MATHTECH

           The Technical Research
          and Consulting Division of
              Mathematica, Inc.
   BENEFIT AND NET BENEFIT ANALYSIS OF


ALTERNATIVE NATIONAL AMBIENT AIR QUALITY


    STANDARDS FOR PABTICULATE MATTER

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     BBNBF1T AMD HE! K8NKVIT ANALYSIS OF

  ALTERNATIYB NATIONAL AMBIENr All QUALITY

      STANDARDS FOR PAETTCULATE VASTER



                  YQUHB 17
                Prepared for:

          Benefits Analysis Program
          Economic Analysis Branch
    Strategies and Air Standards Division
Office of Air Quality Planning and Standards

    U.S. ENVIRONMENTAL PROTECTION AGENCY
   Research Triangle Park.  North Carolina
                 March 1983

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     BENEFIT AND NET BENEFIT ANALYSIS OF ALTERNATIVE
       NATIONAL AMBIENT AIR QUALITY STANDARDS FOR
                   PARTICULATE HATTER
                           By:
Ernest H. Manuel, Jr.
Robert L. Horst, Jr.
Kathleen M. Brennan
Jennifer M. Hobart
Carol D. Harvey
Jerome T. Bentley
Marcus C. Duff
Daniel E. Klingler
Judith K. Tapiero
                 With the Assistance of:
David S. Brookshire
Thomas D. Crocker
Ralph C. d'Arge
A. Myrick Freeman, III
William D. Schulze
James H. Ware
                     MATHTECH, INC.
                      P.O. Box 2392
              Princeton, New Jersey  08540
             EPA Contract Number 68-02-3826
                    Project Officer:
                     Allen C. Basala
                Economic Analysis Branch
          Strategies and Air Standards Division
      Office of Air Quality Planning and Standards
          U.S.  Environmental Protection Agency
      Research Triangle Park, North Carolina  27711
                       March 1983

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The analysis and conclusions presented in this report are
those of the authors and should not be interpreted as necessarily
reflecting the official policies of. the U.S. Environmental
Protection Agency.

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EPA PERSPECTIVE
There has been growing concern with the effectiveness and burden of
regulations imposed by the Federal government. In order to improve the
process by which regulations are developed, Executive Order 12291 was
issued. The order requires that Federal agencies develop and consider, to
the extent permitted by law, Regulatory Impact Analyses (RIA) for the
proposal and promulgation of regulatory actions which are classified as
major. According to the order, a significant component of the RIA is to be
an economic benefit and benefit—cost analysis of the regulatory alternatives
considered. Under the Clean Air Act, the Administrator of EPA may not
consider economic and technological feasibility in setting National Ambient
Air Quality Standards (NAAQS). Although this precludes consideration of
benefit cost analyses in setting NAAQS, it does not necessarily preclude
consideration of benefit analyses for that purpose.
In full support of the Exetutive Order, the EPA commissioned Mathtech,
Inc. to accomplish an economic benefit and benefit-cost analysis of some of
the alternatives that were thought likely to be considered in the development
of proposed revisions to the NAAQS for particulate matter (PM). The report,
entitled “Benefit and Net Benefit Analysis of Alternative National Ambient
Air Quality Standards for Particulate Matter,” documents the results of the
contractor’s study. One of the major objectives of the study was to give a
better understanding of the complex technical issues and the resource
requirements associated with complying with the spirit of the Order for the
NAAQS program. In order to achieve this objective, the contractor was
given a wide range of latitude in the use of data, analytic methods, and
underlying assumptions.
It is important to stress that the benefit analysis portion of the
Mathtech study has not had a role to date in the development of proposed
revisions to the NAAQS for particulate matter. Staff reconniendations
currently under consideration are based on the scientific and technical
information contained in two EPA documents. They are the “Air Quality
Criteria for Particulate Matter and Sulfur Oxides” and the “Review of the
National Ambient Air Quality Standards for Particulate Matter: Assessment
of Scientific and Technical Information, OAQPS Staff Paper.” These documents
have undergone extensive and rigorous review by the public and the Clean
Air Scientific Advisory Committee in accordance with the Agency’s established
scientific review policy. Although the Mathtech study reflects the
“state—of-the-art” in particulate matter benefit analysis, the approach and
results have not been subjected to a comparable extensive peer review
process. In addition, some EPA staff have raised questions regarding the
approach taken In the analysis and the significance of the results for
standard setting purposes under the Act. These circumstances do not
necessarily preclude use of the benefit analysis in some manner after
appropriate peer review and further consideration of the questions that
have been raised.

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PREFAcE
This report was prepared for the U.S. Environmental Protection Agency
by Mathtech, Inc. The report is organized into five volumes containing a
total of 11 sections as follows:
Volume I
Section 1: The Benefit Analysis
Section 2: The Net Benefit Analysis
Volume El
Section 3: Health Effects Studies in the Epidemiology Literature
Section 4: Health Effects Studies in the Economics Literature
Appendix: Valuation of Health Improvements
Volume III
Section 5: Residential Property Value Studies
Section 6: Hedonic Wage Studies
Section 7: Economic Benefits of Reduced Soiling
Section 8: Benefits of National Visibility Standards
Volume IV
Section 9: Air Quality Data and Standards
Section 10: Selected Methodological Issues
Volume V
Section 11: Supplementary Tables
iv

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AcL GXENTS
While preparing this report, we had the benefit of advice, comments
and other assistance from many individuals. Allen Basala, the EPA Project
Officer, and lames Bain, former Chief of the Economic Analysis Branch
(EAB), were especially helpful. They provided both overall guidance on
project direction as well as technical review and comment on the report.
Others in EAB who assisted us included Thomas Walton, George Duggan, and
John O’Connor, the current Chief of EAB.
Others within EPA/OAQPS who reviewed parts of the report and assisted
in various ways included Renry Thomas, Jeff Cohen, John Bachman, John
Names, Joseph Padgett, and Bruce Jordan.
Several individuals within EPA/OPA also provided comments or assis-
tance at various stages of the project. These included Bart Ostro, Alex
Cristofaro, Ralph Luken, Jon Rarford, and Paul Stolpman.
Others outside EPA who reviewed parts of the report and provided
comments included V. Kerry Smith, Paul Portney, Lester Lave, Eugene Seskin,
and William Watsom Other Mathtech staff who assisted us in various ways
were Donald Wise, Gary Labovich, and Robert 3. Anderson. We also
appreciate the assistance of Al Smith and Ken Brubaker of Argonne National
Laboratory who conducted the parallel analysis of control costs and air
quality impacts.
Naturally, it was not possible to incorporate all comments and
suggestions. Therefore, the individuals listed above do not necessarily
endorse the analyses or conclusions of the report.
The production of a report this length in several draft versions, each
under a tight time constraint, is a job which taxes the patience and sanity
of a secretarial staff. Carol Rossell had this difficult task and managed
ably with the assistance of Deborah Piantoni, Gail Gay, and Sally Webb.
Nadine Vogel and Virginia Wyatt, who share the same burden at EAB, also
assisted us on several occasions.
V

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VOLUIIE IV
Section Pane
9. AIR QUALITY DATA AND STANDARDS
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9—1
Sources of Data 9—2
Design Value File 9—2
Pre—Control Air Quality Data File . 9—3
Post—Control Air Quality Data File 9—5
ConsistencyCheckiofData 9—12
Development of Data for Benefits Analysis ... 9—17
Geographical Aggregation 9—19
Temporallnterpolation 9—21
Projections of TSP Annual Arithmetic Means .......... 9—23
Index of Exposure 9—24
Distribution of Air Quality Changes 9—29
Definition of B—Scenario Concentrations ............. 9—35
Benefit Calculations 9—40
TypesofStandards 9—42
Parameters Used in the Benefits Analysis .... 9—42
TabulationofBenefits 9—47
SummaryofAirQualityData............. .... 9—47
References . . 9—49
vi

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CONTENTS (Continued)
Section Page
10. SELECTED METhODOLOGICAL ISSUES
Procedures for Calculation of Aggregate Benefits ......... 10—2
Introduction 10—2
Criteria for Assessing Study Quality 10—4
Application of Study Evaluation Criteria 10—6
Alternative Aggregation Procedures 10—13
Results of the Aggregation Procedures 10—17
Applicable Exposure Ranges for Benefit Calculations 10—17
Introduction . . . . . . . 10—17
Concentration Ranges Suggested by CASAC and the
EPA/OAQPS Staff Paper 10—19
Applicability of Staff Paper Lower Bounds to the
B enef it Analysis . . . . 10—24
Practical Constraints in Applying the Staff Paper
Lower Bounds . 10—26
ResultsoftheCalculations 10—29
Chicago Area Case Study . . . . . . . . . . . . . 10—29
Introduction 10—29
Data 10—35
Approach ... . 1036
P indings 1038
Limitations 1039
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1042
vi i

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FIGURES
VOLUME IV
Figure No, Page
9—i. Air Quality Data . 9—6
9—2. Air Quality Data With Complete Attainment 9—11
9—3. Time Paths for Air Quality Data 9—22
9—4. EqualMagnitude Improvement 9—31
9—5. EqualPercent lmprovement...................... . . .... .. .. 9—32
9—6. Localizedlmprovement 9—34
9—7. Comparison of A and B Scenarios . 9—36
9—8. DevelopmentofB—ScenarioData................ .. 9—39
9—9. Benefits of Ambient Air Quality Improvements 9—45
10—1. Constrained Estimation of Concentration—Response
Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10—28
10—2. EqualMagnitude Improvement 10—31
10—3. EqualPercentimprovement...... .... 10—33
10—4 • Local ized Improvement . . . . . . • . . . . . . . . . . . . , 10—3 4
V Li ].

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TABLRS
VOLUIB IV
Table No. Paae
9—1. AmbientAirQualityStafldardS 9—7
9—2. Counties in Post—Control Files . 9—8
9—3. Swnmary of Consistency Checks for A—Scenario Data 9—18
9—4. Subcounty and Aggregated Observations in Air Quality
Data Files . 9—20
9—5. Distribution of SAROAD Monitors by County 9—26
9—6. CountyAverageCorrectionFactOrS................ . ’ .... 9—27
9—7. Review of Pollution Monitors Used by Studies 9—28
9—8. Problem Counties in B—Scenario Data Base . 9—41
9—9. Ambient Air Quality Standards . 943
9—10. Benefit Parameter Values ..... . ...... .... ..... .. .. .. ..... . 9—46
9—11. EPA Federal Administrative Regions by State 9—48
10—1. Application of Study Evaluation Criteria ...... 10—7
10—2. Alternative AggregatiouProcedures ....................... 10—14
10—3. Incremental Benefits for the PM1O (70, 250) Scenario B
Standard 10—18
10—4. Summary of Concentration Ranges in EPA/OAOPS Staff Paper . 10—20
10—S. Ratio of Census Division Estimates to Cook County
Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10—38
ix

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TABLES (Continued)
Table No. Page
10—6. Benefit Estimates for Two Methods of Air Quality
Aggregation and for Benefit Aggregation Procedure B 10—40
10—7. Benefit Estimates for Two Methods of Air Quality
Aggregation and for Benefit Aggregation Procedure C 10—40
10—8. Benefit Estimates for Two Methods of Air Quality
Aggregation and for Benefit Aggregation Procedure D 10—41
x

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SECTION 9
AIR QUALITY DATA AND STANDARDS

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SECTION 9
AIR QUALITY flAIL AND STANDARDS
Analyses of the benefits and costs of alternative ambient air quality
standards require very different input data. flowever, both analyses use
data on ambient concentrations of air pollution. Consequently, if benefits
and costs are to be compared, care must be taken to ensure that the air
quality data used are consistent across the two analyses.
In the analysis of control costs, the focus is on the control of
source emissions. The analysis identifies the degree of emission control
required to achieve the ambient concentration of the controlled pollutant
allowable under the proposed standard. The analysis of benefits is based
on an examination of pollutant—related impacts with and without the pro-
posed standard. Because the impacts depend primarily on the ambient
concentrations of pollution, the appropriate air pollution data for the
benefit analysis are ambient air quality concentrations. Thus, data on
ambient concentrations provide the linkage between the cost and benefit
analyses.
Clearly, the interdependence between the benefit and cost analysis
data requirements must be recognized if a consistent benefit—cost analysis
is to be completed. It is not enough to assume that promulgation of the
standard will result in every area being just in compliance. In reality,
the limited number and discreteness of available means of controlling
emissions can lead to a range of resultant concentration levels across
geographical areas.
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The analysis in this study uses ambient air quality data generated by
the contractor performing the cost analysis. As a consequence, both the
benefit and cost analyses are based on a common set of air quality data.
The purpose of this section is to describe the air quality data used
in the benefits analysis. The discussion focuses not only on the scope of
available data, but also on the manner in which the data are used in the
analysis. In addition, the section includes a description of the alterna-
tive air quality standards examined in the study and the conditions under
which benefits estimates are derived. If benefits and costs are to be
compared, the standards analyzed and the method of reporting dollar values
must also be defined consistently across the two analyses.
S4RJR S OF DATA
This subsection describes the air quality data provided by the cost
contractor. The discussion begins with an outline of the source and scope
of the historical air quality data used in the analysis. This is followed
by a review of the air quality data files generated for future years, both
with and without the proposed standards. Finally, the results of a variety
of data consistency checks are reported. Additional information on the air
quality data base is available in Reference (1).
Desian Vali.e File
The primary source of air quality data for the analysis is the “design
value” file maintained by the U.S. EPA for the RIA analysis. This file
contains an index of air quality data by county (or subcounty area) for a
variety of averaging times. The index (referred to as the design value) is
defined as the concentration reported at the monitor(s) with the highest
reading of annual mean and 24—hour observed second high for TSP within a
given county. 5 The data used in defining the county design values are
* In some cases, design values are adjusted to be consistent with county
attainment/nonattainment designations in the 1979 Federal Register or EPA
Regional Office classifications.
9 -Z

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taken from EPA ’s Storage and Retrieval of Aerometric Data System (SAROAD).
These data are drawn primarily from 1977 and 1978, but in some cases may
reflect conditions from as far back as 1975. For purposes of this study,
we view the design value data as nominally representative of air quality
conditions in 1978.
The specific data available in the design value file include measures
of TSP in terms of annual geometric means, annual arithmetic means, annual
second highest 24—hour average concentrations observed, and 24—hour
expected value concentrations (expected to occur once per year). The
latter values are generated by assuming the air quality data fit an
exponential distribution.
Data for these statistical measures are available for 1,230 county or
subcounty areas. Although the scope is limited to about 40 percent of all
U.S. counties, the presumption is that the included counties represent
those areas that are likely to be in nonattainment with plausible alterna-
tive standards.
For some county or subcounty entries, one or more of the statistical
measures may not be reported. In this case, the cost contractor developed.
regression equations to generate the necessary data. These transformations
were carried out prior to our acquisition of the data. A discussion of the
process for replacing missing values can be found in Reference (1).
Pro—Control Air Qmality Data Pu .
The design value file is taken to be representative of air quality
conditions in 1978. In order to project future air quality, a modified
linear rollback model was applied to project future emissions growth. 5 The
estimates of future emissions were based on estimates of the growth in new
sources, and on the rate of replacement or retirement of sources in exis-
tence in 1978. These emission estimates are made under the assumption that
$ See Reference (1) for a discussion of rollback models.
9—3

