971 3.5 3600 36 10,000 

Graham 

County 

NORTH 

CAROLINA 7993 4.0 300 31 930,000 

Hinsdale 

County COLORADO 790 1.7 1100 30 4100 

Malheur 

County OREGON 31615 75 9900 30 85,000 

Highland 

County VIRGINIA 2536 4.1 420 29 250,000 

Coconino 

County ARIZONA 116320 260 19,000 29 93,000 

Mono County CALIFORNIA 12853 15 3100 29 59,000 

Pendleton 

County 

WEST 

VIRGINIA 8196 7.8 690 29 380,000 

Greenlee 

County ARIZONA 8547 4.1 1800 28 86,000 

33
 
 

 

 

 

 

 

 

Table 9: Counties with Highest Import/Export Factors for On-road Diesel Sources 

County State 2000 

Population 

Emissions 

Input 

(tons/year) 

County 

Area 

(hectares) 

Import/ 

export 

factor 

Benefits 

Output 

($/ton) 

Mineral 

County NEVADA 5071 0.61 3800 140 220,000 

Esmeralda 

County NEVADA 971 0.36 3600 100 29,000 

Harding 

County NEW MEXICO 810 0.42 2100 95 45,000 

Alpine 

County CALIFORNIA 1208 0.57 730 88 153,000 

Nye County NEVADA 32485 3.9 18,000 77 180,000 

Arthur 

County NEBRASKA 444 0.18 720 66 34,000 

McPherson 

County NEBRASKA 533 0.24 870 62 19,000 

Banner 

County NEBRASKA 819 0.42 750 54 55,000 

Hancock 

County TENNESSEE 6786 0.75 220 46 1,600,000 

Brewster 

County TEXAS 8866 2.3 6100 45 52,000 

McMullen 

County TEXAS 851 1.4 1100 45 48,000 

Inyo County CALIFORNIA 17945 10 10,000 43 89,000 

Skamania 

County WASHINGTON 9872 4.2 1700 42 210,000 

Sierra County CALIFORNIA 3555 1.3 960 42 160,000 

Meagher 

County MONTANA 1932 0.60 2400 42 36,000 

Catron 

County NEW MEXICO 3543 3.1 7000 39 18,000 

Lincoln 

County NEVADA 4165 1.3 11,000 38 14,000 

Highland 

County VIRGINIA 2536 1.2 420 38 320,000 

Mariposa 

County CALIFORNIA 17130 4.3 1500 36 460,000 

Webster 

County 

WEST 

VIRGINIA 9719 1.5 560 36 680,000 

34
 
 

 

 

 

 

 

Table 10: Counties with Highest Import/Export Factors for Non-road Diesel Sources 

County State 2000 

Population 

Emissions 

Input 

(tons/year) 

County 

Area 

(hectares) 

Import/ 

export 

factor 

Benefits 

Output 

($/ton) 

Loving 

County TEXAS 67 0.034 660 520 42,000 

Alpine 

County CALIFORNIA 1208 0.30 730 300 520,000 

Catron 

County 

NEW 

MEXICO 3543 1.4 7000 120 56,000 

Edwards 

County TEXAS 2162 0.70 2100 120 41,000 

Nye County NEVADA 32485 20 18,000 110 250,000 

Inyo County CALIFORNIA 17945 10 10,000 96 200,000 

Mineral 

County NEVADA 5071 2.8 3800 67 100,000 

McMullen 

County TEXAS 851 1.9 1100 59 62,000 

Kimble 

County TEXAS 4468 1.5 1300 52 195,000 

Brooks 

County TEXAS 7976 2.1 970 47 360,000 

Crockett 

County TEXAS 4099 1.4 2800 47 57,000 

Hinsdale 

County COLORADO 790 0.71 1100 46 6200 

King County TEXAS 356 1.6 940 45 8300 

Hamilton 

County NEW YORK 5379 4.0 1800 43 140,000 

Moffat 

County COLORADO 13184 18 4800 42 67,000 

St. Helena 

Parish LOUISIANA 10525 4.2 410 41 960,000 

Coconino 

County ARIZONA 116320 130 19,000 39 120,000 

Clay County WEST 

VIRGINIA 10330 1.6 350 38 1,300,000 

Blanco 

County TEXAS 8418 3.6 720 37 310,000 

Park County COLORADO 14523 8.2 2200 37 130,000 

35
 
 

 

 

Table 11: Counties with Lowest Import/Export Factors for Total Diesel Sources 

County State 

2000 

Populatio 

n 

Emissions 

Input 

(tons/year) 

County 

Area 

(hectares) 

Import/ 

export 

factor 

Benefits 

Output 

($/ton) 

New York 

County NEW YORK 1537195 820 23 0.11 5,200,000 

Norfolk city VIRGINIA 234403 460 48 0.16 590,000 

San Francisco 

County CALIFORNIA 776733 870 47 0.19 2,500,000 

Kings County NEW YORK 2465326 630 60 0.20 6,200,000 

Suffolk 

County MASSACHUSETTS 689807 370 69 0.24 1,700,000 

Denver 

County COLORADO 554636 400 100 0.24 940,000 

Bristol city VIRGINIA 17367 17 5.1 0.27 1,200,000 

San Juan 

County WASHINGTON 14077 52 56 0.28 70,000 

Newport 

News city VIRGINIA 180150 180 75 0.28 510,000 

Arlington 

County VIRGINIA 189453 180 26 0.29 1,300,000 

Bronx 

County NEW YORK 1332650 290 40 0.31 7,700,000 

Emporia city VIRGINIA 5665 10 3.5 0.31 1,000,000 

Fredericksburg city VIRGINIA 19279 26 7.3 0.31 880,000 

Williamsburg 

city VIRGINIA 11998 9.7 4.8 0.32 890,000 

District of 

Columbia 

DISTRICT OF 

COLUMBIA 572059 370 6 0.32 2,400,000 

Dukes 

County MASSACHUSETTS 14987 180 95 0.33 51,000 

Queens 

County NEW YORK 2229379 610 110 0.33 5,000,000 

Manassas 

Park city VIRGINIA 10290 6.6 1.7 0.34 990,000 

Lynchburg 

city VIRGINIA 65269 48 23 0.38 1,200,000 

Richmond 

County NEW YORK 443728 260 48 0.39 3,200,000 

36
 
 

 

 

Table 12: Counties with Lowest Import/Export Factors for On-road Diesel Sources 

County State 2000 

Population 

Emissions 

Input 

(tons/year) 

County 

area 

(hectares) 

Import/ 

export 

factor 

Benefits 

Output 

($/ton) 

San Francisco 

County CALIFORNIA 776733 260 47 0.19 2,500,000 

New York 

County NEW YORK 1537195 91 23 0.20 9,900,000 

Norfolk city VIRGINIA 234403 33 48 0.23 840,000 

Bristol city VIRGINIA 17367 8.5 5.1 0.24 1,100,000 

Denver 

County COLORADO 554636 140 100 0.24 940,000 

Kings County NEW YORK 2465326 100 60 0.27 8,600,000 

Bronx 

County NEW YORK 1332650 94 40 0.28 7,100,000 

Fredericksburg city VIRGINIA 19279 9.7 7.3 0.29 830,000 

Emporia city VIRGINIA 5665 2.9 3.5 0.31 1,000,000 

Danville city VIRGINIA 48411 16 17 0.32 1,200,000 

Lynchburg 

city VIRGINIA 65269 22 23 0.33 1,000,000 

Franklin city VIRGINIA 8346 1.7 3.2 0.33 1,600,000 

Hampton city VIRGINIA 146437 25 51 0.34 750,000 

Harrisonburg 

city VIRGINIA 40468 12 11 0.34 870,000 

Queens 

County NEW YORK 2229379 150 110 0.37 5,700,000 

Suffolk 

County MASSACHUSETTS 689807 84 69 0.37 2,600,000 

District of 

Columbia 

DISTRICT OF 

COLUMBIA 572059 90 66 0.37 2,800,000 

St. Louis city MISSOURI 348189 140 72 0.37 1,700,000 

Arlington 

County VIRGINIA 189453 35 26 0.38 1,700,000 

Pinellas 

County FLORIDA 921482 210 310 0.41 1,400,000 

37
 
 

