The distributive effects are estimated by separating the social cost
into producer surplus and consumer surplus losses. First, the change in
producer surplus is given by

	ΔPS = B – F – (G+H+E)	(C.1)

Producers gain B from the increase in price (a transfer from consumers
to producers), but lose F from the increase in production costs due to
the incremental compliance costs. Furthermore, the reduction of cement
production leads to foregone baseline profits of G+H+E.

The change in consumer surplus is given by

	ΔCS = – (B + C)	(C.2)

This change results from the reduction in consumer surplus from the
baseline value of ABC to the with-regulation value of A. In this case,
consumers lose area B as a transfer to producers through the increase in
the price they pay for the with-regulation level of cement consumption,
while the reduction in cement consumption due to regulation leads to
foregone baseline value of consumption equal to area C.

The social cost or total change in social surplus can then be derived
simply by adding the changes in producer and consumer surplus, i.e.,

al Cost = ΔPS + ΔCS = – (F + G + H + E + C)	(C.3)

This estimate can be compared to the engineering estimate of incremental
compliance cost to demonstrate the difference between these two
estimates of social cost. The incremental compliance cost estimate is
given by the area FGH, which is simply the constant cost per unit times
the baseline output level of cement. The social cost estimate from
Equation (C.3) above, however, exceeds the engineering estimate by the
area EC. In other words, the incremental compliance cost estimate
understates the social costs of the regulation. The reason for this
follows directly from the imperfectly competitive structure of the
markets for cement. A comparison with the outcome under perfect
competition will assist in illustrating this point.

Suppose that the MR curve in Figure C-1 was the demand function for a
competitive market, rather than the marginal revenue function for an
imperfectly competitive producer. Similarly, let the MC function be the
aggregate supply function for all producers in the market. The market
equilibrium is still determined at the intersection of MC and MR, but
given the revised interpretation of MR as the competitive demand
function, the baseline (competitive) market price, P0C, is now equal to
MC and Q0 is now interpreted as the competitive level of cement demand.
In this case, all social surplus goes to the consumer. This is because
producers receive a price that just covers their costs of production.

In the with-regulation perfectly competitive equilibrium, the market
price would rise by the per unit control cost amount to P1c. The social
cost of the regulation is given entirely by the loss in consumer surplus
as given by area FG. As shown in Figure C-1, this estimate of social
cost is less than the incremental compliance cost estimate (i.e., area
FGH) so that the engineering estimate overstates the social cost of the
regulation under perfect competition. The overstatement results from the
fact that the incremental compliance costs are estimated based on the
baseline market level of cement output. With regulation, output is
projected to decline to Q1, so that the actual incremental compliance
costs incurred by the industry are given by area F. Area G represents
the foregone value of cement consumption to consumers, also referred to
as the deadweight loss (analogous to area C under the imperfect
competition scenario).

−[(F+G+H+E+C) – (F+G)] = −(H + E + C)	(C.4)

The difference between these two measures results from the fact that the
price paid by consumers (i.e., marginal value to society for cement)
exceeds the cost of producing cement (i.e., the marginal cost to society
of producing cement). As shown in Figure C-1, this difference in social
cost is equal to the area between the demand curve (D) and the marginal
revenue curve (MR) that exist under imperfectly competitive market
structure. This area does not exist under perfect competition because
the MR curve is interpreted as the demand curve so that the price paid
by consumers equals the marginal cost of producing cement. The
pre-existing social inefficiency of imperfect competition is exacerbated
as the regulation moves society further away from the socially optimal
level of cement production, which results in social costs greater than
the incremental compliance cost imposed on the cement industry.

Appendix D

Summary of Expert Opinions on the Existence of a Threshold in the
Concentration-Response Function for PM2.5-related Mortality

Summary of Expert Opinions on the Existence of a Threshold in the
Concentration-Response Function for PM2.5-related Mortality

Technical Support Document (TSD)

July 2010

Compiled by:

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Health and Environmental Impact Division

Air Benefit-Cost Group

Research Triangle Park, North Carolina

Contents:

A. HES comments on 812 Analysis (2010)

B. American Heart Association Scientific Statement (2010)

C. Integrated Science Assessment for Particulate Matter (2009)

D. CASAC comments on PM ISA and REA (2009)

E. Krewski et al. (2009)

F. Schwartz et al. (2008)

G. Expert Elicitation on PM Mortality (2006, 2008)

H. CASAC comments on PM Staff Paper (2005)

I. HES comments on 812 Analysis (2004)

J. NRC (2002)

A. HES Comments on 812 Analysis (2010)

U.S. Environmental Protection Agency - Science Advisory Board (U.S.
EPA-SAB). 2010. Review of EPA’s DRAFT Health Benefits of the Second
Section 812 Prospective Study of the Clean Air Act. EPA-COUNCIL-10-001.
June. Available on the Internet at
<http://yosemite.epa.gov/sab/sabproduct.nsf/0/72D4EFA39E48CDB28525774500
738776/$File/EPA-COUNCIL-10-001-unsigned.pdf>.

Pg 2: “The HES generally agrees with other decisions made by the EPA
project team with respect to PM, in particular, the PM mortality effect
threshold model, the cessation lag model, the inclusion of infant
mortality estimation, and differential toxicity of PM.”

Pg 2: “Further, the HES fully supports EPA’s use of a no-threshold
model to estimate the mortality reductions associated with reduced PM
exposure.”

Pg 6: “The HES also supports the Agency’s choice of a no-threshold
model for PM-related effects.”

Pg 13: “The HES fully supports EPA’s decision to use a no-threshold
model to estimate mortality reductions. This decision is supported by
the data, which are quite consistent in showing effects down to the
lowest measured levels. Analyses of cohorts using data from more recent
years, during which time PM concentrations have fallen, continue to
report strong associations with mortality. Therefore, there is no
evidence to support a truncation of the CRF.”

HES Panel Members

Dr. John Bailar, Chair of the Health Effects Subcommittee, Scholar in
Residence, The National Academies, Washington, DC

Dr. Michelle Bell, Associate Professor, School of Forestry and
Environmental Studies, Yale

University, New Haven, CT

Dr. James K. Hammitt, Professor, Department of Health Policy and
Management, Harvard School of Public Health, Boston, MA

Dr. Jonathan Levy, Associate Professor, Department of Environmental
Health, Harvard School of Public Health, Boston, MA

Dr. C. Arden Pope, III Professor, Department of Economics, Brigham Young
University, Provo, UT

Mr. John Fintan Hurley, Research Director, Institute of Occupational
Medicine (IOM), Edinburgh, United Kingdom, UK

Dr. Patrick Kinney, Professor, Department of Environmental Health
Sciences, Mailman School of Public Health, Columbia University, New
York, NY

Dr. Michael T. Kleinman, Professor, Department of Medicine, Division of
Occupational and Environmental Medicine, University of California,
Irvine, Irvine, CA

Dr. Bart Ostro, Chief, Air Pollution Epidemiology Unit, Office of
Environmental Health Hazard Assessment, California Environmental
Protection Agency, Oakland, CA

Dr. Rebecca Parkin, Professor and Associate Dean, Environmental and
Occupational Health, School of Public Health and Health Services, The
George Washington University Medical Center, Washington, DC

B. Scientific Statement from American Heart Association (2010)

Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux
AV, Holguin F, Hong Y, Luepker RV, Mittleman MA, Peters A, Siscovick D,
Smith SC Jr, Whitsel L, Kaufman JD; on behalf of the American Heart
Association Council on Epidemiology and Prevention, Council on the
Kidney in Cardiovascular Disease, and Council on Nutrition, Physical
Activity and Metabolism. (2010). “Particulate matter air pollution and
cardiovascular disease: an update to the scientific statement from the
American Heart Association.” Circulation. 121: 2331-2378.

Pg 2338: “Finally, there appeared to be no lower-limit threshold below
which PM10 was not associated with excess mortality across all
regions.”

Pg 2350: “There also appears to be a monotonic (eg, linear or
log-linear) concentration-response relationship between PM2.5 and
mortality risk observed in cohort studies that extends below present-day
regulations of 15 µg/m3 for mean annual levels, without a discernable
“safe” threshold.” (cites Pope 2004, Krewski 2009, and Schwartz
2008)

Pg 2364: “The PM2.5 concentration– cardiovascular risk relationships
for both short- and long-term exposures appear to be monotonic,
extending below 15 µg/m3 (the 2006 annual NAAQS level) without a
discernable “safe” threshold.”

Pg 2365: “This updated review by the AHA writing group corroborates
and strengthens the conclusions of the initial scientific statement. In
this context, we agree with the concept and continue to support measures
based on scientific evidence, such as the US EPA NAAQS, that seek to
control PM levels to protect the public health. Because the evidence
reviewed supports that there is no safe threshold, it appears that
public health benefits would accrue from lowering PM2.5 concentrations
even below present-day annual (15 µg/m3) and 24-hour (35 µg/m3) NAAQS,
if feasible, to optimally protect the most susceptible populations.”

Pg 2366: “Although numerous insights have greatly enhanced our
understanding of the PM-cardiovascular relationship since the first AHA
statement was published, the following list represents broad strategic
avenues for future investigation: ... Determine whether any “safe”
PM threshold concentration exists that eliminates both acute and chronic
cardiovascular effects in healthy and susceptible individuals and at a
population level.”