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unretired plants are controlled at 1978 control levels, One—half of the
replacement sources are controlled at 1978 control levels, while new
sources and the other replacement sources are controlled at BACT levels
(NSPS and other new source control requirements). However, because this
initial (or baseline) situation represents the “without standard” case, we
refer to these data as pre-control data.
Scope of Pre—Control Data —
Data for the pre—control file are provided for a historical year, 1978
(these values correspond to those found in the design value file), and for
four future years, 1985, 1987, 1989, and 1995. Four distinct pollution
measures are reported for each county and each year.* These measures are:
• TSP annual geometric mean.
• TSP 24—hour observed second high.
• PM1O annual arithmetic mean.
• PM1O 24—hour expected value.
The designation PM1O refers to particles which are less than 10
microns in diameter. Thus, concentrations of PM1O represent a subset of
TSP concentrations. As noted in the Staff Paper for PM (2), there is
reason to believe that PM1O concentrations are more relevant for assessing
health impacts than the broader measure of TSP. For this reason, many of
the alternative standards analyzed in this study are defined in terms of
limits on allowable PM1O concentrations.
P1(10 Conyer5ions —
Since all entries in the design value file are expressed in terms of
TSP measures, a conversion factor is required to determine the PM1O
$ The geographic scope of the pre—control data file corresponds to that in
the design value file.
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concentrations for the pre—control data file. This is accomplished by
multiplying the TSP annual arithmetic mean and 24—hour expected value in
the design value file by O.55. This yields estimates (in 1978) for PM1O
annual arithmetic mean and 24—hour expected value, respectively. Estimates
of PM1O for future years are based on proportional relationships discussed
later in this section. These conversion calculations were completed prior
to our acquisition of the data base.**
Post—Control Air Quality Data File
With the implementation and enforcement of an ambient standard, air
quality can be expected to improve relative to the pre—control case. An
example of this situation is portrayed in Figure 9—1. In the figure, the
segment AB represents concentrations across time when no ambient standard
is in place. After a standard is imposed, concentrations decrease to the
level of the standard in year t 1 , but increase over time along the segment
CD because of net growth in emissions (and hence higher concentrations of
the pollutant). The segment AB represents the pre—control data, while the
segment CD represents the post—control data. If all benefits are assumed
to occur within the timeframe of t 1 to t 2 . then the benefits to society due
to imposition of the standard are given by the health and welfare improve-
ments resulting from the air quality change represented by area ABDC.
Aabient Standards —
The data in the post—control file depend on the particular standard
imposed. Table 9—1 lists the six alternative ambient standards considered
• This factor was developed through analysis of data collected by EPA’s
Inhalable Particulate Network (3).
** One conversion made after we acquired the air quality data involved
defining a relationship between TSP and British smoke. A discussion of
this conversion procedure is presented in Appendix 3A of Section 3.
+ The monotonically increasing nature of segment AB in Figure 9—1 is for
expository purposes. The segment could also decrease or remain constant
through some or all of the interval t 1 to t 2 .
9—5

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Figure 9-1. Air Quality Data
Nonattairiment Due
to Emissions
Growth
Concentration
Pre—Control
Data
— —
Benefits
Standard
Post-Control
Data
ti t 2 Time
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Table 9—1
AMBIENT AIR QUALITY STANDARDS
Pollutant
Level
(pg/rn 3 )
Averaging Time
Implementation
Date
Maintenance
Date
P1 110
70
250
Annual Arithmetic Mean
2 4—Hour Expected Value
1989
1989
1995
1995
P1110
55
Annual Arithmetic Mean
1989
1995
P1110
55
250
Annual Arithmetic Mean
24—Hour Expected Value
1989
1989
1995
1995
P1110
55
150
Annual Arithmetic Mean
24—Hour Expected Value
1989
1989
1995
1995
TSP
75
260
Annual Geometric Mean
24—Hour Second High
1987
1987
1995
1995
TSP
150
24—Hour Second High
1987
1995

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in this analysis. The order in the table goes from least stringent (PM1O
70/250) to most stringent (TSP 150). As the standard considered becomes
more strict, the post—control concentrations (segment CD in Figure 9—1)
would fall. Since the pre—control data are unaltered by the choice of
standards, this implies that total benefits become larger the more strict
the standard.
The data available in each of the six post—control files are defined
in a manner consistent with the pre—control data. In particular, data are
available for the same measurements, averaging times, and years as in the
pre—control file. There is, however, a difference in scope. While the
pre—control file contains data for all counties in the design value file,
each of the post—control files contain data only for those counties that
are not in attainment with the relevant standard in at least one of the
years under consideration. As shown in Table 9—1, this timeframe encom-
passes the period 1987 to 1995 for TSP standards, and 1989 to 1995 for the
PM1O standards.
The number of counties included in the analysis for each of the
ambient standards is shown in Table 9—2. These observations represent a
Table 9—2
COTJNrIES IN POST—CONTROL FILES
Standard
Number of Counties
PM1O
(70/250)
93
PM1O
(55)
161
PM1O
(55/250)
163
PM1O
(55/150)
297
TSP
(75/260)
282
TSP
(150)
499
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subset of total available counties. This occurs because aggregation across
county parts was performed and several counties were discarded from the
analysis after consistency checks of the data. Both of these procedures
are discussed more fully below.
*.sidu*1 Nonattainment —
In Figure 9—1, point C is located such that the standard is just
attained in period t 1 . ilowever, in some cases, it may not be possible to
attain the standard in the implementation year. This can occur if all
available means of controlling emissions are applied prior to standard
attainment. This situation does occur with the data provided us, and a
county for which this happens is referred to as being in residual non—
attainment.
Nonattainment Du. to Growth —
Counties may also be in nonattainment with a standard due to growth in
emissions across time. For example, in Figure 9—1, segment CD is drawn
such that the standard is exceeded in the years after t 1 . In the context
of benefits analysis, this implies that the relevant area for benefits
calculations is ABDC. If the analysis were to assume that the standard was
just met in all years t 1 to t 2 , then the estimates of benefits (relative to
control costs) would be overstated by the benefits associated with area DE
in Figure 9—1.
The air quality modeling effort attempted to account for nonattainment
due to growth. This was done by applying all available control strategies
in year t, for example, in order to bring concentration levels in year t 2
as close to the standard as possible. In terms of Figure 9—1, this is
equivalent to shifting segment CD downward. Note that residual nonattain—
ment is still possible because the available control strategies limit the
shift in segment CD. Thus, the data provided in the post—control files
permit both residual nonattainment and nonattainment due to growth. For
purposes of discussion, we will call this situation the “A” scenario. As
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an alternative to this scenario, a “B” scenario was also defined. In this
scenario, air quality was reduced to a sufficient degree in the implementa-
tion year to ensure maintenance of the standard over the time period
analyzed. In order to achieve this degree of control, it was necessary to
apply pseudo—controls at a fixed cost per microgram reduction. These
procedures are discussed more fully in Reference (1). An example of the B—
scenario situation is shown in Figure 9—2. In this figure, if the post—
control data are assumed to lie along the segment FE, then benefits are
evaluated in terms of the area ABEF.
In order to derive the appropriate concentration levels for the B
scenario, the post—control files were transformed using relationships
described later in this section. These transformations are consistent with
those used by the cost contractor in developing B—scenario estimates of
control costs. Although it is possible that there will be nonattainment
when a particular standard is actually implemented, the maintained
assumption in this analysis is that all counties are in attainment
throughout the study period. Consequently, benefits estimates róported in
subsequent sections of this study represent B—scenario benefits. Benefits
estimates from A—scenario post—control data are provided for one of the
standards as a sensitivity check.
Background Data —
In addition to the concentration levels, the post—control data files
contain information on background levels of TSP concentrations. The
background concentrations are defined to be those concentrations that are
not sensitive to control strategies used in the analysis. Thus, the
background levels represent natural sources, small sources, and uncon-
trolled emission contributions from outside the county. While background
* In the benefits analysis, B—scenario refers to the total change from the
pre—control data to complete attainment. In the cost analysis, the B—
scenario concept is defined as the increment to complete attainment,
given A—scenario changes. This difference in definition has been
accounted for in comparisons of benefits and costs.
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Post-Control Data
“Type A”
Post-Control Data
“Type B”
Figure 9-2.
Air Quality Data With Complete Attainment
Pre—Control
Data B
D
Standard
t 1 t 2 Time
9—11

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data are defined for each. county, the data are develo:ped from regional
averages (across states). Thus, all counties in a state will likely be
assigned the same background values.* Furthermore, all background valties
are assumed to remain constant across time.
Background data are available in terms of annual arithmetic means and
24—hour second high measures. The corresponding background levels for PM1O
arithmetic mean and expected value are derived by multiplying the TSP
background values by 0.55. The background value for TSP geometric and
arithmetic means are assumed to be equal.
Definition of the background levels is an important part of the
analysis because it sets a lower bound for the post—control data. This
becomes a binding constraint for several counties in the B—scenario (full
attainment) analysis when all counties are driven into attainment for all
years analyzed.
Cons ist.ncv eckz of D.tz
Prior to using the pre—control and post—control (A scenario) data
files for calculating benefits, a series of consistency checks were carried
out to determine if the data met certain reasonable conditions. The condi-
tions tested were:
• One or more of the standards was violated in each county in
the pro—control data file.
• Post—control data were less than or equal to pre—control
data.
• Concentration levels were greater than background.
• Arithmetic means were greater than or equal to geometric
means.
* A small number of counties are assigned background values based on
county—specific SIP information.
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• 24—hour second high observations were greater than annual
mean measures.
• 24—hour PM1O expected values were less than 24—hour TSP
second highs.
These checks were made only with respect toA—scenario data. Addi-
tional consistency checks were undertaken after B—scenario transformations
were made. The results of the B—scenario consistency checks are presented
in a later subsection. The purpose of these checks is to document the
quality of the data base. In some cases, though inconsistencies are
present. it is notpossible totake corrective stepsbecause of a lack of
supporting information as to what changes are reasonable.
Standard Violation —
The pre—control file contains usuable data for 1,230 counties or
county parts in the United States. Since many of these counties are
currently (1978) in attainment with the proposed standards and are expected
to remain in attainment through 1995, they are not pertinent for the
benefit analysis. 5
The relevant subset of pro—control counties (those counties out of
attainment with one or more of the alternative standards) was determined by
comparing state and county codes in the pro—control file with state and
county codes in a data file defined in terms of a multiple standard of TSP
75 annual geometric mean and 150 24—hour second high. Since this standard
represents the binding combination of the two TSP standards considered, it
was thought that this comparison would yield a complete list of relevant
counties. That is, counties in each of the other post—control files would
be a subset of the counties in this “screen” file. Subsequent consistency
checks, however, showed this to be false in a few cases. Specifically.
$ This presumes that improved air quality in a neighboring county will not
load to air quality improvements in the county already in attainment.
Since there may be cross—county improvements, this assumption may lead to
an underestimate of benefits.
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several counties are in attainment with respect to the TSP standards, but
not in attainment with a PM1O standard. This situation is described more
fully below.
Of the 1,230 counties or county parts available in the pre—control
file, 532 areas remain in the sample after comparison with the screen file.
Each of these observations was then checked against the remaining
consistency criteria.
Post—Control Data Versus Pro—Control Data —
With the imposition of a control strategy in a given year, emissions
should be reduced. As a consequence, post—control data for that year
should be less than the pre—control data. This condition is satisfied for
all but four counties appearing in the various scenarios. In each case,
the negative change (i.e., pre—control concentrations are less than post—
control concentrations) occurs for the TSP annual geometric mean and is
equal to the difference between the post—control value and the background
value. This implies that the pre—control data for the geometric mean are
at background and no further controls are possible. However, controls are
applied because other averaging times have concentration levels not only
above background but above the relevant standard. Given the definition of
background, this can only be viewed as an inconsistency in the air quality
data set. No corrective action was taken for these counties.
Concentration Levels and Backgronnd —
Given that the background data represent a lower bound for the post—
control files, the post—control data should not fall below the given
background levels. To ensure that this condition is not violated, the
projections of pre—control and post—control values by the cost contractor
were made as if the background levels were the origin. Our check of the
data indicated that no county fell belo, background during the 1987 to 1995
* See subsection entitled, “Second Highs Versus Means”.
9-14

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time period. Rowever, the geometric mean for one county was at background.
No action was thought to be necessary in this case. Note that this test
was made for A—scenario data. Transformations for the B scenario do lead
to several violations of the background constraint. B—scenario issues are
discussed in a later subsection.*
Arit]i.etic and G.o.etria Means —
By definition, the arithmetic mean of a set of observations should be
greater than or equal to the corresponding geometric mean. In the original
design value file, however, several counties violate this condition. Since
the design value file is the foundation of the other air quality files,
this problem is reflected in the pre-control and post—control files.
There are ten counties in the various post—control files for which the
arithmetic mean is less than the geometric mean. Since no additional
information is available as to why the inconsistency exists or whether one
of the entries is miscoded, no alteration of the data was attempted.
Second High Valuos Versus Moans —
In the discussion of the standards violation consistency check, it was
noted that a comparison of the pre—control and screen files revealed an
unexpected relationship. In particular, there were 13 counties in viola-
tion of one or more of the PM1O standards but in compliance with both of
the TSP standards. Since PM1O is a fraction (0.55) of TSP, and the TSP
standards appear more stringent than the PM1O standards analyzed, this
suggested another consistency check.
The check involved a comparison of the TSP annual arithmetic mean with
the TSP observed 24—hour second high value. Naturally, one would expect a
measure of central tendency to be less than an extreme value.
* See subsection entitled, “Definition of B—Scenario Concentrations”.
9-15

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Recall that neither the pro—control file nor the post—control file
contains a measure of TSP annual arithmetic mean.. Instead, this measure is
derived (in 1978) by dividing the reported ,PM1O annual arithmetic mean by
O.55. When the arithmetic mean of TSP is calculated in this fashion,
seven of the 13 counties which violate a PM1O standard, but not a TSP
standard, record TSP arithmetic means greater than the observed 24—hour
second high for TSP. Thus, it appears that the PM1O data for these
counties may be suspect. Since these counties were not on the screen file,
they were not included in the benefits analysis. However, given the
implicit relationship among the TSP measures, their omission does not seem
inappropriate. It should be noted that, to our knowledge, these counties
are retained in the cost analysis. Consequently, this is one source of
possible inconsistency. A review of the difference between pre—control and
post—control data indicates that the impact of this inconsistency on the
benefits estimates should be minor.
Observed Versus Expected Values —
Of the remaining six counties which are in violation of a PM1O
standard but in compliance with the TSP standards, three have unusually
high PM1O 24—hour expected value readings in 1978. Since these values are
derived by multiplying the 1978 TSP expected value in the design value file
by 0.55, the implication of this is that the TSP expected value is very
much larger than the observed second highest reading. For the three
counties mentioned above, the expected value would be more than twice as
great as the second high. While this is possible, it does seem unlikely.
As with the seven counties described above, these three counties are
omitted from the analysis because they were not included in the screen
file. Given the data, this may not be inappropriate. However, the basis
for omission is weaker than in the previous case.
* In a sense, this is a circular calculation since the PM1O mean in the
pre—control and post—control files was derived by multiplying the
observed TSP arithmetic mean. in the design value file by 0.55.
9-16

