EPA Report Number: 560/4-82-00 3
June 1982
THE APPLICATION OF DECISION ANALYSIS
TO TOXIC SUBSTANCES:
Proposed Methodology and Two Case Studies
by:
Gregory L. Campbell
David Cohan
D. Warner North
EPA Contract 68-01-6054
Project Officer:
Michael H. Shapiro
Economics and Technology Division
Office of Toxic Substances
Washington, D.C. 20460
OFFICE OF PESTICIDES
U.S. ENVIRONMENTAL
WASHINGTON,
AND TOXIC SUBSTANCES
PROTECTION AGENCY
D.C. 20460
1084-0
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THE APPLICATION OF DECISION ANALYSIS TO TOXIC SUBSTANCES:
Proposed Methodology and Two Case Studies
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Gregory L. Campbell, David Cohan, D. Warner North
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Decision Focus Incorporated
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26. Abstract (UmHT: 200 werd»|
This research report investigates the use of decision analysis as an aid to decisions on
toxic substance regulation. Part I of this report includes a literature survey on deci-
sion analysis and related methods for quantitative analysis and an exposition of the
proposed methodology. Parts II and III illustrate the decision analysis approach on two
chemicals, perchloroethylene (PCE) and di-ethyIhexyl phthalate (DEHP). The PCE case
study illustrates how uncertainties in the potential chronic health effects of a
chemical may be quantitatively described in a way that makes use of bioassay data and
scientific judgment on the extrapolation of the dose response relationship from animals
to humans. The PCE analysis includes a comparison of control options to workers,
service users, and the general public, showing the potential impact on health for each
group and comparing expected health benefits to control costs. The value of better
information to resolve health effect uncertainties is computed and compared to the cost
of large-scale animal tests. The DEHP analysis illustrates an economic analysis of
regulatory costs based on interproduct substitution among DEHP and competing
plasticizers.
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17. Document Analyst m. D»tcrtptor»
Cost-Benefit Analysis, Decision Analysis, Risk Analysis, Risk Assessment, Bayesian
Decision Theory, Perchloroethylene, Tetrachloroethylene, Di-ethyIhexyl Phthalate
b. ltf«ntlfl«rt/Op.n-End.d Tirm>
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This report was prepared under contract to an agency of the
United States Government. Neither the United States Government
nor any of its employees, contractors, subcontractors, or their
employees makes any warranty, expressed or implied, or assumes
any legal liability or responsibility for any third party's use
or the results of such use of any information, apparatus,
product, or process disclosed in this report, or represents
that its use by such third party would not infringe on
privately owned rights.
Publication of the data in this document does not signify Chat
the contents necessarily reflect the Joint or separate views
and policies of each sponsoring agency. Mention of trade names
or commercial products does not constitute endorsement or
recommendation for use.
Preparation of this document was completed prior to January 22,
1982, the effective date of the EPA Administrator's Order 2200
and, consequently, the document did not necessarily undergo the
peer review procedures described therein. The document
received peer review according to procedures in place prior to
that date and received complete administrative review.
¦ »
//
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CONTENTS
Part I
THE APPLICATION OF DECISION ANALYSIS TO TOXIC SUBSTANCES
Section Page
1 INTRODUCTION AND SUMMARY 1-1
2 LITERATURE REVIEW 2-1
3 DECISION ANALYSIS METHODOLOGY FOR UNREASONABLE RISK
DETERMINATION 3-1
4 REFERENCES 4-1
Part II
PERCHLOROETHYLENE: A CASE STUDY OF THE APPLICATION OF DECISION
ANALYSIS TO THE DETERMINATION OF THE RISK POSED BY A TOXIC CHEMICAL
Section Page
1 INTRODUCTION AND SUMMARY 1-1
2 THE DRY CLEANING INDUSTRY 2-1
3 PCE EXPOSURE CALCULATIONS 3-1
4 CONTROL OPTIONS 4-1
5 HEALTH EFFECTS OF PERCHLOROETHYLENE 5-1
6 MODEL FOR DOSE RESPONSE RELATIONSHIP FOR PCE 6-1
7 ANALYSIS OF CONTROL OPTIONS 7-1
8 VALUE OF FURTHER INFORMATION 8-1
9 INSIGHTS FROM THE PERCHLOROETHYLENE CASE STUDY 9-1
10 REFERENCES 10-1
APPENDIX A: CALCULATING AN ANNUAL CAPITAL CHARGE FOR AN INVESTMENT A-l
APPENDIX B: DETAILED CALCULATIONS OF CONTROL COSTS AND EXPOSURE
REDUCTION B-l
APPENDIX C: CALCULATIONS OF PROBABILITY OF INCIDENCE AND EXPECTED
CANCER CASES BY CONTROL OPTION AND DOSE RESPONSE CASE C-l
. « *
m
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CONTENTS (continued)
Part III
DI-ETHYHEXYL PHTHALATE: A CASE STUDY OF THE APPLICATION OF DECISION
ANALYSIS TO THE DETERMINATION OF RISK POSED BY A TOXIC CHEMICAL
Section Page
1 INTRODUCTION AND SUMMARY 1-1
2 PLASTICIZERS 2-1
3 THE PROTOTYPE PLASTICS INDUSTRY MODEL 3-1
4 RESULTS FROM USING THE PROTOTYPE MODEL 4-1
5 POTENTIAL DEVELOPMENT AND USE OF THE ECONOMIC ANALYSIS MODEL 5-1
6 REFERENCES 6-1
APPENDIX A: DATA FOR THE PROTOTYPE PLASTICS INDUSTRY MODEL A-l
APPENDIX B: RESULTS OF POLICY AND SENSITIVITY ANALYSIS CASES USING
THE PROTOTYPE PLASTICS INDUSTRY MODEL B-l
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ABSTRACT
Many of the provisions of the Toxic Substance Control Act (TSCA)
require a finding that a chemical or mixture presents or may present an
"unreasonable risk" to human health or the environment. The Act and its
legislative history leave ambiguous the extent to which methods such as
cost-benefit analysis, risk assessment, and decision analysis are appro-
priate to use in determining whether a chemical or mixture presents an
"unreasonable risk."
Decision analysis provides an extension of cost-benefit methods to
deal explicitly with uncertainty. This research report investigates the
use of decision analysis as an aid to decisions on toxic substance regu-
lation. Fart 1 of this report includes a literature survey on decision
analysis and related methods for quantitative analysis and an exposition of
the proposed methodology. Parts II and III illustrate the decision
analysis approach on two chemicals of current regulatory concern,
perchloroethylene (PCE) and di-ethylhexyl phthalate (DEHP). The PCE case
study illustrates how uncertainties in the potential chronic health effects
of these chemicals may be quantitatively described in a way that makes use
of bioassay data and scientific judgment on the extrapolation of the dose
response relationship from animals to humans. The PCE analysis includes a
comparison of control options to workers, service users, and the general
public, showing the potential impact on health for each group and comparing
expected health benefits to control costs. The value of better information
to resolve health effect uncertainties is computed and compared to the cost
of large-scale animal tests. The DEHP analysis illustrates an economic
analysis of regulatory costs based on interproduct substitution among DEHP
and competing plasticizers.
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PART I
THE APPLICATION OF DECISION ANALYSIS
TO TOXIC SUBSTANCES
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Part I
CONTENTS
Section Page
1 INTRODUCTION AND SUMMARY l-l
"Unreasonable Risk" and the Toxic Substance Control Act 1-1
Decision Analysis as a Methodology for Dealing with
Uncertainty 1-1
Project Purpose, Scope, and History 1-3
Summary of the Results of Literature Review 1-5
Summary of the Decision Analysis Methodology for
Unreasonable Risk Determination 1-6
Summary of the Perchloroethylene Case Study 1-7
Summary of the Case Study of Di-EthyIhexyl Phthalate 1-9
Conclusions from the Case Studies on the Applicability
of the Methodology 1-11
2 LITERATURE REVIEW 2-1
General Concepts 2-1
Risks and Benefits of Chemical Substances 2-14
Application to the Toxic Substances Control Act 2-25
Conclusions 2-30
3 DECISION ANALYSIS METHODOLOGY FOR UNREASONABLE RISK
DETERMINATION 3-1
Overview 3-1
Nature of the Toxic Substances Problem 3-1
Purpose of a Decision Analysis Methodology 3-4
Outline of the Methodology 3-6
Coordination of Decisions 3-42
Summary 3-46
4 REFERENCES 4-1
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Part I
FIGURES
Figure Page
3-1 Components of a Decision 3-8
3-2a Example Probability Density Function 3-17
3-2b Example Cumulative Probability Distribution 3-17
3-3a Example Probability Mass Function 3-19
3-3b Example Decision Tree Probability Node 3-19
3-4 Illustrative Generic Decision Tree 3-20
3-5a Decision Under Uncertainty 3-24
3-5b Decision with Perfect Information 3-25
3-6 Decision Analysis Integration Model 3-28
3-7a Simple Exposure Model 3-29
3-7b Detailed Exposure Model 3-29
3-8 Illustrative Economic Network 3-31
3-9 Using Model to Calculate Uncertain Outcome 3-36
3-10 Relationship of Decision Tree and Integrating Model 3-37
3-11 Example Probabilistic Analysis for "Permit All Users"
Alternative 3-39
3-12 Cumulative Probability Distribution on the Number of
Cases of Cancer 3-4 0
3-13 Overview of a Coordinate Framework for Decision Making 3-44
TABLES
Table Page
3-1 Example Sensitivity Analysis Results 3-34
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Section 1
INTRODUCTION AND SUMMARY
"UNREASONABLE RISK" AND THE TOXIC SUBSTANCE CONTROL ACT
The American public has become increasingly concerned with toxic
chemicals as a threat to human health and the environment. While Congress
had previously passed legislation enabling the regulation of chemicals in
food, in water, in solid waste, and in pesticides applications, the passage
of the Toxic Substance Control Act (TSCA) in 1976 provided a comprehensive
means for regulating the manufacture and use of chemicals in the United
States. Under the language of the Act, the Administrator of EPA has a
broad range of alternatives for imposing controls on the manufacturing and
use of chemicals and for requiring that information on the potential toxic
effects of the chemicals be provided to the federal government.
Most of these provisions of TSCA require a finding that the chemical
substance or mixture in question presents or may present an "unreasonable
risk" of injury to human health or the environment. The term "unreasonable
risk" is not defined in the Act, and both the Act and its legislative
history leave ambiguous the extent to which formal methods such as cost-
benefit analysis, risk assessment, and decision analysis are appropriate
for EPA to use in determining whether a chemical substance or mixture
presents an "unreasonable risk."
DECISION ANALYSIS AS A METHODOLOGY FOR DEALING WITH UNCERTAINTY
This report presents the results of an investigation of decision
analysis as a methodology that could assist EPA in regulatory decisions
under TSCA. The methodology is also applicable to decision making by other
regulatory agencies and by corporations or citizens whose decisions impact
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the extent and manner in which potentially toxic chemicals will be manu-
factured and used. The essence of the decision analysis approach is a
recognition that the toxic impact of a chemical substance is typically
uncertain.
The legislative history of TSCA suggests that determination of
unreasonable risk involves a process of information gathering, analysis,
and judgment that balances the probability of harm and the extent of the
harm from the toxicity against the benefits of chemicals in use. The
assessment of the probability of harm will rarely be possible on the basis
of extensive statistical data, for such data rarely exist even for chemi-
cals that have been widely used for many decades. In most decisions under
TSCA, EPA uust judge the probability of harm on the basis of such infor-
mation as the effect of the chemical on laboratory animals and various
cellular systems (e.g., Samonella typhimurium in the Ames test), the
toxicity of similar chemicals, the extent of potential human exposure, and
epidemiological data whose relationship to past chemical exposures are
difficult to ascertain. The judgments required are therefore exceedingly
complex, involve a wide range of scientific specialties, and require
judgmental extrapolation from the limited data available to assess what
might happen to human health and the environment in the absence of direct
scientific evidence of harm.
Such extrapolation is inherently difficult and subject to various
biases. Scientists and regulators have often advocated the use of con-
servative, "worst-case" assumptions as a way of ensuring that the extrap-
olation will be carried out in a consistent way, and that any errors intro-
duced through the assumptions will be in the direction that increases
protection for human health and the environment. However, it is often
difficult to judge how much conservatism is appropriate. Subgroups within
the population may be especially susceptible to toxic impacts or through
unusual circumstances may be subject to unusually high levels of exposure.
How far should regulators go in denying substantial benefits from the use
of a substance to the public because the possibility exists that under sone
conceivable set of conditions the substance might induce harmful effects?
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The methodology of decision analysis deals with uncertainty
explicitly, rather than submerging the uncertainty by using "worst-case"
assumptions. The "probability of barn" suggested in the legislative
history may be assessed quantitatively as a summary of scientific judgment,
and this probability assessment can be acconpanied by the data and
reasoning of the scientific experts whose judgment is being summarized. The
use of mathematical modeling methods permits the overall probability of
harm to be computed from probability judgments made on specific scientific
issues, such as the appropriate form of a dose response relation or the
appropriate formula for extrapolating from doses in laboratory animals to
equivalent doses in humans.
PROJECT PURPOSE, SCOPE, AND HISTORY
In September of 1978, EPA issued a Request for Proposal for a project
to investigate the use of risk-benefit methods to assist EPA decision mak-
ing under TSCA. The contract was awarded to Decision Focus Incorporated
and work began early in 1980. Initial tasks in the project included a
review of the existing literature on methods for quantitative assessment of
risks and benefits that would be applicable to toxic substances, and an
exposition of the proposed methodology. The revised versions of the task
reports constitute Section 2 and Section 3, the remainder of Part I of this
report.
On the basis of extensive discussions with the SPA project tech-
nical officer and management within the Office of Pesticides and Toxic
Substances, it was decided that the remainder of the project resources
should be devoted to case studies illustrating the application of decision
analysis to specific chemical substances. The specific chemical substances
should be representative of the types of chemicals that would come up for
regulatory decisions by EPA under TSCA. It was suggested that the case
studies should emphasize the determination of unreasonable risk in situa-
tions where extensive information was already available about chemical
toxicity and the extent of human exposure, but where EPA had not estab-
lished specific proposed control options. The case studies would address
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situations in which EPA's regulatory decision would be a choice among
control options rather than requirements for industry to carry out tests
and submit information.
During the summer of 1980, the project team reviewed a number of lists
of priority chemicals that were regarded as potential candidates for future
regulation by the Office of Pesticides and Toxic Substances. Two classes
of chemicals were selected: phthalate ester plasticizers and chlorinated
solvents. During the fall of 1980, the case studies were narrowed to one
chemical representative of each class: di-ethylhexyl phthalate (DEHP), the
largest volume chemical among the phthalate ester plasticizers, and perch-
loroethylene (PCE), a chlorinated solvent widely used by the dry cleaning
industry. In each case, the primary health concern was cancer, because
recent data obtained by the National Cancer Institute indicated that expo-
sure to these widely used chemicals could induce tumors in laboratory
animals.
EPA and its contractors have carried out extensive analytical and
information-gathering activities on both PCE and DEHP, and some of the
resulting information and analysis was made available to our project. We
also benefited from discussions with industry representatives and environ-
mental groups, and we had some consulting assistance from Dr. Lee Husting
of Environmental Health Associates, Berkeley, California, in the area of
toxicology. However, most of the information for each case study was
assembled from public sources by the authors. We did not attempt to assess
judgmental probabilities from EPA scientists, and we did not participate in
or interact with any EPA efforts to develop regulatory proposals for the
two chemicals. The case studies are strictly intended as illustrative.
Their sole purpose is to demonstrate how decision analysis methods might be
used to quantify the degree of risk posed by exposure to a specific chem-
ical, and how that risk might be changed by control options to reduce human
exposure. The reader should draw no policy conclusions or implications
regarding needed regulation from these case studies. Although the authors
have summarized their insights from the analysis regarding the extent of
the risk to human health posed by PCE, it should be recognized that the
information in this report regarding both of the case study chemicals may
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differ in important respects from information currently available to EPA,
and that no attempt has been made by the authors or EPA to obtain scien-
tific peer review of the substantive scientific information. The case
studies are intended solely as a vehicle for illustrating decision analysis
methods that mijrht at a future time he used to assist in reaching regula-
tory decisions. The authors believe that extensive scientific peer review
of data, assumptions, and especially judgmental probabilities should be an
essential part of any future applications of decision analysis methods to
EPA regulation of specific chemical substances and mixtures under TSCA.
SUMMARY OF THE RESULTS OF LITERATURE REVIEW
Although there is an extensive literature on risk-benefit analysis,
there is a lack of specific examples where analytical methods have been
used as a basis for regulatory decisions on health or environmental
hazards. There is general agreement in the literaiture that the role of
analysis should be to organize information, not to provide a rule or
formula that would determine the choice for the decision maker. The
analysis should explicitly state the objectives and the feasible alter-
natives, and it should clearly separate value judgments from scientific
Information. Quantifying elements of the analysis can facilitate discus-
sion and help in focusing debate on the relevant issues. Environmental
legislation, executive orders, and court decisions have generally encour-
aged analysis, but have provided little guidance to regulatory agencies as
to how analysis should be used in their decision processes.
Decision analysis may be regarded as an extension of cost-benefit or
cost-effectiveness analysis to include an explicit, quantitative treatment
of uncertainty. Like cost-benefit analysis, decision analysis frequently
involves use of explicit quantitative values on outcomes such as environ-
mental and property damage, mortality and morbidity, and aesthetic and
political concerns. Mathematical modeling methods and sensitivity analysis
can help to determine which of the many uncertainties and value issues are
most important in determining the best decision alternative. Issues such
«
as valuing human life, distributional effects, discounting, and quantifi-
cation of uncertainty require close attention.
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Risk-benefit analysis methodologies, particularly decision analysis,
appear applicable to the regulation of toxic substances, but examples of
such application are rare. Analysis can aid decision makers by organizing
the available information, and making clear and explicit where assumptions
and value judgments have been introduced. The application of risk-benefit
analysis to chemical hazards is difficult because of the general lack of
understanding of the toxicity of low levels of exposure and the pathways of
exposure. However, because regulatory decisions must be made even in the
face of uncertainty, it makes sense to use a method of analysis that deals
with the uncertainty in a logical way rather than assert that uncertainty
makes analysis impossible or useless. Progress has been made in developing
methodologies to deal with the specific problems of implementing TSCA,
namely, methodologies to determine unreasonable risk and to set priorities.
However, much more development, refinement, and experience in applications
appear to be needed before the methodology can be used as an operational
framework to assist EPA decision making in determination of unreasonable
risk and in setting priorities for allocating testing and regulatory
resources.
SUMMARY OF THE DECISION ANALYSIS METHODOLOGY FOR UNREASONABLE RISK
DETERMINATION
The decision analysis methodology consists of a conceptual framework
within which to define a specific regulatory decision based on determina-
tion of unreasonable risk under TSCA, a mathematical model to assess the
harm to human health and the environment and the costs of control alter-
natives that is tailored to the specific decision situation, and a process
or series of steps for carrying out the analysis. Section 3 of Part I
describes the conceptual framework, the model, and the analysis process in
general terms and illustrates each of these aspects of the methodology with
a hypothetical chemical substance.
The basic concepts of the decision analysis methodology include the
definition of specific decision alternatives, the use of judgmental prob-
ability to quantify uncertainty, the explicit quantitative evaluation of
health and environmental damage and costs of control alternatives, and the
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assessment of che value of additional information that reduces or removes
uncertainty. The methodology is capable of addressing the difficulties of
unreasonable risk determination: complex physical, biological, and economic
interactions, numerous uncertainties, diverse impacted groups, and con-
flicting objectives. Key aspects of the analysis process include the
development and use of models, sensitivity analysis, and explicit assess-
ment of probabilities. The analytical process is iterative and can be
applied at almost any level of detail, from a quick "back-of-the-envelope"
calculation to fully disaggregated nultiregional analysis using elaborate
models to represent exposure pathways and economic substitution effects.
The analysis is not static but permits new information to be factored in as
it is obtained, and the value of developing more information can be esti-
mated in a consistent manner. The concepts of decision analysis not only
apply to individual decisions on toxic chemicals, they also can provide a
basis for priority setting and coordination of decisions affecting many
toxic substances.
SUMMARY OF THE PERCHLOROETHYLENE CASE STUDY
Perchloroethylene, sometimes called tetrachloroethylene and abbre-
viated PCE or PERC, is a solvent used for about 75 percent of the dry
cleaning done in the United States. Dry cleaning accounts for about 50
percent of PCE consumption in the United States, other uses being metal
cleaning, textile processing, and use as a chemical intermediate. PCE is
not a new chemical, but rather one that has been in widespread use for
decades. Its potential for liver damage and other acute toxicity effects
in humans has been known for many years and is the basis for the current
occupational standard of 100 parts per million of PCE vapor in the air.
PCE has recently come under suspicion as a potential carcinogen. Its
chemical structure is similar to a known human carcinogen, vinyl chloride.
Short-term tests indicate that a commercial preparation of PCE is muta-
genic, and there is some epidemiological evidence of higher cancer occur-
rence among dry cleaning workers. However, the strongest evidence impli-
cating PCE as a carcinogen is a recent animal test carried out by the
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National Cancer Institute (NCI) , in which a sensitive strain of T.ouse
showed a high incidence of liver tumors when fed PCE.
The case study presented in Part II uses the r-.ethodology of decision
analysis to develop a quantitative description of the cancer risk posed by
PCE to dry cleaning workers, users of dry cleaning services, and men-.bers of
the public who live or work near dry cleaning plants. The case study
examines a variety of control options that could reduce PCE emissions and
human exposures, such as better maintenance and housekeeping to reduce
spills and fugitive emissions, installation of carbon adsorption units to
recover PCE vapor from the air, placement of dry cleaning machines in
separate rooms, and use of conbined dry-to-dry units instead of separate
cleaning and drying machines.
The effect of such control options would be at most a modest increase
in the cost of providing dry cleaning services. Options such as better
maintenance and housekeeping appear to have a negative cost, because the
saving from reduced purchases of PCE solvent more than offsets the cost of
the maintenance and housekeeping procedures. A major benefit is reduction
in exposure levels for dry cleaning workers. We have therefore formulated
the analysis from the viewpoint of the dry cleaning industry. Costs of
control options were assessed including taxes and tax credits. We intro-
duced an explicit value judgment for the worth of avoiding a case of
cancer, either among the workers, service users, or general public. This
value judgment permitted determination of the best control strategy in the
sense of maximizing benefits from avoided cancers less the costs of the
control.
The impact of uncertainty in this case study is profound. We assessed
uncertainty on the impact of PCE, not by requesting probabilistic judgments
on whether PCE is a carcinogen, but by examining the judgments required to
extrapolate from the results of animal experiments and other available
scientific evidence to a projection of the incidence of human cancer that
PCE exposure may induce. Based upon examination of the literature the
following factors were identified as crucial:
o The basis for extrapolating dose in small laboratory animals to
an equivalent dose in humans.
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o Idencification of the most appropriate laboratory animal to use
as a model for cancer impacts in humans.
o The numerical basis for extrapolation of cancer incidence from
high doses in laboratory animals to lower levels corresponding to
human exposure.
For each factor, we have considered two alternative assumptions, giving a
total of eight combinations. The effect of the alternative assumptions is
great. Using conservative judgments (extrapolation based on surface area,
most sensitive mouse strain as a model for man, linear nonthreshold dose
response relation), the projected increase in national cancer incidence is
about 350 cases per year. Under alternative, less conservative assumptions
(extrapolation based on body weight, rat as the model for man, nonlinear
dose response relation), the projected change in cancer incidence is nearly
five orders of magnitude less, 0.01 cases per year. We therefore examined
each combination of assumptions and developed a probability distribution
over cancer incidence from probabilities assigned to the three crucial
factors in the dose response relationship. From the decision context on
control options, an illustrative calculation indicates it would be worth
several million dollars per year to resolve this uncertainty by such means
as large-scale bioassays of PCE. Such bioassays are in fact being carried
out for PCE by NCI.
SUMMARY OF THE CASE STUDY OF DI-ETHYLHEXYL PHTHALATE
DEHP is a plasticizer, a substance that is combined with plastic
resins to nake flexible plastic products. About 190,000 metric tons of
DEHP are produced and consumed annually in the United States, making it the
most widely used single plasticizer. It is used in a variety of commercial
and consumer products in which it has considerable economic value. It is
not a new chemical and has been in use since the 1930s.
Because of the widespread use of DEHP, a large number of people are
potentially exposed to it. Like other plasticizers, DEHP is not tightly
bound to plastic products and can thus migrate out of the products during
use or after disposal. Small amounts of DEHP have been found in many
samples of food, water, and air. Because DEHP can bioaccumulate, it may be
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passed through the food chain. DEHP has also been found to leach from
plastic blood bags and medical tubinf, such as that used in hemodialysis
machines. Since the toxicity of DEHP is low, the observed concentrations
and resulting human exposures are unlikely to have immediate health
effects. However, in a recent study sponsored by NCI, DEHP was found to
produce a significant number of liver tumors in rats and mice. Thus, EPA
has examined possible human cancer risk for this chemical.
DFI carried out an illustrative calculation of the uncertainty in the
human carcinogenic risk from DEHP using assumptions and methodology similar
to that used for PCE. However, the calculations were based in large pare
on information from EPA internal draft documents. These data, particularly
with respect to exposure, were later determined by EPA to be incorrect. At
EPA's request, we have not included these calculations in this final
report. After considering DEHP as a candidate for designation under
Section 4(f) of TSCA, EPA concluded in early 1982 that no 4(f) funding with
respect to DEHP was warranted [1,2].
The major focus of the DEHP case study is therefore the development
and use of a prototype economic model of the plastics industry with
emphasis on plasticizers and DEHP in particular. The model was developed
to help analyze the economic implications of potential federal actions that
might be taken to regulate the production or use of DEHP. The production
of several different plastic resins, plasticizers, plastics, and their
interactions in the marketplace are represented in the model. The model is
a prototype in the sense that it is intended to provide first-order quanti-
tative estimates of changes in market prices and quantities demanded and
supplied at a national aggregate level of detail. These changes are diffi-
cult to determine, given the complex interaction of substitution among
plasticizers. However, the prototype model illustrates such an estimate,
and it could be refined to give better estimates.
The prototype model was run and solved for equilibrium quantities and
prices for a base case and two regulatory policies. A consumer surplus
calculation was used to estimate the economic impact of prohibiting or
restricting the production and use of DEHP. A complete ban on DEHP would
reduce consumers' surplus on the order of 100 million dollars per year.
Eliminating the use of DEHP from medical applications and automobile
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interiors would have an annual loss of consumers' surplus on the order of
three million dollars.
CONCLUSIONS FROM THE CASE STUDIES ON THE APPLICABILITY OF THE METHODOLOGY
The determination of whether the risk posed by a toxic chemical is
unreasonable generally involves assessment of complex uncertainties.
Although we support the use of quantitative estimates, the use of single
number estimates such as those put forward by EPA's Carcinogen Assessment
Group (CAG) is potentially misleading, for such estimates do not communi-
cate any sense of the uncertainties to nontechnical decision makers. The
use of confidence intervals derived from bioassay sample size is partic-
ularly misleading, since this source of uncertainty in projecting dose
response relationships will often be much less than the uncertainty attend-
ing the basis for extrapolating from small animals to hucnans, the selection
of an appropriate animal model, or the mathematical form of the dose
response relationship.
Suppressing the uncertainty by invoking prudent assumptions is one
basis for regulatory decisions. However, when widely desired products or
services are involved and control will require large expenditures, judg-
ments of prudence by scientists may be unacceptable as a means of determin-
ing public policy. A superior approach in our opinion is to portray the
uncertainty explicitly and incorporate It into the basis for decision. Ue
have illustrated this approach for PCE by calculating cancer incidence not
just for one set of assumptions as CAG has done, but for alternative sets
of assumptions. The methods of decision analysis involve assessment of
probabilities over these alternative cases, permitting the calculation of a
probability distribution on cancer incidence. By using the methods of
cost-benefit analysis, which includes a monetary value on avoiding a case
of cancer, one can then determine the best action in the face of uncer-
tainty. Moreover, the decision analysis approach allows the calculation of
what it would be worth to resolve the uncertainty. This calculation can be
extremely useful for establishing priorities for costly information-
gathering activities such as large-scale bioassays.
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A frequent criticism of cost-benefit analysis is that distributional
effects are suppressed. The PCS case study included calculation of the
health impacts on various affected groups: workers, dry cleaning service
users, and persons who live or work in the surrounding community. While we
have chosen to value a case of cancer avoided as equal in each group, this
assumption can easily be relaxed. Our calculations for PCE show the health
impact as falling primarily on the workers and secondarily on the users of
coin-operated dry cleaners. The risk to the neighboring public is much
smaller by comparison.
Conceptually, the DEHP case study could have proceeded from the
alternative estimates on cancer incidence for different sets of assumptions
through to a probability distribution on cancer incidence for each affected
group under each control option and a value of information calculation in
the sane manner as the PCE study. DFI and EPA concluded that it was not
useful to carry a case study assessment of the health impacts of DEHP. To
provide a meaningful illustrative calculation, we would have needed better
definition of control options for DEHP and a pathway model linking the
impact of emission reduction from the control to reduced levels in the
environment, and especially in food. Developing such a pathway model was
beyond the resources available to our project. Such models typically
involve considerable uncertainty in the relation between specific sources
of the toxic substance and the extent of concentration of the substance in
the human diet, and the methods of decision analysis should be useful in
developing and applying pathway models.
The DEHP case study therefore focused on the economic consequences of
potential regulation. DEHP is typical of a chemical intermediate, and the
consequences of restricting its use are complicated to assess. Many
substitute plasticizers are available at modest additional cost among the
phthalate esters and other classes of chemicals, and both DEHP and its
substitutes can be used in a wide variety of plastics products. How does
one evaluate the economic impacts of regulation in such a complex market
situation, which is typical of many potentially toxic chemicals? Using
existing software developed by DFI for analysis of energy markets, we
constructed a prototype economic model of the plastics industry with
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emphasis on plasticizers , particularly DEHP. The nodel explicitly
represents plasticizers, resins, and plastics production processes; market
competition; and changing demand patterns. Modeling approaches of this
level of detail are needed for EPA to assess the costs imposed on the
industry and on consumers by regulations that change chemical use patterns
within a complex market.
The analytical methods presented in the two case studies should he
viewed as illustrative of applications that must be tailored to the spe-
cifics of the regulatory decision situation and the scientific information
available. If analysis using these methods is to be used as a basis for
public policy decisions, it should receive review and careful scrutiny from
the scientific community through such mechanisms as the EPA Science
Advisory Board. Probabilities such as those we have assessed for PCE on
factors in the dose response relationship should represent a judgment
agreed upon by qualified scientists, and the basis for such a judgment
should be documented and be available for review.
The decision analysis approach presented in this report is not neces-
sarily elaborate computationally or expensive to carry out. The largest
expense is in assembling the information. We expect that the methods
illustrated in the case study for PCE could easily be adapted for many
other chemicals of concern because of their potential carcinogenicity. We
would expect that the conclusions for such analysis would be similar: If
there is widespread human exposure and even a small probability that the
chemical is a carcinogen, information gathering by means such as large-
scale bioassays will have large expected benefits that more than offset the
costs.
The analysis presented in the PCE case study was not representative of
many substances that are candidates for regulation under TSCA. Human
exposure to PCE is well documented. Relatively good data and predictive
models for exposure are available. Such information will not be available
for new chemicals, especially where complex environmental pathways to
people are involved. PCE does not bioaccumulate significantly, whereas
many toxic chemicals do. For such chemicals, assessment of uncertainty on
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human exposure might be as complex as or more complex than the assessment
of uncertainty we have presented on the health impacts of PCE.
The DEHP case study illustrates that assessment of economic impacts of
regulation can involve elaborate and sophisticated calculations, which will
typically be needed when substitution effects in complex markets must be
assessed. Similarly, evidence of bioaccuinulation potential and recognition
that ingestion through food is the leading route for human exposure might
motivate development of an elaborate and sophisticated pathways model for
DEHP and for other chemicals with significant bioaccuinulation potential-
The development and use of large models is consistent with decision
analysis. Large models are in fact a necessary part of the decision
analysis process when sensitivity analysis indicates that the additional
detail in the scientific information that these models embody is important
for regulatory decisions.
Decision analysis provides a potentially useful framework for EPA for
addressing the complexity and uncertainty in the potential harm to human
health and the environment posed by chemical substances. It does not pro-
vide an easy route to regulatory answers, but rather a framework for inte-
grating the available scientific information together with value judgments
fron policy makers to achieve a consistent set of decisions. Use of deci-
sion analysis methods requires skillful tailoring of the methodology to the
application, and EPA will need to acquire the needed skills through experi-
ence. The accomplishments of this project should encourage further experi-
mental applications of decision analysis to chemical regulatory problems.
As decision analysis methods are perceived by policy makers and the scien-
tific community as providing an improved basis for regulatory decisions,
the use of these methods should increase.
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V
Section 2
LITERATURE REVIEW
The primary purpose of this section is to review and summarize the
literature on risk-benefit analysis and priority setting as applied to
toxic substances. A second purpose is to provide a background for the
analysis methodology presented in Section 3. By risk-benefit analysis we
mean the general field of analysis that attempts to balance costs and bene-
fits or risks and benefits. We mean it to include the methods known as
cost-benefit analysis, cost-effectiveness analysis, and decision analysis,
each of which is defined in the section on specific methodologies. Since
the entire body of literature on risk-benefit analysis is vast and has been
reviewed many times before, our discussion will cover only the most per-
tinent works. Additionally, rather than present individual reviews, we
present a review organized by topic, starting with a discussion of general
concepts, followed by a discussion of chemical risks and the implementation
of the Toxic Substances Control Act (TSCA).
GENERAL CONCEPTS
Although there are a number of approaches to risk-benefit analysis,
many of the general concepts are common to the different approaches. We
begin this subsection by discussing the role of analysis in decision making
and the characteristics of analysis recognized as desirable. We then dis-
cuss features of several analytical approaches, methodological issues that
require close attention, and institutional issues related to the use of
risk-benefit analysis.
Role of Analysis in Decision Making
It is generally recognized that the role of analysis should be to
organize information for use by politically responsible decision makers and
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to help focus public discussion on the relevant issues. As stated by
Harold Green:
The object of risk-benefit assessment should be to produce data
for use in political discussion and debate, to elevate the level
of such discussion and debate, and to inform and enlighten, but
not control, those who are charged with responsibility for making
decisions in the public interest ([3),p.290).
A similar view was expressed by the National Research Council in its
report Decision Making for Regulating Chemicals in the Environment [4],
Benefit-cost analysis, at least as we use the terra, is not a rule
or formula which would make the decision or predetermine the
choice for the decision maker. Rather, it refers to the system-
atic analysis and evaluation of alternative courses of action
drawing upon the analytical tools and insights provided by
economics and decision theory. It is a framework and a set of
procedures to help organize the available information, display
trade-offs, and point out uncertainties. In this way benefit-
cost analysis can be a valuable aid; but it does not dictate
choices, nor does it replace the ultimate authority and responsi-
bility of the decision maker CIA],p.39).
Thus, although analysis can be useful in the regulatory decision-making
process, analytical results do not dictate the regulatory choice.
Desirable Characteristics of Analysis
A number of desirable characteristics of analysis have been identi-
fied in the risk-benefit literature. According to the National Research
Council in its report Decision Making in the Environmental Protection
Agency [5], an analysis should explicitly state the objective and feasible
alternatives, as well as identify the possible outcomes of EPA action and
the uncertainties associated with those outcomes:
EPA's decisions on standards and regulations should be supported
by analyses that explictly state the objectives of the decisions,
identify feasible alternatives, evaluate Cquantitatively, to the
extent possible) the consequences of each alternative decision,
explore potential problems in implementation, and indicate and
examine the degree of uncertainty about the effects of EPA
actions. The analysis should be available to the public
([5],p.10).
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Another important characteristic of analysis is the clear identi-
fication and separation of value judgments from objective statements of
information. The National Research Council has recommended:
Value judgments about noncoramensurate factors in a decision such
as life, health, aesthetics, and equity should he explicitly
dealt with by the politically responsible decision makers and not
hidden in purportedly objective data and analysis ([4],p.10).
The need to separate the scientific information from the value judgments
in arriving at a basis for decision is a major theme of William Lowrance's
book, Of Acceptable Risk: Science and the Determination of Safety:
Failure to appreciate how safety determinations resolve into the
two discrete activities...jives rise to the false expectation
that scientists can measure whether something is safe. They
cannot, of course, because the methods of the physical and bio-
logical sciences can assess only the probabilities and conse-
quences of events, not their value to people....Deciding whether
people, with all their peculiarities of need, taste, tolerance,
and adventurousness, might be or should be willing to bear the
estimated risks is a value judgment that scientists are little
becter qualified to make than anyone else ([6],p.9).
Most authors also believe that it is desirable to quantify as many of
the elements of analysis as possible. Quantification facilitates discus-
sion and focuses debate on the relevant issues. However, "Quantification
is not an end in itself, but is rather a means to make the reasoning behind
a regulatory decision clear and explicit" ([7),p.6).
Finally, it is desirable to have systematic procedures for processing
information. As stated by the Conservation Foundation:
Somehow the information must be analyzed and synthesized. This
may be done intuitively, if the decision maker chooses to focus
on only a few of the relevant factors. But ideally this process
should be approached more systematically and predictably
(I71,p.9).
To summarize, analysis should contain explicit statements of alterna-
tives, information, and values; it should contain quantitative elements to
the extent feasible; and it should contain a systematic procedure for
organizing the elements.
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Specific Methodologies
We now present a brief review of three methodologies: cost-benefit
analysis, cost-effectiveness analysis, and decision analysis. Although
each methodology was developed from a slightly different point of view,
many of the specific techniques are similar. In applying each methodology,
there are fundamentally difficult issues, such as valuing noneconoraic
effects and describing distributional effects of regulation. These
difficulties are discussed in later paragraphs.
Cost-Benefit Analysis. Cost-benefit analysis (Prest and Turvey [8], Hill
[9], Mishan [10]) has been widely used in the public sector, particularly
by federal agencies such as the Corps of Engineers. It is most often used
to choose an alternative for a specific project and is less easily applied
to regulatory decisions. Cost-benefit analysis is based on maximizing
economic efficiency, with the assumption that maximum social welfare will
thus be realized. It attempts to take into account all costs and benefits
to all groups in society, whether incurred directly or not.
The major limitation of the approach is that all outcomes to be in-
cluded In the analysis must be specified, in monetary terms or must have
monetary value imputed from an appropriate market. In essence, it is
assumed that society's preferences are expressed by the monetary values
placed on goods and services. This is a standard assumption of welfare
economics, and while it may be appropriate in some cases, it runs into
trouble when dealing with regulatory decisions, where many of the outcomes
are not subject to market pricing. Hill summarizes the approach and
suggests some of the problems:
...cost-benefit analysis can be conceived as a process whereby a
public agency in pursuit of economic efficiency allocates its
resources (land, labor, and capital) in such a way that the most
'profitable' projects are executed and developed to the point
where marginal benefits equal marginal costs. Thus the last
dollar invested in a project brings a marginal economic return at
least as high as that possible were the funds spent in some other
way. However, so direct an extension of the private allocation
model to allocation in the public sector raises a number of
problems. The private and public models are analogous only if
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(1) no barriers exist Co the flow of funds and resources, (2)
benefits and costs can be determined at competitive prices, (3)
there are no external economies or diseconomies, and (4) no other
external effects exist ([9],p.19).
Hill goes on to point out that such conditions rarely exist in the public
sector, noting that "the existence of a public sector in a free enterprise
economy is justified, in part, by the absence of these very factors"
(19),p.19).
Despite the problems and limitations, however, cost-benefit analysis
is being widely advocated as a means of improving regulatory decisions.
The recently released report of the Toxic Substances Strategy Committee
(TSSC) recommends a federal effort to improve methods for estimating costs
and benefits and for evaluating their relative importance:
A shortcoming in federal regulation of toxic substances is the
failure to improve analytical methods for developing cost-benefit
information and for using it as appropriate, as one of several
tools in regulatory decision making despite the inherently
limited analysis. Improvements in analytical methods will help
the agencies to select consistent and cost effective ways to
reduce risks ([ll],p.l01 and p.xxxiii).
Cost-Effectiveness Analysis. Cost-effectiveness analysis is similar to
cost-benefit analysis, except that usually a goal is specified and analysis
is performed to determine the least-cost (most cost-effective) means of
achieving that goal. One possible advantage of this approach is that only
the costs and not the benefits have to be quantified. An example of cost-
effectiveness analysis would be the examination of the least-cost method of
achieving a prespecified pollution emission standard, in which the costs of
meeting the standard are estimated but no attempt is made to quantify the
benefits. Some shortcomings of this approach as applied to TSCA were
identified by the Conservation Foundation:
Determination of whether risk posed by a chemical is unreasonable
involves choosing among goals, and therefore cost-effectiveness
analysis does not help to provide an answer. It may be useful in
choosing among alternative regulatory techniques (prohibition of
uses versus volume limitation, for example), once the decision
about level of control has been made. However, it should be kept
in mind that, even when the goal has been unambiguously specified
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cost-effectiveness analysis poses the same. problems, in quanti-
fying and aggregating factors that cost-benefit analysts does
([7],p.34).
Decision Analysis. Decision analysis (Howard [12,13,14], North [15], and
Raiffa [16]) combines the concepts of decision theory with the oodeling
techniques of systems analysis. Decision theory provides the basis for the
analysis of uncertainty and values of the decision maker, while the use of
explicit structural models gives decision analysis the power to cope with
complex interactions and dynamic behavior. An example of decision analysis
applied to sulfur oxide emission control is given by North and Merkhofer
[17,18]. Other examples include Howard et al. [19] and Keeney and
Robilliard [20].
One of the main features of decision analysis is the explicit incor-
poration of uncertainty into the risk-benefit calculations. As stated by
the National Research Council:
Decision analysis does not supplant or make unnecesary other
methods of analysis such as cost-benefit analysis or cost-
effectiveness analysis. Rather, decision analysis should be
looked upon as a technique for incorporating uncertainty into
these other forms of analysis ([5],p.226).
And as concluded by the Conservation Foundation:
Given that a high degree of uncertainty surrounds most of the
components of decisions about toxic chemicals and that decision
analysis provides the best set of analytical tools for dealing
with uncertainty, the application of such analysis to TSCA
decisions clearly warrants examination ([7],p.34).
Decision analysis employs a quantitative description of the probabil-
ities and values for the consequences or outcomes that may result from each
of the decision alternatives. Assessment of the probabilities and conse-
quences may be carried out directly by encoding the judgment of experts or
indirectly through the use of structural models to predict the consequences
or outcomes of concern.
A structural model is a mathematical representation of how various
physical, biological, and economic factors relate to or determine outcomes
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such as morbidity and mortality; damage to nonhuman species; and economic
welfare of consumers, producers, and workers. In an environmental appli-
cation of decision analysis such as [18], models may be used to describe
the relation of emissions to output of economic goods and services, disper-
sion and conversion of emissions through environmental pathways to reach
receptors, dose response relationships, and economic impacts of controls.
The structural models make it possible to incorporate explicitly complex
information on causal and statistical relationships in assessing probabili-
ties on outcomes.
Decision analysis frequently involves use of explicit quantitative
values assessed on outcomes such as environmental and property damage,
mortality and morbidity, and intangible issues such as aesthetic and
political concerns [18,19]. The central advantage of this practice is that
it allows sensitivity analysis to be carried out to determine which of the
many uncertainties and value issues are most important in determining the
best decision alternative. The efforts of the analysts and the attention
of the decision maker may then be focused on these issues. Calculations of
the value of resolving uncertainty require explicit quantitative value
assessments; these calculations are often useful in providing insight on
the desirability of gathering further information from research programs
[18,19].
Issues That Require Close Attention
At least four issues associated with risk-benefit analysis have been
identified in the literature as requiring close attention: valuing human
life, describing distribution effects, specifying preferences over time,
and quantifying uncertainty.
Value of Life. Three main approaches have been taken to the problem of
deternining what resources should be devoted to reducing statistical risks
to human life. The first is an attempt to place a dollar value on the life
that is lost through the concept of "human capital" ([5],p.233). Human
capital is the present value of an individual's projected future earnings
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taking into account the age, sex, and education of the particular indi-
vidual. A major problem with this approach is that Is does not reflect
social attitudes toward life. For example, its vise implies that retired
people or those unable to work would have no value to society.
An alternative that avoids the problem of assigning a value to life is
determining an individual's "willingness to pay" for a certain reduction
in risk ([5],p.235). The amount that should be spent on a risk-reducing
project would then be the sum of the amount each individual would be will-
ing to pay. Of course, determining this amount may be difficult; some
authors have maintained that the determination can be accomplished through
questionnaires or by observing pay differentials for jobs that pose higher
than average risks ([5],p.236 and [21],p.138).
The third method of valuing risk reductions, developed by Howard [22],
Involves the concept of "small-risk value of life." This method is based
on the ethical view that each individual has the right to make or delegate
decisions that affect his or her life. Most individuals would choose to
make their own decisions when high risks are involved and might choose not
to take some risks, such as playing Russian roulette, even if offered arbi-
trarily large sums of money. However, for decisions involving small risks,
— <4
on the order of one chance in 10 or smaller, an individual might
choose to delegate those decisions to his or her agent (e.g., the govern-
ment), who would use that individual's small-risk value of life in the
expected value sense to determine the best alternative.
The implication of the "snail-risk value of life" approach is that the
judgment of the appropriate tradeoff between probability of death and mone-
tary gain or loss is a subjective choice by the individuals affected. The
value of life is not a quantity susceptible to "objective" measurement by
economists or scientists. The monetary value ascribed by an individual to
reducing his or her probability of death will vary from individual to indi-
vidual; the value will depend on the individual's health, assets, attitude
toward risk, and many other factors. The process of making explicit judg-
ments on the tradeoff between money and risk makes many people uncomfort-
able, but decisions implying such judgments are being made constantly
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both by individuals and by public officials with responsibility to protect
public health and safety.
Distribution Effects. A number of authors have pointed out that the
concept of economic efficiency ignores important distributional effects
that governmental regulations nay impose. As noted by the Conservation
Foundation:
It is important for the decision maker to have information about
how costs and benefits are distributed among various sectors of
the population (e.g., workers, direct consumers of products con-
taining the substance, general urban populations, etc.). In par-
ticular, he should consider whether both the costs and the bene-
fits accrue to the same sectors, or whether some receive the
benefits, while others bear the cost ([7],p.13).
An example of such a distributional effect would be an action that
resulted in the closing of an industrial plant. From the point of view of
society as a whole, if there is full employment, the closure may not be a
cost, since workers may find employment elsewhere and the capital in the
plant may be put to use elsewhere in the economy. However, from the point
of view of the workers and the community in which the plant was located,
the costs may be great.
Distributional effects such as these involve multiparty interactions,
which are traditionally the concern of game theory [23] and social choice
theory [24]. Although these analytical areas are not highly developed in
practical applications, they indicate that one potentially valuable way to
address distribution effects is to identify clearly the consequences to
each party and then let them use that information to come to a resolution
by political or legal means. (For a discussion of multiparty decision
analysis see [25].)
Time Preferences and Discounting. Discounting is a traditional method of
comparing and aggregating costs and benefits that occur at different points
in time. In dealing with strictly economic costs and benefits, it may be
appropriate to discount cost and benefits by a "private opportunity rate,"
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which corresponds roughly with market interest rates ([5],pp.237-238).
However, the application of discounting in the context of chemical
regulation can cause considerable difficulties:
For example, if the discount rate were 5 percent, 100 cases of
poisoning 7 5 years from now would be equivalent to about 3 cases
today; or 1 case today would be valued the same as 1,730 cases
occurring in 200 years, or the same as the current world popula-
tion (more than 3 billion cases) in 450 years. Clearly, inter-
generational effects of these magnitudes are ethically unaccept-
able, yet they might be made to appear acceptable if the tradi-
tional social rate of discount concept were applied. There is as
yet no generally accepted method for weighing the intergenera-
tional incidence of benefits and costs ([4],p.43).
To overcome these problems, it has been proposed that time preferences
involving intergenerational effects be recognized as value judgments and,
whenever possible, they should be addressed directly by assigning present
values to future impacts rather than through a discount rate. (See [26],
p.62 and [27 ].)
Uncertainty. A fundamental characteristic of risks and benefits Is that
their quantification contains many uncertainties. These uncertainties
arise from a lack of knowledge in the mechanisms of the risk (e.g., the
relationship between exposure to a chemical and the effect on health) and
the economics associated with the benefits (e.g., the availability of
adequate substitutes for a particular chemical). However, regulatory
agencies are confronted with the necessity of making decisions, including
the alternative of doing nothing. Therefore, the assessments of risks and
benefits must be made in spite of the uncertainties. In describing the
estimation of cancer risks, the Interagency Regulatory Liaison Group (IRLG)
has stated:
Because extrapolations are involved, uncertainties are necessar-
ily attached to the cancer risk estimates that can be made with
current methodologies. Furthermore, uncertainties arise from
other sources, particularly from attempts to identify accurately
conditions and levels of exposure of the human group or individ-
ual. Despite the uncertainties, risk estimates can be and are
being made not only by some regulatory agencies but by other
scientific bodies ([28],p.39871).
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In a recent report, the Office of Science and Technology Policy
(OSTP) recommended that uncertainties be included in risk estimates in the
first stage of a two-stage decision-making process:
Characterization of the overall risk for humans in quantitative
terms should be the final step in the stage I process. The
characterization should use the most likely value for each term
in the risk equation and should include an appropriate represen-
tation of uncertainty ([29],p.174).
A solution to the problem of representing uncertainty is suggested by
decision theory. As stated by the National Research Council:
One of the contributions of decision theory is to show how prob-
abilities can be used to quantify uncertainties. People are
accustomed to hearing probabilities of rain in predictions of
tomorrow's weather or odds on forthcoming elections and sporting
events. The same concepts may be used to describe uncertainties
related to toxic substances. An example of such a probability
statement would be: "There is a 90 percent probability that the
chemical will produce no increase in the incidence of cancer, and
a probability of 5 percent is assigned that the chemical is
weakly carcinogenic and would result in an annual increase of 1
to 100 additional deaths from cancer. A 5 percent probability is
assessed that the compound is strongly carcinogenic in man and at
least 100 deaths from cancer would result from each year of
unrestricted use" ([4],pp.43-44).
The source of these probabilistic estimates can be either the decision
maker himself or experts in a particular field. Slesin and Sandler [30]
advocate the use of chemical categorization systems to help scientific
researchers assign probabilities to the likelihood that a chemical causes
cancer:
In the analysis of vinyl chloride analogs, for instance, we
suggested several different structure-activity categorization
schemes, each based upon a different family of potential car-
cinogens. Given present knowledge, researchers can assign dif-
ferent subjective probabilities to the likelihood that an un-
tested analog, a member of one of the several possible categor-
ical families, causes cancer or is otherwise toxic ([30],p.389).
To help quantify these probabilities, analysts can use procedures for
encoding probabilities as described by Spetzler and Stael von Holstein [31]
and Tversky and Kahneman [32,33]. EPA is currently sponsoring a review of
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the rich literature on encoding probabilities in the context of possible
use of decision analysis methods in setting ambient standards for criteria
air pollutants [34].
Legal and Institutional Basis for Using Risk-Benefit Analysis
The legal basis for performing risk-benefit analysis comes from three
sources: legislation, executive orders, and court decisions. Here we
briefly review these sources as they apply to the regulation of toxic
substances.
Legislation. There is a wide range of approaches to the use of cost-
benefit or risk-benefit analysis in environmental legislation. The most
restrictive is the Federal Food Drug and Cosmetic Act, which requires a ban
of any food additive that is found to be carcinogenic in animals regardless
of the benefits [35]. At the other extreme is TSCA, which specifically
requires tradeoffs of risks and benefits. Under TSCA, EPA must consider
and make findings on
...the effects of such substance or mixture on health and the
magnitude of the exposure of human beings to such substance or
mixture, the effects of such substance or mixture on the environ-
ment and the magnitude of the exposure of the environment to such
substance or mixture, the benefits of such substance or mixture
for various uses, and the reasonably ascertainable economic con-
sequences of the rule, after consideration of the effects on the
national economy, small business, technological innovation, the
environment and public health [36].
However, actual procedures for making tradeoffs are not included in TSCA
or in other environmental legislation. As stated by Baram:
Congress has frequently called upon the regulatory agencies to
consider and balance multiple societal objectives in their
decision-making, but has usually failed to provide guidance as to
how agency discretion on such substantive matters is to be struc-
tured. At its most basic level, the necessary guidance would
address the relative importance of the various factors to be in-
cluded in the balancing process; the classic problems of valua-
tion, discount rates and distribution; and the analytical frame-
work to be used to reach decisions ([37],p.49).
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Thus, Congress may permit or even encourage the use of risk-benefit
analysts but leaves a great deal of discretion to the implementing agency.
Executive Orders. Both the Ford and Carter administrations have issued
executive orders requiring federal agencies to assess the economic impacts
of major regulations and rules issued by thera. Under President Ford's
Executive Order 11821, amended by Executive Order 11949, federal agencies
were required to prepare "inflationary impact statements" for all major
legislative proposals, regulations, and rules. Similarly, President
Carter's Executive Order 12044 required that "regulatory analysis" be
performed in developing regulations. The analysis must include careful
examination of alternative approaches early in the decision-making process:
Each regulatory analysis shall contain a succinct statement of
the problem; a description of the major alternative ways of
dealing with the problem that were considered by the agency; an
analysis of the economic consequences of each of these alterna-
tives and a detailed explanation of the reasons for choosing one
alternative over the others [38).
Executive Order 12291 issued at the beginning of the Reagan Administration
sets forth the general requirements that:
(Section 2b) Regulatory action shall not be undertaken unless the
potential benefits to society for the regulation out-
weigh the potential costs to society;
(Section 2c) Regulatory objectives shall be chosen to maximize the
net benefits to society;
(Section 2d) Among alternative approaches to any given regulatory
objective, the alternative involving the least net cost
to society shall be chosen; and
(Section 2e) Agencies shall set regulatory priorities with the aim
of maximizing the aggregate net benefits to society,
taking into account the condition of the particular
industries affected by the regulations, the condition of
the national economy, and other regulatory actions con-
templated for the future ([39],p.13193-4 ).
Thus, executive orders have reinforced the legislative requirements for
risk-benefit analysis and have tended to emphasize the economic conse-
quences of regulation.
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Court Decisions. Several recenc court decisions have shaped the use of
risk-benefit analysis. In the recent cotton dust case [40], the court
upheld standards that were based on a logical analysis of the best
available information and rejected standards that were not supported by
analysis. In an earlier case, Ethyl Corporation v. EPA [41], the court
"expressly held that hypothetical and other forms of scientific evidence
not reaching the level of certainty were relevant to a judgment of risk
posed by chemicals" ([30],p.387). In Reserve Mining Company v. EPA, the
court supported the use of hypothetical evidence to predict risk. The
court stated:
These concepts of potential harm, whether they be assessed as
"probabilities and consequences" or "risk and harm," necessarily
must apply in a determination of whether any relief should be
given in cases of this kind in which proof with certainty is
impossible [42].
The recent Supreme Court cases on the Occupational Safety and Health
Administration (OSHA) proposed benzene and cotton dust standards have been
regarded as Important legal tests on the need for cost-benefit analysis in
regulation of toxic chemicals. In the cotton dust decision, the court held
that cost-benefit analysis is required only if explicitly stated in the law
[43). In particular, OSHA is not required to perform such analyses.
Neither decision, however, provided clarification of the Court's position
on use of cost-benefit analysis, treatment of uncertainty, or definition of
"significant risk" [43,44],
In summary, the courts have generally supported the use of risk-
benefit analysis and the use of probabilities to specify risks, but the
courts have provided EPA and other regulatory agencies with little specific
guidance on how risk-benefit analysis should be used in their decision
processes.
RISKS AND BENEFITS OF CHEMICAL SUBSTANCES
Since World War II, the American chemical industry has produced a
proliferation of chemicals that have contributed to 'our standard of living.
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Pesticides and food additives have helped to produce a "cornucopia of
diverse prepared foods and foodstuffs made independent of local growing
seasons or of climate by safe transportation over long distances"
([45],p.5). Synthetic drugs have contributed to the treatment of disease,
and a vast array of chemical products such as plastics have made life
easier and more productive. Toxicological testing for acute effects has
long been an accepted industry practice, but adverse chronic effects are
considerably more difficult to determine, especially when a long latency
period may be present. In recent years there has been an Increasing level
of public concern for potential adverse health and environmental effects
from chronic exposure to manufactured chemicals. As stated by Philip
Handler:
Growing knowledge of the biology of cancer and the diversity of
chemical structures capable of eliciting a neoplastic response in
laboratory animals—together with the thalidomide episode, which
demonstrated that a substance which successfully passed the
screen of assays for acute toxicity could, nevertheless, have
disastrous long-term effects—gave rise to concern for possible
consequences of chronic exposure to low levels of diverse
materials ([45],p.5).
The essence of risk-benefit analysis of chemicals is thus the estima-
tion of the risks from chronic exposure, an estimation of the benefits, and
a balancing of risks and benefits. Handler proposes a sensible approach to
this balancing:
A sensible guide would surely be to reduce exposure to hazard
whenever possible, to accept substantial hazard only for great
benefit, minor hazard for modest benefit, and no hazard at all
when the benefit seems relatively trivial ([45],p.8).
In this subsection, we summarize the current state of the art in
estimating risks and benefits of chemicals. In the following subsection,
we describe some suggested approaches to the balancing process and the
problem of setting priorities in the context of the Toxic Substances
Control Act.
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Characteristics of Chemical Risks and Benefits
Chemical risks and benefits have some characteristics that distin-
guish them from other types of risk-benefit problems. Page [46,47) has
identified at least five characteristics: ignorance of mechanism, potential
for catastrophic costs, relatively modest benefits, low probability of
catastrophe, and difficulty of detecting adverse effects. A sixth charac-
teristic, which is really a consequence of the above five, is the diffi-
culty of establishing that a chemical is safe.
Ignorance of mechanism may apply to a number of stages that relate the
manufacture and use of a chemical to an adverse health or environmental
impact. Either the chemical or a byproduct of chemical manufacture may
possess the toxic effect. The pathway linking the receptor to the emission
is typically complex and often poorly understood. A chemical such as
mercury may be released at a low concentration, transformed by microorgan-
isms into a more toxic form (methyl mercury), and concentrated in the aqua-
tic food chain. Very little knowledge is available on the biochemical
mechanisms that lead to cancer, birth defects, or chronic health impair-
ments, so that dose response relationships must be inferred from
epidemiological investigations or extrapolations from animal experiments
that are typically carried out at very high dose levels.
A potential for catastrophic costs arises when a chemical can be
introduced on a massive scale over a time that is short compared to the
latency period for adverse effects. Thus, many people may be exposed to
irreversible adverse effects before the damage becomes apparent and regula-
tion can be imposed. The increase of lung and other cancers followed by
several decades the increase in smoking, first in men and then in women. A
food additive or consumer product (e.g., flame-resistant children's sleep-
wear) could in principle expose a large number of people to a chemical that
later turned out to be a carcinogen, with the result that an epidemic of
cancer, birth defects, or chronic impairment occurs in the affected group.
Such epidemics have indeed occurred on a limited scale with drugs (thalido-
mide, diethylstilbestrol) and with chemicals in the workplace (vinyl chlor-
ide, beta naphthalamine, bischloromethylether, asbestos).
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Measured against the potential for catastrophe, the benefits of the
chemical nay appear relatively modest. However, when substitutes are not
readily available, the judgment of risks versus benefits may be difficult.
When the accumulated scientific evidence indicated that increasing levels
of fluorocarbons in the atmosphere might deplete the ozone layer, use of
fluorocarbons as aerosol propellants was quiclcly banned. However, fluoro-
carbons continue to be used as refrigerants, in part because good substi-
tute refrigerant materials do not exist. Many persons regard food color-
ings as providing little benefit. The benefits of saccharin compared to
its risk as a carcinogen have been the subject of intense public debate
1481.
A fourth characteristic of chemical risks is the low probability of
the catastrophic outcome. In fact, most chemicals do not cause cancer,
birth defects, or other serious and potentially widespread toxic effects:
For many potentially toxic chemicals, and for nuclear power,
ozone depletion, and recombinant DNA., what little is known about
mechanism suggests that the probability of the catastrophic out-
come is low, much lower than the probabililty of the favorable
outcome ([46] ,p.210).
Even though the potential loss from the catastrophic outcome may be far
greater than the benefits, the low probability of the catastrophic outcome
may make the problem more evenly balanced in the judgment of the public and
the officials who oust assume responsibility for regulatory decisions.
The fifth characteristic is the difficulty of detecting adverse
effects:
As a general observation, it appears that for many toxics
problems, adverse effects, when they exist, are hidden with the
consequences that even expensive, well designed tests have low
probabilities of discovering effects when they exist ([47],p.10).
Detecting low-potency carcinogens is particularly difficult. For example,
consider an animal test for carcinogenicity for which there are 50 animals
in the control group and 50 in each of several treated groups. If the
significance level is set at 5 percent—that is, there is a 5 percent
chance the test will give a false positive reading—then there can be up to
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a 50 percent chance that the test will not detect an effect when it is
actually present ([47],p.8).
A consequence of the difficulty of detecting an adverse effect is the
difficulty of establishing that a chemical is safe. After examining the
methodological difficulties of epidemiology and the low sensitivity of
animal bioassays, Leape concluded that neither procedure could ever "pro-
vide conclusive proof that a substance is not carcinogenic" ([49],p.92).
William Clark [50] has compared regulation with medieval witch hunting.
Once a suspect witch (or a suspect chemical) has been accused, it is im-
possible to assemble enough evidence to conclusively establish innocence.
A potentially serious consequence of the difficulty of proving a chemical
not toxic is that too much attention may be focused on relatively unimpor-
tant chemical risks that have previously been identified, while more
serious risks continue to go unrecognized.
All of these characteristics of chemical risks taken together result in
a complex risk-benefit problem. However, the need for regulatory decisions
requires approaching the complexities and uncertainties with as much logic
and analytical skill as possible.
Chemical Toxicity
A key element in the risk-benefit analysis of chemicals is the
toxicity of the chemical under consideration. Estimates of toxicity for a
particular chemical can be derived from toxicity tests and from comparisons
with chemicals of similar structure and known toxicity. In this section we
summarize the characteristics and limitations of toxicity tests, describe
the use of chemical structure, and point out some of the difficulties in
predicting human response to various levels of chemical concentrations
(dose response).
There are many hazards to human health from toxic chemicals. Chronic
neurological damage, mutations, birth defects, cancer, and many other
adverse health impacts may be caused by chemical substances. Most discus-
sions of toxicity emphasize cancer, for reasons that were articulated in
the TSSC report:
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...cancer captures the most public attention because It Is preva-
lent, It is often irreversible, it is poorly understood, and it
is a disease that often involves considerable pain and suffering.
Many substances that cause cancer have other toxic effects ([11],
p.116).
Our discussion will therefore enphasize testing for carcinogenicity; it
should be recognized, however, that a much wider range of health effects
may be of concern in assessing the risks from toxic substances.
Toxicity Testing. There are three main types of toxicity tests: epidemio-
logic studies, animal bioassays, and short-term tests (usually tests of
single cells). Each type of test has its advantages and disadvantages, and
each can contribute to the overall assessment of toxicity.
Epidemiology is the study of the correlations between disease rates
and environmental factors in a human population. Epidemiological studies
for cancer are usually of two types: "cohort" studies and "case-control"
studies. Cohort studies compare groups differently exposed to a suspect
substance for differences in disease rates. Case-control studies compare
those who contract a certain type of disease with those who do not to
identify differences in their environmental conditions ([49],p.92).
Epidemiology is generally considered to be the soundest basis for
determining cancer risks to humans ([51],p.1200). However, epidemiology
has many limitations:
Theoretically, as direct evidence of human response, epidemiology
can offer compelling evidence of carcinogenicity and the magni-
tude of the risk to humans posed by a particular substance. Un-
fortunately, epidemiology at best is a crude science. A variety
of factors may enhance or disguise the actual effect of a car-
cinogen. A scientist is unlikely to discover whether a popula-
tion was exposed to substances which enhance or inhibit the
response to the studied carcinogen. Nor can the scientist esti-
mate precisely the degree to which demographic differences are
responsible for differences in the cancer rates of the observed
groups ([49],p.92).
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One difficulty is in quantifying the potency of a potential carcinogen:
3ut even when epidemiology indicates that a substance causes
cancer, It often cannot measure the potency of that substance.
It is simply too insensitive and imprecise. The scientist cannot
control the conditions of exposure, and therefore cannot isolate
the effect of the suspected carcinogen for precise measurement.
Nor can he or she determine, in most cases, the level of exposure
of the sample population to a given substance, since information
on ambient concentrations, or even on emissions, is often crude
or completely unavailable ([49],p.92).
Furthermore, epidemiologic evidence is available only after years of expo-
sure to an identifiable group of people such as smokers or workers exposed
to a particular chemical. Because of these limitations, there are only 32
chemicals for which there is some epidemiological evidence of human carcin-
ogenicity ([51],p.1201). Clearly, other methods for determining toxicity
must also be used.
Because of the inherent difficulties of epidemiology, long-term animal
bioassays are most often used for regulatory purposes to determine whether
or not a substance should be considered a carcinogen. An animal bioassay
is a laboratory procedure in which scientists administer the test substance
in one or more dose levels to one or more groups of animals and compare
their cancer incidence to that of a control group that has not been exposed
to the substance. Bioassays have several advantages over epidemiology:
Laboratory testing on rodents avoids many of the pitfalls of
epidemiological studies. Because experimental conditions and
doses can be controlled, scientists can isolate the effect of a
suspected carcinogen. In addition, because of the short lifespan
of these animals, scientists can observe their response to a
carcinogen within one or two years after exposure ([49],p.93).
Under the IRLC guidelines [28], a substance is considered as a
potential human carcinogen once statistically significant quantities of
tumors have been identified in experiments on at least one animal species
[11,28]. However, there may be no direct evidence that the substance does
indeed cause or promote cancer in humans.
The results of animal tests generally agree with human epidemiology.
Of the 82 chemicals for which there is sone epidemiological evidence of
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human carcinogenicity, all but arsenic have been shown to be carcinogenic
in animals ([51],p.1201). However, because test populations cannot always
be found for epidemiologic studies, it is not possible to determine how
many of the chemicals found to be carcinogenic in animals are also carcin-
ogenic in humans.
On the practical side, the use of aninal testing is limited in some
applications because of the time required (about three years) and the cost
($250,000 or more) ([52] ,p.589). Also, because of the limitations of space
and trained personnel, the current capacity for animal carcinogenicity
testing is about 300 chemicals per year ([51],p.1200), which is small com-
pared to the 43,000 chemicals on the TSCA list of chemicals produced in or
imported into the United States in the past four years [11]. However, tnany
of these 43,000 chemicals will not be of interest for testing because human
exposure to thera is essentially negligible.
To overcome the cost and time requirements of animal testing, a number
of short-term (in vitro) mutagenicity tests have been introduced. The most
widely known of these tests is the Ames test, in which a mutant form of
Salmonella bacteria is tested for mutations back to its normal form
( [52 ] ,p.587). Most short-term tests are designed to detect damage to DMA,
the theory being that this damage is a major cause of cancer and genetic
birth defects.
The correlation between the results of short-term tests and animal
tests is good but not perfect. Ames ([52],p.590) reported that of 300
chemicals reported as carcinogenic and noncarcinogenic in animals, about 90
percent (158 out of 176) of the carcinogens were found to be positive in
the Ames test, while only about 12 percent (13 out of 108) of the noncar-
cinogens were positive in the Ames test.
In addition to the Ames test, there are many other short-terra muta-
genicity tests, including tests with cultured animal or human cells and
improved versions of the drosophila (fruit fly) test for mutations after
reproduction. The advantage of the tests with cultured cells is that the
target cell may be more representative for actual cancer formation than a
bacterial cell. On the other hand, a test with a small animal,, even an
insect, has the advantage of detecting possible carcinogenic metabolic
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products of a chemical. Additional short-terra tests that will provide a
more appropriate range of biochemical environment for the test cells are
currently being developed. Many investigators favor the use of a battery
of short-term tests, since no system is perfect and each system detects a
few carcinogens that others do not ([52],p.591). However, with a battery
of tests, the interpretation of a mixture of positive and negative results
may be difficult.
Better understanding of the mechanisms for chemical carcinogenesis is
urgently needed. The reasons a chemical will cause cancer in one species
but not in another are rarely known. In the absence of such knowledge,
regulators must assume that cancer in animals implies a potential for
cancer in hunans:
Most carcinogens appear to undergo a chemical change before they
produce their effect in the body....It appears that some
chemicals cannot be activated to their carcinogenic form until
some such metabolic process has taken place. Susceptibility to
an agent may therefore depend on how it is metabolized. To date,
little evidence suggests that the metabolism of agents to
carcinogenic intermediates in animals is not similar to that in
humans ([11],p.130).
Short-term tests and animal bioassays of suspect carcinogens may add to
knowledge of the mechanism for chemical carcinogenesis. Research
priorities for animal bioassays and cellular level tests should reflect
this consideration in addition to the need to screen chemicals for
regulatory decisions [53,54].
Use of Chemical Structure. Especially in the absence of test data on a
specific substance, its chemical structure can often be used to estimate
its potential toxicity. Even when test data are available, structural
similarities with other known toxic chemicals may indicate that a chemical
should be regarded as nore hazardous than merited on the basis of the test
data alone.
The use of structure to estimate toxicity is a variation of the use of
"structure-activity relationships" (SAR), which have been used for years in
the development of new drugs and pesticides. Basically, scientists start
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with a compound of known structure and activity and predict changes in
biological activity with changes in the functional groups of atoms attached
to specific sites. Structure-activity relationships can be used as a basis
for categorization of chemicals in TSCA ([30],p.372). The IRLG has stated,
"There is a moderately substantial base of empirical data that permits
conclusions about carcinogenic potential on the basis of molecular
structure" ([28],p.39870).
Dose Response Issues. In order to detect a toxic effect in a small number
of animals in a test, typically a very large dose is required. The problem
is first to extrapolate the response to lower doses in the test animal and
then to translate that response to humans. One way of doing this is
through mathematical models of response. Donniger has described some of
the limitations of using such mathematical models:
Limited to observations at unrealistically high doses in unreal-
istically low numbers, the scientist's recourse is to use mathe-
matical models of dose response relationships to extrapolate from
experimental results downward to the effects of low doses.
However, like the theories on which they are based, the models
yield widely divergent estimates of the risk associated with each
low dose. The extent of the differences is astounding. For
example, the major models differ by a factor ox 100,000 on the
size of the dose that creates a risk of one cancer in a million
subjects. The models do provide credible outer limits for the
risk associated with each dose, and they do permit the ranking of
carcinogens in rough order of their potency. But they cannot
provide the-regulator with precise estimates of the risks of low
doses ([55],p.592).
Even stronger criticism of the use of extrapolative predictions of dose-
response relationships is found in a recent National Academy report on
pesticide regulation [56], especially in Appendix A and in the references.
From the literature, there appears to be no general consensus on the shape
of dose response curves. The IRLG advocates the use of a linear model to
give a conservative estimate of risks:
The linear nonthreshold dose response model is most commonly
used at the present time. Of the various models, it appears to
have the soundest scientific basis and is less likely to
understate risk than other plausible models ([28],p.39873).
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Others, however, argue for the existence of thresholds below which no re-
sponse occurs [57]. The more fundamental problem Is, of course, the lack
of knowledge of the basic causal mechanises of cancer, including transport
and metabolism in the body and potential detoxification of the chemical or
repair of damaged cells. Apparently the only way to deal with these un-
knowns is to recognize that the uncertainties may extend over many orders
of magnitude and to reflect the extent of such uncertainties in the con-
clusions to be drawn from analysis.
Exposure
Although it receives less attention in the literature, exposure is
equally important as toxicity in determining risk:
Characterization of risk must also take into account the nature
of human exposure. Several questions must be asked: What are the
sources of the chemical? Does the chemical encountered from each
of these sources undergo any environmental alteration? By what
routes and at what rates do humans come in contact with the
chemical? How do these different routes and rates affect the
risk? How many individuals are so exposed? Although consider-
able strides have been made in recent years in the identification
of potential human carcinogens, little work has been done to
define the nature and extent of actual human exposures to these
chemicals ([29,p.173).
Another aspect in the calculation of risk is a description of the
population exposed to a chemical and the differences in susceptibility in
that population. As recommended by OSTP, "Characterization of risk should
consider individual susceptibility" {[29],p.174). Furthermore, "Cancer
susceptibility varies greatly among individual members of human popula-
tions due to genetic, racial, and ethnic factors; to environmental and
dietary exposure; and to other modifiers" ([28] ,p.39875) .
The Office of Science and Technology Policy report Includes the
following recommendation:
Risk assessment should include consideration of the nature of
human exposure to potential carcinogens. Appropriate research
should be undertaken to determine sources, prevalence, and levels
of exposure, as well as any environmental alteration, of the
chemical under consideration ([29] ,p.!6).
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The LRLG report also strongly stresses the importance of information about
exposure as part of the risk evaluation process ((28],pp.39873-39875).
In addition to routes or "pathways" of exposure through the environ-
ment, it is inportant to consider the raeans by which a chemical nay enter
the body and to investigate the chemical's pathways to the site of actual
damage:
The dose of an ultimate carcinogen at the site of action in the
tissues or cells, which is measured at all times after its
introduction ("target tissue dose") is ideally the dose that
should be estimated and correlated with expected effects
([28] ,p.39874).
APPLICATION TO THE TOXIC SUBSTANCES CONTROL ACT
Risk-benefit analysis can be used for at least two purposes in the
Toxic Substances Control Act. The first purpose is to assist in deter-
mining whether or not a chemical presents (or will present or may present)
an unreasonable risk. Such a determination must be made before testing or
control rules can be promulgated under the act. The second purpose of
risk-benefit analysis is to help set priorities within the toxic substances
office of SPA. Priority setting is related to the determination of un-
reasonable risk in that high priority should be given to chemicals that are
likely to be found to present an unreasonable risk. It is also important
because of the need to manage scarce testing and regulatory resources.
Unreasonable Risk
The determination of unreasonable risk is the key element in the
implementation of TSCA. In order to regulate a chemical under Section 6 of
the Act, the EPA Administrator must find that a chemical "presents or will
present" an unreasonable risk. And, in order to require testing of an
existing chemical under Section A or to regulate a new chemical pending the
development of information under Section 5, the administrator must find
either that the chemical "may present" an unreasonable risk or that the
chemical may enter the environment in substantial quantities or result in
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significant hunan" exposure. Thus, a distinction is drawn between the
finding of "presents or will present" an unreasonable risk and the less
demanding finding of "may present" an unreasonable risk..
In any case, the tern unreasonable risk, is purposely not defined in
the Act. According to the House Report, the term was not defined because
the determination of unreasonable risk is not a factual question but a
complex exercise of judgment that involves
...balancing the probability that harm will occur and the magni-
tude and severity of that harm against the effect of a proposed
regulatory action on the availability to society of the benefits
of the substance of mixture, taking into account the availability
of substitutes for the substance or mixture which do not require
regulation, and other adverse effects which such proposed action
may have on society ([58],p.14).
The determination of unreasonable risk is thus a judgment involving the
"balancing" of risks and benefits where risk is defined as the probability
of harm together with the magnitude of the potential harm and benefit is
defined as the economic usefulness of the substance. An important point in
this stateaent is the use of the concept of probabililty, which indicates
that a chemical may be found to present an unreasonable risk even if the
exact toxicity or exposure of a chemical is uncertain.
Because of the difficulties in assessing toxicity and exposure,
similar difficulties in assessing the economic effects of regulation, and
the difficulties in assigning values to potential outcomes, the determina-
tion of unreasonable risk is extremely complex. However, EPA is required
by TSCA to make such determinations, usually within certain mandated time
limits. For example, after receiving a premanufacture notice for a new
chemical, EPA has at most 135 days to determine whether the chemical should
be allowed to be manufactured or whether more information needs to be sub-
mitted. Similarly, SPA has one year to act on chemicals that have been
identified by the Interagency Testing Committee (ITC) to be of high prior-
ity. In either case, EPA always has the alternative of doing nothing,
i.e., taking no rule-making action. However, this choice should be made
consciously and not by default. To help prevent decisions by default, TSCA
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often requires EPA to publish the reasons for not taking regulatory
actions.
As yet there is no source in the literature claiming to provide a
definitive method of determining unreasonable risk. However, a number of
authors have suggested elements of such a determination. Lowrance [6]
draws a clear distinction between matters of empirical fact and natters of
personal or social value judgment. The determination of unreasonable risk
(insufficient safety) is thus a combination of empirical findings and value
judgments.
Another important aspect of unreasonable risk arises from the uncer-
tainties in the risks and benefits of a chemical. Because of the uncer-
tainties, it is possible to make "regulatory errors" in the sense that a
different regulatory option may have been preferable if the true toxicity,
exposure, and benefits of a substance were known. As described by Page,
"The essence of the balancing process is the willingness to accept some
false positives as the unavoidable means of controlling false negatives"
([47],p.3). By false positives, Page means restricting a substance that is
in fact safe for humans; regulation is not necessary and the benefits are
lost. By false negatives, he means failing to control a chemical that
turns out to be damaging to human health or the environment to an extent
that is not justified by benefits from the chemical. Thus, a necessary
result of the balancing process is the acceptance that errors of both types
are unavoidable. A partial solution to this problem may be to allow a
certain flexibility in the regulatory decision-making process so that
decisions can be changed if new information is discovered, indicating a
chemical restricted by regulation is nontoxic (false positive) or an
unrestricted chemical is inflicting unacceptable damage (false negative).
A key issue that has not received sufficient attention to date is the
formulation of decision criteria for making unreasonable risk determina-
tions. Currently, EPA has elaborate procedures for analyzing information
on chemicals but does not have explicit criteria for the decision points in
the process. As a result, it is necessary to consider each chemical on a
case-by-case basis. It is therefore difficult to check f.or consistency of
decisions, and the decision makers may be subject to political influence
41
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and pressures that could be more easily resisted with explicit criteria for
decision making.
Two attempts at formulating decision criteria were posed by the Con-
servation Foundation and by Page. The Conservation Foundation recommends a
procedure in which the effects of regulatory options are quantified in a
variety of units such as lost lives, lost jobs, and dollar costs. The
estimated effects are then aggregated by "subjectively established weight-
ing numbers" to avoid translating all factors into dollars and to emphasize
the fact that the weights are subjective ([7],p.15). Page recommends use
of quantitative values and probabilities to describe the uncertain effects
of regulation. The net benefits of a regulation are then calculated as an
expected value ([47],p.3).
Priority Setting
Both regulatory staff and facilities for chemical testing are
resources in short supply within the Office of Pesticides and Toxic
Substances (OPTS), and these resource limitations affect the rate at which
TSCA can be implemented. Priority setting is the means of managing these
scarce resources. Suggested principles for priority setting are described
in the recent Office of Science and Technology Policy report:
For testing, highest priority should be given to chemicals for
which preliminary analysis suggests that regulation may be indi-
cated but for which existing data are too uncertain to justify
immediate action. These chemicals would generally include ones
that have a high potential for carcinogenicity (based upon struc-
tural similarity to known carcinogens or the results of short-
term in vitro tests) but have had only minimal prior testing,
ones that offer substantial consumer benefits, and ones that do
not have readily available means of controls ([29] ,p. 174) .
Thus, priority setting involves a preliminary risk-benefit analysis,
presumably of less detail than that required for regulatory action but in
enough detail to indicate the type of regulatory action that may result and
the approximate value of such regulation. One disputable point, hovtever,
is the assumed Inability to regulate if data are uncertain. Given the time
constraints for TSCA, it may be necessary to make a regulatory decision
42
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before testing to reduce uncertainty can be conducted. Furthermore,
because it is impossible to remove 3ll uncertainties, presence of uncer-
tainty cannot prevent decision making.
To aid in rapidly setting priorities, Slesin and Sadler advocate the
use of categories of substances:
The inclusion of a chemical in a category of suspected toxic
substances should raise sufficient "concern" over its safety to
indicate that the substance "may present" an unreasonable risk,
and therefore, categorization may aid in the establishment of
testing priorities ([30),p.390).
Such categorization could help in assessing the probability of harm for a
substance.
Presently, priorities for testing are set by the ITC, which was estab-
lished under TSCA. The ITC has developed a priority-setting methodology in
which chemicals are scored on the basis of exposure and toxicity [59],
Similar methodology could also be used to set regulatory priorities. The
methodology does allow a ranking of chemicals according to 6Core, but it
has been criticized for ignoring the possible regulatory actions that may
be required as well as for ignoring economics:
One difficulty in using the ITC methodology to select existing
chemicals for possible regulatory action is that it considers the
risk of the chemicals in toto. Aggregating all uses, exposures,
and effects into a single final rating, the method fails to dis-
tinguish among different uses of the chemical, its use in differ-
ent geographical areas, or the potential impact of a wide variety
of regulatory options short of banning. Thus, if this analysis
shows that a chemical presents a risk, the implicit assumption is
that the. only regulatory option available under TSCA is a total
ban. While this approach is the most economical one in terms of
resources expended to collect information, it may result in over-
looking situations where regulatory action—controlling certain
uses or production volume, for instance—could have the greatest
payoff, reducing the risk from a chemical to a level considered
reasonable rather than banning it entirely ([7],o.3).
The ITC scoring system can also be criticized as being highly
subjective and potentially inconsistent. The numbers have little meaning
once they are aggregated, and the uncertainty is not quantified in a way
that can be easily communicated to those who have not participated in the
scoring process.
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One possible alternative to the ITC methodology is the ranking system
developed for EPA by SRI [60]. The so-called objective component of this
system does provide an outline for a simple analysis of the risks of a
chemical. However, this system also ignores economic effects and
uncertainty.
Another alternative may be a simplified decision analysis framework
that utilizes a highly aggregated model of exposure and toxicity as well as
a simple market model. Using the etructure, a few regulatory options could
be considered and uncertainties could be explicitly incorporated. This
approach may have promise but is currently undeveloped.
CONCLUSIONS
Risk-benefit analysis methodologies, particularly decision analysis,
appear applicable to the regulation of toxic substances, but examples of
such application are rare. The proper role of analysis is agreed to be an
aid to responsible decision makers, and the objective of analysis should be
to organize information and clearly present all assumptions and value
judgments. The application of risk-benefit analysis to chemical hazards is
difficult because of the general lack of understanding of the toxicity of
low levels of exposure and the pathways of exposure. However, because
regulatory decisions oust be made even in the face of uncertainty, it makes
sense to use a method of analysis that deals with the uncertainty in a
logical way rather than assert that uncertainty makes analysis impossible
or useless. Progress has been made in developing methodologies to deal
with the specific problems of implementing TSCA, namely, methodologies to
determine unreasonable risk and to set priorities. However, much more
development, refinement, and experience in applications appear to be needed
before the methodology can be used as an operational framework to assist
EPA decision making in determination of unreasonable risk and in setting
priorities for allocating testing and regulatory resources.
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Section 3
DECISION ANALYSIS METHODOLOGY FOR UNREASONABLE RISK DETERMINATION
OVERVIEW
Under Section 6 of the Toxic Substances Control Act (TSCA), the EPA
must select from a wide range of regulatory alternatives if there is a
"reasonable basis to conclude" that a substance "presents or will present
an unreasonable risk" to health or the environment. The determination of
unreasonable risk, is also required by several other sections of the Act.
In this section, we will discuss a decision analysis methodology that can
be used to help determine if an unreasonable risk exists or will exist. Of
perhaps greater benefit, use of the methodology will provide information
and Insights that can help guide the EPA in the selection of regulatory
alternatives.
