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Technical Support Document

Estimating the Benefit per Ton of Reducing PM2.5

Precursors from the Petroleum Refineries Sector

U.S. Environmental Protection Agency

Office of Air and Radiation

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

April 2012
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CONTACT INFORMATION

This document has been prepared by staff from the Office of Air Quality Planning 

and Standards, U.S. Environmental Protection Agency. Questions related to this document 

should be addressed to Neal Fann or Charles Fulcher, U.S. Environmental Protection 

Agency, Office of Air Quality Planning and Standards, Health and Environmental Impacts 

Division, Risk and Benefits Group, Research Triangle Park, North Carolina 27711 (email: 

fann.neal@epa.gov, fulcher.charles@epa.gov). 
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Overview

This Technical Support Document (TSD) describes our approach for estimating the avoided 

human health impacts, and monetized benefits, of reducing emissions of PM2.5 and PM2.5

precursors including NOx and SO2 from the Petroleum Refineries sector using the results of 

source apportionment photochemical modeling. We focus in particular on the aspects of 

our approach that represent a change from the benefit per ton calculation methodology

described in Fann, Fulcher and Hubbell (2009) and subsequently applied in several EPA 

RIAs. We also describe the ways in which these new estimates can improve our 

characterization of the PM2.5-related health benefits from the Petroleum Refineries sector

and other sectors that were modeled. We summarize the benefit per ton estimates for both 

Petroleum Refineries sector as well as 12 other emission sectors. 

Summary of Calculations

The procedure for calculating benefit per ton coefficients follows three steps:

1. Use source apportionment photochemical modeling to predict the directly emitted 

PM2.5, nitrate and sulfate attributable to each of 13 emission sectors across the 

Continental U.S., including the Petroleum Refineries sector; see below for a 

summary of the sectors modeled.

2. For each sector, estimate the health impacts, and the economic value of these 

impacts, associated with the directly emitted PM2.5, sulfate and nitrate PM2.5 using 

the environmental Benefits Mapping and Analysis Program (BenMAP v4.0.44)1.

3. For each sector, divide the PM2.5-related health impacts attributable to each type of 

PM2.5, and the monetary value of these impacts, by the level of precursor emissions.

 1 In this stage we estimate the PM2.5-related impacts associated with changes in directly emitted PM2.5, nitrate 

and sulfate separately, so that we may ultimately calculate the benefit per ton reduced of the corresponding 

PM2.5 precursor, or directly emitted PM2.5, in step 3. When estimating these impacts we apply effect 

coefficients that relate changes in total PM2.5 mass to the risk of adverse health outcomes; we do not apply 

effect coefficients that are differentiated by PM2.5 specie. 
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Figure 1: Conceptual overview of the steps for calculating benefit per-ton estimates

The example above depicts the total PM2.5 contribution from the pulp and paper sector, 

though we repeat this process for each of the 13 sectors, which include:

1. Cement Kilns

2. Pulp and Paper Facilities

3. Petroleum Refineries

4. Coke Ovens

5. Iron and Steel Foundries

6. Integrated Iron and Steel Facilities

7. Electric Arc Furnaces

8. Taconite Mining

9. Ferroalloy Production

10. Residential Wood Combustion

11. Other Nonpoint Sources

12. Other Non-EGU Point Sources

13. Electricity Generating Units

The “Other Nonpoint Sources” and “Other Non-EGU Point Sources” categories are

agglomerations of sectors that were not modeled separately. When selecting a benefit per 

ton estimate for use with a sector not specifically modeled, it is necessary to determine 

which composite sector is the best match with respect to the source characteristics that 

would affect the level of benefits. These attributes include the proximity to receptor 

populations, the geographic distribution of sources, and the release parameters of the 

source. 

Readers interested in a full discussion of the air quality modeling performed for this rule 

may consult “Air Quality Modeling Technical Support Document: Source Sector 

Assessments” (U.S. EPA 2011). We project PM2.5 levels for sector to 2016. The appendix to 

this TSD includes plots of the PM2.5 levels attributed to each of these sectors for which we 

estimated benefit per-ton metrics.
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The photochemical modeling used here also produced estimates of ozone levels 

attributable to each sector. However, the complex non-linear chemistry governing ozone 

formation prevented us from developing a complementary array of ozone benefit per ton 

values. This limitation notwithstanding, we anticipate that the ozone-related benefits 

associated with reducing emissions of NOx and VOC for many of these sectors would be 

substantial. 