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Finally, the three remaining counties which did not fail either of the
two PM1O consistency checks were also omitted from the analysis because
they were not on the screen file. This appears inappropriate and leads to
a small inconsistency in benefit and cost comparisons. However, since
benefits will be understated relative to costs, the benefit—cost comparison
will be overly conservative.
S”— ry of Consistency checks —
Table 9—3 summarizes the results of the consistency checks described
above. Although the omission of 13 counties leads to an inconsistency
between the air quality data in the benefit and cost analyses, the direc-
tion of inconsistency implies anunderstatement of benefits relative to
costs.
DEVELoI’1 gwr OF DATA FOl BEN ITS ANALISIS
The discussion of the design value, pre—control, and post—control data
files focused on the scope and consistency of data provided by the cost
contractor. In order to develop benefit estimates, several modifications
to the basic data base are required. In this subsection, five specific
issues are addressed:
• Geographical aggregation
• Temporal interpolation
• Projections of TSP annual arithmetic means
• Index of exposure
• Distribution of air quality changes
• Definition of Scenario B concentration levels.
9-17

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Table 9—3
SUMMARY OF CONSISTENCY CHECKS FOR A-SCENARIO DATA
State County Problem Code Disposition Code
02 0380 5 0
04 0480 3 0
04 1100 3 0
04 1680 3 0
04 2000 5 0
04 2600 3 0
06 1520 2 K
07 0565 2 K
07 1155 2 K
11 1980 2 K
18 2520 2 K
18 3280 2 K
23 3600 2 K
23 3980 2 K
24 3400 2 K
37 0360 3 0
37 1440 3 0
44 0300 2 K
45 0540 4 0
45 0860 3 0
45 2160 5 0
45 3440 4 0
45 5550 4 0
50 1580 1 K
51 0400 1 K
51 2600 1 K
51 3280 1 K
Problem Codes :
1. Post—control data greater than pie—control data.
2. TSP annual arithmetic mean less than TSP annual geometric mean.
3. TSP 24—hour second high less than TSP annual arithmetic mean.
4. PM1O 24—hour expected value greater than TSP 24—hour second high.
5. Not on screen file; no other problems observed.
Disposition Codes :
0 omitted.
kept.
9 . 18

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Geosraphical Agar.patjon
As noted earlier, there are 532 distinct county or subconnty observa-
tions available after the pre—control and screen files are compared. Of
the 532 observations, 31 represent subcounty data for 12 whole Counties. A
list of these counties is shown in Table 9—4(a).
Although the subcounty observations are generated to provide a more
realistic picture of air quality in a given area, the division into county
parts does create some problems for the benefits analysis. In particular,
it is difficult to obtain certain economic/demographic information used in
the analysis at less than the county level. Thus, if the air quality data
are to be merged with economic/demographic data in a consistent manner, an
aggregation procedure must be employed for those observations defined in
terms of subcounty areas.
The aggregation procedure involves selecting the county part (from the
multiple parts in a given county) with the highest annual geometric mean of
TSP in 1978 as representative of the entire county. This choice is consis-
tent with the procedure for selection of design value data, and therefore
is consistent with the data reported for whole counties.
In addition to the aggregation of subcounty observations, it.was also
necessary to disaggregate several observations. Specifically, six counties
in Massachusetts are aggregated into two “counties” in the design value,
pre—control, and post—control files. Rather than aggregate the
economic/demographic data across counties, we disaggregated the air quality
data. The disaggregation procedure involved assigning the same air quality
levels to each of the county constituents in the two aggregate “counties”
of the air quality files. The counties involved in this process are shown
in Table 9—4(b).
9—19

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Table 9—4
StJBCOIJNTY AND AGGREGATED OBSERVATIONS IN AIR QUALITY DATA FILES
a) Subcounties
State Code
State
County Code
County
03
Arizona
0620
Pima
14
Illinois
1540
Cook
14
Illinois
4100
La Salle
16
Iowa
0680
Cerro Gordo
16
Iowa
2280
Linn
16
Iowa
3120
Polk
24
Minnesota
3260
St. Louis
29
Nevada
0280
Humboldt
29
Nevada
0540
Washoe
32
New Mexico
0360
Eddy
33
New York
2000
Erie
52
Wyoming
0700
Sweetwater
b) Aggregate Counties
State
Code
State
Aggregate
County Code
County Code
County
22
22
22
22
22
22
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
1798
1798
1798
1291
1291
1291
0691
0801
0815
1325
1555
2205
Franklin
Hampden
Hampshire
Middlesex
Norfolk
Suffolk
9-20

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After these procedures were completed, there were 517 counties avail-
able for the analysis.* The distribution of these counties for each of the
various standards was shown earlier in Table 9—2.
Temporal Interpolation
Data in the pre—contro]. and post—control files are available only for
five discrete years: 1978, 1985, 1987, 1989 and 1995. In order to
estimate benefits over a range of years, it is necessary to make
assumptions on air quality levels in intervening years.
Figure 9—3 highlights the problem. The format of the figure is
similar to the formats shown in Figures 9—1 and 9—2. However, in this
case, the smooth segments AS and CD have been replaced by a variety of
possible time paths of concentration levels. Clearly, the level of
benefits (area ABDC) will be sensitive to the assumptions made with respect
to concentration levels in intervening years.
There are two straightforward interpolation schemes. One of the
approaches is a linear interpolation. This method assumes that concentra-
tions fall on a straight line drawn between the two points for which data
are available. The other approach is known as geometric interpolation.
This method allows concentrations to change by an annual “growth” factor
which is computed from the total “growth” observed between the years for
which data are available.
In the subsequent sections of this study, benefits estimates are
derived on the basis of geometric interpolation. The rationale behind this
choice can be traced to the manner in which ambient pollution levels are
derived from the growth in source emissions. Specifically, emissions are
assumed to grow at an approximately constant rate g between two time
* was later noted that two of the counties were in compliance with the
standards examined in the benefits analysis. Thus, the total number of
counties for which benefits are calculated is 515.
9-21

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Concentration
Standard
Figure 9-3.
Time Paths for Air Quality Data
A
C
E
t 2 TIME
9—22

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periods, say 1989 and 1995. The interpolated emissions for 1990 are
defined as (1+g) times the observed 1989 emissions. Similarly, emissions
in 1991 are (l+g) times the calculated 1990 emissions. Given that future
air quality concentrations are based on a proportional relationship with
projected emissions, air quality concentrations may be expected to follow a
similar pattern. Thus, the geometric interpolation method seems most
consistent with assumptions in the cost study.
To test the sensitivity of the benefit estimates to this assumption,
benefits were also calculated via the linear interpolation method for the
various studies reviewed in this report. For some studies, linear interpo-
lation resulted in smaller benefits. However, the majority of studies
recorded increased benefits with linear interpolation. For all studies,
the absolute value of the change was never more than one percent, with the
average difference being on the order of one—half of one percent.
Projections of TSP Annual Arithmetic Means
TSP data in the pre—control and post—control files are limited to
measures of 24—hour observed second high and the annual geometric mean.
Since many of the studies reviewed in subsequent sections of this report
are based on TSP annual arithmetic means, this is also a relevant measure.
Earlier, a procedure for determining TSP arithmetic means in 1978 was
described. Here, we are concerned with identifying these arithmetic means
for future years. The procedure used involves proportional relationships
between 1978 values of PM1O arithmetic means and changes in the annual
geometric mean of TSP across time. In particular:
TSP(AAM)t (PMlo u o 78 /o.55)
+ (TSP(AGM) — TSP(AGM) 78 )
( (PMlo . 73 /o.55) — TSP(AAM)b )
(TSPAGM 78 — TSP(AAM)b)
9-23

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where TSP(AAM)t is the year t annual arithmetic mean of TSP; PM1O(AAM) is
the year t (= 1978) annual arithmetic mean of PM1O; TSP(AGM)t is the annual
geometric mean of TSP in year t; and TSP(AAM)b is the TSP annual arithmetic
mean background level. The formula for calculating TSP(AAM)t is consistent
with the equations used by the cost contractor. Similar proportional
relationships are required to determine concentrations for various
averaging times after the B—scenario assumptions are imposed. These
transformations are discussed in more detail below.
Index of Exposure
The individual studies reviewed in subsequent sections of this report
each employ specific indices of air pollution exposure. If the results of
these studies are to be used in the calculation of benefits, the air
quality data in the pre—contro]. and post—control files must be defined
consistently on a study—by—study basis.
In the discussion of the sources and scope of the air quality data,
the design value data for a county was defined, in general, as representa-
tive of the monitor(s) with the highest readings of TSP (annual mean and
24—hour second high) in the county. Consequently, data in the pre—control
and post—control files can be used directly only in those benefit studies
which have air quality indices that are defined in an analogous fashion.
That is, the studies that used monitors that recorded the maximum values in
the geographic area, for a given averaging time and pollutant. Only then
will the units of the air pollution coefficient in the benefit equation be
compatible with the units of the air pollution variable itself. When the
benefit studies use a measure of air quality which differs from a maximum
value, adjustments to the pre—control and post—control data are required
prior to the calculation of benefits.
The studies reviewed in this analysis differ in several ways with
respect to the definition of the air quality indices. These differences
include:
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• All but two studies use a measure of TSP; the two remaining
studies use British Smoke (BS) and Suspended Particulate
Matter (SPM).
• All studies use either an annual average concentration, an
average 24—hour concentration, or a 24—hour second high
concentration.
• The geographic areas include Census tracts, cities,
counties, and SMSAs (Standard Metropolitan Statistical
Areas).
• The number of monitors in each county ranges from one to a
dozen or more; the monitor recording the highest concentra-
tions or an average across monitors was typically used in
the original study.
Because of these differences in study design, a procedure was developed to
ensure a meaningful integration of available air quality data with the
benefits equations of the various studies. This procedure involves
defining proportional relationships between maximum values for a county
(design values) and calculated county averages. County averages were
required because this is the form of the pollution variable in several of
the studies included in the benefits analysis.
Calculation of County Averages —
In order to calculate a county average of air pollution, it was
necessary to use site—specific information available from EPA’s Storage and
Retrieval of Aerometric Data (SAROAD) system. Recall that this is the data
base used, in part, to construct the design value file. However because of
certain adjustments to the data, more counties are available in the design
valu, file than in SAROAD. In fact, of the 515 counties considered in this
analysis, only 421 appear in SAROAD for 1978. A distribution of the number
of monitors per county is shown in Table 9—5.
County averages in 1978 for TSP annual arithmetic mean, annual
geometric mean, and 24—hour observed second high were calculated for each
county with two or more sites. It was then assumed that the ratio between
the county averages and observed design values in 1978 would be maintained
9-25

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Table 9—5
DISTRIBUTION OF SAROAD MONITORS BY COUNTY
No. of Monitors
in County
No. of
Counties
0
94
1
157
2
65
3
36
4
32
5—10
93
11—20
32
21—30
3
> 30
3
Total
515
for all future years. Since the pre—control and post—control data are
generated from design values, the projected equivalent county average value
in year t can be calculated as:
CAt DVt (CA. 78 1DV 78 ) (9.2)
where t is the county average in year t and DVt is the design value (pre—
control or post—control data) in year t. This calculation was performed
for each of the TSP measures mentioned above, for each county with two or
more monitors.’
* Since the design value reading typically represents a maximum value, the
term (CA 78 /DV 78 ) was constrained to be less than or equal to one. The
constraint was binding in only 16 counties.
9-26

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For those counties in SAROAD with no or only one monitor, the factor
(CA 78 /DV 78 ) in Equation (9.2) was calculated as the average of this factor
across all counties which had two or more monitors. These average values
for each of the three pollutant measures are shown in Table 9—6. Numbers
in parenthesis represent standard deviations.
Given the calculations implied by Equation (9.2). new pre—control and
post—control data are defined which can be interpreted in terms of county
averages rather than county maximums. These data can then be used in the
calculation of benefits for those studies utilizing average pollution
readings in the benefit equations. Naturally, the original (design value)
pre—control and post—control files are still appropriate for those studies
using maximum values. A study—by—study summary of the index used is given
in Table 9—7.
Sensitivity check —
Of the 14 separate studies reviewed in this report, 13 use population
or household data in the benefits calculation phase. Because of this, a
more conservative approach for estimating county averages would be to use
only population—based monitors from the SAROAD data system. Ostensibly,
this would yield county averages that are more representative of county
Table 9—6
COUNT! AVERAGE CORRECTION FACTORS
Pollutant Measure (CA 78 /DV 73 )
A nu*1
Geometric

Mean
0.74
(0.16)
Annual
Arithmetic
Mean
0.74
(0.16)
24—Hour
Observed
Second
High
0.70
(0.18)
9-27

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Table 9—7
REVIEW OF POLLUTION MONITORS USED BY STUDIES
Study
Geographic Area
Monitor(s) Used 4n
c ig inal Study
Measure Used for
Benefits Malysis
-
Design Value Avg. Over
Monitor in All Monitors
County in County
Mazi.indar et al.
— —
County of London
Avg. of 7 representative monitors
in county
x
Samet et a!.
Area served by hospital in
Steubenville, OH
Monitor near hospital (not highest
value monitor in area)
x
Saric et al.
P ighborhoods in two cities
Both indoor and outdoor monitors
x
Ferris et al.
Berlin, NH
Avg. of 3 monitors in Berlin
x
Lave & Seskin
SAa
Center city monitor in each SMSA
(NASN data)
X
Crocker et a!.
Counties
County monitor having most
complete data between 1967—75
X
Ostro
Cities
Avg. over all 01 monitors
x
Property Value
Studies
Census tracts in a city
Interpolation of census tract
exposure from isopleth or dis-
persion modeling of city monitors
x
Bosen
SMSAs
Monitor representing worst
incidence within SMSA
x
K. ith
SNSA8
Avg. over all monitors within SMSA
X
Ctunmings et a!.
4 Nzonesn in Phila. SMSA
Avg. of monitors in each zone
x
Watson & Jakach
4 zonesu in Phila. SMSA
Avg. of monitors in each zone
x
Mathtech
(Household)
SMSAs
Highest value monitor in SMSA
X
tlathtech
(Manufacturing)
Counties
Avg. over all monitors in county
x

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population exposures. Of the 2,983 TSP site monitors in SAROAD in 1978,
2,180 are designated as population surveillance monitors. When county
averages are computed from these monitors only, the factor (CA 78 /DV 78 ) in
Equation (9.2) is 0.75, 0.75, and 0.69 for the geometric, arithmetic, and
second high measures, respectively. Comparing these values with those
reported in Table 9—6, there are only minor differences. Thus, the impact
on benefits will be minimal. Consequently, benefits estimates for those
studies using county average air quality data are reported only for the
all—monitor case.
Distribution of Air Qma1it kaues
The pre— and post—control air quality data identify the air quality
changes predicted to occur at a single location in the county. This is the
location of the design value monitor. The assumption made in the cost and
air quality modeling effort is that all sources contribute proportionally
to the concbntrations recorded at the design value monitor. The propor-
tionality factor is based on source emissions rates and takes into account
differences in stack height. Thus, a given reduction in emissions across
all sources is assumed to reduce concentrations at the design value monitor
by a known factor. Although certain stack parameters do affect this
factor, distance of the source from the design value monitor is not
explicitly considered.
Consistency of the cost and benefit analyses would require beüeiits
estimates to be calculated across all parts of the county, since control of
sources is not limited to a specific group of Sources near those monitors
that record concentrations higher than the alternative standards. However,
an alternative approach would be to assume that Stat. Implementation Plans
(SIPs) may in practice require control first on those sources suspected of
contributing most to any observed air quality violations. In this case,
benefits may arise primarily in those areas near controlled sources. If
the population in the county is located in areas away from the controlled
9—29