NATURE OF THE TOXIC SUBSTANCES PROBLEM
The term unreasonable risk appears over thirty times in TSCA, yet it
is not defined in the Act. It seems clear that Congress felt there was no
single, objective definition of unreasonable risk; as stated in the House
Report [61], the determination of unreasonable risk is a complex process of
information gathering, analysis, and judgment which involves
...balancing the probability that harm will occur and the magni-
tude and severity of that hara against the effect of proposed
regulatory action on the availability to society of the benefits
of the substance or mixture, taking into account the availability
of substitutes for the substance or mixture which do not require
regulation, and other adverse effects which such proposed action
may have on society.
Inherent in the statement are two concepts central to a determination of
unreasonable risk: the balancing of risks and benefits and the explicit
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consideration of uncertainty in the risks and benefits, the uncertainty-
being described in teres of the probability that ham (and other uncertain
outcomes) will occur.
Before an EPA decision maker can exercise judgment in the balancing of
risks and benefits, he or she must have a good understanding of what the
important risks and benefits are or nay be. Gaining this understanding
presents a challenging problem: numerous issues are involved, interacting
in a complex manner, and information is always limited. Even for chemicals
produced in large volumes for many years, crucial information on toxicity,
uses of the chemical, and dispersion of the chemical in the environment may
be unavailable.
Factors that must be considered when evaluating a substance for its
potential to cause harm include the techniques of its production, use, and
disposal, pathways of exposure to both humans and the environment, and the
biological responses of humans and other organisms. Chemicals or mixtures
may be acutely or chronically toxic, carcinogenic, mutagenic, or terato-
genic. The dose required to produce the adverse effect varies widely among
toxic substances, and for most substances the dose response relationship is
highly uncertain. Some effects may be due to synergistic interactions
between several substances or metabolic transformation. Harmful effects
may appear only after a period of time has passed, due to bioaccumulation
or latency in the effects, or both.
Even when information is available on a possible hazard, it may be
ambiguous or inconclusive. If a large human population is exposed to low
concentrations of a substance, and the sane substance appears to increase
significantly the number of tumors when administered to rodents in large
doses, what is the extent of the danger? There is uncertainty in extrapo-
lating from high to low doses, from animals to humans, and perhaps from
short- to long-term exposure. The mechanisms of uptake and metabolic
transformation may be poorly understood. Is the substance itself a car-
cinogen? Is some metabolic byproduct of the substance a carcinogen? Or
is the substance a cocarcinogen whose effect depends on the presence of
some other compound? These are but a small sample of the questions and
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uncertainties likely to be raised when assessing the potential for risk to
human health. The processes through which a substance may present a risk
to the environment--whether to individual species or to an entire
ecosystem—are likely to be sioilarly complex and uncertain.
It is often believed that the economic benefits of a substance (or the
economic costs of regulation) are better understood or easier to calculate
than are the risks. Unfortunately, this may not always be the case. An
existing substance nay be involved in a complex network of economic inter-
actions: competing in several market areas, being used as an end product or
a factor of production in diverse ways, and generating a demand for a set
of feedstocks ar.d other input factors. There nay be uncertainty in the
benefits of use, as with certain pesticides where resistant strains of the
pest have developed in some geographic areas but not in others. The avail-
ability of substitutes or the opportunities and costs of developing substi-
tutes can be a critical issue and one fraught with uncertainties. While
vast quantities of data on production and use may be available, such data
may be of little use in assessing the changes that may result from regula-
tory action.
In the case of a new substance or significant new use, the problem can
be even more difficult. The markets in which the substance will compete
may not all be clearly identified, nor will the materials that may be
displaced. Processes for large-scale production are likely to be unproven,
leading to uncertainty in costs, prices, and byproducts. Estimating the
economic benefits of a new substance may be every bit as challenging as
assessing its potential risk.
An important characteristic of the problem of determining unreasonable
risk is that both risks and benefits nay be received in different propor-
tions by many diverse groups in our society. This may result simply from
the nature of production and use of a substance: production workers and
those who use a product are likely to receive greater exposure than will
the general public. Or, it may be for biological reasons. For example,
certain groups, such as the aged, may he more susceptible to a particular
toxic reaction. In terns of economic effects, parties that may feel the
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impact of a regulatory action (or of no action) include the producers of a.
substance, suppliers of existir.g or potential substitutes or complements,
feedstock suppliers, and consumers of the end product. Even within the
production sector, the effects nay vary over diverse groups: workers whose
jobs may be eliminated (or created), shareholders in a firm, or members of
a surrounding conraunity. Assessing the impacts on all concerned parties is
part of what cakes the determination of unreasonable risk such a difficult
problem, and it must be addressed within any analytical methodology.
We have described just a few of the issues, complexities, and uncer-
tainties that make the determination of unreasonable risk—and the choice
of an appropriate regulatory alternative—an extraordinarily challenging
problem. In the remainder of this section we will discuss a decision
analysis methodology that can provide a first step towards meeting the
challenge. Vie will begin by discussing the purpose of such a methodology,
what it can be expected to provide, and what it will not provide. We will
then present the generic structure of the methodology, and finally outline
the steps of the analysis process.
PURPOSE OF A DECISION ANALYSIS METHODOLOGY
At a concrete level, the purpose of a decision analysis methodology
is to provide consistent quantitative estimates of the harm that may result
from the use of a substance as well as the benefits of such use, of the
economic and other costs of regulatory alternatives, and of the degree of
uncertainty in both risks and benefits. A good analysis will clearly
identify the groups or individuals who will receive the benefits and bear
the risks, the timing of various outcomes, and the nature of each impact.
Summarizing the factual results of an analysis may, however, miss its
broader purposes: to provide a way of integrating the diverse issues and
information, to provide a framework for communication, and to improve an
understanding of the critical issues, uncertainties, and impacts.
The problem of determining whether a substance presents an unreason-
able risk involves a vast number of factors. One way in which a decision
analysis methodology can help is by providing a framework with which to
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integrate the important factors of the problem. A cenfral tool in this
regard ts an explicit model representing the interactions between the
various aspects of the problem. The ir.odel allows the economic, physical,
biological, and other factors to be integrated into a comprehensive
analysis. It provides a framework within which the knowledge and infor-
mation of experts from diverse fields can be pulled together in a con-
sistent manner. Beyond the use of an explicit model, the decision analysis
approach allows uncertainties—which may be critical in unreasonable risk
decisions—to be included as an integral part of an analysis. This helps
make the impact of the uncertainties clear and focuses efforts on those
uncertainties of the greatest importance.
As just noted, a model plays a key role in integrating information
from diverse sources. The decision analysis methodology in general and an
integrating model in particular also facilitate communication among the
parties concerned with an unreasonable risk determination. The model
requires assumptions about both processes and data to be made explicit; it
also allows the implications of alternative assumptions to be tested and
compared. The decision analysis approach can help focus debate on the key
issues—the ones that are crucial to a particular unreasonable risk regu-
latory decision. By making assumptions and data explicit, it can clarify
where agreement exists and where it does not.
The need for clear and concise communication is a primary reason for
quantitative analysis. We view numbers as a language, that is, as a tool
for communication. The quantitative results of an analysis should be ouch
more than a single "bottotr.-line" number: they should include ranges, sensi-
tivities, and disaggregation of risks and benefits by type of outcome and
impacted group. Again, this approach will help make clear the critical
issues and uncertainties.
A quantitative decision analysis methodology should not be expected to
provide an objective, "scientific" mechanism to generate an "answer" for an
unreasonable risk determination. Rather, it should help the decision
makers and other interested parties understand the problem better. The
methodology should help to focus attention on what is most important, on
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the issues that are irost critical to the regulatory decision. While using
available information to the fullest extent possible, the decision analysis
methodology will provide guidance as to the need for more information and
will enable new information to be factored in a consistent manner. The
analysis will not make the decision regarding unreasonable risk; it will
give the decision makers the information and insights that should provide a
firm basis for the judgment required to make the regulatory decision.
OUTLINE OF THE METHODOLOGY
The decision analysis methodology consists of a conceptual framework
within which to define a specific unreasonable risk decision, a generic
structural model that is detailed for a specific decision, and a process or
series of steps for carrying out the analysis. In this section we vrill
discuss the general structure of an unreasonable risk decision analysis,
introduce some key methodological concepts, and summarize the analysis
process.
To illustrate the methodology, we will develop a sequence of examples
around an unreasonable risk decision regarding a hypothetical substance,
the chemical XYZ. While being simplified and representing only a small
portion of a complete analysis, the examples will be more specific and
occasionally more technical than the body of the section. To avoid con-
fusion between the necessarily simplified assumptions of the illustra-
tive example and the more general description of the aethodology, all
discussions of the examples will be single spaced and indented.
Our hypothetical substance XYZ is a resin used in the production
of a family of industrial adhesives. These adhesives compete in
the marketplace with other adhesive compounds, and also with
other fastener technologies. A new adhesive containing >CYZ is
being considered for marketing as a consumer product; it would
compete with existing household adhesives.
XYZ can have serious acute toxic effects when ingested in greater
than milligram quantities. Particularly sensitive individuals
may have an acute reaction from surface exposure. The risks of
such acute effects from occupational exposure are relatively
small when common industrial safety procedures are used.
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Consumer users of the new adhesive may be exposed to XYZ through
three possible pathways: direct skin contact with the uncured
adhesive, funes from the uncured adhesive, and leaching from
cured adhesive when it has been used to repair eating utensils.
It is believed that none of these pathways will lead to signifi-
cant acute toxic effects.
Structure-activity relationships indicate that XYZ is a possible
carcinogen. Epidemiological studies carried out on employees of
plants producing and using \rYZ have provided no clear indication
of carcinogenicity, but the number of individuals involved was
small. Furthermore, XYZ has been in regular industrial use for
only about ten years; any adverse effects may not appear for
another ten years.
Defining the Decision
The first step in carrying out a decision analysis is to carefully
define the decision problem. For a particular chemical substance or class
of substances we must characterize the dimensions of the problem of deter-
mining whether or not an unreasonable risk exists, and, if so, what regula-
tory actions should be taken. By dimensions we mean what choices the EPA
must make, the alternatives available, the outcomes of interest, and the
information on which the analysis and decision must be based. One way of
visualizing how these factors fit together is illustrated in Figure 3-1;
the central role of an integrating model is indicated as well. Each speci-
fic decision will have only a certain set of alternatives available, a
unique set of outcomes that are felt to be most important, and a unique
combination of available and unavailable information.
Outcomes and Decision Criteria. To arrive at a more precise definition of
the unreasonable risk decision we must ask what we want. Then we should be
able to identify the important things we would like to know, after a deci-
sion is made, to see whether things turned out well or badly. The measures
by which we judge how well things turned out we choose to call outcomes.
The outcomes of any toxic substance regulatory decision will generally fall
into one of three categories: human health, environmental impacts, and
economic effects.
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u>
I
CO
INFORMATION
9 HisLoriral Data
• "IV St RCMlltS
• A 'j sump t ion -i
I'd Kmployi'i".
outcomes
lo Industry
I nt rt>r;it lur Moil.'l
r
0!
Di'C Is Ion
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l*o the General
Public
ALTERNATIVES
From EPA Stnff .mil
Other Interested Parties
i
i \
JUDGMENT
of EPA
Decision Makers
ot
A1 cornatives
01
to
Figure 3-1. Component, r; of n Dc-r ision
-------
A given toxic substance may cause one or more of a wide variety of
health effects. What we would like to know is how many people will be
affected and to what degree. As the uncertainty is likely to be con-
siderable when estimating health effects, what we might expect from an
analysis is a probability distribution on the nunber of people incurring
each health effect. An EPA decision maker will need to know more than
simply a raw nunber (or range) of people affected; we can provide a more
complete definition of each health impact by describing it (qualitatively
or quantitatively, as appropriate) along each of the following five
dimensions:
1. The nature of an effect: this could range from a mild annoy-
ance (e.g., a simple allergy) to severe distress (a serious
illness) to death.
2. The timing of an effect: how soon after exposure does it
occur? This will range from immediately, as in the case of
acute toxicity, to perhaps ten or twenty years (e.g., with
cancer).
3. The duration of an effect: how long does the effect last?
This might be for a very short period (e.g., an allergy only
upon exposure) or indefinitely (as with a chronic respiratory
illness).
A. The recipient of an effect: the users of the substance (e.g.,
consumers of a final good); employees in manufacturing,
processing, or distribution; the general public; or perhaps
special groups, such as children, the elderly, or those
exposed to some other substance.
5. The number who incur the effect, described in terns of a best
estimate and probabilities on the range of possible outcomes.
When analyzing a specific substance to determine if it presents an
unreasonable risk, there will usually be a small number of health effect
definitions: perhaps one type of disease that affects several distinct
groups in society, or several different diseases affecting the general
public, or some other combination.
The environmental impacts attributable to chemical substances can
appear in a vast number of forms. They can range from a short-term impact
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on a single species to a permanent major modification of an entire eco-
system While environmental impacts are in general aore difficult to cate-
gorize than are human health effects, for a specific regulatory decision
there is likely to be a small set of critical environmental impacts. Each
night be characterized along the following dimensions:
1. The severity of the impact on an individual organism.
2. The number of species affected.
3. The duration of the environmental harm.
4. The geographic extent of the effect.
5. The reversibility of the effect, as for example, elimination of
an endangered species as compared to a temporary reduction in
species population.
We now return to the hypothetical example.
To define outcomes that reflect the health effects of the production
and use of XYZ, we refer back to the general discussion of its use
and characteristics. Two potential health effects were noted:
illness resulting from an acute toxic reaction and cancer. For the
purposes of this simplified example we will assume that the acute
effects are not a central issue. Groups that might be affected
include employees of firms that manufacture XYZ, employees of firms
that use products containing the substance, and consumers who might
use the potential new product. Thus, the health effects outcomes
can be summarized as
o Number of cases of cancer associated with exposure to XYZ
where the effect nay be incurred by
—employees of XYZ production plants
—employees of firms using products containing XYZ
—consumers using an adhesive containing XYZ
We now have three distinct measures of outcomes. Each can occur now
or at any future point. Thus, the outcomes need to be defined along
the time dimension: the number of cancers detected in 1980, 1985,
1990, and so on.
As the analysis of XYZ evolves we may discover that additional out-
comes are necessary or that one previously defined is no longer an
issue. We may, for example, choose to disaggregate the cancer occur-
rences by age group, sex, or both. This illustrates the iterative
nature of the decision analysis process, making successive
improvements and refinements as one gains understanding of the
problem and its key attributes.
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Economic impacts car. be incurred by a variety of parties, the most
important of whom include manufacturers of a chemical or mixture, those who
use it as an end product or factor of production, current or potential
suppliers of substitute substances, and employees of the firms involved.
Which parties receive costs or benefits depends both on the characteristics
of the industrial processes and economic markets, and on the nature of any
regulatory actions. Consuners might bear costs due to higher product
prices, lower quantities, or a poorer selection of products. Suppliers can
incur costs because of lower production quantities, increased production
costs, or unrecovered R&D expenses. Whatever the specific nature of the
economic impacts of a regulatory action, the impacts will generally occur
in one or more ox three forms: net dollar costs or benefits, changes in
employment, and effects on innovation. The first two can be quantified,
while the third is a more qualitative measure. Thus, the economic outcomes
of a toxic substance regulatory decision can be summarized by providing
estimates of
1. The net dollar benefits (or costs) received by each industrial
sector involved to a significant degree (disaggregated geograph-
ically when necessary).
2. The net change in employment (again, it may be important to
distinguish by geographic region).
3. Changes in price, quantity, and quality of products available to
consumers.
4. Impacts on innovation.
These effects nay occur immediately after a regulatory action is taken,
or they may appear gradually. The manner in which the economic outcomes
change over time may be important in an unreasonable risk decision
analysis.
There exist competing materials for both current and proposed uses
of XYZ. Thus, regulatory action with respect to XYZ is unlikely
to eliminate any final goods available to consumers. There will
be both production cost and price effects, which may occur at
several points in the economy. While many of the effects propa-
gate from intermediate producers to final producers to consumers,
we must take care to avoid double counting.
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For the purposes of the example, we will assume that the economic
effects outcomes are as follows:
o !Jet economic benefits to
—firms that produce XYZ
—firms that use XYZ
o Net change in consumers surplus [62]
As with health effects, the economic outcomes must be estimated for
a number of points in time. We will assume that disaggregation by
location (e.g., of impacted firms) is judged to be unnecessary.
The preceding few paragraphs have outlined outcomes that are likely
to be important in judging whether a substance presents an unreasonable
risk. Presenting this information to the EPA decision makers and other
interested parties in the form of qualitative descriptions, quantitative
estimates, and probability ranges does not, however, complete the decision
analysis process. It is clear that it will be necessary to make tradeoffs
when balancing the risks of harm to humans and the environment with the
costs of regulation. Ultimately, these tradeoffs will be reflected in the
judgment of the decision makers. We believe that it would provide useful
insights to develop quantitative tradeoffs that could be used to estimate
the overall impact of each alternative.
As with other aspects of the decision analysis methodology, the
purpose of making these tradeoffs explicit is not to obscure the numerous
factors involved (to reiterate: the full set of disaggregated outcomes
should be made available to all interested parties), but rather to aid in
understanding which factors are most important to the decision.' Primary
uses of an explicit set of tradeoffs will be to facilitate sensitivity
analysis and value of information calculations, both of which will be
discussed below. Alternative tradeoff values can be tried to see if
varying the tradeoffs changes the ranking of the regulatory options. This
can help focus debate on the outcomes and tradeoffs that are most crucial
to the decision, while providing a basis for agreement (at least, an
agreement that they are less critical) on other outcomes:
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To carry out sensitivity analysis and to estimate the value of ob-
taining nore information, it is necessary to specify a tentative
set of tradeoff values between the outcomes. Both economic cut-
cones are in dollar values, while the remaining outcome is measured
in the number of cases of cancer. It does not matter in principle
vjhich measure is "traded off" into the other; for simplicity, we
will use dollars as the "net" outcome.
As a starting point we shall specify the tradeoff as one statis-
tical case of cancer is equivalent to ten million dollars. In a
complete analysis we would test the implications of a wide range of
tradeoff values from, for example, one hundred thousand to one
hundred million dollars. The tradeoffs need not be constant; they
might be a function of the number of cases of cancer, or economic
benefits, or both.
Note that for this simplified example we are simply adding the two
economic (dollar) outcomes, the net benefits to industry, and the
net benefits to consumers. If distributional issues were felt to
be significant, the costs (or benefits) incurred by consumers need
not be considered equivalent to those incurred by industry.
Decisions and Alternatives. The decisions that the EPA must make under
TSCA can be summarized as determining whether or not unreasonable risks
exist or may exist. We find it useful to define the decision as being the
choice of an appropriate regulatory action, where no action is always a
possibility. An explicit assumption is that no further action will be
taken if it is judged that an unreasonable risk does not exist and that
some action will be taken if such a risk does (or will) exist. Thus, in
defining the decision it is useful to define in greater detail the
regulatory alternatives, keeping in mind that "do nothing" is always an
alternative.
For the purposes of structuring a generic decision analysis methodol-
ogy, it is useful to think of an alternative as being defined along four
dimensions:
1. Type of action (record keeping, label warnings, limit, prohibit)
2. Economic stage (manufacturing, processing, distribution, use,
disposal)
3. Type of use (all uses, a set of uses, a single specific use)
4. Geographic extent (everywhere, in specific areas)
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For example, one complete alternative might he to limit a substance's use
to a particular set of applications and prohibit the substance's manufac-
ture for all other purposes. Another alternative could be to prohibit use
in certain ecologically sensitive geographic areas, while requiring warning
labels on distribution of the substance in the rest of the country. Hot
all alternatives will be available for every chemical substance, and many
alternatives will clearly be inappropriate due to the nature of the poten-
tial ham or the characteristics of the economic sector. However, it should
be possible to define any alternative reasonably well using the four attri-
butes just listed.
As an analysis evolves, the alternatives under consideration will
evolve as well, with new alternatives emerging as the problem
becones better structured and better understood. While it is not
necessary to begin with every alternative specified in complete
detail, it is essential to start out with a set of alternatives
that "span" the full range of possible actions. The following is
an initial set of alternatives with respect to the substance XYZ:
1. Take no action, e.g., permit all uses.
2. Prohibit the new consumer use, while allowing industrial
uses to continue with appropriate safeguards.
3. Phase out existing industrial uses over a 10-year period
and prohibit any new uses.
It is clear that an action taken at one stage (e.g., to limit
manufacturing of a substance) will affect the other stages (e.g., its
distribution and use). The purpose of classifying the alternatives as
described is to enable us to identify clearly at what point(s) the
regulatory action has an impact on the physical, biological, and economic
systems. This will be of importance when we begin to think about modeling
the effects of a regulatory action.
Information and Uncertainty
One of the key characteristics of the determination of unreasonable
risk that makes it a difficult problem is that we do not really know
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everything about each aspect of the problem, and in many cases we know very
little- In other words, our information is always incomplete and usually
very limited. The result of the lack of information is uncertainty: we do
not know precisely how much of a substance is produced, or how much escapes
to the environment, or what all the pathways of exposure are, or what the
biological processes are that lead to an illness. As the examples indi-
cate, this uncertainty is often in what we might call "data" or "para-
meters;" something in which the precise answer, if it could be given, would
be in the form of a number (or set of numbers). Sometimes the uncertainty
has to do with the nature of the physical or biological relationships: Is
the dose response relationship for a particular substance linear, or a
probit-log, or in some other form? In such cases, we are uncertain about
the underlying processes as well as numeric values.
While the uncertainty is often very great, it is also important to
recognize that only rarely are we totally without information. We usually
have some idea of the range for a variable, even if we do not know its
exact value. The fraction of employees involved in a manufacturing process
who are exposed to a certain substance may be twenty percent, or it may be
thirty percent, but we are fairly certain that it is greater than one per-
cent and less than fifty percent. In other words, we do know something. A
fundamental concept of decision analysis is that decisions must be made
even when complete information is not available, that is, under uncer-
tainty. There will rarely be enough scientific data to resolve all uncer-
tainty. In order to make good decisions we should make the best possible
use of all available information, however limited that may be.
Representing Uncertainty. A cornerstone of the decision analysis approach
is the idea that uncertainty reflects an individual's state of information
about some quantity or event. The information he or she has includes sub-
jective judgment as well as scientific data. In the decision analysis
process this state of information—and, conversely, state of uncertainty—
is represented by probability assignments that specify explicitly what the
chances are of each of the possible data values (or structural relation-
ships) occurring. The probability assignments are elicited from the
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decision maker or others knowledgeable in the subject using a process
called probability encoding [31]. The encoding procedure is a structured
interview designed to eliminate biases. The probability assignments
thus specified reflect the full information—both quantitative and
qualitative—that is available to the EPA and others involved in the
analysis at the time the decision is made.
Probabilities are key parts of the language we use to deal with
uncertainty. They describe our state of information about the
likelihood of an event. There are several ways of representing
probabilities, depending on the nature of an event. The simplest
case is when a discrete event either will or will not occur (or
when, equivalencly, a statement is or is not true). For example,
suppose that our best judgment indicates a 10 percent chance that
XYZ is a human carcinogen; we could write
P(XYZ a carcinogen) =0.1
P(XYZ not a carcinogen) ¦ 0.9
Even when uncertainty is not an issue, we often wish to qualify
statements such as "B will occur if A occurs." The analog in the
language of uncertainty is the conditional probability. A3 an
example, suppose we believe there is an 85 percent chance that an
Ames test will be positive if XYZ is a carcinogen and a 20 percent
chance for a positive result if XYZ is not a carcinogen. This can
be written as
P(test positiveIXYZ carcinogen) » 0.85
P(test positivejxYZ not carcinogen) = 0.20
Probabilities must always sum to one (including probabilities on
conditioned events), so we also could note that
P(test negative
P(test negative
XYZ carcinogen) * 0.15
XYZ not carcinogen) = 0.80
Probabilities used to represent the likelihood of various values
for a variable are only a bit more complicated. Figure 3-2a shows
a probability density function, giving the relative likelihood of
each value for the number of tons of XYZ used in consumer products
in 1985. (Such a function is always normalized so that the area
under the curve is one.) In Figure 3-2b, we have an equivalent
but perhaps more intuitive representation: a cumulative probabil-
ity distribution. Each point on the curve gives the probability
that the amount of XYZ in adhesives used by consumers is less than
or equal to the value on the horizontal axis. For example, there
is a 70 percent chance that the number of tons/year of XYZ used by
60
3-16
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o
oc
50 100 150
Consumer Use cf XYZ (tor.s/year)
Figure 3-2a. Example Probability Density Function
U
0. 5 - -
i
a
50
100
150
Consumer Use of XYZ (tons/ydar)
Figure 3-2b. Example Cumulative Probability Distribution
3-17
61
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consumers is less than or equal to 100. It is often more conveni-
ent for computation to use a discrete probability mass function
rather than a continuous distribution. An example is shown as
Figure 3-3a.
A convenient way to represent the probabilistic relationships
between the decisions and key uncertainties is with a decision
tree. Such a tree has two kinds of nodes: decisions, represented
by boxes, and uncertainties, represented by circles. A probabil-
ity mass function translates directly to an uncertainty node, as
shown in Figure 3-3b.
A simplified generic decision tree for unreasonable risk decisions
is shown as Figure 3-4. Rather than detailing all decision alter-
natives or uncertain events, only their ranges are indicated. In
this tree the decision and uncertainty, or chance, nodes are not
connected. This convention is a shorthand representation of the
full tree where each node is connected to all branches of the
preceding node.
Each path through the tree represents a scenario, a description of
a possible sequence of events. The product of Che probabilities
along the branches of the uncertainty nodes gives the likelihood
of the scenario's occurring. A probabilistic scenario defines the
values of all critical uncertainties. These values, one for each
uncertainty node, can then be used together with other data to
calculate the values associated with the scenario of the important
outcomes.
An important benefit of this approach is that it is quite straight-
forward to factor new information into the analysis as It becomes avail-
able. Simple rules from probability theory are used to "update" the prob-
ability assignments based on the new information [12,15]. The process is
intuitively reasonable: If the new information tends to confirm the
previous "best guess," the range of uncertainty will decrease; if we are
"surprised" by what we learn, the range of uncertainty can increase.
Either way, the implications of the new information on our estimates of
the outcomes can quickly be evaluated, which may in turn either confirm or
modify our judgment regarding the best regulatory alternative.
Suppose we have a bioassay for carcinogenicity that has an 85
percent chance of being positive if the substance tested is a
carcinogen and a 20 percent chance of indicating positive if the
substance is not a carcinogen. We can summarize this as
3-18
cz
-------
A
0. 6 __
0.5'
•h 0.4
f-H
£ 0.3
o
VJ
eu
0.2.
0.1 .
50 100 150
Consuner 'Jsc of XYZ (cons/year)
Figure 3-3a. Example Probability Mass Function
50 tons/year
100 tons/year
0. 55
150 tons/year
0.15
Figure 3-3b. Example Decision Tree Probability Node
3-19
S3
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Not.lt Ion 1 :1 (1 i (!, i [ e "j r;.LH.;c cf
Decision Alternatives or Kvt-n
D»»e ision
UncertciIn Event
No Tests
H;i rmf u I
No Act Ion
Not Harmful
low
Low
Choice of Subscances Te.st Results Re^nl.itory Kment ot Kxposure Harm to Human Net Economic
to Test and Extent Decision Health lcip.it I
of Testing
Figure 3
-A. T 1 I usrrat" ivo Cnnoric Decision Tree
-------
P(Positive Carcinogen) = 0.85
P(Positive Not Carcinogen) = 0.20
Continuing from our earlier example, based on structure-activity
relationships and other available information prior to carrying out
the test, we felt that
P(Carcinogen) =0.1
Now, suppose the test is performed and the result is positive. How
should we "update" our probability that XYZ is a carcinogen, given
this new information? More precisely, we need to calculate
P(Carcinogen j Positive)
We can calculate this new conditional probability using a simple
rule from probability theory known as Rayes Theorem [64,671. In
terms of arbitrary events A and B, the theorem states that
P(AlB) x P(3)
P(B A)
P( A)
In our example, "B" is "Carcinogen," while "A" is a "Positive" test
result. Since we know P(A|B) and P(B), we must first find P(A), or
P(Positive). This is straightforward, again following the rule of
probability:
P(Positive) = P(Positive and Carcinogen)
+ PCPositive and Not Carciogen)
- P(Positive|Carcinogen) x P(Carcinogen)
+ P(Positive|Not Carcinogen)
x P(Hot Carcinogen)
= 0.85 x 0.1 + 0.2 x 0.9
- 0.265
Now we can use Bayes Theorem to find the updated probability that
XYZ is a carcinogen:
P(Carcinogen | Positive) , Positive |Carcinogen)^* P(CarcinoSen)
= 0.85 x 0.1
0.265
= 0.32
Note that even though the test was positive we still are not
certain that XYZ is carcinogenic. The test itself was uncertain
and does not give us "perfect" information on carcinogenicity.
3-2 L
65
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The Value of Information. We noted earlier that it is a tenet of decision
analysis—and a mandate to the EPA—to make a decision even when uncer-
tainty is great. To do nothing is always an alternative, but the choice of
this alternative should be made explicitly and not by default. Another
alternative is also usually available: to gather more information. This is
particularly relevant to options under TSCA for testing, monitoring, and
data reporting. The decision analysis methodology can give explicit gui-
dance as to when it is worthwhile to obtain aore information and can help
indicate which information will be the most valuable. This is carried out
by calculating the "value of information," based on the idea that new in-
formation has value if it reduces uncertainty and allows us to make better
decisions.
Is the information that will be acquired by carrying out a test
worth the cost of the test? This question will occur repeatedly in
toxic substances unreasonable risk decisions. It is a question
that the decision analysis methodology is well equipped to answer.
We approach the problem by calculating the "expected value of per-
fect information," or EVPI. The idea is, how much would you pay a
clairvoyant to tell you, in advance, what was going to happen (or
whether, for example, a substance really is carcinogenic)? If the
new information would in no case change your decision, you would
not be willing to pay the clairvoyant much, if anything. If it
would help make a better decision, then the information has value.
The EVPI gives an upper bound on this value.
As an example, consider a highly simplified decision: should the
use of the substance XYZ be banned due to its possible carcinogeni-
city? The three alternatives were identified previously: to pro-
hibit all uses, to restrict use to the current industrial applica-
tions, or to permit all uses including the new consumer adhesive.
Assume that a preliminary analysis has indicated that the key
uncertainty is the degree of dose response activity of XYZ. As a
simple approximation, we consider three possibilities: XYZ could be
highly active, inducing cancer at low doses; it could be moderately
active, causing cancer at high dose levels; or it could be weakly
active, with cancerous tumors occurring only when the most suscep-
tible individuals receive very large doses. Based on structure-
activity relationships, the judgment of those knowledgeable about
the properties of XYZ is that there is a 2 percent chance that it
is highly active, a 13 percent change that it is moderately active,
and an 85 percent chance that it is weakly active.
3-22
cs
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The alternatives and possible outcomes are summarized in the deci-
sion tree of Figure 3-5a. Higher activity implies a greater number
of cases of cancer, as does permitting widespread use. The net
value consists of the net economic benefits (we have added bene-
fits to industry and benefits to consumers to simplify the figure)
less the cancer cases times the tradeoff of ten million dollars.
Under each alternative is shown the expected value (the arithmetic
average of the values of the uncertain outcomes) for that
alternative, enclosed in angle brackets. We would choose the
restrict alternative, since it has the greatest expected value
($221 million); we note this by circling the alternative.
How would the decision change if we knew in advance what was the
actual degree of activity of XYZ? If we knew that it was highly
active, to ban its use would clearly be the preferred alternative.
If it was only weakly active, then it would be best to permit all
uses so as not to forego the economic benefits of use. This situ-
ation is summarized in Figure 3-5b. Given our prior state of un-
certainty, we would expect a 2 percent chance that the clairvoyant
would tell us that XYZ was highly active (implying that we would
choose to ban its use) and an 85 percent chance that it was weakly
active (implying we would permit all uses). The average, or ex-
pected value given the clairoyant's information is 761 million. The
increase in expected value due to our receiving the information on
carcinogenicity—the EVPI—is 761 less 221, or 540 million. This
figure provides an upper bound on the value of information about
the dose-response activity of XYZ.
As was noted in the previous example, one can rarely acquire per-
fect information. Tests are not 100 percent precise. Using the
same procedure just outlined, one can calculate the expected value
of the information provided by any test [31,63,64]. Although the
idea is the same, the calculations are somewhat more complicated;
we shall not go through them here.
In light of the vast number of uncertain factors involved in a deter-
mination of unreasonable risk, one might reasonably ask, How can we make
probability assignments for every one? And even if we could, what would we
do with that mass of numbers and judgments? The answer is, We do not
specify probabilities for all of the variables and relationships—only for
those that are the most important. The measure of importance is a simple
one: Does the uncertainty in this variable (or relationship) affect the
3-23
67
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Alternative
Act ivity
Cancer s
Annua 11y
Net Economic
Benefits of Use
($xl0 )
Net Value
(xlO6)
High
<_o
1
rsj
(T)
00
0. 02
Moderate
Permit
<143
Weak
Restrict
Moderate
Weak
0.85
0.02
Moderate
Prohibit
Weak
1 .100
400
700
30
900
900
900
400
400
400
-10,100
-3,100
880
-6,600
1 00
400
0.85
Figure 3-5a. Decision Under Uncertainty
-------
Activity
Alternative
Cancer s
Annuallv
Nut Economic
Benefits of Use
(SxlO6)
Net Value
(>: 1 0 )
Permit
ho
CD
CD
High
Restrict
Prohibit
Permit
Restrict ^
Prohibit
Permi t
3-
Restrict
1 .100
700
400
10
900
400
900
400
900
.00
-10. 100
-6. 600
-3.100
100
880
4 00
Prohibit
Figure 3-5b, Decision witli Perfect Information
-------
outcomes enough to change Che preferred decision alternative? For most
decisions only a relatively snail number of uncertain parameters and
relationships are really critical.
The way we determine the critical uncertainties is by using a key part
of the decision analysis methodology—sensitivity analysis. Before dis-
cussing sensitivity analysis in any detail we will outline the structural
model used to integrate the information, uncertainties, and decision alter-
natives. Such a model is a key tool for carrying out sensitivity analysis
and plays a central role in the entire decision analysis methodology.
Integrating Model
Complexity is all pervasive in the toxic substances unreasonable risk
determination process. There are many ways in which a substance can be
used, many ways in which it night be harmful, and many groups in society
that it can affect. The chemical and biological processes are exceedingly
complex. Even the economic system is complicated, with intermediate
products, substitutes, complements, multistage production processes, and
diverse markets.
In the face of such complexity, intuition is often a poor and mislead-
ing guide. The alternative to relying strictly on intuition is modeling
the complex interactions—representing them explicitly so that our intui-
tion is not burdened with keeping track of a great many different interac-
tions. The decision analysis methodology uses a structural nodel to fill
this role.
The purposes of a structural model are to integrate information from a
variety of sources, to represent critical interactions, to provide a con-
sistent and explicit set of assumptions, and to facilitate sensitivity
analysis. By structural model we mean an explicit representation of one's
understanding and assumptions about the underlying relationships among the
various aspects of the decision problem. Referring back to Figure 3-1, the
model represents how the EPA's decisions interact with the economic, physi-
cal, and biological systems to affect the outcomes. It is "structural" in
the sense that it is based as much as possible on a basic understanding of
3-26
70
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the relationships and interactions involved in the problem—econor.ic market
processes, dose response relationships, and so on.
An important advantage of building an explicit structural nodel is
that it provides an explicit, common framework for experts from diverse
fields to make contributions. Assumptions are clearly evident, and the
overall raodel can be examined and calculations verified by any individual
interested in the analysis.
The nature and content of the raodel depends critically on the decision
being analyzed. Any toxic substances decision will involve economic fac-
tors, physical processes of transport and exposure, and effects on humans
and/or the environment. Thus, we can outline a generic model in more
detail than in Figure 3-1. This is shown in Figure 3-6. The diagram
depicts the key submodels and indicates some of the important variables
through which the submodels interact.
Beginning with the generic model and a specification of the particular
unreasonable risk, decision, the analysts can begin to define each submodel.
Although there are many steps in the development of a final structural
model, starting fron prose statements of assumptions, the model's final
form is usually a set of mathenatical statements representing the basic
relationships. The degree of detail depends on the specific decision and
the stage of the analysis. Initially, a model might have only a few equa-
tions. If sensitivity analysis indicates that a particular aspect of the
problem is critical, it can thea be modeled in more detail, with more fac-
tors represented and more second- or third-order interactions included.
As a simple example, consider a human exposure submodel. Its pur-
pose is to calculate the aggregate dose received by consumers, dur-
ing a given period of time. As shown in Figure 3-7a, inputs to the
exposure model would include total quantity of 5CYZ produced, the
fraction used in consumer products, and the fraction ingested in
consumer use. Such a simple model would incorporate equations to
account for the flow and mass-balance of XYZ, and to compute the
average dose.
A more detailed exposure model is outlined in Figure 3-7b. It dis-
aggregates exposure by different populations, such as consuner ex-
posure and occupational exposure. Total exposure to an individual
is a sum of exposures through several possible pathways, including
3-27
71
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I—
EconomLc Sector Submodel
Conrrn t c.< I Inn
EnvJ.ron-nent.al
Transport and
Transformation
Knv i r orTicn t a 1
Zi tects
Submodel
Em}
Demand and IJ«ip
Submodel
A'm 1 ss Ions
/ f roTi
Pt i'k!ii« t lor
Envlronnental
Ir.p.lCtJi
environment
K.x pnsii r».'
User
Exposure
Human Health
Effects Submodel
>jc i s ion
Criter ion
industry
Su bmodc1
llun.in Health
Kf f ec t -s
Net Imp.ict on Industry
Figure 3-6. Decision Analysis Integration Model
-------
Quant icy
of XYZ Produced
Fraction Used in
Consumer Produces
Fraction of
Material Int'estad in
*
Consumer Use
Simple
-xpo sure
Mocel
Aggregate CoT\sumer Dose/Year
Figure 3-7a. Simple Exposure Model
Total Quantity
of XYZ Produced .
Fraction in
Industrial Usage
Fraction in
Consumer Usage
Chemical Properties
Biological Properties
Deta iled
Exposure
Model
Dose/Year to Consumer Users
Dose/Year to Industrial Users
Dose/Year to Employees of
XYZ Production Plants
Figure 3-7b. Detailed Exposure Model
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73
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consumer use and through environmental residues. Each pathway
would include the relevant steps from the manufacturing of XYZ, to
its use in a product, to its release, and finally to its ingestion,
absorption, or inhalation by an individual. The model would
include equations to represent chemical and biological processes
such as decomposition and bioaccunulation, as well as the simpler
equations for mass-balance accounting.
Structural modeling will play a particularly important role in helping our
understanding of the economic implications of toxic substances decisions.
Issues of substitution, market shares, and effects on feedstocks and final
consumer products will all be important. A central role is played by
economic markets, wherein chemical substances and products using such
substances compete. In order to calculate economic costs and benefits, it
is necessary to estimate market equilibrium prices and quantities. One way
to approach this problem is to use an economic modeling methodology known
as generalized equilibrium modeling [65]. The basic idea is to think about
an economic sector in terms of a network of Industries, consumers, and
markets. An example will illustrate the approach.
To evaluate the economic impact of the possible regulation of XYZ,
it is necessary to understand the processes of market competition
and interproduct substitution. For example, if a product using XYZ
is removed from the market as a result of regulatory action, there
will be direct economic losses, but they will be partially offset
by increased sales of competing products. Three markets are cen-
tral to the analysis of XYZ: the market for the XYZ resin itself,
the market for consumer adhesives, and the market for industrial
adhesives.
A portion of the economic sector in which the resin XYZ plays a
role is depicted by Figure 3-8. Circles represent simple models o
economic markets. Boxes represent models of production or conver-
sion processes, or of a final demand for a good. Final demands are
at the top of the network, while basic resource supplies are at the
bottom. Materials flow "up" the network, undergoing various con-
versions and being traded in explicit markets. Note that both
industrial and the new consumer adhesive manufacturing processes
compete for a supply of XYZ resin, which can be produced by either
of two processes. Adhesives using XYZ compete with alternative
adhesives in both the industrial-use and consumer-use markets. A
network model such as that summarized in Figure 3-8 can be used to
trace the implications of regulatory actions as they propagate
through the economic system.
3-30
74
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Industr in I
Adhesive
Market
Altcrnat ive
Feed stock
Intlustrl.il
Sect.or N
Aillu* s Ivc Ocmnnil
1ndu sl r ia 1
Sector A
Adht"j I wr Ih-m.jnd
I mlu '.trial
Sector B
Adlm^.We Demand
Consumer
XY7. Kits In
Product ion
Process 2
N»tw Consumer
A*i 111• •. i v i'
Production Proves
XYZ Ki:^In
Produc t ion
Process 1
I ndu st r ia I
Adhesive 2
Production Proce-j;
Industrial
Adhesive 1
Product ten Process
Ex l st in^ Con sumer
Ad lies ive
Production Procos.s
roduc t ion Pi mi-
Figure 3-3, Illustrative Economic Network
-------
The simple models of markets and production processes include
representations of both physical processes and economic behavior.
The generalized equilibrium modeling approach can be used to
"solve" the network, providing a set of equilibrium prices and
quantities over time which can he used to calculate net econonic
impacts of regulatory options.
Economic network models are a flexible yet powerful way of custo-
mizing a model to capture the economic interactions important to a
specific unreasonable risk decision. Detail can be added as
necessary, while retaining a good intuitive representation of the
economy.
Once a structural model has been developed, it provides a tool to
estimate "what happens," that is, what outcomes are obtained given each
decision alternative. The model provides the linkage between the infor-
mation we have about the specific problem ("what we know"), the regulatory
alternatives ("what we can do"), and the outcomes that enable the decision
maker to judge the desirability of each alternative ("what we want").
Sensitivity Analysis
Although sensitivity analysis is a key methodological concept, it is
an intuitively simple idea. The basic idea is to find out whether some-
thing in the decision problem about which we are uncertain is important.
Something is important if it affects the decision, that is, if changes in
the parameter or relationship change the preferred decision alternative.
Although sensitivity analysis is used throughout the decision analysis
process, it plays a critical role at two stages. The first is when the
structural model is being developed. There may be several alternative
formulations of a particular relationship that are being considered for use
in the model. A simple version of each can be tried out and the impact on
the outcomes observed. If the outcomes change little, we can assune that
the decision is unlikely to change and that it is reasonable to go ahead
and choose the "most likely" model relationship. If the outcomes change
considerably, this suggests either that more detailed modeling is necessary
or that the uncertainty must be represented explicitly using probability
assignments. As the model is developed, we can continue to use sensitivity
3-32
76
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analysis to help guide our choices in terns of modeling effort and degree
of detail.
Once a first-cut model is complete, including specification of a deci-
sion criterion, sensitivity analysis is carried out on each of the (perhaps
numerous) uncertain variables. As previously noted, although a variable
may be highly uncertain, we will always have enough information to define a
range (perhaps very broad) of possible values. To perform the sensitivity
we vary the value of the variable over its maximum possible range and ob-
serve the impact on the decision. If the outcomes do not change enough to
change the decision, then the uncertainty in the variable is not critical.
A "best estimate" for its value can be used in the remainder of the analy-
sis. If the decision changes, the uncertainty should be represented
explicitly and factored into the analysis.
The sensitivity analysis might be displayed in a form similar to
that demonstrated in Table 3-1. The table shows the sensitivity of
the outcones, net value, and regulatory decision to the uncertainty
in two variables. The first uncertainty is in the number of con-
sumers exposed to XYZ. Upon examining the net value under each
alternative, it is apparent that the "restrict use" alternative is
preferred over the full range of uncertainty in the population
size. Since the decision is not affected, the nominal value or
"best estimate" can be used.
The net value is much more sensitive to variations in the average
dose level, a highly uncertain variable. If the dose is high, the
best alternative is to prohibit all uses, while it would be pre-
ferable to permit all uses if the actual dose value were low. Be-
cause the decision is sensitive to the uncertainty in dosage level,
the uncertainty should be represented explicitly in the analysis.
This can be done using a probability distribution.
It is important to remember that sensitivity analysis is a conceptual
idea as well as a methodological procedure. It can be applied qualita-
tively as well as quantitatively. This simply involves the analyst or
decision maker's asking at each step of the process, "Does it matter to the
specific decision?" This applies when defining the decision and the de-
tails of alternatives, when choosing outcones to examine, and when evalu-
ating informaton that might be factored into the analysis. In many cases
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77
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Table 3-1
EXAMPLE SENSITIVITY ANALYSTS RESULTS
(L) Permit All Uses (2) Restrict Use
i
ijj
¦p-
Number Economic Net Number Economic Net
of Cancers Benefits* Value of Cancers Benefits Value
1. Size of Exposed Population
Hi£h - 8,000 410 900 -3,200 38 400 20
Nominal - 6,000 400 900 -3,100 30 400 100
Low - 3,000 37 0 900 -2,800 15 400 250
2. Average Dose Level
llifth - 0.9 gram/yr.
Nominal - 0.2 gram/yr.
Low - 0.03 grara/yr.
1400 900 -13,100
400 900 -3,100
10 900 800
7 0 400 -300
30 400 100
8 400 320
(3) Prohibit All Uses
Number Economic Net
of Cancers Henefits Value
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
0 0 0
05 *E~conomic benefits and net value are all x 10^
-------
one can do a qualitative sensitivity analysis, note the conclusion, and
simpify the analysis accordingly.
Probabilistic Analysis
In the process of carrying out the sensitivity analysis we will have
identified a set of uncertain variables for which the uncertainty really
matters; that is, the unreasonable risk decision is sensitive to such
uncertainties. Given all available data and the judgments of EPA staff,
probability distributions will be specified to represent each uncertain
variable explicitly.
The next stage in the decision analysis process is to carry out a full
probabilistic analysis, using the integrating model and probability distri-
butions on the critical parameters to generate probability distributions on
the outcomes. One might visualize this as shown in Figure 3-9, which de-
picts only a portion of the integrating model, a simplified cancer occur-
rence submodel. Probability distributions on exposure, number exposed, and
response function parameters are combined using the equations of the model
to produce a probability distribution on the number of cancers. This
latter distribution explicitly represents the uncertainty in a critical
outcome resulting from the uncertainty in the input data. An analysis
summary presented to EPA decision malcers should include for each alterna-
tive a description of the range of uncertainty in each of the important
outcomes.
Outcome probability distributions are calculated by using a decision
tree to outline probabilistic scenarios. One might think of "driving" the
integrating model with the decision tree, as shown in Figure 3-10. Recall
that each path through the tree defines a scenario, whose probability of
occurrence is equal to the product of the probabilities on each uncertainty
node branch. The scenario fixes a set of values for the uncertain para-
meters. These values, combined with deterministic data, are used by the
integrating model to calculate a value for each outcome, a value whose
probability of occurrence is equal to the scenario probability. This pro-
cedure is carried out for each path through the decision tree, often with
3-35
79
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•H
-O
Exposure Level
Cancer
Occurrence
Submodel
>•
Population Exposed
•H
Dosc-Rcsponse Activity
r-H
Number of Cancers
Figure 3-9. Using Model
to Calculate Uncertain Outcome
-------
Values of Uncertain
Pji Defined
by u P..H h Tl-iouf'.h l W Tree
(A Probab 111 s t ic Scenario)
Ou tCninc5. Assoc iat cJ
u 1 ! 11 I III- P.irl icul.Tr
A11t r n;> t I y«.1 .nul
Probatilic Scenario
Intcgrat ing
Model
Rcgulatory
Alternatives
_y
Critical. Uncircaintles
Alcerivit lve
Hf Ing Lx.jm tiled
Figure 3 — 10. Relationship of Decision True and integral; ing Model
-------
the aid of a computer program to carry out the numerous calculations.
Combining all the outcome values and associated probabilities gives the
overall probability distributions on the outcomes.
As an example, suppose that the sensitivity analysis has shown that
the icey uncertainties for the XYZ regulatory decision are dose
response activity, average dose level, and the number of consumers
exposed. These uncertainties are shown in the probability tree of
Figure 3-11 (a decision tree with uncertainty nodes only is known
as a probability tree; in this example we are considering only one
of the three alternatives in the complete decision tree). We
assume that the uncertain parameters are independent.
The first four columns at the right of the tree summarize the out-
cone values for each path or scenario and the probability of the
scenario. The outcomes and the scenario probabilities are combined
to give a set of cumulative probability distributions. The prob-
ability distribution on the number of cancers can be examined by
referring to Figure 3-12 (the horizontal scale is broken at several
points because of the wide range of possible outcomes). Note that
while there is a 76 percent chance that the number of cancers will
be 5 or fewer, there is still a small possibility that it could be
as many as 7000. The mean or expected value for the number of
cancers is 83. Again, it is important to keep in mind that this is
for a single alternative only. With a different alternative, the
number exposed would be different, and perhaps the dose would be
different, leading to a different probability distribution for the
cancer outcome.
While the entire probability distribution shows the range of
outcomes and the likelihood of each outcome, it can be somewhat
cumbersome. There are several ways to summarize the distribution in
more concise terms. The following is one way that retains much of
the information on the range and uncertainty in the net value:
mean net value = $79 million
probability that net value >1.4 billion = 10%
probability that net value > 695 million = 50%
probability that net value < -2.5 billion = 10%
probability that net value < -16 billion = 1%
At this point a sensitivity analysis can be conveniently carried out
on the cancer tradeoff. The results just summarized used the tentative
tradeoff of 10 million. If the tradeoff were one million, the mean net
value would be 827 million. On the other hand, if a tradeoff of 20 million
were used, the mean net value would become -750 million. By carrying out
3-38 82
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e r>| lixpo'.i'd
ru t Po|ii11 00 ,rj(1
3.COO , £, 0r)
Nvt Vj^m- P.il h
_ < * I 0 > rrol.ibl 1 U y
-68,600 U.UU06
! ,200
^0
20
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Figure 3-11,
Example Probabilistic Analysis for "Permit All Uses" Alternative
-------
O.9..
O.H..
Z 0.7..
o
u
p*
0.6--
-T->
a
o
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I 50 00 100 500 I
NunbiT of Cases of Cancer Annually
800
1000
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on
Figure 3-12. Cumulative Probability Distribution on the Number of Cases of Cancer
-------
similar sensitivity tests for each of the other alternatives, sensitivity
of the unreasonable risk determination to the tradeoff value can be
evalua ted.
The final step in the probabilistic analysis is to calculate the value
of information for each uncertain parameter for which information-gathering
alternatives exist.
The "results" of an unreasonable risk decision analysis presented to
EPA decision makers should include, at a minimum, the following:
1. Definition of alternatives considered and outcomes evaluated.
2. Description of the integrating model, including an explicit list
of key assumptions.
3. Documentation of the initial data and of the sensitivity
analysis.
4. Probability distributions for the critical uncertainties
identified in the sensitivity analysis.
5. For each alternative, the expected value, range, and probability
distribution (or summary thereof) for each outcome and for the
overall desirability of the alternative.
6. The expected value of perfect information on relevant parameters.
7. The expected value of the (imperfect) information to be gained
from potential testing or other information-gathering alterna-
tives.
Summary of the Decision Analysis Process
Having discussed the methodology of decision analysis ia some detail,
we would like to step back and review some of the key points made at the
outset. It cannot be overemphasized that the primary purposes of carrying
out an unreasonable risk decision analysis are to improve our understanding
of the decision problem and to gain insights into the critical issues.
While the quantitative results are important, the qualitative understanding
is the real goal. This understanding will come as much from the process of
the analysis--structur ing the problem, developing a ir.odel, testing assump-
tions, carrying out sensitivity analyses, and so on—as from examination of
3-41
S5
-------
the analysis results. For this reason we maintain that the process of the
analysis as well as the conclusions must be documented.
We have noted several times that decision analysis is an iterative and
evolutionary process. As the analysis proceeds and we understand the
problem better, we are likely to go back to a previous step and refine the
model or modify a decision alternative. This is particularly the case with
the sensitivity analysis phase, which often iterates back and forth several
times between model improvements and sensitivity tests. In the probabil-
istic phase, new information can be factored in and the probability distri-
butions recalculated to reflect what has been learned. The process is
iterative and flexible but always carried out in a consistent manner within
the overall decision analysis framework.
Complementing the iterative nature of the process is the fact that the
decision analysis methodology can be applied at almost any level of detail,
from a quick "back-of-the-envelope" calculation to a fully disaggregated,
multiregional analysis. The level of detail used should be appropriate to
the nature of the decision and the time and resources available for the
analysis. Whatever the level of detail, the decision analysis approach
focuses attention and effort on the key issues and critical uncertainties,
providing a maximum of useful, insightful information for the decision
makers .
COORDINATION OF DECISIONS
We have described a decision analysis methodology for evaluating a
toxic substances unreasonable risk decision, or closely related set of
decisions. The problem facing the EPA as it implements TSCA has another
critical dimension: the coordination of an enormous number of individual
decisions, decisions that must be made with respect to different sub-
stances, at different stages in the evaluation of a single suhstaDce, and
at different points in time. It is essential that these decisions be made
in a consistent manner, that analytical resources be optimally utilized,
and that the EPA be able to set priorities for action.
3-4 2
£6
-------
In this section we outline some of the ideas that can provide the
basis for developing a system for coordinating TSCA decision making.
Collectively, these ideas are the fundamental principles of decision system
analysis [66,67], the methodology for analyzing in a coordinated and effi-
cient manner a large number of interrelated decisions. In particular,
decision systeu analysis allows us to deal with the interactions caused by
uncertainty and by the fact that limited regulatory resources must be allo-
cated to a relatively small number of substances selected from an enormous
initial group. The concepts of decision system analysis are especially
useful in situations characterized by multiple levels of decision making,
as are found in the EPA ISCA program.
Decision system analysis is based on three principles: decomposition,
coordination, and iteration. Decomposing the overall toxic substances
problem into a set of semi-independent regulatory decisions considerably
reduces the magnitude of the problem. Since these individual decisions are
not completely independent, they must be coordinated to account for their
interactions. Coordination is provided by common value tradeoffs and
assumptions about such general factors as economic growth and inflation.
Finally, rather than attempt to arrive at an "optimal" decision in one
step, we proceed in an iterative fashion, revising and refining the deci-
sion as more information is received and more factors are worked into the
analysis. This Is a familiar problem-solving process that goes on in an
informal way in Innumerable situations. Decision system analysis provides
the formal principles necessary for applying it in a rigorous and consis-
tent way to such complex problems as toxic substances regulation.
Decision system analysis provides the overall logical approach that
the EPA can use to develop a framework for coordinated decision making with
respect to priority setting and unreasonable risk determination. It can be
applied along two general dimensions: the coordination of a sequence of
decisions for a single substance and the analysis of decisions regarding
different substances. One way in which these decisions might fit together
in a coordinated framework is shown in Figure 3-13.
3-4 3
87
-------
Information oa Coses
Charac C€t* i-T iC ion of
Regulatory KcKmri. o:
Characterize j<">n of
Characterization
of Ti'stS
Kequ ire T>*si i ng
Subiit/uiccs
lor Which
Tnt or ¦ it ion
— ^
C.j 1lit r i nj;
Substances
for Which
Ki'/.u 1 .it ion
While
Eva lull ton
Priority SetrI
for Kcgclotorv
Acl 1 < > 11
• Ext cnl of Tcr.t i iv;
Kr aineu'ork to
Guide Teat inft:
Initial Screening
co Get Priorities
for TtL-tin£
Cliar ;ic t cr l/.at i >mi of Potential Hcy.uloiory /V-*
C r>
CC
Figure 3-13.
Overview of a Coordinated Framework for Decision Making
-------
Coordination Among Decision Stages
Decisions regarding a particular chemical or class of chemicals range
from initial screening and priority setting to a final determination of
unreasonable risks. The decisions at the earlier stages (see Figure 3-13)
must in general be made more quickly and vrith less analytical effort. As we
move from the initial stages of screening and testing decisions to the
final regulatory decision analysis, we are able to add greater detail and
structure to the analysis. Yet it is essential that the assumptions and
approximations used in the initial stages be consistent with the more
detailed analysis at the final stage. One way to do this is to have the
basic core structure of the model be essentially the same at each decision
stage. As we nove toward a more detailed decision, we night add
o Greater detail and disaggregation in the time dimension—a
dynamic rather than static analysis.
o Disaggregation of the parties (or populations) bearing the costs
and receiving the benefits from the use of a substance.
o Greater attention to second-order effects.
Another way in which the initial stages could approximate a detailed
model is by directly assessing probability distributions on key variables
rather than using a model. Later, in the regulatory decision analysis, a
detailed submodel nay be used to calculate the probability distribution for
the variable from probabilities assessed on model inputs. For a screening
decision we nay, for example, directly estimate the number of occurrences
and the severity of a given health effect. If a regulatory decision
analysis is warranted, we would use a more detailed structural model,
requiring specification of a number of parameters in order to calculate
occurrence and severity.
Coordination of the decision stages also requires the use of a con-
sistent decision criterion; again, it may have greater detail at the later
stages.
3-45
83
-------
Coordination of Decisions Regarding Different Substances
It is important that decisions regarding a wide variety of chemicals
be made in a consistent and coordinated manner. Such consistency is
clearly part of the legislative intent of TSCA. At the first level, the
use of an explicit decision analysis methodology will provide a strong
degree of consistency. The coordination will be augmented to the extent
that a common integrating nodel (reflecting common assumptions) can be
used.
At another level, coordination requires the use of a common set of
parameters (those not specific to the substance under analysis) for both
the structural model and the decision criterion. In the language of
decision system analysis, these are "coordinating signals," which may
include
o Sizes of populations that may be exposed to a class of
substances.
o Market information, including denand levels, supply quantities,
and prices.
o Tradeoffs between the economic, health, and environmental
outcomes.
o Tradeoffs between outcomes over time.
o Probabilistic assessments of common planning scenarios, reflect-
ing potentially important parameters ou.side the chemicals sector
(for example, energy prices, inflation rate).
A final level at which coordination may be necessary stems from the
limitations on EPA's regulatory budget. Not all substances that may
warrant regulatory analysis can be given the attention merited. With an
explicit decision system, it may be possible to factor in these budget
limitations so that testing and regulatory priorities are set accordingly.
SUMMARY
In this chapter we have described the basic concepts of a decision
analysis methodology that can be used by the EPA for toxic substances
3-46
90
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unreasonable risk determination and priority setting. The nethodology is
capable of treating the difficult dimensions of unreasonable risk deter-
mination: complex physical, biological, and economic interactions, numerous
uncertainties, diverse impacted groups, and conflicting objectives. Key
components of the methodology include structural modeling, sensitivity
analysis, and explicit treatment of uncertainty. New information can be
factored in as it is obtained, and the economic value of developing more
information can be estimated in a consistent manner. Finally, the concepts
of decision system analysis can provide a basis for priority setting and
the coordination of decisions regarding a wide range of potentially toxic
substances.
3-47
SI
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Section 4
REFERENCES
[1] Pesticide and Toxic Chemical News, February 17, 1982, pp. 10-11.
[2] Chemical and Engineering News, February 22, 1982, p. 27.
[3] Harold P. Green, "Legal and Political Dimensions of Risk-Benefit
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Papers Presented at the Engineering Foundation Workshop, September
22-26, 1975, Asilomar, California, pp. 273-290.
[4] National Research Council, Decision Making for Regulating Chemicals
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1975).
[5] National Research Council, Decision Making in the Environmental
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[6] W. W. Lowrance, Of Acceptable Risk: Science and the Determination of
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[7] J. Davies, S. Gussman, and F. Irwin. Determining Unreasonable Risk
Under the Toxic Substances Control Act (Washington, D.C.: The
Conservation Foundation, 1979).
[8] A. R. Prest and R. Turvey, "Cost-Benefit Analysis: A Survey," The
Economic Journal 75 (1965).
[9] M. Hill, "A Goals-Achievement Matrix for Evaluating Alternative
Plans," Journal of the American Institute of Planners 34, no. 1,
(1968): 19-29.
[10] E. J. Mishan, Cost-Benefit Analysis (New York: Praeger Publishers,
1971).
[11] Toxic Substances Strategy Committee, Toxic Chemicals and Public
Protection (Washington, D.C.: Council on Environmental Quality, May
1980).
4-1
32
-------
[12] R. A. Howard, "The Foundations of Decision Analysis," IEEE
Transactions on Systems Science and Cybernetics SSC-4, no. 3 (1968):
211-219.
[13] R. A. 'toward, "Decision Analysis In Systems Engineering," Systems
Concepts: Lectures on Contemporary Approaches to Systems, ed. R. F.
Miles (New York: John Wiley and Sons, 1973).
[14] R. A. Howard, "Social Decision Analysis," Proceedings of the IEEE 63,
no. 3 (1975): 359-371.
[15] D. W. North, "A Tutorial Introduction to Decision Theory," IEEE
Transactions on Systems Science and Cybernetics SSC-4, no. 3 (1968).
[16] H. Raiffa, Decision Analysis: Introductory Lectures on Choices Under
Uncertainty (Reading, Mass.: Addison-Wesley Publishing Co., 1968).
[17] D. W. North and M. W. Merkhofer," A Methodology for Analyzing
Emission Control Strategies," Computers and Operations Research 3
(1976): 185-207.
[18] D. W. North and M. W. Merkhofer, "Analysis of Alternative Emissions
Control Strategies." Chapter 13 in Air Quality and Stationary Source
Emission Control. Report by the Commission on Natural Resources,
National Academy of Sciences (Washington, D.C.: U.S. Government
Printing Office, 1975).
[19] R. A. Howard, J. E. Matheson, and D. W. North, "The Decision to Seed
Hurricanes," Science 176 (1972): 1191-1202.
[20] R. L. Keeney and G. A. Robilliard, "Assessing and Evaluating Environ-
mental Impacts at Proposed Nuclear Power Plant Sites," Journal of
Environmental Economics and Management 4 (1977): 153-166.
[21] National Research Council, Polychlorinated Biphenyls (Washington,
D.C.: National Academy of Sciences, 1979).
[22] R. A. Howard, Life and Death Decision Analysis. Research Report No.
EES DA-79-2, Department of Engineering-Economic Systems, Stanford,
California, December 1979.
[231 R. D. Luce and H. Raiffa, Games and Decisions (New York: John Wiley
and Sons, 1965).
[24] A. K. Sen, Collective Choice and Social Welfare (San Francisco:
Holden-Day, Inc., 1970).
[25] D. W. Boyd and C. E. Clark, Jr., Multi-Party Decision Analysis'for
Social Decisions. Decision Focus Incorporated Working Paper No. 2,
1978.
4-2
93
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[26] S. M. 3arrager, Bruce R. Judd, and D. Warner North, The Economic and
Social Costs of Coal and Nuclear Electric Generation: A Framework
for Assessment and Illustrative Calculations for the Coal and Nuclear
Fuel Cycles, prepared for the National Science Foundation, U.S.
Government Printing Office Stock Number 038-000-00293-7, March 1976.
[27] P. A. Owen, "Decisions That Affect Outcomes in the Distant Future"
(Ph.D. dissertation, Stanford University, 1979).
[28] Interagency Regulatory Liaison Group, "Scientific Bases for Identi-
fication of Potential Carcinogens and Estimation of Risks," Federal
Register 44, no. 131 (1979); 39858-39879.
[29] D. R. Calkins et al. , "Identification, Characterization, and Control
of Potential Human Carcinogens: A Framework for Federal Decision-
Making," Executive Office of the President, Office of Science and
Technology Policy (1979). Also published in Journal of the National
Cancer Institute 64, no. 1 (1980): 169-176.
[30] L. Slesin and R. Sandler, "Categorization of Chemicals Under the
Toxic Substances Control Act," Ecology Law Quarterly 7, no. 2 (1978):
359-396.
[31] C. S. Spetzler and C.A.S. Stael von Holstein, "Probability Encoding
in Decision Analysis," Management Science 22, no. 3 (1975).
[32] A. Tversky and D. Kahneman, "Availability: A Heuristic for Judging
Frequency and Probability," Cognitive Psychology 5 (1973): 207-232.
[33] Judgment Under Uncertainty: Heuristics and Biases, D. Kahneman et
al., ed. (Cambridge: Cambridge University Press, 1982).
[34] T. S. Vialisten and D. V. Budescu, Encoding Subjective Probabilities:
A Psychological and Psychometric Review. Report prepared for Office
of Air Quality Planning and Standards, U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina, August 1980.
[35] 21 U.S.C. Sec. 301.
[36] 15 U.S.C. Sec. 2605(c)(1).
[37] M. S. Baran, Regulation of Health, Safety, and Environmental Quality
and the Use of Cost-Benefit Analysis. Report to the Administrative
Conference of the United States, March 1979.
[38] Executive Order 12044, Sec. 3.
[39] Federal Register, Presidential Documents 46, no. 33 (1981):
13193-13198.
4-3
94
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[40] AFL-CIO v. Marshall, No. 78-1362; Cotton Warehouse Assn. v. Marshall,
No. 78-1736 (Bazelon opinion).
[41] 541 F.2d 1, B ERC 1785 (D.C. Cir.) (en banc), cert, denied, 426 U.S.
941, 8 ERC 2200.
[42] 5U F.2d 492 (8th Cir. 1975) (en banc).
143] American Textile Manufacturers v. Donovan, No. 79-1429; National
Cotton Council v. Donavan, No. 79-1583 (Brennan opinion).
[44] Business Week, July 21, 1980, pp. 65-66.
[45] P. Handler, "Some Comments on Risk Assessment," The National Research
Council in 1979: Current Issues and Studies (Washington D.C.:
National Academy of Sciences, 1979), pp. 3-24.
[46] T. Page, "A Generic View of Toxic Chemicals and Similar Risks,"
Ecology Law Quarterly 7, no. 2 (1978): 207-244.
[47] T. Page, "A Framework for Unreasonable Risk in the Toxic Substance
Control Act (TSCA)," Annals of New York Academy of Sciences 363
(April 30, 1981): 145-166.
[48] National Research Council, Saccharin: Technical Assessment of Risks
and Benefits, Part 1 (Washington D.C.: National Academy of Sciences,
1978).
[49] J. P. Leape, "Quantitative Risk Assessment in Regulation of Environ-
mental Carcinogens," Harvard Environmental Law Review 4, no. 86, pp.
86-116.
[50] W. C. Clark, "Witches, Floods, and Wonder Drugs: Historical Perspec-
tives on Risk Management," Proceedings of Symposium on Societal Risk
Assessment: How Safe Is Safe Enough? Richard C. Schwing and Walter
A. Albers, Jr., ed. (New York: Plenum Press, 1980).
[51] T. H. Maugh, "Chemical Carcinogens: The Scientific Basis of Regula-
tlon'" Science 201 (1978): 1200-1205.
[52] B. N. Ames, "Identifying Environmental Chemicals Causing Mutations
and Cancer," Science 204 (1979): 587-593.
[53] Harold H. Schmeck, "Cancer Research Criticized," interview with Dr.
Joshua Lederberg, New York Times, March 18, 1980.
[54] R. Bates, "Priority Setting for Carcinogenesis and Mutagenesis
Testing: Approaches of the FDA," Proceedings of the Second Food and
Drug Administration Office of Science Summer Symposium, Annapolis,
Maryland, August 31-September 2, 1977, pp. 211-215.
S5
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[55] D. D. Doniger, "Federal Regulation of Vinyl Chloride: A Short Course
in the Law and Policy of Toxic Substance Control," Ecology Law
Quarterly 7, no. 2 (1978): 497-675.
[56] National Research Council, Regulating Pesticides (Washington, D.C.:
National Academy of Sciences, 1980).
[57] J. Cornfield, "Carcinogenic Risk Assessment," Science 198 (1979):
693-699.
[53] U.S. House Committee on Interstate and Foreign Commerce, Legislative
History of the Toxic Substances Control Act (Washington, D.C.: U.S
Government Printing Office, 1976).
[59] Environmental Protection Agency, Initial Report of the TSCA Inter-
agency Testing Committee (Washington, D.C.: U.S. Government Printing
Office, 1978).
[60] Systems for Rapid Ranking of Environmental Pollutants. Report No.
ZPA-600/5-78-012, Environmental Protection Agency, Washington, D.C.,
June 1978.
[61] U.S. House of Representatives, Committee on Interstate and Foreign
Commerce, Legislative History of the Toxic Substances Control Act,
(Washington, D.C.: U.S. Government Printing Office, December 1976)
p. 14.
[62] R. D. Willig, "Consumer Surplus Without Apology," American Economic
Review, 66(4) (1976).
[63] R. A. Howard, "Information Value Theory," IEEE Transactions on
Systems Science and Cybernetics SSC-2, no. 1 (1966), pp. 22-26.
[64] R. A. Howard, "Value of Information Lotteries," IEEE Transactions on
Systems Science and Cybernetics SSC-3, no. 1 (1967), pp. 54-60.
[65] E. G. Cazalet, Generalized Equilibrium Modeling: The Methodology of
the SRI-Gulf Energy Model. Final Report prepared by Decision Focus
Incorporated for the Federal Energy Administration, May 1977.
[66] D. W. Boyd and E. G. Cazalet, An Illustrative Example of the Appli-
cation of Resource Allocation Using Price Iteration for Decentrali-
zation (Menlo Park, Calif.: Stanford Research Institute,Decision
Analysis Group Report, March 1971).
[67] D. W. Boyd, D. W. North, and S. G. Regulinski, A Resource Allocation
Methodology for Establishing RD&D Budgetary Priorities. Final Report
prepared by Decision Focus Incorporated for U.S. Department of
Energy, February 1979.
4-5
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PART II
PERCHLOROETHYLENE:
A CASE STUDY OF THE APPLICATION OF
DECISION ANALYSIS TO THE DETERMINATION
OF THE RISK POSED BY A TOXIC CHEMICAL
37
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Part II
CONTENTS
Section Page
1 INTRODUCTION AND SUMMARY 1-1
2 THE DRY CLEANING INDUSTRY 2-1
3 PCE EXPOSURE CALCULATIONS 3-1
Worker Exposure 3-2
User Exposure 3-5
Exposure to Urban Population 3-8
Summary of Exposure Calculations 3-12
4 CONTROL OPTIONS 4-1
Description of Control Options 4-2
Determining Costs and Effects of Control Options 4-4
Data for Control Options Analysis 4-8
Results of Costs and Exposure Calculations 4-15
5 HEALTH EFFECTS OF PERCHLOROETHYLENE 5-1
Toxicological Impacts of Perchloroethylene 5-1
Carcinogenic Effects of Perchloroethylene 5-2
6 MODEL FOR DOSE RESPONSE RELATIONSHIP FOR PCE 6-1
Scaling Method 6-2
Species Type for Extrapolation 6-3
Extrapolation Model 6-5
7 ANALYSIS OF CONTROL OPTIONS 7-1
Structure of the Control Decision 7-1
Selection of Best Control Option 7-5
Sensitivity Analysis 7-8
38
-------
CONTENTS (continued)
Section Page
8 VALUE OF FURTHER INFORMATION 8-1
9 INSIGHTS FROM THE PERCHLOROETHYLENE CASE STUDY 9-1
Insights Regarding Perchloroethylene 9-1
Insights Regarding the Methodology 9-2
10 REFERENCES 10-1
APPENDIX A: CALCULATING AN ANNUAL CAPITAL CHARGE FOR AN INVESTMENT A-l
APPENDIX B: DETAILED CALCULATIONS OF CONTROL COSTS AND EXPOSURE
REDUCTION B-l
APPENDIX C: CALCULATIONS OF PROBABILITY OF INCIDENCE AND EXPECTED
CANCER CASES BY CONTROL OPTION AND DOSE RESPONSE CASE C-l
39
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Part II
FIGURES
Figure Page
7-1 Decision Tree for PCE Control Analysis 7-2
7-2 Sensitivity Analysis on Cost per Cancer Case 7-9
8-1 Decision Tree for Calculating Value of Perfect Information
on All Dose Response Uncertainties 8-2
8-2 Decision Tree for Expected Value of Perfect Information
on Scaling Method 8-4
8-3 Decision Tree for Expected Value of Perfect Information
on Species Type 8-5
8-4 Decision Tree for Expected Value of Perfect Information
on Extrapolation Model 8-6
100
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Part II
TABLES
Table Page
2-1 Dry Cleaning Plants Using PCE 2-2
3-1 Exposure Levels in Commercial Dry Cleaners by Type
of Worker 3-2
3-2 Exposure Levels for Machine Operators in Industrial
Dry Cleaners 3-3
3-3 Number of Dry Cleaning Plants by Size of Operation 3-4
3-4 Estimated Number of Workers and PCE Exposure in Dry
Cleaning Plants Using PCE 3-6
3-5 PCE Concentrations in Coin-Operated Dry Cleaners 3-8
3-6 Estimated Number of Users of and PCE Exposure from
Dry Cleaning Services 3-9
3-7 Concentrations of PCE and Urban Populations at Specified
Distances from Typical Dry Cleaning Point Sources 3-11
3-8 Summary of Ambient PCE Concentrations in Three Cities 3-12
3-9 Summary of Calculations of PCE Exposures from the Dry
Cleaning Industry 3-13
4-1 Control Options and Combinations of Options Considered
for Analysis 4-5
4-2 Summary of Control Costs and Exposure Reduction Factors
by Control Option 4-16
5-1 Incidence of Hepatocellular Carcinomas in B6C3F1 Mice
Fed Tetrachloroethylene 5-3
7-1 Enumeration of Dose Response Cases 7-4
7-2 Control Costs and Expected Cancer Cases 7-6
7-3 Selection of Optimal Decision 7-8
7-4 Sensitivity Analysis of Probability Assignments 7-11
8-1 Data for Calculating Expected Value of Perfect Information 8-7
8-2 Expected Value of Perfect Information on All Dose Response
Uncertanties 8-7
8-3 Expected Value of Perfect Information on Each Component of
Dose Response Uncertainty 8-8
IG1
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Section 1
INTRODUCTION AND SUMMARY
Legislation such as the Toxic Substance Control Act (TSCA) requires
regulatory agencies to make decisions on chemicals that may pose an unrea-
sonable risk to human health and the environment. These decisions may be
highly controversial, for there is little agreement on the appropriate
basis for making them. The term unreasonable risk is not defined in the
legislation. While many argue in favor of cost-benefit analysis, there are
few examples to show how cost-benefit analysis methods can be applied to
regulatory decisions on chemicals. The use of quantitative methods is
criticized for lending an air of false precision to a problem area fraught
with uncertainty. Others question whether cost-benefit criteria ignore
Important distributional issues of who receives the benefits and who bears
the costs. This report provides a specific case study example that
addresses some of these difficult issues.
Previous sections of this report have reviewed the literature of
cost-risk-benefit analysis (Part T, Section 2) and have presented a
description of decision analysis as a basis for decision making under TSCA
(Part I, Section 3). This part of the report presents a case study
application of decision analysis to perchloroethylene, a widely used
chemical suggested by staff of the EPA Office of Toxic Substances as
representative of chemicals which may be considered for regulatory
decisions.
Perchloroethylene, sometimes called tetrachloroethylene and abbre-
viated PCE or PERC, is a solvent used for about 75 percent of the dry
cleaning done in the United States. Dry cleaning accounts for about 50
percent of PCE consumption in the United States, other uses being metal
cleanin3, textile processing, and use as a chemical intermediate. PCE is
not a new chemical, but rather one that has- been in Widespread use for
decades. Its potential for liver damage and other acute toxicity effects
1-1
102
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in humans has been known for many years and is the basis for the current
occupational standard of 100 parts per million of PCE vapor in the air.
PCE has recently come under suspicion as a potential carcinogen. Its
cheaical structure is similar to a known human carcinogen, vinyl chloride.
Short-term tests indicate that a commercial preparation of PCE is muta-
genic, and there is some epidemiological evidence of higher cancer occur-
rence among dry cleaning workers. However, the strongest evidence impli-
cating PCE as a carcinogen is a recent animal test carried out by the
National Cancer Institute in which a sensitive strain of mouse showed a
high incidence of liver tumors when fed PCE.
The Carcinogen Assessment Group (CAG) of EPA prepared a summary of the
evidence for the carcinogenicity of PCE 11]. This document was subse-
quently reviewed by the Subcommittee on Airborne Carcinogens of the EPA
Science Advisory Board in a public meeting [2]. Despite the language in
the recent Interagency Regulatory Liaison Group guidelines (3] that a
single positive animal test is ordinarily sufficient to establish that a
chemical should be regarded as a carcinogen for regulatory purposes, the
SAB subcommittee declined to endorse such a judgment for PCE.
The focus of our case study is the determination of risk for a spe-
cific chemical, perchloroethylene. We shall use the methods of decision
analysis to develop a quantitative description of the cancer risk posed by
PCE to workers, users of dry cleaning services, and members of the public
who live or work near dry cleaning plants. We shall examine the change in
cancer risk for a variety of control options that could reduce PCE emis-
sions and human exposure. By introducing an explicit value judgment on the
worth of avoiding a case of cancer, we are able to determine the best stra-
tegy in the sense of maximizing benefits less costs.
The analysis is based on data for 1978. Our impression is that the
volume of dry cleaning and the alternatives and their real dollar costs
associated with lowered perchloroethylene exposure have changed little over
the past three years. The reader should bear in mind that the purpose of
this case study is a demonstration of the methodology. The analysis is
intended to be illustrative. The data are the result of a modest level of
1-2
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effort by the authors, making use of the sources of industry information
brought to our attention by EPA.
The inpact of uncertainty in this example is profound. We believe
that the most effective way to assess uncertainty on the inpact of PCE is
not by requesting probabilistic judgments on whether PCE is a carcinogen
but by examining the judgments required to extrapolate from the results of
animal experiments and other available scientific evidence to a projection
of the incidence of human cancer that PCE exposure may induce. Based upon
our examination of the literature, in particular [1] and [2], we have iden-
tified the following factors as crucial:
o The basis for extrapolating dose in small laboratory animals to
an equivalent dose in humans.
o Identification of the most appropriate laboratory animal to use
as a model for cancer impacts in humans.
o The numerical basis for extrapolation of cancer incidence from
high doses in laboratory animals to lower levels corresponding to
human exposure.
For each factor, we have considered two alternative assumptions, giving a
total of eight combinations. The effect of the alternative assumptions is
great. Using conservative judgments, such as those used by CAG [1] (ex-
trapolation based on surface area, most sensitive mouse strain as a model
for man, linear nonthreshold dose response relation), the projected cancer
incidence is about 350 cases per year. Under alternative, less conserva-
tive assumptions (extrapolation based on body weight, rat as the model for
man, nonlinear dose response relation), the projected cancer incidence is
nearly five orders of magnitude less, 0.01 cases per year. We therefore
examine each combination of assumptions and develop a probability distribu-
tion over cancer incidence from probabilities assigned to the three crucial
factors in the dose response relationship. The methods of decision analy-
sis can be used to determine that it would be worth several million dollars
per year to resolve this uncertainty by such means as large-scale bioassays
of PCE. Such bioassays are in fact being carried out for PCE by NCI.
1-3
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This part of Che report is made up of nine sections. Section 2 gives
an overview of the dry cleaning industry, and Section 3 presents estimates
of PCE exposure based on data and analysis contained in EPA and NIOSH
reports. Section 4 describes control options for reducing PCE exposures
and provides estimates of the exposure reduction and associated annualized
cost. Section 5 discusses the available information (taken from NIOSH and
EPA sources, including the transcript of the SAB meeting [2]) on the health
impacts of PCE. Section 6 describes the specific assumptions used in mod-
eling the relation between human exposure and cancer incidence. Section 7
presents the analysis of control options. For each control option, we
compute the impact in terras of expected cases of cancer and control cost.
The best control option alternative is determined based on a value judgment
of one million dollars per case of cancer avoided; the sensitivity of the
control option choice to this judgment is explored. Section 8 gives the
calculation for the value of additional information to resolve uncertainty
in the relation between exposure to PCE and cancer incidence. Section 9
contains conclusions regarding PCE and the decision analysis methodology.
The scope of this case study is limited to the use of PCE as a solvent
in dry cleaning and the possible health effect of increased human cancer.
We have not included a discussion of human exposure from other PCE uses,
other human health impacts, or adverse effects that PCE may have on the
environment. This choice of scope was motivated by the perception that
human cancer resulting from dry cleaning-related exposure is the major
concern of EPA and other regulatory agencies.
1-4
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Section 2
THE DRY CLEANING INDUSTRY
The dry cleaning industry is composed of several types of firms that
use various types of equipment and solvents to clean clothing and other
items made of fabric or leather. The three main types of dry cleaning
firms are commercial, coin-operated, and industrial. Commercial dry
cleaners are those firms engaged in cleaning and pressing consumer itens
such as garments and drapes. Coin-operated dry cleaners are self-service
installations, usually found in coin-operated laundries. Industrial dry
cleaners are those engaged in cleaning heavily soiled items such as uni-
forms, coveralls, and towels for industrial and commercial users.
In a report done for EPA [4], SRI estimated that in 1978 there were
approximately 22,000 commercial dry cleaners, 11,000 coin-operated laun-
dries with dry cleaning machines, and 700 industrial cleaners with dry
cleaning equipment. The number of commercial dry cleaners can be taken
directly from the Bureau of Census data [5 J. However, because the figures
for coin-operated and industrial cleaners included plants with water-based
washing equipment only, the number of plants with dry cleaning equipment
could only be estimated.
Solvents used in dry cleaning can be categorized into two groups:
petroleum solvents and synthetic solvents. Petroleun solvents, which
include Stoddard and 140F, are mixtures of hydrocarbons similar to
kerosene. Synthetic solvents, such as perchloroethylene (PCE) and
fluorocarbon-113, are halogenated hydrocarbons. Perchloroethylene is by
far the most widely used synthetic solvent in use today. According to
estimates of the International Fabricare Institute (IFI) [6), about three
quarters of the commercial dry cleaners use PCE, with the other one quarter
using petroleum solvents. Almost all (97 percent) of the coin-operated
2-1
106
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cleaners use ?CE,
of the industrial
Table 2-1.)
with the
cleaners
remainder using fluorocarbon-113. About
use PCS and half use petroleum solvents.
half
(See
Table 2-1
DRY CLEANING PLANTS USING PCE
(1978)
Type of
Operation
Number
of Plants
Number
Using PCE
Using PCE
Percent
Commercial
Industrial
Coin-Operated
22,000
7 CO
11,000
16,000
350
11,000
97
50
74
The ir.ain advantage of the synthetic solvents is that they are non-
flammable, unlike the petroleum solvents, which are highly flammable.
The major disadvantage is the cost. PCE costs about three tiaes and
fluorocarbon-113 costs about ten times as much per gallon as petroleum
solvents. Because of the cost, the synthetic solvents must be recycled for
economic operation. The efficiency of the recycling equipment largely
determines both the cost to the dry cleaner and the exposure of workers and
others to PCE.
Dry cleaning equipment consists of cleaning machines and equipment for
recovering and purifying used solvents. In all types of cleaning machines,
there are three cycles, much like the cycles of regular water laundry. The
three cycles are washing, in which solvent is mixed with clothing in a ro-
tating drum; spinning, in which solvent is extracted by centrifugal force;
and drying, in which the clothes are rotated in warm air.
The two main types of cleaning machines are transfer units and dry-
to-dry units. In transfer units, the washing and extraction are done in
one machine. Then the clothes, still damp with solvent, are transferred by
hand to a separate dryer. In a drv-to-dry unit, all three cycles are
2-2
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accomplished in the same machine: the clothes are put in dry and taken out
dry. The dry-to-dry units eliminate the need far the operator to handle
solvent-laden clothing. However, to clean the same volume of garments, the
dry-to-dry unit must have a larger capacity than the corresponding transfer
unit, since the separate washer and dryer can be operated simultaneously.