Scientific Statement Authors

Dr. Robert D. Brook, MD

Dr. Sanjay Rajagopalan, MD

Dr. C. Arden Pope, PhD

Dr. Jeffrey R. Brook, PhD

Dr. Aruni Bhatnagar, PhD, FAHA

Dr. Ana V. Diez-Roux, MD, PhD, MPH

Dr. Fernando Holguin, MD

Dr. Yuling Hong, MD, PhD, FAHA

Dr. Russell V. Luepker, MD, MS, FAHA

Dr. Murray A. Mittleman, MD, DrPH, FAHA

Dr. Annette Peters, PhD

Dr. David Siscovick, MD, MPH, FAHA

Dr. Sidney C. Smith, Jr, MD, FAHA

Dr. Laurie Whitsel, PhD

Dr. Joel D. Kaufman, MD, MPH

C. Integrated Science Assessment for Particulate Matter (2009)

U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated
Science Assessment for Particulate Matter (Final Report).
EPA-600-R-08-139F. National Center for Environmental Assessment – RTP
Division. December. Available on the Internet at
<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

Pg 1-22: “An important consideration in characterizing the public
health impacts associated with exposure to a pollutant is whether the
concentration-response relationship is linear across the full
concentration range encountered, or if nonlinear relationships exist
along any part of this range. Of particular interest is the shape of the
concentration-response curve at and below the level of the current
standards. The shape of the concentration-response curve varies,
depending on the type of health outcome, underlying biological
mechanisms and dose. At the human population level, however, various
sources of variability and uncertainty tend to smooth and
“linearize” the concentration-response function (such as the low
data density in the lower concentration range, possible influence of
measurement error, and individual differences in susceptibility to air
pollution health effects). In addition, many chemicals and agents may
act by perturbing naturally occurring background processes that lead to
disease, which also linearizes population concentration-response
relationships (Clewell and Crump, 2005, 156359; Crump et al., 1976,
003192; Hoel, 1980, 156555). These attributes of population
dose-response may explain why the available human data at ambient
concentrations for some environmental pollutants (e.g., PM, O3, lead
[Pb], ETS, radiation) do not exhibit evident thresholds for health
effects, even though likely mechanisms include nonlinear processes for
some key events. These attributes of human population dose-response
relationships have been extensively discussed in the broader
epidemiologic literature (Rothman and Greenland, 1998, 086599).”

Pg 2-16: “In addition, cardiovascular hospital admission and mortality
studies that examined the PM10 concentration-response relationship found
evidence of a log-linear no-threshold relationship between PM exposure
and cardiovascular-related morbidity (Section 6.2) and mortality
(Section 6.5).”

Pg 2-25: “2.4.3. PM Concentration-Response Relationship

An important consideration in characterizing the PM-morbidity and
mortality association is whether the concentration-response relationship
is linear across the full concentration range that is encountered or if
there are concentration ranges where there are departures from linearity
(i.e., nonlinearity). In this ISA studies have been identified that
attempt to characterize the shape of the concentration-response curve
along with possible PM “thresholds” (i.e., levels which PM
concentrations must exceed in order to elicit a health response). The
epidemiologic studies evaluated that examined the shape of the
concentration-response curve and the potential presence of a threshold
have focused on cardiovascular hospital admissions and ED visits and
mortality associated with short-term exposure to PM10 and mortality
associated with long-term exposure to PM2.5.

“A limited number of studies have been identified that examined the
shape of the PM cardiovascular hospital admission and ED visit
concentration-response relationship. Of these studies, some conducted an
exploratory analysis during model selection to determine if a linear
curve most adequately represented the concentration-response
relationship; whereas, only one study conducted an extensive analysis to
examine the shape of the concentration-response curve at different
concentrations (Section <IP_ADDRESS>). Overall, the limited evidence from
the studies evaluated supports the use of a no-threshold, log-linear
model, which is consistent with the observations made in studies that
examined the PM-mortality relationship.

“Although multiple studies have previously examined the PM-mortality
concentration-response relationship and whether a threshold exists, more
complex statistical analyses continue to be developed to analyze this
association. Using a variety of methods and models, most of the studies
evaluated support the use of a no-threshold, log-linear model; however,
one study did observe heterogeneity in the shape of the
concentration-response curve across cities (Section 6.5). Overall, the
studies evaluated further support the use of a no-threshold log-linear
model, but additional issues such as the influence of heterogeneity in
estimates between cities, and the effect of seasonal and regional
differences in PM on the concentration-response relationship still
require further investigation.

“In addition to examining the concentration-response relationship
between short-term exposure to PM and mortality, Schwartz et al. (2008,
156963) conducted an analysis of the shape of the concentration-response
relationship associated with long-term exposure to PM. Using a variety
of statistical methods, the concentration-response curve was found to be
indistinguishable from linear, and, therefore, little evidence was
observed to suggest that a threshold exists in the association between
long-term exposure to PM2.5 and the risk of death (Section 7.6).”

Pg 6-75: “<IP_ADDRESS>. Concentration Response

The concentration-response relationship has been extensively analyzed
primarily through studies that examined the relationship between PM and
mortality. These studies, which have focused on short- and long-term
exposures to PM have consistently found no evidence for deviations from
linearity or a safe threshold (Daniels et al., 2004, 087343; Samoli et
al., 2005, 087436; Schwartz, 2004, 078998; Schwartz et al., 2008,
156963) (Sections <IP_ADDRESS> and 7.1.4). Although on a more limited basis,
studies that have examined PM effects on cardiovascular hospital
admissions and ED visits have also analyzed the PM
concentration-response relationship, and contributed to the overall body
of evidence which suggests a log-linear, no-threshold PM
concentration-response relationship.

“The results from the three multicity studies discussed above support
no-threshold log-linear models, but issues such as the possible
influence of exposure error and heterogeneity of shapes across cities
remain to be resolved. Also, given the pattern of seasonal and regional
differences in PM risk estimates depicted in recent multicity study
results (e.g., Peng et al., 2005, 087463), the very concept of a
concentration-response relationship estimated across cities and for
all-year data may not be very informative.”

Pg 6-197: “<IP_ADDRESS>. Investigation of Concentration-Response
Relationship

The results from large multicity studies reviewed in the 2004 PM AQCD
(U.S. EPA, 2004, 056905) suggested that strong evidence did not exist
for a clear threshold for PM mortality effects. However, as discussed in
the 2004 PM AQCD (U.S. EPA, 2004, 056905), there are several challenges
in determining and interpreting the shape of PM-mortality
concentration-response functions and the presence of a threshold,
including: (1) limited range of available concentration levels (i.e.,
sparse data at the low and high end); (2) heterogeneity of susceptible
populations; and (3) investigate the PM-mortality concentration-response
relationship.

threshold at around 50 μg/m3 PM10, as shown in Figure 6-35. For
all-cause and cardio-respiratory deaths, based on the Akaike’s
Information Criterion (AIC), a log-linear model without threshold was
preferred to the threshold model and to the spline model.

“The HEI review committee commented that interpretation of these
results required caution, because (1) the measurement error could
obscure any threshold; (2) the city-specific concentration-response
curves exhibited a variety of shapes; and (3) the use of AIC to choose
among the models might not be appropriate due to the fact it was not
designed to assess scientific theories of etiology. Note, however, that
there has been no etiologically credible reason suggested thus far to
choose one model over others for aggregate outcomes. Thus, at least
statistically, the result of Daniels et al. (2004, 087343) suggests that
the log-linear model is appropriate in describing the relationship
between PM10 and mortality.

between 15 and 25 μg/m3, between 25 and 34 μg/m3, between 35 and 44
μg/m3, and 45 μg/m3 and above. In the model, days with concentrations
below 15 μg/m3 served as the reference level. This model was fit using
the single stage method, combining strata across all cities in the
case-crossover design. Figure 6-36 shows the resulting relationship,
which does not provide sufficient evidence to suggest that a threshold
exists. The authors did not examine city-to-city variation in the
concentration-response relationship in this study.

“PM10 and mortality in 22 European cities (and BS in 15 of the cities)
participating in the APHEA project. In nine of the 22 cities, PM10
levels were estimated using a regression model relating co-located PM10
to BS or TSP. They used regression spline models with two knots (30 and
50 μg/m3) and then combined the individual city estimates of the
splines across cities. The investigators concluded that the association
between PM and mortality in these cities could be adequately estimated
using the log-linear model. However, in an ancillary analysis of the
concentration-response curves for the largest cities in each of the
three distinct geographic areas (western, southern, and eastern European
cities): London, England; Athens, Greece; and Cracow, Poland, Samoli et
al. (2005, 087436) observed a difference in the shape of the
concentration-response curve across cities. Thus, while the combined
curves (Figure 6-37) appear to support no-threshold relationships
between PM10 and mortality, the heterogeneity of the shapes across
cities makes it difficult to interpret the biological relevance of the
shape of the combined curves.

“The results from the three multicity studies discussed above support
no-threshold log-linear models, but issues such as the possible
influence of exposure error and heterogeneity of shapes across cities
remain to be resolved. Also, given the pattern of seasonal and regional
differences in PM risk estimates depicted in recent multicity study
results (e.g., Peng et al., 2005, 087463), the very concept of a
concentration-response relationship estimated across cities and for
all-year data may not be very informative.”

