### Technical Support Document: Estimating the Benefit per Ton of Reducing PM 2.5



### 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 PM 2.5 

### and PM 2.5 

### 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 PM 2.5 

### - 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 1 1 other emission sectors.

Summary of Calculations

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

1. Model the directly emitted PM 2.5 

### , 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, e stimate the health impacts, and the economic value of these impacts, associated with the directly emitted PM 2.5 

### , sulfate and nitrate PM 2.5 

### using the environmental Benefits Mapping and Analysis Program (BenMAP) 1



### .

3. For each sector, d ivide the PM 2.5 

### - related health impacts attributable to each type of

PM 2.5 

### , and the monetary value of these impacts, by the level of precursor emission s.

1 In this stage we estimate the PM 2.5 - related impacts associated with changes in directly emitted PM 2.5 , nitrate and sulfate separately, so that we may ultimately calculate the benefit per ton reduced of the corresponding

PM 2.5 precursor, or directly emitted PM 2.5 , in step 3. When estimating these impacts we apply effect coefficients that relate changes in total PM 2.5 mass to the risk of adverse health ou tcomes; we do not apply effect coefficients that are differentiated by PM 2.5 specie.

### Figure 1: Conceptual overview of the steps for calculating benefit per

- ton estimates

The example above depicts the total PM 2.5 

### 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, 2011 a ). All PM 2.5 

### levels are modeled to 2016. The appendix to this TSD

includes plots of the PM 2.5 

### 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 im pacts and economic value of PM 2.5 

### 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 PM 2.5 

### - related health impacts attributable to each sector

In this stage of the analysis we perform ed a Health Impact Assessment (HIA)

, which

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

human exposure to PM 2.5 

### 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 B enefits M apping and A nalysis P rogram (BenMAP) can

systematize health impact analyses by appl ying 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; Dav idson et al. 2007, Tagaris et al. 2009).

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

CAMx

- - generated projections of PM 2.5 

### levels attributed to each sector ; (2) determining the

subsequent change in population

- level expo sure; (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 impa ct function.

A typical health impact function look s as follows:

where y 0 

### 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 populati on 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 t he 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 PM 2.5 

### - 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 PM 2.5 

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

consistent wi th 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 valu e 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 BenM AP Approach

### Table 1: Human health effects of PM 2.5 

### 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 PM 2.5 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 PM 2.5 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 ISA 2

Other respiratory effects (e.g., pulmonary function, non

- asthma ER

visits, non

- bronchitis chronic diseases, other ages and populations) -- -- PM ISA 2

Reproductive and developmental effects (e.g., low birth weight, pre

- term births, etc) -- -- PM ISA 2,3

Cancer, mutagenicity, and genotoxicity

effects -- -- PM ISA 2,3

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 e conomic v alu e of h ealth i mpacts 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 i s 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 val ues 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 exam ple, 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 tru e 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 rea l 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 de ath 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 201 6

. 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 out comes, 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 PM 2.5 

### , NO x 

### and SO 2



### . The result

is incidence per ton and $ benefit per ton estimates for e ach 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 PM 2.5 

### and PM 2.5 

### precursor reduced by each of 12 sectors in 2016 (2008$, 3% discount rate)

Pope et al. (2002) mortality estimate A Laden et al. (2006) mortality estimate A

Sector Directly emitted PM 2.5 SO 2 NO

x Directly emitted PM 2.5 SO 2 NO

x 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 s um of the value of avoided morbidity impacts and mortality impacts quantified using the PM 2.5 mortality risk estimate

noted.

### Table 3 : Dollar value per ton of avoided premature PM 2.5 

### - related deaths from the

Ferroalloy sector for directly emitted PM 2.5 

### and PM 2.5 

### precursor s reduced in 2016 (2008$, 3% discount rate)

Pollutant emitted
Mortality risk estimate NO

x SO 2 Directly emitted PM 2.5

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 PM 2.5 

### and PM 2.5 

### precursors reduced in 2016

Health Endpoint Pollutant emitted

NO

x SO 2 Directly emitted PM 2.5

Premature mortality

Pope et al. (2002) 0.000510 0.0049 00 0.03 1000
Laden et al. (2006) 0.00130 0 0.01 3000 0.079 000
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 a nnual m ean PM 2.5 

### l evels a ttributable to 12 Sectors

### References

Fann

, N, CM Fulcher, and BJ Hubbell. 2009. The influence of location, source, and emission type in estimates of the human health benefits of reducing a ton of air pollution. Air Qual ity and Atmos

pheric Health 2:169

– 176.

Davidson, K, A Hallberg, DR McCubbin, and BJ Hubbell. 2007. Analysis of PM 2.5 

### using the environmental benefits mapping and analysis program (BenMAP). Journal of Toxicology
and Environmental Health, Part A 70:332

- 346.

Hubbell, BJ, A Hallberg, DR McCu bbin, and E Post

. 2005 . Health

- related benefits of attaining

the 8

- hr o zone s tandard. Environmental Health Perspectives 113:73

– 82.

Hubbell

, BJ

, N F ann , and J I Levy . 2009. Methodological considerations in developing local

scale health impact assessments: balancing national, regional, and local data

. Air Qual ity

and Atmos

pheric Health 2: 9 9

– 1 10

.

Levy, JI, LK Baxter, and J Schwartz. 2009. Uncertainty and Variability in Health

- Related Damages from Coal

- Fired Power Plants in the United States. Risk Analysis 29(7):1000

- 1014.

Tagaris, E, K Liao, AJ Delucia, L Deck, P Amar, and AG Russell. 2009. Potential Impact of Climate Change on Air Pollution

- Related Human Health Effects. Environmental Science & Technology 43(13):4979

- 4988.

U.S. Environmental Protection Agency (U.S. EPA) . 2011 a

. Air Quality Modeling Technical

S upport D ocument: Source Sector Assessments (Draft) ; EPA: Research Triangle Park,

NC.

U.S. Environmental Protection Agency (U.S. EPA). 201 1b

. Regulatory Impact Analysis for the

Cross

- State Air Pollution Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC.