EPA-600/5-76-011
September 1976
Socioeconomic Environmental Studies Series
PHYSICAL AND ECONOMIC DAMAGE FUNCTIONS
FOR AIR POLLUTANTS BY RECEPTORS
Environmental Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Corvallis. Oregon 97330
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into five series. These five broad
categories were established to facilitate further development and application of
environmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The five series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
This report has been assigned to the SOCIOECONOMIC ENVIRONMENTAL
STUDIES series. This series includes research on environmental management,
economic analysis, ecological impacts, comprehensive planning and forecast-
ing, and analysis methodologies. Included are tools for determining varying
impacts of alternative policies; analyses of environmental planning techniques at
the regional, state, and local levels; and approaches to measuring environmental
quality perceptions, as well as analysis of ecological and economic impacts of
environmental protection measures. Such topics as urban form, industrial mix,
growth policies, control, and organizational structure are discussed in terms of
optimal environmental performance. These interdisciplinary studies and systems
analyses are presented in forms varying from quantitative relational analyses to
management and policy-oriented reports.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/5-76-011
September 1976
PHYSICAL AND ECONOMIC DAMAGE FUNCTIONS FOR AIR
POLLUTANTS BY RECEPTOR
by
Ben-chieh Liu, Ph.D.
Eden Siu-hung Yu, Ph. D.
Midwest Research Institute
425 Volker Boulevard
Kansas City, Missouri 64110
MRI Project No. 4004-D
EPA Contract No. 68-01-2968
Project Officer
John Jaksch
Criteria and Assessment Branch
Corvallis Environmental Research Laboratory
Corvallis, Oregon 97330
CORVALLIS ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF DEVELOPMENT AND RESEARCH
U.S. ENVIRONMENTAL PROTECTION AGENCY
CORVALLIS, OREGON 97330
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DISCLAIMER
This report has been reviewed by the Corvallis Environmental Research
Laboratory, U.S. Environmental Protection Agency, and approved for publi-
cation. Approval does not signify that the contents necessarily reflect
the views and policies of the U.S. Environmental Protection Agency, nor
does mention of trade names or commercial products constitute endorsement
or recommendation for use.
ii
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FOREWORD
The Clean Air Act of 1970 requires substantial reduction in air
pollution. Under the authority of this and subsequent Acts, the
Environmental Protection Agency has promulgated national ambient air
quality standards for several pollutants. In geographic regions
where ambient standards are exceeded, the states have been required
to undertake action to comply with the standards.
The current energy crisis has resulted in a closer look by society
and the Agency at the tradeoffs between energy conservation and improved
environmental quality. Specifically, the crisis has resulted in the air
quality standards coming under closer scrutiny. The standards in many
instances are viewed by industry as impediments to the use of alterna-
tive fuels which could alleviate the current energy situation.
In order to effectively evaluate the environmental tradeoffs, the
decision maker must have information on the costs and benefits of alterna-
tive environmental control strategies. Providing such information involves
difficult issues of measuring and evaluating the diverse effects of pollu-
tion abatement. One of the results of the energy crisis has been a
renewed call for a reevaluation of and increased emphasis on the delinea-
tion and quantification of the benefits and costs attributable to air
pollution reduction.
As most economists who are familiar with the methodology know, benefit/
cost analysis has its limitations in practical application to decision
making problems. The primary limitations are the difficulties encountered
in placing an economic value on some effect responses, and/or the deriva-
tion of adequate effect responses. While dependable, systematic estimates
of damages resulting from the effects of air pollution are still quite
rare, progress is being made. Within the past decade, several studies
have been completed estimating property and material costs of air pollution
and the effects of air pollution on property values and human health.
However, many of these studies are too specific, and, as a result, do not
lend themselves well for use in formulating decisions having national
implications. The purpose of this study was to see, using existing
studies, whether this limitation could be overcome.
More specifically, the purpose of this study was to examine past
economic, and other related environmental studies, to determine whether the
results could be utilized in estimating composite parametric damage func-
tions. The functions, while providing ballpark estimates, could be used
in evaluating the outcomes of implementing alternative environmental
policies. In the meantime it was hoped that additional economic-environ-
mental studies would be undertaken which would mitigate the shortcomings
and permit a reestimation of more precise damage functions.
m
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This report estimates economic, parametric damage function by receptor
(human health, household soiling, materials, and vegetation) for the sta-
tionary source pollutants - sulfur dioxide and suspended particulates. The
damage functions are based on existing research results. The socio-economic
data used in formulating the damage functions for the different metropolitan
areas are derived from the 1970 census.
The research results have been extensively reviewed by environmental
economists, whose suggestions and comments have been incorporated into the
study. The results should be used with appropriate caution. Some of the
assumptions employed in the study, by necessity, are uncertain. Some of
the methodological-statistical techniques employed are in their infancy
and have not been tested elsewhere. Despite the existence of these diffi-
culties, it is the general consensus of the reviewers that the study re-
presents an important step forward in evaluating alternative pollution
control options. Peer review of the study results by other environmental
economists are welcome, and should be sent to the project officer at the
Corvallis Laboratory.
This study was initiated by the Washington Environmental Research
Center, Office of Research and Development, Washington, D.C., and completed
at the Con/all is Environmental Research Laboratory (CERL), Office of Research
and Development, Corvallis, Oregon.
A. F. Bartsch
Director, CERL
IV
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PREFACE
This is the Final Report for the project entitled "Physical and Economic
Damage Functions for Air Pollutants by Receptor," for U.S. Environmental Pro-
tection Agency, EPA Contract 68-01-2968 and MRI Project No. 4004-D.
The primary objective of this project is to generate some physical and
economic damage functions by receptor for sulfur dioxide and suspended par-
ticulates for the U.S. urban areas so that marginal benefit and marginal cost
principal can be applied to air pollution control decisionmaking. Based on
existing literature and available data on U.S. metropolitan areas, 1970, aver-
age functions are developed for air pollution damages on human health, house-
hold soiling, materials and vegetation. Various types of air pollution damages
are also estimated on a cross section basis for the metropolitan areas included.
It should be noted that the geographic damage estimates are tentative not only
because the assumptions employed in the study are uncertain but also because
the methodology used is in its infant stage of development.
This project was completed under the general supervision of Mr. Bruce
Macy, Assistant Director of Economics and Management Science Division and the
project director was Dr. Ben-chieh Liu, Principal Economist. Research assis-
tance and data process were provided, respectively, by Miss Mary Kies, and
Mr- Jim Miller. Valuable assistance and comments from Dr. Chatten Cowherd,
Messrs. Paul Gorman and Richard Salmon of MRI, Drs. Donald Gillette, Michael
Hay and John Jaksch of EPA, Dr. Fred Able of Energy Research and Development
Administration, Dr. William Watson of Resource for the Future and Dr. Eugene
Seskin at Urban Institute are gratefully acknowledged. Editorial service was
provided by Mrs. Doris Nagel, Mrs. Sharon Wolverton efficiently performed the
report typing and computer work was carried out at MRI's Computation Center.
Nevertheless, the views expressed in this study are those of the authors. They
do not necessarily reflect the opinions of the sponsoring agency.
v
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CONTENTS
Foreword iii
Preface t v
List of Figures ix
List of Tables x
Executive Summary 1
Section I - Introduction 1
Section II - Mortality and Air Pollution 1
Section III - Morbidity and Air Pollution 2
Section IV - Household Soiling and Air Pollution 2
Section V - Material and Air Pollution 2
Section VI - Vegetation and Air Pollution 2
Section VII - Aggregate Damage Losses and Damage Functions: An
Overall View 3
Section I - Introduction 8
Damaging Effects of Air Pollution 9
Section II - Mortality and Air Pollution 14
Introduction: The Problems and the Objectives 14
Estimation of Physical Damage Functions 18
A Linear General Physical Damage Function 26
Values of Air Pollution Damages and Economic Damage Functions. . 28
Premature Mortality Damages and Suspended Particulates 33
Implications and Concluding Remarks 36
Section III - Morbidity and Air Pollution 41
Problems and Objectives 41
Environmental Damage Functions: Some Theoretical Underpinnings. 45
Adult Morbidity and Air Pollution 47
Adult Morbidity Damages and Sulfur Dioxide 49
Adult Morbidity Damages and Total Suspended Particulates .... 60
Section IV - Household Soiling and Air Pollution 66
The Problems and the Objectives 55
Soiling Physical Damage Functions 57
Economic Damages and Economic Damage Functions 59
vii
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CONTENTS (concluded)
Page
Section V - Material and Air Pollution 92
Problems and Objectives 92
A Theoretical Framework 94
Exposition of Methodology 97
Regional Material Damage Costs 99
Economic Damage Functions 102
A Summary of Material Physical Damage Functions 108
Section VI - Vegetation and Air Pollution 120
Problems and Objectives 120
Dose-Response Relationships 122
Economic Damage Functions 124
Concluding Remarks 133
Section VII - Aggregate Economic Damage Costs and Functions: An
Overall View 136
Aggregate Economic Damage Functions 138
Section VIII - References 143
Appendix A - Optimal Policies in the Presence of Environmental
Pollution: A Theoretical Framework 153
Appendix B 158
viii
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LIST OF FIGURES
Number Page
II-1 Hypothetical relationship between mortality rate and
S02 concentration ^. . . 20
2
II-2 Heteroscedastic distribution of the residuals 27
III-l Sample observation from four morbidity studies with respect
to S02 52
III-2 Sample observations from four morbidity studies with respect
to TSP 61
ix
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LIST OF TABLES
Number
S-l Economic Damages Due to Air Pollution, by Receptors for
Selected SMSA'S 4
II-1 Correlation Coefficients 25
II-2 Mortality Costs With S02 by SMSA's, 1970. 31
II-3 Mortality Costs With TSP by SMSA's, 1970 35
II-4 Mortality Costs by SMSA's, 1970 39
III-l Morbidity Dose - Response Observations 48
III-2 Adult Morbidity Linear Damage Functions 50
III-3 Mean Values and Standard Deviations of the Variables. ... 51
III-4 Morbidity Costs With S02 by SMSA's, 1970 58
III-5 Morbidity Costs With TSP by SMSA's, 1970 63
IV-1 Pollution-Related Tasks and Their Unit Cleaning Costs ... 68
IV-2 Mean Frequency, Standard Error and Upper and Lower Limits
of Frequency and Suspended Particulates 70
IV-3 Soiling Physical Damage Functions 71
IV-4 Net Soiling Damage Costs by Large SMSA's 73
IV-5 Net Soiling Damage Costs by Medium SMSA' s 75
IV-6 Gross Soiling Damage Costs by Large SMSA 78
IV-7 Gross Soiling Damage Costs by Medium SMSA's 80
IV-8 Per Capita Net and Gross Soiling Damage Costs ($) by Large
SMSA's, 1970 83
IV-9 Per Capita Net and Gross Soiling Damage Costs ($) by Medium
SMSA's, 1970 85
x
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LIST OF TABLES (concluded)
Number
IV-10 Net and Gross Soiling Damage Costs in 148 SMSA's by
Cleaning Operations, 1970 88
IV-11 Soiling Economic Damage Functions 89
V-l Soiling and Deteriorating Costs of Paint and Zinc 100
V-2 Material Damage by Large SMSA' s, 1970 103
V-3 Material Damage by Medium SMSA1 s, 1970 105
V-4 Economic Damage Functions on Materials 107
V-5 Major Pollutant - Material Interactions 109
V-6 Results of Regression Analysis for Soiling of Building
Materials as a Function of Suspended Particulate Dose . . 116
V-7 Physical Damage Functions for Materials 118
VI-1 Variables Used in Economic Damage Functions 126
VI-2 Economic Damage Functions on Vegetation With Pollution
Relative Severity Indices 127
VI-3 Economic Damage Functions of Vegetation, With Sulfur
Dioxide Annual Mean Level 129
VI-4 Economic Damage Functions on Total Crops, Total Ornamentals
and All Plants 130
VI-5 Estimated Economic Damages of Total Crops, Total Ornamentals
and All Plants 132
VI-6 Mean and Standard Deviations of Variables in Vegetation
Damage Functions 134
VII-1 Economic Damages Due to Air Pollution, by Receptors for
Selected SMSA1s 137
VII-2 Economic Damage Functions 140
VII-3 Gross Economic Damages Changes Resulting From a 10 Percent
Reduction in the Pollution Level 141
xi
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EXECUTIVE SUMMARY
The research delineated in this report is primarily concerned with evaluat-
ing regional economic damages to human health, material, and vegetation and of
property soiling resulting from air pollution. This research also attempts to
develop a more plausible exponential physical dose-response function for pre-
mature mortality and morbidity. The comparable and consistent damage loss esti-
mates for a variety of receptors developed in this research are expected to pro-
vide a data base useful for designing national and regional pollution control
strategies.
The report comprises seven sections. A brief summary of the highlights
from each section follows:
SECTION I - INTRODUCTION
The project involving the determination of regional air pollution damage
losses for mortality, morbidity, household soiling, material and vegetation can
be divided into four distinct phases: (1) problem discussion and refinement;
(2) information and data gathering; (3) damage loss assessment; and (4) physical
and/or economic damage function estimation. Static analyses are performed on
the basis of 1970 data for many metropolitan areas and regions in the United
States.
SECTION II - MORTALITY AND AIR POLLUTION
A two-step econometric model was developed for estimating a nonlinear mor-
tality physical damage function and net damage costs of premature deaths result-
ing from excess air pollution for the 40 Standard Metropolitan Statistical Areas
(SMSA's) which had a sulfur dioxide level above 25 (ig/m^ between 1968 and 1970.
The model circumvents partial'ly the often recognized but largely ignored econo-
metric problems such as heteroscedasticity and multicolinearity and, hence, gives
credence to our damage loss estimate. In addition, an "average" economic damage
function was developed which relates premature mortality damage losses in dollar
terms to socioeconomic, demographic, climatological and air pollution variables—
sulfur dioxide (SO ) and total suspended particulate (TSP). The estimated mor-
tality damage due to SO for 1970 varies from less than $0.1 million in Charleston,
West Virginia to $329 million in New York City, whereas mortality damage attribut-
able to TSP ranges from $1.4 million in Lawrence, Massachusetts to $155 million
in New York City. On a per capita basis, the highest damage due to SO and TSP
is $28.4 in New York City and $27.6 in Detroit, respectively.
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SECTION III - MORBIDITY AND AIR POLLUTION
The damage costs and physical and economic damage functions were developed
and estimated. Regional physical damage functions on adult morbidity were de-
rived by resorting to the classical least-squares linear regression. A Monte
Carlo technique was then used to derive an "average" nonlinear morbidity physi-
cal damage function for adults. Low estimates for total annual morbidity costs
due to SO range from less than $1,000 in Cincinnati to a maximum of $22 mil-
lion in New York City. Low estimates on morbidity damages attributable to TSP,
however, range from $152,000 in Bridgeport to more than $21 million in Chicago.
On a per capita basis, the highest damage due to S02 and TSP is respectively
$1.9 in Chicago and $3.7 in Cleveland.
SECTION IV - HOUSEHOLD SOILING AND AIR POLLUTION
A system of soiling physical damage functions relating various types of
cleaning frequencies to air pollution was developed. Net and gross soiling dam-
age costs for the 148 SMSA's were estimated. Finally, national "average" eco-
nomic damage functions for household soiling were developed by relating soiling
damages to air pollution, dempgraphic, socioeconomic, and climatological vari-
ables. Total net soiling costs for 1970 attributable to air pollution over the
148 SMSA's were estimated to be more than $5 billion, while total gross soiling
costs were about $17 billion over the 148 SMSA's.
SECTION V - MATERIAL AND AIR POLLUTION
This section develops economic damage estimates on the two most economically
important materials, i.e., zinc and paint, for the 148 SMSA's in the United
States. Economic damage functions relating material damages to air pollution
and other socioeconomic and climatological variables were derived. The state
of the art regarding the physical damage functions on materials was also re-
viewed and summarized. The soiling damage costs of zinc for 1970 range from
less than $0.5 million in Dayton, Ohio to $1.7 billion in Chicago, whereas the
deteriorating damage costs of zinc range from less than $0.5 million in Dayton
to $57 million in Chicago. The soiling damage costs of paint for 1970 range from
$19 million in Fayetteville, North Carolina, to $2.3 billion in New York City,
while the deteriorating damage cost of paint is $0.7 million in Fayetteville
and $79 million in New York City.
SECTION VI - VEGETATION AND AIR POLLUTION
Dose-response relationships for vegetation were reviewed. A set of national
"average" economic damage functions for 10 economically important crops in the
United States and regional economic damages to vegetation were derived. The eco-
nomic damage functions will be useful to policymakers for forecasting possible
gains as a result of pollution control programs.
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SECTION VII - AGGREGATE DAMAGE LOSSES AND DAMAGE FUNCTIONS: AN OVERALL VIEW
Range estimates of economic damage losses over some broader categories
of receptors were derived. A number of aggregate economic damage functions were
also developed and summarized for the major pollutants. The aggregate as well
as the disaggregate damage functions developed in the previous sections can be
useful to national and regional policymakers in their quest for obtaining esti-
mates of possible benefits brought about by various pollution abatement strate-
gies.
The numerically large values of aggregate damage estimates provided by
the experts in this area point to the need for effective control of pollutant
emissions. The question naturally arises as to what constitutes economically
optimal and politically feasible pollution control programs. As an effort in
providing some useful clues for understanding the above question, this study
attempts to estimate net as well as gross economic damages to human health,
material, vegetation and household soiling attributable to and in the presence
of air pollution for the urban areas in the United States. Economic and physi-
cal damage functions relating economic (physical) damages to air pollution,
demographic, socioeconomic, and climatological variables were also developed
for the United States urban areas. It is hoped that the generalized economic
damage functions in this report are informative and useful for predicting pos-
sible marginal (average) benefits resulting from various air pollution abate-
ment programs.
Any study of this nature is bound to have a few inherent limitations. The
notable limitations are the uncertainty associated with estimating the physical
damage function and in translating it into economic terms, and the uncertainty
of selecting the most relevant measure of air pollution and the "correct" form
of relating damages to pollution.
To provide the reader an overall view of the economic damages of various
receptors due to air pollution, a summary of the damage estimates for the effect
categories of human health, material deterioration, and household soiling is
presented in Table S-l. The selected 40 SMSA1 s which had an SO level equal to
or greater than the threshold 25 |a,g/m are listed in Column 1. The low and high
damage estimates of human health are presented, respectively, in Column 2 (HNC1)
and Column 3 (HNC2). Column 4 (MDC) presents the material deteriorating damage
estimates of both zinc and paint; Column 5 (TNSCO) contains the aggregate net
household soiling damages. On the basis of the low and high damage estimates of
human health presented, respectively, in Columns 2 and 3, two sets of low and high
aggregate damage estimates for the three effect categories, i.e., human health,
material deterioration and household soiling, were derived and summarized in
Column 6 (TNCl) and Column 7 (TNC2), respectively. The further details on the
estimations of the economic damages of each of the effect categories are con-
tained in the subsequent Sections II, III, IV, V and VI. The formulas used for
deriving the estimates presented in Table S-l will be discussed in Section VII.
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TABLE S-l . ECONOMIC DAMAGES DUE TO AIR POLLUTION, BY
RECEPTORS FOR SELECTED SMSA's
(in $ million, 1970)
(1)
SMSA' s
1. Akron, OH
2. Allen town, PA
3. Baltimore, MD
4. Boston, MA
5. Bridgeport, CT
6. Canton, OH
7. Charleston, WV
8. Chicago, IL
9. Cincinnati, OH
10. Cleveland, OH
11. Dayton, OH
12. Detroit, MI
13. Evansville, IN
14. Gary, IN
15. Hartford, CT
16. Jersey City, NJ
17. Johnstown, PA
18. Lawrence, MA
19. Los Angeles, CA
20. Minneapolis, MN
21. New Haven, CT
22. New York, NY
23. Newark, NJ
24. Norfolk, VA
25. Paterson, NJ
26. Peoria, IL
27. Philadelphia, PA
28. Pittsburgh, PA
29. Portland, OR
30. Providence, RI
31. Reading, PA
32. Rochester, NY
33. St. Louis, MO
34. Scranton, PA
35. Springfield, MA
36. Trenton, NJ
37. Washington, DC
38. Worcester, MA
39. York, PA
40. Youngstown, OH
Total
(2)
HNC1
10
8
48
49
3
6
3
191
22
55
18
129
2
12
12
11
4
3
123
21
3
352
39
13
7
4
107
45
13
16
5
13
44
5
12
3
48
3
4
9
1,475
(3)
HNC2
18
15
80
52
5
6
3
360
22
93
18
161
2
24
19
17
4
5
147
32
5
527
48
13
7
4
158
79
13
25
5
15
61
5
15
3
88
4
4
10
2,166
(4)
MDC
7
3
17
26
6
11
4
105
12
49
9
55
2
8
5
8
1
7
76
12
4
111
14
3
13
9
33
30
8
9
4
7
24
2
3
2
21
8
2
8
736
(5)
TNSCO
16
16
137
117
3
14
10
516
57
216
39
294
5
24
16
17
10
3
388
37
4
418
112
29
9
8
104
147
30
20
15
27
119
23
7
5
86
6
9
23
3,134
(6)
TNC1
33
27
202
192
12
31
17
812
91
320
66
478
9
44
33
36
15
13
587
70
11
881
165
45
29
21
244
222
51
45
24
47
187
30
22
10
155
17
15
40
5,349
(7)
TNC2
41
34
234
195
14
31
17
981
91
358
66
510
9
56
40
42
15
15
611
81
13
1,056
174
45
29
21
295
256
51
54
24
49
204
30
25
10
195
18
15
41
6,045
Note—individual figure may not add to totals due to rounding.
4
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Table S-l reveals that the largest aggregate air pollution damage, in the
order of $1 billion, occurred in New York and Chicago SMSA's in 1970. The small-
est air pollution damage occurred in Evansville and Trenton, both SMSA's damages
were in the magnitude of $10 million in 1970. Human
health damage estimates (mortality and morbidity) ranged from $1.5 to $2.2 bil-
lion for the 40 SMSA's. Total material deterioration damages were about 0.7
billion, and total household soiling costs were about 3 billion for the 40
SMSA's under study.
The implication of our study for pollution abatement strategies is obvious.
Any effort to reduce the current pollution level appears to have a varyingly
significant impact on the economic damages resulting from the harmful effects
of air pollution. Admittedly, the implication of this study must be qualified
by several theoretical and empirical factors. The major difficulties often en-
countered in estimating air pollution damages involve the lack of knowledge
regarding the shapes of functions describing the relationship between air pol-
lution and various receptors, and the lack of a satisfactory theoretical model
specifying the way air pollution affects various receptors. The impossibility
of accounting for all major factors which might affect various receptors, the
lack of reliable formulations used for translating physical damages into mone-
tary terms, and the presence of numerous econometric problems have also caused
concern to investigators.
Despite the existence of these difficulties, this study represents a step
forward in our knowledge of pollution damages. It seems to be the first attempt
to construct essential frameworks of the physical and economic damage functions
which can be used for calculating comparable regional damage estimates for the
several important receptors--human health, material, and household soiling--
however tentative the damage estimates may appear to be. More importantly, ag-
gregate economic damage functions instrumental for transforming the multifarious
aspects of the pollution problem into a single, homogeneous monetary unit are
tentatively derived and illustrated. It is hoped that these results will be of
some use to guide policymakers as they make decisions on the implementation of
programs to achieve "optimal" pollution levels for this country. Given the ex-
perimental nature of the methodological and statistical procedures and the de-
gree of uncertainty associated with the study results, a great deal of caution
should be exercised in using the products of this research.
Finally, it should be noted that although the availability of information
on average or marginal damages is instrumental in determining the optimal na-
tional or regional pollution control strategies, the current problem is far
more complex than the question of balancing the benefits to polluters with dam-
ages inflicted on the receptors. The issues are pressing and not yet well speci-
fied. The basic difficulty in applying the recent research findings to accurately
estimate the air pollution damage cost stems from our ignorance about the recep-
tors at risk to air pollution. So far, few attempts have been made to identify
who suffers, to what extent, from which sources, and in what regions. At this
moment, updating and expansion of the available crude estimates, which are
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generally restricted to certain regions, are urgently needed. To identify the
population at risk to air pollution, and to measure the damage specifically
for polluted regions are apparently the most logical steps in the area of future
research.
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MAJOR NOTATIONS AND VARIABLES
A
C
CC
CMR
CRMR
CROPL
CROPV
DTS
DDCZ
DDCP
EXP or e
GSCO
MR
MB
MBC
MDC
NSCO
OXID
PAGE
PYAP
POOL
PWOP
POP
PDS
RHM
RMR
SDCP
SDCZ
SMSA
SO
TSP
TMBCSO
TMBCTSP
TEMB
TEMA
u
Air pollutants
Conventional mortality rate
Computed conventional mortality rate
Computed mortality rate
Computed residual mortality rate
Economic loss of a particular type of crop
The output value of a particular type of crop
Number of days with thunderstorms
Deteriorating damage cost of zinc
Deteriorating damage cost of paint
Exponential
Elasticity of variable i with respect to variable j
Gross household soiling damage cost
Mortality rate
Morbidity rate
Morbidity cost
Material deteriorating cost
Net household soiling damage cost
Oxidant relative severity index
Percentage of population 65 or older
Percentage of population with income above poverty level
Percent of persons 25 or older who have completed 4 years
of college
Percentage of white to total population
Population in the area
Population density
Relative humidity
Residual mortality rate
Soiling damage cost of paint
Soiling damage cost of zinc
Standard Metropolitan Statistical Areas
Sulfur dioxide
Possible annual sunshine days (percent)
Total suspended particulates
Total morbidity cost due to SO
Total morbidity cost due to TSP
Number of days in a year with temperature below 33° F
Number of days in a year with temperature above 89° F
The disturbance term
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SECTION I
INTRODUCTION
Deterioration in urban air quality constitutes one of the major problems
confronting most American cities today. Air pollution has inflicted a multitude
of damaging effects on human health, material, vegetation, animals, household
and industrial property. In the past decades, numerous research studies have
been conducted to ascertain and to quantify, if possible, the physical and
monetary damage losses to the various receptors due to the presence of exces-
sive concentration levels of the major air pollutants, e.g., sulfur dioxide,
total suspended particulate matter, oxidants, carbon monoxide and other sub-
stances in the urban areas.i/
The numerical values of aggregate damage estimates provided by the experts
in this area point to the need for effective control of pollutant emissions ..£'
The question naturally arises as to what constitutes economically optimal and
politically feasible pollution control programs. The issues surrounding the
control strategies have been hotly argued and debated. Implementation of some
of the proposed control programs has been postponed for either political or
economic reasons.
According to estimates prepared by the Bureau of Economic Analysis,
(Cremeans and Segel, 1975) a total of $18.7 billion was spent on domestic air,
water, solid waste and other pollution abatement and control programs in 1972.
The expenditure was -about 1.6 percent of our GNP in that year. Of the total
figure, 35 percent was accounted for by control and abatement activities of
air pollution. This expenditure figure is indicative of the magnitude of sac-
rifice the society has made for the purpose of reducing the problem of air
degradation.
Is this amount of expenditure sufficient, from an economic point of view,
to attain optimal air quality for this country? The inquiry into this question
is handicapped without information about the corresponding benefit accruable
to the society because of the existing pollution control programs.
From economic theory, it is well-known that the control policy is optimal
if the marginal benefit due to pollution abatement is matched by the marginal
expenditure incurred to implement the control. In the absence of national mar-
ginal or "average" damage functions of air pollution by receptors and the
marginal (average) damage estimate for each effect category, it is difficult,
if not totally impossible, to estimate the marginal (average) benefits stemming
from the abatement of the last unit of air pollution in each metropolitan area
and the nation as a whole.
\J For a background information on the cost of air pollution damage, see
Barrett and Waddell (1973) and Waddell (1974).
2/ For details on the damage estimates and the references, see the beginning
paragraphs of each of the later sections.
8
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For purposes of analysis the effects of air pollution are customarily
classified into six broad categories: (1) detrimental effects on human health;
(2) damage to vegetation; (3) deterioration of materials; (4) soiling of house-
holds and business establishments; (5) injury to animals; and (6) reduction
of visibility and other atmospheric effects of an aesthetic nature. Since each
of these categories has direct and indirect economic value, whenever one's
ability and opportunity to enjoy these benefits is reduced, economic damages
result. It is unfortunate that the magnitude and measurement of the resulting
economic damages is probably the most controversial point in the entire pollu-
tion control issue.
The basic objectives of this study were to estimate net as well as gross
economic damages to human health, material, vegetation and household soiling
attributable to and in the presence of air pollution for the urban areas in
the United States. Economic and physical damage functions relating economic
(physical) damages to air pollution, demographic, socioeconomic, and climate-
logical variables were also developed for the United States urban areas. It
is hoped that the generalized economic damage functions in this report are
informative and useful for predicting possible marginal (average) benefits
resulting from various air pollution abatement programs.
Any study of this nature is bound to have a few inherent limitations.
The notable limitations are the uncertainty associated with estimating the
physical damage function and in translating it into economic terms, and the
uncertainty of selecting the most relevant measure of air pollution and the
"correct" form of relating damages to pollution.
Since this study is primarily concerned with the estimation of the economic
damages of air pollution in the United States urban areas, a brief, but criti-
cal, review of the economic effects of air pollution is in order. Accumulating
evidence suggests that air pollution results in a number of noticeable and
substantial economic effects. Some of the more obvious of these effects include
the soiling of materials by dustfall, necessitating additional expenditures
for cleaning; corrosion of materials, requiring replacement and application
of protective coatings; atmospheric haze, reducing visibility and causing aes-
thetic blight; and various respiratory and other health problems associated
with the inhalation of noxious fumes and particles from the atmosphere.
DAMAGING EFFECTS OF AIR POLLUTION
Effects on Human Health
According to the 1974 National Academy of Sciences reports, two major
pollutants, i.e., total suspended particulates and sulfur dioxide, are responsi-
ble for the bulk of the deleterious effects on human health. Other pollutants,
like carbon monoxide, nitrogen oxides and photochemical oxidants and ozone
also exert damaging effects. Exposure to high concentrations of carbon monoxide
damages the function of oxygen-dependent tissues and exposure to low concen-
trations of carbon monoxide results in adverse effects both in normal people
-------
and in patients with heart disease. Acute exposure to Low concentrations of
nitrogen oxide can cause visual and olfactory abnormalities. Tentative evidences
indicate that long term exposure to photochemical oxidants can result in eye
irritation and a decrease in lung tissue elasticity. At any one time, several
pollutants are present in the air- Thus, it is difficult to determine the inter-
action of pollutants and the specific health damages caused by a single pollut-
ant. Nevertheless, it has been established that air pollutants can accelerate
disease and death, even at levels generally considered safe and used as the
basis for setting standards. Each of the major air pollutants presents a health
hazard in itself, and harmful effects may be greatly amplified when they occur
in combination. Unfortunately, the degree of the synergistic effects among
the pollutants is not clearly known.
Particulate emissions include a wide variety of pollutants, each of which
may exert different effects on human health. Carbon or soot particles are the
most commonly emitted kinds of particles. However, even when these are the
only particulates emitted—such as in coal combustion--there are indications
that the toxic effects of sulfur dioxide (also released in the coal combustion
process) are enhanced by their association with the particulate matter. Other
contaminants can absorb on the surface of the particles, thereby coming into
contact with the inner surfaces of the lungs and mucous membranes in far greater
concentrations than would otherwise be possible. The site and extent of parti-
cle deposition in the respiratory tract, and therefore its ultimate effect
on human health, depend upon both physical and physiological factors.
Sulfur dioxide is highly soluble in body fluids. The principal effect
of this gas is irritation of the tissues lining the upper respiratory tract.
This results in bronchial constriction which, in turn, produces an increase
in respiratory flow resistance. Persons suffering from respiratory or cardiac
diseases may be unable to withstand the increased body burden caused by this
respiratory flow resistance. Adverse effects on ciliary activity and mucous
flow may also result from prolonged exposure to sulfur dioxide. Sulfur dioxide
and other oxides of sulfur can, under certain conditions, combine with water,
soot particles and other aerosols in the atmosphere to produce toxic acid aero-
sols and other contaminants far more dangerous than any of the individual ingre-
dients.
The damage to human health depends not only on the concentration level
of pollutants, but also on the physical conditions of each individual. There
is virtually no single threshold of pollutant concentration below which health
damages will not occur. At every level of a pollutant concentration, someone
could be adversely affected. In view of a wide range of physical conditions
of human beings the threshold of pollutants may be viewed as a symmetrical
distribution. The "mean" level of this distribution is used in the present
study to calculate the economic damages resulting from air pollution.
10
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While the exact role of air pollution in causing illnesses is not known,
there is substantial evidence that air pollution does aggravate existing ill-
nesses, even to the point of causing premature death..I/While high rates of
asthma attacks have been reported on days with high air pollution surface con-
centrations, greatly increased mortality rates from influenza, bronchitis,
and pneumonia have been noted during periods of high sulfur dioxide and partic-
ulate levels.
In estimating the damage cost of morbidity, it should be noted that the
direct, out-of-pocket cost of treating an illness or disease is probably far
less than the value of avoiding the necessity for treatment. When someone
suffers from a pollution-related chronic illness, the cost of pollution to
him is almost infinite; the value of avoiding the pollution-induced discomfort
is, for this person, immeasurably high. For this reason, it should be cautioned
that the health damage of air pollution estimated in this study, like other
major studies on the basis of the health costs of treating pollution-related
illnesses, may understate the true economic costs or benefits of reducing the
responsible pollutants. Sections II and III present a thorough analysis of
the air pollution effects on human health, i.e., mortality and morbidity,
respectively.
Effects on Materials
Many external factors influence the reaction rate between pollutants
and materials, with moisture the most important in accelerating corrosion.—
Inorganic gases are likely to cause tarnishing and corrosion of metals; can
attack various building materials such as stone, marble, slate, and mortar;
and may deteriorate a variety of natural and synthetic fibers.
The most noticeable effect of particulate pollutants is soiling of the
surfaces on which they are deposited. They may also act as catalysts increasing
the corrosive reactions between metals and acid gases. Additional damages to
surfaces and textiles are incurred by the wear and tear imposed by the extra
cleanings made necessary because of particulate soiling.
The true economic damage to materials caused by air pollution is difficult
to ascertain. First, it is difficult to scientifically distinguish between
natural deterioration and deterioration caused by air pollution. Secondly,
it is uncertain regarding indirect costs of early replacement of materials
worn out by pollutant soiling.
The most comprehensive analysis of the economic effects of air pollution
on materials was conducted by Midwest Research Institute (Salmon 1969). In that
study, the damages caused by interactions between specific pollutants and spe-
cific materials were identified. The estimated economic loss resulting from the
various pollutant-material interactions totaled $3.8 billion in 1968.
I/ For details, see Section III.
~2f See Section V for further details.
11
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Detailed analyses of soiling costs and material damages by region are con-
tained in Sections IV and V, respectively.
Effects on Vegetation
The air pollutants having the greatest deleterious effects on vegetation
are sulfur dioxide, hydrogen fluoride, photochemical smog and oxidants, ethyl-
ene, and herbicides and fungicides. Sulfur dioxide enters a leaf through the
stoma, causing injury to the blade of the leaf in the form of intervenal col-
lapsed areas. Fluorides may be absorbed from the surface of the leaf and can
be toxic to some plants at extremely low concentrations. Other pollutants may
damage only certain susceptible types of plants.
Based upon a Stanford Research Institute study (Benedict et a 1., 1973),
the national damage cost of air pollution on vegetation is estimated to be $150
million. This damage cost amounts to approximately more than one-half of 1 per-
cent of the total value of crops produced in the United States in 1970. This
figure represents mainly the visible damage to agricultural crops, and does not
fully recognize the real economic losses due to growth suppression, delayed ma-
turity, reduced yields, and increased costs of crop production.
Section VI describes and estimates the air pollution damages on vegetation
on a regional basis for different types of crops.
Other Damaging Effects
Aesthetic damage caused by air pollution is the most difficult to quantify;
yet, intuitively at least, it represents one of the important categories of eco-
nomic loss suffered as a result of degraded air quality. The aesthetic category
encompasses a number of different effects ranging from impaired atmospheric
visibility to decreased property values resulting from the presence of air pol-
lutants .
Reduction in visibility creates a heavy economic burden on most communities.
Some of the community operations which are most affected by pollution-related
visibility problems include airports, highways, and homes. When an airport's
traffic pattern is slowed due to delays in take-offs and landings caused by re-
duced atmospheric visibility, operational costs are increased, additional safety
hazards are imposed, passengers are inconvenienced, and businesses may be indi-
rectly affected. Similar effects occur on highways where reduced visibility slows
traffic, causes congestion, and increases the likelihood of injuri/ous and expen-
sive accidents. Additional lighting—both on the streets and in the home—is
required when the sunlight is unable to penetrate a polluted atmosphere.
Aesthetic damage can sometimes be partially measured indirectly, such as
by comparing property values in comparable residential neighborhoods having dif-
ferent air pollution levels. In other cases, aesthetic damages may be reflected
in the costs that are incurred in connection with their prevention or avoidance,
such as special precautions taken to protect certain values from aesthetic damage
12
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by air pollutants. Still other cases may require a willingness-to-pay approach,
estimating the amount that individuals would be willing to pay in order to pre-
vent or avoid the threatened aesthetic damages due to the soiling effect or,
conversely, how much additional they would have to be paid to willingly endure
the aesthetic blight.
Due to data deficiency, air pollution effects on aesthetics are not studied.
Considerable damage to animals caused by air pollution has been noted. How-
ever, most cases are localized, the sources are easily identified, and the eco-
nomic consequences are relatively minor- Poisoning of livestock from heavy mef
als--arsenic, lead, and molybdenum--has been reported on numerous occasions,
and cattle and sheep are particularly susceptible to fluorine poisoning. In
addition to the direct economic losses resulting from animal mortality, signifi-
cant losses may come from such effects as decreased reproductivity, decreased
growth, and lower output of milk, eggs and wool.
No studies of the economic impact of air pollution on animals have been
reported in the literature. The value of all livestock and livestock products
produced during 1968 was $21 billion; out of this total, perhaps $10 million
could reasonably be attributed to losses of all kinds from air pollution damage
(Park, 1974).
Due to data deficiency, air pollution effects on animals are not studied.
In summary, this air pollution damage function project involves four dis-
tinct phases common to each of the five studies regarding the damaging effects
of air pollution on mortality, morbidity, household soiling, materials and vege-
tation. The four phases are as follows: (1) problem refinement; (2) data and
information gathering; (3) estimation of regional economic damages; and (4)
development of physical and economic damage functions.
Data on air pollution, demographic, socioeconomic and climatological vari-
ables were collected by a thorough literature search. Most of the data utilized
for developing the economic damage functions were attained from a comprehensive
quality of life study for the United States Standard Metropolitan Statistical
Areas (SMSA's) recently completed by Liu (1975).
Following the selection of the needed data, regression models were devel-
oped to determine the physical and economic damage functions for all these major
air pollutants as well as the various categories of the damaging effects. Econo-
metric problems and technical difficulties are discussed and dealt with as much
as possible during the process of damage estimation. Furthermore, several method-
ologies were developed to evaluate the economic damages by air pollutants and
effect categories for the SMSA's in the United States.
13
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SECTION II
MORTALITY AND AIR POLLUTION
INTRODUCTION: THE PROBLEMS AND THE OBJECTIVES
Two issues in the area of pollution control have attracted much attention
recently. The first problem is to evaluate from the control efficiency viewpoint
the appropriate governmental policies for handling pollution abatement. While
Kneese (1972), Peltzman and Tideman (1972), and Lerner (1974) opted for regional
regulation of pollution, Stein (1974) stressed the role of the federal govern-
ment for controlling various pollution. Another problem involves the determina-
tion of the optimal level of pollution abatement at which the marginal benefits
are matched by the marginal expenditures incurred to implement the control. Esti-
mation of the marginal benefits of pollution control at regional levels, however,
requires information on damage functions and damage estimates for the various
regions in the United States.
Empirical works in this area for the United States have been advanced sub-
stantially by Ridker (1967), Lave and Seskin (1970, 1973), Jaksch and Stoevener
(1974), R. K. and M. Koshal (1974), among others. They confirmed the existence
of a close association between health and air pollution. 1>.2/ The conventional
ordinary least squares, linear or log-linear regression method has been employed
to quantify the damaging effect of air pollution on mortality. However, often
the major difficulties encountered in estimating such a physical damage function
involve the problems of errors in variables, nonnormality, heteroscedasticity,
and multicolinearity among air pollution and other explanatory socioeconomic,
demographic and climatological variables, and the lack of knowledge regarding
the shape of the function which depicts the relationship between air pollution
and health.