Authors of ISA

Dr. Lindsay Wichers Stanek (PM Team Leader)—National Center for
Environmental Assessment (NCEA), U.S. Environmental Protection Agency
(U.S. EPA), Research Triangle Park, NC

Dr. Jeffrey Arnold—NCEA, U.S. EPA, Research Triangle Park, NC (now at
Institute for Water Resources, U.S. Army Corps of Engineers, Washington,
D.C)

Dr. Christal Bowman—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. James S. Brown—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Barbara Buckley—NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Allen Davis—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Jean-Jacques Dubois—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Steven J. Dutton—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Tara Greaver—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Erin Hines—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Douglas Johns—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Ellen Kirrane—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Dennis Kotchmar—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Thomas Long—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Thomas Luben—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Qingyu Meng—Oak Ridge Institute for Science and Education,
Postdoctoral Research Fellow to NCEA, U.S. EPA, Research Triangle Park,
NC

Dr. Kristopher Novak—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Joseph Pinto—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Jennifer Richmond-Bryant—NCEA, U.S. EPA, Research Triangle Park,
NC

Dr. Mary Ross—NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Jason Sacks—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Timothy J. Sullivan—E&S Environmental Chemistry, Inc., Corvallis,
OR

Dr. David Svendsgaard—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Lisa Vinikoor—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. William Wilson—NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Lori White— NCEA, U.S. EPA, Research Triangle Park, NC (now at
National Institute for Environmental Health Sciences, Research Triangle
Park, NC)

Dr. Christy Avery—University of North Carolina, Chapel Hill, NC

Dr. Kathleen Belanger —Center for Perinatal, Pediatric and
Environmental Epidemiology, Yale University, New Haven, CT

Dr. Michelle Bell—School of Forestry & Environmental Studies, Yale
University, New Haven, CT

Dr. William D. Bennett—Center for Environmental Medicine, Asthma and
Lung Biology, University of North Carolina, Chapel Hill, NC

Dr. Matthew J. Campen—Lovelace Respiratory Research Institute,
Albuquerque, NM

Dr. Leland B. Deck— Stratus Consulting, Inc., Washington, DC

Dr. Janneane F. Gent—Center for Perinatal, Pediatric and Environmental
Epidemiology, Yale University, New Haven, CT

Dr. Yuh-Chin Tony Huang—Department of Medicine, Division of Pulmonary
Medicine, Duke University Medical Center, Durham, NC

Dr. Kazuhiko Ito—Nelson Institute of Environmental Medicine, NYU
School of Medicine, Tuxedo, NY

Mr. Marc Jackson—Integrated Laboratory Systems, Inc., Research
Triangle Park, NC

Dr. Michael Kleinman—Department of Community and Environmental
Medicine, University of California, Irvine

Dr. Sergey Napelenok—National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Dr. Marc Pitchford—National Oceanic and Atmospheric Administration,
Las Vegas, NV

Dr. Les Recio—Genetic Toxicology Division, Integrated Laboratory
Systems, Inc., Research Triangle Park, NC

Dr. David Quincy Rich—Department of Epidemiology, University of
Medicine and Dentistry of New Jersey, Piscataway, NJ

Dr. Timothy Sullivan— E&S Environmental Chemistry, Inc., Corvallis, OR

Dr. George Thurston—Department of Environmental Medicine, NYU, Tuxedo,
NY

Dr. Gregory Wellenius—Cardiovascular Epidemiology Research Unit, Beth
Israel Deaconess Medical Center, Boston, MA

Dr. Eric Whitsel—Departments of Epidemiology and Medicine, University
of North Carolina, Chapel Hill, NC

Peer Reviewers

Dr. Sara Dubowsky Adar, Department of Epidemiology, University of
Washington, Seattle, WA

Mr. Chad Bailey, Office of Transportation and Air Quality, Ann Arbor, MI

Mr. Richard Baldauf, Office of Transportation and Air Quality, Ann
Arbor, MI

Dr. Prakash Bhave, National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Mr. George Bowker, Office of Atmospheric Programs, U.S. EPA, Washington,
D.C.

Dr. Judith Chow, Division of Atmospheric Sciences, Desert Research
Institute, Reno, NV

Dr. Dan Costa, U.S. EPA, Research Triangle Park, NC

Dr. Ila Cote, NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Robert Devlin, National Health and Environmental Effects Research
Laboratory, U.S. EPA, Research Triangle Park, NC

Dr. David DeMarini, National Health and Environmental Effects Research
Laboratory, U.S. EPA, Research Triangle Park, NC

Dr. Neil Donahue, Department of Chemical Engineering, Carnegie Mellon
University, Pittsburgh, PA

Dr. Aimen Farraj, National Health and Environmental Effects Research
Laboratory, U.S. EPA, Research Triangle Park, NC

Dr. Mark Frampton, Department of Environmental Medicine, University of
Rochester Medical Center, Rochester, NY

Mr. Neil Frank, Office of Air Quality Planning and Standards, U.S. EPA,
Research Triangle Park, NC

Mr. Tyler Fox, Office of Air Quality Planning and Standards, U.S. EPA,
Research Triangle Park, NC

Dr. Jim Gauderman, Department of Environmental Medicine, Department of
Preventive Medicine, University of Southern California, Los Angeles, CA

Dr. Barbara Glenn, National Center for Environmental Research, U.S. EPA,
Washington, D.C.

Dr. Terry Gordon, School of Medicine, New York University, Tuxedo, NY

Mr. Tim Hanley, Office of Air Quality Planning and Standards, U.S. EPA,
Research Triangle Park, NC

Dr. Jack Harkema, Department of Pathobiology and Diagnostic
Investigation, Michigan State University, East Lansing, MI

Ms. Beth Hassett-Sipple, Office of Air Quality Planning and Standards,
U.S. EPA, Research Triangle Park, NC

Dr. Amy Herring, Department of Biostatistics, University of North
Carolina, Chapel Hill, NC

Dr. Israel Jirak, Department of Meteorology, Embry-Riddle Aeronautical
University, Prescott, AZ

Dr. Mike Kleeman, Department of Civil and Environmental Engineering,
University of California, Davis, CA

Dr. Petros Koutrakis, Exposure, Epidemiology and Risk Program, Harvard
School of Public Health, Boston, MA

Dr. Sagar Krupa, Department of Plant Pathology, University of Minnesota,
St. Paul, MN

Mr. John Langstaff, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Dr. Meredith Lassiter, Office of Air Quality Planning and Standards,
U.S. EPA, Research Triangle Park, NC

Mr. Phil Lorang, Office of Air Quality Planning and Standards, U.S. EPA,
Research Triangle Park, NC

Dr. Karen Martin, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Ms. Connie Meacham, NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Tom Pace, Office of Air Quality Planning and Standards, U.S. EPA,
Research Triangle Park, NC

Dr. Jennifer Peel, Department of Environmental and Radiological Health
Sciences, College of Veterinary Medicine and Biomedical Sciences,
Colorado State University, Fort Collins, CO

Dr. Zackary Pekar, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Mr. Rob Pinder, National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Mr. Norm Possiel, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Dr. Sanjay Rajagopalan, Division of Cardiovascular Medicine, Ohio State
University, Columbus, OH

Dr. Pradeep Rajan, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Mr. Venkatesh Rao, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Ms. Joann Rice, Office of Air Quality Planning and Standards, U.S. EPA,
Research Triangle Park, NC

Mr. Harvey Richmond, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Ms. Victoria Sandiford, Office of Air Quality Planning and Standards,
U.S. EPA, Research Triangle Park, NC

Dr. Stefanie Sarnat, Department of Environmental and Occupational
Health, Emory University, Atlanta, GA

Dr. Frances Silverman, Gage Occupational and Environmental Health,
University of Toronto, Toronto, ON

Mr. Steven Silverman, Office of General Council, U.S. EPA, Washington,
D.C.

Dr. Barbara Turpin, Department of Environmental Sciences, Rutgers
University, New Brunswick, NJ

Dr. Robert Vanderpool, National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Dr. John Vandenberg (Director)—NCEA-RTP Division, U.S. EPA, Research
Triangle Park, NC

Dr. Alan Vette, National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Ms. Debra Walsh (Deputy Director)—NCEA-RTP Division, U.S. EPA,
Research Triangle Park, NC

Mr. Tim Watkins, National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Dr. Christopher Weaver, NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Lewis Weinstock, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Ms. Karen Wesson, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC

Dr. Jason West, Department of Environmental Sciences and Engineering,
University of North Carolina, Chapel Hill, NC

Mr. Ronald Williams, National Exposure Research Laboratory, U.S. EPA,
Research Triangle Park, NC

Dr. George Woodall, NCEA, U.S. EPA, Research Triangle Park, NC

Dr. Antonella Zanobetti, Department of Environmental Health, Harvard
School of Public Health, Boston, MA

D. CASAC comments on PM ISA and REA (2009)

U.S. Environmental Protection Agency - Science Advisory Board (U.S.
EPA-SAB). 2009. Review of EPA’s Integrated Science Assessment for
Particulate Matter (First External Review Draft, December 2008).
EPA-COUNCIL-09-008. May. Available on the Internet at
<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006b
e86e/73ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pd
f>.

Pg 9: “There is an appropriate discussion of the time-series studies,
but this section needs to have an explicit finding that the evidence
supports a relationship between PM and mortality that is seen in these
studies. This conclusion should be followed by the discussion of
statistical methodology and the identification of any threshold that may
exist.”

U.S. Environmental Protection Agency Science Advisory Board (U.S.
EPA-SAB). 2009. Consultation on EPA’s Particulate Matter National
Ambient Air Quality Standards: Scope and Methods Plan for Health Risk
and Exposure Assessment. EPA-COUNCIL-09-009. May. Available on the
Internet at
<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006b
e86e/723FE644C5D758DF852575BD00763A32/$File/EPA-CASAC-09-009-unsigned.pd
f>.

Pg 6: “On the issue of cut-points raised on 3-18, the authors should
be prepared to offer a scientifically cogent reason for selection of a
specific cut-point, and not simply try different cut-points to see what
effect this has on the analysis. The draft ISA was clear that there is
little evidence for a population threshold in the C-R function.”

U.S. Environmental Protection Agency - Science Advisory Board (U.S.
EPA-SAB). 2009. Review of Integrated Science Assessment for Particulate
Matter (Second External Review Draft, July 2009). EPA-CASAC-10-001.
November. Available on the Internet at
<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006b
e86e/151B1F83B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pd
f>.

Pg 2: “The paragraph on lines 22-30 of page 2-37 is not clearly
written. Twice in succession it states that the use of a no-threshold
log-linear model is supported, but then cites other studies that suggest
otherwise. It would be good to revise this paragraph to more clearly
state – well, I’m not sure what. Probably that more research is
needed.”

CASAC Panel Members

Dr. Jonathan M. Samet, Professor and Chair, Department of Preventive
Medicine, University of Southern California, Los Angeles, CA

Dr. Joseph Brain, Philip Drinker Professor of Environmental Physiology,
Department of Environmental Health, Harvard School of Public Health,
Harvard University, Boston, MA

Dr. Ellis B. Cowling, University Distinguished Professor At-Large
Emeritus, Colleges of Natural Resources and Agriculture and Life
Sciences, North Carolina State University, Raleigh, NC

Dr. James Crapo, Professor of Medicine, Department of Medicine, National
Jewish Medical and Research Center, Denver, CO

Dr. H. Christopher Frey, Professor, Department of Civil, Construction
and Environmental Engineering, College of Engineering, North Carolina
State University, Raleigh, NC

Dr. Armistead (Ted) Russell, Professor, Department of Civil and
Environmental Engineering, Georgia Institute of Technology, Atlanta, GA

Dr. Lowell Ashbaugh, Associate Research Ecologist, Crocker Nuclear Lab,
University of California, Davis, Davis, CA

Prof. Ed Avol, Professor, Preventive Medicine, Keck School of Medicine,
University of Southern California, Los Angeles, CA

Dr. Wayne Cascio, Professor, Medicine, Cardiology, Brody School of
Medicine at East Carolina University, Greenville, NC

Dr. David Grantz, Director, Botany and Plant Sciences and Air Pollution
Research Center, Riverside Campus and Kearney Agricultural Center,
University of California, Parlier, CA

Dr. Joseph Helble, Dean and Professor, Thayer School of Engineering,
Dartmouth College, Hanover, NH

Dr. Rogene Henderson, Senior Scientist Emeritus, Lovelace Respiratory
Research Institute, Albuquerque, NM

Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department
of Chemical Engineering, Clarkson University, Potsdam, NY

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental
Medicine, New York University School of Medicine, Tuxedo, NY

Dr. Helen Suh MacIntosh, Associate Professor, Environmental Health,
School of Public Health, Harvard University, Boston, MA

Dr. William Malm, Research Physicist, National Park Service Air
Resources Division, Cooperative Institute for Research in the
Atmosphere, Colorado State University, Fort Collins, CO

Mr. Charles Thomas (Tom) Moore, Jr., Air Quality Program Manager,
Western Governors' Association, Cooperative Institute for Research in
the Atmosphere, Colorado State University, Fort Collins, CO

Dr. Robert F. Phalen, Professor, Department of Community & Environmental
Medicine; Director, Air Pollution Health Effects Laboratory; Professor
of Occupational & Environmental Health, Center for Occupation &
Environment Health, College of Medicine, University of California
Irvine, Irvine, CA

Dr. Kent Pinkerton, Professor, Regents of the University of California,
Center for Health and the Environment, University of California, Davis,
CA

Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control
Division, Department of Environmental Conservation, Vermont Agency of
Natural Resources, Waterbury, VT

Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing
Laboratory, Harvard Medical School, Boston, MA

Dr. Sverre Vedal, Professor, Department of Environmental and
Occupational Health Sciences, School of Public Health and Community
Medicine, University of Washington, Seattle, WA

Dr. Donna Kenski, Data Analysis Director, Lake Michigan Air Directors
Consortium, Rosemont, IL

Dr. Kathy Weathers, Senior Scientist, Cary Institute of Ecosystem
Studies, Millbrook, NY

E. Krewski et al. (2009)

Krewski, Daniel, Michael Jerrett, Richard T. Burnett, Renjun Ma, Edward
Hughes, Yuanli Shi, Michelle C. Turner, C. Arden Pope III, George
Thurston, Eugenia E. Calle, and Michael J. Thun with Bernie Beckerman,
Pat DeLuca, Norm Finkelstein, Kaz Ito, D.K. Moore, K. Bruce Newbold, Tim
Ramsay, Zev Ross, Hwashin Shin, and Barbara Tempalski. (2009). Extended
follow-up and spatial analysis of the American Cancer Society study
linking particulate air pollution and mortality. HEI Research Report,
140, Health Effects Institute, Boston, MA.

Pg 119: [About Pope et al. (2002)] “Each 10-μg/m3 increase in
long-term average ambient PM2.5 concentrations was associated with
approximately a 4%, 6%, or 8% increase in risk of death from all causes,
cardiopulmonary disease, and lung cancer, respectively. There was no
evidence of a threshold exposure level within the range of observed
PM2.5 concentrations. “

Krewski (2009). Letter from Dr. Daniel Krewski to HEI’s Dr. Kate Adams
(dated July 7, 2009) regarding “EPA queries regarding HEI Report
140”. Dr. Adams then forwarded the letter on July 10, 2009 to EPA’s
Beth Hassett-Sipple. (letter placed in docket #EPA-HQ-OAR-2007-0492).

Pg 4: “6. The Health Review Committee commented that the Updated
Analysis completed by

Pope et al. 2002 reported “no evidence of a threshold exposure level
within the range of observed PM2.5 concentrations” (p. 119). In the
Extended Follow-Up study, did the analyses provide continued support for
a no-threshold response or was there evidence of a threshold?

“Response: As noted above, the HEI Health Review Committee commented
on the lack of evidence for a threshold exposure level in Pope et al.
(2002) with follow-up through the year 1998. The present report, which
included follow-up through the year 2000, also does not appear to
demonstrate the existence of a threshold in the exposure-response
function within the range of observed PM2.5 concentrations.”

HEI Health Review Committee Members

Dr. Homer A. Boushey, MD, Chair, Professor of Medicine, Department of
Medicine, University of California–San Francisco

Dr. Ben Armstrong, Reader, in Epidemiological Statistics, Department of
Public Health and Policy, London School of Hygiene and Tropical
Medicine, United Kingdom

Dr. Michael Brauer, ScD, Professor, School of Environmental Health,
University of British Columbia, Canada

Dr. Bert Brunekreef, PhD, Professor of Environmental Epidemiology,
Institute of Risk Assessment Sciences, University of Utrecht, The
Netherlands

Dr. Mark W. Frampton, MD, Professor of Medicine & Environmental
Medicine, University of Rochester Medical Center, Rochester, NY

Dr. Stephanie London, MD, PhD, Senior Investigator, Epidemiology Branch,
National Institute of Environmental Health Sciences

Dr. William N. Rom, MD, MPH, Sol and Judith Bergstein Professor of
Medicine and Environmental Medicine and Director of Pulmonary and
Critical Care Medicine, New York University Medical Center

Dr. Armistead Russell, Georgia Power Distinguished Professor of
Environmental Engineering, School of Civil and Environmental
Engineering, Georgia Institute of Technology

Dr. Lianne Sheppard, PhD, Professor, Department of Biostatistics,
University of Washington

F. Schwartz et al. (2008)

Schwartz J, Coull B, Laden F. (2008). The Effect of Dose and Timing of
Dose on the Association between Airborne Particles and Survival.
Environmental Health Perspectives. 116: 64-69.

15 μg/m3.”

Pg 68: “In conclusion, penalized spline smoothing and model averaging
represent reasonable, feasible approaches to addressing questions of the
shape of the exposure–response curve, and can provide valuable
information to decisionmakers. In this example, both approaches are
consistent, and suggest that the association of particles with mortality
has no threshold down to close to background levels.”

G. Expert Elicitation on PM-Mortality (2006, 2008)

Industrial Economics, Inc., 2006. Expanded Expert Judgment Assessment of
the Concentration-Response Relationship Between PM2.5 Exposure and
Mortality. Prepared for the U.S.EPA, Office of Air Quality Planning and
Standards, September. Available on the Internet at
<http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf>.

Pg v: “Each expert was given the option to integrate their judgments
about the likelihood of a causal relationship and/or threshold in the
C-R function into his distribution or to provide a distribution
"conditional on" one or both of these factors.”

Pg vii: “Only one of 12 experts explicitly incorporated a threshold
into his C-R function.3 The rest believed there was a lack of empirical
and/or theoretical support for a population threshold. However, three
other experts gave differing effect estimate distributions above and
below some cut-off concentration. The adjustments these experts made to
median estimates and/or uncertainty at lower PM2.5 concentrations were
modest.”

μg/m3, and a 20% chance that it would fall between 5 and 10 μg/m3.”

Pg ix: “Compared to the pilot study, experts in this study were in
general more confident in a causal relationship, less likely to
incorporate thresholds, and reported higher mortality effect estimates.
The differences in results compared with the pilot appear to reflect the
influence of new research on the interpretation of the key
epidemiological studies that were the focus of both elicitation studies,
more than the influence of changes to the structure of the protocol.”

Pg 3-25: “3.1.8 THRESHOLDS

The protocol asked experts for their judgments regarding whether a
threshold exists in the PM2.5 mortality C-R function. The protocol
focused on assessing expert judgments regarding theory and evidential
support for a population threshold (i.e., the concentration below which
no member of the study population would experience an increased risk of
death).32 If an expert wished to incorporate a threshold in his
characterization of the concentration-response relationship, the team
then asked the expert to specify the threshold PM2.5 concentration
probabilistically, incorporating his uncertainty about the true
threshold level.

“From a theoretical and conceptual standpoint, all experts generally
believed that individuals exhibit thresholds for PM-related mortality.
However, 11 of them discounted the idea of a population threshold in the
C-R function on a theoretical and/or empirical basis. Seven of these
experts noted that theoretically one would be unlikely to observe a
population threshold due to the variation in susceptibility at any given
time in the study population resulting from combinations of genetic,
environmental, and socioeconomic factors.33 All 11 thought that there
was insufficient empirical support for a population threshold in the C-R
function. In addition, two experts (E and L) cited analyses of the ACS
cohort data in Pope et al. (2002) and another (J) cited Krewski et al.
(2000a & b) as supportive of a linear relationship in the study range.