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sources, then their exposure may not change appreciably and few benefits
would be generated. 5
The potential for overestimating benefits becomes clearer when the
different air quality indices used in the benefits analysis are considered.
These are the average of readings at air quality monitors in the county and
the reading at the highest valued monitor in the county. Figures 9—4 and
9—5 show how the changes at the design value monitor may affect the
exposure of the county population.
In Figure 9—4, the pre—control distribution of air quality is labelled
as A. For expository purposes, it is assumed that the county is one—
dimensional and that population is distributed uniformly. If the air
quality index is based on the highest valued monitor, then a reduction in
air pollution of I Lg/m 3 at the design value monitor results in an assumed
reduction in PM concentrations of I ig/m 3 throughout the county (subject to
a possible background constraint). This post—control distribution is
labelled as curve B. Benefits would be estimated as the health and welfare
effects associated with the shaded area.
In Figure 9—5, the pre—control curve is the same (again labelled as
A). flowever, it is now assumed that PM concentrations in the county are
reduced by the same percentage as concentrations at the design value
monitor. Since the average value of PM concentrations in the county is
less than at the design value monitor, the shaded area in Figure 9—5 will
be less than that in Figure 9—4.
Figure 9—5 is probably more representative of reality in view of the
dispersion properties of PM and the likelihood that SIPs may focus on
specific sources contributing to violations. Thus, Figure 9—5 is used in
the benefit analysis.
* This is a simplification since population data reflect only where people
reside. Since some individuals will work in the vicinity of the
controlled sources, they would realize an exposure reduction during the
work day. Data available for this study did not permit this distinction.
9-30

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pg/rn 3
Standard
Figure 9—4.
Equal Magnitude Improvement
Pre-Contro].
Post-Control
A
B
nitor 2 Design D itor bnitor 3
9—31

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3
igAn
Figure 9—5.
Equal Percent Improvement
Standard
Post-Control
A
B
nit r 2 Design Monitor nitor 3
9-32

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Depending on actual dispersion properties and the location of
controlled sources, it is possible that air quality improvements may be
even more narrowly focused than suggested by Figure 9—5. For example, it
is possible that air quality may change as shown in Figure 96. In Figure
9—6, curve A again identifies the pre—control distribution. In the post—
control case, the distribution of the improvement is limited to the area
near the monitor out of compliance.
Although the third case is not consistent with the assumptions under-
lying the cost and air quality modeling effort, it is possible that air
quality improvements may follow this pattern in reality. Thus, it would be
informative to identify the extent to which benefits are sensitive to
assumptions about the changes in the air quality distribution. In order to
provide such a check, it is necessary to have detailed information on air
quality dispersion characteristics. An area study that compares the
benefit estimates obtained using dispersion modeling results with the
estimates calculated assuming air quality improvements proportiona l, to the
change at the design value monitor is described in Section 10. Because of
time and resource constraints, it is not practical to develop dispersion
modeling results in the nationwide study. In the absence of dispersion
modeling, the assumption of equal proportional air quality changes in the
benefit analysis (Figure 9—5) is the best that can be done with the avail-
able information.
Note that the lack of dispersion modeling also hinders the use of
studies which measure air quality effects at the city or census tract
level. For moat appropriate use of this. studios, it would be desirable to
have dispersion modeling results for suboounty areas. Alternatively, for
studies based on county or SMSA data, using county air quality indices
provides a relatively good match with the units of the variables used in
the original studies.
9-33

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pg/rn 3
Figure 9-6.
Localized Improvement
Post-c trol
nitor 2 Design nitor nitor 3
9-34

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Definition of B—Scenario Concentrations
The final adjustment made to the pro—control and post—control data
provided by the cost contractor involve.s the definition of B—scenario
concentrations. As defined earlier in this Section, A—scenario data may
leave some counties in nonattainment after imposition of the standard. The
data in the pro—control and post—control files are of this type. On the
other hand, the B scenario was defined to be the situation where every
county would be in attainment with the relevant standard in each year
considered. The relationship between A and B scenario data is demonstrated
in Figure 9—7. In this subsection, the procedures used to derive the B—
scenario concentrations are discussed. It is important to note that the
transformations used are consistent with those used by the cost contractor
in a corresponding analysis of B—scenario control costs.
B-S..nario Calculations —
There are two major issues that arise in the calculation of the B—
scenario values. These are:
• Between implementation of the standard and 1995, which year
in the post—control file is furthest from the relevant
standard (i.e., what is the controlling year)?
• In the case where a standard is stated in terms of two
averaging times, which is the most restrictive (binding)
averaging time?
The rationale behind the B—scenario calculations is that if the data
that an, furthest from attainment (i.e., a liven year and averaging time)
can be brought into attainment, then all other years and averaging times
will also be in attainment. Note that this is just the definition of the B
scenario.
The procedure used to determine the controlling year and averaging
time (standard) is described in detail in the technical appendices to the
9-35

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Concentrations
Figure 9—7.
Pre-Control
Data
A Scenario
Comparison of A and B Scenarios
A
Standard
E
Data
B Scenario
t-Control Data
t 2
Time
9—36

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cost analysis (1). Only a brief discussion of the major equations will be
given here.
The first step in defining B—scenario concentrations is to calculate a
set of factors of the form:
Zjj (x 1 — sj)/(x? — X j) (9.3)
where is the A—scenario post—control data in year i for standard j;
is the level of standard 3; and X’ j is the pre—control data in year i for
standard 3. Z 1 is th. ratio of the additional improvement required to
obtain the standard to the improvement obtained with available controls.
This proportionality factor is used to alter the A—scenario data so that
attainment is guaranteed in all years.
The controlling year and standard are determined by computing the
maximum of all Zjj. That is,
ZB — max (0, ij (9.4)
i,j
Note that if Zjj is negative for all i, then the standard is attained in
all years and B—scenario data are equivalent to the A—scenario data.
Given a positive 5 B’ adjustments to the QQntrolljg standard are made
by assuming a proportionate shift relative to the A—scenario data and the
controlling stAndard level;
— (z — x 3 )(z 8 Zjj) (95)
where is the B—scenario concentration in year i for controlling
standard 3. In the case where 5 B equals Zjji x will be set equal to the
standard $j. In all other oases, j will be less than the standard.
9-37

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Figure 9—8 portrays the adjustments implied by Equations (9.3) to
(9.5). For ease of exposition, assume that there are only two distinct
time periods, t 1 and t 2 . Furthermore, let us assume the following
numerical values (in tg/m 3 ): x j 100; ‘ 2j 130; x 1 = 65; = 90;
and s. 55. Then, by Equation (9.3), 0.286 and 0.875.
According to Equation (9.4), this implies that LB e nals z 2 (= 0.875).
Finally, Equation (9.5) is used to find the B—scenario concentrations. For
i equal to t 2 , ‘ 2j is just equal to the standard s . In year t 1 , X jj is
equal to 34.4 pg/ms. These concentrations are indicated as the lower line
s egment in Figure 9—8.
Knowledge of for the controlling standard permits a straightf or—
ward adjustment of the concentration levels in each year of the other
standards (in a multiple standard case). Specifically,
= — A(x 1 — X j) (9.6)
where
A = 18,k — xb,k)/(x 8,j — x 1 , ) (9.7)
Here, ‘8,k is the 1978 (A scenario) concentration for standard (averaging
time) k, and xbk is the background concentration level for averaging time
k.
Consistency 1secks —
Two consistency checks are made as the B—scenario data are con-
structed.
• In the case of multiple standards, a check is made to
determine if one standard controls in one year while the
second standard controls in another year.
• A comparison is made between the calculated B—scenario
values and the background values.
9-38

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Concentrations 0
Si_
S —
.t]. J
I I
t 1 t 2 Time
Figure 9—8. Development of B—Scenario Data
9-39

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The first check is made to determine if zB (the maximum of the Z 13 )’ is
sensitive to averaging time. Given that the data are related via propor-
tionality factors, it was believed a uriori that a controlling standard in
one year would control in all years. This is true for all but three
counties in the data base. With ZB computed from all years and averaging
times, no correction is required. The most binding standard is always
selected for control to the standard level so that complete attainment is
ensured.
Violation of the background constraint is a relatively serious problem
in the construction of the B—scenario data. This is especially true for
the stringent TSP standards. Overall, calculation of and Xj from
Equations (9.6) and (9.7) led to 12 counties having predicted concentra-
tions lower than background in at least one year or averaging time. In
fact, because of the linear shift implicit in the equations, seven counties
had predicted concentration levels below zero. For all of these counties,
post—control B—scenario data were set equal to the appropriate background
concentration.
Although the concentration levels predicted from the B—scenario
transformations are based on very general proportionality assumptions,
these data are used as the principal air quality indicators in the benefits
analysis. As noted earlier, equivalent assumptions are used in the cost
analysis. See Reference (1) for further caveats related to the use of B—
scenario data.
Table 9—8 reports the counties that failed the two consistency checks
for the B—scenario data. Corrections were made as described above. None
of these counties are omitted from the benefits analysis.
I IT CAL JLATIONS
Given the air quality data base described in the previous subsections,
we next review the manner in which these data are used in the benefits
analysis. Three issues are addressed:
9-40

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Table 9—8
PROBLEM COUNTIES IN B-SCENARIO DATA BASE
State County Problem Code
01 2400 2
03 0300 2
05 5440 1
10 1800 2
13 0080 2
13 0820 2
13 1420 2
14 1540 1
23. 1340 2
29 0080 2
29 0280 2
32 0640 1
37 2620 2
45 1710 2
52 0700 2
Problem Code :
1. Different standards control in different years.
2. B—scenario concentrations predicted to be less than background.
9-41

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• Alternative standards to be analyzed.
• Parameters used in reporting the stream of benefits.
• The manner in. which benefit estimates are tabulated.
Tynos of Standards
Table 9—9 shows the alternative standards analyzed in this study.
This table is like Table 9—1, except an additional column is added. This
column identifies a particular standard as being primary or secondary. In
the Clean Air Act, primary standards are set to protect human health with
an adequate margin of safety; secondary standards are set to protect the
public welfare. 5 Note, in this report, that the reported benefits for the
secondary standards represent the benefits that would accrue with a change
from current levels to the secondary standard. In order to determine the
incremental benefits of the secondary standard relative to a particular
primary standard, it is necessary to subtract the primary standard benefits
from the reported secondary standard benefits. In this study, the PM1O
secondary standard of 55 )ig/m 3 is associated with the PM1O 70/250
primary standard. In addition, the two TSP standards of (75/260) and 150
represent the current primary and secondary standards, respectively.
The choice of standards to be analyzed was made by EPA. The rationale
for selection of the alternative levels is based on information contained
in the Criteria Document (4) and the Staff Paper for PM (2).
Paraa.ters Used I a tks Ban.fits Analysis
The benefit models described in subsequent sections of this report
each calculate benefits in terms of functions of changes in air quality.
* The distinction is somewhat unfortunate in that some health effects may
be realized at levels below the alternative primary standards, and
welfare benefits are generated in the course of attaining alternative
primary standards.
9-42

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Table 9—9
AMBIENT AIR QUALITY STANDARDS
Pollutant
Loyal
Type
Averaging Time
Implementation
Date
Maintenance
Date
P110
70
250
Primary
Primary
Annual Arithmetic Mean
24—Hour Expected Value
1989
1989
1995
1995
P110
55
Secondary
Annual Arithmetic Mean
1989
1995
P110
55
250
Primary
Primary
Annual Arithmetic Mean
24—Hour Expected Value
1989
1989
1995
1995
P110
55
150
Primary
Primary
Annual Arithmetic Mean
24—flour Expected Value
1989
1989
1995
1995
TSP
75
260
Primary
Primary
Annual Geometric Mean
24—Hour Second High
1987
1987
1995
1995
TSP
150
Secondary
24—Hour Second High
1987
1995

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In general, the relationship between benefits and air quality can be
expressed as:
B = F(AQ 1 ) — F(AQ 0 ) (9.8)
where B is the dollar benefit in year i for a particular benefit category;
AQ is the air quality in year i (post—control file data); AQ 0 is the pre—
control air quality; and F is a function. In this report, the functional
form F is assumed to remain constant across years.
Diagrammatically, the yearly benefits represented by Equation (9.8)
can be shown as in Figure 9—9. This figure is similar to those shown
earlier. In Figure 9—9, the air quality associated with the post—control
data is pictured as in attainment for all years between t 1 and t 2 , and
background levels are assumed tobe binding. This latter feature is for
illustrative purposes only. In fact, in most counties, the post—control
data for the B scenario were above background.
Equation (9.8) indicates that benefits are calculated for each year in
the time horizon beingconsidered. This is 1989 to 1995 for PM1O and 1987
to 1995 for TSP. As a consequence, there is a series of anticipated yearly
bencf its. In order to provide a consistent summary of the total value of
benefits, it is convenient to summarize the stream of benefits in terms of
a discounted present value.
Disconnt.d Present Vain. —
The formula for calculating the discounted present value of benefits
is expressed as:
1 N
DPV £ B /(1 + r)i (9.9)
(1 + r) i1
where DPV is the discounted present value; r is the prescribed rate of
discount, and B represents the benefits in year i. Note that all benefit
9 44

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Concentrations
(i g/ni 3 )
Standard
Background
Figure 9-9.
Benefits of Ambient Air Quality Improvements
Pre—Control
Data
Due
to Standard
Scenario B
Post-Control
Data
t 2 Time
9-45

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estimates are reported in 1980 dollars, and benefits are assumed to accrue
at the end of each year the standard is in effect. The parameters for the
indices and N, and the assumed value for r are provided in Table 9—10.
Given these parameter values, the DPV is interpreted as a discounted
present value in 1982, in 1980 dollars, ‘with a 10 percent discount rate.
The timeframe assumptions were provided by EPAe The choiCe of discount
rate is guided by 0MB requirements.’
Annualized Benefits —
Another way to report the stream of benefit estimates is to calculate
the constant (levelIzed) annual benefits during the period of the standard.
This number is derived by converting the 1982 DPV value forward to a DPV in
the implementation year (1987 or 1989) and then multiplying the 1987 or
1989 DPV by an annualizing factor. Because of the different timeframes for
PM1O and TSP, the conversion and annualizing factors differ for the two
pollutants. For PM1O, the combined factor is:
Table 9—10
BENEFIT PARAMETER VALUES
Parameter
Standard (Pollutant)
—— ———— . ——
PM1O TSP
r

N
0.10
7
7
0.10
5
9
* The choice of 10 percent may be high for some of the benefit categories
(economic sectors) analyzed in this report. See Reference (5) for a
clear discussion of the rationale and problems associated with choosing a
discount rate.
9 -46

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(1 + r) 7 ] (9.10)
For TSP, the combined factor is:
(1 + r) 5 r (9.11)
1 — 1/(1+r)
Benefit estimates in terms of the DPV and annualized benefits are reported
in subsequent sections of this study.
Tabm1 tjon of B.nef its
Benefits are calculated on a county—by—county basis. This is consis-
tent with the level of detail available in the air quality data. For
reporting purposes, however, benefits are aggregated to the level of the
ten EPA Federal administrative regions. While aggregation hides some
detail, this breakdown provides an indication of the distribution of
benefits across the country: Table 9—li. shows the assignment of states to
the ten EPA regions.
S11 ARY OP A QUAIX1 DATL
This section has reviewed the source, scope, and use of the sir
quality data for benefits analysis. One of the major concerns involved
consistency with the companion cost analysis. For the most part, the data
used in the two studies are equivalent. For the. small number of counties
excluded from the benefits analysis, but retained in the cost analysis, the
impact on benefit—cost comparisons should be minimal. In fact, the exclu-
sions tend to make the benefits estimates smaller relative to costs.
r
9-47

