1 

# Technical Support Document

**Estimating the Benefit per Ton of Reducing PM 2.5**  

# 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 20122 **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

Agen cy, 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 ).3 Overview

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

human health impacts, and monetized benefits, of reducing emissions of PM 2.5 and PM 2.5

precursors including NO
x and SO 2 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 estimat es can improve our

characterization of the PM 2.5 - 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 1 2 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

PM 2.5 , nitrate and sulfate attributable to each of 1 3 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, 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 v4. 0.44 ) 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 thes e 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 ma ss to the risk of adverse health outcomes; we do not apply effect coefficients that are differentiated by PM 2.5 specie.4 **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 1 3 sectors, which include:

1

. Cement K ilns

2

. Pulp and Paper Facilities

3

. Petroleum Refineries

4

. Coke O vens 5

. Iron and Steel Foundries

6

. Integrated Iron and Steel Facilities

7

. E lectric A rc F urnaces 8

. Taconite Mining

9

. Ferroalloy Production

10

. Residential Wood Combustion

11

. Other Nonpoint Sources

12

. Other N on

- EGU Point S ources 13

. Electric ity Generating Units

The “Other Nonpoint Sources” and “O ther N on

- EGU Point Sources ” categories are agglomeration s of sectors that we re not model ed 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 attrib utes 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 Mod eling Technical Support Document: Source Sector

Assessments” (U .S. EPA 2011)

. We project PM 2.5 levels for sector 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.5 The photochemical modeling used here also produce d 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 m ost VOCs emitted are oxidized to carbon dioxide (CO 2 ) rather than to PM, a portion of VOC emission contributes to ambient PM 2.5 levels as organic carbon aerosols (US EPA 2009)

. T herefore, reducing these emissions would reduce

PM 2.5 formation, human exposure to PM 2.5 , and the incidence of PM 2.5 - related health effects. However, we have not quantified a VOC benefit per ton estimate in this analysis. An alysis 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 contrib ution 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 μ g/m 3

. For this reason, we have not rep orted 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 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 bene fits analysis may consult the Cross

- State

Air Pollution Rule (CSAPR) (US EP A 2011)

.

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 en efits M apping and A nalysis P rogram (BenMAP) can systematize health impact analyses by applying a database of key input parameters, including health impact functions and population projections. Anal ysts 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 projection s of PM 2.5 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 po pulation exposure (Hubbell et al. 2009)

. This procedure is operationalized6 within BenMAP usin g a health impact function.

A typical health impact function look s as follows:

∆ y = y o ∙ � e β

∙ ∆ x

− 1 �

∙ Pop

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 1 3 sectors. The PM 2.5 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.7 **Figure 2**  **: Illustration of BenMAP Approach**8 **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

Monetized 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.9 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 da ta 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 t o 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 av oided 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 t o 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 t his 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 201 6

. As with the selection of health studies,

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

the CSAPR RIA.10 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, attribu table to the directly emitted PM 2.5 , nitrate and sulfate from each sector by the emissions of directly emitted PM 2.5 , NO x and SO 2

. 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 1 3 sectors (Table 2 ), as well as more detailed health impacts per ton for the Petroleum Refineries sector

.11 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 1**  **3 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 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

Electri city 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 PM 2.5 mortality risk estimate noted. Estimates rounded to two significant figures in this table, but all calculations are performed with the unrounded estimates.12 **Table 3**  **: Dollar value**  **(mortality and morbidity) per ton of directly emitted PM**  **2.5 and** 

**PM 2.5**  **precursor s**  **reduced in 2016 for the**  **Petroleum Refineries s**  **ector (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 A 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 PM 2.5 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 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) 0.000760 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 0.01489413 Lowest Measured Air Quality Level Exposure Assessment

Assessments quantifying PM 2.5 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 a ir quality improvements below the lowest levels of PM 2.5 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 PM 2.5 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 . I n general, our confidence in the estimated number of premature deaths

diminishes as we estimate the se impacts in locations whose PM 2.5 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. W e maintain a high confidence in the

PM 2.5 - related impacts estimated from air quality changes down to the LML of th e two major

cohort studies used to quantify PM

- related premature deaths

. That is

— o ur 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 PM 2.5 air quality changes for every pol icy scenario for which these source apportionment model results might be used , we cannot quantify the distribution of mortality impacts across baseline PM 2.5 levels for each rule. As an

alternative , we provide the percentage of the population exposed at ba seline PM 2.5 level s

. 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. F igure 3 shows a bar chart of the percentage of the population exposed to various air q uality 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 PM 2.5 levels at or above the LML of 7.5 μg/m 3 . Using the Laden et al. ( 2006)

st udy, 25 % of the population is exposed above the LML of 10 μg/m 3

.14 **Figure 3:Percentage of Adult Population According to Annual Mean PM 2.5**  **Exposure in**