Finally, it is important to note that while most VOCs emitted are oxidized to carbon dioxide 

(CO2) rather than to PM, a portion of VOC emission contributes to ambient PM2.5 levels as 

organic carbon aerosols (US EPA 2009). Therefore, reducing these emissions would reduce 

PM2.5 formation, human exposure to PM2.5, and the incidence of PM2.5-related health effects. 

However, we have not quantified a VOC benefit per ton estimate in this analysis. Analysis of 

organic carbon measurements suggest only a fraction of secondarily formed organic carbon 

aerosols are of anthropogenic origin. The current state of the science of secondary organic 

carbon aerosol formation indicates that anthropogenic VOC contribution to secondary 

organic carbon aerosol is often lower than the biogenic (natural) contribution. 

Photochemical models typically estimate secondary organic carbon from anthropogenic 

VOC emissions to be less than 0.1 μg/m3. For this reason, we have not reported a VOC 

benefit per ton estimate. 

Below we provide an expanded discussion of each of the latter two steps to the 

calculation—estimating health impacts and economic value of PM2.5 attributable to each 

sector and calculating the benefit per ton coefficients. The discussion of these topics is not 

intended to be exhaustive, and readers interested in learning more about our approach to 

performing an air pollution health impact and benefits analysis may consult the Cross-State 

Air Pollution Rule (CSAPR)(US EPA 2011).

Estimating the number of PM2.5-related health impacts attributable to each sector

In this stage of the analysis we performed a Health Impact Assessment (HIA), which 

quantifies the changes in the incidence of adverse health impacts resulting from changes in 

human exposure to PM2.5 from each sector. HIAs are a well-established approach for 

estimating the retrospective or prospective change in adverse health impacts expected to 

result from population-level changes in exposure to pollutants (Levy et al. 2009). PC-based 

tools such as the environmental Benefits Mapping and Analysis Program (BenMAP) can 

systematize health impact analyses by applying a database of key input parameters, 

including health impact functions and population projections. Analysts have applied the 

HIA approach to estimate human health impacts resulting from hypothetical changes in 

pollutant levels (Davidson et al. 2007; Hubbell et al. 2004; Tagaris et al. 2009). 

The HIA approach used in this analysis involves three basic steps: (1) utilizing CAMx--

generated projections of PM2.5 levels attributed to each sector; (2) determining the 

subsequent change in population-level exposure; (3) calculating health impacts by applying 

concentration-response relationships drawn from the epidemiological literature to this 

change in population exposure (Hubbell et al. 2009). This procedure is operationalized 
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within BenMAP using a health impact function. 

A typical health impact function looks as follows:

∆y = yo ∙ �eβ∙∆x − 1� ∙ Pop

where y0 is the baseline incidence rate for the health endpoint being quantified (for 

example, a health impact function quantifying changes in mortality would use the baseline, 

or background, mortality rate for the given population of interest); Pop is the population 

affected by the change in air quality, whose size and distribution we have projected to 2016 

for this analysis; ∆x is the change in air quality; and β is the effect coefficient drawn from 

the epidemiological study. Tools such as BenMAP can systematize the HIA calculation 

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

population data and health impact functions. 

Figure 2 provides a simplified overview of this approach, using PM2.5-related premature 

mortality as an example, though the procedure is generally the same for other health 

endpoints. This sequence of steps is repeated for each of the 13 sectors. The PM2.5 health 

endpoints quantified and the health impact functions applied in this analysis are consistent 

with the CSAPR RIA. That RIA includes a detailed discussion of each of the data inputs, 

analytical assumptions and sources of uncertainty. In the interest of brevity, we do not 

repeat these here in detail. However, it is worth noting that we exclude the value of several 

important non-health endpoints, including recreational and residential visibility, climaterelated impacts and ecological endpoints. Table 1 below summarizes the endpoints 

quantified in this benefit per ton TSD.
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Figure 2: Illustration of BenMAP Approach
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Table 1: Human health effects of PM2.5 quantified and not quantified in this analysis

Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

(refers to 

CSAPR RIA)

Improved Human Health

Reduced incidence of 

premature mortality 

from exposure to PM2.5

Adult premature mortality based on 

cohort study estimates and expert 

elicitation estimates (age >25 or age 

>30)