Currently, about 80 percent of conmercial and virtually all industrial dry
cleaners have transfer units, although there seems to be a trend toward
installing dry-to-dry units [7], Virtually all coin-operated dry cleaning
units are dry-to-dry [8].
Solvent recovery and recycling equipment consists of filters, dis-
tillers, and adsorbers. All plants have some equipment for filtering and
distilling used solvent to remove dirt and water. There are several types
of filters, including single-charge tube type and multicharge filters,
which use a powder such as diatoaiaceous earth. If a single-charge filter
is used, the distiller can operate at room temperature, since the PCE is
highly volatile conpared to water. However, most plants that use a powder
type of filter have a combination cooker and still designed to remove PCE
from the filter residue or "muck."
About half of the commercial dry cleaners have a carbon adsorption
unit in addition to a filter and still [8], These units, which are also
called "sniffers," remove PCE vapors from the air in the work area. They
can both reduce cost by recovering solvent and reduce PCE exposure.
In all of the equipment just described, including the cleaning
machines and the solvent recovery systems, maintenance is extremely
important for low solvent emissions. Leaky gaskets and clogged filters
can result in large PCE losses. Therefore, good maintenance can minimize
solvent cost as well as exposure resulting from emissions. A recent EPA
report [8] contains an excellent discussion of cleaning equipment, solvent
recovery systems, and maintenance procedures.
2-3
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Section 3
PCS EXPOSURE CALCULATION'S
Because of the widespread use of PCE in dry cleaning, many people are
potentially exposed. To estimate the extent of the exposure, three classes
of people are considered:
1. Workers in the dry cleaning industry..
2. People who use dry cleaning services.
3. People who live or work near dry cleaning facilities.
Inhalation of vapors is the primary route of exposure to PCE. Absorption
through the skin is not considered to be significant [1], Following the
procedures of EPA (9J and NIOSH [10], the method of determining exposure to
PCE consists of estimating the average exposure to each category of people
under consideration and then estimating the number of people in each
category. The base year of 1978 is chosen for the analysis to be
consistent with the industry data discussed in Section 2.
Exposure to workers and users of dry cleaning services is estimated
from measured air samples in dry cleaning facilities and from assumptions
concerning the amount of time spent in the facilities. Additional exposure
to service users from PCE vapors from freshly cleaned clothing is also
estimated. An annual average exposure level is then calculated from the
following formula [11]:
For example, workers are assuned to work 40 hours per week and 50 weeks
per year, or 2000 hours. Therefore, their average annual exposure is com-
puted to be the average exposure during work multiplied by 2000/8760. In
Average
Annual
Exposure
(Exposure Level)(Hours of Exposure Per Year)
8760 Hours Per Year
(3.1)
3-1
109
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this case study, we use the average annual PCE exposure to workers to esti-
mate the expected number of annual cancer cases. It would also be possible
to consider the average nunber of year? worked in a lifetime to calculate
an average lifetime exposure. The use of average annual exposure gives
somewhat higher cancer incidence estimates than would average lifetime
exposure.
The number of workers employed in each type of dry cleaning firm is
estimated from U.S. Bureau of Census data [5] as summarized in 14], The
nunber of users of dry cleaning services is estimated from the volume of
dry cleaning business and from estimates of the average number of cleaning
loads per user per year [11|.
The exposure to urban residents is calculated from a simplified
Gaussian dispersion model for airborne pollution by PCE [111. Dry cleaning
facilities are modeled as point sources evenly distributed over urban
areas. The annual average PCE concentration is calculated for concentric
areas around each source, and the number of people living in each area is
calculated from the average population density of urban areas.
WORKER EXPOSURE
Exposure Levels
In a survey of 44 commercial dry cleaners, uIOSH [10] measured con-
centrations of PCE as shown in Table 3-1. In that study, the average
exposure to dry cleaning machine operators was found Co be 31 parts per
Table 3-1
EXPOSURE LEVELS IN COMMERCIAL DRY CLEANERS BY TYPE OF WORKER
Concentration of PCE (ppm)
Job Description
Range
Mean
Machine Operator
Presser
<4.0 - 149
31.0
0.1 - 37
5.9
Seamstress
0.6 - 29
6.6
Counter Worker
0.3 - 26
5.9
Source: NIOSH [10]
3-2
110
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million (ppn), and the exposures at different plants ranged from 4 to 149
ppm. Exposure to other workers, such as pressers, seamstresses, and
counter workers, was found to vary from 0.1 to 37 ppm, with an average of
about 6 ppm. Clearly, with such a wide range of exposures, the average
(mean) value does not fully describe the exposure to workers. However, for
our analysis, the total number of workers will be divided into two groups
defined by different exposure levels: machine operators and all other
workers. The workers in each of the two groups are assumed to be exposed
to the average level for that group.
To calculate potential health effects of exposures to PCE, it is
convenient to express the concentrations in units of micrograms per
cubic meter (ug/m^) rather than in parts per million. For PCE, the
factor for converting ppm to ug/ra^ is 6.7 x 10^. Therefore, the
average PCE concentration in the workplace for machine operators is
taken to be 31 x 6.7 x 1(P, or 200 x 10^ Jg/m^, and for other workers
6 x 6.7 x 10^, or 40 x 10^ To express these concentrations as
average annual exposures, we use equation (3.1), which implies multiplying
the above concentrations by 2000/8760. The resulting average annual
exposures are thus approximately 45 x 10^ tg/m^ for machine operators
and 10 x 10^ for other workers.
Exposure data for industrial and coin-operated dry cleaners are
available but are not so extensive as data for commercial cleaners. In
an assessment of engineering controls done for N10SH [12], exposures to
machine operators were measured in three industrial dry cleaners that use
PCE. (See Table 3-2.) From these samples, there appears to be almost no
Table 3-2
EXPOSURE LEVELS FOR MACHINE OPERATORS IN INDUSTRIAL DRY CLEANERS
Case Study Number
Range of PCE Exposure (ppm)
13
2
25 - 30
13 - 40'
17
30 --35
Source: Reference [12
3-3
ill
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difference in exposures between industrial and commercial cleaners. We
therefore assure the same levels, that is, 31 ppm for machine operators and
6 ppm for other workers. These concentrations are then converted to the
"1 'j
same average annual exposure levels (i.e., 45 x 10 ug/m for machine
operators and 10 x 10^ ug/na^ for other workers in industrial plants).
In an EPA survey [13] of a single combination laundry and cleaner in
Raleigh, North Carolina, the exposure to an operator/maintenance person was
found to be 4.4 ppm. In the following analysis, we assume all workers in
coin-operated cleaners are exposed to about 4 ppm of PCE, which is equiva-
lent to an average annual exposure of 6 x 10 ug/m .
Number of Workers in Each Exposure Category
Once exposure levels have been determined, the second part of the
assessment of worker exposure is to estimate the number of workers in each
category. The numbers of workers in commercial, industrial, and coin-
operated cleaners are estimated from the data in [4], These data give the
number of dry cleaning firms in the United States by the number of em-
ployees in each firm. (See Table 3-3.) Although the details of these data
are not discussed in [4], the data appear to represent only a fraction of
all dry cleaning establishments. Since the number of plants is signifi-
cantly smaller than in Table 2-1, we assume that this sample is represen-
tative of the entire industry.
Table 3-3
NUMBER OF DRY CLEANING PLANTS BY SIZE OF OPERATION
Number of Plants
Number of
Employees
1 - 4
5-9
10 - 19
20 - 49
50 +
Source: Reference [4]
Commercial
4194
1906
846
691
119
3-4
Industrial
16
11
18
98
112
Coin-Op
3695
499
119
. 57
12
112
-------
To calculate the average number of employees per type of firm, we assume
the following average value for each of the ranges for the number of
employees:
Range
Assumed Average
5-9
7
10 - 19
20 - 49
30
50 +
100
Multiplying these assumed averages by number of plants in each size range,
the average number of workers is computed to be eight per commercial plant,
sixty per industrial plant, and three per coin-operated plant. Based on
the typical plants described in [10] and [12], we assume that for commer-
cial plants about one out of eight workers is a machine operator, for in-
dustrial plants about two out of sixty workers are machine operators, and
for coin-operated plants about one out of three workers is a machine opera-
tor. In industrial plants, many of the workers are involved with laundry
rather than with dry cleaning. However, because the laundry and dry clean-
ing equipment is usually located in the same building, these workers are
exposed to PCE. Multiplying these estimates by the estimated number of
plants usin3 PCE gives an estimate of the total number of workers. These
estimates, together with the previously described exposure estimates, are
given in Table 3-4.
USER EXPOSURE
Three types of users of dry cleaning services are considered in this
analysis:
1. Users of commercial dry cleaners.
2, Users of coin-operated dry cleaners
3. Users of coin-operated laundries located in facilities-with
coin-operated dry cleaning machines.
3-5
113
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Table 3-4
ESTIMATED NUMBER OF WORKERS AND PCE EXPOSURE
IN DRY CLEANING PLANTS USING PCE
Nurr.be r
of Uorkers
Average Annual
PCE Exposure Level
Worker Category
Machine Ooerators
Commercial
Industrial
Coin-Op
16,000
700
11,000
45,000
45,000
6,000
Other Workers
Coin-Op
Commercial
Industrial
110,000
20,000
22,000
10,000
10,000
6,000
Users of industrial dry cleaners were not considered because of their
relatively small numbers, although the exposures may be comparable to the
above groups. PCE exposure to users of dry cleaning services in categories
1 and 2 is assumed to come from two sources: visiting cleaning establish-
ments and having dry cleaned clothing at home. Exposure to people in
category 3 is assumed to arise from visits to laundries with dry cleaning
machines. These people are exposed to PCE even though they do not directly
use dry cleaning services.
Exposure from Visits to Cleaners
The method for estimating exposure from visits to dry cleaners in-
volves estimating the total number of cleaning loads per year in the United
States and dividing this total into the expected number of users and the
average number of loads per user per year. Exposure times corresponding
to cleaning loads are estimated, and the exposure levels are taken from
measurements similar to those used in determining worker exposure.
The annual volume of commercial dry cleaning is about two billion
pounds [8,10]. Assuming an average of 5 pounds per average single load
114
3-6
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implies a total of 4C0 nillion loads per year, -and assuming an average of
8 loads per user per year results in an estimate of 50 million users. Of
course some users will make more and some will nake fewer than eight
visits. However, these numbers appear to be representative. To estimate
user exposure, we assume an average of one load per visit to a dry cleaner
at five minutes per visit at an exposure of six ppm, which corresponds to
the average measured concentration at the counter area [10].
Data for estimating the number of loads of cleaning in coin-operated
laundries come from the National Automatic Laundry Cleaning Council as
reported by Mara (11). According to these data, the gross income from
coin-operated washing is one billion dollars at an average cost of $0.50
per load, while the gross income from coin-operated dry cleaning is $400
million at an average cost of $4.00 per load. Therefore, approximately 2
billion loads are washed and 100 million loads are dry cleaned at coin-
operated laundromats. Assuming an average of two loads per trip and twenty
trips per year implies about 50 million users of coin-operated laundries,
and assuming one load per trip and four trips per year implies about 25
million users of coin-operated dry cleaning. According to SRI estimates
[4], about 75 percent of the laundromats in the United States also have
coin-operated dry cleaners, almost all of which use PCE. Therefore, assum-
ing that about 7 5 percent of all laundromat users are exposed to PCE im-
plies that 75 percent of 50 nillion, or 37 million, laundromat users are
exposed to PCE.
To estimate exposure to users of coin-operated dry cleaning machines,
we use the average of the PCE concentration measurements summarized in
Table 3-5. We assume one load per visit to the cleaner at 30 minutes per
visit at a PCE concentration of 5 ppm, which is an approximate average of
concentrations near the dry cleaning machines. For laundromat users, we
assume one hour per trip at a PCE concentration of 2.5 ppra.
Exposure from Cleaned Clothing
In an experiment to measure PCE exposure from cleaned clothing, PEDCO
Environmental [14,15] placed three loads of clothing cleaned by an atten-
dant in a coin-operated dry cleaner in a bedroom closet. After seven days
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Table 3-5
PCE CO NC ENT RAT IONS IN CO IN-OPliRATF.D DRY CLEANERS
PCE ConceatraCion (ppn)
Nearest Dry Average of
Source Cleaning Machines Other Locations
Howie 114]
Site
B
0.4
0.3
Site
C
1.5
1.2
Site
D
3.0
0.6
Site
E
9.6
8.0
Site
F
1.6
1.1
Bumgarner
[13]
16.6
4.7
Average
5.5
2.7
of monitoring PCE concentration at the closet door and two locations in the
room, they found concentrations of about 100 parts per billion (ppb) for
one day and about 20 ppb for an additional four to five days.
For our analysis, we assume that three loads of cleaning would cause
about three tines the concentration of a single load. In the absence of
data concerning emissions from clothes cleaned by a commercial dry cleaner,
we have assumed that the PCE concentration caused by a single load would be
about half that caused by a load cleaned in a coin-op since clothes usually
remain longer after being cleaned in a commercial cleaner and are thus pre-
sumably drier. For exposure time, we assume that one person is exposed for
10 hours per day over a six-day period in which PCE vapors are present. The
assumed exposure per cleaning load is thus 10 hours at 33 ppb for a coin-op
load or 17 ppb for a commercial load and 50 hours at 6.7 ppb for a coin-op
load or 3.3 ppb for a commercial load.
Converting the above concentrations to ug/m^ and calculating average
annual exposure with (3.1) for visits to the cleaners and from cleaned
clothing gives the estimates shown in Table 3-6.
EXPOSURE TO URBAN POPULATION
The PCE exposure from air pollution to the general population is
estimated using a general point source dispersion model as described in [9]
3-8
116
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Table 3-6
ESTLMATED KUKBSR Or USERS OF a::D PCE EXPOSURE FROM DRY CLEANING SERVICES
Type of Service
Commercial Dry Cleaning
Coin-Op Dry Cleaning
Coin-Op Laundry
number
of Users
50 nillion
25 nillion
37 million
Average Annual Exposure (ig/a^)
Visits Co Cleaned
Cleaners Clothing Total
2
3
8
38
5
13
35
and (4). The results of the model are checked against actual measurements
of ambient levels to verify that the results are reasonable. To estimate
the number of people exposed, we use the average population density for
urban areas, including urban fringe as reported in [14]. We assume that
small towns in rural areas that may have dry cleaners have roughly the same
population density as this urban area. In the analysis, we consider people
who live near dry cleaning facilities and assume 24 hour per day exposure.
This is a simplification since people who live in one location usually work
in another. A more detailed analysis might consider residents and non-dry-
cleaning workers separately.
Since there are a large number of dry cleaning firms in the United
States and their locations are not known, it is impractical to estimate
emissions and resultant exposures originating from each individual firm.
With the general point source methodology, exposure from a typical plant in
a typical area is considered. Then the total U.S. exposure is calculated
by multiplying by the number of dry cleaning plants.
Considering a single point source of PCE with an average emission race
of E grams/second, the average annual concentration C in ,Jg/m^ at a
distance D from the source in km is given by [4]
-1.48
C = 0.659 E D
(3.2)
This dispersion formula was developed by regression analysis to represent
dispersion from three source categories: ground level (zero meter), build-
ing level (L0 meters), and stock level (20 meters). The meteorological
conditions assumed were wind speed equals 4 meters per second and neutral
stability class (Pasquill-Gifford "D") [16].
3-9
J17
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The concentration calculated from (3.2) is greatest near the source
and decreases as one moves away from the source. To calculate the range of
exposures in urban areas, we consider concentric rings about the point
source. We assume that nobody lives within a 25-meter circle of a dry
cleaner. Starting at 25 meters, we consider areas from 25 to 20C meters,
200 to 500 meters, and 500 to 1000 meters. Using (3.2) and the formulas
for the circumference and area of a circle, the average concentration in a
ring of inside radius Rj and outside radius R2 can be found from the
formula
The average emission rate E can be calculated from the estimate of total
PCE consumption in dry cleaning and by the number of dry cleaning firms.
It is estimated that almost all of the PCE consumed is emitted [4], and the
remaining is discarded either as liquid or solid waste. From [4], the
estimated consumption of PCE in dry cleaning in 1978 was 160 oillion kg.
With 27,350 sources (the total of commercial, industrial, and coin-operated
facilities), this implies an average source emission of 5900 kg per year,
or 0.19 g/s.
To calculate the average number of people who live in each ring about
a typical source, it is necessary to estimate the average population den-
sity of urban areas. From Census Bureau data [14], the U.S. urban popula-
tion in 1970 was 149 million and the urban area was 140,000 km2, for an
average urban population density of 1004 persons/km2. The total popula-
tion in 1978 was 218 million compared to 203 million in 1970, which is a
7.4 percent increase. If we assume that the urban population increased at
this same rate and that urban area increased at half that rate, or 3.7 per-
cent, the 1978 urban population would be 160 million and the urban area
145,000 km2. The implied population density is thus 1100 persons per
km2 in 1978. The number of people in a ring of inside radius R^ and
outside radius R2 is given by the fornula
Average
Concentration
(R2*52 " Ri'52)
2.53 E — ^
(3.3)
Avg. No.
of People
in Ring
1100 ~ (r22 - rl2)
(3.4)
3-10
118
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The concentrations in each ring about a source calculated by equation
(3.3) and the average number of people who live in each ring as calculated
by equation (3.4) are listed in Table 3-7.
It is fairly difficult to check the estimates of the above dispersion
model with measurements of ambient levels of PCE because the detection
limit for PCS is about 0.1 parts per billion (ppb), which is 0.67 „g/m^.
However, the upper end of the concentration scale can be checked. In a
recent study, -.PA [14] measured ambient levels in three cities suspected of
having unusually high PCE concentrations: New York, Detroit, and Houston.
New York is a center for clothing manufacturing and has a large number of
dry cleaning facilities; Houston has a major PCE production facility; and
Detroit has a large number of metal cleaning operations, many of which use
PCE. The 24-hour average concentrations of PCE found at each sample site
are summarized in Table 3-8. New York appears to have PCE concentrations
significantly above those predicted by the dispersion model that we have
used; the measurements in the other cities appear to be more consistent.
Recall that we are considering only emissions froui dry cleaning, not from
metal cleaning or PCE production. Since dry cleaning accounts for about
half the total PCE consumption, one would expect that the results of the
dispersion model would be about haLf the measured PCE concentrations.
In the exposure analysis we shall onit the background due to sources
other than dry cleaning. These exposures appear to be negligible compared
to the exposures received by dry cleaning workers, service users, and
people living or working within 200 meters of dry cleaning plants. Even in
able 3-7
CONCENTRATIONS OF PCE AND URBAN POPULATIONS AT SPECIFIED
DISTANCES FROM TYPICAL DRY CLEANING POINT SOURCES
Distance
From Source
(meters)
Number of People
Average PCE
Concentrations
(ug/m^)
25 - 200
3.7 million
3.5
200 - 500
20.0 million
0.6
500 - 1000
71.0 million
0.2
3-11
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Table 3-8
SUMMARY OF AMBIENT °CE CONCENTRATIONS IN THREE CITIES
Concentration
(ppb)
Concentration
(ug/a3)
Number of
Citv
Satnoles
Min
Max
I'.ed j an
Min
Max
Median
New York
95
0.16
10.61
1.00
1.07
71 .1
6.70
Houston
96
<0.10
4.52
0.11
<0.67
30.3
0.74
Detroit
100
<0.10
2.16
0.35
<0.67
14.5
2.40
Source: EPA [1A]
Che case o: a linear dose response carve, our analysis shows that the
health impacts are expected to occur nostly in the classes of people listed
above rather than urban residents or workers located more than 200 meters
from the dry cleaning plants. In the case of a nonlinear dose response
curve, the importance of low dose exposure to urban populations is reduced
even further.
SUMMARY OF EXPOSURE CALCULATIONS
Table 3-9 is a summary of the exposure calculations. Average concen-
trations are converted to average daily dose by assuming an inhalation rate
of 20 nJ per cav, a body weight of 70 kg, and 50 percent adsorption. Note
that workers, particulary dry cleaning machine operators, are exposed to
the greatest concentrations. There are many more service users, but they
are exposed to much lower concentrations. Urban residents within 200
meters of a dry cleaner are exposed to levels comparable to commercial dry
cleaning service users. Urban residents at a distance greater than 200
meters are exposed to about one-fifth to one-twentieth of the level inside
200 meters. The implications of these exposure levels will be explored in
Section 7.
3-12
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Table 3-9
SUMMARY OF CALCULATIONS OF PCE EXPOSURES
FROM THE DRY CLEANING INDUSTRY
Type of Person
Numbers in U.S.
Pooulation
Average
Annual Exposure
(mg/kg/day)
Workers
Machine Operators
Commercial
16,000
45,000
6.43
Industrial
7 00
4 5,000
6.43
Coin-Op
11,000
6,000
0.86
Other Workers
Commercial
110,000
10,000
L .43
Industrial
20,000
10,000
1.43
Coin-Op
22,000
6,000
0.86
Service Users
Commercial Dry Cleaning
50,000,000
5
7.1 x 10"4
Coin-Op Dry Cleaning
25,000,000
10
1.4 x 10"3
Coin-Op Laundry
37,000,000
38
5.4 x 10"3
Urban Residents
(distance from dry
cleaners in meters)
25 - 200
3,700,000
3.5
5.0 x 10-4
200 - 500
20,000,000
0.6
8.6 x 10-5
500 - 1000
71 ,000,000
0.2
2.9 x 10"5
3-13
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Section 4
CONTROL OPTIONS
In this section we consider control options available to reduce human
PCE exposure. Since our case study concentrates on the dry cleaning indus-
try, each control option should be expected to reduce exposures to one or
more of the groups of people identified in Section 3. The implementation
of the option generally has a cost that must then be weighed against the
value of the exposure reduction. The cost-benefit tradeoff is discussed in
Section 7.
This section presents six control option alternatives and the esti-
mated effect of each alternative on PCE exposures and the costs associated
with its implementation. The analysis is based on prices and exposure
levels as of 1978. The exposures estimated in Section 3 are considered the
base case for which costs and exposure changes are compared.
While most applications of cost-benefit analysis examine control
options from the viewpoint of the government agency, we have chosen to
examine the decisions on control options from the point of the dry cleaning
industry. We shall compute the cost of a control option, including tax
effects, as it would be perceived by the individual dry cleaning plant
owners. The results obtained in Section 3 indicate that benefits of
reduced PCE exposure will accrue largely to the workers in the dry cleaning
plants, and often the workers are in fact the owners and their immediate
families. We have therefore formulated our analysis by assuming the
decision makers to be private entrepreneurs who are willing to spend money
to protect the health of workers (perhaps including themselves and their
family) and members of the general public.
A more general approach would involve a multiparty formulation in
which costs incurred for equipment purchase and operation,, changes in
government tax revenue, and health benefits to workers, customers, and
4-1
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members of Che public would be separately assessed over time as the basis
for decisions both by public agencies and private sector entrepreneurs.
While taxes nay be regarded as transfer payment by public agencies, they
represent a real cost of doing business to a private sector enterprise.
Moreover, tax credits or tax incentives can be an important mechanism for
motivating private sector decision making to accomplish public goals such
as protection of health and the environment. In this analysis we have not
used the general multiparty formulation in assessing costs, but we do show
which segment of the population receives the benefits. The assessment of
cost including taxes is the appropriate basis for decisions by an
individual dry cleaner.
DESCRIPTION OF CONTROL OPTIONS
Most of the control options considered here are discussed in the EPA
study of dry cleaning performance standards [8]. All of the options are
aimed at either reducing PCE emissions, which would in turn reduce expo-
sure, or reducing exposure directly through changes in equipment or the
physical layout of cleaning establishments. Options such as banning seg-
ments of the industry or banning the use of PCE were not considered.
The control options in approximate order of increasing cost are as
follows:
1. Implement better maintenance and housekeeping procedures in all
commercial and industrial dry cleaning establishments.
2. Implement better maintenance and housekeeping procedures in all
coin-operated dry cleaning establishments.
3. Install carbon adsorption units in all commercial and industrial
dry cleaning plants that do not currently have them.
4. Install carbon adsorption units in all coin-operated dry cleaners
that do not already have them.
5. Replace all transfer type of cleaning machines with dry-to-dry
units in all commercial dry cleaning plants when the cleaning
machine normally needs to be replaced.
6. Move all coin-operated dry cleaning machines to rooms separate
from the coin-operated laundry facilities.
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Better maintenance and housekeeping procedures, including record
keeping of PCE consumption, was identified in [8] to be one of the riost
effective xear.s of reducing PCE emissions at little or no additional cost
to dry cleaners. As indicated in the next section, experiments at IFI and
by equipment manufacturers indicate that better maintenance can reduce PCE
consumption in both transfer and dry-to-dry cleaning machines to levels
substantially below the current industry average.
A sonewhat more costly but still effective means of reducing PCE con-
sumption is the addition of a carbon adsorption unit or "sniffer" to plants
that do not already have one. This involves a capital investment by the
dry clear.er but can actually save money in the long run due to the reduc-
tion of ?CE consumption and thus can lower the cost of buying solvent. As
we show in the following sections, the costs and PCE savings are less
advantageous for coin-operated facilities than for commercial or industrial
cleaners. However, the addition of a carbon adsorption unit in a coin-
operated cleaner could reduce PCE exposure to employees and customers.
Data for calculating the costs and effects of replacing transfer units
by dry-to-dry units in commercial dry cleaners are contained in [8] and
[10], It is generally believed that with equally good maintenance, dry-to-
dry units would have lower PCE consumption than transfer units, since they
are completely enclosed systems. However, there is little data to support
this contention [8]. In fact, the five plants with dry-to-dry units moni-
tored by Ludwig [10] had slightly higher PCE consumption than the plants
with transfer units, even though the PCE exposure to workers was lower for
those five plants. The approach taken in this analysis is to assume that
transfer and dry-to-dry machines have the same rate of PCE consumption, but
that exposure to machine operators is reduced with dry-to-dry machines by
eliminating the necessity to handle solvent-laden clothing.
Finally, the last option, that of placing coin-operated cleaning
machines in rooms separate from coin-operated laundry is motivated by the
relatively high exposure to laundry users as estimated in Section 3. If
PCE at these exposure levels is harmful, then it seems reasonable to pro-
tect those customers who are not even using the service from that exposure.
Such a change in arrangement of the cleaning facility would probably
4-3
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require a remodelling expense and some additional floor space to nake room
for additional partitions and doors.
The above control options can be analyzed separately or in combina-
tion. For example, the combination of options 1, 2, and 3 (better main-
tenance in conrcercial, industrial, and coin-operated cleaners, plus the
installation of carbon adsorption units in commercial and industrial
plants) rr.ight be an appropriate combination of options to consider. A
complete list of options and combinations of options considered in our
analysis is shown in Table 4-1.
DETERMINING COSTS AND EFFECTS OF CONTROL OPTIONS
In the following analysis, control costs are considered to be the
additional annual expenses associated with the control as compared with the
base case (existing levels of expenses). These expenses are small compared
to the total cost of providing the dry cleaning services, and they would
normally be passed on froci dry cleaners to consumers. Assuming that the
resulting change in the amount of dry cleaning purchased by consumers is
small, the impact on consumers is that they are paying more for the same
service. This amount can be considered what society is paying for the
exposure reductions brought about by the control.* We have not included
costs that the government might incur in implementing these control
options. Adding in these costs would account more fully for the total
costs to society.
Control costs are considered to have three components: capital expen-
ditures, increases in operating costs for maintenance and supplies, and
credit for reduction in PCE use. The capital expenditures are required for
the purchase of PCE control equipment and remodeling of dry cleaning
facilities. To add capital expenditures to recurring annual costs, the
capital expenditures are annualized by a formula that takes into account
the return on the investment, corporate income tax, investment tax credits,
*If the demand for dry cleaning services decreased substantially with an
Increase in price resulting from a PCE control option, then the relevant
measure of cost to society would be the reduction in consumer surplus. See
the DEHP case study, Part III of this report, for a discussion of consumer
surplus.
4-4
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Table £-1
CONTROL OPTIONS AND COMBINATIONS OF OPTIONS CONSIDERED FOR ANALYSIS
Option
Number Description
1 Inplenent better maintenance and housekeeping in all existing
commercial and industrial plants.
2 Implement better maintenance and housekeeping in all coin-operated
dry cleaners.
3 Install carbon adsorption units on all commercial and industrial
plants that do not currently have them.
4 Install carbon adsorption units on all coin-operated dry cleaners
that do not currently have them.
5 Replace all transfer units in commercial plants with dry-to-dry
unit s.
6 Move all coin-operated dry cleaning machines to separate rooms
from laundry.
7 Options 1 and 2.
8 Options 1, 2, and 3.
9 Options 1, 2, 3, and 4.
10 Options 1, 2, 3, and 5.
11 Options 1, 2, 3, and 6.
12 Options L, 2, 3, 4, and 5.
13 Options 1, 2, 3, 4, and 6.
14 Options 1, 2, 3, 5, and 6.
15 Options 1, 2, 3, 4, 5, and 6.
4-5
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inflation, and the tax and book life of the investment. (See Appendix A.)
The result is the equivalent annual charge for the capital expenditure.
Increases in operating costs might result from increases in labor and
repair parts to achieve high levels of maintenance. Additional equipment
will also require maintenance, which will be a recurring expense.
The credit for reduction in PCE use is the estimated annual savings in
PCE purchases resulting from the control measure if the control measure
reduces PCE consumption. This credit is calculated for a typical plant by
subtracting the annual PCE consumption, in gallons, after the control from
the consumption before the control and multiplying by the price per gallon
of PCE.
The effects of the options on PCE exposures are estimated along with
the cost of the control options. There are two types of controls; each is
treated somewhat differently. The first type includes options 1 through 4,
which are directed primarily at reducing PCE consumption in the cleaning
process. Since most of the PCE consumed is emitted in the vicinity of the
dry cleaning machines, PCE consumption should be related to PCE concentra-
tion in the facility. We assume that a reduction in PCE consumption causes
a proportionate reduction in PCE concentration. For example, if PCE con-
sumption in a typical cleaner is cut in half, the average PCE concentration
is assumed to be cut in half. Furthermore, the workers in that type of
plant and the users of services who visit that plant are assumed to be
exposed to half the base case dose of PCE. This type of control also
reduces PCE emissions to the environment and thus reduces exposure to the
urban population. In the model for PCE concentration near dry cleaners
(equation (3.2) of Section 3), the concentration is proportional to E, the
average emission rate. We therefore assume that a reduction in the total
PCE emissions for all types of cleaners results in a proportionate decrease
in exposure to urban residents. For example, if in the base case, half the
total PCE were consumed by commercial dry cleaners, and after the control,
consumption by commercial cleaners were cut in half, total PCE consumption
would be three fourths of the base case. Thus, the exposure to the urban
population would be cut to three fourths of the base case levels.
4-6
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Exposure Co users of dry cleaning services through possession of
cleaned clothing is handled as a special case. We assume that through
better operating procedures, commercial dry cleaners could reduce the
anount of PCE left in the clothing and the resulting exposure by 50
percent. Since coin-operated cleaners have less control over the actual
cleaning, we assume that better maintenance would reduce the exposure from
cleaned clothing by only 25 percent.
The other type of control, which includes options 5 and 6, is assumed
to act directly on PCE exposure without changing PCE consumption. For op-
tion 5 (replacing transfer type of dry cleaning machines with dry-to-dry
units in commercial dry cleaners), it is assumed that PCE consumption
remains the sane but exposure to the machine operators is reduced. For
option 6 (moving coin-operated cleaning machines to a separate room), it is
assumed that PCE consumption, exposure to workers, and exposure to users of
the dry cleaning machines remains the same, but exposure to users of the
coin-operated laundry machines is reduced. In each case, since PCE
consumption does not change, the exposure to urban population does not
change.
For combinations of options, the capital costs and increases in main-
tenance costs of the individual options are simply added together. How-
ever, PCE consumption and the resulting exposure levels are calculated from
the cumulative effect of all the options. Assume for example, chat better
maintenance in a particular type of plant would result in PCE consumption
of 60 percent of the base case. Also assume that the addition of a carbon
adsorption unit by itself would reduce PCE consumption to 70 percent of the
base case. Then, it is assumed that in combination, the carbon adsorber
would reduce PCE consumption to 7 0 percent of the level achieved after good
maintenance. Therefore, after the combination, PCE consumption would be
0.6 x 0.7, or 0.42 times the base case. The PCE credit for operating cost
and the exposure to workers, users, and urban population would thus be
based on PCE consumption of 42 percent of the base case.
Finally, to calculate PCE consumption, it is useful to change the form
of the data that are contained in most industry sources. In the dry clean-
ing trade, PCE consumption is expressed in pounds of cleaning per 55-gallon
4-7
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drum of PCE, which is called the "mileage" of the cleaning process. A typ-
ical mileage nijht be 6,000 pounds or cleaning per drum of PCE. Mileage
can be converted to a consumption rate, expressed in pounds (or kg) of PCE
consumed per pound (or kg) of cleaning, hy dividing the weight of a drum of
PCK (715 pounds) by the nileaf-e as given by equation (4.1).
Consumption Rate = ———-— (4.1)
roileage
The total PCE consumption of a sector of the dry cleaning industry can
then be calculated by multiplying the average consumption rate of that
sector by the total volume of cleaning done by that sector.
DATA FOR CONTROL OPTIONS ANALYSIS
This section presents the data and assumptions used in the analysis
of the control options along with a description of their sources.
PCE consumption rates are obviously a key data element. In the EPA
performance standards report 18], the authors concluded that the mileage
obtained in PCE plants appeared to be more strongly affected by the com-
petence of the operator than by any other factor. The following evidence,
as quoted from that report, was used to support that conclusion:
Mileage data collected by Dow Chemical—a major perc manufacturer—
in a customer survey indicate that the average perc plant without
an adsorber achieves a mileage of 6,940 pounds per drum, and an
adsorber increases this figure to about 8,370 pounds per drum.
However, in both adsorber-equipped plants and non-adsorber-equipped
plants, the range of mileages reported was enormous, and plants in
each category showed both operators with mileages less than 2,0UQ
pounds per drum and others with mileages greater than 15,000 pounds
per drum.
The International Fabricare Institute (IFI) gives most of the
credit for either poor mileage performance or exceptional mileage
performance to the plant operator. One plant (without an adsorber)
visited had a mileage of 2,900 pounds per drum. The plant showed
obvious mechanical problems which even superficial maintenance
should have corrected. In marked contrast, TFI personnel have
obtained mileages of 11,000 pounds per drum in transfer machines,
without adsorbers. ([8] p. 4-9)
4-8
129
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Combining the above data with their own plant visits, the authors
used the following estimates of plant mileages in their analysis:
of
[81
o Average Mileage of Co-nrrercial Plants
—uncontrolled plant
—housekeeping procedures
--housekeeping plus adsorber
6,000 lb/drum
lO.COO lb/drum
15,COO lb/drum
o Average Mileage of Industrial Plants
—uncontrolled plant
—housekeeping procedures
—housekeeping plus adsorber
10,000 lb/drum
15,000 lb/drum
20,000 lb/drum
o Average Mileage of Coin-Operated Plants
—uncontrolled plant
—housekeeping procedures
—housekeeping plus adsorber
4,400 lb/drum
8,000 lb/druo
12,0CO lb/drum
We also use these data with the following interpretations: The mileage for
uncontrolled plants of each type is assumed to represent the current aver-
age mileage of plants without carbon adsorbers. If all cleaners in each
category currently without adsorbers were able to implement the improved
maintenance and housekeeping procedures, the average mileage for those
plants would increase to the next higher level. For plants that currently
have adsorbers, their mileage is calculated from the uncontrolled rate by
assuming a reduction in consumption rate equal to the reduction obtained by
adding an adsorber to a plant with good housekeeping procedures. To illus-
trate this calculation, note that through equation (4.1), the following
mileages and consumption rates are equivalent:
Mileage
4,400
6,000
8,000
10,000
12,000
15,000
20,000
Consumption Rate
0.163
0.119
0.089
0.072
0.060
0.048
0.036
4-9
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Adding a carbon adsorber to a plant with good maintenance reduces the
consumption rate as shown in the following table:
Consumption Rate
Ratio:
without Adsorber With Adsorber Rate With/Rate Without
o Commercial 0.072 0.048 0.67
o Industrial O.O^S 0.036 0.75
o Coin-Operated 0.089 0.060 0.67
The current average consumption rates for dry cleaning plants with carbon
adsorbers are thus taken to be
o Commercial 0.119 x 0.67 = 0.080
o Industrial 0.072 x 0.75 ¦ 0.054
o Coin-Operated 0.163 x 0.67 = 0.109
which correspond to mileages of
o Commercial 8,940
o Industrial 13,200
o Coin-Operated 6,600
Then, if better maintenance and housekeeping procedures were implemented in
all of the plants, the mileage would increase to the levels indicated in
the table of estimated plant mileages for housekeeping plus adsorber.
To determine the current average PCE consumption rate, it is necessary
to estimate how many cleaners currently have carbon adsorbers. In a survey
of its members, the International Fabricare Institute [15) found that about
half of all dry cleaners have carbon adsorbers. We assume this to mean
that half of the current commercial and coin-operated dry cleaners have ad-
sorbers. However, because of the obvious economic benefits to industrial
cleaners, It is assumed that 90 percent of industrial cleaners have adsorb-
ers. Thus, the current average PCE consumption rates are taken to be as
follows:
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131
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o Coinercial 0.5
o Industrial 0.1
o Coin-Ooerated 0.5
x 0.119 + 0.5 x 0.080 = 0.099
x 0.072 + 0.9 x C.054 = 0.056
x 0.163 + 0.5 x 0.109 = 0.136
The next data elem
type of plant. In thei
values, which were repr
o Commercial
o Industrial
o Coin-Operated
nts to consider are the a
analysis, the authors of
sentative of survey resul
100,000 pounds/year
1,000,000 pounds/year
80,000 pounds/year
erage cleaning volumes by
13] used the following
s :
However, data from Ludwig [10] indicated that the average volume or commer-
cial cleaners was 2,900 pounds per week, or about 150,000 pounds per year.
We shall use the average of the two values, which implies the following
average cleaning volumes:
o Commercial 125,000 pounds/year
o Industrial 1,000,000 pounds/year
o Coin-Operated 80,000 pounds/year
Taking the estimated number of plants that use PCE from Section 2, we can
estimate the total volume of cleaning by type of cleaner.
Cleaning Volume
Type of Cleaner Number (pounds) (kg)
Commercial 16,000 2000 x 10^ 900 x 10^
Industrial 350 350 x 10^ 160 x 10^
Coin-Op 11,000 880 x 10^ 400 x 10^
Recall that multiplying the average consumption rate by the total cleaning
volume will give total PCE consumption. Carrying out this multiplication
gives the following accounting of PCE consumption:
4-11
132
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Consumption Cleaning PCE
Volume Consumption
Rate (kg) (kg)
Commercial 0.C99 x 900 x 106 = 90 x 10^
Industrial 0.056 x 100 x 10^ = 9 x 10°
Coin-Op 0.136 x AGO x 1 0^ = 54 x IP1*
Total 152 x 106
This total agrees well with the independent estimate of 160 x 10^ kg of
PCE [4]. For calculating reductions in PCE exposure to urban residents,
reductions in total PCE consumption resulting from controls are compared to
the assumed base case of 152 x 10^ kg.
Data required to estimate the cost of control options include the cost
of control equipment, estimated increases in maintenance costs, and the
price of PCE. Since the equipment costs in [8] correspond to prices in
1975, we assume price increases to convert to 1978 dollars. For cleaning
equipment, we use the producer price index for general purpose machinery
[13], which increased by a factor of 1.21 between 1975 and 1978. From [8],
the estimated costs of carbon adsorbers including installation (in 1978
dollars) were
o Commercial $5,000
o Industrial $9,100
o Coin-Operated $7,300
and the costs of cleaning machines were
o Commercial (40-pound capacity)
—Transfer Unit $ 21,000
—Dry-to-dry unit $ 28,000
o Industrial (200-pound capacity)
—Transfer Unit $114,000
o Coin-Operated (25-pound capacity)
—4 dry-to-dry units $ 39,000
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From Appendix A, the capital charge rate for dry cleaners is assumed to be
0.097. That is, the annual charge for a capital expenditure is 0.097 times
that expenditure. Thus, the annual costs of adding a carbon adsorber are
o Conmercial $485
o Industrial $883
o Coin-Operated $708
As in [8], we assume that the cost of annual maintenance of the car-
bon adsorbers would be 5 percent ox their cash price. Thus, annual main-
tenance for the adsorbers is taken to be
o Commercial $250
o Industrial $455
o Coin-Operated $365
Using the sane estimate of 5 percent of the equipment cost for maintenance
of the dry cleaning machines and assuming that implementing improved main-
tenance and housekeeping would increase this cost by 50 percent, the addi-
tional annual cost of improved maintenance would be
o Conmercial $ 525
o Industrial $2,850
o Coin-Operated S 975
For conmercial dry cleaners, this estimate is based on a transfer unit
rather than on a dry-to-dry unit.
Finally, to complete the cost estimates for options 1 through 4, it is
necessary to have a price for PCE. From [8], the price of PCE to dry
cleaners in 1975 was $2.80 per gallon. Using the price index for chemicals
and allied products that increased by a factor of 1.10 from 1975 to 1978,
we have the estimated PCE price in 1978 of $3.10 per gallon. With 5.9 kg
of PCE per gallon, the PCE price is SO.53 per kg.
To complete the cost and exposure estimates for option 5, we need che
difference in cost between transfer and dry-to-dry units of equivalent
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134
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capacity. From Che above data, this difference is S7,Q00, which is annual-
ized to 0.097 x 7000 = $680. This cost would apply to about 80 percent of
existing commercial cleaners, since about 20 percent already have dry-to-
dry units. Recall that in option 5, the replacement of transfer units by
dry-to-dry units is assumed to be accomplished when the cleaning machine
needs to be replaced anyway so that no additional expense besides the dif-
ference in machine costs needs to be considered. The resulting annual cost
of the option can thus be viewed as the long-run increase in annual costs.
To complete the exposure analysis of option 5, it is necessary to es-
timate the reduction in worker exposure by switching to a dry-to-dry unit.
From Ludwig [10], the average PCE concentration in the processing area was
31 ppm, but during a 15-minute period after the transfer operation, the
average concentration was 64 ppm. Assuming 8 transfers per day in an
8-hour day, the concentration during the other 45 minutes per hour must
have been 20 ppm in order for the daily average to be 31 ppm. That is
15 x 64 + 45 x 20
60 " JI
By eliminating the transfer process, we nay assume that the concentration
in the machine area would average 20 ppo for the entire day. This is about
a one-third reduction in PCE exposure to machine operators, which is
assumed for the remainder of the analysis.
To complete the analysis of option 6 (moving coin-operated dry clean-
ing machines to a room separate from coin-operated laundry facilities), we
assume the following. We assume that the rearrangement of equipment in a
typical coin-operated laundry would require an additional 50 square feet at
a monthly rental cost of $0.50 per square foot, or an annual cost of $300.
We assume that remodeling would cost $1500, which would be capitalized and
charged at the sane annual rate as other dry cleaning investments; this
comes to $145 per year. We assume that the effect on exposure is to reduce
exposure to coin-operated laundry users by 90 percent, without changing
exposure to any other group.
This completes the data and assumptions needed to apply the cost and
exposure calculations described in the previous subsection.
4-14
•35
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RESULTS OF COST AND EXPOSURE CALCULATIONS
Appendix B contains the detailed calculation of costs and exposure
reductions. The results are summarized in Table 4-2. The control costs
are expressed in millions of dollars per year, and the exposure reduction
estimates are multipliers applied to the exposures calculated in Section 3.
Note that many of the control costs are actually negative. These cost
reductions result from the large PCE credits that can be obtained with
options 1, 2, and 3 and their combinations. Because of the cost savings,
these options would be desirable even if exposures were not reduced.
Section 7 analyzes the value of all options including those that actually
result in a net cost.
4-15
136
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Table 4-2
SUMMARY OF CONTROL COSTS AND EXPOSURE REDUCTION FACTORS BY CONTROL OPTION
Control Cost
(millions of dollars)
Exposure Reduction
(mult iplier )
Workers
Machine Operators
Commercial
Industrial
Coin-Operated
Other Workers
Commercial
Industrial
Coin-Operated
Service Users
Commercial Dry Cleaners
Coin-Operated Dry Cleaners
Coin-Operated Laundry
Urban Residents (distance
from dry cleaners, meters)
25 - 200
200 - 500
500 - 1000
Control Option
1
2
_ A_
4
5
11.5
-2. 2
-3.3
0. 3
8. 7
0.60
1.00
0.81
1.00
0.66
0.67
1.00
0. 96
1.00
1.00
1.00
0.55
1.00
0.67
1.00
0.60
1.00
0. 81
1.00
1.00
0.67
1.00
0.96
1.00
1.00
1.00
0. 55
1.00
0.67
1.00
0. 56
1.00
0.89
1.00
1.00
1.C0
0. 59
1.00
0. 74
1.00
1.00
0.55
1.00
0.67
1.00
0. 72
0.84
0.89
0.93
1.00
0. 72
0.84
0.89
0.93
1.00
0.72
0.84
0.89
0.93
1.00
137
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Table 4-2 (continued)
Control Option
10
Control Cost
(millions of dollars)
Exposure Reduction
(multiplier)
4.9
-13.5 -L3.0
¦10. 1
-4.3
Workers
Machine Operators
Commercial
1.00
0.60
0.49
0.49
0.32
Industrial
1.00
0.67
0.64
0.64
0.64
Coin-Operated
1.00
0.55
0.55
0.44
0.55
Other Workers
Commercial
1.00
0.60
0. 49
0.49
0.49
Industrial
L.00
0.67
0.64
0.64
0.64
Co in-Operated
1.00
0. 55
0. 55
0. 44
0. 55
Service Users
Commercial Dry Cleaners
1.00
0.56
0.49
0.49
0.49
Coin-Operated Dry Cleaners
1.00
0. 59
0. 59
0.50
0. 59
Coin-Operated Laundry
0.10
0.55
0.55
0.44
0.55
Urban Residents (distance
from dry cleaners, meters)
25 - 200
1.00
0.57
0.52
0.48
0.52
200 - 500
1. 00
0. 57
0. 52
0.48
0. 52
500 - 1000
1.00
0.57
0.52
0.48
0.52
4-17
138
-------
Table 4-2 (cor.tinued)
1 1
12
Control Option
13 b
Control Cost
(millions or dollars)
Exposure Reduction
(nult ipli e r)
Workers
Machine Operators
Commercial
Industrial
Coin-Operated
Other Workers
Commercial
Indus trial
Coin-Operated
Service Users
Commercial Dry Cleaners
Coin-Operated Dry Cleaners
Coin-Operated Laundry
Urban Residents (distance
from dry cleaners, meters)
25 - 200
200 - 500
500 - 1000
¦1.4
-5.2
0.6
15
3. 5
0.49
0.64
0. 55
0.49
0.64
0.55
0.49
0.59
0.055
0.52
0. 52
0.52
0.49
0.64
0.44
0. 49
0.64
0.44
0.49
0. 50
0.44
0.48
0.48
0.48
0.49
0.64
0. 44
0. 49
0.64
0. 44
0.49
0. 50
0.044
0.48
0.48
0.48
0. 32
0.64
0.55
0. 49
0.64
0. 55
0.49
0. 59
0.055
0.52
0.52
0.52
0. 32
0.64
0.44
0.49
0.64
0.44
0.49
0. 50
0.044
0.48
0. 43
0.48
4-18
139
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Section 5
HEALTH EFFECTS OF PERCHLOROETHYLENE
This section of the report presents an overview of the extensive
literature on the health effects of perchloroethylene. The first part of
the section discusses the noncarcinogenic health impacts and is taken
primarily from a recent NIOSH report [10]. The second part summarizes
information from the CAG assessment [lj and the SAB review meeting [2]
regarding the potential carcinogenicity of PCE.
T0XIC0L0GICAL IMPACTS OF PERCHLOROETHYLENE
Perchloroethylene is a volatile liquid at room temperature with an
odor threshold in the range of 5 to 50 parts per million in air. Its use
as a dry cleaning solvent was motivated primarily by its nonrlammability,
as petroleum solvents pose a risk of explosion and fire.
Toxic effects from PCE are well established, with the principal target
organ being the liver [10]. Liver enlargement and abnormal liver function
tests, pathological changes in the kidneys, and depressive effects on the
central nervous system have been observed in animal experiments [19-24].
Some animal evidence for skin irritation and adverse impacts on fetal
development is also available [25,26]. Clinical evidence demonstrates that
PCE is toxic to the liver in humans [27-30], Additional PCE effects on
humans are irritation of the eyes, upper respiratory tract, and sinus
cavities; skin burns; headache; and central nervous system depression
[19,27,29,31,32].
The most common exposure route for PCE is inhalation. If ingested,
PCE enters the body through the intestines. Absorption through the skin is
considered to be of lesser importance. PCE is retained in body fat and
metabolized relatively slowly. The half-life for urinary excretion is
estimated to be six days [33].
5-1
140
-------
Occupational standards for PCE exposure reflect concern for the acute
toxic effects. The allowed eight-hour time-averaged concentration is 100
ppm, with a ceiling concentration of 200 ppm, not to exceed a maximum peak
of 300 ppm for 5 minutes in a 3-hour period [10]. NIOSH recommended in
1976 that occupational levels be reduced to a maximum of 50 ppm, averaged
over a 10-hour workday or 4 0-hour work week, and a ceiling concentration of
100 ppm determined by 15-minute samples 134].
CARCINOGENIC EFFECTS OF PERCHLOROETHYLENE
Summary of Information
The major evidence for the potential carcinogenicity of PCE is a
rodent bioassay carried out by the National Cancer Institute (NCI) using
B6C3F1 mice and Osborne-Mendel rats [35]. The animals were given PCE in
corn oil through a stomach tube five days a week for eighteen months. The
B6C3F1 mice showed a statistically significant incidence of liver tumors
(hepatocellular carcinomas), as shown in Table 5-1, which is reproduced
from [1). The bioassay on rats gave negative results, which NCI considered
to be inconclusive because of high mortality. While chronic respiratory
and kidney disorders were observed, there were no hepatocellular carcinomas
observed in any of the exposed rats and no statistically significant tumor
incidence at any anatomical site.
In addition to the NCI bioassays, a test was carried out on Sprague-
Dawley rats by Dow Chemical 136]. This test involved inhalation of PCE
vapor at 300 and 600 ppm. Although many tumors were found in both treated
and control animals, there was no statistically significant increase in
tumor incidence excepting an unusual tumor (adrenal pheochromocytoma) in
the low dose female rats.
The results for short-term tests of PCF. are inconclusive. In six bac-
terial systems and one mammalian cytogenic study, only one bacterial system
indicated that PCE is mutagenic. A cell transformation study of Fischer
rat embryo cells indicates that these highly sensitive cells can be trans-
formed by PCE into tunor-producing cells.
5-2
141
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Tabic 5-1
INCIDENCE OF HEPATOCELLULAR CARCINOMAS
IN B6C3F1 MICE FED TETRACHLOROETHYLKNE
Dose (ng/kg/dav)*
Hepatocellular Carcinomas
Male
vehicle control
unt reated
536
1072
2/17 (12%)
2/20 (10%)
32/49 (65%)
27/48 (56%)
F eicale
772
untreated
vehicle control
386
2/20 (10%)
0/20 (0%)
19/48 (40%)
19/48 (40%)
*Time-weighted average doses.
Source: [1]
Limited epidemiological data have been obtained for dry cleaning and
laundry workers. Prelininary results by Blair et al. [37] for 330 former
members of dry cleaning union locals indicate an excess of lung, cervix,
uterine, and skin cancer compared to the proportionate mortality for the
U.S. population. A very recent study by Lin and Kessler [38] using a
case-controlled survey method indicates increased incidence of pancreatic
cancer aaong workers in garages and dry cleaning establishments. (This
latter study was not included in the CAG review.) The small samples and
methodological limitations of these epidemiological studies make them at
most suggestive and not conclusive evidence that PCE induces cancer in
humans.
5-3
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The SAB Review
The CAG assessment of PCE was critically reviewed by the SAB Subcom-
mittee on Airborne Carcinogens [2]. Many objections were raised regarding
the evidence for the carcinogenicity of PCE, and the subcommittee declined
to endorse the implication from che IRLG guidelines that the positive bio-
assay in B6C3F1 nice should be sufficient to inply that PCE be regarded as
a carcinogen. The specific issues raised by the scientists at this meeting
included the purity of the PCE, the absence of evidence for a genetic
mechanism to induce the liver tumors observed in the B6C3F1 mice, and the
appropriateness of the nouse as a species for extrapolating a carcinogenic
risk to humans.
The first area of concern expressed by the scientists was whether PCE
was in fact responsible by the observed mutagenic or carcinogenic effects.
The PCE for the NCI bioassay was described as more than 99 percent pure,
but with unknown impurities. Ames test data [39] showed that perchloro-
ethylene containing a stabilizing chemical was mutagenic while unstabil-
ized, purified PCE was not mutagenic in the activated bacteria system.
Dr. Weinhouse, the chairman of the SAB subcommittee, expressed doubt based
on the Ames data that the PCE used in the NCI oioassay "may have been
stabilized with a substance that may he carcinogenic" ([2], p.141). The
epidemiological data were similarly inconclusive, because dry cleaning
workers are exposed to a number of potentially toxic chemicals in addition
to PCE.
The second issue is thac of genetic mechanism. Dr. Marvin Kuschner,
the Dean of the School of Medicine at the State University of New York at
Stoneybrook, described an alternative hypothesis: PCE is a liver toxin that
produces extensive liver damage, and replication errors in regenerating
liver cells in a strain of mouse prone to liver tumors result in a higher
incidence of the tumors. Dr. Kuschner remarked that the available evidence
indicates that PCE interacts with protein and not with the cell DMA. Ke
noted that binding to protein may be a mechanism for toxicity to the cell,
not alteration of the cell to cause a tumor. PCE may therefore cause a
chemical hepatectomy, a removal of part of the liver. The implication of
5-4
143
-------
this hypothesis is that a threshold or strong nonlinearity in the dose
response relationship should exist. Exposure to PCE at levels below that
at which cell destruction takes place should not result in tumors.
The third issue is the basis for extrapolating from bioassav data to
hunans in assessing risk. Dr. Raymond Harbison of Vanderbilt Medical
Center and Dr. Jessie Norris of Dow Chemical made presentations to the SA3
meeting [2], stressing the metabolic differences between nice, rats, and
hunans. Perchloroethylene is transformed to an epoxide intermediate
through a metabolic activation process. This reaction appears to go much
faster in the oouse than in the rat or in man, based on studies that use
carbon-14 labelled PCE to track its metabolic pathways [2,40,41]. Gross
changes in the liver of mice occur well below maximum tolerated dosage
levels, while rats appear to be much less sensitive to liver damage fron
PCE even at high doses. A dose-related increase of liver cell DNA
synthesis was observed in mice, but not in rats. The ability of the mouse
to metabolize PCE saturates at high doses, providing a possible explanation
for the apparent saturation in tumor response that was observed in the
B6C3F1 mouse data.
The limited human clinical data suggests that man metabolizes less PCE
than either mice or rats. Dr. Norris of Dow asserted that the rat and not
the mouse was the appropriate species on which to base a risk assessment.
She presented a risk assessment based on the Dow rat study [36], taking 300
parts per million or 253 milligrams per kilogram of body weight per day as
a level for no observed adverse effect in the rat [40]. Although Dr.
Norris derived a margin of safety of 118,000 for humans, this assessment
assumes an atmospheric concentration of one part per billion PCE for human
exposure. Occupational exposures can be four to five orders of magnitude
higher, reducing the margin of safety in this calculation to a factor of
ten or less for exposed wokers.
A related problem in assessing the human risk based on rodent bio-
assays is the calculation of an equivalent human dose corresponding to
rodent exposures. While the IRLG guidelines cite a National Academy of
Sciences study [42] in support of using total dose per body weight as a
basis for extrapolating from animals to humans, the CAG methodology for air
5-5
144
-------
pollutants uses relative surface area [43]. CAG's procedure appears to be
based on a suggestion of Mantel and Schneiderraan, which is based on the
observation that the effective dose of drugs scales better with surface
area than body weight weight [44]. As a caveat CAG notes
...the concept of equivalent doses for humans compared to animals on a
nig/surface area basis is virtually without experimental verification
regarding carcinogenic response. ([43], p.15)
CAG's use of surface area was challenged at the SAB meeting by Dr. A. M.
Schumann of Dow in the context of trichloroethylene:
Rodents metabolize trichloroethylene (TCE) to a greater extent than do
humans on a weight basis and are thus exposed to much more reactive
TCE intermediate than humans. Therefore, the corrected equation for
trichloroethylene species comparison is the reciprocal of that used in
the CAG assessment. ([2], p. 163)
The surface area extrapolation procedure was not discussed specifically
for PCE in the SAB meeting, but the issues for PCE and TCE are very
similar. Dr. Norris used an extrapolation on body weight in her calcula-
tion [40].
5-6
145
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Section 6
MODEL FOR DOSE RESPONSE RELATIONSHIP FOR PCE
This section presents a model for the dose response relationship for
PCE. Our objective is to develop a representation of the uncertainty in
human cancer incidence given PCE dose that might be used as a basis for
determining unreasonable risk under the language of the Toxic Substance
Control Act. The calculation is intended to be illustrative, based on the
data and discussion from the CAG Assessment (1] and the SAB meeting
transcript 12).
It was pointed out by Dr. Altshuler in [2] that an estimate of cancer
risk to the population can be obtained by multiplying an exposure estimate
by CAG's unit risk estimate. Dr. Albert and Walter Barber of EPA re-
sponded that EPA has not yet determined how to use these estimates in the
regulatory process. A recent National Academy of Sciences study [45] was
strongly critical of the CAG unit risk approach because a single number
could provide an illusion of certainty, whereas in reality the health
effects of a suspected carcinogen might be uncertain over many orders of
magnitude.
In developing our approach, we shall endeavor to account for major
sources of uncertainty in estimating the human health impact of a given
level of PCE exposure. After a review of the discussion in [2], we con-
cluded that the roost important sources of uncertainty are as follows:
1. Should one extrapolate from rodents to humans based on surface
area as suggested by CAG, by body weight as suggested by the
National Academy of Sciences, or on some other basis?
2. Should the extrapolation be from the. most sensitive species, the
B6C3F1 mouse, or from a species metabolically more similar to
humans, such as the rat used for Dow's inhalation studies?
3. Should a linear nonthreshold dose response relationship be used,
or should a nonlinear or nonzero threshold relationship be used
-------
that is more representative of the hypothesis that PCE acts in-
directly in causing mouse liver tumors through a cell toxicity
nechanism?
For our illustrative calculations, we shall examine two alternative
cases for each of these questions. To develop a probability distribution
on health impacts, we shall assess probabilities for these cases. Ideally,
these probabilities should reflect the judgment of scientific specialists
on the issues, but for our purposes, the authors will use three sets of
illustrative values:
1. A probability assessment of 20 percent to the assumptions used by
CAG and 80 percent to the alternative.
2. A probability assessment of 33 percent to the CAG assumptions and
67 percent to the alternative.
3. A probability assessment of 50 percent to each alternative case.
We shall consider that the probability assignments for each of the three
uncertain factors are independent: Resolution of uncertainty on one factor
would not affect the probabilities assigned to the alternatives for the
other two factors. Two representative outcomes for each of three factors
give a total of eight cases that we shall consider. Additional factors and
additional representative cases for each factor could be included without
changing the methodology, but for simplicity, we have chosen to limit the
analysis to eight alternative cases for the dose response relationship.
We now describe the specific assumptions used in determining the
quantitative descriptions for the representative outcomes for each of the
three factors treated as uncertain.
SCALING METHOD
The two cases correspond to the surface area extrapolation method
proposed by CAG [1] and the method of extrapolation by body weight. In the
CAG surface area method, the equivalent dosage for humans measured in
milligrams per kilogram of body weight is reduced from the dosage measured
6-2
147
-------
in milligrams per kilogram of body weight that was actually administered to
rodents by a factor equal to the cube root of human body weight divided by
rodent body weight. Thus, for example, the reduction factor in going from
a 30-gran mouse* to a 7 0-kilograa human is
In Che body weight extrapolation method, no such reduction factor is used.
The dosage to rodents in milligrams per kilogram of body weight is con-
sidered equivalent to the same dosage in milligrams per kilogram of body
weight ingested in a human. The ratio of absolute dosage is the same as
the ratio of body weights, for example, (70/0.03) = 2333 for human to
SPECIES TYPE FOR EXTRAPOLATION
Laboratory animal strains are highly inbred, genetically similar and,
therefore, homogeneous populations for which reproducible results may be
obtained. Human populations are genetically heterogeneous and may exhibit
a much broader range of sensitivities to biological insults than the animal
strains used in bioassays. While some scientists and regulators believe
that use of the most sensitive species and genetic strain is appropriate to
avoid underestimating the potential risk to sensitive human subpopulations,
other scientists stress the biological and metabolic differences between
humans and the small rodents typically used as laboratory animals. For
some chemicals there may be substantial scientific grounds for believing
that an effect observed, for example, in mice but not in rats, is highly
unlikely to occur in humans because the way the rat metabolizes the
chemical is similar to humans and the metabolic process for the mouse is
significantly different. For our analysis we consider two cases, one based
on mouse data and the other on rat data. Both the mouse and rat are of a
genetic strain that is particularly sensitive to liver tumors.
*In the CAG documentation [1,43] the mouse is assumed- to weigh 30 gram9 and
the rat 350 grams.
13.26
mouse
6-3
148
-------
Our two representative cases are based on the results of the NCI test
using B6C3F1 mice and the Dow tests with Sprague-Dawley rats.* We prefer
the use of the naxixun likelihood estimate for reasons articulated by
Altshuler in [2] for the frequency of tumors in the exposed animals. A
correction tenn should be employed if there is a substantial incidence of
the tumors in the control group. For our analysis, we use the observed
incidence in male mice of 32/49 or 65 percent at the dose level of 536
milligrams/kilogram/day for five days per week over 78 weeks [ll.** The
test was terminated after 90 weeks resulting in a lifetime exposure of 332
rag/kg/day.
For the rat results, we have used a conservative figure of a five
percent incidence. No statistically significant elevated tunor incidence
was observed in the Dow rat study. However, the small sample size of the
experiment makes it difficult to conclude that a small incidence of tumors
is not present. An experimental result of no tumors in 100 animals has an
upper 99 percent confidence limit for the incidence rate of 3.6 percent.
Since both the Dow study and the NCI rat study are considered by CAG [1,2]
to have deficiencies as the basis for a negative finding (no tumor inci-
dence), we have used as an estimate a low (five percent) rate of incidence
at the lower (300 ppa or 253 mg/kg/day) administered dose in the Dow study.
This judgment by the authors is illustrative and should be subjected to
review by qualified experts. The estimate of five percent in the rat leads
*The MCI bioassay in Osborne-Mendel rats suffered from high mortality. The
CAG assessment [1] notes that no hepatocellular carcinomas were observed in
any exposed rats, and that no statistically significant tumor incidence was
observed at any anatomical site, but provides no further data. The CAG
assessment [1] does provide the data for the Dow study, but concludes that
the study has limitations that make it "inadequate to assess the carcino-
genicity or noncarcinogenicity" of PCE because (1) the exposure was for 12
months rather than a full lifetime of 24 months, (2) the exposure levels
were too low, and (3) control animals were not properly separated from
treated animals. The Dow study appeared to us to provide a better basis
for a representative case than the NCI study.
**An upper 95 percent confidence limit value would correspond to an inci-
dence rate of 73.5 percent tumors in the exposed mice. The difference in
the analysis from using this figure would be to increase the cancer inci-
dence by about 10 percent in the four cases extrapolated from the mouse
data.
6-4
119
-------
to nuch lower numbers for cancer incidence in humans than do the estimates
from the B6C3F1 mouse data.
EXTRAPOLATION MODEL
We employ a nodel for extrapolating from the high doses administered
in aninal experiments to the lower doses corresponding to human exposure.
Two alternative models are used. The first is a linear, nonthreshold dose
response model fitted to the single bioassay data point available, either
for the B6C3F1 mouse or the Sprague-Dawley rat as previously described.
For the alternative model, we assume a quadratic model of the form inci-
dence proportional to dose squared. Such a model is representative of
nonlinear relationships without assuming a threshold for tumor incidence.
It is consistent with observed nonlinearities observed for some carcinogens
[46] but does not assume that tumor incidence rate changes rapidly with
dose.
The form of the model is the multistage model described in the IRLG
guidelines [3].
P(d) is the probability of cancer occurrence given a dose level d. We
have assumed no background incidence Oq = 0) and we assume either a
linear (k » 1) or pure quadratic form: (k = 2, " 0). Therefore, for
each case we compute the parameter A (aj for the linear case or '2 ^or
the quadratic case) from the animal test data relating the observed life-
time cancer incidence to dosage level in milligrams/kilogram/day. We then
use the above formula to predict human lifetime cancer incidence corres-
ponding to various PCE human exposure levels, translating appropriately
from micrograms per cubic meter to milligrams/kilogram/day for a 70-
kilogram person. The value of the parameter X and the calculations of
human cancer incidence for each case are given in Appendix C.
(6.1)
150
6-5
-------
The attribution of the linear model to CAG deserves additional
comroent. The CAG assessment procedures use the multistage model, equation
(6.1), and a computer program to estimate the coefficients for the
upper confidence limit. As noted in [43] this is equivalent to using a
linear relation for low dose extrapolation. A discussion of this
equivalence is found in Guess and Crump (47].
6-6
151
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Section 7
ANALYSIS OF CONTROL OPTIONS
This section brings together the cost and exposure calculations of
Sections 3 and 4 with the dose response calculations of Sections 5 and 6 to
choose a best alternative control option. To choose the best option,
probabilities must be assigned to uncertain outcomes, and values must be
assigned to trade off control costs with expected cancer cases. The best
choice depends on these probabilities and value assignments. Following a
base case selection of an option, sensitivity analysis is performed to
explore the effect of changes in probabilities and values on the choice of
the best option.
STRUCTURE OF THE CONTROL DECISION
The decision of selecting a PCE control option for the dry cleaning
industry is represented in Figure 7-1. The alternative decisions, as shown
at the left of the figure, are to do nothing or to select one of the
options 1 through 15. From our analysis of Section 4, the selection of one
of these alternatives determines the control cost and the average level of
exposure to the twelve population groups used in that analysis. Of course,
uncertainties exist in both the cost and the exposure estimates. However,
it is reasonable to expect that these uncertainties are far less
significant than the uncertainty in the nu;nber of cancer cases that may
result from those exposures. The uncertainty in possible cancer cases is
modeled explicitly through the selection of alternative dose response
models for extrapolating test results from animals to humans and from high
to low doses. From Section 6, the difference in predicted cancer incidence
among the alternative models can be five orders of magnitude. These
uncertainties clearly dominate uncertainties in exposure levels, which are
probably less than one order of magnitude.
7-1
152
-------
N3
Do Nothing
Option 1
Option 15
Surface Area
Body Weight
Scaling
Method
Mouse
Rat
Species
Type
Linear
Quadratic
Extrapolation
Model
Values
of
Outcomes
Decision
Uncertainty in Dose-Response
Figure 7-1. Decision Tree for PCE Control Analysis
C/l
Co
-------
The uncertainty in dose response Ls represented in the center of the
decision tree of Figure 7-1. The dose response is assumed to have three
independent components, each with two alternatives. The components are
scaling method, species type, and extrapolation model. The scaling nethod
determines how ar. equivalent dose is detemined for different sized
animals. The representative alternatives we have chosen are scaling
methods based on dose per surface area or dose per body weight. The choice
of species type involves using the observed response from either the mouse
or the rat tests as the basis for extrapolation to humans. The extrapola-
tion model is the method of extrapolating from high to low doses and can be
taken to be either linear or quadratic, as described in Section 6.
Since the correct choice of each dose response component is not known,
probabilities are assigned to each possibility. In Figure 7-1, rep-
resents the probability that the true scaling method is surface area.
Thus, is the probability that the true scaling method is body
weight. Similarly, p^ is the probability that the true representative
species is the raouse, and p^ is the probability that the correct
extrapolation model is linear. The probabilities l~p2 and 1-P3 are
assigned to the other possibilities of species and extrapolation model,
respectively.
Each one of the eight possible combinations of dose response compo-
nents gives a method for estimating the lifetime cancer incidence in
humans, given an average daily dose of PCE. Taking the average life
expectancy of 70 years, the annual cancer incidence rate for a group of
people with the same average daily exposure would be
Annual Cancer Cases = Number of People Exposed x Lifetime Incidence
° (7.1)
Adding the number of cases for each population group would theh give the
total annual cancer cases resulting from PCE exposure.
The next step is to find the expected annual number of cancer cases
using the probabilities of each dose response combination. Assuming prob-
abilistic independence of the components of dose response, the probability
of any combination is simply the product of the probabilities of the com-
ponents. Each dose response combination and its probability are listed in
Table 7-1.
7-3
154
-------
Table 7-1
ENUMERATION OF DOSE RESPONSE CASES
Definition of Case
Dose Response Scaling Species Extrapolation
Combinations Method Type Model Probability
1
surface area
mouse
linear
x p
2 x P3
2
surface area
mouse
quadratic
Pl x P
2 * <1'?3)
3
surface area
rat
linear
Px X (l-p2) x P3
4
surface area
rat
quadratic
p^ X (1"P2) X (1"P3>
5
body weight
mouse
linear
(l-p^
X P2 X P3
6
body weight
mouse
quadratic
(I-Pj)
x P2 X (1-P3)
7
body weight
rat
linear
n-Pj)
x (1 -P2) x p3
8
body weight
rat
quadratic
(I-Pj)
x (l-p9) x (l-p3)
Using the notations prob^ to represent the probability of dose response
combination i(i = 1 to 8) and cases^ to represent the annual number of
cases predicted by dose response combination i, then the expected annual
number of cancer cases is given by
8
Expected Annual Cancer Cases = ^ prob^ x cases^ (7.2)
i=l
The final part of the decision tree is the value component. Using
the criterion of expected cost, the total annual cost of a control option
and its impact on cancer incidence is given by
Total Cost = Control Cost (*7.3)
+ Expected Annual Cancer Cases
x Cost per Case
7-4
155
-------
where the expected number of cancer cases is given by (7.2), the control
cost is the cost calculated in Section 4, and the cost per case is a value
judgment made by the decision maker. Once the probabilities p^,
P2, and p^ are specified and the value judgment of cost per cancer
case is made, each control option can be evaluated. The best control
option is then simply the one with the least expected total annual cost.
SELECTION OF BEST CONTROL OPTION
Because of the large number of calculations required, the expected
number of cancer cases for each control option and dose response combin-
ation was calculated with a computer program. The results are in Table
7-2, while the details of each computation are in Appendix C.* Note the
wide differences in expected number of cancer cases as a function of dose
response model. In the base case, no controls, the expected annual number
of cases is 348, if we assume the dose response model consisting of surface
area scaling, mouse species, and linear extrapolation. On the other hand,
the expected number of cases is only 0.01 if we assume the dose response
model of body weight scaling, rat species, and quadratic extrapolation.
Intermediate values correspond to other combinations.
Examining the control options, we find that all of the options reduce
the expected number of cancer cases as compared with the base case. Option
15, the combination of all control options, results in the least number of
cancer cases for all dose response combinations. In examining the cost of
controls, note that options 1, implementing better maintenance in commer-
cial and industrial cleaners; 2, implementing better maintenance in coin-
operated dry cleaners; and 3, installing carbon adsorbers in commercial dry
cleaners, actually save money as well as reduce expected cancer cases.
Option 7, which is a combination of options 1 and 2, is the least cost
(greatest savings) of any of the options.
Since option 7 saves money and lowers expected cancer cases, it would
always be preferred to the base case no matter what cost per cancer case is
*By expressing cancer cases and costs on an annual basis, we have avoided
the controversial problem of discounting health effects that may occur in
the future. A more detailed analysis could address this issue.
7-5
15S
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Table 7-2
CONTROL COSTS AND EXPECTED CANCER CASES
Expected Number of
Cancc.r Case
s Pe. r
Year
Cost of
Control
Combination
by
Dose Response Combination
Control Option
Op L i on
of Options
1
2
3
4
5
6
7
8
($ nillion)
Base Case
-
347.49
22.91
10.15
0.38
27.05
0. 13
1.70
0.01
0.00
1
-
275. 22
8. 92
7.87
0. 15
21.06
0. 05
1. 32
0.00
-11.30
2
-
276.67
22.47
8.08
0.38
21.62
0. 13
1.36
0.01
-2.20
3
-
317.92
15.80
9. 2 5
0. 26
24. 52
0. 09
1. 55
0.01
-3. 30
4
-
296.61
22. 56
8.71
0.38
23. 12
0. 13
1 .46
0.01
0. 30
5
-
330.74
14.41
9. 57
0. 24
25.49
0. 08
1. 59
0.01
8. 70
6
-
239.21
22.88
7. 10
CO
m
o
18.89
0. 1 3
1.19
0.01
4.90
7
1,2
204.41
8.48
5.87
0. 14
15.71
0.05
0.99
0.00
-13.50
8
1,2,3
186.31
5.91
5. 33
0. 10
14. 28
0. 03
0.90
0. 00
-13.00
9
1,2,3,4
169.25
5.85
4.87
o
d
13.01
0.03
0.81
0.00
-10.10
10
1,2,3,5
176.92
3. 79
5. 03
0. 06
1 3. 50
0. 02
0.85
0. 00
1
-O
o
11 1,2,3,6 126. 73 5.91 3.66 0. 10 9.81 0.03 0.62 0.00 -8.10
12 1,2,3,4,5 169.25 5.85 4.87 0.10 13.01 0.03 0.81 0.00 -1.40
13 1,2,3,4,6 121.59 5.85 3. 51 0. 10 9.42 0.03 0.59 0.00 -5.20
14 1,2,3,5,6 117. 34 3.76 3. 36 0.06 9.02 0.02 0.57 0.00 0.60
15 1,2,3.4,5,6 112. 19 3.70 3.22 0.06 8.64 0.02 0. 54 0.00 3.50
-------
assumed. The question is whether or not there is another option that gives
enough reduction in cancer to be worth the additional cost as compared with
this option.
To illustrate the process of selecting the best option, we night
assess the probabilities in the dose response model as follows:
p^ = 0.2 (20% chance that surface area is the appropriate basis
for extrapolation and 80% chance that body weight is the
approriate basis for extrapolation)
~ 0*2 (20% chance that the mouse is appropriate as a model for
humans and 80% chance that the less sensitive rat is appropriate)
p^ = 0.2 (20% chance that the dose response relation is linear and
80% chance that it is nonlinear [quadratic])
The probabilities of each dose response combination are then computed to be
Dose Response
Combination Probability
1 0.008
2 0.032
3 0.032
4 0.128
5 0.032
6 0.128
7 0.128
8 0.512
We have also assumed a value of one million dollars per cancer case. Then
using (7.3), we compute the expected annual cost and display it in Table
7-3. We find that option 7, with a net savings of $10.75 million, is found
to be the least cost option, including both control costs and cancer costs
valued at one million dollars per case. Note that option 8, which includes
option 7 plus the installation of carbon adsorption units in commercial dry
cleaners, is very close to option 7 in total cost. Increasing the value
assigned per case of cancer by about 50 percent would ca,use the least-cost
option to switch to option 8. The sensitivity of the optimal option to
these values is explored in the next subsection.
7-7
158-
-------
Table 7-3
SELECTION OF OPTIMAL DECISION
Control
Opt i or.
Expected Number
of Cancer Cases
per Year
Assumed Cost
per Case
($ million)
Annual
Control Cost
($ million)
Expected
Annual Cost
($ million)
Base Case
4.99
1.0
0.0
4.99
I
3.61
1.0
-1 1.3
-7.69
2
4.13
1.0
-2.2
1.93
3
¦C-
o->
00
1.0
-3.3
1.08
4
4.37
1.0
0.0
4.67
5
4.48
1.0
8.7
13.18
6
3.70
1.0
4.9
8.60
7
2.75
1.0
-13.5
-10.75*
8
2.44
1.0
-13.0
-10.56
9
2.23
1.0
-10.1
-7.87
10
2.25
1.0
-4.3
-2.05
11
1.73
1.0
-8.1
-6.37
12
2.23
1.0
-1.4
0.83
13
1.67
1.0
-5.2
-3.53
14
1.54
1.0
0.6
2.14
15
1.47
1.0
3.5
4.97
*Least cost alternative in this example.
SENSITIVITY ANALYSIS
The optimal control option depends on the assumed cancer cost and the
probabilities assigned to the dose response model. With the probability
assignments as made in the previous subsection, the optimal decision de-
pends on cancer cost as shown in Figure 7-2. For any value less than $1.61
million, option 7 is the least cost. For values between $1.61 and $6.90
million, the least cost option is option 8, and for values over $6.90
million up to at least $20 million, option II is optimal. Recall that
option 11 consists of option 8 in addition to moving all coin-operated dry
7-8
159
-------
15 r
10
- 5
-10
-15
\
Option 7
Option 8
1.61
Option 11
6.90
10
COST PER CANCER CASE
(Millions of Dollars)
Figure 7-2. Sensitivity Analysis on Cost Per Cancer Case (p1 = p2 = P3 = 0.2)
7-9
160
-------
rooms separate from coin-operated laundries. Option 11 would cost about
$4.9 million more annually than would option 8. However, the value of
reducing exposure to coin-operated laundry users is worthwhile with a high
cost per expected cancer case.
The sensitivity of the optimal decision to changes in probability
assessments is shown in Table 7-4. Here, the cancer cost is again assumed
to be one million dollars, though the probabilities are varied. For a
probability assignment of p-^ " P2 ^ P3 = 0.33, the optimal option switches
from option 7 to option 8. For the probability assignment of p^ = p? =
= 0.5, the optimal option becomes option 11. Clearly, if higher probabil-
ities are placed on dose response models that give higher levels of cancer
incidence, nore controls become attractive.
7-10
161
«
-------
Table 7-4
SENSITIVITY ANALYSIS OF PROBABILITY ASSIGNMENTS
Expected Annual Cose of Option
(aiillions of dollars)
Control Option (p^=p^=p^=0.2) (p^=p0=p^=0.33) (p{=p?=p3=0.5)
;e Case
4.99
17.20
51.23
1
-7.69
1.58
28.02
2
1.93
11.83
39. 14
3
1.08
12.03
42.88
4
4.67
15.22
44.42
5
13.18
24.48
56.47
6
8.60
17.32
41.12
7
-10.75*
-3.79
15.96
8
-10.56
-4.29*
13.61
9
-7.87
-2.15
14.14
10
-2.05
3.82
20.72
11
-6.37
-2.02
10.26*
12
0.83
6 .55
22.84
13
-3.53
0.65
12.44
14
2.14
6.09
17.37
15
4.97
8.76
19.54
*Least cost alternatives for each set of probability assignments.
7-11
162
-------
Section 8
VALUE OF FURTHER INFORMATION
In addition to having the choice of control option, the dry cleaning
industry has the option of gathering more information. Under TSCA, EPA can
require that industry obtain better information through such means as
animal tests. Decision analysis can provide guidance as to the best time to
obtain more information and can indicate which type of information would be
most valuable. The value of information comes from its effect on
decisions. In the analysis of PCE in dry cleaning, if we knew which dose
response model were correct, in the sense of giving the best estimate of
the true cancer risk, we might find a superior option to the one previously
selected. The difference between the cost of this superior option and the
expected cost of the best option without the information is the measure of
the value of that information. However, before we find out which dose
response model is correct, we do not know which decision would be chosen on
the basis of this new information. To account for this difficulty, we can
calculate the expected value of obtaining perfect information using the
previously assessed probabilities of each dose response model.
The expected value of perfect information (EVPI) found in this way
will be an upper limit to the value of further information gathering. Real
experiments, such as using larger groups of animals at lower PCE doses, do
not give perfect information. Given a quantification of the possible re-
sults of real tests, one can calculate the expected value of information
provided by any test [48,49]. Those calculations, which are only slightly
more complicated than the ones below, are left for later analysis.
The expected value of perfect information on the entire dose response
model is calculated using the decision tree in Figure 8-1. Here, the un-
certainties are shown to be resolved before the decision is made. The
8-1
163
-------
Surface Area
Body Weight
Mouse
Rat
Linear
Quadratic
Do Nothing
Option 1
Values
of
Outcomes
Option 15
Scaling
Method
Species
Type
Extrapolation
Model
Decision
Figure 8-1. Decision Tree for Calculating Value of Perfect
Information on All Dose Response Uncertainties
-------
expected value for this decision problem is calculated in the usual way
with the probabilities on each of the eight possible branches.
Mathematically, the expected value of perfect information is simply
EVPI - ECwo - ECW (8.1)
where ECwo is the expected cost of the optinal control option without
any additional information as calculated in (7.3) and ECW is the expected
cost of the eventual optimal control option with perfect information.
EC^ is computed from
ECw - £ prob^ x cost^ (8.2)
i
where probi is the probability that dose response case i is true and
cost^ is the cost of the optimal control option given the knowledge that
case i is true.
The data required to calculate the EVPI are in Table 8-1. The table
shows the probability of the dose response model, the optimal decision
given that model, and the expected annual cost of that optimal choice.
Using the three sets of probabilities from the sensitivity analysis of
Section 7-3 and a cost per cancer case of one million dollars, we calculate
the corresponding expected values of perfect information, as shown in Table
8-2. For the base case probability assignments, the EVPI is SO.7 million
per year. With the other probability assignments, it varies from $2.4
million to about $4 million per year. From these calculations, one may
conclude that under reasonable probability assignments and cost per cancer
case, it would be worthwhile to spend up to $4 million per year on tests to
resolve all the dose response uncertainties.
In addition to considering all the dose response uncertainties
together, we can examine the expected value of perfect information on each
one separately. These values of EVPI are calculated using the decision
trees of Figures 8-2 through 8-4. In each of the figures, a single com-
ponent of the dose response model is resolved before the decision is made,
and the other two are resolved after the decision. The decisions are thus
made with the knowledge of the first component but not of the others. The
expected value of perfect information is then calculated in the same way as
in Tables 8-1 and 8-2. The results of these calculations are in Table 8-3.
165
8-3
-------
Surface Area
Body Weight
Scaling
Method
Do Nothing
Option 1
Option 15
Decision
Mouse
Rat
Species
Type
Linear
Quadratic
Values
of
Outcomes
Extrapolation
Model
Figure 8-2. Decision Tree for Expected Value of
Perfect Information on Scaling Method
-------
Mouse
Species
Type
Do Nothing
Option 1
Option 15
Decision
Surface Area
Body Weight
Scaling
Method
Linear
Values
of
Outcomes
Quadratic
Extrapolation
Model
Figure 8-3. Decision Tree for Expected Value of
Perfect Information on Species Type
-------
Linear
Quadratic
Extrapolation
Model
Do Nothing
Option 1
Option 15
Decision
Surface Area
Body Weight
Scaling
Method
Mouse
Rat
Species
Typu
Values
of
Outcomes
Figure 8-4. Decision Tree for Expected Value of
Perfect Information on Extrapolation Model
-------
These values vary from $0.15 million per year for Che scaling method
component under the base case probability assignments to S2.46 million per
year for the extrapolation r.odel component under the probability assignment
pl = P2 = p3 =
Table
DATA FOR CALCULATING EXPECTED
Dose Response
Combination Probability
1
P1XP2XP3
2
Pixp2x(1—p 3)
3
Pix(l-p2)xp3
4
p^x(l-p2)x(1—P3)
5
(l-pl)xp2xp3
6
(1-p; )xp2X(1 —P3)
7
(1-pi)x(l-p2)xp3
8
(1—pj_)x( 1—P2)x( 1—P3>
8-1
VALUE OF PERFECT INFORMATION
Expected Annual
Optimal Cost of Option
Options (S million/year)
15 115.69
8 -7.09
8 -7.67
7 -13.36
8 1.28
7 -13.45
7 -12.51
7 -13.50
Table 8-2
EXPECTED VALUE OF PERFECT INFORMATION ON ALL DOSE RESPONSE UNCERTAINTIES
Expected Cost Expected Cost Expected Value
Without Perfect With Perfect of Perfect
Probability Information Information Information
Assignment ($ oillion/yr) ($ million/vr) ($ rcillion/yr)
pi=p2=p330.2 -10.75 —11.45 0.70
Pl=p2=P3=0.33 -4.29 -6.71 2.42
Pl=P2=P3=0.5 10.26 6.17 4.09
8-7
169
-------
Table 8-3
EXPECTED VALUE OF PERFECT INFORMATION ON EACH COMPONENT
OF DOSE RESPONSE UNCERTAINTY
Component of Dose Response Uncertainty
Extrapola tion
Probability Scaling Method Species Type Model
Assignment ($ million/yr) ($ pjllion/yr) ($ nillion/yr)
Pl=P2=P3=0.2 0.15 C.17 0.12
Pj=P2=P3=0.33 0.86 1.13 1.15
Pl=P2=P3=0.5 1.92 2.38 2.46
8-8
170
-------
Section 9
INSIGHTS FROM THE PERCRLOROETHYLENE CASE STUDY
INSIGHTS REGARDING PERCHLOROETHYLENE
In this case study, we have employed the methods of decision analysis
to evaluate the risk to human health posed by the use of perchloroethylene
in the dry cleaning industry. The evaluation includes an explicit charac-
terization of uncertainty in the relation between the level of PCS exposure
and the number of cases of cancer that night be induced. Different assump-
tions in extrapolation from the meager animal test data now available lead
to different projections for human cancer incidence, ranging from about 350
cases per year to about one case every hundred years.
While it may be questioned [2] whether current information supports a
finding that PCE should be considered as a human carcinogen for regulatory
purposes, on the basis of its acute toxicity alone, it is clear that PCE is
clearly a dangerous chemical that can inflict serious damage to human
health. Some options for reducing PCE exposure are desirable purely on
economic grounds: The savings in costs of PCE more than offsets the cost of
better maintenance practices. It is in the interest of the owners and the
workers of a dry cleaning establishment and in the interest of the public
to implement better maintenance practices throughout the industry. This
would result in reductions in PCE exposure by about 40 percent and would
reduce operating costs. Additional reductions can be achieved by install-
ing a carbon adsorption "sniffer" unit or using dry-to-dry machines. Such
actions night seem prudent to commercial dry cleaning plant owners as a
measure to protect workers (in the dry cleaning industry, owners and
workers are often the same individuals). Although dry-to-dry machines and
adsorption units provide additional health protection for workers and
customers, our analysis does not indicate that the health benefits clearly
offset the additional costs.
9-1
171
-------
The wide range of uncertainty in the potential incidence of cancer
from PCE gives a high value to information that would resolve this uncer-
tainty. The calculation in Section 8 shows that resolving the uncertainty
could be worth from one to four million dollars per year, depending on the
probabilities assessed on the factors in the dose response extrapolation
model. This result justifies additional epidemiological studies and an
animal testing program, such as the large-scale bioassays now underway at
NCI, to determine the response of several strains of mice and rats to a
range of PCE doses. PCE is a chemical whose human exposure is widespread.
Given even a very small chance that PCE could induce cancer in humans, it
becomes important to discover the carcinogenic potential of PCE. On the
other hand, implenentation of even a relatively inexpensive control option
such as carbon adsorption units implies costs on the order of millions of
dollars per year.
INSIGHTS REGARDING THE METHODOLOGY
The determination of whether the risk posed by a toxic chemical is
unreasonable generally involves assessment of complex uncertainties.
Although we support the use of quantitative estimates, the use of single
number estimates such as those put forward by CAG is potentially mislead-
ing, for such estimates do not communicate any sense of the uncertainties
to nontechnical decision makers. The use of confidence intervals derived
from bioassay sample size is particularly misleading, since this source of
uncertainty in projecting dose response relationships will often be much
less than the uncertainty attending the basis for extrapolating from small
animals to humans, the selection of an appropriate animal model, or the
mathematical form of the dose response relationship.
Suppressing the uncertainty by invoking prudent assumptions is one
basis for regulatory decisions. However, when widely desired products or
services are involved and control will require large expenditures, judg-
ments of prudence by scientists may be unacceptable as a means of determin-
ing public policy. A superior approach in our opinion is to p.ortray the
uncertainty explicitly and incorporate it into the basis for decision. We
9-2
172
-------
have Illustrated this approach for PCE by calculating cancer incidence not
just for one set of assumptions as CAG has done, but for eight alternative
sets of assumptions. The methods of decision analysis involve assessment
of probabilities over these alternative cases, permitting the calculation
of a probability distribution on cancer incidence. By using the methods of
cost-benefit analysis, which includes a monetary value on avoiding a case
of cancer, one can then determine the best action in the face of uncer-
tainty. Moreover, the decision analysis approach allows the calculation of
what it would be worth to resolve the uncertainty. This calculation can be
extremely useful for establishing priorities for costly information-
gathering activities such as large-scale bioassays.
A frequent criticism of cost-benefit analysis is that distributional
effects are suppressed. This case study has included calculation of the
health impacts on various affected groups: workers, dry cleaning service
users, and persons who live or work in the surrounding community. While we
have chosen to value a case of cancer avoided as equal in each group, this
assumption can easily be relaxed. Our calculations for PCE show the health
impact as falling primarily on the workers and secondarily on the users of
coin-operated dry cleaners. The risk to the neighboring public is much
smaller by comparison.
The analysis of PCE that we have presented should be viewed as
illustrative of a methodology. If such analysis is to be used as a basis
for public policy decisions, it should receive review and careful scrutiny
from the scientific community through such mechanisms as the EPA Science
Advisory Board. Probabilities such as those we have assessed on factors in
the dose response relationship should represent a judgment agreed upon by
qualified scientists, and the basis for such a judgment should be docu-
mented and be available for review.
The decision analysis approach presented in this case study is not
necessarily elaborate computationally or expensive to carry out. The larg-
est expense is in assembling the information. We expect that our analysis
for PCE could easily be adapted for trichloroethylene (TCE), toluene, or
other solvents of concern because of their potential carcinogenicity. We
9-3
173
-------
would expect that the conclusions for such analysis would he similar: If
there is widespread human exposure and even a snail probability that the
solvent is a carcinogen, information gathering by means such as lar^e-scale
bioassays will have large expected benefits that more than offset the
costs.
The analysis presented in this case study was not representative of
many substances that are candidates for regulation under TSCA. Human
exposure to PCE is well documented. Relatively good data and predictive
models for exposure are available. Such information will not be available
for new chemicals, especially where complex environmental pathways to
people are involved. PCE does not bioaccumulate significantly, whereas
many toxic chemicals do. For such chemicals, assessment of uncertainty on
human exposure might be as complex as or more complex than the assessment
of uncertainty we have presented on the health inpacts of PCE.
The PCE case study was also atypical in that it did not involve com-
plex control options or analysis to determine the economic impact of con-
trol options. PCE is used directly to provide a consumer service, and
control option costs are easily translated into their effect on the cost of
dry cleaning. The control options for PCE involved equipment maintenance,
purchase of new equipment of a standard kind, and repositioning of equip-
ment. In contrast, many chemicals are used as intermediates in complex
manufacturing processes. For these chemicals, one must examine the econom-
ics of substitution and the impact of higher prices in a variety of end-use
markets. The analytical methods for doing so will be demonstrated in the
case study on phthalate esters presented in Part III of this report.
174
-------
Section 10
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10-1
175
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[32] R. D. Stewart et al. , Experimental Human Exposure to Tetrachloro-
ethylene, Archives of Environmental Health 20 (1970): 224-229.
[33] M. Ikeda, "Metabolism of Trichlorethylene and Tetrachloroethylene in
Hunan Subjects," Environmental Health Perspectives 21 (1977):
239-245.
[34 ] Criteria for a Recommended Standard...Occupational Exposure to
Tetrachloroethylene (Perchloroethylene), U.S. Department of Health,
Education, and Welfare, Public Health Service, Center for Disease
Control, Mational Institute for Occupational Safety and Health
(1976).
10-3
177
-------
[35] National Cancer Institute, "Bioassay of Tetrachloroethylene for
Possible Carcinogenicity," DHEW Publication No. 77-813, U.S.
Department of Health, Education, and Welfare, Public Health Service,
National Institute of Health (1977).
[36] L. W. Rampy et al., "Results of Long-Term Inhalation Toxicity
Studies on Rats of 1,1,1-Trichloroethane and Perchloroethylene
Formulations," Proceedings of the First International Congress on
Toxicology, ed. Plaa and Duncan, (New York: Academic Press, 1978).
[37] A. Blair, P. Drople, ar.d D. Grarrman, "Causes of Death Among Laundry
ana Dry Cleaning Workers," American Journal of Public Health 69,
no. 5, (1979): 508-511.
[38] R. S. Lin and I. I. Kessler, "A Multifactorial Model for Pancreatic
Cancer in Man," Journal of the American Medical Association 245,
no. 2, (1981): 147-152.
[39] W. Magard, "In Vitro Bioassay of Chlorinated Hydrocarbon Solvents,"
unpublished proprietary document for Detrex Chemical Industries,
Battelle Laboratories, 1978 (cited in [1]).
[40] Jessie M. Norris, "Assessment of Carcinogenic Potential of
Perchloroethylene for Man," paper presented at the 1980 Annual
Meeting of the Toxicology Forum, Aspen, Colorado, July 28-August 1,
1980, The Dow Chemical Company, Midland, Michigan, 1980.
[41] "Tetrachloroethylene," IARC Monographs on the Evaluation of the
Carcinogenic Rise of Chemicals to Humans, International Agency for
Research on Cancer, Lyon, Volume 20, (1979): 491-514.
[42] Environmental Studies Board, National Research Council, National
Academy of Sciences, "Carcinogenesis in Man and Laboratory Animals,"
Pest Control: An Assessment of Present and Alternative Technologies,
Vol. 1., National Academy of Sciences, Washington, D.C. (1975):
66-82.
[43] Roy E. Albert et al., "The Carcinogen Assessment Group's Method for
Determining the Unit Risk Estimate for Air Pollutants" (1980).
[44] N. Mantel and M. A. Schneiderman, "Estimating 'Safe' Levels, A
Hazardous Undertaking," Cancer Research 35, (1975): 1379-86.
[45] The National Research Council, Regulating Pesticides (Washington,
D.C.: National Academy of Sciences, 1980).
[46] Food Safety Council, Toward a Better Way to Make Food Safety
Decisions (Washington^ D.C.: Food Safety Council., 1980).
10-4
ITS
-------
[47 J H. A. Guess and K. Crump, "Can We Use Animal Data to Estiiate 'Safe'
Doses for Chemical Carcinogens?", Environmental Health: Quantitative
Methods. Proceedings of a conference sponsored by SLAM Institute
for Mathematics and Society, Alta, Utah, July 5-9, 1976.
A. Whittenore, ed., SIAM, Philadelphia, 1977, pp. 13-30.
[43] H. Raiffa, Decisions Analysis, Introductory Lectures on Choices
Under Uncertainty (Reading, Mass.: Addison-Wesley, 1970).
[49] C. Holloway, Decision Making Under Uncertainty: Models and Choices
(Englewood Cliffs, N.J.: Prentice-Hall, 1979).
[50] D. W. Boyd, D. Cohan, and S. G. Regulinski, Abbreviated RDSD Progran
Portfolio Selection Workbook. P^eport prepared by Decision Focus
Incorporated for U.S. Department of Energy (1979).
10-5
179
-------
Appendix A
CALCULATING AM ANNUAL CAPITAL CHARC'i FOR AN INVESTMENT
To compare annual costs with capital expenditures, either the annual
costs need to be converted to net present value or the capital investment
needs to he converted to an annual equivalent. The approach taken in this
analysis is to convert to annual equivalent. This is done by calculating a
capital charge rate (CCR) appropriate for the dry cleaning industry. The
annual equivalent of a capital investment is, then:
Annual Equivalent = CCR x Capital Investment of Capital Cost (A.I)
The financial parameters used to calculate the capital charge rate are
in Table A-I. The following are the basic assumptions:
o Return on equity is 10 percent above the inflation rate,
o Return on debt is 4 percent above inflation,
o Inflation rate is 8 percent,
o Marginal income tax rate is 17 percent,
a Investment tax credt is 10 percent,
o Tax life of investment is 10 years,
o Book life of investment is 15 years.
o Investment is financed by 25 percent equity and 75 percent debt.
o Property tax and insurance are 2 percent of the value of the
investment.
Using these assumptions, the capital charge rate is computed in Table A-2
with the following result:
CCR = 0.097 (A.2)
ISO
A-l
-------
This value is used in the analysis to convert all types of investments
annual equivalents. For a discussion of the equations used in Table A
see Appendix B of [50].
Table A-l
DATA FOR CALCULATING THE CAPITAL CHARGE RATE
For
the market being considered enter the
appropriate values
u
o
U-t
a)
return on common equity
rE =
0.18
b)
return to preferred equity
r? =
c)
return to debt
rD =
0.12
d)
fraction financed by common equity
fE =
0.25
e)
fraction financed by preferred equity
fP =
f)
fraction financed by debt
f D =
0.75
g)
inflation rate
inf =
0.08
h)
income tax rate
TAX =
0.17
2. Conpute
a) Current-Dollar After-Tax Cost of Capital (r)
r = r„f + r_f + rf (1 - TAX) = 0.120
EE P P D D
b) Constant-Dollar After-Tax Cost of Capital (r0)
r - y " ln* - 0.037;
o 1 + inf
131
A-2
-------
Table A-l (continued)
3. Eater the appropriate values for
a) current-dollar after-tax cost of capital r = 0.11
b) constant-dollar after-tax cost of capital r = 0.03
o
c) inflation rate inf = 0.08
d) income tax rate TAX = 0.17
e) investment tax credit rate ITC = 0.10
f) AFUDC rate (if needed) a =
A. From the process description, enter the appropriate value for
a) construction lead time CL ® 0;
b) property tax and insurance PTI = 0.02
c) tax life TL = 10;
d) book life BL = 15;
A-3
182
-------
Table A-2
CALCULATION OF'THE CAPITAL CHARGE RATS
1. Compute the present value factors:
a) PV( r ,BL) =» —
o r
1 -
1
1+r
BI,
b) PV(r,BL) = - 11 - BL
r 1+r
c) PV(r.TL) =7(1- ^ TL
d) DCinf , CL) =
1
1+inf
CL/3
= 11.36;
= 6.81;
= 5.65;
= 1.0;
Compute appropriate construction cost financing and tax factors:
Regulated Utility Unregulated Industry
a)
1+a
1+inf
CL/3
1+r
1+inf
CL/3
v ITC x D(inf,CL)
:) ns
ITC
1+r
1+inf
CL/3
= 1.0;
, \ TAX x D(inf,CL) nu, . ., . . n nac
b) — '¦ PV(r,TL) same as utility = 0.096;
X Lj
= 0.10;
d) PTI(1-TAX)PV(r,BL)D(inf,CL) same as utility = 0.113;
3. Compute Effective Capital Cost Factor:
(line 2a) - (line 2b) - (line 2c) + (line 2d) = ECCF = 0.917;
4. Compute Capital Charge Rate:
1 1
CCR =
1-TAX PV(r ,BL)
0
ECCF
0.097;
133
A-4
-------
Appendix 3
DETAILED CALCULATIONS OF CONTROL COSTS AND EXPOSURE REDUCTION
B-l
134
-------
CONTROL OPTION COMPUTATIONS
Option Number Description
1 Implement better maintenance and housekeeping in all
commercial and industrial plants.
A. EMISSIONS
Item Commercial Industrial Coin-Op
Base emission rate 0.099 0.056 0.136
Rate after option 0.059 0.033 0.136
Fraction 0.60 0.67 l.C
kg of cleaning 900 x 10^ 110 x 10^ 400 x 10^
PCF. consumption 50 x 10^ 6 x 10^ 54 x 10^
Total Emission Fraction 110/152 = 0.72
Multipliers for Service Users
Commercial Dry Cleaning 0.56
Coin-Operated Dry Cleaning 1.00
Coin-Operated Laundry 1.00
B. COSTS
Item
Capital
Ma Lntenance
PCE credit
Total
Commercial
0
525
-1200
- 675
Industrial
0
2850
-4340
-1490
Coin-Op
0
0
0
Number of plants
Total dollars
16,000
-10.8 x 10(
350
-0.5 x 106
11,000
0
Total Cost = -11.3 million dollars per year
XS5
B-2
-------
Option Analysis
Option Nuxber Description.
2 Implement better maintenance and housekeeping in all
coin-operated dry cleaners.
A. EMISSIONS
Item Commercial Industrial CoIn-Op
Base emission rate 0.099 0.056 0.136
Rate after option 0.099 0.056 0.075
Fraction 1.0 1.0 C.55
kg of cleaning 900 x 10® 160 x 10° 400 x 10°
PCE consumption 89 x 1C6 9 x 106 30 x L06
Total Emission Fraction 128/152 = 0.84
Multipliers for Service Users
Commercial Dry Cleaning 1.00
Coin-Operated Dry Cleaning 0.59
Coin-Operated Laundry 0.55
B. COSTS
Item
Capital
Maintenance
PCE credit
Total
Connercial
0
0
_0_
0
Industrial
0
0
0
Coin-Op
0
975
-H7_7
- 202
Number of plants
Total dollars
.16, 000
0
350
0
11,000
-2.2 x 106
Total Cost
million dollars per year
B-3
156
-------
Option Auk lysis
Option Number Description
3 Install carbon adsorber units in all comrqercial and
industrial plants that do not already have them.
A. EMISSIONS
Item
Commercial
Industrial
Coin-Op
Base emission rate
0.099
0.056
0. 136
Rate after option
0. 030
0.054
0. 136
Fraction
0.31
0.96
1.0
kg of cleaning
900 x 10fi
160 x 106
400 x 106
PCE consumption
72 >: i o6
9 x 106
54 x 106
Total
Emission Fraction
135/152 =
0.39
Multipliers for Service Users
Commercial Dry Cleaning 0.89
Coin-Operated Dry Cleaning I.00
Coin-Operated Laundry 1.00
B. COSTS
Item. Commerc ial* Industrial** Coin-Op
Capital 243 88 0
Maintenance 125 46 0
PCE credit -570 -480 _0_
Total -202 -346 0
Number of plants 16,000 350 11,000
Total dollars -3.2 x 10^ -0.1 x 10^
Total Cost = -3.3 million dollars per year
*Average cost per plant, 50% of plants have expense.
**Average cost per plant, 10% of plants have expense.
157
B-4
-------
Option Analysis
Option Number
Description
Install carbon adsorber units in all coin-operated
plants that do not already have them.
Item
Base emission rate
Rate after option
Fraction
kg of cleaning
PCE consumption
A. EMISSION'S
Commercial Industrial
0. 099
0.C99
1.0
900 x 106
89 x 106
0.056
0.056
1.0
160 x 106
9 x 106
Coin-Op
0. 136
0. 109
0.67
400 x 106
ii X 10^
Total Emission Fraction 143/152 =
Multipliers for Service Users
Commercial Dry Cleaning
Coin-Operated Dry Cleaning
Coin-Operated Laundry
0.93
1.00
0. 74
0.67
B. COSTS
Item
Capital
Ma intenance
PCS credit
Total
Commercial
0
0
0
Industrial
0
0
0
Co in-Op
354
183
-512
24
Number of plants
Total dollars
16,000
0
350
0
11,000
0.3 x
10c
Total Cost = 0.3 million dollars per.year
B-5
1SS
-------
Option Analysis
Option Number Description
5 Replace all transfer type cleaning machines in
commercial plants vrith dry-to-crv units as the
machines normally need to be replaced.
A. EMISSIONS
Item Commercial Industrial Co jr.-Op
Base e.x.ission rate 0.099 0.056 0. 13b
Rate after option 0.099 0.056 0.136
Fraction I.0 1.0 1.0
kg of cleaning 900 x 106 160 x I06 400 x 106
PCE consumption 89 x 10^ 9 x lO^5 54 x 10^
Total Emission Fraction 152/152 = 1.0
Multiply exposure to commercial machine operators by 0.6b
Multipliers for Service Users
Commercial Dry Cleaning 1.00
Coin-Operated Dry Cleaning 1.00
Coin-Operated Laundry 1.00
B. COSTS
Item Commercial Industrial Coin-Op
Capital 54 5 0 0
Maintenance 0 0 0
PCE credit 0 __0_ 0
Total 545 0 0
Total dollars 8.7 x 10^ 0 0
Total Cost = 8.7 million dollars per year
B-6
1.59
-------
Option Analysis
Option Number Description
6 Move all coin-operated dry cleaning machines to rooms
separate from laundry.
A. EMISSIONS
Item Commercial Industrial Coin-Op
Base emission rate 0.099 0.056 0.136
Rate after option 0.J99 0.056 0.136
Fraction 1.0 1.0 1.0
kg of cleaning 900 x 106 160 x 106 400 x 106
PCE consumption 39 x 10^ 9 x 10^ 54 x 10^
Total Emission Fraction 152/152 = 1.0
Multipliers for Service Users
Commercial Dry Cleaning 1.00
Coin-Operated Dry Cleaning 1,00
Coin-Operated Laundry 0.10
B. COSTS
Item Commercial Industrial Coin-Op
Capital 0 0 445
Maintenance 0 0 0
PCEcredit 0 0 0
Total 0 0 445
Number of plants 16,000 350 11,000
Total dollars 0 0 4.9 x 10^
Total Cost = 4.9 million dollars per year
130
B-7
-------
Oct ion Analysis
Option Number
Description
Options 1 and Z.
A. EMISSIONS
Item
Base emission rate
Rate after option
Fraction
kg of cleaning
PCE consumption
Commercial
0.099
0.059
0.60
900 x 106
50 x 106
Industrial
0.056
0.03S
0.67
160 x 106
6 x 106
Coir.-Op
0. 136
0.075
0. 55
400 x !06
30 x 106
Total Emission Fraction
Multipliers for Service Users
Commercial Dry Cleaning
Coin-Operated Dry Cleaning
Coin-Operated Laundry
36/152 = 0.57
0. 56
0.59
0. 55
B. COSTS
Item
Capi tal
Ma ir. tenance
PCE credit
Total
Commercial
0
52 5
-1200
Industrial
0
2350
-4340
- 675
•1490
Coin-Op
0
975
-1177
- 202
Number of plants
Total dollars
16,000
-10.8 x 106
350
-0.5 x 106
11,000
-2.2 x 106
Total Cost = -13.5 million dollars per year
131
B-8
-------
Option Analysis
Option Number
Description
Options 1, 2, and 3.
Item
Base emission rate
Rate after option
Fraction
kg of cleaning
PCE consumption
A. EMISSIONS
Commercial Industrial
0.099
0.043
0.49
900 x 106
43 x 106
0.056
0.036
C. 64
160 x !06
6 x 106
Coin-Op
0. 136
0.075
0. 55
400 x 106
30 x 106
Total Emission Fraction
Multipliers for Service Users
Commercial Dry Cleaning
Coin-Operated Dry Cleaning
Co'n-Operated Laundry
79/15:
0.52
0.49
0.59
0. 55
B. COSTS
Item
Capital
Maintenance
PCE credit
Total
Commercial
243
650
-1530
- 637
Industrial
88
2396
-4800
-1816
Coin-Op
0
975
-1177
- 202
Number of plants
Total dollars
16,000
-10.2 x 106
350
-0.6 x 106
11,000
-2.2 x 1
Total Cost = -13.0 million dollars per year
132
B-9
-------
Option Analysis
Option Number
9
Description
Options 1, 2, 3, and 4.
[ten
Base emission rate
Rate after option
Fraction
kg of cleaning
PCE consuaption
A. EMISSIONS
Commercial Industrial
0.099
0.048
0. 49
900 x 106
43 x 10°
0.056
0.036
0.64
150 x 106
6 x 106
Coin-Op
0. 136
0.060
0. 44
400 x 106
24 x 106
Total Emission Fraction
Multipliers for Service Users
Commercial Dry Cleaning
Coin-Operated Dry Cleaning
Coin-Operated Laundry
73/151
0.48
0. 49
0.50
0. 44
B. COSTS
Item Commercial Industrial Coin-Op
Capital 243 88 354
Maintenance 650 2896 1158
PCE credit -1530 -4800 -1444
Total - 637 -1316 68
Number of plants 16,000 350 11,000
Total dollars -10.2 x 106 -0.6 x 106 0.7 x 1
Total Cost = -10.1 million dollars per year
B-10
193
-------
Option Analysis
Option Number
10
Description
Options 1, 2, 3, and 5.
A. liMISS LOKS
Item
Base emission rate
Rate after option
Frac t ion
kg of cleaning
PCE consunption
Conraercia1
0.099
0.043
0. 49
900 x L0b
43 x 106
Industrial
0.056
0.036
0.64
160 x I06
6 x 106
Coin-Op
0. 136
0.075
0. 55
400 x 106
30 x 106
Total Emission Fraction
79/152
0.5i
Multiply exposure to commercial machine operators by 0.49 x 0.66 =
0.32
Multipliers for Service Users
Commercial Dry Cleaning
Coin-Operated Dry Cleaning
Coin-Operated Laundry
0.49
0.59
0. 55
B. COSTS
Item
Capi tal
Ma i ntenance
PCE credit
Total
Commercial
788
650
-1530
- 92
Indus trial
88
2896
-4800
-1316
Coin-Op
0
975
-1177
- 202
Number of plants
Total dollars
16,000
-1.5 x 10£
350
-0.6 x 106
11,000
-2.2 x 106
Total Cost = -4.3 million dollars per year
194
B-11
-------
Option Analysis
Option Number
11
Description
Options 1, 2, 3, and 6.
iMI.SSIONS
Item
Base emission rate
Rate after option
F raction
kg of cleaning
PCE consumption
Commercial
0.099
0.043
0. 69
900 x ie6
43 x 106
Industrial
0.056
0.036
0. 64
160 x 106
6 x 106
Coin-Op
0. 136
0.075
0. 55
~00 x 106
30 x iO6
Total Emission Fraction
Multipliers for Service Users
Commercial Dry Cleaning
Coin-Operated Dry Cleaning
Coin-Operated Laundry
79/152
0.52
0.49
0. 59
0. 055
B. COSTS
Item Commercial Industrial Coin-Op
Capital 243 88 445
Maintenance 650 2896 975
PCE credit -1530 -4S00 -1177
Total - 637 -1S16 243
Number of plants 16,000 350 11,000
Total dollars -10.2 x 10^ -0.6 x 10^ 2.7 x 10^
Total Cost = -8.1 million dollars per year
B-12
195
-------
Option Analysis
Option Number Description
12 Options 1, 2, 3, 4, ar.d 5.
I tem
Base emission rate
Rate after option
Fraction
kg of cleaning
PCE consumption
A. EMISSION'S
Commercial Industrial
0.099
0.043
0. 49
900 x 106
43 x 106
0.056
0.036
0. 64
160 x 106
6 x 106
Coin-Op
0. 136
0.060
0.44
400 x 106
24 x 106
Total Emission Fraction
Multipliers for Service Users
Commercial Dry Cleaning
Coin-Operated Dry Cleaning
Coin-Operated Laundry
73/152
0.49
0.50
0. 44
B. COSTS
Item
Ca p 11 a 1
Maintenance
PCE credit
Total
Commercial
788
650
~l530 _
- 92
Industrial
88
2896
-4800
-1816
Coin-Op
354
1158
-1444
68
Number of plants
Total dollars
16,000
-1.5 x 10e
350
-0.6 x 106
11,000
0.7 x 1.06
Total Cost = -1.4 million dollars per year
B-13
1SS
-------
Ontion Analvsi:
Option Number
13
Description
Options 1, 2, 3, 4, and 6.
Item
Base eraission rate
Rate after option
F rac t i on
kg of cleaning
PCE consumption
A. EMISSIONS
Commercial Industrial
0.099
0.043
0.49
900 x 106
4 3 x 10&
0.056
0.036
0.64
l&C x 106
6 x 10b
Coin-Op
0. 136
0. 060
0. 44
400 x 106
24 x I06
Total Emission Fraction
Multipliers for Service Users
Commercial Dry Cleaning
Coin-Operated Dry Cleaning
Coin-Ooerated Laundrv
73/15:
0.48
0.49
0. 50
0.044
3. COSTS
Costs
Cap i cal
Ma intenance
PCE credit
Total
Commerc ia1
243
650
-1530
- 63;
Industria1
88
2896
-4800
-1816
Coin-Op
799
1158
-1444
513
Number of plants
Total dollars
16,000
-10.2 x 106
350
-0.6 x 106
11,000
5.6 x 106
Total Cost = -5.2 million dollars per year
B-14
137
-------
Option Analysis
Option Number
Description
14
Opt ions 1, 2, 3, 5, .and 6 ,
Item
Base emission rate
Rate after option
Fract ion
kg of cleaning
PCE consumption
A. EMISSIONS
Commercial Industrial
0.099
0.048
C. 49
900 x 106
43 x 10&
0.056
0.036
C. 64
:60 x iO6
6 x 106
Coin-Op
0. 136
0.075
0. 55
400 x 106
30 x 106
Total Emission Fraction
79/152 = 0.52
Multiply exposure to commercial machine operators by 0.32
Multipliers for Service Users
Commercial Dry Cleaning
Coin-Operated Dry Cleaning
Coin-Operated Laundry
0. 49
0. 59
0.055
B. COSTS
Costs
Capi tal
Ma intenance
PCE credit
Total
Commerc ial
788
650
^1.5J30_
- 92
Industrial
88
2896
-4800
-1816
Coin-Op
445
975
-1177
243
Number of plants
Total dollars
16,000
-1.5 x 1C6
350
-0.6 x 10^
11,000
2.7 x 106
Total Cost = 0.6 million dollars per year
B-15
138
-------
Option Analysis
Option Number
15
Description
Options 1, 2, 3, 4, 5, and 6.
A. EMISSION'S
Item
Base emission rate
Rate after option
Fraction
k.g of cleaning
PCE consumption
Commercial
0.099
0. 043
0. 49
900 x 106
43 x 106
Industrial
0.056
0.036
0. 64
160 x 106
6 x 106
Coin-Op
0. 136
0. 0 b 0
0. 44
400 x 106
24 x 106
Total Emission Fraction
73/152 =
0.
Multiply exposure to commercial machine operators by 0.32
Multipliers for Service I'sers
Commercial Dry Cleaning
Coin-Operated Dry Cleaning
Coin-Operated Laundry
0.49
0.50
0.D44
B. COSTS
Item
Capital
Ma intenance
PCE credit
Total
Commercial
788
650
-l_530
-92
Industrial
88
2896
-48_00_
-1316
Coin-Op
799
1158
-1444
513
Number of plants
Total dollars
16,000
-1.5 x 1C6
350
-0.6 x 10°
11,000
5.6 x
Total Cost
3.5 million dollars per year
133
3-16
-------
Appendix C
CALCULATIONS OF PROBABILITY OF INCIDENCE AND EXPECTED CANCER CASES
BY COKTROL OPTION AND DOSE RESPONSE CASE
The calculations of lifetime cancer incidence in humans resulting
from doses of PCE are outlined in this appendix. The detailed computed
results for cancer incidence by exposure category follow.
ANIMAL DATA
From Chapter 6, the observed cancer incidence in mice and rats are
taken to be
1. Incidence of 0.65 in mice at a lifetime dose of 332 mg/kg/day.
We followed the practice used by CAG ([l],p.24) in considering
the dose of 536 aiiligrams/kilogram/day for five days per week
over 78 weeks as equivalent to an average lifetime dose of
536 x 5/7 x (78/90) = 332 miligrams/kilogram/day.
2. Incidence of 0.05 in rats at a lifetine dose of 253 mg/kg/day.
EXTRAPOLATION BY BODY WEIGHT
In extrapolating from animals to humans by body weight, dosages in
miligrams of PCE per kilogram of body weight are considered equivalent.
Therefore, it is assumed that
1. If the mouse is chosen as the representative species, then a dose
of 332 mg/kg/day would result in a lifetime cancer incidence of
0.65 in humans.
2. If the rat is chosen as the representative species, then a dose
of 253 mg/kg/day would result in a lifetime cancer Incidence of
0.05 in humans.
EXTRAPOLATION BY SURFACE AREA
In extrapolating by surface area, equivalent doses in different sized
animals are adjusted by the cube root of the ratio of body weights as
follows:
200
C-l
-------
1. In extrapolating from mice to humans, the scaling factor is
1/3
a
70 1 13.26
03
A dose of 332 mg/kg/day for mice is considered to be equivalent
to 332/13.26 = 25.0 mg/kg/day for humans.
In extrapolating from rats to humans, the scaling factor is
G&r--
A dose of 253 mg/kg/day for rats is considered to be equivalent
to 253/5.85 = 43.3 rag/kg/day for humans.
EXTRAPOLATION WITH A LINEAR MODEL
The linear model for predicting cancer indidence as a function of
dose level is
P(d) = 1 - exp(-Xd) X > 0 (C.l)
where P(d) is the lifetime probability of cancer occurrence given a dose
level d, and X is a coefficient that must be estimated. The model is
called linear because incidence is proportional to dose at low doses.
Rewriting (C.l), the coefficient A can be found by
-ln(1 - P(d) ) (C.2)
d
Using (C.2), X is calculated as follows:
1. Using mouse data and extrapolation by body weight
X = "ln(1332Q'65) = 0-00316
2. Using mouse data and extrapolation by surface area
A - ~ln(1 " °'63) = 0.0419
Z J • u
3. Using rat data and extrapolation by body weight
X = ~ln(l253°<05) = 0.000203
C-2
201
-------
4. Using rat data and extrapolation by surface area
A = "In(1 " °,5) = 0.001L9
43.3
EXTRAPOLATION WITH A QUADRATIC MODEL
The quadratic model for predicting cancer incidence as a function of
dose level is
P(d) = 1 - exp(- Xd2) X > 0 (C.3)
Rewriting to solve for the coefficient X gives
, _ -ln(1 - P(d>) (C.4)
7
Using (C.4) and the same doses and incidence probabilities as in the
calculations for the linear model, X is calculated as follows:
1. Using mouse data and extropolation by body weight,
X = 9.52 x 10"6
2. Using mouse data and extropolation by surface area,
X = 0.00167
3. Using rat data and extrapolation by body weight,
X = 8.01 x 10-7
4. Using rat data and extrapolation by surface area,
X = 2.74 x 10"5
EXPECTED ANNUAL CANCER CASES
The expected annual number of cancer cases is taken to be the number
of people exposed times the expected lifetime incidence divided by an
average life span of 70 years.
202
C-3
-------
BASE CASE
Seel I nq i 3URFACE_A«EA Speclesl HOUSE Model I LINEAR
Response of 65 percent corresponds to dose In iron of 25,0 mg/kg/day which Implies lambda = 0.G419
Option cost (millions of dollars} a 0,00 Cost Per cancer case (millions of dollars) a 1.00
Type of
Person
Number In
U. 3, Population
Average Annual Exposure
(micro qms/cublc n) (mO/ku/day)
Expected LIfe11 me
Incidence
(orobablI11 y / 11fet1 me)
Expected
Annual Cases
(nuir.tcr/year)
Machine Operators
O
l
Codinerc' 8'
Indust rial
Coln»0p
Other Workers
CommercIe1
Induit rial
Coln-Op
Service Users
Commercial Dry Clean
Co1n-Up Dry Clean
Coin-Op Laundry
Urban Residents
16000,
700.
>1000.
110000.
20000.
22000.
50000000.
25000000.
37000000,
4 5000.
45000.
6000.
10000.
10000.
6000.
5.00
10.00
36.00
6.43
6,13
0.U571
1.13
1,43
0.8571
0.000714
0.00142V
0.005429
0.2363
0.2363
0.0353
0,0562
0.0582
0.0353
2.99HE-05
5.990E-05
0,000228
54,02
2.36
5.55
91 .39
16.62
11.to
21.42
21,39
120,
fO
o
w
25 - 200 *
200 - 500 •
500 - 1000 «
3700000.
20000000.
71 000000.
3.50
0,6000
0.2000
» Distance from dry cleaner In meters
Total cost of option and expected cancer (millions of dollars) =
0,000500
6.571E-05
2.857t-05
347,51
2.098E-05
3.595E-06
1 . 19BE-06
Total
1.11
1,03
I .22
• N • « • ¦
3«8,
-------
flASE CASE
Seal Ingi 3URF»CE.AREA Spedesl HUUSE Modal I QUAOKAMC
Response of 65 percent corresponds to dose In nan of 25.0 mg/kg/dey which Implies lambda '
Option cost (millions of dollars) 3 0,00 Coat per cancer case (olllloni of dollera) = 1,00
0.00167b
tvoa of
Person
Number 1n
3, Population
Average Annual Exposure
(micro Qdia/cublc m) (mg/kg/day)
Expected Lifetime
IncIdence
(probabl1 Ity/1IfetI me)
txpec ted
Annua I Cases
(nuTitor/year)
0
1
U1
Machine Operators
Commercial
Industrial
C o I n»flp
Other Workers
Comme rc i a I
Industrial
Co In-Oo
Service Users
16000.
700,
11000.
110000.
20000.
22000.
A5000.
15000.
6000.
10000.
10000.
6000.
6,u3
6.US
o.isn
1.13
l.u
0.8571
0.0669
0.0669
0.001210
0.003UI3
0,00311J
0.001230
15.29
0.6690
0.1V31
5. 3b
0.9753
0.J066
Commercial Dry Clean 50000000,
Coln-Cp Dry Clean 250000i)0.
Coln-Qp Laundry 37COOOOO,
Urban Residents
5,00
10.00
36.00
0.000711
0.001129
0. 005129
8.518E-10
3. 'I I 9E-09
1.9 J7t"«0A
0,000611
0.001221
0.02b!
to
o
25 - ?00 «
200 - 500 *
500 - 1000 •
3700000,
20000000,
71000000,
3.50
0,6000
0,2000
• Olatance fro»i dry cleaner In meter*
Total coat of ootlon and expected cancar (millions of dollars) ¦
0.000500
8,571E-05
2.857E-05
22,91
1,18'E~ 1 0
1.2J1E-U
1, 368E-12
Total
2,211t-05
3,517t-06
1 , 3S7E-06
22.91
-------
BASE CASE
Sea Mngi SuRF ACE_are A Speelest RAT Modelt LINEAR
Response of S oarcent correioonds to dose In man of 43.3 mg/kg/day ».h1ch Implies lambda = Q,00lie<>
Option cost (mil Ilont of dollars) b 0,00 Coat per cancer case (millions of dollars) = 1,00
Expected Lifetime E»pectad
Type of Number In Average Annual Exposure Incidence Annual Cases
Parson U, 3, Population (micro gmj/cgblc m) (mg/kg/day) (probao1tity/IIfettme) (numoar/year)
0
1
Machine Operators
Comme re 1 a I
Indus trial
C o I n - 0 p
Other workers
Commercial
I ndustrial
Coln-Up
Service Users
16000,
7C0,
11000.
uoooo,
20000,
22000,
45000,
45000.
6000.
10000.
10000.
6000.
6,13
6. 43
o .65 ft
1,43
I.Hi
0.B571
0.007593
0.007593
0,0010)6
0,001692
0.001692
0.001016
1 .71
0.0759
0.1596
2,66
0.UB35
0.3192
Commercial Dry Clean 50000000.
Co1n«CJp Pry Clean 25000000,
Co1n«(jp Laundry 37000000,
5.00
10,00
36,00
0.000714
0.001429
0.005429
8,469fc-0 7
I .694E-06
6,136E-06
0.6049
0.6049
3,40
Urban Healdents
o
V\
25 - 200 •
200 - 500 *
500 - 1000 •
3700000,
20000000.
71000000 .
* Distance from dry cleaner In meters
3.50
0.6000
0.2000
0.000500
8.5ME-05
2.B57E-05
5.926E-07
1.016E-07
3,3d 7£-0 6
Total
0.0313
0,0290
0.0j3«4
10,14
Total coat of ontlon ontf expected cancer (mllllona of dollars) ¦ 10,14
-------
BASE CASE
ScaMncM SU^FACE.AHEA Species! BAT Model! QUADRATIC
Kesponse of 5 percent correiponds to dose In mn of 43,3 mg/kg/day whlc* Implies Ienbda = 2.710E-U5
Option coat (millions of dollars) ¦ 0.00 Cost oer cancer case (mil lions of collars) = 1.00
Expected Lifetime E*pected
Type of Number In Average Annual Exposure Incidence Annual Cases
Person U, S. Population (micro o^s/cublc m) (mg/kg/day) (probab11ity/Mfe11 me) (nuir.be r/year)
rs
I
Machine Operators
Compere 1 a 1
Inaustrial
C oIn-Go
Other Workers
Commercial
Indus t rial
C o1n-Up
Service Users
16000.
700.
1 1000,
U0000,
20000.
22000.
05000,
15000.
~ 000.
10000,
10000 ,
6000,
6,13
6.(13
0.65,71
l.«3
1.13
o.Bbn
0.001132
0.001132
2,01 5E-G5
5.591E-05
5,591E-05
2,0 15E-05
0,2587
0,01 13
0.005166
0,0 879
0,0160
0.0U6332
Commercial Dry Clean 50000000.
Coin-Up Dry Clean 25000000,
Coin-Op Laundry 37000000,
5,00
10,00
38.00
0,000711
0.001129
0.00*129
t .398t-l1
S.593E-11
8.0766-10
9.987E-06
1 .997E-05
0.000427
Urban Hesldenta
<\)
o
&
25 - 200 *
200 - 500 •
500 - 1000 •
3700000.
20000000.
71000000,
* Distance from dry cleaner In meters
3,50
0.6000
0.2000
Total cost of option and expected cancer (millions o1 dollars) =
0,000500
8.571E-05
2 ,6i»7t-05
0.38
6,851E-1 2
2,013E-13
2.237E-H
Total
3.621E-07
5 , 753E-06
2,26VE-08
0,38 36
-------
BASE CASE
Seel Inoi BOUY_wEICHT Speetest MOUSE Model I LINEAN
Response of 65 percent corresponds to oose In men of 332.0 irig/kq/dey which Implies l«*bda a
Dotlon cost (irllllom of dollars) a 0.00 Cost per cancer case (.nil lions of dollars) a 1.00
0.003162
Type of
Person
U.
Number In
S. Population
Average Annua) Exposure
(micro gns/eublc m) (ma/kg/dey)
Expect ed Lifetime
I nc1 donee
(probab I Mty/I 1 fo11mc)
E xoec t ed
Annuel Cases
tnu'ear/year)
Machine Uperetora
ra
i
00
Conmerc1e1
Industrial
C o(n»0p
Other worker!
Co"merc i »1
Indua t rial
Co 1n-Up
16000.
700.
11000.
110000.
20000.
22000.
15000.
<15000,
6000.
10000.
100b0t
6000.
6,13
6.43
0.6571
1.13
1.13
0.U571
0.0201
0.0201
0,002707
0.00150/
0.001507
0,002707
1,60
0.2012
0 , '12b 5
7.08
1.21?
0,6507
Service User*
Coinrerciel Dry Clean 50000000,
Coln-Oo Dry Clean 25000000,
Coln-Oo Laundry 37000000,
5,00
10.00
ja.oo
0,000711
0,001129
0.005429
2.259E-06
1.517E-06
1.717E-05
1,61
1,61
9.07
c
Urben Residents
25 - 200 *
200 - 50J *
S00 • 1000 •
3700000.
20000000,
71000000.
• Olitence from dry cleaner In meters
3.50
0.6000
0,2000
0,000500
B, 5 7 1E-05
2.857E-05
1.581E-06
2,710E-07
V.035E-08
total
0 ,0936
0.0771
0,0916
27.00
Total cost of option and expected cancer (millions of dollar*) = 27,00
-------
BASE CASE
Sc a 11ngl BOOY.WEIGHT 3pgc
-------
BASE CASE
Scaling: BODY..HEIGMT Specfesl RAT Model! LINEAR
"eipomo of 5 percent corrtipond! to doaa 1n man of 253.0 ma/kg/day which ImpHoa lambda =
Option coat [millions of ciollari) a 0.00 Cost par cancer case (millions of dollars) e 1.00
0.000203
Type of
H«fson
Number In
U. S. Population
Average Annual Expoaure
(micro u^a/cublc (mo/kg/davl
Expected Lltetlmo
Incidence
(probae1 I lty/1 I f e11 me )
E
25 - 200
20n - 500
500 - 1000
50000000.
25000000.
37000000.
3700000.
20000000.
71000000.
5.00
10.00
38. 00
3.50
0.6000
0,2000
0.000714
0.001429
0,005a2«j>
0,000500
6.571E-05
2.B57E-05
1 .448E-07
2.896E-07
1, 101E-06
1.014E-07
1.739E-08
5. 793E-G9
Total
• Distance fro* dry cleaner In meters
0.1034
0. 1034
0.5B17
0.005 358
0,004965
0 .005675
1 . '«
Total cost of option and expected cancer (millions of dollars) a 1.74
-------
A
BASE CASE
Scaling: P0DY_WEIGHT Speclesl "AT Modal: QUADRATIC
Response of 5 percent corresponds to dose 1n man of 253.0 my/kg/dey which Implies ton-bdn = 6,013t-07
Option cost (millions of dollars) B 0.00 Cost per cenci'r case (nil I I Ions of dollars} a 1.00
Eiipected Lifetime Expected
Typ« of Number In Average Annual Exposure Incidence Annual Cases
Person U, 3, Population (micro gmj/cuDlc m) (mg/kg/day) (probablHty/I Ife11 me) (numbii r/y ear)
Machine Operators
n
i
Compere IeI
Industrial
Co In-Uo
Other "orkers
C o»«i( r c i a 1
Industrial
Co 1n«Up
16000,
700,
HOOO.
110000.
20000,
2 £ 0 0 0 .
«5000,
«b000.
6000.
1 oooo.
10000.
bOOO,
6.43
6.H3
0.057 I
1.13
1.43
0.B571
3.31'ir»05
3.314E-05
5.&87E-07
1 i 6 35E "06
1 ,635fc-06
5,6876-07
0,007S7S
0.000331
9.25
-------
OPTION J
Scaling} 3URMCE_AKE* Species! HOUSE Model! LINEAR
Suponie of 65 percent corresponds to do»e In men of 25.0 mg/kg/day which Implies lambda a
Option cett (million! of dollar*) »-ll,30 Coat per cancer cue (millions of dollars) a 1,00
0,0419
Type of
Person
Number In
U. S. Populat 1 on
Average Annual Eipoaure
(micro qmi/cublc m) (mg/kg/dey)
E *pec t nci lifetime
IncIdc nce
(proeablI ity/Hfutlne)
Expected
Annual Coses
I nguoar/ve sr)
Machine Operator*
C omir.'e rc 1 a I
lndust rial
Coln-Op
16000.
700.
1 1000,
27000,
30150.
6000.
3,66
1.31
0.8571
0. 1«9U
0.I6SJ
0.0353
34,14
1.65
5.55
0
1
Other Horkeri
Comme rc1 a 1
1 ndu.s trial
C oIn-Qp
110000,
20000.
22000.
bOOO.
6700.
6000.
0.8571
0,9571
0.8571
0.0353
0.0393
0.0353
55.49
11,24
11,10
Service liters
Commercial Dry Clean 50000000,
Coin-On Dry Clean 25000000.
Coin-Op laundry 37000000.
2.80
10,00
38.00
0.000400
0.001429
0.005429
1.675E-05
5.99QE-05
0,00022b
11.96
21,39
120.
Urban Residents
25 -
200 • •
500 -
200
500
1000
370000ft,
20000000,
71000000,
• Distance from dry cleaner 1n meters
2.52
0,4J20
0,1440
0,000360
6.17U-05
2.05 7E-05
1.50BE-05
2.S8OE-06
B.626E-07
Total
0,7971
0,7395
0,8751
275,
Total cost of option and expected cancer (millions of dollars) ¦ 263.96
-------
OPTION J
9cal
-------
OPTION 1
Seal 1 ng | SURFACE.AREA Specie#I RAT
Response of 5 oercent corresponds to dose
Option cost (millions of aollars) »-11.30
Model I LINEAR
1n man of "3.3 mg/kg/day which Implies lambda *
Cost per cancer case (millions of dollars) » 1,00
0.0011H6
Type of
Parson
U.
Number In
S. Population
Average Annual Exposure
(micro gms/cublc m) (mg/kg/day)
Expect B'l Lifetime
Incidence
(probabl)lty/11feti»e)
txpec ted
Annual Lases
(number/year)
Machine Operators
Co««»rcI a 1
Industrial
C o1n-Oo
16000.
7C0,
11000,
27000,
30150,
6000,
3.86
1.31
0, b571
0.004563
0.0050V)
0,001016
I . 04
0,0509
0,1596
0
1
Other Workers
Commere1 a I
Industrial
C o1n-Up
llOOOO,
200 0 0,
22000,
6000,
6700,
60 0 0,
0.8571
0,9571
0,8571
0,001016
0,001134
0,001016
I .60
0, 3240
0,3192
Service Users
Commercial Dry Clean 50000000,
Cotn-Op Orv Clean 25000000,
Coin-Gp Laundry 37000000,
2.80
10,00
38,00
0,000400
0,001429
0,005429
4,7426-07
1.694E-06
6,436fc-06
0,3387
0,6049
3,40
Urban Residents
H-
CJ
25 - 200 •
200 - 500 •
500 - 1000 *
3700000,
20000000,
71000000,
» D1stance from dry cleaner 1n metsra
2,52
0,4320
0.1440
0,000360
6,1 71E-05
2.057E-05
4.268E-07
7,317E-0b
2, 439E-08
Total
0,0226
0. 0209
0,0247
7.91
Total cost of option and expected cancer (millions of dollars) ¦
-3,39
-------
OPTION 1
Sea H nc] | SURF»CE_AREA SoeclasJ RAT Modeli QUADRATIC
Response of 5 percent corrtipondi to dose In man of <43,1 irg/tcg/day which Implies lambda a
Option cost (millions of aollars) =-11,JC Coat par cancer case (nil Hons of dollars} s 1,00
2.740E-05
Type of
Person
Number In
U, S. Population
Average Annual Exposure
(micro gms/cublc m) (mg/kg/day)
txpect ed L1f e 11 me
lnc1dence
(probability/lifetime)
t «pec ted
Annual Cases
(nu-her/year)
Machine Operator*
Coir.me rc 1 a 1
Indus t rial
C o 1 n - 0 o
16000,
700,
liooo.
27000,
50150.
6000,
3,8b
«.31
0.6571
0,000406
0.00050a
2.0I5E-05
0,09 38
0.005082
0,00 3166
Other workers
Co-
-------
OPTION 1
ScaHngi HODY_wE1Ght Speclesl MOUSE Model i LINEAK
Pesponsa of 65 percent corresponds to dose I r> man of 332.0 wg/kg/day which Implle* lambda »
Option cost (ulllloni of dollars) *-11.30 Coat oar cancer casa (nil Hon* of dollars) a 1,00
0,003162
lype of
Person
U.
Number Cowmere1al
1nduitrial
Coln-Oo
16000.
700.
11000.
110000.
20000,
22000.
27000.
30 150.
6000 .
60 00 ,
6700.
6000.
3.86
«.31
0.8571
0.0571
0.9S71
0.8571
0 .0 1 21
0.01J5
0.002707
0.002707
0.003022
0.002707
2.77
0.1 J53
0.u 25J
<4.25
0.bb iy
0.8507
Service User*
Commercial Dry Clean 50000000,
Coin-Up Dry Clean 25000000.
Coin-Op Laundrv 37000000.
2.80
10.00
38 .00
0.000400
0.001429
0.005429
1 .265E-06
4.51 7E-06
1.71 7E-05
0.9035
I .61
9.07
Urban Heddents
r,
01
25 .« 2 00 ~
200 - 500 «
500 - 1000 *
3700000,
20000000.
71000000.
• Distance from dry cleaner In meter*
2.52
0.4320
0.1440
0.000360
6,1 ME-05
2.057E-05
1.13BE-06
1.951E-07
6.505E-00
Total
0,0602
0,0553
0 ,0663
21.07
Total coat of option and orpected cencer (millions of dollars) *
9,77
-------
UPTION 1
Scaling: BODY.nEIGHT Special! MOUSE Models QUADRATIC
Response 6f 65 percent corresponds to dose 1n man of Ji2,0 mg/ko/dsy which Upllei lambda s
Option coat (millions of dollars) =-11.30 Cost Per cancer caaa (millions of dollars) = l.OQ
9.524E-C6
Tvds of
Person
Number in
U. 3, Population
Average Annual Exposure
(micro oms/cuolc m) (mO/kg/dSy)
t xpsc t ijo L If e 11 ire
Ine tdence
(probab11f ty/1If e t1ms)
txpacted
Annual Casai
(number/year)
Machine Operators
Commere i a I
Inrtust rial
Coln-Op
16000,
700.
11000.
27000.
30150.
oOOO.
3.6b
".31
0,8571
O.OOOM2
0.000177
6.V98E-06
0,0 32"
0.001767
O.OuUOO
Othep Workers
Compere1al
Indust rial
Coln-Qp
UOOOO.
20000,
22000.
6000,
e700,
6000.
0.8571
0.957)
0.8571
6.99BE-06
6.726E-06
6.998E-06
0.0110
0.002D9J
0.002199
Service Users
Commercial Dry Clean 50000000,
Coin-Op Dry Clean 2SOOOAUO,
Coln-Gp Laundry 37000000.
2,80
10,00
38,00
0.000400
0,001129
0 , 0 05H29
1 .520E-12
1 ,9
-------
OPTION |
Scaling! BODY_WEIGHT Speclesl HAT Model I LINEAR
Response of 5 percent corresponds to dose in man o f 253.0 mg/kg/day which UpHei lambda -
Option cost (millions of dollars) *-11.JO Cost per cancer case (millions of dollars) = 1,00
0,000203
Type of
Person
Number In
U. S, Population
Average Annual Exposure
(micro gms/cublc n) (mO/kg/dey)
E xpec ted Lifetime
Inc1oenc e
(probaoi111 y / I1fetlme)
Expected
Annual Cases
(nuitibjp/year)
Machine Operators
Co™i»«rc I a 1
Industrial
Coi n»L)p
16000.
700,
11000.
27000.
30150,
6000.
3.86
".31
0.8571
0.000782
0.00087J
o.oooi ;a
0,1767
0.008729
0.027 J
O
I
OD
Other Workers
ComnercI el
Inrtust rlal
Coln-0o
1 10000.
20000.
22000,
6000.
6700,
6000.
0,8571
0.9571
0,8571
0.00017U
0.000194
0,000174
0,2730
0,0551-
0,0546
Service Users
Comnerclel Dry Clean 50000000,
Coin»Up Dry Clean 25000000,
Co1n-0p Laundry 37000000,
2,60
10,00
36,00
0.000400
0,001429
0 .005429
S. 1 10E-06
2.896E-07
1 ,101006
0,0579
0,1034
0,5617
Urban Residents
-J
25 - 200
200 - 500
500 - 1000
3700000,
20000000,
710 J00 00,
2.52
0,4320
0,1440
* 01st»nc« from dry cleaner In metera
Total cost of option and expected cancer (millions of dollars)
0,000360
6,171E-05
?.057E-05
¦9.95
7.299E-08
1.251E-08
4, 17IE-09
Total
0,003858
0.003575
0.004230
1 .35
-------
OPTION 1
3celfngl BQ0Y_*E IGHT Sd«c1«H "AT Model l QUADRATIC
Response of 5 percent corresponds to dose In min of 25 3.0 mg/ke) liunoer/yeJf)
Machine Operator*
Conmerc I a I
Indust rial
C oIn-Uo
Other Workers
Cohere ( a 1
1 ndus t rial
Coln-Cp
Service Users
16000.
700.
11000.
110000.
20000.
2*000,
27000.
30150,
6000,
6000.
6700.
6000.
3.86
0.8571
0.6S71
0.9571
0.6571
1 ¦ I926-05
1.18QE-05
5,&87t-07
5.867fc-07
7.illE-0 7
5.887E-07
0,002725
O.OOOHd
9.C52E-05
0,000925
0.000210
0.0001B5
Cohere I »1 Dry Clean 50000000.
Coin-Op Dry Clean 25000000.
Coln-Gp Laundry 37000000,
2.60
10.00
38.00
O.OvOlOO
0.001*129
0,005429
1 .282E-13
1.635E-12
2.J62E-11
9. I56E-08
5.8<4lt-07
1.2U8E-C5
UrOan Residents
2S - 200 •
200 • • '500 •
500 - 1000 «
3700000,
20000000.
71000000.
2.52
0,4320
0. 1440
0.000360
6.171E-05
2.0S7E-05
1 .039E-13
3.052E-15
3, J91E-16
5.UB9E-09
8.720E-10
3.440E-10
» Distance from dry cleaner 1n meters
1 ot a 1
o.oouav9
Total cost of option and expected cancer (million# of dollars) = -11.30
-------
OPTION 2
3c eI 1nq; SURMCE.AREA 9ptc<«St MOUSE Model! LINEAR
Re»ponse of 65 percent corresponds to dose In man of 25,0 mg/kg/day which implies lambda =
Option cost I'llHoni of dollars) a -2,20 Cost per cancer case (millions of dollars) = 1,00
0.0419
Type of
Pfrson
Number In Average Annuel Exposure
U, 3. Population (micro g">s/cub i c m) (ng/kg/day)
Expected Li f e 11 ire
Incidonee
(probeoi I 1 ty/1 1 fet i^e)
Exoacted
Annuel Case*
(numDar/year)
Machine Operator*
Coifne rc I a 1
Indus trial
Coln-0o
16000,
700,
11000,
45000.
15000,
3300,
6.43
6,43
0.4714
0,2363
0.2363
0 ,0 H6
sa.02
2.3b
J.08
O
I
N)
o
Other Workers
Commercial
Industrial
Co 1n-Uo
UOOOO,
20000,
22000,
10000,
10000,
3300,
l.«3
1,43
0,4711
0,0582
0,0582
0,0196
VI ,3V
I t> . b?
6.15
3ervIee Uaers
Commercial Dry Clean 50000000,
Coin-Op Dry Clean 25900000,
Coin-Op Laundry 37000000,
5,00
5,90
20,90
0,000714
0,009643
0,002986
2.998E-05
3.535E-05
0,00012$
21 ,42
12,62
66, 19
Urhan Residents
!S \
'
CO
25 - 200
200 - 500
500 - 1000
3700000,
20000000,
71000000,
• Distance from dry cleaner in motera
2,94
0.5040
0,1680
0,000420
7.200E-05
2.400L-05
1,761E-05
3.020E-06
1.00/E-06
Total
0,9326
0 ,8628
1.02
27 7 ,
Total cost of option and expected cancer (millions of dollars) ¦ 274,46
-------
OPTION 2
3c•1 Ing i SUSFACE..AREA Speclesl ML)H3E Model! UUADHATIC
Hesponse of 65 oar cent correapondi to aoia 1n nan of 25.0 mg/kg/day which Ua lambda * 0.001675
Cotton cost (ml 1 I ions of dollars) = -2.20 Cost per cancer ense (million* of dollars) = 1.00
Eapected Lifeline t»i>ected
Typo of Number 1n Average Annual Exposure Incidence Annual Cases
Person II, S, Population (micro qms/cublc m) (mo/kg/oay) (probab1111y/I 1fe11 me) (number/year)
Machine Operators
D
I
j-o
Comtrerc I a I
I ndustrial
C o1n-uo
Other norkers
Conusrc1«I
I "rlust rial
Coin-Dp
Service Users
Commere tal Dry Clean
Co1n»Up Dry Clean
Co1n-Op Laundry
Urban Residents
16000.
700.
1 1000.
110000.
20000,
23000.
50000000.
25000000.
37000000.
45000,
45000,
3300,
10000.
10000.
3300.
5.00
5.90
20,90
6,43
6,43
0,1714
1.43
1.43
0, M714
0.000714
0,0006'13
0,002986
0.0669
0,0669
0,000 372
0.00ji4l3
0.003413
0,000372
8.548E-10
1.190E-09
1.494E-06
15.29
0,6690
O.ObaS
5.36
0.9753
0.1170
0.00061 1
0,000425
0,007695
\
ly
J\j
o
25 -
200 -
500 -
200
500
1000
3700000,
20000000,
71000000,
• Distance from dry cleaner In maters
2.94
0,5040
0.1680
0,000420
7,20oE-05
2.400E-05
2.956E-10
8.686E-12
9.65IE-1J
Total
1.562E-05
2.4B2E-06
9.769E-07
22.48
Total cost of option eno expected cancer (millions of dollars) a 20,28
-------
OPTION 2
ScaHnql SUHMCE_*REA SpecleSl RAT Hodell IINEAN
Hesponsc of 5 percent correspond* to dose In -nan of 43,3 mg/kg/day which Inpllat Ianbda a 0,001106
Option cost (mil Horn of dollars) s -2,20 Coat pep cancer case (mil Mont of dollars) c 1,0U
Expected Lifetime t*pected
Type of Number In Averago Annual Exposure Incidence Annual Cases
Person U, S, Population Cmlcro o^s/cublc m) (mO/kg/dey) (probabl I) ty/H fet 1«e) ( nuntor/y ea r)
Machine Ooeratore
0
1
(si
M
Coinmerc I a 1 1600 0,
Industrial 700,
Co1n-Up 11000,
Other workers
Coimerci »I 11 0000,
I ndu s t rial 20000.
Co1n«Dp 22000,
Service Ueera
Compere I a 1 Dry Clean 50000000,
Coin-Do Dry Clean £5000000,
Co 1n-Oo Laundry 37000000,
Urban Resident*
25 - 200 * 3700000,
200 - SCO • ?OOOOGOO,
500 - 1000 * 7l00u000,
15000,
05000,
33 00,
10000,
10000,
3300,
5,00
5,90
20,90
2,94
0,5040
0,1680
ro
» Distance from dry cleaner tn meters
Total coat of option and expected cancer (millions o' dollars) a
6.13
6,43
0,4714
1.43
1.43
0,4714
0,000714
0,000043
0,0 02996
0,000420
7.200E-05
2.40 0E-05
5,9}
0.007593
0.007593
0,000559
0.001692
0.001692
0,000559
8, 469E-07
9,993E-07
3.540E-O6
4.90OE-O7
8,536E-08
2.B45E-06
total
1.74
0,0759
0,0070
2.66
0,4035
0,1756
0,6049
0,3569
1.87
0.0263
0.0244
0,0289
6.13
-------
UPTION 2
3c • M no I 3URFACC_AKEA Specleai RAT Model I QUADRATIC
Response of 5 percent correspond# to dose In man of «3.3 mg/kg/dey which Implies lambda b
Option cost (millions of dollars) a -2.20 Cost per ceneor case (millions of poller*) = I,00
2 . 7<40t -OS
type of
Parson
Number In
U. S. Population
Average Annual Exposure
(micro Qms/cublc m) ("000.
115000,
3300.
6. a J
b.«3
a.i7u
0,001132
0,001132
6,091t-U6
0,2587
0.0113
0.000957
n
I
ro
Other Workers
Cohere I e 1
Indust rial
C o1n-Op
I 10000.
20000.
22000.
toono,
10000 .
3300 ,
J.13
1.13
0.U7H
S.S91E-05
5.591E-05
6.091E-06
0.0879
0,0160
o,oom«
Service Users
Commercial Dry Clean
Coln-Uo Dry C1een
Co1n-Oo Laundry
50000000.
25000000.
17000000.
5.00
5.90
20.90
0.000714
O.OGOBU3
0.002966
1 .398E-1I
I .907E-11
2.113E-10
9.987E-06
6.95JE-06
0.000129
Urban Res I dent s
?0
JO
25 «
200 -
500 -
200
500
1000
3700000.
20000000.
71000000.
• Distance from dry cleaner In meters
2,9i|
0.5010
0.1680
Total cost of option and expected cancer (millions of dollars) a
0.000420
7,2006-05
2.100E-05
-1.82
<1.8316-12
1.1216-13
1,5796-H
Total
i. 555E-07
1.05VE-0B
I . 601E-08
0.3/6V
-------
OPTION 2
ScaHngj BOOY.WfclCHT SpeeiasI Nil
-------
OPTION 2
ScallngI BODY.welGMT Speclesl MOUSE
Response of 65 percent corresponds to dose
Option cost Inllllooi of dollars) s -2,29
Model I QUADRATIC
1n "an of 532,0 mg/kq/dey which Implies 1 ambdi *
Cost par cancer case (millions of aollars) * 1,09
9.524E-06
Type of
Hereon
Numoar 1n
U. 3. Populat 1 on
Average Annual Exposure
(micro gms/cublc m) (wg/kg/day)
fipecua Lifetime
Incidence
(probability/lifetime)
Exoected
Annual Cases
(numciar/year )
Hachlne Operators
Cotime r c I a I
I nduitrial
C o1n-Op
Other Workers
16000,
700,
11000,
45000.
<15000.
33U0 ,
b.m
6.41
0,4714
0.000394
0.000394
2.1 I 7E-06
0,0899
0.0039J5
O.OUOJ13
Co»«crc1 a 1
Industrial
Coln-Uo
Service Users
Commercial Dry Clean
Coin-Up Dry Clean
Coln-Gp Laundry
Urban Residents
25 - 200
200 - 500
500 - 1000
110000,
20000,
22000,
50000000,
25000000.
37000000.
3700000,
20000000,
71000000,
10000,
10000,
3300,
5.00
5.90
20,90
2,94
0,50«0
0,1630
l.«3
1.43
0,4714
0.000714
0.000943
0.002986
0.000420
7.200E-05
2.400E-05
1.943E-05
1 ,9436-05
2• 1 I 7i"0 6
4,859E-I2
6.766E-12
6,491£-11
1.680E-12
4.937E-14
5.486E-15
0,0305
0.005552
0.000665
3.471E-06
2.417E-06
4.480E-0S
B.8B1E-08
1.41 IE-OS
5.56
-------
OPTION 2
JciUngi BODr_"EIGHT Spedesi KAT Modeli LINEAR
Response of 5 percent correspond* to dot* 1n nan of 253,0 mg/kg/dey which Impllei 1airbda ¦ O.OG02U1
Option coit (illlioni of dollira) « -2.20 Coat per cancer case (millions of dollars) s l.OO
Exoeeted Lifetime Expected
Type of Number In Average Annual Exposure Incidence Annual Cases
Person U, 3. Population (micro gps/cublc «n) (iro/kg/day) (probablI 11y/1 Ifettme) (nunuer/year)
r>
I
to
Maehlne Operator!
Conrerc i a 1
Indust rial
C o1n-up
Other Workers
Co"»erc1 a 1
Induj t rial
Co1n-Op
Service Uaers
16000.
700,
11000.
UOOOO,
20000.
22000.
15000.
15000.
3300.
10000,
10000.
3300 ,
6.13
6,13
0.1711
1.13
1.13
0,1711
0,001302
0.001302
9.555E-05
0.000290
0.000290
9.555E-05
0,2977
0,0130
0,0150
0,1550
0.0627
0,0300
Conrerclal Dry Clean 50000000.
Coin-Up Dry Clean 25000000.
Coin-Op Laundry 37010000.
5,00
5.90
20.90
0,000711
O.O0O813
0,00296b
I .118E-07
1 .709E-07
b.053E-G7
0, 1031
0,0610
0.3200
Urban Residents
~c.)
10
CT
25 - 200
200 - 500
50(3 - 1000
3700000.
20000000,
71000000.
2,91
0,5010
0.1680
• Olatonca from dry cleaner In meters
Total coat of option and expected cancer (millions of dollars) *
0 .000120
7.200E-US
2.1O0t-O5
¦0.81
8.515E-08
1 .160E-08
1.S66E-09
Total
0, 001501
0,001171
0,001935
1.39
-------
OPTION 2
Sea 11nqt BODr_wEIGHT Species: RAT Model I QUADRATIC
Response of 5 percent corresponds to dose In man of 253,0 rrg/kg/day which 1mpl(«9 Ia^bds ¦
Option cost (*4 1 Hons of dollars) a -2,20 Cost per csncer case (millions of dollars) = 1,00
B.013E-07
Type of
Herson
Number |n
U, S, Population
Average Annual £KPO*ura
(micro gin»/cubIc m) (ir.Q/kg/day)
Expected Lifetime
Inc I iJence
(prooao 41 Itv/l4 fet I me)
Expect ed
Annual Cases
(nuf,Dor/year)
Machine Operators
Co*«>e rc 4 a I
I ndus trial
Coln-Up
Other Korkors
Co«»erc 4 a 1
I ndua t r 4 a 1
Coln-Up
Service Users
16000,
700,
11000,
110000,
20000,
2100 0,
15000,
<(5000,
130 0,
10000,
10000 .
i300.
6.<43
6.<13
0,4/14
1,^3
1-03
0 , 4 7 1 <4
3.314E-05
3.314E-05
1.781E-07
1.635E-06
1.635E-06
1 . 781E-07
0.007575
0,000331
2,7<*vE-05
0. 002570
0.000467
5.597t-05
ComrercI el Dry Clean 50000000,
Coin-Up Dry Clean 25000000,
Co4n-0p Laundry 37000000,
Urban Hesldents
5,00
5,90
20,90
0,000714
0,000043
0,002986
il.OaaE-l J
5,693E-13
7,144E-12
2,920E-07
2.033E-07
3,77oE-06
25 - ^0lJ *
200.- 500 »
500 - 1000 *
3700000,
20000000,
71000000,
2.94
0.5010
0,1660
• Distance from dry cleaner In meters
Totsl coat of option and expected cancer (millions of dollars) *
0,000420
7.200E-05
2.400E-OS
*2,19
1.414E-I3
4.154E-I5
4,616E"16
Total
7.472E-09
1 , 187E-09
4.&82E-10
0.011U
-------
OPTION 3
Scaling: SURF»CE_ArEa Speclesl MOUSE Model I LINEAR
Response of 65 percent correlponds to dose In man 0f 25.0 mg/kfl/day which Implies lambda »
Option cost (millions of dollars) « *3,30 Cost per c»nccr case (millions of dollars) a 1,00
0.0119
Type of
Person
Number In
tl, S. Population
Average Annuel
{micro gms/cub1c m)
Exposure
(iro/kg/day)
£x pec t «'J Lifetime
Incidence
(probability/lifetime)
t «pec t ed
Annuel Cases
(number/ye a r)
Machine Operators
Comme rc1sI
Indust rial
Coln»(Jp
Other Horkers
16000,
700,
11000.
36450,
43200,
6000.
5,21
6,17
0,6571
0,1962
o.22eo
0.0351
«u,8«
2,26
5,55
0
1
NJ
Oc
Comme rc1 a 1
Jndust rial
Co)n-Op
Service Users
Commercial Dry Clean
Coln-Uo Dry Clean
Coin-Op Laundry
110000.
20000,
22000,
50000000,
2500OO0O,
3 ronuooo,
8100,
9600,
60 00,
«.'45
10.00
36.00
1,16
1,37
0.8*71
0,000636
0.001029
0.005429
0.0M74
0,0559
0.0355
2.664E-05
5.990E-05
0.000220
74,44
lb.97
11.10
19,03
21 ,3V
12 0.
Urban Residents
fo
?0
kS - 200 »
200 - 500 *
500 - 1000 •
5700000,
20000000.
71000000,
3.12
0.5340
0,1780
* Distance from dry cleaner 1n meter*
Total cost of option end expected cancer (millions of dollars)
0,000415
7.629E-0S
2.543E-05
314,60
1.866E-05
3 . 199t"06
1 . 066E-06
Total
0.9861
0.9141
1 ,08
3ld,
-------
OPTION 3
ScaHnql SURF*CE_«REA SdccUiI MOUSE Modal I QUADRATIC
Reaponie of 65 percent correioonds to doae In men of 25,0 mg/ko/day which Implies lambda ¦
Option colt (>n I 1 1 I on i of aollara) = -5,10 Coat per Cancer caae (millions of aollars) * 1.00
0.001675
Type of
Peraon
U.
Number in
S. Population
Average Annual Exposure
(micro awa/cublc *) (mQ/kg/dayJ
t'xpec ted LI fet 1 m«
Incloence
(prob
-------
OPTION 5
Scallnoi 8URFACE_AR£a Speclesl RAT Model l
Response of 5 percent correspond# to dose In man of
LINEAR
13.i mu/kg/diy which implies lambda =
0.00110b
Option cost (»t)l lonj of dollars) = -3,30 Cost per cancer case (mil Horn of dollars) » 1,00
Iype of
Person
U.
Number In
S. Population
Average Annual
(micro gms/eublc m)
Exposure
(no/kg/dey)
tupected Lifetime
Inc1aenc e
(probebiI1ty/11fetI me)
t »oect ed
Annual Cases
(nuir.ba r/ year)
Machine Operator*
0
1
u>
o
Comma re 1 a 1
1ndust r t a 1
Co 1n-Op
Other Workers
Commerc 1 a I
Induat rial
Coln-Op
Service User*
Commercial Dry Clean
Coin-Op Dry Clean
Co1n»0p Laundry
Urban Resident*
16000.
700.
t J000,
110000,
200 00,
23000,
50000000,
25000000,
57000000.
36450,
13200,
6000 ,
6100.
9600,
6000,
1,15
10,00
36,00
5.21
6,17
0,6571
1,16
1,37
0,6571
0,000636
0 , 0 0 1 <129
0.0054(29
0,006155
0,007290
0,001016
0,on 117 1
0,001625
0,001016
7.537E-07
1,691E-06
6.13&E-06
1.11
0,0729
0, 1596
2,15
0,1612
0.3192
0,5301
0,6019
3,10
tfO
cr
25 - 200 »
200 - 500 *
500 - 1000 *
3700000,
20000000,
71000000,
3,12
0,5310
0.1780
• Distance from dry cleaner In meters
Total cost o> option and e*pected cancer (millions of dollars) s
0.000115
7.629E-05
2,513E-05
5,91
5.276E-07
9.O15E-O0
3,0l5£>06
Total
0,0279
0,0258
O,0f306
9,21
-------
OPTION 3
Scallnal 3URFACE_*HEA SdccUii RAT Modeli QUADRATIC
Response of 5 percent correspond* to doee In men of <43.5 fg/kg/day which Implies I nmbde -
Option cost (millions of dollars) c -3.30 Cost o«f cancer c>>« (mil Hons of ooHars) a 1.00
2.740E-05
Type of
Person
Number 1n
U, 3. Population
Average Annuel Exposure
(micro ams/cublc m) (mO/kg/doy)
Eipee t «d L1 f «t1 me
1nc1 denes
(probed I 1 tv/1 I fet Ime)
t xpec t ed
Annual Cases
Inumno r/year)
Machine Operators
Commercial
Industrial
Co 1n-0o
OtNer workers
C Of*e rc i a 1
Indus t rial
Co 1n-Up
Service Users
Commercial Ory Clean
Co1n-OD Dry Clean
Coin-Up Laundry
Urban Residents
25 - 200 •
200 - 500 »
500 - 1000 •
16000.
700.
11000.
110000.
20 0 00.
22000.
SOOOOOOO.
25000000.
370000C0.
3700000.
20000000.
71000000,
36450,
13200,
bOOO ,
8100.
•<600.
6000.
«.«5
10.00
3B.O0
3.12
0,53"0
0.1700
• Distance from dry cleaner In meters
Total cost of option and expected cencar (millions of dollars)
5.21
6.17
0.B571
1.16
1.37
0.US71
0.000636
0.OU«29
0.005429
0.0004US
7.6£9t-05
2.543t-0S
-3.00
0.000703
0.001043
2.0I5t-05
3,67?E-05
S.1S6E-05
2.015E-05
1.108E-U
5.593E-11
6.076E-10
5.Q27E-12
1 .595E-13
1.772E-11
Total
0,1698
0.0104
0.003166
0,0577
0 , 0 I <17
0.006332
7,91lt-06
1 .997E-05
0.000127
2.668E-07
1.5576-00
1.797E-08
U,2t>26
-------
OPTION 3
Seal IngI BODY_HEIGHT Sprclesl HOUSE Model| LINEAR
Hesponse of 65 percent corresponds to dose In man of 332.3 ">g/kfj/day wMc* Implies lambda a 0.001162
Option coat (ftllloni of dollars) = -3.30 Coat por cancer cue (nil I Ions of dollars) a 1,00
lyoe of
Pe r son
U.
Number in
S. Copulation
Average Annual
(micro gma/cubic m)
Exposure
(mO/kQ/dey J
Expected Lifetime
I nc1dence
(probab1 Mtv/1 I ~ e t 1 me J
Expected
Annua I Celts
tngnper/ya a r )
Machine Operators
0
1
w
N>
w
cj
Comire rc I a 1
Inrigst p1e1
Co 1n-Qp
Other horkers
Co»ir,orc i el
Induit rial
Co In-Op
Service user*
Cominerc I el Dry Clean
Coln-Uo Dry Clean
Coin-Up Laundry
Urban Residents
25 - 2 00 *
200 - 590 *
500 - 1000 »
16000.
700.
I 1000.
110000.
20 C 00.
22000.
50000000.
25000000,
37000000,
3700000.
20000000,
71000000.
36^50.
43200,
6000.
8100.
9600.
6000 .
4,45
10,00
36,00
3,12
0.5340
0.1/B0
5.21
6.17
0.6571
1.16
1.37
0,057 1
0.000636
O.OOU29
0.005429
0,000445
7.605
0.0163
0.0193
0.0027 u 7
0.003652
0,004 327
0.002707
2.010E-06
4,517E-06
I. H7t-05
1, 407E-06
2.412E-07
6.041E-08
• Distance fro* dry cleaner In mettri
Total coat of option and expected cancer (million* of dollars)
total
3.73
0,1933
0,4253
5,74
1,24
0 ,05 07
1 ,44
1,61
9.07
0,0740
0.0639
0.0816
2U.53
21.23
-------
OPTION 3
Scaling! BOO*_*EIGMT Speclesi MOUSE Modall UUADHATIC
Response of 65 percent corresponds to dose In man of 332,0 mg/kg/dav which Inpllei lambda »
Option cost (tilllions of dollars) » -3.30 Cost par cancer case Ijillllcni of dollars) = I • U 0
9.520E-06
Type of
Person
U.
Number 1n
S. Population
Average Annual
(micro g!"s/cub1c m)
Exposure
(mg/kg/day)
Expect cd lifetime
Incidence
(orobeb11i ty/l1tet1 me)
Expee ted
Annual Cases
I numtJu r/yea r )
Machine Operators
Cgmnerc1 a I
Indust rial
Coln-Uo
16000.
700,
11000.
J6«50.
«3200,
O 0 0 0 .
5.21
6.17
0.657 1
0.000258
0.000363
6.99BE-U6
0.0590
0,003b27
0 .00 1 1 00
O
U)
Other workers
Coifs re Ial
InduStrial
Coln-Op
110000.
200 0 0,
220 0 0.
B 1 00 .
V600 .
OOOO.
1.16
1.37
0.8571
1.27bE-05
1.791E-05
b.9VHt-06
0.0200
0,005126
0,002199
Service Users
Commercial Ory Clean 50000000.
Coin-Op Dry Clean 25000000.
Coin-Op Laundry 37000000.
H.HS
10.00
36.00
0.000636
0.001129
0.005129
3.B49E-12
I ,9«
-------
OPTION J
Scallnql 8nOY_wEInHT Species! RAT Model I
Response of 5 percent corresponds to dose In men of
LINEAR
253.0 mo/kq/day whlc>» Implies 1embda =
0.000203
Uotlon cost hi IItonj of dollars) a -3.30 Coat par cancer case (millions of dollars) " 1,00
Type of
Corson
Machine Operators
U.
Numbar In
S. Population
Average Annual Exposure
(micro gms/cubic m) (my/kg/day)
Expected Lifetime
Inc1 dene e
(probabl I I tv/l I fet lire)
Expect ert
Annual Casus
(numoer/year)
Comma rela)
Indust rial
Coln-Oo
16000.
700,
liooo.
36150,
<13200,
6000 ,
5.21
6.17
0.8571
0.001055
O.OOU50
0,000174
0,2112
0,0125
0.0273
f-j Other Horkers
^ Comma rc 1 a 1
Indus trial
Co In-Up
110000,
20000,
22000,
8100,
9600,
6000,
1,16
1.37
0,8571
0,000235
0,000278
0.00017a
0.3686
0,0 791
0.0516
Service Users
Commercial Dry Clean 50000000,
Coin-Op Dry Clean 25000000,
Coln-Cp Laundry 37000000.
«.«5
10,00
38,00
0,000636
0,001129
0,005129
1.289E-07
2,8VoE-07
1.101E-06
0.0V21
0.1031
O.'idlT
Urban Healdenta
w
Co
CJ
25 - 200 •
200 - 500 •
50 0 - 10 00 *
3700000,
20000000,
71000000,
• Dlatanca from dry cleaner In maters
3.12
0,5310
0,1730
0,000115
7.629E-05
2,513fc-05
9,0226-08
1.517E-08
5, 155E-09
I ot a 1
0.OU1769
0,001119
0,005229
1 .58
Total coat of option ana expected cancer (nil Iloni of dollara) a -1,72
-------
OPTION 3
Sea 1 Ingi liODy_*EIGMT Speclasl RAT Modal I QUADWATIC
Raspoma of 5 percent eofresBondj to dose In man of 253,0 mg/kg/dsy which lupl 1st lambda =
Option cost (millions of dollars) a -J.40 Cost Per cancer case (millions of dollars) * 1.00
8.013E -0 7
Type of
Person
Machine Operators
Number In
U, S, Population
Average Annual Exposure
(micro gms/cwbic (mg/ltg/dey)
Expected LI fet ime
Inc i aonca
(probabi I ity/1 i fer i ire)
t »pec t ed
Annual Cases
( auh.co r/ v ea r )
ConnercI a 1
I noustrial
Coi n-0p
16000,
700,
110C0.
36<150,
'11200,
6000 ,
5.2!
6.17
0.B571
2.176E-05
3.O52E-0S
S.6H7E-07
0.001973
0,000305
9,
Ul
Ot^er Workere
CommereIal
1 ndu»trial
Co t n»0p
110000.
20000.
22000.
S100,
9600 ,
6000 ,
1.16
1.37
0.BS71
1.073E-06
1.507E-06
5t687E-07
0.001686
0,000 431
0 , 0 00 11»5
Service Users
Compereial Dry Clean
Coln»0o Dry Clean
Coin-Op tagnrlry
50000000,
25000000,
57000000,
4.15
10.00
38,00
0.000636
0.001129
0,005129
3.238E-13
1 .635E-12
2.362£>l 1
2.313E-07
5.841E-07
1.21OE-0S
t'O
CO
H-*
Urban Residents
25 - 200 «
200 - 500 *
500 - 1000 *
3700000.
20000000,
711/00000,
» Distance from dry eleaner In maters
5.12
0,5310
0.1760
0.000115
7.629E-05
2,5136-05
1,587E-1 3
1.663E-15
5, 182E-16
Total
d,388E-09
1 ,332E-09
5,i:5<>£-l0
0,C 0 7606
Total cost of option and expected cancer (millions of dollars) * -3,29
-------
OPTION a
9ca 1 Ingi suftF4CE_*HEA Spectesi MOUSE Model I L1N£AH
Response of 65 percent correspond# to dose In frsn of 25.0 mg/kg/day xhlc* Implies lambda = 0.0U19
Option colt (niltlon» of dollar*) ¦ 0.30 Cost Par* cancer case (millions of dollars) s 1,00
Expected Lifetime txnected
Type of Number In Avar-age Annual Exposure Incidence An-ual Cases
Person U, S, Population -(micro gma/cublc m) (mg/kg/day) (proheb(11ty/1If e 11 we ) (number/year)
Machine Operators
Comite rc 1 a 1
Indust rial
Coln-lip
O Other Workers
(jj
<7> Compere t al
Indust rial
Co In»0o
Service Users
16000,
700.
11000.
110000.
20000,
22000,
ISO00 ,
#5000 .
1020.
10000.
10000.
•4020.
6.43
6.43
0.5743
1.43
1.43
0,5743
0.2363
0.2363
0. 02ib
0.0582
0,0582
0.0238
54,02
2.36
91 .39
16.62
7.48
ro
co
Ol
Commercial Dry Clean
Coin-Up Ory Clean
Coin-Op Laundry
Urban Residents
25 - 200 •
?J0 - 500 «
500 - 1000 *
50000000,
25000000,
37GOUOOO,
3700000,
20000000,
7lonoooo.
5.00
7,«0
25,46
3.26
0,5580
0,1160
• Distance from dry cleaner in meters
Total cost of option end expected cancer (millions of dollars) =
0,000714
0.00 1057
0,00363/
0.O00465
7,9 716-05
2.65 7 E-05
2.99BE-05
4,4 35E-05
0,000153
1.949E-05
3,3436-06
J.U4E-06
Total
21.42
IS.64
80.62
I ,03
0,9552
1.13
2V7.
296,89
-------
option a
3C»H npi SURFACE,AREA Species! MOU3E Model» QUADHATIC
ftJDonij of 6b percent correspond! to aose In nan of 25,0 mg/tco/dey which Implies lambda e
Option coat (millions of dollar*) ¦ 0.30 Coat per cancer case (illlions of dollars] * 1,00
0.00157b
Type of
Person
U.
Number In
S. Popu1 at 4 on
Average Annua) E«po*ure
(micro gns/cuble >n) (irO/kg/day)
Expected Ufet ime
Inc1dence
(proban tI1 t y / I1fe t1 me)
txpected
Annual Cases
(nuir.to r/ y ear J
Machine Operator!
Co»"nerc 1 »1
Industrie)
Coin-Op
16000,
700.
11000.
15000,
15000.
•4020,
6,43
6.13
0.5713
0.0669
0,0669
0,000552
15,29
0,6690
0 , 0«68
n
i
-O
Other Horkere
Comma rc 1 a 1
I nflu s t r 1 a I
Coln-Op
110000,
20000,
22000.
10000.
10000,
1020,
i,13
1,13
0,5713
0,003113
0,003 /ill
0,00 0 552
5.36
0.9753
0,1736
Service Uaers
Commercial Dry Clean 50000000,
Co1n-Up Ory Cleon 25000000,
Coin-Op Laundry 37000000,
5,00
7,10
25,16
0,000711
0,001057
0,003637
8.518E-I0
1 .(J72E-09
2.216E-08
0,000611
0,000669
0.0117
Urban Resloente
fo
Co
cs
25 - 200 *
200 - 500 *
500 - 1000 •
3700000,
20000000,
71000000,
* Distance from dry cleaner In meter*
3,26
0,5580
0 . 1b60
0,000065
7.971E-05
2.b57E-05
3.623E-10
1 .065E-I I
1.1 83E-1 2
Total
1 ,915E-05
3.012E-06
I,200fc-0b
22,57
Total cost of option end expected cancer (million* of dollars) ¦ 22,87
-------
OPTION 4
5c«lt no: SUR*ACE_A*EA Speclesi H*T Model I LINEAR
Response of 5 percent corresponds to dose In nan of 13.3 mg/kg/dey which ImpHas lambda =
Option coat (million* of dollars) b 0,30 Coat Per cancer cata (millions of dollars) a 1.00
0,0011«6
Type of
Person
Number In
U. S. Population
Average Annual Exposure
(Hero gms/cub1c (mg/kg/day)
txpected LI f et |ne
Inc1 dene e
(p robao1I11 y / 1i fot1 me )
Expected
Annua I Cases
(nuTb« r/yea r)
Machine Operators
0
1
u>
00
Comme rc1 a 1
tndustMa I
Coin-Op
Other Workers
Commercial
InduSIr1 a I
Coln-Up
Service Users
Comma re 1 a I Ory Clean
Coin-Op Ory Clean
Coin-Op Laundry
Urban Residents
160 00,
700.
1 1000.
110000,
20000,
22000.
50000000,
25000000.
37000000,
45000.
45000.
4020.
10000.
10000.
4020.
5.00
7.40
25.46
6,43
6,43
0,5743
1.43
1,43
0,5743
0.000714
0.001U57
0.003637
0,007593
0.007593
O.OOU681
0,001692
0,00169?
0,000681
8.469E-07
1.253E-06
M.312E-06
1,74
0,0759
0. 1070
2,66
0,4835
0,2139
0,6049
0.4476
2,2a
C-
25 - 200 •
200 - 500 »
500 - 1000 •
3700000,
20000000,
71000000,
3.26
0,5500
0,IBS0
* Distance fro« dry cleaner In meters
Total cost of option and expected cancer (millions of dollars) -
0.000465
7.971E-05
2.65 76-05
9,00
5.513E-07
9.45IE-06
3, 150E-08
Total
0,0291
0,0270
0.0320
8,70
-------
OPTION 4
Scallngi SURF ACE_ANE A Speclesl HAT Model! QUADRATIC
R»»ponn of 5 percent corraioerd) to dose tn man of "3.5 mg/kg/day whtc& Implies lambda "
Option cost Cut I I lens of dollars) = O.JO Cost par cancer case (millions of dollars) a 1,00
a.7«oE-os
type o*
Person
U.
Number In
3. PopuI a tI on
Average Annual
(micro gms/cublc m)
Exposure
(mO/kg/day)
Eipee ted Lifetime
I nc1dencc
(probabI1 Ity/1If e t(me)
Expect ed
Annual Cases
(numoe r/year)
Machine Optratori
Comirerc i a 1
Indust rial
Coln-Uo
16000,
700.
U000.
05001,
<4500 0 ,
14020 .
6,43
6,03
0.5743
0.001132
0,001132
9.03OE-06
0.2587
0.0113
0.001420
o Other aorkera
I
vO
C o "> <"> o r c I a 1
Induit rial
Coin-Up
110000,
20000,
22000,
10000,
1000U,
ao2o,
i,«3
1,43
0,5703
5.591E-05
5.59lt-0S
9,0 iHE- 0b
0,0879
0,0160
0,00284 1
Service Users
Comirerclel Dry Clean 50000000,
Coin-Op Dry Clean 25000000,
Coin-Op Laundry 37000000,
5,00
7,(40
25, <46
0,0007144
0,001057
0,003637
I.396E-I1
3.063E-11
3.625E-10
9.987E-06
1.09UE-05
0.000192
Urban Residents
W
CO
uo
25
200
500
20u •
500 •
1000 «
3700000,
20000000,
71000000,
• Distance from dry cleaner In metera
3.26
0.5590
0,1660
O,O0O«65
7,9' 1E-05
2.657E-05
5.926E-12
1.711E-13
l,VJ5t-11
Total
3.132E-07
4,9 75t-06
1 ,V6 3£-0B
0 , i 7 81
Total cost of option and expocted cancer (mllHona of dollars) ¦
0,66
-------
OPTION o
SciMnql BODY^HEIGHT Species! MOUSE Modal l
Raiponte of 65 percent corresponds to oose In man of
LINEAR
my/kg/day nMch InpMti lambda =
0,003162
Option coit (millions of collars) e 0,30 Cost per concer co9e (ml I Horn of dollars) = 1,00
lype of
Person
Number In
U. S. Population
Average Annual Exposure
(micro gmj/cuble n) (mg/ky/day)
Expeetao L1 fat 1 me
Incidence
(probanI 1|t v /1 t fot1 me)
Exoeeted
Annual Cases
(nu™be r/ye « r)
•fc-
O
Machine Operetore
Comma re f a 1
Indus t rial
Co1 n»l)D
Other workers
Co^merc 1 a 1
Industrial
C o1n-Oo
Service Users
16000.
700,
11000.
110000,
20000.
22000.
05000,
15000.
1020,
10000,
10000 ,
4080.
6.13
6.43
0.5713
1.13
1.13
0.5713
0.020!
0.0201
0.00 Id 1 4
0.0OU507
0,004507
0,001614
1,60
0.2012
0,2851
7,08
1,29
0,5702
Commercial Dry Clean 50000000.
Coln»0p Dry Cleen 25000000.
Coin-Op Launrtry 37000000.
Urban Nestdents
5.00
7.40
25.16
0.000711
0.001057
0.003637
2.259E-06
3.343E-06
1 . 150t'05
1,61
1.19
6.08
Co
C3
25 - 2Oti *
200 - 500 »
500 • 1000 •
3700000.
20000000.
71000000,
3.26
0,5580
0,1860
* Distance from dry cleaner In meter*
Total cost of option end expected cancer (millions of dollars)
0.000165
7.97lt»05
2. 6571-05
23,45
1.170E-06
2.521E-07
b,402E-08
Total
0,0777
0.0720
0,0a52
23.15
-------
OPTION 1
Seel I rigl BODr.wFIGHT Species! MUUSE Mode) I QUADHATIC
Response of 6b percent correaponds to dose In men of 332.0 mo/kg/day which Implies Iembda a
Option coat (millions of dollars) ¦ O.iO Cost per cancer case (millions of aollars) = 1,00
9.521E-06
Type of
Person
U.
Number In
S( Popu1 a11 on
Average Annuel Exposure
(micro qms/cublc m) (mg/kq/dey)
Expected L IIet * me
Inc 1 der-ce
(probabl11ty/l1 let1«e)
Expected
Annuo) Cases
(nijir.be r/year)
Machine Operators
Commercial
Inriust r I el
Coln-Op
IbOQO.
700.
11000.
15000,
IbOOO,
1020,
6.as
6,13
0,571J
0,000191
0.000391
3. 111E-06
0, 0899
0,00J935
0.000191
n
I
Other Workers
CoimercI»1
Industrial
Col n-l)D
Service Users
110000.
20000,
22001.
10000,
10000.
1020.
>.«3
1.13
0.5713
1,913E-05
1.913E-05
i. 111E-06
0,0JOi
0.005552
O.Ono9
-------
OPTION 4
ScaHnox 80DT_WEIGNT Speelest RAT Modeli LINEAR
"tloonie of S percent correseonds to dose In n>an of 253,0 mg/kg/day which ImpMas lambda = 0,000203
Option cost (millions of dollars) ¦ 0,30 Cost per cancer ease (millions of dollars) 2 1.00
Expected lifetime Expected
Type of Number In Average Annual Exposure Incidence Annual Cesos
Herson U, 3, Population (micro g^s/cublc m) (mO/kg/dey) (probtblljty/l1fet1"t) Ingmber/vejr)
Machine Operators
O
I
-T-
CoT.mercial
1nfluit r1 a I
Co I n-l)n
Other Workers
CoT»erc1 a I
Industrial
Coln-Op
16000,
700.
11000,
110000,
20000,
22000.
45000.
15000.
«&20.
10000,
10000,
4020,
6,43
6, <4 3
0.5743
t .«3
1.43
0.S743
0,001302
0.001302
0,000116
0,000290
0.0O0290
0,000 1 16
0.2977
0.0130
0.018J
0,4550
0.062 7
0,0366
Service Users
Commercial Dry Cleen 50000000,
Coln-Qp Dry Clean 25000000,
Coin-up Laundry 37000000,
5,00
7,40
25,46
0,000714
0,001057
0,00i637
1.4481-07
2.143E-07
7.3746-07
0,1034
0,0765
0,J893
Urban Residents
ro
25 - 200 »
200 - 500 *
500 - 1000 •
3700000,
20000000,
71000000.
« Distance from dry cleaner In meters
3.26
0.5580
0. 1S60
0,000465
7.971E-05
2.657E-0S
9.427E-0S
1.616E-08
5.Jd/E-OV
Total
0,004983
0.004618
0,005464
1 ,49
Total cost of option and expected cancer (millions of dollars) ¦ 1.79
-------
OPTION «
Scaling: 801) Y_KEIGHT Sneclesi KAT Modell QUADRATIC
Response of 5 percent corresponds to dose In man of 253,0 trg/ltg/day which Implies lambda t
Option cost (millions of dollars) * 0,30 Cost per cancer case (Mlllons of dollars) ¦ 1.00
8,01 3E-0 7
Type of
Person
Machine Operators
Number In
I). 8. Population
Average Annual Exposure
(micro gms/eub1c m) (nig/ kg/day)
Expected lifetime
Inc1dence
(probabI11ty/l1fet1 me)
Expected
Annual Cases
(ngibof/fe#rl
Commercial
Indust rial
Coln-Op
16000,
700,
11000,
15000,
<45000,
1020,
6.U3
6,13
0,5/«3
3,31«E-05
3.3ME-05
2, 6<43£-07
0,007575
0,000331
a, i5it-05
Other Markers
Co«i«erctel
Industrial
Coln-Up
110000.
20000,
220 00,
10000,
10000,
1020.
1.13
1.1J
0,5 7«3
1 ,6356-06
1 ,635E-06
2.6M3E-07
0.002570
0.000467
b.30&t>05
Service Users
Commercial Dry Clean 50000000,
Coin-Up Ory Clean 25000000,
Coin-Op Laundry 37000000,
5,00
7.10
25.16
0,000714
0,001057
0.003637
<4,086E-13
8.955t-13
I.060E-11
i.V20E-07
3,1Vflt-07
5,60iE-06
Urban Hesldants
25 - 200 •
200 - 500 •
500 • 1000 »
3700000.
20000000,
71000000,
3,26
0,5583
0,i860
0,000465
7.9ME-05
2.6S7E-05
1 . 733E-1 3
5.092E-15
5.65SE-16
9. l59E-0<5
1 .M55E-09
5,73SE-1 0
* Distance from dry cleaner In meters
fot al
O.ul 1 1
Total cost of option and expected cancer (trillions of dollara) a
0,31
-------
OPTION 5
ScaHngi SURFACE_AKEA Species! MOUSE Model I LINEAR
Hesponse of *>5 percent corrtioond) to dose In iron of 25.0 mg/kg/dey kMc" ImpHe* lambda =
Option cost (million* of dollar*) e 8,70 Cost Per cancer case (millions of dollars) - l.UJ
u.oaiw
Type of
Person
Number In
U, S, PopuI at 1 on
Average Annual Exposure
(micro gms/cubie m) (mO/kn/day)
Expected Lifetime
Ine1 dene e
(proo*b i)It y /11fetl»n)
t" *pec t ed
Annual Cases
(number/year)
Machine Operator*
Con«e rcia I
Indus t rial
C o1n-Op
16000.
700.
11000,
29700,
15000.
faOOO.
1.34
6,13
0.8571
0.16JO
0.2363
0.0353
37,26
2.36
5 ¦ 55
¦p-
.e-
Other Workers
Commerc1 a I
Indus t r|el
C oIn-Oo
110000.
20000,
220 C 0,
10000,
10000,
6000,
1.03
l.«3
0.857J
0.0582
0,0582
0,0353
91 .39
16,62
11,10
Service Users
Commercial Dry Clean 50000000,
Co1n-0p Dry Clean 25000000,
Coin-Op Laundry 37000000,
5,00
10,00
38.00
0.0U0711
0.001129
0,005U2V
2.998E-0S
5.990E-05
0,000228
21.12
21.39
120.
Urban Residents
Co
.25 - 200 *
200 - 500 •
500 - 1000 *
3700000,
20000000.
71000000,
3.50
0.6000
0,2000
* Distance fro* dry cleaner In meter*
Totel cost of option and expected cancer (millions of dollars)
0,000500
B.571E-05
2.b57E-05
339.15
2,0986-05
3.595E-06
1, 198E-06
I ot«I
1.11
1.03
1.22
331 .
-------
OPTIUN 5
Seal 1ngI 3URFACE_»REA 3pec1»a: MOUSE Modelj QllADHATIC
Wesponsa of 65 percent corresponda to dose 1n man of 25,0 mg/kg/day *Mch Upl l#i lambda s 0,101675
Option coat (millions of dollars) 3 8.70 Cost pep cancer case (millions of dollars) a l.OO
Type of
Ha rson
Number In
II, 3, Population
Average Annual Exposure
(micro o^s/cviblc m) (iry/kg/dav)
Eipec t ed Lifetime
IncI denee
(probahlI1ty/lIf e t i«f)
Expactad
Annual Cases
(nvjmncr/yeor)
Machine Operator*
0
1
Ul
Cohere I a I
Indgst rial
Co 1n-Jp
Other norkera
Co*«trciil
Industrial
Co1n-Qp
Service Users
Commercial Dry Clean
Coin-Op Dry Clean
Coin-Up Laundry
Urben Residents
• • •m mmmmmmmm•* a
25 - 200 »
2on - 500 «
500 - 1000 *
16000,
700.
1inoo.
110000.
20000.
22000,
50000000,
25000000,
37000000.
3700000,
20000000,
71000000.
29700.
150 0 0,
6000.
loooo,
10000,
6003 .
5,00
10.00
38.00
5.50
0,6000
0,2000
• Distance from dry cleaner In meters
Total coat of option end expected cancer ImHHoni of dollars) *
«.2«
6.UJ
0.6571
1.13
1,11
0.B571
0.000714
0.00102?
0 .0051429
0,000500
8.S71E-0S
2.65 7E-05
23.11
0,0297
0,066V
0.301230
0,0034 I 3
0.003a I 3
0.001230
8.5O0E-1O
3,
-------
L
OPTION 5
SciHnqi 3URF ACE_AKE A Speclest RAT Hodels LINEAR
Response of 5 percent cerrespondu to dose In man of 43,3 mg/ko/day which Inpllei lambda =
Option coat (ml It tons of dollars) * 8,f0 Cost par cancer case (millions of dollars) = 1,00
0 , 00I 1Bfa
Type of
Pe rion
U.
Number In
3, Population
Avoraqa Annual Exposure
(micro omj/eublc m) (mg/kg/d«y)
Expected L(fet1 me
Incidence
(probab1I< ty/l1f e11 me)
Expect ed
Annua I Cases
(nuT.Dc r/y e ar )
Machine Operators
0
1
01
Commerc i a I
Inouitrial
Co1n-0p
Ot her Workers
Co»mrc 1 a 1
Industrial
C o1n»Up
Service Uaers
<;S 20« *
200 - 500 *
500 - 1000 »
16000,
700,
11000.
1 10000.
20000.
22000,
Commercial Dry Clean 50000000,
Co1n»0p Dry Clean 25000000,
Co 1n-Op Laundry J7000000,
Urban Wealdents
3700000.
20000000.
71000000,
29700.
45000,
6000,
10000,
10000,
6000,
5.00
10,00
id,00
5,50
0,6000
0,2000
* Distance from dry cleaner in meters
Total coat of option and expected cancer (millions of dollars)
i,2a
6.43
0,6571
l,«3
1.-43
0,0571
0,000714
0,001429
0,005429
O.OOOSOO
8.5 M t-05
2.6b7t-05
0,005018
0.007S9J
0.001016
0,001692
0.001692
0,001016
8.469E-07
1.694E-06
6.436E-06
5,928t-07
1.016E-07
3.387E'0a
1ot«l
1.1*
0.0759
0, 1596
2.66
0,4 635
0.3192
0,6049
0,6049
1.40
0,0313
0,0290
0,0344
9.55
18,25
-------
OPTION 5
SccHngt SURF»CE_ANEA Spedeat HAT Model I ClUAORATIC
Response of S percent corresponds to coit In men of <43,3 ng/kp/day which Implies lambda =
Option cost fit 11 Ions of dollars) s 8,70 Co »t Per cancer case (r.|11 Ions of dollars) a 1,00
2, 7u0f>0b
Type of
Person
U.
Number In
S, Population
Average Annual
(micro gma/eublc m)
Exposure
(mo/kg/day)
Expected Lifetime
Irc I (fence
(pronabIIlty/lifetlne)
t *pec t ed
Annual Cases
(nu-rnijr/year)
Machine Operators
Comma re I a I
Indus t rial
Coln«0p
16000,
700.
» 1 0 0 0 .
29700.
45000.
6000.
4,2a
6.43
0 . tt!> 7 1
0,000493
o.oojise
a.oist-os
0,1127
0 , 0 1 1 J
0.00 316b
n
I
¦e-
Other Workers
Comm® rcI a I
Indust rial
Co In-Qp
110000.
20000,
22000,
10000,
10000,
6000.
i,«J
t.«s
0,8571
5.591E-05
5.591E-05
2.015E-05
0.0879
0 , 0 I b 0
0,006312
Service Users
Commercial Dry Clean 50000000.
Coin-Up Dry Clean 25000000.
Coin-Op Laundry 37000000,
5,00
JO,00
38.00
0,000714
0,0011429
0,005429
1.396E-11
5,593E-11
8.076E-10
9.987E-06
1.997E-QS
O.OOOU27
iN
cn
Urban Resident*
»99+9mmmm9mmmwm
25 - 200 «
200 - 500 •
500 - 1000 *
3700000.
20000000.
71oooooo.
* Distance from dry cleaner In meter*
3.50
0,6000
0.2000
0.000500
t) ,5 71E-0S
2.057E-05
6.851E-12
2.013E-13
2,237E-l<4
Tot e I
3.621E-07
S.75iE-00
2.269E-08
0.2376
Total cost of option and expected cancer (million* of dollars) ¦ 8,94
-------
OPTION 5
3c¦1 Ingl B0DY_*E1GHT Spec I as I HOUSE Model! LINEAH
Response of 65 percent corresponds to dota In man of 332,0 ng/kp/dey nMc* Implies lambda a
Option coat (millions of dolla'i) * b.70 Cost per cancer case (millions of dollars) = 1,00
0,0 0 J I 62
Type of
Person
Number 1n
U, S. Population
Average Annuel Exposure
(micro gms/cunlc «i) (no/ kg/day )
txpeeted L1fet1 me
Ine 1 'lence
(probaoI Htv
1.61
1 .61
9.07
Urban Residents
-J
25 - 200 *
200 • 500 «
500 - 1000 «
3700000.
20000000,
71000000.
• Distance from dry cleaner 1n iretere
3.50
0.6 00 0
0.2000
0. 000500
».5ME-05
2.C57E-05
1.501E-06
2. 710E-07
9.O35E-08
Total
0.0636
0.0774
0.0916
25.45
Total coat of option and expected cancer (millions of dollars) s 34,15
-------
OPTION 5
Scallngi B00Y_kEIGHT Speciesi MUU3E Moduli QUADRATIC
Response of 65 percent corresponds to dost in man of 332.0 ma/ke'day which Implies lambda a
Option cost (trillions of dollara) » 6.70 Cost per cancer case (millions of dollars) b 1,00
9,52«E-06
Ivpa of
Person
Number in
U, S, Population
Average Annual Exposure
(micro a^s/cubie m) (mo/kg/day)
Expected Li tet |».e
Inc i dene e
(probabl1ity/11fe11 me)
6 «nec ted
Annual Cases
(nufiber/yesr J
Machine Operators
Conine rc i a 1
Industrla)
Coln-Op
16000,
700.
11000.
29700.
05000,
b 0 0 0 ,
«. 21
6.43
0,8'j7 J
0,0001 7 I
0 . 0 30 J 1, 9U2E-0 6
0,0U01'4 8
Urban Residents
CD
2 5 - 20 0
200 - 500
500 - 1000
3700000.
20000000,
71000000.
* Distance from dry cleaner in meters
3,50
0,6000
0,2300
0.000500
8.571E-05
2.857E-05
2.381E-12
6, 99bE-l1
7.775E-15
lot o I
1.259E-07
1 ,<499t-08
7 .ISHbE-09
0,0(427
Total coat of option and expected cancer (millions of dollars) * 8,78
-------
OPTION 5
ScaHngj BOCV_WtICHT Speclasi RAT Modell
Response of 5 percent corresponds to dose In men of
LINEAR
253,0 mg/Wg/dsy which Implies Iemhde
0.000203
Option coat (millions of dollars)
B.70 Cost per cencer ceia (nil Hons of dollars) ¦ 1.00
Type of
He rson
Number In
U, 3. Population
Average Annuel Exposure
(micro oms/cutHc m) (mfl/kg/day )
Expected lifetime
I nc1aenee
(erob at> I I 1 ty/1 1 fet I ma)
t «pee t od
Annuel Ceiei
( nu.T.Do r/y ee r)
Machine Operators
a
I
Ul
o
r
K-*
Co»'«rc1 el
Inoust rial
C o1n-0o
Other Workers
Commercial
Indust rial
Coln-Uo
Service Users
Commercial Dry Clean
Coin-Op Dry Clean
Coln-Qo Laundry
Urben Residents
25 - 20u •
200 - 500 «
500 - 1000 •
16000,
700.
UOOO.
110000,
20000.
22000.
50000000,
25000000.
37000000.
3700000.
<£0000000.
71000000.
29700.
«5000,
O000,
10000.
10000.
bOOO,
5.0U
10.00
38.00
3,50
0.6000
0,2000
• Distance from dry cleaner In meters
Totel cost of option end expected cencer (millions of dollars) a
4,24
6.03
0,8571
l.«3
l.«3
0,8571
0,0007111
0,001929
0,005129
0.000500
U.571E-05
2.B57E-05
10.33
0,000860
o.oaiJ02
0.000170
0,000290
0,000290
0.00017«
1.U48E-07
2.896E-07
1 . 101E-06
1.011E-07
1 .730E-O8
5. 793E-09
Total
0.1965
0.0130
0.0273
0.4550
0.0627
0.05H6
0,103m
0.103V
0.5817
0.005358
O.OC'1965
0.00567b
s*«a*«a«a«
1.63
-------
UPTIUN 5
3calingi BODY_HEIGHT Specieal HAT Modal! QUADRATIC
Response of 5 percent correspond! to doao in man of 253.0 mo/ko/day which Implies lambda s
Option coat (millions of dollars) s 8.70 Cost par cancer case (mlllloni of dollars) s 1.00
8.013E-07
Type of
Person
Number In Avarana Annual Expoaure
U. 3. Population (micro g^s/cubic m) (mg/tcg/day)
Expected LHet I'll
IncIUence
(prohabI1 Ity/11fet(me)
L"«pected
Annua I Coses
1ftu"ber/year)
Machine Operators
O
I
Ln
w
C'l
o
Commercial
Indus t rial
Coln-Op
Other horkera
Commercial
Indultrial
C o1n»Op
Service Users
Commercial Ory Clean
Coin-Up Ory Cl*on
Coln-Qp Laundry
Urban Resident*
25 - 200 •
200 - 500 »
500 - 1000 •
tbOOO.
700,
11000.
110000,
20000,
22000.
50000000,
25000000,
37000000.
3700000,
20000000,
71000000,
29700.
asooo,
6000,
10000,
10000,
6000.
5,00
10.00
38,00
3,50
0,6000
0,2000
<. 21
6.U3
0.8571
I.#3
1.13
0.8571
0,000711
0.001129
0.005429
0.000500
8.571E-05
2.B57E-05
1.112E-05
3.311E-05
5.O87E-07
1.63St-06
1 ,635t"-0fc
5.887E-07
1.088E-1 3
1.635E-12
2.362E-U
2.00JE-13
5.887E-15
6,512E-lb
• Distance fro* dry cleaner in mat art
Total coat of option and expected cancer (millions of dollars)
Total
0.003297
0.000331
9 .252E-05
0, 002570
0.000467
O.OOOld'i
2.920E-07
5.b11t-07
1 .^OE-OS
1 .O59E-08
1.662E-09
b.6iSE-l0
0,OUbVsb
8,71
-------
OPTION *
Seal 1ngt SURFACE,APE* Speclesr MOUSE ModeH
Response of 65 percent correspond* to dose 4n man of
LINEAR
25,0 *g/kg/day kMc* Imp)lai lambda
0.0119
Option cost (ml I Hons of dollars) * 1.90 Coat Par cancer cist (ilI I Ions of dollars) * 1,00
Type of
Person
0.
Nuntoer In
S. Population
Average Annual
(micro gms/cublc *)
Exposure
(mg/kg/day)
Expected Lifetime
Incidence
(probao11itv/1Ifetl««)
txrected
Annual Coses
(numbn r/year )
HecMne Operators
0
1
i-n
ho
C Ofnmf rc 1 » 1
Industrial
Co 1n»Up
Other Workers
Con-me rc 1 a I
Indust rial
Coln»0p
16000.
700.
11000,
1 10000.
20000.
22000.
15000,
15000.
6000.
loooo,
laooo.
6000.
6.15
6.03
0,8571
1.13
1.13
0,057 1
0,2363
0,2363
0,0353
0,0582
0.0582
0.0353
51.02
2.36
5.55
91.39
1 ft«62
11.10
Service Users
Cohere 1 a 1 Dry Clean 50000000,
Co1n»Uo Ory Clean 25000000,
Co1n«0p Laundry 37000000,
5,00
10,00
3,60
0,000711
O.OOH29
0.00 0513
2.998E"05
5.990E-05
2,277t-0b
21 ,12
21,39
12.01
Urban Mesldents
cn
v*
25 - 20 u •
2'00 - 500 •
500 - 1000 «
37 0 U 000 ,
20000000,
71000000,
• Distance fro" dry cleaner In meters
3.50
0.6000
0,2000
0,000500
6.57 lt-05
2.B57E-0S
2.098E-05
3.5<»5E-06
1 , 198f"06
Total
1,11
1,03
1 ,22
239,
Total cost of option end expected cancer (millions of dollars) a 211,12
-------
OPTION 6
Seal 1 ngl SURFACE_AREA Spec lest MOU5E Model I QUADRATIC
Response of 65 percent corresponds to dosa In man of 25,0 mg/kg/day which Implies lambda a
Option cost (millions of dollars) - 4,90 Cost Per cancer case (millions of dollars) « 1,00
0.001675
Type of
Harson
Number I r»
U, 3, Population
Average Annual Exposure
(micro
-------
OPTION 6
Selling; 3URFACE_AREA SpecleSI «AT Hoclelj LINEAR
Hesponse of 5 percent correspond* to dose In nan of 43,3 mg/kg/day whlc* Implies Iembdo =
Option cott (i| I I Ions of dollars) : <(,90 Cost oer csnctf case (HI lions of dollars) - 1.00
0,001tttb
Type of
Person
Number in
U. 3, Hopulstlon
Average Annual Exposure
(micro gms/cublc m) (mg/k;j/d«iy)
E*uact ed Lifetime
IncI denee
(probabl Hty/Hfetlme)
Expected
Annual Ceaas
lnu"in or/year)
Machine Operators
Corime rc I 8 I
Indust rial
Co1n-Uo
16000,
700,
11000,
45000,
15000,
6000.
6,43
6.as
0.«S71
0.0075^3
0,007593
0 . 0 0 1 C 1 6
1.74
0 , 0 7 'j 9
0,lS9b
n
i
ui
Other Workers
CoT-e rc I a I
Indust rial
Co 1n-Op
110000,
20000,
22000.
10000,
10000,
6000,
1.43
1.43
U.#57l
0,001692
0.001692
0. 0 0 1 0 16
2.66
0.«H35
G.119*
Service Users
Conmarciel Dry Clean 50000000.
Coin-Op Dry Clean 25000000.
Coln-Oo Laundry 37000000.
5.00
10.00
3,80
0,000714
0,001429
0,000543
8.469E-07
1.694E-06
b.436E-07
0.6049
0.6049
0,3402
Urban Has I dent¦
w
CI
CO
25 - 200
200 - 500
500 - 1U00
3700000,
20000000.
71000000.
» Distance from dry cleaner In maters
3,50
0,6000
0,2000
0,000500
8.57lE-05
2.6t>7fc-05
5.928E-07
1.016E-07
J.3B7E-08
Total
0.0313
0 .029C
0,0J4U
7 ,0b
Total cost of option and expected cancer (millions of dollars) * 11,98
-------
OPTION b
Scaling: SURFACE.AREA Species: RAT Modelr QUADRATIC
Response of 5 percent corresponds to dose 1n »«n of 13,3 mo/ko'dey which Implies lambda * 2.7UOI-OS
Option coat (millions of do liars) a 1, YO Coat Per cancer case ("I I I 1 ona of dollars) c 1,00
Expected lifetime txoected
Typa of Number In Average Annual Exposure Incidence Annual Cases
Person U, S, Population (micro gms/cublc m) (mQ/kg/oey) (probau111ty/1(fetI mo) (numoor/ycar)
n
<-n
t_n
Machine Operators
Co""ierc 1 * I
Industrlsi
Coln-On
Ot^er Workers
Co-mere 1 a I
Inoust rial
Coln-Gp
Service Users
16000.
7U0.
11000.
110000.
20000,
22000.
15000.
15000.
6000.
100 JO.
10000.
6000.
6.13
6,Mi
0tt)5M
1,13
1 ,13
0.8571
0.001132
0.001132
2,O|5E-05
5.591E-05
5.591E-05
2.015E-05
0.2587
0.0113
0.00 3166
0.0 879
0,0160
0.00(S3i2
Commercial Dry Clean 50000000,
Coin-Up Ory Clean 25000000,
Co1n-Gp Laundry 37000000,
5,00
10,00
3.60
0,000711
0.001129
0.000513
1 .39BE-U
5.593E-U
6.076E-12
9.V87E-06
1.V97E-0S
1.269E-06
01
Urban. Resldenta
25 -
200 -
500 -
200 *
500 •
1000 •
3700000.
20000000,
71000000,
• Distance from dry eleener 1n meter*
3.50
0,6000
0,2000
0,000500
fl.5Mt-0S
2.857E-05
6.851E-12
2.013E-13
2.237E-14
Total
i,621E-07
5.751E-0H
2.26VE-0B
O.iBjl
Totel coat of option end expected cancer (millions of dollars) * 5,26
-------
OPTION b
9c«Mng: ijnDY_wtIGH7 Species: MOUSE Modal I
Response of 65 percent corresponds to (foie 1n man of
LINE4H
333.0 mq/kg/dsy xhlch Irplle* lambda s
0.003162
Option cost (ml I Hons of dollar*) * 4,90 Co«t por ctncor case (ml 11 Ions of dollars) e 1,00
type of
Parson
Number in
U. S. Population
Average Annual Exposure
(micro gms/cuotc m)
-------
OPTION 6
3c a 11ng t 80DY_*EIGHT Sp«cl«lt MOUSE Model I QUAORATIC
Response of 65 percent corresponds to doae 1n iran of 332.0 mg/ko/dey which Implies lambda =
Option coat (millions of dollars) a u.>»0 Cost par cancer case (tlllloni of dollars) 3 1.00
9.sa
i
v
~4
Other horkera
Comma rc1 a 1
InrtuB trial
C oIn-Uo
1 10000.
20000.
220UO,
10000.
10000.
6000.
l.«3
l.«3
0.U571
i .VU3E-05
1.9M3E-05
b,v9a£-06
0.0305
0,005552
0,002199
3»rv1ce Users
Commercial Dry Clean 50000000,
Coin-Up Dry Clean 25000000,
Co1n-Up Laundry 37000000,
5.00
10,00
3.60
0, 000 7 la
0.O0IK29
0.1005M3
H.859E-I2
I.9*»«fc»l I
2.S07E-12
S,«7IE-06
6.9U2E-06
1, <4(3 ^E«06
Urban Residents
w
C/l
a
25 - 200 •
200 - 500 •
500 • 1000 *
3700000,
20000000.
71000000,
* Distance from dry claaner in maters
3,50
o.tooo
0,2000
0,000500
8,57IE-05
2.857E-0S
2 .38IE-12
6.99UE-1U
7.775E-IS
total
l,2S9t-0T
1.999E-0B
7 .B86E-09
0.I3ii
Total cost of option and expected cancar (millions of dollars) ¦ 5,03
-------
OPTION 6
ScsHnos BODY_ta£ IGHT Soeclesl RAT Model J LINEAR
Response of 5 percent corresponds to dose in ran of 253,0 mg/kg/day which Icplles lambda «
Option eo»t (Ml Hons of dollars) a 4,V0 Cost pep cancer ease (nil 1 torn of dollara) = 1,00
0. 00020 J
Type of
Person
Number In
U. S, Populat 1 on
Average Annual
(micro TBS/cubic m)
Exposure
(mg/kg/da y)
Expected LI fet1 me
I ncIoance
(probab1I 1t y / 1Ifet1 me )
txDected
Annual Cases
Inumber/yeo r)
t ~ • « m m
Machine Operators
Co^merc1 a I
1 ndustrial
C o1n«Up
16000,
7 00,
11000,
45000,
15000,
6000,
6.4J
b.m
0,6571
0,001302
0,001302
0.000174
0,8977
0.0130
0,0273
n
I
OO
Other norkeri
C oiF">e r c 1 a I
I ndus trial
Co 1n-Op
110000 ,
20000,
22000,
10000,
10000.
6000,
1,43
1,13
0 .657 I
0,000290
0,000290
0.000174
0, 4550
0,0b2 7
0.0516
Service Users
Ccerdil Dry Clean 50000000,
Co1n-0p Dry Clean 25000000,
Coin-Dp Laundry 37000000,
5,00
10,00
3.BO
0.000714
3,001429
0,000543
1.448E-07
Z.S96E-07
i.ioie-07
0.1034
0.1034
0.05B2
Urban Residents
<\3
Cl
-J
*5 - 20 0 «
200 - 500 «
500 - 1000 »
3700000.
20000000,
71000000,
• Distsnce fro* dry Cleaner In meters
3.50
0,6000
0.2UOO
0.000500
6.57 it-OS
2.857E-05
1.014E-07
1 ,738£-0«
5. 7936-09
Total
0,005358
0.004965
0,005b 75
i.ai
Total cost of option and expected cancer (millions of dollars) 1 6,11
-------
1/
OPTION 6
SeaHngs H00V_hE IGHT Speclesl HA1 Model l QUADRATIC
"espouse of S percent corresponds to doae In men of 253.0 n,g/kg/day wh1ch linplles lambda a
Option cost (ml I Hona of oollers) a #,90 Cost par cancer case (millions of oollars) = l.oO
8,014E-0 7
Type of
Ho r son
U.
Number In
S. Population
Average Annuel Exposure
(micro gmj/cublc i*>) (ir.O/kg/day)
E xpec t nd Lifetime
Incidence
(probao1 I i ty/1 I 1 e 11 ">e )
Expect ed
Annual Cases
(njn.oer/year)
Machine Uoerators
n
i
Ln
vO
Co»»«rcIbI
Indus t rial
Coin-Op
Other Workers
Co»merc(al
Indust rial
Co In-Uo
ServIce Users
16000.
700.
11000,
UOOOO.
20000.
22000,
<45000 ,
<15000.
6000,
10000,
10000,
6000,
6.UJ
6.4J
0.«57l
l.«3
l,«i
0.0571
3.314E-05
3,31'4 £-05
5.687E-07
1 .635E-06
1 .635E-06
5,687£-07
0,007575
0,000351
9,c?52l-05
0.002570
0,000467
0,000185
C.-1
CO
Cohere I a I Dry Cleen
Coln-Oo Dry Clean
Coin-Op laundry
Urban Residents
25 - 200 »
200 - 500 *
5u0 - 1&00 *
50000000.
250O00C0.
37000000.
3700000,
2000C000,
71000000,
5.00
10.00
3,80
3,50
0.6000
0.2000
• Distance from dry cleaner In meter*
Total cost of option and e>pected cancer (mllllona of dollars) a
0.000714
0.001429
0,0005 05
2.H57E-05
a,08»E-l3
1,635t> 12
2,i62£-li
2,O0iE-l3
5.687E-15
6.5<(2E-16
Total
2.920E-07
5.61 lE-07
X , 2
-------
OPTION 7
Scaling! SURF ACE_AREA Specie*: HOUSE Model t
Resoonse of t>S otrctnt corresponds to aot« In man of
LINEAR
25,0 rog/kq/day which l«pHei lambda s
0.04 19
Option cost (#1IIloni of dollars) =-13.50 Cost oar cancer case (millions of dollars) « 1,00
Typo of
Person
U.
Number In
S, Population
Average Annual
(micro ami/cubic
Exposure
(mQ/ko/day)
Expected LHethe
Incidence
(probab t1(t y /11fet 1 me)
E xpectea
Annual Cases
(number/year)
Machine Operator#
O
I
O
O
C/l
CoTierclal
Indust rial
Coln-Oo
Other Morkere
Commercial
I ndus t rial
Co In-Op
Service Users
Commercial Dry Clean
Co1n-Op Dry Clean
Coin-Op Laundry
Urban Healdenta
25 - 200 *
2(i0 - 500 •
500 - loao *
16000.
700.
11000.
110000,
20000.
22000,
sooooooo,
25000000,
37000000,
3700000,
20000000,
71000000,
27000.
10150.
3300.
6000.
6700,
3300,
2.80
5.SO
20.90
2.00
0.3420
0,1140
3.86
1.31
0.4714
0.8571
0,9571
0,471 4
0.000100
0.000343
0.00296b
0.000285
4.886E-05
1 .029E-05
0, 1494
0,1653
0.0196
0,0353
0.0193
0.0196
1 .675E-05
3.535E-05
0.000125
I , 198E-05
2.U49E-06
6.830E-07
Total
* Dlitance from dry cleaner 1n metere
Total coat of option and expected cancer (mllllona of dollar*) *
34, !<4
1,65
3.08
55.49
11,24
6,15
1 1 ,96
1?,62
06, 19
0.6333
0.5854
0.6928
204,
190,94
-------
OPTION 7
Scillnol SURF ACE_*SEA Species: HOUSE Modelt QUADRATIC
Response of 65 percent correspond* to dose in man of 25,0 mg/kg/day which Implies lambda =
Option cost (millions of dollars) *-13.SO Cost Por cancer esse (millions of dollars} = l.OO
0,001675
Type of
Pa rson
U.
Number in
3, Populat 1 On
Average Annual Exposure
(¦Hero gms/cublc m) (mo/kq/day)
Expected Lifetime
Inc1dence
(probal'l 1 i ty/1 1 fet 1 m o)
Expect ea
Annual Cases
I-umho r/yea r)
n
I
cr
''•Chine Operators
Co«*ercI a 1 16000,
Industrial 700.
Co 1n-Do 1JOOO,
Other Korksrs
Coirme rc I e 1 11 000 0 ,
I ndus t rial 20000 .
Coin-Op 22000,
Service Users
Commercial Dry Clean 50000000,
Coin-up Dry Clean 25000000,
Coin-Op Laundry 37000000,
Urban Residents
27000,
30150.
3300.
6000,
6700 ,
3300 .
2.do
5.90
20, SO
3,86
«.31
0.47U
0,6571
0.9571
o.47l«
0,000400
0,000643
0,002966
0.0246
0,0306
0,000372
0,001230
0,001534
0,000372
2.681E-10
1,190E-09
1 ,494E-0b
5.63
1.3060
0. 05o5
1.93
0,«Jd2
0.1170
0,000 191
0,000(125
0,007895
Ci
o
25 - 200 •
200 - 500 ~
500 - 1000 *
3700000,
20000000,
71000000,
« Distance from dry cleaner in meters
2,00
0 , 3 'J 2 0
0,1140
0,000285
«,886E-05
1.629E-0S
1.361E-10
3.9996-12
-------
OPTION 7
Sc»Hng! SURFACE_akEA Species! RAT Model!
Response of 5 percent corresponds to dose 1n man of
LINEAR
43.3 mg/kg/day which Implies lambda =
Option cost (millions of dollars) =-13,50 Cost per cancer case (millions of dollars) = 1,00
0,0011tib
Type of
Parson
U.
Number In
S, Population
Average Annual Exposure
(micro gns/cub1c m) (mg/kg/day)
E xpec t ed Lifetime
Inct dence
(proootil I 1 ty/1 i fet * me)
Expected
Annual Cases
(ogmnar/year)
Machine Operators
Comm»re I a 1
Indust M sI
Coin-Op
16000,
700,
11000,
27000,
30150.
530 0.
1.96
«,31
0 . « / 1 'I
0,004563
0.005044
0,00055V
l .04
0,0509
o.oare
n
i
(T-
Other Workers
Con-me rc 1 a I
Indus trial
Col r>-0p
110000,
20000,
22000.
6000.
6700.
3300.
0,6571
0.9571
0,4714
0,001016
0.001134
0.0005b9
1 ,60
0.32«0
0. W56
Service users
Commercial Dry Clean 50000000,
Coin-Op Dry Clean 250O000C,
Coin-Up Laundry 37000000,
2,80
5.40
20.90
0,000400
o.oooeaj
0,002986
4,7 42E-07
9.993E-07
3.540E-36
0.33a7
0.3569
I. d 7
Urban Hesldenta
&
25 - 200 *
200 - 500 *
500 - 1000 «
3700000,
20000000.
71000000,
• Distance fro" dry cleaner 1n meters
2.00
0,3420
0,1140
0,000285
4.866E-05
1 .629E-05
3.379E-07
5.793E-08
1,9316-08
Total
0.0179
0,0166
0.0196
S.VO
Total cost of option and expected cancer (millions of dollars) a -7,60
-------
;
OPTION 7
Seallngt 3UNMC£_*hEA Speclesl NAT Modell uuaDratic
Response of 5 percent correspond# to dosa In men of 43,1 mg/k <3/day «Mc* Implies lambda a 2.740E-H5
Option cost (millions of dollars) Coat par cancer case (millions of dollars) * 1,00
Expected Lifetime Expecte
Type of Number In Avarage Annual E*po3ure Inclaence Annual Co
Peraon U, S, Population (micro oms/cuble m) (mg/kg/dey) (probeb1111y/111etI me) UuTher/y
MecMna Operator*
n
1
o
to
Conmerc» a I
1ndus trial
Co f n-Op
Other Workers
Comma rc1 a I
1 noustrial
Cot n-Op
Service Usera
Co««
-------
OPTION 7
SdHngi 0(lDV_wEIGHT Soeclesi MOUSE Modal t LINEAR
Response of 65 percent corresponds to oose 1n man of 352.0 mg/ko/day which Implies lambda -
Option cost (millions of dollar*) c-|3,S0 Coat Per cancer case (millions of doll»r9) - 1,00
0,003162
Type of
Person
Number In
U. 5, Population
Average Annual Exposure
(micro gns/cublc m) (mg/kg/day)
Expectea Lifetime
IncIdence
(probability/lifetime)
Expected
Annual Cases
(nu•¦•••«¦
15,71
Total cost of option and expected cancer (millions of dollars) ¦ 2,21
-------
OPTION 7
Sealing: BOO Y_*/E IGHT Speclesi MUUSE Model| QUADRATIC
Response of 65 percent corresponds to dosa In men of 33?,0 *<3/kQ/d«v whlc'i lambda =
Option cost (nllHoni of dollars) «»13,50 Cost per cancer case (millions of dollars) s 1,00
9.520 3 33 3
0,0110
0.002U93
0.0006o5
I ,039E-06
2.U17E-06
4.<(8
-------
OPTION 7
SceH not 60DY_*EIGmT Species: HAT Hodeli LIHEAK
Response of 5 percent correspond* to dose In man of 253,0 mq/tcn/day which Implies lambda c 0,000203
Option cost (millions of dollars) «»13.50 Cost per cancer case (millions of dollars) a 1,00
Type of
Person
Number 1n
U. S. Population
Avarago Annual Exposure
(micro Qms/cublc m) (iro/ltg/day)
Expected Lifetime
Incidence
(probabl 1 I ty/11 fet li»e)
t »pec t ed
Annual Cjses
(numoer/year)
Machine Operators
O
I
a*
o
CD
C/l
Commercia I
Industrial
Co In-Uo
Other Hor«eri
Comme re i a I
Industrial
C o1n-Co
Service Users
Coirmerciel Ory Clean
Coin-Op Ory Clean
Coin-Go (.sundry
Urban Residents
25 - 200 *
200 - 500 «
500 - tooo *
16000.
700.
11000,
110000,
20000,
22000,
50000000,
25000000,
37000000 ,
3700000,
20000000,
71000000,
27000,
30150,
3300,
(>000.
6700,
3300.
2.80
5.90
20.90
2.00
0.3420
0.1 140
3,86
1,31
0,4714
0.8571
0.9571
0,47m
,000400
,000843
. 1)02986
0,000285
4.8O6E-05
1.629C-05
0,000762
0.000873
9.5S5E-U5
0.000 S 74
0.000194
9.555E-05
8,1 I0E-08
I.709E-07
6.U53E-07
5.77BE-08
9.905E-09
3.302E-09
Total
• Distance from dry cleaner In meter#
Total cost of option and expected cancer (millions of dollars)
0.1787
0,008729
0,0150
0.2730
0,0'j5 4
0,0300
0.0579
0,Obi 0
0.3200
0,003054
0,002830
0.003349
1,01
-12.
-------
OPTION 7
SceHnqi BOOY_wtIGHT Speclesl HAT Model! QUADRATIC
Response of 5 percent correspond* to dose In men of 253,0 ng/kg/day which Implies lambda =» 8.0I3E-07
Option cost (millions of dollars) *-13.50 Cost per cancer case (millions of dollars) = 1.00
E*nectod Lifetime txDected
Iype of Number In Average Annual Exposure Incloa^ce Annuo! Cases
Herson U. S. Population (micro gms/cuolc m) t-nQ/kg/day) (probab 1 II t y/H f e t i <*e) (nun, oa r/year)
Machine Operator#
O
I
en
Co"m»rc1al
1nduitrial
Co 1n-Op
Other rtorveri
Coif»erc i a I
1ndust ri a I
Co 1 n»0p
Service Users
Commercial Ory Clean
Coin-Op Dry Clean
Coin»Jp Laundry
Urban Residents
25 - 200 •
200 - 500 «
500 - 1000 «
16000,
700.
11000,
110000,
20000,
22000.
50000000.
25000000.
37000000.
3700000.
20000000.
71000000,
27000.
30150.
1300.
6000.
6700.
1300,
2,80
5.90
20,9«
2,00
0.3D20
0.1 140
3.8b
1.31
0.47(4
0.857J
0.9571
0,0714
0,000400
0.000343
0,002966
0.000285
4.886E-05
1.629E-05
1 . 192E-05
I .484E-05
1.781E-07
5.88/L-07
7.341E-07
1.781E-07
1.282E-13
5.693E-13
7.144E-12
6.509E-14
1.913E-15
2.125E-16
Total
* Olatanee from dry cleaner In meters
Total cost of option and expected cancer (millions of dollars) »
0.002725
o, o o o i«a
d.l 99E-05
0.000925
0,000210
5.S97E-0S
9.158E-08
2.033E-07
3.776t-06
3.440E-09
5.465E-10
156E-I 0
0.004096
-13,50
-------
OPTION B
3e»Hnqt SURF«CE_ARE* Sprcies: MOUSE Mods) t tIN£*8
Response of 65 percent correspond* to dose in man of 25.0 mo/kg/day which ii*olles lawbda *
Option cost (millions of collars) =-13.00 Cost per cancer esse (millions of dollars)'* 1,00
0.OH19
Type of
Pe r son
U.
Number In
S. Population
Average Annua I Exposure
(mi c ro g.-s/cub i c *) (-y/ko/day)
Expected LI fetimc
1nci dene e
(orociuI 1ity/l1fet t#c)
k spec t ed
Annual Cases
(nu'oer/ve*')
Machine Operator#
Comma rcia I
Industri al
Co i n -Op
16000.
TOO,
11CQ0,
22050.
26800,
1300,
3.15
1.11
o,«n«
0.1238
0,1585
0.0196
28,29
1.58
3.08
Other Workers
Co»*e re 1 a I
Industrial
Co In-Qp
1 10000,
20000,
22000,
-------
OPTION 8
Sea 1 t rig: 3URFACE_*REA Species! MOUSE Modal I 8UADHATIC
Hesponse of 65 percent corresponds to dose In ma" of 25,0 mrj/k>¦)
a
CO
Commercial Dry Clean
Coin-Up Dry Clean
Co)n»up Laundry
Urban Residents
25 - 200 »
200 - 500 *
500 » 100C «
50000000.
25000000.
37000000.
3700000.
20000000.
71000000.
2.45
5.90
20.90
1.62
0.3120
0.1010
• Distance from dry cleaner In metera
Total cost of option and expected cancer (millions of dollars)
0.000350
0.000843
0,002966
0.000260
1.157E-05
I.186E-05
2.052E-10
1 . 190E-09
1.191E-08
1.133E-10
3,320E-12
3.698E-13
Total
0,000117
0,000125
0.Otf7895
5.V87E-06
9,Sli)t-D7
3.7S1E-07
' 5.9,i
•7,08
-------
V
OPTION B
ScaMngi 3UKFACE_»REA Speclesl RAT Med«ll
Response of 5 percent corresponds to doss In man of
LINE AH
13.1 mg/kg/day which Implies lambda
0,001186
Option cost (it,Hltons of dollars) *-13,00 Cost per cancer case (millions of collars] e 1,00
Type of
Pa rson
Number In Average Annual Exposure
U. 3, Population (micro gn>s/cuolc m) (mg/kg/day)
Expected Lifetime
I nc I aence
(prooatH I I ty/1 1 fet (me)
t «pec ted
Annual C 3 S 6 9
(number/year)
MaeMne Ooerators
0
1
o
Cor.mercl al
t nduj t rial
Coln»0o
Other Workers
Co"i"|erc t al
I ndus trial
Coln-Un
16000,
700,
11000,
110000,
20000,
22000,
22050 ,
29000,
3100,
4900 ,
6«00.
3300,
3.15
*.11
0.U71U
0.7000
0,9103
0,4710
0,003728
0.03M666
0.0 00 559
0.000fl30
0.001001
0.000559
0.6521
0.0U&7
0,C6?U
I .30
0,3095
0.17 56
Service Usera
Commercial Dry Clean 50000000,
Co1n-Oo Dry Clean 25000000,
Co1n»0p Laundry 37000000,
2,as
5,90
20,90
0.000350
0.00 060 J
0.002966
«.150E-07
9.99JE-07
3.5«3E-06
0.2964
0.3569
1.37
Urban Realdertts
-------
OPTION 8
Scaling: dOOY.HEICHT SPtcUsl MUU5E HodaU LINEAR
Response of 65 percent corresponds to dose in man of 332.0 mn/kg/dav which implies lambda ° 0,005162
Option cost (»IIHont of dollars) *-13.00 Cost per cancer esse (million* of aollara) a 1,00
Expected lifetime txoeeted
Type of Number In Average Annual Exposure Ineluerce Annual C«sea
Heraon U. S. Population (wlcro g*>s/cub1c ml (mo/kg/day) (probibl lltv/H(ttiff>e) I numb a r / yea r >
w»eh1na Operators
0
1
¦»j
N>
Comma rcI a I
Indust rI el
Coln-Op
Other workers
Coimarc t al
Indust rial
Coln-Uo
Service Users
Commercial Dry Cloan
Coin-Up Dry Clean
Co1i"0n Laundry
Urban Residents
16000.
700,
noon.
110000.
20000 ,
22000.
sooooooo.
25000000.
17000000.
22050,
2H8O0,
330J.
4900,
6400.
3 300.
2.«5
5.90
20.93
3.15
«.U
0.4714
0,7000
0.91U3
0.4/14
0,0 00350
0.O0Q643
o.oo2vet>
0.009911
0.0129
0.001490
0,00221 1
0.002(187
0.001490
1.107E-06
2.665E-06
V.441E-Q6
2.27
0, 1293
0,2341
3.47
0,B24t»
0.4602
0. 7905
0 , 9i I 9
4.99
•v]
o
25 - 200 «
200 • 500 *
500 - 1000 *
3700000,
20000000.
71000000.
1.62
0.3120
0 , 1 0 " 0
* Distance from dry cleaner In meteri
Total cost of option and expected cancer (millions of dollars)
0.000260
4.157E-05
1.4B6E-0S
1.26
8.221E-07
1.409E-07
4,t>9aE-08
Total
0,043-1
0.0403
0.0477
14.2b
-------
OPTION 8
Soling: 3URFACP_4KEA Spaclesl HAT Model 1 QUADRATIC
Response of S oarctnt correspond* to dose In ran of 43,1 mg/kg/day which InplU« lambda =
Option coat (million* of dollars) =-13,00 Coat per concar case (millions of dollar*) - 1,00
2.740E-U5
Tvpe of
Person
Nuifbar In
U, 3, Population
Average Annuel Exposure
(micro qms/cublc m) (mg/kg/day)
E xpec t ed LI 1 a11 me
Inc1dence
(proDablllty/llfetlme)
Expected
Annual Cases
(number/year)
Machine Operators
n
-~4
w
"N*
Cotimerc I a 1
Indu st rial
Col n-Up
Other rtorkera
Comrnerc I al
Indust rial
Coln-Oo
Service User*
Commercial Dry Clean
Coin-Do Dry Clean
Coin-Op Laundry
Urban Residents
25 - 200 •
2tf0 - 500 *
500 - 1000 •
16000.
700,
11000.
110000.
20000,
220 00,
50000000.
2500u000.
37000000,
3700000.
20000000.
71000000.
22050.
24600,
3300,
4900,
6400,
3300 ,
2,15
5,90
20,90
1 ,82
0,3120
0.1U40
3.15
«.ll
0,4714
0,7000
0.9143
0,4714
0,000350
0,000643
0,00296b
0,000260
4.457E-05
i.4«te-os
0,000272
0,090464
6.091E-06
1 .341E-05
2.289E-05
b.091£-06
3.35/E-12
1 .947E-11
2.443E-10
1 .053E-12
5.444E-14
6.049E-15
Total
0,0621
0.00463a
0,000957
0,0211
0,0065JV
0,001914
i.i9tjE-0b
6.951E-06
O.0U0129
9.792E-08
1 ,5Sb£-0a
6 , I i>>E -09
0.0974
• Distance from dry cleaner In meter*
Total cost of option and expected cancer (million* of dollars) o
-12,90
-------
OPTION a
Scaling! BOOY_wEIGHT Specleal MOUSE Model! QUADRATIC
Retnonta of 65 percent corresponds to dose In men of 332.0 "Kj/kq/doy uhlch Implies lambda a
Option cost (millions of dollars) **13,00 Lost per cancer ease (millions of dollars) * 1,00
9.52«E-C6
Type of
Pe r son
Machine Operators
Number In
U, 9, Podu1 at I on
Average Annual Exposure
(micro ans/cuolc ml (ron/kg/day1
Expect ed Lifetime
IncIriencc
(probablII ty/lIfetl"e)
Expec t ed
Annua I Cases
I numbe r/ve ar)
ConnercI a I
Indust rial
Co In-Op
16000,
700,
11000,
22050.
28800.
4300 .
1.15
'Ml
0,4711
9,4176-05
O.orio 161
2.H7E-06
0,0216
0.001612
0,000333
O
Other Workers
CommercIal
Industrlal
C oIn-Jp
110000,
20000,
22000,
1900.
6400.
3300.
0.7000
0 , V 1 4 3
0.4714
1.66/E-06
7.V62E-06
2.1 17E-06
0,007334
0.00227S
0,000665
Service Users
Commercial Dry Clean 50000000.
Coin-Op Dry Clean 25000000.
Coin-Up Laundry 37000000,
2,15
5,90
20.90
0,000350
0,000613
0.O02V86
1,167E-12
6.766E-12
8.191E-1 1
8.334E-07
2.417E-06
4.486E-05
Urban Residents
25 - 200 •
250 - 500 *
500 - 1000 *
3700000.
20000000.
71000000.
• Distance from dry cleaner In meters
1.82
0.3120
0.1040
0.000260
4.457E-0S
1,4«6E-05
6.139E-13
I.892E-14
2.102E-IS
1 ot a 1
3.103E-08
5.406E-09
2.1 J2E-09
0,0339
Total cost of option end expected cancer (millions of dollars) ¦ -12,97
-------
OPTION 6
ScaHnq: bOOV_kEK,ht Species: HAT Model! L1NEAK
Response of 5 percent corresponds to dose in man of 253,0 ma/lcg/day which Implies lambda =
Uption cost (millions of dollars) s-13.00 Cost per cancer case (millions Of dollars) « 1,1)0
0,000201
Type of
Person
Number in
U, 3. Population
Average Annual Exposure
(micro flnis/cubic m) (ing/kg/day )
fcxpoctod Lifetime
I nc1dc nc e
(prohabi 1 1 t y / I i fetl«f)
txpec t ed
Annual Cases
InuJbor/year)
Machine Operetora
Comnerc i al
I ndust r t al
Co I n-Op
16000,
700.
unoo.
22050.
2M8C0.
3300,
5.15
0.11
0.4714
0.000636
0.000634
9.555E-05
0,1459
0,008338
0,0150
ft
i
Other rtorkert
Comme rc i a I
I ndus t rial
Co i n-llo
110000.
20000.
22000.
19 0 0,
64Q0,
3300.
0.7000
0.91^3
0.1714
0,000142
O.00C185
9.S55E-05
0,2230
0. 0530
0 . 03 0 0
Service Users
Commercial Dry Clean 50000000.
Coin-Do Dry Clean 25000000.
Coin-Uo Laundry 37000000.
2.IS
5.90
20.90
0.0UO350
0.000833
0.002966
7.096E-06
1.709E-0J
6.U53E-07
0.0507
0.0610
0.3200
Urban Hesidents
N
•vi
co
25 - 200
200 - 500
500 - 1000
3700000.
20000000,
71000000.
• Distance from dry cleaner in meters
t .92
0.3120
0.1040
0.000260
4.157E-05
1 .486E-05
5.271E-06
9.036E-09
3.012E-09
Total
0.002786
0.002562
0,003055
0,9151
Total cost of option and expected cancer (millions of dollars) * *12,06
-------
OPTION 8
Scallnql B0DY_ViEIGH7 Speclesl RAT Modal I QUADRATIC
Response of 5 narcant correspond# to dose In man of 253,0 mg/kg/day whlcl Implies lambda =
Option cost (trillions of dollars) *-13.00 Coat par cancer case (millions of dollars) a 1,00
8.013E-0/
Type of
Person
U.
Number In
S. Population
Average Annual
(micro gni/cublc m)
Eipolura
(ma/kg/day)
Expected L 1 f e tI mo
IncIaence
(probat)i 1 lt^/1 I feline)
Expected
Annual Cases
(nunDer/yur)
Machine Operator!
ComrercI«I
Industrial
Co In-0o
Othar Workers
Coi'trcI a 1
Industrial
Coln«0p
Service Users
Cohere I al Dry C'een
Coin-Dp Dry Clean
Coln»0o Launary
Urban Patidrnts
16000,
700.
11000,
110000.
20000.
22000.
50000000.
25000000.
J7000000,
22050,
2ts800.
JJOO.
<1900.
b000.
3300.
2.15
5.90
20.90
3.15
".11
0,4714
0.7000
0 , 9 1 <| 1
0.4714
0,000350
o. oooe
-------
OPTION 9
Seal 4 rial SURFACE_AREA Speclesi MOUSE Modeli
Response of *>5 percent corresponds to dose In mar* of
LINEAR
25,0 mq/kg/day which (npl l«i lembde c
0,0
-------
OPTION 9
Scaling! SUKFACE.AREA Speclesl HOUSE Model I OUAOHAT1C
Response of 65 percent corresponds to dose In man of 25.0 mo/kg/day which Implies lambda e
Option coat (millions of dollars) =-10,13 Cost per cancer case (ml 1)Ions of dollars) * 1.00
0.00167^
Ivoe of
Person
U.
Number In
3, Populotion
Average Annual
(micro
25 - 20o *
ZOO - 500 »
500 - 1000 •
5700000.
20000000,
71000000,
• Distance from dry cleaner In meters
1.68
0.2080
0,0960
0.000240
«. 1116-05
1.371E-05
9.651E-1 1
2,8 3ot-1 2
1,151E-13
Total
5.1 0 1E-06
8.103E-07
3,1961-07
v«*i«a»w«a4B«a
5 , 6b
Total cost of option and expected cancer (millions of dollars) »
2a
-------
OPTION 9
Seal 1 nq | SURFACE.AKEA Species! HAT Modal: LINEAR
Htipsrie of 5 pccent corresponds to dose In man of 43.3 mg/kg/dey wrtlcl Implies lambda s
Option cost l«lll(om of do liars) =-10.10 Cost per cancer case (millions of do Pars) = 1.00
0.0U1 186
Type of
Hereon
U.
Number in
3. Population
Average Annual
(micro 0*ie/cub1c ft.)
Exposure
(nig/kg/day )
txpected LIfe11 me
Inc1dene e
(orobab1Iity/l1f e 11 me )
t xpected
Annual Caaea
Inunbu r/yea r)
Machine Operators
CommercI a'
Indust rial
Coln-0p
16000,
700.
11000.
22050,
26800.
2640.
3.15
«.ll
0.5771
0.003728
0,004066
0. OOOM I
0 .8521
0.0187
0.0702
0
1
'JO
Other Morkera
Cohere 1 al
Industrial
Coln-Uo
110000,
20000,
22000.
U900.
6M00,
26'J0.
0.7000
0 . «# 114 i
0.3771
0,000630
0.001083
0,0004^7
1.30
0.3095
0. U05
Service Users
Commercial Dry C'ean 50000000,
Co 1n-Op Dry Clean 25000000,
Coln»Op Laundry 37000000,
2.15
5,00
16,72
0.000350
0,000/ltt
0,002389
«, 150E-07
6,«6<)E-07
2,«32£-0h
0,2961
0,3025
I.SO
Urban Residents
A}
-J
25 • 2 00 *
2i)0 - 500 •
500 - 1000 «
3700000,
20000000.
71000000,
• Distance from dry cleaner In meters
1.68
0.2UBO
0,0960
0,000200
U.llUt-05
1.3716-05
2,8«5E-u7
4.878E-08
I .626E-08
Total
0.0150
0,0139
0.0165
1,67
Total cost of option and expected cancer (millions of dollars) ¦
-5,23
-------
OPTION 9
Seal 4noi SuRFACE_AREA Species: RaT Moduli QUADRATIC
Response oI 5 percent corresponds to do3« In man of «1.3 mg/kg/day which Implies lambda =
Option cost (millions of dollars) = — 10. 10 Cost per cancer case (millions of dollars) s 1,00
2,7«OE-05
type of
Person
Machine Operator*
Number In
U. S. Population
Average Annual Exposure
(micro gma/cublc m) (nfl/kg/day)
txpected Lifetime
Inc1 dance
(probability/11fet1me)
Expected
Annual Cases
(number/year)
Conmerc1eI
I noultrial
Coln-Op
16000,
700,
11000,
22050,
28600,
£6*0.
3,15
1,11
0,3771
0,000272
0,000461
3.898t-06
0,0621
0,000638
0,000613
0
1
sO
Other Workers
Commerc1•1
Induatrial
C o1n-Op
110000,
20000.
22000,
4900,
6400,
26a0,
0,7000
0.9141
0.3771
1.341E-0S
2.209E-05
3,d9bE-06
0.0211
0,006519
0,001225
Service User*
Co*merct»1 Dry Clean 50000000,
Coin-Op Dry Clean 25000000,
Coin»0p Laundry 37000000.
2.«5
5.00
16.72
0.000350
0.000714
0,002189
3.J57E-12
1.398E-1I
1.564E-10
2 • 398E-06
4.99«£-06
8,261E-05
X S
i'v
CC
Urban Hesident*
-------
OPTION 9
Scaling: 800Y_wElGHT Species! MOUSE Model!
Response of 65 percent correspond# to dost in nan of
L INEA«
332.0 mn/kg/dey which implies 1ambda -
0,003162
nDtlon cost (millions of dollars) =-10.10 Cost par cancer cose (
-------
OPTION 9
ScaHnqi UODY.rfEIGHT Scxdesi HOUSE Mod# 1 I QUADRAT IC
Response of 65 percent corresponds to Ooaa 10,10 Cost por c«nc»r case (nilllonl of dollars) = 1.00
9.52«E-06
lype of
Person
Machine Uperetors
U.
Number In
S. Population
Average Annual txoosure
(micro gms/cublc ml (mg/kg/day)
Expected lifetime
1ncI dene a
(p robabI1lty/11fctlmo)
E xpec ted
Annual Cases
(nunosr/ytir)
Comm# rcI a 1
Inoust rial
Coln»l)p
16000.
700.
11000.
22050.
2B800,
2640.
3.15
t,U
0,3771
9.4«7E-05
0.000161
1.355C-06
0,0216
0,001612
0,000211
0
1
00
Other Workers
Commercial
Industrial
Co In-Op
110000.
20000.
22P00.
M900 ,
6400.
26U0,
0.7000
0.S14J
0.3/71
M.667E-06
7.962E-06
1.355E-G6
0.0 07 J 31
0,00 2275
0,000«26
Service Users
Commercial Dry Clean 50000000.
Coin-On Dry Clean 25000000,
Coln-Gp Laundry 37000000.
2.
-------
OPTION 9
SceMnqt BODY.KEIGHT Speciesj HAT Models LINEAR
Rnoonse of 5 percent corresponds to dose 1n man of 253.0 mg/kg/day which l«pll(> lambda a
Option cost (millions of dollar*) =-10,10 Cost per cancer case (•lllloni of collars) = I,DO
0,000203
Type of
Person
U.
Number In
S, Population
Average Annual Exposure
(micro
-------
OPTION 9
SesHnqt BODY_KE IGH T Spedesl RAT Modelt UUADHATIC
Response of 5 percent corresponds to dose In man of 2b3.0 ng/kg/day which lupllei lambda -
Option cost (*1 I 1Ions of dollar*) >*10.10 Cost per cancer case (millions of dolltri) > 1.00
8.0131-9/
Type of
He r»on
Machine Operators
Number In
U. S, Population
Average Annual Exposure
(micro qms/cublc m> (*i0/kg/day)
Ewpectert Lifetime
lncIdcnce
(probability/lifetime)
t» pec t ed
Annual Cases
(nufflDor/ycar)
0
1
OD
u>
Co-«merc1 al
Industrial
Col n-0p
Other Korkers
Conirerc I a I
Indust rial
Co 1 n-Up
Service Osera
16000,
700 ,
11000,
110000,
20000,
28000.
22050.
28600,
abuo.
«900,
6100.
2640,
3.15
1.11
0.3771
0.7000
0,9i«S
0.3771
7.951£-0 6
1.359E-05
1,l«0E-07
3.927E-07
6.699E-07
1 , li|0E-07
0.001817
0.000136
1.791E-05
0.000617
0.000191
3.5821-05
Commercial Dry Clean
Co1n-Qo 0 ry Clean
Co1n-0p Laundry
50000000.
25000000.
37000000,
2.as
5.00
16.72
0,000350
0,000714
0,0 923B9
9.816E-1U
4,00bE-13
4,5 7 2 E -1 2
7.012E-06
I .U60E-0)
2.11 /t -06
Urban Resident*
M
CD
N
25 - 200 «
2u0 - 500 *
500 - 1000 *
3700000.
20000000.
71000000,
1.68
0.2660
O.OV60
• Dlatance from dry cleaner In meters
Total cost of option and expected cancer (million* of dollars)
0.000240
<1.1 14E-05
1. 371E-05
-10.10
U.616E-I4
1.356E-15
1,507E-16
Total
2,"106 - 09
3,676E-10
I,529E-10
l),002bte
-------
OPTION 10
ScBllngi SURMCE_AHEA Specfeal HOUSE Mociell LINEAR
K«(pofij« of 65 percent corresponds to dose In me* of 25.0 mg/krj/day which Implies lambda = 0,041V
Option cost (mil Hon* of dollars) e -4.30 Coat Dor cancer case (million) of dollars) a 1.00
Expected Lifetime Expected
Type of Number In Average Annual Exposure Incloence Annual Cn4es
Person U. 3. Population (micro gms/cublc m) (mg/kQ/day) (probaBfIfty/1 I Ietfme) (numcor/ycar)
Machine Operators
O
ao
¦C^
CommercI a 1
f nriust rial
CoI n-l)p
Other Workers
Co»"erci a 1
Industrlel
Co In-0p
Service Users
16000,
700.
110GO.
110000.
20000.
22000,
liaoo.
28800.
1300.
4900.
6400 .
3500.
2.06
«.U
0,*714
0,7000
0,9111
o,47»4
0.0827
0.1505
0.019b
0.0289
0.Oi 76
0,0196
18.89
i .58
3,08
M5.46
10,75
6,15
Commercial Dry Clean 50000000.
Cofn-Oo Dry Clean 25000000.
Cofn-0n Laundry 37000000,
Urban Kesldents
2,IS
5.90
20,90
0.000350
0,000643
0.002986
1.466E-05
3, 535E-05
0,000125
10,47
12,62
66. 19
*0
00
CO
. 25 - 200 •
200 - 500 •
500 » 1000 *
3700000.
20000000.
71000000,
1.B2
0.3120
0,1040
0.000260
4.4!>7E-05
1.466E-05
• Distance from dry cleaner In meters
Total cost of option end expected cancer (ullllom of dollars) *
172.65
1.091E-0S
1.869E-06
6.231E-07
Total
0,5765
0,5341
3,6320
177.
-------
OPTION 10
Seal < "01 SURMCE_»REA Speclesl MOUSE Model: QU4DNATIC
Response of 65 percent correspond* to doae In man of 25.0 mg/kg/day which implies Iembda "
Option cost (millions of dollars) * -«,J0 Cost per cancer case (millions of dollars) * 1.00
0,001675
Type of
He r son
Number 1n
U, S, Population
Average Annual Eipoaura
(micro qms/cub I c m) (mO/kg/day)
Expected Lifetime
1nc1dence
(probablI11 y / I1fe 11 me J
Expacted
Annuel Cases
I numbe r/ye a r )
Machine Operators
0
1
ao
v/l
CoTme rc1 a 1
IndustH al
Coln-Qp
Other Workers
Cc"«nrc i a 1
Industrlal
Coln-0p
Service Users
16000.
700,
liouo.
I 10000,
20000.
22000.
moo.
2d«00.
3300.
1900.
6ioo.
3300.
2.06
1.11
0.47|l
0,7 0 00
0,9141
0.1714
0.007065
0.0260
0.0OU372
0.000621
0.001400
0.000 $72
1.61
0.2796
0,0565
1.29
0.3999
0,1170
Commercial Ory Clean 50000000,
Coin-Up Ory Clean 25000000.
Co1n»0p Laundry 37000000.
2.05
5.90
20.90
0.000350
0.000643
0.002986
2.0 52t-10
1 . 190E-09
1 .494E-06
0.000117
0.000125
0.007bV5
Urban Residents
ro
Ifi"
25 - 20 0 *
200 - 500 «
500 - 1 000 ~
3700000,
20000000,
71000000,
* Distance from dry cleaner In maters
1.92
0.3120
0,1040
O.DOO260
4.457E-05
1.466E-05
1.133E-10
3.328E-12
3.69BE-13
Total
5.987E-06
9.510E-0?
i , 7 5 I E - 0 7
1. 77
Total cost of ootlon and eapeeted cancer (million# of dollar#) a
-0,53
-------
OPTION 10
ScaHnai SUPF&CS.AREa Spedeaj RAT Model: LINEAR
Response of 5 oerceM corresponds to dose In mm of 43,3 mq/kg/day which Implies lambda c
Dotlon coat tMIMons of dollars) ¦ -4,30 Coat per cancer case (millions of dollars) * 1,00
0,001166
Type of
Parson
Number In
U, S. Population
Averaoe Annual
(micro gms/cuble m)
Exposure
(mg/ko/day)
fcxpected L1fot1me
Inc 1 dence
(ProbaiHI 1t y/1 If e11 me 1
Expected
Annual Cases
t numbo r/year)
Machine Operators
0
1
CD
a*
Commercial
Irriust rial
Co 1n-Uo
Ot^er horktri
Comma re i al
I nous trial
Coln-0p
16000,
700.
UOOO.
110000.
20000,
22000,
14100,
26A0O,
1300,
«900,
6400,
1300,
2,06
4,11
0 , 47 I <4
0,7000
0,9143
0,4714
0.002436
0.0 04 866
0,000559
0 , 000830
0,001083
0,000559
0,5568
0,0487
0,0878
1,30
0,3095
0,1756
Service Users
Commercial Dry Clean 50000000,
Coin-Op Dry Clean 25000000,
Coln-Qp Laundry J7000000,
2.45
5.90
20,90
0.000350
0¦00064 3
0,002906
4, 150E-07
9.993E-07
3,54 0E-06
0.2964
0,3569
1,87
Urban Residents
a)
01
25 - 200 •
200 - 500 *
500 - 1000 *
3700000,
20000000.
71000000,
• Distance from dry cleaner In meters
1,62
0,3120
0, 1040
0,000260
4.457E-05
1.4B6E-0S
3.0B3E-07
5.284E-00
1 ,761008
Total
0,0163
0,0151
0 . 0 1 7 V
5,06
Total cost of option and expected cancer (millions of dollars) * 0,76
-------
OPTION 10
Seal < nq: 3URFACE_AREA SpecUsi RAT Model l QUADRATIC
Response of 5 percent corre*ponris to dot* In man of 05
6,391E-06
0,0265
0,0n«G3e
o.ooovsr
0.0211
0,0 06b 39
o.ooi9ia
Commercial Dry Clean SOOOOOOO,
Coin-Op Dry Clean 2SOOOOOO,
Coin-Op Laundry 37000000,
2,15
5,90
20.90
0,00 0350
0.000613
0 ,002966
3.357E-12
1.947C-11
2,«U3t>10
2,39BE-06
b.9ilE-06
0,000129
ro
C?)
o
Urban Residents
25 - 200 •
200 - 500 *
500 - 1300 *
3700000,
20000000.
71000000,
• Distance from dry cleaner in meters
1,82
0,3120
0,1000
0,000260
4.457E-0S
1.4O6E-05
1.853E-12
6.04*E-15
Total
9.792E-06
1 .bS-st'-Ott
6,136t-09
U.UfclB
Total cost of option and expected cancer (million* of dollars) « -4,24
-------
OPTION 10
Sesl1n
-------
DPT ION 10
Sealing! BODY_WE IGHT Specleai HUUSE Podelj QUaOHATIC
Response of 65 percent corresponds to doae In man of 332.0 mg/kq/day which Impllea lambda s
Option cost (millions of dollara) a -4,30 Coat Per cancer cuse UllMom of dollars) a 1,00
9,524t-06
Type of
Person
U.
Number In
S. Popu1 a 11 on
Average Annual Exposure
(micro (jrps/cublc m) (irg/kg/day)
fcxpected Llfutlme
Incidence
(prooab1 I1ty/li fct 1 me)
t*nccted
Annua 1 Cases
(numtar/year)
Machine Uperators
00
v£>
Comma rc1 a I
Industrial
Co 1n-Up
Other Horkera
Comne rcia I
Industrial
C o1n-Op
Service Uaera
Commercial Dry Clean
Coin-Up Dry Clean
Coin-Op taundry
Urban Nealdents
16000.
700.
1 1 0 0 0,
110000.
aooto,
2^000,
50000000.
25000000.
371/00000 .
14400.
?B8 00 ,
3300,
4900.
6100,
3301,
Z.15
5.90
20.SO
2,06
0,4/14
0,7000
0 , 91 4 J
0,4714
0.000350
0.000843
0.002986
4.029E-05
0.000161
2.117E-06
4.667E-06
7.962E-06
2.11 li'Qb
1.167E-12
6.766E-12
tt.491E- 11
0,009210
0,001612
0,000333
0,007334
0,002275
0,000665
8.3346.-07
2.41 /E-06
4.4HBE-05
c r.
OD
25 - 200 »
200 - 500 •
500 - 1000 •
3700000.
20OOCOOO.
71000000.
* Distance from dry eleener in metera
Total coat of option and expected cancer (m
1.82
0.3120
0.1040
ona of dollars) *
0.000260
4.4571.-05
1.466E-05
"4,28
6.439E-11
1.892E-14
2.102E-15
Total
3.403E-08
5,4066-09
2 . 1 32 £ • 0 9
0.0215
-------
OPTION 10
ScaHnqi tt(iDY_«EIGHT Specleal NAT Nodeli LINEAR
Response of 5 percent correaponda to doae In man of 253,0 mg/kg/day which Implies lambda '
Option coat (m111t ona of dollars) s -1,30 Coat Per cancer ease (millions of dollars) = 1,00
0,000205
Tvpis of
Parson
Number In
U, S, Population
Average Annual Expoaure
(micro gioi/cubic m)
(mg/ leg/day)
tipected Lifetime
Inc1dence
(probablIity/11f e11 me)
E xoec t ed
Annus I Cases
(nunoor/year)
Machine Operatort
Comme rc 1 6000,
700,
>1000.
1HOO,
20600.
3300.
2,06
1.11
0,1711
0.0001)17
0.000631
9.555E-05
0,0953
n.008333
0.0150
0
1
*£>
o
Other nor leer a
C o"in>e rc 1 a 1
Iniua t r1 a 1
Coln»0p
110000,
20000,
22000,
190 0 ,
6«O0,
3300 ,
0,7000
0.9ta3
0,1/11
0,000112
0,0 00185
9.555E-05
0,2230
0,0530
0,0300
Service Uaera
Comporclal Dry Clean 50000000,
Coln-Oo Dry Clean 25000000,
Co1n«0p Laundry 37000000.
2,15
5,90
20.90
0,000350
0. 000613
0,002986
7 ,O96t>08
l,709£-07
6.053t-0 7
0.0507
O.OblO
0.3200
Urban Resident*
fvy
a.)
<3
25 - 200 •
200 - 500 «
5.00 - 1000 *
3700000.
20000000.
71000000,
» Distance from dry cleaner In meters
1,82
0.3120
o.ioio
0.000260
1.157E-0S
1,«86E-0S
5.271E-08
9.036E-09
3.012E-09
Total
0.0U27B6
0,002582
0,003055
U.86UB
Total cost of option and expected cancer (mil Hons of dollars) »
- 3, *ia
-------
OPTION 10
Sea M no! BODV_WEIGH T Speclesi HAT Mooeli QUADRATIC
Reiponu of 5 percent corresponds to dove In nan of 253.0 mg/xg/day which Implies lambda a 8.013E-07
Option cost (^i)Hona of dollars) = -4,30 Cost Per cancer case (millions of dollars) ¦ 1,00
Expected Lifetime Expected
Type of Number In Average Annual Exposure Incidence Annual Csses
Person U, S, Population (micro gus/cublc m) (f^O/kg/day) (probability/lifetime) (ng«tnf/ynr)
Machine Operators
n
i
\o
to
O
Comma re 1 a I
Industrial
C o 1 n - 0 p
Other Workers
Comrre rc 1 al
Inau s t r1 a I
Co 1n-Op
Service Users
Commercial Dry Clean
Coin-Op Dry Clean
Coln-Op Laundry
Urban Residents
25 - 200 *
200 - h0o *
500 - 1000 *
16000,
700.
11000.
UOOOO,
20000,
22000,
50000000,
25000000,
J7000000,
5700000,
20000000,
71000000.
14400,
2(4600,
3300,
<4900,
6400.
3100.
2.15
5.90
20.90
1.82
0.3120
o.ioao
* Distance from dry Cleaner In meters
Total cost of option and expected cancer (millions of dollars) t
2.06
4.11
0.1711
0,7000
0.9113
0.4714
0.000350
0.000643
0.002966
0,0O0260
1.157E-05
1 ,1661-05
•4.30
3.391E-06
1.35VE-05
1.781E-07
3.927E-07
6.699E-07
1 .78IE-07
9.816E-11
5.693E-13
7.111E-12
5.117E-14
1.592E-15
1,769E-16
Total
0.000775
0,000136
2,79lt-03
0.OOQ617
0.000191
5.59/E-U5
7.012E-08
2.033E-07
3. 776E-06
2.863E-09
4,54tsc-10
1.79«E-1 0
U.UU1B07
-------
OPTION U
Seal I ng i SlJRFACE.ARE A Soecleai MOUSE Modell LINEAR
Response of 6S percent corresponds to doae In men of 25,0 my/ko/d«y which IiidIIts lambda »
Option cost (nlI Ilon» of dollars) = -8,10 Coat par cancer case (>1 I Hon) o~ dollars) = 1.00
O.OHS
type of
Person
Machine Operators
Number In
U. S, Population
Average Annual Exposure
(micro g*s/cublc m) (iro/*g/aay)
Expected Lifetime
Incidence
(probablI Ity/1IfetI me)
Expec ted
Snnjsl Cases
(nuncer/vear)
Coimerclal
Industrial
Co In-Op
1600(1,
700,
11000,
22050,
26800,
3300.