**the Baseline**15 **Figure 4:Cumulative Distribution of Adult Population According to Annual Mean** 

**PM 2.5**  **Exposure in the Baseline**

Limitations

This analysis includes many data sources as inputs, incl uding emission inventories, air quality data from models (with their associated parameters and i nputs), population data,

health effect estimates from epidemiology studies, and economic data f or monetizing

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

When the uncertainties from each stage of the a nalysi s are compounded, small

uncertainties can have large effects on the total quantified benefits. T his 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 w e assume that all fine particles, regardless of t heir ch emical composition, are equally potent in causing premature mortality . This is an important assumpt ion,

because PM2.5 produced via transported precursors emitted from EGUs may differ16 significantly from direct PM2.5 released from other industr ial sou rces. However, the

scientific evidence is not yet sufficient to allow differentiation of eff ect 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 an alysis. Thu s, 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 benef it

- per

- ton estimates u sed here reflect specific geographic patterns of emissions and sp ecific air quality and benefits modeling assumptions. Great care should be taken in applying these e stimates to emission reductions occurring in any specific loca tion, a s these are all based on national emission reduction assumptions and therefore represent an average benefits

- per

- ton over the enti re 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 PM 2.5 levels.17 **Appendix :**  **Modeled a nnual**  **m ean PM**  **2.5 l**  **evels a**  **ttributable to 1 3**  **Sectors in 2016** 18192021 **References**

Davidson K, Hallberg A, McCubbin D, Hubbell B. 2007. Analysis of PM2.5 using the Environmental Benefits Mapping and Analysis Program (BenMAP). Journal of toxicology and environmental health. Part A 70:332

- 46; doi:10.1080/15287390600884982.

Fann N, Fulcher C, Hubbell B. 2009. The influence of location, source, and emission type in estimates of the human health benefits of reducing a ton of air pollution. Air Quality, Atmosphere & Health 2:169

- 176; doi:10.1007/s11869

- 009

- 0044

- 0.

Hubbell B, Fann N, Levy J. 2009. Methodological considerations in developing local

- scale health impact assessments: balancing national, regional, and local data. Air Quality, Atmosp here & Health 2:99

- 110; doi:10.1007/s11869

- 009

- 0037

- z.

Hubbell BJ, Hallberg A, McCubbin DR, Post E. 2004. Health

- Related Benefits of Attaining the

8

- Hr Ozone Standard. Environmental Health Perspectives 113:73

- 82; doi:10.1289/ehp.7186.

Laden F, Schwartz J, Speizer FE, Dockery DW. 2006. Reduction in fine particulate air pollution and mortality: Extended follow

- up of the Harvard Six Cities study. American journal of respiratory and critical care medicine 173:667

- 72; doi:10.1164/rccm.200503

- 443OC.

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.

Mansfield, Carol; Paramita Sinha; Max Henrion. 2009. Influence Analysis in Support of

Char acterizing Uncertainty in Human Health Benefits Analysis: Final Report. Prepared for U.S. EPA, Office of Air Quality Planning and Standards. November. Available on the

internet at <http://www.epa.gov/ttn/ecas/regdata/Benefits/influence_analysis_final_repor t_psg. pdf>.

Pope III C a. 2002. Lung Cancer, Cardiopulmonary Mortality, and Long

- term Exposure to Fine Particulate Air Pollution. JAMA: The Journal of the American Medical Association

287:1132

- 1141; doi:10.1001/jama.287.9.1132.

Roman H a, Walker KD, Walsh TL, Conner L, Richmond HM, Hubbell BJ, et al. 2008. Expert judgment assessment of the mortality impact of changes in ambient fine particulate matter in the U.S. Environmental science & technology 42: 2268

- 74.

Tagaris E, Liao K

- J, Delucia AJ, Deck L, Amar P , Russell AG. 2009. Potential impact of climate change on air pollution

- related human health effects. Environmental science & technology 43: 4979

- 88.22 U.S. EPA. 2011. Air Quality Modeling Technical Support Document: Source Sector

Assessments. EPA: Research Triangle Park, NC.

US EPA. 2009. Integrated Science Assessment for Particulate Matter (Final Report) EPA

600

- R

- 08

- 139F. National Center for Environmental Assessment

— RTP Division. <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>. Accessed March 30, 2011

.

US EPA. 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particulate Matter, Chapter 5. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205

-- Benefits.pdf>

.

US EPA. 2011. Regulatory Impact Assessment for the Cross

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