  Section 5.4

Infant mortality (age <1)   Section 5.4

Reduced incidence of 

morbidity from exposure 

to PM2.5

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

Hospital admissions—respiratory (all 

ages)   Section 5.4

Hospital admissions—cardiovascular 

(age >20)   Section 5.4

Emergency room visits for asthma (all 

ages)   Section 5.4

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

Lower respiratory symptoms (age 7-14)   Section 5.4

Upper respiratory symptoms 

(asthmatics age 9-11)   Section 5.4

Asthma exacerbation (asthmatics age 6-

18)   Section 5.4

Lost work days (age 18-65)   Section 5.4

Minor restricted-activity days (age 18-

65)   Section 5.4

Chronic Bronchitis (age >26)   Section 5.4

Emergency room visits for 

cardiovascular effects (all ages) -- -- Section 5.4

Strokes and cerebrovascular disease 

(age 50-79) -- -- Section 5.4

Other cardiovascular effects (e.g., other 

ages) -- -- PM ISA2

Other respiratory effects (e.g., 

pulmonary function, non-asthma ER 

visits, non-bronchitis chronic diseases, 

other ages and populations)

-- -- PM ISA2

Reproductive and developmental effects 

(e.g., low birth weight, pre-term births, 

etc)

-- -- PM ISA2,3

Cancer, mutagenicity, and genotoxicity

effects -- -- PM ISA2,3

1 We assess these benefits qualitative due to time and resource limitations for this analysis.

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

3 We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other 

significant concerns over the strength of the association.
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Estimating the economic value of health impacts attributable to each sector

After quantifying the change in adverse health impacts, the next step is to estimate the 

economic value of these avoided impacts. The appropriate economic value for a change in a 

health effect depends on whether the health effect is viewed ex ante (before the effect has 

occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations 

of air pollution generally lower the risk of future adverse health effects by a small amount 

for a large population. The appropriate economic measure is therefore ex ante Willingness 

to Pay (WTP) for changes in risk. However, epidemiological studies generally provide 

estimates of the relative risks of a particular health effect avoided due to a reduction in air 

pollution. A convenient way to use this data in a consistent framework is to convert 

probabilities to units of avoided statistical incidences. This measure is calculated by 

dividing individual WTP for a risk reduction by the related observed change in risk. For 

example, suppose a measure is able to reduce the risk of premature mortality from 2 in 

10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction 

is $100, then the WTP for an avoided statistical premature mortality amounts to $1 million 

($100/0.0001 change in risk). Using this approach, the size of the affected population is 

automatically taken into account by the number of incidences predicted by epidemiological 

studies applied to the relevant population. The same type of calculation can produce values 

for statistical incidences of other health endpoints. 

For some health effects, such as hospital admissions, WTP estimates are generally not 

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

estimate. For example, for the valuation of hospital admissions we use the avoided medical 

costs as an estimate of the value of avoiding the health effects causing the admission. These 

cost of illness (COI) estimates generally (although not in every case) understate the true 

value of reductions in risk of a health effect. They tend to reflect the direct expenditures 

related to treatment but not the value of avoided pain and suffering from the health effect.

We express the economic value of the avoided impacts using constant year 2008 dollars, 

adjusted for growth in real income out to 2016 (the year of the air quality change) using 

projections provided by Standard and Poor’s. Economic theory argues that WTP for most 

goods (such as environmental protection) will increase if real income increases. Many of 

the valuation studies used in this analysis were conducted in the late 1980s and early 

1990s. Because real income has grown since the studies were conducted, people’s 

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

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

current costs. For these two reasons, these cost of illness estimates may underestimate the 

economic value of avoided health impacts in 2016. As with the selection of health studies, 

the economic valuation estimates applied in this analysis are consistent with those used in 

the CSAPR RIA. 
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Calculating the benefit per ton estimate

The final step is to divide the incidence of adverse health outcomes, and the economic value 

of those outcomes, attributable to the directly emitted PM2.5, nitrate and sulfate from each 

sector by the emissions of directly emitted PM2.5, NOx and SO2. The result is a suite of

incidence per ton and $ benefit per ton estimates for each sector. Below we summarize the 

total $ per ton estimates for each of the 13 sectors (Table 2), as well as more detailed 

health impacts per ton for the Petroleum Refineries sector. 
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Results