Two major approaches have been suggested in the literature for estimating
a pollution damage function..3' The first approach involves the assumption that
consumers are explicitly or implicitly knowledgeable about the potential bene-
fit of pollution control. Therefore, the estimation problem boils down to one
of inducing the consumers to reveal their "true" preferences about abatement.
Often, unsatisfactory results were obtained in this approach because consumers
generally are not willing to pay their share of cost for abatement, and, hence,
tend to provide misleading information about the benefit accruable to them if
air quality is improved.
_!_/ These and earlier studies are subject to a number of limitations. For a de-
tailed discussion see, for example, J. R. Goldsmith (1969).
_2/ Contrary results have also been obtained, for example, by Toyama (1964) and *
Petrilli, Agnese and Kanitz (1966). There were no controls for socioeconomic
factors in their studies. Hence, their results are subject to bias.
.37 See, for example, Lave (1972), p. 213, for a detailed exposition of the two
approaches.
14
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The second method, on the other hand, involves explicit quantification of
the physical damage function and translation of the physical damage into mone-
tary terms. The advantage of this explicit approach is that it requires no inter-
personal utility comparison and cooperation from the consumers. However, the con-
siderable extent of uncertainty present in estimating the physical damage func-
tion and in converting it into an economic damage function casts doubt on the
reliability of the damage estimates.
The damaging effects on human health by air pollution in New York City have
been well documented by Glasser _et al. (1967), Greenburg et al. (1962a, 1962b),
Hodgson (1970), and McCarroll and Bradley (1966). Recently, Schimmel and
Greenburg (1972) performed a time-series study based on mortality rate and pol-
lution for New York City covering the period between January 1, 1963, to
December 31, 1968. The excess mortality rate was regressed on two daily mean
pollution variates, SO and smoke shade, for both the same and previous day. They
showed that approximately 80 percent of the excess deaths were attributed to
the effects of smoke shade while only 20 percent were attributed to SO . Again,
methodological problems encountered in national estimates are also prevailing
in these regional estimates.
Damage costs of premature death and morbidity due to air pollution have
been estimated for the whole nation previously. Ridker (1965) estimated the to-
tal costs of a specific disease and then attributed 20 percent of these costs
to air pollution. Lave and Seskin (1970, 1973) related the amount of mortality
for specific diseases to air pollution and some socioeconomic variables. They
found that the association between air pollution and mortality is significant
and of substantial magnitude; e.g., a 10 percent decrease in the biweekly mini-
mum level of sulfates is associated with a 0.3 percent decrease in mortality
rate per 10,000 live births. Koshal (1974) established a quantitative relation-
ship between respiratory mortality rates and the level of air pollution and two
climatic variables. They estimated a reduction of about 50 percent in the air
pollution would imply a social saving on the order of about $1.9 to $2.2 billion
per year in terms of respiratory disease alone.
It is noteworthy that although most of these air pollution damage studies
draw tentative conclusions, they suffer from a certain inherent difficulty in
evaluating their results. Difficulty arises because either the statistical pro-
cedures employed are less than perfect or the results obtained are inadequate
for generating statistical inferences needed. With the exception, perhaps, of
those of Lave and Seskin and the Koshals, most of the studies are time-series
analyses with sample observations restricted to a specific area or a small num-
ber of areas. As a result, little information can be deduced from the existing
studies for designing a general air pollution control policy which requires
the knowledge of an "average" damage function expressed in both physical and
economic terms and applicable to all metropolitan areas in the nation. From eco-
nomic theory, it is well known that the control policy is optimal if the marginal
benefits resulting from pollution abatement are matched by the marginal expendi-
tures incurred to implement the control. In the absence of national average dam-
age functions by pollutants and the marginal damages for each pollutant, it is
15
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difficult to estimate the marginal benefits stemming from the abatement of the
last unit of air pollution in each metropolitan area and the nation as a whole.
Lave and Seskin (1973 p. 290) in a well-known article, noted possible spec-
ification errors in the empirical estimates of mortality and air pollution rela-
tion. They cautioned the reader that "[their] analysis is beset by a vast number
of problems including little a. priori knowledge of the true specification of
the relations, omitted variables, and errors of measurement in the variables."
This observation has been recently verified by Smith (1975) by reestimating a
set of mortality air pollution relationships with a new data base. The Ramsey
tests were utilized with the data on mortality rates and suspended particulates
for 50 SMSA's.l/ The research findings indicate that the errors in specification
and heteroscedasticity could constitute technical problems in estimation.
While the multicolinearity problem between air pollution and other indepen-
dent variables in the damage function makes it difficult, if not totally impos-
sible, to disentangle their influences so as to obtain reasonably precise esti-
mates of their separate independent effects on mortality, the presence of the
heteroscedasticity problem violating one of the assumptions used in the normal
linear regression model (i.e., the disturbances were independently distributed
with constant variances) renders the ordinary least-squares estimates ineffi-
cient ..£/ Despite the fact that these specification errors were observed by Lave
and Seskin, the econometric problems remain largely unexplored in the prior stu-
dies.
This section attempts to achieve two basic objectives. First, a stepwise
econometric model will be developed to estimate a dose-response relation for
mortality and pollution. Second, "average" economic and physical damage func-
tions for the United States Metropolitan Areas will be constructed by relating
mortality economic damages and mortality rates, respectively, to air pollution,
demographic, socioeconomic, and climatological variables. Although the method-
ological and statistical procedures used are experimental, and the statistical
results are subject to a great deal of uncertainty, it is hoped that the gener-
alized economic damage function and the cost estimates presented in this sec-
tion are informative. They can be useful for predicting possible benefits in
the urban areas resulting from various air pollution abatement programs and to
shed light on the major issues in current and future air pollution research.
Technically, the heteroscedasticity and multicolinearity problems that emerged
in estimating the relationships between mortality and pollution damage are par-
tially circumvented via the two-step econometric model. In the first step, ob-
served mortality rates are regressed on several relevant socioeconomic, demo-
graphic and climatological variables. In the second step, the residual mortality
rates obtained by subtracting the computed mortality rates from the observed
I/ The logic underlying the Ramsey tests was succinctly outlined in Smith (1975),
pp. 341-342. For a detailed discussion on the tests, see Ramsey (1969, 1970,
1974).
21 See, for example, Johnston (1963), pp. 207-211, and Goldberger (1964), pp.
192-194.
16
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mortality rates are again regressed nonlinearly on air pollution variable only
to derive the physical damage function. The estimated dose-response relation
is then utilized to derive net damage costs of premature deaths due to excessive
air pollution for 40 Standard Metropolitan Statistical Areas (SMSA's) in the
United States.
In order to estimate physical and economic damages associated with air pol-
lution the effects of air pollution on human health are classified as: (1) mor-
tality effect; (2) morbidity effect; and (3) combination effect.I/ The mortality
effect refers to the increase in the excess deaths resulting from increased con-
tamination in the air, or the decrease in the survival probability of all ages.
The premature mortality affects an individual's probability of being accessible
to future earning opportunities and nonmarket leisure activities, but it will
not alter the nature of the existing economic and leisure activities. The mor-
bidity effect, which will be dealt with in the next section, however, directly
changes the nature of economic and leisure activities. The combination effect
can be viewed as earlier mortality because of increased severity in morbidity.
In this case, both the survival probability and the nature of activities of the
victim are affected. Schrimper (1975) has shown that this interaction effect
can be conveniently ignored because of its small magnitude.
It may be worth pointing out, at the outset, that the physical dose-response
relation derived in the present study is probably the first of its kind ever
estimated in the pollution effect studies. Four distinguishing features in the
dose-response relation differentiate our study from the earlier studies, say,
Lave and Seskin (1970, 1972, 1973) and Koshals (1974). First, the technique of
residualizing the dependent variable (mortality rates) is used in estimating
the dose-response function. Second, the pollution variable is the sole explana-
tory variable included in the dose-response relation. Third, the dose-response
function is specified as a nonlinear relation in accord with both a^ priori judg-
ment and empirical results regarding human responses to increased pollution
doses. Fourth, a threshold level is adopted before damages are estimated.
This section, which represents a preliminary effort to estimate empirically
a nonlinear dose-response function and a linear "average" pollution damage func-
tion, is presented in the following subsections: Estimation of Physical Damage
Functions, A Linear General Physical Damage Function, Values of Air Pollution
Damages and Economic Damage Functions, Premature Mortality Damages and Suspended
Particulates, and Implications and Concluding Remarks.
I/ For a detailed discussion on the effect of air pollution on human health,
~~ see Schrimper (1975).
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ESTIMATION OF PHYSICAL DAMAGE FUNCTIONS
For analytical purposes, two types of physical damage functions can be
posited: (1) dose-response or stimulus-effect relations; (2) general physical
damage functions which relate mortality not only to pollution, but also to other
relevant socioeconomic, demographic and climatological variables.
A long-term, generalized physical damage function has been specified, for
example, by Lave and Seskin (1970), Goldsmith (1965), and Ferris and Whittenberger
(1966) as
MR = F(D, S, E, W, A; e) (II-l)
where MR is mortality rate per 10,000 population and is related to D (demo-
graphic factors such as age, sex, racial and genetic), S (the social factor
such as individual's exercise and other habits, nutrition, occupational struc-
ture, population density, and housing conditions), E (the economic variables
such as income and the level and quality of medical care received), W (weather),
A (the air pollutants), and e (the disturbance term). To measure the damage
effect of air pollution and other independent variables on mortality, the con-
ventional least-squares linear regression has been the common technique.
If the objective is to estimate a short-term, day-to-day, physical damage
function for a given study region, demographic, social and economic factors can
then be reasonably assumed to be stable. Hence, the short-term physical damage
function can be specified as
MR = f(W, A; e) (II-l1)
Lave and Seskin (1972) utilized (II-l') to derive acute, day-to-day mortality-
pollution relationships. Lags up to 5 days in the pollution variables were in-
corporated into the regression equations. Results obtained in their study were
generally negative because no discernible, consistent pattern of statistically
significant coefficients was observed. One of the several questions examined
by Lave and Seskin which has bearing on policymaking is whether deaths are
merely shifted by a few days by pollution episodes. Their finding indicates
that the reallocation of mortality extends over a period longer than 10 days.
Nonlinear Dose-Response Function
A dose-response relation which includes the pollution variables as the sole
explanatory variable can be written as
MR = g(A; e) (II-2)
The dose-response functions may be estimated via controlled laboratory experi-
ments on human bodies. However, ethical and legal considerations prohibit the
use of human bodies for experimental purposes. Because of this, epidemiological
studies so far have not been fruitful in identifying the true cause-effect re-
lationship underlying mortality and air pollution.
18
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The clinical, laboratory, and experimental studies at relatively high con-
centration levels of sulfur dioxide or other pollutants suggest that tentative
dose-response relationships are available. However, information on such rela-
tionships is largely lacking at concentration levels in the range of current
standards. The best available tentative dose-response function ever produced
by epidemiological studies is presented in Buechley (1971). The function can
be approximated by a nonlinear, flat "S" shape curve, as shown in Figure II-l.
The relation indicates that while the air pollutant SO is a contributing fac-
tor of premature mortality, the damaging effect is nonproportional. As the SO
concentration level increases, the excess mortality rate increases initially
at an increasing rate and continues to increase, but at a decreasing rate af-
ter a certain inflection level.
It is generally the opinion of medical experts that the true a_ priori dose-
response is nonlinear. This hypothesis is also recently used by Leung (1974)
who studied the exposure-effect relation between human health and mobile source
air pollution best described by a nonlinear curve as shown in Figure II-l.
On the basis of _a priori judgment of medical experts and the two empirical
results produced respectively by Buechley and Leung concerning the human health
damage responses to pollutant doses, the exposure-effect function relating mor-
tality rate (MR) to sulfur dioxide (SO ) in the present study is hypothesized
as an exponential function alternatively specified as follows:
MR = C + e(a-b/S°2) (II_3)
MR- c-e (H-3-)
In (MR - C) = a - b/S02 (II-4)
where C is the "conventional" mortality rate in that the mortality rate is
independent of the pollutants and a and b are parameters determining the
shape of the nonlinear function. Since both coefficients a and b can take
any real values, the semilog, reciprocal equation (II-3) covers a wide range
of nonlinear functions with positive first derivatives.
The conventional mortality rate (C) is determined by a host of socioeconomic,
demographic, climatological and personal factors. It is recognized that many of
the factors known to affect mortality are not amenable to quantification. Factors
such as nutrition, exercise, personal habits, etc., are difficult to measure
conceptually> while data on smoking habits have not been collected and on medi-
cal care are poorly measured. The exclusion of these relevant factors from the
regression equation because of insufficient data may result in specification
errors and, hence, biased estimates. Thus, careful interpretation of the regres-
sion results is warranted.
19
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o
o
o
0)
o_
D
O
C
<
>-
Zj
<
o
0
0
50
SO2(fig/m3)
75
Figure II-l. Hypothetical relationship between mortality rate
and SO concentration.
20
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A number of regressions with data on more than 25 potential explanatory
variables collected from the 40 SMSA's which had a sulfur dioxide level equal
to or greater than 25 iig/m^ between 1968 and 1970 were run during the course
of this study. The selection of 25 ^.g/m^ as the threshold is based on two
considerations: First, this concentration level is the average level prevail-
ing in rural areas. Second, this level is considered to be the "mean" of the
tolerable threshold distribution of all individuals in the SMSA. Available evi-
dence suggests that no matter how small the concentration is, adverse health
effects may still occur (National Academy of Science, 1974). Thus, threshold
in a strict sense should be zero concentration. However, the threshold levels
with respect to all individuals in a given region could be reasonably viewed
as a symmetrical distribution with a mean level possibly at 25 (J-g/m^. The use
of this mean level of thresholds will probably result in more accurate damage
estimates than using zero or other threshold levels.
It should be noted that damage estimates cited previously in other pollu-
tion studies were derived on the basis of a zero threshold. The use of a zero
threshold level tends to overstate the damages.
It is noteworthy that many of the determinants of mortality are difficult
to quantify, and data are not readily available for some of the variables.JL'
The data for the above mentioned variables for 40 SMSA's which had a sulfur
dioxide level equal to or greater than 25 [ig/m^ between 1968 and 1970 were
taken from a comprehensive quality of life study about U.S. SMSA's recently
completed by Liu (1975). Variables of no statistical significance or with
wrong signs were accordingly eliminated, and the best regression results with
the remaining seven independent variables were obtained as follows:
CC = 229.6 + 741.8 PAGE - 119.7 PYAP - 0.12 PCOL - 76.58 PWPO
(50.5)* (96.4)* (62.8)** (0.04)* (21.6)*
(II-5)
- 0.54 SUN + 0.23 RUM + 0.04 DTS
(0.24)* (0.22) (0.07)
R = 0.82
If In the studies of Lave and Seskin (1970, 1973) and the Koshals (1974), a
portion of the explanatory variables was used to estimate a general physi-
cal damage. Lave and Seskin regressed mortality rates against air pollu-
tions—particulates and sulfates—population density, proportions of non-
white, proportions of people over age 64, and proportion of poor families,
The Koshals selected the population density, the percentage of relative
humidity and the pollutants-suspended particulate matter and benzene
soluble organic matter as the explanatory variables in their mortality
equation.
21
-------
where CC denotes the computed conventional mortality rates, PAGE the percent-
age of population 65 or older, PYAP percentage of population with income above
poverty level, PCOL percent of persons 25 or older who have completed 4 years
of college, PWPO percentage of white to total population, SUN possible annual
sunshine days, RHM relative humidity, and DTS number of days with thunder-
storms. The figures in the parentheses are standard errors of the estimates.
The estimated coefficients shown in the equation have the correct signs, and
with * and ** to indicate that they are statistically significant at the 1 and
5 percent level.
The dose-response function embodying the effect of the threshold level of
25 (ig/m-^ is expressed as:
(MR - CC) = EXP (a - b/(SO - 25))
or
RMR = EXP (a - b/(S02 - 25)) (II-6)
where CC is the computed value of conventional mortality rate from equation
(II-5), and RMR = MR - CC is the residual mortality rate.
The residuals, i.e., MR - CC = RMR, take both positive and negative val-
ues. Since the logarithm of a negative number is undefined, RMR was squared
prior to its logarithmic transformation. The resultant regression equation was
then adjusted by dividing the coefficients by 2. This adjustment is demonstrated
as follows:
The regression equation takes the form
2
In (RMR) = 2 a - 2b/SC>2
By virtue of a property of logarithm, we also obtain
2 In (RMR) = 2a - 2b/SO (II-7)
or
In (RMR) = a - b/SO
Note that the coefficients in equation (II-7) are twice as large as those in
equation (II-4) which is the initially specified nonlinear dose-response function.
22
-------
The regression result for equation (II-4) is shown as follows:
RMR2 = EXP (2.50 - 51.04/SO )
(1.34) (4.22)*
or (II-8)
RMR = EXP (1.25 - 25.52/SO )
R = 0.03
The figures below the coefficients are standard errors with * indicating that
the coefficient of SO is significant at the 1 percent level. Though SO ex-
plains only 3 percent of the residual mortality rate, the nonlinear fit showed
an explanatory power 150 times larger than the linear fit. Generally comparison
of R when the dependent variables are different may not be meaningful. However,
the purpose of comparing R2 associated with RMR and In (RMR) equations here is
to determine which of the two specifications is more suitable for the estima-
tion of the physical damage function. For comparison purposes, such a linear
regression equation is presented as follows:
RMR = 29.65 - 0.034 SO (II-9)
(20.28) (0.35)
R2 = 0.0002
The linear fit showed not only very low explanatory power, but also an incor-
rect sign for S02- Thus, the nonlinear specification of the dose-response rela-
tion seems to be superior and tends to support the _a priori judgment regarding
human responses to pollution dose variations.
To recapitulate, the methodological procedures for estimating the function
between mortality rate and SO are summarized as follows:
1. A linear multiple regression model represented by equation (II-5)
was developed for estimating the effects of the socioeconomic, demographic, and
climatological factors with the exclusion of air pollution on the conventional
mortality rate, C, expressed in deaths per 10,000 population.
2. The computed values of C, i.e., CC, were subtracted from the ob-
served gross mortality rate. The residual, RMR = MR - CC, was then regressed
on S02 alone according to the specification in equation (II-4). The regression
result was shown in equation (II-8). The nonlinear, exponential dose-response
function was transformed into a linear function with logarithm on RMR and
reciprocal on SO for empirical estimation.
23
-------
The nonlinear physical dose-response function between residual mor-
tality and SO derived from this stepwise econometric technique is characterized
by the following features:
1. The nonlinear dose-response function is consistent with the £
priori judgment about dose-response relationship between air pollution and mor-
tality rate. It can also be easily adjusted with whatever is the threshold
level of the SO concentration when estimating the economic damages.
2. For the purpose of predicting and computing the marginal mortality
damages due to SO , this nonlinear equation has the right sign and higher ex-
planatory power than its counterpart linear equation in view of its goodness
of fit.
3. The nonlinear specification circumvents at least partially some
of the econometric problems such as multicolinearity and heteroscedasticity
•
which are to be discussed next.
Technical Problems in Estimation--
Although detecting and treating econometric problems which are often en-
countered in the pollution effect studies are not the main purpose of this
study, the problem of multicolinearity and heteroscedasticity are examined dur-
ing the course of research.
Multicolinearity--j:/It is well known that multicolinearity problems occur
when some or all of the explanatory variables are highly correlated and that
it becomes difficult!, if not totally impossible, to disentangle their separate
influences. Of the nine explanatory variables used in this study, PWPO is cor-
related with the pollution variables, SO and TSP. RHM is correlated with PAGE,
PYAP, PWPO, and SUN. The correlation coefficients are presented in Table II-1.
On the basis of this correlation coefficient table, one may be led to conclude
that not too "strong" multicolinearity appear to be present in this study. How-
ever, it should be noted that the usefulness of partial correlation coefficients
as a diagnosis of multicolinearity is questionable. Wichers (1975) has recently
shown that a given value of partial correlation coefficient may be compatible
with two very different multicolinearity patterns. Less obtusely stated, a simple
correlation coefficient may not be the appropriate measure of multicolinearity.
\l For a detailed discussion on multicolinearity see Johnston (1963), p. 207,
Goldberger (1964), pp. 192-193, Farrar and Glauber (1967), and Haitovsky
(1969). The three-stage test for the detection of multicolinearity patterns
in the classical regression model was criticized by Kumar (1975), Wichers
(1975), and O'Hagen and McCabe (1975). Kumar cast doubt on the x^ test sug-
gested by Farrar and Glauber for the existence of multicolinearity and on
the F and t tests to localize the problem. Wichers showed that the third
stage of the Farrar-Glauber test is ineffective. O'Hagan and McCabe pointed
out a fundamental error which renders meaningless the contribution of
Farrar-Glauber to multicolinearity as a sample problem.
24
-------
TABLE II-l. CORRELATION COEFFICIENTS!/
PAGE
PYAP
PCOL
PWPO
SUN
RHM
DTS
S02
TSP
0.
-0.
0.
0.
-0.
0.
0,
0.
0.
74
26
61
36
25
23
05
13
24
-0.12
-0.41
0.72
-0.09
0.35
-0.20
0.05
-0.09
0.25
0.33
-0.01
0.45
-0.19
-0.10
-0.23
-0.38
0.18
-0.04
-0.19
0.08
-0.15
-0.26
0.42
-0.13
-0.27
-0.33
-0
-0
0
-0
.36
.17
.08
.23
0.
-0.
-0.
00
08 -0.04
01 0.06
0.04
MR
PAGE PYAP * PCOL PWPO SUN RHM DTS
SO,
_a/ Correlation coefficients are statistically significant at 5 percent level
if r^0.32 for 40 observations.
Thus, diagnosis of multicolinearity could be guided by _a priori judgment
with respect to the interactions among the explanatory variables. Furthermore,
the existence of multicolinearity poses little problem if the model is correctly
specified, because in such a case least-squares estimates will be unbiased re-
gardless of the extent of multicolinearity. The estimates will be biased if a
relevant variable is omitted and inefficient if a nonrelevant variable is in-
cluded in the regression analysis. The extent of the biases is dependent on the
degree of correlation between the misspecified variable and the variables with
significant coefficient.
In the presence of multicolinearity, no cut-and-dried technique has been
discovered to treat the problem. The residualization technique was first used
by Ridker (1965, p. 127-135) in a study of property value and pollution to al-
leviate the multicolinearity problem by attributing to all the nonpollution
variables the covariance between them and the pollution variable. The two-stage
estimation procedure is known to bias the pollution coefficients toward zero
and reduce their significance in the presence of multicolinearity.
Residualization technique was later employed by Lave and Seskin (1973) to
examine the multicolinearity problem. However, the estimated results obtained
by Lave and Seskin indicate that the estimated coefficients of the air pollut-
ants retain their significance and the parameter estimates are similar to those
in the one-stage regression equation.
Following Ridker and Lave and Seskin, the residuals rather than the gross
mortality rates were regressed on the air pollution variables. In doing so,
not only the nonlinear dose-response function can be estimated, but also the
25
-------
possible multicolinearity problem existing among the explanatory variables can be
alleviated. The low R for the dose-response function is expected from using
this two-stage residualization technique. However, the important result is that
the nonlinearity of dose-response function represents a better fit than the lin-
ear specification as pointed out previously.
Heteroscedasticity- The violation of the condition of a constant variance
in the disturbance term in any regression analysis is called heteroscedasticity.
The effect of heteroscedasticity is not on the biasness of the estimated regres-
sion coefficient itself, but rather on efficiency of the variance of the coefficient
estimated. It is recognized that the existence of heteroscedasticity often occurs
in the cross-section data. In the present study, heteroscedasticity is detected
by using the eyeballing -method. In terms of Figure II-2, the residuals are
plotted against the dependent variables. The shape of the residual distribution
pattern suggests that the variance of the error term is variable, i.e., there
is likely a problem of heteroscedasticity. Glejser (1969) and Park (1966) dis-
cussed alternative methods for detecting heteroscedasticity. These methods have
been applied by Smith and Deyak (1975) for testing heteroscedasticity in estimat-
ing air pollution and property value relation.
The common treatment for heteroscedasticity is to use the weighted regres-
sion method designed to reduce the nonhomogeneity of the variance. The use of
semilog on the dependent variable in this study is a sort of the weighted re-
gression method. The semilog transformation reduces the nonhomogeneous spread
of the variance in the error term (e.g., along the mortality rate axis in Figure
II-2, on page 27), and, hence, partially alleviates the heteroscedasticity
problem.
A LINEAR GENERAL PHYSICAL DAMAGE FUNCTION
As noted earlier, reliable and useful average damage functions on mortality
rate and air pollution for the United States metropolitan areas are still lack-
ing. To close this gap in the air pollution damage investigation, a generalized
average damage function is developed by regressing jointly, in a linear form,
the sum of the estimated mortality rates from both equations (II-5) and (II-8)
on the four socioeconomic and demographic variables, the three climatological
variables and the SO . It should be stressed that the results of this gener-
alized average damage function should only be used for prediction purposes,
and any statistical interpretations would be meaningless. Otherwise stated,
this damage function so derived serves to yield a more accurate prediction with
respect to the changes in the mortality rates in response to a ceretis paribus
change in any of its determinants. Based on the data of the 40 SMSA's with SO
exceeding 25 |_lg/m3 between 1968 and 1970, the linear regression analysis was
conducted to ascertain the generalized average damage function, estimated ass
26
-------
10
/
•H
CO
/
*
-1
-2
-3
-4
-5
\ 70
L \
\
\
-211
\
\
\
\
\
\
\
\
\
-*-* 1 1
80 • 90 W100 110 120 130
\
\
\
\
Mortality
Rate
\
Figure II-2. Heteroscedastic distribution of the residuals.
27
-------
CMR = CC + CRMR
= 226.2 + 735.4 PAGE - 113.8 PYAP - 0.12 PCOL - 77.5 PWPO
(3.4)* (8.7)* (5.3)* (0.003)* (2.0)*
(11-10)
- 0.55 SUN + 0.23 RHM + 0.03 DTS + 0.023 S02
(0.02)* (0.02)* (0.006)* (0.003)*
where CMR is the computed mortality rate, which is the sum of the computed
conventional mortality rate (CC) and the computed residual mortality rate
(CRMR) from equations (II-5) and (II-8), respectively. All independent vari-
ables on the right-hand side of equation (11-10) were defined previously.
Admittedly, a usual statistical interpretation for the generalized damage
function summarized by equation (11-10) is not meaningful. However, the purpose
of deriving this equation is to demonstrate that the stepwise econometric model
ameliorates some technical problems of estimation. The advantage of this approach
is clear if equation (11-10) is compared with the similar physical damage func-
tion using the actual rather than Computed mortality rates as the dependent
variable. Such a physical damage function is summarized as follows:
MR = 230.1 + 746.4 PAGE - 119.3 PYAP - 0.12 PCOL - 77.7 PWPO
(51.5) (10.59) (63.9) (0.035) (24.3)
(II-ll)
- 0.54 SUN + 0.23 RHM + 0.04 DTS - 0.004 SO
(0.25) (0.22) (0.07) (0.033)
It is noteworthy that the coefficient of SO in equation (II-ll) is negative
despite the fact that the simple correlation coefficient between MR and SO is
positive and equal to 0.13. The negativity of the SO coefficient is probably
due to multicolinearity and other econometric problems discussed earlier. The two-
step econometric method seems to have partially overcome these technical problems
and yields, if not coincidentally, the expected positive coefficient of SO in
equation (11-10).
VALUES OF AIR POLLUTION DAMAGES AND ECONOMIC DAMAGE FUNCTIONS
Air pollution damage to human health in this country has been roughly es-
timated by Ridker (1965), Lave and Seskin (1970, 1973), Jaksch and Stoevener
(1974), Koshal and Koshal (1974), Park (1974), and others. However, their es-
timates vary considerably; from $443 million by Ridker to $2.4 billion by Lave
and Seskin, and $6.8 billion by Park, partially because their study scopes and
period are not commensurate with each other. In order to estimate an average
economic damage function for the United States urban areas, it is not meaning-
ful to borrow the national damages estimated by the above authors not only
28
-------
because of this great disparity but also the different methods of estimation.
A method will be developed to quantify regional damage separately for each met-
ropolitan area so that regional control costs and benefits can be evaluated.
Since we considered 25 (ig/m^ as the threshold of SO , only those SMSA's with
average annual SO levels eual to or greater than 25 i/m^ between 1968 and
levels equal to or greater than
1970 and with data on other relevant factors were selected.
Air pollution has caused high morbidity rates in addition to premature
mortality in this country. This section, however, is mainly concerned with the
mortality damages. The morbidity damages due to air pollution will be discussed
in Section III. To estimate the mortality damages of SO and the percentage of
pollution-caused damage to total mortality losses, an expected average permanent
income method was developed. Specifically, we computed via equations (II-5) and
(II-8) the conventional and the residual mortality rate for the selected SMSA's.
Assume that each individual in any of the SMSA's is equally affected by air pol-
lution and that the growth in median earnings from 1960 to 1970 represents an
expected normal income rate. The expected future income streams are computed
by a simple formula computed for the conventional and air pollution victims in
the labor force- -between 18 and 64 years of age. The present value of the eco-
nomic damages was derived by discounting the future incomes at a rate of 4 per-
cent which is the long-term bond rate. Finally, we regressed the computed economic
losses of both conventional and pollution victims on the demographic, socioeco-
nomic, and weather variables, and SO for the selected SMSA's to derive the so-
called "average" economic damage function.
In functional form, this part of the work for each SMSA can be succinctly
expressed as follows:.!;/
V = Y
V = Y
n
fc/(i + i)fc • L •
n
(1 + r)
(1 + r) /(I + i!
[cc + CEMR] (11-12)
t=l
L • CG
(11-12')
I/ A somewhat different formula was developed and employed by Ridker for esti-
~~ mating damage costs due to premature death. A drawback of his method, as
noted by Ridker himself, is the lack of adjustment for increase in labor
productivity over time. A similar framework was also used by Schrimper
(1975) to calculate mortality costs for Chicago. The expected income formula
developed here considers the improvement in labor productivity, though all
workers are assumed to live through and be employed until the age of 65.
The bias in the resulting estimates is believed to be negligible.
29
-------
where V and V are, respectively, the computed value of regional economic
damages with or without air pollution;
Y is the weighted median income of 1970 between males and females with
the weights being their respective share in the labor force;
r is the expected family income growth rate which partially reflects the
growth in labor productivity assumed to be equal to the average from
1960 to 1970;
i is the discount rate, set at 4 percent per year, a rather conservative
rate;
L is the labor force or population between 18 and 64 years of age;
CRMR and CC are the computed excess mortality rates and the computed
conventional mortality rate, respectively;
n is the difference between regional median age and 64; this assumes that
the number of deaths due to air pollution with age younger than the me-
dian age is offset by those who fall short of reaching the age of 64.
The damage costs without and with air pollution and the per capita damage
costs for 1970 by SMSA are estimated using equations (II-8), (11-12), and (II-
12') and are contained in Table II-2. All dollars reported in the table are in
1970 value. Under the heading of mortality damage due to SO , total and per
capita mortality cost for each SMSA can be found in Columns 1 and 2. Mortality
damages in the absence of air pollution is presented in Column 3, and Column
4 presents the ratio or the relative magnitude of total mortality cost attribut-
able to SO and the mortality damage with and without SO . The higher the ratio,
the more serious is the pollution damage.
It should be noted that the damage estimates presented in the table depend
vitally on the assumptions made in this study. The most critical assumptions
are the threshold levels of SO the natural mortality rate, the growth in in-
come and the discount rates. Change in any of these assumptions would result
in modification in the damage estimate.
As readily revealed in the table, total mortality costs in the presence
of SO amounted to $887 million for the 40 SMSA's which had average annual SO
concentration beyond 25 (0,g/m^ between 1968 and 1970. Given that total mortality
cost in the absence of air pollution in the 40 SMSA's is $60.2 billion, as mea-
sured, the air pollution damage accounted for 1.4 percent of the total. Among
the 40 SMSA's, New York City had the highest total and per capita mortality air
pollution damage, about $329 million and $28.4 respectively, partially because
it had the highest SO concentration level between 1968 and 1970, i.e., 210
. The highest percentage of air pollution damage was found in Chicago and
New York City; 2.7 percent of total gross mortality values in these areas could
30
-------
TABLE 11-2. MORTALITY COSTS WITH S02 BY SMSA1 s, 1970
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
SMSA
Akron, OH
Allentown, PA
Baltimore, MD
Boston, MA
Bridgeport, CT
Canton, OH
Charleston, WV
Chicago, IL
Cincinnati, OH
Cleveland, OH
Dayton, OH
Detroit, MI
Evansville, IN
Gary, IN
Hartford, CT
Jersey City, NJ
Johnstown, PA
Lawrence , MA
Los Angeles, CA
Minneapolis, MN
New Haven, CT
New York, NY
Newark, NJ
Norfolk, VA
Paterson, NJ
Peoria, IL
Philadelphia, PA
Pittsburgh, PA
Portland, OR
Providence, RI
Reading, PA
Roche ster, NY
St. Louis, MO
Scranton, PA
Springfield, MA
Trenton, NJ
Washington, D.C.
Worchester, MA
York, PA
Youngs town, OH
Total
( g5/™3)
51
57
54
31
40
30
27
120
25
64
25
38
25
58
57
75
25
52
35
38
40
210
37
26
28
26
84
57
26
67
30
32
40
30
87
32
47
31
31
30
Mortality
Due to
Total
(in 106)
(1)
8.4
7.5
28.4
1.3
2.6
0.1
—
178.0
--
34.3
__
26.0
--
10.6
10.5
9.6
—
2.7
15.9
9.1
2.2
329.0
7.0
--
--
--
97.9
30.0
--
14.6
0.1
0.8
13.3
—
10.6
0.2
35.5
0.2
0.1
0.1
886.6
Damage
S02
Per
Capita
(2)
12.4
13.8
13.( 7
0.5
6.7
0.3
..
25.5
.-
16.6
_„
6.2
--
16.7
15.8
15.8
--
11.6
2.3
5.0
6.2
28.4
3.8
--
--
--
20.3
12.5
--
16.0
0.3
0.9
5.6
0.01
20.0
0.7
12.4
0.6
0.3
0.2
Mortality Damage
Without Air
Pollution Ratio
(in 106) (lH((l)+(3)
(3) (4)
570.6
462.5
1891.6
2398.7
353.4
330.9
159.0
6292.0
1160.0
1875.7
398.0
4884.0
214.0
555.4
552.5
529.4
263.0
204.3
4964.1
1380.9
341.8
11671.0
1633.0
511.0
1150.0
295.0
4322.1
2000.0
922.0
777.4
257.9
784.2
2156.7
185.0
458.4
254.8
1194.5
319.8
263.9
482.9
60,221.4
0.0145
0.0160
0.0148
0.0005
0.0073
0.0003
__
0.0275
--
0.0180
__
0.0053
..
0.0187
0.0187
0.0178
--
0.0130
0.0032
0.0065
0.0064
0.0274
0.0043
--
--
--
0.0221
0.0148
--
0.0184
0.0004
0.0010
0.0061
--
0.0226
0.0008
0.0175
0.0006
0.0004
0.0002
Note: individual figure may not add to totals due to rounding.
31
-------
be attributed to SO . In order of magnitude, New York City, Chicago, and
Philadelphia all had air pollution damages of more than $50 million. In terms
of the ratio of net mortality damage to gross mortality damage, i.e., Column
4, again New York and Chicago which had ratio values of 2.7 percent lead all
the other SMSA's. As noted earlier, the degree of the pollution damage is par-
tially reflected by the magnitude of this ratio.
Although the economic damage costs derived in this section are more detailed
than prior estimates, they are still crude information and should be used with
caution under the stated conditions. In order to develop a marginal economic
damage function useful for prediction and control purposes, the "total of eco-
nomic costs of mortality" is related not only to SO , but also to various socio-
economic, demographic, and climatological characteristics of different regions.
The stepwise regression technique was used with inputs from the 40 sample ob-
servations to estimate the economic damage function. The regression results are
shown as follows:
V = 10,295 + 47.02 SO - 8,128.4 PWPO + 98.5 RHM + 72.3 SUN
(11,023) (6.97)* (5,195.9) (46.9)* (53.4)
(11-13)
- 15.98 DTS - 16,191.8 PYAP +7.7 PCOL + 3,772 PAGE
(15.99) (13,659.9) (7.6) (22,650)
R2 = 0.74
where V is total mortality cost obtained from equation (11-12) and all the
explanatory variables are defined earlier.
The coefficients and standard errors in (11-13) are reduced by a factor
of 10 . The values of standard error are presented below the coefficients, with
* to indicate that the coefficient is significant at the 1 percent level.
The economic damage function derived can be useful to policymakers in esti-
mating the marginal and average damages (benefits) resulting from a pollution
control program. To serve as an illustration, an example involving the computa-
tion of the partial elasticity of an explanatory variable and the associated
marginal benefit due to the changes in that variable is presented. Suppose the
federal government is considering the implementation of a pollution abatement
program which is expected to reduce the average SO level in the urban areas
by, say, 10 percent. What will then be the dollar worth benefit of the reduced
premature mortality rate as a result of the pollution abatement program? Since
the average total damage cost due to premature mortality is $1,530.8 million
and the average SO level is 47.95 (j/g/m among the 40 SMSA's, the partial elas-
ticity of the damage cost with respect to SO is derived by using the formula
that
32
-------
E. QH = @c/o-(S00)) x (SO./c) = 47.02 x (47.95/1,530.8) = 1.45.
Cjdu / z
Note (9c/d(SO )) in the formula denotes the coefficient of SO in the economic
damage function; SO and c are, respectively, the mean values of SO and the
total damage cost for the 40 SMSA's included in the sample.
The distinguishing property of the concept of elasticity is that it is a
unit-free measure of the percentage change in the dependent variable with re-
spect to the percentage change in any of the explanatory variables while hold-
ing other things equal. Given the computed elasticity of damage cost with re-
spect to SO , EC SQ2 = 1.45, it is in general expected that a 10 percent de-
crease in the SO concentration level will result in a 14.5 percent reduction
in the premature mortality damage cost. Since the mean value of the regional
damage cost for the 40 SMSA's is $1,531 million, when the SO level decreases
from 47.95 (J,g/m3 to 43.15 |j,g/m3, it is expected that on the average the damage
cost will be reduced by the amount of $1,530.8 x 14.5 percent = $221.9 million.
Likewise, the elasticities for the other explanatory variables can be analogously
computed and interpreted.
PREMATURE MORTALITY DAMAGES AND SUSPENDED PARTICULATES
Earlier studies have established a positive qualitative relationship be-
tween mortality and suspended particulates. Recently Lave and Seskin (1970,
1973), and the Koshals (1974) further confirmed the existence of a quantitative
association between mortality and the particulates. As discussed earlier, the
threshold effects of the air pollutant and the heteroscedasticity problems in
the empirical estimation of the relation were, by and large, ignored in the prior
studies. A two-step residualization technique was, however, developed earlier
to cope with these problems in estimating a nonlinear dose-response function.
The same methodology is used in this section to establish a dose-response func-
tion relating mortality to suspended particulates.
The nonlinear dose-response relation (II-3) was used for regressing the
residual mortality rate (MR-C) on total suspended particulates (TSP). To be
consistent with earlier SO estimates, the particulate level is also adjusted
by a threshold of 25 |i.g/m3 in computing the physical damage. As noted earlier,
although 25 (ig/m^ is a reasonable level for capturing the threshold effect,
alternative thresholds may also be considered. Changes in the threshold will
cause modifications in the damage estimates. It is conceivable that, other
things being equal, a lower threshold level implies higher damage cost.
33
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The least-squares regression yields the following nonlinear physical damage
functions for suspended particulates:
RMR = EXP [1.30 - 65.75/TSP]
(0.83) (70.84) (11-14)
R2 = 0.02
The values below the coefficients are standard errors. The explanatory variable
TSP is not statistically significant. With the availability of the physical dam-
age function, the methodological procedures used earlier for estimating the pos-
tulated function relating mortality rate to SO can be employed to estimate
the economic damages and the associated economic damage function for suspended
particulates.
