Technical Support Document: Estimating the Benefit per Ton of Reducing PM2.5

Precursors from the Ferroalloy Sector

Overview

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

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

precursors from the Ferroalloy sector. We focus in particular on the aspects of our

approach that represent a change from the previous benefit per ton calculation 

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

several EPA RIA’s. We also describe the ways in which these new estimates can support 

more reliable estimates of the PM2.5-related health benefits. We summarize the benefit per 

ton estimates for both the Ferroalloy sector that were used in the RIA for this rule, as well 

as 11 other emission sectors. 

Summary of Calculations

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

1. Model the directly emitted PM2.5, nitrate and sulfate attributable to each of 12 

emission sectors, including the Ferroalloy 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)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. 
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 12 sectors, which include:

1. Cement kilns

2. Coke ovens

3. Electric arc furnaces

4. Ferroalloy facilities

5. Integrated iron & steel

6. Iron and steel

7. Other non-EGU sources

8. Other non-point sources

9. Pulp & paper facilities

10.Refineries

11.Residential wood combustion

12.Taconite mining

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” (EPA, 2011a). All PM2.5 levels are modeled 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.

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) (EPA, 2011b).

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 (Hubbell et al. 2005; Davidson et al. 2007, 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 (Hubbell 

et al. 2009) to this change in population exposure. This procedure is operationalized within 

BenMAP using a health impact function. 

A typical health impact function looks as follows:

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 12 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, 

climate-related impacts and ecological endpoints. Table 1 below summarizes the endpoints 

quantified in this benefit per ton TSD.

Figure 2: Illustration of BenMAP Approach
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 

Monetize

d

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.
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. 

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

the total $ per ton estimates for each of the 12 sectors (Table 1), as well as more detailed 

health impacts per ton for the Ferroalloy sector. 
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 12 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 $150,000 $45,000 $3,800 $370,000 $110,000 $9,200 

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

Coke Ovens $470,000 $52,000 $11,000 $1,200,000 $130,000 $26,000 

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

Integrated Iron/Steel $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 $84,000 $34,000 $6,200 $210,000 $84,000 $15,000 

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

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

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

Non-EGU Point 

Other $270,000 $40,000 $6,400 $670,000 $100,000 $16,000 

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

noted. 
Table 3: Dollar value per ton of avoided premature PM2.5-related deaths from the 

Ferroalloy sector for directly emitted PM2.5 and PM2.5 precursors reduced in 2016 

(2008$, 3% discount rate)

Pollutant emitted

Mortality risk estimate NOx SO2 Directly emitted PM2.5

Risk estimates derived from the 

epidemiological literature

Pope et al. (2002) $4,500 $44,000 $280,000 

Laden et al. (2006) $11,000 $110,000 $680,000 

Risk estimates derived from the expert 

elicitation

Expert A $12,000 $110,000 $720,000 

Expert B $290 $19,000 $240,000 

Expert C $9,000 $87,000 $550,000 

Expert D $6,400 $62,000 $390,000 

Expert E $15,000 $140,000 $890,000 

Expert F $290 $17,000 $210,000 

Expert G $5,400 $52,000 $330,000 

Expert H $6,800 $66,000 $410,000 

Expert I $9,000 $86,000 $540,000 

Expert J $7,300 $70,000 $440,000 

Expert K $290 $5,200 $52,000 

Expert L $290 $13,000 $150,000 
Table 4: Incidence per ton of avoided morbidities from the Ferroalloy 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) 0.000510 0.004900 0.031000

Laden et al. (2006) 0.001300 0.013000 0.079000

Expert A 0.001400 0.013000 0.084000

Expert B 0.000000 0.001900 0.027000

Expert C 0.001000 0.010000 0.064000

Expert D 0.000730 0.007100 0.044000

Expert E 0.001700 0.017000 0.110000

Expert F 0.000000 0.001700 0.023000

Expert G 0.000610 0.005900 0.037000

Expert H 0.000780 0.007500 0.047000

Expert I 0.001000 0.010000 0.063000

Expert J 0.000840 0.008100 0.051000

Expert K 0.000000 0.000290 0.004100

Expert L 0.000000 0.001200 0.016000

Infant mortality 0.000002 0.000022 0.000140

Morbidity

Chronic bronchitis 0.000320 0.003100 0.020000

Respiratory emergency 

room visits 0.000300 0.003400 0.020000

Acute bronchitis 0.000670 0.006800 0.043000

Lower respiratory 

symptoms

0.008500 0.086000 0.540000

Upper respiratory 

symptoms

0.006400 0.065000 0.410000

Acute respiratory 

symptoms

0.360000 3.500000 22.000000

Work loss days 0.060000 0.590000 3.700000

Asthma exacerbation 0.014000 0.140000 0.900000

Non-fatal heart attacks 0.000590 0.005700 0.036000

Respiratory hospital 

admissions 0.000096 0.000990 0.006400

Cardiovascular hospital 

admissions 0.000220 0.002100 0.013000
Appendix: Modeled annual mean PM2.5 levels attributable to 12 Sectors


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