THE RCRA RISK-COST ANALYSIS MODEL
PHASE III REPORT
Submitted to the
Office of Solid Waste
Economic Analysis Branch
U.S. Environmental Protection Agency
March 1, 1984
ICF INCORPORATED International Square
1850 K Street, Northwest,Washington, D. C. 20006
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THE RCRA RISK-COST ANALYSIS MODEL
PHASE III REPORT
Submitted to the
Office of Solid Waste
Economic Analysis Branch
U.S. Environmental Protection Agency
March 1, 1984
ICF
INCORPORATED
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TABLE OF CONTENTS
Chapter Page
1. OVERVIEW 1-1
1.1 Introduction 1-1
1.2 Purpose 1-2
1.3 Summary of the Model 1-7
1.4 Project Development 1-15
1.5 Organization of This Report 1-17
2. W-E-T FRAMEWORK 2-1
2.1 Introduction 2-1
2.2 Waste Streams 2-1
2.3 Technologies 2-19
2.4 Environments 2-42
3. HUMAN HEALTH RISKS 3-1
3.1 Introduction 3-1
3.2 Overview of Methodology 3-1
3.3 Inherent Limitations in Risk Assessment 3-3
3.4 Key Toxicological Issues 3-5
3.5 Risk Assessment Methodology 3-6
4. ECORISKS 4-1
4.1 Introduction 4-1
4.2 Definition of Ecorisk 4-1
4.3 General Limitations in Estimating Ecorisk 4-3
4.4 General Approach to Ecorisk Scoring 4-5
4.5 Aquatic Ecorisk Methodology 4-5
4.6 Terrestrial Ecorisk Methodology 4-28
5. SENSORY EFFECTS 5-1
5.1 Introduction 5-1
5.2 Odor and Taste Perception 5-2
5.3 Methods for Measuring Odor and Taste Thresholds 5-4
5.4 Sensory Effects Risk Scoring 5-6
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TABLE OF CONTENTS
(Continued)
Chapter Page
6. COSTS ' 6-1
6.1 Introduction 6-1
6.2 Methodology for Estimating Costs 6-1
6.3 Treatment Technology Cost Elements 6-10
6.4 Transportation Technology Cost Elements 6-12
6.5 Disposal Technology Cost Elements 6-13
6.6 Conclusion 6-16
Appendices
A. Waste Streams
B. Treatment Technologies
C. Transportation Technologies
D. Disposal Technologies
E. Chemical and Physical Processes Affecting the Half-Lives
of Chemicals in the Environment
F. Dose-Response Curves for Non-Cancer Effects
G. Case Studies of Ecosystem Effects
H. Program Logic
I. Comparison of the Waste Data Base Used in the RCRA
Risk-Cost Analysis Model with the Preliminary Results
of the OSW Mail Survey
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PREFACE
ICF Incorporated developed the RCRA Risk-Cost Analysis Model under
contract to the Office of Solid Waste, U.S. Environmental Protection Agency,
to support the development of regulations under the Resource Conservation and
Recovery Act (RCRA) governing the treatment, storage, and disposal of
hazardous waste. This report describes the model. It is the culmination of
the third phase in the development of the model, reflecting additions and
refinements to earlier work described in two previous reports.
This report is supplemented by a series of appendices bound separately as
a second volume. These appendices contain additional data, detailed
descriptions of key components of the model, and background studies.
In place of an executive summary, the first chapter of this report,
entitled "Overview," provides a capsule description of the model for the
general reader.
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ACKNOWLEDGEMENTS
We wish to acknowledge the valuable contributions of our subcontractors
and our clients to the development of the RCRA Risk-Cost Analysis Model.
Subcontractor support for Phase III was provided principally by SCS
Engineers and Environ Corporation. Under the direction of Michael W.
McLaughlin, SCS was responsible for developing the model's disposal
technologies. Michael Scott (now of Environ Corporation) assembled the
model's waste streams and outlined its treatment technologies. Dr. Joseph
Rodricks of Environ Corporation was responsible for the methodologies to
estimate human health risks, ecological risks, and risks of sensory effects.
Pope-Reid Associates, under the direction of Mark Gilbertson, prepared all
cost estimates and reviewed the treatment technologies. Several other
subcontractors contributed to selected aspects of the model. MRI conducted
background studies for the incineration technology. Incineration costs are
based on work by Industrial Economics, Inc. The transportation technologies
are based on a survey of hazardous waste transportation by Dr. Mark Abkowitz,
Dr. Amir Eiger, and Suresh Srinivasan of Rensselaer Polytechnic Institute.
We were fortunate to receive excellent cooperation and judicious guidance
from the three EPA project officers in the Economic Analysis Branch, Office of
Solid Waste, who have successively assumed responsibility for the model.
Curtis Haymore was our first project officer, Arline Sheehan was the project
officer during most of the preparation of this report, and Er.ic Males
currently supervises the project. We wish to thank all three"] together with
the Chief of the Economic Analysis Branch,, Dale Ruhter, for their patience,
diligence, and labor on behalf of this project. We are especially indebted to
Ms. Sheehan for her thoughtful supervision of our work during Phase III and
her outstanding efforts to ensure the quality of this report.
The ICF Principal in charge of this project is Joseph Kirk. Under his
direction, the model was assembled and programmed by Leslie Kostrich, with the
assistance of Thomas Wilson, Mario Kerby, and Rene Pulupa. The principal
authors of the Phase III report were Stephen Bailey and Edwin Berk. Other ICF
staff who contributed to this report include Jean Tilly, Carolyn Atwood, Yueh
Chuang, Lois Epstein, Louise Sheiner, and Zella Williams.
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I Overview
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1. OVERVIEW
1.1 INTRODUCTION
The RCRA Risk-Cost Analysis Model is intended to support the development
of regulations authorized by the Resource Conservation and Recovery Act (RCRA)
to control the treatment, storage, and disposal of hazardous waste. This
report describes the model. It is the culmination of the third phase in the
development of the model, reflecting additions and refinements to earlier work
described in two previous reports.1'2
This overview chapter explains the purpose of the model and provides a
summary description. It is organized as follows:
1.2 Purpose explains the problem that led to the
development of the model, describes the model in general
terms, then clarifies the use of the model in the process
of formulating regulatory policy.
1.3 Summary of the Model is a capsule description of
the model, including central points of approach and
critical assumptions. The subsequent chapters of this
report provide a more detailed description and present the
most important data incorporated in.the model.
1.4 Project Development briefly discusses the relation
of this report to the Phase II report and identifies
aspects of the model still under development.
1.5 Organization of This Report is an introduction to
the subsequent chapters of the report.
1.2 PURPOSE
Subtitle C of RCRA authorizes a national regulatory program for the
management of hazardous wastes from generation to disposal. Rather than
specify technical design or performance standards, as do some environmental
^CF Incorporated, "RCRA Regulatory Policy Project, Phase I Progress
Report," submitted to the Office of Solid Waste, U.S. Environmental Protection
Agency (October 1981).
2ICF Incorporated, "RCRA Risk/Cost Policy Model Project, Phase II
Report," submitted to the Office of Solid Waste, U.S. Environmental Protection
Agency (June 1982).
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statutes,3 RCRA establishes an overriding goalthe protection of human
health and the environment and leaves the task of developing and implementing
specific regulations to EPA. The regulatory program must, however, provide
for the following:
(1) The identification and listing of hazardous wastes
(2) A national manifest system for tracking wastes
(3) Standards for hazardous waste treatment, storage, and
disposal facilities
(A) A permit system for treatment, storage, and disposal
facilities.
EPA began the complex process of developing these regulations following the
enactment of RCRA in 1976 and does not expect to finish implementing the
regulatory system for several years.
1.2.1 The Problem Addressed by the RCRA Risk-Cost Analysis Model
The RCRA Risk-Cost Analysis Model is meant to facilitate the crafting of
regulations of the third type, those governing hazardous waste treatment,
storage, and disposal facilities. Regulations for this purpose must be
written with a high degree of simplification and generality. They must be
general because they must apply to a myriad of individual cases, including
both current waste management practices and future possibilities, all of which
differ to some extent. They must be organized and expressed in a simplified
manner if they are to be comprehensible to the regulated community and
enforceable by the government. Yet these regulations must still ensure the
protection of human health and the environment in every case to which they
apply.
All environmental regulations need to be written with some degree of
simplification and generality, but the consequences are especially acute for
RCRA regulations. Although these regulations may need to be organized simply,
in terms of particular technologies, for example, they do not apply to single
technologies, specific chemicals, or particular environmental media in
isolation, as may regulations under other environmental statutes. Instead,
they ultimately must govern complete waste management practices, which may
involve the successive application of multiple technologies to alter the
characteristics of complex chemical mixtures, with numerous opportunities
along the way for hazardous chemicals to be released to various environmental
media. The regulations must deal with the possible effects of these
management practices on human health and the environment, not the separable
effects of an individual chemical or technology.
'See, for example, Part C of the Clean Air Act on the prevention of
significant deterioration in air quality.
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The problem that led to the development of the model, then, is to design
regulations that incorporate a high degree of simplification and generality
yet produce the desired resultsnamely, the protection of human health and
the environment--in particular "real-world" applications to complete waste
management practices. The regulations must avoid simplifications that permit
dangerous management practices to slip through the regulatory net. Despite a
high degree of generality, they must be able to discriminate between desirable
and undesirable practices. To write regulations that achieve these
objectives, it is necessary to find a way to see what effects the regulations
are likely to have, where they should focus, and in what aspects they must
contain special detail and specificity.
1.2.2 Project History
In response to this problem, EPA's Office of Solid Waste initiated in
March 1981 what was then referred to as the "Regulatory Policy Project."
Contracts were let with three consulting firms and work started in August
1981. In October, EPA made available a Phase I progress report (referenced
above) proposing a general methodology.
The Regulatory Policy Project was conceived as an effort to gather
information on the risks and costs of managing different types of wastes with
the use of different technologies in different environmental settings. The
RCRA Risk-Cost Analysis Model preserves this concept with an important
difference. Originally, risks and costs were to be assigned on the basis of a
qualitative assessment. As work began, it became clear that a defensible
assignment of risks and costs would require a more quantitative grounding.
The result was to develop a computer-based model incorporating not only a
large data base, but also algorithms for calculating magnitudes of risks and
costs.
In June 1982, EPA made available a Phase II report that described the
"RCRA Risk/Cost Policy Model," the outgrowth of the Regulatory Policy Project,
and presented the contractors' first attempts to construct a comprehensive
data base on hazardous waste management and to assess the risks and costs of
various management practices. This report was reviewed by EPA's Science
Advisory Board, industry associations, environmental groups, and other
interested members of the public. Phase III of this project has been an
effort to refine and expand the model (renamed the "RCRA Risk-Cost Analysis
Model") along lines recommended by the reviewers. Section 1.4 below includes
a summary of the major changes in the current model from the version described
in the Phase II report.
1.2.3 What is the RCRA Risk-Cost Analysis Model?
The RCRA Risk-Cost Analysis Model consists, in the first place, of an
array of possible ways to treat, store, and dispose of the hazardous wastes
generated in the U.S. For each possibility, the model calculates the risks
and costs involved. Thus, the model organizes a vast amount of information
and then assigns risk and cost values to that information.
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Following the original lines of the Regulatory Policy Project, there are
three main factors considered in the model's formulation of possible ways to
manage hazardous waste:
The type of waste (and its hazardous chemical
constituents)
The types of technologies used to treat, transport,
store, and dispose of the wastes
The environmental settings in which the wastes are
generated, treated, and disposed.
The model forms all possible combinations of a list of wastes, technologies,
and environmental settings. Thus, the model may be regarded as a
three-dimensional matrix, each cell of which is a combination of wastes, an
environment, and technologies -- a W-E-T cell. Each W-E-T cell may be viewed
as a particular waste management practice. For example, the practice of
landfilling paint application sludges in a low population density area
underlain by a productive aquifer system constitutes a W-E-T cell in the
model. A W-E-T cell does not represent a single waste management facility.
Rather, it is the practice of managing a waste stream by a particular
technology, or combination of technologies in a standard environmental setting.
The model then calculates the risks and costs involved in each
possibility for managing hazardous wastes. That is, it assigns risk and cost
values to each W-E-T cell. In so doing, the model can take into account the
environmental setting in different areas of the United States.
The assignment of risks and costs requires considerable data on national
waste generation, chemical toxicity, the cost and effectiveness of various
technologies, environmental characteristics, and so forth, all of which are
incorporated in the model. Thus, the model may be regarded, in part, as an
extensive data base. As indicated, the data incorporated in the model and the
formulas used to array possibilities and to calculate risks and costs are all
recorded as a complex computer program.
The data in the model are suitable for analysis by linear programming
techniques. Linear programming is a way to determine how to optimize the"
value of a variable by adjusting a number of limited, and interdependent,
inputs. In this case, such optimization techniques can be used to identify
the combinations of wastes, technologies, and environments that will result in
the lowest risks, the lowest costs, or the most favorable ratio of risks to
costs under a certain set of operating conditions.
The model is not, however, a linear programming model nor, more
generally, is it an optimization model. Its output consists of all possible
waste management practices and their risk and cost values, not just the
optimal practices. The model can be applied to identify waste management
practices with optimal risk or cost values, but only subject to additional
constraints or conditions assumed for purposes of a particular analysis.
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This extremely simplified sketch of the RCRA Risk-Cost Analysis Model
will be further developed in Section 1.3 below. It is sufficient, however,
for explaining the purpose of the model and its application to the regulatory
development process.
1.2.4 Purpose of the Model
The purpose of the RCRA Risk-Cost Analysis Model is to respond to the
problem discussed in Section 1.2.1 by providing a basis for testing simplified
and general regulations against the multitude of individual cases to which
they may apply. The model does so by serving as a picture of hazardous waste
management in the United States, including possible as well as current
practices. The model does not describe actual instances of waste management,
which are almost limitless in number and variation. Instead, it depicts a
large number of standardized possibilities for waste management, based upon
simplifying assumptions. The model identifies the risks and costs involved in
each waste management practice so that regulations may be evaluated in terms
of their economic effects and their consequences for human health and the
environment.
Despite simplifying assumptions, the model contains greater
circumscription and complexity than can be incorporated in regulations. For
example, it delineates possible ways to manage 154 individual waste streams;
for each waste stream, it identifies the hazardous constituent chemicals, the
technologies suitable for treating them, and their effects on human health and
the environment. The model also includes greater detail on environmental
settings in relation to management practices than can usually be incorporated
into workable regulations. So far as risks to human health and the
environment depend upon the likelihood of exposure to hazardous chemicals,
which is significantly influenced by environmental setting, the model's
inclusion of environmental setting is central to its utility.
The model functions in the following way as a basis for testing the broad
effects of regulatory options. The model can be structured or altered in
various respects in accordance with possible regulatory specifications. The
different effects of alternative specifications, particularly for risks and
costs, can then be identified. To the extent that the model approximates
actual management practices, the same effects can be expected in the real
world from such regulatory specifications. For example, the model's data on
landfilling can reflect design standards for liners that may be imposed by a
regulation. If the design standards are changed, the estimated cost of
landfilling may increase and, in consequence, the model might indicate shifts
away from the use of landfilling for waste disposal. As another example, the
model could be organized to eliminate the possibility of using certain
technologies to dispose of liquid wastes, in accordance with a regulation
banning the use of those technologies to dispose of such wastes.
The model can also function in alternative but related ways. It can
identify waste management practices that involve especially high or low risks
or costs. Using optimization techniques, the model can indicate strategies
for waste management that may be optimal with respect to risk or cost. The
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model's assumptions and parameters can be varied in a "sensitivity analysis"
to determine the dependence of its results on certain assumptions. The model
can also be used as a well-organized data repository. On a more general
level, the model permits the simultaneous consideration of the numerous
factors and extensive data that should be taken into account in structuring
and drafting a regulation.
The model is a valuable supplement to RCRA regulatory impact analyses
because it can enable policy-makers to evaluate the interplay, or trade-offs,
between different technologies under varying circumstances. The choice of a
technology to treat or dispose of a waste may depend upon the ability to
realize economies of scale with certain technologies, the imposition of
restrictions on the use of technologies, or similar considerations that the
model can represent. For example, landfills may be banned from use for
disposing of some kinds of wastes, necessitating the use of alternative
disposal technologies, possibly requiring wastes to be transported to another
area where population density, hydrology, and hydrogeology may be different.
The trade-offs between waste management technologies are difficult to reflect
in a regulatory impact analysis that focuses on one technology in isolation.
Impact analyses of RCRA regulations often need to be limited to an individual
technology, however, because they are prepared when new regulations are
promulgated, and RCRA regulations, to be workable, must usually address
individual technologies (e.g., landfills, waste piles). The RCRA Risk-Cost
Analysis Model can more accurately reflect shifts in actual waste management
practices by factoring in the interplay of technologies.
The calculation of risks by the model enables RCRA regulations to
incorporate a "degree of hazard" approach even if they are not explicitly
structured according to the degree of hazard of waste streams. For example,
the model may reveal that the treatment of a certain waste by a certain
technology poses unusually high risks. Policy-makers may then consider it
advisable to conduct further technical analyses on whether to restrict that
kind of waste management practice.
By using the model's cost calculations, policy-makers can identify the
lowest cost methods of protecting human health and the environment. The
model's results may suggest that some waste management practices are less
costly than others, yet no less effective.
1.2.5 Use of the Model in the Policy-Making Process
There are four ways in which the functions described above can be applied
to support the development of RCRA regulations.
(a) The model can be used as an inventory of data on waste streams and
on treatment and disposal technologies. The model reflects the most accurate
and up-to-date scientific and engineering information on waste generation and
management available to EPA's Office of Solid Waste.
(b) The model can be used as a screen to identify waste management
practices deserving special regulatory attention. The model can single out'
practices that are especially risky, suggesting a need for strict regulatory
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control. It can also identify practices that are comparatively safe,
suggesting that controls need not be very strict. The model may indicate that
certain regulatory provisions impose unnecessarily high costs and could be
relaxed.
(c) The model can be used to ensure that new RCRA regulations are
consistent with existing RCRA regulations regarding the level of protection
they provide. Both proposed and existing regulations can be tested by the
model to determine the risks and costs involved and to identify ways to
achieve consistency.
(d) The model can be used to test promulgated regulations to determine
the need for revisions. Amendments may be considered if the model reveals
that existing regulations offer inadequate protection, involve unnecessary
costs, are outdated in light of evolving management practices and scientific
understanding, favor undesirable practices, or are unrealistic in their
assumptions- about technology capacity, waste generation, technological
effectiveness, and possible trade-offs among waste management practices.
In these four applications, the use of the model's results in developing
regulations is a matter of policy and priorities. The model cannot serve as
an algorithm that dictates regulations. It may indicate the relative risks of
certain practices, for example, but the determination of a tolerable level of
risks is an independent policy decision. The questions put to the model, and
the areas emphasized in developing regulations, depend upon priorities that
must be specified by the user.
It is important to note that results from the model can not replace the
detailed scientific and technical analyses that must be part of the record on
which a regulation is based. The model's results can narrow the range of
approaches to regulation that must be considered. The model is, however, only
one input to the regulatory development process. It is a supplement to,
rather than a substitute for, public comment and detailed scientific review.
Neither can the model be used as the sole basis for facility siting or
permit decisions. These decisions require additional data on environmental
setting and facility design. Some of these data are site-specific; others are
obtainable from more suitable models.
1.3 SUMMARY OF THE MODEL
This section provides a capsule description of the model, building on the
account in Section 1.2.3. It is organized to match the subsequent chapters in
this report. First, it describes the three factors from which the model
constructs possible ways to manage wastes: wastes, technologies, and
environments.1* It then provides an overview of the methodology used to
''The report describes wastes first, then technologies, and thereafter
environments, rather than following the sequence suggested by the acronym
"W-E-T", because the model calculates releases of waste stream constituents
from technologies before considering the characteristics of the environment to
which they are released.
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determine human health risks, ecological risks, sensory effects, and costs.
Finally, it reviews the major methodological assumptions in the model. The
model's computer program follows an order somewhat different from that used to
describe the model in this report; its logic is explained in Appendix H.
1.3.1 Wastes, Technologies, Environments
(a) Wastes. The model contains data on a total of 154 waste
streams. A "waste stream" is the combination of substances (e.g., unusable
byproducts or residues) generated during a manufacturing process that requires
disposal. Each waste stream in the model is meant to be representative of the
type of waste generated by any facility or plant of the various firms in a
particular industry. These 154 waste streams total about 158 million metric
tons annually (65 million metric tons of which are flue gas desulphurization
fFGD] sludge, fly ash, and wastes included because of their potential effects
on ecosystems).
The waste streams in the model are primarily those identified as
hazardous in 40 CFR 261, with the following additions:
. PCBs (which compete for disposal capacity with RCRA
wastes)
High-volume utility wastes (FGD sludge, fly ash)
Certain wastes that pose ecological hazards (instead
of hazards to human health).
For each waste stream, the model contains extensive data on the physical
characteristics of the chemical constituents that present the greatest toxic
risks, and the quantity of waste generated annually. The model also
identifies feasible treatment and disposal options for the waste.
The model does not include waste streams that are hazardous solely
because of ignitability, reactivity, or corrosivity, nor does it consider the
risks resulting from these characteristics of the waste streams it includes.
Thus, the model does not take into account all the hazards that subject a
waste to regulation under RCRA.
(b) Technologies. The model includes three classes of technologies--
treatment, transportation, and disposal. Treatment technologies alter the
characteristics (e.g., volume, constituent concentrations) of waste streams.
The model contains 15 treatment technologies. Examples are dewatering through
vacuum filtration and chemical transformation through oxidation/reduction.
Transportation technologies are used to transfer wastes from the point of
generation and treatment to the,point of disposal. The model distinguishes
transportation technologies according to on-site, local, or long distance
transport by stake or tanker truck, a total of six types. The model contains
six kinds of disposal technologies, with variants of each: landfills, land
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treatment, surface impoundments, deep well injection, waste piles, and
incineration.5
The model forms W-E-T cells (possible waste management practices) from
every feasible combination of technologies and waste streams. Each waste
stream is managed by the successive application of a series of technologies.
The only constraint is that there be one disposal step, one transportation
step, and no more than three treatment steps in each cell. Some technologies
cannot be used with certain wastes (e.g., waste streams consisting primarily
of metals cannot be incinerated) and such combinations are therefore not
options in the model.
The model includes the following types of data on each technology:
Effects of the technology on waste streams (e.g.,
how a treatment technology alters constituent
concentrations)
Rates of release of waste constituents from the
technology to the environment
Costs of using the technology.
Data on release rates are critical to estimating the risks involved in
using a technology. Releases may occur either routinely or accidentally. For
example, open treatment tanks and incinerators routinely emit contaminants to
the air; leachate may routinely migrate from landfills. The rupture of a
tanker truck in a highway accident may permit an accidental release of
hazardous substances. The model calculates release rates for each technology
that take into account the probability and quantity of both routine and
accidental releases.
(c) Environments. The model also considers environmental setting in
forming possibilities for waste management. To yield a limited range of
environmental settings, the model defines environments by three factors
critical for assessing the likelihood of exposure to human and non-human
populations:
Surface water assimilative capacity
Ground water systems
Population density.
Surface water assimilative capacity is characterized as either high or low.
Ground water systems are divided into areas underlain by productive aquifers
and all other areas. Both of these systems contain ground water; they are
distinguished by the hydrodynamic characteristics of the water-bearing
60ne disposal technology -- waste piles -- is also used for storage.
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formations. Population density may be high (250 people/km2 or above),
medium (25 to 249 people/km2), or low (fewer than 25 people/km2). The
combination of these factors yields 12 standard environments. For purposes of
evaluating ecological risks, environmental setting is defined only in terms of
surface water. Instead of distinguishing two surface water environments based
on assimilative capacity, however, the model defines 7 surface water
environments based on ecosystem type (e.g. marsh, lake).6
In addition to considering the environmental setting of a waste
management practice, a second way in which the model takes the environment
into account is by including data on the movement and fate of waste stream
chemicals when released into air, surface water, or ground water. These data
on fate and transport are used in calculating risks, as explained below. The
behavior of a chemical in an environmental medium is a function of the
characteristics both of the chemical and of the medium. Thus, the model has
data on chemical-specific factors (such as the physical and chemical processes
that affect decay and mobility in an environmental medium) and environment-
specific factors (such as flow velocities, soil permeabilities, and porosity).
1.3.2 Human Health Risks
The model evaluates the relative risks of each possible waste management
practice, that is, each W-E-T cell, and assigns risk scores to each W-E-T
cell. The model calculates two scores indicative of the risk of adverse
effects to the health of humans, one for exposed individuals, the other for
the total population at risk. It separately calculates a score for the risk
of damage to ecosystems and non-human populations in the environment, i.e.,
ecological risks or "ecorisks" (discussed below in Section 1.3.3). It also
estimates the risk of sensory effects, odors and tastes in particular, caused
by a release of chemicals to the environment. Thus, there are four risk
scores for each W-E-T cell. These scores, it should be emphasized^ indicate
the differences in risk involved in managing wastes by different practices,
not the absolute risk. They may suggest, for example, that the risk of
aquatic ecosystem damage from one management practice is greater than from
another. These scores do not, however, indicate the magnitude of differences
in relative risk nor can they be interpreted to identify the probability that
a release will cause a certain extent or type of human health or ecosystem
damage.
ฎThese standard environments are geographically distributed. The model
divides the United States into 559 areas on the basis of postal zip codes and
characterizes each area as having one of the 12 or 7 environments (depending
on whether human health or ecological risk is assessed). The geographic
distribution of environments vastly increases the number of W-E-T cells and
fixes each cell in a particular location (although these locations consist of
relatively large areas for which there is only limited and general data in the
model).
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The human health risk posed by a waste management practice is a function
primarily of two factors:
The toxicity of the chemical constituents of the
waste stream
The extent of human exposure to these chemicals,
Accordingly, the model uses a five-step process to estimate human health risks.
Step 1 is to select chemicals for scoring. The model
identifies those chemical constituents of a waste stream
whose toxicity and concentration are sufficiently great to
have a bearing on the calculation of risks.
Step 2 is to estimate the concentrations of each of the
selected chemicals in each of the three media (air,
surface water, ground water) into which they can be
released in the course of waste management. These
estimates are based on the release rates calculated for
each technology. The model uses different approaches to
predict the transport and fate of released chemicals in
each of these media. Concentrations are estimated at (or
for air, over) a distance of one kilometer from the source
of the release.
Step 3 is to estimate the total human intake, or dose,
of each of the chemicals through inhalation of air and
ingestion of ground water and surface water.
Step 4 is to calculate the risk to an individual from
the doses calculated in the previous step. Individual
risk depends on three factors in addition to dose: (1)
the probability of a toxic effect from a chemical per unit
dose; (2) the relationship of dose to effect (the
"dose-response curve"); and (3) the severity of the toxic
effect.
Step 5 is to estimate the population at risk by
multiplying the individual risk value by the population in
a given area (recall that environmental settings are
characterized by population density). The result is used
to assign a risk score. An integer difference in human
health risk scores indicates an order-of-magnitude
difference in risk.
1.3.3 Ecorisks
The model separately estimates the risks posed by the release of waste
stream constituents to aquatic and to terrestrial ecological systems. These
estimates are combined to yield a unified ecorisk score for each W-E-T cell.
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A broad range of adverse environmental effects is considered in
estimating ecorisks, including diversity (the variety of species),
productivity, accumulation of biomass, resistance and resilience, species
interactions, and flows of energy and nutrients. These effects may be felt by
individual organisms, species, or entire ecological systems. The model's
calculation of the relation of such effects to concentrations of waste stream
constituents is largely qualitative. Consequently, as noted above, a higher
ecorisk score reflects greater risks, but the scores do not indicate absolute
risks, nor can the quantitative difference in risks be determined from the
difference in scores.
The method used to estimate both aquatic and terrestrial ecorisks
consists of five steps that parallel those used to estimate human health
risks. The principal difference is that the environmental concentration of a
waste stream constituent is used to measure exposure in estimating ecorisks,
whereas dose is used to measure exposure in estimating human health risks
(dose, however, is a function of concentration). Because the factors
considered in deriving human health risk and ecorisk scores are substantially
different, the two scores are not comparable.
The model estimates aquatic ecorisks using the following steps. First,
the chemical constituents of concern in a waste stream are identified and the
aquatic environments into which they may migrate are characterized. Then the
concentrations of the chemicals are estimated at a one kilometer distance from
the point of release as a measure of exposure. An exposure-response function
for each chemical is used to relate the concentration of the chemical to the
likelihood and severity of ecosystem damage. The ecorisk score obtained is
subsequently adjusted to take into account the commercial or recreational
importance of the affected environment.
Terrestrial ecorisks are more difficult to estimate because of the
multiple routes of exposure and the diversity of the organisms that may be
affected. The same ecosystem exposure-response function is used for both
aquatic and terrestrial ecorisks; this function is based on case studies that
include terrestrial as well as aquatic ecosystems. The model also assumes
that the threshold concentration below which a chemical will have no adverse
ecological effects is the same for both aquatic and terrestrial ecosystems.
In addition to estimating human health risks and ecorisks, the model also
estimates the risk of sensory effects, i.e., odors and tastes, from releases
of waste stream constituents to the environment. If chemical releases during
waste management result in environmental concentrations exceeding certain
perception thresholds, the model assigns a sensory effect score of 1 to a
W-E-T cell; otherwise a score of 0 is assigned. The simplicity of the sensory
effects scoring system reflects the expectation that in evaluating risk and
cost trade-offs, less weight will usually be given to the risk of sensory
effects than to human health and ecological risks.
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1.3.4 Costs
The model estimates costs for each waste management practice, i.e., each
W-E-T cell. It can also assign a cost score to each cell as an option to
facilitate certain analyses. An integer difference in cost scores reflects a
two-fold difference (i.e., a doubling) in costs. The cost scale is more
precise than the human health risk scale for two reasons. First, the data on
costs, generally based on standard engineering estimates, are better than
those on risks (this also being the reason why the model can derive cost
estimates but not absolute risk estimates). Second, the main purpose of the
cost scoring system is to indicate the point at which differences in costs
will clearly lead to shifts from one treatment or disposal technology to
another, which requires finer resolution than can be expressed by an
order-of-magnitude scale.
The model's estimate of the costs of a waste management practice (and, in
turn, the cost scores) reflect the summed costs of treating, transporting, and
disposing of a waste stream, using typical facilities. Disposal costs are
often the major component. All costs are expressed in terms of the annual
revenue requirements for managing the typical quantity of a waste stream that
generators must dispose of annually. The costs accounted for in estimating
annual revenue requirements include the following:
Capital investment costs, both direct and indirect,
incurred initially and intermittently over the
technology's operating life (e.g., land, equipment)
Annual operation and maintenance costs, both direct
and indirect (e.g., materials, labor, utilities)
Capital costs for facility closure
Annual costs of post-closure maintenance.
These costs are adjusted to a common base of first quarter 1983 dollars and
discounted at a nominal rate of 11.24 percent (based on a real rate of return,
or discount factor, of 3 percent and an inflation of 8.24 percent).
1.3.5 Methodological Assumptions
This section reviews some of the major methodological assumptions of the
RCRA Risk-Cost Analysis Model. Because the model depicts representative
possibilities, it is, in addition, replete with assumed or estimated values,
simplifications, and generalizations. For example, data on a particular
technology are representative of typical facilities, based on assumptions
about size, design, operating expenses, lifetime, routine release rates, etc.
Such assumptions are discussed in subsequent chapters.
Where there are insufficient data to confirm the realism of an
assumption, the model attempts to be conservative by using assumptions that
avoid minimizing the risks and costs of hazardous waste management.
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(a) Estimates and scores are used to compensate for incomplete or
imprecise data. Data required for the model are often imprecise or simply
unavailable. For example, certain data on waste streams are confidential
business information; the current scientific understanding of the toxic
effects of certain chemicals is limited. One way in which the model
compensates for data gaps and weaknesses is by using estimates (e.g., in the
profiles of chemical half-life). Another way is to provide uncertainty
indices in the waste stream data to indicate the possible range of error in
data on the quantity of waste generated, the distribution by size of
generating facilities, and the concentrations of the waste stream's
constituents. Finally, the model expresses risks and costs on different
scales matched to the level of precision of the data upon which the scores are
calculated.
(b) The human health risk methodology is an advance in certain respects
from traditional toxicological methods. The model must estimate the
probability that a chemical will produce a response at dose levels below those,
for which toxicological data are ordinarily available. It must also consider
the effects caused by doses greater than the "acceptable human intakes" that
toxicology is usually limited to determining. Moreover, to ensure the
simplicity necessary if risk estimates are to be useful in policy analyses,
the model must have a unified treatment of the risks associated with both
carcinogens and noncarcinogens. The model's solution to these requirements is
to use dose-response curves for both carcinogens and noncarcinogens that
predict a finite toxic response from exposure at any level. In accordance
with the traditional view that noncarcinogens do not produce toxic effects at
exposure levels below a certain threshold, however, the dose-response curves
discount the risks from exposure to noncarcinogens at low levels. In other
words, the model's dose-response curves are non-threshold curves, but
incorporate the practical equivalent of a threshold for noncarcinogens.
(c) The model calculates two human health risk scores to meet the needs
of policy analyses with differing priorities. One score is for the risk of
adverse effects to the health of individuals, the other is scaled to the total
population at risk. The individual risk score is for analyses that attempt
not to neglect less-populous areas in policy decisions. The score for the
population at risk is intended for analyses that give special consideration to
the number of people affected by a policy decision.
(d) The model values present and future generations equally in
assigning risk scores. It is arguable that greater weight should be given to
effects on people now living than to future generations when analyzing
comparative risks, i.e., that the value of future generations should be
discounted. The model, however, does not take into account the time when
exposure to a waste stream constituent occurs. Hence, it values present and
future generations equally.
To explain more fully, the model estimates risks to human health and
ecosystems based on environmental concentrations that have reached a steady
state, assuming constant management of a constant inflow of wastes. The
length of time before steady-state concentrations are reached will vary
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according to environmental medium. For example, chemicals will disperse more
rapidly through air than through ground water. To estimate risks, the model
adds chemical concentrations in all media without taking into account the
different times by which steady-state concentrations are reached in these
media. The effect is to give equal weight to any exposure, regardless of when
it occurs and, thus, regardless of the generation at risk.
(e) Factors used to define environmental settings are limited primarily
to the assimilative capacity of surface water, differences in groundwater
hydrodynamics, and population density. Climate, soil acidity, and other
environmental factors play a lesser role in determining exposure to migrating
chemicals and, therefore, are not included in defining environmental settings.
(f) Environmental concentration is the primary basis for estimating
exposure. The transport of waste stream constituents through the
environment, and the exposure of individuals to these constituents, depends
upon site-specific circumstances that the model cannot fully reflect. For
example, it may depend upon subsurface geologic structures that affect the
movement of a plume of contaminants, the percentage of the population that
obtains drinking water from a contaminated well, and the amount of water each
person consumes before contamination is detected. Consequently, the model
relies mainly upon environmental concentration to estimate human and ecosystem
exposure. It calculates concentration on the basis of distance from the point
of release, taking into account the attenuation characteristics of certain
media and chemical persistence in the environment. All risk calculations are
based on concentrations at a distance of (or up to) one kilometer from the
source of the release.
(g) The model takes into account real resource costs alone. Transfers
or redistributions of monies are not included. The model takes into account
only direct and indirect expenditures for capital investments and for
operation and maintenance. It does not consider costs for loss of
productivity or for the replacement of natural resources. It also does not
include the costs of health care, although the relative value of these costs
may be inferred from the human health risk scores. All cost values are based
on engineering estimates for constructing and operating waste management
facilities; they do not necessarily represent the price as determined by
facility supply and the demand for facility services.
1.4 PROJECT DEVELOPMENT
1.4.1 Relation of the Phase III Report to the Phase II Report
The model described in this report is a refinement of an earlier version
described in the Phase II report. Numerous changes have been made in response
to reviewers' comments. In addition, the model incorporates new data and
technical improvements that reflect an additional year's work in accordance
with long-standing project plans. The Phase III report is organized along
different lines from the Phase II report and it may be difficult to compare
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the two. The main differences between the model at present and the version
described in the Phase II report are as follows:
The number of waste streams in the model has been
expanded from 83 to 154. These waste streams account
for a correspondingly greater percentage of the
hazardous (and potentially hazardous) waste generated
annually in the U.S. than accounted for previously.
The number of treatment technologies has been
revised by the addition of biological treatment,
containerization, and asphalt encapsulation, and by
deleting technologies that are duplicative or not used
for the model's waste streams. Waste piles have been
added as a disposal technology. The number of
incineration technologies has been reduced from four
to two. The model now includes the option of taking
corrective action when problems are experienced with
land disposal technologies.
Costs are now calculated by a substantially
different method. Instead of considering only direct
capital and operating costs, the model now factors in
tax, depreciation, and related indirect expenses to
calculate annual revenue requirements for each waste
stream.
The model now estimates ecological risks as well as
human health risks. It also estimates the risk of
sensory effects from releases of waste stream
constituents to the environment.
The methodology for evaluating human health risks
has been thoroughly revised. The model now calculates
chemical concentrations in the environment before
assessing the health effects of exposure, reversing
the order used to estimate human health risks in the
model's previous version.
1.4.2 Work in Progress
Projects are now underway to strengthen and expand the model in several
areas. Additional data will be gathered on waste streams, in particular,
those produced by the pesticides industry, and additional waste streams may be
included in the model. The number of environments used to assess human health
risks will be expanded to include oceans, reflecting the addition of ocean
dumping as a disposal technology. (The Phase II report included preliminary
but, for present purposes, insufficient data on the ocean environment and
ocean dumping.) Further work may be conducted on storage technologies. The
characterization of the transport of chemicals though various environmental
media will be improved.
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1.5 ORGANIZATION OF THIS REPORT
The subsequent chapters of this report are organized as follows:
Chapter 2: W-E-T Framework contains separate sections on
waste streams, technologies, and environments.
Chapter 3: Human Health Risks describes the methodology
used to estimate human health risks.
Chapter A: Ecorisks describes the methodology used to
estimate aquatic and terrestrial ecological risks.
Chapter 5: Sensorv Effects describes the methodology
used to estimate the risk of sensory effects from releases.
Chapter 6: Costs describes the methodology used to
estimate the costs of waste management practices.
Much of the data in the model is presented in a series of appendices
bound as the second volume of this report. These appendices also contain
detailed descriptions of every technology in the model and full accounts of
background studies used to determine the values of critical parameters.
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2. W-E-T FRAMEWORK
2.1 INTRODUCTION
This chapter describes the wastes, environments, and technologies that the
model combines to array possible ways to manage hazardous wastes. The model
contains detailed characterizations of 154 waste streams, 12 environmental
settings, 15 treatment technologies, 2 transportation technologies, and 6
disposal technologies. On the basis of these detailed characterizations, the
model calculates the risks and costs of waste management practices.
The chapter is organized in three sections:
2.2 Waste Streams identifies the waste streams in the
model, the industrial sectors and quantities represented,
the data included, data sources, and data limitations.
2.3 Technologies describes the treatment,
transportation, and disposal technologies in the model.
2.4 Environments explains the factors used in the model
to distinguish a range of environmental settings.
The descriptions in this chapter are of a summary and non-mathematical nature.
A series of supporting appendices in the second volume of this report presents
greater detail. The justifications for most of the assumptions in the model's
characterizations of wastes, environments, and technologies are provided in
the appendices, rather than in this chapter.
2.2 WASTE STREAMS
We have characterized the universe of hazardous waste generation in the
United States by identifying and assembling data on 154 industrial waste
streams. Each waste stream in the model is intended to represent, to the
extent possible, wastes generated by an average plant or a facility within an
industry. For example, waste stream No. 01.01.11, lead sludges from battery
production, represents waste lead sludges generated by any facility that
produces batteries.
The data on a waste stream focus on two features:
The physical and chemical characteristics of the waste
stream
The quantities of the waste generated
The waste stream data are assembled in profiles, which are summarized in
Appendix A to this report.
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Assembling data on waste streams is the starting point in tracing the fate
of waste constituents through the waste management practices in the model.
These data are necessary to calculate releases of constituents to the
environment during treatment, transport, and disposal and, in turn, to
estimate the consequent risk to human health and the environment. The
detailed characterization of each waste stream also facilitates the systematic
calculation of the costs of managing hazardous wastes.
The following discussion describes the waste streams included in the
model, the industrial sectors represented, the format used for assembling
data, and data sources and limitations.
2.2.1 Waste Streams Included in the Model
The model's data base includes 154 waste streams, accounting for a total
annual generation of approximately 158 million metric tons. A breakdown of
the major groupings by waste type is shown in Exhibit 2-1. The waste streams
in the model are primarily those considered hazardous or potentially hazardous
under Subtitle C of RCRA. Those included from Subtitle C are F and K wastes
specifically listed in 40 CFR 261; those having the characteristic of EP
toxicity; and those containing significant concentrations of constituents
listed in Appendix VIII to 40 CFR 261. Thus, the model does not include RCRA
P and U wastes. Nontoxic hazardous wastes (i.e., those having the
characteristics of ignitability, corrosivity, or reactivity) are excluded from
the data base because the model cannot assess risks associated solely with
ignitability, corrosivity, and reactivity, but is capable of assessing only
the toxicity hazard.
For wastes other than those specifically listed in 40 CFR 261, it is often
difficult to determine whether a waste is actually hazardous. Comprehensive
test data for EP toxicity are not readily available and wastes generated from
a particular industrial process vary in physical and chemical characteristics.
We have been conservative by including wastes in the model when there is some
doubt as to whether they are hazardous.
We have also extended the scope of the data base beyond Subtitle C
hazardous wastes. For organic waste streams, we included waste streams in the
model if they contained more than one percent by weight of constituents of
concern and their annual generation exceeded 100 metric tons. These criteria
may introduce into the data base certain aqueous waste streams conventionally
considered to be within the jurisdiction of the Clean Water Act (although some
such wastes can be considered hazardous under RCRA by virtue of the mixture
rule). Nonetheless, including these waste streams ensures flexibility in
considering regulatory options by permitting the model to stray over the
border between hazardous and nonhazardous properties. For the same reasons,
we included a limited number of inorganic waste streams containing heavy
metals not listed for the characteristic of EP toxicity in 40 CFR 261.
Additional waste streams specifically included in the data base are the
following:
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EXHIBIT 2-1
PRINCIPAL COMPONENTS OF WASTE DATA BASE
Waste Type
Number of
Waste Streams
Waste Quantity
(million metric tons/yr.)
Listed Wastes from Non-Specific
Sources (F)
19
2.69
Listed Wastes from Specific
Sources (K)
51
8.83
Wastes Containing EP Toxic Metals a/
34
63.30
Unlisted Wastes from Organic
Chemical and Plastics Production b/
39
17.53
Others c/
6
1.20
Subtotal
149
93.55
FGD Sludge and Fly Ash
2
64.64
Ecostreams d/
3
0.09
Subtotal
5
64.73
Total
154
158.28
a/ These wastes are potentially EP toxic only and in many cases may not
be found EP toxic by testing.
b/ Excludes EP toxic wastes.
c/ Includes non-EP toxic metals, pesticide wastes not otherwise specified
and PCB wastes.
d/ Ecostreams are those waste streams included for consideration of their
effects on ecosystems as opposed to human health.
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PCB wastes, since these compete for hazardous waste
disposal capacity with RCRA-regulated wastes1
Wastes that pose a hazard based on potential
ecological effects (as opposed to effects on human
health). Three waste streams included for this reason
are:
Production wastes from quaternary amine
(disinfectants) manufacture
Distillation bottoms from linear alkyl benzene
sulfonate (detergents) production
Off-specification commercial 2,4-dichloropheno-
xyacetic acid, salts, and esters (a herbicide)
High volume utility wastes:
Fly ash from conventional coal combustion
Flue gas desulfurization (FGD) sludge from
conventional coal combustion2
Specifically excluded from the model are the following wastes:
Hazardous wastes from federal and other government
establishments
Discarded products, off-specification products, and
containers (P and U wastes)'
Hazardous wastes from spills and abandoned sites
^CB wastes are also one of the waste streams proposed for listing under
pending RCRA reauthorization legislation (H.R. 2867).
2The utility wastes, fly ash and FGD sludge, differ from the other
wastes included in the model. Their potential for human health or
environmental effects is enhanced by their large volumes and by the common
management practice of storage in uncontrolled waste piles. At this time the
model does not include this management practice and, therefore, has no
provision for estimating the risks involved.
30ff-specification 2,4-dichlorophenoxyacetic acid is an exception to
this exclusion , and is included to incorporate a commonly-used herbicide in
the model for consideration of effects on ecosystems.
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State-designated hazardous wastes
Industry-perceived hazardous wastes
Mining wastes
Asbestos separator wastes from diaphragm cell process
in chlorine production.1*
We have not excluded wastes from small generators, but neither has there
been special effort to include them. Their partial inclusion simply reflects
those instances in which a large number of small generators results in a large
total volume of a particular waste stream with well documented
characteristics. Waste streams from small generators are a small fraction of
the waste data base.
The model accounts for 69 of the 89 hazardous waste streams listed in 40
CFR 261 as hazardous wastes from specific sources (K prefix) and hazardous
wastes from non-specific sources (F prefix). Three of these waste streams
were excluded because they are listed for characteristics other than
toxicity. Most of the 17 remaining waste streams are wastewater treatment
sludges from pesticide production, where industrial confidentiality precluded
data acquisition. To compensate for data gaps, we created three artificial
waste streams for the model based on literature review and engineering
judgment. Two of these represent pesticide waste and the third represents
organic/metal sludges from miscellaneous sources. These three waste streams
comprise a relatively small proportion (0.7 percent) of the total quantity
represented in the waste data base.
2.2.2 Industrial Sectors Represented
The focus of the model's data base is on those industrial sectors that
generate the bulk of hazardous waste in the United States. A 1980 study
conducted for EPA5 estimated that 82 percent of the hazardous waste gene-
rated in the U.S. was generated by four industrial sectors, listed in Exhibit
2-2. These figures are corroborated by data from the 1983 mail survey
conducted for EPA's Office of Solid Waste.6 Accordingly, we concentrated
fcThe asbestos waste stream could not be included in the data base,
because the information available relating "dose" of asbestos to risk is in
the form of risk per unit number of asbestos fibers per unit volume of air.
The waste stream information is in the form of weight of asbestos and we have
no way of converting risk per fiber to risk per unit weight because of the
great variability in the size of different types of asbestos fibers.
5Putnam, Hayes, and Bartlett, Inc. (PHB), "Hazardous Waste Generation
and Commercial Hazardous Waste Management Capacity: An Assessment" (1980).
6Westat Research, "National Survey of Hazardous Waste Generators and
Treatment, Storage and Disposal Facilities Regulation under RCRA in 1981,
Draft Final Report" (January 6, 1984.)
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our initial data collection efforts in these sectors.
EXHIBIT 2-2
INDUSTRIAL HAZARDOUS WASTE GENERATION
BY STANDARD INDUSTRIAL CODE
SIC
Number
Industry
Percent of Total
1980 PHB Study 1983 OSW Survey
28
Chemical
62 71
33
Primary Metals
10 3
29
Petroleum and Coal Products
5 3
34
Fabricated Metals
5 2
82 79
We also concentrated on industry sectors in which the impact of the RCRA
land disposal regulations may be most significant. Based on the preliminary
regulatory impact analysis for the land disposal regulations,7 those
industry sectors are as follows:
Wood preserving (SIC 2491)
Alkalies and chlorine (SIC 2812)
Inorganic pigments (SIC 2816)
Synthetic organic fibers (SIC 2823, 2824)
Gum and wood chemicals (SIC 2861)
Organic chemicals (SIC 2865, 2869)
Agricultural chemicals (SIC 2879)
Explosives (SIC 2892)
Petroleum (SIC 2911)
Iron and steel (SIC 331, 332)
Secondary nonferrous metals (SIC 3341)
Copper drawing and rolling (SIC 3351)
Plating and polishing (SIC 3471, 3479)
In addition, we examined potentially hazardous wastes generated by the
pulp and paper industry (SIC 26). Their inclusion was in response to the
large volumes of potentially hazardous wastes reported in Part A permit
applications by that industry. We did not, however, identify any major
sources of hazardous waste generation in this industry. It would seem that
hazardous waste generation in the pulp and paper industry is limited to small
generators of wastes with varying characteristics.
747 FR 32274 (July 26, 1982).
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2.2.3 Comparison of Waste Quantities to Other Studies
The total quantity of wastes included in the model, 158 million metric
tons, is substantially higher than the 41 million metric tons per year
reported in the 1980 PHB study8 as the total for U.S. hazardous waste
generation. It is substantially lower than the estimate reported in the
survey of state data undertaken for the Office of Technology Assessment9
(OTA), 250 million metric tons and, on the face of it, is comparable with the
estimate currently reported in the preliminary results of the generator
questionnaire of the 1983 mail survey10 160 million metric tons. A
comparison of these figures, however, requires an understanding of the
universe of wastes that the studies represent. Exhibit 2-3 presents a summary
of the waste types and data sources used in each study.11 Also included are
data from a survey undertaken by the Chemical Manufacturers Association (CMA)
of the chemical industry.12
Of the model's waste streams, 65 million metric tons are FGD sludge, fly
ash, and other wastes included for consideration of effects on ecosystems
rather than human health. The remaining 93 million metric tons are considered
potentially hazardous under Subtitle C of RCRA. A major reason for the
discrepancy between this figure and the 41 million metric tons estimated by
PHB is the inclusion in the model of high volume waste waters that are
potentially hazardous under RCRA regulations, but which may currently be
regulated under the Clean Water Act NPDES program. The four largest waste
streams of this type in the model (from electroplating, explosives
manufacture, and ethylene production) account for approximately 41 million
metric tons.
The universe of wastes defined as hazardous by selected states (the 250
million tons reported in the OTA state survey) includes a number of high
quantity wastes, such as fly ash, oil field wastes, mining wastes and waste
oil. If the quantity represented by some of the larger state-defined
hazardous wastes is subtracted, the adjusted total is approximately 115
"Putnam, Hayes and Bartlett, Inc. (PHB), op. cit.
9U.S. Congress, Office of Technology Assessment (OTA), Technologies and
Management Strategies for Hazardous Waste Control (Washington: U.S.
Government Printing Office, 1983), p. 122.
10Westat Research, og. cit.
11An additional study based on EPA's Part A permit application is not
included here. The reliability of this data base is questionable because of
protective filing and other factors. It is superseded by the mail survey.
12Chemical Manufacturers Association, "The CMA Hazardous Waste Survey
for 1981 and 1982" (1983).
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EXHIBIT 2-3
COMPARISON OF SCOPE OF WASTE DATA BASE
WITH OTHER STUDIES
Studv
Waste Quantity
(rdllion metric
tons/year)
Universe of Wastes
Data Sources
Risk-Cost Analysis
Model (1984)
158
OSW Mail Survey
(1984)
PHB (1980)
OTA (1983)
160
265
41
250
(i) Potentially
hazardous wastes
under Subtitle C,
excluding:
corrosive, reac-
tive and igni-
table wastes
discarded com-
mercial chemical
products
(ii) FGD sludge and
fly ash and wastes
selected for effects
on ecosystem
Subtitle C hazardous
wastes
Subtitle C hazardous
wastes excluding
discarded commercial
chemical products
(i) Subtitle C
hazardous wastes
(ii) State desig-
nated hazardous
wastes a/
EPA industry
studies, permit
applications, de-
listing petitions,
state data, trade
associations
Responses to Genera-
tor Questionnaire
Responses to TSD
General Questionnaire
EPA industry studies
State data
CMA (1983)
Subtitle C hazardous Member survey for
wastes in SIC 28 1981
a/ Includes PCBs, waste oil, fly ash, oil field wastes, mining wastes, and
other wastes for selected states.
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million metric tons.13 This may still overestimate the quantity of Subtitle
C wastes, since it contains a number of wastes for some states that are not
hazardous under Subtitle C of RCRA.
CMA developed waste quantity estimates for SIC 28 from a survey of member
companies.1u Seventy companies responded to the survey, providing
information on 535 plants. Based on extrapolation of the survey data to the
whole industry, approximately nine million metric tons of Subtitle C hazardous
wastes were generated in 1981 (the survey records significantly smaller
quantities for 1982). Included in the nine million metric tons are 2.8
million metric tons of F and K listed wastes, compared to 4.3 million metric
tons from SIC 28 in the model. Data on individual waste streams were not
available in the CMA report to allow more specific identification of
discrepancies.
The OSW Mail Survey is the most comprehensive collection of hazardous
waste generation data assembled to date. It is important to recognize,
however, that:
The total figure of 160 million metric tons per year
is extrapolated from responses to questionnaires sent to
a sample of RCRA-regulated generators. An estimate of
265 million metric tons15 has also been developed on
the basis of data from responses to the general
questionnaire sent to a sample of treatment, storage,
and disposal (TSD) facilities.
The initial estimates are preliminary in nature and
subject to statistical uncertainty. The data are
currently undergoing verification and further analysis.
The design of the survey imposes limitations on
detailed comparisons of waste quantity estimates both
between the mail survey data and the model and, within
the survey, between the generator and TSD general
questionnaire data.
For those waste types included in both the model and the Mail Survey, a
comparison of the quantity estimates is shown in Exhibit 2-4.
13The waste quantity was adjusted using data supplied in the OTA report,
op. cit.
1<*Chemical Manufacturers Association, oj>. cit.
15Westat Research, op. cit.
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EXHIBIT 2-4
COMPARISON OF WASTE QUANTITY ESTIMATES
TO OSW MAIL SURVEY DATA
OSW Mail Survey
Risk-Cost
Generator
Analysis Model
Questionnaire a/
(million metric
(million metric
Waste Type
tons/yr.)
tons/yr.)
Listed wastes from
11.5
104.1
specific and non-
specific sources
(F and K)
EP toxic wastes
63.3 b/
14.9
(D004-D017)
a/ Source: Westat Research op. cit.
b/ These wastes are potentially EP toxic only and, in many cases, may not
be found EP toxic by testing.
Based on the data in Exhibit 2-4, it appears that the model overestimates
the quantity of EP toxic wastes by a factor of approximately four. This is
because the model's data base is conservatively designed to include wastes
containing EP toxic metals at relatively low concentrations.
The exhibit above also suggests that the model's quantity estimate for F
and K listed wastes is about a factor of nine smaller than the Mail Survey
estimate. The model's estimate of the number of generators for individual
waste streams is reasonably consistent with the Mail Survey, with exception of
spent solvent wastes (F001, F002, F003, F004, F005) and wood preserving sludge
(K001).16 The Mail Survey, however, indicates that the distribution of
generator size is skewed by some very large generators, such that only one
percent of the generators would account for nearly 90 percent of the hazardous
waste. Because such a large proportion of wastes is generated by a small
fraction of the generators, any variance in the data from these large
16We believe that these exceptions are caused to some extent by the
inclusion of small generators in the model.
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generators may have a significant impact on the total quantity estimate.
Examination of generator questionnaire data for a limited sample of the
largest generators indicates that mixing of hazardous waste with process water
is occurring in some cases and results in high values for waste quantities,
since the mixture is then classified as a hazardous waste (the generation of
aqueous streams of this type is distinct from potentially hazardous or
hazardous wastewaters in the model generated directly by chemical processes).
Also in some cases, inappropriate or overly conservative coding of wastes by
the generator is observed.
It further appears from responses to the TSD general questionnaire that
ma jor discrepancies (greater than one million metric tons per year) between
the model and the Mail Survey, however, occur only for a limited number of
waste codes, specifically:
F001 -- Spent halogenated solvents and sludges from
solvent recovery
F005 -- Spent non-halogenated solvents and still
bottoms from solvent recovery
F006 and F008 -- Wastewater treatment sludges and
plating batch sludges from electroplating
K044 and K046 -- Wastewater treatment sludges from
manufacture of explosives and lead-based initiating
compounds
K048 -- Dissolved air flotation (DAF) float from
petroleum refining
K049 -- Slop oil emulsion solids from petroleum
ref ining
K099 -- Untreated wastewater from production of 2,4-D
(not currently represented in the model)
These discrepancies can be attributed to three possible causes: (1) the
data reflect "real-world" values which are larger than those in the model, (2)
the data reflect high values as a result of mixing with non-hazardous waste,
or (3) the data are inappropriately coded as hazardous by respondents. We
believe that a combination of these factors is the most likely explanation,
based on a number of considerations:
(i) Our review of the largest generators showed clear
evidence of mixing for solvent wastes and instances of
coding as hazardous being questionable
(ii) Treatment and disposal methods for some of the larger
solvent streams, F001 and F005, include surface
impoundments and deep well injection. These are
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technologies more appropriate for dilute aqueous streams
rather than concentrated organics (see Appendix B).
(iii) We have a high degree of confidence in the model's data
for the petroleum industry for which waste generation
has been characterized relatively well.
In addition, statistical analysis of the data indicates that some of these
discrepancies are associated with large quantity outliers. While inconclusive
alone, this is consistent with the possibility either of mixing as an exception
to the normal pattern or of anomalous data points. Resolution of these discre-
pancies and those observed in comparing the number of generators requires
further investigation.
From these findings, three types of issues emerge as to how well the model
represents waste generation practices: 1) for some of the cases where large
discrepancies occur, the model may simply underestimate waste quantities; 2) the
mixing practice may introduce the need for a different type of waste stream in
the model to represent high volume dilute aqueous wastes, and 3) it may be that
such dilute aqueous streams do not have a place in the model in spite of their
classification by the Mail Survey as hazardous. Clarification as to why these
discrepancies exist may provide some further insight into how they can be best
handled in the model. In the meantime, we have elected to retain the data in
the model as developed during our review of secondary sources. Four reasons
support this approach: 1) the causes of the data discrepancies are inconclusive
and require extensive further analysis, 2) the discrepancies'appear to be
concentrated in a limited number of waste streams, 3) we have a high degree of
confidence in the model's data for which some of the discrepancies occur, and 4)
revisions to the model's waste stream data base can be made relatively easily.
.More detailed discussion of the data discrepancies appears in Appendix I.
2.2.4 Waste Stream Data Base
The data on each waste stream in the model are organized into a profile.
These profiles can be used independently of the RCRA Risk-Cost Analysis Model
and can be updated as necessary. A summary version of each profile is presented
in Appendix A and contains the following information:
Waste stream name, a classification number from the
model, an EPA classification number, and a standard
industrial code number
Annual waste quantity, number of generating facilities,
and the average quantity per day of waste produced by each
generating facility
Physical and chemical characteristics of the waste:
fraction non-water, fraction suspended, specific gravity
o'f solids, average specific gravity of waste, heating
value, chlorine fraction, ash fraction, pH, biodegradation
rate, and biological oxygen demand
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Mass fraction and fraction dissolved for each
constituent of concern in the waste stream
Physical and chemical characteristics of the
constituents of concern in the waste stream: molecular
weight, vapor pressure, solubility, biodegradation rate,
and biological oxygen demand.
Reference sources for data on average waste quantity per
facility-and constituent concentrations
Indices of the degree of uncertainty concerning data on
waste quantity per facility and constituent concentrations
A typical data compilation for a waste stream is presented in Exhibit 2-5.
The waste streams in the model have been classified into categories on the
basis of constituents and physical state, as indicated by the model's
numbering system. The system is hierarchical, so that a waste stream forms a
subcategory under a major category. For example, wastewater treatment sludge
from the production of chrome pigments (01.01.06), is part of the metal
sludges subcategory (01.01), which is in turn part of the aqueous inorganics
category (01). The number of waste streams and waste quantity generated in
each major category of the classification scheme are-presented in Exhibit 2-6.
There is a certain amount of ambiguity in the terms commonly used to
describe the physical state of waste streams, particularly in the definition
of sludges and oily wastes, and in the distinction between aqueous organic and
concentrated organic waste streams. For this classification we used the
following criteria (although other criteria are possible):
Sludges contain less than or equal to 50 percent
suspended solids, solid residues contain greater than 50
percent suspended solids
Concentrated organics contain less than or equal to 50
percent water, aqueous organics contain greater than 50
percent water
Oily wastes contain greater 1 percent oil
The waste data base includes a total of 84 constituents of concern. We'
selected the constituents of concern for each waste stream primarily by
reference to the listing of chemicals in Appendix VIII to 40 CFR 261.
Constituents in the list were considered candidates as constituents of concern
if concentration data were available or could be estimated. In addition, we
considered other constituents in the waste streams as candidates by
preliminary assessment of their toxicity.
When multiple constituents of concern were identified, a determination as
to their inclusion was made by consideration of the constituent's contribution
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EXHIBIT 2-5
TYPICAL WASTE STREAM DATA COMPILATION
APPINOIX A
WASTE STREAM HATA BAST
PAC.I : Jป1
()?.()? - WASlfWAHR FROM ORGANIC; CHf.MICAl PRODUCTION
WASH SIR! AM NUMBER ฆ 0? 0? 1)1 f PA NUMBER : K0H9
SK. NUMWR : ?R69
NAME : DI ST 11 I AT I ON BOTTOMS fROM ACT T AIDE HYDE PRODUCTION I ROM f TMYLENE 0XIDA1I0N
OUANI 11Y (1000 MI/YR) : 399.50
I WASTE SIR! AM SPECIFIC INIORMATION I
NUMBER Of F AC 11 I I IES :
QUANT 11 Y PI R r ACUITY
(KG/DAY)
SOURCf
UNCfRIAINIY
36r>000. n
CMARACTE R I ST ICS :
TRACTION TRACTION
nonwaier suspendtn
030 .?OO0E-O3
SOI IDS AVC. Ml AT INC
S.G. S.G VAlUl(KJ/kC)
1.50 1.nn
395.0
FRACTION fRACTION
CI ASH
.01
.0?
BIOPFGRADAI ION
PH RAI r ( PI R DAY J
.0 3.93
BOI){ U )
(M(;/i )
16000. (ill
I CONSTITUENT SPECIUC INFORMATION I
CONST I UIFNT OF
CONCFRN
CHLOROFORM
FORMALDEHYOF
CHARACTERISTICS
CONCENTRA?ION
(PPM) SOURCE
3000.00
3000.00
31
UNCERTAINTY
1
CONST ITUENT
Or CONCERN
CHLOROFORM
FORMALDEHYDE
MASS FRACTION MOLECULAR VAPOR PRESSURE SOLUBILITY BIODrGRADATI ON
FRACTION DISSOLVED WEIGHT (MM.HG)625C. (MG/L)@?5C. RATE(PER DAY) BOD(U|
0.003000
0.003000
1.000
1.000
119.0
30.0
150.5
3266.
8200.
.3000E+06
. 1000
5.000
.280
1 .070
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EXHIBIT 2-6
CLASSIFICATION OF WASTE STREAMS
Waste
Quantity
Waste Number of (million metric
Category No. Waste Category Waste Streams tons per year)
01 INORGANICS 37 73.8
01.01 Metal sludges
01.02 Solutions containing heavy metals
01.03 Cyanide sludge
01.04 Biosludges containing heavy metals
01.05 Metal sludges with organics
02 AQUEOUS ORGANICS 30 22.0
02.01 Phenols
02.02 Wastewater from organic chemical
production
02.03 Wastewater treatment sludge from
pesticide production
02.04 Other aqueous organics
03 CONCENTRATED ORGANICS 63 1.4
03.01 Spent solvents
03.02 Still bottoms from solvent recovery
03.03 Organic/metal sludges
03.04 Liquid residues from organic
chemical production
03.05 Solid residues from organic
chemical production
03.06 Other concentrated organics
04 OILY WASTES 11 0.9
04.01 Oily wastes from petroleum refining
04.02 Other oily wastes
05 INORGANIC SOLID RESIDUES 13 60.2
05.01 Residues from metal smelting
ana refining
05.02 Spent catalyst
05.03 Other inorganic solid residues
TOTAL 154 158.3
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to the overall hazard of the waste stream. We made this determination by
review of the relative toxicity and concentration of the constituents, and
excluded only those constituents that clearly would contribute insignificantly
to the risks to human health posed by the waste stream. On this basis, the
model includes 80 constituents of concern to human health, 77 of which are
listed in Appendix VIII. A further four constituents are included in the
three waste streams identified as being especially hazardous to ecosystems.
2.2.5 Data Sources
Collecting data to compile the waste stream profiles involved several
stages of research, data assembly, and analysis. We first conducted a
feasibility study to investigate potential sources of waste data, focusing
primarily on the following:
State data sources, e.g., manifest systems, permit
requirements, and waste exchanges
Part B permit applications submitted to EPA regional
offices
Delisting petitions filed with the Office of Solid
Waste
Office of Solid Waste sources, specifically studies
undertaken for the Economic Analysis, Waste Treatment,
Waste Characterization, and Land Disposal branches
EPA offices other than the Office of Solid Waste,
specifically the Effluent Guidelines Division of the
Office of Water, the Office of Toxic Substan-ces, and the
Office of Research and Development's Industrial
Environmental Research and Municipal Environmental
Research Laboratories
Other federal agencies and private organizations
involved in environmental or waste-related issues
Trade associations that have undertaken surveys on
waste characterization within their industry
In evaluating the data from these sources, we considered the quality of the
data, both in terms of its age and likely accuracy, and its accessibility.
The most useful sources were existing compilations of hazardous waste
characterization data prepared for the Office of Solid Waste or the Office of
Research and Development. These were supplemented by state data from
Illinois, New York, and California; delisting petitions filed with the Office
of Solid Waste; Part B Permit Applications submitted to EPA regional offices;
trade association documents; and wastewater data from the Effluent Guidelines
Division of the Office of Water. Of the state sources, only Illinois provided
an automated data base; other states were either unable to provide data in the
ICF INCORPORATED
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form required or had only "hard copy" compilations requiring a lengthy time to
abstract the data. In general, the principal limitations in compiling the
waste stream profiles were the limited resources for collecting and compiling
data, the time required to access usable data, and restrictions on data
availability because of industrial confidentiality.
Appendix A contains a complete list of references for the waste stream
data base.
2.2.6 Data Limitations
The quality of the data on waste streams varies. In general, however, it
is far from satisfactory, partly because of the variability of industrial
practices and partly because of incompleteness. Two specific problems deserve
special note.
(a) Complexity and variability of industrial processes. It is difficult
to characterize a typical or representative waste stream for an industry
because of the complexities and variations in process operations at different
plants.
While this is a universal problem in characterizing industrial waste
generation, an example can be drawn from the wood preserving industry. Three
major types of preservatives are used for treating wood. The different
preservatives are used separately, but wastes may not be segregated, so that
sludges produced at individual plants may contain more than one preservative.
Developing three waste profiles for separately defined sludges would not fully
reflect actual practices; similarly, a single mixed sludge profile would be
only partially representative. Accordingly, the model must sometimes make
estimates about waste generation based on simplifying assumptions about the
uniformity of facility size and operations.
(b) Lack of complete data. Data may be incomplete for particular waste
streams because of lack of available source material, either in absolute terms
or in the time frame of this project. Data may be particularly limited for
certain industries in which it is considered confidential business
information, e.g., in the pesticide industry.
In addition, the data may imprecisely record specific information. For
example, in the reporting of total chromium, there is commonly no quantitative
information on the concentrations of hexavalent chromium, which is by far the
more toxic and mobile constituent. In the absence of specific data, we
estimate hexavalent chromium concentrations, partly by analogy to other waste
streams with measured concentrations, and partly by considering waste source
and oxidation/reduction conditions. Such estimates are based on very limited
data.
Because of the variability in the data quality for constituent
concentrations, and for average waste quantity per facility, the model
includes a system of indices for the reliability of this information. The
uncertainty indices are not used in the model; that is, they in no way affect
ICF INCORPORATED
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the model's risk and cost scores. These indices are presented simply to show
the approximate degree of reliability of the data on waste quantity per
facility and constituent concentrations. They may be taken into consideration
when reviewing the supporting data on waste streams to clarify the source of
values in the model's output.
Three uncertainty indices are reported in Appendix A. We assigned a "l"
for waste quantity per facility where data were available on the total
quantity of wastes generated and the number of facilities, and similarly for
concentration of constituents where data were available. A "2" is used where
we calculated these values from partial data. A "3" corresponds to crude
estimates obtained using minimal data.
Existing data are particularly unsatisfactory in specific areas. In the
model, the pesticide industry is the most poorly documented significant
generator of hazardous wastes. Data for cyanide wastes and wastes from
non-ferrous metal production are also inadequate at present.
Additional data sources might be used in the future to strengthen the
model's current data base on waste streams:
The 3007 RCRA questionnaire survey on the organic
chemicals and chlorinated organic chemicals industry
(including pesticides) will create a large amount of
data that address waste generation and constituent
concentrations.
The petroleum industry survey sponsored by the
American Petroleum Institute will be available shortly
and is the most recent comprehensive survey of that
industry.
At the state level, there are also a number of additional data sources
(non-automated) that could be assessed, although it is hard to predict how
much additional data may be obtained. A sample from the New York State
Hazardous Waste Survey indicated that approximately one industry survey
questionnaire in ten yielded quantitative data of assistance to this project.
Other state data sources include those in Oklahoma, Oregon, Pennsylvania, and
Texas.
Additional sources of "real-world" data that have provided limited help
are the delisting petitions filed with the Office of Solid Waste, and the RCRA
Part B permit applications. The permitting process was at a relatively early
stage of development during data collection for this project (April to June
1983), so these applications will likely be an increasingly useful source in
the future.
Finally, to date, this project has concentrated on certain industry
sectors that are, consequently, better characterized than others. It would
likely be advantageous to pursue additional data for industries that are not
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so thoroughly characterized to provide some uniformity of reliability to the
data base.
2.3 TECHNOLOGIES
The model includes three classes of technologies: treatment,
transportation, and disposal. A waste stream is managed by using some
feasible combination of the technologies in these three classes, with the
constraint that there be no more than three treatment steps, one transporta-
tion step, and one disposal step for a single waste stream.17 For example,
a RCRA waste stream resulting from metal plating might first be treated by
reduction to transform hexavalent chromium to trivalent chromium, and
subsequently treated by chemical precipitation. The resultant sludge might
then be put through a vacuum filter (another treatment step) before being
transported to a landfill for disposal. A waste stream, however, need not be
treated prior to transportation and disposal.
The technologies in the model, each of which is characterized with
considerable specificity and detail, are meant to be representative of the
technologies presently available to manage hazardous wastes. The technologies
are characterized in terms of three factors:
Wastes handled. Not all technologies are techni-
cally capable of handling all wastes. The model,
therefore, allows only feasible combinations of waste
streams and technologies.
Design and operating criteria. Typical equipment
and commonlv-used design and operating criteria are
presented for each technology as the basis for
estimating the costs of applying the technology to waste
streams.
Waste releases. The amount of waste released into
the environment (air, surface water, and ground water)
is estimated for each technology. (These release rates
are functions partly of waste stream and constituent
characteristics.)
The following discussion is organized according to the three classes of
technologies in the model: treatments, transportation and disposal.
2.3.1 Treatment Technologies
The purpose of treating a hazardous waste is to render the waste less
hazardous or to alter its characteristics in such a way that the costs of
disposing of its hazardous constituents are reduced.
1'Except when wastes are sent for disposal to waste piles, in which case
the wastes are subsequently sent to an additional disposal technology.
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There are 15 treatment technologies in the model, organized into 6
groups. Exhibit 2-7 lists the treatment technologies in the six groups.
The technologies in the model represent feasible methods for treating the
model's waste streams. The model includes not only technologies commonly used
in waste treatment, but also those that are well developed but less commonly
used industrial processes. Treatment technologies with extremely limited
application to the waste streams in the model, are not included, for example,
reverse osmosis and electrolytic decomposition.
We define the feasibility of a treatment technology for a waste stream in
two ways. First, the technology must be technically feasible for the waste
stream. For example, precipitation can remove dissolved species from solution
but is clearly not feasible for dealing with hazardous constituents in
suspension. Second, to avoid a preponderance of "non-sensible" waste stream
treatment technology combinations, the model also contains a set of
conventions. For example, leaching is used only for waste streams containing
specified heavy metals at a dry weight concentration in excess of 10 percent.
In essence, these conventions are coarse screening criteria to eliminate
extremely high cost options. Conventions of this type are not absolute
constraints, but simply a means of providing a consistent and reasonable
approach for matching technologies to waste streams.
Matching waste streams to technologies is a combination of engineering
analysis and the review of waste management practices currently employed by
industry. In general, our assignment of treatment technologies is based on
the following:
Physical form of the waste stream, e.g., solid,
sludge, or liquid; aqueous/non-aqueous liquid content
Hazardous constituents to be treated and their
physical/chemical properties
Overall chemical composition of the waste stream
Based on these general considerations, the model uses a set of treatment
criteria or rules to define the suitability of a waste stream for each
treatment technology. The factors considered in developing these criteria are
summarized in Exhibit 2-8, and the quantitative criteria are presented in
Appendix B. Exceptions to these treatment rules are incorporated in the model
where our knowledge of current practices indicates them to be appropriate.
In assessing the feasibility of the various technologies for particular
waste streams, it is important to consider the overall chemical composition,
as well as the hazardous constituents to be treated. Treatment technologies
are not always specific to individual constituents, and a given technology may
consequently be a poor choice for treating a hazardous constituent that is a
small fraction of the overall waste composition. For example, steam stripping
of an aqueous organic waste stream is effective, for the purposes of the
model, only if it enables some separation of hazardous from nonhazardous
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EXHIBIT 2-7
TREATMENT TECHNOLOGIES
Group
Technology
Dewatering (Phase Separation)
Chemical Transformation
Component Separation
Biological Treatment
Solidification
Containerization
Vacuum Filtration
Centrifugation
Sludge Drying Beds
Chemical Precipitation
Oxidation/Reduction
Evaporation/Drying
Steam Stripping
Solvent Extraction
Leaching
Distillation
Carbon Adsorption
Conventional Activated Sludge
Chemical Fixation/Stabilization
Asphalt Solidification
Steel Drums/Sorbent
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EXHI13I r 2-8
CRITERIA TOR ASSIGNMENT OF TREATMENT TECHNOLOGIES TO WASTE STREAMS
Acceptable Waste
Category, Subcategory
Cha rac te ri sties
a/
Cons i dc red
Treatment Technology
or Waste Stream
Wa ste
Co;ist i tuent
Vacuum F i1trat ion
01 and 0't
Suspended
so 1 ids
None
Cenlri Tligat ion
01 and 0U
Suspended
so 1 ids
None
Sludge Drying Beds
01
Suspended
so 1i ds
None
Chemical Precipitation
01.02
None
Me ta 1 s
Chemical Destruction
01.02 and 02.02
None
Chromium and cyanide
Evaporat ion/Dryi ng
01, 02 and OU
Non-wa ter
rract ion
Vapor pressure
Steam Stripping
02
Suspended
so I ids
Henry's constant; Hoi1ing
po i nt
Solvent Extraction
02.01.02
02.01.03
02.02.12
None
Phenol extracted by butyl
aceta te
Benzene and toluene
extracted by butylene
Leach i ng
01.01, 01.01, 01.05,
and 05
None
Metals in suspension;
Concent ra t i on
D i st i11 a t i on
03.01
Suspended
so 1 ids
None
Carbon Adsorption
02
Suspended
so 1 ids
Concentra t ion;
Freundlich isotherm
Biological Treatment
02
Di ssoIved
so 1 ids
Biodegradation rate
Chemical Fixation
01.01, 01.02, OI.OU,
and 04 and oh
01.05,
None
Cyanide and chromium excluded
Asphalt Solidification
01, 02, 03.03 and 05
Non-wa ter
rrac t i on
Metals concentration
Conta inerization
Any
Flow rate
None
a/ Detailed values and explanations are included in Appendix B.
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2-23
constituents, or recovery of a portion of the waste stream. Similarly, it may
be desirable to remove the nonhazardous constituents from a waste stream prior
to treatment of its hazardous constituents. For example, oil recovery from
certain petroleum waste streams may be desirable before treatment of the heavy
metal constituents. The removal of nonhazardous constituents, however, is not
included in the current configuration of the model.
The application of each treatment technology modifies the characteristics
of the waste stream. The effects of applying the treatment technologies to a
waste stream ("treatment effectiveness") are identified in Exhibit 2-9. For
containerization, treatment effectiveness is measured by the elimination of
free liquids (by sorbent addition) and prevention of environmental releases of
the waste constituents for a period of five years.
The model often uses "treatment trains," or treatment by a series of
technologies, in order to produce a residual suitable for transportation and
disposal. The effluent from the initial treatment step is the influent to
following treatment steps. Dewatering, chemical transformation, and
solidification commonly produce one solid waste residue for successive
treatment steps. Component separation, however, may produce two separate
streams that are both hazardous. At its current stage of development, the
model is able to track only one of these streams, which is termed the RCRA
stream. Consequently, the use of treatment technologies employing component
separation is limited to situations where one of the residual streams is
either nonhazardous, or alternatively is recovered for re-use. Exhibit 2-9
lists the residual streams generated by the treatment processes, identifying
the RCRA stream to be disposed of and the recovered components.
In addition to quantifying these output streams, the model estimates the
amounts of hazardous constituents that are released into the environment (air,
surface water, and ground water) from each waste stream/technology
combination. We group sources of environmental releases of waste constituents
into four categories:
Continuous aqueous releases to surface water
Continuous releases to air from fugitive emissions
Intermittent releases to surface and ground water
resulting from spills
Intermittent releases to air resulting from spills.
Continuous releases to surface water arise from the discharge of treated
effluent when the effluent is not the RCRA stream. The model considers
routine spillage to be a negligible contribution to continuous aqueous
releases. Intermittent releases to surface and ground water occur from spills
caused by vessel failure or, in the case of sludge drying beds, by liner
failure. The model accounts for this type of release only when the treatment
unit is housed outdoors. For indoor units we assume that spills will be
contained within a closed drainage system.
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treatment technology
Vacuum F iI Ira t i on
Cont r i Fuga t i on
Sludge Drying Bods
Chemical Precipitation
Ox i da t i on/Reduc t ion
Evaporat ion/Dry ing
Steam Stripping
Solvent Extraction
Leach i ng
D i st iI I at i on
Carbon Adsorption
-- Without Regeneration
-- With Regeneration
Biological Treatment
Chem i caI F i xa t i on
Asphalt So I idiTication
Conta inerization
EXIII RI r 2-9
TRtAIMENT rn FXI IVLNLSS AND RCRA F_f FLUENT I 01 N I I F I CAT I ON
treatment FFTectivoness
RCRA FI f Incnt
Recovered Traction
Dewaters to 30% solids
llowaters to 25% solids
Dewaters t.o i|0% solids
Precipitates 98% or metals
Oxidizes 98% of cyan i tin/reduces
98% of chromium
Dries to 30% water content
Strips 90% of constituents
Extracts 95% or constituents
Leaches 70% of metals: dewaters
residue to 30% solids
Removes 95% of constituents
Dewatered sludge
Dewatered sludge
Downterod sludge
Sludge with precipitated
me t a Is
rrriimnt containing ?.% or
cyanide arid/or chromium
Dried erriuent
rrrluent containing 10% of
hazardous constituents
Frrluent containing 5% oF
haza rtfrius const i tuents
Dewatered sludge
Erriuent containing 5% or
hazardous constituents
None
None
None
None
None
None
Stripped constituents
Extracted constituents
Leached constituents
Distilled constituents
Removes 99% or constituents
Removes 99% or constituents
Removes 10-92% or biodegradable
const i tuents
Reduces constituent teachability
by 2 orders or magnitude;
removes water
Reduces constituent teachability
by M orders or magnitude;
removes water
Removes Tree liquids
Erriuent with carbon and 99% None
or hazardous constituents
None None
None None
Solidiried waste None
Sol idiTied waste None
Drummed waste with sorbent None
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2-25
When an accidental surface spill occurs, we assume there is some
volatilization of a constituent to air, with the remainder either running off
to surface water or percolating to ground water. The distribution of the
non-volatilized portion of the spill is approximated by using a runoff
coefficient of 70 percent, representative of industrial areas with impervious
surfaces. Thus, 70 percent of the spill migrates to surface waters,18 and
30 percent percolates to ground water. The volatilized portion of the spill
is the only intermittent release to air considered by the model.
There are a variety of sources of continuous releases to air including
fugitive emissions of both vapor and dusts and the evaporation of volatile
constituents from open vessels. For open vessels, the model distinguishes
between releases from aeration using high energy mixing and evaporation from
vessels under quiescent conditions.
Exhibit 2-10 presents a summary of the types of environmental releases
from each technology. Appendix B provides detailed descriptions of the
functional aspects of the technologies and their treatment of hazardous
constituents.
In the remainder of this section, we provide capsule descriptions of each
treatment technology. We discuss the technologies under the headings of the
groups identified in Exhibit 2-7.
(a) Dewatering (Phase Separation). Dewatering or phase separation
concentrates the suspended solids in a waste stream by the physical removal of
the water fraction. In so doing, it does not change the chemical
characteristics of the waste stream. Three dewatering techniques, vacuum
filtration, centrifugation, and sludge drying beds, are used in the model to
represent the range of configurations commonly used in industrial
applications. Pressure filtration is also a commonly used technique, but is
excluded here, since for the purposes of the model, it is essentially similar
to vacuum filtration.
A prerequisite for the application of any dewatering technique is the
presence of a significant concentration of suspended solids in the waste
stream. In the model, we consider this "significant concentration" to be two
percent, consistent with our definition of a sludge waste stream. Hazardous
wastes of this type may be generated directly by industrial processes or arise
from the treatment of aqueous solutions of heavy metals by chemical
precipitation. The model assumes 100 percent solids capture in the
application of all three dewatering techniques; the resultant dewatered sludge
is the RCRA stream.
18Gordon Maskew Fair, John Charles Geyer, and Daniel Alexander Okun,
Elements of Water Supply and Wastewater Disposal, 2nd edition (New York:
John Wiley & Sons, Inc., 1971), p. 274.
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EXHIBIT ?-10
ENVIRONMENIAL RELEASES TROM TREATMENT TECHNOLOGIES
Treatment Technology
Surface and Ground Watcr
Cont i nuous
A i r
Inte rm i ttent
Cont i nuous
Inte rm i ttent
Vacuum F iIt ra t i on
Cent r i ftiga t i on
Sludge Drying Beds
Chemical Precipitation
Chemical Destruction
Evaporat ion/Drying
Steam Stripping
Solvent Extraction
Leach i ng
Di st iIlat ion
Carbon Adsorption
Biological Treatment
Chemical Fixation
Asphalt Solidification
Corita i ner i za t ion
Discharge of filtrate
Discharge of centrate
Discharge of filtrate
Discharge of clarifier
overflow
None
None
None
None
None
None
Discharge of effluent
Discharge of effluent
None
None
None
None
None
Sp iI Is due to the
liner fa iIure
None
None
None
None
None
None
None
None
None
Sp iIIs due
to vessel failures
Sp iI Is due
to vesseI fa iIures
None
None
None
None
None
None
None
None
None
None
None
Sp iII voI a t iIi za t i on
Sp i I I voI aL i I i za t ion
None
Sp i II voI a L i I i za t i on
Sp iI I voI a t iIi za t i on
None
Fugitive emissions of
cyanide compounds
(cyanide oxidation only)
Fugitive dust emissions
Fugitive emissions
Scrubber emissions
Fug i t i ve emi ss i ons
None
Fugitive emissions
Fugitive emissions
Furnace emissions (for
regeneration only)
Vo I a t i I i za t i on f rom
ecjtia I i za t i on ba s i n
Volatilization from
ae ra t i on ba s i n
Fugitive dust emissions Spill volatilization
Fugitive dust emissions Spill volatilization
None
None
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2-27
Environmental releases from these technologies are limited to releases to
surface and ground water, as the waste streams treated by those technologies
do not contain volatile organics.
There are three dewatering technologies:
Vacuum Filtration separates the suspended solids from the liquid
fraction by drawing the free liquid through a membrane using a vacuum. The
vacuum filter is currently the most commonly used dewatering technique for
waste treatment. In addition to aqueous sludges, it can be used for
non-aqueous liquids such as oily wastes, but it is not suitable for highly
viscous semi-liquids such as tars.
In the model, vacuum filtration is applied to aqueous inorganics and oily
wastes ("waste categories 01 and 04 in the classification described in Section
2.2) either directly or in treatment trains following chemical precipitation.
The model accounts for releases of hazardous constituents from vacuum
filtering by one mechanism only: discharge of filtrate to surface water.
Centrifugation separates the suspended solids from the liquid fraction
by rapidly rotating the waste stream within the confines of a rigid vessel.
Centrifugal force causes the suspended solids to migrate towards the periphery
of the rotating vessel, while the liquid flows out the sides or ends.
Centrifugation is especially effective for sticky or gelatinous sludges.
Since the centrifuge is totally enclosed, it is also suitable for waste
streams that are volatile or flammable or present other hazards if not
enclosed. In practice, the application of centrifugation to hazardous waste
treatment is somewhat limited, although the smaller the size of the stream,
the greater the potential applicability.
The model applies centrifugation to aqueous inorganics or oily wastes
(waste categories 01 and OA) either directly or in treatment trains following
chemical precipitation.
The model accounts for releases of hazardous constituents from
centrifugation by one mechanism only: discharge of centrate (the separated
liquid fraction) to surface water.
Sludge drying beds dewater sludges by gravity drainage of free liquids
through graded sand and gravel layers and by evaporation drying. The dried
sludge is removed from the bed periodically. The technique is widely used for
wastewater treatment sludges and is also suitable for other aqueous inorganic
sludges that are essentially oil-free. Oily wastes are excluded from sludge
drying beds because the oil could clog the bed. Because the drying bed is
exposed to the atmosphere, it is also not suitable for sludges containing
volatile components.
The model applies the sludge drying bed treatment to aqueous inorganics
(waste category 01) either directly or in treatment trains following chemical
precipitation.
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The model accounts for releases of hazardous constituents from the sludge
by two mechanisms: (1) the continuous discharge of filtrate to surface water,
and (2) intermittent spills to ground water due to liner failure.
(b) Chemical Transformation. Chemical transformation converts the
hazardous constituents of a waste stream into a form which is either more
easily handled for subsequent treatment and disposal, or is less hazardous as
a result of modified physical or chemical properties. The model uses two such
chemical transformation processes, chemical precipitation and chemical
destruction (oxidation/reduction). These technologies are applied to
inorganic hazardous constituents in solution. Both processes are considered
to be housed indoors, so there are no intermittent releases to surface or
ground water. The principal environmental releases are the continuous
discharge of treated effluent from chemical precipitation and the continuous
emission of cyanide for chemical destruction.
There are two chemical transformation technologies in the model.
Chemical precipitation transforms a hazardous constituent in solution
into a solid phase which can be removed from solution by subsequent
flocculation and clarification. The most commonly used precipitation agent is
lime, which is used for the removal of heavy metals from aqueous inorganic
waste streams as the metal hydroxide or related compounds. Sulfide
precipitation is also becoming increasingly commonly used for metal
precipitation, for example, in the removal of mercury from chloroalkali
wastewater.
The model uses lime to precipitate metal hydroxides from solutions
containing heavy metals (waste subcategory 01.02), whenever one or more of the
metals lead, cadmium, zinc, nickel, copper or trivalent chromium is present.
The resultant sludge is the RCRA stream. In the model, further treatments
that may be applied to the sludge include dewatering and chemical
stabilization/fixation.
The model accounts for environmental releases of hazardous constituents by
one mechanism only: discharge of clarifier overflow to surface water.
Oxidation/Reduction chemically transforms of hazardous constituents to
less toxic species by changing their oxidation state. It is most commonly
applied to aqueous systems containing relatively dilute concentrations of
hazardous constituents, generally not more than a few thousand parts per
million. For inorganic waste streams, the most common applications are the
oxidation of cyanide solutions to cyanate or to carbon dioxide and nitrogen,
and the reduction of nexavalent chromium to trivalent chromium. These are the
only applications used in the model. Oxidation/ reduction may also be
appropriate for organic waste streams, for example, the oxidation of phenols
in dilute streams. This type of application, however, is less commonly used
for hazardous waste treatment and is not considered in the model.
The model considers the application of oxidation/reduction techniques
whenever cyanide or hexavalent chromium appear in solutions containing heavy
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metals or wastewaters from organic chemical production (waste subcategories
01.02 and 02.02). When both cyanide and hexavalent chromium are present,
chromium reduction follows cyanide oxidation. After treatment, a small
fraction of the initial cyanide or hexavalent chromium remains in solution and
constitutes the RCRA stream.
For chromium reduction, the model does not include any allowance for
environmental releases. For cyanide oxidation, the system is assumed
similarly to be housed indoors, so that liquid spills and leaks can also be
considered negligible. Cyanide oxidation is subject to routinely-occurring
air emissions of cyanide.
(c) Component Separation. Component separation techniques distinguish
the constituents of a waste stream by differences in physical properties. Six
component separation technologies are included in the model: evaporation/
drying, steam stripping, solvent extraction, leaching, distillation, and
carbon adsorption. In general, this class of technologies is more
sophisticated than the other classes in the model and is currently less-
commonly used for hazardous waste management. Applications of component
separation techniques are often associated with recovery or reuse of one of
the separated waste components.18
Evaporation/drying dewaters sludge streams by heating (usually by
steam-heated coils) in a stirred or agitated vessel under vacuum. The process
differs from dewatering techniques such as sludge drying beds in that water is
removed by evaporation only, not by drainage. Consequently, none of the
dissolved constituents are lost from the waste stream, in contrast to
dewatering. Evaporation/drying is used for streams that do not contain
volatile compounds, particularly those with a high concentration of dissolved
solids. Evaporation may also be used to concentrate liquids for recovery, as
in applications to spent cleaning and electroplating process solutions, but
this type of application is not considered in the model.
Applications in the model are restricted to sludge treatment for aqueous
inorganics, aqueous organics, and oily wastes (waste categories 01, 02 and 04)
either directly or in treatment trains following chemical precipitation. The
dried sludge is the RCRA stream.
The model accounts for releases of hazardous constituents from evaporation/
drying by one mechanism only: the release of fugitive dust emissions during
the unloading of the dried waste.
Steam stripping removes volatile organics from aqueous waste streams.
It is essentially a fractional distillation of volatile components from a
wastewater stream. The hazardous constituents to be stripped are generally
fairly soluble in water. Steam stripping is particularly attractive when
19The model currently does not credit the value of recovered or reused
components in estimating the cost of a treatment technology.
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byproducts recovery is possible for the stripped components. In such cases,
the volatile components are usually processed further for recovery.
The model applies steam stripping to aqueous organic streams (waste
category 02), excluding streams containing a significant level of suspended
solids that could plug the stripping column. Steam stripping may be useful in
reducing the load on subsequent waste treatment processes and is used in the
model as a pretreatment to biological treatment. Waste streams containing
high concentrations of volatile organics are excluded since these streams are
more effectively handled by alternate treatment technologies such as
distillation. Steam stripping is only partially effective in removing
volatile organics. The treated waste stream containing residual organics
constitutes the RCRA stream.
The model accounts for environmental releases of hazardous constituents, by
three mechanisms: (1.) fugitive emissions of volatile organics to air, (2)
releases to air from vessel failures or spills, and (3) releases to air of
emissions of volatile organics from the scrubber.
Solvent extraction (or liquid-liquid extraction) separates the
constituents of a liquid solution by contact with another immiscible liquid.
In general, solvent extraction is not competitive with other options unless
some form of materials recovery is involved. Such applications are
potentially feasible for wastes containing one or a limited number of organic
components in concentrations from a few hundred parts per million to several
percent. In practice, the feasibility of solvent extraction depends on a
case-by-case assessment of the individual waste stream and the availability of
a suitable solvent.
For the model, we restrict the use of solvent extraction to a limited
number of aqueous organics (waste category 02) where treatment feasibility has
been demonstrated. Following solvent extraction, a small fraction of the
extractable constituents remain in the treated waste stream, which constitutes
the RCRA stream.
Environmental releases from solvent extraction parallel those from steam
stripping and consist of two types:' (1) fugitive emissions of volatile
organics to air, and (2) releases to air from vessel failures or spills.
Leaching dissolves and separates selected suspended solids from sludges
and solid waste streams. Present applications center around metals recovery
options, where all the metals present in the waste stream are recovered. In
theory, there are no limits to the concentration or amounts of substances that
can be removed by leaching, provided that sufficient reagent is added to
satisfy the stoichiometry requirements, and that sufficient solvent is added
to maintain the concentration of the dissolved species below the solubility
limit.
The model applies leaching to aqueous inorganics (sludges only) and
inorganic solid residues (waste categories 01 and 05). Based on review of
reported applications for metals recovery, the hazardous constituents
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considered suitable for leaching are arsenic, barium, cadmium, chromium,
copper, lead, mercury, nickel, vanadium, and zinc.20 For the process to be
feasible, the model requires that the leachable metal be present in the waste
stream predominantly in suspension and at a relatively high concentration, 10
percent on a dry weight basis. Following leaching of the waste stream, the
model includes separation of the leachate for recovery from the remaining
solids. The residual sludge is the RCRA stream.
Environmental releases from leaching are not considered significant in the
model.
Distillation separates the constituents of a liquid mixture using
differences in vapor pressure. The process involves boiling a liquid solution
and condensing the vapor of the more volatile species. Because of high energy
costs, treatment of wastes by distillation is generally restricted to liquid
organics and involves the recovery of the distillate. Distillation is not
suitable for viscous, polymeric materials such as tars for waste streams with
high concentrations of suspended solids.
The model applies distillation to spent solvents and degreaser sludges
(.waste subcategory 03.01 in the classification described in Section 2.2). The
use of distillation may be possible for other concentrated organics, but the
technical and economic feasibility is highly dependent on individual waste and
plant conditions. For this reason, distillation is restricted to spent
solvents and degreaser sludges in the model. After distillation, the still
bottom contains residual hazardous constituents and is the RCRA stream.
The model accounts for environmental releases of hazardous constituents by
two mechanisms: (1) fugitive emissions of volatile organics to air, and (2)
releases to air from vessel failures or spills.
Carbon adsorption removes selected organic or inorganic species in
solution from aqueous waste streams. Applications of carbon adsorption are
more common for organic waste streams than inorganic waste streams. Granular
activated carbon fGAC) is the predominant type of carbon adsorption treatment
process used for hazardous wastes. For aqueous organics, carbon adsorption is
most suitable for waste streams containing constituents with low water
solubility, high molecular weight, low polarity, and a low degree of
ionization. Concentrations of dissolved constituents of up to five percent
are acceptable when recovery is involved, although in general, a limit of one
percent is more appropriate. Pretreatment is necessary to remove suspended
solids (less than 50 ppm) and oil and grease (less than 10 ppm).
In the model, a granular activated carbon (GAC) system, preceded by a
mixed media filter, is used to treat aqueous organics (waste category 02).
For the waste stream to be suitable, the model requires all hazardous
2ฐA.D. Little, "Physical, Chemical and Biological Treatment Technologies
for Industrial Wastes" (1976).
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constituents to be treatable (see Appendix B-11). Following carbon treatment,
the bulk of the hazardous constituents are associated with the carbon. For
small scale systems, the carbon becomes the RCRA stream, and for large scale
systems, the carbon is regenerated so that there is no RCRA stream.
The model accounts for four types of releases from treatment by carbon
adsorption: (1) discharge of the treated effluent to surface water, (2)
releases to air from vessel failures and spills, (3) releases to air of
fugitive emissions, and (4) releases to air of emissions of organics during
the regeneration process for large scale systems.
(d) Biological Treatment. Aerobic biological treatment decomposes
organic waste streams through the use of aerobic bacteria. It can be applied
to a wide variety of aqueous waste streams, providing the solids content is
low (less than one percent) and the contaminants are predominantly organic and
biodegradable. Pretreatment may be required to adjust pH, reduce the load on
the biological treatment system, or remove toxic or refractory (non-
biodegradable) constituents.
The model uses a conventional activated sludge scheme preceded by an
equalization basin as the representative biological system. The model applies
biological treatment to aqueous organics (waste category 02), the constituents
of which are all biodegradable at a specified minimum rate. The waste
biosludge generated by biological treatment is assumed to be non-hazardous, so
there is no RCRA stream.
The model accounts for releases of hazardous constituents from biological
treatment by three mechanisms: (1) discharge of treated effluent to surface
water, (2) volatilization of organics to air from the equalization basin, and
(3) volatilization of organics to air during aeration from the activated
sludge basins.
(e) Solidification. The term solidification collectively defines
disposal technologies that fixate or encapsulate wastes in a solid matrix.
Solidification processes can generically be classified into five groups,
according to the principal additive: silicate and/or cement-based;
lime-based; thermoplastic based; organic polymer-based; and encapsulation
techniques. Two solidification technologies, cement-based chemical
fixation/stabilization and thermoplastic-based asphalt solidification, are
used in the model.
Chemical fixation/stabilization solidifies sludges by binding the waste
stream in a solid matrix using inorganic reagents. The process may involve
chemical reaction between the waste constituents and the fixation reagents as
well as physical processes. It may also be used to reduce the leachability of
solid residues by first dissolving the material and subsequently precipitating
and fixing the dissolved solids. Chemical fixation/stabilization is generally
suited to inorganic waste streams. Limitations may arise for particular
inorganics, although the use of additives can solve most of these problems.
In general, this technology is not suitable for predominantly organic waste
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streams, although it has been reported to have been used successfully for oily
wastes from petroleum refineries.
In the model, a cement-based chemical fixation/stabilization is applied to
aqueous inorganics, oily wastes and inorganic solid residues (waste categories
01, 04 and 05). The solidified waste stream is the RCRA stream.
The model accounts for three types of environmental releases from chemical
fixation/stabilization: (11 releases to air from vessel failures and spills,
(2) releases to surface and ground water from vessel failures and spills, and
(3) releases of fugitive dust to air from inorganic solid residues.
Asphalt solidification solidifies sludges and solid residues by binding
the waste stream in a solid matrix using a thermoplastic encapsulating medium.
The waste stream is mixed with the hot liquid asphalt so that the solid
particles become encapsulated with asphalt. It is then allowed to cool and
set into a solid mass. The technique is reportedly suitable for a wide
variety of hazardous waste streams, both inorganic and organic, including some
not suitable for lime- or cement-based technologies. Experience with non-
nuclear hazardous waste streams is limited, although asphalt solidification
has been used for electroplating sludges, refinery sludges, arsenic wastes,
paint sludges, and mechanical plating sludges, after evaporating the water
present in the waste stream. This technology is not suitable for wastes
containing solvents that attack asphalt. Strong oxidizers, such as nitrate,
chlorate, perchlorate, and persulfate, which react with the organic matrix
material, as well as salts subject to swelling on hydration, should not be
included in the waste streams. In addition, since the process involves
heating to temperatures of 130 to 230ฐC, it is not appropriate for low flash
materials.
In the model, asphalt solidification is applied to aqueous inorganics
(sludges only), aqueous organics (sludges only), organic/metal sludges, and
inorganic solid residues (waste categories 01, 02, 03.03 and 05). The
solidified waste stream is the RCRA stream.
The model accounts for three types of releases from asphalt
solidification: (1) releases to air from vessel failures and spills, (2)
releases to surface and ground water from vessel failures and spills, and (3)
releases of fugitive dust to air during process mixing.
(f) Containerization. Containerization is often used by generators
of hazardous waste to accumulate and store waste materials prior to transport
and disposal. It is also used by commercial off-site disposal facilities
prior to landfilling to minimize direct handling of the waste, to reduce air
emissions, and to reduce releases to ground and surface water (at least
temporarily, until the containers corrode or fail). A variety of materials
and sizes of containers are used, including 55 gallon steel drums, fiberboard
boxes, and open metal containers.
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The model uses 55 gallon steel drums for containerization and includes the
addition of sorbent for waste streams containing free liquids.
Containerization, therefore, eliminates free liquids as a component of any
environmental release resulting from landfilling. For waste streams generated
at rates in excess of 10,000 kg/day, containerization is not considered an
option.
The model does not account for any environmental releases of hazardous
constituents during waste containerization.
2.3.2 Transportation Technologies
Transportation technologies are used to transfer waste from the point of
generation and treatment to the point of disposal. Both on- and off-site
disposal require a handling or transportation step. By including a
transportation component, the model is able to calculate the risks and costs
of moving waste.
The model considers only truck transport because 90 percent of all current
hazardous waste transport is by truck.21 There are two types of vehicles:
A 6,000 gallon capacity tanker truck for liquid wastes
An 18 ton capacity stake truck for containerized solid
or liquid wastes.
The model includes three travel distances. For on-site disposal, we
assume that 0.25 miles separate the point of generation from the point of
disposal. There are two off-site travel distances: a local, one-way trip of
25 miles, and a long- distance, one-way trip of 250 miles. We selected these
values because they differ by an order of magnitude and fall within the range
of distances typically travelled to transport hazardous waste.
Incidents involving releases of hazardous wastes during transport result
from any number of causes and can occur at shipment terminal points or
enroute. The model considers three sources of releases to the environment:
Releases due to vehicular accidents enroute
Releases occurring enroute from causes other than
vehicular accidents
Releases in the vicinity of shipment terminal points
during loading and unloading of wastes.
21M. Abkowitz, A. Eiger, and S. Srinivasan, "Assessing the Releases and
Costs Associated with Truck Transport of Hazardous Wastes," prepared for the
Office of Solid Waste, U.S. EPA (January 1984).
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The first two classes of release incidents are a function of the distance
travelled. The third class depends on the number of shipments made. All
three classes of release are functions of the type of container used in waste
shipment. The model estimates release rates as the fractions released from
the total waste shipped.
For shipment of wastes by stake truck, the model uses two different types
of containers: 55 gallon steel drums or open metal containers. The tanker
truck represents an additional type of container.
Exhibit 2-11 summarizes the model's estimates of fractions released for
each container type for the three shipment distances. These estimates are
based on incident frequency and release data, information on shipment
distance, and truck volume and accident data.22 The model multiplies these
fractions by the total volume of waste shipped to obtain the total expected
amount released. A more detailed description of the transportation
technologies can be found in Appendix C.
EXHIBIT 2-11
TOTAL EXPECTED RELEASE RATE FRACTIONS
Truck/Container
Type
One-Way Distance
1/4 Mile
25 Miles
250 Miles
Tanker
Steel Drum
Open Metal Container
8 x 10
3 x 10
1 x 10
-U
-3
1 x 10ฐ 3 x 10"5
- Zf -1+
4 x 10 9 x 10
1 x lo"3 3 x 10~3
2.3.3 Disposal Technologies
The model incorporates six disposal technologies:
Landfills
Land treatment
Surface impoundments
22 Ibid.
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Deep well injection
Waste piles
Incineration
The model also provides for the addition of corrective action measures to the
land disposal technologies. Several configurations of landfills, surface
impoundments, and waste piles are included in the model to reflect the range
of designs and operating practices for these technologies. The model contains
two sizes for each technology Cexcept deep well injection) in order to account
for differences in the scale of on- and off-site facilities. In general, the
smaller disposal facility is intended to represent a typical facility
constructed at the site of waste generation. The larger facilities reflect
capacities typical of off-site operations. Only one size deep well injection
facility is in the model because we believe it is representative of most
existing installations.
The following is a synopsis of each disposal technology. We provide a
brief description of the technology, explain any limitations on the wastes it
may handle, and summarize the environmental releases generated using the
technology. Detailed descriptions of the technologies are provided in
Appendix D.
Landfiliing involves placing waste in a specially prepared excavation
or trench and then covering the waste with fill material. Modern landfills
are commonly partitioned into a number of "cells." All waste streams in the
model may be disposed in landfills.
To represent the wide -range of landfill designs, the model includes six
configurations, differing primarily in the type of liner system used. Each
configuration has two sizes of waste capacity: 500 and 60,000 metric tons per
year. Exhibit 2-12 outlines the features of the six configurations. The
first three configurations satisfy the 40 CFR 264 regulations for landfills.
We assume that hazardous substances may be released from landfills by
three means: volatilization, accidental surface spills, and leachate
migration. The model estimates air releases by computing volatilization rates
of hazardous substances for the various operating conditions that exist over
the life of the facility and after closure. Accidental surface spills due to
leachate collection system failure are taken into account by including an
expected fraction release rate and distributing the spilled material between
air, surface water, and ground water. The model accounts for leachate
migration to soil and ground water by first calculating leachate formation
within the fill arising from rainwater infiltration and free waste liquids,
and then considering the rate of migration from the fill. For configurations
with synthetic membrane liners, the rate of migration depends primarily on the
rate of liner failure. For clay-lined configurations, the rate is determined
by permeability limitations.
Land treatment -- also known as land farming, land cultivation, and
soil incorporation -- is a process by which industrial wastes are mixed with
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EXHIBIT 2-12
FEATURES OF LANDFILL CONFIGURATIONS
Configuration
Features
(1) Double Synthetic Liner
(2) Single Synthetic Liner and
Clay Liner
Leachate collection and treatment; leak
detection system; control and
collection of surface runoff; clay/
synthetic membrane cover.
Leachate collection and treatment;
monitoring wells; control and
collection of surface runoff; clay/
svnthetic membrane cover.
(3) Single Synthetic Liner
(4) Clay Liner (1 meter)
Leachate collection and treatment;
monitoring wells; control and
collection of surface runoff; clay/
synthetic membrane cover.
Leachate collection and treatment;
monitoring wells; control and
collection of surface runoff; clay
cover.
(5) Clay Liner (0.3 meter)
(6) Unlined
Leachate collection and treatment;
monitoring wells; control and
collection of surface runoff; clay
cover.
Native soil compacted to 10-5 cm/sec
permeability; control of site runoff;
clav cover.
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surface soils and allowed to decompose. .The objective in land treatment is to
ensure that chemical constituents in the waste are retained or decomposed in
the surface layer. Microbial degradation is the principal decomposition
mechanism, but volatilization and chemical and photochemical degradation are
also important mechanisms. Land treatment is not suitable for wastes too
toxic or too persistent to be degraded. The model accepts only 15 potentially
biodegradable waste streams (generated primarily in the petroleum refining and
wood preserving industries) for land treatment.
The model's design and operating configuration is intended to satisfy the
40 CFR 264 regulations for land treatment. We include 6.5 and 74.3 acre
sites, each with surface water run-on and run-off control, soil pH control,
inspection, monitoring, and wind dispersion control.
The model provides for three sources of environmental releases of
hazardous constituents from land treatment: volatilization to air,
infiltration to ground water, and run-off to surface water. It estimates air
releases by calculating average emission rates of volatile chemicals after the
waste is applied. There are two sources of releases to ground water:
contaminated rainwater infiltration and free-flowing waste liquids containing
dissolved hazardous constituents. For contaminated surface water runoff, the
model computes the expected annual storm flow that exceeds the holding
capacity of the stormwater runoff management system.
Surface impoundments are used to contain liquid wastes or wastes with
free liquids, often for long-term storage. Where evaporation continually
reduces the volume of waste, impoundments can serve as disposal facilities and
be closed like a landfill. In the model, we assume that only moderate
evaporation occurs and that intermittent dredging is conducted over the
operating life of the facility. As a result, hazardous constituents in the
impoundments not released to the environment are removed after a period of
time and disposed elsewhere. Any waste with less than 10 percent solids
concentration may be placed in an impoundment.
The model includes six different configurations for surface impoundment
designs, based up on essentially the same liner systems used for landfills.
Each configuration has two sizes: one-quarter acre and two acres. Unlike
most other disposal technologies, the model assumes both sizes correspond to
on-site operations, since most surface impoundments are on-site. Exhibit 2-13
outlines the features of the six configurations. As with landfills, the first
three configurations satisfy the 40 CFR 264 regulations.
The model accounts for three sources of releases from surface
impoundments: volatilization to air, accidental spillage and overtopping, and
leaching to ground water. The model estimates air emission release rates by
calculating a steady-state, mass transfer rate of volatilization for hazardous
constituents in the waste stream. Accidental spillage and overtopping are
accounted for by calculating the probability of a storm that could cause such
occurrences. The model computes the rate of release of constituents to ground
water by combining liner failure probabilities with the saturated flow
limitations imposed by underlying clays and soil.
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EXHIBIT 2-13
FEATURES OF SURFACE IMPOUNDMENT CONFIGURATIONS
Configuration Features
(1) Double Synthetic Liner
(2) Single Synthetic Liner and
Clay Liner
(3) Single Synthetic Liner
(A) Clay Liner (1 meter)
(5) Clay Liner (0.3 meter)
(6) Unlined
Leachate collection; leak detection;
run-on, run-off control system; final
cover.
4 monitoring wells; run-on, run-off
control system; final cover.
4 monitoring wells; run-on, run-off
control system; final cover.
4 monitoring wells; run-on, run-off
control system; final cover.
4 monitoring wells; run-on, run-off
control systemfinal cover.
Compacted native soil; 4 monitoring
wells; run-on, run-off control system;
final cover.
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Deep well injection is a disposal method whereby liquid wastes are
pumped under pressure into underground geologic formations, so that the
materials are isolated from the usual effects of the hydrologic cycle. It is
a technology that is highly dependent upon site-specific geologic conditions.
Consequently, the model restricts the use of deep well injection to favorable
geologic environments. The model further considers only "injectable" waste
streams, defined as those with flow rates of less than 2 million gallons per
day (mgd) and total suspended solids (TSS) concentrations of less than 5,000
ppm.
The model uses a single design and operating configuration that conforms
to the 40 CFR 146 regulations for underground injection. The well's capacity
is 138,000 cubic meters per year and it has a depth of 3,600 feet. Wastes of
between 10 and 5,000 ppm are pretreated by dual-media filtration prior to
injection so that the injected waste's TSS concentration is less than 10 ppm.
The well's operation is monitored to ensure detection of failure.
The model estimates releases from three types of injection well failure.
Releases from well-head/piping failure are discharged to the ground surface
and may volatilize to air, run off to surface water, or infiltrate to ground
water. The failure of the well casing or grout seal can lead to the leaking
of waste directly into aquifers. Finally, failure of the geologic formation
into which wastes are injected can allow upward migration of fluids to
groundwater formations. The model derives expected failure rates for each of
these conditions to compute the amount of waste constituents released to the
environment.
Waste piles are above-grade accumulations of solid, non-flowing waste
material. They are more properly considered a storage technology than a
disposal technology, since material must be removed after a period of time for
permanent disposal elsewhere. Nevertheless, the model considers waste piles
with other disposal technologies because of the parallel considerations in
evaluating associated risks and costs. Waste piles can manage only high
solids content waste streams, those greater than 40 percent solids by weight.
The model includes five waste pile configurations, all of which conform to
the 40 CFR 264 regulations. There are two sizes of each configuration: 60
and 2830 cubic meters. Exhibit 2-14 summarizes the features of the five
configurations.
The model computes air releases, surface water releases from the run-off
and leachate collection system, and ground water releases from failure of the
liner or concrete base. Releases to air result from the volatilization of
waste constituents and emissions of particulate caused by wind erosion. The
model calculates the quantity of leachate formed in the waste pile from
rainwater infiltration, and uses this to estimate accidental releases to
surface water due to failure of run-off and leachate collection and treatment
systems. The model also uses these estimates of the quantity of leachate
formed, in combination with estimates of failure of synthetic liners or cracks
in the concrete base, to calculate the rate of leachate released to ground
water.
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EXHIBIT 2-14
FEATURES OF WASTE PILE CONFIGURATIONS
Configuration
Features
(1) Double Synthetic Liner
(2) Synthetic and Clay Liner
Leachate collection in sand base and
treatment; leak detection; run-off
control.
Leachate collection in sand base and
treatment; four monitoring wells;
run-off control.
(3) Concrete Base with Ground
Water Monitoring
Leachate collection on concrete slab
and treatment; four monitoring wells;
run-off control.
(4) Concrete Base with Inspection
(5) Indoor Pile
Leachate collection on concrete slab
and treatment; periodic inspections;
run-off control.
Sloped floor slab drains free liquids.
Incineration is an engineered process using thermal oxidation of waste
material to produce a less bulky, less toxic, or less noxious material. A
waste must be combustible to some extent for incineration to be a viable
disposal method. In the model, the predominantly organic waste streams,
including aqueous wastes with an organic fraction greater than 10 percent, can
be incinerated. Hazardous constituents in these waste streams that are not
destroyed and that appear in the bottom ash are disposed with the ash in a
landfill.
The model includes two configurations of incineration: liquid injection
and combination rotary kiln/multiple hearth facilities. Each configuration
has two sizes: an on-site, 1400 pounds waste per hour capacity unit, and an
off-site, 7000 pounds per hour commercial scale facility. Waste streams of
less than 10 percent solids are incinerated in a liquid injection unit. All
other wastes are incinerated in a rotary kiln/hearth. The incinerators
operate at 99.993 percent destruction and removal efficiencies (DRE) for
organic hazardous constituents. Hazardous metals present in a waste stream
are not destroyed; they are distributed to the exit gas, ash, and scrubber
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effluent. Each incinerator in the model has wet scrubbers to remove metals
from the outlet gases before they are released to the atmosphere.
In the model, incinerators release hazardous substances to the environment
by two mechanisms: air emissions and the discharge of scrubber effluents to
surface waters. The model estimates air emission release rates based on the
DRE for organics and the removal efficiency of the wet scrubber for metals.
The model also estimates the amount of constituents released to surface water
via scrubber effluent discharge by using the efficiency of the wet scrubber
for removing metals.
Corrective action must be taken at land disposal facilities where
hazardous constituents are detected in ground water. The model includes three
types of corrective actions: slurry trench cut-off walls; groundwater
counterpumping; and a combination of slurry wall and counterpumping.
Corrective actions reduce releases to ground water according to the respective
effectiveness of the three corrective action technologies.
2.4 ENVIRONMENTS
The third and final part of the W-E-T framework is the environmental
setting in which the waste stream is managed. The risk posed by a release of
a hazardous substance to an environmental medium -- air, surface water, or
ground water -- depends on the differing capacities of the environmental
settings to dilute, degrade and transport the chemical, and on the nature of
the human populations and ecosystems that are exposed to the substance.
To account for these differences, we have defined environmental settings
in terms of three factors:
Population density
Surface water environment
Groundwater system.
We distinguished three ranges of population density, two surface water
environments, and two ground water environments, then formed all possible
combinations to yield 12 environmental settings for purposes of estimating
human health risks. As explained below, we distinguished seven surface water
environments, on the basis of ecosystem type, for purposes of estimating
ecological risks.
We chose not to use other factors in developing environmental categories,
for example, soil alkalinity or acidity, average precipitation, average
temperature; incidence of tornadoes, hurricanes, and thunderstorms, and
evaporation potential. These factors are less significant than those listed
above for determining the potential for human and ecosystem exposure to
hazardous chemicals.
We did not use varying atmosphere characteristics in defining
environmental settings because we have assumed the capacity of the air to
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transport, dilute, and degrade chemicals does not vary. Thus, although the
risks from environmental releases to air are considered by the model, we have
chosen to consider the air medium as a constant factor in the characterization
of environmental settings.
The remainder of this section presents a discussion of the distinctions
within each of the three environmental factors and the resultant distribution
of the environmental settings throughout the United States.
2.4.1 Population Density
We distinguished three ranges of population densities to characterize
environmental settings in terms of this factor:
High (250 people/km2 and over)
Medium (25-249 people/km2)
Low (fewer than 25 people/km2).
These ranges vary by one order of magnitude.
Using data from the I960 census, we characterized each of the 559
three-digit zip-code areas according to one of the three population
densities. We estimated the area covered by each zip code by measuring the
proportional area of the state represented by the zip code and multiplying
that fraction by the total area of the state. Exhibit 2-15, which summarizes
the distribution of zip-code areas by population density, indicates that
almost two-thirds of the areas of the country have medium densities.
EXHIBIT 2-15
DISTRIBUTION OF THREE-DIGIT ZIP CODES
BY POPULATION DENSITY
Population
Number of
Percentage
Density-
Zip Code Areas
of Total
Low
141
25%
Medium
342
61%
High
76
14%
2.4.2 Surface Water Environment
We used two different schemes to classify surface water environments in
the model, depending on whether human health risks or ecosystem risks are to
be assessed. Each of these schemes is discussed below.
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(a) Human Health Risk Classification. For assessing human health risks,
we divided differences in surface water assimilative capacity into two
categories: low and high. Given equal releases of wastes to each of these
areas, the low assimilative capacity environment poses higher risks than does
the high assimilative capacity environment. Areas with low assimilative
capacity are characterized by relatively low-flow streams (e.g., flows of
106 m3/day). Areas with high assimilative capacity are those with
high-flow or high-volume surface waters (e.g. stream flows of 107 m3/day
and lakes greater than 3xl010m3).
We simplified the task of distributing zip code areas into the two
categories by assuming that if a zip-code area borders the coast, the Great
Lakes, the lower Mississippi River, or the Chesapeake Bay, either completely
or partially, it is an area of high assimilative capacity. Exhibit 2-16
summarizes the distribution of the assimilative capacity categories used in
the model.
EXHIBIT 2-16
DISTRIBUTION OF THREE-DIGIT ZIP CODES BY
SURFACE WATER ASSIMILATIVE CAPACITY
Assimilative Number of Percentage
Capacity Zip Code Areas of Total
Low
High
440
119
79%
21%
(b) Ecorisk Classification. For assessing ecological risks, we
classified surface waters into seven groups, based on ecosystem type, to
represent the aquatic environments in the United States:
Large rivers
Medium rivers
Small streams
Rivers with large drainage basins, but low flow
Marshes
Sea coasts
Lakes.
Using a hydrologic map of the United States, we identified types of
surface water within each three-digit zip code.23 The classification of a
23nWater Resources Aggregated Subregions, Second National Assessment of
Water and Related Land Resources," U.S. Water Resources Council, U.S.
Department of Interior (1977).
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single representative form of surface water was often complicated by the
presence of more than one aquatic system. In such cases, we chose the form of
surface water in contact with the largest area of the zip code as the
representative type. There were three exceptions to this procedure:
Zip-code areas located along a Great Lake were
classified as lakes.
Zip-code areas along the sea coast and with a marsh
were classified as marshes.
Zip-code areas containing large or medium rivers were
classified as rivers unless the area had only marginal
contact with the river.
Exhibit 2-17 summarizes the distribution of zip codes among the seven surface
water subenvironments in the model.
EXHIBIT 2-17
DISTRIBUTION OF THREE-DIGIT ZIP CODES BY
SURFACE WATER ECOSYSTEMS
Type of
Number of
Percentage
Ecosystem
Zip Code Areas
of Total
Large River
225
40%
Medium River
91
16%
Small Stream
66
12%
Rivers--Low Flow
24
4%
Marshes
76
14%
Sea Coasts
8
1%
Lakes
69
13%
2.4.3 Ground Water System
We have found no satisfactory source of hydrogeologic information that
covers the entire country. For selected regions, some data exist on the
geologic and hydrodynamic factors necessary to predict plume migration, but
the extreme variability of these factors makes generalized mapping difficult.
Consequently, we distinguished two types of ground water systems into which
hazardous wastes may be released: areas underlain by productive ground water
formations, and all other areas. We distinguished these two types of
environments primarily on the basis of hydrodynamic conditions, i.e., whether
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the underlying geologic formation acts as a hydrologic unit and is capable of
transmitting significant quantities of water. Areas underlain by productive
formations are generally, capable of yielding at least 50 gallons per minute
(gpmj of water containing less than 2000 ppm dissolved solids. All other
areas are classified as non-productive areas in the model.
We believe this is the most reasonable approach for segregating ground
water environmental settings for two reasons. First, the risk of exposure to
polluted ground water is determined to a large degree by the lateral movement
of the contamination from the source of release. Second, the dominant
direction of flow in productive systems is horizontal, whereas in other
water-bearing units, vertical flow is' the dominant influence on the advective
disposition of the contaminant, and horizontal flows are low.
We recognize there are other site-specific phenomena that affect the fate
and transport of chemicals in ground water, but chose not to incorporate them
because of the lack of a unified national data base on these characteristics.
As discussed in Chapter 3, we do consider chemical-specific factors in
evaluating the fate and transport of chemicals in the various environmental
settings described here.
We distributed zip-code areas into productive areas and non-productive
areas based on a generalized map indicating productive aquifers in the
Continental United States.2U The map indicates locations of four productive
aquifer regions: alluvial valley; coastal plain sands and gravel; alluvial
basins, high plains, and glaciated regions; consolidated rock. If a zip code
overlies these locations by one-third or more, we designated it as being
underlain by a productive formation. We resolved questionable situations by
designating the area as containing a productive formation. Exhibit 2-18,
which summarizes the distribution of zip-code areas among the two categories
of'ground water, shows that the country is divided fairly evenly in terms of
productive and non-productive groundwater formation areas.
2.4.4 Summary
When all combinations of the various environmental settings used in the
model are considered, a total of twelve standard environments are available
forevaluating human health risks. Exhibit 2-19 lists the twelve possible
combinations and indicates the number of corresponding zip-code areas. For
risks to ecological systems, there are a total of seven environments, since
only surface water environments are used in estimating these risks.
2i*C.L. McGuinness, "The Role of Ground Water in the National Water
Situation," U.S. Geological Survey -- Water Supply Paper 1800, Plate 1 (1963).
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EXHIBIT 2-18
DISTRIBUTION OF THREE-DIGIT ZIP CODES BY
GROUND WATER SYSTEM
Ground Water Number of Percentage
System Zip Code Areas of Total
Non-Productive
Formation 295 53%
Productive
Formation 264 U7%
EXHIBIT 2-19
STANDARD ENVIRONMENTS FOR HUMAN HEALTH RISK EVALUATION
Surface Water
Population Assimilative Ground Waterฆ Number of
Density Capacity System Zip-Code Areas
High
Low
Productive
10
High
Lou-
Non-Product ive
22
High
High
Productive
25
High
High
Non-Productive
19
Medium
Low
Productive
135
Medium
Low
Non-Productive
141
Medium
High
Productive
49
Medium
High
Non-Productive
17
Low
Low
Productive
39
Low
Low
Non-Productive
94
Low
High
Productive
6
Low
High
Non-Productive
2
Total 559
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3. HUMAN HEALTH RISKS
3.1 INTRODUCTION
In this chapter, we describe the methodology the model uses to assess the
risks to human health posed by particular waste management practices. The
methodologies used to assess risks to non-human ecological systems, and to
assess the risk of sensory effects, are presented in Chapters 4 and 5
respectively.
The model uses the releases of hazardous constituents to the environment
that occur during waste management as the key input in estimating human health
risks. The model calculates the mass rate of release from a combination of
management technologies, then converts the release rate to risk values within
the context of one of twelve standard environments.1
The remainder of this chapter is organized into the following four
sections:
3.2 Overview of the risk assessment methodology
3.3 Inherent limitations in risk assessment
3.4 Key toxicological issues
3.5 Description of the methodology
Throughout the chapter, we refer to a number of appendices and other
supporting documentation. Appendix E describes the natural physical and
chemical processes affecting the fate and transport of pollutants in various
environmental media. Appendix F presents an empirical study of dose-response
curves for substances inducing non-carcinogenic effects. A background
document under separate cover, entitled "Exposure Profiles" presents data for
each of the hazardous chemical constituents in the model on the behavior of
that chemical in each environmental medium. Another background document,
"Toxicity Profiles," contains a profile for each chemical identifying the
toxic effects attributed to that chemical.
3.2 OVERVIEW OF METHODOLOGY
The RCRA Risk-Cost Analysis Model assigns two human health risk scores to
each combination of wastes, technologies, and environments. One score is for
risks to individual humans; the other incorporates the total population at
*As discussed in Section 2.4. there are twelve standard environments for
estimating human health risks, based on combinations of the two surface water
environments, two groundwater environments, and three types of population
densities in the model. The model distinguishes seven surface water
subenvironments to estimate ecological risks (see Chapter 4).
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risk. These scores indicate the relative risk of managing the waste stream by
certain technologies in a particular environmental setting.
The model does not calculate separate scores for each of the many
different types of health effects because a multitude of risk scores would
obscure the differences between W-E-T cells and reduce the utility of the
model as an aid to analyzing regulatory options. Moreover, the available data
on health effects are not yet sufficiently comprehensive to draw connections
between most chemicals and specific effects.
The human health risk scores take into account only the toxic constituents
of a waste stream, not the hazards posed by corrosivity, ignitability, or
reactivity. The scores are derived by five steps:
Step 1. Select Highest Toxicity Chemicals Released. Our
methodology assumes that the risk from managing a waste
stream is represented by the waste stream constituents of
highest toxicity and highest concentration. The model
calculates the risk associated with each constituent
separately, based on a constant release of the constituent
to various environmental media.
Step 2. Predict Chemical Concentrations in the
Environment. The model uses different approaches to
determine the fate and transport of releases in each of the
three environmental media: surface water, air, and ground
water. On this basis, the model calculates the steady-state
concentration of the chemical in the different media within
a certain distance from the point of release.
Step 3. Estimate Human Intake of Chemicals. The model
calculates the total human lifetime intake of each chemical
constituent from each route of exposure for the nearby
population and then adds all media-specific doses of the
chemical to give a total daily dose for that chemical. The
model expresses the human dose in milligrams of chemical per
kilograms of body weight per day.
Step 4. Calculate Individual Risk. The model calculates
chronic individual risk by combining three factors with the
dose calculated in the previous step: 1) chronic risk per
unit of dose; 2) a factor that distinguishes between the
dose-response relationship for carcinogens and other types
of substances; and 3) severity of toxic effect.
Step 5. Estimate Population-At-Risk. The model
multiplies the individual risks for each waste stream
constituent by the numbers of people experiencing the
constituent exposures'to estimate population-at-risk. The
model uses the largest population-at-risk value for any
cons.tituent in the waste stream to represent the total risk
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for that waste stream. This risk is converted to a whole
number risk score. The model also derives a score for the
risk to individuals based on the estimate of individual risk
obtained in step 4.
The final risk scores represent approximately ten-fold differences in risks.
3.3 INHERENT LIMITATIONS IN RISK ASSESSMENT
The risk to human health from managing a waste stream is a function of two
factors:
(1) The toxicity of the constituents of the waste stream,
which determines the type of adverse effects the waste
stream might be capable of causing (e.g., cancer, birth
defects, kidney damage), and the relationships between
exposure and magnitude of effect;
(2) The magnitude of human exposure to the constituents
of the waste stream, which determines whether and to
what extent the adverse effects identified in (1) are
likely to occur.
Unfortunately, there is considerable uncertainty associated with both of
these factors and it is not possible to produce wholly accurate estimates of
human risk for any waste management practice. There are numerous sources of
uncertainty, but the seven most important are the following:
Most waste streams are complex mixtures of chemicals,
many of which have not been chemically characterized.
No data are available on the toxic properties of any of
the complex mixtures comprising hazardous waste streams.
In many cases where toxicity data on the individual
chemical constituents of waste streams are available,
the data are highly limited and do not afford a picture
of the full range of the adverse effects a chemical
might cause.
Most of the toxicity data on the individual
constituents of waste streams have been collected in
experimental studies involving laboratory animals. For
the hazards of concern in this modeldelayed or chronic
toxic effects--there is very little information
reflecting direct human experience.
There is great variation in the type and severity of
toxic effects produced by the individual constituents of
waste streams. Thus, for example, some of the
substances are carcinogenic, others cause birth defects
or reproductive disorders, and others cause damage to
particular organs or systems.
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Although there is considerable experimental
information on the relationships between exposure to
waste stream constituents and the magnitude of resulting
adverse effects (dose-response relationships), the data
are limited to a relatively narrow range. The range for
which data are available is ordinarily higher than the
range of human exposures likely to be encountered in the
environment.
There are no useful data on the actual concentrations
of waste stream constituents in the media (primarily air
and water) likely to be the major sources of human
exposure to inadequately managed wastes.
Methodologies for predicting the environmental
behavior and concentrations of waste stream constituents
do not have substantial empirical support, and the
results of their application are subject to additional
uncertainty because of incomplete data on specific
constituents.
Given these uncertainties (many of which pertain not only to the risks of
hazardous waste streams, but to all other types of chemical risks), it would
seem rash to attempt to predict the human health risks from managing hazardous
waste streams. At the same time, the evaluation of alternative policies for
hazardous waste management must incorporate some consideration of the risks to
human health posed by pollutants released to the environment. What we
describe in this chapter is a methodology that systematically incorporates
toxicity and exposure data to provide a single relative measure of human risk,
expressed as two risk scores. The methodology adopts, with some modification,
the traditional methods of toxicology and exposure analysis. We believe the
methodology provides an approximation of the relative level of risk among
different waste management practices that is sufficiently precise to analyze
trade-offs among costs and risks.
Before describing the methodology, we note that it incorporates a variety
of simplifying assumptions to deal with the seven sources of uncertainty
listed above. In every case, we have revealed explicitly the nature of the
assumption and its justification, and have indicated the influence of the
assumption upon the outcome of the analysis. The use of assumptions for such
purposes is a common feature of all risk assessments and is necessary if data
on health effects are to be given any consideration in decision-making.2
2The issue of incorporating assumptions into risk assessment is
thoroughly discussed in the recent National Research Council report Risk
Assessment in the Federal Government: Managing the Process (Washington:
National Academy Press, 1983). The broad principles set forth by the NRC
Committee are adhered to in this methodology.
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3.4 KEY TOXICOLOGICAL ISSUES
The model's risk assessment methodology includes a procedure for
estimating the probability of toxic response as a function of a chronic dose
of the chemical producing the response. This dose-response curve is
applicable at all finite doses. Moreover, it predicts a finite toxic response
to all doses--i.e., it is a non-threshold curve. Except for chemical
carcinogens, such a curve varies from traditional methods of toxicology.
Traditional methods of toxicology have, with few exceptions, avoided
confronting the central question for the RCRA model: what is the probability
that a given chemical will produce a response at exposure levels (or doses)
outside of, and ordinarily below, the range of doses for which data are
available? This question needs to be answered because environmental exposures
to chemicals are rarely in the range of doses for which direct experimental or
epidemiologic evidence exists. If risk is defined as the probability of an
adverse effect, then a usable risk model must incorporate a measure of the
probability of response in the exposure region of interest.
The traditional means for treating toxicity data depend, as the present
methodology does, on the availability of experimental or other types of
toxicity data relating dose to response over a range of doses. Under
traditional schemes, these data are used to estimate the maximum dose at which1
no adverse effects of the chemical are detectable. At doses above this level,
the experimental threshold, effects may increase in incidence or in
severity; and in some cases both may increase. If incidence increases, then
responses are usually measured as the fraction of the experimental population
affected at each dose. This fraction is the risk, and in most experiments,
risks below 0.1 (10 percent) are not detectable because of the small numbers
of experimental subjects typically used. If the severity of the effect
increases with dose, or if both severity and incidence increase, the measure
of response cannot be readily reduced to a simple and universally applicable
scale, although the various types of responses can usually be described
qualitatively, and in some incidences quantitatively. The absence of a
severity scale is one of the problems the present methodology attempts to
address.
The traditional means of using toxicological data have been such that the
problem of defining a severity scale has not created difficulties. Once
having determined a threshold dose, the toxicologist has used this information
to define "acceptable human intakes" of various environmental agents by
dividing the threshold dose by a "safety factor" or, more recently, an
"uncertainty factor."3
'There are differences in meaning between these two terms, but the
difference is not important to the present discussion.
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Under this system, the fact that different chemicals produced a variety of
toxic effects of greatly varying severity had no bearing on determining
acceptable intake. Any effect, regardless of its severity, could be protected
against because a threshold could be> estimated.
This safety assessment scheme was not applied to chemicals displaying
carcinogenic properties, but was and continues to be applied to all other
toxic agents. It is applied not only to food additives and pesticides, but to
drinking water contaminants and, in somewhat modified form, to occupational
exposures. The scheme does not attempt to define risk at any dose below the
experimental threshold, although it is claimed that the risk at the acceptable
intake level is either zero or very low.
Because they appear to possess certain unique biological properties,
carcinogens have been treated differently from other toxic agents. Before the
early 1970s, they were either banned completely or controlled to the extent
technically feasible. These policies were based on the notion that no safe
level of a carcinogen could be identified. During the 1970s, when regulatory
agencies realized that these policies were not applicable to many classes of
carcinogenic agents, new methods were developed to estimate carcinogenic risk
in the dose region of interest (i.e., below the experimental dose-response
range). Carcinogenic responses are measured on the incidence scale; various
models were used to extrapolate downward from the region of high dose/high
response to the region of probable human exposure. The models commonly used
include no response threshold, and assume direct proportionality between dose
and risk in the low-dose region (i.e., they are linear at low doses).
Although this method of projecting low dose carcinogenic risk is still
controversial, it has become widely adopted, and its use continues to grow.
These methods for incorporating toxicity data into the regulatory process
were the only ones available when the present methodology was developed. The
method for carcinogens could be directly adopted for the model because it was
designed specifically for the type of risk-cost analysis model envisioned.
For substances exhibiting other forms of toxicity, the traditional scheme was
clearly unsuitable (the model could make no use of schemes that simply defined
"acceptable intake" and did not consider the dependence of risk upon
exposure). The methodology we have developed for non-carcinogens is thus
without precedent. For this reason, we provide extensive discussion below of
the basis for estimating human risk scores.
3.5 RISK ASSESSMENT METHODOLOGY
In this section, we describe each step of the methodology for estimating
and scoring the risks to human health presented by managing waste streams.
3.5.1 Selection of Chemicals
The waste streams in the model and their principal constituents are
described in Section 2.2 and Appendix A. These streams are typically complex
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mixtures and many of their constituents have not been identified. Because the
only information on the toxicity of waste streams concerns their individual
constituents, it is necessary to make inferences about the inherent hazards of
waste streams from what is known about the toxicological properties of their
constituents. We thus identify the constituents of each waste stream that
have the highest toxicity and yield the highest environmental concentrations
and assume that the risk posed by a stream is represented by the risks of
those constituents.1"
Because the risks of every component of a waste stream cannot be
estimated, the risks predicted under this methodology may understate the true
risk, even without considering the possibility that chemicals may interact
synergisticallv to yield more serious risks than would be predicted from the
individual properties of the chemicals.ฎ Selecting the highest risk
chemicals as indicators of the risk of a waste stream, however, ensures that
the relative risks of different streams have been identified and distinguished
and permits the assignment of "order-of-magnitude" risk scores. In addition,
the risks posed by the other constituents would be too small to affect the
risk scores. Thus, the model takes into account all constituents that have a
bearing on the risk scores. The waste stream constituents on which risk
estimates are based are listed in Exhibit 3-1.
3.5.2 Prediction of Environmental Concentrations
The extent of human exposure to a chemical depends largely upon the
concentration of that chemical in the environment. The chemical's
environmental concentration, in turn, depends largely upon the transport of
the chemical through an environmental medium, and its eventual fate in that
medium.
There are many mathematical models available to represent the fate and
transport of chemicals released into the environment. The choice depends on
the purposes to which the model will be put. The RCRA Risk-Cost Analysis
Model uses different fate and transport models, or approaches, for different
environmental media. The approaches selected yield results of sufficient
accuracy to permit relative risks to be assessed, but they do not require
extensive site-specific data, as appropriate to a model of the management
practices of representative waste streams in representative environments.
''The risk posed by a given amount of a chemical in a given environment
reflects both its inherent hazard and its persistence and dispersibility in
that environment. Since chemicals vary in their persistence and
dispersibility, it is necessary to calculate their risks individually. It
would not be appropriate, as an alternative, to calculate an average score for
the inherent hazard of a waste stream because, for example, the more hazardous,
components may degrade rapidly, before there is opportunity for significant
human exposure.
5We also recognize the possibility that chemicals in a mixture may
interact to produce less risk than would be predicted by adding the risks from
individual chemicals.
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3-8
EXHIBIT 3-1
WASTE STREAM CONSTITUENTS USED TO ESTIMATE RISKS
Organic Chemicals
Acenaphthene
Acetaldehyde
Acetonitrile
Acrolein
Acrylonitrile
Allyl alcohol
Aniline
Benzalkonium chloride
Benzene
Benzo(a)anthracene
Benzo(a)pyrene
Benzotrichloride
Benzyl chloride
Bis(chloromethyl) ether
Carbon tetrachloride
Chlordane
Chloroacetaldehyde
Chlorobenzene
Chloroform
2-Chlorophenol
Chrysene
Cyclohexane
2,4-D'
1,2-Dichlorobenzene
1,4-Dichlorobenzene
1,2-Dichloroethane
1.1-Dichloroethene
Dichloromethane
1.2-Dichloropropane
1.3-Dichloropropan-l-ol
1,3-Dichloropropene
Dimethylalkylamines
1.3-Dinitrobenzene
2.4-Dinitrotoluene
Epichlorohydrin
Ethylene oxide
Formaldehyde
Hexachlorobenzene
Hexachlorobutadiene
Hexachloroethane
Hydroquinone
Linear alkylbenzene sulfonates
Maleic anhydride
Methyl chloride
(LAS)
Organic Chemicals (continued)
Methyl ethyl ketone
Methyl methacrylate
Naphthalene
1,4-Naphthoquinone
Nitrobenzene
4-Nitrophenol
PCB-1254
Paraldehyde
Parathion
Pentachlorophenol
Phenol
Phthalic anhydride
Pyridine
2,3,7,8-Tetrachlorodibenzo-p-dioxin
1,1,1,2-Tetrachloroethane
1,1,2,2-Tetrachloroethane
Tetrachloroethene
Toluene
Toluene diamine
Toluene diisocyanate
Toxaphene
1,2,4-Trichlorobenzene
1.1.1-Trichloroethane
1.1.2-Trichloroethane
Trichloroethene
Vinyl chloride
Inorganic Chemicals
Antimony
Arsenic
Barium
Cadmium
Chromium (VI)
Copper
Cyanide (hydrogen cyanide)
Fluorides
Lead
Mercury
Nickel
Thallium
Vanadium
Zinc
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3-9
The model's approaches for predicting chemical behavior in surface water,
air, and ground water, while specific to the medium in question, parallel one
another in basic assumptions. We assume in all three cases that hazardous
chemicals are released at a continuous rate. These rates represent the
releases of constituents that occur as a result of continually treating or
disposing waste streams that are continuously generated. The result of using
constant release rates as inputs is that in all three environmental media,
there will eventually be steady-state concentrations of chemicals at points
where populations may be exposed to these substances. The model does not
account for the differences in the time it may take for these releases to
yield steady-state concentrations in air, surface water, or ground water. We
simply assume that wastes are being managed so as to maintain steady-state
concentrations. The model then uses these concentrations to assess lifetime
risk to an individual or to a single generation of individuals residing near
the waste management site.
We also assume consistently that the potential population at risk is
comprised of those people residing within a one-kilometer radius of the point
of release. Accordingly, the model calculates chemical concentrations within
a one-kilometer radius or at a point one kilometer from the point of release,
according to the medium in question (as explained in the discussions below).
We selected the one-kilometer radius because some limit had to be set on the
population for which risks are calculated, and one kilometer is a reasonable
value for establishing steady-state concentrations of a chemical in different
environmental media.
The three approaches for predicting the behavior of released chemicals in
the environment are explained below, including additional assumptions and
values for key parameters.
(a) Surface Water. The model uses a simplified equation to represent the
behavior of a chemical released to a surface water environment. With a
constant streamflow, constant cross-sectional area, first-order kinetics, and
steady-state conditions in one-dimensional river or stream systems, the
equation is:6
- K X
sw
C = CQ exp (1)
V
where C = concentration of constituent C at distance X from the point of
release (mg/fc)
C = initial constituent concentration in the surface water due to
o
the release of the constituent (mg/fc)
6D.P. Loucks, J.R. Stedinger, and D.A. Haith, Water Resource Systems
Planning and Analysis (Englewood Cliffs, N.J.: Prentice-Hall, Inc., 1981),
pp. 435-437.
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K = overall decay rate coefficient of the chemical in surface
sw
, ฆ
water (time )
X = distance from the point of release (1000 meters)
V = constant downstream velocity of the water body (m/day).
This equation accounts for natural decay due to a variety of physical and
chemical processes that reduce the aquatic concentration of a chemical as it
moves downstream. It assumes complete mixing at the point of discharge and
small and negligibile dispersion. The equation provides a reasonably accurate
description of the behavior of a chemical in an average river system when
released in small amounts and present at extremely low concentrations.
Although equation (1) is relatively simple, the underlying factors that
govern the values of its components are complex. The equation indicates that
once a chemical is released into surface water, its behavior is influenced by
both environmental factors and factors specific to the chemical. The flow, Q,
of the surface water dilutes the released constituent to some initial
concentration, level, C^. The downstream velocity, V, of the water body
determines the time it will take for the chemical to reach the exposure site
at distance X (we assume 1000 meters for X). Characteristics of the chemical
that influence its degradation rate coefficient, K, during this time period
concern the strength of the chemical's bonds and their susceptibility to
various natural physical and chemical processes.
The initial concentration, C , of a chemical is that concentration
o
attained when the released chemical mixes instantaneously with the surface
water. We assume instantaneous mixing, although we recognize that such an
assumption is more reasonable for fast-moving, highly turbulent rivers than
for slower-moving river systems. To calculate this concentration, the model
divides the mass of chemical released per day, Re, for each waste stream/
technology combination by the average daily flow, Q, of the surface water.7
We assigned a different value of Q for the two types of surface water environ-
ments in the model: high- and low-assimilative capacity. We assume that these
two environments differ only in flow volumes and not in velocities of flow.
The decay rate coefficient, K , is determined from the half-life of the
sw
chemical in the aquatic environment (Kgw = 0.693/half-life).8
'Section 2.3 of this report discusses in general how these release rates
are calculated for all combinations of waste streams and technologies.
Appendices B, C, and D discuss in detail how release rates are calculated for
each technology in the model.
8Half-life is the time required for the concentration of a chemical to
decline to one-half its original value.
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EXHIBIT 3-2
FLOW AND
VELOCITY VALUES FOR
SURFACE WATER
Flow, Q
Velocity, V
Assimilative Capacity
(liters/day)
(meters/day)
High
io10
5.5 x 10*
Low
io9
5.5 x 104
Source: United States Geological Survey, Water Resources Division, Large
Rivers of the United States (1981).
Examples of physical and chemical processes that determine a chemical's
half-life include hydrolysis, volatilization, and biodegradation. Appendix E
describes these and other processes and presents the methods we used to
calculate chemical half-lives in surface water; it also lists half-life values
for each waste stream constituent in surface water. While a number of
chemical or physical processes may cause a chemical to decay in the
environment, one process (or in some cases, two) usually predominates. The
model thus calculates a chemical's half-life in surface water using the rates
of decay of the one or two predominant processes.
The distance from the point of release, X, is assumed to be one kilometer.
Thus, the model calculates the concentration of a constituent one mile down-
stream from the point of release and uses this concentration to estimate risk
to human health. In effect, the model assumes there is a drinking water
intake at this location and that -this serves as the point of exposure for a
nearby population. The number of people obtaining their drinking water from
this intake, and therefore exposed to the constituent by this route, is
determined by the population within a one kilometer radius of the point of
release. Comparable assumptions are used for air and ground water, as
explained below.
(b) Air. The model calculates the environmental concentrations of
substances released to air by relying on atmospheric advection and dispersion.
These are the predominant mechanisms that affect chemical fate and transport
within relatively short distances from a ground level source with no effective
plume rise. The model uses the following equation:9
9B.D. Turner, Workbook of Atmospheric Dispersion Estimates (PB-191
482), (Cincinnati: U.S. Department of Health, Education, and Welfare, 1970).
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3-12
Re
C = (2)
no o V
y 2
where C = concentration of constituent C at distance X from the point of
release (mg/m3)
Re = Point source release rate of constituent (mg/day)
a = Dispersion in lateral direction (meters)
= Dispersion in vertical direction (meters)
V = Mean wind speed (meters/day).
We believe it important to acknowledge that estimating atmospheric
concentrations of point source emissions of chemicals using this equation
introduces a great deal of uncertainty, although it appears to be the most
accurate way to obtain engineering estimates of ground level pollution
concentrations.10 The uncertainty is due to wide variations in geographical
and meteorological conditions such as terrain, wind speed, turbulence, and
temperature that may change drastically in a short period of time.
Equation (2) describes downwind Gaussian distributions in both the
vertical and horizontal directions for material in a plume. We assume a
neutral atmospheric stability class in specifying values of the standard
deviations (o and o ) as a function of downwind distance from the
y z
source and assign an average wind velocity of 5.5 x 10^ meters/day. We also
assume that the wind blows towards the exposure point 30 percent of the time.
Exhibit 3-3 lists the lateral and vertical dispersion values, or standard
deviations at 100 meter intervals to 1 kilometer.
The model uses equation (2) to calculate a constituent's downwind
concentrations along the centerline of a plume at intervals of 100 meters from
the point of release, to a maximum distance of 1 kilometer. The model assumes
that the exposed population is the downwind population in the area defined by
a 45 degree angle extending from the release source for 1 kilometer. The
population in the angular space between any two intervals breathes air
containing the constituent at this centerline concentration. Thus, one-eighth
of the total population surrounding the release source within a one kilometer
radius of the point of release is exposed through inhalation to the
constituent.
(c) Ground Water. The model predicts constituent concentrations in
ground water assuming that constituent releases are continually flowing into a
ground water formation underlying the release source. The model uses an
equation that describes a steady-state plume of pollution developing
downstream of the source as a function of groundwater seepage, velocity,
dispersion, thickness of the ground- water formation, effective porosity,
10G.T. Canady, Turbulent Diffusion in the Environment (Dordrecht: D.
Reidel Publishing Company, 1973), p. 75.
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INCORPORATED
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3-13
EXHIBIT 3-3
AIR DISPERSION CONSTANT VALUES a/
Distance from Release Lateral Dispersion Vertical Dispersion
(meters) o (meters) a (meters)
y z
100
8
4
200
15
8
300
22
12
400
29
15
500
36
19
600
42
22
700
50
24
800
55
27
900
62
29
1,000
68
31
a/ Assumes a neutral atmospheric stability class (Class D).
Source: F.A. Gifford, Nuclear Safety, Vol. 2, No. 47 (1961).
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3-14
retardation due to adsorption on soil, and the natural decay rate of the
chemical. The equation is as follows:11
Re exp (-J7)
C =
2irnm (D D )0,5
x y
VD-0"5
xv y
(3)
where C = Concentration of constituent C at distance X from the point of
release (mg/ฃ)
Re = Release rate of constituent to ground water (mg/day)
n = Effective porosity of the ground water formation (dimensionless)
m = Vertical thickness of ground water layer (meters)
= Longitudinal dispersion coefficient (meters2/day)
Dy = Transverse dispersion coefficient (meters2/day)
V = Ground water seepage velocity in the direction of flow
(meters/day)
X = Distance from release point (meters)
4 D R,X
x d
Z = 1 + (dimensionless)
V2
R^ = Chemical-specific retardation factor (dimensionless)
X = Chemical-specific decay rate (days *).
Using this equation, which assumes a balance between the rate at which
pollution disperses and the rate of release, the model calculates a single
steady-state concentration to represent the concentration of a constituent in
ground water. The model assumes that the drinking water supply of the entire
population within a one kilometer radius of the point of release contains this
constituent in this concentration, much as if that population's drinking water
supply was obtained from a single well drilled one kilometer downgrade from
the source of the release.12
11J.L. Wilson and P.J. Miller, "Two-Dimensional Plume in Uniform Ground
Water Flow," Journal Hyd. Div., ASCE, AY4 (1978), pp. 503-514.
12The average distance from land disposal facilities to ground water
wells in a sample visited by U.S. EPA's OSW in 1982 was 1300 meters for those
facilities with any wells identified.
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3-15
Equation (3) does not account for how long it will take the steady-state
concentration to develop at the one kilometer well, even though this process
could be lengthy because of the relatively slow movement of ground water. We
assume that the releases of most hazardous constituents have been occurring
long enough for steady-state concentrations to be reached.13
Some of these variables in equation (3) have values specific to an
environmental setting; others have values specific to each chemical. Values
for the environmental-specific variables in the model are presented in Exhibit
3-4 for the two groundwater environmental settings. The chemical-specific
variables include the retardation factors and natural decay rates of the
chemicals in ground water. The retardation factor, R^, measures the effect
that sorption processes in the soil have on a chemical's mobility. For
organic chemicals, retardation is primarily a function of the soil's organic
adsorption properties. The retardation mechanisms for inorganic constituents
are a combination of sorption processes, which depend not only on soil type
and soil conditions (e.g., pH, clay content, ionic strength, etc.), but also
on absorbate concentration. Exhibit 3-5 lists the R, values for the
a
constituents used in the model. Decay rates are expressed as half-lives of
the chemical in a ground water environment. However, many of the chemicals in
the model do not decay significantly in ground water, i.e., they are
persistent.
EXHIBIT 3-4
VALUES FOR COMPUTING CHEMICAL CONCENTRATIONS IN
TWO GROUNDWATER ENVIRONMENTS
Ground Water Formation
Parameter Productive Non-Productive
Longitudinal Dispersion (meters2/day) (Dx)
20
0.05
Transverse Dispersion (meters2/day) (D )
2
0.005
Groundwater Velocity (meter/day) (V)
2
0.02
Porosity (dimensionless) (n)
0.2
0.05
Layer Thickness (meters) (m)
25
25
13There are five hazardous constituents with extremely low mobility in
soil and ground water for which we assume steady-state concentrations are
never reached. These are the organics benzo(a)pyrene, hexachlorobenzene,
PCB-1254, and TCDD (dioxin), and the inorganic barium. For the organics, the
degree of adsorption of the chemicals to soil is such that they will never
(i.e., within thousands of years) be advected through ground water. The low
mobility of barium is based on its removal from ground water by precipitation
as barium sulfate. (At a sulfate concentration of 50 mg/H, solubility
product limits concentration of barium to 0.14 ppm). Consequently, for
releases of these chemical constituents to ground water, the model calculates
no resulting concentrations in drinking water at the one kilometer distance.
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EXHIBIT 3-5
PARAMETERS FOR CONCFNI RAT I ON AND
Chemical Retardation Factor
Acenaphthene
921
AcetaIdehyde
1.08
AceLoni trile
1.06
Ac roIe i n
1. 10
Ac ry1 on i t r i1e
1.17
Ally a Icohol
1. 19
An i1i ne
1. U 5
Benza 1 konitim chloride
210
Benzene
11.0
Benzo(a)anthracene
10,000
Benzoj a jpyrene
1 . 1110,000
Benzotrichloride
106
Benzyl chloride
26.2
Bis (chloromethy1) ether
1 .21
Carbon tetrachloride
89.0
Chlordane
28,000
ChloroacetaIdehyde
1 .05
Chlorobenzene
62.0
Ch1o roform
9.8
2-Chlorophenol
15.6
Chrysene
140,000
Cyc1ohexane
318
2,1) - D
5.0
1,2-Di ch1orobenzene
311
1,1-Dichlorobenzene
311
1.2-Di chloroethane
3.8
1,1-Dichloroethene
11.0
D i ch1oromethane
2.8
1,2-Dichloropropane
11.2
1,3-Dichloropropan-1-ol
1 . 0*4
1,3-Dichloropropene
10.6
D i methy la 1ky1 am i nes
127
1,3-Di n itrobenzene
14.91
2,1-Din i troto1uene
10.0
Epichlorohydrin
1.05
Ethylene oxide
1.06
Forma 1dehyde
1.02
Hexach1o robenzene
2'40, 000
Mexachlorobutad i ene
5,800
Hexachloroethane
1,0U0
Hyd roqu i none
1.15'
Linear aIkylbenzene
12
su1fonates (LAS)
Maleic anhydride
1.001
Methyl chloride
1.86
Methyl ethyl ketone
1.23
OE T LRMI NAT ION
B i oconcont ra t i on
factor (BCF) Source Tor BCF
2'I2 a/
.05
.014
215 a/
30 a/
. i 1
. 5 c
N/A d/
5.21
30
3()
?'4 c/
7.1 c
.628
18. /5
1 'I . ooo
. O'l
10.3 a
3. 75
1 3'l a/
30
66 c/
N/A d/
55.6 a
3 7.ii a
1.2 a
5.61
0.9.c
1.11
.03
1.91
28 c/,
1. 'I c
3.8 a
.O'l
. 0'4
.01
8690 a/
2. 78
86.9 a
.23
N/A d/
b/. c/
c/
c/
/
a/
/
1/
a/
a/
b/,
/
n/
c/
/
/
/
3/
/
a/
b/, c/
ซ/
ง/
/
/
b/, c/
ฃ/
b/, c/
a/
/
s/
AWQCD r/
osl i ina ted
est ima ted
AWQCI)
AWQCD
est ima Led
os t i ma tod
AWQCD
AWQCD
AWQCD
est ima ted
est i ma tod
AWQCD
AWQCD
AWQCD
est ima ted
AWQCD
AWQCD
AWQCD
AWQCD
est i ma ted
AWQCD
AWQCD
AWQCD
AWQCD
est imated
AWQCD
est i ma ted
AWQCD
est i ma ted
AWQCD
est i ma Led
est i ina ted
est imated
AWQCD
AWQCD
AWQCD
est i ma ted
w
ฆ
On
.0002 b/, c/
2.0
0.13 c/
est imated
AWQCD
est i ma ted
-------
EXHIBIT 3-5
PARAMETERS FOR CONCENTRATION AND DOSE DETERMINATION
( cont i ruied )
Chem i caI
Retardation factor
B i oconcent rn t i on
factor (ncr)
Source for BCf
Methyl methacrylate
NapthaIcne
1, ii-Nnphthoquinone
N i t robenzene
1-N i tropheno I
PCB-12514
ParaIdehyde
Pa ra th i on
PentachlorophenoI
PhenoI
Phthalic anhydride
Pyr i d i ne
2,3.7,8-Tetrachloro
dibenzo-p-dioxin
1,1,1,2-Tetrachloroethane
1,1,2,2-TetrachIoroethane
Tetrachloroethene
To Inene
Toluene diamine
Toluene di i socynate
Toxaphene
1.2,1-Tr i ch I o robenzene
1.1.1-Trichloroethane
1.1.2-TrichIoroethane
T richloroethene
Vinyl chloride
Inorganics
Ant imony
Arsen i c
Ba r i urn
Cadmium
Chromium (VI)
Copper
Cyan i de
FIuorides
Lead
Mercury
N i eke I
ThaII Ium
1.68
189
8.01
8.20
10.0
106,000
1.25
2, 120
10,600
3.8'ป
1.03
1.57
660,000
110
21.6
73.8
61
2.00
N/A
191
1,810
31.1
12.2
26.2
67.0
10,000
2.0
1,000,000
100
2.0
10,000
2.0
2.0
10,000
100
100
100
0.003 c/
10.5 a/
2.1 0/
2.89 a/
3.31 a/
31,200 a/
(ป. 11 c/
21 c/
I 1 a/
1.'i a/
n.?1 c/
.28 c/
5800 a/
30 b/, c/
5.0 a/
30.6 a/
10.7 a/
91
probably low due to
ra p i d hyd ro I y s i s
13,100 a/
II a/
10.6 a/
1.51 a/
10.6 a/
1.17 a/
1 a/
11 a/
no evidence Tor
b i oconcent ra t i on
61 a/
16 a/
36 a/
close to zero
not significant
19 a/
3,750-13,000 a/
17 a/
116 a/
cstimntod
AWQCO
est i ma ted
AWQCD
AWQCD
AWQCD
est i mn ted
est i ma ted
AWQCD
AWQCD
es t i ma ted
est i ma ted
AWQCO
est i ma ted
est i ma ted
AWQCD
AWQCD
Ve i th et a I., 1979
AWQCD
AWQCD
AWQCD
AWQCD
AWQCD
AWQCD
AWQCD
AWQCD
AWQCD
AWQCD
AWQCD
AWQCD
NAS 1971
AWQCD
AWQCD
AWQCD
AWQCD
-------
exhibit 3-5
PARAMETERS I OR CONCENTRA11 ON AND DOSE DETERMINATION
(cont i nued )
Chem i caI
Retardation Factor
Vanad i urn
Z i nc
2.0
100
B i oconcent ra t i on
factor (BCF)
Source for BCF
no evidence Tor
b i oconcent ra t i on
147 a/
AWQCD
a/ Weighted average BCF Tor the edible portion of all freshwater and estuarine aquatic organisms consumed by
Ame r i cans.
b/ Octanol-to-water partition coefficient calculated to Leo's fragment constant method as described in W.J.
Lyman, W.F. Reehl, and D.H. Rosenblatt, Handbook of Chemical Property Estimation Methods (New York: McGraw-Hill Book
Company, 1982).
c/ BCF estimated according to U.S. EPA method described in the Ambient Water Quality Criteria Documents: log
BCF = (0.85 log P) - 0.70, Tor aquatic organisms that contain about 7.6 percent lipids, where P = octanol-water
partition coefficient. For aquatic organisms that contain 3.0 percent lipids (the weighted average for fish and
shellfish consumed by Americans), the BCF was adjusted by 3.0/7.6 = 0.395.
d/ Data for these chemicals are included only for ecorisk parameters.
e/ Octanol-to-water partition coefficient was estimated by analogy to structuraMy-related compounds.
f/ AWQCD: Ambient Water Quality Criteria Document, U.S. EPA.
-------
3-19
(d) Summary. The key input variable for determining the concentration
of a chemical constituent in ground water is the mass rate of release for a
combination of a waste stream and a specific waste management technology. The
mass rate of release is also the key input variable for determining
constituent concentrations in surface water and air. In all three media,
concentrations are calculated at a distance (or for air, over a distance) of
one kilometer from the point of release. The model is capable, however, of
varying this distance to determine the sensitivity of risk estimations to the
distance from the point of release used in calculating exposure.
3.5.3 Estimation of Dose
The dose of a chemical received by an exposed individual must be
determined to estimate the likelihood that the individual will be adversely
affected by the release of the chemical. In the previous step, the model
estimates the concentration of each chemical in each medium as the basis for
estimating dose. In this step, the model separately calculates human intake
from each of the three media to estimate dose by each route of exposure, and
then adds the three doses to yield a total dose.
In general, dose (in mg/kg body weight-day) is obtained by multiplying the
human intake of a medium by the concentration of the chemical in the medium.
We have adopted standard assumptions about human intake of air (17 m3/day,
assuming 8 hours light work and 16 hours rest per day); ground water and
surface water as a source of drinking water (2 ฃ/day); and fish (6.5
g/day). Assuming an average body weight of 65 kg, dose is estimated for air
and water exposures as follows:
d . = 0.28 [C . 1 (4)
air 1 airJ
d = 0.033 [C ] (5)
water 1 water
where d . is dose through air, d is dose through ground or surface
air 6 ' water 6 6
water, C . is concentration in air, and C is concentration in ground
air ' water 6
or surface water. The values for C . (mg/mJ) and C (mg/ฃ) are
air b water 6
computed in the previous step.
The model also estimates the dose received through fish consumption. It
calculates concentrations of contaminants in fish by combining data on surface
water concentration (C __ ) with chemical-specific bioconcentration factors
water r
(BCF) listed in Exhibit 3-5. Fish concentration in mg/kg can
estimated by:
C*. u = (C ) (BCF) (6)
fish water J
Now, using the daily fish intake value of 6.5 g/day-person, we derive the dose
received through fish consumption:
d,. . = (0.0001) (C,. . , ...
fish fish) (7)
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Finally, total daily dose, d, for an individual is computed by adding
doses from applicable routes of exposure. For downwind populations, the total
dose received is the sum of the dose through air and the dose through water.
The dose for other individuals is just the dose through water. Consequently,
for each W-E-T cell, there are two dose, or exposure scenarios, that are
carried through the analysis.
In the following discussion, we explain how risk is determined from the
measure of human exposure to a toxic chemical, d.
3.5.4 Calculation of Individual Risk
Risks to human health are a function not only of exposure, but also of the
toxicity of the chemicals to which humans are exposed. We define toxicity in
terms of three factors. The first is the probability of harm, or risk, per
unit dose of a chemical (called "H"). The second is K, which specifies the
shape of the dose-response curve. The third is the "severity index," S, which
is a measure of the degree to which a chemical's effects are likely to
threaten survival or cause irreversible, progressive damage to health. Taken
together, these three factors describe the toxicity of a chemical constituent.
They are later combined with the measure of human exposure, dose (d), to yield
risk, according to the following equation:
Risk = [H] x [S] x [d]K (8)
This equation describes risk to an individual. The following step extends
individual risk to a population to yield a score for population-at-risk.
(a) Principles for Assessing Toxicity. The risk assessment methodology
developed for the model incorporates, with some modifications, traditional
methods for addressing uncertainties associated with measuring the toxicity of
chemicals. This section describes five principal sources of uncertainty and
the simplifying assumptions used in the model to deal with them.
Interspecies differences. It is a widely accepted principle of
toxicology that, with certain exceptions, data on the toxicity of chemicals in
animals can be extrapolated to humans to predict likely adverse effects.
However, it is also a truism that humans are not simply big rats. In addition
to the obvious differences in size and life span, there are interspecies
differences in physiology, metabolism, and pharmacokinetics that can influence
susceptibility to some toxic effects. For example, differences in the
structure of the placenta in different mammals may alter the embryotoxic or
teratogenic efficacy of some chemicals, and quantitative and qualitative
differences in metabolism can affect the rate of excretion of a toxicant or
the amount of an active metabolite present. Absent data on such factors, we
have adopted the traditional principle that humans and test animals respond
similarly to toxic chemicals. Thus, for both carcinogens and non-carcinogens,
we assume humans and test animals are at equal risk at the same dose in mg/kg
body weight-day.
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Influence of route of exposure. Exposure to waste stream constituents is
likely to occur either by inhalation of volatile components or particulates,
or ingestion from drinking water or contaminated food. Wherever possible,
toxicity studies involving the most appropriate route of exposure are used as
the basis for scoring. In some cases where the main toxic effect of a
chemical occurs at places distant from where it enters the body, it is
reasonable to use data derived from one route of exposure to assess toxicity
from exposure through a different route. For example, toxicity data on a
chemical that causes liver damage upon inhalation may be used to assess
hazards not only by inhalation but also by ingestion. For chemicals that
cause their toxic effects at the site of entry to the body, such conversions
are not appropriate. For example, data on a substance that causes damage only
to the upper respiratory track when inhaled at high concentrations are not
used to predict the effects of ingestion at low concentrations.
Effects of different lengths of exposure. In the present context we are
interested largely in the potential adverse effects of long-term, low-level
exposure to constituents of waste streams. Hence, the most useful type of
data for assessing toxicity comes from chronic studies (studies that cover a
large portion of the subject's lifespan, whether animal or human). Where data
from chronic studies are not available for a particular chemical, it may be
possible to use data from subchronic studies (generally animal studies of one
to six months duration). Subchronic studies, however, are usually less
sensitive than chronic studies because of the shorter exposure and shorter
time allowed for development of progressive adverse effects, a point that must
be considered when comparing the inherent hazards of two chemicals if only
subchronic data are available for one of them. Our approach to this problem
is the traditional one of adjusting the subchronic dose-response data by a
factor corresponding to the difference in sensitivity of subchronic and
chronic studies; generally two- to five-fold.
Types of toxic effects (severity index). If all toxic substances caused
the same types of adverse effects, it would be relatively simple to construct
a scale ranking different chemicals according to their potencies; this can be
done for individual types of effects such as cancer. For present purposes,
however, we need a scale capable of ranking chemicals that cause cancer or
birth defects along with those, for example, that may, in the low-dose region,
cause alterations in blood enzyme levels or liver lipid levels. We address
this problem by incorporating a scaling factor for effects of different
11 . . It
severity.
There is as yet no universally agreed upon ranking of effects, but it is
possible that a consensus could be reached.1'' From the point of view of the
toxicologist and pathologist, effects may be ranked according to the degree to
1''National Research Council (NRC) , Steering Committee on Identification
of Toxic and Potentially Toxic Chemicals for Consideration by the National
Toxicology Program, Strategies to Determine Needs and Priorities for Toxicity
Testing. Volume 2: Development (Washington, D.C.: National Academy Press,
1982).
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which they are likely to threaten the survival and quality of life of
individuals. It is not clear, however, that such a ranking would be
universally satisfactory. Economists, for example, might wish to rank the
severities of different effects as a function of the extent to which they
contribute to economic, social, and individual costs. Individual perceptions
of different illnesses no doubt vary widely, and may be at considerable
variance from biological or economic "realities."
A "toxicologist's severity model" is least likely to be subject to some of
the social criticisms usually encountered by models that attempt to place
economic costs on illnesses and to use these costs as a basis for policy. To
explain the idea in more detail, the toxicologist's model would be based on
the premise that the most severe effects are those that cause death or
markedly reduce life span, and that risk scoring must place great weight on
some effects. Perhaps of somewhat less concern are effects that, while not
clearly life threatening, result in functional damage to various organs,
especially if the damage is irreversible. Organ dysfunction, which is
reversible upon cessation of exposure, might be considered less severe yet.
There are, however, a number of questions that need to be addressed before any
such scale can be formulated, for example, whether the relative ranking of
effects would find wide acceptance in the community of toxicologists, and
whether the numerical rating scale is appropriate (e.g., is cancer only 10
times worse than enzyme induction?). Accordingly, the RCRA Risk-Cost Analysis
Model does not include a detailed severity model. It does, however, make a
severity adjustment for effects at the two extremes of the toxicologist1s
severity scale. Thus, clearly life-threatening effects are given considerably
more weight than effects that appear to have no adverse health consequences or
that are more clearly temporary. How these adjustments are made in the model
is discussed in the next section.
Dose-response curves. Responses to hazardous chemicals may be classified
as "dichotomous" if an individual either does or does not respond (if he dies,
develops cancer, or exhibits birth defects), or "graded" if the response is
measured on a continuous scale (if it affects the weight of an organ or the
activity of an enzyme), or a combination of the two. At least in the case of
many carcinogens there is some evidence, largely theoretical, that risk
declines monotonically with dose, reaching zero only at zero dose, i.e., with
no threshold. Further, it is widely accepted that for most carcinogens risk
is linearly related to dose at low doses. The low-dose risk extrapolation
model currently used by EPA for carcinogens (the linearized multi-stage model)
incorporates these assumptions, at least for the upper confidence limit on
risk.
For most types of adverse effects, however, the incidence, or more
commonly the severity of the effect, declines with dose until a threshold is
reached at a dose greater than zero. Below the threshold, no adverse effect
is thought to occur. The position of this threshold is, however, likely to
vary to some extent due to variation in individual susceptibility. As
mentioned at the opening of this section, one measure of toxicity is the
probability of harm (H) at a particular dose. In the case of effects believed
to display thresholds, the "probability of harm" may be considered to be the
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probability that an individual's threshold for that effect is exceeded at a
particular dose. Unlike the probability of cancer, it is unlikely that the
probability of exceeding an individual's threshold will be a linear function
of dose. Unfortunately, how this probability varies with dose has not been
widely considered in the scientific community. Indeed, as we noted in the
opening sections of this chapter, traditional means for using toxicologic data
on threshold-type effects have been to identify an experimental "threshold" or
"no observed effect level" and to apply a "safety" or "uncertainty" factor to
define a dose below this level at which the probability of harm is considered
to be zero, or at least insignificantly small. That this traditional approach
has generally served its intended purpose well, and that the "safety factors"
used are relatively small, would imply that the probability of harm falls more
rapidly with decreasing dose than a linear function would. We have taken this
into account in the treatment of non-carcinogenic agents.
(b) Procedures Used for Assessing Toxicity. Against the background
above, we present the toxicity assessment procedure applied to each chemical.
Recall that risk is given by the following relation:
R = [H] x [S] x [d]K (8)
The risk represented by Equation (8) is the result of a lifetime of exposures
to a total daily dose, d, of a hazardous constituent. For reasons to be
explained, K is 1 for carcinogens, and as 2 for all other types of toxic
substances except copper, fluoride, and lead, for which a value of 4 is used.
Further, (S) is set at 1 for carcinogens. Equation 10 can thus be written as:
R = [H] x [d] (carcinogens) (9)
cdr
or,
^other = X (other agents) (10)
where K = 2 or 4.
The toxicity assessment procedure thus requires only a means for deriving [H]
for carcinogens, [H] for other agents, and |S] for non-carcinogens.
We now describe how each of these measures of toxicity is derived for each
chemical. For ease of presentation, we do not describe the basis for
selecting K = 2 for non-carcinogens (and K = 4 for the special case of
fluoride, copper, and lead) until the end of this section.
Derivation of [HI. The key feature of the assessment scheme is the
definition of [H] as the probability of response per unit dose of a chemical
over a lifetime. For carcinogenic chemicals, this definition coincides
exactly with the definition of "unit risk" as used by EPA. The widely adopted
assumption is that dose-response relationships for carcinogenic effects are
likely to be linear and non-threshold at low doses; thus, the slope of the
dose-response relationship is independent of dose and is defined as the unit
risk. We have calculated unit risks for a number of carcinogens, primarily on
the basis of animal data, although linear dose-response relationships can also
be fitted to human data. We used estimates of unit risks derived by EPA where
they are available. In other cases, we estimated unit risks ourselves by
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employing a simple approximation. This approximation uses the "one-hit
model," which provides a close approximation to the unit risk when the
observed incidence of cancer is less than 50 percent, but progressively
underestimates the risk as the observed incidence of cancer increases.
The risk equation for non-carcinogens assumes no absolute threshold (i.e.,
in equation (10), R = 0 only at values of zero dose). Although this may at
first appear to diverge from traditional assumptions of toxicology, we note
that under the non-linear model of dose-response assumed here, risk declines
relatively quickly below the experimental dose range and reaches a "practical
threshold" (i.e.. a risk so low that it is negligible for policy purposes) at
doses much higher than those representing negligible risks under a linear
model of dose response.15 Thus, the model chosen here for non-carcinogens
is the practical equivalent of a threshold model, thereby meeting the
scientific requirements described earlier, and also preserving the simplicity
required by the RCRA model.
For non-carcinogens, H is derived from observed (experimental)
dose-response data by assuming that in the region close to and below the low
end of the dose-response curve, response declines as dose to the Kth power:
R = H x dK (11)
A value for H for a specific chemical can be derived from the quantitative
relationship between d, the experimental dose producing the minimum level of
effect (referred to as the MED, minimum effective dose), and Ro, the observed
response at the MED. Thus,
H = Ro/[MED]K (12)
This value of H can then be used in equation (12) above to estimate non-cancer
risks for various values of d.
Derivation of H for non-carcinogens requires further discussion. Because
there are no sources similar to EPA's unit risks values for carcinogens, H for
non-carcinogens must be derived from experimental data on toxicity. As shown
in equation (12), the critical experimental values needed to derive H are MED,
and Ro, the risk (response) observed at the MED. These two experimental
factors have somewhat different meanings for dichotomous and graded
responses. Our concern in both cases is for chronic toxicity.
For dichotomous responses, the observed responses are recorded as the
fraction of the experimental population affected at a given dose. Typically,
the MED is the dose producing about a 10 percent increase in the incidence of
lsNote, for example, that for H =
non-linear model with K = 2 at a do.se
this risk is reached under the linear
_2
10 , R will equal 10 under the
that is 100 times the dose at which
mode.
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adverse effects. For such responses, the MED and corresponding Ro can be
selected from the experimental data; H is then simply calculated as
Ro/(MED) . If Ro is not specified in the experimental data, we assume it is
10 percent.
For graded effects such as liver enlargement and body weight depression,
the MED may be the dose at which all subjects in the experimental population
are minimally affected, rather than the dose at which a minimal number of
subjects is affected. In many cases, toxicity data are reported only as group
averages, so that it is impossible to determine the incidence of effects.
Therefore, unless the data indicate otherwise, we assume that most members of
an exposed population are minimally affected at the MED. Immediately below
the MED. the fraction of the population whose individual thresholds are
K
exceeded declines as d . H is calculated by assuming, unless the data show
otherwise, that Ro = 1.0 at the MED. That effects are minimal at the MED is
further considered by including a severity index, discussed below.
Finally, some provision must be made for the fact that, for many of the
chemicals scored, the toxicity data do not reveal the chronic MED. Several
types of limitations are common, including:
Chronic toxicity data are available but the lowest
dose observed to cause toxicity is not the MED.
No toxicity data are available reflecting the effects
of chronic exposure; rather, subchronic data, reflecting
the effects of partial lifetime exposures are the only
data available.
In reviewing the chemicals scored, we encountered numerous examples of these
limitations. In Exhibit 3-6, we describe the decision rules we used to derive
chronic MEDs from limited data. These rules are based on practices long used
in toxicology.
Severity index. In an earlier section, we described some of the problems
with incorporating a measure of severity into the risk equation. We concluded
that it is probably prudent to avoid establishing fine distinctions among
effects of varying degrees of severity. Instead, we have developed the
simpler severity scale presented in Exhibit 3-7.
The effects considered are those occurring at the MED. In many cases,
effects become more severe above the MED, but this is not likely to be a
problem because it is improbable that environmental concentrations will reach
levels that, on a chronic basis, exceed the experimental MEDs.16 We note
16Because of the way H is estimated for graded responses (1/MED ),
chronic human doses exceeding the MED will produce "risks" greater than 100
percent. If such "risks" appear for any chemical, this would suggest an
extremely dangerous situation.
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EXHIBIT 3-5
RULES FOR DERIVING CHRONIC MEDS FROM LIMITED DATA a/
To derive chronic MED from subchronic MED, divide subchronic MED by 5
i.e., chronic MED = subchronic MED/5.
In the special case where the effect occurring at the lowest dose level
is teratogenic or fetotoxic, this procedure is not appropriate and no
adjustment is made, since such effects depend on treatment occurring
during a short period (gestation).
To derive chronic MED from an effective dose presumably higher than the
MED, scientific judgment is used. If the effects reported are of minimal
degree or incidence no adjustment is made, but if the effects are of
moderate or severe degree, or greater than about 10 percent incidence
(above control incidence), the experimental dose is divided by 5 to
estimate a MED.
ce: B.P. McNamara, Concepts in health evaluation of commercial and
industrial chemicals. In M.A. Mehlman, R.E. Shapiro, and H.
Blumenthal, eds., Advances in Modern Toxicology, Volume 1, Part 1:
New Concepts in Safety Evaluations (New York: John Wiley and Sons,
1976), pp. 61-141.
a/ McNamara examined 74 sets of data on no-effect levels in subchronic
and chronic studies. In about half of the cases, the difference in
sensitivity was two-fold or less, and in about 95 percent of the
cases the difference was five-fold or less.
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EXHIBIT 3-7
SEVERITY SCALE
Severity Index
[S] Effects
(listed below)
0.1
0.5
1.0
1, 2
3-7
8, 9, 10
TOXIC EFFECTS
1 Enzyme induction or other biochemical change with no pathologic changes
and no change in organ weights.
2 Enzyme induction and subcellular proliferation or other changes in
organanelles but no other apparent effects.
3 Hyperplasia, hypertrophy or atrophy but no change in organ weights.
4 Hyperplasia, hypertrophy, or atrophy with changes in organ weights.
4 Reversible cellular changes: cloudy swelling, hydropic change, or fatty
changes.
6 Necrosis, or metaplasia with no apparent decrement of organ function.
Any neuropathy without apparent behavioral, sensory, or physiologic
changes.
7 Necrosis, atrophy, hypertrophy, or metaplasia with a detectable
decrement of organ functions. Any neuropathy with a measurable change
in behavioral, sensory, or physiologic activity.
8 Necrosis, atrophy, hypertrophy, or metaplasia with definitive organ
dysfunction. Any neuropathy with gross changes in behavior, sensory, or
motor performance. Any decrease in reproductive capacity, any evidence
of fetotoxicity.
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EXHIBIT 3-7 (continued)
SEVERITY SCALE
9 Pronounced pathologic changes with severe organ dysfunction. Any
neuropathy with loss of behavioral or motor control or loss of sensory
ability. Reproductive dysfunction. Any teratogenic effect with maternal
toxicity.
10 Death or pronounced life shortening. Any teratogenic effect without signs
of maternal toxicity.
Source: Environmental Criteria and Assessment Office, U.S. Environmental
Protection Agency, "Methodology and Guidelines for Reportable Quantity
Determinations Based on Chronic Toxicity Data," ECA0-CIN-R245 (August
1983 Draft).
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further that the apparently overcautious assumption that the MED for graded
effects corresponds to an incidence of 100 percent, which was needed to
"dichotomize" the data so that all risks could be presented in the same units)
is offset by incorporating the severity index: by definition, "minimal
effects" for graded responses are assigned an [S] of 0.1 (or at most, 0.5).
Data sources. We relied primarily on secondary sources to determine the
lowest exposure level at which each waste stream constituent exerts an adverse
effect on health. Some of the principal secondary sources were the monograph
series of the International Agency for Research on Cancer (IARC), the National
Academy of Sciences (NAS) series Drinking Water and Health and Medical and
Biologic Effects of Environmental Pollutants, NIOSH Criteria Documents,
Casarett and Doull's Toxicology, and EPA's Ambient Water Quality Criteria
Documents. In addition, we consulted carcinogenesis bioassays of the
National Cancer Institute (NCI). After identifying the secondary sources that
indicated hazardous effects at the lowest dose levels, we obtained and briefly
reviewed the primary reference in most cases.
Exhibit 3-8 presents values for H, S, and K for the waste stream
constituents in the model. These values are taken from a series of toxicity
profiles we developed under separate cover for each chemical. The profiles
present the critical studies upon which these values are based as well as the
MED and Ro values.
Note on Selection of Value for K. In Appendix F, we present the results
of an evaluation of dose-response curves for a large number of non-carcinogenic
effects. We collected the data for these dose-response curves from the
primary literature, and analyzed them statistically. The results of this
analysis must still be considered preliminary because the number of data sets
analyzed, while relatively large, is still too small to provide a basis for
strong generalizations.
Data for teratogenic and reproductive toxicity were analyzed, as were data
for a variety of other toxic effects. The dose-response data were analyzed
statistically to determine the range of values of K compatible with
non-carcinogenic dose-response data. The analysis revealed that it is
appropriate to use values of K greater than 1 for the effects studied.
The median values for K computed under various classifications of data
were similar, ranging from 1.70 for teratogenic effects to 2.26 for
non-reproductive forms of toxicity. The median value for the combined data
sets was 1.74.
Although not examined in this study, many carcinogenesis data sets
probably exhibit values of K greater than 1. The assumption of K = 1 for the
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EXHIBIT 3-8
TOXICITY VALUES
Chemical
Unit Risk,
H(mg/kg-day)
Severity
Index, S
Dose-Response
Curve Factor K
Acenapthene
Acetaldehyde
Acetonitrile
Acrolein
Acrylonitrile
Allyl alcohol
Aniline
Benzalkonium chloride a/
Benzene
Benzo(a)anthracene
Benzo(a)pyrene
Benzotrichloride
Benzyl chloride
Bis(chloromethyl) ether
Carbon Tetrachloride
Chlordane
Chloroacetaldehyde
Chlorobenzene
Chloroform
Chrysene
2-Chlorophenol
Cyclohexane
2,4-D a/
1,2-Dichlorobenzene
1,4-Dichlorobenzene
1,2-Dichloroethane
1.1-Dichloroethene
Dichloromethane
1.2-Dichloropropane
1.3-Dichloropropan-l-ol
1,3-Dichloropropene
Dimethylalkylamines a/
1.3-Dinitrobenzene
2.4-Dinitrotoluene
Epichlorohydrin
Ethylene oxide
11.5
I.59 x 10
0.036
0.66
0.24
6.27
0.019
0.052
II.5
11.5
12.1
-4
1.8 x 10
9.3 x 10
0.13
I.61
37.6
2.36 x 10
0.11
II.5
0.01
-2
-3
-3
5.9 x 10
-5
-4
-3
3.4 x 10
4.3 x 10"
0.0584
1.04
4.5 x 10
0.039
0.039
9.4
4.05 x 10
8.86 x 10
0.31
0.024
0.63
b/
-2
1
0.5
0.5
0.5
1
0.5
1
1
1
1
0.5
0.5
1
1
1
0.5
0.5
1
1
0.5
0.5
0.5
0.5
0.5
0.5
0.1
1
1
1
2
2
2
1
2
1
1
1
1
2
1
1
1
2
2
1
1
2
2
2
2
2
2
1
1
1
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EXHIBIT 3-8 (continued)
TOXICITY VALUES
Chemical
Unit Risk,
H(mg/kg-day)
-K
Severity
Index, S
Dose-Response
Curve Factor K
Formaldehyde - ingestion
Formaldehyde - inhalation
Hexachlorobenzene
Hexachlorobutadiene
Hexachloroethane
Hydroquinone
Linear alkylbenzene
sulfonates (LAS) a/
Maleic anhydride
Methyl ethyl ketone
Methyl chloride
Methyl methacrylate
Naphthalene
1,4-Naphthoquinone .
Nitrobenzene
4-Nitrophenol
PCB-1254 - carcinogenic c/
PCB-1254 - neurological
Paraldehyde
Parathion
Pentachlorophenol
Phenol
Phthalic anhydride
Pyridine
Tetrachloroethene
1,1,1,2-Tetrachloroethane
1,1,2,2-Tetrachloroethane
2,3,7,8-Tetrachlorodibenzo-
p-dioxin
Toluene
Toluene diamine
Toluene diisocyanate - inh.
Toluene diisocynate - ing.
Toxaphene
0.77
0.0214
1.67
7.75 x 10'
0.0142
0.25
2.78 x 10
1.1 x 10"7
-6
9.85 x 10
2.5 x 10~
io-4
0.25
0.018
0.018
4.34
io4
1.59 x 10
6.94 x 10
1.6 x 10'
0.01
4 x 10"?
-4
-4
5
2
6.6 x 10
-2
5.31 x 10
8.1 x IO-3
0.20
4.25 x 105
9.4 x 10
0.29
-4
6.25 x 10"
4.3 >
1.13
4.3 x 10"3 a/
0.5
1
1
1
1
0.5
0.5
1
0.5
0.1
0.5
0.5
0.5
0.5
1
0.5
0.5
0.1
1
0.5
0.1
0.5
1
0.5
1
0.1
1
0.5
1
1
2
1
1
1
1
2
2
2
2
2
2
2
2
2
1
2
2
2
2
2
2
2
1
2
1
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EXHIBIT 3-8 (continued)
TOXICITY VALUES
Chemical
Unit Risk,
H(mg/kg-day) K
Severity
Index, S
Dose-Response
Curve Factor K
1,1,1-Trichloroethane
9.9 x 10"4 b/
1
1
1,1,2-Trichloroethane
0.68
1
1'
1,2,4-Trichlorobenzene
0.02
0.5
2
Trichloroethene
0.0126
1
1
Vinyl chloride
1.75 x 10~2
1
1
Inorganics
Antimony
1.8 x 10~4
0.5
2
Arsenic - ingestion
1A ""
1
1
Arsenic - inhalation
49.3
1
1
Barium
0.30
0.5
2
Cadmium
6.65
1
1
Chromium (VI) ingestion
0.02
0.5
2
Chromium (VI) inhalation
41
1
1
Copper
25.6
0.5
4
Cyanide
0.1
0.5
2
Fluorides
1.5 x 107
0.5
4
Lead
1.25 x 109
0.1
4
Mercury
0.69
0.5
2
Nickel - ingestion
9.0
1
2
Nickel - inhalation
1.15
1
1
Thallium
0.54
1
2
Vanadium - ingestion
0.03
0.5
2
Vanadium - inhalation
1.56 x 104
0.5
2
Zinc
1 x 10"4
0.5
2
a/ Data for these chemicals are included only for ecorisk parameters.
b/ Estimate based on preliminary data.
c/ The use of the carcinogenic or neurological values depends on the
dose level, thus both are included.
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low dose region is retained for carcinogens for theoretical reasons.17
Assuming there are no theoretical bases for considering non-carcinogenic
dose-response curves to be linear in the low-dose region (and we know of
none), the median K value determined in this study would be said to apply in
the low dose region.
With a few exceptions, we adopted K = 2 as a sufficiently close
approximation of the observed median value of 1.74.
We assigned a value of 4 for three substances (fluorides, copper and lead)
because we are confident that risk to humans becomes extremely small at doses
not far below the MED. Fluoride and copper are beneficial to human health at
doses within one or two orders of magnitude below the MED.
3.5.5 Estimation of Population at Risk and Scoring
The total number of people adversely affected by a waste management
practice depends upon the size of the population within the area defined by a
one kilometer radius from the release source. The number of people in this
area is obtained by multiplying the population density (as described in
Section 2.4) by the area covered. The model calculates the population at risk
by multiplying the individual risk, calculated by equation (8) in the previous
step, by the total number of people in the area that experience these adverse
effects. Because constituent concentrations in air (and, consequently, dose
through inhalation) vary according to distance from the release source, this
multiplication is performed in a series of steps for successive concentric
areas around the release source.
The scoring system for risk is based on taking the logarithm of the
estimate of the total population at risk. Logarithmic values are rounded to
the nearest whole integer to ensure that the estimated risk values are not
accorded greater precision and accuracy than is warranted. A constant integer
is added to each to ensure that the scores are positive. The resulting
positive whole integer scores separate risk by about one order of magnitude.
The model also derives a score for the risk to the health of individual
humans. This score is obtained by dividing the estimate for population-at-risk
by the number of people in the area defined by a one-kilometer radius from the
release source. The result is converted to a score by the same procedure used
to convert estimated population-at-risk into a score: rounding the logarithm
17The major theoretical reason is based on the existence of a background
incidence of tumors to which additional carcinogenic simply add. Hoel (1980)
has demonstrated that if any of this background is additive (i.e., occurs by
the same mechanism as the added carcinogen) low dose linearity will occur.
Cf. K.S. Crump, D. Hoel, C. Langley, and R. Peto, "Fundamental Carcinogenic
Processes and Their Implications for Low Dose Risk Assessment," Cancer Res.
36 (1976), pp. 2973-2979, also D.G. Hoel. Incorporation of background
response in dose-response models. Fed. Proc. 39 (1980), pp. 73-75.
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and adding a constant. The individual risk score is calculated after the
population-at-risk score because constituent concentrations in air vary with
distance, the risk to individuals varies according to their distance from the
release source. In order to obtain an individual risk score representative of
the average risk to individuals residing within one kilometer of the release
source, the model derives the individual risk score from the estimated
popu1at ion-at-risk.
As explained in the following chapter, the methodology for scoring
ecological risks parallels the five steps for scoring human health risks.
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0 Human Health Risks
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4 Ecofsks
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4. ECORISKS
4.1 INTRODUCTION
This chapter describes the methodology the model uses to estimate the
risks of adverse ecological effects, or "ecorisks," from managing hazardous
wastes. These are risks to non-human populations in the environment. This
methodology is an extension of that used to estimate risks to human health, as
described in the preceding chapter.
Despite the similarity of methodological approach, the human health and
ecological risk scores are not directly comparable. They are based on
different types of effects and calculated in ways that differ in important
details. Consequently, instead of providing an integrated or unified risk
score for each W-E-T combination, the model presents separate scores for risks
to human health and for ecological risks.
This chapter has six sections after the introduction:
4.2 Definition of Ecorisk discusses the properties
of ecosystems that may be affected by exposure to
pollutants.
4.3 General Limitations in Estimating Ecorisks
describes the limitations of inferring ecorisks from
experimental toxicity studies of single species.
4.4 General Approach to Ecorisk Scoring describes
important characteristics of the scoring methodology,
including its precision.
4.5 Aquatic Ecorisks Methodology presents a
five-step methodology for estimating risks to aquatic
ecosystems.
4.6 Terrestrial Ecorisks Methodology describes a
methodology for estimating risks to terrestrial
ecosystems.
4.7 Total Ecorisk Scores describes how the aquatic
and terrestrial scores are added to yield an ecorisk
score for each combination of waste, environment, and
technology.
Appendix G presents case studies supporting the exposure-response
relationships used in the ecorisk scoring methodology.
4.2 DEFINITION OF ECORISK
An ecosystem is a set of physical, chemical, and biological components and
processes that interact in complex ways. There are many diverse types of
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ecosystems, with components that interact in unique, system-specific ways.
Our knowledge of the "normal" structure of most ecosystems, and of the
processes that sustain them over time, is highly limited. Consequently,
attempts to understand the impact on ecosystems of various types of stress,
including those produced by the introduction of toxic chemicals, are subject
to substantial uncertainties.1 In fact, estimating ecorisk, which we
characterize as the nature and magnitude of damage an ecosystem sustains under
given conditions of exposure to a hazardous chemical, is subject to much
greater uncertainty than estimating human health risk.
That various hazardous chemicals can adversely affect ecosystems and the
populations and individuals within them is, of course, well established from
field studies and laboratory investigations. This knowledge has permitted the
development of broad generalizations about the many ways in which ecosystems
and the populations and individual organisms within them can be affected by
hazardous chemicals. These generalizations have been reviewed in a recent
report prepared by an expert committee of the National Research Council
(NRC).2
The NRC committee cited numerous examples of the ways in which populations
of various species of plants, animals, and microorganisms can be affected by
environmental pollutants. Changes in mortality, fecundity, growth rates, and
age distribution have been documented in many investigations. More subtle
effects, including alteration in behavior, sexual activity, predator-prey
interactions, and chromosomes, have also been associated with exposure to
pollutants. There is substantial evidence that the migration patterns of
certain populations and individual organisms can be altered by chemicals.
The vulnerability of certain ecosystem properties to environmental
pollutants is also well documented. Diversity (a measure of the variety and
number of species in a system), levels of productivity and accumulation of
biomass, resistance and resilience, interactions of species, and flows of
energy and essential nutrients are among the many properties of ecosystems
altered by the introduction of pollutants.
A comprehensive characterization of ecorisk should include a description
of how these attributes, whether of ecosystems, populations, or individuals,
are affected by a pollutant. It should also include a description of the
relation of magnitude and types of changes to pollutant concentration
(exposure-response relations) and duration of exposure.
xNational Research Council (NRC), Environmental Study Board and the
Committee on Toxicology, Principles for Evaluating Chemicals in the
Environment (Washington: National Academy of Sciences, 1975). National
Research Council, Committee on Natural Resources, Testing for Effects of
Chemicals on Ecosystems (Washington: National Academy Press, 1981).
2NRC (1981), ibid.
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Ecosystem exposure-response relations are highly complex and, for the same
pollutant, may vary considerably from one ecosystem to another. Typically, at
a relatively low exposure, a few highly sensitive species may be affected with
no accompanying damage to other species or to any ecosystem process. As
exposure increases, less sensitive species may become affected, and some
ecosystem processes may be altered. Greater exposures may not only severely
affect many individual species, but may also substantially damage a variety of
ecosystem properties. At very high exposure levels an entire ecosystem may be
destroyed. Because so many species and system properties can be affected in
many different ways, well-defined exposure-response relations for ecosystem
toxicity are extremely difficult to identify. The picture can become
extraordinarily complex if the most highly sensitive species play a critical
role in ecosystem processes that other species depend on. In such cases, the
dependent species may sustain extensive injury even if they are not directly
affected by a pollutant.
4.3 GENERAL LIMITATIONS IN ESTIMATING ECORISKS
No methodology is available to allow the precise prediction of ecorisk, as
defined above, for any pollutant in any ecosystem. As for human health risks,
it is necessary to infer ecorisks from limited sets of data, using plausible
models of exposure-response relationships. There are, however, major
differences in the types of inference needed to estimate the two kinds of
risks.
The major inferences needed to estimate human health risks result from the
fact that most toxicity data are from studies of experimental animals at
exposure levels considerably higher than those to which humans are likely to
encounter. Because such inferences are needed, and because of uncertainties
in exposure assessment, the human health risk scores in the model distinguish
only rough "order-of-magnitude" differences in risk.
Most ecotoxicity data relate pollutant concentration to toxic response in
individual species under carefully controlled laboratory conditions. The
concentrations of the pollutants are usually in the range producing observable
toxicity (broadly defined); in addition, these studies involve many species
encountered in natural settings. It may thus appear that there are less
serious problems in inferring ecorisks than in inferring human health risks.
In fact, the contrary is true. Inferences of ecorisk carry inherently greater
uncertainty than inferences about human health risk.
The NRC committee that recently reviewed ecosystem toxicity test
procedures has identified several reasons why test results from single species
are of limited value in predicting ecorisks:3
J Ibid.
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Results from single-species tests do not reflect
the complexity of ecosystem structures and the many
processes contributing to the maintenance of a
natural system
The absence of natural stresses.(predation and
competition) creates a highly artificial response
Organisms in natural settings are often
constrained in ways that cannot be duplicated in
single-species tests
Single-species tests do not permit identification
of indirect effects, such as those on populations
that depend on interactions with other species for
survival
Single-species tests do not measure the adaptive
capacity of a natural system.
The committee report cited numerous examples to support these generaliza-
tions. No clear trend is discernible in the examples. In some cases, results
from single-species tests appear to predict less severe effects than those
encountered in natural settings, but in others, more severe effects are seen.
One of the major conclusions of the NRC review is that
Single-species tests can provide much information on
the concentrations and durations of exposures to
chemicals that result in changes in survival,
reproduction, physiology, biochemistry, and behavior of
individuals within particular species, but results
from such tests cannot predict or be used to evaluate
aspects of chemical impacts beyond this level of
biological organization. *
The committee described more complex test systems that might yield more
useful data for ecorisk. prediction, but noted that such tests are still in the
developmental stages. In fact, it is not at all clear how data from such
experimental test systems can be used. Data from field studies provide
extensive documentation that chemicals can damage ecosystems and their member
populations and individuals, but most lack the type of quantitative
information needed for risk prediction.5
* Ibid., p. 6 (emphasis added).
SNRC (1981), op. cit.; Cf. F.J. Cairns, Jr., K.L. Dickson, and A.W.
Maki, eds., Estimating the Hazard of Chemical Substances to Aquatic Life,
ASTM Special Technical Publication 657 (Philadelphia: American Society for
Testing Materials, 1981).
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4.4 GENERAL APPROACH TO ECORISK SCORING
A means for systematically incorporating ecotoxicity data, mostly obtained
in single-species tests, into a scheme that provides some measure of ecorisk
is described below. The measure may not be an accurate index of "real world"
effects, but it does provide a rough indication of the relative magnitudes of
ecorisks caused by different waste management practices.
The ecorisk scoring methodology has these important characteristics:
It is relatively simple, to permit application
to a broad range of circumstances
It incorporates the well-established fact that
different components of waste streams display
different toxic potencies to individual species
within ecosystems
It incorporates the notion, based on a substantial
body of literature, that there is an environmental
concentration (a threshold concentration) for
each chemical below which no effect is likely to
occur on an ecosystem or on member populations or
individuals. (The adoption of ecosystem threshold
concentrations is a significant departure from the
methodology used for scoring human health risks.)
It incorporates the notion that as environmental
concentrations increase above the threshold
concentration, the' number of individual organisms
and populations and ecosystem properties affected
will increase, and the seriousness of the effects
will also increase (i.e., an exposure-response
relationship of some type holds for ecosystem
damage)
It provides a means for systematic and
consistent incorporation of toxicity,
exposure-response, and threshold information into
the ecorisk score.
The methodology results initially in separate risk scores for the effects of
chemical pollutants on aquatic and terrestrial ecosystems. These scores are
then combined to yield a single ecorisk score.
4.5 AQUATIC ECORISK METHODOLOGY
This section describes a five-step methodology for characterizing the
risks to aquatic ecosystems from managing hazardous wastes. These steps
parallel those in the methodology for scoring human health risks.
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Step 1 is to select waste stream chemicals for scoring.
Step 2 is to estimate the concentration of each chemical in surface water
at various distances from the point of release to serve as a measure of
exposure. Concentration is a function of chemical-specific factors, the
release rate, and the characteristics of the aquatic subenvironment to which
the chemical is released.
Step 3 is to derive an operational aquatic ecosystem6 exposure-response
function for each chemical. The function is qualitative and is designed to
incorporate both the likelihood that an ecosystem will be damaged at certain
environmental concentrations and the severity of the damage.
Step 4 is to develop a first-stage aquatic ecorisk score by combining the
data from steps 2 and 3, taking into account the severity of the expected
ecosystem damage as well as the area over which damage is expected. The score
is the rounded logarithm of the integrated risk value. It is a first-stage
score because it does not take into account the importance of the ecosystem
that is vulnerable to damage.
Step 5 is to derive a final aquatic ecorisk score by modifying the
first-stage score to reflect the commercial or recreational importance of the
aquatic environment.
Although this methodology results in numerical scores for ecorisk, no way
exists to determine the quantitative relationships between the different
degrees of damage represented by the scores. As noted in Section 4.1,
chemicals damage ecosystems in many ways and at many different levels of
biological organization with different degrees of seriousness. At this stage,
it is impossible to provide anything but a qualitative scale of relative
ecorisks. It can be shown that a score of 8, for example, reflects a greater
ecorisk than one of 7, but the magnitude of the difference cannot be
quantified.
In the following sections, we expand on each of the above steps, and
describe the basis for and application of the aquatic ecorisk scoring method.
4.5.1 Selection of Chemicals
The waste streams and their constituents scored are the same as those used
for human health risk scoring, except that three waste streams with four
chemical constituents likely to be especially hazardous to ecosystems are
scored for ecorisks but not for human health risks. The additional waste
streams and the corresponding constituents of concern are listed below in
Exhibit 4-1.
6Hereafter the term "ecosystem" refers to individual organisms,
populations of individual species, and system properties (structural and
functional).
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EXHIBIT 4-1
ADDITIONAL WASTE STREAMS AND CONSTITUENTS SCORED FOR ECORISKS
Waste Stream
Number
Name
Constituent(s)
03.04.31 Production wastes from
disinfectants manufacture
03.05.08 Distillation bottoms from
LAS Production
Benzalkonium chloride
Dimethyl Alkylamine
LAS (Linear alkylbenzene
sulfonate)
03.06.04 Off-specification commer-
cial 2,4-D acid, salts,
and esters
2,4-D (A herbicide)
These three waste streams represent broad classes of substances either
designed to be selectively toxic to non-human targets (e.g., disinfectants and
herbicides) or known to result in environmental damage at high
concentrations. All waste streams and constituents are described in Chapter 2
and Appendix A.
To estimate ecorisks, we assume the risk posed by a waste stream is
represented by the risks of a few key constituents, the same assumption made
for predicting human health risks.
4.5.2 Exposure Assessment
The model's approach to characterizing the release of a chemical from a
waste site and its subsequent behavior in the environment is discussed in
Chapter 3 and is not repeated here. To assess aquatic risks, it is necessary
to estimate the surface water concentrations of each of the released chemicals
at a distance of one kilometer from the point of release. The model predicts
concentration as a function of distance. The exposure-response relations for
aquatic ecosystem toxicity depend on concentration, and not on intake (i.e.,
dose), as they do for human health effects. Thus, the surface water
concentration for each chemical is used as the measure of exposure.
As described in Chapter 3, surface water concentration (C ) is
sw
predictable if the following parameters are specified:
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Release Rate. The mass of chemical released per
day to surface water for a specific waste management
practice.
Surface Water Flow Rate. The daily volume of
surface water flow into which the chemical is
released. Flows depend on the characteristics of the
surface water subenvironment.
Initial Concentration. The initial concentra-
tion of the chemical in surface water, obtained by
dividing the release rate by the surface water flow.
Decay Rate Coefficient. The chemical's decay
rate due to physical and chemical degradation
processes.
Velocity. The velocity of the surface water
into which the chemical is released.
Distance. The distance from the point of
release to the potential aquatic ecosystem at risk.
Given a value for each of these parameters, C is estimated by equation 1
from Chapter 3:
-K , X
C = C exp su
sw o
The values of flow and velocity are specific to a subenvironment and are
listed in Exhibit 4-2. The values of the decay rate for each chemical are
presented in terms of half-life values in Appendix E. The distance from the
point of release, X, is one kilometer.
4.5.3 Ecosystem Damage Functions
The ecosystem damage function (i.e., exposure-response relation) is a
single, simple measure of damage as a function of environmental
concentration. As explained earlier in this chapter, empirical information is
not available to construct this measure in a rigorous way. Indeed, the
available data strongly suggest that the notion that any such measure could
conceivably be "simple" or "single" is almost certainly in error. But it is
necessary to assume such a measure so that at least our broad and general
knowledge about the ecotoxicity of chemicals and the ways in which ecosystems
are vulnerable to damage can be reflected in the model. In the following
discussion, we explain how we construct ecosystem damage functions.
(a) Types of Data Used. The data used for constructing the
operational ecosystem damage functions for individual chemicals are based on
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EXHIBIT 4-2
FLOW AND VELOCITY VALUES FOR AQUATIC ECOSYSTEM SUBENVIRONMENTS
SUBENVIRONMENT FLOW (Q) VELOCITY (V)
(Liters/Day) (Meters/Day)
Large River
1011
1.0
X
io4
Medium River
IO10
5.5
X
io4
Small River
109
5.5
X
io4
Rivers With Large Drainage, Low Flow
109
5.5
X
io4
Marshes-^
109
1.0
X
io3
2 /
Sea Coast-
10s
1.0
X
io4
Lakes-^
109
1.0
X
io3
a/ No data available. We assume that flows for these bodies of water
approximate those of rivers with large drainage basins and low flows.
Source: United States Geological Survey, Water Resources Division, Large
Rivers of the United States (1981).
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ecotoxicity studies on single species of aquatic organisms. The choice of
species for testing has traditionally been based on convenience, rather than
on an attempt to construct an ecologically realistic test system.7
Exhibit 4-3 organizes aquatic organisms into eleven groups. The exhibit
indicates, for each group, the number of freshwater and saltwater species that
have been used as subjects for testing chemical toxicity (or more accurately,
for how many species toxicity test data that are useful for the purposes of
the model are available). The exhibit shows the wide variation in the amount
of testing on different groups of aquatic organisms.
Toxicity data for some of the waste stream constituents in the model are
available from tests conducted on many aquatic species. For other chemical
constituents, data are available from tests on just a few species. Exhibit
4-4 lists the constituents in the model and indicates, for each constituent,
the number of species for which the results of toxicity testing are available.
The information in Exhibits 4-3 and 4-4 is indicative of the completeness
of the data on chemical toxicity. The model takes data completeness into
account in estimating ecorisks, as explained below in the discussion of the
derivation of ecosystem damage thresholds.
(b) Derivation of Ecosystem Damage Function. Because few
quantitative toxicity tests have been performed on an ecosystem level, we must
construct an ecosystem damage function E(C) on the basis of exposure-response
relationships obtained from single-species toxicity tests. This goal is
illustrated in Exhibit 4-5, which depicts exposure-response functions (H(c))
for many individual species, each characterized by a threshold concentration
(C^,), and concentrations giving a 50 percent (M) and a 100 percent (C)
response rate. The measured response is ordinarily mortality, or a more
sensitive measure of toxicity, such as an effect on growth or reproduction.
These functions represent chronic exposures, the types of exposure situations
in the model.
The first theoretical consideration is that E(C) spans part of, but not
all of the range of concentrations shown in Exhibit 4-5. The first detectable
ecosystem effect is likely to be close to C , the threshold concentration
1
for the most sensitive species in the ecosystem. However, it may be above
C_ , if species 1 is resilient in the natural ecosystem, or below C ,
1 :1
if species 1 is more sensitive in the natural ecosystem than in the test
environment. At the other end of the scale, major (catastrophic) effects on
ecosystems are not expected until a substantial fraction of the species is
severely affected. For example, total elimination of the most sensitive
species will usually have only minor effects on an ecosystem. However,
7NRC (1981), 02- cit.
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EXHIBIT 4-3
GROUPS OF AQUATIC ORGANISMS
Groups of
Organisms
Number of Species
Useful Data Are
for Which
Available
Freshwater
Saltwater
Fish
48
24
Molluscs
10
12
Crustaceans
17
40
Insects
14
-
Annelids
5
10
Aschelminthes
2
1
Echinoderms
-
6
Macrophytes
6
7
Amphibians
3
-
Phytoplankton
24
24
Other Microorganisms
_J2
7
Total
131
131
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EXHIBIT 4-4
NUMBER OF AQUATIC SPECIES FOR WHICH TOXICITY DATA
ON WASTE STREAM CONSTITUENTS ARE AVAILABLE
NUMBER OF SPECIES
CHEMICAL Freshwater Saltwater
Organic Compounds
Acenaphthene
3
3
Acetaldehyde
5
2
Acetonitrile
6
0
Acrolein
16
4
Acrylonitrile
4
1
Allyl alcohol
2
0
Aniline
7
0
Benzalkonium chloride
9
0
Benzene
10
11
Benzo(a)anthracene
1
0
Benzo(a)pyrene
0
0
Benzotrichloride
4
0
Benzyl Chloride
10
1
Bis (chloromethyl) ether
0
0
Carbon tetrachloride
3
2
Chlordane
22
11
Chloroacetaldehyde
0
0
Chlorobenzene
8
3
Chloroform
4
1
2-Chlorophenol
8
0
Chrysene
0
0
Cyclohexane
8
0
2,4-D
21
0
1,2-Dichlorobenzene
6
7
1,4-Dichlorobenzene
6
5
1,2-Dichloroethane
3
3
1,1-Dichloroethene
4
4
Dichloromethane
4
3
1,2-Dichloropropane
3
2
1,3-Dichloropropan-l-ol
0
0
1,3-Dichloropropene
5
3
Dimethylalkylamines
0
0
1-3-Dinitrobenzene
6
0
2,4-Dinitritoluene
2
0
Epichlorohydrin
6
3
Ethylene oxide
1
0
Formaldehyde
21
4
Hexachlorobenzene
5
4
Hexachlorobutadiene
4
4
Hexachloroethane '
6
3
Hydroquinone
18
0
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NUMBER OF AQUATIC SPECIES FOR WHICH TOXICITY DATA
ON WASTE STREAM CONSTITUENTS ARE AVAILABLE
EXHIBIT 4-4
mc SPECIE*
IEAM CONST
(continued)
NUMBER OF SPECIES
LAS 8 1
Maleic anhydride 2 0
Methyl chloride 6 1
Methyl ethyl ketone 5 1
Methyl methacrylate 9 0
Naphthalene 7 12
1,4-Naphthoquinone 0 0
Nitrobenzene 3 2
4-Nitrophenol 11 4
PCB-1254 12 16
Paraldehyde 0 0
Parathion 34 6
Pentachlorophenol 16 12
Phenol 28 5
Phthalic anhydride 0 0
Pyridine 15 0
2 ,3,7,8-Tetrachlorodibenzo-p-dioxin 3 0
1,1,1,2-Tetrachloroethane 2 0
1,1,2,2-Tetrachloroethane 4 2
Tetrachloroethene 6 4
Toluene 7 13
Toluene diamine 0 0
Toluene diisocyanate 1 2
Toxaphene 30 23
1,2,4-Trichlorobenzene 3 3
1.1.1-Trichloroethane 4 3
1.1.2-Trichloroethane 6 0
Trichloroethene 5 5
Vinyl Chloride 0 0
Inorganic Compounds
Antimony 4 3
Arsenic 23 10
Barium 15 0
Cadmium 56 46
Chromium (VI) 27 25
Copper 82 58
Cyanide 29 5
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EXHIBIT 4-4
NUMBER OF AQUATIC SPECIES FOR WHICH TOXICITY DATA
ON WASTE STREAM CONSTITUENTS ARE AVAILABLE
(continued)
NUMBER OP SPECIES
CHEMICAL Freshwater Saltwater
Inorganic Compounds (continued)
Fluoride
5
2
Lead
35
18
Mercury
24
63
Nickel
34
23
Thallium
7
6
Vanadium
9
6
Zinc
48
45
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elimination of most plants will be catastrophic even if animals are not
directly affected. In practice, hazard functions for individual species are
usually relatively steep compared to the range of sensitivities among species,
or in the notation of Exhibit 4-5, (C^, - C^. ) ).
max 1 1
Hence, the ecosystem damage function E(C) will be flatter than those for
individual species.
Scientific literature on ecosystem effects of pollutants is extensive but,
as noted, most is general and non-quantitative. We have found several case
studies that provide some quantitative measures of exposure-response
relationships at an ecosystem (or community, or multi- species microcosm)
level. These include the following studies:
Effects of zinc on a forest ecosystem
Effects of radiation on terrestrial ecosystems
Effects of toxaphene on estuarine organisms
Effects of acidification on freshwater ecosystems
These four case studies, summarized in Appendix G, differ in the types of
ecosystem studied and have several limitations. Only two of the chemicals are
on the model's list of constituents and only one of the studies is of a
hazardous waste disposal situation. The "ecosystems" studied range from a
three-species microcosm to mixed terrestrial-aquatic systems including both
animals and plants.
In addition, the units of the damage function for ecosystems raise
conceptual problems because there is no simple measure of "damage." Most of
the case studies measured changes in species composition, species diversity,
or other structural properties of ecosystems. This is appropriate for most
circumstances, given a concern with the loss of species variety. Empirically,
structural measures of effect are probably more sensitive than functional
measures in natural ecosystems.8 These studies, however, do not provide any
common measures of the various types of ecosystem damages. Such measures must
be defined to achieve consistency in scoring.
The case studies support two generalizations. First, there is no
discernible trend in the discrepancies between the observed ecosystem
threshold concentration and the experimentally determined chronic threshold
value for the most sensitive species. For this reason, and pending the
development of more information, we assume the ecosystem threshold (C )
E
is equal to the threshold concentration for the most sensitive species
"NRC (1981), op. cit.
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EXHIBIT 4-5
EXPOSURE-RESPONSE RELATIONS FOR SPECIES
AND DAMAGE FUNCTION FOR ECOSYSTEM
H.(o) J 1
Ccc CC CCCCCC C /. .ป
T T T T T. T T T T T T T (tog scaM
E 1 2 34 66789 10 "*ซ*
Surface Water Concentration
H.(C) =
i
Hazard function for the ith species.
= Threshold concentration for the ith species
ik
M | = Concentration giving a 50 percent response rate for the I species.
C, = Concentration giving a 100 percent response rate for the i species
E(c) = Damage function for the ecosystem.
C^. = Threshold concentration for the ecosystem.
Catastrophic concentration for the ecosystem.
ICF
INCORPORATED
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4-17
(C_ ). Second, the range of concentrations between the threshold and the
1
catastrophic level is typically two to three orders of magnitude (depending on
how "catastrophic" is defined and how carefully the threshold is measured).
These generalizations are adopted as the basis for a generalized damage
function for ecosystems.
Thus, the model's ecosystem damage function E(C) varies in magnitude
between 0 (i.e., no damage) when the concentration of the pollutant is at or
below a threshold, C , and 1.0 (i.e., 100 percent damage) at pollutant
E
concentrations (C^.) 2.5 orders of magnitude higher. E(C) is assumed to be a
linear function of log C between C_ and C_ (Exhibit 4-5).
E
Having assigned an operational ecosystem damage function, the response
scale is divided into six "damage zones" (none, small effect, moderate,
substantial, severe, and catastrophic; see Exhibit 4-6). It then becomes
possible to relate the extent of ecosystem damage to the concentration of a
given surface water pollutant (C ) If the ecosystem threshold
concentration is C_ and the surface water concentration of the pollutant
E
is Cgw> the relationships in Exhibit 4-6 hold.
Thus, when C equals C_ (or is less than C_ ), the threshold
sw 1 _ l_
E E
concentration is not exceeded, and no damage is expected. If Cgw is between
1.0 and 3.16 times the value of C_ , then a "small" level of damage is
E
predicted. Moderate damage is expected when Cgw is between 10 and 31.6
times the value of C_ , and so on up the scale. When C is greater
1ฃ sw
than 316 times C_ , catastrophic damage is expected. This simple scale
E
follows directly from the imposed linear relationship between E(C) and log C
and the imposed slope.
(c) Derivation of Ecosystem Thresholds. The critical step in
producing the ecosystem damage function is deriving the appropriate value for
C_ for each chemical to be scored. We used the exposure concentration at
E
which no effects were observed after prolonged (chronic) exposure as the
threshold concentration for each species. The threshold concentration of the
most sensitive species was selected as the ecosystem threshold, C_ . We
E
separately derived a C_ value for freshwater organisms and salt water
E
organisms for each chemical.9
90nly two of the seven subenvironments, marshes and sea coasts, contain
predominantly saltwater organisms; the saltwater toxicity data apply only to
those two subenvironments.
ICF INCORPORATED
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4-18
EXHIBIT 4-6
ECOSYSTEM DAMAGE FUNCTION, E(C)
Degree of Damage
E(C)
Log (C /C ) a/
sw 1ฃ
c /cT
sw Te
None
0.0
<0
<1.0
Small
0.2
0.5
3.16
Moderate
0.4
1.0
10
Substantial
0.6
1.5
31.6
Severe
0.8
2.0
100
Catastrophic
1.0
2.5
316
a/ C is surface
- sw
water concentration of pollutant;
C is
Ar
ecosystem threshold concentration for the same pollutant.
Five major issues were considered in deriving the C_ : data quality,
E
completeness of data, exposure duration, absence of experimental threshold,
and missing data. The model factors three of these issues -- completeness,
exposure duration, and absence of experimental threshold -- into the
calculation of C_ values by the procedures explained below. Exhibit 4-8
E
is a summary of the derivation of CT values for each waste stream
E
constituent.
Data Quality. We relied primarily on secondary data sources. The
only readily available sources are EPA's Ambient Water Quality Criteria
Documents. When the criteria documents did not reveal sufficient information
(e.g., whether a threshold concentration had been identified in a particular
experiment), we consulted the primary literature. A literature search was
conducted for chemicals for which no criteria documents exist.
Completeness of Data. The completeness of the data available to
determine ecosystem damage thresholds for each of the model's waste stream
constituent varies widely. We regard completeness of data as a function of
two factors: quantity and representativeness. We judge the quantity of data
on the basis of the number of different species, N, on which a chemical's
toxicity has been tested, as identified in Exhibit 4-4 above. We use a value
of N=30 as the standard for data quantity, this being the average number of
freshwater species on which the toxicity of the best-studied constituents,
ICF INCORPORATED
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4-19
inorganic chemicals, has been studied. We judge the representativeness of
data on the basis of the number of groups of aquatic organisms, G, represented
among the species on which a chemical's toxicity has been tested. The 11
groups into which we divide aquatic organisms are listed in the left column of
Exhibit 4-3. Because there are a total of 11 groups, we use a value of G=ll
as the standard (or ideal) for data representativeness. Exhibit 4-8
identifies the number of groups represented among the species on which each
constituent's toxicity has been tested.
Thus, we can obtain a numerical measure of the completeness of data on
each chemical constituent and using that measure, take data completeness into
account in deriving values for a constituent's ecosystem damage threshold.
Data for a chemical is considered to be "complete" if G and N both exceed
two-thirds of 11 and 30, respectively. We reduced the experimental threshold
for the "most sensitive" species for which data are available by dividing by a
"completeness factor,"because we assume that the less "complete" the data, the
less likely it is that the most sensitive species has been identified. The
completeness factors are presented in Exhibit 4-7. For example, the experi-
mental threshold for a substance studied in tests on fewer than ten species
and in fewer than three groups, is divided by a completeness factor of 10.
EXHIBIT 4-7
COMPLETENESS FACTORS FOR EXPERIMENTAL THRESHOLDS
Representativeness
(G/ll)
<0.33
0.33 - 0.67
>0.67
<0.33
0.33 - 0.67
>0.67
10
i
7.5 |
i
5 |
7.5
j
5
2.5 -
1
1
5
2.5 |
i
1 |
i
Exposure Duration. Most available data reflect acute exposures; we
are interested in the effect of chronic exposure. If only acute data were
available, a factor of 100 was applied to derive a chronic value. The only
exceptions were compounds structurally related to substances for which
ICF INCORPORATED
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0ซnillซlli NrMi
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bMU
li.w, lปrr ir.U
ซ/ Ao^tMt fm.lur li eliซ.l aป
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4-26
empirical chronic/acute ratio data were available. In such cases, the
empirical ratio was used for the structurally-related chemical.
Absence of Experimental Threshold. If the lowest concentration at
which a chemical was tested produced toxic effects, a threshold concentration
was estimated by dividing the lowest concentration by 10. This factor does
not have empirical support; it is based on our judgment of the likely
exposure-response relationship.
Missing Data. For some chemicals, no data were available and
default values were selected. In most of these cases, data were adopted from
the most thoroughly studied, structurally-related chemicals. Where data were
available only for freshwater organisms, they were assumed to applied to
saltwater organisms, and vice versa.
The ecosystem threshold concentrations, C_ , for each chemical are
E
presented in Exhibit 4-8. The exhibit also includes the data and assumptions
used to derive C_ (i.e., it reveals the adjustments described above).
E
These values of C are used to derive the first-stage ecorisk score.
E
4.5.4 First-Stage Score
The subenvironment-specific concentration, C , is estimated as a
sw
function of distance from the point of release for the various combinations of
waste streams and technologies. The ratios of C to C_ at these
sw lฃ
distances are the basis for first-stage ecorisk scoring.
Using the operational ecosystem damage function (linear in log concentra-
tion and spanning 2.5 orders of magnitude), the degree of ecosystem damage can
be related to the concentration, Cgw, of a given surface water pollutant.
C can, in turn, be related to distance from the point of release.
SW
As described in Section 3.5.2, at the source of a release, the initial
concentration, Co, is determined by the release rate and the initial volume of
water into which a chemical mixes. The concentration decreases with distance
from the source depending on the rate of degradation of the chemical and the
rate of movement in the water into which it is released (e.g., velocity of the
river). For any particular chemical release, at any particular site, the
initial concentration, Co, the half-life, t^, and the rate of movement of
the water, v, are constant for a given chemical and subenvironment (ignoring
intermittent or seasonal fluctuations).
Since C is expressed as a function of distance from the source of
sw
discharge, E(C) can also, by substitution, be expressed as a function of
distance. The mathematical equation relating E(C) to distance can then be
integrated for all values of distance for which the concentration, C , is
' sw
greater than C_ (i.e., E(C) is greater than 0). This produces a value
E
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indicating the extent of damage to the ecosystem combining both the degree of
damage and the distance over which it occurs. Thus, for example, a release
producing only a moderate degree of damage, but over a very great distance,
may be more hazardous to the ecosystem as a whole than one producing severe
damage, but only immediately adjacent to the source of release.
The integrated measure of ecosystem damage is used for the first-stage
scoring. The score is derived by taking the logarithm (base 10) of the
damage. We use a distance of one kilometer from the point of release in
deriving this score.
4.5.5 Final Score
The first-stage ecorisk score does not reflect the fact that different
subenvironments have different numbers of biological targets that may be
affected. Nor does it take into account the presence of economically valuable
resources and recreational areas. These two factors are described below.
(a) Difference in Number, Type, and Diversity of Aquatic Organisms. The
ecorisk methodology could, theoretically, incorporate a measure of the number
of organisms (e.g., population or biomass density) in different
subenvironments, but the variation in number is not likely to be extreme and
perhaps depends more upon region of the country than upon subenvironment.
Moreover, there are few data available to make such distinctions.
However, the ecorisk methodology is not species specific. It is designed
to reflect generalized freshwater and saltwater aquatic environments and not
the fact that, for example, a freshwater environment in North Dakota contains
different types of species than a freshwater environment in Florida. The
ecorisk methodology reflects toxic responses among a wide range of species and
is sufficiently generalized to apply to any freshwater or saltwater
environment. Consequently, the ecorisk score does not take into account
differences in subenvironment populations, species types, or diversity.
(b) Differences in Importance of Aquatic Resources. Most people
place different values on different regions, either for economic or aesthetic
reasons, as reflected, for example, in the designation of certain areas as
especially valuable natural regions requiring protection. The ecorisk score
is adjusted to incorporate such values.
Each three-digit zip code area in the model is designated as of greater or
lesser "importance." Zip code areas of "greater importance" have at least one
of the following:
All or part of a national or state park, seashore,
wilderness area, monument, river, lakeshore, or historic
site
A commercial fishing area
A public or private beach.
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Any zip code area containing a seashore or the shore of a Great Lake is
automatically considered of "greater importance," even if it is uncertain
whether fishing or swimming is available in the shore area. All other zip
code areas are considered to be of "lesser importance." The ecorisk scoring
obtained in the previous section is adjusted upward by one (1) log if the zip
code area to which it is applied is of "greater importance." No adjustment is
made for areas of "lesser importance." The result is the final ecorisk score.
This adjustment reflects a qualitative judgment. No precise quantitative
significance is attached to the absolute values of the final scores; they
simply reflect relative degrees of aquatic ecorisk.
4.6 TERRESTRIAL ECORISK METHODOLOGY
This section describes the methodology for estimating risks to terrestrial
ecosystems from hazardous waste management practices. First, however, we
present some general observations on calculating risks to terrestrial
ecosystems from toxic substances and discuss some of the additional
limitations of constructing this component of the ecorisk score.
Terrestrial ecosystems differ from aquatic systems not only in content but
also in diversity and complexity. For example, aquatic ecosystems are
generally divided into two categories: saltwater and freshwater. By
contrast, terrestrial ecosystems are conventionally divided into at least
eleven categories or "biomes" -- tundra, northern coniferous forest, temperate
deciduous and rain forest, temperate grassland, chaparral, desert, tropical
rain forest, tropical deciduous forest, tropical scrub forest, tropical
grassland and savanna, and mountains.10
Despite our ability to distinguish among the various terrestrial
ecosystems, our knowledge of the structure of specific ecosystems and of the
processes that sustain them over time is limited. Consequently, there is
great uncertainty in determining the effects of toxic substances on an entire
ecosystem.
It is possible, however, to make some general observations about the
effects of toxic substances on terrestrial ecosystems.11 Pollutants tend to
simplify plant and animal communities by causing a progressive loss of
species. At the extreme, the result is erosion and loss of soil fertility.
Mutagenesis can be caused by some common pollutants, and reproduction may also
be affected. Behavioral changes can be caused by relatively low levels of
contaminants. At certain exposures below toxic levels, a biostimulatory
10E.J. Kormandy, Concepts of Ecology (Englewood Cliffs, New Jersey:
Prentice-Hall, Inc., 1969).
^W.H. Stickel, "Some Effects of Pollutants in Terrestrial Ecosystems,"
Proceedings of the NATO Science Committee Conference on Ecotoxicology, Mont-
Gabriel, Quebec, Canada, Mav 6-10, 1974 (New York: Plenum Press, 1974).
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effect can be expected. There are, however, substantial differences in
susceptibility among species. Resistant individuals can carry toxicant loads
that make them dangerous food for other animals. Food chain accumulations
occur when persistent chemicals enter organisms that eliminate them poorly.
Terrestrial food chains often begin with a high level of contamination, because
of accumulation.
Determining ecorisks to terrestrial systems is limited by the same
problems outlined for aquatic systems: toxicity data are derived from single-
species laboratory tests and quantitative data are usually unavailable for the
few field studies that have been conducted. Terrestrial ecorisk
determinations are even more complicated, however, because the data base for
toxic effects on terrestrial organisms is much scantier than that available on
aquatic organisms. For example, of the chemical constituents in the model's
waste streams, there were none for which data were available on at least one
species in each of six categories of terrestrial organisms (mammals; birds;
amphibians and reptiles; arthropods, annelids and other invertebrates; plants;
and microorganisms). Data were available on species in at least three of
these categories for only nine chemicals.
The data base on terrestrial toxicity is far less extensive than that on
aquatic toxicity for the following reasons:
Relatively few hazardous wastes have been studied for
terrestrial ecosystem damage. Among the classes of
pollutants for which terrestrial ecosystem effects have
been measured are metals, pesticides, PCBs, mine
tailings, and six criteria air pollutants. These
pollutants account for relatively few of the model's
waste stream constituents.
Available data are fragmented and relatively
unsystematized. Documents comparable to the 1980 EPA
Ambient Water Quality Criteria Documents -- which serve
as a major source of data in developing aquatic ecorisk
scores -- do not exist for the majority of constituents
in the model. EPA Air Criteria Documents, which include
data on terrestrial ecosystem effects, have been
prepared for only six chemicals; of these, only lead is
among the pollutants scored in the model.
Because of these limitations, there is at present no rigorous method for
quantifying risks of waste stream constituents to terrestrial ecosystems. It
is important, however, that they be considered. The following sections
present a five-step methodology for assigning a score to the terrestrial
ecorisks from managing hazardous wastes. It must be emphasized that this
score does not represent a quantitative measure of risk. Rather, it is an
attempt to make use of the available data in a simple manner for which there-
is some justification. The methodology parallels that used for aquatic
ecosystems.
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Because of the manner in which terrestrial ecosystem risks are determined,
the model does not rely on a separate series of terrestrial subenvironments.
Instead, the seven surface water subenvironments used for estimating aquatic
ecorisks are also used for estimating terrestrial ecorisks.
The five steps are as follows:
Step 1 is to select waste stream chemicals for scoring.
Step 2 is to estimate the concentrations of chemicals at
various distances from the point of release to serve as a
measure of exposure.
Step 3 is to take into account an operational
exposure-response function (or damage function) for each
chemical. A special damage function is used for compounds
that bioconcentrate.
Step 4 is to derive a first-stage terrestrial ecorisk score
by combining the data from steps 2 and 3 and taking into
account the severity of the expected ecosystem damage.
Step 5 is to derive a final score by modifying the
first-stage score to reflect the economic and recreational
importance of the area.
4.6.1 Selection of Chemicals
The waste stream constituents for which terrestrial ecorisk scores are
derived are the same used for aquatic ecorisk scoring, as described in section
4.5.1.
4.6.2 Exposure Assessment
Perhaps the most problematic aspect of developing an ecological risk score
for terrestrial ecosystems is the difficulty of deriving a single exposure
estimate for an entire ecosystem. The extreme diversity in the types of
organisms present in a terrestrial ecosystem -- simple and complex plants,
herbivorous and carnivorous mammals and birds, lower vertebrates,
invertebrates -- also means great variability in routes of exposure and
uptake. Stickel has discussed some of the possible routes of exposure to
toxic substances in terrestrial ecosystems.12 He notes that contaminated
foods, especially animal foods in which chemicals are concentrated, are the
chief sources of trouble. Litter feeders, such as snails, slugs, and
earthworms, may accumulate high concentrations of persistent chemicals, which
are then ingested by predators. Vegetation at the beginning of the food chain
12 Ibid.
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may pick up lead, certain pesticides, and other chemicals from the soil.
Because fish pick up pesticides efficiently and store them for long periods,
some of the most serious food chain contamination of air-breathing animals
arises from the aquatic environment. The oral route is thus the main route of
exposure, but it is not the only one. Animals may inhale toxic chemicals
through polluted air; certain chemicals can be absorbed through the skin.
Because of the multiple routes of exposure possible for each species in
an ecosystem and the large number of species found in a given terrestrial
system, construction of an exposure model is much too complicated an
undertaking for purposes of the model (if, indeed, it can be done at all).
Therefore, in order to develop a score for terrestrial systems, we make the
simplifying assumption that total exposure to a terrestrial ecosystem is equal
to total exposure in an aquatic system. The rationale for this assumption is
that although exposure of terrestrial organisms to a concentration of a
chemical in surface water (C ) would be less than for aquatic organisms
exposed to the same C , terrestrial organisms are also exposed through
sw
other media (air, contaminated soil, contaminated food).
The model calculates C as a function of distance from the point of
sw
release, according to the procedures described in Chapter 3 and in section
4.5.2 above. Chemical concentration in the environment is thus used as the
measure of exposure in estimating terrestrial as well as aquatic ecorisks.
(In contrast, the estimation of human health risks uses dose to determine
exposure; dose, however, is a function of concentration.)
4.6.3 Ecosystem Risk Score Assignment
As described in Section 4.5, an aquatic ecorisk score is determined by
first deriving an ecosystem damage function, E(C). Because the case studies
(see Appendix G) used to develop this function include terrestrial ecosystems
as well as aquatic systems, we shall use the same ecosystem damage function
for both types of ecosystems.
Obviously, the most important data point for determining an ecorisk score
(given the concentration of the chemical) is the C_ value (the threshold
1
for the most sensitive species) used for determining the ecosystem threshold
concentration, C_ . Because data on toxic effects on terrestrial
E
organisms are sparse or nonexistent for many chemicals, we use the lower of
the two aquatic ecosystem thresholds (freshwater or saltwater) as the
terrestrial ecosystem threshold.
A major reason for assuming the equivalency of ecosystem damage functions
concerns the relative toxicities of compounds in aquatic and terrestrial
systems. We reviewed toxicity data for terrestrial species and compared the
lowest observable toxic doses against those for the most sensitive aquatic
species. In no case was terrestrial toxicity for a waste stream constituent
greater than aquatic toxicity. The equating of aquatic and terrestrial
thresholds may tend to overestimate risk to terrestrial organisms. However,
because data on toxicity to terrestrial ecosystems are even more sparse than
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on aquatic systems, the "true" relationship between the thresholds is
unclear. Given this uncertainty, equating aquatic and terrestrial thresholds
appears to be a reasonable, though perhaps conservative, assumption for
ecorisk estimation. Terrestrial ecosystem threshold concentrations are
presented in Exhibit 4-9.
An exception to this assumption exists for compounds that bioconcentrate.
In general, the ecological damage function derives from the observation that
in both aquatic and terrestrial systems the C^. (the concentration required
for a catastrophic effect) is approximately 2.5 orders of magnitude greater
than C,p. However, empirical evidence from case studies of effects of toxic
substances on ecosystems suggests that the ecosystem damage function should be
reconsidered for compounds that bioconcentrate in order to accommodate
top-of-food-chain terrestrial organisms. Because of the increased exposure
from consuming organisms containing the bioconcentrated substance, the
threshold concentration (C^.) for a toxic effect in top-of-food-chain species
is roughly two orders of magnitude below the threshold concentration (C_ )
measured for the aquatic ecosystem.
This relationship holds true, for example, with dieldrin, a pesticide that
is highly bioconcentrated in the environment. The C_ for dieldrin in
1
fish is .1 ppb; however, bald eagles and blue herons are affected by surface
water concentrations as low as 0.001 ppb. The catastrophic concentration
threshold for dieldrin in a salt marsh ecosystem is approximately 100 ppb,
five orders of magnitude greater than the for the top-of-food-chain avian
species and three orders of magnitude greater than the C^. for fish. Thus,
when top-of-food-chain organisms are considered -- as they would be in
terrestrial, but not necessarily aquatic ecosystems -- the ecosystem damage
function should span five orders of magnitude.
The damage function, E(c), for compounds that bioconcentrate and its
relationship to the original damage function is depicted in Exhibit 4-10. No
empirical information is available to construct ecosystem damage functions for
either bioconcentrated or nonbioconcentrated compounds in a rigorous way, nor
to permit precise comparison of the two. In fact, available ecological data
suggest that this simple representation of exposure-response relationships for
ecosystems is almost certainly in error. Nonetheless, we use the damage
functions depicted in Exhibit 4-10 to ensure that the relative ecotoxicity of
highly bioaccumulated chemicals is considered in the risk score.
Rather than reconstruct the ecosystem damage function for the five highly
bioconcentrated chemicals in the model (chlordane, hexachlorobenzene,
PCB-1254, toxaphene, and mercury15) the estimated surface water
13A compound is considered highly bioconcentrated if the bioconcentra-
tion factor, as calculated by EPA in their 1980 Ambient Water Quality Criteria
Documents, is 1,000 or greater.
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EXHIBIT 4-9
DERIVATION OF TERRESTRIAL ECOSYSTEM THRESHOLD CONCENTRATIONS
Lowest
Fresh- Salt- Aquatic =
water water Terrestrial
Chemical (yg/1) (yg/1) (yg/1)
Acenaphthene
14
71
14
Acetaldehyde
6000
583
583
Acetonitrile
6182
6182
6181
Acrolein
4.2
0.46
0.46
Acrylonitrile
130
204
130
Allyl alcohol
1000
1000
1000
Aniline
3.7
3.7
3.7
Benzalkonium chloride
57
57
57
Benzene
45
53
45
Benzo(a)anthracene
50
50
50
Benzo(a)pyrene
50
50
50
Benzotrichloride
560
560
560
Benzyl chloride
960
33
33
Bis (chloromethyl) ether
2000
942
942
Carbon Tetrachloride
228
417
228
Chlordane
0.13
0.02
0.02 a/
Chloroacetaldehyde
583
583
583
Chlorobenzene &
2.5
88
2.5
Chloroform
62
679
62
2-Chlorophenol
390
390
390
Chrysene
50
50
50
Cyclohexane
5000
5000
5000
2,4-D
8
8
8
1,2-Dichlorobenzene
267
17
17
1,4-Dichlorobenzene
102
17
17
1, 2-Dichloroet.hane
2000
942
942
1,1-Dichloroethene
1450
1867
1450
Dichloromethane
1608
2133
1608
1,2-Dichloropropane
810
8200
810
1,3-Dichloropropan-l-ol
810
8200
810
1,3-Dichloropropene
24
6.6
6.6
Dimethylalkylamines
273
273
273
1-3-Dinitrobenzene
13
13
13
2,4-Dinitrotoluene
258
258
258
Epichlorohydrin
6000
98
98
Ethylene oxide
ฆ750
750
750
Formaldehyde
1000
150
150
Hexachlorobenzene
0.10
0.10
0.10 a/
Hexachlorobutadiene
0.93
0.27
0.27
Hexachloroethane
72
7.8
7.8
Hydroquinone
40
40
40
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EXHIBIT 4-9 (continued)
DERIVATION OF TERRESTRIAL ECOSYSTEM THRESHOLD CONCENTRATIONS
Lowest
Fresh- Salt- Aquatic =
water water Terrestrial
Chemical (Ug/1) (Vg/1) (vg/1)
Organic Compounds
LAS
1.0
17
1
Maleic Anhydride
1150
1150
1150
Methyl chloride
50,000
2250
2250
Methyl Ethyl Ketone
1727
16250
1727
Methyl methacrylate
4933
4933
4933
Naphthalene
62
20
20
1,4-Naphthoquinone
40
40
40
Nitrobenzene
355
80
80
4-Nitrophenol
178
1687
178
PCB-1254
0.16
0.02
0.02 a/
Paraldehyde
1000
150
150
Parathion
0.04
0.15
0.04
Pentachlorophenol
0.64
13
0.64
Phenol
2560
0.91
0.91
Phthalic anhydride
1150
1150
1150
Pyridine
8000
8000 ป
8000
2,3,7,8-Tetrachlorodibenzo-
ฆp-dioxin 0.0000056
0.0000056
0.0000056
1,1,1,2-Tetrachloroethane
163
163
163
1,1,2,2-Tetrachloroethane
240
52
52
Tetrachloroethene
112
45
45
Toluene
106
1000
106
Toluene Diamine
1371
4621
1371
Toluene Diisocyanate
1371
4621
1371
Toxaphene
0.015
0.06
0.06 a/
1,2,4-Trichlorobenzene
29
22
22
1,1,1-Trichloroethane
440
260
260
1,1,2-Trichloroethane
940
940
940
Trichloroethene
67
67
67
Vinyl Chloride
67
67
67
Inorganic Compounds
Antimony 160 63 63
Arsenic 365 252 252
Barium 1600 1600 1600
Cadmium 0.15 2.2 0.15
Chromium (VI) 73 10 10
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EXHIBIT 4-9 (continued)
DERIVATION OF TERRESTRIAL ECOSYSTEM THRESHOLD CONCENTRATIONS
Lowest
Fresh-
Salt-
Aquatic =
water
water
Terrestrial
Chemical
Cvg/l)
(vg/l)
(Vg/D
Inorganic Compounds
Copper
3.9
54
3.9
Cyanide
3.1
0.25
0.25
Fluoride
150
415
150
Lead
12
5
5
Mercury
1.3
1.2
1.2 a/
Nickel
15
37
15
Thailium
5 .6
8
7.6
Vanadium
8.0
25
8.0
Zinc
47
166
47
a/ The Cgw must be adjusted using the method described in the text for
highly bioconcentrated substances.
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EXHIBIT 4-10
DAMAGE FUNCTION FOR COMPOUNDS THAT BIOCONCENTRATE
H(c)
'1
(Terrestrial)
(Aquatic) (Aquatic C
corresponding to
actual risk)
SURFACE WATER CONCENTRATION
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concentration, Cgwi is adjusted so that the risk predicted using the aquatic
ecosystem damage function spanning 2.5 orders of magnitude is the same as
would be predicted at the unadjusted concentration on a damage function that
extending five orders of magnitude below the concentration causing
catastrophic damage (C^.). Since the slopes of these two functions on a log
scale differ by a factor of two, the adjustment is performed by increasing the
log of the concentration C by a factor equal to half of the difference
between the log of the catastrophic concentration and the log of the actual
concentration (C ). Thus:
sw
(1) log C1^ = log Cgw + (log Cฃ - log Cgw)
2
where C1 is the adjusted surface water concentration. Since C_ is
SW L
defined as being 2.5 orders of magnitude above the aquatic ecosystem threshold
(C ), we substitute for log C_ in equation (1) as follows:
E L
(2) log Clgw = log Csfc, + (log + 2.5 - log Csw)
2
This adjustment is warranted because at the aquatic threshold
concentration the predicted risk would be 0, but the terrestrial ecorisk at
this concentration would be some value greater than 0. The simplest
adjustment is to change the C value to the concentration corresponding to
that risk.
4.6.4 First-Stage Score
This step is the same as described in section 4.5.4 for aquatic ecorisks.
4.6.5 Final Score
The first-stage terrestrial ecorisk score does not reflect the variation
in number of susceptible organisms and species in different subenvironments.
Nor does it take into account the presence of economically valuable resources
(e.g., livestock) and recreational (or wilderness) areas. Consequently, the
first-stage score is adjusted to reflect the "importance" of an area using the
same procedure described in section 4.5.5 for aquatic ecorisks. The
"importance" of an area is judged by some of the same criteria used in
adjusting aquatic ecorisk scores, namely, the presence of a national or state
park, seashore, wilderness area, monument, lakeshore, historic site, or beach.
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4.6.6 Total Ecorisks
The total ecorisk score, combining both aquatic and terrestrial
environments, is derived simply by adding the final scores derived in steps 4
and 5. The result indicates the relative risks of different waste management
practices to non-human populations. The ecorisk score may be considered in
conjunction with the two human health risk scores, derived by the methodology
explained in Chapter 3, and the score for the risk of sensory effects, derived
by the methodology explained in the following chapter, in analyzing the
relative risks and costs of waste management practices.
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REFERENCES TO EXHIBIT 4-8
1. Kemps et al., 1973; as cited in the Oil and Hazardous Materials Technical
Assistance Data System; NIH/EPA Chemical Information System.
2. Bringmann, G. and Kuhn, R. 1980a. Comparison of the Toxicity Thresholds
of Water Pollutants to Bacteria, Algae, and Protozoa in the Cell Multi-
plication Inhibition Test. Water Research 14:231-241.
3. DeGraeve et al. , 1980; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Benzene. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-018. PB81-117293.
4. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Toluene. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-075. PB81-117855.
5. Brown et al., 1975; as cited in the Oil and Hazardous Materials Technical
Assistance Data System; NIH/EPA Chemical Information System.
6. USEPA, 1978; as.cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Carbon Tetrachloride. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-026. PB81-117376.
7. Birge et al., 1979; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Chloroform. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-033. PB81-117442.
8. Alexander et al., 1978; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Halomethanes. Office
of Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-051. PB81-117624.
9. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Chlorinated Ethanes. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-029. PB81-117400.
10. Dill et al., manuscript; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Dichloroethylenes.
Office of Water Regulations and Standards, Criteria and Standards
Division, Washington, D.C. EPA 440/5-80-041. PB81-117525.
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REFERENCES TO EXHIBIT 4-8
(continued)
11. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Chlorinated Ethanes. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-029. PB81-117400.
12. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Chlorinated Ethanes. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-029. PB81-117400.
13. Pearson and McConnell, 1975; as cited in U.S. Environmental Protection
Agency (USEPA). 1980. Ambient Water Quality Criteria for Trichloro-
ethylene. Office of Water Regulations and Standards, Criteria and
Standards Division, Washington, D.C. EPA 440/5-80-077. PB81-117871.
14. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Dichloropropanes/propenes.
Office of Water Regulations and Standards, Criteria and Standards
Division, Washington, D.C. EPA 440/5-80-043. PB81-117541.
15. Birge et al., 1979; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Chlorinated
Benzenes. Cffice of Water Regulations and Standards, Criteria and
Standards Division, Washington, D.C. EPA 440/5-80-028. PB81-117392.
16. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Dichlorobenzenes. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-039. PB81-117509.
17. Geike and Prasher, 1976; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Chlorinated Benzenes.
Office of Water Regulations and Standards, Criteria and Standards
Division, Washington, D.C. EPA 440/5-80-028. PB81-117392.
18. Henderson et al., 1961; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Acrylonitrile.
Office of Water Regulations and Standards, Criteria and Standards
Division, Washington, D.C. EPA 440/5-80-017. PB81-117285.
19. U.S. Army, 1976; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Dinitrotoluene. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-045. PB81-117566.
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4-41
REFERENCES TO EXHIBIT 4-8
(continued)
20. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Nitrobenzene. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-061. PB81-117723.
21. Caldwell, 1977; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Nitrobenzene. Office of Water
Regulations and Standards. Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-027. PB81-117384.
22. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Hexachlorobutadiene. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-053. PB81-117640.
23. McKee, J.E. and Wolf, H.W. 1963; as cited in the Oil and Hazardous
Materials Technical Assistance Data System; NIH/EPA Chemical Information
System.
24. Nebeker and Puglisi; 1974; as cited in U.S. Environmental Protection
Agency (USEPA). 1980. Ambient Water Quality Criteria for Polychlori-
nated Biphenyls. Office of Water Regulations and Standards, Criteria and
Standards Division, Washington, D.C. EPA 440/5-80-068. PB81-117798.
25. Spacie, A., A.G. Vilkas, G.F. Doebbler, W.J. Kuc and G. R. Iwan, 1981.
Acute and Chronic Parathion Toxicity to Fish and Invertebrates. Office
of Research and Monitoring, U.S. Environmental Protection Agency,
Washington, D.C. EPA 600/3-81-047. PB81-245862. ฆ
26. Webb and Brett, 1973; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Pentachlorophenol.
Office of Water Regulations and Standards, Criteria and Standards
Division, Washington, D.C. EPA 440/5-80-065. PB81-117764.
27. Holcombe et al., 1980; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Phenol. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-066. PB81-117772.
28. Curtis, M.W., Copeland, T.L. and Ward, C.H. 1979. Acute Toxicity of 12
Industrial Chemicals to Freshwater and Saltwater Organisms. Water
Research 13:137-141.
29. Mayer et al., 1977; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Toxaphene. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-076. PB81-117863.
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4-42
REFERENCES TO EXHIBIT 4-8
(continued)
30. Kimball, Manuscript; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Antimony. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-020. PBS1-117319.
31. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Arsenic. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-021. PB81-117327.
32. Chapman, Manuscript; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Cadmium. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-025. PB81-117368.
33. Sauter et al., 1976; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Chromium. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-035. PB81-117467.
34. Sauter et al., 1976; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Copper. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-036. PB81-117475.
35. Koenst et al., 1977; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Cyanides. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-037. PB81-117483.
36. Ellis et al., 1946; as cited in McKee, J.E. and H.W. Wolf, 1963. Water
Quality Criteria. The Resources Agency of California, State Water
Resources Control Board. Sacramento, California. Publication No. 3-A.
PB82-188244.
37. Chapman et al., Manuscript; as cited in U.S. Environmental Protection
Agency (USEPA). 1980. Ambient Water Quality Criteria for Lead. Office
of Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-057. PB81-117681.
38. Bluesinger et al., Manuscript; as cited in U.S. Environmental Protection
Agency (USEPA). 1980. Ambient Water Quality Criteria for Mercury.
Office of Water Regulations and Standards, Criteria and Standards
Division, Washington, D.C. EPA 440/5-80-058. PB81-117699.
39. Chapman et al., Manuscript; as cited in U.S. Environmental Protection
Agency (USEPA). 1980. Ambient Water Quality Criteria for Nickel.
Office of Water Regulations and Standards, Criteria and Standards
Division, Washington, D.C. EPA 440/5-80-060. PB81-117715.
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4-43
REFERENCES TO EXHIBIT 4-8
(continued)
40. Kimball, Manuscript; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Thallium. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-074. PB81-117848.
41. Holdway and Sprague, 1979; as cited in Chemical Abstracts 91:205233.
42. Spehar, 1976; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Zinc. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-079. PB81-117897.
43. Struhsaker, J.W., 1977. Effects of Benzene on Spawning Pacific Herring.
Fish Bulletin 75:43-49.
44. Pearson and McConnell, 1975; as cited in U.S. Environmental Protection
Agency (USEPA). 1980. Ambient Water Quality Criteria for Carbon
Tetrachloride. Office of Water Regulations and Standards. Criteria and
Standards Divis-ion, Washington, D.C. EPA 440/5-80-026. PB81-117376.
45. Bently et al., 1975; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Chloroform. Office
of Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-033. PB81-117442.
46. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Halomethanes. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-051. PB81-117624.
47. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Chlorinated Ethanes. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-029. PB81-117400.
48. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Dichloroethylenes. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-041. PB81-117525.
49. USEPA, 1978; as.cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Tetrachloroethene. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-073. PB81-117830.
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REFERENCES TO EXHIBIT 4-8
(continued)
50. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Dichloropropanes/propenes.
Office of Water Regulations and Standards, Criteria and Standards
Division, Washington, D.C. EPA 440/5-80-043. PB81-117541.
51. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Chlorinated Benzenes. Office
of Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-028. PB81-117392.
52. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Dichlorobenzenes. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-039. PB81-117509.
53. Daugherty and Garrett, 1951; as cited in U.S. Environmental Protection
Agency (USEPA). 1980. Ambient Water Quality Criteria for Acrylonitrile.
Office of Water Regulations and Standards, Criteria and Standards
Division, Washington, D.C. EPA 440/5-80-017. PB81-117285.
54. Dow Chemical, 1974; as cited in the Oil and Hazardous Materials Technical
Assistance Data System; NIH/EPA Chemical Information System.
55. Price, K.S., Waggy, G.T. and Conway, R.A. 1974. Brine Shrimp Bioassay
and Seawater BOD of Petrochemicals. Journal of the Water Pollution
Control Federation 46(l):63-77.
56. Schimmel et al., 1974; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Polychlorinated
Biphenyls. Office of Water Regulations and Standards, Criteria and
Standards Division, Washington, D.C. EPA 440/5-80-068. PB81-117798.
57. Lowe, 1970; as cited in the Oil and Hazardous Materials Technical
Assistance Data System; NIH/EPA Chemical Information System.
58. Parush, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Pentachlorophenol. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-065. PB81-117764.
59. Fries and Tripp, 1977; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Phenol. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-066. PB81-117772.
60. Ukeles, R., 1962. Growth of Pure Cultures of Marine Phytoplankton in the
Presence of Toxicants. Applied Microbiology 10:532-537.
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4-45
REFERENCES TO EXHIBIT 4-8
(continued)
61. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Antimony. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-020. PB81-117319.
62. Holland et al., 1960; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Arsenic. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-021. PB81-117327.
63. Nimms et al., 1977; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Cadmium. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-025. PB81-117368.
64. Oshida, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Chromium. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-035. PB81-117467.
65. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Copper. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-036. PB81-117475.
66. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Cyanides. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-037. PB81-117483.
67. Connell, A.D. and Airey, D.D. 1982. The Chronic Effects of Fluoride on
the Estuarine Amphipods Grandidierella Lutosa and G. Lignorum. Water
Research 16:1313-1317.
68. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Lead. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-057. PB81-117681.
69. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Mercury. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-058. PB81-117699.
70. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Nickel. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-060. PB81-117715.
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4-46
REFERENCES TO EXHIBIT 4-8
(continued)
71. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Thallium. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-074. PB81-117848.
72. Miramand, P. and linsal, M. 1978; as cited in Chemical Abstracts 90:67376.
73. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Zinc. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-079. PB81-117897.
74. McKee, J.E. and Wolf, H.W. 1963. Water Quality Criteria. Second
Edition. Publication 3-A. California State Water Resources Control
Board. NTIS PB82-188244.
75. Maki, A.W., and Bishop, W.E. 1979. Acute toxicity studies of surfactants
to Daphnia magna and Daphnia pulex. Arch. Environ. Contam. Toxicol.
8:599-612.
76. USEPA, 1976; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Acenaphthene Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-015. PB81-117269.
77. Macek et al., 1976; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Acrolein. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/-80-016. PB81-117277.
78. Hubault, 1955; as cited in McKee, J.E. and H.W. Wolf, 1963. Water
Quality Criteria. The Resources Agency of California, State Water
Resources Control Board. Sacramento, California. Publication No. 3-A.
PB82-188244.
79. Bringmann and Kuhn, 1959; as cited in McKee, J.E. and H.W. Wolf, 1963.
Water Quality Criteria. The Resources Agency of California, State Water
Resources Control Board. Sacramento, California. Publication No. 3-A.
PB82-188244.
80. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for 2-Chlorophenol. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-034. PB81-117459.
81. Bringmann and Kuhn, 1980b; as cited in Verschueren, 1983. Handbook of
Environmental Data on Organic Chemicals, Second Edition. Van Nostrand
Reinhold Co., New York.
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REFERENCES TO EXHIBIT 4-8
(continued)
82. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Dichloropropane and
Dichloropropene. Office of Water Regulations and Standards, Criteria and
Standards Division, Washington, D.C. EPA 440/5-80-043. PB81-117541.
83. Bringmann and Kuhn, 1976; as cited in Verschueren, 1983. Handbook of
Environmental Data on Organic Chemicals, Second Edition. Van Nostrand
Reinhold Co., New York.
84. Shell Chemie, 1975; as cited in Verschueren, 1983. Handbook of
Environmental Data on Organic Chemicals, Second Edition. Van Nostrand
Reinhold Co., New York.
85. Bandt, 1955; as cited in McKee and Wolf, 1963. Water Quality Criteria.
The Resources Agency of California, State Water Resources Control Board.
Sacramento, California. Publication No. 3-A. PB82-188244.
86. DeGraeve et al., 1980; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Benzene. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-059. PB81-117707.
87. Miller et al., 1973; as cited in USEPA, 1979. Ambient Water Quality
Criterion for 2,3,7,8-Tetrachlorodibenzo-p-dioxin. Draft report. Office
of Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C.
88. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Chlorinated Ethanes. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-029. PB81-117400.
89. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Chlorinated Benzenes. Office
of Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-028. PB81-117392.
90. USEPA, 1980; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Chlorinated Ethanes. Office of
Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-029. PB81-117400.
91. Butler, 1965; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Acrolein. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 440/5-80-016. PB81-117277.
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REFERENCES TO EXHIBIT 4-8
(continued)
92. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Dichloropropane and
Dichloropropene. Office of Water Regulations and Standards, Criteria and
Standards Division, Washington, D.C. EPA 043/5-80-043. PB81-117541.
93. Dawson et al, 1977; as cited in U.S. Environmental Protection Agency
(USEPA). 1980. Ambient Water Quality Criteria for Halomethanes. Office
of Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-051. PB81-117624.
94. Korn et al., 1979; as cited in Verschueren, 1983. Handbook of
Environmental Data on Organic Chemicals, Second Edition. Van Nostrand
Reinhold Co., New York.
95. USEPA, 1978; as cited in U.S. Environmental Protection Agency (USEPA).
1980. Ambient Water Quality Criteria for Nitrophenols. Office of Water
Regulations and Standards, Criteria and Standards Division, Washington,
D.C. EPA 043/5-80-063. PB81-117749.
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5 5ensory Effects
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5. SENSORY EFFECTS
5.1 INTRODUCTION
This chapter describes the methodology used to estimate the risk of
adverse sensory effects from managing hazardous wastes.
Adverse sensory effects may conceivably encompass a broad range of
environmental changes, including offensive odors and tastes, visually
objectionable structures or waste piles, the discoloration, turbidity, or
foaming of streams, the tainting of fish, and the reduction of atmospheric
visibility. The difficulty and subjectivity involved in quantifying a
relationship between chemical concentrations and many of these effects,
however, have led us to focus our analysis on two effects that can be readily
and objectively measured: taste and odor. Because the sense of smell is
particularly acute, the measurement of offensive odors should serve as a
reliable index of sensory effects in general. Our methodology, therefore,
includes estimates of the risks of sensory effects due to offensive odors
caused by airborne pollutants and to odors and tastes caused by pollutants in
surface and ground water.
It is clear that not all odors and tastes are unpleasant. It is also
recognized that humans have the capacity to adapt to the presence of certain
odors and tastes. Furthermore, different members of the population show
substantial variation in their sensitivities to and perceptions of the degree
of damage caused by odors and tastes. However, we have assumed that any
concentration of a chemical in air or water whose odor or taste can be
detected by a significant proportion of the population is unpleasant. A
chemical at or above this threshold concentration (to be defined below) is
considered to present a risk of adverse sensory effects.
The model assigns a score of either 1 or 0 to each waste management
practice to indicate the risk of sensory effects. As explained below, a score
of 1 means that chemicals released into the environment during waste
management reach concentrations that exceed a perception threshold; a score of
0 means the concentrations do not exceed the threshold. The model's scores
for the risk of sensory effects are not comparable, in either derivation or
significance, to the scores for human health and ecological risks and are
therefore not combined to yield a unified risk score. Instead, the model
provides four separate risk scores for each waste management practice.
Before outlining the procedure for quantifying sensory effects, we discuss
the phenomenon of odor and taste perception as well as standard testing
procedures for determining odor and taste thresholds.
5.2 ODOR AND TASTE PERCEPTION
Receptors of taste and smell tend to react to the presence of specific
molecular structures and thereby set in motion a chain of biochemical and
physiological processes that create chemical-specific sensations of odor and
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5-2
taste. The two senses are closely related; for instance, what we refer to as
a taste sensation is often produced by a compound's stimulation of.both taste
and odor receptors. For a chemical to be detected, it must go into solution
in the film of liquid coating the membranes of taste and odor receptors. The
major functional difference between the two kinds of receptors is that taste
receptors detect chemicals present in the mouth itself, whereas odor receptors
detect vapors from distant sources and can be as much as 3,000 times more
sensitive than taste receptors.1
5.2.1 Taste Perception
Receptor cells for taste are located in taste buds on the upper surface of
the tongue, and to a lesser extent, on the pharynx and larynx. When a
receptor cell is stimulated, it generates nerve impulses in the neighboring
nerve fiber. Receptors for four basic taste senses -- sweet, sour, salt, and
bitter -- are located on different areas of the tongue. Substances stimulate
from one to all four of these receptors to varying degrees. Thus, it is the
blending of the four basic sensations in different relative intensities that
produces a taste sensation.
The taste response to a substance is not uniform in the population.
Individuals vary in sensitivity to chemicals and can respond differently to
the same stimulus (e.g., a chemical may primarily stimulate sweet receptors in
one individual and bitter receptors in the next). Odor receptors also
interact in the production of "taste" sensations.2 Therefore, as discussed
in greater detail below, a taste threshold is described more appropriately as
a distribution than as a single concentration value.
5.2.2 Odor Perception
The receptor cells for olfaction are neurons located in the upper part of
the nasal passages. Impulses pass along nerve fibers to the olfactory bulb
and from there to other areas of the brain.3
An odor can be characterized by its threshold, its intensity, its quality,
and its hedonic tone (pleasure or displeasure associated with the odor)."
The threshold odor characteristics of individual chemicals are described in
greater detail below.
1W.T. Keeton, Biological Science, 2nd edition (New York: W.W. Norton
and Company, Inc., 1972).
2 Ibid.
3A. Turk and A.M. Hyman, "Odor measurement and control," in G.D. Clayton
and F.E. Clayton, eds., Patty's Industrial Hygiene and Toxicology. Volume I:
General Principles, 3rd revised edition (New York: John Wiley and Sons,
1978) .
k Ibid.; Cf. K. Verschueren, Handbook of Environmental Data on Organic
Chemicals, 2nd edition (New York, Van Nostrand Reinhold Company, 1983).
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Some chemicals stimulate olfactory receptor cells as well as other
sensitive cells, producing "the common chemical sense" which results in the
sensation of heat, cold, pain, irritation, and pungency. The primary mediator
of common chemical sensations is the trigeminal nerve. These sensations are
frequently perceived as elements of odor intensity, although the two types of
sensations are actually distinct.6 Failure to distinguish between the two
types of effects can result in incorrect odor threshold determination.
Another important phenomenon relevant to odor threshold determination is
olfactory adaptation. A decrease in sensitivity to a chemical's odor may
occur following the use of excessively strong stimuli, short intervals between
exposures, and the presence of other odors.6
Research on the mechanism of olfaction has failed to elucidate the
physiologic basis of olfactory stimulation. To date, no precise relationship
has been established between the olfactory quality of a chemical and its
physical or chemical properties. Theories relating the two based on molecular
structure, size and shape, intermolecular interactions, electron affinities,
vapor pressure, molar volume, intramolecular vibrations, or a combination of
these factors have been found to be inadequate. Even optical enantiomers,
which have identical spectra, can have different odors. While it is generally
assumed that most odor-causing chemicals are gases or vapors, recent evidence
indicates that odors are also associated with particulate matter. Because of
the inability to correlate physical-chemical properties with odor thresholds,
predictions of odor thresholds for chemicals for which experimental data are
not available cannot be made with a reliable degree of accuracy.
5.2.3 Definition of Odor Threshold Concentration
Three different types of odor thresholds are commonly reported in the
literature: (1) the absolute perception threshold, (2) the recognition
threshold, and (3) the objectionability threshold. The perception threshold
is the minimum concentration of an odorant that can be distinguished from an
environment free of the odorant; at this concentration, the odor is too faint
to identify further. The recognition threshold is the minimum concentration
at which a chemical's odor can be individually identified, and the
objectionability threshold is the minimum concentration at which a chemical's
odor becomes unpleasant.
Because sensitivity to odors varies among members of the population,
threshold concentrations must be defined in terms of statistical averages.
Thresholds typically reported are those for 50 or 100 percent of an odor panel.
sTurk and Hyman, op. cit.
6 Ibid.
7 Ibid.
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Verschueren8 lists the following definitions relating to
Absolute Odor Threshold: the concentration at which
percent of a panel of observers detect the odor.
50 Percent Recognition Threshold: the concentration
which 50 percent of an odor panel define the odor as
representative of the odorant being studied.
One-hundred Percent Recognition Threshold: the
concentration at which 100 percent of the odor panel
the odor as being representative of the odorant being
studied.
PPT (Population Perception Threshold): the concentration at
which 50 percent of the people who have a capable sense of
smell are able to detect an odor.
PIT,q (Population Identification Threshold): the
concentration at which 50 percent of the population can
identify and describe the odor, or at least compare its
quality with another odor.
IPT (Individual Perception Threshold): the lowest
concentration of a particular odor at which a subject gave
both an initial positive response and a repeated response
when the same stimulus was given a second time.
T.O.N. (Threshold Odor Number): the number of times a given
volume of a gas sample has to be diluted with clean,
odorless air to bring it to the threshold level (detected by
50 percent of a panel of observers). The T.O.N, is thus the
value of the intensity of an odor expressed in odor units.
5.3 METHODS FOR MEASURING ODOR AND TASTE THRESHOLDS
5.3.1 Measurement of Odor Thresholds
Several methods for determining odor thresholds have been developed. The
first three techniques described below are used for dilutions of chemicals in
air; the last technique is used for determining odor thresholds for chemicals
in water. In all cases, rigorous environmental controls (e.g., removal of
extraneous odors and other environmental distractions) are required.
odor thresholds:
50
at
being
define
8 Ojd . cit.
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5-5
1. Odor Room. Odorous air is admitted into a room pf
known volume (V) until the volume of odorous air (S) is
found that just allows the odor to be detected within
the room. The ratio V/S at this point is the threshold
dilution.8
2. ASTM Syringe Method (ASTM D 1391-57). A series of
dilutions of the test chemical in air are stored in
syringes. Injections are made into the nostrils of
each panel member in order to identify the detection
threshold.10
3. Dynamic Dilution Method. This method involves the
mixing of sample air supplied at a known flow rate and
chemical concentration with odor-free air at a known
flow rate. After each change in dilution flow rate,
panel members are presented with the dilution and asked
to register the presence or absence of odor.11
4. Odor of Chemicals in Solution (ASTM Method D-1292).
Dilutions of a chemical are made in odor-free water and
are presented to panelists in flasks. Odor thresholds
are determined by comparison with samples of odor-free
water. Concentrations are increased or decreased for
each panelist in order to identify the lowest
concentration detected.12
5.3.2 Measurement of Taste Thresholds
Most methods for evaluating taste thresholds have been concerned with
taste (flavor) in foods. Alexander et al.13 describe a method for
determining the taste thresholds of compounds in water. As in odor tests,
carefully controlled environmental conditions (e.g., odor-free room and
equipment) are required. Panelists are presented with three flasks for test
evaluation, one containing the diluted chemical and two containing water.
Odor and taste-free water at about 40ฐ C (body temperature) must be used. A
9 Ibid.
10 Ibid.
11 Ibid.
12H.C. Alexander, W.M. McCarty, E.A. Bartlett, and A.N. Syverud,
"Aqueous odor and taste threshold values of industrial chemicals," J. Am.
Water Works Assoc. (1982), p. 595-599.
15 Ibid.
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5-6
sample of each is taken into the mouth, held for a few seconds, and
discharged. Concentrations of the chemical are increased or decreased to
determine the lowest concentrations detectable for each panelist.
5.3.3 Experimental Variability
Taste and odor thresholds are subject to significant variability.
Individuals vary greatly in their odor and taste sensitivities. Experimental
techniques, sample temperature, treatment of data, panelist experience, and
chemical purity have a substantial impact on threshold determination.
Phosphine, for example, for which odor detection thresholds reported in the
literature range from 0.2 to 3 ppm, has been shown to be odorless when pure;
the reported odors are presumably due to organic phosphine derivatives present
as impurities.114 Presenting samples with insufficient intertrial intervals
can lead to odor or taste adaptation and subsequent errors in threshold
determination. Experimental bias can occur when administering the
experiments.15
Consequently, there is a wide range in reported recognition thresholds for
some chemicals. The odor thresholds for chloroform and nitrobenzene in air
range over two orders of magnitude, and for benzene, over three orders of
magnitude. Chemicals in water can display similar variability. Odor
recognition thresholds for acrylonitrile range from 0.0031 to 50.4, greater
than four orders of magnitude; the range of thresholds for 1,2-dichloroethane
spans three orders of magnitude.
5.4 SENSORY EFFECTS RISK SCORING
5.4.1 Overview of the Scoring Methodology
The model calculates risks of sensory effects for the three major media as
follows: (1) risk of odor caused by chemicals in the air; (2) risk of odor or
taste caused by chemicals in surface water; and (3) risk of odor or taste
caused by chemicals in ground water. Because the same data and principles are
used in determining potential effects in both surface water and ground water,
the methodology for determining odor and taste thresholds distinguishes only
between air and water.
In calculating risks to human health, unit risks are determined for
carcinogens and noncarcinogens from mathematical dose-response relationships.
In calculating aquatic and terrestrial ecorisks, scores are derived from a
theoretical damage function, which relates environmental concentrations of a
chemical to various degrees of ecosystem damage. Although dose-response
ll* Turk and Hyman, op. cit.
15 Ibid. ; V'erschueren, og. cit.
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5-7
relationships exist for the sensations of odor and taste, dose-response data
for these effects are not well developed. Therefore, the model does not use a
dose-response methodology to estimate risk of sensory effects, but rather,
bases scoring on odor and taste thresholds. We define sensory risk as the
risk to a population posed by a chemical at a concentration in air or water
that can be detected (either by odor or taste) by a substantial proportion of
the population. If chemicals are present at detectable concentrations, a
W-E-T cell is assigned a sensory effects score of 1. If chemicals are present
at concentrations below the "threshold" concentration, a W-E-T cell is
assigned a sensory effects score of zero.
These risk scores have no meaning in absolute terms. Rather, they are
used to determine the greater or lesser potential for one waste management
practice to pose odor or taste problems when compared to a second waste
management practice. As a relative risk, no units are associated with the
sensory effects scores.
5.4.2 Odor and Taste Threshold Identification
Earlier in this chapter, we noted that odor and taste thresholds for a
single chemical can cover a wide range of values as a result of the inherent
biological variability in the .population. Accordingly, odor and taste
thresholds are better represented by a statistical distribution than by a
single value.
Not all threshold values reported in the literature represent the same
point in the statistical distribution. For instance, threshold values may
represent the lowest level of detection or perception for an individual, for
50 percent of the individuals tested, or for 100 percent of the individuals
tested. Even for the same statistical average (e.g., or 50 percent
population perception thresholds), experimental differences introduce
additional variability. In general, the 100 percent recognition threshold
displays less variation than does the lower absolute perception threshold.17
To further complicate the problem of determining the threshold, secondary
sources do not always clearly identify the type of threshold measured.
Verschueren18 presents data as ranges identified as "detection,"
"recognition," and "not specified" thresholds. In the OHM-TADS data base,
thresholds are typically reported as "low", "medium", "upper", or
"recognition." Verschueren states that when a threshold odor concentration is
given without any qualifications, it usually represents the 50 percent
recognition threshold.
16 Op. cit.
17Verschueren, ojd. cit.
18 Ibid.
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Given the above problems of threshold identification, the following
criteria were used to select a single odor or taste threshold concentration.
The threshold values chosen for air and water
and C_.) were the lowest reported recognition
I VI
threshold concentrations. Recognition thresholds are
the most frequently reported thresholds and are more
reliable than absolute perception thresholds. Where
recognition thresholds were unavailable, the lowest
reported value for other types of thresholds was
selected as the C_, and C_..
TA TV'
When both taste and odor threshold data were
available, the lower of the two thresholds.determined
Sv
Where no odor or taste threshold data were available,
the following default values were assigned for organic
and inorganic compounds in air and water:
Organic Compounds: for chemicals in the air, a
default value was calculated as the average of the
threshold values (CyA) for chemicals for which
data were available. This default value is 70
mg/m3.
Similarly, for chemicals in surface water and ground
water, a default value was calculated as the mean of
the threshold values for chemicals in water (C_.)
TW
for which odor or taste threshold data were
available. This default value is 5 mg/H.
Inorganic Compounds: Inorganic compounds typically
do not present odor or taste problems. Therefore, a
default value of zero was assigned for C^ and
C^. for these compounds.
In selecting the lowest reported odor and taste recognition threshold,
values that appeared to be outliers were eliminated. Eliminating outliers
involved some degree of judgment. In general, a value was considered to be an
outlier if it was roughly two orders of magnitude lower than a cluster of
other odor or taste recognition thresholds reported for that chemical.
Exhibits 5-1 and 5-2 summarize the lowest reported odor and taste
threshold data and identify the threshold values for each chemical in air and
water (C_ and C_,). Default values, where assigned, are noted.
1 A 1 Vs
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5-9
5.4.3 Scoring Procedure
For each waste stream constituent, the media-specific concentrations in
air, surface water, and ground water resulting from releases during waste
management are estimated as a function of distance from the point of release,
as described in Chapter 3. The ratios of these media-specific concentrations
to the threshold values for air and water (C_. and C_.) at a 1 kilometer
TA TW
distance from the point of release provide the basis for a score for the risk
of sensory effects. If the ambient concentration of a chemical at 1 kilometer
from the point of release in any medium is equal to or greater than the
threshold value for the medium in question, the waste management practice
presents a risk of adverse sensory effects and is assigned a sensory effects
score of 1. For example, if the concentration in surface water, C , is
sw
greater than the threshold value for water, the W-E-T cell receives a
sensory effects score of 1; otherwise, it receives a score of 0. Thus, so
long as the threshold is exceeded in any one medium, even if not exceeded in
the others, the sensory effects score for a W-E-T cell is 1.
5.4.4 Conclusion
This chapter concludes our presentation of the methodologies by which the
model estimates the risks associated with waste management practices. In the
following chapter, we turn to the costs of managing wastes by different
combinations of technologies, the counterbalance to risks in evaluating waste
management practices.
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5-10
EXHIBIT 5-1
ODOR THRESHOLDS IN AIR
LOWEST REPORTED ODOR FINAL THRESHOLD
RECOGNITION THRESHOLD VALUE, CTA
(mg/m3) (mg/m3)
COMPOUND Source
Organic
Acenaphthene
-
-
70.00 c/
Acetaldehyde
0.01
a/
.01
Acetonitrile
68.00
a/
68.00
Acrolein
-
70.00 c/
Acrylonitrile
3.72
a/
3.70
Allyl alcohol
-
-
70.00 cj
Aniline
-
-
70.00 c/
Benzalkonium chloride
-
-
70.00 c/
Benzene
0.516
a/
0.52
Benzo(a") anthracene
-
-
70.00 c/
Benzo(a)pyrene
-
-
70.00 c/
Benzotrichloride
-
-
70.00 c/
Benzyl chloride
-
-
70.00 c/
Bis(chloromethyl) ether
-
-
70.00 c/
Carbon tetrachloride
100.00
a/
100.00
Chlordane
-
-
70.00 c/
Chloroacetaldehyde
-
-
70.00 c/
Chlorobenzene
1.00
' a/, b/
1.00
Chloroform
10.00
a/
10.00
2-Chlorophenol
-
-
70.00 c/
Chrysene
-
-
70.00 c/
Cyclohexane
-
-
70.00 c/
2,4-D
-
-
70.00 c/
1,2-Dichlorobenzene
0. 12
a/
0.12
1,4-Dichlorobenzene
90.00
ฆat, b/
90.00
1,2-Dichloroethane
20.00
a/
20.00
1,1-Dichloroethene
500.00
a/
500.00
Dichloromethane
21.00
ฃ/
21.00
1,2-Dichloropropane
235.00
a/
235.00
1,3-Dichloropropan-l-ol
-
70.00 c/
1,3-Dichloropropene
-
-
70.00 c/
Dimethylalkylamines
-
-
70.00 c/
1-3-Dinitrobenzene
-
-
70.00 c/
2,4-Dinitrotoluene
0.30
y
0.30
Epichlorohydrin
0.30
a/
0.30
Ethylene oxide
-
-
70.00 c/
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5-11
EXHIBIT 5-1 (continued)
ODOR THRESHOLDS IN AIR
LOWEST REPORTED ODOR FINAL THRESHOLD
RECOGNITION THRESHOLD VALUE, CTA
COMPOUND
(mg/m3)
Source
(mg/m3)
Organic (continued)
Formaldehyde
0.03
5/
0.03
Hexachlorobenzene
-
-
70.00 c/
Hexachlorobutadiene
0.06
b/
0.06
Hexachloroethane
-
-
70.00 c/
Hydroquinone
-
-
70.00 c/
Linear alkylbenzene
-
-
70.00 c/
sulfonates (LAS)
Maleic anhydride
1.30
5/
1.30
Methyl chloride
-
-
70.00 c/
Methyl ethyl ketone
5.50
ง./
5.50
Methyl methacrylate
-
-
70.00 c/
Naphthalene
-
-
70.00 c/
1^-Naphthoquinone
-
-
70.00 c/
Nitrobenzene
0.005
2/
0.005
4-Nitrophenol
-
-
70.00 c/
PCB-1254
-
-
70.00 c/
Paraldehyde
-
-
70.00 c/
Parathion
-
-
70.00 c/
Pentachlorophenol
9.50
9.50
Phenol
0.20
a/
0.20
Phthalic anhydride
-
-
70.00 c/
Pyridine
0.04
a/
0.04
2,3,7,8-Tetrachlorodibenzo-p-
ฆdioxin
-
70.00 c/
1,1,1,2-Tetrachloroethane
-
-
70.00 c/
1,1,2,2-Tetrachloroethane
3.00
3.00
Tetrachloroethene
32.00
*/
32.00
Toluene
3.00
1/
3.00
Toluene diamine
-
-
70.00 c/
Toluene diisocyanate
1.00
a /
1.00
Toxaphene
0.09
b/
0.09
1,2,4-Trichlorobenzene
-
-
70.00 c/
1,1,1-Trichloroethane
1,000.00
5/
1,000.00
1,1,2-Trichloroethane
-
-
70.00 c/
Trichloroethene
100.00
5/
100.00
Vinyl chloride
-
-
70.00 c/
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5-12
EXHIBIT 5-1 (continued)
ODOR THRESHOLDS IN AIR
LOWEST REPORTED ODOR FINAL THRESHOLD
RECOGNITION THRESHOLD VALUE, CTA
(mg/m ) (mg/m )
COMPOUND Source
Inorganic
Antimony
0.
00
ฃ/
Arsenic
0.
.00
ฃ/
Barium
0.
,00
ฃ/
Cadmium
0.
.00
ฃ/
Chromium (VI)
0.
.00
ฃ/
Copper
0.
,00
ฃ/
Cyanides
0.
,00
Fluorine
0.
,00
ฃ/
Lead
0.
,00
Mercury
0.
,00
ฃ/
Nickel
0.
.00
Sl!
Thallium
0.
.00
Vanadium
0.
.00
Zinc
0.
.00
ฃ/
a/ Source: K. Verschueren, Handbook of Environmental Data on Organic
Chemicals, 2nd edition (New York: Van Nostrand Reinhold Company, 1983).
b/ Source: Oil and Hazardous Materials - Technical Assistance Data
Systems (OHM-TADS). Database prepared by U.S." Environmental Protection
Agency, Oil and Special Materials Controls Division, Office of Water Program
Operations, Washington, D.C. (1983).
c/ Default value.
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5-13
EXHIBIT 5-2
ODOR AND TASTE THRESHOLDS IN WATER
LOWEST REPORTED ODOR
LOWEST REPORTED
TASTE
ASSIGNED THRES-
RECOGNITION
THRESHOLD
RECOGNITION THRESHOLD
HOLD VALUE, CTW
(mg/e)
Cmg/ฃ)
(mg/ฃ)
COMPOUND
Source
Source
Organic
Acenaphthene
-
-
-
-
5.0000 f/
Acetaldehyde
0.004
-
-
0.0040
Acetonitrile
-
-
-
-
5.0000 f/
Acrolein
-
-
-
-
5.0000 f/
Acrylonitrile
0.0031
5/
-
-
0.0031
Allyl alcohol
-
-
-
-
5.0000 f/
Aniline
-
-
-
-
5.0000 f/
Benzalkonium chloride
-
-
-
-
5.0000 f/
Benzene
0.5000
5/
0.50
y
0.5000
Benzo(a)anthracene
-
-
-
-
5.0000 f/
Benzo(a)pyrene
-
-
-
-
5.0000 f/
Benzotrich loride
-
-
-
-
5.0000 f/
Benzyl chloride
-
-
-
-
5.0000 f/
Bis(chloromethyl) ether
-
-
-
-
5.0000 f/
Carbon tetrachloride
50.0000
3.60
b/
3.6000
Chlordane
0.0005
ฃ/
-
-
0.0005
Chloroacetaldehyde
-
-
-
-
5.0000 f/
Chlorobenzene
0.1000
a/
-
-
0.1000
Chloroform
0.1000
5/
12.00
y
0.1000
2-Chlorophenol
-
-
-
-
5.0000 f/
Chrysene
-
-
-
-
5.0000 f/
Cyclohexane
-
-
-
-
5.0000 f/
2,4-D
3.1300
a/
-
-
3.1000
1,2-Dichlorobenzene
0:0100
f/
-
-
0.0100
1,4-Dichlorobenzene
0.0003
S/
-
-
0.0003
1,2-Dichloroethane
20.0000
a/
20.00
b/
20.0000
1,1-Dichloroethene
-
-
-
-
5.0000 f/
Dichloromethane
-
-
24.00
y
24.0000
1,2-Dichloropropane
0.0014
a/
-
-
0.0014
1,3-Dichloropropan-l-ol
-
-
-
-
5.0000 f/
1,3-Dichloropropene
-
-
-
-
5.0000 f/
DimethylaIky 1amines
-
-
-
-
5.0000 f/
1-3-Dinitrobenzene
-
-
-
-
5.0000 f/
2,4-Dinitrotoluene
-
-
-
-
5.0000 f/
Epichlorohydrin
-
-
-
-
5.0000 f/
Ethylene oxide
-
-
-
-
5.0000 f/
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5-14
EXHIBIT 5-2 (continued)
ODOR AND TASTE THRESHOLDS IN WATER
LOWEST REPORTED ODOR LOWEST REPORTED TASTE ASSIGNED THRES-
RECOGNITIOS THRESHOLD . RECOGNITION THRESHOLD HOLD VALUE, CTW
fmg/e) (mg/e) (mg/fc)
COMPOUND Source Source
Organic (continued)
Formaldehyde
Hexachlorobenzene 3.0000 a/
Hexachlorobutadiene 0.0060 a/
Hexachloroethane
Hydroquinone
Linear alkylbenzene
sulfonates (LAS)
Maleic anhydride
Methyl chloride
Methyl ethyl ketone 1.0000 a/
Methyl methacrylate
Naphthalene
1,4-Naphthoquinone
Nitrobenzene
4-Nitrophenol
PCB-1254
Paraldehyde
Parathion
Pentachlorophenol 0.86 a/
Phenol
Phthalic anhydride
Pyridine
2,3,7,8-Tetrachlorodibenzo-
p-dioxin
1,1,1,2-Tetrachloroethane
1,1,2,2-Tetrachloroethane 0.5000 a/
Tetrachloroethene 0.3000 a/
Toluene 1.0000 a/
Toluene diamine -
Toluene diisocyante
Toxaphene 0.1400 a/
1,2,4-Trichlorobenzene
1.1.1-Trichloroethane 50.0000 a/
1.1.2-Trichloroethane
Trichloroethene 0.5000 a/
Vinyl chloride
0.03
0.0001
2.8000
0.1400
0.0100
2.6000
a/
c/
b/
b/
c /
b/
5.0000 f/
3.0000
0.0060
5.0000 f/
5.0000 {/
5.0000 f/
5.0000 f/
5.0000 f/
1.0000
0000 f/
0000 f/
0000 f/
0000 f/
0000 f/
0000 f/
5.0000 f/
5.0000 f/
0.03
0.0001
5.0000 f/
5.0000 f/
5.0000 f/
5.0000 f/
0.5000
0.3000
0.1400
5.0000 f/
5.0000 f/
0.0100
5.0000 f/
50.0000
5.0000 f/
0.5000
5.0000 f/
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5-15
EXHIBIT 5-2 (continued)
ODOR AND TASTE THRESHOLDS IN WATER
LOWEST REPORTED ODOR
LOWEST REPORTED TASTE
ASSIGNED THRES-
RECOGNITION THRESHOLD
RECOGNITION THRESHOLD
HOLD VALUE, CTW
(rng/H)
(mg/fc)
(rng/H)
COMPOUND Source
Source
Inorganic
Antimony
-
0.0000 f/
Arsenic
-
0.0000 f/
Barium
-
0.0000 f/
Cadmium
-
0.0000 f/
Chromium (VI)
-
0.0000 f/
Copper
1.0000 e/
1.0000
Cyanides
-
0.0000 f/
Fluorine
-
0.0000 f/
Lead
-
0.0000 f/
Mercury
-
0.0000 f/
Nickel
-
0.0000 f/
Thallium
-
0.0000 f/
Vanadium
-
0.0000 f/
Zinc
5.0000 f/
5.0000
a/ Source: K. Verschueren, Handbook of
Environmental Data on Org
anic Chemicals,
2nd edition (New York: Van Nostrand Reinhold Company, 1983).
b/ Source: H.C. Alexander, W.M. McCarty,
E.A. Bartlett, and A.N.
Syverud, "Aqueous
odor and taste threshold values of industrial
chemicals," J. Am. Water
Works Assoc.
(1982), p. 595-599.
c/ Source: Oil and Hazardous Materials - Technical Assistance Data Systems
(OHM-TADS). Database prepared by U.S. Environmental Protection Agency, Oil and Special
Materials Controls Division, Office of Water Program Operations, Washington, D.C.
(1983).
d/ Source: U.S. Environmental Protection Agency. Ambient Water Quality Criteria
for Copper. Office of Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-036, PB81-117475 (1980).
e/ Source: U.S. Environmental Protection Agency. Ambient Water Quality Criteria
for Zinc. Office of Water Regulations and Standards, Criteria and Standards Division,
Washington, D.C. EPA 440/5-80-079, PB81-117897 (1980).
f/ Default value.
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6 Costs
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6. COSTS
6.1 INTRODUCTION
In this chapter, we describe the methodology the model uses to estimate
the cost of a waste management practice and to assign a cost score to each
practice, that is, to each W-E-T cell. The model may be used to generate
either cost estimates or cost scores. The cost scores indicate the relative
cost of using one set of technologies to manage a waste stream rather than
another set. They are scaled to show when one waste management practice has a
clear cost advantage over another, facilitating analyses of the trade-offs
between risks and costs.
The cost of a waste management practice is determined primarily by the
technologies employed. The only waste stream characteristic that affects the
cost of using a disposal or transportation technology is its flow, i.e., the
quantity to be disposed of. The cost of using a treatment technology,
however, is affected by a variety of waste stream characteristics, as
explained below.
Because of the centrality of technologies to costs, this chapter first
outlines the methodology the model uses to estimate costs and derive cost
scores, then separately discusses treatment, transportation, and disposal
technologies. The discussion of each of these types of technologies is an
overview of the various items the model considers in estimating the cost of
any technologies of that type. Usually, the precise cost estimates or cost
values the model uses depend on the specific size and configuration of a
technology. Although we present summary figures on costs in this chapter, we
reserve detailed listings of cost estimates for the individual descriptions of
each of the model's technologies in Appendices B, C, and D to this report.
These appendices also explain the design features assumed in calculating a
technology's costs.
6.2 METHODOLOGY FOR ESTIMATING COSTS
The model derives a cost score for each waste management practice by three
steps:
Step 1 is to estimate the individual costs involved in
using each technology in a waste management practice. These
costs are grouped into "elements," for example, capital
investment costs or operation and maintenance costs.
Step 2 is to calculate, on the basis of these costs
estimates, the annual revenue requirements for using a
technology to manage the quantity generated annually of the
waste stream in question. The annual revenue requirements
of each technology in the waste management practice are
calculated separately, then summed.
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6-2
Step 3 is to assign an integer cost score to represent
the management practice's annual revenue requirements.
Each step is discussed separately.
6.2.1 Cost Elements
The first step in deriving a cost score is to estimate the various costs
involved in using a technology. These costs are grouped under the headings of
four cost "elements":
Capital investment costs
Operation and maintenance (O&M) costs
Closure costs
Post-closure maintenance costs.
There may not be any costs in the last element for some technologies.
The model adjusts all costs to first quarter 1983 dollars, based on
appropriate indices. Exhibit 6-1 lists these indices. It also identifies the
values the model uses for the costs of labor, energy, land, water, and
chemicals.
All costs in the model are real resource costs. They do not consider
transfers or redistributions of monies. The model does not include costs for
the loss of productivity and the replacement of natural resources. It also
does not include the costs of health care, although the relative health care
costs of different waste management practices may be inferred from the human
health risk scores.
The model's cost values are engineering estimates for constructing and
operating hazardous waste management facilities. They do not necessarily
represent prices as determined by facility supply and demand for facility
services.
We explain below the types of costs included in each cost element.
(a) Capital Investment Costs. Capital investment costs include both
direct and indirect costs. Direct costs are for land, equipment (trucks,
tanks, etc.), auxiliaries (instruments, controls), and related facilities
(laboratories). Installation costs are also included. The model's estimates
of these costs are based on information on the type and size of equipment at
standard facilities; we believe the estimates are accurate to within + 30
percent.1
^'.M. Vatavuk, "Factors for Estimating Capital and Operating Costs,"
Chemical Engineering (November 3, 1980).
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6-3
EXHIBIT 6-1
COST VALUES
Item
Assumed Value
Cost and Construction Index (CCI) a/
Construction Materials Producer Price Index (PPI) b/
Labor Rate b/
Energy Costs b/
Electrical Power c/
Fuel Oil (No. 2) d/
Steam Cost
Diesel Fuel/Gasoline d/
Natural Gas
Land
Cooling Water
Chemicals e/
Asphalt
Granular Activated Carbon f/
Ferrous Sulfate (FeSO.
7H20)
Lime
Polymer
97% Potassium Permanganate
Sodium Chloride
98.9% Sodium Hydroxide
93% Sulfuric Acid
Sodium hypochlorite
Chlorine
Quicklime
Sodium metabisulfite
Cement
n-Butyl acetate
Fly ash
4006
295.5
$14.56/hour
SO.05/kwh
$0.81/gallon
$1.55 x 10 ^/Btu
$1.20/gallon
$5.73 x 10 ^/Btu
$5,000/acre
$2.5 x 10 ^/gallon
$170/ton
$0.80/lb.
$0.065/lb.
-2
$1.63 x 10 /lb.
$3.. 50/lb.
$4.45/lb.
$0.025/lb.
$0.74/lb.
$0.40/gallon
$0.4/lb
$0.08/lb
$30/ton
$0.28/lb
$0.0325/lb
$0.5/lb
$0.016/lb
a/ Engineering News Record (June 1983)
b/ Monthly Labor Review (September 1983). The labor rate includes
fringe benefits and supervision.
c/ Monthly Energy Review (September 1983)
d/ Oil and Gas Journal (July 1983)
e/ Chemical Marketing Reporter (September 1983)
f/ Vendor quote (confidential)
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6-4
Indirect costs are not included in the equipment or installation cost, but
are necessary to provide a functioning system. The model includes the
following indirect costs:2
Engineering and supervision
Construction and field expense
Contractor's fees
Interest during construction
Start-up expenses
Spare-parts inventory
Permit and legal fees
Contingency
Working capital.
Indirect costs are estimated as fixed percentages of direct or total capital
costs. Exhibit 6-2 describes these costs as well as the ranges of percentages
that were used to calculate indirect capital costs for the treatment and
disposal technologies. The exact percentages used are identified below in the
discussions of cost elements for each type of technology.
(b) Operation and Maintenance Costs. The model considers both direct
and indirect annual operation and maintenance (O&M) costs. Direct O&M costs
include raw materials, labor, utilities, and equipment maintenance. Estimates
of these costs are determined by applying the unit costs listed in Exhibit 6-1
to information on the design and operating characteristics of the model's
treatment and disposal technologies and estimates of operating man-hours.
Indirect O&M costs are incurred regardless of whether the technology is
operating. They include overhead, taxes, and administration and are
determined as percentages of capital or operating costs. Exhibit 6-3 contains
descriptions of these costs as well as the percentages used to calculate them
in the model.3
(c) Closure and Post-Closure Costs. Costs are incurred for technologies
following their period of operation, including capital costs at facility
closure for both treatment and disposal facilities and annual maintenance
costs for post-closure care for some disposal facilities (landfills, surface
impoundments, and land treatment facilities).
6.2.2 Annual Revenue Requirements
As the second step in deriving cost scores, the model converts the cost
estimates from the preceding step to annual revenue requirements. The reason
2 M.S. Peters and K.D. Timmerhaus, Plant Design and Economics for
Chemical Engineers, 3d edition (New York: McGraw-Hill Book Company, 1980),
pp. 174-176.
3 Ibid, pp. 202-204.
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6-5
EXHIBIT 6-2
INDIRECT CAPITAL COSTS
Item
Description
Cost Ranges
(Percent)
Engineering and
Supervision
Construction and
Field Expense
Contractor's Fee
Start-up Expense
Spare-parts
Inventory
Interest During
Construction
Permit and Legal
Fees
Cost for design and engineering,
drafting, purchasing, accounting,
construction and cost engineering,
travel, reproductions, communication,
field expense for supervision,
and home office expense including
overhead.
Cost for temporary construction
and operation, construction
tools and rentals, home office
personnel located at the con-
struction site, construction
payroll preparation expense,
travel and living, taxes and
insurance, and other construc-
tion overhead.
Profit contractor makes on
system construction.
Cost for testing, labor,
materials, and equipment changes
required to bring a system into
operation at design conditions.
Cost for equipment/parts stored
on-site for routine and emergency
maintenance.
Interest paid on loans that are
repaid when the project is
capitalized.
Authorization costs for land-
related facilities.
5-20 a/
5-15 a/
7-10 a/
0-10 a/
1-2 a/
10 a/
10-25 a/
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6-6
EXHIBIT 6-2 (continued)
INDIRECT CAPITAL COSTS
Item
Description
Cost Ranges
(Percent)
Contingency
Working Capital
Compensation for unpredictable
events such as storms, floods,
strikes, price changes, small
design changes, errors in esti-
mation, and other unforeseen
expenses.
Funds necessary for normal
conduct of business.
5-25 b/
5-18 b/, c/
a/ Expressed as percentages of direct capital costs.
b/ Expressed as percentages of the sum of direct and indirect capital
costs.
c/ The working capital is retrieved in year 20 when the facility is
closed.
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6-7
EXHIBIT 6-3
INDIRECT OPERATION AND MAINTENANCE COSTS
Item Description Cost
Insurance, Taxes,
General Administration
Cost for insurance premiums,
property taxes, executive
and clerical wages, office
supplies, engineering and
legal expenses, upkeep on
office buildings, and
general communications.
5 percent of the total
capital cost.
System Overhead
Cost for plant management,
general supervision of main-
tenance, personnel, plant
protection, storerooms,
accounting, purchasing, traf-
fic, and other service items.
Also includes the deprecia-
tion, operation, and main-
tenance costs of railroads,
roads, sewers, parking lots,
cafeterias, and other system-
serving facilities.
75 percent of direct
labor, or 10 percent of
the total annual cost, a/
a/ The total annual cost is defined as the sum of the total capital
cost multiplied by the capital recovery factor (as defined in Section 6.2.2)
and the total operation and maintenance costs. Total annual costs are
calculated for treatment and transportation technologies, cf. Peters and
Timmerhaus, og. cit., p. 208.
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6-8
for doing so is to permit the costs involved in using the different
technologies in a waste management practice to be added. An annual revenue
requirement is not a cost per se, but the revenue needed annually to offset
the total aggregate costs of using a technology to manage the quantity of a
waste stream generated annually. Thus, the annual revenue requirements for,
say, using landfills to dispose of a particular waste stream are the revenues
required annually to offset the costs of using landfills to dispose of the
quantity of that waste generated annually. The model reflects economies of
scale by offering a range of facility capacities to manage the varying
quantities of waste streams in the model. The annual revenue requirements for
a waste management practice are the sum of the annual revenue requirements for
the treatment, transportation, and disposal steps in that practice. We
believe annual revenue requirements more accurately reflect the economic
impact of managing hazardous wastes than do standard cost stream analyses.
The model uses several assumptions and simplifications to calculate annual
revenue requirements:
Operating Life. All technologies in the model have
20-year operating lives (except injection wells, which
have 17-year operating lives, and trucks, which have 8-
year operating lives). Initial capital costs are
incurred in year zero, and O&M costs are incurred in
years 1 through 20 (or 17 for injection wells and 8 for
trucks). Intermittent capital costs may also be
incurred during the facility's operating life, depending
on the technology (e.g., a new cell is constructed every
year of a landfill's operating life).
Closure/Post-Closure. For treatment and disposal
facilities, the capital costs of closure are incurred in
year 20. Post-closure maintenance costs for landfills,
surface impoundments, and land treatment facilities are
incurred annually during the post-closure period.
Investment Tax Credit. An investment tax credit
equal to 10 percent of all capital expenditures is
incurred in year zero.
Depreciation Method. Direct initial capital costs
are depreciated over five years using the "150 percent
declining balance" method as recommended by the Economic
Recovery Tax Act of 1981. Exhibit 6-4 presents this
method.
Tax Rate. The effective tax rate is 50 percent.
Inflation Rate. All costs incurred after year zero
are inflated at an annual rate of 8 percent.
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6-9
Discount Rate. A real rate of return, or discount
factor, of 3 percent is used. When adjusted for
inflation, the discount rate is 11.24 percent.
Capital Recovery Factor. The annuity factor for
repaying a present value is 0.0672 (i = 3ป; n = 20
years).
EXHIBIT 6-4
DEPRECIATION METHOD:
"150 PERCENT DECLINING BALANCE"
Year
Direct Initial Capital Cost
Depreciated by Year (Percent)
0
0
1
15
2
22
3
21
4
21
5
21
The model calculates annual revenue requirements for a technology by first
calculating the after-tax cash flows required each year,- as follows:
Initial Capital Cost - Investment Tax Credit - [(Tax Rate)
(Depreciation)] + [(1-Tax Rate) (Non-Depreciable Costs)]
where Initial Capital Cost and Investment Tax Credit are only
incurred in year zero.
Depreciation is incurred during the first five years of
operation according to the schedule in Exhibit 6-4.
Non-Depreciable Costs include operation and maintenance
costs (incurred annually during the operating life of the
facility), intermittent capital costs (incurred annually,
e.g., the construction of a new landfill cell, or
periodically, e.g., the dredging of surface impoundments),
closure costs (incurred at the end of the last year of
operation) and post-closure costs (incurred during the
post-closure period.)
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6-10
The model calculates the present value of the total after-tax cost by
first discounting the after-tax cash flows at a nominal discount rate of 11.24
percent, then adding the discounted cash flows. It determines the annualized
after-tax cost by multiplying the present value by the capital recovery factor
f0.0672 for 20 years at 3 percent). Finally, the model obtains the pre-tax
annual revenue requirements by dividing the annualized after-tax cost by
(1-Tax Rate).
The total annual revenue requirements of a waste management practice are
the sum of the annual revenue requirements of the treatment, transportation,
and disposal steps in the management practice.
6.2.3 Cost Scores
Although the cost estimates obtained by the preceding two steps may be
sufficient for purposes of most analyses, the model can also, as an option,
derive a cost score for each waste management practice from these cost
estimates. The cost score reflects the cost of managing the total quantity of
the waste generated annually. The scores are determined by the range within
which annual revenue requirements fall. They distinguish two-fold differences
in annual revenue requirements. That is, an integer difference in cost scores
indicates that using one management practice requires twice the annual revenue
as using the other.
In the remaining sections of this chapter, we discuss aspects of the cost
scoring methodology that are specific to treatment, transportation, or
disposal technologies.
6.3 TREATMENT TECHNOLOGY COST ELEMENTS
The cost of using a treatment technology varies according to the
characteristics of the waste stream being treated (e.g., quantity, fraction of
non-water components, fraction of suspended solids, specific gravity). In
addition, the model takes into account the economies of scale realized as
these technologies are used to treat increasing quantities of waste.
Consequently, it is not possible to calculate annual revenue requirements for
treatment technologies per unit of waste, expressed in dollars per metric ton
of waste, as can be calculated for disposal and transportation technologies
(see Exhibits 6-6 and 6-8). Instead, the model's estimates of treatment
technology costs are in the form of equations that relate costs to waste
stream characteristics. The cost equations for treatment technologies are
presented in Appendix B.
The model calculates indirect capital costs as a percentage of either the
direct capital cost or the sum of direct and indirect capital costs. Exhibit
6-5 presents the percentages used for each treatment technology. There are no
permit and legal fees costs for treatment technologies. The value of the
contractor's fee and interest during construction is the same for each
treatment. However, the values of the other indirect cost items differ based
on the technical sophistication of the treatment system.
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id i re:
ists
Work
Cap i
15
15
12
12
15
18
15
18
18
15
18
12
12
12
18
EXIIIBI I 6-5
INDIRECT CAPITAL COSTS FOR TREATMENT TECHNOLOGIES
Engineering Construction
and and
Supervision Eiold Expense
Percent or Direct Capi taI Costs
Spa re
Contractors Parts
ee Startup Inventory
20
20
8
10
20
16
12
20
16
8
12
16
10
10
8
5
5
15
10
10
10
10
10
10
5
10
15
10
10
5
Sta rtup
5
8
2
5
8
5
5
5
5
5
5
2
5
5
0
2
2
2
1
2
2
1
2
2
2
2
1
2
2
1
Into res t
Dur i ng
Cons t ruet i on
10
10
10
10
10
10
10
10
1()
10
10
10
10
10
10
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6-12
O&M costs are functions both of waste stream characteristics and of the
concentrations of hazardous constituents in waste streams. The size of the
waste stream determines the labor and power requirements. The quantity of
hazardous constituent present determines the amount of chemicals necessary to
treat the waste. For technologies comprised of several components, the model
sums the O&M costs for each component to obtain the direct O&M costs for the
technology.
The model calculates indirect O&M costs as a percentage of either the
total capital cost or the total annual cost. Exhibit 6-3 presents
descriptions and percentages of the different indirect O&M items.
Closure costs are calculated as a function of the volume of waste
generated or stored. For most of the treatment technologies comprised of
treatment and storage tanks, the closure costs of each component were summed
to yield closure cost estimates for a system of a given capacity. Cost
equations were developed from estimates of closure costs for several different
sizes of facilities that were curve-fitted.k Appendix B contains the
closure cost equations for each treatment technology.
In cases where wastes are treated by distillation or leaching, most of the
hazardous constituents are recovered. The model does not give credit for the
value of these recovered materials and does not consider the benefits derived
from chemical recovery. For additional details on the derivation of cost
estimates for treatment technologies, see the descriptions of each treatment
technology in Appendix B.
6.4 TRANSPORTATION TECHNOLOGY COST ELEMENTS
The model estimates the cost of hazardous waste transportation for two
types of transporting vehicles: a tanker truck with a 6,000 gallon capacity
and a stake truck (for containerized waste) with an 18 ton capacity.
Transportation costs are taken into account only when wastes are disposed of
off-site. Where wastes are managed at the generating facility, on-site
transportation costs are included in the cost of the disposal technology.
The model includes two possible travel distances: a local, one-way trip
of 25 miles, and a long-distance, one-way trip of 250 miles. We selected
these values because they differ by an order of magnitude and fall within the
range of distances typically traveled to transport hazardous vaste.5
""Pope-Reid Associates, Inc., "Average and Maximum Engineering Cost
Estimates for Closure," prepared for the Office of Solid Waste, U.S.
Environmental Protection Agency (October 1983).
5M. Abkowitz, A. Eiger, and S. Srinivasan, "Assessing the Releases and
Costs Associated with Truck Transport of Hazardous Wastes," prepared for the
Office of Solid Waste, U.S. Environmental Protection Agency (January 1984).
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The model estimates, for each travel distance, capital and O&M costs for
the two vehicle types. The only capital cost item is the truck. The O&M
costs include the driver's salary, supervision, insurance, licenses, fuel,
oil, tires, maintenance, and repair. The model calculates "indirect O&M costs
as a percentage of the total annual cost.
The model combines and discounts capital and O&M costs to obtain annual
revenue requirements. It then divides these annual revenue requirements by
the capacity of the truck to yield a transportation cost per unit of waste,
assuming an average tanker load of 3218.4 gallons and a stake truck load of
10.8 tons.6 Exhibit 6-6 presents the unit annual revenue requirements for
transportation technologies. Appendix C presents additional details on
transportation costs.
EXHIBIT 6-6
UNIT ANNUAL REVENUE REQUIREMENTS FOR TRANSPORTATION
Vehicle Type
One-Way 6,000 18 Ton
Trip Length Gallon Tanker Stake Truck
25 miles Sll/metric ton $19/metric ton
250 miles S58/metric ton $82/metric ton
6.5 DISPOSAL TECHNOLOGY COST ELEMENTS
The costs of disposal technologies are independent of waste stream
characteristics. They depend solely on the capacity and design of the
technology. For example, the cost of building and operating a landfill is the
same regardless of the type of waste disposed of in it. Consequently, instead
of providing cost estimates in the form of equations, as for the treatment
technologies, the model's initial cost estimates for disposal technologies are
point estimates of cost per unit of waste. These estimates are obtained by
first calculating costs for a given size and configuration of a technology,
s Ibid.
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6-14
then dividing by the size (expressed as disposal capacity). When annual
revenue requirements for disposal technologies are calculated, one waste
stream characteristic -- namely, the flow, or quantity generated annually --
has to be taken into account. The model's cost estimates for disposal
technologies are based on EPA's regulatory analysis of the RCRA land disposal
regulations, supplemented by other EPA documents.7
The model estimates costs in up to four elements for each of the six
disposal technologies. These elements are the capital costs of construction,
O&M costs, capital costs of closure, and post-closure maintenance costs. If
corrective action is taken, additional costs are incurred. (Corrective action
is not available for waste piles, deep well injection, and incineration.)
Capital costs include both direct and indirect initial capital investment
costs. The major direct costs are for land and equipment (pumps, leachate
collection system, membranes, etc.). For landfills, the model also includes a
capital cost incurred over the operating life of the landfill: the cost of
constructing a new cell at the beginning of each operating year. The model
calculates indirect capital costs as a percentage of either the direct capital
cost or the sum of direct and indirect capital costs. Exhibit 6-2 presents
descriptions of the different indirect capital cost items; Exhibit 6-7
presents the percentages used for each disposal technology. The values of
these percentages are similar for most of the disposal technologies and vary
according to the technical sophistication of the technology.
Direct O&M costs include operating labor, utilities, and maintenance. The
model also calculates additional O&M costs for deep well injection and surface
impoundments: the cost of periodic (approximately once every four years)
"workover" of the well casing system for injection wells, and the intermittent
costs of dredging surface impoundments and transporting and disposing of the
dredged material. The model calculates indirect O&M costs as a percentage of
either the total capital cost, the direct labor, or the total annual cost.
The system overhead is equal to 75 percent of the direct labor for all the
disposal technologies except incineration. The system overhead for
incineration is the same as for treatment technologies, 10 percent of the
total annual cost.
Closure costs are incurred for the decontamination of facility equipment
and structures, and for certification and supervision, as required by RCRA
regulations (40 CFR 264, Subpart G). Closure costs are incurred at the end of
the operating life of the facility.
The model includes annual costs for post-closure care for landfills,
surface impoundments, and land treatment facilities for 30 years after
7U.S. EPA, "Docket Report, Supporting Documents for the Regulatory
Analysis of the Part 264 Land Disposal Regulations," Volume II, Appendix C
(August 24, 1982); disposal facility design and cost models prepared by
Pope-Reid Associates, Inc. for the U.S. EPA Office of Solid Waste (1983).
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EXHIBIT 6-7
INDIRECT CAPITAL COSTS FOR DISPOSAL TECHNOLOGIES
Surface
Land Impound- Deep Well Waste Incinera-
Item Landfill Treatment ment Injection Piles tion
(Percent of Direct Capital Costs)
Engineering/
Supervision
10
10
10
10
5
20
Construction/FieId
Expenses
5
5
5
10
5
15
Contractor's Fee
10
10
10
10
10
7
Start-up
1
2
1
3
1
10
Spare-Parts
Inventory
1
1
1
2
1
2
Permit and Legal
Fees
15
15
15
25
10
0
Interest During
Construction
10
10
10
10
10
10
Working Capital a/
10
10
10
10
5
18
Contingency a/
15
15
10
20
5
25
a/ The percentages given for working capital and contingency represent
fractions of the sum of direct and indirect capital costs.
b/ The costs of permits and legal fees for incineration are included in
engineering/supervision.
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6-16
closure. Because the RCRA regulations do not require post-closure care for
properly closed injection wells, waste piles, and incineration facilities, the
model does not include post-closure costs for these technologies.
The model calculates capital and O&M costs for three different types of
corrective actions to land disposal facilities (landfills, surface
impoundments, and land treatment). These costs are based on the assumption
that if corrective action is necessary, it is initiated 50 years after the
facility started to operate and continues for 150 years.
The model combines and discounts capital, O&M, closure and post-closure
costs to obtain annual revenue requirements. It then divides these annual
revenue requirements by the capacity of the disposal facility to yield a
disposal cost per unit of waste. Exhibit 6-8 presents the unit annual revenue
requirements for disposal technologies. These unit annual revenue
requirements are multiplied by the quantity of waste generated annually to
determine the annual revenue requirements for using a particular disposal
technology to manage a specific waste stream.
Appendix D, which consists of detailed descriptions of each disposal
technology in the model, identifies the individual costs on which the totals
in Exhibit 6-8 are based.
6.6 CONCLUSION
The preceding is an overview of the elements the model takes into account
in determining costs and the methodology the model uses to convert these cost
estimates to a cost score for each W-E-T cell. For details on specific cost
values for each technology, the reader is referred to the detailed
descriptions of each treatment, transportation, and disposal technology in
Appendices B, C, and D respectively, included in the second volume of this
report.
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EXHIBIT 6-8
UNIT ANNUAL REVENUE REQUIREMENTS FOR DISPOSAL TECHNOLOGIES
($/metric ton or $/cubic meter)
Disposal Technology
Small Facilities a/ Large Facilities b/
Landfills (S/raetric ton)
Double Synthetic
Synthetic/Clay
Single Synthetic
1.0 m Clay
0. 0- m Clay
Unlined
Surface Impoundments(S/m1)
Double Synthetic
Synthetic/Clay
Single Synthetic
1.0m CLay
0.3 m Clay
Unlined
$466.8
465.0
444.9
449.6
422.9
315.9
194.4
200.6
185.9
191.9
170.7
111.1
48.53
44.46
41.55
41.36
37.85
23.18
26.24
25.85
24.64
24.95
22.62
17.42
Land Treatment (S/metric ton)
Waste Piles (5/m1)
. Double Synthetic
Synthetic/Clay
Concrete/Monitoring
Concrete/Inspection
Indoor
Incineration (3/metric ton)
Liquid injection
heat content ฃ 3,000 Btu/lb
heat content between 3,000 and
7,500 Btu/lb
heat content t 7,500 Btu/lb
Solids kiln/hearth
heat content ฃ 3,000 Btu/lb
heat content ฃ 3,000 Btu/lb
228.3
58.33
60.09
63.41
57.74
56.56
123.90
83.82
115.20
206.44
164.95
42.83
5.23
5.40
5.42
5.43
5.09
79.82
79.82
79.82
123.19
123.19
Injection wells c/
S6.35
a/ Landfills = 500 MT/yr; surface impoundments =0.25 acre; land
treatment =6.5 acre site; waste piles = 60 cubic meters volume; incineration
= 3,900 MT/yr.
b/ Landfills = 60,000 MT/yr; surface impoundments = 2.0 acre; land
treatment = 74;3 acre site; waste piles = 2,830 cubic meters volume,
incineration = 22,250 MT/yr.
c/ Injection wells have a capacity of 100,000 gallons of waste per day.
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