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TabiC 9—11
EPA FEDERAL ADMINISTRATIVE REGIONS BY STATE
State
Code
State
EPA
Region
State
Code
State
EPA
Region
01
Alabama
IV
27
Montana
VIII
02
Alaska
1
28
Nebraska
VII
03
Arizona
I X
29
Nevada
IX
04
Arkansas
VI
30
New Hampshire
I
05
California
II
31
New Jersey
II
06
Colorado
VIII
32
New Mexico
VI
07
Connecticut
I
33
New York
II
08
Delaware
III
34
North Carolina
IV
09
Washington, DC
III
35
North Dakota
VIII
10
Florida
IV
36
Ohio
V
11
Georgia
IV
37
Oklahoma
VI
12
Hawaii
IX
38
Oregon
X
13
Idaho
I
39
Pennsylvania
III
14
Illinois
V
40
Rhode Island
I
15
Indiana
V
41
South Carolina
IV
16
Iowa
VII
42
South Dakota
VIII
17
Kansas
VII
43
Tennessee
IV
18
Kentucky
IV
44
Texas
VI
19
Louisiana
VI
45
Utah
VIII
20
Maine
I
46
Vermont
I
21
Maryland
III
47
Virginia
III
22
Massachusetts
I
48
Washington
I
23
Michigan
V
49
West Virginia
III
24
Minnesota
V
50
Wisconsin
V
25
Mississippi
IV
51
Wyoming
VIII
26
Missouri
VII

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1. Argonne National Laboratory. Costs and Air Quality Impacts of
Alternative National Ambient Air Quality Standards for Particulate
Matter, Draft Technical Support Document, June 1982.
2. U.S. Environmental Protection Agency. Review of the National Ambient
Air Quality Standards for Particulate Matter. Draft Staff Paper,
October 1981.
3. U.S. Environmental Protection Agency. Review of the Relationships of
IP1O, IP1S, and TSP. Memo to N. Thomas of EPA, July 1981.
4. U.S. Environmental Protection Agency. Air Quality Criteria for
Particulate Matter and Sulfur Oxides — Volume IV. Review Draft No. 2,
December 1980.
5. Provenzano, G. j al. Methods for Evaluating the Equity Impacts of
Proposed Environmental Regulations. Draft background report of the
equity impacts analysis team of the Regulatory Impacts Analysis
workgroup, September 1981.
9-49

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SECTION 10
SELECTED METHODOLOGICAL ISSUES

-------
SEcI’ION 10
S RCI E ODOLOGIChL ISSUES
This section discusses several methodological, issues which arise in
the benefit analysis. The issues are sufficiently important that they
require fairly detailed discussion. They also affect several parts of the
benefit analysis and are thus most conveniently considered in one central
place rather than at each point where they arise.
The first issue concerns how to combine the benefit estimates from
individual health and welfare studies and categories. Some combining or
aggregation is required in order to obtain a total benefit estimate for
comparison with costs. Aggregation is difficult because it cannot be
accomplished by simply summing all categories. Simple summation would run
the risk of double counting and would not give adequate consideration to
study quality. Aggregation procedures are discussed in the first
subs e c t ion.
The second issue is the question of applicable PM concentration ranges
for benefit calculations. Three possible ranges are available. These
include: 1) the range of air quality improvements experienced under
alternative st.ndards 2) the rang. of air quality included in the data
samples of the original health and welfare studies; and 3) the range of
concentrat4ons within which the Clean Air Scientific Advisory Committee
(CASAC) has concluded that quantitative evidence of health effects exists.
The lower bounds of the first two ranges are generally below the lower
bound identified by CASAC. The issues this raises for the benefit analysis
are discussed in the second subsection.
10-1

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The third issue concerns air quality data. The benefit analysis
relies on air quality data •developed as part of the cost analysis. Use of
common data helps insure that the cost and benefit analyses are as
c-onsistent with one another as possible. However, the benefit analysis
requires more spatial detail than is available from the cost analysis.
Approximations are made to provide this detail. The uncertainties
introduced into the benefit estimates by these approximations are the
subject of a detailed sensitivity analysis discussed in the third
subsection. The sensitivity analysis is based on a case study of air
quality dispersion modeling focused on the Chicago area.
PZO URE$ FOt CALCULATION OF AGGREGATE B EFITS
Introduct ioa
Sections 2 through 9 of the report have developed methods for
estimating incremental benefits in a variety of health and welfare
categories. Those sections do not discuss how to combine the estimates for
the individual benefit categories in order to develop an estimate of
aggregate incremental benefits. It is the purpose of this section to
describe how aggregation is done in the study.
Aggregate numbers are required in order for any benefit—cost analysis
to proceed. Aggregation requires combining benefit estimates from
individual health and welfare effects studies and categories. However,
aggregation cannot be accomplished by simply summing all of the categories
because this might result in double counting and other problems. One of
the most difficult problems is that the available health and welfare
effects studies vary widely in scope and quality. Thus, aggregation forces
judgments to be made about study scope and quality.
Some of the issues which must be considered in choosing an aggregation
procedure are:
• Does the procedure avoid double counting of benefits?
10-a

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• Does the procedure provide complete coverage of benefits?
• Does the procedure give due consideration to the relative
strength of the evidence across different benefit
categories?
The double counting problem is perhaps best illustrated by the property
value studies. It is generally believed that properly value studies
measure a combination of effects which may include health, soiling, and
aesthetic benefits. However, the proportion of benefits in each category
is not generally known. Nor is the relationship to benefits estimated from
specific health and soiling studies known. Consequently, it is generally
not appropriate to add property value estimates together with estimates
from health and soiling studies in developing aggregate benefit numbers.
The problem of benefits coverage ii perhaps best illustrated by
various health studies from the medical epidemiology literature used in the
benefit analysis. Morbidity studies in this category generally measure
only the respiratory disease effects of particulate matter. They do not
capture other potential health impacts of particulate matter such as
possible cardiovascular effects or other forms of acute or chronic illness
that might be pollution related. As a consequence, if one relies solely on
the medical epidemiology studies for estimates of morbidity—related
benefits, benefits that might arise from reductions in non—respiratory
diseases would be omitted.
It is unfortunately the case that no one aggregation procedure best
satisfies all three criteria above. As a consequence, this analysis
follows a specific strategy. The first element of the strategy is to use
the individual health and welfare effects studies to develop the basic
benefit estimates. Second, the property value and hedonic wage studies are
used as cross—checks on the basic benefit estimates. Recall that the
property value and wage studies measure aggregate effects. Third, the
analysis develops several alternative aggregation procedures. These
• See Section 5 for additional discussion of property value studies.
10-3

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alternatives are described in detail in the paragraphs that follow, Each
alternative gives differing weight to the three criteria above. In
particular, each reflects different possibilities of double counting versus
under—counting. Each reflects different assessments of study quality.
Criteria for Assossina Stu4 Qunlitv
Study quality is one of the most important issues in the process of
aggregation. Therefore, this section sets forth the criteria that are used
in assessing the quality of the studies available for developing aggregate
benefit estimates. The criteria are similar to those used by the Clean Air
Scientific Advisory Committee (CASAC) in evaluating studies for use in the
Criteria Document (1). ifowever, there are differences in the two sets of
criteria since CASAC was not specifically concerned with ns of studies in
a benefit analysis.
Study Sample —
The first criterion considered is: Did the study use well defined and
appropriately selected study populations? At least two issues are
important in this regard. First, did the study use microdata on
receptors as opposed to aggregate data for cities, counties, or SMSAS.
Microdata is preferred because it avoids the problems that arise when using
grouped data. One important problem is loss of efficiency in the
estimation of statistical parameters. Also, with macrodata, proper
statistical controls are loss directly applied.
The second issue in terms of sample selection is whether the study
sample can be considered ropresentativó of the U.S population. A repre-
sentative sample reflects the range of differences in the U.S. in personal
characteristics (age, sex, race, occupation, etc.), environmental charac-
teristics (climate, methods of home heating, etc.), and other influences on
health status. A representative sample provides greater assurance that the
estimates of effects developed can be extrapolated to the U.S. population
for developing national benefit estimates. A loss representative sample,
10-4

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on the other hand, may provide a less valid basis for calculation of
national benefits.
Study Controls —
The second criterion used in assessing study quality is the extent to
which the study controlled for potentially confounding or covarying
factors. One issue here is the extent to which variables were included in
the analysis to control for covarying effects. Example variables include
age, sex, race, diet, smoking habits, occupational exposure, and method of
home heating. The second issue here is whether the study properly
controlled for behavioral adjustments that individuals may make to
compensate for pollution effects. As an example, some individuals may stay
indoors on particularly polluted days in order to avoid health—related
problems. If this is done, and not considered during study design, then
the measurement of health effects with respect to levels of pollution could
be biased downwards.
*easure snt of Pollution Exposure —
The third criterion considered in the study assessment is the quality
of the measurement of pollution exposure, One attribute considered in this
regard is whether the study controls for both particulate matter and sulfur
oxides. If only one pollutant was considered or used in the analysis, the
potential exists for the estimated effect of that pollutant to be biased.
A second characteristic considered is the quality of th. monitoring of
pollutiou. This might take two forms. In the case of a large geographic
area, a dense monitoring network may be required to provide an accurate
estimate of pollution exposure. Or in the case of a relatively small
geographic area, a small number of monitors may be adequate for measurement
purposes.
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Statistical Techniques —
A fourth criterion considered in assessing study quality is the extent
to which studies used appropriate and properly performed statistical
techniques. This issue includes consideration of adjustments that may have
been performed on the raw data, methods used in analyzing the data, and
conclusions drawn from the analyses performed.
Xessureaent of Effects —
The types of effects measured in individual health and welfare studies
vary widely. Some are ideally suited for benefit analysis, while others
are difficult to apply in a practical way. A good example of the latter is
the common practice in medical epidemiology studies to measure health
endpoints such as reductions in lung function. Although these measures may
be useful for medical diagnostic purposes, they have little practical
application for estimating economic benefitg. In some respects then, this
criterion helps to separate studies which may be useful for Criteria
Document purposes from those which may be useful for benefit analysis
purposes.
Consist.*cy YIth Otker Research —
The final criterion considered is whether the study obtained results
which are consistent with other research in the ares. The degree of
consistency enables judgments to be made about the degree of confidence
that one can have in using the study for benefit calculations.
Luolipation of Stidy Evaliatiom Criteria
Table 10—1 summarizes how the study evaluation criteria were applied
to the individual studies selected for estimating benefits.* The table is
* Complete bibliographic citations for the studies can be found in Section
1.
10-6

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APPLICATION OF
Table 10—1
STUDY EVALUATION CRITERIA
Micro
Data
Repres.
S sap 1.
Covar.
Controls
Behavioral
Controls
Both
Pill SO 1
Network
or Saall
Are a
Approp.
Stat.
Approp. Other
Effects Research
HEALTh EFFECTS
Crocker at *1.
Ferris at J. •l•
Lave & Seskin/
Lipfe rt
Mazuadar at a1 . *+
Ostro
Saaet at al +
Sane ii.
1
1
0
0
1
0
1
<1
0
1
1
1
0
0
<1
Long it.
(1
Longit.
<1
Long it.
<1
<1
0
0
0
0
0
0
<1
0
1
1
1
0
0
0
1
1
1
1
<1
1
<1
NM
1
0
1
<1
NH
1
1
1
1
1
1
1
(1
1
1
1
<1
1
1
WELFARE EFFECTS
Cuaings j.
1
1
0
0
0
1
(1
1
<1
Matbtech
Household
0
1
1
1
1
(
1
1
1
1
Mathtechilanufaot.
0
1
<1
1
1
<1
1
1
<1
Watson&Jaksoh
1
1
0
1
0
1
NM
1
<1
* CASAC “quantitative” study.
+ EPA staff Paper “quantitative” study.
NM Not meaningful.

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organized as follows. Across the top of the table are the various study
evaluation criteria described previously. The rows in. the table correspond
to the individual studies available for assessing health and welfare
effects. Note that studies have been grouped into separate health and
welfare effects categories, and. within those categories, listed alphabeti-
cally. Entries in the table are primarily l’s and 0’s. A “1” is assigned
if the study met the listed criterion, and a “0” is assigned if the study
Was deficient in that criterion. In some cases, a clear judgment was not
possible. These cases are generally shown as < 1, not meaningful (NM), or
by some other designation.
The table also identifies studies which CASAC (2) has indicated
provide quantitative evidence of pollution—related effects. These are
identified in. the table by an asterisk. They include the studies by Ferris
et ii. and Mazumdar j gj. (CASAC also identified other studies providing
quantitative evidence, but they could not be adapted for benefit calcula-
tions.) Also shown. in the table are the studies judged in the EPA Staff
Paper (3) to provide quantitative evidence of concentration—response rela-
tionships. These include the Ferris g.. gj.. study, the Mazumdar
study, and the study by Sam.t jj. The latter studies are identified by
a plus symbol in the table.
To highlight the differences between study assessments made for
purposes of standard setting and criteria document development, and studies
used for benefit analysis, it is useful to compare the quality assessments
made for the CASAC “quantitative” studies and some of the other studies
available for benefit analysis. This is perhaps best illustrated by
comparing the two C.&SAC—approved studies by Ferris j gj,. and Mazunidar j
ii. with the study by Ostro. The Ostro study appeared in preliminary form
prior to CASAC closure of the Criteria Document, but has not been formally
reviewed for purposes of inclusion in the Criteria Document.
In the first column of the table, which is concerned with whether the
study used microdata, note that the Ferris jtgJ,. study is assigned a “1”.
The Mazumdar j jj.. study, which used aggregate data for the County of
10-8

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London is assigned a “0”. The Ostro study, which used microdata from the
National Health Interview Survey, is assigned a “1”.
The second column is concerned with whether the study used a represen-
tative sample of the U.S. population. The studies by Ferris j jj. used a
small sample of people from a small town, Berlin, New Hampshire. This
sample may not be representative in terms of the desired characteristics
listed previously. Factors behind this judgment include the small size of
the sample, the fact that Berlin is a small rural town, and the fact that
the major pollution source in the area is a wood pulp mill, which is
atypical of most other areas. This study was therefore assigned a “0”.
The Mazumdar j,. study, as mentioned previously, worked with data for
the County of London. As a large city and sample size, the Mazuisdar
study sample is likely to be more representative of the U.S. conditions.
However, as a European city, where methods of home heating are often quite
different, it is not completely representative. That is, different methods
of home heating can generate indoor and outdoor pollutants that may differ
from those experienced by U.S. populations. On balance, the Mazumdar j
J.. study was assigned a rating of”< 1”. Finally, the Ostro study makes
use of data for 90 mediumsized cities (populations of 100,000 to 600,000).
The Ostro study is assigned a “1” on this criteria.
The third column is concerned with the extent to which the studies
controlled for oovarying factors. The Ferris it ii.. study was a longi-
tudinal study which looked at pollution—related respiratory disease at
three different points in time (1961, 1967 and 1973) in Berlin, New
Hampshire. As a longitudinal study, the Ferris !. gJ.. analysis probably
benefits from a high degree of control for such factors as sample popula-
tion, cultural differeaces, regional differences, occupational exposure,
etc. However, longitudinal studies also suffer from a variety of other
problems. For example, characteristics of the sample may change over time
due to deaths, aging, migration, changes in life style, and other factors.
10-9