Economic Damage Functions
For policymakers, economic damage functions may be more relevant than physi-
cal damage functions. An economic damage function, or a monetary damage function,
relates levels of pollution to the amount of compensation which would be needed
in order that the society is not worse off than before the deterioration of the
air quality. The economic damage function is useful to decision makers since
the multiple dimensions of the decision problem are reduced into one dimension
only, i.e., money. It should be noted, however, that transformation of a physi-
cal damage function into an economic damage function often involves value judg-
ment on the part of the policymaker- A related question as to the degree of
conformity of the values of the policymaker with those of the consumer sover-
eignty is largely unresolved.
The expected permanent income method delineated earlier was employed to
estimate premature mortality damages due to total suspended particulates. The
damage costs associated with total suspended particulates are presented in
Table II-3. Columns (1) and (2) present total and per capita mortality damage
attributable to TSP. Mortality damage without air pollution is presented in Col-
umn (3). Column (4) presents ratio of total mortality damage due to TSP to total
mortality damage with TSP. This ratio reflects the relative magnitude of the
damage attribute TSP to total mortality damage. An examination of the table re-
veals that the mortality damages range from $1.4 million in Lawrence,
Masssachusetts, to $155 million in New York City. The air pollution damage in
Lawrence is 0.7 percent of the total gross mortality damage, while in New York
City suspended particulate causes about 1.3 percent of total mortality damage.
The highest ratio of pollution damage to total mortality damage of the magnitude
of 4.0 percent is observed for Dayton, Ohio.
Generalized economic damage functions were derived by regressing the pre-
mature mortality damage costs associated with TSP (TMRCT) which is the sum of
Columns (1) and (3) in Table II-2 on the demographic, socioeconomic, and
34
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TABLE II-3. MORTALITY COSTS WITH TSP BY SMSA's,
(in dollars)
1970
Mortality Damage
Due to TSP
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
SMSA
Akron, OH
Allentown, PA
Baltimore, MD
Boston, MA
Bridgeport, CT
Canton, OH
Charleston, WV
Chicago, IL
Cincinnati, OH
Cleveland, OH
Dayton, OH
Detroit, Ml
Evansville, IN
Gary, IN
Hartford, CT
Jersey City, NJ
Johnston, PA
Lawrence, MA
Los Angeles, CA
Minneapolis, MN
New Haven, CT
New York, NY
Newark, NJ
Norfolk, VA
Paterson, NJ
Peoria, IL
Philadelphia, PA
Pittsburgh, PA
Portland, OR
Providence, RI
Reading, PA
Rochester, NY
St. Louis, MO
Scranton, PA
Springfield, MA
Trenton, NJ
Washington, B.C.
Worchester, MA
York, PA
Youngs town, OH
Total
TSP
(ug/tn3)
80
87
147
108
57
103
105
155
106
201
114
153
75
105
74
83
103
65
118
76
60
95
134
113
56
78
78
135
86
77
117
90
120
189
64
71
90
72
85
110
Total
(in 106)
(1)
7.2
6.0
42.0
42.9
2.0
5.3
2.5
147.0
18.9
47.8
16.4
116.0
2.0
10.6
6.4
5.4
3.7
1.4
106.0
18.8
1.9
155.0
33.6
11.2
6.0
3.3
45.8
38.5
11.1
8.0
4.3
•11.7
38.5
3.7
3.1
2.4
43.3
2.8
3.3
8.2
1044.0
Per
Capita
(2)
10.6
11.0
20.3
15.6
5.1
14.2
10.9
21.1
13.6
23.2
19.3
27.6
8.6
16.7
9.6
8.9
14.1
6.0
15.1
10.4
5.3
13.4
18.1
16.5
4.4
9.6
9.5
16.0
11.0
8.8
14.5
13.3
16.3
15.8
5.9
7.9
15.1
8.1
10.0
15.3
Mortality Damage
Without Air
Pollution Ratio
(in 106) (l)-K(D+(3)
(3) (4)
570.6
462.5
1891.6
2398.7
353.4
330.9
159.0
6292.0
1160.0
1875.7
398.0
4884.0
214.0
555.4
552.5
529.4
263.0
204.3
4964.1
1380.9
341.8
11671.0
1633.0
511.0
1150.0
295.0
4322.1
2000.0
922.0
777.4
257.9
784.2
2156.7
185.0
458.4
254.8
1994.5
319.8
263.9
482.9
60,221.4
0.0125
0.0128
0.0217
0.0176
0.0056
0.0158
0.0155
0.0228
0.0160
0.0249
0.0396
0.0232
0.0093
0.0187
0.0115
0.0101
0.0139
0.0068
0.0209
0.0134
0.0055
0.0131
0.0202
0.0214
0.0052
0.0111
0.0105
0.0189
0.0119
0.0102
0.0164
0.0147
0.0175
0.0196
0.0067
0.0093
0.0212
0.0087
0.0124
0.0167
Note: — individual figure may not add to totals due to rounding.
35
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climatological variables and the suspended participate level for the 40 SMSA's.
The stepwise regression result for the generalized economic damage function is
summarized below:
TMRCT = 13,109 + 7.63 TSP - 20,225 PWPO + 85.05 RHM + 6.85 SUN
(18,034) (11.68) (8,339)* (72.85) (87.49)
- 14.88 DTS - 10,554 PYAP + 11.39 POOL + 54,606 PAGE
(24.89) (21,076) (12.25) (33,019)
R2 = 0.38 (11-15)
The values below the coefficients are standard errors. The symbol * indi-
cates that the coefficient is significant at the 1 percent level. The coeffi-
cients and standard errors are reduced by a factor of 106. The explanatory
variables are the same as those appearing in the sulfur dioxide economic damage
function and explain about 38 percent of the variations in the dependent vari-
ables .
Given the mean values of total damage cost and total suspended particulate
level are $1,543.5 million and 100.9 (j,g/m^, respectively, the partial elasticity
of damage cost with respect to total suspended particulate is:
Ec TSP = ?*63 X (100-9/1»543) = 0.49
Thus, a 10 percent decrease in the average TSP level as a result of pollu-
tion control programs will cause a reduction of 4.9 percent in the premature
mortality damage cost. That is, when the TSP is reduced from 100.9 [^g/m3 to
89.81 |j,g/m3 the damage cost, on the average, will be reduced by the amount of
$1,543 x 4.9 percent = $75.6 million.
IMPLICATIONS AND CONCLUDING REMARKS
This study is the first attempt to estimate a physical-nonlinear damage
function between excess mortality rates and the SO concentration with consid-
erations of circumventing certain econometric problems such as multicolinearity
and heteroscedasticity, and accounting for the effects of the threshold levels.
Through a two-step adjustment procedure, the average physical mortality.function
was generalized with a rather complete specification. That is, the generalized
average mortality model includes not only the major demographic, socioeconomic,
and climatological determinants but also air pollution variables. The two-step
econometric model developed here represents a constructive response to the call
recently made by Lave and Seskin (1973) and Ferris (1970) in connection with
the urgent need to improve on the existing studies in the area of air pollution
and human health.
36
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This study is also the first attempt to present comparable estimates for
premature mortality damages due to "excessive" air pollution—sulfur dioxide
and total suspended particulates—for individual urban areas in the United
States. To assist the policymakers in estimating possible marginal damage
(benefit) resulting from a given pollution control strategy, "average" economic
damage functions which transform the multiaspect of the problems into a single,
homogeneous monetary unit were also developed separately for the two major pol-
lutants.
It should be noted that the present federal standards were derived on the
assumption that threshold levels for various pollutants exist. These threshold
levels are considered to be the safe levels below which essentially no person
is hurt. This threshold level concept has been attacked by many medical experts
on the grounds that evidence has failed to support a genuine clear-cut lower
limit. It is our contention that the threshold model of health effects, however,
should not be taken literally, as some experts suggested.
The threshold of 25 |j,g/m3 was used in this study in deriving the damage
estimates because it is viewed as the mean level of the underlying distribution
of tolerable threshold levels of all the individuals in a given SMSA. Further-
more, it is also the average concentration level in the rural areas where lit-
tle air pollution damage on human health is observed. Thus, while the annual
average concentration level is below the threshold level, the majority of the
population in the rural areas is assumed not hurt from the presence of air pol-
lution. In order to derive more accurate "average" damages of pollution in a
given region, it is imperative to establish threshold level which is the mean
level of the actual threshold distribution. Our model is easily adaptable for
any threshold levels that one would like to consider as tolerable.
Another issue which merits discussion is the possible chemical interactions
among the pollutants. It is generally recognized that the total effect of sev-
eral pollutants present at the same time in the air may be greater or less than
the sum of their individual effects. In other words, the interaction effect may
be additive, synergistic or even antagonistic. Two types of interactions should
be noted: (1) physiological, and (2) chemical. Both types of interactions are
expected to occur. The crucial question is how and to what extent air pollutants
interact with each other. Stated differently, the question is whether the inter-
action effects are of sufficient magnitude to negate the present method of estab-
lishing the air quality standards. All panel experts, according to a recent Na-
tional Academy of Sciences study (1974), found that synergistic effects are not
important enough to invalidate the current methods which set air quality stan-
dards for each major pollutant.
Since the synergism occurs when SO and TSP are present at the same time,
the independency assumptions employed in this study may result in underestimating
the damages. However, it has been well recognized that both SO and TSP may be
merely convenient indexes of all major damaging pollutants. This measurement
problem of the pollutants contributes to overestimating the damages. In view
of these two opposing factors, we are unable to judge whether our procedure
tends to result in upward or downward biased estimates.
37
-------
Other conceptual and empirical problems often encountered in estimating
air pollution damage also should be noted. The major difficulties include the
lack of knowledge regarding the shape of the function which describes the re-
lationship between air pollution and health, the lack of a theoretical model
specifying the way air pollution affects health, the virtual impossibility of
accounting for all factors that might affect human health, and errors of ob-
servations in the data. Some of these problems, however, have been tackled in
the present paper. For example, the nonlinear dose-response relation was spec-
ified for the excess mortality rate and the pollutant concentration level. The
specification of this more plausible physical dose-response function would par-
tially account for the credibility in our air pollution damage estimates. The
semilog transformation reduces the heteroscedasticity while the use of the re-
siduals ameliorates the multicolinearity problem.
Although the income foregone or productivity models have been employed by
economists, their application to mortality or deaths in individual metropolitan
areas due to SO , TSP, and other causes opens up another avenue for air pollu-
tion damage quantification which seems to be much more desirable than earlier
studies unveiling only some aggregate figures for the nation as a whole. Fur-
thermore, the average air pollution damage functions derived in this study with
observations from a selected set of SMSA's with pollution level above the thresh-
old are conceivably more meaningful than prior studies which included all SMSA's
as sample observations regardless of the concentration level of air pollution.
This section presents a set of more recent estimates of air pollution dam-
age for each of the 40 SMSA's with concentration levels higher than 25 p-g/m3.
Based on the conservative assumption employed, it is found that while SO alone
in 1970 costs approximately $887 million, or about 1.4 percent of total mortality
costs in these areas, TSP imposes about $1,047 million damages, or about 1.7 per-
cent on the total mortality costs in the same areas.
The mortality damages due to SO and TSP and the mortality damages without
air pollution from Tables II-2 and II-3 are reproduced in Table II-4. Column
4 of Table II-4 presents the total mortality damage costs which are the sum of
the three component damages listed in Columns 1, 2, and 3. The SO damage esti-
mates derived in this section should replace the earlier estimates reported by
Liu (1975).
The results presented in this section are only suggestive and tentative.
Given the tentativeness and experimental nature of the methodological and
statistical procedures, and the degree of uncertainty associated with the
study results, a great deal of caution should be exercised in using the prod-
ucts of this research. However, the availability of average or marginal damages
is instrumental in determining the optimal national or regional pollution con-
trol strategies.
The current problem seems far more complex than the question of balancing
the benefits to polluters against damages inflicted on the receptors. The issues
are pressing and not yet well specified. The basic difficulty in applying the
38
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TABLE II-4. MORTALITY COSTS BY SMSA's, 1970
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
SMSA
Akron, OH
Allentown, PA
Baltimore, MD
Boston, MA
Bridgeport, CT
Canton, OH
Charleston, WV
Chicago, IL
Cincinnati, OH
Cleveland, OH
Dayton, OH
Detroit, MI
Evansville, IN
Gary, IN
Hartford, CT
Jersey City, NJ
Johnstown, PA
Lawrence, MA
Los Angeles , CA
Minneapolis, MN
New Haven, CT
New York, NY
Newark, NJ
Norfolk, VA
Paterson, NJ
Peoria, IL
Philadelphia, PA
Pittsburgh, PA
Portland, OR
Providence, RI
Read ing , PA
Rochester, NY
St. Louis, MO
Scranton, PA
Springfield, MA
Trenton, NJ
Washington, D.C.
Worchester, MA
York, PA
Youngs town, OH
Total
Total Mortality
Damage Due to
S02 (106) (1)
8.4
7.5
28.4
1.3
2.6
0.1
--
178.0
--
34.3
--
26.0
--
10.6
10.5
9.6
--
2.7
15.9
9.1
2.2
329.0
7.0
--
--
--
97.9
30.0
--
14.6
0.1
0.8
13.3
--
10.6
0.2
35.5
0.2
0.1
0.1
886.6
Total Mortality
Damage Due to
TSP (in 106) (2)
7.2
6.0
42.0
42.9
2.0
5.3
2.5
147.0
18.9
47.8
16.4
116.0
2.0
10.6
6.4
5.4
3.7
1.4
106.0
18.8
1.9
155.0
33.6
11.2
6.0
3.3
45.8
38.5
11.1
8.0
4.3
11.7
38.5
3.7
3.1
2.4
43.3
2.8
3.3
8.2
1044.0
Mortality Damage
Without Air
Pollution
(in 106) (3)
570.6
462.5
1891.6
2398.7
353.4
330.9
159.0
6292.0
1160.0
1875.7
398.0
4884.0
214.0
555.4
552.5
529.4
263.0
204.3
4964.1
1380.9
341.8
11671.0
1633.0
511.0
1150.0
295.0
4322.1
2000.0
922.0
777.4
257.9
784.2
2156.7
185.0
458.4
254.8
1994.5
319.8
263.9
482.9
60,221.4
Total Mortality
Damage
(in 106)
586.2
476.0
1962.0
2442.9
358.0
336.3
161.5
6617.0
1178.9
1957.8
414.4
5026.0
216.0
576.6
569.4
544.4
266.7
208.4
5086.0
1408.8
345.9
12155.0
1673.6
522.2
1156.0
298.3
4465.8
2068.5
933.1
800.0
262.3
796.7
2208.5
188.7
472.1
257.4
2073.3
322.8
267.3
491.2
62,152.0
Note: -- denotes less than $0.1 million.
Note: — individual figure may not add to totals due to rounding.
39
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recent research findings to accurately estimate the air pollution damage cost
stems from our ignorance about the populations at risk to air pollution. So
far, few attempts have been made to identify who suffers, to what extent, from
which sources, and in what regions. At this moment, updating and expanding the
available crude estimates which are generally restricted to certain regions are
urgently needed.
40
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SECTION III
MORBIDITY AND AIR POLLUTION
PROBLEMS AND OBJECTIVES
A great number of epidemiological studies have suggested that there is a
significant relationship between various morbidity rates and air pollution.
Even in the early 17th century it was quite generally suspected that sulfur
dioxide in coal smoke was responsible for the high morbidity and mortality
associated with the notorious smoke disasters such as those that later occurred
in Belgium's Meuse Valley in 1930, in Donora, Pennsylvania in 1948 and in London
in 1952.
The relationship between air pollution and health can be acute response--
dramatic increases in air pollution concentration exert an immediate adverse
effect on human health. However, it is well known that air pollutants contin-
uously react dynamically in the environment. The effect of pollutants on health
should also be examined over an extended period. Lave and Seskin (1973b, p. 17)
remarked that "a long, or chronic exposure to low concentrations might be just
as harmful to health as a short, or episodic exposure to high concentrations."I/
The diseases which are known to be related to air pollution include the
following: bronchitis and emphysema; pneumonia, tuberculosis and asthma; total
respiratory diseases; lung cancer; nonrespiratory-tract cancers; and cardiovas-
cular diseases. A review of the existing literature on the diseases attributable
to air pollution is given in the following paragraphs for better understanding
of the problems under study.
Bronchitis and Emphysema
Six specific bronchitis rates have been found by Stocks (1959) to be cor-
related with a deposit index and smoke. This result was corroborated by
Ashley (1969) who found a positive correlation between deaths due to bronchitis
and sulfur dioxide and smoke. However, a contrary result was obtained by Burgess
and Shaddick (1959) who failed to reveal a significant relationship between bron-
chitis death and air pollution.
Holland and Reid (1965) and Reid (1968) found that the health status of
postmen was inversely affected by fog and air pollution. Cornwall and Raffle
(1961) found a positive correlation between sickness absence and fog.
I/ A comprehensive literature review on the effect of air pollution on human
health was provided, for example, by Lave and Seskin (1975).
41
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Higgins (1966) found lower peak expiratory flow rate in urban areas than
in rural areas. Hammond (1967) confirmed that heavy smokers in cities suffered
a much higher morbidity rate than those in the rural areas. Ishikawa et al. (1969)
found that the incidence and severity of emphysema was higher in St. Louis than
in Winnipeg, which had a lower pollution level than St. Louis.
Petrilli et al. (1966) also discovered that the incidence of bronchitis
was signficantly correlated with pollution. Toyama (1964) and Yoshida et al.
(1966) confirmed the positive relationship between bronchitis and pollution.
Pneumonia, Tuberculosis, and Asthma
Stocks (1960) discovered a high correlation between smoke index and pneu-
monia mortality. Mills (1943) found substantial correlation between pneumonia
mortality and pollution levels. Significant sample correlations for pneumonia
mortality and fuel consumption, and for tuberculosis mortality and fuel con-
sumption were reported by Daly (1969).
Sultz et al. (1969) found a significant relation between air pollution
levels and the incidence of asthma and eczema among boys under 5 years of age.
Yoshida et al. (1969) found that bronchial asthma among Japanese residents was
proportional to the sulfur dioxide levels.
Total Respiratory Disease
Skalpe (1964) found that pulp mill workers under 50 years of age exposed
to sulfur dioxide suffered from a significantly lower maximal expiratory flow
rate. Speizer and Ferris (1963) reported more prevalent chronic respiratory
disease in those working in the tunnel for more than 10 years than for those
with shorter employment periods.
Winkelstein and Kantor (1969) discussed a positive reaction between cough
with phlegm and suspended particulates. However, the association was not found
between cough and sulfur dioxide. Rosenbaum (1961) found that British servicemen
from an industrial region exhibited a greater liability to respiratory diseases.
Feidbert et al. (1967) discovered that total respiratory disease mortality
in Nashville was directly related to the degree of sulfation and soiling. Lepper
et al. (1969) found that total respiratory deaths were related to the levels of
sulfur dioxide across areas of Chicago with various socioeconomic variables being
controlled.
Lung Cancer
Dean (1966) discovered that lung cancer death rates are higher in urban
areas than in rural areas. Gardner et al. (1969) found the lung cancer death
rate in males is positively related to air pollution when other social and en-
vironmental factors are controlled. Somewhat inconsistent results regarding the
relationship between sulfur dioxide and lung cancer were obtained by Buck and
Brown (1964).
42
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Stocks (1966) discovered a significant correlation between lung cancer and
air pollution. Clemmesen and Nielsen (1951) reported the lung cancer morbidity
for males in Copenhagen was about four times greater than in rural areas in
Denmark.
Manos and Fisher (1959) and Griswold et al. (1955) found that urban lung
cancer rates are significantly higher than rates in rural or nonmetropolitan
areas. Greenburg et al. (1967) reported correlation between lung cancer and air
pollution. However, negative results were obtained by Zeidberg et al. (1967)
and Winkelstein et al. (1967).
Nonrespiratory-Tract Cancers and Cardiovascular Disease
Winkelstein and Kantor (1969) found that stomach cancer mortality was
twice as high in high pollution areas as in low pollution areas.
Levin et al. (1960) discovered that the incidence rate for both sexes for
each of 16 categories of cancer was higher in urban than in rural areas. Contrary
results have also been reported by Greenburg et al. (1967a), among others.
Higher incidence rates of cardiovascular diseases in urban than in rural
areas were reported by Enterline et al. (1960). Zeidberg et al. found heart dis-
ease rates were correlated with air pollutants in Nashville. Manos and Fisher
(1959) also found positive relationships between heart disease and air pollution.
The results of many of the epidemiological studies discussed above indicate
that incidence rates of various kinds of diseases are generally much higher in
the urban areas than in the rural areas. Many of these disparities in morbidity
rates between urban and rural areas can be attributed to air pollution. The ratio
of urban incidence to rural incidence of morbidity has been termed the urban
factor. This urban factor has been used for estimating health damage due to air
pollution. The rationale for the urban factor technique is that if air pollution
levels in the urban areas could be reduced to the rural levels, then the dif-
ferences between the urban and rural morbidity rates adjusted for smoking, age,
sex, and race should be eliminated.
The crucial question is what portion of this urban factor is attributable
to air pollution. In a pioneering study of air pollution damage, Ridker (1965)
assumed that 100 percent of the urban factor is attributable to air pollution
and derived a damage value of $2 billion for 1958. Williams and Justus (1974)
assumed that a minimum of 10 percent and a maximum of 50 percent of the urban
factor is due to air pollution and estimated that the total 1970 nationwide
health cost due to air pollution was between $62 million and $311 million. The
43
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figures are much lower than the estimate of $6.22 billion for respiratory dis-
ease in the United States.JL' The damage estimates derived by using the urban
factor of health deterioration due to air pollution are apparently subject to
a large margin of error because of the difficult assignment problem of the ur-
ban factor. The urban factor method is also replete with several other concep-
tual and practical difficulties. For example, the distinction between urban
and rural pollution levels is hard to define because there exists a continuous
scale of pollution intensity instead of a simple dichotomy between urban and
rural pollution levels. Thus, after all, the question as to what percentage of
this urban factor is actually accounted for by air pollution remains largely
unresolved.
A recent study performed by Shy et al. (1974) on the Community Health and
Environmental Surveillance System (CHESS) examined the adverse effects of air
pollution on acute and chronic respiratory disease. The methodological proce-
dures employed in the CHESS study involve statistical analysis with varying pol-
lutant gradients and concentration levels. Each CHESS set which consists of a
group of communities selected to represent an exposure gradient for designated
pollutants generally includes High, Intermediate, and Low exposure communities.
The community selection is subject to the following criteria: The communities
have similar climates and are made up of a predominantly white, middle-class
population with as much homogeneity in socioeconomic and other demographic fac-
tors as possible. The research findings point to a clear trend toward excess
illness in the High exposure community.
Since the national and regional annual damage cost figures greatly assist
policymakers in determining optimal pollution control strategies, the effort
to derive a set of internally consistent and relatively accurate damage estimates
is warranted. The primary purpose of this study is to derive such damage esti-
mates. Specifically, physical and economic damage functions will be derived re-
lating morbidity rate and morbidity costs to air pollution, socioeconomic, demo-
graphic, and climatological variables. The morbidity damage costs will be esti-
mated for the 40 SMSA's included in the preceding section on mortality and air
pollution.
The balance of this section, which represents an exploratory effort to
estimate morbidity dose-response functions for adult morbidity damage costs
for the 40 SMSA's selected in our study, discusses the following subjects:
_!/ For a detailed discussion on some of the problems in using the urban factor
for calculating health costs, see J. R. William and C. F. Justus, "Evalu-
ation of Nationwide Health Costs of Air Pollution and Cigarette Smoking,"
Journal of the Air Pollution Control Association (November 1974), pp. 1063-
1066. The figure $6.22 billion was derived by William and Justus by ad-
justing Ridker's value of $2 billion for 1958.
44
-------
Environmental Damage Functions: Some Theoretical Underpinnings, Adult Morbidity
and Air Pollution, Adult Morbidity Damage and Sulfur Dioxide, Economic Damages
and Economic Damage Functions, and Adult Morbidity Damages and Total Suspended
Particulates.
ENVIRONMENTAL DAMAGE FUNCTIONS: SOME THEORETICAL UNDERPINNINGS
An economic damage function, which is usually derived on the basis of a
physical damage function, is defined, for example, by Maler (1974) as the com-
pensating variation or the amount the individual (or society) should be com-
pensated so as to maintain his initial preference level in the presence of a
deterioration in the environment. This definition is clearly applicable to any
situations in which the effect of environmental degradation enters directly into
the individual's utility function.
We assume that the consumer's preferences can be represented by a twice
differentiable, concave utility function, defined on Rm + n
U = U(C,H(A)) (III-l)
where C is an m-vector representing m private commodities and services, with
positive components indicating consumption, and negative ones, supply of labor
services. H denotes the health status, which is influenced by air pollution;
A is an n-vector characterizing environmental quality, which is exogeneously
given to the community. H can be viewed as the dose-response function.
Each individual wants to maximize (III-l) subject to the following budget
constraint:
PC
where P is the price vector associated with C, and Y is the individual's
income.
The economic damage function as registered in the compensation variations
due to changes in the individual's health condition because of changes in A
can be derived by minimizing the total expenditures subject to a given utility
level, say U.
45
-------
The familiar first order necessary conditions are
a U. = P., i = l,...,m (III-3)
where & is the Langrangean multiplier.
Solving (III-3) yields the following compensated demand functions
C = C(P,H(A);U) (III-4)
The minimum income required to maintain the same utility level when one
or several components in A changes is denoted by±'
I = I(P,A; U) (IH-5)
Assuming the individual always exhausts his budget, the economic damage
function is simply the difference between (III-5) and the individual's initial
income, Y,
D = I - Y = (P,H(A); U) (III-6)
Regional economic damages and the economic damage function can be opera-
tionally expressed as:
MBC j = MB (A) x PC j + HSj(A) x HC j + DUj(A) x DC j x POP j (IH-7)
MBC = f(H(E,D,S,W,A;e),P) (III-8)
where MBC. denotes total morbidity cost in the jth urban area, MB is the
morbidity rate, HS hospitalization rate, DU drug use rate, PC physician
cost, HC hospitalization cost, DC drug cost, and POP is the population in
the area. The notations in equation (III-8) were defined in Section II. That is,
_!/ Equation 5 was labeled by Maler as the expenditure function. The analytical
properties of such expenditure functions are delineated in K. G. Maler,
Studies in Environmental Economics, in press.
46
-------
E for the economic factors, D the demographic factors, S the social fac-
tors, W climatological factors, A air pollution, e error term, and P
the commodity prices.
ADULT MORBIDITY AND AIR POLLUTION
Physical damage functions on adult morbidity are derived by the classical
least-squares linear regression technique and the random sampling, simulation
technique. The few aggregated dose-response observations obtained from the CHESS
study (1974) form the data base for the regression analysis in this study. The
dose-response observation reported in the CHESS study related morbidity prevalence
rate to particulates and sulfur dioxide in 1971 for four regions: Salt Lake
Basin, Chicago, Rocky Mountain, and New York.JV
The CHESS communities in the Salt Lake Basin are located near the major
copper smelter, and the local meteorological pattern provides an area gradient
of exposure to sulfur oxides. The selected communities include Magna, Kearns,
Salt Lake City, and Ogden. Magna was designated the high exposure area because
it had a high sulfur dioxide level due to its proximity to the smelter. Kearns,
Salt Lake City, and Ogden were designated as Intermediate II, Intermediate I
and Low exposure areas. These three cities had a descending exposure gradient
to sulfur oxides.
The CHESS communities in the Chicago area include urban core, suburban
areas and the relatively clean area, designated as High I, High II and Low pol-
lution exposure areas for 1969-1970. The five communities selected in the Rocky
Mountain area for the CHESS study are Anacenda, Kellogg, East Helena, Bozeman
and.Helena, designated, respectively, as High I, High II, Low III, Low I and
Low II exposure areas. For the New York City area, Riverhead, Long Island was
chosen as a Low exposure community, the Howard Beach section of Queens as the
Intermediate exposure community, and the Westchester section of the Bronx as
a High exposure community.
The dose-response observations collected from the 15 CHESS communities
in the four selected regions are summarized in Table III-I. The adjusted
bronchitis prevalence rates expressed in percentages for the selected exposure
areas are presented in Column 3 of the table. The annual average sulfur dioxide
and total suspended particulates levels for the same set of communities are
presented respectively in Column 4 and Column 5. It should be noted that the
bronchitis prevalence rates presented in the CHESS report for Utah, Rocky Moun-
tain and New York were adjusted for smoking status (e.g., nonsmoker, ex-smoker
and smoker) and sex (e.g., mother and father), while the rates for Chicago were
adjusted for education level, race and smoking status.
I/ For a general description about the EPA's CHESS Program, see Shy and Finkles
(1973).
47
-------
TABLE III-l. MORBIDITY DOSE - RESPONSE OBSERVATIONS
oo
Area
Salt Lake Basin
Chicago
Rocky Mountain
New York
Adjusted Bronchitis
Community Prevalence Rate (%)
Low
Intermediate I
Intermediate II
High
Low
High I
High II
Low I
Low II
Low III
High I
High II
Low
Intermediate I
Intermediate II
6.71
6.92
8.54
10.77
25.97
25.30
21.22
1.78
5.10
4.88
4.23
3.98
9.17
16.49
13.93
Pollution Levels (ug/m3)
S02 (1971)
8
15
22
62
19
96
217
10
26
67
177
374
23
51
51
TSP (1971)
78
81
45
66
71
155
103
50
45
115
65
102
34
63
86
-------
The adjusted bronchitis prevalence rates were regressed on the two pollut-
ants to derive the dose-response functions for Salt Lake Basin, Chicago, Rocky
Mountain and New York separately by the least-squares technique. The regression
results are summarized in Table III-2. The regression fit between morbidity and
SO for New York, Chicago and Salt Lake Basin is fairly good, with R having
the values of 0.50, 0.88 and 0.94, respectively. Furthermore, SO is significant
at the 1 percent level for the New York and Salt Lake Basin regression equations.
For total suspended particulates, good regression fit was obtained for Chicago
and New York. However, TSP is consistently insignificant in expressing the vari-
ations in morbidity- These regression equations, coupled with the mean values
and standard deviations of the pollutants and the morbidity prevalence rates
presented in Table III-3, were used for a random sampling and simulation study
to generate a "national" dose-response function which can be used for estimating
morbidity damage costs in the various SMSA's.
ADULT MORBIDITY DAMAGES AND SULFUR DIOXIDE
Epidemiological studies have demonstrated that deterioration in air quality
results in increased consumption of medical services and, hence, in economic
loss to the pollution victims. To estimate such damage loss for the 40 SMSA's
and to estimate an average economic damage function on adult morbidity, a ran-
dom sampling technique for deriving a "representative" dose-response function
was employed.
Random Sampling Simulation Study and the Physical Damage Function
"Simulation" is the technique of setting up a stochastic model of a real
situ.ation so that sampling experiments can be performed upon the model (Harling,
1958). Simulation study differs from the classical sampling experiment in that
the former involves the construction of an abstract model, while the latter in-
volves direct experiment with the new data. The term "simulation" is often used
interchangeably with the term "Monte Carlo" technique.
The Monte Carlo technique, which was employed to generate the "average"
nonlinear dose-response damage function vis-a-vis existing time series and cross-
section studies, involves the study of probability models. As described by
Dienemann (1966) the Monte Carlo technique can be defined as follows:
Assume a system planner can describe each parameter with
a probability distribution. This distribution is then treated
as a theoretical population from which random samples are
obtained. The method of taking such samples, as well as
problems which rely on these sampling techniques, are often
referred to as Monte Carlo methods.
49
-------
TABLE III-2. ADULT MORBIDITY LINEAR DAMAGE FUNCTIONS
I. S02
(1) Rocky Mountain
MB (7») = 3.84 + 0.001 S02 R2 = 0.016
(0.94)*(0.005)
(2) Chicago
MB (7o) =
(2.49)* (0.023)
MB (7o) = 22.14 + 0.018 S02 R2 = 0.50
(3) New York
Salt Lake Basin
MB (7o) = 4.2 + 0.21 S02 R2 = 0.88
(3.46) (0.083*
MB (7=) = 6.22 + 0.075 S02 R2 = 0.94
(0.46)* (0.013)*
II. TSP
(1) Rocky Mountain
(2) Chicago
(3) New York
(4) Salt Lake Basin
MB (7o) = 2.94 + 0.014 TSP R2 = 0.109
(1.84) (0.023)
MB (70) = 18.42 + 0.05 TSP R2 = 0.74
(3.52)* (0.03)
MB (7o) = 7.19 + 0.098 TSP R2 = 0.47
(6.66) (0.10)
MB (7=) = 11.97 - 0.05 TSP R2 = 0.23
(4.90)* (0.07)
50
-------
TABLE III-3. MEAN VALUES AND STANDARD DEVIATIONS OF THE VARIABLES
Mean Value (X) Standard Deviation (S)
Utah
Prevalence Rate 8.2 1.9
S02 26.8 24.2
TSP 67.5 16.3
Chicago
Prevalence Rate 24.2 2.6
S02 110.6 99.8
TSP 109.6 42.4
Rocky Mountain
Prevalence Rate 4.0 1.3
S02 132.8 150.8
TSP 75.4 31.4
New York
Prevalence Rate 13.2 3.7
S02 41.2 16.2
TSP 61.0 26.1
A random sampling experiment was performed on the four sample regions in
this study for deriving an "average" morbidity dose-response function. These
four sample regions were constructed in the two dimensional space with the aid
of the four regional dose-response functions shown in Part I of Table III-2,
coupled with the data on the mean values and the standard deviations of the
dependent and independent variable (see Table III-3). The four regional blocks
are shown in Figure III-2, the vertical axis represents the morbidity rate ex-
pressed in number of incidences per 100 residents, and the horizontal axis de-
notes SO pollutant concentrations level expressed in fig/m . For each sample
block, the height of the block is the difference between the morbidity rate com-
puted from the dose-response function with the coefficient of SO in the function
taking the value of (b + s) and (b - s), where b is the coefficient of SO and
s the associated standard error. The width of the block is, however, measured
by the mean value of_ SO plus and_minus one standard deviation of the mean,
i.e., (X + S) and (X - S) where X denotes the mean value of SO and S the
associated standard deviation.
Thus, the four sample blocks shown in Figure III-2 were defined on the
basis of the four prior studies regarding the morbidity effect of SO in the
four different regions. The construction of these four blocks permits us to
51
-------
Oi
to
>
5
en
0£.
i
Figure III-1. Sample observation from four morbidity studies
with respect to SO .
-------
perform random sampling experiments. A random sample of 800 observations
with 200 chosen from each block was obtained. To eliminate possible bias
in the probability of being randomly selected resulting from the overlapping
of the blocks, another random sampling was performed on the basis that two
sorting schemes yield better results than one sorting procedure. A smaller
sample of 81 observations, i.e., 10 percent of 800, was chosen. These 81
observations were used to develop a nonlinear "average" dose-response function
specified alternatively as follows:
MB = C + EXP (a-b/SO ) (III-9)
or
MB - C = EXP (a-b/S02)
(MB-C) = a-b/S02 (111-10)
As in the mortality study reported in Section II, the physical dose-
response function in this morbidity study is again expressed as an exponential
function which is consistent with _a priori judgment and empirical results of
medical experts regarding plausible human dose-responses to changes in pollution
levels. The geometrical counterpart of this exponential relation is a long flat
"S" curve, implying that while the air pollutant contributes to the morbidity
incidence rate, the damaging effect is not proportional. In the presence of in-
creased SO level, the morbidity rate initially increases at an increasing rate
and continues to increase, but at a decreasing rate after a certain inflection
level.
Unlike the mortality study in which the intercept term C, conventional
mortality, is expressed as a function of a number of socioeconomic, demographic
and climatological variables, no such conventional morbidity function was esti-
mated due to the lack of a systematic collection of morbidity data by the var-
ious SMSA's. Of necessity, the C term in equation (III-9) above is assumed
to take the value of 11 since 11 is the arithmetic mean of the morbidity rates
calculated from the four regional dose-response functions with the explanatory
variable, SO being at the threshold of 25 )J,g/m for the sake of consistency
with the earlier mortality study.
In estimating equation (III-9), the classical least-squares technique was
applied. Since (MB - .11) may be negative, and the logarithm of a negative number
is undefinable, (MB - 11) was therefore squared prior to its logarithm transfor-
mation. The resultant regression equation was then adjusted by dividing the co-
efficients by 2. A detailed discussion on the rationale of this procedure was
presented in Section II.
53
-------
The regression results for equation (III-9) look as follows:
MB = 11 + EXP(0.65 - 4.96/SO )
(0.11)* (1.99)* (111-11)
R2 = 0.072
The figures below the coefficients are standard errors, with * indicating
that the coefficient of SO is significant at the 1 percent level. However,
the pollution variable SO explains only about 7 percent of the variations in
the residual morbidity rate, i.e., (MB - 11).
A linear morbidity equation was also fitted, with the regression result
shown as follows:
MB = 12.06 - 0.01 SO-
(1.28)* (0.01) (111-12)
R2 = 0.011
Comparing the result of equation (III-ll) to that of (111-12) the exponen-
tial dose-response function is apparently a better fit than the linear one be-
cause the former showed an explanatory power seven times larger than the latter
equation. Furthermore, the coefficient of SO in the exponential equation is
statistically significant, whereas it is insignificant and has a wrong sign in
the linear equation. Thus, the empirical results suggest that the nonlinearity
in the dose-response relation is more consistent with
-------
c. These 81 randomly selected observations were fitted to an exponen-
tial reciprocal equation to derive an "average" dose-response function for the
four regions.
Like the mortality dose-response function, the nonlinear morbidity dose-
response function has a number of distinguishing features: (1) the nonlinear
dose-response function is not only more in accord with _a priori judgment re-
garding human morbidity response to pollution doses, but also it is more amen-
able to being adjusted with whatever the assumed threshold level of SO is in
estimating the economic damages than the linear functions; and (2) for the pur-
pose of predicting and estimating the marginal morbidity damages due to SO ,
the nonlinear equation has shown better fit and hence, will yield more accurate
prediction over the linear one.
Economic Damages and Economic Damage Functions
Given the preceding nonlinear physical damage function, the economic costs
of diseases related to air pollution can be estimated by transforming the addi-
tional morbidity rate into monetary units. Economic damages of morbidity, as
discussed earlier, represent the amount that an individual or a society is will-
ing to spend so as to maintain the previous preference level in the presence
of the deterioration of air quality,.
Morbidity damages generally are comprised of two parts: direct and indirect
costs of illness. Included in the direct costs of illnesses are the expenditures
for prevention, detection, treatment, rehabilitation, research, training, and
capital investment in medical facilities. Indirect costs of illness include the
loss of output to the economy because of disability and the imputed costs such
as opportunities foregone. A comprehensive framework for calculating the direct
and indirect economic costs of illness and disability has been developed by Rice
(1966) and others.
Both direct and indirect morbidity costs were estimated in the present study.
Direct morbidity costs were computed by summing up the costs of physician visits,
hospitalization costs, and drug costs. According to a recent study by Jaksch
(1975), the average cost per physician visit for all ages combined in 1970 was
$14, and the average cost of a hospital day for all ages combined was $82. To
estimate total morbidity costs, further information is needed on the average
number of physician visits and the average length of hospital stay per pollution-
related disease incidence. A number of assumptions were made to obtain conserva-
tive morbidity damage estimates, as follows: (1) each pollution-related morbidity
incidence results in one visit to consult a physician; (2) 1 of 8.3 physician
visits, i.e., 12 percent, results in hospitalization; (3) drug costs run about
50 percent of the physician costs; (4) if hospitalization is required, each
patient stays 1 day in the hospital for treatment.I/
I/ Various information on national data about the number of visits to doctors
and the hospital days stayed per treatment can be obtained from Public
Health Service (1973).
55
-------
The conservative nature of both assumptions (1) and (4) leads to under-
estimations of the morbidity costs. The bias could be partially removed by as-
suming a greater number of physician visits and a longer hospital stay, however.
The estimates presented in this study can be regarded as low estimates for mor-
bidity costs. Assumption (2) is based on the calculated proportion of physician
visits resulting in hospital discharge for four categories of diseases related
to pollution (Jaksch, 1975). The figure 12 percent is the average of such pro-
portions of physician visits in the four disease categories. Assumption (3)
is, however, based on a ratio of total drug costs to total physician costs at-
tributable to the use of oxidation catalyst as estimated by (Jaksch, 1975),
i.e., 11.4/23.2 = 0.5.
The direct morbidity costs attributable to SO were estimated with the aid
of the following formulas:
PCS02 = $14 x EXP [0.65 - 4.96/(S02 -25)] x POP x NPV (111-13)
HCS02 = $82 x EXP [0.65 - 4.96/(SO -25)] x 0.12 x POP x HSD (111-14)
DCS02 = 0.5 x PCS02 (111-15)
where PCSO = physician cost atttibutable to S0_.
HCSO = hospitalization cost attributable to SO .
DCS02 = drug cost attributable to SO .
POP = SMSA population.
NPV = number of physician visits per incidence
= 1 (by assumption (1))
HSD = number of hospital stay days = 1 (by assumption (4))
Recall the physical dose-response function for SO as expressed in equa-
tion (III-ll) which has an intercept value of 11. If the exponential term in
equations (111-13) and (111-14) is replaced by the value of the intercept of
the dose-response function, then we can derive another set of cost estimates
for morbidity in the absence of SO .
2
Another dimension of morbidity health costs is the indirect component re-
garding the changes in earnings and leisure opportunities because of disability
and debility. A shortcut to estimate the indirect morbidity cost attributable
to pollution was found by applying to the direct morbidity cost a multiplier
56
-------
of 2.4, which is the ratio of the best estimates of total indirect net costs and
the total direct costs of morbidity (Jaksch, 1975). Hence, the following formula
was used for estimating the indirect morbidity costs attributable to SO :
IMBCSO = 2.4 x (PCSO + HCSO + DGSO ) (111-16)
The estimated morbidity costs for the 40 SMSA's with an SO level equal
to or greater than 25 p,g/m , i.e., the threshold level, are presented in Table
III-4. Columns 1, 2, and 3 present, respectively, the physician costs, hospital
costs and drug costs attributable to SO . Indirect morbidity costs due to SO
are presented in Column 4. It should be noted that the figures in Column 4 are
2.4times the sum of Columns 1, 2, and 3. Total morbidity costs due to SO cal-
culated by summing Columns 1, 2, 3 and 4 are presented in Column 5, and per
capita total morbidity costs are in Column 6. Total morbidity costs in the
absence of SO , direct and indirect, are presented in Column 7. The cost figures
in this column were estimated with the aid of equations (111-13) to (111-16)
with the modification of replacing the exponential term by the intercept term
of the dose-response function. Finally, Column 8 presents the ratio of total
morbidity cost attributable to SO to total morbidity cost with and without SO ,
that is, Column 8 = Column 5/(Column 5 + Column 7). The extent of pollution
damage to human health is partially reflected by the magnitude of this ratio.
Upon examination of the low estimates of morbidity costs in Table III-4,
it is readily revealed that the annual morbidity costs due to SO range from
a minimum value of less than $1,000 in Cincinnati, Dayton, Evansville and
Johnstown to a maximum of $22 million in New York City. Per capita morbidity
costs attributable to SO in 1970 vary between cost of negligible magnitude to
$1.96 in New York City- Total morbidity damages attributable to SO over the
40 SMSA's were at least $99 million in 1970.
It should be stressed that the cost figures presented in the table repre-
sent low estimates for the morbidity damages due to the two conservative as-
sumptions made for the calculation of the costs. If five instead of one is the
average number of doctor visits, and the average number of days in the hospital
is 5 days rather than 1 day per pollution-related disease incident, then by
assuming the same costs incurred per visit to consult doctors and per hospital
day for treatment, the cost figures in Columns 1 to 7 should be revised accord-
ingly. In other words, the direct and indirect morbidity costs and the per cap-
ita total morbidity cost attributable to SO should be five times as large as
the low cost estimates calculated for the SMSA's.
An "average" economic damage function was derived for the purpose of predict-
ing marginal and average changes in the morbidity costs in response to changes
in the pollution or in other variables. The morbidity cost in the presence of
SO , which is the sum of morbidity costs due to SO and morbidity cost in the
absence of pollution, was regressed on a host of socioeconomic, demographic and
climatological variables. The stepwise regression results are shown as follows:
57
-------
TABLE III-4. MORBIDITY COSTS WITH S02 BY SMSA's, 1970
Indirect
Morbidity Costs Total Morbidity
Direct Morbidity Costs Due Due to S02 Morbidity Cost Due Cost Ratio
to SO? (in $103) (in S103) to SO? Without S02 (in S1Q3) (8) = (5 U( (5K(71 )
SMSA
1 AKR
2 ALL
3 BAL
4 BOS
5 BRI
6 CAN
7 CHA
8 CHI
9 CIN
10 CLE
11 DAY
12 DET
13 EVA
14 GAR
15 HAR
16 JER
17 JOH
18 LAW
19 LOS
20 MIN
21 NHA
22 NYO
23 NEW
24 NOR
25 PAT
26 PEO
27 PHI
28 PTB
29 FOR
30 PRO
31 REA
32 ROC
33 STL
34 SCR
35 SPR
36 TRE
37 WAS
38 WOR
39 YOR
40 YOU
Total
PCS02
(1)
151
125
468
323
75
37
5
1775
--
487
--
769
--
146
152
148
52
1149
332
69
3021
329
1
70
1
1188
551
2
218
29
117
455
23
131
40
612
40
39
53
13,183
HCS02
(2)
106
88
329
227
53
26
4
1248
--
343
--
541
--
103
107
104
--
36
808
233
48
2123
231
1
49
--
835
388
1
153
21
82
320
16
92
28
430
28
27
37
9,266
DCS02
(3)
75
62
234
162
38
19
3
888
--
244
--
385
--
73
76
74
--
26
575
166
34
1511
165
1
35
—
594
276
1
109
15
58
228
12
66
20
306
20
19
27
6,597
1MB C SO 2
(4)
796
660
2474
1708
397
196
27
9386
--
2577
--
4066
--
773
806
782
--
274
6075
1756
362
15900
1741
7
369
3
6280
2916
10
1150
156
616
2407
123
694
212
3238
214
204
282
69,637
Total
(51 (in $103)
1127
935
3505
2420
563
277
39
13200
--
3651
--
5760
--
1095
1142
1108
--
389
8607
2487
513
22600
2467
10
522
5
8897
4131
14
1629
221
873
3410
174
982
301
4587
303
290
399
98,633
Per Capita
(6) ($)
1.66
1.72
1.69
0.88
1.44
0.74
0.17
1.91
--
1.77
--
1.37
--
1.73
1.72
1.82
--
1.67
1.22
1.37
1.44
1.96
1.33
0.01
0.38
0.01
1.85
1.72
0.01
1.78
0.74
0.99
1.44
0.74
1.85
0.99
1.60
0.88
0.88
0.74
(7)
7834
6269
23883
31763
4500
4293
2647
80240
15973
23809
9807
48280
2685
7305
7656
7027
3031
2681
80920
20919
4120
133280
21414
7850
15673
3944
55420
27696
11639
10530
3419
10181
27255
2700
6112
3506
33001
3975
3801
6182
783,202
(8)
0.13
0.13
0.13
0.07
0.11
0.06
0.01
0.14
..
0.13
—
0.11
--
0.13
0.13
0.14
--
0.13
0.10
0.11
0.11
0.14
0.10
0.03
0.14
0.13
0.13
0.06
0.08
0.11
0.06
0. 14
0.08
0.12
0.07
0.07
0. 06
Note: -- denotes less than $1,000.
Note: — individual figure may not add to totals due to rounding.
58
-------
TMBCSO = 52.4+ 0.60 SO - 135.0 PWPO + 1.4 SUN + 1.3 RHM -
(80.3) (0.09)* (67.9)* (0.7)** (0.6)*
0.3 DTS + 0.09 PCOL + 34.4 AGE (111-17)
(0.2) (0.10) (310.4)
R2 = 0.73
where TMBCSO denotes the morbidity cost in the presence of SO , and all
seven explanatory variables are the same as those defined previously in Sec-
tion II. The values below the coefficients are standard errors, with * and **
to indicate that the coefficients are significant at the 1 and 5 percent level,
respectively. All coefficients and the corresponding standard errors are re-
duced by a factor of 10 . It should be pointed out that the primary use of equa-
tion (111-17) is only for prediction. "Wrong" signs as well as other statistical
questions do not constitute a great problem if they are understood and accounted
for.
In predicting and estimating the responsiveness of morbidity damages to
changes in any one of the explanatory variables, the partial elasticity of the
morbidity cost with respect to the variable of interest merits some discussion.
Suppose a policymaker would like to estimate what the marginal changes will be
in the morbidity cost if the pollution level of SO in the SMSA' s is lowered,
on the average, by, say, 1 percent. In order to aid this policymaker to make
the prediction, the partial elasticity of the morbidity cost with response to
SO (K,__ ,,„ ) is calculated as follows:
2. MCB,bU
n = °'6 x 10 x (47.95/22.7 x 106) = 1.27 (111-18)
where (0.6 x 10 ) is the coefficient of SO in the economic damage function,
and 47.95 and (22.7 x 10°) are, respectively, the mean level of SO and total
morbidity cost.
In view of the SO partial elasticity value of 1.27, the estimated morbidity
cost would decrease by 1.27 percent, for every 1 percent reduction in SO level,
other things being equal. Stated differently, if the air pollution control program
lowers the SO level by 4.7 |ig/m from 47.9 to 43.2 [ig/m^ (10 percent reduction),
adult morbidity costs on the average would decrease by $2.72 million, from $22.7
million to $19.98 million. In a like manner, the coefficients of other variables
in equation (111-17) can be used to compute the partial elasticities associated
with the variables and can be analogously interpreted as conditional marginal
inpact when others are held constant.
59
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ADULT MORBIDITY DAMAGES AND TOTAL SUSPENDED PARTICULATES
Total suspended particulates are directly harmful to human health. The
poisonous substances or hydrocarbons contained in the particulates may cause
cancer. Other particulates multiply the potential harm of irritant gases. For
example, the interaction of sulfur dioxide gas with particulate matter will
penetrate deep into the lungs and cause much greater harm. Some particulates
expedite chemical reactions in the atmosphere to form harmful substances.
Arsenic, a well-known poison, may also cause cancer. Asbestos fiber is re-
sponsible for chronic lung disease. Beryllium has produced malignant tumors in
monkeys. Cadmium, a respiratory poison, induces high blood pressure and heart
disease. Lead, a cumulative poison, impairs the functioning of the nervous sys-
tem in adults.
Adult morbidity costs attributable to TSP were estimated by invoking the
same methodology delineated above for deriving morbidity costs due to SO . The
aggregate dose-response observations relating morbidity rate to TSP are presented
in Table III-l, page 48. The observations, obtained from the report on the CHESS
study, were used to estimate four separate regional, dose-response functions
for the four study regions, i.e., Salt Lake Basin, Chicago, Rocky Mountain and
New York. The regression results for the regional dose-response relations are
shown in the lower half of Table III-2, page 50. The mean values and standard devi-
ations of suspended particulates and the morbidity prevalence rates are pre-
sented in Table III-3, page 51.
The random sampling and simulation techniques delineated above were again
applied to derive an "average" nonlinear dose-response function relating mor-
bidity rates to suspended particulate levels. A total of 82 observations was
randomly selected in the two-round sampling experiments from the four "blocks"
defined in the two-dimensional morbidity and suspended particulate space as
shown in Figure III-3. Given these 82 observations, least-squares regressions
were run and the results are shown as follows:
MB = 11 + EXP (1.75 - (87.7/TSP))
(0.22)* (15.7)* (111-19)
2
R = 0.28
Again, the values below the coefficients are standard errors with * to in-
dicate that the coefficients are significant at the 1 percent level. It should
be noted that the intercept term 11 in equation (111-19) is the arithmetic mean
of the morbidity rates calculated from the four regional dose-response functions
with the dependent variable TSP being at the threshold level of 25
As in the case of SO , (MB - 11) was squared prior to its logarithmic trans-
formation when the regression was run. The coefficients in equation (111-19)
60
-------
30 -
20
£
(JU
i
.
O
10
25
50
75
100
125
150
175
TSP
Figure III-2. Sample observations from four morbidity studies
with respect to TSP.
-------
were obtained by dividing also the regression coefficients Log 2. The coefficient
of TSP in this nonlinear dose-response function is also statistically significant
at the 1 percent level and has a correct sign.
The direct morbidity costs attributable to TSP were estimated with the aid
of the following formulas:
PCTSP = $14 x EXP [1.75 - 87.7/(TSP - 25)] x POP x NPV (111-20)
HCTSP = $82 x EXP [1.75 - 87.7/(TSP - 25] x POP x HSD (111-21)
DCTSP = 0.5 x PCTSP (III-22)
where PCTSP = physician cost attributable to TSP.
HCTSP = hospitalization cost attributable to TSP.
DCTSP = drug costs attributable to TSP.
POP, NPV and HSD are the same as those defined in (111-13)
and (111-14).
Applying the same multiplier of 2.4 used in the case of SO , the indirect
morbidity costs due to TSP (IMBCTSP) were computed by
IMBCTSP = 2.4 x (PCTSP + HCTSP + DCTSP) (III-23)
Morbidity costs for the 40 SMSA's with a TSP level equal to or greater
than 25 |Jig/m3 are tabulated in Table III-5. Physician costs, hospital costs,
and drug costs attributable to TSP are presented in Columns 1 to 3, and indirect
morbidity costs due to TSP in Column 4. Total and per capita morbidity costs at-
tributable to TSP are presented in Columns 5 and 6. The ratio of total morbidity
cost attributable to TSP to total morbidity cost associated with or without TSP
is given in Column 8.
It should be again noted that the cost figures presented in this table,
as those in the case of SO , are low estimates for the morbidity damage associ-
ated with TSP. If each pollution-related incidence results in, on the average,
five rather than one visit to doctors, and the patients, if admitted to a hos-
pital, will stay in the hospital for 5 days instead of 1 day, then, by assuming
a constant cost for consuming medical services, the morbidity cost estimates
in Columns 1 to 7 in Table III-5 will be magnified five times. Consequently,
the total morbidity costs over the 40 SMSA's for each category (column) will
also increase five times.
62
-------
TABLE III-5. MORBIDITY COSTS WITH TSP BY SMSA's, 1970
Indirect
Morbidity Costs Total Morbidity
Direct Morbidity Cost Due Due to TSP Morbidity Cost Due Cost
to TSP (in 1103) (in S103) to TSP Without TSP (in $103
SMS A
1 ARK
2 ALL
3 BAL
4 BOS
5 BRI
6 CAN
7 CHA
8 CHI
9 CIN
10 CLE
11 DAY
12 DET
13 EVA.
14 GAR
15 HAR
16 JER
17 JOH
18 LAW
19 LOS
20 MIN
21 NHA
22 NYO
23 NEW
24 NOR
25 PAT
26 PEO
27 PHI
28 PTB
29 POR
30 PRO
31 REA
32 ROC
33 STL
34 SCR
35 SPR
36 TRE
37 WAS
38 WOR
39 YOR
40 YOU
PC TSP
(1)
111
106
813
771
20
97
62
2862
378
1010
256
1705
32
170
89
108
69
21
2208
262
23
2663
669
202
65
53
742
872
193
136
92
134
756
110
45
36
598
43
62
154
HC TSP
(2)
78
75
571
542
14
68
43
2012
266
710
180
1199
23
120
63
76
48
15
1552
184
16
1872
470
142
45
37
521
613
136
96
65
130
532
78
32
26
420
30
43
108
DC TSP
(3)
56
53
406
386
10
49
31
1431
189
505
128
853
16
85
45
54
34
10
1104
131
12
1332
334
101
32
26
371
436
97
68
46
92
378
55
23
18
299
21
31
77
IMBCTSP
(4)
587
563
4298
4077
107
515
327
15100
1998
5342
1352
9016
172
901
472
572
364
111
11600
1384
124
14000
3537
1070
342
278
3923
4608
1021
720
487
975
3998
584
238
192
3162
227
325
814
Total
(5) (in 103)
832
797
6089
5776
152
730
463
21400
2830
7567
1915
12700
243
1277
669
810
515
157
16500
1960
175
19900
5011
1516
484
394
5557
6528
1446
1020
689
1382
5664
828
337
253
4479
322
461
1153
Per Capita
(6)' ($)
1.22
1.47
2.94
2.10
0.39
1.96
2.02
3.07
2.04
3.67
2.25
3.04
1.04
2.02
1.01
1.33
1.96
0.67
2.35
1.08
0.49
1.72
2.70
2.23
0.36
1.15
1.15
2.72
1.43
1.12
2.33
1.57
2.40
3.53
0.64
0.90
1.57
0.93
1.40
2.15
(7)
7834
6269
23883
31763
4500
4293
2647
8240
15973
23809
9807
48280
2685
7305
7656
7027
3031
2681
80920
20919
4102
133280
21414
7850
15673
3944
55420
27696
11639
10530
3419
10181
27255
2700
6112
3506
33001
3975
3801
6182
Ratio
) (S) = (5M(5U(7))
(8)
0.10
0.11
0.20
0.16
0.15
0.15
0.15
0.21
0.15
0.24
0.16
0.21
0.08
0.15
0.08
0.10
0.15
0.06
0.17
0.09
0.04
0.13
0.19
0.16
0.03
0.09
0.09
0.19
0.11
0.09
0.17
0.12
0.17
0.24
0.05
0.07
0.12
0.08
0.11
0.16
Total
18,848
13,251
9,425
99,483
140,981
711,202
Note:
— individual figure may not add to totals due to rounding.
63
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The table reveals that the low estimate for morbidity damages attributable
to TSP range from $0.15 million in Bridgeport to more than $21 million in
Chicago. On a per capita basis, the low damage estimates for morbidity range
from $360 in Paterson, New Jersey to $3,000 in Chicago. Total morbidity dam-
ages due to TSP over the 40 SMSA's were estimated to be at least $140 million
in 1970.
Comparison of Tables III-4 and III-5 reveals that the morbidity costs as-
sociated with TSP are larger than the costs associated with SO . The total mor-
bidity cost due to TSP is $141.2 million, while the total morbidity cost attrib-
utable to SO is $98.4 million. The ratio between these two costs is 1.43. The
larger morbidity cost due to TSP is attributable to the fact that the average
TSP level (100.87 |j,g/m is larger than the average S02 level (47.95 p,g/m ) and
that TSP has a more responsive dose-response function than SO .
Note that an important assumption on the independency between SO and TSP
is made so that we can estimate the damage cost separately. In reality, the
costs of SO and TSP may be larger than the sum of the two component damages
because of the possible interaction effects between the two pollutants.
However, another note of caution is warranted in interpreting the cost es-
timates presented in this study. The effect of SO as indicated in the regres-
sion equation may represent the effect of not only the single pollutant SO
but also the effect of other pollutants, say TSP, as well. The prior pollution
studies suggested that the variable SO may serve as a proxy variable for air
pollution. If this is the case, then the pollution damage estimates yielded by
summing the two computed damages attributable to SO and TSP may not necessarily
be smaller than the actual pollution damages, even if the effect of interaction
is accounted for. Whether the sum of the two component damages estimates is
larger or smaller than the actual damages attributable to the concomitant pres-
ence of the two major pollutants depends on the balance of the magnitudes of
the two opposing factors, i.e., the interaction effect versus the double count-
ing effect.
An "average" economic damage function for TSP with respect to the 40 SMSA's
was developed by the least-squares technique. Morbidity costs in the presence
of TSP, i.e., the sum of the morbidity costs due to TSP, and the morbidity costs
in the absence of pollution, were regressed against the same set of socioeconomic,
demographic and climatological variables appearing earlier in the SO economic
damage function. The regression results are shown as follows:
TMBCTSP = -43 + 0.55 TSP - 131.7 PWO + 1.3 SUN + 1.2 RHM
(74) (0.09)* (63.3)* (0.7)** (0.6)**
- 0.2 DTS + 0.07 PCOL + 35.0 AGE
(0.2) (0.09) (289.7) (111-24)
R2 = 0.72
64
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where TMBCTSP denotes the total morbidity cost in the presence of TSP, and
all seven explanatory variables are identical to those defined previously in
Section II. The values below the coefficients are standard errors, with * and
** to denote that the coefficients are significant at the 1 and 5 percent levels.
All coefficients and the corresponding standard errors are reduced by a factor
of 106.
Since equation (111-24) is developed mainly for prediction purposes, the
"unexpected" signs and possible colinearity among the independent variables
should not present a problem to the use of this equation for estimating TMBCTSP
provided that the signs and the multicolinearity will persist in the future.
However, the use of partial elasticity between the dependent and the independent
variable with wrong signs does cause difficulty in interpreting the results.
This average economic damage function again is useful for forecasting and
estimating the changes in adult morbidity costs in response to changes in any
of the climatological, demographic, and socioeconomic characteristics, and the
suspended particulate variable. The partial elasticity of the morbidity damages
with respect to suspended particulates is computed as follows: El qp = 0.55
x (100.87/708) = 0.08, as measured from the respective mean levels 6f total mor-
bidity costs and suspended particulates. Thus, if the suspended particulate level
in the air is lowered by 10.1 (Jig/m from 100.87 to 90.76 ^g/m (i.e., 10 percent
reduction), gross adult morbidity costs on the average would reduce by $5.66
million from $708 to $702.3 million nationwide.
65
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SECTION IV
HOUSEHOLD SOILING AND AIR POLLUTION
THE PROBLEMS AND THE OBJECTIVES
In addition to human health, air pollution has also a multitude of damag-
ing effects on material, vegetation, animals, and residential and commercial
establishments, etc. Ronald Ridker (1967) designed a framework for identifying
and quantifying these damage costs. He suggested that the effects of air pollu-
tion and their costs can be categorized into: (1) cost of direct effects, (2)
adjustment costs, and (3) market effect costs. The damage costs of human health
derived in the previous two chapters are costs of direct effects of air pollu-
tion. The present section is concerned with the second category; i.e., adjustment
costs or the cost of individual adjustments to the effects of air pollution.
The best known and the pioneering contribution to the estimation of soiling
loss due to air pollution is the Mellon Institute Study of the Pittsburgh smoke
nuisance (1913). The $20.00 per capita soiling cost figure of the Mellon Insti-
tute Study has been used as a basis for extrapolating to the $11 billion na-
tional damage estimate. The validity of this damage estimate, often quoted by
public officials, has been questioned by Jones (1969) and others. A serious
problem with the national damage estimate arises because of the strong assump-
tion that the air pollution level in Pittsburgh is representative of the entire
nation.
The two studies of quantifying the soiling costs in the Upper Ohio River
Valley and Washington, D. C. carried out by Michelson and Tourin (1966) have
also attracted public attention. Their methodology is based on the hypothesis
that significant soiling due to air pollution may be reflected in shortened
time intervals between successive cleaning and maintenance operations. Michelson
and Tourin established a positive relationship between frequency of cleaning
operations and the levels of air pollution in both studies. However, the prob-
lems with the sample survey design and the lack of a statistically reliable
technique cast doubt on the reliability of their findings. Michelson and Tourin
(1968) employed the same methodology and estimated the extra household soiling
costs due to air pollution in Connecticut. They found that an average household
spent about $600 each year for coping with the effect of suspended particulates,
with the range from $230 per year in Fairfield to $725 per year in Bridgeport.
These cost estimates are conservative since the cleaning operations studied did
not cover the full gamut of operations affected by air pollution.
Ridker (1967) conducted interurban studies to determine the relation between
per capita soiling costs and air pollution level for 144 cities in the United
States. Soiling damage costs were approximated by per capita expenditures on
laundry and dry cleaning services. Ridker found that no discernible patterns
between soiling costs and the suspended particulate levels were detected,
whether the effects of climate, per capita income, and price differentials were
66
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controlled for or not. The problem often encountered in identifying the soiling
damages, as noted by Ridker, is that cleaning and maintenance operations are
often undertaken on a rigid schedule which is independent of the location of
the operation. This is especially true for commercial and industrial buildings.
Furthermore, nonpollution factors which could not be controlled for may be im-
portant in explaining the cleaning and maintenance procedures.
The primary objectives of this study are threefold: a system of soiling
physical damges functions which relate various types of cleaning frequencies
to air pollution level are derived. The physical damage functions are then uti-
lized to estimate net and gross soiling damage costs for the 148 SMSA's. Finally,
"average" economic damage functions over the United States metropolitan areas
are developed by relating soiling damages to air pollution, demographic, socio-
economic, and climatological variables. It is hoped that the generalized eco-
nomic damage functions presented in this section are informative and useful for
predicting possible benefits as a result of the reduction in air pollution when
air pollution abatement programs are implemented.
This section, which represents a first exploratory effort to estimate aver-
age air pollution soiling damage functions and soiling damage costs for the 148
SMSA's individually, contains subsections: Soiling Physical Damage Function, and
Economic Damages and Economic Damage Functions.
SOILING PHYSICAL DAMAGE FUNCTIONS
Soiling as a result of falling total suspended particulates compels house-
holds as well as business and industrial establishments to increase cleaning
activities. Thus, soiling has resulted in extra economic losses not only to house-
holds but to business and industrial firms as well. As noted above, a number
of attempts have been undertaken to identify and quantify the soiling damages
due to air pollution. However, a recent study by Booz, Allen and Hamilton, Inc.
(1970), offers the needed data base for our purpose of developing the soiling
physical damage functions.
Sophisticated and rigorous statistical survey techniques were employed by
Booz-Allen researchers. The Renjerdel area around Philadelphia, Pennsylvania,
was used as the data gathering area. Frequency of cleaning by the residents was
determined by a carefully developed questionnaire containing queries regarding
cleaning operations and a set of self-referent statements with respect to clean-
ing attitudes. Among the 27 cleaning and maintenance operations, the study shows
that 11 were somewhat sensitive to air-suspended particulate levels. Because
of the lack of certain needed information for evaluating the costs, only 9 of
these 11 cleaning tasks were considered in this study. A list of these nine
pollution-related cleaning tasks together with the information on unit cleaning
costs is contained in Table IV-1.
67
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TABLE IV-1. POLLUTION-RELATED TASKS AND THEIR UNIT CLEANING COSTS
Tasks Unit Market Value ($)
1
2
3
4
5
6
7
8
9
Replace air conditioner filter
Wash floor surface
Wash inside window
Clean Venetian blinds/shades
Clean/repair screens
Wash outside windows
Clean/repair storm windows
Clean outdoor furniture
Clean gutters
1.00
6.00
0.50
3.50
0.20
1.50
2.00
10.00
15.00
A set of physical damage functions was derived via the technique delineated
in Section III above, which combines the simulation and regression analysis.
The areas under study were divided into four zones according to their air pollu-
tion levels. This breakdown in the study areas allows one to construct four pop-
ulation "blocks" for each pollution-related cleaning task in the two-dimensional
pollution level and cleaning frequency spaces. For ease of description, let X
and Y denote respectively the suspended particulate level and cleaning fre-
quency. The vertices of each "block" then consist of the following four combina-
tions: [Max X, Max Y]; [Max X, Min Y]; [Min X, Max YJ; and [Min X, Min Y], where
Max and Min denote the upper and^ower limits of the two variables. The annual
average particulate levels ((j,g/m ) in the four sampling zones were given in the
Booz-Allen report as follows:
Zone 1 x < 75
Zone 2 75 < X < 100
Zone 3 100 < X < 125
Zone 4 125 < X
68
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Thus, the suspended particulate levels, X, vary from 75 (J,g/m to 100
3 in Zone 2 and from 100 |ig/m3 to 125 |0,g/m3 in Zone 3. The upper limit of
X in Zone 1 is 75 [ig/m3 and the lower limit of X in Zone 4 is 125 u.g/m3. Assuming
that 25 [ig/m3 of suspended particulate is the background concentration level
and 175 u.g/m3 is the upper limit in the study areas then the values of Min X
and Max X (in (j,g/m3) for the four study zones are tabulated as follows:
Zone 1
Zone 2
Zone 3
Zone 4
The minimum and the maximum values for the dependent variable Y (Min Y
and Max Y) for each zone were calculated by subtracting and adding one standard
error of the mean from the mean value of the cleaning frequency. The computed
values for Min Y and Max Y, the mean frequency of cleaning and the standard er-
ror of the means are presented in Table IV-2.
The Monte Carlo sampling technique, delineated in Section III, was applied
to the four blocks for generating a random sample for the regression analysis.
A total of 800 such random observations for each cleaning task were selected.
For the sake of computational simplicity, a smaller random sample, about 20 per-
cent of the 800 random observations, was further obtained. The 160 observations
included in this sample were fitted via both linear and nonlinear least-squares
techniques. The linear fit is more superior than the nonlinear fit in all cases
except for Task 8. The linear regression results for Task 1 through 7 and Task
9 and the nonlinear regression result for Task 8 are summarized in Table IV-3.
ECONOMIC DAMAGES AND ECONOMIC DAMAGE FUNCTIONS
Given the preceding nine physical damage functions for the nine pollution-
related cleaning tasks and the associated unit cleaning costs which were ob-
tained through telephone conversations with various cleaning firms in Kansas
City, the economic costs of soiling or of individual household adjustment to
air pollution can be derived by transforming the increased cleaning frequency
into monetary units, via the following two formulas:!.'
_!/ For Task 8, NSC08 = EXP(0.85 - 0.015/(TSP - 45)) . UC . U . HU and
GSCOg = 2 + EXP(0.85 - 0.015/(TSP - 45)) . UC . U . HU.
69
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TABLE IV-2. MEAN FREQUENCY, STANDARD ERROR AND UPPER AND LOWER LIMITS OF
FREQUENCY AND SUSPENDED PARTICULATES
Mean Frequency Standard Error
of Cleaning of Means Mtn Y
Task 1
Task 2
Task 3
Task 4
Task 5
Task 6
Task 7
Task 8
Task 9
Zone 1
Zone 2
Zone 3
Zone 4
Zone 1
Zone 2
Zone 3
Zone 4
Zone 1
Zone 2
Zone 3
Zone 4
Zone 1
Zone 2
Zone 3
Zone 4
Zone 1
Zone 2
Zone 3
Zone 4
Zone 1
Zone 2
Zone 3
Zone 4
Zone 1
Zone 2
Zone 3
Zone 4
Zone 1
Zone 2
Zone 3
Zone 4
Zone 1
Zone 2
Zone 3
Zone 4
0.36
0.50
0.30
0.98
40.55
42.06
42.74
45.17
10.06
11.78
12.74
18.45
4.04
6.17
9.13
9.21
0.80
0.93
0.79
1.50
4.25
4.59
6.17
10.09
2.07
1.60
2.12
3.69
2.50
4.29
3.52
1.19
1.12
1.54
1.35
2.80
0.06
0.08
0.07
0.34
0.84
0.84
0.98
0.93
0.61
0.70
0.82
1.10
0.53
0.66
0.91
0.49
0.07
0.16
0.10
0.32
0.35
0.38
0.60
0.88
0.28
0.23
0.39
0.63
0.45
0.65
0.71
0.47
0.22
0.33
0.44
0.69
0.30
0.42
0.23
0.64
39.71
41.22
41.77
44.24
9.45
11.08
11.93
17.85
3.51
5.51
8.22
8.22
0.75
0.77
0.70
1.18
3.90
4.21
5.57
9.21
1.79
1.37
1.73
3.60
2.05
3.64
2.81
0.72
0.91
1.21
0.91
2.11
Max Y
0.42
0.58
0.37
1.32
41.39
42.90
43.72
46.10
10.17
12.48
13.55
20.05
4.57
6.87
10.04
10.20
0.87
1.09
0.86
1.82
4.60
4.97
6.77
10.97
2.35
1.83
2.51
4.32
2.95
4.94
4.23
1.66
1.34
1.87
1.79
3.49
Min X
25
75
100
125
25
75
100
125
25
75
100
125
25
75
100
125
25
75
100
125
25
75
100
125
25
75
100
125
25
75
100
125
25
75
100
125
Max X
75
100
125
175
75
100
125
175
75
100
125
175
75
100
125
175
75
100
125
175
75
100
125
175
75
100
125
175
75
100
125
175
75
100
125
175
70
-------
TABLE IV-3. SOILING PHYSICAL DAMAGE FUNCTIONS!/
»——•— - . __ _ .
A. Frequency = a + b TSP
Task a b R2
1
2
3
4
5
6
7
9
0.03
(0.05)
38.6
(0.18)
5.6
(0.4)
2.3
(0.2)
0.42
(0.06)
1.00
(0.28)
0.85
(0.15)
0.27
(0.12)
0.00510
(0.00048)*
0.0400
(0.0017)*
0.078
(0.036)*
0.048
(0.002)*
0.0059
(0.0049)*
0.0530
(0.0025)*
0.015
(0.001)*
0.0140
(0.0011)*
0.43
0.80
0.76
0.79
0.48
0.74
0.48
0.55
B. Frequency = c +
8
(c = 2)
0.67
(0.10)
53.2
(7.4)*
0.26
a/ The values below the coefficients are standard
errors, with * to indicate that the coefficient
is significant at the 1 percent level.
71
-------
NSCO. = b.(TSP-45) • UC • U • HU (IV-1)
GSCO. = a. + b.(TSP-45) • UC • U * HU (IV-2)
where NSCO. and GSCO. are, respectively, the net (extra) and gross soiling
damage cost1for the it^type of cleaning task. Coefficients a± and b± are the
estimated coefficients in the physical damage functions in Table IV-3. i = 1
through 7, and 9. Variables UC, U and HU stand for the unit market value, num-
ber of cleaning objects per household and number of households in a metropolitan
area, respectively.
To capture the "real" effect of suspended particulates on soiling damages,
the suspended particulate level was adjusted by a threshold level because a low
level of suspended particulate might have a negligible effect on the household
cleaning activities. A threshold level of 45 |j,g/m3 for suspended particulate
was assumed as the background concentration level in this study because the low-
est 1970 annual mean level for total suspended particulates was 46.7 (j,g/nP for
Charleston, South Carolina. Alternative reasonable threshold levels can also
be considered. Other things being equal, a higher threshold level is generally
associated with a lower damage cost, and the marginal changes in the damage cost
in response to a unit change in the threshold level is the value of b. for
the ith type of cleaning task.
Given the data collected for the variables in the formula (IV-1) and (IV-2)
the net and gross household soiling costs for each of the nine cleaning operations
by the 65 large SMSA's (with population greater than 500,000) in the United States
were derived and presented in Tables IV-4 and IV-6. Similar damage costs for
each of the nine cleaning operations by the 83 medium SMSA's (200,000 to 500,000
people) were presented in Tables IV-6 and IV-7- An examination of the table re-
veals that Chicago, New York, and Los Angeles, in order of magnitude, suffered
the most in terms of total net soiling damages. The net soiling damages in these
three SMSA's in 1970 are, respectively, $516 million, $418 million, and $388 mil-
lion. It is noteworthy that the cleaning activities of Tasks 4 and 6 in response
to air pollution had resulted in an economic damage of about $1,956 million and
$925.7 million, respectively, in the 40 metropolitan areas. These two tasks con-
stitute the largest damage categories among the nine pollution-related clean-
ing tasks.
Per capita net and gross soiling damage costs in the presence of air pollu-
tion for large SMSA's and medium SMSA's for 1970 are presented, respectively,
in Tables IV-8 and IV-9. Per capita net soiling costs (PCNSCO) and per capita
gross soiling costs (PCGSCO) are summarized in the second and the third columns
of the tables. These cost figures indicate that the soiling damages attributable
to air pollution in large SMSA's range from $5 per person in San Antonio, Texas,
to $104 per person in Cleveland, Ohio, whereas the net soiling damages in medium
SMSA's vary from less than a dollar per person in Charleston, South Carolina,
to $67.35 per person in Wichita, Kansas. These estimates for individual SMSA's
appear to be compatible with the overall per capita soiling damage estimates
of $20.00 by Mellon Institute and of $200 by Michelson and Tourin.
72
-------
TABLE IV-lt. NET SOILING DAMAGE COSTS BY LARGE SMSA's£/
(million $)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
Large SMSA' s
AKR, OH
ALB, NY
ALL, NJ
ANA, CA
ATL, GA
BAL, MD
BIR, AL
BOS, MA
BUF, NY
CHI, IL
CIN, OH-KY-IN
CLE, OH
COL, OH
DAL, TX
DAY, OH
DEN, CO
DET, MI
FOR, FL
FOR, TX
GAR, IN
GRA, MI
GRE, NC
HAR, CT
HON, HI
HOU, TX
IND, IN
JAC, FL
JER, NJ
KAN, MO-KS
LOS, CA
LOU, KY-IN
MEM, TN-AR
MIA, FL
MIL, WI
MINN.MN
NSC01
..
0.1
--
0.1
0.1
0.3
0.2
0.3
0.2
1.2
0.1
0.5
0.1
0.1
0.1
0.2
0.7
--
0.1
0.1
__
--
--
--
0.1
0.1
--
--
0.1
0.9
0.1
0.1
--
0.1
0.1
NSC02
1.7
4.0
1.8
6.1
3.8
15.3
7.7
12.9
8.1
57.5
6. .3
24.3
2.3
6.8
4.4
10.1
32.7
0.9
2.6
2.7
1.1
1.9
1.8
1.2
6.4
2.5
1.2
1.9
4.0
42.8
6.3
2.6
1.7
4.8
4.1
NSC03
1.4
3.2
1.4
5.0
3.1
12.4
6.2
10.5
6.6
46.7
5.1
IV. 7
1.9
5.5
3.5
8.2
26.6
0.7
2.1
2.2
0.9
1.5
1.4
1.0
5.2
2.0
0.9
1.5
3.3
34.8
5.1
2.1
1.4
3.9
3.3
NSC04
6.3
14.0
6.2
21.3
13.1
53.6
26.7
45.3
28.3
201.0
21.9
85.0
8.2
23.7
15.2
35.3
114.0
3.2
9.2
9.3
4.0
6.5
6.2
4.1
22.4
8.9
3.8
6.7
14.1
150.0
21.9
9.2
6.0
16.9
14.4
NSC05
..
0.1
--
0.2
0.1
0.4
0.2
0.3
0.2
1.4
0.2
0.6
0.1
0.2
0.1
0.2
0.8
--
0.1
0.1
__
--
--
--
0.2
0.1
--
--
0.1
1.1
0.2
0.1
--
0.1
0.1
NSC 06
2.9
6.7
2.9
10.1
6.2
25.3
12.6
21.4
12.4
95.2
10.4
40.2
3.9
11.2
7.2
16.7
54.3
1.5
4.3
4.4
1.9
3.1
2.9
2.0
10.6
4.2
1.8
3.2
6.7
71.0
0.2
4.4
2.9
8.0
6.8
. NSC07
1.1
2.5
1.1
3.8
2.4
9.6
4.7
8.1
5.1
35.9
3.9
15.1
1.5
4.2
2.7
6.3
20.4
0.6
1.6
1.7
0.7
1.2
1.1
0.7
4.0
1.6
0.7
1.2
2.5
2.7
10.3
1.6
1.1
3.0
2.6
NSC08
0.9
2.2
1.0
3.4
2.0
7.3
3.1
7.2
4.2
26.2
3.5
9.0
1.2
3.8
2.4
4.7
15.1
0.2
1.5
1.5
0.5
1.0
0.8
0.5
3.5
1.2
4.7
1.0
2.2
2.3
3.9
1.5
0.3
2.7
1.9
NSC09
1.5
3.5
1.5
5.3
2.2
13.4
6.7
11.3
7.1
50.3
5.5
21.2
2.1
5.9
3.8
8.8
28.6
0.8
2.3
2.3
1.0
1.6
1.6
1.0
5.6
2.2
0.9
1.7
3.5
3.8
5.5
2.3
1.5
4.2
3.6
TNSCO
15.5
36.4
15.9
55.4
34.0
137.0
68.2
117.0
73.1
516.0
57.0
216.0
21.1
61.4
39.4
90.7
294.0
7.8
23.7
24.2
10.1
16.9
15.8
10.5
58.1
22.7
9.7
17.2
36.6
383.0
56.3
23.8
15.0
43.9
36.9
a/ NSC01 stands for the net soiling cost for the 1th type of operation, i = 1, 2,. . .,9.
TOTNETSL Is the sum of NESOC01 over 1 and "--•' Indicates that the figure Is less than 0.05.
-------
TABLE IV-4 (Concluded)
•vl
-P-
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
Large SMSA's
NAS, TN
NEW, LA
NEW, NY
NEW, NJ
NOR, VA
OKL, OK
OMA, NE-LA
PAT, NJ
Pill, PA-NJ
PI10, AZ
PIT, PA
FOR, OR-WA
PRO, RI-MA
RIG, VA
ROC, NY
SAC, CA
SAI, MO-IL
SAL, UT
SAN, TX
SAN, CA
SAN, CA
SAN, CA
SAN, CA
SEA, WA
SPR, MC-CT
SYR, NY
TAM, FL
TOL, OH-MI
WAS, DC-MD-VA
YOU, OH
Total
NSC01
0.1
0.1
1.0
0.3
0.1
__
0.1
--
0.2
0.2
0.3
0.1
—
0.1
0.1
--
0.3
--
--
0.1
—
0.1
--
--
--
0.1
0.1
0.1
0.2
0.1
9.9
NSC02
3.2
2.7
46.0
12.4
3.2
1.2
3.8
1.1
11.5
10.4
16.3
3.3
2.2
2.7
3.1
1.0
13.1
1.9
0.5
7.9
1.4
4.0
1.2
1.4
0.8
3.0
2.7
4.0
9.7
2.5
474.5
NSC03
2.6
2.2
37.4
10.1
0.6
0.9
3.1
0.9
9.4
8.5
13.2
2.7
1.8
2.2
2.4
0.8
10.6
1.5
0.4
6.3
1.1
3.2
0.9
1.2
0.6
2.5
2.2
3.3
7.8
2.0
382.7
NSC04
11.1
9.3
161.0
43.5
11.0
3.8
13.2
3.8
40.4
36.4
57.1
11.6
7.7
9.4
10.2
3.5
46.0
6.6
1.8
27.5
4.8
14.0
4.0
5.0
2.7
10.6
9.4
14.1
37.7
8.9
1.662.0
NSC05
0.1
0.1
1.1
0.3
0.1
..
0.1
—
0.3
0.3
0.4
0.1
0.1
0.1
0.1
__
0.2
--
--
0.2
—
0.1
--
--
--
0.1
0.1
0.1
0.2
0.1
10.6
NSC06
5.3
4.4
76.3
20.6
5.2
1.8
6.3
1.8
19.1
17.2
27.0
5.5
3.6
4.4
4.9
1.7
21.7
3.1
0.8
13.0
2.3
6.7
1.9
2.3
1.3
5.0
4.5
6.7
15.9
4.2
774.0
NSCO7
2.0
1.7
28.7
7.8
2.0
6.8
2.4
0.7
7.2
6.5
10.2
2.1
1.4
1.7
1.8
0.6
8.2
1.2
0.3
4.9
0.9
2.5
0.7
0.9
0.5
1.9
1.7
2.5
6.0
1.6
284.7
NSC08
1.7
1.3
25.8
6.3
1.7
0.3
1.8
0.1
5.6
4.1
8.2
1.8
1.1
1.5
1.6
0.2
7.0
1.2
--
3.9
0.2
0.6
0.2
0.1
0.2
1.7
1.3
2.1
5.42
1.4
216.8
NSC09
2.8
2.3
40.3
10.8
2.8
1.0
3.3
1.0
10.1
9.1
14.2
3.0
1.9
2.3
2.5
0.9
11.5
1.7
0.4
6.9
1.2
3.5
1.0
1.2
0.7
2.7
2.4
3.5
8.4
2.2
379.7
TNSCO
28.9
23.9
418.0
112.0
28.5
9.6
34.0
9.3
104.0
92.8
147.0
30.1
19.7
24.2
26.5
8.8
119.0
17.2
4.3
70.8
11.9
34.8
10.0
12.1
6.7
27.4
24.2
36.4
85.5
23.0
2,465.9
Note: — individual figure may not add to totals due to rounding.
-------
TABLE IV-5. NET SOILING DAMAGE COSTS BY MEDIUM SMSA'
(million $)
Medium SMSA' 8
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
ALB,
ANN,
AFP,
AUG,
AUS,
BAK,
BAT,
BEA,
BIN,
BRI,
CAN,
CI1A,
CHA,
CHA,
CIIA,
COL,
COL,
COL,
COR,
DAV,
DBS,
DUL,
ELF,
ERI,
EUG,
EVA,
FAY,
FLI,
FOR,
FRE,
GRE,
HAM,
HAR,
HUN,
IRJN,
MM
MI
WI
GA-SC
TX
CA
LA
TX
NY^PA
CN
OH
SC
WV
NC
TN-CA
CO
SC
GA-AL
TX
IA-IL
IA
MN-WI
TX
PA
OR
IN-KY
NC
MI
IN
CA
SC
OH
PA
WV-KY.OH
AL
NSC01 NSC02
I.I
0.5
0.9
0.3
0.5
2.2
0.3
0.3
0.3
0.4
1.6
..
1.1
1.6
1.4
0.8
0.4
0.1
1.1
2.3
0.9
0.5
2.2
1.1
0.7
0.5
0.3
0.1 3.0
0.6
2.1
0.7
0.6
1.0
1.0
0.3
NSC03
0.9
0.4
0.7
0.3
0.4
1.8
0.3
0.3
0.2
0.3
1.3
--
0.9
1.3
1.2
0.7
0.4
0.1
0.9
1.8
0.7
0.4
1.9
0.9
0.5
0.4
0.2
2.4
0.5
1.7
0.6
0.5
0.8
0.9
0.2
NSC04 NSC05
3.
1.
3.
1.
1.
7
1
1
1
1
5
0.
3.
5,
5.
1.
1.
0.
4,
8.
3.
1.
7.
3,
2.
2.
0.
10.
2,
7.
2.
2,
3,
3.
1,
.7
.7
1
,1
9
.7 0.1
.1
.2
.0
.2
.6
.1
.7
.7
.0
,9
3
.3
.0
.0 0.1
,1
,9
,9
.9
,3
.0
.9
.2 0.1
.2 00
.5 0.1
.4
.0 0.1
.6
.5
.0
NSC06
1.8
0.8
1.4
0.5
0.9
3.7
0.5
0.6
0.4
0.6
2.6
0.1
1.8
2.7
2.4
1.4
0.6
0.1
1.9
3.8
1.5
0.9
3.8
1.9
1.1
0.9
0.4
4.9
1.0
3.5
1.1
1.0
1.7
1.7
0.5
NSC07
0.7
0.3
0.5
0.2
0.3
1.4
0.2
0.2
0.2
0.2
1.0
--
0.7
1.0
0.9
0.5
0.2
--
0.7
1.4
0.6
0.3
1.4
0.7
0.4
0.3
0.2
1.8
0.4
1.3
0.4
0.4
0.6
0.6
0.2
NSC08
0.6
0.2
0.5
0.1
0.2
1.1
0.1
0.1
--
--
0.9
--
0.6
0.9
0.8
0.5
0.1
--
0.6
1.1
0.5
0.2
1.1
0.6
0.4
0.3
0.1
1.5
0.3
1.2
0.3
0.3
0.5
0.6
0.1
NSC09
0.9
0.4
0.8
0.3
0.5
1.9
0.3
0.3
0.2
0.3
1.4
--
0.9
1.4
1.2
0.7
0.3
0.1
1.0
2.0
0.8
0.5
2.0
1.0
0.6
0.5
0.2
2.6
0.5
1.9
0.6
0.5
0.9
0.9
0.9
TNSCO
9.7
4.3
7.9
2.7
4.8
19.9
2.7
3.0
2.5
3.0
14.4
0.3
9.6
14.6
12.9
7.6
3.2
0.7
10.2
20.4
8.1
4.8
20.1
10.0
6.0
5.0
2.2
26.5
5.6
19.3
6.2
5.3
9.2
9.1
9.7
-------
TABLE IV-5 (Continued)
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
117.
118,
119.
120.
121.
122.
123.
124.
125.
126.
127.
128.
129.
130.
JAC,
JOH,
KAL,
KNO,
LAN,
LAN,
LAS,
LAW,
LITT
LOR,
LOW,
MAC,
MAD,
MOB,
MON,
NEW,
NEW,
NEW,
ORL,
OXN,
PEN,
PEO,
RAL,
RE A,
ROC,
SAG,
SAL,
SAN,
SAN,
SCR,
MS
PA
MI
TN
PA
MI
NV
MA-NH
, AK
OH
MA
GA
WI
AL
AL
CN
CN
VA
FL
CA
FL
IL
NC
PA
IL
MI
CA
CA
CA
PA
NSC01 NSC02
1.1
1.1
0.2
1.7
1.5
0.9
1.2
0.4
0.7
0.1 2.7
0.1
0.5
0.6
1.6
0.7
0.4
0.2
0.2
1.0
1.9
1.0
0.8
0.2
1.7
1.2
1.3
1.2
1.5
1.2
0.1 2.6
NSC03
0.9
0.9
0.2
1.3
1.2
0.7
1.0
0.3
0.6
2.2
0.1
0.4
0.5
1.3
0.6
0.4
0.2
0.1
0.8
1.5
0.8
0.7
0.1
1.4
1.0
1.0
1.0
1.2
1.0
2.1
NSC04 NSC05
3
4
0
5
5
3
4
1
2
9
0
1
2
5
2
1
0
0
3
6
3
3
0
6
4
4
4
5
4
9
.8
.0
.7
.8
.2
.1
.1
.2
.5
.5 0.1
.3
.9
.2
.6
.6
.4
.9
.6
.4
.5
.7
.0
.6
.0
.2
.5
.2
.2
.2
.2 0.1
NSC06
1.8
1.9
0.3
2.7
2.4
1.5
1.9
0.6
1.2
4.5
0.1
0.9
1.0
2.7
1.2
0.7
0.4
0.3
1.6
3.1
1.7
1.4
0.3
2.8
2.0
2.1
2.0
2.5
2.0
4.3
NSC07
0.7
0.7
0.1
10.
0.9
0.5
0.7
0.2
0.4
1.7
..
0.3
0.4
1.0
0.5
0.2
0.2
0.1
0.6
1.2
0.7
0.5
0.1
1.0
0.8
0.8
0.7
0.9
0.7
1.6
NSC08
0.6
0.6
--
0.9
0.8
0.4
0.7
0.1
0.3
1.0
__
0.3
0.3
0.9
0.4
0.1
0.1
--
0.5
1.0
0.6
0.4
--
0.9
0.7
0.7
0.6
0.8
0.6
1.0
NSC 09
0.9
1.0
0.2
1.5
1.3
0.8
1.0
0.3
0.6
2.4
0.1
0.5
0.5
1.4
0.7
0.3
0.2
0.2
0.9
1.6
0.9
0.-7
0.1
1.5
1.0
1.2
1.0
1.3
1.0
2.3
TNSCO
9.7
10.1
1.7
15.0
13.3
7.9
10.6
3.1
6.4
24.1
0.7
4.9
5.5
14.5
6.8
3.5
2.1
1.5
8.8
16.9
9.5
7.6
1.4
15.3
10.9
11.6
10.8
13.4
10.8
23.3
-------
TABLE IV-5 (Concluded)
131.
132.
133.
134.
135.
136.
137.
138.
139.
140.
141.
142.
143.
144.
145.
146.
147.
148.
SHR,
SOU,
SPO,
STA,
STO,
TAC,
TRE,
TUC,
TUL,
UTI,
VAL,
WAT,
WES,
WIC,
WIL,
WIL,
WOR,
YOR,
LA
IN
WA
CN
CA
WA
NJ
AZ
OK
NY
CA
CN
FL
KS
PA
DE.NJ.MD
MA
PA
Total
NSC01 NSC02
1.3
0.6
1.2
0.2
0.3
1.4
0.6
1.4
1.5
0.7
0.3
0.5
0.5
0.1 2.9
2.2
0.1 2.9
0.7
1.0
1.9 84.3
NSC03
1.1
0.5
1.0
0.2
0.3
1.2
0.5
1.1
1.2
0.5
0.2
0.4
0.4
2.4
1.8
2.3
0.6
0.8
71.4
NSC04
4.6
2.2
4.2
0.7
1.1
5.1
2.0
4.9
5.1
2.3
0.9
1.9
1.7
10.2
7.8
10.0
2.4
3.5
294.3
NSC05 NSC06
2
1
2
0
.2
.1
.0
.3
0.5
2
1
2
2
1
0
0
0
0.1 4
0.1 3
0.1 4
1
1
.4
.0
.3
.4
.1
.4
.9
.8
.8
.7
.7
.1
.7
1.3 151.7
NSC07
0.8
0.4
0.7
0.1
0.2
0.9
0.4
0.9
0.9
0.4
0.2
0.3
0.3
1.8
1.4
1.8
0.4
0.6
64.5
NSC08
0.7
0.3
0.7
—
--
0.8
0.2
0.8
0.8
0.3
--
0.3
0.1
1.4
1.1
1.5
0.3
0.5
58.3
NSC09
1.2
0.6
1.0
0.2
0.3
1.3
0.5
1.2
1.3
0.6
0.2
0.5
0.4
2.6
1.9
2.5
0.6
0.9
109.2
TNSCO
11.9
5.7
10.8
1.6
2.8
13.0
5.2
12.8
13.1
8.9
2.3
4.9
4.3
26.2
20.0
25.9
6.2
9.0
767.2
Note: — individual figure may not add to totals due to rounding.
-------
TABLE IV-6. GROSS SOILING DAMAGE COSTS BY LARGE SMSA*/
(million $)
oo
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
Large SMSA's
AKR, OH
ALB, NY
ALL, NJ
ANA, CA
ATL, GA
BAL, MD
BIR, AL
BOS, MA
BUF, NY
CHI, IL
CIN, OH-KY-IN
CLE, OH
COL, OH
DAL, TX
DAY, OH
DEN, CO
DET, MI
FOR, FL
FOR, TX
GAR, IN
GRA, MI
GRE, NC
HAR, CT
HON, HI
HOU, TX
IND, IN
JAC,, FL
JER, NJ
KAN, MO-KS
LOS, CA
LOU, KY-IN
MEM, TN-AR
MIA, FL
MIL, WI
MINN, MN
GSC01
0.1
—
--
0.1
0.3
0.2
0.3
0.2
1.3
__
0.5
0.1
0.2
0.1
0.2
0.7
--
0.1
0.1
„.
--
--
--
0.2
0.1
--
--
0.1
1.0
„_
0.1
--
0.1
0.1
GSC02
49.4
57.2
42.2
107.0
103.0
159.0
61.3
212.0
104.0
563.0
106.0
174.0
67.8
120.0
65.2
100.0
326.0
52.5
58.4
45.0
38.1
45.1
60.8
39.3
147.0
82.8
38.5
49.8
98.7
606.0
67.8
55.2
100.0
105.0
133.0
GSC03
0.9
1.3
0.8
2.2
1.8
4.2
1.2
4.5
2.5
15.4
2.2
5.8
1.2
2.5
1.4
2.7
8.9
0.8
1.1
1.0
0.6
0.8
4.1
0.7
4.8
1.4
0.6
0.9
1.8
1.4
1.7
1.1
1.5
2.0
2.2
GSC04
14.3
23.3
13.2
38.9
20.5
78.8
36.1
80.1
45.2
289.0
39.4
111.0
19.6
43.5
25.8
51.2
166.0
12.1
18.9
16.8
10.4
14.0
16.5
10.8
47.1
22.8
10.3
15.0
30.7
248.0
32.3
18.3
23.3
34.4
36.9
GSC05
0.1
0.2
0.1
0.3
0.3
0.6
0.3
0.7
0.4
2.3
0.3
0.9
0.2
0.4
0.2
0.4
1.3
0.1
0.2
0.1
0.1
0.1
0.2
0.1
0.4
0.2
0.1
0.1
0.3
2.1
0.3
0.2
0.2
0.3
0.3
GSCO6
4.4
8.4
4.2
13.3
9.5
30.0
14.4
27.9
16.5
111.0
13.6
45.1
6.0
14.9
9.2
19.6
63.8
3.2
6.2
5.8
3.1
4.5
4.8
3.2
15.2
6.8
3.0
4.7
9.8
89.2
12.3
6.1
6.1
11.2
10.9
GSC07
2.8
4.5
2.6
7.5
6.0
14.8
6.8
15.4
8.6
54.5
7.6
20.7
3.9
8.4
5.0
9.7
31.2
2.5
3.7
3.2
2.1
2.8
3.3
2.1
9.2
4.5
2.1
3.0
6.0
4.7
6.1
3.6
4.7
6.7
7.3
GSC08
5.0
6.8
4.5
12.1
10.5
19.7
7.7
24.4
12.5
69.9
12.1
22.0
6.8
13.6
7.6
12.5
40.4
4.6
9.6
5.2
3.7
4.8
5.9
3.8
15.7
8.1
3.7
5.2
10.3
71.6
8.1
6.0
8.9
11.3
13.0
GSC09
2.3
4.5
2.3
7.1
5.0
15.9
7.6
14.8
8.8
59.1
7.2
23.9
3.2
7.9
4.9
10.1
33.8
1.7
3.3
3.1
1.6
2.4
2.6
1.7
8.1
3.6
1.6
2.5
5.2
47.3
6.5
3.2
3.2
6.0
5.9
TGSCO
79.3
106.0
69.9
188.0
166.0
324.0
136.0
380.0
199.0
1160.0
188.0
405.0
108.0
212.0
119.0
207.0
672.0
77.5
98.0
80.8
59.8
74.6
95.1
61.8
246.0
130.0
60.0
81.2
163.0
1,120.0
133.0
93.7
148.0
117.0
209.0
a/ GSCOi denotes the gross soiling damage cost for the ith type of cleaning operation, i = 1, 2,.
TGSCO is the sum of GSCOj over i.
9,
-------
TABLE IV-6 (Concluded)
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
Large SMSA's
NAS, TN
NEW, LA
NEW, NY
NEW, NJ
NOR, VA
OKL, OK
DMA, NE-IA
PAT, NJ
PHI, PA-NJ
PHO, AZ
PIT, PA
FOR, OR-WA
PRO, RI-MA
RIC, VA
ROC, NY
SAC, CA
SAI, MO-IL
SAL", UT
SAN, TX
SAN, CA
SAN, CA
SAN, CA
SAN, CA
SEA, WA
SPR, MC-CT
SYR, NY
TAM, FL
TOL, OH-MI
WAS, DC-MD-VA
YOU, OH
Total
GSC01
0.1
0.1
0.1
0.3
0.1
0.1
--
0.3
0.2
0.4
0.1
0.1
0.1
0.1
0.3
--
—
0.2
—
0.1
--
--
--
0.1
0.1
0.1
0.2
0.1
9.5
GSC02
42.3
76.3
939.0
147.0
47.6
49.7
42.0
99.9
354.0
80.5
192.0
82.5
67.9
40.4
65.6
60.3
183.0
38.7
57.0
91.9
99.5
265.0
75.9
110.0
38.9
47.4
88.3
53.3
247.0
40.0
8,063.0
GSC03
1.0
1.3
18.2
3.7
1.1
0.8
1.1
1.4
6.0
2.5
4.8
1.5
1.2
0.9
1.2
0.9
4.2
0.6
0.8
2.3
1.4
3.8
1.1
1.6
0.6
1.0
1.5
1.3
4.1
0.9
169.8
GSC04
18.0
22.1
317.0
67.1
18.8
12.3
19.9
21.0
100.0
48.7
87.8
25.4
19.1
15.9
21.1
13.8
75.8
13.0
11.6
42.1
21.9
59.7
17.0
24.0
9.3
18.3
24.4
22.7
70.1
15.4
2,967.8
GSC05
0.2
0.2
2.8
0.6
0.2
0.1
0.2
0.2
0.9
0.4
0.7
0.2
0.2
0.1
0.2
0.1
0,6
0.1
0.1
0.3
0.2
0.6
0.2
0.2
0.1
0.2
0.2
0.2
0.6
0.1
25.4
GSC06
6.6
6.8
105.0
25.0
6.7
3.4
7.5
5.0
30.2
19.5
32.7
8.1
5.7
5.7
6.9
3.6
27.3
4.3
2.7
15.7
5.5
15.1
4.3
5.9
2.5
6.5
7.2
8.3
22.7
5.4
1,029.7
GSC07
3.4
4.3
61.5
12.7
3.6
2.5
3.8
4.3
19.8
9.1
16.6
5.0
3.8
3.1
4.1
2.8
14.4
2.5
2.4
8.0
4.5
12.1
3.5
4.9
1.9
3.4
4.8
4.3
13.6
3.0
531.3
GSC08
5.1
7.7
103.0
17.9
5.6
4.6
5.1
8.6
32.2
10.1
23.3
8.6
6.7
4.7
7.0
5.3
21.7
4.2
4.9
11.1
8.6
23.1
6.6
9.6
3.5
4.4
8.7
6.4
23.3
4.6
883.8
GSC09
3.5
3.6
55.9
13.2
3.5
1.8
4.0
2.7
16.1
10.3
17.3
4.3
3.1
3.0
3.7
1.9
14.4
2.3
1.4
8.3
2.9
8.1
2.3
3.2
1.3
3.4
3.9
4.4
12.0
2.9
546.6
IE SCO
80.1
122.0
1,600.0
288.0
87.0
75.1
83.6
143.0
563.0
181.0
375.0
135.0
107.0
73.9
110.0
88.8
342.0
66.1
80.9
180.0
144.0
388.0
111.0
160.0
58.2
86.0
129.0
101.0
364.0
72.4
14,162.8
Note: — individual figure may not add to totals due to rounding.
-------
TABLE IV-7. GROSS SOILING DAI1AGE COSTS BY MEDIUM SMSA's
(million $)
00
O
Medium SMSA's
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
ALB,
ANN,
APP,
AUG,
AUS,
BAR,
BAT,
BF,A,
BIN,
BRI,
CAN,
CHA,
CHA,
CHA,
CHA,
COL.
COL,
COL,
COR,
DAY,
DES,
DUL,
ELP,
ERI,
BUG,
EVA,
FAY,
FLI,
FOR,
FRF,,
GRE,
HAM,
HAR,
Hl'N,
HUN,
NH
MI
WI
GA-SC
TX
CA
LA
TX
NY-PA
CN
OH
SC
WV
NC
TN-GA
CO
SC
GA-AL
TX
IA-IL
IA
MN-Wr
TX
PA
OR
IN-KY
NC
MI
IN
CA
SC
OH
PA
WV-KY-OH
AL
GSC01 GSC02
22.8
16.4
18.9
16.7
21.6
25.8
19.0
22.8
21.8
28.1
28.2
19.2
18.1
30.5
24.1
16.5
20.2
15.5
19.6
28.9
22.4
19.9
24.4
19.4
16.4
17.9
12.2
0.1 36.2
20.5
31.5
21.7
16.1
31.5
19.7
15.3
GSC03
0.4
0.3
0.4
.0.2
0.3-
0.6
0.3
0.3
0.3
0.4
0.6
0.2
0.4
0.6
0.5
0.3
0.3
0.2
0.4
0.7
0.4
0.3
0.6
0.4
0.3
0.3
0.2
0.9
0.3
0.7
0.4
0.3
0.5
0.4
U.2
GSC04
7.5
4.5
6.2
0.3
5.6
11.8
4.4
5.1
4.8
6.1
10.2
3.5
6.7
10.7
8.9
5.7
4.8
3.0
7.1
12.5
7.0
5.3
11.7
7.1
5.1
5.0
3.0
16.1
5.7
12.5
0.1
4.8
9.0
6.8
3.6
GSC05
0.1
--
0.1
--
0.1
0.1
—
--
--
0.1
0.1
--
0.1
0.1
0.1
__
--
--
--
0.1
--
--
0.1
0.1
--
--
--
0.1
0.1
0.1
1.8
--
0.1
0.1
--
GSC06
2.5
1.3
2.0
1.0
1.6
4.4
1.1
1.3
1.2
1.5
3.5
0.7
2.3
3.6
3.1
1.9
1.3
0.6
2.5
4.6
2.1
1.6
4.4
2.5
1.6
1.5
0.8
6.0
1.7
4.5
1.2
1.5
2.7
2.3
1.0
GSC07
1.5
0.9
1.2
0.8
1.1
2.2
0.9
1.0
1.0
1.2
2.0
0.7
1.3
201
1.7
1.1
1.0
0.6
1.4
2.4
1.3
1.0
2.2
1.4
1.0
1.0
0.6
3.1
1.1
2.4
1.2
0.9
1.8
1.3
0.7
GSC08
2.5
1.6
2.0
1.5
2.0
3.1
1.7
2.0
1.9
2.4
3.2
1.7
2.1
3.4
2.8
1.8
1.8
1.3
2.2
3.5
2.3
1.9
3.0
2.2
4.7
1.8
1.1
4.4
2.0
3.7
2.2
1.6
3.1
2.2
1.4
GSC09
1.3
0.7
1.0
0.6
0.8
2.3
0.6
0.7
0.6
0.8
1.9
0.4
1.2
1.9
1.6
1.0
0.6
0.3
1.3
2.4
1.1
0.9
2.4
1.3
0.9
0.8
0.4
3.2
0.9
2.4
1.0
0.8
1.4
1.2
0.5
TGSCO
38.6
25.7
31.9
24.8
33.1
50.6
28.0
33.3
31.5
40.6
49.6
26.4
32.2
53.0
42.8
28.5
30.1
21.7
34.7
55.2
36.8
30.9
49.0
34.4
27.0
28.1
18.4
70.0
32.3
57.9
34.4
26.0
50.2
33.9
22.7
-------
TABLE IV-7 (Continued)
00
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126
127.
128.
129.
130.
JAC,
JOH,
KAL,
KNO,
LAN,
LAN,
LAS,
LAW,
LITT
LOR,
LOW,
MAC,
MAD,
MOB,
MON,
NEW
NEW,
NEW,
ORL,
OXN,
PEN,
PEO,
RAL,
REA,
ROC,
SAG,
SAL,
SAN,
SAN,
SCR,
MS
PA
MI
TK
PA
MI
NV
MA, NH
, AK
OH
MA
GA
WI
AL
AL
CN
CN
VA
FL
CA
FL
IL
NC
PA
IL
MI
CA
CA
CA.
PA
GSC01 GSC02
18.2
19.8
13.8
21.0
24.1
26.5
21.5
17.4
24.5
0.1 19.6
13.9
14.9
21.2
26.8
14.6
26.5
14.3
19.3
32.0
26.4
17.4
25.8
15.9
24.3
20.6
15.8
17.6
20.9
16.9
0.1 20.2
GSC03
0.4
0.4
0.2
0.6
0.5
0.5
0.4
0.3
0.4
0.6
0.3
0.3
0.3
0.6
0.3
0.4
0.2
0.3
0.5
0.6
0.4
0.4
0.2
0.5
0.4
0.4
0.4
0.5
0.4
0.6
GSC04
5.7
7.2
5.3
10.9
9.1
7.6
7.7
4.2
5.7
12.4
3.7
5.4
5.8
10.0
5.0
6.0
3.3
4.0
8.8
10.8
6.5
7.3
3.3
9.9
7.6
7.1
7.0
8.6
6.9
12.2
GSC05
0.1
0.1
--
0.1
0.1
0.1
0.1
--
0.1
0.1
..
0.3
0.1
0.1
--
0.1
--
==
0.1
0.1
0.1
0.1
--
0.1
0.1
0.1
0.1
0.1
0.1
0.1
GSC06
2.3
2.5
0.8
3.7
3.2
2.3
2.6
1.1
2.0
5.1
0.6
1.4
1.7
3.5
1.7
1.5
0.9
0.9
2.6
3.9
2.3
2.2
0.8
3.6
2.5
2.6
2.5
3.1
2.5
4.9
GSC07
1.3
1.4
0.6
2.1
1.8
1.5
1.5
0.9
1.3
2.3
0.6
0.9
1 .1
1.9
1.0
1.2
0.7
0.8
1.8
2.1
1.3
1.4
0.7
1.9
1.5
1.3
1.3
1.6
1.3
2.3
GSC08
2.1
2.3
1.2
3.5
2.8
2.7
1.4
1.6
2.4
2.5
1.2
1.5
2.1
3.1
1.6
2.3
1.3
1.7
3.1
3.1
2.0
2.6
1.4
2.9
2.4
1.9
2.1
2.5
2.0
2.5
GSCP?
1.2
1.3
0.4
2.0
1.7
1.2
1.4
0.6
1.1
2.7
0.3
0.7
0.9
1.8
0.9
0.8
0.5
0.5
1.4
2.1
1.2
1.2
0.4
1.9
1.4
1.4
1.3
1.6
1.3
2.6
TGSCO
32.3
35.0
20.1
54.0
43.2
42.3
37.6
26.2
38.4
45.4
19.5
24.1
33.1
47.8
25.2
38.8
21.2
27.5
50.3
49.1
31.1
41.1
22.7
45.1
36.6
30.6
32.4
38.9
31.5
45.6
-------
TABLE IV-7. (Concluded)
00
CO
GSC01 GSC02
131.
132.
133..
134.
135.
136.
137.
138.
139.
140.
141.
142.
143.
144.
145.
146
147.
148
SHR,
SOU,
SPO,
STA,
STO,
TAG,
TRE,
TUG,
TUL,
UTI,
VAL,
WAT,
WES,
WIG,
WIL,
WIL,
WOR,
YOR,
LA
IN
WA
CN
CA
WA
NJ
AZ
OK
NY
CA
CN
FL
KS
PA
DE, NJ, MD
MA
PA
Total
22
20
22
15
21
29
22
27
38
24
17
15
28
0.1 31
28
0.1 37
25
25
3.3 1,821
.3
.7
.9
.0
.6
.9
.1
.1
.2
.5
.8
.3
.9
.8
.1
.3
.0
.3
.9
GSC03
0.5
0.3
0.5
0.2
0.3
0.6
0.4
0.5
0.7
0.4
0.3
0.3
0.4
0.8
0.7
0.9
0.4
0.5
34.2
GSC04
8.3
5.7
8.0
3.2
4.9
10.0
5.8
9.4
11.5
6.5
4.0
4.5
6.7
15.2
12.2
16.0
6.7
7.7
616.3
GSC05
0.1
0.1
0.1
—
--
0.1
0.1
0.1
0.1
0.1
_.
--
0.1
0.1
0.1
0.1
0.1
0.1
5.2
GSC06
2.9
1.7
2.7
0.8
1.21
3.3
1.7
3.2
3.6
1.9
1.0
1.4
1.7
5.8
4.5
5.9
1.9
2.4
198.2
GSC07
1.6
1.1
1.5
0.7
1.0
1.9
1.2
1.8
2.3
1.3
0.8
0.9
1.4
2.9
2.3
3.1
1.3
1.5
160.3
GSC08
2.6
2.0
2.6
1.3
1.9
3.3
2.1
3.1
3.9
2.3
1.6
1.6
2.6
3.9
3.4
4.5
2.4
2.6
195.4
GSC09
1.5
0.9
1.4
0.4
0.7
1.8
0.9
1.7
1.9
1.0
0.5
0.7
0.9
3.1
2.4
3.1
1.0
1.3
105.4
TGSCO
39.7
32.7
39.6
21.6
31.6
50.9
34.1
46.9
62.3
37.9
26.1
24.7
42.7
63.7
53.8
71.0
38.8
41.4
3,204.3
Note: — individual figure may not add to totals due to rounding,
-------
TABLE IV-8. PER CAPITA NET AND GROSS SOILING DAMAGE COSTS ($) BY LARGE SMSA1 s,
1970
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
SMSA
AKR, OH
ALB, NY
ALL, NJ
ANA, CA
ATL, GA
BAL, MD
BIR, AL
BOS, MA
BUF, NY
CHI, IL
CIN, OH-KY-IN
CLE, OH
COL, OH
DAL, TX
DAY, OH
DEN, CO
DET, MI
FOR, FL
FOR, TX
GAR, IN
GRA, MI
GRE, NC
HAR, CT
HON, HI
HOU, TX
IND, IN
JAC, FL
JER, NJ
KAN, MO-KS
LOS, CA
LOU, KY-IN
MEM, TN-AR
MIA, FL
MIL, WI
MINN, MN
NAS, TN
NEW, LA
NEW, NY
NEW, NJ
NOR, VA
PCNSCO
22.83
50.49
29.23
39.01
24.46
66.15
92.29
42.48
54.19
73.94
41.16
104.65
23.03
39.46
46.35
73.86
70.00
12.58
31.10
38.23
18.74
27.98
23.80
16.69
29.27
20.45
18.34
28.24
29.19
55.18
68.08
20.91
11.83
31.27
20.34
53.42
22.85
36.26
60.31
41.85
PCGSCO
116.79
147.02
128.49
132.39
119.42
156.45
184.03
137.98
147.52
166.21
135.74
196.22
117.90
136.25
140.00
168.57
160.00
125.00
128.61
127.65
110.95
123.51
143.22
98.25
123.98
117.12
113.42
133.33
129.98
159.27
160.82
121.69
116.72
83.33
115.21
148.06
116.63
138.78
155.09
127.75
83
-------
TABLE IV-8 (Concluded)
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
SMSA ' s
OKL, OK
OMA, NE-LA
PAT, NJ
PHI, PA-NJ
PHO, AZ
PIT, PA
POR, OR-WA
PRO, RI-MA
RIG, VA
ROC, NJ
SAC, CA
SAI, MO-IL
SAL, UT
SAN, TX
SAN, CA
SAN, CA
SAN, CA
SAN, CA
SEA, WA
SPR, MC-CT
SYR, NY
TAM, FL
TOL, OH- MI
WAS, DC-MD-VA
YOU, OH
PCNSCO
14.98
62.96
6.84
21.59
95.87
61.22
29.83
21.62
46.72
39.01
10.99
50.36
30.82
4.98
61.94
8.76
11.19
9.39
8.51
12.64
43.08
23.89
52.53
29.88
42.91
PCGSCO
117.16
154.81
105.22
116.85
186.98
156.18
133.80
117.45
142.66
124.58
110.86
144.73
118.46
93.63
157.48
106.04
124.76
104.23
112.52
109.81
135.22
127.34
145 . 74
127.23
135.07
84
-------
TABLE IV-9. PER CAPITA NET AND GROSS SOILING DAMAGE COSTS ($) BY MEDIUM
SMSA's, 1970
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
ALB
ANN
APP
AUG
AUS
BAK
BAT
BEA
BIN
BRI
CAN
CHA
CHA
CHA
CHA
COL
COL
COL
COR
DAV
DES
DUL
ELP
ERI
EUG
EVA
FAY
FLI
FOR
FRE
GRE
HAM
HAR
HUN
HUN
SMSA'S
, NM
, MI
, WI
, GA-SC
, TX
, CA
, LA
, TX
, NY- PA
, CN
, OH
, SC
, wv
, NC
, TN-GA
, CO
, SC
,GA-AL
, TX
, IA-IL
, IA
, MN-WI
, TX
, PA
, OR
, IN-KY
, NC
, MI
, IN
, CA
, sc
, OH
, PA
, WV-KY.OH
, AL
PCNSCO
30.
18.
28.
10.
16.
60.
9.
9.
8.
7.
38.
0.
41.
35.
42.
32.
9.
2.
35.
56.
28.
18.
55.
37.
28.
21.
10.
53.
20.
46.
20.
23.
22.
35.
42.
70
38
52
67
22
49
47
49
25
71
71
99
74
70
30
20
91
93
79
20
32
11
99
88
17
46
38
32
00
73
67
45
38
83
54
PCGSCO
122.15
109.83
115.16
98.02
111.82
153.80
98.25
105.38
103.96
104.37
133.33
86.84
140.00
129.58
140.33
120.76
93.19
90.79
121.75
152.07
128.67
116.60
136.49
130.30
126.76
120.60
86.79
140.85
115.36
140.19
114.67
115.04
122.14
133.46
99.56
85
-------
TABLE IV-9 (Continued)
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126.
127.
128.
129.
130.
131.
132.
133.
134.
135.
136.
137.
138.
139.
140.
SMSA
JAC, MS
JOH, PA
KAL, MI
KNO, TN
LAN, PA
LAN, MI
LAS, NV
ALW, MA-NH
LITT, AK
LOR, OH
LOW, MA
MAC, GA
MAD, WI
MOB, AL
MON, AL
NEW, CN
NEW, CN
NEW, VA
ORL, FL
OXN, CA
PEN, FL
PEO, IL
RAL, NC
RE A, PA
ROC, IL
SAG, MI
SAL, CA
SAN, CA
SAN, CA
SCR, PA
SHR, LA
SOU, IN
SPO, WA
STA, CN
STO, CA
TAG, WA
TRE, NJ
TUG, AZ
TUL, OK
UTI, NY
PCNSCO
37.45
38.401
8.42
37.50
41.56
20.90
38.83
13.36
19.81
93.77
3.29
23.79
18.97
38.46
33.83
9.83
10.10
5.14
20.56
62.13
39.09
22.22
6.14
51.69
40.07
52.73
43.20
50.76
52.68
99.57
40.34
20.36
37.63
7.77
9.66
31.63
17.11
36.36
27.46
26.18
PCGSCO
124.71
133.08
99.50
135.00
135.00
111.90
137.73
112.93
118.89
176.65
91.55
116.99
114.14
126.79
125.37
108.99
101.92
94.18
117.52
180.51
127.98
120.18
99.56
152.36
134.56
139.09
129.60
147.35
153.66
194.87
134.58
116.79
137.98
104.85
108.97
123.84
112.17
133.24
130.61
111.47
86
-------
TABLE IV-9 (Concluded)
. SMSA PCNSCO PCGSCO
141. VAL, CA 9.24 104.82
142. WAT, CN 23.44 118.18
143. WES, FL 12.32 122.35
144. WIC, KS 67.35 163.75
145. WIL, PA 58.48 157.31
146. WIL, DE, NJ, MD 51.90 142.28
147. WOR, MA 18.02 112.79
148. YOR, PA 27.27 125.45
87
-------
A summary of the net and gross soiling damages by cleaning operations is
contained in Table IV-10. The total net soiling damage as a result of falling
suspended particulates for the 148 SMSA's in 1970 amounts to $5 billion. This
damage figure is far smaller than the $11 billion and $30 billion national esti-
mate extrapolated from the per capita damage figures reported respectively in
the Mellon Institute study and the study by Michelson and Tourin. As noted
earlier, the validity of the $11 billion and $30 billion estimates is seriously
undermined by the assumptions used in the extrapolation technique. Regarding
the gross soiling damage costs, New York, Chicago and Los Angeles had the high-
est damages among the 148 large SMSA's, about $1.6 billion, $12 billion, and
$1.1 billion, respectively, partially because of the relatively high suspended
particulate levels and a large number of household units in these three cities.
Total gross soiling damage which is the sum of soiling damages attributable to
air pollution and other factors amounts to $17.4 billion per year for the 148
SMSA's.
TABLE IV-10. NET AND GROSS SOILING DAMAGE COSTS IN 148
SMSA's BY CLEANING OPERATIONS, 1970
Net Gross Net/Gross
Soiling Damages Soiling Damages Soiling Damage
Tasks
1
2
3
4
5
6
7
8
9
Total
(million $)
11.8
558.8
454.1
1,956.3
13.6
925.7
349.2
275.1
488.9
5,033.0
(million $)
12.8
9,884.9
204.0
3,584.1
30.6
1,227.9
691.6
1,079.2
652.0
17,367.1
Cost
0.91
0.05
0.45
0.55
0.44
0.75
0.50
0.25
0.75
0.28
Note: — individual figure may not add to totals due to rounding.
88
-------
00
TABLE IV-11
TABLE IV-11. SOILING ECONOMIC DAMAGE FUNCTIONS£iW
Dependent
Variable
GSC01
GSC02
GSC03
GSC04
GSC05
GSC06
GSC07
GSC08
GSC09
TGSCO y
MANFV
66.18
(2.02)*
42.9
(1.45)*
957.6
(25.9)*
17.2
(0.42)*
144.3
(3.90)*
6.12
(0.17)*
3.29
(0.08)*
4.92
(0.15)*
3.23
(0.08)*
78.9
(2.3)*
TSP
1,128.83
(147.36)*
-108.5
(105.5)
6,802.6
(1,887.5)*
160.0
(33.1)*
1,031.6
(284.2)*
84.0
(12.5)*
27.7
(6.4)*
9.07
(10.9)
44.6
(6.5)*
226.4
(166.9)
PCOL
2,377.55
(1,288.8)
2,252.2
(915.7)*
42,426.0
(16,507.0)*
727.4
(29?.. 3)*
6,384.9
(2,485.6)*
236.9
(109.9)*
142.0
(56.3)*
223.0
(94.9)*
126.9
(579.4)*
3.766.0
(1,460.0)*
RUM
-995.17
(538.89)
670.6
(383.4)
-14,677.0
(6,902.0)*
-262.6
(122.4)*
-2,210.7
(1,039.3)*
-92.6
(45.9)*
-50.3
(23.5)*
-74.8
(39.7)
-48.9
(24.2)*
-1,219.8
(610.7)*
DTS
271.69
(193.52)
1,946.0
(2,478.0)
42.577
(43.8)
294.6
(373.2)
20.9
(16.5)
7.6
(8.4)
11.2
(8.6)
90.9
(219.3)
PDS
-1.78
(3.81)
2.2
(2.7)
12.4
(48.9)
1.85
(7.36)
-0.083
(0.325)
0.025
(0.166)
0.17
(0.28)
2.3
(4.3)
PAGE
764.7
(1,940.9)
2,401.1
(1,392.6)
32,626.0
(24,859.0)
501.9
(439.4)
4,898.9
(3,743.2)
116.6
(165.6)
102.2
(84.8)
212.9
(144.4)
60.4
(87.0)
3,432.3
(2,199.5)
a
-100,181.3
(46,192.6)
5,400.0
(32,763.0)
652,046.0
(591,650.0)
14,825.0
(10,445.0)
98,799.0
(89,087.0)
-7,565.5
(3,941.2)
-2,607.0
(2,018.7)
877.7
(3,396.3)
4,047.7
(2,070.1)
-25,621.0
(52,347.0)
R2
0.92
0.89
0.93
0.93
0,93
0.93
0.93
0.91
0.93
0.92
a/ All coefficients and standard errors are reduced by a factor of 103, except equation GSC01, GSC03 and GSC05. The
~ standard errors are the values below the coefficients, and * indicates that the coefficient is significant at
the I percent level.
b/ TGSCO denotes the total gross soiling cost for the ith cleaning task, and TGSCO is the sum of GSCOi over i, i = 1, 2,
-------
The regional soiling damage costs and the national damage cost deduced in
this study should be used, however, only as crude estimates. There are uncertain-
ties embodied in the two major assumptions: (1) the physical damage functions
for the variety of cleaning tasks estimated on the basis of the Philadelphia
study are "representative" of the physical damage functions of the 148 SMSA's;
(2) the unit market value figures obtained in the Kansas City area are applicable
to other SMSA's.
In order to develop "average" soiling economic damage functions for each
of the nine cleaning tasks which can be used for prediction and control purposes,
the individual metropolitan damage costs were regressed against not only the
SO , but also to several socioeconomic, demographic, and climatological charac-
teristics of different regions. The independent variables include MANFV (value
of manufacturing), PCOL (percentage of persons 25 or older who have completed
4 years of college), RHM (relative humidity), DTS (number of days with thunder-
storm), PDS (population density), PAGE (percentage of population 65 or older)
and TSP- The inclusion of these variables is to account for the variations in
educational level, economic and age structure and density differentials among
the study regions. The stepwise regression technique was used with inputs from
the 148 sample observations for the purpose of estimating the economic damage
functions. The regression results are summarized in Table IV-11. It is note-
worthy that all the coefficients of TSP are of correct signs except the one in
the second regression equation. Since the partial correlation coefficient be-
tween GSC02 and TSP is positive and equal to 0.18, the negative coefficient ob-
tained for TSP in the regression equation may be attributable to multicolinearity
between TSP and other independent variables or other econometric problems or
data deficiency.
It is interesting to note that aside from total suspended particulates PCOL,
RHM, and MANFV are significant factors in determining the household soiling costs.
While the effect of educational level on soiling adjustment cost is ambiguous
£ priori , relative humidity is likely to have a cleansing effect which reduces
the soiling costs.
The soiling economic damage functions derived in this study are useful to
policymakers at either the local or national level in estimating the marginal
and average benefits of implementing a particular pollution abatement program.
The responsiveness of gross soiling damages for a particular cleaning task to
changes in climatological, demographic, and socioeconomic variables and the con-
centration level of suspended particulates can be easily estimated. The partial
elasticity of the gross soiling costs of, say, cleaning Task 4, i.e., cleaning
Venetian blinds and shades, with respect to suspended particulate level, can
be estimated by
Esc4jTSP = |1SC41 . TSP
o-(TSP) SC4
90
-------
where 8(SC4)/B(TSP) is the coefficient of TSP in the soiling economic damage
function with GSC04 as the dependent variable, and is equal to 160,000. TSP
and SC4 are respectively the mean values of total suspended particulates and
of soiling damage cost associated with cleaning Venetian blinds and shades. Given
TSP = 94.5 M-g/™3> and SC4 = $24.2 million,
ESC4,TSP = °'16 X <94-5/24-2> = °-62
Thus, for every 1 percent reduction in suspended particulate level, the soiling
damage cost of cleaning Venetian blinds would decrease by 0.62 percent, holding
other characteristics unchanged. Thus, if the suspended particulate level in
the air is lowered by 9.45 p,g/m3 from 94.5 to 85.5 l-lg/m^ (i.e., 10 percent re-
duction), gross soiling damage cost associated with cleaning Venetian blinds
alone, would reduce, on the average, by $1.5 million from $24.2 to $22.7 million
nationwide. Of particular policy interest is the estimation of possible benefit
in terms of the reduction in the overall soiling damage cost as a result of a
pollution control program. Note that the coefficient of TSP in the overall soil-
ing economic damage function is 226,400 and the mean value of overall soiling
damage cost is $117.3 million.
E = 226400 x (94.5/117.300000) = 0.18
SC,TSP
Thus, if the suspended particulate level is lowered, on the average, by 10 per-
cent, from 94.5 to 85.5 [j,g/m , overall gross soiling damage cost would reduce
by 1.8 percent or by $2.1 million, from $117.3 million to $115.2 million.
91
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SECTION V
MATERIAL AND AIR POLLUTION
PROBLEMS AND OBJECTIVES
The damaging effects of air pollution to materials have been well recognized
by the Air Pollution Control Office of the Environmental Protection Agency for
some time. Effects of air pollution on materials range from soiling to chemical
alteration. Corrosion of metals has attracted the most attention, while many
other important areas were found to have been largely neglected. Most materials
exhibit a high degree of chemical resistance to oxides of nitrogen, while sulfur
dioxides were found to seriously attack about a third of the materials. And
while some materials (such as glass) are highly resistant to chemical attack
by most air pollutants, certain plastics and metals are highly susceptible to
damage by a number of different commonly encountered air pollutants.
Among the adverse effects of air pollution on material included are the
corrosion of metals, the deterioration of rubber, the fading of paint and soil-
ing of materials. Many external factors influence the reaction rate between pol-
lutants and materials, with moisture the most important in accelerating corro-
sion. Inorganic gases are likely to cause tarnishing and corrosion of metals;
they can attack various building materials such as stone, marble, slate, and
mortar and may deteriorate a variety of natural and synthetic fibers.
The most noticeable effect of particulate pollutants is soiling of the
surfaces on which they are deposited. They may also act as catalysts increasing
the corrosive reactions between metals and acid gases. Additional damages to
surfaces and textiles are incurred by the wear and tear imposed by the extra
cleaning made necessary because of particulate soiling. The true economic dam-
age to materials caused by air pollution is difficult to ascertain because of
the difficulty of distinguishing between natural deterioration and deterioration
caused by air pollution and the uncertainty regarding indirect costs of early
replacement of materials worn out by excessive cleaning.
Some of the material damage estimates attributable to air pollution in this
country are as follows: In a pilot study by Uhlig (1950), the total corrosion
bill was estimated at $5.4 billion, though air pollution was merely implicated
as a causal agent of corrosion. Uhlig's estimate was updated and estimated by
the Rust-Oleum Corporation (1974) to be $7.5 billion in 1958. Stickney, Mueller
and Spence (1971) estimated that the pollution damage cost of rubber to U.S.
consumers amounts to at least $398 million. Haynie (1973) estimated a value of
$1.4 billion for the cost of corrosion of galvanized steel. The total damage
cost due to air pollution inflicted on textiles and fibers was estimated to be
$2 billion annually by Salvin (1970).
92
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Robbins (1970) of Stanford Research Institute conducted one of the first
major material damage studies and found that the air pollution damage with re-
spect to electric contacts was not as serious as originally estimated. Two
types of major costs were investigated. They are the direct cost associated
with the plating of contacts with precious metals and the indirect cost incurred
because of the preventive measures of air conditioning and air purification.
It was estimated that about $65 million is spent annually on electric contacts
because of air pollution.
Economic impact of air pollution on electric components was estimated by
International Telephone and Telegraph (ITT) Electro-Physics Laboratories (1971).
The damage costs to the following electric components were estimated: semi-
conductor devices, integrated circuits, television picture tubes, connectors,
transformers, relays, receiving tubes, and crystals. Total damages to these
categories amounted to $15.5 million.
A most comprehensive study on pollution damage on materials was conducted
by Midwest Research Institute (Salmon, 1970)- The MRI study presented a systematic
analysis of all of the physical and chemical interactions between materials, pol-
lutants and environmental parameters. Fifty-three economically important mate-
rials which represent about 40 percent of the economic value of all materials
exposed to air pollution were identified and selected for the study. An esti-
mated $100 billion in added cleaning costs would be necessary to keep these mate-
rials in polluted areas as clean as they would be in a nonpolluted environment,
while deterioration of these materials causes yearly direct damage losses of
approximately $4 billion. Paint and zinc are the two materials most affected
by both soiling and deterioration caused by air pollution, accounting for more
than half of the total losses in each category. Based on the MRI estimate,
Barrett and Waddell (1974) estimated an annual material deterioration damage
in the United States to be $4.75 billion.
Some of the methodological procedures for estimating material effect was
critically reviewed by Gillette and Upham (1973). It is generally understood
that in order to develop reasonable estimates of pollution damages, the follow-
ing information is very relevant: (1) geographical and temporal distribution
of air quality levels and receptors' exposure to various pollution levels; (2)
physical damage functions on important receptors; and (3) data on other socio-
economic, demographic and environmental factors on a regional basis.
Pollution level varies within any given SMSA. In the absence of population-
at-risk information, it is usually assumed that the entire SMSA population was
exposed to the same pollution level as recorded by the station(s) which in all
probability is (are) located in the central city of the SMSA. Cost estimates
derived under this assumption tend to overestimate the actual damage due to air
pollution, since the pollution concentration level is likely to be higher in
the central city than in the suburban areas.
Physical damage functions relating material damage to air pollution have
recently been derived in a series of in-house experiments. Haynie and his
93
-------
associates (1974, 1975) obtained such physical dose-response relations sep-
arately for different kinds of steels, zinc, oil-base house paint and selected
fabrics. Economic damage functions for materials which translate material phys-
ical loss into monetary terms, however, are still lacking. Although some damage
estimates for certain types of materials at the national level are now avail-
able, detailed regional cost estimates on material damages due to air pollu-
tion are virtually nonexistent. Since the information on such regional damage
costs of air pollution is indispensable for providing guidance for establishing
pollution controls, it is imperative to develop a set of comparable damage cost
estimates for each as well as a system of economic damage functions on differ-
ent types of materials.
This section represents a first exploratory effort to estimate not only
urban material damages attributable to air pollution for all 148 SMSA's with
population greater than 250,000, but also the "average" air pollution damage
functions on materials for this country. This section contains the following
subsections: A Theoretical Framework, Exposition of Methodology, Regional Mate-
rial Damage Costs, Economic Damage Functions, and A Summary of Material Phys-
ical Damage Functions.
A THEORETICAL FRAMEWORK
A theoretical framework is developed in this section for defining and de-
veloping urban economic costs and economic damage functions of materials. The
economic costs of a material are defined as the decrease in the values of this
particular material as a result of increased contamination in the environment.
An economic damage function of material which relates the economic costs to a
host of relevant variables including pollution, socioeconomic, demographic and
climatological variables is also estimated.
It is noteworthy that materials generally do not directly affect an individ-
ual's utility or preferences. Thus, materials can be regarded as "pure" inter-
mediate commodities which are differentiable from the traditional interindustry
flows. The pure intermediate commodities utilized the primary inputs, i.e., la-
bor and capital, in their production and are themselves solely utilized as in-
puts in the production of "final" commodities which enter into one's utility
function. Interindustry flows, however, refer to those commodities which are
intermediate inputs to be used in other industries as well as final outputs
for consumers.
The degree of the effect of air pollution on materials depends on a number
of factors: (1) the extent of the reduction in the normal service or use life
of material; (2) the frequency of maintenance and preventive measures on the
part of users; and (3) the changes in the quality and quantity of the services
rendered by the product which contains the materials being affected by air pol-
lution.
94
-------
Let D. represent the level of physical deterioration of the ith type of
material. &L. will be the service life, SP. the service performance, ME the
maintenance effort of the same ith type of material, and A the air pollution
concentration level to which the material is being exposed.
Thus, we can write
D± = D [SLXA), SPi(A), MEi(A); e]
(V-l)
Equation (V-l) states that the deterioration of the ith type of material
is functionally related to SL., SP. and ME., directly. Each of the explanatory
variables is, however, being influenced by the prevailing pollution levels.
The plausible signs of the partial derivations are as follows:
Thus,
o-(SL) d(A)
.
'
BO . d(SP)
a(SP) d(A)
.
' *
a(ME) d(A)
> 0.
Assuming noninteractions among the independent variables, total physical
damage of material as a result of an increased pollution is expressed asi—'
dD =
5D
a(sL)
d(SL)
d(A)
+ 5D
a(sp)
d(SP)
d(A)
+ 5D
9 (ME)
, d(ME)
d(A)
d(A)
(V-2)
Note that changes in SL and SP of the product represent direct damages
attributable to pollution, whereas pollution-induced changes in ME are indirect
damages due to pollution.
I/ The assumption of noninteraction is made for the sake of simplicity. In the
real world, it is observed that service life, performance and maintenance
effort are interrelated. Increased maintenance should also increase service
life and performance.
-------
A simplified and yet commonly adopted formulation of material physical dam-
age function is written as follows:
D. = D.(A, RHM; u) (V-3)
where RHM is relative humidity, D and A are the same as in (V-l) and u
is the error term.
In order to develop a theoretical framework for estimating economic damage
costs of materials, let us assume for the sake of simplicity, but without loss
of generality, that there is only one type of material. The initial endowment
of this material in a given urban area is M . Further, it is assumed that the
material stock grows at an exogenously determined rate of r over the planning
time horizon.JV Thus, total stock of this material in the absence of air pollution
in the area at time t is given by:
t
g
M = / M er dt (V-4)
J o
t=0
If the area in question is subject to an air pollution level which is above
the threshold level, and if the material depreciates at a rate i because of
the air pollution, then the net existing material stock at the time t is given
by:
M = /* M e dt
n */ o
Note that i = dD/D and D = D(A; RHM) with 9D/d(A) > 0. dD/d(RHM) > 0.
The economic damage of this material (ED) is defined to be that portion
of the material loss attributable to air pollution evaluated at the prevailing
market prices of the material. Let P be the market price of the material,
which is determined by the supply ana demand conditions for this material.
JL/ A more realistic approach is to consider that the growth of stock of a mate-
rial within an area is endogeneously determined. It is determined by the
need for that material within an area and the ability to acquire it. One
would expect that the stock growth rate to be a function of population
growth rate, per capita income growth rate and the change in demand for
that material with respect to replacement material.
-------
Thus,
f X. *~V* ^*-i-r I i> \fjLmj.\.iU.J./ . .
ED = P (M -M ) = P /Me dt (V-6)
m g n m S o
& t=o
It may be remarked that (V-6) reflects the economic damage associated with
the air pollution level through a change in demand for the material. It does
not reflect the economic damage associated with increased flow of the material
through the area caused by pollution-induced decreased service life.
It follows from (V-6) that a general economic damage function for material
can be expressed as
ED = ED(P , A, RHM; u) (V-7)
m
Those socioeconomic and other variables which influence P could be in-
cluded in the general economic damage function in addition to A and RHM.
EXPOSITION OF METHODOLOGY
Ideally, the information on the distribution of materials, of pollutants,
the value of the products made from the materials, the service life of the prod-
ucts in the absence of pollution and the physical dose-response function should
be gathered in order to accurately assess economic costs of material deteriora-
tion due to excessive air pollution. Empirical data on the distributions of both
materials and pollution are unavailable for most urban areas. However, sketchy
estimates for product values and the service life have been derived by Fink et
al. (1971) by resorting to the annual production figures in the Standard Indus-
trial Classification statistics and product useful life statistics issued by
Internal Revenue Service. Dose-response functions for a variety of materials
have recently been estimated via the technique of in-house controlled experi-
ment s.
In the absence of the relevant material and pollution distribution data,
an alternative "top-down" method is developed in this section to derive a con-
sistent set of urban economic costs of material damage as a result of air pol-
lution. The existing valuable information on national material damage and the
dose-response functions were obtained through literature survey. The national
damage estimates were then allocated down to various SMSA's by utilizing the
dose-response functions and other relevant regional data.
For the sake of illustration, but without loss of generality, let the phys-
ical dose-response function for the ith type of material be written as:
97
-------
i i '
where D, A and C denote, respectively, the physical damage, the air pollu-
tion level and climatological conditions.
Given the physical damage function and the national damage estimates, the
regional damage costs for the ith type of material can be estimated by using
the following formula:
D. . SE.
ij j
RED = NED. • • (V-9)
I
j/
n SE.n
J
where RED. . and NED. are, respectively, the regional and national economic
damage costs for the ith type of material. SEj stands for the relevant socioeco-
nomic characteristics which are thought to affect material damages, e.g., manu-
facturing establishments and D-M is the dose-response relation for the ith type
of material. The subscript j denotes the jth SMSA.
Substituting (V-8) into (V-9) yields:
RED =NED. . W V • Jfj CV-10)
1J X SD..(A., C.)
ij j j SSE^ n
Equation (V-10) can be used to derive the regional economic cost for the
ith type of material. The data on NED is available from an earlier material
damage study conducted by Salmon (1970) at Midwest Research Institute, and AJ ,
Cj and SEj can be respectively secured from the Air Quality Data, published
by the Environmental Protection Agency; Local Climatological Data, published
by the National Oceanic and Atmospheric Administration; and 1972 County and City
Data Book. Substituting the values for NED-^, A j, Cj, and SEj into equation
(V-10), a series of consistent estimates for material damages for various SMSA's
due to air pollution is obtained.
According to the earlier MRI study, material damage as a result of air pol-
lution can be categorized into two major effects: (1) soiling effects attribut-
able to particulate pollutants; and (2) chemical effects attributable to gaseous
pollutants. The national soiling cost (SC) and deterioration cost (DC) of various
materials were estimated with the aid of the following formulas:
98
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SC = SIF . Q (V-ll)
DC = DIP . Q (V-12)
Q = P . N . F . R (V-13)
where SC represents material soiling costs, SIF the soiling interaction fac-
tor, Q in-place unprotected material value, DC material deterioration cost,
DIF deterioration interaction factor, P annual production value of the mate-
rial, N economic life of the material based on usage, F weighted average fac-
tor for the percentage of the material exposed to air pollution, and R labor
factor reflecting the in-place or as-used value of the material.
The rate of soiling interaction factor, SIF, is computed by complex for-
mulas which are different for fibers and nonf ibers .JL/ The rate of deterioration,
DIF, is computed by estimating the difference between the deterioration rate
in polluted and unpolluted environments divided by the average thickness of the
material.
Finally, it is noted that two methods are generally feasible for estimating
regional damage costs. The first method is the "top-down" technique which is to
allocate via weighting and adjusting schemes a national damage estimate down
to various regions. The second method is the "bottom-up" technqiue which involves
direct estimation of the regional damages. The bottom-up method, which incorpo-
rates uncertainties of the assumptions into regional estimates generated, was
used to derive the damage costs for human health and household soiling adjustment
in the preceding three sections. The top-down technique was employed, however,
in this study to estimate material damage costs because of the lack of distribu-
tion information about materials and pollutants and the technical difficulties
encountered in direct estimation of the regional damages.
REGIONAL MATERIAL DAMAGE COSTS
This section is concerned with estimating regional material damages by us-
ing the top-down method delineated above. In view of the fact that there are
virtually infinite categories of materials, and the fact that zinc and paint
are most important from an economic point of view, only these two materials were
selected for this study. The damage costs of zinc and paint account for over 50
percent of the total economic damage losses of the 53 economically important
materials selected in the earlier MRI study (Salmon, 1970).
I/ The formulas are: SIFfibers = °-10 Af/Rw and SIFnonfibers = O.lOAf/Rwpt
where Af is the increased frequency of cleaning due to pollution, R
the labor factor, w the material price per pound, p the density and t
the average thickness.
99
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The soiling and deterioration costs of paint and zinc, and the percentage
of these two costs in terms of total costs, are summarized in Table V-l-i/
These figures are admittedly artificial because they were calculated on the
assumption that the material would be maintained completely clean at all times.
In practice, each individual will have an acceptable level of soiling for each
material.
TABLE V-l. SOILING AND DETERIORATING COSTS OF PAINT AND ZING
Paint
Zinc
53 Materials
Soiling
Cost (SC)
(billion $)
35.0
24.0
100.0
SC/Total SC
0.35
0.24
0.59
Deterioration
Cost (DC)
(billion $)
1.2
0.8
3.8
DC/Total DC
0.31
0.20
0.51
The physical dose-response functions for zinc and oil-base house paint are
obtained from two recent studies by Haynie and Upham (1970), Spence, Haynie and
Upham (1975).
Zinc - Corrosion = 0.001028 (RH - 48.8) SO (V-14)
Paint - Erosion = 14.32 + 0.01506 SO + 0.3884 RH (V-15)
Recalling equation (V-10), and substituting the above physical dose-response
function for zinc, and the relevant socioeconomic data into equation (V-10)
yields the estimates of soiling and deterioration cost of zinc for the jth SMSA.
Thus,
O.OOr028(RH.-48.8)50 ME
zinc. zinc * —• 1 *
j 148
S (0.001028(RH.-48.8)SO /148) ZME./148
j=l J j j j
/148
J S POP ,
V J
*
NED . = NED . Jj E POP /POP .
zinc zinc 1 . j' us I (V-17)
y The estimates are taken from Table XII and Table XIII of the MRI research
report, "System Analysis of the Effects of Air Pollution on Materials,"
Kansas City (January 1970).
100
-------
where NED* . is the total damage cost of zinc over the 148 SMSA's included
7 1 T) f
for the present study, RHj and MEj the relative humidity and manufacturing
establishment in the jth SMSA, POPj and POPus represent, respectively, the
population in the jth SMSA and in the whole country.
The earlier MRI study estimated the national soiling and deterioration dam-
age costs of zinc to be $24 billion and $778 million, respectively. Given the
ratio of 0.63 which represents the ratio of the 148 SMSA population to the na-
tionwide population in 1974, the soiling and deterioration damage costs of zinc
for the 148 SMSA's are calculated as follows:
$24 x 0.63 = $15.12 billion (soiling)
$778 x 0.63 = $496 million (deterioration)
A threshold level of zero (J,g/m3 for SC>2 is implicit in the physical dose-
response functions for zinc and paint. Thus, a zero threshold level is used in
estimating the regional damage costs of materials. It should be noted that the
value of RED*z£nc as calculated from the weighting scheme expressed by (V-16)
can be greater than NED*z:j_nc. Thus, the damage costs for each SMSA are further
J 148
adjusted by the ratio (REDzj;nCj j .J5. REDz^nCj j) to preserve the equality be-
tween the sum of the regional damage costs obtained via equation (V-16) over
the 148 SMSA's and the total damage costs evaluated for the same 148 SMSA's,
i.e., NED*.
Similarly, the soiling or deterioration damage costs of paint for the jth
SMSA are computed by using the following formula:
(14.3 + 0.01506 S02 + 0.3884 RH.)
RED = NED* . j J
paint, j paint ^
S (14.3 + 0.01506 SO + 0.3884 RH.)/148
J=l J J
HU. YP.
J J
SHU /148 * EYP./148
j j j
.
J (V-18)
NED* = NED .
paint paint
POP /POP (V-19)
j us
101
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where NED*paint is the total cost of zinc over the 148 SMSA's, HUj and YPj
are housing units and per capita income in the jth SMSA's. POP, S02> RH, NED
and RED are the same as those in equation (V-15).
The nationwide soiling and deterioration damage costs of paint were esti-
mated in 1970 to be $35 billion and $1.2 billion, respectively. Thus, applying
the population ratio of 0.63 to these two estimates, NED* is calculated to be
$22 billion and $753 million for soiling and deterioration, respectively. The
damage estimates are further adjusted by RED ./ (S RED ) such
pirint jj j
that the sum of the damage costs over the 148 SMSA's equals the total damage
cost evaluated for these SMSA's by using (V-19).
The soiling and deterioration damage costs of zinc and paint for the 65 large
SMSA's and the 83 medium SMSA's are computed by using equations (V-16) through
(V-19). The results of the regional damage costs are summarized in Tables V-2
and V-3. Since the national damage figures employed are extremely large because
they were estimated on the stringent assumption that the materials would be main-
tained completely clean at all times, it is not surprising that the top-down
method yields relatively large damage figures for the study regions. An examina-
tion of the tables reveals that Chicago scores the highest damage costs on zinc
among all the 148 SMSA's in both the soiling and deterioration damages (an an-
nual soiling damage of $1.7 billion and deterioration damage of $57 million in
1970). However, regarding the paint damages, New York City, which had an annual
soiling and deterioration damage cost of paint of $2.3 billion and $79 million,
respectively, surpassed all the SMSA's included in this study.
It should be noted again that the assumptions made in deriving the national
damage figures and the physical damage functions in the earlier studies are in-
herited by this study, especially the theoretically maximum national damage esti-
mates in both categories of soiling and deterioration. Should there be changes
in these assumptions, the estimates developed and presented in the table would
be modified accordingly. Thus, the results presented in this section are only
suggestive and tentative. Given the tentativeness and experimental nature of
the methodological and statistical procedures, and the degree of uncertainty
associated with the estimates, a great deal of caution should be exercised in
using the product of this research.
ECONOMIC DAMAGE FUNCTIONS
In order to develop marginal equivalent economic damage functions which can
be used for damage (benefit) prediction and for designing pollution control
strategies, the economic costs of material soiling and deterioration are re-
gressed not only against S02 and relative humidity, but also against other rele-
vant socioeconomic and climatological variables. The stepwise regression tech-
nique was used with inputs from the 148 sample observations for estimating the
economic damage functions. The regression results for soiling and deterioration
damages of zinc and paint are presented in Table V-4. Consistent with a priori
expectation, the coefficients of ME, S02, TSP, RH, HU and YP are all posi-
tive, and the coefficient of SUN has ambiguous signs depending on the type
102
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TABLE V-2. MATERIAL DAMAGE BY LARGE SMSA's,
(in million $)
1970
I
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26..
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
•arge
AKR,
ALB,
ALL,
ANA,
ATL,
BAL,
BIR,
BOS,
BUF,
CHI,
CIN,
CLE,
COL,
DAL,
DAY,
DEN,
DET,
FOR,
FOR,
GAR,
GRA,
GRE,
HAR,
HON,
HOU,
IND,
JAC,
JER,
KAN,
LOS,
LOU,
MEM,
MIA,
MIL,
MINN
NAS,
NEW,
NEW,
NEW,
NOR,
SMSA's
OH
NY
NJ
CA
GA
MD
AL
MA
NY
IL
OH-KY-IN
OH
OH
TX
OH
CO
MI
FL
TX
IN
MI
NC
CT
HI
TX
IN
FL
NJ
MO-KS
CA
KY-IN
TN-AR
FL
WI
, MN
TN
LA
NY
NJ
VA
Soiling Damage
Cost of Zinc
(SDCZ)'
108.0
54.0
16.5
62.6
25.2
190.0
12.9
289.0
32.8
1,770.0
134.0
1,110.0
91.5
15.3
125.0
--
910.0
8.8
11.9
144.0
29.4
18.8
19.1
2.3
28.6
79.3
2.1
128.0
122.0
730.0
171.0
17.3
24.6
178.0
32.5
12.6
25.6
1,040.0
94.9
10.3
Deteriorating Dama
Cost of Zinc
(DDCZ)
3.533
1.751
0.537
2.029
0.818
6.166
0.421
9.395
1.064
57.600
4.372
36.000
2.969
0.496
4.080
--
29.500
0.284
0.387
4.680
0.955
0.611
0.621
0.078
0.929
2.572
0.068
4.152
3.956
23.600
5.545
0.563
0.800
5.799
1.054
0.411
0.832
34.000
3.077
0.335
ige Soiling Damage
Cost of Paint
(SDCP)
107.0
121.0
85.6
279.0
224.0
320.0
98.8
480.0
219.0
1,350.0
225.0
392.0
153.0
273.0
145.0
167.0
741.0
158.0
127.0
94.0
81.9
86.0
140.0
83.7
338.0
192.0
70.8
101.0
229.0
1,530.0
127.0
94.5
239.0
247.0
307.0
80.8
147.0
2,310.0
327.0
81.4
Deteriorating Damage
Cost of Paint
(DDCP)
3.684
4.141
2.925
9.542
7.667
10.900
3.374
16.300
7.503
46.300
7.716
13.300
5.239
9.343
4.952
5.728
25.300
5.414
4.352
3.217
2.798
2.937
4.792
2.860
11.500
6.563
2.418
3.475
7.824
52.300
4.345
3.227
8.164
8.435
10.500
2.759
5.035
79.100
11.100
2.781
Note:
-- denotes damage of value less than $0.5 million.
103
-------
TABLE V-2 (Concluded)
Large SMSA's
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
OKL,
DMA,
PAT,
PHI,
PHO,
PIT,
FOR,
PRO,
RIC,
ROC,
SAC,
SAI,
SAL,
SAN,
SAN,
SAN,
SAN,
SAN,
SEA,
SPR,
SYR,
TAM,
TOL,
WAS,
YOU,
OK
NE-IA
NJ
PA-NJ
AZ
PA
OR-WA
RI-MA.
VA
NY
CA
MO-IL
UT
TX
CA
CA
CA
CA
WA
MC-CT
NY
FL
OH -MI
DC-MD-VA
OH
Total
SDCZ
2.
13.
101.
520.
--
504.
36.
137.
18.
57.
__
308.
--
1.
12.
9.
57.
26.
143.
15.
40.
13.
28.
38.
152.
10,114
6
3
0
0
0
3
0
7
1
0
8
1
8
3
7
0
3
4
3
4
4
0
.4
DDCZ
0.
0.
3.
16.
-
16.
1.
4.
0.
1.
_
10.
-
0.
0.
0.
1.
0.
4.
0.
1.
0.
0.
1.
4.
328.
083
434
292
800
-
300
780
51&
061
852
_
000
-
060
393
316
858
868
641
498
311
432
922
246
946
651
SDCP
106
85
271
767
90
386
179
140
78
151
114
411
58
97
198
224
725
199
293
75
104
175
108
578
84
18,272
.0
.1
.0
.0
.5
.0
.0
.0
.7
.0
.0
.0
.8
.2
.0
.0
.0
.0
.0
.6
.0
.0
.0
.0
.9
.3
DDCP
3.
2.
9.
26.
3.
13.
6.
4.
2.
5.
3.
14.
2.
3.
6.
7.
24.
6.
10.
2.
3.
5.
3.
19.
2.
624.
641
907
271
200
091
200
116
798
688
516
900
000
Oil
321
760
670
700
826
000
584
557
975
712
700
900
854
!-• • -
Note: — individual figure may not add to totals due to rounding,
104
-------
TABLE V-3. MATERIAL DAMAGE BY MEDIUM SMSA's, 1970
(In million $)
Medium SMSA's
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
ALB, NM
ANN, MI
APP, WI
AUG, GA-SC
AUS, TX
BAR, CA
BAT, LA
BEA, TX
BIN, NY-PA
BRI, CN
CAN, OH
CHA, SC
CHA, WV
CHA, NC
CHA, TN-GA
COL, CO
COL, SC
COL, GA-AL
COR, TX
DAY, IA-IL
DES, IA
DDL, MN-WI
ELP, TX
ERI, PA
BUG, OR
EVA, IN-KY
FAY, NC
FLI, MI
FOR, IN
FRE, CA
GRE, SC
HAM, OH
EAR, PA
HUN, WV-KY.OH
HUN, AL
JAC, MS
JOH, PA
KAL, MI
KNO, TN
LAN, PA
LAN, MI
LAS, NV
LAW, MA-NH
LITT, AK
LOR, OH
SDCZ
..
572.0
0.0
2.3
3.3
-2.9
75.8
164.0
93.4
155.0
287.0
3.8
76.6
119.0
96.6
-.
7.7
20.5
5.9
34.6
28.1
31.4
--
131.0
41.0
104.0
4.6
0.0
67.2
--
30.2
22.2
6.7
91.4
38.8
2.7
2.4
45.2
46.9
62.6
118.0
--
162.0
12.3
17.8
DDCZ
18.500
0.000
0.076
0.107
-0.095
2.459
5.342
3.030
5.041
9.332
0.122
2.485
3.859
3.134
--
0.251
0.665
0.192
1.123
0.912
1.020
--
3.924
1.332
3.389
0.150
0.0
2.180
--
0.979
0.722
0.218
2.965
1.260
0.086
0.081
1.466
1.523
2.032
3.839
--
5.270
0.399
5.771
SDCP
29.6
46.2
39.7
26.5
44.8
35.9
38.5
49.1
47.9
70.3
59.0
32.1
33.6
65.1
43.9
24.8
34.8
26.5
112.0
60.3
52.0
43.9
24.3
37.0
33.2
34.7
19.1
76.4
46.5
46.7
37.6
32.3
62.7
34.8
31.1
28.7
32.3
32.3
55.3
45.0
61.0
29.7
42.3
46.2
37.9
DDCP
1.014
1.580
1.357
0.905
1.532
1.228
1.317
1.678
1.638
2.404
2.016
1.096
1.148
2.224
1.501
0.849
1.189
0.905
3.831
2.060
1.778
1.501
0.830
1.264
1.135
1.186
0.653
2.609
1.591
1.596
1.287
1.106
2.143
1.190
1.065
0.980
1.104
1.106
1.889
1.539
2.085
1.015
1.444
1.579
1.297
105
-------
TABLE V-3 (Concluded)
Medium SMSA's
111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126.
127.
128.
129.
130.
131.
132.
133.
134.
135.
136.
137.
138.
139.
140.
141.
142.
143.
144.
145.
146.
147.
148.
Total
LOW -MA
MAC, GA
MAD, WI
MOB, AL
MON, AL
NEW, CN
NEW, CN
NEW, VA
ORL, FL
OXN, CA
PEN, FL
PEO, IL
RAL, NC
REA, PA
ROC , IL
SAG, MI
SAL, CA
SAN, CA
SAN, CA
SCR, PA
SHR, LA
SOU, IN
SPO, WA
STA, CN
STO, CA
TAC, -WA
TRE, NJ
TUC, AZ
TUL, OK
UTI, NY
VAL, CA
WAT, CN
WES, FL
WIG, KS
WIL, PA
WIL, DE-NJ-MD
WOR, MA
YOR, PA
SDCZ
151.0
2.8
18.5
34.7
2.9
66.3
42.8
5.3
18.5
22.5
34.6
240.0
10.9
90.6
56.8
56.3
5.0
11.6
14.9
25.8
38.5
154.0
--
84.6
--
35.0
18.6
--
473.0
97.7
11.0
8.7
3.8
25.4
60.3
96.1
201.0
9.7
5 , 005 . 5
DDCZ
4.924
0.089
0.602
1.126
0.092
2.152
1.388
0.174
0.601
0.732
1.122
7.782
0.355
2.939
1.843
1.828
0.164
0.377
0.485
0.838
1.249
4.993
--
2.744
--
1.137
0.604
--
15.300
3.169
0.357
0.282
0.122
0.824
1.956
3.117
6.537
0.313
168.309
SDCP
31.2
25.5
49.1
42.8
24.7
64.4
32.3
39.8
62.4
57.2
30.3
61.4
30.6
48.3
48.3
32.1
36.9
44.8
38.9
31.1
38.3
46.4
38.6
63.0
37.8
63.7
48.3
32.9
97.4
50.7
39.3
31.8
79.1
62.5
46.0
78.0
56.1
49.6
3,777.8
DDCP
1.067
0.871
1.678
1.464
0.845
2.200
1.106
1.359
2.132
1.954
1.037
2.099
1.046
1.650
1.651
1.099
1.261
1.531
1.329
1.065
1.310
1.587
1.318
2.153
1.292
2.176
1.651
1.126
3.328
1.733
1.345
1.087
2.702
2.136
1.572
2.663
1.918
1.695
127.996
Note: — individual figure may not add to totals due to rounding.
106
-------
TABLE V-4. ECONOMIC DAMAGE FUNCTIONS ON MATERIALS^/
SDCZ -23,328.4 + 43.1 ME + 943.3 S02 + 148.1 TSP - 235.0 SUN
(19,929) (3.4)* (171.6)* (356.0)* (1820.4)
(V-20)
+ 2,679.3 RHM + 21.9 YP R2 = 0.64
(1,750.2) (18.9)
DDCZ = 7,562.2 + 1.4 ME + 30.5 S02 + 47.9 TSP - 76.2 SUN
(6,460.4) (0.1)* (5.5)* (11.5)* (59.0)
+ 86.8 RHM + 712.6 YP R = 0.63
(56.7) (615.5)
SDCP - -141,199.7 + 577.2 HU + 15.2 YP + 911.3 RHM + 69.1 S02
(259.861.3) (3.4)* (2.6)* (235.3)* (23.2)*
(V-22)
+ 305.3 SUN R = 0.995
(245.9)
DDCP = -4,820.1 + 19.7 HU + 0.5 YP + 31.1 RHM + 2.3 S02 + 10.4 SUN
(887.2)* (0.1)* (0.08)* (8.0)* (0.8)* (8.4)
9 (V-23)
RZ = 0.995
a/ The values below the coefficients are standard errors with * to indicate
that the coefficients are significant at the 1 percent level. All coefficients
and standard errors are reduced by a factor of 10 .
107
-------
of material. The coefficients of ME, SC>2, and TSP are significant at the 1 per-
cent Level in equations (V-20) and (V-21), whereas the coefficients of HU, YP,
RH and S02 are significant at the 1 percent level in equations (V-22) and (V-23).
The economic damage functions on zinc and paint summarized in Table V-4 were
estimated simply for national decisionmaking. They offer some short-cut tech-
niques for rough computations and can be used in determining the marginal as
well as average damages (benefits) resulting from a pollution control strategy.
To serve as an illustration, an example involving the computation of the par-
tial elasticity of SDCZ with respect to SC>2, and the associated marginal
benefit due to a reduction in SC>2, is presented. Suppose the federal government
is contemplating the implementation of a pollution abatement program which is
expected to reduce the average S02 level in the urban areas by, say, 10 percent.
A question arises as to what the dollar benefit will be for the reduction in
soiling damage of zinc as a result of the pollution control program. Since the
average, gross soiling damage due to S02 is $102 million and the average SC>2
level is 55.73 [ig/m3 among the 148 SMSA's, the partial elasticity of the damage
cost with respect to S02 is obtained:
E __ = 0.9433 x (55.73/102) = 0.52.
c,SO
Thus, it is in general expected that a 10 percent decrease in the SC>2 con-
centration level will result in a 5.2 percent reduction in the soiling damage cost
cost of zinc. Since the mean value of the regional damage cost for the 148 SMSA's
included in this study is $102 million, when the 862 level decreases from 55.73
(J,g/m3 to 50.16 |ig/m3, it is also expected that on the average the damage cost
will be. reduced by the amount of $102 million x 5.27o = $5.73 million. Likewise,
the elasticities for the other dependent variables with respect to S02 and other
explanatory variables can be analogously computed and interpreted.
A SUMMARY OF MATERIAL PHYSICAL DAMAGE FUNCTIONS
After a careful review of the literature, some recent publications as well
as many unpublished manuscripts were identified as major sources providing use-
ful information for our future on material damage*. The damage resulting from
air pollution includes corrosion of metals, deterioration of paints and materi-
als, fading of fabric dyes, etc. It is also worth noting that the physical damage
functions expressed in equations (V-30) and (V-32) below were utilized in deriv-
ing the economic damage estimates in the preceding section.
Major Pollution and Material Interactions
Fred H. Haynie (1974), based on an MRI study, summarized in Table V-5 the
relative extent to which effects of pollutants on materials are known. Haynie
considered an effect (1) "well established" when evidence was corroborated by
several high quality references; (2) "some evidence" in the presence of one or
two references; and (3) "suspected" when interactions are based on behavior of
material at pollutant level much higher than the ambient level.
108
-------
TABLE V-5. MAJOR POLLUTANT - MATERIAL INTERACTIONS
Material
Pollutants
Metals Paints Textile Elastomers Plastics
Sulfur dioxide
Particulate
Ozone
Nitrogen dioxide
Hydrocarbons
1
2
2
2
--
2
1
2
3
3
1
2
1
1
__
_ _
1
3
--
3
—
3
3
—
Metals--
Steel, S02_ and Photochemical Oxidant--Haynie and Upham (1971) conducted
an in-house field study of the effect of atmospheric pollutants on steel cor-
rosion, Good dose-response relationships were estimated as follows:
Carbon steel y = A.013
0.00161 SO,
0.7512 - 0.00582 OX
(4.768t)
(V-24)
Copper bearing steel
y = 8.341
0.00171 SO,
0.8151 - 0.00642 OX
(4.35U)
(V-25)
Weathering steel
y = 8.876
0.0045 SO,
0.6695 - 0.00544 OX
(3.389t)
(V-26)
is the depth of corrosion in microns (m);
up t is time.
S02 and OX are expressed in
Enameling Steel, Sulfate in Suspended Particulate--Havnie and Upham (1974)
in a companion paper examined correlation between erosion behavior of steel and
gaseous S02, total suspended particulate, sulfate in suspended particulate, and
nitrate in suspended particulate. Multiple linear regression and nonlinear curve
fitting techniques were used to analyze the relationship between corrosion of
enameling steel and the atmospheric data. The resulting best empirical function
has the form:
109
-------
Corrosion = 183.5 x/TEXP [0.06421 Sul - 163.21/RH ] (V-27)
where
corrosion = depth of corrosion, p-m
t = time, years
Sul = average level of sulfate in suspended particulate
(p.g/m ) or average level of sulfur dioxide (|J,g/m )
RH = average relative humidity
The statistical analysis shows that differences in average temperature, average
total suspended particulate, and average nitrate in suspended particulate exert
insignificant effects on steel's corrosion behavior. Covariance between sulfate
and S02 and the relative accuracies of the two sets of data make it impossible
to statistically identify the causative agent. Laboratory experiments suggest
that S02 is the major cause.
Galvanized Steel and S0?--In an unpublished paper, Spence, Upham, and
Haynie (1975) derived a dose-response relation for galvanized steel, S02, N02
and Ozone in controlled environmental chambers. Of the three pollutants, S02
was shown to be a major factor in determining the corrosion rate of galvanized
steel. The corrosion of the galvanized panels fits the relationship:
•u p /RT
Corr = (d. SOo + e ) ft~ (V-28)
0 ^ v w
where Corr = corrosion in micrometer ((im)
dQ = 0.0187, b = 41.85, E = 23,240
t = time of wetness
w
Weathering Steel, S02, Relative Humidity and Temperature—A similar cham-
ber study for weathering steel was conducted by Spence, Haynie, and Upham
(1975b) who developed a corrosion function that accounts for 99 percent of the
variability for the clean air and pollutant experimental data.
r (55 44 _ 31.150 "I _.
Corr = 5.64 \/ SO. + e * RT J V w (V-28)
where
S02 is
R is 1.9872 cal/g - mole °K
110
-------
T is the goemetric mean temperature of the specimen when wet
in °K
t is time of wetness in years
w
This chamber has shown that S02 is a major factor determining the corrosion
of weathering steel.
Zinc and S02—Haynie and Upham (1970) have shown that the amount of S02
in the air is the major factor in determining the rate of corrosion of zinc.
They found that little zinc corrosion would occur in an environment in which
SC>2 was not present.
The dose-response relationship between zinc corrosion rate and SC>2 was
estimated as follows:
Y= 0.00104 (RH - 49.4) S02 - 0.00664 (RH - 76.5) (V-30)
or alternatively, Y = 0.001028 (RH - 48.8) S02
where
Y = zinc corrosion rate, |j,m/year
RH = average relative humidity, percentage
SO = average sulfur dioxide concentration, \},g/m.
Pitting of Galvanized Steel--J. W. Spence and F. H. Haynie (1974) exposed
specimens of galvanized steel to polluted and clean air in controlled environ-
mental chambers. They found that corrosion of the zinc films was essentially
a linear function of time for polluted and clean air condition. Uniform corro-
sion of the zinc occurred in the polluted exposures, whereas pitting corrosion
of the zinc was observed ^.n the clean air exposures.
The pitting corrosion, expressed as a uniform thickness loss, fits the re-
lationship:
Corr = t exp |30.53 - (16,020/RT )1 (V-31)
w [_ m J
where
Corr = amount of pitting corrosion (|im)
111
-------
t = time of wetness, years
w
T = geometric mean specimen temperature when wet, °K
m
Catastrophic Failure of Metals--Air pollution has contributed to the cata-
strophic failure of metal structure. John Gerhard and Fred H. Haynie (1974) es-
timated the loss of metal failure to be between $50 million and $100 million
annually for the United States. The dose-response relationship between air pol-
lution and the occurrence of catastrophic failure of metals has not been estab-
lished in the literature.
Three types of catastrophic failure of metals that are associated with en-
vironmental corrosion were identified: (1) stress-corrosion cracking, (2) cor-
rosion fatigue, and (3) hydrogen embrittlement. Notable examples of problem
areas involve failure of essential structures, aircraft and aerospace components,
and communication equipment.
In the case of a 40 percent reduction in pollution, the per capita cost
would drop from the present level of $7.10 to $4.36 by 1980. Assuming a 60 per-
cent reduction in pollution, the per capita cost will drop from $7.10 to $2.20
in 1980.
Air Pollution Corrosion Costs on Metals--Fink, Buttner and Boyd (1971)
examined air pollution corrosion costs on metals in the United States from both
technical and economic viewpoints. They calculated corrosion costs for the nine
major categories which were most sensitive to and most damaged by air pollution
corrosion. The grand total cost was estimated at $1.45 billion, or approximately
$7.10 per person per year.
Fink, Buttner and Boyd considered SC>2 as the most important pollutant from
a corrosion point of view. They projected the damage costs of metals due to SO
under a variety of SO concentration levels for 1980. The annual loss would in-
crease from the present $1.45 billion to $2.1 billion by 1980 if there is a 55
percent increase in pollution. A 10 percent increase in SO level would result
in an increase in annual loss by $0.3 billion to $1.73 billion in 1980.
Paint
Paint technology and Air Pollution--
J. W. Spence and F. H. Haynie (1972a) in their recent survey on paint tech-
nology and air pollution, identified the characteristics of pollutant attacks
on exterior paints, and estimated the annual cost of air pollutant damage to
such paints. They assessed the chemical damage of air pollutants for four classes
of exterior paints: (1) household, (2) automotive refinishing, (3) coil coating,
and (4) maintenance. The cost at the consumer level is more than $0.7 billion
annually. Household paints sustain damage representing 75 percent of the total
annual dollar loss.
112
-------
Exterior Paint, SO?, and Particulates--
Spence and Haynie (1972b) investigated the deterioration of exterior paints
due to 862 and particulate matter, and the associated potential economic loss
to manufacturers and consumers. A breakdown of the damage loss of exterior
paints is summarized as follows:
Loss at Consumer
Level (million $)
Coil coating 16
Automotive refinishing 88
Maintenance 60
Household 540
Total 704
Oil Base House Paint, Acrylic Latex House Paint, Vinyl Coil Coating and Acrylic
Coil Coating--
A chamber study of the effects of gaseous pollutants on paints was carried
out by J. Spence, F. Haynie, and J. Upham (1975c). Regression analysis showed
that S02 concentration and relative humidity accounted for 61 percent of the
variability in the case of oil base house paint which experienced the highest
erosion rates. Vinyl and acrylic coil coatings experienced very low erosion
rates.
The multiple linear regression of oil base house paint on SO concentration
and relative humidity gave the relationship:
erosion rate = 14.323 + 0.01506 SO + 0.3884 RH (V-32)
where erosion rate is (j,m/year
3
S02 is
RH is percent relative humidity
In the case of vinyl and acrylic coil coating, the regression equations
are respectively given by:
113
-------
erosion rate = 2.511 + 1.597 x 10"5 RH x SC>2 (V-33)
and
erosion rate = 0.159 + 0.000714 03 (V-34)
where 0 is in (J,g/m .
Fabric Fading
Selected Drapery Fabrics and N02--
Upham, Haynie and Spence (1975) have studied and assessed the fading charac-
teristics of three drapery fabrics after exposure to air pollutants and other
environmental factors in the chamber. The experimental results indicated that
N02 is a major factor in determining the fading rate for one of the fabrics,
a plum-colored cotton duck material. The other two fabrics did not fade signifi-
cantly in the presence of the air pollutants.
The dose-response relationship for the plum fabric was estimated as follows:
A E = 30
1 - EXP(-(257 + 3.38 x IQ-^M x N02>t)
(V-35)
where
A E = amount of fading, fading units
M = amount of moisture, (J,g/m , at 25 C and one atmosphere
N02 =
t = exposure time, year
R2 = 0.70
Dyed Fabrics, N02, Ozone, S02 and Nitric Oxide--
Beloin (1973) assessed 20 dye-fabric combinations which were exposed to
two levels each of N02, ozone, S02 and nitric oxide, at four combinations of
temperature and humidity for a period of 12 weeks. The study showed that NC^,
ozone and, to a lesser extent, S02, can cause appreciable dye fading, and that
nitric oxide has little or no effect. In an earlier article, Beloin (1972)
evaluated the color fastness of 67 dye-fabric combinations exposed to atmos-
pheric gases. Multiple regression analysis of pollutant concentrations indicated
that SC>2, N02 and ozone are major factors determining fabric fading.
Rubber, Ozone, Nitrogen
Stickney, Mueller and Spence (1971) estimated the yearly cost of air pol-
lution damage to rubber industry. The total estimated cost at the consumer level
114
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is at least $500 million yearly. Of this, $398 million can be accounted for in
detail. Mueller and Stickney (1970) identified ozone as the only major pollutant
to shorten the life of rubber products. S02 is not known to have harmful effects
on rubber products.
Soiling and Suspended Particulates
Beloin and Haynie (1975) studied the soiling of building materials. Six
building materials were exposed at five sites in Birmingham, Alabama, to deter-
mine the rate of soiling by different levels of suspended particulate. Excellent
dose-response relationships were obtained for the white-surfaced painted cedar
siding and asphalt shingles. Similar regressions for brick can account for 34
to 50 percent of variability. Poor correlations were obtained for concrete,
limestone, and window glass. The regression results are summarized in Table V-6.
Material Damage
Material Damage From SO 2--
An overall assessment of material damage from S02 was made by Gillette and
Upham (1973) and is summarized as follows:
Me ta Is-- Cor ro si on of metals by acids derived from airborne S02 is most im-
portant. Zinc and steel are particularly vulnerable to attack by atmospheric
S02.
Cotton Fabrics--Not significant since most cotton fabrics are not exposed
continuously to the external environment.
Synthetic Fabrics and Blends--Not significant with the exception of nylon
hosiery.
Dye Fading- -Unimportant at present SO concentrations.
Paper and Leather Products- -Strongly influenced by S02» tend to disintegrate
or discolor after prolonged exposure to relatively high levels of
Plastics- -Little is known about the effects of S02 on plastics.
Concrete, Marble, Roofing Slate, Mortar and Other Limestone—Subject to
attack from acids derived from S02- Most of the concrete and limestone used in
the construction of highways and buildings in the United States is not seriously
affected by the present level of atmospheric S02«
In assessing the damage loss resulting from S02> the following observations
are noteworthy:
- Most materials are not substantilly damaged when pollution levels are
less than 250 |j,g/m3.
115
-------
TABLE V-6. RESULTS OF REGRESSION ANALYSIS FOR SOILING OF BUILDING MATERIALS
AS A FUNCTION OF SUSPENDED PARTICULATE DOSE
_. Material Independent variable
Oil Base Paint JsP(pg/m3) x t (months)
V
Tint Base Paint Ditto
Sheltered Acrylic "
Emulsion Paint
Acrylic Emulsion "
Faint
Shingles SP(ug/m ) x t (years)
10
Shingles -JsP(ug/m ) x t (months)
Concrete Y Ditto
Coated Limestone "
Uncoated Limestone "
Coated Red Brick "
Uncoated Red Brick "
Coated Yellow Brick "
Uncoated Yellow Brick "
Glass "
N Number of data sets (dependent upon the
A Intercept of linear regression
B Slope of linear regression
N
400
400
400
720
48
48
160
80
80
80
80
80
80
45 0
number
A
89.43
86.13
91.54
90.79
41.69
43.50
41.45
44.57
46.99
12.95
14.88
45.05
43.21
.2806
B
-0.2768
-0.2618
-0.
-0.
-0.
-0.
-0.
+0.
-0.
-0.
-0.
-0.
-0.
+0.
593
4131
331
199
0458
0779
0503
0296
0374
1133
1133
0314
of controlled
s2,
0.0641
0.0571
0.1156
0.0497
0.1895
0.5771
0.1338
0.2464
0.1500
0.0223
0.0331
0.5337
0.2740
0.008077
variables
S2B
0.000069
0.000061
0.000123
0.000026
0.000312
0.000258
0.000080
0.000164
0.000089
0.000013
0.000020
0.000317
0.000168
0.000007
S2
E
7.6510
6.8265
13.8143
8.3791
3.8685
7.6992
7.5011
6.9046
4.2035
0.6255
0.9274
14.9533
7.6773
0.6851
in the factorial
0.
0.
0-.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
P2
745
738
880
902
884
769
143
347
266
459
477
342
503
340
Remarks
Excludes all data
beyond 12 months
Ditto
11
Excludes 2 24 month
Tarrant readings
Ditto
Haze readings include
ing 3 periods for
right panes prior
12 months
to
experiment)
Estimated variance of intercept
Estimated variance of slope
Residual variance (error)
Correlation index (fraction of variability accounted for by regression)
Suspended particulata
-------
- Given the present 862 levels, damage to most materials other than certain
ferrous and nonferrous metals is probably not significant.
- The only important materials adversely affected by 862 are iron, steel
and zinc products.
- There are two important factors determining the corrosion rate for gal-
vanized products: (1) relative humidity, and (2) S02 level.
- The relationship between SO and damage to paint is not as clear as it
is between SO and corrosion to galvanized products.
- The threshold or minimum level of SO required to produce an economic
loss ^ (10 |j,g/m3). For lack of better data, it is reasonable to assume a thresh-
old level of 20 p,g/m before any loss is achieved.
Finally, to recapitulate, the various physical dose-response relationships
for metals, paints, fabrics, and building materials are summarized in Table V-
7.
117
-------
TABLE V-7. PHYSICAL DAMAGE FUNCTIONS FOR MATERIALS
Material
- Response Relationships
1. Metals
A. Steel - Carbon steel
Weathering steel B
Enameling steel A
Galvanized steel
B. Zinc
2. Paints
A. Oil base house paint
Y = 9 013
Copper-bearing steel Y = 8 341
Weathering steel A Y = 8 876
0.00161 S02
. 00171
0.0045 S02
0.7512 - 0.00582 OX
(4.768t)
0.8151 - 0.00642 OX
(4.351t)
0.6695 - 0.00544 OX
corr =|5.64 \|S02 + e
(3.389t)
(55.44 - 31,150/RT)
'Enameling steel B corr = 325
[0.06421 Sul-163.21/RH]
corr = 183.5 >jte
[0.00275 S02 - (163.2/RH)]
corr =
41.85 - 23,240/RT
0.0187 S02 + e
Y* = 0.001028 (RH - 48.8) S02
erosion rate = 14.323 + 0.01506 S02 + 0.3884 RH
0.91
0.91
0.91
0.91
0.9
0.9:
0.6
-------
TABLE V-7 (Concluded
-5
B. Vinyl coil coating
C. Acrylic coil coating
3. Fabrics - Plain fabric
4. Soiling of Building Material
A. Oil base paint
B. Tint base paint
C. Sheltered acrylic emulsion
paint
D. Acrylic emulsion paint
E. Shingles
F. Coated yellow brick
erosion rate = 2.511 + 1.597 X10 RH x S02
erosion rate = 0.159 + 0.000714 03
-(2.57 + 3.38 x 10" MX N02)t
AE = 30
L-e
Reflectance = 89.43 - 0.2768 \JSP x t*
Reflectance = 86.13 - 0.2618 ^SP x t*
Reflectance = 91.54- - 0.593 \JSP x t*
Reflectance = 90.79 - 0.4131 NJSP x t*
Reflectance = 43.50 - 0.199 XJSP x t*
Reflectance = 43.21 - 0.1133 \(SP x t*
0.34
0.70
0.74
R2 = 0.738
R2 = 0.88
R2 = 0.902
R
0.769
R2 = 0.503
where Y = depth of corrosion in microns (u)
13
S02 =ug/nr
OX = ug/m3
t = time, years
corr = depth of corrosion in micrometer (um)
sul = average level of sulfate in suspended particulate (ug/m ) t* = time, months
RH = average relative humidity, percent
tw = time of wetness
R = 1.9872 cal/g - mole °K
T = geometric mean temperature of the specimen when wet in °K
Y* = Zinc corrosion rate,/urn/year
Erosion rate = urn/year
03 = Ozone, ug/m3
/\E = amount of fading, fading units
A
M = amount of moisture, ug/m at 25°c and one
atmosphere
N02 = ug/m3
Reflectance = a measure of soiling, percent
SP = suspended particulate (ug/m3)
-------
SECTION VL
VEGETATION AND AIR POLLUTION
PROBLEMS AND OBJECTIVES
Air pollution is a fact of contemporary life. It is not only deleterious
to human health, material, and household and commercial establishments as dis-
cussed in the preceding sections, but it is also recognized as a causal agent
of damage to vegetation. Urban expansion and industrialization have resulted
in deteriorated air quality in many major cities in the United States. Though
social concern with the problem of contaminated air can be dated back to as
early as the 13th century, the biological effects of degraded air are not thor-
oughly understood even now. Some progress has been made, however, in recent
years. According to Naegele (1973), laboratory and chamber studies of individual
plants under somewhat controlled environments have contributed to the awareness
of the complexity of plant response to toxicants. Acute and even chronic responses
of plants to deteriorated air are being studied and documented.
There are three principal air pollutants of major interest to agricultural
plants; namely, sulfur dioxide, fluorine compounds and smog. Regarding smog,
there are two distinct types, with numerous intermediate grades: the London
type, which is a mixture of coal smoke, fog and sulfur dioxide, and the Los
Angeles type which is a mixture of ozone and peroxidized organic compounds.!.'
Studies on the effect of sulfur dioxide (S02) on vegetation are voluminous.
Stoeckhard (1871) reported SO injury to plants as early as 1871. Since then
more than 700 articles have been published regarding the effects of SO upon
vegetation. The documents point to a great variation in plant responses to the
pollutant. This variation in plant responses can be accounted for by such fac-
tors as genetic composition, stage of development, climatic factors, interactions
between pollutants, the time of day of exposure, and soil moisture.
The effects of air pollution are customarily classified into two categories:
(1) visible effects, which are identifiable pigmented foliar patterns as a re-
sult of major physiological disturbances to plant cells, and (2) subtle effects,
which are not visibly identifiable, and may be identified when physiological
change occurs in the plant. The disturbance of biochemical processes at the
molecular level is the cause of both the visible and subtle effects. Within the
category of visible effects, acute and chronic injury can be identified. Acute
injury is a severe injury as a result of a short-term, but high concentration
of the pollutant. Chronic injury is light to severe injury; it develops from
exposure to long-term, low pollutant concentration.
_!/ For a detailed discussion on the types of air pollutants causing damage to
vegetation, see Thomas (1961).
120
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The effects of oxidant on vegetation have been studied since the early part
of this century. Oxidant or smog type symptoms were identified with the reac-
tion product of ozone and reactive hydrocarbons. The symptoms were also associ-
ated with a new toxicant, peroxiyacetyl nitrate (PAN), which was generated ex-
perimentally by photochemical reaction of a mixture of nitrogen dioxide and re-
active hydrocarbon (Stephen et al., 1960). Nitrogen dioxide is also a phototoxi-
cant at high concentration levels. Benedict and Breen (1955) found tissue col-
lapse with nitrogen dioxide concentration above 20 ppm.
Generally speaking, agricultural plants are adversely affected by air pol-
lution vis-a-vis reductions in the quantity of output and/or degradation of the
quality of the product. With the information on the determinants of the biologi-
cal response of a plant to contaminated air, a reasonable, physical dose-response
relationship could be constructed. In translating the physical damage function
into a monetary damage function, the following factors should be considered:
time and growing season, market value and price of the plant, the possibility
of growing a different crop and the opportunity cost of the site for growing
the plant.
Waddell (1974) identified two general approaches to assess the economic
loss of plants due to air pollution-}J One approach is to survey the damage loss
on a statewide basis. Included in this category are the studies by Middleton
and Paulus (1956), Weidensaul and Lacasse (1970), Feliciano (1972), Pell (1973),
Naegele et al. (1972), and Millecan (1971).
Another approach is to construct predictive models by relating data on crop
losses to crop values, pollution emission and meteorological parameters. The
landmark study by Benedict and his associates (1971, 1973) at Stanford Research
Institute (SRI) is probably the only study undertaken so far which provided some
essential background material for further investigation. The SRI study estimated
plant losses caused by air pollution in those U.S. counties where major pollutants
(oxidants, SO , and fluorides) are expected to produce adverse effects on plants.
The major contribution of the SRI study is the provision of a wealth of
data for the development of economic damage functions or of more sophisticated
predictive models when better dose-response data are available. However, the
study also contains the following weaknesses: (1) the damage factors were at
best educated guesses and are subject to criticism; (2) yearly variations in
climate and meteorology were not allowed for; (3) ornamentals were undervalued
since only replacement costs were used as a proxy for aesthetic values; and (4)
the subtle effects of air pollution which causes no visible injury were ignored.
However, some subtle injuries were indeed included, contrary to most critics.
The amount was a rough guess and, with the exception of citrus and grapes, could
II See Waddell (1974) for a detailed discussion.
121
-------
have been much larger or much smaller depending on the plant species. Latest
information shows that such losses to forests and perhaps cotton in California
are much greater than previously realized.i'
A review of some previous damage estimates at both the national and state
levels would give us a rough idea as to how serious the damage loss is because
of air pollution. Benedict and his associates estimated the national total damage
of visible injury to vegetation to be $132 million each year. Lacasse-Weidensaul
estimated the amount of direct losses uncovered in the survey to be more than
$3.5 million in Pennsylvania in 1969. Indirect losses were estimated to be $8
million. Feliciano reported the losses to agriculture in New Jersey due to air
pollution were about $1.19 million in 1971. Naegele estimated direct economic
losses for the 1971-72 season at $1.1 million. Finally, Millecan estimated a
monetary loss of $26 million in crops in California in 1970.
In summary, the problems in the field of vegetation and air pollution are
similar to those delineated previously in other categories, i.e., the lack of
reliable scientific damage functions and the presence of a wide range of damage
estimates. The primary objective of this section is to review the state of the
art and derive, through existing documentation and data, an integrated economic
damage function of air pollution on vegetation for purpose of prediction. The
remaining part of this section contains the following subsections: Dose-Response
Relationships, Economic Damage Functions, and Concluding Remarks.
DOSE-RESPONSE RELATIONSHIPS
Some crude dose-response relationships for various types of crops have been
derived. O'Gara (1972) estimated the first such function for alfalfa under condi-
tions of maximum sensitivity, as follows:
(C-0.33O = 0.92 (VI-1)
where C is the concentration level to be estimated with respect to time t
in hours. The constant 0.33 ppm represents a concentration that presumably can
be endured indefinitely, i.e., the threshold level, without prolonged fumigation.
That is to say that C = 1.25 ppm for t = 1.0.
The O'Gara equation was generalized by Thomas and Hill (1935) for any degree
of leaf destruction and any degree of susceptibility. The generalized equation
can be specified as:
t(c-a) = b (VI-2)
JY Personal correspondence with Dr. H. M. Benedict.
122
-------
where t = time, hours, c = pollutant concentrations above a, a = threshold concen-
tration below which no injury occurs, and b = constant.
W^th maximum susceptibility, the generalized equations were shown as follows:
t(c-0.24) = 0.94 traces of leaf destruction
t(c-1.4) =2.1 50 percent leaf destruction
t(c-2.6) = 3.2 100 percent leaf destruction
Zahn (1963) developed an equation which modified the O'Gara equation and
provides better fit over a longer period of time. The equation is shown as fol-
lows:
t = 1 + 0.5C (VI-3)
C(C-a)
The threshold level a was given as 0.1 for alfalfa; b is the dimensional
resistance factor which incorporates the influence of environmental conditions,
An alternative experimental formula was suggested by Guderian, Van Haut
(1960) and Stratmann (1963). The formula gives best fit to their observations
for either short- or long-term exposures.
t = Ke-b(C - a) (VI-4)
where K = vegetation life time, in hours, t; a, b, and C are the same as
in (VI-3). These parameters may vary with species, environmental conditions,
and degree of injury.!.'
Although several physical dose-response relationships have been determined,
economic damage functions for vegetations are largely nonexistent. The economic
damage functions described in the following section employed input data on vege-
tation losses obtained from the Benedict study (1971,1973).
I/ The dose-response equations developed by Zahn, and Guderian, Van Haut and
Stratmann were summarized in Environmental Protection Agency, Effect of
Sulfur Oxides in the Atmosphere on Vegetation, op cit. The references were
contained therein.
123
-------
Benedict et al. derives crop Loss estimates by using the product of three
i Y- C _ T . (^ . -
factors, i.e
Crop Loss = crop value . crop sensitivity to the pollutant .
regional pollution potential (VI-5)
The regional pollution potential is a relative severity index of pollution,
estimated for each county selected in the Benedict study on the basis of emission
rates which are, in turn, derived from fuel consumption data. The relative sensi-
tivity of various plant species to the pollutants was determined from a litera-
ture review. Each crop or ornamental was classified as to whether the part of
the plant directly affected by the pollutants had high, medium or no economic
value.
Despite the fact that the ceteris paribus type of dose-response functions
has been developed and refined for certain types of vegetation, such functions
are still unavailable for a majority of vegetations even now. Furthermore, the
multivariate physical damage functions relating plant damage to several relevant
explanatory factors are yet to be developed. In the absence of reliable plant
dose-response functions, only rough estimates of economic damages for various
plants can be derived.
ECONOMIC DAMAGE FUNCTIONS
Of more relevance to policymakers at both the national and local levels,
however, are the monetary or economic damage functions which transform all as-
pects of dose-response relationships into one common unit of measurement, i.e.,
money. An attempt was made in this study to estimate such economic damage func-
tions which relate economic losses of a variety of crops to air pollution con-
centration levels and climatological variables.
The crops and agricultural products for which the economic damage functions
were estimated include corn grain, soybean, cotton, vegetable, other vegetable,
nursery, floral, forestry, field crop, fruit and nuts, total crops, total orna-
mentals, and all plants. The selection of the crops is based mainly on the eco-
nomic importances of these crops to the United States. However, it is understood
that different cultivating procedures and methods as well as relocation of crop
growing patterns in the United States will result in reduction in air pollution
damage to crops.
A stepwise linear multivariate regression model was developed for determin-
ing the economic damage functions for the selected crops and plants, as follows:
CROPLi= a + b CROPVi + c TEMB + d TEMA + e SUN + f RHM + g DTS
+ h S0 + j OXID (VT-6)
124
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where CROP denotes the economic loss (in $1,000) of the ith type of crops
by county from the Benedict study; CROPVi the output value (in $1,000) of the
ith type of crops by county; TEMB and TEMA stand for, respectively, the
number of days in a year with temperature below 33°F and above 89°F; SUN rep-
resents possible annual sunshine days; RHM, relative humidity; DTS number of
days with thunderstorm; S02 sulfur dioxide concentration or relative severity
index; and OXID the oxidant relative severity index.
Data used for the regression analysis were obtained from prior studies on
vegetation losses and the official publication on climatological data. As noted
earlier, the disaggregated data on the vegetation losses and the values of the
crops by county were obtained from the Benedict study. It should be pointed out
that only the aggregate data on vegetation by regions are presented in Benedict
et al. (1973). The crop data in the published form were integrated so as to pre-
serve some anonymity about certain single sources of pollution. The data for
CS02 and OXID were taken from Table 7 of Benedict et al. (1973), and the data
for TEMB, TEMA, SUN, RHM, DTS were secured from the U.S. Department of Commerce,
Local Climatological Data. Since the climatological data were not available for
all counties or cities, data for a nearby city were, hence, substituted for the
missing information for a number of counties. Finally, the annual mean level
for S02 was taken from the U.S. Environmental Protection Agency, Air Quality
Data - 1972 Annual Statistics.
Although estimates on crop values and crop losses are available for a total
of 679 counties in the United States, a thorough examination of the data reveals
that some counties have zero crop damage estimates and, hence, are not suitable
for inclusion in the study sample. In addition, both climatological and pollution
data are unavailable for a number of counties, but for which positive crop loss
estimates were available. Only 74 counties have both positive crop loss esti-
mates and data on climate and pollution levels. Thus they were selected for
this study for deriving the vegetation economic damage functions.
The dependent and explanatory variables used in the regression analysis
are described in Table VI-1. It should be noted that for sulfur dioxide two al-
ternative measures were available: the first measure is the relative severity
index constructed on the basis of pollutant emissions, concentration rate factor
and episode days by Benedict et a1.. (1971), i.e., CS02«.The second alternative
measure, S02, is the annual mean level for sulfur dioxide (fig/m^). Both measures
were used in the regression analysis, and the regression results are separately
reported in Tables VI-2 and VI-3. With regard to oxidants, the relative severity
index for oxidants was also provided by Benedict et al. (1971). However, data
on the annual mean level of oxidants are insufficient for this study. Thus, only
the former measure was used in the regression analysis. The regression results
containing oxidants are presented in Tables VI-2 and VI-4.
Some remarks on the regression results are in order. The values below the
regression coefficients are standard errors with * indicating that they are
significant at the 1 percent level. The signs of the regression coefficients
125
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TABLE VI-1. VARIABLES USED IN ECONOMIC DAMAGE FUNCTIONS
A. Dependent variables - vegetation loss (in $1,000)
CORNL
SOYBL
COTNL
OVGTL
NUSRL
FLORL
FRSTL
FCROL
FRNTL
VEGTL
TOCRL
TOORL
ALPLL
Corn grain loss.
Soybean loss.
Cotton loss.
Other vegetable loss.
Nursery loss.
Floral loss.
Forestry loss.
Field crops loss.
Fruit and nuts loss.
Vegetable loss.
Total crop loss.
Total ornamentals loss.
All plant loss.
B. Explanatory Variables
CROPV
TEMB
TEMA
SUN
RHM
DTS
so2
OXID
CS00
The value of the vegetation in question (in $1,000)
Number of days with temperature 32°F or below.
Number of days with temperature 90°F or above.
Possible annual sunshine days.
Relative humidity.
Number of days with thunderstorm.
Annual mean level for sulfur dioxide (ug/m ).
The relative piant-damaging oxidant pollution
potential index.
The relative plant-damaging sulfur dioxide
pollution potential index.
126
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TABLE VI-2. ECONOMIC DAMAGE FUNCTIONS ON VEGETATION WITH POLLUTION
RELATIVE SEVERITY INDICES (in $1,000)
Ni
-J
(1) CORNL
(2) SOYBL
(3) COTNL
(4) OVGTL
(5) NUSRL
(6) FLORL
(7) FRSTL
(8) FCROL
a
4.4
(32.1)
-2.2
(0.3)
-5.8
(6.9)
133.6
(58.5)*
-113.1
(300.2)
-616.4
(485.2)
-616.4
(485.2)
520.5
(222.3)*
CROPV
0.001
(0.001)
0
(0
0
(0
0
(0
0
(0
9
(0
0.
(0.
.003
.001)*
.0063
.0002)*
.006
.001)*
.11
.02)*
.10
.01)*
071
003)*
0.003
(0.002)
TEMB
0.02
(0.04)
0.01
(0.03)
0.0006
(0.0094)
-0.03
(0.08)
1.12
(0.42)*
0.93
(0.57)
1.93
(0.70)*
0.28
(0.32)
TEMA
0.09
(0.10)
0.04
(0.07)
-0.054
(0.028^
-0.44
(0.22)
-0.19
(1.03)
-0.30
(1.41)
-2.33
(1.63)
1.17
(0.82)
SUM
-0.13
(0.35)
-0.04
(0.28)
0.067
(0.077)
2.02
(0.63)*
0.35
(3.27)
-0.79
(4.37)
5.20
(5.34)
-5.61
(2.44)*
RHM
0.16
(0.34)
0.03
(0.07)
0.10
(0.65)
-2.95
(3.26)
-6.7
(4.4)
-1.88
(5.23)
-3.26
(2.44)
DTS CS09
-0
(0
0
(0
0
(0
0
(0
2
(1
3
(1
4,
(1.
-1
(0
.041 6.73
.10) (1.84)*
.05 3.58
.74) (1.49)*
.03 0.05
.02) (0.40)
.06
.21)
.34
.02)*
.03
.37)*
.77
.71)*
.20
• 77)
OXID
-0.85
(2.18)
0.24
(1.65)
0.57
(0.48)
97.73
(3.71)*
191.51
(33.09)*
356.3
(30.8)*
370.52
(30.71)*
54.07
(14.20)*
R?
0.28
0.26
0.98
0.96
0.90
0.93
0.96
0.35
-------
TABLE VI-2 (Concluded)
(9) FRNTL
(10) VEGTL
-90.9
(281.2)
-308.8
(168.4)
0.061
(0.006)*
0.011
(0.002)*
0.83
(0.43)*
-0.33
(0.23)
0.43
(1.00)
-1.66
(0.64)*
-2.28
(3.18)
4.92
(1.80)*
0.28
(3.09)
1.05
(1.85)
1
(0
0.
(0.
.74
.98)
08
60)
121
(18
136.
(10.
.3
.02)*
02
69)*
0.82
0.89
N5
oo
-------
VO
TABLE VI-3. ECONOMIC DAMAGE FUNCTIONS OF VEGETATION, WITH SULFUR DIOXIDE
ANNUAL MEAN LEVEL (In $1,000)£/
a
(1)
(2)
(3)
(4)
(5)
CORNL 10.
(31.
COTNL -9
(6
OVGTL -803
(181
NUSRL -780.
(350.
FRSTL-3,315
(785
.6
,0)
.4
•2)
.0
.1)*
,2
,6)*
.6
.0)*
CROPV
0.0013
(0.0007)
0.0063
(0.0002)*
0.009
(0.003)*
0.20
(0.01)*
0.065
(0.005)*
TEMB
0.
(0.
-0
(0
-0
(0
0.
(0.
015
045)
.0004
.0089)
.72
.27)*
77
51)
-3.52
(2.90)
TEMA
0.11
(0.09)
-0.05
(0.03)
-1.05
(0.77)
-0.98
(1.25)
-1.29
(1.17)
SUN
0.38
(0.32)
0.11
(0.66)
9.44
(1.95)*
7.79
(3.81)*
36.53
(8.57)*
RHM
0.21
(0.30)
0.07
(0.07)
6.82
(2.00)*
1.19
(3.86)
24.3
(8.63)*
DTS
0.02
(0.02)
-1.58
(0.67)*
1.87
(1-25)
-3.05
(2.82)
S02 (ug/m3)
0.0008
(0.0960)
0.0005
(0.0195)
0.27
(0.57)
0.40
(1.06)
2.30
(2.44)
R2
0.10
0.98
0.60
0.85
0.87
a/ For the 10 types of vegetations, the economic damage functions for CORNL COTNL OVGTL NUSRL and
FRSTC yields a positive S02, while the remaining regression equations contain a negative S02.
Only those five damage functions with a positive S02 are reported here.
-------
TABLE VI-4. ECONOMIC DAMAGE FUNCTIONS ON TOTAL CROPS, TOTAL
ORNAMENTALS AND ALL PLANTS (in $1,000)£/
CO
o
(1) TOCRL
(2) TOORL
(3) ALPLL
(4) TOCRL
(5) TOORL
(6) ALPLL
a
-375.7
(762.8)
-519.7
(965.5)
-2,251.3
(1,908.9)
-8,247.2
(2,302.4)
-5,927.4
(1,630.8)*
-14,350.5
(3,835.7)*
CROPV
0.011
(0.003)*
0.074
(0.004)
0.039
(0.006)*
0.032
(0.011)*
0.069
(0.008)*
0.05
(0.01)*
TEMB
-0.66
(1.07)
3.18
(1.38)
0.50
(2.73)
-9.07
(3.38)
-3.039
(2.41)
012.53
(5.69)*
TEMA
-6.81
(2.80)*
-1.61
(3.28)
-16.02
(6.76)*
-16.40
(9.03)
-4.03
(6.03)
-24.98
(14.52)
SUN
12.93
(8.45)
-0.91
(10.59)
18.02
(21.49)
93.30
(26.04)*
RHM
-8.12
(8.40)
-6.36
(10.63)
7.84
(20.83)
74.72
(25.07)*
61.38 46.27
(17.82)* (17.97)*
150.45
(43.62)*
128.37
(41.68)*
DTS OXID S09(ug/m3)
1.25 1,262.5
(2.77) (50.26)*
9.09 769.31
(3.42) (60.87)
9.34 1,892.46
(7.21) (121.58)*
-15.44 3.15
(8.76) (7.19)
-6.23 4.29
(5.89) (5.07)
-20.13 6.87
(15.06 (11.84)
R2
0.96
0.92
0.92
0.59
0.74
0.6
al Equations (1) through (3) are economic damage functions of total crops, total ornamentals and
all plants with OXID as the sole pollution variable, while equations (4) to (6) are similar
economic damage functions with S09 rather than OXID as the sole pollution variable.
-------
are mostly compatible with a. priori expectations. Specifically, the signs of
the pollution variables are mostly correct except in equation (1) of Table VI-
2 in which a negative sign for OXID appears. The negativity of OXID may be
substantially attributable to the multicolinearity between the two pollution
variables, CSC>2 and OXID (r = 0.31) because OXID changes sign from positive
to negative immediately when CSO was picked up by the regression equation,±.'
Utilizing pollution severity indexes in the regression, a wide range of
R^ is obtained, ranging from 0.25 for soybeans to 0.98 for cotton. However,
when the annual mean level of S02 was included as the sole pollution variable,
the independent variables explain a minimum of about 10 percent of the variations
in corn losses and a maximum of 98 percent of the variations in cotton losses.
The coefficients for the pollution severity indexes, i.e., CS02 which were
constructed on the basis of pollutant emissions, concentration rate factor and
episode days and OXID, are mostly significant at the 1 percent level whereas
no coefficients for S02 are significant even at the 10 percent level. This re-
sult lends support to the hypothesis that it may not be appropriate to use pollu-
tion measures mostly recorded in the central city to represent countywide pollu-
tion level. Furthermore, it should be noted that the variable DTS was intention-
ally excluded from equation (1) of Table VT-3 to preserve the positive sign of
S02-.2/
Using Equation (4), (5) and (6) in Table VI-4, economic damages of total
crops, total ornamentals and all plants were estimated for the 74 counties. The
results are presented in Table VI-5. The table reveals that while total crop
damages reached about $4 million in Los Angeles, Orange and San Diego counties
all in California, San Bernadino suffered the largest ornamental damages and
all plant damages in the order of $8.5 million and $10.6 million, respectively.
Intercorrelation among explanatory variables may not constitute a serious
problem if prediction is the primary objective, provided, of course, the inter-
correlation is expected to persist in the future. However, if multicolinearity
results in an incorrect sign of the key variable, S02, a statistical interpreta-
tion of the S02 coefficient would be meaningless, and the exclusion of DTS is,
hence, warranted.
I/ It should be noted that RHM was intentionally excluded from equation
2 in Table VI-2 because the inclusion of RHM resulted in a negative
OXID.