“Seven of the experts favored epidemiological studies as ideally the
best means of addressing the population threshold issue, because they
are best able to evaluate the full range of susceptible individuals at
environmentally relevant exposure levels. However, those who favored
epidemiologic studies generally acknowledged that definitive studies
addressing thresholds would be difficult or impossible to conduct,
because they would need to include a very large and diverse population
with wide variation in exposure and a long follow-up period.
Furthermore, two experts (B and I) cited studies documenting
difficulties in detecting a threshold using epidemiological studies
(Cakmak et al. 1999, and Brauer et al., 2002, respectively). The experts
generally thought that clinical and toxicological studies are best
suited for researching mechanisms and for addressing thresholds in very
narrowly defined groups. One expert, B, thought that a better
understanding of the detailed biological mechanism is critical to
addressing the question of a threshold.

“One expert, K, believed it was possible to make a conceptual argument
for a population threshold. He drew an analogy with smoking, indicating
that among heavy smokers, only a proportion of them gets lung cancer or
demonstrates an accelerated decline in lung function. He thought that
the idea that there is no level that is biologically safe is
fundamentally at odds with toxicological theory. He did not think that a
population threshold was detectable in the currently available
epidemiologic studies. He indicated that some of the cohort studies
showed greater uncertainty in the shape of the C-R function at lower
levels, which could be indicative of a threshold.

“Expert K chose to incorporate a threshold into his C-R function. He
indicated that he was 50% sure that a threshold existed. If there were a
threshold, he thought that there was an 80% chance that it would be less
than or equal to 5 μg/m3, and a 20% chance that it would fall between 5
and 10 μg/m3.”

Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey
M. Richmond, Bryan J. Hubbell, and Patrick L. Kinney. (2008). “Expert
Judgment Assessment of the Mortality Impact of Changes in Ambient Fine
Particulate Matter in the U.S.” Environ. Sci. Technol.,
42(7):2268-2274.

 was log-linear across the entire study range (ln(mortality) ) β ×
PM). Four experts (B, F, K, and L) specified a “piecewise”
log-linear function, with different β coefficients for PM
concentrations above and below an expert-specified break point. This
approach allowed them to express increased uncertainty in mortality
effects seen at lower concentrations in major epidemiological studies.
Expert K thought the relationship would be log-linear above a
threshold.”

T, to his function, which he described probabilistically. He specified
P(T > 0) = 0.5. Given T > 0, he indicated P(T ≤ 5 μg/m3) = 0.8 and
P(5 μg/m3 < T ≤ 10 μg/m3) = 0.2. Figure 3 does not include the
impact of applying expert K’s threshold, as the size of the reduction
in benefits will depend on the distribution of baseline PM levels in a
benefits analysis.”

Experts:

Dr. Doug W. Dockery, Harvard School of Public Health

Dr. Kazuhiko Ito, Nelson Institute of Environmental Medicine, NYU School
of Medicine, Tuxedo, NY

Dr. Dan Krewski, University of Ottawa

Dr. Nino Künzli, University of Southern California Keck School of
Medicine

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental
Medicine, New York University School of Medicine, Tuxedo, NY

Dr. Joe Mauderly, Lovelace Respiratory Research Institute

Dr. Bart Ostro, Chief, Air Pollution Epidemiology Unit, Office of
Environmental Health Hazard Assessment, California Environmental
Protection Agency, Oakland, CA

Dr. Arden Pope, Professor, Department of Economics, Brigham Young
University, Provo, UT

Dr. Richard Schlesinger, Pace University

Dr. Joel Schwartz, Harvard School of Public Health

Dr. George Thurston—Department of Environmental Medicine, NYU, Tuxedo,
NY

Dr. Mark Utell, University of Rochester School of Medicine and
Dentistry

H. CASAC comments on PM Staff Paper (2005)

U.S. Environmental Protection Agency - Science Advisory Board (U.S.
EPA-SAB). 2005. EPA’s Review of the National Ambient Air Quality
Standards for Particulate Matter (Second Draft PM Staff Paper, January
2005). EPA-SAB-CASAC-05-007. June. Available on the Internet at
<http://yosemite.epa.gov/sab/sabproduct.nsf/E523DD36175EB5AD8525701B0073
32AE/$File/SAB-CASAC-05-007_unsigned.pdf>.

h impacts, the Panel favored the primary use of an assumed threshold of
10 μg/m3. The original approach of using background or LML, as well as
the other postulated thresholds, could still be used in a sensitivity
analysis of threshold assumptions.

“The analyses in this chapter highlight the impact of assumptions
regarding thresholds, or lack of threshold, on the estimates of risk.
The uncertainty associated with threshold or nonlinear models needs more
thorough discussion. A major research need is for more work to determine
the existence and level of any thresholds that may exist or the shape of
nonlinear concentration-response curves at low levels of exposure that
may exist, and to reduce uncertainty in estimated risks at the lowest PM
concentrations.”

CASAC Panel Members

Dr. Rogene Henderson, Scientist Emeritus, Lovelace Respiratory Research
Institute, Albuquerque, NM

Dr. Ellis Cowling, University Distinguished Professor-at-Large, North
Carolina State University, Colleges of Natural Resources and Agriculture
and Life Sciences, North Carolina State University, Raleigh, NC

Dr. James D. Crapo, Professor, Department of Medicine, Biomedical
Research and PatientCare, National Jewish Medical and Research Center,
Denver, CO

Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department
of Chemical Engineering, Clarkson University, Potsdam, NY

Dr. Jane Q. Koenig, Professor, Department of Environmental Health,
School of Public Health and Community Medicine, University of
Washington, Seattle, WA

Dr. Petros Koutrakis, Professor of Environmental Science, Environmental
Health , School of Public Health, Harvard University (HSPH), Boston, MA

Dr. Allan Legge, President, Biosphere Solutions, Calgary, Alberta

Dr. Paul J. Lioy, Associate Director and Professor, Environmental and
Occupational Health Sciences Institute, UMDNJ - Robert Wood Johnson
Medical School, NJ

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental
Medicine, New York University School of Medicine, Tuxedo, NY

Dr. Joe Mauderly, Vice President, Senior Scientist, and Director,
National Environmental Respiratory Center, Lovelace Respiratory Research
Institute, Albuquerque, NM

Dr. Roger O. McClellan, Consultant, Albuquerque, NM

Dr. Frederick J. Miller, Consultant, Cary, NC

Dr. Gunter Oberdorster, Professor of Toxicology, Department of
Environmental Medicine, School of Medicine and Dentistry, University of
Rochester, Rochester, NY

Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control
Division, Department of Environmental Conservation, Vermont Agency of
Natural Resources, Waterbury, VT

Dr. Robert D. Rowe, President, Stratus Consulting, Inc., Boulder, CO

Dr. Jonathan M. Samet, Professor and Chair, Department of Epidemiology,
Bloomberg School of Public Health, Johns Hopkins University, Baltimore,
MD

Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing
Laboratory, Harvard Medical School, Boston, MA

Dr. Sverre Vedal, Professor of Medicine, School of Public Health and
Community Medicine University of Washington, Seattle, WA

Mr. Ronald White, Research Scientist, Epidemiology, Bloomberg School of
Public Health, Johns Hopkins University, Baltimore, MD

Dr. Warren H. White, Visiting Professor, Crocker Nuclear Laboratory,
University of California -Davis, Davis, CA

Dr. George T. Wolff, Principal Scientist, General Motors Corporation,
Detroit, MI

Dr. Barbara Zielinska, Research Professor, Division of Atmospheric
Science, Desert Research Institute, Reno, NV

I. HES Comments on 812 Analysis (2004)

U.S. Environmental Protection Agency - Science Advisory Board (U.S.
EPA-SAB). 2004. Advisory on Plans for Health Effects Analysis in the
Analytical Plan for EPA’s Second Prospective Analysis – Benefits and
Costs of the Clean Air Act, 1990-2020. Advisory by the Health Effects
Subcommittee of the Advisory Council on Clean Air Compliance Analysis.
EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at
<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400
433D5D/$File/council_adv_04002.pdf>.

Pg 20: “The Subcommittee agrees that the whole range of uncertainties,
such as the questions of causality, shape of C-R functions and
thresholds, relative toxicity, years of life lost, cessation lag
structure, cause of death, biologic pathways, or susceptibilities may be
viewed differently for acute effects versus long-term effects.

“For the studies of long-term exposure, the HES notes that Krewski et
al. (2000) have conducted the most careful work on this issue. They
report that the associations between PM2.5 and both all-cause and
cardiopulmonary mortality were near linear within the relevant ranges,
with no apparent threshold. Graphical analyses of these studies (Dockery
et al., 1993, Figure 3 and Krewski et al., 2000, page 162) also suggest
a continuum of effects down to lower levels. Therefore, it is reasonable
for EPA to assume a no threshold model down to, at least, the low end of
the concentrations reported in the studies.”

HES Panel Members

Dr. Bart Ostro, California Office of Environmental Health Hazard
Assessment (OEHHA), Oakland, CA

Mr. John Fintan Hurley, Institute of Occupational Medicine (IOM),
Edinburgh, Scotland

Dr. Patrick Kinney, Columbia University, New York, NY

Dr. Michael Kleinman, University of California, Irvine, CA

Dr. Nino Künzli, University of Southern California, Los Angeles, CA

Dr. Morton Lippmann, New York University School of Medicine, Tuxedo, NY
Dr. Rebecca Parkin, The George Washington University, Washington, DC

Dr. Trudy Cameron, University of Oregon, Eugene, OR

Dr. David T. Allen, University of Texas, Austin, TX

Ms. Lauraine Chestnut, Stratus Consulting Inc., Boulder, CO

Dr. Lawrence Goulder, Stanford University, Stanford, CA

Dr. James Hammitt, Harvard University, Boston, MA

Dr. F. Reed Johnson, Research Triangle Institute, Research Triangle
Park, NC

Dr. Charles Kolstad, University of California, Santa Barbara, CA

Dr. Lester B. Lave, Carnegie Mellon University, Pittsburgh, PA

Dr. Virginia McConnell, Resources for the Future, Washington, DC

Dr. V. Kerry Smith, North Carolina State University, Raleigh, NC

Other Panel Members

Dr. John Evans, Harvard University, Portsmouth, NH Dr. Dale Hattis,
Clark University, Worcester, MA Dr. D. Warner North, NorthWorks Inc.,
Belmont, CA Dr. Thomas S. Wallsten, University of Maryland, College
Park, MD

J. NRC – Committee on Estimating the Health Risk Reduction Benefits of
Proposed Air Pollution Regulations (2002)

National Research Council (NRC). 2002. Estimating the Public Health
Benefits of Proposed Air Pollution Regulations. Washington, DC: The
National Academies Press.

Pg 109: “Linearity and Thresholds

“The shape of the concentration-response functions may influence the
overall estimate of benefits. The shape is particularly important for
lower ambient air pollution concentrations to which a large portion of
the population is exposed. For this reason, the impact of the existence
of a threshold may be considerable.

“In epidemiological studies, air pollution concentrations are usually
measured and modeled as continuous variables. Thus, it may be feasible
to test linearity and the existence of thresholds, depending on the
study design. In time-series studies with the large number of repeated
measurements, linearity and thresholds have been formally addressed with
reasonable statistical power. For pollutants such as PM10 and PM2.5,
there is no evidence for any departure of linearity in the observed
range of exposure, nor any indication of a threshold. For example,
examination of the mortality effects of short-term exposure to PM10 in
88 cities indicates that the concentration-response functions are not
due to the high concentrations and that the slopes of these functions do
not appear to increase at higher concentrations (Samet et al. 2000).
Many other mortality studies have examined the shape of the
concentration-response function and indicated that a linear
(nonthreshold) model fit the data well (Pope 2000). Furthermore, studies
conducted in cities with very low ambient pollution concentrations have
similar effects per unit change in concentration as those studies
conducted in cities with higher concentrations. Again, this finding
suggests a fairly linear concentration-response function over the
observed range of exposures.

“Regarding the studies of long-term exposure, Krewski et al. (2000)
found that the assumption of a linear concentration-response function
for mortality outcomes was not unreasonable. However, the statistical
power to assess the shape of these functions is weakest at the upper and
lower end of the observed exposure ranges. Most of the studies examining
the effects of long-term exposure on morbidity compare subjects living
in a small number of communities (Dockery et al. 1996;
Ackermmann-Liebrich 1997; Braun-Fahrländer et al. 1997). Because the
number of long-term effects studies are few and the number of
communities studied is relatively small (8 to 24), the ability to test
formally the absence or existence of a no-effect threshold is not
feasible. However, even if thresholds exist, they may not be at the same
concentration for all health outcomes.

“A review of the time-series and cohort studies may lead to the
conclusion that although a threshold is not apparent at commonly
observed concentrations, one may exist at lower levels. An important
point to acknowledge regarding thresholds is that for health benefits
analysis a key threshold is the population threshold (the lowest of the
individual thresholds). However, the population threshold would be very
difficult to observe empirically through epidemiology, because
epidemiology integrates information from very large groups of people
(thousands). Air pollution regulations affect even larger groups of
people (millions). It is reasonable to assume that among such large
groups susceptibility to air pollution health effects varies
considerably across individuals and depends on a large set of underlying
factors, including genetic makeup, age, exposure measurement error,
preexisting disease, and simultaneous exposures from smoking and
occupational hazards. This variation in individual susceptibilities and
the resulting distribution of individual thresholds underlies the
concentration-response function observed in epidemiology. Thus, until
biologically based models of the distribution of individual thresholds
are developed, it may be productive to assume that the population
concentration-response function is continuous and to focus on finding
evidence of changes in its slope as one approaches lower concentrations.

EPA’s Use of Thresholds

“In EPA’s benefits analyses, threshold issues were discussed and
interpreted. For the PM and ozone National Ambient Air Quality Standards
(NAAQS), EPA investigated the effects of a potential threshold or
reference value below which health consequences were assumed to be zero
(EPA 1997). Specifically, the high-end benefits estimate assumed a
12-microgram per cubic meter (µg/m3) mean threshold for mortality
associated with long-term exposure to PM2.5. The low-end benefits
estimate assumed a 15-µg/m3 threshold for all PM-related health
effects. The studies, however, included concentrations as low as 7.5
µg/m3. For the Tier 2 rule and the HD engine and diesel-fuel rule, no
threshold was assumed (EPA 1999, 2000). EPA in these analyses
acknowledged that there was no evidence for a threshold for PM.

“Several points should be noted regarding the threshold assumptions.
If a threshold is assumed where one was not apparent in the original
study, then the data should be refit and a new curve generated with the
assumption of a zero slope over a segment of the concentration-response
function that was originally found to be positively sloped. The
assumption of a zero slope over a portion of the curve will force the
slope in the remaining segment of the positively sloped
concentration-response function to be greater than was indicated in the
original study. A new concentration-response function was not generated
for EPA’s benefits analysis for the PM and ozone NAAQS for which
threshold assumptions were made. The generation of the steeper slope in
the remaining portion of the concentration-response function may fully
offset the effect of assuming a threshold. These aspects of assuming a
threshold in a benefits analysis where one was not indicated in the
original study should be conveyed to the reader. The committee notes
that the treatment of thresholds should be evaluated in a consistent and
transparent framework by using different explicit assumptions in the
formal uncertainty analyses (see  HYPERLINK
"http://www.nap.edu/openbook.php?record_id=10511&page=126" \l
"p20005c9f9970126001" Chapter 5 ).”

Pg 117: “Although the assumption of no thresholds in the most recent
EPA benefits analyses was appropriate, EPA should evaluate threshold
assumptions in a consistent and transparent framework using several
alternative assumptions in the formal uncertainty analysis.”

Pg 136: “Two additional illustrative examples are thresholds for
adverse effects and lag structures. HYPERLINK
"http://www.nap.edu/openbook.php?record_id=10511&page=136" \l
"p20005c9f8960136001" 2  EPA considers implausible any threshold for
mortality in the particulate matter (PM) exposure ranges under
consideration (EPA 1999a, p. 3-8). Although the agency conducts
sensitivity analyses incorporating thresholds, it provides no judgment
as to their relative plausibility. In a probabilistic uncertainty
analysis, EPA could assign appropriate weights to various threshold
models. For PM-related mortality in the Tier 2 analysis, the committee
expects that this approach would have resulted in only a slight widening
of the probability distribution for avoided mortality and a slight
reduction in the mean of that distribution, thus reflecting EPA’s
views about the implausibility of thresholds. The committee finds that
such formal incorporation of EPA’s expert judgments about the
plausibility of thresholds into its primary analysis would have been an
improvement.

“Uncertainty about thresholds is a special aspect of uncertainty about
the shape of concentration-response functions. Typically, EPA and
authors of epidemiological studies assume that these functions are
linear on some scale. Often, the scale is a logarithmic transformation
of the risk or rate of the health outcome, but when a rate or risk is
low, a linear function on the logarithmic scale is approximately linear
on the scale of the rate or risk itself. Increasingly, epidemiological
investigators are employing analytic methods that permit the estimation
of nonlinear shapes for concentration-response functions (Greenland et
al. 1999). As a consequence, EPA will need to be prepared to incorporate
nonlinear concentration-response functions from epidemiological studies
into the agency’s health benefits analyses. Any source of error or
bias that can distort an epidemiological association can also distort
the shape of an estimated concentration -response function, as can
variation in individual susceptibility (Hattis and Burmaster 1994;
Hattis et al. 2001).”

Pg 137: “In principle, many components of the health benefits model
need realistic probabilistic models (see Table 5-1 for a listing of such
components), in addition to concentration-response thresholds and time
lags between exposure and response. For example, additional features of
the concentration-response function—such as projection of the results
from the study population to the target populations (which may have
etiologically relevant characteristics outside the range seen in the
study population) and the projection of baseline frequencies of
morbidity and mortality into the future—must be characterized
probabilistically. Other uncertainties that might affect the probability
distributions are the estimations of population exposure (or even
concentration) from emissions, estimates of emissions themselves, and
the relative toxicity of various classes of particles. Similarly, many
aspects of the analysis of the impact of regulation on ambient
concentrations and on population exposure involve considerable
uncertainty and, therefore, may be beneficially modeled in this way.
Depending on the analytic approach used, joint probability distributions
will have to be specified to incorporate correlations between model
components that are structurally dependent upon each other, or the
analysis will have to be conducted in a sequential fashion that follows
the model for the data-generating process.

“EPA should explore alternative options for incorporating expert
judgment into its probabilistic uncertainty analyses. The agency
possesses considerable internal expertise, which should be employed as
fully as possible. Outside experts should also be consulted as needed,
individually or in panels. In all cases, when expert judgment is used in
the construction of a model component, the experts should be identified
and the rationales and empirical bases for their judgments should be
made available.”