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Some of these factors are not easily controlledt In view of this mix of
advantages and disadvantages, the Ferris ji,. and Mazumdar
studies, which are both longitudinal, are simply denoted as such rather
than assigned a specific rating. The Ostro study, in contrast, is cross—
sectional. It makes use of data on a large number of cities and attempts
to control for covariation by explicit inclusion of other variables. For
example, the Ostro study controls for age, race, sex, marital status,
general occupational class, family income, population density, weather
variables, and smoking habits. The Ostro study does not control for diet
or method of home heating and has limited control for exposure to occupa—
tiona.l hazards. Kowever, the other controls are sufficiently thorough that
on balance the study is assigned a “< 1,’.
The fourth column is concerned with control for behavioral
adjustments. All three studies are assigned a “0” on this criteria. None
of them adequately account for possible behavioral adjustments that
individuals may mak. in order to reduce pollution—related health effects.
All three studies may thus measure pollution—related effects with a
downard bias.
The fifth column is concerned with the degree of control for both
particulate matter and sulfur oxides. The Ferris j gJ.. study is assigned
a “0” on this criteria. The Ferris study considered conditions in
Berlin, New Bampshire, at three points in time (1961, 1967 and 1973).
Between 1961 and 1967, particulate matter, sulfation, and measured health
effects declined. Between 1967 and 1973, particulate matter declined,
sulfation increased (yet s ill remained at relatively low levels), and no
health effect differences were observed. Because both pollutants were
changing during both periods, while health effects changed only during the
* For example, in the Ferris j study, 16 percent of the men and 7
percent of the women in the 1967 sample were dead by 1973. Those who
died were among those in the 1967 sample with more symptoms and. poorer
pulmonary function (4). Comparisons of health status between 1967 and
1973 using the surviving population were thus based on a subsample that
was known to be biased in favor of healthier individuals. No correction
for this bias was possible.
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first period, at least four data points are required to identify the
separate effects of the two pollutants and test for the apparent existence
of a no—effects threshold.* Only the three data points are available. The
Mazumdar , j. study in contrast used daily data on particulate matter and
sulfur dioxide for 14 London winters. Both pollutants were thus measured
and in considerable detail, so the study is assigned a “1”. The Ostro
study also receives a “1”. It measured both particulate matter (in the
form of TSP) and sulfates (SO 4 ) in all 90 cities in the analysis.
The next column is concerned with the quality of the pollution
measurement. In the case of the Ferris j Al.. study, three monitors were
used to represent air pollution conditions in the Berlin, New Hampshire.
area. This approach is assigned a “1”. The Mazuadar et j. study used
seven monitors in the County of London. This study is also assigned a “1”.
The Ostro study used an average of the population—oriented monitors in each
of the study cities. The number of monitors in each city varied. This
study is also assigned a “1”.
The next column is concerned with the application and appropriateness
of the statistical techniques used in the studies. In the.csse of the
Ferris Al. study, no meaningful assessment can be made since with the
limited data available, no concentration—response functions were estimated.
The Mazumdar et si. study in contrast used extensive statistical
techniques. Unfortunately, one of the early adjustments made to the data
was to remove weather—related variation. This is an inappropriate proce-
dure because in removing the weather variation in the mortality data, the
Mazumdar j Al . study also removes all pollution—related effects that are
correlated with weather. Thus, the study is likely to provide a downward—
biased estimate of pollution—related effects. The study is thus assigned a
“0”. The Ostro study utilizes multiple regression techniques. These
techniques appear to have been applied properly and the study is assigned a
t Il
* The threshold issue is discussed in more detail in a later subsection.
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The next column is concerned ‘with the appropriateness of the effects
measured in terms of their use in benefit analysis. As might be expected,
all of the studies in this column receive a “1” because they reflect those
studies which have passed preliminary screening for use in benefit
analysis. It is worth noting, however, that there are still variations in
the type of effects measured. For example, the Ferris j aj. study makes
measurements of respiratory disease incidence. The Mazamdar j. study
utilizes data on total mortality rates. The Ostro study measures work—loss
days. Although the latter is not traditionally considered a health effect,
and thus may not be as useful for Criteria Document purposes, it is none-
theless a usable measure for benefit analysis.
The final column is concerned with the availability of other research
which provides validation or confirming evidence of the results obtained by
the listed studies. As discussed in Section 3, the Ferris et j. study
results are confirmed in general by a number of studies. These include
studies by Lunn and by Colley and Brasser. The Mazumdar j
study is an analysis of London data which has been extensively analyzed by
other researchers who have found similar results. Thus,both the Ferris
and Mazumdar studies ire assigned “1” on this criteria. The Ostro study is
consistent ‘with other general research in the area. However, no specific
studies are yet available which confirm the type and magnitude of effects
measured. Thus, the Ostro study is assigned a rating of “< 1”,
The above discussion compares the studies by Ferris j gj ., Maznmdar
j]., and Ostro on the basis of a variety of criteria. A review of the
ratings in Table 10—1 suggests that the Ostro study must be judged
favorably in terms of its use in a benefit analysis. This is clear because
the Ostro study rates “1” or “< 1” on all criteria except behavioral
controls, where the Ferris g.. , and Mazuadar j,. studies also rate
“0”. The latter studies also rate “0” on other criteria. Thus, the Ostro
study is given considerable weight in some of the benefit analysis. In
contrast, the Ostro study was available only in preliminary form prior to
closure of the Criteria Document and was not formally considered by CASAC.
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Alternative Azarosation Procedures
Table 10—2 incorporates the judgments about study quality and study
coverage discussed previously. Each of the aggregation procedures shown in
the table reflects different tradeoffs between the possibility of double
counting versus undercounting. The table also incorporates assessments of
study quality. The different procedures are each described below.
Procedure A —
The first column in the table includes only those studies judged by
CASAC to be in the quantitative category (5). This includes the studies by
Mázumdar 1 j 1 . and Ferris jt,il. (Other studies were judged by CASAC as
providing quantitative evidence, but they could not be adapted for benefit
calculations.) It also excludes all non—health studies since the Criteria
Document is noncommital regarding the availability of quantitative evidence
of non—health effects (6). Procedure A is thus a conservative alternative.
It probably understates benefits because of its incomplete coverage of
benefit categories. For example, it excludes the possibility of welfare
effects, acute morbidity effects, chronic mortality effects, and non—
respiratory related diseases. By the same token, it is unlikely that
Procedure A involves any double counting of benefits.
Procedure B —
This procedure is similar to the previous one with the exception that
the health study by Samet ii ii. has been included also. This modification
is consistent with the conclusions of the EPA Staff Paper (7) concerning
studies providing roasonable evidence of concentration—response relation-
ships for health effects. The Staff Paper includes the Samet j jj. study
in this category, as well as the studies in Procedure A.
As with Procedure A, no non—health studies are included. The Staff
Paper, like the Criteria Document, is noncommittal regarding the availa-
bility of quantitative non—health effects evidence.
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0
Table 10—2
ALTERNATIVE AGGREGATION PROCEDURES
Procedure
Benefit Category - - - - -
A B C U E F
Mortality
.
Mazumdar
at al.
Mazuadat
at al.
Mazumdar
et a l.
Lave & Seskin;
Lipfert
Lave & Seskin;
Lipfert
Col. C +•
Col. D
Col. B +
Col. C
Crocker
et al.
Acute Morbidity
——
Samet
et al.
Ostro
Ostro
Ostro
Chronic Morbidity
Ferris
ct al.
Ferris
et al.
Ferris
at *1.
Ferris
at al.
Crocker
et at.
household Sector
Soiling & Materials
——
——
Matbtech
Mathtech
Geom. mean of
Cola. D & P
at county
level
Watson &
Jaksch +
Cummings
et al.
Manufacturing Sector
Soiling & Materials
Mathtech
Other Sectors
——
— —
——
——
—
Soiling & Materials
Math tech

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Procedure C —
Procedure C brings in the study of acute morbidity by Ostro. As noted
previously, the Ostro study compares very favorably with the studies
included in Procedures A and B.
With the addition of the Ostro study in Procedure C, the study by
Samet j gJ,. is omitted to avoid possible double counting of benefits. The
Saiset j j,. study is concerned with acute respiratory effectsb which may
be a subset of the effects captured in the Ostro study. The Ostro study is
also given preference over the acute morbidity study by Saric j j. The
latter study provides less complete coverage of diseases (respiratory
disease only) and limited concentration—response information (e.g., no
separation of PM/SO 2 effects, no information on PM effects below 200 Lg/m 3
annual mean, etc.).
Procedure C also incorporates the Mathteoh study of household soiling
effects. The Criteria Document and Staff Paper reference this recent study
but are noncommittal as to its use for quantitative purposes. The study is
included here in order to provide some coverage of soiling effects, in
recognition of its strong analytical features, and because of the favorable
peer review whic h the study has received.
Note that Procedure C excludes possible chronic exposure mortality
effects, provides limited coverage of chronic morbidity effects (respira-
tory illness only), and offers limited coverage of soiling effects.
Pro..dur. D —
Procedure D addresses some of the possible incompleteness of coverage
provided by Procedure C. It adds the chronic exposure mortality studies by
Lave and Seskin and by Lipfert. The Lipfert studies are significant in
that they addressed some of the criticisms ascribed to the work of Lays and
Seskin (e.g., omission of data on smoking habits), and yet found comparable
results. These studies are discussed in more detail in Section 4,
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With the addition of the chronic exposure studies, the acute exposure
study by Mazumdar . j,. is dropped from this procedure. This is because
of the possibility for overlap between those estimates. In particular, the
chronic exposure studies are based on annual mortality rates. Annual
mortality rates will include all deaths during the year, including those
deaths that may be due to acute exposures. Thus, it is possible for the
chronic exposure studies to be capturing the mortality effects of both
acute and chronic exposure. The extent to which this may happen is
unknown, however. It depends on a variety of factors such as the
functional forms for the acute and chronic dose—response relationships, the
statistical correlation between the measures of acute and chronic exposure,
and so on. To be conservative, the acute exposure study is eliminated from
this procedure.
Procod res E and 1 —
Procedures E and F are most easily considered together. Procedure E
addresses the incomplete coverage of the chronic morbidity category and the
underestimation of soiling effects. In Procedures A through D, chronic
morbidity estimates arà based on the study by Ferris j aj,. which iucludes
only respiratory diseases. In Procedure E, the Ferris j j. study is
replaced by the Crocker j ,. study which includes more chronic illnesses.
In Procedure E, the Mathtech study of soiling and materials damage in
parts of the manufacturing sector is included. The coverage of household
sector soiling is also expanded. The latter is done by taking into con-
sideration the results of studies by Watson and Iaksch and by Cummings
al. The sum of these two studies is used in Procedure F, in view of the
possibility that together they may overestimate benefits. In contrast, the
Matbtech household study probably underestimates benefits. Procedure E
thus uses a compromise estimate for household soiling: the geometric mean
of the Mathtech estimate and the sum of the estimates based on Cummings j
j. and Watson and Jaksch. The geometric mean is used as a conservative
measure of the average of the two estimates.
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The remainder of the estimates used in Procedure F also seek to
provide more complete coverage of benefits, with the possible risk of some
double counting. In particular 1 the benefits for the acute and chronic
exposure studies for mortality are added together; the same is also done
with the estimates from the acute and chronic exposure studies of acute
morbidity. As a result, Procedure F provides the most complete estimate of
benefits possible with the available studies. However, it may involve some
double counting. The possibility of double counting is not likely to arise
with any of the procednzes A through E.
Resalts of the Aureaation Procedures
Applying the procedures outlined in Table 10—2 leads to the results
shown in Table 10—3. The benefit estimates shown in the table are for the
PM1O (70, 250) Scenario B standard. This table also appears in Section 1
and is discussed in detail there. Corresponding tables for the other
standards can be found in Section 11.
APPLICARLR CON l1tATIOt4 RM E$ P01 BEN ?IT C&L LATIGNS
Introduction
The results of health effects studies can often be expressed in the
form of concentratiOn—resPOnSe functions. Such functions represent a quan-
titative statement of the relationship between health status and ambient
concentrations of an environmental pollutant such as particulate matter.
Concentration-respOnse functions are useful in a benefit analysis because
they provide a mathematical basis for calculating health improvements
associated with air quality changes over different concentration ranges.
This subsection is concerned with the ranges over which the concentration—
response functions should be applied.
EPA’s Staff Paper and EPA’s Clean Air Scientific Advisory Committee
(CASAC) addressed the question of concentration ranges over which health
effects may occur. These ranges are identified and discussed in the next
10—17

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I-.
Table
INCREMENTAL BENEFITS FOR I1IE PM1O
10—3
(70, 250) SCENARIO B STANDARD*
* 1982 discounted present values in billions of 1980 dollars for a 7—year time
horizon (1989—95) and a 10 percent discount rate. Individual entries may not sum
to subtotals due to independent rounding.
Benefit Category
A B
C D
E F
Mortality
1.12 1.12
1.12 12.72
12.72 13.84
Acute Morbidity
0.0 1.32
10.65 10.65
10.65 11.97
Chronic Morbidity
0.12 0.12
0.12 0.12
11.40 11.40
Household Sector
Soiling & Materials
0.0 0.0
0.73 0.73
3.14 13.85
Subtotal 1
1.24 2.56
12.63 24.24
37.92 51.07
Manufacturing Sector
0.0 0.0
0.0 0.0
1.30 1.30
Subtotal 2
1.24 2.56
12.63 24.24
39.22 52.36
Minimum
Midpoint
Maiimum
Property Value Studies
3.43
6.85
11.42
Hedonic Wage Studies
9.81
19.81
37.21