Table 2: Summary of the total dollar value (mortality and morbidity) per ton of directly emitted PM2.5 and PM2.5

precursor reduced by each of 13 sectors in 2016 (2008$, 3% discount rate)

Pope et al. (2002) mortality estimateA Laden et al. (2006) mortality estimateA

Sector

Directly emitted 

PM2.5 SO2 NOx

Directly emitted 

PM2.5 SO2 NOx

Cement Kilns $360,000 $43,000 $5,700 $890,000 $110,000 $14,000

Pulp & Paper Facilities $150,000 $45,000 $3,800 $370,000 $110,000 $9,200

Petroleum Refineries $330,000 $69,000 $6,800 $810,000 $170,000 $17,000

Coke Ovens $480,000 $52,000 $11,000 $1,200,000 $130,000 $26,000

Iron and Steel Foundries $520,000 $420,000 $17,000 $1,300,000 $1,000,000 $41,000

Integrated Iron and Steel Facilities $520,000 $90,000 $14,000 $1,300,000 $220,000 $34,000

Electric Arc Furnaces $440,000 $81,000 $9,700 $1,100,000 $200,000 $24,000

Taconite Mining $85,000 $34,000 $6,200 $210,000 $84,000 $15,000

Ferroalloys Production $280,000 $44,000 $4,500 $680,000 $110,000 $11,000

Residential Wood Combustion $370,000 $100,000 $13,000 $910,000 $250,000 $33,000

Other Nonpoint Sources $330,000 $49,000 $7,900 $800,000 $120,000 $19,000

Other Non-EGU Point Sources $270,000 $40,000 $6,400 $670,000 $100,000 $16,000

Electricity Generating Units $130,000 $36,000 $5,300 $330,000 $87,000 $13,000

A Value represents sum of the value of avoided morbidity impacts and mortality impacts quantified using the PM2.5 mortality risk estimate noted. 

Estimates rounded to two significant figures in this table, but all calculations are performed with the unrounded estimates.
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Table 3: Dollar value (mortality and morbidity) per ton of directly emitted PM2.5 and 

PM2.5 precursors reduced in 2016 for the Petroleum Refineries sector (2008$, 3% 

discount rate)

Pollutant emitted

Mortality risk estimate NOx SO2

Directly emitted 

PM2.5

Risk estimates derived from the 

epidemiological literatureA

Pope et al. (2002) $6,800 $69,000 $330,000 

Laden et al. (2006) $17,000 $170,000 $810,000 

A Value represents sum of the value of avoided morbidity impacts and mortality impacts quantified 

using the PM2.5 mortality risk estimate noted. Estimates are rounded to two significant digits in 

this table, but all calculations are performed with the unrounded estimates.

Table 4: Incidence per ton of avoided mortalities and morbidities from the 

Petroleum Refineries sector for directly emitted PM2.5 and PM2.5 precursors reduced 

in 2016 

Health Endpoint

Pollutant emitted

NOx SO2

Directly 

emitted PM2.5

Premature mortality

Pope et al. (2002) 0.000760 0.007675 0.036763

Laden et al. (2006) 0.001953 0.019712 0.094568

Morbidity

Chronic bronchitis 0.000578 0.005768 0.026906

Respiratory emergency room visits 0.000574 0.005813 0.027938

Acute bronchitis 0.001307 0.012884 0.060960

Lower respiratory symptoms 0.016629 0.164146 0.776130

Upper respiratory symptoms 0.012553 0.124335 0.586745

Acute respiratory symptoms 0.674979 6.739813 31.414966

Work loss days 0.113843 1.136584 5.296836

Asthma exacerbation 0.027679 0.274946 1.292597

Non-fatal heart attacks 0.000821 0.008186 0.039306

Respiratory hospital admissions 0.000134 0.001374 0.006694

Cardiovascular hospital admissions 0.000308 0.003109 0.014894
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Lowest Measured Air Quality Level Exposure Assessment

Assessments quantifying PM2.5 related health impacts generally find that cases of avoided 

mortality represent the majority of the monetized benefits. For this reason, EPA has 

historically performed a series of analyses that characterize the uncertainty associated 

with the PM-mortality relationship and the economic value of reducing the risk of 

premature death (Mansfield et al. 2009; Roman et al. 2008; US EPA 2011). Here we focus 

on the level of uncertainty associated with the avoided premature deaths estimated to

occur due to air quality improvements below the lowest levels of PM2.5 observed in the 

epidemiological studies used to quantify such risks. 