NRC members

Dr. JOHN C. BAILAR, III (Chair), (emeritus) University of Chicago,
Chicago, Illinois

Dr. HUGH ROSS ANDERSON, University of London, London, England

Dr. MAUREEN L. CROPPER, University of Maryland, College Park

Dr. JOHN S. EVANS, Harvard University, Boston, Massachusetts

Dr. DALE B. HATTIS, Clark University, Worcester, Massachusetts

Dr. ROGENE F. HENDERSON, Lovelace Respiratory Research Institute,
Albuquerque, New Mexico

Dr. PATRICK L. KINNEY, Columbia University, New York, New York

Dr. NINO KÜNZLI, University of Basel, Basel, Switzerland; as of
September 2002, University of Southern California, Los Angeles

Dr. BART D. OSTRO, California Environmental Protection Agency, Oakland

Dr. CHARLES POOLE, University of North Carolina, Chapel Hill

Dr. KIRK R. SMITH, University of California, Berkeley

Dr. PETER A. VALBERG, Gradient Corporation, Cambridge, Massachusetts

Dr. SCOTT L. ZEGER, Johns Hopkins University, Baltimore, Maryland

 USGS notes these shipment data include cement imports (primarily Types
I, II, and V).

 Wages paid to production workers were $0.8 billion (8% of the value of
shipments) at an average hourly rate of $27.

 Throughout this report, we use PCA’s method to calculate labor and
energy efficiency. This measure is a weighted sum of clinker and
finished cement production. Weights for labor are 85% clinker and 15%
finished cement production. Weights for energy are 92% clinker and 8%
finished cement production (PCA, 2005  XE "PCA, 2005"  ).

 The labor costs reported in Figure 2-3 were calculated by first
multiplying the number of employee hours per metric ton of cement
reported in Table 2-4 by the average hourly earnings of production
workers for each year (BLS, 2007a and 2007b  XE "BLS, 2007"  ). Next,
these cost estimates were adjusted for inflation and expressed in 2005
dollars by using the consumer price index (CPI) (BLS, 2008  XE "DOC,
BLS, 2008"  ).

 The 2002 Economic Census reports that the national Herfindahl-Hirschman
Index (HHI) for cement (North American Industry Classification System
[NAICS] 32731) is 568. However, this measure is likely not
representative of actual concentration that exists in regional markets.

 A recent USITC study of California cement markets found more than 75%
of gray Portland cement shipments in the state were shipped to customers
within 200 miles of the cement producer (USITC, 2006  XE "USITC, 2006"
).

 In cases where no employment data were available, we used information
from previous EPA analyses to determine firm size.

 The 2002 Economic Census reports that the national Herfindahl-Hirschman
Index (HHI) for cement—North American Industry Classification System
(NAICS) 32731—is 568. However, this measure is likely not
representative of actual concentration that exists in regional markets.

 A recent USITC study of California cement markets found more than 75%
of gray Portland cement shipments in the state were shipped to customers
within 200 miles of the cement producer (USITC, 2006  XE "USITC, 2006"
).

 In addition, large plants are typically more economical because they
can produce cement at lower unit costs; this reduces entry incentives
for smaller capacity cement plants.

 This economic model is formally known as a multi-firm Cournot oligopoly
model.

 This ultimately influences the partial-equilibrium model’s estimates
of the social cost of the regulatory program since bigger existing
market distortions tend to widen the gap between price and marginal cost
in these markets and lead to higher deadweight loss estimates than under
the case of perfectly competitive markets. The Office of Management and
Budget (OMB) explicitly mentions the need to consider market
power–related welfare costs in evaluating regulations under Executive
Order 12866 (EPA, 1999a  XE "EPA, 1999a"  ).

 The per-unit compliance costs were calculated by dividing the total
annualized cost per kiln by the kiln’s estimated cement production
within the economic impact model.

 To place this reduction in context, it is a similar to the decline
experienced during the latest economic downturn; approximately 2,000
jobs (see Appendix A, Table A-3).

 Since Morgenstern’s analysis reports environmental expenditures in
1987 dollars, we make an inflation adjustment to the engineering cost
analysis using the consumer price index (195.3/113.6) = 0.6)

 In addition, large plants are typically more economical because they
can produce cement at lower unit costs; this reduces entry incentives
for smaller capacity cement plants.

 For this analysis, we used BenMAP version 3.0 (Abt Associates, 2008).
This model is available for free download on the Internet at
<http://www.epa.gov/air/benmap>.

 These two studies specify multi-pollutant models that control for SO2,
among other pollutants.

 Please see the Section 5.2 of the proposal RIA for this rule for more
information regarding the change in the presentation of benefits
estimates.

The benefits methodology has also been updated since the proposal RIA to
incorporate a revised VSL, as discussed in the next section.

It is important to note that uncertainty regarding the shape of the
concentration-response function is conceptually distinct from an assumed
threshold.  An assumed threshold (below which there are no health
effects) is a discontinuity, which is a specific example of
non-linearity.

 The comment period for the proposed rule closed on September 4, 2009
(Docket ID No. EPA–HQ–OAR–2002–0051 available at  HYPERLINK
"http://www.regulations.gov" http://www.regulations.gov ).  All public
comments received as well as the responses to those comments are
available in this docket.

 To comply with Circular A-4, EPA provides monetized benefits using
discount rates of 3% and 7% (OMB, 2003  XE "OMB, 2003"  ). These
benefits are estimated for a specific analysis year (i.e., 2013), and
most of the PM benefits occur within that year with two exceptions:
acute myocardial infarctions (AMIs) and premature mortality. For AMIs,
we assume 5 years of follow-up medical costs and lost wages. For
premature mortality, we assume that there is a “cessation” lag
between PM exposures and the total realization of changes in health
effects. Although the structure of the lag is uncertain, EPA follows the
advice of the SAB-HES to assume a segmented lag structure characterized
by 30% of mortality reductions in the first year, 50% over years 2 to 5,
and 20% over the years 6 to 20 after the reduction in PM2.5 (U.S.
EPA-SAB, 2004  XE "U.S. EPA-SAB, 2004"  ). Changes in the lag
assumptions do not change the total number of estimated deaths but
rather the timing of those deaths. Therefore, discounting only affects
the AMI costs after the analysis year and the valuation of premature
mortalities that occur after the analysis year. As such, the monetized
benefits using a 7% discount rate are only approximately 10% less than
the monetized benefits using a 3% discount rate.

After adjusting the VSL to account for a different currency year (2005$)
and to account for income growth to 2015, the $5.5 million VSL is $7.2
million.

In the (draft) update of the Economic Guidelines (U.S. EPA, 2006  XE
"U.S. EPA, 2008"  ), EPA retained the VSL endorsed by the SAB with the
understanding that further updates to the mortality risk valuation
guidance would be forthcoming in the near future. Therefore, this report
does not represent final agency policy.

In this analysis, we adjust the VSL to account for a different currency
year (2005$) and to account for income growth to 2015.  After applying
these adjustments to the $6.3 million value, the VSL is $8.3 million.

 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document:
Social Cost of Carbon for Regulatory Impact Analysis Under Executive
Order 12866, Interagency Working Group on Social Cost of Carbon, with
participation by Council of Economic Advisers, Council on Environmental
Quality, Department of Agriculture, Department of Commerce, Department
of Energy, Department of Transportation, Environmental Protection
Agency, National Economic Council, Office of Energy and Climate Change,
Office of Management and Budget, Office of Science and Technology
Policy, and Department of Treasury (February 2010).  Also available at
HYPERLINK "http://www.epa.gov/otaq/climate/regulations.htm"
http://www.epa.gov/otaq/climate/regulations.htm

 The interagency group decided that these estimates apply only to CO2
emissions.  Given that warming profiles and impacts other than
temperature change (e.g. ocean acidification) vary across GHGs, the
group concluded “transforming gases into CO2-equivalents using GWP,
and then multiplying the carbon-equivalents by the SCC, would not result
in accurate estimates of the social costs of non-CO2 gases” (SCC TSD,
pg 13).

 It is possible that other benefits or costs of proposed regulations
unrelated to CO2 emissions will be discounted at rates that differ from
those used to develop the SCC estimates.

 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment.
http://www.epa.gov/ttn/atw/nata2002/

 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment.
http://www.epa.gov/ttn/atw/nata2002/

 Chronic exposure is defined in the glossary of the Integrated Risk
Information (IRIS) database (http://www.epa.gov/iris) as repeated
exposure by the oral, dermal, or inhalation route for more than
approximately 10% of the life span in humans (more than approximately 90
days to 2 years in typically used laboratory animal species).

 Defined in the IRIS database as exposure to a substance spanning
approximately 10% of the lifetime of an organism.

 Defined in the IRIS database as exposure by the oral, dermal, or
inhalation route for 24 hours or less.

 The NATA modeling framework has a number of limitations which prevent
its use as the sole basis for setting regulatory standards. These
limitations and uncertainties are discussed on the 2002 NATA website.
Even so, this modeling framework is very useful in identifying air toxic
pollutants and sources of greatest concern, setting regulatory
priorities, and informing the decision making process.  U.S. EPA. (2009)
2002 National-Scale Air Toxics Assessment.
http://www.epa.gov/ttn/atw/nata2002/

 National Research Council (NRC). 2000. Toxicological Effects of
Methylmercury. Committee on the Toxicological Effects of Methylmercury,
Board on Environmental Studies and Toxicology. National Academies Press.
Washington, DC. pp.168-173.

Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. “Intake of mercury
from fish lipid peroxidation, and the risk of myocardial infarction and
coronary, cardiovascular and any death in Eastern Finnish men.”
Circulation, 91 (3):645-655.

Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. Weihe, and  Grandjean,
P., 1999. “Prenatal Methylmercury Exposure As A Cardiovascular Risk
Factor At Seven Years of Age”, Epidemiology, pp370-375.