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section. The lower bounds of these concentration ranges will be referred
to hereafter as the “Staff Paper lower bounds”. Some of the alternative PM
standards under consideration are below the Staff Paper lower bounds, or
may result in ambient PM concentrations below them. The below—lower bound
concentrations can result for several reasons. One example is that
controls required in an area to attain the 24—hour standard may lead to
associated reductions in annual average concentrations below the annual
standard. The reverse is also possible. Other examples can also arise due
to differences between the statistical form of the standard and the statis-
tical form of the threshold, due to the timing of control implementation,
or due to the dispersion pattern of PM in the vicinity of an emissions
source being incrementally controlled.
The existence of air quality improvements below the Staff Paper lower
bounds poses difficult problems for the benefit analysis. These problems
include such issues as the appropriateness of attributing benefits to air
quality improvements below the lower bounds, statistical procedures for
applying lower bounds in a benefit analysis, and uncertainty about the
level of the lower bounds. In view of these uncertainties, calculations
and results are included for two cases —— one without consideration of the
Staff Paper lower bounds, the other with benefits below the lower bounds
excluded.
The section begins ‘with a review of the evidence the Staff Paper cited
In. determining specific lower bounds. This is followed by a discussion of
the applicability of the lower bounds to a benefit analysis in general and
to this analysis in particular. Also discussed are other difficulties with
restricting the allowable concentration ranges for the benefit calcuja—
tions. Finally, the results of the calculations are presented.
Concentration Ianus Sua..stsd by and tiii EPA/OAQPS Staff Panor
The EPA/OAQPS Staff Paper explicitly addressed the question of concen-
tration ranges over which health effects may occur. The Staff Paper
identified one set of ranges at or above which health effects are likely to
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occur among sensitive groups in the population; it identified a second,
lower set of ranges over which effects may be possible. but for which the
evidence and level of risk was less certain. These ranges were derived
from the EPA Criteria Document and reflect consideration of the epidemi-
ology studies judged tb provide the most reliable quantitative evidence.
The numerical values of these ranges are identified in Table 10—4.
EPA’s Clean Air Scientific Advisory Committee (CASAC) also addressed
the question of concentration ranges over which health effects may occur.
Their conclusions were summarized inthe CASAC Chairman s closure letter (8)
on the Staff Paper. CASAC concluded that detectable health effects occur
at the upper bound of the “Effects Possible” ranges idàntified in the Staff
Paper. These ranges are 150 to 350 pig/m 3 for 24—hour PM1O concentrations
(150 to 250 ig/m 3 BS) and 55 to 110 ig/m 3 for annual average PM1O concen-
trations (110 to 180 g/ra 3 TSP). The closure letter also made it clear
that these conclusions were based solely on a review of currently available
quantitative evidence from epidemiology studies. That is, CASAC did
say that there are no health effects at the lower end or below these
Table 10—4
SUMMARY OF CONCENTRATION RANGES IN EPA! OAQPS STAFF PAPERS
Short-To rin Long-Term
BS PM1O TSP PM1O
“Effects
Likely”
250—500
350—600
2.
180
90110
“Effects
Possible”
150250
150—350
110—180
55110
* All entries are in ig/m 3 . Concentrations are given in both original
study units and approximately equivalent PM1O units.
Source: EPA/OAQPS Staff Paper, 2.2. iii.’ Tables 1 and 2.
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concentration ranges; it merely said that there was currently no quantita-
tive evidence of effects.
The evidence for the lower bounds is discussed in. the EPA Staff Paper,
which CASAC has reviewed. The evidence is summarized below, first for the
24—hour lower bound and then for the annual lower bound.
The 24—Hour Lower Bound —
The EPA Staff Paper (9) concludes that there is no strong scientific
evidence of health risks of consequence at 24—hour PM1O levels below 150
j ig/rn 3 (approximately 150 iig/m 3 BS). This conclusion is based on the
results of Martin and Bradley (10), Ware j j.. (11), Mazumdar al. (12),
and Lawther it ii. (13). The first three sets of studies are mortality
studies, while Lawther it. .&1. is concerned with morbidity effects.
Martin and Bradley and Ware it. ii .. examine mortality data for London
for the winter of 1958—59. The lowest average daily pollution level in
their data was 150 ig/rn 3 BS. Therefore, the Martin and Bradley and Ware j
],. studies provide no information on effects of BS below 150 118/rn 3 .
Mazurndar gJ,. examine London data for 14 winters, including the
winter of 1958—59. Their data include daily pollution values as low as 13.
BS. Significant effects of BS on mortality are observed for both
episodic days (days with BS levels over 500 jig/rn 3 and seven days on each
side), and non—episodic days. No formal tests for evidence of a threshold
are conducted.
Ostro (14) reanalyzes the Mazumdar it . J,. data using specific statis-
tical tests for the existence of a threshold. He finds strong evidence of.
effects at ES levels below 100 jig/m 3 and some evidence of effects below 75
118/rn 3 , the lowest threshold Ostro considered. Thus, Ostro’s reanalysis of
the London mortality data provides no evidence of a threshold at 150 g/m 3 .
In fact, Ostro’s analysis indicates that effects may have occurred at BS
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levels of 75 xg/m 3 with effects possible at lower levels. Ostro’s analysis
was not yet available at the time the EPA Staff Paper was completed.
In a series of studies, Lawther et j. examined the relationship
between changes in morbidity and BS levels. Lawther suggests that signifi-
cant effects occur at BS levels over 250 tgJm 3 . In the winter of 1967—68,
however, a correlation between changes in symptoms and BS levels is
observed although smoke levels average 68 Lg/m 3 and exceed 250 g/m 3 only
once. In addition, Lawther et perform very limited statistical
analysis. Most conclusions are derived from visual inspection of graphs
which does not permit systematic examination of the influence of each
variable.
The Annual Lower Bound —
The PM Staff Paper’s analysis of the chronic exposure lower bound is
based on the studies of Ferris . j. (1516), Bouhuys e J. (17), and
Lunn j gi,. (18). These studies examine differences in health status
between a few time periods or areas. As the EPA Staff Paper discusses,
When only two geographic areas or time periods are involved in a
study, the levels that happened to exist in the more polluted
area or time period usually are listed as the ‘effects’ levels.
Effects levels thus derived are somewhat arbitrary, but do
represent concentrations where effects can be ascribed with some
certainty. Because a gradient of effects is often observed for
multiple area studies, however, some risk exists that effects
decrease but do not necessarily disappear at concentrations
substantially below those of the more polluted area. Therefore,
‘effects’ levels derived from two—area (or time) studies cannot
be regarded as thresholds or ‘no detectable effects’ levels
(19).
In fact, effects may continue to occur at concentrations below those in the
clean area. For example, even the 97 Lg/m 3 BS level and 40 Lg/m 3 TSP in
the clean areas in Lunn et al. and Bouhuys j gJ. cannot be interpreted as
threshold or “no effects” levels.
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In Ferris ., decreases in respiratory disease incidence accompany
decreases in annual TSP in Berlin, New Uampshire of 180 to 130 j.ig/rn 3
between 1961 and 1967. No decrease in incidence is observed when annual
TSP declines from 130 to 80 Lg/m 3 between 1967 and 1973. Based on these
results, the Staff Paper selects a “no observed effects” level of 130
TSP. The lack of observed effects, however, may result from the increase
in su.lfation or other changing influences between 1967 and 1973. A
decrease in disease incidence from the decline in TSP and an increase in
incidence from increased sulfation could have the net effect of leaving
incidence unchanged.
The annual threshold of 110 tg/rn 3 TSP is based largely on a study of
two Connecticut towns by Bouhuys, Beck and Schoenberg. The study found
evidence of increased respiratory symptoms (cough, phlegm and dyspnea) in
the town with dirtier air (Ansonia), but no differences in prevalence rates
for chronic bronchitis or lung function. Concentrations of TSP in the
cleaner town averaged 40 fLg/m 3 annual mean. Concentrations of TSP in
Ansonia averaged 63 fig/rn 3 annual mean during the year of the study and up
to 152 ig/m 3 annual mean during the previous seven years. The EPA Staff
Paper characterizes pollution in Ansonia by a median value of 110 pg/rn 3 TSP
annual mean (20) and this value is the basis for the lower—bound annual
concentration (21).
Alternative conclusions are also possible from the Bouhuys j
study. For example, the reported increase in symptoms could be related to
both current and historical pollution levels. In this case, 63 pg/rn 3 TSP
rather than 110 ig/rn 3 TSP would be a safer estimate of the lower bound.
Also, the presence of symptom differences between the two towns implies
effects at concentrations at least as low as in the dirtier town, and
possibly lower. Thus, the possibility of symptoms appearing at levels
below 63 pg/rn 3 cannot be ruled out. The choice of 110 pg/rn 3 TSP as a
threshold level is thus not a uniq ue choice.
S Despite the increase, sulfation was still at relatively low levels.
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Snumary of the Evidence —
As discussed above and in the EPA Staff Paper (22), there is no clear
evidence of thresholds below which effects do not occur. The 24—hour
threshold of 150 jig/rn 3 BS (approximately 150 jig/rn 3 PM1O) is contradicted by
more recent evidence suggesting effects at BS levels of 75 jig/rn 3 and
possibly lower. The annual threshold of 110 jig/rn 3 TSP (approximately 55
jig/rn 3 PM1O) is a choice from the range of 63 jig/rn 3 to 152 jig/rn 3 in the
dirtier of two towns. In addition, as noted previously 1 one cannot rule
out effects below this range on the basis of a two—area study.
Ap- licability of Staff Pacer Lower Bounds to the Benefit £ual sis
Applicability of Staff Paper Lower Bounds in General —
As the EPA Staff Paper’s use of the Bouhuys j J. study and others
suggests, selection of the “Effects Possible” concentration range for the
primary standard emphasizes identification of concentrations at which
effects of a medically significant or permanent nature are detectable.
Effects in the form of increased symptoms by themselves, appear to be less
influential in determining that range. Hence, symptomatic effects may
still exist at concentrations below the Staff Paper lower bounds.
In contrast, the benefit analysis is concerned with the benefits of
reducing ambient air pollutiom Benefits are determined by individuals’
willingness to pay for pollution reductions. Individuals may value a
reduction in symptoms as well as valuing reductions in more significant
health problems. In this case, then, ‘benefits may exist at concentrations
below the lower bounds selected-during Staff Paper development. Applica-
tion of the lower bounds in the calculation of benefits may thus be
inappropriate because of the differing objectives of benefit analysis
compared to Staff Paper and Criteria Document development.
The distinction between symptoms and significant health effects
becomes particularly important if there are separate concentration—response
10-24

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functions for the two categories. In this case, the concentration—response
functions for the significant health effects could be used to calculate
benefits down to the lower bound concentrations prescribed by the Staff
Paper. In addition, the concentration—response functions for symptoms
could be used to calculate benefits below the lower bound concentrations.
The only major constraint on the latter calculations would be those
suggested by the sample ranges in the original studies.
However, benefit calculations below the Staff Paper lower bounds may
be appropriate even when separate concentration—response functions don’t
exist. The reason for this is the uncertainty surrounding the level and
existence of a no—effects threshold. That is, the location of the thres-
hold is not known precisely and is perhaps better characterized by a proba-
bility distribution. Or alternatively, the concentration—response
functions themselves are perhaps better represented by probability distri-
butions. The existence of uncertainty raises the possibility that lay
individuals, as well as informed experts, may assess the risks differently,
i.e., subscribe to different probability distributions. In this case, the
benefit analysis should account for the differing probability assessments.
That is, it may not be appropriate to constrain the benefit analysis
by Staff Paper and CASAC risk assessment. This is because economic
principles prescribe that the benefit analysis should measure the sum of
individuals’ willingness to pay for reduced risk. This means that the
benefit analysis should reflect lay assessments of risk and risk valuation.
These lay assessments may not be substantially different from Staff Paper
and CASAC assessments. Nonetheless, restricting the benefit analysis to
consistency with their determinations would violate the fundamental
principle of benefit—cost analysis —— namely, that benefit estimates should
reflect individuals’ preferences.
Applicability of Staff Paper Lover sounds to This Analysis —
The above discussion summarizes the conceptual problems associated
with use of Staff Paper lower bounds in a benefit analysis. Those problems
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arise in connection with this benefit analysis. First, separate
concentration—response functions for health effects and symptoms are not
generally available or equally amenable to benefit calculations. Thus, it
is not possible to treat these two categories differently with respect to
restrictions on below—lower bound benefit calculations. Benefits below the
lower bounds must be excluded either in both cases or in neither case.
Second, PM health risk assessments by individuals in the population are not
available. This would require extensive survey work which was not
practical for this study. Thus, the analysis must rely either on CASAC and
Staff Paper assessment of the health risk (i.e., CASAC and Staff Paper
study selection and lower bound determination) or on other interpretations
of the health risk suggested by available studies.
In view of the above uncertainties, several sets of benefit calcula-
tions are developed. One set allows health benefit calculations below the
Staff Paper lower bounds, the other excludes health benefits below the
lower bounds. Each of these sets is further distinguished by several
alternative interpretations of health risk. These alternative interpreta-
tions are based on different assessments of the available health risk
literature. One assessment is consistent with CASAC’s view, while others
provide alternative interpretations.
Practical Constraints in Auclvina the Staff Psoer Lower Bounds
There are additional practical constraints to imposing the Staff Paper
lower bounds on the benefit analysis. These constraints include: 1) data
limitations which limit attention to the annual lower bound only; and 2)
the possibility of introducing statistical bias by imposing the lower bound
without proper adjustments to the concentrationrespOflse function. These
problems are discussed further below.
Data Liaitations —
The Staff Paper identified both 24—hour and annual mean lower bounds.
Application of these lower bounds requires pre—control. and post—control air
io-26

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quality data on both a daily and annual basis. Air quality data available
from the control cost and air quality analysis does not include daily data,
other than for the second—highest day per year. Thus, the benefit analysis
can apply only the annual mean lower bound. The annual mean lower bound is
directly applied to those benefit models based on studies that used the
annual mean as the pollution exposure index. This includes the studies by
Ferris et gj., Ostro, Crocker j j . , and Lipfert/Lave and Seskin.
The studies by Mazumdar j gJ. and Samet j . j . both make use of daily
pollution data. As explained in Section 3, however, these studies can be
used for equivalent or approximately equivalent benefit calculations based
on annual data. The annual mean lower bound can be imposed on these
calculations. The 24—hour lower bound, however, cannot be applied in the
absence of daily data.
Statistical Bias —
Practical constraints limit the benefit analysis to use of
concentration—response functions which in most cases are taken directly
from the original research studies. None of these studies imposed the
Staff Paper lower bounds at the time the concentration—response functions
were statistically estimated, either by the authors or by Mathtech.
Estimates of the slopes of the conoentration—response functions reflect the
full range of data available in each study. If this range includes a no—
effects region. the slope estimatei may be downward—biased estimates of the
slope in the effects—present region.
The possibility is illustrated in Figure 10—1. In Panel A. linear
regression leads to line segment AA as the best fit to the data. In Panel
B, the same data are split into two parts around the concentration Q*.
Linear regression leads to separate line segments BB and CC in each subset
of the data. Line segment BB has approximately zero slope. Line segment
CC has a positive slope, and more importantly, a slope that exceeds the
slope of AA.
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Panel A
Morbidity
Rate
PM Concentration
B
C
Morbidity
Rate
Q PM Concentration
Figure 10—1. Constrained Estimation of Concentration—ResPOnse Function
Panel B
B
C
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If Q 5 in the figure represents the Staff Paper lower bound, then
benefits above the lower bound should be estimated using the concentration—
response function given by CC. If AA is used to estimate the above—lower
bound benefits, the estimate will be downward biased. Note that the degree
of bias is likely to be more severe for linear concentration—response
functions. Other functional forms such as the quadratic may be less
biased.
Correct application of the Staff Paper lower bounds would require: 1)
re—estimating all of the concentration—response functions as shown in Panel
B (or, at a minimum, all of the linear ones); and 2) using the revised
slope estimates, analogous to CC in the figure, to calculate benefits. Re—
estimation was not possible as part of this analysis. Thus, our estimates
of the health benefits above the Staff Paper lower bounds are likely to be
downward biased.
R*sults of the Calculations
The above discussion summarizes the potential problems with applying
the Staff Paper lower bounds to the benefit analysis. Despite these
problems, it is useful to know how sensitive the benefit estimates are to
the imposition of the lower bounds. For this reason, benefit calculations
are developed for all standards, first without any lower bounds imposed,
and then with the annual lower bound imposed. The results of these calcu-
lations are summarized in Section 1 and reported in more detail in Section
11.
mICA O AREA C&SE wuT
Introduction
The cost and air quality analysis (23) estimates the ambient air
quality conditions at the “design value” pollution monitoring site in each
county. The design value monitor in a county typically is, the monitor that
recorded the highest ambient concentration in 1977 and 1978. The
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concentrations at other monitors in the county are thus generally lower
than the design value monitor. Non—monitored areas in a county may
experience higher or lower concentrations than the design value monitor,
but lower concentrations are more likely. Air quality conditions at these
other monitors and locations are not estimated in the cost analysis.
Estimates were not developed because it would require detailed dispersion
modeling, an impractical task in the context of a national analysis.
Benefit calculations require more detailed information about the
spatial distribution of the air quality improvement in each county than is
available from the cost analysis. Given the limited information available,
there are two alternative assumptions that can be made concerning the
spatial distribution of the air quality improvement:
• The improvement throughout the county is equal in magnitude
to the improvement at the design monitor.
• The improvement throughout the county is equal in
percentage terms to the improvements at the design monitor.
The situation with an equal magnitude improvement is illustrated in Figure
10—2. Prior to the implementation of controls, Curve A illustrates the
ambient concentration in the county. The county in this case is repre-
sented as a one—dimensional county which has three monitors, a design
monitor and two others. Prior to implementation of controls, the ambient
air quality in the county exceeds the standard by an amount 1. After
implementation of controls, air quality at the design monitor is reduced by
an amount X and thus brought into compliance. The air quality improvement
throughout the rest of the county in this case is also assumed to improve
by an amount I.
The case with an equal percentage improvement is shown in Figure 10—3.
In this case, Curve A again illustrates the ambient concentrations in the
county before controls are applied. Conditions at the, design monitor are
as in Figure 10—2, namely, exceeding the standard by an amount x.
Following implementation of controls, air quality improves to the level
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i -’g/m
Standard
Figure 10—2.
Equal Magnitude Improvement
Post-Control
A
B
nitor 2 Design nitor rv cnitor 3
10—3 1