We include a “Lowest Measured air quality Level” (LML) assessment, which identifies the 

baseline (i.e. pre-rule) annual mean PM2.5 levels at which populations are exposed and the 

minimum observed air quality level of each long-term cohort study we use to quantify 

mortality impacts. In general, our confidence in the estimated number of premature deaths 

diminishes as we estimate these impacts in locations whose PM2.5 levels are below this 

minimum level. While an LML assessment provides some insight into the level of 

uncertainty in the estimated PM mortality benefits, EPA does not view this minimum value

as a concentration threshold. The central benefits estimates reported in this RIA reflect a 

full range of modeled air quality concentrations. We maintain a high confidence in the 

PM2.5-related impacts estimated from air quality changes down to the LML of the two major 

cohort studies used to quantify PM-related premature deaths. That is—our confidence in 

the estimated benefits above the LML should not imply an absence of confidence in benefits 

estimated below the LML.

Because we cannot estimate PM2.5 air quality changes for every policy scenario for which 

these source apportionment model results might be used, we cannot quantify the 

distribution of mortality impacts across baseline PM2.5 levels for each rule. As an 

alternative, we provide the percentage of the population exposed at baseline PM2.5 levels. It 

is important to note that baseline exposure is only one parameter in the health impact 

function, along with baseline incidence rates population, and change in air quality. In other 

words, the percentage of the population exposed to air pollution below the LML is not the 

same as the percentage of the population experiencing health impacts as a result of a 

specific emission reduction policy. A very large proportion of the population is exposed at 

or above the lowest LML of the cohort studies (Figures 3 and 4), increasing our confidence 

in the PM mortality analysis. Figure 3 shows a bar chart of the percentage of the population 

exposed to various air quality levels in the baseline. Figure 4 shows a cumulative 

distribution function of the same data. Both figures identify the LML for each of the major 

cohort studies. Using the Pope et al. (2002) study, the 77% of the population is exposed to 

annual mean PM2.5 levels at or above the LML of 7.5 μg/m3. Using the Laden et al. (2006)

study, 25% of the population is exposed above the LML of 10 μg/m3. 
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Figure 3:Percentage of Adult Population According to Annual Mean PM2.5 Exposure in 

the Baseline
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Figure 4:Cumulative Distribution of Adult Population According to Annual Mean 

PM2.5 Exposure in the Baseline

Limitations

This analysis includes many data sources as inputs, including emission inventories, air 

quality data from models (with their associated parameters and inputs), population data, 

health effect estimates from epidemiology studies, and economic data for monetizing 

benefits. Each of these inputs may be uncertain and would affect the benefits estimate.

When the uncertainties from each stage of the analysis are compounded, small 

uncertainties can have large effects on the total quantified benefits. This analysis does not 

include the type of detailed uncertainty assessment found in the PM NAAQS RIA (US EPA, 

2006). However, the results of the Monte Carlo analyses of the health and welfare benefits 

presented in Chapter 5 of the PM RIA can provide some evidence of the uncertainty 

surrounding the benefits results presented in this analysis.

In this analysis we assume that all fine particles, regardless of their chemical composition, 

are equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 produced via transported precursors emitted from EGUs may differ 
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significantly from direct PM2.5 released from other industrial sources. However, the 

scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle 

type. We also assume that the health impact function for fine particles is linear down to the 

lowest air quality levels modeled in this analysis. Thus, the estimates include health 

benefits from reducing fine particles in areas with varied concentrations of PM2.5, 

including both regions that are in attainment with fine particle standard.

It is also important to note that the monetized benefit-per-ton estimates used here reflect 

specific geographic patterns of emissions and specific air quality and benefits modeling 

assumptions. Great care should be taken in applying these estimates to emission reductions 

occurring in any specific location, as these are all based on national emission reduction 

assumptions and therefore represent an average benefits-per-ton over the entire United 

States. The benefits-per-ton for emission reductions in specific locations may be very 

different from the estimates presented here. The maps in the Appendix that follows 

provide an indication of the location of the facilities that were modeled as well as the 

associated PM2.5 levels. 
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Appendix: Modeled annual mean PM2.5 levels attributable to 13 Sectors in 2016
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19
20
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