National Research Council (NRC). 2000. Toxicological Effects of
Methylmercury. Committee on the Toxicological Effects of Methylmercury,
Board on Environmental Studies and Toxicology. National Academies Press.
Washington, DC.  p. 229.

 See Section 5 of this RIA for more information on the air quality
modeling.

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Toxic Substances and Disease Registry (ATSDR). Medical Management
Guidelines for Hydrogen Chloride (HCl). CAS#: 7647-01-0. Atlanta, GA:
U.S. Department of Health and Human Services, Public Health Service.
Available on the Internet at < HYPERLINK
"http://www.atsdr.cdc.gov/Mhmi/mmg173.html"
http://www.atsdr.cdc.gov/Mhmi/mmg173.html >.

 All health effects language for this section came from: U.S. EPA. 2005.
“Full IRIS Summary for Toluene (CASRN 108-88-3)” Environmental
Protection Agency, Integrated Risk Information System (IRIS), Office of
Health and Environmental Assessment, Environmental Criteria and
Assessment Office, Cincinnati, OH. Available on the Internet at <
HYPERLINK "http://www.epa.gov/iris/subst/0118.htm"
http://www.epa.gov/iris/subst/0118.htm >.

 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and
Certain Home Residents from Exposure to Formaldehyde, Office of
Pesticides and Toxic Substances, April 1987.

 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A.
2003. Mortality from lymphohematopoetic malignancies among workers in
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1615-1623.

 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A.
2004. Mortality from solid cancers among workers in formaldehyde
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 Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Hayes,
R. B.; Hoover, R. N.; Hauptmann, M. 2009. Mortality from
lymphohematopoietic malignancies among workers in formaldehyde
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 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers
exposed to formaldehyde: an update. Occup. Environ. Med. 61: 193-200.

 Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Extended follow-up of a
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Cancer Inst. 95:1608-1615.

 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston,
and FJ Miller. 2003. Biologically motivated computational modeling of
formaldehyde carcinogenicity in the F344 rat. Tox Sci 75: 432-447.

 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston,
and FJ Miller. 2004. Human respiratory tract cancer risks of inhaled
formaldehyde: Dose-response predictions derived from
biologically-motivated computational modeling of a combined rodent and
human dataset. Tox Sci 82: 279-296.

 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde:
Hazard characterization and dose-response assessment for carcinogenicity
by the route of inhalation. CIIT, September 28, 1999. Research Triangle
Park, NC.

 U.S. EPA. Analysis of the Sensitivity and Uncertainty in 2-Stage Clonal
Growth Models for Formaldehyde with Relevance to Other
Biologically-Based Dose Response (BBDR) Models. U.S. Environmental
Protection Agency, Washington, D.C., EPA/600/R-08/103, 2006

 Subramaniam, R; Chen, C; Crump, K; .et .al. (2006) Uncertainties in
biologically-based modeling of formaldehyde-induced cancer risk:
identification of key issues. Risk Anal 28(4):907-923.

 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in
the CIIT 2-stage model for formaldehyde-induced nasal cancer in the F344
rat: a limited sensitivity analysis-I. Risk Anal 27:1237

 Crump, K; Chen, C; Fox, J; .et .al. (2006) Sensitivity analysis of
biologically motivated model for formaldehyde-induced respiratory cancer
in humans. Ann Occup Hyg 52:481-495.

 Crump, K; Chen, C; Fox, J; .et .al. (2006) Sensitivity analysis of
biologically motivated model for formaldehyde-induced respiratory cancer
in humans. Ann Occup Hyg 52:481-495.

 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in
the CIIT 2-stage model for formaldehyde-induced nasal cancer in the F344
rat: a limited sensitivity analysis-I. Risk Anal 27:1237

 International Agency for Research on Cancer (2006) Formaldehyde,
2-Butoxyethanol and 1-tert-Butoxypropan-2-ol. Monographs Volume 88.
World Health Organization, Lyon, France.

 Agency for Toxic Substances and Disease Registry (ATSDR). 1999.
Toxicological profile for Formaldehyde. Atlanta, GA: U.S. Department of
Health and Human Services, Public Health Service.
http://www.atsdr.cdc.gov/toxprofiles/tp111.html

 WHO (2002) Concise International Chemical Assessment Document 40:
Formaldehyde. Published under the joint sponsorship of the United
Nations Environment Programme, the International Labour Organization,
and the World Health Organization, and produced within the framework of
the Inter-Organization Programme for the Sound Management of Chemicals.
Geneva.

 All health effects language for this section came from: Agency for
Toxic Substances and Disease Registry (ATSDR). 1999. ToxFAQs for
Chlorinated Dibenzo-p-dioxins (CDDs) (CAS#: 2,3,7,8-TCDD 1746-01-6).
Atlanta, GA: U.S. Department of Health and Human Services, Public Health
Service. Available on the Internet at  HYPERLINK
"http://www.atsdr.cdc.gov/tfacts104.html"
http://www.atsdr.cdc.gov/tfacts104.html .

 All health effects language for this section came from: Agency for
Toxic Substances and Disease Registry (ATSDR). 1995. ToxFAQs™ for
Chlorodibenzofurans (CDFs). Atlanta, GA: U.S. Department of Health and
Human Services, Public Health Service. Available on the Internet at
<http://www.atsdr.cdc.gov/tfacts32.html>.

 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment.
http://www.epa.gov/ttn/atw/nata2002/

 U.S. EPA. 2000. Integrated Risk Information System File for Benzene.
This material is available electronically at:
http://www.epa.gov/iris/subst/0276.htm.

International Agency for Research on Cancer, IARC monographs on the
evaluation of carcinogenic risk of chemicals to humans, Volume 29, Some
industrial chemicals and dyestuffs, International Agency for Research on
Cancer, World Health Organization, Lyon, France, p. 345-389, 1982.

Irons, R.D.; Stillman, W.S.; Colagiovanni, D.B.; Henry, V.A. (1992)
Synergistic action of the benzene metabolite hydroquinone on
myelopoietic stimulating activity of granulocyte/macrophage
colony-stimulating factor in vitro, Proc. Natl. Acad. Sci. 89:3691-3695.

 International Agency for Research on Cancer (IARC). 1987. Monographs on
the evaluation of carcinogenic risk of chemicals to humans, Volume 29,
Supplement 7, Some industrial chemicals and dyestuffs, World Health
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U.S. Department of Health and Human Services National Toxicology Program
11th Report on Carcinogens available at:
http://ntp.niehs.nih.gov/go/16183.

 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene.
Environ. Health Perspect. 82: 193-197.

Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State
of the Art Reviews. 3: 541-554.

 Rothman, N., G.L. Li, M. Dosemeci, W.E. Bechtold, G.E. Marti, Y.Z.
Wang, M. Linet, L.Q. Xi, W. Lu, M.T. Smith, N. Titenko-Holland, L.P.
Zhang, W. Blot, S.N. Yin, and R.B. Hayes (1996) Hematotoxicity among
Chinese workers heavily exposed to benzene. Am. J. Ind. Med. 29:
236-246.

U.S. EPA 2002 Toxicological Review of Benzene (Noncancer Effects).
Environmental Protection Agency, Integrated Risk Information System
(IRIS), Research and Development, National Center for Environmental
Assessment, Washington DC. This material is available electronically at
http://www.epa.gov/iris/subst/0276.htm.

 Qu, O.; Shore, R.; Li, G.; Jin, X.; Chen, C.L.; Cohen, B.; Melikian,
A.; Eastmond, D.; Rappaport, S.; Li, H.; Rupa, D.; Suramaya, R.;
Songnian, W.; Huifant, Y.; Meng, M.; Winnik, M.; Kwok, E.; Li, Y.; Mu,
R.; Xu, B.; Zhang, X.; Li, K. (2003). HEI Report 115, Validation &
Evaluation of Biomarkers in Workers Exposed to Benzene in China.

Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B. Cohen, et al. (2002).
Hematological changes among Chinese workers with a broad range of
benzene exposures. Am. J. Industr. Med. 42: 275- 285.

Lan, Qing, Zhang, L., Li, G., Vermeulen, R., et al. (2004).
Hematotoxically in Workers Exposed to Low Levels of Benzene. Science
306: 1774-1776. Turtletaub, K.W. and Mani, C. (2003). Benzene metabolism
in rodents at doses relevant to human exposure from Urban Air. Research
Reports Health Effect Inst. Report No.113.

 U.S. EPA Integrated Risk Information System (IRIS) database is
available at: www.epa.gov/iris

 For more information on the annualized costs, please refer to Section 4
of this RIA.

 There are different commonly used models of oligopoly in the economics
literature. They differ with respect to the assumption about how a
company believes competing companies will react to its own production
decision. EPA selected the Cournot model where the company assumes
competing companies’ output is fixed in its own production decision.

 To highlight and make transparent the assumptions regarding seller
behavior, this equation is formally derived in Appendix B.

  PAGE  iii

  PAGE  1-1

  PAGE  2-1

  PAGE   \* MERGEFORMAT  iii

  PAGE   \* MERGEFORMAT  7-1

A-  PAGE  6

B-  PAGE  2

C-  PAGE   \* MERGEFORMAT  4

  PAGE   \* MERGEFORMAT  5-1

D-  PAGE   \* MERGEFORMAT  30

Added number to table without track changes.

Blue identifies a user-selected input within the BenMAP model

Green identifies a data input generated outside of the BenMAP model

Monetized PM2.5-related Benefits

Background Incidence and Prevalence Rates

Woods & Poole Population Projections

PM2.5 Related Health Impacts

PM2.5 Incremental Air Quality Change

Population Projections

Economic Valuation Functions

PM2.5 Health Functions

Baseline and Post-Control PM2.5 Concentrations

Census Population Data