3.15
1.U
0 . « 7 11
0.1230
0,1085
0,0196
28.29
1,56
i.oa
0
1
\D
to
01 * e r Workers
C o"M«e rc I a I
I nouitrial
C oIn-Op
110000,
20000,
22000,
4900 ,
6uno,
3300.
0,7000
0,9143
0,471V
0,02b9
0,0376
0,0196
45,46
1 J. 75
6.15
Service Users
Co*ii>erc I a I Dry Clean 50000000,
Coin-Up Dry Clean 250CC00C.
Coin-Dp Laundry 37000000,
2.«5
5.90
2.09
0.000350
0.000843
0,000299
I , 466E-05
3.S35E-05
1 .2S2E-05
10,47
12,62
6.62
Urban Residents
to
|r^
25 - 200 •
200 - bOO •
500 - 1000 *
3700000,
20000 00 0,
7100000C ,
* Distance from dry cleaner In maters
1.82
0.3120
0.1U40
0.000260
4.V57E-05
1,«66E-05
1, 091E"05
1 .669L-06
6,2 31E-07
Total
0.5765
0,5341
u. 6320
127.
Total cost of option and expected cancer (millions of dollars) * 118,67
-------
OPTION 11
Sealing! SuRFACE_«HEA Special HOUSE Model! QUADRATIC
Response et 6b percent correspond# to uose In men of 25.0 mg/ka/day wh1ch IrpHtt lambda -
CpMon Co M (millions of ao11 a rI) * -B. I 0 L'oa t per cancer case (m 1 1 I 1 on 8 Of dollars) = 1.00
0,0016/b
Ivpe of
Person
Number 1n
II, S, Population
Average Annual
(micro oms/cublc it)
Exposure
(ng/ko/dav)
Expected LIfetIne
Incidence
(probab1 I ity/1 1 f et 1 n>e )
Expect ed
Annual Coses
( nymbe r/year)
n
i
o
u>
Machine Uoerators
Co»»(rc1 a I
I nous t r1 a 1
Co 1n-Op
Other Workera
Coirmerc 1 e 1
Industrial
Co1n-up
Service Uaers
16000.
700 ,
11000,
1 10000,
20000.
22000,
22050.
ateoo,
3300.
n9oo,
6100,
iluo.
3.15
0.4714
0.7000
0.91UJ
0 . M / i <*
0.0165
o. o i; u o
0.000372
0.000821
0.001400
0.000372
3.77
0,2796
0.0S«i
1.2V
0.3999
0.1170
Commercial Dry Clean 50000000,
Coin-Op Dry Clean 25000000,
Co1n-Cp Laundry 37000000,
2.85
5,90
2,09
0,000350
0.0008U3
0,000299
2.052E-10
1 , 1 9 0 fc. - 0 9
1 .«9JIE-10
0.000147
0.OOOM2S
7.B9bt-05
Urban Residents
CO
cO
. 25 - 200 *
200 - $00 »
500 - 1000 •
3700000.
20000000,
71000000,
1,82
0.3120
0.IO«0
• Dlatance from dry cleaner in meters
Total cost of option and expected cancer (millions of dollars] ¦
0,000260
«.«i7fc-05
1 .Q86E-05
-2.19
1.133E-10
3.328E-12
3.698E-1J
Total
S.987E-06
9.S10E-07
3 , 7b | £ - 07
b.9i
-------
OPTICN U
ScaHnoi SUPKACE.AREA Speeieai RAT Model I LINEAR
B«»i)0nie of 5 percent corrticondi to doae In man of 13,3 mQ/Wo/dey which Implies 1 embda c
Option cost (millions of dollars) s -8,10 Cost pgr career case (millions Of dollars) s 1,00
0,001106
Type of
Person
Number 1n
U, S, PopuI a 11 on
Average Annuel Exposure
(micro gms/cubic m) (mO/kg/day)
Enpec ted U I f otI me
Incidence
(oroe9b1 Ii t y / 1Ife11 me )
Expec ted
Annua I Cases
(nyirpep/ycarj
Machlne Operators
0
1
VO
Co«m»re 1 a I
Indus trial
Coln-Oo
Other Werners
Co^ime rc I a 1
1ndu s t ria 1
Coln-Up
16000.
700.
11000.
110000.
20000.
220 JO,
22050.
28800.
3300,
U900.
6100.
3300,
3.15
1.11
0,1711
0. 7000
0,9103
0 , <4 711
0,00372a
0.031866
0,000559
0.000830
0,001083
0.000559
0.8521
0,0187
0,0678
1.30
0.309S
0,1756
Service Users
Commercial Dry Clean 50000000,
Coin-Op Dry Clean 25000000,
Coin-Op Laundry 37000000,
2.45
5.90
2.09
0.000350
o. oooe«3
0.000299
1.150E-07
9.993E-07
3,b'40t-07
0,2961
0,35b9
0.1871
Urban Residents
JO
c.0
Co
2b - 200 •
200 - 500 •
500 - 1C00 *
3700000,
2QOOUOOO,
71000000,
• Distance from dry cleaner in meters
1.02
0,3120
0,1010
0.000260
1,15 7E-05
1.186E-05
3.083E-07
5.28UE-08
1.761E-08
Total
0,0163
0.0151
0,0179
h.bl
Total cost of option and expected cancer (millions of dollars) ¦ -1.13
-------
OPTION 11
Scallno: SURf ACE_AREA Speclesl RAT Model I QUAORATIC
Rnoon»c of 5 percent corr«s»ondi to dose In man of <<3,3 mg/kg/day which Implies lambda s
Uotion cost t»l I I loni of dollars) 3 -0,10 Cost per Cancer caaa (rrllllonS of dollars) s 1.00
2, 7<40E-OS
Type of
Person
Number in
U, S, Population
Average Annual Exposure
(micro gma/cub1c ml (mg/kg/dey)
t*Dected LIfet1me
Inc1 dance
(p ronab t11ty/l1 feline)
t *t>ec t ed
Annual Cases
C numb er/y ear)
Machine Operators
0
1
\C
L/l
TO
CO
Commercial 160 00.
Industrial 700,
Coln-Cp 11000,
Other Horkers
ComwR rc1 a 1 110000.
Industrial 20000,
Coin-On 22000,
Service Users
Commercial Dry C1 eon 50000000,
Coin-Up Dry Clean 25000000,
Coln-Qo Laundry 37000000,
Urban Residents
25 - 200 * 3700000.
200 - b00 » 20000000,
500 - 1000 « 71000000,
22050,
26800,
3300.
«930,
6000.
3300,
2.45
5.90
2,0?
1.02
0,3120
0.10'»0
» Distance from dry cleaner In meters
Total cost of option and axoected cancer (millions of dollars) °
3.15
4.11
0,0714
0.7000
0.9H3
0 , 17 1
-------
UPTIDN 11
SesHngi dOOY.WEIOHT Speclesl MOUSE Model! LINEAR
Response of 65 percent corresponds to dosa 1n man of 332.0 mg/kg/day which Imp)lei lambda =
Option cost (millions of dollars) « •6,10 Cost Per cancer case (million* of oollars) * 1.00
0 , 0 0 iI 62
Type of
Person
Number In
U, S, Population
E*peet*d Lifetime Expected
Average Annual EapoSura Incidence Annual Cases
(micro gms/cubtc m) (-rig/kg/day) (probab 1 11 t v/ 1 1 f e 11 me) ( numoe r/y aa r )
Machine Operators
n
i
v£>
CT-
0
01
Com* rc 1 a I
Indus trial
C o1n-Op
Other Horkers
Connerci a 1
I ndust rial
Co1n-Op
Service Users
Conmnrclil Dry Clean
Coin-Op Dry Clean
Co1n-0p Laundry
Urban Residents
25 - 300 *
200 - 500 •
500 - 1000 *
1600 0,
700 ,
11000,
110000,
20000,
22000,
50000000.
25000000.
37000000,
3700000,
20000000,
71000000.
22050,
26600,
3300,
<1900,
6100 .
3300.
2.«5
5.90
2,09
1,82
0,3120
0 , 11) « 0
* Distance from dry cleaner In meters
Total coat of option and expected cancer (millions of dollars)
3.15
«.U
0 ,17 1 <4
0,7000
0,910 3
0,«71«
0,000350
0,000b03
0,000299
0,000260
1.157E.-05
1 .4S6E-05
>.67
0.009911
0.0129
0.001490
0.00221 1
o,oo;?aa7
0.00ia90
1.107E-06
2.665E-06
9,
-------
OPTION 11
Scaling: BODY_*EICHT Snecleaj M0U5E Modal: QUADRATIC
Response of 65 percent corresponds to dose 06
2,117E-06
0,0216
0,001612
0,Ot)OJ13
0.007331
0.002275
0, 00366b
Commercial Dry Clean 50000000.
Co1n-Op Dry Clean 25000000.
Coin-Dp laundry J7u00000,
Urban Rasloents
2.05
5,90
2,09
0,000350
o.oooeai
0,00029V
1.167E-12
6.766E-12
8 , « 9 I E- 11
07
cc
25
200
500
200 «
*00 «
1000 «
3700000,
20000000.
71000000.
I ,82
0.3120
0.10<40
• Distance from dry cleaner In meters
Total coat of option and expected cancer (millions of dollars)
0,000260
«.457E-05
l.«B6t-05
"6.07
6,«J9E-13
1.892E-M
2. 102E-IS
Total
3. <10 3E-08
b,«0fe£-09
2 . 112c-0 9
0,0338
-------
OPTION U
Scellncj: BOOY_wElGMT Speclesi «*T Mode) I L1NEAN
Response of 5 percent correspond# to dose In man of 253,0 m^/tcg/dey which Inollet lambda =
Option cost (million* of dollars) c -8,10 Cost per cancer can (millions of dollars) e 1,00
0,000203
Type of
Person
Number in
U, 3, Popu1 at 1 on
Average Annual
(micro oos/cublc m)
Exposure
(mo/kg/day)
E »pect ed Lifetime
I nc1 dene e
(probabtI1 ty/11fetlme)
t*pected
Annual Cases
(nuribcr/year)
Machine Operators
Co*me rc1 »1
I ndu s t rI a 1
Coln-Un
16000.
700.
11000,
22050.
26800.
33t>0.
3.15
«. It
0. H 7 X 'I
0,0 0 0638
O,000BJ<4
9.05SE-05
0, 1159
O.OOUJiS
C.0150
0
1
Oo
Other Workers
C a ("ire rc i a I
Industrial
Co In-Oo
110000,
HOC 0 0 ,
22000,
4900,
fcaoo.
33u0.
0,7000
0,91«J
0.1711
0,000142
0,000165
9.555E-0S
0.2230
0.0530
0.0300
Service Users
Commercial Dry Cleen 50000000,
Coin-Up Ory Clean 25000000,
Coin-Op Laundry 37COOOOO,
2.15
5.90
2.09
0,000550
0.0008U3
0.000299
7.096E-08
1 .709E-07
6.053E-08
0.0507
0.0610
0.0320
fO
CO
Urban Residents
25 • ?00 •
200 - 500 *
500 - 1000 •
3700000,
20000000.
71 000000 ,
* Olstsnce from dry cleaner In meters
1.92
0,3120
0.1040
0.000260
«.1b76-05
1 .1B6E-05
5.271E-0B
9.036E-09
5.012E-09
Total
0.0027B6
0.002562
0,003055
0.627 <4
Total cost of option and expected cancer (millions of dollers) » -7.47
-------
OPTION 11
3c e 1 I nql BOOY_>»EICHT Seeciesi HAT Model I QUADRATIC
Response of 5 nercent corratooridi to dote In man of 253,0 mg/kg/day wMc* ImpHei lambda s
Option cost (it. II Hons of oollars) * -8,10 Cost Per cancer case (millions of dollars) s 1,00
8.013E-07
Type of
Person
Number In
U, S. Population
Average Annual
(micro Qirs/cublc m)
EipoSure
(mU/kg/day)
Expected Lifetime
IncIdence
(probab1 I It y/I 1fetlme)
fc xoec t ed
Annual CasflS
(nuTOor/ye*r)
Machine Operators
CoTine rc i a 1
Industrlal
Co 1n-Or
16000.
700.
t1000,
28050,
26800,
3300.
3.15
1.11
0.4714
7.951E-06
1.359E-05
1 .781E-07
0,001817
0,000136
2.799E-05
Other Workers
Com-iie rc I e 1
Inoutt rial
Col n-0r>
1 10000,
20000,
22000,
4900.
6100,
3300.
0.7000
0 . 9 I 4 i
0.4714
3.927E-07
6.699E-07
1.781E-07
0.000617
0.000191
5.597E-0S
Service Users
Commercial Dry Clean 50000000.
Coln-tjp Dry Clean 25000000.
Coin-Op Laundry 57000000,
2.45
5.90
2,09
0.000350
0,000843
0,000299
9.816E-14
5.693E-13
7.1U4E-14
7 ,0 1 2E-08
d.. 033E-07
3.776E-08
Urban Residents
25 « 200 •
200 - 500 *
500 - 1000 •
3700000.
20000000,
71000000.
1.62
0,3120
0.1040
0,000260
4.4576-05
J.486E-05
5.417E-14
1.592E-15
1.769E-16
2,d63E-09
4 , 5ue£«10
1,7946-10
• Distance from dry cleaner in meters
Total
0,0028'16
Total cost of option ond expected cancer (millions of dollars) *
-8.10
-------
OPTION 12
Scat I rigi SUKMCE_»HEA Speclesl HOUSE ("odell LINEAR
Wesponse of 65 percent corresponds to dose In man of 25.0 mg/kg/day *hlc* implies lemode »
Option cost (millions of dollars) s -1,10 Cost oer cancer case (millions of dollars) « 1.00
0.0119
Type of
Person
Number In
U. 3, Population
Average Annual Exposure
(micro QB'3/cublc m) Cn>0/Wo/day)
Expected Lifetime
I ncI dene e
Cprooab 1 I ity/Mfetlme)
fc «pec t ed
Annual Cases
(numoer/year)
Machine Operators
0
1
o
o
CoinorcI a 1
Indus t rIe 1
Coln»Up
OtHer workers
Commercial
Industrlal
Coln-Oo
Service Users
16000,
700,
11000.
1 1 0000.
20000.
2*000.
22050.
23800.
2640.
1900.
6100.
2610.
3.15
0.11
0.3771
0.7000
0.911i
0.3771
0. 1238
0.>585
0 .0 1 b 7
0.02U9
0.0376
0.0157
28.29
1 .56
2.17
15. 16
10.75
4.93
fO
CO
o
Con'orcial Dry Clean 50000000.
Coln-Oo Dry Clean 25000000,
Co1n«Up Laundry 37000000.
Urban Residents
25 - 200 *
2u0 - 500 •
5g0 - 1 000 »
3700000,
20000000.
7 1 OOuOOO.
2.«5
5.O0
16.72
1.68
0,2860
0,0 460
0,000350
0,000711
0.002389
0,000210
1.1 llt-05
1.371E-05
» Distance fro« dry cleaner In meters
Total cost of option end expected cancer (millions of dollars) a
1 .466E-05
2.99BE-05
0,000100
J.007E-05
1.726E-06
5.752E-07
Total
10,17
10,71
52,96
0,5321
0,1930
0,5831
16V.
167,03
-------
OPTION 12
Sealing! SuflF ACE_AKEA 3pec1est MOUSE Modeli QUAOwATIC
Response of 4b percent corresponds to dose 1n men of 2S.0 (n
-------
OPTION 12
Sealing: SUHF ACF..ARE A Species! HAT Modal t L1NEAK
Pesponse of 5 percent corresponds to dose in fan of "3,3 mg/kg/dav wh(e" Implies lambda =
Option cost (mil Hons of dollars) « •l.uo Coat per cancer case (nlllions of dollars) s 1,00
0,001166
Type of
Person
U.
Number in
S. Population
Average Annual
(micro o^a/cubic m)
Exooeure
(mg/ko/dey)
Expected Lifetime
IncIoence
(probabi11ty/li fetlne)
E *pec t eo
Anngol Cases
(numbar/yejr)
Hechina Operator#
n
i
o
ro
CO
c
Compare 1 » I 1 60 0 0 ,
Industrial 700,
Co in«Op 1 1 0 00,
Other Workers
Con«e rc i a I 1 10000 ,
In-iustrlal 20 00 0 .
Coin-Op 22000,
Service Users
Commercial Dry Clean 50000000,
Coin-Op Dry Clean 25000000,
Co1n«L)p Laundry 37000000,
Urban Hesidenta
25 • 200 • 3700000,
• 200 - 500 * 20000000,
500 - 1000 « 71000000,
2205O,
268 0 0,
2610.
«900,
6«00,
26«0,
2.15
5.00
16.72
1.66
0.2090
0,0960
• Distance from dry cleanar 1n meters
Total cost of option and expected cancer (trillions of dollars) a
3,15
«. n
0,3771
0.7000
0,9l«J
0,3771
0.000350
0,000714
0,00 2 389
0.000240
-------
OPTION
Seal 1ng: SURFACE.AKEA Speclest RAT Modell QUADRATIC
Response of 5 percent corresponds to doae In man of 43.3 roo/kg/dey which Implies lambda =
Option cost (millions of dollars) * -1,10 Cost nor cancer case (millions of dollars) s 1,(J0
2.7«0E-0b
Type of
Herson
U.
Number In
S, Population
Average Annual
(micro gns/cublc m)
Exposure
(mQ/kg/day)
txoected L I (at 1ne
Incidence
(probrtblIit y/I 1*etlm«)
txpec ted
Annual Cases
(nunbor/year)
Machine Operators
Corome re 1al
1ndus trial
Coln-Up
16000,
TOO,
11000,
220SO.
26800.
abno.
3,15
«.ll
0,3771
0.000272
0.000<46«
3,89t)E-0b
0.0621
0.004630
0.UU0613
I
3
Other Worker#
Coume re I a I
I ndustrial
C o1n-Qp
110000.
20000.
22000,
4900.
feuao.
2 6 '10 .
0.7000
0,<>ia3
0,3771
1.311E-05
2.269E-05
3.S9&E-06
0.0211
0 i 0D6539
0,00122^
Service Users
Commercial Ory Clean 50000000.
Co1n«0p Ory Clean 25000000,
Coln-Gp Ldundry 37000000,
2.«5
5,00
16.72
0.000350
0.000714
0.002369
3.35TE-12
1.398E-11
1.564E-10
2.39f)t-06
4.99ME-C6
6,26
-------
OPT I On 12
Sea 11nqt BODY_hEIGnr Speelesi MOUSt Modal I LINEAR
Reaponsc of 65 percent eorresoonda to dose In msn of 332,0 mg/kg/day which Implies lambda a
Option cost (nllllom o< dollars) s -1,m0 Cost per cancer casa (*lI Hons of dollars) = 1.00
0.0 Q 3162
Type o<
Person
Number 1n
U, S, Population
Average Annual Exposure
(micro gms/cublc m) (my/kected Lifetime
IneI donee
(probaolIi t v' 11fet1*e)
t*oected
Annual Cases
Inu»oer/year)
Heehlne Operators
.T
I
t-
Coirme rc 1 a I
Indus t rial
Coln-Uo
Other Workers
CoTiire pc 111
1ndustrial
Coln»Oo
16000,
700,
1 1000,
1)0000,
£0000.
22000.
22050,
23800,
26«0.
D9O0 ,
6«00,
2640.
3.15
4. 1 1
0.3771
0.7000
0 .V 1 (13
0.3771
0.00991 1
0.0129
0.001192
0,00221)
0,002687
0,001 192
2,27
0,1293
0.1873
3,17
0 .8248
0 , 3 7 <46
Service Users
Commcrelal Dry Clean
Coln-Uo Dry Clean
Coin-up Laundry
Urban Kealdents
50000000,
25000000.
37000000.
2,15
5,00
16.72
0,000350
0,000714
0,002369
1,107E-06
2.259E-06
7.553E-06
0.7905
0,8067
3.9V
CO
O
Co
25 - 200 •
200 - 500 *
500 - 1000 •
3700000,
20000000,
71000000,
1,68
0,2060
0,0960
• Distance from dry cleaner In meters
Total cost of option and expected cancer (millions of dollars) a
0,000240
4.114E-05
1.371E-05
11.57
7 »i»89t-07
1.301E-07
4.337E-O0
total
0,0401
0.0372
0.0440
12.97
-------
OPTION 12
Seal I no I BOOY.WEIGHT Specleal MOUSE Modal I QUADRATIC
Response of 65 oereent corresponds to dose In man of 332,0 mg/kg/day which Inpl(«S 1embda a
Option cost (minions of dollars) » -1,«0 Coat par cancer case (millions of collars) = 1.00
9.52«E-06
Type ot
Person
Machine Operators
Number |n
U. S. Population
Averaqe Annual
(micro gms/cublc m)
Exposure
(mg/kg/day)
Expected li f a11ne
IncI dene e
(probab1Ii ty/1Ifet1 me)
Expec ted
Annual Cases
(numbu r/yea r)
Compereiel
Indus trial
Coln-Qp
16000,
700.
11000.
?2050.
2:1600.
ibkO.
3.15
«.ll
O.J771
9.
-------
option ia
Seel Ingi 60UY_*EIGHT Speclesl RAT M0deli linear
KeSponsa of 5 percent correIponds to dose 1n men of 253.0 fq/kg/day which Implies lambda =
Option cost (millions of dollars) a »1.«0 Cost per cancer case (millions of dollars) ¦ 1,00
0 .000203
Type of
Pa rson
Number In
U. 3. Population
AvaPaaa Annual Exposure
(micro 0">s/cublc m) («fl/kg/dav)
Expected Lifetime
Inc1 dene*
(probabl11ty/l1f ct1 me)
lupected
Annu sI C-lses
(nuie«r/yeer)
I
3
>
Machine Operators
Ccirerc I a I 16000,
Industrial 700,
Co1n»0p 11000,
Other Workers
Commercial 110000,
I ndus trial 20000,
Coin-Up 22000,
Service Users
Con-merclal Dry Clean 50000000,
Coin-Op Dry Clean 25000000,
Co1n-Uo Laundry J700JOOO,
Urban Residents
22050.
28B0O.
2bl0,
<1900,
6100,
2640,
2.IS
5.00
16.72
3.15
1.11
0. 377 1
0.7000
0,
-------
OPTION 12
Seal 1 nq l PDDY_WEICHT 3nec1esJ RAT Hodell CJUADhAT tC
Httpons« of 5 percent corresponds to dose In nai> of 253.0 mq/ltg/day which (mpl In lambda s
Op11 on cost (millions of dollars) = -1.40 Coat par cancer case trillions of dollars) = 1.00
8.01 3E-07
Typi» of
Person
Humoer In
U. S. Hopulotlon
Average Annual Exposure
(micro yms/cublc m) (mg/kg/day)
txpected L1fe11 me
Inc1dence
(probaDlllty/llfetlme)
i » pec t ed
Annual Cases
(numoa r/yea r)
Machine Uperators
C omme rc 1 a I
Industrial
Coln-Up
16000,
700.
11000.
22050,
2d80O.
2610.
3.15
«.ll
0.3771
7 , 951E"0 6
1 .35VE-05
1.140E-07
0.001817
0,000136
I. /91E-05
O
Other Workers
o
-J
CofmercI a 1
Industrial
Co1n»Uo
110000,
20000,
2200 0.
4900,
6400.
26(10.
0,7000
0,9143
0.3771
3.927E-07
6.69VE-07
1 , 140E-07
0,000617
0,000191
3.562E-U5
Service Users
Commercial Dry Clean 50000000,
Co1n»Up Dry Clean 25000000,
Coin-Op Laundry 37000000,
2.15
5.00
16.72
0.000350
0.000714
0.002309
9.816E-14
4 , 0Q6E- I 3
4.S72E-12
7.012E-08
1 ,a60E-07
2.ai'7£-06
Urban Residents
CO
o
0*5
25 - 200 ~
200 - 500 •
50 0 - 100 0 *
3700000.
20000000.
71000000,
* Distance from dry cloener In maters
1,68
0.2060
0.0960
0,000240
4,114t-05
1.37 lt-05
4.616E-14
1.356E-15
1.507E-16
Total
2, 440E-09
i,b76£- I 0
1.b29c- 1 0
0.002810
Total cost of option and expected cancer (millions of dollars) = '1.40
-------
OPTION 13
9c a H ng| SURF ACE—AREA Species! HOUSE Modell LINEAR
Response of h5 percent corresponds to dose 1 n man of 25.U it.g/kg/day which Implies lambda -
Option cnst (millions of dollars) " -5.20 Cost per cancer cue (millions of aollars) ¦ 1,00
y.oaiy
Typo of
Person
Number In
U. 3. Population
Average Annual EnpoSyre
(micro gms/cublc m) (mo/kg/day)
Eiptct od lifetime
IncI dene o
(prohabt1 11 y/11fc11"e )
E«pected
Annual Cases
(ngmber/vea r)
HacMne Operators
Commerc1al
Industrial
Coi n-Op
t 6000.
700.
11000.
22050,
28800.
2M0.
3.15
«.U
0.3/71
0.1238
0.1535
0.U157
28.29
1 .58
2.«7
Other Worker*
O
OD
Commerc1al
Industrial
C o1n-Oo
110000,
20000,
22000.
<*900 ,
6100.
26 40.
0,700 0
0 , 9 1
-------
UPTION 15
Sc* H no: SURF»CE_AHEA Speciest RAT Model I LINEAR
fcesponje of 5 percent correspond! to doae In man of 13,3 nig/kg/day which (mo!lea 1am&da »
Option cost trillions of dollar*) ~ *5.20 Coat per cancer case (millions of dollars) c 1,00
0.001186
Type of
Person
Number in
U, S, Population
Average Annual
(micro gna/cublc m)
Exposure
(mg/kg/day)
Expected LifetIme
i nc i dance
(proDabi11ty/I 1fet1me)
t » pec t ed
Annual Cases
(numoe r/y ea r )
n
I
o
Machine Operators
Con"nerc1al
I nous trial
Coln-Op
Ot her Workers
Conift re 1 s I
Industrial
Coi n»0o
16000,
700,
11000,
110000,
20000,
22000,
?20 50,
aaeoo.
2640,
4900.
6400,
2610,
3.IS
«.n
0,3771
0,7000
0,9113
0.3771
0,00J72P
0,00 4 066
0.000117
0.000810
0.001083
0.000517
0,8521
0,0167
o.o;o2
1.30
0. 3095
0, 1105
Service Users
Co<-u>e re 1 a 1 Dry Clean 50000000,
Coin-Op Dry Clean 25000000,
Co1n-0p Laundry 37000000,
2,05
5,00
1.67
0,000350
0.000711
0,0 00239
1.150E-07
8.169E-07
2.B32E-07
0.2964
0.3025
0, 1197
Urban fesldents
CO
o
GO
25 - 200
200 - SCO
500 - 1000
3700000,
20000000,
71000000,
l ,6a
0,2080
0,0963
* Distance from dry cleaner In meters
Total cost of option and expected cancer (millions of dollars)
0,000200
1.111E-05
1.3/1E-05
-1,66
2.815E-07
<1,8 7 6L-08
1.626E-08
lota!
0,0150
0,0139
0.0l6b
-------
OPT ION 13
9 e•11na: 3URFACE_arEa Speciesi mat Modelt quaDkatic
Response of S percent corresponds to dose 1n man of 43.S mg/ko/dey which IhpHh lambda s
Option cost (millions of dollars) = «5.20 Cost per cancer case (millions of dollars) = 1,00
2.740E-05
Type of
Person
U.
Number in
S, Population
Average Annual
(micro o'ns/cublc m)
Exposure
(mO/kp/dey)
Expected Lifeline
1ncIdenct
(pro&abII 1t */I 1fet1 me)
E »pec t ed
Annual Cases
I nun-be r/y ear)
Machine Operators
O
Commerc1 a I
Indus trial
Coln-Uo
Other rtorkera
CompereI a 1
Indust r i a 1
Col n-Op
Service 'isera
Comrerctal Dry C'ean
Coin-up Dry Clean
Coin-Up laundry
16000,
700.
11000.
110000.
20000.
22000.
50000000,
2SOOOOOO,
37000000,
22050,
26800.
2640,
4900 ,
feaoo.
2640.
2,45
5.00
1,67
3.15
1.11
0 . 3 V 7 1
0,7000
0.9113
0.3771
0,000350
0,0007 1'!
O.U00239
0,000272
0.000464
3.096E-06
1.3«lt-05
2.289E-05
3,898E»0 6
3, 357E-12
1.39BE-11
1,564t-12
0,0621
0 .004630
0.0O0613
0,0211
0.006539
0,001225
2.398E-06
4.994E-06
6.264E-07
CO
O
cr>
Urban Healdents
25 - 200 •
200 - 500 *
500 - 1000 »
3700000,
20000000,
71000000,
1,68
o.2aeo
0,0960
* Distance from dry cleaner In meters
Total cost of option and expected cancer (millions of dollars) *
0,000240
4.114E-05
1 , 3' 1E.-C5
-5,10
1 .579E-12
4.639E-14
5,154E-15
Total
8.34UE-08
1, 32SE-08
5.228E-09
0.0962
-------
OPTION 13
Scaling; bODY_w£IGHT SpecUsl MUUSE Model: LINEAR
Response o* 65 percent corresponds to aose tn fjn of 332,0 nig/teg/day ««Mch Implies lambda =
Option cost IHMIcni of dollers) * -5,20 Cost per cancer case (million* of dollars) s 1,00
3.003162
Type of
Person
Number In
U, S, Population
Average Annual Exposure
(nicro pus/cubic n>) (mg/kg/dey)
t*oect ea Lifetime
Inc1rience
(probohiI 1ty/1i fet 1 me)
Expected
Annual Cases
t ngmhe r/year)
Machine Operators
n
Comrerc1 a 1
Industrial
Co i n-Op
DtKer Workers
16000,
700,
1 1000,
22050,
26800,
tbnO,
3,15
«.ll
0,i771
0.009911
0.0129
o.ooi im
2.27
0,1293
0.1873
Comwercl*1
I ndus trial
Coln-Up
110000,
200 u&,
22000,
4900.
6U00,
260 0,
0,7000
o. v i a 3
0.3771
0,00221 1
0.002007
0,001192
3.M7
0.8246
0 , 3 7 <46
Service Users
Commercial Dry Clean 50000000,
Coln-Uo Dry Clean 25000000,
Coin-Op Laundry 37000000,
2 • AS
S.oa
1,67
0,000350
0.000714
0,000239
1 . I07E-06
2.259E-06
7,55 4E-0 7
0.7905
0.8067
0,3992
Urban Residents
CO
H*
O
25- 200
200 - 500
50 0 - 1000
3700000,
20000000,
71000000,
• Distance from dry cleaner in meters
1 ,6ft
0,2660
0,0960
0,000200
U.lllt-05
1,371E-0S
7.509F-O7
1, 3016-07
'1,33/E-OU
I ot a 1
0,0401
0,0372
o.ouao
9.17
Total cost of option oid expected cancer (millions of dollars) *
".17
-------
OPTION 13
Scaling: BOOV^EIGMT Speclesl HOUSE Modal I UUAOWATIC
Response of 65 percent corresponds to ooaa In men of 312.0 ng/kg/day which implies lambda = 9,b2«E-0b
Option cost (million* of dollars) = -5.20 tost per cancer case (millions of collars! * 1,00
tooected Lifetime Expected
Type of Number In Average Annuel Exposure Incidence Annual Cases
Person U, S. Population (micro gms/cublc m) (mg/ko/day) (nrobab1IItv/1 Ife11 me) (numhor/year)
Machine Operators
Ci
I
Commercial
Industrial
C oIn-Up
Other Workers
Commarc1 a I
Industrial
Co In-Op
Service Users
16000,
700.
UOOO,
110000.
20000,
22000,
22050.
26800,
2640.
<1900,
6100.
2610,
3.15
<4.11
0,3771
0,700 0
0,9143
0,3771
9.UU7E-05
O.OOOifcl
1.355E-06
4 .6676-06
7.962E-06
1 .355E-06
0,0216
0.001612
0,00021i
0,007330
0,00227b
0.00J426
CoTierclal Dry Clean 50000000.
Coin-Up Orv Clean 25000000,
Coin-Op Laundry 37000000,
Urban Hesldente
2,45
5,00
1.67
0,000350
0.O0071U
0,000239
1,167E-12
4.U59E-12
b,«3«e-t3
b. 334E-07
1 . 7 36E-06
2.« 7 2t-0 7
w
Y*
25 - 200 *
200 - 500 •
50 0 - 10 0U *
3700000,
20000000,
71000000,
1,66
0.26SO
0,0960
• Distance from dry cleaner In meters
Total coat of option ond e*Pected cancer (million# of dollars) a
0.000240
4.114E-05
1,37 |E-05
-5,17
5.486E-13
1 .612E-14
1.791E-15
Total
2.900E-08
« .606E-09
1,B1 /t-09
0,0335
-------
OPTION 13
ScaMngI BOD*_KEIGHT Specie*: RAT Model! LINEAH
Response of 5 percent corresponds to dose In man of 253,0 rig/kg/day which lambda r
Option cost (mi I Hon* of dollars) - -5,20 Co*t per cancer case (millions of dollars) = 1,00
0.000203
Type of
Her*on
Number In
U. S, Population
Average Annual Exposure
(micro gms/cublc m) (irg/kg/da y)
Expected Lifetime
1ncIdence
(probata 1 I i t y /1 I f et 1 me)
t xpec t ed
Annual Cases
(number/year)
Machine Operators
Comme rc1 a 1
IndustM al
Coln-Up
16000.
700.
11000.
22050.
2t)flU 0 ,
2640.
3,15
«.ll
0 » 3 7 71
0. 000636
0,000834
7,647E-0 5
0,1459
o.oo a 116
0,0120
0
1
Other workers
Comme rc i a 1
Industrial
Co 1n-Uo
110000,
20000•
22000,
1900.
6400,
2640,
0,7000
0.91UJ
0.3771
0,000142
0.000185
7.O47E-05
0.2230
0,0530
0,02m0
Service Users
Commercial Dry Clean 50000000,
Coin-Up Dry Clean 25000000.
Coln»L)p Laundry 37000000 .
2,05
5.00
1.67
0.000350
0,000714
0.000239
7.096E-08
1,44 BE-0 7
'1.64 3E-0B
0,0507
0,0517
0,0256
CO
Urban Resident*
25 - 200 •
200 - 500 •
500 - 1000 *
3700000,
20000000,
71000000.
• Distance from dry cleaner in meters
1,66
0.2660
0.0V6Q
0.000240
4 .114E-05
1.371E-05
4.866E-06
8,34 11.-09
2.7B0E-0V
Total
0.002572
0.002383
0,002820
0,6021
Total coat of option end expected cancer (millions of dollars) ¦
-4.60
-------
OPTION 13
Scalinol 0DDY_W£IGHT Soecies! HAT Model I QUADRATIC
Peaponse of 5 percent corresponds to dose In n-.an of 2S3.0 mq/kg/day which liipl let lambda -
Option cost (millions of dollar*) * -5.20 Cost pit cancer caoo (millions of collars) = 1.J0
a.01iE-07
lye* of
Per Bon
Number in
U, S, Population
Average Annual Exposure
(micro Bms/cubic m) (mg/kg/day)
Expected LIfet ime
Incidence
(probability/lifetime)
t «nec ted
Annual Cases
(nuTber/ycar)
Hachina Cioeratora
I
w
CJ
C 0»«0rc i »I
I nous t rial
Co In-Up
Other »>orkera
dS - 200
200 - 500
500 - 1000
16000.
700.
11000,
UOOOO.
20000.
220 00.
Conixerciel
1 ndya trial
C oIn-Up
Service Users
Commercial Ury Clean 50000000.
Coin-Op Dry Clean 25000000.
Coin-Op Laundry 37000000.
Urban Heaidenti
3700000.
20000000.
71COOOOO.
22050.
26600,
2640.
4900.
6400.
2640.
2.45
5.00
1 .67
1 .68
0.2tt»0
0.0960
* Distance from dry cleaner in maters
Total cost of option and expected cancer (millions of dollars)
3.15
4.11
0.3771
0.7000
0.9143
0.3771
0.000350
0.000714
0.00023?
0.000240
4 . 114E-05
1.3T1E-0S
"5.20
7.951E-06
i ,35')E-0i>
1.140E-07
3.927E-07
6.694E-07
1 . 140E-07
9.816E-14
4.088E-13
4.572E-14
4.616E-14
1 .356E-15
1 .507E-16
I ot a I
0.001817
0.000136
1 , 7 71E-05
0.0O0617
0, 000191
3.582E-05
7.012E-08
1 .460E-07
2.11 7E-08
2.440E-09
3.876E-I0
1 ,5.29E-tO
0.gu2816
-------
OPTION 1 (I
ScaHmt SUHFACE_ARtA Specltll HOUSE Model l
KosDonse of 65 percent correspond* to dose In (ran of
Option cost (nil Hers of dollars) » 0,60 Cost per cancer case (m
LINEAR
25,0 mg/kg/day whlc* Implies lambda
0.6419
oni of aollars) s 1,00
Tyoa of
Person
U.
Number In
S, Population
Average Annual
(micro H^s/coblc m)
Exposure
(m9/kq/day)
Expected LIfet(ma
Incidence
(probabl1ity/11fettme)
E xpec t ed
Annual Cases
I nuTibe r/y e a r )
Machine Operetors
O
I
Co«i>erc 1 • 1
Indus t rial
Coln-Up
Other Worker*
Co»"ie rc 1 a 1
Industrial
C o1n-Op
Service Users
16000,
700.
11000,
110000.
200 0 0,
22000,
11100,
28600.
3300,
1900,
6400,
3300,
2,06
«.ll
o,«»7m
0,7000
0,9m J
0,1711
0 , 0tt2 7
0.1585
0,3196
0,0289
0,0376
0,0196
18,89
1 .58
3,0B
15,16
10.75
6.15
CO
Commercial Dry Clean
Co1n»0p Dry Clean
Co1n-Op Laundry
Urban Healdent*
25 £00 *
200 - 500 «
500 - 1000 •
50000000,
25000000,
37000000,
3700000,
20000000,
71000O00,
2,15
5,90
2,09
1,82
0,3120
0,1010
• Distance fro« dry cleaner In meters
Total cost of option end expected cancer (million* of dollars)
0,000350
o,oooai3
0,000299
0,000260
1,157E-05
t .lHbfc-05
1.166E-05
3.535E-05
I.252E-05
1.091E-05
1.66VE-06
6.231E-07
Total
10.17
12.62
6 ,b2
0,5765
0.5311
0.6320
117.
117,97
-------
OPTION I 4
Sctllngi 3URFACE.AREA Speclest HOUSE Modal J QUADRATIC
"ssDonse of 65 percent corresponds to doss In »in of 25.0 mq/kg/day which Implies lambda
0,00167b
Option cost (ml I I ion of dollars) s 0,6U tost per cancer c>>« ('
ona of oollsrs) 2 1.00
Ivdc of
Person
Number In
U, 3, Population
Average Annuel
(micro gmj/cubtc t>)
Exposure
(ng/kg/day)
Expectad L1fet1 me
Incidence
(prooeblI 1t y / Ii fetime)
Expected
Annual Cases
(nunoer/year)
Machine Operators
Commerc1«1
Industrial
Co In-Op
16000.
700.
11000,
1««00,
28800.
i300.
2.06
1 1
0 .1 / 1 U
0,007065
0,0260
0.000372
1,61
0.2796
0, U565
Other Workers
Co»"»e rc 1 a 1
Indust r1al
Coin-Up
110000.
20000,
22000.
4900,
6 400.
3300.
0.7000
O.VldJ
0,«7 1 U
0.000821
0.0011U0
0,000372
1.2V
0,3999
0. 1170
Service User*
Commtrclal Dry Clean 50000000.
Coin-Op Dry Clean 25000000,
Coin-Op Laundry J7DCOOOO.
2.15
5.90
2.09
0.000350
0.000643
0.000299
2.052E-10
1.190E-09
1 .191E-10
0.0001U7
0.0 00125
7,695c-05
Urban Resident*
25 - IS)0 •
200 - 500 •
500 - 1000 *
3700000 ,
20OOOO00.
71000000.
1.62
0.3120
0. I 010
0.000260
1.157E-05
i.
-------
OPTIOH 14
Scsllnqi SU9FACE_ANEA Speclesl "AT Model!
Response of 5 percent corresponds to dose In men of
LINEAR
43.J mU-oa
Total
0,0163
0,0151
0,0179
3.37
Total cost of option and expected cancer (millions of dollars) a
3,97
-------
OPTION 11
SceHngj SURFACE_A1EA Species: NAT Model I QUADKA TIC
Response of 5 percent corresponds to dose In ««n of «"3.3 mg/kg/dey which Implies lambda s
OoMon cost (millions of dollara) » 0,60 Cost par cancer case Ullllom of dollars) - 1,00
2, 710E-05
Type of
Person
Number In
U, S, Populat 1 on
Average Annuel Exposure
(micro gma/cublc m) (mQ/kg/day)
Expected Lifetime
1ncIde nce
(probablI1t v / I1fet *me)
tipect ed
Annual Cases
Cnymber/yeer)
Machine Operators
Comme rcia I
1 ndus t rial
Co 1n-Up
16000.
700.
11000.
11«00,
26600,
1300,
2,06
1,11
0 , 1 7 1 <1
0,00011b
0.000M61
6, 091E-0&
0.0£65
0.004 638
0tC00Vb7
Other MorKers
Commerc1 a 1
Indus t rial
Co 1n«Up
110000.
20000,
22000,
1900.
6100.
3300,
0,7000
0.911J
0. q 71 *(
1 .3HE-05
2.289E-05
6.091E.-06
0.0211
0,006539
0.00 1911
Service Users
Commercial Dry Clean 50000000,
Co1n«Qo Dry Clean 25000000,
Coln-Oo Laundry 37000000,
Urban Residents
2.15
5.90
2,09
0,000350
0.000613
0.000299
3.3S7E-12
1.917E-11
2.113E-12
2, 398E-06
6.9iJ£«06
1 .291E-06
25
200
500
200 «
500 •
1C0O .
3700000.
20000000,
71000000,
1.S2
0.3120
0,10'40
0.000260
1,1b7E-05
1,186E-05
1.853E-12
5,iiie-ii
6.019E-1S
9.792E-08
1.55SE-08
b,I 361-39
• Distance from dry cleaner In iretere
Total
0.0616
Total coat of option end expected cancer (ullllom of dollars) *
0,66
-------
0P1IUN 14
Sc ¦ H nq | BODY_*EIGHT Spectesi MOUSE Model: LINEAR
Response of bb percent corresponds to aose In man of 332,0 mg/kg/day which lirplltt lambda a
Option cost (mil Hons of dollars) ' 0.60 Cost per cancer case (millions of aollars) a l.QO
0 , 0 0 3 1 b ^
Typa of
Parson
Number In
IJ, S. Population
Average Annua) Exposure
(micro gmo/euBlc «) (mp/kg/day)
Expec ted Lifetime
IncI dene e
(P rebau1 I 1 t y / 1 1 fet I «ji e J
t xpec t ed
Annual Cases
(ng.Tber/year)
Machine Operators
r>
I
CO
f--s
CD
Commercial
I ndu atrial
Coln-Op
Other nopkeri
C o»'er c1 a 1
Indust rial
C oIn-Oo
Sarvlca Users
Conmerelal 0ry Clean
Co I n-Clp Dry CI eon
Coln-Uo Linjn^py
Urban Kesldents
25 - "200 «
200 - 500 •
500 - 1000 *
IbOOO.
700.
11000.
110000.
20000,
22000,
50000000.
25COOOOO,
J7GOOO&0,
3700000,
2O000000,
71COOOOO,
liioo.
26600.
3300,
<4900.
olOO,
3300.
2,15
5.90
2.09
1.82
0.3120
o.ioao
* Olstance from dry elaaner in mater#
Total cost of option and expected eancar (millions of dollars)
2.06
0,1/11
0,7000
0.9113
0.17H
0.000350
o.oooau
0.000299
0,000260
1.157E-0S
1 .186E-05
9.59
0.006181
0.0129
0 . 00 11)9 0
0.01I22H
0 ¦ 002otJ 7
0,001490
1.107E-06
2.665E-06
9.111E-07
8.221E-07
1.109E-G7
1.69BE-O0
Total
1 .08
0.1J9J
0,2311
3.17
0.8218
0,«662
0.7905
0,9519
0.1990
0.0135
0,0*03
0.0*77
8,99
I
-------
OPTION 10
Scaling! bOOY_wEIGHT Species! MOUSE Mode 1 I QUADRATIC
Rolponn of 65 percent corresponds to dose 1n nan of 3.12,0 mg/ka/dsv which lirpHoa lambda a
Option cost (million* of dollars) a 0.60 Cost per cancer case (rr1l Hone of collars] a 1.00
V.524E-06
Type of
Person
U,
Number 1n
S. Popul at I on
Average Annua) Exposure
(micro gms/cublc m) (irg/kg/aay)
Expected Lifetime
Inc 1 aar.ee
(probabl I i t v / 1 H e t1 me )
Expected
Annual Casos
( numne r/yla r )
Machine Operator!
Commare1 a I
I ndu s t ria I
Co In«Up
16000,
700,
11000.
14400,
?seno.
iiOO,
2,0b
«,ll
U.4714
4.029E-05
0,000161
e.int-06
0,009210
0,001613
0.000333
O
I
to
O
Other Workers
Commercial 110 0 0 0,
Industrial 20000,
Coin-Op 22000.
Service Users
Commercial Dry Clean 50000000,
Co1n-0p Dry Clean 25000000,
Coin-Up Laundry 37000000,
Urban Residents
<1900,
6400,
530 0,
2.15
5.90
2.0
-------
OPTION 1«
Sealing: BODY_wCI&HT Speclesi HAT Modell LINEAH
Response of 5 percent corresponds to dose 1n man of 253.0 mg/kg/day which Implies lamoda s
Option coit (millions of oollars) * 0,60 Coat par cancer case (tllllom of dollars) s 1,00
0.000203
Tyoe of
Person
U.
Number In
3. Population
Average Annui
(micro gn>s/cublc n)
Eipoiuri
(mfl/ko/day)
bipac tad LIfet1 me
Inc 1 oence
(probao! I 1 t y / 1 1 fet I ma)
t «p«ctcd
Annual Coses
(nuir.Dnr/year)
Machine Operators
Coirme rc 1 a I
Inous trial
Co 1 n-Op
16000.
700.
tiooo.
14400.
26600.
3300.
2.06
4.U
0,4714
0,000417
0.000634
9.555E-05
0.0953
0.005336
0.0150
Other Workers
ColoreIal
Inous t r i a I
Co In-Up
110000.
20000,
22000,
4900,
6400.
3300.
0 , 7 0 0 0
0.9143
0,4714
0,000142
0,000185
9.555t-05
o.2230
0.0530
0,0300
Service Users
Commercial Dry Clean 50000000,
Co1n»Uo Dry Clean 25000000.
Coin-Op Laundry 37000000.
2,45
5,90
2,09
0,"00350
0.O0J643
0.0C0299
7.096E-08
1.7G9E-07
b,053E-0a
0,0507
0,0610
0,0320
Urban Residents
25 -
200 «
500 -
200 *
500 *
1000 •
J700000,
20000000.
71000000,
1,62
0.3120
0.1040
0,000260
4.457E-05
1.1&6E-Q5
5,271E-0U
9.036E-09
3.012E-09
0,002786
0.002582
0,003055
* Distance fro* dry cleaner In metert
total
0,5 Ibii
Total cost of option ana expected cancer (millions of dollars) = 1.18
-------
OPTION 14
Scalinql BODY_WEIGHT 3pecio#l KAT Model! QUADRATIC
Response of 5 percent correspond# to dose In man of 253,0 mg/ko/day which Implies lambda =
Option co»t (nlllloni of dollars) » 0,60 Cost oar cancer case tillllopii of dollars) = 1,00
a.ciit-07
lype of
Person
Machine llperators
Number in
U, 5. Population
Average Annua) E*po»ure
(micro Qua/cubic m) (mO/kg/dey)
Expactod Lifetime
I "cIUenco
CproDabIIity/lIf etI me)
Expected
Annual Cases
(nuiroe r/y e s r )
Coirr-ere I al
Industrial
Coln-Op
16000.
700.
11000.
liaoo.
26900.
3300 .
2,06
<•.11
0.471M
i. J91E-06
1 . 359E-05
1 . 781E-07
0, 0 0 0 7 75
0.000136
2.799E-0S
C")
I
t-J
NJ
Other Workers
Commercial
Induat rial
Co In-Op
110000.
20 000.
22000.
«
-------
OPTION 15
Scaling! 3URFACE.AKEA Speclasl HOUSE
Response of b5 percent corresponds to dose
Option cost (millions of dollars) » 3.50
Mode) I UMEAW
In man o1 25.0 mg/kg/dey
Cost per cancer case ()
EnpoSure
(mg/kg/day)
Expected Lifetime
I ncIUcnce
(prohabII Ity/li fetlme)
t > pec t ed
Annual Cases
tnyHDer/year)
Machine Oueratore
O
I
r-j
u>
CO
Co«i*»pc 1 al
Indust rial
Coin-Op
Other Workers
Con'frc1 a 1
Industrial
Coln-Op
Serv1ce Users
Commercial Dry Clean
Coln»Up Dry Cle«n
Coin-Op Leundry
Urban Residents
25 - 200 «
200 - 500 *
500 - 1000 •
16000.
700.
11000.
110000.
20000.
2
-------
OPT I UN 15
SciHno: SURF *CE_AR£ A Speclesi
Response of *5 percent corrsaponds
Option cost (millions of do 1 I or81 a
MUUSE Model: QUADRATIC
to dose In man of 25.0 mg/kq/day which Implies lambda »
3,50 Coat Per cancer cast lnllMgm of dollars) = 1,00
0,01)1675
Tvpe of
Person
U.
Number In
S. Population
Average Annual ExpoSuro
(micro «*s/cuble m) Cirg/kq/oey)
Expected Lifetime
Inc t 'lenco
(orobablI1 ty / IIfetlmc)
txpacted
Annual Cases
C unbar/year)
Machine Operatora
Ci
I
i—•
ts>
•O
Comma rc1 a 1
Indu s t r1 a I
Coln-Gp
Other Horkers
Coi"»(! rc 1 a 1
1ndu# trial
Coln»Up
Service Users
16000.
700.
11000,
110000.
20000.
22000.
14«00.
28600.
2610,
4900.
6" 0 0 .
2610.
2.0b
a. 11
0.3771
0,7000
0.91
-------
OPTIUN 15
Seal I ng l SURFACE.AHEA Speclesl RAT Modell LINEAR
Response of 5 percent corresponds to dote In man of 43.1 mo/kQ/dav *Mef< ImpHts lambda =
Option cost (million* of dollars) ¦ J,SO Cost per cancer case (millions of dollars) e 1,00
o.ooi lab
Type of
Person
Number In
U, S . Poou1et1 on
Average Annuel Exposure
(micro Ofi's/cublc m) (mg/kg/oay)
fc*pected L Met i««
Incidence
(probab(Iitv/lIfetlme)
tupecteo
Annuol Cases
(riurher/ye a r )
M»chIne Ope rat ors
Commerc i a I
Industrial
Co In-Up
16000.
700.
11000.
14000.
26800.
2640,
2,06
4.11
0,3771
0.002436
0,004d6b
0.0004«7
0.S568
O.Oub"/
0.07U2
CI
I
to
Ln
Other Workers
CommercI a 1
Indus t rial
Co I n»l'p
110000,
20000,
22000,
4900,
6400.
2640,
0,7000
0,^143
0,1771
0,000850
0,0011)81
0.0O0447
1.30
O.lO^b
0.140b
Service User*
Commercial Dry Clean 50000000,
Coin-Up Dry Clean 25000000,
Coin-Op Laundry 37000000,
2,4b
5.00
1,67
0,000350
0,000714
0,00023V
4,150E-07
8,46
-------
OPTION 15
Scat Ingi SURFACE_AHE.A Speciesl "*1 Modelt QUADrtATIC
Response of 5 percent corresponds to dosa In mon of 4 3,3 mg/kg/day which lnpHas 1am&da = 2,7401-05
Option cost (nillllons of dollars) c 3.50 Coat per cancer ense lnl|l|oni of dollars)" 1,00
Type of
Person
U.
Number 1n
S. Population
Average Annual Exposure
(micro gms/cuble m) (my/ko/dey)
t*pcc tea Li fet1 me
1nc1dene e
(prohaDi1i ty/1i fet (me)
t *oec t ed
Annuo 1 Coses
(number/year)
Machine Operators
Coirim#rc \ * 1
I noust rial
C o t n-Oo
Other Workers
C'ctimerc i al
Xndustri el
Colh-ud
Se rv i ca Uaera
160 00,
700.
11000,
110000,
20000.
22000.
I«fl00,
2APO0,
2640.
#900 ,
6100 .
2640,
2.0b
".It
0.5771
0.7000
0,91flJ
0.1771
0.000116
0,000064
J.8
-------
UPTIUN 15
Scsllngi 610Y_»iE ICHT Spoc
-------
OPTION 1*
Seal 1 no I 800r_HEICHT SpecleSJ MOUSE Model I QUADRATIC
Response of 65 percent corresponds to dose 1n man of 332,0 «ifj/*g/day which (""Piles lambda a
Option cost (millions of dollar*) = 3.50 Coat oer cancer caie (millions of dollars) = 1.00
9.52«t-06
Type of
Parson
Number In
U. S. Population
Average Annual Exposure
(micro gms/cublc m) (irO/kg/dey)
E *pect ed Lifetime
Incidence
(probab!1ity/l1fetlne)
t*pec t ed
Annual Cnaes
(nu'Cer/year)
Machine Operators
Comirerc i a I
Inrtuatrial
Coi n»0p
16000,
700.
11000.
H«00.
26600.
2630,
2.06
1.11
0.3771
u.02
-------
OPTION 15
ScaHngt BnDY_*E 1 liH T Speclesl Rat Model I LI SEAR
Response of 5 percent corresponds to dose In nan of 253.0 mg/kg/dey wnich Implies 1amBda = 0,000203
Ootton cost (rlllloni of dollars) c J,50 Cost par cancer cose (;
lO
cc
Co-me rc i a 1
Indus t r I s 1
Co 1n~0p
Service Users
Commercial Dry Clesn
Co1n«pp Dry Clean
Coin-Dp laundry
Urban Residents
25 - 200 «
200 - 500 »
500 - 1000 •
UOOOO,
20000,
2*000,
50000000,
25000000,
37000000,
3700000,
20000000,
71000000,
<4900 ,
610 0,
2640,
2.«5
5,00
1.67
1,60
0,2600
0,0969
• Distance from dry cleaner in meters
Total cost of option and expected cancer (millions of dollars) s
0,7000
0.91U3
0,3/71
0,000350
0. 0007U
0.000239
0,000210
0,110E-05
U371E-05
a,05
0,000142
0,000185
/,bi»7£-0S
7.096E-OB
1.
4, 8
-------
OPTION IS
3c« 1 I ng I BDOY_wtIGHT Speclesl R*I Model I HUAPM A r1C
Patpomt of S percent corresponds to rlose In man of 253.0 mg/kg/day which Implies lambda
Option colt (millions of dolItrt) a 3.50 Coat per cancer casa (millions of dollars) s 1,
B, 0 Iit-07
00
fype of
Pa r son
U.
Number In
3, Population
Average Annual
(micro ui'S'euMc rr.J
Expoiure
(mg/ku/day)
Exoected Lifetime
1 ncI dene e
(probab i111 y / 1ife t1 me)
t*pected
Annual Cases
(number/year)
Machine Operators
Compere I a I
Industrial
Col n»l>p
16000.
TOO.
11000,
1 <41 00 .
28800.
26:10.
2.06
1.11
0.3771
3.391E-06
1.359E-05
1 , 14 0E-0 7
0,000775
0,000136
1 , /V1E-G5
Other Workers
Co*f«rci a 1
Indus:rI a 1
Coln-Op
110000,
20000.
22000,
<4900.
6«00,
<;6qo.
0.7000
0.9143
0,3771
3.927E-07
6.69VE-07
l.HOc-07
0.00061 7
0,000191
3.562t-0'j
Service Users
Commercial Dry Clean 50000000,
Coin-Op Dry Clean 25000000,
Coin-Op Laundry 37000000,
2,45
5.00
1.67
0.000350
0.000714
0.000239
V.816E-11
M.0U8E-1J
«,572E-1
-------
PART [II
DI-ETHYLHEXYL PHTHALATE:
A CASE STUDY OF THE APPLICATION
OF DECISION ANALYSIS TO THE DETERMINATION
OF RISK POSED BY A TOXIC CHEMICAL
/"/'
330
-------
Part III
CONTENTS
Section Page
1 INTRODUCTION AND SUMMARY 1-1
2 PLASTICIZERS 2-1
Phthalate Ester Plasticizers 2-1
Manufacturing 2-4
Uses of Phthalate Esters 2-6
3 THE PROTOTYPE PLASTICS INDUSTRY MODEL 3-1
Purpose of the Model 3-1
Structure of the Plastics Industry 3-2
Structure of the Prototype Model 3-4
Implementation of the Prototype Model 3-9
Data for the Prototype Model 3-18
4 RESULTS FROM USING THE PROTOTYPE MODEL 4-1
Base Case Results 4-1
Regulatory Policy Run Results 4-10
Sensitivity Analysis Results 4-18
5 POTENTIAL DEVELOPMENT AND USE OF THE ECONOMIC ANALYSIS MODEL 5-1
Additional Policy and Sensitivity Analysis 5-1
Further Development of the Prototype Model 5-2
Summary 5-3
6 REFERENCES 6-1
APPENDIX A: DATA FOR THE PROTOTYPE PLASTICS INDUSTRY MODEL A-l
APPENDIX B: RESULTS OF POLICY AND SENSITIVITY ANALYSIS CASES USING
THE PROTOTYPE PLASTICS INDUSTRY MODEL B-l
I*
/_ /f
-------
Pare III
FIGURES
Figure Page
2-1 Formation of a Phthalate Ester from Phthalic Acid
and Alcohol 2-2
2-2 Production of Phthalic Anhydride from Naphthalene or
Ortho-xylene 2-5
3-1 General Structure of the Plastics Industry 3-3
3-2 Detail of the Plasticizer Sector and Portions of the
Feedstock, and Plastics Sectors 3-6
3-3 Structure of the Prototype Plastics Industry Model 3-8
3-4 Modeling the Production of DEHP 3-10
3-5 Market Share Curve 3-16
4-1 Base Case Plastics Production 4-4
4-2 Base Case Plasticizer Production 4-5
4-3 Base Case Plastics Demand for Medical Supplies 4-11
4-4 Plastics Production with "Prohibit" Policy 4-15
4-5 Plasticizer Production with "Prohibit" Policy 4-16
4-6 Plastics Production with High Feedstock Prices and
"Prohibit" Policy 4-21
4-7 Plasticizer Production with High Feedstock. Prices and
"Prohibit" Policy 4-22
-------
Part III
TABLES
Table Page
2-1 Consumption of Plasticizers by Type 2-2
3-1 Estimates of Quantities Produced and Demanded 3-19
3-2 Derivation of Process Economics for PVC Resin 3-21
3-3 Derivation of Process Economics for DEHP 3-21
3-4 Process Economics and Financial Parameters 3-22
3-5 Economic Market Parameters 3-25
3-6 Feedstock Prices 3-26
4-1 Base Case Plastics and Plasticizer Quantities 4-2
4-2 Base Case Plastics and Plasticizer Prices 4-3
4-3 Base Case Average Plastics Prices by End-Use Category 4-6
4-4 Plasticizer and Plastics Prices 4-7
4-5 Base Case Plastics Usage by End-Use Category 4-8
4-6 Base Case Plastics Usage by End-Use Category 4-9
4-7 New Plant Capacity for DEHP in the Base Case 4-12
4-8 Costs to Consumers 4-17
4-9 Sensitivity Test Feedstock Prices 4-19
l-tv
333
-------
Section 1
INTRODUCTION AND SUMMARY
In this case study we demonstrate a method for estimating the
economic costs of regulating di-ethylhexyl phthalate (DEHP), a widely used
plasticizer. Our case study on perchloroethylene addressed a chemical
whereby a single predominant exposure route made analysis of control
options relatively straightforward. DEHP is representative of chemicals
whose uses and potential for substitution are extremely complex. In this
case study, we illustrate a methodology for addressing the complex use
structure and analyzing the economics of substitution. The economic
analysis is developed using a prototype economic model of the plastics
industry.
DEHP is a plasticizer, a substance that is combined with plastic resins
to make flexible plastic products. About 190,000 metric tons of DEHP are
produced and consumed annually in the United States, making it the most
widely used single plasticizer. It is used in a variety of commercial and
consumer products in which it has considerable economic value. It is not a
new chemical and has been in use since the 1930s. DEHP was selected for
possible designation under Section 4(f) of TSCA because of bioassay results
from the National Cancer Institute (NCI) indicating that DEHP produces a
significant number of liver tumors in rats and mice [1].* Subsequent to
our case study on DEHP, EPA has decided that DEHP is not an appropriate
candidate for designation under Section A(f) of TSCA at the present time
[2|.
*Under this section, when the Administrator receives information indicating
that a chemical substance or mixture presents or will present a significant
risk of serious or widespread harm to humans from cancer, gene mutations,
or birth defects, he or she must act within 180 days of receipt of such
information to initiate appropriate ac,tion to prevent or 'reduce, such risk
or find that the risk is not unreasonable. In determining whether to
designate a chemical under Section 4(f), EPA must decide whether the
underlined criteria have been met.
1-1
-------
The major focus of this case study is the. development and use of a
prototype economic model of the plastics industry with emphasis on
plasticizers and DEHP in particular. The model was developed to help
analyze the economic implications of potential federal actions that might
be taken to regulate the production or use of DEHP. The production of
several different plastic resins, plasticizers, plastics, and their
interactions in the narketplace are represented in the model. The model is
a prototype in the sense that it is intended to provide first-order quanti-
tative estimates of changes in market prices and quantities demanded and
supplied at a national aggregate level of detail. These changes are diffi-
cult to determine, given the complex interaction of substitution among
plasticizers. However, the prototype model illustrates such an estimate,
and it could be refined to give better estimates.
The prototype model was run and solved for equilibrium quantities and
prices for a base case and two regulatory policies. A consumer surplus
calculation was used to estimate the economic impact of prohibiting or
restricting the production and use of DEHP. A complete ban on DEHP would
reduce consumers' surplus on the order of 100 million dollars per year.
Eliminating the use of DEHP from medical applications and automobile
interiors would have an annual loss of consumers' surplus on the order of
three million dollars.
This part of the report consists of five sections. Section 2 gives an
overview of the plasticizer industry. Section 3 describes the prototype
plastics industry model, and Section 4 presents the results of the model.
Section 5 describes potential development and use of the economic model.
1-2
pi rr
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Section 2
PLASTICIZERS
Plasticizers are substances that are mixed with plastic resins to
make flexible products. Several different types of plasticizers and indi-
vidual plasticizers within each type are on the market today. They are
used either singly or in combination to achieve desirable processing char-
acteristics and product properties. Table 2-1 lists the major types of
plasticizers and their estimated consumption in the United States. In
1979, the estimated consumption of plasticizers was 752,000 metric tons
[3], At an average price of SO.46 per pound [4], the value of these
plasticizers in 1979 was $763 million.
From Table 2-1, phthalates (also called phthalate esters) accounted
for 509,000 metric tons or about two thirds of total plasticizer consump-
tion. The single most widely used plasticizer, DEHP, is estimated to
comprise about 25 percent of the consumption of phthalate esters [4J.
PHTHALATE ESTER PLASTICIZERS
An ester is an organic compound formed by the reaction of an acid
with an alcohol. The use of different alcohols, such as methanol, ethanol,
or butanol, results in different esters. Phthalate esters are formed from
phthalic acid, which consists of a single benzene ring with two adjacent
carboxylic acid groups. Figure 2-1 shows formation of a generic phthalate
ester [5]. In the figure, the letter R stands for an alkyl radical, such
as methyl or ethyl, which i9 a carbon chain having the general formula
CnH2n+l•
The names of the phthalate esters are derived from the names of the
alkyl groups from which they are composed. If the two alkyl groups are the
2-1
336
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Table 2-1
CONSUMPTION OK PLASTIC l/.ERS BY TYPE (IN THOUSAND METRIC TONS)
Plasticizcrs 1977 19 7_8 19 79
Adipates 28 29 32
Azelates 5 5 5
Epoxy 54 57 59
Linear Phthalates 142 154 157
Phthalates, Other 336 346 352
Polyesters 24 24 25
Trinellitates 12 14 14
Others 103 105 108
Total 704 734 752
Source: Modern Plastics [3]
CATALYST
PHTHALIC ACID ALCOHO
PHTHALA7E ESTER
ure 2-1. Formation of a Phthalate Ester from Phthalic Acid and Alcohol
2-2 337
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sane, the name is preceded by the prefix di. For example, diethyl phtha-
late (DEP) has two ethyl radicals. If the radicals are different, the two
names are given separately; e.g., butyl decvl phthalate (3DP) contains a
butyl (4 carbons) and a decyl (10 carbons) radical. Commercial prepara-
tions of mixed esters such as BDP also contain significant quantities of
the diesters (DBP and DDP), since they are all produced together and are
impractical to separate.
The most commonly used group of plasticizers is dioctyl phthalate
(DOP), which contains two octyl radicals, each consisting of eight carbon
atoms and seventeen hydrogen atoms. Actually, the radicals with longer
chains, such as the octyl radical, can have structures of their own, called
branching. The ethylhexyl radical is a form of the octyl radical with a
main body of six carbons (hexyl) and a branch with two carbons (ethyl).
Another form is the isooctyl radical, which has random branching. The
corresponding esters are diethylhexyl phthalate (DEHP) and diisooctyl
phthalate (DIQP). The form of DOP with straight alcohols is called
di-n-octyl phthalate (DNOP).
The basic differences among the phthalate esters are their molecular
weight (corresponding to the number of carbon atoms in the alkyl radicals)
and their extent of branching. When phthalate esters are used as plasti-
cizers, increasing the molecular weight generally decreases the compatibil-
ity of the ester with the plastic resin and decreases its efficiency of
imparting flexibility to the plastic at roon temperature. Conversely,
increasing molecular weight lowers volatility, thus improving retention of
the plasticizer in the product. Increased branching improves compatibility
but increases volatility, slightly reduces efficiency, and reduces the low
temperature properties of the product. Because of volatility, dibutyl
phthalate (DBP) is the lowest molecular weight ester used as a general
plasticizer; and because of low compatibility, ditridecylphthalate (DTDP)
is about the highest molecular weight ester used as a plasticizer. In many
applications, the osters that have radicals with seven to ten carbons have
the most useful combination of properties.
Of course, another commercially important property is the cost. Manu-
facturing branched alcohols is generally less costly than manufacturing
2-3
33S
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straight alcohols, since the corresponding feedstocks are less costly.
Therefore, the branched phthalate esters, such as DEHP, are less expensive
than the straight (normal) esters. The branched esters were also intro-
duced earlier into the plasticizer market, since the process for manufac-
turing straight alcohols from petroleum is relatively new. Previously the
straight esters could be made only from coconut oil, which is high in
straight chain hydrocarbons.
MANUFACTURING
This section briefly describes the process of manufacturing phthalate
esters and products containing phthalate esters.
Manufacturing Phthalate Esters
As just described, phthalate esters can be formed by combining phtha-
lic acid with alcohols. The commercial process actually uses phthalic
anhydride, which is phthalic acid ninus one water molecule [5]. Phthalic
anhydride is produced from either of two feedstocks: naphthalene or ortho-
xylene. Naphthalene and ortho-xylene occur naturally in coal tar. How-
ever, most of these feedstocks are manufactured from crude oil. Through
the process of "catalytic reforming," petroleum fractions of aliphatic
hydrocarbons (straight carbon chains) are converted to the desired aromatic
compounds (compounds with carbon rings) [6j. The naphthalene and ortho-
xylene are then separated out of the products by distillation or crystalli-
zation.
The production of phthalic anhydride follows the formulas shown in
Figure 2-2. Either naphthalene or ortho-xylene is oxidized, usually as a
vapor mixed in air and in the presence of a catalyst, to produce phthalic
anhydride and combustion products such as carbon dioxide and water.
The alcohols used to produce phthalate esters are made in a variety of
ways, depending on size and branching [6]. The smallest molecular weight
alcohol, methanol, can be made from many feedstocks, such as coal or
natural gas. The next smallest, ethanol, is usually made fron such vege-
table products as grain. The higher molecular weight alcohols, such as
2-4
333
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NAPHTHALENE
PHTHALIC ANHYDRIDE
Figure 2-2. Production of phthalic anhydride from naphthalene or
ortho-xylene.
octanol, are made from propylene and butylene, which are products of crude
oil cracking. Randomly branched alcohols can be produced by a process
called hydroforraulation, in which high molecular weight olefins are con-
verted directly to alcohols. Alcohols with specifically desired structures
are usually built from ethylene.
Manufacturing Plasticized Products
Because 80 percent of all plasticizers are used in polyvinyl chloride
(PVC) [7], we will concentrate on the production and uses of plasticized
PVC. By itself, PVC is a rigid plastic that becomes brittle at 0°C (32°F).
However, when mixed and fused with a plasticizer, such as a phthalate
ester, it becomes a tough but flexible product. The plasticizer acts as an
internal lubricant, overcoming attractive forces between the chains of PVC
2-5
3.10
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and separating them to prevent intermeshing. The degree of flexibility is
determined by the concentration of plasticizers, which may coopose 30-40
percent of a final PVC product [7],
The action of the plasticizer can be illustrated by considering water
as a plasticizer for gelatin. In nonplasticized form, gelatin is a pow-
dered, nonflexible solid. However, when mixed with water, heated, and then
cooled, it becomes a soft, flexible solid.
Several ways exist for making plasticized PVC products. Most com-
monly, powdered PVC and plasticizers are mixed and heated to 150-170°C, the
temperature at which the materials fuse or dissolve. While hot, they are
formed by calendering (rolling), extrusion, injection molding, spreading,
dipping, or pressing. Another procedure is to mix very finely powdered PVC
with the plasticizer at roon temperature and then use the liquid mixture,
called a vinyl dispersion, to spray, pour, spread, or dip a product. After
it is formed, the product can then be heated to fuse the PVC and plasti-
cizer, creating a flexible solid.
USES OF PHTHALATE ESTERS
Phthalate esters, particularly those with higher molecular weights,
are used primarily as plasticizers. The following are but a few of the
many PVC plasticized products containing phthalate esters, including DEHP
[8 J:
o
Shower curtains
o
Rainwear
o
Inflatable toys
o
Tablecloths
0
Floor covering and floor tiles
o
Insulation (roof, water tank.)
o
Adhesive tape and film
0
Food packaging
0
Garden hoses
o
Wire covering
0
Footwear
2-6
«5 ~
-------
o Upholstery
o Handbags
o Coated tubing (bed frames, chairs, plastic racks)
o Gloves
o Gaskets
o Automobile components (crash pads, instrument panels, vinyl tops)
o Medical tubing
o Blood bags
In addition to functioning as plasticizers, some of the lower molecu-
lar weight phthalate esters have other uses. Dimethyl phthalate is used as
an insect repellent. Diethyl phthalate, because of its bitter taste, is
used as a denaturant for alcohol, especially in cosmetics. Several phtha-
late esters can also be used as solvents [9],
o ".O
l* 1
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Seccior. 3
THE PROTOTYPE PLASTICS INDUSTRY MODEL
In this section, we discuss the purpose of the plastics industry
economic model, describe its structure and underlying assumptions, and
summarize the data used and their sources. Section 4 includes a full
discussion of the results obtained from using the model. A key insight is
that although the economic costs of a total ban on DEHP would be great due
to the large quantities in use, a restriction on its use in selected
sensitive markets would be significantly less costly because of the avail-
ability of substitute materials. In Section 5, we discuss the possible
extensions to the prototype model and summarize the capabilities of the
modeling approach used in this case study.
PURPOSE OF THE MODEL
What would be the economic impact of prohibiting the production or
use of DEHP, restricting its uses, or limiting the quantities used? What
would be the costs of using substitute materials? Our prototype model is
intended to help answer questions such as these--questions that are cri-
tical to a regulatory decision regarding DEHP. The model will also be
useful in understanding the implications on the plastics industry of
changes in the prices of feedstocks derived from fossil fuels and of vari-
ations in plastics demand growth over time.
We use the questions one wishes to answer, together with our informa-
tion about the plastics industry, to guide the development of the model.
The level of detail and required areas of emphasis are defined largely by
the three regulatory alternatives considered:
1. No action.
2. Prohibit all manufacturing and use of DEHP.
3-1
343
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3. Prohibit the use of DEHP in plastics intended for medical or
automobile interior uses.
The focus on DEHP leads to the need for relatively more detail when model-
ing plasticizers and relatively less for other sectors of the plastics
industry. Potential substitutes for DEHP are of particular importance, as
are other plasticizers currently competing with DEHP. Polyvinyl chloride
(PVC) resins and plastics must be modeled carefully, as the bulk of DEH? is
used to plasticize PVC. Because we are interested in decisions concerning
plasticizers, we will emphasize flexible plastics.
We chose to develop the prototype model on a national aggregate quan-
tity level. This is sufficient to demonstrate the modeling approach and to
provide first-order estiaates of the economic impacts. It would be a
straightforward task to expand the prototype model in order to examine re-
gional effects or international trade impacts.
STRUCTURE OF THE PLASTICS INDUSTRY
The structure of the model is meant to represent the structure of the
relevant portion of the plastics industry. The industry itself can be-
thought of as composed of three distinct sectors: the production of plastic
resins, the production of plasticizers, and the production of plastic
products using either plasticized or unplasticized resins. Figure 3-i
depicts these three sectors, together with a sector representing the supply
of feedstocks to the plastics industry and a sector representing the demand
for plastic products. As the figure indicates, one can think of materials
flowing up the economic network, from fossil fuel or other feedstocks,
through conversion to plasticizers and resins, to the manufacturing of
plastic products, and finalLy to the end users. This represents the se-
quence of industrial stages and market transactions from raw materials to
final goods.
Within each sector is a variety of industrial and economic processes.
Plastic resins, for example, include PVC (which can be produced In several
grades), polyethylene (PE), polypropylene (PP), and polystyrene (PS).
3-2
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SECTOR
PLASTICS
SECTOR
PLASTICIZER
RESIN
SECTOR
SECTOR
FLOW OF
MATERIALS
feedstock
SECTOR
Figure 3-1. General Structure of the Flastics Industry
3-3 345
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They are produced from feedstocks such as ethane, propane, and 3TX
(butylene-toluene ~xv1ene)-
PVC resins and a portion of other resins are combined x^ith plasti-
cizers in the plastics sector. Plasticizers compete on the basis of price
and properties, including volatility, ease of processing, and plasticity
effectiveness. Volatility is of particular importance, as it determines in
part both the degree to which the plasticity lasts and the extent to which
people are exposed to the plasticizer. Different types of plastics
products represent different markets for plasticizers, depending on the
nature of the goods and their intended uses. For many uses, such as in
construction, the ratio of plastici2er price to its effectiveness is the
dominant criterion. For others, including medical uses, low volatility is
important.
STRUCTURE OF THE PROTOTYPE MODEL
In our description of the structure of the model on a sector-by-
sector basis, we shall first discuss the degree of detail and assumptions
used in each sector and then describe the representation of market
interactions among sectors.
Guided by the purposes of the model just summarized, we chose to
represent plasticizers by the following categories:
1. DEHP--the most widely used phthalate ester plasticizer and the
object of toxic substances regulatory analysis.
2. Other large molecular weight phthalate esters—these
plast icizers, which include di-isodecyl (DIDP), iso-octyl, and
di-isononyl phthalate esters, have properties roughly similar to
DEHP and compete primarily on the basis of price and plasticizing
abili ty.
3. Small phthalate esters--thcse plasticizers include di-ethyl and
di-methyl phthalate ester, are more volatile than DEHP, and are
used to a significant degree to plasticize resins other than
PVC.
4. Very large molecular weight phthalate esters--this category
includes both polymerized phthalates and such large molecular
size substances as di-tridecyl phthalate ester. They are more
expensive but have significantly lower volatility.
-------
5. Epoxidized soy oil—this plasticizer, while relatively expensive,
has several desirable properties, including low volatility. It
is produced in part from a renewable resource and thus may be
less affected by increases in the price of fossil feedstocks.
6. Other primary plasticizers—these include adipic acid esters and
phosphoric acid esters. They compete primarily with DEHP and the
other large molecular weight phthalate esters.
A portion of the prototype model, Including the plasticizer sector,
is shown in Figure 3-2. Boxes represent industrial production or conver-
sion processes. Circles represent economic markets, that is, the competi-
tion among various products and users of products. The term allocation ,1s
used to reflect the market process of allocating goods among the competing
firms and industries. The equations used to represent each industrial or
market process are discussed in the following subsection.
Only three distinct categories of plastic resins were included in the
resins sector of the prototype model. This was felt to be adequate, given
the purpose of analyzing toxic substances regulatory decisions concerning
plasticizers. The resins represented are PVC, polyethylene, and a category
called "other resins." PVC resin is almost always plasticized, thus
forming a major part of the demand for DEHP and other plasticizers. PE is
the resin used in greatest quantities to produce flexible plastics, and PE
plastics often compete with plasticized PVC. The "other resins" include
polypropylene and polystyrene, both of which are used to produce plastic
products that compete with PVC and PE.
We included five types of plastic in the plastics sector:
1. PVC plastic using DEHP—this material is the focus of the
analysis and is important to examine separately.
2. Regular PVC—this includes all PVC plastic other than that made
using DEHP and the special low-volatility/low-migration PVC
plastics. These materials compete against PVC with DEHP.
3. Low-migration PVC—this is PVC plastic optimized to cut down on
the volatility of the plasticizer. This increases the life of
the material and cuts down on the potential for human exposure to
the plasticizers. It is plasticized by either epoxidized soy oil
or the very large molecular weight phthalate esters.
A. Polyethylene plastic—PE does not need to be plasticized to be
flexible. It is the most used plastic in the markets studied.
3-5
1
u I j
-------
To PVC
yji d e With
DEHP
DIHP
To Lo-
Migration PvC
To ocher
Pl.i'jt l(
Snail PAZ
Alloc a C ion
Epoxldi?ed
Soy Oil
Other
Primary
tPlasC lc Izeri
I Large
I Molecular
Wei2ht
?'nt h/i I,i t c
Esters
very -jr^e
Molecular
'-.'eigh*
Phthalace
Escers
Saua 11
Mo ledular
Veighc
Phthalatt
Escer s
To Resin
Foss 11
Liquid s
Allocac ion
vcgtable
Supply
Fossil
Liqu Ld s
Feedstoc k
Supply
Figure 3-2. Detail of the Plasticizer Sector and Portions of the Feedstock
and Plastics Sectors
348
3-6
-------
5. Other plastics—the other flexible (sometimes requiring plasti-
cizers) plastics modeled are polypropylene and polystyrene.
Each of the plastics competes in several end-use market categories in
the demand sector. The manner in which we disaggregated plascic demand was
determined directly by the nature of the decisions under study. The
categories are as follows:
1. Food wrapping and packaging—includes plastic wrap and bags,
bottles, cartons, disposable cups and plates, and other products.
These products have a relatively high potential for human
exposure through skin contact and ingestion of food containing
plasticizers.
2. Medical supplies—tubing, blood bags, and so forth. These
products have a very high potential for human exposure through
contamination of blood or other fluids.
3. Automobile interiors—primarily vinyl seat cushions, trim, and
interior linings. These products can have a moderate potential
for human exposure under certain conditions.*
A. Consumer products—all consumer products not included in the
first or third categories, including housewares, appliances, hone
siding, luggage, toys, apparel, and recreational products. In
most cases, these goods present a low potential for human
exposure to plasticizers.
5. All other uses for flexible plastics—primarily industrial
(particularly packaging) and construction. The potential for
human exposure from these uses is judged to be low.
The low migration PVC plastics play an increasingly important role in the
first three demand categories, while plastics other than PVC have only a
minimal portion of the medical and automobile interior markets.
The structure of the entire prototype model is outlined in Figure 3-3.
It can be thought of as a detailed implementation of the basic structure
shown in Figure 3-1. The feedstock sector is quite simple; the fossil
liquids supply model represents a generic feedstock. A simplifying
assumption was made to aggregate the feedstocks rather than to include
*Information available at the time the prototype, model was developed indi-
cated that DEHP was used in a significant portion of automobile interior
plastics. This use may have since been replaced by alternative materials.
3-7
3>13
-------
.J
L
! I
XSL
/ i
i
is'
/ \ / \ \A-^
i
\>
; ,\ //\ //' \\ ^
/ / aX^
-------
individual hydrocarbons such as ethane, propane, and BTX. Such detail at
the feedstock level was unnecessary due to the enphasis on plasticizers and
PVC plastics.
The plasticizer and resin sectors have no direct interactions, but
both are drawn on the same feedstock market and are input factors to the
plastics sector processes. The plastics and demand sectors have a rich set
of interactions: each plastic competes in several end-use markets, while
each market draws on several types of plastics.
A key simplifying assumption that helped keep the prototype model at a
manageable size was to represent what might be a sequence of industrial
processes (which may or may not be in the same physical plant) as a single
process of converting one or two primary inputs to a single primary output.
This was particularly important in modeling the resin and plasticizer sec-
tors. As an example, consider the production of DEHP. The top portion of
Figure 3-4 shows the full sequence of steps required to produce DEHP. As
noted, the fossil liquids supply model is used to represent several feed-
stocks, including propane, ethane, and BTX. The DEHP conversion process
represents the combination of all the steps from primary feedstocks through
production of D3HP. The process economics represented in the prototype
model DEHP conversion process is the net result of the full sequence of
steps. We felt that the detail required to model all steps individually
would not lead to any significant additional insights. Such detail could
be added to the model if it were felt to be necessary.
IMPLEMENTATION OF THE PROTOTYPE MODEL
The prototype plastics industry model represents a portion of the
national economy. A number of economic markets in which products compete
are included explicitly. To answer the questions presented earlier in this
section, it is necessary first to solve for equilibrium prices and quanti-
ties in each of the key markets and then to see how these equilibrium
values change over time and under different regulatory alternatives.
Given the nature of the plastics industry and the regulatory issues,
we chose to implement the prototype model using an approach known as
generalized equilibrium modeling [10]. This methodology characterizes the
351
3-9
-------
r o s s i 1
i'.CLDnrccs
Fi: edf.COC'iC
StparotIon
Hani
Propane
.in J for
C I lLlllC
Propylene
,nid/or
Kl.hylrne
McXiiti'i I
O-xyltne
r^thaL ic
H'fX
At; li ydt iJf
[Ts-'tui't ion
ni-..ip
I
lull Sequent n of Sti'p;. !< <¦• rjn j t t'd to Produce I) l-it h y I brx v1 I'M lm late Ester
S Imp 1 It I imI oiicnt at t on Used in the KroLotyp- T-h u i«* I
U*
CI
w
Ko s s i 1
I. i qu Id 3
dt:iip
Feedstock
w-
Couvrr j> ton
Sup ji 1 y
Proci bs
i'i oc i-ss
di:iip
Figure 3-4.