2/ When DTS is included, the regression equation, however, changes
to read as follows:
CORNL = 9.6 + 0.0013 CROW + 0.02 TEMB + 0.14 TEMA - 0.41 SUN
(31.3) (0.0007) (0.05) (0.12) (0.33)
+ 0.27 RHM + 0.05 DTS - 0.004 SO
(0.35) (0.10) (0.097)
R2 =0.10
131
-------
TABLE VI-5. ESTIMATED ECONOMIC DAMAGES OF TOTAL CROPS, TOTAL
ORNAMENTAL AND ALL PLANTS2/
(In $1,000)
(1)
Counties
Jefferson, Alabama
Maricopa, Arizona
Alameda, California
Los Angelesi California
Orange, California
San Bernadino, California
San Diego, California
Fairfield, Connecticut
New Haven, Connecticut
New Castle, Delaware
Santa Rosa, Florida
Chatham, Georgia
Fulton Georgia
Honolulu, Hawaii
Cook, Illinois
Lake, Indiana
Marion, Indiana
St. Joseph, Indiana
Vanderburgh, Indiana
Polk, Iowa
Se dgwl ck , Kan sa s
Shawnee, Kansas
Wyandotte, Kansas
Boone , Kentucky
McCracken, Kentucky
Cumberland, Maine
Anne Arundel, Maryland
Bal timore, Maryland
Harford, Maryland
Howard, Maryland
Montgomery, Maryland
Prince Georges, Maryland
Berkshire, Massachusetts
Bristol, Massachusetts
Middlesex, Massachusetts
Worcester, Massachusetts
St. Louis, Missouri
Douglas, Nebraska
Lancaster, Nebraska
Rockinghara, New Hampshire
Mercer, New Jersey
•Bernalillo, New Mexico
Albany, New York
Erie, New York
Monroe, New York
Niagara, New York
Oneida, New York
Forsyth, North Carolina
Clark, Ohio
Cuyahoga, Ohio
Franklin, Ohio
Hamilton, Ohio
Jefferson, Ohio
Mahoning, Ohio
Mon tgomery , Ohio
Stark, Ohio
Sunmit, Ohio
Mul tnomah, Oregon
Indiana, Pennsylvania
Washington, Rhode Island
Greenville, South Carolina
Hamilton, Tennessee
Shelby, Tennessee
Tom Green, Texas
Nanseraond, Virginia
York, Virginia
King, Washington
Pierce, Washington
Spokane, Washington
Dane , Wisconsin
Milwaukee, Wisconsin
Natrona, Wyoming
(2)
Estimated
Total
Crop b/
Damages"
..
1,947
3,708
4,591
4,330
3,780
4,008
703
847
136
—
—
139
3,178
149
161
356
442
407
522
159
122
375
—
281
381
144
281
148
84
—
—
—
280
894
185
196
327
563
117
--
—
—
294
..
257
—
114
187
151
—
..
..
281
72
..
-.
102
..
175
—
--
267
303
995
803
834
711
—
1,070
340
(3)
Estimated
Total
Ornamental
Damaees^
„..
605
1,527
3,658
1,249
8,481
2,434
530
423
2
—
28
185
885
766
82
144
106
277
58
103
38
231
—
217
209
139
1,257
—
--
—
--
—
87
444
423
230
227
265
333
--
—
—
389
—
—
—
51
—
322
—
„
—
124
42
..
„
„
—
--
~
--
77
387
224
327
1,249
837
286
132
147
d)
Estimated
All
Plant
Damages^'
3,214
5,677
8,350
6,675
10,606
6,908
1,140
1,290
89
—
—
250
4,596
888 .
335
602
692
633
841
301
222
565
—
407
549
177
1,192
83
--
—
--
—
311
1,359
533
405
527
990
286
--
—
-.
835
-.
405
142
266
458
--
,_
„
494
180
..
__
-.
21
—
—
393
595
1,364
1,079
1,876
1,407
..
1,759
517
_§_/ "--" denotes that the estimates are ei ther Insignificant or unreliable.
t>/ Estimates based on equation (4) in Table VI-4.
_£/ Estimates based on equation (5) in Table VI-4.
Al Estimates based on equation (6) in Table VI-4»
132
-------
Utilizing the "average" economic damage functions presented in this section,
the changes in crop losses brought about by changes in the pollution or climato-
logical variables can be easily estimated. For the sake of illustration; but
without loss of generality, consider equation (6) of Table VI-4. The partial
elasticity of ALPLL with respect to S00 evaluated at their mean values (see
Table VI-6) is l
EPLSO = 6'87 x (20-5/79°) = °-18-
Thus, if the SC>2 level in the air is lowered on the average by 2 yg/
m
from 20.5 yg/m3 to 18.5 yg/m (i.e., 10 percent reduction), then economic damage
to all plants, on the average, could reduce by $14,220, $790,00 x 1.8 percent
from $790,000 to $775,780. The partial elasticities for other variables of in-
terest in the economic damage functions can be similarly computed, and the results
are amenable to analogous interpretation. It should be noted, however, that the
estimates are based on the assumption that the presence of any S02 is harmful to
vegetation regardless of its level of concentration. Although California has
been reported to have very low S02, Equations (4) to (6) do indicate the positive,
though not statistically significant, damaging effect of SC>2 on crop losses.
CONCLUDING REMARKS
Economic damage functions estimated in this section are replete with con-
ceptual difficulties. The task of translating physical damage functions into
monetary damage functions involves a rather anthropocentric-egocentric evalua-
tion procedure. This is generally the case because the evaluation, and subse-
quently adoption, of the physical damage functions by Benedict et al. is mainly
based on our own value judgments rather than on any scientific substance. Fur-
thermore, the damages suffered or anticipated by the receptors may well lead
to changes in the market behavior, and hence, the market prices may not correctly
reflect the welfare loss associated with the physical damages.!/
In spite of the various conceptual difficulties associated with translating
physical damages into dollar worth equivalents, economic damage functions were
estimated for a variety of vegetation in this study. In view of the numerous
inherent weaknesses in the prior study and other conceptual and empirical diffi-
culties associated with the estimation of economic damage functions, the damage
functions presented in this section, though useful for estimating possible damage
reductions brought about by pollution abatement programs, should be interpreted
and employed with proper caution.
Finally, it is widely recognized that the best way to determine the occur-
rence and severity of an air pollution episode is to install a network of re-
corders to measure the daily and hourly concentration of various pollutants and
the physical effects simultaneously. Although such nationwide networks have been
JL/ For a detailed discussion on some conceptual difficulties with economic dam-
age functions, see Hans Opschoor, "Damage Functions, Some Theoretical and
Practical Problems," in Environmental Damage Costs, Paris, OECD (1974).
133
-------
TABLE VI-6. MEAN AND STANDARD DEVIATIONS OF VARIABLES
IN VEGETATION DAMAGE FUNCTIONS^/
Variable
CORNL
SOYBL
COTNL
OVGTL
NUSRL
FLORL
FRSTL
FCROL
FRNTL
VEGTL
TOCRL
TOORL
ALPLL
CORNV
SOYBV
COTNV
OVGTV
NUSRV
FLORV
FRSTV
FCROV
•FRNTV
VEGTV
TOCRV
TOORV
ALPLV
S02
SUN
DTS
TEMA
TEMB
OXID
SC02
RHM
Mean
6.3000
3.8838
2.8176
32.3865
72.2946
150.8000
208.3392
48.7608
56.2436
58.2176
436.6257
353.8257
790.4486
1199.6392
562.8405
439.0622
992.6432
728.7108
1441.2243
2734.4284
6435.3541
1154.7284
1660.6703
11905.0000
5576.9757
17473.4527
20.4595
59.5135
34.5811
26.2027
82.4595
0.4586
0.7927
58.8108
Standard
Deviation
13.5973
11.0622
18.7760
120.5110
370.8508
622.7670
894.3120
106.6920
257.2594
197.5723
1502.4422
1334.1816
2651.6519
2347.7278
1330.7283
3088.3767
4300.7229
1755.0623
3055.6803
10586.1798
8530.7033
3037.2926
5341.7627
16015.2235
12421.3958
22764.5429
17.7148
6.6647
18.8999
24.2735
40.6061
1.0726
0.9200
6.9394
zil The values of crop losses and crop values are
expressed in $1,000.
134
-------
set up, the individual stations are unfortunately mostly located in the center
of large metropolitan areas or industrialized areas. Few stations have been lo-
cated in agricultural areas or in suburban areas where most of the vegetation
is grown. Furthermore, a substantial amount of S02 is produced by power plants
and various smelter operations which are generally located outside of SMSA's.
This difficulty of a lack of meaningful information on pollution levels in subur-
ban or rural areas has motivated earlier investigators to resort to fuel consump-
tion, number of pollution episodes, and the tendency of atmospheric conditions
to derive the air pollution damaging potential estimates. After all, it is imper-
ative to conduct research directed at obtaining information on vegetation-at-
risk isopleths for various counties in the United States, so that more reliable
economic damage estimates for vegetation can be derived for policy decisions.
135
-------
SECTION VII
AGGREGATE ECONOMIC DAMAGE COSTS AND FUNCTIONS: AN OVERALL VIEW
Air pollution constitutes a modern problem which goes beyond the technology
of simply controlling the pollutants. The need for effective control is generally
recognized, but arguments against control proposals also prevail. These arguments
are mainly based on economic grounds—whether or not the cost of attaining a
specified level of ambient air quality exceeds the economic benefit that would
be realized from a control program. The regional damage estimates developed in
the preceding six sections provide some of this much needed information, how-
ever crude it may be, for evaluating the economic feasibility of a specific air
pollution control program.
This final section presents an overall view of the economic damages and
damage functions of various receptors that were derived in the preceding six
sections. Further, "aggregate" economic damage functions defined with respect
to several effect categories are developed by regressing the aggregate damages
to the same set of explanatory variables used earlier in the development of
the "individual" effect economic damage functions. Aggregate damage estimates
for selected categories of damaging effects are also computed and presented.
The economic damage estimates for the effect categories of human health,
material, and household soiling are summarized in Table VII-1, for the 40 SMSA's
having an S02 level equal or greater than 25 (j,g/m3- These 40 SMSA's are listed
in Column 1. Column 2 (HNC1) and Column 3 (HNC2) present, respectively, the low
and the high damage estimates of human health; the material deterioration damage
estimates of both paint and zinc as derived in Section V are summarized in Column
4 (MDC). Column 5 (TNSCO) contains the total net household soiling damages as
described in Section IV. Based upon the low and high damage estimates of human
health presented in Columns 2 and 3, respectively, two sets of low and high ag-
gregate damage estimates for the three effect categories were estimated and pre-
sented in Column 6 (TNC1) and Column 7 (TNC2).
Specifically, the following two equations were used for computing HNC1
and HNC2 for the 40 SMSA's.
HNC1 = Maximum of (HNCSO , HNCTSP) (VII-1)
HNC2 = HNCS02 + HNCTSP (VII-2)
where HNCS02 and HNCTSP are, respectively, the net health damages attributable
to S02 and TSP. These two aggregate damage estimates were computed by summing the
mortality and morbidity costs due to S02 and TSP derived in Sections II and III;
namely,
136
-------
TABLE VII-1. ECONOMIC DAMAGES DUE TO AIR POLLUTION, BY
RECEPTORS FOR SELECTED SMSA's
(in $ million, 1970)
(1)
SMSA1 s
1. Akron, OH
2. Allentown, PA
3. Baltimore, MD
4. Boston, MA
5. Bridgeport, CT
6. Canton, OH
7. Charleston, WV
8. Chicago, IL
9. Cincinnati, OH
10. Cleveland, OH
11. Dayton, OH
12. Detroit, MI
13. Evansville, IN
14. Gary, IN
15. Hartford, CT
16. Jersey City, NJ
17. Johnstown, PA
18. Lawrence, MA
19. Los Angeles, CA
20. Minneapolis, MN
21. New Haven, CT
22. New York, NY
23. Newark, NJ
24. Norfolk, VA
25. Paterson, NJ
26. Peoria, IL
27. Philadelphia, PA
28. Pittsburgh, PA
29. Portland, OR
30. Providence, RI
31. Reading, PA
32. Rochester, NY
33. St. Louis, MO
34. Scranton, PA
35. Springfield, MA
36. Trenton, NJ
37- Washington, DC
38. Worcester, MA
39. York, PA
40. Youngstown, OH
Total
(2)
HNC1
10
8
48
49
3
6
3
191
22
55
18
129
2
12
12
11
4
3
123
21
3
352
39
13
7
4
107
45
13
16
5
13
44
5
12
3
48
3
4
9
1,475
(3)
HNC2
18
15
80
52
5
6
3
360
22
93
18
161
2
24
19
17
4
5
147
32
5
527
48
13
7
4
158
79
13
25
5
15
61
5
15
3
88
4
4
10
2,166
(4)
MDC
7
3
17
26
6
11
4
105
12
49
9
55
2
8
5
8
1
7
76
12
4
111
14
3
13
9
33
30
8
9
4
7
24
2
3
2
21
8
2
8
736
(5)
TNSCO
16
16
137
117
3
14
10
516
57
216
39
294
5
24
16
17
10
3
388
37
4
418
112
29
9
8
104
147
30
20
15
27
119
23
7
5
86
6
9
23
3,134
(6)
TNC1
33
27
202
192
12
31
17
812
91
320
66
478
9
44
33
36
15
13
587
70
11
881
165
45
29
21
244
222
51
45
24
47
187
30
22
10
155
17
15
40
5,349
(7)
TNG 2
41
34
234
195
14
31
17
981
91
358
66
510
9
56
40
42
15
15
611
81
13
1,056
174
45
29
21
295
256
51
54
24
49
204
30
25
10
195
18
15
41
6,045
137
-------
HNCSO~ = Mortality cost due to SO 2 + morbidity cost due to SO ^
HNCTSP = Mortality cost due to TSP + morbidity cost due to TSP
Total material damages (MDC) in Column 4 is the sum of deterioration dam-
ages on both materials, zinc and paint. Specifically, it was calculated as
follows:
MDC = DDCZ + DDCP (VII-3)
with DDCZ, and DDCP defined and computed previously in Section V.
Finally, Column 6 (TNCl) and Column 7 (TNC2), which represent the low and
high human health damages, respectively, plus other damages, were calculated
as follows:
TNCl = HNC1 + MDC + TNSCO (VII-4)
TNC2 = HNC2 + MDC + TNSCO (VII-5)
An inspection of Table VII-1 reveals that while New York and Chicago SMSA's
had the largest aggregate air pollution damages, in the order of $1 billion,
the smallest air pollution damages occurred in Johnstown and York, Pennsylvania,
in the magnitude of $15 million in 1970.
Total material deterioration damage, including deterioration for zinc and
paint, amounted to $0.7 billion for the selected 48 SMSA's under study. The
corresponding figures for net household soiling was estimated at $3 billion,
respectively. The damage on vegetation for this nation was estimated, according
to Benedict, to be $132 million. These damage figures employed in this study
were taken from earlier studies which were completed under various stringent
assumptions.
AGGREGATE ECONOMIC DAMAGE FUNCTIONS
In order to develop marginal equivalent economic damage functions for the
purpose of predicting damage or benefit, and for designing pollution control
strategies, the overall economic costs of human health in the presence of S02
(HCS02) and that in the presence of TSP (HCTSP) were respectively regressed
not only against pollution and relative humidity, but also against other
relevant socioeconomic and climatological variables, e.g., PWPO, PAGE, PCOL,
PDS, DTS, SUN, etc. The least-squares regression technique was used with
input from the 40 sample observations for estimating the economic damage
138
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functions. The regression results pertaining to overall human health damage
are presented in Column 1 to Column 4 in Table VII-2. The overall economic
damage functions for zinc and paint, for household soiling and for plants
derived in the previous sections are also presented in the table in Columns
5, 6, 7, 8, 9, and 10.
The existence of an economic damage function does not in itself provide
us with sufficient information to make any policy recommendations. Quantitative
estimates of the magnitudes of the relationship are required. As discussed ear-
lier, this information can be obtained directly from the estimated regression
coefficients. The coefficients in the regression equation indicate the changes
in the dependent variable in response to a one unit change in the associated
explanatory variable ceteris paribus. The coefficients can be used for computing
the elasticities under given conditions. A distinguishing feature of the concept
of elasticity is that it is a unit free measure of the percentage change in the
dependent variable with respect to the percentage change in the independent var-
iable. Given the elasticity estimates, we are able to answer the question, "What
would the effect of a reduction in the pollution level be, ceteris paribus, on
the level of economic damages of various receptors?"
Table VII-3 contains estimates of a hypothetical reduction in the air pollu-
tion concentration level for the several pollution receptors analyzed and presented
in Table VII-2. The first column in this table presents the dependent variables.
Column 2 shows the estimated values of the coefficients of the S0£ or TSP var-
iables. The next two columns list the mean values of SC^j TSP, and the economic
'damages of the various receptors. The estimated elasticity of economic damages
of a particular receptor with respect to SC>2 or TSP; evaluated at the means of
both variables, is found in Column 5. These elasticities indicate the percentage
change in the economic damages that would result, on an average, from a 1 per-
cent change in S02 or TSP.
Of particular interest to the policymaker is the effect of a given discrete
change in the pollution level on the economic damages of a particular receptor.
Assuming that the federal government is considering the implementation of a pol-
lution control program which is expected to lower the pollution level, on the
average, by 10 percent, the average benefit of a receptor can be calculated
by multiplying the coefficient of S02 or TSP by 0.10 times the mean value of
S02 and TSP. These estimates can be found in Column 6.
The study of Table VII-3 reveals that the partial elasticities of gross
economic damages of the receptors included in our study vary from 0.004 to 1.28.
Furthermore, a 10 percent reduction of the air pollution level would result in
a decrease in the annual economic damages in the range of $0.01 million for plants
(ALPLL) to $5.26 million for the soiling effect of zinc (SDCZ).
The implication of our study for pollution abatement strategies is obvious.
Any effort to reduce the current pollution level appears to have a varyingly
significant impact on the economic damages resulting from the harmful effects
of air pollution. Admittedly, the implication of this study must be qualified
139
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TABLE VII-2. ECONOMIC DAMAGE FUNCTIONSa»b,c/
Dependent Variables HCS02
Intercept
FWPO
PAGE
COL
PD
DTS
RHM
SON
S02
TSP
MANFV
ME
YP
HU
CROPV
TEMA
TEMB
2
R
(1)
37,775
(35,512)
0.02
(0.14)
189,112
(207,266)
70
(89)
94
(196)
222
(489)
593
(78)*
0.66
HCTSP
(2)
-9,939
(10,525)
74,828
(36,937)*
5
(16)
39
(35)
156
(94)
179
(114)
0.0003
(0.002)
0.25
HCAP1
(3)
-54,687
(42,107)
100,580
(179,860)
70
(83)
54
(156)
120
242
(563)
611
(74)*
0.00006
(0.00009)
0.69
HCAP2
(4)
-46,751
(57,923)
146,324
(204,915)
70
(89)
75
(195)
120
(518)
139
(632)
601
(77)*
0.00004
(0.00012)
0.68
SDCZ
(5)
-23,328.
.4
(19,929)
2,679.
(1,750.
-235.
(1,820.
943.
(171.
148.
(356.
43.
(3.
21.
(18.
0.
3
2)
0
4)
3
6)*
1
0)*
1
4)*
9
9)
64
DDCZ
(6)
7,562.2
(6,640.4)
86.8
(56.7)
-76.0
(59.0)
30.5
(5.5)*
47.9
(11.5)*
1.4
(0.1)*
712.6
(615.5)
0.63
SDCP
(7)
-141,199.7
(259.8)*
911.3
(235.3)*
305.3
(245.9)
69.1
(23.2)*
15.2
(2.6)*
577.2
(3.4)*
0.99
DDCP GRSOC
(8) (9)
-4,820.1 -25,621
*887.2 *(52,347
3,432
(2,199
3,766
(1,460
2
(4
90
(219
31.1 -1,219
(8.0)* (610
10.4
(8.4)
2.3
(0.8)*
226
(166
78
(2
0.50
(0.08)*
19.7
(0.1)*
0.099 0
.0
.0
.3
• 5)
.0
.0)*
.3
• 3)
.9
.3)
.8
.7)*
.4
.9)
.9
.3)*
.92
ALPLL
(10)
-14,350.5
(3,835.7)*
-20.13
(15.06)
128.37
(41.68)*
150.45
(43.62)*
6.87
(11.84)
0.05
(0.01)*
-24.98
(14.52)
12.53
(5.69)*
0.64
at The values in the brackets are standard errors of the coefficients, with * to indicate that the coefficient Is significant at
the 1 percent level. The coefficients and standard errors in equations (5), (6), (7), (8) (9) and (10) are reduced by a
factor of 10
b/ HCSO2 = Overall health cost In the presence of S02, HCTSP = overall health cost in the presence of TSP.
HCAP1 = HCS02 + HCTSP = high health damage estimates.
HCAP2 = Maximum (HCS02, HCTSP) = low health damage estimates, SDCZ, DDCZ, SDCP, DDCP, GRSOC and ALPLL are defined previously in
Chapters IV, V, and VI.
cj The sample observations for HCS02, HCTSP, HCAP1 and HCAP2 are the 40 SMSA's with SO2 level equal or greater than 25 g/m , whereas
the sample observations for SDCZ, DDCZ, SDCP, DDCP and GRSOC are the 148 SMSA's with population greater than 250,000. In the
case of ALPLL, 74 counties were selected in the sample observation.
-------
TABLE VII-3. GROSS ECONOMIC DAMAGES CHANGES RESULTING FROM A
10 PERCENT REDUCTION IN THE POLLUTION T,F.VF.T,a,b/
(2) (3) (4)
(1) Coefficients Mean Values of Mean Value of
Dependent S02, TSP S02, TSP Economic Damages
Variables (103) (ug/m3) ($ million)
HCS02 593 47.25
HCTSP 0.0003 100.87
HCAPl(a) 611 47.25
(b) 0.00006 100.87
HCAP2(a) 601 47.25
(b) 0.00004 100.87
SDCZ (a) 943.3 55.73
(b) 148.1 93.81
DDCZ (a) 30.5 55.73
(b) 47.9 93.81
SDCP 69.1 55.73
DDCP 2.3 55.73
GRSOC 226.4 93.81
ALPLL 6.87 20.45
5,575.7
2,431.7
8,007.4
8,007.4
6,789.2
6,789.2
107.3
107.3
3.5
3.5
150.0
3.3
434.2
0.8
(5)
Partial Elasticity
E = (2)-(3)/(4)
0.050
— —
0.004
—
0.004
— —
0.480
0.130
0.480
1.280
0.026
0.039
0.049
0.180
(6)
Economic Damage
Reduction
= 0.1-(2)-(3)
($ million)
2.80
— —
2.89
—
2.83
~—
5.26
1.39
0.17
0.45
0.39
0.01
2.12
0.01
a/ This table is calculated on the basis of the 10 economic damage equations presented in Table VII-2.
"b/ "__" denotes value smaller than $10,000.
-------
by several theoretical and empirical factors. As discussed in the previous sec-
tions, the major difficulties often encountered in estimating air pollution dam-
ages include the lack of knowledge regarding the shapes of functions describing
the relationship between air pollution and various receptors, and the lack of
a satisfactory theoretical model specifying the way air pollution affects various
receptors. The impossibility of accounting for all major factors which might
affect various receptors, the lack of reliable formulations used for translating
physical damages into monetary terms, and the presence of numerous econometric
problems have also caused concern to investigators.
Despite the existence of these difficulties, this study represents a major
step forward in our knowledge of pollution damages in that it seems to be the
first attempt to construct essential frameworks of the physical and economic
damage functions to calculate comparable regional damage estimates for the sev-
eral important receptors—human health, material, and household soiling, however
tentative they may be. More importantly, various aggregate economic damage func-
tions instrumental for transforming the multifarious aspects of the pollution
problem into a single, homogeneous monetary unit are tentatively derived and
illustrated. It is hoped that these will be useful to policymakers as they make
decisions on the implementation of programs to achieve "optimal" (where social
MR = social MC) pollution levels for this country, although proper caution must
be exercised in interpreting and employing the various economic damage functions
presented in this study.
Finally, it should be noted that although the availability of information
on average or marginal damages is instrumental in determining the optimal na-
tional or regional pollution control strategies, the current problem is far more
complex than the question of balancing the benefits to polluters against damages
inflicted on the receptors. The issues are pressing and not yet well specified.
The basic difficulty in applying the recent research findings to accurately
estimate the air pollution damage cost stems from our ignorance about the recep-
tors at risk to air pollution. So far, few attempts have been made to identify
who suffers, to what extent, from which sources, and in what regions.JL^ At this
moment, updating and expansion of the available crude estimates, which are gener-
ally restricted to certain regions, are urgently needed. To identify the popula-
tion at risk to air pollution, and to measure the damage specifically for pol-
luted regions are apparently the most logical steps in the area of future re-
search.
_!/ We are aware of only one study in the area of estimating population at risk.
Namely, 1stvan Jakaces and G. Bradford Shea, Estimation of Human Population^
at-Risk to Existing Levels of Air Quality, Enviro Control, Inc., Rockville,
Maryland (February 1975). This study reports the number of people within
each major social and economic classification who were exposed to 1973 levels
of various air pollutants within each standard metropolitan statistical
area and EPA regions. Estimates of the population at risk for other major
receptors, e.g., material and vegetation, have not been derived to date.
142
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SECTION VIII
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152
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APPENDIX A
OPTIMAL POLICIES IN THE PRESENCE OF ENVIRONMENTAL POLLUTION: A THEORETICAL
FRAMEWORK
Before we systematically present the economic damage and damage function
of air pollution for a variety of receptors, a general equilibrium framework
explicitly incorporating the effect of environmental pollution is described in
this section. Optimal intervention policies are also derived in this framework
for policy consideration. More importantly, optimal policy prescriptions are
suggested for meeting the acceptable pollution levels predetermined by the author-
ity.
For analytical purposes, the following assumptions are made:—
1. Air pollution adversely affects social welfare.
2. There are two types of industries; pollution emitting and pollution
nonemitting, and air pollution is a joint product of the commodities produced
by the pollution emitting industry.
3. Air pollution adversely affects the productivity of the labor input used
in other industries.
4. By holding capital constant, labor is the only variable factor of produc-
tion in all industries in the short run.
The social utility function for the economy under consideration is written
as
U = U(XL, X2, A) (A-l)
where X^ and X£ denote, respectively, the vectors of commodities produced by
the first and the second industries. The first industry refers to one in which
the labor productivity is adversely affected by air pollution, and the second
industry consists of those firms which, in the process of producing commodities
X2, emit pollution into the air- A represents a vector of n pollutants existing
in the air, i.e.,{A= [a^, . . a , . -j^}.
The partial derivatives of U are subject to the following sign restric-
tions:
I/ The assumptions are made mainly for facilitating the exposition. Relaxation
~~ of any of the postulates will not affect the conclusions.
153
-------
2 2
U = 3U/BX > 0; U = a U/ax < 0
U2 = SU/3X2 > 0; U22 = a U/BX2 < 0
U = au/aA < 0
A
In view of assumption (2), the amount of air pollution emitted to the air,
Ae is proportional to X£.
A = aX0 (A-2)
e 2
where a is a matrix with elements showing the quantity of each type of pollut-
ant being emitted per unit of the commodities produced by the industry 2.
Assumption (3) permits the production function of the first industry to
be represented by
X, = F, [L, - bAL,] (A-3)
where L-^ is the amount of labor employed in industry 1, and b is the vector
with elements indicating the loss of efficiency in LI due to a unit of the
jth pollutant produced by industry 2, j = l,...,n. To ensure that net labor in-
put is positive, it is imposed that bA < 1.
Since industry 2 is assumed to be unaffected by, or at least compensated
for, air pollution, an externality or by-product, its production function is
represented by
X2 = F2 (V (A"4)
where L is the amount of labor utilized in industry 2.
Also assume that there is a pollution control sector with the following
production function
-------
where A^, is the quantity of air pollution abated and 1/3 the amount of labor
utilized in the pollution control activities.
Thus, the pollution existing in the air at any point of time is simply the
difference between the quantity of pollution emitted and quantity of pollution
abated.
A = A - A = aX0 - A (X) (A-6)
e c 2 c 3
Finally, the economy is subject to a labor availability constraint
Ll + L2 + L3 * L (A"7)
The first order optimality conditions for this economy which is subject
to an environmental externality are.derived by maximizing (A-l) subject to the
constraints (A-2), through (A-7) and
Lt, L2, L3, Xlf X2, A> 0 (A-8)
Form the Lagrangean:
- Y [aX2 - aF2(L2)] - u [A -aX£ + A^)] - w (1^ + 1^ + 1^- I) (A-9)
Partially differentiating (A-9) with respect to X , X , A, L , L and L
yields: 2 3
U - X = 0
M = UA -x aF; - bLj_ - u = 0
155
-------
- w
50 = (p+ya) S_F2 - w = 0 (A-14)
(A-15)
50 = -u 5A - w = 0
Note that the shadow prices of X , X and A are, respectively, X, (3
and jj,. Both X and (j, are positive by assuming nonsatiation in consumption
of both X and X . (j, is negative since 5U/5A < 0. The interpretation
of equations (A-10) through (A-15) is straightforward. The optimality in the
presence of the pollution externality requires that U /U =X/[J3+ a(y-u)];
U /U = X/(u + bLn + ^dF, ) and w = X(l-bA) 5_F = -u 5_Ac = 0+ ya) 5F0 .
1 A l X
In view of (A-ll), and remembering a > 0 the optimal policy is to impose
a consumption tax of aCii+y) per unit of X£. From (A-12), it is clear that
a subsidy of n, + bL^ +X5F should be given to consumers who suffer from the
5L*
air pollution. In view of (A-13), a production subsidy of XbA per unit of
Xl is required for efficient production. Also in view of (A-14), a production
tax of ya per unit of X£ should be imposed. In short, the optimal policies
in the presence of the environmental pollution involve a consumption and prod-
uction tax on X2, a consumption subsidy on A and a production subsidy on X,.
ACCEPTABLE POLLUTION LEVEL
Suppose the pollution level is constrained by the authority not to exceed
the statutory acceptable level. This problem amounts to introducing an addi-
tional constraint in the model.
A
-------
** 5Fo
If = O + Ya + eva)ol? - w=o
o-'-'n
_J? = (-|j, -ec )
- w = o
where a is the shadow price associated with the acceptable pollution constraint.
The constraint will be binding because otherwise the objective can be attained
without statutory regulation. This means a > 0. It is clear, in view of (A-14')
and (A-151) that the optimal interventions to constrain the pollution in the
air not to exceed the acceptable level are to apply an additional tax of aa
per unit of X£ and a subsidy of o; per unit of AC to the pollution control
sector of the economy. Thus, a penalty on the pollution producing industry coupled
with a subsidy on the pollution abatement industry is the second best optimal
combination of policies to achieve the objective of reducing the pollution concen-
tration below the "threshold" level.
157
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APPENDIX B
LIST A
SMSA'S WITH POPULATION OVER 500,000
01
oo
SMSA
1 Akron, Ohio
2 Albany-Schenectady-Troy, N.Y.
3 Allentown-Bethlehem-Easton, Pa.-N.J.
4 Anaheim-Santa Ana-Garden Grove, Calif.
5 Atlanta, Ga.
6 Baltimore, Md.
7 Birmingham, Ala.
8 Boston, Mass.
9 Buffalo, N.Y.
10 Chicago, 111.
11 Cincinnati, Ohio-Ky.-Ind.
12 Cleveland, Ohio
13 Columbus, Ohio
14 Dallas, Texas
15 Dayton, Ohio
16 Denver, Colo.
17 Detroit, Mich.
18 Fort Lauderdale-Hollywood, Fla.
19 Fort Worth, Texas
20 Gary-Hammond-East Chicago, Ind.
21 Grand Rapids, Mich.
22 Greensboro-Winston-Salem-High Point,
N.C.
23 Hartford, Conn.
24 Honolulu, Hawaii
25 Houston, Texas
26 Indianapolis, Ind.
27 Jacksonville, Fla.
,28 Jersey City, N.J.
29 Kansas City, Mo.-Kans.
30 Los Angeles-Long Beach, Calif.
Code
Population, 1970
(in 1.000)
AKR
ALB
ALL
ANA
ATL
BAL
BIR
BOS
BUF
CHI
GIN
CLE
COL
DAL
DAY
DEN
DET
FOR
FOR
GAR
GRA
CRE
HAR
HON
HOU
IND
JAC
JER
KAN
LOS
679
721
544
1,420
1,390
2,071
739
2,754
1,349
6,979
1,385
2,064
916
1,556
850
1,228
4,200
620
762
633
539
604
664
629
1,985
1,110
529
609
1,254
7,032
SMSA
31 Louisville, Ky.-Ind.
32 Memphis, Tenn.-Ark.
33 Miami, Fla.
34 Milwaukee, Wis.
35 Minneapolis-St. Paul, Minn.
36 Nashville-Davidson, Tenn.
37 New Orleans, La.
38 New York, N.Y.
39 Newark, N.J.
40 Norfolk-Portsmouth, Va.
41 Oklahoma City, Okla.
42 Omaha, Nebraska-Iowa
43 Paterson-Clifton-Passaic, N.J.
44 Philadelphia, Pa.-N.J.
45 Phoenix, Ariz.
46 Pittsburgh, Pa.
47 Portland, Oreg.-Wash.
48 Providence-Pawtucket-Warwick, R.I.-Mass.
49 Richmond, Va.
50 Rochester, N.Y.
51 Sacramento, Calif.
52 St. Louis, Mo.-111.
53 Salt Lake City, Utah
54 San Antonio, Texas
55 San Bernadino-Riverside-Ontario, Calif.
56 San Diego, Calif.
57 San Francisco-Oakland, Calif.
58 San Jose, Calif.
59 Seattle-Everett, Wash.
60 Springfleld-Chicopee-Holyoke, Mass.-Conn.
61 Syracuse, N.Y.
62 Tampa-St. Petersburg, Fla.
63 Toledo, Ohio-Mich.
64 Washington, D.C.-Md.-Va.
65 Youngstown-Warren, Ohio
Code
Population, 1970
(in 1.000)
LOU
MEM
MIA
MIL
MIN
NAS
NEW
NEW
NEW
NOR
OKL
OMA
PAT
PHI
PHO
PIT
POR
PRO
RIC
ROC
SAC
STL
SAL
SAN
SAN
SAN
SAN
SAN
SEA
SPR
SYR
TAM
TOL
WAS
YOU
827
770
1,268
1,404
1,814
541
1,046
11,529
1,857
681
641
540
1,359
4,818
968
2,401
1,009
911
518
883
801
2,363
558
864
1,143
1,358
3,110
1,065
1,422
530
636
1,013
693
2,861
536
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LIST B
SMSA'S WITH POPULATION 200,000-500,000 (M)
SMSA
66 Albuquerque, N. Hex.
67 Ann Arbor, Mich.
68 Appleton-Oshkosh, His.
69 Augusta, Ga.-S.C.
70 Austin, Texas
71 Bakersfield, Calif.
72 Baton Rouge, La.
73 Beaumont-Port Authur-Orange, Texas
74 Blnghamton, N.Y.-Pa.
75 Bridgeport, Conn.
76 Canton, Ohio
77 Charleston, S.C.
78 Charleston, W. Va.
79 Charlotte, N.C.
80 Chattanooga, Tenn.-Ga.
81 Colorado Springs, Colo.
82 Columbia, S.C.
83 Columbus, Ga.-Ala.
84 Corpus Christ!, Texas
85 Davenport-Rock Island-Moline, Iowa-Ill.
86 Dee Moines, Iowa
87 Duluth-Superior, Minn.-Wis.
88 El Paso, Tex.
89 Erie, Pa.
90 Eugene, Oreg.
91 Evansville, Ind.-Ky.
92 Fayetteville, N.C.
93 Flint, Mich.
94 Fort Wayne, Ind.
95 Fresno, Calif.
96 Greenville, S.C.
97 Hamilton-Middleton, Ohio
98 Harrisburg, Pa.
99 Huntington-Ashland, W. Va.-Ky.-Ohio
100 Huntsville, Ala.
101 Jackson, Miss.
102 Johnstown, Pa.
103 Kalamazoo, Mich.
104 Knoxvllle, Tenn.
105 Lancaster, Pa.
Population, 1970
Code (in 1.000)
ALB 316
ANN 234
APP 277
AUG 253
AUS 296
BAK 329
BAT 285
BEA 316
BIN 303
BRI 389
CAN 372
CHA 304
CHA 230
CHA 409
CHA 305
COL 236
COL 323
COL 239
COR 285
DAV 363
DES 286
DUL 265
ELP 359
ERI 264
BUG 213
EVA 233
FAY 212
FLI 497
FOR 280
FRE 413
GRE 300
HAM 226
BAR 411
HUN 254
HUN 228
JAC 259
JOB 263
KAL 202
KNO 400
LAN 320
SMSA
106 Lansing, Mich.
107 Las Vegas, Nev.
108 Lawrence-Haverhill, Mass.-N.H.
109 Little Rock-North Little Rock, Ark.
110 Lorain-Elyria, Ohio
111 Lowell, Mass.
112 Macon, Ga.
113 Madison, Wis.
114 Mobile, Ala.
115 Montgomery, Ala.
116 New Haven, Conn.
117 New London-Groton-Norwich, Conn.
118 Newport News-Hampton, Va.
119 Orlando, Fla.
120 Oxnard-Ventura, Calif.
121 Pensacola, Fla.
122 Peoria, 111.
123 Raleigh, N.C.
124 Reading, Pa.
125 Rockford, 111.
126 Saginaw, Mich.
127 Salinas-Monterey, Calif.
128 Santa Barbara, Calif.
129 Santa Rosa, Calif.
130 Scranton, Pa.
131 Shreveport, La.
132 South Bend, Ind.
133 Spokane, Wash.
134 Stamford, Conn.
135 Stockton, Calif.
136 Tacoma, Wash.
137 Trenton, N.J.
138 Tucson, Ariz.
139 Tulsa, Okla.
140 Utica-Rome, N.Y.
141 Vallejo-Napa, Calif.
142 Waterbury, Conn.
143 West Palm Beach, Fla.
144 Wichita, Kans.
145 Wilkes-Barre-Hazleton, Pa.
146 Wilmington, Del.-N.J.-Md.
147 Worcester, Mass.
148 York, Pa.
Population, 1970
Code (in 1,000)
LAN
LAS
LAW
LIT
LOR
LOW
MAC
MAD
MOB
MON
NEW
NEW
NEW
ORL
OXN
PEN
FED
RAL
REA
ROC
SAG
SAL
SAN
SAN
SCR
SHR
SOU
SPO
STA
STO
TAC
TRE
TUC
TUL
UTI
VAL
WAT
WES
WIC
WIL
WIL,
WOR
YOR
378
273
232
323
257
213
206
290
377
201
356
208
292
428
376
243
342
228
296
272
220
250
264
205
234
295
280
287
206
290
411
304
352
477
340
249
209
349
389
342
499
344
330
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/5-76-011
2.
3. RECIPIENT'S ACCESSION NO.
4. TITLE AND SUBTITLE
Physical and Economic Damage Functions for Air
Pollutants by Receptors
5. REPORT DATE
6. PERFORMING ORGANIZATION CODE
September 1976
7. AUTHOR(S)
Ben-chieh Liu and Eden Siu-hung Yu
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Midwest Research Institute
425 Volker Boulevard
Kansas City, Missouri 64110
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
EPA Contract No. 68-01-2968
12. SPONSORING AGENCY NAME AND ADDRESS
Washington Environmental Research Center
Office of Research and Development
Environmental Protection Agency
Washington, D.C. 20460
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
EPA/ORD
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This study is primarily concerned with evaluating regional economic damages to human
health, material, and vegetation and of property soiling resulting from air pollution.
This study represents a step forward in methodological development of air pollution
damage estimation. It attempts to construct essential frameworks of the physical and
economic damage functions which can be used for calculating comparable regional damage
estimates for the several important receptors—human health, material, and household
soiling—however,tentative the damage estimates may appear to be. More importantly,
aggregate economic damage functions instrumental for transforming the multifarious as-
pects of the pollution problem into a single, homogeneous monetary unit are tentatively
derived and illustrated. It is hoped that these results will be of some use to guide
policymakers as they make decisions on the implementation of programs to achieve "op-
timal" pollution levels for this country. Given the experimental nature of the method-
ological and statistical procedures and the degree of uncertainty associated with the
study results, a great deal of caution should be exercised in using the products of
this research.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Physical Damage Functions
Economic Damage Functions
Air Pollutants - S02, Suspended Particulates
Receptors - Health, Materials, Household
Soiling, Vegetations
Standard Metropolitan Statistical Areas
egions
8. DISTRIBUTION STATEMENT
Unlimited
19. SECURITY CLASS (ThisReport)
Unclassified
21. NO. OF PAGES
172
20. SECURITY CLASS {Thispage)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
150
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