 

 

Table 13: Counties With Lowest Import/Export Factors for Non-road Diesel Sources 

County State 2000 

Population 

Emissions 

Input 

(tons/year) 

County 

area 

(hectares) 

Import/ 

export 

factor 

Benefits 

Output 

($/ton) 

New York 

County NEW YORK 1537195 730 23 0.094 4,600,000 

Norfolk city VIRGINIA 234403 430 48 0.16 580,000 

Kings County NEW YORK 2465326 530 60 0.18 5,800,000 

San Francisco 

County CALIFORNIA 776733 610 47 0.20 2,500,000 

Suffolk 

County MASSACHUSETTS 689807 280 69 0.20 1,400,000 

San Juan 

County WASHINGTON 14077 51 56 0.22 55,000 

Denver 

County COLORADO 554636 260 100 0.24 940,000 

Newport 

News city VIRGINIA 180150 150 75 0.25 450,000 

Arlington 

County VIRGINIA 189453 140 26 0.27 1,200,000 

Dukes 

County MASSACHUSETTS 14987 170 95 0.28 43,000 

Williamsburg 

city VIRGINIA 11998 8.6 4.8 0.29 800,000 

Bristol city VIRGINIA 17367 8.1 5.1 0.29 1,300,000 

District of 

Columbia 

DISTRICT OF 

COLUMBIA 572059 280 66 0.31 2,300,000 

Emporia city VIRGINIA 5665 7.2 3.5 0.31 1,000,000 

Queens 

County NEW YORK 2229379 460 110 0.31 4,800,000 

Manassas 

Park city VIRGINIA 10290 5.3 1.7 0.32 920,000 

Bronx 

County NEW YORK 1332650 190 40 0.32 8,100,000 

Fredericksburg city VIRGINIA 19279 17 7.3 0.33 920,000 

Salem city VIRGINIA 24747 17 10 0.35 940,000 

Richmond 

County NEW YORK 443728 220 48 0.36 2,900,000 

38
 
 

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
 
 

Figure 3: Distribution of County-Level Benefit-per-Ton of Diesel PM Emission Reductions: Onroad Sources (Laden et al. mortality estimate, 2006$) 

40
 
 

Figure 4: Distribution of County-Level Benefit-per-Ton of Diesel PM Emission Reductions: 

Non-road sources (Laden et al. mortality estimate, 2006$) 


 


 

41
 
 

 

 

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 annual tons 

diesel PM 

reduction 

annualized costs annual benefits 

Cook County, IL 0.44 $15,203 $1,000,000 

Anderson County, TX 0.45 $15,203 $224,000 

42
 
 

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 benefitper-ton estimates are averages, they may not reflect non-linear relationships between air 

quality changes and background PM2.5 levels. For example, because the concentrationresponse 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
 
 

VII. LITERATURE CITED 

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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 

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Ito, K. 2003. “Associations of Particulate 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
 
 

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, J.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 PM10 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/projections/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.html#ria2007 

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
 
 

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, 47:1238-1249 

Woodruff, T.J., J. Grillo, and K.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. 

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VIII. WEBSITE INDEX/INTERNET RESOURCES 

AERMOD model: www.epa.gov/scram001/dispersion_prefrec.htm#aermod 

Assessment System for Population Exposure Nationwide (ASPEN) user's guide is available at 

www.epa.gov/scram001/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/420b10033.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 (NATA): www.epa.gov/ttn/atw/natamain/ 

EPA’s Draft Guidance for Discounting Future Costs and Benefits 

http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE-0516-06.pdf/$File/EE-0516­

06.pdf?OpenElement 

NATA’s use of the Hazardous Air Pollutant Exposure Model 5 (HAPEM5) can be found at 

www.epa.gov/ttn/atw/nata1999/ted/teddraft.html 

2002 NATA and past results summarized: 

www.epa.gov/ttn/atw/nata1999/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 

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Technology Transfer Network 1999 National-Scale Air Toxic Assessment: 

www.epa.gov/ttn/atw/nata1999/background.html 

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 

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