Modeling the Production of DF.HP
-------
economy (or a portion thereof) as a system of economic agents who interact
through explicit markets and who make economic decisions at each step fron
feedstock production to end use of a product by consumers.
At any time, the state of an industry is determined by the results of
the decisions nade by the agents in the industry. It is the interactions
of these uncoordinated decisions that determine the prices and quantitites
of the chemical substances or other materials produced by each available
industrial process. The decisions made by these agents will determine the
reaction of the industry both to government policies such as taxes, regula-
tions, and subsidies and to other changes in the economy, such as those
caused by inflation, embargoes, or changes in the cost of various input
factors.
Generalized equilibrium models consist of three basic elements:
1. Process submodels that describe the decision-making behavior of
agents in the economy.
2. A network that describes the way that agents in the economy
interact.
3. An algorithm that solves the model to obtain the equilibrium set
of prices, quantities, and capacity additions.
A process is defined as a model of a subsystem or sector of the
overall system being modeled and is characterized by a set of mathematical
relations or equations that are formalized as computer code. These rela-
tions are derived from the underlying decision-making behavior of the
economic agent or agents being modeled. In each process, two types of
relationships can be distinguished. The first consists of physical rela-
tionships that describe how physical flows interact over time. An example
of a physical relationship is the amount of plastic resin produced per ton
of petroleum feedstock for a particular chemical process. The second type
consists of behavioral relationships that describe the choices that deci-
sion makers make in operating plants. An example would be the decision of
a plasticizer producer of whether to shut down production or continue oper-
ating plants if a regulatory action substantially changes his process
economics.
•jrro
3-11
-------
A network defines the economic and material links among the processes
in a model. Figure 3-3, the diagrara we used to describe the structure of
the plastics industry, is already in the network form required. In this
network, the links represent flows of chemical products. Associated with
each flow is a price and quantity of the product. At the top of the net-
work are processes that describe the end-use demands; at the bottom are
processes that describe feedstock production and supply. In between is a
network of other processes that describe market behavior, conversion, and
(if important) transportation in the entire economic system. One can think
of materials flowing "up" the network, with prices being communicated
"down" the links.
The algorithm used to solve a generalized equilibrium model must find
the set of price and quantities that simultaneously satisfy all the physi-
cal and behavioral relations embodied in the processes and the links among
the variables as defined by the network. The algorithm used to solve the
problem should take advantage of the natural structure of a particular
model rather than impose arbitrary restrictions on the problem structure.
The particular algorithm used is a form of successive approximations
with relaxation. The first step in the algorithm is to provide a guess of
all quantities of goods and materials produced or used by each process for
each time period. Prices at which the required quantities of primary
resources or feedstocks will be supplied are computed by resource process
models or, as in the case of the plastics industry model, supplied as in-
puts. The algorithm then works its way "up" the network, using the process
models to calculate output prices (of resins, plastics, and so on) based on
factor input prices (from "lower down" the network) and other process data.
When the "top" of the network is reached, prices of end-use goods are used
to compute demand quantities. The quantities then flow "down" the network,
with quantities of plastics being used to compute required quantities of
resins and plasticizers, and the quantities of the latter two sets of
substances used to compute required quantities of feedstocks. The quan-
tities calculated on this "downpass" through the network now form a new
guess for the equilibrium quantities. This improved guess is used as the
basis for another pass "up" the network computing prices and market shares.
354
3-12
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The algorithm continues in this manner, going up (computing prices) and
down (computing quantities) the network until the prices and quantities do
not change (within a tolerance of a few percent) from one iteration to the
next. At this point, the algorithm has converged to a set of prices and
quantities that balance supply and demand and satisfy all physical and
behavioral relations in the model. A simple relaxation scheme is used to
speed convergence.
DFI has developed a set of computer software to facilitate the imple-
mentation of economic market models. These software tools are known as the
Generalized Equilibrium Modeling System (GEMS) and the Model Data Manage-
ment System (MDMS) [11,12,13]. The GEMS software includes the capability
to specify economic networks, a successive approximations algorithm used to
solve for equilibrium, and the necessary process models. MDMS provides the
tools to enter data for the model and to record and display the results of
running the model. Both data and results will be discussed in the follow-
ing paragraphs.
Three GEMS process models were used to implement the prototype plas-
tics industry model:
1. The conversion process, which represents a firm (or aggregation
of similar firms) producing an output product from a set of
inputs.
2. The allocation process, which models the allocation of supply
and demand in a competitive market.
3. The end-use demand process.
The conversion process uses a set of equations developed from the
assumption that the owners of a plant will make decisions that tend to
maximize discounted cash flow or some other measure of discounted profit.
Decisions made at each time point are based on forecasts of market prices
for both output products and factors of production. Necessary additions to
productive capacity are modeled explicitly, and the different "vintages" of
plants (which may have different operating costs and efficiencies) are
tracked individually. The basic equation used to determine product prices
over time is as follows:
3-13
-------
p(t)
ECC(t)
CF(t)
+ voc( t ) +
E
IOi(t) * Pi(t)
(1)
where
P(t) = output price at time t (of, e.g., a particular resin,
plasticizer, or plastic).
Pj(t) = price of input factor i at tine t.
ECC(t) = effective capital cost per unit of output at time t. ECC
is a function of plant cost, plant capacity, and financial
parameters including book and debt life, cost of capital,
and tax rates. It represents the effective after-tax cost
to the firm of the amount of capacity needed to produce
one unit of output per year.
CF(t) = capacity factor, that is, the ratio of actual production
to the capacity of the plant. This reflects the fact that
the plant is not operated at full capacity all the time,
due to breakdowns or other factors.
IOj(t) = the input-output coefficient for primary input factor i
(e.g., the number of units of the factor i needed to
produce one unit of the output good). These are the input
factors of production explicitly modeled by the links in
the economic network.
VOC(t) = all other variable operating costs, including secondary
inputs, maintenance, and so on.
Operating and capital costs as well as input-output coefficients can
change over time due to technological improvements and other factors. The
capacity factor adjusts the effective capital cost ECC(t) to reflect less
than full-time use of a plant. The capacity factor is computed based on
the cost of operating plants of different ages. Less efficient older
plants may be operated less and eventually shut down when, they are not
economically competitive with new plants. Given the economically efficient
level of production (at the price p(t)) from existing plants, new capacity
is added to meet demand. If demand is less than the potential production
from existing plants (e.g., excess capacity exists), plants are shut down
in order of decreasing operating costs. The full set of equations used in
the conversion process are documented in.(11]. In the prototype plastics
3-14
356
-------
industry model we used the conversion process (1), with appropriate data
and parameters, to represent the production of plasticizers, resins, and
plas tic products.
The allocation process model represents a market where buyers and
sellers trade for a commodity at a price. Specifically, demands for a
commodity are aggregated and allocated among suppliers of that commodity
according to their various selling prices.
An allocation process that responds sharply to small changes in prices
(as would be the case if the demand were allocated entirely to the lowest
price source) would overstate the market response to prices. Thus, the
model described here produces allocations that respond continuously to
changes in product prices while retaining the flexibility to represent
markets ranging from the highly competitive to those where allocation of
demand is independent of price. The process also has the flexibility to
allow for product discrimination in the marketplace and for market response
lags. Thus, by suitable choice of parameters, a wide range of different
market situations can be represented.
With no lag in the market response, the market share of the ith
supplier is given by
fi * Pi(t) b
(2)
ail j
where
MSj_(t) = market share for supplier i
Pj(t) = price for the commodity from supplier i
b
narket-price sensitivity parameter
fraction of market captured by supplier j when all supplier
prices are equal, reflecting buyer's preferences for the
product offerings of the different suppliers
The form of this function is depicted in Figure 3-3. The parameter b
determines the market-share sensitivity to changes in price. By setting
-------
1.0
MS ,
D.5
biincreasing \
V b increasing
1.0
P./P
l ave
Note: p
ave
average price of competing goods.
Figure 3-5. Market Share Curve
3-16
3oS
-------
the value of this parameter—the price sensitivity of each allocation
process—the user selects the price ser.sitivty of each process in the
model, from a very price sensitive limit to a price insensitive limit. The
larger the value of b, the more sensitivity the market share is to price.
The fraction at equal prices parameters fj provide for the possibil-
ity that the product from one supplier is more attractive than that from
another for reasons other than price. Low volatility of a plasticizer is
an example in the prototype model of a characteristic that may affect buyer
preferences among alternative suppliers. The parameters fj can be set to
reflect this kind of product differentiation.
The allocation of demand given by the market share equation assumes
that decision makers reallocate demand instantly in response to changes in
relative prices. In reality, the response to price changes is slowed by
planning lead times and a natural reluctance on the part of the decision
makers to change. This aspect of demand allocation is modeled by a
behavioral lag coefficient that represents the number of years it takes a
market to make one-half of its ultimate response to a change in prices.
The calculation of the prices of the outputs of a market process
should reflect the relative share of each source and its price. The allo-
cation of process model further assumes that the price of the outputs of an
allocation process are identical and are given by
p(t) = Pi(t) MSi(t) (3)
all i
where p(t) is the average price of the output of an allocation process and
other terms are as defined earlier.
The end-use demand process represents the changes in demand over time
and as a result of price changes. In effect, it is simply a demand
function. Input data is used to specify a reference demand quantity in
each period given a reference real price. The reference demand is then
adjusted for price changes using a constant elasticity demand function.
Different short- and long-run elasticities can be specified with the
long-run response lagged behind the short-run adjustments.
3-17
359
-------
The form of the demand function is
where
Q(t) = quantity of product demanded at year t
P(t) = price of product at year t
Q(t) = value of reference demand path at t
P(t) = value of reference price path at t
a = annual lag coefficient
e = short-run (one year) price elasticity
The lag coefficient depends on the relative values of the short- and
long-run price elasticity parameters.
DATA FOR THE PROTOTYPE MODEL
Data for the model were derived from a number of sources, including
industry and government publications. The data can be grouped in five
rough categories: estimates of quantities supplied and demand during the
first period of the model; process economics and financial parameters;
market price sensitivity and behavioral lag parameters; prices of feed-
stocks; and parameters defining the time structure of the aodel.
Estimates of initial quantities arc used by the GEMS software as a
starting point from which to solve for supply-demand equilibrium. Quan-
tities of plasticizers, resins, and plastics as well as demand estimates
were developed from data in the SRI Chemical Economics Handbook [14],
Modern Plastics [15], and the U.S. government publication, Synthetic
Organic Chemicals [4]. Initial quantity estimates as of 1978 are sum-
marized in Table 3-1.
3-18
3 GO
-------
Table 3-1
ESTIMATES OF QUANTITIES PRODUCED AND DEMANDED
(1973)
Plasticizer Quantities (1000s of metric tons)
DEHP
Large Molecular Weight Phthalate Esters
Very Large Molecular Weight Phthalate Esters
Small Molecular Weight Phthalate Esters
Epoxidized Soy Oil
Other Primary Plasticizers
Resin Quantities (1000s of metric tons)
PVC Resin
PE Resin
Other Resins
Plastics Quantities (1000s of metric tons)
PVC made with DEHP
Regular PVC Plastic (non-DEHP)
Low Migration PVC Plastic
PE Plastic
Other Plastics
Plastics Demand Quantities (1000s of metric tons)
Food Packaging, Wrapping, and Containers /
Medical Supplies
Automobile Interiors
Other Consumer Products
Other Flexible Plastics Uses
189
260
18
135
34
76
1, 780
4,995
1,640
703
1,451
156
4,755
1,652
1,505
34
199
2, 127
5,021
3-19
3G1
-------
Process economics data were developed primarily from the SRI Chemical
Economics and Process Economics Handbooks [14,161. The process parameters
for many industries represented in the model were derived from a sequence
of nanufacturing steps. A relatively simple example for PVC resin is shown
in Table 3-2. Capital and operating costs for the first stage are scaled
by the appropriate input-output coefficient and added to those for the
second stage.
A more complex example is the DEHP production process. The sequence
of production steps was shown previously in Figure 3-1. The net process
economics parameters are derived in Table 3-3. Input-output coefficients
were adjusted for relative heat content to account for the single generic
feedstock (modeled as an average of propane and ethane).
The process economics and financial parameters for a number of the
chemical processes are summarized in Table 3-4. This table is in the form
generated by the MDMS software [L2] used to store and update data for the
prototype model. The feedstock input-output coefficients in column 3 are
entered as inverses. The planning lead times indicate how long it takes
from the point a decision is made to expand capacity to the point at which
the new capacity is available. The year available defines when the par-
ticular process technology is available for use; if less than the initial
year of the model (as in this case), it has no effect. Columns 6 and 7
contain limit parameters that can be used to model significant technolog-
ical changes over time; the settings to 1.00 indicate that this capability
was not used in the prototype model. The plant life reflects the actual
useful life of a plant, while tax and book lives are financial parameters.
The equity fraction is used to determine the cost of capital. Availability
is the fraction of time the plant is available for production, net of un-
planned shutdowns and failures.
The parameters used to define the characteristics of the economic
markets were based on a combination of information contained in [14] and
[151, together with DFI experience with similar models. The parameters are
summarized in MDMS data tables as Table 3-5. The row names correspond to
allocation processes (circles) shown in Figure 3-3. Price sensitivity and
behavioral lag were defined in the discussion of the allocation process
3-20
n O
-------
Table 3-2
DERIVATION OF PROCESS ECONOMICS FOR PVC RESIN
Stage SCC VOC I/O
VC Monomer $240 $16.3 ethane 0.53
chlorine 0.59
Polymerization $564 $136 1.025
Net for PVC Resin $810 $303 ethane 0.54
chlorine 0.61
Stream Factor (Availability) = 0.9
SCC = specific capital cost, the cost of one unit (metric ton per
year) of capacity
VOC = variable operating cost (per metric ton of output)
I/O = input-output coefficient(s), the units of input(s) required
to produce one unit of output
Table 3-3
DERIVATION OF PROCESS ECONOMICS FOR DEHP
Stage SCC VOC I/O
Ethylene $ 363 $ 58 1.40
2-Ethyl Hexanol from ethylene 636 97 1.79
Met for 2-EH from feedstock 1286 201 2.51
0-xylene 132 37 1.12
Phthalate anhydride from o-xylene 407 96 1.02
Net for Phthalate anhydride 542 134 1.14
from feedstock
DEHP from 2-EH and 137 71 2-EH 0.69
Phthalate anhydride P.A. 0.39
Net for DEHP from feedstock 1236 262 2.18
Steam Factor (availability) = 0.9
Note: See Table 3-2 for definitions of process parameters
3G3
3-21
-------
Table 3-4
PROCESS ECONOMICS AND FINANCIAL PARAMETERS
GP =3 CONVERSION
SPECIFIC PROCESS
ATTRIBUTE
I SPECIFIC | VARIABLE
! CAPITAL | OPTT>'G
I COST | COST
I
NOT CURRENTLY USED
! i 1
0.00 !
0.00 |
O.CO 1
2
DEHP
1 2 I
1236.00 !
262.00 I
0.46 1
2
LG. MOLEC WT. PHTHAL EST
1 3 !
1187.00 I
283.00 |
0.45 1
2
VY LG. MCL.WT. PAE
1 4 |
1187.00 I
314.00 I
0.41 |
2
SM MOL.WT. PAE
1 5 I
1069.00 |
41C.00 |
0.51 1
2
PE resd:
1 5 !
1100.00 I
340.00 I
0.73 i
2
OTHER RESIM
1 7 |
900.00 I
330.00 |
0.62 '
2
PE PLASTIC
I 3 |
100.00 |
50.00 1
0.97 t
2
OTHER PRIMARY PLASTICIZE
1 9 1
1001.00 I
306.00 |
0.42 i
2
FEEDS7K.
I/O
COEFF.
PLANNING I
LEAD |
TIME |
GP = 3 CONVERSION'
SPECIFIC PROCESS
ATTRIBUTE
YEAR | CAPITAL | THERMAL
AVAILABL | TECH CHG | EFFIC.
I LIMIT | LTMTT
CHAR.
PLANT
LIFE
8
NOT CURRENTLY USED
1 1 1
19^0 |
1.00 I
1.00 1
30
DEHP
1 2 |
1970 |
1.00 1
1.00 1
30
LG. MOLEC WT. PHTHAL EST
! 3 1
1970 1
1.00 I
' 1.00 1
30
VY LG. MCL.WT. PAE
1 4 |
1970 |
1.00 |
1.00 1
30
SM MCL.WT. PAE
1 5 1
1970 |
1.00 1
1.00 1
30
PE RESIN
1 6 1
1970 |
1.00 1
1.00 1
30
OTHER RESIN
1 7 |
1970 |
1.00 I
1.00 1
30
PE PLA5TTC
1 8 I
1970 |
1.00 1
1.00 [
30
OTHER PRIMARY PLASTICIZE
1 9 1
1970 I
1.00 |
1.00 1
30
Ou'i
3-2?
-------
Table 3-4
(continued)
ATTRIBUTE
TAX
LIFE
GP = 3 CONVERSION
SPECIFIC PROCESS
1
g
1 10 I
11 1
12
NOT CURRENTLY USED
1 i 1
11
! 20 |
0.75 '
0.90
DEHP
1 2 |
11
1 2C |
0.75 |
0.90
LG. MCLEC WT. PHTKAL EST
1 3 I
11
1 2C I
0.75 [
0.90
VY LG. MCL.WT. PAE
1 4 |
11
1 2C |
0.75 |
0.90
SM MOL.WT. PAE
1 5 I
11
1 2C |
0.75 |
0.90
PE RESIN
1 6 |
;i
1 20 1
0.75 1
0.90
OTHER RESIN
1 7 ;
:i
1 20 |
0.75 I
0.90
PE PLASTIC
1 S '
n
! 20 |
0.75 1
0.90
OTHER PRIMARY PLASTICIZE
1 9 1
n
1 20 |
0.75 1
0.90
DEBT
LIFE
I EQUITY
! FRACTION
AVAIL-
ABILITY
3-2 3
-------
Table 3-5
ECONOMIC MARKET PARAMETERS
ATTRIBUTE
CP = 1 BASIC ALLOC.
SPECIFIC PROCESS
FCS/tlQ ALLOC
EPOX SCY OIL ALLCC
PVC RESIN ALLCC
PVC RESICN ALLOC.
PVC/REG PLASTC ALLOC
PVC/LOW MIG ALLOC
PE PLASTIC ALLCC
OTHER PLASTICS ALLOC
PVC/DEHP ALLOC
I SHARE
! PRICE
I SENSITVY
.+
I 1
1
2
3
4
5
6
5.00
5.00
5. CO
5.00
5.00
5.00
5.00
5.00
5.00
BEHAVIOR
LAG
3.00
3.CO
3.00
3.00
3.00
3.00
3.00
3.00
3.00
I MIN.CUAN
I FCR CCNV
I CHECK
0.C2
0.C2
0. 02
0.02
0.C2
0.02
0.02
0.02
0.02
ATTRIBLTE
+-
1
SHARE
BEHAVIOR 1
MIN
.QUAN |
1
PRICE
LAG |
FOR
CONV |
GP = 2 SPECIAL ALLCC.
1
SENSITW
1
CHECK |
SPECIFIC PROCESS
1
1
2 I
3 1
—— — '¦ I
VLG/FOS LIQ. ALLCC.
I 1
1
5.00
3.00 1
0.02 1
CELLULOSE SUPPLY ALLOC.
1 2
1
5.00
4.00 I
0.02 I
INPUT FACTOR ALLOC-1
1 3
1
10.00
2.00 1
0.02 I
INPUT FACTOR ALLCC-2
I 4
1
10.00
2.CO 1
0.02 1
INPUT FACTOR ALLCC-3
1 5
1
10.00
2.00 |
0.02 |
FCOD DEMAND ALLOC
1 6
I
10.00
2.00 I
0.02 1
MLDIC\L DEMAND ALLOC
1 7
1
10.00
2.00 |
0.02 |
AUTO DEMAND ALLOC
1 8
I
10.00
2.00 I
0.02 |
CCN3.VR PROD CMND ALLOC
1 9
l
10.00
2. 00 !
0.02 |
OTHER DEMAND ALLOC
1 10
1
10.CO
2.00 |
0.02 |
ulJO
3-24
-------
model. The minimum quantity for convergence check is used by the iterative
convergence algorithm; it avoids checking for convergence in markets with
insignificantly small quantities. Larger values of share price sensitivity
imply a more price-sensitive market. Smaller values for behavioral lag
reflect a more rapid adjustment to price changes.
Future price estimates for feedstocks were developed from information
in Synthetic Organic Chemicals [4], Agricultural Statistics [17], and three
national energy-economic system models: the Long-Range Energy Analysis
Program (LEAP) used by the Energy Information Agency of the U.S. Department
of Energy [18], the Integrated Forecasting Model (IFM) used by the Electric
Power Research Institute [19], and the Salant Energy Reference Model [20].
Prices over the next twenty years are listed in Table 3-6. The prices for
the generic fossil liquid feedstock reflect the cost of conversion from
crude oil and natural gas to an ethane/propane mix. All prices are in
constant 1978 dollars (that is, they do not include inflation).
Demand was modeled as increasing at an average of three percent per
year in the food and consumer products markets, at five percent per year In
the medical and automobile markets, and at two percent annually in the
market for other plastic goods [15]. Short-run price elasticity is quite
low in the plastics end-use markets, with the long-run elasticity being
somewhat greater (-0.1 and -0.2, respectively).
The prototype model was set up to run from 1978 to the year 2000, at
two-year intervals. This gives both sufficient resolution over time and
an adequately long time horizon over which to examine the implications of
regulatory actions carried out in the near future.
Complete documentation of the data used in the prototype model is
provided in Appendix A. The relative desirability of different piasti-
cizers and plastics are defined by the market fractions and equal prices
parameters. These are listed in Table A-5 in Appendix A.
3-25
3G7
-------
Year
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
Table 3-6
FEEDSTOCK PRICES
(all prices -in $/metric ton)
Fossil Liquid
S 172
265
288
313
337
358
381
402
424
446
467
489
Chlorine
S 294
312
331
352
372
395
419
436
454
472
491
51!
Vegetable Oils
$ 620
620
620
620
620
620
620
620
620
620
620
620
Cellulose
$ 260
275
290
305
320
340
360
385
410
435
460
490
L*
3-26
-------
Section 4
RESULTS FROM USING THE PROTOTYPE MODEL
In this section we will present and discuss the results obtained
using the model. Discussion of a nominal or "base case" will be followed
by policy cases and feedstock price sensitivity test cases.
BASE CASE RESULTS
The base case assumes no new regulatory actions with respect to plas-
ticizers or plastic resins. The model was run for eleven two-year periods
and solved for estimates of resin, plasticizers, and plastics prices and
quantities through the year 2000.
Aggregate results for plastics and plasticizers are shown in Tables
4-1 and 4-2 and Figures 4-1 and 4-2. These tables and plots were produced
by the MDMS software. The average prices of the nix of plastics used by
each demand category are shown in Table 4-3. A portion of the price re-
sults are translated into cents per pound in Table 4-4, for the convenience
of readers more familiar with these units. The breakdown of types of plas-
tics used by each end-use demand are detailed in Tables 4-5 and 4-6. All
quantities are given in thousands of metric tons per year and all prices
are in 1978 dollars per metric ton. The quantities shown for the first
period differ slightly from the estimates given in Table 3-1. These
estimates represent an incomplete description of the market based on the
available data. The quantities in Tables 4-1 and 4-2 describe a complete
supply-demand balance consistent with the assumptions of the prototype
model.
In the base case the use of polyethylene is rising more rapidly than
is the use of any other flexible plastic (see Table 4-1 or Figure 4-1).
This is consistent with recent trends [15]. The greatest percentage in-
crease is seen for low migration PVC, a more than tripling of use by the
4-1
'tnrk
U J '• J
-------
Table 4-1
BASE CASE PLASTICS AND PLASTICIZER QUANTITIES
REPORT * 1
PLASTICS P3CCJCTICN
MATERIAL
AF =
CSS =
SUM
QUANTITY
I PVC WITH | REGULAR
I DEIIP I PVC
TIME
I LOW MIG. i ?E
1 PVC | PLASTIC
3 I 4
1978
1 - 1
713.92 1
1483.57 !
143.29 1
4860.78 1
1679.44
1980
1 2 !
774.22
1523.17 [
177.10 |
4956.74 |
1732.27
19=2
1 2 1
326..3 1
1577.32 1
205.SC 1
5118.52 1
1776.41
19£4
1 4 1
379."2 1
1642.62 1
232.62 1
5729.31 1
1817.59
15S6
1 5 t
930.40 1
1712.53 1
260.41 I
5569.24 1
1858.40
1908
1 6 I
976.23 1
1777.76 :
295.81 1
5844.02 1
1906.15
152C
1 7 1
1020.69 1
184:. 13 ,
311.04 1
6142.21 |
1957. 4"'
1992
1 9
1071.2'
1916.5®! i
378.05 1
645C.11 1
200?.69
1994
1 9 1
1126. r;9 |
20U0.7S ;
367. 27 |
6762.93 1
2060.64
: 99 C
1 10
nee. :3 :
2092.22 !
3?9.16 1
7:99.45 I
2114.5?
1998
11
124a..5 1
2137.;6 1
43?. ">5 1
7449.76 1
2173.57
2:00
1 1
1212.25 1
2236.29 1
471.79 |
7
-------
Table L-2
BASE CASE PLASTICS AND PLASTICIZER PRICES
REPC.TT * 7
PLASTICS PRICES
MATERIAL
AF-
AVE SACS
PRICE |
PVC WITH 1
REOJIAR |
IC.< MIC. 1
PE '
OTHER 1
CES =
PRICES
1
DEI1P 1
PVC 1
PVC 1
PLASTIC '
PLASTICS 1
time
1
- 1
. ....
^ i
A
1
5 1
i9?a
1 1
8C8.54 I
827.20 1
8PC.73
an i.1
833.06 1
1980
: 2 I
912.49 1
932.05 1
979.55
932.": 1
995.52 1
19=2
! 2 1
945.2: 1
955.16 1
i'JCS. 63 1
965.42 '
1035.18 1
19S4
i 4 1
983.91 1
1001.54 i
.343.81 1
:003.?5 '
1C9.18 1
1986
: 5 I
1015.10 1
1036.55 '•
:z~-9.02 1
:c35.:i !
1120.32 1
1988
1 6 1
iC-r.65 1
1069.?: 1
1113.10 1
:o6-.92 ;
1156.64 I
1590
; t i
1032.^3 1
1105.30 !
1149.36 1
1C9".60
1196.28 1
1592
; 8 i
1112.59 1
1135.33 1
1 IF 1. 16 :
1127.47
1232.97 I
199^
1 9 1
1143. 64 1
1163.<2 !
12J3.75 1
1158.80 '
1271.42 1
1996
i :: i
1175. |
1637.76 1
200C
1 12 1
1479.02 i
1517.90 I
1 503. 14 [
1655.41 |
ISO"1. 16 |
1593.26 I
average ptuces in ixllars per metric tcn.
^71
o
4-3
-------
F L A 3
'ICS 3DODJC:T
i
-r DC_YETHV_ENE
o
o
n M . I I i o i* s o t If t r r. T r r.
5?c;si3h
Fipur?. 4-1. Ease Case Plastics Production
U i fw
4-4
-------
Pi
1 c^>
^ T
3
T
-------
Table 4-3
BASE CASE AVERACE PLASTICS PRICES BY END-USE CATEGORY
REPORT ft 13
AVERAGE PLASTICS PRICES
FLAS71
CS
AF=
AVERAGE PRICE
| < w mm- . - |
I FOOD |
MEDICAL •
! auto
L . m m 1 ¦ 1 ¦ 1 ¦ |
I CONSUMER !
OTHER |
GSS =
PRICES
I USES |
SUPPLIES
I USES
I PRODUCTS |
DEMAND 1
TIME
! 1 1
2
1 3
1 4 !
5 1
1978
1
1
| _ _ —p _ _ j
1 818.03 j
852.16
—f- — — — — .
I 841.00
1 017.07 |
811.08 I
1980
1
2
1 954.31 |
955.17
I 941.81
I 944.19 1
940.52 I
1982
1
3
1 988. ~n 1
938.54
I 973.85
1 978.13 1
974.28 1
1984
1
4
1 1026.21 |
1025."74
! 1009.98
1 1015.06 1
1010.89 !
1986
1
5
I 1062.05 1
1061.81
! 1045.0?
1 1050.42 1
1045.93 1
1988
1
6
1 1093.87 |
1096.22
1 1078-67
I 1082.47 |
1077.25 !
1990
1
7
1 1128.56 1
1133.04
1 1114.73
1 1117.32 I
1111.34 :
1992
1
8
I 1159.93 |
1164.23
I 1145.45
! 1148.30 !
1141.95 1
1994
1
9
1 1 J. 92.11 |
1196.81
1 1177.57
1 1180.78 '
1174.04 I
1996
1
10
I 1226.16 |
1229.66
I 1210.15
! 1213.82 1
1206.70 1
1998
1
11
I 1256.91 |
1260.70
I 1241.01
1 1244.50 I
1236.S5 1
2000
1
12
I 1289.36 |
1291.84
I 1272.25
1 1275.90 |
1267.77 1
PRICES IN DOLLARS
PER METRIC TON.
374
4-6
-------
Table 4-4
PLASTICIZER AND
(cents pi
Material
DEHP
Large Molecular Weight
Phthalate Esters
Small Molecular Weight
Phthalate Esters
Very Large Molecular Weight
P'nthlate Esters
Epoxidized Soy Oil
Other Plasticizers
PVC with DEHP
Regular Grade PVC
Low Migration PVC
PE Plastics
Other Plastics
PLASTICS PRICES
r pound)
1980 1990 2000
45 57 67
46 58 69
• 48 59 68
51 63 75
65 74 32
53 65 77
41 49 56
42 50 57
45 52 59
42 50 57
45 54 63
o e a
4-7
-------
Table 4-5
BASE CASE PLASTICS USAGE 3Y END-USE CATEGORY
S£rOHT \ 3
~0.:z PACKAGING AI-D CONTAINERS
material
Af- S'JM ! PVC WITH | REGULAR | LOW N*G. I PS I CTH£R 1
GSS= QU/V.TITY I O'EKP | PVC ' PVC I PLASTIC ' PLASTICS !
1978
1 : l
59. :M 1
61.1) 1
65.05 1
8*7.75 '
48:.95
195 0
1 2 1
6'3.~5 1
62.63 1
3!.33 I
863.43
<92.35
1 982
1 3 1
73.2i 1
65.1C 1
96.-'2 1
902.C6
503.92
1924
1 4 I
79.:: I
63.21 1
110.00 1
951.21 :
515.c:
19S6
1 5 I
8': .": 1
11.i: 1
122.07 I
1CC7. ,8
531.23
1938
1 6 i
09.56 1
75.C2 1
132.C6 1
::7o.33 ;
548.23
1990
1 7 I
9-5.07 1
78.29 1
140.94 1
1139.17 :
566.73
1992
1 a '
99.19 !
82.20 1
150.31 !
1212.04 :
586.
:9S4
1 9 1
1C4.73 |
86.60 1
160.29 1
I2E-.94 !
605. 36
1996
1 io :
110.^9 !
91.39 1
:">:.03 |
1369.38 1
627.53
1995
1 n .
116.34 !
96.36 1
•32.24 I
.^56.04 1
650.42
2000
1 12 '
122.20 '
101.55 1
194.27 |
154-*. 33 1
674.67
Cua:jtities in thousands of ketric tons
MEDICAL SUPPLIES
fWERIALS
AF=
G53 =
SIM
QUANTITY
' PVC WITH 1 REOJIA1 1
I r.EHP I PVc 1
Lav MIC. '
pvc :
TIME
1 1
1 2 !
3 1
w
~
.4— k-
1
197 a
1 1
I 7.46
1 9-95 1
16.59 1
1983
1 2
1 7.38
l e. 57 1
21.05 1
1982
1 3
1 7.46
1 7.78 1
25.14 1
1934
I 4
I 7.70
1 7.42 1
29.03 1
1986
1 5
! 8. 10
; 7.33!
32.^4 I
I98S
1 6
1 8.64
! 7. 59 1
36.70 I
1990
; 7
; . 9. 7.0
: 7.961
40." 1 1
1992
I s
: 10.07
8.49 '
44.93 1
1S9 4
1 9
1 10.94
9-J3 :
49.SO 1
1 01,'
1 10
i
! 9.a1 '
54.62 1
199 c
I 1:
1 12.95
1 10.70 1
CC. 13
20CO
1 12
! 14.09
! 11.62 1
6G.20 1
QUANTITIES IN THOUSANDS CF K.CT"C TCNS.
37G
4-8
-------
Table -4-6
BASE CASE PLASTICS USAGE BY END-USE CATEGORY
PXPCP.T s 5
ALTCM03 ILE USES
MATERIAL
AF= S'J-, I PVC WITH | REGULVt I LC.V MrC. !
GSS= QUALITY I DC IP I PVC I PVC
TIME
1
1
2 1
3
19? 3
1 1 1
6;.70 1
66.65
66.65
1980
1 2 1
72.?9 1
68.53 :
74.72
19S2
1 3 1
80.S3 I
71.64 :
84. 23
1984
1 4 1
80.06 1
75.99 '
9*.. 50
1936
1 5 1
95.15 1
81.46
105.50
19S8
1 6 1
1C5.C4 |
37.99 !
117.04
1090
1 7 |
114.75 1
95.17 !
129.25
1392
I 8 I
125.5: I
:o:.?6 1
142.75
199s
' 9 1
12-.33 I
11".29 1
157.3?
1596
: io i
150.19 !
123.-7 |
173.51
1993
: n i
J-A.72 |
::s.is [
191.39
20C0
1 12 1
-79.<4 I
i47.60 !
211.32
quantities thousands cf rm:c tons.
REPORT * 6
OTHER
ccas f" in psc:
:ucts
MATERIAL
f-S= SIM
1
PVC WITH
REGULAR I
PE I
CTKER
GS3= QU.W
•:ty i
DLHP I
PVC I
PLASTIC |
PLASTICS
TIKE
1
1 1
2 1
3 1
«/
1970
1 i I
208.S3 :
517.10 1
827.57 1
493.4C
123 C
1 2 1
307.04 '
530.54 '
377.49 1
501.95
1532
1 3 1
325.no .
552.33 '
932.90 1
512.SO
lOS'I
1 4 1
345.45
57e.a7
994.26 1
525.03
153C
1 3 :
365.SO
608.51 1
1059.46 !
539.22
1530
1 5 1
2C4.92
653.-3 1
1132.31 1
557.14
155C
1 7
403.72 !
667.73 1
1210.91 I
577.23
-222
1 9 '
425.13 !
702.C2 1
1251.55 1
59".52
: £04
1 9 :
448. r.n .
719.50 1
1375. if, 1
618.71
1S96
1 10
473. W
7yo.95 1
1-762. 29 1
6?9.91
i i:
500.30 '
^23.55 I
1555. f-S 1
663.5?
20 CO
1 12 !
527.75 ¦
COS.53 I
1654.55 1
680."9
QUANTITIES IN THOUSANDS CF M£Tn:C tons.
ii—U
vi £ .
-------
year 2000. This reflects an increased willingness by the uses of plastics
products to pay a premium for low volatility, particularly in the key
markets for food packaging, medical supplies, and automobile interiors.
As indicated in Table 4-4, PVC with DEHP retains a snail price advan-
tage over regular PVC with other plasticizers, due to the slightly lower
costs of DEHP [15,4]. Polypropylene and polystyrene, aggregrated as "other
plastics," show a somewhat greater percentage increase in price over time.
This results from the larger requirements for fossil-based feedstocks.
In the plasticizer sector, the greatest percentage increase is in the
quantity of epoxidized soy oil used, followed by that of the very large
molecular weight phthalate esters. The relative price premium for epoxi-
dized soy oil decreases over time due to its being produced in part from a
renewable resource.
In the detailed breakdown of plastics usage shown in Tables 4-5 and
4-6, the general trends are an increase in the use of low migration PVC and
a decrease in the market share for PP and PS (other plastics). The medical
supplies and automobile markets show dramatic increases in the use of low
migration PVC. This trend is shown clearly in Figure 4-3.
The model can provide economic information beyond market equilibrium
prices and quantities. Required additions to production capacity (that is,
the capacity of new plants built to serve changing demands) can be of con-
siderable importance in assessing economic impacts. As an example, the
estimates of required new capacity for DEHP are summarized in Table 4-7;
these reflect economic decisions modeled in the DEHP conversion process
model.
Of perhaps greater interest than the absolute results from the base
case are the relative changes observed when trying several alternative
regulatory policies. These are discussed in the next subsection.
REGULATORY POLICY RUN RESULTS
The implications of two different regulatory policies were examined
using the prototype model. These cases were:
4-10
-------
n
u. ;
MEDICAL SUPPLIES USAGE
o
o
^ LOW IIGRATn Pv
960 13R4 13SR
lire
33 nvc WITH DEHP
0 REGULAR PVC
1 99c
iOOO
Quantities i r Thousands n T Metric Tons
lnx
• DECISION FOCUS 11-.-'
Figure 4-3. Base Case Plastics Demand for Medical Supplies
4-11
379
-------
Table 4-7
new CAPACITY FOR DEHP IN THE BASE CASE
(Capacity in 1000s of metric tons per year)
1980 106
1982 76
1984 69
1986 67
1988 62
1990 59
1992 66
1994 72
1996 78
1998 84
20CO 100
4-12
o
u
-------
1. All production and use of DZHP is prohibited (the "prohibit"
case).
2. The use of PVC with DEH? for medical supplies or in automobile
interiors is prohibited (the "restrict" case).
The two policy cases together with the "no action" base case form the
three regulatory decision alternatives discussed earlier in this section.
A key goal of the plastics industry economic model was to estimate the
economic cost of each regulatory alternative. The primary measure we use
to define the cost is the change in consumers surplus. Consumers surplus
is a widely accepted method of estimating economic benefits to consumers
[21,22 J - When a regulatory action causes consumers surplus to decrease,
the net change is a cost to consumers.*
Suppose some action causes the price of a good to increase. Not only
will consumers pay more for each unit (for example, an automobile seat
cover), but in the aggregate they will purchase fewer units due to the
higher price. A good approximation to the loss in consumers surplus (and
thus the net cost to consumers) is given by
f\- r, ¦ m j v. • \ j. New Quantity + Old Quantity
CC6 = (New Price - Old Price) * z—^ -
where
CCS = change in consumers surplus
Old Price = product price without regulatory action
Old Quantity = quantity purchased given no regulatory action
New Price = product price given the effects of a regulatory action.
New Quantity = quantity purchased given the effects of a regulatory
action (e.g., given the new price)
*This measure only reflects costs to consumers. Costs may also be borne by
producers, and such costs should be addressed in a full-scale regulatory
decision analysis.
4-13
331
-------
The consumers surplus measure thus takes into account the two ways in which
consumers (as a group) bear the impact of a regulatory action. They may
have to pay more for a product whose intrinsic value has not increased, and
they will tend to purchase a smaller amount of a product whose price has
increased. It should be noted that ir. this section we are addressing only
the economic costs of a regulatory action. No iaplication is intended with
regard to the net worth of such an action after health benefits and other
outcomes are balanced with economic costs.
The first policy examined is that of a full ban on the manufacturing
and use of DEHP and PVC with DEHP starting in 1982. The qualitative
effects are shown in Figures 4-4 and 4-5. These can be contrasted with
Figures 4-1 and 4-2, which summarized the base case (note that the scales
are different). As may be expected, the market share of DEHP is taken over
primarily by other large and small phthalate ester plasticizers. The use
of more specialized plasticizers such as epoxidized soy oil does not change
significantly. The quantities of regular PVC and other plastics have
increased, taking the place of the PVC with DEHP. The short-run response
to a ban on DEHP is relatively smooth. This is consistent with an assump-
tion that shifting between producing different types of plastics and
plasticizers is relatively straightforward. To the extent that this
assumption is not true, the short-run effects, especially on prices, may
exceed those estimated by the prototype model.
The full set of quantitative results for the "prohibit" case are in-
cluded in Appendix B. The effect on plastics prices to end users ranges
from an increase of about one-half of a percent for the "other demand"
category in 1982 to about one and one-half percent for automobile uses in
the year 2000. This corresponds to price increases of less than one cent
per pound. The impact is greatest on the food packaging, medical supplies,
and automobile markets, as these currently use the greatest fraction of PVC
with DEHP.
The net cost to consumers was calculated for each market using the
formula discussed above. The net cost summed over all markets is shown in
Table 4-8. The total cost to the economy is probably greater than shown in
the near terra due to conversion and shutdown costs incurred by industry.
4-14
382
-------
PL AST ICS DROCUCTI ON
o
o
o ..
o
+ POLYETHYLENE
I 992
T i ME
X OTHER PL A S t i CS
O REGULAR PVC
a l::w migratn pvc
~ PVC WITH DFHP
i 980
2003
Oudnt i t i . n Millions of Metric Tors
1 W
i
>
sec;>:icn racus :no
Figure 4-4. Plastics Production with "Prohibit" Policy
4-15
333
-------
o
c:
c
C"
; c I ZE
op n
! ¦ s kJ
D JCT I 0!
^ O LRG ^IDLEC WT PAE
o
Quant
Th
o u s a n a s
Me ti
O n
\ \
r
\
i
L
r
1]
1
;
D£C!S;CN F3CUS iNCUPPOPi*
Figure 4-5. Plasticizer Production with "Prohibit" Policy
4-16
334
-------
Table 4-8
COSTS TO CONSUMERS
(measured by change in consumers surplus,
in trillions of 197 8 dollars)
Year
Prohibit
DEHP
Restrict
DEHP
1982
54.2
2.6
1984
76.8
2.7
1986
80.7
2.8
1988
90.7
3.1
1990
101.4
3.4
1992
114.4
3.8
1994
124.0
4.3
1996
142.8
4.8
1998
157.0
5.3
2000
171.3
5.9
In the longer terra, the prototype model may overestimate costs as it does
not currently reflect innovation or technological change stimulated by a
regulatory action (it would be straightforward to examine such effects in a
refinement of the model). On the whole, we believe that the net cost to
the economy of a complete ban on DEHP would on the order of 100 million
dollars per year.
The second policy tested would prohibit the use of PVC with DEHP for
medical supplies or automobile interiors, two categories with relatively
large opportunities for human exposure to DEHP. The prototype model
suggests that such an action would have no significant effects on other
uses for plastics. Average plastics prices for medical and automobile uses
would increase on the order of one percent. Complete results are included
in Appendix B.
The net cost to consumers, summarized in Table 4-8, would increase
from about 2.6 million dollars in 1982 to 5.9 million dollars in 2000. As
-------
with Che prohibit policy discussed earlier, the effects nay be underesti-
mated in the near tern and overestimated in the long terra, with the net
cost being on the order of three or four million dollars per year. This is
considerably less than the cost of the complete ban on DEHP, due to the
relatively small quantities used in the affected markets.
In addition, we examined a case that included the restrictions on the
use of DEHP discussed above, plus a limit constraining the production of
PVC with DEHP to not significantly exceed the 1980 level (roughly 800
thousand metric tons). This case was not carried to the point of computing
changes in consumers surplus, but serves to demonstrate the capability of
the modeling approach to investigate the impact of production constraints.
The results (also included in Appendix B) show an economic impact falling
between the two policy cases just discussed. Plastics prices increase in
all market segments, but never by more than about three-fourths of a
percent. In the near term, the limit on DEHP production has little effect,
while in the long tern, the markets have adjusted with other products
replacing the loss of PVC with DEHP. The changes in consumers surplus
would fall between the prohibit and restrict cases.
SENSITIVITY ANALYSIS RESULTS
Plastic resins and plasticizers are produced in large part using
feedstocks derived from fossil fuels. The future prices of such fuels, and
thus of the chemical feedstocks, are the source of considerable uncer-
tainty. In order to investigate the sensitivity of the plastics industry
and potential regulatory actions to such uncertainty, we developed test
cases with higher and lower feedstock price forecasts, as compared to the
base case.
Price forecasts used in the sensitivity tests are shown in Table 4-9.
As the production of chlorine (a major input to PVC production) is highly
energy-intensive, its prices were adjusted as well. In the base case the
price of fossil feedstocks increases by an average of 3.1 percent per year,
while chlorine shows an average 2.5 percent increase. The "higher prices"
sensitivity case has an average annual increase of six percent for fossil
4-18
-------
Table 4-9
SENSITIVITY TEST fEEDSTOCK PRICES
(all prices in 1978 dollars per metric ton)
Higher Prices _C_as_e
Year Fossi1 Liquids Chlorine
1973 172 294
1980 265 312
1982 298 337
1984 335 365
1986 376 395
1988 422 427
1990 475 462
1992 533 500
1994 599 540
1996 673 584
1998 756 632
2000 850 684
Lower Prices Case
Year Fossil Liquids Chlorine
1978 172 294
1980 265 312
1982 270 318
1984 276 325
1986 281 331
1988 287 338
1990 293 345
1992 299 352
1994 305 359
1996 311 366
1998 317 373
2000 323 381
4-19
-------
liquids and four percent for chlorine. The "lower prices" case uses one
percent annual increases for both feedstocks.
Four test cases were run. These were the high and low price forecasts
with no regulatory action (the base case), and the high and low price fore-
casts with a full ban on DEHP (the prohibit case). Complete results for
the four sensitivity cases are included in Appendix B. The impact on
plasticizer and plastic production for the case with high feedstock prices
and a ban on DEHP is summarized in Figures 4-6 and 4-7. These figures can
be compared to Figures 4-4 and 4-5, which show analogous results for the
prohibit case with nominal feedstock, prices. The use of polyethylene,
which uses relatively more fossil derived feedstocks per ton of plastic,
increases less rapidly (given high feedstock prices) and peaks around 1995.
The "other plastics" (polypropylene and polystyrene) increase their market
share significantly. In the plasticizer sector large molecular weight
phthalate esters lose market share and the use of small phthalate esters
increases less rapidly, while the demand for epoxidized soy oil increases
dramatically. The soy oil, based in large part on a renewable resource,
incurs a considerably smaller price increase resulting from the higher
fossil feedstock prices.
An interesting observation is that the net economic impact of banning
DEHP is actually slightly less under the high feedstock scenario than it is
with nominal feedstock prices. This is probably due to the somewhat
smaller market shares for DEHP and PVC with DEHP when feedstock prices are
greater. The plastics prices increases are never more than two-thirds of a
cent per pound, or less than one-half of one percent. The overall cost to
consumers of a DEHP ban is still on the order of 50 to 100 million dollars
annually. Under the low feedstock price scenario, the economic cost of the
prohibit action is very close to that incurred in with nominal feedstock
prices.
The overall impact of the uncertainty in feedstock prices on a regu-
latory decision depends on health effects and other factors in addition to
the economic costs. In this case study, we are only demonstrating the use
of the prototype economic model to analyze the sensitivity of economic
impacts to such uncertainties.
4-20
333
-------
p
TTCS 3 R 00UC TIo N
o
o
PGLYiTMvLENE
8r
-¦¦¦" i
X 0THE3 PLASTICS
O REG'.^nR FVC
o ,
O
"9--
cJrJL
J r, ¦-
7 i rii
—
19c-:
H ¥ 3-
9r-».' 1 3 96
A _ (J W 11 G R 4 T N P V ;
~ PVC WITH l! F HP
:cco
G j a n t ; t i
e s ¦ n
Millions of (Metric Ton
>
ofciion focuj!: in(:uRfu'
-------
r>
• -)
Pi_ AST i c; ZER PRODUCT I GN
O LRG MClFC Wr ~ A R
X EPOX IDZD f10V CiL
^ A SI^L '-IOlEC W" PnE
O GTHE^ plastic;
+ VL EC «T PAi
~ DEHP
3?4 1933
VEAR
i 3
¦:0C3
0 u n n i i t i ® "i Thoi) sands of fit |r , c T o n
\ \
1
^FC i . CN Y2"m: I h: :;ft| ;.1: \1 m
Figure &-7. Plasticizer Production with High Feedstock Prices
and "Prohibit" Policy
4-22
330
-------
Section 5
POTENTIAL DEVELOPMENT AND USE OF THE ECONOMIC ANALYSIS MODEL
ADDITIONAL POLICY AND SENSITIVITY ANALYSIS
The prototype plastics industry model would be useful in the analysis
of a wide range of potential regulatory actions concerning DEHP, other
plasticizers, and PVC. First-order quantitative estimates of the economic
implications of policies including use restrictions, production controls,
and modifications to production processes could be determined. Regulatory
actions that impose costs on producers but do not directly limit or re-
strict production on use could also be examined. This could include, for
example, rules mandating the use of certain health and safety protection
equipment or procedures in plants using DEHP.
The current model can be used to assess secondary economic impacts
such as plant closings or reduced economic growth in the plastics industry.
Estimates of plant closings can be derived from the changes in productive
capacity calculated by the model. Effects on employment can be inferred
from production quantities and manufacturing and from capacity, both of
which are standard model outputs.
Section U discusses an example sensitivity analysis on feedstock
prices using the prototype model. Sensitivity analyses could be carried
out on a wide range of other data, including demand growth rates, process
economics parameters such as capital costs, financial parameters including
interest rates, and market behavior parameters. Such sensitivity analysis
could be used in a full regulatory decision analysis to determine to which
uncertainties the decision is most sensitive. The important uncertainties
could then be factored explicitly into the analysis, as discussed in
Part I, Section 3.
The results of policy and sensitivity analyses using a model of plas-
tics industry economic market behavior such as our prototype model can then
5-1
-------
be used as inputs to other models in a more extensive regulatory analysis.
Production quantities and manufacturing capacity nay be key inputs to human
exposure and environmental fate raodels, for example.
FURTHER DEVELOPMENT OF THE PROTOTYPE MODEL
The prototype model is sufficient to provide a good estimate of the
quantitative economic impact of regulatory actions. In particular, it can
help screen potential actions to determine which ones merit further study.
To determine the economic effects of particular actions in greater detail
and with more accuracy, it would be necessary to extend the prototype
model.
It would be straightforward to add greater detail to the model in key
sectors or to model in more detail the links or pathways between the sec-
tors. For example, the manufacturing of specific plastics products, such
as automobile seat covers, blood bags, plastic cups, or plastic wrap, could
be modeled explicitly. This would be implemented as an additional set of
conversion processes placed between the plastics production stage and the
end-use demands. Such detail would allow further disaggregation of impor-
tant market sectors to identify economic impacts more precisely.
If process economics and the details of resin or plasticizer produc-
tion were critical to a regulatory decision, the model could be expanded to
provide more resolution in such areas. The production of DEHP could, for
example, be represented by a full set of conversion process models, one for
each stage in the sequence from chemical feedstocks to the plasticizer com-
pound .
As a final example, the feedstock sector—and particularly the uncer-
tainties in price and product availability—could be highly important to
the reguLatory decisions. Our assumption for the prototype model of a
generic fossil liquid feedstock could easily be relaxed, with each relevant
feedstock (such as ethane, propane, and BTX) represented explicitly. The
production of these feedstocks from fossil resources such as crude oil or
natural gas could also be added to the model.
5-2
-------
SUMMARY
The prototype plastics industry model is potentially useful for a
wide variety of regulatory policy sensitivity analyses. It provides a firm
basis for further development of a more detailed model. The GEMS and MDMS
software nakes adding detail or otherwise modifying the model or data base
a reasonably easy task. Any further model use and development should be
guided by the needs of a regulatory analysis, focusing model detail and
analytical effort on key issues in the regulatory decision.
5-3
' 5 "~i
-------
Section 6
REFERENCES
t1] National Cancer Institute, "Carcinogenesis Bioassay of
Di-(2-Ethylhexyl) Phthalate (Draft Report)," DHHS Publication No.
(NTH) 81-1773 (1980).
[2] Chemical and Engineering News, February 22, 1982.
[3] "Plasticizers," Modern Plastics (September 1979).
[4] Synthetic Organic Chemicals: U.S. Production and Sales, 1977 and
1979, U.S. International Trade Commission, Publication 1099.
[5] Encyclopedia of Polymer Science and Technology (New York.: John Wiley
and Sons, 1969), S.v. "Acids and Derivatives, Aromatic," and
"Plasticizers."
[6] L. I. Nass, Encyclopedia of PVC (New York: Marcel Dekker, 1976),
Chapter 11.
[7] J. Frados, ed., Plastics Engineering Handbook (New York: Van Nostrand
Reinhold, 1976), pp. 365-367, 842-843.
[8] Chemical Technology: An Encyclopedia Treatment (New York: Barnes and
Noble, 1973), Vol. VI, S.v. "Polyvinyl Chloride (PVC)."
[9] R, L. Gosselin et al., Clinical Toxicology of Commercial Products
(Baltimore: The Williams and Wilikins Company, 1974), pp. 137-138.
[10] E. G. Cazalet, Generalized Equilibrium Modeling: The Methodology of
the SRI-Gulf Energy Model. Final report prepared by Decision Focus
Incorporated for the Federal Energy Administration (1977).
[11] R. J. Adler et al., The DFI Generalized Equilibrium Modeling System
(Palo Alto, Calif.: Decision Focus Incorporated, 1979).
[12] R. A. Flint et al., Users Guide to the DFI Model Data Management
System (MDMS), Decision Focus Incorporated (1980).
[13] C. E. Clark, Jr., et al., Modeler's Guide to Building Generalized
Equilibrium Models, Decision Focus Incorporated (1979).
6-1
334
-------
[14] Chemical Economics Handbook, SRI International (1980 and preceding
years).
[15] "Special Reports on ?taterials 1980," and "In Plasticizers, Less Means
More," Modern Plastics (January 1980, December 1979).
[16] Process Economics Handbook, SRI International (1980 and preceding
years).
[17] Agricultural Statistics 1978, U.S. Government Printing Office.
[18] Annual Report to Congress 1979, Volume Three: Projections, U.S.
Department of Energy, Energy Information Agency. Publication
D0E/EIA-0173(79)/3 (1979).
[19] S. M. Barrager et al., Integrated Models for R&D Planning, Electric
Power Research Institute (1980). EA-1A62.
[20] S. Salant, "Exhaustible Resources and Industrial Structure," Journal
of Political Economy 84, no. 5 (1976).
[21] R. D. Willig, "Consumer's Surplus Without Apology," The American
Economic Review 66, no. 4 (1976).
[22] D. M. Nesbitt et al., An Analytical Framework for Assessing Federal
R&D Strategies on Liquid and Gaseous Fuels. Final report prepared by
Decision Focus Incorporated for U.S. Department of Energy (1980).
[23] "Data for the TVA Regional Energy-Economy Model," DFI project notes
and memoranda (1978).
[24] Technical Assessment Guide, Electric Power Research Institute (1978),
[25] S. Salant, "Imperfect Competition in the International Energy Market:
A Computerized Nash Cournot Model," ICF Incorporated (1979).
6-2
n.'-.rr
O U iJ
-------
Appendix A
DATA FOR THE PROTOTYPE PLASTICS INDUSTRY MODEL
This appendix documents the sources and derivation of data used in
the prototype economic model of the plastics industry. Data sources
include government and industry publications, trade journals, results of
other models, and previous work by Decision Focus. Most data require
aggregation, conversion to different units, or some other adjustment.
Data for the prototype model can be thought of as falling into six
categories:
1. Parameters describing the financial environment of the industries
represented in the model.
2. Data describing the process economics of the modeled industries.
3. Parameters defining the nature of the economic markets in which
the industries compete.
4. Demand information.
5. Feedstock price projections.
6. Initial estimates of quantities of materials demanded and
supplied.
FINANCIAL PARAMETERS
Financial data are used in the model to calculate depreciation,
interest, and other charges against capital stock, as well as tax effects
(11,22]. We found no sources of data for financial parameters specific to
the plastics industry. The parameters used, listed in Table A-l, were
based on previous DFI experience in modeling a variety of industrial
A-l
326
-------
Table A-l
FINANCIAL PARAMETERS
1. Real return on equity financing
2. Real return on debt financing
3. Income tax rate (including st3te)
4. Investment tax credit rate
0.10
0.04
0.52
0. 10
5. Property tax rate (on capital stock)
6. Planning lead time (years)*
7. Plant lifetime (years)
2.00
30.00
0.02
8. Tax life (years)
9. Debt life (years)
10. Equity fraction^
20.00
11.00
0.75
1. Average time required to construct new capacity after initial go-ahead.
2. Fraction of new capacity financed by equity; remainder is debt financed.
sectors (22,23,24). Some parameters, such as the property and income tax
rates, are averages of values that vary from one state to another. We
assumed, having no information to the contrary, that the same set of
financial parameters applied to all industrial processes in the resin,
plasticizer, and plastics sectors.
PROCESS ECONOMICS
Process economics parameters summarize the technology of converting
one or more inputs to a primary output. The parameters include capital
cost of new capacity (expressed as dollars per unit of capacity), variable
operating costs, input-output coefficients, and plant availability.
Process economics parameters for industrial processes in the resin,
plasticizer, and plastics industries were developed primarily from data in
the Chemical Economics Handbook [14] and the Process Economics Handbook
A-2
-------
[16]. Where data were unavailable, parameters used in the prototype model
represent DFI estimates based on comparison with similar technologies.
The data used generally required several steps to derive the actual
model parameters from the source data. Necessary adjustments included
converting all costs and prices to a common basis (1978 dollars), aggregat-
ing several items to form one model parameter (e.g., adding labor, electri-
city, and secondary material inputs to get the variable operating cost),
and combining a sequence of industrial processes or process steps into one
overall industrial conversion process.
The raw data available in the Process and Chemical Economic Handbooks
are broken down in varying, and often considerable, degrees of detail. The
data for the production of 2-ethylhexanol (a factor in the production of
di-ethylhexyl phthalate plasticizer) are provided in Table A-2 as an
example (from [16]). The data are provided for a base size plant (120
million pounds per year in the example). The SRI process economics cost
index is used to adjust to common year dollars.
To maintain consistency, a set of standard assumptions was used in
converting the raw data to a form appropriate for the prototype model.
Process economics parameters required by the model for each industrial pro-
cess are specific capital cost (SCC), variable operating cost (VOC), input-
output coefficient(s) (I/O), and plant availability (or stream factor).
The assumptions used are as follows:
1. All costs are converted to 1978 dollars using the cost indexes
provided [14,16]. The index for 1978 is 275; the index
corresponding to the data in Table A-2 is 268. Therefore, all
costs in the 2-ethylhexanol example were multiplied by 27 5/268.
2. Total plant cost is assumed to include site, working capital, and
start-up costs. It was adjusted, if necessary, to deduct
interest charges on capital used during plant construction, as
this is calculated by the model. The total cost was divided by
plant capacity and adjusted by the cost index to provide the
specific capital cost. When necessary, the SCC in dollars per
pound was multiplied by 2200 to convert to dollars per metric
ton. For 2-ethylhexanol, we have $33.8 million divided by 120
million pounds per year times 275/268 times 2200 equals $636 per
metric ton capacity.
339
A-3
-------
Tabl-2 A-2
PROCESS ECONOMICS DATA
Cost: data for plant vith capacity
Data reported May 1979, cost inde
Plant Cost
(millions)
Basic Plant S18.03
Offsite Costs 3.93
General Services 4.39
Start-up 1.96
Working Capital 5.49
Total Plant Cost $33.80
FOR 2-ETHYLHEXAN0L
of 120 million pounds per year.
= 268, stream factor = 0.9.
Production Costs
(cents/pound)
Labor 0.95
Other Materials 1.70
Maintenance 0.49
Utilities 1.35
Overhead 0.76
G&A 2.4 0
Depreciation 2.20
Interest on
Working Capital 0.43
Byproduct Credits -3.37
Input-Output Coefficients
Propylene 0.87 pounds/pound of output
Synthetic Gas 21.85 scf/pound of output
333
A-4
-------
3. Variable operating cost is assumed to include labor, overhead on
labor, secondary (or "other") material inputs, maintenance,
utilities, and (when appropriate) a deduction for byproduct
credits. The sum is converted to a dollars-per-metric-ton figure
and adjusted to 1978 dollars. For our example, we have 4.3 cents
per pound tines 2200 divided by 100 (cents per dollar) times
275/268 equals $97 per metric ton. Depreciation and interest are
calculated internally by the model (based on the SCC), and are
not included in the VOC.
4. Input-output coefficients usually required no adjustment. How-
ever, in the prototype model, we have assumed a single generic
fossil feedstock. In some cases, the feedstock input-output
coefficient(s) required adjustment to maintain a consistent ratio
of heat content to product price. In the 2-ethylhexanol example,
21.85 scf of synthetic gas is the Btu equivalent of 0.92 pounds
of propylene. Added to the coefficient for propylene, this re-
sults in a net input-output coefficient for feedstock to product
of 1.79.
Interest on capital invested in the plant is calculated by the model and
included in the effective capital cost (ECC) discussed in Section 5.
Parameters for all necessary industrial processes were derived using
these assumptions. The process economics parameters are summarized in
Table A-3. All parameters are derived from data in [14] and [16] unless
otherwise noted. The stream factor for all processes was 0.9.
As described in Section 5, in many cases we combined several processes
to form the aggregate conversion (or production) process represented in the
model. For example, the PVC resin process in the prototype model repre-
sents the composition of VC monomer production and a polymerization stage,
resulting in PVC resin. A detailed example, for di-ethylhexyl phthalate,
was discussed in Section 5. In each case, to fully reflect the process
economics of all steps in the production of a material or compound, the SCC
and VOC of the earlier stages are multiplied by the input-output coeffi-
cients) at the next stage and summed over all stages. Final input-output
coefficients are the product of all intermediate steps. The derivation of
the final process economics parameters are summarized in Tables A-4(a)
through A-4(g). Notes regarding special cases and assumptions are included
in the tables.
A-5
4 -JO
-------
Table A-3
PROCESS ECONOMICS PARAMETERS FOR INDIVIDUAL INDUSTRIAL PRODUCTION PROCESSES
see
VOC
I/O
Product
($/metric ton/year)
($/raetric ton)
Feedstock
Coet1icie
Ethylene/propylene
$ 363
$ 58
Ethane/propane mix
1 .40
2-Ethylhexonal
636
97
F-t hylene
1.79
Phthalic Anhydride
407
96
O-xylene
1.02
O-xylene
132
37
BTX
1.12
Di-ethylhexyl Phthalate
137
71
2-Ethy1hexano1
0.69
Phthalic Anhydride-
0.39
Adipic Acid
1200
300
Fossil Liquids
4.70
Di-ethylhexyl Adipate
384
209
Adipic Acid
0.41
2-EH
0.72
Epoxidized Soy Oil
271
213
Soy Oil
0.93
h2o2
0.37
Large Alcohols
449
150
Fossil Liquids
1.92
Vinyl Chloride Monomer
240
163
Ethane
0.53
Chlorine
0.59
VC Polymerization
564
136
VC Monomer
1.03
Polyethylene Resin
1 100
250
Fossi1 Liquids
1.82
Polystyrene Resin
900
200
Fossil Liquids
3.12
PVC Plastic
100
50
PVC
0.77
(from res in) *
Plasticizer
0.28
Polyethylene Plastic
100
50
PE Resin
1.03
(from resin)'
Other Plastics'
100
50
Resin
0.98
Plasticizer
0.07
1. Plastic process parameters are inferred from information in Modern Plastics, a trade journal [16).
"Other Plastics" is an average of ftolypropylene and polystyrene. As little specific data was
available, we assumed that capital and operating c.osLs were the same for all three plastics formation
processes.
-------
Table A-4(a)
DERIVATION OF PROCESS ECONOMICS FOR DEHP
Stage SCC__ VOC I/O
Ethylene
$ 363
$ 58
1.40
2-Ethylhexanol from ethylene
636
97
1.79
Net for 2-EH from feedstock
12S6
201
2.51
0-xylene
132
37
1.12
Phthalate anhydride from o-xvlene
407
96
1.02
Net for phthalate anhydride fron feedstock
542
134
1.14
DEHP from 2-EH and phthalate anhydride
137
71
2-EH 0.69
P.A. 0.39
Net for DEHP from feedstocK
1235
262
2. 13
Table A-4(b)
DERIVATION OF PROCESS ECONOMICS FOR LARGE1
MOLECULAR WiIGHT PHTHALATE ESTERS
Stage SCC VOC I/O
Ethylene S 363 $ 58 1.40
Large alcohols from ethylene 543 124 1.85
Net for large alcohols 1215 231 2.59
Net for phthalate anhydride 542 134 1.14
(see Table 4(a))
Phthalate esters from alcohols and P.A. 137 71 Ale. 0.69
P.A. 0.39
Net for large molecular weight phthalate 1187 283 2.23
esters
I. Large molecular weight phthalate esters were assumed to be those with
molecular weights similar to DEHP.
A-7
A1.r>
Jt —•
-------
Table A-4(c)
DERIVATION OF PROCESS ECONOMICS FOR
VERY LARCE MOLECULAR WEIGHT PHTHALATE ESTERS
Stage
Ethylene
Very large molecular weight alcohols
from ethylene
Net for very large molecular weight
alcohols
Net for phthalate anhydride
(see Table 4(a))
Phthalate esters froo alcohols and P.A.
Net for very large molecular weight
phthalate esters
1
see
$ 363
449
1146
542
137
1187
VOC
$ 58
150
261
134
71
314
I/O
1 .40
1.92
2.69
1.14
Ale. 0.77
P.A. 0.31
2.42
1. I/O coefficients for alcohol and P.A. were adjusted from detailed data
for DEHP by the ratio of molecular weights (assuming average of 12 carbon
size for very large molecular weight alcohols) and renornalized.
Table A-4(d)
DERIVATION Or PROCESS ECONOMICS FOR
SMALL MOLECULAR IrtlGHT PHTHALATE ESTERS
Stage
Ethylene
Small molecular weight alcohols from
ethylene
Net for small molecular weight alcohols
Phthalate anhydride (see Table 4(a))
Small molecular weight phthalate esters
from alcohols and P.A.
r
Net for small molecular weight phthalate
esters
SCC
$ 363
600
1253
542
100
1069
VOC
$ 58
150
244
134
200
410
I/O
1.40
1.80
2.52
1.14
Ale. 0.54
P.A. 0.54
1.98
1. I/O coefficients for alcohol and P.A. were adjusted from detailed data
for DEHP by the ratio of molecular weights (assuming average size of four
carbons for small alcohols) and renormalized. The process economics were
assumed to be less capital intensive and more operating cost intensive due
to smaller quantities being produced.
A-8
403
-------
Tabl
DERIVATION OF PROCESS ECONOMICS
Stage
Adipic acid
2-Ethylhexanol (see Table 4(a))
Di-2-ethylhexyl adipate from
adipic acid and 2-EH
Net for Di-2-ethylhexyl adipate
1. Based on process economics for di-
A-4(e)
FOR OTHER PRIMARY PLASTICIZERS1
SCC VOC I/O
S12C0 $300 0.48
1286 201 2.51
384 209 A.A. 0.41
2-EH 0.7 2
1801 476 3.73
-ethylhexyl adipate.
Table A-4(f)
DERIVATION OF PROCESS ECONOMICS FOR EPOXIDIZED SOY OIL
Stage
1
H2°2
Epoxidization
Net for epoxidized soy oil
SCC
$1200
271
713
VOC
$300
213
323
I/O
Soy
h,o2
4.50
0.93
0.37
Soy 0.93
fossil 1.66
1. The process economics parameters for H^02 were chosen to
calibrate with product cost data in [4].
Table A-4(g)
DERIVATION OF PROCESS ECONOMICS FOR PVC RESIN
Stage SCC VOC
VC Monomer
Polymerization
$240
564
$163
136
I/O
ethane 0.53
chlorine 0.59
1.025
Net for PVC Resin
810
303 ethane 0.54
chlorine 0.61
A-9
404
-------
MARKET PARAMETERS
The models of economic markets (also known as allocation processes)
used In the prototype plastics industry model are described by three types
of parameters: the share price sensitivity, the behavioral lag, and the
fractions of oarket share at equal product prices.
The market share price sensitivity is analogous to the standard
economic idea of an elasticity. It defines the extent to which the market
shares received by competing products, such as plasticizers, are determined
by price differences. Large values for the share price sensitivity para-
meter imply that the market is highly sensitive to price. A good needs
only a snail edge in price to gain a very large share of the market. Con-
versely, low values of the price sensitivity parameter are used to model
markets that respond less drastically to price differences. An extremely
low value (close to zero) would imply that all competing products would get
roughly similar market shares, regardless of price.
A moderately high share price sensitivity parameter value of 10.0 was
used for all end-use markets (food wrapping and packaging, medical sup-
plies, automobile interiors, consumer products, and other) and for plas-
ticizer input factor markets to PVC plastics production processes. Thus,
we have assumed these markets to be relatively sensitive to prices. A
moderate parameter value of 5.0 was used for all other markets in the
prototype model, reflecting an assumption of somewhat reduced sensitivity
to small changes in price.
The behavioral lag parameter defines the average number of years it
takes for a potential change in market shares induced by a change in prices
to actually be realized in the marketplace. This reflects the fact that
price changes do not result in immediate changes in demand patterns. Con-
sumer buying patterns, inventories, long lead times for capital equipment,
and other effects contribute to this lag. The average lag was assumed to
be two years in all end-use demand markets and in all markets for plasti-
cizers as input factors to PVC production. The average lag in the material
input market for "other resins" was assumed to be four years. An average
lag of three years was used in all other markets in the prototype model.
A-10
435
-------
These values are based on DFI experience with nodels of similar industrial
sectors [22,23] and on qualitative descriptions in trade journals of the
plastics industry [15].
The market share fractions at equal product price parameters are used
as a first-order method of modeling products that compete on the basis of
characteristics other than price. For example, plasticizers compete on the
basis of ease of use, plasticizing efficiency, and volatility, as well as
price. The fractions at equal price parameters define the market shares
that each product would receive were they all at the same price (the de-
fault, for homogenous goods, is equal shares). The parameters used (as
summarized in Table A-5) were selected so as to calibrate market shares to
estimates of initial quantities in the relevant markets.
INITIAL QUANTITY ESTIMATES
The GEMS modeling approach [10] used to develop the prototype model
solves for market equilibrium prices and quantities such that supply and
demand are in balance. The solution algorithm requires that initial esti-
mates of equilibrium quantities be provided.
Estimates of the quantities of flexible plastics used in 1978 were
developed from data in Modern Plastics [15] and checked against data in the
Chemical Economics Handbook [14]. The estimates are summarized at the top
of Table A-6. Food packaging and wrapping is assumed to include bags, food
wrap, bottles, eating utensils and containers, closures, food tubs, and
packaging for drugs and personal care items. Medical supplies primarily
include blood bags and tubing. Automobile interior uses are dominated by
upholstery and seat covers but include mats, liners, and trim. The con-
sumer products category includes all non-food- or drug-related consumer
uses: housewares, toys, trash bags, clothing, recreation goods, hoses,
flooring, and furniture. The "other" flexible plastics demand is composed
of flexible pipe, other construction and industrial materials, and indus-
trial packaging.
The raw data gave quantities of resin used and were adjusted by the
plastics process input-output coefficients to provide the estimates of
plastics quantities.
A-ll
-------
Table A-5
MARKET SHARE FRACTIONS AT EQUAL PRODUCT PRICES
ir.c-Use Market
Plastic Food Medical
PVC with DEHP 0.03 0. 10
Regular PVC 0.03 0.10
Low Migration PVC 0.08 0.80
Polyethylene C.42
Other Plastics 0.44
ftUtO
0. 25
0. 25
0. 50
Consumer
0. 10
0.20
0.35
0. 35
Other
0.05
0. 15
0. 5 5
0.25
Input Factor Market
Plast icizer
Plasticizer Used
Plasticizer Used in Low Migration Plasticizer Used
in Regular PVC PVC in Other Plastics
Large Molecular Weight
Phthalats Esters
Very Large Molecular
Weight Phthalate Esters
Small Molecular Weight
Phthalate Esters
Epoxidized Soy Oil
Other Primary
Plasticizers
0.40
0.03
0. 17
0. 40
0. 25
0. 75
0. 50
0. 50
A-12
437
-------
Table A-6
ESTIMATES OF QUANTITIES PRODUCED AND DEMANDED (1973)
(all quantities in thousands of metric tons)
Plastics Demand Quantities
Food Packaging, Wrapping, and Containers 1,505
Medical Supplies 34
Automobile Interiors 199
Other Consumer Products 2,127
Other Flexible Plastics Uses 5,021
Plastics Quantities
PVC made with DEHP 703
Regular PVC Plastic (non-DEHP) 1,451
Low Migration PVC Plastic 156
PE Plastic 4,755
Other Plastics 1,652
Plasticizer Quantities
DEHP 189
Large Molecular Weight Phthalate Esters 260
Very Large Molecular Weight Phthalate Esters 18
Small Molecular Weight Phthalate Esters 135
Epoxidized Soy Oil 34
Other Primary Plasticizers 76
Resin Quantities
PVC Resin 1,780
PE Resin 4,995
Other Resins 1,640
Feedstock Quantities
Fossil Liquids 12,000
Cellulose 30
A-13
-ICS
-------
Plasticizer quantities, also listed in Table A-6, were derived fron
data in [14] and [15]. These data were adjusted to be consistent with
initial plasticizer demand from the plastics sector.
Estimates of initial quantities of feedstocks used were developed by
inferring the quantities required to support resin and plasticizer produc-
tion. The GEMS algorithm did not require estimates of quantities of vege-
table oil or chlorine since these materials do not pass through an economic
market.
FLEXIBLE PLASTICS DEMAND
The demand for flexible plastics is described in the prototype model
by three types of data: quantities demanded in the first year (1978), the
rate of demand growth given constant real prices, and price elasticities.
Quantities demanded (used) in 1978 in each end-use market were taken
from the initial quantity estimates listed in Table A-6. These data
required no further adjustment.
Rates of growth in demand are summarized in Table A-7. These growth
rates are those projected if prices in constant dollars remain unchanged,
that is, if prices move up only by the annual inflation rate. Estimates of
growth in demand for PVC were found in [14], for plasticizers in [3], and
for plastics end uses in [15].
The demand model represents both short- and long-run responses of
plastics consumers to changes in plastics prices. These characteristics
Table A-7
DEMAND GROWTH RATES
End Use
Food-Related
Medical
Autoraobile
Consumer Products
Other
Average Annual
Growth Rate
32
5%
3%
2%
A-14
439
-------
are modeled using short- and long-run price elasticities. The short-run
elasticity for all end uses was assumed to be -0.10, while the long-run
elasticity was -9.20. The long-run elasticity shows a greater sensitivity
to price changes, reflecting the increased ability over the long run to
change capital equipment, manufacturing techniques, and buying patterns.
The elasticities used are baaed on DFI experience in modeling similar
industrial sectors [19,23].
FEEDSTOCK PRICES
Future price estimates for feedstocks were developed from information
in Synthetic Organic Chemicals [4], the Agricultural Statistics [17], and
three national energy-econonic system models: the Long-Range Energy
Analysis Program (LEAP) used by the Energy Information Agency of the U.S.
Department of Energy [18], the Integrated Forecasting Model (IFM) used by
the Electric Power Research Institute [19], and the Salant Energy Reference
Model [20,25]. Prices over the next twenty years are listed in Table A-8.
The prices for the generic fossil liquid feedstock reflect the cost of
conversion from crude oil and natural gas to an ethane/propane mix. All
prices are in constant 1978 dollars (that is, they do not include
inflation).
The fossil liquids price estimates used in the prototype model repre-
sent an average of projections from the three energy models. Prices for
vegetable oils (primarily soy oil) are assumed to remain constant in con-
stant dollars; the price used is based on data in [17], The cellulose
supply was included in the prototype model design as a feedstock for "other
resins" but does not play a significant role in the results. Chlorine
prices, data for which were also found in [16], are assumed to rise quite
rapidly due to the high use of energy in producing chlorine.
SUMMARY
We have presented a description of the data used in the prototype
plast ics industry model. The purpose of the model and data base is to
A-15
410
-------
Year
1973
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
Table A-S
FEEDSTOCK PRICES
(alL prices in S/netric ton)
Fossi1 Liquid Chlorine Vegetable Oils Cellulose
172
$ 294
S 620
S 26C
265
312
620
275
233
331
620
290
313
352
620
305
337
372
620
320
358
395
62 0
340
381
419
620
360
4 02
436
620
385
424
4 54
620
410
446
472
620
435
46 7
491
620
46C
469
51 1
620
.490
411
A-16
-------
Illustrate the application of detailed market models to the analysis of the
economic impacts of potential toxic substances regulatory decisions. We
believe the data used are adequate to meet that goal and to give a reason-
able indication of the economic behavior of the plastics industry. The
amount of effort devoted to developing the data base was deliberately
limited, as the primary emphasis of this project was on the methodology of
modeling and decision analysis. The data, as well as the prototype model,
would require careful refinement before being used in an actual regulatory
decision analysis.
A-17
A °
-------
Appendix B
RESULTS OF POLICY AND SENSITIVITY ANALYSIS CASES
USING THE PROTOTYPE PLASTICS INDUSTRY MODEL
This appendix contains the complete quantitative results for the
policy and sensitivity analysis cases using the prototype plastics industry
model. An index to the results follows.
The cases involving a complete ban on DEHP require a special
explanation. The results for these cases show very high prices for DEHP,
although production and use of the material has been prohibited. This is
because the GEMS software implements a ban or prohibition by setting prices
high enough that the demand for the material is zero. These DEHP prices
have no effect on other model results and can be ignored.
Index to the Results
Case
Page
Prohibit DEHP
Restrict DEHP
Restrict DEHP and constrain production
High fossil feedstock, prices
Low fossil feedstock prices
Prohibit DEHP, with high fossil feedstock prices
Prohibit DEHP, with low fossil feedstock prices
B-2
B-8
B-14
B-20
B-26
B-32
B-38
B-l
413
-------
RESULTS FOR CASE WITH A COMPLETE PROHIBITION
ON THE PRODUCTION AND USE OF DEHP
414
B-2
-------
REPCRT 1
PLASTICS FRCDUCTTON'
."•ATERTAL
AF=
SUM
1
PVC WITH 1
REGULAR
—r—
I
LOW MTC.
! pr
| OTHER
GSS=
QUANTITY
1
DE HP |
PVC
1
PVC
' PLASTIC
I PLASTICS '
TIXS
1 !
L.
i
i
3
1 4
1 5
1978
I 1 !
o.cc 1
1654.29
188.46
[ 5222.95
I 1320.3C '
1980
1 2 i
o.cc !
165:.CS
'
234.03
I 5195.46
I 20^5.31 |
1982
1 3 :
0.0C 1
1683.97
!
274.37
1 5276.99
1 2262.00 I
1984
I 4 |
O.CC !
1736.33
1
i
311.54
1 5433.17
1 2410.09 1
1986
I 5 |
0-00 |
1801.70
1
346.85
I 5633.50
1 2534.76 |
1988
1 6 I
O.CC I
18"7!. ]9
!
381.!0
1 5871.59
1 2649.99 1
1990
1 7 |
O.CC !
1944.77
1
415.61
1 6135.56
1 2~753.92 1
1992
1 3 1
0.C0 !
2G29.99
1
452.18
I 6418.98
I 2864.13 I
1994
1 9 I
0.C0 1
212':.21
1
1
49J. 3"7
1 6719.57
! 296^.02 1
1996
1 10 |
0.00 |
2226.22
1
1
533.71
1 7036.35
I 3072.21 I
1998
1 11 1
0.00 1
2333.66
1
579.51
1 7373.52
1 3180.89 1
2000
1 12 1
0.00 !
2447.0"7
1
629.48
1 7729.3"
1 3293.74 1
QUANTITIES 31 THOUSANDS OF METRIC TC-NS.
REPORT # 2
PLASTICIZER PRODUCTION
AF=
GS5=
SUM
QUANTITY
]
DEHP !
LG.MOL.
SML.MOL.
VL.MOL.
EPOXID. 1
OTHER
i
1
WT.PAE
WT.PAE
WT.PAE
SOY OIL 1
PLASTCRS
i
1 1
2
3
4
5 1
6
i i i
C.00 ]
320.30
155. tn
27.94
46.50 !
93.63
) 2 |
0.00 1
325.28
164.14
40.84
45.C5 '
98.36
1 3 |
0.00 |
332.86
172.09
50.93
46.89 i
103.85
1 4 |
0.00 1
342.29
179.22
58.43
52.09 I
110.03
1 5 1
0.00 1
352.82
185.70
63.86
60.06 |
116.60
1 6 !
O.CC !
363.32
192.04
67. 80
69.92 1
123.06
1 7 1
0.C0 !
373.88
198.16
70.61
31.81 |
129.56
1 8 |
0.00 1
306.23
204.27
72.97
95.41 1
136.63
i 9 I
0.00 I
399.78 I
210.35
74.^5 1
110.88 [
144.16
1 10 |
0.00 |
414.33 |
216.4^
76.70 |
128.24 |
152.09
1 IJ 1
0.00 I
429.68 |
222.97 |
78.53 !
146.98 I
160.1?
1 12 |
0.00 |
445.56 1
229.56 |
30.24 '
168.C2 !
168.62
THOUSANDS
OF METRIC
TONS.
B-3
'' C
'f jlO
TIME
1978
1980
1982
1984
1986
1908
1990
1992
1994
1996
1998
2000
-------
REPORT # 3
FCOD PACKAGING AND CONTAINERS
MATERIAL
AF=
SUM
j
PVC WITH :
REGULAR |
LCW MIG. 1
PE |
OTHER
GSS =
QUANTITY
i
DEHP :
PVC |
PVC 1
PLASTIC 1
PLASTICS
A ,
TIME
1 1
2 I
3 I
4 1
5
_ _ i _
1
'
1978
1 1 1
0.00 1
63.61 '
67.71 |
872.02 [
501.57
1980
1 2 |
0.00 1
62.27 |
89.71 |
859.10 |
554.67
1982
1 3 |
0.C0 !
62.83 1
107.63 1
871.20 I
597.51
1904
! 4 1
0.00 I
64.65 |
122.72 1
900.81 1
635.16
1906
! 5 :
0.CC !
67.27 i
135.76 I
941.56 1
669.84
19S8
1 6 1
O.CO 1
70.20 1
146.94 I
991.26 !
703.96
1990
I 7 !
0.00 !
71.41 1
157.12 '
1047.63 1
737.82
1992
1 8 !
O.CO I
77.19 |
167.49 I
1109.61 !
771.94
1994
1 9 1
0.00 1
81.41 |
178.26 1
1176.58 1
806.61
1996
1 10 1
0.00 1
86.01 |
189.60 1
1248.23 1
842.21
1998
1 11 1
0.00 1
90.88 |
201.^0 !
1325.18 !
879.55
2000
1 12 1
0.C0 1
96.C3 !
213.95 1
1407.17 |
918.56
QUAN1
'ITIES IN
THOUSANDS
OF METRIC
TONS
REPORT }< 4
MEDICAL SUPPLIES
MATERIALS
H 1 1 ("
AF= SUM | PVC WITH | REGULAR I LOW —- mm mm mm* mm — mm -mm mm mm mmmmmmm mm mm mm mm mm mm mm mm mm *mmm «¦» mm mm mm mm mm Mm *mm^*
TIME | I| 2 1 3 !
. 1_ + (. +
1978 I i | 0.00 | 12.75 | 21.25 1
1980 12 1 0.00 | 10.35 I 2G.58 [
1982 | 3 I 0.00 I 8.95 I 31.25 I
1904 I 4 | 0.00 | 8.21 I 35.86 I
1986 I5| 0.00 | 7.93 I 40.31 I
1988 I 6| 0.00 I 7.99 ! 44.84 I
1990 I 7 | 0.00 | 8.28 I 49.58 I
1992 I 8 1 0.00 | 8.77 | 54.66 I
1994 I 9 I 0.00 ! 9.40 I 60.14 !
1996 | 10 I 0.00 | 10.J6 I 66.09 !
1998 I 11 I 0.00 | 11.03 I 72.61 !
2000 j 12 | 0.00 | 12.01 | 79.75 I
QUANTITIES IN THOUSANDS OF METRIC TONS.
B-4
4—o
-------
REPORT ;• 5
AUT0MC3ILE 'J3Z5
MATERIAL
AF= SUM
PVC WITH
REGULAR I
LOW MIG.
GSS= QUANTITY
DEHP
PVC 1
PVC
TI>"E
1
2 1
3
1978
1 1
o.co
99.50 1
99. 50
1SSC
1 2
o.co
93.24 1
11*7.74
1982
1 3
0.00
100.32 1
135.39
1981
1 4
0.00
104.83 |
152.96
1936
! 5
0.00
111.40 1
170.78
19SD
1 6
G. 00
11S.74 |
IB 9.32
:990
1 7
0.00
129.61 1
208.91
1992
1 3
0.00
141.01 I
230.04
1994
1 9
0.00
153.32 |
252.97
1995
1 10
0.00
168.03 I
278.01
1998
1 11
0.00
183.73 |
305.50
2000
1 12
0.00
20C.92 1
335.79
QUANTITIES THOUSANDS OF METRIC TONS.
REPORT # 6
OTHER CONSUMER PRODUCTS
MATERIAL
•+ + + +
AF=
SUM
I PVC WITH |
REGULAR
1 PE |
OTHER
GSS=
QUANTITY
I DSHP I
PVC
I PLASTIC |
PLASTICS
TIME
1" i t
2
T 3 T
4
1978
1 1
1 0.00 1
598.37
1 957.63 |
571.01
1930
1 2
I 0.03 I
594.23
1 982.40 !
638.30
1932
1 3
1 0.00 I
605.38
I 1022.57 !
691.35
1984
1 4
1 O.OO I
626. "72
1 1074.59 1
737.97
1936
I. 5
1 0.00 I
654.42
! 1134.24 !
779.69
1938
! 6
1 0.00 1
684.67
! 120L.69 !
820.61
1S90
1 7
1 0.00 1
717.24
! 1275.23 !
861.07
1992
I 8
! 0.00 !
754.77
! 1353.60 1
901.25
1994
! 9
1 0.00 !
796.25
1 1436.79 1
941.71
1996
1 10
! 0.00 1
341.24
1 1524.90 I
933.06
1993
1 11
! 0.00 !
388.79
! 1619.27 |
1026.49
2000
1 12
1 0.00 1
939.15
1 1719.72 I
1071.94
QUANTITIES IN THOUSANDS OF METRIC TONS.
-------
REPORT i! 7
PLASTICS PR]
AF= AVERAGE PRICE
GS3= PRICES
MATERIAL
' PVC WITH ! REGULAR | LO.v MIG. 1 PE
; DIH? | PVC | PVC I PLASTIC
_i- + + (.
: I 2 ! 3 1 4
! CTHER |
I PLASTICS I
.j
TIiXE
_i +_
1978
¦ 1
o.on i
827.28 I
885.73 1
801.16 1
838.06
1230
2
1 ********* j
932.05 |
979.55 !
932.71 |
996.52
1982
1 3
|********* |
965.16 ;
1003.63 1
965.42 1
1036.18
1934
1 4
|********* |
10C1.54 |
1043.81 !
1000.95 |
1079.18
1936
1 5
|********* |
1036.55 |
1079.02 i
1035.04 1
1120.32
1230
1 6
1949686.19 1
1069.90 1
1113.10 1
1064.92 I
1156.64
1990
! n
'890317.R3 I
1105.SO |
1149.86 1
1097.60 1
1196.28
1292
: 8
,'327662. B8 I
1136.33 |
1151.16 1
1127.47 I
1232.97
1994
1 9
.766962.25 |
1168.42 1
1212.75 1
1158.80 1
1271.42
1996
1 10
!"707350. 00 I
1201.09 1
1246.61 I
1190.73 1
1310.54
1998
1 11
I 653002.44 I
1232.06 I
1277.64 I
1219.97 |
1346.71
2000
1 12
1593652.00 I
1253.GO |
1303.54 I
1250.07 |
1383.47
AVERAGE PRICES IN DOLLARS PER METRIC TON.
REPORT ft 8
PLASTICIZER PRICES
4——— — ——4—
AF=
AVERAGE
PRICE
1 DEHP |
LG.MOL. 1
SML.KOL.
I VL.MOL.
I EPOXID. 1
OTHER 1
GSS=
PRICES
1 1
WT.PAE |
WT.PAE
I WT.PAE
1 SOY OIL 1
O-.
PLASTCRS |
TIME
1 1 1
2 I
3
1 4
1 5 1
6 1
,
.
1978
1 1
1 o.co 1
815.72 1
334.25
I 834.74
I 1280.59 f
940.7Q !
1980
1 2
1 59223.98 '
1022.67 !
1C66.S8
1 1111.39
1 1435.12 1
1162.45 1
1982
1 3
I 53627.52 1
1074.09 I
1112.27
1 1168.33
1 1473.46 1
1217.49 I
1984
1 4
1 6C019.69 |
1129.94 |
1161.57
I 1229.63
1 1515.11 !
127-.29 I
1986
1 5
1 60015.22 I
1193.5? 1
1208.88
I 1238.45
1 1555.07 !
1334.67 I
1988
1 6
1 59430.19 1
1230.48 1
1250.32
I 1339.98
1 1590.07
1334.95 i
1990
1 7
1 53856.46 1
1281.85 1
1295.66
1 1396.35
1 1628.37 1
1439.97 |
1992
1 8
I 57874.85 1
1323.78 1
1337.08
I 1447.35
1 1663.35 1
1490.24 1
1994
1 9
1 56815.89 1
1377.92 !
1330.45
I 1551.79
1 1S99.99 1
1542.90 |
1996
1 10
I 55534.59 I
1427.10 !
1423.92
I 1555.81
1 1736.49 1
1595.70 :
1998
1 11
1 54295.27 I
1466.19 1
1457.22
I 1593.71
1 1764.43 I
1637.76 !
2000
1 12
I 52314.66 |
1517.90 1
1503.14
! 1655.41
I 1803.16 1
1693.26
AVERAGE PRICES IN DOLLARS PER METRIC TON.
B-6
418
-------
REPORT 13
AVERAGE PLASTICS PRICES
PLASTICS
AF=
AVERAGE
PRICE
GSS =
PRICES
TIME
1973
I i
1980
1 2
1932
1 3
1984
1 4
1936
1 5
1988
i 6
1990
1 7
1992
1 8
1994
1 9
1996
1 10
1998
1 li
2000
1 12
FOOD
USES
818.42
957.98
994.05
1032.87
1069.89
1102.59
1138.11
1170.35
1204.05
1233.27
1259.75
1301.86
MEDICAL | AUTO I CONSUMER ! OTHER
SUPPLIES | USES | PRODUCTS | DEMAM3
2 1 3 1 4 1 5
864.43 I
857.00 I
818.41 I
811.23
956.23 1
957.93 !
950.93 1
943.59
998.98 |
990.13 1
986.46 !
978.46
1035.94 |
1026.63 1
1024.78 1
1016.01
1072.04 |
1062.26 1
1061.33 I
1051.8?
1106.57 |
1096.37 |
1094.CO |
1083.60
1143.56 1
1133.00 |
1129.46 1
1118.12
1174.97 |
1164.13 1
1161.31 !
1149.27
1207.63 |
1196.62 |
1194.65 1
1181.88
1240.55 I
1229.47 |
1228.54 I
1215.C4
1271.63 |
1260.53 |
1259.86 I
12^5.59
1302.66 |
1291.72 |
1291.P6 |
1276.86
PRICES IN DOLLARS PER METRIC TON.
B-7
410
-------
RESJLTS FOR THE CASE WITH RESTRICTIONS ON THE USE OF DEKP
B-8
420
-------
..~PCRT i| i
PLASTICS PRODIJCTJ.cn
MATERIAL
AF= SUM
i
I
?VC WTTH 1
REGULAR
LOW MIC.
PE
CTKER
GSS= QUANTITY
I
DEHP !
?VC
PVC
PLASTIC
PLASTICS
TIME
: 1
2
2
/
5
1S73
1 1 1
6^0.76 |
1519.22
1P5.00
4860.78
1679.44
1930
1 2 1
693.46 |
1576.60
203.95
4956.74
1732.27
1982
1 3 I
740.12 I
1645.21
226.01
5113.53
1776.41
1984
1 4 |
783.96 |
1 "'21.73
249.73
5329.81
1317.59
198G
! 5 1
326.15 1
1802.93
274.10
5569.34
1853.40
1938
I 5 1
362.70 I
1373.47
298.19
5844.02
1906.15
1990
) 7 |
396.63 i
1952.78
322.89
6142.21
195"7. 47
1992
1 3 1
935.64 |
2039.55 1
349.92
5450.11
2CCS.59
1994
1 9 I
973.32 |
2136.00 I
3^9.56
6763.93
2C6C.64
1996
1 10 1
1024.10 I
2240.51
412. 17
7099.45
2114.52
1993
1 11 !
1070.98 1
2349.50
447.73
7449.76
2173. 5"1
2000
1 12 1
1119.?2 |
2463.86 1
486.92
7818.59
2236.56
QUALITIES IN
THOUSANDS
OF METRIC
TONS.
REPORT if 2
PLASTIC12ER PRODUCTION
4—
-+-
——-"4"~
-H
_i
-i
AF=
SUM
i
DEHP
1
LG.MOL. |
SML.MOL.
! VL.MOL.
! SPOXID.
| OTHER
GSS=
QUANTITY
l
1
WT.PAE |
WT.PAE
1 WT.PAE
i. . . .. . _ .
1 SOY OIL
I PLASTCRS
TIME
l
1
1
2 1
3
1 4
1 5
I 6
.
1973
l i I
179.41
1
294.15 1
142.37
1 27.54
44.42
35. 98
1930
! 2 1
194.17
1
310.29 1
140.49
I 35.59
1 39."0
1 93.74
1982
1 3 |
207.23
1
325.30 1
140.01
I 42.01
! 39.62
1 1C1.34
1984
1 4 |
2J.9.51
1
339.55 1
140.82
1 46.90
1 43.03
1 ICS.95
19C6
i 5 1
231.32
1
353.20 I
142.43
I 50.49
1 48.99
I 116.49
1988
1 6 !
241.55
1
364.91 I
145.02
! 53.08
1 56.55
1 123.34
1990
1 7 |
251.06
!
375.59 1
148.05
I 54.39
1 65.79
1 129.89
1992
1 S 1
261.98
1
3P8.25 1
151.38
1 56.50
1 76.50
1 137.C6
1994
1 9 I
273.93
1
402.J9 1
L54.94
1 57.93
I 88.53
' 144.74
1996
1 10 I
286.75
1
417.19 |
158.72
1 59.28
i 102.73
! 152.84
1998
1 11 1
299.37
1
4'32. 72 |
162.91
I 60.63
1 117.98
! 161.C9
2000
! 12 I
313.41
1
448.7[ |
167.32
1 62.0"7
I 135.06
] 169.62
QUANTITIES IN THOUSANDS CF ME7TRIC TONS
A CM
B-9
-------
REPORT if 3
FCCD PACKAGING AND CONTAINERS
MATERIAL
AF=
SUM
|
PVC WITH
| REGULAR
1 LOW MJG. 1
PE I
OTHER
GSS=
QUANTITY
DEHP
1 PVC
! PVC |
PLASTIC |
PLASTICS
TIME
1
1
1 2
3 !
4 I
5
1973
1 1 1
59- :4
1 61.11
1 65.05 I
337.75 |
431.95
1980
1 2 |
66.75
I 62.63
81.33 !
863.48 I
492.35
1982
1 3 1
73.21
I 65.JO
! 96.42 I
902.06 1
503.32
1984
! 4 |
79. 11
! 68.21
1 110.00 1
951.21 I
516.81
1936
1 5 |
84.71
1 71.73
; 122.07 |
1007.18 I
531.23
1988
1 6 !
39.56
1 75.02
I 132.06 1
1070.39 1
548.23
1990
: 7 l
94.07
! "8.29
1 140.94 !
1139.17 |
565. "78
1992
1 8 |
99.19
I 02.20
1 150.31 1
1212.04 1
526.17
1994
! 9 1
104."9
1 86.60
1 160.29 I
1288.94 |
606.36
1996
! 1C i
110.79
! 91.39
I 171.03 1
1369.88 [
627.53
1998
I 11 1
116.94
] 96.36
I 182.24 I
1456.04 I
650.42
2COO
I 12 |
123.30
I 1C1.56
I 194.27 1
1547.33 I
674.67
QUANTITIES IN THOUSANDS OF METRIC TONS
REPORT # 4
MEDICAL SUPPLIES
MATERIALS
H + 4- !-
AF = SUM | PVC WITH | REGULAR | LCW MIG. |
GSS= Q-LJANTITY I DEHP | PVC | PVC |
+ + + +
TIME | 1| 2 ! 3 !
1
1
1
+
1
1
1
1
+
1
1
1
1
1
1
1
1
1—
1973
1 1 1
0.00 1
12.75 I
21.25
1980
1 2 |
0.00 !
12.55 I
24.40
1982
1 3 1
c.oo 1
12.65 1
27.67
1934
1 4 1
0.00 1
13.06 |
31.05
19S6
1 5 |
0.00 1
13.73 I
34.55
1988
1 5 |
0.00 1
14.64 |
38.25
1990
1 7 |
0.00 1
15.74 |
42.19
1992
1 3 |
0.00 1
17.03 I
46.46
1994
1 9 |
0.00 1
18.49 1
51.12
1996
1 10 |
0.00 1
20.10 1
56.22
1998
1 11 1
0. GO 1
21. S"7 |
61.84
20C0
1 12 |
0.00 1
23.79 1
68.05
QUANTITIES IN THOUSANDS OF "ETRTC TONS.
B-10
422
-------
REPORT 2 5
AUTOMOBILE USES
MATERIAL
AF=
S L"'
— — — — — — — —
I PVC WITH |
REGULAR !
LCW MT3.
CSS-
QUANTITY
! DEHP |
PVC 1
PVC
TIME
1 1
2 !
3
+
„+ 1-
1_
1973
I
1
I C.CO 1
99.50 1
99. 50
1980
2
I C.00 1
117.99 1
98.22
1982
3
1 0.00 1
134.04 1
101.91
1984
4
1 0.00 1
149.45 1
108.63
1936
5
1 0.00 I
165.12 1
117.48
1933
6
1 0.00 I
131.66 1
127.83
1993
7
1 0.00 1
199.20 1
139.76
1992
I
a
! 0.00 1
218.49 !
153.15
1994
i
9
; o.co I
239.29 1
16R.16
1996
I
10
1 0.00 1
261.R3 I
184.92
1993
1
11
I 0.00 I
286-?4 I
203.65
20CC
i
i
12
! 0.GO 1
312.90 '
224.61
QUANTITIES THOUSAM)S OF METRIC TONS.
REPORT If 6
OTHER COMSUMER PRODUCTS
MATER L^L
AF= SIX"!
GSS = QUANTITY
PVC WITH
DEHP
REGULAR
PVC
PE
PLASTIC
OTHER
PLASTICS
TIME
+ +—
(._
+_
+_.
197S
1 1 I
238.88 |
517.10 |
827.57 |
493.46
1980
1 2 1
307.04 |
530.54 I
1
501.95
1982
1 3 1
325.80 I
552.03 |
932.90 1
512.50
1984
1 4 |
345.45 1
573.87 |
994.26 |
525.03
1586
! 5 1
365.80 1
608.94 !
1059.46 1
539.22
1988
1 6 1
384.92 !
638.23 1
1132.31 1
557.14
1990
1 7 |
40^.72 |
667.78 |
L210.91 1
57".23
1S92
1 8 1
425.18 I
702.02 1
1291.55 1
597.53
1994
1 9 1
448.68 |
739.90 I
1375.16 1
610.31
1996
1 10 1
473.99 1
780.95 I
1462.29 I
639.91
1998
I 11 1
500.30 !
823.85 1
1555.45 1
663.53
200C
I 12 ;
52-7.75 1
868.93 |
1654.55 1
688.79
QUANTITIES IN THOUSANDS OF METRIC TONS.
B— 11
,1
\r 'wO
-------
REPORT * 7
PLASTICS FRICES
MATERIAL
AF=
AVERAGE PRICE
I PVC WITH
' REGULAR
' LOW MIG.
FE |
OTHER
GSS=
PRICES
1 DEH?
' PVC
I PVC
PLASTIC |
PLASTICS
,
TIME
1 2
1 3
4 I
5
,
1973
! 1
! SCO.54
; 877.28
1 886.73
801.15 I
838.05
1900
! 2
1 912.49
932.05
! 979.55
932.71 |
996.52
1902
1 3
1 945.21
I 965.16
1 1008.63
965.42 |
1036.18
1984
1 4
1 980.91
1 1001.55
1 1043.81
1000.35 ]
1079.19
1986
1 5
1 1C15.10
1 1036.55
1 1079.02
1035.04 |
1120.32
1988
1 6
1 104?.65
1 1069.90
I 1113.10
1C64.92 1
1156.64
1990
! 7
I 1032.63
1 1105.80
I 1149.86
1C97.60 1
1196.22
1992
1 8
1 1112.39
I 1136.33
1 1181.16
1127.41 |
1232.97
1994
1 9
1 1143.64
1 1163.42
1 1213.75
1158.80 |
12-1.42
1996
I 10
1 1175.47
1 1201.09
I 1246.61
1190.73 |
1310.54
1998
1 11
1 1205.58
1 1232.06
1 1277.64
1219.97 |
1346.71
2000
1 12
1 1236.58
I 1263.60
I 1308.54
1250.07 |
1383.47
AVERAGE PRICES IN DOLLARS PER iMETRIC TCN.
REPORT # 8
PLASTICIZER PRICES
AF= AVERAGE PRICE
GSS= PRICES
TIME
DEHP
DG.MCL.
WT. PAE
I SML.MCL.
! WT. PAE
VL.MCL.
WT.PAE
EPC-XID.
SOY OIL
1973
1 1 !
791.98 |
815.72 1
804.25 1
804.74 I
1230.59 I
940.79
19S0
1 2 1
994.42 I
1022.67 |
1066.88 !
1111.89 !
1435.12 I
1162.45
1982
1 3 1
1044.71 I
1074.09 1
1112.27 I
1160.33 1
1473.46 |
1217.49
1904
! 4 |
1099.35 1
1129.94 '
1161.57 |
1229.63 1
1515.11 1
1277.29
1986
1 5 1
1151.77 !
1103.53 !
1200.08 I
1283.45 1
1555. 0"7 |
1334.67
1988
1 6 1
1197.70 !
1230.48 |
1250.32 i
1339.98 1
1590.07 I
13B4.95
1990
1 7 |
1247.94 |
1281.85 1
1295.66 !
1396.35 1
1628. 37 |
1439.97
1992
1 8 1
1293.85 1
1328.78 1
1337.03 1
1447.85 1
1663.35 1
1490.24
1994
1 9 1
13'. 1.94 1
1377.92 1
1380.45 1
1501.79 1
1699-99 1
1542.90
1996
1 10 |
1390.13 |
1427.18 |
1423.92 |
1555.81 ]
1736.49 1
1595.70
1993
1 11 1
1428.42 |
1466.19 I
1457.23 !
1598.71 1
1764.43 1
1637.76
2000
1 12 1
1479.02 1
1517.90 !
1503.14 1
1655.41 !
1803.16 1
1693.26
OTHER
PLASTCRS
6
AVERAGE PRICES IN DOLLARS PER METRIC TCN.
B-12
424
-------
REPORT # 13
AVERAGE PLASTICS PRICES
PLASTICS
AF= AVERAGE PRICE
G5S= PRICES
TIME
1973
1980
1982
1984
1906
1988
1990
1992
1994
1996
1998
2000
1
2
3
4
5
6
7
8
9
10
11
12
FOOD
USES
818.03
954.31
988.77
1026.21
1062.05
1093.87
1123.56
1159.93
1192.77
1226.16
1256.91
1288.36
MEDICAL
SUPPLIES
364.43
963.42
994.99
1031.30
1C66.95
1101.15
1137.89
1169.14
1201.71
123^.63
1265.73
1296.90
AUTO
USES
3
857.00
953.63
983.94
1019.34
1054.21
1087.75
1123.96
1154.81
1187.13
1219.94
1251.01
1282.39
CONSUMER
PRODUCTS
317.07
944.19
978.13
1015.06
1050.42
1082. 4*7
1117.32
1148.30
1180.78
1213.82
1244.50
1275.90
OTHER
DEMAND
811.CS
940.52
974.28
1010.89
1045.93
1077.25
1111.34
1141.95
1174.04
1206.70
1236.85
126"7. 77
PRICES IN DOLLARS PER METRIC TON'.
B-13
4"
-------
RESULTS "OR THE CASE WITH RESTRICTIONS
ON THE USE OF DEHP AND A CONSTRAINT
ON THE TOTAL QUANTITY OF DEHP
THAT MAY BE PRODUCED
B-14
43S
-------
REPORT i? 1
PLASTICS PRODUCTION
MATERIAL
U
+-
AF=
SUM
! PVC WITH
1
REGULAR !
LOW MTG. !
PE
OTHER
GSS =
QUANTITY
1 DEflP
I
PVC !
PVC |
PLASTIC !
PLASTICS
TIME
| i
I
2 I
3 1
4 I
5
1970
! 1
1 640.76
I
1519. 72 |
185.30 !
4860.70 I
1679.44
1530
' 2
1 795.95
1
1553.36 |
206.37 !
4930.16 !
1667.18
1982
1 3
1 803.06
1
1656.84 1
230.48 1
5121.62 1
1693.51
1984
1 4
1 303.88
1
176C.07 |
252.89 1
5362.18 I
1722.43
1986
1 £
I 304.81
1
1860.67 |
27".03 1
5626.20 !
1760.40
1988
' 6
! 805.36
1
1944.91 1
299.39 1
5925.46 1
1812.18
1990
! 7
1 805.97
1
2011.99 !
322.44 1
6260.76 !
1867.81
1992
1 s
I 306.72
1
2130.71 |
343.01 I
6583.^4 |
1911.12
1994
1 9
1 CO7.8G
1
2240.87 |
376.90 !
6925.95 1
1967.:o
1996
1 10
1 SOS.76
1
2359.77 |
409.34 :
7279.70 1
2027.17
1998
1 11
I SC9.17
1
2488.60 1
443.G4 I
7652.16 1
2050.99
200C
1 12
1 810.44
1
2608.67 |
'80.14 1
8058.88 1
2159.14
QUANTITIES IN THOUSANDS OF METRIC TCNS.
REPORT * 2
PLASTICIZER PRODUCTION
AF=
SUM
1
DEHP
1 LG.MOL. 1
SML.MCL. 1
VL.MOL.
1 EPOXID. t
OTHER i
GSS=
QUANTITY
I
1 WT.PAE I
WT.PAE I
WT.PAE
1 SOY OIL |
PLASTCRS 1
TIME
1
1
1 2 |
3 I
4
1 5 I
6 1
1978
179.41
1 276.3C !
135.98 I
26.01
1 55.51 I
99.96 1
19C0
1 2 1
222.37
1 3J6.S5 1
136.50 |
35.83
1 45.30 |
77.95 1
19S2
1 3 |
224.86
1 354.4J 1
139.70 |
43.74
! 42.0.! |
67.JO 1
1934
1 4 |
225.C9
i 386.59 I
143.45 1
49.29
I 43.JO I
61. 1
1986
1 5 |
225.35
1 413.11 1
147.87 |
53.60
1 47.59 I
59.61 1
1988
1 6 |
225.50
1 432.74 1
J 52.93 I
56.49
I 54.03 |
59-0? I
1990
1 7 |
225.67
1 446.19 !
157.91 1
58.55
1 62.^0 I
59. .">3 1
1992
1 8 |
225.88
¦ 469.53 !
163.07 |
60.41
1 72.84 |
61.9" I
1994
1 9 1
226.20
: 409.67 ;
168.84 I
62.13
1 85.10 1
64.87 |
1996
1 10 I
226.45
1 510.66 !
174.97 I
6?. 94
1 99.35 1
68.33 I
1998
1 11 1
276.51
1 533.20 1
181.65 1
65.69
I J 14. 68 |
72.19 I
2COO
1 12 |
226.92
1 552.85 ]
188.21 1
67.13
! 131.89 1
75.92 |
QUANTITIES IN THOUSANDS OF METRIC TCNS.
B-15
42?
-------
REPORT ft
FCOD PACKAGING AND CONTAINERS
AF= SUM
GSS= QUANTITY
TIME
MATERIAL
+ 1- +
! PVC WITH ! REGULAR | LOW MIG.
I DEHP | PVC I PVC
I : I 2 i 3
PE
PLAS1
OTHER
PLASTICS
1978
1 1 1
59.14 I
61.11 1
65.C5 1
837.75 1
481.95 ;
1980
1 2 |
€8.26 1
61.82 I
89-92 1
360.58 I
485.84 1
1982
1 3 j
68.61 1
65.27 |
107.07 |
903.96 1
495.47 !
1984
1 4 |
69.44 1
69.64 |
118.31 |
959.97 |
507.69 I
1986
1 5 1
69.95 1
74.11 1
129.41 1
1020.16 1
522.92 1
1988
! 6 1
69.32 1
77.73 I
137.33 1
.1088.81 |
541.16 !
1990
I 7 |
66.35 !
31.39 !
•
00
1163.C9 I
553.27 '
1992
1 8 1
72.73 1
85.Pi 1
152.99 '
1236.59 1
561.22 :
1934
1 9 1
71.5S 1
90.49 1
152.73 1
1316.36 1
605.11 :
199G
1 10 |
72.1? |
96.39 |
173.96 !
1401.52 1
625.76 1
1993
1 U 1
72.18 1
101.84 |
184.72 !
1189.21 1
653.0"7 i
2000
! 12 1
73.47 |
106.82 |
194.78 !
1586.12 I
678.83 '
QUANTITIES IN THOUSANDS OF METRIC TONS
REPORT If 4
MEDICAL SUPPLIES
MATERIALS
AF=
SUM
1
PVC WITH |
REGULAR |
LOW MIG.
GSS=
QUANTITY
1
DEHP |
PVC |
PVC
TIME
!
1 |
2 I
3
J 978
1 1 1
0.00 |
12.75 !
21.25
1980
1 2 |
0.00 |
11.91 1
25.c:
J 982
1 3 1
0.00 I
11.94 1
28.36
1984
1 4 |
C.00 1
12.50 1
31.6C
1986
1 5 |
0.00 |
13.39 1
34.38
1988
! 6 I
0.00 |
14.52 ]
38.36
1990
1 7 |
0.00 1
15.03 1
42.10
1992
1 8 |
0.00 |
l"7.?,! |
46.18
1994
1 9 1
0.00 1
18.93 1
50.63
1996
1 10 I
0.00 1
20.68 1
55.64
1998
1 U 1
0.00 I
22.59 I
61.12
2000
1 12 1
0.00 I
24.62 1
67.21
QUANTITIES IN THOUSANDS OF METRIC TON'S.
B-16
-------
REPORT 5
AUTOMOBILE USES
MATERIAL
H h + -1
AF= SUM I PVC WITH | REGULAR I LCW MIG. I
GSS= QUANTITY 1 D£HP I PVC I PVC
TIME
1
i
2 1
3
1970
1 1 '
0.00 I
99.50 1
99.50
1980
1 2 I
0.C0 :
124.70 !
91.44
1932
1 3 1
0.C0 !
140.91 1
95.06
1984
1 4 1
0.00 1
155.22 1
102.99
1986
1 5 1
0.00 1
169.99 1
112.73
1988
1 6 |
0.00 1
186.00 1
123.69
1990
1 7 |
0.00 1
203.56 1
135.67
1992
! 8 !
0.00 1
222.98 |
14B.84
199 4
i s :
0.C0 1
244.15 1
163.49
1996
1 10 !
o.cc '
26"'. 21 I
179.73
1998
1 11 1
0.00 1
292.39 1
197.80
2C00
1 12 1
0.00 1
319.56 1
218.14
QUANTITIES THOUSANDS OF METRIC TCNS.
REPORT rf 6
OTHER CONSUMER PRODUCTS
MATERIAL
AF= SUM
GSS= QUANTITY
— — — —4-———————-
PVC WITH | REGULAR | PE | OTHER
DEHP I PVC I PLASTIC I PLASTICS
TIME
I
3
— —
(.
1978
1 1 1
283.88 |
517.10 |
827.57 [
493.46 |
1980
1 2 I
403.20 !
511.41 1
874.83 1
428.15 1
1982
1 3 1
419.18 |
542.17 !
932.55 I
429.68 1
1984
1 4 |
426.43 I
582.83 !
996.29 1
4??.09 1
1986
1 5 1
424.66 I
623.20 1
1071.20 !
454.02 !
1238
1 6 1
432.42 !
659.08 I
1150.13 1
470.36 |
1990
1 7 1
445.16 I
677.35 I
1239.90 |
495.69 |
1992
1 8 '
444.52 1
739.22 l
1318.05 |
513.04 |
1994
1 9 1
445.72 I
782.35 !
1420.38 I
531.53 !
1996
1 10 j
455.02 |
831.79 !
1513.30 |
554.29 |
1998
1 11 !
46C.54 1
837.74 I
1611.95 1
579.61 1
2000
1 12 !
456.49 1
940.98 |
1737.04 1
601.66 1
QUANTITIES IN THOUSANDS GF METRIC TONS.
B-17
429
-------
REPORT a 7
PLASTICS PRICES
MATERIAL
AF=
AVERAGE
FRICS |
PVC WITH |
REGULAR
LOW M7G. 1
?E !
OTHER :
GSS=
PRICES
1
DSHP 1
PVC
PVC |
PLASTIC |
PLASTICS 1
'
TIY.E
1
' 1 1
: 1
2
1
4 1
5 !
1978
808.54 I
331.33
895.22 1
SOI.16 1
839.25 1
1990
' 2 1
912.49 1
933.09
983. ""9 1
932.71 1
997.13 1
1982
1 2 |
962.78 1
964.00
io:o."?7 1
965.42 1
1036.47 |
1984
1 4 1
1005.17 |
958.98
1044.92 1
1000.95 I
1079.28 1
1936
! 5 1
1045.28 1
1033.08
1079.73 1
1035.04 !
1120.31 I
1588
1 6 1
1083.87 |
1065.85
1113.79 1
1064.92 1
1156.5"? 1
1990
1 7 |
1125.91 |
1101.35
1150.SO 1
1097.60 |
1196.17 |
1592
' 8 '
1161.87 |
ii?i.62
1182.48 1
1127.47 |
1232.84 |
1994
1 9 1
12G4.C4 '
1153.51
1215.55 |
1158.80 1
1271.29 I
1996
i io ;
]246.61 :
1196.C3
1248.96 !
1190.73 1
1310.41 1
1998
i 11 I
1283.88 1
1226.88
1280.52 !
1219.97 |
1346.59 1
2000
1 12 ]
1323.81 !
1258.34
1311.94 !
1250.07 |
1383.37 !
AVERAGE PRICES IN DOLLARS PER METRIC TON.
REPORT j} S
PLASTICIZER PRICES
AF=
AVERAGE
PRICE
| DEHP |
LG. POL. 1
SML.MOL. I
VL.MOL. 1
EPOXID.
!
OTHER
GSS=
PRICES
1 1
WT.PAE I
WT.PAE !
I V.T. PAE 1
SCY OIL
1
PLASTCRS
TIME
1 1 1
2 !
3 1
4 1
5
1
6
1978
1 1
I 791.98 1
015.72 I
884.25
! 884.74 1
1 1317.94
1
961.06
1980
! 2
! 994.42 |
1022.67 I
1066.88
1 1111.89 !
1472.4*7
1
1132.74
1982
1 3
I 104^.71 |
1074.09 !
1112.27
1 1163.33 1
1510.80
1
1237.78
1984
i 4
I 1099.35 !
! 1129.94 1
1161.57
I 1229.63 1
1 1552.45
1
1297. S3
1986
1 5
! 1151.77 !
! 1133.53 !
1200.88
! 12SP.45 1
! 1592.41
1
1
1354 .9"'
1988
1 6
; 1197.70 '
[ 1230.40 1
1250.32
I 1339.98 1
1 162~\ 40
1
1405.26
1990
1 7
! 1247.94
1 1281.85 1
1295.66
1 1396.35 1
I 1665. "'O
!
1460.28
1992
1 8
! 1293.85
! 1323.78 |
1337.08
| 1447.85 1
I 1700.68
3
1510.55
1994
1 9
I 1341.94
! 1377.92 1
1380.45
| 1501.79 1
I 1737.31
1563.21
1996
1 ic
I 1390.13
I 1427.18 i
M23.92
1 1555.81 I
I 1773.80
I
1616.02
1998
1 11
| 1428.42
! 1466.19 1
1457.23
I 1598.71 ]
1 1801.71
r
1657.S2
2000
1 12
I 1479.02
I 1517.90 I
1503.14
I 1655.41
1 1040.45
p
1713.37
AVERAGE PRTCES IN DOLLARS PER METRIC TCN.
B-18
430
-------
REPORT ft 13
AVERAGE PLASTICS PRICES
AF = AVERAGE
GSS= PRICES
TIME
PRICE
FCOD
USES
PLASTICS
MEDICAL I A'JTC
SUPPLIES I USES
CONSUMER
PRODUCTS
1
1978
1 1
818.96 |
871.45 |
863.53 |
813.46 1
812.00
1980
1 2
954.67 |
967.42 1
954.56 1
941.97 |
940.85
1982
1 3
989.65 |
996.90 |
982.84 1
977.87 |
975.38
1984
1 4
1027.16 1
1031.89 1
1017.30 1
1015.30 1
1012.G7
1986
1 5
1063.12 1
1066.79 1
1051.68 |
1051.24 |
1047.21
1988
1 6
1095.10 1
1100.62 1
1084.99 1
1084.05 1
1078.67
1990
1 7
1129.99 1
1137.23 |
1121.12 |
1119.75 I
1112.95
1992
1 8
1161.57 !
1168.61 1
1151.97 |
1151.40 |
1143.75
1994
1 9
1124.78 1
1201.39 1
1184.37 |
1185.13 1
1176.22
1996
1 10
1228.52 I
1234.61 |
1217.30 I
1219.30 |
1209.23
1998
1 11
1259.48 |
1256.03 I
1248.51 1
1250.59 1
1239.56
2000
i 12
1291.20 |
1297.56 |
1280.07 |
1282.81 1
1270.72
PRICES
IN DOLLARS PER
METRIC TON.
OTHER
DEMAND
B-19
-------
RESULTS FOR THE CASE WITH HIGH FOSSIL FEEDSTOCK PRICES
M
B-20
-------
REPORT i 1
PLASTICS PRODUCTION
MATERIAL
AF=
SUM
I PVC WTTK
I REGULAR
I LOT MTG.
1 PE
I OTHER 1
GSS=
QUANTITY
I DEHP 1 PVC
I PVC
1 PLASTIC
1 PLASTICS :
TIME
1 1 ! 2
! 3
| IIM. IMI
1 4
1 5
.
.
1970
1 1
! 713.92
' 1483.57
1 148.29
1 4860.78
! 1679.44 !
1930
1 2
1 774.21
1 1523.C8
1 177.09
1 4955.61
1 1732.47 l
1952
1 3
! 828.85
! 1579.68
I 206.13
I 5108.73
I 1764.52 '
1984
1 4
I 881.79
1 1647.54
I 234.36
1 5303.54
I 1788.36 I
19B6
1 5
! 933.84
1 1721.24
I 261.69
I 5513.26
1 1813.10 1
19BS
1 6
I 986.06
1 1799.41
[ 289.03
1 5720.37
! 1849.64 1
1990
1 7
! 1037.55
1 13^9.J 5
1 317.49
1 5902.96
1 1919.41 '
1992
1 a
! 1085.93
1 1955.67
I 347.60
i 6050.41
I 2C42.55 '
1994
1 9
I 1120.92
1 2025.58
1 379.96
I 6131.74
1 2254.80 '
1996
1 10
1 1159.55
1 2076.93
I 413.65
I 6115.R4
! 26C8.17 '
1993
I i:
1 1169.93
1 2096.01
I 447.06
1 5970.43
1 3161.76
20U0
1 12
! 1153.64
1 2030.39
I 480.22
! 5693.27
I 3932.67 '
QUANTITIES IM THOUSANDS OF METRIC TONS.
REPORT S 2
PLASTICIZER mODUCTION
+-
AF =
SUM
1
DEHP I
LG.MCL.
I SML.MCL.
1 VL.MCL. !
EPOXTD.
: OTHER
GSS=
QUANTITY
1
1
WT.PAE
! WT.PAE
I WT.PAE !
SOY OIL
I PLASTCRS
'
—— —«—¦— —
TIME
1
1 1
2
1 3
1 4 |
5
1 6
1978
1 1
1
199.90 |
2S*7. 24
1 142.08
1 21.93 I
39.21
1 83.95
1980
1 2
1
216.78 |
2.99.76
I 139.55
1 30.91 !
36.54
90.5?
1982
I 3
1
232.on |
311.66
1 138.19
1 37.91 |
33.21
97.53
1934
1 4
1
246.90 |
322.97
I 137.69
I 42.71 |
43.82
104.92
1986
1 5
1
261.48 |
333.25
I 13".98
1 45.40 I
53. 15
1 112.36
1983
1 6
1
276.10 I
342.49
1 139.32
1 46.31 ]
66. 30
1 119.83
1990
I 7
1
290.51 |
350.16
1 142.63
1 45.78 |
83.76
! 127.09
1992
1 8
1
304.06 1
355.51
I 148.68
I 44.25 |
105.74
1 133.71
1994
1 9
1
316.:o 1
35"?. G 9
1 15S.98
I 42.00 I
133.19
1 139.33
1996
1 10
1
324.67 |
355.30
] 175.44
1 39.19 1
166.95
\ 147.05
1998
1 11
1
327.53 1
345.73
1 199.91
I . 36.00 1
207.63
1 143.93
2000
! 12
1
324.42 |
329.7C
I 231.50
1 32.67 |
256.65
1 141.74
QUANTITIES IN THOUSANDS OF METRIC TONS.
B-21
-------
REPORT 3
FOOD PACKAGING AxVD CONTAINSRS
MATERI
AL
AF=
SI"*-'
1
PVC WITH '
REGULAR
—— -f- .
! LCW MIG. 1
PE |
CTHSR 1
GSS =
QUANTITY
1
DEHP !
PVC
1 PVC
PLASTIC 1
PLASTICS 1
TIME
1
1 '
2
3 1
4 !
5 1
1978
1 1 1
59.14 |
6i. n
1 65.C5 I
837.1
481.95 1
1980
1 2 1
56.75 |
62. 62
I 81.32 |
853.26 I
492.40 I
1982
! 3 |
73.?7 1
65.23
! 96.87 |
901.01 I
500.81 1
1984
1 4 I
79.56 1
68.59
1 111.10 |
948.27 |
509.36 !
1986
1 5 !
85.47 1
72.37
1 124.05 |
999.71 |
519.57 |
1S38
1 6 1
91.2? 1
76.41
! 136.?3 1
1051.31 |
533.83 1
1990
1 7 i
96.67 1
SO.45
1 143.41 |
1097.21 !
557.49 1
1992
1 8 1
101.^2 1
84.11
1 160.35 1
1133.57 |
595.G5 I
1994
1 9 1
105.09 !
87.04
1 172.13 1
1152.20 1
657.40 I
1996
1 10 1
1C6.68 1
88.43
1 182.31 1
1144.52 |
754.92 |
1999
1 11 1
1C5.27 |
87.52
1 188.89 I
1103.43 |
899.95 1
2000
! 12 1
1C0.94 1
GO
J*
•
O
1 191.34 |
1031.65 |
1092.37 1
QUANTITIES IN
THOUSANDS
OF METRIC
TONS
REPORT # 4
MEDICAL SUPPLIES
MATERIALS
+•
AF=
SUM
1
PVC WITH |
REGULAR
LCW MIG.
GSS=
QUANTITY
1
DEHP |
PVC
PVC
TIME
1
1 |
2
3
1978
1 i :
7.46 !
9.95
16. 59
1980
1 2 ;
7.38 !
8.57
21. 05
1932
1 3 !
7.44 !
7.76
25. 11
1284
1 4 1
7.65 1
7.33
28.93
1986
1 5 1
8.01 [
7.30
32.63
1988
1 6 I
8.47 I
7.44
36.36
1990
1 7 !
8.99 I
7.-'!
40.25
1992
1 8 1
9.55 1
8. 07
44.-12
1994
1 9 1
10.11 |
8.46
48.99
1995
1 10 1
10.G3 I
8. 87
54.03
1998
! 11 1
11.11 I
9. 23
59.60
2G00
! 12 I
11.55 |
9. 69
65. SI
quant
ITIES IN
THOUSANDS
OF METRIC
TONS.
B-22
<134 .
-------
REPORT 5
ALTCMC5ILE USES
'MATERIAL
AF -
S'JM
I PVC WITH
I REGULAR
La-; MIC. 1
GSS =
QUANTITY
I DEHP
! PVC
pvc ;
TIME
I 1
1 2
4-
1973
1 -
I 65.7C
1 66.65
66.65 !
1980
! 2
1 73.:e
1 68.52
74.72 |
1992
1 3
1 80.28
1 71.47
34.16 |
19S4
1 4
1 87.55
i 75.58
94.33 1
1S86
1 5
! 95.15
1 80.65
105.01 |
128S
1 6
i 103.25
1 86.53
116.39 |
1390
n
111.75
I 93.00
128.84 1
1392
I s
1 120.56
1 99.94
142.82 |
1991
! 9
! 129.51
1 1C7.20
158.84 !
1996
1 :o
I 138.41
I 114.66
177.31 1
1993
: ii
! 147.13
I 122.31
198.55 |
2000
! 12
! 155.72
I 130.25
223.07 |
QUANTITIES THOUSANDS OF METRIC TONS.
REFCRT if 6
OTHER CONSUMER PRODUCTS
MATERIAL
AF=
GSS =
SUM
QUANTITY
-i +.
I PVC WITH |
1 DEHP |
REGULAR
PVC
1 PE |
[ PLASTIC |
OTHER
PLASTICS
TIME
! : 1
2
1 3 |
4
1970
! 1
288.83 I
517.10
827.57 I
493.46
1930
! 2
: 307.02 I
570.50
: 877.25 |
502.00
1982
1 ->
1 326.J3 |
552.66
i 930.88 !
508.96
1934
1 4
' 346.41 |
580.62
' 988.63 1
516.21
1986
1 5
' 367.39 |
611.80
i 1047. 1.1 |
525.25
1988
! 6
! 388.98 !
645.16
! 1103.64 1
538.54
1990
i 7
! 410.43 |
579.09
! 1153.98 1
561.97
1992
1 8
1 430.51 I
711.50
I 1196.21 |
601.55
1994
1 9
! 447.89 1
740.31
1 1223.29 1
667.75
1996
1 10
1 459.21 I
760.27
1 1228.36 I
776.38
1993
1 11
1 460.66 |
765.43
i 1204.25 I
945.28
2000
1 12
1 45.1.50 1
754.81
1 1150.4" |
1180.03
QUANTITIES IN THOUSANDS OF MCTRIC TCNS.
B-23
435
-------
REPORT r- 7
PLASTICS PRICES
MATERIAL
AF=
AVERAGE
PRICE
PVC WITH
REGULAR
LOT MIG. 1
PE !
OTHER
GSS=
PRICES
DEllP
PVC
PVC I
PLASTIC I
PLASTICS
TIV.E
i
-
2
7 1
5
1978
, 1
808.99
827.77
887.23 |
801.82 |
838.44
1980
1 2
913.10
932.70
980.21 |
933-. 55 I
997.04
1982
1 3
959.07
979.31
1023.12 I
980.57 |
1053.76
1984
1 4
1010.57
1031.88
1075.03 1
1033.29 |
1116.69
1986
I 5
1067.14
1089.81
1133.57 1
1091.69 1
1184.48
1988
1 6
1129.82
1154.C6
1198.46 |
1157.22 1
1257.43
199C
1 7
1201.11
1 . 1
1271.21 1
1232.66 I
1334.68
1992
1 8
1278.97
1306.75
1349.06 1
1315.23 I
1411.70
1994
1 9
1366.1C
1395.65
1434.16 I
1409.24 1
1487.42
1996
1 10
1464.36
1495.52
1528.01 I
1515.^4 I
1558.20
199S
1 11
1575.67
1608.03
1632.68 I
1636.04 1
1622.68
2C00
1 12
1689.82
1722."7
1737.49 1
1762.23 I
1682.31
AVERAGE PRICES IN DOLLARS PER METRIC TON.
REPORT # 3
PLASTICIZER PRICES
+.
AF= AVERAGE
GSS= PRICES
PRICE | DEHP | LG.MCL. I SML.MGL. | VL.MQL.
I | WT. PAE | WT.PAE | WT.PAE
TIME
1
EPCXID.
SOV OIL
OTHER
PLASTCRS
1973
IS80
1982
1994
1986
19G8
1990
1992
1991
1996
1998
2000
1 I
2 I
3 I
4 I
5 1
G I
7 I
8 I
9 I
10 I
11 I
12 |
791.
994.
1066.
1147.
1237.
1337.
1453.
1580.
1724.
18J?6.
2058.
2266.
53
68
59
26
87
"7 ^
55
80
52
12
92
815.78
1022.P3
1096.60
1179.31
1270.97
1373.81
1492.24
1621-84
1769.26
1934.50
2109.90
2323.33
I 884.75 |
I 1067.50 |
I 1132.63 I
I 1205.64 |
! 1286.54 i
I 1377.29 I
! 1401.78 i
I 1596. 13 |
I 1726.19 I
I 1871.93 |
I 2024.S7 |
I 2214.04 I
834.85
1112. 11
1L93.G9
1237.87
1384.45
1497.30
162~\ 26
1769.46
193L.2J
21J 2.45
2305.44
2539.35
1230.82
1435.41
1490.43
1552.09
1620.41
1697.04
1785.31
¦ISfli.S9
1991.75
2114.66
2245.33
2404.40
940.7"? |
1162.51 I
1241.47 |
1330.03 I
1428.20 I
1538.39 I
1665.33 I
1804.27 |
1962.36 I
2139."5 I
2326.03 I
2E56.C9 I
AVERAGE PRICES IN DOLLARS PER MCTRIC TON.
fl-24
4 JO
-------
REPORT a 13
AVERAGE PLASTICS PRICES
AF= AVERAGE PRICE
GSS= PRICES
TIME
FOOD
'JSES
PLASTICS
MECICAL | AUTO
SUPPLIES | USES
CCNSLMER
PRC-DUCTS
I 1 I
OTHER
DEMAND
1578
I 1 1
818.58 1
852.65 1
841.48 1
817.60 1
811.66 1
198C
1 2 1
955. 03 1
955.82 I
942.45 1
944.90 |
941.2S 1
1982
1 3 |
1004.48 1
1002.87 |
980.04 |
993.32 1
989.43 1
1984
1 4 |
1059.65 ]
1056. 57 |
1040.45 1
1047.44 1
1C43.32 1
1986
I 5 1
1120.15 1
1115.32 1
1093. 50 1
1106.84 |
1102.55 1
1988
1 6 1
113",.12 !
1:31.03 1
1162.77 ]
1172.68 |
1168.43 !
1990
1 7 1
1262. 57 |
1254.17 1
1235.44 |
124"7.18 1
1243.41 1
1992
1 8 |
1343.49 1
1332.70 1
1314.18 1
1327.56 1
1324.75 1
1994
1 9 1
1432.29 1
1419-17 1
1401.46 1
1415.70 1
1415.59 1
1996
1 10 1
1527.65 !
1514.90 1
1493.90 1
1513.92 1
1515.85 1
1998
1 11 1
1627.03 1
1621.52 1
1608.34 1
1617.72 1
1624.46 1
2000
1 12 1
1721. 1J |
1729. 54 1
1719.16 |
1717.86 I
1731.22 1
PRICES IN DOLLARS PER METRIC TON.
B-25
437
-------
RESULTS FOR THE CASE WITH LOW FOSSIL FEEDSTOCK PRICES
B-26
438
-------
REPORT ji 1
FUSTICS PRODUCTION
MATERIAL
AF= SUM j PVC WTTH | REGULAR I LCN MIG. | ?E ! CTHER !
GSS= QUALITY I DEKP | PVC | PVC 1 PLASTIC I PLASTICS !
TIME I i I 2 1 3 | 4 ! 5
—f- — — — — -f—— — — — ——— ——-i-———— — — — — — —— — — — — —.4 »¦—— ———— — — > ———————— — f
1973
1 1 1
713.92 |
1483.57 |
148.29 1
4860.78 1
1679.44
1980
1 2 |
774. 27, |
1523.19 I
177.11 !
4957.12 |
1732.53
1982
1 3 |
828.94 |
1578.58 |
205.54 !
5127.87 I
1799.33
1984
1 4 |
891.70 I
1644.72 I
233.53 1
5351.75 I
1876.08
198G
1 5 1
933.35 1
1716.50 1
260.89 !
5604.54 I
1953.67
1938
1 6 |
984.7B '
1791.90 1
257.96 !
5879.07 |
2044.50
1S90
l 7 |
1C27.20 I
1870.88 1
315.22 |
6172.44 1
22 34.31
1992
! 3 i
1C91.54 1
1953.75 !
343.24 |
6483.96 I
2228.47
1994
1 9 '
1148.43 !
2C40.80 1
372.48 1
6813.16 |
2327. "^0
1996
; 1C 1
1208.26 1
2132.20 ;
403.39 1
7159.80 1
2472.29
1993
! 11 1
1271.51 !
2228.54 '
436.47 |
7523.98 1
254 "*¦.63
2000
i 12 1
1337.2G I
2327.62 !
471.83 1
7911.58 1
2561.80
QUANTITIES IN THOUSANDS OF METRIC TONS.
REPORT # 2
PLASTICIZER PRODUCTION
AF=
SUM
DEHP
LG.MOL.
SML.MOL.
VL.MOL.
EPCXID.
CTHER
GSS=
QUAOTITY
WT.PAE
WT.PAE
WT.PAE
SCY OIL
PL^STCRS
TIME
1
2
3
4
5
6
1978
1 1
199.90
237.24
142.08
21.98
39. 21
83.96
1980
1 2
216.79
299."9
i 39.57
30.91
36.54
90. 55
1982
1 3
232. IC
313."8
140.60
38.94
35. 37
96.71
1S84
1 4
246.88
327.73
143.86
46. 0"
36.79
102.75
1986
! 5
261.34
342.50
148.44
52.44
3C.76
103. 6-1
1988
! 6
275.74
357.34
153.81
53. 19
41.66
114.43
1990
1 7
290.42
372.47
159.81
63.53
45. 31
120.39
1992
1 8
305.63
388.03
166.33
68.65
49.62
126.46
1994
1 9
321.56
404.29
173. 36
73.72
54. 55
132.74
1996
1 :o
378.31
421.17
180.84
7R.84
60. 10
139.29
1998
1 ii
356.02
438.94
188.36
84. 15
66.23
146.C"
2C00
1 12
374.43
457.05
197.35
89.66
73.02
153.C9
QUALITIES IN THOUSANDS OF METRIC TONS.
B-27
439
-------
REPORT (f 3
FOOD FACKAGINC AM) CONTAINERS
MATERIAL
H 4- + +- +
AF= SUM | PVC WITH I REGULAR | LOW MIG. | PE ! OTHER
GSS= QUANTITY | DEHP | PVC I PVC | PLASTIC I PLASTICS
1 (. + + +
TIME ! i| 2| 3 ! 4 1 5
f- — — + — — — — — + — — — — — +— — — — — ——4..
1978
1 : !
59.14 |
61.11 |
65.05 !
837.75 1
4C1.95
1980
I 2 ]
66.75 1
62.63 !
01.32 |
863.53 1
492.42
1982
1 3 I
73.14 |
65.03 |
95.96 I
902.60 1
509.74
1984
1 4 1
78.95 1
68.04 |
109.15 I
952.14 !
531.98
1986
1 5 I
84.38 1
71.42 |
12C.96 |
1008.17 !
557.37
1983
1 6 I
99.63 |
"75.04 |
131-64 !
1069-64 |
584.85
1990
1 7 1
94.?,4 I
73.90 I
141.52 |
1136.12 1
614.37
1992
1 3 |
ICG.14 |
32.99 |
150.93 |
1207.57 |
645.9C
1994
1 9 I
ICS.61 I
87.31 |
160.11 !
1283.95 1
679.53
1996
1 :o 1
111.28 1
91.86 |
169.25 ¦
1365.29 I
715.32
1993
1 u I
117.20 |
96.66 1
178. 56 !
1451.62 1
753.62
2000
1 12 1
123.25 I
101.59 1
187.89 !
1544.07 |
794.37
QUANTITIES
IN THOUSANDS
OF METRIC
TONS
REPORT # 4
MEDICAL SUPPLIES
MATERIALS
AF = SUM
+
! PVC WITH
-+ H
1 REGULAR
LOW MIG.
GSS= QUANTITY
1 DEHP
I PVC
PVC
TIME
1 I
! 2
3
1978
1 I
! 7.46
1 9.95
16. 59
1980
1 2
1 7.38
1 3.57
21.05
1982
1 3
1 7.50
! 7.81
25. 20
1984
1 4
! 7.79
1 7.50
29. 24
1936
1 5
1 8.24
1 7.50
33. 25
1988
I 6
i 8.82
! 7.74
37.34
1990
1 7
• 9.53
1 3. 16
41.61
1992
1 8
1 10.37
I 8.75
46.13
1994
1 9
I 11.33
! 9.47
50.99
1996
1 10
12.42
1 10.31
. 56.25
1998
1 -1
i 13.64
I 11.28
61.99
2000
1 12
I 14.99
1 12.38
68. 27
QUANTITIES IN THOUSANDS OF METRIC TONS.
B-28
44'-
-------
MATERIAL
AF =
SUM
1
PVC WITH |
REGULAR j
LOW MIG. 1
GSS-
QUANTITY
1
DEHP |
PVC. !
PVC |
TIME
1
1 1
2 I
2 |
1-
(.
1978
! 1 !
65.70 1
66.65 I
66.65 I
1930
1 2 :
73.39 :
68. 53 |
74.73 |
1982
1 3 1
80.95 |
71.92 |
84.37 |
1984
1 4 |
88.95 !
76.69 1
95.14 1
1936
1 5 |
97.50 |
82.62 I
106.69 1
193S
1 6 1
107.03 1
89.65 !
113.98 1
1990
1 7 !
117.53 !
97.74 !
132.09 1
1992
1 8 |
129.11 ;
106.93 1
146.17 j
±994
1 9 :
141.92 |
117.25 !
161.38 [
1996
1 10 |
156.08 I
128.75 |
177.89 1
1993
1 11 1
171.71 |
141.51 !
195.92 1
2000
1 12 1
183.95 |
155.63 I
215.67 |
QUANTITIES THOUSANDS OF METRIC TONS.
REPORT # 6
OTHER CONSUMER FRODUCTS
MATERIAL
AF= sum
GSS= QUANTITY
PVC WITH
DEHP
REGULAR
PVC
PE
PLASTIC
OTHER
PLASTICS
TIME
1
1 1
2
1978
1 1 1
288.88 1
517.10
1980
1 2 |
307.04 1
530.55
1982
1 3 |
325.94 1
552.01
1984
1 4 1
345.79 1
573.97
1986
1 5 1
366.20 1
609.10
1988
I 6 i
387.11 |
641.42
1990
1 7 |
408.73 |
675.73
1992
i 8 |
431.27 |
712.02
1994
1 9 |
454.S7 |
750.33
1996
1 10 I
479.60 I
790.69
1998
1 11 1
505.6! |
833.29
2CC0
1 12 I
532.47 |
877.36
I 82".5?
I 877.55
I 974. 41
I 997.B5
I 1065.34
i 1136.81
I 1212.47
I 1292.70
I 1377.82
I 1468.07"
I 1563.69
I 1666.53
493. 46
502.C3
519.C4
541.85
568.32
597.17
623.27
661.51
697.00
734.79
775.25
813.60
QUANTITIES IN THOUSANDS OF METRIC TONS.
-------
REPORT = 7
PLASTICS PRICES
MATERIAL
+ + +
AF=
AVERAGE
PRICE |
PVC WITH I
REGULAR |
LOW MJG. I
PE
1 OTHER
GSS=
PRICES
1
DEHP |
PVC |
PVC I
PLASTIC
I PLASTICS
TIME
1
1 1
2 1
3 1
4
1 5
1978
1 L 1
308.32 |
827.04 I
-------
REPORT # 13
AVERAGE PLASTICS PRICES
PLASTICS
AF=
AVERAGE
PRICE |
FOOD |
MEDICAL
AUTO
CONSUMER
OTHER |
GSS=
PRICES
1
USES |
SUPPLIES
USES
PRODUCTS
DEMAND |
. — — _ I
TIME
1
i !
3
4
— — — - !
5 1
1978
! l I
817.70 |
851.92
840.76
816.77
810.ln [
1980
1 2 |
953.81 1
954.76
941.40
943.73
940.05 |
1982
1 3 |
961.26 1
952.57
948. 14
951.28
947.68 1
1984
1 4 |
970.31 1
972.14
957.15
960.43
956.75 |
1986
1 5 1
977.91 1
980.44
965.07
96R.14
964.34 |
1988
1 6 |
9S7.06 1
920.42
974.74
977.39
973.3P 1
1990
1 7 |
995.22 |
1000.52
984.54
986.65
982.41 |
1992
1 8 1
1005.39 1
1010.67
994.40
995.93
991.43 1
1994
1 9 1
1014.58 |
1020.87
1004.33
1C05.23
1000.47 |
1996
1 10 |
1024.23 1
1031. 54
1014.71
1G14.99
1009.95 [
1998
1 11 1
1033.37 |
1041.59
1024.57
1024.27
1019.01 1
2000
1 12 |
1041.92 |
1051.52
1034.27
1033.05
1027.44 1
PRICES IN DOLLARS PER METRIC TON.
IIS
-------
RESULTS FOR THE CASE WITH HIGH FOSSIL FEEDSTOCK
PRICES AND A COMPLETE BAN ON D2KP
3-32
-------
REPORT If- 1
PLASTICS PRODUCTION
MATERIAL
AF= SUM I PVC WJ7H I REGULAR I LOT MIG. I PE I OTHER I
GSS= QUANTITY I DEHP I PVC | PVC I PLASTIC 1 PLASTICS |
TIME I 1 I 2| 2 1 4 [ 5 1
1978
1 1 I
0.00 1
1654.29 |
188.46 1
5222.95 1
1820.3C
1930
1 2 1
0.00 |
1652.96 |
234.02 i
5194.10 |
2C75.76
1982
I 3 |
0.00 |
1683.64 |
274.28 1
5267.33 |
2252.98
1984
I 4 i
0.00 I
1735.87 |
311.18 |
5409.46 I
2386.34
1986
i 5 1
0.00 [
1800.45 I
346.01 i
5586.18 I
2495.25
1988
1 6 1
0.00 1
1873.21 [
380.18 1
5777.53 |
2596.33
1990
1 7 |
0.00 1
1949.78 |
414.79 1
5963.24 !
2710.22
1992
1 8 1
0.00 |
2025.95 |
450.62 |
6132.34 1
2854.63
1994
1 9 I
0.00 I
2098.23 I
483.19 1
6262.85 i
3054.61
1996
I 10 1
0.00 [
2159.07 |
527.24 1
6333.47 I
3340.42
1993
I U 1
0.00 1
2200.82 1
567.10 I
6325.54 1
3741.60
20C0
i 12 I
c.oo 1
2226.97 |
608.72 ]
6243.52 |
4258.94
QUANTITIES
IN THOUSANDS
OF METRIC
TONS.
REPORT # 2
PLASTICIZER PRODUCTION
AF= SUM
GSS = QUANTITY
TIME
DEHP
LG.MOL.
WT. PAE
SML.MOL.
WT.PAE
1979
! 1
1930
1 2
1982
1 3
19S4
1 4
1986
1 5
1988
1 6
1990
1 7
1992
1 8
1994
1 9
1996
1 10
1990
1 11
20C0
1 12
O.CC
0.C0
o.co
o.co
o.co
0.00
0.00
0.00
0.00
0.00
0.00
o.co
320.30
325.25
332.C6
340.15
348.42
356.37
363.
360.
370.
369-20
362.94
352.82
. 15
12
57
155.03
164.15
1"?1.36
177.23
182.12
186.5S
191.46
197.45
205.54
21C.75
232.05
250.16
I VL.MOL.
I WT.PAE
I 4
I 27.94
I 40.33
50.43
! 56.69
I 59.99
! 60.87
I 59.77
I C^"J
I 53.91
i 49.90
I 45.64
I 41.39
EPOXID, | CTHER
SCY OIL i PLASTOS
46.50 I
45.06 I
47.04 |
55.42 |
67.40 I
83.91 I
105.34 1
131.66 I
163.50 I
201.30 I
244.93 1
295.83 i
93.63
98. 37
104.14
110.73
117.74
124.56
132.09
138.72
144.51
148.35
151.
151.
QUANTITIES IN THOUSANDS OF METRIC TONS.
B-33
.-1 " rr
'jt-jtO
-------
REPORT a 3
FOOD PACiCAGING AND CONTAINERS
•\ATZRIAL
+-
- + 4-
+.
AF=
SUM
!
PVC WITH
: REGULAR 1
LOT MIG. !
PE |
¦OTHER !
GS3 =
QUANTITY
i
CEH?
1 PVC !
PVC |
PLASTIC !
PLASTICS l
TIME
i
•i. i
-
1 2 I
3 I
4 !
5
1S7S
: i i
O.CO
1 63.61 !
67.71 |
872.02 |
501.67 |
193C
! 2 :
O.CO
1 62. 26 I
89.71 |
858.85 |
554.76 !
1932
1 3 1
0.00
I 62.84 I
JO"7.81 1
869.85 |
595.31 |
1984
1 4 1
0.00
1 64.71 |
123.13 I
897.58 I
629.39 1
1986
1 5 1
0.00
1 67.34 1
136.42 !
934.69 |
660.11 I
1908
! 6 I
O.CO
1 70.46 1
140.51 1
976.56 |
690.56 |
1990
i 7 ;
O.CO
1 72.73 I
159.89 1
1018.19 1
725.12 1
1992
1 3 I
0.03
1 77.02 I
170.76 !
1056.28 1
767.74 |
1994
1 9 |
0.00
1 79.32 |
181.16 [
ICS5.75 I
824.21 I
1996
1 10 1
O.CO
1 82.06 1
190.43 1
1100.93 1
901.41 |
1998
1 11 1
0.00
1 83.02 1
197.63 1
1097.65 |
1005.65 i
2000
1 12 |
O.CO
I 83.00 1
203.C7 |
1077.59 1
1136.40 |
QUANTITIES IN THOUSANDS OF METRIC TONS
REPORT # 4
MEDICAL SUPPLIES
MATERIALS
AF=
SUM
-1
1
PVC WITH I
REGULAR 1
Lav MIG.
GSS=
QUANTITY
1
DEHP 1
PVC 1
PVC
TIME
1
1 1
2 1
3
1978
! 1 !
O.CO 1
12.75 1
21.25
1SGC
1 2 1
0.00 1
10.35 1
26. 58
1932
1 3
I 0.00
1 8.93 |
31.29
1984
1 4
| 0.00
! 8.16 1
35.71
19G6
1 5
I 0.00
I 7.85 |
40.00
1988
1 6 !
! 0.00
I 7.85 1
44.32
1990
1 7
I 0.00
| 8. 07 |
48. ^9
1992
! 3
1 0.00
| 8.42 1
53.52
1994
1 9
1 0.00
| 3.86 1
58.60
1996
1 10
1 0.00
| 9.37 i
64.08
1993
1 11
1 0.00
| 9.90 !
70.01
2000
! 12
1 O.CO
1 10.48 !
76.50
QUANTITIES IN THOUSANDS OF METRIC TONS.
B-34
-------
REPORT # 5
A'JTCMOBILZ USES
MATERIAL
AF= SUM
GSS= QUANTITY
TIME
| PVC WITH
I DEIIP
1 1
REGULAR
PVC
2
I LOW MIG.
I PVC
1 3
1978
1 1
I 0.00
99-50
[ 99.50
1980
1 2
I 0.00
93.33
I 117.73
1982
1 3
I 0.00
100.09
1 135.18
1934
1 4
I 0.00
104.27
! 152.35
1936
1 5
1 0.00
110.32
1 169.59
193S
1 5
I 0.00
117.84
1 187.26
199C
1 7
I 0.00
126.45
1 2C6.11
1992
1 3
1 0.00
135.92
1 226.33
1994
1 9
I o.oc
145.03
1 240.4?
1996
1 10
1 O.C'O
156.60
I 272.73
1S93
1 11
1 u.co
167.55
! 299.47
20C0
1 12
1 0.00
179.01
1 329.15
QUANTITIES THOUSANDS OF METRIC TONS.
REPORT # 6
OTHER CONSUMER PRODUCTS
MATERIAL
AF= SUM
!
PVC
WITH |
REGULAR
PE
I OTHER
GSS= QUANTITY
i
DEHP |
PVC
PLASTIC
1 PLASTICS
TIME
;
1 1
2
3
1 4
197S
i i i
0.00 !
593.37
957.63
1 571.CI
1900
1 2 |
O.OC !
594.17
982.10
I 630.42
1932
! 3 1
O.OC '
605.34
1020.70
1 689.09
1984
! 4 I
0.C0 I
626.80
1069.91
1 730.65
1936
1 5 !
0.00 I
654.32
1124.51
1 767.34
1933
I 6 1
0.00 |
6S5.73
11 SI.24
1 003.21
1990
1 7 |
0.00 1
718.93
1235.96
! 843.95
1992
1 8 |
0.00 I
751.89 1
1236.35
1 894.S5
1994
1 9 1
0.00 |
782.54 I
1327.03
1 963.38
1996
1 10 I
0.00 !
007.32 I
1352.61
1 1059.00
1998
1 11 !
O.C'O :
022.60 |
1358.<54
1190.35
2000
! 12 !
O.CO 1
£29.73 !
1345.45
1359.13
QUANTITIES IN
THOUSANDS
OF
METRIC
TONS.
•:
B
-35
X
-------
REPORT J:f
PLASTICS PRICES
AF =
GSS =
TIME
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
AVERAGE PP. I1'
PRICES
I PVC WI
I DEHP
MATERIAL
I 0.00
I "kick -kit* ***
[ *********
| *********
| *********
| *********
| *********
j *********
*********
10 1990078.06
11 1971379.00
12 1944124.69
j
4
5
6
1
8
9
REGULAR
PVC
027.77
932.70
979.31
1031.88
1089.81
1154.06
1227.:c
1306.75
1295.65
1495.52
1608.03
1722.77
low m:g.
PVC
SB"7. 23
900.21
1023.12
1075.03
1133.57
1193.<6
1271.21
1349.06
1434.16
1528.01
1632.68
1737.49
PE | OTHER
PLASTIC | PLASTIC:
001.82
933.55
980.57
1033.29
1091.69
1157.22
1232.66
1315.23
1409.24
1515.74
1636.04
1762.28
938.44 I
997.04 I
1C53.76 I
1116.69 I
1184.48 I
1257.43 |
1334.63 I
1411.70 I
1437.42 I
1558.20 |
1622.63 |
1632.31 |
AVERAGE PRICES IN DOLLARS PER METRIC TON.
REPORT # 8
PLASTICTZER PRICES
AF= AVERAGE PRICE
GSS= PRICES
TIME
I DEHP
I
I 1
j LC.y.OL.
! WT.PAE
I 2
.+ +
! SML.MOL. | VL.MOL.
I WT.PAE | WT.PAE
13 14
-+ +
1978
1 1
0.00 1
315.73 I
884.75 1
834.85 1
1280.02 1
940.
•' t
1980
1 2
59531.70 |
1022.8" |
106".50 |
1112.11 1
1425.41 1
1162.
51
1932
1 3
61456.16 I
1096.60 |
1132.63 1
11 93-CO 1
1490.43 |
1241.
1904
1 4
63466.27 |
1179.31 |
1205.64 |
1283.37 |
1552.09 1
1330.
03
19C6
1 5
65547.48 I
1270.97 |
12G6.54 |
1384.45 |
1620.41 |
1428.
20
1988
1 6
67748.52 1
1373.SI 1
1 "'77. 29 I
149*7.30 1
1697.04 :
1528.
39
1990
I 7
70197.37 1
1492.24 |
1431."73 1
1627.26 |
1785.31 !
1665.
23
1992
1 o
72590.16 I
1621.84 |
1596.13 |
L769.45 I
1831.89 !
18C4.
2 /
1994
1 9
75145.20 |
1769.26 |
1726.19 |
1931.21 |
1991.75 1
1962.
26
1996
1 10
77747.75 I
1934.50 |
1371.93 I
2112.45 I
2114.66 |
2139.
"" —
1998
1 U
8CS01.38 1
2109.90 I
2024.8" I
2305.44 1
2245.32 I
2326.
C3
2000
1 12
33897.37 I
2223.33 I
2214.04 I
2539.36 i
2404.40 |
2556.
C9
EPOXID.
SOY OIL
OTHER
PLASTCaS
AVERAGE PRICES IN DOLLARS PER METRIC TON.
B-36
443
-------
REPORT I? 13
AVERAGE PLASTICS PRICES
PLASTICS
AF=
AVERAGE
PRICE
+-
1
FOCD |
MEDICAL |
AUTO
CONSUMER |
OTHER I
GSS=
PRICES
1
USES |
SUPPLIES I
USES
PRODUCTS |
DEMAND j
TINE
1
1 1
2 1
3
4 I
5 I
,
1978
I :
I
018.97 1
864.93 |
35'1. 50
818.95 I
811.82 !
1980
1 2
1
953.70 |
966.89 1
958.59
951.63 1
944.34 I
1932
1 3
1
1009-98 |.
1013.40 |
1004.49
1002.04 |
993.87 i
198*1
1 4
1
1066.87 |
1067.01 |
1057.51
1058.06 |
1040.92 |
193G
1 5
1
1128.89 1
1126.39 I
1116.33
1119.21 |
1109.23 !
1938
1 6
1
1197.09 1
1191.78 1
1181.33
1186.60 1
1176.04 '
19S0
1 7
1
1273.04 1
1264.95 [
1254.44
1262.04 |
1251.55 !
1992
1 8
1
1353.50 1
1343.32 1
1333.20
1342.54 |
1332.85 !
1994
I 9
1
1440.54 1
1429.11 1
1419.91
1430.33 I
1422.89 !
1996
1 10
1
1532.89 I
1523.87 |
1516.17
1524.66 1
1521.46 I
1998
1 11
1
1629.14 1
1629.63 |
1623.85
1624.49 I
1628.08 1
2000
1 12
1
1722.58 !
1735.72 |
1732.31
1722.23 1
1734.62 !
PRICES IN DOLLARS PER METRIC TON.
•i :3
B-37
-------
RES'JLTS FOR THE CASE WITH LOW FEEDSTOCK PRICES
AND A COMPLETE BAN ON DEHP
B-38
1ZO
-------
REPORT |! i
PLASTICS PRODUCTION
MATERIAL
AF=
SUM
PVC WITH
REGULAR
LOW MIG.
PE I
OTHER
GSS =
QUANTITY
DEHP
PVC 1
PVC
PLASTIC 1
PLASTICS
TIME
1
2
3
* 1
"1 I
5
1978
1 i
C.00
1654.29
180.46
5222.25 I
1320.30
i960
I 2
0.00
1653.10
231.03
5195.83 1
20^5.56
1982
1 3
0.00
1686.58
274.72
5239.00 |
2280.75
1934
1 4
o.co
1742.94
312.87
5451.68 |
2453.79
1986
1 5
0.00
1812.59
349.60
5679.43 |
2619.18
19C8
1 6
o.cc
1891.19
385.99
5928.13 I
2768.40
1990
1 7
o.co
1977.15
423.02
6201.43 1
2912.78
1992
1 8
0.00
2069.83
461.53
6496.39 1
3056.44
199-1
1 9
o.co
2163.88
502.20
6011.31 !
3202.33
1996
1 10
0.00
227'.. 13
545.60
7145.15 !
3352.18
1998
1 11
C.00
2385.91
592.35
7497.01 !
3503.99
2000
1 12
0.00
2502.95
642.76
78-1.37 I
3672.15
QUANTITIES IN THOUSANDS CF METRIC TONS.
REPORT ft 2
PLASTTCIZER PRODUCTION
AF= SUM
DEKP 1
LG.MOL.
I SML.MOL.
I VL.MCL.
EPOXTD.
I OTHER
GSS= QUANTITY
1
WT.PAE
I V/T. PAE
I WT.PAE
SOY OIL
I PLASTCRS
TIME
1 1
2
1 3
5
1 6
1978
1 1
O.CO 1
320.30
I 155.03
1 27.94
46.50
1 93.63
193C
1 2
O.CO !
325.30
I 164.17
1 40.84
45.04
1 98.33
1982
1 3
0.00 1
334.74
1 173.55
1 52.03
45.07
I 103.43
1934
1 4
0.00 I
347.24
! 183.07
I 6J.70
46.72
1 109.01
1936
I 5
0.00 I
361.54
1 192.55
f 70.25
49.55
; 114.S7
1988
1 6
0.00 1
376.99
1 201.94
1 77.93
53.49
! 120.99
1990
1 7
0.00 1
393.T
I 211.43
I 85.23
58.40
1 127.41
1992
! 8
O.CO 1
410.9"7
I 221.13
1 92.20
64.18
1 134.16
1994
1 9
0.00 !
429.48
! 231.16
1 99.36
70.79
I 141.23
1996
1 10
o.co ;
449.CI
I 241.55
I -106.59
70.25
1 148.73
1998
! 11
O.CO !
469.83
1 252.55
I 114.20
36.45
I 156.52
2000
! 12
0.00 I
491.42
263.98
1 122.n
95.61
1 164.72
QUANTITIES IN THOUSANDS OF METRIC TONS.
B-39
-------
RSFCRT fi
FCCD PACKAGING AND CONTAINER?
MATERIAL
+—
AF=
GSS-
TIME
1973
1980
1932
1984
1986
1988
1990
1992
1994
1996
1998
2000
SUM
QUANTITY
PVC WITH
DEHP
+ h-
I 1 I
I 2 I
I 3 I
I 4 I
I 5 I
I 6 |
I 7 |
I 8 I
I 9 I
I 1C |
I 11 I
I 12 I
0.00
0.00
C. 00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
o.cc
0.00
REGULAR
PVC
63.51
62. 27
62.88
64.77
67.44
70.64
74.25
78.21
82. 50
87.08
91.96
9"\08
Lav m:
DW C
' PE
! PLASTIC
67.71 ;
89.7C '
107.49 I
122.60 I
135.77 |
147.63 I
158.70 !
169.37 I
179.94 !
190.59 1
201.54 I
212.73 I
872.02
859.17
8 "72.66
904.C7
946.50
996.95
1053.92
1116.67
1J 84.78
1258.02
1336. 16
1420.12
! OTHER
! PLASTICS
501.67 l
554.73 I
602.05 !
646.98 :
690.39 I
723.CS I
776.12 I
820.2^ I
866.02 1
913.79 I
964.26 !
1017.25 !
QUANTITIES IN THOUSANDS CF METRIC TONS
REPORT 3 4
MEDICAL SUPPLIES
MATERIALS
AF=
SUM
1
1
PVC WITH I
REGULAR
I LOW MIG.
GSS =
QUANTITY
1
DEHP |
PVC
[ PVC
Tiy.s
1 !
2
1 3
1978
1 1 :
o.oo :
12.75
1 21.25
1980
1 2 !
0.00 I
10.35
1 26.58
1932
1 3 I
0.00 i
8.98
! 31.45
1984
1 4 I
0.00 i
8.28
! 36.15
1986
1 5 I
U.00 1
8.C5
1 40.84
1988
1 6 |
0.00 1
8.14
1 45.65
1990
I 7 |
0.00 I
8.43
I 50.71
1992
1 8 |
0.C0 1
9.02
! 56.11
1994
1 9 1
0.00 !
9.72
1 61.94
1996
1 10 |
0.00 [
10.57
i 6S.27
1998
1 11 1
0.00 I
11.55
I 75.20
2000
1 12 I
0.00 1
12. 67
J 82.79
QUANTITIES IN THOUSANDS CF MCTRIC TONS.
B-40
>» r—
j 'JlV
-------
REPORT !f 5
AirrOMC3ILE USES
MATERIAL
AF=
GSS-
SUM
QUANTITY
+-
1
1
PVC with
D5HP
I REGULAR !
I PVC |
LCW MIG.
PVC
TIME
1
i i
1
I 2 I
3
1970
1 1 1
0.00
I 99.50 I
99.50
1950
1 2 |
0.00
I 98.35 !
117.75
1992
1 3 I
0.00
I 100.72 1
135.78
1984
1 4 |
0.00
I 105.30 1
154.12
1986
1 5 I
0.00
1 112.99 !
173.00
1983
1 6 1
0.00
1 121.98 |
192.71
1990
1 7 ;
o.co
1 132.65 |
213.61
1992
I S :
o.co
! 144.94 1
236.05
1994
1 9 1
0.00
1 158.88 1
260.32
1396
1 10 1
0.00
1 174.50 1
286.73
1993
1 11 1
o.co
1 191.90 |
315.61
2C30
! 12 1
o.co
1 211.22 |
347.24
QUANTITIES THOUSANDS OF METRIC TONS.
REPORT if 6
OTHER CONSUMER PRODUCTS
MATERIAL
AF=
SUM
1
PVC WITH |
REGULAR
PE
I OTHER
GSS=
QUANTITY
1
DEHP |
PVC
PLASTIC
I PLASTICS
TIME
— — imr — _ [
1
1 1
2
3
i 4
1978
1 1 !
0.00 1
59S.37
957.63
1 571.01
1980
1 2 |
O.OC [
594.23
982.4°
I 638.38
1982
1 3 1
0.00 1
606.07
1024.59
I 697.42
1S34
1 4 1
0.00 I
620.45
1079.42
I 752.53
1936
1 5 1
0.00 1
657.18
1142.06
! 805.12
1933
1 6 1
0.00 I
690.26
1210.38
I 856.74
1990
1 7 |
0.00 I
726.35
1285.31
1 907.61
1992
1 8 1
0.00 I
766.56
1365.27
1 959.89
1994
I 9 1
O.CO 1
809.15
1450.79
1 1013.92
1996
1 10 1
O.CO 1
854.51
1541.95
1 1070.17
1993
1 11 1
0.00 1
902.71
1638.74
1 1129.53
2000
1 12 |
O.OC 1
953.28
1742.53
I 1191.91
QUANTITIES IN THOUSANDS OF METRIC TONS.
B-41
153
-------
PFASTICS PRICES
MATERIAL
AF=
AVERAGE PRICE
I PVC KITH
REGULAR |
LOT MIG. !
PE |
OTHER
GSS =
PRICES
I DEHP
PVC
PVC |
PLASTIC 1
PLASTICS
TIME
1 1
2
3 I
4 1
5
197S
| i
I 0.00
827.04 1
836.46 I
300.35 |
837.63
1980
1 2
1 *********
931.64 |
979.12 I
932.23 |
995.23
1982
1 3
1 *********
939. 57 |
982.33 !
939.35 |
1004.73
1984
1 4
1970071.31
949.12 1
989.38 1
947.37 |
1015.31
1SS6
1 5
138*3319. 94
957.24 1
996.27 |
954.99 1
1024.14
1958
1 6
1309119.23
966.92 |
1005.52 1
963.51 1
1034.69
1990
I 7
1725331.75
976.53 I
1015.26 I
972.04 1
1045.18
1992
1 3
1567925.56
986.36 1
1025.29 1
930.58 1
1055.63
1994
1 9
1603926.63
996.13 1
1035.52 I
989.14 1
1066.15
1998
1 10
1544300.50
1006.37 |
1046.29 I
998.15 I
107-.C
1998
1 11
149!633.72
1016.10 1
1056.43 1
1006.86 1
1087.04
2003
1 12
1436359.94
1025.66 I
1066.51 1
1014.72 I
1096.73
--+
-+
AVERAGE PRICES DJ DOLLARS PER METRIC TON.
REPORT # 8
PLASTICIZER PRICES
AF= AVERAGE PRICE | DEHP
GSS= PRICES |
TIME | 1
LC.MOL. ! SML.MOL. I VL.KOL.
WT.PAE | WT.PAE | WT.PAE
2!3|4
EPCXID.
SOY OIL
OTHER
PLASTCRS
1973
1 1
0.00 |
815.59 :
083.91 |
884.56 |
1280.42 ]
940.73
1980
1 2
58347.85 ]
1022.22 |
1066.33 |
1111.46 I
1434.83 |
1162.21
1932
1 3
56746.71 !
1033.50 !
1076.20 I
1123.74 I
1443.17 |
1174.18
1584
1 4
54766.67 !
1046.90 !
1088.03 |
1138.45 t
1453.17 |
1188.52
1936
1 5
52656.48 !
1058.03 !
1097.91 |
1150.73 I
1461.51 |
1200.49
1988
1 6
50660.21 I
1071.49 1
1109.74 1
1165.44 1
147J.51 |
1214.84
1990
1 7
48655.76 i
1084.90 !
1121.58 |
1180.16 1
1481.51 |
1229.19
1992
1 8
46*47.40 :
1098.21 1
1133.42 |
1194.88 1
1491.51 I
1243.54
1994
1 9
44639. 51 1
1111.72 1
1145.25 I
1209.60 1
1501.51 I
1257.89
1996
1 10
42634.89 !
1125.22 :
1157.19 1
1224.43 1
1511.44 |
1272.32
1998
1 U
40725.38 !
1133.10 |
1163.12 |
1232.96 |
1515.04 |
1281.39
20C0
1 12
38703.60 1
1147.91 1
1176.41 |
1249.24 |
1526.63 1
1297.10
AVERAGE PRICES IN DOLLARS PER METRIC TON.
B-42
iftA
-------
REPORT # 13
AVERAGE PLASTICS PRICES
PLASTICS
AF= AVERAGE PRICE | FOOD | MEDICAL I AUTO | CONSUMER | OTHER |
GS3= PRICES | USES | SUPPLIES I USES | PRODUCTS | DEMAND |
TIME | 11 2 ! 3 1 4 1 5 1
——— — —1 —
H
127G
1 1 1
S1S.08 !
864.18 !
856.75 1
818.10
1 810.92
1980
1 2 ;
957.47 |
965.81 1
957.51 1
950.44
1 943.10
1932
1 3 1
966.12 1
972.83 1
964.12 1
959.02
1 951.55
1984
1 4 1
975.96 1
981.88 |
973.00
968.83
I 961.15
1286
1 5 |
984.C7 ]
989.35 1
980.86
976.95
1 969.06
1988
1 6 1
993.62 1
999.68 1
990.56
986.49
1 978.33
1990
1 7 !
1C03.C7 i
10C9.73 1
1000.47 |
995.94
1 9S7.50
1992
1 8 '
1012.47 !
1019.91 1
1010.49 1
1005.35
I 996.63
1994
1 9 :
1021.85 1
1030.18 1
1020.60 1
1014.74
1 1005.74
1996
1 10 !
1031.65 1
1040.94 1
1031.19
1024.56
I 1015.28
1998
1 11 !
1040.88 !
1051.07 1
1041.19
1037.83
i 1024.35
2000
1 12 1
1049.52 1
1061.09 1
1051. on
1042.58
] 1032.78
PRICES IN DOLLARS PER METRIC TON.
B-43
455
------- |