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given by Curve B. At the design monitor 1 the improvement is an amount X.
Elsewhere in the county, air quality is assumed to improve by the same
percentage amount as occurred at the design monitor.
The benefit estimates reported in Section 3 through 7 of the report
are based on air quality data which incorporate the equal percentage
improvement concept illustrated in Figure 10—3. This approach yields more
conservative estimates of benefits than would calculations based on the
equal absolute improvement shown in Figure 10—2. The conservative approach
is used in the benefit analysis because it is believed to be more
realistic. That is, the dispersion properties of PM may attenuate the air
quality improvement occurring at various distances from the emission
sources being incrementally controlled. However, the degree of attenuation
cannot be assessed without detailed dispersion modeling of the PM emissions
controls applied in each county. As noted previously, such modeling is
very time and resource intensive and thus not practical in a nationwide
study.
There is a possibility that air quality improvements could be even
more localized than is suggested by the equal percentage improvement
approach. For example, if control sources are concentrated in one part of
a county or only selected “hot” sources are targeted for control, changes
in air quality may be limited to localized improvements. An example of
this possibility is illustrated in Figure 10—4. Note in this case that the
improvement at the design monitor is the same as in the previous figures.
However, the improvement near the other monitors is substantially reduced
compared to the earlier figures.
To clarify the implications of possible localized improvement, it was
decided that a detailed analysis should be undertaken for one county using
detailed dispersion modeling. The county selected for the analysis was
Cook County, Illinois. Cook County, which includes the City of Chicago,
was selected for two reasons: 1) it accounts for the largest fraction of
total national benefits among all counties in the analysis; 2) it is an
area that already has undergone detailed dispersion modeling.
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Figure 10—3. Equal Percent Improvement
Standard
Post-Control
A
B
Monitor 2 Design nitor nitor 3
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pg/rn 3
Standard
Figure 10—4.
Localized Improvement
pre-Control
Monitor 2 Design Monitor Monitor 3
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The remainder of this subsection is concerned with the results of the
Chicago area case study. Two specific analyses are described:
• Benefit estimates based on detailed dispersion modeling
results for Cook County.
• Benefit calculations based on the same dispersion modeling
results but using benefit calculation procedures analogous
to those done in the national analysis.
In the first analysis, Cook County is divided into 31 subareas corres-
ponding to the Census divisions within Cook County. In the second
analysis, Cook County as a whole is analyzed using procedures similar to
that in the national analysis. The following subsections describe the
data, methods, findings, and limitations of these analyses.
Dispersion modeling for the Chicago area was done previously by Pechan
and Associates (24). The modeling resulted in estimates of TSP concentra-
tions for over 1,400 modeled receptors in the Chicago air quality control
region (AQcR). Attributes of the data include:
• A TSP standard of 150 jig/na 3 observed 24—hour second high is
analyzed.
• Pro—control (baseline) data are representative of modeled
conditions in 1987.
• Post—control TSP levels are also assumed to occur in 1987.
• Some receptors are not in attainment after available
controls are implemented.
• The pro— and post—control data measure total concentrations
and not just contributions from selected sources.
• Estimates are provided for the 24—hour second high measure
as ‘well as the equivalent levels for TSP annual arithmetic
mean and geometçic mean.
• The location of each modeled receptor is defined by UTM
coordinates.
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• Pro— and post—control concentration levels are also
provided for each monitor site in the Chicago SMSA that
meets the EPA summary criteria.
Ago ro a oh
To test the sinsitivity of the benefit estimates to the level of
spatial aggregation in the air quality data, the geographic area of Cook
County, Illinois, was considered at two levels of detail. In the aggregate
version, Cook County is analyzed as a single area. In the disaggregate
approach, Cook County is divided into 31 Census divisions. For each level
of aggregation, one file of economic/demographic data (ED) and six files of
air quality data are constructed. The attributes of the ED file include:
• ED data for Cook County are available from the nationwide
PM benefit analysis.
• 1980 population and population growth rates by Census
division are obtained from (25). No other ED variables are
currently available for 1980 for this area.
• Data by Census division for the number of households,
employment, the size of the work force, and mortality rates
are calculated as population—weighted averages from the
Cook County estimates.
• Data by Census division for average real income, average
property values, employment growth rates, and cost of
living indices are assumed to be equal to the Cook County
values.
The six air quality data files for the two levels of aggregation are
similar in that they represent information on pre— and post—control levels
(two alternatives) and three measurement types (arithmetic, mean, geometric
mean. 24—hour second high). However, the data development procedures for
the two aggregation schemes are different. In particular:
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• For the Cook County analysis, a single index of TSP for
each file is created under the following assumptions:
— For each measurement type, the single j or
recording the highest pre—control concentration is
designated as the “design value” monitor.
— A pro—control average index of TSP concentrations is
calculated for each measurement typo. The average is
based on the available pro—control monitored data.
— The percentage change in the average measure of con-
centrations is assumed to be equal to the percentage
change observed at the design value monitor. This
yields the post—control average level of TSP for Cook
County.
• For the Census division analysis, indices of air quality
are created for each division. The assumptions include:
— For each measurement type, the modeled receptor
recording the highest pro—control concentration in the
Census division is identified.
— A pro—control average index of TSP concentrations is
calculated for the three measurement types in each
Census division. The average is based on the avail-
able pro—control modeled receptor data.
— Thó post—control maximum value is the concentration
level occurring at the modeled receptor reporting the
highest value in the pro—control case.
— A post—control index of average TSP levels in a Census
division is calculated from the available post—control
modeled receptor data.
Given these data files, benefit calculations are carried out according
to the procedures described previously in Sections 3 through 7. While the
structure of the individual dose—response function and valuation procedures
does not change, one change is made in the scenario definition. Specif i—
ashy, the time horizon is only one year, 1987, since this is the only year
for which air quality data are available.
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FindinKs
The results of the analysis indicate that benefits are reduced in the
Chicago area when more detailed information on population and air quality
distributions is available. This result is relative to a situation in
which data from the highest monitor in a county are used to characterize
levels and changes in. exposure. Table 10—5 identifies the ratio of
benefits calculated for the Census divisions to the Cook County study. The
relatively high ratio for Lipfert/Lave and Seskin, Crocker gJ., and
Mathtech are due to the fact that the air quality variable in each of these
studies is defined in terms of maximum rather than average concentrations.
In addition, the small variation around 0.27 for several of the studies is
due to nonliuearities in the model dose—response functions.
Table 10—5
RATIO OF NSUS DIVISION ESTIMATES TO COOL COlThfl’! ESTIMATES
Study
Ratio
Mazumdar et al.
0.33
Lipfert/Lave and Seskin
0.94
Ssmet jj .
0.27
Saric j.
.
Ostro
0.27
Ferris j ii..
Crooker
0.80
Property Value
0.28
Hedonic Wage
0.29
Cummings .&.k.
0.27
Watson and Jaksch
0.26
Mathtecb. Household
0.62
* Zero benefits calculated for point estimate.
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Tables 10—6 through 10—8 present the comparative findings for three of
the benefit aggregation procedures discussed previously in this section.
The three benefit aggregation procedures are designated B, C, and D. The
benefit estimates reported represent point estimates, as defined in Section
1. Note that these benefit estimates are based only on individuals
residing or working in Cook County. Although air quality improvements also
occur in neighboring counties, benefits are not calculated for these areas.
Limitations
The degree of generality that can be attached to the above findings is
limited by several factors. These include:
• Limitations with the assumptions implicit in the current
analysis.
• Limitations in applying these results to the PM NAAQS
analysis.
With respect to the approach limitations, we note the following attributes:
• Air quality improvements occur outside Cook County but are
not included in the previous tables. These improvements
may be generated in part by controls on sources in Cook
County.
• Estimates of exposure are still approximate. The daily
movements of people between Census divisions are not
identified.
• Much of the economic/demographic data is attributed. In
particular, population—weighted averages are used to define
several data series.
• Statistical issues may arise when models are used at levels
of spatial aggregation that differ from those levels used
in the original study.
• One of the Census divisions in Cook County accounts for a
large percentage of the total county population and is
relatively large in area.
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Table 10—6
BENEFIT ESTIMATES FOR TWO METHODS OF AIR QUALITY AGGREGATION
AND FOR BENEFIT AGGREGATION PROCEDURE B
Study
Census Divisions
Cook County
Mazumdar et al.
0.90
2.74
Samet j ii, .
1.98
7.24
Ferris et j..
0.00
0.00
Total
2.88
9.98
* Discounted present values in 1982 in millions of 1980 dollars.
Table 10—7
BENEFIT ESTIMATES FOR TWO METHODS OF AIR QUALITY AQGREGA ION
AND FOR BENEFIT AGGREGATION PROCEDURE C
Study
Census Divisions
Cook County
Mazumdar j jj.
0.90
2.74
Ostro
15.88
57.79
Ferris j . ,.
0.00
0.00
Msthtech Household
4.66
7.51
Total
21.45
68.05
* Discounted present values in 1982 in millions of 1980 dollars.
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Table 10—8
BENEFIT ESTIMATES FOR TWO METHODS OF AIR QUALITY AGGREGATION
AND BENEFIT AGGREGATION PROCEDURE D 5
Study
Census Divisions
Cook County
Lave and Seskin
Ostro
Ferris j..
Mathtech Household
Total
77.41
15.88
0.00
4.66
97.96
82.40
57.79
0.00
7.51
147.71
* Discounted present values in 1982 in millions of 1980 dollars.
With respect to the PM NAAQS analysis, the following points should be
considered prior to any comparisons:
• The cost—of—control routines differ in the two analyses.
In the PM NAAQS study, a modified rollback approach is
employed. In the Chicago area case study, a least—cost
algorithm is used.
• Differences in study design and data files in the cost
analysis lead to differences in air quality levels and
changes. For example, the Chicago model predicts a county—
leve’ change in the design value geometric mean. of 9.78
g/m from a baseline level of 99.17. On the other hand,
the PM NAAQS results are based on predicqd changes at the
design value of 25.9 (geometric mean) Lg/m from a baseline
of 132.2.
• The scenarios differ in the two studies. The Chicago area
study is based on a one—year benefit stream; the PM NAAQS
study is based on a 9—year benefit stream (for TSP).
• The air quality and population distributions implicit in
the Chicago area analysis are likely to be different in
other areas. As a consequence, extrapolation of the
results reported here to other areas is tenuous at best.
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• Given the above caveats, if these results are to be used to
benchmark the PM NAAQS results, the results of this
analysis suggest the following:
1) Detailed dispersion modeling may result in benefits
estimates that are between a factor of 2 to 4 below
those reported in the PM NAAQS study.
2) Detailed dispersion modeling may also lead to changes
in cost of control estimates relative to a county—wide
rollback approach.
3) Comparisons of adjusted benefits to costs cannot be
made until more complete information is developed with
respect to the different cost—of—control approaches
and impacts on resultant air quality.
1. U.S. Environmental Protection Agency, Office of Research and Develop-
ment. Air Quality Criteria for Particulate Matter and Sulfur Oxides:
External Review Draft No. 4. Research Triangle Park, North Carolina,
December 1981, p. 14—5.
2. Ibid., pp. 14—49 to 14—54.
3. U.S. Environmental Protection Agency, Office of Air Quality Planning
and Standards. Rev’iew of the National Ambient Air Quality Standards
for Particulate Matter: Assessment of Scientific and Technical
Information. OAQPS Staff Paper (EPA—450/5—82—OO 1 ), Research Triangle
Park, North Carolina. January 1982, pp. 48 — 63 .
4. Ferris, B. G., Jr., H. Chen, S. Puleo and R. L. H. Murphy, It.
Chronic Nonspecific Respiratory Disease in Berlin, New Hampshire,
1967—1973: A Further Follow—Up Study. American Re’ iew of Respiratory
Disease, 113:475—485, 1976, p. 480.
5. EPA Criteria Document, . çj ., pp. 14—49 to 1454.
6. Ibid., p. 10—73.
7. EPA Staff Paper, . jt., pp. 48—63.
8. EPA Staff Paper, . ., Appendix E, p. E—4. (Appendix E is the
letter from the chairman of CASAC to the EPA Administrator concerning
CASAC review and closure of the EPA Staff Paper.)
9. Ibid., pp. xiii—xv.
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10. Martin, A. E. and W. H. Bradley. Mortality, Fog and Atmospheric
Pollution —— An Investigation During the Winter of 1958—59. Monthly
Bulletin of the Ministry of Health, Laboratory Services, 19:56—73,
1960.
ii. Ware, .7., L. A. Thibodeau, F. E. Speizer, S. Colome and B. G. Ferris,
3r. Assessment of the Health Effects of Sulfur Oxides and Particulate
Matter: Analysis of the Exposure—Response Relationship. Environ-
mental Health Perspectives, 1981 (in press).
12. Mazumdar, S., H. Schimmel and I. Higgins; Relation of Air Pollution
to Mortality: An Exploration Using Daily Data for 14 London Winters,
1958—1972. Electric Power Research Institute, Palo Alto, 1980.
13. Lawther, P. 3., R. E. Wailer and M. Henderson. Air Pollution and
Exacerbations of Bronchitis. Thorax, 25:525—539, 1970.
14. Ostro, B. D. A Search for a Threshold in the Relationship of Air
Pollution to Mortality in London: A Reanalysis of Martin and Bradley.
Working Paper, September 21, 1982.
15. Ferris, j iL., .22 Lit.
16. Ferris, B. G., Jr., I. Higgins, M. W. Higgins and I. M. Peters.
Chronic Non—Specific Respiratory Disease in Berlin, New Hampshire,
1961—1967: A Follow—Up Study. American Review of Respiratory
Disease, 107:110—112, 1973.
17. Bouhuys, A., G. I. Beck and 3. B. Schoenberg. Do Present Levels of
Air Pollution Outdoors Affect Respiratory Health? Nature, 276:466—
471, 1978.
18. Lunn. I ., I. Knowelden and A. 3. Handyside. Patterns of Respiratory
Illness in Sheffield Infant School Children. British Journal of
Preventive Social Medicine, 21:7—16, 1967.
19. EPA Staff Paper, 22. p. 104.
20. Ibid., p. 62.
21. Ibid., p. xvi.
22. Ibid., p. 88.
23. A. E. Smith and L L. Brubaker. Costs and Air Quality Impacts of
Alternative National Ambient Air Quality Standards for Particulate
Matter. Technical Support Document. Argonne National Laboratory,
Argonne, Illinois. October 1982.
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24.   The  data were developed for  this  study by Pechan and Associates,
     Washington, DC using  the AIRMOD model.  A description of AIRMOD is
     available  in:   Putnam,  Hayes and  Bartlett,  Inc.   Analysis  of
     Alternative TSP and PM10 Ambient  Standards:   A Case  Study of the
     Chicago Metropolitan Area.  Draft report prepared for EPA, August
     1982.

25.   U.S.  Bureau of Census.  1980 Census  of  Population:  Illinois.  PHC80-
     15.
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